StationAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Divvy allows us to see what stations are being used most. This notebook will examine the data and produce maps tracking how usage has changed over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getData(year,months=['01','02','03','04','05','06','07','08','09','10','11','12']):\n",
    "    # procedure accepts year (as string) and optional list of months (strings) for which to retreive data\n",
    "    # will return a df with the DIVVY data for that year and month(s)\n",
    "    import pandas as pd\n",
    "\n",
    "    if not isinstance(year,str):\n",
    "        year = str(year)\n",
    "\n",
    "    if not isinstance(months,list):\n",
    "        months = [months]\n",
    "\n",
    "    if int(year) >= 2020:\n",
    "        workDir = '/home/alp/Google Drive/Python/DataSets/Data_Divvy/'\n",
    "    else:\n",
    "        workDir = '/home/alp/Google Drive/Python/DataSets/Data_Divvy/pre2020/'\n",
    "\n",
    "    fileName = year + months[0] + '-divvy-tripdata.json'\n",
    "    print(fileName)\n",
    "    retData = pd.read_json(workDir+fileName,orient='index')\n",
    "\n",
    "    if len(months) > 0:\n",
    "        for month in months[1:]:\n",
    "                fileName = year + month + '-divvy-tripdata.json'\n",
    "                print(fileName)\n",
    "                newData = pd.read_json(workDir+fileName,orient='index')\n",
    "                retData = pd.concat([retData,newData])\n",
    "\n",
    "    return retData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "201901-divvy-tripdata.json\n",
      "201902-divvy-tripdata.json\n",
      "201903-divvy-tripdata.json\n",
      "201904-divvy-tripdata.json\n",
      "201905-divvy-tripdata.json\n",
      "201906-divvy-tripdata.json\n",
      "201907-divvy-tripdata.json\n",
      "201908-divvy-tripdata.json\n",
      "201909-divvy-tripdata.json\n",
      "201910-divvy-tripdata.json\n",
      "201911-divvy-tripdata.json\n",
      "201912-divvy-tripdata.json\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(3818004, 16)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data19 = getData('2019')\n",
    "data19.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data19.drop(columns = ['trip_id', 'bikeid', 'from_station_name',\n",
    "       'to_station_name', 'gender', 'birthyear','tripduration'],inplace=True)\n",
    "data19['tripduration'] = data19['end_time'] - data19['start_time']\n",
    "data19.drop(data19[data19['tripduration'] < '0:00:00'].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data19.rename(columns={'from_station_id':'start_station_id','to_station_id':'end_station_id'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "202001-divvy-tripdata.json\n",
      "202002-divvy-tripdata.json\n",
      "202003-divvy-tripdata.json\n",
      "202004-divvy-tripdata.json\n",
      "202005-divvy-tripdata.json\n",
      "202006-divvy-tripdata.json\n",
      "202007-divvy-tripdata.json\n",
      "202008-divvy-tripdata.json\n",
      "202009-divvy-tripdata.json\n",
      "202010-divvy-tripdata.json\n",
      "202011-divvy-tripdata.json\n",
      "202012-divvy-tripdata.json\n"
     ]
    }
   ],
   "source": [
    "data20 = getData('2020')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data20.drop(columns=['ride_id', 'rideable_type', 'start_station_name', 'end_station_name'],inplace=True)\n",
    "data20.rename(columns={'started_at':'start_time','ended_at':'end_time','member_casual':'usertype'},inplace=True)\n",
    "data20['tripduration'] = data20['end_time'] - data20['start_time']\n",
    "data20.drop(data20[data20['tripduration'] < '0:00:00'].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "202101-divvy-tripdata.json\n",
      "202102-divvy-tripdata.json\n",
      "202103-divvy-tripdata.json\n",
      "202104-divvy-tripdata.json\n",
      "202105-divvy-tripdata.json\n",
      "202106-divvy-tripdata.json\n",
      "202107-divvy-tripdata.json\n",
      "202108-divvy-tripdata.json\n",
      "202109-divvy-tripdata.json\n",
      "202110-divvy-tripdata.json\n",
      "202111-divvy-tripdata.json\n",
      "202112-divvy-tripdata.json\n"
     ]
    }
   ],
   "source": [
    "data21 = getData('2021')\n",
    "data21.drop(columns=['ride_id', 'start_station_name', 'end_station_name'],inplace=True) # 'rideable_type',\n",
    "data21.rename(columns={'started_at':'start_time','ended_at':'end_time','member_casual':'usertype'},inplace=True)\n",
    "data21['tripduration'] = data21['end_time'] - data21['start_time']\n",
    "data21.drop(data21[data21['tripduration'] < '0:00:00'].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 12883947 entries, 0 to 247539\n",
      "Data columns (total 10 columns):\n",
      " #   Column            Dtype          \n",
      "---  ------            -----          \n",
      " 0   start_time        datetime64[ns] \n",
      " 1   end_time          datetime64[ns] \n",
