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Data Science Functions


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