Question

我正在分析Excel中包含多个列的数据。我已经从正在分析的那些列中提取出来。根据现有列的某些条件，我想创建一些新列。

首先，我的示例数据框架如下：

df = pd.DataFrame()
df['Match'] = ['A','A','A','A','A','B','B','B','B','B',]
df['HomeGoal'] = [ 0,1,2,3,4,0,1,2,3,4]
df['AwayGoal'] = [ 0,1,2,3,4,0,1,2,3,4]
df ['AOS'] = [0.12,0.12,0.12,0.12,0.12,0.06,0.06,0.06,0.06,0.06]
df ['% Prob'] = [0.15,0.12,0.10,0.08,0.05,0.18,0.15,0.10,0.08,0.05]

数据框包含匹配， HomeGoal ， AwayGoal ， AOS 和％Prob 。

我要创建以下列

Homegoal <1
HomeGoal <2
HomeGoal <3
HomeGoal >=1
HomeGoal >=2
HomeGoal >=3

每列包含满足以下条件的％prob的总和：

Homegoal <1 ==> sum of the colums % Prob where Homegoal less than 1
HomeGoal <2 ==> sum of the colums % Prob where Homegoal less than 2
HomeGoal <3 ==> sum of the colums % Prob where Homegoal less than 3
HomeGoal >=1 ==> sum of the colums % Prob and AOS where Homegoal 1 goals and above
HomeGoal >=2 ===> sum of the colums % Prob and AOS where Homegoal 2 goals and above
HomeGoal >=3 ==> sum of the colums % Prob and AOS where Homegoal 2 goals and above

上述所有这些计算都是基于每个匹配项。

请问您如何做？

我附上了预期的结果，如下：

Answer 1

使用：

L = [1,2,3]

for v in L:
    #new column name 
    col = 'HG>={}'.format(v)
    #filter by condition
    df1 =  df[df['HomeGoal'] >= v]
    #new Series filled by aggregated values per groups and added column AOS
    df[col] = df1.groupby('Match')['% Prob'].transform('sum') + df['AOS']
    #only first non missing value per group
    mask = ~df.dropna(subset=[col]).duplicated(subset=[col, 'Match']) 
    df[col] = df[col].mask(~mask, 0)

for v in L:
    col = 'HG>{}'.format(v)
    df[col] = df[df['HomeGoal'] < v].groupby('Match')['% Prob'].transform('sum')
    mask = ~df.dropna(subset=[col]).duplicated(subset=[col, 'Match']) 
    df[col] = df[col].mask(~mask, 0)

print (df)

  Match  HomeGoal  AwayGoal   AOS  % Prob  HG>=1  HG>=2  HG>=3  HG>1  HG>2  \
0     A         0         0  0.12    0.15   0.00   0.00   0.00  0.15  0.27   
1     A         1         1  0.12    0.12   0.47   0.00   0.00  0.00  0.00   
2     A         2         2  0.12    0.10   0.00   0.35   0.00  0.00  0.00   
3     A         3         3  0.12    0.08   0.00   0.00   0.25  0.00  0.00   
4     A         4         4  0.12    0.05   0.00   0.00   0.00  0.00  0.00   
5     B         0         0  0.06    0.18   0.00   0.00   0.00  0.18  0.33   
6     B         1         1  0.06    0.15   0.44   0.00   0.00  0.00  0.00   
7     B         2         2  0.06    0.10   0.00   0.29   0.00  0.00  0.00   
8     B         3         3  0.06    0.08   0.00   0.00   0.19  0.00  0.00   
9     B         4         4  0.06    0.05   0.00   0.00   0.00  0.00  0.00   

   HG>3  
0  0.37  
1  0.00  
2  0.00  
3  0.00  
4  0.00  
5  0.43  
6  0.00  
7  0.00  
8  0.00  
9  0.00

Answer 2

您可以使用numpy.where
1的示例：

import numpy as np
df['HG>=1'] = np.where(df['HomeGoal']>=1, 
    'insert your pass condition logic calculation here', 
    'insert your fail condition logic calculation here')

我不了解您的通过/失败逻辑计算，因此您将必须提供或自己输入。

基于列约束处理熊猫数据框中的聚合

2 个答案: