exploretransform 1.0.4

Explore and transform your data

Exploretransform

Explore and transform your datasets

Exploretransform is a collection of data exploration functions and custom pipline trasformers. It's aims to streamline exploratory data analysis and extend some of scikit's data transformers.  

Package Guide

Examples of using the exploretransform functions and classes are contained in examples.ipynb. Details about each function or class (docstrings) can be accessed using ?name

Installation

Python PYPI:

!pip install exploretransform

Import the exploretransform package:

import exploretransform as et

 

Summary of Functions and Classes

Function / Class	Description
loadboston	loads the Boston housing dataset
peek	returns dtype, levels, # of observations, and first five observations for a dataframe
explore	provides various statistics on a dataframe (zeros, inf, missing, levels, dtypes)
nested	takes a list, series or dataframe and returns the location of nested objects
freq	for categorical or ordinal features, provides the count, percent, and cumulative percent for each level
plotfreq	generates a bar plot using the data generated by freq
corrtable	generates a table of all pairwise correlations and uses the average correlation for the row and column in to decide on potential drop/filter candidates
calcdrop	analyzes corrtable output determines which features should be filtered/drop
skewstats	returns the skewness statistics and magnitude for each numeric feature
ascores	calculates various association scores (kendall, pearson, mic, dcor, spearman) between predictors and target
ColumnSelect	custom transformer that selects columns for pipeline
CategoricalOtherLevel	custom transformer that creates "other" level in categorical / ordinal data based on threshold
CorrelationFilter	custom transformer that filters numeric features based on pairwise correlation

 

How to use exploretransform

More examples of using the exploretransform functions and classes are contained in examples.ipynb. Details about each function or class (docstrings) can be accessed using ?

?et.explore

loadboston()

df, X, y = et.loadboston()

explore()

et.explore(X)

	variable	obs	q_zer	p_zer	q_na	p_na	q_inf	p_inf	dtype
0	town	506	0	0.00	0	0.0	0	0.0	object
1	lon	506	0	0.00	0	0.0	0	0.0	float64
2	lat	506	0	0.00	0	0.0	0	0.0	float64
3	crim	506	0	0.00	0	0.0	0	0.0	float64
4	zn	506	372	73.52	0	0.0	0	0.0	float64
5	indus	506	0	0.00	0	0.0	0	0.0	float64
6	chas	506	0	0.00	0	0.0	0	0.0	category
7	nox	506	0	0.00	0	0.0	0	0.0	float64
8	rm	506	0	0.00	0	0.0	0	0.0	float64
9	age	506	0	0.00	0	0.0	0	0.0	float64
10	dis	506	0	0.00	0	0.0	0	0.0	float64
11	rad	506	0	0.00	0	0.0	0	0.0	category
12	tax	506	0	0.00	0	0.0	0	0.0	int64
13	ptratio	506	0	0.00	0	0.0	0	0.0	float64
14	b	506	0	0.00	0	0.0	0	0.0	float64
15	lstat	506	0	0.00	0	0.0	0	0.0	float64

 

Column	Description
variable	name of variable
obs	number of observations
q_zer	number of zeros
p_zer	percentage of zeros
q_na	number of missing
p_na	percentage of missing
q_inf	number of infinity
p_inf	percentage of infinity
dtype	Python dtype

 

Release History

• 0.1.0
  • First release

 

Meta

Brian Pietracatella – [e-mail]

Distributed under the MIT license. See LICENSE for more information.

https://github.com/bxp151/exploretransform