淘宝APP用户行为模式分析.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分析指标:\n",
    "日PV 日UV分析\n",
    "付费率分析\n",
    "复购行为分析\n",
    "漏斗流失率分析\n",
    "用户价值RFM分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用户行为分析过程：\n",
    "\n",
    "说明\n",
    "一、数据集与分析目的\n",
    "1、数据集\n",
    "2、分析目的\n",
    "\n",
    "二、数据处理\n",
    "1、数据预览\n",
    "2、数据处理\n",
    "\n",
    "三、数据提取与分析\n",
    "1、网站流量分析\n",
    "2、用户分析\n",
    "3、商品销售分析\n",
    "\n",
    "四、总结\n",
    "1、关于平台流量与转化率：\n",
    "2、关于网站用户粘性以及用户消费习惯\n",
    "3、关于商品销售情况"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "说明\n",
    "本次数据分析基于阿里云天池数据集（用户行为数据集），使用转化漏斗，AARRR模型，对常见电商分析指标，包括转化率，PV,UV,留存率，复购率等进行分析，分析过程中使用python进行数据清洗及可视化。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  二、理解数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style='darkgrid',font_scale=1.5)\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>behavior_type</th>\n",
       "      <th>user_geohash</th>\n",
       "      <th>item_category</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>98047837</td>\n",
       "      <td>232431562</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4245</td>\n",
       "      <td>2014-12-06 02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>97726136</td>\n",
       "      <td>383583590</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5894</td>\n",
       "      <td>2014-12-09 20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>98607707</td>\n",
       "      <td>64749712</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2883</td>\n",
       "      <td>2014-12-18 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>98662432</td>\n",
       "      <td>320593836</td>\n",
       "      <td>1</td>\n",
       "      <td>96nn52n</td>\n",
       "      <td>6562</td>\n",
       "      <td>2014-12-06 10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>98145908</td>\n",
       "      <td>290208520</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13926</td>\n",
       "      <td>2014-12-16 21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id    item_id behavior_type user_geohash item_category           time\n",
       "0  98047837  232431562             1          NaN          4245  2014-12-06 02\n",
       "1  97726136  383583590             1          NaN          5894  2014-12-09 20\n",
       "2  98607707   64749712             1          NaN          2883  2014-12-18 11\n",
       "3  98662432  320593836             1      96nn52n          6562  2014-12-06 10\n",
       "4  98145908  290208520             1          NaN         13926  2014-12-16 21"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user = pd.read_csv('tianchi_mobile_recommend_train_user.csv',dtype=str)\n",
    "data_user.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 802757 entries, 0 to 802756\n",
      "Data columns (total 6 columns):\n",
      "user_id          802757 non-null object\n",
      "item_id          802757 non-null object\n",
      "behavior_type    802757 non-null object\n",
      "user_geohash     252627 non-null object\n",
      "item_category    802757 non-null object\n",
      "time             802756 non-null object\n",
      "dtypes: object(6)\n",
      "memory usage: 36.7+ MB\n"
     ]
    }
   ],
   "source": [
    "data_user.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 若数据量太大可以抽样\n",
    "# data_user = data_user.sample(frac=0.2,replace=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "user_id          用户ID\n",
    "item_id          商品ID\n",
    "behavior_type    字符串，枚举类型，包括(‘pv’, ‘buy’, ‘cart’, ‘fav’)\n",
    "user_geohash     地理位置\n",
    "item_category    序列化后的商品所属类目ID\n",
    "time             行为发生的时间戳"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 三、数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.1 统计缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id          0.000000\n",
       "item_id          0.000000\n",
       "behavior_type    0.000000\n",
       "user_geohash     0.685301\n",
       "item_category    0.000000\n",
       "time             0.000001\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.apply(lambda x: sum(x.isnull())/len(x),axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id               0\n",
       "item_id               0\n",
       "behavior_type         0\n",
       "user_geohash     550130\n",
       "item_category         0\n",
       "time                  1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.apply(lambda x: sum(x.isnull()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "发现缺失值大部分是地理信息，暂时不用理会,并将时间类型的一个缺失值删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_user.dropna(subset=['time'],how='any',inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.2日期格式处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_user['date'] = data_user['time'].str[0:10]\n",
    "data_user['hour'] = data_user['time'].str[11:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>behavior_type</th>\n",
       "      <th>user_geohash</th>\n",
       "      <th>item_category</th>\n",
       "      <th>time</th>\n",
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       "      <th>hour</th>\n",
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       "      <td>2014-12-06 10</td>\n",
       "      <td>2014-12-06</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>98145908</td>\n",
       "      <td>290208520</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13926</td>\n",
       "      <td>2014-12-16 21</td>\n",
       "      <td>2014-12-16</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id    item_id behavior_type user_geohash item_category  \\\n",
       "0  98047837  232431562             1          NaN          4245   \n",
       "1  97726136  383583590             1          NaN          5894   \n",
       "2  98607707   64749712             1          NaN          2883   \n",
       "3  98662432  320593836             1      96nn52n          6562   \n",
       "4  98145908  290208520             1          NaN         13926   \n",
       "\n",
       "            time        date hour  \n",
       "0  2014-12-06 02  2014-12-06   02  \n",
       "1  2014-12-09 20  2014-12-09   20  \n",
       "2  2014-12-18 11  2014-12-18   11  \n",
       "3  2014-12-06 10  2014-12-06   10  \n",
       "4  2014-12-16 21  2014-12-16   21  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data_user['hour'].fillna(25)   # 缺失值数据类型转换时候的第二种方法，即填充\n",
    "\n",
    "data_user['time'] = pd.to_datetime(data_user['time'])\n",
    "data_user['date'] = pd.to_datetime(data_user['date'])\n",
    "data_user['hour'] = data_user['hour'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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       "      <th>item_id</th>\n",
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       "      <th>hour</th>\n",
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       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>2014-12-18 11:00:00</td>\n",
