如何编写自定义函数对 python 中的数据框进行排序和透视

Question

在下面的数据框中

我想编写一个 def 函数，它接受一个数据框并执行以下操作：

select Location , Group, Income_Yr1 : Income_Yr3 列
使用从 lowest 到 highest 值
的 Group 列对数据框进行排序
为 mean、median 和 standard deviation 创建一个枢轴 table（使每个 [=24 = 3 table 秒=] 或者可能将它们合并为一个 ) for Income_Yr1, Income_Yr2 and Income_Yr3

# DataFrame using arrays.
import pandas as pd
import numpy as np
 
# initialise data of lists.
data = {'Gender':['F', 'F', 'M', 'F','M', 'F', 'M', 'M','F', 'F', 'M', 'F','M', 'F', 'M', 'M','M','F', 'F', 'M'],
        'UID':[1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1012,1013,1014,1015,1016,1017,1018,1019,1020],
        'Location':['PHX','PHX','PHX','PHX','ATL','ATL','ATL','ATL','HOU','HOU','HOU','MIA','MIA','MIA','MIA','MIA','DEN','DEN','DEN','DEN'],
         'Group':[3,3,3,3,4,4,4,4,1,1,1,1,2,2,2,2,5,5,5,5],
        'Income_Yr1':[32112,34214,45575,22106,32612,34216,47515,22906,32112,34511,45525,12106,52112,54214,45015,22986,32112,34214,47518,22175],
         'Income_Yr2':[52112,54215,65515,72109,52616,64217,77515,52906,52145,38512,65516,32157,63152,57218,51017,42997,38125,36253,49589,32598],
        'Income_Yr3':[52143,54239,65557,72116,52660,64273,77551,52969,52500,38201,65169,32795,63288,57180,51173,42970,38205,36301,59591,32580]}

df = pd.DataFrame(data)

下面是我的尝试，我对其他方法持开放态度

# read in the dataset 


def pivot_table (data): 
    
     #1. import dataset and select the desired columns, I want to include all column names with string 'Income'

    df1 = df[['Group','Location','Income_Yr1':'Income_Yr3']]
     #2 sort the data using 'Group' column
    df1 = df1.sort_values('Group')
     #3a create pivot table for mean
    pd.pivot_table(df1,index = ['Group','Location'],values ='Income_Yr1',columns = 'Location',margins = True)
     #3b create pivot table for median
    pd.pivot_table(df1,index = ['Group','Location'],values ='Income_Yr1',columns = 'Location',aggfunc = 'median', margins = True)
     #3c create pivot table for std
    pd.pivot_table(df1,index = ['Group','Location'],values ='Income_Yr1',columns = 'Location',aggfunc = np.std, margins = True)
    #3d Income_Yr2: create pivot table for mean 
    pd.pivot_table(df1,index = ['Group','Location'],values ='Income_Yr2',columns = 'Location',margins = True)
     #3e Income_Yr2: create pivot table for median
    pd.pivot_table(df1,index = ['Group','Location'],values ='Income_Yr2',columns = 'Location',aggfunc = 'median', margins = True)
     #3f Income_Yr2 create pivot table for std
    pd.pivot_table(df1,index = ['Group','Location'],values ='Income_Yr2',columns = 'Location',aggfunc = np.std, margins = True)
      #3g Income_Yr3: create pivot table for mean 
    pd.pivot_table(df1,index = ['Group','Location'],values ='Income_Yr3',columns = 'Location',margins = True)
     #3h Income_Yr3: create pivot table for median
    pd.pivot_table(df1,index = ['Group','Location'],values ='Income_Yr3',columns = 'Location',aggfunc = 'median', margins = True)
     #3i Income_Yr3 create pivot table for std
    pd.pivot_table(df1,index = ['Group','Location'],values ='Income_Yr3',columns = 'Location',aggfunc = np.std, margins = True)
##########
#test code 

pivot_table(df)

谢谢

Answer 1

让我们使用 melt 然后 groupby

然后我们可以使用字典推导式对数据帧进行排序和拆分。

df1 = pd.melt(df,
             id_vars=['Group','Location'],
             value_vars=df.filter(like='Income').columns.tolist()
            ).sort_values('value') # default is lowest to highest.

df2 = df1.groupby(['Group','Location','variable'])['value'].agg(['mean','median','std'])

#now for your split dataframes.
out = {income_yr : frame for income_yr, frame in df2.groupby(level=-1)}

print(out['Income_Yr2'])

                                   mean   median           std
Group Location variable
1     HOU      Income_Yr2  52057.666667  52145.0  13502.211831
      MIA      Income_Yr2  32157.000000  32157.0           NaN
2     MIA      Income_Yr2  53596.000000  54117.5   8629.910428
3     PHX      Income_Yr2  60987.750000  59865.0   9466.207878
4     ATL      Income_Yr2  61813.500000  58561.5  11779.239888
5     DEN      Income_Yr2  39141.250000  37189.0   7333.583770

函数

不确定这里函数的好处，除非你有非常复杂的数据管道或需要在很多地方重复使用这段代码，但这应该可行，

import pandas as pd
from typing import Dict

def transform_and_split_data(data: pd.DataFrame) -> Dict[str,pd.DataFrame]:

    df1 = pd.melt(data,
            id_vars=['Group','Location'],
            value_vars=data.filter(like='Income').columns.tolist()
        ).sort_values('value') # default is lowest to highest.

    
    df2 = df1.groupby(['Group','Location','variable'])['value'].agg(['mean','median','std'])

    return {income_yr : frame for income_yr, frame in df2.groupby(level=-1)}

如何编写自定义函数对 python 中的数据框进行排序和透视

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