This repo contains all of the code I wrote while learning Python. I started learning Python in 2023, and I'm still learning today. I've learned a lot from following tutorials online , Courses and reading atricles.

I hope you enjoy the codes! It's a mix of simple and complex tutorials.

This section covers the basics of using Python.

• Containers (List, Tuple, Set, Dictionary)
• Loops
• Functions
• Recursion
• Lambda and Built-in Functions
• File Handling
• Exception Handling
• Type Hinting
• Generators and Decorators
• Modules and Date-Time
• Operators (Boolean, Assignment, Logical)
• Regex
• Strings and Numbers

• Elzero Web School: Mastering Python
• W3Schools: Python Getting Started

This section covers the basics of using OOP inPython

• OOP (Class, Instance Methods & Attributes, Class Methods, Static Method, Magic Method)
• Setter, Getter & Property Decorator
• Polymorphism & Encapsulation
• Inheritance & Multiple Inheritance & Overriding & MRO
• Abstract Base Class

• Elzero Web School: Object-Oriented Programming in Python
• DataCamp: Object-Oriented Programming in Python

This section contains my solutions to some of the Hackerrank challenges in Python.

• Any or All
• Athlete Sort
• Capitalize!
• Company Logo
• Compress the String!
• Dealing with Complex Numbers
• Default Arguments
• DefaultDict Tutorial
• Exceptions
• Find Angle MBC
• Incorrect Regex
• Input()
• No Idea!
• The Captain's Room
• Time Delta
• Triangle Quest 2
• Triangle Quest
• Words Score
• Write a function
• ginortS
• itertools.permutations()
• itertools.product()

This is just a small selection of the challenges I have solved. You can find the full list of my solutions on Hackerrank.

I hope you find these solutions helpful.

This section covers the basics of using Python to interact with a SQLite database.

• Create Database
• Create Table
• Insert
• Retrieve
• Update
• Delete
• Practice1
• Skills Application

• Elzero Web School: Database with Python (SQLite)

This section covers the basics of the NumPy library, which provides a high-performance array data type and a wide range of mathematical functions.

• Data types
• Arithmetic and Useful Operations
• Array Shape and Reshape
• Comparison between list and array
• Slicing and indexing
• Notes from Kaggle Numpy Tutorial

• Elzero Web School: Numpy
• Kaggle Numpy Tutorial

This section covers the basics of the Pandas library, which provides a high-level interface for data analysis and manipulation and by using Jupyter.

• Series
• DataFrame
• Indexing and Selection
• Conditional Selection & Assigning Data
• Combining data
• Handling Missing Values
• Aggregation functions & group-by
• Pivot Table
• Time series analysis

• Pandas Playlist
• Pandas Course From Kaggle

In this section, I've gained valuable insights into the Matplotlib library, a widely-used data visualization tool in Python. Through this learning journey, I've acquired the skills to create a variety of informative and visually appealing plots.

• Drawing Plots

I've learned how to create different types of plots using Matplotlib, which is crucial for visually representing data patterns and trends.

• Bar Chart

Understanding how to construct bar charts has allowed me to effectively display and compare categorical data.

• Pie Chart

Pie charts are now a part of my visualization toolkit, enabling me to showcase the proportions of different categories in a dataset.

• Histogram Charts

Histograms are an essential tool for illustrating the distribution of continuous data, providing insights into the data's spread and central tendencies.

• Scatter Plots

Scatter plots have proven invaluable in showcasing relationships and correlations between two continuous variables.

• Subplots

Learning how to create subplots within a single figure has enabled me to display multiple plots in a structured and organized manner.

• Matplotlib Playlist

In this section, I've explored the Seaborn library in Python, which is a powerful tool for data visualization and exploration. Seaborn simplifies the process of creating visually appealing and informative plots.

Topics Covered:

• Line Plots

I learned the art of creating line plots with Seaborn. Line plots are particularly useful for visualizing trends and patterns in data over time.

• Scatter Plots

Seaborn's scatter plots enabled me to explore relationships between two variables, highlighting patterns and correlations within the data.

• Regression Plots

Regression plots allowed me to visualize the linear relationship between variables, making it easier to understand correlations and fit regression models.

• Distribution Plots

I learned to create distribution plots that provide insights into the distribution of data, whether it's a histogram, kernel density estimate, or combination of both.

• Seaborn Playlist