semvec/pypi-packages huggingface dataset:
quickpanda-aidroid 0.0.1
quick analysis and dataanalysis pipleline
Requires Python: >=3.6
Keywords: computer vision

#a pipeline for quick data analysis and machine learning

##file_operate:

***
--------
> <u>__*~~123~~*__</u>

* read file and do base operate like auto asdtype etc.
* it support multi type files operates together.

##preprocess:

    create a preprocessor to preprocessing(dropna,fillna,dropoutlyers...)
    you can select the file and cols by passing dict-type args.
##analysis:
    base on the previous manipulations,we get clean datas,we can now acutally start the analysis tasks:
    correlation:
        get correlations between value-type features and labels. 
        compare the correlation between class-type features and labels.
        

##modeling:
    we provide base ml models to complete classification or regression tasks
    listing:
        gbdt:xgboost,light gbm,radom forests
        norm:svc,linear,logistic,bayes
        timesequence:ARMA,ARIMA
        nn:
        DeepLearning:\

statistic_test:

[//]: # (    [test func]&#40;https://blog.csdn.net/weixin_46271668/article/details/123981051&#41;)

    normality test
    correlation test
    significance test
    parametric test
    nonparametric test

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![alt 属性文本](C:\Users\23920\Desktop\avatar.jpg)
text-chunker 0.2.2
A Python class that takes in long text as input and divides it into shorter chunks no longer than a specified length.
Requires Python: >=3.5

# TextChunker

TextChunker is a Python class that takes in long text as input and divides it into shorter chunks no longer than a specified length. The purpose of this project is to provide a simple and useful tool for data processing tasks such as natural language processing and information extraction.

## Install
``` sh
pip intsll text_chunker
```

## Usage

You can use the `TextChunker` class in your Python code as follows:

``` python
from text_chunker import TextChunker

# Create a new TextChunker object with a maximum chunk length of 50 characters
chunker = TextChunker(maxlen=1000)

# Chunk a long text string into smaller chunks
text = "This is a long text string..."
for chunk in chunker.chunk(text):
    print(chunk)
```

The `chunk` method attempts to split paragraphs first while keeping chunk length below `maxlen`. If a paragraph is longer than `maxlen`, the method attempts to split the paragraph into sentences. If a sentence is longer than `maxlen`, it is split into smaller chunks no longer than `maxlen`.

There are also functions called `paragraphs` and `sentences` that divide the text into paragraphs and sentences, respectively.

``` python
from text_chunker import paragraphs

for p in paragraphs(text):
    print(p)
```
``` python
from text_chunker import sentences

for s in sentences(text):
    print(s)
```

The `sentences` function utilizes a tokenizer from the [`nltk`](https://pypi.org/project/nltk/) library.


## License

This project is distributed under the MIT license. See the LICENSE file for details.
bettersleepy 0.1.2
Makes the Python sleep module easier a bit.

BetterSleePy
============

Makes the Python sleep module easier a bit.

Installation
============

::

   pip3 install bettersleepy

Usage
=====

I recommend importing it as wait to make it shorter.

Seconds:

::

   import bettersleepy as wait

   wait.sec(1)
   print("Waited 1 second.")

Minutes:

::

   import bettersleepy as wait

   wait.min(1)
   print("Waited 1 minute.")

Hours:

::

   import bettersleepy as wait

   wait.hour(1)
   print("Waited 1 hour.")
lotrpy 1.0.1
Lord Of the Rings Python SDK
Requires Python: >=3.10.6

# dinesh_ketumakha-SDK

This is the python sdk for accessing https://the-one-api.dev/ server's endpoints related to Lord Of the Rings movies & quotes.

# Installation
To install using ``pip`` do the following in a virtual environment

    $ mkdir my_lotr_test_dir
    $ cd my_lotr_test_dir
    $ python3 -m venv venv
    $ pip install lotrpy


# Usage

The following is how you can use this SDK

    from lotrpy.lotr import LOTR
    
    token = "bearer_token_value"
    base_url = "https"//server.com/api/v1"

    lotr = LOTR(token, base_url)
    
    # get all movies
    lotr.get_movies()

    # get all movies on page 2 if there was more than 1 page 
    # in the response above
    lotr.get_movies(page=2)

    # get all movies, limit each response to 5  items
    lotr.get_movies(limit = 5)

    # get info on a single movie
    movie_id = "123lkdlfs"
    lotr.get_movie(movie_id)

    # get all quotes. paging and limit apply just like above
    lotr.get_quotes()

    # get all quotes from one specific movie
    lotr.get_quotes_from_movie(movie_id)

    # get one specific quote. Need to get the quote_id from 
    # response of get_quotes()
    lotr.get_quote(quote_id) 


# Testing

To run the tests, install pytest in the venv created above

    $ pip install pytest

Clone this repo and cd into the root of the directory

    $ git clone https://github.com/gypsyx/dinesh_ketumakha-SDK.git

    $ cd dinesh_ketumakha-SDK
    $ pytest # runs all tests

To run just unit tests

    $ pytest -k unit