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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.

Data Science Functions

The Sports Watch Data Set

The max() function

The min() function

The mean() function

Data Science Functions

The Sports Watch Data Set

The max() function

The min() function

The mean() function

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

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

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

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

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

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