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 pd
import numpy as np
import plotly.plotly as py
import plotly.graph_objs as go
path = '/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
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持脚本之家。
