{"id":20817538,"url":"https://github.com/greed2411/ndl","last_synced_at":"2026-04-19T05:35:02.690Z","repository":{"id":125880285,"uuid":"95579702","full_name":"greed2411/NDL","owner":"greed2411","description":"Numbers Don't Lie,  attempt on Data Analysis using pandas and matplotlib.","archived":false,"fork":false,"pushed_at":"2017-08-16T06:19:19.000Z","size":1761,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"master","last_synced_at":"2025-09-14T07:29:06.478Z","etag":null,"topics":["cities","data-analysis","data-science","data-visualization","india","kaggle"],"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/greed2411.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":"2017-06-27T16:38:24.000Z","updated_at":"2023-04-11T15:20:48.000Z","dependencies_parsed_at":"2023-07-08T04:46:32.939Z","dependency_job_id":null,"html_url":"https://github.com/greed2411/NDL","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/greed2411/NDL","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/greed2411%2FNDL","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/greed2411%2FNDL/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/greed2411%2FNDL/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/greed2411%2FNDL/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/greed2411","download_url":"https://codeload.github.com/greed2411/NDL/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/greed2411%2FNDL/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31996445,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-18T20:23:30.271Z","status":"online","status_checked_at":"2026-04-19T02:00:07.110Z","response_time":55,"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":["cities","data-analysis","data-science","data-visualization","india","kaggle"],"created_at":"2024-11-17T21:42:47.560Z","updated_at":"2026-04-19T05:35:02.674Z","avatar_url":"https://github.com/greed2411.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Numbers Don't Lie\n\nAnalysis on cities of India, and what they do have to say about our nation.\n**Kaggle submission.**\n\nData set : [here](https://www.kaggle.com/zed9941/top-500-indian-cities/downloads/cities_r2.csv) or use the one from repo.\n\n### Dependencies required\n\n  * [pandas](https://pandas.pydata.org/pandas-docs/stable/) - For data analysing and manipulation.\n  * [matplotlib](https://matplotlib.org/) - For data visualisation.\n  \n### Actual data set used : December 2016 version, which only had 493 cities.\n\n### Analysis made on the data set as of June 2017\n\n  * Number of cities : **493**\n  * Number of states : **29**\n  * Most number of cities are in the state:  **UTTAR PRADESH**\n  * Number of cities in **UTTAR PRADESH** :  **63**\n  * Least number of cities belong to these states and their counts\n  \n        HIMACHAL PRADESH             1\n        CHANDIGARH                   1\n        TRIPURA                      1\n        MIZORAM                      1\n        NAGALAND                     1\n        MANIPUR                      1\n        MEGHALAYA                    1\n        ANDAMAN \u0026 NICOBAR ISLANDS    1\n\n  * There are two **Aurangabad**(s) in the nation, \n  \n       * one belonging to **BIHAR**\n       * second one belonging to **MAHARASHTRA**\n    \n  * Top 5 States with the maximum number of Cities\n      \n          UTTAR PRADESH     63\n          WEST BENGAL       61\n          MAHARASHTRA       43\n          ANDHRA PRADESH    42\n          TAMIL NADU        32\n    \n  * States vs City counts\n      \n      ![States\u0026citycounts](/../images/figure_1.png?raw=true \"States and city counts\")\n\n  * States vs District counts\n\n      ![States\u0026districtcounts](/../images/figure_1-1.png?raw=true \"States and district counts\")\n\n  * Each city and it's district number plot\n    \n      ![City\u0026districtnumber](/../images/figure_1-2.png?raw=true \"City and district number\")\n      \n    This graph made me analyse and conclude that\n      \n     * The most common district numbers are `11`, `9` and `12` not the conventional `1`, `2` and `3`.\n     \n     * District Number, and their occurences and percentage they contribute to the total district count.\n        Example : District Number `11`, there are `37` districts in our India numbered `11`, which contributes to `7.51%` of                   total number of districts in India.\n        \n                                        District Counts  Percentage Index\n            District Number                                   \n            11                            37              7.51\n            9                             26              5.27\n            12                            24              4.87\n            1                             22              4.46\n            3                             22              4.46\n            21                            21              4.26\n            \n    * `95.94%` of districts have their district number value which is less than `50`\n    \n  * District numbers and their frequency\n    \n      ![districtcounts](/../images/figure_1-3.png?raw=true \"District frequency\")\n      \n     * The above graphs tells us that there are no district numbers from 72 to 98 and few numbers here and there in the 40s \u0026 50s\n            \n          Actual missing district numbers : \n            \n            40, 42, 43, 45, 51, 53, 55, 56, 58, 67, 69, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98\n            \n   * Missing State Codes\n     \n          11, 12, 25, 26, 30, 31\n      \n    \n      \n      \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgreed2411%2Fndl","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgreed2411%2Fndl","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgreed2411%2Fndl/lists"}