This repository contains various Python projects and exercises related to data analysis. Each folder focuses on a specific concept or technique, providing examples, code snippets, and explanations. The goal is to provide a comprehensive overview of Python's data analysis capabilities, ranging from basic control structures to advanced libraries.
This folder includes detailed explanations and examples of Python's built-in data structures such as lists, tuples, and sets. It shows how to manipulate these structures for efficient data handling and processing.
- This folder contains Python examples on how to create and use functions, including anonymous functions (lambda), higher-order functions, and decorators. Functions are essential building blocks for organizing code, especially in larger data analysis projects.
- In this folder, you will find examples of how to handle file input and output in Python, including reading from and writing to files in various formats such as .txt, .csv, and .json.
- This folder contains examples of how to extract data from websites using Python libraries like BeautifulSoup and requests. It covers the basics of web scraping, handling HTML, and exporting the scraped data for analysis.
- This section covers how to import and use various Python libraries and packages that are commonly used in data analysis. It includes best practices for working with third-party packages and creating your own Python packages.
- The Pandas folder contains exercises and examples using the pandas library, one of the most powerful tools in Python for data manipulation. It covers loading datasets, data cleaning, data transformation, and data aggregation techniques.
- This folder deals with Python's error handling capabilities, using try-except blocks and custom exceptions. The examples focus on managing runtime errors gracefully during data analysis processes to ensure your programs run smoothly.
- In this folder, you’ll find examples of various data visualization techniques using libraries like matplotlib and seaborn. The focus is on creating insightful graphs and plots to represent data trends and patterns.
- This folder contains examples and exercises related to Python’s control flow structures like conditionals (if-else), loops (for, while), and iteration techniques. It demonstrates how control structures are essential in decision-making and iterative operations within data analysis workflows.
Clone the repository to your local machine:
git clone https://github.com/yourusername/Xaltius_Python_DataAnalysis.git
- Navigate through the folders and explore the example code.
- Each folder contains .py files demonstrating key concepts, with comments and explanations where necessary.
- Python 3.x
- Libraries such as:
- pandas
- matplotlib
- seaborn
- requests
- BeautifulSoup (install using
pip)