README.md

PyRat

R-like vectors and functions in Python.

Warning

This is not Pandas, or scikit-learn, or numpy. This is not R, either. Do not use this for industry purposes. Do not use this unless you want your project to break.

Simply have fun with it, or use this for educational purposes.

Installation

Clone this repository to your local machine with git, then install with Python.

git clone https://github.com/shanedrabing/pyrat.git
cd pyrat
python setup.py install

Getting Started

Import the libraries with Python.

import pyrat.base
import pyrat.stats
import pyrat.utils
import pyrat.closure

The closure library is the only not inspired by R. All other libraries contain familiar functions. If you want to locate a function within R, look into the find function, determine the namespace, and see if PyRat contains the function!

Motivating Examples

So, why use PyRat? If you're a fan of R (like myself), you may have learned that the capabilities of vectors and functions that operate on them are immensely powerful.

Linear Modeling

Let's try modeling a line of best fit between horsepower and miles per gallon from the mtcars dataset:

import matplotlib.pyplot as plt

from pyrat.base import log10, seq
from pyrat.stats import lm, predict
from pyrat.utils import read_csv, struct

df = read_csv("data/mtcars.csv")

x = df["hp"]
y = df["mpg"]

m = lm(log10(x), log10(y))

x_new = seq(min(x), max(x), length_out=101)
y_new = 10 ** predict(m, log10(x_new))

plt.scatter(x, y, color="black")
plt.plot(x_new, y_new, color="red")
plt.xlabel("Horsepower")
plt.ylabel("Miles per Gallon")
plt.tight_layout()
plt.savefig("data/fit.png")
plt.clf()

A plot of the fitted linear model:

Functional Programming

PyRat has its own version of "piping", whereby the items of a vector (or the vector itself) can be continuously operated on. All intermediates are immutable, as the vector is just a tuple on steroids.

from pyrat.base import c, paste

taxa = c("Canis lupus", "Felis catus", "Ursidae", "Anura (order)")

print(
    taxa
    .apply("https://en.wikipedia.org/w/index.php?search={}".format)
    .thread(requests.get)
    .apply(getattr, "content")
    .apply(bs4.BeautifulSoup, "lxml")
    .apply(bs4.BeautifulSoup.select_one, "h1")
    .apply(getattr, "text")
    .transform(paste, "(" + taxa + ")", collapse="\n")
)

Here's the output:

Wolf (Canis lupus)
Cat (Felis catus)
Bear (Ursidae)
Frog (Anura (order))