SimplifiedDivvyAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Questions for Divvy Data:\n",
    "\n",
    "Points:\n",
    "(1) Covid allegedly affected the number of bicyclists, because public transit was restricted and/or seen as dangerous. Many people tried to buy bicycles, but because of supply chain issues, many dealers couldn't get enough stock to satisify demand, so people may have turned to DIVVY as a stopgap measure.\n",
    "\n",
    "(2) Covid also drastically reducted tourism throughout the summer of 2020, especially in Chicago, which had strong public health directives requiring masking.\n",
    "\n",
    "Questions:\n",
    "A) From 2019 to 2020, did Divvy use drop (because of business shut-downs) or increase (because people wanted to bike rather than public transit)? \n",
    "\n",
    "A1) Did the path of bike usage shift? (How to measure this? Aggregrate where bikes went from given stations?)\n",
    "\n",
    "B) Did the proportion of \"casual\" (=non-member) users drop (#2) in 2020? Did it pick up in 2021?"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "In 2020, Divvy changed the sort of information it collected. For this analysis, let's drop the columns we don't care about and combine the two DFs.\n",
    "\n",
    "Eliminate: 2019: ride_id, bikeid, from_station_id, from_station_name, to_station_id, to_station_name, gender, birthyear\n",
    "    2020: ride_id, ridable_type, start_station_name, start_station_id, end_station_name, end_station_id\n",
    "\n",
    "Rename: 2020: started_at -> start_time, ended_at -> end_time\n",
    "\n",
    "Finally, for some rows, the end time is before the start time. This results in negative \"tripduration\" values. These errors seem to be when bikes are removed or added to the system (the station ID fields are often blank or the start and end locations are identical). In any case, they represent a very small proportion of the total number of rows, so we will just drop them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 13 entries, 384478 to 384497\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count  Dtype          \n",
      "---  ------        --------------  -----          \n",
      " 0   start_time    13 non-null     datetime64[ns] \n",
      " 1   end_time      13 non-null     datetime64[ns] \n",
      " 2   usertype      13 non-null     object         \n",
      " 3   start_lat     13 non-null     float64        \n",
      " 4   start_lng     13 non-null     float64        \n",
      " 5   end_lat       13 non-null     float64        \n",
      " 6   end_lng       13 non-null     float64        \n",
      " 7   tripduration  13 non-null     timedelta64[ns]\n",
      "dtypes: datetime64[ns](2), float64(4), object(1), timedelta64[ns](1)\n",
      "memory usage: 936.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "data19.drop(columns = ['trip_id', 'bikeid', 'from_station_id', 'from_station_name', 'to_station_id',\n",
    "       'to_station_name', 'gender', 'birthyear','tripduration'],inplace=True)\n",
    "data19['tripduration'] = data19['end_time'] - data19['start_time']\n",
    "\n",
    "prob = data19[data19['tripduration'] < '0:00:00']\n",
    "prob.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before: 3817965\n",
      "after:  3817965\n",
      "dropped:  0 0.0\n"
     ]
    }
   ],
   "source": [
    "b = len(data19)\n",
    "print('before:',len(data19))\n",
    "data19.drop(data19[data19['tripduration'] < '0:00:00'].index, inplace=True)\n",
    "print('after: ',len(data19))\n",
    "print('dropped: ',b-len(data19),(b-len(data19))/b*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(3541683, 13)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data20 = getData('2020')\n",
    "data20.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_lat</th>\n",
       "      <th>start_lng</th>\n",
       "      <th>end_lat</th>\n",
       "      <th>end_lng</th>\n",
       "      <th>tripduration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10665.000000</td>\n",
       "      <td>10665.000000</td>\n",
       "      <td>10639.000000</td>\n",
       "      <td>10639.000000</td>\n",
       "      <td>10665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>41.905145</td>\n",
       "      <td>-87.640899</td>\n",
       "      <td>41.905472</td>\n",
       "      <td>-87.642753</td>\n",
       "      <td>-1 days +07:05:44.387998125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.038737</td>\n",
       "      <td>0.021870</td>\n",
       "      <td>0.039757</td>\n",
       "      <td>0.022618</td>\n",
       "      <td>3 days 16:05:51.065799012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>41.691300</td>\n",
       "      <td>-87.774446</td>\n",
       "      <td>41.700000</td>\n",
       "      <td>-87.774446</td>\n",
       "      <td>-21 days +19:50:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>41.884241</td>\n",
       "      <td>-87.652310</td>\n",
       "      <td>41.883668</td>\n",
       "      <td>-87.654020</td>\n",
       "      <td>-1 days +23:59:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>41.900363</td>\n",
       "      <td>-87.636829</td>\n",
       "      <td>41.899930</td>\n",
       "      <td>-87.638973</td>\n",
       "      <td>-1 days +23:59:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>41.929499</td>\n",
       "      <td>-87.626761</td>\n",
       "      <td>41.930585</td>\n",
       "      <td>-87.627691</td>\n",
       "      <td>-1 days +23:59:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>42.064313</td>\n",
       "      <td>-87.552812</td>\n",
       "      <td>42.064854</td>\n",
       "      <td>-87.549386</td>\n",
       "      <td>-1 days +23:59:59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          start_lat     start_lng       end_lat       end_lng  \\\n",
       "count  10665.000000  10665.000000  10639.000000  10639.000000   \n",
       "mean      41.905145    -87.640899     41.905472    -87.642753   \n",
       "std        0.038737      0.021870      0.039757      0.022618   \n",
       "min       41.691300    -87.774446     41.700000    -87.774446   \n",
       "25%       41.884241    -87.652310     41.883668    -87.654020   \n",
       "50%       41.900363    -87.636829     41.899930    -87.638973   \n",
       "75%       41.929499    -87.626761     41.930585    -87.627691   \n",
       "max       42.064313    -87.552812     42.064854    -87.549386   \n",
       "\n",
       "                      tripduration  \n",
       "count                        10665  \n",
       "mean   -1 days +07:05:44.387998125  \n",
       "std      3 days 16:05:51.065799012  \n",
       "min             -21 days +19:50:02  \n",
       "25%              -1 days +23:59:35  \n",
       "50%              -1 days +23:59:47  \n",
       "75%              -1 days +23:59:54  \n",
       "max              -1 days +23:59:59  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data20.drop(columns=['ride_id', 'rideable_type', 'start_station_name', 'start_station_id', 'end_station_name',\n",
    "       'end_station_id'],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",
    "prob = data20[data20['tripduration'] < '0:00:00']\n",
    "prob.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before: 3541683\n",
      "after:  3472163\n",
      "dropped =  69520 1.9629085945862461\n"
     ]
    }
   ],
   "source": [
    "b = len(data20)\n",
    "\n",
    "print('before:',b)\n",
    "data20.drop(data20[data20['tripduration'] < '0:00:00'].index, inplace=True)\n",
    "print('after: ',len(data20))\n",
    "print('dropped = ',b-len(data20),(b-len(data20))/b*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the same boolean mask, the prob df has 10665 records but when I drop the problem records, it gets rid of 69520 rows, not the expected 10665.\n",
    "\n",
    "WTF?\n",
    "\n",
    "Still, only 2% of the total, so it probaby won't affect the interpretation much. But 2020 has a lot more \"problem\" rows than 19 or 21.\n",
    "\n",
    "Could this be because of the ebikes? (Need to chart WHEN the problem rows appear and the TYPE of bike used -- question for further reserach.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(5595063, 13)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data21 = getData('2021')\n",
    "data21.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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_lat</th>\n",
       "      <th>start_lng</th>\n",
       "      <th>end_lat</th>\n",
       "      <th>end_lng</th>\n",
       "      <th>tripduration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>147.000000</td>\n",
       "      <td>147.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>41.908401</td>\n",
       "      <td>-87.647183</td>\n",
       "      <td>41.907911</td>\n",
       "      <td>-87.649534</td>\n",
       "      <td>-1 days +23:44:29.564625851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.041060</td>\n",
       "      <td>0.021769</td>\n",
       "      <td>0.042781</td>\n",
       "      <td>0.025698</td>\n",
       "      <td>0 days 00:20:31.832180729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>41.684961</td>\n",
       "      <td>-87.720000</td>\n",
       "      <td>41.670000</td>\n",
       "      <td>-87.770000</td>\n",
       "      <td>-1 days +23:01:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>41.891023</td>\n",
       "      <td>-87.654460</td>\n",
       "      <td>41.894498</td>\n",
       "      <td>-87.658404</td>\n",
       "      <td>-1 days +23:23:32.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>41.912616</td>\n",
       "      <td>-87.648830</td>\n",
       "      <td>41.919909</td>\n",
       "      <td>-87.648830</td>\n",
       "      <td>-1 days +23:58:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>41.921094</td>\n",
       "      <td>-87.633772</td>\n",
       "      <td>41.925592</td>\n",
       "      <td>-87.633693</td>\n",
       "      <td>-1 days +23:59:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>42.040000</td>\n",
       "      <td>-87.594747</td>\n",
       "      <td>42.039713</td>\n",
       "      <td>-87.570000</td>\n",
       "      <td>-1 days +23:59:59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        start_lat   start_lng     end_lat     end_lng  \\\n",
       "count  147.000000  147.000000  146.000000  146.000000   \n",
       "mean    41.908401  -87.647183   41.907911  -87.649534   \n",
       "std      0.041060    0.021769    0.042781    0.025698   \n",
