Data Science

Data Science Functions

We will in this tutorial
show three commonly used functions
when working with Data Science: max(), min(), and mean().

Let us explain this example:

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?

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)

Output:



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)

Output:



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)

Output:


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

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