IndegoEDAAndModeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading PICKLED Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load in all of the libraries we'll be using for our analysis and set our random seed for reproduceability \n",
    "\n",
    "from datetime import datetime as dt \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import linear_model\n",
    "from sklearn.preprocessing import scale\n",
    "from sklearn.model_selection import cross_validate\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error as MSE\n",
    "import matplotlib.pyplot as plt\n",
    "import holidays\n",
    "\n",
    "np.random.seed(17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in our pickled dataframes created in the previous script \n",
    "\n",
    "import pickle\n",
    "\n",
    "pickle_off = open(\"Weather.pickle\",\"rb\")\n",
    "PhillyWeatherPickle = pickle.load(pickle_off)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>temperature</th>\n",
       "      <th>precip_intensity</th>\n",
       "      <th>precip_probability</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>humidity</th>\n",
       "      <th>mean_temperature</th>\n",
       "      <th>temperature6am</th>\n",
       "      <th>temperatureNoon</th>\n",
       "      <th>temperature6pm</th>\n",
       "      <th>...</th>\n",
       "      <th>summary_Possible Drizzle and Humid</th>\n",
       "      <th>summary_Possible Flurries</th>\n",
       "      <th>summary_Possible Light Rain</th>\n",
       "      <th>summary_Possible Light Rain and Humid</th>\n",
       "      <th>summary_Possible Light Snow</th>\n",
       "      <th>summary_Rain</th>\n",
       "      <th>summary_Rain and Humid</th>\n",
       "      <th>summary_Rain and Windy</th>\n",
       "      <th>summary_Snow</th>\n",
       "      <th>summary_Windy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>63.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.81</td>\n",
       "      <td>70.4629</td>\n",
       "      <td>61.41</td>\n",
       "      <td>76.3</td>\n",
       "      <td>79.39</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-10-02</td>\n",
       "      <td>68.41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.32</td>\n",
       "      <td>0.92</td>\n",
       "      <td>70.3371</td>\n",
       "      <td>66.77</td>\n",
       "      <td>78.05</td>\n",
       "      <td>80.33</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-10-03</td>\n",
       "      <td>65.33</td>\n",
       "      <td>0.0686</td>\n",
       "      <td>0.92</td>\n",
       "      <td>3.72</td>\n",
       "      <td>0.97</td>\n",
       "      <td>69.0888</td>\n",
       "      <td>64.79</td>\n",
       "      <td>71.29</td>\n",
       "      <td>77.14</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-10-04</td>\n",
       "      <td>65.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.79</td>\n",
       "      <td>0.77</td>\n",
       "      <td>71.2529</td>\n",
       "      <td>62.8</td>\n",
       "      <td>72.78</td>\n",
       "      <td>82.43</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-10-05</td>\n",
       "      <td>73.57</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.02</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>66.0721</td>\n",
       "      <td>64.93</td>\n",
       "      <td>63.89</td>\n",
       "      <td>67.52</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date temperature precip_intensity precip_probability wind_speed  \\\n",
       "0 2018-10-01        63.2                0                  0       1.59   \n",
       "1 2018-10-02       68.41                0                  0       2.32   \n",
       "2 2018-10-03       65.33           0.0686               0.92       3.72   \n",
       "3 2018-10-04       65.01                0                  0       1.79   \n",
       "4 2018-10-05       73.57           0.0002               0.02        2.6   \n",
       "\n",
       "  humidity mean_temperature temperature6am temperatureNoon temperature6pm  \\\n",
       "0     0.81          70.4629          61.41            76.3          79.39   \n",
       "1     0.92          70.3371          66.77           78.05          80.33   \n",
       "2     0.97          69.0888          64.79           71.29          77.14   \n",
       "3     0.77          71.2529           62.8           72.78          82.43   \n",
       "4      0.9          66.0721          64.93           63.89          67.52   \n",
       "\n",
       "   ... summary_Possible Drizzle and Humid summary_Possible Flurries  \\\n",
       "0  ...                                  0                         0   \n",
       "1  ...                                  0                         0   \n",
       "2  ...                                  0                         0   \n",
       "3  ...                                  0                         0   \n",
       "4  ...                                  0                         0   \n",
       "\n",
       "  summary_Possible Light Rain summary_Possible Light Rain and Humid  \\\n",
       "0                           0                                     0   \n",
       "1                           0                                     0   \n",
       "2                           0                                     0   \n",
       "3                           0                                     0   \n",
       "4                           0                                     0   \n",
       "\n",
       "   summary_Possible Light Snow  summary_Rain  summary_Rain and Humid  \\\n",
       "0                            0             0                       0   \n",
       "1                            0             0                       0   \n",
       "2                            0             1                       0   \n",
       "3                            0             0                       0   \n",
       "4                            0             0                       0   \n",
       "\n",
       "   summary_Rain and Windy  summary_Snow  summary_Windy  \n",
       "0                       0             0              0  \n",
       "1                       0             0              0  \n",
       "2                       0             0              0  \n",
       "3                       0             0              0  \n",
       "4                       0             0              0  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PhillyWeatherPickle.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1710 entries, 0 to 1711\n",
      "Data columns (total 43 columns):\n",
      "Date                                     1710 non-null datetime64[ns]\n",
      "temperature                              1710 non-null object\n",
      "precip_intensity                         1710 non-null object\n",
      "precip_probability                       1710 non-null object\n",
      "wind_speed                               1710 non-null object\n",
      "humidity                                 1710 non-null object\n",
      "mean_temperature                         1710 non-null object\n",
      "temperature6am                           1710 non-null object\n",
      "temperatureNoon                          1710 non-null object\n",
      "temperature6pm                           1710 non-null object\n",
      "mean_precip_chance                       1710 non-null object\n",
      "cumulative_rain                          1710 non-null object\n",
      "mean_wind_speed                          1710 non-null object\n",
      "mean_humidity                            1710 non-null object\n",
      "Year                                     1710 non-null int64\n",
      "Month                                    1710 non-null int64\n",
      "Day                                      1710 non-null int64\n",
      "summary_Clear                            1710 non-null uint8\n",
      "summary_Drizzle                          1710 non-null uint8\n",
      "summary_Foggy                            1710 non-null uint8\n",
      "summary_Heavy Rain                       1710 non-null uint8\n",
      "summary_Heavy Snow                       1710 non-null uint8\n",
      "summary_Humid                            1710 non-null uint8\n",
      "summary_Humid and Mostly Cloudy          1710 non-null uint8\n",
      "summary_Humid and Overcast               1710 non-null uint8\n",
      "summary_Humid and Partly Cloudy          1710 non-null uint8\n",
      "summary_Light Rain                       1710 non-null uint8\n",
      "summary_Light Rain and Humid             1710 non-null uint8\n",
      "summary_Light Snow                       1710 non-null uint8\n",
      "summary_Mostly Cloudy                    1710 non-null uint8\n",
      "summary_Overcast                         1710 non-null uint8\n",
      "summary_Partly Cloudy                    1710 non-null uint8\n",
      "summary_Possible Drizzle                 1710 non-null uint8\n",
      "summary_Possible Drizzle and Humid       1710 non-null uint8\n",
      "summary_Possible Flurries                1710 non-null uint8\n",
      "summary_Possible Light Rain              1710 non-null uint8\n",
      "summary_Possible Light Rain and Humid    1710 non-null uint8\n",
      "summary_Possible Light Snow              1710 non-null uint8\n",
      "summary_Rain                             1710 non-null uint8\n",
      "summary_Rain and Humid                   1710 non-null uint8\n",
      "summary_Rain and Windy                   1710 non-null uint8\n",
      "summary_Snow                             1710 non-null uint8\n",
      "summary_Windy                            1710 non-null uint8\n",
      "dtypes: datetime64[ns](1), int64(3), object(13), uint8(26)\n",
      "memory usage: 283.9+ KB\n"
     ]
    }
   ],
   "source": [
    "PhillyWeatherPickle.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <td>3025.0</td>\n",
       "      <td>2015-06-30 23:49:00</td>\n",
       "      <td>Indego30</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-75.1875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3022.0</td>\n",
       "      <td>2015-06-30 23:33:00</td>\n",
       "      <td>4150097</td>\n",
       "      <td>One Way</td>\n",
       "      <td>2015-06-30</td>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119556</th>\n",
       "      <td>3689</td>\n",
       "      <td>NaN</td>\n",
       "      <td>420</td>\n",
       "      <td>39.93750</td>\n",
       "      <td>-75.1875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3045.0</td>\n",
       "      <td>2015-06-30 23:48:00</td>\n",
       "      <td>Indego30</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-75.1875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3023.0</td>\n",
       "      <td>2015-06-30 23:41:00</td>\n",
       "      <td>4150099</td>\n",
       "      <td>One Way</td>\n",
       "      <td>2015-06-30</td>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119557</th>\n",
       "      <td>3709</td>\n",
       "      <td>NaN</td>\n",
       "      <td>480</td>\n",
       "      <td>39.93750</td>\n",
       "      <td>-75.1875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3045.0</td>\n",
       "      <td>2015-06-30 23:56:00</td>\n",
       "      <td>Indego30</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-75.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3026.0</td>\n",
       "      <td>2015-06-30 23:48:00</td>\n",
       "      <td>4150100</td>\n",
       "      <td>One Way</td>\n",
