Bike-share-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read In Dataset 😚"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('/home/robot/Desktop/SoftRice/LASharedBikeAnalysis-main/Final_Large_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Trip ID</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Start Time</th>\n",
       "      <th>End Time</th>\n",
       "      <th>Starting Station ID</th>\n",
       "      <th>Starting Station Latitude</th>\n",
       "      <th>Starting Station Longitude</th>\n",
       "      <th>Ending Station ID</th>\n",
       "      <th>Ending Station Latitude</th>\n",
       "      <th>Ending Station Longitude</th>\n",
       "      <th>Bike ID</th>\n",
       "      <th>Plan Duration</th>\n",
       "      <th>Trip Route Category</th>\n",
       "      <th>Passholder Type</th>\n",
       "      <th>bike_type</th>\n",
       "      <th>Starting Lat-Long</th>\n",
       "      <th>Ending Lat-Long</th>\n",
       "      <th>start station name</th>\n",
       "      <th>end station name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165226338</td>\n",
       "      <td>19</td>\n",
       "      <td>2021-07-01T00:12:00</td>\n",
       "      <td>2021-07-01T00:31:00</td>\n",
       "      <td>4548</td>\n",
       "      <td>34.026829</td>\n",
       "      <td>-118.393517</td>\n",
       "      <td>4555</td>\n",
       "      <td>34.005871</td>\n",
       "      <td>-118.429161</td>\n",
       "      <td>20122</td>\n",
       "      <td>30</td>\n",
       "      <td>One Way</td>\n",
       "      <td>Monthly Pass</td>\n",
       "      <td>standard</td>\n",
       "      <td>{'longitude': -118.393517, 'latitude': 34.026829}</td>\n",
       "      <td>{'longitude': -118.429161, 'latitude': 34.005871}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     Trip ID  Duration           Start Time             End Time  \\\n",
       "0  165226338        19  2021-07-01T00:12:00  2021-07-01T00:31:00   \n",
       "\n",
       "   Starting Station ID  Starting Station Latitude  Starting Station Longitude  \\\n",
       "0                 4548                  34.026829                 -118.393517   \n",
       "\n",
       "   Ending Station ID  Ending Station Latitude  Ending Station Longitude  \\\n",
       "0               4555                34.005871               -118.429161   \n",
       "\n",
       "  Bike ID  Plan Duration Trip Route Category Passholder Type bike_type  \\\n",
       "0   20122             30             One Way    Monthly Pass  standard   \n",
       "\n",
       "                                   Starting Lat-Long  \\\n",
       "0  {'longitude': -118.393517, 'latitude': 34.026829}   \n",
       "\n",
       "                                     Ending Lat-Long  start station name  \\\n",
       "0  {'longitude': -118.429161, 'latitude': 34.005871}                 NaN   \n",
       "\n",
       "   end station name  \n",
       "0               NaN  "
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(58411, 19)"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select Necessary Columns to Analyze 😁"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Process Station Infomation 😦"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Starting Station ID</th>\n",
       "      <th>Starting Station Latitude</th>\n",
       "      <th>Starting Station Longitude</th>\n",
       "      <th>Ending Station ID</th>\n",
       "      <th>Ending Station Latitude</th>\n",
       "      <th>Ending Station Longitude</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4548</td>\n",
       "      <td>34.026829</td>\n",
       "      <td>-118.393517</td>\n",
       "      <td>4555</td>\n",
       "      <td>34.005871</td>\n",
       "      <td>-118.429161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4441</td>\n",
       "      <td>34.026619</td>\n",
       "      <td>-118.281807</td>\n",
       "      <td>4441</td>\n",
       "      <td>34.026619</td>\n",
       "      <td>-118.281807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4555</td>\n",
       "      <td>34.005871</td>\n",
       "      <td>-118.429161</td>\n",
       "      <td>4555</td>\n",
       "      <td>34.005871</td>\n",
       "      <td>-118.429161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4441</td>\n",
       "      <td>34.026619</td>\n",
       "      <td>-118.281807</td>\n",
       "      <td>4454</td>\n",
       "      <td>34.017899</td>\n",
       "      <td>-118.291718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4564</td>\n",
       "      <td>34.035351</td>\n",
       "      <td>-118.434143</td>\n",
       "      <td>4561</td>\n",
       "      <td>34.020061</td>\n",
       "      <td>-118.422684</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Starting Station ID  Starting Station Latitude  Starting Station Longitude  \\\n",
       "0                 4548                  34.026829                 -118.393517   \n",
       "1                 4441                  34.026619                 -118.281807   \n",
       "2                 4555                  34.005871                 -118.429161   \n",
       "3                 4441                  34.026619                 -118.281807   \n",
       "4                 4564                  34.035351                 -118.434143   \n",
       "\n",
       "   Ending Station ID  Ending Station Latitude  Ending Station Longitude  \n",
       "0               4555                34.005871               -118.429161  \n",
       "1               4441                34.026619               -118.281807  \n",
       "2               4555                34.005871               -118.429161  \n",
       "3               4454                34.017899               -118.291718  \n",
       "4               4561                34.020061               -118.422684  "
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "station_info = data[['Starting Station ID','Starting Station Latitude','Starting Station Longitude','Ending Station ID','Ending Station Latitude','Ending Station Longitude']]\n",
    "station_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_station = station_info[['Starting Station ID','Starting Station Longitude','Starting Station Latitude']]\n",
    "#start_station.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "end_station = station_info[['Ending Station ID','Ending Station Longitude','Ending Station Latitude']]\n",
    "#end_station.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Process Bike Infomation 😄"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Duration</th>\n",
       "      <th>Start Time</th>\n",
       "      <th>End Time</th>\n",