       "      <td>2014-12-18</td>\n",
       "      <td>11</td>\n",
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       "      <td>3</td>\n",
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       "      <td>96nn52n</td>\n",
       "      <td>6562</td>\n",
       "      <td>2014-12-06 10:00:00</td>\n",
       "      <td>2014-12-06</td>\n",
       "      <td>10</td>\n",
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       "      <td>4</td>\n",
       "      <td>98145908</td>\n",
       "      <td>290208520</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13926</td>\n",
       "      <td>2014-12-16 21:00:00</td>\n",
       "      <td>2014-12-16</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    user_id    item_id behavior_type user_geohash item_category  \\\n",
       "0  98047837  232431562             1          NaN          4245   \n",
       "1  97726136  383583590             1          NaN          5894   \n",
       "2  98607707   64749712             1          NaN          2883   \n",
       "3  98662432  320593836             1      96nn52n          6562   \n",
       "4  98145908  290208520             1          NaN         13926   \n",
       "\n",
       "                 time       date  hour  \n",
       "0 2014-12-06 02:00:00 2014-12-06     2  \n",
       "1 2014-12-09 20:00:00 2014-12-09    20  \n",
       "2 2014-12-18 11:00:00 2014-12-18    11  \n",
       "3 2014-12-06 10:00:00 2014-12-06    10  \n",
       "4 2014-12-16 21:00:00 2014-12-16    21  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对数据根据time来排序\n",
    "data_user.sort_values(by='time',ascending=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 充值索引\n",
    "data_user.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
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       "      <td>71146691</td>\n",
       "      <td>212152444</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1863</td>\n",
       "      <td>2014-11-18 00:00:00</td>\n",
       "      <td>2014-11-18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>88271974</td>\n",
       "      <td>25265374</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1488</td>\n",
       "      <td>2014-11-18 00:00:00</td>\n",
       "      <td>2014-11-18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>106634442</td>\n",
       "      <td>44026159</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11955</td>\n",
       "      <td>2014-11-18 00:00:00</td>\n",
       "      <td>2014-11-18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>802751</td>\n",
       "      <td>132966536</td>\n",
       "      <td>175578758</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1083</td>\n",
       "      <td>2014-12-18 23:00:00</td>\n",
       "      <td>2014-12-18</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>802752</td>\n",
       "      <td>88826481</td>\n",
       "      <td>24201365</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2261</td>\n",
       "      <td>2014-12-18 23:00:00</td>\n",
       "      <td>2014-12-18</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>802753</td>\n",
       "      <td>92746744</td>\n",
       "      <td>174717351</td>\n",
       "      <td>3</td>\n",
       "      <td>956mmpt</td>\n",
       "      <td>437</td>\n",
       "      <td>2014-12-18 23:00:00</td>\n",
       "      <td>2014-12-18</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>802754</td>\n",
       "      <td>79266183</td>\n",
       "      <td>15955333</td>\n",
       "      <td>1</td>\n",
       "      <td>9rneew6</td>\n",
       "      <td>10567</td>\n",
       "      <td>2014-12-18 23:00:00</td>\n",
       "      <td>2014-12-18</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>802755</td>\n",
       "      <td>44462249</td>\n",
       "      <td>299263526</td>\n",
       "      <td>1</td>\n",
       "      <td>9rg2ntw</td>\n",
       "      <td>730</td>\n",
       "      <td>2014-12-18 23:00:00</td>\n",
       "      <td>2014-12-18</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>802756 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          user_id    item_id behavior_type user_geohash item_category  \\\n",
       "0       130270245  253791580             2          NaN          9261   \n",
       "1        71146691  373475577             1          NaN          4370   \n",
       "2        71146691  212152444             1          NaN          1863   \n",
       "3        88271974   25265374             1          NaN          1488   \n",
       "4       106634442   44026159             1          NaN         11955   \n",
       "...           ...        ...           ...          ...           ...   \n",
       "802751  132966536  175578758             1          NaN          1083   \n",
       "802752   88826481   24201365             1          NaN          2261   \n",
       "802753   92746744  174717351             3      956mmpt           437   \n",
       "802754   79266183   15955333             1      9rneew6         10567   \n",
       "802755   44462249  299263526             1      9rg2ntw           730   \n",
       "\n",
       "                      time       date  hour  \n",
       "0      2014-11-18 00:00:00 2014-11-18     0  \n",
       "1      2014-11-18 00:00:00 2014-11-18     0  \n",
       "2      2014-11-18 00:00:00 2014-11-18     0  \n",
       "3      2014-11-18 00:00:00 2014-11-18     0  \n",
       "4      2014-11-18 00:00:00 2014-11-18     0  \n",
       "...                    ...        ...   ...  \n",
       "802751 2014-12-18 23:00:00 2014-12-18    23  \n",
       "802752 2014-12-18 23:00:00 2014-12-18    23  \n",
       "802753 2014-12-18 23:00:00 2014-12-18    23  \n",
       "802754 2014-12-18 23:00:00 2014-12-18    23  \n",
       "802755 2014-12-18 23:00:00 2014-12-18    23  \n",
       "\n",
       "[802756 rows x 8 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>behavior_type</th>\n",
       "      <th>user_geohash</th>\n",
       "      <th>item_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>802756</td>\n",
       "      <td>802756</td>\n",
       "      <td>802756</td>\n",
       "      <td>252627</td>\n",
       "      <td>802756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>unique</td>\n",
       "      <td>6471</td>\n",
       "      <td>554441</td>\n",
       "      <td>4</td>\n",
       "      <td>132483</td>\n",
       "      <td>6753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>top</td>\n",
       "      <td>36233277</td>\n",
       "      <td>97655171</td>\n",
       "      <td>1</td>\n",
       "      <td>94ek6lw</td>\n",
       "      <td>1863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>freq</td>\n",
       "      <td>3117</td>\n",
       "      <td>108</td>\n",
       "      <td>756386</td>\n",
       "      <td>101</td>\n",
