{"id":14977406,"url":"https://github.com/aivanf/lemuras","last_synced_at":"2025-10-28T03:32:04.373Z","repository":{"id":62575636,"uuid":"122880311","full_name":"AivanF/Lemuras","owner":"AivanF","description":"A small Python library to deal with big tables","archived":false,"fork":false,"pushed_at":"2020-05-02T17:55:25.000Z","size":761,"stargazers_count":3,"open_issues_count":1,"forks_count":2,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-02-01T11:24:11.502Z","etag":null,"topics":["bigdata","data-analysis","html","ipython-notebook","join-tables","json","jupyter-notebook","pandas","pivot-tables","python","sql","table"],"latest_commit_sha":null,"homepage":"https://pypi.org/project/Lemuras/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/AivanF.png","metadata":{"files":{"readme":"README-pypi.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}},"created_at":"2018-02-25T21:45:42.000Z","updated_at":"2022-04-18T16:48:56.000Z","dependencies_parsed_at":"2022-11-03T18:57:27.562Z","dependency_job_id":null,"html_url":"https://github.com/AivanF/Lemuras","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AivanF%2FLemuras","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AivanF%2FLemuras/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AivanF%2FLemuras/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AivanF%2FLemuras/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AivanF","download_url":"https://codeload.github.com/AivanF/Lemuras/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":238590596,"owners_count":19497350,"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":["bigdata","data-analysis","html","ipython-notebook","join-tables","json","jupyter-notebook","pandas","pivot-tables","python","sql","table"],"created_at":"2024-09-24T13:55:36.112Z","updated_at":"2025-10-28T03:32:03.907Z","avatar_url":"https://github.com/AivanF.png","language":"Python","readme":"# Lemuras\n\nSometimes you cannot or don't want to use Pandas or similar advanced tool for data analysis, but still have a need to manipulate large tables with code. In such cases you can use **Lemuras** – it is a *pure Python* library without external dependencies. And if you have some experience of Pandas or SQL, then you can easily work with Lemuras.\n\nAgain, this library may be considered as a simplified analogue of Pandas, but not as a replacement. However, Lemuras is capable of processing an operation on a few tables with several thousands of rows in less than a second on a simple web server. So, if you need a tiny library to generate analytical reports or convert table formats, Lemuras is a good choice!\n\n## Features\n\n- Integration with *Jupyter IPython Notebook*: Lemuras objects are printed as nice tables.\n- Save / load **CSV files**, **JSON**, **HTML** tables, **SQL** (both query result and table creation code).\n- Automatic columns types detection, simple type conversion.\n- Access, add, edit, delete cells, rows, columns. Apply custom of built-in functions, lambdas, sort the data, iterate over rows.\n- Advanced processing of **columns**: you can take any table column, apply any function or lambda, do math with several columns and discrete values, compare them, check existing in other columns or lists, filter a table by it, or add it to a table, etc... In other words, you can do anything!\n- **Grouping by** *none*, *one*, or *multiple columns*, **aggregation** with *built-in* or *user-defined functions and lambdas* for specified or just all the columns.\n- **Merge** (**Join**): *inner / left / right / outer*.\n- **Tables concatenation** and **appending**.\n- **Pivot tables** creation.\n\nIt is tasted on Python **2.7** and Python **3.4-3.7**\n\n## Examples\n\nAll the features are described in notebook examples:\n\n1) [Basic things](https://github.com/AivanF/Lemuras/blob/master/examples/Example%201%20-%20Basic%20things.ipynb) – access to columns, cells, rows; add, delete, change their values; also filtering and sorting.\n1.5) [Functions applying](https://github.com/AivanF/Lemuras/blob/master/examples/Example%201.5%20-%20Functions%20Applying.ipynb) – apply functions or lambda expressions to columns or tables, change types, aggregate values, use your own or one of lots predefined useful functions (oncluding statistical ones).\n2) [Group by](https://github.com/AivanF/Lemuras/blob/master/examples/Example%202%20-%20Group%20By.ipynb) – grouping and combining (aggregating).\n3) [Merge / Join](https://github.com/AivanF/Lemuras/blob/master/examples/Example%203%20-%20Merge%20Join.ipynb) – such types: inner, outer, left, right.\n4) [Pivot table](https://github.com/AivanF/Lemuras/blob/master/examples/Example%204%20-%20Pivot%20table.ipynb) – create new tables with columns, rows and cells from another table.\n5) [Tables Concatenate / Append](https://github.com/AivanF/Lemuras/blob/master/examples/Example%205%20-%20Tables%20Concatenate%20Append.ipynb) – simple tables concatenation and appending.\n6) [Types, Read/Write, CSV, SQL, JSON, HTML](https://github.com/AivanF/Lemuras/blob/master/examples/Example%206%20-%20Types%20Read%20Write%20CSV%20SQL%20JSON%20HTML.ipynb) – description of Lemuras supported data types, saving to and loading from CSV, SQL, JSON, HTML formats.\n\nIn addition, there are several complex examples of solving a real world problems:\n\n1) [Retargeting results report](https://github.com/AivanF/Lemuras/blob/master/examples/Complex%20Example%20-%20Retargeting%20results%20report.ipynb)\n\n2) [Filtration by location area](https://github.com/AivanF/Lemuras/blob/master/examples/Complex%20Example%202%20-%20Filtering%20by%20Location%20Area.ipynb)\n\n3) [OLAP reports parser](https://github.com/AivanF/Lemuras/blob/master/examples/Complex%20Example%203%20-%20Parsing%20OLAP%20Reports.ipynb) with CSV, TSV, and even XLS support\n\nThe code of Lemuras is well-commented, also there are many unit-tests, so, you can easily find useful information there. Contributions are welcome.\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faivanf%2Flemuras","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faivanf%2Flemuras","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faivanf%2Flemuras/lists"}