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Visualization

This guide covers the essentials of using Matplotlib, a powerful plotting library in Python, particularly for users familiar with MATLAB. It discusses the key features and functions, including how to create and modify plots, manage figures, and save visualizations. Examples illustrate plotting simple mathematical functions like x^2 and its negative, while incorporating best practices for labeling and customizing graph appearance. Ideal for students and professionals looking to enhance their data visualization skills.

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Visualization

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  1. Visualization UW CSE 140 Winter 2014

  2. matplotlib • Strives to emulate MATLAB • Pro: familiar to MATLAB users • Pro: powerful • Con: not the best design for a plotting library • One important function for HW7: plot(xvalues, yvalues)

  3. Plot import matplotlib.pyplot as plt xs = [1,2,3,4,5] # ys= [x**2 for x in xs] ys = [] for x in xs: ys.append(x**2) plt.plot(xs, ys) no return value? Has a side effect on the figure (like “print” statement)

  4. Incrementally modify the figure. Save your figure to a file Show it on the screen import matplotlib.pyplotasplt xs = range(-100,100,10) x2 = [x**2 for x inxs] negx2 = [-x**2 for x inxs] plt.plot(xs, x2) plt.plot(xs, negx2) plt.xlabel("x”) plt.ylabel("y”) plt.ylim(-2000, 2000) plt.axhline(0) # horiz line plt.axvline(0) # vert line plt.savefig("quad.png") plt.show()

  5. We can group these options into functions as usual, but remember that they are operating on a global, hidden variable (the figure)

  6. 1854 London cholera epidemic Map by Dr. John Snow

  7. Napoleon’s Russian Campaign of 1812 Graphic by Charles Joseph Minard

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