       "      <td>2015-06-30</td>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119558</th>\n",
       "      <td>3701</td>\n",
       "      <td>NaN</td>\n",
       "      <td>240</td>\n",
       "      <td>39.93750</td>\n",
       "      <td>-75.1875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3063.0</td>\n",
       "      <td>2015-06-30 23:53:00</td>\n",
       "      <td>Walk-up</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-75.1875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3063.0</td>\n",
       "      <td>2015-06-30 23:49:00</td>\n",
       "      <td>4150101</td>\n",
       "      <td>Round Trip</td>\n",
       "      <td>2015-06-30</td>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3503654 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       bike_id bike_type  duration   end_lat  end_lon  end_station  \\\n",
       "0        11908  standard        30  39.93750 -75.1875       3063.0   \n",
       "1        05382  standard         7  39.93750 -75.1875       3064.0   \n",
       "2        11930  standard         6  39.93750 -75.1875       3064.0   \n",
       "3        05225  standard         6  39.93750 -75.1875       3007.0   \n",
       "4        03379  standard         6  39.96875 -75.1875       3035.0   \n",
       "...        ...       ...       ...       ...      ...          ...   \n",
       "119554    2523       NaN       480  39.93750 -75.1250          NaN   \n",
       "119555    2629       NaN       960  39.93750 -75.1875          NaN   \n",
       "119556    3689       NaN       420  39.93750 -75.1875          NaN   \n",
       "119557    3709       NaN       480  39.93750 -75.1875          NaN   \n",
       "119558    3701       NaN       240  39.93750 -75.1875          NaN   \n",
       "\n",
       "        end_station_id            end_time passholder_type  plan_duration  \\\n",
       "0                  NaN 2018-10-01 00:33:00        Indego30           30.0   \n",
       "1                  NaN 2018-10-01 00:13:00        Indego30           30.0   \n",
       "2                  NaN 2018-10-01 00:13:00        Indego30           30.0   \n",
       "3                  NaN 2018-10-01 00:20:00        Indego30           30.0   \n",
       "4                  NaN 2018-10-01 00:23:00        Indego30           30.0   \n",
       "...                ...                 ...             ...            ...   \n",
       "119554          3028.0 2015-06-30 23:33:00        Indego30           30.0   \n",
       "119555          3025.0 2015-06-30 23:49:00        Indego30           30.0   \n",
       "119556          3045.0 2015-06-30 23:48:00        Indego30           30.0   \n",
       "119557          3045.0 2015-06-30 23:56:00        Indego30           30.0   \n",
       "119558          3063.0 2015-06-30 23:53:00         Walk-up            0.0   \n",
       "\n",
       "        ...  start_lon  start_station  start_station_id          start_time  \\\n",
       "0       ...   -75.1875         3038.0               NaN 2018-10-01 00:03:00   \n",
       "1       ...   -75.1875         3032.0               NaN 2018-10-01 00:06:00   \n",
       "2       ...   -75.1875         3032.0               NaN 2018-10-01 00:07:00   \n",
       "3       ...   -75.1875         3025.0               NaN 2018-10-01 00:14:00   \n",
       "4       ...   -75.1875         3029.0               NaN 2018-10-01 00:17:00   \n",
       "...     ...        ...            ...               ...                 ...   \n",
       "119554  ...   -75.1250            NaN            3026.0 2015-06-30 23:25:00   \n",
       "119555  ...   -75.1875            NaN            3022.0 2015-06-30 23:33:00   \n",
       "119556  ...   -75.1875            NaN            3023.0 2015-06-30 23:41:00   \n",
       "119557  ...   -75.1250            NaN            3026.0 2015-06-30 23:48:00   \n",
       "119558  ...   -75.1875            NaN            3063.0 2015-06-30 23:49:00   \n",
       "\n",
       "          trip_id  trip_route_category        Date  Year  Month  Day  \n",
       "0       299955254              One Way  2018-10-01  2018     10    0  \n",
       "1       299955253              One Way  2018-10-01  2018     10    0  \n",
       "2       299955252              One Way  2018-10-01  2018     10    0  \n",
       "3       299955251              One Way  2018-10-01  2018     10    0  \n",
       "4       299955250              One Way  2018-10-01  2018     10    0  \n",
       "...           ...                  ...         ...   ...    ...  ...  \n",
       "119554    4150090              One Way  2015-06-30  2015      6    1  \n",
       "119555    4150097              One Way  2015-06-30  2015      6    1  \n",
       "119556    4150099              One Way  2015-06-30  2015      6    1  \n",
       "119557    4150100              One Way  2015-06-30  2015      6    1  \n",
       "119558    4150101           Round Trip  2015-06-30  2015      6    1  \n",
       "\n",
       "[3503654 rows x 21 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pickle_off_bike = open(\"Bikes.pickle\",\"rb\")\n",
    "BikesPickle = pickle.load(pickle_off_bike)\n",
    "BikesPickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get a count of the number of rides per day (based on the date during the end time of the ride)\n",
    "\n",
    "Daily_count = BikesPickle.groupby('Date')[['end_time']].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead 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>end_time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-04-23</th>\n",
       "      <td>398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-24</th>\n",
       "      <td>723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-25</th>\n",
       "      <td>1108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-26</th>\n",
       "      <td>1277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-27</th>\n",
       "      <td>809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-27</th>\n",
       "      <td>1318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-28</th>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29</th>\n",
       "      <td>543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30</th>\n",
       "      <td>577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>1208</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1712 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            end_time\n",
       "Date                \n",
       "2015-04-23       398\n",
       "2015-04-24       723\n",
       "2015-04-25      1108\n",
       "2015-04-26      1277\n",
       "2015-04-27       809\n",
       "...              ...\n",
       "2019-12-27      1318\n",
       "2019-12-28      1000\n",
       "2019-12-29       543\n",
       "2019-12-30       577\n",
       "2019-12-31      1208\n",
       "\n",
       "[1712 rows x 1 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Daily_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge this count dataframe with our weather dataframe \n",
    "\n",
    "PhillyWeatherCount = pd.merge(PhillyWeatherPickle, Daily_count, how = 'inner', \n",
    "                              left_on = PhillyWeatherPickle['Date'].dt.date, right_on = 'Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "PhillyWeatherCount = PhillyWeatherCount.rename(columns = {'end_time' : 'Count'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1710 entries, 0 to 1709\n",
      "Data columns (total 44 columns):\n",
      " #   Column                                 Non-Null Count  Dtype         \n",
      "---  ------                                 --------------  -----         \n",
      " 0   Date                                   1710 non-null   datetime64[ns]\n",
      " 1   temperature                            1710 non-null   object        \n",
      " 2   precip_intensity                       1710 non-null   object        \n",
      " 3   precip_probability                     1710 non-null   object        \n",
      " 4   wind_speed                             1710 non-null   object        \n",
      " 5   humidity                               1710 non-null   object        \n",
      " 6   mean_temperature                       1710 non-null   object        \n",
      " 7   temperature6am                         1710 non-null   object        \n",
      " 8   temperatureNoon                        1710 non-null   object        \n",
      " 9   temperature6pm                         1710 non-null   object        \n",
      " 10  mean_precip_chance                     1710 non-null   object        \n",
      " 11  cumulative_rain                        1710 non-null   object        \n",
      " 12  mean_wind_speed                        1710 non-null   object        \n",
      " 13  mean_humidity                          1710 non-null   object        \n",
      " 14  Year                                   1710 non-null   int64         \n",
      " 15  Month                                  1710 non-null   int64         \n",
      " 16  Day                                    1710 non-null   int64         \n",
      " 17  summary_Clear                          1710 non-null   uint8         \n",
      " 18  summary_Drizzle                        1710 non-null   uint8         \n",
      " 19  summary_Foggy                          1710 non-null   uint8         \n",
      " 20  summary_Heavy Rain                     1710 non-null   uint8         \n",
      " 21  summary_Heavy Snow                     1710 non-null   uint8         \n",
      " 22  summary_Humid                          1710 non-null   uint8         \n",
      " 23  summary_Humid and Mostly Cloudy        1710 non-null   uint8         \n",
      " 24  summary_Humid and Overcast             1710 non-null   uint8         \n",
      " 25  summary_Humid and Partly Cloudy        1710 non-null   uint8         \n",
      " 26  summary_Light Rain                     1710 non-null   uint8         \n",
      " 27  summary_Light Rain and Humid           1710 non-null   uint8         \n",
      " 28  summary_Light Snow                     1710 non-null   uint8         \n",
      " 29  summary_Mostly Cloudy                  1710 non-null   uint8         \n",
      " 30  summary_Overcast                       1710 non-null   uint8         \n",
      " 31  summary_Partly Cloudy                  1710 non-null   uint8         \n",
      " 32  summary_Possible Drizzle               1710 non-null   uint8         \n",
      " 33  summary_Possible Drizzle and Humid     1710 non-null   uint8         \n",
      " 34  summary_Possible Flurries              1710 non-null   uint8         \n",
      " 35  summary_Possible Light Rain            1710 non-null   uint8         \n",
      " 36  summary_Possible Light Rain and Humid  1710 non-null   uint8         \n",
      " 37  summary_Possible Light Snow            1710 non-null   uint8         \n",
      " 38  summary_Rain                           1710 non-null   uint8         \n",
      " 39  summary_Rain and Humid                 1710 non-null   uint8         \n",
      " 40  summary_Rain and Windy                 1710 non-null   uint8         \n",
      " 41  summary_Snow                           1710 non-null   uint8         \n",
      " 42  summary_Windy                          1710 non-null   uint8         \n",
      " 43  Count                                  1710 non-null   int64         \n",
      "dtypes: datetime64[ns](1), int64(4), object(13), uint8(26)\n",
      "memory usage: 297.2+ KB\n"
     ]
    }
   ],
   "source": [
