Introduction to NumPy

Introduction to NumPy

• NumPy is a powerful Python library for numerical computations.
• It excels in handling numerical data, particularly arrays.
• Key advantage: Allows faster mathematical operations compared to standard Python lists.

NumPy Arrays: Advantages

• Enables multiple mathematical operations efficiently.
• Significantly faster processing than Python lists, crucial for large datasets.

NumPy Import

• Standard import statement: import numpy as np
• Time library import for performance measurements: from time import process_time

Example Code (List Operations and Performance Comparison)

• Demonstrates how creating/modifying a large list in python takes longer than the equivalent numpy operation

• List operation code: Creates a list of 10000 integers and then applies a modification to each element.

• NumPy operation code: Creates a NumPy array of 10000 integers and performs the same transformation.

• Note that the implementation to use process_time() is given to calculate processing time

NumPy Functions

• Examples of diverse NumPy array operations are showcased below.

Creating and Displaying a List

• Creates a list list1 containing the integers [1,2,3,4,5]
• Prints the list: print(list1)
• Prints the data type: type(list1)

Creating a NumPy Array from a List

• Creates a NumPy array np_array from a list [1,2,3,4,5]
• Prints the array np_array: print(np_array)
• Prints the data type: type(np_array)

Creating a 1-Dimensional NumPy Array

• Demonstrates how to create a 1-dimensional NumPy array.

• Code example: a = np.array([1, 2, 3, 4])

• Display of array example: print(a)

Creating a 2-Dimensional NumPy Array

• Demonstrates how to create a two-dimensional NumPy array.

• Example Code -b = np.array([(1, 2, 3, 4), (5, 6, 7, 8)]) This creates a 2D array.

• Display example: print(b)

• Accessing dimensions.

  • The .shape attribute returns the shape (dimensions) of an array.

Array with Data Type float

• Displays how to specify the data type of an array.

c = np.array([(1,2,3,4), (5,6,7,8)], dtype=float)
print(c)

Initial Placeholders (Zeroes, Ones, Full, Eye)

• Creates arrays filled with zeroes (np.zeros), ones (np.ones).
• Creates arrays filled with a specific value (np.full).
• Creates an identity matrix (np.eye)
• Examples of creation with shape attributes: np.zeros((4, 5)) , np.ones((3, 3)) and np.full((5, 4), 5)

Creating Arrays with Random Values

• Generates arrays with random numbers (np.random.random)
• Generates arrays with random integers within a range (np.random.randint)
• Example Code: b = np.random.random((3, 4)), c = np.random.randint(10, 100, (3, 5))

Evenly Spaced Values

• Creates arrays with evenly spaced values (np.linspace).
• Creates arrays with evenly spaced values with a specified increment (np.arange)

Converting a List to a NumPy Array

list2 = [10, 20, 20, 20, 50]
np_array = np.asarray(list2)
print(np_array)
type(np_array) 

Analyzing a NumPy Array

• Demonstrates how to determine various attributes of a NumPy array.

• Accesses the size (c.size) and data type (c.dtype) of the array.

• Prints the shape (c.shape) of NumPy array.

• Prints the number of dimensions (c.ndim) of the NumPy array.