A structured collection of Matplotlib practice notebooks covering the core plotting techniques used in data analysis and scientific visualization. Each concept is implemented in Python with annotated examples and output figures.
This repository is designed to build a practical foundation in data visualization using Matplotlib. It progresses from basic single-plot construction to multi-panel layouts, covering the chart types most frequently used in exploratory data analysis and reporting.
| Topic | Description |
|---|---|
| Plot Customization | Titles, axis labels, legends, colors, markers, and line styles |
| Line Plot | Single-variable trends over a continuous axis |
| Double Line Plot | Comparative multi-series visualization on a shared axis |
| Histogram | Distribution of numerical data across bins |
| Bar Chart | Categorical comparison using vertical bars |
| Scatter Plot | Relationship and correlation between two variables |
| Pie Chart | Proportional breakdown of categorical data |
| Box Plot | Distribution summary with quartiles and outliers |
| Subplot | Multi-panel figures using plt.subplot() and plt.subplots() |
- Python
- Numpy
- Matplotlib
- Jupyter Notebook
# Clone the repository
git clone https://github.com/pranay-surya/Matplotlib.git
cd Matplotlib
# Install dependencies
pip install matplotlib numpy
# Launch Jupyter Notebook
jupyter notebookMatplotlib/
|-- notebook1 # Practice notebooks for each plot type
|-- notebook2
|--
|--
|-- Figure_2_mat.png # Sample visualization output
|-- README.md