{"id":25019677,"url":"https://github.com/djeada/data-visualization","last_synced_at":"2026-01-08T16:06:09.251Z","repository":{"id":114371669,"uuid":"471960976","full_name":"djeada/Data-Visualization","owner":"djeada","description":"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.","archived":false,"fork":false,"pushed_at":"2023-06-04T12:48:35.000Z","size":9499,"stargazers_count":0,"open_issues_count":2,"forks_count":1,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-02-05T11:51:52.609Z","etag":null,"topics":["altair","data-visualization","matplotlib","plotly"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/djeada.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-03-20T11:36:33.000Z","updated_at":"2023-05-29T17:46:03.000Z","dependencies_parsed_at":null,"dependency_job_id":"246712db-bbf9-4e70-b27d-1cdd0292ff4e","html_url":"https://github.com/djeada/Data-Visualization","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/djeada%2FData-Visualization","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/djeada%2FData-Visualization/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/djeada%2FData-Visualization/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/djeada%2FData-Visualization/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/djeada","download_url":"https://codeload.github.com/djeada/Data-Visualization/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246301997,"owners_count":20755514,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["altair","data-visualization","matplotlib","plotly"],"created_at":"2025-02-05T11:51:24.353Z","updated_at":"2026-01-08T16:06:09.245Z","avatar_url":"https://github.com/djeada.png","language":null,"readme":"# Data Visualization Guide and Code Snippets\n\nWelcome! 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. \n\n![data_visualization](https://github.com/user-attachments/assets/656c44b9-3bed-4ac5-9622-935a4f8563ca)\n\n## 🎯 Purpose\n\nThe 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.\n\n## 📚 Data Sources\n\nLooking for datasets to use for your visualization practices? Here are a few online sources to obtain public datasets:\n\n- [Scikit-Learn Toy Datasets](https://scikit-learn.org/stable/datasets/toy_dataset.html)\n- [Tableau Public Data Sets](https://www.tableau.com/learn/articles/free-public-data-sets)\n- [Kaggle Datasets](https://www.kaggle.com/datasets)\n- [Google Cloud Public Datasets](https://console.cloud.google.com/marketplace/browse?filter=solution-type:dataset)\n- [U.S. Government's Open Data](https://data.gov/)\n- [Awesome Public Datasets on GitHub](https://github.com/awesomedata/awesome-public-datasets)\n\n## 🛠️ Requirements\n\nEnsure that you have Python 3.8 or above installed to execute the notebooks.\n\n## 🚀 Running Notebooks\n\nTo run these notebooks, you have two options:\n\n1. **Online:** You can use the official Jupyter Notebooks online platform without installing anything on your local machine. Try it out here:\n\n    [Jupyter Notebook Demo](https://jupyter.org/try)\n\n2. **Locally:** If you wish to run notebooks on your local machine, follow the steps below:\n\n    - Clone the repository: \n      ```\n      git clone https://github.com/djeada/Data-Visualization.git\n      ```\n    - Navigate to the cloned repository:\n      ```\n      cd Data-Visualization\n      ```\n    - Install Jupyter Notebook if you haven't done so already:\n      ```\n      pip install notebook\n      ```\n    - Run Jupyter Notebook:\n      ```\n      jupyter notebook\n      ```\n\n## Notes\n\n| # | Description | Notes |\n| --- | --- | --- |\n| 1 | Introduction to data visualization, including its importance and use cases. | \u003ca href=\"https://github.com/djeada/Data-Visualization/blob/main/notes/why_visualize_data.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e  |\n| 2 | Covers the fundamental elements of visual representation in data visualization. | \u003ca href=\"https://github.com/djeada/Data-Visualization/blob/main/notes/visual_grammar.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e  |\n| 3 | Explains how to extract and process data for visualization. | \u003ca href=\"https://github.com/djeada/Data-Visualization/blob/main/notes/extracting_data.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e  |\n| 4 | Guidance on selecting a visualization framework best suited for your specific use case. | \u003ca href=\"https://github.com/djeada/Data-Visualization/blob/main/notes/choosing_framework.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e  |\n| 5 | Quick reference guide and cheat sheet for the Matplotlib data visualization library. | \u003ca href=\"https://github.com/djeada/Data-Visualization/blob/main/notes/matplotlib_cheat_sheet.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e  |\n| 6 | Quick reference guide and cheat sheet for the Altair data visualization library. | \u003ca href=\"https://github.com/djeada/Data-Visualization/blob/main/notes/altair_cheat_sheet.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e  |\n| 7 | Quick reference guide and cheat sheet for the Plotly data visualization library. | \u003ca href=\"https://github.com/djeada/Data-Visualization/blob/main/notes/plotly_cheat_sheet.