      " 2   start_station_id  object         \n",
      " 3   end_station_id    object         \n",
      " 4   usertype          object         \n",
      " 5   start_lat         float64        \n",
      " 6   start_lng         float64        \n",
      " 7   end_lat           float64        \n",
      " 8   end_lng           float64        \n",
      " 9   tripduration      timedelta64[ns]\n",
      "dtypes: datetime64[ns](2), float64(4), object(3), timedelta64[ns](1)\n",
      "memory usage: 1.1+ GB\n"
     ]
    }
   ],
   "source": [
    "bigData = pd.concat([data19,data20,data21],axis=0)\n",
    "bigData['usertype'].replace(['Subscriber','Customer'],['member','casual'],inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data21['usertype'].replace(['Subscriber','Customer'],['member','casual'],inplace = True)\n",
    "data21['month'] = pd.DatetimeIndex(data21['start_time']).month\n",
    "data21['year'] = pd.DatetimeIndex(data21['start_time']).year\n",
    "# DELETE THIS CELL (Test for mapping problem in other tab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "bigData['month'] = pd.DatetimeIndex(bigData['start_time']).month\n",
    "bigData['year'] = pd.DatetimeIndex(bigData['start_time']).year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the busiest stations in 2019?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15 entries, 0 to 14\n",
      "Data columns (total 2 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   index             15 non-null     object\n",
      " 1   start_station_id  15 non-null     int64 \n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 368.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "#DELETE THIS CELL (test for mapping problem)\n",
    "busy_stations = data21['start_station_id'].value_counts().head(15).to_frame().reset_index()\n",
    "busy_stations.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15 entries, 0 to 14\n",
      "Data columns (total 2 columns):\n",
      " #   Column            Non-Null Count  Dtype\n",
      "---  ------            --------------  -----\n",
      " 0   index             15 non-null     int64\n",
      " 1   start_station_id  15 non-null     int64\n",
      "dtypes: int64(2)\n",
      "memory usage: 368.0 bytes\n"
     ]
    }
   ],
   "source": [
    "temp = bigData[bigData['year'] == 2019]\n",
    "busy_stations = temp['start_station_id'].value_counts().head(15).to_frame().reset_index()\n",
    "busy_stations.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>13022</td>\n",
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      "text/plain": [
       "   start_station_id  count\n",
       "0             13022  82698\n",
       "1            LF-005  47914\n",
       "2             13300  46139\n",
       "3             13042  44343\n",
       "4      TA1308000050  43602\n",
       "5             13008  42220\n",
       "6      TA1307000039  41212\n",
       "7      KA1504000135  37684\n",
       "8      TA1308000001  36834\n",
       "9      KA1503000043  33577\n",
       "10            13179  33380\n",
       "11            13146  32898\n",
       "12     TA1307000117  32778\n",
       "13            13045  32149\n",
       "14     TA1306000012  31414"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "busy_stations.rename(columns={'start_station_id':'count','index':'start_station_id'},inplace=True)\n",
    "busy_stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_167548/367422790.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  station_list.drop(columns=['start_time', 'end_time', 'usertype','tripduration','month','year'],inplace=True)\n"
     ]
    }
   ],
   "source": [
    "# DELTE CELL \n",
    "station_list = data21.drop_duplicates(subset='start_station_id')\n",
    "station_list.drop(columns=['start_time', 'end_time', 'usertype','tripduration','month','year'],inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_8047/3172797893.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  station_list.drop(columns=['start_time', 'end_time', 'usertype','tripduration','month','year'],inplace=True)\n"
     ]
    }
   ],
   "source": [
    "station_list = bigData.drop_duplicates(subset='start_station_id')\n",
    "station_list.drop(columns=['start_time', 'end_time', 'usertype','tripduration','month','year'],inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 616 entries, 0 to 844636\n",
      "Data columns (total 6 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   start_station_id  616 non-null    int64  \n",
      " 1   end_station_id    616 non-null    int64  \n",
      " 2   start_lat         609 non-null    float64\n",
      " 3   start_lng         609 non-null    float64\n",
      " 4   end_lat           609 non-null    float64\n",
      " 5   end_lng           609 non-null    float64\n",
      "dtypes: float64(4), int64(2)\n",
      "memory usage: 33.7 KB\n"
     ]
    }
   ],
   "source": [
