Python is known for its powerful and easy-to-use built-in data structures that make it ideal for everything from simple scripts to large-scale software projects. Whether you’re a beginner or looking to strengthen your foundation, understanding how to use Python’s built-in collections—such as lists, tuples, and dictionaries—is essential.
In this post, you’ll learn how to work with these collections, perform common operations like looping, searching, and even pattern matching. We’ll break down each concept with clear explanations and beginner-friendly examples.
1. Python Lists
Description:
A list is an ordered, mutable collection of items. You can store elements of different data types in a list, and Python provides many methods to manipulate them easily.
Example:
fruits = ['apple', 'banana', 'cherry']
fruits.append('orange')
print(fruits) # ['apple', 'banana', 'cherry', 'orange']
2. Python Tuples
Description:
Tuples are ordered and immutable collections. Once created, you cannot change their values. They are often used for fixed data and can be more memory-efficient than lists.
Example:
person = ('Alice', 30, 'Engineer')
print(person[0]) # Alice
3. Loops Over Sequences
Description:
Python allows you to loop through sequences like lists, tuples, and strings using for loops. This is a common way to process data one item at a time.
Example:
colors = ['red', 'green', 'blue']
for color in colors:
print(color)Output:
red
green
blue
4. Python Dictionaries
Description:
Dictionaries are unordered collections of key-value pairs. They are ideal when you need to associate pieces of information, like a name and a phone number.
Example:
contact = {'name': 'Bob', 'phone': '123-4567'}
print(contact['phone']) # 123-4567
5. Searching for Values in Collections
Description:
You can use the in keyword to check whether an item exists in a list, tuple, or dictionary. This is especially useful in conditionals.
Example:
numbers = [10, 20, 30, 40]
if 20 in numbers:
print("Found!")
Output:
Found!
6. Pattern Matching with Collections (Python 3.10+)
Description:
Starting with Python 3.10, you can use structural pattern matching (match / case) to match data shapes, such as list contents or dictionary keys.
Example:
def describe_point(p):
match p:
case (0, 0):
return "Origin"
case (0, y):
return f"Y={y} axis"
case (x, 0):
return f"X={x} axis"
case (x, y):
return f"Point at X={x}, Y={y}"
print(describe_point((0, 5))) # Y=5 axis
