{"id":20315392,"url":"https://github.com/henrylin03/china-gdp","last_synced_at":"2026-05-01T22:35:13.653Z","repository":{"id":159029663,"uuid":"497957339","full_name":"henrylin03/china-gdp","owner":"henrylin03","description":"Analysis and visualisation of China GDP data using Python.","archived":false,"fork":false,"pushed_at":"2022-06-15T14:20:23.000Z","size":17901,"stargazers_count":1,"open_issues_count":1,"forks_count":2,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-12-04T22:55:44.589Z","etag":null,"topics":["data","data-analysis","data-visualisation","dataset","kaggle","pandas"],"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/henrylin03.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":"2022-05-30T13:33:50.000Z","updated_at":"2023-07-30T14:32:34.000Z","dependencies_parsed_at":"2023-07-26T13:15:15.034Z","dependency_job_id":null,"html_url":"https://github.com/henrylin03/china-gdp","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/henrylin03/china-gdp","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/henrylin03%2Fchina-gdp","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/henrylin03%2Fchina-gdp/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/henrylin03%2Fchina-gdp/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/henrylin03%2Fchina-gdp/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/henrylin03","download_url":"https://codeload.github.com/henrylin03/china-gdp/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/henrylin03%2Fchina-gdp/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32515838,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-30T13:12:12.517Z","status":"online","status_checked_at":"2026-05-01T02:00:05.856Z","response_time":64,"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":["data","data-analysis","data-visualisation","dataset","kaggle","pandas"],"created_at":"2024-11-14T18:19:00.768Z","updated_at":"2026-05-01T22:35:13.620Z","avatar_url":"https://github.com/henrylin03.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# China GDP: Data Analysis\n\u003cp align=\"center\"\u003e\n    \u003cimg width=\"500\" src=\"http://www.hydrogenfuelnews.com/wp-content/uploads/2021/08/Fuel-cell-vehicles-growth-china.jpg\"\u003e\n\u003c/p\u003e\n\nIn this project, I analyse annual GDPs of China's provincial-level regions, from 1992-2020 inclusive. \n\n## Description\nIn 2020, China was ranked 2nd in the world by norminal GDP ([IMF 2021](https://www.imf.org/en/Publications/WEO/weo-database/2021/October/weo-report?c=512,914,612,171,614,311,213,911,314,193,122,912,313,419,513,316,913,124,339,638,514,218,963,616,223,516,918,748,618,624,522,622,156,626,628,228,924,233,632,636,634,238,662,960,423,935,128,611,321,243,248,469,253,642,643,939,734,644,819,172,132,646,648,915,134,652,174,328,258,656,654,336,263,268,532,944,176,534,536,429,433,178,436,136,343,158,439,916,664,826,542,967,443,917,544,941,446,666,668,672,946,137,546,674,676,548,556,678,181,867,682,684,273,868,921,948,943,686,688,518,728,836,558,138,196,278,692,694,962,142,449,564,565,283,853,288,293,566,964,182,359,453,968,922,714,862,135,716,456,722,942,718,724,576,936,961,813,726,199,733,184,524,361,362,364,732,366,144,146,463,528,923,738,578,537,742,866,369,744,186,925,869,746,926,466,112,111,298,927,846,299,582,487,474,754,698,\u0026s=NGDPD,PPPGDP,\u0026sy=2020\u0026ey=2021\u0026ssm=0\u0026scsm=0\u0026scc=0\u0026ssd=1\u0026ssc=0\u0026sic=0\u0026sort=country\u0026ds=.