metal_processing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"border:solid green 2px; padding: 20px\">\n",
    "<b>Алексей, привет!</b>\n",
    "\n",
    "Меня зовут Евгений Д., и я буду проверять твой проект. Предлагаю общаться на «ты» 😊 Но если это не удобно - дай знать, и мы перейдем на \"вы\". \n",
    "\n",
    "Моя основная цель — не указать на совершенные тобою ошибки, а поделиться своим опытом и помочь тебе стать аналитиком данных. Ты уже проделал большую работу над проектом, но давай сделаем его еще лучше. Ниже ты найдешь мои комментарии - пожалуйста, не перемещай, не изменяй и не удаляй их. Я буду использовать цветовую разметку:\n",
    "\n",
    "<div class=\"alert alert-danger\">\n",
    "<b>Комментарий ревьюера:</b> Так выделены самые важные замечания. Без их отработки проект не будет принят. </div>\n",
    "\n",
    "<div class=\"alert alert-warning\">\n",
    "<b>Комментарий ревьюера:</b> Так выделены небольшие замечания. Одна-два таких замечания в проекте допустимы, но если их будет больше - тебе будет необходимо внести исправления. Это как тестовое задание при приеме на работу: очень много мелких ошибок могут стать причиной отказа кандидату. \n",
    "\n",
    "</div>\n",
    "\n",
    "<div class=\"alert alert-success\">\n",
    "<b>Комментарий ревьюера:</b> Так я выделяю все остальные комментарии.</div>\n",
    "\n",
    "Давай работать над проектом в диалоге: если ты что-то меняешь в проекте или отвечаешь на мои комменатри — пиши об этом. Мне будет легче отследить изменения, если ты выделишь свои комментарии:\n",
    "<div class=\"alert alert-info\"> <b>Комментарий студента:</b> Например, вот так.</div>\n",
    "\n",
    "Всё это поможет выполнить повторную проверку твоего проекта оперативнее. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Описание проекта\n",
    "\n",
    "Подготовьте прототип модели машинного обучения для «Цифры». Компания разрабатывает решения для эффективной работы промышленных предприятий.\n",
    "\n",
    "Модель должна предсказать коэффициент восстановления золота из золотосодержащей руды. В вашем распоряжении данные с параметрами добычи и очистки. \n",
    "\n",
    "Модель поможет оптимизировать производство, чтобы не запускать предприятие с убыточными характеристиками.\n",
    "\n",
    "Вам нужно:\n",
    "\n",
    "1. Подготовить данные;\n",
    "2. Провести исследовательский анализ данных;\n",
    "3. Построить и обучить модель.\n",
    "\n",
    "Чтобы выполнить проект, обращайтесь к библиотекам *pandas*, *matplotlib* и *sklearn.* Вам поможет их документация."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Содержание\n",
    "* [Подготовка данных](#prepare_data)\n",
    "  * [Изучение данных из файлов](#prepare_data.open_file)\n",
    "  * [Проверка правильности вычисленной эффективности обогащения](#prepare_data.check_efficiency)\n",
    "  * [Анализ признаков, недоступных в тестовой выборке](#prepare_data.analyse_test)\n",
    "  * [Предобработка данных](#prepare_data.data_preprocessing)\n",
    "* [Анализ данных](#analyse_data)\n",
    "  * [Анализ изменения концентрация металлов (Au, Ag, Pb) на различных этапах очистки](#analyse_data.analyse_concentration)\n",
    "  * [Сравнение распределения размеров гранул сырья на обучающей и тестовой выборках](#analyse_data.compare_training_and_test)\n",
    "  * [Исследование суммарной концентрации всех веществ на разных стадиях: в сырье, в черновом и финальном концентратах](#analyse_data.analyse_sum_concentration)\n",
    "* [Построение модели](#model_creation)\n",
    "  * [Функция для вычисления итоговой sMAPE](#model_creation.create_function)\n",
    "  * [Обучение моделей и оценка их качества кросс-валидацией](#model_creation.train_models)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Комментарий ревьюера:</b>\n",
    "\n",
    "Здорово, когда есть вступление в сам проект, каждый сможет быстрее разобраться с тем о чем сам проект. Можно также привести описание используемых столбцов. \n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "from sklearn.model_selection import cross_val_score, GridSearchCV\n",
    "from sklearn.metrics import mean_absolute_error, make_scorer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from collections import namedtuple\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "RANDOM_STATE = 12345\n",
    "\n",
    "TARGETS = ['rougher.output.recovery', 'final.output.recovery']\n",
    "\n",
    "METALS = ['au', 'ag', 'pb']\n",
    "\n",
    "CONCENTRATE_METRICS = [\n",
    "    'rougher.input.feed',\n",
    "    'rougher.output.concentrate',\n",
    "    'primary_cleaner.output.concentrate',\n",
    "    'final.output.concentrate',\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyze_df(df):\n",
    "    rows_count, columns_count = df.shape\n",
    "    print('Количество стобцов:', columns_count)\n",
    "    print('Количество строк:', rows_count)\n",
    "    display(df.head(5))\n",
    "    display(df.describe())\n",
    "    df.info()\n",
    "    \n",
    "def print_df(df):\n",
    "    with pd.option_context('display.max_rows', None):\n",
    "        display(df)\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Комментарий ревьюера:</b>\n",
    "\n",
    "Отличные функции для проверки наших данных 👍\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"prepare_data\"></a>\n",
    "## Подготовка данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"prepare_data.open_file\"></a>\n",
    "### Изучение данных из файлов"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Откроем датасеты с данными и проанализируем их"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "gold_recovery_train = pd.read_csv('/datasets/gold_recovery_train.csv', index_col='date')\n",
    "gold_recovery_test = pd.read_csv('/datasets/gold_recovery_test.csv', index_col='date')\n",
    "gold_recovery_full = pd.read_csv('/datasets/gold_recovery_full.csv', index_col='date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество стобцов: 86\n",
      "Количество строк: 16860\n"
     ]
    },
    {
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       "      <th>rougher.calculation.sulfate_to_au_concentrate</th>\n",
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       "      <th>rougher.input.feed_sol</th>\n",
       "      <th>rougher.input.feed_au</th>\n",
       "      <th>rougher.input.floatbank10_sulfate</th>\n",
       "      <th>rougher.input.floatbank10_xanthate</th>\n",
       "      <th>rougher.input.floatbank11_sulfate</th>\n",
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       "      <th>rougher.output.concentrate_sol</th>\n",
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       "      <th>rougher.output.recovery</th>\n",
       "      <th>rougher.output.tail_ag</th>\n",
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       "      <th>rougher.output.tail_au</th>\n",
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       "      <th>rougher.state.floatbank10_a_level</th>\n",
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       "      <th>rougher.state.floatbank10_c_level</th>\n",
       "      <th>rougher.state.floatbank10_d_air</th>\n",
       "      <th>rougher.state.floatbank10_d_level</th>\n",
       "      <th>rougher.state.floatbank10_e_air</th>\n",
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       "      <th>rougher.state.floatbank10_f_air</th>\n",
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       "      <th>secondary_cleaner.output.tail_ag</th>\n",
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       "      <td>14.016835</td>\n",
       "      <td>-502.488007</td>\n",
       "      <td>12.099931</td>\n",
       "      <td>-504.715942</td>\n",
       "      <td>9.925633</td>\n",
       "      <td>-498.310211</td>\n",
       "      <td>8.079666</td>\n",
       "      <td>-500.470978</td>\n",
       "      <td>14.151341</td>\n",
       "      <td>-605.841980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-15 01:00:00</td>\n",
       "      <td>6.029369</td>\n",
       "      <td>9.968944</td>\n",
       "      <td>5.257781</td>\n",
       "      <td>42.701629</td>\n",
       "      <td>69.266198</td>\n",
       "      <td>10.462676</td>\n",
       "      <td>0.927452</td>\n",
       "      <td>16.634514</td>\n",
       "      <td>2.224930</td>\n",
       "      <td>125.629232</td>\n",
       "      <td>10.296251</td>\n",
       "      <td>7.25</td>\n",
       "      <td>1.002663</td>\n",
       "      <td>8.558743</td>\n",
       "      <td>10.497069</td>\n",
       "      <td>19.369147</td>\n",
       "      <td>34.118526</td>\n",
       "      <td>16.250511</td>\n",
       "      <td>3.049646</td>\n",
       "      <td>6.733907</td>\n",
       "      <td>2.353017</td>\n",
       "      <td>1576.166671</td>\n",
       "      <td>-500.904965</td>\n",
       "      <td>1575.950626</td>\n",
       "      <td>-499.865889</td>\n",
       "      <td>1575.994189</td>\n",
       "      <td>-499.315107</td>\n",
       "      <td>1574.479259</td>\n",
       "      <td>-498.931665</td>\n",
       "      <td>42050.861809</td>\n",
       "      <td>3498.370979</td>\n",
       "      <td>3489.981857</td>\n",
       "      <td>2.858998</td>\n",
       "      <td>6.161113</td>\n",
       "      <td>2.266033</td>\n",
       "      <td>525.290581</td>\n",
       "      <td>57.278666</td>\n",
       "      <td>35.753385</td>\n",
       "      <td>6.478583</td>\n",
       "      <td>11.971193</td>\n",
       "      <td>6.005766</td>\n",
       "      <td>11.996163</td>\n",
       "      <td>6.012594</td>\n",
       "      <td>11.615865</td>\n",
       "      <td>7.278807</td>\n",
       "      <td>28.067063</td>\n",
       "      <td>20.050975</td>\n",
       "      <td>86.843261</td>\n",
       "      <td>4.955378</td>\n",
       "      <td>0.536689</td>\n",
       "      <td>18.965234</td>\n",
       "      <td>1.184827</td>\n",
       "      <td>1000.286398</td>\n",
       "      <td>-400.065196</td>\n",
       "      <td>1600.754587</td>\n",
       "      <td>-449.953435</td>\n",
       "      <td>1600.479580</td>\n",
       "      <td>-449.830646</td>\n",
       "      <td>1600.527589</td>\n",
       "      <td>-449.953649</td>\n",
       "      <td>1399.227084</td>\n",
       "      <td>-450.869848</td>\n",
       "      <td>1399.719514</td>\n",
       "      <td>-450.119001</td>\n",
       "      <td>14.265474</td>\n",
       "      <td>4.592547</td>\n",
       "      <td>9.001494</td>\n",
       "      <td>2.488248</td>\n",
       "      <td>25.880539</td>\n",
       "      <td>-499.989656</td>\n",
       "      <td>23.889530</td>\n",
       "      <td>-500.372428</td>\n",
       "      <td>23.970550</td>\n",
       "      <td>-500.085473</td>\n",
       "      <td>22.085714</td>\n",
       "      <td>-499.446897</td>\n",
       "      <td>13.992281</td>\n",
       "      <td>-505.503262</td>\n",
       "      <td>11.950531</td>\n",
       "      <td>-501.331529</td>\n",
       "      <td>10.039245</td>\n",
       "      <td>-500.169983</td>\n",
       "      <td>7.984757</td>\n",
       "      <td>-500.582168</td>\n",
       "      <td>13.998353</td>\n",
       "      <td>-599.787184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-15 02:00:00</td>\n",
       "      <td>6.055926</td>\n",
       "      <td>10.213995</td>\n",
       "      <td>5.383759</td>\n",
       "      <td>42.657501</td>\n",
       "      <td>68.116445</td>\n",
       "      <td>10.507046</td>\n",
       "      <td>0.953716</td>\n",
       "      <td>16.208849</td>\n",
       "      <td>2.257889</td>\n",
       "      <td>123.819808</td>\n",
       "      <td>11.316280</td>\n",
       "      <td>7.25</td>\n",
       "      <td>0.991265</td>\n",
       "      <td>8.603505</td>\n",
       "      <td>10.354494</td>\n",
       "      <td>19.167579</td>\n",
       "      <td>33.969464</td>\n",
       "      <td>16.491823</td>\n",
       "      <td>3.124664</td>\n",
       "      <td>6.471768</td>\n",
       "      <td>2.416800</td>\n",
       "      <td>1601.556163</td>\n",
       "      <td>-499.997791</td>\n",
       "      <td>1600.386685</td>\n",
       "      <td>-500.607762</td>\n",
       "      <td>1602.003542</td>\n",
       "      <td>-500.870069</td>\n",
       "      <td>1599.541515</td>\n",
       "      <td>-499.827444</td>\n",
       "      <td>42018.101184</td>\n",
       "      <td>3495.348910</td>\n",
       "      <td>3502.359819</td>\n",
       "      <td>2.945989</td>\n",
       "      <td>6.116455</td>\n",
       "      <td>2.159622</td>\n",
       "      <td>530.026610</td>\n",
       "      <td>57.510649</td>\n",
       "      <td>35.971630</td>\n",
       "      <td>6.362222</td>\n",
       "      <td>11.920603</td>\n",
       "      <td>6.197377</td>\n",
       "      <td>11.920305</td>\n",
       "      <td>6.204633</td>\n",
       "      <td>11.695753</td>\n",
       "      <td>7.216833</td>\n",
       "      <td>27.454037</td>\n",
       "      <td>19.737170</td>\n",
       "      <td>86.842308</td>\n",
       "      <td>4.843460</td>\n",
       "      <td>0.546416</td>\n",
       "      <td>18.808533</td>\n",
       "      <td>1.162562</td>\n",
       "      <td>999.719565</td>\n",
       "      <td>-400.074028</td>\n",
       "      <td>1599.337330</td>\n",
       "      <td>-450.008530</td>\n",
       "      <td>1599.672797</td>\n",
       "      <td>-449.954491</td>\n",
       "      <td>1599.849325</td>\n",
       "      <td>-449.954185</td>\n",
       "      <td>1399.180945</td>\n",
       "      <td>-449.937588</td>\n",
       "      <td>1400.316682</td>\n",
       "      <td>-450.527147</td>\n",
       "      <td>14.115662</td>\n",
       "      <td>4.624775</td>\n",
       "      <td>8.842896</td>\n",
       "      <td>2.458187</td>\n",
       "      <td>26.005245</td>\n",
       "      <td>-499.929616</td>\n",
       "      <td>23.886657</td>\n",
       "      <td>-499.951928</td>\n",
       "      <td>23.913535</td>\n",
       "      <td>-499.442343</td>\n",
       "      <td>23.957717</td>\n",
       "      <td>-499.901982</td>\n",
       "      <td>14.015015</td>\n",
       "      <td>-502.520901</td>\n",
       "      <td>11.912783</td>\n",
       "      <td>-501.133383</td>\n",
       "      <td>10.070913</td>\n",
       "      <td>-500.129135</td>\n",
       "      <td>8.013877</td>\n",
       "      <td>-500.517572</td>\n",
       "      <td>14.028663</td>\n",
       "      <td>-601.427363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-15 03:00:00</td>\n",
       "      <td>6.047977</td>\n",
       "      <td>9.977019</td>\n",
       "      <td>4.858634</td>\n",
       "      <td>42.689819</td>\n",
       "      <td>68.347543</td>\n",
       "      <td>10.422762</td>\n",
       "      <td>0.883763</td>\n",
       "      <td>16.532835</td>\n",
       "      <td>2.146849</td>\n",
       "      <td>122.270188</td>\n",
       "      <td>11.322140</td>\n",
       "      <td>7.25</td>\n",
       "      <td>0.996739</td>\n",
       "      <td>7.221879</td>\n",
       "      <td>8.496563</td>\n",
       "      <td>15.978538</td>\n",
       "      <td>28.260743</td>\n",
       "      <td>16.024359</td>\n",
       "      <td>2.960362</td>\n",
       "      <td>6.843791</td>\n",
       "      <td>2.262051</td>\n",
       "      <td>1599.968720</td>\n",
       "      <td>-500.951778</td>\n",
       "      <td>1600.659236</td>\n",
       "      <td>-499.677094</td>\n",
       "      <td>1600.304144</td>\n",
       "      <td>-500.727997</td>\n",
       "      <td>1600.449520</td>\n",
       "      <td>-500.052575</td>\n",
       "      <td>42029.447991</td>\n",
       "      <td>3498.578313</td>\n",
       "      <td>3499.162912</td>\n",
       "      <td>3.002340</td>\n",
       "      <td>6.043309</td>\n",
       "      <td>2.037807</td>\n",
       "      <td>542.590390</td>\n",
       "      <td>57.792734</td>\n",
       "      <td>36.862241</td>\n",
       "      <td>6.118189</td>\n",
       "      <td>11.630094</td>\n",
       "      <td>6.203177</td>\n",
       "      <td>11.692450</td>\n",
       "      <td>6.196578</td>\n",
       "      <td>11.915047</td>\n",
       "      <td>7.175616</td>\n",
       "      <td>27.341277</td>\n",
       "      <td>19.320810</td>\n",
       "      <td>87.226430</td>\n",
       "      <td>4.655288</td>\n",
       "      <td>0.542525</td>\n",
       "      <td>19.330200</td>\n",
       "      <td>1.079755</td>\n",
       "      <td>999.814770</td>\n",
       "      <td>-400.200179</td>\n",
       "      <td>1600.059442</td>\n",
       "      <td>-450.619948</td>\n",
       "      <td>1600.012842</td>\n",
       "      <td>-449.910497</td>\n",
       "      <td>1597.725177</td>\n",
       "      <td>-450.130127</td>\n",
       "      <td>1400.943157</td>\n",
       "      <td>-450.030142</td>\n",
       "      <td>1400.234743</td>\n",
       "      <td>-449.790835</td>\n",
       "      <td>13.732071</td>\n",
       "      <td>4.482453</td>\n",
       "      <td>9.122857</td>\n",
       "      <td>2.322062</td>\n",
       "      <td>25.942508</td>\n",
       "      <td>-499.176749</td>\n",
       "      <td>23.955516</td>\n",
       "      <td>-499.848796</td>\n",
       "      <td>23.966838</td>\n",
       "      <td>-500.008812</td>\n",
       "      <td>23.954443</td>\n",
       "      <td>-499.944710</td>\n",
       "      <td>14.036510</td>\n",
       "      <td>-500.857308</td>\n",
       "      <td>11.999550</td>\n",
       "      <td>-501.193686</td>\n",
       "      <td>9.970366</td>\n",
       "      <td>-499.201640</td>\n",
       "      <td>7.977324</td>\n",
       "      <td>-500.255908</td>\n",
       "      <td>14.005551</td>\n",
       "      <td>-599.996129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-15 04:00:00</td>\n",
       "      <td>6.148599</td>\n",
       "      <td>10.142511</td>\n",
       "      <td>4.939416</td>\n",
       "      <td>42.774141</td>\n",
       "      <td>66.927016</td>\n",
       "      <td>10.360302</td>\n",
       "      <td>0.792826</td>\n",
       "      <td>16.525686</td>\n",
       "      <td>2.055292</td>\n",
       "      <td>117.988169</td>\n",
       "      <td>11.913613</td>\n",
       "      <td>7.25</td>\n",
       "      <td>1.009869</td>\n",
       "      <td>9.089428</td>\n",
       "      <td>9.986786</td>\n",
       "      <td>19.199864</td>\n",
       "      <td>33.044932</td>\n",
       "      <td>16.480185</td>\n",
       "      <td>3.112100</td>\n",
       "      <td>6.550154</td>\n",
       "      <td>2.277073</td>\n",
       "      <td>1601.339707</td>\n",
       "      <td>-498.975456</td>\n",
       "      <td>1601.437854</td>\n",
       "      <td>-500.323246</td>\n",
       "      <td>1599.581894</td>\n",
       "      <td>-500.888152</td>\n",
       "      <td>1602.649541</td>\n",
       "      <td>-500.593010</td>\n",
       "      <td>42125.354245</td>\n",
       "      <td>3494.800771</td>\n",
       "      <td>3506.679315</td>\n",
       "      <td>3.169615</td>\n",
       "      <td>6.060915</td>\n",
       "      <td>1.786875</td>\n",
       "      <td>540.531893</td>\n",
       "      <td>56.047189</td>\n",
       "      <td>34.347666</td>\n",
       "      <td>5.663707</td>\n",
       "      <td>10.957755</td>\n",
       "      <td>6.198826</td>\n",
       "      <td>10.960521</td>\n",
       "      <td>6.194897</td>\n",
       "      <td>12.411054</td>\n",
       "      <td>7.240205</td>\n",
       "      <td>27.041023</td>\n",
       "      <td>19.216101</td>\n",
       "      <td>86.688794</td>\n",
       "      <td>4.552800</td>\n",
       "      <td>0.515438</td>\n",
       "      <td>19.267422</td>\n",
       "      <td>1.012642</td>\n",
       "      <td>999.678690</td>\n",
       "      <td>-399.752729</td>\n",
       "      <td>1600.208824</td>\n",
       "      <td>-449.599614</td>\n",
       "      <td>1600.357732</td>\n",
       "      <td>-450.034364</td>\n",
       "      <td>1599.759049</td>\n",
       "      <td>-449.909799</td>\n",
       "      <td>1401.560902</td>\n",
       "      <td>-448.877187</td>\n",
       "      <td>1401.160227</td>\n",
       "      <td>-450.407128</td>\n",
       "      <td>14.079996</td>\n",
       "      <td>4.470737</td>\n",
       "      <td>8.871028</td>\n",
       "      <td>2.330448</td>\n",
       "      <td>26.024787</td>\n",
       "      <td>-500.279091</td>\n",
       "      <td>23.955345</td>\n",
       "      <td>-500.593614</td>\n",
       "      <td>23.985703</td>\n",
       "      <td>-500.083811</td>\n",
       "      <td>23.958945</td>\n",
       "      <td>-499.990309</td>\n",
       "      <td>14.027298</td>\n",
       "      <td>-499.838632</td>\n",
       "      <td>11.953070</td>\n",
       "      <td>-501.053894</td>\n",
       "      <td>9.925709</td>\n",
       "      <td>-501.686727</td>\n",
       "      <td>7.894242</td>\n",
       "      <td>-500.356035</td>\n",
       "      <td>13.996647</td>\n",
       "      <td>-601.496691</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     final.output.concentrate_ag  final.output.concentrate_pb  \\\n",
       "date                                                                            \n",
       "2016-01-15 00:00:00                     6.055403                     9.889648   \n",
       "2016-01-15 01:00:00                     6.029369                     9.968944   \n",
       "2016-01-15 02:00:00                     6.055926                    10.213995   \n",
       "2016-01-15 03:00:00                     6.047977                     9.977019   \n",
       "2016-01-15 04:00:00                     6.148599                    10.142511   \n",
       "\n",
       "                     final.output.concentrate_sol  \\\n",
       "date                                                \n",
       "2016-01-15 00:00:00                      5.507324   \n",
       "2016-01-15 01:00:00                      5.257781   \n",
       "2016-01-15 02:00:00                      5.383759   \n",
       "2016-01-15 03:00:00                      4.858634   \n",
       "2016-01-15 04:00:00                      4.939416   \n",
       "\n",
       "                     final.output.concentrate_au  final.output.recovery  \\\n",
       "date                                                                      \n",
       "2016-01-15 00:00:00                    42.192020              70.541216   \n",
       "2016-01-15 01:00:00                    42.701629              69.266198   \n",
       "2016-01-15 02:00:00                    42.657501              68.116445   \n",
       "2016-01-15 03:00:00                    42.689819              68.347543   \n",
       "2016-01-15 04:00:00                    42.774141              66.927016   \n",
       "\n",
       "                     final.output.tail_ag  final.output.tail_pb  \\\n",
       "date                                                              \n",
       "2016-01-15 00:00:00             10.411962              0.895447   \n",
       "2016-01-15 01:00:00             10.462676              0.927452   \n",
       "2016-01-15 02:00:00             10.507046              0.953716   \n",
       "2016-01-15 03:00:00             10.422762              0.883763   \n",
       "2016-01-15 04:00:00             10.360302              0.792826   \n",
       "\n",
       "                     final.output.tail_sol  final.output.tail_au  \\\n",
       "date                                                               \n",
       "2016-01-15 00:00:00              16.904297              2.143149   \n",
       "2016-01-15 01:00:00              16.634514              2.224930   \n",
       "2016-01-15 02:00:00              16.208849              2.257889   \n",
       "2016-01-15 03:00:00              16.532835              2.146849   \n",
       "2016-01-15 04:00:00              16.525686              2.055292   \n",
       "\n",
       "                     primary_cleaner.input.sulfate  \\\n",
       "date                                                 \n",
       "2016-01-15 00:00:00                     127.092003   \n",
       "2016-01-15 01:00:00                     125.629232   \n",
       "2016-01-15 02:00:00                     123.819808   \n",
       "2016-01-15 03:00:00                     122.270188   \n",
       "2016-01-15 04:00:00                     117.988169   \n",
       "\n",
       "                     primary_cleaner.input.depressant  \\\n",
       "date                                                    \n",
       "2016-01-15 00:00:00                         10.128295   \n",
       "2016-01-15 01:00:00                         10.296251   \n",
       "2016-01-15 02:00:00                         11.316280   \n",
       "2016-01-15 03:00:00                         11.322140   \n",
       "2016-01-15 04:00:00                         11.913613   \n",
       "\n",
       "                     primary_cleaner.input.feed_size  \\\n",
       "date                                                   \n",
       "2016-01-15 00:00:00                             7.25   \n",
       "2016-01-15 01:00:00                             7.25   \n",
       "2016-01-15 02:00:00                             7.25   \n",
       "2016-01-15 03:00:00                             7.25   \n",
       "2016-01-15 04:00:00                             7.25   \n",
       "\n",
       "                     primary_cleaner.input.xanthate  \\\n",
       "date                                                  \n",
       "2016-01-15 00:00:00                        0.988759   \n",
       "2016-01-15 01:00:00                        1.002663   \n",
       "2016-01-15 02:00:00                        0.991265   \n",
       "2016-01-15 03:00:00                        0.996739   \n",
       "2016-01-15 04:00:00                        1.009869   \n",
       "\n",
       "                     primary_cleaner.output.concentrate_ag  \\\n",
       "date                                                         \n",
       "2016-01-15 00:00:00                               8.547551   \n",
       "2016-01-15 01:00:00                               8.558743   \n",
       "2016-01-15 02:00:00                               8.603505   \n",
       "2016-01-15 03:00:00                               7.221879   \n",
       "2016-01-15 04:00:00                               9.089428   \n",
       "\n",
       "                     primary_cleaner.output.concentrate_pb  \\\n",
       "date                                                         \n",
       "2016-01-15 00:00:00                              10.389648   \n",
       "2016-01-15 01:00:00                              10.497069   \n",
       "2016-01-15 02:00:00                              10.354494   \n",
       "2016-01-15 03:00:00                               8.496563   \n",
       "2016-01-15 04:00:00                               9.986786   \n",
       "\n",
       "                     primary_cleaner.output.concentrate_sol  \\\n",
       "date                                                          \n",
       "2016-01-15 00:00:00                               19.529297   \n",
       "2016-01-15 01:00:00                               19.369147   \n",
       "2016-01-15 02:00:00                               19.167579   \n",
       "2016-01-15 03:00:00                               15.978538   \n",
       "2016-01-15 04:00:00                               19.199864   \n",
       "\n",
       "                     primary_cleaner.output.concentrate_au  \\\n",
       "date                                                         \n",
       "2016-01-15 00:00:00                              34.174427   \n",
       "2016-01-15 01:00:00                              34.118526   \n",
       "2016-01-15 02:00:00                              33.969464   \n",
       "2016-01-15 03:00:00                              28.260743   \n",
       "2016-01-15 04:00:00                              33.044932   \n",
       "\n",
       "                     primary_cleaner.output.tail_ag  \\\n",
       "date                                                  \n",
       "2016-01-15 00:00:00                       14.936526   \n",
       "2016-01-15 01:00:00                       16.250511   \n",
       "2016-01-15 02:00:00                       16.491823   \n",
       "2016-01-15 03:00:00                       16.024359   \n",
       "2016-01-15 04:00:00                       16.480185   \n",
       "\n",
       "                     primary_cleaner.output.tail_pb  \\\n",
       "date                                                  \n",
       "2016-01-15 00:00:00                        2.534912   \n",
       "2016-01-15 01:00:00                        3.049646   \n",
       "2016-01-15 02:00:00                        3.124664   \n",
       "2016-01-15 03:00:00                        2.960362   \n",
       "2016-01-15 04:00:00                        3.112100   \n",
       "\n",
       "                     primary_cleaner.output.tail_sol  \\\n",
       "date                                                   \n",
       "2016-01-15 00:00:00                         7.476074   \n",
       "2016-01-15 01:00:00                         6.733907   \n",
       "2016-01-15 02:00:00                         6.471768   \n",
       "2016-01-15 03:00:00                         6.843791   \n",
       "2016-01-15 04:00:00                         6.550154   \n",
       "\n",
       "                     primary_cleaner.output.tail_au  \\\n",
       "date                                                  \n",
       "2016-01-15 00:00:00                        2.106679   \n",
       "2016-01-15 01:00:00                        2.353017   \n",
       "2016-01-15 02:00:00                        2.416800   \n",
       "2016-01-15 03:00:00                        2.262051   \n",
       "2016-01-15 04:00:00                        2.277073   \n",
       "\n",
       "                     primary_cleaner.state.floatbank8_a_air  \\\n",
       "date                                                          \n",
       "2016-01-15 00:00:00                             1549.775757   \n",
       "2016-01-15 01:00:00                             1576.166671   \n",
       "2016-01-15 02:00:00                             1601.556163   \n",
       "2016-01-15 03:00:00                             1599.968720   \n",
       "2016-01-15 04:00:00                             1601.339707   \n",
       "\n",
       "                     primary_cleaner.state.floatbank8_a_level  \\\n",
       "date                                                            \n",
       "2016-01-15 00:00:00                               -498.912140   \n",
       "2016-01-15 01:00:00                               -500.904965   \n",
       "2016-01-15 02:00:00                               -499.997791   \n",
       "2016-01-15 03:00:00                               -500.951778   \n",
       "2016-01-15 04:00:00                               -498.975456   \n",
       "\n",
       "                     primary_cleaner.state.floatbank8_b_air  \\\n",
       "date                                                          \n",
       "2016-01-15 00:00:00                             1551.434204   \n",
       "2016-01-15 01:00:00                             1575.950626   \n",
       "2016-01-15 02:00:00                             1600.386685   \n",
       "2016-01-15 03:00:00                             1600.659236   \n",
       "2016-01-15 04:00:00                             1601.437854   \n",
       "\n",
       "                     primary_cleaner.state.floatbank8_b_level  \\\n",
       "date                                                            \n",
       "2016-01-15 00:00:00                               -516.403442   \n",
       "2016-01-15 01:00:00                               -499.865889   \n",
       "2016-01-15 02:00:00                               -500.607762   \n",
       "2016-01-15 03:00:00                               -499.677094   \n",
       "2016-01-15 04:00:00                               -500.323246   \n",
       "\n",
       "                     primary_cleaner.state.floatbank8_c_air  \\\n",
       "date                                                          \n",
       "2016-01-15 00:00:00                             1549.873901   \n",
       "2016-01-15 01:00:00                             1575.994189   \n",
       "2016-01-15 02:00:00                             1602.003542   \n",
       "2016-01-15 03:00:00                             1600.304144   \n",
       "2016-01-15 04:00:00                             1599.581894   \n",
       "\n",
       "                     primary_cleaner.state.floatbank8_c_level  \\\n",
       "date                                                            \n",
       "2016-01-15 00:00:00                               -498.666595   \n",
       "2016-01-15 01:00:00                               -499.315107   \n",
       "2016-01-15 02:00:00                               -500.870069   \n",
       "2016-01-15 03:00:00                               -500.727997   \n",
       "2016-01-15 04:00:00                               -500.888152   \n",
       "\n",
       "                     primary_cleaner.state.floatbank8_d_air  \\\n",
       "date                                                          \n",
       "2016-01-15 00:00:00                             1554.367432   \n",
       "2016-01-15 01:00:00                             1574.479259   \n",
       "2016-01-15 02:00:00                             1599.541515   \n",
       "2016-01-15 03:00:00                             1600.449520   \n",
       "2016-01-15 04:00:00                             1602.649541   \n",
       "\n",
       "                     primary_cleaner.state.floatbank8_d_level  \\\n",
       "date                                                            \n",
       "2016-01-15 00:00:00                               -493.428131   \n",
       "2016-01-15 01:00:00                               -498.931665   \n",
       "2016-01-15 02:00:00                               -499.827444   \n",
       "2016-01-15 03:00:00                               -500.052575   \n",
       "2016-01-15 04:00:00                               -500.593010   \n",
       "\n",
       "                     rougher.calculation.sulfate_to_au_concentrate  \\\n",
       "date                                                                 \n",
       "2016-01-15 00:00:00                                   41885.707031   \n",
       "2016-01-15 01:00:00                                   42050.861809   \n",
       "2016-01-15 02:00:00                                   42018.101184   \n",
       "2016-01-15 03:00:00                                   42029.447991   \n",
       "2016-01-15 04:00:00                                   42125.354245   \n",
       "\n",
       "                     rougher.calculation.floatbank10_sulfate_to_au_feed  \\\n",
       "date                                                                      \n",
       "2016-01-15 00:00:00                                        3481.779053    \n",
       "2016-01-15 01:00:00                                        3498.370979    \n",
       "2016-01-15 02:00:00                                        3495.348910    \n",
       "2016-01-15 03:00:00                                        3498.578313    \n",
       "2016-01-15 04:00:00                                        3494.800771    \n",
       "\n",
       "                     rougher.calculation.floatbank11_sulfate_to_au_feed  \\\n",
       "date                                                                      \n",
       "2016-01-15 00:00:00                                        3520.337158    \n",
       "2016-01-15 01:00:00                                        3489.981857    \n",
       "2016-01-15 02:00:00                                        3502.359819    \n",
       "2016-01-15 03:00:00                                        3499.162912    \n",
       "2016-01-15 04:00:00                                        3506.679315    \n",
       "\n",
       "                     rougher.calculation.au_pb_ratio  rougher.input.feed_ag  \\\n",
       "date                                                                          \n",
       "2016-01-15 00:00:00                         2.838687               6.100378   \n",
       "2016-01-15 01:00:00                         2.858998               6.161113   \n",
       "2016-01-15 02:00:00                         2.945989               6.116455   \n",
       "2016-01-15 03:00:00                         3.002340               6.043309   \n",
       "2016-01-15 04:00:00                         3.169615               6.060915   \n",
       "\n",
       "                     rougher.input.feed_pb  rougher.input.feed_rate  \\\n",
       "date                                                                  \n",
       "2016-01-15 00:00:00               2.284912               523.546326   \n",
       "2016-01-15 01:00:00               2.266033               525.290581   \n",
       "2016-01-15 02:00:00               2.159622               530.026610   \n",
       "2016-01-15 03:00:00               2.037807               542.590390   \n",
       "2016-01-15 04:00:00               1.786875               540.531893   \n",
       "\n",
       "                     rougher.input.feed_size  rougher.input.feed_sol  \\\n",
       "date                                                                   \n",
       "2016-01-15 00:00:00                55.486599               36.808594   \n",
       "2016-01-15 01:00:00                57.278666               35.753385   \n",
       "2016-01-15 02:00:00                57.510649               35.971630   \n",
       "2016-01-15 03:00:00                57.792734               36.862241   \n",
       "2016-01-15 04:00:00                56.047189               34.347666   \n",
       "\n",
       "                     rougher.input.feed_au  rougher.input.floatbank10_sulfate  \\\n",
       "date                                                                            \n",
       "2016-01-15 00:00:00               6.486150                          11.986616   \n",
       "2016-01-15 01:00:00               6.478583                          11.971193   \n",
       "2016-01-15 02:00:00               6.362222                          11.920603   \n",
       "2016-01-15 03:00:00               6.118189                          11.630094   \n",
       "2016-01-15 04:00:00               5.663707                          10.957755   \n",
       "\n",
       "                     rougher.input.floatbank10_xanthate  \\\n",
       "date                                                      \n",
       "2016-01-15 00:00:00                            6.007990   \n",
       "2016-01-15 01:00:00                            6.005766   \n",
       "2016-01-15 02:00:00                            6.197377   \n",
       "2016-01-15 03:00:00                            6.203177   \n",
       "2016-01-15 04:00:00                            6.198826   \n",
       "\n",
       "                     rougher.input.floatbank11_sulfate  \\\n",
       "date                                                     \n",
       "2016-01-15 00:00:00                          11.836743   \n",
       "2016-01-15 01:00:00                          11.996163   \n",
       "2016-01-15 02:00:00                          11.920305   \n",
       "2016-01-15 03:00:00                          11.692450   \n",
       "2016-01-15 04:00:00                          10.960521   \n",
       "\n",
       "                     rougher.input.floatbank11_xanthate  \\\n",
       "date                                                      \n",
       "2016-01-15 00:00:00                            6.005818   \n",
       "2016-01-15 01:00:00                            6.012594   \n",
       "2016-01-15 02:00:00                            6.204633   \n",
       "2016-01-15 03:00:00                            6.196578   \n",
       "2016-01-15 04:00:00                            6.194897   \n",
       "\n",
       "                     rougher.output.concentrate_ag  \\\n",
       "date                                                 \n",
       "2016-01-15 00:00:00                      11.500771   \n",
       "2016-01-15 01:00:00                      11.615865   \n",
       "2016-01-15 02:00:00                      11.695753   \n",
       "2016-01-15 03:00:00                      11.915047   \n",
       "2016-01-15 04:00:00                      12.411054   \n",
       "\n",
       "                     rougher.output.concentrate_pb  \\\n",
       "date                                                 \n",
       "2016-01-15 00:00:00                       7.101074   \n",
       "2016-01-15 01:00:00                       7.278807   \n",
       "2016-01-15 02:00:00                       7.216833   \n",
       "2016-01-15 03:00:00                       7.175616   \n",
       "2016-01-15 04:00:00                       7.240205   \n",
       "\n",
       "                     rougher.output.concentrate_sol  \\\n",
       "date                                                  \n",
       "2016-01-15 00:00:00                       28.029297   \n",
       "2016-01-15 01:00:00                       28.067063   \n",
       "2016-01-15 02:00:00                       27.454037   \n",
       "2016-01-15 03:00:00                       27.341277   \n",
       "2016-01-15 04:00:00                       27.041023   \n",
       "\n",
       "                     rougher.output.concentrate_au  rougher.output.recovery  \\\n",
       "date                                                                          \n",
       "2016-01-15 00:00:00                      19.793808                87.107763   \n",
       "2016-01-15 01:00:00                      20.050975                86.843261   \n",
       "2016-01-15 02:00:00                      19.737170                86.842308   \n",
       "2016-01-15 03:00:00                      19.320810                87.226430   \n",
       "2016-01-15 04:00:00                      19.216101                86.688794   \n",
       "\n",
       "                     rougher.output.tail_ag  rougher.output.tail_pb  \\\n",
       "date                                                                  \n",
       "2016-01-15 00:00:00                5.008018                0.508728   \n",
       "2016-01-15 01:00:00                4.955378                0.536689   \n",
       "2016-01-15 02:00:00                4.843460                0.546416   \n",
       "2016-01-15 03:00:00                4.655288                0.542525   \n",
       "2016-01-15 04:00:00                4.552800                0.515438   \n",
       "\n",
       "                     rougher.output.tail_sol  rougher.output.tail_au  \\\n",
       "date                                                                   \n",
       "2016-01-15 00:00:00                19.154297                1.170244   \n",
       "2016-01-15 01:00:00                18.965234                1.184827   \n",
       "2016-01-15 02:00:00                18.808533                1.162562   \n",
       "2016-01-15 03:00:00                19.330200                1.079755   \n",
       "2016-01-15 04:00:00                19.267422                1.012642   \n",
       "\n",
       "                     rougher.state.floatbank10_a_air  \\\n",
       "date                                                   \n",
       "2016-01-15 00:00:00                       999.706909   \n",
       "2016-01-15 01:00:00                      1000.286398   \n",
       "2016-01-15 02:00:00                       999.719565   \n",
       "2016-01-15 03:00:00                       999.814770   \n",
       "2016-01-15 04:00:00                       999.678690   \n",
       "\n",
       "                     rougher.state.floatbank10_a_level  \\\n",
       "date                                                     \n",
       "2016-01-15 00:00:00                        -404.066986   \n",
       "2016-01-15 01:00:00                        -400.065196   \n",
       "2016-01-15 02:00:00                        -400.074028   \n",
       "2016-01-15 03:00:00                        -400.200179   \n",
       "2016-01-15 04:00:00                        -399.752729   \n",
       "\n",
       "                     rougher.state.floatbank10_b_air  \\\n",
       "date                                                   \n",
       "2016-01-15 00:00:00                      1603.011353   \n",
       "2016-01-15 01:00:00                      1600.754587   \n",
       "2016-01-15 02:00:00                      1599.337330   \n",
       "2016-01-15 03:00:00                      1600.059442   \n",
       "2016-01-15 04:00:00                      1600.208824   \n",
       "\n",
       "                     rougher.state.floatbank10_b_level  \\\n",
       "date                                                     \n",
       "2016-01-15 00:00:00                        -434.715027   \n",
       "2016-01-15 01:00:00                        -449.953435   \n",
       "2016-01-15 02:00:00                        -450.008530   \n",
       "2016-01-15 03:00:00                        -450.619948   \n",
       "2016-01-15 04:00:00                        -449.599614   \n",
       "\n",
       "                     rougher.state.floatbank10_c_air  \\\n",
       "date                                                   \n",
       "2016-01-15 00:00:00                      1602.375000   \n",
       "2016-01-15 01:00:00                      1600.479580   \n",
       "2016-01-15 02:00:00                      1599.672797   \n",
       "2016-01-15 03:00:00                      1600.012842   \n",
       "2016-01-15 04:00:00                      1600.357732   \n",
       "\n",
       "                     rougher.state.floatbank10_c_level  \\\n",
       "date                                                     \n",
       "2016-01-15 00:00:00                        -442.204468   \n",
       "2016-01-15 01:00:00                        -449.830646   \n",
       "2016-01-15 02:00:00                        -449.954491   \n",
       "2016-01-15 03:00:00                        -449.910497   \n",
       "2016-01-15 04:00:00                        -450.034364   \n",
       "\n",
       "                     rougher.state.floatbank10_d_air  \\\n",
       "date                                                   \n",
       "2016-01-15 00:00:00                      1598.937256   \n",
       "2016-01-15 01:00:00                      1600.527589   \n",
       "2016-01-15 02:00:00                      1599.849325   \n",
       "2016-01-15 03:00:00                      1597.725177   \n",
       "2016-01-15 04:00:00                      1599.759049   \n",
       "\n",
       "                     rougher.state.floatbank10_d_level  \\\n",
       "date                                                     \n",
       "2016-01-15 00:00:00                        -451.294128   \n",
       "2016-01-15 01:00:00                        -449.953649   \n",
       "2016-01-15 02:00:00                        -449.954185   \n",
       "2016-01-15 03:00:00                        -450.130127   \n",
       "2016-01-15 04:00:00                        -449.909799   \n",
       "\n",
       "                     rougher.state.floatbank10_e_air  \\\n",
       "date                                                   \n",
       "2016-01-15 00:00:00                      1404.472046   \n",
       "2016-01-15 01:00:00                      1399.227084   \n",
       "2016-01-15 02:00:00                      1399.180945   \n",
       "2016-01-15 03:00:00                      1400.943157   \n",
       "2016-01-15 04:00:00                      1401.560902   \n",
       "\n",
       "                     rougher.state.floatbank10_e_level  \\\n",
       "date                                                     \n",
       "2016-01-15 00:00:00                        -455.462982   \n",
       "2016-01-15 01:00:00                        -450.869848   \n",
       "2016-01-15 02:00:00                        -449.937588   \n",
       "2016-01-15 03:00:00                        -450.030142   \n",
       "2016-01-15 04:00:00                        -448.877187   \n",
       "\n",
       "                     rougher.state.floatbank10_f_air  \\\n",
       "date                                                   \n",
       "2016-01-15 00:00:00                      1416.354980   \n",
       "2016-01-15 01:00:00                      1399.719514   \n",
       "2016-01-15 02:00:00                      1400.316682   \n",
       "2016-01-15 03:00:00                      1400.234743   \n",
       "2016-01-15 04:00:00                      1401.160227   \n",
       "\n",
       "                     rougher.state.floatbank10_f_level  \\\n",
       "date                                                     \n",
       "2016-01-15 00:00:00                        -451.939636   \n",
       "2016-01-15 01:00:00                        -450.119001   \n",
       "2016-01-15 02:00:00                        -450.527147   \n",
       "2016-01-15 03:00:00                        -449.790835   \n",
       "2016-01-15 04:00:00                        -450.407128   \n",
       "\n",
       "                     secondary_cleaner.output.tail_ag  \\\n",
       "date                                                    \n",
       "2016-01-15 00:00:00                         14.500184   \n",
       "2016-01-15 01:00:00                         14.265474   \n",
       "2016-01-15 02:00:00                         14.115662   \n",
       "2016-01-15 03:00:00                         13.732071   \n",
       "2016-01-15 04:00:00                         14.079996   \n",
       "\n",
       "                     secondary_cleaner.output.tail_pb  \\\n",
       "date                                                    \n",
       "2016-01-15 00:00:00                          4.694824   \n",
       "2016-01-15 01:00:00                          4.592547   \n",
       "2016-01-15 02:00:00                          4.624775   \n",
       "2016-01-15 03:00:00                          4.482453   \n",
       "2016-01-15 04:00:00                          4.470737   \n",
       "\n",
       "                     secondary_cleaner.output.tail_sol  \\\n",
       "date                                                     \n",
       "2016-01-15 00:00:00                           8.764648   \n",
       "2016-01-15 01:00:00                           9.001494   \n",
       "2016-01-15 02:00:00                           8.842896   \n",
       "2016-01-15 03:00:00                           9.122857   \n",
       "2016-01-15 04:00:00                           8.871028   \n",
       "\n",
       "                     secondary_cleaner.output.tail_au  \\\n",
       "date                                                    \n",
       "2016-01-15 00:00:00                          2.606185   \n",
       "2016-01-15 01:00:00                          2.488248   \n",
       "2016-01-15 02:00:00                          2.458187   \n",
       "2016-01-15 03:00:00                          2.322062   \n",
       "2016-01-15 04:00:00                          2.330448   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank2_a_air  \\\n",
       "date                                                            \n",
       "2016-01-15 00:00:00                                 25.853109   \n",
       "2016-01-15 01:00:00                                 25.880539   \n",
       "2016-01-15 02:00:00                                 26.005245   \n",
       "2016-01-15 03:00:00                                 25.942508   \n",
       "2016-01-15 04:00:00                                 26.024787   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank2_a_level  \\\n",
       "date                                                              \n",
       "2016-01-15 00:00:00                                 -498.526489   \n",
       "2016-01-15 01:00:00                                 -499.989656   \n",
       "2016-01-15 02:00:00                                 -499.929616   \n",
       "2016-01-15 03:00:00                                 -499.176749   \n",
       "2016-01-15 04:00:00                                 -500.279091   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank2_b_air  \\\n",
       "date                                                            \n",
       "2016-01-15 00:00:00                                 23.893660   \n",
       "2016-01-15 01:00:00                                 23.889530   \n",
       "2016-01-15 02:00:00                                 23.886657   \n",
       "2016-01-15 03:00:00                                 23.955516   \n",
       "2016-01-15 04:00:00                                 23.955345   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank2_b_level  \\\n",
       "date                                                              \n",
       "2016-01-15 00:00:00                                 -501.406281   \n",
       "2016-01-15 01:00:00                                 -500.372428   \n",
       "2016-01-15 02:00:00                                 -499.951928   \n",
       "2016-01-15 03:00:00                                 -499.848796   \n",
       "2016-01-15 04:00:00                                 -500.593614   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank3_a_air  \\\n",
       "date                                                            \n",
       "2016-01-15 00:00:00                                 23.961798   \n",
       "2016-01-15 01:00:00                                 23.970550   \n",
       "2016-01-15 02:00:00                                 23.913535   \n",
       "2016-01-15 03:00:00                                 23.966838   \n",
       "2016-01-15 04:00:00                                 23.985703   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank3_a_level  \\\n",
       "date                                                              \n",
       "2016-01-15 00:00:00                                 -495.262817   \n",
       "2016-01-15 01:00:00                                 -500.085473   \n",
       "2016-01-15 02:00:00                                 -499.442343   \n",
       "2016-01-15 03:00:00                                 -500.008812   \n",
       "2016-01-15 04:00:00                                 -500.083811   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank3_b_air  \\\n",
       "date                                                            \n",
       "2016-01-15 00:00:00                                 21.940409   \n",
       "2016-01-15 01:00:00                                 22.085714   \n",
       "2016-01-15 02:00:00                                 23.957717   \n",
       "2016-01-15 03:00:00                                 23.954443   \n",
       "2016-01-15 04:00:00                                 23.958945   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank3_b_level  \\\n",
       "date                                                              \n",
       "2016-01-15 00:00:00                                 -499.340973   \n",
       "2016-01-15 01:00:00                                 -499.446897   \n",
       "2016-01-15 02:00:00                                 -499.901982   \n",
       "2016-01-15 03:00:00                                 -499.944710   \n",
       "2016-01-15 04:00:00                                 -499.990309   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank4_a_air  \\\n",
       "date                                                            \n",
       "2016-01-15 00:00:00                                 14.016835   \n",
       "2016-01-15 01:00:00                                 13.992281   \n",
       "2016-01-15 02:00:00                                 14.015015   \n",
       "2016-01-15 03:00:00                                 14.036510   \n",
       "2016-01-15 04:00:00                                 14.027298   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank4_a_level  \\\n",
       "date                                                              \n",
       "2016-01-15 00:00:00                                 -502.488007   \n",
       "2016-01-15 01:00:00                                 -505.503262   \n",
       "2016-01-15 02:00:00                                 -502.520901   \n",
       "2016-01-15 03:00:00                                 -500.857308   \n",
       "2016-01-15 04:00:00                                 -499.838632   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank4_b_air  \\\n",
       "date                                                            \n",
       "2016-01-15 00:00:00                                 12.099931   \n",
       "2016-01-15 01:00:00                                 11.950531   \n",
       "2016-01-15 02:00:00                                 11.912783   \n",
       "2016-01-15 03:00:00                                 11.999550   \n",
       "2016-01-15 04:00:00                                 11.953070   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank4_b_level  \\\n",
       "date                                                              \n",
       "2016-01-15 00:00:00                                 -504.715942   \n",
       "2016-01-15 01:00:00                                 -501.331529   \n",
       "2016-01-15 02:00:00                                 -501.133383   \n",
       "2016-01-15 03:00:00                                 -501.193686   \n",
       "2016-01-15 04:00:00                                 -501.053894   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank5_a_air  \\\n",
       "date                                                            \n",
       "2016-01-15 00:00:00                                  9.925633   \n",
       "2016-01-15 01:00:00                                 10.039245   \n",
       "2016-01-15 02:00:00                                 10.070913   \n",
       "2016-01-15 03:00:00                                  9.970366   \n",
       "2016-01-15 04:00:00                                  9.925709   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank5_a_level  \\\n",
       "date                                                              \n",
       "2016-01-15 00:00:00                                 -498.310211   \n",
       "2016-01-15 01:00:00                                 -500.169983   \n",
       "2016-01-15 02:00:00                                 -500.129135   \n",
       "2016-01-15 03:00:00                                 -499.201640   \n",
       "2016-01-15 04:00:00                                 -501.686727   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank5_b_air  \\\n",
       "date                                                            \n",
       "2016-01-15 00:00:00                                  8.079666   \n",
       "2016-01-15 01:00:00                                  7.984757   \n",
       "2016-01-15 02:00:00                                  8.013877   \n",
       "2016-01-15 03:00:00                                  7.977324   \n",
       "2016-01-15 04:00:00                                  7.894242   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank5_b_level  \\\n",
       "date                                                              \n",
       "2016-01-15 00:00:00                                 -500.470978   \n",
       "2016-01-15 01:00:00                                 -500.582168   \n",
       "2016-01-15 02:00:00                                 -500.517572   \n",
       "2016-01-15 03:00:00                                 -500.255908   \n",
       "2016-01-15 04:00:00                                 -500.356035   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank6_a_air  \\\n",
       "date                                                            \n",
       "2016-01-15 00:00:00                                 14.151341   \n",
       "2016-01-15 01:00:00                                 13.998353   \n",
       "2016-01-15 02:00:00                                 14.028663   \n",
       "2016-01-15 03:00:00                                 14.005551   \n",
       "2016-01-15 04:00:00                                 13.996647   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank6_a_level  \n",
       "date                                                             \n",
       "2016-01-15 00:00:00                                 -605.841980  \n",
       "2016-01-15 01:00:00                                 -599.787184  \n",
       "2016-01-15 02:00:00                                 -601.427363  \n",
       "2016-01-15 03:00:00                                 -599.996129  \n",
       "2016-01-15 04:00:00                                 -601.496691  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>final.output.concentrate_ag</th>\n",
       "      <th>final.output.concentrate_pb</th>\n",
       "      <th>final.output.concentrate_sol</th>\n",
       "      <th>final.output.concentrate_au</th>\n",
       "      <th>final.output.recovery</th>\n",
       "      <th>final.output.tail_ag</th>\n",
       "      <th>final.output.tail_pb</th>\n",
       "      <th>final.output.tail_sol</th>\n",
       "      <th>final.output.tail_au</th>\n",
       "      <th>primary_cleaner.input.sulfate</th>\n",
       "      <th>primary_cleaner.input.depressant</th>\n",
       "      <th>primary_cleaner.input.feed_size</th>\n",
       "      <th>primary_cleaner.input.xanthate</th>\n",
       "      <th>primary_cleaner.output.concentrate_ag</th>\n",
       "      <th>primary_cleaner.output.concentrate_pb</th>\n",
       "      <th>primary_cleaner.output.concentrate_sol</th>\n",
       "      <th>primary_cleaner.output.concentrate_au</th>\n",
       "      <th>primary_cleaner.output.tail_ag</th>\n",
       "      <th>primary_cleaner.output.tail_pb</th>\n",
       "      <th>primary_cleaner.output.tail_sol</th>\n",
       "      <th>primary_cleaner.output.tail_au</th>\n",
       "      <th>primary_cleaner.state.floatbank8_a_air</th>\n",
       "      <th>primary_cleaner.state.floatbank8_a_level</th>\n",
       "      <th>primary_cleaner.state.floatbank8_b_air</th>\n",
       "      <th>primary_cleaner.state.floatbank8_b_level</th>\n",
       "      <th>primary_cleaner.state.floatbank8_c_air</th>\n",
       "      <th>primary_cleaner.state.floatbank8_c_level</th>\n",
       "      <th>primary_cleaner.state.floatbank8_d_air</th>\n",
       "      <th>primary_cleaner.state.floatbank8_d_level</th>\n",
       "      <th>rougher.calculation.sulfate_to_au_concentrate</th>\n",
       "      <th>rougher.calculation.floatbank10_sulfate_to_au_feed</th>\n",
       "      <th>rougher.calculation.floatbank11_sulfate_to_au_feed</th>\n",
       "      <th>rougher.calculation.au_pb_ratio</th>\n",
       "      <th>rougher.input.feed_ag</th>\n",
       "      <th>rougher.input.feed_pb</th>\n",
       "      <th>rougher.input.feed_rate</th>\n",
       "      <th>rougher.input.feed_size</th>\n",
       "      <th>rougher.input.feed_sol</th>\n",
       "      <th>rougher.input.feed_au</th>\n",
       "      <th>rougher.input.floatbank10_sulfate</th>\n",
       "      <th>rougher.input.floatbank10_xanthate</th>\n",
       "      <th>rougher.input.floatbank11_sulfate</th>\n",
       "      <th>rougher.input.floatbank11_xanthate</th>\n",
       "      <th>rougher.output.concentrate_ag</th>\n",
       "      <th>rougher.output.concentrate_pb</th>\n",
       "      <th>rougher.output.concentrate_sol</th>\n",
       "      <th>rougher.output.concentrate_au</th>\n",
       "      <th>rougher.output.recovery</th>\n",
       "      <th>rougher.output.tail_ag</th>\n",
       "      <th>rougher.output.tail_pb</th>\n",
       "      <th>rougher.output.tail_sol</th>\n",
       "      <th>rougher.output.tail_au</th>\n",
       "      <th>rougher.state.floatbank10_a_air</th>\n",
       "      <th>rougher.state.floatbank10_a_level</th>\n",
       "      <th>rougher.state.floatbank10_b_air</th>\n",
       "      <th>rougher.state.floatbank10_b_level</th>\n",
       "      <th>rougher.state.floatbank10_c_air</th>\n",
       "      <th>rougher.state.floatbank10_c_level</th>\n",
       "      <th>rougher.state.floatbank10_d_air</th>\n",
       "      <th>rougher.state.floatbank10_d_level</th>\n",
       "      <th>rougher.state.floatbank10_e_air</th>\n",
       "      <th>rougher.state.floatbank10_e_level</th>\n",
       "      <th>rougher.state.floatbank10_f_air</th>\n",
       "      <th>rougher.state.floatbank10_f_level</th>\n",
       "      <th>secondary_cleaner.output.tail_ag</th>\n",
       "      <th>secondary_cleaner.output.tail_pb</th>\n",
       "      <th>secondary_cleaner.output.tail_sol</th>\n",
       "      <th>secondary_cleaner.output.tail_au</th>\n",
       "      <th>secondary_cleaner.state.floatbank2_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank2_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank2_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank2_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank3_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank3_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank3_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank3_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>16788.000000</td>\n",
       "      <td>16788.000000</td>\n",
       "      <td>16490.000000</td>\n",
       "      <td>16789.000000</td>\n",
       "      <td>15339.000000</td>\n",
       "      <td>16794.000000</td>\n",
       "      <td>16677.000000</td>\n",
       "      <td>16715.000000</td>\n",
       "      <td>16794.000000</td>\n",
       "      <td>15553.000000</td>\n",
       "      <td>15598.000000</td>\n",
       "      <td>16860.000000</td>\n",
       "      <td>15875.000000</td>\n",
       "      <td>16778.000000</td>\n",
       "      <td>16502.000000</td>\n",
       "      <td>16224.000000</td>\n",
       "      <td>16778.000000</td>\n",
       "      <td>16777.000000</td>\n",
       "      <td>16761.000000</td>\n",
       "      <td>16579.000000</td>\n",
       "      <td>16777.000000</td>\n",
       "      <td>16820.000000</td>\n",
       "      <td>16827.000000</td>\n",
       "      <td>16820.000000</td>\n",
       "      <td>16833.000000</td>\n",
       "      <td>16822.000000</td>\n",
       "      <td>16833.000000</td>\n",
       "      <td>16821.000000</td>\n",
       "      <td>16833.000000</td>\n",
       "      <td>1.683300e+04</td>\n",
       "      <td>1.683300e+04</td>\n",
       "      <td>1.683300e+04</td>\n",
       "      <td>15618.000000</td>\n",
       "      <td>16778.000000</td>\n",
       "      <td>16632.000000</td>\n",
       "      <td>16347.000000</td>\n",
       "      <td>16443.000000</td>\n",
       "      <td>16568.000000</td>\n",
       "      <td>16777.000000</td>\n",
       "      <td>15816.000000</td>\n",
       "      <td>16514.000000</td>\n",
       "      <td>16237.000000</td>\n",
       "      <td>14956.000000</td>\n",
       "      <td>16778.000000</td>\n",
       "      <td>16778.000000</td>\n",
       "      <td>16698.000000</td>\n",
       "      <td>16778.000000</td>\n",
       "      <td>14287.000000</td>\n",
       "      <td>14610.000000</td>\n",
       "      <td>16778.000000</td>\n",
       "      <td>14611.000000</td>\n",
       "      <td>14611.000000</td>\n",
       "      <td>16807.000000</td>\n",
       "      <td>16807.000000</td>\n",
       "      <td>16807.000000</td>\n",
       "      <td>16807.000000</td>\n",
       "      <td>16807.000000</td>\n",
       "      <td>16814.000000</td>\n",
       "      <td>16802.000000</td>\n",
       "      <td>16809.000000</td>\n",
       "      <td>16257.000000</td>\n",
       "      <td>16809.000000</td>\n",
       "      <td>16802.000000</td>\n",
       "      <td>16802.000000</td>\n",
       "      <td>16776.000000</td>\n",
       "      <td>16764.000000</td>\n",
       "      <td>14874.000000</td>\n",
       "      <td>16778.000000</td>\n",
       "      <td>16497.000000</td>\n",
       "      <td>16751.000000</td>\n",
       "      <td>16705.000000</td>\n",
       "      <td>16748.000000</td>\n",
       "      <td>16763.000000</td>\n",
       "      <td>16747.000000</td>\n",
       "      <td>16752.000000</td>\n",
       "      <td>16750.000000</td>\n",
       "      <td>16731.000000</td>\n",
       "      <td>16747.000000</td>\n",
       "      <td>16768.000000</td>\n",
       "      <td>16767.000000</td>\n",
       "      <td>16775.000000</td>\n",
       "      <td>16775.000000</td>\n",
       "      <td>16775.000000</td>\n",
       "      <td>16776.000000</td>\n",
       "      <td>16757.000000</td>\n",
       "      <td>16775.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>4.716907</td>\n",
       "      <td>9.113559</td>\n",
       "      <td>8.301123</td>\n",
       "      <td>39.467217</td>\n",
       "      <td>67.213166</td>\n",
       "      <td>8.757048</td>\n",
       "      <td>2.360327</td>\n",
       "      <td>9.303932</td>\n",
       "      <td>2.687512</td>\n",
       "      <td>129.479789</td>\n",
       "      <td>8.625021</td>\n",
       "      <td>7.297285</td>\n",
       "      <td>0.843812</td>\n",
       "      <td>7.422640</td>\n",
       "      <td>8.620294</td>\n",
       "      <td>9.171453</td>\n",
       "      <td>29.164596</td>\n",
       "      <td>14.916716</td>\n",
       "      <td>3.165957</td>\n",
       "      <td>6.746209</td>\n",
       "      <td>3.590534</td>\n",
       "      <td>1521.291691</td>\n",
       "      <td>-505.811103</td>\n",
       "      <td>1523.880015</td>\n",
       "      <td>-507.346105</td>\n",
       "      <td>1514.351131</td>\n",
       "      <td>-507.094373</td>\n",
       "      <td>1468.599116</td>\n",
       "      <td>-501.835392</td>\n",
       "      <td>-1.137202e+04</td>\n",
       "      <td>3.784836e+03</td>\n",
       "      <td>2.017512e+03</td>\n",
       "      <td>2.327953</td>\n",
       "      <td>7.832347</td>\n",
       "      <td>3.218931</td>\n",
       "      <td>435.963431</td>\n",
       "      <td>58.676444</td>\n",
       "      <td>32.636662</td>\n",
       "      <td>7.170717</td>\n",
       "      <td>11.198142</td>\n",
       "      <td>5.366609</td>\n",
       "      <td>10.550080</td>\n",
       "      <td>5.792970</td>\n",
       "      <td>10.580475</td>\n",
       "      <td>6.880322</td>\n",
       "      <td>25.388996</td>\n",
       "      <td>17.401541</td>\n",
       "      <td>82.394201</td>\n",
       "      <td>5.566579</td>\n",
       "      <td>0.596493</td>\n",
       "      <td>17.865309</td>\n",
       "      <td>1.763930</td>\n",
       "      <td>1077.201302</td>\n",
       "      <td>-384.109359</td>\n",
       "      <td>1252.285954</td>\n",
       "      <td>-475.443395</td>\n",
       "      <td>1229.567445</td>\n",
       "      <td>-474.713811</td>\n",
       "      <td>1146.785992</td>\n",
       "      <td>-476.180417</td>\n",
       "      <td>1033.886871</td>\n",
       "      <td>-475.545309</td>\n",
       "      <td>983.384202</td>\n",
       "      <td>-475.980531</td>\n",
       "      <td>13.014971</td>\n",
       "      <td>5.320902</td>\n",
       "      <td>6.181742</td>\n",
       "      <td>3.935796</td>\n",
       "      <td>28.134535</td>\n",
       "      <td>-517.777116</td>\n",
       "      <td>23.619503</td>\n",
       "      <td>-519.562134</td>\n",
       "      <td>27.913358</td>\n",
       "      <td>-493.470026</td>\n",
       "      <td>21.663507</td>\n",
       "      <td>-501.521561</td>\n",
       "      <td>19.101874</td>\n",
       "      <td>-494.164481</td>\n",
       "      <td>14.778164</td>\n",
       "      <td>-476.600082</td>\n",
       "      <td>15.779488</td>\n",
       "      <td>-500.230146</td>\n",
       "      <td>12.377241</td>\n",
       "      <td>-498.956257</td>\n",
       "      <td>18.429208</td>\n",
       "      <td>-521.801826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>2.096718</td>\n",
       "      <td>3.389495</td>\n",
       "      <td>3.825760</td>\n",
       "      <td>13.917227</td>\n",
       "      <td>11.960446</td>\n",
       "      <td>3.634103</td>\n",
       "      <td>1.215576</td>\n",
       "      <td>4.263208</td>\n",
       "      <td>1.272757</td>\n",
       "      <td>45.386931</td>\n",
       "      <td>3.698633</td>\n",
       "      <td>0.619166</td>\n",
       "      <td>0.419098</td>\n",
       "      <td>3.105117</td>\n",
       "      <td>3.815415</td>\n",
       "      <td>4.901822</td>\n",
       "      <td>10.983718</td>\n",
       "      <td>5.940559</td>\n",
       "      <td>1.747066</td>\n",
       "      <td>2.999423</td>\n",
       "      <td>2.028115</td>\n",
       "      <td>380.064453</td>\n",
       "      <td>77.640285</td>\n",
       "      <td>381.245324</td>\n",
       "      <td>79.830738</td>\n",
       "      <td>396.493106</td>\n",
       "      <td>78.823157</td>\n",
       "      <td>444.329353</td>\n",
       "      <td>87.257010</td>\n",
       "      <td>5.754320e+06</td>\n",
       "      <td>7.088720e+04</td>\n",
       "      <td>4.836941e+04</td>\n",
       "      <td>0.899125</td>\n",
       "      <td>3.139014</td>\n",
       "      <td>1.477263</td>\n",
       "      <td>156.592162</td>\n",
       "      <td>23.922591</td>\n",
       "      <td>11.661981</td>\n",
       "      <td>3.002113</td>\n",
       "      <td>3.964080</td>\n",
       "      <td>1.940766</td>\n",
       "      <td>4.560428</td>\n",
       "      <td>1.294180</td>\n",
       "      <td>4.451490</td>\n",
       "      <td>2.928757</td>\n",
       "      <td>10.400528</td>\n",
       "      <td>6.921875</td>\n",
       "      <td>15.096808</td>\n",
       "      <td>1.045456</td>\n",
       "      <td>0.330819</td>\n",
       "      <td>3.455588</td>\n",
       "      <td>0.731518</td>\n",
       "      <td>291.800894</td>\n",
       "      <td>112.408685</td>\n",
       "      <td>331.076715</td>\n",
       "      <td>70.051458</td>\n",
       "      <td>344.840251</td>\n",
       "      <td>67.065122</td>\n",
       "      <td>327.311834</td>\n",
       "      <td>64.118377</td>\n",
       "      <td>293.456164</td>\n",
       "      <td>68.781915</td>\n",
       "      <td>275.426261</td>\n",
       "      <td>70.132801</td>\n",
       "      <td>5.960323</td>\n",
       "      <td>3.191103</td>\n",
       "      <td>4.460622</td>\n",
       "      <td>2.675785</td>\n",
       "      <td>8.320744</td>\n",
       "      <td>89.551883</td>\n",
       "      <td>8.144236</td>\n",
       "      <td>89.374849</td>\n",
       "      <td>8.170477</td>\n",
       "      <td>85.296428</td>\n",
       "      <td>6.738952</td>\n",
       "      <td>74.723250</td>\n",
       "      <td>6.883163</td>\n",
       "      <td>84.803334</td>\n",
       "      <td>5.999149</td>\n",
       "      <td>89.381172</td>\n",
       "      <td>6.834703</td>\n",
       "      <td>76.983542</td>\n",
       "      <td>6.219989</td>\n",
       "      <td>82.146207</td>\n",
       "      <td>6.958294</td>\n",
       "      <td>77.170888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.080000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-800.037388</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-800.409770</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-800.362914</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-800.166222</td>\n",
       "      <td>-6.643891e+08</td>\n",
       "      <td>-2.023459e+06</td>\n",
       "      <td>-4.014502e+06</td>\n",
       "      <td>-0.010548</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>9.659576</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.594562</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.014648</td>\n",
       "      <td>0.020676</td>\n",
       "      <td>-0.345168</td>\n",
       "      <td>-658.223080</td>\n",
       "      <td>-0.759568</td>\n",
       "      <td>-650.437575</td>\n",
       "      <td>-0.378521</td>\n",
       "      <td>-650.151190</td>\n",
       "      <td>-1.197435</td>\n",
       "      <td>-649.300693</td>\n",
       "      <td>-2.163619</td>\n",
       "      <td>-650.196204</td>\n",
       "      <td>-2.636171</td>\n",
       "      <td>-650.072915</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-799.841677</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-799.967956</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-799.926254</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-809.421923</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-799.920713</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-800.021781</td>\n",
       "      <td>-0.423260</td>\n",
       "      <td>-799.741097</td>\n",
       "      <td>0.427084</td>\n",
       "      <td>-800.258209</td>\n",
       "      <td>0.024270</td>\n",
       "      <td>-810.473526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>3.971262</td>\n",
       "      <td>8.825748</td>\n",
       "      <td>6.939185</td>\n",
       "      <td>42.055722</td>\n",
       "      <td>62.625685</td>\n",
       "      <td>7.610544</td>\n",
       "      <td>1.641604</td>\n",
       "      <td>7.870275</td>\n",
       "      <td>2.172953</td>\n",
       "      <td>103.064021</td>\n",
       "      <td>6.004886</td>\n",
       "      <td>6.940000</td>\n",
       "      <td>0.568934</td>\n",
       "      <td>6.612584</td>\n",
       "      <td>7.146794</td>\n",
       "      <td>6.477601</td>\n",
       "      <td>29.631129</td>\n",
       "      <td>13.283872</td>\n",
       "      <td>2.162262</td>\n",
       "      <td>5.530088</td>\n",
       "      <td>2.664223</td>\n",
       "      <td>1503.883158</td>\n",
       "      <td>-500.414946</td>\n",
       "      <td>1502.956269</td>\n",
       "      <td>-500.491143</td>\n",
       "      <td>1500.685486</td>\n",
       "      <td>-500.859360</td>\n",
       "      <td>1418.756530</td>\n",
       "      <td>-500.643616</td>\n",
       "      <td>3.898865e+04</td>\n",
       "      <td>2.502308e+03</td>\n",
       "      <td>2.498158e+03</td>\n",
       "      <td>1.979046</td>\n",
       "      <td>6.889426</td>\n",
       "      <td>2.551143</td>\n",
       "      <td>396.667936</td>\n",
       "      <td>47.575879</td>\n",
       "      <td>32.170423</td>\n",
       "      <td>6.203083</td>\n",
       "      <td>9.366078</td>\n",
       "      <td>4.802531</td>\n",
       "      <td>8.903492</td>\n",
       "      <td>5.097006</td>\n",
       "      <td>9.970467</td>\n",
       "      <td>6.346373</td>\n",
       "      <td>24.792988</td>\n",
       "      <td>17.443663</td>\n",
       "      <td>79.818372</td>\n",
       "      <td>4.918713</td>\n",
       "      <td>0.413711</td>\n",
       "      <td>15.646980</td>\n",
       "      <td>1.311610</td>\n",
       "      <td>999.636979</td>\n",
       "      <td>-499.908427</td>\n",
       "      <td>1196.415722</td>\n",
       "      <td>-500.284895</td>\n",
       "      <td>1100.084257</td>\n",
       "      <td>-500.329171</td>\n",
       "      <td>1044.848978</td>\n",
       "      <td>-500.594910</td>\n",
       "      <td>949.615568</td>\n",
       "      <td>-500.395113</td>\n",
       "      <td>900.029475</td>\n",
       "      <td>-500.738649</td>\n",
       "      <td>11.367185</td>\n",
       "      <td>3.466609</td>\n",
       "      <td>2.045824</td>\n",
       "      <td>2.820390</td>\n",
       "      <td>25.045244</td>\n",
       "      <td>-500.333800</td>\n",
       "      <td>20.110141</td>\n",
       "      <td>-500.348178</td>\n",
       "      <td>24.972364</td>\n",
       "      <td>-500.236661</td>\n",
       "      <td>19.007706</td>\n",
       "      <td>-500.141064</td>\n",
       "      <td>14.508299</td>\n",
       "      <td>-500.837689</td>\n",
       "      <td>10.741388</td>\n",
       "      <td>-500.269182</td>\n",
       "      <td>10.977713</td>\n",
       "      <td>-500.530594</td>\n",
       "      <td>8.925586</td>\n",
       "      <td>-500.147603</td>\n",
       "      <td>13.977626</td>\n",
       "      <td>-501.080595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>4.869346</td>\n",
       "      <td>10.065316</td>\n",
       "      <td>8.557228</td>\n",
       "      <td>44.498874</td>\n",
       "      <td>67.644601</td>\n",
       "      <td>9.220393</td>\n",
       "      <td>2.453690</td>\n",
       "      <td>10.021968</td>\n",
       "      <td>2.781132</td>\n",
       "      <td>131.783108</td>\n",
       "      <td>8.016531</td>\n",
       "      <td>7.285000</td>\n",
       "      <td>0.844271</td>\n",
       "      <td>7.997143</td>\n",
       "      <td>9.618910</td>\n",
       "      <td>9.295954</td>\n",
       "      <td>32.648867</td>\n",
       "      <td>15.554321</td>\n",
       "      <td>3.082745</td>\n",
       "      <td>7.380058</td>\n",
       "      <td>3.404941</td>\n",
       "      <td>1601.139322</td>\n",
       "      <td>-499.951772</td>\n",
       "      <td>1601.125754</td>\n",
       "      <td>-499.985447</td>\n",
       "      <td>1600.908740</td>\n",
       "      <td>-499.950634</td>\n",
       "      <td>1599.709469</td>\n",
       "      <td>-499.908024</td>\n",
       "      <td>4.316793e+04</td>\n",
       "      <td>2.854641e+03</td>\n",
       "      <td>2.815062e+03</td>\n",
       "      <td>2.229144</td>\n",
       "      <td>7.938193</td>\n",
       "      <td>3.297050</td>\n",
       "      <td>488.629971</td>\n",
       "      <td>54.104257</td>\n",
       "      <td>36.354079</td>\n",
       "      <td>7.443530</td>\n",
       "      <td>11.179351</td>\n",
       "      <td>5.767015</td>\n",
       "      <td>11.001027</td>\n",
       "      <td>5.995942</td>\n",
       "      <td>11.459862</td>\n",
       "      <td>7.583773</td>\n",
       "      <td>28.797785</td>\n",
       "      <td>19.644663</td>\n",
       "      <td>85.235997</td>\n",
       "      <td>5.718038</td>\n",
       "      <td>0.594959</td>\n",
       "      <td>17.996971</td>\n",
       "      <td>1.747907</td>\n",
       "      <td>1001.310190</td>\n",
       "      <td>-300.233403</td>\n",
       "      <td>1299.649429</td>\n",
       "      <td>-499.837368</td>\n",
       "      <td>1298.324087</td>\n",
       "      <td>-499.775884</td>\n",
       "      <td>1199.211289</td>\n",
       "      <td>-499.640478</td>\n",
       "      <td>1049.693712</td>\n",
       "      <td>-499.724295</td>\n",
       "      <td>999.828611</td>\n",
       "      <td>-499.576077</td>\n",
       "      <td>14.773117</td>\n",
       "      <td>5.164745</td>\n",
       "      <td>6.194420</td>\n",
       "      <td>3.863798</td>\n",
       "      <td>29.988519</td>\n",
       "      <td>-499.986132</td>\n",
       "      <td>26.981647</td>\n",
       "      <td>-500.037663</td>\n",
       "      <td>27.999437</td>\n",
       "      <td>-499.908346</td>\n",
       "      <td>21.996298</td>\n",
       "      <td>-499.980536</td>\n",
       "      <td>19.986958</td>\n",
       "      <td>-499.778379</td>\n",
       "      <td>14.943933</td>\n",
       "      <td>-499.593286</td>\n",
       "      <td>15.998340</td>\n",
       "      <td>-499.784231</td>\n",
       "      <td>11.092839</td>\n",
       "      <td>-499.933330</td>\n",
       "      <td>18.034960</td>\n",
       "      <td>-500.109898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>5.821176</td>\n",
       "      <td>11.054809</td>\n",
       "      <td>10.289741</td>\n",
       "      <td>45.976222</td>\n",
       "      <td>72.824595</td>\n",
       "      <td>10.971110</td>\n",
       "      <td>3.192404</td>\n",
       "      <td>11.648573</td>\n",
       "      <td>3.416936</td>\n",
       "      <td>159.539839</td>\n",
       "      <td>11.437819</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>1.097080</td>\n",
       "      <td>9.399463</td>\n",
       "      <td>11.131134</td>\n",
       "      <td>12.747802</td>\n",
       "      <td>35.011318</td>\n",
       "      <td>18.310868</td>\n",
       "      <td>4.190997</td>\n",
       "      <td>8.728101</td>\n",
       "      <td>4.467152</td>\n",
       "      <td>1699.304472</td>\n",
       "      <td>-499.412584</td>\n",
       "      <td>1699.997294</td>\n",
       "      <td>-499.436173</td>\n",
       "      <td>1700.221778</td>\n",
       "      <td>-498.901452</td>\n",
       "      <td>1699.222198</td>\n",
       "      <td>-498.716100</td>\n",
       "      <td>4.781106e+04</td>\n",
       "      <td>3.644408e+03</td>\n",
       "      <td>3.511394e+03</td>\n",
       "      <td>2.637776</td>\n",
       "      <td>9.829857</td>\n",
       "      <td>4.181842</td>\n",
       "      <td>540.548376</td>\n",
       "      <td>65.051064</td>\n",
       "      <td>39.043343</td>\n",
       "      <td>8.965193</td>\n",
       "      <td>13.203315</td>\n",
       "      <td>6.501621</td>\n",
       "      <td>13.004085</td>\n",
       "      <td>6.695277</td>\n",
       "      <td>13.280031</td>\n",
       "      <td>8.503498</td>\n",
       "      <td>31.542677</td>\n",
       "      <td>21.224486</td>\n",
       "      <td>90.131691</td>\n",
       "      <td>6.308490</td>\n",
       "      <td>0.779544</td>\n",
       "      <td>19.933255</td>\n",
       "      <td>2.190839</td>\n",
       "      <td>1299.577654</td>\n",
       "      <td>-299.976784</td>\n",
       "      <td>1432.326657</td>\n",
       "      <td>-400.865979</td>\n",
       "      <td>1448.560325</td>\n",
       "      <td>-401.277897</td>\n",
       "      <td>1307.829439</td>\n",
       "      <td>-402.354963</td>\n",
       "      <td>1199.711798</td>\n",
       "      <td>-401.349388</td>\n",
       "      <td>1099.734617</td>\n",
       "      <td>-402.026493</td>\n",
       "      <td>17.134159</td>\n",
       "      <td>7.562139</td>\n",
       "      <td>10.252499</td>\n",
       "      <td>4.845187</td>\n",
       "      <td>34.771800</td>\n",
       "      <td>-499.620568</td>\n",
       "      <td>28.094066</td>\n",
       "      <td>-499.780945</td>\n",
       "      <td>34.967470</td>\n",
       "      <td>-495.885262</td>\n",
       "      <td>25.071338</td>\n",
       "      <td>-499.778014</td>\n",
       "      <td>24.983961</td>\n",
       "      <td>-494.648754</td>\n",
       "      <td>20.023751</td>\n",
       "      <td>-400.137948</td>\n",
       "      <td>20.000701</td>\n",
       "      <td>-496.531781</td>\n",
       "      <td>15.979467</td>\n",
       "      <td>-498.418000</td>\n",
       "      <td>24.984992</td>\n",
       "      <td>-499.565540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>16.001945</td>\n",
       "      <td>17.031899</td>\n",
       "      <td>18.124851</td>\n",
       "      <td>53.611374</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>19.552149</td>\n",
       "      <td>6.086532</td>\n",
       "      <td>22.317730</td>\n",
       "      <td>9.789625</td>\n",
       "      <td>251.999948</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>10.470000</td>\n",
       "      <td>6.027907</td>\n",
       "      <td>16.081632</td>\n",
       "      <td>17.081278</td>\n",
       "      <td>22.280845</td>\n",
       "      <td>45.933934</td>\n",
       "      <td>29.459575</td>\n",
       "      <td>9.634565</td>\n",
       "      <td>20.620833</td>\n",
       "      <td>18.528821</td>\n",
       "      <td>2079.531340</td>\n",
       "      <td>-330.134203</td>\n",
       "      <td>2361.414084</td>\n",
       "      <td>-336.237097</td>\n",
       "      <td>2042.072583</td>\n",
       "      <td>-221.857683</td>\n",
       "      <td>2398.903635</td>\n",
       "      <td>-20.654343</td>\n",
       "      <td>8.332404e+07</td>\n",
       "      <td>6.211433e+06</td>\n",
       "      <td>1.537564e+06</td>\n",
       "      <td>39.379272</td>\n",
       "      <td>14.869652</td>\n",
       "      <td>7.142594</td>\n",
       "      <td>717.508837</td>\n",
       "      <td>484.967466</td>\n",
       "      <td>48.363177</td>\n",
       "      <td>14.093363</td>\n",
       "      <td>36.118275</td>\n",
       "      <td>31.493331</td>\n",
       "      <td>37.980648</td>\n",
       "      <td>9.698407</td>\n",
       "      <td>24.480271</td>\n",
       "      <td>18.394042</td>\n",
       "      <td>40.089993</td>\n",
       "      <td>28.522811</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.719177</td>\n",
       "      <td>3.778064</td>\n",
       "      <td>66.117188</td>\n",
       "      <td>9.688980</td>\n",
       "      <td>1521.980372</td>\n",
       "      <td>-19.383282</td>\n",
       "      <td>1809.194008</td>\n",
       "      <td>9.457822</td>\n",
       "      <td>2499.129174</td>\n",
       "      <td>-10.242658</td>\n",
       "      <td>2077.222699</td>\n",
       "      <td>21.365035</td>\n",
       "      <td>2330.310425</td>\n",
       "      <td>-2.406020</td>\n",
       "      <td>1950.493751</td>\n",
       "      <td>-21.745268</td>\n",
       "      <td>23.264738</td>\n",
       "      <td>17.042505</td>\n",
       "      <td>33.058594</td>\n",
       "      <td>26.811643</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>-127.875565</td>\n",
       "      <td>51.093216</td>\n",
       "      <td>-212.064665</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>-191.678810</td>\n",
       "      <td>39.487633</td>\n",
       "      <td>-139.771483</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>-127.692333</td>\n",
       "      <td>28.003828</td>\n",
       "      <td>-71.472472</td>\n",
       "      <td>63.116298</td>\n",
       "      <td>-275.073125</td>\n",
       "      <td>39.846228</td>\n",
       "      <td>-120.190931</td>\n",
       "      <td>54.876806</td>\n",
       "      <td>-39.784927</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       final.output.concentrate_ag  final.output.concentrate_pb  \\\n",
       "count                 16788.000000                 16788.000000   \n",
       "mean                      4.716907                     9.113559   \n",
       "std                       2.096718                     3.389495   \n",
       "min                       0.000000                     0.000000   \n",
       "25%                       3.971262                     8.825748   \n",
       "50%                       4.869346                    10.065316   \n",
       "75%                       5.821176                    11.054809   \n",
       "max                      16.001945                    17.031899   \n",
       "\n",
       "       final.output.concentrate_sol  final.output.concentrate_au  \\\n",
       "count                  16490.000000                 16789.000000   \n",
       "mean                       8.301123                    39.467217   \n",
       "std                        3.825760                    13.917227   \n",
       "min                        0.000000                     0.000000   \n",
       "25%                        6.939185                    42.055722   \n",
       "50%                        8.557228                    44.498874   \n",
       "75%                       10.289741                    45.976222   \n",
       "max                       18.124851                    53.611374   \n",
       "\n",
       "       final.output.recovery  final.output.tail_ag  final.output.tail_pb  \\\n",
       "count           15339.000000          16794.000000          16677.000000   \n",
       "mean               67.213166              8.757048              2.360327   \n",
       "std                11.960446              3.634103              1.215576   \n",
       "min                 0.000000              0.000000              0.000000   \n",
       "25%                62.625685              7.610544              1.641604   \n",
       "50%                67.644601              9.220393              2.453690   \n",
       "75%                72.824595             10.971110              3.192404   \n",
       "max               100.000000             19.552149              6.086532   \n",
       "\n",
       "       final.output.tail_sol  final.output.tail_au  \\\n",
       "count           16715.000000          16794.000000   \n",
       "mean                9.303932              2.687512   \n",
       "std                 4.263208              1.272757   \n",
       "min                 0.000000              0.000000   \n",
       "25%                 7.870275              2.172953   \n",
       "50%                10.021968              2.781132   \n",
       "75%                11.648573              3.416936   \n",
       "max                22.317730              9.789625   \n",
       "\n",
       "       primary_cleaner.input.sulfate  primary_cleaner.input.depressant  \\\n",
       "count                   15553.000000                      15598.000000   \n",
       "mean                      129.479789                          8.625021   \n",
       "std                        45.386931                          3.698633   \n",
       "min                         0.000003                          0.000000   \n",
       "25%                       103.064021                          6.004886   \n",
       "50%                       131.783108                          8.016531   \n",
       "75%                       159.539839                         11.437819   \n",
       "max                       251.999948                         40.000000   \n",
       "\n",
       "       primary_cleaner.input.feed_size  primary_cleaner.input.xanthate  \\\n",
       "count                     16860.000000                    15875.000000   \n",
       "mean                          7.297285                        0.843812   \n",
       "std                           0.619166                        0.419098   \n",
       "min                           1.080000                        0.000005   \n",
       "25%                           6.940000                        0.568934   \n",
       "50%                           7.285000                        0.844271   \n",
       "75%                           7.700000                        1.097080   \n",
       "max                          10.470000                        6.027907   \n",
       "\n",
       "       primary_cleaner.output.concentrate_ag  \\\n",
       "count                           16778.000000   \n",
       "mean                                7.422640   \n",
       "std                                 3.105117   \n",
       "min                                 0.000000   \n",
       "25%                                 6.612584   \n",
       "50%                                 7.997143   \n",
       "75%                                 9.399463   \n",
       "max                                16.081632   \n",
       "\n",
       "       primary_cleaner.output.concentrate_pb  \\\n",
       "count                           16502.000000   \n",
       "mean                                8.620294   \n",
       "std                                 3.815415   \n",
       "min                                 0.000000   \n",
       "25%                                 7.146794   \n",
       "50%                                 9.618910   \n",
       "75%                                11.131134   \n",
       "max                                17.081278   \n",
       "\n",
       "       primary_cleaner.output.concentrate_sol  \\\n",
       "count                            16224.000000   \n",
       "mean                                 9.171453   \n",
       "std                                  4.901822   \n",
       "min                                  0.000000   \n",
       "25%                                  6.477601   \n",
       "50%                                  9.295954   \n",
       "75%                                 12.747802   \n",
       "max                                 22.280845   \n",
       "\n",
       "       primary_cleaner.output.concentrate_au  primary_cleaner.output.tail_ag  \\\n",
       "count                           16778.000000                    16777.000000   \n",
       "mean                               29.164596                       14.916716   \n",
       "std                                10.983718                        5.940559   \n",
       "min                                 0.000000                        0.000000   \n",
       "25%                                29.631129                       13.283872   \n",
       "50%                                32.648867                       15.554321   \n",
       "75%                                35.011318                       18.310868   \n",
       "max                                45.933934                       29.459575   \n",
       "\n",
       "       primary_cleaner.output.tail_pb  primary_cleaner.output.tail_sol  \\\n",
       "count                    16761.000000                     16579.000000   \n",
       "mean                         3.165957                         6.746209   \n",
       "std                          1.747066                         2.999423   \n",
       "min                          0.000000                         0.000000   \n",
       "25%                          2.162262                         5.530088   \n",
       "50%                          3.082745                         7.380058   \n",
       "75%                          4.190997                         8.728101   \n",
       "max                          9.634565                        20.620833   \n",
       "\n",
       "       primary_cleaner.output.tail_au  primary_cleaner.state.floatbank8_a_air  \\\n",
       "count                    16777.000000                            16820.000000   \n",
       "mean                         3.590534                             1521.291691   \n",
       "std                          2.028115                              380.064453   \n",
       "min                          0.000000                                0.000000   \n",
       "25%                          2.664223                             1503.883158   \n",
       "50%                          3.404941                             1601.139322   \n",
       "75%                          4.467152                             1699.304472   \n",
       "max                         18.528821                             2079.531340   \n",
       "\n",
       "       primary_cleaner.state.floatbank8_a_level  \\\n",
       "count                              16827.000000   \n",
       "mean                                -505.811103   \n",
       "std                                   77.640285   \n",
       "min                                 -800.037388   \n",
       "25%                                 -500.414946   \n",
       "50%                                 -499.951772   \n",
       "75%                                 -499.412584   \n",
       "max                                 -330.134203   \n",
       "\n",
       "       primary_cleaner.state.floatbank8_b_air  \\\n",
       "count                            16820.000000   \n",
       "mean                              1523.880015   \n",
       "std                                381.245324   \n",
       "min                                  0.000000   \n",
       "25%                               1502.956269   \n",
       "50%                               1601.125754   \n",
       "75%                               1699.997294   \n",
       "max                               2361.414084   \n",
       "\n",
       "       primary_cleaner.state.floatbank8_b_level  \\\n",
       "count                              16833.000000   \n",
       "mean                                -507.346105   \n",
       "std                                   79.830738   \n",
       "min                                 -800.409770   \n",
       "25%                                 -500.491143   \n",
       "50%                                 -499.985447   \n",
       "75%                                 -499.436173   \n",
       "max                                 -336.237097   \n",
       "\n",
       "       primary_cleaner.state.floatbank8_c_air  \\\n",
       "count                            16822.000000   \n",
       "mean                              1514.351131   \n",
       "std                                396.493106   \n",
       "min                                  0.000000   \n",
       "25%                               1500.685486   \n",
       "50%                               1600.908740   \n",
       "75%                               1700.221778   \n",
       "max                               2042.072583   \n",
       "\n",
       "       primary_cleaner.state.floatbank8_c_level  \\\n",
       "count                              16833.000000   \n",
       "mean                                -507.094373   \n",
       "std                                   78.823157   \n",
       "min                                 -800.362914   \n",
       "25%                                 -500.859360   \n",
       "50%                                 -499.950634   \n",
       "75%                                 -498.901452   \n",
       "max                                 -221.857683   \n",
       "\n",
       "       primary_cleaner.state.floatbank8_d_air  \\\n",
       "count                            16821.000000   \n",
       "mean                              1468.599116   \n",
       "std                                444.329353   \n",
       "min                                  0.000000   \n",
       "25%                               1418.756530   \n",
       "50%                               1599.709469   \n",
       "75%                               1699.222198   \n",
       "max                               2398.903635   \n",
       "\n",
       "       primary_cleaner.state.floatbank8_d_level  \\\n",
       "count                              16833.000000   \n",
       "mean                                -501.835392   \n",
       "std                                   87.257010   \n",
       "min                                 -800.166222   \n",
       "25%                                 -500.643616   \n",
       "50%                                 -499.908024   \n",
       "75%                                 -498.716100   \n",
       "max                                  -20.654343   \n",
       "\n",
       "       rougher.calculation.sulfate_to_au_concentrate  \\\n",
       "count                                   1.683300e+04   \n",
       "mean                                   -1.137202e+04   \n",
       "std                                     5.754320e+06   \n",
       "min                                    -6.643891e+08   \n",
       "25%                                     3.898865e+04   \n",
       "50%                                     4.316793e+04   \n",
       "75%                                     4.781106e+04   \n",
       "max                                     8.332404e+07   \n",
       "\n",
       "       rougher.calculation.floatbank10_sulfate_to_au_feed  \\\n",
       "count                                       1.683300e+04    \n",
       "mean                                        3.784836e+03    \n",
       "std                                         7.088720e+04    \n",
       "min                                        -2.023459e+06    \n",
       "25%                                         2.502308e+03    \n",
       "50%                                         2.854641e+03    \n",
       "75%                                         3.644408e+03    \n",
