bikesharing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=’intro’></a>\n",
    "\n",
    "# Introduction of the dataset and Analysis Plan\n",
    "\n",
    "**Structure of the dataset**\n",
    "\n",
    "- I have 12 csv's, for the 12 months of 2020. Ideally, I would like to see all 12 in one dataset, so I can have a wider range in time to draw conclusions from.\n",
    "\n",
    "- Most of the cvs's have 15 columns with a few having only 14. \n",
    "- Need to make sure I'm not missing data so will reduce the ones with 15 rows to 14 before I merge them together. \n",
    "- Different months have different amount of entries, but that's normal and I will not manipulate that.\n",
    "\n",
    "- I will look at the datasets' statistics, datatypes, missing and duplicated values as well as visually assess them to see if there is any need for cleaning.\n",
    "\n",
    "**Columns**\n",
    "- **tripduration**: trip duration in seconds\n",
    "- **starttime**: the time the rental started\n",
    "- **stoptime**: the time teh rental ended\n",
    "- **start station id**: where the rental started\n",
    "- **end station id**: where the rental ended\n",
    "- **start station latitude**: gps coordinates of start station\n",
    "- **start station longitude**: gps coordinates of start station\n",
    "- **end station latitude**: gps coordinates of end station\n",
    "- **end station longitude**: gps coordinates of end station\n",
    "- **bikeid**: identificator of the bike used\n",
    "- **usertype**: if the user has subscription or not\n",
    "\n",
    "\n",
    "**Insights to explore**\n",
    "\n",
    "- How long does the average trip takes?\n",
    "- Which are the most used start and end stations?\n",
    "- When are the most trips taken in terms of time of day, day of the week, or month of the year?\n",
    "- Does these depend on if a user is a subscriber or customer?\n",
    "\n",
    "**Features in the dataset the will help support the investigation?**\n",
    "\n",
    "Trip duration, Start time, End time, Start station, End Station, Usertype\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Udacity Project - Bikesharing Dataset\n",
    "\n",
    "## Data Wrangling and Data Visualisation\n",
    "\n",
    "This data set includes information about individual rides made in a bike-sharing system covering the greater Boston area. \n",
    "\n",
    "Table of Contents\n",
    "\n",
    "<ul>\n",
    "<li><a ref=\"#intro\">Introduction of the dataset and Analysis Plan</a></li>\n",
    "<li><a ref=\"#2\">Data Wrangling</a></li>\n",
    "<li><a ref=\"#65\">Explore Univariate Variables</a></li>\n",
    "<li><a ref=\"#100\">Explore Bivariate Variables</a></li>\n",
    "<li><a ref=\"#105\">Explore Multivariate Variables</a></li>\n",
    "\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=’2’></a>\n",
    "\n",
    "# Data Wrangling\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# import libraries \n",
    "import numpy as np\n",
    "import pandas as pandas\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>usertype</th>\n",
       "      <th>birth year</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>478</td>\n",
       "      <td>2020-01-01 00:04:05.8090</td>\n",
       "      <td>2020-01-01 00:12:04.2370</td>\n",
       "      <td>366</td>\n",
       "      <td>Broadway T Stop</td>\n",
       "      <td>42.342781</td>\n",
       "      <td>-71.057473</td>\n",
       "      <td>93</td>\n",
       "      <td>JFK/UMass T Stop</td>\n",
       "      <td>42.320340</td>\n",
       "      <td>-71.051180</td>\n",
       "      <td>6005</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1969</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>363</td>\n",
       "      <td>2020-01-01 00:04:45.6990</td>\n",
       "      <td>2020-01-01 00:10:49.0400</td>\n",
       "      <td>219</td>\n",
       "      <td>Boston East - 126 Border St</td>\n",
       "      <td>42.373312</td>\n",
       "      <td>-71.041020</td>\n",
       "      <td>212</td>\n",
       "      <td>Maverick Square - Lewis Mall</td>\n",
       "      <td>42.368844</td>\n",
       "      <td>-71.039778</td>\n",
       "      <td>3168</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>284</td>\n",
       "      <td>2020-01-01 00:06:07.0630</td>\n",
       "      <td>2020-01-01 00:10:51.9240</td>\n",
       "      <td>219</td>\n",
       "      <td>Boston East - 126 Border St</td>\n",
       "      <td>42.373312</td>\n",
       "      <td>-71.041020</td>\n",
       "      <td>212</td>\n",
       "      <td>Maverick Square - Lewis Mall</td>\n",
       "      <td>42.368844</td>\n",
       "      <td>-71.039778</td>\n",
       "      <td>3985</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2001</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>193</td>\n",
       "      <td>2020-01-01 00:06:13.8550</td>\n",
       "      <td>2020-01-01 00:09:27.8320</td>\n",
       "      <td>396</td>\n",
       "      <td>Main St at Beacon St</td>\n",
       "      <td>42.409330</td>\n",
       "      <td>-71.063819</td>\n",
       "      <td>387</td>\n",
       "      <td>Norman St at Kelvin St</td>\n",
       "      <td>42.409859</td>\n",
       "      <td>-71.066319</td>\n",
       "      <td>2692</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1978</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>428</td>\n",
       "      <td>2020-01-01 00:07:25.2950</td>\n",
       "      <td>2020-01-01 00:14:33.7800</td>\n",
       "      <td>60</td>\n",
       "      <td>Charles Circle - Charles St at Cambridge St</td>\n",
       "      <td>42.360793</td>\n",
       "      <td>-71.071190</td>\n",
       "      <td>49</td>\n",
       "      <td>Stuart St at Charles St</td>\n",
       "      <td>42.351146</td>\n",
       "      <td>-71.066289</td>\n",
       "      <td>4978</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1987</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration                 starttime                  stoptime  \\\n",
       "0           478  2020-01-01 00:04:05.8090  2020-01-01 00:12:04.2370   \n",
       "1           363  2020-01-01 00:04:45.6990  2020-01-01 00:10:49.0400   \n",
       "2           284  2020-01-01 00:06:07.0630  2020-01-01 00:10:51.9240   \n",
       "3           193  2020-01-01 00:06:13.8550  2020-01-01 00:09:27.8320   \n",
       "4           428  2020-01-01 00:07:25.2950  2020-01-01 00:14:33.7800   \n",
       "\n",
       "   start station id                           start station name  \\\n",
       "0               366                              Broadway T Stop   \n",
       "1               219                  Boston East - 126 Border St   \n",
       "2               219                  Boston East - 126 Border St   \n",
       "3               396                         Main St at Beacon St   \n",
       "4                60  Charles Circle - Charles St at Cambridge St   \n",
       "\n",
       "   start station latitude  start station longitude  end station id  \\\n",
       "0               42.342781               -71.057473              93   \n",
       "1               42.373312               -71.041020             212   \n",
       "2               42.373312               -71.041020             212   \n",
       "3               42.409330               -71.063819             387   \n",
       "4               42.360793               -71.071190              49   \n",
       "\n",
       "               end station name  end station latitude  end station longitude  \\\n",
       "0              JFK/UMass T Stop             42.320340             -71.051180   \n",
       "1  Maverick Square - Lewis Mall             42.368844             -71.039778   \n",
       "2  Maverick Square - Lewis Mall             42.368844             -71.039778   \n",
       "3        Norman St at Kelvin St             42.409859             -71.066319   \n",
       "4       Stuart St at Charles St             42.351146             -71.066289   \n",
       "\n",
       "   bikeid    usertype  birth year  gender  \n",
       "0    6005    Customer        1969       0  \n",
       "1    3168  Subscriber        2000       1  \n",
       "2    3985  Subscriber        2001       1  \n",
       "3    2692  Subscriber        1978       1  \n",
       "4    4978  Subscriber        1987       1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load in each month's dataset into a pandas dataframe\n",
    "df_01 = pandas.read_csv('202001-bluebikes-tripdata.csv')\n",
    "df_01.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128598, 15)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assess shape\n",
    "df_01.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 128598 entries, 0 to 128597\n",
      "Data columns (total 15 columns):\n",
      " #   Column                   Non-Null Count   Dtype  \n",
      "---  ------                   --------------   -----  \n",
      " 0   tripduration             128598 non-null  int64  \n",
      " 1   starttime                128598 non-null  object \n",
      " 2   stoptime                 128598 non-null  object \n",
      " 3   start station id         128598 non-null  int64  \n",
      " 4   start station name       128598 non-null  object \n",
      " 5   start station latitude   128598 non-null  float64\n",
      " 6   start station longitude  128598 non-null  float64\n",
      " 7   end station id           128598 non-null  int64  \n",
      " 8   end station name         128598 non-null  object \n",
      " 9   end station latitude     128598 non-null  float64\n",
      " 10  end station longitude    128598 non-null  float64\n",
      " 11  bikeid                   128598 non-null  int64  \n",
      " 12  usertype                 128598 non-null  object \n",
      " 13  birth year               128598 non-null  int64  \n",
      " 14  gender                   128598 non-null  int64  \n",
      "dtypes: float64(4), int64(6), object(5)\n",
      "memory usage: 14.7+ MB\n"
     ]
    }
   ],
   "source": [
    "# assess column names in each data set to see which column is missing\n",
    "df_01.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(133235, 15)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_02 = pandas.read_csv('202002-bluebikes-tripdata.csv')\n",
    "df_02.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 133235 entries, 0 to 133234\n",
      "Data columns (total 15 columns):\n",
      " #   Column                   Non-Null Count   Dtype  \n",
      "---  ------                   --------------   -----  \n",
      " 0   tripduration             133235 non-null  int64  \n",
      " 1   starttime                133235 non-null  object \n",
      " 2   stoptime                 133235 non-null  object \n",
      " 3   start station id         133235 non-null  int64  \n",
      " 4   start station name       133235 non-null  object \n",
      " 5   start station latitude   133235 non-null  float64\n",
      " 6   start station longitude  133235 non-null  float64\n",
      " 7   end station id           133235 non-null  int64  \n",
      " 8   end station name         133235 non-null  object \n",
      " 9   end station latitude     133235 non-null  float64\n",
      " 10  end station longitude    133235 non-null  float64\n",
      " 11  bikeid                   133235 non-null  int64  \n",
      " 12  usertype                 133235 non-null  object \n",
      " 13  birth year               133235 non-null  int64  \n",
      " 14  gender                   133235 non-null  int64  \n",
      "dtypes: float64(4), int64(6), object(5)\n",
      "memory usage: 15.2+ MB\n"
     ]
    }
   ],
   "source": [
    "# assess column names in each data set to see which column is missing\n",
    "df_02.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(107350, 15)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_03 = pandas.read_csv('202003-bluebikes-tripdata.csv')\n",
    "df_03.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 107350 entries, 0 to 107349\n",
      "Data columns (total 15 columns):\n",
      " #   Column                   Non-Null Count   Dtype  \n",
      "---  ------                   --------------   -----  \n",
      " 0   tripduration             107350 non-null  int64  \n",
      " 1   starttime                107350 non-null  object \n",
      " 2   stoptime                 107350 non-null  object \n",
      " 3   start station id         107350 non-null  int64  \n",
      " 4   start station name       107350 non-null  object \n",
      " 5   start station latitude   107350 non-null  float64\n",
      " 6   start station longitude  107350 non-null  float64\n",
      " 7   end station id           107350 non-null  int64  \n",
      " 8   end station name         107350 non-null  object \n",
      " 9   end station latitude     107350 non-null  float64\n",
      " 10  end station longitude    107350 non-null  float64\n",
      " 11  bikeid                   107350 non-null  int64  \n",
      " 12  usertype                 107350 non-null  object \n",
      " 13  birth year               107350 non-null  int64  \n",
      " 14  gender                   107350 non-null  int64  \n",
      "dtypes: float64(4), int64(6), object(5)\n",
      "memory usage: 12.3+ MB\n"
     ]
    }
   ],
   "source": [
    "# assess column names in each data set to see which column is missing\n",
    "df_03.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(46793, 15)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_04 = pandas.read_csv('202004-bluebikes-tripdata.csv')\n",
    "df_04.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 46793 entries, 0 to 46792\n",
      "Data columns (total 15 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   tripduration             46793 non-null  int64  \n",
      " 1   starttime                46793 non-null  object \n",
      " 2   stoptime                 46793 non-null  object \n",
      " 3   start station id         46793 non-null  int64  \n",
      " 4   start station name       46793 non-null  object \n",
      " 5   start station latitude   46793 non-null  float64\n",
      " 6   start station longitude  46793 non-null  float64\n",
      " 7   end station id           46793 non-null  int64  \n",
      " 8   end station name         46793 non-null  object \n",
      " 9   end station latitude     46793 non-null  float64\n",
      " 10  end station longitude    46793 non-null  float64\n",
      " 11  bikeid                   46793 non-null  int64  \n",
      " 12  usertype                 46793 non-null  object \n",
      " 13  birth year               46793 non-null  int64  \n",
      " 14  gender                   46793 non-null  int64  \n",
      "dtypes: float64(4), int64(6), object(5)\n",
      "memory usage: 5.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# assess column names in each data set to see which column is missing\n",
    "df_04.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(124879, 14)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_05 = pandas.read_csv('202005-bluebikes-tripdata.csv')\n",
    "df_05.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 124879 entries, 0 to 124878\n",
      "Data columns (total 14 columns):\n",
      " #   Column                   Non-Null Count   Dtype  \n",
      "---  ------                   --------------   -----  \n",
      " 0   tripduration             124879 non-null  int64  \n",
      " 1   starttime                124879 non-null  object \n",
      " 2   stoptime                 124879 non-null  object \n",
      " 3   start station id         124879 non-null  int64  \n",
      " 4   start station name       124879 non-null  object \n",
      " 5   start station latitude   124879 non-null  float64\n",
      " 6   start station longitude  124879 non-null  float64\n",
      " 7   end station id           124879 non-null  int64  \n",
      " 8   end station name         124879 non-null  object \n",
      " 9   end station latitude     124879 non-null  float64\n",
      " 10  end station longitude    124879 non-null  float64\n",
      " 11  bikeid                   124879 non-null  int64  \n",
      " 12  usertype                 124879 non-null  object \n",
      " 13  postal code              110498 non-null  object \n",
      "dtypes: float64(4), int64(4), object(6)\n",
      "memory usage: 13.3+ MB\n"
     ]
    }
   ],
   "source": [
    "# assess column names in each data set to see which column is missing\n",
    "df_05.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(191843, 14)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_06 = pandas.read_csv('202006-bluebikes-tripdata.csv')\n",
    "df_06.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 191843 entries, 0 to 191842\n",
      "Data columns (total 14 columns):\n",
      " #   Column                   Non-Null Count   Dtype  \n",
      "---  ------                   --------------   -----  \n",
      " 0   tripduration             191843 non-null  int64  \n",
      " 1   starttime                191843 non-null  object \n",
      " 2   stoptime                 191843 non-null  object \n",
      " 3   start station id         191843 non-null  int64  \n",
      " 4   start station name       191843 non-null  object \n",
      " 5   start station latitude   191843 non-null  float64\n",
      " 6   start station longitude  191843 non-null  float64\n",
      " 7   end station id           191843 non-null  int64  \n",
      " 8   end station name         191843 non-null  object \n",
      " 9   end station latitude     191843 non-null  float64\n",
      " 10  end station longitude    191843 non-null  float64\n",
      " 11  bikeid                   191843 non-null  int64  \n",
      " 12  usertype                 191843 non-null  object \n",
      " 13  postal code              170883 non-null  object \n",
      "dtypes: float64(4), int64(4), object(6)\n",
      "memory usage: 20.5+ MB\n"
     ]
    }
   ],
   "source": [
    "# assess column names in each data set to see which column is missing\n",
    "df_06.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(259726, 14)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_07 = pandas.read_csv('202007-bluebikes-tripdata.csv')\n",
    "df_07.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 259726 entries, 0 to 259725\n",
      "Data columns (total 14 columns):\n",
      " #   Column                   Non-Null Count   Dtype  \n",
      "---  ------                   --------------   -----  \n",
      " 0   tripduration             259726 non-null  int64  \n",
      " 1   starttime                259726 non-null  object \n",
      " 2   stoptime                 259726 non-null  object \n",
      " 3   start station id         259726 non-null  int64  \n",
