{"id":22445478,"url":"https://github.com/anas436/analyzing-real-world-data-set-with-sqlite3-and-sqlmagic-using-python","last_synced_at":"2026-05-03T12:34:33.959Z","repository":{"id":110394351,"uuid":"498418876","full_name":"Anas436/Analyzing-Real-World-Data-Set-with-SQLite3-and-SQLMagic-using-Python","owner":"Anas436","description":null,"archived":false,"fork":false,"pushed_at":"2022-05-31T16:48:50.000Z","size":20,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-08-01T05:58:27.026Z","etag":null,"topics":["csv","jupyter-notebook","matplotlib","pandas","python3","seaborn","sql-magic","sqlite3"],"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/Anas436.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":"2022-05-31T16:43:44.000Z","updated_at":"2022-05-31T16:52:57.000Z","dependencies_parsed_at":null,"dependency_job_id":"361d0ee9-ab41-43c0-982e-c68f4455a319","html_url":"https://github.com/Anas436/Analyzing-Real-World-Data-Set-with-SQLite3-and-SQLMagic-using-Python","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Anas436/Analyzing-Real-World-Data-Set-with-SQLite3-and-SQLMagic-using-Python","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Anas436%2FAnalyzing-Real-World-Data-Set-with-SQLite3-and-SQLMagic-using-Python","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Anas436%2FAnalyzing-Real-World-Data-Set-with-SQLite3-and-SQLMagic-using-Python/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Anas436%2FAnalyzing-Real-World-Data-Set-with-SQLite3-and-SQLMagic-using-Python/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Anas436%2FAnalyzing-Real-World-Data-Set-with-SQLite3-and-SQLMagic-using-Python/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Anas436","download_url":"https://codeload.github.com/Anas436/Analyzing-Real-World-Data-Set-with-SQLite3-and-SQLMagic-using-Python/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Anas436%2FAnalyzing-Real-World-Data-Set-with-SQLite3-and-SQLMagic-using-Python/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32569714,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-03T06:36:36.687Z","status":"ssl_error","status_checked_at":"2026-05-03T06:36:09.306Z","response_time":103,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":["csv","jupyter-notebook","matplotlib","pandas","python3","seaborn","sql-magic","sqlite3"],"created_at":"2024-12-06T03:14:38.681Z","updated_at":"2026-05-03T12:34:33.939Z","avatar_url":"https://github.com/Anas436.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Analyzing-Real-World-Data-Set-with-SQLite3-and-SQLMagic-using-Python\n\n\n\nThe city of Chicago released a dataset of socioeconomic data to the Chicago City Portal. This dataset contains a selection of six socioeconomic indicators of public health significance and a “hardship index,” for each Chicago community area, for the years 2008 – 2012.\n\nScores on the hardship index can range from 1 to 100, with a higher index number representing a greater level of hardship.\n\nA detailed description of the dataset can be found on the city of Chicago's website, but to summarize, the dataset has the following variables:\n\n\nCommunity Area Number (ca): Used to uniquely identify each row of the dataset\n\nCommunity Area Name (community_area_name): The name of the region in the city of Chicago\n\nPercent of Housing Crowded (percent_of_housing_crowded): Percent of occupied housing units with more than one person per room\n\nPercent Households Below Poverty (percent_households_below_poverty): Percent of households living below the federal poverty line\n\nPercent Aged 16+ Unemployed (percent_aged_16_unemployed): Percent of persons over the age of 16 years that are unemployed\n\nPercent Aged 25+ without High School Diploma (percent_aged_25_without_high_school_diploma): Percent of persons over the age of 25 years without a high school education\n\nPercent Aged Under 18 or Over 64:Percent of population under 18 or over 64 years of age (percent_aged_under_18_or_over_64): (ie. dependents)\n\nPer Capita Income (per_capita_income_): Community Area per capita income is estimated as the sum of tract-level aggragate incomes divided by the total population\n\nHardship Index (hardship_index): Score that incorporates each of the six selected socioeconomic indicators\n\nIn this Lab, we'll take a look at the variables in the socioeconomic indicators dataset and do some basic analysis with Python.\n\n\nConnect to the database\n\nLet us first load the SQL extension and establish a connection with the database\n\nThe syntax for connecting to magic sql using sqllite is\n%sql sqlite://DatabaseName\n\nwhere DatabaseName will be your .db files\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanas436%2Fanalyzing-real-world-data-set-with-sqlite3-and-sqlmagic-using-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fanas436%2Fanalyzing-real-world-data-set-with-sqlite3-and-sqlmagic-using-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanas436%2Fanalyzing-real-world-data-set-with-sqlite3-and-sqlmagic-using-python/lists"}