       "      <td>26360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         user_id   item_id behavior_type user_geohash item_category\n",
       "count     802756    802756        802756       252627        802756\n",
       "unique      6471    554441             4       132483          6753\n",
       "top     36233277  97655171             1      94ek6lw          1863\n",
       "freq        3117       108        756386          101         26360"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 字符型变量的数据的描述\n",
    "data_user.describe(include=['object'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>behavior_type</th>\n",
       "      <th>user_geohash</th>\n",
       "      <th>item_category</th>\n",
       "      <th>time</th>\n",
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       "      <th>hour</th>\n",
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       "    <tr>\n",
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       "      <td>802756</td>\n",
       "      <td>802756</td>\n",
       "      <td>802756</td>\n",
       "      <td>252627</td>\n",
       "      <td>802756</td>\n",
       "      <td>802756</td>\n",
       "      <td>802756</td>\n",
       "      <td>802756.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>unique</td>\n",
       "      <td>6471</td>\n",
       "      <td>554441</td>\n",
       "      <td>4</td>\n",
       "      <td>132483</td>\n",
       "      <td>6753</td>\n",
       "      <td>744</td>\n",
       "      <td>31</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>top</td>\n",
       "      <td>36233277</td>\n",
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       "      <td>1</td>\n",
       "      <td>94ek6lw</td>\n",
       "      <td>1863</td>\n",
       "      <td>2014-12-11 22:00:00</td>\n",
       "      <td>2014-12-12 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>freq</td>\n",
       "      <td>3117</td>\n",
       "      <td>108</td>\n",
       "      <td>756386</td>\n",
       "      <td>101</td>\n",
       "      <td>26360</td>\n",
       "      <td>3480</td>\n",
       "      <td>45197</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>first</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-11-18 00:00:00</td>\n",
       "      <td>2014-11-18 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>last</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-12-18 23:00:00</td>\n",
       "      <td>2014-12-18 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.783176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.516358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         user_id   item_id behavior_type user_geohash item_category  \\\n",
       "count     802756    802756        802756       252627        802756   \n",
       "unique      6471    554441             4       132483          6753   \n",
       "top     36233277  97655171             1      94ek6lw          1863   \n",
       "freq        3117       108        756386          101         26360   \n",
       "first        NaN       NaN           NaN          NaN           NaN   \n",
       "last         NaN       NaN           NaN          NaN           NaN   \n",
       "mean         NaN       NaN           NaN          NaN           NaN   \n",
       "std          NaN       NaN           NaN          NaN           NaN   \n",
       "min          NaN       NaN           NaN          NaN           NaN   \n",
       "25%          NaN       NaN           NaN          NaN           NaN   \n",
       "50%          NaN       NaN           NaN          NaN           NaN   \n",
       "75%          NaN       NaN           NaN          NaN           NaN   \n",
       "max          NaN       NaN           NaN          NaN           NaN   \n",
       "\n",
       "                       time                 date           hour  \n",
       "count                802756               802756  802756.000000  \n",
       "unique                  744                   31            NaN  \n",
       "top     2014-12-11 22:00:00  2014-12-12 00:00:00            NaN  \n",
       "freq                   3480                45197            NaN  \n",
       "first   2014-11-18 00:00:00  2014-11-18 00:00:00            NaN  \n",
       "last    2014-12-18 23:00:00  2014-12-18 00:00:00            NaN  \n",
       "mean                    NaN                  NaN      14.783176  \n",
       "std                     NaN                  NaN       6.516358  \n",
       "min                     NaN                  NaN       0.000000  \n",
       "25%                     NaN                  NaN      10.000000  \n",
       "50%                     NaN                  NaN      16.000000  \n",
       "75%                     NaN                  NaN      20.000000  \n",
       "max                     NaN                  NaN      23.000000  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 所有的数据的描述\n",
    "data_user.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 四、构建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "分析  PV访问量，UV独立访客"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 每天的访问量\n",
    "pv_daily = data_user.groupby('date').count()['user_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2014-11-18    23943\n",
       "2014-11-19    23250\n",
       "2014-11-20    23372\n",
       "2014-11-21    21888\n",
       "2014-11-22    23832\n",
       "Name: user_id, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv_daily.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 每天不同独立访客量\n",
    "uv_daily = data_user.groupby('date')['user_id'].apply(lambda x: x.drop_duplicates().count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2014-11-18    3286\n",
       "2014-11-19    3268\n",
       "2014-11-20    3296\n",
       "2014-11-21    3202\n",
       "2014-11-22    3157\n",
       "Name: user_id, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uv_daily.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "pv_uv_daily = pd.concat([pv_daily,uv_daily],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <td>2014-11-18</td>\n",
       "      <td>23943</td>\n",
       "      <td>3286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-11-19</td>\n",
       "      <td>23250</td>\n",
       "      <td>3268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-11-20</td>\n",
       "      <td>23372</td>\n",
       "      <td>3296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-11-21</td>\n",
       "      <td>21888</td>\n",
       "      <td>3202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-11-22</td>\n",
       "      <td>23832</td>\n",
       "      <td>3157</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            user_id  user_id\n",
       "date                        \n",
       "2014-11-18    23943     3286\n",
       "2014-11-19    23250     3268\n",
       "2014-11-20    23372     3296\n",
       "2014-11-21    21888     3202\n",