       "min     41.684961  -87.720000   41.670000  -87.770000   \n",
       "25%     41.891023  -87.654460   41.894498  -87.658404   \n",
       "50%     41.912616  -87.648830   41.919909  -87.648830   \n",
       "75%     41.921094  -87.633772   41.925592  -87.633693   \n",
       "max     42.040000  -87.594747   42.039713  -87.570000   \n",
       "\n",
       "                      tripduration  \n",
       "count                          147  \n",
       "mean   -1 days +23:44:29.564625851  \n",
       "std      0 days 00:20:31.832180729  \n",
       "min              -1 days +23:01:58  \n",
       "25%       -1 days +23:23:32.500000  \n",
       "50%              -1 days +23:58:50  \n",
       "75%              -1 days +23:59:52  \n",
       "max              -1 days +23:59:59  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data21.drop(columns=['ride_id', 'rideable_type', 'start_station_name', 'start_station_id', 'end_station_name',\n",
    "       'end_station_id'],inplace=True)\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",
    "prob = data21[data21['tripduration'] < '0:00:00']\n",
    "prob.describe()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before: 5595063\n",
      "after:  5593819\n",
      "dropped =  1244 0.022233887268114764\n"
     ]
    }
   ],
   "source": [
    "b = len(data21)\n",
    "\n",
    "print('before:',b)\n",
    "data21.drop(data21[data21['tripduration'] < '0:00:00'].index, inplace=True)\n",
    "print('after: ',len(data21))\n",
    "print('dropped = ',b-len(data21),(b-len(data21))/b*100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "change from 19 to 20  -0.09057233369085363\n",
      "change from 20 to 21  0.611047350023602\n"
     ]
    }
   ],
   "source": [
    "print('change from 19 to 20 ',(len(data20) - len(data19))/len(data19) )\n",
    "print('change from 20 to 21 ',(len(data21) - len(data20))/len(data20) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 12883947 entries, 0 to 247539\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Dtype          \n",
      "---  ------        -----          \n",
      " 0   start_time    datetime64[ns] \n",
      " 1   end_time      datetime64[ns] \n",
      " 2   usertype      object         \n",
      " 3   start_lat     float64        \n",
      " 4   start_lng     float64        \n",
      " 5   end_lat       float64        \n",
      " 6   end_lng       float64        \n",
      " 7   tripduration  timedelta64[ns]\n",
      "dtypes: datetime64[ns](2), float64(4), object(1), timedelta64[ns](1)\n",
      "memory usage: 884.7+ MB\n"
     ]
    }
   ],
   "source": [
    "bigData = pd.concat([data19,data20,data21],axis=0)\n",
    "bigData.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "bigData['month'] = pd.DatetimeIndex(bigData['start_time']).month\n",
    "bigData['year'] = pd.DatetimeIndex(bigData['start_time']).year\n",
    "# I added these to facilitate doing a pivot table based on year and month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>year</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>103214</td>\n",
       "      <td>140778</td>\n",
       "      <td>96802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>96146</td>\n",
       "      <td>136326</td>\n",
       "      <td>49603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>165390</td>\n",
       "      <td>141147</td>\n",
       "      <td>228434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>264690</td>\n",
       "      <td>83250</td>\n",
       "      <td>337139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>366456</td>\n",
       "      <td>195874</td>\n",
       "      <td>531502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>473985</td>\n",
       "      <td>335568</td>\n",
       "      <td>729448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>555213</td>\n",
       "      <td>541377</td>\n",
       "      <td>822263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>588101</td>\n",
       "      <td>611788</td>\n",
       "      <td>804205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>491935</td>\n",
       "      <td>522988</td>\n",
       "      <td>756000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>371180</td>\n",
       "      <td>380472</td>\n",
       "      <td>631085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>177013</td>\n",
       "      <td>253786</td>\n",
       "      <td>359864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>154926</td>\n",
       "      <td>128809</td>\n",
       "      <td>247474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year     2019    2020    2021\n",
       "month                        \n",
       "1      103214  140778   96802\n",
       "2       96146  136326   49603\n",
       "3      165390  141147  228434\n",
       "4      264690   83250  337139\n",
       "5      366456  195874  531502\n",
       "6      473985  335568  729448\n",
       "7      555213  541377  822263\n",
       "8      588101  611788  804205\n",
       "9      491935  522988  756000\n",
       "10     371180  380472  631085\n",
       "11     177013  253786  359864\n",
       "12     154926  128809  247474"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot1920 = bigData.pivot_table(index='month',columns='year',values='start_lat',aggfunc = 'count')\n",
    "pivot1920"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 792x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pivot1920.plot(kind = 'bar',figsize=(11,9))\n",
    "plt.xticks(rotation=0,horizontalalignment = 'center')\n",
    "plt.ylabel('Number of Rides')\n",
    "plt.xlabel('Month of year')\n",
    "plt.title('Number of Divvy rides per month in 2019, 2020, and 2021')\n",
    "plt.grid(axis='y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "bigData['usertype'].replace(['Subscriber','Customer'],['member','casual'],inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2019</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2020</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2021</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usertype</th>\n",
       "      <th>casual</th>\n",
       "      <th>member</th>\n",
       "      <th>casual</th>\n",
       "      <th>member</th>\n",
       "      <th>casual</th>\n",
       "      <th>member</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4602</td>\n",
       "      <td>98670</td>\n",
       "      <td>7626</td>\n",
       "      <td>133152</td>\n",
       "      <td>18113</td>\n",
       "      <td>78689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2638</td>\n",
       "      <td>93548</td>\n",
       "      <td>12593</td>\n",
       "      <td>123733</td>\n",
       "      <td>10129</td>\n",
       "      <td>39474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15923</td>\n",
       "      <td>149688</td>\n",
       "      <td>27294</td>\n",
       "      <td>113853</td>\n",
       "      <td>84010</td>\n",
       "      <td>144424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47744</td>\n",
       "      <td>217566</td>\n",
       "      <td>23207</td>\n",
       "      <td>60043</td>\n",
       "      <td>136559</td>\n",
       "      <td>200580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>81620</td>\n",
       "      <td>285825</td>\n",
       "      <td>85009</td>\n",
       "      <td>110865</td>\n",
       "      <td>256852</td>\n",
       "      <td>274650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>130218</td>\n",
       "      <td>345177</td>\n",
       "      <td>151356</td>\n",
       "      <td>184212</td>\n",
       "      <td>370605</td>\n",
       "      <td>358843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>175630</td>\n",
       "      <td>381672</td>\n",
       "      <td>264670</td>\n",
       "      <td>276707</td>\n",
       "      <td>441980</td>\n",
       "      <td>380283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>186889</td>\n",
       "      <td>403295</td>\n",
       "      <td>284958</td>\n",
       "      <td>326830</td>\n",
       "      <td>412595</td>\n",
       "      <td>391610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>129173</td>\n",
       "      <td>364046</td>\n",
       "      <td>226793</td>\n",
       "      <td>296195</td>\n",
       "      <td>363834</td>\n",
       "      <td>392166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>71035</td>\n",
       "      <td>300751</td>\n",
       "      <td>142191</td>\n",
       "      <td>238281</td>\n",
       "      <td>257184</td>\n",
       "      <td>373901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>18723</td>\n",
       "      <td>158440</td>\n",
       "      <td>86345</td>\n",
       "      <td>167441</td>\n",
       "      <td>106881</td>\n",
       "      <td>252983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>16430</td>\n",
       "      <td>138662</td>\n",
       "      <td>29505</td>\n",
       "      <td>99304</td>\n",
       "      <td>69716</td>\n",
       "      <td>177758</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year        2019            2020            2021        \n",
       "usertype  casual  member  casual  member  casual  member\n",
       "month                                                   \n",
       "1           4602   98670    7626  133152   18113   78689\n",
       "2           2638   93548   12593  123733   10129   39474\n",
       "3          15923  149688   27294  113853   84010  144424\n",
       "4          47744  217566   23207   60043  136559  200580\n",
       "5          81620  285825   85009  110865  256852  274650\n",
       "6         130218  345177  151356  184212  370605  358843\n",
       "7         175630  381672  264670  276707  441980  380283\n",
       "8         186889  403295  284958  326830  412595  391610\n",
       "9         129173  364046  226793  296195  363834  392166\n",
       "10         71035  300751  142191  238281  257184  373901\n",
       "11         18723  158440   86345  167441  106881  252983\n",
       "12         16430  138662   29505   99304   69716  177758"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivotCust = bigData.pivot_table(index=['month'],columns=['year','usertype'],values='start_time',aggfunc = 'count')\n",
    "pivotCust"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>usertype</th>\n",
       "      <th>casual</th>\n",
       "      <th>member</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>880625</td>\n",
       "      <td>2937340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>1341547</td>\n",