      " 4   start station name       259726 non-null  object \n",
      " 5   start station latitude   259726 non-null  float64\n",
      " 6   start station longitude  259726 non-null  float64\n",
      " 7   end station id           259726 non-null  int64  \n",
      " 8   end station name         259726 non-null  object \n",
      " 9   end station latitude     259726 non-null  float64\n",
      " 10  end station longitude    259726 non-null  float64\n",
      " 11  bikeid                   259726 non-null  int64  \n",
      " 12  usertype                 259726 non-null  object \n",
      " 13  postal code              230479 non-null  object \n",
      "dtypes: float64(4), int64(4), object(6)\n",
      "memory usage: 27.7+ MB\n"
     ]
    }
   ],
   "source": [
    "# assess column names in each data set to see which column is missing\n",
    "df_07.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(289033, 14)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_08 = pandas.read_csv('202008-bluebikes-tripdata.csv')\n",
    "df_08.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 289033 entries, 0 to 289032\n",
      "Data columns (total 14 columns):\n",
      " #   Column                   Non-Null Count   Dtype  \n",
      "---  ------                   --------------   -----  \n",
      " 0   tripduration             289033 non-null  int64  \n",
      " 1   starttime                289033 non-null  object \n",
      " 2   stoptime                 289033 non-null  object \n",
      " 3   start station id         289033 non-null  int64  \n",
      " 4   start station name       289033 non-null  object \n",
      " 5   start station latitude   289033 non-null  float64\n",
      " 6   start station longitude  289033 non-null  float64\n",
      " 7   end station id           289033 non-null  int64  \n",
      " 8   end station name         289033 non-null  object \n",
      " 9   end station latitude     289033 non-null  float64\n",
      " 10  end station longitude    289033 non-null  float64\n",
      " 11  bikeid                   289033 non-null  int64  \n",
      " 12  usertype                 289033 non-null  object \n",
      " 13  postal code              264273 non-null  object \n",
      "dtypes: float64(4), int64(4), object(6)\n",
      "memory usage: 30.9+ MB\n"
     ]
    }
   ],
   "source": [
    "# assess column names in each data set to see which column is missing\n",
    "df_08.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307853, 14)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_09 = pandas.read_csv('202009-bluebikes-tripdata.csv')\n",
    "df_09.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 307853 entries, 0 to 307852\n",
      "Data columns (total 14 columns):\n",
      " #   Column                   Non-Null Count   Dtype  \n",
      "---  ------                   --------------   -----  \n",
      " 0   tripduration             307853 non-null  int64  \n",
      " 1   starttime                307853 non-null  object \n",
      " 2   stoptime                 307853 non-null  object \n",
      " 3   start station id         307853 non-null  int64  \n",
      " 4   start station name       307853 non-null  object \n",
      " 5   start station latitude   307853 non-null  float64\n",
      " 6   start station longitude  307853 non-null  float64\n",
      " 7   end station id           307853 non-null  int64  \n",
      " 8   end station name         307853 non-null  object \n",
      " 9   end station latitude     307853 non-null  float64\n",
      " 10  end station longitude    307853 non-null  float64\n",
      " 11  bikeid                   307853 non-null  int64  \n",
      " 12  usertype                 307853 non-null  object \n",
      " 13  postal code              284701 non-null  object \n",
      "dtypes: float64(4), int64(4), object(6)\n",
      "memory usage: 32.9+ MB\n"
     ]
    }
   ],
   "source": [
    "# assess column names in each data set to see which column is missing\n",
    "df_09.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(248424, 14)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_10 = pandas.read_csv('202010-bluebikes-tripdata.csv')\n",
    "df_10.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 248424 entries, 0 to 248423\n",
      "Data columns (total 14 columns):\n",
      " #   Column                   Non-Null Count   Dtype  \n",
      "---  ------                   --------------   -----  \n",
      " 0   tripduration             248424 non-null  int64  \n",
      " 1   starttime                248424 non-null  object \n",
      " 2   stoptime                 248424 non-null  object \n",
      " 3   start station id         248424 non-null  int64  \n",
      " 4   start station name       248424 non-null  object \n",
      " 5   start station latitude   248424 non-null  float64\n",
      " 6   start station longitude  248424 non-null  float64\n",
      " 7   end station id           248424 non-null  int64  \n",
      " 8   end station name         248424 non-null  object \n",
      " 9   end station latitude     248424 non-null  float64\n",
      " 10  end station longitude    248424 non-null  float64\n",
      " 11  bikeid                   248424 non-null  int64  \n",
      " 12  usertype                 248424 non-null  object \n",
      " 13  postal code              230070 non-null  object \n",
      "dtypes: float64(4), int64(4), object(6)\n",
      "memory usage: 26.5+ MB\n"
     ]
    }
   ],
   "source": [
    "# assess column names in each data set to see which column is missing\n",
    "df_10.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(161712, 14)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_11 = pandas.read_csv('202011-bluebikes-tripdata.csv')\n",
    "df_11.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 161712 entries, 0 to 161711\n",
      "Data columns (total 14 columns):\n",
      " #   Column                   Non-Null Count   Dtype  \n",
      "---  ------                   --------------   -----  \n",
      " 0   tripduration             161712 non-null  int64  \n",
      " 1   starttime                161712 non-null  object \n",
      " 2   stoptime                 161712 non-null  object \n",
      " 3   start station id         161712 non-null  int64  \n",
      " 4   start station name       161712 non-null  object \n",
      " 5   start station latitude   161712 non-null  float64\n",
      " 6   start station longitude  161712 non-null  float64\n",
      " 7   end station id           161712 non-null  int64  \n",
      " 8   end station name         161712 non-null  object \n",
      " 9   end station latitude     161712 non-null  float64\n",
      " 10  end station longitude    161712 non-null  float64\n",
      " 11  bikeid                   161712 non-null  int64  \n",
      " 12  usertype                 161712 non-null  object \n",
      " 13  postal code              151233 non-null  object \n",
      "dtypes: float64(4), int64(4), object(6)\n",
      "memory usage: 17.3+ MB\n"
     ]
    }
   ],
   "source": [
    "# assess column names in each data set to see which column is missing\n",
    "df_11.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(74002, 14)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_12 = pandas.read_csv('202012-bluebikes-tripdata.csv')\n",
    "df_12.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 74002 entries, 0 to 74001\n",
      "Data columns (total 14 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   tripduration             74002 non-null  int64  \n",
      " 1   starttime                74002 non-null  object \n",
      " 2   stoptime                 74002 non-null  object \n",
      " 3   start station id         74002 non-null  int64  \n",
      " 4   start station name       74002 non-null  object \n",
      " 5   start station latitude   74002 non-null  float64\n",
      " 6   start station longitude  74002 non-null  float64\n",
      " 7   end station id           74002 non-null  int64  \n",
      " 8   end station name         74002 non-null  object \n",
      " 9   end station latitude     74002 non-null  float64\n",
      " 10  end station longitude    74002 non-null  float64\n",
      " 11  bikeid                   74002 non-null  int64  \n",
      " 12  usertype                 74002 non-null  object \n",
      " 13  postal code              71109 non-null  object \n",
      "dtypes: float64(4), int64(4), object(6)\n",
      "memory usage: 7.9+ MB\n"
     ]
    }
   ],
   "source": [
    "# assess column names in each data set to see which column is missing\n",
    "df_12.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of Assessment\n",
    "\n",
    "### Quality Issues\n",
    "*All datasets*\n",
    "- `starttime` and `stopttime` column is in object format, it should be datetime\n",
    "\n",
    "### Tidiness Issues\n",
    "\n",
    "- merge datasets (concat)\n",
    "- remove birthyear, postcalcode and gender columns as it only has partial month's data coverage, not the full year\n",
    "\n",
    "I have to state here, that I haven't gone through all the assessment like checking for missing values, etc. I would like to do that after merging the datasets together as it will be a lot easier like that so I assume that there will be a Cleaning Part 2 upon succeeding to merge the datasets together.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define \n",
    "- merge datasets (concat)\n",
    "\n",
    "Action: find best method to merge datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a copy of all datasets to be sure I can revert if things go wrong\n",
    "df_01_copy = df_01.copy()\n",
    "df_02_copy = df_02.copy()\n",
    "df_03_copy = df_03.copy()\n",
    "df_04_copy = df_04.copy()\n",
    "df_05_copy = df_05.copy()\n",
    "df_06_copy = df_06.copy()\n",
    "df_07_copy = df_07.copy()\n",
    "df_08_copy = df_08.copy()\n",
    "df_09_copy = df_09.copy()\n",
    "df_10_copy = df_10.copy()\n",
    "df_11_copy = df_11.copy()\n",
    "df_12_copy = df_12.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check each copied datasets shape so I can see if I got all the data after merging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128598, 15)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_01_copy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(133235, 15)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_02_copy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(107350, 15)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_03_copy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(46793, 15)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_04_copy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(124879, 14)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_05_copy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(191843, 14)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_06_copy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(259726, 14)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_07_copy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(289033, 14)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_08_copy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307853, 14)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_09_copy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(248424, 14)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_10_copy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(161712, 14)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_11_copy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(74002, 14)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_12_copy.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check if copies have the right columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>usertype</th>\n",
       "      <th>birth year</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>478</td>\n",
       "      <td>2020-01-01 00:04:05.8090</td>\n",
       "      <td>2020-01-01 00:12:04.2370</td>\n",
       "      <td>366</td>\n",
       "      <td>Broadway T Stop</td>\n",
       "      <td>42.342781</td>\n",
       "      <td>-71.057473</td>\n",
       "      <td>93</td>\n",
       "      <td>JFK/UMass T Stop</td>\n",
       "      <td>42.320340</td>\n",
       "      <td>-71.051180</td>\n",
       "      <td>6005</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1969</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>363</td>\n",
       "      <td>2020-01-01 00:04:45.6990</td>\n",
       "      <td>2020-01-01 00:10:49.0400</td>\n",
       "      <td>219</td>\n",
       "      <td>Boston East - 126 Border St</td>\n",
       "      <td>42.373312</td>\n",
       "      <td>-71.041020</td>\n",
       "      <td>212</td>\n",
       "      <td>Maverick Square - Lewis Mall</td>\n",
       "      <td>42.368844</td>\n",
       "      <td>-71.039778</td>\n",
       "      <td>3168</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>284</td>\n",
       "      <td>2020-01-01 00:06:07.0630</td>\n",
       "      <td>2020-01-01 00:10:51.9240</td>\n",
       "      <td>219</td>\n",
       "      <td>Boston East - 126 Border St</td>\n",
       "      <td>42.373312</td>\n",
       "      <td>-71.041020</td>\n",
       "      <td>212</td>\n",
       "      <td>Maverick Square - Lewis Mall</td>\n",
       "      <td>42.368844</td>\n",
       "      <td>-71.039778</td>\n",
       "      <td>3985</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2001</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>193</td>\n",
       "      <td>2020-01-01 00:06:13.8550</td>\n",
       "      <td>2020-01-01 00:09:27.8320</td>\n",
       "      <td>396</td>\n",
       "      <td>Main St at Beacon St</td>\n",
       "      <td>42.409330</td>\n",
       "      <td>-71.063819</td>\n",
       "      <td>387</td>\n",
       "      <td>Norman St at Kelvin St</td>\n",
       "      <td>42.409859</td>\n",
       "      <td>-71.066319</td>\n",
       "      <td>2692</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1978</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>428</td>\n",
       "      <td>2020-01-01 00:07:25.2950</td>\n",
       "      <td>2020-01-01 00:14:33.7800</td>\n",
       "      <td>60</td>\n",
       "      <td>Charles Circle - Charles St at Cambridge St</td>\n",
       "      <td>42.360793</td>\n",
       "      <td>-71.071190</td>\n",
       "      <td>49</td>\n",
       "      <td>Stuart St at Charles St</td>\n",
       "      <td>42.351146</td>\n",
       "      <td>-71.066289</td>\n",
       "      <td>4978</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1987</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration                 starttime                  stoptime  \\\n",
       "0           478  2020-01-01 00:04:05.8090  2020-01-01 00:12:04.2370   \n",
       "1           363  2020-01-01 00:04:45.6990  2020-01-01 00:10:49.0400   \n",
       "2           284  2020-01-01 00:06:07.0630  2020-01-01 00:10:51.9240   \n",
       "3           193  2020-01-01 00:06:13.8550  2020-01-01 00:09:27.8320   \n",
       "4           428  2020-01-01 00:07:25.2950  2020-01-01 00:14:33.7800   \n",
       "\n",
       "   start station id                           start station name  \\\n",
       "0               366                              Broadway T Stop   \n",
       "1               219                  Boston East - 126 Border St   \n",
       "2               219                  Boston East - 126 Border St   \n",
       "3               396                         Main St at Beacon St   \n",
       "4                60  Charles Circle - Charles St at Cambridge St   \n",
       "\n",
       "   start station latitude  start station longitude  end station id  \\\n",
       "0               42.342781               -71.057473              93   \n",
       "1               42.373312               -71.041020             212   \n",
       "2               42.373312               -71.041020             212   \n",
       "3               42.409330               -71.063819             387   \n",
       "4               42.360793               -71.071190              49   \n",
       "\n",
       "               end station name  end station latitude  end station longitude  \\\n",
       "0              JFK/UMass T Stop             42.320340             -71.051180   \n",
       "1  Maverick Square - Lewis Mall             42.368844             -71.039778   \n",
       "2  Maverick Square - Lewis Mall             42.368844             -71.039778   \n",
       "3        Norman St at Kelvin St             42.409859             -71.066319   \n",
       "4       Stuart St at Charles St             42.351146             -71.066289   \n",
       "\n",
       "   bikeid    usertype  birth year  gender  \n",
       "0    6005    Customer        1969       0  \n",
       "1    3168  Subscriber        2000       1  \n",
       "2    3985  Subscriber        2001       1  \n",
       "3    2692  Subscriber        1978       1  \n",
       "4    4978  Subscriber        1987       1  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_01_copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>usertype</th>\n",
       "      <th>postal code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1160</td>\n",
       "      <td>2020-06-01 00:00:25.6240</td>\n",
       "      <td>2020-06-01 00:19:45.7010</td>\n",
       "      <td>192</td>\n",
       "      <td>Purchase St at Pearl St</td>\n",
       "      <td>42.354659</td>\n",
       "      <td>-71.053181</td>\n",
       "      <td>366</td>\n",
       "      <td>Broadway T Stop</td>\n",
       "      <td>42.342781</td>\n",
       "      <td>-71.057473</td>\n",
       "      <td>2831</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1419</td>\n",
       "      <td>2020-06-01 00:01:09.3800</td>\n",
       "      <td>2020-06-01 00:24:48.9520</td>\n",
       "      <td>355</td>\n",
       "      <td>Bennington St at Constitution Beach</td>\n",
       "      <td>42.385224</td>\n",
       "      <td>-71.010631</td>\n",
       "      <td>355</td>\n",
       "      <td>Bennington St at Constitution Beach</td>\n",
       "      <td>42.385224</td>\n",
       "      <td>-71.010631</td>\n",
       "      <td>5696</td>\n",
       "      <td>Customer</td>\n",
       "      <td>02128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1093</td>\n",
       "      <td>2020-06-01 00:01:29.4790</td>\n",
       "      <td>2020-06-01 00:19:43.3530</td>\n",
       "      <td>131</td>\n",
       "      <td>Jackson Square T Stop</td>\n",
       "      <td>42.322931</td>\n",
       "      <td>-71.100141</td>\n",
       "      <td>331</td>\n",
       "      <td>Huntington Ave at Mass Art</td>\n",
