这篇文章主要介绍了python基于plotly实现画饼状图代码实例,文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值,需要的朋友可以参考下

代码

import pandas as pdimport numpy as npimport plotly.plotly as pyimport plotly.graph_objs as gopath = '/home/v-gazh/PycharmProjects/us_data/limit_code.csv'df = pd.read_csv(path)df.set_index(['code'], inplace=True)# ST 占比total_count = len(df)st_count = len(df[df['isST']==1])print(f'禁投池总数：{total_count}')print(f'禁投池中ST个数：{st_count}') # f'禁投池中ST个数：{}'# 成分股占比sz50_count = len(df[df['isSz50']==1])print(f'禁投池中上证50个数：{sz50_count}')hs300_count = len(df[df['isHs300']==1])print(f'禁投池中沪深300个数：{hs300_count}')zz500_count = len(df[df['isZz500']==1])print(f'禁投池中中证500个数：{zz500_count}')# 退市占比outdate_count = len(df['outDate'].dropna())print(f'禁投池中退市股票个数：{outdate_count}')# 非股票not_stock = len(df[df['type']!=1])print(f'禁投池中非股票个数：{not_stock} 【SZ006415 为基金：F006415 | SZ000000 代码错误】')# 次新股delta_df = pd.DataFrame((pd.to_datetime(df['date']) - pd.to_datetime(df['ipoDate'])))new_stock = len(delta_df[delta_df[0] < pd.Timedelta('365 days')]) # 上市不满一年为次新股 print(f'禁投池中次新股个数：{new_stock}')# 市值小于30亿的股票maketValue = len(df[df['maketValue'] < 3000000000])print(f'市值小于30亿股票个数：{maketValue}')# 画图labels = ['股票总数', 'ST股票', '深证50', '沪深300', '中证500', '退市股票', '非股票', '次新股', '小市值']values = [total_count, st_count, sz50_count, hs300_count, zz500_count, outdate_count, not_stock, new_stock, maketValue]trace = go.Pie(labels=labels, values=values,textfont=dict(size=15),)py.iplot([trace], filename='basic_pie_chart')

注：上面代码中，起主要作用的主要是

# 画图labels = ['股票总数', 'ST股票', '深证50', '沪深300', '中证500', '退市股票', '非股票', '次新股', '小市值']values = [total_count, st_count, sz50_count, hs300_count, zz500_count, outdate_count, not_stock, new_stock, maketValue]trace = go.Pie(labels=labels, values=values,textfont=dict(size=15),)py.iplot([trace], filename='basic_pie_chart')values = [total_count, st_count, sz50_count, hs300_count, zz500_count, outdate_count, not_stock, new_stock, maketValue]

values 列表里的内容为int数值，对应上面的labels

图示

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