Remap values in pandas column with a dict, preserve NaNs

Question

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 :-/

Answer 1

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).