ggcorrplot 0.1.0
Visualization of a Correlation Matrix using plotnine
Requires Python: >=3.11
Keywords: correlation matrix, matrix of correlation p-values, plotnine, ggplot

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# ggcorrplot: Visualization of a correlation matrix using plotnine

ggcorrplot is an open source [Python](https://www.python.org/) package dedicated to matrix of correlation visualization. It os distributed under the [MIT License](https://github.com/enfantbenidedieu/ggcorrplot/blob/master/LICENSE.txt).

# Contents

**1. [Overview](#overview)**

**2. [Installation](#installation)**

* [2.1 Global environmen](#genv)
* [2.2 Virtual environment](#venv)
* [2.3 Version](#version)
* [2.4 Dependencies](#dependencies)

**3. [Example](#example)**

**4. [Documentation](#doc)**

**5. [About us](#about_us)**

* [5.1 Authors](#authors)
* [5.2 Feedbacks](#authors)
* [5.3 Citing discrimintools](#citing)

## Overview <a name="overview"></a>

The **ggcorrplot** package can be used to **visualize easily a correlation matrix** using [plotnine](https://plotnine.org/). It provides a solution for **reordering** the correlation matrix and displays the **significance level** on the correlogram. It includes also a function for computing a matrix of **correlation p-values**.

ggcorrplot package provides three functions:

* ``cor_pmat`` which computes a matrix of correlation p-values.
* ``get_melt`` which convert DataFrama from wide to long 
* ``ggcorrplot`` for correlation matrix visualization

## Installation <a name="installation"></a>

### Global environment <a name="genv"></a>

You can directly install discrimintools using pip :

```bash
pip install ggcorrplot
```

or set a virtual environment.

### Virtual environment <a name="venv"></a>

Install the 64-bit version of Python 3, for instance from the [official website](https://www.python.org/). Now create a [virtual environment (venv)](https://docs.python.org/3/tutorial/venv.html) and install ggcorrplot.

The virtual environment is optional but strongly recommended, in order to avoid potential conflicts with other packages.

```{bash}
PS C:\> python -m venv ggcorrplot-env # create virtual env
PS C:\> ggcorrplot-env\Scripts\activate  # activate
PS C:\> pip install -U ggcorrplot  # install ggcorrplot
```

### Version <a name="version"></a>

In order to check your installation, you can use.

```{python}
import ggcorrplot
print(ggcorrplot.__version__)
```

Using an isolated environment such as *pip venv* or *conda* makes it possible to install a specific version of discrimintools with pip and conda and its dependencies independently of any previously installed Python packages.

You should always remember to activate the environment of your choice prior to running any Python command whenever you start a new terminal session.

### Dependencies <a name="dependencies"></a>

ggcorrplot is compatible with python version which supports both dependencies :

| Packages  |  Version |
| :-------- | :------: |
| numpy     |  2.3.4   |
| pandas    |  2.3.3   | 
| scipy     |  1.16.3  |
| plotnine  |  0.15.1  |

## Example <a name="example"></a>

See [examples](https://ggcorrplot.readthedocs.io/en/latest/source/examples/index.html)

## Documentation <a name="doc"></a>

The official documentation is hosted on [https://discrimintools.readthedocs.io](https://discrimintools.readthedocs.io).

## About Us <a name="about_us"></a>

### Authors <a name="authors"></a>

ggcorrplot is developed and maintained by [Duvérier DJIFACK ZEBAZE](https://www.linkedin.com/in/duv%C3%A9rier-djifack-z-030097118/), the founder 
of djifacklab (*Djifack Laboratory of Mathematics, Statistics and Economics books and packages production using Python Programming Language*).

The djifacklab laboratory maintains others python librairies such as [scientisttools](https://pypi.org/project/scientisttools/), [scientistmetrics](https://pypi.org/project/scientistmetrics/), [scientistshiny](https://pypi.org/project/scientistshiny/), [scientisttseries](https://pypi.org/project/scientistshiny/) and [discrimintools]( https://pypi.org/project/discrimintools/).

### Feedbacks <a name="feedbacks"></a>

If you have found ggcorrplot useful in your work, research, or company, please let us know by writing to email [djifacklab@gmail.com](mailto:djifacklab@gmail.com).

### Citing ggcorrplot <a name="citing"></a>

If ggcorrplot has been significant in your research, and you would like to acknowledge the project in your academic publication, we suggest citing it using the following *BibTeX format*:

```
@misc{DJIFACK ZEBAZE_2023, 
    url = {https://github.com/enfantbenidedieu/ggcorrplot}, 
    journal = {ggcorrplot}, 
    publisher = {ggcorrplot: Visualization of a correlation matrix using plotnine}, 
    author = {DJIFACK ZEBAZE, Duvérier}, 
    year = {2023}
}
```