pandas：将多个列映射到一列

Question

pandas：将多个列映射到一列

5

我有两列数据，想要使用同一字典将它们映射到一个新的单独的列中（如果字典中没有匹配的键，则返回0）。

>> codes = {'2':1,
            '31':1,
            '88':9,
            '99':9}

>> df[['driver_action1','driver_action2']].to_dict()    
{'driver_action1': {0: '1',
  1: '1',
  2: '77',
  3: '77',
  4: '1',
  5: '4',
  6: '2',
  7: '1',
  8: '77',
  9: '99'},
 'driver_action2': {0: '31',
  1: '99',
  2: '31',
  3: '55',
  4: '1',
  5: '5',
  6: '99',
  7: '2',
  8: '4',
  9: '99'}}

我以为我只需要这样做：

但实际上：

>> df['driver_reckless_remapped'] = df[['driver_action1','driver_action2']].applymap(lambda x: codes.get(x,0))

预期输出：

  driver_action1 driver_action2   driver_reckless_remapped
0              1             31                          1
1              1             99                          9
2             77             31                          1
3             77             55                          0
4              1              1                          0
5              4              5                          0
6              2             99                          1
7              1              2                          1
8             77              4                          0
9             99             99                          9

但是我得到的却是：

TypeError: ("'dict' object is not callable", 'occurred at index driver_action1')

有没有办法将多列映射到一个新列？

- ale19

1个回答

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可以查看英文原文，
原文链接

- MaxU - stand with Ukraine · Accepted Answer

如果我理解正确的话，您可以使用 combine_first() 方法。

df['new'] =  = \
    df.driver_action1.map(codes).combine_first(df.driver_action2.map(codes)).fillna(0)

检查：

In [106]: df['new'] = df.driver_action1.map(codes).combine_first(df.driver_action2.map(codes)).fillna(0)

In [107]: df
Out[107]:
  driver_action1 driver_action2 driver_reckless_remapped  new
0              1             31                        1  1.0
1              1             99                        9  9.0
2             77             31                        1  1.0
3             77             55                        0  0.0
4              1              1                        0  0.0
5              4              5                        0  0.0
6              2             99                        1  1.0
7              1              2                        1  1.0
8             77              4                        0  0.0
9             99             99                        9  9.0