demand_score.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "N3tscw0S49Jy",
    "outputId": "ff2f0bf0-77f2-4261-d4c8-6c36e0fb5fb3"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "W4uNiLw4_y9h",
    "outputId": "d4b85440-9cd2-46b1-9469-d78ca4b75ed6"
   },
   "outputs": [],
   "source": [
    "data_directory = 'data/'\n",
    "bike_trips = pd.read_csv(f'{data_directory}/combined_bike_trips_2023.csv')\n",
    "stations = pd.read_csv(f'{data_directory}/manhattan_stations.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "z75vso93DrtG"
   },
   "outputs": [],
   "source": [
    "bike_df = bike_trips.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "6ek19MgxAQL8"
   },
   "outputs": [],
   "source": [
    "# Convert string to datetime objects\n",
    "bike_df['started_at'] = pd.to_datetime(bike_df['started_at'])\n",
    "bike_df['ended_at'] = pd.to_datetime(bike_df['ended_at'])\n",
    "\n",
    "# Get trip_duration\n",
    "bike_df['trip_duration'] = (bike_df['ended_at'] - bike_df['started_at']).dt.total_seconds()/60\n",
    "bike_df = bike_df[bike_df['trip_duration'] > 0].copy() # ensure start_date is before end_date\n",
    "\n",
    "# Get week for each observation\n",
    "bike_df['week_started'] = bike_df['started_at'].dt.isocalendar().week\n",
    "bike_df['week_ended'] = bike_df['ended_at'].dt.isocalendar().week\n",
    "\n",
    "# Get month for each observation\n",
    "bike_df['month_started'] = bike_df['started_at'].dt.month\n",
    "bike_df['month_ended'] = bike_df['ended_at'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "JQYnVI_qFNOm",
    "outputId": "12deba8e-f19e-4edd-f68b-1f548978d583"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ride_id</th>\n",
       "      <th>rideable_type</th>\n",
       "      <th>started_at</th>\n",
       "      <th>ended_at</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>start_lat</th>\n",
       "      <th>start_lng</th>\n",
       "      <th>end_lat</th>\n",
       "      <th>end_lng</th>\n",
       "      <th>member_casual</th>\n",
       "      <th>trip_duration</th>\n",
       "      <th>week_started</th>\n",
       "      <th>week_ended</th>\n",
       "      <th>month_started</th>\n",
       "      <th>month_ended</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DC1CB984821DFFF7</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2023-01-07 15:36:53.430</td>\n",
       "      <td>2023-01-07 15:39:45.406</td>\n",
       "      <td>Vesey St &amp; Church St</td>\n",
       "      <td>5216.06</td>\n",
       "      <td>Albany St &amp; Greenwich St</td>\n",
       "      <td>5145.02</td>\n",
       "      <td>40.712220</td>\n",
       "      <td>-74.010472</td>\n",
       "      <td>40.709267</td>\n",
       "      <td>-74.013247</td>\n",
       "      <td>member</td>\n",
       "      <td>2.866267</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C00CA02971557F16</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2023-01-04 19:23:01.234</td>\n",
       "      <td>2023-01-04 19:34:57.151</td>\n",
       "      <td>Lispenard St &amp; Broadway</td>\n",
       "      <td>5391.06</td>\n",
       "      <td>St Marks Pl &amp; 1 Ave</td>\n",
       "      <td>5626.13</td>\n",
       "      <td>40.719392</td>\n",
       "      <td>-74.002472</td>\n",
       "      <td>40.727791</td>\n",
       "      <td>-73.985649</td>\n",
       "      <td>member</td>\n",
       "      <td>11.931950</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E4415A543C1972A7</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2023-01-24 10:38:01.135</td>\n",
       "      <td>2023-01-24 10:41:40.749</td>\n",
       "      <td>E 5 St &amp; Ave A</td>\n",
       "      <td>5626.06</td>\n",
       "      <td>E 1 St &amp; 1 Ave</td>\n",
       "      <td>5593.01</td>\n",
       "      <td>40.724790</td>\n",
       "      <td>-73.984301</td>\n",
       "      <td>40.723356</td>\n",
       "      <td>-73.988650</td>\n",
       "      <td>member</td>\n",
       "      <td>3.660233</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4FC76B63F9FE631F</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2023-01-26 19:17:23.200</td>\n",
       "      <td>2023-01-26 19:47:05.287</td>\n",
       "      <td>W 59 St &amp; 10 Ave</td>\n",
       "      <td>7023.04</td>\n",
       "      <td>E 1 St &amp; 1 Ave</td>\n",
       "      <td>5593.01</td>\n",
       "      <td>40.770513</td>\n",
       "      <td>-73.988038</td>\n",
