{"id":18606084,"url":"https://github.com/tonyhollaar/projects","last_synced_at":"2025-11-02T11:30:31.182Z","repository":{"id":151327349,"uuid":"582435444","full_name":"tonyhollaar/projects","owner":"tonyhollaar","description":"data analytics projects repository tony hollaar","archived":false,"fork":false,"pushed_at":"2023-11-30T21:40:14.000Z","size":5509,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-12-27T00:11:57.525Z","etag":null,"topics":["analytics","data-visualization","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/tonyhollaar.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2022-12-26T20:34:42.000Z","updated_at":"2023-02-18T18:00:50.000Z","dependencies_parsed_at":null,"dependency_job_id":"0fa51c8f-fd0a-4380-ab2b-df83a28f0abd","html_url":"https://github.com/tonyhollaar/projects","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/tonyhollaar%2Fprojects","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tonyhollaar%2Fprojects/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tonyhollaar%2Fprojects/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tonyhollaar%2Fprojects/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/tonyhollaar","download_url":"https://codeload.github.com/tonyhollaar/projects/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":239394716,"owners_count":19631122,"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":["analytics","data-visualization","python"],"created_at":"2024-11-07T02:24:14.616Z","updated_at":"2025-11-02T11:30:31.136Z","avatar_url":"https://github.com/tonyhollaar.png","language":"Jupyter Notebook","readme":"# Projects\n\n## Webscraping\n\u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tonyhollaar/projects/blob/main/Example_1_Web_Scraping_Public_Dataset%20-%20US%20Labor%20Statistics.ipynb\"\u003e\n  \u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\n\u003c/a\u003e\n\n- [Example 1: US Labor Statistics](https://github.com/tonyhollaar/projects/blob/102e74fe13c980e7d694f4904db9fa0553eaa47e/Example%201:%20Web%20Scraping%20Public%20Dataset%20-%20US%20Labor%20Statistics.ipynb)\n\u003cbr\u003e \u003cb\u003e Description: \u003c/b\u003e Webscraping historical oil prices, utilizing US Labor Statistics API and creating stylized visualizations with Plotly Package \n\u003cbr\u003e **Programming language**: Python\n\u003cbr\u003e **Packages used**: Pandas, Math, Numpy, JSON, Requests, Re, Matplotlib, Seaborn, Plotly\n\n## SQL\n\u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tonyhollaar/projects/blob/67851d175b0f23fbde9a2ada0b8ba190ee559928/SQLite3_Example.ipynb\"\u003e\n  \u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\n\n- [SQLite3 Dummy Dataset - Events Data](https://github.com/tonyhollaar/projects/blob/67851d175b0f23fbde9a2ada0b8ba190ee559928/SQLite3_Example.ipynb)\n\u003cbr\u003e \u003cb\u003e  Description: \u003c/b\u003e Use cases for utilizing SQL queries such as aggregatations (count/sum) and flattening of SQL tables with e.g. 1 row per user utilizing Python package SQLite3\n\u003cbr\u003e **Programming language**: Python\n\u003cbr\u003e **Packages used**: Pandas, SQLite3\n\n\n## Visualizations \n\u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tonyhollaar/projects/blob/3fabc8900c74a55d9879b6bc0a4bf03a3cc1b60e/Visualizations_Bar_Chart_Race.ipynb\"\u003e\n  \u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\n\u003c/a\u003e\n\n- [Bar Chart Race](https://github.com/tonyhollaar/projects/blob/3fabc8900c74a55d9879b6bc0a4bf03a3cc1b60e/Visualizations_Bar_Chart_Race.ipynb)\n- [Bar Chart Example GIF](https://github.com/tonyhollaar/projects/blob/12a1514c76c25a08b0c03dabd9712fee72e20f33/Visualizations_Bar_Chart_Race_Example.gif)\n\u003cbr\u003e \u003cb\u003e Description: \u003c/b\u003e Example of animated bar charts of select countries yearly population changes between 1960-2021\n\u003cbr\u003e **Programming language**: Python\n\u003cbr\u003e **Packages used**: Pandas, WBGAPI, Bar_Chart_Race, RegEx\n\n- [Polar