This chapter shows three commonly used functions when working with Data Science: max(), min(), and mean().

The Sports Watch Data Set

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

The data set above consists of 6 variables, each with 10 observations:

• Duration - How long lasted the training session in minutes?
• Average_Pulse - What was the average pulse of the training session? This is measured by beats per minute
• Max_Pulse - What was the max pulse of the training session?
• Calorie_Burnage - How much calories were burnt on the training session?
• Hours_Work - How many hours did we work at our job before the training session?
• Hours_Sleep - How much did we sleep the night before the training session?

We use underscore (_) to separate strings because Python cannot read space as separator.

The max() function

The Python max() function is used to find the highest value in an array.

Example

Average_pulse_max = max(80, 85, 90, 95, 100, 105, 110, 115, 120, 125)

print (Average_pulse_max)

The min() function

The Python min() function is used to find the lowest value in an array.

Example

Average_pulse_min = min(80, 85, 90, 95, 100, 105, 110, 115, 120, 125)

print (Average_pulse_min)

The mean() function

The NumPy mean() function is used to find the average value of an array.

Example

import numpy as np

Calorie_burnage = [240, 250, 260, 270, 280, 290, 300, 310, 320, 330]

Average_calorie_burnage = np.mean(Calorie_burnage)

print(Average_calorie_burnage)

Note: We write np. in front of mean to let Python know that we want to activate the mean function from the Numpy library.