       "      <td>42.336586</td>\n",
       "      <td>-71.098870</td>\n",
       "      <td>3922</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>10570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1067</td>\n",
       "      <td>2020-06-01 00:01:35.8540</td>\n",
       "      <td>2020-06-01 00:19:23.3250</td>\n",
       "      <td>131</td>\n",
       "      <td>Jackson Square T Stop</td>\n",
       "      <td>42.322931</td>\n",
       "      <td>-71.100141</td>\n",
       "      <td>331</td>\n",
       "      <td>Huntington Ave at Mass Art</td>\n",
       "      <td>42.336586</td>\n",
       "      <td>-71.098870</td>\n",
       "      <td>3361</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>10570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1391</td>\n",
       "      <td>2020-06-01 00:01:51.0390</td>\n",
       "      <td>2020-06-01 00:25:02.8460</td>\n",
       "      <td>355</td>\n",
       "      <td>Bennington St at Constitution Beach</td>\n",
       "      <td>42.385224</td>\n",
       "      <td>-71.010631</td>\n",
       "      <td>355</td>\n",
       "      <td>Bennington St at Constitution Beach</td>\n",
       "      <td>42.385224</td>\n",
       "      <td>-71.010631</td>\n",
       "      <td>3621</td>\n",
       "      <td>Customer</td>\n",
       "      <td>01902</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration                 starttime                  stoptime  \\\n",
       "0          1160  2020-06-01 00:00:25.6240  2020-06-01 00:19:45.7010   \n",
       "1          1419  2020-06-01 00:01:09.3800  2020-06-01 00:24:48.9520   \n",
       "2          1093  2020-06-01 00:01:29.4790  2020-06-01 00:19:43.3530   \n",
       "3          1067  2020-06-01 00:01:35.8540  2020-06-01 00:19:23.3250   \n",
       "4          1391  2020-06-01 00:01:51.0390  2020-06-01 00:25:02.8460   \n",
       "\n",
       "   start station id                   start station name  \\\n",
       "0               192              Purchase St at Pearl St   \n",
       "1               355  Bennington St at Constitution Beach   \n",
       "2               131                Jackson Square T Stop   \n",
       "3               131                Jackson Square T Stop   \n",
       "4               355  Bennington St at Constitution Beach   \n",
       "\n",
       "   start station latitude  start station longitude  end station id  \\\n",
       "0               42.354659               -71.053181             366   \n",
       "1               42.385224               -71.010631             355   \n",
       "2               42.322931               -71.100141             331   \n",
       "3               42.322931               -71.100141             331   \n",
       "4               42.385224               -71.010631             355   \n",
       "\n",
       "                      end station name  end station latitude  \\\n",
       "0                      Broadway T Stop             42.342781   \n",
       "1  Bennington St at Constitution Beach             42.385224   \n",
       "2           Huntington Ave at Mass Art             42.336586   \n",
       "3           Huntington Ave at Mass Art             42.336586   \n",
       "4  Bennington St at Constitution Beach             42.385224   \n",
       "\n",
       "   end station longitude  bikeid    usertype postal code  \n",
       "0             -71.057473    2831    Customer         NaN  \n",
       "1             -71.010631    5696    Customer       02128  \n",
       "2             -71.098870    3922  Subscriber       10570  \n",
       "3             -71.098870    3361  Subscriber       10570  \n",
       "4             -71.010631    3621    Customer       01902  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_06_copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# concat the data sets\n",
    "df_concatenated_columns_all = pandas.concat([df_01_copy, df_02_copy, df_03_copy, df_04_copy, df_05_copy, df_06_copy, df_07_copy, df_08_copy, df_09_copy, df_10_copy, df_11_copy, df_12_copy,])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "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>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>usertype</th>\n",
       "      <th>birth year</th>\n",
       "      <th>gender</th>\n",
       "      <th>postal code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>478</td>\n",
       "      <td>2020-01-01 00:04:05.8090</td>\n",
       "      <td>2020-01-01 00:12:04.2370</td>\n",
       "      <td>366</td>\n",
       "      <td>Broadway T Stop</td>\n",
       "      <td>42.342781</td>\n",
       "      <td>-71.057473</td>\n",
       "      <td>93</td>\n",
       "      <td>JFK/UMass T Stop</td>\n",
       "      <td>42.320340</td>\n",
       "      <td>-71.051180</td>\n",
       "      <td>6005</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1969.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>363</td>\n",
       "      <td>2020-01-01 00:04:45.6990</td>\n",
       "      <td>2020-01-01 00:10:49.0400</td>\n",
       "      <td>219</td>\n",
       "      <td>Boston East - 126 Border St</td>\n",
       "      <td>42.373312</td>\n",
       "      <td>-71.041020</td>\n",
       "      <td>212</td>\n",
       "      <td>Maverick Square - Lewis Mall</td>\n",
       "      <td>42.368844</td>\n",
       "      <td>-71.039778</td>\n",
       "      <td>3168</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>284</td>\n",
       "      <td>2020-01-01 00:06:07.0630</td>\n",
       "      <td>2020-01-01 00:10:51.9240</td>\n",
       "      <td>219</td>\n",
       "      <td>Boston East - 126 Border St</td>\n",
       "      <td>42.373312</td>\n",
       "      <td>-71.041020</td>\n",
       "      <td>212</td>\n",
       "      <td>Maverick Square - Lewis Mall</td>\n",
       "      <td>42.368844</td>\n",
       "      <td>-71.039778</td>\n",
       "      <td>3985</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>193</td>\n",
       "      <td>2020-01-01 00:06:13.8550</td>\n",
       "      <td>2020-01-01 00:09:27.8320</td>\n",
       "      <td>396</td>\n",
       "      <td>Main St at Beacon St</td>\n",
       "      <td>42.409330</td>\n",
       "      <td>-71.063819</td>\n",
       "      <td>387</td>\n",
       "      <td>Norman St at Kelvin St</td>\n",
       "      <td>42.409859</td>\n",
       "      <td>-71.066319</td>\n",
       "      <td>2692</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1978.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>428</td>\n",
       "      <td>2020-01-01 00:07:25.2950</td>\n",
       "      <td>2020-01-01 00:14:33.7800</td>\n",
       "      <td>60</td>\n",
       "      <td>Charles Circle - Charles St at Cambridge St</td>\n",
       "      <td>42.360793</td>\n",
       "      <td>-71.071190</td>\n",
       "      <td>49</td>\n",
       "      <td>Stuart St at Charles St</td>\n",
       "      <td>42.351146</td>\n",
       "      <td>-71.066289</td>\n",
       "      <td>4978</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration                 starttime                  stoptime  \\\n",
       "0           478  2020-01-01 00:04:05.8090  2020-01-01 00:12:04.2370   \n",
       "1           363  2020-01-01 00:04:45.6990  2020-01-01 00:10:49.0400   \n",
       "2           284  2020-01-01 00:06:07.0630  2020-01-01 00:10:51.9240   \n",
       "3           193  2020-01-01 00:06:13.8550  2020-01-01 00:09:27.8320   \n",
       "4           428  2020-01-01 00:07:25.2950  2020-01-01 00:14:33.7800   \n",
       "\n",
       "   start station id                           start station name  \\\n",
       "0               366                              Broadway T Stop   \n",
       "1               219                  Boston East - 126 Border St   \n",
       "2               219                  Boston East - 126 Border St   \n",
       "3               396                         Main St at Beacon St   \n",
       "4                60  Charles Circle - Charles St at Cambridge St   \n",
       "\n",
       "   start station latitude  start station longitude  end station id  \\\n",
       "0               42.342781               -71.057473              93   \n",
       "1               42.373312               -71.041020             212   \n",
       "2               42.373312               -71.041020             212   \n",
       "3               42.409330               -71.063819             387   \n",
       "4               42.360793               -71.071190              49   \n",
       "\n",
       "               end station name  end station latitude  end station longitude  \\\n",
       "0              JFK/UMass T Stop             42.320340             -71.051180   \n",
       "1  Maverick Square - Lewis Mall             42.368844             -71.039778   \n",
       "2  Maverick Square - Lewis Mall             42.368844             -71.039778   \n",
       "3        Norman St at Kelvin St             42.409859             -71.066319   \n",
       "4       Stuart St at Charles St             42.351146             -71.066289   \n",
       "\n",
       "   bikeid    usertype  birth year  gender postal code  \n",
       "0    6005    Customer      1969.0     0.0         NaN  \n",
       "1    3168  Subscriber      2000.0     1.0         NaN  \n",
       "2    3985  Subscriber      2001.0     1.0         NaN  \n",
       "3    2692  Subscriber      1978.0     1.0         NaN  \n",
       "4    4978  Subscriber      1987.0     1.0         NaN  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_concatenated_columns_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2073448 entries, 0 to 74001\n",
      "Data columns (total 16 columns):\n",
      " #   Column                   Dtype  \n",
      "---  ------                   -----  \n",
      " 0   tripduration             int64  \n",
      " 1   starttime                object \n",
      " 2   stoptime                 object \n",
      " 3   start station id         int64  \n",
      " 4   start station name       object \n",
      " 5   start station latitude   float64\n",
      " 6   start station longitude  float64\n",
      " 7   end station id           int64  \n",
      " 8   end station name         object \n",
      " 9   end station latitude     float64\n",
      " 10  end station longitude    float64\n",
      " 11  bikeid                   int64  \n",
      " 12  usertype                 object \n",
      " 13  birth year               float64\n",
      " 14  gender                   float64\n",
      " 15  postal code              object \n",
      "dtypes: float64(6), int64(4), object(6)\n",
      "memory usage: 268.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df_concatenated_columns_all.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2073448, 16)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_concatenated_columns_all.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all = df_concatenated_columns_all.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2073448, 16)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define\n",
    "- remove birth year, gender and postal code columns\n",
    "\n",
    "Action: \n",
    "drop these columns permanently"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all.drop(['birth year', 'postal code', 'gender'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>usertype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>478</td>\n",
       "      <td>2020-01-01 00:04:05.8090</td>\n",
       "      <td>2020-01-01 00:12:04.2370</td>\n",
       "      <td>366</td>\n",
       "      <td>Broadway T Stop</td>\n",
       "      <td>42.342781</td>\n",
       "      <td>-71.057473</td>\n",
       "      <td>93</td>\n",
       "      <td>JFK/UMass T Stop</td>\n",
       "      <td>42.320340</td>\n",
       "      <td>-71.051180</td>\n",
       "      <td>6005</td>\n",
       "      <td>Customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>363</td>\n",
       "      <td>2020-01-01 00:04:45.6990</td>\n",
       "      <td>2020-01-01 00:10:49.0400</td>\n",
       "      <td>219</td>\n",
       "      <td>Boston East - 126 Border St</td>\n",
       "      <td>42.373312</td>\n",
       "      <td>-71.041020</td>\n",
       "      <td>212</td>\n",
       "      <td>Maverick Square - Lewis Mall</td>\n",
       "      <td>42.368844</td>\n",
       "      <td>-71.039778</td>\n",
       "      <td>3168</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>284</td>\n",
       "      <td>2020-01-01 00:06:07.0630</td>\n",
       "      <td>2020-01-01 00:10:51.9240</td>\n",
       "      <td>219</td>\n",
       "      <td>Boston East - 126 Border St</td>\n",
       "      <td>42.373312</td>\n",
       "      <td>-71.041020</td>\n",
       "      <td>212</td>\n",
       "      <td>Maverick Square - Lewis Mall</td>\n",
       "      <td>42.368844</td>\n",
       "      <td>-71.039778</td>\n",
       "      <td>3985</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>193</td>\n",
       "      <td>2020-01-01 00:06:13.8550</td>\n",
       "      <td>2020-01-01 00:09:27.8320</td>\n",
       "      <td>396</td>\n",
       "      <td>Main St at Beacon St</td>\n",
       "      <td>42.409330</td>\n",
       "      <td>-71.063819</td>\n",
       "      <td>387</td>\n",
       "      <td>Norman St at Kelvin St</td>\n",
       "      <td>42.409859</td>\n",
       "      <td>-71.066319</td>\n",
       "      <td>2692</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>428</td>\n",
       "      <td>2020-01-01 00:07:25.2950</td>\n",
       "      <td>2020-01-01 00:14:33.7800</td>\n",
       "      <td>60</td>\n",
       "      <td>Charles Circle - Charles St at Cambridge St</td>\n",
       "      <td>42.360793</td>\n",
       "      <td>-71.071190</td>\n",
       "      <td>49</td>\n",
       "      <td>Stuart St at Charles St</td>\n",
       "      <td>42.351146</td>\n",
       "      <td>-71.066289</td>\n",
       "      <td>4978</td>\n",
       "      <td>Subscriber</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration                 starttime                  stoptime  \\\n",
       "0           478  2020-01-01 00:04:05.8090  2020-01-01 00:12:04.2370   \n",
       "1           363  2020-01-01 00:04:45.6990  2020-01-01 00:10:49.0400   \n",
       "2           284  2020-01-01 00:06:07.0630  2020-01-01 00:10:51.9240   \n",
       "3           193  2020-01-01 00:06:13.8550  2020-01-01 00:09:27.8320   \n",
       "4           428  2020-01-01 00:07:25.2950  2020-01-01 00:14:33.7800   \n",
       "\n",
       "   start station id                           start station name  \\\n",
       "0               366                              Broadway T Stop   \n",
       "1               219                  Boston East - 126 Border St   \n",
       "2               219                  Boston East - 126 Border St   \n",
       "3               396                         Main St at Beacon St   \n",
       "4                60  Charles Circle - Charles St at Cambridge St   \n",
       "\n",
       "   start station latitude  start station longitude  end station id  \\\n",
       "0               42.342781               -71.057473              93   \n",
       "1               42.373312               -71.041020             212   \n",
       "2               42.373312               -71.041020             212   \n",
       "3               42.409330               -71.063819             387   \n",
       "4               42.360793               -71.071190              49   \n",
       "\n",
       "               end station name  end station latitude  end station longitude  \\\n",
       "0              JFK/UMass T Stop             42.320340             -71.051180   \n",
       "1  Maverick Square - Lewis Mall             42.368844             -71.039778   \n",
       "2  Maverick Square - Lewis Mall             42.368844             -71.039778   \n",
       "3        Norman St at Kelvin St             42.409859             -71.066319   \n",
       "4       Stuart St at Charles St             42.351146             -71.066289   \n",
       "\n",
       "   bikeid    usertype  \n",
       "0    6005    Customer  \n",
       "1    3168  Subscriber  \n",
       "2    3985  Subscriber  \n",
       "3    2692  Subscriber  \n",
       "4    4978  Subscriber  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define\n",
    "- `starttime` and `stopttime` column is in object format, it should be datetime\n",
    "\n",
    "Action: convert these to datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert starttime field into datetime\n",
    "df_all['starttime'] = pandas.to_datetime(df_all['starttime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert stoptime field into datetime\n",
    "df_all['stoptime'] = pandas.to_datetime(df_all['stoptime'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tripduration                        int64\n",
       "starttime                  datetime64[ns]\n",
       "stoptime                   datetime64[ns]\n",
       "start station id                    int64\n",
       "start station name                 object\n",
       "start station latitude            float64\n",
       "start station longitude           float64\n",
       "end station id                      int64\n",
       "end station name                   object\n",
       "end station latitude              float64\n",
       "end station longitude             float64\n",
       "bikeid                              int64\n",
       "usertype                           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning part 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first check if there are any outliers in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>tripduration</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>bikeid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.073448e+06</td>\n",
       "      <td>2.073448e+06</td>\n",
       "      <td>2.073448e+06</td>\n",
       "      <td>2.073448e+06</td>\n",
       "      <td>2.073448e+06</td>\n",
       "      <td>2.073448e+06</td>\n",
       "      <td>2.073448e+06</td>\n",
       "      <td>2.073448e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.831438e+03</td>\n",
       "      <td>1.634481e+02</td>\n",
       "      <td>4.235597e+01</td>\n",
       "      <td>-7.108868e+01</td>\n",
       "      <td>1.623836e+02</td>\n",
       "      <td>4.235589e+01</td>\n",
       "      <td>-7.108840e+01</td>\n",
       "      <td>4.263332e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.425891e+04</td>\n",
       "      <td>1.357767e+02</td>\n",
       "      <td>3.452189e-02</td>\n",
       "      <td>5.628081e-02</td>\n",
       "      <td>1.358793e+02</td>\n",
       "      <td>3.455072e-02</td>\n",
       "      <td>5.626398e-02</td>\n",
