You can use the following methods to select rows without NaN values in pandas:

Method 1: Select Rows without NaN Values in All Columns

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

Method 2: Select Rows without NaN Values in Specific Column

df[~df['this_column'].isna()]

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'],
                   'points': [np.nan, 12, 15, 25, np.nan, 22, 30],
                   'assists': [4, np.nan, 5, 9, 12, 14, 10]})

#view DataFrame
print(df)

  team  points  assists
0    A     NaN      4.0
1    B    12.0      NaN
2    C    15.0      5.0
3    D    25.0      9.0
4    E     NaN     12.0
5    F    22.0     14.0
6    G    30.0     10.0

Example 1: Select Rows without NaN Values in All Columns

We can use the following syntax to select rows without NaN values in every column of the DataFrame:

#create new DataFrame that only contains rows without NaNs
no_nans = df[~df.isnull().any(axis=1)]

#view results
print(no_nans)

  team  points  assists
2    C    15.0      5.0
3    D    25.0      9.0
5    F    22.0     14.0
6    G    30.0     10.0   

Notice that each row in the resulting DataFrame contains no NaN values in any column.

Example 2: Select Rows without NaN Values in Specific Column

We can use the following syntax to select rows without NaN values in the points column of the DataFrame:

#create new DataFrame that only contains rows without NaNs in points column
no_points_nans = df[~df['points'].isna()]

#view results
print(no_points_nans)

  team  points  assists
1    B    12.0      NaN
2    C    15.0      5.0
3    D    25.0      9.0
5    F    22.0     14.0
6    G    30.0     10.0

Notice that each row in the resulting DataFrame contains no NaN values in the points column.

There is one row with a NaN value in the assists column, but the row is kept in the DataFrame since the value in the points column of that row is not NaN.