       "max                                         6.211433e+06    \n",
       "\n",
       "       rougher.calculation.floatbank11_sulfate_to_au_feed  \\\n",
       "count                                       1.683300e+04    \n",
       "mean                                        2.017512e+03    \n",
       "std                                         4.836941e+04    \n",
       "min                                        -4.014502e+06    \n",
       "25%                                         2.498158e+03    \n",
       "50%                                         2.815062e+03    \n",
       "75%                                         3.511394e+03    \n",
       "max                                         1.537564e+06    \n",
       "\n",
       "       rougher.calculation.au_pb_ratio  rougher.input.feed_ag  \\\n",
       "count                     15618.000000           16778.000000   \n",
       "mean                          2.327953               7.832347   \n",
       "std                           0.899125               3.139014   \n",
       "min                          -0.010548               0.000000   \n",
       "25%                           1.979046               6.889426   \n",
       "50%                           2.229144               7.938193   \n",
       "75%                           2.637776               9.829857   \n",
       "max                          39.379272              14.869652   \n",
       "\n",
       "       rougher.input.feed_pb  rougher.input.feed_rate  \\\n",
       "count           16632.000000             16347.000000   \n",
       "mean                3.218931               435.963431   \n",
       "std                 1.477263               156.592162   \n",
       "min                 0.000000                 0.000044   \n",
       "25%                 2.551143               396.667936   \n",
       "50%                 3.297050               488.629971   \n",
       "75%                 4.181842               540.548376   \n",
       "max                 7.142594               717.508837   \n",
       "\n",
       "       rougher.input.feed_size  rougher.input.feed_sol  rougher.input.feed_au  \\\n",
       "count             16443.000000            16568.000000           16777.000000   \n",
       "mean                 58.676444               32.636662               7.170717   \n",
       "std                  23.922591               11.661981               3.002113   \n",
       "min                   9.659576                0.000000               0.000000   \n",
       "25%                  47.575879               32.170423               6.203083   \n",
       "50%                  54.104257               36.354079               7.443530   \n",
       "75%                  65.051064               39.043343               8.965193   \n",
       "max                 484.967466               48.363177              14.093363   \n",
       "\n",
       "       rougher.input.floatbank10_sulfate  rougher.input.floatbank10_xanthate  \\\n",
       "count                       15816.000000                        16514.000000   \n",
       "mean                           11.198142                            5.366609   \n",
       "std                             3.964080                            1.940766   \n",
       "min                             0.000002                            0.000003   \n",
       "25%                             9.366078                            4.802531   \n",
       "50%                            11.179351                            5.767015   \n",
       "75%                            13.203315                            6.501621   \n",
       "max                            36.118275                           31.493331   \n",
       "\n",
       "       rougher.input.floatbank11_sulfate  rougher.input.floatbank11_xanthate  \\\n",
       "count                       16237.000000                        14956.000000   \n",
       "mean                           10.550080                            5.792970   \n",
       "std                             4.560428                            1.294180   \n",
       "min                             0.000049                            0.000010   \n",
       "25%                             8.903492                            5.097006   \n",
       "50%                            11.001027                            5.995942   \n",
       "75%                            13.004085                            6.695277   \n",
       "max                            37.980648                            9.698407   \n",
       "\n",
       "       rougher.output.concentrate_ag  rougher.output.concentrate_pb  \\\n",
       "count                   16778.000000                   16778.000000   \n",
       "mean                       10.580475                       6.880322   \n",
       "std                         4.451490                       2.928757   \n",
       "min                         0.000000                       0.000000   \n",
       "25%                         9.970467                       6.346373   \n",
       "50%                        11.459862                       7.583773   \n",
       "75%                        13.280031                       8.503498   \n",
       "max                        24.480271                      18.394042   \n",
       "\n",
       "       rougher.output.concentrate_sol  rougher.output.concentrate_au  \\\n",
       "count                    16698.000000                   16778.000000   \n",
       "mean                        25.388996                      17.401541   \n",
       "std                         10.400528                       6.921875   \n",
       "min                          0.000000                       0.000000   \n",
       "25%                         24.792988                      17.443663   \n",
       "50%                         28.797785                      19.644663   \n",
       "75%                         31.542677                      21.224486   \n",
       "max                         40.089993                      28.522811   \n",
       "\n",
       "       rougher.output.recovery  rougher.output.tail_ag  \\\n",
       "count             14287.000000            14610.000000   \n",
       "mean                 82.394201                5.566579   \n",
       "std                  15.096808                1.045456   \n",
       "min                   0.000000                0.594562   \n",
       "25%                  79.818372                4.918713   \n",
       "50%                  85.235997                5.718038   \n",
       "75%                  90.131691                6.308490   \n",
       "max                 100.000000               12.719177   \n",
       "\n",
       "       rougher.output.tail_pb  rougher.output.tail_sol  \\\n",
       "count            16778.000000             14611.000000   \n",
       "mean                 0.596493                17.865309   \n",
       "std                  0.330819                 3.455588   \n",
       "min                  0.000000                10.014648   \n",
       "25%                  0.413711                15.646980   \n",
       "50%                  0.594959                17.996971   \n",
       "75%                  0.779544                19.933255   \n",
       "max                  3.778064                66.117188   \n",
       "\n",
       "       rougher.output.tail_au  rougher.state.floatbank10_a_air  \\\n",
       "count            14611.000000                     16807.000000   \n",
       "mean                 1.763930                      1077.201302   \n",
       "std                  0.731518                       291.800894   \n",
       "min                  0.020676                        -0.345168   \n",
       "25%                  1.311610                       999.636979   \n",
       "50%                  1.747907                      1001.310190   \n",
       "75%                  2.190839                      1299.577654   \n",
       "max                  9.688980                      1521.980372   \n",
       "\n",
       "       rougher.state.floatbank10_a_level  rougher.state.floatbank10_b_air  \\\n",
       "count                       16807.000000                     16807.000000   \n",
       "mean                         -384.109359                      1252.285954   \n",
       "std                           112.408685                       331.076715   \n",
       "min                          -658.223080                        -0.759568   \n",
       "25%                          -499.908427                      1196.415722   \n",
       "50%                          -300.233403                      1299.649429   \n",
       "75%                          -299.976784                      1432.326657   \n",
       "max                           -19.383282                      1809.194008   \n",
       "\n",
       "       rougher.state.floatbank10_b_level  rougher.state.floatbank10_c_air  \\\n",
       "count                       16807.000000                     16807.000000   \n",
       "mean                         -475.443395                      1229.567445   \n",
       "std                            70.051458                       344.840251   \n",
       "min                          -650.437575                        -0.378521   \n",
       "25%                          -500.284895                      1100.084257   \n",
       "50%                          -499.837368                      1298.324087   \n",
       "75%                          -400.865979                      1448.560325   \n",
       "max                             9.457822                      2499.129174   \n",
       "\n",
       "       rougher.state.floatbank10_c_level  rougher.state.floatbank10_d_air  \\\n",
       "count                       16814.000000                     16802.000000   \n",
       "mean                         -474.713811                      1146.785992   \n",
       "std                            67.065122                       327.311834   \n",
       "min                          -650.151190                        -1.197435   \n",
       "25%                          -500.329171                      1044.848978   \n",
       "50%                          -499.775884                      1199.211289   \n",
       "75%                          -401.277897                      1307.829439   \n",
       "max                           -10.242658                      2077.222699   \n",
       "\n",
       "       rougher.state.floatbank10_d_level  rougher.state.floatbank10_e_air  \\\n",
       "count                       16809.000000                     16257.000000   \n",
       "mean                         -476.180417                      1033.886871   \n",
       "std                            64.118377                       293.456164   \n",
       "min                          -649.300693                        -2.163619   \n",
       "25%                          -500.594910                       949.615568   \n",
       "50%                          -499.640478                      1049.693712   \n",
       "75%                          -402.354963                      1199.711798   \n",
       "max                            21.365035                      2330.310425   \n",
       "\n",
       "       rougher.state.floatbank10_e_level  rougher.state.floatbank10_f_air  \\\n",
       "count                       16809.000000                     16802.000000   \n",
       "mean                         -475.545309                       983.384202   \n",
       "std                            68.781915                       275.426261   \n",
       "min                          -650.196204                        -2.636171   \n",
       "25%                          -500.395113                       900.029475   \n",
       "50%                          -499.724295                       999.828611   \n",
       "75%                          -401.349388                      1099.734617   \n",
       "max                            -2.406020                      1950.493751   \n",
       "\n",
       "       rougher.state.floatbank10_f_level  secondary_cleaner.output.tail_ag  \\\n",
       "count                       16802.000000                      16776.000000   \n",
       "mean                         -475.980531                         13.014971   \n",
       "std                            70.132801                          5.960323   \n",
       "min                          -650.072915                          0.000000   \n",
       "25%                          -500.738649                         11.367185   \n",
       "50%                          -499.576077                         14.773117   \n",
       "75%                          -402.026493                         17.134159   \n",
       "max                           -21.745268                         23.264738   \n",
       "\n",
       "       secondary_cleaner.output.tail_pb  secondary_cleaner.output.tail_sol  \\\n",
       "count                      16764.000000                       14874.000000   \n",
       "mean                           5.320902                           6.181742   \n",
       "std                            3.191103                           4.460622   \n",
       "min                            0.000000                           0.000000   \n",
       "25%                            3.466609                           2.045824   \n",
       "50%                            5.164745                           6.194420   \n",
       "75%                            7.562139                          10.252499   \n",
       "max                           17.042505                          33.058594   \n",
       "\n",
       "       secondary_cleaner.output.tail_au  \\\n",
       "count                      16778.000000   \n",
       "mean                           3.935796   \n",
       "std                            2.675785   \n",
       "min                            0.000000   \n",
       "25%                            2.820390   \n",
       "50%                            3.863798   \n",
       "75%                            4.845187   \n",
       "max                           26.811643   \n",
       "\n",
       "       secondary_cleaner.state.floatbank2_a_air  \\\n",
       "count                              16497.000000   \n",
       "mean                                  28.134535   \n",
       "std                                    8.320744   \n",
       "min                                    0.000000   \n",
       "25%                                   25.045244   \n",
       "50%                                   29.988519   \n",
       "75%                                   34.771800   \n",
       "max                                   60.000000   \n",
       "\n",
       "       secondary_cleaner.state.floatbank2_a_level  \\\n",
       "count                                16751.000000   \n",
       "mean                                  -517.777116   \n",
       "std                                     89.551883   \n",
       "min                                   -799.841677   \n",
       "25%                                   -500.333800   \n",
       "50%                                   -499.986132   \n",
       "75%                                   -499.620568   \n",
       "max                                   -127.875565   \n",
       "\n",
       "       secondary_cleaner.state.floatbank2_b_air  \\\n",
       "count                              16705.000000   \n",
       "mean                                  23.619503   \n",
       "std                                    8.144236   \n",
       "min                                    0.000000   \n",
       "25%                                   20.110141   \n",
       "50%                                   26.981647   \n",
       "75%                                   28.094066   \n",
       "max                                   51.093216   \n",
       "\n",
       "       secondary_cleaner.state.floatbank2_b_level  \\\n",
       "count                                16748.000000   \n",
       "mean                                  -519.562134   \n",
       "std                                     89.374849   \n",
       "min                                   -799.967956   \n",
       "25%                                   -500.348178   \n",
       "50%                                   -500.037663   \n",
       "75%                                   -499.780945   \n",
       "max                                   -212.064665   \n",
       "\n",
       "       secondary_cleaner.state.floatbank3_a_air  \\\n",
       "count                              16763.000000   \n",
       "mean                                  27.913358   \n",
       "std                                    8.170477   \n",
       "min                                    0.000000   \n",
       "25%                                   24.972364   \n",
       "50%                                   27.999437   \n",
       "75%                                   34.967470   \n",
       "max                                   60.000000   \n",
       "\n",
       "       secondary_cleaner.state.floatbank3_a_level  \\\n",
       "count                                16747.000000   \n",
       "mean                                  -493.470026   \n",
       "std                                     85.296428   \n",
       "min                                   -799.926254   \n",
       "25%                                   -500.236661   \n",
       "50%                                   -499.908346   \n",
       "75%                                   -495.885262   \n",
       "max                                   -191.678810   \n",
       "\n",
       "       secondary_cleaner.state.floatbank3_b_air  \\\n",
       "count                              16752.000000   \n",
       "mean                                  21.663507   \n",
       "std                                    6.738952   \n",
       "min                                    0.000000   \n",
       "25%                                   19.007706   \n",
       "50%                                   21.996298   \n",
       "75%                                   25.071338   \n",
       "max                                   39.487633   \n",
       "\n",
       "       secondary_cleaner.state.floatbank3_b_level  \\\n",
       "count                                16750.000000   \n",
       "mean                                  -501.521561   \n",
       "std                                     74.723250   \n",
       "min                                   -809.421923   \n",
       "25%                                   -500.141064   \n",
       "50%                                   -499.980536   \n",
       "75%                                   -499.778014   \n",
       "max                                   -139.771483   \n",
       "\n",
       "       secondary_cleaner.state.floatbank4_a_air  \\\n",
       "count                              16731.000000   \n",
       "mean                                  19.101874   \n",
       "std                                    6.883163   \n",
       "min                                    0.000000   \n",
       "25%                                   14.508299   \n",
       "50%                                   19.986958   \n",
       "75%                                   24.983961   \n",
       "max                                   60.000000   \n",
       "\n",
       "       secondary_cleaner.state.floatbank4_a_level  \\\n",
       "count                                16747.000000   \n",
       "mean                                  -494.164481   \n",
       "std                                     84.803334   \n",
       "min                                   -799.920713   \n",
       "25%                                   -500.837689   \n",
       "50%                                   -499.778379   \n",
       "75%                                   -494.648754   \n",
       "max                                   -127.692333   \n",
       "\n",
       "       secondary_cleaner.state.floatbank4_b_air  \\\n",
       "count                              16768.000000   \n",
       "mean                                  14.778164   \n",
       "std                                    5.999149   \n",
       "min                                    0.000000   \n",
       "25%                                   10.741388   \n",
       "50%                                   14.943933   \n",
       "75%                                   20.023751   \n",
       "max                                   28.003828   \n",
       "\n",
       "       secondary_cleaner.state.floatbank4_b_level  \\\n",
       "count                                16767.000000   \n",
       "mean                                  -476.600082   \n",
       "std                                     89.381172   \n",
       "min                                   -800.021781   \n",
       "25%                                   -500.269182   \n",
       "50%                                   -499.593286   \n",
       "75%                                   -400.137948   \n",
       "max                                    -71.472472   \n",
       "\n",
       "       secondary_cleaner.state.floatbank5_a_air  \\\n",
       "count                              16775.000000   \n",
       "mean                                  15.779488   \n",
       "std                                    6.834703   \n",
       "min                                   -0.423260   \n",
       "25%                                   10.977713   \n",
       "50%                                   15.998340   \n",
       "75%                                   20.000701   \n",
       "max                                   63.116298   \n",
       "\n",
       "       secondary_cleaner.state.floatbank5_a_level  \\\n",
       "count                                16775.000000   \n",
       "mean                                  -500.230146   \n",
       "std                                     76.983542   \n",
       "min                                   -799.741097   \n",
       "25%                                   -500.530594   \n",
       "50%                                   -499.784231   \n",
       "75%                                   -496.531781   \n",
       "max                                   -275.073125   \n",
       "\n",
       "       secondary_cleaner.state.floatbank5_b_air  \\\n",
       "count                              16775.000000   \n",
       "mean                                  12.377241   \n",
       "std                                    6.219989   \n",
       "min                                    0.427084   \n",
       "25%                                    8.925586   \n",
       "50%                                   11.092839   \n",
       "75%                                   15.979467   \n",
       "max                                   39.846228   \n",
       "\n",
       "       secondary_cleaner.state.floatbank5_b_level  \\\n",
       "count                                16776.000000   \n",
       "mean                                  -498.956257   \n",
       "std                                     82.146207   \n",
       "min                                   -800.258209   \n",
       "25%                                   -500.147603   \n",
       "50%                                   -499.933330   \n",
       "75%                                   -498.418000   \n",
       "max                                   -120.190931   \n",
       "\n",
       "       secondary_cleaner.state.floatbank6_a_air  \\\n",
       "count                              16757.000000   \n",
       "mean                                  18.429208   \n",
       "std                                    6.958294   \n",
       "min                                    0.024270   \n",
       "25%                                   13.977626   \n",
       "50%                                   18.034960   \n",
       "75%                                   24.984992   \n",
       "max                                   54.876806   \n",
       "\n",
       "       secondary_cleaner.state.floatbank6_a_level  \n",
       "count                                16775.000000  \n",
       "mean                                  -521.801826  \n",
       "std                                     77.170888  \n",
       "min                                   -810.473526  \n",
       "25%                                   -501.080595  \n",
       "50%                                   -500.109898  \n",
       "75%                                   -499.565540  \n",
       "max                                    -39.784927  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 16860 entries, 2016-01-15 00:00:00 to 2018-08-18 10:59:59\n",
      "Data columns (total 86 columns):\n",
      "final.output.concentrate_ag                           16788 non-null float64\n",
      "final.output.concentrate_pb                           16788 non-null float64\n",
      "final.output.concentrate_sol                          16490 non-null float64\n",
      "final.output.concentrate_au                           16789 non-null float64\n",
      "final.output.recovery                                 15339 non-null float64\n",
      "final.output.tail_ag                                  16794 non-null float64\n",
      "final.output.tail_pb                                  16677 non-null float64\n",
      "final.output.tail_sol                                 16715 non-null float64\n",
      "final.output.tail_au                                  16794 non-null float64\n",
      "primary_cleaner.input.sulfate                         15553 non-null float64\n",
      "primary_cleaner.input.depressant                      15598 non-null float64\n",
      "primary_cleaner.input.feed_size                       16860 non-null float64\n",
      "primary_cleaner.input.xanthate                        15875 non-null float64\n",
      "primary_cleaner.output.concentrate_ag                 16778 non-null float64\n",
      "primary_cleaner.output.concentrate_pb                 16502 non-null float64\n",
      "primary_cleaner.output.concentrate_sol                16224 non-null float64\n",
      "primary_cleaner.output.concentrate_au                 16778 non-null float64\n",
      "primary_cleaner.output.tail_ag                        16777 non-null float64\n",
      "primary_cleaner.output.tail_pb                        16761 non-null float64\n",
      "primary_cleaner.output.tail_sol                       16579 non-null float64\n",
      "primary_cleaner.output.tail_au                        16777 non-null float64\n",
      "primary_cleaner.state.floatbank8_a_air                16820 non-null float64\n",
      "primary_cleaner.state.floatbank8_a_level              16827 non-null float64\n",
      "primary_cleaner.state.floatbank8_b_air                16820 non-null float64\n",
      "primary_cleaner.state.floatbank8_b_level              16833 non-null float64\n",
      "primary_cleaner.state.floatbank8_c_air                16822 non-null float64\n",
      "primary_cleaner.state.floatbank8_c_level              16833 non-null float64\n",
      "primary_cleaner.state.floatbank8_d_air                16821 non-null float64\n",
      "primary_cleaner.state.floatbank8_d_level              16833 non-null float64\n",
      "rougher.calculation.sulfate_to_au_concentrate         16833 non-null float64\n",
      "rougher.calculation.floatbank10_sulfate_to_au_feed    16833 non-null float64\n",
      "rougher.calculation.floatbank11_sulfate_to_au_feed    16833 non-null float64\n",
      "rougher.calculation.au_pb_ratio                       15618 non-null float64\n",
      "rougher.input.feed_ag                                 16778 non-null float64\n",
      "rougher.input.feed_pb                                 16632 non-null float64\n",
      "rougher.input.feed_rate                               16347 non-null float64\n",
      "rougher.input.feed_size                               16443 non-null float64\n",
      "rougher.input.feed_sol                                16568 non-null float64\n",
      "rougher.input.feed_au                                 16777 non-null float64\n",
      "rougher.input.floatbank10_sulfate                     15816 non-null float64\n",
      "rougher.input.floatbank10_xanthate                    16514 non-null float64\n",
      "rougher.input.floatbank11_sulfate                     16237 non-null float64\n",
      "rougher.input.floatbank11_xanthate                    14956 non-null float64\n",
      "rougher.output.concentrate_ag                         16778 non-null float64\n",
      "rougher.output.concentrate_pb                         16778 non-null float64\n",
      "rougher.output.concentrate_sol                        16698 non-null float64\n",
      "rougher.output.concentrate_au                         16778 non-null float64\n",
      "rougher.output.recovery                               14287 non-null float64\n",
      "rougher.output.tail_ag                                14610 non-null float64\n",
      "rougher.output.tail_pb                                16778 non-null float64\n",
      "rougher.output.tail_sol                               14611 non-null float64\n",
      "rougher.output.tail_au                                14611 non-null float64\n",
      "rougher.state.floatbank10_a_air                       16807 non-null float64\n",
      "rougher.state.floatbank10_a_level                     16807 non-null float64\n",
      "rougher.state.floatbank10_b_air                       16807 non-null float64\n",
      "rougher.state.floatbank10_b_level                     16807 non-null float64\n",
      "rougher.state.floatbank10_c_air                       16807 non-null float64\n",
      "rougher.state.floatbank10_c_level                     16814 non-null float64\n",
      "rougher.state.floatbank10_d_air                       16802 non-null float64\n",
      "rougher.state.floatbank10_d_level                     16809 non-null float64\n",
      "rougher.state.floatbank10_e_air                       16257 non-null float64\n",
      "rougher.state.floatbank10_e_level                     16809 non-null float64\n",
      "rougher.state.floatbank10_f_air                       16802 non-null float64\n",
      "rougher.state.floatbank10_f_level                     16802 non-null float64\n",
      "secondary_cleaner.output.tail_ag                      16776 non-null float64\n",
      "secondary_cleaner.output.tail_pb                      16764 non-null float64\n",
      "secondary_cleaner.output.tail_sol                     14874 non-null float64\n",
      "secondary_cleaner.output.tail_au                      16778 non-null float64\n",
      "secondary_cleaner.state.floatbank2_a_air              16497 non-null float64\n",
      "secondary_cleaner.state.floatbank2_a_level            16751 non-null float64\n",
      "secondary_cleaner.state.floatbank2_b_air              16705 non-null float64\n",
      "secondary_cleaner.state.floatbank2_b_level            16748 non-null float64\n",
      "secondary_cleaner.state.floatbank3_a_air              16763 non-null float64\n",
      "secondary_cleaner.state.floatbank3_a_level            16747 non-null float64\n",
      "secondary_cleaner.state.floatbank3_b_air              16752 non-null float64\n",
      "secondary_cleaner.state.floatbank3_b_level            16750 non-null float64\n",
      "secondary_cleaner.state.floatbank4_a_air              16731 non-null float64\n",
      "secondary_cleaner.state.floatbank4_a_level            16747 non-null float64\n",
      "secondary_cleaner.state.floatbank4_b_air              16768 non-null float64\n",
      "secondary_cleaner.state.floatbank4_b_level            16767 non-null float64\n",
      "secondary_cleaner.state.floatbank5_a_air              16775 non-null float64\n",
      "secondary_cleaner.state.floatbank5_a_level            16775 non-null float64\n",
      "secondary_cleaner.state.floatbank5_b_air              16775 non-null float64\n",
      "secondary_cleaner.state.floatbank5_b_level            16776 non-null float64\n",
      "secondary_cleaner.state.floatbank6_a_air              16757 non-null float64\n",
      "secondary_cleaner.state.floatbank6_a_level            16775 non-null float64\n",
      "dtypes: float64(86)\n",
      "memory usage: 11.2+ MB\n"
     ]
    }
   ],
   "source": [
    "analyze_df(gold_recovery_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество стобцов: 52\n",
      "Количество строк: 5856\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>primary_cleaner.input.sulfate</th>\n",
       "      <th>primary_cleaner.input.depressant</th>\n",
       "      <th>primary_cleaner.input.feed_size</th>\n",
       "      <th>primary_cleaner.input.xanthate</th>\n",
       "      <th>primary_cleaner.state.floatbank8_a_air</th>\n",
       "      <th>primary_cleaner.state.floatbank8_a_level</th>\n",
       "      <th>primary_cleaner.state.floatbank8_b_air</th>\n",
       "      <th>primary_cleaner.state.floatbank8_b_level</th>\n",
       "      <th>primary_cleaner.state.floatbank8_c_air</th>\n",
       "      <th>primary_cleaner.state.floatbank8_c_level</th>\n",
       "      <th>primary_cleaner.state.floatbank8_d_air</th>\n",
       "      <th>primary_cleaner.state.floatbank8_d_level</th>\n",
       "      <th>rougher.input.feed_ag</th>\n",
       "      <th>rougher.input.feed_pb</th>\n",
       "      <th>rougher.input.feed_rate</th>\n",
       "      <th>rougher.input.feed_size</th>\n",
       "      <th>rougher.input.feed_sol</th>\n",
       "      <th>rougher.input.feed_au</th>\n",
       "      <th>rougher.input.floatbank10_sulfate</th>\n",
       "      <th>rougher.input.floatbank10_xanthate</th>\n",
       "      <th>rougher.input.floatbank11_sulfate</th>\n",
       "      <th>rougher.input.floatbank11_xanthate</th>\n",
       "      <th>rougher.state.floatbank10_a_air</th>\n",
       "      <th>rougher.state.floatbank10_a_level</th>\n",
       "      <th>rougher.state.floatbank10_b_air</th>\n",
       "      <th>rougher.state.floatbank10_b_level</th>\n",
       "      <th>rougher.state.floatbank10_c_air</th>\n",
       "      <th>rougher.state.floatbank10_c_level</th>\n",
       "      <th>rougher.state.floatbank10_d_air</th>\n",
       "      <th>rougher.state.floatbank10_d_level</th>\n",
       "      <th>rougher.state.floatbank10_e_air</th>\n",
       "      <th>rougher.state.floatbank10_e_level</th>\n",
       "      <th>rougher.state.floatbank10_f_air</th>\n",
       "      <th>rougher.state.floatbank10_f_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank2_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank2_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank2_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank2_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank3_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank3_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank3_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank3_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_level</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2016-09-01 00:59:59</td>\n",
       "      <td>210.800909</td>\n",
       "      <td>14.993118</td>\n",
       "      <td>8.080000</td>\n",
       "      <td>1.005021</td>\n",
       "      <td>1398.981301</td>\n",
       "      <td>-500.225577</td>\n",
       "      <td>1399.144926</td>\n",
       "      <td>-499.919735</td>\n",
       "      <td>1400.102998</td>\n",
       "      <td>-500.704369</td>\n",
       "      <td>1399.001695</td>\n",
       "      <td>-499.485144</td>\n",
       "      <td>13.128569</td>\n",
       "      <td>5.636710</td>\n",
       "      <td>489.793655</td>\n",
       "      <td>62.710452</td>\n",
       "      <td>42.022440</td>\n",
       "      <td>12.084103</td>\n",
       "      <td>16.922877</td>\n",
       "      <td>6.153479</td>\n",
       "      <td>16.867518</td>\n",
       "      <td>6.150709</td>\n",
       "      <td>1001.849317</td>\n",
       "      <td>-350.301265</td>\n",
       "      <td>1249.738612</td>\n",
       "      <td>-399.107531</td>\n",
       "      <td>1249.750520</td>\n",
       "      <td>-399.396730</td>\n",
       "      <td>1198.287855</td>\n",
       "      <td>-399.488594</td>\n",
       "      <td>999.472250</td>\n",
       "      <td>-399.530962</td>\n",
       "      <td>949.566189</td>\n",
       "      <td>-398.180807</td>\n",
       "      <td>24.938200</td>\n",
       "      <td>-500.490963</td>\n",
       "      <td>14.947607</td>\n",
       "      <td>-500.013837</td>\n",
       "      <td>20.018166</td>\n",
       "      <td>-450.395995</td>\n",
       "      <td>13.987058</td>\n",
       "      <td>-449.831513</td>\n",
       "      <td>12.023554</td>\n",
       "      <td>-497.795834</td>\n",
       "      <td>8.016656</td>\n",
       "      <td>-501.289139</td>\n",
       "      <td>7.946562</td>\n",
       "      <td>-432.317850</td>\n",
       "      <td>4.872511</td>\n",
       "      <td>-500.037437</td>\n",
       "      <td>26.705889</td>\n",
       "      <td>-499.709414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-09-01 01:59:59</td>\n",
       "      <td>215.392455</td>\n",
       "      <td>14.987471</td>\n",
       "      <td>8.080000</td>\n",
       "      <td>0.990469</td>\n",
       "      <td>1398.777912</td>\n",
       "      <td>-500.057435</td>\n",
       "      <td>1398.055362</td>\n",
       "      <td>-499.778182</td>\n",
       "      <td>1396.151033</td>\n",
       "      <td>-499.240168</td>\n",
       "      <td>1399.508091</td>\n",
       "      <td>-500.416324</td>\n",
       "      <td>13.036280</td>\n",
       "      <td>5.526065</td>\n",
       "      <td>490.104534</td>\n",
       "      <td>61.961163</td>\n",
       "      <td>41.187903</td>\n",
       "      <td>11.918977</td>\n",
       "      <td>17.002532</td>\n",
       "      <td>5.999043</td>\n",
       "      <td>16.996063</td>\n",
       "      <td>6.002315</td>\n",
       "      <td>998.690785</td>\n",
       "      <td>-350.425704</td>\n",
       "      <td>1248.395457</td>\n",
       "      <td>-399.946025</td>\n",
       "      <td>1249.514451</td>\n",
       "      <td>-399.630810</td>\n",
       "      <td>1200.506459</td>\n",
       "      <td>-399.942434</td>\n",
       "      <td>1000.002114</td>\n",
       "      <td>-399.491528</td>\n",
       "      <td>950.200009</td>\n",
       "      <td>-405.787514</td>\n",
       "      <td>24.923466</td>\n",
       "      <td>-499.813963</td>\n",
       "      <td>14.930873</td>\n",
       "      <td>-500.764452</td>\n",
       "      <td>19.988503</td>\n",
       "      <td>-450.114024</td>\n",
       "      <td>14.092981</td>\n",
       "      <td>-450.059163</td>\n",
       "      <td>12.058140</td>\n",
       "      <td>-498.695773</td>\n",
       "      <td>8.130979</td>\n",
       "      <td>-499.634209</td>\n",
       "      <td>7.958270</td>\n",
       "      <td>-525.839648</td>\n",
       "      <td>4.878850</td>\n",
       "      <td>-500.162375</td>\n",
       "      <td>25.019940</td>\n",
       "      <td>-499.819438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-09-01 02:59:59</td>\n",
       "      <td>215.259946</td>\n",
       "      <td>12.884934</td>\n",
       "      <td>7.786667</td>\n",
       "      <td>0.996043</td>\n",
       "      <td>1398.493666</td>\n",
       "      <td>-500.868360</td>\n",
       "      <td>1398.860436</td>\n",
       "      <td>-499.764529</td>\n",
       "      <td>1398.075709</td>\n",
       "      <td>-502.151509</td>\n",
       "      <td>1399.497088</td>\n",
       "      <td>-499.715479</td>\n",
       "      <td>13.138412</td>\n",
       "      <td>5.426561</td>\n",
       "      <td>489.617969</td>\n",
       "      <td>66.903807</td>\n",
       "      <td>42.546294</td>\n",
       "      <td>12.091332</td>\n",
       "      <td>16.992520</td>\n",
       "      <td>5.850632</td>\n",
       "      <td>16.982096</td>\n",
       "      <td>5.853594</td>\n",
       "      <td>998.516720</td>\n",
       "      <td>-349.783538</td>\n",
       "      <td>1247.440748</td>\n",
       "      <td>-400.264330</td>\n",
       "      <td>1248.207204</td>\n",
       "      <td>-401.074405</td>\n",
       "      <td>1199.770026</td>\n",
       "      <td>-400.790582</td>\n",
       "      <td>999.925519</td>\n",
       "      <td>-399.236999</td>\n",
       "      <td>950.320481</td>\n",
       "      <td>-400.864340</td>\n",
       "      <td>24.908732</td>\n",
       "      <td>-500.304035</td>\n",
       "      <td>14.996511</td>\n",
       "      <td>-500.993821</td>\n",
       "      <td>20.039689</td>\n",
       "      <td>-450.263164</td>\n",
       "      <td>14.077881</td>\n",
       "      <td>-449.660997</td>\n",
       "      <td>11.962366</td>\n",
       "      <td>-498.767484</td>\n",
       "      <td>8.096893</td>\n",
       "      <td>-500.827423</td>\n",
       "      <td>8.071056</td>\n",
       "      <td>-500.801673</td>\n",
       "      <td>4.905125</td>\n",
       "      <td>-499.828510</td>\n",
       "      <td>24.994862</td>\n",
       "      <td>-500.622559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-09-01 03:59:59</td>\n",
       "      <td>215.336236</td>\n",
       "      <td>12.006805</td>\n",
       "      <td>7.640000</td>\n",
       "      <td>0.863514</td>\n",
       "      <td>1399.618111</td>\n",
       "      <td>-498.863574</td>\n",
       "      <td>1397.440120</td>\n",
       "      <td>-499.211024</td>\n",
       "      <td>1400.129303</td>\n",
       "      <td>-498.355873</td>\n",
       "      <td>1401.065165</td>\n",
       "      <td>-501.038738</td>\n",
       "      <td>12.400507</td>\n",
       "      <td>5.114758</td>\n",
       "      <td>476.618130</td>\n",
       "      <td>59.865919</td>\n",
       "      <td>41.060195</td>\n",
       "      <td>12.181597</td>\n",
       "      <td>16.532144</td>\n",
       "      <td>5.801935</td>\n",
       "      <td>16.515277</td>\n",
       "      <td>5.803657</td>\n",
       "      <td>1000.276604</td>\n",
       "      <td>-350.172597</td>\n",
       "      <td>1251.322675</td>\n",
       "      <td>-398.655289</td>\n",
       "      <td>1250.493702</td>\n",
       "      <td>-399.746728</td>\n",
       "      <td>1199.398691</td>\n",
       "      <td>-397.503052</td>\n",
       "      <td>1001.930679</td>\n",
       "      <td>-400.437573</td>\n",
       "      <td>950.735324</td>\n",
       "      <td>-399.802986</td>\n",
       "      <td>24.893998</td>\n",
       "      <td>-499.382374</td>\n",
       "      <td>14.916674</td>\n",
       "      <td>-499.862299</td>\n",
       "      <td>20.031100</td>\n",
       "      <td>-449.372570</td>\n",
       "      <td>14.013968</td>\n",
       "      <td>-449.526900</td>\n",
       "      <td>12.033091</td>\n",
       "      <td>-498.350935</td>\n",
       "      <td>8.074946</td>\n",
       "      <td>-499.474407</td>\n",
       "      <td>7.897085</td>\n",
       "      <td>-500.868509</td>\n",
       "      <td>4.931400</td>\n",
       "      <td>-499.963623</td>\n",
       "      <td>24.948919</td>\n",
       "      <td>-498.709987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-09-01 04:59:59</td>\n",
       "      <td>199.099327</td>\n",
       "      <td>10.682530</td>\n",
       "      <td>7.530000</td>\n",
       "      <td>0.805575</td>\n",
       "      <td>1401.268123</td>\n",
       "      <td>-500.808305</td>\n",
       "      <td>1398.128818</td>\n",
       "      <td>-499.504543</td>\n",
       "      <td>1402.172226</td>\n",
       "      <td>-500.810606</td>\n",
       "      <td>1399.480508</td>\n",
       "      <td>-499.374053</td>\n",
       "      <td>11.327427</td>\n",
       "      <td>4.767383</td>\n",
       "      <td>488.248117</td>\n",
       "      <td>63.315324</td>\n",
       "      <td>41.269161</td>\n",
       "      <td>11.290425</td>\n",
       "      <td>13.607257</td>\n",
       "      <td>5.738081</td>\n",
       "      <td>13.650053</td>\n",
       "      <td>5.739634</td>\n",
       "      <td>996.541302</td>\n",
       "      <td>-350.562517</td>\n",
       "      <td>1304.658848</td>\n",
       "      <td>-399.511519</td>\n",
       "      <td>1306.455515</td>\n",
       "      <td>-399.050685</td>\n",
       "      <td>1248.699349</td>\n",
       "      <td>-400.876894</td>\n",
       "      <td>1058.839106</td>\n",
       "      <td>-398.988239</td>\n",
       "      <td>949.645313</td>\n",
       "      <td>-399.277632</td>\n",
       "      <td>24.886905</td>\n",
       "      <td>-499.357375</td>\n",
       "      <td>14.979233</td>\n",
       "      <td>-500.186804</td>\n",
       "      <td>19.962364</td>\n",
       "      <td>-450.638828</td>\n",
       "      <td>14.009245</td>\n",
       "      <td>-450.022259</td>\n",
       "      <td>12.025367</td>\n",
       "      <td>-500.786497</td>\n",
       "      <td>8.054678</td>\n",
       "      <td>-500.397500</td>\n",
       "      <td>8.107890</td>\n",
       "      <td>-509.526725</td>\n",
       "      <td>4.957674</td>\n",
       "      <td>-500.360026</td>\n",
       "      <td>25.003331</td>\n",
       "      <td>-500.856333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     primary_cleaner.input.sulfate  \\\n",
       "date                                                 \n",
       "2016-09-01 00:59:59                     210.800909   \n",
       "2016-09-01 01:59:59                     215.392455   \n",
       "2016-09-01 02:59:59                     215.259946   \n",
       "2016-09-01 03:59:59                     215.336236   \n",
       "2016-09-01 04:59:59                     199.099327   \n",
       "\n",
       "                     primary_cleaner.input.depressant  \\\n",
       "date                                                    \n",
       "2016-09-01 00:59:59                         14.993118   \n",
       "2016-09-01 01:59:59                         14.987471   \n",
       "2016-09-01 02:59:59                         12.884934   \n",
       "2016-09-01 03:59:59                         12.006805   \n",
       "2016-09-01 04:59:59                         10.682530   \n",
       "\n",
       "                     primary_cleaner.input.feed_size  \\\n",
       "date                                                   \n",
       "2016-09-01 00:59:59                         8.080000   \n",
       "2016-09-01 01:59:59                         8.080000   \n",
       "2016-09-01 02:59:59                         7.786667   \n",
       "2016-09-01 03:59:59                         7.640000   \n",
       "2016-09-01 04:59:59                         7.530000   \n",
       "\n",
       "                     primary_cleaner.input.xanthate  \\\n",
       "date                                                  \n",
       "2016-09-01 00:59:59                        1.005021   \n",
       "2016-09-01 01:59:59                        0.990469   \n",
       "2016-09-01 02:59:59                        0.996043   \n",
       "2016-09-01 03:59:59                        0.863514   \n",
       "2016-09-01 04:59:59                        0.805575   \n",
       "\n",
       "                     primary_cleaner.state.floatbank8_a_air  \\\n",
       "date                                                          \n",
       "2016-09-01 00:59:59                             1398.981301   \n",
       "2016-09-01 01:59:59                             1398.777912   \n",
       "2016-09-01 02:59:59                             1398.493666   \n",
       "2016-09-01 03:59:59                             1399.618111   \n",
       "2016-09-01 04:59:59                             1401.268123   \n",
       "\n",
       "                     primary_cleaner.state.floatbank8_a_level  \\\n",
       "date                                                            \n",
       "2016-09-01 00:59:59                               -500.225577   \n",
       "2016-09-01 01:59:59                               -500.057435   \n",
       "2016-09-01 02:59:59                               -500.868360   \n",
       "2016-09-01 03:59:59                               -498.863574   \n",
       "2016-09-01 04:59:59                               -500.808305   \n",
       "\n",
       "                     primary_cleaner.state.floatbank8_b_air  \\\n",
       "date                                                          \n",
       "2016-09-01 00:59:59                             1399.144926   \n",
       "2016-09-01 01:59:59                             1398.055362   \n",
       "2016-09-01 02:59:59                             1398.860436   \n",
       "2016-09-01 03:59:59                             1397.440120   \n",
       "2016-09-01 04:59:59                             1398.128818   \n",
       "\n",
       "                     primary_cleaner.state.floatbank8_b_level  \\\n",
       "date                                                            \n",
       "2016-09-01 00:59:59                               -499.919735   \n",
       "2016-09-01 01:59:59                               -499.778182   \n",
       "2016-09-01 02:59:59                               -499.764529   \n",
       "2016-09-01 03:59:59                               -499.211024   \n",
       "2016-09-01 04:59:59                               -499.504543   \n",
       "\n",
       "                     primary_cleaner.state.floatbank8_c_air  \\\n",
       "date                                                          \n",
       "2016-09-01 00:59:59                             1400.102998   \n",
       "2016-09-01 01:59:59                             1396.151033   \n",
       "2016-09-01 02:59:59                             1398.075709   \n",
       "2016-09-01 03:59:59                             1400.129303   \n",
       "2016-09-01 04:59:59                             1402.172226   \n",
       "\n",
       "                     primary_cleaner.state.floatbank8_c_level  \\\n",
       "date                                                            \n",
       "2016-09-01 00:59:59                               -500.704369   \n",
       "2016-09-01 01:59:59                               -499.240168   \n",
       "2016-09-01 02:59:59                               -502.151509   \n",
       "2016-09-01 03:59:59                               -498.355873   \n",
       "2016-09-01 04:59:59                               -500.810606   \n",
       "\n",
       "                     primary_cleaner.state.floatbank8_d_air  \\\n",
       "date                                                          \n",
       "2016-09-01 00:59:59                             1399.001695   \n",
       "2016-09-01 01:59:59                             1399.508091   \n",
       "2016-09-01 02:59:59                             1399.497088   \n",
       "2016-09-01 03:59:59                             1401.065165   \n",
       "2016-09-01 04:59:59                             1399.480508   \n",
       "\n",
       "                     primary_cleaner.state.floatbank8_d_level  \\\n",
       "date                                                            \n",
       "2016-09-01 00:59:59                               -499.485144   \n",
       "2016-09-01 01:59:59                               -500.416324   \n",
       "2016-09-01 02:59:59                               -499.715479   \n",
       "2016-09-01 03:59:59                               -501.038738   \n",
       "2016-09-01 04:59:59                               -499.374053   \n",