       "2014-11-22    23832     3157"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv_uv_daily.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "pv_uv_daily.columns = ['pv','uv']\n",
    "# pv_uv_daily.rename(columns={'user_id':'pv','user_id':'uv'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>pv</th>\n",
       "      <th>uv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2014-11-18</td>\n",
       "      <td>23943</td>\n",
       "      <td>3286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-11-19</td>\n",
       "      <td>23250</td>\n",
       "      <td>3268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-11-20</td>\n",
       "      <td>23372</td>\n",
       "      <td>3296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-11-21</td>\n",
       "      <td>21888</td>\n",
       "      <td>3202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-11-22</td>\n",
       "      <td>23832</td>\n",
       "      <td>3157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               pv    uv\n",
       "date                   \n",
       "2014-11-18  23943  3286\n",
       "2014-11-19  23250  3268\n",
       "2014-11-20  23372  3296\n",
       "2014-11-21  21888  3202\n",
       "2014-11-22  23832  3157"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv_uv_daily.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>pv</th>\n",
       "      <th>uv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>pv</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.964318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>uv</td>\n",
       "      <td>0.964318</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          pv        uv\n",
       "pv  1.000000  0.964318\n",
       "uv  0.964318  1.000000"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算pv和uv的相关系数\n",
    "pv_uv_daily.corr(method='pearson')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可知日访问量和日访客数是成正向相关的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "plt.subplot(211)\n",
    "plt.xticks(rotation=30,fontsize=15)\n",
    "plt.plot(pv_daily,color='red')\n",
    "plt.title('每天访问量')\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.xticks(rotation=30)\n",
    "plt.plot(uv_daily,color='gold')\n",
    "plt.title('每天访客数量')\n",
    "plt.suptitle('uv和pv的变化趋势')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "pv_hour = data_user.groupby('hour').count()['user_id']\n",
    "uv_hour = data_user.groupby('hour')['user_id'].apply(lambda x: x.drop_duplicates().count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "pv_uv_hour = pd.concat([pv_hour,uv_hour],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>pv</th>\n",
       "      <th>uv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>35088</td>\n",
       "      <td>3095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>18433</td>\n",
       "      <td>1937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>10035</td>\n",
       "      <td>1254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>6552</td>\n",
       "      <td>947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5006</td>\n",
       "      <td>843</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         pv    uv\n",
       "hour             \n",
       "0     35088  3095\n",
       "1     18433  1937\n",
       "2     10035  1254\n",
       "3      6552   947\n",
       "4      5006   843"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv_uv_hour.columns = ['pv','uv']\n",
    "pv_uv_hour.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>pv</th>\n",
       "      <th>uv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>pv</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.879253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>uv</td>\n",
       "      <td>0.879253</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          pv        uv\n",
       "pv  1.000000  0.879253\n",
       "uv  0.879253  1.000000"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv_uv_hour.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "\n",
    "pv_uv_hour['pv'].plot(color='steelblue',label='每个小时访问量')\n",
    "plt.legend(loc='upper right')\n",
    "plt.ylabel('访问量')\n",
    "\n",
    "pv_uv_hour['uv'].plot(color='red',label='每个小时不同访客量',secondary_y=True)\n",
    "plt.ylabel('访客数量')\n",
    "\n",
    "plt.xticks(range(0,24))\n",
    "plt.legend(loc='upper center')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 不同行为类型下每个小时的访问量\n",
    "pv_detail = pd.pivot_table(columns='behavior_type',index='hour',data=data_user,values='user_id',aggfunc=np.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>点击</th>\n",
       "      <th>收藏</th>\n",
       "      <th>加入购物车</th>\n",
       "      <th>购买</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>33078</td>\n",
       "      <td>747</td>\n",
       "      <td>917</td>\n",
       "      <td>346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>17436</td>\n",
       "      <td>447</td>\n",
       "      <td>424</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>9511</td>\n",
       "      <td>224</td>\n",
       "      <td>242</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>6238</td>\n",
       "      <td>125</td>\n",
       "      <td>158</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4733</td>\n",
       "      <td>126</td>\n",
       "      <td>123</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>5498</td>\n",
       "      <td>140</td>\n",
       "      <td>149</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>9699</td>\n",
       "      <td>235</td>\n",
       "      <td>260</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>17581</td>\n",
       "      <td>370</td>\n",
       "      <td>458</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>23797</td>\n",
       "      <td>487</td>\n",
       "      <td>697</td>\n",
       "      <td>247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>29886</td>\n",
       "      <td>694</td>\n",
       "      <td>836</td>\n",
       "      <td>374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>34007</td>\n",
       "      <td>743</td>\n",
       "      <td>1130</td>\n",
       "      <td>456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>32258</td>\n",
       "      <td>745</td>\n",
       "      <td>995</td>\n",
       "      <td>467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>32726</td>\n",
       "      <td>670</td>\n",
       "      <td>957</td>\n",
       "      <td>460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>36709</td>\n",
       "      <td>793</td>\n",
       "      <td>1100</td>\n",
       "      <td>467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>36357</td>\n",
       "      <td>761</td>\n",
       "      <td>1116</td>\n",
       "      <td>452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>36760</td>\n",
       "      <td>831</td>\n",
       "      <td>1201</td>\n",
       "      <td>459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>35434</td>\n",
