淘宝用户行为数据分析.ipynb

{
 "cells": [
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   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#项目背景\" data-toc-modified-id=\"项目背景-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>项目背景</a></span></li><li><span><a href=\"#项目目标\" data-toc-modified-id=\"项目目标-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>项目目标</a></span><ul class=\"toc-item\"><li><span><a href=\"#分析目的\" data-toc-modified-id=\"分析目的-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>分析目的</a></span></li><li><span><a href=\"#分析思路\" data-toc-modified-id=\"分析思路-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>分析思路</a></span></li></ul></li><li><span><a href=\"#数据准备与清洗\" data-toc-modified-id=\"数据准备与清洗-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>数据准备与清洗</a></span><ul class=\"toc-item\"><li><span><a href=\"#数据字段说明\" data-toc-modified-id=\"数据字段说明-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>数据字段说明</a></span></li><li><span><a href=\"#缺失值处理\" data-toc-modified-id=\"缺失值处理-3.2\"><span class=\"toc-item-num\">3.2&nbsp;&nbsp;</span>缺失值处理</a></span></li><li><span><a href=\"#数据全字段去重\" data-toc-modified-id=\"数据全字段去重-3.3\"><span class=\"toc-item-num\">3.3&nbsp;&nbsp;</span>数据全字段去重</a></span></li><li><span><a href=\"#统计每个字段的取值个数\" data-toc-modified-id=\"统计每个字段的取值个数-3.4\"><span class=\"toc-item-num\">3.4&nbsp;&nbsp;</span>统计每个字段的取值个数</a></span></li><li><span><a href=\"#时间戳处理\" data-toc-modified-id=\"时间戳处理-3.5\"><span class=\"toc-item-num\">3.5&nbsp;&nbsp;</span>时间戳处理</a></span></li></ul></li><li><span><a href=\"#数据分析\" data-toc-modified-id=\"数据分析-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>数据分析</a></span><ul class=\"toc-item\"><li><span><a href=\"#描述分析\" data-toc-modified-id=\"描述分析-4.1\"><span class=\"toc-item-num\">4.1&nbsp;&nbsp;</span>描述分析</a></span><ul class=\"toc-item\"><li><span><a href=\"#整体维度--查看观察窗口内UV、PV、人均浏览次数、成交量的总体情况\" data-toc-modified-id=\"整体维度--查看观察窗口内UV、PV、人均浏览次数、成交量的总体情况-4.1.1\"><span class=\"toc-item-num\">4.1.1&nbsp;&nbsp;</span>整体维度--查看观察窗口内UV、PV、人均浏览次数、成交量的总体情况</a></span></li><li><span><a href=\"#时间维度--按日期维度查看UV、PV、人均浏览次数、成交量\" data-toc-modified-id=\"时间维度--按日期维度查看UV、PV、人均浏览次数、成交量-4.1.2\"><span class=\"toc-item-num\">4.1.2&nbsp;&nbsp;</span>时间维度--按日期维度查看UV、PV、人均浏览次数、成交量</a></span><ul class=\"toc-item\"><li><span><a href=\"#日均UV和PV变化折线图\" data-toc-modified-id=\"日均UV和PV变化折线图-4.1.2.1\"><span class=\"toc-item-num\">4.1.2.1&nbsp;&nbsp;</span>日均UV和PV变化折线图</a></span></li></ul></li><li><span><a href=\"#用户维度\" data-toc-modified-id=\"用户维度-4.1.3\"><span class=\"toc-item-num\">4.1.3&nbsp;&nbsp;</span>用户维度</a></span><ul class=\"toc-item\"><li><span><a href=\"#查看每个用户的行为数据\" data-toc-modified-id=\"查看每个用户的行为数据-4.1.3.1\"><span class=\"toc-item-num\">4.1.3.1&nbsp;&nbsp;</span>查看每个用户的行为数据</a></span></li><li><span><a href=\"#查看购买用户数、复购用户数以及复购率\" data-toc-modified-id=\"查看购买用户数、复购用户数以及复购率-4.1.3.2\"><span class=\"toc-item-num\">4.1.3.2&nbsp;&nbsp;</span>查看购买用户数、复购用户数以及复购率</a></span></li><li><span><a href=\"#用户跳失率\" data-toc-modified-id=\"用户跳失率-4.1.3.3\"><span class=\"toc-item-num\">4.1.3.3&nbsp;&nbsp;</span>用户跳失率</a></span></li></ul></li></ul></li><li><span><a href=\"#用户流量行为转化分析\" data-toc-modified-id=\"用户流量行为转化分析-4.2\"><span class=\"toc-item-num\">4.2&nbsp;&nbsp;</span>用户流量行为转化分析</a></span></li><li><span><a href=\"#用户行为习惯分析\" data-toc-modified-id=\"用户行为习惯分析-4.3\"><span class=\"toc-item-num\">4.3&nbsp;&nbsp;</span>用户行为习惯分析</a></span></li><li><span><a href=\"#用户类目偏好分析\" data-toc-modified-id=\"用户类目偏好分析-4.4\"><span class=\"toc-item-num\">4.4&nbsp;&nbsp;</span>用户类目偏好分析</a></span><ul class=\"toc-item\"><li><span><a href=\"#查看pv—top20的商品类目\" data-toc-modified-id=\"查看pv—top20的商品类目-4.4.1\"><span class=\"toc-item-num\">4.4.1&nbsp;&nbsp;</span>查看pv—top20的商品类目</a></span></li><li><span><a href=\"#将上诉过程定义为函数\" data-toc-modified-id=\"将上诉过程定义为函数-4.4.2\"><span class=\"toc-item-num\">4.4.2&nbsp;&nbsp;</span>将上诉过程定义为函数</a></span></li><li><span><a href=\"#比较所有行为的top20的商品类别\" data-toc-modified-id=\"比较所有行为的top20的商品类别-4.4.3\"><span class=\"toc-item-num\">4.4.3&nbsp;&nbsp;</span>比较所有行为的top20的商品类别</a></span></li></ul></li></ul></li><li><span><a href=\"#结论\" data-toc-modified-id=\"结论-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>结论</a></span></li></ul></div>"
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   "cell_type": "markdown",
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   "source": [
    "# 项目背景  \n",
    "UserBehavior是阿里巴巴提供的一个淘宝用户行为数据集，数据集包含了2017年11月25日至2017年12月3日之间，有行为的约一百万随机用户的所有行为（行为包括点击、购买、加购、喜欢），用以进行隐式反馈推荐问题的研究。  \n",
    "注：隐式反馈推荐问题\n",
    "通过对2017年11月25日至2017年12月3日之间的用户行为数据分析，为客户提供更精准的隐式反馈推荐。  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 项目目标"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析目的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1.从用户角度：提高用户忠诚度，帮助用户快速找到商品，减轻信息过载带来的问题。**   \n",
    "**2.从网站角度：提高网站交叉销售能力，提高成交转化率， 从而提高成交额。**  \n",
    "**3.最终目的：能使得推荐系统能将更多的物品推荐给用户， 能给用户推荐他们真正感兴趣的物品。**  \n",
    "注：数据来源为下面所示的天池数据集，但是由于数据量过大（约有1亿条用户行为），所以抽样约**10000000**的用户行为进行分析。 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "参考资料  \n",
    "> https://zhuanlan.zhihu.com/p/105766780 《数据分析项目实战——淘宝用户行为分析》-------作者：（知乎）何书365"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析思路  \n",
    "主要进行以下四个专题分析：\n",
    "1. <font color=red>描述分析</font>:从整体维度和日期维度查看观察窗口内的UV PV PV/UV 成交量， 从用户维度查看用户的行为数据。\n",
    "2. <font color=red>用户流量行为分析</font>:分析网站用户的各行为转化率。  \n",
    "3. <font color=red>用户类目偏好分析</font>:从商品的点击、收藏、加购、购买的频率，探索用户对商品的购买偏好，找到针对不同商品的营销策略(什么产品以及产品类目的购买率最高，找出最受欢迎的产品，优化产品推荐)。   \n",
    "4. <font color=red>用户行为习惯分析</font>:从时间维度分析观察窗口内平均而言一天当中的哪段时间用户数最多， 找到一天当中用户最活跃的时刻。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据准备与清洗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#全部行都能输出\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn')\n",
    "#%matplotlib inline  \n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('max_rows', 100) \n",
    "pd.set_option('max_columns', 50) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif']=['Simhei']  #显示中文,解决图中无法显示中文的问题\n",
    "plt.rcParams['axes.unicode_minus']=False    #设置显示中文后,负号显示受影响。解决坐标轴上乱码问题 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.io.parsers.TextFileReader at 0x261ce6a2340>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# csv的压缩数据\n",
    "# header=None不以第一行作为标题\n",
    "df = pd.read_csv(r\"D:\\资料\\项目实战\\淘宝用户行为数据分析\\data\\UserBehavior.csv.zip\", compression='zip', iterator=True, header=None)\n",
    "df\n",
    "# 由于这里数据量很大， 直接全部加载进来会直接占用内存， 故这里只抽取20000000行数据做分析\n",
    "#  iterator=True是产生一个TextFileReader对象， 不会直接全部把数据读进来\n",
    "# 如果想用所有数据做分析， 就利用chunksize=int, 指定生成一个迭代器， 每次指定读取int行数据， 批量进行处理也行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TextFileReader对象的get_chunk(10000000)指定获取10000000行数据\n",
    "df = df.get_chunk(10000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000000, 5)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape  # 一共10000000， 5列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <td>pv</td>\n",
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       "      <th>3</th>\n",
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       "      <td>4181361</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511593493</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
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       "      <td>2520377</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511596146</td>\n",
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       "   0        1        2   3           4\n",
       "0  1  2268318  2520377  pv  1511544070\n",
       "1  1  2333346  2520771  pv  1511561733\n",
       "2  1  2576651   149192  pv  1511572885\n",
       "3  1  3830808  4181361  pv  1511593493\n",
       "4  1  4365585  2520377  pv  1511596146"
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000000 entries, 0 to 9999999\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Dtype \n",
      "---  ------  ----- \n",
      " 0   0       int64 \n",
      " 1   1       int64 \n",
      " 2   2       int64 \n",
      " 3   3       object\n",
      " 4   4       int64 \n",
      "dtypes: int64(4), object(1)\n",
      "memory usage: 381.5+ MB\n"
     ]
    }
   ],
   "source": [
    "# 查看数据信息\n",
    "df.info()\n",
    "\n",
    "# 抽取出来的1000万行数据有381.5+ MB， 其实不是很大， 虽然行数很多， 但是列数只有5列， 数据类型都很简单， 没有长文本\n",
    "# 所以完全可以考虑分析更大量的数据， 此处， 我们就以1000万行数据分析即可"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据字段说明  \n",
    "本数据集包含了2017年11月25日至2017年12月3日之间，有行为的**约一百万**随机用户的所有行为（行为包括点击、购买、加购、喜欢）。数据集的组织形式和MovieLens-20M类似，即数据集的每一行表示一条用户行为，由用户ID、商品ID、商品类目ID、行为类型和时间戳组成，并以逗号分隔。关于数据集中每一列的详细描述如下："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|    | 列名称     | 说明                                               |\n",
    "|---:|:-----------|:---------------------------------------------------|\n",
    "|  0 | 用户ID     | 整数类型，序列化后的用户ID                         |\n",
    "|  1 | 商品ID     | 整数类型，序列化后的商品ID                         |\n",
    "|  2 | 商品类目ID | 整数类型，序列化后的商品所属类目ID                 |\n",
    "|  3 | 行为类型   | 字符串，枚举类型，包括('pv', 'buy', 'cart', 'fav') |\n",
    "|  4 | 时间戳     | 行为发生的时间戳                                   |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['用户ID', '商品ID', '商品类目ID', '行为类型', '时间戳']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 添加列名\n",
    "columns = ['用户ID', '商品ID', '商品类目ID', '行为类型', '时间戳']\n",
    "columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "      <th>商品ID</th>\n",
       "      <th>商品类目ID</th>\n",
       "      <th>行为类型</th>\n",
       "      <th>时间戳</th>\n",
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       "      <td>1511561733</td>\n",
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       "      <td>1</td>\n",
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       "      <td>pv</td>\n",
       "      <td>1511593493</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2520377</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999995</th>\n",
       "      <td>536993</td>\n",
       "      <td>95420</td>\n",
       "      <td>3253210</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511934240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999996</th>\n",
       "      <td>536993</td>\n",
       "      <td>1702210</td>\n",
       "      <td>1689352</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511934277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999997</th>\n",
       "      <td>536993</td>\n",
       "      <td>3023810</td>\n",
       "      <td>2832009</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511934294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999998</th>\n",
       "      <td>536993</td>\n",
       "      <td>1702210</td>\n",
       "      <td>1689352</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511934330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999999</th>\n",
       "      <td>536993</td>\n",
       "      <td>4505862</td>\n",
       "      <td>1689352</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511934431</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           用户ID     商品ID   商品类目ID 行为类型         时间戳\n",
       "0             1  2268318  2520377   pv  1511544070\n",
       "1             1  2333346  2520771   pv  1511561733\n",
       "2             1  2576651   149192   pv  1511572885\n",
       "3             1  3830808  4181361   pv  1511593493\n",
       "4             1  4365585  2520377   pv  1511596146\n",
       "...         ...      ...      ...  ...         ...\n",
       "9999995  536993    95420  3253210   pv  1511934240\n",
       "9999996  536993  1702210  1689352   pv  1511934277\n",
       "9999997  536993  3023810  2832009   pv  1511934294\n",
       "9999998  536993  1702210  1689352   pv  1511934330\n",
       "9999999  536993  4505862  1689352   pv  1511934431\n",
       "\n",
       "[10000000 rows x 5 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 缺失值处理"
   ]
  },
  {
   "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>用户ID</th>\n",
       "      <th>商品ID</th>\n",
       "      <th>商品类目ID</th>\n",
       "      <th>行为类型</th>\n",
       "      <th>时间戳</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [用户ID, 商品ID, 商品类目ID, 行为类型, 时间戳]\n",
       "Index: []"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.isna().values]  # 无缺失值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据全字段去重"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>用户ID</th>\n",
       "      <th>商品ID</th>\n",
       "      <th>商品类目ID</th>\n",
       "      <th>行为类型</th>\n",
       "      <th>时间戳</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2293745</th>\n",
       "      <td>18711</td>\n",
       "      <td>3324300</td>\n",
       "      <td>1464116</td>\n",
       "      <td>buy</td>\n",
       "      <td>1511676439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3542227</th>\n",
       "      <td>242650</td>\n",
       "      <td>3967698</td>\n",
       "      <td>796856</td>\n",
       "      <td>buy</td>\n",
       "      <td>1512097481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4294120</th>\n",
       "      <td>277134</td>\n",
       "      <td>3404251</td>\n",
       "      <td>149192</td>\n",
       "      <td>buy</td>\n",
       "      <td>1512224835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4294121</th>\n",
       "      <td>277134</td>\n",
       "      <td>1697974</td>\n",
       "      <td>982926</td>\n",
       "      <td>buy</td>\n",
       "      <td>1512224835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4849518</th>\n",
       "      <td>30295</td>\n",
       "      <td>1081025</td>\n",
       "      <td>895939</td>\n",
       "      <td>buy</td>\n",
