jupyter 用pyecharts进行数据分析

一、jupyter和pyecharts下载和打开

��为我是用的pycharm，所以我直接在pycharm项目终端中下载pip install jupyter,pip install pyecharts

在你下载的项目路径中输入jupyter notebook

 

之后会进入页面

 

Jupyter 具体使用参考这个链接：Jupyter Notebook基本使用_jupyter notebook有哪些优点和缺点?-CSDN博客 

二、jupyter 利用pyecharts常见问题

 最常见的问题是不出图

解决方法：

在导入包时候，导入下面的代码：

from pyecharts.globals import CurrentConfig,NotebookTypeCurrentConfig.NOTEBOOK_TYPE = NotebookType.JUPYTER_LAB

接着写代码

 

一定要另起一行写展示代码

 

 

三、爬取懂车帝数据并进行数据分析详细代码

爬虫代码：

import csvimport requestsf = open('懂车帝.csv',mode='w',encoding='utf-8',newline='')csv_writer = csv.writer(f)csv_writer.writerow(['车辆名称','系列名称','品牌','城市','过户次数','售价','官方指导价','年份','行驶里程','汽车源类型','认证'])headers = {    "User-Agent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36"}url = 'https://www.dongchedi.com/motor/pc/sh/sh_sku_list?aid=1839&app_name=auto_web_pc'for page in range(1,21):    data = {        'sh_city_name':' 全国',        'page': page,        'limit': '20',    }    response = requests.post(url=url,json=data,headers=headers)    json_data = response.json()['data']['search_sh_sku_info_list']    print(f'正在打印第{page}页数据')    for item in json_data:        car_name = item['car_name']        series_name = item['series_name']        brand_name = item['brand_name']        car_source_city_name = item['car_source_city_name']        transfer_cnt = item['transfer_cnt']        sh_price = item['sh_price']        official_price = item['official_price']        productionTime = item['sub_title'].split('|')[0]        car_source_type = item['car_source_type']        authentication_method = item['authentication_method']        try:            mileage = item['sub_title'].split('|')[1]        except:            mileage = '无数据'        csv_writer.writerow([car_name, series_name, brand_name, car_source_city_name, transfer_cnt, sh_price, official_price,productionTime, mileage, car_source_type, authentication_method])

分析代码:

import pandas as pd# 配置pyecharts的代码# 读取数据df = pd.read_csv('懂车帝.csv')# 显示前五行df.head()

info = df['品牌'].value_counts().index.to_list()  # x轴的内容,分类型num = df['品牌'].value_counts().to_list() # y轴的数据

from pyecharts import options as optsfrom pyecharts.charts import Piefrom pyecharts.faker import Fakerfrom pyecharts.globals import CurrentConfig,NotebookTypeCurrentConfig.NOTEBOOK_TYPE = NotebookType.JUPYTER_LABc = (    Pie()    .add(        "",        [            list(z)            for z in zip(                # 传入的x和y轴数据                info,                num,            )        ],        center=["40%", "50%"],    )    .set_global_opts(        # 设置标题        title_opts=opts.TitleOpts(),        legend_opts=opts.LegendOpts(type_="scroll", pos_left="80%", orient="vertical"),    )    .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))    # 把可视化图保存为html文件    #.render("pie_scroll_legend.html"))c.load_javascript()

c.render_notebook()

 

结果展示：

 

 

 

 

四、pyecharts官方示例代码链接:Document 

官方链接:pyecharts - A Python Echarts Plotting Library built with love.