social-distancing-aggregate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Social Distancing Aggregate\n",
    "\n",
    "This notebook aggregates the data generated by the notebook `social-distancing.ipynb`. See email `2021-07-08 16:19`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATE_FORMAT = \"%a %b %d %H:%M:%S %z %Y\"\n",
    "INPUT_DATA_FILE = \"social-distancing-2020.csv\"\n",
    "# times are in UTC/GMT; summer time changes to winter time on the last Sunday in October\n",
    "SURVEY_DATES = [[\"Mon Jun 22 22:00:00 +0000 2020\", \"Mon Jun 29 22:00:00 +0000 2020\"],\n",
    "                [\"Mon Jun 29 22:00:00 +0000 2020\", \"Mon Jul 06 22:00:00 +0000 2020\"],\n",
    "                [\"Mon Jul 06 22:00:00 +0000 2020\", \"Mon Jul 13 22:00:00 +0000 2020\"],\n",
    "                [\"Mon Jul 13 22:00:00 +0000 2020\", \"Mon Jul 20 22:00:00 +0000 2020\"],\n",
    "                [\"Mon Jul 20 22:00:00 +0000 2020\", \"Mon Jul 27 22:00:00 +0000 2020\"],\n",
    "                [\"Mon Jul 27 22:00:00 +0000 2020\", \"Mon Aug 03 22:00:00 +0000 2020\"],\n",
    "                [\"Mon Aug 03 22:00:00 +0000 2020\", \"Mon Aug 10 22:00:00 +0000 2020\"],\n",
    "                [\"Mon Aug 10 22:00:00 +0000 2020\", \"Mon Aug 17 22:00:00 +0000 2020\"],\n",
    "                [\"Mon Aug 17 22:00:00 +0000 2020\", \"Mon Aug 24 22:00:00 +0000 2020\"],\n",
    "                [\"Mon Aug 24 22:00:00 +0000 2020\", \"Mon Aug 31 22:00:00 +0000 2020\"],\n",
    "                [\"Mon Aug 31 22:00:00 +0000 2020\", \"Mon Sep 07 22:00:00 +0000 2020\"],\n",
    "                [\"Mon Sep 07 22:00:00 +0000 2020\", \"Mon Sep 14 22:00:00 +0000 2020\"],\n",
    "                [\"Mon Sep 14 22:00:00 +0000 2020\", \"Mon Sep 21 22:00:00 +0000 2020\"],\n",
    "                [\"Mon Sep 21 22:00:00 +0000 2020\", \"Mon Sep 28 22:00:00 +0000 2020\"],\n",
    "                [\"Mon Sep 28 22:00:00 +0000 2020\", \"Mon Oct 05 22:00:00 +0000 2020\"],\n",
    "                [\"Mon Oct 05 22:00:00 +0000 2020\", \"Mon Oct 12 22:00:00 +0000 2020\"],\n",
    "                [\"Mon Oct 12 22:00:00 +0000 2020\", \"Mon Oct 19 22:00:00 +0000 2020\"],\n",
    "                [\"Mon Oct 19 22:00:00 +0000 2020\", \"Mon Oct 26 23:00:00 +0000 2020\"],\n",
    "                [\"Mon Oct 26 23:00:00 +0000 2020\", \"Mon Nov 02 23:00:00 +0000 2020\"],\n",
    "                [\"Mon Nov 02 23:00:00 +0000 2020\", \"Mon Nov 09 23:00:00 +0000 2020\"],\n",
    "                [\"Mon Nov 09 23:00:00 +0000 2020\", \"Mon Nov 16 23:00:00 +0000 2020\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_data_df = pd.read_csv(INPUT_DATA_FILE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "320770"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(input_data_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label                                  REJECTS\n",
       "id_str                     1274099821041930240\n",
       "user_id                              453382300\n",
       "created_at      Fri Jun 19 22:00:43 +0000 2020\n",
       "location                                   NaN\n",
       "municipality                               NaN\n",
       "province                                   NaN\n",
       "country                                    NaN\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_data_df.loc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Aggregate data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import clear_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def squeal(text=None):\n",
    "    clear_output(wait=True)\n",
    "    if not text is None: print(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_counts(input_data_df, datetime_start, datetime_end, last_index=0):\n",
    "    municipality_data = {}\n",
    "    province_data = {}\n",
    "    ids_part_municipalities = []\n",
    "    ids_part_provinces = []\n",
    "    for i in range(last_index, len(input_data_df)):\n",
    "        tweet_datetime = datetime.datetime.strptime(input_data_df.loc[i][\"created_at\"], DATE_FORMAT)\n",
    "        if tweet_datetime < datetime.datetime.strptime(datetime_start, DATE_FORMAT):\n",