       "      <th>Trip Route Category</th>\n",
       "      <th>Plan Duration</th>\n",
       "      <th>Passholder Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>2021-07-01T00:12:00</td>\n",
       "      <td>2021-07-01T00:31:00</td>\n",
       "      <td>One Way</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>2021-07-01T00:23:00</td>\n",
       "      <td>2021-07-01T00:45:00</td>\n",
       "      <td>Round Trip</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>2021-07-01T00:34:00</td>\n",
       "      <td>2021-07-01T01:00:00</td>\n",
       "      <td>Round Trip</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>2021-07-01T00:50:00</td>\n",
       "      <td>2021-07-01T01:07:00</td>\n",
       "      <td>One Way</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>2021-07-01T02:38:00</td>\n",
       "      <td>2021-07-01T02:53:00</td>\n",
       "      <td>One Way</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Duration           Start Time             End Time Trip Route Category  \\\n",
       "0        19  2021-07-01T00:12:00  2021-07-01T00:31:00             One Way   \n",
       "1        22  2021-07-01T00:23:00  2021-07-01T00:45:00          Round Trip   \n",
       "2        26  2021-07-01T00:34:00  2021-07-01T01:00:00          Round Trip   \n",
       "3        17  2021-07-01T00:50:00  2021-07-01T01:07:00             One Way   \n",
       "4        15  2021-07-01T02:38:00  2021-07-01T02:53:00             One Way   \n",
       "\n",
       "   Plan Duration Passholder Type  \n",
       "0             30    Monthly Pass  \n",
       "1             30    Monthly Pass  \n",
       "2             30    Monthly Pass  \n",
       "3             30    Monthly Pass  \n",
       "4             30    Monthly Pass  "
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_info = data[['Duration','Start Time','End Time','Trip Route Category','Plan Duration','Passholder Type']]\n",
    "bike_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Passholder Type\n",
       "Monthly Pass    25423\n",
       "Walk-up         19830\n",
       "One Day Pass     7081\n",
       "Annual Pass      6076\n",
       "Testing             1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Passholder Type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Process Trip Information 😃"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Duration</th>\n",
       "      <th>Start Time</th>\n",
       "      <th>End Time</th>\n",
       "      <th>Starting Station ID</th>\n",
       "      <th>Ending Station ID</th>\n",
       "      <th>Trip Route Category</th>\n",
       "      <th>Plan Duration</th>\n",
       "      <th>Passholder Type</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>2021-07-01T00:12:00</td>\n",
       "      <td>2021-07-01T00:31:00</td>\n",
       "      <td>4548</td>\n",
       "      <td>4555</td>\n",
       "      <td>One Way</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>2021-07-01T00:23:00</td>\n",
       "      <td>2021-07-01T00:45:00</td>\n",
       "      <td>4441</td>\n",
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       "      <td>Round Trip</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>2021-07-01T00:34:00</td>\n",
       "      <td>2021-07-01T01:00:00</td>\n",
       "      <td>4555</td>\n",
       "      <td>4555</td>\n",
       "      <td>Round Trip</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>2021-07-01T00:50:00</td>\n",
       "      <td>2021-07-01T01:07:00</td>\n",
       "      <td>4441</td>\n",
       "      <td>4454</td>\n",
       "      <td>One Way</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>2021-07-01T02:38:00</td>\n",
       "      <td>2021-07-01T02:53:00</td>\n",
       "      <td>4564</td>\n",
       "      <td>4561</td>\n",
       "      <td>One Way</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Duration           Start Time             End Time  Starting Station ID  \\\n",
       "0        19  2021-07-01T00:12:00  2021-07-01T00:31:00                 4548   \n",
       "1        22  2021-07-01T00:23:00  2021-07-01T00:45:00                 4441   \n",
       "2        26  2021-07-01T00:34:00  2021-07-01T01:00:00                 4555   \n",
       "3        17  2021-07-01T00:50:00  2021-07-01T01:07:00                 4441   \n",
       "4        15  2021-07-01T02:38:00  2021-07-01T02:53:00                 4564   \n",
       "\n",
       "   Ending Station ID Trip Route Category  Plan Duration Passholder Type  \n",
       "0               4555             One Way             30    Monthly Pass  \n",
       "1               4441          Round Trip             30    Monthly Pass  \n",
       "2               4555          Round Trip             30    Monthly Pass  \n",
       "3               4454             One Way             30    Monthly Pass  \n",
       "4               4561             One Way             30    Monthly Pass  "
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trip_info = data[['Duration','Start Time','End Time','Starting Station ID','Ending Station ID','Trip Route Category','Plan Duration','Passholder Type']]\n",
    "trip_info.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Station Infomation 😮"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Starting Station ID</th>\n",
       "      <th>Starting Station Longitude</th>\n",
       "      <th>Starting Station Latitude</th>\n",
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       "      <td>34.026829</td>\n",
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       "      <th>1</th>\n",
       "      <td>4441</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4555</td>\n",
       "      <td>-118.429161</td>\n",
       "      <td>34.005871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4441</td>\n",
       "      <td>-118.281807</td>\n",
       "      <td>34.026619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4564</td>\n",
       "      <td>-118.434143</td>\n",
       "      <td>34.035351</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Starting Station ID  Starting Station Longitude  Starting Station Latitude\n",
       "0                 4548                 -118.393517                  34.026829\n",
       "1                 4441                 -118.281807                  34.026619\n",
       "2                 4555                 -118.429161                  34.005871\n",
       "3                 4441                 -118.281807                  34.026619\n",