    "PhillyWeatherCount.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>temperature</th>\n",
       "      <th>precip_intensity</th>\n",
       "      <th>precip_probability</th>\n",
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       "      <th>humidity</th>\n",
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       "      <th>temperatureNoon</th>\n",
       "      <th>temperature6pm</th>\n",
       "      <th>...</th>\n",
       "      <th>summary_Possible Flurries</th>\n",
       "      <th>summary_Possible Light Rain</th>\n",
       "      <th>summary_Possible Light Rain and Humid</th>\n",
       "      <th>summary_Possible Light Snow</th>\n",
       "      <th>summary_Rain</th>\n",
       "      <th>summary_Rain and Humid</th>\n",
       "      <th>summary_Rain and Windy</th>\n",
       "      <th>summary_Snow</th>\n",
       "      <th>summary_Windy</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>63.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.81</td>\n",
       "      <td>70.4629</td>\n",
       "      <td>61.41</td>\n",
       "      <td>76.3</td>\n",
       "      <td>79.39</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-10-02</td>\n",
       "      <td>68.41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.32</td>\n",
       "      <td>0.92</td>\n",
       "      <td>70.3371</td>\n",
       "      <td>66.77</td>\n",
       "      <td>78.05</td>\n",
       "      <td>80.33</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-10-03</td>\n",
       "      <td>65.33</td>\n",
       "      <td>0.0686</td>\n",
       "      <td>0.92</td>\n",
       "      <td>3.72</td>\n",
       "      <td>0.97</td>\n",
       "      <td>69.0888</td>\n",
       "      <td>64.79</td>\n",
       "      <td>71.29</td>\n",
       "      <td>77.14</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-10-04</td>\n",
       "      <td>65.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.79</td>\n",
       "      <td>0.77</td>\n",
       "      <td>71.2529</td>\n",
       "      <td>62.8</td>\n",
       "      <td>72.78</td>\n",
       "      <td>82.43</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-10-05</td>\n",
       "      <td>73.57</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.02</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>66.0721</td>\n",
       "      <td>64.93</td>\n",
       "      <td>63.89</td>\n",
       "      <td>67.52</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2643</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date temperature precip_intensity precip_probability wind_speed  \\\n",
       "0 2018-10-01        63.2                0                  0       1.59   \n",
       "1 2018-10-02       68.41                0                  0       2.32   \n",
       "2 2018-10-03       65.33           0.0686               0.92       3.72   \n",
       "3 2018-10-04       65.01                0                  0       1.79   \n",
       "4 2018-10-05       73.57           0.0002               0.02        2.6   \n",
       "\n",
       "  humidity mean_temperature temperature6am temperatureNoon temperature6pm  \\\n",
       "0     0.81          70.4629          61.41            76.3          79.39   \n",
       "1     0.92          70.3371          66.77           78.05          80.33   \n",
       "2     0.97          69.0888          64.79           71.29          77.14   \n",
       "3     0.77          71.2529           62.8           72.78          82.43   \n",
       "4      0.9          66.0721          64.93           63.89          67.52   \n",
       "\n",
       "   ... summary_Possible Flurries summary_Possible Light Rain  \\\n",
       "0  ...                         0                           0   \n",
       "1  ...                         0                           0   \n",
       "2  ...                         0                           0   \n",
       "3  ...                         0                           0   \n",
       "4  ...                         0                           0   \n",
       "\n",
       "  summary_Possible Light Rain and Humid summary_Possible Light Snow  \\\n",
       "0                                     0                           0   \n",
       "1                                     0                           0   \n",
       "2                                     0                           0   \n",
       "3                                     0                           0   \n",
       "4                                     0                           0   \n",
       "\n",
       "   summary_Rain  summary_Rain and Humid  summary_Rain and Windy  summary_Snow  \\\n",
       "0             0                       0                       0             0   \n",
       "1             0                       0                       0             0   \n",
       "2             1                       0                       0             0   \n",
       "3             0                       0                       0             0   \n",
       "4             0                       0                       0             0   \n",
       "\n",
       "   summary_Windy  Count  \n",
       "0              0   2784  \n",
       "1              0   2794  \n",
       "2              0   2811  \n",
       "3              0   2478  \n",
       "4              0   2643  \n",
       "\n",
       "[5 rows x 44 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PhillyWeatherCount.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "m , b = np.polyfit(PhillyWeatherCount['mean_temperature'].astype(float).values , PhillyWeatherCount['Count'].astype(float).values , 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x129331590>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Very clear positive trend between temperature and number of rides even if there is a fairly siginificant variance to the data\n",
    "\n",
    "plt.scatter(PhillyWeatherCount['mean_temperature'], PhillyWeatherCount['Count'])\n",
    "\n",
    "plt.plot(PhillyWeatherCount['mean_temperature'], PhillyWeatherCount['mean_temperature'] * m + b, color = 'y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45.78245252011727"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for every one degree increase we're looking at an average increase of 46 rides \n",
    "\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "quick_linear_model = linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quick_linear_model.fit(PhillyWeatherCount['mean_temperature'].astype(float).values.reshape(-1,1) ,\n",
    "                       PhillyWeatherCount['Count'].astype(float).values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4710762637175355"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Like mentioned earlier there is variability in the data leading to a low r^2 value but the trend is still visible none the less \n",
    "\n",
    "quick_linear_model.score(PhillyWeatherCount['mean_temperature'].astype(float).values.reshape(-1,1) , \n",
    "                         PhillyWeatherCount['Count'].astype(float).values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Observation : all days exceeding count of 5000 were 60 degrees or warmer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x129bdbc50>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# stronger correlation for chance of rain than actual amount of rain\n",
    "# (large spread in data for days with no rain, seeing as with the absence of rain many other factors play a larger role)\n",
    "\n",
    "plt.scatter(PhillyWeatherCount['cumulative_rain'], PhillyWeatherCount['Count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x129e3e850>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(PhillyWeatherCount['mean_precip_chance'], PhillyWeatherCount['Count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x12a3e1410>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Virtually no correlation between wind speeds and number of rides \n",
    "\n",
    "plt.scatter(PhillyWeatherCount['mean_wind_speed'], PhillyWeatherCount['Count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x12a3e1750>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# no correlation between humidity and number of rides \n",
    "\n",
    "plt.scatter(PhillyWeatherCount['mean_humidity'], PhillyWeatherCount['Count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1710 entries, 0 to 1709\n",
      "Data columns (total 44 columns):\n",
      " #   Column                                 Non-Null Count  Dtype         \n",
      "---  ------                                 --------------  -----         \n",
      " 0   Date                                   1710 non-null   datetime64[ns]\n",
      " 1   temperature                            1710 non-null   object        \n",
      " 2   precip_intensity                       1710 non-null   object        \n",
      " 3   precip_probability                     1710 non-null   object        \n",
      " 4   wind_speed                             1710 non-null   object        \n",
      " 5   humidity                               1710 non-null   object        \n",
      " 6   mean_temperature                       1710 non-null   object        \n",
      " 7   temperature6am                         1710 non-null   object        \n",
      " 8   temperatureNoon                        1710 non-null   object        \n",
      " 9   temperature6pm                         1710 non-null   object        \n",
      " 10  mean_precip_chance                     1710 non-null   object        \n",
      " 11  cumulative_rain                        1710 non-null   object        \n",
      " 12  mean_wind_speed                        1710 non-null   object        \n",
      " 13  mean_humidity                          1710 non-null   object        \n",
      " 14  Year                                   1710 non-null   int64         \n",
      " 15  Month                                  1710 non-null   int64         \n",
      " 16  Day                                    1710 non-null   int64         \n",
      " 17  summary_Clear                          1710 non-null   uint8         \n",
      " 18  summary_Drizzle                        1710 non-null   uint8         \n",
      " 19  summary_Foggy                          1710 non-null   uint8         \n",
      " 20  summary_Heavy Rain                     1710 non-null   uint8         \n",
      " 21  summary_Heavy Snow                     1710 non-null   uint8         \n",
      " 22  summary_Humid                          1710 non-null   uint8         \n",
      " 23  summary_Humid and Mostly Cloudy        1710 non-null   uint8         \n",
      " 24  summary_Humid and Overcast             1710 non-null   uint8         \n",
      " 25  summary_Humid and Partly Cloudy        1710 non-null   uint8         \n",
      " 26  summary_Light Rain                     1710 non-null   uint8         \n",
      " 27  summary_Light Rain and Humid           1710 non-null   uint8         \n",
      " 28  summary_Light Snow                     1710 non-null   uint8         \n",
      " 29  summary_Mostly Cloudy                  1710 non-null   uint8         \n",
      " 30  summary_Overcast                       1710 non-null   uint8         \n",
      " 31  summary_Partly Cloudy                  1710 non-null   uint8         \n",
      " 32  summary_Possible Drizzle               1710 non-null   uint8         \n",
      " 33  summary_Possible Drizzle and Humid     1710 non-null   uint8         \n",
      " 34  summary_Possible Flurries              1710 non-null   uint8         \n",