    "station_list.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "bigData = data21\n",
    "df = bigData[bigData['rideable_type']=='classic_bike']\n",
    "col = 'start_station_id'\n",
    "temp = df\n",
    "busy_stations = temp[col].value_counts().head(20).to_frame().reset_index()\n",
    "busy_stations.rename(columns={col:'count','index':col},inplace=True)\n",
    "bs = busy_stations.merge(station_list,how='left',left_on=col,right_on=['start_station_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 20 entries, 0 to 19\n",
      "Data columns (total 8 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   start_station_id  20 non-null     object \n",
      " 1   count             20 non-null     int64  \n",
      " 2   rideable_type     20 non-null     object \n",
      " 3   end_station_id    20 non-null     object \n",
      " 4   start_lat         20 non-null     float64\n",
      " 5   start_lng         20 non-null     float64\n",
      " 6   end_lat           20 non-null     float64\n",
      " 7   end_lng           20 non-null     float64\n",
      "dtypes: float64(4), int64(1), object(3)\n",
      "memory usage: 1.4+ KB\n"
     ]
    }
   ],
   "source": [
    "bs.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "map = busy_stations.merge(station_list,how='left',on='start_station_id')\n",
    "map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "                initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
       "            &lt;style&gt;\n",
       "                #map_596e72e9abd9f6559dc74c506bd9a24f {\n",
       "                    position: relative;\n",
       "                    width: 100.0%;\n",
       "                    height: 100.0%;\n",
       "                    left: 0.0%;\n",
       "                    top: 0.0%;\n",
       "                }\n",
       "            &lt;/style&gt;\n",
       "        \n",
       "&lt;/head&gt;\n",
       "&lt;body&gt;    \n",
       "    \n",
       "             &lt;h3 align=&quot;center&quot; style=&quot;font-size:16px&quot;&gt;&lt;b&gt;Most popular start (green) and end (red) stations in 2019&lt;/b&gt;&lt;/h3&gt;\n",
       "             \n",
       "    \n",
       "            &lt;div class=&quot;folium-map&quot; id=&quot;map_596e72e9abd9f6559dc74c506bd9a24f&quot; &gt;&lt;/div&gt;\n",
       "        \n",
       "&lt;/body&gt;\n",
       "&lt;script&gt;    \n",
       "    \n",
       "            var map_596e72e9abd9f6559dc74c506bd9a24f = L.map(\n",
       "                &quot;map_596e72e9abd9f6559dc74c506bd9a24f&quot;,\n",
       "                {\n",
       "                    center: [41.903823824095, -87.631090895505],\n",
       "                    crs: L.CRS.EPSG3857,\n",
       "                    zoom: 11,\n",
       "                    zoomControl: true,\n",
       "                    preferCanvas: false,\n",
       "                }\n",
       "            );\n",
       "\n",
       "            \n",
       "\n",
       "        \n",
       "    \n",
       "            var tile_layer_038f46c9840e6c66f05253caf78ab120 = L.tileLayer(\n",
       "                &quot;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,\n",
       "                {&quot;attribution&quot;: &quot;Data by \\u0026copy; \\u003ca href=\\&quot;http://openstreetmap.org\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca href=\\&quot;http://www.openstreetmap.org/copyright\\&quot;\\u003eODbL\\u003c/a\\u003e.&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
       "            ).addTo(map_596e72e9abd9f6559dc74c506bd9a24f);\n",
       "        \n",
       "    \n",
       "            var circle_marker_7d65199dd247d696ada9c08df251bc56 = L.circleMarker(\n",
       "                [41.892278, -87.612043],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;green&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;green&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 10.1976, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_596e72e9abd9f6559dc74c506bd9a24f);\n",
       "        \n",
       "    \n",
       "            var circle_marker_9f582146ae8f2f9346478b5c65176c03 = L.circleMarker(\n",
       "                [41.911722, -87.62680399999999],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;green&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;green&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 6.8342, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_596e72e9abd9f6559dc74c506bd9a24f);\n",
       "        \n",
       "    \n",
       "            var circle_marker_150855bfdc07e89d20391809bcea4d52 = L.circleMarker(\n",
       "                [41.912095, -87.6348903333],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;green&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;green&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 6.0122, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_596e72e9abd9f6559dc74c506bd9a24f);\n",
