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Online Python

🔍 Finding and Replacing Text

Python provides powerful methods to search for and replace text within strings. These operations are essential for text processing and data cleaning.

# Basic find and replace
text = "Hello World"
new_text = text.replace("World", "Python")
print(f"Original: {text}")
print(f"Replaced: {new_text}")

🎯 Finding Text

Python offers several methods to locate text within strings.

Basic Examples

# Different ways to find text
text = "Python is fun, Python is great"

# Find first occurrence
first_pos = text.find("Python")
print(f"First 'Python' at: {first_pos}")

# Find last occurrence
last_pos = text.rfind("Python")
print(f"Last 'Python' at: {last_pos}")

# Count occurrences
count = text.count("Python")
print(f"'Python' appears {count} times")

# Using index (raises error if not found)
try:
    pos = text.index("Java")
except ValueError:
    print("'Java' not found!")

🔄 Replacing Text

The .replace() method is the primary tool for text replacement.

# Basic replacement
text = "Hello World"
new_text = text.replace("World", "Python")
print(f"Replaced: {new_text}")

# Multiple replacements
text = "cat, dog, cat, bird"
new_text = text.replace("cat", "mouse")
print(f"Replaced: {new_text}")

# Limiting replacements
text = "cat, dog, cat, bird"
new_text = text.replace("cat", "mouse", 1)  # Replace only first occurrence
print(f"Limited replacement: {new_text}")

🎨 Common Use Cases

Here are some practical examples of finding and replacing text.

# 1. Cleaning data
text = "User123@email.com"
cleaned = text.replace("@", " [at] ")
print(f"Cleaned email: {cleaned}")

# 2. Formatting text
text = "hello world"
formatted = text.replace("h", "H").replace("w", "W")
print(f"Formatted: {formatted}")

# 3. Multiple replacements
text = "The price is $100.00"
cleaned = text.replace("$", "").replace(".00", "")
print(f"Cleaned price: {cleaned}")

📝 Quick Practice

Let's practice finding and replacing text in a real-world scenario!

Hands-on Exercise

Clean up a text containing sensitive information.

python
# Text Cleaning Practice
sensitive_text = """
User: john.doe@company.com
Phone: 555-123-4567
ID: 123-45-6789
"""

# TODO: Clean up the sensitive information
# 1. Mask email addresses
# 2. Mask phone numbers
# 3. Mask social security numbers

# Print the result
print("Cleaned text:")
print(cleaned_text)

Solution and Explanation 💡

Click to see the solution
# Text Cleaning Practice Solution
sensitive_text = """
User: john.doe@company.com
Phone: 555-123-4567
ID: 123-45-6789
"""

# Clean up the sensitive information
cleaned_text = sensitive_text

# Mask email
email_pos = cleaned_text.find("@")
if email_pos != -1:
    username = cleaned_text[:email_pos].split(": ")[1]
    domain = cleaned_text[email_pos:].split("\n")[0]
    masked_email = f"User: {username[0]}***@{domain}"
    cleaned_text = cleaned_text.replace(cleaned_text.split("\n")[1], masked_email)

# Mask phone
phone_pos = cleaned_text.find("555-")
if phone_pos != -1:
    phone = cleaned_text[phone_pos:].split("\n")[0]
    masked_phone = f"Phone: ***-***-{phone[-4:]}"
    cleaned_text = cleaned_text.replace(cleaned_text.split("\n")[2], masked_phone)

# Mask SSN
ssn_pos = cleaned_text.find("123-45-")
if ssn_pos != -1:
    ssn = cleaned_text[ssn_pos:].split("\n")[0]
    masked_ssn = f"ID: ***-**-{ssn[-4:]}"
    cleaned_text = cleaned_text.replace(cleaned_text.split("\n")[3], masked_ssn)

print("Cleaned text:")
print(cleaned_text)

Key Learning Points:

  • 📌 Used .find() to locate sensitive information
  • 📌 Used string slicing to preserve parts of the data
  • 📌 Used .replace() to update the text
  • 📌 Handled multiple types of sensitive data

🎯 Key Takeaways

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