03. Python绘制电商网站转化漏斗图.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python绘制电商网站转化漏斗图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "漏斗图：适用于业务流程环节多的流程分析，通过漏斗各环节业务数据的比较，能够直观地发现问题所在。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "实例：数据来自kaggle网站的\"E-commerce website Funnel analysis\"  \n",
    "地址为：https://www.kaggle.com/aerodinamicc/ecommerce-website-funnel-analysis\n",
    "\n",
    "网站很简单，有四个页面数据：\n",
    "1. home_page_table.csv，首页用户访问数据\n",
    "2. search_page_table.csv，搜索页用户访问数据\n",
    "3. payment_page_table.csv，支付信息页用户访问数据\n",
    "4. payment_confirmation_table.csv，支付成功页用户访问数据\n",
    "5. user_table.csv，用户信息数据\n",
    "\n",
    "目标：绘制转化漏斗，查看是否正常"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 读取5个数据表到df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_home_page = pd.read_csv(\"./datas/ecommerce-website-funnel-analysis/home_page_table.csv\")\n",
    "df_search_page = pd.read_csv(\"./datas/ecommerce-website-funnel-analysis/search_page_table.csv\")\n",
    "df_payment_page = pd.read_csv(\"./datas/ecommerce-website-funnel-analysis/payment_page_table.csv\")\n",
    "df_payment_confirmation_page = pd.read_csv(\"./datas/ecommerce-website-funnel-analysis/payment_confirmation_table.csv\")\n",
    "df_user_table = pd.read_csv(\"./datas/ecommerce-website-funnel-analysis/user_table.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 查看首页数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>page</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>313593</td>\n",
       "      <td>home_page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>468315</td>\n",
       "      <td>home_page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>264005</td>\n",
       "      <td>home_page</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   user_id       page\n",
       "0   313593  home_page\n",
       "1   468315  home_page\n",
       "2   264005  home_page"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_home_page.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 查看搜索页数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15866</td>\n",
       "      <td>search_page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>347058</td>\n",
       "      <td>search_page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>577020</td>\n",
       "      <td>search_page</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   user_id         page\n",
       "0    15866  search_page\n",
       "1   347058  search_page\n",
       "2   577020  search_page"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "df_search_page.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 查看支付信息页数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>page</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>253019</td>\n",
       "      <td>payment_page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>310478</td>\n",
       "      <td>payment_page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>304081</td>\n",
       "      <td>payment_page</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   user_id          page\n",
       "0   253019  payment_page\n",
       "1   310478  payment_page\n",
       "2   304081  payment_page"
      ]
     },
     "execution_count": 6,
     "metadata": {},
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   "source": [
    "df_payment_page.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 查看支付成功页数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>user_id</th>\n",
       "      <th>page</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>123100</td>\n",
       "      <td>payment_confirmation_page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>704999</td>\n",
       "      <td>payment_confirmation_page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>407188</td>\n",
       "      <td>payment_confirmation_page</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   user_id                       page\n",
       "0   123100  payment_confirmation_page\n",
       "1   704999  payment_confirmation_page\n",
       "2   407188  payment_confirmation_page"
      ]
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     "execution_count": 7,
     "metadata": {},
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   "source": [
    "df_payment_confirmation_page.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 查看用户信息表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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>date</th>\n",
       "      <th>device</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>450007</td>\n",
       "      <td>2015-02-28</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>756838</td>\n",
       "      <td>2015-01-13</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>568983</td>\n",
       "      <td>2015-04-09</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   user_id        date   device     sex\n",
       "0   450007  2015-02-28  Desktop  Female\n",
       "1   756838  2015-01-13  Desktop    Male\n",
       "2   568983  2015-04-09  Desktop    Male"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_user_table.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 查看设备的类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Desktop    60200\n",
       "Mobile     30200\n",
       "Name: device, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_user_table[\"device\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 查看性别的类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      45325\n",
       "Female    45075\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_user_table[\"sex\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 关联5个数据表为一个大表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merge = df_user_table\n",
    "\n",
    "for df_inter in [df_home_page, df_search_page, df_payment_page, df_payment_confirmation_page]:\n",
    "    # 每次循环都会忘df_merge中添加新列\n",
    "    df_merge = pd.merge(\n",
    "        left=df_merge, \n",
    "        right=df_inter, \n",
    "        left_on=\"user_id\", \n",
    "        right_on=\"user_id\", \n",
    "        how=\"left\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
