Notebook3-DataAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook focuses reading data from files and analyzing large data sets with the python package [\"pandas\"](http://pandas.pydata.org/pandas-docs/version/0.15/tutorials.html). In particular, we are going to analyze 22 years (collected and recorded every 15 minutes) of water quality data collected at Elkhorn Slough, an estuary in Monterey Bay. To do this notebook, you will also need to download the \"ELKSMWQ.csv\" file. First things first, import necessary packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's import the data set, a csv file that contains various measurements of water at Elkhorn slough. Pandas has the ability to read a variety of different types of data files, so we have to use the correct function to read our file, which is a csv (comma separated values) file. Be sure to change the path to whatever diretory you put the data file (ELKSMWQ.csv) in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path = \"/Users/erikmenke/UC-Merced/Computations/JupyterNotebooks/DataAnalysis/ElkhornSlough/338524/\"\n",
    "WaterFilename = \"ELKSMWQ.csv\"\n",
    "WaterData = pd.read_csv(path+WaterFilename, header=2, skipfooter=51, engine='python')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's quite a bit of data in the file that we don't need or care about, so let's get rid of some of the data columns. In addition, because of the way the data was collected, the pandas import can't tell what type of data is in the file. So, let's define what data type each column is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "WaterDataAbrev = WaterData.drop(['isSWMP','Historical','ProvisionalPlus','F_Record','F_Temp','F_SpCond','F_Sal','F_DO_pct','F_DO_mgl','F_Depth','F_cDepth','F_Level','cLevel','F_cLevel','F_pH','F_Turb','cDepth','Level','Turb','Unnamed: 28'], axis=1)\n",
    "WaterDataAbrev=WaterDataAbrev.astype({'Temp':'float','SpCond':'float','Sal':'float','DO_pct':'float','DO_mgl':'float','Depth':'float','pH':'float'})\n",
    "WaterDataAbrev.DateTimeStamp = pd.to_datetime(WaterDataAbrev.DateTimeStamp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what's left!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Sal</th>\n",
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       "      <th>0</th>\n",
       "      <td>elksmwq</td>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>elksmwq</td>\n",
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       "      <td>16.2</td>\n",
       "      <td>46.19</td>\n",
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       "      <th>3</th>\n",
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       "      <td>16.4</td>\n",
       "      <td>45.68</td>\n",
       "      <td>29.6</td>\n",
       "      <td>114.8</td>\n",
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       "      <th>4</th>\n",
       "      <td>elksmwq</td>\n",
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       "      <td>16.4</td>\n",
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       "      <td>1996-04-01 01:15:00</td>\n",
       "      <td>16.4</td>\n",
       "      <td>45.61</td>\n",
       "      <td>29.6</td>\n",
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       "      <th>6</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>1996-04-01 01:30:00</td>\n",
       "      <td>16.4</td>\n",
       "      <td>45.62</td>\n",
       "      <td>29.6</td>\n",
       "      <td>114.2</td>\n",
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       "      <td>0.99</td>\n",
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       "      <th>7</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>1996-04-01 01:45:00</td>\n",
       "      <td>16.5</td>\n",
       "      <td>45.65</td>\n",
       "      <td>29.6</td>\n",
       "      <td>115.0</td>\n",
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       "      <td>0.93</td>\n",
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       "      <th>8</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>1996-04-01 02:00:00</td>\n",
       "      <td>16.5</td>\n",
       "      <td>46.33</td>\n",
       "      <td>30.1</td>\n",
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       "      <th>9</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>1996-04-01 02:15:00</td>\n",
       "      <td>16.5</td>\n",
       "      <td>45.65</td>\n",
       "      <td>29.6</td>\n",
       "      <td>110.4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.83</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>1996-04-01 02:30:00</td>\n",
       "      <td>16.5</td>\n",
       "      <td>46.86</td>\n",
       "      <td>30.5</td>\n",
       "      <td>106.6</td>\n",
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       "      <th>11</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>1996-04-01 02:45:00</td>\n",
       "      <td>16.5</td>\n",
       "      <td>46.54</td>\n",
       "      <td>30.3</td>\n",
       "      <td>94.1</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.81</td>\n",
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       "      <th>12</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>1996-04-01 03:00:00</td>\n",
       "      <td>16.4</td>\n",
       "      <td>45.69</td>\n",
       "      <td>29.6</td>\n",
       "      <td>109.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>0.84</td>\n",
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       "      <th>13</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>1996-04-01 03:15:00</td>\n",
       "      <td>16.3</td>\n",
       "      <td>45.87</td>\n",
       "      <td>29.8</td>\n",
       "      <td>106.9</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0.87</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>1996-04-01 03:30:00</td>\n",
       "      <td>16.2</td>\n",
       "      <td>45.78</td>\n",
       "      <td>29.7</td>\n",
       "      <td>105.5</td>\n",
       "      <td>8.7</td>\n",
       "      <td>0.90</td>\n",
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       "      <th>15</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>1996-04-01 03:45:00</td>\n",
       "      <td>16.1</td>\n",
       "      <td>45.81</td>\n",
       "      <td>29.7</td>\n",
       "      <td>102.7</td>\n",
       "      <td>8.4</td>\n",
       "      <td>0.92</td>\n",
       "      <td>8.4</td>\n",
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       "      <th>16</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>1996-04-01 04:00:00</td>\n",
       "      <td>16.1</td>\n",
       "      <td>45.94</td>\n",
       "      <td>29.8</td>\n",
       "      <td>103.1</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.95</td>\n",
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       "      <th>17</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>1996-04-01 04:15:00</td>\n",
       "      <td>16.0</td>\n",
       "      <td>45.90</td>\n",
       "      <td>29.8</td>\n",
       "      <td>99.1</td>\n",
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       "      <td>0.98</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>1996-04-01 04:30:00</td>\n",
       "      <td>16.0</td>\n",
       "      <td>46.27</td>\n",
       "      <td>30.1</td>\n",
       "      <td>96.9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.01</td>\n",
       "      <td>8.3</td>\n",
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       "      <th>19</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>1996-04-01 04:45:00</td>\n",
       "      <td>15.9</td>\n",
       "      <td>46.86</td>\n",
       "      <td>30.5</td>\n",
       "      <td>95.4</td>\n",
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       "      <td>elksmwq</td>\n",
       "      <td>1996-04-01 05:00:00</td>\n",
       "      <td>15.9</td>\n",
       "      <td>45.67</td>\n",
       "      <td>29.6</td>\n",
       "      <td>95.5</td>\n",
       "      <td>7.9</td>\n",
       "      <td>1.09</td>\n",
       "      <td>8.3</td>\n",
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       "      <th>21</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>1996-04-01 05:15:00</td>\n",
       "      <td>15.8</td>\n",
       "      <td>45.63</td>\n",
       "      <td>29.6</td>\n",
       "      <td>96.7</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.14</td>\n",
       "      <td>8.3</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>1996-04-01 05:30:00</td>\n",
       "      <td>15.7</td>\n",
       "      <td>47.30</td>\n",
       "      <td>30.8</td>\n",
       "      <td>96.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.19</td>\n",
       "      <td>8.3</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>1996-04-01 05:45:00</td>\n",
       "      <td>15.6</td>\n",
       "      <td>46.49</td>\n",
       "      <td>30.2</td>\n",
       "      <td>95.6</td>\n",
       "      <td>7.9</td>\n",
       "      <td>1.25</td>\n",
       "      <td>8.3</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>1996-04-01 06:00:00</td>\n",
       "      <td>15.5</td>\n",
       "      <td>45.87</td>\n",
       "      <td>29.8</td>\n",
       "      <td>93.6</td>\n",
       "      <td>7.8</td>\n",
       "      <td>1.30</td>\n",
       "      <td>8.3</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>1996-04-01 06:15:00</td>\n",
       "      <td>15.4</td>\n",
       "      <td>45.72</td>\n",
       "      <td>29.7</td>\n",
       "      <td>92.7</td>\n",
       "      <td>7.7</td>\n",
       "      <td>1.35</td>\n",
       "      <td>8.3</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>1996-04-01 06:30:00</td>\n",
       "      <td>15.2</td>\n",
       "      <td>46.05</td>\n",
       "      <td>29.9</td>\n",
       "      <td>91.3</td>\n",
       "      <td>7.6</td>\n",
       "      <td>1.41</td>\n",
       "      <td>8.3</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>1996-04-01 06:45:00</td>\n",
       "      <td>15.1</td>\n",
       "      <td>46.35</td>\n",
       "      <td>30.1</td>\n",
       "      <td>90.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>1.47</td>\n",
       "      <td>8.2</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>1996-04-01 07:00:00</td>\n",
