Instant search for and access to many datasets in Pyspark.

SparkDataset

Provides instant access to many datasets right from Pyspark (in Spark DataFrame structure).

Drop a star if you like the project. 😃 Motivates 💪 me to keep working on such projects

What?

The idea is simple. There are various datasets available out there, but they are scattered in different places over the web. Is there a quick way (in Pyspark) to access them instantly without going through the hassle of searching, downloading, and reading ... etc? SparkDataset tries to address that question :)

Usage:

Start with importing data():

from sparkdataset import data

• To load a dataset:

titanic = data('titanic')

• To display the documentation of a dataset:

data('titanic', show_doc=True)

• To see the available datasets:

data()

• To search for datasets with terms

data('ab')

Did you mean:
crabs, abbey, Vocab

That's it.

Go to this notebook for a demonstration of the functionality

Why?

In R, there is a very easy and immediate way to access multiple statistical datasets, in almost no effort. All it takes is one line > data(dataset_name). This makes the life easier for quick prototyping and testing. Well, I am jealous that Pyspark does not have a similar functionality. Thus, the aim of sparkdataset is to fill that gap.

Currently, sparkdataset has about 757 (mostly numerical-based) datasets, that are based on RDatasets. In the future, I plan to scale it to include a larger set of datasets. For example,

1. include textual data for NLP-related tasks, and
2. allow adding a new dataset to the in-module repository.

Installation:

$ pip install sparkdataset

Uninstall:

• $ pip uninstall sparkdataset
• $ rm -rf $HOME/.sparkdataset

Changelog

1.0.0

• Added search dataset by name similarity.
• Example:

>>> data('heat')
Did you mean:
Wheat, heart, Heating, Yeast, eidat, badhealth, deaths, agefat, hla, heptathlon, azt

• Added support to Windows.

Dependency:

• pandas
• pyspark :: 3.1.2

Miscellaneous:

• Tested on OSX and Linux (debian).
• Supports both Python 3 (3.8.8 and above).

TODO:

• add textual datasets (e.g. NLTK stuff).
• add samples generators.

Thanks to:

• RDatasets: R's datasets collection.