Visualization 1: Matplotlib Basics Exercises

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In [48]:

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

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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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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()

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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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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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Out[66]:

<matplotlib.text.Text at 0x7f31cc0844d0>

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