       "      <td>14.9</td>\n",
       "      <td>47.39</td>\n",
       "      <td>30.9</td>\n",
       "      <td>89.4</td>\n",
       "      <td>7.5</td>\n",
       "      <td>1.53</td>\n",
       "      <td>8.2</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>1996-04-01 07:15:00</td>\n",
       "      <td>14.8</td>\n",
       "      <td>46.61</td>\n",
       "      <td>30.3</td>\n",
       "      <td>88.1</td>\n",
       "      <td>7.4</td>\n",
       "      <td>1.58</td>\n",
       "      <td>8.2</td>\n",
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       "    <tr>\n",
       "      <th>618348</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>2018-05-22 16:30:00</td>\n",
       "      <td>16.2</td>\n",
       "      <td>50.25</td>\n",
       "      <td>33.0</td>\n",
       "      <td>81.3</td>\n",
       "      <td>6.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
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       "      <td>elksmwq</td>\n",
       "      <td>2018-05-22 16:45:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.21</td>\n",
       "      <td>32.9</td>\n",
       "      <td>79.4</td>\n",
       "      <td>6.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>618350</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>2018-05-22 17:00:00</td>\n",
       "      <td>-296.6</td>\n",
       "      <td>50.16</td>\n",
       "      <td>32.9</td>\n",
       "      <td>78.7</td>\n",
       "      <td>6.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>618351</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>2018-05-22 17:15:00</td>\n",
       "      <td>15.5</td>\n",
       "      <td>-912.28</td>\n",
       "      <td>32.9</td>\n",
       "      <td>77.7</td>\n",
       "      <td>6.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618352</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>2018-05-22 17:30:00</td>\n",
       "      <td>15.2</td>\n",
       "      <td>50.08</td>\n",
       "      <td>32.8</td>\n",
       "      <td>78.1</td>\n",
       "      <td>6.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>618353</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>2018-05-22 17:45:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.07</td>\n",
       "      <td>32.8</td>\n",
       "      <td>79.3</td>\n",
       "      <td>6.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618354</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>2018-05-22 18:00:00</td>\n",
       "      <td>-296.6</td>\n",
       "      <td>50.08</td>\n",
       "      <td>32.8</td>\n",
       "      <td>80.1</td>\n",
       "      <td>6.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618355</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>2018-05-22 18:15:00</td>\n",
       "      <td>14.7</td>\n",
       "      <td>-912.28</td>\n",
       "      <td>32.8</td>\n",
       "      <td>77.8</td>\n",
       "      <td>6.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618356</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>2018-05-22 18:30:00</td>\n",
       "      <td>14.6</td>\n",
       "      <td>50.07</td>\n",
       "      <td>32.8</td>\n",
       "      <td>75.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618357</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>2018-05-22 18:45:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.08</td>\n",
       "      <td>32.8</td>\n",
       "      <td>76.9</td>\n",
       "      <td>6.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618358</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>2018-05-22 19:00:00</td>\n",
       "      <td>-296.6</td>\n",
       "      <td>50.08</td>\n",
       "      <td>32.8</td>\n",
       "      <td>79.2</td>\n",
       "      <td>6.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618359</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>2018-05-22 19:15:00</td>\n",
       "      <td>14.8</td>\n",
       "      <td>-912.28</td>\n",
       "      <td>32.8</td>\n",
       "      <td>78.9</td>\n",
       "      <td>6.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618360</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>2018-05-22 19:30:00</td>\n",
       "      <td>15.5</td>\n",
       "      <td>50.29</td>\n",
       "      <td>33.0</td>\n",
       "      <td>82.5</td>\n",
       "      <td>6.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618361</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>2018-05-22 19:45:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.22</td>\n",
       "      <td>32.9</td>\n",
       "      <td>82.9</td>\n",
       "      <td>6.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618362</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>2018-05-22 20:00:00</td>\n",
       "      <td>-296.6</td>\n",
       "      <td>50.35</td>\n",
       "      <td>33.0</td>\n",
       "      <td>84.8</td>\n",
       "      <td>6.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618363</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>2018-05-22 20:15:00</td>\n",
       "      <td>16.2</td>\n",
       "      <td>-912.28</td>\n",
       "      <td>33.1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618364</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>2018-05-22 20:30:00</td>\n",
       "      <td>16.3</td>\n",
       "      <td>50.59</td>\n",
       "      <td>33.2</td>\n",
       "      <td>86.9</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618365</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>2018-05-22 20:45:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.57</td>\n",
       "      <td>33.2</td>\n",
       "      <td>85.7</td>\n",
       "      <td>6.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618366</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>2018-05-22 21:00:00</td>\n",
       "      <td>-296.6</td>\n",
       "      <td>50.62</td>\n",
       "      <td>33.2</td>\n",
       "      <td>86.7</td>\n",
       "      <td>6.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618367</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>2018-05-22 21:15:00</td>\n",
       "      <td>16.5</td>\n",
       "      <td>-912.28</td>\n",
       "      <td>33.3</td>\n",
       "      <td>87.2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618368</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>2018-05-22 21:30:00</td>\n",
       "      <td>16.6</td>\n",
       "      <td>50.78</td>\n",
       "      <td>33.4</td>\n",
       "      <td>86.8</td>\n",
       "      <td>6.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618369</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>2018-05-22 21:45:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.80</td>\n",
       "      <td>33.4</td>\n",
       "      <td>86.8</td>\n",
       "      <td>6.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618370</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>2018-05-22 22:00:00</td>\n",
       "      <td>-296.6</td>\n",
       "      <td>50.85</td>\n",
       "      <td>33.4</td>\n",
       "      <td>85.4</td>\n",
       "      <td>6.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618371</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>2018-05-22 22:15:00</td>\n",
       "      <td>16.7</td>\n",
       "      <td>-912.28</td>\n",
       "      <td>33.4</td>\n",
       "      <td>84.5</td>\n",
       "      <td>6.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618372</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>2018-05-22 22:30:00</td>\n",
       "      <td>16.7</td>\n",
       "      <td>50.89</td>\n",
       "      <td>33.5</td>\n",
       "      <td>84.5</td>\n",
       "      <td>6.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618373</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>2018-05-22 22:45:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.95</td>\n",
       "      <td>33.5</td>\n",
       "      <td>83.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618374</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>2018-05-22 23:00:00</td>\n",
       "      <td>-296.6</td>\n",
       "      <td>50.98</td>\n",
       "      <td>33.5</td>\n",
       "      <td>83.2</td>\n",
       "      <td>6.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618375</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>2018-05-22 23:15:00</td>\n",
       "      <td>16.8</td>\n",
       "      <td>-912.28</td>\n",
       "      <td>33.5</td>\n",
       "      <td>82.1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618376</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>2018-05-22 23:30:00</td>\n",
       "      <td>16.8</td>\n",
       "      <td>51.00</td>\n",
       "      <td>33.5</td>\n",
       "      <td>80.4</td>\n",
       "      <td>6.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618377</th>\n",
       "      <td>elksmwq</td>\n",
       "      <td>2018-05-22 23:45:00</td>\n",
       "      <td>16.8</td>\n",
       "      <td>51.00</td>\n",
       "      <td>33.5</td>\n",
       "      <td>79.8</td>\n",
       "      <td>6.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>618378 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Station_Code       DateTimeStamp   Temp  SpCond   Sal  DO_pct  DO_mgl  \\\n",
       "0           elksmwq 1996-04-01 00:00:00   16.1   46.12  30.0   114.9     9.4   \n",
       "1           elksmwq 1996-04-01 00:15:00   16.1   45.69  29.6   114.8     9.4   \n",
       "2           elksmwq 1996-04-01 00:30:00   16.2   46.19  30.0   114.5     9.4   \n",
       "3           elksmwq 1996-04-01 00:45:00   16.4   45.68  29.6   114.8     9.4   \n",
       "4           elksmwq 1996-04-01 01:00:00   16.4   45.85  29.8   114.7     9.4   \n",
       "5           elksmwq 1996-04-01 01:15:00   16.4   45.61  29.6   115.1     9.4   \n",
       "6           elksmwq 1996-04-01 01:30:00   16.4   45.62  29.6   114.2     9.3   \n",
       "7           elksmwq 1996-04-01 01:45:00   16.5   45.65  29.6   115.0     9.4   \n",
       "8           elksmwq 1996-04-01 02:00:00   16.5   46.33  30.1   113.8     9.3   \n",
       "9           elksmwq 1996-04-01 02:15:00   16.5   45.65  29.6   110.4     9.0   \n",
