Remap values in pandas column with a dict

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:

Answer 1

As an extension to what have been proposed by Nico Coallier (apply to multiple columns) and U10-Forward(using apply style of methods), and summarising it into a one-liner I propose:

df.loc[:,['col1','col2']].transform(lambda x: x.map(lambda x: {1: "A", 2: "B"}.get(x,x))

The .transform() processes each column as a series. Contrary to .apply()which passes the columns aggregated in a DataFrame.

Consequently you can apply the Series method map().

Finally, and I discovered this behaviour thanks to U10, you can use the whole Series in the .get() expression. Unless I have misunderstood its behaviour and it processes sequentially the series instead of bitwisely.
The .get(x,x)accounts for the values you did not mention in your mapping dictionary which would be considered as Nan otherwise by the .map() method