semvec/pypi-packages huggingface dataset:
ML-Education-Tools 24.1.18
ML Education Tools for Teaching
Requires Python: >=3.5

<img src="https://wqxuetang.oss-cn-beijing.aliyuncs.com/cover/3/236/3236048/3236048.jpg!wqb" alt="Python与数据分析及可视化" style="zoom:25%;" />

# Prof.Li's ： ML Education Tools

###### Author: 道法自然  lilluqun@gmail.com success@shnu.edu.cn

## **模块安装：pip install -U ml-education-tools**

* AI Education Tools模块为《零基础入门机器学习-从机器模型到Web应用集成实践》配套教学工具。
* 模块由作者Prof.Luqun Li团队自主开发 。
* 其他教学资源可以参见GitHub：https://github.com/liluqun。
* 作者会对模块不断更新，确保为读者提供最佳的代码工具、教学案例与服务。
* 如果您有好的建议，请发邮件到liluqun@gmail.com联系我。

<img src="https://bkimg.cdn.bcebos.com/pic/8694a4c27d1ed21b0ef4a2a7ac20cac451da81cb9169?x-bce-process=image/format,f_auto" alt="hah" style="zoom:25%;" />

`````

提前备好：
    1.学生名单： **.xlsx , 从教务处系统导出学生名单，然后一定另存为xlsx文件。
    2.指明作业目录：homework_dir ='./期末试卷'
    3.指明作业排序目录：sorted_homework_dir=./期末试卷-ok
*******************************************
编辑一个文本文件：
shnu.ini,内容样本为：
name_list=人工智能.xlsx 
homework_dir =./期末试卷
sorted_homework_dir=./期末试卷-ok
直接在操作系统下运行：shnu， 如：c:\>shnu

如果用户没有shnu.ini文件，不用担心，直接运行c:\>shnu，便可在当前目录下创建shnu.ini
编辑好shnu.ini
再次运行，就会得到 排序的作业目录，和作业统计Excel文件。
******************************************

api调用使用方法:
from shnu.tools import Assistant
ast=Assistant()
df=ast.homework_check()
 返回3个DataFrame；
      0：统计学生作业的Dataframe:
      1：作业提交次数为零的Dataframe:
      2：作业次数大于0的DataFrame

使用用例1：
	ast=Assistant(name_list='人工智能.xlsx', homework_dir ='./期末试卷',sorted=False)
	result=ast.homework_check()
	print(result[0],result[1],result[2])
使用用例2：
    ast=Assistant(sorted=True,sorted_homework_dir='e:/ok')
    result=ast.homework_check()
    print(result[0],result[1],result[2])
`````

![hah](https://www.drbataipei.org/wisdom/165/images/165_4.jpg)
dvu 0.0.2
Functions for data visualization in matplotlib.
Requires Python: >=3.6

<h1 align="center"> Data-viz utils 📈</h1>
<p align="center"> Functions for data visualization in matplotlib
</p>
<p align="center">
  <img src="https://img.shields.io/badge/license-mit-blue.svg">
  <img src="https://img.shields.io/badge/python-3.6--3.8-blue">
  <img src="https://img.shields.io/github/checks-status/csinva/data-viz-utils/master">
  <img src="https://img.shields.io/pypi/v/dvu?color=orange">
</p>  
<p align="center" fontsize=40><a href="https://csinva.io/data-viz-utils/docs/dvu.html"> 📚 API </a>
</p>


Can be installed using `pip install dvu` and then imported with `import dvu`. 

You can also just copy the relatively short source code for the functions (easily viewable [here](https://csinva.io/data-viz-utils/docs/dvu.html)). 

Helps create a bunch of different plots such as these:

![](https://csinva.io/data-viz-utils/img/plots_screenshot.png)



One particularly useful function is `dvu.line_legend()` which replaces a typical matplotlib legend with labels for each line:


| Using `plt.legend()`                                | Using `dvu.line_legend()`                      |
| --------------------------------------------------- | ---------------------------------------------- |
| ![plt_legend](docs/img/plot_labeled_lines_orig.png) | ![dvu_legend](docs/img/plot_labeled_lines.png) |



Another one is `dvu.invert_plot()` which can be called after generating a plot to invert everything besides the line colors

| Original plot                                  | After `dvu.invert_plot()`                           |
| ---------------------------------------------- | --------------------------------------------------- |
| ![plt_legend](docs/img/plot_labeled_lines.png) | ![dvu_legend](docs/img/plot_labeled_lines_dark.png) |



# Reference

- for updates, star the repo or follow [@csinva_](https://twitter.com/csinva_)
- super-related and wonderful [matplotlib-label-lines](https://github.com/cphyc/matplotlib-label-lines) project
- [PR](https://t.co/lTe19vdETE?amp=1) for implementing line-labeling into matplotlib
- feel free to use openly!
- built with jekyll + github pages
- theme from [here](https://github.com/inded/Jekyll_modern-blog)
    - based off of this [article from Codrops](http://tympanus.net/codrops/?p=24222)