Autolib_hypotheses_testing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Statement\n",
    "### The problem that I am trying to solve is whether the bluecars taken in month 5 are greater than the bluecars taken in month 1\n",
    "### I am tasked with veryfing a hypothesis from the autolib car sharing service about their electric blue cars from the above problem.\n",
    "### This hypothesis would help the company know whether demand for the bluecars is increasing and thus act in informing a decision on whether to increase the bluecar fleet.\n",
    "\n",
    "### H0:There is no difference in blucar usage between month 1 and month 5\n",
    "### H1:There is a clear increase in bluecar usage in month 5 as compared to month 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### step one: Previewing and cleaning the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing relevant libraries\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas_profiling\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>Postal code</th>\n",
       "      <th>date</th>\n",
       "      <th>n_daily_data_points</th>\n",
       "      <th>dayOfWeek</th>\n",
       "      <th>day_type</th>\n",
       "      <th>BlueCars_taken_sum</th>\n",
       "      <th>BlueCars_returned_sum</th>\n",
       "      <th>Utilib_taken_sum</th>\n",
       "      <th>Utilib_returned_sum</th>\n",
       "      <th>Utilib_14_taken_sum</th>\n",
       "      <th>Utilib_14_returned_sum</th>\n",
       "      <th>Slots_freed_sum</th>\n",
       "      <th>Slots_taken_sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75001</td>\n",
       "      <td>1/1/2018</td>\n",
       "      <td>1440</td>\n",
       "      <td>0</td>\n",
       "      <td>weekday</td>\n",
       "      <td>110</td>\n",
       "      <td>103</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75001</td>\n",
       "      <td>1/2/2018</td>\n",
       "      <td>1438</td>\n",
       "      <td>1</td>\n",
       "      <td>weekday</td>\n",
       "      <td>98</td>\n",
       "      <td>94</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>75001</td>\n",
       "      <td>1/3/2018</td>\n",
       "      <td>1439</td>\n",
       "      <td>2</td>\n",
       "      <td>weekday</td>\n",
       "      <td>138</td>\n",
       "      <td>139</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75001</td>\n",
       "      <td>1/4/2018</td>\n",
       "      <td>1320</td>\n",
       "      <td>3</td>\n",
       "      <td>weekday</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75001</td>\n",
       "      <td>1/5/2018</td>\n",
       "      <td>1440</td>\n",
       "      <td>4</td>\n",
       "      <td>weekday</td>\n",
       "      <td>114</td>\n",
       "      <td>117</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16080</th>\n",
       "      <td>95880</td>\n",
       "      <td>6/15/2018</td>\n",
       "      <td>1440</td>\n",
       "      <td>4</td>\n",
       "      <td>weekday</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16081</th>\n",
       "      <td>95880</td>\n",
       "      <td>6/16/2018</td>\n",
       "      <td>1440</td>\n",
       "      <td>5</td>\n",
       "      <td>weekend</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16082</th>\n",
       "      <td>95880</td>\n",
       "      <td>6/17/2018</td>\n",
       "      <td>1440</td>\n",
       "      <td>6</td>\n",
       "      <td>weekend</td>\n",
       "      <td>33</td>\n",
       "      <td>35</td>\n",
       "      <td>1</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>16083</th>\n",
       "      <td>95880</td>\n",
       "      <td>6/18/2018</td>\n",
       "      <td>1440</td>\n",
       "      <td>0</td>\n",
       "      <td>weekday</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16084</th>\n",
       "      <td>95880</td>\n",
       "      <td>6/19/2018</td>\n",
       "      <td>1370</td>\n",
       "      <td>1</td>\n",
       "      <td>weekday</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16085 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Postal code       date  n_daily_data_points  dayOfWeek day_type  \\\n",
       "0            75001   1/1/2018                 1440          0  weekday   \n",
       "1            75001   1/2/2018                 1438          1  weekday   \n",
       "2            75001   1/3/2018                 1439          2  weekday   \n",
       "3            75001   1/4/2018                 1320          3  weekday   \n",
       "4            75001   1/5/2018                 1440          4  weekday   \n",
       "...            ...        ...                  ...        ...      ...   \n",
       "16080        95880  6/15/2018                 1440          4  weekday   \n",
       "16081        95880  6/16/2018                 1440          5  weekend   \n",
       "16082        95880  6/17/2018                 1440          6  weekend   \n",
       "16083        95880  6/18/2018                 1440          0  weekday   \n",
       "16084        95880  6/19/2018                 1370          1  weekday   \n",
       "\n",
       "       BlueCars_taken_sum  BlueCars_returned_sum  Utilib_taken_sum  \\\n",
       "0                     110                    103                 3   \n",
       "1                      98                     94                 1   \n",
       "2                     138                    139                 0   \n",
       "3                     104                    104                 2   \n",
       "4                     114                    117                 3   \n",
       "...                   ...                    ...               ...   \n",
       "16080                  15                     10                 0   \n",
