Question

我有一个pandas数据框，其中包含两个分类变量（在我的示例中，城市和颜色），一个包含百分比的列，另一个包含权重。我想做一个城市和颜色的交叉表，显示两者的每个组合，perc的加权平均值。

我已经设法使用下面的代码，我首先使用权重x perc创建一个列，然后使用（权重x perc）之和创建一个交叉表，使用权重之和创建另一个交叉表，然后最后划分首先是第二个。

它有效，但有更快/更优雅的方式吗？谢谢！

import pandas as pd
import numpy as np
np.random.seed(123)
df=pd.DataFrame()
myrows=10
df['weight'] = np.random.rand(myrows)*100

np.random.seed(321)
df['perc']=np.random.rand(myrows)
df['weight x perc']=df['weight']*df['perc']
df['colour']=np.where( df['perc']<0.5, 'red','yellow')

np.random.seed(555)
df['city']=np.where( np.random.rand(myrows) <0.5,'NY','LA' )


num=pd.crosstab( df['city'], df['colour'], values=df['weight x perc'], aggfunc='sum', margins=True)
den=pd.crosstab( df['city'], df['colour'], values=df['weight'], aggfunc='sum', margins=True)

out=num/den

print(out)

Answer 1

这里使用带有apply（）的groupby并使用numpy加权平均方法。

df.groupby(['colour','city']).apply(lambda x: np.average(x.perc, weights=x.weight)).unstack(level=0)

给出了

colour       red    yellow
city                      
LA      0.173870  0.865636
NY      0.077912  0.687400

虽然我没有全部收益。

这将产生总数

df.groupby(['colour']).apply(lambda x: np.average(x.perc, weights=x.weight))
df.groupby(['city']).apply(lambda x: np.average(x.perc, weights=x.weight))

当然仍未打包成单帧

Answer 2

我有同样的问题。我现在找到了解决方案！是！我是编程的初学者，我的代码可以运行，但是可以改进。我希望它可以帮助某人。谢谢大家的帮助。

import pandas as pd
import numpy as np

np.random.seed(123)
df=pd.DataFrame()
myrows=10
df['weight'] = np.random.rand(myrows)*100

np.random.seed(321)
df['perc']=np.random.rand(myrows)
#df['weight x perc']=df['weight']*df['perc']
df['colour']=np.where( df['perc']<0.5, 'red','yellow')

np.random.seed(555)
df['city']=np.where( np.random.rand(myrows) <0.5,'NY','LA' )

df.head()

和标签创建：

grouped = df.groupby(['colour','city'])
ci = df.groupby('city')
co =df.groupby('colour')

def wavg(group):
    d = group['perc']
    w = group['weight']
    return (d * w).sum() / w.sum()

# adding 1 columns of aggregate the weighted average by row
result = pd.concat([grouped.apply(wavg).unstack(level=0),ci.apply(wavg).rename('weighted average')],axis = 1)
# change index because unstack get city is index, i want city in columns
result.index.name='city'
result.reset_index(inplace=True)
# adding 1 row in bottom of aggregate the weighted average by columns and weighted average of the dataframe (total)
# be careful about co.ndim i'm not sure is alway ok
result.loc['WAvg'] = ['weighted average'] + [co.apply(wavg).values.round(1)[i] for i in range(0,co.ndim)] + [((df['perc'] * df['weight']).sum() / df['weight'].sum()).round(1)] 
result

请参阅结果：

I had this result

pandas交叉表：如何计算加权平均值？以及如何添加行和列总计？

2 个答案: