戒酒的李白
805 lines
24 KiB
Plaintext
805 lines
24 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# yf_amazon 说明\n",
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"0. **下载地址：** [百度网盘](https://pan.baidu.com/s/1SbfpZb5cm-g2LmnYV_af8Q)\n",
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"1. **数据概览：** 52 万件商品，1100 多个类目，142 万用户，720 万条评论/评分数据\n",
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"2. **推荐实验：** 推荐系统、情感/观点/评论 倾向性分析\n",
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"2. **数据来源：** [亚马逊](https://www.amazon.cn/)\n",
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"3. **原数据集：** [JD.com E-Commerce Data](http://yongfeng.me/dataset/)，Yongfeng Zhang 教授为 WWW 2015 会议论文而搜集的数据\n",
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"4. **加工处理：**\n",
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" 1. 将全角字符转换为半角字符，并采用 UTF-8 编码\n",
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" 2. 整理成与 [MovieLens](https://grouplens.org/datasets/movielens/) 兼容的格式\n",
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" 3. 进行脱敏操作，以保护用户隐私"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"path = 'yf_amazon_文件夹_所在_路径'"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 1. products.csv"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 加载数据"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"产品数目：525619\n"
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]
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}
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],
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"source": [
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"products = pd.read_csv(path + 'products.csv')\n",
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"\n",
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"print('产品数目：%d' % products.shape[0])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 字段说明\n",
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"\n",
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"| 字段 | 说明 |\n",
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"| ---- | ---- |\n",
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"| productId | 产品 id (从 0 开始，连续编号) |\n",
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"| name | 产品名称 |\n",
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"| catIds | 类别 id（从 0 开始，连续编号，从左到右依次表示一级类目、二级类目、三级类目） |"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>productId</th>\n",
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" <th>name</th>\n",
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" <th>catIds</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>331420</th>\n",
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" <td>331420</td>\n",
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" <td>欧意金狐狸 女式 皮手套 QT602</td>\n",
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" <td>802,143,996</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>130945</th>\n",
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" <td>130945</td>\n",
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" <td>YESO TOT 中性 单肩包/斜挎包 均码 9411</td>\n",
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" <td>1111,864,781</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>179886</th>\n",
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" <td>179886</td>\n",
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" <td>李斯特论柏辽兹与舒曼</td>\n",
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" <td>832,552,337</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>504123</th>\n",
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" <td>504123</td>\n",
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" <td>Tuscarora 途斯卡洛拉 中性 烈焰驰骋无缝头巾 PSU3083</td>\n",
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" <td>1111,522,720</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>387785</th>\n",
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" <td>387785</td>\n",
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" <td>我们的故事:一百个北大荒老知青的人生形态</td>\n",
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" <td>832,519,599</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>406231</th>\n",
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" <td>406231</td>\n",
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" <td>图读周易</td>\n",
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" <td>832,723,724</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>199072</th>\n",
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" <td>199072</td>\n",
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" <td>Barbie 芭比 女童 运动休闲鞋 A22993</td>\n",
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" <td>802,777,601</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>518528</th>\n",
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" <td>518528</td>\n",
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" <td>HiVi 惠威 多媒体音箱 D1080MKII 2.0声道 棕色</td>\n",
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" <td>1057,439,1064</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>446621</th>\n",
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" <td>446621</td>\n",
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" <td>HALTI 男式 JUOVAJACKET 芬兰国家队系列 羽绒滑雪服 H0591922</td>\n",
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" <td>1111,651,693</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>379960</th>\n",
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" <td>379960</td>\n",
