Introduction to NumPy Arrays

NumPy is one of Python’s most powerful and popular libraries for mathematical and scientific data manipulation. In this guide, we will explore the basics of NumPy arrays, their creation, properties, and common operations.

1. What is a NumPy Array?

• A NumPy Array is a data structure similar to a Python list but optimized for numerical data and matrices.
• It is more memory-efficient and faster for computation than regular Python lists.
• Enables mathematical operations directly on whole arrays without loops.

2. Creating Basic Arrays

import numpy as np

array_1d = np.array([1, 2, 3, 4, 5])           # Creating a 1D array
array_2d = np.array([[1, 2, 3], [4, 5, 6]])    # Creating a 2D array (matrix)
float_array = np.array([1, 2, 3], dtype=float) # Array with a specific data type

3. Creating Arrays with Built-in Functions

zeros = np.zeros((3, 3))  # Matrix of zeros with size 3x3
ones = np.ones((2, 4))    # Matrix of ones with size 2x4
empty_array = np.empty((2, 3))  # Array with size 2x3
sequence = np.arange(0, 10, 2)  # [0, 2, 4, 6, 8]

• Array with evenly spaced values:

  linspace = np.linspace(0, 1, 5)  # [0. , 0.25, 0.5 , 0.75, 1. ]
  

4. Key Properties of Arrays

• Shape: Dimensions of the array.
• Data type (dtype): Type of values stored in the array.
• Number of dimensions (ndim):

  array = np.array([[1, 2, 3], [4, 5, 6]])
  print(array.shape)  # (2, 3)
  print(array.ndim)   # 2
  print(array.dtype)  # int64
  

5. Operations on Arrays

Mathematical operations:

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])

print(a + b)  # [5, 7, 9]   ~~~ a.plus(b)
print(a * b)  # [4, 10, 18] ~~~ a.multiply(b)
print(a ** 2) # [1, 4, 9]   

Transpose of a matrix:

[[1, 2], ===>[[1, 3],
 [3, 4]] ===> [2, 4]]

code example:

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

6. Slicing and Indexing

• Slicing works similarly to Python lists:

  array = np.array([10, 20, 30, 40, 50])
  print(array[1:4])  # [20, 30, 40]
  

• Conditional indexing:

  array = np.array([1, 2, 3, 4, 5])
  print(array[array > 3])  # [4, 5]
  print(array[(array > 2) & (array < 5)]) # [3, 4]
  

7. Multi-dimensional Arrays

• Complex indexing:

  #           indexes 0  1  2        
  matrix = np.array([[1, 2, 3],   # index 0 
                   [4, 5, 6]])  # index 1
  print(matrix[1, 2])  # Value: 6
  

• Multi-dimensional slicing:

  print(matrix[:, 1])  # Second column
  print(matrix[1, :])  # Second row
  

8. Advanced Usage

Statistical operations:

data = np.array([10, 20, 30, 40])
print(data.mean())   # Mean ==>  25
print(data.sum())    # Sum  ==> 100
print(data.std())    # Standard deviation ==>11.180339887498949

# works the same on matrix
matrix = np.array([[10, 20], [30, 40]])
print(matrix.mean())   # Mean ==>  25
print(matrix.sum())    # Sum  ==> 100
print(matrix.std())    # Standard deviation ==>11.180339887498949

Matrix computations:

matrix = np.array([[1, 2],   #  [1  2    X [3
                   [3, 4]])  #   3  4]      4]
vector = np.array([5, 6])    #
result = matrix.dot(vector)  # Matrix-vector multiplication

NumPy vs 3D List of Lists Performance Comparison

Take a look at this program: numpy_vs_lists_perfomance_test.py

Creation has big differences but other operations, surprisingly, do not differ as I expected.