       "16081                  19                     19                 0   \n",
       "16082                  33                     35                 1   \n",
       "16083                  11                     14                 3   \n",
       "16084                   2                      2                 2   \n",
       "\n",
       "       Utilib_returned_sum  Utilib_14_taken_sum  Utilib_14_returned_sum  \\\n",
       "0                        2                   10                       9   \n",
       "1                        1                    8                       8   \n",
       "2                        0                    2                       2   \n",
       "3                        2                    9                       8   \n",
       "4                        3                    6                       6   \n",
       "...                    ...                  ...                     ...   \n",
       "16080                    0                    2                       3   \n",
       "16081                    0                    2                       1   \n",
       "16082                    1                    0                       0   \n",
       "16083                    5                    2                       2   \n",
       "16084                    1                    0                       0   \n",
       "\n",
       "       Slots_freed_sum  Slots_taken_sum  \n",
       "0                   22               20  \n",
       "1                   23               22  \n",
       "2                   27               27  \n",
       "3                   25               21  \n",
       "4                   18               20  \n",
       "...                ...              ...  \n",
       "16080                0                0  \n",
       "16081                0                0  \n",
       "16082                0                0  \n",
       "16083                0                0  \n",
       "16084                0                0  \n",
       "\n",
       "[16085 rows x 13 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#importing the dataset\n",
    "df = pd.read_csv('autolib_daily_events_postal_code.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16085, 13)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The dataset has 16085 rows and 13 columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### checking the unique dates in the autolib dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1/1/2018', '1/2/2018', '1/3/2018', '1/4/2018', '1/5/2018',\n",
       "       '1/6/2018', '1/7/2018', '1/8/2018', '1/9/2018', '1/10/2018',\n",
       "       '1/11/2018', '1/12/2018', '1/13/2018', '1/14/2018', '1/15/2018',\n",
       "       '1/16/2018', '1/17/2018', '1/18/2018', '1/19/2018', '1/20/2018',\n",
       "       '1/21/2018', '1/22/2018', '1/23/2018', '1/24/2018', '1/25/2018',\n",
       "       '1/26/2018', '1/27/2018', '1/28/2018', '1/29/2018', '1/30/2018',\n",
       "       '1/31/2018', '2/1/2018', '2/2/2018', '2/3/2018', '2/4/2018',\n",
       "       '2/5/2018', '2/6/2018', '2/7/2018', '2/8/2018', '2/9/2018',\n",
       "       '2/10/2018', '2/11/2018', '2/12/2018', '2/13/2018', '2/14/2018',\n",
       "       '2/15/2018', '2/16/2018', '2/17/2018', '2/18/2018', '2/19/2018',\n",
       "       '2/20/2018', '2/21/2018', '2/22/2018', '2/23/2018', '2/24/2018',\n",
       "       '2/28/2018', '3/1/2018', '3/2/2018', '3/3/2018', '3/4/2018',\n",
       "       '3/5/2018', '3/6/2018', '3/7/2018', '3/8/2018', '3/9/2018',\n",
       "       '3/10/2018', '3/11/2018', '3/12/2018', '3/13/2018', '3/14/2018',\n",
       "       '3/15/2018', '3/16/2018', '3/17/2018', '3/18/2018', '3/19/2018',\n",
       "       '3/20/2018', '3/21/2018', '3/22/2018', '3/23/2018', '3/24/2018',\n",
       "       '3/25/2018', '3/26/2018', '3/27/2018', '3/28/2018', '3/29/2018',\n",
       "       '3/30/2018', '3/31/2018', '4/1/2018', '4/2/2018', '4/3/2018',\n",
       "       '4/4/2018', '4/5/2018', '4/6/2018', '4/7/2018', '4/8/2018',\n",
       "       '4/9/2018', '4/10/2018', '4/11/2018', '4/12/2018', '4/13/2018',\n",
       "       '4/14/2018', '4/15/2018', '4/16/2018', '4/17/2018', '4/18/2018',\n",
       "       '4/19/2018', '4/20/2018', '4/21/2018', '4/22/2018', '4/23/2018',\n",
       "       '4/24/2018', '4/25/2018', '4/26/2018', '4/27/2018', '4/28/2018',\n",
       "       '4/29/2018', '4/30/2018', '5/1/2018', '5/2/2018', '5/3/2018',\n",
       "       '5/4/2018', '5/5/2018', '5/6/2018', '5/7/2018', '5/8/2018',\n",
       "       '5/20/2018', '5/21/2018', '5/22/2018', '5/23/2018', '5/24/2018',\n",
       "       '5/25/2018', '5/26/2018', '5/27/2018', '5/28/2018', '5/29/2018',\n",
       "       '5/30/2018', '5/31/2018', '6/1/2018', '6/2/2018', '6/3/2018',\n",
       "       '6/4/2018', '6/5/2018', '6/6/2018', '6/7/2018', '6/8/2018',\n",
       "       '6/9/2018', '6/10/2018', '6/11/2018', '6/12/2018', '6/13/2018',\n",
       "       '6/14/2018', '6/15/2018', '6/16/2018', '6/17/2018', '6/18/2018',\n",
       "       '6/19/2018'], dtype=object)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.date.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the above data I can tell that the data only runs until the sixth month of the year and that on data on the 6th month is incomplete"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Inorder to select the specific months I will be working with I will need to access the datetime column but it is in string format.\n",
    "\n",
    "#### I will first need to change it to the datetime format to access the 1st and 5th months and create 2 new datafames each month in a different dataframe, I will change it back to string format later as I will not be doing any operation on the dates during this analysis\n",