    "            continue\n",
    "        if tweet_datetime >= datetime.datetime.strptime(datetime_end, DATE_FORMAT):\n",
    "            break\n",
    "        if not pd.isna(input_data_df.loc[i][\"province\"]):\n",
    "            province = input_data_df.loc[i][\"province\"]\n",
    "            if province not in province_data:\n",
    "                province_data[province] = { \"SUPPORTS\": 0, \"REJECTS\": 0 }\n",
    "            province_data[province][input_data_df.loc[i][\"label\"]] +=1\n",
    "            ids_part_provinces.append([input_data_df.loc[i][\"id_str\"], input_data_df.loc[i][\"label\"]])\n",
    "        if not pd.isna(input_data_df.loc[i][\"municipality\"]):\n",
    "            municipality = input_data_df.loc[i][\"municipality\"]\n",
    "            if municipality not in municipality_data:\n",
    "                municipality_data[municipality] = { \"SUPPORTS\": 0, \"REJECTS\": 0 }\n",
    "            municipality_data[municipality][input_data_df.loc[i][\"label\"]] +=1\n",
    "            ids_part_municipalities.append([input_data_df.loc[i][\"id_str\"], input_data_df.loc[i][\"label\"]])\n",
    "    return province_data, municipality_data, ids_part_provinces, ids_part_municipalities, i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n"
     ]
    }
   ],
   "source": [
    "province_data = {}\n",
    "municipality_data = {}\n",
    "ids_municipalities = []\n",
    "ids_provinces = []\n",
    "last_index = 0\n",
    "for i in range(0, len(SURVEY_DATES)):\n",
    "    province_data[i], municipality_data[i], ids_part_provinces, ids_part_municipalities, last_index = get_counts(input_data_df, SURVEY_DATES[i][0], SURVEY_DATES[i][1], last_index)\n",
    "    ids_municipalities.extend(ids_part_municipalities)\n",
    "    ids_provinces.extend(ids_part_provinces)\n",
    "    squeal(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1275187044424331266, 'SUPPORTS']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids_municipalities[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save ids for Chao et al. paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(sorted(ids_municipalities)).to_csv(\"20211207_municipalities.csv\", header=[\"id_str\", \"label\"], index=None)\n",
    "pd.DataFrame(sorted(ids_provinces)).to_csv(\"20211207_provinces.csv\", header=[\"id_str\", \"label\"], index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'SUPPORTS': 1364, 'REJECTS': 1094}\n",
      "{'SUPPORTS': 690, 'REJECTS': 532}\n",
      "{'SUPPORTS': 591, 'REJECTS': 352}\n",
      "{'SUPPORTS': 737, 'REJECTS': 213}\n",
      "{'SUPPORTS': 1629, 'REJECTS': 460}\n",
      "{'SUPPORTS': 1008, 'REJECTS': 268}\n",
      "{'SUPPORTS': 1222, 'REJECTS': 393}\n",
      "{'SUPPORTS': 695, 'REJECTS': 219}\n",
      "{'SUPPORTS': 1007, 'REJECTS': 259}\n",
      "{'SUPPORTS': 941, 'REJECTS': 464}\n",
      "{'SUPPORTS': 699, 'REJECTS': 385}\n",
      "{'SUPPORTS': 427, 'REJECTS': 150}\n",
      "{'SUPPORTS': 670, 'REJECTS': 234}\n",
      "{'SUPPORTS': 910, 'REJECTS': 320}\n",
      "{'SUPPORTS': 1135, 'REJECTS': 250}\n",
      "{'SUPPORTS': 789, 'REJECTS': 228}\n",
      "{'SUPPORTS': 993, 'REJECTS': 260}\n",
      "{'SUPPORTS': 479, 'REJECTS': 132}\n",
      "{'SUPPORTS': 509, 'REJECTS': 127}\n",
      "{'SUPPORTS': 321, 'REJECTS': 66}\n",
      "{'SUPPORTS': 276, 'REJECTS': 95}\n"
     ]
    }
   ],
   "source": [
    "for i in province_data:\n",
    "    print(province_data[i]['Noord-Holland'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'SUPPORTS': 779, 'REJECTS': 665}\n",
      "{'SUPPORTS': 395, 'REJECTS': 343}\n",
      "{'SUPPORTS': 373, 'REJECTS': 228}\n",
      "{'SUPPORTS': 464, 'REJECTS': 134}\n",
      "{'SUPPORTS': 1020, 'REJECTS': 277}\n",
      "{'SUPPORTS': 558, 'REJECTS': 162}\n",
      "{'SUPPORTS': 709, 'REJECTS': 225}\n",
      "{'SUPPORTS': 417, 'REJECTS': 146}\n",
      "{'SUPPORTS': 607, 'REJECTS': 154}\n",
      "{'SUPPORTS': 576, 'REJECTS': 256}\n",
      "{'SUPPORTS': 416, 'REJECTS': 245}\n",
      "{'SUPPORTS': 256, 'REJECTS': 100}\n",
      "{'SUPPORTS': 361, 'REJECTS': 146}\n",