       "      <td>1511594724</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           用户ID     商品ID   商品类目ID 行为类型         时间戳\n",
       "2293745   18711  3324300  1464116  buy  1511676439\n",
       "3542227  242650  3967698   796856  buy  1512097481\n",
       "4294120  277134  3404251   149192  buy  1512224835\n",
       "4294121  277134  1697974   982926  buy  1512224835\n",
       "4849518   30295  1081025   895939  buy  1511594724"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 提取出重复的行\n",
    "df[df.duplicated()]  # 有5行是重复的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 删除重复的行\n",
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9999995, 5)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 重置行索引\n",
    "df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>用户ID</th>\n",
       "      <th>商品ID</th>\n",
       "      <th>商品类目ID</th>\n",
       "      <th>行为类型</th>\n",
       "      <th>时间戳</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2268318</td>\n",
       "      <td>2520377</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511544070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2333346</td>\n",
       "      <td>2520771</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511561733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2576651</td>\n",
       "      <td>149192</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511572885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3830808</td>\n",
       "      <td>4181361</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511593493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4365585</td>\n",
       "      <td>2520377</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511596146</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   用户ID     商品ID   商品类目ID 行为类型         时间戳\n",
       "0     1  2268318  2520377   pv  1511544070\n",
       "1     1  2333346  2520771   pv  1511561733\n",
       "2     1  2576651   149192   pv  1511572885\n",
       "3     1  3830808  4181361   pv  1511593493\n",
       "4     1  4365585  2520377   pv  1511596146"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9999995, 5)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 统计每个字段的取值个数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "用户ID        97811\n",
       "商品ID      1561016\n",
       "商品类目ID       7967\n",
       "行为类型            4\n",
       "时间戳        760501\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(lambda x:x.value_counts().size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从中可以看出：\n",
    "1. 所用数据中的用户一共有97811个\n",
    "2. 商品共有1561016个\n",
    "3. 商品一共有7967类\n",
    "4. 行为类型有四个"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pv      8950064\n",
       "cart     559132\n",
       "fav      291657\n",
       "buy      199142\n",
       "Name: 行为类型, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对行为类型进行统计\n",
    "df.行为类型.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意到，**用户行为类型 共有 四种**，它们分别是:   \n",
    "\n",
    "|    | 行为类型   |    计数 | 说明                     |\n",
    "|---:|:-----------|--------:|:-------------------------|\n",
    "|  0 | pv         | 8950064 | 商品详情页pv，等价于点击 |\n",
    "|  1 | buy        |  199142 | 商品购买                 |\n",
    "|  2 | cart       |  559132 | 将商品加入购物车         |\n",
    "|  3 | fav        |  291657 | 收藏商品                 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>用户ID</th>\n",
       "      <th>商品ID</th>\n",
       "      <th>商品类目ID</th>\n",
       "      <th>行为类型</th>\n",
       "      <th>时间戳</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2268318</td>\n",
       "      <td>2520377</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511544070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2333346</td>\n",
       "      <td>2520771</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511561733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2576651</td>\n",
       "      <td>149192</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511572885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3830808</td>\n",
       "      <td>4181361</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511593493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4365585</td>\n",
       "      <td>2520377</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511596146</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   用户ID     商品ID   商品类目ID 行为类型         时间戳\n",
       "0     1  2268318  2520377   pv  1511544070\n",
       "1     1  2333346  2520771   pv  1511561733\n",
       "2     1  2576651   149192   pv  1511572885\n",
       "3     1  3830808  4181361   pv  1511593493\n",
       "4     1  4365585  2520377   pv  1511596146"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 时间戳处理  \n",
    "在计算机中，时间实际上是用数字表示的。  \n",
    "我们把1970年1月1日 00:00:00 UTC+00:00时区的时刻称为epoch time(纪元)，记为0（1970年以前的时间timestamp为负数），当前时间就是相对于epoch time的秒数，称为timestamp。   \n",
    "timestamp的值与时区毫无关系，因为timestamp一旦确定，其UTC时间就确定了，转换到任意时区的时间也是完全确定的，这就是为什么计算机存储的当前时间是以timestamp表示的，因为全球各地的计算机在任意时刻的timestamp都是完全相同的（假定时间已校准）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-15T06:56:39.482475Z",
     "start_time": "2020-08-15T06:56:39.477530Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1597544038.7362134"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import time\n",
    "time.time()  # 现在的时间戳， # 整数位10是秒， 即1970年1月1日 00:00:00以来的秒数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-15T06:56:40.998726Z",
     "start_time": "2020-08-15T06:56:40.994779Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2020-08-16 02:14:00.013303280')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 把时间戳转换成日期时间的方法\n",
    "pd.Timestamp(time.time(), unit='s')   # 日期是对的， 但是时区不对， 需要加上8个小时"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         2017-11-24 17:21:10\n",
       "1         2017-11-24 22:15:33\n",
       "2         2017-11-25 01:21:25\n",
       "3         2017-11-25 07:04:53\n",
       "4         2017-11-25 07:49:06\n",
       "                  ...        \n",
       "9999990   2017-11-29 05:44:00\n",
       "9999991   2017-11-29 05:44:37\n",
       "9999992   2017-11-29 05:44:54\n",
       "9999993   2017-11-29 05:45:30\n",
       "9999994   2017-11-29 05:47:11\n",
       "Name: 时间戳, Length: 9999995, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将时间戳转换成日期时间\n",
    "pd.to_datetime(df.时间戳, unit='s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-11-24</td>\n",
       "      <td>17:21:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-11-24</td>\n",
       "      <td>22:15:33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>01:21:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>07:04:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>07:49:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>9999990</th>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>05:44:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999991</th>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>05:44:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999992</th>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>05:44:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999993</th>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>05:45:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999994</th>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>05:47:11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9999995 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0         1\n",
       "0        2017-11-24  17:21:10\n",
       "1        2017-11-24  22:15:33\n",
       "2        2017-11-25  01:21:25\n",
       "3        2017-11-25  07:04:53\n",
       "4        2017-11-25  07:49:06\n",
       "...             ...       ...\n",
       "9999990  2017-11-29  05:44:00\n",
       "9999991  2017-11-29  05:44:37\n",
       "9999992  2017-11-29  05:44:54\n",
       "9999993  2017-11-29  05:45:30\n",
       "9999994  2017-11-29  05:47:11\n",
       "\n",
       "[9999995 rows x 2 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 分离日期和时间\n",
    "a = pd.to_datetime(df.时间戳, unit='s').astype(str).str.split(' ', expand=True)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.columns = ['日期', '时间']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>9999991</th>\n",
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       "      <td>05:44:37</td>\n",
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       "    <tr>\n",
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       "      <td>2017-11-29</td>\n",
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       "      <td>05:47:11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9999995 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 日期        时间\n",
       "0        2017-11-24  17:21:10\n",
       "1        2017-11-24  22:15:33\n",
       "2        2017-11-25  01:21:25\n",
       "3        2017-11-25  07:04:53\n",
       "4        2017-11-25  07:49:06\n",
       "...             ...       ...\n",
       "9999990  2017-11-29  05:44:00\n",
       "9999991  2017-11-29  05:44:37\n",
       "9999992  2017-11-29  05:44:54\n",
       "9999993  2017-11-29  05:45:30\n",
       "9999994  2017-11-29  05:47:11\n",
       "\n",
       "[9999995 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 拼接到原表中\n",
    "df = pd.concat([df, a], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>商品类目ID</th>\n",
       "      <th>行为类型</th>\n",
       "      <th>时间戳</th>\n",
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       "      <td>pv</td>\n",
       "      <td>1511572885</td>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>01:21:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3830808</td>\n",
       "      <td>4181361</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511593493</td>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>07:04:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
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       "      <td>2520377</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511596146</td>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>07:49:06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   用户ID     商品ID   商品类目ID 行为类型         时间戳          日期        时间\n",
       "0     1  2268318  2520377   pv  1511544070  2017-11-24  17:21:10\n",
       "1     1  2333346  2520771   pv  1511561733  2017-11-24  22:15:33\n",
       "2     1  2576651   149192   pv  1511572885  2017-11-25  01:21:25\n",
       "3     1  3830808  4181361   pv  1511593493  2017-11-25  07:04:53\n",
       "4     1  4365585  2520377   pv  1511596146  2017-11-25  07:49:06"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-12-02    1399859\n",
       "2017-12-03    1201017\n",
       "2017-12-01    1122808\n",
       "2017-11-26    1053119\n",
       "2017-11-30    1046205\n",
       "2017-11-25    1045084\n",
       "2017-11-29    1024392\n",
       "2017-11-27     999106\n",
       "2017-11-28     984832\n",
       "2017-11-24     122028\n",
       "2017-11-23        578\n",
       "2017-11-22        183\n",
       "2017-11-21         84\n",
       "2017-11-03         70\n",
       "2017-11-19         66\n",
       "2017-11-20         62\n",
       "2017-11-18         56\n",
       "2017-11-17         37\n",
       "1905-07-13         28\n",
       "2019-02-18         24\n",
       "2017-11-12         23\n",
       "2017-11-11         19\n",
       "2017-04-13         18\n",
       "2016-10-20         17\n",
       "2018-08-28         16\n",
       "2017-11-16         16\n",
       "2020-05-09         15\n",
       "2019-02-25         14\n",
       "2019-02-24         12\n",
       "2017-10-26         12\n",
       "2012-12-03         12\n",
       "2017-04-12         12\n",
       "2025-10-11         11\n",
       "2017-11-14         11\n",
       "2020-10-18         11\n",
       "2017-12-06         11\n",
       "2019-02-20         11\n",
       "2017-04-11          9\n",
       "2019-02-26          9\n",
       "2017-11-15          9\n",
       "2017-10-29          8\n",
       "2017-12-04          8\n",
       "2018-06-20          6\n",
       "2019-02-23          6\n",
       "2017-11-10          6\n",
       "2017-11-13          5\n",
       "2030-01-16          4\n",
       "2017-10-10          3\n",
       "2017-11-01          3\n",
       "2019-02-19          2\n",
       "2017-09-01          2\n",
       "2016-01-06          2\n",
       "2019-02-21          2\n",
       "2016-10-22          2\n",
       "2017-11-02          2\n",
       "2017-10-24          2\n",
       "2017-07-03          2\n",
       "2017-11-05          2\n",
       "2017-11-06          2\n",
       "2016-01-05          1\n",
       "2017-05-15          1\n",
       "2017-08-03          1\n",
       "2010-01-01          1\n",
       "2025-11-02          1\n",
       "2017-11-07          1\n",
       "2017-10-31          1\n",
       "2017-09-16          1\n",
       "2021-10-19          1\n",
       "2037-04-09          1\n",
       "1970-01-01          1\n",
       "2017-09-15          1\n",
       "2017-09-11          1\n",
       "2015-02-06          1\n",
       "2017-10-07          1\n",
       "2017-10-30          1\n",
       "1919-09-19          1\n",
       "2020-11-03          1\n",
       "2017-08-15          1\n",
       "2017-10-27          1\n",
       "Name: 日期, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对日期进行分类计数\n",
    "df.日期.value_counts()   # 我们只取2017年11月25日至2017年12月3日之间的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         2017-11-24\n",
       "1         2017-11-24\n",
       "2         2017-11-25\n",
       "3         2017-11-25\n",
       "4         2017-11-25\n",
       "             ...    \n",
