You can check if any value is NaN in a dataframe in Pandas in Python by using the following 2 methods.

**Method 1: **check if any value is NaN by columns:

`df.isnull().any()`

**Method 2: **Check if any value is NaN in the whole dataframe:

`df.isnull().any().any() `

## Example for Method 1

The following checks if any value is NaN in the dataframe by columns using `df.isnull().any()`

.

```
import pandas as pd
import numpy as np
# Create a dataframe with NaN
df = pd.DataFrame({'Col_1': [100, np.nan, 200, np.nan, 500],
'Col_2': [np.nan, 30, 100, 88, 55],
'Col_3': [88, 87, 79, 88, 55]})
# print out the dataframe with NaN
print('Dataframe with NaN: \n', df)
# check if any value is NaN in dataframe by columns
df.isnull().any()
```

The following is the output showing there is NaN in the first two columns.

Dataframe with NaN: Col_1 Col_2 Col_3 0 100.0 NaN 88 1 NaN 30.0 87 2 200.0 100.0 79 3 NaN 88.0 88 4 500.0 55.0 55 Col_1 True Col_2 True Col_3 False dtype: bool

## Example for Method 2

The following checks if any value is NaN in the whole dataframe using `df.isnull().any().any()`

.

```
import pandas as pd
import numpy as np
# Create a dataframe with NaN
df = pd.DataFrame({'Col_1': [100, np.nan, 200, np.nan, 500],
'Col_2': [np.nan, 30, 100, 88, 55],
'Col_3': [88, 87, 79, 88, 55]})
# print out the dataframe with NaN
print('Dataframe with NaN: \n', df)
# check if any value is NaN in the whole dataframe
df.isnull().any().any()
```

The following is the output showing there is at least one NaN in the whole dataframe.

Dataframe with NaN: Col_1 Col_2 Col_3 0 100.0 NaN 88 1 NaN 30.0 87 2 200.0 100.0 79 3 NaN 88.0 88 4 500.0 55.0 55 True

## Further Reading

The following are other tutorials related to NaN.