python基于plotly实现画饼状图代码实例
人气:0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
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