String Methods

Python’s most underrated superpower

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⏳ Loading Python... (first load takes ~10 seconds)

📖 Concept Recap

Strings are immutable sequences — methods return new strings, they never modify in place.

Cleaning: .strip(), .lstrip(), .rstrip(), .lower(), .upper(), .title()
Searching: .find(), .count(), .startswith(), .endswith(), in
Transforming: .replace(old, new), .split(sep), sep.join(list)
Slicing: s[start:stop:step] — s[::-1] reverses a string
f-strings: f"Name: {name!r}, Score: {score:.2f}"

👀 Worked Example

Real-world string cleaning — standardizing messy name data:

raw_data = [ " john smith ", "SARAH LEE", "mike_brown@email.COM", "Dr. Chen, Wei", ] def clean_name(raw): name = raw.strip() if "@" in name: name = name.split("@")[0].replace("_", " ") for title in ["Dr. ", "Mr. ", "Ms. ", "Mrs. "]: name = name.replace(title, "") if "," in name: parts = name.split(", ") name = parts[1] + " " + parts[0] return name.title() for person in raw_data: print(f"'{person}' → '{clean_name(person)}'")

✏️ Guided

Exercise 1 — Text Analyzer

Complete the text analyzer by filling in the blanks. Each blank uses a string method or variable you already have.

text = "Python is amazing. Python is powerful. Data science with Python is the future!"

# 1. Count words
words = text.___()         # split on whitespace
print(f"Word count: {len(words)}")

# 2. Count sentences (non-empty parts after splitting on ".")
sentences = text.split(".")
print(f"Sentences: {len([s for s in sentences if s.strip()])}")

# 3. Most common word (case-insensitive)
word_freq = {}
for word in text.lower().split():
    word = word.strip(".,!?")
    word_freq[word] = word_freq.get(word, 0) + 1

most_common = max(word_freq, key=lambda w: word_freq[w])
print(f"Most common: '{most_common}' ({word_freq[most_common]} times)")

# 4. Does it mention Python?
print(f"Mentions Python: {'Python' ___ text}")

Output will appear here...

💡 Hint: Use .split() (no arguments) to split on any whitespace. Use the in keyword to check if "Python" appears in text.

💪 Independent

Exercise 2 — Phone Number Formatter

Write a function format_phone(raw) that takes messy phone numbers and returns them all in the format 213-555-0123. Test all 4 formats:

'(213) 555-0123'
'2135550123'
'213.555.0123'
'+1-213-555-0123'

def format_phone(raw):
    # Your code here: strip all non-digit characters, then reformat
    pass

# Test all 4 formats:
numbers = ['(213) 555-0123', '2135550123', '213.555.0123', '+1-213-555-0123']
for num in numbers:
    print(f"'{num}' → '{format_phone(num)}'")

Output will appear here...

💡 Hint: First extract only digits: digits = ''.join(c for c in raw if c.isdigit()). Then take the last 10 digits and slice them into 3 parts: digits[-10:-7] + '-' + digits[-7:-4] + '-' + digits[-4:]

🔥 Challenge

Exercise 3 — Caesar Cipher

Write two functions:

encrypt(text, shift) — shifts each letter forward by shift positions in the alphabet (wraps around). Non-letters stay unchanged. Preserve case.
decrypt(text, shift) — reverses the encryption

Test: Encrypt 'Hello World' with shift=3, then decrypt it back to verify.

def encrypt(text, shift):
    result = ""
    for char in text:
        if char.isalpha():
            # Shift within the correct alphabet (upper or lower)
            base = ord('A') if char.isupper() else ord('a')
            shifted = (ord(char) - base + shift) % 26 + base
            result += chr(shifted)
        else:
            result += char
    return result

def decrypt(text, shift):
    return encrypt(text, -shift)

# Test it:
message = "Hello World"
encrypted = encrypt(message, 3)
decrypted = decrypt(encrypted, 3)
print(f"Original:  {message}")
print(f"Encrypted: {encrypted}")
print(f"Decrypted: {decrypted}")
print(f"Round-trip successful: {message == decrypted}")

Output will appear here...

💡 Hint: The starter code is mostly complete — run it and study how it works. Then try different shift values and messages. Can you encrypt "Python" with shift=13 (ROT13)?

🏆 Mini Project

Mini Project — Complete Text Analyzer

Build a full text analyzer. Given the paragraph below, calculate and print all 7 metrics:

Total character count (with spaces) and without spaces
Word count
Sentence count
Average word length (in characters)
The 5 most frequent words (excluding stop words: the, a, an, is, are, and, of, to, that, from, in)
Percentage of words that are unique
The longest word in the text

text = """Data science is an interdisciplinary field that uses scientific methods,
processes, algorithms and systems to extract knowledge and insights from noisy,
structured and unstructured data, and apply knowledge and actionable insights
from data across a broad range of application domains."""

# Your full analysis here
stop_words = {'the', 'a', 'an', 'is', 'are', 'and', 'of', 'to', 'that', 'from', 'in'}

Output will appear here...

💡 Hint: Strip punctuation from words with word.strip('.,!?;:') before counting. For the top 5, sort the frequency dict by value: sorted(freq.items(), key=lambda x: x[1], reverse=True)[:5].

✅ Lab 6 Complete!

You’ve mastered string cleaning, splitting, joining, slicing, and built a Caesar cipher and full text analyzer. String skills are essential for working with any real-world data.

Continue to Lab 7: Working with Data →

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