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       "      <th>0</th>\n",
       "      <td>450007</td>\n",
       "      <td>2015-02-28</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Female</td>\n",
       "      <td>home_page</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>756838</td>\n",
       "      <td>2015-01-13</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Male</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>568983</td>\n",
       "      <td>2015-04-09</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Male</td>\n",
       "      <td>home_page</td>\n",
       "      <td>search_page</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>190794</td>\n",
       "      <td>2015-02-18</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Female</td>\n",
       "      <td>home_page</td>\n",
       "      <td>search_page</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>537909</td>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Male</td>\n",
       "      <td>home_page</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>90395</th>\n",
       "      <td>307667</td>\n",
       "      <td>2015-03-30</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Female</td>\n",
       "      <td>home_page</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>90396</th>\n",
       "      <td>642989</td>\n",
       "      <td>2015-02-08</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Female</td>\n",
       "      <td>home_page</td>\n",
       "      <td>search_page</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90397</th>\n",
       "      <td>659645</td>\n",
       "      <td>2015-04-13</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Male</td>\n",
       "      <td>home_page</td>\n",
       "      <td>search_page</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90398</th>\n",
       "      <td>359779</td>\n",
       "      <td>2015-03-23</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Male</td>\n",
       "      <td>home_page</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90399</th>\n",
       "      <td>438929</td>\n",
       "      <td>2015-03-26</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Female</td>\n",
       "      <td>home_page</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>90400 rows × 8 columns</p>\n",
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      ],
      "text/plain": [
       "       user_id        date   device     sex     page_x       page_y page_x  \\\n",
       "0       450007  2015-02-28  Desktop  Female  home_page          NaN    NaN   \n",
       "1       756838  2015-01-13  Desktop    Male  home_page          NaN    NaN   \n",
       "2       568983  2015-04-09  Desktop    Male  home_page  search_page    NaN   \n",
       "3       190794  2015-02-18  Desktop  Female  home_page  search_page    NaN   \n",
       "4       537909  2015-01-15  Desktop    Male  home_page          NaN    NaN   \n",
       "...        ...         ...      ...     ...        ...          ...    ...   \n",
       "90395   307667  2015-03-30  Desktop  Female  home_page          NaN    NaN   \n",
       "90396   642989  2015-02-08  Desktop  Female  home_page  search_page    NaN   \n",
       "90397   659645  2015-04-13  Desktop    Male  home_page  search_page    NaN   \n",
       "90398   359779  2015-03-23  Desktop    Male  home_page          NaN    NaN   \n",
       "90399   438929  2015-03-26   Mobile  Female  home_page          NaN    NaN   \n",
       "\n",
       "      page_y  \n",
       "0        NaN  \n",
       "1        NaN  \n",
       "2        NaN  \n",
       "3        NaN  \n",
       "4        NaN  \n",
       "...      ...  \n",
       "90395    NaN  \n",
       "90396    NaN  \n",
       "90397    NaN  \n",
       "90398    NaN  \n",
       "90399    NaN  \n",
       "\n",
       "[90400 rows x 8 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>date</th>\n",
       "      <th>device</th>\n",
       "      <th>sex</th>\n",
       "      <th>home_page</th>\n",
       "      <th>search_page</th>\n",
       "      <th>payment_page</th>\n",
       "      <th>confirmation_page</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>450007</td>\n",
       "      <td>2015-02-28</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Female</td>\n",
       "      <td>home_page</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>756838</td>\n",
       "      <td>2015-01-13</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Male</td>\n",
       "      <td>home_page</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>568983</td>\n",
       "      <td>2015-04-09</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Male</td>\n",
       "      <td>home_page</td>\n",
       "      <td>search_page</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>190794</td>\n",
       "      <td>2015-02-18</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Female</td>\n",
       "      <td>home_page</td>\n",
       "      <td>search_page</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>537909</td>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Male</td>\n",
       "      <td>home_page</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90395</th>\n",
       "      <td>307667</td>\n",
       "      <td>2015-03-30</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Female</td>\n",
       "      <td>home_page</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90396</th>\n",
       "      <td>642989</td>\n",
       "      <td>2015-02-08</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Female</td>\n",
       "      <td>home_page</td>\n",
       "      <td>search_page</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90397</th>\n",
       "      <td>659645</td>\n",
       "      <td>2015-04-13</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Male</td>\n",
       "      <td>home_page</td>\n",
       "      <td>search_page</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90398</th>\n",
       "      <td>359779</td>\n",
       "      <td>2015-03-23</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>Male</td>\n",