       "4                 4564                 -118.434143                  34.035351"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_station.head().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped = start_station.groupby('Starting Station ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_value = start_station['Starting Station ID'].value_counts().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "st_info = grouped.max()\n",
    "st_info.insert(2,'Counts',count_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "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>Starting Station Longitude</th>\n",
       "      <th>Starting Station Latitude</th>\n",
       "      <th>Counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Starting Station ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3000</th>\n",
       "      <td>-118.238220</td>\n",
       "      <td>34.025890</td>\n",
       "      <td>3279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3005</th>\n",
       "      <td>-118.258537</td>\n",
       "      <td>34.048500</td>\n",
       "      <td>2485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3006</th>\n",
       "      <td>-118.256668</td>\n",
       "      <td>34.045540</td>\n",
       "      <td>2410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3007</th>\n",
       "      <td>-118.254593</td>\n",
       "      <td>34.050480</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3008</th>\n",
       "      <td>-118.262733</td>\n",
       "      <td>34.046612</td>\n",
       "      <td>1634</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Starting Station Longitude  Starting Station Latitude  \\\n",
       "Starting Station ID                                                          \n",
       "3000                                -118.238220                  34.025890   \n",
       "3005                                -118.258537                  34.048500   \n",
       "3006                                -118.256668                  34.045540   \n",
       "3007                                -118.254593                  34.050480   \n",
       "3008                                -118.262733                  34.046612   \n",
       "\n",
       "                     Counts  \n",
       "Starting Station ID          \n",
       "3000                   3279  \n",
       "3005                   2485  \n",
       "3006                   2410  \n",
       "3007                   2022  \n",
       "3008                   1634  "
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st_info.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discover Bike Infomation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "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>Duration</th>\n",
       "      <th>Start Time</th>\n",
       "      <th>End Time</th>\n",
       "      <th>Trip Route Category</th>\n",
       "      <th>Plan Duration</th>\n",
       "      <th>Passholder Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>2021-07-01T00:12:00</td>\n",
       "      <td>2021-07-01T00:31:00</td>\n",
       "      <td>One Way</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>2021-07-01T00:23:00</td>\n",
       "      <td>2021-07-01T00:45:00</td>\n",
       "      <td>Round Trip</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>2021-07-01T00:34:00</td>\n",
       "      <td>2021-07-01T01:00:00</td>\n",
       "      <td>Round Trip</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>2021-07-01T00:50:00</td>\n",
       "      <td>2021-07-01T01:07:00</td>\n",
       "      <td>One Way</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>2021-07-01T02:38:00</td>\n",
       "      <td>2021-07-01T02:53:00</td>\n",
       "      <td>One Way</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Duration           Start Time             End Time Trip Route Category  \\\n",
       "0        19  2021-07-01T00:12:00  2021-07-01T00:31:00             One Way   \n",
       "1        22  2021-07-01T00:23:00  2021-07-01T00:45:00          Round Trip   \n",
       "2        26  2021-07-01T00:34:00  2021-07-01T01:00:00          Round Trip   \n",
       "3        17  2021-07-01T00:50:00  2021-07-01T01:07:00             One Way   \n",
       "4        15  2021-07-01T02:38:00  2021-07-01T02:53:00             One Way   \n",
       "\n",
       "   Plan Duration Passholder Type  \n",
       "0             30    Monthly Pass  \n",
       "1             30    Monthly Pass  \n",
       "2             30    Monthly Pass  \n",
       "3             30    Monthly Pass  \n",
       "4             30    Monthly Pass  "
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['One Way', 'Round Trip'], dtype=object)"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_info['Trip Route Category'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Monthly Pass', 'Walk-up', 'Annual Pass', 'One Day Pass',\n",
       "       'Testing'], dtype=object)"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_info['Passholder Type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 30,   1, 365, 999])"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_info['Plan Duration'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "scrolled": true
   },
   "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>Duration</th>\n",
       "      <th>Plan Duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>58411.000000</td>\n",
       "      <td>58411.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>45.731455</td>\n",
       "      <td>52.412799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>135.685078</td>\n",
       "      <td>108.874373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1440.000000</td>\n",
       "      <td>999.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Duration  Plan Duration\n",
       "count  58411.000000   58411.000000\n",
       "mean      45.731455      52.412799\n",
       "std      135.685078     108.874373\n",
       "min        1.000000       1.000000\n",
       "25%        9.000000       1.000000\n",
       "50%       18.000000      30.000000\n",
       "75%       35.000000      30.000000\n",
       "max     1440.000000     999.000000"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_info.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Methodology 😜"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Distribution of Hot Stations**\n",
    "- **Rush Hour Analysis**\n",
    "- **Round Trip/One Way Comparison Analysis**\n",
    "- **relevancy analysis between Duration&Passholder Type**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis 😁"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1）**Distribution of Hot Stations**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pic = st_info.plot(kind=\"scatter\", x=\"Starting Station Longitude\", y=\"Starting Station Latitude\",s = st_info['Counts']/20,\n",
