{"id":21865030,"url":"https://github.com/mr-chang95/sf_data_visualization","last_synced_at":"2026-05-04T08:36:39.547Z","repository":{"id":136750107,"uuid":"446678979","full_name":"Mr-Chang95/SF_Data_Visualization","owner":"Mr-Chang95","description":"In this personal project, I am interested in examining all of the active businesses in the San Francisco Bay Area while performing some simple data visualizations, mainly on categorical variables.","archived":false,"fork":false,"pushed_at":"2022-02-07T20:27:03.000Z","size":24396,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-21T21:12:17.948Z","etag":null,"topics":["business","data-analysis","data-visualization","jupyter-notebook","pandas","python","san-francisco"],"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/Mr-Chang95.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":"2022-01-11T04:38:13.000Z","updated_at":"2022-02-07T20:18:24.000Z","dependencies_parsed_at":"2023-07-17T19:42:09.031Z","dependency_job_id":null,"html_url":"https://github.com/Mr-Chang95/SF_Data_Visualization","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Mr-Chang95/SF_Data_Visualization","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mr-Chang95%2FSF_Data_Visualization","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mr-Chang95%2FSF_Data_Visualization/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mr-Chang95%2FSF_Data_Visualization/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mr-Chang95%2FSF_Data_Visualization/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Mr-Chang95","download_url":"https://codeload.github.com/Mr-Chang95/SF_Data_Visualization/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mr-Chang95%2FSF_Data_Visualization/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32600964,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-03T22:12:39.696Z","status":"online","status_checked_at":"2026-05-04T02:00:06.625Z","response_time":58,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["business","data-analysis","data-visualization","jupyter-notebook","pandas","python","san-francisco"],"created_at":"2024-11-28T04:13:52.065Z","updated_at":"2026-05-04T08:36:39.529Z","avatar_url":"https://github.com/Mr-Chang95.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# San Francisco Business Data Analysis And Visualization\n## by Daniel Chang\n\n## Project Description\nFor this project, I am interested in analyzing businesses that have physical locations in the Bay Area. Mainly, those located in San Francisco. I am going to put a lot of focus on the industries of these businesses. For somewhere like SF, my initial thought was that the most popular industry would be financial services. I will then examine the locations of these businesses and industries. Perhaps, each area has a higher concentration of a particular industry than another. \n\n## Dataset\nThis dataset contains 28,3369 loans with 32 variables for each business, including 'City', 'Source Zipcode', 'Location Start Date','Location End Date', 'NAICS Code Description', 'Neighborhoods - Analysis Boundaries'.\n\n\n## Summary of Findings\nDuring my project, I found that the most popular industry in SF/Bay Area is real estate, rental \u0026 leasing services, then the professional, scientific and technical services. The most popular zip code is 94110, while the most popular boundary is the Financial District/South Beach. In most zip codes and boundaries, the real estate, rental \u0026 leasing industry is the most dominant. We also found that the most businesses (active) opened their location in the 1990s.  \n\n## Licensing, Authors, Acknowledgements\nI would like to give special thanks to the San Francisco government for making this dataset free on their [website](https://data.sfgov.org/widgets/g8m3-pdis).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmr-chang95%2Fsf_data_visualization","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmr-chang95%2Fsf_data_visualization","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmr-chang95%2Fsf_data_visualization/lists"}