Welcome! This repository is dedicated to the exploration of various data visualization frameworks through bite-sized code snippets, as well as providing insights on effective data visualization techniques and principles.

The goal of this repository is to serve as a practical guide for understanding the strengths and drawbacks of diverse data visualization frameworks. Additionally, it encompasses my own reflections on the topic of data visualization.

Looking for datasets to use for your visualization practices? Here are a few online sources to obtain public datasets:

• Scikit-Learn Toy Datasets
• Tableau Public Data Sets
• Kaggle Datasets
• Google Cloud Public Datasets
• U.S. Government's Open Data
• Awesome Public Datasets on GitHub

Ensure that you have Python 3.8 or above installed to execute the notebooks.

To run these notebooks, you have two options:

1. Online: You can use the official Jupyter Notebooks online platform without installing anything on your local machine. Try it out here:

   Jupyter Notebook Demo

2. Locally: If you wish to run notebooks on your local machine, follow the steps below:

   • Clone the repository:

     git clone https://github.com/djeada/Data-Visualization.git
     

   • Navigate to the cloned repository:

     cd Data-Visualization
     

   • Install Jupyter Notebook if you haven't done so already:

     pip install notebook
     

   • Run Jupyter Notebook:

     jupyter notebook
     

#	Description	Notes
1	Introduction to data visualization, including its importance and use cases.
2	Covers the fundamental elements of visual representation in data visualization.
3	Explains how to extract and process data for visualization.
4	Guidance on selecting a visualization framework best suited for your specific use case.
5	Quick reference guide and cheat sheet for the Matplotlib data visualization library.
6	Quick reference guide and cheat sheet for the Altair data visualization library.
7	Quick reference guide and cheat sheet for the Plotly data visualization library.
8	Detailed guide on selecting the appropriate type of plot based on the nature of the data.
9	In-depth discussion on representing uncertainty in data through error bars.
10	Special topic focusing on creating and interpreting racing charts.
11	Discusses the ethics of data visualization and how to avoid data misrepresentation.
12	Covers advanced topics on how to create dashboards for presenting multiple visualizations.

Description	Altair	Plotly	Matplotlib
Plotting a single line, typically the simplest form of data visualization.
Plotting two lines, slightly more complex than a single line.
Bar plots represent categorical data with rectangular bars.
Pie charts represent proportions or percentages in a whole.
Line charts represent continuous data with lines connecting data points.
Histograms display frequency distributions using bins and frequencies.
Area charts are similar to line charts but with the area under the line filled in.
Stacked area charts involve layering multiple datasets.
Grouped bar charts involve grouping bars based on categories.
Box plots show the distribution of data using a five-number summary.
Density plots display data distribution using kernel density estimation.
Error bar plots show the error or uncertainty associated with data points.
Bubble charts represent data using marker size as the third dimension.
Correlation heatmaps display complex multi-dimensional data and correlations.
Anscombe's Quartet explores datasets with the same statistical properties but different visual appearances.

• Scientific-looking matplotlib graphs: SciencePlots
• Cyberpunk style matplotlib graphs: MPLCyberpunk

Find more detailed insights on data visualization from the resources listed below:

• Introduction to Computational Thinking and Data Science
• Storytelling with Data
• Types of Charts
• Uncertainty in Visualization
• Data Visualization: A Practical Introduction
• Matplotlib CheatSheets
• Styling with Matplotlib

Contributions are warmly welcomed. If you are considering large changes, please open an issue first to discuss your ideas. Remember to update tests as required for your changes.

This project is licensed under the terms of the MIT license.