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Ultimate NumPy
course content

Course Content

Ultimate NumPy

Ultimate NumPy

1. NumPy Basics
Introduction to NumPyBasic Array CreationCreating Higher Dimensional ArraysCreation Functions for 1D ArraysCreation Functions for 2D ArraysGeneral Array Creation FunctionsRandom Arrays
2. Indexing and Slicing
Basic IndexingMultidimensional IndexingSlicingSlicing in 2D ArraysInteger Array IndexingInteger Array Indexing in 2D ArraysBoolean IndexingMore about Comparisons Boolean Indexing in 2D ArraysAssigning Values to Indexed ElementsAssigning Values to Indexed Subarrays
3. Commonly used NumPy Functions
Sorting ArraysSorting 2D ArraysCopying ArraysReshaping ArraysFlattening ArraysArray Concatenation
4. Math with NumPy
BroadcastingBasic Mathematical OperationsStatistical OperationsBasic Linear Algebra with NumPy

bookIntroduction to NumPy

In order to feel confident and successfully complete this course, we strongly recommend you complete the following courses beforehand (just click on them to start):

In a world full of data, working with matrices and arrays is extremely important. That's where NumPy comes in handy. With its blazing speed and relatively easy-to-use interface, it has become the most used Python library for working with arrays.

Let's take a look at the most common applications of NumPy:

Let's now discuss the speed of NumPy and where it comes from. Despite being a Python library, it is mostly written in C, a low-level language that allows for fast computations.

Another contributing factor to NumPy's speed is vectorization. Essentially, vectorization involves transforming an algorithm from operating on a single value at a time to operating on a set of values (vector) at once, which is performed under the hood at the CPU level.

Task

To use NumPy, you should first import it, so import numpy using the alias np.

Once you've completed this task, click the button below the code to check your solution.

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Section 1. Chapter 1
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bookIntroduction to NumPy

In order to feel confident and successfully complete this course, we strongly recommend you complete the following courses beforehand (just click on them to start):

In a world full of data, working with matrices and arrays is extremely important. That's where NumPy comes in handy. With its blazing speed and relatively easy-to-use interface, it has become the most used Python library for working with arrays.

Let's take a look at the most common applications of NumPy:

Let's now discuss the speed of NumPy and where it comes from. Despite being a Python library, it is mostly written in C, a low-level language that allows for fast computations.

Another contributing factor to NumPy's speed is vectorization. Essentially, vectorization involves transforming an algorithm from operating on a single value at a time to operating on a set of values (vector) at once, which is performed under the hood at the CPU level.

Task

To use NumPy, you should first import it, so import numpy using the alias np.

Once you've completed this task, click the button below the code to check your solution.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 1. Chapter 1
toggle bottom row

bookIntroduction to NumPy

In order to feel confident and successfully complete this course, we strongly recommend you complete the following courses beforehand (just click on them to start):

In a world full of data, working with matrices and arrays is extremely important. That's where NumPy comes in handy. With its blazing speed and relatively easy-to-use interface, it has become the most used Python library for working with arrays.

Let's take a look at the most common applications of NumPy:

Let's now discuss the speed of NumPy and where it comes from. Despite being a Python library, it is mostly written in C, a low-level language that allows for fast computations.

Another contributing factor to NumPy's speed is vectorization. Essentially, vectorization involves transforming an algorithm from operating on a single value at a time to operating on a set of values (vector) at once, which is performed under the hood at the CPU level.

Task

To use NumPy, you should first import it, so import numpy using the alias np.

Once you've completed this task, click the button below the code to check your solution.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

In order to feel confident and successfully complete this course, we strongly recommend you complete the following courses beforehand (just click on them to start):

In a world full of data, working with matrices and arrays is extremely important. That's where NumPy comes in handy. With its blazing speed and relatively easy-to-use interface, it has become the most used Python library for working with arrays.

Let's take a look at the most common applications of NumPy:

Let's now discuss the speed of NumPy and where it comes from. Despite being a Python library, it is mostly written in C, a low-level language that allows for fast computations.

Another contributing factor to NumPy's speed is vectorization. Essentially, vectorization involves transforming an algorithm from operating on a single value at a time to operating on a set of values (vector) at once, which is performed under the hood at the CPU level.

Task

To use NumPy, you should first import it, so import numpy using the alias np.

Once you've completed this task, click the button below the code to check your solution.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 1. Chapter 1
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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