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" <td>塑料回收再生术:百工百技</td>\n",
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" <td>832,1096,509</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" productId name catIds\n",
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"331420 331420 欧意金狐狸 女式 皮手套 QT602 802,143,996\n",
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"130945 130945 YESO TOT 中性 单肩包/斜挎包 均码 9411 1111,864,781\n",
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"179886 179886 李斯特论柏辽兹与舒曼 832,552,337\n",
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"504123 504123 Tuscarora 途斯卡洛拉 中性 烈焰驰骋无缝头巾 PSU3083 1111,522,720\n",
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"387785 387785 我们的故事:一百个北大荒老知青的人生形态 832,519,599\n",
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"406231 406231 图读周易 832,723,724\n",
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"199072 199072 Barbie 芭比 女童 运动休闲鞋 A22993 802,777,601\n",
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"518528 518528 HiVi 惠威 多媒体音箱 D1080MKII 2.0声道 棕色 1057,439,1064\n",
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"446621 446621 HALTI 男式 JUOVAJACKET 芬兰国家队系列 羽绒滑雪服 H0591922 1111,651,693\n",
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"379960 379960 塑料回收再生术:百工百技 832,1096,509"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"products.sample(10)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 2. categories.csv"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 加载数据"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"类别数目：1175\n"
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]
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}
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],
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"source": [
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"categories = pd.read_csv(path + 'categories.csv')\n",
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"\n",
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"print('类别数目：%d' % categories.shape[0])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 字段说明\n",
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"\n",
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"| 字段 | 说明 |\n",
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"| ---- | ---- |\n",
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"| catId | 类别 id (从 0 开始，连续编号) |\n",
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"| category | 类别名称 |"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>catId</th>\n",
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" <th>category</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>947</th>\n",
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" <td>947</td>\n",
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" <td>理发器</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>818</th>\n",
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" <td>818</td>\n",
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" <td>电脑硬件</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>212</th>\n",
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" <td>212</td>\n",
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" <td>帐篷</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>815</th>\n",
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" <td>815</td>\n",
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" <td>路由器/中继器</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>829</th>\n",
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" <td>829</td>\n",
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" <td>拉杆箱/包</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>391</th>\n",
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" <td>391</td>\n",
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" <td>女鞋</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>756</th>\n",
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" <td>756</td>\n",
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" <td>大型健身器械</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>11</th>\n",
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" <td>11</td>\n",
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" <td>其他运动器材</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>633</th>\n",
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" <td>633</td>\n",
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" <td>垂钓用品</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>115</th>\n",
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" <td>115</td>\n",
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" <td>卡通</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" catId category\n",
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"947 947 理发器\n",
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"818 818 电脑硬件\n",
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"212 212 帐篷\n",
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"815 815 路由器/中继器\n",
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"829 829 拉杆箱/包\n",
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"391 391 女鞋\n",
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"756 756 大型健身器械\n",
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"11 11 其他运动器材\n",
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"633 633 垂钓用品\n",
