Violin plots are a powerful tool for visualizing the distribution of data. They are similar to box plots, but instead of displaying only summary statistics, violin plots also show the …

Box plots (or box-and-whisker plots) are useful for displaying the distribution of data based on a five-number summary: minimum, first quartile (Q1), median, third quartile (Q3), and maximum. They provide …

Contour plots are useful for visualizing three-dimensional data on a two-dimensional plane. They represent levels or “contours” that correspond to different heights or values of a variable. This makes contour …

Histograms are a powerful tool for visualizing the distribution of a dataset. They allow us to see the frequency of data points within specified ranges or bins, making them useful …

Pie charts are a common way to represent the composition of a whole divided into parts. They are useful for visualizing data in categories, showing proportions, and illustrating percentages. Matplotlib …

Scatter plots are a powerful way to visualize the relationship between two variables, making them a staple in data analysis and visualization. Matplotlib provides several ways to customize scatter plots, …

Bar graphs are a versatile and commonly used plot type in data visualization. They are useful for comparing categorical data, showing distribution, or illustrating changes over time. Matplotlib’s bar function …

The CheckButtons widget in Matplotlib allows you to create checkboxes that can be toggled independently. Unlike radio buttons, multiple check buttons can be selected at once, making this widget useful …

The RadioButtons widget in Matplotlib is a tool for creating a list of options where only one can be selected at a time. This widget is particularly useful for switching …

The SpanSelector widget in Matplotlib provides an easy way to select a range within a plot by clicking and dragging. This widget is particularly useful for selecting ranges on one …