Mobike.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "81c7f004",
   "metadata": {},
   "outputs": [],
   "source": [
    "#加载工具包\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a0caa98a",
   "metadata": {},
   "outputs": [],
   "source": [
    "mobike = pd.read_csv('mobike.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3bc44474",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6427 entries, 0 to 6426\n",
      "Data columns (total 15 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Unnamed: 0         6427 non-null   int64  \n",
      " 1   user_id            6427 non-null   int64  \n",
      " 2   start_time         6427 non-null   object \n",
      " 3   end_time           6427 non-null   object \n",
      " 4   timeduration       6427 non-null   int64  \n",
      " 5   bikeid             6427 non-null   int64  \n",
      " 6   tripduration       6427 non-null   int64  \n",
      " 7   from_station_id    6427 non-null   int64  \n",
      " 8   from_station_name  6427 non-null   object \n",
      " 9   to_station_id      6427 non-null   int64  \n",
      " 10  to_station_name    6427 non-null   object \n",
      " 11  usertype           6427 non-null   object \n",
      " 12  gender             5938 non-null   object \n",
      " 13  birthyear          5956 non-null   float64\n",
      " 14  age                6427 non-null   object \n",
      "dtypes: float64(1), int64(7), object(7)\n",
      "memory usage: 753.3+ KB\n"
     ]
    }
   ],
   "source": [
    "mobike.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "59b6c978",
   "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>Unnamed: 0</th>\n",
       "      <th>user_id</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>timeduration</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>from_station_id</th>\n",
       "      <th>from_station_name</th>\n",
       "      <th>to_station_id</th>\n",
       "      <th>to_station_name</th>\n",
       "      <th>usertype</th>\n",
       "      <th>gender</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>439283</td>\n",
       "      <td>21499218</td>\n",
       "      <td>11/14/2018 7:37</td>\n",
       "      <td>11/14/2018 7:44</td>\n",
       "      <td>7</td>\n",
       "      <td>2631</td>\n",
       "      <td>436</td>\n",
       "      <td>319</td>\n",
       "      <td>Greenview Ave &amp; Diversey Pkwy</td>\n",
       "      <td>67</td>\n",
       "      <td>Sheffield Ave &amp; Fullerton Ave</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Male</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>603317</td>\n",
       "      <td>21694389</td>\n",
       "      <td>12/18/2018 19:02</td>\n",
       "      <td>12/18/2018 19:10</td>\n",
       "      <td>7</td>\n",
       "      <td>1565</td>\n",
       "      <td>445</td>\n",
       "      <td>164</td>\n",
       "      <td>Franklin St &amp; Lake St</td>\n",
       "      <td>195</td>\n",
       "      <td>Columbus Dr &amp; Randolph St</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Male</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>109957</td>\n",
       "      <td>21110722</td>\n",
       "      <td>10/9/2018 12:37</td>\n",
       "      <td>10/9/2018 12:55</td>\n",
       "      <td>18</td>\n",
       "      <td>2231</td>\n",
       "      <td>1090</td>\n",
       "      <td>163</td>\n",
       "      <td>Damen Ave &amp; Clybourn Ave</td>\n",
       "      <td>69</td>\n",
       "      <td>Damen Ave &amp; Pierce Ave</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Male</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>428082</td>\n",
       "      <td>21485409</td>\n",
       "      <td>11/12/2018 12:30</td>\n",
       "      <td>11/12/2018 12:40</td>\n",
       "      <td>9</td>\n",
       "      <td>4226</td>\n",
       "      <td>581</td>\n",
       "      <td>226</td>\n",
       "      <td>Racine Ave &amp; Belmont Ave</td>\n",
       "      <td>308</td>\n",
       "      <td>Seeley Ave &amp; Roscoe St</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Female</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>395437</td>\n",
       "      <td>21445994</td>\n",
       "      <td>11/7/2018 7:29</td>\n",
       "      <td>11/7/2018 7:35</td>\n",
       "      <td>6</td>\n",
       "      <td>3475</td>\n",
       "      <td>390</td>\n",
       "      <td>77</td>\n",
       "      <td>Clinton St &amp; Madison St</td>\n",
       "      <td>621</td>\n",
       "      <td>Aberdeen St &amp; Randolph St</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Male</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>73632</td>\n",
       "      <td>21068843</td>\n",
       "      <td>10/6/2018 12:04</td>\n",
       "      <td>10/6/2018 12:17</td>\n",
       "      <td>12</td>\n",
       "      <td>2173</td>\n",
       "      <td>772</td>\n",
       "      <td>321</td>\n",
       "      <td>Wabash Ave &amp; 9th St</td>\n",
       "      <td>134</td>\n",
       "      <td>Peoria St &amp; Jackson Blvd</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Male</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>317778</td>\n",
       "      <td>21354901</td>\n",
       "      <td>10/29/2018 17:26</td>\n",
       "      <td>10/29/2018 17:35</td>\n",
       "      <td>8</td>\n",
       "      <td>1735</td>\n",
       "      <td>505</td>\n",
       "      <td>90</td>\n",
       "      <td>Millennium Park</td>\n",
       "      <td>255</td>\n",
       "      <td>Indiana Ave &amp; Roosevelt Rd</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Female</td>\n",
       "      <td>1971.0</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>371796</td>\n",
       "      <td>21417733</td>\n",
       "      <td>11/3/2018 16:32</td>\n",
       "      <td>11/3/2018 16:54</td>\n",
       "      <td>22</td>\n",
       "      <td>983</td>\n",
       "      <td>1347</td>\n",
       "      <td>31</td>\n",
       "      <td>Franklin St &amp; Chicago Ave</td>\n",
       "      <td>315</td>\n",
       "      <td>Elston Ave &amp; Wabansia Ave</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>333577</td>\n",
       "      <td>21373047</td>\n",
       "      <td>10/31/2018 6:15</td>\n",
       "      <td>10/31/2018 6:29</td>\n",
       "      <td>13</td>\n",
       "      <td>2178</td>\n",
       "      <td>819</td>\n",
       "      <td>77</td>\n",
       "      <td>Clinton St &amp; Madison St</td>\n",
       "      <td>331</td>\n",
       "      <td>Halsted St &amp; Clybourn Ave (*)</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Male</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>261523</td>\n",
       "      <td>21288683</td>\n",
       "      <td>10/23/2018 18:11</td>\n",
       "      <td>10/23/2018 18:14</td>\n",
       "      <td>3</td>\n",
       "      <td>3888</td>\n",
       "      <td>225</td>\n",
       "      <td>74</td>\n",
       "      <td>Kingsbury St &amp; Erie St</td>\n",
       "      <td>84</td>\n",
       "      <td>Milwaukee Ave &amp; Grand Ave</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Male</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0   user_id        start_time          end_time  timeduration  \\\n",
       "0      439283  21499218   11/14/2018 7:37   11/14/2018 7:44             7   \n",
