Data Visualisation using Python
Data visualization is an essential tool in data analysis as it enables us to explore data, identify trends, patterns and relationships, and communicate insights to stakeholders effectively. Matplotlib and Seaborn are two popular Python libraries used for data visualization. In this introduction, we will look at the basic syntax and features of both libraries. Matplotlib is a powerful data visualization library that allows us to create a wide range of plots, including line, scatter, bar, histogram, and more. The library is highly customizable, allowing us to tweak almost every aspect of the plot to match our requirements. Matplotlib works well with Pandas dataframes and NumPy arrays, making it easy to integrate with data analysis workflows.
Seaborn is a high-level data visualization library built on top of Matplotlib that provides a simpler interface for creating attractive and informative plots. Seaborn has built-in themes and color palettes, making it easy to create aesthetically pleasing plots. Seaborn also provides specialized plots for exploring relationships between variables, such as scatterplots, pairplots, and heatmaps.
In summary, Matplotlib and Seaborn are powerful Python libraries for data visualization. Matplotlib provides a low-level interface for creating highly customizable plots, while Seaborn provides a high-level interface for creating attractive and informative plots with built-in themes and color palettes.