       "      <td>1.272965e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.100000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-7.122627e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-7.122627e+01</td>\n",
       "      <td>3.100000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.750000e+02</td>\n",
       "      <td>5.500000e+01</td>\n",
       "      <td>4.234522e+01</td>\n",
       "      <td>-7.110594e+01</td>\n",
       "      <td>5.500000e+01</td>\n",
       "      <td>4.234522e+01</td>\n",
       "      <td>-7.110567e+01</td>\n",
       "      <td>3.175000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.280000e+02</td>\n",
       "      <td>1.100000e+02</td>\n",
       "      <td>4.235560e+01</td>\n",
       "      <td>-7.108981e+01</td>\n",
       "      <td>1.080000e+02</td>\n",
       "      <td>4.235560e+01</td>\n",
       "      <td>-7.108822e+01</td>\n",
       "      <td>4.256000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.443000e+03</td>\n",
       "      <td>2.390000e+02</td>\n",
       "      <td>4.236567e+01</td>\n",
       "      <td>-7.106959e+01</td>\n",
       "      <td>2.390000e+02</td>\n",
       "      <td>4.236567e+01</td>\n",
       "      <td>-7.106894e+01</td>\n",
       "      <td>5.406000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.879352e+06</td>\n",
       "      <td>4.990000e+02</td>\n",
       "      <td>4.241608e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.990000e+02</td>\n",
       "      <td>4.241608e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.724000e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       tripduration  start station id  start station latitude  \\\n",
       "count  2.073448e+06      2.073448e+06            2.073448e+06   \n",
       "mean   1.831438e+03      1.634481e+02            4.235597e+01   \n",
       "std    2.425891e+04      1.357767e+02            3.452189e-02   \n",
       "min    6.100000e+01      1.000000e+00            0.000000e+00   \n",
       "25%    4.750000e+02      5.500000e+01            4.234522e+01   \n",
       "50%    8.280000e+02      1.100000e+02            4.235560e+01   \n",
       "75%    1.443000e+03      2.390000e+02            4.236567e+01   \n",
       "max    3.879352e+06      4.990000e+02            4.241608e+01   \n",
       "\n",
       "       start station longitude  end station id  end station latitude  \\\n",
       "count             2.073448e+06    2.073448e+06          2.073448e+06   \n",
       "mean             -7.108868e+01    1.623836e+02          4.235589e+01   \n",
       "std               5.628081e-02    1.358793e+02          3.455072e-02   \n",
       "min              -7.122627e+01    1.000000e+00          0.000000e+00   \n",
       "25%              -7.110594e+01    5.500000e+01          4.234522e+01   \n",
       "50%              -7.108981e+01    1.080000e+02          4.235560e+01   \n",
       "75%              -7.106959e+01    2.390000e+02          4.236567e+01   \n",
       "max               0.000000e+00    4.990000e+02          4.241608e+01   \n",
       "\n",
       "       end station longitude        bikeid  \n",
       "count           2.073448e+06  2.073448e+06  \n",
       "mean           -7.108840e+01  4.263332e+03  \n",
       "std             5.626398e-02  1.272965e+03  \n",
       "min            -7.122627e+01  3.100000e+01  \n",
       "25%            -7.110567e+01  3.175000e+03  \n",
       "50%            -7.108822e+01  4.256000e+03  \n",
       "75%            -7.106894e+01  5.406000e+03  \n",
       "max             0.000000e+00  6.724000e+03  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assess df_all statistics\n",
    "df_all.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There must be some zero values in the start latitude and end latitude columns, but since I'm not going to explore the data in those columns, I will not clean the data there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assess df_all's duplicated values\n",
    "sum(df_all.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tripduration               0\n",
       "starttime                  0\n",
       "stoptime                   0\n",
       "start station id           0\n",
       "start station name         0\n",
       "start station latitude     0\n",
       "start station longitude    0\n",
       "end station id             0\n",
       "end station name           0\n",
       "end station latitude       0\n",
       "end station longitude      0\n",
       "bikeid                     0\n",
       "usertype                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assess df_all's null values\n",
    "df_all.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems like the data set is clean enough to start my exploration towards the initial questions, but to be able to draw conclusions from the starttime column, I would like to extract a couple of values and create new columns.\n",
    "\n",
    "Q: When are the most trips taken in terms of time of day, day of the week, or month of the year?\n",
    " \n",
    " ### Tidiness Issues\n",
    "\n",
    "There is no way to see\n",
    "- which day of the week the data was recorded\n",
    "- if the day is a weekday or a weekend\n",
    "- which months of the year the data was recorded\n",
    "- which time of the day the data was recorded\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define \n",
    "There is no way to see which day of the week the data was recorded\n",
    "\n",
    "Action:\n",
    "- Create a column for the days of the week \n",
    "- Convert values to date time and then to week day names\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a column for the days of the week using the starttime column\n",
    "\n",
    "df_all['weekday'] = df_all['starttime'].dt.dayofweek   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>usertype</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>478</td>\n",
       "      <td>2020-01-01 00:04:05.809</td>\n",
       "      <td>2020-01-01 00:12:04.237</td>\n",
       "      <td>366</td>\n",
       "      <td>Broadway T Stop</td>\n",
       "      <td>42.342781</td>\n",
       "      <td>-71.057473</td>\n",
       "      <td>93</td>\n",
       "      <td>JFK/UMass T Stop</td>\n",
       "      <td>42.320340</td>\n",
       "      <td>-71.051180</td>\n",
       "      <td>6005</td>\n",
       "      <td>Customer</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>363</td>\n",
       "      <td>2020-01-01 00:04:45.699</td>\n",
       "      <td>2020-01-01 00:10:49.040</td>\n",
       "      <td>219</td>\n",
       "      <td>Boston East - 126 Border St</td>\n",
       "      <td>42.373312</td>\n",
       "      <td>-71.041020</td>\n",
       "      <td>212</td>\n",
       "      <td>Maverick Square - Lewis Mall</td>\n",
       "      <td>42.368844</td>\n",
       "      <td>-71.039778</td>\n",
       "      <td>3168</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>284</td>\n",
       "      <td>2020-01-01 00:06:07.063</td>\n",
       "      <td>2020-01-01 00:10:51.924</td>\n",
       "      <td>219</td>\n",
       "      <td>Boston East - 126 Border St</td>\n",
       "      <td>42.373312</td>\n",
       "      <td>-71.041020</td>\n",
       "      <td>212</td>\n",
       "      <td>Maverick Square - Lewis Mall</td>\n",
       "      <td>42.368844</td>\n",
       "      <td>-71.039778</td>\n",
       "      <td>3985</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>193</td>\n",
       "      <td>2020-01-01 00:06:13.855</td>\n",
       "      <td>2020-01-01 00:09:27.832</td>\n",
       "      <td>396</td>\n",
       "      <td>Main St at Beacon St</td>\n",
       "      <td>42.409330</td>\n",
       "      <td>-71.063819</td>\n",
       "      <td>387</td>\n",
       "      <td>Norman St at Kelvin St</td>\n",
       "      <td>42.409859</td>\n",
       "      <td>-71.066319</td>\n",
       "      <td>2692</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>428</td>\n",
       "      <td>2020-01-01 00:07:25.295</td>\n",
       "      <td>2020-01-01 00:14:33.780</td>\n",
       "      <td>60</td>\n",
       "      <td>Charles Circle - Charles St at Cambridge St</td>\n",
       "      <td>42.360793</td>\n",
       "      <td>-71.071190</td>\n",
       "      <td>49</td>\n",
       "      <td>Stuart St at Charles St</td>\n",
       "      <td>42.351146</td>\n",
       "      <td>-71.066289</td>\n",
       "      <td>4978</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32947</th>\n",
       "      <td>130</td>\n",
       "      <td>2020-02-08 07:26:10.301</td>\n",
       "      <td>2020-02-08 07:28:21.117</td>\n",
       "      <td>195</td>\n",
       "      <td>Child St at North St</td>\n",
       "      <td>42.371505</td>\n",
       "      <td>-71.072493</td>\n",
       "      <td>90</td>\n",
       "      <td>Lechmere Station at Cambridge St / First St</td>\n",
       "      <td>42.370677</td>\n",
       "      <td>-71.076529</td>\n",
       "      <td>3844</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32948</th>\n",
       "      <td>157</td>\n",
       "      <td>2020-02-08 07:27:17.874</td>\n",
       "      <td>2020-02-08 07:29:54.959</td>\n",
       "      <td>116</td>\n",
       "      <td>359 Broadway - Broadway at Fayette Street</td>\n",
       "      <td>42.370803</td>\n",
       "      <td>-71.104412</td>\n",
       "      <td>76</td>\n",
       "      <td>Central Sq Post Office / Cambridge City Hall a...</td>\n",
       "      <td>42.366426</td>\n",
       "      <td>-71.105495</td>\n",
       "      <td>3684</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32949</th>\n",
       "      <td>629</td>\n",
       "      <td>2020-02-08 07:28:25.079</td>\n",
       "      <td>2020-02-08 07:38:54.861</td>\n",
       "      <td>118</td>\n",
       "      <td>Linear Park - Mass. Ave. at Cameron Ave.</td>\n",
       "      <td>42.397828</td>\n",
       "      <td>-71.130516</td>\n",
       "      <td>115</td>\n",
       "      <td>Porter Square Station</td>\n",
       "      <td>42.387995</td>\n",
       "      <td>-71.119084</td>\n",
       "      <td>4985</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32950</th>\n",
       "      <td>525</td>\n",
       "      <td>2020-02-08 07:29:50.489</td>\n",
       "      <td>2020-02-08 07:38:35.699</td>\n",
       "      <td>407</td>\n",
       "      <td>Craigie at Summer St</td>\n",
       "      <td>42.383634</td>\n",
       "      <td>-71.108558</td>\n",
       "      <td>328</td>\n",
       "      <td>Grove St at Community Path</td>\n",
       "      <td>42.396387</td>\n",
       "      <td>-71.120113</td>\n",
       "      <td>5624</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32951</th>\n",
       "      <td>283</td>\n",
       "      <td>2020-02-08 07:30:46.284</td>\n",
       "      <td>2020-02-08 07:35:29.419</td>\n",
       "      <td>280</td>\n",
       "      <td>Main St at Baldwin St</td>\n",
       "      <td>42.380857</td>\n",
       "      <td>-71.070629</td>\n",
       "      <td>171</td>\n",
       "      <td>Bunker Hill Community College</td>\n",
       "      <td>42.374090</td>\n",
       "      <td>-71.069060</td>\n",
       "      <td>3994</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>161550 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       tripduration               starttime                stoptime  \\\n",
       "0               478 2020-01-01 00:04:05.809 2020-01-01 00:12:04.237   \n",
       "1               363 2020-01-01 00:04:45.699 2020-01-01 00:10:49.040   \n",
       "2               284 2020-01-01 00:06:07.063 2020-01-01 00:10:51.924   \n",
       "3               193 2020-01-01 00:06:13.855 2020-01-01 00:09:27.832   \n",
       "4               428 2020-01-01 00:07:25.295 2020-01-01 00:14:33.780   \n",
       "...             ...                     ...                     ...   \n",
       "32947           130 2020-02-08 07:26:10.301 2020-02-08 07:28:21.117   \n",
       "32948           157 2020-02-08 07:27:17.874 2020-02-08 07:29:54.959   \n",
       "32949           629 2020-02-08 07:28:25.079 2020-02-08 07:38:54.861   \n",
       "32950           525 2020-02-08 07:29:50.489 2020-02-08 07:38:35.699   \n",
       "32951           283 2020-02-08 07:30:46.284 2020-02-08 07:35:29.419   \n",
       "\n",
       "       start station id                           start station name  \\\n",
       "0                   366                              Broadway T Stop   \n",
       "1                   219                  Boston East - 126 Border St   \n",
       "2                   219                  Boston East - 126 Border St   \n",
       "3                   396                         Main St at Beacon St   \n",
       "4                    60  Charles Circle - Charles St at Cambridge St   \n",
       "...                 ...                                          ...   \n",
       "32947               195                         Child St at North St   \n",
       "32948               116    359 Broadway - Broadway at Fayette Street   \n",
       "32949               118    Linear Park - Mass. Ave. at Cameron Ave.    \n",
       "32950               407                         Craigie at Summer St   \n",
       "32951               280                        Main St at Baldwin St   \n",
       "\n",
       "       start station latitude  start station longitude  end station id  \\\n",
       "0                   42.342781               -71.057473              93   \n",
       "1                   42.373312               -71.041020             212   \n",
       "2                   42.373312               -71.041020             212   \n",
       "3                   42.409330               -71.063819             387   \n",
       "4                   42.360793               -71.071190              49   \n",
       "...                       ...                      ...             ...   \n",
       "32947               42.371505               -71.072493              90   \n",
       "32948               42.370803               -71.104412              76   \n",
       "32949               42.397828               -71.130516             115   \n",
       "32950               42.383634               -71.108558             328   \n",
       "32951               42.380857               -71.070629             171   \n",
       "\n",
       "                                        end station name  \\\n",
       "0                                       JFK/UMass T Stop   \n",
       "1                           Maverick Square - Lewis Mall   \n",
       "2                           Maverick Square - Lewis Mall   \n",
       "3                                 Norman St at Kelvin St   \n",
       "4                                Stuart St at Charles St   \n",
       "...                                                  ...   \n",
       "32947        Lechmere Station at Cambridge St / First St   \n",
       "32948  Central Sq Post Office / Cambridge City Hall a...   \n",
       "32949                              Porter Square Station   \n",
       "32950                         Grove St at Community Path   \n",
       "32951                      Bunker Hill Community College   \n",
       "\n",
       "       end station latitude  end station longitude  bikeid    usertype  \\\n",
       "0                 42.320340             -71.051180    6005    Customer   \n",
       "1                 42.368844             -71.039778    3168  Subscriber   \n",
       "2                 42.368844             -71.039778    3985  Subscriber   \n",
       "3                 42.409859             -71.066319    2692  Subscriber   \n",
       "4                 42.351146             -71.066289    4978  Subscriber   \n",
       "...                     ...                    ...     ...         ...   \n",
       "32947             42.370677             -71.076529    3844  Subscriber   \n",
       "32948             42.366426             -71.105495    3684  Subscriber   \n",
       "32949             42.387995             -71.119084    4985  Subscriber   \n",
       "32950             42.396387             -71.120113    5624  Subscriber   \n",
       "32951             42.374090             -71.069060    3994  Subscriber   \n",
       "\n",
       "       weekday  \n",
       "0            2  \n",
       "1            2  \n",
       "2            2  \n",
       "3            2  \n",
       "4            2  \n",
       "...        ...  \n",
       "32947        5  \n",
       "32948        5  \n",
       "32949        5  \n",
       "32950        5  \n",
       "32951        5  \n",
       "\n",
       "[161550 rows x 14 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.head(161550)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define \n",
    "There is no way to see which day of the month the data was recorded\n",
    "\n",
    "Action:\n",
    "- Create a column for the months of the year\n",
    "- Convert values to date time and then to month names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a column for the months of the year using the starttime column\n",
    "df_all['month'] = pandas.DatetimeIndex(df_all['starttime']).month\n",
    "\n",
    "# convert values to date time and then month names\n",
    "\n",
    "df_all['month'] = pandas.to_datetime(df_all['month'], format='%m').dt.month_name()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>usertype</th>\n",
       "      <th>weekday</th>\n",
       "      <th>month</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>478</td>\n",
       "      <td>2020-01-01 00:04:05.809</td>\n",
       "      <td>2020-01-01 00:12:04.237</td>\n",
       "      <td>366</td>\n",
       "      <td>Broadway T Stop</td>\n",
       "      <td>42.342781</td>\n",
       "      <td>-71.057473</td>\n",
       "      <td>93</td>\n",
       "      <td>JFK/UMass T Stop</td>\n",
       "      <td>42.320340</td>\n",
       "      <td>-71.051180</td>\n",
       "      <td>6005</td>\n",
       "      <td>Customer</td>\n",
       "      <td>2</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>363</td>\n",
       "      <td>2020-01-01 00:04:45.699</td>\n",
       "      <td>2020-01-01 00:10:49.040</td>\n",
       "      <td>219</td>\n",
       "      <td>Boston East - 126 Border St</td>\n",
       "      <td>42.373312</td>\n",
       "      <td>-71.041020</td>\n",
       "      <td>212</td>\n",
       "      <td>Maverick Square - Lewis Mall</td>\n",
       "      <td>42.368844</td>\n",
       "      <td>-71.039778</td>\n",
       "      <td>3168</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>284</td>\n",
       "      <td>2020-01-01 00:06:07.063</td>\n",
       "      <td>2020-01-01 00:10:51.924</td>\n",