       "      <td>home_page</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90399</th>\n",
       "      <td>438929</td>\n",
       "      <td>2015-03-26</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Female</td>\n",
       "      <td>home_page</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90400 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id        date   device     sex  home_page  search_page  \\\n",
       "0       450007  2015-02-28  Desktop  Female  home_page          NaN   \n",
       "1       756838  2015-01-13  Desktop    Male  home_page          NaN   \n",
       "2       568983  2015-04-09  Desktop    Male  home_page  search_page   \n",
       "3       190794  2015-02-18  Desktop  Female  home_page  search_page   \n",
       "4       537909  2015-01-15  Desktop    Male  home_page          NaN   \n",
       "...        ...         ...      ...     ...        ...          ...   \n",
       "90395   307667  2015-03-30  Desktop  Female  home_page          NaN   \n",
       "90396   642989  2015-02-08  Desktop  Female  home_page  search_page   \n",
       "90397   659645  2015-04-13  Desktop    Male  home_page  search_page   \n",
       "90398   359779  2015-03-23  Desktop    Male  home_page          NaN   \n",
       "90399   438929  2015-03-26   Mobile  Female  home_page          NaN   \n",
       "\n",
       "      payment_page confirmation_page  \n",
       "0              NaN               NaN  \n",
       "1              NaN               NaN  \n",
       "2              NaN               NaN  \n",
       "3              NaN               NaN  \n",
       "4              NaN               NaN  \n",
       "...            ...               ...  \n",
       "90395          NaN               NaN  \n",
       "90396          NaN               NaN  \n",
       "90397          NaN               NaN  \n",
       "90398          NaN               NaN  \n",
       "90399          NaN               NaN  \n",
       "\n",
       "[90400 rows x 8 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge.columns = [\n",
    "    \"user_id\", \"date\", \"device\", \"sex\", \n",
    "    \"home_page\", \"search_page\", \"payment_page\", \"confirmation_page\"]\n",
    "df_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90400, 8)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 计算每个页面的用户数目\n",
    "\n",
    "目的是给漏斗图填充数据，pyecharts需要的格式为：数据格式为[(key1, value1), (key2, value2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('home_page', 90400), ('search_page', 45200), ('payment_page', 6030), ('confirmation_page', 452)]\n"
     ]
    }
   ],
   "source": [
    "datas = []\n",
    "for column in [\"home_page\", \"search_page\", \"payment_page\", \"confirmation_page\"]:\n",
    "    user_count = df_merge[column].dropna().size\n",
    "    datas.append((column, user_count))\n",
    "print(datas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('home_page', 100.0),\n",
       " ('search_page', 50.0),\n",
       " ('payment_page', 6.67),\n",
       " ('confirmation_page', 0.5)]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 方便查看对比，进行归一化\n",
    "max_count = datas[0][1]\n",
    "datas_norm = [\n",
    "              (x, round(y*100/max_count, 2)) \n",
    "              for x,y in datas\n",
    "             ]\n",
    "datas_norm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. 绘制漏斗图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Funnel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pyecharts.charts.basic_charts.funnel.Funnel at 0x212409cd188>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "funnel = Funnel()\n",
    "funnel.add(\"用户比例\", datas_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"63f15ab2c2c64f8daa6c6ffbe26640af\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_63f15ab2c2c64f8daa6c6ffbe26640af = echarts.init(\n",
       "                    document.getElementById('63f15ab2c2c64f8daa6c6ffbe26640af'), 'white', {renderer: 'canvas'});\n",
       "                var option_63f15ab2c2c64f8daa6c6ffbe26640af = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
       "        \"#ca8622\",\n",
       "        \"#bda29a\",\n",
       "        \"#6e7074\",\n",
       "        \"#546570\",\n",
       "        \"#c4ccd3\",\n",
       "        \"#f05b72\",\n",
       "        \"#ef5b9c\",\n",
       "        \"#f47920\",\n",
       "        \"#905a3d\",\n",
       "        \"#fab27b\",\n",
       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"funnel\",\n",
       "            \"name\": \"\\u7528\\u6237\\u6bd4\\u4f8b\",\n",
       "            \"data\": [\n",
       "                {\n",
       "                    \"name\": \"home_page\",\n",
       "                    \"value\": 100.0\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"search_page\",\n",
       "                    \"value\": 50.0\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"payment_page\",\n",
       "                    \"value\": 6.67\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"confirmation_page\",\n",
       "                    \"value\": 0.5\n",
       "                }\n",
       "            ],\n",
       "            \"sort\": \"descending\",\n",
       "            \"gap\": 0,\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"home_page\",\n",
       "                \"payment_page\",\n",
       "                \"confirmation_page\",\n",
       "                \"search_page\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"home_page\": true,\n",
       "                \"search_page\": true,\n",
       "                \"payment_page\": true,\n",
       "                \"confirmation_page\": true\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0\n",
       "    }\n",
       "};\n",
       "                chart_63f15ab2c2c64f8daa6c6ffbe26640af.setOption(option_63f15ab2c2c64f8daa6c6ffbe26640af);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x212407c5a08>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "funnel.render_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
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