Data Visualization with Python

Data visualisation is the process of representing data in visual formats like charts, graphs, or maps to make it easier to understand. It helps people quickly grasp patterns, trends, and insights from data. By turning complex data into visual elements, data visualization makes analysis more intuitive and accessible for everyone.

To learn the basic forms of data visualization you can already check the following entry introduction to statistical figures.

Data visualization in data analysis

In Python, data visualization enhances statistical analysis by enabling the graphical representation of statistical summaries and distributions, such as histograms, box plots, and scatter plots. These visualizations help in assessing the distribution of data, identifying correlations, and detecting anomalies. Advanced visualizations, like heatmaps or pair plots, provide deeper insights into multivariate relationships and the impact of statistical measures such as correlation coefficients or regression lines.

For a first overview of data visualization workflow, check this entry.

Introduction to Seaborn

Specific graphics and figures

If you want to know only the most important command to create specific figures, click on this image.

• Scatterplots