       "      <td>40.723356</td>\n",
       "      <td>-73.988650</td>\n",
       "      <td>member</td>\n",
       "      <td>29.701450</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>153486234EA3E427</td>\n",
       "      <td>electric_bike</td>\n",
       "      <td>2023-01-10 08:29:04.191</td>\n",
       "      <td>2023-01-10 08:33:49.274</td>\n",
       "      <td>Barrow St &amp; Hudson St</td>\n",
       "      <td>5805.05</td>\n",
       "      <td>Washington St &amp; Gansevoort St</td>\n",
       "      <td>6039.06</td>\n",
       "      <td>40.731724</td>\n",
       "      <td>-74.006744</td>\n",
       "      <td>40.739323</td>\n",
       "      <td>-74.008119</td>\n",
       "      <td>member</td>\n",
       "      <td>4.751383</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>D24D370D9CE13CB4</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2023-01-11 08:32:01.889</td>\n",
       "      <td>2023-01-11 08:38:26.498</td>\n",
       "      <td>Barrow St &amp; Hudson St</td>\n",
       "      <td>5805.05</td>\n",
       "      <td>Washington St &amp; Gansevoort St</td>\n",
       "      <td>6039.06</td>\n",
       "      <td>40.731724</td>\n",
       "      <td>-74.006744</td>\n",
       "      <td>40.739323</td>\n",
       "      <td>-74.008119</td>\n",
       "      <td>member</td>\n",
       "      <td>6.410150</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>98E7D95A8B9B2249</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2023-01-01 16:40:29.769</td>\n",
       "      <td>2023-01-01 16:48:04.256</td>\n",
       "      <td>Barrow St &amp; Hudson St</td>\n",
       "      <td>5805.05</td>\n",
       "      <td>Washington St &amp; Gansevoort St</td>\n",
       "      <td>6039.06</td>\n",
       "      <td>40.731724</td>\n",
       "      <td>-74.006744</td>\n",
       "      <td>40.739323</td>\n",
       "      <td>-74.008119</td>\n",
       "      <td>member</td>\n",
       "      <td>7.574783</td>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9ABD445191B6E224</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2023-01-29 14:27:25.595</td>\n",
       "      <td>2023-01-29 14:38:48.023</td>\n",
       "      <td>Barrow St &amp; Hudson St</td>\n",
       "      <td>5805.05</td>\n",
       "      <td>St Marks Pl &amp; 1 Ave</td>\n",
       "      <td>5626.13</td>\n",
       "      <td>40.731724</td>\n",
       "      <td>-74.006744</td>\n",
       "      <td>40.727791</td>\n",
       "      <td>-73.985649</td>\n",
       "      <td>member</td>\n",
       "      <td>11.373800</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>01D7ACD6AE19CC69</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2023-01-09 09:01:26.183</td>\n",
       "      <td>2023-01-09 09:11:28.461</td>\n",
       "      <td>6 Ave &amp; W 33 St</td>\n",
       "      <td>6364.07</td>\n",
       "      <td>E 47 St &amp; Park Ave</td>\n",
       "      <td>6584.12</td>\n",
       "      <td>40.749013</td>\n",
       "      <td>-73.988484</td>\n",
       "      <td>40.755103</td>\n",
       "      <td>-73.974987</td>\n",
       "      <td>member</td>\n",
       "      <td>10.037967</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>044C13BF6237E402</td>\n",
       "      <td>classic_bike</td>\n",
       "      <td>2023-01-26 07:22:25.970</td>\n",
       "      <td>2023-01-26 07:30:34.377</td>\n",
       "      <td>6 Ave &amp; W 33 St</td>\n",
       "      <td>6364.07</td>\n",
       "      <td>E 27 St &amp; 1 Ave</td>\n",
       "      <td>6004.06</td>\n",
       "      <td>40.749013</td>\n",
       "      <td>-73.988484</td>\n",
       "      <td>40.739445</td>\n",
       "      <td>-73.976806</td>\n",
       "      <td>member</td>\n",
       "      <td>8.140117</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ride_id  rideable_type              started_at  \\\n",
       "0  DC1CB984821DFFF7   classic_bike 2023-01-07 15:36:53.430   \n",
       "1  C00CA02971557F16   classic_bike 2023-01-04 19:23:01.234   \n",
       "2  E4415A543C1972A7   classic_bike 2023-01-24 10:38:01.135   \n",
       "3  4FC76B63F9FE631F   classic_bike 2023-01-26 19:17:23.200   \n",
       "4  153486234EA3E427  electric_bike 2023-01-10 08:29:04.191   \n",
       "5  D24D370D9CE13CB4   classic_bike 2023-01-11 08:32:01.889   \n",
       "6  98E7D95A8B9B2249   classic_bike 2023-01-01 16:40:29.769   \n",
       "7  9ABD445191B6E224   classic_bike 2023-01-29 14:27:25.595   \n",