       "\n",
       "                     rougher.input.feed_ag  rougher.input.feed_pb  \\\n",
       "date                                                                \n",
       "2016-09-01 00:59:59              13.128569               5.636710   \n",
       "2016-09-01 01:59:59              13.036280               5.526065   \n",
       "2016-09-01 02:59:59              13.138412               5.426561   \n",
       "2016-09-01 03:59:59              12.400507               5.114758   \n",
       "2016-09-01 04:59:59              11.327427               4.767383   \n",
       "\n",
       "                     rougher.input.feed_rate  rougher.input.feed_size  \\\n",
       "date                                                                    \n",
       "2016-09-01 00:59:59               489.793655                62.710452   \n",
       "2016-09-01 01:59:59               490.104534                61.961163   \n",
       "2016-09-01 02:59:59               489.617969                66.903807   \n",
       "2016-09-01 03:59:59               476.618130                59.865919   \n",
       "2016-09-01 04:59:59               488.248117                63.315324   \n",
       "\n",
       "                     rougher.input.feed_sol  rougher.input.feed_au  \\\n",
       "date                                                                 \n",
       "2016-09-01 00:59:59               42.022440              12.084103   \n",
       "2016-09-01 01:59:59               41.187903              11.918977   \n",
       "2016-09-01 02:59:59               42.546294              12.091332   \n",
       "2016-09-01 03:59:59               41.060195              12.181597   \n",
       "2016-09-01 04:59:59               41.269161              11.290425   \n",
       "\n",
       "                     rougher.input.floatbank10_sulfate  \\\n",
       "date                                                     \n",
       "2016-09-01 00:59:59                          16.922877   \n",
       "2016-09-01 01:59:59                          17.002532   \n",
       "2016-09-01 02:59:59                          16.992520   \n",
       "2016-09-01 03:59:59                          16.532144   \n",
       "2016-09-01 04:59:59                          13.607257   \n",
       "\n",
       "                     rougher.input.floatbank10_xanthate  \\\n",
       "date                                                      \n",
       "2016-09-01 00:59:59                            6.153479   \n",
       "2016-09-01 01:59:59                            5.999043   \n",
       "2016-09-01 02:59:59                            5.850632   \n",
       "2016-09-01 03:59:59                            5.801935   \n",
       "2016-09-01 04:59:59                            5.738081   \n",
       "\n",
       "                     rougher.input.floatbank11_sulfate  \\\n",
       "date                                                     \n",
       "2016-09-01 00:59:59                          16.867518   \n",
       "2016-09-01 01:59:59                          16.996063   \n",
       "2016-09-01 02:59:59                          16.982096   \n",
       "2016-09-01 03:59:59                          16.515277   \n",
       "2016-09-01 04:59:59                          13.650053   \n",
       "\n",
       "                     rougher.input.floatbank11_xanthate  \\\n",
       "date                                                      \n",
       "2016-09-01 00:59:59                            6.150709   \n",
       "2016-09-01 01:59:59                            6.002315   \n",
       "2016-09-01 02:59:59                            5.853594   \n",
       "2016-09-01 03:59:59                            5.803657   \n",
       "2016-09-01 04:59:59                            5.739634   \n",
       "\n",
       "                     rougher.state.floatbank10_a_air  \\\n",
       "date                                                   \n",
       "2016-09-01 00:59:59                      1001.849317   \n",
       "2016-09-01 01:59:59                       998.690785   \n",
       "2016-09-01 02:59:59                       998.516720   \n",
       "2016-09-01 03:59:59                      1000.276604   \n",
       "2016-09-01 04:59:59                       996.541302   \n",
       "\n",
       "                     rougher.state.floatbank10_a_level  \\\n",
       "date                                                     \n",
       "2016-09-01 00:59:59                        -350.301265   \n",
       "2016-09-01 01:59:59                        -350.425704   \n",
       "2016-09-01 02:59:59                        -349.783538   \n",
       "2016-09-01 03:59:59                        -350.172597   \n",
       "2016-09-01 04:59:59                        -350.562517   \n",
       "\n",
       "                     rougher.state.floatbank10_b_air  \\\n",
       "date                                                   \n",
       "2016-09-01 00:59:59                      1249.738612   \n",
       "2016-09-01 01:59:59                      1248.395457   \n",
       "2016-09-01 02:59:59                      1247.440748   \n",
       "2016-09-01 03:59:59                      1251.322675   \n",
       "2016-09-01 04:59:59                      1304.658848   \n",
       "\n",
       "                     rougher.state.floatbank10_b_level  \\\n",
       "date                                                     \n",
       "2016-09-01 00:59:59                        -399.107531   \n",
       "2016-09-01 01:59:59                        -399.946025   \n",
       "2016-09-01 02:59:59                        -400.264330   \n",
       "2016-09-01 03:59:59                        -398.655289   \n",
       "2016-09-01 04:59:59                        -399.511519   \n",
       "\n",
       "                     rougher.state.floatbank10_c_air  \\\n",
       "date                                                   \n",
       "2016-09-01 00:59:59                      1249.750520   \n",
       "2016-09-01 01:59:59                      1249.514451   \n",
       "2016-09-01 02:59:59                      1248.207204   \n",
       "2016-09-01 03:59:59                      1250.493702   \n",
       "2016-09-01 04:59:59                      1306.455515   \n",
       "\n",
       "                     rougher.state.floatbank10_c_level  \\\n",
       "date                                                     \n",
       "2016-09-01 00:59:59                        -399.396730   \n",
       "2016-09-01 01:59:59                        -399.630810   \n",
       "2016-09-01 02:59:59                        -401.074405   \n",
       "2016-09-01 03:59:59                        -399.746728   \n",
       "2016-09-01 04:59:59                        -399.050685   \n",
       "\n",
       "                     rougher.state.floatbank10_d_air  \\\n",
       "date                                                   \n",
       "2016-09-01 00:59:59                      1198.287855   \n",
       "2016-09-01 01:59:59                      1200.506459   \n",
       "2016-09-01 02:59:59                      1199.770026   \n",
       "2016-09-01 03:59:59                      1199.398691   \n",
       "2016-09-01 04:59:59                      1248.699349   \n",
       "\n",
       "                     rougher.state.floatbank10_d_level  \\\n",
       "date                                                     \n",
       "2016-09-01 00:59:59                        -399.488594   \n",
       "2016-09-01 01:59:59                        -399.942434   \n",
       "2016-09-01 02:59:59                        -400.790582   \n",
       "2016-09-01 03:59:59                        -397.503052   \n",
       "2016-09-01 04:59:59                        -400.876894   \n",
       "\n",
       "                     rougher.state.floatbank10_e_air  \\\n",
       "date                                                   \n",
       "2016-09-01 00:59:59                       999.472250   \n",
       "2016-09-01 01:59:59                      1000.002114   \n",
       "2016-09-01 02:59:59                       999.925519   \n",
       "2016-09-01 03:59:59                      1001.930679   \n",
       "2016-09-01 04:59:59                      1058.839106   \n",
       "\n",
       "                     rougher.state.floatbank10_e_level  \\\n",
       "date                                                     \n",
       "2016-09-01 00:59:59                        -399.530962   \n",
       "2016-09-01 01:59:59                        -399.491528   \n",
       "2016-09-01 02:59:59                        -399.236999   \n",
       "2016-09-01 03:59:59                        -400.437573   \n",
       "2016-09-01 04:59:59                        -398.988239   \n",
       "\n",
       "                     rougher.state.floatbank10_f_air  \\\n",
       "date                                                   \n",
       "2016-09-01 00:59:59                       949.566189   \n",
       "2016-09-01 01:59:59                       950.200009   \n",
       "2016-09-01 02:59:59                       950.320481   \n",
       "2016-09-01 03:59:59                       950.735324   \n",
       "2016-09-01 04:59:59                       949.645313   \n",
       "\n",
       "                     rougher.state.floatbank10_f_level  \\\n",
       "date                                                     \n",
       "2016-09-01 00:59:59                        -398.180807   \n",
       "2016-09-01 01:59:59                        -405.787514   \n",
       "2016-09-01 02:59:59                        -400.864340   \n",
       "2016-09-01 03:59:59                        -399.802986   \n",
       "2016-09-01 04:59:59                        -399.277632   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank2_a_air  \\\n",
       "date                                                            \n",
       "2016-09-01 00:59:59                                 24.938200   \n",
       "2016-09-01 01:59:59                                 24.923466   \n",
       "2016-09-01 02:59:59                                 24.908732   \n",
       "2016-09-01 03:59:59                                 24.893998   \n",
       "2016-09-01 04:59:59                                 24.886905   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank2_a_level  \\\n",
       "date                                                              \n",
       "2016-09-01 00:59:59                                 -500.490963   \n",
       "2016-09-01 01:59:59                                 -499.813963   \n",
       "2016-09-01 02:59:59                                 -500.304035   \n",
       "2016-09-01 03:59:59                                 -499.382374   \n",
       "2016-09-01 04:59:59                                 -499.357375   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank2_b_air  \\\n",
       "date                                                            \n",
       "2016-09-01 00:59:59                                 14.947607   \n",
       "2016-09-01 01:59:59                                 14.930873   \n",
       "2016-09-01 02:59:59                                 14.996511   \n",
       "2016-09-01 03:59:59                                 14.916674   \n",
       "2016-09-01 04:59:59                                 14.979233   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank2_b_level  \\\n",
       "date                                                              \n",
       "2016-09-01 00:59:59                                 -500.013837   \n",
       "2016-09-01 01:59:59                                 -500.764452   \n",
       "2016-09-01 02:59:59                                 -500.993821   \n",
       "2016-09-01 03:59:59                                 -499.862299   \n",
       "2016-09-01 04:59:59                                 -500.186804   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank3_a_air  \\\n",
       "date                                                            \n",
       "2016-09-01 00:59:59                                 20.018166   \n",
       "2016-09-01 01:59:59                                 19.988503   \n",
       "2016-09-01 02:59:59                                 20.039689   \n",
       "2016-09-01 03:59:59                                 20.031100   \n",
       "2016-09-01 04:59:59                                 19.962364   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank3_a_level  \\\n",
       "date                                                              \n",
       "2016-09-01 00:59:59                                 -450.395995   \n",
       "2016-09-01 01:59:59                                 -450.114024   \n",
       "2016-09-01 02:59:59                                 -450.263164   \n",
       "2016-09-01 03:59:59                                 -449.372570   \n",
       "2016-09-01 04:59:59                                 -450.638828   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank3_b_air  \\\n",
       "date                                                            \n",
       "2016-09-01 00:59:59                                 13.987058   \n",
       "2016-09-01 01:59:59                                 14.092981   \n",
       "2016-09-01 02:59:59                                 14.077881   \n",
       "2016-09-01 03:59:59                                 14.013968   \n",
       "2016-09-01 04:59:59                                 14.009245   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank3_b_level  \\\n",
       "date                                                              \n",
       "2016-09-01 00:59:59                                 -449.831513   \n",
       "2016-09-01 01:59:59                                 -450.059163   \n",
       "2016-09-01 02:59:59                                 -449.660997   \n",
       "2016-09-01 03:59:59                                 -449.526900   \n",
       "2016-09-01 04:59:59                                 -450.022259   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank4_a_air  \\\n",
       "date                                                            \n",
       "2016-09-01 00:59:59                                 12.023554   \n",
       "2016-09-01 01:59:59                                 12.058140   \n",
       "2016-09-01 02:59:59                                 11.962366   \n",
       "2016-09-01 03:59:59                                 12.033091   \n",
       "2016-09-01 04:59:59                                 12.025367   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank4_a_level  \\\n",
       "date                                                              \n",
       "2016-09-01 00:59:59                                 -497.795834   \n",
       "2016-09-01 01:59:59                                 -498.695773   \n",
       "2016-09-01 02:59:59                                 -498.767484   \n",
       "2016-09-01 03:59:59                                 -498.350935   \n",
       "2016-09-01 04:59:59                                 -500.786497   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank4_b_air  \\\n",
       "date                                                            \n",
       "2016-09-01 00:59:59                                  8.016656   \n",
       "2016-09-01 01:59:59                                  8.130979   \n",
       "2016-09-01 02:59:59                                  8.096893   \n",
       "2016-09-01 03:59:59                                  8.074946   \n",
       "2016-09-01 04:59:59                                  8.054678   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank4_b_level  \\\n",
       "date                                                              \n",
       "2016-09-01 00:59:59                                 -501.289139   \n",
       "2016-09-01 01:59:59                                 -499.634209   \n",
       "2016-09-01 02:59:59                                 -500.827423   \n",
       "2016-09-01 03:59:59                                 -499.474407   \n",
       "2016-09-01 04:59:59                                 -500.397500   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank5_a_air  \\\n",
       "date                                                            \n",
       "2016-09-01 00:59:59                                  7.946562   \n",
       "2016-09-01 01:59:59                                  7.958270   \n",
       "2016-09-01 02:59:59                                  8.071056   \n",
       "2016-09-01 03:59:59                                  7.897085   \n",
       "2016-09-01 04:59:59                                  8.107890   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank5_a_level  \\\n",
       "date                                                              \n",
       "2016-09-01 00:59:59                                 -432.317850   \n",
       "2016-09-01 01:59:59                                 -525.839648   \n",
       "2016-09-01 02:59:59                                 -500.801673   \n",
       "2016-09-01 03:59:59                                 -500.868509   \n",
       "2016-09-01 04:59:59                                 -509.526725   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank5_b_air  \\\n",
       "date                                                            \n",
       "2016-09-01 00:59:59                                  4.872511   \n",
       "2016-09-01 01:59:59                                  4.878850   \n",
       "2016-09-01 02:59:59                                  4.905125   \n",
       "2016-09-01 03:59:59                                  4.931400   \n",
       "2016-09-01 04:59:59                                  4.957674   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank5_b_level  \\\n",
       "date                                                              \n",
       "2016-09-01 00:59:59                                 -500.037437   \n",
       "2016-09-01 01:59:59                                 -500.162375   \n",
       "2016-09-01 02:59:59                                 -499.828510   \n",
       "2016-09-01 03:59:59                                 -499.963623   \n",
       "2016-09-01 04:59:59                                 -500.360026   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank6_a_air  \\\n",
       "date                                                            \n",
       "2016-09-01 00:59:59                                 26.705889   \n",
       "2016-09-01 01:59:59                                 25.019940   \n",
       "2016-09-01 02:59:59                                 24.994862   \n",
       "2016-09-01 03:59:59                                 24.948919   \n",
       "2016-09-01 04:59:59                                 25.003331   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank6_a_level  \n",
       "date                                                             \n",
       "2016-09-01 00:59:59                                 -499.709414  \n",
       "2016-09-01 01:59:59                                 -499.819438  \n",
       "2016-09-01 02:59:59                                 -500.622559  \n",
       "2016-09-01 03:59:59                                 -498.709987  \n",
       "2016-09-01 04:59:59                                 -500.856333  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>primary_cleaner.input.sulfate</th>\n",
       "      <th>primary_cleaner.input.depressant</th>\n",
       "      <th>primary_cleaner.input.feed_size</th>\n",
       "      <th>primary_cleaner.input.xanthate</th>\n",
       "      <th>primary_cleaner.state.floatbank8_a_air</th>\n",
       "      <th>primary_cleaner.state.floatbank8_a_level</th>\n",
       "      <th>primary_cleaner.state.floatbank8_b_air</th>\n",
       "      <th>primary_cleaner.state.floatbank8_b_level</th>\n",
       "      <th>primary_cleaner.state.floatbank8_c_air</th>\n",
       "      <th>primary_cleaner.state.floatbank8_c_level</th>\n",
       "      <th>primary_cleaner.state.floatbank8_d_air</th>\n",
       "      <th>primary_cleaner.state.floatbank8_d_level</th>\n",
       "      <th>rougher.input.feed_ag</th>\n",
       "      <th>rougher.input.feed_pb</th>\n",
       "      <th>rougher.input.feed_rate</th>\n",
       "      <th>rougher.input.feed_size</th>\n",
       "      <th>rougher.input.feed_sol</th>\n",
       "      <th>rougher.input.feed_au</th>\n",
       "      <th>rougher.input.floatbank10_sulfate</th>\n",
       "      <th>rougher.input.floatbank10_xanthate</th>\n",
       "      <th>rougher.input.floatbank11_sulfate</th>\n",
       "      <th>rougher.input.floatbank11_xanthate</th>\n",
       "      <th>rougher.state.floatbank10_a_air</th>\n",
       "      <th>rougher.state.floatbank10_a_level</th>\n",
       "      <th>rougher.state.floatbank10_b_air</th>\n",
       "      <th>rougher.state.floatbank10_b_level</th>\n",
       "      <th>rougher.state.floatbank10_c_air</th>\n",
       "      <th>rougher.state.floatbank10_c_level</th>\n",
       "      <th>rougher.state.floatbank10_d_air</th>\n",
       "      <th>rougher.state.floatbank10_d_level</th>\n",
       "      <th>rougher.state.floatbank10_e_air</th>\n",
       "      <th>rougher.state.floatbank10_e_level</th>\n",
       "      <th>rougher.state.floatbank10_f_air</th>\n",
       "      <th>rougher.state.floatbank10_f_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank2_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank2_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank2_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank2_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank3_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank3_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank3_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank3_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>5554.000000</td>\n",
       "      <td>5572.000000</td>\n",
       "      <td>5856.000000</td>\n",
       "      <td>5690.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5816.000000</td>\n",
       "      <td>5834.000000</td>\n",
       "      <td>5789.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5599.000000</td>\n",
       "      <td>5733.000000</td>\n",
       "      <td>5801.000000</td>\n",
       "      <td>5503.000000</td>\n",
       "      <td>5839.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5839.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5839.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5839.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5839.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5839.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5836.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5833.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5822.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>170.515243</td>\n",
       "      <td>8.482873</td>\n",
       "      <td>7.264651</td>\n",
       "      <td>1.321420</td>\n",
       "      <td>1481.990241</td>\n",
       "      <td>-509.057796</td>\n",
       "      <td>1486.908670</td>\n",
       "      <td>-511.743956</td>\n",
       "      <td>1468.495216</td>\n",
       "      <td>-509.741212</td>\n",
       "      <td>1486.398557</td>\n",
       "      <td>-510.723606</td>\n",
       "      <td>8.736170</td>\n",
       "      <td>3.552722</td>\n",
       "      <td>458.485472</td>\n",
       "      <td>55.937535</td>\n",
       "      <td>35.105834</td>\n",
       "      <td>8.700930</td>\n",
       "      <td>13.439933</td>\n",
       "      <td>6.169553</td>\n",
       "      <td>13.056047</td>\n",
       "      <td>6.423566</td>\n",
       "      <td>1016.294703</td>\n",
       "      <td>-404.977728</td>\n",
       "      <td>1270.598553</td>\n",
       "      <td>-482.154567</td>\n",
       "      <td>1253.495045</td>\n",
       "      <td>-480.945868</td>\n",
       "      <td>1169.675564</td>\n",
       "      <td>-482.152253</td>\n",
       "      <td>1018.386311</td>\n",
       "      <td>-476.551547</td>\n",
       "      <td>962.005375</td>\n",
       "      <td>-477.795380</td>\n",
       "      <td>25.479808</td>\n",
       "      <td>-513.714941</td>\n",
       "      <td>21.083475</td>\n",
       "      <td>-512.325717</td>\n",
       "      <td>24.345831</td>\n",
       "      <td>-519.417733</td>\n",
       "      <td>20.068079</td>\n",
       "      <td>-515.028316</td>\n",
       "      <td>15.636031</td>\n",
       "      <td>-516.266074</td>\n",
       "      <td>13.145702</td>\n",
       "      <td>-476.338907</td>\n",
       "      <td>12.308967</td>\n",
       "      <td>-512.208126</td>\n",
       "      <td>9.470986</td>\n",
       "      <td>-505.017827</td>\n",
       "      <td>16.678722</td>\n",
       "      <td>-512.351694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>49.608602</td>\n",
       "      <td>3.353105</td>\n",
       "      <td>0.611526</td>\n",
       "      <td>0.693246</td>\n",
       "      <td>310.453166</td>\n",
       "      <td>61.339256</td>\n",
       "      <td>313.224286</td>\n",
       "      <td>67.139074</td>\n",
       "      <td>309.980748</td>\n",
       "      <td>62.671873</td>\n",
       "      <td>313.725248</td>\n",
       "      <td>65.417633</td>\n",
       "      <td>2.985805</td>\n",
       "      <td>1.326113</td>\n",
       "      <td>155.958272</td>\n",
       "      <td>22.724254</td>\n",
       "      <td>10.793249</td>\n",
       "      <td>2.803046</td>\n",
       "      <td>3.997715</td>\n",
       "      <td>1.673606</td>\n",
       "      <td>4.533948</td>\n",
       "      <td>1.120371</td>\n",
       "      <td>226.494775</td>\n",
       "      <td>100.953782</td>\n",
       "      <td>285.247715</td>\n",
       "      <td>55.399631</td>\n",
       "      <td>290.703157</td>\n",
       "      <td>53.759053</td>\n",
       "      <td>285.407244</td>\n",
       "      <td>54.967985</td>\n",
       "      <td>231.675756</td>\n",
       "      <td>67.002595</td>\n",
       "      <td>220.862435</td>\n",
       "      <td>68.342305</td>\n",
       "      <td>5.450424</td>\n",
       "      <td>62.448366</td>\n",
       "      <td>5.940017</td>\n",
       "      <td>63.376776</td>\n",
       "      <td>7.950733</td>\n",
       "      <td>73.968109</td>\n",
       "      <td>7.644391</td>\n",
       "      <td>60.457838</td>\n",
       "      <td>4.660835</td>\n",
       "      <td>62.756748</td>\n",
       "      <td>4.304086</td>\n",
       "      <td>105.549424</td>\n",
       "      <td>3.762827</td>\n",
       "      <td>58.864651</td>\n",
       "      <td>3.312471</td>\n",
       "      <td>68.785898</td>\n",
       "      <td>5.404514</td>\n",
       "      <td>69.919839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000103</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>5.650000</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-799.773788</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-800.029078</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-799.995127</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-799.838846</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002986</td>\n",
       "      <td>0.046369</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000154</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>-0.123194</td>\n",
       "      <td>-657.949759</td>\n",
       "      <td>-0.821921</td>\n",
       "      <td>-650.349159</td>\n",
       "      <td>-0.282087</td>\n",
       "      <td>-650.000000</td>\n",
       "      <td>-1.047388</td>\n",
       "      <td>-648.390665</td>\n",
       "      <td>-2.076798</td>\n",
       "      <td>-650.152533</td>\n",
       "      <td>-2.593390</td>\n",
       "      <td>-650.034631</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-799.608557</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-798.467243</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-799.755798</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-809.333008</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-799.798523</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-800.836914</td>\n",
       "      <td>-0.223393</td>\n",
       "      <td>-799.661076</td>\n",
       "      <td>0.528083</td>\n",
       "      <td>-800.220337</td>\n",
       "      <td>-0.079426</td>\n",
       "      <td>-809.859706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>143.340022</td>\n",
       "      <td>6.411500</td>\n",
       "      <td>6.885625</td>\n",
       "      <td>0.888769</td>\n",
       "      <td>1497.190681</td>\n",
       "      <td>-500.455211</td>\n",
       "      <td>1497.150234</td>\n",
       "      <td>-500.936639</td>\n",
       "      <td>1437.050321</td>\n",
       "      <td>-501.300441</td>\n",
       "      <td>1498.407528</td>\n",
       "      <td>-500.553630</td>\n",
       "      <td>7.598683</td>\n",
       "      <td>3.079145</td>\n",
       "      <td>399.781738</td>\n",
       "      <td>43.890852</td>\n",
       "      <td>33.381580</td>\n",
       "      <td>7.763634</td>\n",
       "      <td>11.006487</td>\n",
       "      <td>5.884416</td>\n",
       "      <td>10.999216</td>\n",
       "      <td>5.994363</td>\n",
       "      <td>998.970605</td>\n",
       "      <td>-499.971279</td>\n",
       "      <td>1199.500725</td>\n",
       "      <td>-500.327705</td>\n",
       "      <td>1195.460415</td>\n",
       "      <td>-500.310780</td>\n",
       "      <td>1004.580472</td>\n",
       "      <td>-500.582886</td>\n",
       "      <td>998.870710</td>\n",
       "      <td>-500.268985</td>\n",
       "      <td>900.518560</td>\n",
       "      <td>-500.769058</td>\n",
       "      <td>24.908789</td>\n",
       "      <td>-500.244425</td>\n",
       "      <td>19.964880</td>\n",
       "      <td>-500.252962</td>\n",
       "      <td>22.033603</td>\n",
       "      <td>-500.368334</td>\n",
       "      <td>15.979792</td>\n",
       "      <td>-500.172778</td>\n",
       "      <td>12.057838</td>\n",
       "      <td>-501.054741</td>\n",
       "      <td>11.880119</td>\n",
       "      <td>-500.419113</td>\n",
       "      <td>10.123459</td>\n",
       "      <td>-500.879383</td>\n",
       "      <td>7.991208</td>\n",
       "      <td>-500.223089</td>\n",
       "      <td>13.012422</td>\n",
       "      <td>-500.833821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>176.103893</td>\n",
       "      <td>8.023252</td>\n",
       "      <td>7.259333</td>\n",
       "      <td>1.183362</td>\n",
       "      <td>1554.659783</td>\n",
       "      <td>-499.997402</td>\n",
       "      <td>1553.268084</td>\n",
       "      <td>-500.066588</td>\n",
       "      <td>1546.160672</td>\n",
       "      <td>-500.079537</td>\n",
       "      <td>1552.089348</td>\n",
       "      <td>-500.045560</td>\n",
       "      <td>9.628324</td>\n",
       "      <td>3.679706</td>\n",
       "      <td>489.533016</td>\n",
       "      <td>50.002004</td>\n",
       "      <td>37.336634</td>\n",
       "      <td>9.449623</td>\n",
       "      <td>14.000240</td>\n",
       "      <td>6.401502</td>\n",
       "      <td>13.998778</td>\n",
       "      <td>6.496105</td>\n",
       "      <td>1000.327062</td>\n",
       "      <td>-399.817250</td>\n",
       "      <td>1300.983516</td>\n",
       "      <td>-499.875575</td>\n",
       "      <td>1298.509443</td>\n",
       "      <td>-499.810503</td>\n",
       "      <td>1202.899254</td>\n",
       "      <td>-499.757267</td>\n",
       "      <td>1001.670995</td>\n",
       "      <td>-499.801509</td>\n",
       "      <td>999.088194</td>\n",
       "      <td>-499.754468</td>\n",
       "      <td>26.862173</td>\n",
       "      <td>-500.017410</td>\n",
       "      <td>22.909020</td>\n",
       "      <td>-500.031103</td>\n",
       "      <td>25.000337</td>\n",
       "      <td>-500.053137</td>\n",
       "      <td>20.028317</td>\n",
       "      <td>-500.023768</td>\n",
       "      <td>17.001867</td>\n",
       "      <td>-500.160145</td>\n",
       "      <td>14.952102</td>\n",
       "      <td>-499.644328</td>\n",
       "      <td>12.062877</td>\n",
       "      <td>-500.047621</td>\n",
       "      <td>9.980774</td>\n",
       "      <td>-500.001338</td>\n",
       "      <td>16.007242</td>\n",
       "      <td>-500.041085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>207.240761</td>\n",
       "      <td>10.017725</td>\n",
       "      <td>7.650000</td>\n",
       "      <td>1.763797</td>\n",
       "      <td>1601.681656</td>\n",
       "      <td>-499.575313</td>\n",
       "      <td>1601.784707</td>\n",
       "      <td>-499.323361</td>\n",
       "      <td>1600.785573</td>\n",
       "      <td>-499.009545</td>\n",
       "      <td>1600.781243</td>\n",
       "      <td>-499.612913</td>\n",
       "      <td>10.568373</td>\n",
       "      <td>4.241195</td>\n",
       "      <td>568.111571</td>\n",
       "      <td>61.638434</td>\n",
       "      <td>41.388612</td>\n",
       "      <td>10.377337</td>\n",
       "      <td>16.674640</td>\n",
       "      <td>7.002358</td>\n",
       "      <td>16.375267</td>\n",
       "      <td>7.008034</td>\n",
       "      <td>1005.088855</td>\n",
       "      <td>-300.074857</td>\n",
       "      <td>1403.256153</td>\n",
       "      <td>-497.613901</td>\n",
       "      <td>1401.907624</td>\n",
       "      <td>-497.343773</td>\n",
       "      <td>1357.767099</td>\n",
       "      <td>-495.583327</td>\n",
       "      <td>1100.884720</td>\n",
       "      <td>-497.815261</td>\n",
       "      <td>1050.044643</td>\n",
       "      <td>-495.576639</td>\n",
       "      <td>28.052589</td>\n",
       "      <td>-499.798265</td>\n",
       "      <td>24.951835</td>\n",
       "      <td>-499.836572</td>\n",
       "      <td>29.994405</td>\n",
       "      <td>-499.794288</td>\n",
       "      <td>26.958680</td>\n",
       "      <td>-499.895641</td>\n",
       "      <td>18.030985</td>\n",
       "      <td>-499.441529</td>\n",
       "      <td>15.940011</td>\n",
       "      <td>-401.523664</td>\n",
       "      <td>15.017881</td>\n",
       "      <td>-499.297033</td>\n",
       "      <td>11.992176</td>\n",
       "      <td>-499.722835</td>\n",
       "      <td>21.009076</td>\n",
       "      <td>-499.395621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>274.409626</td>\n",
       "      <td>40.024582</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>5.433169</td>\n",
       "      <td>2212.432090</td>\n",
       "      <td>-57.195404</td>\n",
       "      <td>1975.147923</td>\n",
       "      <td>-142.527229</td>\n",
       "      <td>1715.053773</td>\n",
       "      <td>-150.937035</td>\n",
       "      <td>1913.256561</td>\n",
       "      <td>-32.449456</td>\n",
       "      <td>14.406130</td>\n",
       "      <td>6.905684</td>\n",
       "      <td>707.364020</td>\n",
       "      <td>477.445473</td>\n",
       "      <td>53.477742</td>\n",
       "      <td>13.731330</td>\n",
       "      <td>24.280636</td>\n",
       "      <td>13.037750</td>\n",
       "      <td>28.949609</td>\n",
       "      <td>18.709478</td>\n",
       "      <td>1423.274687</td>\n",
       "      <td>-273.776101</td>\n",
       "      <td>1706.640419</td>\n",
       "      <td>-229.396203</td>\n",
       "      <td>1809.889686</td>\n",
       "      <td>-253.054587</td>\n",
       "      <td>1775.218584</td>\n",
       "      <td>-76.397327</td>\n",
       "      <td>1467.184843</td>\n",
       "      <td>-139.753025</td>\n",
       "      <td>1476.588107</td>\n",
       "      <td>-218.472824</td>\n",
       "      <td>59.944409</td>\n",
       "      <td>-300.343670</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>-211.999273</td>\n",
       "      <td>40.041997</td>\n",
       "      <td>-313.866425</td>\n",
       "      <td>32.039365</td>\n",
       "      <td>-202.277366</td>\n",
       "      <td>30.051797</td>\n",
       "      <td>-401.565212</td>\n",
       "      <td>31.269706</td>\n",
       "      <td>-6.506986</td>\n",
       "      <td>25.258848</td>\n",
       "      <td>-244.483566</td>\n",
       "      <td>14.090194</td>\n",
       "      <td>-126.463446</td>\n",
       "      <td>26.705889</td>\n",
       "      <td>-29.093593</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       primary_cleaner.input.sulfate  primary_cleaner.input.depressant  \\\n",
       "count                    5554.000000                       5572.000000   \n",
       "mean                      170.515243                          8.482873   \n",
       "std                        49.608602                          3.353105   \n",
       "min                         0.000103                          0.000031   \n",
       "25%                       143.340022                          6.411500   \n",
       "50%                       176.103893                          8.023252   \n",
       "75%                       207.240761                         10.017725   \n",
       "max                       274.409626                         40.024582   \n",
       "\n",
       "       primary_cleaner.input.feed_size  primary_cleaner.input.xanthate  \\\n",
       "count                      5856.000000                     5690.000000   \n",
       "mean                          7.264651                        1.321420   \n",
       "std                           0.611526                        0.693246   \n",
       "min                           5.650000                        0.000003   \n",
       "25%                           6.885625                        0.888769   \n",
       "50%                           7.259333                        1.183362   \n",
       "75%                           7.650000                        1.763797   \n",
       "max                          15.500000                        5.433169   \n",
       "\n",
       "       primary_cleaner.state.floatbank8_a_air  \\\n",
       "count                             5840.000000   \n",
       "mean                              1481.990241   \n",
       "std                                310.453166   \n",
       "min                                  0.000000   \n",
       "25%                               1497.190681   \n",
       "50%                               1554.659783   \n",
       "75%                               1601.681656   \n",
       "max                               2212.432090   \n",
       "\n",
       "       primary_cleaner.state.floatbank8_a_level  \\\n",
       "count                               5840.000000   \n",
       "mean                                -509.057796   \n",
       "std                                   61.339256   \n",
       "min                                 -799.773788   \n",
       "25%                                 -500.455211   \n",
       "50%                                 -499.997402   \n",
       "75%                                 -499.575313   \n",
       "max                                  -57.195404   \n",
       "\n",
       "       primary_cleaner.state.floatbank8_b_air  \\\n",
       "count                             5840.000000   \n",
       "mean                              1486.908670   \n",
       "std                                313.224286   \n",
       "min                                  0.000000   \n",
       "25%                               1497.150234   \n",
       "50%                               1553.268084   \n",
       "75%                               1601.784707   \n",
       "max                               1975.147923   \n",
       "\n",
       "       primary_cleaner.state.floatbank8_b_level  \\\n",
       "count                               5840.000000   \n",
       "mean                                -511.743956   \n",
       "std                                   67.139074   \n",
       "min                                 -800.029078   \n",
       "25%                                 -500.936639   \n",
       "50%                                 -500.066588   \n",
       "75%                                 -499.323361   \n",
       "max                                 -142.527229   \n",
       "\n",
       "       primary_cleaner.state.floatbank8_c_air  \\\n",
       "count                             5840.000000   \n",
       "mean                              1468.495216   \n",
       "std                                309.980748   \n",
       "min                                  0.000000   \n",
       "25%                               1437.050321   \n",
       "50%                               1546.160672   \n",
       "75%                               1600.785573   \n",
       "max                               1715.053773   \n",
       "\n",
       "       primary_cleaner.state.floatbank8_c_level  \\\n",
       "count                               5840.000000   \n",
       "mean                                -509.741212   \n",
       "std                                   62.671873   \n",
       "min                                 -799.995127   \n",
       "25%                                 -501.300441   \n",
       "50%                                 -500.079537   \n",
       "75%                                 -499.009545   \n",
       "max                                 -150.937035   \n",
       "\n",
       "       primary_cleaner.state.floatbank8_d_air  \\\n",
       "count                             5840.000000   \n",
       "mean                              1486.398557   \n",
       "std                                313.725248   \n",
       "min                                  0.000000   \n",
       "25%                               1498.407528   \n",
       "50%                               1552.089348   \n",
       "75%                               1600.781243   \n",
       "max                               1913.256561   \n",
       "\n",
       "       primary_cleaner.state.floatbank8_d_level  rougher.input.feed_ag  \\\n",
       "count                               5840.000000            5840.000000   \n",
       "mean                                -510.723606               8.736170   \n",
       "std                                   65.417633               2.985805   \n",
       "min                                 -799.838846               0.000000   \n",
       "25%                                 -500.553630               7.598683   \n",
       "50%                                 -500.045560               9.628324   \n",
       "75%                                 -499.612913              10.568373   \n",
       "max                                  -32.449456              14.406130   \n",
       "\n",
       "       rougher.input.feed_pb  rougher.input.feed_rate  \\\n",
       "count            5840.000000              5816.000000   \n",
       "mean                3.552722               458.485472   \n",
       "std                 1.326113               155.958272   \n",
       "min                 0.000000                 0.002986   \n",
       "25%                 3.079145               399.781738   \n",
       "50%                 3.679706               489.533016   \n",
       "75%                 4.241195               568.111571   \n",
       "max                 6.905684               707.364020   \n",
       "\n",
       "       rougher.input.feed_size  rougher.input.feed_sol  rougher.input.feed_au  \\\n",
       "count              5834.000000             5789.000000            5840.000000   \n",
       "mean                 55.937535               35.105834               8.700930   \n",
       "std                  22.724254               10.793249               2.803046   \n",
       "min                   0.046369                0.000000               0.000000   \n",
       "25%                  43.890852               33.381580               7.763634   \n",
       "50%                  50.002004               37.336634               9.449623   \n",
       "75%                  61.638434               41.388612              10.377337   \n",
       "max                 477.445473               53.477742              13.731330   \n",
       "\n",
       "       rougher.input.floatbank10_sulfate  rougher.input.floatbank10_xanthate  \\\n",
       "count                        5599.000000                         5733.000000   \n",
       "mean                           13.439933                            6.169553   \n",
       "std                             3.997715                            1.673606   \n",
       "min                             0.000154                            0.000052   \n",
       "25%                            11.006487                            5.884416   \n",
       "50%                            14.000240                            6.401502   \n",
       "75%                            16.674640                            7.002358   \n",
       "max                            24.280636                           13.037750   \n",
       "\n",
       "       rougher.input.floatbank11_sulfate  rougher.input.floatbank11_xanthate  \\\n",
       "count                        5801.000000                         5503.000000   \n",
       "mean                           13.056047                            6.423566   \n",
       "std                             4.533948                            1.120371   \n",
       "min                             0.000014                            0.000055   \n",
       "25%                            10.999216                            5.994363   \n",
       "50%                            13.998778                            6.496105   \n",
       "75%                            16.375267                            7.008034   \n",
       "max                            28.949609                           18.709478   \n",
       "\n",
       "       rougher.state.floatbank10_a_air  rougher.state.floatbank10_a_level  \\\n",
       "count                      5839.000000                        5840.000000   \n",
       "mean                       1016.294703                        -404.977728   \n",
       "std                         226.494775                         100.953782   \n",
       "min                          -0.123194                        -657.949759   \n",
       "25%                         998.970605                        -499.971279   \n",
       "50%                        1000.327062                        -399.817250   \n",
       "75%                        1005.088855                        -300.074857   \n",
       "max                        1423.274687                        -273.776101   \n",
       "\n",
       "       rougher.state.floatbank10_b_air  rougher.state.floatbank10_b_level  \\\n",
       "count                      5839.000000                        5840.000000   \n",
       "mean                       1270.598553                        -482.154567   \n",
       "std                         285.247715                          55.399631   \n",
       "min                          -0.821921                        -650.349159   \n",
       "25%                        1199.500725                        -500.327705   \n",
       "50%                        1300.983516                        -499.875575   \n",
       "75%                        1403.256153                        -497.613901   \n",
       "max                        1706.640419                        -229.396203   \n",
       "\n",
       "       rougher.state.floatbank10_c_air  rougher.state.floatbank10_c_level  \\\n",
       "count                      5839.000000                        5840.000000   \n",
       "mean                       1253.495045                        -480.945868   \n",
       "std                         290.703157                          53.759053   \n",
       "min                          -0.282087                        -650.000000   \n",
       "25%                        1195.460415                        -500.310780   \n",
       "50%                        1298.509443                        -499.810503   \n",
       "75%                        1401.907624                        -497.343773   \n",
       "max                        1809.889686                        -253.054587   \n",
       "\n",
       "       rougher.state.floatbank10_d_air  rougher.state.floatbank10_d_level  \\\n",
       "count                      5839.000000                        5840.000000   \n",
       "mean                       1169.675564                        -482.152253   \n",
       "std                         285.407244                          54.967985   \n",
       "min                          -1.047388                        -648.390665   \n",
       "25%                        1004.580472                        -500.582886   \n",
       "50%                        1202.899254                        -499.757267   \n",
       "75%                        1357.767099                        -495.583327   \n",
       "max                        1775.218584                         -76.397327   \n",
       "\n",
       "       rougher.state.floatbank10_e_air  rougher.state.floatbank10_e_level  \\\n",
       "count                      5839.000000                        5840.000000   \n",
       "mean                       1018.386311                        -476.551547   \n",
       "std                         231.675756                          67.002595   \n",
       "min                          -2.076798                        -650.152533   \n",
       "25%                         998.870710                        -500.268985   \n",
       "50%                        1001.670995                        -499.801509   \n",
       "75%                        1100.884720                        -497.815261   \n",
       "max                        1467.184843                        -139.753025   \n",
       "\n",
       "       rougher.state.floatbank10_f_air  rougher.state.floatbank10_f_level  \\\n",
       "count                      5839.000000                        5840.000000   \n",
       "mean                        962.005375                        -477.795380   \n",
       "std                         220.862435                          68.342305   \n",
       "min                          -2.593390                        -650.034631   \n",
       "25%                         900.518560                        -500.769058   \n",
       "50%                         999.088194                        -499.754468   \n",
       "75%                        1050.044643                        -495.576639   \n",
       "max                        1476.588107                        -218.472824   \n",
       "\n",
       "       secondary_cleaner.state.floatbank2_a_air  \\\n",
       "count                               5836.000000   \n",
       "mean                                  25.479808   \n",
       "std                                    5.450424   \n",
       "min                                    0.000000   \n",
       "25%                                   24.908789   \n",
       "50%                                   26.862173   \n",
       "75%                                   28.052589   \n",
       "max                                   59.944409   \n",
       "\n",
       "       secondary_cleaner.state.floatbank2_a_level  \\\n",
       "count                                 5840.000000   \n",
       "mean                                  -513.714941   \n",
       "std                                     62.448366   \n",
       "min                                   -799.608557   \n",
       "25%                                   -500.244425   \n",
       "50%                                   -500.017410   \n",
