{"id":20809556,"url":"https://github.com/amhsirak/ickle","last_synced_at":"2026-04-22T17:01:42.515Z","repository":{"id":42498038,"uuid":"431492668","full_name":"amhsirak/ickle","owner":"amhsirak","description":"DataFrame, analysis \u0026 manipulation library for tiny labeled datasets","archived":false,"fork":false,"pushed_at":"2024-01-31T18:23:24.000Z","size":155,"stargazers_count":14,"open_issues_count":2,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-05-07T08:03:53.551Z","etag":null,"topics":["data-analysis","dataframe","datascience","ickle","pandas","python"],"latest_commit_sha":null,"homepage":"https://pypi.org/project/ickle/","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/amhsirak.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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}},"created_at":"2021-11-24T13:20:34.000Z","updated_at":"2024-10-17T18:02:19.000Z","dependencies_parsed_at":"2023-11-10T13:08:39.976Z","dependency_job_id":"8e220642-24c9-43ff-81e4-993f769eb64e","html_url":"https://github.com/amhsirak/ickle","commit_stats":{"total_commits":159,"total_committers":3,"mean_commits":53.0,"dds":0.08176100628930816,"last_synced_commit":"7adf7240b6a9ef54ff9723d2d5acd57ece9cf610"},"previous_names":["amhsirak/ickle","karishmashuklaa/ickle"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amhsirak%2Fickle","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amhsirak%2Fickle/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amhsirak%2Fickle/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amhsirak%2Fickle/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/amhsirak","download_url":"https://codeload.github.com/amhsirak/ickle/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252839293,"owners_count":21812089,"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":["data-analysis","dataframe","datascience","ickle","pandas","python"],"created_at":"2024-11-17T20:14:23.469Z","updated_at":"2026-04-22T17:01:32.502Z","avatar_url":"https://github.com/amhsirak.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003ch1 align=\"center\"\u003e 📈 Ickle - Data Analysis Library\u003c/h1\u003e\n\n\u003ch3 align=\"center\"\u003e\n  A tiny DataFrame, statistics and analysis library for Python\n\u003c/h3\u003e\n\n\u003cdiv align=\"center\"\u003e\n\n[![PyPI version](https://badge.fury.io/py/ickle.svg)](https://badge.fury.io/py/ickle)\n[![Downloads](https://static.pepy.tech/personalized-badge/ickle?period=total\u0026units=international_system\u0026left_color=grey\u0026right_color=orange\u0026left_text=Downloads)](https://pepy.tech/project/ickle)\n[![Package Status](https://img.shields.io/static/v1?label=status\u0026message=stable\u0026color=brightgreen)](https://pypi.org/project/ickle/)\n  \n\u003c/div\u003e\n\n## Installation\n\nIckle can be installed via pip through PyPi\n\n```\npip install ickle\n```\n\n## Features\n- [x]  DataFrame along with Visual Representation\n- [x]  Basic properties (len, columns, shape, etc)\n- [x]  Subset Selection\n- [x]  Basic Methods (head, tail)\n- [x]  Aggregation Methods (min, max, median, sum, etc)\n- [x]  Non-Aggregation Methods (abs, copy, clip, cummin, etc)\n- [x]  Additional Methods (isna, count, unique, etc)\n- [x]  String-Only Methods (capitalize, center, count, find, etc)\n- [x]  Pivot Table\n- [ ]  CSV\n    - [x]  read_csv\n    - [ ]  to_csv\n- [ ]  Excel\n    - [x]  read_excel\n    - [ ]  to_excel\n    \n... and more. 🚀 Checkout [PATH.md](PATH.md) to see the roadmap.\n\n## How To Contribute?\nSee [CONTRIBUTION.md](CONTRIBUTION.md) to know more.\n\n## Getting Started\n\n### DataFrame\nA DataFrame holds two dimensional heterogenous data. It accepts dictionary as input, with Numpy arrays as values and strings as column names.\n\n```py\nimport numpy as np\nimport ickle as ick\n\nname = np.array(['John', 'Sam', 'Tina', 'Josh', 'Jack', 'Jill'])\nplace = np.array(['Kolkata', 'Mumbai', 'Delhi', 'Mumbai', 'Mumbai', 'Mumbai'])\nweight = np.array([57, 70, 54, 59, 62, 70])\nmarried = np.array([True, False, True, False, False, False])\n\ndata = {'name': name, 'place': place, 'weight': weight, 'married': married}\ndf = ick.DataFrame(data)\n```\n\n## Documentation\n\nRead the documentation \u003ca href=\"https://nbviewer.org/github/karishmashuklaa/ickle/blob/master/Ickle%20Documentation.ipynb\"\u003ehere\u003c/a\u003e\n\n## Authors\n\u003ca href=\"https://github.com/karishmashuklaa\"\u003e@karishmashuklaa\u003c/a\u003e\n\n\u003ca href=\"https://github.com/psy-pri\"\u003e@psy-pri\u003c/a\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famhsirak%2Fickle","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Famhsirak%2Fickle","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famhsirak%2Fickle/lists"}