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

Given map is faster than replace (@JohnE's solution) you need to be careful with Non-Exhaustive mappings where you intend to map specific values to NaN. The proper method in this case requires that you mask the Series when you .fillna, else you undo the mapping to NaN.

import pandas as pd
import numpy as np

d = {'m': 'Male', 'f': 'Female', 'missing': np.NaN}
df = pd.DataFrame({'gender': ['m', 'f', 'missing', 'Male', 'U']})

---

keep_nan = [k for k,v in d.items() if pd.isnull(v)]
s = df['gender']

df['mapped'] = s.map(d).fillna(s.mask(s.isin(keep_nan)))

---

    gender  mapped
0        m    Male
1        f  Female
2  missing     NaN
3     Male    Male
4        U       U