    "                        alpha=1)\n",
    "plt.axis([-118.50,-118.20,34.01,34.07])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- If there is a **L.A. map** should be better to analysis..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2）**Rush Hour Analysis**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Use Regular Expression to Extract the Time*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "bike_info = bike_info.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "from pandas import Series,DataFrame\n",
    "\n",
    "pattern_time = '[0-9][0-9]:[0-9][0-9]:[0-9][0-9]'\n",
    "pattern_hour = '[0-9][0-9]'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "timelist = []\n",
    "for item in bike_info['Start Time']:\n",
    "    found_times = re.findall(pattern_time, item)\n",
    "    if found_times:  # Check if the list is not empty\n",
    "        timelist.append(found_times[0])\n",
    "    else:\n",
    "        # Handle the case where no time is found\n",
    "        # You could append a default value or continue\n",
    "        # For example:\n",
    "        timelist.append(\"No time found\")\n",
    "        # or simply continue to the next iteration\n",
    "        # continue\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['00:12:00', '00:23:00', '00:34:00', '00:50:00', '02:38:00']"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timelist[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Extract the Hour*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "hourlist = []\n",
    "for item in timelist:\n",
    "    hour = re.findall(pattern_hour,item)[0]\n",
    "    hourlist.append(hour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['00', '00', '00', '00', '02']"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hourlist[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "timeSeries = Series(timelist)\n",
    "hourSeries = Series(hourlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "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>Duration</th>\n",
       "      <th>Start Time</th>\n",
       "      <th>End Time</th>\n",
       "      <th>Trip Route Category</th>\n",
       "      <th>Plan Duration</th>\n",
       "      <th>Passholder Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>2021-07-01T00:12:00</td>\n",
       "      <td>2021-07-01T00:31:00</td>\n",
       "      <td>One Way</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>2021-07-01T00:23:00</td>\n",
       "      <td>2021-07-01T00:45:00</td>\n",
       "      <td>Round Trip</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>2021-07-01T00:34:00</td>\n",
       "      <td>2021-07-01T01:00:00</td>\n",
       "      <td>Round Trip</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>2021-07-01T00:50:00</td>\n",
       "      <td>2021-07-01T01:07:00</td>\n",
       "      <td>One Way</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>2021-07-01T02:38:00</td>\n",
       "      <td>2021-07-01T02:53:00</td>\n",
       "      <td>One Way</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Duration           Start Time             End Time Trip Route Category  \\\n",
       "0        19  2021-07-01T00:12:00  2021-07-01T00:31:00             One Way   \n",
       "1        22  2021-07-01T00:23:00  2021-07-01T00:45:00          Round Trip   \n",
       "2        26  2021-07-01T00:34:00  2021-07-01T01:00:00          Round Trip   \n",
       "3        17  2021-07-01T00:50:00  2021-07-01T01:07:00             One Way   \n",
       "4        15  2021-07-01T02:38:00  2021-07-01T02:53:00             One Way   \n",
       "\n",
       "   Plan Duration Passholder Type  \n",
       "0             30    Monthly Pass  \n",
       "1             30    Monthly Pass  \n",
       "2             30    Monthly Pass  \n",
       "3             30    Monthly Pass  \n",
       "4             30    Monthly Pass  "
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "bike_info.insert(3,'Start Hour',hourSeries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "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>Duration</th>\n",
       "      <th>Start Time</th>\n",
       "      <th>End Time</th>\n",
       "      <th>Start Hour</th>\n",
       "      <th>Trip Route Category</th>\n",
       "      <th>Plan Duration</th>\n",
       "      <th>Passholder Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>2021-07-01T00:12:00</td>\n",
       "      <td>2021-07-01T00:31:00</td>\n",
       "      <td>00</td>\n",
       "      <td>One Way</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>2021-07-01T00:23:00</td>\n",
       "      <td>2021-07-01T00:45:00</td>\n",
       "      <td>00</td>\n",
       "      <td>Round Trip</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>2021-07-01T00:34:00</td>\n",
       "      <td>2021-07-01T01:00:00</td>\n",
       "      <td>00</td>\n",
       "      <td>Round Trip</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>2021-07-01T00:50:00</td>\n",
       "      <td>2021-07-01T01:07:00</td>\n",
       "      <td>00</td>\n",
       "      <td>One Way</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>2021-07-01T02:38:00</td>\n",
       "      <td>2021-07-01T02:53:00</td>\n",
       "      <td>02</td>\n",
       "      <td>One Way</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Duration           Start Time             End Time Start Hour  \\\n",
       "0        19  2021-07-01T00:12:00  2021-07-01T00:31:00         00   \n",
       "1        22  2021-07-01T00:23:00  2021-07-01T00:45:00         00   \n",
       "2        26  2021-07-01T00:34:00  2021-07-01T01:00:00         00   \n",
       "3        17  2021-07-01T00:50:00  2021-07-01T01:07:00         00   \n",
       "4        15  2021-07-01T02:38:00  2021-07-01T02:53:00         02   \n",
       "\n",
       "  Trip Route Category  Plan Duration Passholder Type  \n",
       "0             One Way             30    Monthly Pass  \n",
       "1          Round Trip             30    Monthly Pass  \n",
       "2          Round Trip             30    Monthly Pass  \n",
       "3             One Way             30    Monthly Pass  \n",
       "4             One Way             30    Monthly Pass  "
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "Start_Hour_counts = bike_info['Start Hour'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]\n",
      "[586, 297, 233, 155, 142, 148, 484, 1221, 1844, 2460, 2802, 3243, 3812, 4268, 4342, 4414, 4711, 5402, 5713, 4424, 3027, 2142, 1613, 928]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'/home/robot/Desktop/SoftRice/LASharedBikeAnalysis-main/Process_data/rush-hour-line.html'"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from pyecharts.charts import Line\n",