       "1      603317  21694389  12/18/2018 19:02  12/18/2018 19:10             7   \n",
       "2      109957  21110722   10/9/2018 12:37   10/9/2018 12:55            18   \n",
       "3      428082  21485409  11/12/2018 12:30  11/12/2018 12:40             9   \n",
       "4      395437  21445994    11/7/2018 7:29    11/7/2018 7:35             6   \n",
       "5       73632  21068843   10/6/2018 12:04   10/6/2018 12:17            12   \n",
       "6      317778  21354901  10/29/2018 17:26  10/29/2018 17:35             8   \n",
       "7      371796  21417733   11/3/2018 16:32   11/3/2018 16:54            22   \n",
       "8      333577  21373047   10/31/2018 6:15   10/31/2018 6:29            13   \n",
       "9      261523  21288683  10/23/2018 18:11  10/23/2018 18:14             3   \n",
       "\n",
       "   bikeid  tripduration  from_station_id              from_station_name  \\\n",
       "0    2631           436              319  Greenview Ave & Diversey Pkwy   \n",
       "1    1565           445              164          Franklin St & Lake St   \n",
       "2    2231          1090              163       Damen Ave & Clybourn Ave   \n",
       "3    4226           581              226       Racine Ave & Belmont Ave   \n",
       "4    3475           390               77        Clinton St & Madison St   \n",
       "5    2173           772              321            Wabash Ave & 9th St   \n",
       "6    1735           505               90                Millennium Park   \n",
       "7     983          1347               31      Franklin St & Chicago Ave   \n",
       "8    2178           819               77        Clinton St & Madison St   \n",
       "9    3888           225               74         Kingsbury St & Erie St   \n",
       "\n",
       "   to_station_id                to_station_name    usertype  gender  \\\n",
       "0             67  Sheffield Ave & Fullerton Ave  Subscriber    Male   \n",
       "1            195      Columbus Dr & Randolph St  Subscriber    Male   \n",
       "2             69         Damen Ave & Pierce Ave    Customer    Male   \n",
       "3            308         Seeley Ave & Roscoe St  Subscriber  Female   \n",
       "4            621      Aberdeen St & Randolph St  Subscriber    Male   \n",
       "5            134       Peoria St & Jackson Blvd  Subscriber    Male   \n",
       "6            255     Indiana Ave & Roosevelt Rd  Subscriber  Female   \n",
       "7            315      Elston Ave & Wabansia Ave    Customer     NaN   \n",
       "8            331  Halsted St & Clybourn Ave (*)  Subscriber    Male   \n",
       "9             84      Milwaukee Ave & Grand Ave  Subscriber    Male   \n",
       "\n",
       "   birthyear age  \n",
       "0     1982.0  37  \n",
       "1     1988.0  31  \n",
       "2     1989.0  30  \n",
       "3     1989.0  30  \n",
       "4     1979.0  40  \n",
       "5     1994.0  25  \n",
       "6     1971.0  48  \n",
       "7        NaN      \n",
       "8     1991.0  28  \n",
       "9     1989.0  30  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobike.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c5511920",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>timeduration</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>usertype</th>\n",
       "      <th>gender</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11/14/2018 7:37</td>\n",
       "      <td>11/14/2018 7:44</td>\n",
       "      <td>7</td>\n",
       "      <td>436</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Male</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12/18/2018 19:02</td>\n",
       "      <td>12/18/2018 19:10</td>\n",
       "      <td>7</td>\n",
       "      <td>445</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Male</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10/9/2018 12:37</td>\n",
       "      <td>10/9/2018 12:55</td>\n",
       "      <td>18</td>\n",
       "      <td>1090</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Male</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11/12/2018 12:30</td>\n",
       "      <td>11/12/2018 12:40</td>\n",
       "      <td>9</td>\n",
       "      <td>581</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Female</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11/7/2018 7:29</td>\n",
       "      <td>11/7/2018 7:35</td>\n",
       "      <td>6</td>\n",
       "      <td>390</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Male</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         start_time          end_time  timeduration  tripduration    usertype  \\\n",
       "0   11/14/2018 7:37   11/14/2018 7:44             7           436  Subscriber   \n",
       "1  12/18/2018 19:02  12/18/2018 19:10             7           445  Subscriber   \n",
       "2   10/9/2018 12:37   10/9/2018 12:55            18          1090    Customer   \n",
       "3  11/12/2018 12:30  11/12/2018 12:40             9           581  Subscriber   \n",
       "4    11/7/2018 7:29    11/7/2018 7:35             6           390  Subscriber   \n",
       "\n",
       "   gender  birthyear age  \n",
       "0    Male     1982.0  37  \n",
       "1    Male     1988.0  31  \n",
       "2    Male     1989.0  30  \n",
       "3  Female     1989.0  30  \n",
       "4    Male     1979.0  40  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除对业务分析没有实际用处的值\n",
    "mobike.drop(['Unnamed: 0','user_id','bikeid','from_station_id','to_station_id',\n",
    "             'from_station_name','to_station_name'],axis=1,inplace=True)\n",
    "mobike.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9ca7df01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_time      0.000000\n",
       "end_time        0.000000\n",
       "timeduration    0.000000\n",
       "tripduration    0.000000\n",
       "usertype        0.000000\n",
       "gender          0.076085\n",
       "birthyear       0.073285\n",
       "age             0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除空值\n",
    "mobike.isnull().sum()/len(mobike)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "afd352e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_time      False\n",
       "end_time        False\n",
       "timeduration    False\n",
       "tripduration    False\n",
       "usertype        False\n",
       "gender          False\n",
       "birthyear       False\n",
       "age             False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobike.dropna(inplace=True)\n",
    "mobike.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "47e73492",
   "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>timeduration</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>birthyear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5938.000000</td>\n",
       "      <td>5938.000000</td>\n",
       "      <td>5938.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.806837</td>\n",
       "      <td>791.862243</td>\n",
       "      <td>1982.504715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.464567</td>\n",
       "      <td>3334.022596</td>\n",
       "      <td>11.106755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>1918.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>337.250000</td>\n",