       "10          elksmwq 1996-04-01 02:30:00   16.5   46.86  30.5   106.6     8.7   \n",
       "11          elksmwq 1996-04-01 02:45:00   16.5   46.54  30.3    94.1     7.7   \n",
       "12          elksmwq 1996-04-01 03:00:00   16.4   45.69  29.6   109.0     8.9   \n",
       "13          elksmwq 1996-04-01 03:15:00   16.3   45.87  29.8   106.9     8.8   \n",
       "14          elksmwq 1996-04-01 03:30:00   16.2   45.78  29.7   105.5     8.7   \n",
       "15          elksmwq 1996-04-01 03:45:00   16.1   45.81  29.7   102.7     8.4   \n",
       "16          elksmwq 1996-04-01 04:00:00   16.1   45.94  29.8   103.1     8.5   \n",
       "17          elksmwq 1996-04-01 04:15:00   16.0   45.90  29.8    99.1     8.2   \n",
       "18          elksmwq 1996-04-01 04:30:00   16.0   46.27  30.1    96.9     8.0   \n",
       "19          elksmwq 1996-04-01 04:45:00   15.9   46.86  30.5    95.4     7.8   \n",
       "20          elksmwq 1996-04-01 05:00:00   15.9   45.67  29.6    95.5     7.9   \n",
       "21          elksmwq 1996-04-01 05:15:00   15.8   45.63  29.6    96.7     8.0   \n",
       "22          elksmwq 1996-04-01 05:30:00   15.7   47.30  30.8    96.8     8.0   \n",
       "23          elksmwq 1996-04-01 05:45:00   15.6   46.49  30.2    95.6     7.9   \n",
       "24          elksmwq 1996-04-01 06:00:00   15.5   45.87  29.8    93.6     7.8   \n",
       "25          elksmwq 1996-04-01 06:15:00   15.4   45.72  29.7    92.7     7.7   \n",
       "26          elksmwq 1996-04-01 06:30:00   15.2   46.05  29.9    91.3     7.6   \n",
       "27          elksmwq 1996-04-01 06:45:00   15.1   46.35  30.1    90.0     7.5   \n",
       "28          elksmwq 1996-04-01 07:00:00   14.9   47.39  30.9    89.4     7.5   \n",
       "29          elksmwq 1996-04-01 07:15:00   14.8   46.61  30.3    88.1     7.4   \n",
       "...             ...                 ...    ...     ...   ...     ...     ...   \n",
       "618348      elksmwq 2018-05-22 16:30:00   16.2   50.25  33.0    81.3     6.5   \n",
       "618349      elksmwq 2018-05-22 16:45:00    NaN   50.21  32.9    79.4     6.4   \n",
       "618350      elksmwq 2018-05-22 17:00:00 -296.6   50.16  32.9    78.7     6.4   \n",
       "618351      elksmwq 2018-05-22 17:15:00   15.5 -912.28  32.9    77.7     6.3   \n",
       "618352      elksmwq 2018-05-22 17:30:00   15.2   50.08  32.8    78.1     6.4   \n",
       "618353      elksmwq 2018-05-22 17:45:00    NaN   50.07  32.8    79.3     6.5   \n",
       "618354      elksmwq 2018-05-22 18:00:00 -296.6   50.08  32.8    80.1     6.6   \n",
       "618355      elksmwq 2018-05-22 18:15:00   14.7 -912.28  32.8    77.8     6.5   \n",
       "618356      elksmwq 2018-05-22 18:30:00   14.6   50.07  32.8    75.9     6.3   \n",
       "618357      elksmwq 2018-05-22 18:45:00    NaN   50.08  32.8    76.9     6.4   \n",
       "618358      elksmwq 2018-05-22 19:00:00 -296.6   50.08  32.8    79.2     6.6   \n",
       "618359      elksmwq 2018-05-22 19:15:00   14.8 -912.28  32.8    78.9     6.5   \n",
       "618360      elksmwq 2018-05-22 19:30:00   15.5   50.29  33.0    82.5     6.7   \n",
       "618361      elksmwq 2018-05-22 19:45:00    NaN   50.22  32.9    82.9     6.8   \n",
       "618362      elksmwq 2018-05-22 20:00:00 -296.6   50.35  33.0    84.8     6.9   \n",
       "618363      elksmwq 2018-05-22 20:15:00   16.2 -912.28  33.1    89.0     7.2   \n",
       "618364      elksmwq 2018-05-22 20:30:00   16.3   50.59  33.2    86.9     7.0   \n",
       "618365      elksmwq 2018-05-22 20:45:00    NaN   50.57  33.2    85.7     6.9   \n",
       "618366      elksmwq 2018-05-22 21:00:00 -296.6   50.62  33.2    86.7     6.9   \n",
       "618367      elksmwq 2018-05-22 21:15:00   16.5 -912.28  33.3    87.2     7.0   \n",
       "618368      elksmwq 2018-05-22 21:30:00   16.6   50.78  33.4    86.8     6.9   \n",
       "618369      elksmwq 2018-05-22 21:45:00    NaN   50.80  33.4    86.8     6.9   \n",
       "618370      elksmwq 2018-05-22 22:00:00 -296.6   50.85  33.4    85.4     6.8   \n",
       "618371      elksmwq 2018-05-22 22:15:00   16.7 -912.28  33.4    84.5     6.7   \n",
       "618372      elksmwq 2018-05-22 22:30:00   16.7   50.89  33.5    84.5     6.7   \n",
       "618373      elksmwq 2018-05-22 22:45:00    NaN   50.95  33.5    83.0     6.6   \n",
       "618374      elksmwq 2018-05-22 23:00:00 -296.6   50.98  33.5    83.2     6.6   \n",
       "618375      elksmwq 2018-05-22 23:15:00   16.8 -912.28  33.5    82.1     6.5   \n",
       "618376      elksmwq 2018-05-22 23:30:00   16.8   51.00  33.5    80.4     6.4   \n",
       "618377      elksmwq 2018-05-22 23:45:00   16.8   51.00  33.5    79.8     6.3   \n",
       "\n",
       "        Depth   pH  \n",
       "0        1.40  8.4  \n",
       "1        1.34  8.4  \n",
       "2        1.28  8.4  \n",
       "3        1.20  8.4  \n",
       "4        1.13  8.4  \n",
       "5        1.06  8.4  \n",
       "6        0.99  8.4  \n",
       "7        0.93  8.4  \n",
       "8        0.86  8.4  \n",
       "9        0.83  8.4  \n",
       "10       0.81  8.4  \n",
       "11       0.81  8.4  \n",
       "12       0.84  8.4  \n",
       "13       0.87  8.4  \n",
       "14       0.90  8.4  \n",
       "15       0.92  8.4  \n",
       "16       0.95  8.4  \n",
       "17       0.98  8.4  \n",
       "18       1.01  8.3  \n",
       "19       1.05  8.3  \n",
       "20       1.09  8.3  \n",
       "21       1.14  8.3  \n",
       "22       1.19  8.3  \n",
       "23       1.25  8.3  \n",
       "24       1.30  8.3  \n",
       "25       1.35  8.3  \n",
       "26       1.41  8.3  \n",
       "27       1.47  8.2  \n",
       "28       1.53  8.2  \n",
       "29       1.58  8.2  \n",
       "...       ...  ...  \n",
       "618348    NaN  8.0  \n",
       "618349    NaN  8.0  \n",
       "618350    NaN  8.0  \n",
       "618351    NaN  8.0  \n",
       "618352    NaN  8.0  \n",
       "618353    NaN  8.0  \n",
       "618354    NaN  8.0  \n",
       "618355    NaN  8.0  \n",
       "618356    NaN  8.0  \n",
       "618357    NaN  8.0  \n",
       "618358    NaN  8.0  \n",
       "618359    NaN  8.0  \n",
       "618360    NaN  8.0  \n",
       "618361    NaN  8.0  \n",
       "618362    NaN  8.0  \n",
       "618363    NaN  8.1  \n",
       "618364    NaN  8.0  \n",
       "618365    NaN  8.0  \n",
       "618366    NaN  8.0  \n",
       "618367    NaN  8.0  \n",
       "618368    NaN  8.0  \n",
       "618369    NaN  8.0  \n",
       "618370    NaN  8.0  \n",
       "618371    NaN  8.0  \n",
       "618372    NaN  8.0  \n",
       "618373    NaN  8.0  \n",
       "618374    NaN  8.0  \n",
       "618375    NaN  8.0  \n",
       "618376    NaN  8.0  \n",
       "618377    NaN  8.0  \n",
       "\n",
       "[618378 rows x 9 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WaterDataAbrev"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "A few things to note about the data. First, there are 618,378 rows of data. That's a lot of data! Enough that even using a conventional program, like Excel, will be very slow and won't be able to handle some of what we might want to do. Second, there are 9 columns of data. The first column is the station code, just an identifier of where the data was collected. The second column is the time and date that the data was collected. The third column is the water temperature, in $^{o}$C. Note that some of the data entries are NaN (NaN = Not a Number), and others are -296.6 $^{o}$C. Those are obvious errors that we might want to take care of at some point. The fourth column is the specific conductivity of the water (in milliSiemens(mS)/cm), the fifth column is the salinity of the water (in practical salinity units(psu)), the sixth column is the amount of dissolved oxygen (in percent saturation), the seventh column is the amount of dissolved oxygen (in mg/L), the eighth column is the water depth of the measurement (in meters), and the ninth column is the pH of the water.\n",
    "\n",
    "Let's see if there's any correlation between the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temp</th>\n",
       "      <th>SpCond</th>\n",
       "      <th>Sal</th>\n",
       "      <th>DO_pct</th>\n",
       "      <th>DO_mgl</th>\n",
       "      <th>Depth</th>\n",
       "      <th>pH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Temp</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.129813</td>\n",
       "      <td>-0.094820</td>\n",
       "      <td>0.085629</td>\n",
       "      <td>-0.336145</td>\n",
       "      <td>-0.623368</td>\n",
       "      <td>0.330008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpCond</th>\n",
       "      <td>0.129813</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.026048</td>\n",
       "      <td>0.093891</td>\n",
       "      <td>-0.261187</td>\n",
       "      <td>-0.719658</td>\n",
       "      <td>0.281159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sal</th>\n",
       "      <td>-0.094820</td>\n",
       "      <td>-0.026048</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.268967</td>\n",
       "      <td>0.484354</td>\n",
       "      <td>0.575597</td>\n",
       "      <td>-0.535952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DO_pct</th>\n",
       "      <td>0.085629</td>\n",
       "      <td>0.093891</td>\n",
       "      <td>-0.268967</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.213712</td>\n",
       "      <td>-0.383397</td>\n",
       "      <td>0.469576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DO_mgl</th>\n",
       "      <td>-0.336145</td>\n",
       "      <td>-0.261187</td>\n",
       "      <td>0.484354</td>\n",
       "      <td>-0.213712</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.898521</td>\n",
       "      <td>-0.911232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Depth</th>\n",
       "      <td>-0.623368</td>\n",
       "      <td>-0.719658</td>\n",
       "      <td>0.575597</td>\n",
       "      <td>-0.383397</td>\n",
       "      <td>0.898521</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.842757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pH</th>\n",
       "      <td>0.330008</td>\n",
       "      <td>0.281159</td>\n",
       "      <td>-0.535952</td>\n",
