str accessor for string methods in Pandas - Mini Project: Build & Apply

Using str Accessor for String Methods in pandas

📖 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 this list to make it neat and uniform.

🎯 Goal: Clean the book titles by removing extra spaces and making all letters lowercase using pandas str accessor methods.

📋 What You'll Learn

Create a pandas DataFrame with a column named titles containing the exact book titles given.

Create a variable clean_titles that stores the cleaned titles.

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

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

Print the clean_titles variable to show the cleaned titles.

💡 Why This Matters

🌍 Real World

Cleaning text data is a common task in data science, especially when working with user input, product names, or any text data that may have inconsistent formatting.

💼 Career

Data scientists and analysts often need to preprocess and clean text data before analysis or machine learning. Knowing how to use pandas string methods efficiently helps in preparing clean datasets.

1

Create the DataFrame with book titles

Create a pandas DataFrame called df with one column named titles. The column should have these exact values in this order: ' The Great Gatsby ', 'To Kill a Mockingbird', ' 1984', 'Pride and Prejudice ', 'The Catcher in the Rye'.

Pandas

# Create the DataFrame df with the titles column
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary where the key is 'titles' and the value is the list of book titles.

2

Create a variable for cleaned titles

Create a variable called clean_titles and set it equal to the titles column from the DataFrame df.

Pandas

import pandas as pd

df = pd.DataFrame({
    'titles': ['  The Great Gatsby  ', 'To Kill a Mockingbird', '  1984', 'Pride and Prejudice  ', 'The Catcher in the Rye']
})

# Assign the titles column to clean_titles
# Your code here

Need a hint?

Use df['titles'] to get the titles column and assign it to clean_titles.

3

Clean the titles using str accessor methods

Update the variable clean_titles by applying the str.strip() method to remove extra spaces, then apply the str.lower() method to convert all titles to lowercase.

Pandas

import pandas as pd

df = pd.DataFrame({
    'titles': ['  The Great Gatsby  ', 'To Kill a Mockingbird', '  1984', 'Pride and Prejudice  ', 'The Catcher in the Rye']
})

clean_titles = df['titles']

# Use str.strip() and str.lower() to clean the titles
# Your code here

Need a hint?

Chain the string methods using clean_titles.str.strip().str.lower() to clean the text.

4

Print the cleaned titles

Print the variable clean_titles to display the cleaned book titles.

Pandas

import pandas as pd

df = pd.DataFrame({
    'titles': ['  The Great Gatsby  ', 'To Kill a Mockingbird', '  1984', 'Pride and Prejudice  ', 'The Catcher in the Rye']
})

clean_titles = df['titles']
clean_titles = clean_titles.str.strip().str.lower()

# Print the cleaned titles
# Your code here

Need a hint?

Use print(clean_titles) to show the cleaned titles.