The **`isnull`** function in Pandas is used to denote whether null values are present in an object or not. An object can be in the form of a series, data frame, or single element.

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

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

# Syntax

We can use the `isnull` function with a data frame, series, or single element. The syntax for using `isnull` with a data frame is as follows:

```python
pd.DataFrame.isnull()
```

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

```python
pd.Series.isnull()
```

The syntax for using `isnull` with a single element is as follows:

```python
pd.isnull(value)
```

# Return value

The `isnull` function returns a mask of the same length as the data frame or series. It returns true for every null value present and false for every non-null value.

> `isnull` returns a boolean value when used on a single element.

# Example

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

> The `isnull` function has been used on a data frame, series, and individual objects.

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")
print('\n')
print("On a Dataframe")
print(df.isnull())
print('\n')
print("On a Series")
print(df["Sports"].isnull())
print('\n')
print("On a single element")
print(df.iloc[[0],[0]].isnull())
print('\n')
print("Other examples on single elements")
print(pd.isnull("Cat"))
print(pd.isnull(np.NAN))

What is the isnull function in Pandas?

Indicates the presence of null values in data frames, series, or single elements in Pandas.