semvec/pypi-packages huggingface dataset:
senzu 4.3.1
Foreign tables app.
Requires Python: >=3.8,<4.0

# Senzu

![Senzu](docs/senzu.png)

This repository contains both the microservice and the django app !
All the code relative to the microservice is located inside `./main`.
The senzu app is located under the `./senzu`.

## First run
```bash
python manage.py migrate_senzu
python manage.py migrate_schemas
python manage.py init_db
```

## Remount all tables
```bash
python manage.py foreign_tables --remount=True
```

## Reconfigure servers
```bash
python manage.py foreign_tables --reconfigure-servers=True
```
blocksim-quadcopter 0.2.1
A set of computers to simulate a quadcopter
Requires Python: >=3.6

==============================================================================
blocksim_quadcopter
==============================================================================


.. image:: https://img.shields.io/pypi/v/blocksim_quadcopter.svg
        :target: https://pypi.python.org/pypi/blocksim_quadcopter

.. image:: https://readthedocs.org/projects/blocksim_quadcopter/badge/?version=latest
        :target: https://blocksim_quadcopter.readthedocs.io/en/latest/?badge=latest
        :alt: Documentation Status

.. image:: https://gitlab.com/manawenuz/blocksim_quadcopter/badges/master/pipeline.svg
   :target: https://gitlab.com/manawenuz/blocksim_quadcopter/pipelines

.. image:: https://codecov.io/gl/manawenuz/blocksim_quadcopter/branch/master/graph/badge.svg
  :target: https://codecov.io/gl/manawenuz/blocksim_quadcopter

.. image:: https://img.shields.io/pypi/dm/blocksim_quadcopter
  :target: https://pypi.python.org/pypi/blocksim_quadcopter

A set of computers to simulate a quadcopter

Free software: MIT license

Development
-----------

In your virtual env::

    python setup.py develop

Features
--------

* TODO
autohyp 0.0.2
A simple hypothesis testing package
Requires Python: >=3.6

# Data Analysis

### Why did I write this code?
I'm often handed a dataset and asked to do the same thing - determine if the experimental group is different than the control group, and generate some descriptive statistics along the way. As an alternative to writing all operations manually for each project I've created a Python module using Object Oriented Programming (OOP). This eliminates the need to search for old projects, copy and paste long lines of code and cross my fingers that it works. 

### What libraries does this code use?

This code uses a [Scipy](https://scipy.org/), a widely accepted statistical package, to perform hypothesis testing.

I also use [Pandas](https://pandas.pydata.org/), an extremely popular data analysis and manipulation library for organizing the data and providing descriptive statistics.

For data visualization I use the [Seaborn](https://seaborn.pydata.org/) library (built on top of matplotlib)

### Hypothesis Testing
My main task is often to determine if the results are statistically significant. For this we need to implement a hypthesis test, however there are many to [choose from](https://help.xlstat.com/s/article/which-statistical-test-should-you-use?language=en_US). The decision tree of this code looks something like this:
1. Is the data Normally Distributed and do the experimental and control groups have Equal Variance? Student's t-test
2. Is the data Normally Distributed but do the experimental and control groups have Unequal Variance? Welches' t-test
3. Is the data not Normally Distributed? Mann-Whitney U test

![](decision_tree.png)

### Where to Start

See [This Notebook](https://github.com/Gabriel-Aspen/data_analysis_example/blob/main/demo.ipynb) for a demo on the Breast Cancer Wisconsin (Diagnostic) Dataset found [Here](https://www.kaggle.com/uciml/breast-cancer-wisconsin-data)
# autohyp