Pandas isnull() and notnull() Method
Last Updated :
31 Oct, 2025
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Missing or null values are common in real-world datasets. Pandas provides isnull() and notnull() to detect such values in a DataFrame or Series.
- isnull(): Returns True for missing (NaN) values and False for non-missing values.
- notnull(): Returns True for non-missing values and False for missing values.
These methods are essential for locating, filtering, or counting missing values during data cleaning.
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Detecting Missing Values with isnull()
isnull() identifies NULL or NaN values and returns a boolean Series or DataFrame.
Syntax:
pd.isnull(obj)
Parameters:
- obj: can be a Series, DataFrame, or scalar value.
- Returns: Boolean Series/DataFrame with True for missing values.
Example:
import pandas as pd
data = pd.read_csv(r'enter the path to dataset here')
bool_series = pd.isnull(data["Team"])
missing_values_count = bool_series.sum()
filtered_data = data[bool_series]
print("Count of missing values in 'Team':", missing_values_count)
print("Rows with missing 'Team':")
print(filtered_data)
Output
Explanation:
- pd.isnull(data["Team"]): Creates a boolean Series marking missing values
- bool_series.sum(): Counts missing entries.
- data[bool_series]: Filters rows with missing values in the Team column
Detecting Non-Missing Values with notnull()
notnull() method is the inverse of isnull(). It identifies non-missing (valid) values.
Syntax:
pd.notnull(obj)
Parameters:
- obj: can be a scalar, Series, or DataFrame.
- Returns: Boolean Series/DataFrame where True indicates non-null values.
Example:
import pandas as pd
data = pd.read_csv(r'enter path to dataset here')
bool_series = pd.notnull(data["Gender"])
filtered_data = data[bool_series]
print(filtered_data)
Output
Explanation:
- pd.notnull(data["Gender"]): marks all non-missing values as True.
- data[bool_series]:Filters rows that have non-null values in Gender
Filtering Data Based on Null Values
You can combine isnull() and notnull() for efficient filtering in data cleaning tasks.
Syntax:
pd.notnull(obj)
Parameters:
- obj: Can be a scalar value, Series, or DataFrame. It is the input to check for non-missing (non-NaN) values.
- Returns: A boolean Series or DataFrame of the same shape as the input, where True indicates non-null (valid) entries.
Example:
import pandas as pd
data = pd.read_csv("employees.csv")
bool_series = pd.notnull(data["Gender"])
filtered_data = data[bool_series]
print("Data with non-null 'Gender' values:")
print(filtered_data)
Output
Explanation:
- bool_series = pd.notnull(data["Gender"]): Creates a Boolean Series; True for non-missing "Gender" values.
- filtered_data = data[bool_series]: Filters rows where "Gender" is not null.
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