       "75%                                   -499.798265   \n",
       "max                                   -300.343670   \n",
       "\n",
       "       secondary_cleaner.state.floatbank2_b_air  \\\n",
       "count                               5833.000000   \n",
       "mean                                  21.083475   \n",
       "std                                    5.940017   \n",
       "min                                    0.000000   \n",
       "25%                                   19.964880   \n",
       "50%                                   22.909020   \n",
       "75%                                   24.951835   \n",
       "max                                   60.000000   \n",
       "\n",
       "       secondary_cleaner.state.floatbank2_b_level  \\\n",
       "count                                 5840.000000   \n",
       "mean                                  -512.325717   \n",
       "std                                     63.376776   \n",
       "min                                   -798.467243   \n",
       "25%                                   -500.252962   \n",
       "50%                                   -500.031103   \n",
       "75%                                   -499.836572   \n",
       "max                                   -211.999273   \n",
       "\n",
       "       secondary_cleaner.state.floatbank3_a_air  \\\n",
       "count                               5822.000000   \n",
       "mean                                  24.345831   \n",
       "std                                    7.950733   \n",
       "min                                    0.000000   \n",
       "25%                                   22.033603   \n",
       "50%                                   25.000337   \n",
       "75%                                   29.994405   \n",
       "max                                   40.041997   \n",
       "\n",
       "       secondary_cleaner.state.floatbank3_a_level  \\\n",
       "count                                 5840.000000   \n",
       "mean                                  -519.417733   \n",
       "std                                     73.968109   \n",
       "min                                   -799.755798   \n",
       "25%                                   -500.368334   \n",
       "50%                                   -500.053137   \n",
       "75%                                   -499.794288   \n",
       "max                                   -313.866425   \n",
       "\n",
       "       secondary_cleaner.state.floatbank3_b_air  \\\n",
       "count                               5840.000000   \n",
       "mean                                  20.068079   \n",
       "std                                    7.644391   \n",
       "min                                    0.000000   \n",
       "25%                                   15.979792   \n",
       "50%                                   20.028317   \n",
       "75%                                   26.958680   \n",
       "max                                   32.039365   \n",
       "\n",
       "       secondary_cleaner.state.floatbank3_b_level  \\\n",
       "count                                 5840.000000   \n",
       "mean                                  -515.028316   \n",
       "std                                     60.457838   \n",
       "min                                   -809.333008   \n",
       "25%                                   -500.172778   \n",
       "50%                                   -500.023768   \n",
       "75%                                   -499.895641   \n",
       "max                                   -202.277366   \n",
       "\n",
       "       secondary_cleaner.state.floatbank4_a_air  \\\n",
       "count                               5840.000000   \n",
       "mean                                  15.636031   \n",
       "std                                    4.660835   \n",
       "min                                    0.000000   \n",
       "25%                                   12.057838   \n",
       "50%                                   17.001867   \n",
       "75%                                   18.030985   \n",
       "max                                   30.051797   \n",
       "\n",
       "       secondary_cleaner.state.floatbank4_a_level  \\\n",
       "count                                 5840.000000   \n",
       "mean                                  -516.266074   \n",
       "std                                     62.756748   \n",
       "min                                   -799.798523   \n",
       "25%                                   -501.054741   \n",
       "50%                                   -500.160145   \n",
       "75%                                   -499.441529   \n",
       "max                                   -401.565212   \n",
       "\n",
       "       secondary_cleaner.state.floatbank4_b_air  \\\n",
       "count                               5840.000000   \n",
       "mean                                  13.145702   \n",
       "std                                    4.304086   \n",
       "min                                    0.000000   \n",
       "25%                                   11.880119   \n",
       "50%                                   14.952102   \n",
       "75%                                   15.940011   \n",
       "max                                   31.269706   \n",
       "\n",
       "       secondary_cleaner.state.floatbank4_b_level  \\\n",
       "count                                 5840.000000   \n",
       "mean                                  -476.338907   \n",
       "std                                    105.549424   \n",
       "min                                   -800.836914   \n",
       "25%                                   -500.419113   \n",
       "50%                                   -499.644328   \n",
       "75%                                   -401.523664   \n",
       "max                                     -6.506986   \n",
       "\n",
       "       secondary_cleaner.state.floatbank5_a_air  \\\n",
       "count                               5840.000000   \n",
       "mean                                  12.308967   \n",
       "std                                    3.762827   \n",
       "min                                   -0.223393   \n",
       "25%                                   10.123459   \n",
       "50%                                   12.062877   \n",
       "75%                                   15.017881   \n",
       "max                                   25.258848   \n",
       "\n",
       "       secondary_cleaner.state.floatbank5_a_level  \\\n",
       "count                                 5840.000000   \n",
       "mean                                  -512.208126   \n",
       "std                                     58.864651   \n",
       "min                                   -799.661076   \n",
       "25%                                   -500.879383   \n",
       "50%                                   -500.047621   \n",
       "75%                                   -499.297033   \n",
       "max                                   -244.483566   \n",
       "\n",
       "       secondary_cleaner.state.floatbank5_b_air  \\\n",
       "count                               5840.000000   \n",
       "mean                                   9.470986   \n",
       "std                                    3.312471   \n",
       "min                                    0.528083   \n",
       "25%                                    7.991208   \n",
       "50%                                    9.980774   \n",
       "75%                                   11.992176   \n",
       "max                                   14.090194   \n",
       "\n",
       "       secondary_cleaner.state.floatbank5_b_level  \\\n",
       "count                                 5840.000000   \n",
       "mean                                  -505.017827   \n",
       "std                                     68.785898   \n",
       "min                                   -800.220337   \n",
       "25%                                   -500.223089   \n",
       "50%                                   -500.001338   \n",
       "75%                                   -499.722835   \n",
       "max                                   -126.463446   \n",
       "\n",
       "       secondary_cleaner.state.floatbank6_a_air  \\\n",
       "count                               5840.000000   \n",
       "mean                                  16.678722   \n",
       "std                                    5.404514   \n",
       "min                                   -0.079426   \n",
       "25%                                   13.012422   \n",
       "50%                                   16.007242   \n",
       "75%                                   21.009076   \n",
       "max                                   26.705889   \n",
       "\n",
       "       secondary_cleaner.state.floatbank6_a_level  \n",
       "count                                 5840.000000  \n",
       "mean                                  -512.351694  \n",
       "std                                     69.919839  \n",
       "min                                   -809.859706  \n",
       "25%                                   -500.833821  \n",
       "50%                                   -500.041085  \n",
       "75%                                   -499.395621  \n",
       "max                                    -29.093593  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 5856 entries, 2016-09-01 00:59:59 to 2017-12-31 23:59:59\n",
      "Data columns (total 52 columns):\n",
      "primary_cleaner.input.sulfate                 5554 non-null float64\n",
      "primary_cleaner.input.depressant              5572 non-null float64\n",
      "primary_cleaner.input.feed_size               5856 non-null float64\n",
      "primary_cleaner.input.xanthate                5690 non-null float64\n",
      "primary_cleaner.state.floatbank8_a_air        5840 non-null float64\n",
      "primary_cleaner.state.floatbank8_a_level      5840 non-null float64\n",
      "primary_cleaner.state.floatbank8_b_air        5840 non-null float64\n",
      "primary_cleaner.state.floatbank8_b_level      5840 non-null float64\n",
      "primary_cleaner.state.floatbank8_c_air        5840 non-null float64\n",
      "primary_cleaner.state.floatbank8_c_level      5840 non-null float64\n",
      "primary_cleaner.state.floatbank8_d_air        5840 non-null float64\n",
      "primary_cleaner.state.floatbank8_d_level      5840 non-null float64\n",
      "rougher.input.feed_ag                         5840 non-null float64\n",
      "rougher.input.feed_pb                         5840 non-null float64\n",
      "rougher.input.feed_rate                       5816 non-null float64\n",
      "rougher.input.feed_size                       5834 non-null float64\n",
      "rougher.input.feed_sol                        5789 non-null float64\n",
      "rougher.input.feed_au                         5840 non-null float64\n",
      "rougher.input.floatbank10_sulfate             5599 non-null float64\n",
      "rougher.input.floatbank10_xanthate            5733 non-null float64\n",
      "rougher.input.floatbank11_sulfate             5801 non-null float64\n",
      "rougher.input.floatbank11_xanthate            5503 non-null float64\n",
      "rougher.state.floatbank10_a_air               5839 non-null float64\n",
      "rougher.state.floatbank10_a_level             5840 non-null float64\n",
      "rougher.state.floatbank10_b_air               5839 non-null float64\n",
      "rougher.state.floatbank10_b_level             5840 non-null float64\n",
      "rougher.state.floatbank10_c_air               5839 non-null float64\n",
      "rougher.state.floatbank10_c_level             5840 non-null float64\n",
      "rougher.state.floatbank10_d_air               5839 non-null float64\n",
      "rougher.state.floatbank10_d_level             5840 non-null float64\n",
      "rougher.state.floatbank10_e_air               5839 non-null float64\n",
      "rougher.state.floatbank10_e_level             5840 non-null float64\n",
      "rougher.state.floatbank10_f_air               5839 non-null float64\n",
      "rougher.state.floatbank10_f_level             5840 non-null float64\n",
      "secondary_cleaner.state.floatbank2_a_air      5836 non-null float64\n",
      "secondary_cleaner.state.floatbank2_a_level    5840 non-null float64\n",
      "secondary_cleaner.state.floatbank2_b_air      5833 non-null float64\n",
      "secondary_cleaner.state.floatbank2_b_level    5840 non-null float64\n",
      "secondary_cleaner.state.floatbank3_a_air      5822 non-null float64\n",
      "secondary_cleaner.state.floatbank3_a_level    5840 non-null float64\n",
      "secondary_cleaner.state.floatbank3_b_air      5840 non-null float64\n",
      "secondary_cleaner.state.floatbank3_b_level    5840 non-null float64\n",
      "secondary_cleaner.state.floatbank4_a_air      5840 non-null float64\n",
      "secondary_cleaner.state.floatbank4_a_level    5840 non-null float64\n",
      "secondary_cleaner.state.floatbank4_b_air      5840 non-null float64\n",
      "secondary_cleaner.state.floatbank4_b_level    5840 non-null float64\n",
      "secondary_cleaner.state.floatbank5_a_air      5840 non-null float64\n",
      "secondary_cleaner.state.floatbank5_a_level    5840 non-null float64\n",
      "secondary_cleaner.state.floatbank5_b_air      5840 non-null float64\n",
      "secondary_cleaner.state.floatbank5_b_level    5840 non-null float64\n",
      "secondary_cleaner.state.floatbank6_a_air      5840 non-null float64\n",
      "secondary_cleaner.state.floatbank6_a_level    5840 non-null float64\n",
      "dtypes: float64(52)\n",
      "memory usage: 2.4+ MB\n"
     ]
    }
   ],
   "source": [
    "analyze_df(gold_recovery_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество стобцов: 86\n",
      "Количество строк: 22716\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>final.output.concentrate_ag</th>\n",
       "      <th>final.output.concentrate_pb</th>\n",
       "      <th>final.output.concentrate_sol</th>\n",
       "      <th>final.output.concentrate_au</th>\n",
       "      <th>final.output.recovery</th>\n",
       "      <th>final.output.tail_ag</th>\n",
       "      <th>final.output.tail_pb</th>\n",
       "      <th>final.output.tail_sol</th>\n",
       "      <th>final.output.tail_au</th>\n",
       "      <th>primary_cleaner.input.sulfate</th>\n",
       "      <th>primary_cleaner.input.depressant</th>\n",
       "      <th>primary_cleaner.input.feed_size</th>\n",
       "      <th>primary_cleaner.input.xanthate</th>\n",
       "      <th>primary_cleaner.output.concentrate_ag</th>\n",
       "      <th>primary_cleaner.output.concentrate_pb</th>\n",
       "      <th>primary_cleaner.output.concentrate_sol</th>\n",
       "      <th>primary_cleaner.output.concentrate_au</th>\n",
       "      <th>primary_cleaner.output.tail_ag</th>\n",
       "      <th>primary_cleaner.output.tail_pb</th>\n",
       "      <th>primary_cleaner.output.tail_sol</th>\n",
       "      <th>primary_cleaner.output.tail_au</th>\n",
       "      <th>primary_cleaner.state.floatbank8_a_air</th>\n",
       "      <th>primary_cleaner.state.floatbank8_a_level</th>\n",
       "      <th>primary_cleaner.state.floatbank8_b_air</th>\n",
       "      <th>primary_cleaner.state.floatbank8_b_level</th>\n",
       "      <th>primary_cleaner.state.floatbank8_c_air</th>\n",
       "      <th>primary_cleaner.state.floatbank8_c_level</th>\n",
       "      <th>primary_cleaner.state.floatbank8_d_air</th>\n",
       "      <th>primary_cleaner.state.floatbank8_d_level</th>\n",
       "      <th>rougher.calculation.sulfate_to_au_concentrate</th>\n",
       "      <th>rougher.calculation.floatbank10_sulfate_to_au_feed</th>\n",
       "      <th>rougher.calculation.floatbank11_sulfate_to_au_feed</th>\n",
       "      <th>rougher.calculation.au_pb_ratio</th>\n",
       "      <th>rougher.input.feed_ag</th>\n",
       "      <th>rougher.input.feed_pb</th>\n",
       "      <th>rougher.input.feed_rate</th>\n",
       "      <th>rougher.input.feed_size</th>\n",
       "      <th>rougher.input.feed_sol</th>\n",
       "      <th>rougher.input.feed_au</th>\n",
       "      <th>rougher.input.floatbank10_sulfate</th>\n",
       "      <th>rougher.input.floatbank10_xanthate</th>\n",
       "      <th>rougher.input.floatbank11_sulfate</th>\n",
       "      <th>rougher.input.floatbank11_xanthate</th>\n",
       "      <th>rougher.output.concentrate_ag</th>\n",
       "      <th>rougher.output.concentrate_pb</th>\n",
       "      <th>rougher.output.concentrate_sol</th>\n",
       "      <th>rougher.output.concentrate_au</th>\n",
       "      <th>rougher.output.recovery</th>\n",
       "      <th>rougher.output.tail_ag</th>\n",
       "      <th>rougher.output.tail_pb</th>\n",
       "      <th>rougher.output.tail_sol</th>\n",
       "      <th>rougher.output.tail_au</th>\n",
       "      <th>rougher.state.floatbank10_a_air</th>\n",
       "      <th>rougher.state.floatbank10_a_level</th>\n",
       "      <th>rougher.state.floatbank10_b_air</th>\n",
       "      <th>rougher.state.floatbank10_b_level</th>\n",
       "      <th>rougher.state.floatbank10_c_air</th>\n",
       "      <th>rougher.state.floatbank10_c_level</th>\n",
       "      <th>rougher.state.floatbank10_d_air</th>\n",
       "      <th>rougher.state.floatbank10_d_level</th>\n",
       "      <th>rougher.state.floatbank10_e_air</th>\n",
       "      <th>rougher.state.floatbank10_e_level</th>\n",
       "      <th>rougher.state.floatbank10_f_air</th>\n",
       "      <th>rougher.state.floatbank10_f_level</th>\n",
       "      <th>secondary_cleaner.output.tail_ag</th>\n",
       "      <th>secondary_cleaner.output.tail_pb</th>\n",
       "      <th>secondary_cleaner.output.tail_sol</th>\n",
       "      <th>secondary_cleaner.output.tail_au</th>\n",
       "      <th>secondary_cleaner.state.floatbank2_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank2_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank2_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank2_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank3_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank3_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank3_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank3_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_level</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2016-01-15 00:00:00</td>\n",
       "      <td>6.055403</td>\n",
       "      <td>9.889648</td>\n",
       "      <td>5.507324</td>\n",
       "      <td>42.192020</td>\n",
       "      <td>70.541216</td>\n",
       "      <td>10.411962</td>\n",
       "      <td>0.895447</td>\n",
       "      <td>16.904297</td>\n",
       "      <td>2.143149</td>\n",
       "      <td>127.092003</td>\n",
       "      <td>10.128295</td>\n",
       "      <td>7.25</td>\n",
       "      <td>0.988759</td>\n",
       "      <td>8.547551</td>\n",
       "      <td>10.389648</td>\n",
       "      <td>19.529297</td>\n",
       "      <td>34.174427</td>\n",
       "      <td>14.936526</td>\n",
       "      <td>2.534912</td>\n",
       "      <td>7.476074</td>\n",
       "      <td>2.106679</td>\n",
       "      <td>1549.775757</td>\n",
       "      <td>-498.912140</td>\n",
       "      <td>1551.434204</td>\n",
       "      <td>-516.403442</td>\n",
       "      <td>1549.873901</td>\n",
       "      <td>-498.666595</td>\n",
       "      <td>1554.367432</td>\n",
       "      <td>-493.428131</td>\n",
       "      <td>41885.707031</td>\n",
       "      <td>3481.779053</td>\n",
       "      <td>3520.337158</td>\n",
       "      <td>2.838687</td>\n",
       "      <td>6.100378</td>\n",
       "      <td>2.284912</td>\n",
       "      <td>523.546326</td>\n",
       "      <td>55.486599</td>\n",
       "      <td>36.808594</td>\n",
       "      <td>6.486150</td>\n",
       "      <td>11.986616</td>\n",
       "      <td>6.007990</td>\n",
       "      <td>11.836743</td>\n",
       "      <td>6.005818</td>\n",
       "      <td>11.500771</td>\n",
       "      <td>7.101074</td>\n",
       "      <td>28.029297</td>\n",
       "      <td>19.793808</td>\n",
       "      <td>87.107763</td>\n",
       "      <td>5.008018</td>\n",
       "      <td>0.508728</td>\n",
       "      <td>19.154297</td>\n",
       "      <td>1.170244</td>\n",
       "      <td>999.706909</td>\n",
       "      <td>-404.066986</td>\n",
       "      <td>1603.011353</td>\n",
       "      <td>-434.715027</td>\n",
       "      <td>1602.375000</td>\n",
       "      <td>-442.204468</td>\n",
       "      <td>1598.937256</td>\n",
       "      <td>-451.294128</td>\n",
       "      <td>1404.472046</td>\n",
       "      <td>-455.462982</td>\n",
       "      <td>1416.354980</td>\n",
       "      <td>-451.939636</td>\n",
       "      <td>14.500184</td>\n",
       "      <td>4.694824</td>\n",
       "      <td>8.764648</td>\n",
       "      <td>2.606185</td>\n",
       "      <td>25.853109</td>\n",
       "      <td>-498.526489</td>\n",
       "      <td>23.893660</td>\n",
       "      <td>-501.406281</td>\n",
       "      <td>23.961798</td>\n",
       "      <td>-495.262817</td>\n",
       "      <td>21.940409</td>\n",
       "      <td>-499.340973</td>\n",
       "      <td>14.016835</td>\n",
       "      <td>-502.488007</td>\n",
       "      <td>12.099931</td>\n",
       "      <td>-504.715942</td>\n",
       "      <td>9.925633</td>\n",
       "      <td>-498.310211</td>\n",
       "      <td>8.079666</td>\n",
       "      <td>-500.470978</td>\n",
       "      <td>14.151341</td>\n",
       "      <td>-605.841980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-15 01:00:00</td>\n",
       "      <td>6.029369</td>\n",
       "      <td>9.968944</td>\n",
       "      <td>5.257781</td>\n",
       "      <td>42.701629</td>\n",
       "      <td>69.266198</td>\n",
       "      <td>10.462676</td>\n",
       "      <td>0.927452</td>\n",
       "      <td>16.634514</td>\n",
       "      <td>2.224930</td>\n",
       "      <td>125.629232</td>\n",
       "      <td>10.296251</td>\n",
       "      <td>7.25</td>\n",
       "      <td>1.002663</td>\n",
       "      <td>8.558743</td>\n",
       "      <td>10.497069</td>\n",
       "      <td>19.369147</td>\n",
       "      <td>34.118526</td>\n",
       "      <td>16.250511</td>\n",
       "      <td>3.049646</td>\n",
       "      <td>6.733907</td>\n",
       "      <td>2.353017</td>\n",
       "      <td>1576.166671</td>\n",
       "      <td>-500.904965</td>\n",
       "      <td>1575.950626</td>\n",
       "      <td>-499.865889</td>\n",
       "      <td>1575.994189</td>\n",
       "      <td>-499.315107</td>\n",
       "      <td>1574.479259</td>\n",
       "      <td>-498.931665</td>\n",
       "      <td>42050.861809</td>\n",
       "      <td>3498.370979</td>\n",
       "      <td>3489.981857</td>\n",
       "      <td>2.858998</td>\n",
       "      <td>6.161113</td>\n",
       "      <td>2.266033</td>\n",
       "      <td>525.290581</td>\n",
       "      <td>57.278666</td>\n",
       "      <td>35.753385</td>\n",
       "      <td>6.478583</td>\n",
       "      <td>11.971193</td>\n",
       "      <td>6.005766</td>\n",
       "      <td>11.996163</td>\n",
       "      <td>6.012594</td>\n",
       "      <td>11.615865</td>\n",
       "      <td>7.278807</td>\n",
       "      <td>28.067063</td>\n",
       "      <td>20.050975</td>\n",
       "      <td>86.843261</td>\n",
       "      <td>4.955378</td>\n",
       "      <td>0.536689</td>\n",
       "      <td>18.965234</td>\n",
       "      <td>1.184827</td>\n",
       "      <td>1000.286398</td>\n",
       "      <td>-400.065196</td>\n",
       "      <td>1600.754587</td>\n",
       "      <td>-449.953435</td>\n",
       "      <td>1600.479580</td>\n",
       "      <td>-449.830646</td>\n",
       "      <td>1600.527589</td>\n",
       "      <td>-449.953649</td>\n",
       "      <td>1399.227084</td>\n",
       "      <td>-450.869848</td>\n",
       "      <td>1399.719514</td>\n",
       "      <td>-450.119001</td>\n",
       "      <td>14.265474</td>\n",
       "      <td>4.592547</td>\n",
       "      <td>9.001494</td>\n",
       "      <td>2.488248</td>\n",
       "      <td>25.880539</td>\n",
       "      <td>-499.989656</td>\n",
       "      <td>23.889530</td>\n",
       "      <td>-500.372428</td>\n",
       "      <td>23.970550</td>\n",
       "      <td>-500.085473</td>\n",
       "      <td>22.085714</td>\n",
       "      <td>-499.446897</td>\n",
       "      <td>13.992281</td>\n",
       "      <td>-505.503262</td>\n",
       "      <td>11.950531</td>\n",
       "      <td>-501.331529</td>\n",
       "      <td>10.039245</td>\n",
       "      <td>-500.169983</td>\n",
       "      <td>7.984757</td>\n",
       "      <td>-500.582168</td>\n",
       "      <td>13.998353</td>\n",
       "      <td>-599.787184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-15 02:00:00</td>\n",
       "      <td>6.055926</td>\n",
       "      <td>10.213995</td>\n",
       "      <td>5.383759</td>\n",
       "      <td>42.657501</td>\n",
       "      <td>68.116445</td>\n",
       "      <td>10.507046</td>\n",
       "      <td>0.953716</td>\n",
       "      <td>16.208849</td>\n",
       "      <td>2.257889</td>\n",
       "      <td>123.819808</td>\n",
       "      <td>11.316280</td>\n",
       "      <td>7.25</td>\n",
       "      <td>0.991265</td>\n",
       "      <td>8.603505</td>\n",
       "      <td>10.354494</td>\n",
       "      <td>19.167579</td>\n",
       "      <td>33.969464</td>\n",
       "      <td>16.491823</td>\n",
       "      <td>3.124664</td>\n",
       "      <td>6.471768</td>\n",
       "      <td>2.416800</td>\n",
       "      <td>1601.556163</td>\n",
       "      <td>-499.997791</td>\n",
       "      <td>1600.386685</td>\n",
       "      <td>-500.607762</td>\n",
       "      <td>1602.003542</td>\n",
       "      <td>-500.870069</td>\n",
       "      <td>1599.541515</td>\n",
       "      <td>-499.827444</td>\n",
       "      <td>42018.101184</td>\n",
       "      <td>3495.348910</td>\n",
       "      <td>3502.359819</td>\n",
       "      <td>2.945989</td>\n",
       "      <td>6.116455</td>\n",
       "      <td>2.159622</td>\n",
       "      <td>530.026610</td>\n",
       "      <td>57.510649</td>\n",
       "      <td>35.971630</td>\n",
       "      <td>6.362222</td>\n",
       "      <td>11.920603</td>\n",
       "      <td>6.197377</td>\n",
       "      <td>11.920305</td>\n",
       "      <td>6.204633</td>\n",
       "      <td>11.695753</td>\n",
       "      <td>7.216833</td>\n",
       "      <td>27.454037</td>\n",
       "      <td>19.737170</td>\n",
       "      <td>86.842308</td>\n",
       "      <td>4.843460</td>\n",
       "      <td>0.546416</td>\n",
       "      <td>18.808533</td>\n",
       "      <td>1.162562</td>\n",
       "      <td>999.719565</td>\n",
       "      <td>-400.074028</td>\n",
       "      <td>1599.337330</td>\n",
       "      <td>-450.008530</td>\n",
       "      <td>1599.672797</td>\n",
       "      <td>-449.954491</td>\n",
       "      <td>1599.849325</td>\n",
       "      <td>-449.954185</td>\n",
       "      <td>1399.180945</td>\n",
       "      <td>-449.937588</td>\n",
       "      <td>1400.316682</td>\n",
       "      <td>-450.527147</td>\n",
       "      <td>14.115662</td>\n",
       "      <td>4.624775</td>\n",
       "      <td>8.842896</td>\n",
       "      <td>2.458187</td>\n",
       "      <td>26.005245</td>\n",
       "      <td>-499.929616</td>\n",
       "      <td>23.886657</td>\n",
       "      <td>-499.951928</td>\n",
       "      <td>23.913535</td>\n",
       "      <td>-499.442343</td>\n",
       "      <td>23.957717</td>\n",
       "      <td>-499.901982</td>\n",
       "      <td>14.015015</td>\n",
       "      <td>-502.520901</td>\n",
       "      <td>11.912783</td>\n",
       "      <td>-501.133383</td>\n",
       "      <td>10.070913</td>\n",
       "      <td>-500.129135</td>\n",
       "      <td>8.013877</td>\n",
       "      <td>-500.517572</td>\n",
       "      <td>14.028663</td>\n",
       "      <td>-601.427363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-15 03:00:00</td>\n",
       "      <td>6.047977</td>\n",
       "      <td>9.977019</td>\n",
       "      <td>4.858634</td>\n",
       "      <td>42.689819</td>\n",
       "      <td>68.347543</td>\n",
       "      <td>10.422762</td>\n",
       "      <td>0.883763</td>\n",
       "      <td>16.532835</td>\n",
       "      <td>2.146849</td>\n",
       "      <td>122.270188</td>\n",
       "      <td>11.322140</td>\n",
       "      <td>7.25</td>\n",
       "      <td>0.996739</td>\n",
       "      <td>7.221879</td>\n",
       "      <td>8.496563</td>\n",
       "      <td>15.978538</td>\n",
       "      <td>28.260743</td>\n",
       "      <td>16.024359</td>\n",
       "      <td>2.960362</td>\n",
       "      <td>6.843791</td>\n",
       "      <td>2.262051</td>\n",
       "      <td>1599.968720</td>\n",
       "      <td>-500.951778</td>\n",
       "      <td>1600.659236</td>\n",
       "      <td>-499.677094</td>\n",
       "      <td>1600.304144</td>\n",
       "      <td>-500.727997</td>\n",
       "      <td>1600.449520</td>\n",
       "      <td>-500.052575</td>\n",
       "      <td>42029.447991</td>\n",
       "      <td>3498.578313</td>\n",
       "      <td>3499.162912</td>\n",
       "      <td>3.002340</td>\n",
       "      <td>6.043309</td>\n",
       "      <td>2.037807</td>\n",
       "      <td>542.590390</td>\n",
       "      <td>57.792734</td>\n",
       "      <td>36.862241</td>\n",
       "      <td>6.118189</td>\n",
       "      <td>11.630094</td>\n",
       "      <td>6.203177</td>\n",
       "      <td>11.692450</td>\n",
       "      <td>6.196578</td>\n",
       "      <td>11.915047</td>\n",
       "      <td>7.175616</td>\n",
       "      <td>27.341277</td>\n",
       "      <td>19.320810</td>\n",
       "      <td>87.226430</td>\n",
       "      <td>4.655288</td>\n",
       "      <td>0.542525</td>\n",
       "      <td>19.330200</td>\n",
       "      <td>1.079755</td>\n",
       "      <td>999.814770</td>\n",
       "      <td>-400.200179</td>\n",
       "      <td>1600.059442</td>\n",
       "      <td>-450.619948</td>\n",
       "      <td>1600.012842</td>\n",
       "      <td>-449.910497</td>\n",
       "      <td>1597.725177</td>\n",
       "      <td>-450.130127</td>\n",
       "      <td>1400.943157</td>\n",
       "      <td>-450.030142</td>\n",
       "      <td>1400.234743</td>\n",
       "      <td>-449.790835</td>\n",
       "      <td>13.732071</td>\n",
       "      <td>4.482453</td>\n",
       "      <td>9.122857</td>\n",
       "      <td>2.322062</td>\n",
       "      <td>25.942508</td>\n",
       "      <td>-499.176749</td>\n",
       "      <td>23.955516</td>\n",
       "      <td>-499.848796</td>\n",
       "      <td>23.966838</td>\n",
       "      <td>-500.008812</td>\n",
       "      <td>23.954443</td>\n",
       "      <td>-499.944710</td>\n",
       "      <td>14.036510</td>\n",
       "      <td>-500.857308</td>\n",
       "      <td>11.999550</td>\n",
       "      <td>-501.193686</td>\n",
       "      <td>9.970366</td>\n",
       "      <td>-499.201640</td>\n",
       "      <td>7.977324</td>\n",
       "      <td>-500.255908</td>\n",
       "      <td>14.005551</td>\n",
       "      <td>-599.996129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-15 04:00:00</td>\n",
       "      <td>6.148599</td>\n",
       "      <td>10.142511</td>\n",
       "      <td>4.939416</td>\n",
       "      <td>42.774141</td>\n",
       "      <td>66.927016</td>\n",
       "      <td>10.360302</td>\n",
       "      <td>0.792826</td>\n",
       "      <td>16.525686</td>\n",
       "      <td>2.055292</td>\n",
       "      <td>117.988169</td>\n",
       "      <td>11.913613</td>\n",
       "      <td>7.25</td>\n",
       "      <td>1.009869</td>\n",
       "      <td>9.089428</td>\n",
       "      <td>9.986786</td>\n",
       "      <td>19.199864</td>\n",
       "      <td>33.044932</td>\n",
       "      <td>16.480185</td>\n",
       "      <td>3.112100</td>\n",
       "      <td>6.550154</td>\n",
       "      <td>2.277073</td>\n",
       "      <td>1601.339707</td>\n",
       "      <td>-498.975456</td>\n",
       "      <td>1601.437854</td>\n",
       "      <td>-500.323246</td>\n",
       "      <td>1599.581894</td>\n",
       "      <td>-500.888152</td>\n",
       "      <td>1602.649541</td>\n",
       "      <td>-500.593010</td>\n",
       "      <td>42125.354245</td>\n",
       "      <td>3494.800771</td>\n",
       "      <td>3506.679315</td>\n",
       "      <td>3.169615</td>\n",
       "      <td>6.060915</td>\n",
       "      <td>1.786875</td>\n",
       "      <td>540.531893</td>\n",
       "      <td>56.047189</td>\n",
       "      <td>34.347666</td>\n",
       "      <td>5.663707</td>\n",
       "      <td>10.957755</td>\n",
       "      <td>6.198826</td>\n",
       "      <td>10.960521</td>\n",
       "      <td>6.194897</td>\n",
       "      <td>12.411054</td>\n",
       "      <td>7.240205</td>\n",
       "      <td>27.041023</td>\n",
       "      <td>19.216101</td>\n",
       "      <td>86.688794</td>\n",
       "      <td>4.552800</td>\n",
       "      <td>0.515438</td>\n",
       "      <td>19.267422</td>\n",
       "      <td>1.012642</td>\n",
       "      <td>999.678690</td>\n",
       "      <td>-399.752729</td>\n",
       "      <td>1600.208824</td>\n",
       "      <td>-449.599614</td>\n",
       "      <td>1600.357732</td>\n",
       "      <td>-450.034364</td>\n",
       "      <td>1599.759049</td>\n",
       "      <td>-449.909799</td>\n",
       "      <td>1401.560902</td>\n",
       "      <td>-448.877187</td>\n",
       "      <td>1401.160227</td>\n",
       "      <td>-450.407128</td>\n",
       "      <td>14.079996</td>\n",
       "      <td>4.470737</td>\n",
       "      <td>8.871028</td>\n",
       "      <td>2.330448</td>\n",
       "      <td>26.024787</td>\n",
       "      <td>-500.279091</td>\n",
       "      <td>23.955345</td>\n",
       "      <td>-500.593614</td>\n",
       "      <td>23.985703</td>\n",
       "      <td>-500.083811</td>\n",
       "      <td>23.958945</td>\n",
       "      <td>-499.990309</td>\n",
       "      <td>14.027298</td>\n",
       "      <td>-499.838632</td>\n",
       "      <td>11.953070</td>\n",
       "      <td>-501.053894</td>\n",
       "      <td>9.925709</td>\n",
       "      <td>-501.686727</td>\n",
       "      <td>7.894242</td>\n",
       "      <td>-500.356035</td>\n",
       "      <td>13.996647</td>\n",
       "      <td>-601.496691</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     final.output.concentrate_ag  final.output.concentrate_pb  \\\n",
       "date                                                                            \n",
       "2016-01-15 00:00:00                     6.055403                     9.889648   \n",
       "2016-01-15 01:00:00                     6.029369                     9.968944   \n",
       "2016-01-15 02:00:00                     6.055926                    10.213995   \n",
       "2016-01-15 03:00:00                     6.047977                     9.977019   \n",
       "2016-01-15 04:00:00                     6.148599                    10.142511   \n",
       "\n",
       "                     final.output.concentrate_sol  \\\n",
       "date                                                \n",
       "2016-01-15 00:00:00                      5.507324   \n",
       "2016-01-15 01:00:00                      5.257781   \n",
       "2016-01-15 02:00:00                      5.383759   \n",
       "2016-01-15 03:00:00                      4.858634   \n",
       "2016-01-15 04:00:00                      4.939416   \n",
       "\n",
       "                     final.output.concentrate_au  final.output.recovery  \\\n",
       "date                                                                      \n",
       "2016-01-15 00:00:00                    42.192020              70.541216   \n",
       "2016-01-15 01:00:00                    42.701629              69.266198   \n",
       "2016-01-15 02:00:00                    42.657501              68.116445   \n",
       "2016-01-15 03:00:00                    42.689819              68.347543   \n",
       "2016-01-15 04:00:00                    42.774141              66.927016   \n",
       "\n",
       "                     final.output.tail_ag  final.output.tail_pb  \\\n",
       "date                                                              \n",
       "2016-01-15 00:00:00             10.411962              0.895447   \n",
       "2016-01-15 01:00:00             10.462676              0.927452   \n",
       "2016-01-15 02:00:00             10.507046              0.953716   \n",
       "2016-01-15 03:00:00             10.422762              0.883763   \n",
       "2016-01-15 04:00:00             10.360302              0.792826   \n",
       "\n",
       "                     final.output.tail_sol  final.output.tail_au  \\\n",
       "date                                                               \n",
       "2016-01-15 00:00:00              16.904297              2.143149   \n",
       "2016-01-15 01:00:00              16.634514              2.224930   \n",
       "2016-01-15 02:00:00              16.208849              2.257889   \n",
       "2016-01-15 03:00:00              16.532835              2.146849   \n",
       "2016-01-15 04:00:00              16.525686              2.055292   \n",
       "\n",
       "                     primary_cleaner.input.sulfate  \\\n",
       "date                                                 \n",
       "2016-01-15 00:00:00                     127.092003   \n",
       "2016-01-15 01:00:00                     125.629232   \n",
       "2016-01-15 02:00:00                     123.819808   \n",
       "2016-01-15 03:00:00                     122.270188   \n",
       "2016-01-15 04:00:00                     117.988169   \n",
       "\n",
       "                     primary_cleaner.input.depressant  \\\n",
       "date                                                    \n",
       "2016-01-15 00:00:00                         10.128295   \n",
       "2016-01-15 01:00:00                         10.296251   \n",
       "2016-01-15 02:00:00                         11.316280   \n",
       "2016-01-15 03:00:00                         11.322140   \n",
       "2016-01-15 04:00:00                         11.913613   \n",
       "\n",
       "                     primary_cleaner.input.feed_size  \\\n",
       "date                                                   \n",
       "2016-01-15 00:00:00                             7.25   \n",
       "2016-01-15 01:00:00                             7.25   \n",
       "2016-01-15 02:00:00                             7.25   \n",
       "2016-01-15 03:00:00                             7.25   \n",
       "2016-01-15 04:00:00                             7.25   \n",
       "\n",
       "                     primary_cleaner.input.xanthate  \\\n",
       "date                                                  \n",
       "2016-01-15 00:00:00                        0.988759   \n",
       "2016-01-15 01:00:00                        1.002663   \n",
       "2016-01-15 02:00:00                        0.991265   \n",
       "2016-01-15 03:00:00                        0.996739   \n",
       "2016-01-15 04:00:00                        1.009869   \n",
       "\n",
       "                     primary_cleaner.output.concentrate_ag  \\\n",
       "date                                                         \n",
       "2016-01-15 00:00:00                               8.547551   \n",
       "2016-01-15 01:00:00                               8.558743   \n",
       "2016-01-15 02:00:00                               8.603505   \n",
       "2016-01-15 03:00:00                               7.221879   \n",
       "2016-01-15 04:00:00                               9.089428   \n",
       "\n",
       "                     primary_cleaner.output.concentrate_pb  \\\n",
       "date                                                         \n",
       "2016-01-15 00:00:00                              10.389648   \n",
       "2016-01-15 01:00:00                              10.497069   \n",
       "2016-01-15 02:00:00                              10.354494   \n",
       "2016-01-15 03:00:00                               8.496563   \n",
       "2016-01-15 04:00:00                               9.986786   \n",
       "\n",
       "                     primary_cleaner.output.concentrate_sol  \\\n",
       "date                                                          \n",
       "2016-01-15 00:00:00                               19.529297   \n",
       "2016-01-15 01:00:00                               19.369147   \n",
       "2016-01-15 02:00:00                               19.167579   \n",
       "2016-01-15 03:00:00                               15.978538   \n",
       "2016-01-15 04:00:00                               19.199864   \n",
       "\n",
       "                     primary_cleaner.output.concentrate_au  \\\n",
       "date                                                         \n",
       "2016-01-15 00:00:00                              34.174427   \n",
       "2016-01-15 01:00:00                              34.118526   \n",
       "2016-01-15 02:00:00                              33.969464   \n",
       "2016-01-15 03:00:00                              28.260743   \n",
       "2016-01-15 04:00:00                              33.044932   \n",
       "\n",
       "                     primary_cleaner.output.tail_ag  \\\n",
       "date                                                  \n",
       "2016-01-15 00:00:00                       14.936526   \n",
       "2016-01-15 01:00:00                       16.250511   \n",
       "2016-01-15 02:00:00                       16.491823   \n",
       "2016-01-15 03:00:00                       16.024359   \n",
       "2016-01-15 04:00:00                       16.480185   \n",
       "\n",
       "                     primary_cleaner.output.tail_pb  \\\n",
       "date                                                  \n",
       "2016-01-15 00:00:00                        2.534912   \n",
       "2016-01-15 01:00:00                        3.049646   \n",
       "2016-01-15 02:00:00                        3.124664   \n",
       "2016-01-15 03:00:00                        2.960362   \n",
       "2016-01-15 04:00:00                        3.112100   \n",
       "\n",
       "                     primary_cleaner.output.tail_sol  \\\n",
       "date                                                   \n",
       "2016-01-15 00:00:00                         7.476074   \n",
       "2016-01-15 01:00:00                         6.733907   \n",
       "2016-01-15 02:00:00                         6.471768   \n",
       "2016-01-15 03:00:00                         6.843791   \n",
       "2016-01-15 04:00:00                         6.550154   \n",
       "\n",
       "                     primary_cleaner.output.tail_au  \\\n",
       "date                                                  \n",
       "2016-01-15 00:00:00                        2.106679   \n",
       "2016-01-15 01:00:00                        2.353017   \n",
       "2016-01-15 02:00:00                        2.416800   \n",
       "2016-01-15 03:00:00                        2.262051   \n",
       "2016-01-15 04:00:00                        2.277073   \n",
       "\n",
       "                     primary_cleaner.state.floatbank8_a_air  \\\n",
       "date                                                          \n",
       "2016-01-15 00:00:00                             1549.775757   \n",
       "2016-01-15 01:00:00                             1576.166671   \n",
       "2016-01-15 02:00:00                             1601.556163   \n",
       "2016-01-15 03:00:00                             1599.968720   \n",
       "2016-01-15 04:00:00                             1601.339707   \n",
       "\n",
       "                     primary_cleaner.state.floatbank8_a_level  \\\n",
       "date                                                            \n",
       "2016-01-15 00:00:00                               -498.912140   \n",
       "2016-01-15 01:00:00                               -500.904965   \n",
       "2016-01-15 02:00:00                               -499.997791   \n",
       "2016-01-15 03:00:00                               -500.951778   \n",
       "2016-01-15 04:00:00                               -498.975456   \n",
       "\n",
       "                     primary_cleaner.state.floatbank8_b_air  \\\n",
       "date                                                          \n",
       "2016-01-15 00:00:00                             1551.434204   \n",
       "2016-01-15 01:00:00                             1575.950626   \n",
       "2016-01-15 02:00:00                             1600.386685   \n",
       "2016-01-15 03:00:00                             1600.659236   \n",
       "2016-01-15 04:00:00                             1601.437854   \n",
       "\n",
       "                     primary_cleaner.state.floatbank8_b_level  \\\n",
       "date                                                            \n",
       "2016-01-15 00:00:00                               -516.403442   \n",
       "2016-01-15 01:00:00                               -499.865889   \n",
       "2016-01-15 02:00:00                               -500.607762   \n",
       "2016-01-15 03:00:00                               -499.677094   \n",
       "2016-01-15 04:00:00                               -500.323246   \n",
       "\n",
       "                     primary_cleaner.state.floatbank8_c_air  \\\n",
       "date                                                          \n",
       "2016-01-15 00:00:00                             1549.873901   \n",
       "2016-01-15 01:00:00                             1575.994189   \n",
       "2016-01-15 02:00:00                             1602.003542   \n",
       "2016-01-15 03:00:00                             1600.304144   \n",
       "2016-01-15 04:00:00                             1599.581894   \n",
       "\n",
       "                     primary_cleaner.state.floatbank8_c_level  \\\n",
       "date                                                            \n",
       "2016-01-15 00:00:00                               -498.666595   \n",
       "2016-01-15 01:00:00                               -499.315107   \n",
       "2016-01-15 02:00:00                               -500.870069   \n",
       "2016-01-15 03:00:00                               -500.727997   \n",
       "2016-01-15 04:00:00                               -500.888152   \n",
       "\n",
       "                     primary_cleaner.state.floatbank8_d_air  \\\n",
       "date                                                          \n",
       "2016-01-15 00:00:00                             1554.367432   \n",
       "2016-01-15 01:00:00                             1574.479259   \n",
       "2016-01-15 02:00:00                             1599.541515   \n",
       "2016-01-15 03:00:00                             1600.449520   \n",
       "2016-01-15 04:00:00                             1602.649541   \n",
       "\n",
       "                     primary_cleaner.state.floatbank8_d_level  \\\n",
       "date                                                            \n",
       "2016-01-15 00:00:00                               -493.428131   \n",
       "2016-01-15 01:00:00                               -498.931665   \n",
       "2016-01-15 02:00:00                               -499.827444   \n",
       "2016-01-15 03:00:00                               -500.052575   \n",
       "2016-01-15 04:00:00                               -500.593010   \n",
       "\n",
       "                     rougher.calculation.sulfate_to_au_concentrate  \\\n",
       "date                                                                 \n",
       "2016-01-15 00:00:00                                   41885.707031   \n",
       "2016-01-15 01:00:00                                   42050.861809   \n",
       "2016-01-15 02:00:00                                   42018.101184   \n",
       "2016-01-15 03:00:00                                   42029.447991   \n",
       "2016-01-15 04:00:00                                   42125.354245   \n",
       "\n",
       "                     rougher.calculation.floatbank10_sulfate_to_au_feed  \\\n",
       "date                                                                      \n",
       "2016-01-15 00:00:00                                        3481.779053    \n",
       "2016-01-15 01:00:00                                        3498.370979    \n",
       "2016-01-15 02:00:00                                        3495.348910    \n",
       "2016-01-15 03:00:00                                        3498.578313    \n",
       "2016-01-15 04:00:00                                        3494.800771    \n",
       "\n",
       "                     rougher.calculation.floatbank11_sulfate_to_au_feed  \\\n",
       "date                                                                      \n",
       "2016-01-15 00:00:00                                        3520.337158    \n",
       "2016-01-15 01:00:00                                        3489.981857    \n",
       "2016-01-15 02:00:00                                        3502.359819    \n",
       "2016-01-15 03:00:00                                        3499.162912    \n",
       "2016-01-15 04:00:00                                        3506.679315    \n",
       "\n",
       "                     rougher.calculation.au_pb_ratio  rougher.input.feed_ag  \\\n",
       "date                                                                          \n",
       "2016-01-15 00:00:00                         2.838687               6.100378   \n",
       "2016-01-15 01:00:00                         2.858998               6.161113   \n",
       "2016-01-15 02:00:00                         2.945989               6.116455   \n",
       "2016-01-15 03:00:00                         3.002340               6.043309   \n",
       "2016-01-15 04:00:00                         3.169615               6.060915   \n",
       "\n",
       "                     rougher.input.feed_pb  rougher.input.feed_rate  \\\n",
       "date                                                                  \n",
       "2016-01-15 00:00:00               2.284912               523.546326   \n",
       "2016-01-15 01:00:00               2.266033               525.290581   \n",
       "2016-01-15 02:00:00               2.159622               530.026610   \n",
       "2016-01-15 03:00:00               2.037807               542.590390   \n",
       "2016-01-15 04:00:00               1.786875               540.531893   \n",
       "\n",
       "                     rougher.input.feed_size  rougher.input.feed_sol  \\\n",
       "date                                                                   \n",
       "2016-01-15 00:00:00                55.486599               36.808594   \n",
       "2016-01-15 01:00:00                57.278666               35.753385   \n",
       "2016-01-15 02:00:00                57.510649               35.971630   \n",
       "2016-01-15 03:00:00                57.792734               36.862241   \n",
       "2016-01-15 04:00:00                56.047189               34.347666   \n",
       "\n",