       "      <td>219</td>\n",
       "      <td>Boston East - 126 Border St</td>\n",
       "      <td>42.373312</td>\n",
       "      <td>-71.041020</td>\n",
       "      <td>212</td>\n",
       "      <td>Maverick Square - Lewis Mall</td>\n",
       "      <td>42.368844</td>\n",
       "      <td>-71.039778</td>\n",
       "      <td>3985</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>193</td>\n",
       "      <td>2020-01-01 00:06:13.855</td>\n",
       "      <td>2020-01-01 00:09:27.832</td>\n",
       "      <td>396</td>\n",
       "      <td>Main St at Beacon St</td>\n",
       "      <td>42.409330</td>\n",
       "      <td>-71.063819</td>\n",
       "      <td>387</td>\n",
       "      <td>Norman St at Kelvin St</td>\n",
       "      <td>42.409859</td>\n",
       "      <td>-71.066319</td>\n",
       "      <td>2692</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>428</td>\n",
       "      <td>2020-01-01 00:07:25.295</td>\n",
       "      <td>2020-01-01 00:14:33.780</td>\n",
       "      <td>60</td>\n",
       "      <td>Charles Circle - Charles St at Cambridge St</td>\n",
       "      <td>42.360793</td>\n",
       "      <td>-71.071190</td>\n",
       "      <td>49</td>\n",
       "      <td>Stuart St at Charles St</td>\n",
       "      <td>42.351146</td>\n",
       "      <td>-71.066289</td>\n",
       "      <td>4978</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration               starttime                stoptime  \\\n",
       "0           478 2020-01-01 00:04:05.809 2020-01-01 00:12:04.237   \n",
       "1           363 2020-01-01 00:04:45.699 2020-01-01 00:10:49.040   \n",
       "2           284 2020-01-01 00:06:07.063 2020-01-01 00:10:51.924   \n",
       "3           193 2020-01-01 00:06:13.855 2020-01-01 00:09:27.832   \n",
       "4           428 2020-01-01 00:07:25.295 2020-01-01 00:14:33.780   \n",
       "\n",
       "   start station id                           start station name  \\\n",
       "0               366                              Broadway T Stop   \n",
       "1               219                  Boston East - 126 Border St   \n",
       "2               219                  Boston East - 126 Border St   \n",
       "3               396                         Main St at Beacon St   \n",
       "4                60  Charles Circle - Charles St at Cambridge St   \n",
       "\n",
       "   start station latitude  start station longitude  end station id  \\\n",
       "0               42.342781               -71.057473              93   \n",
       "1               42.373312               -71.041020             212   \n",
       "2               42.373312               -71.041020             212   \n",
       "3               42.409330               -71.063819             387   \n",
       "4               42.360793               -71.071190              49   \n",
       "\n",
       "               end station name  end station latitude  end station longitude  \\\n",
       "0              JFK/UMass T Stop             42.320340             -71.051180   \n",
       "1  Maverick Square - Lewis Mall             42.368844             -71.039778   \n",
       "2  Maverick Square - Lewis Mall             42.368844             -71.039778   \n",
       "3        Norman St at Kelvin St             42.409859             -71.066319   \n",
       "4       Stuart St at Charles St             42.351146             -71.066289   \n",
       "\n",
       "   bikeid    usertype  weekday    month  \n",
       "0    6005    Customer        2  January  \n",
       "1    3168  Subscriber        2  January  \n",
       "2    3985  Subscriber        2  January  \n",
       "3    2692  Subscriber        2  January  \n",
       "4    4978  Subscriber        2  January  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define \n",
    "There is no way to see which time of the day the data was recorded\n",
    "\n",
    "Action:\n",
    "- Create a column for the time of the day\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a column for the time of the day using the starttime column\n",
    "df_all['timeoftheday']= df_all['starttime'].dt.strftime('%H')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>usertype</th>\n",
       "      <th>weekday</th>\n",
       "      <th>month</th>\n",
       "      <th>timeoftheday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>478</td>\n",
       "      <td>2020-01-01 00:04:05.809</td>\n",
       "      <td>2020-01-01 00:12:04.237</td>\n",
       "      <td>366</td>\n",
       "      <td>Broadway T Stop</td>\n",
       "      <td>42.342781</td>\n",
       "      <td>-71.057473</td>\n",
       "      <td>93</td>\n",
       "      <td>JFK/UMass T Stop</td>\n",
       "      <td>42.320340</td>\n",
       "      <td>-71.051180</td>\n",
       "      <td>6005</td>\n",
       "      <td>Customer</td>\n",
       "      <td>2</td>\n",
       "      <td>January</td>\n",
       "      <td>00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>363</td>\n",
       "      <td>2020-01-01 00:04:45.699</td>\n",
       "      <td>2020-01-01 00:10:49.040</td>\n",
       "      <td>219</td>\n",
       "      <td>Boston East - 126 Border St</td>\n",
       "      <td>42.373312</td>\n",
       "      <td>-71.041020</td>\n",
       "      <td>212</td>\n",
       "      <td>Maverick Square - Lewis Mall</td>\n",
       "      <td>42.368844</td>\n",
       "      <td>-71.039778</td>\n",
       "      <td>3168</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2</td>\n",
       "      <td>January</td>\n",
       "      <td>00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>284</td>\n",
       "      <td>2020-01-01 00:06:07.063</td>\n",
       "      <td>2020-01-01 00:10:51.924</td>\n",
       "      <td>219</td>\n",
       "      <td>Boston East - 126 Border St</td>\n",
       "      <td>42.373312</td>\n",
       "      <td>-71.041020</td>\n",
       "      <td>212</td>\n",
       "      <td>Maverick Square - Lewis Mall</td>\n",
       "      <td>42.368844</td>\n",
       "      <td>-71.039778</td>\n",
       "      <td>3985</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2</td>\n",
       "      <td>January</td>\n",
       "      <td>00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>193</td>\n",
       "      <td>2020-01-01 00:06:13.855</td>\n",
       "      <td>2020-01-01 00:09:27.832</td>\n",
       "      <td>396</td>\n",
       "      <td>Main St at Beacon St</td>\n",
       "      <td>42.409330</td>\n",
       "      <td>-71.063819</td>\n",
       "      <td>387</td>\n",
       "      <td>Norman St at Kelvin St</td>\n",
       "      <td>42.409859</td>\n",
       "      <td>-71.066319</td>\n",
       "      <td>2692</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2</td>\n",
       "      <td>January</td>\n",
       "      <td>00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>428</td>\n",
       "      <td>2020-01-01 00:07:25.295</td>\n",
       "      <td>2020-01-01 00:14:33.780</td>\n",
       "      <td>60</td>\n",
       "      <td>Charles Circle - Charles St at Cambridge St</td>\n",
       "      <td>42.360793</td>\n",
       "      <td>-71.071190</td>\n",
       "      <td>49</td>\n",
       "      <td>Stuart St at Charles St</td>\n",
       "      <td>42.351146</td>\n",
       "      <td>-71.066289</td>\n",
       "      <td>4978</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2</td>\n",
       "      <td>January</td>\n",
       "      <td>00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration               starttime                stoptime  \\\n",
       "0           478 2020-01-01 00:04:05.809 2020-01-01 00:12:04.237   \n",
       "1           363 2020-01-01 00:04:45.699 2020-01-01 00:10:49.040   \n",
       "2           284 2020-01-01 00:06:07.063 2020-01-01 00:10:51.924   \n",
       "3           193 2020-01-01 00:06:13.855 2020-01-01 00:09:27.832   \n",
       "4           428 2020-01-01 00:07:25.295 2020-01-01 00:14:33.780   \n",
       "\n",
       "   start station id                           start station name  \\\n",
       "0               366                              Broadway T Stop   \n",
       "1               219                  Boston East - 126 Border St   \n",
       "2               219                  Boston East - 126 Border St   \n",
       "3               396                         Main St at Beacon St   \n",
       "4                60  Charles Circle - Charles St at Cambridge St   \n",
       "\n",
       "   start station latitude  start station longitude  end station id  \\\n",
       "0               42.342781               -71.057473              93   \n",
       "1               42.373312               -71.041020             212   \n",
       "2               42.373312               -71.041020             212   \n",
       "3               42.409330               -71.063819             387   \n",
       "4               42.360793               -71.071190              49   \n",
       "\n",
       "               end station name  end station latitude  end station longitude  \\\n",
       "0              JFK/UMass T Stop             42.320340             -71.051180   \n",
       "1  Maverick Square - Lewis Mall             42.368844             -71.039778   \n",
       "2  Maverick Square - Lewis Mall             42.368844             -71.039778   \n",
       "3        Norman St at Kelvin St             42.409859             -71.066319   \n",
       "4       Stuart St at Charles St             42.351146             -71.066289   \n",
       "\n",
       "   bikeid    usertype  weekday    month timeoftheday  \n",
       "0    6005    Customer        2  January           00  \n",
       "1    3168  Subscriber        2  January           00  \n",
       "2    3985  Subscriber        2  January           00  \n",
       "3    2692  Subscriber        2  January           00  \n",
       "4    4978  Subscriber        2  January           00  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define \n",
    "There is no way to see if the day of the week is a weekday or a weekend\n",
    "\n",
    "Action:\n",
    "- Create a column to state if it's a weekday or weekend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all['type'] = df_all.starttime.dt.weekday.apply(lambda x: 'Weekday' if x < 5 else 'Weekend')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>usertype</th>\n",
       "      <th>weekday</th>\n",
       "      <th>month</th>\n",
       "      <th>timeoftheday</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>478</td>\n",
       "      <td>2020-01-01 00:04:05.809</td>\n",
       "      <td>2020-01-01 00:12:04.237</td>\n",
       "      <td>366</td>\n",
       "      <td>Broadway T Stop</td>\n",
       "      <td>42.342781</td>\n",
       "      <td>-71.057473</td>\n",
       "      <td>93</td>\n",
       "      <td>JFK/UMass T Stop</td>\n",
       "      <td>42.320340</td>\n",
       "      <td>-71.051180</td>\n",
       "      <td>6005</td>\n",
       "      <td>Customer</td>\n",
       "      <td>2</td>\n",
       "      <td>January</td>\n",
       "      <td>00</td>\n",
       "      <td>Weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>363</td>\n",
       "      <td>2020-01-01 00:04:45.699</td>\n",
       "      <td>2020-01-01 00:10:49.040</td>\n",
       "      <td>219</td>\n",
       "      <td>Boston East - 126 Border St</td>\n",
       "      <td>42.373312</td>\n",
       "      <td>-71.041020</td>\n",
       "      <td>212</td>\n",
       "      <td>Maverick Square - Lewis Mall</td>\n",
       "      <td>42.368844</td>\n",
       "      <td>-71.039778</td>\n",
       "      <td>3168</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2</td>\n",
       "      <td>January</td>\n",
       "      <td>00</td>\n",
       "      <td>Weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>284</td>\n",
       "      <td>2020-01-01 00:06:07.063</td>\n",
       "      <td>2020-01-01 00:10:51.924</td>\n",
       "      <td>219</td>\n",
       "      <td>Boston East - 126 Border St</td>\n",
       "      <td>42.373312</td>\n",
       "      <td>-71.041020</td>\n",
       "      <td>212</td>\n",
       "      <td>Maverick Square - Lewis Mall</td>\n",
       "      <td>42.368844</td>\n",
       "      <td>-71.039778</td>\n",
       "      <td>3985</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2</td>\n",
       "      <td>January</td>\n",
       "      <td>00</td>\n",
       "      <td>Weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>193</td>\n",
       "      <td>2020-01-01 00:06:13.855</td>\n",
       "      <td>2020-01-01 00:09:27.832</td>\n",
       "      <td>396</td>\n",
       "      <td>Main St at Beacon St</td>\n",
       "      <td>42.409330</td>\n",
       "      <td>-71.063819</td>\n",
       "      <td>387</td>\n",
       "      <td>Norman St at Kelvin St</td>\n",
       "      <td>42.409859</td>\n",
       "      <td>-71.066319</td>\n",
       "      <td>2692</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2</td>\n",
       "      <td>January</td>\n",
       "      <td>00</td>\n",
       "      <td>Weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>428</td>\n",
       "      <td>2020-01-01 00:07:25.295</td>\n",
       "      <td>2020-01-01 00:14:33.780</td>\n",
       "      <td>60</td>\n",
       "      <td>Charles Circle - Charles St at Cambridge St</td>\n",
       "      <td>42.360793</td>\n",
       "      <td>-71.071190</td>\n",
       "      <td>49</td>\n",
       "      <td>Stuart St at Charles St</td>\n",
       "      <td>42.351146</td>\n",
       "      <td>-71.066289</td>\n",
       "      <td>4978</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>2</td>\n",
       "      <td>January</td>\n",
       "      <td>00</td>\n",
       "      <td>Weekday</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32947</th>\n",
       "      <td>130</td>\n",
       "      <td>2020-02-08 07:26:10.301</td>\n",
       "      <td>2020-02-08 07:28:21.117</td>\n",
       "      <td>195</td>\n",
       "      <td>Child St at North St</td>\n",
       "      <td>42.371505</td>\n",
       "      <td>-71.072493</td>\n",
       "      <td>90</td>\n",
       "      <td>Lechmere Station at Cambridge St / First St</td>\n",
       "      <td>42.370677</td>\n",
       "      <td>-71.076529</td>\n",
       "      <td>3844</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>5</td>\n",
       "      <td>February</td>\n",
       "      <td>07</td>\n",
       "      <td>Weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32948</th>\n",
       "      <td>157</td>\n",
       "      <td>2020-02-08 07:27:17.874</td>\n",
       "      <td>2020-02-08 07:29:54.959</td>\n",
       "      <td>116</td>\n",
       "      <td>359 Broadway - Broadway at Fayette Street</td>\n",
       "      <td>42.370803</td>\n",
       "      <td>-71.104412</td>\n",
       "      <td>76</td>\n",
       "      <td>Central Sq Post Office / Cambridge City Hall a...</td>\n",
       "      <td>42.366426</td>\n",
       "      <td>-71.105495</td>\n",
       "      <td>3684</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>5</td>\n",
       "      <td>February</td>\n",
       "      <td>07</td>\n",
       "      <td>Weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32949</th>\n",
       "      <td>629</td>\n",
       "      <td>2020-02-08 07:28:25.079</td>\n",
       "      <td>2020-02-08 07:38:54.861</td>\n",
       "      <td>118</td>\n",
       "      <td>Linear Park - Mass. Ave. at Cameron Ave.</td>\n",
       "      <td>42.397828</td>\n",
       "      <td>-71.130516</td>\n",
       "      <td>115</td>\n",
       "      <td>Porter Square Station</td>\n",
       "      <td>42.387995</td>\n",
       "      <td>-71.119084</td>\n",
       "      <td>4985</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>5</td>\n",
       "      <td>February</td>\n",
       "      <td>07</td>\n",
       "      <td>Weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32950</th>\n",
       "      <td>525</td>\n",
       "      <td>2020-02-08 07:29:50.489</td>\n",
       "      <td>2020-02-08 07:38:35.699</td>\n",
       "      <td>407</td>\n",
       "      <td>Craigie at Summer St</td>\n",
       "      <td>42.383634</td>\n",
       "      <td>-71.108558</td>\n",
       "      <td>328</td>\n",
       "      <td>Grove St at Community Path</td>\n",
       "      <td>42.396387</td>\n",
       "      <td>-71.120113</td>\n",
       "      <td>5624</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>5</td>\n",
       "      <td>February</td>\n",
       "      <td>07</td>\n",
       "      <td>Weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32951</th>\n",
       "      <td>283</td>\n",
       "      <td>2020-02-08 07:30:46.284</td>\n",
       "      <td>2020-02-08 07:35:29.419</td>\n",
       "      <td>280</td>\n",
       "      <td>Main St at Baldwin St</td>\n",
       "      <td>42.380857</td>\n",
       "      <td>-71.070629</td>\n",
       "      <td>171</td>\n",
       "      <td>Bunker Hill Community College</td>\n",
       "      <td>42.374090</td>\n",
       "      <td>-71.069060</td>\n",
       "      <td>3994</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>5</td>\n",
       "      <td>February</td>\n",
       "      <td>07</td>\n",
       "      <td>Weekend</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>161550 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       tripduration               starttime                stoptime  \\\n",
       "0               478 2020-01-01 00:04:05.809 2020-01-01 00:12:04.237   \n",
       "1               363 2020-01-01 00:04:45.699 2020-01-01 00:10:49.040   \n",
       "2               284 2020-01-01 00:06:07.063 2020-01-01 00:10:51.924   \n",
       "3               193 2020-01-01 00:06:13.855 2020-01-01 00:09:27.832   \n",
       "4               428 2020-01-01 00:07:25.295 2020-01-01 00:14:33.780   \n",
       "...             ...                     ...                     ...   \n",
       "32947           130 2020-02-08 07:26:10.301 2020-02-08 07:28:21.117   \n",
       "32948           157 2020-02-08 07:27:17.874 2020-02-08 07:29:54.959   \n",
       "32949           629 2020-02-08 07:28:25.079 2020-02-08 07:38:54.861   \n",
       "32950           525 2020-02-08 07:29:50.489 2020-02-08 07:38:35.699   \n",
       "32951           283 2020-02-08 07:30:46.284 2020-02-08 07:35:29.419   \n",
       "\n",
       "       start station id                           start station name  \\\n",
       "0                   366                              Broadway T Stop   \n",
       "1                   219                  Boston East - 126 Border St   \n",
       "2                   219                  Boston East - 126 Border St   \n",