       "      <td>0.469576</td>\n",
       "      <td>-0.911232</td>\n",
       "      <td>-0.842757</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Temp    SpCond       Sal    DO_pct    DO_mgl     Depth        pH\n",
       "Temp    1.000000  0.129813 -0.094820  0.085629 -0.336145 -0.623368  0.330008\n",
       "SpCond  0.129813  1.000000 -0.026048  0.093891 -0.261187 -0.719658  0.281159\n",
       "Sal    -0.094820 -0.026048  1.000000 -0.268967  0.484354  0.575597 -0.535952\n",
       "DO_pct  0.085629  0.093891 -0.268967  1.000000 -0.213712 -0.383397  0.469576\n",
       "DO_mgl -0.336145 -0.261187  0.484354 -0.213712  1.000000  0.898521 -0.911232\n",
       "Depth  -0.623368 -0.719658  0.575597 -0.383397  0.898521  1.000000 -0.842757\n",
       "pH      0.330008  0.281159 -0.535952  0.469576 -0.911232 -0.842757  1.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WaterDataAbrev.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There seems to be some correlation, in particular between the pH and dissolved oxygen and salinity, as well as between temperature and water depth. Now, let's try out some of the cooler features of pandas. First, let's set the date/time column as our index (the independent variable)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "OrderedWaterData=WaterDataAbrev.set_index('DateTimeStamp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can have pandas do all sorts of statistical analysis for us, without us needing to worry about the details or rewrite algorithms. For example, let's look at the weekly average of the salinity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DateTimeStamp\n",
       "1996-04-07    30.505060\n",
       "1996-04-14    30.767708\n",
       "1996-04-21    31.256250\n",
       "1996-04-28    30.792932\n",
       "1996-05-05    33.284226\n",
       "1996-05-12    32.844196\n",
       "1996-05-19    31.301339\n",
       "1996-05-26    31.848645\n",
       "1996-06-02    32.493006\n",
       "1996-06-09    31.615030\n",
       "1996-06-16    31.741220\n",
       "1996-06-23    31.461050\n",
       "1996-06-30    34.475000\n",
       "1996-07-07    34.753571\n",
       "1996-07-14    33.079018\n",
       "1996-07-21    31.834077\n",
       "1996-07-28    32.492846\n",
       "1996-08-04    30.465625\n",
       "1996-08-11    30.288839\n",
       "1996-08-18    29.712946\n",
       "1996-08-25    29.581193\n",
       "1996-09-01    32.983036\n",
       "1996-09-08    31.143006\n",
       "1996-09-15    30.770238\n",
       "1996-09-22    30.541144\n",
       "1996-09-29    31.085500\n",
       "1996-10-06    29.222619\n",
       "1996-10-13    29.083117\n",
       "1996-10-20          NaN\n",
       "1996-10-27    33.010196\n",
       "                ...    \n",
       "2017-11-05    33.464583\n",
       "2017-11-12    32.788690\n",
       "2017-11-19    32.459821\n",
       "2017-11-26    32.269494\n",
       "2017-12-03    32.919643\n",
       "2017-12-10    33.414732\n",
       "2017-12-17    33.535565\n",
       "2017-12-24    33.438542\n",
       "2017-12-31    33.148065\n",
       "2018-01-07    32.537500\n",
       "2018-01-14    30.606399\n",
       "2018-01-21    31.338839\n",
       "2018-01-28    32.804018\n",
       "2018-02-04    33.636756\n",
       "2018-02-11    34.044345\n",
       "2018-02-18    34.245833\n",
       "2018-02-25    33.920685\n",
       "2018-03-04    32.407292\n",
       "2018-03-11    31.962946\n",
       "2018-03-18    32.114340\n",
       "2018-03-25    32.468269\n",
       "2018-04-01    29.298732\n",
       "2018-04-08    31.667560\n",
       "2018-04-15    30.308185\n",
       "2018-04-22    30.677976\n",
       "2018-04-29    30.875149\n",
       "2018-05-06    30.944196\n",
       "2018-05-13    31.826935\n",
       "2018-05-20    33.272321\n",
       "2018-05-27    33.359896\n",
       "Freq: W-SUN, Name: Sal, Length: 1156, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OrderedWaterData.resample('W')['Sal'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also graph the data for a certain period of time. For example, how did the salinity change from May, 1996, through June, 1996?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sB0fEiIgYEhFDOnTokJeCzcx2R3kPiIiYD8yS1C9tOg54B3gEGJa2DQMezndtZma2WYMC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119c6deb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SalinityPlot = OrderedWaterData['1996-May':'1996-Jun']['Sal']\n",
    "SalinityPlot.plot()\n",
    "plt.title(\"Figure 1 - Elkhorn slough salinity over time\")\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Salinity')\n",
    "ax = plt.gca()\n",
    "ax.set_ylim(0,100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Knowing that we can do some neat stuff, let's do that. I'm going to create a new data table for the data from the weekly averages, and put in the standard deviations. I'm going to ignore the depth column, though, because that doesn't seem very important to me, and a lot of the data entries are NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weekly_summary = pd.DataFrame()\n",
    "weekly_summary['Temp'] = OrderedWaterData.resample('W')['Temp'].mean()\n",
    "weekly_summary['TempErr'] = OrderedWaterData.resample('W')['Temp'].std()\n",
    "weekly_summary['SpCond'] = OrderedWaterData.resample('W')['SpCond'].mean()\n",
    "weekly_summary['SpCondErr'] = OrderedWaterData.resample('W')['SpCond'].std()\n",
    "weekly_summary['Sal'] = OrderedWaterData.resample('W')['Sal'].mean()\n",
    "weekly_summary['SalErr'] = OrderedWaterData.resample('W')['Sal'].std()\n",
    "weekly_summary['DO_pct'] = OrderedWaterData.resample('W')['DO_pct'].mean()\n",
    "weekly_summary['DO_pctErr'] = OrderedWaterData.resample('W')['DO_pct'].std()\n",
    "weekly_summary['DO_mgl'] = OrderedWaterData.resample('W')['DO_mgl'].mean()\n",
    "weekly_summary['DO_mglErr'] = OrderedWaterData.resample('W')['DO_mgl'].std()\n",
    "weekly_summary['pH'] = OrderedWaterData.resample('W')['pH'].mean()\n",
    "weekly_summary['pHErr'] = OrderedWaterData.resample('W')['pH'].std()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What does it look like?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temp</th>\n",
       "      <th>TempErr</th>\n",
       "      <th>SpCond</th>\n",
       "      <th>SpCondErr</th>\n",
       "      <th>Sal</th>\n",
       "      <th>SalErr</th>\n",
       "      <th>DO_pct</th>\n",
       "      <th>DO_pctErr</th>\n",
       "      <th>DO_mgl</th>\n",
       "      <th>DO_mglErr</th>\n",
       "      <th>pH</th>\n",
       "      <th>pHErr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DateTimeStamp</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",
       "      <th>1996-04-07</th>\n",
       "      <td>16.591220</td>\n",
       "      <td>1.724669</td>\n",
       "      <td>46.870030</td>\n",
       "      <td>1.073929</td>\n",
       "      <td>30.505060</td>\n",
       "      <td>0.773155</td>\n",
       "      <td>95.431399</td>\n",
       "      <td>29.499533</td>\n",
       "      <td>7.707143</td>\n",
       "      <td>2.247789</td>\n",
       "      <td>8.252083</td>\n",
       "      <td>0.135045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-04-14</th>\n",
       "      <td>16.328571</td>\n",
       "      <td>1.377676</td>\n",
       "      <td>47.235060</td>\n",
       "      <td>1.351055</td>\n",
       "      <td>30.767708</td>\n",
       "      <td>0.976375</td>\n",
       "      <td>25.684524</td>\n",
       "      <td>24.457572</td>\n",
       "      <td>2.077083</td>\n",
       "      <td>1.963302</td>\n",
       "      <td>7.831399</td>\n",
       "      <td>0.141039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-04-21</th>\n",
       "      <td>15.392857</td>\n",
       "      <td>1.371873</td>\n",
       "      <td>47.927768</td>\n",
       "      <td>1.691262</td>\n",
       "      <td>31.256250</td>\n",
       "      <td>1.223682</td>\n",
       "      <td>11.230655</td>\n",
       "      <td>17.229722</td>\n",
       "      <td>0.926637</td>\n",
       "      <td>1.416011</td>\n",
       "      <td>7.780655</td>\n",
       "      <td>0.148256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-04-28</th>\n",
       "      <td>18.311579</td>\n",
       "      <td>1.788897</td>\n",
       "      <td>47.253549</td>\n",
       "      <td>1.029468</td>\n",
       "      <td>30.792932</td>\n",
       "      <td>0.742489</td>\n",
       "      <td>5.923379</td>\n",
       "      <td>16.266982</td>\n",
       "      <td>0.464706</td>\n",
       "      <td>1.281977</td>\n",
       "      <td>7.836241</td>\n",
       "      <td>0.080628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-05-05</th>\n",
       "      <td>17.938244</td>\n",
       "      <td>2.592252</td>\n",
       "      <td>50.679836</td>\n",
       "      <td>0.504622</td>\n",
       "      <td>33.284226</td>\n",
       "      <td>0.381097</td>\n",
       "      <td>96.279911</td>\n",
       "      <td>23.266789</td>\n",
       "      <td>7.437649</td>\n",
       "      <td>1.596941</td>\n",
       "      <td>7.545238</td>\n",
       "      <td>0.112715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-05-12</th>\n",
       "      <td>16.535714</td>\n",
       "      <td>1.848229</td>\n",
       "      <td>50.092679</td>\n",
       "      <td>0.328509</td>\n",
       "      <td>32.844196</td>\n",
       "      <td>0.266168</td>\n",
       "      <td>79.885119</td>\n",
       "      <td>17.651854</td>\n",
       "      <td>6.370833</td>\n",
       "      <td>1.290125</td>\n",
       "      <td>7.492708</td>\n",
       "      <td>0.111849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-05-19</th>\n",
       "      <td>17.548512</td>\n",
       "      <td>2.017974</td>\n",
       "      <td>47.957098</td>\n",
       "      <td>1.486571</td>\n",
       "      <td>31.301339</td>\n",
       "      <td>1.080479</td>\n",
       "      <td>95.481548</td>\n",
       "      <td>24.384607</td>\n",
       "      <td>7.551339</td>\n",
       "      <td>1.846921</td>\n",
       "      <td>7.541518</td>\n",
       "      <td>0.139253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-05-26</th>\n",
       "      <td>17.883886</td>\n",
       "      <td>1.218781</td>\n",
