You can drop rows or columns with missing data (e.g., with NaN) using `dropna()`

in Pandas.

**Drop rows with NaN:**

df.dropna()

**Drop columns with NaN:**

df.dropna(axis=”columns”)

## Example of dropping rows with NaN

By default, `dropna()`

will drop rows that at least have 1 `NaN`

. The following is an example.

```
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('Original dataframe with NaN: \n', df)
# drop rows with NaN
df_new=df.dropna()
# print out updated dataframe removed rows with NaN
print('Dataframe removed rows with NaN: \n',df_new)
```

The following shows the original dataframe with NaN and the updated dataframe after being removed rows with NaN.

Original 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 Dataframe removed rows with NaN: Col_1 Col_2 Col_3 2 200.0 100.0 79 4 500.0 55.0 55

## Example of dropping columns with NaN

You can drop columns that have at least 1 `NaN`

by using `dropna(axis='columns')`

. The following is the example showing that.

```
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('Original dataframe with NaN: \n', df)
# drop columns with NaN
df_new=df.dropna(axis="columns")
# print out updated dataframe removed columns with NaN
print('Dataframe removed columns with NaN: \n',df_new)
```

The following shows the original dataframe with NaN and the updated dataframe after being removed columns with NaN.

Original 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 Dataframe removed columns with NaN: Col_3 0 88 1 87 2 79 3 88 4 55

## Further Reading

The following is a tutorial showing how to drop rows or columns with NaN in Pandas.