       "        \n",
       "    \n",
       "            var circle_marker_3c481be81ae5173e94cdbc89be481dc5 = L.circleMarker(\n",
       "                [41.902973, -87.63128],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;green&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;green&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 5.8702, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_596e72e9abd9f6559dc74c506bd9a24f);\n",
       "        \n",
       "    \n",
       "            var circle_marker_a76d694f18dfa3d453d945ea9a48a05c = L.circleMarker(\n",
       "                [41.9010013333, -87.6237325],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;green&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;green&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 5.729, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_596e72e9abd9f6559dc74c506bd9a24f);\n",
       "        \n",
       "    \n",
       "            var circle_marker_15632393223852e9cec3584f4c9ba745 = L.circleMarker(\n",
       "                [41.880958, -87.616743],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;green&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;green&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 5.5, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_596e72e9abd9f6559dc74c506bd9a24f);\n",
       "        \n",
       "    \n",
       "            var circle_marker_1df29328085dc24cb2d5c7cc3827bf65 = L.circleMarker(\n",
       "                [41.926277, -87.630834],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;green&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;green&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 5.3988, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_596e72e9abd9f6559dc74c506bd9a24f);\n",
       "        \n",
       "    \n",
       "            var circle_marker_be3b0c9c66848e8b384b6984a4d15c9e = L.circleMarker(\n",
       "                [41.9029363333, -87.634573],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;green&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;green&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 5.2738, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_596e72e9abd9f6559dc74c506bd9a24f);\n",
       "        \n",
       "    \n",
       "            var circle_marker_886841f41ac7e7a434d7820180e52873 = L.circleMarker(\n",
       "                [41.881032, -87.624084],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;green&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;green&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 4.8474, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_596e72e9abd9f6559dc74c506bd9a24f);\n",
       "        \n",
       "    \n",
       "            var circle_marker_8d7cf512283fd1877b4abafdf1d7fe19 = L.circleMarker(\n",
       "                [41.9156916667, -87.634593],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;green&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;green&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 4.707, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_596e72e9abd9f6559dc74c506bd9a24f);\n",
       "        \n",
       "    \n",
       "            var circle_marker_8785f652d975f207d49b8aa88f591d31 = L.circleMarker(\n",
       "                [41.8891768326, -87.6385057718],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;green&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;green&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 4.648, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_596e72e9abd9f6559dc74c506bd9a24f);\n",
       "        \n",
       "    \n",
       "            var circle_marker_96341a8102e72ca70bf071aa1bf5d2e1 = L.circleMarker(\n",
       "                [41.918306, -87.636282],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;green&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;green&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 4.6062, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_596e72e9abd9f6559dc74c506bd9a24f);\n",
       "        \n",
       "    \n",
       "            var circle_marker_c527073c112201a2ee487e92e787addb = L.circleMarker(\n",
       "                [41.937582316, -87.644097805],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;green&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;green&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 4.4308, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_596e72e9abd9f6559dc74c506bd9a24f);\n",
       "        \n",
       "    \n",
       "            var circle_marker_ea876c6ed9fcf5feea7b0c65ce6606d4 = L.circleMarker(\n",
       "                [41.891466, -87.626761],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;green&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;green&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 4.3842, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_596e72e9abd9f6559dc74c506bd9a24f);\n",