    "from pyecharts import options as opts\n",
    "\n",
    "# # Use your data\n",
    "# x_data = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]\n",
    "# y_data = [1771, 1337, 864, 445, 417, 559, 1735, 4684, 6805, 6210, 5356, 7546, 9920, 9628, 8356, 8231, 9863, 11606, 10347, 7876, 5946, 4845, 3799, 2757]\n",
    "attr = list(map(int, Start_Hour_counts.index))\n",
    "print(attr)\n",
    "v = Start_Hour_counts.values.tolist()\n",
    "print(v)\n",
    "# Create a line chart\n",
    "line = Line(init_opts=opts.InitOpts(width='1000px', height='400px'))\n",
    "line.add_xaxis(attr)\n",
    "line.add_yaxis(\"\", v, markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_='max'), opts.MarkPointItem(type_='min')]))\n",
    "line.set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n",
    "line.set_global_opts(title_opts=opts.TitleOpts(title='Rush Hour Analysis'))\n",
    "\n",
    "# Save the chart to an HTML file\n",
    "line.render('rush-hour-line.html')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Shared-bike riders usually choose to start their trip at **7a.m.-23p.m.**\n",
    "- **7a.m.-10a.m.**morning peak period, **10a.m.-13a.m.**afternoon peak period and **16a.m.-18a.m.**evening peak period shows significant increase in shared-bike use\n",
    "- The peak of use is **18p.m.**, which is presumed to be **the evening peak**\n",
    "- The lowest point of use is **4a.m**,few shared-bikes are used in mid-night\n",
    "- From the peak to the lowest point is a **complete downward trend**, with the vehicle gradually reduced"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## 3）**Relevancy Analysis Between Duration&Passholder Type**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "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>Duration</th>\n",
       "      <th>Trip Route Category</th>\n",
       "      <th>Plan Duration</th>\n",
       "      <th>Passholder Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>One Way</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>Round Trip</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>Round Trip</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>One Way</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>One Way</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Duration Trip Route Category  Plan Duration Passholder Type\n",
       "0        19             One Way             30    Monthly Pass\n",
       "1        22          Round Trip             30    Monthly Pass\n",
       "2        26          Round Trip             30    Monthly Pass\n",
       "3        17             One Way             30    Monthly Pass\n",
       "4        15             One Way             30    Monthly Pass"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_trip_info = bike_info[['Duration','Trip Route Category','Plan Duration','Passholder Type']]\n",
    "bike_trip_info.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Cluster by Duration*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "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>Duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Duration\n",
       "0        19\n",
       "1        22\n",
       "2        26\n",
       "3        17\n",
       "4        15"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duration = bike_trip_info[['Duration']]\n",
    "duration.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=4)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=4)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=4)"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans \n",
    "k=4 \n",
    "kmodel = KMeans(n_clusters=k)\n",
    "kmodel.fit(duration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  21.05876792],\n",
       "       [1357.48583878],\n",
       "       [ 144.60272805],\n",
       "       [ 684.34224599]])"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmodel.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "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>Duration</th>\n",
       "      <th>Duration Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21.058768</td>\n",
       "      <td>short-time</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>144.602728</td>\n",
       "      <td>medium-time</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>684.342246</td>\n",
       "      <td>long-time</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1357.485839</td>\n",
       "      <td>very-long-time</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Duration  Duration Class\n",
       "0    21.058768      short-time\n",
       "2   144.602728     medium-time\n",
       "3   684.342246       long-time\n",
       "1  1357.485839  very-long-time"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "c = pd.DataFrame(kmodel.cluster_centers_).sort_values(0)\n",
    "c['Duration Class'] = ['short-time','medium-time','long-time','very-long-time']\n",
    "c.columns=['Duration','Duration Class']\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "short = c[c['Duration Class']=='short-time'].index[0]\n",
    "medium = c[c['Duration Class']=='medium-time'].index[0]\n",
    "long = c[c['Duration Class']=='long-time'].index[0]\n",
    "verylong = c[c['Duration Class']=='very-long-time'].index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "duration_map = {\n",
    "    short:'short-time',\n",
    "    medium:'medium-time',\n",
    "    long:'long-time',\n",
    "    verylong:'very-long-time'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = kmodel.labels_\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Insert the Results Into the** ***Bike_Trip_Info***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "bike_trip_info.insert(1,'Duration Class',labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "bike_trip_info['Duration Class'] = bike_trip_info['Duration Class'].map(duration_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "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>Duration</th>\n",
       "      <th>Duration Class</th>\n",
       "      <th>Trip Route Category</th>\n",
       "      <th>Plan Duration</th>\n",
       "      <th>Passholder Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>short-time</td>\n",
       "      <td>One Way</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>short-time</td>\n",
       "      <td>Round Trip</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>short-time</td>\n",
       "      <td>Round Trip</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>short-time</td>\n",
