I have 3 columns in the dataframe. houses = ["house 1", "house 2", "house 3", "house 4", "house 5", "house 6", "house 7", "house 8", "house 9"] room = ["Kitchen", "Bathroom", "Bedroom"] m2 = [8.4.7, NaN, NaN, NaN, 6.3.7]. What I want to do is fill in the blanks with a pattern I set, which would be: If the room column is Kitchen, m2 is 5. If the room column is Bathroom, m2 is 2. If the room column is Bedroom and m2 is 4.
Input:
houses room m2
0 house 1 Kitchen 8
1 house 2 Bathroom 4
2 house 3 Bedroom 7
3 house 4 Kitchen NaN
4 house 5 Bathroom NaN
5 house 6 Bedroom NaN
6 house 7 Kitchen 6
7 house 8 Bathroom 3
8 house 9 Bedroom 7
Tried df.loc[(df["m2"].isnull() & df["room"] == "Kitchen"), "m2"] == 5
df.loc [(df ["m2"]. isnull () & df ["room"] == "Bathroom"), "m2"] == 2
df.loc [(df ["m2"]. isnull () & df ["room"] == "Bedroom"), "m2"] == 4
but it did not work.
FutureWarning: elementwise comparison failed; returning scalar, but in the future will perform elementwise comparison
Expected output:
houses room m2
0 house 1 Kitchen 8
1 house 2 Bathroom 4
2 house 3 Bedroom 7
3 house 4 Kitchen 5
4 house 5 Bathroom 2
5 house 6 Bedroom 4
6 house 7 Kitchen 6
7 house 8 Bathroom 3
8 house 9 Bedroom 7
df['m2'] = df['m2'].fillna(
df['room'].map({'Kitchen': 5, 'Bathroom': 2, 'Bedroom': 4})
).astype(int)
df['m2'] = np.where(
df['m2'].isna(),
df['room'].replace({'Kitchen': 5, 'Bathroom': 2, 'Bedroom': 4}),
df['m2']
).astype(int)
houses room m2
0 house 1 Kitchen 8
1 house 2 Bathroom 4
2 house 3 Bedroom 7
3 house 4 Kitchen 5
4 house 5 Bathroom 2
5 house 6 Bedroom 4
6 house 7 Kitchen 6
7 house 8 Bathroom 3
8 house 9 Bedroom 7