       "        \n",
       "    \n",
       "            var circle_marker_814bef7f9c6a7c9df3634232f73c7a39 = L.circleMarker(\n",
       "                [41.8941976667, -87.6294278333],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;green&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;green&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 4.2762, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_596e72e9abd9f6559dc74c506bd9a24f);\n",
       "        \n",
       "    \n",
       "            var circle_marker_e947d188b48d9b8a021b82425768cbe9 = L.circleMarker(\n",
       "                [41.894722, -87.634362],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;green&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;green&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 4.188, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_596e72e9abd9f6559dc74c506bd9a24f);\n",
       "        \n",
       "    \n",
       "            var circle_marker_7be7864f831cd966c938f5427f1ef993 = L.circleMarker(\n",
       "                [41.9218428333, -87.6440661667],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;green&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;green&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 4.0212, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_596e72e9abd9f6559dc74c506bd9a24f);\n",
       "        \n",
       "    \n",
       "            var circle_marker_ee7d3d124f8e9e4470cf274ad07c9521 = L.circleMarker(\n",
       "                [41.94018, -87.65304],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;green&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;green&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 3.9396, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_596e72e9abd9f6559dc74c506bd9a24f);\n",
       "        \n",
       "    \n",
       "            var circle_marker_95017b112a914b7238178c8fb6b2b728 = L.circleMarker(\n",
       "                [41.8941505, -87.6226575],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;green&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;green&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 3.92, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_596e72e9abd9f6559dc74c506bd9a24f);\n",
       "        \n",
       "    \n",
       "            var circle_marker_751b1a44c2fa30b51011d1438489993f = L.circleMarker(\n",
       "                [41.867888, -87.623041],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;green&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: true, &quot;fillColor&quot;: &quot;green&quot;, &quot;fillOpacity&quot;: 0.2, &quot;fillRule&quot;: &quot;evenodd&quot;, &quot;lineCap&quot;: &quot;round&quot;, &quot;lineJoin&quot;: &quot;round&quot;, &quot;opacity&quot;: 1.0, &quot;radius&quot;: 3.7922, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_596e72e9abd9f6559dc74c506bd9a24f);\n",
       "        \n",
       "&lt;/script&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7f4e19193d30>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import folium\n",
    "\n",
    "m1 = folium.Map(location = [map['start_lat'].mean(),map['start_lng'].mean()],\n",
    "                tiles='OpenStreetMap',zoom_start = 11)\n",
    "\n",
    "title = 'Most popular start (green) and end (red) stations in 2019'\n",
    "title_html = '''\n",
    "             <h3 align=\"center\" style=\"font-size:16px\"><b>{}</b></h3>\n",
    "             '''.format(title)\n",
    "m1.get_root().html.add_child(folium.Element(title_html)) \n",
    "\n",
    "for index, row in map.iterrows():\n",
    "    lat1, lon1 = row['start_lat'],row['start_lng']\n",
    "    folium.CircleMarker(location=[lat1,lon1],\n",
    "        radius = row['count']/5000,\n",
    "        color = 'green',\n",
    "        fill = True\n",
    "                ).add_to(m1)\n",
    "m1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "def createMapdf(year,col,df):\n",
    "    temp = df[df['year'] == year]\n",
    "    busy_stations = temp[col].value_counts().head(20).to_frame().reset_index()\n",
    "    busy_stations.rename(columns={col:'count','index':col},inplace=True)\n",
    "    return busy_stations.merge(station_list,how='left',left_on=col,right_on=['start_station_id'])\n",
    "    # because I created the station_list by removing dups of the start_station_id, when merging on it \n",
    "    # with 'end_station_id' would return multiple rows. So we will always match on the start_station_id to avoid dups.\n",
    "\n",
    "def mapBusyStations(year,df):\n",
    "    start_stations = createMapdf(year,'start_station_id',df)\n",
    "    end_stations = createMapdf(year,'end_station_id',df)\n",
    "\n",
    "    m1 = folium.Map(location = [start_stations['start_lat'].mean(),start_stations['start_lng'].mean()],\n",
    "                tiles='cartodbpositron',zoom_start = 11)\n",
