Data Science in the Cloud with Microsoft Azure Machine Learning and Python by Stephen F. Elston

Author:Stephen F. Elston
Language: eng
Format: mobi, epub, pdf
Publisher: O'Reilly Media, Inc.
Published: 2016-01-04T05:00:00+00:00

## Explore bike demand for certain times on working and nonworking days labels = ["Boxplots of bike demand at 0900 \n\n", "Boxplots of bike demand at 1800 \n\n"] times = [8, 17] for lab, tms in zip(labels, times): temp = BikeShare[BikeShare.hr == tms] fig = plt.figure(figsize=(8, 6)) fig.clf() ax = fig.gca() temp.boxplot(column = ['cnt'], by = ['isWorking'], ax = ax) plt.xlabel('') plt.ylabel('Number of bikes') plt.title(lab) plt.show() if(Azure == True): fig.savefig('timeplot' + str(tms) + '.png') return BikeShare

This code is nearly identical to the code we already discussed for creating box plots. The only difference is the use of the by argument to create a separate box plot for working and nonworking days.

Note the return statement at the end — Python functions require a return statement.

The result of running this code can be seen in Figures 16 and 17.

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