       "9999990   2017-11-29\n",
       "9999991   2017-11-29\n",
       "9999992   2017-11-29\n",
       "9999993   2017-11-29\n",
       "9999994   2017-11-29\n",
       "Name: 日期, Length: 9999995, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = pd.to_datetime(df.日期)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          False\n",
       "1          False\n",
       "2           True\n",
       "3           True\n",
       "4           True\n",
       "           ...  \n",
       "9999990     True\n",
       "9999991     True\n",
       "9999992     True\n",
       "9999993     True\n",
       "9999994     True\n",
       "Name: 日期, Length: 9999995, dtype: bool"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = a.apply(lambda x:x>=pd.Timestamp('2017-11-25') and x<=pd.Timestamp('2017-12-03'))\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[a]  # 取满足条件的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>3023810</td>\n",
       "      <td>2832009</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511934294</td>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>05:44:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999993</th>\n",
       "      <td>536993</td>\n",
       "      <td>1702210</td>\n",
       "      <td>1689352</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511934330</td>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>05:45:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999994</th>\n",
       "      <td>536993</td>\n",
       "      <td>4505862</td>\n",
       "      <td>1689352</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511934431</td>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>05:47:11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9876422 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           用户ID     商品ID   商品类目ID 行为类型         时间戳          日期        时间\n",
       "2             1  2576651   149192   pv  1511572885  2017-11-25  01:21:25\n",
       "3             1  3830808  4181361   pv  1511593493  2017-11-25  07:04:53\n",
       "4             1  4365585  2520377   pv  1511596146  2017-11-25  07:49:06\n",
       "5             1  4606018  2735466   pv  1511616481  2017-11-25  13:28:01\n",
       "6             1   230380   411153   pv  1511644942  2017-11-25  21:22:22\n",
       "...         ...      ...      ...  ...         ...         ...       ...\n",
       "9999990  536993    95420  3253210   pv  1511934240  2017-11-29  05:44:00\n",
       "9999991  536993  1702210  1689352   pv  1511934277  2017-11-29  05:44:37\n",
       "9999992  536993  3023810  2832009   pv  1511934294  2017-11-29  05:44:54\n",
       "9999993  536993  1702210  1689352   pv  1511934330  2017-11-29  05:45:30\n",
       "9999994  536993  4505862  1689352   pv  1511934431  2017-11-29  05:47:11\n",
       "\n",
       "[9876422 rows x 7 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-12-02    1399859\n",
       "2017-12-03    1201017\n",
       "2017-12-01    1122808\n",
       "2017-11-26    1053119\n",
       "2017-11-30    1046205\n",
       "2017-11-25    1045084\n",
       "2017-11-29    1024392\n",
       "2017-11-27     999106\n",
       "2017-11-28     984832\n",
       "Name: 日期, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 再对日期进行统计检查一下\n",
    "df.日期.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 重置行索引\n",
    "df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>用户ID</th>\n",
       "      <th>商品ID</th>\n",
       "      <th>商品类目ID</th>\n",
       "      <th>行为类型</th>\n",
       "      <th>时间戳</th>\n",
       "      <th>日期</th>\n",
       "      <th>时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2576651</td>\n",
       "      <td>149192</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511572885</td>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>01:21:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3830808</td>\n",
       "      <td>4181361</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511593493</td>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>07:04:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>4365585</td>\n",
       "      <td>2520377</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511596146</td>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>07:49:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4606018</td>\n",
       "      <td>2735466</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511616481</td>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>13:28:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>230380</td>\n",
       "      <td>411153</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511644942</td>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>21:22:22</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9876417</th>\n",
       "      <td>536993</td>\n",
       "      <td>95420</td>\n",
       "      <td>3253210</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511934240</td>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>05:44:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9876418</th>\n",
       "      <td>536993</td>\n",
       "      <td>1702210</td>\n",
       "      <td>1689352</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511934277</td>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>05:44:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9876419</th>\n",
       "      <td>536993</td>\n",
       "      <td>3023810</td>\n",
       "      <td>2832009</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511934294</td>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>05:44:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9876420</th>\n",
       "      <td>536993</td>\n",
       "      <td>1702210</td>\n",
       "      <td>1689352</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511934330</td>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>05:45:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9876421</th>\n",
       "      <td>536993</td>\n",
       "      <td>4505862</td>\n",
       "      <td>1689352</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511934431</td>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>05:47:11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9876422 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           用户ID     商品ID   商品类目ID 行为类型         时间戳          日期        时间\n",
       "0             1  2576651   149192   pv  1511572885  2017-11-25  01:21:25\n",
       "1             1  3830808  4181361   pv  1511593493  2017-11-25  07:04:53\n",
       "2             1  4365585  2520377   pv  1511596146  2017-11-25  07:49:06\n",
       "3             1  4606018  2735466   pv  1511616481  2017-11-25  13:28:01\n",
       "4             1   230380   411153   pv  1511644942  2017-11-25  21:22:22\n",
       "...         ...      ...      ...  ...         ...         ...       ...\n",
       "9876417  536993    95420  3253210   pv  1511934240  2017-11-29  05:44:00\n",
       "9876418  536993  1702210  1689352   pv  1511934277  2017-11-29  05:44:37\n",
       "9876419  536993  3023810  2832009   pv  1511934294  2017-11-29  05:44:54\n",
       "9876420  536993  1702210  1689352   pv  1511934330  2017-11-29  05:45:30\n",
       "9876421  536993  4505862  1689352   pv  1511934431  2017-11-29  05:47:11\n",
       "\n",
       "[9876422 rows x 7 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df  # 现在只剩下9876422行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('清洗好的数据.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 描述分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 整体维度--查看观察窗口内UV、PV、人均浏览次数、成交量的总体情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户ID</th>\n",
       "      <th>商品ID</th>\n",
       "      <th>商品类目ID</th>\n",
       "      <th>行为类型</th>\n",
       "      <th>时间戳</th>\n",
       "      <th>日期</th>\n",
       "      <th>时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2576651</td>\n",
       "      <td>149192</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511572885</td>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>01:21:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3830808</td>\n",
       "      <td>4181361</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511593493</td>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>07:04:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>4365585</td>\n",
       "      <td>2520377</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511596146</td>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>07:49:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4606018</td>\n",
       "      <td>2735466</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511616481</td>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>13:28:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>230380</td>\n",
       "      <td>411153</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511644942</td>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>21:22:22</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9876417</th>\n",
       "      <td>536993</td>\n",
       "      <td>95420</td>\n",
       "      <td>3253210</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511934240</td>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>05:44:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9876418</th>\n",
       "      <td>536993</td>\n",
       "      <td>1702210</td>\n",
       "      <td>1689352</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511934277</td>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>05:44:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9876419</th>\n",
       "      <td>536993</td>\n",
       "      <td>3023810</td>\n",
       "      <td>2832009</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511934294</td>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>05:44:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9876420</th>\n",
       "      <td>536993</td>\n",
       "      <td>1702210</td>\n",
       "      <td>1689352</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511934330</td>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>05:45:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9876421</th>\n",
       "      <td>536993</td>\n",
       "      <td>4505862</td>\n",
       "      <td>1689352</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511934431</td>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>05:47:11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9876422 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           用户ID     商品ID   商品类目ID 行为类型         时间戳          日期        时间\n",
       "0             1  2576651   149192   pv  1511572885  2017-11-25  01:21:25\n",
       "1             1  3830808  4181361   pv  1511593493  2017-11-25  07:04:53\n",
       "2             1  4365585  2520377   pv  1511596146  2017-11-25  07:49:06\n",
       "3             1  4606018  2735466   pv  1511616481  2017-11-25  13:28:01\n",
       "4             1   230380   411153   pv  1511644942  2017-11-25  21:22:22\n",
       "...         ...      ...      ...  ...         ...         ...       ...\n",
       "9876417  536993    95420  3253210   pv  1511934240  2017-11-29  05:44:00\n",
       "9876418  536993  1702210  1689352   pv  1511934277  2017-11-29  05:44:37\n",
       "9876419  536993  3023810  2832009   pv  1511934294  2017-11-29  05:44:54\n",
       "9876420  536993  1702210  1689352   pv  1511934330  2017-11-29  05:45:30\n",
       "9876421  536993  4505862  1689352   pv  1511934431  2017-11-29  05:47:11\n",
       "\n",
       "[9876422 rows x 7 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>UV</th>\n",
       "      <th>PV</th>\n",
       "      <th>人均浏览次数</th>\n",
       "      <th>成交量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>97809.0</td>\n",
       "      <td>8838199.0</td>\n",
       "      <td>90.361817</td>\n",
       "      <td>197534.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        UV         PV     人均浏览次数       成交量\n",
       "0  97809.0  8838199.0  90.361817  197534.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "UV = len(df.用户ID.unique())\n",
    "PV = df.行为类型.value_counts()['pv']\n",
    "pd.DataFrame(pd.Series({'UV' :UV, \n",
    "                        'PV' :PV, \n",
    "                        '人均浏览次数' : PV/UV, \n",
    "                        '成交量' : df.行为类型.value_counts()['buy'] \n",
    "             \n",
    "                        })).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 时间维度--按日期维度查看UV、PV、人均浏览次数、成交量"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. UV = Unique Visitor独立访客, 每个日期下的不同用户ID计数\n",
    "2. PV = page view页面浏览量, 每个日期下pv的计数\n",
    "3. 人均浏览次数 = PV/UV\n",
    "4. 成交量 = 每个日期buy的求和"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_UV_PV_UVPV_cj(DF):\n",
    "    # 独立访客\n",
    "    UV = DF['用户ID'].value_counts().size\n",
    "    # 页面浏览量\n",
    "    PV = DF['行为类型'].value_counts()['pv']\n",
    "    # 人均浏览次数\n",
    "    人均浏览次数 = float(format(PV/UV, '.2f'))\n",
    "    # 成交量\n",
    "    成交量 = DF['行为类型'].value_counts()['buy']\n",
    "    return pd.Series({'UV':UV, \n",
    "                         'PV':PV, \n",
    "                         '人均浏览次数':人均浏览次数, \n",
    "                         '成交量':成交量\n",
    "                        })\n",
    "a = df.groupby('日期').apply(data_UV_PV_UVPV_cj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>UV</th>\n",
       "      <th>PV</th>\n",
       "      <th>人均浏览次数</th>\n",
       "      <th>成交量</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日期</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-25</th>\n",
       "      <td>69730.0</td>\n",