       "3                   396                         Main St at Beacon St   \n",
       "4                    60  Charles Circle - Charles St at Cambridge St   \n",
       "...                 ...                                          ...   \n",
       "32947               195                         Child St at North St   \n",
       "32948               116    359 Broadway - Broadway at Fayette Street   \n",
       "32949               118    Linear Park - Mass. Ave. at Cameron Ave.    \n",
       "32950               407                         Craigie at Summer St   \n",
       "32951               280                        Main St at Baldwin St   \n",
       "\n",
       "       start station latitude  start station longitude  end station id  \\\n",
       "0                   42.342781               -71.057473              93   \n",
       "1                   42.373312               -71.041020             212   \n",
       "2                   42.373312               -71.041020             212   \n",
       "3                   42.409330               -71.063819             387   \n",
       "4                   42.360793               -71.071190              49   \n",
       "...                       ...                      ...             ...   \n",
       "32947               42.371505               -71.072493              90   \n",
       "32948               42.370803               -71.104412              76   \n",
       "32949               42.397828               -71.130516             115   \n",
       "32950               42.383634               -71.108558             328   \n",
       "32951               42.380857               -71.070629             171   \n",
       "\n",
       "                                        end station name  \\\n",
       "0                                       JFK/UMass T Stop   \n",
       "1                           Maverick Square - Lewis Mall   \n",
       "2                           Maverick Square - Lewis Mall   \n",
       "3                                 Norman St at Kelvin St   \n",
       "4                                Stuart St at Charles St   \n",
       "...                                                  ...   \n",
       "32947        Lechmere Station at Cambridge St / First St   \n",
       "32948  Central Sq Post Office / Cambridge City Hall a...   \n",
       "32949                              Porter Square Station   \n",
       "32950                         Grove St at Community Path   \n",
       "32951                      Bunker Hill Community College   \n",
       "\n",
       "       end station latitude  end station longitude  bikeid    usertype  \\\n",
       "0                 42.320340             -71.051180    6005    Customer   \n",
       "1                 42.368844             -71.039778    3168  Subscriber   \n",
       "2                 42.368844             -71.039778    3985  Subscriber   \n",
       "3                 42.409859             -71.066319    2692  Subscriber   \n",
       "4                 42.351146             -71.066289    4978  Subscriber   \n",
       "...                     ...                    ...     ...         ...   \n",
       "32947             42.370677             -71.076529    3844  Subscriber   \n",
       "32948             42.366426             -71.105495    3684  Subscriber   \n",
       "32949             42.387995             -71.119084    4985  Subscriber   \n",
       "32950             42.396387             -71.120113    5624  Subscriber   \n",
       "32951             42.374090             -71.069060    3994  Subscriber   \n",
       "\n",
       "       weekday     month timeoftheday     type  \n",
       "0            2   January           00  Weekday  \n",
       "1            2   January           00  Weekday  \n",
       "2            2   January           00  Weekday  \n",
       "3            2   January           00  Weekday  \n",
       "4            2   January           00  Weekday  \n",
       "...        ...       ...          ...      ...  \n",
       "32947        5  February           07  Weekend  \n",
       "32948        5  February           07  Weekend  \n",
       "32949        5  February           07  Weekend  \n",
       "32950        5  February           07  Weekend  \n",
       "32951        5  February           07  Weekend  \n",
       "\n",
       "[161550 rows x 17 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.head(161550)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tripduration                        int64\n",
       "starttime                  datetime64[ns]\n",
       "stoptime                   datetime64[ns]\n",
       "start station id                    int64\n",
       "start station name                 object\n",
       "start station latitude            float64\n",
       "start station longitude           float64\n",
       "end station id                      int64\n",
       "end station name                   object\n",
       "end station latitude              float64\n",
       "end station longitude             float64\n",
       "bikeid                              int64\n",
       "usertype                           object\n",
       "weekday                             int64\n",
       "month                              object\n",
       "timeoftheday                       object\n",
       "type                               object\n",
       "dtype: object"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "[Errno 30] Read-only file system: '/bikesharing_full_year.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-111-6a483d5c0727>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#save the DF to a new csv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf_all\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr'/bikesharing_full_year.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mto_csv\u001b[0;34m(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, line_terminator, chunksize, date_format, doublequote, escapechar, decimal, errors)\u001b[0m\n\u001b[1;32m   3165\u001b[0m             \u001b[0mdecimal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdecimal\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3166\u001b[0m         )\n\u001b[0;32m-> 3167\u001b[0;31m         \u001b[0mformatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3169\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpath_or_buf\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/io/formats/csvs.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    183\u001b[0m             \u001b[0mclose\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m             f, handles = get_handle(\n\u001b[0m\u001b[1;32m    186\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath_or_buf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors)\u001b[0m\n\u001b[1;32m    491\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 493\u001b[0;31m             \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnewline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    494\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mis_text\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    495\u001b[0m             \u001b[0;31m# No explicit encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mOSError\u001b[0m: [Errno 30] Read-only file system: '/bikesharing_full_year.csv'"
     ]
    }
   ],
   "source": [
    "#save the DF to a new csv\n",
    "df_all.to_csv(r'/bikesharing_full_year.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=’65’></a>\n",
    "\n",
    "\n",
    "## Explore Univariate Variables\n",
    "\n",
    "In this section, I'll investigate distributions of individual variables. My aim is to check if there are any unusual points or outliers, take a deeper look to clean things up and prepare myself to look at relationships between variables.\n",
    "\n",
    "I will look into the following:\n",
    "- How long does the average trip take?\n",
    "- Which are the most used start and end stations?\n",
    "- When are the most trips taken in terms of time of day, day of the week, or month of the year?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1831.438460477427"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# asses how long does the average trip take\n",
    "df_all['tripduration'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average trip takes 1831 seconds, which is approx. 30 minutes. I would like to see how the most frequent trip durations and the min and max value compare to the average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "397    1897\n",
       "382    1884\n",
       "410    1879\n",
       "399    1878\n",
       "366    1873\n",
       "376    1871\n",
       "361    1866\n",
       "391    1865\n",
       "393    1863\n",
       "380    1857\n",
       "360    1857\n",
       "352    1853\n",
       "405    1851\n",
       "368    1850\n",
       "346    1847\n",
       "Name: tripduration, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assess trip duration column's 15 most frequent values in descending order\n",
    "df_all['tripduration'].value_counts().sort_values(ascending=False)[:15]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most frequent trip duration shows values between 1847 and 1897 seconds which is around 30 minutes as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all['tripduration'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3879352"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all['tripduration'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems like both minimum and maximum values are mistakes done by the users, either they didn't ride the bike just started the rental (minimum duration is around 1 minute) or they left the bike without closing the rental (maximum duration is 64655 minutes)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, I will investigate the most frequently used start and end stations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68     34220\n",
       "60     33094\n",
       "67     29301\n",
       "46     27899\n",
       "107    23308\n",
       "342    22866\n",
       "178    22503\n",
       "47     22194\n",
       "74     21111\n",
       "53     20713\n",
       "80     20239\n",
       "179    19741\n",
       "Name: start station id, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assess start station id column's 12 most frequent values in descending order\n",
    "df_all['start station id'].value_counts().sort_values(ascending=False)[:12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "184      Central Square at Mass Ave / Essex St\n",
       "475      Central Square at Mass Ave / Essex St\n",
       "536      Central Square at Mass Ave / Essex St\n",
       "559      Central Square at Mass Ave / Essex St\n",
       "594      Central Square at Mass Ave / Essex St\n",
       "                         ...                  \n",
       "73823    Central Square at Mass Ave / Essex St\n",
       "73854    Central Square at Mass Ave / Essex St\n",
       "73855    Central Square at Mass Ave / Essex St\n",
       "73925    Central Square at Mass Ave / Essex St\n",
       "73960    Central Square at Mass Ave / Essex St\n",
       "Name: start station name, Length: 34220, dtype: object"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.loc[df_all['start station id'] == 68, 'start station name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4        Charles Circle - Charles St at Cambridge St\n",
       "268      Charles Circle - Charles St at Cambridge St\n",
       "425      Charles Circle - Charles St at Cambridge St\n",
       "426      Charles Circle - Charles St at Cambridge St\n",
       "470      Charles Circle - Charles St at Cambridge St\n",
       "                            ...                     \n",
       "73645    Charles Circle - Charles St at Cambridge St\n",
       "73716    Charles Circle - Charles St at Cambridge St\n",
       "73752    Charles Circle - Charles St at Cambridge St\n",
       "73891    Charles Circle - Charles St at Cambridge St\n",
       "73912    Charles Circle - Charles St at Cambridge St\n",
       "Name: start station name, Length: 33094, dtype: object"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.loc[df_all['start station id'] == 60, 'start station name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "171      MIT at Mass Ave / Amherst St\n",
       "172      MIT at Mass Ave / Amherst St\n",
       "216      MIT at Mass Ave / Amherst St\n",
       "234      MIT at Mass Ave / Amherst St\n",
       "279      MIT at Mass Ave / Amherst St\n",
       "                     ...             \n",
       "73383    MIT at Mass Ave / Amherst St\n",
       "73485    MIT at Mass Ave / Amherst St\n",
       "73511    MIT at Mass Ave / Amherst St\n",
       "73519    MIT at Mass Ave / Amherst St\n",
       "73653    MIT at Mass Ave / Amherst St\n",
       "Name: start station name, Length: 29301, dtype: object"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.loc[df_all['start station id'] == 67, 'start station name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "118      Christian Science Plaza - Massachusetts Ave at...\n",
       "187      Christian Science Plaza - Massachusetts Ave at...\n",
       "219      Christian Science Plaza - Massachusetts Ave at...\n",
       "254      Christian Science Plaza - Massachusetts Ave at...\n",
       "283      Christian Science Plaza - Massachusetts Ave at...\n",
       "                               ...                        \n",
       "73488    Christian Science Plaza - Massachusetts Ave at...\n",
       "73705    Christian Science Plaza - Massachusetts Ave at...\n",
       "73736    Christian Science Plaza - Massachusetts Ave at...\n",
       "73737    Christian Science Plaza - Massachusetts Ave at...\n",
       "73812    Christian Science Plaza - Massachusetts Ave at...\n",
       "Name: start station name, Length: 27899, dtype: object"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.loc[df_all['start station id'] == 46, 'start station name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58       Ames St at Main St\n",
       "65       Ames St at Main St\n",
       "66       Ames St at Main St\n",
       "72       Ames St at Main St\n",
       "149      Ames St at Main St\n",
       "                ...        \n",
       "73804    Ames St at Main St\n",
       "73814    Ames St at Main St\n",
       "73818    Ames St at Main St\n",
       "73852    Ames St at Main St\n",
       "73896    Ames St at Main St\n",
       "Name: start station name, Length: 23308, dtype: object"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.loc[df_all['start station id'] == 107, 'start station name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51       Boylston St at Jersey St\n",
       "60       Boylston St at Jersey St\n",
       "64       Boylston St at Jersey St\n",
       "126      Boylston St at Jersey St\n",
       "127      Boylston St at Jersey St\n",
       "                   ...           \n",
       "73849    Boylston St at Jersey St\n",
       "73850    Boylston St at Jersey St\n",
       "73902    Boylston St at Jersey St\n",
       "73905    Boylston St at Jersey St\n",
       "73966    Boylston St at Jersey St\n",
       "Name: start station name, Length: 22866, dtype: object"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df_all.loc[df_all['start station id'] == 342, 'start station name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82       MIT Pacific St at Purrington St\n",
       "165      MIT Pacific St at Purrington St\n",
       "182      MIT Pacific St at Purrington St\n",
       "291      MIT Pacific St at Purrington St\n",
       "296      MIT Pacific St at Purrington St\n",
       "                      ...               \n",
       "73801    MIT Pacific St at Purrington St\n",
       "73856    MIT Pacific St at Purrington St\n",
       "73887    MIT Pacific St at Purrington St\n",
       "73904    MIT Pacific St at Purrington St\n",
       "73994    MIT Pacific St at Purrington St\n",
       "Name: start station name, Length: 22503, dtype: object"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df_all.loc[df_all['start station id'] == 178, 'start station name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16       Cross St at Hanover St\n",
       "100      Cross St at Hanover St\n",
       "263      Cross St at Hanover St\n",
       "323      Cross St at Hanover St\n",
       "376      Cross St at Hanover St\n",
       "                  ...          \n",
       "73876    Cross St at Hanover St\n",
       "73877    Cross St at Hanover St\n",
       "73879    Cross St at Hanover St\n",
       "73943    Cross St at Hanover St\n",
       "73945    Cross St at Hanover St\n",
       "Name: start station name, Length: 22194, dtype: object"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df_all.loc[df_all['start station id'] == 47, 'start station name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21       Harvard Square at Mass Ave/ Dunster\n",
       "24       Harvard Square at Mass Ave/ Dunster\n",
       "25       Harvard Square at Mass Ave/ Dunster\n",
       "80       Harvard Square at Mass Ave/ Dunster\n",
       "81       Harvard Square at Mass Ave/ Dunster\n",
       "                        ...                 \n",
       "72838    Harvard Square at Mass Ave/ Dunster\n",
       "72841    Harvard Square at Mass Ave/ Dunster\n",
       "72887    Harvard Square at Mass Ave/ Dunster\n",
       "73626    Harvard Square at Mass Ave/ Dunster\n",
       "74000    Harvard Square at Mass Ave/ Dunster\n",
       "Name: start station name, Length: 21111, dtype: object"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df_all.loc[df_all['start station id'] == 74, 'start station name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1976      Beacon St at Massachusetts Ave\n",
       "2015      Beacon St at Massachusetts Ave\n",
       "2441      Beacon St at Massachusetts Ave\n",
       "2733      Beacon St at Massachusetts Ave\n",
       "2978      Beacon St at Massachusetts Ave\n",
       "                       ...              \n",
       "100637    Beacon St at Massachusetts Ave\n",
       "100658    Beacon St at Massachusetts Ave\n",
       "100674    Beacon St at Massachusetts Ave\n",
       "100737    Beacon St at Massachusetts Ave\n",
       "100808    Beacon St at Massachusetts Ave\n",
       "Name: start station name, Length: 20713, dtype: object"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df_all.loc[df_all['start station id'] == 53, 'start station name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "220      MIT Stata Center at Vassar St / Main St\n",
