- Matplotlib Basics
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- Matplotlib Plotting
- Matplotlib - Bar Graphs
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- Matplotlib - Violin Plot
- Matplotlib - Contour Plot
- Matplotlib - 3D Plotting
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- Matplotlib - Quiver Plot
- Matplotlib Useful Resources
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- Matplotlib - Useful Resources
- Matplotlib - Discussion
Matplotlib - Violin Plot
Violin plots are similar to box plots, except that they also show the probability density of the data at different values. These plots include a marker for the median of the data and a box indicating the interquartile range, as in the standard box plots. Overlaid on this box plot is a kernel density estimation. Like box plots, violin plots are used to represent comparison of a variable distribution (or sample distribution) across different "categories".
A violin plot is more informative than a plain box plot. In fact while a box plot only shows summary statistics such as mean/median and interquartile ranges, the violin plot shows the full distribution of the data.
import matplotlib.pyplot as plt import numpy as np np.random.seed(10) collectn_1 = np.random.normal(100, 10, 200) collectn_2 = np.random.normal(80, 30, 200) collectn_3 = np.random.normal(90, 20, 200) collectn_4 = np.random.normal(70, 25, 200) ## combine these different collections into a list data_to_plot = [collectn_1, collectn_2, collectn_3, collectn_4] # Create a figure instance fig = plt.figure() # Create an axes instance ax = fig.add_axes([0,0,1,1]) # Create the boxplot bp = ax.violinplot(data_to_plot) plt.show()
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