A small Python library to deal with big tables
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Type Name Latest commit message Commit time
Failed to load latest commit information.
lemuras Minor fixes Feb 21, 2019
.travis.yml Update .travis.yml Feb 21, 2019


Lemuras Build Status Code coverage

Sometimes 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.

Again, 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!



  • Integration with Jupyter IPython Notebook: Lemuras objects are printed as nice tables.
  • Save / load CSV files, JSON, HTML tables, SQL (both query result and table creation code).
  • Automatic columns types detection, simple type conversion.
  • Cells access, rows, columns adding, deleting, columns renaming, functions/lambdas applying, rows sorting.
  • 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!
  • Grouping by none, one, or multiple columns, aggregation with built-in or user-defined functions and lambdas for specified or just all the columns.
  • Merge (Join): inner / left / right / outer.
  • Tables concatenation and appending.
  • Pivot tables creation.

It was tasted on both Python 2.7 and Python 3.6


There are 2 simple options:


All the features are described in notebook examples:

  1. Basic things – access to columns, cells, rows; add, delete, change their values; also filtering and sorting. 1.5) Functions applying – 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).
  2. Group by – grouping and combining (aggregating).
  3. Merge / Join – such types: inner, outer, left, right.
  4. Pivot table – create new tables with columns, rows and cells from another table.
  5. Tables Concatenate / Append – simple tables concatenation and appending.
  6. Types, Read/Write, CSV, SQL, JSON, HTML – description of Lemuras supported data types, saving to and loading from CSV, SQL, JSON, HTML formats.

In addition, there are complex examples of solving a real world problems:

The code of Lemuras is well-commented, also there are many unit-tests, so, you can easily find useful information there. Contributions are welcome.


The list is available on GitHub.


This software is provided 'as-is', without any express or implied warranty. You may not hold the author liable.

Permission is granted to anyone to use this software for any purpose, including commercial applications, and to alter it and redistribute it freely, subject to the following restrictions:

The origin of this software must not be misrepresented. You must not claim that you wrote the original software. When use the software, you must give appropriate credit, provide an active link to the original file, and indicate if changes were made. This notice may not be removed or altered from any source distribution.