Visualization 1: Matplotlib Basics Exercises



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%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np

Scatter plots

Learn how to use Matplotlib's plt.scatter function to make a 2d scatter plot.

Generate random data using np.random.randn.
Style the markers (color, size, shape, alpha) appropriately.
Include an x and y label and title.



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N = 50
x = np.random.rand(N)#random x vals
y = np.random.rand(N)#random y vals
colors = np.random.rand(N)#makes random colors
area = np.pi * (15 * np.random.rand(N))**2 # 0 to 15 point radiuses

plt.ylabel("y")
plt.xlabel("x")
plt.scatter(x, y, s=area, c=colors, alpha=0.5)# makes scatter









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<matplotlib.collections.PathCollection at 0x7f249de3d610>

Histogram

Learn how to use Matplotlib's plt.hist function to make a 1d histogram.

Generate randpom data using np.random.randn.
Figure out how to set the number of histogram bins and other style options.
Include an x and y label and title.



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mu, sigma = 200, 25
x = mu + sigma*np.random.randn(1000)

n, bins, patches = P.hist(x, 50, normed=1, histtype='bar')
P.setp(patches, 'facecolor', 'r', 'alpha', 0.75)

y = P.normpdf( bins, mu, sigma)
l = P.plot(bins, y, 'k--', linewidth=1.5)



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