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.

# 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())

Introduction to Data Analysis in Python

Aggregate Functions

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