# Check if Any Value is NaN in a DataFrame

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```