semvec/pypi-packages huggingface dataset:
owid-repack 0.1.3
Pack Pandas data frames into smaller, more memory-efficient data types.
License: MIT
Requires Python: >=3.8.1

# owid-repack-py

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![](https://img.shields.io/badge/python-3.8|3.9|3.10|3.11-blue.svg)

_Pack Pandas DataFrames into smaller, more memory efficient types._

## Overview

When you load data into Pandas, it will use standard types by default:

- `object` for strings
- `int64` for integers
- `float64` for floating point numbers

However, for many datasets there is a much more compact representation that Pandas could be using for that data. Using a more compact representation leads to lower memory usage, and smaller binary files on disk when using formats such as Feather and Parquet.

This library does just one thing: it shrinks your data frames to use smaller types.

## Installing

`pip install owid-repack`

## Usage

The `owid.repack` module exposes two methods, `repack_series()` and `repack_frame()`.

`repack_series()` will detect the smallest type that can accurately fit the existing data in the series.

```ipython
In [1]: from owid import repack

In [2]: pd.Series([1, 2, 3])
Out[2]:
0    1
1    2
2    3
dtype: int64

In [3]: repack.repack_series(pd.Series([1.5, 2, 3]))
Out[3]:
0    1.5
1    2.0
2    3.0
dtype: float32

In [4]: repack.repack_series(pd.Series([1, None, 3]))
Out[4]:
0       1
1    <NA>
2       3
dtype: UInt8

In [5]: repack.repack_series(pd.Series([-1, None, 3]))
Out[5]:
0      -1
1    <NA>
2       3
dtype: Int8
```

The `repack_frame()` method simply does this across every column in your DataFrame, returning a new DataFrame.

## Releases

- `0.1.3`:
    - Improve performance on float dtypes
- `0.1.2`:
    - Shrink columns with all NaNs to Int8
- `0.1.1`:
    - Fix Python support in package metadata to support 3.8.1 onwards
- `0.1.0`:
  - Migrate first version from `owid-catalog-py` repo
MyPakage 0.2.8
this code is used for my learning, because other wheels are not enough or perfect author:nwkami
License: MIT

# 简介
this code is used for my learning, 
because other wheels are not enough or perfect  
author: nwkami.   
init.py should put in a path named as your package name,and "name = "example_pkg""in it
again I move the code py into the path where init in. and in init we write "from xxx import *"  
then this version is the first usable version
README文件中有中文的时候，setup需要有特殊的设置。  
这一版我加上了  
库函数的代码我没有改
# 更新记录
~~~~~~~~~~~~~~~~~~~~
加上了matrixlizing_function函数
~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~
加上了get_location_by_name
~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~
把get_location_by_name改进了  
现在一二参数的函数都可使用
~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~
把readme重写了一遍
~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~
原来install_requires中的库可以在导入的时候同时导入  
其他库
~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~
增加了install_requires中的库
~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~
增加了cartesian_product函数
~~~~~~~~~~~~~~~~~~~~
# 该版本更新记录
~~~~~~~~~~~~~~~~~~~~
增加了shortest_path_with_inf函数
~~~~~~~~~~~~~~~~~~~~
# 函数列表
~~~~~~~~~~~~~~~~~~~~
1.calc_azimuth
2.int_matrix
3.save_variable
4.load_variable
5.ndarray2img
6.argsort_matrix
7.matrixlizing_function
8.get_location_by_name
9.cartesian_product
10.shortest_path_with_inf
~~~~~~~~~~~~~~~~~~~~
cardia 0.0.8
Python library for intelligent code card generation.
License: MIT

# Cardia

Welcome to Cardia, a Python-based library designed for building large AI-executable systems. Code cards, compact files containing a single class or function along with unit tests, serve as executable building blocks for AI agents, enabling them to execute tasks more efficiently.

## Features

- **Effortless System Building**: Cardia simplifies the process of constructing large, AI-executable systems by breaking them down into manageable code cards.

- **Intelligent Code Execution**: The library enables AI agents to execute tasks with precision and efficiency, thanks to the modular nature of code cards.

- **Seamless Code Transformation**: Easily convert existing code into code cards, allowing for swift integration into your AI-powered projects.

- **Enhanced Code Reusability**: Code cards promote reusability, accelerating the development of complex systems and fostering collaborative coding practices.

- **Robust Unit Testing Support**: Each code card includes comprehensive unit tests, ensuring code integrity and reliability throughout development.

## Installation

You can install Cardia using pip:

```bash
pip install cardia
```

This command will download and install the latest version of Cardia along with any required dependencies. If you want to keep Cardia up-to-date, simply run the same command with the --upgrade flag:

```bash
pip install cardia --upgrade
```

Now you're ready to start using Cardia to intelligently generate and manage your code cards.

*Disclaimer: This project is under active development. Please report any issues or suggestions you may have. Thank you!*