       "8  01D7ACD6AE19CC69   classic_bike 2023-01-09 09:01:26.183   \n",
       "9  044C13BF6237E402   classic_bike 2023-01-26 07:22:25.970   \n",
       "\n",
       "                 ended_at       start_station_name  start_station_id  \\\n",
       "0 2023-01-07 15:39:45.406     Vesey St & Church St           5216.06   \n",
       "1 2023-01-04 19:34:57.151  Lispenard St & Broadway           5391.06   \n",
       "2 2023-01-24 10:41:40.749           E 5 St & Ave A           5626.06   \n",
       "3 2023-01-26 19:47:05.287         W 59 St & 10 Ave           7023.04   \n",
       "4 2023-01-10 08:33:49.274    Barrow St & Hudson St           5805.05   \n",
       "5 2023-01-11 08:38:26.498    Barrow St & Hudson St           5805.05   \n",
       "6 2023-01-01 16:48:04.256    Barrow St & Hudson St           5805.05   \n",
       "7 2023-01-29 14:38:48.023    Barrow St & Hudson St           5805.05   \n",
       "8 2023-01-09 09:11:28.461          6 Ave & W 33 St           6364.07   \n",
       "9 2023-01-26 07:30:34.377          6 Ave & W 33 St           6364.07   \n",
       "\n",
       "                end_station_name  end_station_id  start_lat  start_lng  \\\n",
       "0       Albany St & Greenwich St         5145.02  40.712220 -74.010472   \n",
       "1            St Marks Pl & 1 Ave         5626.13  40.719392 -74.002472   \n",
       "2                 E 1 St & 1 Ave         5593.01  40.724790 -73.984301   \n",
       "3                 E 1 St & 1 Ave         5593.01  40.770513 -73.988038   \n",
       "4  Washington St & Gansevoort St         6039.06  40.731724 -74.006744   \n",
       "5  Washington St & Gansevoort St         6039.06  40.731724 -74.006744   \n",
       "6  Washington St & Gansevoort St         6039.06  40.731724 -74.006744   \n",
       "7            St Marks Pl & 1 Ave         5626.13  40.731724 -74.006744   \n",
       "8             E 47 St & Park Ave         6584.12  40.749013 -73.988484   \n",
       "9                E 27 St & 1 Ave         6004.06  40.749013 -73.988484   \n",
       "\n",
       "     end_lat    end_lng member_casual  trip_duration  week_started  \\\n",
       "0  40.709267 -74.013247        member       2.866267             1   \n",
       "1  40.727791 -73.985649        member      11.931950             1   \n",
       "2  40.723356 -73.988650        member       3.660233             4   \n",
       "3  40.723356 -73.988650        member      29.701450             4   \n",
       "4  40.739323 -74.008119        member       4.751383             2   \n",
       "5  40.739323 -74.008119        member       6.410150             2   \n",
       "6  40.739323 -74.008119        member       7.574783            52   \n",
       "7  40.727791 -73.985649        member      11.373800             4   \n",
       "8  40.755103 -73.974987        member      10.037967             2   \n",
       "9  40.739445 -73.976806        member       8.140117             4   \n",
       "\n",
       "   week_ended  month_started  month_ended  \n",
       "0           1              1            1  \n",
       "1           1              1            1  \n",
       "2           4              1            1  \n",
       "3           4              1            1  \n",
       "4           2              1            1  \n",
       "5           2              1            1  \n",
       "6          52              1            1  \n",
       "7           4              1            1  \n",
       "8           2              1            1  \n",
       "9           4              1            1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_df.head(10)\n",
    "# bike_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "sB3vxm3xfp-q"
   },
   "outputs": [],
   "source": [
    "# Extract the Autumn Season Months (September, October, November)\n",
    "autumn_bike_df = bike_df[bike_df['month_started'].isin([9, 10, 11]) & bike_df['month_ended'].isin([9, 10, 11])].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Fq9TLoGCwNe-",
    "outputId": "cff4fecc-d8dc-4b41-b64b-21ac400fd0bd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 9 10 11]\n",
      "[ 9 10 11]\n"
     ]
    }
   ],
   "source": [
    "# Verify extracted months are 9, 10, 11\n",
    "print(autumn_bike_df['month_started'].unique())\n",
    "print(autumn_bike_df['month_ended'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<IntegerArray>\n",
      "[36, 37, 35, 38, 39, 41, 40, 42, 43, 44, 47, 48, 46, 45]\n",
