Pandas pivot 多索引表排序

Question

輸入代碼：

import pandas as pd
import numpy as np

#Dummy df:
df = pd.DataFrame({'Name': ['John', 'Boby', 'Mina', 'Peter',
'Nicky','Peter','Mina','Peter'],
           'City': ['London','NY','LA','London','NY','HK','NY','HK'],

'Stage': ['Masters', 'Graduate', 'Graduate', 'Masters',
'Graduate','Masters','Graduate','Graduate'],
'Year':[2020,2019,2020,2019,2020,2019,2020,2020],
'Month':[202001,201902,202003,201904,202005,201902,202007,202012],
'Earnings': [27, 23, 21, 66, 24,22,34,65]})

df_pivot=pd.pivot_table(df,values = 'Earnings', index=
['Name','City','Stage'], columns = ['Year','Month'], aggfunc=np.sum,
fill_value=0, margins = True).sort_values('All', ascending=False)
print(df_pivot)

Output pivot表：

Year                    2019          2020                              
All
Month                 201902 201904 202001 202003 202005 202007 202012     
Name  City   Stage                                                         
All                       45     66     27     21     24     34     65  282
Peter London Masters       0     66      0      0      0      0      0   66
      HK     Graduate      0      0      0      0      0      0     65   65
Mina  NY     Graduate      0      0      0      0      0     34      0   34
John  London Masters       0      0     27      0      0      0      0   27
Nicky NY     Graduate      0      0      0      0     24      0      0   24
Boby  NY     Graduate     23      0      0      0      0      0      0   23
Peter HK     Masters      22      0      0      0      0      0      0   22
Mina  LA     Graduate      0      0      0     21      0      0      0   21

所需的 output 首先按第一列排序，然后在組內按第二列排序，最后在組內按第三列排序：

Year                    2019          2020                              All
Month                 201902 201904 202001 202003 202005 202007 202012     
Name  City   Stage                                                         
All                       45     66     27     21     24     34     65  282
Peter HK     Graduate      0      0      0      0      0      0     65   65
             Masters      22      0      0      0      0      0      0   22
      London Masters       0     66      0      0      0      0      0   66
Mina  NY     Graduate      0      0      0      0      0     34      0   34
      LA     Graduate      0      0      0     21      0      0      0   21
John  London Masters       0      0     27      0      0      0      0   27
Nicky NY     Graduate      0      0      0      0     24      0      0   24
Boby  NY     Graduate     23      0      0      0      0      0      0   23

請注意 Peter-HK 如何高於 Peter-London，因為 Peter-HK 的總和 (65+22) > Peter-London 的總和 (66)。

換句話說：首先給我最大總數的名稱，然后在該名稱中給我最大總數的城市，然后在該名稱和城市中給我最大總數的階段。

謝謝帕維爾

Answer 1

在更好地理解問題后進行編輯。

您想對一個人（由Name定義）獲得的最高分數進行排序。 然后在該人中，您要對該人獲得的個人分數進行排序。

在您的示例中，我可以通過以下方式獲取具有所需Name序列的列表：

import pandas as pd
import numpy as np

#Dummy df:
df = pd.DataFrame({'Name': ['John', 'Boby', 'Mina', 'Peter', 
'Nicky','Peter','Mina','Peter'],
               'City': ['London','NY','LA','London','NY','HK','NY','HK'],

  'Stage': ['Masters', 'Graduate', 'Graduate', 'Masters', 
  'Graduate','Masters','Graduate','Graduate'],
  'Year':[2020,2019,2020,2019,2020,2019,2020,2020],
  'Month':[202001,201902,202003,201904,202005,201902,202007,202012],
  'Earnings': [27, 23, 21, 23, 24,22,34,65]})

# Make the pivot table
df_pivot=pd.pivot_table(df,values = 'Earnings', index= 
  ['Name','City','Stage'], columns = ['Year','Month'], aggfunc=np.sum, 
  fill_value=0, margins = True).sort_values('All', ascending=False)
print('Original table')
print(df_pivot)

def sort_groups(df, group_by_col, sort_by_col, F_asc):
    """Sort a dataframe by a certain level of the MultiIndex

    Args:
        df (pd.DataFrame): Dataframe to sort
        group_by_col (str): name of the index level to sort by
        sort_by_col (str): name of the value column to sort by
        F_asc (bool): Ascending sort - True/False

    Returns:
        pd.Dataframe: Dataframe sorted on given multiindex level
    """

    # Make a list of the desired index sequence based on the max value found in each group
    ind = df.groupby(by=group_by_col).max().sort_values(sort_by_col, ascending=F_asc).index.to_list()

    # Return re-indexed dataframe
    return df.reindex(ind, level=df.index.names.index(group_by_col))

# First level sorting: Name
df_pivot_1 = sort_groups(df_pivot, 'Name', 'All', False)
print('\nSort groups at name level:')
print(df_pivot_1)

# Second level sorting : City
#df_pivot_2 = df_pivot_1.groupby(by='Name').apply(lambda x : sort_groups(x, 'City', 'All', False))
df_pivot_2 =pd.concat([sort_groups(group, 'City', 'All', False) for index, group in df_pivot_1.groupby(by=['Name'])])
print('\nSort groups at city level:')
print(df_pivot_2)

# Third level sorting : Stage
df_pivot_3 = df_pivot_2.groupby(by = ['Name', 'City']).apply(lambda x : sort_groups(x, 'Stage', 'All', False))
print('\nSort groups at stage level:')
print(df_pivot_3)

但是，此解決方案不會將All行放置在您指定的位置。 這對你來說很嚴格嗎？

問候，

簡

Answer 2

這是一種將 groupby 與 pivot 結合起來的超級干凈的方法

  df = pd.DataFrame({'Name': ['John', 'Boby', 'Mina', 'Peter', 
  'Nicky','Peter','Mina','Peter'],
           'City': ['London','NY','LA','London','NY','HK','NY','HK'],

 'Stage': ['Masters', 'Graduate', 'Graduate', 'Masters', 
 'Graduate','Masters','Graduate','Graduate'],
 'Year':[2020,2019,2020,2019,2020,2019,2020,2020],
 'Month':[202001,201902,202003,201904,202005,201902,202007,202012],
 'Earnings': [27, 23, 21, 23, 24,22,34,65]})

grouped=df.groupby(['Name','City','Stage','Year','Month'])['Earnings'].sum()
#print(grouped)
grouped=grouped.reset_index(name='Sum')
fp=grouped.pivot(index=['Name','City','Stage'],columns=['Year','Month'],values='Sum').fillna(0)
fp['Totals'] = fp.sum(axis='columns')
fp["Rank"] = fp.groupby(['Name','City'])['Totals'].sum()

fp = fp.sort_values(by=['Name','Rank','City','Totals'],ascending=[False,False,False,False])

print(fp)