       "      <td>730</td>\n",
       "      <td>1051</td>\n",
       "      <td>459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>31154</td>\n",
       "      <td>622</td>\n",
       "      <td>954</td>\n",
       "      <td>339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>33685</td>\n",
       "      <td>691</td>\n",
       "      <td>985</td>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>44797</td>\n",
       "      <td>930</td>\n",
       "      <td>1190</td>\n",
       "      <td>394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>57728</td>\n",
       "      <td>1082</td>\n",
       "      <td>1631</td>\n",
       "      <td>498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>67096</td>\n",
       "      <td>1271</td>\n",
       "      <td>2057</td>\n",
       "      <td>613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>67555</td>\n",
       "      <td>1336</td>\n",
       "      <td>2113</td>\n",
       "      <td>615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>52663</td>\n",
       "      <td>1213</td>\n",
       "      <td>1766</td>\n",
       "      <td>438</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         点击    收藏  加入购物车   购买\n",
       "hour                         \n",
       "0     33078   747    917  346\n",
       "1     17436   447    424  126\n",
       "2      9511   224    242   58\n",
       "3      6238   125    158   31\n",
       "4      4733   126    123   24\n",
       "5      5498   140    149   25\n",
       "6      9699   235    260   52\n",
       "7     17581   370    458  105\n",
       "8     23797   487    697  247\n",
       "9     29886   694    836  374\n",
       "10    34007   743   1130  456\n",
       "11    32258   745    995  467\n",
       "12    32726   670    957  460\n",
       "13    36709   793   1100  467\n",
       "14    36357   761   1116  452\n",
       "15    36760   831   1201  459\n",
       "16    35434   730   1051  459\n",
       "17    31154   622    954  339\n",
       "18    33685   691    985  342\n",
       "19    44797   930   1190  394\n",
       "20    57728  1082   1631  498\n",
       "21    67096  1271   2057  613\n",
       "22    67555  1336   2113  615\n",
       "23    52663  1213   1766  438"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv_detail.columns = ['点击','收藏','加入购物车','购买']\n",
    "pv_detail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "sns.lineplot(data = pv_detail)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 剔除点击行为\n",
    "plt.figure(figsize=(16,9))\n",
    "sns.lineplot(data = pv_detail.iloc[:,1:])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_user_buy = data_user[data_user.behavior_type == '4'].groupby('user_id').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id\n",
       "100012968    2\n",
       "100024529    2\n",
       "100027681    4\n",
       "100035725    1\n",
       "100067745    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3326.000000\n",
       "mean        2.359290\n",
       "std         2.614917\n",
       "min         1.000000\n",
       "25%         1.000000\n",
       "50%         2.000000\n",
       "75%         3.000000\n",
       "max        82.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A27Zto6GhgYSEBACysrIoLy/n0KFDAJw6dYodO3YwfPhwANLS0nC5XKxfvx64Ei7FxcWkpqYSEBDQnOmJiMhNatZlpYceeoglS5YwdepUxo4dy6lTp3jvvfdITEzkgQceACA1NZWUlBQmT55s3KIaERHBhAkTAOjatSu5ubksXryYffv2UV1dTVVVFfPnz2/+6kRE5KY068whLCyMd955B6/Xy/z589m0aRM5OTmsXr3a2KAOCAigqKiISZMmUVlZydChQ1m/fj1hYT9d55o2bRoLFizAbrfTrVs31q5da9rLEBGR1hPg9Xob38G9TTRnQ3pswYeNHrel8NEOt4GmTUP/qVZNo3r577bbkBYRkV8mhYOIiJgoHERExEThICIiJgoHERExUTiIiIiJwkFEREwUDiIiYqJwEBERE4WDiIiYKBxERMRE4SAiIiYKBxERMVE4iIiIicJBRERMFA4iImKicBAREROFg4iImCgcRETEROEgIiImCgcRETFROIiIiInCQURETBQOIiJionAQERGTJoXD559/zqhRo0ztW7duJTMzk+TkZObMmYPT6fTpr6ioIDs7m6SkJHJycjh9+rRPf01NDfn5+QwZMoQxY8awe/fum1iKiIi0FL/D4ZtvvqGgoACPx+PTXlZWRkFBAcOGDeP111+nqqqKhQsXGv0Oh4OcnBwCAwNZunQpffr0YerUqbhcLmNMXl4ee/fuZeHChYwfP57c3Fyqq6tbYHkiInIzOvsz6ODBg0yZMoXo6GjOnTvn07ds2TKSk5OZN28eAP369SMrK4u8vDzuvPNOVq9ejdvtpqioCKvVyogRI8jIyGD79u2MHj2a0tJS9uzZw7p160hOTgbg6NGjrFq1irlz57bwckVExB9+nTns2bOHF154gUmTJvm0X7x4kQMHDvDII48YbdHR0QwYMIBdu3YBV84sRo4cidVqvfIDAwNJS0ujvLzc6O/Vq5cRDADp6emUlZU1b2UiInLT/AqHZ599lnHjxpnaz549i8fjISYmxqe9d+/eHDt2DAC73U5sbOz/7B80aJBPf1RUFCdOnMDtdvu7DhERaUF+hUNg4PWHNTQ0ABhnBVcFBwdTU1MDgNPpbLQ/PDzcpz8kJITLly9TV1fnz/RERKSF+bXncCMWiwWATp06+bQHBQUZwREUFGTqt1gsRv/V1/j58YDprqfGREaG+T22Rw9r44Na8LjbWUdc881SrZpG9fJfa9eqWeEQGRkJXLk01K9fP6O9traWvn37GmPsdrvPcefPnyc0NBSAiIgIKioqfPpra2sBjDH+cjgu4PF4Gx3Xo4eVM2fqfb7317XHdQQ/r5XcmGrVNKqX/25VrQIDA274S3WzPgQXHh5O//792b9/v9Hm9Xqx2Wz07NkTgISEBJ9+gCNHjhj9iYmJ2Gw2n7OEw4cPExISYrrcJCIiraPZn5DOzMykuLjY2B/Ytm0bZ8+eZfjw4QBkZWVRXl7OoUOHADh16hQ7duww+tPS0nC5XKxfvx4Al8tFcXExqampBAQENHd6IiJyE5p1WQng6aefZuPGjTzxxBMkJiZSUlJCeno6cXFxAKSmppKSksLkyZONW1QjIiKYMGECAF27diU3N5fFixezb98+qqurqaqqYv78+c2dmoiI3KRmnzlERkayceNGEhMTqays5JlnnmHJkiVGf0BAAEVFRUyaNInKykqGDh3K+vXrCQv76TrXtGnTWLBgAXa7nW7durF27Vri4+ObOzUREblJAV6vt/Ed3NtEczakxxZ82OhxWwof7XAbaNo09J9q1TSql/9uuw1pERH5ZVI4iIiIicJBRERMFA4iImKicBAREROFg4iImCgcRETEROEgIiImCgcRETFROIiIiInCQURETBQOIiJionAQEREThYOIiJgoHERExEThICIiJgoHERExUTiIiIiJwkFEREwUDiIiYqJwEBERE4WDiIiYKBxERMRE4SAiIiYKBxERMWmRcNiyZQuxsbE+XyNHjjT6t27dSmZmJsnJycyZMwen0+lzfEVFBdnZ2SQlJZGTk8Pp06dbYloiInKTWiQcbDYbDzzwAP/85z+Nr7fffhuAsrIyCgoKGDZsGK+//jpVVVUsXLjQONbhcJCTk0NgYCBLly6lT58+TJ06FZfL1RJTExGRm9C5JV7kq6++YujQoQwePNjUt2zZMpKTk5k3bx4A/fr1Iysri7y8PO68805Wr16N2+2mqKgIq9XKiBEjyMjIYPv27YwePbolpiciIk3UYmcOcXFxpvaLFy9y4MABHnnkEaMtOjqaAQMGsGvXLuDKmcXIkSOxWq1XJhQYSFpaGuXl5S0xNRERuQnNDocTJ05QW1vLm2++SVJSEqmpqcybN48LFy5w9uxZPB4PMTExPsf07t2bY8eOAWC324mNjb1hv4iItL5mX1Y6dOgQAQEB3H///cycOZNjx45RWFjIjz/+SE5ODoBxVnBVcHAwNTU1ADidzv/Z3xSRkWF+j+3Rw9r4oBY87nbWEdd8s1SrplG9/NfatWp2OKSmpvLRRx8ZZwfDhw/HYrHw6quvkp2dDUCnTp18jgkKCqKhocH498/7LRaL0d8UDscFPB5vo+N69LBy5ky9z/f+uva4juDntZIbU62aRvXy362qVWBgwA1/qW72ZaXu3bubLhslJSVx6dIl3G43cOXS0bVqa2sJDQ0FIDIy0tR//vx5o19ERFpfs8Phm2++4dtvv/VpO3/+PHDlklH//v3Zv3+/0ef1erHZbPTs2ROAhIQEn36AI0eOGP0iItL6mh0Oq1atYvHixT5tmzdvJigoiHvuuYfMzEyKi4upq6sDYNu2bZw9e5bhw4cDkJWVRXl5OYcOHQLg1KlT7Nixw+gXEZHW1+w9h4kTJzJp0iReeeUVEhIS+OKLL9i8eTO///3vCQ8P5+mnn2bjxo088cQTJCYmUlJSQnp6unHra2pqKikpKUyePJn09HTKysqIiIhgwoQJzV5cS7vkcvu1P9HgvEx93Y+tMCMRkVuj2eEQHx/PG2+8QWFhIZs2baJv37689tprjBs3Driyp7Bx40YKCwuprKzkmWeeYfr06cbxAQEBFBUVsWLFCnbu3MnQoUOZNWsWYWH+33nUWixBnRhb8GGj47YUPoq22UTkdtYin5AeNWoUo0aNumF/r169+Otf/3rDfovFQn5+Pvn5+S0xHRERaSY9lVVEREwUDiIiYqJwEBERE4WDiIiYKBxERMRE4SAiIiYKBxERMVE4iIiIicJBRERMFA4iImKicBAREROFg4iImCgcRETEROEgIiImCgcRETFROIiIiInCQURETBQOIiJionAQEREThYOIiJgoHERExEThICIiJp3begK/RJdcbnr0sDY6rsF5mfq6H1thRiIiTaNwuAUsQZ0YW/Bho+O2FD5KfSvMR0SkqXRZSURETNpNOHg8Ht544w1+85vf8MADD7BmzZq2npKISIfVbi4rLV++nHfffZdZs2bRp08fXnnlFe68804efvjhtp6aiEiH0y7C4cKFC7z33nvk5uaSk5NjtK1YsULhICLSBtpFOHz55Zc4nU4eeeQRoy09PZ0//elP/PDDD/Tq1asNZ3fr+HtXE+jOJhFpXe0iHOx2O8HBwfTt29do69atG126dOH777/3OxwCAwP8/pk/H9vzjlC/jmvJcZagTvxuwf/59XrvvZzBxSasryU1pa4dnWrVNKqX/25Frf7XawZ4vV5vi//EJlq7di1FRUV8/vnnPu2pqan8+c9/JjMzs41mJiLSMbWLu5UsFguBgeapBAUF0dDQ0AYzEhHp2NpFOERGRnLu3DncbrdPe11dHV26dGmjWYmIdFztIhwGDx6M2+3m4MGDRts333zDjz/+SM+ePdtwZiIiHVO7CIdevXqRlJTEO++8Y7StW7eObt26ce+997bhzEREOqZ2sSENsGvXLn73u99x3333ERoayr///W9mz55tfO5BRERaT7sJB4D9+/fz5ptvUldXx4QJExg/fnxbT0lEpENqV+EgIiLtQ7vYcxARkfZF4SAiIiYdKhz0WPAbczgczJgxg6SkJOLj48nNzcXhcBj9W7duJTMzk+TkZObMmYPT6WzD2bYPbrebcePGsXz5cqNt165dPPbYY9x3333MmDGD2traNpxh++HxeBg3bhyzZs3yadf76ic//PADzz33HMnJyaSkpJCfn8+ZM2eM/tauVYcKh6uPBZ8yZQqvvPIKb731Flu3bm3rabULM2fO5NChQ+Tn51NQUMDevXuZPXs2AGVlZRQUFDBs2DBef/11qqqqWLhwYRvPuO29++67HDlyxPi+qqqK5557jrvvvptly5bhcrlM/xl2VP/4xz/47rvv+MMf/mC06X3lq6CggMDAQJYvX86rr75KZWUl06dPB9qoVt4Oor6+3jt48GDv8uXLjbZNmzZ5H3744TacVftQVlbmTUxM9J4+fdpo+9vf/ub99a9/7b1w4YJ34sSJ3qeeesro+/7777333HOP98yZM20x3Xbh66+/9sbFxXmTkpK8y5Yt83q9Xu/zzz/vzcjI8LpcLq/Xe+U9l5SU5D1w4EBbTrXN2e1275AhQ7xvvfWWT7veVz/5/vvvvTExMT5rLy0t9cbExHjtdnub1KrDnDnc6LHgX3/9NT/88EMbzqztxcXFsWHDBu666y6jrXv37ng8Hurr6zlw4IBP3aKjoxkwYAC7du1qi+m2OY/Hw4svvkhWVpbPhzTLysrIysqic+crDzsOCwtj2LBhlJeXt9VU24XXXnuNbt26MXnyZKPt4sWLel9d49y5cwB4r7l51OVyAXD58uU2qVWHCYfGHgvekVmtVgYOHOjT9tlnnzFw4ECcTicej4eYmBif/t69e3Ps2LFWnGX78f7773Py5Elefvllo+3y5cucO3dOdfqZXbt28fHHHxMVFcXs2bNZsmQJ586d4+zZs3pfXSMmJoaIiAj+8pe/UFNTQ3V1NW+++Sa//e1vuXTpUpvUqsOEQ0NDA1arlYAA3+eXh4SEGKktV1RVVVFSUkJOTo7xVFyr1fePEgUHB1NTU9MW02tTx48fZ9myZSxYsIBu3boZ7arT9S1evBi4csODw+Fg1apVPPbYY8ZGq+p1RWhoKMuXL2fr1q2kpKQwatQo/vvf/7J48eI2e291mHDQY8H943a7efHFF4mJieHxxx/HYrEA0KlTJ59xHbFuXq+Xl156idGjR5OWlubTpzqZHT58mMOHDzNmzBhKSkp4//332bx5M/X19RQVFQGq11WXLl3i1VdfJSkpicWLF/Pyyy9TX1/PjBkzCAoKAlq/Vu3iL8G1hmsfC35tkfVYcF8rV67k6NGjbNiwgc6dOxMZGQlcuSzXr18/Y1xtba3PJbqO4O9//zvV1dW89dZbpj6LxYLVasVut/u019bWEhrq318P/KW5esnj2WefNdoGDBjAkCFDjH0+va+u+PTTT3E4HHzwwQfG+2XYsGGMHTuWr776Cmj9WnWYMwc9FrxxpaWlrFy5ktmzZxMbGwtAeHg4/fv3Z//+/cY4r9eLzWbrcHX75JNP+M9//sP9999PbGwssbGxfPHFF7z55pvExsaSkJDgUyeAI0eOdLg6XXX1P7m7777bpz04OJjIyEi9r65x9c8hX/uLxKBBgwgKCqK2trZNatVhwkGPBf/fKisrmTlzJmPGjCE7O9unLzMzk+LiYurq6gDYtm0bZ8+eZfjw4W0x1TazYMECNm/e7PN17733MnHiRDZv3kxWVhYff/wxJ0+eBODgwYMcPHiww9Xpqri4OAICAqioqDDanE4nBw8eJCEhQe+ra0RERPDtt99y8eJFo62srAyXy0WvXr3apFYd6sF7eiz49blcLsaOHUtdXR1Lly4lJCTE6Ovfvz9Op5NHH32ULl26kJiYSElJCSNGjLju5ZWO5qmnnmLo0KHk5eXR0NDA+PHjqampYcSIEXz66adER0fzwQcfGLe3djR//OMf+fLLLykoKCA8PJzVq1ezb98+/vWvf2GxWPS++v9OnjzJ6NGj6du3LyNHjuT8+fNs3bqVnj17smXLFurr61u9Vh0qHECPBb+eI0eOMG7cuOv2rVmzhmHDhvHDD0Dh3PoAAACjSURBVD9QWFhIZWUlI0aMYPr06T4h0lFdGw4A9fX1LFmyhH379hEfH8/zzz9P9+7d23iWbefSpUusWLGCLVu2cPbsWQYOHMhLL73EkCFDAPS+usaBAwcoLCzk6NGjNDQ0EB8fz8svv2xc4m3tWnW4cBARkcZ1mD0HERHxn8JBRERMFA4iImKicBAREROFg4iImCgcRETEROEgIiImCgcRETH5f3EyaJGBMK0LAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x=data_user_buy,bins=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 不同'date'，不同的'user_id'的购买的次数\n",
    "data_user_buy1 = data_user[data_user.behavior_type=='4'].groupby(['date','user_id'])\n",
    "data_user_buy1 = data_user_buy1.count()['behavior_type'].reset_index().rename(columns={'behavior_type':'total'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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>date</th>\n",
       "      <th>user_id</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2014-11-18</td>\n",
       "      <td>100024529</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2014-11-18</td>\n",
       "      <td>100128093</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2014-11-18</td>\n",
       "      <td>101538639</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2014-11-18</td>\n",
       "      <td>102014015</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2014-11-18</td>\n",
       "      <td>103962421</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6376</td>\n",
       "      <td>2014-12-18</td>\n",
       "      <td>98607707</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6377</td>\n",
       "      <td>2014-12-18</td>\n",
       "      <td>99418963</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6378</td>\n",
       "      <td>2014-12-18</td>\n",
       "      <td>99674599</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6379</td>\n",
       "      <td>2014-12-18</td>\n",
       "      <td>99785045</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6380</td>\n",
       "      <td>2014-12-18</td>\n",
       "      <td>99796276</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6381 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date    user_id  total\n",
       "0    2014-11-18  100024529      1\n",
       "1    2014-11-18  100128093      1\n",
       "2    2014-11-18  101538639      4\n",
       "3    2014-11-18  102014015      1\n",
       "4    2014-11-18  103962421      1\n",
       "...         ...        ...    ...\n",
       "6376 2014-12-18   98607707      1\n",
       "6377 2014-12-18   99418963      1\n",
       "6378 2014-12-18   99674599      1\n",
       "6379 2014-12-18   99785045      1\n",
       "6380 2014-12-18   99796276      2\n",
       "\n",
       "[6381 rows x 3 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2014-11-18    1.232432\n",
       "2014-11-19    1.241206\n",
       "2014-11-20    1.262857\n",
       "2014-11-21    1.192308\n",
       "2014-11-22    1.314286\n",
       "2014-11-23    1.247191\n",
       "2014-11-24    1.209677\n",
       "2014-11-25    1.244565\n",
       "2014-11-26    1.263804\n",
       "2014-11-27    1.207071\n",
       "2014-11-28    1.241573\n",
       "2014-11-29    1.236364\n",
       "2014-11-30    1.133005\n",
       "2014-12-01    1.220183\n",
       "2014-12-02    1.170984\n",
       "2014-12-03    1.277512\n",
       "2014-12-04    1.205742\n",
       "2014-12-05    1.189189\n",
       "2014-12-06    1.208333\n",
       "2014-12-07    1.165829\n",
       "2014-12-08    1.200000\n",
       "2014-12-09    1.167568\n",
       "2014-12-10    1.150000\n",
       "2014-12-11    1.191860\n",
       "2014-12-12    1.400000\n",
       "2014-12-13    1.188482\n",
       "2014-12-14    1.169154\n",
       "2014-12-15    1.151899\n",
       "2014-12-16    1.195238\n",
       "2014-12-17    1.194737\n",
       "2014-12-18    1.200000\n",
       "Name: total, dtype: float64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 每天的人均消费次数 = 每日消费总次数 / 每日消费人数\n",
    "data_user_buy2 = data_user_buy1.groupby('date').sum()['total'] / data_user_buy1.groupby('date').count()['total']\n",
    "data_user_buy2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_user_buy2.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    31.000000\n",
       "mean      1.215260\n",
       "std       0.053003\n",
       "min       1.133005\n",
       "25%       1.188835\n",
       "50%       1.205742\n",
       "75%       1.241390\n",
       "max       1.400000\n",
       "Name: total, dtype: float64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy2.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "日ARPU : ARPU是衡量产答品的盈利能力，也是从某个方面衡量产品的发展活力。\n",