       "      <td>1977.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>1986.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>856.000000</td>\n",
       "      <td>1991.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>59.000000</td>\n",
       "      <td>233732.000000</td>\n",
       "      <td>2002.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       timeduration   tripduration    birthyear\n",
       "count   5938.000000    5938.000000  5938.000000\n",
       "mean      10.806837     791.862243  1982.504715\n",
       "std        8.464567    3334.022596    11.106755\n",
       "min        0.000000      61.000000  1918.000000\n",
       "25%        5.000000     337.250000  1977.000000\n",
       "50%        8.000000     527.000000  1986.000000\n",
       "75%       14.000000     856.000000  1991.000000\n",
       "max       59.000000  233732.000000  2002.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除与实际情况不符的异常值\n",
    "mobike.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "beb63073",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_time       object\n",
       "end_time         object\n",
       "timeduration      int64\n",
       "tripduration      int64\n",
       "usertype         object\n",
       "gender           object\n",
       "birthyear       float64\n",
       "age              object\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobike.dtypes\n",
    "# 发现age本该是数值型变量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a43e8200",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_time       object\n",
       "end_time         object\n",
       "timeduration      int64\n",
       "tripduration      int64\n",
       "usertype         object\n",
       "gender           object\n",
       "birthyear       float64\n",
       "age               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobike.age = pd.to_numeric(mobike.age)\n",
    "mobike.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3a278554",
   "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>timeduration</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5938.000000</td>\n",
       "      <td>5938.000000</td>\n",
       "      <td>5938.000000</td>\n",
       "      <td>5938.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.806837</td>\n",
       "      <td>791.862243</td>\n",
       "      <td>1982.504715</td>\n",
       "      <td>36.495285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.464567</td>\n",
       "      <td>3334.022596</td>\n",
       "      <td>11.106755</td>\n",
       "      <td>11.106755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>1918.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>337.250000</td>\n",
       "      <td>1977.000000</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>527.000000</td>\n",
       "      <td>1986.000000</td>\n",
       "      <td>33.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>856.000000</td>\n",
       "      <td>1991.000000</td>\n",
       "      <td>42.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>59.000000</td>\n",
       "      <td>233732.000000</td>\n",
       "      <td>2002.000000</td>\n",
       "      <td>101.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       timeduration   tripduration    birthyear          age\n",
       "count   5938.000000    5938.000000  5938.000000  5938.000000\n",
       "mean      10.806837     791.862243  1982.504715    36.495285\n",
       "std        8.464567    3334.022596    11.106755    11.106755\n",
       "min        0.000000      61.000000  1918.000000    17.000000\n",
       "25%        5.000000     337.250000  1977.000000    28.000000\n",
       "50%        8.000000     527.000000  1986.000000    33.000000\n",
       "75%       14.000000     856.000000  1991.000000    42.000000\n",
       "max       59.000000  233732.000000  2002.000000   101.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobike.describe()\n",
    "# 年龄101过大、tripduration过大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "608e1b52",
   "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>timeduration</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5900.000000</td>\n",
       "      <td>5900.000000</td>\n",
       "      <td>5900.000000</td>\n",
       "      <td>5900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.657966</td>\n",
       "      <td>679.307119</td>\n",
       "      <td>1982.537797</td>\n",
       "      <td>36.462203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.158206</td>\n",
       "      <td>526.060766</td>\n",
       "      <td>11.037259</td>\n",
       "      <td>11.037259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>1940.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>336.750000</td>\n",
       "      <td>1977.000000</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>1986.000000</td>\n",
       "      <td>33.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>846.250000</td>\n",
       "      <td>1991.000000</td>\n",
       "      <td>42.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>59.000000</td>\n",
       "      <td>4899.000000</td>\n",
       "      <td>2002.000000</td>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       timeduration  tripduration    birthyear          age\n",
       "count   5900.000000   5900.000000  5900.000000  5900.000000\n",
       "mean      10.657966    679.307119  1982.537797    36.462203\n",
       "std        8.158206    526.060766    11.037259    11.037259\n",
       "min        0.000000     61.000000  1940.000000    17.000000\n",
       "25%        5.000000    336.750000  1977.000000    28.000000\n",
       "50%        8.000000    525.000000  1986.000000    33.000000\n",
       "75%       14.000000    846.250000  1991.000000    42.000000\n",
       "max       59.000000   4899.000000  2002.000000    79.000000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mobike[mobike.tripduration>5000])\n",
    "mobike = mobike[(mobike.age<80) & (mobike.tripduration<=5000)]\n",
    "mobike.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4d61bdae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 处理时间数据，转换变量格式，以便进行计算\n",
    "# 先处理时间数据，否则时间数据还是object，在get——dummies时也会被转化\n",
    "mobike.start_time = pd.to_datetime(mobike.start_time)\n",
    "mobike.end_time = pd.to_datetime(mobike.end_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b2e9884a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>timeduration</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>age</th>\n",
       "      <th>usertype_Customer</th>\n",
       "      <th>usertype_Subscriber</th>\n",
       "      <th>gender_Female</th>\n",
       "      <th>gender_Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-11-14 07:37:00</td>\n",
       "      <td>2018-11-14 07:44:00</td>\n",
       "      <td>7</td>\n",
       "      <td>436</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-12-18 19:02:00</td>\n",
       "      <td>2018-12-18 19:10:00</td>\n",
       "      <td>7</td>\n",
       "      <td>445</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-10-09 12:37:00</td>\n",
       "      <td>2018-10-09 12:55:00</td>\n",
       "      <td>18</td>\n",
       "      <td>1090</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-11-12 12:30:00</td>\n",