       "      <td>2130616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>2528458</td>\n",
       "      <td>3065361</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "usertype   casual   member\n",
       "year                      \n",
       "2019       880625  2937340\n",
       "2020      1341547  2130616\n",
       "2021      2528458  3065361"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivotCustAnnual = bigData.pivot_table(index=['year'],columns = ['usertype'],values='start_time',aggfunc='count')\n",
    "pivotCustAnnual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pivotCustAnnual.plot(kind ='bar')\n",
    "plt.xticks(rotation=0,horizontalalignment = 'center')\n",
    "plt.ylabel('Number of Rides (in millions)')\n",
    "plt.xlabel('Year')\n",
    "plt.grid(axis='y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's dig into the different types of riders and see when they show up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"Number of 'casual' Divvy rides per month\")"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pivotCasual = bigData[bigData['usertype']== 'casual'].pivot_table(index=['month'],columns=['year'],values='start_time',aggfunc = 'count')\n",
    "import numpy as np\n",
    "pivotCasual.plot(kind='bar',figsize=(6,4))\n",
    "plt.xticks(rotation=0,horizontalalignment = 'center')\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Rides per month')\n",
    "plt.yticks(np.arange(0,450000,100000))\n",
    "plt.grid(axis='y')\n",
    "plt.title(\"Number of 'casual' Divvy rides per month\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "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>year</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4602</td>\n",
       "      <td>7626</td>\n",
       "      <td>18113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2638</td>\n",
       "      <td>12593</td>\n",
       "      <td>10129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15923</td>\n",
       "      <td>27294</td>\n",
       "      <td>84010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47744</td>\n",
       "      <td>23207</td>\n",
       "      <td>136559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>81620</td>\n",
       "      <td>85009</td>\n",
       "      <td>256852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>130218</td>\n",
       "      <td>151356</td>\n",
       "      <td>370605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>175630</td>\n",
       "      <td>264670</td>\n",
       "      <td>441980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>186889</td>\n",
       "      <td>284958</td>\n",
       "      <td>412595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>129173</td>\n",
       "      <td>226793</td>\n",
       "      <td>363834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>71035</td>\n",
       "      <td>142191</td>\n",
       "      <td>257184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>18723</td>\n",
       "      <td>86345</td>\n",
       "      <td>106881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>16430</td>\n",
       "      <td>29505</td>\n",
       "      <td>69716</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year     2019    2020    2021\n",
       "month                        \n",
       "1        4602    7626   18113\n",
       "2        2638   12593   10129\n",
       "3       15923   27294   84010\n",
       "4       47744   23207  136559\n",
       "5       81620   85009  256852\n",
       "6      130218  151356  370605\n",
       "7      175630  264670  441980\n",
       "8      186889  284958  412595\n",
       "9      129173  226793  363834\n",
       "10      71035  142191  257184\n",
       "11      18723   86345  106881\n",
       "12      16430   29505   69716"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivotCasual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"Number of 'member' Divvy rides per month\")"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LX/xic9n48eMZP358rXUffvjhWmWQjdOYmXV0niqmDS1atIg999yTESNGsNdee7V3dczMcuUWTBsaNGgQL7zwQntXw8ysTbgFY2ZmuXALxqzMeeoW66icYMy6AJ/VZe3BXWRmZpYLt2CaoLgroqVK6cpYvnw555xzDq+88gpbbLEF5513HuPHj2ft2rWceeaZLFu2jMrKSmbMmEHv3r1Zs2YNp59+Ok8++SRjx47lhhtu2Lyvu+66i6uuuooPPviAE088kWuvvbZVj8fMrJBbMB1c9+7d+cEPfsDixYuZO3cuP/nJT1i0aBGTJ09mxIgRLFmyhBEjRjB58mQgm0Zm0qRJXHfddTX2s2bNGr7xjW8we/ZsFi5cyKpVq5g9e3Z7HJJZ2Rs8dXCNH6ubE0wH179/fw466CAAevXqxb777suKFSt44IEHGDMmm4ZtzJgx3H///QBss802HHXUUVRUVNTYzwsvvMDee+/NTjvtBMAxxxzDPffc03YHYmZdjhNMGVm2bBlPP/00hx56KKtWraJ///5AloRWr17d4LZ77rknzz33HMuWLeP999/n/vvvZ/ny5Q1uY2bWEh6DKRNvvfUWp512Gj/60Y/Ybrvtmrx97969ufHGGznzzDPZYostOOKII3zRp5nlyi2YMvDee+9x2mmnMXr0aE499VQguwPnypUrAVi5ciU777xzQ7sA4DOf+QyPP/44jz32GPvss4+nqzGzXLkF08FFBOPGjWPffffla1/72ubyk08+malTpzJx4kSmTp3KqFGjGt3X6tWr2XnnnVm3bh0//elPmTFjRp5VN2sVvpC0fDnBNEF7vLH/+Mc/cttttzF48GAOPPBAAK6++momTpzIGWecwS233MLuu+9eY3bmyspK3nzzTTZt2sT999/PzJkzGTRoEOPHj+fZZ58F4LLLLmPvvfdu8+Mxs67DCaaDO+qoo8hu0llbfacZL1u2rM7yO++8s7WqZWbWKCcYs3bgqVusK/Agv5mZ5cIJxszMcuEEY2ZmuXCCMTOzXDjBmJlZLnwWWVNcsX0r7++NRldp6nT9s2bNYuLEiWzatImePXvy/e9/n09/+tMAzJs3j7Fjx7JhwwZOOOEErr/+eiS17jGZmSVuwXRwTZ2uf8cdd+SXv/wl8+fPZ+rUqXzhC1/YvK/zzz+fm2++mSVLlrBkyRIeeeSR9jqsTq1y4q9q/Zh1RU4wHVxTp+sfMmQIu+yyCwD77bcfGzdu5N1332XlypW8+eabHH744UjinHPO2byNmVkenGDKSFOn67/nnnsYMmQIW265JStWrGDAgAGblw0YMIAVK1a0Wd3NrOvxGEyZaOp0/QsXLmTChAnMnDkToM7pZjz+YmZ5cgumDDR1uv6qqipOOeUUpk2bxh577AFkLZaqqqoa61R3pZmZ5cEJpoNrbLp+oMZ0/a+//jonnngi3/ve9zjyyCM3r9+/f3969erF3LlziQimTZtW0hT/ZmbN5S6ypijhtOLW1tTp+m+44QaWLl3KpEmTmDRpEgAzZ85k55135sYbb9x8mvLxxx/P8ccf3+bHY2ZdhxNMB9fU6fovvfRSLr300jrXHzp0KAsWLGjV+pmZ1cddZGZmlgu3YMysZeqa4WLg7m1fD+tw3IIxM7NcOMGYmVkunGDMzCwXTjBmZpYLD/I3weCpg1t1f/PHzG90ndacrv+SSy5h2rRprFu3jrfeeqtVj8XMrJhbMB1ca07X/5nPfIYnnniivQ7FzLoYJ5gOrrWm6wc47LDDNs/AbGaWNyeYMtKS6frNzNqax2DKREun6zcza2tuwZSB1piu38ysreWWYCRVSHpC0rOSFkr6TirvI2mWpCXpd++CbS6WtFTS85KOLSg/WNL8tOzHSnfKkrSlpLtS+eOSKgu2GZNiLJE0Jq/jzFtrTddvZtbW8uwiexf4dES8JakH8AdJDwOnArMjYrKkicBEYIKkQcBZwH7ALsBvJO0dER8ANwLnAXOB/wGOAx4GxgHrImJPSWcB1wBnSuoDXA4MBQKYJ+nBiFjXkgMq5bTi1taa0/V/85vf5I477uCdd95hwIABnHvuuVxxxRVtfkxm1jXklmAim2O++mKLHukngFHAsFQ+FZgDTEjl0yPiXeBFSUuBQyQtA7aLiMcAJE0DPkuWYEYBV6R93Q3ckFo3xwKzImJt2mYWWVK6M5eDzVFrTtd/7bXXcu2117Zq/cw6m8qJv6rxfNnkE9upJuVP9X14tcrOpW7APGBP4CcRMUHS6xGxQ8E66yKit6QbgLkRcXsqv4UsiSwDJkfEMan8U8CEiDhJ0gLguIioSsv+ChwKjAUqIuK7qfzbwIaIuK6ofueRtYzo16/fwdOnT69R/+23354999yzNV+SsrB06VLeeKPtb67WWcxfUfu1G7xrzRmHF61ZVGudQX0HtUq84li5x9vixdrxevbML15bH18J8ZobqzMYPnz4vIgYWteyXM8iS91bB0raAbhP0v4NrK66dtFAeXO3KazfzcDNAEOHDo1hw4bVWL548WJ69epVf407qYqKCoYMGdLe1ShbY4u+AQMsGz2sxvOLpl5Ua535pzWvC7Y4XnGs3ONVXF47Xh3T9Zft8ZUQr7mxOrs2OU05Il6XNIesm2qVpP4RsVJSf6D6Ao4qYLeCzQYAL6fyAXWUF25TJak7sD2wNpUPK9pmTjPrTjqnoEvIs0Vr1ip8/5mykedZZDullguStgKOAZ4DHgSqz+oaAzyQHj8InJXODBsI7AU8ERErgfWSDkvjK+cUbVO9r9OBR9PYz6+BkZJ6p7PURqayJqmoqGDNmjVd5kM3IlizZg0VFRXtXRUz6wTybMH0B6amcZgtgBkR8ZCkx4AZksYBLwGfA4iIhZJmAIuA94ELUxcbwPnAFGArsnGZh1P5LcBt6YSAtWRnoRERayVNAp5M611ZPeDfFAMGDKCqqopXX321qZuWrYqKCgYMGND4imZmjcjzLLK/ALU68iNiDTCinm2uAq6qo/wpoNb4TURsJCWoOpbdCtzatFrX1KNHDwYOHNiSXZiZdVmNJhhJOwFfBioL14+IL+VXLTMzK3eltGAeAH4P/Ab4oJF1zTqc4usawNc2mLWFUhLM1hExIfeamJlZp1LKWWQPSToh95qYmVmnUm8LRtJ6Prpo8VuS3gXeS88jIhqfM97MzLqsehNMRHS9S9jNzKzVlHIW2eyIGNFYmZl1EL7S3TqIhrrIKoBtgB3T1fDV86VsRzadvlmnMXjq4Fpl7XF7BrPOpKEWzD8DXyVLJvP4KMG8Cfwk32qZmVm5a2gM5nrgekkXRcR/tGGdzMysE2h0DCYi/kPSEdS+kn9ajvUyM7MyV8og/23AHsAzfHQlfwBOMGZmVq9SruQfCgyKrjJnvZmZtYpSruRfAPxd3hUxM7POpZQWzI7AIklPAO9WF0bEybnVyszMyl4pCeaKvCthZmadTylnkf1WUj/gk6noiYhYnW+1zMys3DU6BiPpDOAJsjtHngE8Lun0vCtmZmblrZQuskuAT1a3WtIdLn8D3J1nxczMLFM8lVG5TGNUyllkWxR1ia0pcTszM+vCSmnBPCLp18Cd6fmZwMP5VcnMzDqDUgb5vyHpNOBIsgkvb46I+3KvmZmZlbVSWjBExD2SZlWvL6lPRKzNtWZmZlbWSpmL7J+BK4ENwIekWyYDH8+3amZmVs5KacH8P2C/iHgt78qYmVnnUUqC+SvwTt4VMTPrkHwL6mYrJcFcDPxJ0uPUnIvsK7nVyszMyl4pCeY/gUeB+WRjMGZmZo0qJcG8HxFfy70mZmbWqZRyRf7/SjpPUn9Jfap/cq+ZmZmVtVJaMP+Ufl9cUObTlM3MrEGlXMk/sC0qYmZmnYsnrTQzs1w4wZiZWS4aTDDK7NZWlTEzs86jwQQTEQHc3zZVMTOzzqSUs8jmSvpkRDyZe22sS6ic+KtaZcsmn9gONTGzPJWSYIYD/yJpGfA2aTbliDggz4qZmVl5KyXBHJ97LczMrNNp9CyyiPgbsBvw6fT4nVK2MzOzrq3RRCHpcmACH13J3wO4Pc9KmZlZ+SulJXIKcDLZ+AsR8TLQK89KmZlZ+SslwWxKpysHgKRtStmxpN0k/a+kxZIWShqfyvtImiVpSfrdu2CbiyUtlfS8pGMLyg+WND8t+7EkpfItJd2Vyh+XVFmwzZgUY4mkMSW9GmZm1mpKSTAzJP0nsIOkLwO/Af6rhO3eB74eEfsChwEXShoETARmR8RewOz0nLTsLGA/4Djgp5K6pX3dCJwH7JV+jkvl44B1EbEn8EPgmrSvPsDlwKHAIcDlhYnMzMzyV8og/3XA3cA9wN7AZRHxHyVstzIi/pwerwcWA7sCo4CpabWpwGfT41HA9Ih4NyJeBJYCh0jqD2wXEY+lltS0om2q93U3MCK1bo4FZkXE2ohYB8zio6RkZmZtQNlndiMrSX9H1hII4MmIeKVJQbKuq98B+wMvRcQOBcvWRURvSTcAcyPi9lR+C/AwsAyYHBHHpPJPARMi4iRJC4DjIqIqLfsrWatlLFAREd9N5d8GNqRkWViv88haRvTr1+/g6dOnN+WwrJnmr3ijVtngXWve93zRmkW11hnUd5DjlRBv8BYv1lpnUc+ejpdjvObGKlXx+yXveE0xfPjweRExtK5ljV4HI+lc4DKy2yYL+A9JV0bEraUEl7QtWevnqxHxZho+qXPVOsqigfLmbvNRQcTNwM0AQ4cOjWHDhtVXN2tFY+u6kn/0sBrPL5p6Ua115p823/FKiLes4vJa61w0cHfHyzFec2OVqvj9kne81lLKhZbfAIZExBoASX2BPwGNJhhJPciSy88j4t5UvEpS/4hYmbq/VqfyKrLrbaoNAF5O5QPqKC/cpkpSd2B7YG0qH1a0zZwSjtXMzFpJKYP8VcD6gufrgeWNbZTGQm4BFkfEvxcsehCoPqtrDPBAQflZ6cywgWSD+U9ExEpgvaTD0j7PKdqmel+nA4+mcZpfAyMl9U6D+yNTmZmZtZFSWjArgMclPUDWzTQKeELS1wCKkkehI4EvAPMlPZPKvgVMJjszbRzwEvC5tJ+FkmYAi8jOQLswIj5I250PTAG2IhuXeTiV3wLcJmkpWcvlrLSvtZImAdUTdF4ZEWtLOFYzM2slpSSYv6afatWthwYvtoyIP1D3WAjAiHq2uQq4qo7yp8hOECgu30hKUHUsu5USuvHMzCwfjSaYiPhOW1TEzMw6F09aaWZmuSili8zMWuqKmtfBUMdptWadjVswZmaWi1Km679W0naSekiaLek1SZ9vi8qZmVn5KqUFMzIi3gROIrsmZm+yiy/NzMzqVcoYTI/0+wTgznSNSY5VMjOz9jR46uAaz+ePad7UNKUkmF9Keg7YAFwgaSdgY7OimZlZl1HKdP0TgcOBoRHxHvAO2dX8ZmZm9SplkH9r4EKym34B7ALUOTWzmZlZtVIG+X8GbAKOSM+rgO/mViMzM+sUShmD2SMizpR0NkBEbJBH+c3M8lF8US6U7YW5pbRgNknainTDLkl7AO/mWiszMyt7pbRgLgceAXaT9HOyafjH5lkpMzMrf6XMpjxL0p+Bw8im3x8fEa/lXjMzMytr9SYYSQcVFa1Mv3eXtHtE/Dm/apmZdX6VE39Vq2xZRTtUJCcNtWB+kH5XkJ2W/CxZC+YA4HHgqHyrZmZm5azeQf6IGB4Rw4G/AQdFxNCIOBgYAixtqwqamVl5KuUssr+PiM0T0UTEAuDA3GpkZmadQilnkS2W9N/A7WSnKn8eWJxrrczMrOyVkmC+CJwPjE/Pf8dH08ZYJ1DnQOPkE9uhJmbWmZRymvJG4Ifpx8zMrCQNnaY8IyLOkDSfdBV/oYg4INeamZlZWWuoBVPdJXZSW1TEOr7WugmRmXUN9SaYiFiZfv+tsFxSN+AsstOXzczM6lTvacqStpN0saQbJI1U5iLgBeCMtquimZmVo4a6yG4D1gGPAecC3wB6AqMi4pn8q2ZmZq2prc8YbSjBfDwiBgOk62BeA3aPiPW51cbMzDqNhhLMe9UPIuIDSS86uRSo66ZAV7zR9vUwM+ugGkown5D0ZnosYKv0XEBExHa5187MzMpWQ2eRdWvLipiZWedSymSXZmZmTeYEY2ZmuShlskvrCHxSgZmVGbdgzMwsF04wZmaWCycYMzPLhROMmZnlwoP8Vre6TioYuHvb18PMypZbMGZmlgu3YKxrKm6huXVm1urcgjEzs1zklmAk3SpptaQFBWV9JM2StCT97l2w7GJJSyU9L+nYgvKDJc1Py34sSal8S0l3pfLHJVUWbDMmxVgiaUxex2hmZvXLswUzBTiuqGwiMDsi9gJmp+dIGkR2G+b90jY/TbdmBrgROA/YK/1U73McsC4i9gR+CFyT9tUHuBw4FDgEuLwwkZmZWdvILcFExO+AtUXFo4Cp6fFU4LMF5dMj4t2IeBFYChwiqT+wXUQ8FhEBTCvapnpfdwMjUuvmWGBWRKyNiHXALGonOjMzy5myz+2cdp51Wz0UEfun569HxA4Fy9dFRG9JNwBzI+L2VH4L8DCwDJgcEcek8k8BEyLipNT1dlxEVKVlfyVrtYwFKiLiu6n828CGiLiujvqdR9Y6ol+/fgdPnz699INb+Uztsv4Hlr59U+UYb/6K2nOaDd7ixVpli3r2rPF8UN9BrRdv15qD7ovWLKq1TqvGKzq+4mMr53il/O0cr3Xj5fleyT3errUvSSj+/2so3vDhw+dFxNC6lnWUs8hUR1k0UN7cbWoWRtwM3AwwdOjQGDZsWJ2Vq/M+1hWX117x7Bwnn7xiVG7xxpZ4fBcVnWk1/7T5rRdv9LCasaZeVGudVo1XdHzFx1bO8Ur52zle68bL872Se7wltT9bWiteW59Ftip1e5F+r07lVcBuBesNAF5O5QPqKK+xjaTuwPZkXXL17cvMzNpQWyeYB4Hqs7rGAA8UlJ+VzgwbSDaY/0RErATWSzosja+cU7RN9b5OBx5N4zS/BkZK6p0G90emMjMza0O5dZFJuhMYBuwoqYrszK7JwAxJ44CXgM8BRMRCSTOARcD7wIUR8UHa1flkZ6RtRTYu83AqvwW4TdJSspbLWWlfayVNAp5M610ZEcUnG3RodXfJtUNFzMxaILcEExFn17NoRD3rXwVcVUf5U8D+dZRvJCWoOpbdCtxacmXNzKzV+Up+MzPLhROMmZnlwgnGzMxy4QRjZma5cIIxM7NcdJQr+TuFwVMH13g+f0zzrn41M+sM3IIxM7NcOMGYmVkunGDMzCwXTjBmZpYLJxgzM8uFE4yZmeXCCcbMzHLhBGNmZrlwgjEzs1z4Sn7rGK7YvubzOu6xbmblxS0YMzPLhROMmZnlwgnGzMxy4QRjZma5cIIxM7NcOMGYmVkunGDMzCwXTjBmZpYLJxgzM8uFE4yZmeXCU8WUscFTB9d4Pn/M/HaqiZlZbW7BmJlZLpxgzMwsF04wZmaWCycYMzPLhROMmZnlwgnGzMxy4QRjZma5cIIxM7NcOMGYmVkunGDMzCwXTjBmZpYLJxgzM8uFE4yZmeXCCcbMzHLhBGNmZrlwgjEzs1x06gQj6ThJz0taKmlie9fHzKwr6bQJRlI34CfA8cAg4GxJg9q3VmZmXUenTTDAIcDSiHghIjYB04FR7VwnM7MuQxHR3nXIhaTTgeMi4tz0/AvAoRHxrwXrnAecl57uAzzfjFA7Aq+1sLqO53iO53jtGasl8T4WETvVtaB7y+rToamOshrZNCJuBm5uURDpqYgY2pJ9OJ7jOZ7jtWesvOJ15i6yKmC3gucDgJfbqS5mZl1OZ04wTwJ7SRooqSdwFvBgO9fJzKzL6LRdZBHxvqR/BX4NdANujYiFOYRqUReb4zme4zleB4iVS7xOO8hvZmbtqzN3kZmZWTtygjEzs1w4wTSTpFslrZa0oI3i7SbpfyUtlrRQ0vic41VIekLSsyned/KMl2J2k/S0pIfyjpXiLZM0X9Izkp7KOdYOku6W9Fz6Gx6eY6x90jFV/7wp6at5xUsx/y29TxZIulNSRc7xxqdYC/M4trr+vyX1kTRL0pL0u3fO8T6Xju9DSa16+nA98b6f3p9/kXSfpB1aGscJpvmmAMe1Ybz3ga9HxL7AYcCFOU998y7w6Yj4BHAgcJykw3KMBzAeWJxzjGLDI+LANrje4HrgkYj4e+AT5HicEfF8OqYDgYOBd4D78oonaVfgK8DQiNif7KSas3KMtz/wZbLZOj4BnCRpr1YOM4Xa/98TgdkRsRcwOz3PM94C4FTgd60Yp6F4s4D9I+IA4P+Ai1saxAmmmSLid8DaNoy3MiL+nB6vJ/uA2jXHeBERb6WnPdJPbmeESBoAnAj8d14x2ouk7YCjgVsAImJTRLzeRuFHAH+NiL/lHKc7sJWk7sDW5HvN2b7A3Ih4JyLeB34LnNKaAer5/x4FTE2PpwKfzTNeRCyOiObMLtLceDPT6wkwl+zawRZxgilDkiqBIcDjOcfpJukZYDUwKyLyjPcj4JvAhznGKBbATEnz0rRBefk48Crws9QF+N+StskxXqGzgDvzDBARK4DrgJeAlcAbETEzx5ALgKMl9ZW0NXACNS+qzku/iFgJ2Rc+YOc2iNlevgQ83NKdOMGUGUnbAvcAX42IN/OMFREfpG6WAcAhqWui1Uk6CVgdEfPy2H8DjoyIg8hm3L5Q0tE5xekOHATcGBFDgLdp3e6VOqULjE8GfpFznN5k3+4HArsA20j6fF7xImIxcA1Zl84jwLNkXcjWCiRdQvZ6/ryl+3KCKSOSepAll59HxL1tFTd158whvzGnI4GTJS0jm/X605JuzynWZhHxcvq9mmyM4pCcQlUBVQUtwLvJEk7ejgf+HBGrco5zDPBiRLwaEe8B9wJH5BkwIm6JiIMi4miyrp4lecZLVknqD5B+r26DmG1K0hjgJGB0tMJFkk4wZUKSyPrwF0fEv7dBvJ2qzyKRtBXZh8hzecSKiIsjYkBEVJJ16TwaEbl9AwaQtI2kXtWPgZFkXS+tLiJeAZZL2icVjQAW5RGryNnk3D2WvAQcJmnr9D4dQc4na0jaOf3enWwgvC2O80FgTHo8BnigDWK2GUnHAROAkyPinVbZaUT4pxk/ZG/olcB7ZN9Qx+Uc7yiyMYO/AM+knxNyjHcA8HSKtwC4rI1e12HAQ20Q5+NkXSvPAguBS3KOdyDwVHo97wd65xxva2ANsH0b/d2+Q/YFZAFwG7BlzvF+T5aknwVG5LD/Wv/fQF+ys8eWpN99co53Snr8LrAK+HXO8ZYCyws+X25qaRxPFWNmZrlwF5mZmeXCCcbMzHLhBGNmZrlwgjEzs1w4wZiZWS6cYMzaiKSQdFvB8+6SXm3u7NFphuYLCp4Pa6uZqM1K4QRj1nbeBvZPF64C/COwogX72wG4oLGVzNqLE4xZ23qYbNZoKLrSPt1v5P50P465kg5I5Vek+3fMkfSCpK+kTSYDe6R7vnw/lW1bcN+Zn6cr683ahROMWduaDpyVbsh1ADVnxP4O8HRk9+P4FjCtYNnfA8eSzZd2eZqXbiLZVPwHRsQ30npDgK8Cg8hmKzgyx2Mxa5ATjFkbioi/AJVkrZf/KVp8FNk0K0TEo0BfSdunZb+KiHcj4jWySRb71RPiiYioiogPyab7qGzVAzBrgu7tXQGzLuhBsvunDCOb36paXd1Z1XM5vVtQ9gH1/++Wup5Z7tyCMWt7twJXRsT8ovLfAaMhOyMMeC0avufPeqBXHhU0aw3+dmPWxiKiCri+jkVXkN318i/AO3w0NXx9+1kj6Y+SFpCdPPCr1q6rWUt4NmUzM8uFu8jMzCwXTjBmZpYLJxgzM8uFE4yZmeXCCcbMzHLhBGNmZrlwgjEzs1z8f2iOyTuqz+zjAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bigData[bigData['usertype']== 'member'].pivot_table(index=['month'],columns=['year'],values='start_time',aggfunc = 'count').plot(kind='bar',figsize=(6,4))\n",
    "plt.xticks(rotation=0,horizontalalignment = 'center')\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Rides per month')\n",
    "plt.yticks(np.arange(0,450000,100000))\n",
    "plt.grid(axis='y')\n",
    "plt.title(\"Number of 'member' Divvy rides per month\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "different usage pattern over the calendar month.\n",
    "How does the length of ride vary?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2019</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2020</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2021</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usertype</th>\n",
       "      <th>casual</th>\n",
       "      <th>member</th>\n",
       "      <th>casual</th>\n",
       "      <th>member</th>\n",
       "      <th>casual</th>\n",
       "      <th>member</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0 days 00:47:19.483485441</td>\n",
       "      <td>0 days 00:15:39.002584372</td>\n",
       "      <td>0 days 02:44:14.224495148</td>\n",
       "      <td>0 days 00:11:07.096063145</td>\n",
       "      <td>0 days 00:25:41.257605035</td>\n",
       "      <td>0 days 00:12:52.351548501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0 days 02:25:36.291129643</td>\n",
       "      <td>0 days 00:13:24.217332278</td>\n",
       "      <td>0 days 02:07:52.678551576</td>\n",
       "      <td>0 days 00:12:50.609336232</td>\n",
       "      <td>0 days 00:49:22.716556422</td>\n",
       "      <td>0 days 00:18:01.486421441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0 days 00:52:18.354079005</td>\n",
       "      <td>0 days 00:13:02.420942226</td>\n",
       "      <td>0 days 01:03:19.231369531</td>\n",
       "      <td>0 days 00:14:21.617471652</td>\n",
       "      <td>0 days 00:38:09.611343887</td>\n",
       "      <td>0 days 00:13:58.293254583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0 days 00:50:56.547147285</td>\n",
       "      <td>0 days 00:13:30.767528014</td>\n",
       "      <td>0 days 01:13:34.635799543</td>\n",
       "      <td>0 days 00:21:33.099462052</td>\n",
       "      <td>0 days 00:38:01.419144838</td>\n",
       "      <td>0 days 00:14:41.359043773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0 days 00:51:14.379282038</td>\n",
       "      <td>0 days 00:13:51.001619872</td>\n",
       "      <td>0 days 00:51:04.373642790</td>\n",
       "      <td>0 days 00:19:46.572813782</td>\n",
       "      <td>0 days 00:38:13.854795757</td>\n",
       "      <td>0 days 00:14:38.285042781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0 days 00:45:55.202391374</td>\n",
       "      <td>0 days 00:14:32.846478763</td>\n",
       "      <td>0 days 00:51:49.666778984</td>\n",
       "      <td>0 days 00:18:43.822416563</td>\n",
       "      <td>0 days 00:37:07.396915853</td>\n",
       "      <td>0 days 00:14:40.668334062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0 days 00:59:47.058742811</td>\n",
       "      <td>0 days 00:16:25.998891718</td>\n",
       "      <td>0 days 00:59:44.317448142</td>\n",
       "      <td>0 days 00:17:46.309840372</td>\n",
       "      <td>0 days 00:32:47.517742884</td>\n",
       "      <td>0 days 00:14:14.360705053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0 days 01:07:00.451342775</td>\n",
       "      <td>0 days 00:16:11.132429115</td>\n",
       "      <td>0 days 00:44:59.836589953</td>\n",
       "      <td>0 days 00:16:50.276789156</td>\n",
       "      <td>0 days 00:28:47.273275245</td>\n",
       "      <td>0 days 00:14:06.110571742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0 days 00:51:40.002183118</td>\n",
       "      <td>0 days 00:14:07.838915961</td>\n",
       "      <td>0 days 00:38:11.977702133</td>\n",
       "      <td>0 days 00:15:33.097594490</td>\n",
       "      <td>0 days 00:27:49.010798880</td>\n",
       "      <td>0 days 00:13:44.135761386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0 days 00:58:59.529020905</td>\n",
       "      <td>0 days 00:13:01.853250695</td>\n",
       "      <td>0 days 00:30:15.354670830</td>\n",
       "      <td>0 days 00:14:02.341403636</td>\n",
       "      <td>0 days 00:28:40.507694879</td>\n",
       "      <td>0 days 00:12:30.120630862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0 days 01:07:01.965817443</td>\n",
       "      <td>0 days 00:12:25.924255238</td>\n",
       "      <td>0 days 00:31:50.813087034</td>\n",
       "      <td>0 days 00:13:35.213734987</td>\n",
       "      <td>0 days 00:23:08.050588972</td>\n",
       "      <td>0 days 00:11:18.571239964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0 days 01:03:19.959403530</td>\n",
       "      <td>0 days 00:11:24.909838311</td>\n",
       "      <td>0 days 00:26:55.189357735</td>\n",
       "      <td>0 days 00:12:44.148956738</td>\n",
       "      <td>0 days 00:23:29.724338745</td>\n",
       "      <td>0 days 00:11:00.294208980</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year                          2019                            \\\n",
       "usertype                    casual                    member   \n",
       "month                                                          \n",
       "1        0 days 00:47:19.483485441 0 days 00:15:39.002584372   \n",
       "2        0 days 02:25:36.291129643 0 days 00:13:24.217332278   \n",
       "3        0 days 00:52:18.354079005 0 days 00:13:02.420942226   \n",
       "4        0 days 00:50:56.547147285 0 days 00:13:30.767528014   \n",
       "5        0 days 00:51:14.379282038 0 days 00:13:51.001619872   \n",
       "6        0 days 00:45:55.202391374 0 days 00:14:32.846478763   \n",
       "7        0 days 00:59:47.058742811 0 days 00:16:25.998891718   \n",
       "8        0 days 01:07:00.451342775 0 days 00:16:11.132429115   \n",
       "9        0 days 00:51:40.002183118 0 days 00:14:07.838915961   \n",
       "10       0 days 00:58:59.529020905 0 days 00:13:01.853250695   \n",
       "11       0 days 01:07:01.965817443 0 days 00:12:25.924255238   \n",
       "12       0 days 01:03:19.959403530 0 days 00:11:24.909838311   \n",
       "\n",
       "year                          2020                            \\\n",
       "usertype                    casual                    member   \n",
       "month                                                          \n",
       "1        0 days 02:44:14.224495148 0 days 00:11:07.096063145   \n",
       "2        0 days 02:07:52.678551576 0 days 00:12:50.609336232   \n",
       "3        0 days 01:03:19.231369531 0 days 00:14:21.617471652   \n",
       "4        0 days 01:13:34.635799543 0 days 00:21:33.099462052   \n",
       "5        0 days 00:51:04.373642790 0 days 00:19:46.572813782   \n",
       "6        0 days 00:51:49.666778984 0 days 00:18:43.822416563   \n",
       "7        0 days 00:59:44.317448142 0 days 00:17:46.309840372   \n",
       "8        0 days 00:44:59.836589953 0 days 00:16:50.276789156   \n",
       "9        0 days 00:38:11.977702133 0 days 00:15:33.097594490   \n",
