 # Check if a cell in Pandas DataFrame is NaN

In this tutorial, you will learn to check if a cell in a dataframe has a NaN value or not. It is recommended that you have good exposure to Python, but this is not necessary.

## How to check if a dataframe’s cell has a NaN value?

In Pandas, all numerical values on which mathematical operations cannot be applied are represented as NaN, in addition to empty cells. These values can be Python’s `inf` or `None`, and NumPy’s `np.nan`. To check if a cell has a NaN value, we can use Pandas’ inbuilt function `isnull()`. The syntax is-

```cell = df.iloc[index, column]
is_cell_nan = pd.isnull(cell)```

Here,

• `df` – A Pandas DataFrame object.
• `df.iloc` – A dataframe’s property to extract a cell, a row, or a column.
• `pd.isnull()` – Pandas inbuilt function to check if a value passed to it is null or not.

Here, we have extracted the cell’s value by passing the index and column of that cell to `df.iloc`. Then we passed the obtained value to the `isnull()` function. This function returns `True` if the cell contains a NaN value; else, it returns `False`.

## Examples

Let’s understand the above syntax with some examples. For our examples, we create a sample dataframe for the sale of items at a store for a week.

```import pandas as pd
import numpy as np

#Create data for dataframe
d = {
"Product" : ["Chilli Powder (100 g)", "Mayonnaise", "Wheat Flour (25 kg)", "Rice (25 kg)", "Salt (500g)"],
"Price/unit" : [10, 120, 450, 1100, 22],
"Units sold" : [55, np.nan, 23, 27, np.nan],
}

#Create the dataframe
df = pd.DataFrame(d)

#Print the dataframe
print(df)```

Output:

```                 Product  Price/unit  Units sold
0  Chilli Powder (100 g)          10        55.0
1             Mayonnaise         120         NaN
2    Wheat Flour (25 kg)         450        23.0
3           Rice (25 kg)        1100        27.0
4            Salt (500g)          22         NaN```

In the data, there are two nan values. You can notice that they are represented as NaN in the above output. We will use these nan values for the demonstrations.

### Example 1: Apply isnull() function on a cell containing nan value

To check if a cell contains a null value, we first obtain the cell value using `iloc`. Then we pass this value to the `isnull()` function. Now, we will use the cell with the column ‘Units sold’ and index 1. Note that it has a nan value.

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```cell = df.iloc[1, 2]
is_cell_nan = pd.isnull(cell)
print(is_cell_nan)```

Output:

`True`

Since the cell had a nan value, the `isnull()` function returns `True`, which is seen in the output.

### Example 2: Apply isnull() function on a cell which doesn’t contain nan value

Now let’s apply the `isnull()` function on the cell with column ‘Units sold’ and index 2. This cell doesn’t have a nan value.

```cell = df.iloc[2, 2]
is_cell_nan = pd.isnull(cell)
print(is_cell_nan)```

Output:

`False`

The `isnull()` function returns `False` in this case, as was expected.

## Summary

In this tutorial, we have learned the following:

• Nan, none, and inf are represented as NaN values in Pandas.
• Use Pandas inbuilt function `isnull()` to check if a cell contains a nan value or not.

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