Why text data requires special handling in Data Analysis Python - See It in Action

Why Text Data Requires Special Handling

📖 Scenario: Imagine you work at a company that collects customer feedback as text messages. You want to analyze these messages to find common words customers use. But text data is tricky because it has spaces, punctuation, and different letter cases.

🎯 Goal: You will create a simple program to prepare text data by cleaning and counting words. This shows why text data needs special handling before analysis.

📋 What You'll Learn

Create a variable with a sample text string containing uppercase letters and punctuation.

Create a variable to hold a list of punctuation marks to remove.

Write code to clean the text by making it lowercase and removing punctuation.

Split the cleaned text into words and count how many times each word appears.

Print the word counts.

💡 Why This Matters

🌍 Real World

Companies analyze customer feedback, social media posts, or reviews which are all text data. Cleaning text helps find useful insights.

💼 Career

Data analysts and scientists must clean and prepare text data before applying machine learning or statistics.

1

Create a sample text string

Create a variable called text and set it to the string: 'Hello! This is a sample text. Text data needs special handling.'

Data Analysis Python

# Create the variable text with the exact string
# Your code here

Hint

Use single or double quotes to create the string exactly as shown.

2

Create a list of punctuation marks

Create a variable called punctuation and set it to the list: ['!', '.', ',']

Data Analysis Python

text = 'Hello! This is a sample text. Text data needs special handling.'
# Create the list punctuation with ['!', '.', ',']
# Your code here

Hint

Make sure to include the exact punctuation marks in the list.

3

Clean the text by lowering case and removing punctuation

Create a variable called clean_text that stores the text converted to lowercase and with all characters in punctuation removed using a for loop.

Data Analysis Python

text = 'Hello! This is a sample text. Text data needs special handling.'
punctuation = ['!', '.', ',']
# Create clean_text by lowering text and removing punctuation
# Your code here

Hint

Use text.lower() to make all letters lowercase. Then use a for loop to replace each punctuation mark with an empty string.

4

Count the words and print the result

Split clean_text into words using split(). Create a dictionary called word_counts to count how many times each word appears. Then print word_counts.

Data Analysis Python

text = 'Hello! This is a sample text. Text data needs special handling.'
punctuation = ['!', '.', ',']
clean_text = text.lower()
for p in punctuation:
    clean_text = clean_text.replace(p, '')
# Split clean_text into words, count them in word_counts, and print word_counts
# Your code here

Hint

Use split() to get words. Use a for loop to count each word in a dictionary. Use print() to show the counts.