Datatile: A Library for AutoEDA

Datatile: A Library for AutoEDA

A library called Datatile is used to organize, summarize, and display data. The goal of DataTile is to serve as a library for organizing, summarizing, and displaying data. It was formerly known as pandas-summary and has since changed its name to DataTile. This more ambitious project has several planned features and enhancements, including support for visualizations, quality checks, linking summaries to versions, and integrations with third-party libraries. Let's look at the currently available features since many of them are still under development.

Earlier versions of the Datatile AutoEDA library

• The first version of the DataTile AutoEDA library was made available in 2020.?
• The library was developed by DataTile, a startup company that focuses on developing data science tools to help businesses make better decisions.
• Making the EDA process easier for data scientists and analysts was the main objective when creating DataTile AutoEDA.?
• The founders of DataTile wanted to create a tool that would automate many of the repetitive EDA tasks because doing so can be time-consuming and monotonous.
• Because of how simple it is to use and how many features are customizable, DataTile AutoEDA has become increasingly well-liked among data scientists and analysts since its release.?
• The library is frequently updated with new features and improvements based on user feedback.

For data scientists and analysts, exploratory data analysis (EDA) is made easier with the help of the Python library DataTile AutoEDA. For tasks related to EDA, such as feature engineering, data profiling, and visualization, it provides a set of features and tools.

Several significant features of DataTile AutoEDA include:

Data Profiling: DataTile AutoEDA allows you to quickly understand the structure and content of your dataset by generating a comprehensive report that includes summary statistics, data type information, missing value analysis, and more.

Feature Engineering: With DataTile AutoEDA, you can easily create new features from existing ones using a variety of techniques, such as scaling, one-hot encoding, and binning.

Visualization: The library provides a set of visualization functions that allow you to explore your data visually, such as histograms, box plots, and scatter plots.

Customizable: DataTile AutoEDA is highly customizable, allowing you to modify the default settings and parameters to suit your specific needs.

Overall, DataTile AutoEDA is a helpful tool for data analysts and scientists who want to streamline their EDA process and spend less time on manual tasks.

• Saves time: DataTile AutoEDA can automate many of the repetitive and time-consuming tasks involved in EDA, such as data profiling and feature engineering. This can save data scientists and analysts a significant amount of time.
• Simplifies EDA: The library provides a user-friendly interface and a suite of functions that make it easier for data scientists and analysts to perform EDA.
• Customizable: DataTile AutoEDA is highly customizable, allowing users to modify the default settings and parameters to suit their specific needs.
• Visualizations: The library provides a set of visualization functions that allow users to explore their data visually, making it easier to identify patterns and relationships in the data.
• Free and Open-source: DataTile AutoEDA is a free and open-source library, which means that anyone can use it and contribute to its development.

• Limited functionality: DataTile AutoEDA is primarily focused on automating the EDA process, and may not be suitable for more advanced data analysis tasks.
• Dependence on Python: DataTile AutoEDA is a Python library, which means that users must be proficient in Python in order to use it effectively.
• Lack of documentation: The library is relatively new, and as such, may not have extensive documentation or support available.
• Limited compatibility: DataTile AutoEDA may not be compatible with all types of data sources, and may require some additional configuration to work with certain datasets.

Installation:

The module can be easily installed with pip:

> pip install datatile

This module depends on NumPy and Pandas. Optionally, you can also get some nice visualizations if you have Matplotlib installed.

Tests:

To run the tests, execute the command python setup.py test

Usage:

DataFrameSummary

An extension to pandas' data frames describes the function. The module contains a DataFrameSummary object that extends describe() with:

properties:

dfs.columns_stats: counts, uniques, missing, missing_perc, and type per column

dsf.columns_types: a count of the types of columns

dfs[column]: more in-depth summary of the column

Function:

summary(): extends the describe() function with the values with columns_stats

The DataFrameSummary expects a Pandas DataFrame to summarize.

from datafile.df.summary import DataFrameSummary

dfs = DataFrameSummary(df)

getting the types of the columns

getting the column's stats

getting a single-column summary, e.g. numerical column

# We can also access the column using numbers A[1]

Future development:

Summaries:

• Add summary analysis between columns, i.e.. dfs[[1, 2]]

Visualizations:

• Add summary visualization with matplotlib.
• Add summary visualization with Plotly.
• Add summary visualization with Altair.
• Add predefined profiling.

Catalog and Versions:

?Add the possibility of persisting the summary and linking to a specific version.

?Integrate with quality libraries.

Conclusion:

In this article, we examine how to use DataTile to summarize a Pandas DataFrame with a few lines of code. DataTile is still in its early stages, therefore, it may need more advanced features compared to other low-code EDA packages such as Pandas-Profiling, AutoViz, and SweetViz. Nonetheless, DataTile’s future plan sounds exciting and optimistic, it’s definitely something to look out for.