Python 9: NumPy for Scientific Computing

Goal:

By the end of this lesson, you’ll:

• Understand what NumPy is and why it’s fast
• Work with arrays instead of lists
• Perform fast mathematical operations
• Manipulate data like a data scientist

What Is NumPy?

NumPy (Numerical Python) is a powerful library used for:

• Fast calculations with large datasets
• Handling numerical matrices (tables of numbers)
• Vectorized operations (no more for-loops!)

First, install it:

pip install numpy

And import it:

import numpy as np

Creating Arrays

arr = np.array([1, 2, 3, 4])
print(arr)          # [1 2 3 4]
print(arr.shape)    # (4,)

2D array:

matrix = np.array([[1, 2], [3, 4]])
print(matrix)

Generate useful arrays:

np.zeros(5)        # [0. 0. 0. 0. 0.]
np.ones((2, 3))    # 2 rows, 3 cols of ones
np.arange(0, 10, 2)  # [0 2 4 6 8]
np.linspace(0, 1, 5)  # 5 evenly spaced values from 0 to 1

Array Math (Vectorized)

a = np.array([1, 2, 3])
b = np.array([10, 20, 30])

print(a + b)     # [11 22 33]
print(a * b)     # [10 40 90]
print(a ** 2)    # [1 4 9]

✅ Way faster than using Python for loops!

Reshaping and Indexing

x = np.arange(12)     # [0 to 11]
x = x.reshape(3, 4)   # 3 rows, 4 columns
print(x)

print(x[0, 1])  # value at row 0, column 1
print(x[:, 2])  # all rows, column 2

Basic Statistics

data = np.array([10, 20, 30, 40, 50])

print(data.mean())     # 30.0
print(data.std())      # Standard deviation
print(data.max())      # 50
print(data.min())      # 10

🧪 6. Practice Time

Try these in your blog_env notebook:

• Create an array of daily sales: [2500, 2700, 2600, 3000, 3100]
• Calculate total, average, and standard deviation
• Create a 2D array of stock levels by product and day
• Reshape a 1D array of 12 numbers into 3 rows and 4 columns
• Use slicing to get the second column of that matrix

✅ Summary

• NumPy is fast, memory-efficient, and the foundation of data science
• Arrays replace lists for calculations
• Use .mean(), .std(), and .reshape() often
• Avoid loops — vectorize your code!