You can use the following methods to select rows with NaN values in pandas:

Method 1: Select Rows with NaN Values in Any Column

df.loc[df.isnull().any(axis=1)]

Method 2: Select Rows with NaN Values in Specific Column

df.loc[df['this_column'].isnull()]

The following examples show how to use each method in practice with the following pandas DataFrame:

import pandas as pd
import numpy as np

#create DataFrame
df = pd.DataFrame({'team': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'],
                   'points': [18, np.NaN, 19, 14, 14, 11, 20, 28],
                   'assists': [5, 7, 7, 9, np.NaN, 9, 9, np.NaN],
                   'rebounds': [11, 8, 10, 6, 6, 5, 9, np.NaN]})

#view DataFrame
print(df)

Example 1: Select Rows with NaN Values in Any Column

We can use the following syntax to select rows with NaN values in any column of the DataFrame:

#create new DataFrame that only contains rows with NaNs in any column
df_nan_rows = df.loc[df.isnull().any(axis=1)]

#view results
print(df_nan_rows)

  team  points  assists  rebounds
1    B     NaN      7.0       8.0
4    E    14.0      NaN       6.0
7    H    28.0      NaN       NaN   

Notice that each row in the resulting DataFrame contains a NaN value in at least one column.

Example 2: Select Rows with NaN Values in Specific Column

We can use the following syntax to select rows with NaN values in the assists column of the DataFrame:

#create new DataFrame that only contains rows with NaNs in assists column
df_assists_nans = df.loc[df['assists'].isnull()]
#view results
print(df_assists_nans)

  team  points  assists  rebounds
4    E    14.0      NaN       6.0
7    H    28.0      NaN       NaN   

Notice that each row in the resulting DataFrame contains a NaN value in the assists column.

There is one row with a NaN value in the points column, but this row is not selected since it doesn’t have a NaN value in the assists column as well.