Advanced Dashboard with Matplotlib

A visually rich, professional-level static dashboard built using Matplotlib, Seaborn, and Pandas. This project demonstrates how to combine multiple chart types into a single dashboard layout and render it as a high-quality .png image for reporting and analysis.

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Overview

This project uses Python’s Matplotlib GridSpec layout to display multiple data visualizations—line plot, bar chart, pie chart, and scatter plot—all in one structured layout. It is ideal for static reporting, analytics presentations, and building your portfolio as a data analyst or Python developer.

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Dashboard Preview

Output: Dashboard_With_Matplotlib.png

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Project Structure

dashboard_with_matplotlib/
├── data.csv                      # Input dataset used for visualization
├── dashboard.py                  # Main Python script that generates the dashboard
├── Dashboard_With_Matplotlib.png  # Output image of the generated dashboard (auto-created)
└── README.md                     # Project documentation and usage guide

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📂 Dataset Format (data.csv)

This project expects a basic CSV format with the following sample structure. You can replace this with your own relevant dataset and modify the plotting logic accordingly:

Year,Sales,Profit,Region,Category
2018,120,30,North,Tech
2019,150,45,South,Office
2020,180,60,East,Furniture
2021,200,70,West,Tech
2022,210,90,North,Furniture

You can use any dataset, just make sure the columns match your visualization requirements in dashboard.py.

Setup Instructions

1. Clone the Repository

git clone https://github.com/dvanhu/Dashboard-with-Matplotlib.git
cd Dashboard-with-Matplotlib

2. Install Required Packages

pip install matplotlib pandas seaborn

3. Run the Dashboard Script

python dashboard.py

The output will be saved as Dashboard_With_Matplotlib.png in the same directory.

Tech Stack

Technology	Purpose
Python 3.x	Main programming language
Pandas	CSV data handling
Matplotlib	Core charting & layout framework
Seaborn	Chart styling & enhancement

What You'll Learn

• Using GridSpec to organize multiple subplots
• Loading and preprocessing CSV data using pandas
• Enhancing chart readability with seaborn
• Exporting static charts as .png for offline use
• Styling charts using custom color palettes, titles, and legends

Sample Visualizations

• Line Plot: Sales trend over years
• Bar Chart: Profit by region
• Pie Chart: Category distribution
• Scatter Plot: Correlation between sales and profit

All visualizations are customized for better clarity and are arranged in a clean dashboard layout.