    "\n",
    "#### note that I am only using the weekdays and not the weekends in the analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting the date column datatype to datetime \n",
    "df['date'] = pd.to_datetime(df['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2369, 13)"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating month 1 dataframe \n",
    "january = df[(df.date >= '1/1/2018')& (df.date <= '1/31/2018') & (df.day_type == 'weekday')]\n",
    "january.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1558, 13)"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating month 3 dataframe\n",
    "may = df[(df.date >= '5/1/2018')& (df.date <= '5/31/2018') & (df.day_type == 'weekday')]\n",
    "may.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reconverting the date column datatype back to str\n",
    "may.date = may.date.apply(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "january.date = january.date.apply(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I will then remove any outliers that might affect the final calculation \n",
    "\n",
    "#### NB: I discovered that running the outlier algorithm a second or third time resulted in more outliers being removed improving on the overall quality of the data,thus I ran it 5 times for each dataframe to get a more coherent dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(744, 13)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#removing outliers in january dataframe\n",
    "q1 = january.quantile(0.25)\n",
    "q3 = january.quantile(0.75)\n",
    "IQR = q3-q1\n",
    "january = january[~((january < (q1 - 1.5 * IQR)) |(january > (q3 + 1.5 * IQR))).any(axis=1)]\n",
    "january.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(518, 13)"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#removing outliers in march dataframe\n",
    "q1 = may.quantile(0.25)\n",
    "q3 = may.quantile(0.75)\n",
    "IQR = q3-q1\n",
    "may = may[~((may < (q1 - 1.5 * IQR)) | (may > (q3 + 1.5 * IQR))).any(axis=1)]\n",
    "may.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Postal code               0\n",
       "date                      0\n",
       "n_daily_data_points       0\n",
       "dayOfWeek                 0\n",
       "day_type                  0\n",
       "BlueCars_taken_sum        0\n",
       "BlueCars_returned_sum     0\n",
       "Utilib_taken_sum          0\n",
       "Utilib_returned_sum       0\n",
       "Utilib_14_taken_sum       0\n",
       "Utilib_14_returned_sum    0\n",
       "Slots_freed_sum           0\n",
       "Slots_taken_sum           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for null values\n",
    "may.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Postal code               0\n",
       "date                      0\n",
       "n_daily_data_points       0\n",
       "dayOfWeek                 0\n",
       "day_type                  0\n",
       "BlueCars_taken_sum        0\n",
       "BlueCars_returned_sum     0\n",
       "Utilib_taken_sum          0\n",
       "Utilib_returned_sum       0\n",
       "Utilib_14_taken_sum       0\n",
       "Utilib_14_returned_sum    0\n",
       "Slots_freed_sum           0\n",
       "Slots_taken_sum           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "january.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### checking the frequency of days in 'days of the week' column in march and january"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    132\n",
       "0    126\n",
       "2     91\n",
       "3     88\n",
       "4     81\n",
       "Name: dayOfWeek, dtype: int64"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "may['dayOfWeek'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13eeca1b348>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAD3CAYAAAAXDE8fAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAQ4UlEQVR4nO3df+xddX3H8WcLQufybWXLrWSJSbfM73uGBBxlwPihdTgJM6YbWTazgGFkwPyaedUZVArp14XFwKSbwMDZ0qFEIxHXZJIw0NBVZEFcxSmMvBEccWayXn/QfrWhWPjuj3ua+7Ur33t77v1yTj99PpIm55x77uF93mlf58O558ey+fl5JEnlWd50AZKkpWHAS1KhDHhJKpQBL0mFMuAlqVDHNl3AQr3eXOOX9Jxwwiv58Y/3Nl1GK9iLAXsxYC8G2tCLTmdq2Ut95gj+IMcee0zTJbSGvRiwFwP2YqDtvVh0BB8RrwC2AmuA44Frgf8EbgfmgUeBd2XmixFxGXAFsB+4NjPvXrqyJUnDDBvBXwT8MDPPBS4AbgY2AVdXy5YB6yPiRODdwNnA+cBHIuL4pStbkjTMsHPwnwPuWjC/H1gL7Kjm7wHeArwAPJiZ+4B9EfEkcDLwtWEFRMQssBFgZmaGbrd7OPUviU5nqukSWsNeDNiLAXsx0OZeLBrwmfkTgIiYoh/0VwMfzcwDP4bOAauAlcDuBV89sHyozJwFZqH/I2uvNzd69Uug05mi6Rrawl4M2IsBezHQhl4sdoAZ+iNrRLwG2A7ckZmfAV5c8PEU8Cywp5o+eLkkqSGLBnxEvBq4D/hAZm6tFj8SEeuq6QuAB4CHgXMjYkVErAJeR/8HWElSQ4adg78KOAG4JiKuqZZ1gRsj4jjgceCuzHwhIm6kH/bLgQ2Z+dxSFS1JGm5Zmx4X3IYbndpwTq0t7MWAvRiwFwNt6IU3OknSUahVjyoY1+pbVjZdAgC7ZvY0XYIkOYKXpFIZ8JJUKANekgplwEtSoQx4SSpUUVfRaKCzejJXFHXG/H5vl1cUSU1xBC9JhTLgJalQBrwkFcqAl6RCGfCSVCgDXpIKZcBLUqEMeEkqlAEvSYUy4CWpUCM9qiAizgCuy8x1EfFZ4MTqozXAQ5n59uqdrGcDB95ftT4zd0+6YEnSaIYGfERcCVwM/BQgM99eLT8B2A68t1r1VOD8zPzB0pQqSToco5yieQq48BDLPwzclJnfj4jlwGuBT0TEgxFx6SSLlCQdvqEj+Mz8fESsWbgsIlYD5zEYvf8icBOwCTgG2B4R/56Z3xy2/YiYBTYCzMzM0O12D6f+Vup0ppouoTVK6kVJ+zIuezHQ5l7UfVzwHwKfycwXqvm9wMcycy9ARNwPnAIMDfjMnAVmAXq9ufleb27R9Y8EbdiHcR/zOylt6MUkdDpTxezLuOzFQBt6sdgBpu5VNG8G7lkwPw18JSKOiYhXAOcAX6+5bUnSBNQN+AC+c2AmMx8HPg08BOwAPpWZj41fniSprpFO0WTm08CZC+ZPOsQ61wPXT6wySdJYvNFJkgplwEtSoQx4SSqUAS9JhTLgJalQBrwkFcqAl6RCGfCSVCgDXpIKZcBLUqEMeEkqlAEvSYWq+zx46YixevWkXsgw3nZ27fIZ6np5OYKXpEIZ8JJUKANekgplwEtSoQx4SSqUAS9JhRrpMsmIOAO4LjPXRcSpwBeAb1cf35qZd0bEZcAVwH7g2sy8e0kqliSNZGjAR8SVwMXAT6tFpwKbMvOGBeucCLwbOA1YAXwlIr6YmfsmX7IkaRSjjOCfAi4E7qjm1wIREevpj+LfA5wOPFgF+r6IeBI4Gfja5EuWJI1iaMBn5ucjYs2CRQ8DWzJzZ0RsADYC3wB2L1hnDlg1SgERMVttg5mZGbrd7miVt1inM6k7J4