      " 35  summary_Possible Light Rain            1710 non-null   uint8         \n",
      " 36  summary_Possible Light Rain and Humid  1710 non-null   uint8         \n",
      " 37  summary_Possible Light Snow            1710 non-null   uint8         \n",
      " 38  summary_Rain                           1710 non-null   uint8         \n",
      " 39  summary_Rain and Humid                 1710 non-null   uint8         \n",
      " 40  summary_Rain and Windy                 1710 non-null   uint8         \n",
      " 41  summary_Snow                           1710 non-null   uint8         \n",
      " 42  summary_Windy                          1710 non-null   uint8         \n",
      " 43  Count                                  1710 non-null   int64         \n",
      "dtypes: datetime64[ns](1), int64(4), object(13), uint8(26)\n",
      "memory usage: 297.2+ KB\n"
     ]
    }
   ],
   "source": [
    "PhillyWeatherCount.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "us_holidays = holidays.UnitedStates()\n",
    "\n",
    "PhillyWeatherCount['Holiday'] = PhillyWeatherCount.apply(lambda x: 1 if x.Date in us_holidays else 0, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 1710 artists>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fairly constant number of rides throughout week with slightly greater amount on Saturdays\n",
    "\n",
    "plt.bar(PhillyWeatherCount['Day'], PhillyWeatherCount['Count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 1710 artists>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# July, August and September clearly have greatest amounts of rides \n",
    "\n",
    "plt.bar(PhillyWeatherCount['Month'], PhillyWeatherCount['Count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 1710 artists>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Strong growth from 2015 to 2016 but struggles over the span from 2017-2019\n",
    "\n",
    "plt.bar(PhillyWeatherCount['Year'], PhillyWeatherCount['Count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 1710 artists>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD4CAYAAAAAczaOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAUfUlEQVR4nO3dcayd9X3f8fenuJAt7WI7uB6zWUxUrxGZFkItoGnULaE1hlQx01JG1C4u8+R2Y1WjTVph+YMNikY6abSoKxMCNybrIJQuwmtY6Y1JVE0ZhEtDSIAQXwgIe4BvsUOXotKSfvfH+d3k4Nzrey4+91x7v/dLOjq/5/v8znO+z8Plc859znOuU1VIkvrwfSvdgCRpcgx9SeqIoS9JHTH0Jakjhr4kdWTVSjdwLKeffnpt2rRppduQpJPKww8//CdVtW6+dSd06G/atInp6emVbkOSTipJnl1onad3JKkjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpIyf0N3KP16arPrPSLegE9cwNH1jpFqQV4Tt9SeqIoS9JHTH0Jakjhr4kdWTR0E/yI0keGbr9aZKPJlmbZCrJ/na/ps1PkpuSzCR5NMm5Q9va0ebvT7JjOXdMkvS9Fg39qnqyqs6pqnOAHwVeAT4NXAXsq6rNwL62DHAxsLnddgE3AyRZC1wDnA+cB1wz90IhSZqMpZ7euRB4qqqeBbYDe1p9D3BpG28Hbq+BB4DVSc4ALgKmqupwVR0BpoBtx70HkqSRLTX0LwfuaOP1VfV8G78ArG/jDcBzQ4850GoL1SVJEzJy6Cc5Ffgg8LtHr6uqAmocDSXZlWQ6yfTs7Ow4NilJapbyTv9i4I+r6sW2/GI7bUO7P9TqB4Ezhx63sdUWqr9OVd1SVVuqasu6dfP+u76SpDdoKaH/Yb57agdgLzB3Bc4O4J6h+kfaVTwXAC+300D3AVuTrGkf4G5tNUnShIz0t3eSvBn4KeAXhso3AHcl2Qk8C1zW6vcClwAzDK70uQKgqg4nuQ54qM27tqoOH/ceSJJGNlLoV9WfAW89qvYSg6t5jp5bwJULbGc3sHvpbUqSxsFv5EpSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSMjhX6S1UnuTvK1JE8k+bEka5NMJdnf7te0uUlyU5KZJI8mOXdoOzva/P1JdizXTkmS5jfqO/3fAP6gqt4BvAt4ArgK2FdVm4F9bRngYmBzu+0CbgZIsha4BjgfOA+4Zu6FQpI0GYuGfpK3AD8B3AZQVX9RVd8EtgN72rQ9wKVtvB24vQYeAFYnOQO4CJiqqsNVdQSYAraNdW8kScc0yjv9s4BZ4LeTfCnJrUneDKyvqufbnBeA9W28AXhu6PEHWm2h+usk2ZVkOsn07Ozs0vZGknRMo4T+KuBc4OaqejfwZ3z3VA4AVVVAjaOhqrqlqrZU1ZZ169aNY5OSpGaU0D8AHKiqB9vy3QxeBF5sp21o94fa+oPAmUOP39hqC9UlSROyaOhX1QvAc0l+pJUuBB4H9gJzV+DsAO5p473AR9pVPBcAL7fTQPcBW5OsaR/gbm01SdKErBpx3i8Bv5PkVOBp4AoGLxh3JdkJPAtc1ubeC1wCzACvtLlU1eEk1wEPtXnXVtXhseyFJGkkI4V+VT0CbJln1YXzzC3gygW2sxvYvZQGJUnj4zdyJakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI6MFPpJnknylSSPJJlutbVJppLsb/drWj1Jbkoyk+TRJOcObWdHm78/yY7l2SVJ0kKW8k7/fVV1TlXN/QPpVwH7qmozsK8tA1wMbG63XcDNMHiRAK4BzgfOA66Ze6GQJE3G8Zze2Q7saeM9wKVD9dtr4AFgdZIzgIuAqao6XFVHgClg23E8vyRpiUYN/QL+MMnDSXa12vqqer6NXwDWt/EG4Lmhxx5otYXqr5NkV5LpJNOzs7MjtidJGsWqEee9t6oOJvkhYCrJ14ZXVlUlqXE0VFW3ALcAbNmyZSzblCQNjPROv6oOtvtDwKcZnJN/sZ22od0fatMPAmcOPXxjqy1UlyRNyKKhn+TNSX5wbgxsBb4K7AXmrsDZAdzTxnuBj7SreC4AXm6nge4DtiZZ0z7A3dpqkqQJGeX0znrg00nm5v+3qvqDJA8BdyXZCTwLXNbm3wtcAswArwBXAFTV4STXAQ+1eddW1eGx7Yl0Etp01WdWugWdoJ654QPLst1FQ7+qngbeNU/9JeDCeeoFXLnAtnYDu5fepiRpHPxGriR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktSRkUM/ySlJvpTk99vyWUkeTDKT5FNJTm3109ryTFu/aWgbV7f6k0kuGvfOSJKObSnv9H8ZeGJo+ePAjVX1w8ARYGer7wSOtPqNbR5JzgYuB94JbAN+K8kpx9e+JGkpRgr9JBuBDwC3tuUA7wfublP2AJe28fa2TFt/YZu/Hbizql6tqm8AM8B549gJSdJoRn2n/+vAvwH+qi2/FfhmVb3Wlg8AG9p4A/AcQFv/cpv/nfo8j5EkTcCioZ/kp4FDVfXwBPohya4k00mmZ2dnJ/GUktSNUd7p/zjwwSTPAHcyOK3zG8DqJKvanI3AwTY+CJwJ0Na/BXhpuD7PY76jqm6pqi1VtWXdunVL3iFJ0sIWDf2qurqqNlbVJgYfxN5fVT8LfA74UJu2A7injfe2Zdr6+6uqWv3ydnXPWcBm4Itj2xNJ0qJWLT5lQb8C3JnkV4EvAbe1+m3AJ5PMAIcZvFBQVY8luQt4HHgNuLKqvn0czy9JWqIlhX5VfR74fBs/zTxX31TVnwM/s8DjrweuX2qTkqTx8Bu5ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqyKKhn+RNSb6Y5MtJHkvy71v9rCQPJplJ8qkkp7b6aW15pq3fNLStq1v9ySQXLddOSZLmN8o7/VeB91fVu4BzgG1JLgA+DtxYVT8MHAF2tvk7gSOtfmObR5KzgcuBdwLbgN9Kcso4d0aSdGyLhn4NfKstfn+7FfB+4O5W3wNc2sbb2zJt/YVJ0up3VtWrVfUNYAY4byx7IUkayUjn9JOckuQR4BAwBTwFfLOqXmtTDgAb2ngD8BxAW/8y8Nbh+jyPGX6uXUmmk0zPzs4ufY8kSQsaKfSr6ttVdQ6wkcG783csV0NVdUtVbamqLevWrVuup5GkLi3p6p2q+ibwOeDHgNVJVrVVG4GDbXwQOBOgrX8L8NJwfZ7HSJImYJSrd9YlWd3Gfw34KeAJBuH/oTZtB3BPG+9ty7T191dVtfrl7eqes4DNwBfHtSOSpMWtWnwKZwB72pU23wfcVVW/n+Rx4M4kvwp8Cbitzb8N+GSSGeAwgyt2qKrHktwFPA68BlxZVd8e7+5Iko5l0dCvqkeBd89Tf5p5rr6pqj8HfmaBbV0PXL/0NiVJ4+A3ciWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOLBr6Sc5M8rkkjyd5LMkvt/raJFNJ9rf7Na2eJDclmUnyaJJzh7a1o83fn2TH8u2WJGk+o7zTfw3411V1NnABcGWSs4GrgH1VtRnY15YBLgY2t9su4GYYvEgA1wDnM/gH1a+Ze6GQJE3GoqFfVc9X1R+38f8FngA2ANuBPW3aHuDSNt4O3F4DDwCrk5wBXARMVdXhqjoCTAHbxro3kqRjWtI5/SSbgHcDDwLrq+r5tuoFYH0bbwCeG3rYgVZbqH70c+xKMp1kenZ2dintSZIWMXLoJ/kB4PeAj1bVnw6vq6oCahwNVdUtVbWlqrasW7duHJuUJDUjhX6S72cQ+L9TVf+9lV9sp21o94da/SBw5tDDN7baQnVJ0oSMcvVOgNuAJ6rqPw2t2gvMXYGzA7hnqP6RdhXPBcDL7TTQfcDWJGvaB7hbW02SNCGrRpjz48A/Ab6S5JFW+7fADcBdSXYCzwKXtXX3ApcAM8ArwBUAVXU4yXXAQ23etVV1eCx7IUkayaKhX1X/C8gCqy+cZ34BVy6wrd3A7qU0KEkaH7+RK0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHVk0dBPsjvJoSRfHaqtTTKVZH+7X9PqSXJTkpkkjyY5d+gxO9r8/Ul2LM/uSJKOZZR3+p8Ath1VuwrYV1WbgX1tGeBiYHO77QJuhsGLBHANcD5wHnDN3AuFJGlyFg39qvoj4PBR5e3AnjbeA1w6VL+9Bh4AVic5A7gImKqqw1V1BJjie19IJEnL7I2e019fVc+38QvA+jbeADw3NO9Aqy1U/x5JdiWZTjI9Ozv7BtuTJM3nuD/IraoCagy9zG3vlqraUlVb1q1bN67NSpJ446H/YjttQ7s/1OoHgTOH5m1stYXqkqQJeqOhvxeYuwJnB3DPUP0j7SqeC4CX22mg+4CtSda0D3C3tpokaYJWLTYhyR3APwBOT3KAwVU4NwB3JdkJPAtc1qbfC1wCzACvAFcAVNXhJNcBD7V511bV0R8OS5KW2aKhX1UfXmDVhfPMLeDKBbazG9i9pO4kSWPlN3IlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktSRiYd+km1Jnkwyk+SqST+/JPVsoqGf5BTgPwMXA2cDH05y9iR7kKSeTfqd/nnATFU9XVV/AdwJbJ9wD5LUrVUTfr4NwHNDyweA84cnJNkF7GqL30ry5IR6e6NOB/5kpZsYgX0OycfHshmP6XidLH3CBHo9zp/Rty20YtKhv6iqugW4ZaX7GFWS6arastJ9LMY+x+9k6dU+x+9k6vVokz69cxA4c2h5Y6tJkiZg0qH/ELA5yVlJTgUuB/ZOuAdJ6tZET+9U1WtJ/iVwH3AKsLuqHptkD8vgZDkVZZ/jd7L0ap/jdzL1+jqpqpXuQZI0IX4jV5I6YuhLUkcM/REkWZtkKsn+dr9mnjnnJPnfSR5L8miSfzy07hNJvpHkkXY7Z8z9HfNPWyQ5Lcmn2voHk2waWnd1qz+Z5KJx9vUG+vxXSR5vx29fkrcNrfv20PFb1g//R+jz55PMDvXzz4bW7Wg/J/uT7FjhPm8c6vHrSb45tG6Sx3N3kkNJvrrA+iS5qe3Ho0nOHVo3yeO5WJ8/2/r7SpIvJHnX0LpnWv2RJNPL2edxqypvi9yAXwOuauOrgI/PM+fvAJvb+G8BzwOr2/IngA8tU2+nAE8BbwdOBb4MnH3UnH8B/Jc2vhz4VBuf3eafBpzVtnPKCvb5PuCvt/E/n+uzLX9rQv+tR+nz54HfnOexa4Gn2/2aNl6zUn0eNf+XGFw4MdHj2Z7rJ4Bzga8usP4S4H8CAS4AHpz08Ryxz/fMPT+DPyXz4NC6Z4DTJ3VMj+fmO/3RbAf2tPEe4NKjJ1TV16tqfxv/H+AQsG4CvY3ypy2G+78buDBJWv3Oqnq1qr4BzLTtrUifVfW5qnqlLT7A4Hsck3Y8fyrkImCqqg5X1RFgCth2gvT5YeCOZerlmKrqj4DDx5iyHbi9Bh4AVic5g8kez0X7rKovtD5g5X4+j5uhP5r1VfV8G78ArD/W5CTnMXj39dRQ+fr2q+GNSU4bY2/z/WmLDQvNqarXgJeBt4742En2OWwng3d/c96UZDrJA0m+50V3jEbt8x+1/553J5n7wuEJeTzbabKzgPuHypM6nqNYaF8meTyX6uifzwL+MMnD7U/JnLBOuD/DsFKSfBb4m/Os+tjwQlVVkgWvc23vUD4J7Kiqv2rlqxm8WJzK4PreXwGuHUff/z9K8nPAFuDvD5XfVlUHk7wduD/JV6rqqfm3sOz+B3BHVb2a5BcY/Bb1/hXqZRSXA3dX1beHaifS8TypJHkfg9B/71D5ve14/hAwleRr7TeHE47v9Juq+smq+rvz3O4BXmxhPhfqh+bbRpK/AXwG+Fj7NXVu28+3X11fBX6b8Z5CGeVPW3xnTpJVwFuAl0Z87CT7JMlPMnih/WA7XgBU1cF2/zTweeDdK9VnVb001NutwI+O+thJ9jnkco46tTPB4zmKhfblhPuzLUn+HoP/5tur6qW5+tDxPAR8muU7TXr8VvpDhZPhBvxHXv9B7q/NM+dUYB/w0XnWndHuA/w6cMMYe1vF4AOus/juB3rvPGrOlbz+g9y72vidvP6D3KdZvg9yR+nz3QxOiW0+qr4GOK2NTwf2c4wPLSfQ5xlD438IPNDGa4FvtH7XtPHaleqzzXsHgw8ZsxLHc+g5N7HwB6Qf4PUf5H5x0sdzxD7/NoPPvd5zVP3NwA8Ojb8AbFvOPo9rH1e6gZPhxuD89772P8dn537wGJyCuLWNfw74S+CRods5bd39wFeArwL/FfiBMfd3CfD1Fpgfa7VrGbxbBngT8LvtB/aLwNuHHvux9rgngYuX+Tgu1udngReHjt/eVn9PO35fbvc7V7jP/wA81vr5HPCOocf+03acZ4ArVrLPtvzvOOpNxgoczzsYXM32lwzOy+8EfhH4xbY+DP5xpadaP1tW6Hgu1uetwJGhn8/pVn97O5Zfbj8XH1vOPo/35p9hkKSOeE5fkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SO/D8C2FjGVzgvWAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(PhillyWeatherCount['Holiday'], PhillyWeatherCount['Count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PhillyWeatherCount['Holiday'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Indego30, Walk-up, Day Pass, Indego365, IndegoFlex, One Day Pass, Two Day Pass, NaN]\n",
       "Categories (7, object): [Indego30, Walk-up, Day Pass, Indego365, IndegoFlex, One Day Pass, Two Day Pass]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BikesPickle['passholder_type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>passholder_type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>46543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>35663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     passholder_type\n",
       "Day                 \n",
       "0              18086\n",
       "1              14983\n",
       "2              15737\n",
       "3              16162\n",
       "4              21530\n",
       "5              46543\n",
       "6              35663"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of day passes purchased is much greater on weekends\n",
    "\n",
    "BikesPickle[BikesPickle['passholder_type'] == 'Day Pass'].groupby('Day')[['passholder_type']].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>passholder_type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>403885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>441231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>451070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>431428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>416693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>283572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>261776</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     passholder_type\n",
       "Day                 \n",
       "0             403885\n",
       "1             441231\n",
       "2             451070\n",
       "3             431428\n",
       "4             416693\n",
       "5             283572\n",
       "6             261776"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of rides for subscription holders is much greater during the week \n",
    "\n",
    "BikesPickle[BikesPickle['passholder_type'] == 'Indego30'].groupby('Day')[['passholder_type']].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>passholder_type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>38247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>22890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20573</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     passholder_type\n",
       "Day                 \n",
       "0              38247\n",
       "1              42902\n",
       "2              42990\n",
       "3              41104\n",