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"115 115 卡通"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"categories.sample(10)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 3. ratings.csv"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 加载数据"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"用户 数目：1424596\n",
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"评分/评论 数目（总计）：7202921\n",
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"\n"
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]
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}
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],
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"source": [
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"pd_ratings = pd.read_csv(path+'ratings.csv')\n",
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"\n",
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"print('用户 数目：%d' % pd_ratings.userId.unique().shape[0])\n",
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"print('评分/评论 数目（总计）：%d\\n' % pd_ratings.shape[0])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 字段说明\n",
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"\n",
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"| 字段 | 说明 |\n",
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"| ---- | ---- |\n",
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"| userId | 用户 id (从 0 开始，连续编号) |\n",
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"| productId | 即 products.csv 中的 productId |\n",
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"| rating | 评分，[1,5] 之间的整数 |\n",
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"| timestamp | 评分时间戳 |\n",
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"| title | 评论的标题 |\n",
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"| comment | 评论的内容 |"
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]
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},
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{
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"cell_type": "code",
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||
"execution_count": 8,
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||
"metadata": {
|
||
"scrolled": false
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||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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||
" <thead>\n",
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||
" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>userId</th>\n",
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" <th>productId</th>\n",
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" <th>rating</th>\n",
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" <th>timestamp</th>\n",
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" <th>title</th>\n",
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" <th>comment</th>\n",
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" </tr>\n",
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||
" </thead>\n",
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||
" <tbody>\n",
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||
" <tr>\n",
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" <th>4287636</th>\n",
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" <td>230944.0</td>\n",
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" <td>394505</td>\n",
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" <td>5.0</td>\n",
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" <td>1393084800</td>\n",
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" <td>赞!</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3940838</th>\n",
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" <td>16628.0</td>\n",
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" <td>84789</td>\n",
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" <td>5.0</td>\n",
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" <td>1389715200</td>\n",
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" <td>喜欢</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4064284</th>\n",
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" <td>325829.0</td>\n",
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" <td>94108</td>\n",
|
||
" <td>3.0</td>\n",
|
||
" <td>1384531200</td>\n",
|
||
" <td>磨脚</td>\n",
|
||
" <td>右脚小脚趾磨掉一块皮</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4802616</th>\n",
|
||
" <td>586385.0</td>\n",
|
||
" <td>254002</td>\n",
|
||
" <td>5.0</td>\n",
|
||
" <td>1383408000</td>\n",
|
||
" <td>哦~</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>292946</th>\n",
|
||
" <td>842028.0</td>\n",
|
||
" <td>231449</td>\n",
|
||
" <td>5.0</td>\n",
|
||
" <td>1369324800</td>\n",
|
||
" <td>致我们终将逝去的青春</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2306551</th>\n",
|
||
" <td>933226.0</td>\n",
|
||
" <td>219015</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>1341763200</td>\n",
|
||
" <td>有点大 不过很漂亮</td>\n",
|
||
" <td>外观很精致的说 就是外形有点偏大</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1707442</th>\n",
|
||
" <td>402851.0</td>\n",
|
||
" <td>228321</td>\n",
|
||
" <td>5.0</td>\n",
|
||
" <td>1374076800</td>\n",
|
||
" <td>给宝宝讲讲挺好的,内容简单,便于宝宝理解。</td>\n",
|
||
" <td>给宝宝讲讲挺好的,内容简单,便于宝宝理解。</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3641724</th>\n",
|
||
" <td>123473.0</td>\n",
|
||
" <td>515623</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>1305475200</td>\n",
|
||
" <td>书很好,但居然没有包装!?!?!?</td>\n",
|
||
" <td>书很好,但居然没有包装!?!?!?这么好的书却没有包装!?!?!?</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1921912</th>\n",