       "      <td>48.692907</td>\n",
       "      <td>2.223044</td>\n",
       "      <td>31.848645</td>\n",
       "      <td>1.623767</td>\n",
       "      <td>83.615813</td>\n",
       "      <td>16.830409</td>\n",
       "      <td>6.556777</td>\n",
       "      <td>1.310227</td>\n",
       "      <td>7.786145</td>\n",
       "      <td>0.182791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-06-02</th>\n",
       "      <td>16.170089</td>\n",
       "      <td>2.017113</td>\n",
       "      <td>49.622426</td>\n",
       "      <td>0.534047</td>\n",
       "      <td>32.493006</td>\n",
       "      <td>0.407707</td>\n",
       "      <td>76.541071</td>\n",
       "      <td>15.146332</td>\n",
       "      <td>6.152232</td>\n",
       "      <td>1.046886</td>\n",
       "      <td>7.852083</td>\n",
       "      <td>0.080211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-06-09</th>\n",
       "      <td>16.769048</td>\n",
       "      <td>2.147551</td>\n",
       "      <td>48.403110</td>\n",
       "      <td>0.253365</td>\n",
       "      <td>31.615030</td>\n",
       "      <td>0.211369</td>\n",
       "      <td>71.468155</td>\n",
       "      <td>13.089708</td>\n",
       "      <td>5.729315</td>\n",
       "      <td>0.982327</td>\n",
       "      <td>7.836458</td>\n",
       "      <td>0.097638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-06-16</th>\n",
       "      <td>15.950000</td>\n",
       "      <td>2.028603</td>\n",
       "      <td>48.593214</td>\n",
       "      <td>0.307621</td>\n",
       "      <td>31.741220</td>\n",
       "      <td>0.260025</td>\n",
       "      <td>74.801339</td>\n",
       "      <td>15.180237</td>\n",
       "      <td>6.088690</td>\n",
       "      <td>1.171503</td>\n",
       "      <td>7.811310</td>\n",
       "      <td>0.092119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-06-23</th>\n",
       "      <td>16.367177</td>\n",
       "      <td>1.985710</td>\n",
       "      <td>48.201335</td>\n",
       "      <td>0.242704</td>\n",
       "      <td>31.461050</td>\n",
       "      <td>0.205018</td>\n",
       "      <td>77.920788</td>\n",
       "      <td>17.010351</td>\n",
       "      <td>6.281619</td>\n",
       "      <td>1.212983</td>\n",
       "      <td>7.803501</td>\n",
       "      <td>0.107752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-06-30</th>\n",
       "      <td>17.547837</td>\n",
       "      <td>2.670300</td>\n",
       "      <td>52.302572</td>\n",
       "      <td>1.592687</td>\n",
       "      <td>34.475000</td>\n",
       "      <td>1.186531</td>\n",
       "      <td>63.434615</td>\n",
       "      <td>23.768141</td>\n",
       "      <td>4.920673</td>\n",
       "      <td>1.793363</td>\n",
       "      <td>7.843269</td>\n",
       "      <td>0.085604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-07-07</th>\n",
       "      <td>17.316964</td>\n",
       "      <td>2.176944</td>\n",
       "      <td>52.669673</td>\n",
       "      <td>1.086938</td>\n",
       "      <td>34.753571</td>\n",
       "      <td>0.796875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.851935</td>\n",
       "      <td>0.123036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-07-14</th>\n",
       "      <td>17.875446</td>\n",
       "      <td>1.563435</td>\n",
       "      <td>50.384315</td>\n",
       "      <td>1.268334</td>\n",
       "      <td>33.079018</td>\n",
       "      <td>0.931846</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.900298</td>\n",
       "      <td>0.131709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-07-21</th>\n",
       "      <td>18.722768</td>\n",
       "      <td>1.854073</td>\n",
       "      <td>48.681354</td>\n",
       "      <td>0.463180</td>\n",
       "      <td>31.834077</td>\n",
       "      <td>0.338026</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.828869</td>\n",
       "      <td>0.120233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-07-28</th>\n",
       "      <td>19.580924</td>\n",
       "      <td>1.671123</td>\n",
       "      <td>49.580402</td>\n",
       "      <td>0.968568</td>\n",
       "      <td>32.492846</td>\n",
       "      <td>0.710713</td>\n",
       "      <td>73.803869</td>\n",
       "      <td>13.586579</td>\n",
       "      <td>5.647619</td>\n",
       "      <td>1.190257</td>\n",
       "      <td>7.448137</td>\n",
       "      <td>0.191285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-08-04</th>\n",
       "      <td>18.408482</td>\n",
       "      <td>1.869148</td>\n",
       "      <td>46.796027</td>\n",
       "      <td>0.953800</td>\n",
       "      <td>30.465625</td>\n",
       "      <td>0.694535</td>\n",
       "      <td>75.951339</td>\n",
       "      <td>14.955581</td>\n",
       "      <td>5.964881</td>\n",
       "      <td>1.267989</td>\n",
       "      <td>7.379315</td>\n",
       "      <td>0.115314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-08-11</th>\n",
       "      <td>18.354464</td>\n",
       "      <td>1.567824</td>\n",
       "      <td>46.551071</td>\n",
       "      <td>0.276126</td>\n",
       "      <td>30.288839</td>\n",
       "      <td>0.210691</td>\n",
       "      <td>74.701339</td>\n",
       "      <td>13.937114</td>\n",
       "      <td>5.861458</td>\n",
       "      <td>1.100773</td>\n",
       "      <td>7.318155</td>\n",
       "      <td>0.108770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-08-18</th>\n",
       "      <td>19.232143</td>\n",
       "      <td>1.619988</td>\n",
       "      <td>45.757783</td>\n",
       "      <td>0.397759</td>\n",
       "      <td>29.712946</td>\n",
       "      <td>0.288264</td>\n",
       "      <td>82.862202</td>\n",
       "      <td>13.436599</td>\n",
       "      <td>6.407589</td>\n",
       "      <td>0.962587</td>\n",
       "      <td>7.424107</td>\n",
       "      <td>0.066524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-08-25</th>\n",
       "      <td>18.598553</td>\n",
       "      <td>1.178751</td>\n",
       "      <td>45.566618</td>\n",
       "      <td>1.227730</td>\n",
       "      <td>29.581193</td>\n",
       "      <td>0.886538</td>\n",
       "      <td>82.477758</td>\n",
       "      <td>10.320463</td>\n",
       "      <td>6.467089</td>\n",
       "      <td>0.806721</td>\n",
       "      <td>7.421338</td>\n",
       "      <td>0.110074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-09-01</th>\n",
       "      <td>18.163988</td>\n",
       "      <td>1.854880</td>\n",
       "      <td>50.252798</td>\n",
       "      <td>0.776457</td>\n",
       "      <td>32.983036</td>\n",
       "      <td>0.579951</td>\n",
       "      <td>87.811607</td>\n",
       "      <td>12.439326</td>\n",
       "      <td>6.813839</td>\n",
       "      <td>1.026824</td>\n",
       "      <td>7.830060</td>\n",
       "      <td>0.090641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-09-08</th>\n",
       "      <td>18.703274</td>\n",
       "      <td>1.403059</td>\n",
       "      <td>47.729449</td>\n",
       "      <td>0.484047</td>\n",
       "      <td>31.143006</td>\n",
       "      <td>0.358435</td>\n",
       "      <td>78.583333</td>\n",
       "      <td>9.801044</td>\n",
       "      <td>6.086905</td>\n",
       "      <td>0.699249</td>\n",
       "      <td>7.786012</td>\n",
       "      <td>0.061629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-09-15</th>\n",
       "      <td>18.464583</td>\n",
       "      <td>1.528467</td>\n",
       "      <td>47.215565</td>\n",
       "      <td>0.687070</td>\n",
       "      <td>30.770238</td>\n",
       "      <td>0.504650</td>\n",
       "      <td>80.235565</td>\n",
       "      <td>9.475410</td>\n",
       "      <td>6.265179</td>\n",
       "      <td>0.761778</td>\n",
       "      <td>7.801190</td>\n",
       "      <td>0.058023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-09-22</th>\n",
       "      <td>18.792804</td>\n",
       "      <td>1.093031</td>\n",
       "      <td>46.894723</td>\n",
       "      <td>0.681098</td>\n",
       "      <td>30.541144</td>\n",
       "      <td>0.497447</td>\n",
       "      <td>87.061439</td>\n",
       "      <td>11.217120</td>\n",
       "      <td>6.751292</td>\n",
       "      <td>0.766626</td>\n",
       "      <td>7.865867</td>\n",
       "      <td>0.065182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-09-29</th>\n",
       "      <td>16.174500</td>\n",
       "      <td>0.989730</td>\n",
       "      <td>47.664967</td>\n",
       "      <td>1.880399</td>\n",
       "      <td>31.085500</td>\n",
       "      <td>1.372083</td>\n",
       "      <td>53.983667</td>\n",
       "      <td>12.942636</td>\n",
       "      <td>4.403667</td>\n",
       "      <td>1.075646</td>\n",
       "      <td>7.723333</td>\n",
       "      <td>0.061605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-10-06</th>\n",
       "      <td>18.253423</td>\n",
       "      <td>1.346648</td>\n",
       "      <td>45.074152</td>\n",
       "      <td>0.135877</td>\n",
       "      <td>29.222619</td>\n",
       "      <td>0.103271</td>\n",
       "      <td>38.529167</td>\n",
       "      <td>22.695935</td>\n",
       "      <td>3.035417</td>\n",
       "      <td>1.756901</td>\n",
       "      <td>7.692708</td>\n",
       "      <td>0.064992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-10-13</th>\n",
       "      <td>19.702597</td>\n",
       "      <td>1.171928</td>\n",
       "      <td>44.890519</td>\n",
       "      <td>0.240503</td>\n",
       "      <td>29.083117</td>\n",
       "      <td>0.181661</td>\n",
       "      <td>44.322078</td>\n",
       "      <td>11.870769</td>\n",
       "      <td>3.412987</td>\n",
       "      <td>0.899759</td>\n",
       "      <td>7.605195</td>\n",
       "      <td>0.022338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-10-20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-10-27</th>\n",
       "      <td>13.601176</td>\n",
       "      <td>0.827593</td>\n",
       "      <td>50.403627</td>\n",
       "      <td>0.354352</td>\n",
       "      <td>33.010196</td>\n",
       "      <td>0.276474</td>\n",
       "      <td>82.763655</td>\n",
