Qt Dataset Explorer

Interactive desktop GUI application for exploring datasets and performing statistical analysis. Built using Python, PyQt5, and scientific libraries (pandas, matplotlib, seaborn, scipy).

Features

• Dataset Loading: Support for CSV and Excel files
• Dataset Preview: Interactive table view with up to 1000 rows displayed
• Data Filtering: Apply filters using various operators (==, !=, >, <, >=, <=, contains, startswith, endswith)
• Descriptive Statistics: Comprehensive statistical analysis including:
  • Mean, median, standard deviation, min, max
  • Variance, skewness, kurtosis
  • Missing value counts
  • Categorical value distributions
• Visualization: Multiple plot types:
  • Histograms
  • Box plots
  • Scatter plots
  • Correlation heatmaps
  • Bar plots
• Hypothesis Testing: Statistical tests including:
  • Independent T-Test
  • Paired T-Test
  • Chi-Square Test
  • ANOVA
  • Correlation Test (Pearson & Spearman)
• Export Tools:
  • Export filtered data to CSV/Excel
  • Export statistics to text file
  • Save plots as PNG/PDF

Installation

1. Clone the repository:

git clone https://github.com/BaseMax/qt-dataset-explorer.git
cd qt-dataset-explorer

2. Install required dependencies:

pip install -r requirements.txt

Usage

Run the application:

python dataset_explorer.py

Quick Start Guide

1. Load a Dataset:

   • Click "Load Dataset" button
   • Select a CSV or Excel file
   • The dataset will be loaded and displayed in the Preview tab

2. Filter Data:

   • Go to the "Preview & Filter" tab
   • Select a column from the dropdown
   • Choose an operator (==, !=, >, <, contains, etc.)
   • Enter a value to filter by
   • Click "Apply Filter"
   • Click "Reset Filter" to show all data again

3. View Statistics:

   • Go to the "Statistics" tab
   • Statistics are automatically calculated when you load data
   • Click "Refresh Statistics" to update after filtering

4. Generate Plots:

   • Go to the "Plots" tab
   • Select a plot type (Histogram, Box Plot, Scatter Plot, etc.)
   • Choose columns for X and Y axes (if applicable)
   • Click "Generate Plot"
   • Click "Save Plot" to export as PNG or PDF

5. Run Hypothesis Tests:

   • Go to the "Hypothesis Testing" tab
   • Select a test type
   • Choose columns to test
   • Click "Run Test"
   • View detailed results including p-values and conclusions

6. Export Data:

   • Click "Export Data" to save filtered dataset
   • Click "Export Statistics" to save statistical summary

Sample Data

A sample dataset (sample_data.csv) is included for testing the application. It contains employee information with columns: Name, Age, Gender, Salary, Department, and Experience.

Requirements

• Python 3.7+
• PyQt5 5.15.10
• pandas 2.0.3
• matplotlib 3.7.2
• seaborn 0.12.2
• scipy 1.11.2
• numpy 1.24.3
• openpyxl 3.1.2

License

See LICENSE file for details.

Author

• GitHub: @BaseMax

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.