Removing titles and taking names

While collecting survey respondent metadata in the airlines DataFrame, the full name of respondents was saved in the full_name column. However upon closer inspection, you found that a lot of the different names are prefixed by honorifics such as "Dr.", "Mr.", "Ms." and "Miss".

Your ultimate objective is to create two new columns named first_name and last_name, containing the first and last names of respondents respectively. Before doing so however, you need to remove honorifics.

The airlines DataFrame is in your environment, alongside pandas as pd.

This is a part of the course

“Cleaning Data in Python”

Exercise instructions

Remove "Dr.", "Mr.", "Miss" and "Ms." from full_name by replacing them with an empty string "" in that order.
Run the assert statement using .str.contains() that tests whether full_name still contains any of the honorifics.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Replace "Dr." with empty string ""
airlines['full_name'] = airlines['full_name'].____.____("____","")

# Replace "Mr." with empty string ""
airlines['full_name'] = ____

# Replace "Miss" with empty string ""
____

# Replace "Ms." with empty string ""
____

# Assert that full_name has no honorifics
assert airlines['full_name'].str.contains('Ms.|Mr.|Miss|Dr.').any() == False

Edit and Run Code

This exercise is part of the course

Cleaning Data in Python

IntermediateSkill Level

4.4+

Learn to diagnose and treat dirty data and develop the skills needed to transform your raw data into accurate insights!

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