在数据框中，将具有多个条件且不在同一行的一列中的值替换到另一列

Question

我正在尝试将值从数据框中的一列传输到另一列，具有多个条件且不在同一行中。

从列“BEGUZ_H”和“ENDUZ_H”到列“BEGUZ”和“ENDUZ”的值，

如果“PERNR_H”、“WORKDATE_H”、“AWART_H”列中的值

与“PERNR”、“WORKDATE”、“AWART”列中的值相等。

data = {'PERNR': [138888, 138432, 138432, 138437, 138437, 126162, 222829, 234740], 
        'WORKDATE': ['2024-01-25', '2024-01-10', '2024-01-09', '2024-01-26', '2024-01-24', '2024-01-29', '2024-01-15', '2024-01-09'],
        'AWART': [2235, 2235, 2235, 2235, 2235, 2235, 2235, 2235], 
        'BEGUZ': ['00:00:00', '00:00:00', '00:00:00', '00:00:00', '00:00:00', '00:00:00', '00:00:00', '00:00:00'],
        'ENDUZ': ['00:00:00', '00:00:00', '00:00:00', '00:00:00', '00:00:00', '00:00:00', '00:00:00', '00:00:00'],
        'PERNR_H': [0, 0, 126162, 222829, 234740, 0, 0, 138888], 
        'WORKDATE_H': ['NaN', 'NaN', '2024-01-29', '2024-01-15', '2024-01-09', 'NaN', 'NaN', '2024-01-25'],
        'AWART_H': [2235, 2235, 2235, 2235, 2235, 2235, 2235, 2235], 
        'BEGUZ_H': ['NaN', 'NaN', '07:59:00', '07:50:00', '07:03:00', 'NaN', 'NaN', '07:45:00'],
        'ENDUZ_H': ['NaN', 'NaN', '17:30:00', '17:35:00', '18:28:00', 'NaN', 'NaN', '18:59:00']}
df = pd.DataFrame(data)
df
Out[11]: 
    PERNR    WORKDATE  AWART     BEGUZ     ENDUZ  PERNR_H  WORKDATE_H  AWART_H   BEGUZ_H   ENDUZ_H
0  138888  2024-01-25   2235  00:00:00  00:00:00        0         NaN     2235       NaN       NaN
1  138432  2024-01-10   2235  00:00:00  00:00:00        0         NaN     2235       NaN       NaN
2  138432  2024-01-09   2235  00:00:00  00:00:00   126162  2024-01-29     2235  07:59:00  17:30:00
3  138437  2024-01-26   2235  00:00:00  00:00:00   222829  2024-01-15     2235  07:50:00  17:35:00
4  138437  2024-01-24   2235  00:00:00  00:00:00   234740  2024-01-09     2235  07:03:00  18:28:00
5  126162  2024-01-29   2235  00:00:00  00:00:00        0         NaN     2235       NaN       NaN
6  222829  2024-01-15   2235  00:00:00  00:00:00        0         NaN     2235       NaN       NaN
7  234740  2024-01-09   2235  00:00:00  00:00:00   138888  2024-01-25     2235  07:45:00  18:59:00

我的方法是 'np.where' ，但这只适用于相同的行。

np.where((df['PERNR']==df['PERNR_H'])&(df['WORKDATE']==df['WORKDATE_H'])&(df['AWART']==df['AWART_H']),df['BEGUZ_H'], df['ENDUZ_H'])

我期望：

    PERNR    WORKDATE  AWART     BEGUZ     ENDUZ  PERNR_H  WORKDATE_H  AWART_H   BEGUZ_H   ENDUZ_H
0  138888  2024-01-25   2235  07:45:00  18:59:00        0         NaN     2235       NaN       NaN
1  138432  2024-01-10   2235  00:00:00  00:00:00        0         NaN     2235       NaN       NaN
2  138432  2024-01-09   2235  00:00:00  00:00:00   126162  2024-01-29     2235  07:59:00  17:30:00
3  138437  2024-01-26   2235  00:00:00  00:00:00   222829  2024-01-15     2235  07:50:00  17:35:00
4  138437  2024-01-24   2235  00:00:00  00:00:00   234740  2024-01-09     2235  07:03:00  18:28:00
5  126162  2024-01-29   2235  07:59:00  17:30:00        0         NaN     2235       NaN       NaN
6  222829  2024-01-15   2235  07:50:00  17:35:00        0         NaN     2235       NaN       NaN
7  234740  2024-01-09   2235  07:03:00  18:28:00   138888  2024-01-25     2235  07:45:00  18:59:00

Answer 1

根据列名称将数据框分成两部分，然后

merge

将它们放回到

'PERNR' , 'WORKDATE' , 'AWART'

和

'PERNR_H' , 'WORKDATE_H' , 'AWART_H'

列上。然后你可以简单地替换这些值。

df_h = df.filter(regex=r"_H$")
df = df.filter(regex=r"^(?!.*?_H)")

df = df.merge(
    df_h,
    left_on=["PERNR", "WORKDATE", "AWART"],
    right_on=["PERNR_H", "WORKDATE_H", "AWART_H"],
    how="left",
)

df.loc[df["BEGUZ_H"].notna(), "BEGUZ"] = df["BEGUZ_H"]
df.loc[df["ENDUZ_H"].notna(), "ENDUZ"] = df["ENDUZ_H"]

df = pd.concat([df.filter(regex=r"^(?!.*?_H)"), df_h], axis=1)

    PERNR    WORKDATE  AWART     BEGUZ     ENDUZ  PERNR_H  WORKDATE_H  AWART_H   BEGUZ_H   ENDUZ_H
0  138888  2024-01-25   2235  07:45:00  18:59:00        0         NaN     2235       NaN       NaN
1  138432  2024-01-10   2235  00:00:00  00:00:00        0         NaN     2235       NaN       NaN
2  138432  2024-01-09   2235  00:00:00  00:00:00   126162  2024-01-29     2235  07:59:00  17:30:00
3  138437  2024-01-26   2235  00:00:00  00:00:00   222829  2024-01-15     2235  07:50:00  17:35:00
4  138437  2024-01-24   2235  00:00:00  00:00:00   234740  2024-01-09     2235  07:03:00  18:28:00
5  126162  2024-01-29   2235  07:59:00  17:30:00        0         NaN     2235       NaN       NaN
6  222829  2024-01-15   2235  07:50:00  17:35:00        0         NaN     2235       NaN       NaN
7  234740  2024-01-09   2235  07:03:00  18:28:00   138888  2024-01-25     2235  07:45:00  18:59:00