Plot](https://github.com/tonyhollaar/projects/blob/main/Polar_Plot_Country_Debt_%25_GDP.ipynb)\n\u003cbr\u003e \u003cb\u003e Description: \u003c/b\u003e Example of a Polar Plot of Countries Government Debt as % of GDP\n\u003cbr\u003e **Programming language**: Python\n\u003cbr\u003e **Packages used**: Pandas, Numpy, Math, MatPlotLib, Seaborn, PIL\n\n## Dashboards\n[![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://bls-connection-demo.streamlit.app/)\n1. [Dashboard - GasPriceWatcher](https://bls-connection-demo.streamlit.app/) \n\u003cbr\u003e \u003cb\u003e Description: \u003c/b\u003e Dashboard to compare U.S. gasoline prices vs. electric vehicle electricity costs 🚗⚡\n\u003cbr\u003e **Programming language**: Python\n\u003cbr\u003e **Packages used**: Streamlit, Pandas, Plotly, Json, Base64, Numpy, Requests, PIL \n\u003cbr\u003e **Source Code**: [Link](https://github.com/tonyhollaar/streamlit_connection)\n\n3. [Dashboard - Youtube Metrics](https://tonyhollaar-dashboards-dashboard-youtube-9g4la2.streamlit.app/)\n\u003cbr\u003e \u003cb\u003e Description: \u003c/b\u003e Example of Dashboard with Youtube Metrics created with Streamlit Package\n\u003cbr\u003e Special thanks to Ken Jee for the tutorial, for tutorial click [link](https://30days.streamlit.app/?challenge=Day+4#install-the-streamlit-library)\n\u003cbr\u003e **Programming language**: Python\n\u003cbr\u003e **Packages used**: Streamlit, Pandas, Plotly\n\u003cbr\u003e **Source Code**: [Link](https://github.com/tonyhollaar/dashboards)\n\n## API\n- [Fiscal Calendar](https://github.com/tonyhollaar/fiscal_calendar)\n\u003cbr\u003e \u003cb\u003e Description: \u003c/b\u003e The fiscal calendar package is a 4-5-4 calendar used commonly to ensures sales comparability between years by dividing the year into months based on a 4 weeks – 5 weeks – 4 weeks format.\n  - *Sales Comparability:* The layout of the calendar strategically aligns holidays and guarantees an equal number of Saturdays and Sundays in comparable months. This approach ensures a fair comparison of like days for accurate sales reporting.\n  - *Date Features:* This package encompasses a comprehensive set of 44 features tailored to fiscal dates, providing versatility in managing and analyzing fiscal timeframes.\n  - *PDF Export:* Seamlessly generate and save your fiscal calendar in PDF format with the package's user-friendly functionality.\n  - *Pandas Integration:* Easily output Fiscal Calendar data to a Pandas Dataframe for efficient analysis and integration with other data processing workflows.\n\n- [Streamlit Connection to Public Bureau of Labor Statistics Data](https://github.com/tonyhollaar/streamlit_bls_connection)\n\u003cbr\u003e \u003cb\u003e Description: \u003c/b\u003e The `streamlit-bls-connection` Python package allows you to easily interact with the U.S. Bureau of Labor Statistics (BLS) API and retrieve data as pandas dataframes and display them in [`Streamlit`](https://docs.streamlit.io/) !\n\n## Games\n- [Pong Game](https://github.com/tonyhollaar/pong_game/)\n\u003cbr\u003e\u003cb\u003eDescription:\u003c/b\u003e The \"Pong Game\" project is a classic arcade-style game implemented in Python.\n\u003cbr\u003e\u003cb\u003eUse Case:\u003c/b\u003e This project is a practical illustration of OOP in action. It demonstrates how classes, objects, and inheritance can be used to create modular and maintainable code. It's not just a game; it's a codebase that reflects good programming practices.\n\u003cbr\u003e **Programming language**: Python\n\u003cbr\u003e **Packages used**: Turtle, Time\n\n#\n\u003cbr\u003e \u003cb\u003e Author: \u003c/b\u003e Tony Hollaar\n\u003cbr\u003e \u003cb\u003e Subject: \u003c/b\u003e Data Analytics/Science Projects Repository \n\u003cbr\u003e \u003cb\u003e Note: \u003c/b\u003e Projects to be added soon and existing to be updated...\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftonyhollaar%2Fprojects","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftonyhollaar%2Fprojects","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftonyhollaar%2Fprojects/lists"}