       "10       0 days 00:30:15.354670830 0 days 00:14:02.341403636   \n",
       "11       0 days 00:31:50.813087034 0 days 00:13:35.213734987   \n",
       "12       0 days 00:26:55.189357735 0 days 00:12:44.148956738   \n",
       "\n",
       "year                          2021                            \n",
       "usertype                    casual                    member  \n",
       "month                                                         \n",
       "1        0 days 00:25:41.257605035 0 days 00:12:52.351548501  \n",
       "2        0 days 00:49:22.716556422 0 days 00:18:01.486421441  \n",
       "3        0 days 00:38:09.611343887 0 days 00:13:58.293254583  \n",
       "4        0 days 00:38:01.419144838 0 days 00:14:41.359043773  \n",
       "5        0 days 00:38:13.854795757 0 days 00:14:38.285042781  \n",
       "6        0 days 00:37:07.396915853 0 days 00:14:40.668334062  \n",
       "7        0 days 00:32:47.517742884 0 days 00:14:14.360705053  \n",
       "8        0 days 00:28:47.273275245 0 days 00:14:06.110571742  \n",
       "9        0 days 00:27:49.010798880 0 days 00:13:44.135761386  \n",
       "10       0 days 00:28:40.507694879 0 days 00:12:30.120630862  \n",
       "11       0 days 00:23:08.050588972 0 days 00:11:18.571239964  \n",
       "12       0 days 00:23:29.724338745 0 days 00:11:00.294208980  "
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rideLengthPivot = bigData.pivot_table(index=['month'],columns=['year','usertype'],values='tripduration',aggfunc = 'mean')\n",
    "rideLengthPivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2019</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2020</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2021</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usertype</th>\n",
       "      <th>casual</th>\n",
       "      <th>member</th>\n",
       "      <th>casual</th>\n",
       "      <th>member</th>\n",
       "      <th>casual</th>\n",
       "      <th>member</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0 days 01:03:25.768668780</td>\n",
       "      <td>0 days 00:13:58.159505546</td>\n",
       "      <td>0 days 01:03:39.358291116</td>\n",
       "      <td>0 days 00:15:44.517156900</td>\n",
       "      <td>0 days 00:32:36.528400116</td>\n",
       "      <td>0 days 00:13:48.836396927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0 days 00:26:52.418887968</td>\n",
       "      <td>0 days 00:01:31.068454133</td>\n",
       "      <td>0 days 00:41:41.899494578</td>\n",
       "      <td>0 days 00:03:11.954026761</td>\n",
       "      <td>0 days 00:07:47.268081331</td>\n",
       "      <td>0 days 00:01:50.023383299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0 days 00:45:55.202391374</td>\n",
       "      <td>0 days 00:11:24.909838311</td>\n",
       "      <td>0 days 00:26:55.189357735</td>\n",
       "      <td>0 days 00:11:07.096063145</td>\n",
       "      <td>0 days 00:23:08.050588972</td>\n",
       "      <td>0 days 00:11:00.294208980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0 days 00:51:09.921248349</td>\n",
       "      <td>0 days 00:13:02.279019343</td>\n",
       "      <td>0 days 00:36:36.686548358</td>\n",
       "      <td>0 days 00:13:24.062635298</td>\n",
       "      <td>0 days 00:27:17.072500418</td>\n",
       "      <td>0 days 00:12:46.793819091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0 days 00:55:38.941549955</td>\n",
       "      <td>0 days 00:13:40.884573943</td>\n",
       "      <td>0 days 00:51:27.020210887</td>\n",
       "      <td>0 days 00:14:57.357533071</td>\n",
       "      <td>0 days 00:30:47.395509064</td>\n",
       "      <td>0 days 00:14:02.201913162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0 days 01:04:15.082388341</td>\n",
       "      <td>0 days 00:14:49.385505165</td>\n",
       "      <td>0 days 01:05:53.082477034</td>\n",
       "      <td>0 days 00:18:00.687984419</td>\n",
       "      <td>0 days 00:38:03.467194600</td>\n",
       "      <td>0 days 00:14:38.880865601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0 days 02:25:36.291129643</td>\n",
       "      <td>0 days 00:16:25.998891718</td>\n",
       "      <td>0 days 02:44:14.224495148</td>\n",
       "      <td>0 days 00:21:33.099462052</td>\n",
       "      <td>0 days 00:49:22.716556422</td>\n",
       "      <td>0 days 00:18:01.486421441</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year                           2019                             \\\n",
       "usertype                     casual                     member   \n",
       "count                            12                         12   \n",
       "mean      0 days 01:03:25.768668780  0 days 00:13:58.159505546   \n",
       "std       0 days 00:26:52.418887968  0 days 00:01:31.068454133   \n",
       "min       0 days 00:45:55.202391374  0 days 00:11:24.909838311   \n",
       "25%       0 days 00:51:09.921248349  0 days 00:13:02.279019343   \n",
       "50%       0 days 00:55:38.941549955  0 days 00:13:40.884573943   \n",
       "75%       0 days 01:04:15.082388341  0 days 00:14:49.385505165   \n",
       "max       0 days 02:25:36.291129643  0 days 00:16:25.998891718   \n",
       "\n",
       "year                           2020                             \\\n",
       "usertype                     casual                     member   \n",
       "count                            12                         12   \n",
       "mean      0 days 01:03:39.358291116  0 days 00:15:44.517156900   \n",
       "std       0 days 00:41:41.899494578  0 days 00:03:11.954026761   \n",
       "min       0 days 00:26:55.189357735  0 days 00:11:07.096063145   \n",
       "25%       0 days 00:36:36.686548358  0 days 00:13:24.062635298   \n",
       "50%       0 days 00:51:27.020210887  0 days 00:14:57.357533071   \n",
       "75%       0 days 01:05:53.082477034  0 days 00:18:00.687984419   \n",
       "max       0 days 02:44:14.224495148  0 days 00:21:33.099462052   \n",
       "\n",
       "year                           2021                             \n",
       "usertype                     casual                     member  \n",
       "count                            12                         12  \n",
       "mean      0 days 00:32:36.528400116  0 days 00:13:48.836396927  \n",
       "std       0 days 00:07:47.268081331  0 days 00:01:50.023383299  \n",
       "min       0 days 00:23:08.050588972  0 days 00:11:00.294208980  \n",
       "25%       0 days 00:27:17.072500418  0 days 00:12:46.793819091  \n",
       "50%       0 days 00:30:47.395509064  0 days 00:14:02.201913162  \n",
       "75%       0 days 00:38:03.467194600  0 days 00:14:38.880865601  \n",
       "max       0 days 00:49:22.716556422  0 days 00:18:01.486421441  "
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rideLengthPivot.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "casual_length = bigData[bigData['usertype']== 'casual'].pivot_table(index=['month'],columns=['year','usertype'],values='tripduration',aggfunc = 'mean')\n",
    "member_length = bigData[bigData['usertype']== 'member'].pivot_table(index=['month'],columns=['year','usertype'],values='tripduration',aggfunc = 'mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>2019</th>\n",
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       "      <th>2021</th>\n",
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       "    <tr>\n",
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       "      <th>casual</th>\n",
       "      <th>casual</th>\n",
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       "    </tr>\n",
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       "      <th>month</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0 days 00:47:19</td>\n",
       "      <td>0 days 02:44:14</td>\n",
       "      <td>0 days 00:25:41</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0 days 02:25:36</td>\n",
       "      <td>0 days 02:07:53</td>\n",
       "      <td>0 days 00:49:23</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0 days 00:52:18</td>\n",
       "      <td>0 days 01:03:19</td>\n",
       "      <td>0 days 00:38:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0 days 00:50:57</td>\n",
       "      <td>0 days 01:13:35</td>\n",
       "      <td>0 days 00:38:01</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0 days 00:51:14</td>\n",
       "      <td>0 days 00:51:04</td>\n",
       "      <td>0 days 00:38:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0 days 00:45:55</td>\n",
       "      <td>0 days 00:51:50</td>\n",
       "      <td>0 days 00:37:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0 days 00:59:47</td>\n",
       "      <td>0 days 00:59:44</td>\n",
       "      <td>0 days 00:32:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0 days 01:07:00</td>\n",
       "      <td>0 days 00:45:00</td>\n",
       "      <td>0 days 00:28:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0 days 00:51:40</td>\n",
       "      <td>0 days 00:38:12</td>\n",
       "      <td>0 days 00:27:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0 days 00:59:00</td>\n",
       "      <td>0 days 00:30:15</td>\n",
       "      <td>0 days 00:28:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0 days 01:07:02</td>\n",
       "      <td>0 days 00:31:51</td>\n",
       "      <td>0 days 00:23:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0 days 01:03:20</td>\n",
       "      <td>0 days 00:26:55</td>\n",
       "      <td>0 days 00:23:30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year                2019            2020            2021\n",
       "usertype          casual          casual          casual\n",
       "month                                                   \n",
       "1        0 days 00:47:19 0 days 02:44:14 0 days 00:25:41\n",
       "2        0 days 02:25:36 0 days 02:07:53 0 days 00:49:23\n",
       "3        0 days 00:52:18 0 days 01:03:19 0 days 00:38:10\n",
       "4        0 days 00:50:57 0 days 01:13:35 0 days 00:38:01\n",
       "5        0 days 00:51:14 0 days 00:51:04 0 days 00:38:14\n",
       "6        0 days 00:45:55 0 days 00:51:50 0 days 00:37:07\n",
       "7        0 days 00:59:47 0 days 00:59:44 0 days 00:32:48\n",
       "8        0 days 01:07:00 0 days 00:45:00 0 days 00:28:47\n",
       "9        0 days 00:51:40 0 days 00:38:12 0 days 00:27:49\n",
       "10       0 days 00:59:00 0 days 00:30:15 0 days 00:28:41\n",
       "11       0 days 01:07:02 0 days 00:31:51 0 days 00:23:08\n",
