Pandas isnull() and notnull() Method (original) (raw)
Last Updated : 31 Oct, 2025
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:
Python `
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:
Python `
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:
Python `
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.