      "Length: 14, dtype: UInt32\n",
      "<IntegerArray>\n",
      "[36, 37, 35, 38, 39, 41, 40, 42, 43, 44, 47, 48, 46, 45]\n",
      "Length: 14, dtype: UInt32\n"
     ]
    }
   ],
   "source": [
    "# See the week numbers\n",
    "print(autumn_bike_df['week_started'].unique())\n",
    "print(autumn_bike_df['week_ended'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "605 Starting stations\n",
      "605 Ending stations\n"
     ]
    }
   ],
   "source": [
    "# See number of unique stations\n",
    "print(len(autumn_bike_df[\"start_station_id\"].unique()), \"Starting stations\")\n",
    "print(len(autumn_bike_df[\"end_station_id\"].unique()), \"Ending stations\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "LeJXnLLHwyFB"
   },
   "outputs": [],
   "source": [
    "# Calculate the mean weekly trips for each stations\n",
    "mean_weekly_trips = autumn_bike_df.groupby(['start_station_id', 'week_started']).size().groupby(level=0).mean()\n",
    "mean_weekly_trips = mean_weekly_trips.reset_index(name='MEAN_WEEKLY_TRIPS')\n",
    "mean_weekly_trips['MEAN_WEEKLY_TRIPS'] = mean_weekly_trips['MEAN_WEEKLY_TRIPS'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 444
    },
    "id": "yURFHAKlxCKk",
    "outputId": "c1743621-a026-427e-855b-42e76932f978"
   },
   "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>start_station_id</th>\n",
       "      <th>MEAN_WEEKLY_TRIPS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4846.01</td>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4889.06</td>\n",
       "      <td>248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4920.13</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4953.04</td>\n",
       "      <td>405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4962.01</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>8679.03</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>8696.05</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>8715.01</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>8715.06</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>8732.04</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>605 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     start_station_id  MEAN_WEEKLY_TRIPS\n",
       "0             4846.01                313\n",
       "1             4889.06                248\n",
       "2             4920.13                 27\n",
       "3             4953.04                405\n",
       "4             4962.01                241\n",
       "..                ...                ...\n",
       "600           8679.03                 11\n",
       "601           8696.05                  4\n",
       "602           8715.01                 17\n",
       "603           8715.06                 14\n",
       "604           8732.04                 11\n",
       "\n",
       "[605 rows x 2 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Glimpse at the values\n",
    "mean_weekly_trips\n",
    "\n",
    "# Note only 605 stations have recorded trips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nByZKEPd2GPW"
   },
   "outputs": [],
   "source": [
    "# Add mean weekly trips of each station to the stations data set\n",
    "merged_df = pd.merge(stations, mean_weekly_trips, left_on='station_id', right_on='start_station_id', how='left')\n",
    "# Replace empty demand with 0 mean weekly trips\n",
    "merged_df[\"MEAN_WEEKLY_TRIPS\"].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "662"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check total number of unique stations\n",
    "len(stations[\"station_id\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>station_id</th>\n",
       "      <th>station_name</th>\n",
       "      <th>capacity</th>\n",
       "      <th>lng</th>\n",
       "      <th>lat</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>MEAN_WEEKLY_TRIPS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5772.05</td>\n",
       "      <td>Morton St &amp; Greenwich St</td>\n",
       "      <td>56</td>\n",
       "      <td>-74.008870</td>\n",
       "      <td>40.731150</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6560.15</td>\n",
       "      <td>Broadway &amp; W 38 St</td>\n",
       "      <td>68</td>\n",
       "      <td>-73.987349</td>\n",
       "      <td>40.752973</td>\n",
       "      <td>6560.15</td>\n",
       "      <td>511.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7727.07</td>\n",
