The **`isna`** function in Pandas denotes whether null values are present in an object or not. An object can be in the form of a series or dataframe.

> Null values are represented by `None` or `NAN` (not a number) in Pandas. 

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

# Syntax

We can use the `isna` function with a dataframe or series. The syntax for using it with a dataframe is as follows:

```python
pd.DataFrame.isna()
```

The syntax for using `isna` with a series is as follows:

```python
pd.Series.isna()
```
# Return value
The `isna` function returns a mask of the same length as the dataframe or series. `isna` returns `true` for every null value present and `false` for every non-null value.

# Example

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

import pandas as pd
import numpy as np

# Creating a dataframe
df = pd.DataFrame({'Sports': ['Football', 'Cricket', 'Baseball', 'Basketball',
      'Tennis', None, 'Archery', None, 'Boxing'], 
      'Player': ["Messi", "Afridi", "Chad", "Johnny", "Federer",
      "Yong", "Mark", None, "Khan"],
      'Rank': [1, 9, 7, np.NAN, 1, 2, 11, np.NAN, 1] })

print("Original Dataframe")
print(df)
print('\n')
print("Null Values in dataframe")
print(df.isna())
print('\n')
print("Null Values in series")
print(df["Rank"].isna())

> Values that are `None` or `NAN` return `true`. Others return `false`.

What is the isna function in Pandas?

Identifies null values in Pandas objects, returning true for null and false otherwise. Available for dataframes and series.