CSV Analyzer

CSV Analyzer is a lightweight desktop GUI application built with Python and Tkinter. It allows users to quickly load, inspect, visualize, and export insights from CSV files — all without writing a single line of code. It's built for data analysts, students, and anyone working with tabular data.

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Features

• Load and preview any CSV file via a graphical interface
• Instantly view key metadata: number of rows/columns, column names, and data types
• Get descriptive statistics (mean, min, max, std, etc.) for each column
• Count and display missing (NaN) values across the dataset
• Column wise stats: For any selected column, view data type, non-null count, unique value count, most frequent (mode) value, min/max value (for numeric columns)
• Data cleaning tool: Instantly fill all NaN values with 0, drop rows/columns with any NaN values
• Data visualization: View bar plot for categories, pie chart of distributions, histogram for numeric columns
• Export results: Save analysis report of current window to a txt or csv file
• Fully local — no data is sent or stored externally

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Screenshots

App UI	Data Analysis	NaN Summary

Column Statistics	Data Cleaning	Data Visualizer

Bar Chart

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Requirements

Install the required packages using:

pip install -r requirements.txt

Contents of requirements.txt:

pandas
matplotlib
tabulate

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How to Run

1. Clone the repository:

   git clone https://github.com/AyaanNadeem12/CSV-Analyzer.git
   cd csv-analyzer

2. Install the dependencies:

   pip install -r requirements.txt

3. Run the app:

   python main.py

Ensure Python 3.8 or above is installed on your system.

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Project Structure

CSV-Analyzer/
├── main.py                # Main application file
├── requirements.txt       # Python dependencies
├── README.md              # Project documentation
├── assets/                # Icons, screenshots
└── .gitignore             # Files/folders to exclude from Git

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Roadmap

Planned features for future versions:

• “Top N frequent values” analysis for selected columns
• Column filtering by data type (numeric, object, etc.)
• Support for Excel (.xlsx) files
• Correlation heatmaps
• Drag-and-drop CSV support
• Column-specific cleaning tools

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License

This project is open-source and available under the MIT License.

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Acknowledgements

Built using:

• Tkinter
• pandas
• matplotlib
• tabulate