Exploring Data Types and Data Structures in Python

Exploring Data Types and Data Structures in Python: Lists, Tuples, Sets, Dictionaries, and More

Python offers a variety of data types and data structures, each optimized for different kinds of tasks. Whether you're storing a simple value or managing complex relationships, Python has a structure that fits your needs. In this post, we’ll dive into Lists, Tuples, Sets, Dictionaries, and other common Python data types, discussing their major methods and differences.

1. Data Types in Python

Before we get into the various data structures, let's quickly review the fundamental data types in Python:

1.1 Numbers

Python supports different types of numbers:

• Integers (int): Whole numbers, positive or negative.

  x = 10
  

• Floating Point (float): Decimal numbers.

  y = 10.5
  

• Complex Numbers (complex): Numbers with real and imaginary parts.

  z = 3 + 4j
  

Major Methods:

• Integers/Float:

  • .bit_length() - Returns the number of bits required to represent the number in binary.

  • .real, .imag - Returns the real and imaginary parts of a complex number.

  x = 5
  print(x.bit_length())  # Output: 3 (5 is 101 in binary)
  

1.2 Strings

A string (str) is a sequence of characters, enclosed in single, double, or triple quotes.

my_string = "Hello, Python!"

Major Methods:

• .upper() - Converts the string to uppercase.

• .lower() - Converts the string to lowercase.

• .strip() - Removes leading and trailing whitespace.

• .replace() - Replaces a substring with another substring.

• .split() - Splits the string into a list of substrings.

my_string = " hello world "
print(my_string.strip())  # Output: "hello world"

2. Data Structures in Python

2.1 Lists: The Flexible Collection

Lists are ordered, mutable, and allow duplicate values. They are commonly used when you need to store an ordered collection of items.

Major Methods:

• .append() - Adds an item to the end of the list.

• .insert() - Inserts an item at a specific index.

• .remove() - Removes the first occurrence of a value.

• .pop() - Removes and returns the element at a given index.

• .sort() - Sorts the list.

• .reverse() - Reverses the order of the list.

• .extend() - Adds elements of another list to the end of the current list.

my_list = [1, 2, 3]
my_list.append(4)  # Adds 4 at the end of the list

Differences:

• Mutable: Lists can be changed (elements can be added, modified, or deleted).

• Order: Lists preserve the order of elements, i.e., elements are indexed.

---

2.2 Tuples: The Immutable Sequence

Tuples are ordered, immutable collections that cannot be changed after creation. They are typically used to store related pieces of information.

Major Methods:

• .count() - Returns the number of occurrences of an element in the tuple.

• .index() - Returns the index of the first occurrence of a value.

my_tuple = (1, 2, 2, 3)
print(my_tuple.count(2))  # Output: 2

Differences:

• Immutable: Once created, tuples cannot be modified.

• Hashable: Tuples can be used as dictionary keys due to their immutability, whereas lists cannot.

2.3 Sets: The Unordered Collection

A set is an unordered collection of unique items. It doesn’t allow duplicates and is great for membership testing and performing set operations (union, intersection, etc.).

Major Methods:

• .add() - Adds an element to the set.

• .remove() - Removes an element (raises an error if the element doesn't exist).

• .discard() - Removes an element (does not raise an error if the element is missing).

• .union() - Returns the union of two sets.

• .intersection() - Returns the intersection of two sets.

• .difference() - Returns the difference between two sets.

my_set = {1, 2, 3}
my_set.add(4)  # Adds 4 to the set

Differences:

• Unordered: Sets do not maintain the order of elements.

• Unique: Sets automatically remove duplicates.

• Faster membership testing: Sets are optimized for fast lookups.

2.4 Dictionaries: The Key-Value Store

A dictionary (dict) is an unordered collection of key-value pairs. It allows you to store data in a way where each element has a unique key associated with a value.

Major Methods:

• .get() - Returns the value for a given key.

• .update() - Updates the dictionary with elements from another dictionary or iterable.

• .keys() - Returns all keys in the dictionary.

• .values() - Returns all values in the dictionary.

• .items() - Returns all key-value pairs.

my_dict = {"name": "Alice", "age": 25}
print(my_dict.get("name"))  # Output: Alice

Differences:

• Key-Value: Dictionaries store pairs, allowing access to values via keys.

• Unordered: Dictionaries do not maintain the order of insertion (although order is preserved in Python 3.7+).

• Mutable: Dictionaries can be modified (keys and values can be added or changed).

2.5 Other Data Types

2.5.1 Strings

Strings are a sequence of characters and are immutable in Python. You can manipulate them with several built-in methods like .replace(), .find(), and .split().

2.5.2 Boolean

The bool type represents True or False values. It's commonly used in conditionals and loops.

Major Methods:

• .not() - Inverts the boolean value.

• .and() - Logical AND operation.

x = True
print(not x)  # Output: False

Wrap Up:

Python’s built-in data types and data structures—Lists, Tuples, Sets, Dictionaries, Strings, and Booleans—are powerful tools to help you organize and manipulate data in your programs. Each data structure comes with its own set of methods and functionalities, making it important to choose the right one for the task at hand.

Quick Recap:

• Lists: Ordered, mutable, allow duplicates.

• Tuples: Ordered, immutable, allow duplicates.

• Sets: Unordered, mutable, no duplicates.

• Dictionaries: Unordered, key-value pairs, mutable.

• Strings: Immutable sequence of characters.

• Booleans: Represents True or False values.