Aggregate Functions

You can also calculate aggregate statistics of NumPy arrays, like minimum, maximum, mean, product, sum, etc. These ase realized in NumPy as arrays methods.

Method	Description
`.mean()`	Returns the arithmetic mean
`.sum()`	Returns the sum of elements
`.prod()`	Returns the product of all elements
`.min()`	Returns the minimum of an array
`.max()`	Returns the maximum of an array
`.std()`	Returns the standard deviation of array elements
`.var()`	Returns the variance of array elements

For example, assume we have two arrays: prices and sales, representing goods' prices and quantity of each good being sold, respectively. Using multiplication and .sum() method we can easily calculate the total revenue.


              12345678910
            
# Import the library
import numpy as np

# Two arrays
prices = np.array([15, 60, 40, 5])
sales = np.array([7, 3, 5, 15])
# Revenue per good
rev_per_good = prices * sales
# Total revenue
print("Total revenue is", rev_per_good.sum())

War alles klar?

Danke für Ihr Feedback!

Abschnitt 5. Kapitel 4

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Kursinhalt

Introduction to Data Analysis in Python

Aggregate Functions

You can also calculate aggregate statistics of NumPy arrays, like minimum, maximum, mean, product, sum, etc. These ase realized in NumPy as arrays methods.

Method	Description
`.mean()`	Returns the arithmetic mean
`.sum()`	Returns the sum of elements
`.prod()`	Returns the product of all elements
`.min()`	Returns the minimum of an array
`.max()`	Returns the maximum of an array
`.std()`	Returns the standard deviation of array elements
`.var()`	Returns the variance of array elements


              12345678910
            
# Import the library
import numpy as np

# Two arrays
prices = np.array([15, 60, 40, 5])
sales = np.array([7, 3, 5, 15])
# Revenue per good
rev_per_good = prices * sales
# Total revenue
print("Total revenue is", rev_per_good.sum())

War alles klar?

Danke für Ihr Feedback!

Abschnitt 5. Kapitel 4