       "12       0 days 01:03:20 0 days 00:26:55 0 days 00:23:30"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = temp.columns\n",
    "for c in cols:\n",
    "    temp[c] = temp[c].dt.round(freq='S')\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usertype</th>\n",
       "      <th>casual</th>\n",
       "      <th>casual</th>\n",
       "      <th>casual</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>145.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>51.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>45.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>59.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>67.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>51.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>58.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>67.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>63.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year       2019   2020   2021\n",
       "usertype casual casual casual\n",
       "month                        \n",
       "1          47.0  164.0   25.0\n",
       "2         145.0  127.0   49.0\n",
       "3          52.0   63.0   38.0\n",
       "4          50.0   73.0   38.0\n",
       "5          51.0   51.0   38.0\n",
       "6          45.0   51.0   37.0\n",
       "7          59.0   59.0   32.0\n",
       "8          67.0   44.0   28.0\n",
       "9          51.0   38.0   27.0\n",
       "10         58.0   30.0   28.0\n",
       "11         67.0   31.0   23.0\n",
       "12         63.0   26.0   23.0"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for c in cols:\n",
    "    temp[c] = temp[c].astype('timedelta64[m]')\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotRideLength(df):\n",
    "    cols = df.columns\n",
    "    for c in cols:\n",
    "        df[c] = df[c].astype('timedelta64[m]')\n",
    "    # round the timedelta data to minues\n",
    "\n",
    "    df.plot(kind='bar',figsize=(6,4))\n",
    "    plt.xticks(rotation=0,horizontalalignment = 'center')\n",
    "    plt.xlabel('Month')\n",
    "    plt.ylabel('Length of Ride (in minutes)')\n",
    "    #plt.yticks(np.arange(0,450000,100000))\n",
    "    plt.grid(axis='y')\n",
    "    plt.title(\"Length of ride\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotRideLength(member_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotRideLength(casual_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['start_time', 'end_time', 'usertype', 'start_lat', 'start_lng',\n",
       "       'end_lat', 'end_lng', 'tripduration', 'month', 'year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigData.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob = bigData.loc[ (bigData['usertype']== 'casual') & (bigData['month'] == 1) & (bigData['year'] == 2020)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7626 entries, 99279 to 86287\n",
      "Data columns (total 10 columns):\n",
      " #   Column        Non-Null Count  Dtype          \n",
      "---  ------        --------------  -----          \n",
      " 0   start_time    7626 non-null   datetime64[ns] \n",
      " 1   end_time      7626 non-null   datetime64[ns] \n",
      " 2   usertype      7626 non-null   object         \n",
      " 3   start_lat     7626 non-null   float64        \n",
      " 4   start_lng     7626 non-null   float64        \n",
      " 5   end_lat       7626 non-null   float64        \n",
      " 6   end_lng       7626 non-null   float64        \n",
      " 7   tripduration  7626 non-null   timedelta64[ns]\n",
      " 8   month         7626 non-null   int64          \n",
      " 9   year          7626 non-null   int64          \n",
      "dtypes: datetime64[ns](2), float64(4), int64(2), object(1), timedelta64[ns](1)\n",
      "memory usage: 655.4+ KB\n"
     ]
    }
   ],
   "source": [
    "prob.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "209\n"
     ]
    }
   ],
   "source": [
    "map_prob = prob[prob['tripduration'] > pd.Timedelta(5, unit='h')]\n",
    "print(len(map_prob))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "it looks like most of these are starting / ending at the same location (almost)\n",
    "let's map it and see if that's correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n",
       "&lt;head&gt;    \n",
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       "    &lt;style&gt;#map {position:absolute;top:0;bottom:0;right:0;left:0;}&lt;/style&gt;\n",
       "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.6.0/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n",
       "    &lt;script src=&quot;https://code.jquery.com/jquery-1.12.4.min.js&quot;&gt;&lt;/script&gt;\n",
       "    &lt;script src=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js&quot;&gt;&lt;/script&gt;\n",
       "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js&quot;&gt;&lt;/script&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.6.0/dist/leaflet.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css&quot;/&gt;\n",
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       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css&quot;/&gt;\n",
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       "            &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
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       "                #map_4ec97162433c29d4e1d47166d08b7f5a {\n",
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       "    \n",
       "            var map_4ec97162433c29d4e1d47166d08b7f5a = L.map(\n",
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       "\n",
       "            \n",
       "\n",
       "        \n",
       "    \n",
       "            var tile_layer_c037d62abe3c8131b8674275f6e6b86c = 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_4ec97162433c29d4e1d47166d08b7f5a);\n",
       "        \n",
       "    \n",
       "            var circle_marker_98660097ae75989002be34bd5db66a7f = L.circleMarker(\n",
       "                [41.8795, -87.6257],\n",
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       "            ).addTo(map_4ec97162433c29d4e1d47166d08b7f5a);\n",
       "        \n",
       "    \n",
       "            var circle_marker_d133ddf1f82674eaf66959adc9bfaf1c = L.circleMarker(\n",
       "                [41.8772, -87.6278],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#1a9641ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;#1a9641ff&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;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_4ec97162433c29d4e1d47166d08b7f5a);\n",
       "        \n",
       "    \n",
       "            var circle_marker_ba08cec9be52ef676c9ee09e39812bc6 = L.circleMarker(\n",
       "                [41.8923, -87.612],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#1a9641ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;#1a9641ff&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, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_4ec97162433c29d4e1d47166d08b7f5a);\n",
       "        \n",
       "    \n",
       "            var circle_marker_7f09fde7159d94256454c09c5c13ba6c = L.circleMarker(\n",
       "                [41.8923, -87.612],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#1a9641ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;#1a9641ff&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;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_4ec97162433c29d4e1d47166d08b7f5a);\n",
       "        \n",
       "    \n",
       "            var circle_marker_617e78750b6791416b094c1473e6b76d = L.circleMarker(\n",
       "                [41.9445, -87.6547],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#1a9641ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;#1a9641ff&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, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_4ec97162433c29d4e1d47166d08b7f5a);\n",
       "        \n",
       "    \n",
       "            var circle_marker_4be093b9fecffd6875e95f4c0db42825 = L.circleMarker(\n",
       "                [41.8915, -87.6268],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#1a9641ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;#1a9641ff&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;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_4ec97162433c29d4e1d47166d08b7f5a);\n",
       "        \n",
       "    \n",
       "            var circle_marker_d4b7db439e267ccc165733ffe2582274 = L.circleMarker(\n",
       "                [41.8915, -87.6268],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#1a9641ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;#1a9641ff&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, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_4ec97162433c29d4e1d47166d08b7f5a);\n",
       "        \n",
       "    \n",
       "            var circle_marker_d539122261c2783b8c702058313d06ed = L.circleMarker(\n",
       "                [41.8915, -87.6268],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#1a9641ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;#1a9641ff&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;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_4ec97162433c29d4e1d47166d08b7f5a);\n",
       "        \n",
       "    \n",
       "            var circle_marker_095115f2f961989c48ddee48c23102ea = L.circleMarker(\n",
       "                [41.9263, -87.6308],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#1a9641ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;#1a9641ff&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, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_4ec97162433c29d4e1d47166d08b7f5a);\n",
       "        \n",
       "    \n",
       "            var circle_marker_6bc6943239234af441eb7718e07386fb = L.circleMarker(\n",
       "                [41.9263, -87.6308],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#1a9641ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;#1a9641ff&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;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_4ec97162433c29d4e1d47166d08b7f5a);\n",
       "        \n",
       "    \n",
       "            var circle_marker_ec67cd246f6611cd28de40387ad53751 = L.circleMarker(\n",
       "                [41.8847, -87.6277],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#1a9641ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;#1a9641ff&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, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_4ec97162433c29d4e1d47166d08b7f5a);\n",
