我有一个pandas数据帧,我正在调用一个函数来填充不符合条件的列中的na。
以下是我的代码:
def clean_feedback(DF):
feed_id = DF.id_y.unique()
for ID in feed_id:
Min = np.argmin(np.abs(DF[DF.id_y == ID].created_at_x - DF[DF.id_y == ID].created_at_y))
print(Min)
DF[DF.id_y == ID].loc[DF[DF.id_y == ID].index != Min, 'comments'] = np.nan
return DF[DF.id_y == ID]
示例数据框是:
id_x user_id merchant_id amount_spent bill_number created_at_x checked_in chain_id id_y feedback_setting_id comments created_at_y updated_at feedback_type
1097 268868 975 42 149 None 2016-12-14 12:11:14 1 NaN 219 194 Lovely cafe! 2017-03-22 12:55:05 2017-10-05 06:45:49 1
2150 468876 975 42 278 None 2017-06-04 10:51:47 1 NaN 219 194 Lovely cafe! 2017-03-22 12:55:05 2017-10-05 06:45:49 1
6535 5020 975 42 200 None 2015-03-25 12:37:36 1 NaN 219 194 Lovely cafe! 2017-03-22 12:55:05 2017-10-05 06:45:49 1
9228 476314 975 42 676 None 2017-06-09 14:34:03 1 NaN 219 194 Lovely cafe! 2017-03-22 12:55:05 2017-10-05 06:45:49 1
9601 293308 975 42 438 None 2017-01-22 13:03:18 1 NaN 219 194 Lovely cafe! 2017-03-22 12:55:05 2017-10-05 06:45:49 1
10215 781647 975 42 335 None 2017-08-21 13:36:43 1 NaN 219 194 Lovely cafe! 2017-03-22 12:55:05 2017-10-05 06:45:49 1
20405 5441 975 42 200 None 2015-03-29 14:24:32 1 NaN 219 194 Lovely cafe! 2017-03-22 12:55:05 2017-10-05 06:45:49 1
24117 277853 975 42 220 None 2016-12-25 12:57:53 1 NaN 219 194 Lovely cafe! 2017-03-22 12:55:05 2017-10-05 06:45:49 1
24432 949216 975 42 219 None 2017-10-05 10:22:52 1 NaN 219 194 Lovely cafe! 2017-03-22 12:55:05 2017-10-05 06:45:49 1
24475 289288 975 42 109 None 2017-01-15 08:49:55 1 NaN 219 194 Lovely cafe! 2017-03-22 12:55:05 2017-10-05 06:45:49 1
32318 767980 975 42 293 None 2017-08-16 09:41:30 1 NaN 219 194 Lovely cafe! 2017-03-22 12:55:05 2017-10-05 06:45:49 1
32820 343502 975 42 387 None 2017-03-22 12:52:48 1 NaN 219 194 Lovely cafe! 2017-03-22 12:55:05 2017-10-05 06:45:49 1
每当我调用该函数时:
clean_feedback(Transaction[Transaction.id_y == 219])
,没有任何变化。我确定这是一个愚蠢的错误,但我完全被难倒了。
EDIT1:我也尝试过使用.where函数,但它会使整个数据帧变得很难。有没有办法为“评论”栏目指定?
答案 0 :(得分:1)
请改为尝试:
DF.loc[(DF.id_y == ID) & (DF.index != Min), 'comments'] = np.nan
<强>解释强>
pd.DataFrame.loc
接受基于标签的或布尔索引。id_y
等于ID
和index
!= Min
。&
运算符组合了2个布尔系列以形成单个布尔索引器