       "4              38970\n",
       "5              22890\n",
       "6              20573"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BikesPickle[BikesPickle['passholder_type'] == 'Indego365'].groupby('Day')[['passholder_type']].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bike_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2679</th>\n",
       "      <td>3580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2706</th>\n",
       "      <td>3370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5351</th>\n",
       "      <td>3334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6725</th>\n",
       "      <td>3282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3724</th>\n",
       "      <td>3274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3560</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03722</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14635</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11914</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3926 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       bike_id\n",
       "2679      3580\n",
       "2706      3370\n",
       "5351      3334\n",
       "6725      3282\n",
       "3724      3274\n",
       "...        ...\n",
       "3560         1\n",
       "03722        1\n",
       "14635        1\n",
       "11914        1\n",
       "0            1\n",
       "\n",
       "[3926 rows x 1 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking at the number of rides per bike could be beneficial to help see when bikes are on the verge of being to worn and damaged to use\n",
    "\n",
    "pd.DataFrame(BikesPickle.bike_id.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015-07-20\n"
     ]
    }
   ],
   "source": [
    "print(BikesPickle.groupby('bike_id')['Date'].get_group('2679').min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5956.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BikesPickle.groupby('bike_id')['duration'].get_group('3690').sum()/60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bike introduction date 2015-04-25\n",
      "Total Trips 3186\n",
      "Trips in 2015 379\n",
      "Trips in 2016 1093\n",
      "Trips in 2017 757\n",
      "Trips in 2018 353\n",
      "Trips in 2019 604\n",
      "Total hours of usage 5956.0\n"
     ]
    }
   ],
   "source": [
    "# simple function can be used to get the yearly break down of rides by bike ID along with total hourly usage \n",
    "\n",
    "def BikeUsageReport(ID):\n",
    "    print(\"Bike introduction date {}\".format(BikesPickle.groupby('bike_id')['Date'].get_group(ID).min()))\n",
    "    print(\"Total Trips {}\".format(BikesPickle[BikesPickle['bike_id'] ==  ID].bike_id.count()))\n",
    "    print(\"Trips in 2015 {}\".format(BikesPickle[(BikesPickle['bike_id'] ==  ID) & (BikesPickle['Year'] == 2015)].bike_id.count()))\n",
    "    print(\"Trips in 2016 {}\".format(BikesPickle[(BikesPickle['bike_id'] ==  ID) & (BikesPickle['Year'] == 2016)].bike_id.count()))\n",
    "    print(\"Trips in 2017 {}\".format(BikesPickle[(BikesPickle['bike_id'] ==  ID) & (BikesPickle['Year'] == 2017)].bike_id.count()))\n",
    "    print(\"Trips in 2018 {}\".format(BikesPickle[(BikesPickle['bike_id'] ==  ID) & (BikesPickle['Year'] == 2018)].bike_id.count()))\n",
    "    print(\"Trips in 2019 {}\".format(BikesPickle[(BikesPickle['bike_id'] ==  ID) & (BikesPickle['Year'] == 2019)].bike_id.count()))\n",
    "    print(\"Total hours of usage {}\".format(BikesPickle.groupby('bike_id')['duration'].get_group(str(ID)).sum()/60))\n",
    "    \n",
    "BikeUsageReport(3690)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding a column calculating the amount of time a given ride went over the alloted time could help for calculating the amount of\n",
    "# revenue brought in this way as well as what characteristics are related to rides going over the alloted time \n",
    "\n",
    "def OverDuration(duration, plan_type):\n",
    "    threshold = 0\n",
    "    if plan_type in ['One Day Pass', 'Two Day Pass', 'Day Pass' 'Walk Up']:\n",
    "        threshold += 30\n",
    "    elif plan_type in ['Indego365', 'Indego30', 'IndegoFlex']:\n",
    "        threshold += 60\n",
    "    else:\n",
    "        pass\n",
    "    if duration > threshold:\n",
    "        time_over = duration - threshold\n",
    "    else:\n",
    "        time_over = 0\n",
    "    return time_over"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "BikesPickle['Time_Over'] = BikesPickle.apply(lambda x: OverDuration(x['duration'],x['passholder_type']),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bike_id</th>\n",
       "      <th>bike_type</th>\n",
       "      <th>duration</th>\n",
       "      <th>end_lat</th>\n",
       "      <th>end_lon</th>\n",
       "      <th>end_station</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_time</th>\n",
       "      <th>passholder_type</th>\n",
       "      <th>plan_duration</th>\n",
       "      <th>...</th>\n",
       "      <th>start_station</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_time</th>\n",
       "      <th>trip_id</th>\n",
       "      <th>trip_route_category</th>\n",
       "      <th>Date</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time_Over</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11908</td>\n",
       "      <td>standard</td>\n",
       "      <td>30</td>\n",
       "      <td>39.93750</td>\n",
       "      <td>-75.1875</td>\n",
       "      <td>3063.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-10-01 00:33:00</td>\n",
       "      <td>Indego30</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3038.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-10-01 00:03:00</td>\n",
       "      <td>299955254</td>\n",
       "      <td>One Way</td>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>2018</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>05382</td>\n",
       "      <td>standard</td>\n",
       "      <td>7</td>\n",
       "      <td>39.93750</td>\n",
       "      <td>-75.1875</td>\n",
       "      <td>3064.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-10-01 00:13:00</td>\n",
       "      <td>Indego30</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3032.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-10-01 00:06:00</td>\n",
       "      <td>299955253</td>\n",
       "      <td>One Way</td>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>2018</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11930</td>\n",
       "      <td>standard</td>\n",
       "      <td>6</td>\n",
       "      <td>39.93750</td>\n",
       "      <td>-75.1875</td>\n",
       "      <td>3064.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-10-01 00:13:00</td>\n",
       "      <td>Indego30</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3032.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-10-01 00:07:00</td>\n",
       "      <td>299955252</td>\n",
       "      <td>One Way</td>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>2018</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>05225</td>\n",
       "      <td>standard</td>\n",
       "      <td>6</td>\n",
       "      <td>39.93750</td>\n",
       "      <td>-75.1875</td>\n",
       "      <td>3007.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-10-01 00:20:00</td>\n",
       "      <td>Indego30</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3025.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-10-01 00:14:00</td>\n",
       "      <td>299955251</td>\n",
       "      <td>One Way</td>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>2018</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03379</td>\n",
       "      <td>standard</td>\n",
       "      <td>6</td>\n",
       "      <td>39.96875</td>\n",
       "      <td>-75.1875</td>\n",
       "      <td>3035.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-10-01 00:23:00</td>\n",
       "      <td>Indego30</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3029.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-10-01 00:17:00</td>\n",
       "      <td>299955250</td>\n",
       "      <td>One Way</td>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>2018</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  bike_id bike_type  duration   end_lat  end_lon  end_station  end_station_id  \\\n",
       "0   11908  standard        30  39.93750 -75.1875       3063.0             NaN   \n",
       "1   05382  standard         7  39.93750 -75.1875       3064.0             NaN   \n",
       "2   11930  standard         6  39.93750 -75.1875       3064.0             NaN   \n",
       "3   05225  standard         6  39.93750 -75.1875       3007.0             NaN   \n",
       "4   03379  standard         6  39.96875 -75.1875       3035.0             NaN   \n",
       "\n",
       "             end_time passholder_type  plan_duration  ...  start_station  \\\n",
       "0 2018-10-01 00:33:00        Indego30           30.0  ...         3038.0   \n",
       "1 2018-10-01 00:13:00        Indego30           30.0  ...         3032.0   \n",
       "2 2018-10-01 00:13:00        Indego30           30.0  ...         3032.0   \n",
       "3 2018-10-01 00:20:00        Indego30           30.0  ...         3025.0   \n",
       "4 2018-10-01 00:23:00        Indego30           30.0  ...         3029.0   \n",
       "\n",
       "   start_station_id          start_time    trip_id trip_route_category  \\\n",
       "0               NaN 2018-10-01 00:03:00  299955254             One Way   \n",
       "1               NaN 2018-10-01 00:06:00  299955253             One Way   \n",
       "2               NaN 2018-10-01 00:07:00  299955252             One Way   \n",
       "3               NaN 2018-10-01 00:14:00  299955251             One Way   \n",
       "4               NaN 2018-10-01 00:17:00  299955250             One Way   \n",
       "\n",
       "         Date  Year Month  Day  Time_Over  \n",
       "0  2018-10-01  2018    10    0          0  \n",
       "1  2018-10-01  2018    10    0          0  \n",
       "2  2018-10-01  2018    10    0          0  \n",
       "3  2018-10-01  2018    10    0          0  \n",
       "4  2018-10-01  2018    10    0          0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BikesPickle.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   30,     7,     6, ..., 44400, 76920, 63000])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It is clear to see that future cleaning in regards to the duration column would be needed to get a more accurate\n",
    "# depiction of rides going over time and full hourly usage for bikes \n",
    "\n",
    "BikesPickle['duration'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'temperature', 'precip_intensity', 'precip_probability',\n",
       "       'wind_speed', 'humidity', 'mean_temperature', 'temperature6am',\n",
       "       'temperatureNoon', 'temperature6pm', 'mean_precip_chance',\n",
       "       'cumulative_rain', 'mean_wind_speed', 'mean_humidity', 'Year', 'Month',\n",
       "       'Day', 'summary_Clear', 'summary_Drizzle', 'summary_Foggy',\n",
       "       'summary_Heavy Rain', 'summary_Heavy Snow', 'summary_Humid',\n",
       "       'summary_Humid and Mostly Cloudy', 'summary_Humid and Overcast',\n",
       "       'summary_Humid and Partly Cloudy', 'summary_Light Rain',\n",
       "       'summary_Light Rain and Humid', 'summary_Light Snow',\n",
       "       'summary_Mostly Cloudy', 'summary_Overcast', 'summary_Partly Cloudy',\n",
       "       'summary_Possible Drizzle', 'summary_Possible Drizzle and Humid',\n",