       "      <td>Amsterdam Ave &amp; W 119 St</td>\n",
       "      <td>29</td>\n",
       "      <td>-73.959621</td>\n",
       "      <td>40.808625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6569.08</td>\n",
       "      <td>W 35 St &amp; Dyer Ave</td>\n",
       "      <td>41</td>\n",
       "      <td>-73.997402</td>\n",
       "      <td>40.754692</td>\n",
       "      <td>6569.08</td>\n",
       "      <td>206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7023.04</td>\n",
       "      <td>W 59 St &amp; 10 Ave</td>\n",
       "      <td>117</td>\n",
       "      <td>-73.988038</td>\n",
       "      <td>40.770513</td>\n",
       "      <td>7023.04</td>\n",
       "      <td>341.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>8416.10</td>\n",
       "      <td>W 186 St &amp; St Nicholas Ave</td>\n",
       "      <td>26</td>\n",
       "      <td>-73.931308</td>\n",
       "      <td>40.852253</td>\n",
       "      <td>8416.10</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>4846.01</td>\n",
       "      <td>South St &amp; Whitehall St</td>\n",
       "      <td>55</td>\n",
       "      <td>-74.012342</td>\n",
       "      <td>40.701221</td>\n",
       "      <td>4846.01</td>\n",
       "      <td>313.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>6890.01</td>\n",
       "      <td>W 53 St &amp; 10 Ave</td>\n",
       "      <td>29</td>\n",
       "      <td>-73.990617</td>\n",
       "      <td>40.766697</td>\n",
       "      <td>6890.01</td>\n",
       "      <td>424.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>6483.06</td>\n",
       "      <td>W 36 St &amp; 7 Ave</td>\n",
       "      <td>38</td>\n",
       "      <td>-73.989539</td>\n",
       "      <td>40.752149</td>\n",
       "      <td>6483.06</td>\n",
       "      <td>173.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>6257.06</td>\n",
       "      <td>W 27 St &amp; 7 Ave</td>\n",
       "      <td>75</td>\n",
       "      <td>-73.993920</td>\n",
       "      <td>40.746650</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>662 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     station_id                station_name  capacity        lng        lat  \\\n",
       "0       5772.05    Morton St & Greenwich St        56 -74.008870  40.731150   \n",
       "1       6560.15          Broadway & W 38 St        68 -73.987349  40.752973   \n",
       "2       7727.07    Amsterdam Ave & W 119 St        29 -73.959621  40.808625   \n",
       "3       6569.08          W 35 St & Dyer Ave        41 -73.997402  40.754692   \n",
       "4       7023.04            W 59 St & 10 Ave       117 -73.988038  40.770513   \n",
       "..          ...                         ...       ...        ...        ...   \n",
       "657     8416.10  W 186 St & St Nicholas Ave        26 -73.931308  40.852253   \n",
       "658     4846.01     South St & Whitehall St        55 -74.012342  40.701221   \n",
       "659     6890.01            W 53 St & 10 Ave        29 -73.990617  40.766697   \n",
       "660     6483.06             W 36 St & 7 Ave        38 -73.989539  40.752149   \n",
       "661     6257.06             W 27 St & 7 Ave        75 -73.993920  40.746650   \n",
       "\n",
       "     start_station_id  MEAN_WEEKLY_TRIPS  \n",
       "0                 NaN                0.0  \n",
       "1             6560.15              511.0  \n",
       "2                 NaN                0.0  \n",
       "3             6569.08              206.0  \n",
       "4             7023.04              341.0  \n",
       "..                ...                ...  \n",
       "657           8416.10                8.0  \n",
       "658           4846.01              313.0  \n",
       "659           6890.01              424.0  \n",
       "660           6483.06              173.0  \n",
       "661               NaN                0.0  \n",
       "\n",
       "[662 rows x 7 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iJTdsYao2bNm",
    "outputId": "51946b98-c992-420b-e966-f5794832474d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column names: \n",
      "Index(['station_id', 'station_name', 'capacity', 'lng', 'lat',\n",
      "       'MEAN_WEEKLY_TRIPS'],\n",
      "      dtype='object')\n",
      "\n",
      "Checking for null values \n",
      "station_id           0\n",
      "MEAN_WEEKLY_TRIPS    0\n",
      "capacity             0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Select wanted columns\n",
