Python Programming: Basic and Advanced Topics with EDA

Project Overview

This repository provides a comprehensive set of Python scripts and notebooks covering both basic and advanced programming topics. It includes practical examples and exercises on fundamental Python concepts, advanced techniques, and Exploratory Data Analysis (EDA). The aim is to facilitate learning and mastery of Python for various applications, including data analysis and visualization.

Objectives

• Basic Python Concepts: Learn and practice fundamental Python programming topics.
• Advanced Python Topics: Explore advanced programming techniques and concepts.
• Exploratory Data Analysis (EDA): Perform EDA to uncover insights and patterns in datasets.
• Data Visualization: Use Python libraries to create visualizations that support data-driven decisions.

Content

Basic Python Topics

1. Data Types and Structures:

   • Integers, floats, strings, booleans
   • Lists, tuples, dictionaries, sets

2. Conditional Statements:

   • If, elif, else statements
   • Nested conditions

3. Loops:

   • For loops, while loops
   • Nested loops
   • List and dictionary comprehensions

4. Functions:

   • Defining and calling functions
   • Lambda functions
   • Function arguments and return values

5. File Handling:

   • Reading from and writing to files
   • Handling different file formats (e.g., CSV, JSON)

Advanced Python Topics

1. Object-Oriented Programming (OOP):

   • Classes and objects
   • Inheritance and polymorphism
   • Encapsulation and abstraction

2. Error Handling:

   • Try, except, finally
   • Custom exceptions

3. Decorators and Generators:

   • Function decorators
   • Generator functions and expressions

4. Data Manipulation and Analysis:

   • Pandas for data manipulation
   • Numpy for numerical operations

5. Web Scraping and API Integration:

   • Using BeautifulSoup and requests for web scraping
   • Accessing and interacting with APIs

Exploratory Data Analysis (EDA)

1. Data Loading and Cleaning:

   • Loading data using Pandas
   • Handling missing values and outliers

2. Data Exploration:

   • Summary statistics
   • Correlation analysis

3. Data Visualization:

   • Using Matplotlib and Seaborn for visualizations
   • Creating plots such as histograms, scatter plots, and box plots

4. Feature Engineering:

   • Creating new features
   • Encoding categorical variables

5. Model Evaluation:

   • Basic evaluation metrics
   • Train-test split and cross-validation

How to Use

1. Setup:

   • Ensure you have Python installed.
   • Install required libraries using pip install -r requirements.txt.

2. Run Scripts:

   • Execute Python scripts in your preferred IDE or command line.
   • Open Jupyter notebooks for interactive analysis and visualization.

3. Explore Topics:

   • Follow the notebooks and scripts to understand basic and advanced concepts.
   • Practice exercises to reinforce your learning.

Prerequisites

• Python (3.x recommended)
• Basic knowledge of programming concepts
• Libraries: Pandas, Numpy, Matplotlib, Seaborn, BeautifulSoup, requests

Conclusion

This repository serves as a comprehensive resource for learning Python programming, from fundamental concepts to advanced techniques. It also includes practical examples of Exploratory Data Analysis (EDA) to help you gain insights from data.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

• Python Documentation
• Pandas Documentation
• Matplotlib Documentation
• Seaborn Documentation