{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Future question for Dr. Soto\n",
    "\n",
    "Should I concatinate the dataframes on integer indices or Date-Time indices? I dont think it will make a difference.. not sure which one is easier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 777,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 778,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "    }\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>Timestamp</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>CO2</th>\n",
       "      <th>Noise</th>\n",
       "      <th>Pressure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.014300e+04</td>\n",
       "      <td>90143.000000</td>\n",
       "      <td>90143.000000</td>\n",
       "      <td>90137.000000</td>\n",
       "      <td>90132.000000</td>\n",
       "      <td>90143.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.469613e+09</td>\n",
       "      <td>22.766724</td>\n",
       "      <td>51.291637</td>\n",
       "      <td>550.204799</td>\n",
       "      <td>38.964730</td>\n",
       "      <td>1011.348933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.900773e+06</td>\n",
       "      <td>1.592268</td>\n",
       "      <td>6.929620</td>\n",
       "      <td>318.321732</td>\n",
       "      <td>7.100703</td>\n",
       "      <td>4.217541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.455917e+09</td>\n",
       "      <td>17.900000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>995.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.462765e+09</td>\n",
       "      <td>21.700000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>354.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>1008.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.469657e+09</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>416.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>1011.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.476459e+09</td>\n",
       "      <td>23.800000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>639.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1014.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.483257e+09</td>\n",
       "      <td>28.500000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>2777.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>1027.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 778,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv('NetAtmo_2016.csv', parse_dates = True,)\n",
    "df1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 779,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_index1 = pd.Series(range(1,90144))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 780,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['Numbered_index'] = new_index1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 781,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Timestamp</th>\n",
       "      <th>Timezone : America/Los_Angeles</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>CO2</th>\n",
       "      <th>Noise</th>\n",
       "      <th>Pressure</th>\n",
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       "    <tr>\n",
       "      <th>Numbered_index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1455917199</td>\n",
       "      <td>2/19/16 13:26</td>\n",
       "      <td>18.8</td>\n",
       "      <td>76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1015.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1455917255</td>\n",
       "      <td>2/19/16 13:27</td>\n",
       "      <td>19.2</td>\n",
       "      <td>75</td>\n",
       "      <td>718.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1015.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1455917257</td>\n",
       "      <td>2/19/16 13:27</td>\n",
       "      <td>19.9</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1015.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1455917513</td>\n",
       "      <td>2/19/16 13:31</td>\n",
       "      <td>20.3</td>\n",
       "      <td>73</td>\n",
       "      <td>337.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1015.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1455917814</td>\n",
       "      <td>2/19/16 13:36</td>\n",
       "      <td>21.2</td>\n",
       "      <td>70</td>\n",
       "      <td>332.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1015.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 781,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.set_index('Numbered_index', inplace = True)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 782,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.drop(df1.columns[[0,2,3,5,6]], axis =1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 783,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Timezone : America/Los_Angeles</th>\n",
       "      <th>CO2</th>\n",
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       "    <tr>\n",
       "      <th>Numbered_index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>1</th>\n",
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       "      <td>NaN</td>\n",
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     "execution_count": 783,
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   ],
   "source": [
    "df1.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 784,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_csv('NetAtmo_2017.csv', parse_dates = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 785,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_index2 = pd.Series(range(90144, 100992))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 786,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['numbered_index'] = new_index2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 787,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.set_index('numbered_index', inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 788,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.drop(df2.columns[[0,2,3,5,6]], axis =1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 789,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>90144</th>\n",
       "      <td>1/1/17 0:00</td>\n",
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       "      <th>90145</th>\n",
       "      <td>1/1/17 0:05</td>\n",
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       "      <th>90146</th>\n",
       "      <td>1/1/17 0:11</td>\n",
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       "      <th>90147</th>\n",
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       "      <th>90148</th>\n",
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     },
     "execution_count": 789,
     "metadata": {},
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   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 790,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ]
     },
     "execution_count": 790,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 791,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ]
     },
     "execution_count": 791,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df1.rename(columns = {'Timezone : America/Los_Angeles':'Time'})\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 792,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2/12/17 18:42</td>\n",
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      ]
     },
     "execution_count": 792,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 793,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2/19/16 13:27</td>\n",
       "      <td>718.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2/19/16 13:27</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2/19/16 13:31</td>\n",
       "      <td>337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2/19/16 13:36</td>\n",
       "      <td>332.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 793,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.concat([df1,df2])\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 794,
   "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>Time</th>\n",
       "      <th>CO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100987</th>\n",
       "      <td>2/12/17 18:27</td>\n",
       "      <td>484.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100988</th>\n",
       "      <td>2/12/17 18:32</td>\n",
       "      <td>486.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100989</th>\n",
       "      <td>2/12/17 18:37</td>\n",
       "      <td>469.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100990</th>\n",
       "      <td>2/12/17 18:42</td>\n",
       "      <td>485.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100991</th>\n",
       "      <td>2/12/17 18:47</td>\n",
       "      <td>480.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 794,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 795,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc30f81eb00>"
      ]
     },
     "execution_count": 795,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 795,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 796,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df3.set_index('Time', inplace = True)\n",
    "#df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 797,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df3.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 798,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df3.plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 799,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time     object\n",
       "CO2     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 799,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 800,
   "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>Time</th>\n",
       "      <th>CO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2/19/16 13:26</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2/19/16 13:27</td>\n",
       "      <td>718.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2/19/16 13:27</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2/19/16 13:31</td>\n",
       "      <td>337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2/19/16 13:36</td>\n",
       "      <td>332.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 800,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 801,
   "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>Time</th>\n",
       "      <th>CO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 801,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.isnull().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 802,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3['Time'] = pd.to_datetime(df3.Time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 803,
   "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>Time</th>\n",
       "      <th>CO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-02-19 13:26:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-02-19 13:27:00</td>\n",
       "      <td>718.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-02-19 13:27:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-02-19 13:31:00</td>\n",
       "      <td>337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-02-19 13:36:00</td>\n",
       "      <td>332.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 803,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://pandas.pydata.org/pandas-docs/stable/api.html#datetimelike-properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 804,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    Friday\n",
       "2    Friday\n",
       "3    Friday\n",
       "4    Friday\n",
       "5    Friday\n",
       "Name: Time, dtype: object"
      ]
     },
     "execution_count": 804,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.Time.dt.weekday_name.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 805,
   "metadata": {},
   "outputs": [],
   "source": [
    "#isolating the seonc day\n",
    "Firstday = pd.to_datetime('2/20/2016 23:59:59')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 806,
   "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>Time</th>\n",
       "      <th>CO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>2016-02-20 23:39:00</td>\n",
       "      <td>400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>2016-02-20 23:44:00</td>\n",
       "      <td>419.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>2016-02-20 23:49:00</td>\n",
       "      <td>407.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>2016-02-20 23:54:00</td>\n",
       "      <td>417.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>2016-02-20 23:59:00</td>\n",
       "      <td>417.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 806,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.loc[df3.Time <= Firstday, :].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 807,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('359 days 05:21:00')"
      ]
     },
     "execution_count": 807,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#almost a full year of data!\n",
    "(df3.Time.max() - df3.Time.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 808,
   "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>Time</th>\n",
       "      <th>CO2</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-02-19 13:26:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-02-19 13:27:00</td>\n",
       "      <td>718.0</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-02-19 13:27:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-02-19 13:31:00</td>\n",
       "      <td>337.0</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-02-19 13:36:00</td>\n",
       "      <td>332.0</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 808,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['Day'] = df3.Time.dt.weekday_name\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 809,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Saturday     14598\n",
       "Tuesday      14589\n",
       "Sunday       14539\n",
       "Monday       14488\n",
       "Wednesday    14472\n",
       "Friday       14438\n",
       "Thursday     13867\n",
       "Name: Day, dtype: int64"
      ]
     },
     "execution_count": 809,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# so many questions\n",
    "df3.Day.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 810,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc30f0ed1d0>"
      ]
     },
     "execution_count": 810,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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"
     },
     "execution_count": 810,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.Day.value_counts().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Switching to df2 because it is still note recognized by datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 811,
   "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>Time</th>\n",
       "      <th>CO2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numbered_index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>90144</th>\n",
       "      <td>1/1/17 0:00</td>\n",
       "      <td>482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90145</th>\n",
       "      <td>1/1/17 0:05</td>\n",
       "      <td>491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90146</th>\n",
       "      <td>1/1/17 0:11</td>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90147</th>\n",
       "      <td>1/1/17 0:16</td>\n",
       "      <td>486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90148</th>\n",
       "      <td>1/1/17 0:21</td>\n",
       "      <td>490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 811,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 812,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df3['Time2'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 813,
   "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>Time</th>\n",
       "      <th>CO2</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-02-19 13:26:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-02-19 13:27:00</td>\n",
       "      <td>718.0</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-02-19 13:27:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-02-19 13:31:00</td>\n",
       "      <td>337.0</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-02-19 13:36:00</td>\n",
       "      <td>332.0</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 813,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 814,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3['Time2'] = df3.Time.shift(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 815,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>CO2</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-02-19 13:26:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2016-02-19 13:27:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-02-19 13:27:00</td>\n",
       "      <td>718.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2016-02-19 13:27:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-02-19 13:27:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2016-02-19 13:31:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-02-19 13:31:00</td>\n",