       "                     rougher.input.feed_au  rougher.input.floatbank10_sulfate  \\\n",
       "date                                                                            \n",
       "2016-01-15 00:00:00               6.486150                          11.986616   \n",
       "2016-01-15 01:00:00               6.478583                          11.971193   \n",
       "2016-01-15 02:00:00               6.362222                          11.920603   \n",
       "2016-01-15 03:00:00               6.118189                          11.630094   \n",
       "2016-01-15 04:00:00               5.663707                          10.957755   \n",
       "\n",
       "                     rougher.input.floatbank10_xanthate  \\\n",
       "date                                                      \n",
       "2016-01-15 00:00:00                            6.007990   \n",
       "2016-01-15 01:00:00                            6.005766   \n",
       "2016-01-15 02:00:00                            6.197377   \n",
       "2016-01-15 03:00:00                            6.203177   \n",
       "2016-01-15 04:00:00                            6.198826   \n",
       "\n",
       "                     rougher.input.floatbank11_sulfate  \\\n",
       "date                                                     \n",
       "2016-01-15 00:00:00                          11.836743   \n",
       "2016-01-15 01:00:00                          11.996163   \n",
       "2016-01-15 02:00:00                          11.920305   \n",
       "2016-01-15 03:00:00                          11.692450   \n",
       "2016-01-15 04:00:00                          10.960521   \n",
       "\n",
       "                     rougher.input.floatbank11_xanthate  \\\n",
       "date                                                      \n",
       "2016-01-15 00:00:00                            6.005818   \n",
       "2016-01-15 01:00:00                            6.012594   \n",
       "2016-01-15 02:00:00                            6.204633   \n",
       "2016-01-15 03:00:00                            6.196578   \n",
       "2016-01-15 04:00:00                            6.194897   \n",
       "\n",
       "                     rougher.output.concentrate_ag  \\\n",
       "date                                                 \n",
       "2016-01-15 00:00:00                      11.500771   \n",
       "2016-01-15 01:00:00                      11.615865   \n",
       "2016-01-15 02:00:00                      11.695753   \n",
       "2016-01-15 03:00:00                      11.915047   \n",
       "2016-01-15 04:00:00                      12.411054   \n",
       "\n",
       "                     rougher.output.concentrate_pb  \\\n",
       "date                                                 \n",
       "2016-01-15 00:00:00                       7.101074   \n",
       "2016-01-15 01:00:00                       7.278807   \n",
       "2016-01-15 02:00:00                       7.216833   \n",
       "2016-01-15 03:00:00                       7.175616   \n",
       "2016-01-15 04:00:00                       7.240205   \n",
       "\n",
       "                     rougher.output.concentrate_sol  \\\n",
       "date                                                  \n",
       "2016-01-15 00:00:00                       28.029297   \n",
       "2016-01-15 01:00:00                       28.067063   \n",
       "2016-01-15 02:00:00                       27.454037   \n",
       "2016-01-15 03:00:00                       27.341277   \n",
       "2016-01-15 04:00:00                       27.041023   \n",
       "\n",
       "                     rougher.output.concentrate_au  rougher.output.recovery  \\\n",
       "date                                                                          \n",
       "2016-01-15 00:00:00                      19.793808                87.107763   \n",
       "2016-01-15 01:00:00                      20.050975                86.843261   \n",
       "2016-01-15 02:00:00                      19.737170                86.842308   \n",
       "2016-01-15 03:00:00                      19.320810                87.226430   \n",
       "2016-01-15 04:00:00                      19.216101                86.688794   \n",
       "\n",
       "                     rougher.output.tail_ag  rougher.output.tail_pb  \\\n",
       "date                                                                  \n",
       "2016-01-15 00:00:00                5.008018                0.508728   \n",
       "2016-01-15 01:00:00                4.955378                0.536689   \n",
       "2016-01-15 02:00:00                4.843460                0.546416   \n",
       "2016-01-15 03:00:00                4.655288                0.542525   \n",
       "2016-01-15 04:00:00                4.552800                0.515438   \n",
       "\n",
       "                     rougher.output.tail_sol  rougher.output.tail_au  \\\n",
       "date                                                                   \n",
       "2016-01-15 00:00:00                19.154297                1.170244   \n",
       "2016-01-15 01:00:00                18.965234                1.184827   \n",
       "2016-01-15 02:00:00                18.808533                1.162562   \n",
       "2016-01-15 03:00:00                19.330200                1.079755   \n",
       "2016-01-15 04:00:00                19.267422                1.012642   \n",
       "\n",
       "                     rougher.state.floatbank10_a_air  \\\n",
       "date                                                   \n",
       "2016-01-15 00:00:00                       999.706909   \n",
       "2016-01-15 01:00:00                      1000.286398   \n",
       "2016-01-15 02:00:00                       999.719565   \n",
       "2016-01-15 03:00:00                       999.814770   \n",
       "2016-01-15 04:00:00                       999.678690   \n",
       "\n",
       "                     rougher.state.floatbank10_a_level  \\\n",
       "date                                                     \n",
       "2016-01-15 00:00:00                        -404.066986   \n",
       "2016-01-15 01:00:00                        -400.065196   \n",
       "2016-01-15 02:00:00                        -400.074028   \n",
       "2016-01-15 03:00:00                        -400.200179   \n",
       "2016-01-15 04:00:00                        -399.752729   \n",
       "\n",
       "                     rougher.state.floatbank10_b_air  \\\n",
       "date                                                   \n",
       "2016-01-15 00:00:00                      1603.011353   \n",
       "2016-01-15 01:00:00                      1600.754587   \n",
       "2016-01-15 02:00:00                      1599.337330   \n",
       "2016-01-15 03:00:00                      1600.059442   \n",
       "2016-01-15 04:00:00                      1600.208824   \n",
       "\n",
       "                     rougher.state.floatbank10_b_level  \\\n",
       "date                                                     \n",
       "2016-01-15 00:00:00                        -434.715027   \n",
       "2016-01-15 01:00:00                        -449.953435   \n",
       "2016-01-15 02:00:00                        -450.008530   \n",
       "2016-01-15 03:00:00                        -450.619948   \n",
       "2016-01-15 04:00:00                        -449.599614   \n",
       "\n",
       "                     rougher.state.floatbank10_c_air  \\\n",
       "date                                                   \n",
       "2016-01-15 00:00:00                      1602.375000   \n",
       "2016-01-15 01:00:00                      1600.479580   \n",
       "2016-01-15 02:00:00                      1599.672797   \n",
       "2016-01-15 03:00:00                      1600.012842   \n",
       "2016-01-15 04:00:00                      1600.357732   \n",
       "\n",
       "                     rougher.state.floatbank10_c_level  \\\n",
       "date                                                     \n",
       "2016-01-15 00:00:00                        -442.204468   \n",
       "2016-01-15 01:00:00                        -449.830646   \n",
       "2016-01-15 02:00:00                        -449.954491   \n",
       "2016-01-15 03:00:00                        -449.910497   \n",
       "2016-01-15 04:00:00                        -450.034364   \n",
       "\n",
       "                     rougher.state.floatbank10_d_air  \\\n",
       "date                                                   \n",
       "2016-01-15 00:00:00                      1598.937256   \n",
       "2016-01-15 01:00:00                      1600.527589   \n",
       "2016-01-15 02:00:00                      1599.849325   \n",
       "2016-01-15 03:00:00                      1597.725177   \n",
       "2016-01-15 04:00:00                      1599.759049   \n",
       "\n",
       "                     rougher.state.floatbank10_d_level  \\\n",
       "date                                                     \n",
       "2016-01-15 00:00:00                        -451.294128   \n",
       "2016-01-15 01:00:00                        -449.953649   \n",
       "2016-01-15 02:00:00                        -449.954185   \n",
       "2016-01-15 03:00:00                        -450.130127   \n",
       "2016-01-15 04:00:00                        -449.909799   \n",
       "\n",
       "                     rougher.state.floatbank10_e_air  \\\n",
       "date                                                   \n",
       "2016-01-15 00:00:00                      1404.472046   \n",
       "2016-01-15 01:00:00                      1399.227084   \n",
       "2016-01-15 02:00:00                      1399.180945   \n",
       "2016-01-15 03:00:00                      1400.943157   \n",
       "2016-01-15 04:00:00                      1401.560902   \n",
       "\n",
       "                     rougher.state.floatbank10_e_level  \\\n",
       "date                                                     \n",
       "2016-01-15 00:00:00                        -455.462982   \n",
       "2016-01-15 01:00:00                        -450.869848   \n",
       "2016-01-15 02:00:00                        -449.937588   \n",
       "2016-01-15 03:00:00                        -450.030142   \n",
       "2016-01-15 04:00:00                        -448.877187   \n",
       "\n",
       "                     rougher.state.floatbank10_f_air  \\\n",
       "date                                                   \n",
       "2016-01-15 00:00:00                      1416.354980   \n",
       "2016-01-15 01:00:00                      1399.719514   \n",
       "2016-01-15 02:00:00                      1400.316682   \n",
       "2016-01-15 03:00:00                      1400.234743   \n",
       "2016-01-15 04:00:00                      1401.160227   \n",
       "\n",
       "                     rougher.state.floatbank10_f_level  \\\n",
       "date                                                     \n",
       "2016-01-15 00:00:00                        -451.939636   \n",
       "2016-01-15 01:00:00                        -450.119001   \n",
       "2016-01-15 02:00:00                        -450.527147   \n",
       "2016-01-15 03:00:00                        -449.790835   \n",
       "2016-01-15 04:00:00                        -450.407128   \n",
       "\n",
       "                     secondary_cleaner.output.tail_ag  \\\n",
       "date                                                    \n",
       "2016-01-15 00:00:00                         14.500184   \n",
       "2016-01-15 01:00:00                         14.265474   \n",
       "2016-01-15 02:00:00                         14.115662   \n",
       "2016-01-15 03:00:00                         13.732071   \n",
       "2016-01-15 04:00:00                         14.079996   \n",
       "\n",
       "                     secondary_cleaner.output.tail_pb  \\\n",
       "date                                                    \n",
       "2016-01-15 00:00:00                          4.694824   \n",
       "2016-01-15 01:00:00                          4.592547   \n",
       "2016-01-15 02:00:00                          4.624775   \n",
       "2016-01-15 03:00:00                          4.482453   \n",
       "2016-01-15 04:00:00                          4.470737   \n",
       "\n",
       "                     secondary_cleaner.output.tail_sol  \\\n",
       "date                                                     \n",
       "2016-01-15 00:00:00                           8.764648   \n",
       "2016-01-15 01:00:00                           9.001494   \n",
       "2016-01-15 02:00:00                           8.842896   \n",
       "2016-01-15 03:00:00                           9.122857   \n",
       "2016-01-15 04:00:00                           8.871028   \n",
       "\n",
       "                     secondary_cleaner.output.tail_au  \\\n",
       "date                                                    \n",
       "2016-01-15 00:00:00                          2.606185   \n",
       "2016-01-15 01:00:00                          2.488248   \n",
       "2016-01-15 02:00:00                          2.458187   \n",
       "2016-01-15 03:00:00                          2.322062   \n",
       "2016-01-15 04:00:00                          2.330448   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank2_a_air  \\\n",
       "date                                                            \n",
       "2016-01-15 00:00:00                                 25.853109   \n",
       "2016-01-15 01:00:00                                 25.880539   \n",
       "2016-01-15 02:00:00                                 26.005245   \n",
       "2016-01-15 03:00:00                                 25.942508   \n",
       "2016-01-15 04:00:00                                 26.024787   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank2_a_level  \\\n",
       "date                                                              \n",
       "2016-01-15 00:00:00                                 -498.526489   \n",
       "2016-01-15 01:00:00                                 -499.989656   \n",
       "2016-01-15 02:00:00                                 -499.929616   \n",
       "2016-01-15 03:00:00                                 -499.176749   \n",
       "2016-01-15 04:00:00                                 -500.279091   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank2_b_air  \\\n",
       "date                                                            \n",
       "2016-01-15 00:00:00                                 23.893660   \n",
       "2016-01-15 01:00:00                                 23.889530   \n",
       "2016-01-15 02:00:00                                 23.886657   \n",
       "2016-01-15 03:00:00                                 23.955516   \n",
       "2016-01-15 04:00:00                                 23.955345   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank2_b_level  \\\n",
       "date                                                              \n",
       "2016-01-15 00:00:00                                 -501.406281   \n",
       "2016-01-15 01:00:00                                 -500.372428   \n",
       "2016-01-15 02:00:00                                 -499.951928   \n",
       "2016-01-15 03:00:00                                 -499.848796   \n",
       "2016-01-15 04:00:00                                 -500.593614   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank3_a_air  \\\n",
       "date                                                            \n",
       "2016-01-15 00:00:00                                 23.961798   \n",
       "2016-01-15 01:00:00                                 23.970550   \n",
       "2016-01-15 02:00:00                                 23.913535   \n",
       "2016-01-15 03:00:00                                 23.966838   \n",
       "2016-01-15 04:00:00                                 23.985703   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank3_a_level  \\\n",
       "date                                                              \n",
       "2016-01-15 00:00:00                                 -495.262817   \n",
       "2016-01-15 01:00:00                                 -500.085473   \n",
       "2016-01-15 02:00:00                                 -499.442343   \n",
       "2016-01-15 03:00:00                                 -500.008812   \n",
       "2016-01-15 04:00:00                                 -500.083811   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank3_b_air  \\\n",
       "date                                                            \n",
       "2016-01-15 00:00:00                                 21.940409   \n",
       "2016-01-15 01:00:00                                 22.085714   \n",
       "2016-01-15 02:00:00                                 23.957717   \n",
       "2016-01-15 03:00:00                                 23.954443   \n",
       "2016-01-15 04:00:00                                 23.958945   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank3_b_level  \\\n",
       "date                                                              \n",
       "2016-01-15 00:00:00                                 -499.340973   \n",
       "2016-01-15 01:00:00                                 -499.446897   \n",
       "2016-01-15 02:00:00                                 -499.901982   \n",
       "2016-01-15 03:00:00                                 -499.944710   \n",
       "2016-01-15 04:00:00                                 -499.990309   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank4_a_air  \\\n",
       "date                                                            \n",
       "2016-01-15 00:00:00                                 14.016835   \n",
       "2016-01-15 01:00:00                                 13.992281   \n",
       "2016-01-15 02:00:00                                 14.015015   \n",
       "2016-01-15 03:00:00                                 14.036510   \n",
       "2016-01-15 04:00:00                                 14.027298   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank4_a_level  \\\n",
       "date                                                              \n",
       "2016-01-15 00:00:00                                 -502.488007   \n",
       "2016-01-15 01:00:00                                 -505.503262   \n",
       "2016-01-15 02:00:00                                 -502.520901   \n",
       "2016-01-15 03:00:00                                 -500.857308   \n",
       "2016-01-15 04:00:00                                 -499.838632   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank4_b_air  \\\n",
       "date                                                            \n",
       "2016-01-15 00:00:00                                 12.099931   \n",
       "2016-01-15 01:00:00                                 11.950531   \n",
       "2016-01-15 02:00:00                                 11.912783   \n",
       "2016-01-15 03:00:00                                 11.999550   \n",
       "2016-01-15 04:00:00                                 11.953070   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank4_b_level  \\\n",
       "date                                                              \n",
       "2016-01-15 00:00:00                                 -504.715942   \n",
       "2016-01-15 01:00:00                                 -501.331529   \n",
       "2016-01-15 02:00:00                                 -501.133383   \n",
       "2016-01-15 03:00:00                                 -501.193686   \n",
       "2016-01-15 04:00:00                                 -501.053894   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank5_a_air  \\\n",
       "date                                                            \n",
       "2016-01-15 00:00:00                                  9.925633   \n",
       "2016-01-15 01:00:00                                 10.039245   \n",
       "2016-01-15 02:00:00                                 10.070913   \n",
       "2016-01-15 03:00:00                                  9.970366   \n",
       "2016-01-15 04:00:00                                  9.925709   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank5_a_level  \\\n",
       "date                                                              \n",
       "2016-01-15 00:00:00                                 -498.310211   \n",
       "2016-01-15 01:00:00                                 -500.169983   \n",
       "2016-01-15 02:00:00                                 -500.129135   \n",
       "2016-01-15 03:00:00                                 -499.201640   \n",
       "2016-01-15 04:00:00                                 -501.686727   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank5_b_air  \\\n",
       "date                                                            \n",
       "2016-01-15 00:00:00                                  8.079666   \n",
       "2016-01-15 01:00:00                                  7.984757   \n",
       "2016-01-15 02:00:00                                  8.013877   \n",
       "2016-01-15 03:00:00                                  7.977324   \n",
       "2016-01-15 04:00:00                                  7.894242   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank5_b_level  \\\n",
       "date                                                              \n",
       "2016-01-15 00:00:00                                 -500.470978   \n",
       "2016-01-15 01:00:00                                 -500.582168   \n",
       "2016-01-15 02:00:00                                 -500.517572   \n",
       "2016-01-15 03:00:00                                 -500.255908   \n",
       "2016-01-15 04:00:00                                 -500.356035   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank6_a_air  \\\n",
       "date                                                            \n",
       "2016-01-15 00:00:00                                 14.151341   \n",
       "2016-01-15 01:00:00                                 13.998353   \n",
       "2016-01-15 02:00:00                                 14.028663   \n",
       "2016-01-15 03:00:00                                 14.005551   \n",
       "2016-01-15 04:00:00                                 13.996647   \n",
       "\n",
       "                     secondary_cleaner.state.floatbank6_a_level  \n",
       "date                                                             \n",
       "2016-01-15 00:00:00                                 -605.841980  \n",
       "2016-01-15 01:00:00                                 -599.787184  \n",
       "2016-01-15 02:00:00                                 -601.427363  \n",
       "2016-01-15 03:00:00                                 -599.996129  \n",
       "2016-01-15 04:00:00                                 -601.496691  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>final.output.concentrate_ag</th>\n",
       "      <th>final.output.concentrate_pb</th>\n",
       "      <th>final.output.concentrate_sol</th>\n",
       "      <th>final.output.concentrate_au</th>\n",
       "      <th>final.output.recovery</th>\n",
       "      <th>final.output.tail_ag</th>\n",
       "      <th>final.output.tail_pb</th>\n",
       "      <th>final.output.tail_sol</th>\n",
       "      <th>final.output.tail_au</th>\n",
       "      <th>primary_cleaner.input.sulfate</th>\n",
       "      <th>primary_cleaner.input.depressant</th>\n",
       "      <th>primary_cleaner.input.feed_size</th>\n",
       "      <th>primary_cleaner.input.xanthate</th>\n",
       "      <th>primary_cleaner.output.concentrate_ag</th>\n",
       "      <th>primary_cleaner.output.concentrate_pb</th>\n",
       "      <th>primary_cleaner.output.concentrate_sol</th>\n",
       "      <th>primary_cleaner.output.concentrate_au</th>\n",
       "      <th>primary_cleaner.output.tail_ag</th>\n",
       "      <th>primary_cleaner.output.tail_pb</th>\n",
       "      <th>primary_cleaner.output.tail_sol</th>\n",
       "      <th>primary_cleaner.output.tail_au</th>\n",
       "      <th>primary_cleaner.state.floatbank8_a_air</th>\n",
       "      <th>primary_cleaner.state.floatbank8_a_level</th>\n",
       "      <th>primary_cleaner.state.floatbank8_b_air</th>\n",
       "      <th>primary_cleaner.state.floatbank8_b_level</th>\n",
       "      <th>primary_cleaner.state.floatbank8_c_air</th>\n",
       "      <th>primary_cleaner.state.floatbank8_c_level</th>\n",
       "      <th>primary_cleaner.state.floatbank8_d_air</th>\n",
       "      <th>primary_cleaner.state.floatbank8_d_level</th>\n",
       "      <th>rougher.calculation.sulfate_to_au_concentrate</th>\n",
       "      <th>rougher.calculation.floatbank10_sulfate_to_au_feed</th>\n",
       "      <th>rougher.calculation.floatbank11_sulfate_to_au_feed</th>\n",
       "      <th>rougher.calculation.au_pb_ratio</th>\n",
       "      <th>rougher.input.feed_ag</th>\n",
       "      <th>rougher.input.feed_pb</th>\n",
       "      <th>rougher.input.feed_rate</th>\n",
       "      <th>rougher.input.feed_size</th>\n",
       "      <th>rougher.input.feed_sol</th>\n",
       "      <th>rougher.input.feed_au</th>\n",
       "      <th>rougher.input.floatbank10_sulfate</th>\n",
       "      <th>rougher.input.floatbank10_xanthate</th>\n",
       "      <th>rougher.input.floatbank11_sulfate</th>\n",
       "      <th>rougher.input.floatbank11_xanthate</th>\n",
       "      <th>rougher.output.concentrate_ag</th>\n",
       "      <th>rougher.output.concentrate_pb</th>\n",
       "      <th>rougher.output.concentrate_sol</th>\n",
       "      <th>rougher.output.concentrate_au</th>\n",
       "      <th>rougher.output.recovery</th>\n",
       "      <th>rougher.output.tail_ag</th>\n",
       "      <th>rougher.output.tail_pb</th>\n",
       "      <th>rougher.output.tail_sol</th>\n",
       "      <th>rougher.output.tail_au</th>\n",
       "      <th>rougher.state.floatbank10_a_air</th>\n",
       "      <th>rougher.state.floatbank10_a_level</th>\n",
       "      <th>rougher.state.floatbank10_b_air</th>\n",
       "      <th>rougher.state.floatbank10_b_level</th>\n",
       "      <th>rougher.state.floatbank10_c_air</th>\n",
       "      <th>rougher.state.floatbank10_c_level</th>\n",
       "      <th>rougher.state.floatbank10_d_air</th>\n",
       "      <th>rougher.state.floatbank10_d_level</th>\n",
       "      <th>rougher.state.floatbank10_e_air</th>\n",
       "      <th>rougher.state.floatbank10_e_level</th>\n",
       "      <th>rougher.state.floatbank10_f_air</th>\n",
       "      <th>rougher.state.floatbank10_f_level</th>\n",
       "      <th>secondary_cleaner.output.tail_ag</th>\n",
       "      <th>secondary_cleaner.output.tail_pb</th>\n",
       "      <th>secondary_cleaner.output.tail_sol</th>\n",
       "      <th>secondary_cleaner.output.tail_au</th>\n",
       "      <th>secondary_cleaner.state.floatbank2_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank2_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank2_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank2_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank3_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank3_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank3_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank3_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>22627.000000</td>\n",
       "      <td>22629.000000</td>\n",
       "      <td>22331.000000</td>\n",
       "      <td>22630.000000</td>\n",
       "      <td>20753.000000</td>\n",
       "      <td>22633.000000</td>\n",
       "      <td>22516.000000</td>\n",
       "      <td>22445.000000</td>\n",
       "      <td>22635.000000</td>\n",
       "      <td>21107.000000</td>\n",
       "      <td>21170.000000</td>\n",
       "      <td>22716.000000</td>\n",
       "      <td>21565.000000</td>\n",
       "      <td>22618.000000</td>\n",
       "      <td>22268.000000</td>\n",
       "      <td>21918.000000</td>\n",
       "      <td>22618.000000</td>\n",
       "      <td>22614.000000</td>\n",
       "      <td>22594.000000</td>\n",
       "      <td>22365.000000</td>\n",
       "      <td>22617.000000</td>\n",
       "      <td>22660.000000</td>\n",
       "      <td>22667.000000</td>\n",
       "      <td>22660.000000</td>\n",
       "      <td>22673.000000</td>\n",
       "      <td>22662.000000</td>\n",
       "      <td>22673.000000</td>\n",
       "      <td>22661.000000</td>\n",
       "      <td>22673.000000</td>\n",
       "      <td>2.267200e+04</td>\n",
       "      <td>2.267200e+04</td>\n",
       "      <td>2.267200e+04</td>\n",
       "      <td>21089.000000</td>\n",
       "      <td>22618.000000</td>\n",
       "      <td>22472.000000</td>\n",
       "      <td>22163.000000</td>\n",
       "      <td>22277.000000</td>\n",
       "      <td>22357.000000</td>\n",
       "      <td>22617.000000</td>\n",
       "      <td>21415.000000</td>\n",
       "      <td>22247.000000</td>\n",
       "      <td>22038.000000</td>\n",
       "      <td>20459.000000</td>\n",
       "      <td>22618.000000</td>\n",
       "      <td>22618.000000</td>\n",
       "      <td>22526.000000</td>\n",
       "      <td>22618.000000</td>\n",
       "      <td>19597.000000</td>\n",
       "      <td>19979.000000</td>\n",
       "      <td>22618.000000</td>\n",
       "      <td>19980.000000</td>\n",
       "      <td>19980.000000</td>\n",
       "      <td>22646.000000</td>\n",
       "      <td>22647.000000</td>\n",
       "      <td>22646.000000</td>\n",
       "      <td>22647.000000</td>\n",
       "      <td>22646.000000</td>\n",
       "      <td>22654.000000</td>\n",
       "      <td>22641.000000</td>\n",
       "      <td>22649.000000</td>\n",
       "      <td>22096.000000</td>\n",
       "      <td>22649.000000</td>\n",
       "      <td>22641.000000</td>\n",
       "      <td>22642.000000</td>\n",
       "      <td>22616.000000</td>\n",
       "      <td>22600.000000</td>\n",
       "      <td>20501.000000</td>\n",
       "      <td>22618.000000</td>\n",
       "      <td>22333.000000</td>\n",
       "      <td>22591.000000</td>\n",
       "      <td>22538.000000</td>\n",
       "      <td>22588.000000</td>\n",
       "      <td>22585.000000</td>\n",
       "      <td>22587.000000</td>\n",
       "      <td>22592.000000</td>\n",
       "      <td>22590.000000</td>\n",
       "      <td>22571.000000</td>\n",
       "      <td>22587.000000</td>\n",
       "      <td>22608.000000</td>\n",
       "      <td>22607.000000</td>\n",
       "      <td>22615.000000</td>\n",
       "      <td>22615.000000</td>\n",
       "      <td>22615.000000</td>\n",
       "      <td>22616.000000</td>\n",
       "      <td>22597.000000</td>\n",
       "      <td>22615.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>4.781559</td>\n",
       "      <td>9.095308</td>\n",
       "      <td>8.640317</td>\n",
       "      <td>40.001172</td>\n",
       "      <td>67.447488</td>\n",
       "      <td>8.923690</td>\n",
       "      <td>2.488252</td>\n",
       "      <td>9.523632</td>\n",
       "      <td>2.827459</td>\n",
       "      <td>140.277672</td>\n",
       "      <td>8.587607</td>\n",
       "      <td>7.288872</td>\n",
       "      <td>0.969830</td>\n",
       "      <td>7.691652</td>\n",
       "      <td>8.921110</td>\n",
       "      <td>9.573672</td>\n",
       "      <td>29.212289</td>\n",
       "      <td>14.876219</td>\n",
       "      <td>3.175822</td>\n",
       "      <td>7.208474</td>\n",
       "      <td>3.670333</td>\n",
       "      <td>1511.162809</td>\n",
       "      <td>-506.647591</td>\n",
       "      <td>1514.351654</td>\n",
       "      <td>-508.478882</td>\n",
       "      <td>1502.534056</td>\n",
       "      <td>-507.776133</td>\n",
       "      <td>1473.186236</td>\n",
       "      <td>-504.124775</td>\n",
       "      <td>-6.294614e+06</td>\n",
       "      <td>3.559534e+03</td>\n",
       "      <td>2.294828e+03</td>\n",
       "      <td>2.386287</td>\n",
       "      <td>8.065715</td>\n",
       "      <td>3.305676</td>\n",
       "      <td>441.873650</td>\n",
       "      <td>57.959166</td>\n",
       "      <td>33.276016</td>\n",
       "      <td>7.565838</td>\n",
       "      <td>11.784263</td>\n",
       "      <td>5.573526</td>\n",
       "      <td>11.209718</td>\n",
       "      <td>5.962586</td>\n",
       "      <td>10.874484</td>\n",
       "      <td>6.900646</td>\n",
       "      <td>26.095508</td>\n",
       "      <td>17.879538</td>\n",
       "      <td>83.078122</td>\n",
       "      <td>5.587861</td>\n",
       "      <td>0.593620</td>\n",
       "      <td>18.040138</td>\n",
       "      <td>1.821193</td>\n",
       "      <td>1061.497265</td>\n",
       "      <td>-389.490702</td>\n",
       "      <td>1257.007638</td>\n",
       "      <td>-477.174011</td>\n",
       "      <td>1235.736890</td>\n",
       "      <td>-476.320380</td>\n",
       "      <td>1152.689097</td>\n",
       "      <td>-477.720243</td>\n",
       "      <td>1029.790755</td>\n",
       "      <td>-475.804766</td>\n",
       "      <td>977.870710</td>\n",
       "      <td>-476.448631</td>\n",
       "      <td>13.375349</td>\n",
       "      <td>5.304107</td>\n",
       "      <td>6.465460</td>\n",
       "      <td>4.041218</td>\n",
       "      <td>27.440808</td>\n",
       "      <td>-516.727003</td>\n",
       "      <td>22.963160</td>\n",
       "      <td>-517.691199</td>\n",
       "      <td>26.993715</td>\n",
       "      <td>-500.178956</td>\n",
       "      <td>21.251091</td>\n",
       "      <td>-505.013347</td>\n",
       "      <td>18.205125</td>\n",
       "      <td>-499.878977</td>\n",
       "      <td>14.356474</td>\n",
       "      <td>-476.532613</td>\n",
       "      <td>14.883276</td>\n",
       "      <td>-503.323288</td>\n",
       "      <td>11.626743</td>\n",
       "      <td>-500.521502</td>\n",
       "      <td>17.976810</td>\n",
       "      <td>-519.361465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>2.030128</td>\n",
       "      <td>3.230797</td>\n",
       "      <td>3.785035</td>\n",
       "      <td>13.398062</td>\n",
       "      <td>11.616034</td>\n",
       "      <td>3.517917</td>\n",
       "      <td>1.189407</td>\n",
       "      <td>4.079739</td>\n",
       "      <td>1.262834</td>\n",
       "      <td>49.919004</td>\n",
       "      <td>3.611361</td>\n",
       "      <td>0.617357</td>\n",
       "      <td>0.548083</td>\n",
       "      <td>3.109306</td>\n",
       "      <td>3.706314</td>\n",
       "      <td>4.788433</td>\n",
       "      <td>10.539303</td>\n",
       "      <td>5.654342</td>\n",
       "      <td>1.652177</td>\n",
       "      <td>3.087892</td>\n",
       "      <td>1.985206</td>\n",
       "      <td>363.801472</td>\n",
       "      <td>73.798002</td>\n",
       "      <td>365.280954</td>\n",
       "      <td>76.785239</td>\n",
       "      <td>376.634842</td>\n",
       "      <td>75.003917</td>\n",
       "      <td>414.691424</td>\n",
       "      <td>82.278973</td>\n",
       "      <td>9.826921e+07</td>\n",
       "      <td>6.116649e+04</td>\n",
       "      <td>4.177895e+04</td>\n",
       "      <td>0.839999</td>\n",
       "      <td>3.125250</td>\n",
       "      <td>1.446905</td>\n",
       "      <td>156.736078</td>\n",
       "      <td>23.644823</td>\n",
       "      <td>11.494135</td>\n",
       "      <td>3.026954</td>\n",
       "      <td>4.093125</td>\n",
       "      <td>1.908123</td>\n",
       "      <td>4.685205</td>\n",
       "      <td>1.280682</td>\n",
       "      <td>4.377924</td>\n",
       "      <td>2.806948</td>\n",
       "      <td>10.140120</td>\n",
       "      <td>6.790112</td>\n",
       "      <td>14.661880</td>\n",
       "      <td>1.114614</td>\n",
       "      <td>0.315295</td>\n",
       "      <td>3.470514</td>\n",
       "      <td>0.695663</td>\n",
       "      <td>277.718805</td>\n",
       "      <td>109.946946</td>\n",
       "      <td>319.983248</td>\n",
       "      <td>66.645938</td>\n",
       "      <td>331.886940</td>\n",
       "      <td>63.957539</td>\n",
       "      <td>317.187293</td>\n",
       "      <td>61.942486</td>\n",
       "      <td>278.544787</td>\n",
       "      <td>68.327502</td>\n",
       "      <td>262.604257</td>\n",
       "      <td>69.678409</td>\n",
       "      <td>5.768719</td>\n",
       "      <td>3.092536</td>\n",
       "      <td>4.277993</td>\n",
       "      <td>2.605738</td>\n",
       "      <td>7.763001</td>\n",
       "      <td>83.411562</td>\n",
       "      <td>7.715298</td>\n",
       "      <td>83.492128</td>\n",
       "      <td>8.262921</td>\n",
       "      <td>83.293510</td>\n",
       "      <td>7.018935</td>\n",
       "      <td>71.552885</td>\n",
       "      <td>6.560700</td>\n",
       "      <td>80.273964</td>\n",
       "      <td>5.655791</td>\n",
       "      <td>93.822791</td>\n",
       "      <td>6.372811</td>\n",
       "      <td>72.925589</td>\n",
       "      <td>5.757449</td>\n",
       "      <td>78.956292</td>\n",
       "      <td>6.636203</td>\n",
       "      <td>75.477151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.080000</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-800.037388</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-800.409770</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-800.362914</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-800.166222</td>\n",
       "      <td>-2.248017e+09</td>\n",
       "      <td>-2.023459e+06</td>\n",
       "      <td>-4.014502e+06</td>\n",
       "      <td>-0.010548</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.046369</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.594562</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.014648</td>\n",
       "      <td>0.020676</td>\n",
       "      <td>-0.345168</td>\n",
       "      <td>-658.223080</td>\n",
       "      <td>-0.821921</td>\n",
       "      <td>-650.437575</td>\n",
       "      <td>-0.378521</td>\n",
       "      <td>-650.151190</td>\n",
       "      <td>-1.197435</td>\n",
       "      <td>-649.300693</td>\n",
       "      <td>-2.163619</td>\n",
       "      <td>-650.196204</td>\n",
       "      <td>-2.636171</td>\n",
       "      <td>-650.072915</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-799.841677</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-799.967956</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-799.926254</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-809.421923</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-799.920713</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-800.836914</td>\n",
       "      <td>-0.423260</td>\n",
       "      <td>-799.741097</td>\n",
       "      <td>0.427084</td>\n",
       "      <td>-800.258209</td>\n",
       "      <td>-0.079426</td>\n",
       "      <td>-810.473526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>4.018525</td>\n",
       "      <td>8.750171</td>\n",
       "      <td>7.116799</td>\n",
       "      <td>42.383721</td>\n",
       "      <td>63.282393</td>\n",
       "      <td>7.684016</td>\n",
       "      <td>1.805376</td>\n",
       "      <td>8.143576</td>\n",
       "      <td>2.303108</td>\n",
       "      <td>110.177081</td>\n",
       "      <td>6.018085</td>\n",
       "      <td>6.920000</td>\n",
       "      <td>0.626346</td>\n",
       "      <td>6.771102</td>\n",
       "      <td>7.834690</td>\n",
       "      <td>7.009980</td>\n",
       "      <td>29.374406</td>\n",
       "      <td>13.464756</td>\n",
       "      <td>2.269103</td>\n",
       "      <td>5.961535</td>\n",
       "      <td>2.741534</td>\n",
       "      <td>1500.593119</td>\n",
       "      <td>-500.426010</td>\n",
       "      <td>1500.500790</td>\n",
       "      <td>-500.558891</td>\n",
       "      <td>1499.146216</td>\n",
       "      <td>-500.953774</td>\n",
       "      <td>1447.698210</td>\n",
       "      <td>-500.616787</td>\n",
       "      <td>3.988478e+04</td>\n",
       "      <td>2.506366e+03</td>\n",
       "      <td>2.501745e+03</td>\n",
       "      <td>2.021360</td>\n",
       "      <td>6.944415</td>\n",
       "      <td>2.658814</td>\n",
       "      <td>398.222097</td>\n",
       "      <td>46.687088</td>\n",
       "      <td>32.524264</td>\n",
       "      <td>6.485009</td>\n",
       "      <td>9.915698</td>\n",
       "      <td>5.046782</td>\n",
       "      <td>9.281388</td>\n",
       "      <td>5.353321</td>\n",
       "      <td>10.126646</td>\n",
       "      <td>6.374692</td>\n",
       "      <td>25.815139</td>\n",
       "      <td>17.928729</td>\n",
       "      <td>80.941306</td>\n",
       "      <td>4.887758</td>\n",
       "      <td>0.427513</td>\n",
       "      <td>15.788283</td>\n",
       "      <td>1.403951</td>\n",
       "      <td>999.482192</td>\n",
       "      <td>-499.930417</td>\n",
       "      <td>1197.548268</td>\n",
       "      <td>-500.297595</td>\n",
       "      <td>1100.701647</td>\n",
       "      <td>-500.323902</td>\n",
       "      <td>1040.497698</td>\n",
       "      <td>-500.590442</td>\n",
       "      <td>950.272126</td>\n",
       "      <td>-500.357323</td>\n",
       "      <td>900.173576</td>\n",
       "      <td>-500.749731</td>\n",
       "      <td>11.802936</td>\n",
       "      <td>3.451435</td>\n",
       "      <td>2.658971</td>\n",
       "      <td>2.877554</td>\n",
       "      <td>25.011462</td>\n",
       "      <td>-500.303776</td>\n",
       "      <td>20.018733</td>\n",
       "      <td>-500.318700</td>\n",
       "      <td>24.020017</td>\n",
       "      <td>-500.273049</td>\n",
       "      <td>18.070333</td>\n",
       "      <td>-500.150167</td>\n",
       "      <td>14.095940</td>\n",
       "      <td>-500.896232</td>\n",
       "      <td>10.882675</td>\n",
       "      <td>-500.309169</td>\n",
       "      <td>10.941299</td>\n",
       "      <td>-500.628697</td>\n",
       "      <td>8.037533</td>\n",
       "      <td>-500.167897</td>\n",
       "      <td>13.968418</td>\n",
       "      <td>-500.981671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>4.953729</td>\n",
       "      <td>9.914519</td>\n",
       "      <td>8.908792</td>\n",
       "      <td>44.653436</td>\n",
       "      <td>68.322258</td>\n",
       "      <td>9.484369</td>\n",
       "      <td>2.653001</td>\n",
       "      <td>10.212998</td>\n",
       "      <td>2.913794</td>\n",
       "      <td>141.330501</td>\n",
       "      <td>8.018173</td>\n",
       "      <td>7.280000</td>\n",
       "      <td>0.905286</td>\n",
       "      <td>8.265643</td>\n",
       "      <td>9.921116</td>\n",
       "      <td>9.851136</td>\n",
       "      <td>32.359813</td>\n",
       "      <td>15.600454</td>\n",
       "      <td>3.154022</td>\n",
       "      <td>7.873945</td>\n",
       "      <td>3.513008</td>\n",
       "      <td>1600.079008</td>\n",
       "      <td>-499.963174</td>\n",
       "      <td>1600.032196</td>\n",
       "      <td>-500.001770</td>\n",
       "      <td>1599.749504</td>\n",
       "      <td>-499.982485</td>\n",
       "      <td>1598.993577</td>\n",
       "      <td>-499.961283</td>\n",
       "      <td>4.405929e+04</td>\n",
       "      <td>2.940838e+03</td>\n",
       "      <td>2.897014e+03</td>\n",
       "      <td>2.286493</td>\n",
       "      <td>8.302613</td>\n",
       "      <td>3.432054</td>\n",
       "      <td>488.968597</td>\n",
       "      <td>53.405122</td>\n",
       "      <td>36.559955</td>\n",
       "      <td>7.884832</td>\n",
       "      <td>11.998801</td>\n",
       "      <td>5.999078</td>\n",
       "      <td>11.946466</td>\n",
       "      <td>6.034551</td>\n",
       "      <td>11.785127</td>\n",
       "      <td>7.572855</td>\n",
       "      <td>29.382638</td>\n",
       "      <td>20.003202</td>\n",
       "      <td>86.144121</td>\n",
       "      <td>5.759630</td>\n",
       "      <td>0.590746</td>\n",
       "      <td>18.082595</td>\n",
       "      <td>1.808567</td>\n",
       "      <td>1000.874850</td>\n",
       "      <td>-300.401147</td>\n",
       "      <td>1299.976586</td>\n",
       "      <td>-499.847233</td>\n",
       "      <td>1298.358825</td>\n",
       "      <td>-499.786527</td>\n",
       "      <td>1200.061465</td>\n",
       "      <td>-499.674320</td>\n",
       "      <td>1049.208185</td>\n",
       "      <td>-499.749602</td>\n",
       "      <td>999.693303</td>\n",
       "      <td>-499.631519</td>\n",
       "      <td>15.222165</td>\n",
       "      <td>5.074145</td>\n",
       "      <td>6.966645</td>\n",
       "      <td>3.956171</td>\n",
       "      <td>28.927709</td>\n",
       "      <td>-499.996457</td>\n",
       "      <td>24.978758</td>\n",
       "      <td>-500.035682</td>\n",
       "      <td>27.959889</td>\n",
       "      <td>-499.957251</td>\n",
       "      <td>21.971564</td>\n",
       "      <td>-499.993182</td>\n",
       "      <td>18.007326</td>\n",
       "      <td>-499.917108</td>\n",
       "      <td>14.947646</td>\n",
       "      <td>-499.612292</td>\n",
       "      <td>14.859117</td>\n",
       "      <td>-499.865158</td>\n",
       "      <td>10.989756</td>\n",
       "      <td>-499.951980</td>\n",
       "      <td>18.004215</td>\n",
       "      <td>-500.095463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>5.862593</td>\n",
       "      <td>10.929839</td>\n",
       "      <td>10.705824</td>\n",
       "      <td>46.111999</td>\n",
       "      <td>72.950836</td>\n",
       "      <td>11.084557</td>\n",
       "      <td>3.287790</td>\n",
       "      <td>11.860824</td>\n",
       "      <td>3.555077</td>\n",
       "      <td>174.049914</td>\n",
       "      <td>11.003573</td>\n",
       "      <td>7.680000</td>\n",
       "      <td>1.200734</td>\n",
       "      <td>9.697896</td>\n",
       "      <td>11.266100</td>\n",
       "      <td>13.180304</td>\n",
       "      <td>34.770726</td>\n",
       "      <td>17.931084</td>\n",
       "      <td>4.123574</td>\n",
       "      <td>9.348743</td>\n",
       "      <td>4.559485</td>\n",
       "      <td>1696.691067</td>\n",
       "      <td>-499.458089</td>\n",
       "      <td>1698.933079</td>\n",
       "      <td>-499.414187</td>\n",
       "      <td>1699.151169</td>\n",
       "      <td>-498.932758</td>\n",
       "      <td>1698.142189</td>\n",
       "      <td>-499.135326</td>\n",
       "      <td>4.814479e+04</td>\n",
       "      <td>3.613983e+03</td>\n",
       "      <td>3.529913e+03</td>\n",
       "      <td>2.711070</td>\n",
       "      <td>10.135202</td>\n",
       "      <td>4.204960</td>\n",
       "      <td>546.587401</td>\n",
       "      <td>64.096151</td>\n",
       "      <td>39.675470</td>\n",
       "      <td>9.668064</td>\n",
       "      <td>14.437582</td>\n",
       "      <td>6.700493</td>\n",
       "      <td>14.015696</td>\n",
       "      <td>6.799526</td>\n",
       "      <td>13.615299</td>\n",
       "      <td>8.477358</td>\n",
       "      <td>32.002766</td>\n",
       "      <td>21.564238</td>\n",
       "      <td>89.987561</td>\n",
       "      <td>6.386301</td>\n",
       "      <td>0.763219</td>\n",
       "      <td>20.084427</td>\n",
       "      <td>2.215317</td>\n",
       "      <td>1210.446131</td>\n",
       "      <td>-299.992579</td>\n",
       "      <td>1407.132038</td>\n",
       "      <td>-403.034804</td>\n",
       "      <td>1437.004925</td>\n",
       "      <td>-403.386144</td>\n",
       "      <td>1342.550632</td>\n",
       "      <td>-443.310570</td>\n",
       "      <td>1198.762174</td>\n",
       "      <td>-403.199939</td>\n",
       "      <td>1099.189914</td>\n",
       "      <td>-403.810536</td>\n",
       "      <td>17.231611</td>\n",
       "      <td>7.585281</td>\n",
       "      <td>10.152612</td>\n",
       "      <td>5.006944</td>\n",
       "      <td>32.892271</td>\n",
       "      <td>-499.686110</td>\n",
       "      <td>27.999758</td>\n",
       "      <td>-499.798350</td>\n",
       "      <td>32.981390</td>\n",
       "      <td>-499.386548</td>\n",
       "      <td>25.961958</td>\n",
       "      <td>-499.821199</td>\n",
       "      <td>22.998194</td>\n",
       "      <td>-498.361545</td>\n",
       "      <td>17.977502</td>\n",
       "      <td>-400.224147</td>\n",
       "      <td>18.014914</td>\n",
       "      <td>-498.489381</td>\n",
       "      <td>14.001193</td>\n",
       "      <td>-499.492354</td>\n",
       "      <td>23.009704</td>\n",
       "      <td>-499.526388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>16.001945</td>\n",
       "      <td>17.031899</td>\n",
       "      <td>19.615720</td>\n",
       "      <td>53.611374</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>19.552149</td>\n",
       "      <td>6.086532</td>\n",
       "      <td>22.861749</td>\n",
       "      <td>9.789625</td>\n",
       "      <td>274.409626</td>\n",
       "      <td>40.024582</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>6.027907</td>\n",
       "      <td>16.081632</td>\n",
       "      <td>17.081278</td>\n",
       "      <td>22.458114</td>\n",
       "      <td>45.933934</td>\n",
       "      <td>29.459575</td>\n",
       "      <td>9.634565</td>\n",
       "      <td>22.276364</td>\n",
       "      <td>18.528821</td>\n",
       "      <td>2212.432090</td>\n",
       "      <td>-57.195404</td>\n",
       "      <td>2361.414084</td>\n",
       "      <td>-142.527229</td>\n",
       "      <td>2042.072583</td>\n",
       "      <td>-150.937035</td>\n",
       "      <td>2398.903635</td>\n",
       "      <td>-20.654343</td>\n",
       "      <td>8.332404e+07</td>\n",
       "      <td>6.211433e+06</td>\n",
       "      <td>1.537564e+06</td>\n",
       "      <td>39.379272</td>\n",
       "      <td>14.869652</td>\n",
       "      <td>7.142594</td>\n",
       "      <td>717.508837</td>\n",
       "      <td>484.967466</td>\n",
       "      <td>53.477742</td>\n",
       "      <td>14.093363</td>\n",
       "      <td>36.118275</td>\n",
       "      <td>31.493331</td>\n",
       "      <td>37.980648</td>\n",
       "      <td>18.709478</td>\n",
       "      <td>24.480271</td>\n",
       "      <td>18.394042</td>\n",
       "      <td>40.089993</td>\n",
       "      <td>28.824507</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.719177</td>\n",
       "      <td>3.778064</td>\n",
       "      <td>66.117188</td>\n",
       "      <td>9.688980</td>\n",
       "      <td>1521.980372</td>\n",
       "      <td>-19.383282</td>\n",
       "      <td>1809.194008</td>\n",
       "      <td>9.457822</td>\n",
       "      <td>2499.129174</td>\n",
       "      <td>-10.242658</td>\n",
       "      <td>2077.222699</td>\n",
       "      <td>21.365035</td>\n",
       "      <td>2330.310425</td>\n",
       "      <td>-2.406020</td>\n",
       "      <td>1950.493751</td>\n",
       "      <td>-21.745268</td>\n",
       "      <td>23.264738</td>\n",
       "      <td>17.042505</td>\n",
       "      <td>33.058594</td>\n",
       "      <td>26.811643</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>-127.875565</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>-211.999273</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>-191.678810</td>\n",
       "      <td>39.487633</td>\n",
       "      <td>-139.771483</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>-127.692333</td>\n",
       "      <td>31.269706</td>\n",
       "      <td>-6.506986</td>\n",
       "      <td>63.116298</td>\n",
       "      <td>-244.483566</td>\n",
       "      <td>39.846228</td>\n",
