Visualization 1: Matplotlib Basics Exercises



In [50]:

    
%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 [51]:

    
?plt.scatter()



In [60]:

    
from matplotlib import markers
markers.MarkerStyle.markers.keys()

x = np.random.rand(100)
y = np.random.rand(100)
plt.scatter(x, y, label = 'The Dots', c = u'r', marker = u'o')
plt.grid(True)
plt.box(False)
plt.xlabel('The X-Axis')
plt.ylabel('The Y-Axis')
plt.legend(loc=0) ##I have no idea if you wanted a legend... but I tried to find the best place for it









    Out[60]:





<matplotlib.legend.Legend at 0x7fdea880d750>

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]:

    
data = np.random.rand(100)
data

?plt.hist()

plt.hist(data, bins = 30, histtype = u'step', color = 'g')
plt.box(True)
plt.xlabel('The X-Axis for Histograms')
plt.ylabel('The Y-Axis for Histograms')









    Out[66]:





<matplotlib.text.Text at 0x7fdea85bf2d0>



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