Getting A `(key, value) dict` from your Categorical Variables

main.md

#Intro

If you're dealing with categorical variables, it's highly valuable to have:

1. Number representations for the category (so you can do things like sklearn algorithms)
2. A dictionary mapping of the numbers to the categories (for groupby and other such methods)

I couldn't find something built-in, but the solve is pretty easy (and sexy), and a on-liner

main.py

import pandas
import numpy

#create some categorical data
In [ 1]: categories = list( 'abcdefghi')
In [ 2]: category_series = pandas.Series( map(lambda x: categories[x], numpy.random.randint(
                                           low = 0, high = len(categories), size = [100, ])))

In [ 3]: category_series.head()
Out[78]: 
0    e
1    d
2    f
3    h
4    a
dtype: object

#Create a pandas.Categorical object
In [79]: cat_obj = pandas.Categorical(category_series)

#here are the integer values
In [80]: cat_obj.labels
Out[80]: 
array([4, 3, 5, 7, 0, 6, 8, 1, 2, 1, 1, 2, 3, 4, 6, 2, 6, 2, 7, 4, 4, 3, 0,
       3, 7, 1, 6, 1, 7, 3, 7, 5, 4, 0, 8, 0, 8, 4, 3, 7, 4, 0, 8, 3, 3, 8,
       7, 6, 4, 0, 4, 7, 0, 0, 7, 8, 1, 1, 0, 2, 2, 7, 5, 1, 3, 7, 1, 3, 7,
       3, 4, 0, 0, 6, 6, 4, 6, 4, 3, 1, 3, 8, 5, 3, 7, 0, 7, 7, 3, 1, 7, 6,
       7, 3, 1, 1, 2, 6, 6, 0])

#here's a dictionary mapping
dict(zip(cat_obj, cat_obj.labels) )
Out[81]: {'a': 0, 'b': 1, 'c': 2, 'd': 3, 'e': 4, 'f': 5, 'g': 6, 'h': 7, 'i': 8}