# Visualization 1: Matplotlib Basics Exercises

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In [1]:

%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 [31]:

x=np.random.randn(22)
y=np.random.randn(22)
plt.xlabel('x')
plt.ylabel('y')
plt.title('Scatter')
plt.scatter(x,y,s=22.0,c='g',marker='x',alpha=.7,linewidths=2.2)
#plt.xlim(0,1)
#plt.ylim()

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Out[31]:

<matplotlib.collections.PathCollection at 0x7f7f11cdf1d0>

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## Histogram

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

• Generate random 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 [34]:

data=np.random.randn(22)
plt.hist(data)

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Out[34]:

(array([ 1.,  0.,  2.,  1.,  3.,  1.,  3.,  4.,  4.,  3.]),
array([-3.06904602, -2.59684524, -2.12464446, -1.65244368, -1.1802429 ,
-0.70804212, -0.23584134,  0.23635944,  0.70856022,  1.18076101,
1.65296179]),
<a list of 10 Patch objects>)

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