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Overview of Python Data Structures

Overview of Python Data Structures

Overview of Python Data Structures

Lists and tuples are two commonly used data structures in Python. Both are used for storing a collection of items, but they differ in properties and behavior.

Attributes of Lists:

  1. Mutable: Lists are mutable, meaning you can modify their elements after creation. You can add, remove, or change parts in a list.
  2. Ordered: Lists maintain the order of their elements. You can access features in a list by their index position.
  3. Dynamic: Lists are dynamic, meaning you can add or remove elements from a list anytime.
  4. Homogeneous: Lists can contain elements of different types.

Attributes of Tuples:

  1. Immutable: Tuples are immutable, meaning you cannot modify their elements after creation. Once a tuple is created, it cannot be changed.
  2. Ordered: Tuples maintain the order of their elements. You can access elements in a tuple by their index position.
  3. Static: Tuples are fixed, which means you cannot add or remove elements from a tuple.
  4. Heterogeneous: Tuples can contain elements of different types.

When to use a Tuple instead of a List?

  1. When you want to store a collection of items that should not be changed once created, tuples are immutable, so they help keep data that should not be modified accidentally.
  2. When you want to maintain the order of the items, tuples are ordered, so the order of the elements in a tuple is always preserved.
  3. When you want to use less memory, tuples are smaller than lists because they are immutable and can be stored more efficiently in memory.
  4. To utilize a group of objects as a key in a dictionary, tuples can be employed as they are immutable. In contrast, lists cannot serve as dictionary keys due to their mutable nature.

In summary, lists and tuples are used for storing a collection of items, but they differ in their properties and behavior. Lists are mutable and dynamic and can contain elements of different types, while tuples are immutable and static and have details of various kinds. You would use a tuple instead of a list to store a collection of items that should not be changed, maintain their order, use less memory, or use a dictionary key.

Python has a built-in data structure called a dictionary with a set of key-value pairs. In other words, rather than using an index, you can retrieve values by their associated keys thanks to dictionaries, which map keys to matching values.

Dictionary definitions in Python are made with curly braces {}, and colons separate key-value pairs:

Here is an illustration of a Python dictionary:

student = {'name': 'John', 'age': 21, 'grade': 'A', 'major': 'Computer Science'}

In this example, the dictionary `student` contains four key-value pairs, where the keys are `’name’`, `’age’`, `’grade’`, and `’major’` and the corresponding values are `’John’`, `21`, `’A’`, and `’Computer Science’` respectively.

We can access the values in a dictionary by their keys using square brackets `[].` For example, to access the value associated with the legend `’age’` in the `student` dictionary, we can write:

age = student['age']

This will output `21`.

A dictionary can also have new key-value pairs added to it or existing fundamental values changed. For instance, you could type the following to add a fresh key-value combination to the “student” dictionary:

student['email'] = ''

This will add a new key `’email’` with the value `’` to the `student` dictionary.

Dictionaries help store and organize data in a way that allows for quick and efficient lookups based on keys. They are commonly used in many applications, such as web development, data analysis, and machine learning. For example, you could use a dictionary to store customer information in an e-commerce application or to store feature vectors for different data points in a machine-learning model.

An unordered group of distinct elements is referred to in Python as a set. Using curly braces or the location () method, settings are defined. While lists, tup, less, and backgrounds are related, groups do not support duplicate elements like those other data types.

Union, intersection, and difference operations in mathematics can be carried out using sets. Duplicate items can be eliminated from a list or other collection of elements using them as well.

Example of a Set in Python:

# Define a set of even numbers
even_numbers = {2, 4, 6, 8, 10}

# Define a set of prime numbers
prime_numbers = {2, 3, 5, 7, 11}

# Find the union of the two sets
all_numbers = even_numbers.union(prime_numbers)

# Find the intersection of the two sets
common_numbers = even_numbers.intersection(prime_numbers)

In this example, we define two sets of numbers and then use the union and intersection methods to find the combined set of all numbers and the set of numbers in both groups. Settings can also be used for other operations, such as adding and removing elements, checking for membership, and iterating through the details.


Python documentation on Lists:

Python documentation on Tuples:

Python documentation on Dictionaries:

Python documentation on Sets:


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Compare lists and tuples. What are the attributes of each? When would you use a tuple instead of a list?

Overview of Python Data Structures

Overview of Python Data Structures

How would you describe a dictionary in Python? Share an example of data that would be included in a dictionary.

What are sets? How are sets used?

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