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Apr 26th, 2019
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  1. import sys
  2. !{sys.executable} -m pip install matplotlib sklearn pandas requests seaborn --user
  3. import matplotlib.pyplot as plt
  4. import numpy as np
  5. from sklearn import datasets
  6. import pandas as pd
  7. import matplotlib as mpl
  8. import io
  9. import requests
  10.  
  11. mpl.rcParams['figure.figsize'] = (12, 8)
  12. %matplotlib inline
  13. from sklearn import datasets
  14. figure=datasets.load_boston()
  15. fig=plt.figure()
  16. axes=fig.add_axes([0.1, 0.1, 0.8, 0.8])
  17. fig, axes = plt.subplots(nrows=1, ncols=2)
  18.  
  19. for ax in axes:
  20. ax.plot(CRIM, PRICE, 'r')
  21. ax.plot(NOX, PRICE, 'g')
  22. ax.set_xlabel('crime')
  23. ax.set_ylabel('price')
  24. ax.set_xlabel('nox')
  25. ax.set_title('You think this bad neighborhood?')
  26.  
  27. fig.tight_layout()
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