Remove a row in pandas using Chain rule

April 4, 2022

I have below code

import pandas as pd
df = pd.DataFrame({"A":[12, 4, 5, None, 1],
                   "B":[7, 2, 54, 3, None],
                   "C":[20, 16, 11, 3, 8],
                   "D":[14, 3, None, 2, 6]})
df
df[df['A'].notna()]

Last line remove entire row of df for which A is None.

However to improve readability, I was achieve this final dataframe is one line where I created df, using chain rule.

Is there any way to achieve this?

>Solution :

Use loc with a function/lambda:

df = (pd.DataFrame({"A":[12, 4, 5, None, 1],
                    "B":[7, 2, 54, 3, None],
                    "C":[20, 16, 11, 3, 8],
                    "D":[14, 3, None, 2, 6]})
        .loc[lambda d: d['A'].notna()]
     )

output:

      A     B   C     D
0  12.0   7.0  20  14.0
1   4.0   2.0  16   3.0
2   5.0  54.0  11   NaN
4   1.0   NaN   8   6.0

Documentation:

Allowed inputs are:

[…]

A callable function with one argument (the calling Series or
DataFrame) and that returns valid output for indexing (one of the
above)