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'''
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Now, we can first plot the values of Average_Pulse against Calorie_Burnage using the matplotlib library.
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The plot() function is used to make a 2D hexagonal binning plot of points x,y:
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'''
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import pandas as pd
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import matplotlib.pyplot as plt
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health_data = pd.read_csv('data-linear-functions.csv', header=0, sep=',')
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health_data.columns = health_data.columns.str.strip()
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health_data.plot(x='Average_Pulse', y='Calorie_Burnage', kind='line')
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plt.xlim(xmin=0)
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plt.ylim(ymin=0)
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plt.title('Average Pulse vs Calorie Burnage')
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plt.xlabel('Average Pulse')
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plt.ylabel('Calorie Burnage')
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plt.show()
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'''
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Example Explained
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Import the pyplot module of the matplotlib library
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Plot the data from Average_Pulse against Calorie_Burnage
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kind='line' tells us which type of plot we want. Here, we want to have a straight line
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plt.ylim() and plt.xlim() tells us what value we want the axis to start on. Here, we want the axis to begin from zero
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plt.show() shows us the output
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Why is The Line Not Fully Drawn Down to The y-axis?
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==> The reason is that we do not have observations where Average_Pulse or Calorie_Burnage are equal to zero. 80 is the first observation of Average_Pulse and 240 is the first observation of Calorie_Burnage.
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We can use the diagonal line to find the mathematical function to predict calorie burnage.
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As it turns out:
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If the average pulse is 80, the calorie burnage is 240
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If the average pulse is 90, the calorie burnage is 260
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If the average pulse is 100, the calorie burnage is 280
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There is a pattern. If average pulse increases by 10, the calorie burnage increases by 20.
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''' |