开发环境
解释器版本: python 3.8
代码编辑器: pycharm 2021.2
第三方模块
requests: pip install requests
csv
爬虫案例的步骤
1.确定url地址(链接地址)
2.发送网络请求
3.数据解析(筛选数据)
4.数据的保存(数据库(mysql\mongodb\redis), 本地文件)
爬虫程序全部代码
分析网页
打开开发者工具，搜索关键字，找到正确url
导入模块
import requests # 发送网络请求 import csv
请求数据
url = f'https://xueqiu.com/service/v5/stock/screener/quote/list?page=1&size=30&order=desc&order_by=amount&exchange=CN&market=CN&type=sha&_=1637908787379'
# 伪装
headers = {
# 浏览器伪装
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.45 Safari/537.36'
}
response = requests.get(url, headers=headers)
json_data = response.json()
解析数据
data_list = json_data['data']['list']
for data in data_list:
data1 = data['symbol']
data2 = data['name']
data3 = data['current']
data4 = data['chg']
data5 = data['percent']
data6 = data['current_year_percent']
data7 = data['volume']
data8 = data['amount']
data9 = data['turnover_rate']
data10 = data['pe_ttm']
data11 = data['dividend_yield']
data12 = data['market_capital']
print(data1, data2, data3, data4, data5, data6, data7, data8, data9, data10, data11, data12)
data_dict = {
'股票代码': data1,
'股票名称': data2,
'当前价': data3,
'涨跌额': data4,
'涨跌幅': data5,
'年初至今': data6,
'成交量': data7,
'成交额': data8,
'换手率': data9,
'市盈率(TTM)': data10,
'股息率': data11,
'市值': data12,
}
csv_write.writerow(data_dict)
翻页
对比1、2、3页数据url，找到规律
for page in range(1, 56):
url = f'https://xueqiu.com/service/v5/stock/screener/quote/list?page={page}&size=30&order=desc&order_by=amount&exchange=CN&market=CN&type=sha&_=1637908787379'
保存数据
file = open('data2.csv', mode='a', encoding='utf-8', newline='')
csv_write = csv.DictWriter(file, fieldnames=['股票代码','股票名称','当前价','涨跌额','涨跌幅','年初至今','成交量','成交额','换手率','市盈率(TTM)','股息率','市值'])
csv_write.writeheader()
file.close()
实现效果
数据可视化全部代码
导入数据
import pandas as pd from pyecharts import options as opts from pyecharts.charts import Bar
读取数据
data_df = pd.read_csv('data2.csv')
df = data_df.dropna()
df1 = df[['股票名称', '成交量']]
df2 = df1.iloc[:20]
print(df2['股票名称'].values)
print(df2['成交量'].values)
可视化图表
c = (
Bar()
.add_xaxis(list(df2['股票名称']))
.add_yaxis("股票成交量情况", list(df2['成交量']))
.set_global_opts(
title_opts=opts.TitleOpts(title="成交量图表 - Volume chart"),
datazoom_opts=opts.DataZoomOpts(),
)
.render("data.html")
)
print('数据可视化结果完成,请在当前目录下查找打开 data.html 文件!')
效果展示
以上就是Python爬取股票交易数据并数据可视化的详细内容，更多关于Python股票数据爬取的资料请关注服务器之家其它相关文章！
原文链接：https://blog.csdn.net/m0_48405781/article/details/121640081
本文链接：https://my.lmcjl.com/post/12765.html
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