Data Visualization with Python

Data visualization is the process of representing data in visual formats like charts, graphs, or maps to convey information quickly by revealing patterns and trends that may be difficult to discern when data is presented in tables or text formats. By turning complex data into visual elements, data visualization makes analysis more intuitive and accessible for everyone and ultimately lead to more informed decision making processes.

To get into data visualization it is necessary to learn some fundamentals of it, such as data formats and basic statistical figures thar are commonly used.

Data visualization in Python

Python has become the language for data scientists and analysts due to its simplicity, versatility and robust ecosystem of libraries specifically tailored for data visualization purposes. These libraries offer tools for creating dynamic and interactive visualizations that cater to diverse needs.

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

Contributors of this entry are: Abraham and Gustavo Rodriguez | Back to Python Landscape