Python Pandas – Check for Null values using notnull()

The notnull() method in Pandas returns a Boolean DataFrame where True indicates non-null values and False indicates null (NaN) values. This method is essential for identifying missing data in your DataFrame.

Syntax

DataFrame.notnull()

Creating Sample Data

Let's create a DataFrame with some null values to demonstrate notnull() ?

import pandas as pd
import numpy as np

# Create sample data with null values
data = {
    'Car': ['Audi', 'Porsche', 'RollsRoyce', 'BMW', 'Mercedes'],
    'Place': ['Bangalore', 'Mumbai', 'Pune', 'Delhi', None],
    'UnitsSold': [80.0, 110.0, np.nan, 200.0, 80.0]
}

df = pd.DataFrame(data)
print("Original DataFrame:")
print(df)

Original DataFrame:
         Car      Place  UnitsSold
0       Audi  Bangalore       80.0
1    Porsche     Mumbai      110.0
2  RollsRoyce       Pune        NaN
3        BMW      Delhi      200.0
4   Mercedes       None       80.0

Using notnull() Method

The notnull() method returns a Boolean DataFrame showing where values are not null ?

import pandas as pd
import numpy as np

data = {
    'Car': ['Audi', 'Porsche', 'RollsRoyce', 'BMW', 'Mercedes'],
    'Place': ['Bangalore', 'Mumbai', 'Pune', 'Delhi', None],
    'UnitsSold': [80.0, 110.0, np.nan, 200.0, 80.0]
}

df = pd.DataFrame(data)

# Check for non-null values
result = df.notnull()
print("Boolean DataFrame (True = Not Null, False = Null):")
print(result)

Boolean DataFrame (True = Not Null, False = Null):
     Car  Place  UnitsSold
0   True   True       True
1   True   True       True
2   True   True      False
3   True   True       True
4   True  False       True

Checking Specific Columns

You can check for non-null values in specific columns ?

import pandas as pd
import numpy as np

data = {
    'Car': ['Audi', 'Porsche', 'RollsRoyce', 'BMW', 'Mercedes'],
    'Place': ['Bangalore', 'Mumbai', 'Pune', 'Delhi', None],
    'UnitsSold': [80.0, 110.0, np.nan, 200.0, 80.0]
}

df = pd.DataFrame(data)

# Check non-null values in UnitsSold column
print("Non-null values in UnitsSold column:")
print(df['UnitsSold'].notnull())

Non-null values in UnitsSold column:
0     True
1     True
2    False
3     True
4     True
Name: UnitsSold, dtype: bool

Practical Applications

Common use cases for notnull() include filtering data and counting non-null values ?

import pandas as pd
import numpy as np

data = {
    'Car': ['Audi', 'Porsche', 'RollsRoyce', 'BMW', 'Mercedes'],
    'Place': ['Bangalore', 'Mumbai', 'Pune', 'Delhi', None],
    'UnitsSold': [80.0, 110.0, np.nan, 200.0, 80.0]
}

df = pd.DataFrame(data)

# Filter rows with no null values
complete_rows = df[df.notnull().all(axis=1)]
print("Rows with no null values:")
print(complete_rows)

# Count non-null values per column
print("\nCount of non-null values per column:")
print(df.notnull().sum())

Rows with no null values:
      Car      Place  UnitsSold
0    Audi  Bangalore       80.0
1  Porsche     Mumbai      110.0
3     BMW      Delhi      200.0

Count of non-null values per column:
Car          5
Place        4
UnitsSold    4
dtype: int64

Comparison with isnull()

Conclusion

The notnull() method is essential for data validation in Pandas. Use it to identify complete records, filter out missing data, or count valid entries in your DataFrame columns.