Working with Strings and Lists

# CHAPTER 7

Working with Strings and Lists #

1. Chapter Introduction #

2. String Operations and Methods #

A String is a sequence of characters. Python has powerful built-in methods to clean and manipulate text.

text = "   Data Science is AWESOME!   "

# 1. Stripping whitespace (Crucial for data cleaning!)
clean_text = text.strip()
print(clean_text) # "Data Science is AWESOME!"

# 2. Case formatting
print(clean_text.lower()) # "data science is awesome!"
print(clean_text.upper()) # "DATA SCIENCE IS AWESOME!"
print(clean_text.title()) # "Data Science Is Awesome!"

# 3. Replacing characters
no_spaces = clean_text.replace(" ", "_")
print(no_spaces) # "Data_Science_is_AWESOME!"

# 4. Splitting strings into a List
words = clean_text.split(" ")
print(words) # ['Data', 'Science', 'is', 'AWESOME!']

3. List Operations #

A List is an ordered, mutable (changeable) collection of items, defined by square brackets [].

fruits = ["Apple", "Banana", "Cherry"]

# Adding items
fruits.append("Orange") # Adds to the end
fruits.insert(1, "Mango") # Inserts at index 1

# Removing items
fruits.remove("Banana") # Removes by value
popped = fruits.pop() # Removes and returns the LAST item

print(fruits) # ['Apple', 'Mango', 'Cherry']

4. Indexing and Slicing (The Zero-Index Rule) #

Python is Zero-Indexed. The first item is at index 0. You can extract subsets of lists and strings using Slicing: [start : stop : step]. *Note: The stop index is NOT included.*

# Indexes:  0    1    2    3    4
numbers = [10, 20, 30, 40, 50]

# Indexing
print(numbers[0])  # First item: 10
print(numbers[-1]) # Last item: 50 (Negative indexing is very useful!)

# Slicing
print(numbers[0:3]) # Indexes 0, 1, 2 (Output: [10, 20, 30])
print(numbers[2:])  # Index 2 to the end (Output: [30, 40, 50])
print(numbers[:3])  # Start to index 2 (Output: [10, 20, 30])

# Step (Get every 2nd item)
print(numbers[0:5:2]) # Output: [10, 30, 50]

*Fun Fact: Slicing works the exact same way on Strings! "Python"[0:2] returns "Py".*

5. List Comprehensions (The Pythonic Way) #

If you have a list of prices and want to double them, the standard way uses a for loop. A List Comprehension does it in a single, elegant line.

prices = [10, 20, 30, 40]

# The Old Way (for loop)
doubled_old = []
for p in prices:
    doubled_old.append(p * 2)

# The Pythonic Way (List Comprehension)
# Syntax: [expression for item in list]
doubled_new = [p * 2 for p in prices]

print(doubled_new) # [20, 40, 60, 80]

# You can even add IF statements! (Only double if price > 20)
filtered = [p * 2 for p in prices if p > 20]
print(filtered) # [60, 80]

6. Mini Project: Text Cleaner #

Let's write a script that takes a list of messy email addresses, cleans them, and extracts the domain names using list comprehensions and string methods.

messy_emails = ["  Alice@Gmail.com", "BOB@yahoo.com  ", "  charlie@GMAIL.com"]

# 1. Clean the emails (lowercase and strip spaces)
clean_emails = [email.strip().lower() for email in messy_emails]
print("Cleaned:", clean_emails)

# 2. Extract domains (split by '@' and take the second part [1])
domains = [email.split('@')[1] for email in clean_emails]
print("Domains:", domains)

*Output:* Cleaned: ['alice@gmail.com', 'bob@yahoo.com', 'charlie@gmail.com'] Domains: ['gmail.com', 'yahoo.com', 'gmail.com']

7. Common Mistakes #

• Zero-Index Confusion: Trying to get the 3rd item using list[3]. The 3rd item is list[2]. list[3] gets the 4th item.

• Slicing Stop Index: Remembering that list[0:2] returns TWO items (index 0 and 1), not three. The stop index is exclusive.

• String Immutability: You cannot change a string in place. text.replace("A", "B") does nothing unless you reassign it: text = text.replace("A", "B").

8. MCQs #

Q10. In slicing [start:stop:step], is the stop index included in the output? a) Yes b) No, it stops right before that index — Answer: b

9. Interview Questions #

• Q: Explain how List Comprehensions work and why they are preferred over standard for loops in Python.

• Q: You have a string "2023-10-15". Write a one-liner to extract just the year "2023" using slicing.

10. Summary #

11. Next Chapter Recommendation #