Performing Mathematical Operations

One of NumPy's major advantages is vectorization. Instead of writing a loop to process every value, you can apply an operation directly to an entire array.

Element-wise Arithmetic

import numpy as np

prices = np.array([100, 200, 300, 400])

discounted_prices = prices * 0.90

taxed_prices = prices * 1.18

print(discounted_prices)

print(taxed_prices)

Each operation is applied to every element in the array.

You can also perform calculations between arrays of compatible shapes.

units_sold = np.array([10, 20, 15, 25])

unit_prices = np.array([100, 150, 200, 120])

revenue = units_sold * unit_prices

print(revenue)

print("Total revenue:", np.sum(revenue))

The multiplication calculates revenue for each product, while np.sum() calculates the combined revenue.

Aggregation Functions

NumPy provides functions to calculate common statistical measurements.

sales = np.array([12000, 15000, 18000, 22000, 25000])

print("Total:", np.sum(sales))

print("Mean:", np.mean(sales))

print("Median:", np.median(sales))

print("Minimum:", np.min(sales))

print("Maximum:", np.max(sales))

print("Standard deviation:", np.std(sales))

These operations help summarize a dataset quickly. By default, np.std() calculates the population standard deviation (ddof=0). Use np.std(sales, ddof=1) when the sample standard deviation is required and the sample contains enough observations.