使用熊貓數據框如何聚合和分組並引入非聚合/分組列

Question

鑒於：

import pandas as pd

d = {'month': pd.Series(['jan', 'jan', 'feb', 'feb']),
     'week' : pd.Series(['wk1', 'wk2', 'wk1', 'wk2']),
     'high_temp' : pd.Series([10, 20, 30, 20]), 
     'low_temp' : pd.Series([4, 5, 23, 40])} 

df = pd.DataFrame(d)
df

    high_temp  low_temp month week
0         10         4   jan  wk1
1         20         5   jan  wk2
2         30        23   feb  wk1
3         20        40   feb  wk2

我想得到一個包含這些數據的新數據框

    month    high_temp   high_temp_week  low_temp  low_temp_week
0   Jan         20            wk2           4          wk1
1   Feb         30            wk1           23         wk1

我可以輕松地獲得按月分組的臨時值的最大值，但我無法弄清楚如何從具有最大值的行中提取周列。

Answer 1

您可以根據情況通過sort_values來完成， drop_duplicates並保持 last of first ，然后merge 。 您僅在月份進行合並，並指定后綴以重命名兩個數據框中的列周。

new_df = df[['month', 'high_temp', 'week']].sort_values('high_temp').drop_duplicates('month', keep='last')\
            .merge(df[['month', 'low_temp', 'week']].sort_values('low_temp').drop_duplicates('month', keep='first'), 
                   on='month', suffixes=('_high_temp', '_low_temp'))

print (new_df)
  month  high_temp week_high_temp  low_temp week_low_temp
0   jan         20            wk2         4           wk1
1   feb         30            wk1        23           wk1

Answer 2

我覺得我們可以做到

s1=df.sort_values('high_temp').drop_duplicates('month',keep='last')
s2=df.sort_values('low_temp').drop_duplicates('month')
df=s1.drop('low_temp',1).merge(s2.drop('high_temp',1),on='month',suffixes=('_high','_low'))
  month week_high  high_temp week_low  low_temp
0   jan       wk2         20      wk1         4
1   feb       wk1         30      wk1        23