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?

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: 