This repository contains my collection of Jupyter Notebooks and Python scripts dedicated to learning and mastering data visualization with Matplotlib. I believe that the ability to effectively visualize data is just as important as the ability to model it.
The files here document my hands-on practice in creating a wide variety of plots and charts to represent data, uncover trends, and communicate insights clearly.
This collection demonstrates my practical skills in creating and customizing various plot types, including:
- Basic Plots:
- Line plots to show trends over time.
- Bar charts for comparing categorical data.
- Scatter plots to investigate relationships between variables.
- Statistical Plots:
- Histograms to understand data distribution.
- Box plots to visualize statistical summaries.
- Pie charts for representing proportions.
- Plotting Architecture:
- Using the object-oriented approach (
plt.figure,ax.plot). - Creating complex layouts with
subplotsfor multiple charts in a single figure.
- Using the object-oriented approach (
- Customization & Styling:
- Adding titles, labels (
xlabel,ylabel), and legends. - Controlling colors, line styles, and markers.
- Adjusting axes limits and ticks.
- Saving high-quality figures to files.
- Adding titles, labels (
This repository is a key part of my self-directed curriculum in data science and AI.
- Step 1: Python Foundations
- Step 2: Data Analysis with
Pandas&NumPy - ➡️ Step 3: Data Visualization with
Matplotlib(You are here) - Step 4: Computer Vision with
OpenCV
Feel free to explore the notebooks and scripts to see my process for turning raw data into meaningful visualizations.