The **`notna`** function detects non-missing values in a series or a data frame. **Non-missing** values are values that are not null.

> Null values are denoted by `None` or `NAN` in Pandas.

Non-null values get mapped to `True`, whereas null values get mapped to `False`.

The illustration below shows how the `notna` function works in Pandas:

# Return value

The `notna` function returns a mask of boolean values, which indicates whether each value is null or non-null.

* Null values are denoted by `False`.

* Non-null values are denoted by `True`.

# Example

The code snippet below shows how we can use the `notna` function in Pandas:

import pandas as pd
import numpy as np

# notna function on Series
ser = pd.Series([5, 6, np.NaN])
print("Original series")
print(ser)
print('\n')

print("Non-null Values")
print(ser.notna())
print('\n')

# notna function on Dataframe
df = pd.DataFrame(dict(age=[5, 6, np.NaN],
    born=[pd.NaT, pd.Timestamp('1939-05-27'),
    pd.Timestamp('1940-04-25')],
    name=['Alfred', 'Batman', ''],
    toy=[None, 'Batmobile', 'Joker']))

print("Original dataframe")
print(df)
print('\n')

print("Non-null Values")
print(df.notna())
print('\n')

What is the notna function in Pandas?

Detects non-missing values, mapping them to True and null values to False in Pandas.