使用 dict 重新映射 pandas 列中的值，保留 NaN

Remap values in pandas column with a dict, preserve NaNs(使用 dict 重新映射 pandas 列中的值，保留 NaN)

问题描述

I have a dictionary which looks like this: di = {1: "A", 2: "B"}

I would like to apply it to the col1 column of a dataframe similar to:

     col1   col2
0       w      a
1       1      2
2       2    NaN

to get:

     col1   col2
0       w      a
1       A      2
2       B    NaN

How can I best do this? For some reason googling terms relating to this only shows me links about how to make columns from dicts and vice-versa :-/

You can use .replace. For example:

>>> df = pd.DataFrame({'col2': {0: 'a', 1: 2, 2: np.nan}, 'col1': {0: 'w', 1: 1, 2: 2}})
>>> di = {1: "A", 2: "B"}
>>> df
  col1 col2
0    w    a
1    1    2
2    2  NaN
>>> df.replace({"col1": di})
  col1 col2
0    w    a
1    A    2
2    B  NaN

or directly on the Series, i.e. df["col1"].replace(di, inplace=True).

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