使用条件列表来过滤Pandas中的DataFrame

Question

我希望有一个函数，它可以获取任意长度的条件列表，并在所有条件之间放置一个＆符号。下面的示例代码。

df = pd.DataFrame(columns=['Sample', 'DP','GQ', 'AB'], 
     data=[ 
       ['HG_12_34', 200, 35, 0.4], 
       ['HG_12_34_2', 50, 45, 0.9], 
       ['KD_89_9', 76, 67, 0.7], 
       ['KD_98_9_2', 4, 78, 0.02], 
       ['LG_3_45', 90, 3, 0.8], 
       ['LG_3_45_2', 15, 12, 0.9] 
       ]) 


def some_func(df, cond_list): 

    # wrap ampersand between multiple conditions 
    all_conds = ? 

    return df[all_conds] 

cond1 = df['DP'] > 40 
cond2 = df['GQ'] > 40 
cond3 = df['AB'] < 0.4 


some_func(df, [cond1, cond2]) # should return df[cond1 & cond2] 
some_func(df, [cond1, cond3, cond2]) # should return df[cond1 & cond3 & cond2] 

我将不胜感激任何帮助。

Answer 1

您可以使用该functools.reduce：

from functools import reduce 

def some_func(df, cond_list): 
    return df[reduce(lambda x,y: x&y, cond_list)]

或者，像@AryaMcCarthy说，你可以使用and_从运营商包：

from functools import reduce 
from operator import and_ 

def some_func(df, cond_list): 
    return df[reduce(and_, cond_list)]

或numpy的 - 像@ayhan说 - 其中有还有一个逻辑和缩减：

from numpy import logical_and 

def some_func(df, cond_list): 
    return df[logical_and.reduce(cond_list)]

所有三个版本制作 - 为您样品输入 - 以下的输出：

>>> some_func(df, [cond1, cond2]) 
     Sample DP GQ AB 
1 HG_12_34_2 50 45 0.9 
2  KD_89_9 76 67 0.7 
>>> some_func(df, [cond1, cond2, cond3]) 
Empty DataFrame 
Columns: [Sample, DP, GQ, AB] 
Index: []