Skip to main content
Help improve PyPI by participating in a 5-minute user interface survey!

Categorical variable friendly pandas data frames

Project Description

Quickstart

$ pip install dummipy

Let it out of the box…

from sklearn.linear_model import LinearRegression
from dummipy import cereal

type(cereal)
# CategoricalDataFrame

cereal.head()
reg = LinearRegression()
reg.fit(cereal[['mfr', 'vitamins', 'fat']], cereal.calories)

Installation

You’ll need `pandas <http://pandas.pydata.org/>`__, but any old version will do the trick. There is no pandas version pegged in the setup.py file so installing dummipy won’t mess up your existing sci-py setup.

$ pip install dummipy

Use

Just use it like any old data frame. That’s really all there is to it.

import dummipy as dp

df = dp.CategoricalDataFrame({
    "x": range(5),
    "y": ["a", "b", "c", "a", "b"]
})


df = pd.read_csv("foo.csv")
df = dp.CategoricalDataFrame(df)

Release history Release notifications

This version
History Node

0.0.1

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
dummipy-0.0.1-py2.7.egg (8.9 kB) Copy SHA256 hash SHA256 Egg 2.7 May 18, 2015
dummipy-0.0.1.tar.gz (5.7 kB) Copy SHA256 hash SHA256 Source None May 18, 2015

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging CloudAMQP CloudAMQP RabbitMQ AWS AWS Cloud computing Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page