Select rows with NA values in any pandas DataFrame column

# 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 that have at least one NA value

##Filter out rows with NA values

Select rows that have at least one NA value

Filter out rows with NA values