       "      <td>938025.0</td>\n",
       "      <td>13.45</td>\n",
       "      <td>19694.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-26</th>\n",
       "      <td>70690.0</td>\n",
       "      <td>942188.0</td>\n",
       "      <td>13.33</td>\n",
       "      <td>21060.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-27</th>\n",
       "      <td>70170.0</td>\n",
       "      <td>893529.0</td>\n",
       "      <td>12.73</td>\n",
       "      <td>21408.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-28</th>\n",
       "      <td>70177.0</td>\n",
       "      <td>881044.0</td>\n",
       "      <td>12.55</td>\n",
       "      <td>20953.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-29</th>\n",
       "      <td>71210.0</td>\n",
       "      <td>916202.0</td>\n",
       "      <td>12.87</td>\n",
       "      <td>21834.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30</th>\n",
       "      <td>72459.0</td>\n",
       "      <td>935968.0</td>\n",
       "      <td>12.92</td>\n",
       "      <td>21836.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-01</th>\n",
       "      <td>74572.0</td>\n",
       "      <td>1004899.0</td>\n",
       "      <td>13.48</td>\n",
       "      <td>21285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-02</th>\n",
       "      <td>93371.0</td>\n",
       "      <td>1251569.0</td>\n",
       "      <td>13.40</td>\n",
       "      <td>26049.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-03</th>\n",
       "      <td>90887.0</td>\n",
       "      <td>1074775.0</td>\n",
       "      <td>11.83</td>\n",
       "      <td>23415.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 UV         PV  人均浏览次数      成交量\n",
       "日期                                             \n",
       "2017-11-25  69730.0   938025.0   13.45  19694.0\n",
       "2017-11-26  70690.0   942188.0   13.33  21060.0\n",
       "2017-11-27  70170.0   893529.0   12.73  21408.0\n",
       "2017-11-28  70177.0   881044.0   12.55  20953.0\n",
       "2017-11-29  71210.0   916202.0   12.87  21834.0\n",
       "2017-11-30  72459.0   935968.0   12.92  21836.0\n",
       "2017-12-01  74572.0  1004899.0   13.48  21285.0\n",
       "2017-12-02  93371.0  1251569.0   13.40  26049.0\n",
       "2017-12-03  90887.0  1074775.0   11.83  23415.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导出数据\n",
    "a.reset_index().to_csv('UV_PV_GBK.csv', index=False, encoding='gbk')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 日均UV和PV变化折线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 利用pyecharts进行可视化, 用matplotlib更简单， 但是图不怎么好看\n",
    "# 导入全局配置项\n",
    "from pyecharts import options as opts\n",
    "# 导入背景主题类\n",
    "from pyecharts.globals import ThemeType\n",
    "# 导入线图的类\n",
    "from pyecharts.charts import Line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\pyecharts\\charts\\chart.py:14: PendingDeprecationWarning: pyecharts 所有图表类型将在 v1.9.0 版本开始强制使用 ChartItem 进行数据项配置 :)\n",
      "  super().__init__(init_opts=init_opts)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<pyecharts.charts.basic_charts.line.Line at 0x1cc9b1d6f40>"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<pyecharts.charts.basic_charts.line.Line at 0x1cc9b1d6f40>"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<pyecharts.charts.basic_charts.line.Line at 0x1cc9b1d6f40>"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<pyecharts.charts.basic_charts.line.Line at 0x1cc9b1d6f40>"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/echarts.min', 'macarons':'https://assets.pyecharts.org/assets/themes/macarons'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"7ab6032f93d744b880a8eb8c927e1366\" style=\"width:1000px; height:330px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts', 'macarons'], function(echarts) {\n",
       "                var chart_7ab6032f93d744b880a8eb8c927e1366 = echarts.init(\n",
       "                    document.getElementById('7ab6032f93d744b880a8eb8c927e1366'), 'macarons', {renderer: 'canvas'});\n",
       "                var option_7ab6032f93d744b880a8eb8c927e1366 = {\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",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"line\",\n",
       "            \"name\": \"UV\",\n",
       "            \"connectNulls\": false,\n",
       "            \"symbol\": \"pin\",\n",
       "            \"symbolSize\": 20,\n",
       "            \"showSymbol\": true,\n",
       "            \"smooth\": false,\n",
       "            \"clip\": true,\n",
       "            \"step\": false,\n",
       "            \"data\": [\n",
       "                [\n",
       "                    \"2017-11-25\",\n",
       "                    69730.0\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-11-26\",\n",
       "                    70690.0\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-11-27\",\n",
       "                    70170.0\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-11-28\",\n",
       "                    70177.0\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-11-29\",\n",
       "                    71210.0\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-11-30\",\n",
       "                    72459.0\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-12-01\",\n",
       "                    74572.0\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-12-02\",\n",
       "                    93371.0\n",
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       "                [\n",
       "                    \"2017-12-03\",\n",
       "                    90887.0\n",
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       "            \"hoverAnimation\": true,\n",
       "            \"label\": {\n",
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       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
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       "            \"lineStyle\": {\n",
       "                \"show\": true,\n",
       "                \"width\": 1,\n",
       "                \"opacity\": 1,\n",
       "                \"curveness\": 0,\n",
       "                \"type\": \"solid\"\n",
       "            },\n",
       "            \"areaStyle\": {\n",
       "                \"opacity\": 0\n",
       "            },\n",
       "            \"markPoint\": {\n",
       "                \"label\": {\n",
       "                    \"show\": true,\n",
       "                    \"position\": \"inside\",\n",
       "                    \"color\": \"#fff\",\n",
       "                    \"margin\": 8\n",
       "                },\n",
       "                \"data\": [\n",
       "                    {\n",
       "                        \"name\": \"\\u6700\\u5927\\u503c\",\n",
       "                        \"type\": \"max\",\n",
       "                        \"symbol\": \"circle\"\n",
       "                    }\n",
       "                ]\n",
       "            },\n",
       "            \"markLine\": {\n",
       "                \"silent\": false,\n",
       "                \"precision\": 2,\n",
       "                \"label\": {\n",
       "                    \"show\": true,\n",
       "                    \"position\": \"top\",\n",
       "                    \"margin\": 8\n",
       "                },\n",
       "                \"data\": [\n",
       "                    {\n",
       "                        \"name\": \"\\u5e73\\u5747\\u503c\",\n",
       "                        \"type\": \"average\"\n",
       "                    }\n",
       "                ]\n",
       "            },\n",
       "            \"zlevel\": 0,\n",
       "            \"z\": 0\n",
       "        },\n",
       "        {\n",
       "            \"type\": \"line\",\n",
       "            \"name\": \"PV\",\n",
       "            \"connectNulls\": false,\n",
       "            \"symbol\": \"diamond\",\n",
       "            \"symbolSize\": 20,\n",
       "            \"showSymbol\": true,\n",
       "            \"smooth\": false,\n",
       "            \"clip\": true,\n",
       "            \"step\": false,\n",
       "            \"data\": [\n",
       "                [\n",
       "                    \"2017-11-25\",\n",
       "                    938025.0\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-11-26\",\n",
       "                    942188.0\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-11-27\",\n",
       "                    893529.0\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-11-28\",\n",
       "                    881044.0\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-11-29\",\n",
       "                    916202.0\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-11-30\",\n",
       "                    935968.0\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-12-01\",\n",
       "                    1004899.0\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-12-02\",\n",
       "                    1251569.0\n",
       "                ],\n",
       "                [\n",
       "                    \"2017-12-03\",\n",
       "                    1074775.0\n",
       "                ]\n",
       "            ],\n",
       "            \"hoverAnimation\": true,\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"lineStyle\": {\n",
       "                \"show\": true,\n",
       "                \"width\": 1,\n",
       "                \"opacity\": 1,\n",
       "                \"curveness\": 0,\n",
       "                \"type\": \"solid\"\n",
       "            },\n",
       "            \"areaStyle\": {\n",
       "                \"opacity\": 0\n",
       "            },\n",
       "            \"markPoint\": {\n",
       "                \"label\": {\n",
       "                    \"show\": true,\n",
       "                    \"position\": \"inside\",\n",
       "                    \"color\": \"#fff\",\n",
       "                    \"margin\": 8\n",
       "                },\n",
       "                \"data\": [\n",
       "                    {\n",
       "                        \"name\": \"\\u6700\\u5927\\u503c\",\n",
       "                        \"type\": \"max\",\n",
       "                        \"symbol\": \"diamond\"\n",
       "                    }\n",
       "                ]\n",
       "            },\n",
       "            \"markLine\": {\n",
       "                \"silent\": false,\n",
       "                \"precision\": 2,\n",
       "                \"label\": {\n",
       "                    \"show\": true,\n",
       "                    \"position\": \"top\",\n",
       "                    \"margin\": 8\n",
       "                },\n",
       "                \"data\": [\n",
       "                    {\n",
       "                        \"name\": \"\\u5e73\\u5747\\u503c\",\n",
       "                        \"type\": \"average\"\n",
       "                    }\n",
       "                ]\n",
       "            },\n",
       "            \"zlevel\": 0,\n",
       "            \"z\": 0\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"UV\",\n",
       "                \"PV\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"UV\": true,\n",
       "                \"PV\": true\n",
       "            },\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"xAxis\": [\n",
       "        {\n",
       "            \"name\": \"\\u65e5\\u671f\",\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 20,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": false,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            },\n",
       "            \"data\": [\n",
       "                \"2017-11-25\",\n",
       "                \"2017-11-26\",\n",
       "                \"2017-11-27\",\n",
       "                \"2017-11-28\",\n",
       "                \"2017-11-29\",\n",
       "                \"2017-11-30\",\n",
       "                \"2017-12-01\",\n",
       "                \"2017-12-02\",\n",
       "                \"2017-12-03\"\n",
       "            ]\n",
       "        }\n",
       "    ],\n",
       "    \"yAxis\": [\n",
       "        {\n",
       "            \"name\": \"\\u6570\\u91cf\",\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": false,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\\u65e5\\u5747UV\\u548cPV\\u53d8\\u5316\\u8d8b\\u52bf\\u7ebf\",\n",
       "            \"left\": 30,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_7ab6032f93d744b880a8eb8c927e1366.setOption(option_7ab6032f93d744b880a8eb8c927e1366);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x1cc9b1d6250>"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 实例化对象\n",
    "line = Line(init_opts=opts.InitOpts(theme=ThemeType.MACARONS, width='1000px', height='330px'))\n",
    "# x轴\n",
    "line.add_xaxis(a.index.to_list())\n",
    "# y轴\n",
    "line.add_yaxis(series_name='UV', \n",
    "               y_axis=a.UV.values.tolist(), \n",
    "               color='green', \n",
    "               symbol='pin', # 标记\n",
    "               symbol_size=20, #标记的大小\n",
    "               # 标记线配置项\n",
    "               markline_opts = opts.MarkLineOpts(data = [opts.MarkLineItem(type_ =\"average\" , name = '平均值')]), \n",
    "               # 标记点配置项\n",
    "               markpoint_opts = opts.MarkPointOpts(data = [opts.MarkPointItem(type_='max', name = '最大值', \n",
    "                                                           symbol = 'circle'\n",
    "                                                                             )])\n",
    "              )\n",
    "line.add_yaxis(series_name='PV', \n",
