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Visualization of Spending Data realized with Streamlit, Pandera and Plotly

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Spending Dashboard

Every quarter I would sit down to pull together all the different transactions from financial instituations, unify them into a format and then try to make an overview of different charts to understand what has happened during the last quarter to my finances.

The last part of this whole process is tackled by this small project.

Overview

A web-based dashboard that visualizes spending data, providing users with insights into their financial habits.

Dashboard Preview

The dashboards consists of five sections

  1. Profit & Loss Profit & Loss
  2. In- vs. Outflows In- vs. Outflows
  3. Fix vs. Var (for In- and Outflows) Fix vs. Var
  4. Categories (for In- and Outflows) Categories
  5. Biggest Transactions Biggest Transactions
  6. Notes (see above)

Features (mostly provided by plotly)

  • Interactive charts and graphs for visualizing spending patterns.
  • Filter and categorize spending data for detailed analysis.
  • User-friendly interface with intuitive navigation.
  • Responsive design for seamless usage on various devices.

Getting Started

Follow these steps to set up and run the Spending Dashboard locally.

Installation & Usage

  1. Clone the repository: git clone https://github.com/your-username/spending-dashboard.git
  2. Navigate to the project directory: cd spending-dashboard
  3. Install dependencies: python -m pip install -r requirements.txt
  4. Configure the user input (see Configuration)
  5. Navigate into reporting directory: cd reporting
  6. Run streamlit: streamlit run streamlit.py
  7. Open web browser (might be done automatically by streamlit) at http://localhost:8501/ (can differ, look at console output)

Configuration

  1. Edit the config.json file to customize dashboard settings.
    {
    "year": "2023",
    "quarter" : "Q1",
    "path_to_data": "../data/sample_data.csv",
    "notes": "This is a test note."
    }
    
  2. Make sure your data adheres to the minimal requirements defined in QuarterlyInputData here (this will also be checked by the application)

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Visualization of Spending Data realized with Streamlit, Pandera and Plotly

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