{
 "cells": [
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   "cell_type": "markdown",
   "id": "58041ec7-6bbf-4fa8-b76d-1f4af1a3cccd",
   "metadata": {},
   "source": [
    "Importing pandas, a leading data manipulation python library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7c04b7c9-fd43-4f0c-91e5-bfca643d0bb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d05092-b42f-4943-bc7d-eff9db6198eb",
   "metadata": {},
   "source": [
    "Reading in the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "32b6b294-acf7-411a-aee6-affd819fbc04",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('raw/data.csv', names=['station_id', 'bike_id', 'from', 'to'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3980c0e3-1d94-4ccd-b863-c63e11561963",
   "metadata": {},
   "source": [
    "Observing the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c35503ec-e37c-4397-8e0d-8968fa18c70a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "baaa8207-6042-43cf-8433-d371238eac86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c812ac6a-47cc-4f61-8612-a8e704fcfb66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 4)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8987a405-9b2b-43bf-a049-aed7c21880f1",
   "metadata": {
    "tags": []
   },
   "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>station_id</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>from</th>\n",
       "      <th>to</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>212</td>\n",
       "      <td>2294</td>\n",
       "      <td>2020-12-13T11:26:54Z</td>\n",
       "      <td>2020-12-17T16:13:54Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>418</td>\n",
       "      <td>3441</td>\n",
       "      <td>2020-08-25T11:37:11Z</td>\n",
       "      <td>2020-08-31T11:18:11Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>301</td>\n",
       "      <td>6467</td>\n",
       "      <td>2021-04-10T17:05:16Z</td>\n",
       "      <td>2021-04-11T15:00:16Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>560</td>\n",
       "      <td>9386</td>\n",
       "      <td>2021-04-28T12:10:24Z</td>\n",
       "      <td>2021-05-02T07:31:24Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>87</td>\n",
       "      <td>8755</td>\n",
       "      <td>2021-01-10T10:40:53Z</td>\n",
       "      <td>2021-01-11T20:58:53Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>651</td>\n",
       "      <td>1525</td>\n",
       "      <td>2020-08-08T06:44:20Z</td>\n",
       "      <td>2020-08-11T01:15:20Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>40</td>\n",
       "      <td>8238</td>\n",
       "      <td>2021-01-16T10:20:47Z</td>\n",
       "      <td>2021-01-16T14:06:47Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>97</td>\n",
       "      <td>2437</td>\n",
       "      <td>2021-06-21T15:52:08Z</td>\n",
       "      <td>2021-06-24T00:56:08Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>362</td>\n",
       "      <td>8428</td>\n",
       "      <td>2021-03-12T16:38:10Z</td>\n",
       "      <td>2021-03-17T05:07:10Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>234</td>\n",
       "      <td>7266</td>\n",
       "      <td>2021-01-02T19:17:57Z</td>\n",
       "      <td>2021-01-08T01:42:57Z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   station_id  bike_id                  from                    to\n",
       "0         212     2294  2020-12-13T11:26:54Z  2020-12-17T16:13:54Z\n",
       "1         418     3441  2020-08-25T11:37:11Z  2020-08-31T11:18:11Z\n",
       "2         301     6467  2021-04-10T17:05:16Z  2021-04-11T15:00:16Z\n",
       "3         560     9386  2021-04-28T12:10:24Z  2021-05-02T07:31:24Z\n",
       "4          87     8755  2021-01-10T10:40:53Z  2021-01-11T20:58:53Z\n",
       "5         651     1525  2020-08-08T06:44:20Z  2020-08-11T01:15:20Z\n",
       "6          40     8238  2021-01-16T10:20:47Z  2021-01-16T14:06:47Z\n",
       "7          97     2437  2021-06-21T15:52:08Z  2021-06-24T00:56:08Z\n",
       "8         362     8428  2021-03-12T16:38:10Z  2021-03-17T05:07:10Z\n",
       "9         234     7266  2021-01-02T19:17:57Z  2021-01-08T01:42:57Z"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b96093e2-356d-46e8-8c09-7b0ae547886f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 4 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   station_id  1000 non-null   int64 \n",
      " 1   bike_id     1000 non-null   int64 \n",
      " 2   from        1000 non-null   object\n",
      " 3   to          1000 non-null   object\n",
      "dtypes: int64(2), object(2)\n",
      "memory usage: 31.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "715ff011-2d0c-473a-a672-f9b632e69ad8",
   "metadata": {},
   "source": [
    "Using the datetime objects in Pandas DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bd204a28-f525-4f4e-9ef8-ba7fa30158ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['from'] = pd.to_datetime(df['from'])\n",
    "df['to'] = pd.to_datetime(df['to'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "932734b3-4155-4491-924e-57fcd8fc24d2",
   "metadata": {},
   "source": [
    "Subracting the two date columns and return a Series of timedelta objects with the time diff between each row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "74b0da85-5b62-4b81-a8a9-6a3f4196539c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     4 days 04:47:00\n",
       "1     5 days 23:41:00\n",
       "2     0 days 21:55:00\n",
       "3     3 days 19:21:00\n",
       "4     1 days 10:18:00\n",
       "            ...      \n",
       "995   5 days 02:16:00\n",
       "996   0 days 20:08:00\n",
       "997   5 days 14:35:00\n",
       "998   3 days 04:50:00\n",
       "999   3 days 00:12:00\n",
       "Length: 1000, dtype: timedelta64[ns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "difference = (df['to'] - df['from'])\n",
    "difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c6fc4dd7-cefd-4d1f-b26e-32865137521f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 days 13:43:53.760000 is the average (mean) journey duration across all bikes and all stations for this reporting period.\n"
     ]
    }
   ],
   "source": [
    "avg_journey_duration = difference.mean()\n",
    "print(f'{avg_journey_duration} is the average (mean) journey duration across all bikes and all stations for this reporting period.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cb0be5f-6889-4d04-9704-9c4a7567ac66",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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