989mKgpF6UtC/janMv6jyqYFtmPntgGrgJ+DI/fx/3FPDswV88lMycBWYBer25+V7vyL+duw370Gm6gEobejHuIwYmpR29GF+nM1XMvoyrDb1Y7ABT5yqaeyPi9Gr6PGAn/VH9uRGxIiJWAa8DHq2xbUnShNQZwb8TuDkingeeAS7PzD0RcSPwAP2DxobMfG6CdUqSDtNIAZ+ZTwNnVtNfB846xDqbgc2TLE6SVJ83OklSoQx4SSqUAS9JhTLgJalQBrwkFcqAl6RCGfCSVCgDXpIKZcBLUqEMeEkqlAEvSYUy4CWpUAa8JBXKgJekQhnwklQoA16SCmXAS1KhDHhJKpQBL0mFGumdrBFxBnBdZq6LiNcDNwEvAPuAd2Tm/1Yv3T4bmKu+tj4zdy9F0ZKk4YYGfERcCVwM/LRa9DHgLzLzGxFxBfAB4H3AqcD5mfmDpSpWkjS6UUbwTwEXAndU82/PzO8v+P5zEbEceC3wiYh4NXBbZm4dpYCImAU2AszMzNDtdg+j/HbqdKaaLqE17MVASb0oaV/G1eZeLJufnx+6UkSsAT6bmWcuWHYWcBvwBuA5oAtsAo4BtgOXZuY3D6eYXm9ueDGLWH3LynG+PjG7ZvY0XQKd1e3oRW9X871Yvbod/wB37ZobvtIRoNOZotcrY1/G1YZedDpTy17qs1o/skbEHwMfB96amT1gL/CxzNybmXPA/cApdbYtSZqMkX5kXSgiLgKuANZl5o+qxdPAZyPiVPoHjXOAT06sSknSYTusgI+IY4Abge8C/xQRADsyc2NEfBp4CPgZ8KnMfGzSxUqSRjdSwGfm08CB8++/9BLrXA9cP5myJEnj8kYnSSqUAS9JhTLgJalQBrwkFcqAl6RCHfZ18JKOXI+t3tl0CQCctGtt0yUcFRzBS1KhDHhJKpQBL0mFMuAlqVAGvCQVyoCXpEIZ8JJUKANekgplwEtSoQx4SSqUAS9JhTLgJalQIz1sLCLOAK7LzHUR8evA7cA88Cjwrsx8MSIuo/8y7v3AtZl59xLVLEkawdARfERcCWwBVlSLNgFXZ+a5wDJgfUScCLwbOBs4H/hIRBy/NCVLkkYxyimap4ALF8yvBXZU0/cAbwZOBx7MzH2ZuRt4Ejh5koVKkg7P0FM0mfn5iFizYNGyzJyvpueAVcBKYPeCdQ4sHyoiZoGNADMzM3S73VG+1mqdzlTTJbSGvRiwFwMl9aLN+1LnhR8vLpieAp4F9lTTBy8fKjNngVmAXm9uvtebq1FSu7RhHzpNF1BpQy9+/q9mc9rRi3YopRedzlTj+7LYAabOVTSPRMS6avoC4AHgYeDciFgREauA19H/AVaS1JA6I/i/BDZHxHHA48BdmflCRNxIP+yXAxsy87kJ1ilJOkwjBXxmPg2cWU0/AbzxEOtsBjZPsjhJUn3e6CRJhTLgJalQBrwkFcqAl6RCGfCSVCgDXpIKZcBLUqEMeEkqVJ07WSXpiPfYYyubLgGAk07as2TbdgQvSYUy4CWpUAa8JBXKgJekQhnwklQoA16SCmXAS1KhDHhJKpQBL0mFMuAlqVC1HlUQEZcAl1SzK4DXA2cBXwC+XS2/NTPvHLM+SVJNtQI+M28HbgeIiL8HtgKnApsy84ZJFSdJqm+sUzQRcRpwUmZ+AlgLvDUivhwRt0XE1EQqlCTVMu7TJK8CPlxNPwxsycydEbEB2Ai8f9gGImK2WpeZmRm63e6YJTWv0/HYdoC9GLAXA/ZiYCl7UTvgI+JVwG9k5vZq0bbMfPbANHDTKNvJzFlgFqDXm5vv9ebqltQabdiHTtMFVNrQC2hHmLSjF+1gLwbG7cViB4hxTtG8AfjSgvl7I+L0avo8YOcY25YkjWmcUzQBfGfB/DuBmyPieeAZ4PJxCpMkjad2wGfm3xw0/3X6l0pKklrAG50kqVAGvCQVyoCXpEIZ8JJUKANekgplwEtSoQx4SSqUAS9JhTLgJalQBrwkFcqAl6RCGfCSVCgDXpIKZcBLUqEMeEkqlAEvSYUy4CWpUAa8JBXKgJekQtV+J2tEPALsrmb/C/hr4HZgHngUeFdmvjhugZKkemoFfESsAMjMdQuW/TNwdWb+a0R8HFgPbJtEkZKkw1d3BH8K8MqIuK/axlXAWmBH9fk9wFsYIeAjYhbYCDAzM0O3261ZUnt0OlNNl9Aa9mLAXgzYi4Gl7EXdgN8LfBTYAryWfqAvy8z56vM5YNUoG8rMWWAWoNebm+/15mqW1B5t2IdO0wVU2tALaEeYtKMX7WAvBsbtxWIHiLoB/wTwZBXoT0TED+mP4A+YAp6tuW1J0gTUvYrmUuAGgIj4FWAlcF9ErKs+vwB4YOzqJEm11R3B3wbcHhFfoX/VzKXAD4DNEXEc8Dhw12RKlCTVUSvgM/N54E8O8dEbxytHkjQp3ugkSYUy4CWpUAa8JBXKgJekQhnwklQoA16SCmXAS1KhDHhJKpQBL0mFMuAlqVAGvCQVyoCXpEIZ8JJUKANekgplwEtSoQx4SSqUAS9JhTLgJalQtV7ZFxGvALYCa4DjgWuB7wFfAL5drXZrZt45gRolSTXUfen2RcAPM/PiiPhl4BHgr4BNmXnDxKqTJNVWN+A/B9y1YH4/sBaIiFhPfxT/nsycG7M+SVJNtQI+M38CEBFT9IP+avqnarZk5s6I2ABsBN4/bFsRMVuty8zMDN1ut05JrdLpTDVdQmvYiwF7MWAvBpayF3VH8ETEa4BtwC2Z+ZmIeFVmPlt9vA24aZTtZOYsMAvQ683N93pH/qC/DfvQabqASht6Ae0Ik3b0oh3sxcC4vVjsAFHrKpqIeDVwH/CBzNxaLb43Ik6vps8DdtbZtiRpMuqO4K8CTgCuiYhrqmXvA/4uIp4HngEun0B9kqSa6p6D7wKHOll+1njlSJImxRudJKlQBrwkFcqAl6RCGfCSVCgDXpIKZcBLUqEMeEkqlAEvSYUy4CWpUAa8JBXKgJekQhnwklQoA16SCmXAS1KhDHhJKpQBL0mFMuAlqVAGvCQVyoCXpELVfen2IUXEcuAW4BRgH/BnmfnkJP8bkqTRTHoE//vAisz8beCDwA0T3r4kaUSTDvhzgH8ByMyHgNMmvH1J0qjm5+cn9md6enrL9PT0BQvmvzs9PX3skO/MTk9Pz1d/ZidZT819aLyGtvyxF/bCXhzZvZj0CH4PMLVgfnlm7l/sC5k5m5nLqj+zE66njo1NF9Ai9mLAXgzYi4FW92LSAf8g8HsAEXEm8K0Jb1+SNKKJXkUDbAN+NyL+DVgG/OmEty9JGtFEAz4zXwT+fJLbbMCHmy6gRezFgL0YsBcDre7Fsvn5+aZrkCQtAe9klaRCGfCSVCgDXpIKZcBLUqEMeEkqlAEvSYUy4PX/VI991kEi4vima2haRKxuuoa2iIhfiIjjmq5jMV4HLwAi4teATfSfALqf/sH/W8B7M/OJJmt7uUXE24CbgZ8BGzLzzmr5/Zn5O40W9zKLiOmDFn0KeAfAUfj34leBvwWeAe4CtgAvAN3MvLvJ2l7KpB9VoCPXFuBDmfnVAwuq5wn9I3B2Y1U1YwPwm/Qft/G5iFiRmZ+s5o82XwL2Av9Df/8D+AdgHjiqDnb0/y1sBNbQD/hp4DngHsCAb6OI2A4c/L/ey4D5zDyrgZKasmJhuEP/mf4R0VQ9TXo+M38EEBHrgfsj4rv0Q+1ocxrwceDWzPxiRGzPzDc1XVRDjs3MHcCOiHhTZu4CiIhFn5jbpKM+4Om/eWoz8Af0T00crf4jIrbSf2HLbvqPff494JuNVtWMpyNiE3BNZs5FxIXAvcCrGq7rZZeZuyLij4CPRsRvNV1PwzIitgCXZ+YlABHxQfqnbFrpqA/4zPxqRNwBnJyZ25qup0Ez9F+5eA6wkv6z/e+m/4TQo82lwEVUI/bM/O+IeBPwoUarakj1Tof3RMQlHN0XZlwGvK16qOIB3wNubKieofyRVZIKdTQfjSWpaAa8JBXKgJekQhnwklSo/wPum53+yoKxXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['g','r','b','m','y']\n",
    "january.dayOfWeek.value_counts().plot.bar(color = colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The data on january is quite different than the one for march as most bluecars tend to be taken on Tuesday"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### checking the info of the january dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 744 entries, 3900 to 15959\n",
      "Data columns (total 13 columns):\n",
      "Postal code               744 non-null int64\n",
      "date                      744 non-null object\n",
      "n_daily_data_points       744 non-null int64\n",
      "dayOfWeek                 744 non-null int64\n",
      "day_type                  744 non-null object\n",
      "BlueCars_taken_sum        744 non-null int64\n",
      "BlueCars_returned_sum     744 non-null int64\n",
      "Utilib_taken_sum          744 non-null int64\n",
      "Utilib_returned_sum       744 non-null int64\n",
      "Utilib_14_taken_sum       744 non-null int64\n",
      "Utilib_14_returned_sum    744 non-null int64\n",
      "Slots_freed_sum           744 non-null int64\n",
      "Slots_taken_sum           744 non-null int64\n",
      "dtypes: int64(11), object(2)\n",
      "memory usage: 81.4+ KB\n"
     ]
    }
   ],
   "source": [
