jupyter 用pyecharts进行数据分析

一、jupyter和pyecharts下载和打开

因为我是用的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,NotebookType
CurrentConfig.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 csv
import requests
f = 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 opts
from pyecharts.charts import Pie
from pyecharts.faker import Faker
from pyecharts.globals import CurrentConfig,NotebookType
CurrentConfig.NOTEBOOK_TYPE = NotebookType.JUPYTER_LAB

c = (
    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(title="二手车数据类型分布"),
        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()