    "               y_axis=a.PV.values.tolist(), \n",
    "               color='green', \n",
    "               symbol='diamond', # 标记\n",
    "               symbol_size=20, #标记的大小\n",
    "               # 标记线配置项\n",
    "               markline_opts = opts.MarkLineOpts(data = [opts.MarkLineItem(type_ =\"average\" , name = '平均值')]), \n",
    "               # 标记点配置项\n",
    "               markpoint_opts = opts.MarkPointOpts(data = [opts.MarkPointItem(type_='max', name = '最大值', \n",
    "                                                           symbol = 'diamond'\n",
    "                                                                             )])\n",
    "              )\n",
    "# 全局配置项          # 标题\n",
    "line.set_global_opts( title_opts = opts.TitleOpts(title = '日均UV和PV变化趋势线', pos_left=30), \n",
    "                      # x轴配置                  # x轴名字\n",
    "                      xaxis_opts = opts.AxisOpts(name='日期', \n",
    "                                                 # 名字所放的位置\n",
    "                                                 name_location='end', \n",
    "                                                 # 坐标轴名称与轴线之间的距离\n",
    "                                                 name_gap=20\n",
    "                                                ), \n",
    "                     # y轴配置\n",
    "                     yaxis_opts = opts.AxisOpts(name='数量')\n",
    "                    \n",
    "                    )\n",
    "line.render_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2017年12月1号到2号， 数据明显向上波动，查了查可能是因为双12领红包优惠券开始时间是12月1号开始   \n",
    "所以导致这天pv上升明显"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 用户维度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 查看每个用户的行为数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户ID</th>\n",
       "      <th>商品ID</th>\n",
       "      <th>商品类目ID</th>\n",
       "      <th>行为类型</th>\n",
       "      <th>时间戳</th>\n",
       "      <th>日期</th>\n",
       "      <th>时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2576651</td>\n",
       "      <td>149192</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511572885</td>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>01:21:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3830808</td>\n",
       "      <td>4181361</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511593493</td>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>07:04:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>4365585</td>\n",
       "      <td>2520377</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511596146</td>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>07:49:06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   用户ID     商品ID   商品类目ID 行为类型         时间戳          日期        时间\n",
       "0     1  2576651   149192   pv  1511572885  2017-11-25  01:21:25\n",
       "1     1  3830808  4181361   pv  1511593493  2017-11-25  07:04:53\n",
       "2     1  4365585  2520377   pv  1511596146  2017-11-25  07:49:06"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = df.groupby('用户ID').apply(lambda x:x.行为类型.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = a.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.columns = ['购买', '加入购物车', '收藏', '点击浏览']   # 出现很多缺失值是正常的， 比如用户1， 只看， 其他什么也没做\n",
    "    # 再比如用户13， 处了， 看， 还加入购物车了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>购买</th>\n",
       "      <th>加入购物车</th>\n",
       "      <th>收藏</th>\n",
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       "      <th>1017994</th>\n",
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       "      <td>29.0</td>\n",
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       "      <td>80.0</td>\n",
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       "      <th>1018000</th>\n",
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       "      <td>41.0</td>\n",
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       "<p>97809 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          购买  加入购物车   收藏   点击浏览\n",
       "用户ID                           \n",
       "1        NaN    NaN  NaN   53.0\n",
       "5        NaN    NaN  NaN   64.0\n",
       "13       NaN    4.0  NaN    7.0\n",
       "19       NaN    1.0  NaN   74.0\n",
       "21       NaN    1.0  6.0  310.0\n",
       "...      ...    ...  ...    ...\n",
       "1017990  NaN    3.0  NaN   56.0\n",
       "1017994  NaN    3.0  3.0   29.0\n",
       "1017997  2.0    NaN  3.0   80.0\n",
       "1018000  NaN    4.0  NaN  177.0\n",
       "1018011  1.0    NaN  NaN   41.0\n",
       "\n",
       "[97809 rows x 4 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1           53\n",
       "5           64\n",
       "13          11\n",
       "19          75\n",
       "21         317\n",
       "          ... \n",
       "1017990     59\n",
       "1017994     35\n",
       "1017997     85\n",
       "1018000    181\n",
       "1018011     42\n",
       "Name: 用户ID, Length: 97809, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.用户ID.value_counts().sort_index()  # 用户行为数， 并按照用户ID进行升序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "a['用户行为数'] = df.用户ID.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "      <td>59</td>\n",
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       "    <tr>\n",
       "      <th>1018011</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>41.0</td>\n",
       "      <td>42</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>97809 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          购买  加入购物车   收藏   点击浏览  用户行为数\n",
       "用户ID                                  \n",
       "1        NaN    NaN  NaN   53.0     53\n",
       "5        NaN    NaN  NaN   64.0     64\n",
       "13       NaN    4.0  NaN    7.0     11\n",
       "19       NaN    1.0  NaN   74.0     75\n",
       "21       NaN    1.0  6.0  310.0    317\n",
       "...      ...    ...  ...    ...    ...\n",
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       "1017994  NaN    3.0  3.0   29.0     35\n",
       "1017997  2.0    NaN  3.0   80.0     85\n",
       "1018000  NaN    4.0  NaN  177.0    181\n",
       "1018011  1.0    NaN  NaN   41.0     42\n",
       "\n",
       "[97809 rows x 5 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a  # 缺失的地方代表用户没有该行为， 可以用0代替缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导出数据 a.reset_index().to_csv('用户行为数据.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 查看购买用户数、复购用户数以及复购率  \n",
    "复购的定义：这里复购是对网站而不是商品而言， 只要有2次及以上的购买行为， 我们可以称该用户为复购客户。  \n",
    "复购率的定义：是指有两次及以上购买行为的用户数/有购买行为的用户数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "          用户ID   购买  加入购物车   收藏   点击浏览  用户行为数\n",
       "0            1  NaN    NaN  NaN   53.0     53\n",
       "1            5  NaN    NaN  NaN   64.0     64\n",
       "2           13  NaN    4.0  NaN    7.0     11\n",
       "3           19  NaN    1.0  NaN   74.0     75\n",
       "4           21  NaN    1.0  6.0  310.0    317\n",
       "...        ...  ...    ...  ...    ...    ...\n",
       "97804  1017990  NaN    3.0  NaN   56.0     59\n",
       "97805  1017994  NaN    3.0  3.0   29.0     35\n",
       "97806  1017997  2.0    NaN  3.0   80.0     85\n",
       "97807  1018000  NaN    4.0  NaN  177.0    181\n",
       "97808  1018011  1.0    NaN  NaN   41.0     42\n",
       "\n",
       "[97809 rows x 6 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 节省内存， 重新启动下， 所以这里， 我们重新导入数据\n",
    "a = pd.read_csv('用户行为数据.csv')\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 填充缺失值\n",
    "a.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>用户ID</th>\n",
       "      <th>购买</th>\n",
       "      <th>加入购物车</th>\n",
       "      <th>收藏</th>\n",
       "      <th>点击浏览</th>\n",
       "      <th>用户行为数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>317</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97804</th>\n",
       "      <td>1017990</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97805</th>\n",
       "      <td>1017994</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97806</th>\n",
       "      <td>1017997</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97807</th>\n",
       "      <td>1018000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97808</th>\n",
       "      <td>1018011</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97809 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          用户ID   购买  加入购物车   收藏   点击浏览  用户行为数\n",
       "0            1  0.0    0.0  0.0   53.0     53\n",
       "1            5  0.0    0.0  0.0   64.0     64\n",
       "2           13  0.0    4.0  0.0    7.0     11\n",
       "3           19  0.0    1.0  0.0   74.0     75\n",
       "4           21  0.0    1.0  6.0  310.0    317\n",
       "...        ...  ...    ...  ...    ...    ...\n",
       "97804  1017990  0.0    3.0  0.0   56.0     59\n",
       "97805  1017994  0.0    3.0  3.0   29.0     35\n",
       "97806  1017997  2.0    0.0  3.0   80.0     85\n",
       "97807  1018000  0.0    4.0  0.0  177.0    181\n",
       "97808  1018011  1.0    0.0  0.0   41.0     42\n",
       "\n",
       "[97809 rows x 6 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>购买用户数</th>\n",
       "      <th>复购用户数</th>\n",
       "      <th>复购率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>66248.0</td>\n",
       "      <td>43632.0</td>\n",
       "      <td>0.658616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     购买用户数    复购用户数       复购率\n",
       "0  66248.0  43632.0  0.658616"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(pd.Series(dict(购买用户数 = (a.购买>0).sum(),\n",
    "                            复购用户数 = (a.购买>1).sum(),\n",
    "                            复购率 = (a.购买>1).sum()/(a.购买>0).sum()))).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 用户跳失率  \n",
    "跳失率的定义， 用户更具链接点击进入淘宝网页， 我们并不知道统计规则里面这个进来的页面是否会算作pv  \n",
    "安全起见，我们认为这个算作pv， 但不是主动浏览的， 可能是广告渠道等等。  \n",
    "所以， 我们定义， **只有pv>=2的客户才不是跳失客户**。但是很奇怪的是有些用户pv没有， 但是却有其他行为， 甚至是购买行为， 这些客户就不能算是跳失客户， 所以， 还同时要求其他行为均为0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>用户ID</th>\n",
       "      <th>购买</th>\n",
       "      <th>加入购物车</th>\n",
       "      <th>收藏</th>\n",
       "      <th>点击浏览</th>\n",
       "      <th>用户行为数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   用户ID   购买  加入购物车   收藏   点击浏览  用户行为数\n",
       "0     1  0.0    0.0  0.0   53.0     53\n",
       "1     5  0.0    0.0  0.0   64.0     64\n",
       "2    13  0.0    4.0  0.0    7.0     11\n",
       "3    19  0.0    1.0  0.0   74.0     75\n",
       "4    21  0.0    1.0  6.0  310.0    317"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>用户ID</th>\n",
       "      <th>购买</th>\n",
       "      <th>加入购物车</th>\n",
       "      <th>收藏</th>\n",
       "      <th>点击浏览</th>\n",
       "      <th>用户行为数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>74891</th>\n",
       "      <td>436370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79195</th>\n",
       "      <td>458889</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85372</th>\n",
       "      <td>490686</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         用户ID   购买  加入购物车   收藏  点击浏览  用户行为数\n",
       "74891  436370  0.0    0.0  0.0   1.0      1\n",
       "79195  458889  0.0    0.0  0.0   1.0      1\n",
       "85372  490686  0.0    0.0  0.0   1.0      1"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[(a.点击浏览<2)&(a.购买==0)&(a.加入购物车==0)&(a.收藏==0)]\n",
    "\n",
    "# 也就是说有3个客户刚点进来就退出网页了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>跳失率</th>\n",
       "      <td>0.00307%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0\n",
       "跳失率  0.00307%"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(pd.Series(dict(跳失率 = format(3/len(a), '.5%'))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当统计时间为上诉观察窗口时，用户的复购率为65.86%且跳失率接近0，说明淘宝拥有你足够的吸引力让用户停留且用户的忠诚度非常高。  \n",
    "可以进一步培养用户的忠诚度，鼓励用户更高频次的消费。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 用户流量行为转化分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8837973.0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "总浏览点击 = (a[a.点击浏览>2]['点击浏览']).sum()  # 网站一共获取的点击量\n",
    "总浏览点击"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "287873.0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "总收藏 = a.收藏.sum()                       # 获取的收藏量\n",
    "总收藏"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "552816.0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "总加入购物车 = a.加入购物车.sum()        # 获取的加入购物车的量\n",
    "总加入购物车"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "197534.0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "总购买 = a.购买.sum()\n",
    "总购买"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.09512237704279024"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(总收藏+总加入购物车)/总浏览点击     # 产生购买倾向"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.022350600075379274"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "总购买/总浏览点击      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\pyecharts\\charts\\chart.py:14: PendingDeprecationWarning: pyecharts 所有图表类型将在 v1.9.0 版本开始强制使用 ChartItem 进行数据项配置 :)\n",
      "  super().__init__(init_opts=init_opts)\n"
     ]
    },
    {
     "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=\"e58620763e24467d8349fa223a39036f\" style=\"width:600px; height:300px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_e58620763e24467d8349fa223a39036f = echarts.init(\n",
       "                    document.getElementById('e58620763e24467d8349fa223a39036f'), 'white', {renderer: 'canvas'});\n",
       "                var option_e58620763e24467d8349fa223a39036f = {\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",
       "            \"data\": [\n",
       "                {\n",
