Exploring US Bikeshare Data - Data set details section.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "#imposting Chicago city data\n",
    "df = pd.read_csv(\"chicago.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed: 0           Start Time             End Time  Trip Duration  \\\n",
      "0     1423854  2017-06-23 15:09:32  2017-06-23 15:14:53            321   \n",
      "1      955915  2017-05-25 18:19:03  2017-05-25 18:45:53           1610   \n",
      "2        9031  2017-01-04 08:27:49  2017-01-04 08:34:45            416   \n",
      "3      304487  2017-03-06 13:49:38  2017-03-06 13:55:28            350   \n",
      "4       45207  2017-01-17 14:53:07  2017-01-17 15:02:01            534   \n",
      "\n",
      "                   Start Station                   End Station   User Type  \\\n",
      "0           Wood St & Hubbard St       Damen Ave & Chicago Ave  Subscriber   \n",
      "1            Theater on the Lake  Sheffield Ave & Waveland Ave  Subscriber   \n",
      "2             May St & Taylor St           Wood St & Taylor St  Subscriber   \n",
      "3  Christiana Ave & Lawrence Ave  St. Louis Ave & Balmoral Ave  Subscriber   \n",
      "4         Clark St & Randolph St  Desplaines St & Jackson Blvd  Subscriber   \n",
      "\n",
      "   Gender  Birth Year  \n",
      "0    Male      1992.0  \n",
      "1  Female      1992.0  \n",
      "2    Male      1981.0  \n",
      "3    Male      1986.0  \n",
      "4    Male      1975.0  \n"
     ]
    }
   ],
   "source": [
    "#viewing the first few rows of the dataset\n",
    "print(df.head())  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300000, 9)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#how many rows and columns are there in the dataset?\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Start Time', 'End Time', 'Trip Duration',\n",
       "       'Start Station', 'End Station', 'User Type', 'Gender', 'Birth Year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Returning column names\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 300000 entries, 0 to 299999\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count   Dtype  \n",
      "---  ------         --------------   -----  \n",
      " 0   Unnamed: 0     300000 non-null  int64  \n",
      " 1   Start Time     300000 non-null  object \n",
      " 2   End Time       300000 non-null  object \n",
      " 3   Trip Duration  300000 non-null  int64  \n",
      " 4   Start Station  300000 non-null  object \n",
      " 5   End Station    300000 non-null  object \n",
      " 6   User Type      300000 non-null  object \n",
      " 7   Gender         238948 non-null  object \n",
      " 8   Birth Year     238981 non-null  float64\n",
      "dtypes: float64(1), int64(2), object(6)\n",
      "memory usage: 20.6+ MB\n"
     ]
    }
   ],
   "source": [
    "#What are the different types of values in each column in the dataset?\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#How many duplicates if exist? \n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0           0\n",
       "Start Time           0\n",
       "End Time             0\n",
       "Trip Duration        0\n",
       "Start Station        0\n",
       "End Station          0\n",
       "User Type            0\n",
       "Gender           61052\n",
       "Birth Year       61019\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#How many missing values if exist?\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Unnamed: 0</th>\n",
       "      <th>Trip Duration</th>\n",
       "      <th>Birth Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.000000e+05</td>\n",
       "      <td>300000.000000</td>\n",
       "      <td>238981.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.763458e+05</td>\n",
       "      <td>936.239290</td>\n",
       "      <td>1980.858223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.481464e+05</td>\n",
       "      <td>1548.792767</td>\n",
       "      <td>11.003329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>1899.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.871368e+05</td>\n",
       "      <td>393.000000</td>\n",
       "      <td>1975.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.771035e+05</td>\n",
       "      <td>670.000000</td>\n",
       "      <td>1984.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.164065e+06</td>\n",
       "      <td>1125.000000</td>\n",
       "      <td>1989.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.551500e+06</td>\n",
       "      <td>86224.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0  Trip Duration     Birth Year\n",
       "count  3.000000e+05  300000.000000  238981.000000\n",
       "mean   7.763458e+05     936.239290    1980.858223\n",
       "std    4.481464e+05    1548.792767      11.003329\n",
       "min    4.000000e+00      60.000000    1899.000000\n",
       "25%    3.871368e+05     393.000000    1975.000000\n",
       "50%    7.771035e+05     670.000000    1984.000000\n",
       "75%    1.164065e+06    1125.000000    1989.000000\n",
       "max    1.551500e+06   86224.000000    2016.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#a brief description of the data \n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Subscriber    238889\n",
       "Customer       61110\n",
       "Dependent          1\n",
       "Name: User Type, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['User Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Subscriber', 'Customer', 'Dependent'], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['User Type'].unique()"
   ]
  }
 ],
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