    "january.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### creating a boxplot of the bluecars column to check if most of the outliers were removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13eecd152c8>"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/ayCf5jhHwAIgM39COzU/buDqcdqoccJe3cf5wLu7+eMjaEX2FtoUwi9GxOBylUTEayPi5Ig4irbi2zza4iVXDW4/M+/tPt4APIU2In0prdx21uXAi2h/zulPt/ncV7ucawa+jusHvo5jgBcP3H7il8m2x+ERMvM13X030ea8T9nO/fYa+P/WbTbRt2VaNQssYAEPzo0up81HTriTVqhDEXEI7bQZ4FrgZRGxqFtg/1PAisy8jTZifc/En+XpRrFvoI0inw2s6+ZkbwZ+i1aC22Z5B3BKZl4C/DHwqzuIP8pDZ3PPpU1H/ANtmcCJrw3gG7TT/l+KiBfQRtzPiIhDu8+/ibaQ+qRExHBE/AdwZ2a+lbYu8ZG04/fkiNgnIpYCx09225rbnILYsx0dETfRRmp78VBBrew+fw1tPjZpTxhdD5CZn4mIw2kFNp92qn5Jd59V3TZuioj7aU++nZKZ34qITbSphYl1oW9k+6fb7wE+FhGn0Z7UWrmd2wy6BviLiPgRsBq4PiLu676eWwf3kZlbI+JVXd6ndnkv70r6Dh6ak95p3V8MOQ/4UkTcC/wQODUzfxgRnwX+vctRtZ6tesrlKCWpiCNg7RYi4qO0Eeu2Pp2Z581ShnfSpji2dWNmnj4bGTS3OAKWpCI+CSdJRSxgSSpiAUtSEQtYkopYwJJU5P8Bbo9zJxc//JYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(may['BlueCars_taken_sum'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13eecacb088>"
      ]
     },
     "execution_count": 139,
     "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": [
    "sns.boxplot(january['BlueCars_taken_sum'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### creating a histogram of the the bluecars taken column for both januray and march"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13eecf41f88>"
      ]
     },
     "execution_count": 185,
     "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": [
    "sns.distplot(may['BlueCars_taken_sum'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13eecb7c6c8>"
      ]
     },
     "execution_count": 141,
     "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": [
    "sns.distplot(january['BlueCars_taken_sum'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As you can observe from the above histograms the column of interest (bluecar taken sum) has an irregular normal distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### next I am going to create samples and use those samples in calculating the t-statistic and p-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "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>Postal code</th>\n",
       "      <th>date</th>\n",
       "      <th>n_daily_data_points</th>\n",
       "      <th>dayOfWeek</th>\n",
       "      <th>day_type</th>\n",
       "      <th>BlueCars_taken_sum</th>\n",
       "      <th>BlueCars_returned_sum</th>\n",
       "      <th>Utilib_taken_sum</th>\n",
       "      <th>Utilib_returned_sum</th>\n",
       "      <th>Utilib_14_taken_sum</th>\n",
       "      <th>Utilib_14_returned_sum</th>\n",
       "      <th>Slots_freed_sum</th>\n",
       "      <th>Slots_taken_sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7642</th>\n",
       "      <td>92320</td>\n",
       "      <td>2018-01-02 00:00:00</td>\n",
       "      <td>1438</td>\n",
       "      <td>1</td>\n",
       "      <td>weekday</td>\n",
       "      <td>51</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7177</th>\n",
       "      <td>92290</td>\n",
       "      <td>2018-01-05 00:00:00</td>\n",
       "      <td>1440</td>\n",
       "      <td>4</td>\n",
       "      <td>weekday</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13288</th>\n",
       "      <td>94110</td>\n",
       "      <td>2018-01-11 00:00:00</td>\n",
       "      <td>1440</td>\n",
       "      <td>3</td>\n",
       "      <td>weekday</td>\n",
       "      <td>34</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8108</th>\n",
       "      <td>92350</td>\n",
       "      <td>2018-01-09 00:00:00</td>\n",
       "      <td>1439</td>\n",
       "      <td>1</td>\n",
       "      <td>weekday</td>\n",
       "      <td>26</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11662</th>\n",
       "      <td>93360</td>\n",
       "      <td>2018-01-16 00:00:00</td>\n",
       "      <td>1440</td>\n",
       "      <td>1</td>\n",
       "      <td>weekday</td>\n",
       "      <td>15</td>\n",
       "      <td>13</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Postal code                 date  n_daily_data_points  dayOfWeek  \\\n",
       "7642         92320  2018-01-02 00:00:00                 1438          1   \n",
       "7177         92290  2018-01-05 00:00:00                 1440          4   \n",
       "13288        94110  2018-01-11 00:00:00                 1440          3   \n",
       "8108         92350  2018-01-09 00:00:00                 1439          1   \n",
       "11662        93360  2018-01-16 00:00:00                 1440          1   \n",
       "\n",
       "      day_type  BlueCars_taken_sum  BlueCars_returned_sum  Utilib_taken_sum  \\\n",
       "7642   weekday                  51                     53                 0   \n",
       "7177   weekday                  20                     15                 1   \n",
       "13288  weekday                  34                     39                 0   \n",
       "8108   weekday                  26                     24                 0   \n",
       "11662  weekday                  15                     13                 0   \n",
       "\n",
       "       Utilib_returned_sum  Utilib_14_taken_sum  Utilib_14_returned_sum  \\\n",
       "7642                     1                    2                       3   \n",
       "7177                     2                    2                       3   \n",
       "13288                    0                    5                       5   \n",
       "8108                     0                    2                       3   \n",
       "11662                    0                    0                       1   \n",
       "\n",
       "       Slots_freed_sum  Slots_taken_sum  \n",
       "7642                 0                0  \n",
       "7177                 0                0  \n",
       "13288                0                0  \n",
       "8108                 0                0  \n",
       "11662                0                0  "
