Python-Based-CSV-Data-Reader

Overview

Python-Based-CSV-Data-Reader is a Python application designed to read, manipulate, and visualize CSV data using the powerful Pandas library. This tool allows users to efficiently filter, sort, and display results in an interactive manner. It's an ideal utility for handling large datasets and performing exploratory data analysis (EDA) with ease.

Features

• Read CSV Files: Easily import CSV files for analysis.
• Data Filtering: Filter rows based on specific conditions such as column values.
• Data Sorting: Sort data by one or more columns in ascending or descending order.
• Visualization: Generate basic visualizations such as bar charts, line charts, or scatter plots to explore data trends.
• Exporting Data: Export manipulated data to a new CSV file.
• Interactive Interface: User-friendly interaction through the command-line interface (CLI) or a GUI-based tool.
• Error Handling: Ensures the application handles invalid inputs or errors gracefully.

Technologies

• Python: The core programming language used for the application.
• Pandas: A Python library used for data manipulation and analysis.
• Matplotlib/Seaborn: Libraries used for data visualization (optional features).
• Tkinter: (Optional) Used for building a graphical user interface (GUI).
• CSV: The format of the data files being read and manipulated.

Getting Started

Prerequisites

Before you begin, ensure you have the following installed:

• Python 3.x: Download Python
• Pandas: Install using pip:

  pip install pandas
  
  Matplotlib/Seaborn (for optional visualizations):
  

pip install matplotlib seaborn Installation Clone this repository to your local machine:

git clone https://github.com/mmb0710/Python-Based-CSV-Data-Reader.git

cd Python-Based-CSV-Data-Reader

Install the required dependencies:

pip install -r requirements.txt

Run the application:

Once you have your CSV file ready, you can load it into the application. For example:

Load CSV data

df = pd.read_csv('your_file.csv')

Example: Filter rows where 'column1' value is greater than 10

filtered_df = df[df['column1'] > 10]

Example: Sort the DataFrame by 'column2' in descending order

sorted_df = df.sort_values(by='column2', ascending=False)

Example: Display first 5 rows of the DataFrame

print(filtered_df.head())

Save the manipulated data to a new CSV file

filtered_df.to_csv('filtered_output.csv', index=False)

Optional Features Visualization: You can generate plots to visualize data trends:

GUI (Tkinter): If you wish to implement a GUI, the code supports Tkinter for a user-friendly interface. You can extend the project by adding buttons, file pickers, and data display areas.

Contributing If you'd like to contribute to this project:

Fork the repository.

Create a new branch: git checkout -b feature-branch

Make your changes and commit them:

git commit -m 'Add some feature' Push to the branch:

git push origin feature-branch Submit a pull request.

License This project is licensed under the MIT License. See the LICENSE file for details.

Contact For any inquiries or issues, feel free to open an issue on GitHub or contact the Me.