semvec/pypi-packages huggingface dataset:
scikit-highs 0.6.0
Cython interface to HiGHS.
License: MIT

pyHiGHS
=======

Cython wrappers over `HiGHS <https://github.com/ERGO-Code/HiGHS>`_.  This project is a fork of the main repo: `fork <https://github.com/mckib2/HiGHS/>`_.

The package is available on pypi as `scikit-highs <https://pypi.org/project/scikit-highs/>`_ and is tested on the following:

- Ubuntu 18, gcc 7.5.0, Python 3.6.9 and 3.7
- Windows 10, VS 2019, Python 3.8.2 x64

It has dependencies on numpy and Cython that require these to be installed before installation of scikit-highs.  Once numpy and Cython are installed, install using pip like this:

.. code:: bash

    pip install scikit-highs

Example usage from script:

.. code:: python

    from _highs.highs_wrapper import highs_wrapper
    from _highs.highs_wrapper import CONST_INF, CONST_I_INF

    # Call like this (usually using numpy arrays and A is a scipy.sparse.csc_matrix):
    res = highs_wrapper(c, A.indptr, A.indices, A.data, lhs, rhs, lb, ub, options)

`options` is a dict that supports all options of the HiGHS C++ API.

It will take a second to compile when installing, I did not create any wheels, etc, so always compiles from source.
fast-csv 1.3.4
reduce memory usage
License: MIT

[![Build Status](https://travis-ci.com/YUX-IO/fast_csv.svg?branch=master)](https://travis-ci.com/YUX-IO/fast_csv)
![PyPI - Downloads](https://img.shields.io/pypi/dm/fast_csv?color=4AC41C)
[![Coverage Status](https://coveralls.io/repos/github/YUX-IO/fast_csv/badge.svg?branch=master)](https://coveralls.io/github/YUX-IO/fast_csv?branch=master)
[![codebeat badge](https://codebeat.co/badges/82ca9fd2-b2e1-439e-b0ac-54fc948127f7)](https://codebeat.co/projects/github-com-yux-io-fast_csv-master)
## requirements

- Pandas
- Numpy

## installation

`pip install fast_csv`

`!pip install fast_csv` for Google Colab or Kaggle notebook

## usage

```python
import fast_csv as fc
data = fc.read_csv('$PATH/$FILE.csv')
```

```python
import pandas as pd
import fast_csv as fc

data = fc.reduce_df(pd.DataFrame())
```

## vs pd.read_csv

See `data.info()`

- it reduces 50% of memory usage on average
- it reduces 90%+ of memory usage on a good day