       "                    \"name\": \"pv\",\n",
       "                    \"value\": 100\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u52a0\\u8d2d\\u7269\\u8f66+\\u6536\\u85cf\",\n",
       "                    \"value\": 95.12\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u8d2d\\u4e70\",\n",
       "                    \"value\": 2.24\n",
       "                }\n",
       "            ],\n",
       "            \"sort\": \"descending\",\n",
       "            \"gap\": 2,\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"inside\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"tooltip\": {\n",
       "                \"show\": true,\n",
       "                \"trigger\": \"item\",\n",
       "                \"triggerOn\": \"mousemove|click\",\n",
       "                \"axisPointer\": {\n",
       "                    \"type\": \"line\"\n",
       "                },\n",
       "                \"showContent\": true,\n",
       "                \"alwaysShowContent\": false,\n",
       "                \"showDelay\": 0,\n",
       "                \"hideDelay\": 100,\n",
       "                \"formatter\": \"{a} <br/>{b} : {c}%\",\n",
       "                \"textStyle\": {\n",
       "                    \"fontSize\": 14\n",
       "                },\n",
       "                \"borderWidth\": 0,\n",
       "                \"padding\": 5\n",
       "            },\n",
       "            \"itemStyle\": {\n",
       "                \"borderColor\": \"#fff\",\n",
       "                \"borderWidth\": 1\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u8d2d\\u4e70\",\n",
       "                \"pv\",\n",
       "                \"\\u52a0\\u8d2d\\u7269\\u8f66+\\u6536\\u85cf\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"pv\": true,\n",
       "                \"\\u52a0\\u8d2d\\u7269\\u8f66+\\u6536\\u85cf\": true,\n",
       "                \"\\u8d2d\\u4e70\": true\n",
       "            },\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\\u6d41\\u91cf\\u884c\\u4e3a\\u6f0f\\u6597\\u56fe\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_e58620763e24467d8349fa223a39036f.setOption(option_e58620763e24467d8349fa223a39036f);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x261fe15f4c0>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyecharts.options as opts\n",
    "from pyecharts.charts import Funnel\n",
    "x_data = [\"pv\", \"加购物车+收藏\", \"购买\"]\n",
    "y_data = [100, 95.12, 2.24]\n",
    "\n",
    "data = [[x_data[i], y_data[i]] for i in range(len(x_data))]\n",
    "\n",
    "c = (\n",
    "    Funnel(init_opts=opts.InitOpts(width=\"600px\", height=\"300px\"))\n",
    "    .add(\n",
    "        series_name=\"\",\n",
    "        data_pair=data,\n",
    "        gap=2,\n",
    "        tooltip_opts=opts.TooltipOpts(trigger=\"item\", formatter=\"{a} <br/>{b} : {c}%\"),\n",
    "        label_opts=opts.LabelOpts(is_show=True, position=\"inside\"),\n",
    "        itemstyle_opts=opts.ItemStyleOpts(border_color=\"#fff\", border_width=1),\n",
    "    )\n",
    "    .set_global_opts(title_opts=opts.TitleOpts(title=\"流量行为漏斗图\", subtitle=\"\"))\n",
    ")\n",
    "c.render_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从整个流量上来说， 客户的购买倾向还是很高的， 说明商品展览等相关说明做的不错， 但是最终购买率2.24%很低，可能是要对不同店铺的同种产品进行比较"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 用户行为习惯分析  \n",
    "这里主要分析一天当中那个时间段用户最活跃"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          01\n",
       "1          07\n",
       "2          07\n",
       "3          13\n",
       "4          21\n",
       "           ..\n",
       "9876417    05\n",
       "9876418    05\n",
       "9876419    05\n",
       "9876420    05\n",
       "9876421    05\n",
       "Name: 时间, Length: 9876422, dtype: object"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df = pd.read_csv('清洗好的数据.csv')\n",
    "df.时间.str.split(':').str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-88-83f0792f57ff>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['时间段'] = df.时间.str.split(':').str[0]\n"
     ]
    }
   ],
   "source": [
    "df['时间段'] = df.时间.str.split(':').str[0]  # 警告不用管"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "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>用户ID</th>\n",
       "      <th>商品ID</th>\n",
       "      <th>商品类目ID</th>\n",
       "      <th>行为类型</th>\n",
       "      <th>时间戳</th>\n",
       "      <th>日期</th>\n",
       "      <th>时间</th>\n",
       "      <th>时间段</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2576651</td>\n",
       "      <td>149192</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511572885</td>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>01:21:25</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3830808</td>\n",
       "      <td>4181361</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511593493</td>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>07:04:53</td>\n",
       "      <td>07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>4365585</td>\n",
       "      <td>2520377</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511596146</td>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>07:49:06</td>\n",
       "      <td>07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4606018</td>\n",
       "      <td>2735466</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511616481</td>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>13:28:01</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>230380</td>\n",
       "      <td>411153</td>\n",
       "      <td>pv</td>\n",
       "      <td>1511644942</td>\n",
       "      <td>2017-11-25</td>\n",
       "      <td>21:22:22</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   用户ID     商品ID   商品类目ID 行为类型         时间戳          日期        时间 时间段\n",
       "0     1  2576651   149192   pv  1511572885  2017-11-25  01:21:25  01\n",
       "1     1  3830808  4181361   pv  1511593493  2017-11-25  07:04:53  07\n",
       "2     1  4365585  2520377   pv  1511596146  2017-11-25  07:49:06  07\n",
       "3     1  4606018  2735466   pv  1511616481  2017-11-25  13:28:01  13\n",
       "4     1   230380   411153   pv  1511644942  2017-11-25  21:22:22  21"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x00000263C0A08610>"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = df.groupby('时间段')\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "时间段\n",
       "00    44182\n",
       "01    51714\n",
       "02    57150\n",
       "03    58234\n",
       "04    58843\n",
       "05    59914\n",
       "06    58765\n",
       "07    59941\n",
       "08    59385\n",
       "09    57779\n",
       "10    57727\n",
       "11    61928\n",
       "12    65920\n",
       "13    67647\n",
       "14    64359\n",
       "15    50735\n",
       "16    29232\n",
       "17    14931\n",
       "18     8801\n",
       "19     6242\n",
       "20     5647\n",
       "21     7654\n",
       "22    16593\n",
       "23    30450\n",
       "dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = a.apply(lambda x:len(set(x.用户ID)))  # 每个时间段的UV\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 实例化对象\n",
    "line = Line(init_opts=opts.InitOpts(theme=ThemeType.ESSOS, width='1000px', height='330px'))\n",
    "# x轴\n",
    "line.add_xaxis(a.index.to_list())\n",
    "# y轴\n",
    "line.add_yaxis(series_name='UV', \n",
    "               y_axis=a.values.tolist(), \n",
    "               color='green', \n",
    "               symbol='pin', # 标记\n",
    "               symbol_size=20, #标记的大小\n",
    "               # 标记线配置项\n",
    "               markline_opts = opts.MarkLineOpts(data = [opts.MarkLineItem(type_ =\"average\" , name = '平均值')]), \n",
    "               # 标记点配置项\n",
    "               markpoint_opts = opts.MarkPointOpts(data = [opts.MarkPointItem(type_='max', name = '最大值', \n",
    "                                                           symbol = 'circle'\n",
    "                                                                             )])\n",
    "              )\n",
    "\n",
    "# 全局配置项          # 标题\n",
    "line.set_global_opts( title_opts = opts.TitleOpts(title = '用户行为习惯分布', pos_left=48), \n",
    "                      # x轴配置                  # x轴名字\n",
    "                      xaxis_opts = opts.AxisOpts(name='时间段', \n",
    "                                                 # 名字所放的位置\n",
    "                                                 name_location='end', \n",
    "                                                 # 坐标轴名称与轴线之间的距离\n",
    "                                                 name_gap=20\n",
    "                                                ), \n",
    "                     # y轴配置\n",
    "                     yaxis_opts = opts.AxisOpts(name='数量')\n",
    "                    \n",
    "                    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/echarts.min', 'essos':'https://assets.pyecharts.org/assets/themes/essos'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"41a3435bda65480280e96f618b7c6cfb\" style=\"width:1000px; height:330px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts', 'essos'], function(echarts) {\n",
       "                var chart_41a3435bda65480280e96f618b7c6cfb = echarts.init(\n",
       "                    document.getElementById('41a3435bda65480280e96f618b7c6cfb'), 'essos', {renderer: 'canvas'});\n",
       "                var option_41a3435bda65480280e96f618b7c6cfb = {\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",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"line\",\n",
       "            \"name\": \"UV\",\n",
       "            \"connectNulls\": false,\n",
       "            \"symbol\": \"pin\",\n",
       "            \"symbolSize\": 20,\n",
       "            \"showSymbol\": true,\n",
       "            \"smooth\": false,\n",
       "            \"clip\": true,\n",
       "            \"step\": false,\n",
       "            \"data\": [\n",
       "                [\n",
       "                    \"00\",\n",
       "                    44182\n",
       "                ],\n",
       "                [\n",
       "                    \"01\",\n",
       "                    51714\n",
       "                ],\n",
       "                [\n",
       "                    \"02\",\n",
       "                    57150\n",
       "                ],\n",
       "                [\n",
       "                    \"03\",\n",
       "                    58234\n",
       "                ],\n",
       "                [\n",
       "                    \"04\",\n",
       "                    58843\n",
       "                ],\n",
       "                [\n",
       "                    \"05\",\n",
       "                    59914\n",
       "                ],\n",
       "                [\n",
       "                    \"06\",\n",
       "                    58765\n",
       "                ],\n",
       "                [\n",
       "                    \"07\",\n",
       "                    59941\n",
       "                ],\n",
       "                [\n",
       "                    \"08\",\n",
       "                    59385\n",
       "                ],\n",
       "                [\n",
       "                    \"09\",\n",
       "                    57779\n",
       "                ],\n",
       "                [\n",
       "                    \"10\",\n",
       "                    57727\n",
       "                ],\n",
       "                [\n",
       "                    \"11\",\n",
       "                    61928\n",
       "                ],\n",
       "                [\n",
       "                    \"12\",\n",
       "                    65920\n",
       "                ],\n",
       "                [\n",
       "                    \"13\",\n",
       "                    67647\n",
       "                ],\n",
       "                [\n",
       "                    \"14\",\n",
       "                    64359\n",
       "                ],\n",
       "                [\n",
       "                    \"15\",\n",
       "                    50735\n",
       "                ],\n",
       "                [\n",
       "                    \"16\",\n",
       "                    29232\n",
       "                ],\n",
       "                [\n",
       "                    \"17\",\n",
       "                    14931\n",
       "                ],\n",
       "                [\n",
       "                    \"18\",\n",
       "                    8801\n",
       "                ],\n",
       "                [\n",
       "                    \"19\",\n",
       "                    6242\n",
       "                ],\n",
       "                [\n",
       "                    \"20\",\n",
       "                    5647\n",
       "                ],\n",
       "                [\n",
       "                    \"21\",\n",
       "                    7654\n",
       "                ],\n",
       "                [\n",
       "                    \"22\",\n",
       "                    16593\n",
       "                ],\n",
       "                [\n",
       "                    \"23\",\n",
       "                    30450\n",
       "                ]\n",
       "            ],\n",
       "            \"hoverAnimation\": true,\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"lineStyle\": {\n",
       "                \"show\": true,\n",
       "                \"width\": 1,\n",
       "                \"opacity\": 1,\n",
       "                \"curveness\": 0,\n",
       "                \"type\": \"solid\"\n",
       "            },\n",
       "            \"areaStyle\": {\n",