    "final_df = merged_df[['station_id','station_name','capacity','lng','lat','MEAN_WEEKLY_TRIPS']].copy()\n",
    "print(f\"column names: \\n{final_df.columns}\")\n",
    "print(\"\")\n",
    "print(f\"Checking for null values \\n{final_df[['station_id', 'MEAN_WEEKLY_TRIPS', 'capacity']].isnull().sum()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 355
    },
    "id": "qqJnQPQB2hnJ",
    "outputId": "9249b35d-6288-4864-c75d-53c4d4fb8f32"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    662.000000\n",
       "mean     166.890942\n",
       "std      138.587444\n",
       "min        0.000000\n",
       "25%       40.504833\n",
       "50%      150.014667\n",
       "75%      257.689250\n",
       "max      780.824333\n",
       "Name: DEMAND_SCORE, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 1: Min-max normalize the 'capacity' column\n",
    "min_capacity = final_df['capacity'].min()\n",
    "max_capacity = final_df['capacity'].max()\n",
    "\n",
    "final_df['NORMALISED_CAPACITY'] = (final_df['capacity'] - min_capacity) / (max_capacity - min_capacity)\n",
    "\n",
    "# Step 2: Calculate the demand score (N) for each station\n",
    "small_constant = 0.001\n",
    "\n",
    "# Demand score = mean * (1 - relative_capacity + e)\n",
    "final_df['DEMAND_SCORE'] = final_df['MEAN_WEEKLY_TRIPS'] * (1 - final_df['NORMALISED_CAPACITY'] + small_constant)\n",
    "final_df['DEMAND_SCORE'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 492
    },
    "id": "gc5Wwi3a68ln",
    "outputId": "46ac7185-7769-4628-c801-b4e971aa8d9e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "# Create the Q-Q plot\n",
    "fig = sm.qqplot(final_df['DEMAND_SCORE'], line='s') # 's' for standardized line\n",
    "plt.title('Q-Q Plot of Demand Score')\n",
    "plt.show()\n",
    "\n",
    "# HOW TO READ:\n",
    "# If the data points fall along the red line, you can say that the distribution of the demand score is follows a normal distribution.\n",
    "# If that is the case, a quantile categorisation will be suitable\n",
    "# In our case, there are some mismatch, so I chose K-Means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 492
    },
    "id": "JlDEMhQj5AfK",
    "outputId": "b98e8448-a6d7-4546-e4d1-ad59991d91c7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.kdeplot(final_df['DEMAND_SCORE'])\n",
    "plt.xlabel('Demand Score')\n",
    "plt.ylabel('Density')\n",
    "plt.title('KDE Plot of Demand Score')\n",
    "plt.show()\n",
    "\n",
    "# HOW TO READ:\n",
    "# Just a smooth curve to see the distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-Eat2P4j7R8R"
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# Do a K-Means clustering\n",
    "num_clusters = 4\n",
    "kmeans = KMeans(n_clusters= num_clusters, random_state=42, n_init=10)\n",
    "final_df['DEMAND_CATEGORY'] = kmeans.fit_predict(final_df[['DEMAND_SCORE']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 261
    },
    "id": "PhR3sqqk7WRp",
    "outputId": "ba275063-dd8c-4a4b-a3a6-1c45783aab8f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    252\n",
       "2    221\n",
       "1    158\n",
       "3     31\n",
       "Name: DEMAND_CATEGORY, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df['DEMAND_CATEGORY'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 584
    },
    "id": "0KxVTRgm72Wd",
    "outputId": "991011d4-3c9f-4f15-903d-0fd62ef1baf6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "for cluster in range(num_clusters):\n",
    "    plt.hist(final_df.loc[final_df['DEMAND_CATEGORY'] == cluster, 'DEMAND_SCORE'],\n",
    "              bins=20, alpha=0.5, label=f'{cluster}')\n",
    "plt.xlabel('Demand Score')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title(f'KMeans Binning with {num_clusters} Clusters')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dictionary to map cluster labels to category names\n",
    "# assign according to the cluster numbers on the chart\n",
    "cluster_mapping = {\n",
    "    0: 'Lowest', \n",
    "    2: 'Low',\n",
    "    1: 'Medium',\n",
    "    3: 'Highest'\n",
    "}\n",
    "\n",
    "# Rename the categories in your DataFrame\n",
    "final_df['DEMAND_CATEGORY'] = final_df['DEMAND_CATEGORY'].map(cluster_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 492
    },
    "id": "g_b5jYIP9bo-",