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e  |\n| 8 | Detailed guide on selecting the appropriate type of plot based on the nature of the data. | \u003ca href=\"https://github.com/djeada/Data-Visualization/blob/main/notes/choosing_plot_type.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e  |\n| 9 | In-depth discussion on representing uncertainty in data through error bars. | \u003ca href=\"https://github.com/djeada/Data-Visualization/blob/main/notes/error_bars.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e  |\n| 10 | Special topic focusing on creating and interpreting racing charts. | \u003ca href=\"https://github.com/djeada/Data-Visualization/blob/main/notes/racing_charts.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e  |\n| 11 | Discusses the ethics of data visualization and how to avoid data misrepresentation. | \u003ca href=\"https://github.com/djeada/Data-Visualization/blob/main/notes/data_misrepresentation.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e  |\n| 12 | Covers advanced topics on how to create dashboards for presenting multiple visualizations. | \u003ca href=\"https://github.com/djeada/Data-Visualization/blob/main/notes/dashboards.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e \u003c/a\u003e  |\n\n## Examples\n\n| Description                                                                                                                     | Altair | Plotly | Matplotlib |\n|---------------------------------------------------------------------------------------------------------------------------------|--------| ------ | ---------- |\n| Plotting a single line, typically the simplest form of data visualization.                                                     | ![single_line](https://github.com/djeada/Data-Visualization/assets/37275728/80fb9cdf-7296-4d48-94fa-da181b78fcb0) | | ![single_line](https://github.com/djeada/Data-Visualization/assets/37275728/18b2ebf5-b7fc-45fe-9f3e-c5ab5502b32e) |\n| Plotting two lines, slightly more complex than a single line.                                                                   | ![two_lines](https://github.com/djeada/Data-Visualization/assets/37275728/6645431b-7b15-4581-ba4b-cf73636093c1) | ![two_lines](https://github.com/djeada/Data-Visualization/assets/37275728/5e9f093a-a29a-4330-9632-6f62207a0d75) | ![two_lines](https://github.com/djeada/Data-Visualization/assets/37275728/c54c7388-2473-4077-a392-fb1aad87bb92) |\n| Bar plots represent categorical data with rectangular bars.                                                                     | ![bar_plot](https://github.com/djeada/Data-Visualization/assets/37275728/8334be80-8fb2-476c-81d5-f676892afac8) | ![bar_plot](https://github.com/djeada/Data-Visualization/assets/37275728/eb774bd4-f757-460f-9990-14d25204c3eb) | ![bar_plot](https://github.com/djeada/Data-Visualization/assets/37275728/bbdec8b0-8d1d-4b76-9cf3-90758ce32d9f) |\n| Pie charts represent proportions or percentages in a whole.                                                                     | ![pie_chart](https://github.com/djeada/Data-Visualization/assets/37275728/4142b933-60d6-48ce-adfd-21a39900e590) | ![pie_chart](https://github.com/djeada/Data-Visualization/assets/37275728/df16dbfd-1a69-4c78-bb95-407a98bd0581) | ![pie_chart](https://github.com/djeada/Data-Visualization/assets/37275728/88e15dfb-fff1-4b6f-b26a-40e8000cf98b) |\n| Line charts represent continuous data with lines connecting data points.                                                        | ![line_chart](https://github.com/djeada/Data-Visualization/assets/37275728/0bf77378-4450-4069-a597-161b10935629)| ![line_chart](https://github.com/djeada/Data-Visualization/assets/37275728/36370a60-cec8-4f3c-b89a-301326e186a2) | ![line_chart](https://github.com/djeada/Data-Visualization/assets/37275728/dd299458-1552-459d-b24b-14a6ce46221e) |\n| Histograms display frequency distributions using bins and frequencies.                                                         | ![histogram](https://github.com/djeada/Data-Visualization/assets/37275728/522b2140-4c54-447d-bdf8-abb16d478e70) | ![histogram](https://github.com/djeada/Data-Visualization/assets/37275728/0fee3bf8-29c9-40d1-a2b6-559d42ae1e0b) | ![histogram](https://github.com/djeada/Data-Visualization/assets/37275728/9a974a3a-b285-47ef-aa01-14973653a73d) |\n| Area charts are similar to line charts but with the area under the line filled in.                                              | ![area_chart](https://github.com/djeada/Data-Visualization/assets/37275728/53b37c60-7323-4823-b896-a0c548f30068) | ![area_chart](https://github.com/djeada/Data-Visualization/assets/37275728/cf696a0f-a328-462e-83c5-ddaee1232d53) | ![area_chart](https://github.com/djeada/Data-Visualization/assets/37275728/8df30bc6-68ca-4fe7-aa2f-fe7574ddb043) |\n| Stacked area charts involve layering multiple datasets.                                                                         | ![stacked_area_chart](https://github.com/djeada/Data-Visualization/assets/37275728/397dedc7-6700-49e6-a97b-a85a4301f378) | ![stacked_area_chart](https://github.com/djeada/Data-Visualization/assets/37275728/888dd0da-7c80-4766-ab49-4e2fb962ef90) | ![stacked_area_chart](https://github.com/djeada/Data-Visualization/assets/37275728/bbb5f075-04e4-4af3-a75c-395aa21bde96) |\n| Grouped bar charts involve grouping bars based on categories.                                                                   | ![grouped_bar_chart](https://github.com/djeada/Data-Visualization/assets/37275728/39b2f769-e532-4de5-8571-10381be8c89b) | ![grouped_bar_chart](https://github.com/djeada/Data-Visualization/assets/37275728/593aaa46-353d-4fee-8d7f-f2ac77bb9bfa) | ![grouped_bar_chart](https://github.com/djeada/Data-Visualization/assets/37275728/b2a677ef-b18f-4b1b-9421-fe0939491a9e) |\n| Box plots show the distribution of data using a five-number summary.                                                            | ![box_plot](https://github.com/djeada/Data-Visualization/assets/37275728/10e1a68b-3c49-4991-85f7-04d51d40fc12) | ![box_plot](https://github.com/djeada/Data-Visualization/assets/37275728/69828ec9-01b6-4739-bb30-06ef2d974271) | ![box_plot](https://github.com/djeada/Data-Visualization/assets/37275728/55b64c36-81ec-4d4a-b25e-a246030c0a9e) |\n| Density plots display data distribution using kernel density estimation.                                                       | ![density_plot](https://github.com/djeada/Data-Visualization/assets/37275728/7c632c65-bd1b-458b-b4fc-f0664ca81554) | ![density_plot](https://github.com/djeada/Data-Visualization/assets/37275728/2f0fb43a-db61-4d81-8688-625afae0993d) | ![density_plot](https://github.com/djeada/Data-Visualization/assets/37275728/9f16d907-ae21-4f68-b75e-7136f89440ef) |\n| Error bar plots show the error or uncertainty associated with data points.                                                      | ![error_plot](https://github.com/djeada/Data-Visualization/assets/37275728/6515209d-99c3-4062-850a-47268b807fb4) | ![error_plot](https://github.com/djeada/Data-Visualization/assets/37275728/3e0ff153-0f49-45ad-bbb7-6b26ad7675e5) | ![error_plot](https://github.com/djeada/Data-Visualization/assets/37275728/99c9bd9a-163a-4af6-8b60-ddc75d9202cd) |\n| Bubble charts represent data using marker size as the third dimension.                                                         | ![bubble_chart](https://github.com/djeada/Data-Visualization/assets/37275728/9b995083-3277-4740-baee-aed8ae654621) | ![bubble_chart](https://github.com/djeada/Data-Visualization/assets/37275728/4c5353c5-77f0-41ce-983f-66ebea6be3e6) | ![bubble_chart](https://github.com/djeada/Data-Visualization/assets/37275728/b0a5de18-87a0-484c-89d9-8fa69d17919a) |\n| Correlation heatmaps display complex multi-dimensional data and correlations.                                                   | ![correlation_heatmap](https://github.com/djeada/Data-Visualization/assets/37275728/b9d322dc-d436-424b-8d41-6da1be24fa2e) | ![correlation_heatmap](https://github.com/djeada/Data-Visualization/assets/37275728/a94a421b-cedc-40f9-81ab-1675fd566757) | ![correlation_heatmap](https://github.com/djeada/Data-Visualization/assets/37275728/7bb79b93-5f4b-4335-aade-d132a5defda6) |\n| Anscombe's Quartet explores datasets with the same statistical properties but different visual appearances.                      | ![anscombes_quartet](https://github.com/djeada/Data-Visualization/assets/37275728/d1b3b94f-e3e8-44e2-98e0-77d9c0c4564d) | ![anscombes_quartet](https://github.com/djeada/Data-Visualization/assets/37275728/ed98d0a8-51ff-4444-9f75-3d429e9f8373) |![anscombes_quartet](https://github.com/djeada/Data-Visualization/assets/37275728/27665ea9-88b1-44cc-aa90-7a8bccf7ee99) |\n\n## 📚 Additional Resources\n\n- Scientific-looking matplotlib graphs: [SciencePlots](https://github.com/garrettj403/SciencePlots)\n- Cyberpunk style matplotlib graphs: [MPLCyberpunk](https://github.com/dhaitz/mplcyberpunk)\n\n## 📖 References\n\nFind more detailed insights on data visualization from the resources listed below:\n\n- [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/)\n- [Storytelling with Data](https://github.com/empathy87/storytelling-with-data)\n- [Types of Charts](https://wpdatatables.com/types-of-charts/)\n- [Uncertainty in Visualization](https://advait.org/files/sarkar_2015_uncertainty_vis.pdf)\n- [Data Visualization: A Practical Introduction](https://clauswilke.com/dataviz/index.html)\n- [Matplotlib CheatSheets](https://github.com/matplotlib/cheatsheets)\n- [Styling with Matplotlib](https://jonchar.net/notebooks/matplotlib-styling/)\n\n## 🙏 Contributing\n\nContributions 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.\n\n## 📄 License\n\nThis project is licensed under the terms of the [MIT license](https://choosealicense.com/licenses/mit/).\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdjeada%2Fdata-visualization","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdjeada%2Fdata-visualization","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdjeada%2Fdata-visualization/lists"}