       "      <td>2018-11-12 12:40:00</td>\n",
       "      <td>9</td>\n",
       "      <td>581</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-11-07 07:29:00</td>\n",
       "      <td>2018-11-07 07:35:00</td>\n",
       "      <td>6</td>\n",
       "      <td>390</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           start_time            end_time  timeduration  tripduration  \\\n",
       "0 2018-11-14 07:37:00 2018-11-14 07:44:00             7           436   \n",
       "1 2018-12-18 19:02:00 2018-12-18 19:10:00             7           445   \n",
       "2 2018-10-09 12:37:00 2018-10-09 12:55:00            18          1090   \n",
       "3 2018-11-12 12:30:00 2018-11-12 12:40:00             9           581   \n",
       "4 2018-11-07 07:29:00 2018-11-07 07:35:00             6           390   \n",
       "\n",
       "   birthyear  age  usertype_Customer  usertype_Subscriber  gender_Female  \\\n",
       "0     1982.0   37                  0                    1              0   \n",
       "1     1988.0   31                  0                    1              0   \n",
       "2     1989.0   30                  1                    0              0   \n",
       "3     1989.0   30                  0                    1              1   \n",
       "4     1979.0   40                  0                    1              0   \n",
       "\n",
       "   gender_Male  \n",
       "0            1  \n",
       "1            1  \n",
       "2            1  \n",
       "3            0  \n",
       "4            1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将类别型变量转换成数字行变量\n",
    "mobike = pd.get_dummies(mobike)\n",
    "mobike.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2c84ba2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>timeduration</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>age</th>\n",
       "      <th>usertype_customer</th>\n",
       "      <th>usertype_subscriber</th>\n",
       "      <th>gender_female</th>\n",
       "      <th>gender_male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-11-14 07:37:00</td>\n",
       "      <td>2018-11-14 07:44:00</td>\n",
       "      <td>7</td>\n",
       "      <td>436</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-12-18 19:02:00</td>\n",
       "      <td>2018-12-18 19:10:00</td>\n",
       "      <td>7</td>\n",
       "      <td>445</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-10-09 12:37:00</td>\n",
       "      <td>2018-10-09 12:55:00</td>\n",
       "      <td>18</td>\n",
       "      <td>1090</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-11-12 12:30:00</td>\n",
       "      <td>2018-11-12 12:40:00</td>\n",
       "      <td>9</td>\n",
       "      <td>581</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-11-07 07:29:00</td>\n",
       "      <td>2018-11-07 07:35:00</td>\n",
       "      <td>6</td>\n",
       "      <td>390</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           start_time            end_time  timeduration  tripduration  \\\n",
       "0 2018-11-14 07:37:00 2018-11-14 07:44:00             7           436   \n",
       "1 2018-12-18 19:02:00 2018-12-18 19:10:00             7           445   \n",
       "2 2018-10-09 12:37:00 2018-10-09 12:55:00            18          1090   \n",
       "3 2018-11-12 12:30:00 2018-11-12 12:40:00             9           581   \n",
       "4 2018-11-07 07:29:00 2018-11-07 07:35:00             6           390   \n",
       "\n",
       "   birthyear  age  usertype_customer  usertype_subscriber  gender_female  \\\n",
       "0     1982.0   37                  0                    1              0   \n",
       "1     1988.0   31                  0                    1              0   \n",
       "2     1989.0   30                  1                    0              0   \n",
       "3     1989.0   30                  0                    1              1   \n",
       "4     1979.0   40                  0                    1              0   \n",
       "\n",
       "   gender_male  \n",
       "0            1  \n",
       "1            1  \n",
       "2            1  \n",
       "3            0  \n",
       "4            1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 相关性\n",
    "mobike.columns = mobike.columns.str.lower()\n",
    "mobike.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e507121e",
   "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>timeduration</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>age</th>\n",
       "      <th>usertype_subscriber</th>\n",
       "      <th>gender_male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>timeduration</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.929463</td>\n",
       "      <td>0.031558</td>\n",
       "      <td>-0.188221</td>\n",
       "      <td>-0.054500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tripduration</th>\n",
       "      <td>0.929463</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.028558</td>\n",
       "      <td>-0.250622</td>\n",
       "      <td>-0.056631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.031558</td>\n",
       "      <td>0.028558</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.050092</td>\n",
       "      <td>0.073693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usertype_subscriber</th>\n",
       "      <td>-0.188221</td>\n",
       "      <td>-0.250622</td>\n",
       "      <td>0.050092</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.016852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender_male</th>\n",
       "      <td>-0.054500</td>\n",
       "      <td>-0.056631</td>\n",
       "      <td>0.073693</td>\n",
       "      <td>0.016852</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     timeduration  tripduration       age  \\\n",
       "timeduration             1.000000      0.929463  0.031558   \n",
       "tripduration             0.929463      1.000000  0.028558   \n",
       "age                      0.031558      0.028558  1.000000   \n",
       "usertype_subscriber     -0.188221     -0.250622  0.050092   \n",
       "gender_male             -0.054500     -0.056631  0.073693   \n",
       "\n",
       "                     usertype_subscriber  gender_male  \n",
       "timeduration                   -0.188221    -0.054500  \n",
       "tripduration                   -0.250622    -0.056631  \n",
       "age                             0.050092     0.073693  \n",
       "usertype_subscriber             1.000000     0.016852  \n",
       "gender_male                     0.016852     1.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobike[['timeduration','tripduration','age','usertype_subscriber','gender_male']].corr()\n",
    "# timeduration 和tripduration 强相关，模型中选其一即可"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ec454910",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.448417</td>\n",
       "      <td>0.048730</td>\n",
       "      <td>0.18291</td>\n",
       "      <td>0.528724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.448417</td>\n",
       "      <td>-0.494930</td>\n",
       "      <td>0.18291</td>\n",
       "      <td>0.528724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.900033</td>\n",
       "      <td>-0.585540</td>\n",
