In programming, a data type is a classification that specifies which type of value a variable can hold. Python, being a dynamically typed language, allows variables to change their data type during runtime, providing flexibility without sacrificing readability.
1. Numeric Types:
Python supports various numeric types, including integers, floats, and complex numbers. Let's delve into a few examples:
# Integers
age = 25
# Floats
height = 5.9
# Complex Numbers
complex_num = 3 + 4j
2. String Type:
Strings are sequences of characters and play a crucial role in handling textual data. Here's a glimpse of string manipulation in Python:
greeting = "Hello, Python!"
# String concatenation
full_greeting = greeting + " You are amazing!"
# String slicing
substring = greeting[7:13]
3. List Type:
Lists are versatile, allowing the storage of multiple values in a single variable. They are mutable and can contain elements of different data types:
fruits = ["apple", "banana", "orange"]
# Adding elements to the list
fruits.append("grape")
# Accessing elements
second_fruit = fruits[1]
4. Tuple Type:
Tuples are similar to lists but are immutable, meaning their elements cannot be modified after creation:
coordinates = (3, 4)
# Unpacking tuple
x, y = coordinates
5. Dictionary Type:
Dictionaries store key-value pairs, enabling efficient data retrieval:
person = {"name": "Alice", "age": 30, "city": "Wonderland"}
# Accessing values
alice_age = person["age"]
# Adding a new key-value pair
person["occupation"] = "Software Engineer"
6. Set Type:
Sets are unordered collections of unique elements, offering powerful operations like union and intersection:
prime_numbers = {2, 3, 5, 7, 11}
# Adding elements to a set
prime_numbers.add(13)
# Set operations
odd_numbers = {3, 5, 7, 9}
common_odd_primes = prime_numbers.intersection(odd_numbers)