What is .notnull in Pandas?

What is .notnull in Pandas?

In Pandas, the .notnull() method is used to check for missing or null values in a Series or DataFrame.

This method returns a boolean value for each element in the Series or DataFrame, indicating whether the element is not null.

To use the .notnull() method in Pandas, we first need to import the Pandas library and create a Series or DataFrame object.

Then, we can use the .notnull() method on the object to check for missing or null values.

Example

Here is an example of how to use the .notnull() method to check for missing or null values in a Series:

import pandas as pd

# Create a Series
s = pd.Series([10, 20, 30, 40, 50])

# Use .notnull() to check for missing or null values
s.notnull()

0    True
1    True
2    True
3    True
4    True
dtype: bool

This will return a Series containing a boolean value for each element in the original Series.

The boolean value will be True if the element is not null, and False if the element is null.

Replace missing or null values

To replace missing or null values in a Series or DataFrame, we can use the .fillna() method.

This method takes a value as an argument, and it will replace all the missing or null values in the Series or DataFrame with this value.

Here is an example of how to use the .fillna() method to replace missing or null values in a Series:

# Replace missing or null values with 0
s.fillna(0)

## Replace missing or null values with an empty string
s.fillna("")

This will return a new Series containing the same data as the original Series, but with all the missing or null values replaced with the value 0.

Conclusion

The .notnull() method is a useful tool for checking for missing or null values in a Series or DataFrame, and the .fillna() method can be used to replace these values with a specified value.

By using these methods, we can ensure that our data is complete and accurate, and we can avoid any problems that may arise from missing or null values.