Use a column value if not Null, or another column from a pandas DataFrame

When calculating a new column in a DataFrame, if you want to use the value of column if it is not Null, or fall back on the value of another column, you can do it with a one-liner.

# Import libraries
import pandas as pd
import numpy as np

# Create a sample DataFrame
df = pd.DataFrame(np.random.random(size=(10, 2)), columns=list('AB')).round(3)
df.iloc[[2,3,5,8], 0] = np.nan
df

	A	B
0	0.591	0.394
1	0.573	0.374
2	NaN	0.309
3	NaN	0.254
4	0.231	0.230
5	NaN	0.099
6	0.392	0.703
7	0.447	0.496
8	NaN	0.058
9	0.597	0.658

Here is the code:

# Create a third column, that will take column A value if not Null, or fall back on B
df['C'] = df['A'].fillna(df['B'])
df

	A	B	C
0	0.591	0.394	0.591
1	0.573	0.374	0.573
2	NaN	0.309	0.309
3	NaN	0.254	0.254
4	0.231	0.230	0.231
5	NaN	0.099	0.099
6	0.392	0.703	0.392
7	0.447	0.496	0.447
8	NaN	0.058	0.058
9	0.597	0.658	0.597

You can even chain it, to get a third column value, if the first two columns are Null:

# Add some data
df['C'] = np.random.random(size=(10, 1)).round(3)
df.iloc[[1,4,5,8], 1] = np.nan

# Create a fourth column, that would take column C value if the first two are Null
df['D'] = df['A'].fillna(df['B']).fillna(df['C'])
df

	A	B	C	D
0	0.591	0.394	0.637	0.591
1	0.573	NaN	0.331	0.573
2	NaN	0.309	0.738	0.309
3	NaN	0.254	0.856	0.254
4	0.231	NaN	0.160	0.231
5	NaN	NaN	0.926	0.926
6	0.392	0.703	0.847	0.392
7	0.447	0.496	0.824	0.447
8	NaN	NaN	0.529	0.529
9	0.597	0.658	0.948	0.597