An Introduction to NumPy Library for Python

Do you want to become more expert in using NumPy to perform mathematical computations? This course helps you understand why an array object in 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 introduces you to the different examples of range parameters in NumPy including the start, stop and step parameters. 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 also discover that the shape of any particular array refers to the number of elements found in each dimension. What is NumPy slicing? This course presents 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 study the different methods used for scientific computing in NumPy. We 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 show how you can use stacking to join arrays in NumPy. Finally, you will study NumPy mathematical functions, axes, vertical and horizontal stacking, random choice and bite types. This course will interest developers wanting to learn how to use NumPy with Python.