Matplotlib Data Visualization with Python

Introduction

In this lab, we will use Python's Matplotlib library to compute the mean (mu) and standard deviation (sigma) of 100 data sets and plot mu vs. sigma. We will also add interactivity to the plot so that when you click on one of the (mu, sigma) points, the raw data from the dataset that generated this point will be plotted.

VM Tips

After the VM startup is done, click the top left corner to switch to the Notebook tab to access Jupyter Notebook for practice.

Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook.

If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.

Skills Graph

%%%%{init: {'theme':'neutral'}}%%%% flowchart RL matplotlib(("`Matplotlib`")) -.-> matplotlib/BasicConceptsGroup(["`Basic Concepts`"]) matplotlib(("`Matplotlib`")) -.-> matplotlib/PlottingDataGroup(["`Plotting Data`"]) matplotlib(("`Matplotlib`")) -.-> matplotlib/AdvancedTopicsGroup(["`Advanced Topics`"]) python(("`Python`")) -.-> python/BasicConceptsGroup(["`Basic Concepts`"]) python(("`Python`")) -.-> python/ControlFlowGroup(["`Control Flow`"]) python(("`Python`")) -.-> python/DataStructuresGroup(["`Data Structures`"]) python(("`Python`")) -.-> python/FunctionsGroup(["`Functions`"]) python(("`Python`")) -.-> python/ModulesandPackagesGroup(["`Modules and Packages`"]) python(("`Python`")) -.-> python/PythonStandardLibraryGroup(["`Python Standard Library`"]) python(("`Python`")) -.-> python/DataScienceandMachineLearningGroup(["`Data Science and Machine Learning`"]) matplotlib/BasicConceptsGroup -.-> matplotlib/importing_matplotlib("`Importing Matplotlib`") matplotlib/BasicConceptsGroup -.-> matplotlib/figures_axes("`Understanding Figures and Axes`") matplotlib/PlottingDataGroup -.-> matplotlib/line_plots("`Line Plots`") matplotlib/AdvancedTopicsGroup -.-> matplotlib/event_handling("`Event Handling`") python/BasicConceptsGroup -.-> python/booleans("`Booleans`") python/ControlFlowGroup -.-> python/conditional_statements("`Conditional Statements`") python/ControlFlowGroup -.-> python/for_loops("`For Loops`") python/DataStructuresGroup -.-> python/lists("`Lists`") python/DataStructuresGroup -.-> python/tuples("`Tuples`") python/DataStructuresGroup -.-> python/dictionaries("`Dictionaries`") python/FunctionsGroup -.-> python/function_definition("`Function Definition`") python/ModulesandPackagesGroup -.-> python/importing_modules("`Importing Modules`") python/ModulesandPackagesGroup -.-> python/standard_libraries("`Common Standard Libraries`") python/PythonStandardLibraryGroup -.-> python/math_random("`Math and Random`") python/DataScienceandMachineLearningGroup -.-> python/numerical_computing("`Numerical Computing`") python/DataScienceandMachineLearningGroup -.-> python/data_visualization("`Data Visualization`") python/FunctionsGroup -.-> python/build_in_functions("`Build-in Functions`") subgraph Lab Skills matplotlib/importing_matplotlib -.-> lab-48865{{"`Matplotlib Data Visualization with Python`"}} matplotlib/figures_axes -.-> lab-48865{{"`Matplotlib Data Visualization with Python`"}} matplotlib/line_plots -.-> lab-48865{{"`Matplotlib Data Visualization with Python`"}} matplotlib/event_handling -.-> lab-48865{{"`Matplotlib Data Visualization with Python`"}} python/booleans -.-> lab-48865{{"`Matplotlib Data Visualization with Python`"}} python/conditional_statements -.-> lab-48865{{"`Matplotlib Data Visualization with Python`"}} python/for_loops -.-> lab-48865{{"`Matplotlib Data Visualization with Python`"}} python/lists -.-> lab-48865{{"`Matplotlib Data Visualization with Python`"}} python/tuples -.-> lab-48865{{"`Matplotlib Data Visualization with Python`"}} python/dictionaries -.-> lab-48865{{"`Matplotlib Data Visualization with Python`"}} python/function_definition -.-> lab-48865{{"`Matplotlib Data Visualization with Python`"}} python/importing_modules -.-> lab-48865{{"`Matplotlib Data Visualization with Python`"}} python/standard_libraries -.-> lab-48865{{"`Matplotlib Data Visualization with Python`"}} python/math_random -.-> lab-48865{{"`Matplotlib Data Visualization with Python`"}} python/numerical_computing -.-> lab-48865{{"`Matplotlib Data Visualization with Python`"}} python/data_visualization -.-> lab-48865{{"`Matplotlib Data Visualization with Python`"}} python/build_in_functions -.-> lab-48865{{"`Matplotlib Data Visualization with Python`"}} end

Generate Random Data

First, we need to generate 100 random datasets, each containing 1000 random numbers between 0 and 1. We will use numpy's random module to generate the random data.

import numpy as np

np.random.seed(19680801)

X = np.random.rand(100, 1000)

Compute Mean and Standard Deviation

Next, we will compute the mean and standard deviation of each of the 100 datasets. We will use numpy's mean and std functions to compute these values.

xs = np.mean(X, axis=1)
ys = np.std(X, axis=1)

Plot the Data

Now, we will plot mu vs. sigma using Matplotlib's pyplot module. We will create a scatter plot using the computed values for mu and sigma. We will also add interactivity to the plot by setting the picker parameter to True.

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.set_title('click on point to plot time series')
line, = ax.plot(xs, ys, 'o', picker=True, pickradius=5)

Add Interactivity

When a point on the scatter plot is clicked, we want to plot the raw data from the dataset that generated that point. We will define a function onpick that will be called when a point is clicked. The function will plot the raw data and display the mean and standard deviation for that dataset.

def onpick(event):

    if event.artist != line:
        return

    N = len(event.ind)
    if not N:
        return

    figi, axs = plt.subplots(N, squeeze=False)
    for ax, dataind in zip(axs.flat, event.ind):
        ax.plot(X[dataind])
        ax.text(.05, .9, f'mu={xs[dataind]:1.3f}\nsigma={ys[dataind]:1.3f}',
                transform=ax.transAxes, va='top')
        ax.set_ylim(-0.5, 1.5)
    figi.show()


fig.canvas.mpl_connect('pick_event', onpick)

Display the Plot

Finally, we will display the plot using the show function.

plt.show()

Summary

In this lab, we learned how to use Matplotlib to compute the mean and standard deviation of 100 datasets and plot mu vs. sigma. We also added interactivity to the plot so that when a point is clicked, the raw data from the dataset that generated that point is displayed. This lab demonstrates the power and flexibility of Matplotlib for exploring and visualizing data.