       "      <td>One Way</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>short-time</td>\n",
       "      <td>One Way</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Duration Duration Class Trip Route Category  Plan Duration Passholder Type\n",
       "0        19     short-time             One Way             30    Monthly Pass\n",
       "1        22     short-time          Round Trip             30    Monthly Pass\n",
       "2        26     short-time          Round Trip             30    Monthly Pass\n",
       "3        17     short-time             One Way             30    Monthly Pass\n",
       "4        15     short-time             One Way             30    Monthly Pass"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_trip_info.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Check the Number of Each Duration*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Duration Class\n",
       "short-time        52886\n",
       "medium-time        4692\n",
       "very-long-time      459\n",
       "long-time           374\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_trip_info['Duration Class'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Duration Class Search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### very-long-time Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Plan Duration\n",
       "1      0.923747\n",
       "30     0.065359\n",
       "365    0.010893\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "verylong_counts = bike_trip_info[bike_trip_info['Duration Class']=='very-long-time']['Plan Duration'].value_counts()\n",
    "series1 = verylong_counts/sum(verylong_counts)\n",
    "series1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### long-time Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "long_counts = bike_trip_info[bike_trip_info['Duration Class']=='long-time']['Plan Duration'].value_counts()\n",
    "series2 = long_counts/sum(long_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### medium-time Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "medium_counts = bike_trip_info[bike_trip_info['Duration Class']=='medium-time']['Plan Duration'].value_counts()\n",
    "series3 = medium_counts/sum(medium_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### short-time Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "short_counts = bike_trip_info[bike_trip_info['Duration Class']=='short-time']['Plan Duration'].value_counts()\n",
    "series4 = short_counts/sum(short_counts)\n",
    "series4 = series4.sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Radar Chart**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_length = min(len(series1), len(series2), len(series3), len(series4))\n",
    " \n",
    "series1 = series1[:min_length]\n",
    "series2 = series2[:min_length]\n",
    "series3 = series3[:min_length]\n",
    "series4 = series4[:min_length]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "duration_rate = pd.DataFrame(list(zip(series1,series2,series3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "duration_rate.columns = ['very-long-time','long-time','medium-time']\n",
    "duration_rate.index = ['0','30','365']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "duration_rate = duration_rate[['medium-time','long-time','very-long-time']]\n",
    "duration_rate.insert(0,'short-time',series4.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "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>short-time</th>\n",
       "      <th>medium-time</th>\n",
       "      <th>long-time</th>\n",
       "      <th>very-long-time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.414458</td>\n",
       "      <td>0.873828</td>\n",
       "      <td>0.860963</td>\n",
       "      <td>0.923747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.469727</td>\n",
       "      <td>0.109335</td>\n",
       "      <td>0.101604</td>\n",
       "      <td>0.065359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>0.115796</td>\n",
       "      <td>0.016837</td>\n",
       "      <td>0.037433</td>\n",
       "      <td>0.010893</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     short-time  medium-time  long-time  very-long-time\n",
       "0      0.414458     0.873828   0.860963        0.923747\n",
       "30     0.469727     0.109335   0.101604        0.065359\n",
       "365    0.115796     0.016837   0.037433        0.010893"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duration_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/robot/Desktop/SoftRice/LASharedBikeAnalysis-main/Process_data/duration-rate-radar.html'"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts.charts import Radar\n",
    "from pyecharts import options as opts\n",
    "\n",
    "schema = [(\"0\", 0.81), (\"30\", 0.63), (\"365\", 0.08)]\n",
    "v1 = [duration_rate['short-time'].values.round(2).tolist()]\n",
    "v2 = [duration_rate['medium-time'].values.round(2).tolist()]\n",
    "\n",
    "radar = Radar(init_opts=opts.InitOpts(width='1000px', height='400px'))\n",
    "radar.add_schema(schema=schema, shape='circle')\n",
    "radar.add(\"Short Time\", v1)\n",
    "radar.add(\"Medium Time\", v2)\n",
    "radar.set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n",
    "radar.set_global_opts(title_opts=opts.TitleOpts(title='Duration Rate Analysis'))\n",
    "\n",
    "radar.render('duration-rate-radar.html')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The **majority** of users who use shared-bikes for a **very-long-time** are those who use them **immediately**.\n",
    "- **Over 60%** of users who use shared-bikes for a **short-time** are those who possess **Monthly-Pass**.\n",
    "-  The percentage of those who possess Flex-Pass among **short-time trip** bike users is also the **highest among all types**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4）Round Trip/One Way Comparison Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "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>Duration</th>\n",
       "      <th>Start Time</th>\n",
       "      <th>End Time</th>\n",
       "      <th>Start Hour</th>\n",
       "      <th>Trip Route Category</th>\n",
       "      <th>Plan Duration</th>\n",
       "      <th>Passholder Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>2021-07-01T00:12:00</td>\n",
       "      <td>2021-07-01T00:31:00</td>\n",
       "      <td>00</td>\n",
       "      <td>One Way</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>2021-07-01T00:23:00</td>\n",
       "      <td>2021-07-01T00:45:00</td>\n",
       "      <td>00</td>\n",
       "      <td>Round Trip</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>2021-07-01T00:34:00</td>\n",
