Text cleaning pipeline in Data Analysis Python - Mini Project: Build & Apply

Text cleaning pipeline

📖 Scenario: You work in a company that collects customer feedback. The feedback text often has extra spaces, uppercase letters, and punctuation that make it hard to analyze. You want to clean this text to prepare it for analysis.

🎯 Goal: Build a simple text cleaning pipeline that removes punctuation, converts text to lowercase, and strips extra spaces from a list of feedback messages.

📋 What You'll Learn

Create a list of feedback messages with exact given texts

Create a variable for punctuation characters to remove

Use a loop to clean each message by removing punctuation, converting to lowercase, and stripping spaces

Print the cleaned list of messages

💡 Why This Matters

🌍 Real World

Cleaning text data is important before analyzing customer feedback, reviews, or social media posts to get accurate insights.

💼 Career

Data analysts and data scientists often clean text data as a first step in text analysis or natural language processing tasks.

1

Create the list of feedback messages

Create a list called feedback with these exact strings: ' Great product! ', 'Needs improvement...', 'Excellent service!!!', ' Will buy again. '

Data Analysis Python

# Create the list called feedback with the exact messages
# Your code here

Hint

Use square brackets to create a list and include the exact strings with quotes.

2

Create the punctuation string

Create a variable called punctuation and set it to the string '!.,' which contains punctuation characters to remove.

Data Analysis Python

feedback = [' Great product!  ', 'Needs improvement...', 'Excellent service!!!', '  Will buy again. ']
# Create the punctuation string
# Your code here

Hint

Assign the exact string '!.,' to the variable punctuation.

3

Clean the feedback messages

Create an empty list called cleaned_feedback. Use a for loop with variable message to go through each item in feedback. Inside the loop, remove all characters in punctuation from message, convert it to lowercase, and strip spaces. Append the cleaned message to cleaned_feedback.

Data Analysis Python

feedback = [' Great product!  ', 'Needs improvement...', 'Excellent service!!!', '  Will buy again. ']
# Create cleaned_feedback list and clean each message
# Your code here

Hint

Use nested loops: outer for each message, inner for each punctuation character to remove. Then lowercase and strip spaces.

4

Print the cleaned feedback list

Write a print statement to display the cleaned_feedback list.

Data Analysis Python

feedback = [' Great product!  ', 'Needs improvement...', 'Excellent service!!!', '  Will buy again. ']
punctuation = '!.,'
cleaned_feedback = []
for message in feedback:
    for p in punctuation:
        message = message.replace(p, '')
    message = message.lower().strip()
    cleaned_feedback.append(message)
# Print the cleaned_feedback list
# Your code here

Hint

Use print(cleaned_feedback) to show the final cleaned list.