semvec/pypi-packages huggingface dataset:
presamples 0.2.7
Package to write, load, manage and verify numerical arrays, called presamples.

# presamples ![](presamples_logo.png) 

 Package to write, load, manage and verify numerical arrays, called `presamples`.

 Initially written for scenario analysis and for the reuse of sampled data in Monte Carlo Simulations
 in the [Brightway LCA framework](https://brightwaylca.org/).

 However, the presamples package is software-generic and built on the datapackage	standard	by	
 Open	Knowledge	Foundation. 

 Presamples are useful anytime values for named parameters or matrix elements need to be saved and reused.

 **Installation**: Via `pip` or `conda` (`conda install --channel pascallesage presamples`).

 **Documentation**: We are in the process of writing better documentation.  
 - To read our documentation "under construction", go to our [readthedocs](https://presamples.readthedocs.io/en/latest/?badge=latest) page. 
 - If you can't find what you are looking for, you can also try the Jupyter Notebook [here](https://github.com/PascalLesage/presamples/tree/master/docs/notebooks/presamples%20docs%20and%20examples.ipynb)


[![Linux/OS X build status](https://travis-ci.org/PascalLesage/presamples.svg?branch=master)](https://travis-ci.org/PascalLesage/presamples) 
[![Windows build status](https://ci.appveyor.com/api/projects/status/pb519kt7kbs188oi/branch/master?svg=true)](https://ci.appveyor.com/project/PascalLesage/presamples/branch/master)
[![Coverage Status](https://coveralls.io/repos/github/PascalLesage/presamples/badge.svg?branch=master)](https://coveralls.io/github/PascalLesage/presamples?branch=master)
[![Documentation Status](https://readthedocs.org/projects/presamples/badge/?version=latest)](https://presamples.readthedocs.io/en/latest/?badge=latest)
outliers-mnv 0.0.5
OUTLIER REMOVAL Package
Requires Python: >=3.6

In statistics, an outlier is a data point that differs significantly from other observations. An outlier may be due to variability in the measurement or it may indicate experimental error; the latter are sometimes excluded from the data set. An outlier can cause serious problems in statistical analyses. Therefore, it is important to detect and remove outliers. This package detects the outliers in the input data file using IQR method and then removes the rows having outliers.

How to make it work :
Command for running the package will look like - 
python outlier_mnv.py <inputdata> <outputdata>
e.g. python outlier_mnv.py "mydata.csv" "newdata.csv"
youtube-unlimited-search 0.3.2
Allow you to search in youtube without care about requests limitation
Requires Python: >=3.6

# Youtube Unlimited Search
Allow you to search in youtube without care about requests limitation
## Example

you can use the search tool as following:

```pip install youtube-unlimited-search```

```python
from youtube_unlimited_search import YoutubeUnlimitedSearch

results = YoutubeUnlimitedSearch('search terms', max_results=10).get()

print(results)
# returns a dictionary