semvec/pypi-packages huggingface dataset:
ctxpy 0.1.3
A minimal but opinionated dict/object combo (like Bunch).
License: MIT
Keywords: ctx,ctxpy

===
ctx
===

ctx - A minimal but opinionated dict/object combo (like Bunch).

.. image:: https://travis-ci.org/figlief/ctx.png?branch=master
        :target: https://travis-ci.org/figlief/ctx

.. contents::
   :backlinks: none

Requirements
------------
The ctx module should work with  all versions of Python.

Features
--------

The ctx module provides the `Ctx` class which is a subclass of
the python 'dict' object.

    Ctx modifies 'dict' in the following ways:

        1  The dictionary items can be read or set using attribute
        access notation.

            'ctx.a' is identical to 'ctx["a"]' and
            'ctx.a = 5' is identical to 'ctx["a"] = 5'

        2  The objects attributes can be read using item access notation.

            'ctx["__doc__"]' is identical to 'ctx.__doc__'

        3  The objects attributes can not be set under any circumstances.

        4  The dictionary can not have a key with the same name as an
        objects attribute.


    'ctx.name' and 'ctx["name"]' are resolved using the following three steps.

        a)  if the object has an attribute 'name' then return the attribute.

        b)  if the dictionary has a key 'name' then return the value
        associated with the key.

        c)  raise LookupError.


    'ctx.name = 5' and 'ctx["name"] = 5'

        a) If name is an attribute of the class then raise AttributeError.

        b) set the dictionary item as usual.


Installation
------------
You can install this package using pip with the following command. ::

    pip install ctx


Support
-------
To report any bugs, or ask any question, please visit ::

    https://github.com/figlief/ctx/issues.


Resources
---------
Here is a list of useful links about this project.

- `Latest release on PyPI <https://pypi.python.org/pypi/ctx>`_
- `Source code on GitHub <https://github.com/figlief/ctx>`_
- `Issue tracker on GitHub <https://github.com/figlief/ctx/issues>`_
- `Changelog on GitHub <https://github.com/figlief/ctx/blob/master/History.rst>`
impexpr 0.0.5
A simple superset of Python with import expressions added in
License: MIT
Requires Python: >=3.7,<4.0
Keywords: python,import,syntax

# Impexpr
A simple superset of Python with import expressions added in. Beware: This project has only been made as a proof of concept and is not intended to ever be used in production. 

## Installation
```bash
pip install impexpr
```
## Quickstart
`impexpr` alias can be used in the same way as `python` alias, a few examples:
* To run an interactive REPL with import expression support, run `impexpr`
* To run a script, run `impexpr script.py` (all imported modules will also support import expressions) 
* To import itertools and use it in the same line:
```python
for x in (import itertools).chain([1, 2], [3, 4], [5, 6]):
    print(x)
```
* To get help about collections.deque:
```python
help((import collections).deque)
```
* If your script must run with python alias but you want to enable import expression support in all imported modules (not in the main script!), you can use:
```python
import impexpr
impexpr.add_hook()
# this will now support import expressions
import my_other_script
```
* For everything else, run `impexpr --help`

## FAQ
* Relative imports not supported yet
multistop 1.3
文本分析停用词表，支持中英德法等15种语言。
License: MIT
Requires Python: >=3.5
Keywords: chinese text analysis,text analysis,text mining,stopwords,natural language processing



# 一、multistop

停用词表, 同时支持中英德等15种语言。

<br>

<br>



# 二、安装

```
pip3 install multistop
```



# 三、使用

初始化停用词类

```
from multistop import Stopwords
#默认选取的中文lang='chinese'
sw = Stopwords()
```



<br>

查看支持的语言

```
sw.languages()
```

Run

```
dict_keys(['dutch', 'german', 'hungarian', 'turkish', 'russian', 'italian', 'english', 'norwegian', 'portuguese', 'finnish', 'danish', 'french', 'swedish', 'spanish', 'chinese'])

```



<br>

选择某种语言的停用词表

```
sw.setlang(lang='chinese')
```

Run

```
set language to chinese
```



<br>



词表长度

```
sw.size()
```



Run

```
778
```



<br>

查看停用词表是否含有某词

```
sw.contains('的')
```

Run

```
True
```



<br>



添加新停用词

```
sw.add('6啊')
sw.size()
```

Run

```
779
```



<br>



将停用词表下载下来

```
sw.download('chinese.txt')
```



<br>

<br>

# 如果

如果您是经管人文社科专业背景，编程小白，面临海量文本数据采集和处理分析艰巨任务，可以参看[《python网络爬虫与文本数据分析》](https://ke.qq.com/course/482241?tuin=163164df)视频课。作为文科生，一样也是从两眼一抹黑开始，这门课程是用五年时间凝缩出来的。自认为讲的很通俗易懂o(*￣︶￣*)o，

- python入门
- 网络爬虫
- 数据读取
- 文本分析入门
- 机器学习与文本分析
- 文本分析在经管研究中的应用

感兴趣的童鞋不妨 戳一下[《python网络爬虫与文本数据分析》](https://ke.qq.com/course/482241?tuin=163164df)进来看看~

[![](img/课程.png)](https://ke.qq.com/course/482241?tuin=163164df)



# 更多

- [B站:大邓和他的python](https://space.bilibili.com/122592901/channel/detail?cid=66008)

- 公众号：大邓和他的python

- [知乎专栏：数据科学家](https://zhuanlan.zhihu.com/dadeng)

<br>

![](img/大邓和他的Python.png)
scanpy-1c 0.0.0
An example project demonstrating various ways to access data files in Python package
License: MIT
Keywords: packaging,resources

## scanpy
> Scientific Anomaly detection using Python

Extreme event in computational (sci) situations are physics-driven anomalies, as opposed to
statistical outliers or measurement noise, and their detection is vital to physical nature of
scientific simulations. For example detecting combustion instabilities in 2D simulation, anomalous event in ecg-records across various patients etc.

The package provides collection of common unsupervised and supervised learning algorithms applied to scientific data settings. 

Sample turbulence dataset link: [Here](http://turbulence.pha.jhu.edu/)



## Algorithms Implemented

- KNN
- Linear Regression
- Logistic Regression
- Naive Bayes
- Perceptron
- SVM
- Decision Tree
- Random Forest
- Principal Component Analysis (PCA)
- K-Means
- AdaBoost
- Linear Discriminant Analysis (LDA)

## Where to get it
The source code is currently hosted on GitHub at: https://github.com/pandas-dev/pandas

Installers for the latest released version are available at the Python Package Index (PyPI) 

pip install 

This project has 2 dependencies.

- `numpy` for the maths implementation and writing the algorithms
- `Scikit-learn` for the data generation and testing.
- `Matplotlib` for the plotting.
- `Pandas` for loading data.
- 'Cantera'To install cantera check [Here] https://cantera.org/install/

**NOTE**: Do note that, Only `numpy` is used for the implementations. Others
help in the testing of code, and making it easy for us, instead of writing that
too from scratch.
pyse2e 0.1.0
Testing capabilities for server-side web APIs
License: MIT

# pyse

Pyse (**Py**thon **s**erver **E**2E testing) provides testing capabilities for
server-side web APIs via the unittest and requests libraries.

Simply subclass PyseTestCase:

```python
import requests

from pyse import PyseTestCase


class PyseTest(PyseTestCase):
    def test_my_api(self):
        response = requests.get("http://127.0.0.1:5000/text")
        self.assert_success(response)
        self.assert_content_equal(response, "Text")
```

## Requirements

This library requires Python >= 3.9.

## Installation

You can install pyse using the pip package manager:

```shell
pip install pyse
```