       "243      MIT Stata Center at Vassar St / Main St\n",
       "529      MIT Stata Center at Vassar St / Main St\n",
       "544      MIT Stata Center at Vassar St / Main St\n",
       "997      MIT Stata Center at Vassar St / Main St\n",
       "                          ...                   \n",
       "73728    MIT Stata Center at Vassar St / Main St\n",
       "73767    MIT Stata Center at Vassar St / Main St\n",
       "73827    MIT Stata Center at Vassar St / Main St\n",
       "73961    MIT Stata Center at Vassar St / Main St\n",
       "73985    MIT Stata Center at Vassar St / Main St\n",
       "Name: start station name, Length: 20239, dtype: object"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df_all.loc[df_all['start station id'] == 80, 'start station name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "299      MIT Vassar St\n",
       "317      MIT Vassar St\n",
       "331      MIT Vassar St\n",
       "352      MIT Vassar St\n",
       "368      MIT Vassar St\n",
       "             ...      \n",
       "73907    MIT Vassar St\n",
       "73908    MIT Vassar St\n",
       "73939    MIT Vassar St\n",
       "73941    MIT Vassar St\n",
       "73973    MIT Vassar St\n",
       "Name: start station name, Length: 19741, dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df_all.loc[df_all['start station id'] == 179, 'start station name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the number of rentals by the most popular start stations\n",
    "ax = df_all['start station id'].value_counts().sort_values(ascending=False)[:12].plot(kind=\"bar\")\n",
    "plt.xlabel('Station Names', fontsize=12)\n",
    "plt.ylabel('Number of rentals', fontsize=12)\n",
    "\n",
    "plt.title('Most Frequently Used Start Stations', fontsize=16)\n",
    "ax.set_xticklabels(['Central Square','Charles Circle','MIT','Christian Science Plaza','Ames St','Boylston St','MIT Pacific St','Cross St','Harvard Square','Beacon St','MIT Stata Center', 'MIT Vassar St'], rotation='90');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most frequently used start stations are: Central Square, Charles Circle, MIT, Christian Science Plaza, Annes St, Boylston St, MIT Pacific St, Cross St, Harward Square, Beacon St, MIT Stata Center, MIT Vassar St."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68     35158\n",
       "60     33450\n",
       "46     28040\n",
       "67     27574\n",
       "107    27111\n",
       "342    22970\n",
       "74     22725\n",
       "47     21995\n",
       "190    21604\n",
       "11     21473\n",
       "178    21351\n",
       "53     20595\n",
       "Name: end station id, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assess end station id column's 12 most frequent values in descending order\n",
    "df_all['end station id'].value_counts().sort_values(ascending=False)[:12]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "165      Central Square at Mass Ave / Essex St\n",
       "457      Central Square at Mass Ave / Essex St\n",
       "483      Central Square at Mass Ave / Essex St\n",
       "487      Central Square at Mass Ave / Essex St\n",
       "542      Central Square at Mass Ave / Essex St\n",
       "                         ...                  \n",
       "73762    Central Square at Mass Ave / Essex St\n",
       "73763    Central Square at Mass Ave / Essex St\n",
       "73764    Central Square at Mass Ave / Essex St\n",
       "73828    Central Square at Mass Ave / Essex St\n",
       "73829    Central Square at Mass Ave / Essex St\n",
       "Name: end station name, Length: 35158, dtype: object"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.loc[df_all['end station id'] == 68, 'end station name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19       Charles Circle - Charles St at Cambridge St\n",
       "203      Charles Circle - Charles St at Cambridge St\n",
       "226      Charles Circle - Charles St at Cambridge St\n",
       "311      Charles Circle - Charles St at Cambridge St\n",
       "524      Charles Circle - Charles St at Cambridge St\n",
       "                            ...                     \n",
       "73723    Charles Circle - Charles St at Cambridge St\n",
       "73801    Charles Circle - Charles St at Cambridge St\n",
       "73848    Charles Circle - Charles St at Cambridge St\n",
       "73964    Charles Circle - Charles St at Cambridge St\n",
       "73965    Charles Circle - Charles St at Cambridge St\n",
       "Name: end station name, Length: 33450, dtype: object"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.loc[df_all['end station id'] == 60, 'end station name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51       Christian Science Plaza - Massachusetts Ave at...\n",
       "149      Christian Science Plaza - Massachusetts Ave at...\n",
       "150      Christian Science Plaza - Massachusetts Ave at...\n",
       "151      Christian Science Plaza - Massachusetts Ave at...\n",
       "154      Christian Science Plaza - Massachusetts Ave at...\n",
       "                               ...                        \n",
       "73506    Christian Science Plaza - Massachusetts Ave at...\n",
       "73715    Christian Science Plaza - Massachusetts Ave at...\n",
       "73736    Christian Science Plaza - Massachusetts Ave at...\n",
       "73737    Christian Science Plaza - Massachusetts Ave at...\n",
       "73909    Christian Science Plaza - Massachusetts Ave at...\n",
       "Name: end station name, Length: 28040, dtype: object"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.loc[df_all['end station id'] == 46, 'end station name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58       MIT at Mass Ave / Amherst St\n",
       "291      MIT at Mass Ave / Amherst St\n",
       "299      MIT at Mass Ave / Amherst St\n",
       "317      MIT at Mass Ave / Amherst St\n",
       "352      MIT at Mass Ave / Amherst St\n",
       "                     ...             \n",
       "73321    MIT at Mass Ave / Amherst St\n",
       "73406    MIT at Mass Ave / Amherst St\n",
       "73582    MIT at Mass Ave / Amherst St\n",
       "73583    MIT at Mass Ave / Amherst St\n",
       "73739    MIT at Mass Ave / Amherst St\n",
       "Name: end station name, Length: 27574, dtype: object"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.loc[df_all['end station id'] == 67, 'end station name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "109      Ames St at Main St\n",
       "115      Ames St at Main St\n",
       "147      Ames St at Main St\n",
       "148      Ames St at Main St\n",
       "179      Ames St at Main St\n",
       "                ...        \n",
       "72850    Ames St at Main St\n",
       "73049    Ames St at Main St\n",
       "73226    Ames St at Main St\n",
       "73706    Ames St at Main St\n",
       "73710    Ames St at Main St\n",
       "Name: end station name, Length: 27111, dtype: object"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.loc[df_all['end station id'] == 107, 'end station name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "108      Boylston St at Exeter St\n",
       "269      Boylston St at Exeter St\n",
       "359      Boylston St at Exeter St\n",
       "371      Boylston St at Exeter St\n",
       "417      Boylston St at Exeter St\n",
       "                   ...           \n",
       "73318    Boylston St at Exeter St\n",
       "73523    Boylston St at Exeter St\n",
       "73671    Boylston St at Exeter St\n",
       "73696    Boylston St at Exeter St\n",
       "73711    Boylston St at Exeter St\n",
       "Name: end station name, Length: 10511, dtype: object"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.loc[df_all['end station id'] == 372, 'end station name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "248      Harvard Square at Mass Ave/ Dunster\n",
       "288      Harvard Square at Mass Ave/ Dunster\n",
       "378      Harvard Square at Mass Ave/ Dunster\n",
       "453      Harvard Square at Mass Ave/ Dunster\n",
       "454      Harvard Square at Mass Ave/ Dunster\n",
       "                        ...                 \n",
       "73161    Harvard Square at Mass Ave/ Dunster\n",
       "73468    Harvard Square at Mass Ave/ Dunster\n",
       "73729    Harvard Square at Mass Ave/ Dunster\n",
       "73753    Harvard Square at Mass Ave/ Dunster\n",
       "73873    Harvard Square at Mass Ave/ Dunster\n",
       "Name: end station name, Length: 22725, dtype: object"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.loc[df_all['end station id'] == 74, 'end station name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "152      Cross St at Hanover St\n",
       "162      Cross St at Hanover St\n",
       "275      Cross St at Hanover St\n",
       "308      Cross St at Hanover St\n",
       "372      Cross St at Hanover St\n",
       "                  ...          \n",
       "73467    Cross St at Hanover St\n",
       "73470    Cross St at Hanover St\n",
       "73656    Cross St at Hanover St\n",
       "73869    Cross St at Hanover St\n",
       "73898    Cross St at Hanover St\n",
       "Name: end station name, Length: 21995, dtype: object"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.loc[df_all['end station id'] == 47, 'end station name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "232      Nashua Street at Red Auerbach Way\n",
       "305      Nashua Street at Red Auerbach Way\n",
       "683      Nashua Street at Red Auerbach Way\n",
       "684      Nashua Street at Red Auerbach Way\n",
       "690      Nashua Street at Red Auerbach Way\n",
       "                       ...                \n",
       "73687    Nashua Street at Red Auerbach Way\n",
       "73779    Nashua Street at Red Auerbach Way\n",
       "73838    Nashua Street at Red Auerbach Way\n",
       "73863    Nashua Street at Red Auerbach Way\n",
       "73956    Nashua Street at Red Auerbach Way\n",
       "Name: end station name, Length: 21604, dtype: object"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.loc[df_all['end station id'] == 190, 'end station name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "114      Longwood Ave at Binney St\n",
       "116      Longwood Ave at Binney St\n",
       "256      Longwood Ave at Binney St\n",
       "272      Longwood Ave at Binney St\n",
       "297      Longwood Ave at Binney St\n",
       "                   ...            \n",
       "72803    Longwood Ave at Binney St\n",
       "73046    Longwood Ave at Binney St\n",
       "73392    Longwood Ave at Binney St\n",
       "73785    Longwood Ave at Binney St\n",
       "73911    Longwood Ave at Binney St\n",
       "Name: end station name, Length: 21473, dtype: object"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.loc[df_all['end station id'] == 11, 'end station name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5        MIT Pacific St at Purrington St\n",
       "61       MIT Pacific St at Purrington St\n",
       "66       MIT Pacific St at Purrington St\n",
       "67       MIT Pacific St at Purrington St\n",
       "69       MIT Pacific St at Purrington St\n",
       "                      ...               \n",
       "73770    MIT Pacific St at Purrington St\n",
       "73816    MIT Pacific St at Purrington St\n",
       "73904    MIT Pacific St at Purrington St\n",
       "73983    MIT Pacific St at Purrington St\n",
       "73985    MIT Pacific St at Purrington St\n",
       "Name: end station name, Length: 21351, dtype: object"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.loc[df_all['end station id'] == 178, 'end station name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015      Beacon St at Massachusetts Ave\n",
       "2347      Beacon St at Massachusetts Ave\n",
       "2669      Beacon St at Massachusetts Ave\n",
       "2723      Beacon St at Massachusetts Ave\n",
       "2762      Beacon St at Massachusetts Ave\n",
       "                       ...              \n",
       "100528    Beacon St at Massachusetts Ave\n",
       "100582    Beacon St at Massachusetts Ave\n",
       "100674    Beacon St at Massachusetts Ave\n",
       "100708    Beacon St at Massachusetts Ave\n",
       "100730    Beacon St at Massachusetts Ave\n",
       "Name: end station name, Length: 20595, dtype: object"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.loc[df_all['end station id'] == 53, 'end station name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the number of rentals by the most popular end stations\n",
    "ax = df_all['end station id'].value_counts().sort_values(ascending=False)[:12].plot(kind=\"bar\")\n",
    "plt.xlabel('Station Names', fontsize=12)\n",
    "plt.ylabel('Number of rentals', fontsize=12)\n",
    "\n",
    "plt.title('Most Frequently Used End Stations', fontsize=16)\n",
    "ax.set_xticklabels(['Central Square','Charles Circle','MIT','Christian Science Plaza','Ames St','Boylston St','Harvard Square','Cross St','Nashua Street','Longwood Ave', 'MIT Pacific St', 'Beacon St']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most frequently used end stations are: Central Square, Charles Circle, MIT, Christian Science Plaza, Annes St - and until here, the most frequent start and end stations are identitical- Boylston St, Harward Square, Cross St, Nashua Street, Longwood Ave, Beacon St, MIT Pacific St, Beacon St."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KOdmYmVnlnGzMzKxyTjZmZlY5JxszM6uck42ZmVXOycbMzCrnZGNmZpXzZaHNl7U2s8q1pWYjaVlJ10u6RdLtkr6Xy1eVNEXSPfl+lcI2h0maJuluSbsVyreSdFtedrwk5fJlJJ2dy6+TNKqwzbj8GvdIGteO92xmZgu0qxntBeAdEbE5sAWwu6RtgEOBSyJiA+CS/BxJGwNjgU2A3YFfSBqS93UCMB7YIN92z+UHArMjYn3gOODovK9VgcOBrYExwOHFpGZmZtVrS7KJ5Jn89DX5FsCewKRcPgnYKz/eEzgrIl6IiPuAacAYSWsBK0bENRERwKlN2zT2dQ6wS6717AZMiYhZETEbmMKCBGVmZm3QtgECkoZIuhl4jPTlfx2wZkQ8DJDv18irjwAeLGw+I5eNyI+byxfaJiLmAU8Bq/WyLzMza5O2JZuImB8RWwAjSbWUTXtZXa120Uv5om6z4AWl8ZKmSpo6c+bMXkIzM7P+avvQ54h4Eric1JT1aG4aI98/llebAaxT2Gwk8FAuH9mifKFtJA0FVgJm9bKv5rgmRsToiBg9fPjwRX+DZmb2Ku0ajTZc0sr58TDgncBdwAVAY3TYOOD8/PgCYGweYbYeaSDA9bmpbY6kbXJ/zP5N2zT2tTdwae7XuRjYVdIqeWDArrnMzMzapF3n2awFTMojypYCJkfEhZKuASZLOhB4ANgHICJulzQZuAOYBxwcEfPzvg4CTgGGARflG8BJwGmSppFqNGPzvmZJOhK4Ia83ISJmVfpuzcxsIW1JNhFxK7Bli/IngF162OYo4KgW5VOBV/X3RMRccrJqsexk4OT+RW3dottPSu32+M3K8HQ1ZmZWOScbMzOrnJONmZlVzhNxmtli6fY+pyrjd3/ZAq7ZmJlZ5ZxszMysck42ZmZWOScbMzOrnJONmZlVzsnGzMwq52RjZmaVc7IxM7PKOdmYmVnlnGzMzKxynq7GzKxLddNUQa7ZmJlZ5ZxszMyscqWa0SRtDDwREY9KWh74OjAfOCYinqsyQDMz635laza/BVbOj48BdgC2BX5VQUxmZjbIlB0gMCoi7pYk4APAJsDzwH2VRWZmZoNG2WTzgqQVgI2BByPicUlDgWWrC83MzAaLssnmt8ClwArAz3LZW3HNxszMSijVZxMRXwb+GzgoIhrJ5mXgy2W2l7SOpMsk3SnpdklfyuVHSPqPpJvz7T2FbQ6TNE3S3ZJ2K5RvJem2vOz43LSHpGUknZ3Lr5M0qrDNOEn35Nu4MjGbmdnAKX1SZ0T8pen51H68zjzgqxFxU26Ou1HSlLzsuIg4prhyHv02ltQ3tDbwV0kbRsR84ARgPHAt8Cdgd+Ai4EBgdkSsL2kscDTwEUmrAocDo4HIr31BRMzuR/xmZrYYekw2kv5G+nLuVUTsUGKdh4GH8+M5ku4ERvSyyZ7AWRHxAnCfpGnAGEnTgRUj4poc46nAXqRksydwRN7+HOBnudazGzAlImblbaaQEtSZfcVtZmYDo7eaza+reMHcvLUlcB2wHfB5SfsDU0m1n9mkRHRtYbMZueyl/Li5nHz/IEBEzJP0FLBasbzFNsW4xpNqTKy77rqL9R7NzGxhPSabiJg00C+WTwj9PXBIRDwt6QTgSFIN6kjgJ8AnAbUKqZdyFnGbBQURE4GJAKNHj+6zRmdmZuWV7rORtCYwBlidwhd4RJxccvvXkBLNGRFxbt720cLyE4EL89MZwDqFzUcCD+XykS3Ki9vMyMOyVwJm5fKdmra5vEzMZmY2MEqNRpO0F/BvYAJp1oAv5Pv9Sm4v4CTgzog4tlC+VmG1DwD/zI8vAMbmEWbrARsA1+e+nzmStsn73B84v7BNY6TZ3sClERHAxcCuklaRtAqway4zM7M2KVuz+T7wiYj4naTZEbGlpE+QRouVsR0pMd0m6eZc9i1gX0lbkJq1pgOfAYiI2yVNBu4gjWQ7OI9EAzgIOAUYRhoYcFEuPwk4LQ8mmEUazUZEzJJ0JHBDXm9CY7CAmZm1R9lks25E/K6pbBLwCPC1vjaOiKto3Xfyp162OQo4qkX5VGDTFuVzgX162NfJQKnmPjMzG3hlJ+J8LPfZAEyXtC3wRmBINWGZmdlgUjbZnAhsnx8fB1wG3EI6wdLMzKxXpZrRIuLowuNTJV0OvDYi7qwqMDMzGzzKjkY7v/g8Ih6IiDslnVtNWGZmNpiUbUbbuYfynQYoDjMzG8R6bUaTNCE/XLrwuOENwP2VRGVmZoNKX302jbP4l2LhM/qDNN/YERXEZGZmg0yvySYiPgEg6eqIOLE9IZmZ2WBTdjTaiZJWAjYClm9admkVgZmZ2eBRKtlIOgD4OfAM8FxhUZD6bszMzHpUdrqao4C9I+KiPtc0MzNrUnbo81DgL32uZWZm1kLZZHM08G1JZdc3MzN7RdlmtC8DrwO+IemJ4oKI8DWUzcysV2WTzccrjcLMzAa1skOfr6g6EDMzG7zKTsS5jKSjJN0r6alctqukz1cbnpmZDQZlO/yPI10d82Okc2sAbiddotnMzKxXZftsPgCsHxHPSnoZICL+I2lEdaGZmdlgUbZm8yJNiUnScOCJ1qubmZktUDbZ/A6YJGk9AElrAT8DzqoqMDMzGzzKJptvAdOB24CVgXuAh4DvldlY0jqSLpN0p6TbJX0pl68qaYqke/L9KoVtDpM0TdLdknYrlG8l6ba87HhJyuXLSDo7l18naVRhm3H5Ne6RNK7kezYzswHSZ7KRNAT4NvDNiFgeWBNYISK+HBEvlnydecBXI+LNwDbAwZI2Bg4FLomIDYBL8nPysrHAJsDuwC9yHAAnAOOBDfJt91x+IDA7ItYnDWg4Ou9rVeBwYGtgDHB4MamZmVn1+kw2ETEfOBh4KT+fGRHR+1av2sfDEXFTfjwHuBMYAewJTMqrTQL2yo/3BM6KiBci4j5gGjAmN9+tGBHX5BhObdqmsa9zgF1yrWc3YEpEzIqI2cAUFiQoMzNrg7LNaJOAzw7EC+bmrS2B64A1I+JhSAkJWCOvNoJ0JdCGGblsRH7cXL7QNhExD3gKWK2XfZmZWZuUHfo8BviCpG+QvrhfqdlExA5lX0zS8sDvgUMi4unc3dJy1RZl0Uv5om5TjG08qXmOddf1dG9mZgOpbLI5Md8WmaTXkBLNGRFxbi5+VNJaEfFwbiJ7LJfPANYpbD6SNCBhRn7cXF7cZoakocBKwKxcvlPTNpc3xxcRE4GJAKNHj+5XM6GZmfWu7Nxok/peq2e57+Qk4M6IOLaw6AJgHPDDfH9+ofy3ko4F1iYNBLg+IuZLmiNpG1Iz3P7A/zbt6xpgb+DSiAhJFwP/UxgUsCtw2OK8HzMz65+yNZvFtR2wH3CbpJtz2bdISWaypAOBB4B9ACLidkmTgTtII9kOzgMVIE2RcwowDLgo3yAls9MkTSPVaMbmfc2SdCRwQ15vQkTMquh9mplZC21JNhFxFa37TgB26WGbo0iXo24un0qap625fC45WbVYdjJwctl4zcxsYPnKm2ZmVrkek42kawuPD29POGZmNhj1VrPZUNKy+fFX2xGMmZkNTr312ZwP/EvSdGCYpCtbrdSf82zMzGzJ1GOyiYhPSNoeGAW8jTTay8zMrN96HY2WR5FdJWnpxT3XxszMllxlT+o8WdLOpHNlRgD/AU6PiEurDM7MzAaHUkOfJX0KOBt4BDgXeJh0hv+nK4zNzMwGibIndX4DeFdE3NIokHQ2aa6zxZozzczMBr+yJ3WuRpo6puhuYNWBDcfMzAajssnmKuBYScsBSHot8GPg6qoCMzOzwaNssvkssBnwlKRHgSeBzYHPVBSXmZkNImVHoz0M7ChpJGnK/4ciYkYfm5mZmQH9nPU5JxgnGTMz6xfP+mxmZpVzsjEzs8r1mWwkLSXpHZKWbkdAZmY2+PSZbCLiZeD8iHixDfGYmdkgVLYZ7UpJ21QaiZmZDVplR6PdD1wk6XzgQSAaCyLiu1UEZmZmg0fZZDMM+EN+PLKaUMzMbLAqe1LnJ6oOxMzMBq/SQ58lvVnSdyT9LD/fSNJmJbc9WdJjkv5ZKDtC0n8k3Zxv7yksO0zSNEl3S9qtUL6VpNvysuMlKZcvI+nsXH6dpFGFbcZJuiffxpV9v2ZmNnDKXs9mH+BK0oXT9s/FKwDHlnydU4DdW5QfFxFb5Nuf8mttDIwFNsnb/ELSkLz+CcB4YIN8a+zzQGB2RKwPHAccnfe1KnA4sDUwBjhc0iolYzYzswFStmYzgXQ9m88C83PZLaTJOPsUEVcCs0q+1p7AWRHxQkTcB0wDxkhaC1gxIq6JiABOBfYqbNO4bPU5wC651rMbMCUiZkXEbGAKrZOemZlVqGyyWYOUXGDBSLQoPF5Un5d0a25ma9Q4RpBGvDXMyGUjWHhetkb5QttExDzgKdI1eHra16tIGi9pqqSpM2fOXLx3ZWZmCymbbG4E9msqGwtcvxivfQLwRmAL0mWmf5LL1WLd6KV8UbdZuDBiYkSMjojRw4cP7yVsMzPrr7JDn78I/EXSgcBrJV0MbAjsuqgvHBGPNh5LOhG4MD+dAaxTWHUk8FAuH9mivLjNDElDgZVIzXYzgJ2atrl8UWM2M7NFU6pmExF3AW8Cfg58G/gN8JaIuGdRXzj3wTR8AGiMVLsAGJtHmK1HGghwfb6mzhxJ2+T+mP2B8wvbNEaa7Q1cmvt1LgZ2lbRKbqbbNZeZmVkblb6eTUQ8J+nvwH2ki6c9U3ZbSWeSahirS5pBGiG2k6QtSM1a08lX/YyI2yVNBu4A5gEHR0RjUMJBpJFtw4CL8g3gJOA0SdNINZqxeV+zJB0J3JDXmxARZQcqmJnZACmVbCStC5wBbAPMBlaRdB3wsYi4v6/tI2LfFsUn9bL+UcBRLcqnApu2KJ8L7NPDvk4GTu4rRjMzq07ZAQKTSIMEVo6INYBVSLWFSb1uZWZmRvlmtK2AXSPiJYCIeEbSN4EnKovMzMwGjbI1m2tJZ+AXjQauGdhwzMxsMOqxZiNpQuHpv4E/Sfoj6STJdYD3AL+tNjwzMxsMemtGW6fp+bn5fg3gBeA8YNkqgjIzs8Glx2TjywqYmdlAKX2ejaTlgPWB5YvlEXH1QAdlZmaDS9nzbPYHfga8CDxfWBTAuhXEZWZmg0jZms2PgA9FxJQqgzEzs8Gp7NDnF/EElmZmtojKJpvvAMdKWr3KYMzMbHAqm2z+BbwfeFTS/Hx7WdL8vjY0MzMr22dzGukyzGez8AABMzOzPpVNNqsB383XiDEzM+uXss1ov+HVl4U2MzMrpWzNZgzweUn/DTxaXBAROwx4VGZmNqiUTTYn5puZmVm/lUo2EeGLpJmZ2SIrO13NJ3tali+7bGZm1qOyzWjNgwNeB7wR+DvgZGNmZr0q24y2c3NZru28ecAjMjOzQafs0OdWTgEOHKA4zMxsECuVbCQt1XRbHhgPPFly+5MlPSbpn4WyVSVNkXRPvl+lsOwwSdMk3S1pt0L5VpJuy8uOl6Rcvoyks3P5dZJGFbYZl1/jHknjysRrZmYDq2zNZh7wUuH2FPAt4KCS258C7N5UdihwSURsAFySnyNpY2AssEne5heShuRtTiAluQ3yrbHPA4HZEbE+cBxwdN7XqsDhwNakc4UOLyY1MzNrj7LJZj3gDYXbmhGxbkRcXGbjiLgSmNVUvCfQGFI9CdirUH5WRLwQEfcB04AxktYCVoyIa/K0Oac2bdPY1znALrnWsxswJSJmRcRsYAqvTnpmZlaxsgME7q/gtdeMiIfz/h+WtEYuHwFcW1hvRi57KT9uLm9s82De1zxJT5Hmc3ulvMU2C5E0nlRrYt11ffFRM7OB1GuykXQZ6dLPPYmI2GVgQ0KtXqeX8kXdZuHCiInARIDRo0d7wlEzswHUV83m9B7KRwBfBJZbjNd+VNJauVazFvBYLp8BrFNYbyTwUC4f2aK8uM0MSUOBlUjNdjOAnZq2uXwxYjYzs0XQa59NRJxUvAF/IJ1b81XgXGDDxXjtC4DG6LBxwPmF8rF5hNl6pIEA1+cmtzmStsn9Mfs3bdPY197Apblf52JgV0mr5IEBu+YyMzNro7LT1awIfB34PHAh8NaI+HfZF5F0JqmGsbqkGaQRYj8EJks6EHgA2AcgIm6XNBm4gzQK7uCIaFwR9CDSyLZhwEX5BnAScJqkaaQazdi8r1mSjgRuyOtNiIjmgQpmZlaxvvpshgGHkGoylwPbR8Tt/X2RiNi3h0Ut+3si4ijgqBblU4FNW5TPJSerFstOxlPqmJnVqq+azX3AEOBHwFRgTUlrFleIiEsris3MzAaJvpLNXNLorZ5O3gzSeTdmZmY96jXZRMSoNsVhZmaD2OJMxGlmZlaKk42ZmVXOycbMzCrnZGNmZpVzsjEzs8o52ZiZWeWcbMzMrHJONmZmVjknGzMzq5yTjZmZVc7JxszMKudkY2ZmlXOyMTOzyjnZmJlZ5ZxszMysck42ZmZWOScbMzOrnJONmZlVrvZkI2m6pNsk3Sxpai5bVdIUSffk+1UK6x8maZqkuyXtVijfKu9nmqTjJSmXLyPp7Fx+naRRbX+TZmZLuNqTTbZzRGwREaPz80OBSyJiA+CS/BxJGwNjgU2A3YFfSBqStzkBGA9skG+75/IDgdkRsT5wHHB0G96PmZkVdEqyabYnMCk/ngTsVSg/KyJeiIj7gGnAGElrAStGxDUREcCpTds09nUOsEuj1mNmZu3RCckmgL9IulHS+Fy2ZkQ8DJDv18jlI4AHC9vOyGUj8uPm8oW2iYh5wFPAas1BSBovaaqkqTNnzhyQN2ZmZsnQugMAtouIhyStAUyRdFcv67aqkUQv5b1ts3BBxERgIsDo0aNftdzMzBZd7TWbiHgo3z8GnAeMAR7NTWPk+8fy6jOAdQqbjwQeyuUjW5QvtI2kocBKwKwq3ouZmbVWa7KR9FpJKzQeA7sC/wQuAMbl1cYB5+fHFwBj8wiz9UgDAa7PTW1zJG2T+2P2b9qmsa+9gUtzv46ZmbVJ3c1oawLn5f76ocBvI+LPkm4AJks6EHgA2AcgIm6XNBm4A5gHHBwR8/O+DgJOAYYBF+UbwEnAaZKmkWo0Y9vxxszMbIFak01E3Ats3qL8CWCXHrY5CjiqRflUYNMW5XPJycrMzOpRe5+NmZkNfk42ZmZWOScbMzOrnJONmZlVzsnGzMwq52RjZmaVc7IxM7PKOdmYmVnlnGzMzKxyTjZmZlY5JxszM6uck42ZmVXOycbMzCrnZGNmZpVzsjEzs8o52ZiZWeWcbMzMrHJONmZmVjknGzMzq5yTjZmZVc7JxszMKudkY2ZmlVtiko2k3SXdLWmapEPrjsfMbEmyRCQbSUOAnwPvBjYG9pW0cb1RmZktOZaIZAOMAaZFxL0R8SJwFrBnzTGZmS0xFBF1x1A5SXsDu0fEp/Lz/YCtI+LzhXXGA+Pz042AuysMaXXg8Qr3XzXHXy/HX69ujr/q2F8fEcNbLRha4Yt2ErUoWyjLRsREYGJbgpGmRsTodrxWFRx/vRx/vbo5/jpjX1Ka0WYA6xSejwQeqikWM7MlzpKSbG4ANpC0nqSlgbHABTXHZGa2xFgimtEiYp6kzwMXA0OAkyPi9hpDaktzXYUcf70cf726Of7aYl8iBgiYmVm9lpRmNDMzq5GTjZmZVc7JxswqJWnTumNYVJJWrTuGwcLJxkrJU/50rW6Pv8v9UtL1kj4naeW6g+mn6yT9TtJ7JLU6X6/jdcqx72TTJpKOkbRJ3XEshmmSftzFc8p1bfySPi9plbrjWFQRsT3wMdK5blMl/VbSu2oOq6wNSSO49iMdQ/8jacOaY+qvjjj2nWza5y5goqTrJH1W0kp1B9RPmwH/An4t6VpJ4yWtWHdQ/dDN8b8OuEHS5Dx7edf9wo6Ie4BvA98EdgSOl3SXpA/WG1nvIpkSEfsCnwLGAddLukLStjWHV1ZHHPse+txmkjYCPgHsC/wdODEiLqs3qv6RtANwJrAycA5wZERMqzWofujG+HOC2ZV07IwGJgMnRcS/aw2sBEmbkeLeA5hCivsmSWsD10TE62sNsBeSVgM+TqrZPAqcRDohfAvgdxGxXn3R9V+dx75rNm2U207flG+PA7cAX5F0Vq2BlSBpiKT3SzoP+CnwE+ANwP8Bf6o1uBK6Pf5Ivwofybd5wCrAOZJ+VGtg5fwMuAnYPCIOjoibACLiIVJtp5NdA6wI7BURe0TEuRExLyKmAr+sObZSOuXYd82mTSQdC7wfuIT0y+76wrK7I2Kj2oIrQdK9wGWk2K9uWnZ8RHyxnsjK6eb4JX2R1HzzOPBr4A8R8ZKkpYB7IuKNtQY4iElSdPmXZKcc+042bSLpk8BZEfFci2UrRcRTNYRVmqTlI+KZuuNYVN0cv6QJpC+K+1sse3NE3FlDWKVJ2gD4AenChcs2yiPiDbUFVZKk4cA3gE1YOPZ31BZUP3XKse9k00Z5RNEGLHzQXllfROVJWhY4kFf/032ytqD6odvjB5C0BgvH/kCN4ZQm6SrgcOA44H2k/htFxOG1BlaCpL8AZwNfAz5LqmHOjIhv1hpYP3TKse8+mzaR9CngStJkoN/L90fUGVM/nUYaFbUbcAXpMg1zao2of7o2fknvk3QPcB8p9unARbUG1T/DIuISUoK5PyKOALqlZrBaRJwEvBQRV+Qv6G3qDqqfOuLYd7Jpny8BbwPuj4idgS2BmfWG1C/rR8R3gGcjYhJpZNFbao6pP7o5/u+TvuD+lUc/7UIaydgt5jb6l/I5Qx8A1qg7qJJeyvcPS9pD0pakL+tu0hHHvpNN+8yNiLkAkpaJiLtIl5/uFo1/uifz9CMrAaPqC6ffujn+lyLiCWApSUvlofJb1BxTfxwCLAd8EdiKNIx4XJ0B9cP38zlxXyU1pf0a+HK9IfVbRxz7S8T1bDrEjDxVxx+AKZJm011XC52Y+5y+QzrPYHngu/WG1C/dHP+TkpYnNcOeIekx0vDnrhARN+SHz5D6a7pGRFyYHz4F7FxnLIuhI459DxCogaQdSb8u/hwRL9Ydj3U2Sa8F5gIiTfuyEnBGru10LEn/B/T4BRMR729jOP0i6X/pPfaOHSrfqVyzqVgPs8belu+XB2a1MZx+k/SV3pZHxLHtimVRdHv8ABHxbOHppNoC6b9j8v0HSR3Up+fn+5IGOXSyqfl+O9KQ7bPz832AG2uJqJ867dh3sqnejaRfSALWBWbnxysDDwCdPt3FCvl+I9IAhwvy8/eRmnU6XdfGL2kOvf+67ui53SLiCgBJR0bEDoVF/yepoz/73JGOpAOAnSPipfz8l8BfagytPzrq2HeyqVhj7qR8kF4QEX/Kz98NvLPO2MqIiO/BK+cbvDUi5uTnRwC/qzG0Uro5/ohYAV45qfMR0hDWRlPaCr1s2mmGS3pDRNwLIGk9YHjNMZW1NumzbrRALJ/LOl6nHftONu3ztoj4bONJRFwk6cg6A+qndYFi/9KLdM9oLuju+HeLiK0Lz0+QdB3QDfOiQRq9dXmeNgXS5/6Z+sLplx8C/5DUmCx3R7rr/DjokGPfyaZ9Hpf0bVK7dZBmku3oDt4mp5GmVj+PFP8HgFPrDalfujn++ZI+BpxFin1fYH69IZUXEX/OU9a8KRfdFREv1BlTWRHxG0kXAY1kf2hEPFJnTIugI459j0ZrkzxQ4HBgB9If/EpgQkR09ACBIklvBf4rP70yIv5RZzz9JWkrYPv8tGvilzSKNFvvdqRj5+/AIRExvcawSpO0D2nk5Zz8g+utwPcbsz93MknbATdHxLOSPk6K/aet5qnrZJ1w7DvZtEG+tMCkiPh43bH0l6QVI+LpHkbV0WXJcgiwJoUafbfML9bNJN0aEZtJ2p40IecxwLeamgY7kqRbgc1JFyA7FTgZ+GBE7FhrYP3UCce+ZxBog4iYT+okXbruWBbBb/P9jaThoI1b43lXkPQF0sWvpgAXAn/M9x1P0o8krSjpNZIukfR4/pXdLRpNfnsAJ0TE+UC3/C/My5cY2BM4PiJ+SncNzuiYY981mzaR9CtSFfwC4JXzJrrhPA9JAtbp5lqApGnA1p1+ImQrkm6OiC3ynGJ7kTrcL4uIzeuNrBxJFwL/IY2+3Ap4Hri+G+KXdAXwZ9LMBzuQ5jO8OSK6ZV69jjn2XbNpn4dIvyaWIv0yatw6Xv5ld17dcSymB0lTjnSj1+T79wBndlPTZfZh0iznu0fEk8CqwNdrjai8jwAvAAfmgQEjgB/XG1K/dcSx79FobdIY897FrpX0tsI8V93mXtLw2z+SvjyA7qhZkk6CvItUI/hcvqDX3JpjKi0insvzuW0P3EOa1+2eeqPqW+7nOD0iXjkfLtfuu2UUY0NHHPtONm2Sx+m/qs2yi674tzPwWUnTSc2AIlV6Nqs1qvIeyLel6Z7+AgAi4lBJRwNPR8R8Sc+R+hC6gqTDgdGkM9l/Q6qpnU4aXdexGp91N1xJtw8dcey7z6ZN8tDDhmWBD5E6H79RU0ilSFo3Ih6Q9PpWy7ttCGg3krQc8BVg3YgYn89Z2agwI3FHk3Qz6fpNN0XElrns1m74oSJpMulaQlNYuK+16ybilLQC6QdiLZeIds2mTSKiefK+v+fOx073B9JUF/dL+n1EfKjugBZFl9csf0Ma/ff2/HwGabqRrkg2wIsREZICXpnFulv8Md+6Vr6GzWmkvjIkPQ7sHxG3tzMOJ5s2aTpPZSnSqJzX1RROf6jw+A21RbH4vlZ4/ErNsqZY+uuNEfERSfsCRMTzeYRgt5icR2OuLOnTwCeBE2uOqZTGhJxdbiLwlXzRPSTtRPr8397LNgPOyaZ9irM/zyNdT/7AWiMqJ3p43FW6uGYJ8KKkYeTPX9IbKXT0drqIOEbSu4CnSf02342IKTWHVYqk+2hdI+6mH16vbSQagIi4vI7apZNN+7y5cVnoBknL1BVMP2wu6WlSkhyWH8OCAQIdPc19Q4ua5Wi6o2YJaZqjPwPrSDqD1LF+QK0R9VNOLl2RYJqMLjxelnQ9m5azaXSweyV9h9SUBmlexvvaHYQHCLSJpJsi4q19lVk1mn6hziNdvGtCRFxVW1D9IGk1Uke1gGsj4vGaQyqt6bo8S5NGoz3bLT9Umkm6KiK273vNzpAvCf090tBzkeZlPCIiZrczDtdsKibpdaQTwYZJ2pIFfSArAsvVFtgSQtLbgAcL1xUaR+qvmQ7cUWNo/bUs6cJ7Q4GNJRERHX0BsobGdXkaJO0FjKknmv7Jk882NGrEXXEydkNOKrWPnnPNpmL5y+0A0kFanEtsDnBKRJxbR1xLCkk3Ae+MiFmSdiBN0/8FYAtS0+bedcZXRj7H5iPA7cDLuTgi4v31RdU3SUMjouUgDEnXRsQ27Y6pvwrXsYEFNeJjIuLueiIqT9IFvS1v9/HjZNMmkj4UEb+vO44ljaRbGnNwSfo5MDMijsjPb46ILWoMrxRJdwObdcs1YBoazcSSPlgobtQOdoyIbWsKbYkgaSZpqpozgetYeGTpK5ftbhc3o7VJRPxe0h7AJqQmkUb5hPqiWiIMKfzC3gUYX1jWLcf/vaR+jq5KNgXv49X9ZR1dK2vIg3g+RLqyZXF6/m74v30d8C7SxfY+Sjpf6Mx2n1/T0C3/bF1P0i9JfTQ7A78G9gaurzWoJcOZwBX5RLbngb8BSFqfDpicsKTngJslXcLCc1vV3g7fhzUkfQX4Z1N5APsB3TAv3fmk4+RGuizZ50ub/Bn4c06a+5LmSJsQEf/b7nicbNrn7fkCUrdGxPck/QRwf03FIuKo/CW9FvCXWNBuvBSp76YbXJBv3WYIsDxNzTddZmRE7F53EIsqJ5k9SIlmFHA8NX3vONm0z/P5/jlJawOzgPVqjGeJERHXtij7Vx2xLIouPov94S5pburN1ZLeEhG31R1If0maBGwKXAR8LyKaa5ht5WTTPhdKWhn4EalKDqk5zawlSbfRy6wNXTCRZdfWaCT9kzTybyjwCUn3kprRumm28/1Ik4duCHyxMMNRLSdkO9lUrHCex5H5+fLAbcBdwHF1xmYd7735/uB83zgD/GOkfpxOt0vdASyGEaTh8V0rIjrq4pge+lyxwXCeh9VL0t8jYru+ymzgeHaPgeeaTfWGFC7j+xFgYj7f5vf5Oh9mfXmtpO0bU+tIejvQTdP0d6PGSLqWuuQKrx3FyaZ6g+E8D6vXgcDJklYi9eE8RZqm36ozGEbSdRR/2VVvMJznYTXKl0fYXNKKpKZvHzfVGwwj6TqK+2zaQNI2LDjP49lctiGwfETcVGtw1vEkrQn8D7B2RLxb0sbAthFxUs2hDVqS/tG4hLUNDCcbsw4n6SLSpaH/OyI2lzQU+EdEvKXm0AYtSasW+lptAHTU0Dgza2n1iJhMnvE59//Nrzekwc2JZuA52Zh1vmfzxdMal4XeBvf3WZdxM5pZh5J0CPB30oioY0lTj9wODAf2iYhb6ovOrH+cbMw6lKRjgLcDbyLNOPEf4HLg7G66LLQZONmYdTxJS5MuOPZ2YNt8ezIiNq41MLN+8Hk2Zp1vGLAisFK+PUSaX8+sa7hmY9ahJE0kXdl1DumyvtcC10bE7FoDM1sEHo1m1rnWBZYBHiH118wAnqwzILNF5ZqNWQdTugjJJqT+mreTRqTNAq6JiMPrjM2sP5xszLqApJHAdqSE815gtYhYudagzPrBycasQ0n6Iim5bAe8RDrn5pp8f1tEvFxjeGb94tFoZp1rFHAO8OWIeLjmWMwWi2s2ZmZWOY9GMzOzyjnZmJlZ5ZxszDqAkt9Imi3p+pLbnCLp+1XHNhAkjZIU+Vo8tgRysrFBT9J0Sc9LmiPpSUlXS/qspE46/rcH3gWMjIgxzQslHSDpqvaHZTYwOumfzaxK74uIFYDXAz8Evgl00mWVXw9Mb1w23GywcbKxJUpEPBURFwAfAcZJ2hRA0h6S/iHpaUkPSjqisY2kP0r6QnE/km6VtFdu/jpO0mOSnsrlm7Z6bUlrS7pA0ixJ0yR9OpcfCPwa2FbSM5K+17Tdm4FfFpY/WVi8So5vjqTrJL2xsN2bJE3Jr3e3pA/3ENfOkm4rPP9rsSlP0lWS9iq8h99LminpvnwuUGO9pSQdKunfkp6QNFnSqj285odyjbPlZ2WDUET45tugvgHTgXe2KH8AOCg/3gl4C+kH2GbAo8BeedmHgesK220OPAEsDewG3AisTLrI2ZuBtXqI4wrgF8CywBbATGCXvOwA4Kpe3sOrlgOnkKauGUM6Z+4M4Ky87LXAg8An8rK3Ao8Dm7TY97LA88Dqed1HSDNLr0Cacfp5YLX82dwIfDe/9zcA9wK75f0cQposdCRpTrdfAWfmZaNIVxodmmOaBqxf97HhW/turtnYkuwhYFWAiLg8Im6LiJcj4lbgTGDHvN75wAaSNsjP9yNdwOxF0pn9K5AucKaIuDNanIApaR1Sv8w3I2JuRNxMqs3st5jv4dyIuD4i5pGSzRa5/L2kZrnfRMS8iLgJ+D2wd/MOImIuMBXYgXTdnFuBq0gzF2wD3BMRTwBvA4ZHxISIeDEi7gVOBMbmXX0G+O+ImBERLwBHAHs3DQo4BPg6sFNETFvM925dxCNDbEk2glQzQNLWpL6cTUm/2pcBfgcQES9Imgx8PDdx7Uv+0o6ISyX9DPg5sK6k84CvRcTTTa+1NjArIuYUyu4nfbkvjkcKj58Dls+PXw9s3dTkNhQ4rYf9XEGq3c3Ij2eTku0L+Xljn2s37XMI8LfC8vMkFafRmQ+sWXj+dWBCRMzo433ZIOOajS2RJL2NlGwaI7x+C1wArBMRK5H6SFTYZBLwMWAX4LmIuKaxICKOj4itSLMzb0j6Qm32ELCqpBUKZeuSLh1QRn+n+ngQuCIiVi7clo+Ig3pYv5FsdsiPryAlmx1ZkGweBO5r2ucKEfGewvJ3Ny1fNiKK73FX4NuSPtTP92NdzsnGliiSVpT0XuAs4PSIaHSMr0CqecyVNAb4aHG7nFxeBn5CoXYg6W2Stpb0GuBZYC7p1zxN2z8IXA38QNKykjYDDiQ1fZXxKDAyXyK6jAuBDSXtJ+k1+fa2PNiglauBjUj9P9dHxO3k2hFwZV7neuBpSd+UNEzSEEmb5sQNKUEfJen1AJKGS9qz6XVuB3YHfi7p/SXfiw0CTja2pPg/SXNIv77/GziW1FHd8DlgQl7nu8DkFvs4lTSI4PRC2YqkfovZpGaxJ4BjeohhX1JH+UPAecDhETGlZPyXkr6oH5H0eF8r5+a6XUn9KQ+RmtuOJjUPtlr/WeAm4PbcFwVphun7I+KxvM584H2kfqH7SAMOfk26VDXAT0m1w7/kz/FaUrJqfq1bSH1KJ0p6d1/vxQYHT8RpVpKk/YHxEbF93bGYdRvXbMxKkLQcqfYzse5YzLqRk41ZHyTtRjon5lHSQAIz6yc3o5mZWeVcszEzs8o52ZiZWeWcbMzMrHJONmZmVjknGzMzq9z/B0ZR/vUpEUl1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the number of rentals by the days of the week\n",
    "ax = df_all['weekday'].value_counts().plot(kind=\"bar\")\n",
    "plt.xlabel('Days of the week', fontsize=12)\n",
    "plt.ylabel('Number of rentals', fontsize=12)\n",
    "\n",
    "plt.title('Most Popular Days of the Week', fontsize=16)\n",
    "ax.set_xticklabels(['Saturday','Friday','Sunday','Wednesday','Tuesday','Thursday','Monday']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most popular days of the week for the riders are Saturday, Frdiay, Sunday, Wednesday, Tuesday, Thursday and not surprisingly, Monday comes the last.\n",
    "\n",
    "Next, I will check the most popular months of the year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "September    307853\n",
       "August       289033\n",
       "July         259726\n",
       "October      248424\n",
       "June         191843\n",
       "November     161712\n",
       "February     133235\n",
       "January      128598\n",
       "May          124879\n",
       "March        107350\n",
       "December      74002\n",
       "April         46793\n",
       "Name: month, dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assess month's column 12 most frequent values in descending order\n",
    "df_all['month'].value_counts().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the most popular months of the year\n",
    "ax = df_all['month'].value_counts().reindex(['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']).plot(kind=\"barh\")\n",
    "ax.set_title('Most Popular Months of the Year', fontsize=16)\n",
    "ax.set_xlabel('Months', fontsize=12)\n",
    "ax.set_ylabel('Number of rentals', fontsize=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most popular months of the year to ride a bike are: \n",
    "\n",
    "September, August, July, October, June, November\n",
    "\n",
    "The least popular ones are:\n",
    "\n",
    "April, December, March, May, January, February"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=’100’></a>\n",
    "\n",
    "## Bivariate Exploration\n",
    "\n",
    "> In this section, I'll investigate relationships between pairs of variables in my data. Make sure the variables that you cover here have been introduced in some\n",
    "fashion in the previous section (univariate exploration).\n",
    "-\n",
    "\n",
    "- Relationship between weekday/weekend and start/end stations\n",
    "- Duration on weekdays/weekends\n",
    "- Duration depending on user type\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# since the first relationship is between two categorical variables, I will use a countplot to visualise\n",
    "ax = sb.countplot(data = df_all, order = pandas.value_counts(df_all['start station name']).iloc[:10].index, x = 'start station name', hue = 'type')\n",
    "plt.xticks(rotation = 90);\n",
    "ax.set_title('Bike Rental Difference Between Weekdays and Weekends on Start stations', fontsize=16)\n",
    "ax.set_xlabel('Station names', fontsize=12)\n",
    "ax.set_ylabel('Number of rentals', fontsize=12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check the same for the end stations as well\n",
    "ax = sb.countplot(data = df_all, order = pandas.value_counts(df_all['end station name']).iloc[:10].index, x = 'end station name', hue = 'type')\n",
    "plt.xticks(rotation = 90);\n",
    "ax.set_title('Bike Rental Difference Between Weekdays and Weekends on End stations', fontsize=16)\n",
    "ax.set_xlabel('Station names', fontsize=12)\n",
    "ax.set_ylabel('Number of rentals', fontsize=12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a clear difference in number of rentals between weekdays and weekends and we can also see that the names of the stations differ on the most popular start and end stations, which likely means there are certain areas where people go to work from and to. To prove this however, I would need to visualise it on a map but that's out of the scope for this project.\n",
    "\n",
    "I will continue looking at the next question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's see how the tripduration is affected by the type of the days in the week on average\n",
    "ax = sb.barplot(data = df_all, x = 'type', y = 'tripduration')\n",
    "plt.xticks(rotation=15);\n",
    "plt.ylabel('Trip duration')\n",
    "plt.xlabel('Type of the Day');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot shows us that on weekdays, the average trip duration is around 1600 seconds and on weekends it's above 2300. This lets us conclude that people use the bikes on weekends for longer rides, perhaps not for the daily commute but for sport purposes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I would like to use the same plot to see the relationship between trip duration and usertype."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's see how the tripduration relates to the type of the user\n",
    "ax = sb.barplot(data = df_all, x = 'usertype', y = 'tripduration')\n",
    "plt.xticks(rotation=15);\n",
    "plt.ylabel('Trip duration')\n",
    "plt.xlabel('Type of the User');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the average usage for a single ride customer is almost tripled compared to the subscribers. This could easily happen due to the number of subscribers versus single ride customers. Let's investigate that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Subscriber    1503053\n",
       "Customer       570395\n",
       "Name: usertype, dtype: int64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the total count of each user type\n",
    "df_all['usertype'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems like there are a lot more subscribers than single ride customers, so looking at our plot let's conclude that single ride customers use the bikes for longer rides, while subscribers probably only use it for daily commute, which makes sense."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=’105’></a>\n",
    "\n",
    "## Multivariate Exploration\n",
    "\n",
    "I'll create plots of three or more variables to investigate my data even\n",
    "further. \n",
    "\n",
    "To specify, I would like to see the relationship between:\n",
    "- trip duration by months, based on user type\n",
    "- number of trips by weekday or weekend, based on user type\n",
    "- trip duration by type of weekday, based on user type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1058.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the trip duration by the months of the year based on if the user is a customer or a subscriber\n",
    "plt.figure(figsize=[14.70, 8.27])\n",
    "sb.stripplot(data = df_all, x = 'month', y = 'tripduration', hue = 'usertype',\n",
    "            jitter = 0.35, dodge = True, palette = 'colorblind')\n",
    "plt.title('Trip duration by Months based on User type', fontsize = 16)\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Trip duration')\n",
    "plt.legend(title=\"User Type\", title_fontsize = 12);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot clearly shows that\n",
    "1, single ride customers are using the bikes for a longer duration than subscribers\n",
    "2, single ride customers are using the bikes more often than subscribers\n",
    "3, both user types are using the bikes more often and for a longer time in the months of summer.\n",
    "\n",
    "Next I'll explore how does the frequency look like by hours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1058.4x595.44 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the number of trips by the hours of the day on weekdays and weekends\n",
    "plt.figure(figsize=[14.70, 8.27])\n",
    "plt.subplot(1,2,1)\n",
    "fig1 = sb.countplot(data = df_all[df_all['type']== 'Weekday'], x = 'timeoftheday', color = 'Grey')\n",
    "plt.title('Number of trips by Hour during Weekdays', fontsize = 16)\n",
    "plt.xlabel('Hour')\n",
    "plt.ylabel('Number of Trips')\n",
    "plt.ylim([0,200000])\n",
    "plt.subplot(1,2,2)\n",
    "fig1 = sb.countplot(data = df_all[df_all['type']== 'Weekend'], x = 'timeoftheday', color = 'Grey')\n",
    "plt.title('Number of trips by Hour during Weekends', fontsize = 16)\n",
    "plt.xlabel('Hour')\n",
    "plt.ylabel('Number of Trips')\n",
    "plt.ylim([0,100000])\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is probably one of the most insightful plots in this analysis.\n",
    "\n",
    "On weekdays, we can see the frequency of usage goes up around rush hours, which makes a lot of sense.\n",
    "On the other hand, weekends show a very balanced and gradually increasing and decreasing usage as people start to wake up, maybe go for a longer ride during the day and go home around the end of the afternoon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1058.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the trip duration by the type of the weekday based on user type\n",
    "plt.figure(figsize=[14.70, 8.27])\n",
    "ax = sb.barplot(data=df_all, x = 'usertype', y='tripduration', hue='type')\n",
    "plt.ylabel('Trip duration')\n",
    "plt.xlabel('Type of the User');\n",
    "plt.legend(title=\"User Type\", title_fontsize = 12);\n",
    "plt.title('Trip duration by Weekday/Weekend based on User Type', fontsize = 16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By the plot above we can conclude that \n",
    "1, Customers use the bikes for longer trips than Subscribers on both type of weekdays, but more on the weekends\n",
    "2, Subscribers also use the bikes for longer trips on weekends, but considerebly shorter rides than single ride customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}