DS Plotting Linear Functions

The Sports Watch Data Set

Let’s examine our health dataset:

Duration	Average_Pulse	Max_Pulse	Calorie_Burnage	Hours_Work	Hours_Sleep
30	80	120	240	10	7
30	85	120	250	10	7
45	90	130	260	8	7
45	95	130	270	8	7
45	100	140	280	0	7
60	105	140	290	7	8
60	110	145	300	7	8
60	115	145	310	8	8
75	120	150	320	0	8
75	125	150	330	8	8

Plot the Existing Data in Python

Next, we can plot the values of Average_Pulse against Calorie_Burnage using the matplotlib library.

The plot() function is used to create a 2D plot of points x and y.

Example

import matplotlib.pyplot as plt

health_data.plot(x =‘Average_Pulse’, y=‘Calorie_Burnage’, kind=‘line’),
plt.ylim(ymin=0)
plt.xlim(xmin=0)

plt.show(

Example Explained:

Import the pyplot module from the matplotlib library.
Plot the data of Average_Pulse against Calorie_Burnage.
kind='line' specifies that we want a line plot.
plt.ylim() and plt.xlim() define the starting values for the axes. In this case, we set them to start from zero.
plt.show() displays the plot.

The code above will generate the following result:

img_linearity2

The Graph Output

As observed, there is a correlation between Average_Pulse and Calorie_Burnage. Calorie_Burnage increases in proportion to Average_Pulse, indicating that Average_Pulse can be used to predict Calorie_Burnage.