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())
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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())
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