       "      <td>24.962280</td>\n",
       "      <td>7.010267</td>\n",
       "      <td>2.095757</td>\n",
       "      <td>7.408235</td>\n",
       "      <td>0.063392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-05</th>\n",
       "      <td>15.433036</td>\n",
       "      <td>0.707219</td>\n",
       "      <td>50.933631</td>\n",
       "      <td>2.028683</td>\n",
       "      <td>33.464583</td>\n",
       "      <td>1.350485</td>\n",
       "      <td>78.256994</td>\n",
       "      <td>11.455595</td>\n",
       "      <td>6.379315</td>\n",
       "      <td>0.947207</td>\n",
       "      <td>7.960565</td>\n",
       "      <td>0.056539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-12</th>\n",
       "      <td>15.054762</td>\n",
       "      <td>0.944625</td>\n",
       "      <td>50.030193</td>\n",
       "      <td>0.668748</td>\n",
       "      <td>32.788690</td>\n",
       "      <td>0.489950</td>\n",
       "      <td>88.731994</td>\n",
       "      <td>7.580751</td>\n",
       "      <td>7.313988</td>\n",
       "      <td>0.619490</td>\n",
       "      <td>8.039583</td>\n",
       "      <td>0.051895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-19</th>\n",
       "      <td>15.184375</td>\n",
       "      <td>0.768421</td>\n",
       "      <td>49.567009</td>\n",
       "      <td>1.993055</td>\n",
       "      <td>32.459821</td>\n",
       "      <td>1.328264</td>\n",
       "      <td>78.102827</td>\n",
       "      <td>10.125995</td>\n",
       "      <td>6.434970</td>\n",
       "      <td>0.841002</td>\n",
       "      <td>7.952381</td>\n",
       "      <td>0.067488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-26</th>\n",
       "      <td>15.973214</td>\n",
       "      <td>0.903988</td>\n",
       "      <td>49.297545</td>\n",
       "      <td>0.640161</td>\n",
       "      <td>32.269494</td>\n",
       "      <td>0.471710</td>\n",
       "      <td>92.766667</td>\n",
       "      <td>9.624094</td>\n",
       "      <td>7.533333</td>\n",
       "      <td>0.742085</td>\n",
       "      <td>8.068899</td>\n",
       "      <td>0.054046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-03</th>\n",
       "      <td>14.011161</td>\n",
       "      <td>0.995224</td>\n",
       "      <td>50.245789</td>\n",
       "      <td>0.475866</td>\n",
       "      <td>32.919643</td>\n",
       "      <td>0.329025</td>\n",
       "      <td>82.477232</td>\n",
       "      <td>8.690243</td>\n",
       "      <td>6.941220</td>\n",
       "      <td>0.750587</td>\n",
       "      <td>8.002530</td>\n",
       "      <td>0.044112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-10</th>\n",
       "      <td>11.228125</td>\n",
       "      <td>0.663885</td>\n",
       "      <td>51.098140</td>\n",
       "      <td>0.338188</td>\n",
       "      <td>33.414732</td>\n",
       "      <td>0.233244</td>\n",
       "      <td>98.471429</td>\n",
       "      <td>7.318833</td>\n",
       "      <td>8.753423</td>\n",
       "      <td>0.694552</td>\n",
       "      <td>8.123214</td>\n",
       "      <td>0.070530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-17</th>\n",
       "      <td>11.250149</td>\n",
       "      <td>0.501859</td>\n",
       "      <td>51.259970</td>\n",
       "      <td>0.467669</td>\n",
       "      <td>33.535565</td>\n",
       "      <td>0.338225</td>\n",
       "      <td>96.409673</td>\n",
       "      <td>8.163649</td>\n",
       "      <td>8.551488</td>\n",
       "      <td>0.668560</td>\n",
       "      <td>8.135268</td>\n",
       "      <td>0.055598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-24</th>\n",
       "      <td>11.062798</td>\n",
       "      <td>0.668818</td>\n",
       "      <td>51.143185</td>\n",
       "      <td>0.212725</td>\n",
       "      <td>33.438542</td>\n",
       "      <td>0.155149</td>\n",
       "      <td>110.435119</td>\n",
       "      <td>8.666806</td>\n",
       "      <td>9.848363</td>\n",
       "      <td>0.801190</td>\n",
       "      <td>8.249851</td>\n",
       "      <td>0.068407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>11.460714</td>\n",
       "      <td>0.489294</td>\n",
       "      <td>50.708229</td>\n",
       "      <td>0.495337</td>\n",
       "      <td>33.148065</td>\n",
       "      <td>0.362919</td>\n",
       "      <td>101.478869</td>\n",
       "      <td>10.669128</td>\n",
       "      <td>8.984077</td>\n",
       "      <td>0.922657</td>\n",
       "      <td>8.205952</td>\n",
       "      <td>0.080573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-07</th>\n",
       "      <td>13.245387</td>\n",
       "      <td>0.836309</td>\n",
       "      <td>49.767753</td>\n",
       "      <td>0.774456</td>\n",
       "      <td>32.537500</td>\n",
       "      <td>0.546354</td>\n",
       "      <td>92.512351</td>\n",
       "      <td>7.864913</td>\n",
       "      <td>7.920982</td>\n",
       "      <td>0.657496</td>\n",
       "      <td>8.055506</td>\n",
       "      <td>0.061521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-14</th>\n",
       "      <td>13.786012</td>\n",
       "      <td>0.485142</td>\n",
       "      <td>47.067500</td>\n",
       "      <td>1.284894</td>\n",
       "      <td>30.606399</td>\n",
       "      <td>0.933642</td>\n",
       "      <td>77.156548</td>\n",
       "      <td>9.350267</td>\n",
       "      <td>6.609226</td>\n",
       "      <td>0.743559</td>\n",
       "      <td>7.919494</td>\n",
       "      <td>0.075201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-21</th>\n",
       "      <td>13.906101</td>\n",
       "      <td>0.659048</td>\n",
       "      <td>48.079509</td>\n",
       "      <td>0.675654</td>\n",
       "      <td>31.338839</td>\n",
       "      <td>0.497350</td>\n",
       "      <td>87.326935</td>\n",
       "      <td>19.638314</td>\n",
       "      <td>7.436607</td>\n",
       "      <td>1.682566</td>\n",
       "      <td>8.095238</td>\n",
       "      <td>0.107156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-28</th>\n",
       "      <td>12.507292</td>\n",
       "      <td>0.541118</td>\n",
       "      <td>50.169866</td>\n",
       "      <td>0.573311</td>\n",
       "      <td>32.804018</td>\n",
       "      <td>0.412508</td>\n",
       "      <td>100.163095</td>\n",
       "      <td>13.690572</td>\n",
       "      <td>8.688690</td>\n",
       "      <td>1.127382</td>\n",
       "      <td>8.152232</td>\n",
       "      <td>0.095943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-04</th>\n",
       "      <td>13.293304</td>\n",
       "      <td>0.891222</td>\n",
       "      <td>51.267351</td>\n",
       "      <td>0.304344</td>\n",
       "      <td>33.636756</td>\n",
       "      <td>0.230409</td>\n",
       "      <td>100.904762</td>\n",
       "      <td>14.964069</td>\n",
       "      <td>8.557887</td>\n",
       "      <td>1.135275</td>\n",
       "      <td>8.047917</td>\n",
       "      <td>0.091168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-11</th>\n",
       "      <td>14.667708</td>\n",
       "      <td>0.892517</td>\n",
       "      <td>51.757827</td>\n",
       "      <td>0.142716</td>\n",
       "      <td>34.044345</td>\n",
       "      <td>0.108770</td>\n",
       "      <td>82.539435</td>\n",
       "      <td>15.323227</td>\n",
       "      <td>6.794940</td>\n",
       "      <td>1.200275</td>\n",
       "      <td>7.966518</td>\n",
       "      <td>0.112793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-18</th>\n",
       "      <td>13.273512</td>\n",
       "      <td>0.767026</td>\n",
       "      <td>52.102693</td>\n",
       "      <td>0.100997</td>\n",
       "      <td>34.245833</td>\n",
       "      <td>0.089279</td>\n",
       "      <td>91.431548</td>\n",
       "      <td>10.820579</td>\n",
       "      <td>7.733185</td>\n",
       "      <td>0.820118</td>\n",
       "      <td>8.026042</td>\n",
       "      <td>0.089328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-25</th>\n",
       "      <td>11.356250</td>\n",
       "      <td>0.955862</td>\n",
       "      <td>51.777664</td>\n",
       "      <td>0.763100</td>\n",
       "      <td>33.920685</td>\n",
       "      <td>0.588036</td>\n",
       "      <td>83.793899</td>\n",
       "      <td>7.828633</td>\n",
       "      <td>7.394345</td>\n",
       "      <td>0.579324</td>\n",
       "      <td>7.954167</td>\n",
       "      <td>0.152517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-04</th>\n",
       "      <td>11.702232</td>\n",
       "      <td>0.919621</td>\n",
       "      <td>49.677515</td>\n",
       "      <td>0.798995</td>\n",
       "      <td>32.407292</td>\n",
       "      <td>0.549209</td>\n",
       "      <td>87.375595</td>\n",
       "      <td>8.563336</td>\n",
       "      <td>7.725149</td>\n",
       "      <td>0.630384</td>\n",
       "      <td>7.756696</td>\n",
       "      <td>0.053351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-11</th>\n",
       "      <td>14.226488</td>\n",
       "      <td>1.287825</td>\n",
       "      <td>48.937753</td>\n",
       "      <td>0.255668</td>\n",
       "      <td>31.962946</td>\n",
       "      <td>0.197553</td>\n",
       "      <td>77.636161</td>\n",
       "      <td>14.445708</td>\n",
       "      <td>6.556845</td>\n",
       "      <td>1.272458</td>\n",
       "      <td>7.706250</td>\n",
       "      <td>0.098980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-18</th>\n",
       "      <td>15.142642</td>\n",
       "      <td>1.018444</td>\n",
       "      <td>49.111321</td>\n",
       "      <td>0.351219</td>\n",
       "      <td>32.114340</td>\n",
       "      <td>0.259556</td>\n",
       "      <td>72.590943</td>\n",
       "      <td>9.105284</td>\n",
       "      <td>5.999245</td>\n",
       "      <td>0.703077</td>\n",
       "      <td>7.613962</td>\n",
       "      <td>0.049173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-25</th>\n",
       "      <td>14.070192</td>\n",
       "      <td>0.765043</td>\n",
       "      <td>49.630481</td>\n",
       "      <td>0.630173</td>\n",
       "      <td>32.468269</td>\n",
       "      <td>0.440852</td>\n",
