https://github.com/djeada/data-visualization
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.
https://github.com/djeada/data-visualization
altair data-visualization matplotlib plotly
Last synced: 5 days ago
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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.
- Host: GitHub
- URL: https://github.com/djeada/data-visualization
- Owner: djeada
- License: mit
- Created: 2022-03-20T11:36:33.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2023-06-04T12:48:35.000Z (over 2 years ago)
- Last Synced: 2025-02-05T11:51:52.609Z (11 months ago)
- Topics: altair, data-visualization, matplotlib, plotly
- Homepage:
- Size: 9.06 MB
- Stars: 0
- Watchers: 2
- Forks: 1
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Data Visualization Guide and Code Snippets
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.
## 🎯 Purpose
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.
## 📚 Data Sources
Looking for datasets to use for your visualization practices? Here are a few online sources to obtain public datasets:
- [Scikit-Learn Toy Datasets](https://scikit-learn.org/stable/datasets/toy_dataset.html)
- [Tableau Public Data Sets](https://www.tableau.com/learn/articles/free-public-data-sets)
- [Kaggle Datasets](https://www.kaggle.com/datasets)
- [Google Cloud Public Datasets](https://console.cloud.google.com/marketplace/browse?filter=solution-type:dataset)
- [U.S. Government's Open Data](https://data.gov/)
- [Awesome Public Datasets on GitHub](https://github.com/awesomedata/awesome-public-datasets)
## 🛠️ Requirements
Ensure that you have Python 3.8 or above installed to execute the notebooks.
## 🚀 Running 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](https://jupyter.org/try)
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
```
## Notes
| # | 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. | |
## Examples
| 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. |  |  | |
## 📚 Additional Resources
- Scientific-looking matplotlib graphs: [SciencePlots](https://github.com/garrettj403/SciencePlots)
- Cyberpunk style matplotlib graphs: [MPLCyberpunk](https://github.com/dhaitz/mplcyberpunk)
## 📖 References
Find more detailed insights on data visualization from the resources listed below:
- [Introduction to Computational Thinking and Data Science](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016/)
- [Storytelling with Data](https://github.com/empathy87/storytelling-with-data)
- [Types of Charts](https://wpdatatables.com/types-of-charts/)
- [Uncertainty in Visualization](https://advait.org/files/sarkar_2015_uncertainty_vis.pdf)
- [Data Visualization: A Practical Introduction](https://clauswilke.com/dataviz/index.html)
- [Matplotlib CheatSheets](https://github.com/matplotlib/cheatsheets)
- [Styling with Matplotlib](https://jonchar.net/notebooks/matplotlib-styling/)
## 🙏 Contributing
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.
## 📄 License
This project is licensed under the terms of the [MIT license](https://choosealicense.com/licenses/mit/).