Course Content
Ultimate NumPy
Ultimate NumPy
Introduction 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.
Swipe to show code editor
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 desktop for real-world practiceContinue from where you are using one of the options belowThanks for your feedback!
Introduction 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.
Swipe to show code editor
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 desktop for real-world practiceContinue from where you are using one of the options belowThanks for your feedback!
Introduction 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.
Swipe to show code editor
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 desktop for real-world practiceContinue from where you are using one of the options belowThanks 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.
Swipe to show code editor
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 desktop for real-world practiceContinue from where you are using one of the options belowSwitch to desktop for real-world practiceContinue from where you are using one of the options below