CSV to Time Series Visualization App

Overview

The CSV to Time Series Visualization App is a web application built using Streamlit, designed to enable users—data analysts, researchers, and anyone working with time-based data—to upload CSV files and create interactive, customizable time series visualizations effortlessly.

Features

• Easy CSV Upload: Upload CSV files directly through the app interface.
• Interactive Visualizations: Generate time series plots based on user-selected date and value columns.
• Data Preview: View the first few rows of the uploaded data to confirm selection.
• Input Validation: Ensures that the selected value column is numeric and handles missing values.
• Plot Customization: Customize line color, plot type (line, bar, or area), and marker visibility.
• Descriptive Statistics: Get basic statistics (mean, median, etc.) for the selected value column.
• Downloadable Plots: Download the generated plots as image files.

Technologies Used

• Python: Core programming language.
• Streamlit: For creating a user-friendly web application.
• Pandas: For data manipulation and analysis.
• Matplotlib: For data visualization.

Installation

Prerequisites

Ensure you have Python installed on your system. You can download it from python.org.

Install the necessary packages:

pip install streamlit pandas matplotlib

Clone the Repository

Clone this repository to your local machine:

git clone https://github.com/kellertree/csv_visualize.git

Install Required Packages

Navigate to the project directory and install all required packages:

cd csv_visualize
pip install -r requirements.txt

Running the Application

You can start the app by running the following command:

streamlit run app.py

This command will launch the application locally, and you can access it in your web browser at http://localhost:8501.

Running with VS Code Terminal

You can also run the app from VS Code’s terminal. Just ensure you're in the project directory before running the streamlit run command.

Future Enhancements

• Support for additional visualization types (e.g., histograms, scatter plots).
• User interface enhancements for improved usability and aesthetics.