       "      <td>-5.46718</td>\n",
       "      <td>0.528724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.203244</td>\n",
       "      <td>-0.585540</td>\n",
       "      <td>0.18291</td>\n",
       "      <td>-1.891347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.571003</td>\n",
       "      <td>0.320559</td>\n",
       "      <td>0.18291</td>\n",
       "      <td>0.528724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5895</th>\n",
       "      <td>-0.448417</td>\n",
       "      <td>-0.041880</td>\n",
       "      <td>0.18291</td>\n",
       "      <td>-1.891347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5896</th>\n",
       "      <td>0.532274</td>\n",
       "      <td>-0.676149</td>\n",
       "      <td>0.18291</td>\n",
       "      <td>0.528724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5897</th>\n",
       "      <td>1.022620</td>\n",
       "      <td>-0.313710</td>\n",
       "      <td>0.18291</td>\n",
       "      <td>-1.891347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5898</th>\n",
       "      <td>-0.325830</td>\n",
       "      <td>-0.132490</td>\n",
       "      <td>0.18291</td>\n",
       "      <td>0.528724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5899</th>\n",
       "      <td>-0.325830</td>\n",
       "      <td>-0.676149</td>\n",
       "      <td>0.18291</td>\n",
       "      <td>-1.891347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5900 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0         1        2         3\n",
       "0    -0.448417  0.048730  0.18291  0.528724\n",
       "1    -0.448417 -0.494930  0.18291  0.528724\n",
       "2     0.900033 -0.585540 -5.46718  0.528724\n",
       "3    -0.203244 -0.585540  0.18291 -1.891347\n",
       "4    -0.571003  0.320559  0.18291  0.528724\n",
       "...        ...       ...      ...       ...\n",
       "5895 -0.448417 -0.041880  0.18291 -1.891347\n",
       "5896  0.532274 -0.676149  0.18291  0.528724\n",
       "5897  1.022620 -0.313710  0.18291 -1.891347\n",
       "5898 -0.325830 -0.132490  0.18291  0.528724\n",
       "5899 -0.325830 -0.676149  0.18291 -1.891347\n",
       "\n",
       "[5900 rows x 4 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#选出合适的特征变量\n",
    "x = mobike[['timeduration','age','usertype_subscriber','gender_male']]\n",
    "# 数据标准化\n",
    "from sklearn.preprocessing import scale\n",
    "x = pd.DataFrame(scale(x))\n",
    "\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "88bc9093",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=3, random_state=10)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1.模型建立\n",
    "from sklearn import cluster\n",
    "\n",
    "# 3类\n",
    "model = cluster.KMeans(n_clusters=3,random_state=10)\n",
    "model.fit(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a4614fd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>timeduration</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>age</th>\n",
       "      <th>usertype_customer</th>\n",
       "      <th>usertype_subscriber</th>\n",
       "      <th>gender_female</th>\n",
       "      <th>gender_male</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-11-14 07:37:00</td>\n",
       "      <td>2018-11-14 07:44:00</td>\n",
       "      <td>7</td>\n",
       "      <td>436</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-12-18 19:02:00</td>\n",
       "      <td>2018-12-18 19:10:00</td>\n",
       "      <td>7</td>\n",
       "      <td>445</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-10-09 12:37:00</td>\n",
       "      <td>2018-10-09 12:55:00</td>\n",
       "      <td>18</td>\n",
       "      <td>1090</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-11-12 12:30:00</td>\n",
       "      <td>2018-11-12 12:40:00</td>\n",
       "      <td>9</td>\n",
       "      <td>581</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-11-07 07:29:00</td>\n",
       "      <td>2018-11-07 07:35:00</td>\n",
       "      <td>6</td>\n",
       "      <td>390</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           start_time            end_time  timeduration  tripduration  \\\n",
       "0 2018-11-14 07:37:00 2018-11-14 07:44:00             7           436   \n",
       "1 2018-12-18 19:02:00 2018-12-18 19:10:00             7           445   \n",
       "2 2018-10-09 12:37:00 2018-10-09 12:55:00            18          1090   \n",
       "3 2018-11-12 12:30:00 2018-11-12 12:40:00             9           581   \n",
       "4 2018-11-07 07:29:00 2018-11-07 07:35:00             6           390   \n",
       "\n",
       "   birthyear  age  usertype_customer  usertype_subscriber  gender_female  \\\n",
       "0     1982.0   37                  0                    1              0   \n",
       "1     1988.0   31                  0                    1              0   \n",
       "2     1989.0   30                  1                    0              0   \n",
       "3     1989.0   30                  0                    1              1   \n",
       "4     1979.0   40                  0                    1              0   \n",
       "\n",
       "   gender_male  cluster  \n",
       "0            1        1  \n",
       "1            1        1  \n",
       "2            1        2  \n",
       "3            0        0  \n",
       "4            1        1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2.可视化和groupby（）评估分群效果\n",
    "# 提取标签，查看分类效果\n",
    "mobike['cluster'] = model.labels_\n",
    "\n",
    "mobike.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a6d55056",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='gender_male', ylabel='usertype_subscriber'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制散点图 查看分群结果\n",
    "# 横坐标为age，纵坐标为usertype——subscriber，类别为分类类别\n",
    "sns.scatterplot(x='gender_male',y='usertype_subscriber',hue='cluster',data=mobike)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "60954413",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "聚类个数为3时，轮廓系数： 0.47352012430664386\n"
     ]
    }
   ],
   "source": [
    "# 3.轮廓系数评估模型效果\n",
    "from sklearn import metrics\n",
    "\n",
    "x_cluster = model.fit_predict(x) #个体与群的距离\n",
    "\n",
    "score = metrics.silhouette_score(x,x_cluster) #评分越高，个体与群越接近；评分越低，个体与群越远\n",
    "print('聚类个数为3时，轮廓系数：',score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e90bc8e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 肘方法评估聚类个数\n",
    "wcss =[]\n",
    "for i in range(1,11):\n",
    "    kmeans = cluster.KMeans(n_clusters=i, init='k-means++',random_state=42)\n",
    "    kmeans.fit(x)\n",
    "    wcss.append(kmeans.inertia_)#每类数据到其中心点的距离之和\n",
    "plt.plot(range(1,11),wcss)\n",
    "plt.title('The Elbow Method')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e8f522aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "聚类个数为5时，轮廓系数： 0.4878488782233543\n"
     ]
    }
   ],
   "source": [
    "# 肘方法评估聚类个数：快速下降趋于平缓下降的转折点时聚类的最好方法\n",
    "model = cluster.KMeans(n_clusters=5,random_state=10)\n",
    "x_cluster = model.fit_predict(x)\n",
    "score = metrics.silhouette_score(x,x_cluster)\n",
    "print('聚类个数为5时，轮廓系数：',score)\n",
    "#相比于个数是3时，有提高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "de94c816",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.196268</td>\n",