      "{'SUPPORTS': 529, 'REJECTS': 196}\n",
      "{'SUPPORTS': 689, 'REJECTS': 169}\n",
      "{'SUPPORTS': 443, 'REJECTS': 159}\n",
      "{'SUPPORTS': 554, 'REJECTS': 167}\n",
      "{'SUPPORTS': 280, 'REJECTS': 70}\n",
      "{'SUPPORTS': 314, 'REJECTS': 73}\n",
      "{'SUPPORTS': 194, 'REJECTS': 38}\n",
      "{'SUPPORTS': 152, 'REJECTS': 58}\n"
     ]
    }
   ],
   "source": [
    "for i in municipality_data:\n",
    "    print(municipality_data[i]['Amsterdam'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Write output files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "MUNICIPALITY_FILE = \"csv/84992NED_Municipalities.csv\"\n",
    "# municipality from tweets: municipality from file\n",
    "ALIASES = { \"Den Haag\": \"'s-Gravenhage\",\n",
    "            \"De Friese Meren\": \"De Fryske Marren\",\n",
    "            \"Dantumadeel\": \"Dantumadiel\",\n",
    "            \"Tietjerksteradeel\": \"Tytsjerksteradiel\",\n",
    "          }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "provinces_dict = { 'Noord-Holland': 'PV27', 'Zuid-Holland': 'PV28', 'Zeeland': 'PV29', 'Noord-Brabant': 'PV30', \n",
    "                   'Utrecht': 'PV26', 'Flevoland': 'PV24', 'Friesland': 'PV21', 'Groningen': 'PV20', \n",
    "                   'Drenthe': 'PV22', 'Overijssel': 'PV23', 'Gelderland': 'PV25', 'Limburg': 'PV31' }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def datetime2file_name(datetime_string):\n",
    "    datetime_date = datetime.datetime.strptime(datetime_string, DATE_FORMAT)\n",
    "    return str(datetime_date.year) + str(datetime_date.month).zfill(2) + str(datetime_date.day).zfill(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_sds(data_df):\n",
    "    sds = []\n",
    "    for area in data_df.index:\n",
    "        if int(data_df.loc[area][\"SUPPORTS\"]) + int(data_df.loc[area][\"REJECTS\"]) > 1:\n",
    "            sds.append(statistics.stdev(int(data_df.loc[area][\"SUPPORTS\"]) * [1] + int(data_df.loc[area][\"REJECTS\"]) * [0]))\n",
    "        else:\n",
    "            sds.append(0.0)\n",
    "    return sds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_municipalities():\n",
    "    municipality_file_data = pd.read_csv(MUNICIPALITY_FILE, sep=\";\")\n",
    "    municipality_dict = {}\n",
    "    for i in range(0, len(municipality_file_data)):\n",
    "        if municipality_file_data.iloc[i][\"Naam_2\"].strip() not in municipality_dict:\n",
    "            municipality_dict[municipality_file_data.iloc[i][\"Naam_2\"].strip()] = municipality_file_data.iloc[i][\"Code_3\"].strip()\n",
    "        elif municipality_file_data.iloc[i][\"Code_3\"].strip() != municipality_dict[municipality_file_data.iloc[i][\"Naam_2\"].strip()]:\n",
    "            print(\"unexpected data in municipality file: {municipality_file_data.iloc[i][\\\"Naam_2\\\"].strip()} {municipality_dict[municipality_file_data.iloc[i][\\\"Naam_2\\\"].strip()]} {municipality_file_data.iloc[i][\\\"Code_3\\\"].strip()}\")\n",
    "    return municipality_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def store_data(data_df, file_name, data_type, statcode_dict={}):\n",
    "    data_df.index.name = \"name\"\n",
    "    data_df[\"stat_code\"] = len(data_df) * [\"\"]\n",
    "    data_df[\"tweet_amount\"] = data_df[\"SUPPORTS\"] + data_df[\"REJECTS\"]\n",
    "    data_df[\"stance_mean\"] = data_df[\"SUPPORTS\"] / (data_df[\"SUPPORTS\"] + data_df[\"REJECTS\"])\n",
    "    data_df[\"stance_sd\"] = compute_sds(data_df)\n",
    "    data_df[\"stance_max\"] = len(data_df) * [1]\n",
    "    data_df[\"stance_min\"] = len(data_df) * [0]\n",
    "    del data_df[\"SUPPORTS\"]\n",
    "    del data_df[\"REJECTS\"]\n",
    "    stat_codes = []\n",
    "    for i in range(0, len(data_df)):\n",
    "        if data_df.iloc[i].name in statcode_dict:\n",
    "            stat_codes.append(statcode_dict[data_df.iloc[i].name])\n",
    "        elif data_df.iloc[i].name in ALIASES and ALIASES[data_df.iloc[i].name] in statcode_dict:\n",
    "            stat_codes.append(statcode_dict[ALIASES[data_df.iloc[i].name]])\n",
    "        else:\n",