|
||
" <td>435946.0</td>\n",
|
||
" <td>63238</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>1357228800</td>\n",
|
||
" <td>嗯</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1475151</th>\n",
|
||
" <td>1612.0</td>\n",
|
||
" <td>139044</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>1316102400</td>\n",
|
||
" <td>一般</td>\n",
|
||
" <td>香味没有前面评价那么香,就是普通的爽肤水,有点黏黏的</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" userId productId rating timestamp title \\\n",
|
||
"4287636 230944.0 394505 5.0 1393084800 赞! \n",
|
||
"3940838 16628.0 84789 5.0 1389715200 喜欢 \n",
|
||
"4064284 325829.0 94108 3.0 1384531200 磨脚 \n",
|
||
"4802616 586385.0 254002 5.0 1383408000 哦~ \n",
|
||
"292946 842028.0 231449 5.0 1369324800 致我们终将逝去的青春 \n",
|
||
"2306551 933226.0 219015 4.0 1341763200 有点大 不过很漂亮 \n",
|
||
"1707442 402851.0 228321 5.0 1374076800 给宝宝讲讲挺好的,内容简单,便于宝宝理解。 \n",
|
||
"3641724 123473.0 515623 4.0 1305475200 书很好,但居然没有包装!?!?!? \n",
|
||
"1921912 435946.0 63238 4.0 1357228800 嗯 \n",
|
||
"1475151 1612.0 139044 4.0 1316102400 一般 \n",
|
||
"\n",
|
||
" comment \n",
|
||
"4287636 NaN \n",
|
||
"3940838 NaN \n",
|
||
"4064284 右脚小脚趾磨掉一块皮 \n",
|
||
"4802616 NaN \n",
|
||
"292946 NaN \n",
|
||
"2306551 外观很精致的说 就是外形有点偏大 \n",
|
||
"1707442 给宝宝讲讲挺好的,内容简单,便于宝宝理解。 \n",
|
||
"3641724 书很好,但居然没有包装!?!?!?这么好的书却没有包装!?!?!? \n",
|
||
"1921912 NaN \n",
|
||
"1475151 香味没有前面评价那么香,就是普通的爽肤水,有点黏黏的 "
|
||
]
|
||
},
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"pd_ratings.sample(10)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# 4. links.csv"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 加载数据"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"links = pd.read_csv(path + 'links.csv')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 字段说明\n",
|
||
"\n",
|
||
"| 字段 | 说明 |\n",
|
||
"| ---- | ---- |\n",
|
||
"| productId | 即 products.csv 和 ratings.csv 中的 productId |\n",
|
||
"| amazonId | 亚马逊的产品编号 |"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"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>productId</th>\n",
|
||
" <th>amazonId</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>436251</th>\n",
|
||
" <td>436251</td>\n",
|
||
" <td>B00F91KYGK</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>194578</th>\n",
|
||
" <td>194578</td>\n",
|
||
" <td>B00GICSVUK</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>336998</th>\n",
|
||
" <td>336998</td>\n",
|
||
" <td>B00GMKUNBI</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>371924</th>\n",
|
||
" <td>371924</td>\n",
|
||
" <td>B008RIA4AS</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>433617</th>\n",
|
||
" <td>433617</td>\n",
|
||
" <td>B00332FJ7Q</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>236918</th>\n",
|
||
" <td>236918</td>\n",
|
||
" <td>060614479X</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>388158</th>\n",
|
||
" <td>388158</td>\n",
|
||
" <td>B008TI5V2C</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>479855</th>\n",
|
||
" <td>479855</td>\n",
|
||
" <td>B002NSML6I</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>311842</th>\n",
|
||
" <td>311842</td>\n",
|
||
" <td>B001DTWV2C</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>445227</th>\n",
|
||
" <td>445227</td>\n",
|
||
" <td>B0055PT83U</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>360465</th>\n",
|
||
" <td>360465</td>\n",
|
||
" <td>B005UTT2QY</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>258363</th>\n",
|
||
" <td>258363</td>\n",
|
||
" <td>0805092919</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>308642</th>\n",
|
||
" <td>308642</td>\n",
|
||
" <td>B0079WMXT8</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>232740</th>\n",
|
||
" <td>232740</td>\n",
|
||
" <td>B0018HKRAW</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>335318</th>\n",
|
||
" <td>335318</td>\n",
|
||
" <td>B00840LWKU</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>497048</th>\n",
|
||
" <td>497048</td>\n",
|
||
" <td>B003ZI61RA</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>388969</th>\n",
|
||
" <td>388969</td>\n",
|
||
" <td>B00BIUYL06</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10448</th>\n",
|
||
" <td>10448</td>\n",
|
||
" <td>B00GMZ9DKK</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>75752</th>\n",
|
||
" <td>75752</td>\n",
|
||
" <td>B002R0DNB4</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>392345</th>\n",
|
||
" <td>392345</td>\n",
|
||
" <td>B0041IY7CE</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" productId amazonId\n",
|
||
"436251 436251 B00F91KYGK\n",
|
||
"194578 194578 B00GICSVUK\n",
|
||
"336998 336998 B00GMKUNBI\n",
|
||
"371924 371924 B008RIA4AS\n",
|
||
"433617 433617 B00332FJ7Q\n",
|
||
"236918 236918 060614479X\n",
|
||
"388158 388158 B008TI5V2C\n",
|
||
"479855 479855 B002NSML6I\n",
|
||
"311842 311842 B001DTWV2C\n",
|
||
"445227 445227 B0055PT83U\n",
|
||
"360465 360465 B005UTT2QY\n",
|
||
"258363 258363 0805092919\n",
|
||
"308642 308642 B0079WMXT8\n",
|
||
"232740 232740 B0018HKRAW\n",
|
||
"335318 335318 B00840LWKU\n",
|
||
"497048 497048 B003ZI61RA\n",
|
||
"388969 388969 B00BIUYL06\n",
|
||
"10448 10448 B00GMZ9DKK\n",
|
||
"75752 75752 B002R0DNB4\n",
|
||
"392345 392345 B0041IY7CE"
|
||
]
|
||
},
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"links.sample(20)"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.6.5"
|
||
},
|
||
"widgets": {
|
||
"state": {},
|
||
"version": "1.1.2"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|