       "      <td>-120.190931</td>\n",
       "      <td>54.876806</td>\n",
       "      <td>-29.093593</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       final.output.concentrate_ag  final.output.concentrate_pb  \\\n",
       "count                 22627.000000                 22629.000000   \n",
       "mean                      4.781559                     9.095308   \n",
       "std                       2.030128                     3.230797   \n",
       "min                       0.000000                     0.000000   \n",
       "25%                       4.018525                     8.750171   \n",
       "50%                       4.953729                     9.914519   \n",
       "75%                       5.862593                    10.929839   \n",
       "max                      16.001945                    17.031899   \n",
       "\n",
       "       final.output.concentrate_sol  final.output.concentrate_au  \\\n",
       "count                  22331.000000                 22630.000000   \n",
       "mean                       8.640317                    40.001172   \n",
       "std                        3.785035                    13.398062   \n",
       "min                        0.000000                     0.000000   \n",
       "25%                        7.116799                    42.383721   \n",
       "50%                        8.908792                    44.653436   \n",
       "75%                       10.705824                    46.111999   \n",
       "max                       19.615720                    53.611374   \n",
       "\n",
       "       final.output.recovery  final.output.tail_ag  final.output.tail_pb  \\\n",
       "count           20753.000000          22633.000000          22516.000000   \n",
       "mean               67.447488              8.923690              2.488252   \n",
       "std                11.616034              3.517917              1.189407   \n",
       "min                 0.000000              0.000000              0.000000   \n",
       "25%                63.282393              7.684016              1.805376   \n",
       "50%                68.322258              9.484369              2.653001   \n",
       "75%                72.950836             11.084557              3.287790   \n",
       "max               100.000000             19.552149              6.086532   \n",
       "\n",
       "       final.output.tail_sol  final.output.tail_au  \\\n",
       "count           22445.000000          22635.000000   \n",
       "mean                9.523632              2.827459   \n",
       "std                 4.079739              1.262834   \n",
       "min                 0.000000              0.000000   \n",
       "25%                 8.143576              2.303108   \n",
       "50%                10.212998              2.913794   \n",
       "75%                11.860824              3.555077   \n",
       "max                22.861749              9.789625   \n",
       "\n",
       "       primary_cleaner.input.sulfate  primary_cleaner.input.depressant  \\\n",
       "count                   21107.000000                      21170.000000   \n",
       "mean                      140.277672                          8.587607   \n",
       "std                        49.919004                          3.611361   \n",
       "min                         0.000003                          0.000000   \n",
       "25%                       110.177081                          6.018085   \n",
       "50%                       141.330501                          8.018173   \n",
       "75%                       174.049914                         11.003573   \n",
       "max                       274.409626                         40.024582   \n",
       "\n",
       "       primary_cleaner.input.feed_size  primary_cleaner.input.xanthate  \\\n",
       "count                     22716.000000                    21565.000000   \n",
       "mean                          7.288872                        0.969830   \n",
       "std                           0.617357                        0.548083   \n",
       "min                           1.080000                        0.000003   \n",
       "25%                           6.920000                        0.626346   \n",
       "50%                           7.280000                        0.905286   \n",
       "75%                           7.680000                        1.200734   \n",
       "max                          15.500000                        6.027907   \n",
       "\n",
       "       primary_cleaner.output.concentrate_ag  \\\n",
       "count                           22618.000000   \n",
       "mean                                7.691652   \n",
       "std                                 3.109306   \n",
       "min                                 0.000000   \n",
       "25%                                 6.771102   \n",
       "50%                                 8.265643   \n",
       "75%                                 9.697896   \n",
       "max                                16.081632   \n",
       "\n",
       "       primary_cleaner.output.concentrate_pb  \\\n",
       "count                           22268.000000   \n",
       "mean                                8.921110   \n",
       "std                                 3.706314   \n",
       "min                                 0.000000   \n",
       "25%                                 7.834690   \n",
       "50%                                 9.921116   \n",
       "75%                                11.266100   \n",
       "max                                17.081278   \n",
       "\n",
       "       primary_cleaner.output.concentrate_sol  \\\n",
       "count                            21918.000000   \n",
       "mean                                 9.573672   \n",
       "std                                  4.788433   \n",
       "min                                  0.000000   \n",
       "25%                                  7.009980   \n",
       "50%                                  9.851136   \n",
       "75%                                 13.180304   \n",
       "max                                 22.458114   \n",
       "\n",
       "       primary_cleaner.output.concentrate_au  primary_cleaner.output.tail_ag  \\\n",
       "count                           22618.000000                    22614.000000   \n",
       "mean                               29.212289                       14.876219   \n",
       "std                                10.539303                        5.654342   \n",
       "min                                 0.000000                        0.000000   \n",
       "25%                                29.374406                       13.464756   \n",
       "50%                                32.359813                       15.600454   \n",
       "75%                                34.770726                       17.931084   \n",
       "max                                45.933934                       29.459575   \n",
       "\n",
       "       primary_cleaner.output.tail_pb  primary_cleaner.output.tail_sol  \\\n",
       "count                    22594.000000                     22365.000000   \n",
       "mean                         3.175822                         7.208474   \n",
       "std                          1.652177                         3.087892   \n",
       "min                          0.000000                         0.000000   \n",
       "25%                          2.269103                         5.961535   \n",
       "50%                          3.154022                         7.873945   \n",
       "75%                          4.123574                         9.348743   \n",
       "max                          9.634565                        22.276364   \n",
       "\n",
       "       primary_cleaner.output.tail_au  primary_cleaner.state.floatbank8_a_air  \\\n",
       "count                    22617.000000                            22660.000000   \n",
       "mean                         3.670333                             1511.162809   \n",
       "std                          1.985206                              363.801472   \n",
       "min                          0.000000                                0.000000   \n",
       "25%                          2.741534                             1500.593119   \n",
       "50%                          3.513008                             1600.079008   \n",
       "75%                          4.559485                             1696.691067   \n",
       "max                         18.528821                             2212.432090   \n",
       "\n",
       "       primary_cleaner.state.floatbank8_a_level  \\\n",
       "count                              22667.000000   \n",
       "mean                                -506.647591   \n",
       "std                                   73.798002   \n",
       "min                                 -800.037388   \n",
       "25%                                 -500.426010   \n",
       "50%                                 -499.963174   \n",
       "75%                                 -499.458089   \n",
       "max                                  -57.195404   \n",
       "\n",
       "       primary_cleaner.state.floatbank8_b_air  \\\n",
       "count                            22660.000000   \n",
       "mean                              1514.351654   \n",
       "std                                365.280954   \n",
       "min                                  0.000000   \n",
       "25%                               1500.500790   \n",
       "50%                               1600.032196   \n",
       "75%                               1698.933079   \n",
       "max                               2361.414084   \n",
       "\n",
       "       primary_cleaner.state.floatbank8_b_level  \\\n",
       "count                              22673.000000   \n",
       "mean                                -508.478882   \n",
       "std                                   76.785239   \n",
       "min                                 -800.409770   \n",
       "25%                                 -500.558891   \n",
       "50%                                 -500.001770   \n",
       "75%                                 -499.414187   \n",
       "max                                 -142.527229   \n",
       "\n",
       "       primary_cleaner.state.floatbank8_c_air  \\\n",
       "count                            22662.000000   \n",
       "mean                              1502.534056   \n",
       "std                                376.634842   \n",
       "min                                  0.000000   \n",
       "25%                               1499.146216   \n",
       "50%                               1599.749504   \n",
       "75%                               1699.151169   \n",
       "max                               2042.072583   \n",
       "\n",
       "       primary_cleaner.state.floatbank8_c_level  \\\n",
       "count                              22673.000000   \n",
       "mean                                -507.776133   \n",
       "std                                   75.003917   \n",
       "min                                 -800.362914   \n",
       "25%                                 -500.953774   \n",
       "50%                                 -499.982485   \n",
       "75%                                 -498.932758   \n",
       "max                                 -150.937035   \n",
       "\n",
       "       primary_cleaner.state.floatbank8_d_air  \\\n",
       "count                            22661.000000   \n",
       "mean                              1473.186236   \n",
       "std                                414.691424   \n",
       "min                                  0.000000   \n",
       "25%                               1447.698210   \n",
       "50%                               1598.993577   \n",
       "75%                               1698.142189   \n",
       "max                               2398.903635   \n",
       "\n",
       "       primary_cleaner.state.floatbank8_d_level  \\\n",
       "count                              22673.000000   \n",
       "mean                                -504.124775   \n",
       "std                                   82.278973   \n",
       "min                                 -800.166222   \n",
       "25%                                 -500.616787   \n",
       "50%                                 -499.961283   \n",
       "75%                                 -499.135326   \n",
       "max                                  -20.654343   \n",
       "\n",
       "       rougher.calculation.sulfate_to_au_concentrate  \\\n",
       "count                                   2.267200e+04   \n",
       "mean                                   -6.294614e+06   \n",
       "std                                     9.826921e+07   \n",
       "min                                    -2.248017e+09   \n",
       "25%                                     3.988478e+04   \n",
       "50%                                     4.405929e+04   \n",
       "75%                                     4.814479e+04   \n",
       "max                                     8.332404e+07   \n",
       "\n",
       "       rougher.calculation.floatbank10_sulfate_to_au_feed  \\\n",
       "count                                       2.267200e+04    \n",
       "mean                                        3.559534e+03    \n",
       "std                                         6.116649e+04    \n",
       "min                                        -2.023459e+06    \n",
       "25%                                         2.506366e+03    \n",
       "50%                                         2.940838e+03    \n",
       "75%                                         3.613983e+03    \n",
       "max                                         6.211433e+06    \n",
       "\n",
       "       rougher.calculation.floatbank11_sulfate_to_au_feed  \\\n",
       "count                                       2.267200e+04    \n",
       "mean                                        2.294828e+03    \n",
       "std                                         4.177895e+04    \n",
       "min                                        -4.014502e+06    \n",
       "25%                                         2.501745e+03    \n",
       "50%                                         2.897014e+03    \n",
       "75%                                         3.529913e+03    \n",
       "max                                         1.537564e+06    \n",
       "\n",
       "       rougher.calculation.au_pb_ratio  rougher.input.feed_ag  \\\n",
       "count                     21089.000000           22618.000000   \n",
       "mean                          2.386287               8.065715   \n",
       "std                           0.839999               3.125250   \n",
       "min                          -0.010548               0.000000   \n",
       "25%                           2.021360               6.944415   \n",
       "50%                           2.286493               8.302613   \n",
       "75%                           2.711070              10.135202   \n",
       "max                          39.379272              14.869652   \n",
       "\n",
       "       rougher.input.feed_pb  rougher.input.feed_rate  \\\n",
       "count           22472.000000             22163.000000   \n",
       "mean                3.305676               441.873650   \n",
       "std                 1.446905               156.736078   \n",
       "min                 0.000000                 0.000044   \n",
       "25%                 2.658814               398.222097   \n",
       "50%                 3.432054               488.968597   \n",
       "75%                 4.204960               546.587401   \n",
       "max                 7.142594               717.508837   \n",
       "\n",
       "       rougher.input.feed_size  rougher.input.feed_sol  rougher.input.feed_au  \\\n",
       "count             22277.000000            22357.000000           22617.000000   \n",
       "mean                 57.959166               33.276016               7.565838   \n",
       "std                  23.644823               11.494135               3.026954   \n",
       "min                   0.046369                0.000000               0.000000   \n",
       "25%                  46.687088               32.524264               6.485009   \n",
       "50%                  53.405122               36.559955               7.884832   \n",
       "75%                  64.096151               39.675470               9.668064   \n",
       "max                 484.967466               53.477742              14.093363   \n",
       "\n",
       "       rougher.input.floatbank10_sulfate  rougher.input.floatbank10_xanthate  \\\n",
       "count                       21415.000000                        22247.000000   \n",
       "mean                           11.784263                            5.573526   \n",
       "std                             4.093125                            1.908123   \n",
       "min                             0.000002                            0.000003   \n",
       "25%                             9.915698                            5.046782   \n",
       "50%                            11.998801                            5.999078   \n",
       "75%                            14.437582                            6.700493   \n",
       "max                            36.118275                           31.493331   \n",
       "\n",
       "       rougher.input.floatbank11_sulfate  rougher.input.floatbank11_xanthate  \\\n",
       "count                       22038.000000                        20459.000000   \n",
       "mean                           11.209718                            5.962586   \n",
       "std                             4.685205                            1.280682   \n",
       "min                             0.000014                            0.000010   \n",
       "25%                             9.281388                            5.353321   \n",
       "50%                            11.946466                            6.034551   \n",
       "75%                            14.015696                            6.799526   \n",
       "max                            37.980648                           18.709478   \n",
       "\n",
       "       rougher.output.concentrate_ag  rougher.output.concentrate_pb  \\\n",
       "count                   22618.000000                   22618.000000   \n",
       "mean                       10.874484                       6.900646   \n",
       "std                         4.377924                       2.806948   \n",
       "min                         0.000000                       0.000000   \n",
       "25%                        10.126646                       6.374692   \n",
       "50%                        11.785127                       7.572855   \n",
       "75%                        13.615299                       8.477358   \n",
       "max                        24.480271                      18.394042   \n",
       "\n",
       "       rougher.output.concentrate_sol  rougher.output.concentrate_au  \\\n",
       "count                    22526.000000                   22618.000000   \n",
       "mean                        26.095508                      17.879538   \n",
       "std                         10.140120                       6.790112   \n",
       "min                          0.000000                       0.000000   \n",
       "25%                         25.815139                      17.928729   \n",
       "50%                         29.382638                      20.003202   \n",
       "75%                         32.002766                      21.564238   \n",
       "max                         40.089993                      28.824507   \n",
       "\n",
       "       rougher.output.recovery  rougher.output.tail_ag  \\\n",
       "count             19597.000000            19979.000000   \n",
       "mean                 83.078122                5.587861   \n",
       "std                  14.661880                1.114614   \n",
       "min                   0.000000                0.594562   \n",
       "25%                  80.941306                4.887758   \n",
       "50%                  86.144121                5.759630   \n",
       "75%                  89.987561                6.386301   \n",
       "max                 100.000000               12.719177   \n",
       "\n",
       "       rougher.output.tail_pb  rougher.output.tail_sol  \\\n",
       "count            22618.000000             19980.000000   \n",
       "mean                 0.593620                18.040138   \n",
       "std                  0.315295                 3.470514   \n",
       "min                  0.000000                10.014648   \n",
       "25%                  0.427513                15.788283   \n",
       "50%                  0.590746                18.082595   \n",
       "75%                  0.763219                20.084427   \n",
       "max                  3.778064                66.117188   \n",
       "\n",
       "       rougher.output.tail_au  rougher.state.floatbank10_a_air  \\\n",
       "count            19980.000000                     22646.000000   \n",
       "mean                 1.821193                      1061.497265   \n",
       "std                  0.695663                       277.718805   \n",
       "min                  0.020676                        -0.345168   \n",
       "25%                  1.403951                       999.482192   \n",
       "50%                  1.808567                      1000.874850   \n",
       "75%                  2.215317                      1210.446131   \n",
       "max                  9.688980                      1521.980372   \n",
       "\n",
       "       rougher.state.floatbank10_a_level  rougher.state.floatbank10_b_air  \\\n",
       "count                       22647.000000                     22646.000000   \n",
       "mean                         -389.490702                      1257.007638   \n",
       "std                           109.946946                       319.983248   \n",
       "min                          -658.223080                        -0.821921   \n",
       "25%                          -499.930417                      1197.548268   \n",
       "50%                          -300.401147                      1299.976586   \n",
       "75%                          -299.992579                      1407.132038   \n",
       "max                           -19.383282                      1809.194008   \n",
       "\n",
       "       rougher.state.floatbank10_b_level  rougher.state.floatbank10_c_air  \\\n",
       "count                       22647.000000                     22646.000000   \n",
       "mean                         -477.174011                      1235.736890   \n",
       "std                            66.645938                       331.886940   \n",
       "min                          -650.437575                        -0.378521   \n",
       "25%                          -500.297595                      1100.701647   \n",
       "50%                          -499.847233                      1298.358825   \n",
       "75%                          -403.034804                      1437.004925   \n",
       "max                             9.457822                      2499.129174   \n",
       "\n",
       "       rougher.state.floatbank10_c_level  rougher.state.floatbank10_d_air  \\\n",
       "count                       22654.000000                     22641.000000   \n",
       "mean                         -476.320380                      1152.689097   \n",
       "std                            63.957539                       317.187293   \n",
       "min                          -650.151190                        -1.197435   \n",
       "25%                          -500.323902                      1040.497698   \n",
       "50%                          -499.786527                      1200.061465   \n",
       "75%                          -403.386144                      1342.550632   \n",
       "max                           -10.242658                      2077.222699   \n",
       "\n",
       "       rougher.state.floatbank10_d_level  rougher.state.floatbank10_e_air  \\\n",
       "count                       22649.000000                     22096.000000   \n",
       "mean                         -477.720243                      1029.790755   \n",
       "std                            61.942486                       278.544787   \n",
       "min                          -649.300693                        -2.163619   \n",
       "25%                          -500.590442                       950.272126   \n",
       "50%                          -499.674320                      1049.208185   \n",
       "75%                          -443.310570                      1198.762174   \n",
       "max                            21.365035                      2330.310425   \n",
       "\n",
       "       rougher.state.floatbank10_e_level  rougher.state.floatbank10_f_air  \\\n",
       "count                       22649.000000                     22641.000000   \n",
       "mean                         -475.804766                       977.870710   \n",
       "std                            68.327502                       262.604257   \n",
       "min                          -650.196204                        -2.636171   \n",
       "25%                          -500.357323                       900.173576   \n",
       "50%                          -499.749602                       999.693303   \n",
       "75%                          -403.199939                      1099.189914   \n",
       "max                            -2.406020                      1950.493751   \n",
       "\n",
       "       rougher.state.floatbank10_f_level  secondary_cleaner.output.tail_ag  \\\n",
       "count                       22642.000000                      22616.000000   \n",
       "mean                         -476.448631                         13.375349   \n",
       "std                            69.678409                          5.768719   \n",
       "min                          -650.072915                          0.000000   \n",
       "25%                          -500.749731                         11.802936   \n",
       "50%                          -499.631519                         15.222165   \n",
       "75%                          -403.810536                         17.231611   \n",
       "max                           -21.745268                         23.264738   \n",
       "\n",
       "       secondary_cleaner.output.tail_pb  secondary_cleaner.output.tail_sol  \\\n",
       "count                      22600.000000                       20501.000000   \n",
       "mean                           5.304107                           6.465460   \n",
       "std                            3.092536                           4.277993   \n",
       "min                            0.000000                           0.000000   \n",
       "25%                            3.451435                           2.658971   \n",
       "50%                            5.074145                           6.966645   \n",
       "75%                            7.585281                          10.152612   \n",
       "max                           17.042505                          33.058594   \n",
       "\n",
       "       secondary_cleaner.output.tail_au  \\\n",
       "count                      22618.000000   \n",
       "mean                           4.041218   \n",
       "std                            2.605738   \n",
       "min                            0.000000   \n",
       "25%                            2.877554   \n",
       "50%                            3.956171   \n",
       "75%                            5.006944   \n",
       "max                           26.811643   \n",
       "\n",
       "       secondary_cleaner.state.floatbank2_a_air  \\\n",
       "count                              22333.000000   \n",
       "mean                                  27.440808   \n",
       "std                                    7.763001   \n",
       "min                                    0.000000   \n",
       "25%                                   25.011462   \n",
       "50%                                   28.927709   \n",
       "75%                                   32.892271   \n",
       "max                                   60.000000   \n",
       "\n",
       "       secondary_cleaner.state.floatbank2_a_level  \\\n",
       "count                                22591.000000   \n",
       "mean                                  -516.727003   \n",
       "std                                     83.411562   \n",
       "min                                   -799.841677   \n",
       "25%                                   -500.303776   \n",
       "50%                                   -499.996457   \n",
       "75%                                   -499.686110   \n",
       "max                                   -127.875565   \n",
       "\n",
       "       secondary_cleaner.state.floatbank2_b_air  \\\n",
       "count                              22538.000000   \n",
       "mean                                  22.963160   \n",
       "std                                    7.715298   \n",
       "min                                    0.000000   \n",
       "25%                                   20.018733   \n",
       "50%                                   24.978758   \n",
       "75%                                   27.999758   \n",
       "max                                   60.000000   \n",
       "\n",
       "       secondary_cleaner.state.floatbank2_b_level  \\\n",
       "count                                22588.000000   \n",
       "mean                                  -517.691199   \n",
       "std                                     83.492128   \n",
       "min                                   -799.967956   \n",
       "25%                                   -500.318700   \n",
       "50%                                   -500.035682   \n",
       "75%                                   -499.798350   \n",
       "max                                   -211.999273   \n",
       "\n",
       "       secondary_cleaner.state.floatbank3_a_air  \\\n",
       "count                              22585.000000   \n",
       "mean                                  26.993715   \n",
       "std                                    8.262921   \n",
       "min                                    0.000000   \n",
       "25%                                   24.020017   \n",
       "50%                                   27.959889   \n",
       "75%                                   32.981390   \n",
       "max                                   60.000000   \n",
       "\n",
       "       secondary_cleaner.state.floatbank3_a_level  \\\n",
       "count                                22587.000000   \n",
       "mean                                  -500.178956   \n",
       "std                                     83.293510   \n",
       "min                                   -799.926254   \n",
       "25%                                   -500.273049   \n",
       "50%                                   -499.957251   \n",
       "75%                                   -499.386548   \n",
       "max                                   -191.678810   \n",
       "\n",
       "       secondary_cleaner.state.floatbank3_b_air  \\\n",
       "count                              22592.000000   \n",
       "mean                                  21.251091   \n",
       "std                                    7.018935   \n",
       "min                                    0.000000   \n",
       "25%                                   18.070333   \n",
       "50%                                   21.971564   \n",
       "75%                                   25.961958   \n",
       "max                                   39.487633   \n",
       "\n",
       "       secondary_cleaner.state.floatbank3_b_level  \\\n",
       "count                                22590.000000   \n",
       "mean                                  -505.013347   \n",
       "std                                     71.552885   \n",
       "min                                   -809.421923   \n",
       "25%                                   -500.150167   \n",
       "50%                                   -499.993182   \n",
       "75%                                   -499.821199   \n",
       "max                                   -139.771483   \n",
       "\n",
       "       secondary_cleaner.state.floatbank4_a_air  \\\n",
       "count                              22571.000000   \n",
       "mean                                  18.205125   \n",
       "std                                    6.560700   \n",
       "min                                    0.000000   \n",
       "25%                                   14.095940   \n",
       "50%                                   18.007326   \n",
       "75%                                   22.998194   \n",
       "max                                   60.000000   \n",
       "\n",
       "       secondary_cleaner.state.floatbank4_a_level  \\\n",
       "count                                22587.000000   \n",
       "mean                                  -499.878977   \n",
       "std                                     80.273964   \n",
       "min                                   -799.920713   \n",
       "25%                                   -500.896232   \n",
       "50%                                   -499.917108   \n",
       "75%                                   -498.361545   \n",
       "max                                   -127.692333   \n",
       "\n",
       "       secondary_cleaner.state.floatbank4_b_air  \\\n",
       "count                              22608.000000   \n",
       "mean                                  14.356474   \n",
       "std                                    5.655791   \n",
       "min                                    0.000000   \n",
       "25%                                   10.882675   \n",
       "50%                                   14.947646   \n",
       "75%                                   17.977502   \n",
       "max                                   31.269706   \n",
       "\n",
       "       secondary_cleaner.state.floatbank4_b_level  \\\n",
       "count                                22607.000000   \n",
       "mean                                  -476.532613   \n",
       "std                                     93.822791   \n",
       "min                                   -800.836914   \n",
       "25%                                   -500.309169   \n",
       "50%                                   -499.612292   \n",
       "75%                                   -400.224147   \n",
       "max                                     -6.506986   \n",
       "\n",
       "       secondary_cleaner.state.floatbank5_a_air  \\\n",
       "count                              22615.000000   \n",
       "mean                                  14.883276   \n",
       "std                                    6.372811   \n",
       "min                                   -0.423260   \n",
       "25%                                   10.941299   \n",
       "50%                                   14.859117   \n",
       "75%                                   18.014914   \n",
       "max                                   63.116298   \n",
       "\n",
       "       secondary_cleaner.state.floatbank5_a_level  \\\n",
       "count                                22615.000000   \n",
       "mean                                  -503.323288   \n",
       "std                                     72.925589   \n",
       "min                                   -799.741097   \n",
       "25%                                   -500.628697   \n",
       "50%                                   -499.865158   \n",
       "75%                                   -498.489381   \n",
       "max                                   -244.483566   \n",
       "\n",
       "       secondary_cleaner.state.floatbank5_b_air  \\\n",
       "count                              22615.000000   \n",
       "mean                                  11.626743   \n",
       "std                                    5.757449   \n",
       "min                                    0.427084   \n",
       "25%                                    8.037533   \n",
       "50%                                   10.989756   \n",
       "75%                                   14.001193   \n",
       "max                                   39.846228   \n",
       "\n",
       "       secondary_cleaner.state.floatbank5_b_level  \\\n",
       "count                                22616.000000   \n",
       "mean                                  -500.521502   \n",
       "std                                     78.956292   \n",
       "min                                   -800.258209   \n",
       "25%                                   -500.167897   \n",
       "50%                                   -499.951980   \n",
       "75%                                   -499.492354   \n",
       "max                                   -120.190931   \n",
       "\n",
       "       secondary_cleaner.state.floatbank6_a_air  \\\n",
       "count                              22597.000000   \n",
       "mean                                  17.976810   \n",
       "std                                    6.636203   \n",
       "min                                   -0.079426   \n",
       "25%                                   13.968418   \n",
       "50%                                   18.004215   \n",
       "75%                                   23.009704   \n",
       "max                                   54.876806   \n",
       "\n",
       "       secondary_cleaner.state.floatbank6_a_level  \n",
       "count                                22615.000000  \n",
       "mean                                  -519.361465  \n",
       "std                                     75.477151  \n",
       "min                                   -810.473526  \n",
       "25%                                   -500.981671  \n",
       "50%                                   -500.095463  \n",
       "75%                                   -499.526388  \n",
       "max                                    -29.093593  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 22716 entries, 2016-01-15 00:00:00 to 2018-08-18 10:59:59\n",
      "Data columns (total 86 columns):\n",
      "final.output.concentrate_ag                           22627 non-null float64\n",
      "final.output.concentrate_pb                           22629 non-null float64\n",
      "final.output.concentrate_sol                          22331 non-null float64\n",
      "final.output.concentrate_au                           22630 non-null float64\n",
      "final.output.recovery                                 20753 non-null float64\n",
      "final.output.tail_ag                                  22633 non-null float64\n",
      "final.output.tail_pb                                  22516 non-null float64\n",
      "final.output.tail_sol                                 22445 non-null float64\n",
      "final.output.tail_au                                  22635 non-null float64\n",
      "primary_cleaner.input.sulfate                         21107 non-null float64\n",
      "primary_cleaner.input.depressant                      21170 non-null float64\n",
      "primary_cleaner.input.feed_size                       22716 non-null float64\n",
      "primary_cleaner.input.xanthate                        21565 non-null float64\n",
      "primary_cleaner.output.concentrate_ag                 22618 non-null float64\n",
      "primary_cleaner.output.concentrate_pb                 22268 non-null float64\n",
      "primary_cleaner.output.concentrate_sol                21918 non-null float64\n",
      "primary_cleaner.output.concentrate_au                 22618 non-null float64\n",
      "primary_cleaner.output.tail_ag                        22614 non-null float64\n",
      "primary_cleaner.output.tail_pb                        22594 non-null float64\n",
      "primary_cleaner.output.tail_sol                       22365 non-null float64\n",
      "primary_cleaner.output.tail_au                        22617 non-null float64\n",
      "primary_cleaner.state.floatbank8_a_air                22660 non-null float64\n",
      "primary_cleaner.state.floatbank8_a_level              22667 non-null float64\n",
      "primary_cleaner.state.floatbank8_b_air                22660 non-null float64\n",
      "primary_cleaner.state.floatbank8_b_level              22673 non-null float64\n",
      "primary_cleaner.state.floatbank8_c_air                22662 non-null float64\n",
      "primary_cleaner.state.floatbank8_c_level              22673 non-null float64\n",
      "primary_cleaner.state.floatbank8_d_air                22661 non-null float64\n",
      "primary_cleaner.state.floatbank8_d_level              22673 non-null float64\n",
      "rougher.calculation.sulfate_to_au_concentrate         22672 non-null float64\n",
      "rougher.calculation.floatbank10_sulfate_to_au_feed    22672 non-null float64\n",
      "rougher.calculation.floatbank11_sulfate_to_au_feed    22672 non-null float64\n",
      "rougher.calculation.au_pb_ratio                       21089 non-null float64\n",
      "rougher.input.feed_ag                                 22618 non-null float64\n",
      "rougher.input.feed_pb                                 22472 non-null float64\n",
      "rougher.input.feed_rate                               22163 non-null float64\n",
      "rougher.input.feed_size                               22277 non-null float64\n",
      "rougher.input.feed_sol                                22357 non-null float64\n",
      "rougher.input.feed_au                                 22617 non-null float64\n",
      "rougher.input.floatbank10_sulfate                     21415 non-null float64\n",
      "rougher.input.floatbank10_xanthate                    22247 non-null float64\n",
      "rougher.input.floatbank11_sulfate                     22038 non-null float64\n",
      "rougher.input.floatbank11_xanthate                    20459 non-null float64\n",
      "rougher.output.concentrate_ag                         22618 non-null float64\n",
      "rougher.output.concentrate_pb                         22618 non-null float64\n",
      "rougher.output.concentrate_sol                        22526 non-null float64\n",
      "rougher.output.concentrate_au                         22618 non-null float64\n",
      "rougher.output.recovery                               19597 non-null float64\n",
      "rougher.output.tail_ag                                19979 non-null float64\n",
      "rougher.output.tail_pb                                22618 non-null float64\n",
      "rougher.output.tail_sol                               19980 non-null float64\n",
      "rougher.output.tail_au                                19980 non-null float64\n",
      "rougher.state.floatbank10_a_air                       22646 non-null float64\n",
      "rougher.state.floatbank10_a_level                     22647 non-null float64\n",
      "rougher.state.floatbank10_b_air                       22646 non-null float64\n",
      "rougher.state.floatbank10_b_level                     22647 non-null float64\n",
      "rougher.state.floatbank10_c_air                       22646 non-null float64\n",
      "rougher.state.floatbank10_c_level                     22654 non-null float64\n",
      "rougher.state.floatbank10_d_air                       22641 non-null float64\n",
      "rougher.state.floatbank10_d_level                     22649 non-null float64\n",
      "rougher.state.floatbank10_e_air                       22096 non-null float64\n",
      "rougher.state.floatbank10_e_level                     22649 non-null float64\n",
      "rougher.state.floatbank10_f_air                       22641 non-null float64\n",
      "rougher.state.floatbank10_f_level                     22642 non-null float64\n",
      "secondary_cleaner.output.tail_ag                      22616 non-null float64\n",
      "secondary_cleaner.output.tail_pb                      22600 non-null float64\n",
      "secondary_cleaner.output.tail_sol                     20501 non-null float64\n",
      "secondary_cleaner.output.tail_au                      22618 non-null float64\n",
      "secondary_cleaner.state.floatbank2_a_air              22333 non-null float64\n",
      "secondary_cleaner.state.floatbank2_a_level            22591 non-null float64\n",
      "secondary_cleaner.state.floatbank2_b_air              22538 non-null float64\n",
      "secondary_cleaner.state.floatbank2_b_level            22588 non-null float64\n",
      "secondary_cleaner.state.floatbank3_a_air              22585 non-null float64\n",
      "secondary_cleaner.state.floatbank3_a_level            22587 non-null float64\n",
      "secondary_cleaner.state.floatbank3_b_air              22592 non-null float64\n",
      "secondary_cleaner.state.floatbank3_b_level            22590 non-null float64\n",
      "secondary_cleaner.state.floatbank4_a_air              22571 non-null float64\n",
      "secondary_cleaner.state.floatbank4_a_level            22587 non-null float64\n",
      "secondary_cleaner.state.floatbank4_b_air              22608 non-null float64\n",
      "secondary_cleaner.state.floatbank4_b_level            22607 non-null float64\n",
      "secondary_cleaner.state.floatbank5_a_air              22615 non-null float64\n",
      "secondary_cleaner.state.floatbank5_a_level            22615 non-null float64\n",
      "secondary_cleaner.state.floatbank5_b_air              22615 non-null float64\n",
      "secondary_cleaner.state.floatbank5_b_level            22616 non-null float64\n",
      "secondary_cleaner.state.floatbank6_a_air              22597 non-null float64\n",
      "secondary_cleaner.state.floatbank6_a_level            22615 non-null float64\n",
      "dtypes: float64(86)\n",
      "memory usage: 15.1+ MB\n"
     ]
    }
   ],
   "source": [
    "analyze_df(gold_recovery_full)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Выводы**:\n",
    "* В данных есть пропуски\n",
    "* В тестовой выборке отсутствуют некоторые признаки, которые есть в обучающей выборке"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"prepare_data.check_efficiency\"></a>\n",
    "### Проверка правильности вычисленной эффективности обогащения"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Напишем функцию для расчета Recovery по формуле  \n",
    "$$\\frac{C\\cdot(F - T)}{F\\cdot(C - T)}\\cdot 100\\%$$\n",
    "где: \n",
    "* C — доля золота в концентрате после флотации/очистки (rougher.output.concentrate_au)\n",
    "* F — доля золота в сырье/концентрате до флотации/очистки (rougher.input.feed_au)\n",
    "* T — доля золота в отвальных хвостах после флотации/очистки (rougher.output.tail_au)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_recovery(rougher_output, rougher_input, rougher_tail):\n",
    "    recovery = (rougher_output * (rougher_input - rougher_tail)) /\\\n",
    "        (rougher_input * (rougher_output - rougher_tail)) * 100\n",
    "    \n",
    "    recovery[recovery < 0] = np.nan\n",
    "    recovery[recovery > 100] = np.nan\n",
    "    return recovery"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посчитаем `recovery` на обучающей выборке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    14287.000000\n",
      "mean        82.394201\n",
      "std         15.096808\n",
      "min         -0.000000\n",
      "25%         79.818372\n",
      "50%         85.235997\n",
      "75%         90.131691\n",
      "max        100.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "recovery = calculate_recovery(\n",
    "    gold_recovery_train['rougher.output.concentrate_au'], \n",
    "    gold_recovery_train['rougher.input.feed_au'], \n",
    "    gold_recovery_train['rougher.output.tail_au'],\n",
    ")\n",
    "print(recovery.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вычилслим MAE между нашими расчётами и значением признака `rougher.output.recovery`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE = 9.444891926100695e-15\n"
     ]
    }
   ],
   "source": [
    "mae_recovery = mean_absolute_error(gold_recovery_train['rougher.output.recovery'].dropna(), recovery.dropna())\n",
    "print('MAE =', mae_recovery)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Вывод**: MAE (средняя абсолютная ошибка) между рассчитаной по формуле эффективностью обогащения и признаком из датасета очень мала, а значит значение эффективности в датасете верное."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"prepare_data.analyse_test\"></a>\n",
    "### Анализ признаков, недоступных в тестовой выборке"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Напишем функцию, которая вернет те столбцы, которые есть в одном датасете, но нет в другом"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fields_diff(first_df, second_df):\n",
    "    return set(first_df.columns.to_list()) - set(second_df.columns.to_list())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим, какие признаки есть в обучающей выборке. но нет в тестовой"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "secondary_cleaner.output.tail_sol                     float64\n",
      "primary_cleaner.output.concentrate_sol                float64\n",
      "rougher.output.recovery                               float64\n",
      "final.output.recovery                                 float64\n",
      "final.output.tail_sol                                 float64\n",
      "final.output.concentrate_sol                          float64\n",
      "final.output.concentrate_au                           float64\n",
      "final.output.tail_ag                                  float64\n",
      "final.output.concentrate_ag                           float64\n",
      "rougher.calculation.floatbank11_sulfate_to_au_feed    float64\n",
      "primary_cleaner.output.tail_au                        float64\n",
      "primary_cleaner.output.concentrate_au                 float64\n",
      "rougher.output.tail_au                                float64\n",
      "rougher.output.tail_sol                               float64\n",
      "final.output.concentrate_pb                           float64\n",
      "rougher.output.concentrate_sol                        float64\n",
      "rougher.output.concentrate_au                         float64\n",
      "rougher.output.concentrate_ag                         float64\n",
      "primary_cleaner.output.tail_sol                       float64\n",
      "final.output.tail_pb                                  float64\n",
      "secondary_cleaner.output.tail_ag                      float64\n",
      "rougher.calculation.au_pb_ratio                       float64\n",
      "rougher.calculation.floatbank10_sulfate_to_au_feed    float64\n",
      "primary_cleaner.output.concentrate_ag                 float64\n",
      "secondary_cleaner.output.tail_au                      float64\n",
      "primary_cleaner.output.tail_ag                        float64\n",
      "secondary_cleaner.output.tail_pb                      float64\n",
      "primary_cleaner.output.concentrate_pb                 float64\n",
