Python provides a myriad of data visualization libraries that give you the flexibility to define every aspect of your visualization. It provides you the option of choosing between static images, which can be helpful for academic papers, and interactive visualizations that can help you delve deeper into your data.
Data Visualization with Python using Seaborn
- Seaborn Boxplot – How to create box and whisker plotsLearn how to create a Seaborn boxplot, including how to add styles, titles, axis labels and add grouped boxplots.
- Seaborn Line Plot – Create Lineplots with Seaborn relplotLearn how to use the Seaborn line plot andrelplot functions to create beautiful line charts, add titles, customize styles, multiple line charts.
- Seaborn Barplot – Make Bar Charts with sns.barplotLearn how to use the Seaborn barplot and countplot functions to create beautiful bar charts, add titles, customize styles, group bar charts.
- Seaborn in Python for Data VisualizationIn this tutorial, you’ll learn how to create a wide variety of different plots using Seaborn, as well as how to apply different styling options to these plots.
- Plotting a Histogram in Python with Matplotlib and PandasLearn what histograms are and how to create them in Python with Matplotlib and Pandas.
Data Visualization with Python using Matplotlib
- Matplotlib Scatter Charts – Learn all you need to knowLearn how to create Matplotlib scatter charts, including how to customize colours, add titles, change transparency and size of markers.
- Matplotlib Bar Charts – Learn all you need to knowLearn how to create Matplotlib bar charts, including how to customize bar colours, add titles, stacked and double bar charts.
- Matplotlib Line Charts – Learn all you need to knowLearn how to make Matplotlib line charts, including how to add multiple lines, customize labels, add data labels, and add a legend.
- Plotting a Histogram in Python with Matplotlib and PandasLearn what histograms are and how to create them in Python with Matplotlib and Pandas.