       "      <td>337.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2016-02-19 13:36:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-02-19 13:36:00</td>\n",
       "      <td>332.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2016-02-19 13:41:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 815,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 816,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>CO2</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time2</th>\n",
       "      <th>TimeDel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-02-19 13:26:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2016-02-19 13:27:00</td>\n",
       "      <td>00:01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-02-19 13:27:00</td>\n",
       "      <td>718.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2016-02-19 13:27:00</td>\n",
       "      <td>00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-02-19 13:27:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2016-02-19 13:31:00</td>\n",
       "      <td>00:04:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-02-19 13:31:00</td>\n",
       "      <td>337.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2016-02-19 13:36:00</td>\n",
       "      <td>00:05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-02-19 13:36:00</td>\n",
       "      <td>332.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2016-02-19 13:41:00</td>\n",
       "      <td>00:05:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 816,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['TimeDel'] = df3.Time2 - df3.Time\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 817,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     60.0\n",
       "2      0.0\n",
       "3    240.0\n",
       "4    300.0\n",
       "5    300.0\n",
       "Name: TimeDel, dtype: float64"
      ]
     },
     "execution_count": 817,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.TimeDel.dt.seconds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 818,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time        datetime64[ns]\n",
       "CO2                float64\n",
       "Day                 object\n",
       "Time2       datetime64[ns]\n",
       "TimeDel    timedelta64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 818,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 819,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3['TimeDel'] = df3.TimeDel /  np.timedelta64(1, 's')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 820,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time       datetime64[ns]\n",
       "CO2               float64\n",
       "Day                object\n",
       "Time2      datetime64[ns]\n",
       "TimeDel           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 820,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 821,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3['CO2_over_TimeDiff'] = (df3.CO2 / df3.TimeDel)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 822,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time                 0\n",
       "CO2                  6\n",
       "Day                  0\n",
       "Time2                1\n",
       "TimeDel              1\n",
       "CO2_over_TimeDiff    7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 822,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of \"not a number\" in each column\n",
    "df3.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 823,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>CO2</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time2</th>\n",
       "      <th>TimeDel</th>\n",
       "      <th>CO2_over_TimeDiff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-02-19 13:26:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2016-02-19 13:27:00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-02-19 13:27:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2016-02-19 13:31:00</td>\n",
       "      <td>240.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2911</th>\n",
       "      <td>2016-02-29 17:03:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2016-02-29 17:05:00</td>\n",
       "      <td>120.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32931</th>\n",
       "      <td>2016-06-14 05:09:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2016-06-14 05:10:00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48678</th>\n",
       "      <td>2016-08-09 05:21:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2016-08-09 05:22:00</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72565</th>\n",
       "      <td>2016-10-31 15:40:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2016-10-31 15:44:00</td>\n",
       "      <td>240.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 823,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[df3.CO2.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 824,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100991, 6)"
      ]
     },
     "execution_count": 824,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 825,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping rows that have \"any\" missing values\n",
    "df3.dropna(how='any', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 826,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100984, 6)"
      ]
     },
     "execution_count": 826,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 827,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "    .dataframe thead th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>CO2</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time2</th>\n",
       "      <th>TimeDel</th>\n",
       "      <th>CO2_over_TimeDiff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-02-19 13:27:00</td>\n",
       "      <td>718.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2016-02-19 13:27:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-02-19 13:31:00</td>\n",
       "      <td>337.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2016-02-19 13:36:00</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1.123333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-02-19 13:36:00</td>\n",
       "      <td>332.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2016-02-19 13:41:00</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1.106667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-02-19 13:41:00</td>\n",
       "      <td>328.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2016-02-19 13:46:00</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1.093333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-02-19 13:46:00</td>\n",
       "      <td>307.0</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2016-02-19 13:51:00</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1.023333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 827,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 828,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CO2</th>\n",
       "      <th>TimeDel</th>\n",
       "      <th>CO2_over_TimeDiff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100984.000000</td>\n",
       "      <td>100984.000000</td>\n",
       "      <td>1.009840e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>547.647548</td>\n",
       "      <td>307.336608</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>304.512740</td>\n",
       "      <td>1369.027580</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>201.000000</td>\n",
       "      <td>-3300.000000</td>\n",
       "      <td>-3.500000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>362.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>1.200000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>438.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>1.453333e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>615.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>2.040000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2777.000000</td>\n",
       "      <td>424320.000000</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 828,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 829,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2         inf\n",
       "4    1.123333\n",
       "5    1.106667\n",
       "6    1.093333\n",
       "7    1.023333\n",
       "Name: CO2_over_TimeDiff, dtype: float64"
      ]
     },
     "execution_count": 829,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.CO2_over_TimeDiff.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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