You can use the pandas notnull() function to test whether or not elements in a pandas DataFrame are null.

If an element is equal to NaN or None, then the function will return False.

Otherwise, the function will return True.

Here are several common ways to use this function in practice:

Method 1: Filter for Rows with No Null Values in Any Column

df[df.notnull().all(1)]

Method 2: Filter for Rows with No Null Values in Specific Column

df[df[['this_column']].notnull().all(1)]

Method 3: Count Number of Non-Null Values in Each Column

df.notnull().sum()

Method 4: Count Number of Non-Null Values in Entire DataFrame

df.notnull().sum().sum()

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, 22, 19, 14, 14, 11, 20, np.nan],
                   'assists': [5, np.nan, 7, 9, 12, 9, 9, np.nan],
                   'rebounds': [11, 8, 10, 6, 6, 5, np.nan, 12]})

#view DataFrame
print(df)

  team  points  assists  rebounds
0    A    18.0      5.0      11.0
1    B    22.0      NaN       8.0
2    C    19.0      7.0      10.0
3    D    14.0      9.0       6.0
4    E    14.0     12.0       6.0
5    F    11.0      9.0       5.0
6    G    20.0      9.0       NaN
7    H     NaN      NaN      12.0

Example 1: Filter for Rows with No Null Values in Any Column

The following code shows how to filter the DataFrame to only show rows with no null values in any column:

#filter for rows with no null values in any column
df[df.notnull().all(1)]


        team	points	assists	rebounds
0	A	18.0	5.0	11.0
2	C	19.0	7.0	10.0
3	D	14.0	9.0	6.0
4	E	14.0	12.0	6.0
5	F	11.0	9.0	5.0

Example 2: Filter for Rows with No Null Values in Specific Column

The following code shows how to filter the DataFrame to only show rows with no null values in the assists column:

#filter for rows with no null values in the 'assists' column
df[df[['assists']].notnull().all(1)]

	team	points	assists	rebounds
0	A	18.0	5.0	11.0
2	C	19.0	7.0	10.0
3	D	14.0	9.0	6.0
4	E	14.0	12.0	6.0
5	F	11.0	9.0	5.0
6	G	20.0	9.0	NaN

Notice that each of the rows in this filtered DataFrame have no null values in the assists column.

Example 3: Count Number of Non-Null Values in Each Column

The following code shows how to count the number of non-null values in each column of the DataFrame:

#count number of non-null values in each column
df.notnull().sum()

team        8
points      7
assists     6
rebounds    7
dtype: int64

From the output we can see:

• The team column has 8 non-null values.
• The points column has 7 non-null values.
• The assists column has 6 non-null values.
• The rebounds column has 7 non-null values.

Example 4: Count Number of Non-Null Values in Entire DataFrame

The following code shows how to count the number of non-null values in the entire DataFrame:

#count number of non-null values in entire DataFrame
df.notnull().sum().sum()

28

From the output we can see there are 28 non-null values in the entire DataFrame.

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