       "      <td>85.324575</td>\n",
       "      <td>6.797546</td>\n",
       "      <td>8.162193</td>\n",
       "      <td>0.926078</td>\n",
       "      <td>7.858979</td>\n",
       "      <td>0.059985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01</th>\n",
       "      <td>11.426606</td>\n",
       "      <td>37.775833</td>\n",
       "      <td>31.711630</td>\n",
       "      <td>114.980654</td>\n",
       "      <td>29.298732</td>\n",
       "      <td>7.205771</td>\n",
       "      <td>117.436012</td>\n",
       "      <td>27.667320</td>\n",
       "      <td>9.950595</td>\n",
       "      <td>1.862661</td>\n",
       "      <td>8.236310</td>\n",
       "      <td>0.185117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-08</th>\n",
       "      <td>17.456399</td>\n",
       "      <td>1.059843</td>\n",
       "      <td>48.441562</td>\n",
       "      <td>1.388793</td>\n",
       "      <td>31.667560</td>\n",
       "      <td>1.007626</td>\n",
       "      <td>68.553125</td>\n",
       "      <td>18.008360</td>\n",
       "      <td>5.424256</td>\n",
       "      <td>1.390287</td>\n",
       "      <td>7.975149</td>\n",
       "      <td>0.174341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-15</th>\n",
       "      <td>16.819196</td>\n",
       "      <td>1.625772</td>\n",
       "      <td>46.589479</td>\n",
       "      <td>0.920041</td>\n",
       "      <td>30.308185</td>\n",
       "      <td>0.656261</td>\n",
       "      <td>93.211310</td>\n",
       "      <td>23.619141</td>\n",
       "      <td>7.501488</td>\n",
       "      <td>1.723485</td>\n",
       "      <td>8.127827</td>\n",
       "      <td>0.170864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-22</th>\n",
       "      <td>15.753423</td>\n",
       "      <td>1.475075</td>\n",
       "      <td>47.115164</td>\n",
       "      <td>0.718002</td>\n",
       "      <td>30.677976</td>\n",
       "      <td>0.508342</td>\n",
       "      <td>71.594792</td>\n",
       "      <td>12.445958</td>\n",
       "      <td>5.894792</td>\n",
       "      <td>0.980793</td>\n",
       "      <td>7.812798</td>\n",
       "      <td>0.214490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-29</th>\n",
       "      <td>16.711012</td>\n",
       "      <td>1.591697</td>\n",
       "      <td>47.369539</td>\n",
       "      <td>0.136077</td>\n",
       "      <td>30.875149</td>\n",
       "      <td>0.103407</td>\n",
       "      <td>79.006399</td>\n",
       "      <td>16.123329</td>\n",
       "      <td>6.359821</td>\n",
       "      <td>1.162539</td>\n",
       "      <td>7.740923</td>\n",
       "      <td>0.119516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-06</th>\n",
       "      <td>16.829464</td>\n",
       "      <td>1.326097</td>\n",
       "      <td>47.462455</td>\n",
       "      <td>0.173550</td>\n",
       "      <td>30.944196</td>\n",
       "      <td>0.143500</td>\n",
       "      <td>72.599405</td>\n",
       "      <td>15.552721</td>\n",
       "      <td>5.843304</td>\n",
       "      <td>1.219556</td>\n",
       "      <td>7.780952</td>\n",
       "      <td>0.106958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-13</th>\n",
       "      <td>17.992411</td>\n",
       "      <td>1.382503</td>\n",
       "      <td>48.661503</td>\n",
       "      <td>0.453646</td>\n",
       "      <td>31.826935</td>\n",
       "      <td>0.338979</td>\n",
       "      <td>75.935714</td>\n",
       "      <td>14.208356</td>\n",
       "      <td>5.938393</td>\n",
       "      <td>1.039481</td>\n",
       "      <td>7.758036</td>\n",
       "      <td>0.110928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-20</th>\n",
       "      <td>-58.772963</td>\n",
       "      <td>134.727590</td>\n",
       "      <td>-137.184003</td>\n",
       "      <td>381.696472</td>\n",
       "      <td>33.272321</td>\n",
       "      <td>0.945556</td>\n",
       "      <td>91.268750</td>\n",
       "      <td>18.128043</td>\n",
       "      <td>7.147768</td>\n",
       "      <td>1.326457</td>\n",
       "      <td>8.020238</td>\n",
       "      <td>0.101881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-27</th>\n",
       "      <td>-84.421918</td>\n",
       "      <td>146.698803</td>\n",
       "      <td>-184.970260</td>\n",
       "      <td>415.162787</td>\n",
       "      <td>33.359896</td>\n",
       "      <td>0.312742</td>\n",
       "      <td>80.702083</td>\n",
       "      <td>6.156450</td>\n",
       "      <td>6.470833</td>\n",
       "      <td>0.489773</td>\n",
       "      <td>7.989583</td>\n",
       "      <td>0.044555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1156 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Temp     TempErr      SpCond   SpCondErr        Sal  \\\n",
       "DateTimeStamp                                                             \n",
       "1996-04-07     16.591220    1.724669   46.870030    1.073929  30.505060   \n",
       "1996-04-14     16.328571    1.377676   47.235060    1.351055  30.767708   \n",
       "1996-04-21     15.392857    1.371873   47.927768    1.691262  31.256250   \n",
       "1996-04-28     18.311579    1.788897   47.253549    1.029468  30.792932   \n",
       "1996-05-05     17.938244    2.592252   50.679836    0.504622  33.284226   \n",
       "1996-05-12     16.535714    1.848229   50.092679    0.328509  32.844196   \n",
       "1996-05-19     17.548512    2.017974   47.957098    1.486571  31.301339   \n",
       "1996-05-26     17.883886    1.218781   48.692907    2.223044  31.848645   \n",
       "1996-06-02     16.170089    2.017113   49.622426    0.534047  32.493006   \n",
       "1996-06-09     16.769048    2.147551   48.403110    0.253365  31.615030   \n",
       "1996-06-16     15.950000    2.028603   48.593214    0.307621  31.741220   \n",
       "1996-06-23     16.367177    1.985710   48.201335    0.242704  31.461050   \n",
       "1996-06-30     17.547837    2.670300   52.302572    1.592687  34.475000   \n",
       "1996-07-07     17.316964    2.176944   52.669673    1.086938  34.753571   \n",
       "1996-07-14     17.875446    1.563435   50.384315    1.268334  33.079018   \n",
       "1996-07-21     18.722768    1.854073   48.681354    0.463180  31.834077   \n",
       "1996-07-28     19.580924    1.671123   49.580402    0.968568  32.492846   \n",
       "1996-08-04     18.408482    1.869148   46.796027    0.953800  30.465625   \n",
       "1996-08-11     18.354464    1.567824   46.551071    0.276126  30.288839   \n",
       "1996-08-18     19.232143    1.619988   45.757783    0.397759  29.712946   \n",
       "1996-08-25     18.598553    1.178751   45.566618    1.227730  29.581193   \n",
       "1996-09-01     18.163988    1.854880   50.252798    0.776457  32.983036   \n",
       "1996-09-08     18.703274    1.403059   47.729449    0.484047  31.143006   \n",
       "1996-09-15     18.464583    1.528467   47.215565    0.687070  30.770238   \n",
       "1996-09-22     18.792804    1.093031   46.894723    0.681098  30.541144   \n",
       "1996-09-29     16.174500    0.989730   47.664967    1.880399  31.085500   \n",
       "1996-10-06     18.253423    1.346648   45.074152    0.135877  29.222619   \n",
       "1996-10-13     19.702597    1.171928   44.890519    0.240503  29.083117   \n",
       "1996-10-20           NaN         NaN         NaN         NaN        NaN   \n",
       "1996-10-27     13.601176    0.827593   50.403627    0.354352  33.010196   \n",
       "...                  ...         ...         ...         ...        ...   \n",
       "2017-11-05     15.433036    0.707219   50.933631    2.028683  33.464583   \n",
       "2017-11-12     15.054762    0.944625   50.030193    0.668748  32.788690   \n",
       "2017-11-19     15.184375    0.768421   49.567009    1.993055  32.459821   \n",
       "2017-11-26     15.973214    0.903988   49.297545    0.640161  32.269494   \n",
       "2017-12-03     14.011161    0.995224   50.245789    0.475866  32.919643   \n",
       "2017-12-10     11.228125    0.663885   51.098140    0.338188  33.414732   \n",
       "2017-12-17     11.250149    0.501859   51.259970    0.467669  33.535565   \n",
       "2017-12-24     11.062798    0.668818   51.143185    0.212725  33.438542   \n",
       "2017-12-31     11.460714    0.489294   50.708229    0.495337  33.148065   \n",
       "2018-01-07     13.245387    0.836309   49.767753    0.774456  32.537500   \n",
       "2018-01-14     13.786012    0.485142   47.067500    1.284894  30.606399   \n",
       "2018-01-21     13.906101    0.659048   48.079509    0.675654  31.338839   \n",
       "2018-01-28     12.507292    0.541118   50.169866    0.573311  32.804018   \n",
       "2018-02-04     13.293304    0.891222   51.267351    0.304344  33.636756   \n",
       "2018-02-11     14.667708    0.892517   51.757827    0.142716  34.044345   \n",
       "2018-02-18     13.273512    0.767026   52.102693    0.100997  34.245833   \n",
       "2018-02-25     11.356250    0.955862   51.777664    0.763100  33.920685   \n",
       "2018-03-04     11.702232    0.919621   49.677515    0.798995  32.407292   \n",
       "2018-03-11     14.226488    1.287825   48.937753    0.255668  31.962946   \n",
       "2018-03-18     15.142642    1.018444   49.111321    0.351219  32.114340   \n",
       "2018-03-25     14.070192    0.765043   49.630481    0.630173  32.468269   \n",
       "2018-04-01     11.426606   37.775833   31.711630  114.980654  29.298732   \n",
       "2018-04-08     17.456399    1.059843   48.441562    1.388793  31.667560   \n",
       "2018-04-15     16.819196    1.625772   46.589479    0.920041  30.308185   \n",
       "2018-04-22     15.753423    1.475075   47.115164    0.718002  30.677976   \n",
       "2018-04-29     16.711012    1.591697   47.369539    0.136077  30.875149   \n",
       "2018-05-06     16.829464    1.326097   47.462455    0.173550  30.944196   \n",
       "2018-05-13     17.992411    1.382503   48.661503    0.453646  31.826935   \n",
       "2018-05-20    -58.772963  134.727590 -137.184003  381.696472  33.272321   \n",
       "2018-05-27    -84.421918  146.698803 -184.970260  415.162787  33.359896   \n",
       "\n",
       "                 SalErr      DO_pct  DO_pctErr    DO_mgl  DO_mglErr        pH  \\\n",
