pyxplorer

Simple Big Data Profiling

Project description

pyxplorer – Easy Interactive Data Profiling for Big Data (and Small Data)

The goal of pyxplorer is to provide a simple tool that allows interactive profiling of datasets that are accessible via a SQL like interface. The only requirement to run data profiling is that you are able to provide a Python DBAPI like interface to your data source and the data source is able to understand simplistic SQL queries.

I built this piece of software while trying to get a better understanding of data distribution in a massive several hundred million record large dataset. Depending on the size of the dataset and the query engine the resposne time can ranging from seconds (Impala) to minutes (Hive) or even hourse (MySQL)

The typical use case is to use `pyxplorer` interactively from an iPython Notebook or iPython shell to incrementally extract information about your data.

Usage

Imagine that you are provided with access to a huge Hive/Impala database on your very own Hadoop cluster and you’re asked to profile the data to get a better understanding for performing more specific data science later on.:

import pyxplorer as pxp
from impala.dbapi import connect
conn = connect(host='impala_server', port=21050)

db = pxp.Database("default", conn)
db.tables()

This simple code gives you access to all the tables in this database. So let’s assume the result shows a `sales_orders` table, what can we do now?:

orders = db["sales_orders"]
orders.size() # 100M
orders.columns() # [ol_w_id, ol_d_id, ol_o_id, ol_number, ol_i_id, ...]

Ok, if we have so many columns, what can we find out about a single column?:

orders.ol_d_id.min() # 1
orders.ol_d_id.max() # 9999
orders.ol_d_id.dcount() # 1000

And like this there are some more key-figures about the data like uniqueness, constancy, most and least frequent values and distribution.

In some cases, where it makes sense, the output of a method call will not be a simple array or list but directly a Pandas dataframe to facilitate plotting and further analysis.

You will find an easier to digest tutorial here:

http://nbviewer.ipython.org/github/grundprinzip/pyxplorer/blob/master/pyxplorer_stuff.ipynb

Supported Features

Column Count (Database / Table)

Table Count

Tuple Count (Database / Table)

Min / Max

Most Frequent / Least Frequent

Top-K Most Frequent / Top-K Least Frequent

Top-K Value Distribution (Database / Table )

Uniqueness

Constancy

Distinc Value Count