Pandas 熊猫系列按百分比分组

我有一个数据框。我已使用将列状态按日期分组

y = news_dataframe.groupby(by=[news_dataframe['date'].dt.date,news_dataframe['status']])['status'].count()

我的输出是--

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现在我想按日期计算每个状态组的百分比。如何使用pandas dataframe实现这一点

计算两个不同的groupbys并将其中一个除以另一个：

y_numerator = news_dataframe.groupby(by=[news_dataframe['date'].dt.date,news_dataframe['status']])['status'].count()


y_denominator = news_dataframe.groupby(by=news_dataframe['date'].dt.date)['status'].count()

y=y_numerator/y_denominator

试试这个：

# just fill the consecutive rows with this
df=df.ffill()
df.df1.columns=['date','status','count']
# getting the total value of count with date and status
df1=df.groupby(['date']).sum().reset_index()
#renaming it to total as it is the sum
df1.columns=['date','status','total']  

# now join the tables to find the total and actual value together
df2=df.merge(df1,on=['date']) 

#calculate the percentage
df2['percentage']=(df2.count/df2.total)*100

如果您需要一个衬垫，则其：

df['percentage']=(df.ffill()['count]/df.ffill().groupby(['date']).sum().reset_index().rename(columns={'count': 'total'}).merge(df,on=['date'])['total'])*100

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我想这是最短的：

news_dataframe['date'] = news_dataframe['date'].dt.date
news_dataframe.groupby(['date','status'])['status'].count()/news_dataframe.groupby(['date'])['status'].count()

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请尝试更好地对齐您的输出，以便我们了解您的意思。我已更新了输出。请尝试：

y['percentage']=y['count'].div（y['count'].sum（））*100

news_dataframe['date'] = news_dataframe['date'].dt.date
news_dataframe.groupby(['date','status'])['status'].count()/news_dataframe.groupby(['date'])['status'].count()