Visualization 1: Matplotlib Basics Exercises



In [48]:

    
%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 [65]:

    
x = np.random.randn(100)
y = np.random.randn(100)
plt.scatter(x,y, s = 20, c = 'b')
plt.xlabel('Random Number 2')
plt.ylabel('Random Number')
plt.title('Random 2d Scatter Plot')
axis = plt.gca()
axis.spines['top'].set_visible(False)
axis.spines['right'].set_visible(False)
axis.get_xaxis().tick_bottom()
axis.get_yaxis().tick_left()
plt.tight_layout()

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 [66]:

    
x = np.random.randn(10)

plt.hist(x,4)
plt.xlabel('X value')
plt.ylabel('Y value')
plt.title('Random Histogram Bins')









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<matplotlib.text.Text at 0x7f31cc0844d0>



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