       "        \n",
       "    \n",
       "            var circle_marker_7f12364867bebc15174e5f5a7202e52b = L.circleMarker(\n",
       "                [41.896, -87.6677],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#1a9641ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;#1a9641ff&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;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_4ec97162433c29d4e1d47166d08b7f5a);\n",
       "        \n",
       "    \n",
       "            var circle_marker_dbe04f022db369728c3adc5e4a4ab226 = L.circleMarker(\n",
       "                [41.8847, -87.6277],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#1a9641ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;#1a9641ff&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, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_4ec97162433c29d4e1d47166d08b7f5a);\n",
       "        \n",
       "    \n",
       "            var circle_marker_d79da2b64eb17a37c6f60f5f1778f495 = L.circleMarker(\n",
       "                [41.894, -87.6293],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#1a9641ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;#1a9641ff&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;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_4ec97162433c29d4e1d47166d08b7f5a);\n",
       "        \n",
       "    \n",
       "            var circle_marker_bdc2cde06245ac602ea2b1f0a3b4ee78 = L.circleMarker(\n",
       "                [41.903, -87.6313],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#1a9641ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;#1a9641ff&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, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_4ec97162433c29d4e1d47166d08b7f5a);\n",
       "        \n",
       "    \n",
       "            var circle_marker_798bd57076f4b5238fad68917175fd90 = L.circleMarker(\n",
       "                [41.8915, -87.6268],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#1a9641ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;#1a9641ff&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;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_4ec97162433c29d4e1d47166d08b7f5a);\n",
       "        \n",
       "    \n",
       "            var circle_marker_95546718bad1b78dc6ca6ba961b069cc = L.circleMarker(\n",
       "                [41.8772, -87.6278],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#1a9641ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;#1a9641ff&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, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_4ec97162433c29d4e1d47166d08b7f5a);\n",
       "        \n",
       "    \n",
       "            var circle_marker_6ca4ed62d97096a854bfb20600e63ddd = L.circleMarker(\n",
       "                [41.8847, -87.6277],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#1a9641ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;#1a9641ff&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;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_4ec97162433c29d4e1d47166d08b7f5a);\n",
       "        \n",
       "    \n",
       "            var circle_marker_f1cb9e3ebaa774ab5deeb8e2319f4abb = L.circleMarker(\n",
       "                [41.8847, -87.6277],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#1a9641ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;#1a9641ff&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, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_4ec97162433c29d4e1d47166d08b7f5a);\n",
       "        \n",
       "    \n",
       "            var circle_marker_f5c4779308e39b303533d3d0d688f8bc = L.circleMarker(\n",
       "                [41.896, -87.6677],\n",
       "                {&quot;bubblingMouseEvents&quot;: true, &quot;color&quot;: &quot;#1a9641ff&quot;, &quot;dashArray&quot;: null, &quot;dashOffset&quot;: null, &quot;fill&quot;: false, &quot;fillColor&quot;: &quot;#1a9641ff&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;: 2, &quot;stroke&quot;: true, &quot;weight&quot;: 3}\n",
       "            ).addTo(map_4ec97162433c29d4e1d47166d08b7f5a);\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 0x7f6d975166b0>"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import folium\n",
    "import branca\n",
    "import branca.colormap as cm\n",
    "\n",
    "sampSize = 10\n",
    "\n",
    "m1 = folium.Map(location = [prob['start_lat'].mean(),prob['start_lng'].mean()],\n",
    "                tiles='OpenStreetMap',zoom_start = 11)\n",
    "\n",
    "colormap = cm.linear.RdYlGn_04.scale(0,sampSize)\n",
    "#.to_step(sampSize)\n",
    "\n",
    "for idx,row in map_prob.sample(n=sampSize).iterrows():\n",
    "    lat1, lon1 = row.start_lat, row.start_lng\n",
    "    folium.CircleMarker(location=[lat1,lon1],\n",
    "        radius = 6,\n",
    "        color = colormap(c)\n",
    "                ).add_to(m1)\n",
    "    lat2, lon2 = row.end_lat, row.end_lng\n",
    "    folium.CircleMarker(location=[lat2,lon2],\n",
    "        radius = 2,\n",
    "        color = colormap(c)\n",
    "                ).add_to(m1)\n",
    "    c += 1\n",
    "\n",
    "m1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So some starting and ending locations are the same, but many are not.\n",
    "Perhaps the problem is that we have a small N of total rides, so a larger-than-normal number of \"long\" rides is pulling the average up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "longRides = bigData[bigData['tripduration'] > pd.Timedelta(5, unit='h')]\n",
    "# rides that are longer than 5 hours"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many rides are longer than 5 hours for each usertype?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "year       2019   2020   2021\n",
      "usertype casual casual casual\n",
      "month                        \n",
      "1            23    209     79\n",
      "2            22    229    120\n",
      "3           122    286    435\n",
      "4           225    218    725\n",
      "5           419    760   1385\n",
      "6           655   1412   2052\n",
      "7           856   2826   1822\n",
      "8          1048   1853   1511\n",
      "9           744    954   1295\n",
      "10          561    536   1051\n",
      "11          282    261    360\n",
      "12          240    101    241\n"
     ]
    }
   ],
   "source": [
    "print(longRides[longRides['usertype']=='casual'].pivot_table(index=['month'],columns=['year','usertype'],values='tripduration',aggfunc = 'count') )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Starting in 11/19, the N of long 'casual' rides does not drop back to its former winter level -- it stays at much higher levels in 2020 for the next year or so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "year       2019   2020   2021\n",
      "usertype member member member\n",
      "month                        \n",
      "1           152     75     74\n",
      "2           105     72    144\n",
      "3            80     86     76\n",
      "4           126     74    145\n",
      "5           193    102    189\n",
      "6           248    162    278\n",
      "7           256    238    261\n",
      "8           216    182    252\n",
      "9           166    161    280\n",
      "10          144    172    283\n",
      "11          120     59    176\n",
      "12           78     59     83\n"
     ]
    }
   ],
   "source": [
    "print(longRides[longRides['usertype']=='member'].pivot_table(index=['month'],columns=['year','usertype'],values='tripduration',aggfunc = 'count') )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the number of \"members\" who use bikes at longer than 5 hours is fairly consistent and consistenly a small number, especially compared to the total number of rides.\n",
    "\n",
    "Let's normalize the data and see what it looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "year       2019   2020   2021\n",
      "usertype member member member\n",
      "month                        \n",
      "1           106     46     41\n",
      "2            64     49    102\n",
      "3            51     56     38\n",
      "4            75     55     69\n",
      "5           105     57     79\n",
      "6           125     81    130\n",
      "7           137    120     96\n",
      "8           120    108    105\n",
      "9           101     76    114\n",
      "10           83     94    116\n",
      "11           86     34     97\n",
      "12           47     33     47\n"
     ]
    }
   ],
   "source": [
    "# this is the number of hours that is considered a \"long ride\"\n",
    "cutoff = 12\n",
    "user_type = 'member'\n",
    "\n",
    "longRides = bigData[bigData['tripduration'] > pd.Timedelta(cutoff, unit='h')]\n",
    "tempLong = longRides[longRides['usertype']==user_type].pivot_table(index=['month'],columns=['year','usertype'],values='tripduration',aggfunc = 'count')\n",
    "tempAll = bigData[bigData['usertype']==user_type].pivot_table(index=['month'],columns=['year','usertype'],values='tripduration',aggfunc = 'count')\n",
    "\n",
    "#print( (tempLong / tempAll).round(4)*100)\n",
    "print(tempLong)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "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": [
    "( (tempLong / tempAll).round(4)*100).plot(kind = 'bar',figsize=(17,10))\n",
    "plt.xticks(rotation=0,horizontalalignment = 'center')\n",
    "plt.xlabel('Month')\n",
    "title = 'Percent of rides longer than ' + str(cutoff) + ' hours'\n",
    "plt.title(title)\n",
    "plt.ylabel('Percent of rides per month')\n",
    "plt.grid(axis='y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "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>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>usertype</th>\n",
       "      <th>start_lat</th>\n",
       "      <th>start_lng</th>\n",
       "      <th>end_lat</th>\n",
       "      <th>end_lng</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2019-01-01 02:21:04</td>\n",
       "      <td>2019-01-02 09:35:30</td>\n",
       "      <td>casual</td>\n",
       "      <td>41.909396</td>\n",
       "      <td>-87.677692</td>\n",
       "      <td>41.925602</td>\n",
       "      <td>-87.653708</td>\n",
       "      <td>1 days 07:14:26</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>2019-01-01 04:07:10</td>\n",
       "      <td>2019-01-02 06:37:40</td>\n",
       "      <td>casual</td>\n",
       "      <td>41.917108</td>\n",