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jansample = january.sample(n=30)\n",
    "jansample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>Postal code</th>\n",
       "      <th>date</th>\n",
       "      <th>n_daily_data_points</th>\n",
       "      <th>dayOfWeek</th>\n",
       "      <th>day_type</th>\n",
       "      <th>BlueCars_taken_sum</th>\n",
       "      <th>BlueCars_returned_sum</th>\n",
       "      <th>Utilib_taken_sum</th>\n",
       "      <th>Utilib_returned_sum</th>\n",
       "      <th>Utilib_14_taken_sum</th>\n",
       "      <th>Utilib_14_returned_sum</th>\n",
       "      <th>Slots_freed_sum</th>\n",
       "      <th>Slots_taken_sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12625</th>\n",
       "      <td>93600</td>\n",
       "      <td>2018-05-21 00:00:00</td>\n",
       "      <td>1440</td>\n",
       "      <td>0</td>\n",
       "      <td>weekday</td>\n",
       "      <td>28</td>\n",
       "      <td>20</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8223</th>\n",
       "      <td>92350</td>\n",
       "      <td>2018-05-07 00:00:00</td>\n",
       "      <td>1437</td>\n",
       "      <td>0</td>\n",
       "      <td>weekday</td>\n",
       "      <td>26</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7296</th>\n",
       "      <td>92290</td>\n",
       "      <td>2018-05-07 00:00:00</td>\n",
       "      <td>1437</td>\n",
       "      <td>0</td>\n",
       "      <td>weekday</td>\n",
       "      <td>10</td>\n",
       "      <td>10</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4341</th>\n",
       "      <td>91400</td>\n",
       "      <td>2018-05-25 00:00:00</td>\n",
       "      <td>1440</td>\n",
       "      <td>4</td>\n",
       "      <td>weekday</td>\n",
       "      <td>21</td>\n",
       "      <td>18</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15284</th>\n",
       "      <td>94500</td>\n",
       "      <td>2018-05-30 00:00:00</td>\n",
       "      <td>1440</td>\n",
       "      <td>2</td>\n",
       "      <td>weekday</td>\n",
       "      <td>25</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Postal code                 date  n_daily_data_points  dayOfWeek  \\\n",
       "12625        93600  2018-05-21 00:00:00                 1440          0   \n",
       "8223         92350  2018-05-07 00:00:00                 1437          0   \n",
       "7296         92290  2018-05-07 00:00:00                 1437          0   \n",
       "4341         91400  2018-05-25 00:00:00                 1440          4   \n",
       "15284        94500  2018-05-30 00:00:00                 1440          2   \n",
       "\n",
       "      day_type  BlueCars_taken_sum  BlueCars_returned_sum  Utilib_taken_sum  \\\n",
       "12625  weekday                  28                     20                 0   \n",
       "8223   weekday                  26                     18                 0   \n",
       "7296   weekday                  10                     10                 0   \n",
       "4341   weekday                  21                     18                 0   \n",
       "15284  weekday                  25                     32                 0   \n",
       "\n",
       "       Utilib_returned_sum  Utilib_14_taken_sum  Utilib_14_returned_sum  \\\n",
       "12625                    0                    0                       0   \n",
       "8223                     0                    2                       2   \n",
       "7296                     0                    0                       0   \n",
       "4341                     0                    0                       0   \n",
       "15284                    0                    3                       4   \n",
       "\n",
       "       Slots_freed_sum  Slots_taken_sum  \n",
       "12625                0                0  \n",
       "8223                 0                0  \n",
       "7296                 0                0  \n",
       "4341                 0                0  \n",
       "15284                0                0  "
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating a sample of 30 variables\n",
    "maysample = may.sample(n= 30)\n",
    "maysample.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### using the describe method in calculating the measures of central tendency "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "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>Postal code</th>\n",
       "      <th>n_daily_data_points</th>\n",
       "      <th>dayOfWeek</th>\n",
       "      <th>BlueCars_taken_sum</th>\n",
       "      <th>BlueCars_returned_sum</th>\n",
       "      <th>Utilib_taken_sum</th>\n",
       "      <th>Utilib_returned_sum</th>\n",
       "      <th>Utilib_14_taken_sum</th>\n",
       "      <th>Utilib_14_returned_sum</th>\n",
       "      <th>Slots_freed_sum</th>\n",
       "      <th>Slots_taken_sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.00000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>93347.333333</td>\n",
       "      <td>1439.233333</td>\n",
       "      <td>1.766667</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>22.566667</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.70000</td>\n",
       "      <td>1.566667</td>\n",
       "      <td>1.766667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1145.870948</td>\n",
       "      <td>0.858360</td>\n",
       "      <td>1.454679</td>\n",
       "      <td>12.906561</td>\n",
       "      <td>13.283859</td>\n",
       "      <td>0.681445</td>\n",
       "      <td>0.83666</td>\n",
       "      <td>1.501340</td>\n",
       "      <td>1.330889</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>91370.000000</td>\n",
