Data Visualization with Matplotlib and Seaborn

Hi everyone! We are back with the new edition "Data Visualization with Matplotlib and Seaborn"! This week we covered Matplotlib and Seaborn libraries which are both popular Python libraries for data visualization, each with its own strengths and features.

Matplotlib

1- Overview: A foundational plotting library in Python, Matplotlib provides a lot of flexibility for creating static, interactive, and animated visualizations.

2- Features:Highly customizable: You can control every aspect of your plots.Supports a variety of plot types (line, scatter, bar, histogram, etc.).Works well with NumPy and Pandas.

3- Usage: Often used for detailed and intricate visualizations where fine control is necessary.

Seaborn

1- Overview: Built on top of Matplotlib, Seaborn provides a high-level interface for drawing attractive statistical graphics.

2- Features:Built-in themes for styling and more aesthetically pleasing plots.Simplifies the creation of complex visualizations (like heatmaps, violin plots, and pair plots).Works seamlessly with Pandas DataFrames, making it easy to visualize data directly.

3- Usage: Ideal for statistical visualizations and for users who want beautiful plots with less code.

Here, you can find the code blocks showing how to use these libraries:

Hope you guys find these data visualization code snippets practical!

Next week will include the last data visualization edition with Advanced Visualizations using Plotly library!

Stay tuned for more!