Factor analysis for in-memory datasets in Python
Project description
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<div align="center">Prince is a factor analysis library for datasets that fit in memory.</div>
<br/>
## Quick start
Prince uses [pandas](http://pandas.pydata.org/) to manipulate dataframes, as such it expects an initial dataframe to work with. In the following example, a [Principal Component Analysis (PCA)](https://www.wikiwand.com/en/Principal_component_analysis) is applied to the iris dataset. Under the hood Prince decomposes the dataframe into two eigenvector matrices and one eigenvalue array thanks to a [Singular Value Decomposition (SVD)](https://www.wikiwand.com/en/Singular_value_decomposition). The eigenvectors can then be used to project the initial dataset onto lower dimensions.
```python
import matplotlib.pyplot as plt
import pandas as pd
import prince
df = pd.read_csv('data/iris.csv')
pca = prince.PCA(df, n_components=4)
fig1, ax1 = pca.plot_cumulative_inertia()
fig2, ax2 = pca.plot_rows(color_by='class', ellipse_fill=True)
plt.show()
```
The first plot displays the rows in the initial dataset projected on to the two first right eigenvectors (the obtained projections are called principal coordinates). The ellipses are 90% confidence intervals.
The second plot displays the cumulative contributions of each eigenvector (by looking at the corresponding eigenvalues). In this case the total contribution is above 95% while only considering the two first eigenvectors.
## Installation
Although it isn't a requirement, using [Anaconda](https://www.continuum.io/downloads) is a good idea in general for doing data science in Python.
**Via PyPI**
```sh
$ pip install prince
```
**Via GitHub for the latest development version**
```sh
$ pip install git+https://github.com/MaxHalford/Prince
```
Prince has the following dependencies:
- [pandas](http://pandas.pydata.org/) for manipulating dataframes
- [matplotlib](http://matplotlib.org/) as a default plotting backend
- [fbpca](http://fbpca.readthedocs.org/en/latest/), [Facebook's randomized SVD implementation](https://research.facebook.com/blog/fast-randomized-svd/)
## Documentation
Please check out the [documentation](http://prince.readthedocs.io) for a list of available methods.
## License
<a href="https://opensource.org/licenses/MIT">
<img src="http://img.shields.io/:license-mit-ff69b4.svg?style=flat-square" alt="mit"/>
</a>
<img src="docs/_static/logo.png" alt="prince_logo"/>
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<img src="https://requires.io/github/MaxHalford/Prince/requirements.svg?branch=master&style=flat-square" alt="Requirements Status"/>
</a>
</div>
<br/>
<br/>
<div align="center">Prince is a factor analysis library for datasets that fit in memory.</div>
<br/>
## Quick start
Prince uses [pandas](http://pandas.pydata.org/) to manipulate dataframes, as such it expects an initial dataframe to work with. In the following example, a [Principal Component Analysis (PCA)](https://www.wikiwand.com/en/Principal_component_analysis) is applied to the iris dataset. Under the hood Prince decomposes the dataframe into two eigenvector matrices and one eigenvalue array thanks to a [Singular Value Decomposition (SVD)](https://www.wikiwand.com/en/Singular_value_decomposition). The eigenvectors can then be used to project the initial dataset onto lower dimensions.
```python
import matplotlib.pyplot as plt
import pandas as pd
import prince
df = pd.read_csv('data/iris.csv')
pca = prince.PCA(df, n_components=4)
fig1, ax1 = pca.plot_cumulative_inertia()
fig2, ax2 = pca.plot_rows(color_by='class', ellipse_fill=True)
plt.show()
```
The first plot displays the rows in the initial dataset projected on to the two first right eigenvectors (the obtained projections are called principal coordinates). The ellipses are 90% confidence intervals.
The second plot displays the cumulative contributions of each eigenvector (by looking at the corresponding eigenvalues). In this case the total contribution is above 95% while only considering the two first eigenvectors.
## Installation
Although it isn't a requirement, using [Anaconda](https://www.continuum.io/downloads) is a good idea in general for doing data science in Python.
**Via PyPI**
```sh
$ pip install prince
```
**Via GitHub for the latest development version**
```sh
$ pip install git+https://github.com/MaxHalford/Prince
```
Prince has the following dependencies:
- [pandas](http://pandas.pydata.org/) for manipulating dataframes
- [matplotlib](http://matplotlib.org/) as a default plotting backend
- [fbpca](http://fbpca.readthedocs.org/en/latest/), [Facebook's randomized SVD implementation](https://research.facebook.com/blog/fast-randomized-svd/)
## Documentation
Please check out the [documentation](http://prince.readthedocs.io) for a list of available methods.
## License
<a href="https://opensource.org/licenses/MIT">
<img src="http://img.shields.io/:license-mit-ff69b4.svg?style=flat-square" alt="mit"/>
</a>
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