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.