       "      <td>2021-07-01T01:00:00</td>\n",
       "      <td>00</td>\n",
       "      <td>Round Trip</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>2021-07-01T00:50:00</td>\n",
       "      <td>2021-07-01T01:07:00</td>\n",
       "      <td>00</td>\n",
       "      <td>One Way</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>2021-07-01T02:38:00</td>\n",
       "      <td>2021-07-01T02:53:00</td>\n",
       "      <td>02</td>\n",
       "      <td>One Way</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Duration           Start Time             End Time Start Hour  \\\n",
       "0        19  2021-07-01T00:12:00  2021-07-01T00:31:00         00   \n",
       "1        22  2021-07-01T00:23:00  2021-07-01T00:45:00         00   \n",
       "2        26  2021-07-01T00:34:00  2021-07-01T01:00:00         00   \n",
       "3        17  2021-07-01T00:50:00  2021-07-01T01:07:00         00   \n",
       "4        15  2021-07-01T02:38:00  2021-07-01T02:53:00         02   \n",
       "\n",
       "  Trip Route Category  Plan Duration Passholder Type  \n",
       "0             One Way             30    Monthly Pass  \n",
       "1          Round Trip             30    Monthly Pass  \n",
       "2          Round Trip             30    Monthly Pass  \n",
       "3             One Way             30    Monthly Pass  \n",
       "4             One Way             30    Monthly Pass  "
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Divide the 'Round Trip'&'One Way'**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_way_trip = bike_info[bike_info['Trip Route Category']=='One Way']\n",
    "round_trip = bike_info[bike_info['Trip Route Category']=='Round Trip']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "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>Duration</th>\n",
       "      <th>Start Time</th>\n",
       "      <th>End Time</th>\n",
       "      <th>Start Hour</th>\n",
       "      <th>Trip Route Category</th>\n",
       "      <th>Plan Duration</th>\n",
       "      <th>Passholder Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>2021-07-01T00:12:00</td>\n",
       "      <td>2021-07-01T00:31:00</td>\n",
       "      <td>00</td>\n",
       "      <td>One Way</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>2021-07-01T00:50:00</td>\n",
       "      <td>2021-07-01T01:07:00</td>\n",
       "      <td>00</td>\n",
       "      <td>One Way</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>2021-07-01T02:38:00</td>\n",
       "      <td>2021-07-01T02:53:00</td>\n",
       "      <td>02</td>\n",
       "      <td>One Way</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10</td>\n",
       "      <td>2021-07-01T03:25:00</td>\n",
       "      <td>2021-07-01T03:35:00</td>\n",
       "      <td>03</td>\n",
       "      <td>One Way</td>\n",
       "      <td>1</td>\n",
       "      <td>Walk-up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16</td>\n",
       "      <td>2021-07-01T03:51:00</td>\n",
       "      <td>2021-07-01T04:07:00</td>\n",
       "      <td>03</td>\n",
       "      <td>One Way</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Duration           Start Time             End Time Start Hour  \\\n",
       "0        19  2021-07-01T00:12:00  2021-07-01T00:31:00         00   \n",
       "3        17  2021-07-01T00:50:00  2021-07-01T01:07:00         00   \n",
       "4        15  2021-07-01T02:38:00  2021-07-01T02:53:00         02   \n",
       "5        10  2021-07-01T03:25:00  2021-07-01T03:35:00         03   \n",
       "6        16  2021-07-01T03:51:00  2021-07-01T04:07:00         03   \n",
       "\n",
       "  Trip Route Category  Plan Duration Passholder Type  \n",
       "0             One Way             30    Monthly Pass  \n",
       "3             One Way             30    Monthly Pass  \n",
       "4             One Way             30    Monthly Pass  \n",
       "5             One Way              1         Walk-up  \n",
       "6             One Way             30    Monthly Pass  "
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_way_trip.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "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>Duration</th>\n",
       "      <th>Start Time</th>\n",
       "      <th>End Time</th>\n",
       "      <th>Start Hour</th>\n",
       "      <th>Trip Route Category</th>\n",
       "      <th>Plan Duration</th>\n",
       "      <th>Passholder Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>2021-07-01T00:23:00</td>\n",
       "      <td>2021-07-01T00:45:00</td>\n",
       "      <td>00</td>\n",
       "      <td>Round Trip</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>2021-07-01T00:34:00</td>\n",
       "      <td>2021-07-01T01:00:00</td>\n",
       "      <td>00</td>\n",
       "      <td>Round Trip</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>2021-07-01T07:50:00</td>\n",
       "      <td>2021-07-01T08:13:00</td>\n",
       "      <td>07</td>\n",
       "      <td>Round Trip</td>\n",
       "      <td>30</td>\n",
       "      <td>Monthly Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>23</td>\n",
       "      <td>2021-07-01T07:52:00</td>\n",
       "      <td>2021-07-01T08:15:00</td>\n",
       "      <td>07</td>\n",
       "      <td>Round Trip</td>\n",
       "      <td>1</td>\n",
       "      <td>One Day Pass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>72</td>\n",
       "      <td>2021-07-01T08:22:00</td>\n",
       "      <td>2021-07-01T09:34:00</td>\n",
       "      <td>08</td>\n",
       "      <td>Round Trip</td>\n",
       "      <td>1</td>\n",
       "      <td>One Day Pass</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Duration           Start Time             End Time Start Hour  \\\n",
       "1         22  2021-07-01T00:23:00  2021-07-01T00:45:00         00   \n",
       "2         26  2021-07-01T00:34:00  2021-07-01T01:00:00         00   \n",
       "23        23  2021-07-01T07:50:00  2021-07-01T08:13:00         07   \n",
       "25        23  2021-07-01T07:52:00  2021-07-01T08:15:00         07   \n",
       "33        72  2021-07-01T08:22:00  2021-07-01T09:34:00         08   \n",
       "\n",
       "   Trip Route Category  Plan Duration Passholder Type  \n",
       "1           Round Trip             30    Monthly Pass  \n",
       "2           Round Trip             30    Monthly Pass  \n",
       "23          Round Trip             30    Monthly Pass  \n",
       "25          Round Trip              1    One Day Pass  \n",
       "33          Round Trip              1    One Day Pass  "