       "                \"opacity\": 0\n",
       "            },\n",
       "            \"markPoint\": {\n",
       "                \"label\": {\n",
       "                    \"show\": true,\n",
       "                    \"position\": \"inside\",\n",
       "                    \"color\": \"#fff\",\n",
       "                    \"margin\": 8\n",
       "                },\n",
       "                \"data\": [\n",
       "                    {\n",
       "                        \"name\": \"\\u6700\\u5927\\u503c\",\n",
       "                        \"type\": \"max\",\n",
       "                        \"symbol\": \"circle\"\n",
       "                    }\n",
       "                ]\n",
       "            },\n",
       "            \"markLine\": {\n",
       "                \"silent\": false,\n",
       "                \"precision\": 2,\n",
       "                \"label\": {\n",
       "                    \"show\": true,\n",
       "                    \"position\": \"top\",\n",
       "                    \"margin\": 8\n",
       "                },\n",
       "                \"data\": [\n",
       "                    {\n",
       "                        \"name\": \"\\u5e73\\u5747\\u503c\",\n",
       "                        \"type\": \"average\"\n",
       "                    }\n",
       "                ]\n",
       "            },\n",
       "            \"zlevel\": 0,\n",
       "            \"z\": 0\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"UV\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"UV\": true\n",
       "            },\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"xAxis\": [\n",
       "        {\n",
       "            \"name\": \"\\u65f6\\u95f4\\u6bb5\",\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 20,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": false,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            },\n",
       "            \"data\": [\n",
       "                \"00\",\n",
       "                \"01\",\n",
       "                \"02\",\n",
       "                \"03\",\n",
       "                \"04\",\n",
       "                \"05\",\n",
       "                \"06\",\n",
       "                \"07\",\n",
       "                \"08\",\n",
       "                \"09\",\n",
       "                \"10\",\n",
       "                \"11\",\n",
       "                \"12\",\n",
       "                \"13\",\n",
       "                \"14\",\n",
       "                \"15\",\n",
       "                \"16\",\n",
       "                \"17\",\n",
       "                \"18\",\n",
       "                \"19\",\n",
       "                \"20\",\n",
       "                \"21\",\n",
       "                \"22\",\n",
       "                \"23\"\n",
       "            ]\n",
       "        }\n",
       "    ],\n",
       "    \"yAxis\": [\n",
       "        {\n",
       "            \"name\": \"\\u6570\\u91cf\",\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": false,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\\u7528\\u6237\\u884c\\u4e3a\\u4e60\\u60ef\\u5206\\u5e03\",\n",
       "            \"left\": 48,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_41a3435bda65480280e96f618b7c6cfb.setOption(option_41a3435bda65480280e96f618b7c6cfb);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x261aa51f820>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line.render_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由此可以看出， 一天当中用户数量最活跃的时候是在13:00左右，之后开始急剧降低，  最低是在晚上19:00-20:00，  \n",
    "之后开始上升， 也就是说人们睡觉之前会喜欢浏览网页。所以如果要做什么活动，打什么广告最好再13:00左右进行"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 用户类目偏好分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 查看pv—top20的商品类目"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4756105    478455\n",
       "4145813    322614\n",
       "2355072    312967\n",
       "3607361    293164\n",
       "982926     283172\n",
       "2520377    197556\n",
       "4801426    186675\n",
       "1320293    174916\n",
       "2465336    154782\n",
       "3002561    142784\n",
       "2735466    114214\n",
       "4181361    101568\n",
       "149192      98842\n",
       "1080785     97608\n",
       "2885642     94869\n",
       "4217906     93132\n",
       "154040      88305\n",
       "3738615     84852\n",
       "1879194     77468\n",
       "2640118     71869\n",
       "Name: 商品类目ID, dtype: int64"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = df[df.行为类型=='pv']['商品类目ID'].value_counts()[0:20]\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'value': 478455, 'name': 4756105},\n",
       " {'value': 322614, 'name': 4145813},\n",
       " {'value': 312967, 'name': 2355072},\n",
       " {'value': 293164, 'name': 3607361},\n",
       " {'value': 283172, 'name': 982926},\n",
       " {'value': 197556, 'name': 2520377},\n",
       " {'value': 186675, 'name': 4801426},\n",
       " {'value': 174916, 'name': 1320293},\n",
       " {'value': 154782, 'name': 2465336},\n",
       " {'value': 142784, 'name': 3002561},\n",
       " {'value': 114214, 'name': 2735466},\n",
       " {'value': 101568, 'name': 4181361},\n",
       " {'value': 98842, 'name': 149192},\n",
       " {'value': 97608, 'name': 1080785},\n",
       " {'value': 94869, 'name': 2885642},\n",
       " {'value': 93132, 'name': 4217906},\n",
       " {'value': 88305, 'name': 154040},\n",
       " {'value': 84852, 'name': 3738615},\n",
       " {'value': 77468, 'name': 1879194},\n",
       " {'value': 71869, 'name': 2640118}]"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = a.to_frame().reset_index()\n",
    "b['name'] = b.pop('index')\n",
    "b = b.rename(columns = {\"商品类目ID\":'value'})\n",
    "b = b.T.to_dict()\n",
    "b = b.values()\n",
    "b = list(b)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\pyecharts\\charts\\chart.py:14: PendingDeprecationWarning: pyecharts 所有图表类型将在 v1.9.0 版本开始强制使用 ChartItem 进行数据项配置 :)\n",
      "  super().__init__(init_opts=init_opts)\n"
     ]
    }
   ],
   "source": [
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import TreeMap\n",
    "from pyecharts.globals import ThemeType\n",
    "data =b\n",
    "c = (\n",
    "    TreeMap(init_opts=opts.InitOpts(theme=ThemeType.LIGHT, width='800px', height='400px'))\n",
    "    .add(\"pv\", data, )\n",
    "    .set_global_opts(title_opts=opts.TitleOpts(title=\"pv-top20商品类目\"))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "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=\"0c68e025d694435aa035cb839afce9b7\" style=\"width:800px; height:400px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_0c68e025d694435aa035cb839afce9b7 = echarts.init(\n",
       "                    document.getElementById('0c68e025d694435aa035cb839afce9b7'), 'light', {renderer: 'canvas'});\n",
       "                var option_0c68e025d694435aa035cb839afce9b7 = {\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",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"treemap\",\n",
       "            \"name\": \"pv\",\n",
       "            \"data\": [\n",
       "                {\n",
       "                    \"value\": 478455,\n",
       "                    \"name\": 4756105\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 322614,\n",
       "                    \"name\": 4145813\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 312967,\n",
       "                    \"name\": 2355072\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 293164,\n",
       "                    \"name\": 3607361\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 283172,\n",
       "                    \"name\": 982926\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 197556,\n",
       "                    \"name\": 2520377\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 186675,\n",
       "                    \"name\": 4801426\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 174916,\n",
       "                    \"name\": 1320293\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 154782,\n",
       "                    \"name\": 2465336\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 142784,\n",
       "                    \"name\": 3002561\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 114214,\n",
       "                    \"name\": 2735466\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 101568,\n",
       "                    \"name\": 4181361\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 98842,\n",
       "                    \"name\": 149192\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 97608,\n",
       "                    \"name\": 1080785\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 94869,\n",
       "                    \"name\": 2885642\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 93132,\n",
       "                    \"name\": 4217906\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 88305,\n",
       "                    \"name\": 154040\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 84852,\n",
       "                    \"name\": 3738615\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 77468,\n",
       "                    \"name\": 1879194\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 71869,\n",
       "                    \"name\": 2640118\n",
       "                }\n",
       "            ],\n",
       "            \"width\": \"80%\",\n",
       "            \"height\": \"80%\",\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"inside\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"upperlabel\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"inside\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"drillDownIcon\": \"\\u25b6\",\n",
       "            \"roam\": true,\n",
       "            \"nodeClick\": \"zoomToNode\",\n",
       "            \"zoomToNodeRatio\": 0.1024,\n",
       "            \"colorMappingBy\": \"index\",\n",
       "            \"visibleMin\": 10\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"pv\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"pv\": true\n",
       "            },\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"pv-top20\\u5546\\u54c1\\u7c7b\\u76ee\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_0c68e025d694435aa035cb839afce9b7.setOption(option_0c68e025d694435aa035cb839afce9b7);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x263c0d50970>"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.render_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们可以看到pv数目最高的商品类目是4756105， 为478455次, 我们再看看其他用户行为的商品类目top20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 将上诉过程定义为函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tree_plot(be):\n",
    "    a = df[df.行为类型==be]['商品类目ID'].value_counts()[0:20]\n",
    "    #print(a)\n",
    "    b = a.to_frame().reset_index()\n",
    "    b['name'] = b.pop('index')\n",
    "    b = b.rename(columns = {\"商品类目ID\":'value'})\n",
    "    b = b.T.to_dict()\n",
    "    b = b.values()\n",
    "    b = list(b)\n",
    "    # b\n",
    "    from pyecharts import options as opts\n",
    "    from pyecharts.charts import TreeMap\n",
    "    from pyecharts.globals import ThemeType\n",
    "    data =b\n",
    "    c = (\n",
    "        TreeMap(init_opts=opts.InitOpts(theme=ThemeType.LIGHT, width='800px', height='400px'))\n",
    "        .add(\"商品类目\", data, )\n",
    "        .set_global_opts(title_opts=opts.TitleOpts(title=\"{}-top20商品类目\".format(be)))\n",
    "    )\n",
    "    return c.render_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\pyecharts\\charts\\chart.py:14: PendingDeprecationWarning: pyecharts 所有图表类型将在 v1.9.0 版本开始强制使用 ChartItem 进行数据项配置 :)\n",
      "  super().__init__(init_opts=init_opts)\n"
     ]
    },
    {
     "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=\"aa06f0d094da4f8586d2181446927600\" style=\"width:800px; height:400px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_aa06f0d094da4f8586d2181446927600 = echarts.init(\n",
       "                    document.getElementById('aa06f0d094da4f8586d2181446927600'), 'light', {renderer: 'canvas'});\n",
       "                var option_aa06f0d094da4f8586d2181446927600 = {\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",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"treemap\",\n",
       "            \"name\": \"\\u5546\\u54c1\\u7c7b\\u76ee\",\n",
       "            \"data\": [\n",
       "                {\n",
       "                    \"value\": 478455,\n",
       "                    \"name\": 4756105\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 322614,\n",
       "                    \"name\": 4145813\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 312967,\n",
       "                    \"name\": 2355072\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 293164,\n",
       "                    \"name\": 3607361\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 283172,\n",
       "                    \"name\": 982926\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 197556,\n",
       "                    \"name\": 2520377\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 186675,\n",
       "                    \"name\": 4801426\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 174916,\n",
       "                    \"name\": 1320293\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 154782,\n",
       "                    \"name\": 2465336\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 142784,\n",
       "                    \"name\": 3002561\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 114214,\n",