       "      <td>1.534257</td>\n",
       "      <td>0.18291</td>\n",
       "      <td>0.491559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.374736</td>\n",
       "      <td>-0.484343</td>\n",
       "      <td>0.18291</td>\n",
       "      <td>0.528724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.069266</td>\n",
       "      <td>-0.181078</td>\n",
       "      <td>0.18291</td>\n",
       "      <td>-1.891347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.029038</td>\n",
       "      <td>-0.273860</td>\n",
       "      <td>-5.46718</td>\n",
       "      <td>-0.092132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.020671</td>\n",
       "      <td>-0.092115</td>\n",
       "      <td>0.18291</td>\n",
       "      <td>0.319737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1        2         3\n",
       "0 -0.196268  1.534257  0.18291  0.491559\n",
       "1 -0.374736 -0.484343  0.18291  0.528724\n",
       "2 -0.069266 -0.181078  0.18291 -1.891347\n",
       "3  1.029038 -0.273860 -5.46718 -0.092132\n",
       "4  2.020671 -0.092115  0.18291  0.319737"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centers = pd.DataFrame(model.cluster_centers_)\n",
    "centers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ed854128",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0.348305\n",
       "2    0.231356\n",
       "1    0.222203\n",
       "4    0.165763\n",
       "0    0.032373\n",
       "Name: cluster, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 提取标签，查看分类结果\n",
    "mobike['cluster'] = model.labels_\n",
    "mobike['cluster'].value_counts(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4a990a97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   timeduration       age  usertype_subscriber  gender_male\n",
      "0     -0.196268  1.534257              0.18291     0.491559\n",
      "1     -0.374736 -0.484343              0.18291     0.528724\n",
      "2     -0.069266 -0.181078              0.18291    -1.891347\n",
      "3      1.029038 -0.273860             -5.46718    -0.092132\n",
      "4      2.020671 -0.092115              0.18291     0.319737\n"
     ]
    }
   ],
   "source": [
    "centers.columns = ['timeduration','age','usertype_subscriber','gender_male']\n",
    "print(centers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "67bfc34a",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1107.0</td>\n",
       "      <td>9.056911</td>\n",
       "      <td>4.965834</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2850.0</td>\n",
       "      <td>7.601053</td>\n",
       "      <td>4.041874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1173.0</td>\n",
       "      <td>10.092924</td>\n",
       "      <td>6.189201</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>191.0</td>\n",
       "      <td>19.052356</td>\n",
       "      <td>13.126806</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>579.0</td>\n",
       "      <td>27.141623</td>\n",
       "      <td>8.374908</td>\n",
       "      <td>17.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count       mean        std   min   25%   50%   75%   max\n",
       "cluster                                                            \n",
       "0        1107.0   9.056911   4.965834   1.0   5.0   8.0  12.0  30.0\n",
       "1        2850.0   7.601053   4.041874   0.0   4.0   7.0  10.0  18.0\n",
       "2        1173.0  10.092924   6.189201   1.0   5.0   9.0  14.0  28.0\n",
       "3         191.0  19.052356  13.126806   0.0   8.0  16.0  27.0  59.0\n",
       "4         579.0  27.141623   8.374908  17.0  21.0  25.0  32.0  59.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobike.groupby(['cluster']).timeduration.describe()\n",
    "#4群是骑行时间最长的用户"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1230a1bd",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1107.0</td>\n",
       "      <td>572.817525</td>\n",
       "      <td>298.445022</td>\n",
       "      <td>80.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>513.0</td>\n",
       "      <td>751.5</td>\n",
       "      <td>1809.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2850.0</td>\n",
       "      <td>490.920351</td>\n",
       "      <td>278.895750</td>\n",
       "      <td>74.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>437.0</td>\n",
       "      <td>654.0</td>\n",
       "      <td>4515.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1173.0</td>\n",
       "      <td>637.494459</td>\n",
       "      <td>381.626481</td>\n",
       "      <td>61.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>541.0</td>\n",
       "      <td>843.0</td>\n",
       "      <td>3692.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>191.0</td>\n",
       "      <td>1400.052356</td>\n",
       "      <td>1094.904066</td>\n",
       "      <td>236.0</td>\n",
       "      <td>573.0</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>1885.0</td>\n",
       "      <td>4899.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>579.0</td>\n",
       "      <td>1657.148532</td>\n",
       "      <td>501.412692</td>\n",
       "      <td>1026.0</td>\n",
       "      <td>1266.5</td>\n",
       "      <td>1531.0</td>\n",
       "      <td>1940.5</td>\n",
       "      <td>3583.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count         mean          std     min     25%     50%     75%  \\\n",
       "cluster                                                                     \n",
       "0        1107.0   572.817525   298.445022    80.0   352.0   513.0   751.5   \n",
       "1        2850.0   490.920351   278.895750    74.0   294.0   437.0   654.0   \n",
       "2        1173.0   637.494459   381.626481    61.0   359.0   541.0   843.0   \n",
       "3         191.0  1400.052356  1094.904066   236.0   573.0  1040.0  1885.0   \n",
       "4         579.0  1657.148532   501.412692  1026.0  1266.5  1531.0  1940.5   \n",
       "\n",
       "            max  \n",
       "cluster          \n",
       "0        1809.0  \n",
       "1        4515.0  \n",
       "2        3692.0  \n",
       "3        4899.0  \n",
       "4        3583.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobike.groupby(['cluster']).tripduration.describe()\n",
    "#4群是骑行路程较长的用户"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5f588df9",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1107.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2850.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1173.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>191.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>579.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count  mean  std  min  25%  50%  75%  max\n",
       "cluster                                            \n",
       "0        1107.0   1.0  0.0  1.0  1.0  1.0  1.0  1.0\n",
       "1        2850.0   1.0  0.0  1.0  1.0  1.0  1.0  1.0\n",
       "2        1173.0   1.0  0.0  1.0  1.0  1.0  1.0  1.0\n",
       "3         191.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
       "4         579.0   1.0  0.0  1.0  1.0  1.0  1.0  1.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobike.groupby(['cluster']).usertype_subscriber.describe()\n",
