Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.
Learn Aggregate Functions | Introduction to NumPy
Introduction to Data Analysis in Python

bookAggregate 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.

MethodDescription
.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())
copy

Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 5. ChapterΒ 4

Ask AI

expand

Ask AI

ChatGPT

Ask anything or try one of the suggested questions to begin our chat

Awesome!

Completion rate improved to 2.7

bookAggregate Functions

Swipe to show menu

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

MethodDescription
.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())
copy

Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 5. ChapterΒ 4
some-alt