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.

**Additional Resources**

The following tutorials explain how to perform other common tasks in pandas:

Pandas: How to Drop Rows with NaN Values

Pandas: How to Replace NaN Values with String

Pandas: How to Fill NaN Values with Mean