    "\n",
    "    title = '20 most popular start (green) and end (red) stations in '+str(year)\n",
    "    title_html = '''\n",
    "             <h3 align=\"center\" style=\"font-size:16px\"><b>{}</b></h3>\n",
    "             '''.format(title)\n",
    "    m1.get_root().html.add_child(folium.Element(title_html)) \n",
    "\n",
    "    for index, row in end_stations.iterrows():   \n",
    "        folium.CircleMarker(location=[row['end_lat'],row['end_lng']],\n",
    "            radius = row['count']/5000,\n",
    "            color = 'red',\n",
    "            tooltip=str(row['count']),\n",
    "            fill = True\n",
    "                    ).add_to(m1)\n",
    "\n",
    "    for index, row in start_stations.iterrows():\n",
    "        folium.CircleMarker(location=[row['start_lat'],row['start_lng']],\n",
    "            radius = row['count']/5000,\n",
    "            color = 'green',\n",
    "            tooltip=str(row['count']),\n",
    "            fill = True\n",
    "                    ).add_to(m1)\n",
    "\n",
    "    return m1\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019member.html\n",
      "2019casual.html\n",
      "2020member.html\n",
      "2020casual.html\n",
      "2021member.html\n",
      "2021casual.html\n"
     ]
    }
   ],
   "source": [
    "for i in range (2019,2022):\n",
    "    for j in ['member','casual']:\n",
    "        fn = str(i)+j+'.html'\n",
    "        mapBusyStations(i,bigData[bigData['usertype']==j]).save(fn)\n",
    "        print(fn)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pctLongRides(cutoff,mTemp,cTemp):\n",
    "    mLong = mTemp[(mTemp['tripduration'] > pd.Timedelta(cutoff  ,unit='h') ) &\n",
    "                    (mTemp['tripduration'] < pd.Timedelta(cutoff+1,unit='h') ) ]\n",
    "    mStartFin = mLong.loc[ (mLong['start_lat'] == mLong['end_lat']) & (mLong['start_lng'] == mLong['end_lng']) ]\n",
    "\n",
    "    cLong = cTemp[(cTemp['tripduration'] > pd.Timedelta(cutoff  ,unit='h') ) &\n",
    "                    (cTemp['tripduration'] < pd.Timedelta(cutoff+1,unit='h') ) ]\n",
    "    cStartFin = cLong.loc[ (cLong['start_lat'] == cLong['end_lat']) & (cLong['start_lng'] == cLong['end_lng']) ]\n",
    "\n",
    "    return len(mLong), round(len(mStartFin)/len(mLong),3), len(cLong), round(len(cStartFin)/len(cLong),3)\n",
    "\n",
    "janfeb = bigData[(bigData['month']==1) | (bigData['month']==2)]\n",
    "\n",
    "mTemp = janfeb[janfeb['usertype']=='member']\n",
    "cTemp = janfeb[janfeb['usertype']=='casual']\n",
    "\n",
    "hours = []\n",
    "NmLong, pctMLong, NcLong, pctCLong  = [],[],[],[]\n",
    "for i in range(2,24):\n",
    "    a,b,c,d = pctLongRides(i,mTemp,cTemp)\n",
    "    hours.append(i)\n",
    "    NmLong.append(a)\n",
    "    pctMLong.append(b*100)\n",
    "    NcLong.append(c)\n",
    "    pctCLong.append(d*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1224x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare = pd.DataFrame({\n",
    "    '# Member'  : NmLong,\n",
    "    '% Member': pctMLong,\n",
    "    '# Casual'  : NcLong,\n",
    "    '% Casual': pctCLong\n",
    "})\n",
    "\n",
    "#ax = plt.gca()\n",
    "ax = compare[['# Member','# Casual']].plot(kind = 'bar',figsize=(17,10))\n",
    "plt.ylabel('Number of Long Rides (log scale)')\n",
    "plt.yscale('log')\n",
    "#compare[['% Member','% Casual']].plot(kind = 'line',secondary_y = True,ax=ax)\n",
    "#plt.ylabel('Percent of long rides')\n",
    "\n",
    "ax.set_xticklabels(hours)\n",
    "plt.xticks(rotation=0,horizontalalignment = 'center')\n",
    "plt.xlabel('Ride length (hours)')\n",
    "title = 'Comparison of number / percent of long rides '\n",
    "plt.title(title)\n",
    "\n",
    "#ax.grid(axis='y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 12883947 entries, 0 to 247539\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Dtype          \n",
      "---  ------            -----          \n",
      " 0   start_time        datetime64[ns] \n",
      " 1   end_time          datetime64[ns] \n",
      " 2   start_station_id  object         \n",
      " 3   end_station_id    object         \n",
      " 4   usertype          object         \n",
      " 5   start_lat         float64        \n",
      " 6   start_lng         float64        \n",
      " 7   end_lat           float64        \n",
      " 8   end_lng           float64        \n",
      " 9   tripduration      timedelta64[ns]\n",
      " 10  month             int64          \n",
      " 11  year              int64          \n",
      "dtypes: datetime64[ns](2), float64(4), int64(2), object(3), timedelta64[ns](1)\n",
      "memory usage: 1.2+ GB\n"
     ]
    }
   ],
   "source": [
    "bigData.info()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.4 64-bit",
   "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": "3.10.6"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}