    "#3群是非订阅的用户，其他是订阅用户"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "968495b3",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1107.0</td>\n",
       "      <td>0.984643</td>\n",
       "      <td>0.123023</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2850.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1173.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>191.0</td>\n",
       "      <td>0.743455</td>\n",
       "      <td>0.437874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>579.0</td>\n",
       "      <td>0.913644</td>\n",
       "      <td>0.281132</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count      mean       std  min  25%  50%  75%  max\n",
       "cluster                                                     \n",
       "0        1107.0  0.984643  0.123023  0.0  1.0  1.0  1.0  1.0\n",
       "1        2850.0  1.000000  0.000000  1.0  1.0  1.0  1.0  1.0\n",
       "2        1173.0  0.000000  0.000000  0.0  0.0  0.0  0.0  0.0\n",
       "3         191.0  0.743455  0.437874  0.0  0.0  1.0  1.0  1.0\n",
       "4         579.0  0.913644  0.281132  0.0  1.0  1.0  1.0  1.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobike.groupby(['cluster']).gender_male.describe()\n",
    "#2群是纯女性用户，1群是纯男性用户，其他是男女都有"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "20c2cd58",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1107.0</td>\n",
       "      <td>53.394761</td>\n",
       "      <td>7.349115</td>\n",
       "      <td>42.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>58.5</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2850.0</td>\n",
       "      <td>31.116842</td>\n",
       "      <td>5.290046</td>\n",
       "      <td>17.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1173.0</td>\n",
       "      <td>34.463768</td>\n",
       "      <td>9.711087</td>\n",
       "      <td>18.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>191.0</td>\n",
       "      <td>33.439791</td>\n",
       "      <td>10.494875</td>\n",
       "      <td>18.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>579.0</td>\n",
       "      <td>35.445596</td>\n",
       "      <td>8.509819</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count       mean        std   min   25%   50%   75%   max\n",
       "cluster                                                            \n",
       "0        1107.0  53.394761   7.349115  42.0  47.0  52.0  58.5  79.0\n",
       "1        2850.0  31.116842   5.290046  17.0  27.0  31.0  35.0  43.0\n",
       "2        1173.0  34.463768   9.711087  18.0  28.0  31.0  39.0  62.0\n",
       "3         191.0  33.439791  10.494875  18.0  25.0  30.0  39.0  63.0\n",
       "4         579.0  35.445596   8.509819  20.0  30.0  34.0  40.0  65.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobike.groupby(['cluster']).age.describe()\n",
    "#0群为较为年长用户"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ae33bedb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[9, 16)     0.326441\n",
       "[16, 19)    0.305424\n",
       "[6, 9)      0.225085\n",
       "[19, 24)    0.116610\n",
       "[0, 6)      0.026441\n",
       "Name: start_level, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据开始时间剥离出小时这个变量，再进行聚类\n",
    "mobike['start_hour'] = mobike['start_time'].apply(lambda x:x.hour)\n",
    "bins = [0,6,9,16,19,24]\n",
    "mobike['start_level'] = pd.cut(mobike.start_hour,bins,right=False)\n",
    "mobike['start_level'].value_counts(1)\n",
    "# 大部分是在6-9和16-19的通勤时间骑车"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "df889f4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>timeduration</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>age</th>\n",
       "      <th>usertype_customer</th>\n",
       "      <th>usertype_subscriber</th>\n",
       "      <th>gender_female</th>\n",
       "      <th>gender_male</th>\n",
       "      <th>cluster</th>\n",
       "      <th>start_hour</th>\n",
       "      <th>start_level_[0, 6)</th>\n",
       "      <th>start_level_[6, 9)</th>\n",
       "      <th>start_level_[9, 16)</th>\n",
       "      <th>start_level_[16, 19)</th>\n",
       "      <th>start_level_[19, 24)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-11-14 07:37:00</td>\n",
       "      <td>2018-11-14 07:44:00</td>\n",
       "      <td>7</td>\n",
       "      <td>436</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-12-18 19:02:00</td>\n",
       "      <td>2018-12-18 19:10:00</td>\n",
       "      <td>7</td>\n",
       "      <td>445</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-10-09 12:37:00</td>\n",
       "      <td>2018-10-09 12:55:00</td>\n",
       "      <td>18</td>\n",
       "      <td>1090</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-11-12 12:30:00</td>\n",
       "      <td>2018-11-12 12:40:00</td>\n",
       "      <td>9</td>\n",
       "      <td>581</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-11-07 07:29:00</td>\n",
       "      <td>2018-11-07 07:35:00</td>\n",
       "      <td>6</td>\n",
       "      <td>390</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           start_time            end_time  timeduration  tripduration  \\\n",
       "0 2018-11-14 07:37:00 2018-11-14 07:44:00             7           436   \n",
       "1 2018-12-18 19:02:00 2018-12-18 19:10:00             7           445   \n",
       "2 2018-10-09 12:37:00 2018-10-09 12:55:00            18          1090   \n",
       "3 2018-11-12 12:30:00 2018-11-12 12:40:00             9           581   \n",
       "4 2018-11-07 07:29:00 2018-11-07 07:35:00             6           390   \n",
       "\n",
       "   birthyear  age  usertype_customer  usertype_subscriber  gender_female  \\\n",
       "0     1982.0   37                  0                    1              0   \n",
       "1     1988.0   31                  0                    1              0   \n",
       "2     1989.0   30                  1                    0              0   \n",
       "3     1989.0   30                  0                    1              1   \n",
       "4     1979.0   40                  0                    1              0   \n",
       "\n",
       "   gender_male  cluster  start_hour  start_level_[0, 6)  start_level_[6, 9)  \\\n",
       "0            1        1           7                   0                   1   \n",
       "1            1        1          19                   0                   0   \n",
       "2            1        3          12                   0                   0   \n",
       "3            0        2          12                   0                   0   \n",
       "4            1        1           7                   0                   1   \n",
       "\n",
       "   start_level_[9, 16)  start_level_[16, 19)  start_level_[19, 24)  \n",
       "0                    0                     0                     0  \n",
       "1                    0                     0                     1  \n",
       "2                    1                     0                     0  \n",
       "3                    1                     0                     0  \n",
       "4                    0                     0                     0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobike =pd.get_dummies(mobike)\n",
