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 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 start coding
To use NumPy, you should first import it, so import numpy using the alias np.
Solution
Switch to desktop for real-world practiceContinue from where you are using one of the options belowThanks for your feedback!
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Completion rate improved to 3.7Introduction 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 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 start coding
To use NumPy, you should first import it, so import numpy using the alias np.
Solution
Switch to desktop for real-world practiceContinue from where you are using one of the options belowThanks for your feedback!
single
Awesome!
Completion rate improved to 3.7Introduction to NumPy
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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 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 start coding
To use NumPy, you should first import it, so import numpy using the alias np.
Solution
Switch to desktop for real-world practiceContinue from where you are using one of the options belowThanks for your feedback!