    "outputId": "8adf1f0a-ac63-47cc-8b38-5e33a9e91458"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Kuan Xin\\anaconda3\\envs\\smu_analytics\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kuan Xin\\anaconda3\\envs\\smu_analytics\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kuan Xin\\anaconda3\\envs\\smu_analytics\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kuan Xin\\anaconda3\\envs\\smu_analytics\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kuan Xin\\anaconda3\\envs\\smu_analytics\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kuan Xin\\anaconda3\\envs\\smu_analytics\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kuan Xin\\anaconda3\\envs\\smu_analytics\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kuan Xin\\anaconda3\\envs\\smu_analytics\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kuan Xin\\anaconda3\\envs\\smu_analytics\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Kuan Xin\\anaconda3\\envs\\smu_analytics\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confirmation of K Values\n",
    "# Use the Elbow Method to see if number of clusters is suitable\n",
    "# Results show K = 3 or K = 4 as suitable.\n",
    "\n",
    "\n",
    "def find_optimal_k(df, max_k=10):\n",
    "    \"\"\"Finds the optimal k using the elbow method.\"\"\"\n",
    "    inertia = []\n",
    "    \n",
    "    for k in range(1, max_k + 1):\n",
    "        kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)\n",
    "        kmeans.fit(df[['DEMAND_SCORE']])\n",
    "        inertia.append(kmeans.inertia_)\n",
    "\n",
    "    # Plot the elbow curve\n",
    "    plt.plot(range(1, max_k + 1), inertia, marker='o')\n",
    "    plt.xlabel('Number of Clusters (k)')\n",
    "    plt.ylabel('Inertia')\n",
    "    plt.title('Elbow Method for Optimal k')\n",
    "    plt.show()\n",
    "\n",
    "# Run the function to find the optimal k\n",
    "find_optimal_k(final_df)\n",
    "\n",
    "# HOW TO READ\n",
    "# Look for drop in inertia as K value increases\n",
    "# We want a reasonably high K value.\n",
    "# Around K = 3 or 4, there is a significant drop in inertia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 584
    },
    "id": "lWZvls1F_Zjs",
    "outputId": "cde99a28-4455-47f2-f823-aeba3bf6ce8d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "for cluster in ['Lowest','Low','Medium','Highest']:\n",
    "    plt.hist(final_df.loc[final_df['DEMAND_CATEGORY'] == cluster, 'DEMAND_SCORE'],\n",
    "              bins=20, alpha=0.5, label=f'{cluster}')\n",
    "plt.xlabel('Demand Score')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title(f'KMeans Binning with {num_clusters} Clusters')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>station_id</th>\n",
       "      <th>station_name</th>\n",
       "      <th>capacity</th>\n",
       "      <th>lng</th>\n",
       "      <th>lat</th>\n",
       "      <th>MEAN_WEEKLY_TRIPS</th>\n",
       "      <th>NORMALISED_CAPACITY</th>\n",
       "      <th>DEMAND_SCORE</th>\n",
       "      <th>DEMAND_CATEGORY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5772.05</td>\n",
       "      <td>Morton St &amp; Greenwich St</td>\n",
       "      <td>56</td>\n",
       "      <td>-74.008870</td>\n",
       "      <td>40.731150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.441667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Lowest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6560.15</td>\n",
       "      <td>Broadway &amp; W 38 St</td>\n",
       "      <td>68</td>\n",
       "      <td>-73.987349</td>\n",
       "      <td>40.752973</td>\n",
       "      <td>511.0</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>234.719333</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7727.07</td>\n",
       "      <td>Amsterdam Ave &amp; W 119 St</td>\n",
       "      <td>29</td>\n",
       "      <td>-73.959621</td>\n",
       "      <td>40.808625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.216667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Lowest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6569.08</td>\n",
       "      <td>W 35 St &amp; Dyer Ave</td>\n",
       "      <td>41</td>\n",
       "      <td>-73.997402</td>\n",
       "      <td>40.754692</td>\n",
       "      <td>206.0</td>\n",
       "      <td>0.316667</td>\n",
       "      <td>140.972667</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7023.04</td>\n",
       "      <td>W 59 St &amp; 10 Ave</td>\n",
       "      <td>117</td>\n",
       "      <td>-73.988038</td>\n",
