课程设计 pycharm python毕设案例教学: 基于数据挖掘在京东客户评价方面的研究与应用

前言

大家早好、午好、晚好吖 ❤ ~欢迎光临本文章

完整源码、素材皆可点击文章下方名片获取此处跳转

开发环境:

Python 3.8 Pycharm

模块使用:

requests >>> pip install requests 第三方模块 csv>>> 内置模块，无需安装

安装模块：win + R 输入cmd 输入安装命令 pip install 模块名

如果出现爆红 可能是因为 网络连接超时 切换国内镜像源

基本思路:

一. 数据来源分析

明确需求:

明确采集网站 明确采集数据 商品评论相关的信息 分析 商品评论相关的信息 请求那个链接可以获取 浏览器自带工具: 开发者工具 <一定要分析清楚采集的数据在什么地方>

打开开发者工具: F12 / 右键点击检查选择network 点击第二页评论内容: 可以直接找到对应数据包

二. 代码实现步骤 --> 实现单页数据采集

发送请求, 模拟浏览器对于url地址发送请求 获取数据, 获取服务器返回响应数据 response 解析数据, 提取我们想要的数据内容 --> 评论信息 保存数据, 把评论信息保存表格文件里面

采集评论数据

（完整源码、素材皆可点击此处+获取)

导入模块

# 导入数据请求模块 --> 需要安装 pip install requests

import requests

# 导入格式化输出模块 --> 内置模块, 不需要安装

from pprint import pprint

# 导入csv模块

import csv

# 导入时间模块

import time

# 导入随机模块

import random

创建文件

f = open('口红20.csv', mode='a', encoding='utf-8', newline='')

csv_writer = csv.DictWriter(f, fieldnames=[

'昵称',

'商品',

'评分',

'购买时间',

'评论时间',

'plus会员',

'内容',

])

写入表头

csv_writer.writeheader()

多页采集

for page in range(0, 20):

print(f'正在采集第{page}页的数据内容')

延时操作 random.randint(1, 2) --> 随机生成 1或者2

time.sleep(random.randint(1, 2))

请求链接

url = 'https://club.jd.com/comment/productPageComments.action?callback=fetchJSON_comment98&productId=100011323932&score=0&sortType=5&page=3&pageSize=10&isShadowSku=0&rid=0&fold=1'

请求参数 --> 字典数据类型

data = {

'callback': 'fetchJSON_comment98',

'productId': '100011323932',

'score': '0',

'sortType': '5',

'page': page,

'pageSize': '10',

'isShadowSku': '0',

'rid': '0',

'fold': '1',

}

模拟浏览器 headers

headers = {

# user-agent 用户代理 表示浏览器基本上身份信息

'user-agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/101.0.0.0 Safari/537.36'

}

发送请求

response = requests.get(url=url, params=data, headers=headers)

print(response.text)

break

# # for循环遍历, 一个一个提取列表里面元素

# for index in response.json()['comments']:

# if index['plusAvailable'] == 201:

# Vip = '是'

# else:

# Vip = '不是'

# dit = {

# '昵称': index['nickname'],

# '商品': index['productColor'],

# '评分': index['score'],

# '购买时间': index['referenceTime'],

# '评论时间': index['creationTime'],

# 'plus会员': Vip,

# '内容': index['content'].replace('\n', ''),

# }

# # 写入数据 字典方式进行保存

# csv_writer.writerow(dit)

# print(dit)

评论分析

import pandas as pd

df = pd.read_csv('data.csv', encoding='gbk')

df.head()

VipNum = df['plus会员'].value_counts().to_list()

VipType = df['plus会员'].value_counts().index.to_list()

from pyecharts import options as opts

from pyecharts.charts import Pie

from pyecharts.faker import Faker

c = (

Pie()

.add(

"",

[list(z) for z in zip(VipType, VipNum)],

radius=["40%", "55%"],

label_opts=opts.LabelOpts(

position="outside",

formatter="{a|{a}}{abg|}\n{hr|}\n {b|{b}: }{c} {per|{d}%} ",

background_color="#eee",

border_color="#aaa",

border_width=1,

border_radius=4,

rich={

"a": {"color": "#999", "lineHeight": 22, "align": "center"},

"abg": {

"backgroundColor": "#e3e3e3",

"width": "100%",

"align": "right",

"height": 22,

"borderRadius": [4, 4, 0, 0],

},

"hr": {

"borderColor": "#aaa",

"width": "100%",

"borderWidth": 0.5,

"height": 0,

},

"b": {"fontSize": 16, "lineHeight": 33},

"per": {

"color": "#eee",

"backgroundColor": "#334455",

"padding": [2, 4],

"borderRadius": 2,

},

},

),

)

