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An Introduction to NumPy Library for Python

In this free online course, learn about the functions and techniques involved in the NumPy library for Python.

Publisher: Kelvin Fosu
Achieve a basic understanding of how Numerical Python (NumPy) works in this free online course introducing you to NumPy. You will be able to master the art of using NumPy to perform different mathematical computations and take advantage of its superiority compared to traditional Python lists. You will also gain an insight into the array object in NumPy that makes all this possible. Expand your NumPy knowledge and skills by studying this course.
An Introduction to NumPy Library for Python
  • Duration

    1.5-3 Hours
  • Students

    38
  • Accreditation

    CPD

Description

Modules

Outcome

Certification

View course modules

Description

Are you a student or working professional seeking to learn and become an expert in using NumPy to perform mathematical computations? This course will help you understand why an array object is NumPy is significantly faster than the standard lists found in Python. You will discover that “ndarray” refers to the array object in NumPy and understand that an array is different from a Python list because it stores values with the same data type. On the other hand, a Python list can store elements and values that have different data types. You will be able to master the keys you use when you want to view the documentation in NumPy and use Booleans to represent the actual value of a Python expression. You will develop your experience of the different examples of array properties in Numpy.

Firstly, the course will introduce you to the different examples of range parameters in NumPy. These range parameters include the start, stop and step parameters, and we will clarify that you have to pass at least one of these parameters. You will find out that you can use 1 as the default value when you do not provide the step parameter. You will discover that the shape of any particular array refers to the number of elements found in each dimension. What is NumPy Slicing? This course will present the steps to take when taking elements from a specific index to another given index. You will learn to use different commands to print other Python elements and exclude certain index elements depending on your desired application. In addition, you will master NumPy shape and reshaping and find out that the shape attribute returns a tuple in NumPy.

Next, you will gain an insight into the different examples of methods used for scientific computing in NumPy. We will explain how to use the linspace function to create numeric sequences and use the max function to find the maximum value of any given array. You will generate random floats between 0 and 1 and use the randint() function to generate random integer values. We will explain how you can use stacking to join arrays in NumPy. Finally, you will study NumPy mathematical functions, NumPy axes, vertical and horizontal stacking, NumPy random choice and NumPy bite types. This course will interest students and working professionals interested in learning how to use NumPy with Python. Why delay! Begin this course today and take your scientific computing skills to the next level using NumPy.

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