semvec/pypi-packages huggingface dataset:
py-wisharetec-utils 1.0.0
郭磊开发的慧享科技工具库
License: MIT
Requires Python: >=3.0

# py_wisharetec_utils

#### 介绍
a python wisharetec utils by guolei

#### 软件架构
软件架构说明


#### 安装教程

1.  xxxx
2.  xxxx
3.  xxxx

#### 使用说明

1.  xxxx
2.  xxxx
3.  xxxx

#### 参与贡献

1.  Fork 本仓库
2.  新建 Feat_xxx 分支
3.  提交代码
4.  新建 Pull Request


#### 特技

1.  使用 Readme\_XXX.md 来支持不同的语言，例如 Readme\_en.md, Readme\_zh.md
2.  Gitee 官方博客 [blog.gitee.com](https://blog.gitee.com)
3.  你可以 [https://gitee.com/explore](https://gitee.com/explore) 这个地址来了解 Gitee 上的优秀开源项目
4.  [GVP](https://gitee.com/gvp) 全称是 Gitee 最有价值开源项目，是综合评定出的优秀开源项目
5.  Gitee 官方提供的使用手册 [https://gitee.com/help](https://gitee.com/help)
6.  Gitee 封面人物是一档用来展示 Gitee 会员风采的栏目 [https://gitee.com/gitee-stars/](https://gitee.com/gitee-stars/)
pyjanitor 0.32.3
Tools for cleaning pandas DataFrames
License: MIT
Requires Python: >=3.6


`pyjanitor` is a Python implementation of the R package [`janitor`][janitor].
It provides a clean user-friendly API for extending pandas with powerful and readable data-cleaning functions .

[janitor]: https://github.com/sfirke/janitor

## Quick start

- Installation: `conda install -c conda-forge pyjanitor`. Read more installation instructions [here](https://pyjanitor-devs.github.io/pyjanitor/#installation).
- Check out the collection of [general functions](https://pyjanitor-devs.github.io/pyjanitor/api/functions/).

## Why janitor?

Originally a port of the R package,
`pyjanitor` has evolved from a set of convenient data cleaning routines
into an experiment with the [`method chaining`][mc] paradigm.

[mc]: https://towardsdatascience.com/the-unreasonable-effectiveness-of-method-chaining-in-pandas-15c2109e3c69

Data preprocessing usually consists of a series of steps
that involve transforming raw data into an understandable/usable format.
These series of steps need to be run in a certain sequence to achieve success.
We take a base data file as the starting point,
and perform actions on it,
such as removing null/empty rows,
replacing them with other values,
adding/renaming/removing columns of data,
filtering rows and others.
More formally, these steps along with their relationships
and dependencies are commonly referred to as a Directed Acyclic Graph (DAG).

The `pandas` API has been invaluable for the Python data science ecosystem,
and implements method chaining of a subset of methods as part of the API.
For example, resetting indexes (`.reset_index()`),
dropping null values (`.dropna()`), and more,
are accomplished via the appropriate `pd.DataFrame` method calls.

Inspired by the ease-of-use
and expressiveness of the `dplyr` package
of the R statistical language ecosystem,
we have evolved `pyjanitor` into a language
for expressing the data processing DAG for `pandas` users.

## Installation

`pyjanitor` is currently installable from PyPI:

```bash
pip install pyjanitor
```

`pyjanitor` also can be installed by the conda package manager:

```bash
conda install pyjanitor -c conda-forge
```

`pyjanitor` can be installed by the pipenv environment manager too. This requires enabling prerelease dependencies:

```bash
pipenv install --pre pyjanitor
```

`pyjanitor` requires Python 3.6+.

## Functionality

Current functionality includes:

- Cleaning columns name (multi-indexes are possible!)
- Removing empty rows and columns
- Identifying duplicate entries
- Encoding columns as categorical
- Splitting your data into features and targets (for machine learning)
- Adding, removing, and renaming columns
- Coalesce multiple columns into a single column
- Date conversions (from matlab, excel, unix) to Python datetime format
- Expand a single column that has delimited, categorical values
  into dummy-encoded variables
- Concatenating and deconcatenating columns, based on a delimiter
- Syntactic sugar for filtering the dataframe based on queries on a column
- Experimental submodules for finance, biology, chemistry, engineering, and pyspark