Introduction
NumPy (Numerical Python) is the ultimate math wizard in the Python world! It’s like a turbocharged calculator that handles large arrays and matrices efficiently. If Python lists are bicycles, then NumPy arrays are rocket-powered race cars!
Installing NumPy
Before we unleash the power of NumPy, let’s install it:
pip install numpy
Check if it’s installed correctly:
import numpy as np
print(np.__version__)
Creating NumPy Arrays
Converting a Python list into a NumPy array:
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(arr)
Boom! Now you have a NumPy array!
Creating a matrix (2D array):
matrix = np.array([[1, 2, 3], [4, 5, 6]])
print(matrix)
Array Operations
NumPy arrays support element-wise operations, making them much faster than Python lists.
arr = np.array([1, 2, 3, 4])
print(arr * 2) # [2 4 6 8]
print(arr + 5) # [6 7 8 9]
print(arr ** 2) # [1 4 9 16]
No need for loops—NumPy does the magic automatically!
Useful NumPy Functions
| Function | Description |
|---|---|
np.zeros((3,3)) |
Creates a 3×3 matrix of zeros |
np.ones((2,2)) |
Creates a 2×2 matrix of ones |
np.arange(0,10,2) |
Creates an array [0, 2, 4, 6, 8] |
np.linspace(0,1,5) |
Generates 5 evenly spaced numbers between 0 and 1 |
np.random.rand(3,3) |
Creates a 3×3 matrix of random numbers |
Example:
rand_matrix = np.random.rand(3,3)
print(rand_matrix)
Indexing & Slicing
NumPy makes it easy to grab specific elements from an array.
arr = np.array([10, 20, 30, 40, 50])
print(arr[1]) # 20 (Indexing)
print(arr[1:4]) # [20 30 40] (Slicing)
print(arr[-1]) # 50 (Negative Indexing)
For 2D arrays:
matrix = np.array([[1, 2, 3], [4, 5, 6]])
print(matrix[1, 2]) # 6 (Row 1, Column 2)
print(matrix[:, 1]) # [2 5] (Second column)
Reshaping & Transposing
Reshaping an array:
arr = np.array([1, 2, 3, 4, 5, 6])
reshaped = arr.reshape(2, 3)
print(reshaped)
Transposing a matrix:
matrix = np.array([[1, 2, 3], [4, 5, 6]])
print(matrix.T)
Summary
| Feature | NumPy Advantage |
|---|---|
| Arrays | Faster than Python lists |
| Math Operations | Element-wise calculations |
| Indexing | Powerful slicing and dicing |
| Reshaping | Easily modify array shapes |
| Random Numbers | Generate matrices with random values |
NumPy is the backbone of numerical computing in Python. Now you’re ready to conquer arrays like a pro!
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