       "      <td>40.770513</td>\n",
       "      <td>341.0</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>17.391000</td>\n",
       "      <td>Lowest</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>8416.10</td>\n",
       "      <td>W 186 St &amp; St Nicholas Ave</td>\n",
       "      <td>26</td>\n",
       "      <td>-73.931308</td>\n",
       "      <td>40.852253</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.191667</td>\n",
       "      <td>6.474667</td>\n",
       "      <td>Lowest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>4846.01</td>\n",
       "      <td>South St &amp; Whitehall St</td>\n",
       "      <td>55</td>\n",
       "      <td>-74.012342</td>\n",
       "      <td>40.701221</td>\n",
       "      <td>313.0</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>177.679667</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>6890.01</td>\n",
       "      <td>W 53 St &amp; 10 Ave</td>\n",
       "      <td>29</td>\n",
       "      <td>-73.990617</td>\n",
       "      <td>40.766697</td>\n",
       "      <td>424.0</td>\n",
       "      <td>0.216667</td>\n",
       "      <td>332.557333</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>6483.06</td>\n",
       "      <td>W 36 St &amp; 7 Ave</td>\n",
       "      <td>38</td>\n",
       "      <td>-73.989539</td>\n",
       "      <td>40.752149</td>\n",
       "      <td>173.0</td>\n",
       "      <td>0.291667</td>\n",
       "      <td>122.714667</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>6257.06</td>\n",
       "      <td>W 27 St &amp; 7 Ave</td>\n",
       "      <td>75</td>\n",
       "      <td>-73.993920</td>\n",
       "      <td>40.746650</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Lowest</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>662 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     station_id                station_name  capacity        lng        lat  \\\n",
       "0       5772.05    Morton St & Greenwich St        56 -74.008870  40.731150   \n",
       "1       6560.15          Broadway & W 38 St        68 -73.987349  40.752973   \n",
       "2       7727.07    Amsterdam Ave & W 119 St        29 -73.959621  40.808625   \n",
       "3       6569.08          W 35 St & Dyer Ave        41 -73.997402  40.754692   \n",
       "4       7023.04            W 59 St & 10 Ave       117 -73.988038  40.770513   \n",
       "..          ...                         ...       ...        ...        ...   \n",
       "657     8416.10  W 186 St & St Nicholas Ave        26 -73.931308  40.852253   \n",
       "658     4846.01     South St & Whitehall St        55 -74.012342  40.701221   \n",
       "659     6890.01            W 53 St & 10 Ave        29 -73.990617  40.766697   \n",
       "660     6483.06             W 36 St & 7 Ave        38 -73.989539  40.752149   \n",
       "661     6257.06             W 27 St & 7 Ave        75 -73.993920  40.746650   \n",
       "\n",
       "     MEAN_WEEKLY_TRIPS  NORMALISED_CAPACITY  DEMAND_SCORE DEMAND_CATEGORY  \n",
       "0                  0.0             0.441667      0.000000          Lowest  \n",
       "1                511.0             0.541667    234.719333             Low  \n",
       "2                  0.0             0.216667      0.000000          Lowest  \n",
       "3                206.0             0.316667    140.972667             Low  \n",
       "4                341.0             0.950000     17.391000          Lowest  \n",
       "..                 ...                  ...           ...             ...  \n",
       "657                8.0             0.191667      6.474667          Lowest  \n",
       "658              313.0             0.433333    177.679667             Low  \n",
       "659              424.0             0.216667    332.557333          Medium  \n",
       "660              173.0             0.291667    122.714667             Low  \n",
       "661                0.0             0.600000      0.000000          Lowest  \n",
       "\n",
       "[662 rows x 9 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "id": "2-yMvyVY_4Z7"
   },
   "outputs": [],
   "source": [
    "# Make sure mean weekly trips is an integer\n",
    "final_df['MEAN_WEEKLY_TRIPS'] = final_df['MEAN_WEEKLY_TRIPS'].astype(int)\n",
    "\n",
    "# Write to file\n",
    "final_df.to_csv('station_with_demand.csv', index=False)"
   ]
  }
 ],
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