      "rougher.output.concentrate_pb                         float64\n",
      "rougher.output.tail_ag                                float64\n",
      "final.output.tail_au                                  float64\n",
      "rougher.output.tail_pb                                float64\n",
      "primary_cleaner.output.tail_pb                        float64\n",
      "rougher.calculation.sulfate_to_au_concentrate         float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "missing_test_fields = fields_diff(gold_recovery_train, gold_recovery_test)\n",
    "print(gold_recovery_train[missing_test_fields].dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Выводы**:\n",
    "* Все признаки имеют тип *float* \n",
    "* Признаки `output` - целевые признаки и поэтому отсутствуют в тестовой выборке\n",
    "* Признаки `rougher.calculation` - это расчетные характеристики и отсутвуют они скорее всего из-за того, что они расчитываются после завершения процесса. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Комментарий ревьюера:</b>\n",
    "\n",
    "Достаточно детально изучили наши первичные данные, давай теперь их обработаем. \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"prepare_data.data_preprocessing\"></a>\n",
    "### Предобработка данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Напишем функцию для расчета того, сколько процентов данных в датасете пропущено"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_na_percent(df):\n",
    "    print_df(df.isnull().sum().sort_values(ascending=False) / df.shape[0] * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Комментарий ревьюера:</b>\n",
    "\n",
    "Отличный способ проверить долю значений в наших данных. \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rougher.output.recovery                               15.260973\n",
       "rougher.output.tail_ag                                13.345196\n",
       "rougher.output.tail_sol                               13.339265\n",
       "rougher.output.tail_au                                13.339265\n",
       "secondary_cleaner.output.tail_sol                     11.779359\n",
       "rougher.input.floatbank11_xanthate                    11.293001\n",
       "final.output.recovery                                  9.021352\n",
       "primary_cleaner.input.sulfate                          7.752076\n",
       "primary_cleaner.input.depressant                       7.485172\n",
       "rougher.calculation.au_pb_ratio                        7.366548\n",
       "rougher.input.floatbank10_sulfate                      6.192171\n",
       "primary_cleaner.input.xanthate                         5.842230\n",
       "primary_cleaner.output.concentrate_sol                 3.772242\n",
       "rougher.input.floatbank11_sulfate                      3.695136\n",
       "rougher.state.floatbank10_e_air                        3.576512\n",
       "rougher.input.feed_rate                                3.042705\n",
       "rougher.input.feed_size                                2.473310\n",
       "final.output.concentrate_sol                           2.194543\n",
       "secondary_cleaner.state.floatbank2_a_air               2.153025\n",
       "primary_cleaner.output.concentrate_pb                  2.123369\n",
       "rougher.input.floatbank10_xanthate                     2.052195\n",
       "rougher.input.feed_sol                                 1.731910\n",
       "primary_cleaner.output.tail_sol                        1.666667\n",
       "rougher.input.feed_pb                                  1.352313\n",
       "final.output.tail_pb                                   1.085409\n",
       "rougher.output.concentrate_sol                         0.960854\n",
       "secondary_cleaner.state.floatbank2_b_air               0.919336\n",
       "final.output.tail_sol                                  0.860024\n",
       "secondary_cleaner.state.floatbank4_a_air               0.765125\n",
       "secondary_cleaner.state.floatbank3_a_level             0.670225\n",
       "secondary_cleaner.state.floatbank4_a_level             0.670225\n",
       "secondary_cleaner.state.floatbank2_b_level             0.664294\n",
       "secondary_cleaner.state.floatbank3_b_level             0.652432\n",
       "secondary_cleaner.state.floatbank2_a_level             0.646501\n",
       "secondary_cleaner.state.floatbank3_b_air               0.640569\n",
       "secondary_cleaner.state.floatbank6_a_air               0.610913\n",
       "primary_cleaner.output.tail_pb                         0.587189\n",
       "secondary_cleaner.state.floatbank3_a_air               0.575326\n",
       "secondary_cleaner.output.tail_pb                       0.569395\n",
       "secondary_cleaner.state.floatbank4_b_level             0.551601\n",
       "secondary_cleaner.state.floatbank4_b_air               0.545670\n",
       "secondary_cleaner.state.floatbank6_a_level             0.504152\n",
       "secondary_cleaner.state.floatbank5_a_level             0.504152\n",
       "secondary_cleaner.state.floatbank5_a_air               0.504152\n",
       "secondary_cleaner.state.floatbank5_b_air               0.504152\n",
       "secondary_cleaner.state.floatbank5_b_level             0.498221\n",
       "secondary_cleaner.output.tail_ag                       0.498221\n",
       "primary_cleaner.output.tail_ag                         0.492289\n",
       "primary_cleaner.output.tail_au                         0.492289\n",
       "rougher.input.feed_au                                  0.492289\n",
       "rougher.output.concentrate_pb                          0.486358\n",
       "rougher.input.feed_ag                                  0.486358\n",
       "secondary_cleaner.output.tail_au                       0.486358\n",
       "rougher.output.tail_pb                                 0.486358\n",
       "primary_cleaner.output.concentrate_ag                  0.486358\n",
       "primary_cleaner.output.concentrate_au                  0.486358\n",
       "rougher.output.concentrate_au                          0.486358\n",
       "rougher.output.concentrate_ag                          0.486358\n",
       "final.output.concentrate_pb                            0.427046\n",
       "final.output.concentrate_ag                            0.427046\n",
       "final.output.concentrate_au                            0.421115\n",
       "final.output.tail_ag                                   0.391459\n",
       "final.output.tail_au                                   0.391459\n",
       "rougher.state.floatbank10_f_level                      0.344009\n",
       "rougher.state.floatbank10_d_air                        0.344009\n",
       "rougher.state.floatbank10_f_air                        0.344009\n",
       "rougher.state.floatbank10_a_air                        0.314353\n",
       "rougher.state.floatbank10_a_level                      0.314353\n",
       "rougher.state.floatbank10_b_air                        0.314353\n",
       "rougher.state.floatbank10_b_level                      0.314353\n",
       "rougher.state.floatbank10_c_air                        0.314353\n",
       "rougher.state.floatbank10_d_level                      0.302491\n",
       "rougher.state.floatbank10_e_level                      0.302491\n",
       "rougher.state.floatbank10_c_level                      0.272835\n",
       "primary_cleaner.state.floatbank8_a_air                 0.237248\n",
       "primary_cleaner.state.floatbank8_b_air                 0.237248\n",
       "primary_cleaner.state.floatbank8_d_air                 0.231317\n",
       "primary_cleaner.state.floatbank8_c_air                 0.225386\n",
       "primary_cleaner.state.floatbank8_a_level               0.195730\n",
       "rougher.calculation.floatbank11_sulfate_to_au_feed     0.160142\n",
       "primary_cleaner.state.floatbank8_b_level               0.160142\n",
       "rougher.calculation.floatbank10_sulfate_to_au_feed     0.160142\n",
       "rougher.calculation.sulfate_to_au_concentrate          0.160142\n",
       "primary_cleaner.state.floatbank8_d_level               0.160142\n",
       "primary_cleaner.state.floatbank8_c_level               0.160142\n",
       "primary_cleaner.input.feed_size                        0.000000\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_na_percent(gold_recovery_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rougher.input.floatbank11_xanthate            6.028005\n",
       "primary_cleaner.input.sulfate                 5.157104\n",
       "primary_cleaner.input.depressant              4.849727\n",
       "rougher.input.floatbank10_sulfate             4.388661\n",
       "primary_cleaner.input.xanthate                2.834699\n",
       "rougher.input.floatbank10_xanthate            2.100410\n",
       "rougher.input.feed_sol                        1.144126\n",
       "rougher.input.floatbank11_sulfate             0.939208\n",
       "rougher.input.feed_rate                       0.683060\n",
       "secondary_cleaner.state.floatbank3_a_air      0.580601\n",
       "secondary_cleaner.state.floatbank2_b_air      0.392760\n",
       "rougher.input.feed_size                       0.375683\n",
       "secondary_cleaner.state.floatbank2_a_air      0.341530\n",
       "rougher.state.floatbank10_a_air               0.290301\n",
       "rougher.state.floatbank10_d_air               0.290301\n",
       "rougher.state.floatbank10_b_air               0.290301\n",
       "rougher.state.floatbank10_c_air               0.290301\n",
       "rougher.state.floatbank10_f_air               0.290301\n",
       "rougher.state.floatbank10_e_air               0.290301\n",
       "primary_cleaner.state.floatbank8_a_air        0.273224\n",
       "primary_cleaner.state.floatbank8_a_level      0.273224\n",
       "primary_cleaner.state.floatbank8_b_air        0.273224\n",
       "primary_cleaner.state.floatbank8_d_level      0.273224\n",
       "rougher.input.feed_au                         0.273224\n",
       "rougher.input.feed_ag                         0.273224\n",
       "primary_cleaner.state.floatbank8_c_air        0.273224\n",
       "primary_cleaner.state.floatbank8_c_level      0.273224\n",
       "rougher.input.feed_pb                         0.273224\n",
       "primary_cleaner.state.floatbank8_d_air        0.273224\n",
       "primary_cleaner.state.floatbank8_b_level      0.273224\n",
       "secondary_cleaner.state.floatbank6_a_level    0.273224\n",
       "rougher.state.floatbank10_a_level             0.273224\n",
       "secondary_cleaner.state.floatbank6_a_air      0.273224\n",
       "secondary_cleaner.state.floatbank5_b_level    0.273224\n",
       "secondary_cleaner.state.floatbank5_b_air      0.273224\n",
       "secondary_cleaner.state.floatbank5_a_level    0.273224\n",
       "secondary_cleaner.state.floatbank5_a_air      0.273224\n",
       "secondary_cleaner.state.floatbank4_b_level    0.273224\n",
       "secondary_cleaner.state.floatbank4_b_air      0.273224\n",
       "secondary_cleaner.state.floatbank4_a_level    0.273224\n",
       "secondary_cleaner.state.floatbank4_a_air      0.273224\n",
       "secondary_cleaner.state.floatbank3_b_level    0.273224\n",
       "secondary_cleaner.state.floatbank3_b_air      0.273224\n",
       "secondary_cleaner.state.floatbank3_a_level    0.273224\n",
       "secondary_cleaner.state.floatbank2_b_level    0.273224\n",
       "secondary_cleaner.state.floatbank2_a_level    0.273224\n",
       "rougher.state.floatbank10_f_level             0.273224\n",
       "rougher.state.floatbank10_e_level             0.273224\n",
       "rougher.state.floatbank10_d_level             0.273224\n",
       "rougher.state.floatbank10_c_level             0.273224\n",
       "rougher.state.floatbank10_b_level             0.273224\n",
       "primary_cleaner.input.feed_size               0.000000\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_na_percent(gold_recovery_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Добавим целевые показатели в датасет `gold_recovery_test` из датасета `gold_recovery_full`, в качестве ключа использовав `date`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5856, 52)\n",
      "(5856, 54)\n"
     ]
    }
   ],
   "source": [
    "print(gold_recovery_test.shape)\n",
    "gold_recovery_test = gold_recovery_test.merge(gold_recovery_full[TARGETS].loc[gold_recovery_test.index], on='date', how='left')\n",
    "print(gold_recovery_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Удалим в обучающей и тестовой выборках строки с пропусками в целевых показателях"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "gold_recovery_train = gold_recovery_train.dropna(subset=TARGETS)\n",
    "gold_recovery_test = gold_recovery_test.dropna(subset=TARGETS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В условии написано, что соседние по времени параметры часто похожи, поэтому заполним пропуски, используя сосдение значения  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "В gold_recovery_train есть пропуски:  False\n",
      "В gold_recovery_test есть пропуски:  False\n"
     ]
    }
   ],
   "source": [
    "gold_recovery_train = gold_recovery_train.fillna(method='ffill')\n",
    "gold_recovery_test = gold_recovery_test.fillna(method='ffill')\n",
    "\n",
    "print('В gold_recovery_train есть пропуски: ', gold_recovery_train.isnull().values.any())\n",
    "print('В gold_recovery_test есть пропуски: ', gold_recovery_test.isnull().values.any())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"analyse_data\"></a>\n",
    "## Анализ данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"analyse_data.analyse_concentration\"></a>\n",
    "### Анализ изменения концентрация металлов (Au, Ag, Pb) на различных этапах очистки"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим, как изменяется концентрация металлов (Au, Ag, Pb) на различных этапах очистки.\n",
    "* Au - золото\n",
    "* Ag - серебро\n",
    "* Pb - свинец"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for metal in METALS:\n",
    "    metrics = ['{}_{}'.format(x, metal) for x in CONCENTRATE_METRICS]\n",
    "\n",
    "    gold_recovery_full[metrics].plot(\n",
    "        title = 'Изменение концентрации на разных этапах очистки для {}'.format(metal.upper()),\n",
    "        kind='hist', \n",
    "        bins=200, \n",
    "        grid=True, \n",
    "        legend=True,\n",
    "        figsize=(20, 5),\n",
    "        alpha=0.5,\n",
    "    ).set(xlabel='concentrate') \n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Поместим средние значения концентрации на каждом из этапов очистки в таблицу и отобразим ее значения в виде графика."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Комментарий ревьюера:</b>\n",
    "\n",
    "Отличный и наглядный график 👍\n",
    "    \n",
    "Здорово, когда он подписан. Так быстрее понять о чем идёт речь на нём.\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rougher.input.feed</th>\n",
       "      <th>rougher.output.concentrate</th>\n",
       "      <th>primary_cleaner.output.concentrate</th>\n",
       "      <th>final.output.concentrate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>au</td>\n",
       "      <td>7.56584</td>\n",
       "      <td>17.8795</td>\n",
       "      <td>29.2123</td>\n",
       "      <td>40.0012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ag</td>\n",
       "      <td>8.06572</td>\n",
       "      <td>10.8745</td>\n",
       "      <td>7.69165</td>\n",
       "      <td>4.78156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pb</td>\n",
       "      <td>3.30568</td>\n",
       "      <td>6.90065</td>\n",
       "      <td>8.92111</td>\n",
       "      <td>9.09531</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rougher.input.feed rougher.output.concentrate  \\\n",
       "au            7.56584                    17.8795   \n",
       "ag            8.06572                    10.8745   \n",
       "pb            3.30568                    6.90065   \n",
       "\n",
       "   primary_cleaner.output.concentrate final.output.concentrate  \n",
       "au                            29.2123                  40.0012  \n",
       "ag                            7.69165                  4.78156  \n",
       "pb                            8.92111                  9.09531  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metals_concentrate = pd.DataFrame(columns=CONCENTRATE_METRICS)\n",
    "\n",
    "for metal in METALS:\n",
    "    metal = metal.lower()\n",
    "        \n",
    "    for metric in CONCENTRATE_METRICS:\n",
    "        metals_concentrate.loc[metal, metric] = gold_recovery_full['{}_{}'.format(metric, metal)].mean()\n",
    "\n",
    "for metal in METALS:\n",
    "    metals_concentrate.loc[metal].plot(\n",
    "        title='Изменение концентрации на разных этапах очистки', \n",
    "        grid=True, \n",
    "        legend=True, \n",
    "        figsize=(11, 5)\n",
    "    ).set(xlabel='stage', ylabel = 'concentrate')\n",
    "plt.show()\n",
    "\n",
    "display(metals_concentrate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Выводы**:\n",
    "* Концентрация Au (золота) растет на протяжеии всех этапов и в конце становится в 5.3 раза больше, чем в начале\n",
    "* Концентрация Ag (серебра) падает начиная с этапа флотации, в 1.6 раз\n",
    "* Концентрация Pb (свинца) медленно растет на протяжении всех этапов очистки, в 2.7 раза"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"analyse_data.compare_training_and_test\"></a>\n",
    "### Сравнение распределения размеров гранул сырья на обучающей и тестовой выборках"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Построим гистограмму по размеру гранул сырья на обучающей и тестовой выборках"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Jcqtkv8byL6BU2mhbpFaYZ3O913yvG1D+6ZnZbNHdnnKLt5Mhl79ugFvQuQVnAaW1uDntOpl5RGOcf2/US6dtaxvKbbsvdhg2j9KK/AYGD3aaBquX91OCgGfWeum/7bZs28zMlwLrULqxHN/MtLbkTKPcuvz6gHmOZB0MLNffKcHQDZR1WwpT7hxsRqNrFrB3LfPmwIER8awh10Bnq7zPNupwWbeQetFwCmU7fwPLB4tQ6nYkLX8LgA2jduXo4IY67w0pF3SDBXPN5Vy3jt/sxrJDzeflwCci4gl1Oc6nHLj/38BlHETHbXqE63O55aJR/yy/zf6VobeNHwFPjYgnU26DL+vmk5k/zszHZuZ6tQxNJwGnU97ksR6le9nA8m1EOV40y7YlpYXp5kGWZTAjOT4B/CelBXeoFtJ+G1LW8UdHWZbRlGsBpcvPaNwAbBbLv298c5bfnz9X62Q6pUX3oMa0g20L/Q4CTqktgsM5pc5nGqV/8ecbw/rPwxtTjrEDWxdvpjRGDCxP/3J0KuvAbWOwYzGU57t2pbQuHtNIfx2lC8kLKd1utqzpze2z/9mN3WP4t9ENdd6DIY5PtRX818BP6ro6ZZh55YDvQ9XnvZR6AZbdUe53K2Vdzm6kjfSuIIwsVhpY1lUxIyIe2fg+6HKyfIC6MmVYQLlTMzBWOaUOu6bD/vyyOu1NrLhNDiszT8zMXSgNy/2NvZ18iXJOP2xAeYcqU0dDBuqZeRflVuKXImLv2go5Jco7W/+zMerTI+KVNeB8DyW4P48S5CT1CjYi3kJpTeo4O0r/r5H2Zf4KcEiUPkNExHoRMdrbK/+PcotuoK8B76wtTRER60TES2rrxWAOiogNotxWP5CH+69NpzxMcFeUvssHNab5PaVf4RF1HlMjYpdRlH+w9f57ylX/ByPikbVV4slR+nMREZsCr2LwPv2DLn88fHt5fmZ2OqCdCLwsInav850a5T2sszuMO5hDKbcIO7UUv5/Sh/B7I8xr/4iYHREbUuq7WS9/A+6sw5btTFF+yONJNRBag/IQUKdbgp8CzsrMge+YHck62C8ito3yfuiPAd+vAckpwEsi4gVR+p6+n1KvnV6D1h/ArEz//7HYZwfzTcqdj5fTCNSjvOf5rYzg3d9ZunL8BDim7ldTIuK5HcZLyt24wY5lL46IZ9eLy49TApJOrUd3Um7ZrlHLug/lwL0y/eKbRrs+vwMcGhEbR8RGlH3txDpsyG2j7i/fpwTev89yC3wkplPuXiyNiJ0owVEnZwDvi4gt67Hw05R+z6O9Izjk8al6PvCPzPzxCPN8D/CNLM9JrKzhyvV14OMRsXU9Lj41ImYMk+fvKC2O/1G34TnAy1j+WaR+D1G2lf79eahtAUq9vYWh+2R38o8B81mmHoPuYsD+VO8IfBf4ZD0PbEHps99fnu8A742IreoF8adYcdv4cI0hnlTL3ezjfXGWO14fBZ4QEa9pLOP9lLv702q+y0TEGyh9pN9MeaHDCXX+K4iIp1AaZDqeO4Y7PkV5beAHgXd1Gj4CQ9XnHyndKbar59jD+yeqdfID4PC6/p5AeT5gpMYiVhqNNShlfUTd3vekHJegPCf0L3X/2oaHn+XrX87FlD7jI/U1yjn+GXWfXDciXhYR61Aamh+IiPfXc/CaEfGUiHh6nfYU4EMRsX5EbE650B9SRDwxInaN8nza3+rfPzqMtz+lm8sb6jmq33Bl6mjYvlaZ+XnKDnko5WSzoC5Qc2M+jdK39g5KK9ors/QpvZxy1X4u5cr6KZSnspueFRFLKAeHVzKClVXL9UPgM8DJUW6JXcroHjoC+HF2uFWWmRcA/0ppVbgDmE85EAzlNEr3iIspLbDfqOkfpTxgeldN/0FjPg9RDtr/RGktW0hZjyM12Hp/iNKith3lqeLbKCeZ9ep011NayH4a5d34SyhByRkjWP5DKf2lX9WpQDUI6n94pX97OYjR/bjWbTz8ENwyEfE4SnAymgPlSZQuItdSblt/oqYfRemveBslQP1pY5o1KS20d1Guup/CgH6TsKyVcoVX5Y1wHXyL0kp/E+Vu0r/Xaa+itEb/Vy3byygPhDa71pxR6+wSyvb0vyNZEQOsyj47pMz8P8rB66L+lr564Pw55dmH4Vqi+r2BcqfhSsqbJN7TGPaYiFgY5YdXPkHphtTJSZSLsNspJ/T9Bgz/dc3j/yjdiC6PiA0oAfq/rkQQupyVWJ+foDzPcAml1fOimkY9Vr2BobeNE+o8Bt7JGMq7KA/k3UMJHgarn09R3hhxLmVfWsKKx+vr6/r8DjC7UUdfAJ4REe8bwfEJym3pgV0Sh7Imne+ajtgIyvUFyrr5OeWu3jcox5Ch8nyAUk971vyOAd6YmVc2RvuPuj/fRDlGfKamD7otVI8Cjs7Bu4cN9Jo6n8WUhwY/1Bj2jEZdvZ7S2DTQgZSLjusoLcsnUfqVU/9/i9KF8DrK2zkOGDD9LynnkrMpdxF+PnAGWfqhvwU4qgaz36Tc2VpEeWPSsrvfNbg6irI+l2TmSZT1tcLFdR33D5T6urRx3oNyPB3J8emrlH7gI7l70clQ+/bVlAabsyjPsAx8Vu3dlO3wJsp6/g4Pd0ca0srEShFxVeNiabQWUlrOb6Qcj97eiLM+R7lIvIWyzZw4YNrDgJOidAt5ZUQ8ttZVx64zWXpD/BvlNxfuoNwN2a8Oe5DyXNhOlJjnNkod9r984bBaxuspjUIrxBwdrE2503cbpS42oDQADvRaygPQN/ZvaxHxHyMoU0exfLA/ehFxOOUVQgNPgKuNiEhg6ywPBozXPA9nJdd7RFyfmVt2SD8rM1/YYZJJKcqvjL09M88abtzxFBF9lNeRDewyM17zP5wu77MR8QvgpIlaxlqG4ymvsBvpmxUmvRqQXAk8JjPvnujyNEXplvDCzDx8osui8VNboq+jvKJ1Qt7RXctwfGbO6TBs0p33IuIzlH18NG/u6boob/L5eqf4QitvNK2c6h2Dvcarq79sp9VD7SqwA8O/vkxjKEo/6PdRXofZqiC9upNyZ0sabw8y+Pmt9ee9iHhC7WoVUbqnvY3hX5utHrGyDzFqEsvMjg8fZmanV4ZJIxYRJ1Aepjwwyyv8NA7qrfubKd0E9hhm9AmR5QHsSye6HFr9ZOZCBnlF4CQ5702ndHfZlLKff57SfVFjLCIeS+me1Mk2mbkqv6mxUla564skSZKksWfXF0mSJKmFDNQlSZKkFrKPerXRRhvllltuOW7zu/fee1lnnXXGbX7qHuuyd1iXvcX67B3WZe8YaV1eeOGFt2XmyvxOSE8xUK+23HJLLrjggnGbX19fH3PmzBm3+al7rMveYV32Fuuzd1iXvWOkdRkRK/vO+p5i1xdJkiSphQzUJUmSpBYyUJckSZJayD7qkiRJLfP3v/+dhQsXsnTp0okuyphab731uOKKK5Z9nzp1KrNnz2bKlCkTWKr2MlCXJElqmYULFzJ9+nS23HJLImKiizNm7rnnHqZPnw5AZrJ48WIWLlzIVlttNcElaye7vkiSJLXM0qVLmTFjRk8F6QNFBDNmzOi5uwZjyUBdkiSphXo5SO+3OizjqjBQlyRJ0nLuvPNOjjnmmFFP9+IXv5g777yzCyVaPdlHXZIkqeWOnHf1mOb33t22GXJ4f6D+rne9a7n0Bx98kLXWGjx8PPPMM8ekfCoM1CVJkrScgw8+mD//+c9st912TJkyhalTp7LBBhtw5ZVXcvXVV7P33nuzYMECli5dyoEHHsjcuXOBh3/pfcmSJey55548+9nP5re//S2zZs3itNNOm+Clmnzs+iJJkqTlHHHEETzucY/j4osv5rOf/SwXXXQRX/ziF7n66tKyf9xxx3HhhRdywQUXcPTRR7N48eIV8rjmmmvYf//9ueyyy1h//fU59dRTx3sxJj1b1DXmBrs9N9xtNkmS1E477bTTcq9QPProo/nhD38IwIIFC7jmmmuYMWPGctNstdVWbLfddgA8/elP5/rrrx+38vYKA3VJkiQNaZ111ln2ua+vj7POOotzzz2XadOmMWfOnI6vWFx77bWXfV5zzTX529/+Ni5l7SV2fZEkSdJypk+fzj333NNx2F133cUGG2zAtGnTuPLKKznvvPPGuXSrD1vUJUmStJwZM2awyy678OQnP5lHPvKRzJw5c9mwPfbYg6985Ss88YlP5PGPfzw777zzBJa0txmoS5IktdxEPOd10kkndUxfe+21+clPftJxWH8/9I022ohLL710WfoHPvABgEFb6dWZXV8kSZKkFjJQlyRJklrIQF2SJElqIQN1SZIkqYUM1CVJkqQWMlCXJEmSWqhVgXpEbBYR50TE5RFxWUQcWNMPj4hFEXFx/XtxY5pDImJ+RFwVEbs30veoafMj4uCJWB5JkqTJ6M477+SYY45ZqWmPOuoo7rvvvjEu0eqpbe9RfxB4f2ZeFBHTgQsjYl4ddmRmfq45ckRsC+wLPAnYFDgrIvpfNPolYDdgIXB+RJyemZePy1JIkiSNpXM+Pbb57XrIkIP7A/V3vetdo876qKOOYr/99mPatGkrWzpVrQrUM/NG4Mb6+Z6IuAKYNcQkewEnZ+b9wHURMR/YqQ6bn5nXAkTEyXVcA/UJdOS8q1dIm4gfcJAkSUM7+OCD+fOf/8x2223HbrvtxqMf/WhOOeUU7r//fl7xilfw0Y9+lHvvvZd99tmHhQsX8tBDD/HhD3+Ym2++mRtuuIFdd92VjTbaiHPOOWeiF2VSa1Wg3hQRWwLbA78DdgHeHRFvBC6gtLrfQQniz2tMtpCHA/sFA9Kf2WEec4G5ADNnzqSvr29Ml2EoS5YsGdf5jadZS+8f8bh9fTd0sSTjo5frcnVjXfYW67N3rI51ud566y33K56PeGDk59aReGCYXwg99NBDueSSS/j1r3/N2WefzWmnncbZZ59NZvKa17yGn/70p9x2221svPHGnHzyyQDcddddrLfeenz+85/njDPOYMaMGSv8EulDDz20QtrSpUtXu/odqVYG6hGxLnAq8J7MvDsivgx8HMj6//PAW1d1Ppl5LHAswI477phz5sxZ1SxHrK+vj/Gc33jq1HI+mH3mTP4W9V6uy9WNddlbrM/esTrW5RVXXMH06dMfTnjE2mOa/9rNvDtYd911WWONNZg+fTq/+c1vOOecc3juc58LlAunRYsW8ZznPIdDDz2UT3ziE7z0pS/lOc95DgARwbrrrrt8+at77rlnhfSpU6ey/fbbj9GS9ZbWBeoRMYUSpH87M38AkJk3N4Z/Dfhx/boI2Kwx+eyaxhDpkiRJGqHM5JBDDuEd73jHCsMuuugizjzzTA499FBe8IIX8JGPfGQCSti72vbWlwC+AVyRmV9opG/SGO0VwKX18+nAvhGxdkRsBWwN/B44H9g6IraKiEdQHjg9fTyWQZIkabKbPn36si4qu+++O8cddxxLliwBYNGiRdxyyy3ccMMNTJs2jf3224+DDjqIiy66aIVptWra1qK+C/AG4E8RcXFN+xDw2ojYjtL15XrgHQCZeVlEnEJ5SPRBYP/MfAggIt4N/AxYEzguMy8bzwWRJEmarGbMmMEuu+zCk5/8ZPbcc09e97rX8axnPQso3WJOPPFE5s+fz0EHHcQaa6zBlClT+PKXvwzA3Llz2WOPPdh00019mHQVtSpQz8zfANFh0JlDTPNJ4JMd0s8cajpJkqRJY5jXKXbDSSedtNz3Aw88cLnvj3vc49h9990Z6IADDuCAAw7oatlWF63q+iJJkiSpMFCXJEmSWshAXZIkSWohA3VJkqQWysyJLkLXrQ7LuCoM1CVJklpm6tSpLF68uKcD2cxk8eLFTJ06daKL0lqteuuLJEmSYPbs2SxcuJBbb711oosyppYuXbpcYD516lRmz549gSVqNwN1SZKklpkyZQpbbbXVRBdjzPX19bH99ttPdDEmDbu+SJIkSS1koC5JkiS1kIG6JEmS1EIG6pIkSVILGahLkiRJLWSgLkmSJLWQgbokSZLUQgbqkiRJUgsZqEuSJEktZKAuSZIktZCBuiRJktRCBuqSJElSCxmoS5IkSS1koC5JkiS1kIG6JEmS1EIG6pIkSVILGSQI4gwAABjHSURBVKhLkiRJLWSgLkmSJLWQgbokSZLUQgbqkiRJUgsZqEuSJEktZKAuSZIktZCBuiRJktRCBuqSJElSCxmoS5IkSS1koC5JkiS1kIG6JEmS1EJrTXQBNLkdOe/qiS6CJElST7JFXZIkSWohA3VJkiSphQzUJUmSpBYyUJckSZJayEBdkiRJaqFWBeoRsVlEnBMRl0fEZRFxYE3fMCLmRcQ19f8GNT0i4uiImB8Rl0TEDo283lTHvyYi3jRRyyRJkiStjFYF6sCDwPszc1tgZ2D/iNgWOBg4OzO3Bs6u3wH2BLauf3OBL0MJ7IHDgGcCOwGH9Qf3kiRJ0mTQqkA9M2/MzIvq53uAK4BZwF7ACXW0E4C96+e9gG9mcR6wfkRsAuwOzMvM2zPzDmAesMc4LookSZK0Slr7g0cRsSWwPfA7YGZm3lgH3QTMrJ9nAQsaky2saYOlD5zHXEpLPDNnzqSvr2/Myj+cJUuWjOv8umXW0vtXafq+vhvGqCQTp1fqUtZlr7E+e4d12Tusy9FpZaAeEesCpwLvycy7I2LZsMzMiMixmE9mHgscC7DjjjvmnDlzxiLbEenr62M859ctq/rLpPvM2WaMSjJxeqUuZV32Guuzd1iXvcO6HJ1WdX0BiIgplCD925n5g5p8c+3SQv1/S01fBGzWmHx2TRssXZIkSZoUWhWoR2k6/wZwRWZ+oTHodKD/zS1vAk5rpL+xvv1lZ+Cu2kXmZ8CLImKD+hDpi2qaJEmSNCm0revLLsAbgD9FxMU17UPAEcApEfE24C/APnXYmcCLgfnAfcBbADLz9oj4OHB+He9jmXn7+CyCJEmStOpaFahn5m+AGGTwCzqMn8D+g+R1HHDc2JVOkiRJGj+t6voiSZIkqTBQlyRJklrIQF2SJElqIQN1SZIkqYUM1CVJkqQWMlCXJEmSWshAXZIkSWohA3VJkiSphQzUJUmSpBYyUJckSZJayEBdkiRJaiEDdUmSJKmFDNQlSZKkFjJQlyRJklrIQF2SJElqIQN1SZIkqYUM1CVJkqQWMlCXJEmSWshAXZIkSWohA3VJkiSphQzUJUmSpBYyUJckSZJayEBdkiRJaiEDdUmSJKmFDNQlSZKkFjJQlyRJklrIQF2SJElqIQN1SZIkqYUM1CVJkqQWMlCXJEmSWqhrgXpEPKVbeUuSJEm9rpst6sdExO8j4l0RsV4X5yNJkiT1nK4F6pn5HOD1wGbAhRFxUkTs1q35SZIkSb2kq33UM/Ma4FDgg8DzgKMj4sqIeGU35ytJkiRNdt3so/7UiDgSuAJ4PvCyzHxi/Xxkt+YrSZIk9YK1upj3fwFfBz6UmX/rT8zMGyLi0C7OV5PIkfOu7pj+3t22GeeSSJIktUs3A/WXAH/LzIcAImINYGpm3peZ3+rifCVJkqRJr5t91M8CHtn4Pq2mSZIkSRpGNwP1qZm5pP9L/Tyti/OTJEmSekY3A/V7I2KH/i8R8XTgb0OMT0QcFxG3RMSljbTDI2JRRFxc/17cGHZIRMyPiKsiYvdG+h41bX5EHDzGyyVJkiR1XTf7qL8H+F5E3AAE8BjgNcNMczzw38A3B6QfmZmfayZExLbAvsCTgE2BsyKi/wnELwG7AQuB8yPi9My8fBWWRZIkSRpXXQvUM/P8iHgC8PiadFVm/n2YaX4VEVuOcBZ7ASdn5v3AdRExH9ipDpufmdcCRMTJdVwDdUmSJE0aXf3BI+AZwFOBHYDXRsQbVzKfd0fEJbVrzAY1bRawoDHOwpo2WLokSZI0aXStRT0ivgU8DrgYeKgmJyt2axnOl4GP12k/DnweeOsYlXEuMBdg5syZ9PX1jUW2I7JkyZJxnV+3zFp6f1fy7eu7oSv5dkOv1KWsy15jffYO67J3WJej080+6jsC22ZmrkommXlz/+eI+Brw4/p1EbBZY9TZNY0h0gfmfSxwLMCOO+6Yc+bMWZWijkpfXx/jOb9uGewHi1bVPnMmzw8e9UpdyrrsNdZn77Aue4d1OTrd7PpyKeUB0lUSEZs0vr6i5gtwOrBvRKwdEVsBWwO/B84Hto6IrSLiEZQHTk9f1XJIkiRJ46mbLeobAZdHxO+BZf0jMvPlg00QEd8B5gAbRcRC4DBgTkRsR+n6cj3wjprPZRFxCuUh0QeB/Ru/gvpu4GfAmsBxmXnZmC+dJEmS1EXdDNQPH+0EmfnaDsnfGGL8TwKf7JB+JnDmaOcvSZIktUU3X8/4y4jYAtg6M8+KiGmUFm5JkiRJw+haH/WI+Ffg+8BXa9Is4Efdmp8kSZLUS7r5MOn+wC7A3QCZeQ3w6C7OT5IkSeoZ3QzU78/MB/q/RMRalAdCJUmSJA2jm4H6LyPiQ8AjI2I34HvAGV2cnyRJktQzuhmoHwzcCvyJ8krFM4FDuzg/SZIkqWd0860v/wC+Vv8kSZIkjULXAvWIuI4OfdIz87HdmqckSZLUK7r5g0c7Nj5PBV4NbNjF+UmSJEk9o2t91DNzceNvUWYeBbykW/OTJEmSekk3u77s0Pi6BqWFvZst+JIkSVLP6Gbg/PnG5weB64F9ujg/SZIkqWd0860vu3Yrb0mSJKnXdbPry/uGGp6ZX+jWvCVJkqTJrttvfXkGcHr9/jLg98A1XZynJEmS1BO6GajPBnbIzHsAIuJw4H8zc78uzlOSJEnqCV17PSMwE3ig8f2BmiZJkiRpGN1sUf8m8PuI+GH9vjdwQhfnpwmw81+PXSHtvM3nTkBJJEmSeks33/ryyYj4CfCcmvSWzPxDt+YnSZIk9ZJudn0BmAbcnZlfBBZGxFZdnp8kSZLUE7oWqEfEYcAHgUNq0hTgxG7NT5IkSeol3WxRfwXwcuBegMy8AZjexflJkiRJPaObgfoDmZlAAkTEOl2clyRJktRTuhmonxIRXwXWj4h/Bc4CvtbF+UmSJEk9o5tvfflcROwG3A08HvhIZs7r1vwkSZKkXtKVQD0i1gTOysxdAYNzSZIkaZS60vUlMx8C/hER63Ujf0mSJKnXdfOXSZcAf4qIedQ3vwBk5r93cZ6SJElST+hmoP6D+idJkiRplMY8UI+IzTPzr5l5wljnrdXHkfOuXiHtvbttMwElkSRJmhjd6KP+o/4PEXFqF/KXJEmSel43AvVofH5sF/KXJEmSel43AvUc5LMkSZKkEerGw6RPi4i7KS3rj6yfqd8zMx/VhXlKkiRJPWXMA/XMXHOs85QkSZJWN135wSNJkiRJq8ZAXZIkSWohA3VJkiSphQzUJUmSpBYyUJckSZJaqFWBekQcFxG3RMSljbQNI2JeRFxT/29Q0yMijo6I+RFxSUTs0JjmTXX8ayLiTROxLJIkSdKqaFWgDhwP7DEg7WDg7MzcGji7fgfYE9i6/s0FvgwlsAcOA54J7AQc1h/cS5IkSZNFqwL1zPwVcPuA5L2AE+rnE4C9G+nfzOI8YP2I2ATYHZiXmbdn5h3APFYM/iVJkqRWa1WgPoiZmXlj/XwTMLN+ngUsaIy3sKYNli5JkiRNGmP+y6TdlJkZETlW+UXEXEq3GWbOnElfX99YZT2sJUuWjOv8uuXe9bZbIW3W0uu6Mq++vhu6ku+q6pW6lHXZa6zP3mFd9g7rcnQmQ6B+c0Rskpk31q4tt9T0RcBmjfFm17RFwJwB6X2dMs7MY4FjAXbcccecM2dOp9G6oq+vj/GcX7ec+40PrJB29eZzuzKvfeZs05V8V1Wv1KWsy15jffYO67J3WJejMxm6vpwO9L+55U3AaY30N9a3v+wM3FW7yPwMeFFEbFAfIn1RTZMkSZImjVa1qEfEdyit4RtFxELK21uOAE6JiLcBfwH2qaOfCbwYmA/cB7wFIDNvj4iPA+fX8T6WmQMfUJUkSZJarVWBema+dpBBL+gwbgL7D5LPccBxY1g0SZIkaVxNhq4vkiRJ0mrHQF2SJElqIQN1SZIkqYUM1CVJkqQWMlCXJEmSWshAXZIkSWohA3VJkiSphQzUJUmSpBYyUJckSZJayEBdkiRJaiEDdUmSJKmFDNQlSZKkFjJQlyRJklrIQF2SJElqobUmugCaZM759ESXQJIkabVgi7okSZLUQgbqkiRJUgvZ9UWT3pHzrl4h7b27bTMBJZEkSRo7tqhLkiRJLWSgLkmSJLWQXV80aXTq4iJJktSrbFGXJEmSWshAXZIkSWohA3VJkiSpheyjrp7kKxslSdJkZ4u6JEmS1EIG6pIkSVILGahLkiRJLWSgLkmSJLWQgbokSZLUQgbqkiRJUgsZqEuSJEktZKAuSZIktZCBuiRJktRCBuqSJElSCxmoS5IkSS1koC5JkiS1kIG6JEmS1EJrTXQBtHra+a/HLvf9vM3nTlBJJEmS2slAXSNy5LyrAdj5r4snuCSSJEmrh0nT9SUiro+IP0XExRFxQU3bMCLmRcQ19f8GNT0i4uiImB8Rl0TEDhNbekmSJGl0Jk2gXu2amdtl5o71+8HA2Zm5NXB2/Q6wJ7B1/ZsLfHncSypJkiStgskWqA+0F3BC/XwCsHcj/ZtZnAesHxGbTEQBJUmSpJUxmQL1BH4eERdGRP+ThzMz88b6+SZgZv08C1jQmHZhTZMkSZImhcjMiS7DiETErMxcFBGPBuYBBwCnZ+b6jXHuyMwNIuLHwBGZ+Zuafjbwwcy8YECecyldY5g5c+bTTz755PFaHJYsWcK66647bvNbVbfccz8A69x/67Dj3rv2xsOOMzCfkUyzqh49fe2u5DvZ6lKDsy57i/XZO6zL3jHSutx1110vbHR1Xm1Nmre+ZOai+v+WiPghsBNwc0Rskpk31q4tt9TRFwGbNSafXdMG5nkscCzAjjvumHPmzOniEiyvr6+P8Zzfqlr21pdb5g077tUjeNXiwHxGMs2q2mfONl3Jd7LVpQZnXfYW67N3WJe9w7ocnUkRqEfEOsAamXlP/fwi4GPA6cCbgCPq/9PqJKcD746Ik4FnAnc1usioy9r6jvT+i42B3rtbdwJ4SZKkVTEpAnVK3/MfRgSUMp+UmT+NiPOBUyLibcBfgH3q+GcCLwbmA/cBbxn/IkuSJEkrb1IE6pl5LfC0DumLgRd0SE9g/3EomiRJktQVk+mtL5IkSdJqw0BdkiRJaiEDdUmSJKmFDNQlSZKkFjJQlyRJklpoUrz1RZPbwPeqS5IkaXgG6mqFTsF8W34oSZIkaSLY9UWSJElqIQN1SZIkqYUM1CVJkqQWMlCXJEmSWsiHSdVaAx8w9eFSSZK0OrFFXZIkSWohA3VJkiSphQzUJUmSpBYyUJckSZJayEBdkiRJaiEDdUmSJKmFDNQlSZKkFjJQlyRJklrIQF2SJElqIQN1SZIkqYXWmugCSCO181+PXSHtvM3nTkBJJEmSus8WdUmSJKmFbFHXau/IeVevkPbe3baZgJJIkiQ9zBZ1SZIkqYUM1CVJkqQWMlCXJEmSWshAXZIkSWohHybVpDbwlY2+rlGSJPUKA3X1FN+1LkmSeoVdXyRJkqQWskVdK+j0XnFJkiSNL1vUJUmSpBayRV09b2UeOB3sroK/WCpJksaLgbpWOz5wKkmSJgMDdQlf8yhJktrHQH01NtxDo51aniVJkjQ+DNSlUeh0cTNr6f0d0+3PLkmSVoWButTBWPRjN3iXJEmroqdfzxgRe0TEVRExPyIOnujySJIkSSPVsy3qEbEm8CVgN2AhcH5EnJ6Zl09sySaGP2K06trywKkt9ZIkrR56NlAHdgLmZ+a1ABFxMrAXsFoG6hp7/YH77ettx863zAOGD95Hc8Fk8D3+brnH5w00MdzuJHXSy4H6LGBB4/tC4JkTVJYV3XMTnPPp5dN2PaTjqKP58Z1O4+7812PZeUBap4DSt7ysupVZh4MF9826bObbX5fN6Ua8jZzzac69dvFKlWc4owkqVvUOz1jMyyDIdSNJbReZOdFl6IqIeBWwR2a+vX5/A/DMzHx3Y5y5QH9U8njgqnEs4kbAbeM4P3WPddk7rMveYn32Duuyd4y0LrfIzI27XZi26+UW9UXAZo3vs2vaMpl5LDAhzcgRcUFm7jgR89bYsi57h3XZW6zP3mFd9g7rcnR6+a0v5wNbR8RWEfEIYF/g9AkukyRJkjQiPduinpkPRsS7gZ8BawLHZeZlE1wsSZIkaUR6NlAHyMwzgTMnuhyD8MnN3mFd9g7rsrdYn73Duuwd1uUo9OzDpJIkSdJk1st91CVJkqRJy0B9AkTEHhFxVUTMj4iDJ7o8GlpEHBcRt0TEpY20DSNiXkRcU/9vUNMjIo6udXtJROwwcSXXQBGxWUScExGXR8RlEXFgTbc+J5mImBoRv4+IP9a6/GhN3yoiflfr7Lv1ZQJExNr1+/w6fMuJLL9WFBFrRsQfIuLH9bt1OQlFxPUR8aeIuDgiLqhpHmNXkoH6OIuINYEvAXsC2wKvjYhtJ7ZUGsbxwB4D0g4Gzs7MrYGz63co9bp1/ZsLfHmcyqiReRB4f2ZuS/ntqP3r/md9Tj73A8/PzKcB2wF7RMTOwGeAIzPzn4A7gLfV8d8G3FHTj6zjqV0OBK5ofLcuJ69dM3O7xmsYPcauJAP18bcTMD8zr83MB4CTgb0muEwaQmb+Crh9QPJewAn18wnA3o30b2ZxHrB+RGwyPiXVcDLzxsy8qH6+hxIUzML6nHRqnSypX6fUvwSeD3y/pg+sy/46/j7wgoiIcSquhhERs4GXAF+v3wPrspd4jF1JBurjbxawoPF9YU3T5DIzM2+sn28CZtbP1u8kUW+Xbw/8DutzUqpdJS4GbgHmAX8G7szMB+sozfpaVpd1+F3AjPEtsYZwFPAfwD/q9xlYl5NVAj+PiAvrL8CDx9iV1tOvZ5TGQ2ZmRPj6pEkkItYFTgXek5l3NxvjrM/JIzMfAraLiPWBHwJPmOAiaSVExEuBWzLzwoiYM9Hl0Sp7dmYuiohHA/Mi4srmQI+xo2OL+vhbBGzW+D67pmlyubn/9lz9f0tNt35bLiKmUIL0b2fmD2qy9TmJZeadwDnAsyi3zvsboZr1tawu6/D1gMXjXFR1tgvw8oi4ntId9PnAF7EuJ6XMXFT/30K5gN4Jj7ErzUB9/J0PbF2fZn8EsC9w+gSXSaN3OvCm+vlNwGmN9DfWJ9l3Bu5q3O7TBKv9WL8BXJGZX2gMsj4nmYjYuLakExGPBHajPHNwDvCqOtrAuuyv41cBv0h/SKQVMvOQzJydmVtSzom/yMzXY11OOhGxTkRM7/8MvAi4FI+xK80fPJoAEfFiSn+8NYHjMvOTE1wkDSEivgPMATYCbgYOA34EnAJsDvwF2Cczb6+B4H9T3hJzH/CWzLxgIsqtFUXEs4FfA3/i4b6wH6L0U7c+J5GIeCrlobQ1KY1Op2TmxyLisZRW2Q2BPwD7Zeb9ETEV+BbluYTbgX0z89qJKb0GU7u+fCAzX2pdTj61zn5Yv64FnJSZn4yIGXiMXSkG6pIkSVIL2fVFkiRJaiEDdUmSJKmFDNQlSZKkFjJQlyRJklrIQF2SJElqIQN1SVoNRMSWEfG6lZhu/Yh4VzfKJEkamoG6JK0etgQ6BuqNX3/sZH3AQF2SJoCBuiR1UUS8MSIuiYg/RsS3asv2L2ra2RGxeR3v+Ig4OiJ+GxHXRsSrGnl8MCL+VPM4oqY9LiJ+GhEXRsSvI+IJw+RzBPCciLg4It4bEW+OiNMj4hfA2RGxbi3PRXVeezWme1yd7rN1HgdFxPl1GT46TqtSklY7Q7WiSJJWQUQ8CTgU+OfMvC0iNqT8muYJmXlCRLwVOBrYu06yCfBs4AmUn9b+fkTsCewFPDMz76t5ABwLvDMzr4mIZwLHAM8fLB/gYOovPtayvRnYAXhq/YXAtYBXZObdEbERcF5EnF6ne3JmblenexGwNbATEMDpEfHczPzVGK8+SVrtGahLUvc8H/heZt4GUAPiZwGvrMO/BfxnY/wfZeY/gMsjYmZNeyHwP5l5XyOPdYF/Br5XfoEbgLWHyaeTeZl5e/0cwKci4rnAP4BZQKdpX1T//lC/r0sJ3A3UJWmMGahLUnvc3/gcg45Vui3e2d/KvQr53Nv4/HpgY+Dpmfn3iLgemNphmgA+nZlfHSJfSdIYsI+6JHXPL4BXR8QMgNpt5bfAvnX464FfD5PHPOAtETGtP4/MvBu4LiJeXdMiIp42TD73ANOHGL4ecEsN0ncFthhkup8Bb62t+kTErIh49DDzliStBFvUJalLMvOyiPgk8MuIeIjSXeQA4H8i4iDgVuAtw+Tx04jYDrggIh4AzgQ+RAnyvxwRhwJTgJOBPw6R1SXAQxHxR+B44I4Bw78NnBERfwIuAK6s818cEf8XEZcCP8nMgyLiicC5tdvNEmA/4JYRrRRJ0ohFZk50GSRJkiQNYNcXSZIkqYUM1CVJkqQWMlCXJEmSWshAXZIkSWohA3VJkiSphQzUJUmSpBYyUJckSZJayEBdkiRJaqH/D4FBoFgKq6UWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 792x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for metric in ('rougher.input.feed_size', 'primary_cleaner.input.feed_size'):\n",
    "    for label, df in (('train', gold_recovery_train), ('test', gold_recovery_test)): \n",
    "        df[metric].plot(\n",
    "                title = 'Сравнение распределения размеров гранул сырья на обучающей и тестовой выборках для {}'.format(metric),\n",
    "                kind='hist', \n",
    "                bins=100, \n",
    "                grid=True, \n",
    "                legend=True,\n",
    "                figsize=(11, 5),\n",
    "                alpha=0.5,\n",
    "                label=label,\n",
    "            ).set(xlabel='concentrate')\n",
    "  \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Вывод**: из графиков можно сделать вывод, что распределения размеров гранул сырья на обучающей и тестовой выборках совпадают а значит оценка модели будет правильной."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Комментарий ревьюера:</b>\n",
    "\n",
    "Обрати внимание, что некоторые графики у нас повторяются.\n",
    "    \n",
    "Это говорит о том, что мы можем создать функцию для постоянного их построения. И с помощью этого повысить оптимизацию своего кода и презентабельность проекта в целом.\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"analyse_data.analyse_sum_concentration\"></a>\n",
    "### Исследование суммарной концентрации всех веществ на разных стадиях: в сырье, в черновом и финальном концентратах"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посчитаем сумму всех веществ (металлы + sol) по каждой из стадий и запишем их в датасет `gold_recovery_train` для дальнейшего поиска аномалий. Я не вижу смысла удалять аномалии из тестовой выборки, поскольку они не могут испортить качество модели, в худшем случае модель вернет для них неверные предсказания."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rougher.input.feed</th>\n",
       "      <th>rougher.output.concentrate</th>\n",
       "      <th>primary_cleaner.output.concentrate</th>\n",
       "      <th>final.output.concentrate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2016-01-15 00:00:00</td>\n",
       "      <td>51.680034</td>\n",
       "      <td>66.424950</td>\n",
       "      <td>72.640924</td>\n",
       "      <td>63.644396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-15 01:00:00</td>\n",
       "      <td>50.659114</td>\n",
       "      <td>67.012710</td>\n",
       "      <td>72.543485</td>\n",
       "      <td>63.957723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-15 02:00:00</td>\n",
       "      <td>50.609929</td>\n",
       "      <td>66.103793</td>\n",
       "      <td>72.095042</td>\n",
       "      <td>64.311180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-15 03:00:00</td>\n",
       "      <td>51.061546</td>\n",
       "      <td>65.752751</td>\n",
       "      <td>59.957723</td>\n",
       "      <td>63.573449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-15 04:00:00</td>\n",
       "      <td>47.859163</td>\n",
       "      <td>65.908382</td>\n",
       "      <td>71.321010</td>\n",
       "      <td>64.004667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     rougher.input.feed  rougher.output.concentrate  \\\n",
       "date                                                                  \n",
       "2016-01-15 00:00:00           51.680034                   66.424950   \n",
       "2016-01-15 01:00:00           50.659114                   67.012710   \n",
       "2016-01-15 02:00:00           50.609929                   66.103793   \n",
       "2016-01-15 03:00:00           51.061546                   65.752751   \n",
       "2016-01-15 04:00:00           47.859163                   65.908382   \n",
       "\n",
       "                     primary_cleaner.output.concentrate  \\\n",
       "date                                                      \n",
       "2016-01-15 00:00:00                           72.640924   \n",
       "2016-01-15 01:00:00                           72.543485   \n",
       "2016-01-15 02:00:00                           72.095042   \n",
       "2016-01-15 03:00:00                           59.957723   \n",
       "2016-01-15 04:00:00                           71.321010   \n",
       "\n",
       "                     final.output.concentrate  \n",
       "date                                           \n",
       "2016-01-15 00:00:00                 63.644396  \n",
       "2016-01-15 01:00:00                 63.957723  \n",
       "2016-01-15 02:00:00                 64.311180  \n",
       "2016-01-15 03:00:00                 63.573449  \n",
       "2016-01-15 04:00:00                 64.004667  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for metric in CONCENTRATE_METRICS:\n",
    "    gold_recovery_train[metric] = 0\n",
    "    for metal in METALS + ['sol']:\n",
    "        gold_recovery_train[metric] += gold_recovery_train['{}_{}'.format(metric, metal)]\n",
    "    \n",
    "gold_recovery_train[CONCENTRATE_METRICS].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Определим класс, который посчитает границы нормальных значений (т.е Q1 - 1.5 * IQR и Q3 + 1.5 * IQR) и затем сможет нарисовать  графики по ним. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "NormalRange = namedtuple('NormalRange', ('min', 'max'))\n",
    "\n",
    "class NormalRangeAnalyzer(object):\n",
    "    MIN_VIEW_BOXPLOT_MUL = 0.8\n",
    "    MAX_VIEW_BOXPLOT_MUL = 1.2\n",
    "    \n",
    "    def __init__(self, df, column_name):\n",
    "        self._series = df[column_name]\n",
    "        self._column_name = column_name\n",
    "        self._normal_range = self._compute_normal_range()\n",
    "    \n",
    "    def _compute_normal_range(self):\n",
    "        min_, q1, q3, max_ = self._series.quantile([0, 0.25, 0.75, 1])\n",
    "        iqr = q3 - q1\n",
    "        return NormalRange(max(min_, q1 - 1.5 * iqr), min(max_, q3 + 1.5 * iqr))\n",
    "    \n",
    "    @property\n",
    "    def column_name(self):\n",
    "        return self._column_name\n",
    "    \n",
    "    @property\n",
    "    def normal_range(self):\n",
    "        return self._normal_range\n",
    "    \n",
    "    def describe(self):       \n",
    "        print('Границы нормальных значений для столбца {}: ({:.2f}, {:.2f})'.format(\n",
    "            self._column_name, \n",
    "            self._normal_range.min,\n",
    "            self._normal_range.max,\n",
    "        ))\n",
    "        display(self._series.describe())\n",
    "        \n",
    "    def plot(self, bins=100):\n",
    "        range_min = self._normal_range.min * self.MIN_VIEW_BOXPLOT_MUL\n",
    "        range_max = self._normal_range.max * self.MAX_VIEW_BOXPLOT_MUL\n",
    "        \n",
    "        fig, axes = plt.subplots(nrows=1, ncols=2)\n",
    "        \n",
    "        self._series.plot(\n",
    "            kind='box',\n",
    "            ax=axes[0],\n",
    "            grid=True,\n",
    "            ylim=(range_min, range_max),\n",
    "            figsize=(20,5),\n",
    "        )\n",
    "        \n",
    "        self._series.plot(\n",
    "            kind='hist', \n",
    "            ax=axes[1],\n",
    "            bins=bins, \n",
    "            title=self._column_name,\n",
    "            grid=True,\n",
    "            figsize=(20,5),\n",
    "        )\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Комментарий ревьюера:</b>\n",
    "\n",
    "Интересный способ обработать наши данные с помощью классов 👍\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выведем гистограммы и диаграммы размаха для всех признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Границы нормальных значений для столбца rougher.input.feed: (40.29, 73.05)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    14149.000000\n",
       "mean        56.242170\n",
       "std          7.029808\n",
       "min          0.040000\n",
       "25%         52.573579\n",
       "50%         56.627635\n",
       "75%         60.763234\n",
       "max         74.458253\n",
       "Name: rougher.input.feed, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Границы нормальных значений для столбца rougher.output.concentrate: (52.14, 86.20)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    14149.000000\n",
       "mean        67.173145\n",
       "std         12.713129\n",
       "min          0.000000\n",
       "25%         64.914390\n",
       "50%         69.175663\n",
       "75%         73.429261\n",
       "max         88.140119\n",
       "Name: rougher.output.concentrate, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Границы нормальных значений для столбца primary_cleaner.output.concentrate: (47.08, 76.32)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    14149.000000\n",
       "mean        60.172546\n",
       "std         10.448165\n",
       "min          0.000000\n",
       "25%         58.046816\n",
       "50%         61.786946\n",
       "75%         65.355951\n",
       "max         76.918076\n",
       "Name: primary_cleaner.output.concentrate, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Границы нормальных значений для столбца final.output.concentrate: (62.44, 76.10)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    14149.000000\n",
       "mean        68.387872\n",
       "std          7.023723\n",
       "min          0.000000\n",
       "25%         67.559819\n",
       "50%         69.141502\n",
       "75%         70.974661\n",
       "max         80.210644\n",
       "Name: final.output.concentrate, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "range_analyzers = []\n",
    "\n",
    "for metric in CONCENTRATE_METRICS:\n",
    "    range_analyzer = NormalRangeAnalyzer(gold_recovery_train, metric)\n",
    "    range_analyzer.describe() \n",
    "    range_analyzer.plot() \n",
    "    range_analyzers.append(range_analyzer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Избавимся от аномальных данных в обучающей выборке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14149, 90)\n",
      "(11931, 90)\n"
     ]
    }
   ],
   "source": [
    "print(gold_recovery_train.shape)\n",
    "\n",
    "for range_analyzer in range_analyzers:    \n",
    "    gold_recovery_train = gold_recovery_train[\n",
    "        (gold_recovery_train[range_analyzer.column_name] >= range_analyzer.normal_range.min) & \n",
    "        (gold_recovery_train[range_analyzer.column_name] <= range_analyzer.normal_range.max)\n",
    "    ]\n",
    "    \n",
    "print(gold_recovery_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Вывод**: Нашли и удалили 15.6% аномалий из обучающей выборки"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Комментарий ревьюера:</b>\n",
    "\n",
    "Что ж, достаточно серьезный объем, однако выбросы нам ни к чему 👍    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"model_creation\"></a>\n",
    "## Построение модели"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Удалим лишние признаки из train, т.е те, которых нет в test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'secondary_cleaner.output.tail_sol', 'primary_cleaner.output.concentrate_sol', 'final.output.tail_sol', 'final.output.concentrate_sol', 'final.output.concentrate_au', 'final.output.tail_ag', 'rougher.output.concentrate', 'final.output.concentrate_ag', 'rougher.calculation.floatbank11_sulfate_to_au_feed', 'primary_cleaner.output.concentrate', 'primary_cleaner.output.tail_au', 'primary_cleaner.output.concentrate_au', 'rougher.output.tail_au', 'rougher.output.tail_sol', 'final.output.concentrate_pb', 'rougher.output.concentrate_sol', 'rougher.output.concentrate_au', 'rougher.input.feed', 'rougher.output.concentrate_ag', 'primary_cleaner.output.tail_sol', 'final.output.tail_pb', 'secondary_cleaner.output.tail_ag', 'rougher.calculation.au_pb_ratio', 'rougher.calculation.floatbank10_sulfate_to_au_feed', 'primary_cleaner.output.concentrate_ag', 'final.output.concentrate', 'secondary_cleaner.output.tail_au', 'primary_cleaner.output.tail_ag', 'secondary_cleaner.output.tail_pb', 'primary_cleaner.output.concentrate_pb', 'rougher.output.concentrate_pb', 'rougher.output.tail_ag', 'final.output.tail_au', 'rougher.output.tail_pb', 'primary_cleaner.output.tail_pb', 'rougher.calculation.sulfate_to_au_concentrate'}\n",
      "set()\n",
      "set()\n"
     ]
    }
   ],
   "source": [
    "missing_test_fields = fields_diff(gold_recovery_train, gold_recovery_test)\n",
    "print(missing_test_fields)\n",
    "gold_recovery_train = gold_recovery_train.drop(missing_test_fields, axis='columns')\n",
    "print(fields_diff(gold_recovery_train, gold_recovery_test))\n",
    "print(fields_diff(gold_recovery_test, gold_recovery_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Отлично, теперь в обучающей и тестовой выборке одни и те же признаки"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"model_creation.create_function\"></a>\n",
    "### Функция для вычисления итоговой sMAPE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Напишем функцию для вычисления итоговой sMAPE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_smape(target_actual, target_predictions):\n",
    "    numerator = np.abs(target_actual - target_predictions)\n",
    "    denominator = (np.abs(target_actual) + np.abs(target_predictions)) / 2 \n",
    "    \n",
    "    return 1 / len(target_actual) * np.sum(numerator / denominator) * 100 \n",
    "    \n",
    "    \n",
    "def calculate_final_smape(target_actual, target_predictions):\n",
    "    target_actual_rougher = target_actual['rougher.output.recovery']\n",
    "    target_actual_final = target_actual['final.output.recovery']\n",
    "    \n",
    "    target_predictions_rougher = target_predictions[:, 0]\n",
    "    target_predictions_final = target_predictions[:, 1]\n",
    "    \n",
    "    return 0.25 * calculate_smape(target_actual_rougher, target_predictions_rougher) \\\n",
    "        + 0.75 * calculate_smape(target_actual_rougher, target_predictions_rougher)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Комментарий ревьюера:</b>\n",
    "\n",
    "👍\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создадим scorer для того, чтобы испольщовать его при кросс-валидации"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "smape_scorer = make_scorer(calculate_final_smape, greater_is_better=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"model_creation.train_models\"></a>\n",
    "### Обучение моделей и оценка их качества кросс-валидацией"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выделим фичи и целевые показатели `rougher.output.recovery` и `final.output.recovery`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_train = gold_recovery_train.drop(TARGETS, axis='columns')\n",
    "target_train = gold_recovery_train[TARGETS]\n",
    "features_test = gold_recovery_test.drop(TARGETS, axis='columns')\n",
    "target_test = gold_recovery_test[TARGETS]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь обучим модели и посмотрим, какая даст лучшее значение sMAPE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Дерево решений**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Лучшее значение sMAPE для дерева решений: 6.886624936629446 при значениях гиперпараметров: {'max_depth': 2}\n"
     ]
    }
   ],
   "source": [
    "param_grid = {'max_depth': range(2, 11)}\n",
    "\n",
    "model = DecisionTreeRegressor(random_state=RANDOM_STATE)\n",
    "search = GridSearchCV(model, param_grid, cv=5, scoring=smape_scorer)\n",
    "search.fit(features_train, target_train)\n",
    "print('Лучшее значение sMAPE для дерева решений: {} при значениях гиперпараметров: {}'.format(-search.best_score_, search.best_params_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Cлучайный лес**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Лучшее значение sMAPE для случайного леса: 6.626027088525869 при значениях гиперпараметров: {'n_estimators': 20}\n"
     ]
    }
   ],
   "source": [
    "param_grid = {'n_estimators': range(10, 31, 10)}\n",
    "\n",
    "model = RandomForestRegressor(max_depth=2, random_state=RANDOM_STATE)\n",
    "search = GridSearchCV(model, param_grid, cv=5, scoring=smape_scorer)\n",
    "search.fit(features_train, target_train)\n",
    "print('Лучшее значение sMAPE для случайного леса: {} при значениях гиперпараметров: {}'.format(-search.best_score_, search.best_params_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Линейная регрессия**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Лучшее значение sMAPE для линейной регрессии: 7.1380816650089995\n"
     ]
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(features_train, target_train)\n",
    "smape = cross_val_score(model, features_train, target_train, cv=5, scoring=smape_scorer).mean()\n",
    "print('Лучшее значение sMAPE для линейной регрессии:', -smape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выяснили, что лучшая модель - **Cлучайный лес**. Проверим ее качество на тестовой выборке.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sMAPE для тестовых данных: 7.804756882032331\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestRegressor(max_depth=2, n_estimators=20, random_state=RANDOM_STATE) \n",
    "model.fit(features_train, target_train)\n",
    "predictions_test = model.predict(features_test)\n",
    "\n",
    "print('sMAPE для тестовых данных:', calculate_final_smape(target_test, predictions_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Выводы**:\n",
    "* случайный лес дал лучшее значение sMAPE среди других моделей \n",
    "* на тестовой выборке случайный лес предсказал значения целевых признаков `rougher.output.recovery` и `final.output.recovery` с sMAPE 7.8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Комментарий ревьюера:</b>\n",
    "    \n",
    "И с этим трудно поспорить 👍\n",
    "    \n",
    "Проект сделан настолько хорошо, что даже добавить нечего, только достаточно мелкие значение, которые я оставил на пути к финалу.\n",
    "\n",
    "На протяжении всей работы чувствуется глубина проработки задачи и это безусловный плюс для нас. Было использовано большое количество методов, которые будут помогать тебе в дальнейших проектах.\n",
    "        \n",
    "Успехов тебе в новых проектах! \n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [x]  Jupyter Notebook открыт\n",
    "- [x]  Весь код выполняется без ошибок\n",
    "- [x]  Ячейки с кодом расположены в порядке выполнения\n",
    "- [x]  Выполнен шаг 1: данные подготовлены\n",
    "    - [x]  Проверена формула вычисления эффективности обогащения\n",
    "    - [x]  Проанализированы признаки, недоступные в тестовой выборке\n",
    "    - [x]  Проведена предобработка данных\n",
    "- [x]  Выполнен шаг 2: данные проанализированы\n",
    "    - [x]  Исследовано изменение концентрации элементов на каждом этапе\n",
    "    - [x]  Проанализированы распределения размеров гранул на обучающей и тестовой выборках\n",
    "    - [x]  Исследованы суммарные концентрации\n",
    "    - [x]  Проанализированы и обработаны аномальные значения\n",
    "- [x]  Выполнен шаг 3: построена модель прогнозирования\n",
    "    - [x]  Написана функция для вычисления итогового *sMAPE*\n",
    "    - [x]  Обучено и проверено несколько моделей\n",
    "    - [x]  Выбрана лучшая модель, её качество проверено на тестовой выборке"
   ]
  }
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