    "ARPU值=收入÷付费用户"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_user['operation'] = 1\n",
    "data_user_buy3 = data_user.groupby(['date','user_id','behavior_type'])['operation'].count().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "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>date</th>\n",
       "      <th>user_id</th>\n",
       "      <th>behavior_type</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2014-11-18</td>\n",
       "      <td>100024529</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2014-11-18</td>\n",
       "      <td>100024529</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2014-11-18</td>\n",
       "      <td>100024529</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2014-11-18</td>\n",
       "      <td>100027681</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2014-11-18</td>\n",
       "      <td>10004287</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>135518</td>\n",
       "      <td>2014-12-18</td>\n",
       "      <td>99796276</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>135519</td>\n",
       "      <td>2014-12-18</td>\n",
       "      <td>99796276</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>135520</td>\n",
       "      <td>2014-12-18</td>\n",
       "      <td>99796276</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>135521</td>\n",
       "      <td>2014-12-18</td>\n",
       "      <td>9996155</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>135522</td>\n",
       "      <td>2014-12-18</td>\n",
       "      <td>99989881</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135523 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date    user_id behavior_type  total\n",
       "0      2014-11-18  100024529             1     27\n",
       "1      2014-11-18  100024529             3      1\n",
       "2      2014-11-18  100024529             4      1\n",
       "3      2014-11-18  100027681             1      8\n",
       "4      2014-11-18   10004287             1     21\n",
       "...           ...        ...           ...    ...\n",
       "135518 2014-12-18   99796276             1      6\n",
       "135519 2014-12-18   99796276             3      1\n",
       "135520 2014-12-18   99796276             4      2\n",
       "135521 2014-12-18    9996155             1      8\n",
       "135522 2014-12-18   99989881             1      1\n",
       "\n",
       "[135523 rows x 4 columns]"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy3.rename(columns={'operation':'total'},inplace=True)\n",
    "data_user_buy3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2014-11-18    0.069385\n",
       "2014-11-19    0.075581\n",
       "2014-11-20    0.067051\n",
       "2014-11-21    0.058089\n",
       "2014-11-22    0.072854\n",
       "2014-11-23    0.067828\n",
       "2014-11-24    0.067004\n",
       "2014-11-25    0.069690\n",
       "2014-11-26    0.063074\n",
       "2014-11-27    0.073245\n",
       "2014-11-28    0.070270\n",
       "2014-11-29    0.063080\n",
       "2014-11-30    0.067508\n",
       "2014-12-01    0.078097\n",
       "2014-12-02    0.066024\n",
       "2014-12-03    0.076570\n",
       "2014-12-04    0.073684\n",
       "2014-12-05    0.066586\n",
       "2014-12-06    0.068884\n",
       "2014-12-07    0.068135\n",
       "2014-12-08    0.057659\n",
       "2014-12-09    0.063269\n",
       "2014-12-10    0.058974\n",
       "2014-12-11    0.054406\n",
       "2014-12-12    0.232478\n",
       "2014-12-13    0.064544\n",
       "2014-12-14    0.067626\n",
       "2014-12-15    0.076772\n",
       "2014-12-16    0.071408\n",
       "2014-12-17    0.065626\n",
       "2014-12-18    0.062259\n",
       "dtype: float64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy3.groupby('date').apply(lambda x: x[x.behavior_type=='4'].total.sum()/len(x.user_id.unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2014-11-18    0.056299\n",
       "2014-11-19    0.060894\n",
       "2014-11-20    0.053095\n",
       "2014-11-21    0.048720\n",
       "2014-11-22    0.055432\n",
       "2014-11-23    0.054384\n",
       "2014-11-24    0.055390\n",
       "2014-11-25    0.055995\n",
       "2014-11-26    0.049908\n",
       "2014-11-27    0.060680\n",
       "2014-11-28    0.056598\n",
       "2014-11-29    0.051020\n",
       "2014-11-30    0.059583\n",
       "2014-12-01    0.064005\n",
       "2014-12-02    0.056383\n",
       "2014-12-03    0.059937\n",
       "2014-12-04    0.061111\n",
       "2014-12-05    0.055993\n",
       "2014-12-06    0.057007\n",
       "2014-12-07    0.058443\n",
       "2014-12-08    0.048049\n",
       "2014-12-09    0.054189\n",
       "2014-12-10    0.051282\n",
       "2014-12-11    0.045648\n",
       "2014-12-12    0.166056\n",
       "2014-12-13    0.054308\n",
       "2014-12-14    0.057842\n",
       "2014-12-15    0.066648\n",
       "2014-12-16    0.059744\n",
       "2014-12-17    0.054929\n",
       "2014-12-18    0.051883\n",
       "dtype: float64"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 付费率\n",
    "# 消费次数 / 活跃客户数\n",
    "b = data_user_buy3.groupby('date').apply(lambda x: x[x.behavior_type=='4'].total.count()/len(x.user_id.unique()))\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000023EB9341F88>"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 同一时间段内\n",
    "data_user_buy4 = data_user[data_user.behavior_type=='4'].groupby(['user_id','date','hour'])\n",
    "data_user_buy4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id    date        hour\n",
       "100012968  2014-11-22  0       1\n",
       "           2014-11-25  21      1\n",
       "100024529  2014-11-18  8       1\n",
       "           2014-11-19  1       1\n",
       "100027681  2014-11-22  10      3\n",
       "                              ..\n",
       "99796276   2014-12-18  19      2\n",
       "99851929   2014-11-29  19      1\n",
       "99900937   2014-12-06  14      1\n",
       "           2014-12-17  14      1\n",
       "99989881   2014-12-04  22      1\n",
       "Name: operation, Length: 6855, dtype: int64"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = data_user_buy4['operation'].sum()\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(c)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "复购行为分析\n",
    "复购率 复购行为用户数 / 购买行为用户数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id\n",
       "100012968    2\n",
       "100024529    2\n",
       "100027681    2\n",
       "100035725    1\n",
       "100067745    2\n",
       "            ..\n",
       "99796192     1\n",
       "99796276     2\n",
       "99851929     1\n",
       "99900937     2\n",
       "99989881     1\n",
       "Name: date, Length: 3326, dtype: int64"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_rebuy = data_user[data_user.behavior_type=='4'].groupby('user_id')['date'].apply(lambda x:len(x.unique()))\n",
