This tutorial uses 2 examples to show how to count the number of NaN in Pandas dataframes.

**Method 1:** count the number of NaN by columns:

`df.isnull().sum()`

**Method 2: **count the number of NaN in the whole dataframe:

`df.isnull().sum().sum()`

## Example for Method 1

The following counts the number of NaN by columns using `df.isnull().sum()`

.

```
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)
# count the number of NaN by columns
df.isnull().sum()
```

The following is the output summarizing the number of NaN by columns in the dataframe. The first column has 2 NaN; second column has 1 NaN; the third column has 0 NaN.

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 2 Col_2 1 Col_3 0 dtype: int64

## Example for Method 2

The following counts the number of NaN by columns using `df.isnull().sum().sum()`

.

```
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)
# counts the number of NaN in the whole dataframe
df.isnull().sum().sum()
```

The following is the output summarizing the total number of NaN in the whole dataframe. In particular, there are 3 NaN in the whold 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 3