# Import libraries
import pandas as pd
import numpy as np

# Create sample dataframe
df = pd.DataFrame({
    'population':[2148000, np.nan, 861635, 232741, np.nan], 
    'area_km':[105.4, 47.87, 240.6, np.nan, 78.26]},
    index=['Paris', 'Lyon',  'Marseille', 'Lille', 'Strasbourg']
)
df

	population	area_km
Paris	2148000.0	105.40
Lyon	NaN	47.87
Marseille	861635.0	240.60
Lille	232741.0	NaN
Strasbourg	NaN	78.26

Select rows that have at least one NA value

# Standard way
df[df.isnull().any(axis=1)]

	population	area_km
Lyon	NaN	47.87
Lille	232741.0	NaN
Strasbourg	NaN	78.26

# With .loc and lambda (useful for method chaining)
df.loc[lambda x: x.isnull().any(axis=1)]

	population	area_km
Lyon	NaN	47.87
Lille	232741.0	NaN
Strasbourg	NaN	78.26

Filter out rows with NA values

# Select rows that do not have any NA value
df.dropna()

	population	area_km
Paris	2148000.0	105.4
Marseille	861635.0	240.6

Select rows with NA values in any pandas DataFrame column

Select rows that have at least one NA value

Filter out rows with NA values