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Pairplotr is a Python library used to graph combinations of numerical and categorical data in a pair plot

Project description

<img src="https://github.com/JaggedParadigm/pairplotr/blob/master/pairplot_demo.png" width="500" />

# How
## Installation
Use:

pip install pairplotr

## Use
See the [demo](https://nbviewer.jupyter.org/github/JaggedParadigm/pairplotr/blob/master/pairplotr_demo.ipynb) for use of pairplotr.

# What
Pairplotr is a Python library used to graph combinations of numerical and categorical data in a pair plot,
similar to Seaborn's pairplot(), given a cleaned Pandas dataframe with a mixture of categorical and numerical
values.

Here are the formats for Row feature|Column feature combinations in either on- or off-diagonal cells:

- On-diagonal:
- Categorical|Categorical:
- Value counts of feature values ordered by ascending value count and colored by feature values
- Numerical|Numerical:
- Histogram of feature w/ no coloring (or by desired label)
- Off-diagonal:
- Categorical|Categorical:
- Stacked value count of row feature values colored by column feature values
- Categorical|Numerical:
- Histograms of column feature for each row feature value colored by row feature value
- Numerical|Numerical:
- Scatter plot of row feature values vs column feature values w/ no coloring (or by desired label)

# Why
The available tools I've found don't seem to be able to combine numerical and categorical feature data
in a quick and easy way and I wanted to customize the comparisons as the plot types I find most useful.

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