How to implement sql coalesce in pandas

Question

I have a data frame like

df = pd.DataFrame({\"A\":[1,2,np.nan],\"B\":[np.nan,10,np.nan], \"C\":[5,10,7]})
     A     B   C
0  1.0   NaN   5
1  2.0  10.0  10         


        

Answer 1

Another approach is to use the combine_first method of a pd.Series. Using your example df,

>>> import pandas as pd
>>> import numpy as np
>>> df = pd.DataFrame({"A":[1,2,np.nan],"B":[np.nan,10,np.nan], "C":[5,10,7]})
>>> df
     A     B   C
0  1.0   NaN   5
1  2.0  10.0  10
2  NaN   NaN   7

we have

>>> df.A.combine_first(df.B).combine_first(df.C)
0    1.0
1    2.0
2    7.0

We can use reduce to abstract this pattern to work with an arbitrary number of columns.

>>> from functools import reduce
>>> cols = [df[c] for c in df.columns]
>>> reduce(lambda acc, col: acc.combine_first(col), cols)
0    1.0
1    2.0
2    7.0
Name: A, dtype: float64

Let's put this all together in a function.

>>> def coalesce(*args):
...     return reduce(lambda acc, col: acc.combine_first(col), args)
...
>>> coalesce(*cols)
0    1.0
1    2.0
2    7.0
Name: A, dtype: float64