       "       'summary_Possible Flurries', 'summary_Possible Light Rain',\n",
       "       'summary_Possible Light Rain and Humid', 'summary_Possible Light Snow',\n",
       "       'summary_Rain', 'summary_Rain and Humid', 'summary_Rain and Windy',\n",
       "       'summary_Snow', 'summary_Windy', 'Count', 'Holiday'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PhillyWeatherCount.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Many combinations of variables were tried and ultimately mean temperature and mean chance of precipitation were the two main \n",
    "# drivers producing the most accurate models, with a binary holiday variable in order to account for that\n",
    "\n",
    "X = PhillyWeatherCount.iloc[:, [6,\n",
    "#                                7,9,\n",
    "                                10,\n",
    "#                                13,\n",
    "#                                15,\n",
    "#                                17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,\n",
    "                                44]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_temperature</th>\n",
       "      <th>mean_precip_chance</th>\n",
       "      <th>Holiday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70.4629</td>\n",
       "      <td>0.000833333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>70.3371</td>\n",
       "      <td>0.00333333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69.0888</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>71.2529</td>\n",
       "      <td>0.0466667</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>66.0721</td>\n",
       "      <td>0.00458333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  mean_temperature mean_precip_chance  Holiday\n",
       "0          70.4629        0.000833333        0\n",
       "1          70.3371         0.00333333        0\n",
       "2          69.0888              0.085        0\n",
       "3          71.2529          0.0466667        0\n",
       "4          66.0721         0.00458333        0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = PhillyWeatherCount[['Count']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2643</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Count\n",
       "0   2784\n",
       "1   2794\n",
       "2   2811\n",
       "3   2478\n",
       "4   2643"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scale our x variable and apply a logarithm to our y variable in order to minimize the data's spread and produce more accurate predictions\n",
    "\n",
    "X_scaled = scale(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_scaled = np.log1p(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "ols = linear_model.LinearRegression(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cross validation shows one very inaccurate iteration and the overall model is not overwhelmingly accurate and this \n",
    "# is found to be largely due to an outlier year in 2016\n",
    "\n",
    "cv = cross_validate(ols, X_scaled, Y_scaled, cv = 5, return_train_score = True, return_estimator = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.53717531, 0.60980703, 0.5938533 , 0.61747199, 0.02261814])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv['test_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.58974261, 0.5937963 , 0.60750165, 0.60607657, 0.65625556])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv['train_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=True)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ols.fit(X_scaled, Y_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.45020555, -0.21627478, -0.05252343]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ols.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping = [*zip(X.columns,ols.coef_.reshape(-1,1))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('mean_temperature', array([0.45020555])),\n",
       " ('mean_precip_chance', array([-0.21627478])),\n",
       " ('Holiday', array([-0.05252343]))]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6107928327974007"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ols.score(X_scaled,Y_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.85628281])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ols.predict(X_scaled)[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Count    7.815611\n",
       "Name: 3, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_scaled.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1710a3750>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(PhillyWeatherCount.Date, PhillyWeatherCount.Count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1706cbe10>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(PhillyWeatherCount.Date, np.expm1(ols.predict(X_scaled)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1025</th>\n",
       "      <td>2016-01-25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1026</th>\n",
       "      <td>2016-01-26</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>2017-03-14</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>2018-03-21</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>2018-01-06</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>2017-01-07</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>2018-03-07</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>2017-12-25</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1045</th>\n",
       "      <td>2016-02-14</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>2017-01-08</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>2018-01-07</td>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1046</th>\n",
       "      <td>2016-02-15</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date  Count\n",
       "1025 2016-01-25      1\n",
       "1026 2016-01-26      6\n",
       "619  2017-03-14     71\n",
       "368  2018-01-04    115\n",
       "444  2018-03-21    128\n",
       "370  2018-01-06    153\n",
       "553  2017-01-07    153\n",
       "430  2018-03-07    155\n",
       "540  2017-12-25    158\n",
       "1045 2016-02-14    178\n",
       "554  2017-01-08    190\n",
       "371  2018-01-07    197\n",
       "545  2017-12-30    205\n",
       "365  2018-01-01    206\n",
       "1046 2016-02-15    206"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PhillyWeatherCount.sort_values('Count')[['Date','Count']].head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# training a model on the more recent data where a clear pattern is emerging gives us much more accurate predictions \n",
    "\n",
    "X_recent = PhillyWeatherCount[PhillyWeatherCount['Year'] >= 2017].iloc[:, [6,\n",
    "#                                7,9,\n",
    "                                10,\n",
    "#                                13,\n",
    "#                                15,\n",
    "#                                17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,\n",
    "                                44]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_recent = PhillyWeatherCount[PhillyWeatherCount['Year'] >= 2017][['Count']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_recent_scaled = scale(X_recent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_recent_scaled = np.log1p(Y_recent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X_recent_scaled, Y_recent_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "ols_recent = linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_recent = cross_validate(ols_recent, X_recent_scaled, Y_recent_scaled, cv = 5, return_train_score = True, return_estimator = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.62627049, 0.65031093, 0.73971217, 0.69867778, 0.35542401])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_recent['test_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.74305166, 0.75071523, 0.71991849, 0.71693874, 0.71283136])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_recent['train_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ols_recent.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7021096608241807"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ols_recent.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1646bec90>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xnkzuX5jRPrioWLlobsPrh7SCUGHALu95/zreDwO4FsAT3vGHAPR7f9/o/Q/v9U8SEXnHH2Pm88z8BoCjAD6aylVkHNXKJmyx+b4Z0yJFBzsFQleHM6mmgWnKCDFB5WVSCtPBx6luBbipQXQBS35PE13K5GCfiJvgcCqwctFcqUpTZzcTO9z+3m7cvnyB9vw6o71sUbHzYBkfXjA7dGx1pWz0bjRGrqXeCv4ggF8C8GcAXgcwwszCqvM2ANFruwEcBwBmHiOiMwB+3ju+33da/2f833UngDsBYMEC/UNsF6IEmPkZqVRx4K1TOD8WPnXJahwAQN/lc4zjGcojFazbPozZJQcznQJGRquY6RRwfmwca7cP464dh9omT79OB7/5qSMYj6GDBi6uUlXPlOCmtN736kntMxceJ2E1KaY6Qs0WfFrnquOYNb2Isxfk/dp/f/sun4PvvHDMSNUXjBNQLSr+4fVToYu51R+aF/6FGcJIGDDzOIAeIuoE8NcAFjWqQcz8IIAHATdraaO+p5EE/cVXLpqL7S8ej2UEe2T/MaOoYH/YvB9/pxZGY935hKtlySni41fNmRTlLFJlv3HyPTzy2x+LdB3NRldkKElKg9ELY7hyw26lqyPD7JmNM08KIgRsnIEMlecXAzh7YVzZn0VqaXGOKEPPv5CIu6sHgJ0Hy+i7fE7bPMNI3kTMPAJgH4CPAegkIiFMLgMg9lZlAPMBwHt9NoCf+o9LPpMbVNvK6dPiOW6Z9mGdkU3kY3lzYDW2rukxUjtVquN16S4Ez79+KvNuc41QdxULhNOjVTDUQWeA+TMTQYSAjTNQYWI8VvVnscqPaoD22yiS2MrazQvPxJtorrcjABGVAPwqgB/AFQqf8952B4Dven/v8v6H9/pedosm7ALwBc/b6EoAVwN4Ma0LyQqqbaVqO5sWYZ1WGMHWbR/GrBnTcPvyBTX9dByy3sl1OvioGVoL5MZfxFUt6RBCxcYZyAnr12FPROyyouDP4xU3Dsj//e2CiZpoHoCHPLtBAcAOZn6aiF4B8BgR3QdgCMC3vPd/C8BfEdFRAKfgehCBmY8Q0Q4ArwAYA/AlT/2UK1rx8MNWvLI8RDsPlmuT44qBvZFVJ+3QyVU6+E2fXRyak8bPBKPhwtzGGcgxyeGlc+EU6rYoz3v7S26Fun2vnqylKyEwRqvR3Q7ayQsvVBgw88sAeiXHfwSJNxAznwPwecW5vgbga9Gb2T6odNIlpwCAUo98LRLVeZ3IKkGpVp39vd3SAVdyivjwgtlKVVE7dfIgUe0ojUZEhKtsHFMZ8azufvJlVGJMxn67iz/DrI7qOE+y+8SNFyCYubZmBRuBnDIqH/axCcbN10TPRaTDXxdZEKUSlFh1qlQqj/z2x7Diqjl1nwtG2bYjwo7S3VlqqSAAkCuX3kbQ39uNH3z1U5FcpYHJGXZFzQ7T8ZdGn7ht+YK2MR4DtgZyYmQeIKpMiKJOahy1TJACueqLbs9bSWxpw9IvB5G5pAbz9AdxCoQtn1/aVh09iHhuSZ9DGhCANwZW457Bw3j0heMYZ0aRCMs/0IU3f1pBeaRSq8E71Wsqm9bw9tf9jvP5pHSWHAxvvK6h36EjTg1kKwwSEKdjicl758Fy6OfCinCnhVMkbPnc0lrBFhP9atYKgEehWROCKWKCN22TaqKbCpgspFQxN4KwxU5SsvB84ggDqyZKQJzsl8J4609eJoOA0CLcUegsOcrvq45zzWvFNClYOxs2ZSkiWgXB7RN37Thk3Kap7GUU1u8KBK0gAFyVkWroEUFb/lWkIxER/kLtJMZWO0eO2+I2CYg7IVaq49j36kltDVaGW1u37/I5kQrgqCDSp70W12KqNmlXw6YuRUQrEE8k6g6wnYWxKTIVbFjQ4AQjtNKYtg8wahH2weDBLKz4G4ndGSQgyYQY5v9MQC2cPg2j8+nRqjamoLPDweBQ2SjuoJ0Nm81YUYuaEJ0lp2H5adpVGJuiCsIzSRInqvqp0PUBcV/v61+CrWt6plSuKGszSICpfl1FZ8nBz85VlaHyQi/fDB13Z8nBrBnTQncG7W7AvHLD7kieInHcTsW99KcjMbERmeK38eQVlW1ABAyG6fvFc5MZ3nV9YNuaHgDhmUyzThybgVUTJaC/t1tbQzWMkUpVuzXzu34eeOuUNOdNh1PA+XFOHB17plJV1jIAJg+SdduHa+6Q7TZIouYmcooEBiKVxRypVGuTld9G9PShE6kYLWcp8lDlCZUazPT+BdVvYmcBqPuAEDTBAE3xOaBeSMiOteuzsWqihJhUPdIxASiNWWLLOjhUxqMvHJeuZrpmzcADEdJcq7i0s6RUPQg1VR5y56j8+W9fvkB6Dy+Mc+L6yKJ2helE5hRJOzB1QjsvzE7Yn2UIw7s/3YSfzyydp0wLsmnXkbr+v/6JQ1j/+KG2HxMCKwwSkobulhnKgCOhIlIZGN8ZqdQCarat6YkUlBP8Ll3gU15y56gC7O7rX4JZMxq3UY4iTqrjjNkdau+vvNgLVJXoAPUCKSnlkQp2v3xC+trDXrEpGSOVal3/r45znYq4HceEwKqJEhLFP1zH/TctqW03Z5ccEAHrtg+HBpHNLjlYMbC3tk2NE5cQNIzJtr3rFB5N7ejVospZFPdaikSYYMbskpOa7/rIaBW3LV8g9WhpV+O9H1m+LKGO6e/tTrzj1tFob7J2HBOAFQaJiZr3REZXh1OboIKDRDe5OwXC2Qtjk/TTUQ2e3Z0lZYEV4dqnE0p5WaUC8Wod+I25vfc+m1pbOjucuqIuBODma/JRAEe30+zv7UZnh5MpF+AoXNpZasvaFFZNlAJ+NU0cN9CNNyyu/W0ayFYkwiUzp9XpsxkwTkutW2UKTymhD5UJgqyvUnVqCBlxrsVvzE1z8jpXHa/rB4z6Gsvtii5L6+BQGe+dG5O+niWKijraHdMLWLd9uO1sCVYYpIhIfiY8b0xYcdXkSkgmK1ORoE61lWagphNX6Z1l2U79bNp1ROoyS4S28LuOUyzGpF5ukEYZc1UZOttVBRFEtaO8tLNkHAWvo0g0KUI4SGfJSRS/091ZwvsUNqYfvnu2bnfeDrYEqyZKiGo7aOJyWnIKdeUjTfIRiUk4LCGeaJ8sPXXYRK5SeTEDbw6s1rYvC4SpIQD5sxNR36ZqP7/Hl47uzhLe8QRTUq7YsBtAeA6eLCOztTkFwuiFsVR2WOPM2P7icTjF+sVQySli02cXS9WyJogdscqOpiLrgtwKgwTIjGBrtw/j8QPHsPGGxVi3fVg7+M9JVn9hgkDo+FVb6WB6aX/u/rT0l1du2J15Pahq4JVHKrWAJr99JehPfn4sPHe+X00myleqEMGDYX0iDP9nT49W6+ootwsidkZkaSW4btZpqtqqE/XePkKAAqg5XswuOTg/Nm5cJ1ksKqLamLJuX7PCIAEq/f7zr5/ClXMvkXqD+JF1jm5NB/NPPqqt9LQC1Yy+/gk76mTRpTHg+dUuQDYnIt1AFcd1W/mwlWJwVa6bxIQ6Ysue12ILAtWOUSQZzOIz0DE4VMbOg+XaNTHQkLKiQTqmu1OefxE3UqnCKRCKhcnBhSWnqOwH74xUsHVNT92uQuXA0Q6FbkxqIM8non1E9AoRHSGi3/GObyKiMhENez+f9n3mbiI6SkSvEdEq3/HrvWNHiWhDYy6peehWBY++cLyW30QWzKQyvqoCYkpOYZJqR7XyrVQnIunJVUbWjTcslm6xJ39XdvWgcWvXvjNSMdrOj1SqWLt9GFds2B3qRSSecxI1gUmSwXYiTsbfNHhnpCL97uoEY9b0aXXxJyq7wqXeDj0Ys3Lb8gV1/Y7QHoVuTHYGYwDuYubvE9H7ABwkoue817Yy8x/530xEH4Rb93gxgEsB/B0R/bL38p8B+FUAbwN4iYh2MfMraVxIsxFJ3VRDVAxemeupTter8haZM2vGpPebblGDevLgNeh8vYGL6iXVdWZ1IpKpx0zul9ithb3XPzfrdgUlp1BrSxzXVSA8P1LW1Q8yWtVvLvVsNzLOVKrSgjQym5sQ8Kpdt79I0a3L5tcyoWYZkxrIJwCc8P7+VyL6AQCdiLsRwGPMfB7AG0R0FBdrJR/1aieDiB7z3tuWwiBsyy+8eGQGqvfOjWHzU0fqVDmAeWH0KMFuqnOGRRUH6yjLJr0sT0TBgSoMryr8gzyNQEKnQLj/pg/V/l+/aiHWP3EocnoL3buzXoJU5WCRdhyBaXyNiKY3rTets7nJrg3AJPXXODMe3n8Mu18+kXljfySbARFdAaAXwAsAVgD4MhH9OoADcHcPp+EKiv2+j72Ni8LjeOD4slitzgBhK5tbl80HIC+kUp3g2kAIrsZNC6PLOmnUCVtnZA3uGJwC1UptCpxCtieiIJ2aCGFZNlYxacTJXFokRWnQFNXiHU4BM5xiZhMHqnaeB946lXocgT9DqQp/TWTdaj94DSpBILu2mU5Buog4PVrNtI0NiCAMiOgSADsBrGXmnxHR1wF8Fe5z+CqABwD8VtIGEdGdAO4EgAULovl8N5OwLf++V0/insHDRqufSnUcd+1wvUJkK35VRw2ufFVupKoJW3cNMgEWpDrBNS+arHZw4OKAlgkCVT3nYCR21AjzCea6c6qM/nGETckpoDrOGK1eXFSsfzxbnkWqnadQoaTNOLPS6CvcSYFwDzt/fWyVx5nq2nS7SZ3KNgsYBZ0RkQNXEDzCzE8CADP/hJnHmXkCwDdwURVUBjDf9/HLvGOq45Ng5geZuY+Z++bOlRtTs0CYgbI8UsEj+48Zn2+cudbRZInUTDqQKgmb6rPrVy00jlZWIVY8WY2u9AefCcQ1d3eW5Kt3CSaupn5kuzHVToyByFlnK9WJOsFSnWBs2qV3cW0mquttVF3vIpG0nGyRqDYRi34qAkTfGFiN5zdcO0kQ+PuLyuMsrs0jqzY2wKC4DRERgIcAnGLmtb7j8zx7AohoHYBlzPwFIloM4DtwhcOlAL4H4Gq4Y/CfAXwSrhB4CcB/YGZl722H4jaiY4QllDOl2YXmw/TopjS73aaoiqSYtNe/QoyCKqhP1RYCMK1IiVNlC7ISFKi6Xp06J0zVo3P3NHndKbhpXEZGq9JYGVWbTdtZcgo4V51Q7vSaNU7iFLcx2RmsAPBrAK4NuJH+IREdJqKXAawEsA4AvMl9B1zD8N8C+JK3gxgD8GUAewD8AMAOnSBoB/yrC1094yjE8TaJy+BQWZmuIng0bAeR1RWPqUE+iGxHYYJI8wGgzmVXtZuMWjynXVClRL912Xyp+yWg3zWIlb/K3VPsAHQIe53K9dqkH5OinU6BMDbBSkGQ9VxeocKAmf87MxMzf4iZe7yfZ5j515h5iXf8s2KX4H3ma8x8FTMvZOa/8R1/hpl/2Xvta426qFZg6lVTcopYcdUc5eui9nGj0dVJKDlF3OblddH5T/tpRDGSNNDlwNElsovrBy8WBbK8SICrAoxTc8KURtVcjoOudoTfh9/UZjLOjJ0Hy9I6yCWnGGtnHoyVCRvHqraqEkcKsp7LC7A1kOuIm3pWl+NEdKBuw3q4zdhK6rbwD9wi16MPDpWxbscwZF2mq8PB0FfqfbRbjey5iOcRHNh+9U5YrWTVpCAmOJ1qKmodZlPasTayqVrGj/D8CsbuAPHTWQjVmq6/6LIDhAm0ZqvubA3khJgEYakQr9+141DdCkV0pOc3XIsVA3tDV5xpq1xkAk5n3AumswDC6zWcHq1mMmeR33Mk6B2iMg7qXHwBtVAPS2Am7nnc4DMdMtfYdiBOXxef8Rv1k8Qs+HdqYZ5GKuF1aWcJPz5zTro7aeROME3szsBHEmOjQLfqM90Op7kzULmbznQK0gEUbKNTcA1lUdPG3L58QeaiLk1WoQTgjYHVRtleVbtIXT8KrmjTQLS5HYm7MwDSta+Zrtx1/WKtJoupyFrbrMVSowzIU4a4xkY/Op2j6Xyqyk8UhkwHrvKHltVdlgmr6kR0QQC49WTTsH1ELVCjw+Q5iudn4qarck9UGU5XLpqLu588nKogAABQc+xMjSBqDimx+0p792zat/p7uye5rwqjdn+v2rBNQFsUurFqIh+m0b8yVIEqcdj+0nH0XYo+u+cAACAASURBVD5HuXpQhcHLVFwqldSZShVb1/REzt0ThaQBNknUdjJMahSPXhjD4FC5FnQW53tUqoZGJWdjRlumsvYvVkzHjCgulnZ/Ne1bwWyrwqjdd/kcacCo7LqyGnxm1UQ+4haCiVMgIwyVqiiq2kflDy07f5wtu46k6os01HYCnfFbRtTCMSaOB40yHPtpl4I3ScaMUySs+cj8UEcMGQVyFwUqG0NY3wrrk8F+oDM4N1K1Zw3ICYlbCEa34tN5negm3qjJ5VTfLwvRV/k7q6pPxbEZAMmT2KWhtgO8es5PHDIWBEB4Lhn/oJ9dcnD2wljNrVC1ykyymjWpgCfa3Q67hCS7pOo4Y9+rJ3H/TUuUu/GSU0SBgLMXJn/HBLs1DUa8WIMgYX0rrE8Gd5Mq4VEgqu1As4K1GXgI3bTwBNm6pmeSHliHrgPJCtQLHaKOqMnlVPjzsoelqJDpybd8fin++JbJNRk6nPBuk0Y2TV2MQBQ2P3UkVlCXql7DPYOHJxU8H6lU684v+2zcGguqQC0VouBNVhkcKifegb4zUqnZbN4cWI2ta3rq+vjoBbmwEQs9GWF9K+xzQRuXLCYCuJh+Jku2A7szQDLd9OBQOTQVhd9H2VQ36tdd+1GtLjtLDs6PTUh3AFF036r3Bo/13vuscqudlqoiStI+HTq3w7DnEbzXg0NlbfU6P0HBHXRz1SHa1dXhgBl4ZP8xzC45IDBGJeVSw7671cRJ9qcjGOQo67e6VNVx+5audvMVG3bXJbbbebCMDy+YjedfP1V3rqzZDuzOAOF5/VXoonj9CH1id2fJWGesSgCn8jT6zNJ5sRPcxWHjDYulK540ddZRE+/58a/QdIRFVgd9xDftOmL8DAtEdV5QYjWr8jwB3OvcuqYH29b04Fx1AiOVam0HwiDcvnxBaGK7LNWZGBwqY/3jh1L1ojrrLZZ0qLy6xAIpTt8Kfq6z5AB0ccEhMxbv/9Fp5fmyJLStARl6w57OP1i3OhZEiWpVfb/foJWmUTUpqtWeidG90e0yMU52lhwMb7wOg0NlrY+4PzpV974w/ILSxFlBZ9AXgW/bXzxel700a5HIaTsmCKIkG2yUj3/Sa2vUuLUG5JioVC9+3X5QdTQ4VA5VPwQ7n+p7hMAxMWilZVRNg/7ebmmdgFZvf02Mk06BJuW3V6kU/Cv4sJ1iV4eDEcUKEZAbpXUTle6ZlkcqeHj/MXQ4BThFqqmOsuhN1Ki+aXLeuO7BabZBBQGJ7WppYtVEkG8ndf7BgH5i6O4s1QUiqb5HbFtNDFrCPhH2vmaSJeFk8t1+w7jpszE57+3LF2DoK9fhjYHV2t2fP6++ThDonrWf0eoEGIRta3rw5sBqDH3lukwJAqBxfTMLqrAkbbht+YJMPSsrDCDXTYcVgNdNDKMXxqQRszo9ZdhkFJZl1P++tCJ2TehUZMls5UDVfbdq4hW7CWEj8D8bcU9VfaKrw4mUeqM8UsH6Jw4po1JNbVECE/tWK4kbUa8jC6vqwaEyzp6PV77TKRD6LldnL24FVk3kYeofLCYaXUFvVX1j2ff4vx9Qqw1Uqg+RP19Xl9V//jQZHCpLa9m2uki7zONDIFP3+d8r4jJUtW6DOEXCxhsWTzrWZVDsXeaKKqqUyZIdhpElQ2SQfa+eTPV8hNavqlX9wjR7qqhKZ3cGbYBupa6aBGVEWbWpct0A6sHur7Ub1ysqiOnuQlXTd9b0aS3t5P4dmIyguk93z8LsD7Jr3XjDYjjF6JkqRypuwFicvPxZUJmoSFNQCU+rVidBVPWLjunTlJ52QUYqVRtn0A7oVuorBvZKJ0EVaQwGk7xJSfX3Mu8g3e5Cdd4zKboQxvUGETswlQdXmLrPRB0IyK81SjxBEF1wnIg5CBrsCY1RxaRFWOS1UyCj8ZSF0qphpVDLIxWs2z6M2SUHM50CRkar2jgkG2eQUYIr4gNv1QeKANEn9zRWbSYGziQRu2LbK/MFV+0uVOcVofZJ8ZeejJvxMeyexH1ddn5//9my5zWsX7UQ29b0hJYMNaHkFLHxhsUY3ngdbl++YNI5GcDOg+VMrTL9qBw0xG8TQZCFkpGmpVBFTMi56gS2runBA7csVb43S+q9UGFARPOJaB8RvUJER4jod7zjc4joOSL6ofe7yztORPSnRHSUiF4mog/7znWH9/4fEtEdjbus6Mgmnof3H5NORKoJQjbo0+rEJkEyJgJDRZg6RNZpVekVxpmxdvswbvvGP4Z+b9Q2RVV7hd2TOK/L3qcSXJufMg9SU+G3CwGuDl7n6ZY1ZH1XBPuZ1vfIQsnIqPmUKtVxrN0+jLt2HFK+J0vqPRM10RiAu5j5+0T0PgAHieg5AL8B4HvMPEBEGwBsAPD7AD4F4GrvZxmArwNYRkRzAGwE0AdXeB4kol3MrA7PayImD1oMuJWL5kpTEgT/L9DkQWoaOatSi4T5TMdNtAeEr1BmlxysGNgrPa/K4Pn866dwz+Dh2PrdNNxWw+5JlNfLI5VawrhgZbGoCQQB1CUQdAoE0GRVkVMkzJo+Deu2D9d2G6qVaZZWmWHsfvlE6HiLWyCpUYFmce+vSkWUhd2On8gRyET0XQD/j/fzCWY+QUTzAPw9My8kov/i/f2o9/7XAHxC/DDz/+Idn/Q+Gc1MYR0lOjg4iE0/EycVdrOieXWRlLJJyjSyukiE1+//dKptyoLuOEjU6HKRTsJfw1d4JYmJrLPDwXvnxiapUXT69SIRJpgzV3o0TrrqsGesmvB1Xj5Jg/F0/RGIVnmt0WVKG17pjIiuANAL4AUA72fmE95LPwbwfu/vbgDHfR972zumOh78jjuJ6AARHTh5Ml2XNB2m27UiUazUuybb+LS8geKgUod0dTi4ZOY0bVZO3b2L4xkT1qZRg7w0gsGhMno2P4srNuzGFRt2o/feZxuiW1fdg86SU3cNToFw9sLYJPvMOS+C2O9R1jF9Wt3Er9OvjzNnsppWnHTVuhWzzpak+i5Vrq+obVKpFKNkpCXAOCNyMzEWBkR0CYCdANYy88/8r7G7vUglyREzP8jMfczcN3du8zwkTB5mySkmmtz820yZ+6ZqG1oeqTQ8gEym1922pgdDX7mulmIhiGivbuAmKQYu2hRMymYysIUQWLt9eNKke3q0irXbh9F777O4Z/BwagF6qoli02cX193XMOEqSKL2yZINIep1dJYc7USpWzTpvivpPdHZ7cLcmf2UDFLAtwIj11IicuAKgkeY+Unv8E+IaJ5PTfSud7wMYL7v45d5x8pwVUX+438fv+np0t/bjQNvncKjLxzHODOKRFj+gS68+dNKXenCuImpRNpdUWzFXwxl/ROHtIFsjQ4gE+eVnVvlGui3IzgFQJZZ+dZl8+sPhhBUAcjkiS7/kYla4vRoFQ/vP1b7P+n9VdkeZMfWKZLdBSeypKUds2JDUF1HySngXHWiriiNyBmlQmdLCrtnSe+Jzm4nXgvrf6PVicwVtgHMvIkIwLcA/ICZ/9j30i4AwiPoDgDf9R3/dc+raDmAM546aQ+A64ioy/M8us47lglktU2/f+wM1q9aOCkITLYCdApUq82qQ6TdlRVbqY4zzlfHtbsTf5RqVJKkqVDtmkYq1dpWXSYIrv7FWdj36slI3ylTAagEZJRqcCaksXL0Bw0CkKozTFN4qPqaaUBbVjxVVi6aW+dp59qcPiQtShOcJIN9V3f/wnb4ad0T1XjSqar8xB3HjcRkZ7ACwK8BOExEYknznwEMANhBRF8E8BaAW7zXngHwaQBHAYwC+E0AYOZTRPRVAC9577uXmeWO/E3CvwKVBYZUquM1t7Aw75PgsZHRC3Ul96rjrE01MFqdwDavSL1qdSOiFqOsKpKmqRDviVqc5Ifvnq39bfqdUSZy1cBu9Gra1FtFpc6YMa1gVIpU19fCUlZkxVNFLLL8LSUAN1/TXTemVJ8P9l0hEIMODf7noEqtHnZPTJ6tajwdeOuUcV3mOOO40UzZegZRPBziePTELX4ucueH5bKP4k2TlldOGnnpw77T9L4Fn0lYZGiU9gl1oEzYy6rVif+DHiKqayG4ZVWjuD8GJ6mVi+bWTTyqdrQSXZ9RuemafL6z5GDWjGlSbyJ/bWoiYGS0anyPTbz5VG0yrVPtf/8DtzSm7oStZxCBKCvQYK4akwEcR9/rN5SuX7VQWUglqt4zrTTTaeigw86hK+spG/xAPNdFGQTgip8v1a361j9+aJJrrSq+JLj70aUQiZJn/57Bw5PiWkQ5xZuv6ca+V09KhZY/LiGNySau777ueYuJM27Kk+GN19W10f/sRipVlJwitq7pMWqrzjBtkicsqnOJqIMMNM4OGIVsmrWbQNSJTXRYXWoEvx7x7PmxSMnK/MVWALdzdKWUHjqtwvJp6FuD5zApIF5yivjM0nnKc0YR7LonwgD+4fVTdeeqTrA2Z5Af/8IhSUS4QFVzuVIdx75XTxrZKJJ6oSVJC2LaZ+KkPAnq65O6ZpsumqKOA51DXZa8vqasMIj6QGXxBSLcfMXAXtwzeHjSgBmpVAF2/fQJehdLWbEVQF5nOI4uOI1JSXUecVWdJSdU+AW/UzbJiBWv36h48zXd2HmwrJyMTAU7AXhjYDW2eUZLGWkoTUV7TFKIhLFlz2uhtTX8721EnEqS80bxv4+a8iTYF5LugE0XTVFjCj7+gTna92fF62vKqol0Oe+DhEUcl0cq0tVbdYLRMX0ahr5yXd1WX5xXNzkkSS/RivPI9NpBNYb/O1WTjFjxClYM7NVu301VcmJQCxXNFRt2R7p+U/yTRxR1kAzdRBGcpBpVdS7KeWV9YMa0gtE4k03GwT6ncvTYsuc1o8y+OmRzgolhX7eAYAAvvnkaaz4yv+a2Hrd9jWbKCoPgA53pFFCR+EeKMPYw46Ru9WbiUaFrZxr6xGacJ+p3mE4yYe8zEeyyQW1q8JOl44jyPUnQ1ecO7rJUqZKTTjamk+zgUBnrHz9Ui5IWyR5NUaXh9verKxUC/J2RCrau6TGazFVEWTT52xTmWFEdZ+x++QQeuGVpovY1mimrJgIu+oVvXdMDlTa5Y7orL6NsDf1c2lmSroAZ6VeAygJR0j+YbsvD3idTx9y+fEGoekYnCPyf3fL5pdjyuaWh0aWNyK6pUs35K32FlclMWuvAVM24adeRSHU+gpiMB11fSEMtF4wVMfmsydxwerRaF6UsVM+iJnarmbI7Az86A6TQSd5/0xLcf9MS7Q4h6HIoBoxpxGm7E1wZAu4gWP/E5FgNgem23OR9ql2JUFvIvGu6FStelftrf293w5Pnybx2RL+Lom7zk3TRYbpijhKDIsNkPIT1hbR2wFEIZrY1ea8sA4H/9VZghQHCO6GQ3v7JRzbxy1z9+nu7lZ1Ets1uROrdZqEqg1kdZ2nqCNNJxvR9Yb74QRdGU2HkJ85nTFEFM91/0xKtoAnrv2ksOpoxyZqos6Lav5o1psT96dn8rFQo+t3GVRkINj/V2prIU0oYqDqGiQFSDEwxUBnmQT4mE0izi9nLSDpwdJOO6v6aTjJh75PdP5VLphBMcQzraRnjZZj6uQcJ67/NMlB2aXJrhRFVt29yv5sxpoJj5jNL52H7i8fr0o773cZV9yjuvUuLKROBrIsuBBBqgFQZG02jCP0RsrLIy1bn7k+jloLOkCaibhsl2KJGR3d3ljKxA/NPJqqRKFxidedQ9V+nQLhk5jTjKNwkDA6V8bs7hhHVbJBGrQEZzVDpycaMSkMgPqMKJgUuZiBISsPrGbQzYauum69Rd0SnqPY6EVGEYQYgoZbwp8FO00c6KWmVmHQUGfsYjU3OFeU+EZB6YFYcgnEWKsJW9jLDJOCpJshdcTbjWvt7u2uZeaPQMX1aQwRUo8dUmGv0GwOra6lNRIDc5qfUYyCYqr3ZTBlhENYxdEa2aQXSepKYTpq6CTetKOG4pFVicsvn1cW/RXIuU2SZIVXZIk3rUgdtPUDrokBNIqfD1CfifggnhW1revD6/Z/GmwOrMWuGWd2ENImj6mjUgqfRYypszETJwAu4kcqt9CrKvc1AbMNVKy/RMfRFMSZw8l/P1WVK9GPSoXWdJ6mPdFJMfMlNg8p0XhVh+m//dwX1vcHttcgbtPmpIzg9WpUa9T+8YDb2/+h0bTdmGs3bDHTfScCkeyqz5wDQ6sRbsduMmqwNaJwjRSON/UD4mImaSl0UbQJa41WUa2EQlsDMKVKtY4QZ4S6EBByZrDbCEpcBjTFMmhA2cGSTs6o4TBpJ9jY/dcRoIFUnuLbakq34n3/dLEt6s6NAdUFiQZ22yhDqBkqqVXuNCkLToRMEqlrajXKkaPSYChszcYSuicNAo8i1MAiTzLN8usr1qxbW+cibEowGVZFFH2lB2MAxWeWIjvz8hmtrq/UgJhPR4FC5qZ4VzY4C1QWJydqiUi+GxcaYnj8tBofKWkcLoUKMGjORZIJs5JgKGzNhGXhVi89WxR/lWhiE3dQzPn9g8QDX7RhGVAcrBoxSBrd69R+GbuCYdlBRr3n1h+bV5ds3nYiaqb8vEqUeNRyGSrCq2hI1FbosqaLAJAWKDpUKJ0zAiesK04m32pEiKroxo1r8bfrsYq0HYatyFeVaGJj6X/s7uCpHURh+bw1AvaVt5eo/CVHqMwgVklMACgRMsDtBmU5EzRz4E8xNfx6q61O1JYoenqBX1Ty8/xh2v3wiliunToWjE3A3X+PakdZuH55k1xF2oM1PHam1J2myuSyhW/wNDpVx9vxY3WdamasoNM6AiL4N4DMA3mXmX/GObQLw2wCEC85/ZuZnvNfuBvBFAOMA/ndm3uMdvx7AnwAoAvgmMw+ENS5pnIHOZiA6pcy7JCnNig1oJmkUkFHFLQRXm6MXxozURB1OIVKtARmteFZR/d+jZldVpdnwI/Ib3de/xPi8unbr4iTCsv6K96hifuJUGswyqrGUZrxFo+IM/gLA9ZLjW5m5x/sRguCDAL4AYLH3mT8noiIRFQH8GYBPAfgggFu99zYUlf+1XwA0IuTOv/JLUog+S+iSwZkic2uUud+9d66+MJBToFptiO7OErat6cErX/0UtnxuKZyYDtKtWoVFrS8R5R4DkK44gzDcXcI9g4eNz6tT4ahW7jqVlR+/XSBpsrmso9pFNSrewpRQNREz/zciusLwfDcCeIyZzwN4g4iOAvio99pRZv4RABDRY957X4nc4ogEt2pxXN8A1+hjmojLr35qdYqJNFGpuKJE/wbfJxsY1QkGwV0pmUTOjhlo9YKrU9MU4o0gqu1IpnvWpdWOkjDuEc8jTFd3QqBT4ajSiEcZa/6iQO04PkzJql0kSdDZl4noZSL6NhF1ece6ARz3vedt75jqeB1EdCcRHSCiAydPJk/xHFx5xhEEwugza7pZCmt/LdpGVJ7KGlHSewcrvqkGAAM4V53A1jU92lTCuhgS/3dmLYV4f68/fbrrgKDaOcpWy6ZptcNguALBJCJbt6MJi+I3wdTTrF122lEDJFttF4lrQP46gK/C7UtfBfAAgN9Ko0HM/CCABwHXZpD0fFEDPwB5PpcDb53C2Qvh5+ksObWJK6srgLQR17tp15HQVek48yQbgcoXHnAF56Zd6kyOg0Pl0B2JTl/d6ucQZeeoWi3393bjyg27E6k7dcn8gt8FqA2i2188jiSEqezaaaeta2ujg+HiEksYMPNPxN9E9A0AT3v/lgHM9731Mu8YNMcbStQBrzLi3LXjUOhnxQ5CkCfPiCAyF8PhjddNSsgno6vDmTQQwnZqI5Uqeu99FhtvcO+r+M7ZJQdnL+h14yWnoK1BEfYcGp3+OC2f+iieXqaoxo1KKKnSl5viX0SpSDsGoZHo2iqcBLLmYh5LGBDRPGY+4f37PwP4J+/vXQC+Q0R/DOBSAFcDeBGuivZqIroSrhD4AoD/kKThpnRGTKursubrJq1g6gBBVlcASRkcKmuLc/j9zoPXzozIO7XTo1Wsf/zQJB25iV58xrRi7XlEfQ7NWIXG2TnKBFSUet6m6NKQyCYu00UXAZgZ2K0FF1Eq2mmnHdZW/y6rPFLBXTsOYe324dB0+I0k1GZARI8C+EcAC4nobSL6IoA/JKLDRPQygJUA1gEAMx8BsAOuYfhvAXyJmceZeQzAlwHsAfADADu89zaUwaEy3jsX7lnhR6XPD+q6/cdVJfLy6hmhK84hUF37mZjVsOK4kIrEeHGeQzPsPVF1xzLPKyGgkurr/fjTtKi+M6irN93tMjDpWXSWHMx0ClqbSdh3ZHGnHdZW/30FIM1k3GxyXc8gao57QJ07/p7Bw9Li3rdH9NXOAzq/d9UuSRDnmSQhThyBLud8WG2BqN8Txadede86Sw7Oj02ktjMgArbe0qONkgUmF3WSlTxVfUY8j6jXn0bNjWYR1tawcZA0/sXWM/BhYlyUoZLo9/Uvwe3LF9R2CEWiKSkIwjDxSGkmUVUIYhCrSHMVGnXHorqWkUo1VRURM7B2+zB6731WO4b8z1mkL9fl5A+q5qLuvtpppy28q/zzhd+VuRmlSqOSy51B3GhZfxlLVXrmqU5YpSY/qtVN773PNi0RXdQVlm7F1upVaLN3VYBZhL7qHofZGlReUGnuvlqFbA7yzy8mUfZJ7Ad2Z+ARx50UmJwz5WFD32sdzfKJbqbvdRSduWp1s/GGxcZxCUmIY6zXrch0gqAZz0Dl59/V0bgKWSZLxfJIRRkfoYulaCcbQFRkc5B/fjljsBhqtv0gl4nqGrHFqlTHa+6lcYuz+L1R0nJbNPF6SdNFMsq9VQ3q/t5uHHjrlNQGkxZxspGG1RjQCYJm+L+r/PwBaHdrcaPuo7B2+zAOvHWqTm3ajv72aRA2TkxTYTbTdTaXwqARftfAxXrHQPggD9OHpjV5hH1PMBAs6USlurdBdYKoM7xiYK9U+CSJ/jWZ3KJmI41aY8BPM/3fVX7+qvoRIomcjrSSNT68/xj6Lp9jXJ8gq/72aZDmHNQs+0Eu1UTrVy2sq32bFqbuhTo/4zTdFlXfIyZ9mT9+EhdJlariNl/SumCaYtlWN24HLzlFPHBLeBqGqKqGqDUG/GTB/12mehNCTHcvxLNzCumMmE27JnuMm/jbi+LxurQj7YBfVXjq7PnUztsstVkuhUF/bzduW75AWgw9DUwGuU4fmubkETdbZNyJSuXRcV//Ejy/4Vp0d5aMCs7H6eB+7xFdPqQoqgYxgFWrOJMdRit030EbBQCl94rqXnV1OLVnt+XzS5WxNFEQsR2CPNsF/ATjMcJqojgFgon89cd8NJpcCgPAdQXduqZn0goyylbYKagDzUw6si6pV5oDRPU9YWqUuIMxaH9YuWgutux5rTYpmZbyi5LcDnAFeXDlOGPaxe4rBpYQglv2vBZqeAsG/shI+qwbgSwQbO32YTy8/1jtuY8zY+fBsjLobtuaHgx95bra/ezv7caty+arvzQC/uDDZt+bVhHFaUUkGpytccMVjCVI8RGVXNoMBEK/2rP52UhpfZ0iYcvn3HqtcQ1cYWmK0zKcqb5Hlx8o7nfJjIF+I3B5pKLUPwcn1Yv34WWjynLB9AjB+1csEIqMWtCTiW0kbACn9azTxnTi8dstVLaGYNLANPDbLpp9b1qF6U7b74a7zsBFmxlNS8aXa2EgiCIIZL69UTqyiedO2gNENdDTrqZkMgnJqsfpJ9X6CahYIIwHVkSjF8Zqq9zNTx2pr4EgSVURZsTVDeCoPt7NzMEfRcUXlucoStLAuOjuTaOTATYLE4NxcByYGpmb5VE0JYSBKbLgmSgdeeWiuZOKwMdJSZwWjViRmU5CjIseP7rax7JJHQDeN8Ptln4hfnq0irufPIwDb52KFLAWpgKKUn4yK0TxVNGpueLG44R5H+mikP20U0rqMFQFiIKp8MMSWapohjPClBAGXQaZS6OqTu4ZPIxH9h+b5DXj/1/QyhS7aQsc00nIX5Rd6K6DLoeDQ2XlMzlTcQdPcEdXqY7Hik247Rv/iEd++2OhwhtoD3226SQSdi1xJxidIHAKZJSBFGivlNRhRF18ib5oKoybYXCfEsJg4w2LJ6VcFsyaXsTohfHIq+bBobJ04lcNkiym2I2D6SRkIhB1rq3CKJoWz79+CvcMHq7bte08WMbN13S3XdoR0T5dMSEifcQ0kH48DgFY89H5xvcvCy65aWK6+IqaLqdZC5QpIQxExGtwAp9gYOuanliBXlG0q3lxoxP38dEXjmOc3TrFRO59DCM4wJs94EWb/VSq49j36slMq4RUiIlHlk3XKRC2fH5paL9ev2qhPnI5YLtxCqTNSspwA8/EvQ6zu+S5+JOf4I509MJYqCDw5zFq1gIlt8JA9gB0/u9RdOu6iSya8bS9GBwqY+fBcm1SZbjeDiYEvYF05S4bgeq72nUVKrivfwn6Lp8TyzYkjPEqdd34BKOrw8HIaBWzSw5+ds7MVuPPzb92+zA2P3VE6rSQ53QUApldREdYCvhGkkthEOUBCKNVFCOWLiXDbcsXtJ3awZS4Bkf/ANelfWgFeViFytQTpl46G29YrN0ddEyfho03LMbdTx422gHKOD1axTpJ7qKp4HYadcy08h7kUhhEeQCySN0wI5ZsRSMEQZ7rG5iuojtLDmbNmCYd4FEHR8kpAKC6e62al2ZMK+D8mFkasLytQgVRvHTCdgeq9ClRYQCPSHIXNdMltxVE3Xm20qMqlxHIpg9AF6mrO4csonPrmp5cCwLAbBXtFF1vElW+maiDgzA50rirw8Ftyxco339+bALbvJTJunNmuTBKUmQuu7p8VBtvUHv/zC45qanSGG5200anWc8ScXaeaZdXNcWkBvK3iehdIvon37E5RPQcEf3Q+93lHSci+lMiOkpELxPRh32fucN7/w+J6I7GXI6LyQOYNb1Ym9CjniMvgTJRMUohEaJKiDo4nlO32wAAGIlJREFURqsTkzxmzoVEK/tz88jo7izlIimaCp3Lrqg7EKy70N/bjVnT5c+VSP/M4uS3a2Wd32azctHcWJ8TGX+beY9MdgZ/AeD6wLENAL7HzFcD+J73PwB8CsDV3s+dAL4OuMIDwEYAywB8FMBGIUAagcmkda46gf7ebqxcNLcuBlanPjAtEK6jmcVo0iS4I5JNuNUJVq5qBofKOHt+rO54ySkqJ6Mgleo4HtHEGog043FSUeeBsBXl+icOSfvu6AW5Guj0aFWZX2jbmh5jB4IgrVr9NpskqdqbLTRDhQEz/zcApwKHbwTwkPf3QwD6fcf/kl32A+gkonkAVgF4jplPMfNpAM+hXsCkhn/SUjHOXPOOCebhV0XMAtHrtgZJQ5i0En/K4YkIKjZx3UG/+K4OBzdf040Lhnp+QL/5UGVrjVPsph0JU+kEY21E0aZOTbW0zU8dwc3XdEtrDycxwLe7J5cJSa+xmUIzrs3g/cx8wvv7xwDe7/3dDeC4731ve8dUx+sgojuJ6AARHTh5Mr5UFZOWSl1QJFKWptNJ86SBMmnWMmg1UbKvqoyQHdOnYd+rJ6X+61HzpulsQFGL3bQrcSbncWa8d65+xyY4PVrFzoNlrF+1sE7FlmSnlQdPrjDSuMa2KW7DzIx0CiWJ8z3IzH3M3Dd3bjx9mx9VWt5bl81XupzGSWksjoepgPIUdRklPbHuulWvMcM4zXXBi7iNYwPKE1FTgwt0wWTAxR3E4FB5Uh/fsue1WHVCpoLKDoj/PPxkvbjNTzz1D7zf73rHywD8s+9l3jHV8YZzX/8S3L58wSTD4u2eC6iumIdqMtdNgCYqoDwV+1AVupGtwHXXrXpNnM9//g5H3mV/bqajLOQSNvG0qw1HhngmsmRxSauZjTPjd7cP19kdTFeCJv0kb4jnEbdwUDOL28SNM9gF4A4AA97v7/qOf5mIHoNrLD7DzCeIaA+A/9tnNL4OwN3xmx2N+/qXSN0+dYFP/skcuOjzqwuUWTGwNzRmIW9Rl6Z+4qqsjqMXxnB6tKqM3A7eb9UTE7aIOAnD8pI5UyCeifB6K49UUCR9KglBWEbSCQATknThYWQ9E2wj6e/tNqpdIKWJsZmhwoCIHgXwCQC/QERvw/UKGgCwg4i+COAtALd4b38GwKcBHAUwCuA3AYCZTxHRVwG85L3vXmYOGqVTw9T1s9sgUVeUlBUmKqCpEHUpI3jds0sOznqCAJhcB8Gfj8U0qZd/5RUlYdhdOw5Jcxa1Y+bMIKpCSjoaMfe082InLeImBRTeec3oi8QZSQsgo6+vjw8cOBDpM6rJQ1bUJUr2wJJTrFvNB7e6qrKPU3lVFMS/WpURvFe6Upqyz5oK1rBnTwDeGFht9L0mNCo2Jey8Ue6fn86SE6kolIxW5tnJGoNDZazbPhxL2Mbpi0R0kJn7onwmdxHIKq8VURzFrw828ZsH9Ckr/EyVeq9xMak5HNxdRZnIorjphqVYSNOG0yh3YpPzxnVMOFOpokvjbhoGwc0ILAR7Xmwycenv7Y6968q6ATmz6Dp/pTqOTbuOTOqYAGp+8w/csjRScXkR0SmIYlCdipjkuAl2/KiGN/GMw9D1k7QFeKPciU3OG3ciubSzhNUfmld33Cm6Dhiij6uqmrHXvnaPq0kTXdyTjmYtJnMnDMI6/0ilOqljrn/8UK1jqiZz3UOU7TZUeXmmCirvnLBVqmwSjpPddKRSDZ1sVP2kEcFpjXInNjmvbLca5lVEcNMobH/xeN1raz4yH/f1L8HzG67F1jU9mDVDbXZUJblr17iapMR1M23WHJI7YRBVilYneNJKUjaZ6x7iVO3YKmQrwXXbh3HP4GGtoFbtouKupkLTMihUeg/cEl4UJiqNcic2Oa9sgXPJTL3fCAN4+tAJqffR04fcWFMTld+lnaVcxdUkxVQt7ce0nnQa5E4Y9Pd2R9Z1hhnK+nu7cfM18YrdTDVUUd2P7D+GlYvmKnPcqHZRcVdTYc+kmSq9RtmSTM8bXOCMKBLZCXTGY3E8TOUn2pGnuJo0MEnnIohSTzoNclnPYPWH5klrFOsQ2Rtlx3W1ZoGp27FlKKOJ4ab5uP+mJQ0rGu7H5Jk0K5d+o9yJ455X5+boFAhnL6hTUwh0wjaoastTXE2a6J6DzPux0eROGMiSz5kgCzQycT21HXsyug7+zkilbgIT6hxZpa4o/vFBsvZMGiV4TM4bdD8V9gCZGmj6tALOKjKYAqjtunXP2Z8HaqrG1ZggC8QUhKVqbwS5EwZxV5LBQCNZoXEZ1ltoMutXLVT6U1/aWTKO+E1SXauz5Nhn4iG73zsPljF9WgFVyaSvEwROkWqFcMKes5+8VzOLi7gnWQl8zJ0wSKK/FysdU0HQ3VmynTxAf283Drx1qk5NJ3ZQKu+STbuOGFdEE8FMKxfNxc6D5ToVRDP1rFlCFoCmut9xWPOR+ZPSXMgEwVTcKccNKBSfi1NtsRHkThiotq9FIkww49LOEk6cqUiLewvr/qMv1LvUBZmKnd6U+/qXoO/yOXWqCV3ksXAHFYNIp4bwD7jg90xVFYRqxxV14u8sOThTqUon+qcPncDTh07U2c9kaUSmCnFzW5moQZtti8ydMFAlgrv/JjdR3ZY9r0kFAXDRpz3Mt70Vxp12IbhK2rqmR7pTkBGW0E8QHHD2OagD0KLgFAhE6vxEKicKIQimYsoVXRyFrl+aemM1k9wJA1lCNCK3EHdYRkbh014kUgqEbWt67OTjITNM+tU2IqjPJFumeP+Kgb218918TTf2vXpSukPISzK5OMjUEqmoFAjK+slhpKXSaLf64nHjKHSvt2qHlbs4A+CiL+/WNT04PzYxKTOmCr8kVhXEAdwSgFMxlD6ILLjskf3H6lY7poJAEDyfrEa1YCrGd6jSO+jKVpoSLIkZBQYS5x1qx9QVceModDU8WpW5IJfCQGDqkRIMOBIFcWST0OnRKtY/cSjTHbQZqILL0kQEq6kmujzGd4QV2lGpJaJUhWsUSSfvdkxdETegMKxIVisS++VOTeTHJOOlStd5X/8SqbEMcFdQU1VFIWjWqpwBnKuOS1OI582Ab2KMVN33M5Uqtq7pqalYChpVZyOJUv8jSDumrohTTMmvwp7pFDAyWq19DkAsg3Qa5FYYDA6VQ20EYROKLuo4yx20Gai8fcLuuQrd5yrVCdy+fAH2vXqybXTJcTAxRqru+6Wem7Nw/ZRFzTsFV0CoNHfBZxD2fhVBTybTCU13bVkmSjEl/30ZqVRRcorY6rND9t77bCyDdBrkVk2k8oMWFIlw8zXxPVGy3kHTQrVlVW1zb/OlNw5LMud/38evmqN979OHTuQ+G2zcLKT+RY2YcIKCoMMpAATtxM646F7d3VnCls8vxR/f0hMrWVocdU/e64GEqcEGh8pKA34zFp+JdgZE9CaAfwUwDmCMmfuIaA6A7QCuAPAmgFuY+TQREYA/gVsWcxTAbzDz95N8v46wmzfOjJ0Hy+i7fI5yYunqcJQPJy8dVIeJ2iJse3zFht3K8wv1nPgeHXGqbrWbZ4rJyjjsvqvsZOfH2EhtNM5cV39a7DbWP3EokZHZJHkgkN/UFWHCXicsm7H4TENNtJKZ/8X3/wYA32PmASLa4P3/+wA+BeBq72cZgK97vxuCSc3RsO3XxhsWSwfA7csX5KaD6ghTW+i2x2Ii1rFiYK8ySjYp7VjoXhUjI8tCqroGVZ+PYj+QjQtVDesowiFLyQNbQZiw1wnLZiw+G2EzuBHAJ7y/HwLw93CFwY0A/pLdosv7iaiTiOYx84kGtEEbtOQn6NseXBEBqEXOiviDpw+dwO6XT0wy/OSxA8c16JkmmSuPVLB2+7BRW6KmJY8bDNRK0lgZ62JkoiCesW53NThUlubVkZEndU9cwoS9Slg0K9dWUmHAAJ4lIgbwX5j5QQDv903wPwbwfu/vbgD+PA9ve8cmCQMiuhPAnQCwYMGC2A0LDiyVdwXh4mpKtnqUpeH1qyzaYcUZl7gGvbRX+v4Eaaa0o2cKkHxlrJuYgx5ZOkySCvb3dmOdgTCfimkqZIQJe5WwaFauraTC4N8wc5mIfhHAc0T0qv9FZmZPUBjjCZQHAaCvry/REsc/sGSrVZkHi2z1GDa5ZX3FGRdTtUWQNCbcpPlu2tUzJSndiusW91FMRCZedmEGzzAnDZEGJm/jIgk6Yd9qm0kiYcDMZe/3u0T01wA+CuAnQv1DRPMAvOu9vQzAH9p7mXesKchutC7vvu5/k8/kgUYUTwlDZCRNOgjiCrJ2R3fd/oloxcBeZUJHMYGrVv0mSfCi5u9qN2N/EnTX2kqbSWxhQESzABSY+V+9v68DcC+AXQDuADDg/f6u95FdAL5MRI/BNRyfaZS9QEXwRvfe+6zUWygY8WoyueV1xRm1cw4OlXH2fHilLBlpJjtr9SrLlLQnQdPrXrlorjRNu0jFsmJgr3LVT6ROghdnJ9eOxv64ZPlak+wM3g/gr12PUUwD8B1m/lsiegnADiL6IoC3ANzivf8ZuG6lR+G6lv5mgu9OBZV6NXg8zBg9FVacJpgajonq73Ej7mHWPVMaNTGortsveAqKYuxPHzpRVyMiiGrcEBBLmLejsT8uWb7W2MKAmX8EYKnk+E8BfFJynAF8Ke73NYIzCt/14HFVJtS8exNFxdhwzG7216yv2htNoyYG2W4DmOwEoTI0x4nnEMTdHbersT8OWb7W3KajMCGKkTHrq8wsYNqh/akTpjKNmBhUu42ZTiH1WA4/SXZ2U8nYr7vWVttNcpmOwjTrn2n4e9Isgq3KQthsTAavValdJG76Yx2q3UbcOgUqCK6RWKQTSeI1lPc0FH5U17py0dyWp+/OnTCIkhO9v7cb99+0ZFKOnGCnTppjvR1ztMdF1tGdAsWaNKaCAG3EJNjcbLIT2LqmJ3GuKP84BFyPJqEuy9tzV805+1492fL03cQtSHNrSl9fHx84cCDSZ1Quc3E9VZKeL+32ZJ00troyQ3RefdbTVg2o+ltnycH5sQmlqihuttk0+/FUeu5BrtywW3r/CcAbA6sjn4+IDjJzX5TP5M5mkLYeNun5smwwagRp2AJUqo5Nu47kzuictu0kLIpVpFYJwognENLsx1n2tGk0WbCb5E4YpH1Tk54vCw85CwwOlbH5qSM13XVnycGmz8qDklQTzEilWvN2yZJ/dpYIizPo7+1WrkJFCmuZp5HqeJr9eKotnPxkIUgydzaDtPWwSc83lYxjKkT6Y78Rc6RSxfrH5eVDTSeYrJdEbBX9vd3a2g+6+rsP3LJU2l9vXTa/4f24EQb1dsHEftlocrczSDvyNOn52iUStpFs2fOaNNVxdYKxadeRunthmnEWmBqrxrQJS1kByPtr3+VzGtqPs7A6biWtdrfOnQHZT6v9di0uugI3gBuAFnwuwWc3emFM6h6ZV0N8o8nq2Mhqu9qNOAbk3AqDqeyZkCUGh8qhNQtMJnRV1tnbli/Aff1L0miqxZIb4giD3NkMBJufOtJyv92pjkk5S8BM1dPf242br+mGP6MOA9h5sJw7X3SLpRXkUhi0urC0xcU0V1EwS6yKfa+elNafuGvHoVwHp1kszSB3BmSg9YWlLS6mgtdUU6k6n3B5tO6mFkt8crkzaHVhaYuLqeBVZY+Ncz6rCrRY4pFLYaCaNOIWlp4KeXIagSzGQoap0DA9n1UFWizRyaUwUAV6xSksPZUSzaVNMAGZCtPdWjAwp6go0GJVgRZLdHIpDNKM5gsrCm7RI6JhVQIh6m7NH12ripa1qkCLJTpNNyAT0fUA/gRAEcA3mXmgEd+TVjTfVM6XkiZhCdTiYKO7LZb0aKowIKIigD8D8KsA3gbwEhHtYuZXmtmOKNhEc+nQqIm71SH8FkteaPbO4KMAjnr1k0FEjwG4EUBmhcFUz5eSJnbitliyS7OFQTeA477/3wawrMltiIRVRVgslqlA5oLOiOhOAHcCwIIFC1rcGhe7orVYLHmn2d5EZQDzff9f5h2rwcwPMnMfM/fNnTu3qY2zWCyWqUqzhcFLAK4moiuJaDqALwDY1eQ2WCwWiyVAU9VEzDxGRF8GsAeua+m3mflIM9tgsVgslnqabjNg5mcAPNPs77VYLBaLmlxGIFssFoslGpmudEZEJwG8leAUvwDgX1JqTju3AbDtCGLbMZkstCMLbQDy0Y7LmTmSB06mhUFSiOhA1NJveWyDbYdtRzu0IwttmMrtsGoii8VisVhhYLFYLJb8C4MHW90AZKMNgG1HENuOyWShHVloAzBF25Frm4HFYrFYzMj7zsBisVgsBlhhYLFYLBaAmTPzAzeJ3T649Q2OAPgd7/gcAM8B+KH3u8s7vgjAPwI4D+D3fOdZCGDY9/MzAGsV33k9gNcAHAWwwdeGE955Ga6/b9Pa4LsXJwBcAFCBm+67p0XtGPLacA7Ai3Aj1yO1w3ttnfdc/wnAowBmKtpxh3feHwK4w9eONwFUAYzH7RspteO0d17xXH6xRe044j2TcwD+LsH9+B2vDUdUfaORYyVJG9IcKym1I8pYuQ3AywAOA/gHAEt15zftG97xr8EtF/Ce8fwbdcJu5A+AeQA+7P39PgD/DOCDAP7Qd8M3APgD7+9fBPAR78J/T3HOIoAfww3CkL32OoAPAJgO4BCAfwvgwwB6ASyGO/l8vMlt+KB3L3YB+FwL78UHAVzqfeaXvXb8FMA9UdsBt5bFGwBK3v87APyGpB1zAPzI+93l/d3l3Y87vN/vxb0fKbXjAIC+JM8lhXZ8EO4EONdrxxkAvxWjHb8Cd/LrgDtx/R2AX2rmWEnYhtTGSgrtiDpWPo6LguFTAF7Qnd+0b3ivLffui7EwyJSaiJlPMPP3vb//FcAP4A6aGwE85L3tIQD93nveZeaX4HZCFZ8E8DozyyKZa5XXmPkCgMcArGDm7zPzELtJ9Kpwb2oz23AjM58AcKrF9+JG73xnmfmfvXa8AuCGmO2YBqBERNPgDrh3JO9ZBeA5Zj7FzKfhrqSu9/rGQ959QcL7kagdcIVRGs8ldjsAzALwCjOf9Npx1PvOqO34H+FOQqPMPAbg/wVwk6QdjRwrSdqQ5lhJ1A5EHyv/4D1TANgPN6W/7vxBVH0DzLzfN1aMyJQw8ENEV8BdcbwA4P2+C/sxgPdHONUX4G7BZcgqr9Wq2HhtmA7gYKvaAOBrRPQDACvhbkGb3Y5/ATCNiPq8+7EUwCVR28HMZQB/BOAY3BXtGWZ+NkI7/BBi9o0U2/FfiegVAP+uRe04CmAhEV1BRFfBncicqO2AuxL+n4jo54moA8CnMbnmSFg7ACQeK6m0AcnHStJ2JBkrXwTwN4bXGdaOWGRSGBDRJQB2wtXZ/cz/Grt7ICN/WK9mwmcBPJ6gDafgrQSb3QYAdwO4Bq4O8iCA/9Tsdnjf8QUAfwJXj7oHgRWVSTuIqAvu6uZKuNvpWUR0e5S2eOe5BMBMxOwbKbXjNgAfg6tzfhPeSq+Z7fBWgv8r3Od5CO6q8HzUdjDzDwD8AYBnAfwtXLvSuO4zQZKOlTTagBTGStJ2xB0rRLQSrjD4fdPvagSZEwZE5MDtWI8w85Pe4Z8Q0Tzv9XkA3jU83acAfJ+Zf+J9dj4RDXs//xGKymv+NgAYbUUbvL//xWvHwwA2w90+tqIdB+AO8v8LwDfg6mSjtuPfA3jDU2tUATwJ4ONEtMzXjs/q2uF7LmMJ+kbidnjfsRPAXwHYhnjPJY12/C3cCfgrAHYj3nMBM3+Lma9h5n8L1zj+z80eK0na4P2dylhJoR2RxgoRfQjAN+Gqu37qHVbd6yh9Izoc0cjbyB+42/+/BLAtcHwLJhtg/jDw+iZIjKZwdW2/qfm+aXCNLlfioqFmsb8NcFd+v9CCNhDcVd827+9tAAZa1I4d3vfPAPA9ANdGbQeAZXBXSx3eOR8C8L9J2jEHrmG1y/t5wztW6xvwGcVa0I5pALZ77XAAPAHgP7bofojn0gV3FfvLcfoHgF/0fi8A8CqAzmaPlYRtSG2spNAO47HifcdRAB83Ob9p3wi8p229if4N3C3Uy7joCvlpAD/v3dgfwrXwz/He/z/A1ZP9DMCI9/fPea/NgmvNnx3ynZ+GK71fB/B/+tpQhuuqxt55/qpZbQjci4r3cwquMatp9yLQDuHCWE7wTDbDHWD/5N3PGYp2/JY3SI7CE2C+dlzw/X6kBe3494HnchLAZ1p4P8RzeTPBc/n/4Bo7DwH4ZIvGSqw2NGCspNEO07HyTbi7DzHXHdCd37RveMf/0LuuCe/3prD516ajsFgsFkv2bAYWi8ViaT5WGFgsFovFCgOLxWKxWGFgsVgsFlhhYLFYLBZYYWCxWCwWWGFgsVgsFgD/P4Zej+m9vqvOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(PhillyWeatherCount[PhillyWeatherCount['Year'] >= 2017].Date, PhillyWeatherCount[PhillyWeatherCount['Year'] >= 2017].Count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1646ab490>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# provide the inverse of the log function so we can more accurately compare our models predictions to the true values \n",
    "\n",
    "plt.scatter(PhillyWeatherCount[PhillyWeatherCount['Year'] >= 2017].Date, np.expm1(ols_recent.predict(X_recent_scaled)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = ols_recent.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "mse = MSE(np.expm1(y_pred), np.expm1(Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse = mse**0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "323875.00087119715\n",
      "569.1001676956326\n"
     ]
    }
   ],
   "source": [
    "# our root mean square error ultimately ends up being relatively low given the spread of the ride counts\n",
    "\n",
    "print(mse)\n",
    "print(rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}