       "                    \"name\": 2735466\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 101568,\n",
       "                    \"name\": 4181361\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 98842,\n",
       "                    \"name\": 149192\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 97608,\n",
       "                    \"name\": 1080785\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 94869,\n",
       "                    \"name\": 2885642\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 93132,\n",
       "                    \"name\": 4217906\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 88305,\n",
       "                    \"name\": 154040\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 84852,\n",
       "                    \"name\": 3738615\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 77468,\n",
       "                    \"name\": 1879194\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 71869,\n",
       "                    \"name\": 2640118\n",
       "                }\n",
       "            ],\n",
       "            \"width\": \"80%\",\n",
       "            \"height\": \"80%\",\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"inside\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"upperlabel\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"inside\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"drillDownIcon\": \"\\u25b6\",\n",
       "            \"roam\": true,\n",
       "            \"nodeClick\": \"zoomToNode\",\n",
       "            \"zoomToNodeRatio\": 0.1024,\n",
       "            \"colorMappingBy\": \"index\",\n",
       "            \"visibleMin\": 10\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u5546\\u54c1\\u7c7b\\u76ee\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"\\u5546\\u54c1\\u7c7b\\u76ee\": true\n",
       "            },\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"pv-top20\\u5546\\u54c1\\u7c7b\\u76ee\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_aa06f0d094da4f8586d2181446927600.setOption(option_aa06f0d094da4f8586d2181446927600);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x263c12c10d0>"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\pyecharts\\charts\\chart.py:14: PendingDeprecationWarning: pyecharts 所有图表类型将在 v1.9.0 版本开始强制使用 ChartItem 进行数据项配置 :)\n",
      "  super().__init__(init_opts=init_opts)\n"
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    {
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       "<script>\n",
       "    require.config({\n",
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       "            'echarts':'https://assets.pyecharts.org/assets/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"57324574098d47809ce96707b56bf114\" style=\"width:800px; height:400px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_57324574098d47809ce96707b56bf114 = echarts.init(\n",
       "                    document.getElementById('57324574098d47809ce96707b56bf114'), 'light', {renderer: 'canvas'});\n",
       "                var option_57324574098d47809ce96707b56bf114 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
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       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
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       "            \"drillDownIcon\": \"\\u25b6\",\n",
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       "            \"nodeClick\": \"zoomToNode\",\n",
       "            \"zoomToNodeRatio\": 0.1024,\n",
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       "        }\n",
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       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u5546\\u54c1\\u7c7b\\u76ee\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"\\u5546\\u54c1\\u7c7b\\u76ee\": true\n",
       "            },\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
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       "    \"tooltip\": {\n",
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       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
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       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
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       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
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       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
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       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
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       "};\n",
       "                chart_57324574098d47809ce96707b56bf114.setOption(option_57324574098d47809ce96707b56bf114);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x263c68ec3a0>"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\pyecharts\\charts\\chart.py:14: PendingDeprecationWarning: pyecharts 所有图表类型将在 v1.9.0 版本开始强制使用 ChartItem 进行数据项配置 :)\n",
      "  super().__init__(init_opts=init_opts)\n"
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      "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=\"f9c388f352b544f48a4c64d261c2410c\" style=\"width:800px; height:400px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_f9c388f352b544f48a4c64d261c2410c = echarts.init(\n",
       "                    document.getElementById('f9c388f352b544f48a4c64d261c2410c'), 'light', {renderer: 'canvas'});\n",
       "                var option_f9c388f352b544f48a4c64d261c2410c = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
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       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
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       "                {\n",
       "                    \"value\": 4426,\n",
       "                    \"name\": 4357323\n",
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       "            ],\n",
       "            \"width\": \"80%\",\n",
       "            \"height\": \"80%\",\n",
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       "                \"position\": \"inside\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"upperlabel\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"inside\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"drillDownIcon\": \"\\u25b6\",\n",
       "            \"roam\": true,\n",
       "            \"nodeClick\": \"zoomToNode\",\n",
       "            \"zoomToNodeRatio\": 0.1024,\n",
       "            \"colorMappingBy\": \"index\",\n",
       "            \"visibleMin\": 10\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u5546\\u54c1\\u7c7b\\u76ee\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"\\u5546\\u54c1\\u7c7b\\u76ee\": true\n",
       "            },\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
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       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"cart-top20\\u5546\\u54c1\\u7c7b\\u76ee\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_f9c388f352b544f48a4c64d261c2410c.setOption(option_f9c388f352b544f48a4c64d261c2410c);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x263c10082e0>"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\pyecharts\\charts\\chart.py:14: PendingDeprecationWarning: pyecharts 所有图表类型将在 v1.9.0 版本开始强制使用 ChartItem 进行数据项配置 :)\n",
      "  super().__init__(init_opts=init_opts)\n"
     ]
    },
    {
     "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=\"22e4d87d59664a8fb1817d14c4215642\" style=\"width:800px; height:400px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_22e4d87d59664a8fb1817d14c4215642 = echarts.init(\n",
       "                    document.getElementById('22e4d87d59664a8fb1817d14c4215642'), 'light', {renderer: 'canvas'});\n",
       "                var option_22e4d87d59664a8fb1817d14c4215642 = {\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",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"treemap\",\n",
       "            \"name\": \"\\u5546\\u54c1\\u7c7b\\u76ee\",\n",
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       "                {\n",
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       "                {\n",
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       "                {\n",
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       "                {\n",
       "                    \"value\": 1785,\n",
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       "                {\n",
       "                    \"value\": 1726,\n",
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       "                {\n",
       "                    \"value\": 1667,\n",
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       "                {\n",
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       "                {\n",
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       "                {\n",
       "                    \"value\": 1558,\n",
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       "                {\n",
       "                    \"value\": 1489,\n",
       "                    \"name\": 903809\n",
       "                },\n",
       "                {\n",
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       "                },\n",
       "                {\n",
       "                    \"value\": 1378,\n",
       "                    \"name\": 570735\n",
       "                },\n",
       "                {\n",
       "                    \"value\": 1242,\n",
       "                    \"name\": 3607361\n",
       "                }\n",
       "            ],\n",
       "            \"width\": \"80%\",\n",
       "            \"height\": \"80%\",\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"inside\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"upperlabel\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"inside\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"drillDownIcon\": \"\\u25b6\",\n",
       "            \"roam\": true,\n",
       "            \"nodeClick\": \"zoomToNode\",\n",
       "            \"zoomToNodeRatio\": 0.1024,\n",
       "            \"colorMappingBy\": \"index\",\n",
       "            \"visibleMin\": 10\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u5546\\u54c1\\u7c7b\\u76ee\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"\\u5546\\u54c1\\u7c7b\\u76ee\": true\n",
       "            },\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"buy-top20\\u5546\\u54c1\\u7c7b\\u76ee\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_22e4d87d59664a8fb1817d14c4215642.setOption(option_22e4d87d59664a8fb1817d14c4215642);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x263c1f42430>"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_plot('pv')\n",
    "tree_plot('fav')\n",
    "tree_plot('cart')\n",
    "tree_plot('buy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过比较可以发现， pv前20里面也有在其他行为中出现"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 比较所有行为的top20的商品类别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [df[df.行为类型==i]['商品类目ID'].value_counts()[0:20] for i in df.行为类型.unique()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['pv', 'buy', 'fav', 'cart'], dtype=object)"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.行为类型.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([4756105, 4145813, 2355072, 3607361,  982926, 2520377, 4801426,\n",
       "            1320293, 2465336, 3002561, 2735466, 4181361,  149192, 1080785,\n",
       "            2885642, 4217906,  154040, 3738615, 1879194, 2640118],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2465336    4\n",
       "4756105    4\n",
       "2885642    4\n",
       "2735466    4\n",
       "1320293    4\n",
       "982926     4\n",
       "4801426    4\n",
       "3002561    4\n",
       "4145813    4\n",
       "4217906    4\n",
       "3607361    4\n",
       "2520377    3\n",
       "1879194    3\n",
       "1464116    3\n",
       "2355072    3\n",
       "2640118    3\n",
       "149192     3\n",
       "4357323    3\n",
       "570735     3\n",
       "4181361    3\n",
       "1080785    2\n",
       "3738615    2\n",
       "154040     1\n",
       "1299190    1\n",
       "4789432    1\n",
       "4159072    1\n",
       "903809     1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(np.array([a[i].index.tolist() for i in range(4)]).flatten()).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过以上对比我们可以发现，pv-top20中前5名的商品在buy-top20中排名分别为5、3、无排名、20、7名，pv-top20、fav-top20、cart-top20的商品品类分别在buy-top20中出现了13、15、15类，说明在点击pv、收藏fav、加购cart环节吸引用户更多注意力的商品没有很好的转化为实际销量，这是销量增长的一个突破点，此外我们也可以看出相比于pv，加购cart、收藏fav与购买的关系更为直接。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 结论"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**一**、通过对用户流量行为的转化分析，可以看出用户从点击到购买的转化率还是比较高的，目前来看可以通过引导用户收藏并加购来提高用户从收藏/加购到购买的转化率。而流量行为从点击到购买的转化率仅有2.24%，故从点击到购买的行为转化是一个提高的重点。针对这一环节的建议有：  \n",
    "- 1 优化电商平台的搜索匹配度和推荐策略，提高筛选精确度，并对搜索和筛选的结果排序的优先级进行优化；  \n",
    "- 2 可以给客户提供同类产品比较的功能，让用户不需要多次返回搜索结果进行点击查看，方便用户确定心仪产品，增加点击到后续行为的转化；  \n",
    "- 3 优化收藏到购买的操作过程，增加用户收藏并加购的频率，以提高购买转化率。  \n",
    "\n",
    "**二**、通过用户行为习惯分析\n",
    "- 1 可以看出用户的活跃时间高峰期主要在13:00-14:00点，此时使用人数最多，活动最容易触达用户，所以可以将营销活动安排在这个时间段内，来进行引流并转化； \n",
    "  \n",
    "**三**、通过对用户的商品偏好分析，可以看出商品销量主要是依靠长尾效应，而非爆款商品的带动。但是通过对商品品类的分析可以看出能吸引用户注意力的商品购买转化率并不高，是一个提高销量的突破口。针对用户关注度高但销量不高的这部分产品，可以从以下几个方面着手：\n",
    "- 1 商家在详情页上的展示突出用户重点关注的信息，优化信息的呈现方式，减少用户的时间成本；  \n",
    "- 2 增加这些产品的质量管控力度，加强对用户反馈建议如评论的管理，认真采纳并根据自身的优劣势进行商品优化  \n",
    "- 3 此外，对于购买top20的商品，可以在电商首页对这些品类的商品优先进行展现，以满足用户的购买需求。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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