    "mobike.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "987bc305",
   "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>timeduration</th>\n",
       "      <th>age</th>\n",
       "      <th>start_level_[6, 9)</th>\n",
       "      <th>start_level_[16, 19)</th>\n",
       "      <th>usertype_subscriber</th>\n",
       "      <th>gender_male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>timeduration</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.031558</td>\n",
       "      <td>-0.025710</td>\n",
       "      <td>0.038946</td>\n",
       "      <td>-0.188221</td>\n",
       "      <td>-0.054500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.031558</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.068588</td>\n",
       "      <td>-0.020403</td>\n",
       "      <td>0.050092</td>\n",
       "      <td>0.073693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_level_[6, 9)</th>\n",
       "      <td>-0.025710</td>\n",
       "      <td>0.068588</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.357386</td>\n",
       "      <td>0.059598</td>\n",
       "      <td>0.025668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_level_[16, 19)</th>\n",
       "      <td>0.038946</td>\n",
       "      <td>-0.020403</td>\n",
       "      <td>-0.357386</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004857</td>\n",
       "      <td>0.002397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usertype_subscriber</th>\n",
       "      <td>-0.188221</td>\n",
       "      <td>0.050092</td>\n",
       "      <td>0.059598</td>\n",
       "      <td>0.004857</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.016852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender_male</th>\n",
       "      <td>-0.054500</td>\n",
       "      <td>0.073693</td>\n",
       "      <td>0.025668</td>\n",
       "      <td>0.002397</td>\n",
       "      <td>0.016852</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      timeduration       age  start_level_[6, 9)  \\\n",
       "timeduration              1.000000  0.031558           -0.025710   \n",
       "age                       0.031558  1.000000            0.068588   \n",
       "start_level_[6, 9)       -0.025710  0.068588            1.000000   \n",
       "start_level_[16, 19)      0.038946 -0.020403           -0.357386   \n",
       "usertype_subscriber      -0.188221  0.050092            0.059598   \n",
       "gender_male              -0.054500  0.073693            0.025668   \n",
       "\n",
       "                      start_level_[16, 19)  usertype_subscriber  gender_male  \n",
       "timeduration                      0.038946            -0.188221    -0.054500  \n",
       "age                              -0.020403             0.050092     0.073693  \n",
       "start_level_[6, 9)               -0.357386             0.059598     0.025668  \n",
       "start_level_[16, 19)              1.000000             0.004857     0.002397  \n",
       "usertype_subscriber               0.004857             1.000000     0.016852  \n",
       "gender_male                       0.002397             0.016852     1.000000  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobike[['timeduration','age','start_level_[6, 9)','start_level_[16, 19)','usertype_subscriber','gender_male']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "514a83bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = mobike[['timeduration','age','start_level_[6, 9)','start_level_[16, 19)','usertype_subscriber','gender_male']]\n",
    "#数据标准化\n",
    "from sklearn.preprocessing import scale\n",
    "x = pd.DataFrame(scale(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76e025a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#肘方法评估聚类个数k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2cc3d213",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wcss = []\n",
    "for i in range(1,15):\n",
    "    kmeans = cluster.KMeans(n_clusters = i,init = 'k-means++',random_state=10)\n",
    "    kmeans.fit(x)\n",
    "    wcss.append(kmeans.inertia_) #每类数据到其中心点的距离之和\n",
    "plt.plot(range(1,15),wcss)\n",
    "plt.title('The Elbow Method')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a297a3bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "聚类个数为5时，轮廓系数： 0.3874598850383234\n"
     ]
    }
   ],
   "source": [
    "# 肘方法评估聚类个数：快速下降趋于平缓下降的转折点时聚类的最好方法\n",
    "model = cluster.KMeans(n_clusters=5,random_state=10)\n",
    "x_cluster = model.fit_predict(x)\n",
    "score = metrics.silhouette_score(x,x_cluster)\n",
    "print('聚类个数为5时，轮廓系数：',score)\n",
    "#相比于个数是3时，有提高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b25d26bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0.348305\n",
       "2    0.231356\n",
       "1    0.222203\n",
       "4    0.165763\n",
       "0    0.032373\n",
       "Name: cluster, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centers = pd.DataFrame(model.cluster_centers_)\n",
    "# 提取标签，查看分类结果\n",
    "mobike['cluster'] = model.labels_\n",
    "mobike['cluster'].value_counts(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2439af3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   timeduration       age  start_level_[6,9)  start_level_[16,19)  \\\n",
      "0      1.029038 -0.273860          -0.325831            -0.026553   \n",
      "1     -0.056533  0.126484           1.855471            -0.663119   \n",
      "2      0.026751  0.000670          -0.538947             1.508025   \n",
      "3     -0.104280  0.012530          -0.538947            -0.663119   \n",
      "4      0.056594 -0.143330          -0.538947             0.182695   \n",
      "\n",
      "   usertype_subscriber  gender_male  \n",
      "0             -5.46718    -0.092132  \n",
      "1              0.18291     0.045079  \n",
      "2              0.18291     0.528724  \n",
      "3              0.18291     0.528724  \n",
      "4              0.18291    -1.891347  \n"
     ]
    }
   ],
   "source": [
    "centers.columns = ['timeduration','age','start_level_[6,9)','start_level_[16,19)','usertype_subscriber','gender_male']\n",
    "print(centers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17d1dba2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 0群是非订阅用户，骑行时间长，可能是临时性骑车需求的用户，占比最小为3.2%\n",
    "# 1群是6-9时骑车，即上班通勤的用户，占比22%；\n",
    "# 2群是16-19时骑车，即下班通勤的用户，占比23%；\n",
    "# 3群占比最高，为非上班高峰时段用户，占比35%\n",
    "# 4群是女性用户，占比16%；"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "b9116d28",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[9, 16)     0.696350\n",
       "[19, 24)    0.247202\n",
       "[0, 6)      0.056448\n",
       "[6, 9)      0.000000\n",
       "[16, 19)    0.000000\n",
       "Name: start_level, dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobike[mobike.cluster==3].start_level.value_counts(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "a11e53be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.800153\n",
       "0    0.199847\n",
       "Name: gender_male, dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mobike[mobike.cluster==1].gender_male.value_counts(1)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py36",
   "language": "python",
   "name": "py36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}