我有一个多级索引pandas数据帧。我想创建一个新列,其中此列中的值基于条件。此条件基于对该索引的另一列求和,然后将其减半。如果这小于存储在单独列表中的最后一个值,则新列中的值将采用与数据帧中另一列相同的值。如果不满足此条件,则新列中的所有值都应为0
。
使用这个问题试图实现这个Sum columns by level in a Multi-Index DataFrame我使用了np.where
和df.sum(level=0, axis=1)
的组合,但这会导致以下错误:
ValueError: operands could not be broadcast together with shapes (2,8) (21,) ()
这是我的数据帧和我迄今使用的代码的示例:
import pandas as pd
import numpy as np
balance = [1400]
data = {'EVENT_ID': [112335580,112335580,112335580,112335580,112335580,112335580,112335580,112335580, 112335582,
112335582,112335582,112335582,112335582,112335582,112335582,112335582,112335582,112335582,
112335582,112335582,112335582],
'SELECTION_ID': [6356576,2554439,2503211,6297034,4233251,2522967,5284417,7660920,8112876,7546023,8175276,8145908,
8175274,7300754,8065540,8175275,8106158,8086265,2291406,8065533,8125015],
'Pot_Bet': [3.236731,2.416966,2.278365,2.264023,2.225353,2.174407, 2.141420,2.122386,2.832997,2.411094,
2.167218,2.138972,2.132137,2.128341,2.116338,2.115239,2.115123,2.114284362,2.113420,
2.113186,2.112729],
'Liability':[3.236731, 2.416966, 12.245492, 12.795112, 15.079176, 23.336171, 50.741182, 571.003118, 2.832997, 6.691736, 15.808607, 27.935834, 35.954927, 43.275250, 147.165537, 193.017915, 199.622454, 265.809019, 405.808678, 473.926781, 706.332594]}
df = pd.DataFrame(data, columns=['EVENT_ID', 'SELECTION_ID', 'Pot_Bet','WIN_LOSE'])
df.set_index(['EVENT_ID', 'SELECTION_ID'], inplace=True) #Selecting columns for indexing
df['Bet'] = np.where(df.sum(level = 0) > 0.5*balance[-1], df['Pot_Bet'], 0)
这导致前面所述的错误。
对于索引112335580
,新列应具有与'Pot_Bet'
相同的值。而对于索引112335582
,新列应具有0
的值。
干杯,桑迪
问题是如果使用df.sum(level=0)
它像df.groupby(level = 0).sum()
一样 - 聚集在第一级MultiIndex
。
解决方案是使用GroupBy.transform
为Series
与原始DataFrame
相同的大小:
df['Bet'] = np.where(df.groupby(level = 0)['Pot_Bet'].transform('sum') > 0.5*balance[-1],
df['Pot_Bet'], 0)
详情:
print (df.groupby(level = 0)['Pot_Bet'].transform('sum'))
EVENT_ID SELECTION_ID
112335580 6356576 18.859651
2554439 18.859651
2503211 18.859651
6297034 18.859651
4233251 18.859651
2522967 18.859651
5284417 18.859651
7660920 18.859651
112335582 8112876 28.611078
7546023 28.611078
8175276 28.611078
8145908 28.611078
8175274 28.611078
7300754 28.611078
8065540 28.611078
8175275 28.611078
8106158 28.611078
8086265 28.611078
2291406 28.611078
8065533 28.611078
8125015 28.611078
Name: Pot_Bet, dtype: float64
如果只需要工作机智列,可以按列名称选择Series
:
print (df['Pot_Bet'].sum(level=0))
EVENT_ID
112335580 18.859651
112335582 28.611078
Name: Pot_Bet, dtype: float64
print (df.groupby(level = 0)['Pot_Bet'].sum())
EVENT_ID
112335580 18.859651
112335582 28.611078
Name: Pot_Bet, dtype: float64