       "      <td>1438.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>92382.500000</td>\n",
       "      <td>1438.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.250000</td>\n",
       "      <td>13.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>93230.000000</td>\n",
       "      <td>1439.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.500000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>94192.500000</td>\n",
       "      <td>1440.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>27.750000</td>\n",
       "      <td>27.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>95880.000000</td>\n",
       "      <td>1440.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Postal code  n_daily_data_points  dayOfWeek  BlueCars_taken_sum  \\\n",
       "count     30.000000            30.000000  30.000000           30.000000   \n",
       "mean   93347.333333          1439.233333   1.766667           21.800000   \n",
       "std     1145.870948             0.858360   1.454679           12.906561   \n",
       "min    91370.000000          1438.000000   0.000000            3.000000   \n",
       "25%    92382.500000          1438.250000   1.000000           13.250000   \n",
       "50%    93230.000000          1439.500000   1.000000           18.500000   \n",
       "75%    94192.500000          1440.000000   3.000000           27.750000   \n",
       "max    95880.000000          1440.000000   4.000000           60.000000   \n",
       "\n",
       "       BlueCars_returned_sum  Utilib_taken_sum  Utilib_returned_sum  \\\n",
       "count              30.000000         30.000000             30.00000   \n",
       "mean               22.566667          0.533333              0.70000   \n",
       "std                13.283859          0.681445              0.83666   \n",
       "min                 4.000000          0.000000              0.00000   \n",
       "25%                13.250000          0.000000              0.00000   \n",
       "50%                22.000000          0.000000              0.00000   \n",
       "75%                27.750000          1.000000              1.00000   \n",
       "max                58.000000          2.000000              2.00000   \n",
       "\n",
       "       Utilib_14_taken_sum  Utilib_14_returned_sum  Slots_freed_sum  \\\n",
       "count            30.000000               30.000000             30.0   \n",
       "mean              1.566667                1.766667              0.0   \n",
       "std               1.501340                1.330889              0.0   \n",
       "min               0.000000                0.000000              0.0   \n",
       "25%               1.000000                1.000000              0.0   \n",
       "50%               1.000000                1.500000              0.0   \n",
       "75%               2.000000                3.000000              0.0   \n",
       "max               5.000000                5.000000              0.0   \n",
       "\n",
       "       Slots_taken_sum  \n",
       "count             30.0  \n",
       "mean               0.0  \n",
       "std                0.0  \n",
       "min                0.0  \n",
       "25%                0.0  \n",
       "50%                0.0  \n",
       "75%                0.0  \n",
       "max                0.0  "
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jansample.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Postal code</th>\n",
       "      <th>n_daily_data_points</th>\n",
       "      <th>dayOfWeek</th>\n",
       "      <th>BlueCars_taken_sum</th>\n",
       "      <th>BlueCars_returned_sum</th>\n",
       "      <th>Utilib_taken_sum</th>\n",
       "      <th>Utilib_returned_sum</th>\n",
       "      <th>Utilib_14_taken_sum</th>\n",
       "      <th>Utilib_14_returned_sum</th>\n",
       "      <th>Slots_freed_sum</th>\n",
       "      <th>Slots_taken_sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.00000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.00000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>93100.000000</td>\n",
       "      <td>1438.50000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>29.733333</td>\n",
       "      <td>29.30000</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.933333</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1003.002389</td>\n",
       "      <td>1.92533</td>\n",
       "      <td>1.531283</td>\n",
       "      <td>19.213740</td>\n",
       "      <td>19.27684</td>\n",
       "      <td>0.727932</td>\n",
       "      <td>0.855006</td>\n",
       "      <td>2.066704</td>\n",
       "      <td>2.040115</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>91400.000000</td>\n",
       "      <td>1435.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>92297.500000</td>\n",
       "      <td>1437.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>14.25000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>93265.000000</td>\n",
       "      <td>1440.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>26.500000</td>\n",
       "      <td>26.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>93750.000000</td>\n",
       "      <td>1440.00000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>37.25000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>95100.000000</td>\n",
       "      <td>1440.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>75.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Postal code  n_daily_data_points  dayOfWeek  BlueCars_taken_sum  \\\n",
       "count     30.000000             30.00000  30.000000           30.000000   \n",
       "mean   93100.000000           1438.50000   2.000000           29.733333   \n",