       "DateTimeStamp                                                                   \n",
       "1996-04-07     0.773155   95.431399  29.499533  7.707143   2.247789  8.252083   \n",
       "1996-04-14     0.976375   25.684524  24.457572  2.077083   1.963302  7.831399   \n",
       "1996-04-21     1.223682   11.230655  17.229722  0.926637   1.416011  7.780655   \n",
       "1996-04-28     0.742489    5.923379  16.266982  0.464706   1.281977  7.836241   \n",
       "1996-05-05     0.381097   96.279911  23.266789  7.437649   1.596941  7.545238   \n",
       "1996-05-12     0.266168   79.885119  17.651854  6.370833   1.290125  7.492708   \n",
       "1996-05-19     1.080479   95.481548  24.384607  7.551339   1.846921  7.541518   \n",
       "1996-05-26     1.623767   83.615813  16.830409  6.556777   1.310227  7.786145   \n",
       "1996-06-02     0.407707   76.541071  15.146332  6.152232   1.046886  7.852083   \n",
       "1996-06-09     0.211369   71.468155  13.089708  5.729315   0.982327  7.836458   \n",
       "1996-06-16     0.260025   74.801339  15.180237  6.088690   1.171503  7.811310   \n",
       "1996-06-23     0.205018   77.920788  17.010351  6.281619   1.212983  7.803501   \n",
       "1996-06-30     1.186531   63.434615  23.768141  4.920673   1.793363  7.843269   \n",
       "1996-07-07     0.796875         NaN        NaN       NaN        NaN  7.851935   \n",
       "1996-07-14     0.931846         NaN        NaN       NaN        NaN  7.900298   \n",
       "1996-07-21     0.338026         NaN        NaN       NaN        NaN  7.828869   \n",
       "1996-07-28     0.710713   73.803869  13.586579  5.647619   1.190257  7.448137   \n",
       "1996-08-04     0.694535   75.951339  14.955581  5.964881   1.267989  7.379315   \n",
       "1996-08-11     0.210691   74.701339  13.937114  5.861458   1.100773  7.318155   \n",
       "1996-08-18     0.288264   82.862202  13.436599  6.407589   0.962587  7.424107   \n",
       "1996-08-25     0.886538   82.477758  10.320463  6.467089   0.806721  7.421338   \n",
       "1996-09-01     0.579951   87.811607  12.439326  6.813839   1.026824  7.830060   \n",
       "1996-09-08     0.358435   78.583333   9.801044  6.086905   0.699249  7.786012   \n",
       "1996-09-15     0.504650   80.235565   9.475410  6.265179   0.761778  7.801190   \n",
       "1996-09-22     0.497447   87.061439  11.217120  6.751292   0.766626  7.865867   \n",
       "1996-09-29     1.372083   53.983667  12.942636  4.403667   1.075646  7.723333   \n",
       "1996-10-06     0.103271   38.529167  22.695935  3.035417   1.756901  7.692708   \n",
       "1996-10-13     0.181661   44.322078  11.870769  3.412987   0.899759  7.605195   \n",
       "1996-10-20          NaN         NaN        NaN       NaN        NaN       NaN   \n",
       "1996-10-27     0.276474   82.763655  24.962280  7.010267   2.095757  7.408235   \n",
       "...                 ...         ...        ...       ...        ...       ...   \n",
       "2017-11-05     1.350485   78.256994  11.455595  6.379315   0.947207  7.960565   \n",
       "2017-11-12     0.489950   88.731994   7.580751  7.313988   0.619490  8.039583   \n",
       "2017-11-19     1.328264   78.102827  10.125995  6.434970   0.841002  7.952381   \n",
       "2017-11-26     0.471710   92.766667   9.624094  7.533333   0.742085  8.068899   \n",
       "2017-12-03     0.329025   82.477232   8.690243  6.941220   0.750587  8.002530   \n",
       "2017-12-10     0.233244   98.471429   7.318833  8.753423   0.694552  8.123214   \n",
       "2017-12-17     0.338225   96.409673   8.163649  8.551488   0.668560  8.135268   \n",
       "2017-12-24     0.155149  110.435119   8.666806  9.848363   0.801190  8.249851   \n",
       "2017-12-31     0.362919  101.478869  10.669128  8.984077   0.922657  8.205952   \n",
       "2018-01-07     0.546354   92.512351   7.864913  7.920982   0.657496  8.055506   \n",
       "2018-01-14     0.933642   77.156548   9.350267  6.609226   0.743559  7.919494   \n",
       "2018-01-21     0.497350   87.326935  19.638314  7.436607   1.682566  8.095238   \n",
       "2018-01-28     0.412508  100.163095  13.690572  8.688690   1.127382  8.152232   \n",
       "2018-02-04     0.230409  100.904762  14.964069  8.557887   1.135275  8.047917   \n",
       "2018-02-11     0.108770   82.539435  15.323227  6.794940   1.200275  7.966518   \n",
       "2018-02-18     0.089279   91.431548  10.820579  7.733185   0.820118  8.026042   \n",
       "2018-02-25     0.588036   83.793899   7.828633  7.394345   0.579324  7.954167   \n",
       "2018-03-04     0.549209   87.375595   8.563336  7.725149   0.630384  7.756696   \n",
       "2018-03-11     0.197553   77.636161  14.445708  6.556845   1.272458  7.706250   \n",
       "2018-03-18     0.259556   72.590943   9.105284  5.999245   0.703077  7.613962   \n",
       "2018-03-25     0.440852   85.324575   6.797546  8.162193   0.926078  7.858979   \n",
       "2018-04-01     7.205771  117.436012  27.667320  9.950595   1.862661  8.236310   \n",
       "2018-04-08     1.007626   68.553125  18.008360  5.424256   1.390287  7.975149   \n",
       "2018-04-15     0.656261   93.211310  23.619141  7.501488   1.723485  8.127827   \n",
       "2018-04-22     0.508342   71.594792  12.445958  5.894792   0.980793  7.812798   \n",
       "2018-04-29     0.103407   79.006399  16.123329  6.359821   1.162539  7.740923   \n",
       "2018-05-06     0.143500   72.599405  15.552721  5.843304   1.219556  7.780952   \n",
       "2018-05-13     0.338979   75.935714  14.208356  5.938393   1.039481  7.758036   \n",
       "2018-05-20     0.945556   91.268750  18.128043  7.147768   1.326457  8.020238   \n",
       "2018-05-27     0.312742   80.702083   6.156450  6.470833   0.489773  7.989583   \n",
       "\n",
       "                  pHErr  \n",
       "DateTimeStamp            \n",
       "1996-04-07     0.135045  \n",
       "1996-04-14     0.141039  \n",
       "1996-04-21     0.148256  \n",
       "1996-04-28     0.080628  \n",
       "1996-05-05     0.112715  \n",
       "1996-05-12     0.111849  \n",
       "1996-05-19     0.139253  \n",
       "1996-05-26     0.182791  \n",
       "1996-06-02     0.080211  \n",
       "1996-06-09     0.097638  \n",
       "1996-06-16     0.092119  \n",
       "1996-06-23     0.107752  \n",
       "1996-06-30     0.085604  \n",
       "1996-07-07     0.123036  \n",
       "1996-07-14     0.131709  \n",
       "1996-07-21     0.120233  \n",
       "1996-07-28     0.191285  \n",
       "1996-08-04     0.115314  \n",
       "1996-08-11     0.108770  \n",
       "1996-08-18     0.066524  \n",
       "1996-08-25     0.110074  \n",
       "1996-09-01     0.090641  \n",
       "1996-09-08     0.061629  \n",
       "1996-09-15     0.058023  \n",
       "1996-09-22     0.065182  \n",
       "1996-09-29     0.061605  \n",
       "1996-10-06     0.064992  \n",
       "1996-10-13     0.022338  \n",
       "1996-10-20          NaN  \n",
       "1996-10-27     0.063392  \n",
       "...                 ...  \n",
       "2017-11-05     0.056539  \n",
       "2017-11-12     0.051895  \n",
       "2017-11-19     0.067488  \n",
       "2017-11-26     0.054046  \n",
       "2017-12-03     0.044112  \n",
       "2017-12-10     0.070530  \n",
       "2017-12-17     0.055598  \n",
       "2017-12-24     0.068407  \n",
       "2017-12-31     0.080573  \n",
       "2018-01-07     0.061521  \n",
       "2018-01-14     0.075201  \n",
       "2018-01-21     0.107156  \n",
       "2018-01-28     0.095943  \n",
       "2018-02-04     0.091168  \n",
       "2018-02-11     0.112793  \n",
       "2018-02-18     0.089328  \n",
       "2018-02-25     0.152517  \n",
       "2018-03-04     0.053351  \n",
       "2018-03-11     0.098980  \n",
       "2018-03-18     0.049173  \n",
       "2018-03-25     0.059985  \n",
       "2018-04-01     0.185117  \n",
       "2018-04-08     0.174341  \n",
       "2018-04-15     0.170864  \n",
       "2018-04-22     0.214490  \n",
       "2018-04-29     0.119516  \n",
       "2018-05-06     0.106958  \n",
       "2018-05-13     0.110928  \n",
       "2018-05-20     0.101881  \n",
       "2018-05-27     0.044555  \n",
       "\n",
       "[1156 rows x 12 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekly_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare the weekly average salinity for 1997 with 2007, to see if there is a difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WVUxvavZ5rV1h6PkOaDOzbbSqYnpdk88CGI0A4eEVrF2u1rJJpFUV05/2KiN9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\nMDOzTA4QZmaWyQHCzMwyOUCYmVmm/w9HzVbHhnquPwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119c68c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "WeeklySalinityPlot1 = weekly_summary['1997-Jan':'1997-Dec']['Sal']\n",
    "WeeklySalinityErr1 = weekly_summary['1997-Jan':'1997-Dec']['SalErr']\n",
    "\n",
    "WeeklySalinityPlot2 = weekly_summary['2007-Jan':'2007-Dec']['Sal']\n",
    "WeeklySalinityErr2 = weekly_summary['2007-Jan':'2007-Dec']['SalErr']\n",
    "\n",
    "plt.errorbar(WeeklySalinityPlot1.index, WeeklySalinityPlot1, yerr = WeeklySalinityErr1, fmt='ro')\n",
    "plt.errorbar(WeeklySalinityPlot1.index, WeeklySalinityPlot2, yerr = WeeklySalinityErr2, fmt='bo')\n",
    "plt.title(\"Figure 1 - Elkhorn slough salinity over time\")\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Salinity')\n",
    "ax = plt.gca()\n",
    "ax.set_ylim(10,40)\n",
    "ax.set_ylim\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks to me that the salinity in 2007 (in blue) is higher than the salinity in 1997, at least in the early months."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, your task is to compare monthly averages of the pH, instead of weekly averages of salinity. First, create a new data frame, similar to above, but named monthly_averages and consisting of monthly averages and standard deviations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that you've done that, graph the monthly average pH for 1998, 2003, and 2008 on the same graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Does the pH vary from year to year? Explain."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Enter answer here..."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}