    "data_rebuy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.46692723992784124"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_rebuy[data_rebuy>=2].count() / data_rebuy.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id          \n",
       "100012968  505602   2014-11-22\n",
       "           505674   2014-11-25\n",
       "100024529  673618   2014-11-18\n",
       "           673642   2014-11-19\n",
       "100027681  476170   2014-11-22\n",
       "                       ...    \n",
       "99796276   765071   2014-12-18\n",
       "99851929   107252   2014-11-29\n",
       "99900937   463252   2014-12-06\n",
       "           462932   2014-12-17\n",
       "99989881   426775   2014-12-04\n",
       "Name: date, Length: 7847, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_day_buy = data_user[data_user.behavior_type=='4'].groupby('user_id').date.apply(lambda x: x.sort_values())\n",
    "data_day_buy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id          \n",
       "100012968  505674    3 days\n",
       "100024529  673642    1 days\n",
       "100027681  476186    0 days\n",
       "           476242    0 days\n",
       "           476154    6 days\n",
       "                      ...  \n",
       "99674599   131263    8 days\n",
       "           192391    6 days\n",
       "99796276   764882    3 days\n",
       "           765071    0 days\n",
       "99900937   462932   11 days\n",
       "Name: date, Length: 4521, dtype: timedelta64[ns]"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_day_buy = data_user[data_user.behavior_type=='4'].groupby('user_id').date.apply(lambda x: x.sort_values().diff(1).dropna())\n",
    "data_day_buy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id          \n",
       "100012968  505674     3\n",
       "100024529  673642     1\n",
       "100027681  476186     0\n",
       "           476242     0\n",
       "           476154     6\n",
       "                     ..\n",
       "99674599   131263     8\n",
       "           192391     6\n",
       "99796276   764882     3\n",
       "           765071     0\n",
       "99900937   462932    11\n",
       "Name: date, Length: 4521, dtype: int64"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_day_buy.map(lambda x:x.days)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "漏斗模型分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "behavior_type\n",
       "1    756386\n",
       "2     16013\n",
       "3     22510\n",
       "4      7847\n",
       "dtype: int64"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_count = data_user.groupby('behavior_type').size()\n",
    "data_user_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "802756"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv_all = data_user['user_id'].count()\n",
    "pv_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.057763504726218176"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 流失率\n",
    "(pv_all-data_user_count[0])/pv_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9702400626135333"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 加入购物车的数量的流失率\n",
    "(data_user_count[0]-data_user_count[2])/data_user_count[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.28862727676588185"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 加入购物车 到 收藏\n",
    "(data_user_count[2]-data_user_count[1])/data_user_count[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.509960656966215"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从收藏到购买\n",
    "(data_user_count[1]-data_user_count[3])/data_user_count[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 用户价值的RFM的分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-293-4a4a2b92d388>, line 6)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-293-4a4a2b92d388>\"\u001b[1;36m, line \u001b[1;32m6\u001b[0m\n\u001b[1;33m    datetime(2014.12.10)\u001b[0m\n\u001b[1;37m                      ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "# from datetime import datetime\n",
    "# R_group = data_user[data_user['behavior_type']=='4'].groupby('user_id')['date']\n",
    "# # R = R_group.apply(lambda x: x.iloc[-1])\n",
    "# R = R_group.apply(lambda x: x.iloc[-1] )\n",
    "# R\n",
    "datetime(2014.12.10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# R = data_user[data_user['behavior_type']=='4'].groupby('user_id')['date'].apply(lambda x: datetime(data_user['date'].max())-x.max())\n",
    "# R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id\n",
       "100012968    2\n",
       "100024529    2\n",
       "100027681    4\n",
       "100035725    1\n",
       "100067745    2\n",
       "            ..\n",
       "99796192     1\n",
       "99796276     3\n",
       "99851929     1\n",
       "99900937     2\n",
       "99989881     1\n",
       "Name: date, Length: 3326, dtype: int64"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F = data_user[data_user['behavior_type']=='4'].groupby('user_id')['date'].count()\n",
    "F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "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>user_id</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>100012968</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>100024529</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>100027681</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>100035725</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>100067745</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3321</td>\n",
       "      <td>99796192</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3322</td>\n",
       "      <td>99796276</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3323</td>\n",
       "      <td>99851929</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3324</td>\n",
       "      <td>99900937</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3325</td>\n",
       "      <td>99989881</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3326 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id  freq\n",
       "0     100012968     2\n",
       "1     100024529     2\n",
       "2     100027681     4\n",
       "3     100035725     1\n",
       "4     100067745     2\n",
       "...         ...   ...\n",
       "3321   99796192     1\n",
       "3322   99796276     3\n",
       "3323   99851929     1\n",
       "3324   99900937     2\n",
       "3325   99989881     1\n",
       "\n",
       "[3326 rows x 2 columns]"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F.reset_index().rename(columns={'date':'freq'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfm = pd.merge(F,M left_on='user_id',right_on='user_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfm['R'] = pd.qcut(rfm.R,2,labels=['2','1'])\n",
    "rfm['F'] = pd.qcut(rfm.F,2,labels=['1','2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfm['rfm'] = rfm['R'].str.cat(rfm['F'])"
   ]
  }
 ],
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