Data visualization in Python involves using various libraries to graphically represent data, enabling easier interpretation, pattern identification, and communication of insights. Python offers a rich ecosystem of libraries for this purpose, each with its own strengths. Key Python Libraries are Matplotlib and Seaborn. Plotly and we will discuss these things.
Matplotlib: This is the foundational library for creating static, animated, and interactive visualizations. It provides a wide range of plot types, including line plots, scatter plots, bar charts, histograms, and pie charts. The pyplot module (commonly imported as plt) offers a convenient interface for creating and customizing plots.
Seaborn: Built on top of Matplotlib, Seaborn specializes in creating aesthetically pleasing and informative statistical graphics. It simplifies the creation of complex plots like heatmaps, violin plots, and pair plots, often with fewer lines of code.
Plotly: Plotly enables the creation of interactive, web-based visualizations that can be embedded in dashboards or web applications. It supports a wide variety of chart types and offers extensive customization options for interactive elements.