Tablexplore

Abstract

1. Introduction

Tablexplore is an application for data analysis and plotting built in Python using the PySide2/Qt toolkit. It uses the pandas DataFrame class to store the table data. Pandas is an open source Python library providing high-performance data structures and data analysis tools.

This application is intended primarily for educational/scientific use and allows quick visualization of data with convenient plotting. The primary goal is to let users explore their tables interactively without any prior programming knowledge and make interesting plots as they do this. One advantage is the ability to load and work with relatively large tables as compared to spreadsheets. The focus is on data manipulation rather than data entry. Though basic cell editing and row/column changes are supported.

2. Objective

Table analysis and plotting application written in PySide2/PyQt5

This project demonstrates how Python, Pandas can be applied to a real-world Data Science problem.

3. Key Features / Modules

• save and load projects
• import csv/hdf/from urls
• delete/add columns
• groupby-aggregate/pivot/transpose/melt operations
• merge tables
• show sub-tables
• plotting mostly works
• apply column functions, resample, transform, string methods and date/time conversion
• python interpreter

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/dmnfarrell/tablexplore.git
cd tablexplore

pip install -e git+https://github.com/dmnfarrell/tablexplore.git#egg=tablexplore

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by dmnfarrell and published on GitHub under the GNU General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.