    "            stat_codes.append(data_df.iloc[i].name)\n",
    "            print(f\"unknown area in {data_type}: {data_df.iloc[i].name}\")\n",
    "    data_df[\"stat_code\"] = stat_codes\n",
    "    data_df.sort_values(by=\"name\").to_csv(f\"tweet_data/{file_name}_{data_type}.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "municipalities_dict = read_municipalities()\n",
    "for i in range(0, len(SURVEY_DATES)):\n",
    "    file_name = datetime2file_name(SURVEY_DATES[i][0])\n",
    "    store_data(pd.DataFrame(province_data[i]).T, file_name, \"provinces\", statcode_dict=provinces_dict)\n",
    "    store_data(pd.DataFrame(municipality_data[i]).T, file_name, \"municipalities\", statcode_dict=municipalities_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_FILE = \"csv/distance.csv\"\n",
    "START_DATE = 20200630\n",
    "NBR_OF_WEEKS = 20\n",
    "NBR_OF_DAYS_PER_WEEK = 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts_per_date = pd.read_csv(DATA_FILE, header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20200630 20903\n",
      "20200707 15613\n",
      "20200714 14964\n",
      "20200721 30486\n",
      "20200728 22780\n",
      "20200804 23370\n",
      "20200811 15129\n",
      "20200818 19198\n",
      "20200825 28551\n",
      "20200901 21147\n",
      "20200908 11634\n",
      "20200915 17141\n",
      "20200922 22343\n",
      "20200929 21223\n",
      "20201006 18129\n",
      "20201013 22256\n",
      "20201020 12477\n",
      "20201027 12126\n",
      "20201103 7713\n",
      "20201110 7668\n"
     ]
    }
   ],
   "source": [
    "week_counts = []\n",
    "for i in range(0, len(counts_per_date)):\n",
    "    if counts_per_date.iloc[i][0] == START_DATE:\n",
    "        for j in range(i, i + NBR_OF_WEEKS * NBR_OF_DAYS_PER_WEEK, NBR_OF_DAYS_PER_WEEK):\n",
    "            initial_week_date = counts_per_date.iloc[j][0]\n",
    "            nbr_of_tweets = 0\n",
    "            for k in range(j, j + NBR_OF_DAYS_PER_WEEK):\n",
    "                nbr_of_tweets += counts_per_date.iloc[k][1]\n",
    "            week_counts.append(nbr_of_tweets)\n",
    "            print(initial_week_date, nbr_of_tweets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checks:\n",
      "first date: 20200630 correct\n",
      "first week count: 20903 correct\n",
      "last week count: 7668 correct\n",
      "number of weeks: 20 correct\n"
     ]
    }
   ],
   "source": [
    "initial_id = 150\n",
    "start_date_check = counts_per_date.iloc[initial_id][0]\n",
    "first_week_count_check = sum([x[1] for i,x in counts_per_date.iloc[initial_id:initial_id+7].iterrows()])\n",
    "last_week_count_check = sum([x[1] for i,x in counts_per_date.iloc[initial_id+19*7:initial_id+20*7].iterrows()])\n",
    "nbr_of_weeks_check = len(week_counts)\n",
    "\n",
    "def check(test):\n",
    "    if test: return \"correct\"\n",
    "    else: return \"wrong\"\n",
    "\n",
    "print(f\"Checks:\\nfirst date: {start_date_check} {check(start_date_check == START_DATE)}\")\n",
    "print(f\"first week count: {first_week_count_check} {check(first_week_count_check == week_counts[0])}\")\n",
    "print(f\"last week count: {last_week_count_check} {check(last_week_count_check == week_counts[-1])}\")\n",
    "print(f\"number of weeks: {nbr_of_weeks_check} {check(nbr_of_weeks_check == NBR_OF_WEEKS)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(1, 1+len(week_counts)), week_counts)\n",
    "plt.xticks(range(1, 1+len(week_counts)))\n",
    "plt.yticks(range(5000, 35000, 5000), [\"5,000\", \"10,000\", \"15,000\", \"20,000\", \"25,000\", \"30,000\"])\n",
    "plt.title(\"Dutch social distancing tweets per week\")\n",
    "plt.xlabel(\"Weeks\")\n",
    "plt.ylabel(\"Number of tweets per week\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"distance-weeks.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total number of tweets: 364851\n"
     ]
    }
   ],
   "source": [
    "print(f\"total number of tweets: {sum(week_counts)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python37",
   "language": "python",
   "name": "python37"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}