\u0026br=1)), and, a year later, overtook USA as the wealthiest nation in the world ([Yahoo Finance 2021](https://finance.yahoo.com/news/report-china-now-worlds-richest-170102566.html?guccounter=1\u0026guce_referrer=aHR0cHM6Ly9lbi53aWtpcGVkaWEub3JnLw\u0026guce_referrer_sig=AQAAAFJGFXnSvLPnX9OtJ9eFiy3Wz1dqzpJ1vRojl8DLBeUsInvpccLX8LYiuPxFr0VGj8MQw57GUTELDRx_ShPSd6ZzN-VEE7nU6Gmuk3C2_cCWRHdnVpa-dSikrUERqfw9QQVvQBoM0ahNF26is6x3gxTI8XKIrrLOtymqVJMqWTvO)). \n\nAs an Australian-born Chinese, this drew my interest into analysing its annual GDP from the early '90s at the provincial level to uncover insights.\n\n## Features \u0026 Insights\nIn this project, I highlights patterns and trends in China's provincial-level GDPs in the `china_province_gdp.ipynb` notebook.\n\nAlthough trending upwards, different regions grew at different rates. ***Guangdong*, *Jiangsu*, and *Shandong* experienced immense growth, consistently contributing a high proportion of China's total, annual GDP.** Contrastingly, ***Tibet*, *Ningxia* and *Qinghai* experienced low growth, and continue to lag in GDP.** These growth differences have led to increasingly greater gaps in GDP.\n\nThe choropleth map (see below) implied that regions' GDPs may differ due to geographical location. \n\n\u003cp align=\"center\"\u003e\n    \u003cimg width=\"1000\" src=\"./images/gdp_2020_choropleth.png\"\u003e\n\u003c/p\u003e\n\nFrom this, I wished to examine China's 'Statistical Regions', which are geographical groupings of provincial-level regions. In the `additional_data.ipynb` Notebook, I scraped and exported this dataset, helping to discover that ***East China* has the highest GDP**, whereas ***Northwest China* continue to experience low GDP**.\n\n\u003cp align=\"center\"\u003e\n    \u003cimg width=\"1000\" src=\"./images/gdp_per_stat_region.png\"\u003e\n\u003c/p\u003e\n\n## How To Use\nTo refresh the data, please:\n1. Fork this repository,\n2. Update the input .csv from **[Kaggle](https://www.kaggle.com/datasets/concyclics/chinas-gdp-in-province?select=Chinas+GDP+in+Province+En.csv)**, and\n3. Re-run the relevant parts of the `additional_data.ipynb` Notebook to load, clean, and export .csv(s) from Wikipedia.\n\n## Frameworks\nThis project is driven by Python in Jupyter Notebooks. The libraries I primarily use are:\n\n| Library      | Use                                    |\n| ------------ | -------------------------------------- |\n| `pandas`     | Loading, cleaning, and displaying data |\n| `matplotlib` | Data visualisation                     |\n| `seaborn`    | Data visualisation                     |\n| `plotly`     | Data visualisation - choropleth maps   |\n| `wikipedia`  | Loading data from Wikipedia            |\n| `json`       | Loading data for choropleth maps       |\n\n## References \u0026 Resources\nThe base data is from **[Kaggle](https://www.kaggle.com/datasets/concyclics/chinas-gdp-in-province?select=Chinas+GDP+in+Province+En.csv)**, with additional data extracted from **[Wikipedia](https://en.wikipedia.org/wiki/Main_Page)**.\n\nTo assist with building my choropleth maps, I relied on the following resources:\n* yg2619, 'Choropleth-Maps-in-Python-Using-Plotly', \u003c[GitHub Repository](https://github.com/yg2619/Choropleth-Maps-in-Python-Using-Plotly)\u003e\n* deldersveld, 'topojson', \u003c[GitHub Repository](https://github.com/deldersveld/topojson)\u003e\n* Ying Li, 'A beginners Guide to Choropleth Map In Python to Visualize China's Aging Problem', \u003c[LinkedIn](https://www.linkedin.com/pulse/beginners-guide-choropleth-map-python-visualize-chinas-ying-li/)\u003e\n\n**Please note that the data does not include Hong Kong SAR nor Macau SAR.**","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhenrylin03%2Fchina-gdp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhenrylin03%2Fchina-gdp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhenrylin03%2Fchina-gdp/lists"}