# Python - Advanced Operations with NumPy Arrays

Python    |    Intermediate
• 13 videos | 1h 7m 19s
• Includes Assessment
• Earns a Badge
Rating 4.5 of 373 users (373)
NumPy is oneof the fundamental packages for scientific computing that allows data to be represented in dimensional arrays. This course covers the array operations you can undertake such as image manipulation, fancy indexing, and broadcasting. To take this Skillsoft Aspire course, you should be comfortable with how to create, index, and slice Numpy arrays, and apply aggregate and universal functions. Among the topics, you will learn about the several options available in NumPy to split arrays. You will learn how to use NumPy to work with digital images, which are multidimensional arrays. Next, you will observe how to manipulate a color image, perform slicing operations to view sections of the image, and use a SciPy package for image manipulation. You will learn how to use masks, an array of index values, to access multiple elements of an array simultaneously, referred to as Sansi indexing. Finally, this course covers broadcasting to perform operations between mismatched arrays.

## WHAT YOU WILL LEARN

• Identify different ways in which arrays can be split up
Describe how grayscale and color images can be represented as multi-dimensional arrays
Perform some basic image manipulation after converting images to arrays
Create a view into a numpy array and learn of the relationship between views and their base arrays
Compare deep copies of arrays with views and know when to use each of them
Use fancy indexing with arrays using an index mask
• Use fancy indexing to analyze real-world data
Apply boolean masks to access array elements which fulfil a specific condition
Use structured arrays in order to store heterogeneous data
Describe how operations can be performed between arrays of mismatched shapes using broadcasting
Perform operations between arrays of mismatched shapes by applying broadcasting rules
Utilize numpy to perform multi-dimensional array operations

## IN THIS COURSE

• During this video, you will learn how to identify different ways arrays can be split up.
• 3.  Images as Arrays
Upon completion of this video, you will be able to describe how grayscale and color images can be represented as multidimensional arrays.
• 4.  Image Manipulation Using NumPy
In this video, you will perform some basic image manipulation after converting images to arrays.
• 5.  Views and NumPy Arrays
Find out how to create a view into a NumPy array and learn about the relationship between views and their base arrays.
• 6.  Deep Copies of Arrays
Find out how to compare deep copies of arrays with views, and know when to use each of them.
• 7.  Introduction to Index Masks
Learn how to use fancy indexing with arrays using an index mask.
• 8.  Applying Index Masks
In this video, learn how to use indexing to analyze real-world data.
• 9.  Indexing with Boolean Masks
In this video, find out how to apply boolean masks to access array elements which fulfill a specific condition.
• 10.  Structured Arrays
In this video, you will use structured arrays to store heterogeneous data.
• 11.  Understanding Array Broadcasting
After completing this video, you will be able to describe how operations can be performed between arrays of mismatched shapes using broadcasting.
• 12.  Applying Broadcasting Rules on Array Operations
In this video, find out how to perform operations between arrays of mismatched shapes by applying broadcasting rules.
• 13.  Exercise: NumPy Multi-dimensional Array Operations
In this video, learn how to use NumPy to perform multi-dimensional array operations.

## EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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