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round_trip.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze 'One Way' and 'Round Starting' Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_way_hour = one_way_trip['Start Hour'].value_counts().sort_index()\n",
    "round_hour = round_trip['Start Hour'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/robot/Desktop/SoftRice/LASharedBikeAnalysis-main/Process_data/departure_time_analysis.html'"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts.charts import Line\n",
    "from pyecharts import options as opts\n",
    "\n",
    "x1 = list(map(int, one_way_hour.index))\n",
    "y1 = one_way_hour.values.tolist()\n",
    "\n",
    "x2 = list(map(int, round_hour.index))\n",
    "y2 = round_hour.values.tolist()\n",
    "\n",
    "line = Line(init_opts=opts.InitOpts(width='1000px', height='400px'))\n",
    "line.add_xaxis(x1)\n",
    "line.add_yaxis(\"one way\", y1, markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_='max')]))\n",
    "line.add_yaxis(\"round\", y2, markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_='max')]))\n",
    "line.set_global_opts(title_opts=opts.TitleOpts(title='One-way/round-trip departure time analysis'))\n",
    "\n",
    "line.render('departure_time_analysis.html')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze the proportion of different membership card holders in One Way and Round"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "oneway_rate = one_way_trip['Passholder Type'].value_counts()/sum(one_way_trip['Passholder Type'].value_counts())\n",
    "#oneway_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "round_rate = round_trip['Passholder Type'].value_counts()/sum(round_trip['Passholder Type'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/robot/Desktop/SoftRice/LASharedBikeAnalysis-main/Process_data/One-way-pie.html'"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts.charts import Pie\n",
    "from pyecharts import options as opts\n",
    "\n",
    "attr1 = oneway_rate.index.tolist()\n",
    "v1 = oneway_rate.values.tolist()\n",
    "\n",
    "pie1 = Pie(init_opts=opts.InitOpts(width='1000px', height='400px'))\n",
    "pie1.add(\"\", [list(z) for z in zip(attr1, v1)], radius=[\"30%\", \"75%\"])\n",
    "pie1.set_global_opts(title_opts=opts.TitleOpts(title=\"One Way\"))\n",
    "pie1.set_series_opts(label_opts=opts.LabelOpts(formatter=\"{b}: {c}\"))\n",
    "\n",
    "pie1.render('One-way-pie.html')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/robot/Desktop/SoftRice/LASharedBikeAnalysis-main/Process_data/Round-pie.html'"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyecharts.charts import Pie\n",
    "from pyecharts import options as opts\n",
    "\n",
    "attr2 = round_rate.index.tolist()\n",
    "v2 = round_rate.values.tolist()\n",
    "\n",
    "pie2 = Pie(init_opts=opts.InitOpts(width='1000px', height='400px'))\n",
    "pie2.add(\"\", [list(z) for z in zip(attr2, v2)], radius=[\"30%\", \"75%\"])\n",
    "pie2.set_global_opts(title_opts=opts.TitleOpts(title=\"Round\"))\n",
    "pie2.set_series_opts(label_opts=opts.LabelOpts(formatter=\"{b}: {c}\"))\n",
    "\n",
    "pie2.render('Round-pie.html')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The proportion of users with membership cards for One Way Trip's shared bicycles is **very high**, accounting for over 70%. Most of them are **monthly card holders**.\n",
    "- The majority of **Round Trip's** shared bicycle users are casual users, with only **thirty percent** having membership cards, and most of these members are also **monthly card holders**.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aanlyze Relation Between'One Way', 'Round' And the Duration of Use of Shared Bike "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "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>Duration</th>\n",
       "      <th>Trip Route Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>One Way</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>Round Trip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>Round Trip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>One Way</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>One Way</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Duration Trip Route Category\n",
       "0        19             One Way\n",
       "1        22          Round Trip\n",
       "2        26          Round Trip\n",
       "3        17             One Way\n",
       "4        15             One Way"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duration_cate_info = bike_info[['Duration','Trip Route Category']]\n",
    "duration_cate_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_way_duration = duration_cate_info[duration_cate_info['Trip Route Category']=='One Way']\n",
    "#one_way_duration\n",
    "round_duration = duration_cate_info[duration_cate_info['Trip Route Category']=='Round Trip']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp_frame1 = one_way_duration.describe()\n",
    "tmp_frame2 = round_duration.describe()\n",
    "tmp_frame1.columns = ['One Way']\n",
    "tmp_frame2.columns = ['Round']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Output Describe Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "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>One Way</th>\n",
       "      <th>Round</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>45631.000000</td>\n",
       "      <td>12780.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>39.475839</td>\n",
       "      <td>68.067136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>136.662651</td>\n",
       "      <td>129.701820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1440.000000</td>\n",
       "      <td>1440.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            One Way         Round\n",
       "count  45631.000000  12780.000000\n",
       "mean      39.475839     68.067136\n",
       "std      136.662651    129.701820\n",
       "min        1.000000      1.000000\n",
       "25%        8.000000     18.000000\n",
       "50%       15.000000     35.000000\n",
       "75%       27.000000     75.000000\n",
       "max     1440.000000   1440.000000"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_frame1.join(tmp_frame2) #collapse frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Based on the **mean**, **median**, and **upper and lower quartiles** in the table above, it is evident that the duration of use for **Round Trip** bicycles is generally longer than that of **One Way Trip** bicycles, which is consistent with expectations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  }
 ],
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