String methods on Series in Data Analysis Python - Mini Project: Build & Apply

String methods on Series

📖 Scenario: You work in a small bookstore. You have a list of book titles, but some titles have extra spaces and inconsistent capitalization. You want to clean up the titles to make them look neat and consistent for your website.

🎯 Goal: You will create a list of book titles, then use string methods on a pandas Series to clean the titles by removing extra spaces and making all letters lowercase.

📋 What You'll Learn

Create a pandas Series from a list of book titles with extra spaces and mixed capitalization

Create a variable to hold the cleaned titles using string methods on the Series

Use the str.strip() method to remove extra spaces

Use the str.lower() method to convert all letters to lowercase

Print the cleaned Series

💡 Why This Matters

🌍 Real World

Cleaning text data is very common in real life, like fixing names, addresses, or product titles before analysis or display.

💼 Career

Data scientists often clean and prepare text data using string methods in pandas to make data consistent and ready for analysis.

1

Create a pandas Series with book titles

Import pandas as pd and create a pandas Series called titles from this list: [" The Great Gatsby", "to Kill a Mockingbird ", "1984", "Pride and Prejudice ", " Moby-Dick"]

Data Analysis Python

# Import pandas and create the Series called titles
# Your code here

Need a hint?

Use pd.Series() to create the Series from the list.

2

Create a variable for cleaned titles

Create a variable called cleaned_titles and set it to titles. You will use this variable to store the cleaned book titles.

Data Analysis Python

import pandas as pd

titles = pd.Series(["  The Great Gatsby", "to Kill a Mockingbird  ", "1984", "Pride and Prejudice  ", "  Moby-Dick"])

# Create cleaned_titles variable
# Your code here

Need a hint?

Just assign titles to cleaned_titles for now.

3

Clean titles by stripping spaces and converting to lowercase

Use string methods on cleaned_titles to remove extra spaces with str.strip() and convert all letters to lowercase with str.lower(). Update cleaned_titles with the cleaned results.

Data Analysis Python

import pandas as pd

titles = pd.Series(["  The Great Gatsby", "to Kill a Mockingbird  ", "1984", "Pride and Prejudice  ", "  Moby-Dick"])

cleaned_titles = titles

# Clean the titles by stripping spaces and converting to lowercase
# Your code here

Need a hint?

Use cleaned_titles.str.strip() first, then .str.lower() to chain the methods.

4

Print the cleaned titles

Print the cleaned_titles Series to see the cleaned book titles.

Data Analysis Python

import pandas as pd

titles = pd.Series(["  The Great Gatsby", "to Kill a Mockingbird  ", "1984", "Pride and Prejudice  ", "  Moby-Dick"])

cleaned_titles = titles
cleaned_titles = cleaned_titles.str.strip().str.lower()

# Print the cleaned_titles Series
# Your code here

Need a hint?

Use print(cleaned_titles) to display the cleaned titles.