.set_global_opts(title_opts=opts.TitleOpts(title="plus会员分布情况"))

# .render("pie_rich_label.html")

)

c.render_notebook()

d = (

Pie()

.add(

series_name="plus会员",

data_pair=[list(z) for z in zip(VipType, VipNum)],

radius=["50%", "70%"],

label_opts=opts.LabelOpts(is_show=False, position="center"),

)

.set_global_opts(legend_opts=opts.LegendOpts(pos_left="legft", orient="vertical"))

.set_series_opts(

tooltip_opts=opts.TooltipOpts(

trigger="item", formatter="{a}
{b}: {c} ({d}%)"

),

# label_opts=opts.LabelOpts(formatter="{b}: {c}")

)

)

d.render_notebook()

scoreNum = df['评分'].value_counts().to_list()

scoreType = df['评分'].value_counts().index.to_list()

from pyecharts import options as opts

from pyecharts.charts import Pie

from pyecharts.faker import Faker

score = (

Pie()

.add(

"",

[list(z) for z in zip(scoreType, scoreNum)],

center=["35%", "50%"],

)

.set_global_opts(

title_opts=opts.TitleOpts(title="评分分布情况"),

legend_opts=opts.LegendOpts(pos_left="15%"),

)

.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))

)

score.render_notebook()

# df['购买时间'] = df['购买时间'].str[:-6]

# BuyDateNum = df['购买时间'].value_counts().to_list()

# BuyDateType = df['购买时间'].value_counts().index.to_list()

BuyDateNum = df['购买时间'].str[:-6].value_counts().to_list()

BuyDateType = df['购买时间'].str[:-6].value_counts().index.to_list()

from pyecharts.charts import Bar

from pyecharts.faker import Faker

from pyecharts.globals import ThemeType

BuyDate = (

Bar({"theme": ThemeType.MACARONS})

.add_xaxis(BuyDateType)

.add_yaxis("购买时间", BuyDateNum)

.set_global_opts(

title_opts={"text": "商品购买时间", "subtext": "2023年"}

)

# .render("bar_base_dict_config.html")

)

BuyDate.render_notebook()

import jieba

string = ' '.join([i for i in df['评论']])

words = jieba.lcut(string)

# 统计词频

word_count = {}

for word in words:

if word not in word_count:

word_count[word] = 1

else:

word_count[word] += 1

word_list = list(zip(word_count.keys(),word_count.values()))

word_list

df['商品'] = df['商品'].replace(regex='【.*?】', value='')

ShopNum = df['商品'].value_counts().to_list()[:7]

ShopType = df['商品'].value_counts().index.to_list()[:7]

ShopType

from pyecharts import options as opts

from pyecharts.charts import Bar

from pyecharts.commons.utils import JsCode

from pyecharts.faker import Faker

c = (

Bar()

.add_xaxis(ShopType)

.add_yaxis("商品", ShopNum, category_gap="60%")

.set_series_opts(

itemstyle_opts={

"normal": {

"color": JsCode(

"""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{

offset: 0,

color: 'rgba(0, 244, 255, 1)'

}, {

offset: 1,

color: 'rgba(0, 77, 167, 1)'

}], false)"""

),

"barBorderRadius": [30, 30, 30, 30],

"shadowColor": "rgb(0, 160, 221)",

}

}

)

.set_global_opts(

xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-15)),

title_opts=opts.TitleOpts(title="口红款式", subtitle="DIRO"),

)

)

c.render_notebook()

import pyecharts.options as opts

from pyecharts.charts import WordCloud

wc = (

WordCloud()

.add(series_name="词云分析", data_pair=word_list, word_size_range=[6, 66])

.set_global_opts(

title_opts=opts.TitleOpts(

title="词云分析", title_textstyle_opts=opts.TextStyleOpts(font_size=23)

),

tooltip_opts=opts.TooltipOpts(is_show=True),

)

# .render("basic_wordcloud.html")

)

wc.render_notebook()

from pyecharts import options as opts

from pyecharts.charts import WordCloud

from pyecharts.globals import SymbolType

c = (

WordCloud()

.add("", word_list, word_size_range=[20, 100], shape=SymbolType.DIAMOND)

.set_global_opts(title_opts=opts.TitleOpts(title="WordCloud-shape-diamond"))

# .render("wordcloud_diamond.html")

)

c.render_notebook()

尾语 

好了，今天的分享就差不多到这里了！

完整代码、更多资源、疑惑解答直接点击下方名片自取即可。

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