Visualization 1: Matplotlib Basics Exercises



In [1]:

    
%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.



In [61]:

    
np.random.seed(33)
plt.figure(figsize=(6,6))
x = np.random.randn(50)
y = np.random.randn(50) + 2 * x
plt.scatter(x,y, c='r', marker='o', alpha=.3, s = 100)
plt.xlabel("Random x value")
plt.ylabel("Random y value")
plt.title("Random Scatter Plot")









    Out[61]:





<matplotlib.text.Text at 0x7f550e95a250>

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.



In [59]:

    
np.random.seed(43)
plt.figure(figsize=(8,4))
x = np.log(np.random.rand(500))
plt.hist(x, alpha = .3, color = 'yellow', normed=True)
plt.xlabel("Log of Random Uniform")
plt.ylabel("Density")
plt.title("Example histogram")









    Out[59]:





<matplotlib.text.Text at 0x7f550f021a90>



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