Python Data Analysis Example with Pandas and Seaborn

Overview

This repository contains Jupyter notebooks designed for easily running on Google Colab and VS Code, focusing on data analysis and visualization examples using pandas and seaborn. It's a practical way to explore datasets, uncover insights, and visualize trends without the need for local environment setup. Specifically, this project utilizes machine learning databases provided from the Dow Jones index, offering a hands-on experience with real-world financial data. We simplify access to these databases using wget, allowing for straightforward data retrieval directly within the notebooks.

Here's a quick demo

Prerequisites

• Ensure you have a Google account to use Google Colab.
• Python 3.6+ is required.
• pandas==2.0.3
• seaborn==0.13.2

Quick Start

1. Open the Notebook in Google Colab

   • Navigate to Google Colab.
   • Choose the "GitHub" tab in the "Open notebook" dialog.
   • Enter the repository URL: https://github.com/maxh33/python_pandas_seaborn and open the desired notebook.
   • Runtime > Run all

Features

• Data Analysis: Using pandas for efficient data manipulation and analysis.
• Data Visualization: Create compelling visualizations with seaborn to represent the data insights graphically.

Contributing

The data manipulation and insights are examples and is open to contributions! If you have suggestions for improving the notebooks or want to add new analyses, please feel free to fork the repository and submit a pull request.