       "      <td>-87.710221</td>\n",
       "      <td>41.917108</td>\n",
       "      <td>-87.710221</td>\n",
       "      <td>1 days 02:30:30</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>2019-01-01 10:22:26</td>\n",
       "      <td>2019-01-02 10:08:20</td>\n",
       "      <td>member</td>\n",
       "      <td>41.883984</td>\n",
       "      <td>-87.624684</td>\n",
       "      <td>41.883984</td>\n",
       "      <td>-87.624684</td>\n",
       "      <td>0 days 23:45:54</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517</th>\n",
       "      <td>2019-01-01 12:44:46</td>\n",
       "      <td>2019-01-02 09:57:16</td>\n",
       "      <td>casual</td>\n",
       "      <td>41.882091</td>\n",
       "      <td>-87.639833</td>\n",
       "      <td>41.881320</td>\n",
       "      <td>-87.629521</td>\n",
       "      <td>0 days 21:12:30</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>2019-01-01 12:45:14</td>\n",
       "      <td>2019-01-02 07:15:36</td>\n",
       "      <td>casual</td>\n",
       "      <td>41.882091</td>\n",
       "      <td>-87.639833</td>\n",
       "      <td>41.889375</td>\n",
       "      <td>-87.627077</td>\n",
       "      <td>0 days 18:30:22</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             start_time            end_time usertype  start_lat  start_lng  \\\n",
       "100 2019-01-01 02:21:04 2019-01-02 09:35:30   casual  41.909396 -87.677692   \n",
       "145 2019-01-01 04:07:10 2019-01-02 06:37:40   casual  41.917108 -87.710221   \n",
       "311 2019-01-01 10:22:26 2019-01-02 10:08:20   member  41.883984 -87.624684   \n",
       "517 2019-01-01 12:44:46 2019-01-02 09:57:16   casual  41.882091 -87.639833   \n",
       "520 2019-01-01 12:45:14 2019-01-02 07:15:36   casual  41.882091 -87.639833   \n",
       "\n",
       "       end_lat    end_lng    tripduration  month  year  \n",
       "100  41.925602 -87.653708 1 days 07:14:26      1  2019  \n",
       "145  41.917108 -87.710221 1 days 02:30:30      1  2019  \n",
       "311  41.883984 -87.624684 0 days 23:45:54      1  2019  \n",
       "517  41.881320 -87.629521 0 days 21:12:30      1  2019  \n",
       "520  41.889375 -87.627077 0 days 18:30:22      1  2019  "
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longRides.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes bikes don't get put into the racks and so although they are not being ridden, they continue to be checked out, according to Divvy. (I had this happen to me personally!) Unfortunately, I don't think there is a way to detect this from the data.\n",
    "\n",
    "Similarly, it might be the case that sometimes when a bike is checked out and returned to the same dock, it might be the case that the bike has been removed for servicing. Let's see how many bikes have the same start/end GPS data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "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>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>usertype</th>\n",
       "      <th>start_lat</th>\n",
       "      <th>start_lng</th>\n",
       "      <th>end_lat</th>\n",
       "      <th>end_lng</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>2019-01-01 04:07:10</td>\n",
       "      <td>2019-01-02 06:37:40</td>\n",
       "      <td>casual</td>\n",
       "      <td>41.917108</td>\n",
       "      <td>-87.710221</td>\n",
       "      <td>41.917108</td>\n",
       "      <td>-87.710221</td>\n",
       "      <td>1 days 02:30:30</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>2019-01-01 10:22:26</td>\n",
       "      <td>2019-01-02 10:08:20</td>\n",
       "      <td>member</td>\n",
       "      <td>41.883984</td>\n",
       "      <td>-87.624684</td>\n",
       "      <td>41.883984</td>\n",
       "      <td>-87.624684</td>\n",
       "      <td>0 days 23:45:54</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4696</th>\n",
       "      <td>2019-01-02 17:58:52</td>\n",
       "      <td>2019-01-03 08:47:38</td>\n",
       "      <td>member</td>\n",
       "      <td>41.925602</td>\n",
       "      <td>-87.653708</td>\n",
       "      <td>41.925602</td>\n",
       "      <td>-87.653708</td>\n",
       "      <td>0 days 14:48:46</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10132</th>\n",
       "      <td>2019-01-03 18:10:51</td>\n",
       "      <td>2019-01-04 14:00:31</td>\n",
       "      <td>member</td>\n",
       "      <td>41.925602</td>\n",
       "      <td>-87.653708</td>\n",
       "      <td>41.925602</td>\n",
       "      <td>-87.653708</td>\n",
       "      <td>0 days 19:49:40</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12284</th>\n",
       "      <td>2019-01-04 08:22:47</td>\n",
       "      <td>2019-01-05 06:51:53</td>\n",
       "      <td>member</td>\n",
       "      <td>41.937582</td>\n",
       "      <td>-87.644098</td>\n",
       "      <td>41.937582</td>\n",
       "      <td>-87.644098</td>\n",
       "      <td>0 days 22:29:06</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129371</th>\n",
       "      <td>2021-12-31 08:14:50</td>\n",
       "      <td>2022-01-01 02:20:45</td>\n",
       "      <td>casual</td>\n",
       "      <td>41.954468</td>\n",
       "      <td>-87.749594</td>\n",
       "      <td>41.954468</td>\n",
       "      <td>-87.749594</td>\n",
       "      <td>0 days 18:05:55</td>\n",
       "      <td>12</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171732</th>\n",
       "      <td>2021-12-19 16:49:38</td>\n",
       "      <td>2021-12-25 17:05:34</td>\n",
       "      <td>casual</td>\n",
       "      <td>41.876535</td>\n",
       "      <td>-87.620473</td>\n",
       "      <td>41.876535</td>\n",
       "      <td>-87.620473</td>\n",
       "      <td>6 days 00:15:56</td>\n",
       "      <td>12</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172492</th>\n",
       "      <td>2021-12-13 16:35:46</td>\n",
       "      <td>2021-12-14 08:01:28</td>\n",
       "      <td>casual</td>\n",
       "      <td>41.876535</td>\n",
       "      <td>-87.620473</td>\n",
       "      <td>41.876535</td>\n",
       "      <td>-87.620473</td>\n",
       "      <td>0 days 15:25:42</td>\n",
       "      <td>12</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173863</th>\n",
       "      <td>2021-12-04 16:28:04</td>\n",
       "      <td>2021-12-06 12:30:32</td>\n",
       "      <td>casual</td>\n",
       "      <td>41.750940</td>\n",
       "      <td>-87.613790</td>\n",
       "      <td>41.750940</td>\n",
       "      <td>-87.613790</td>\n",
       "      <td>1 days 20:02:28</td>\n",
       "      <td>12</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211526</th>\n",
       "      <td>2021-12-23 14:51:20</td>\n",
       "      <td>2021-12-24 12:59:58</td>\n",
       "      <td>casual</td>\n",
       "      <td>41.900960</td>\n",
       "      <td>-87.623777</td>\n",
       "      <td>41.900960</td>\n",
       "      <td>-87.623777</td>\n",
       "      <td>0 days 22:08:38</td>\n",
       "      <td>12</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2279 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                start_time            end_time usertype  start_lat  start_lng  \\\n",
       "145    2019-01-01 04:07:10 2019-01-02 06:37:40   casual  41.917108 -87.710221   \n",
       "311    2019-01-01 10:22:26 2019-01-02 10:08:20   member  41.883984 -87.624684   \n",
       "4696   2019-01-02 17:58:52 2019-01-03 08:47:38   member  41.925602 -87.653708   \n",
       "10132  2019-01-03 18:10:51 2019-01-04 14:00:31   member  41.925602 -87.653708   \n",
       "12284  2019-01-04 08:22:47 2019-01-05 06:51:53   member  41.937582 -87.644098   \n",
       "...                    ...                 ...      ...        ...        ...   \n",
       "129371 2021-12-31 08:14:50 2022-01-01 02:20:45   casual  41.954468 -87.749594   \n",
       "171732 2021-12-19 16:49:38 2021-12-25 17:05:34   casual  41.876535 -87.620473   \n",
       "172492 2021-12-13 16:35:46 2021-12-14 08:01:28   casual  41.876535 -87.620473   \n",
       "173863 2021-12-04 16:28:04 2021-12-06 12:30:32   casual  41.750940 -87.613790   \n",
       "211526 2021-12-23 14:51:20 2021-12-24 12:59:58   casual  41.900960 -87.623777   \n",
       "\n",
       "          end_lat    end_lng    tripduration  month  year  \n",
       "145     41.917108 -87.710221 1 days 02:30:30      1  2019  \n",
       "311     41.883984 -87.624684 0 days 23:45:54      1  2019  \n",
       "4696    41.925602 -87.653708 0 days 14:48:46      1  2019  \n",
       "10132   41.925602 -87.653708 0 days 19:49:40      1  2019  \n",
       "12284   41.937582 -87.644098 0 days 22:29:06      1  2019  \n",
       "...           ...        ...             ...    ...   ...  \n",
       "129371  41.954468 -87.749594 0 days 18:05:55     12  2021  \n",
       "171732  41.876535 -87.620473 6 days 00:15:56     12  2021  \n",
       "172492  41.876535 -87.620473 0 days 15:25:42     12  2021  \n",
       "173863  41.750940 -87.613790 1 days 20:02:28     12  2021  \n",
       "211526  41.900960 -87.623777 0 days 22:08:38     12  2021  \n",
       "\n",
       "[2279 rows x 10 columns]"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testLatLong = longRides.loc[ (longRides['start_lat'] == longRides['end_lat']) &\n",
    "                            (longRides['start_lng'] == longRides['end_lng']) ]\n",
    "testLatLong"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1313090573864946"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(testLatLong) / len(longRides)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So about 13% of the greater than 12 hour rides have the same start/end location.\n",
    "\n",
    "\n",
    "What are the number and proportion of rides for the different hourly breakpoints (2-3, 3-4, etc)?\n",
    "Let's see if we can figure this out..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "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",
    "mTemp = bigData[bigData['usertype']=='member']\n",
    "cTemp = bigData[bigData['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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1224x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "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": "markdown",
   "metadata": {},
   "source": [
    "How do the stations that are busiest change over the three years?\n",
    "I need to reload the DFs and keep the station ID to calculate this, so let's go to a new notebook."
   ]
  }
 ],
 "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
}