       "std     1003.002389              1.92533   1.531283           19.213740   \n",
       "min    91400.000000           1435.00000   0.000000            4.000000   \n",
       "25%    92297.500000           1437.00000   1.000000           17.500000   \n",
       "50%    93265.000000           1440.00000   2.000000           26.500000   \n",
       "75%    93750.000000           1440.00000   3.750000           38.000000   \n",
       "max    95100.000000           1440.00000   4.000000           79.000000   \n",
       "\n",
       "       BlueCars_returned_sum  Utilib_taken_sum  Utilib_returned_sum  \\\n",
       "count               30.00000         30.000000            30.000000   \n",
       "mean                29.30000          0.566667             0.600000   \n",
       "std                 19.27684          0.727932             0.855006   \n",
       "min                  4.00000          0.000000             0.000000   \n",
       "25%                 14.25000          0.000000             0.000000   \n",
       "50%                 26.00000          0.000000             0.000000   \n",
       "75%                 37.25000          1.000000             1.000000   \n",
       "max                 75.00000          2.000000             2.000000   \n",
       "\n",
       "       Utilib_14_taken_sum  Utilib_14_returned_sum  Slots_freed_sum  \\\n",
       "count            30.000000               30.000000             30.0   \n",
       "mean              1.933333                1.900000              0.0   \n",
       "std               2.066704                2.040115              0.0   \n",
       "min               0.000000                0.000000              0.0   \n",
       "25%               0.000000                0.000000              0.0   \n",
       "50%               1.000000                1.500000              0.0   \n",
       "75%               3.000000                2.000000              0.0   \n",
       "max               7.000000                7.000000              0.0   \n",
       "\n",
       "       Slots_taken_sum  \n",
       "count             30.0  \n",
       "mean               0.0  \n",
       "std                0.0  \n",
       "min                0.0  \n",
       "25%                0.0  \n",
       "50%                0.0  \n",
       "75%                0.0  \n",
       "max                0.0  "
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maysample.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### creating a histogram of the samples "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13eec9ebf08>"
      ]
     },
     "execution_count": 146,
     "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": [
    "sns.distplot(jansample['BlueCars_taken_sum'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13eecfd3a48>"
      ]
     },
     "execution_count": 188,
     "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": [
    "sns.distplot(maysample['BlueCars_taken_sum'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bivariate analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### creating a scatterplot of the blucars taken against the postal code from the sampled dataframes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13eecd69188>"
      ]
     },
     "execution_count": 148,
     "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": [
    "x = jansample.BlueCars_taken_sum\n",
    "y = jansample['Postal code']\n",
    "sns.scatterplot(x,y,hue = x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13eed04a708>"
      ]
     },
     "execution_count": 189,
     "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": [
    "r = maysample.BlueCars_taken_sum\n",
    "v = maysample['Postal code']\n",
    "sns.scatterplot(r,v,hue = r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### To conduct the test I will use a t-test\n",
    "#### The reason for using a t-test are due to the fact of the small sample size that I have selected (<30 samples)\n",
    "#### The level of significance is set at 0.05,this will be a two sided t-test\n",
    "\n",
    "#### To run the hypothesis test I will the 'bluecar_taken_sum' column as which should give a clear picture on bluecar usage during the chosen periods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21.8\n",
      "29.733333333333334\n"
     ]
    }
   ],
   "source": [
    "print(jansample.BlueCars_taken_sum .mean())\n",
    "print(maysample.BlueCars_taken_sum .mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### finding the means of the blucar column between Januray and May I can see that the mean for May has increased substantially as compared to the mean for january but does that mean{ pun unintended:) } that more people are using the bluecars?\n",
    "#### Time to find out below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the t-statistic is -1.877311776595369 whilst the p value is 0.0655073917984725\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ttest_ind as t_test\n",
    "x = jansample.BlueCars_taken_sum\n",
    "y = maysample.BlueCars_taken_sum\n",
    "\n",
    "t,p = t_test(x,y, equal_var= True)\n",
    "\n",
    "print('the t-statistic is ' + str(t) + ' whilst the p value is ' + str(p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The above pvalue of 6.5% is greater than the level of signifance of 5% and thus indicates a clear rejection of the alternative hypothesis that bluecar usage has increased over the 4 month period between January and May.\n",
    "#### A conclusion to be drawn from this would be to look at the factors that are causing bluecar usage to not increase and to formulate solutions on how this can be countered."
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}