A Quick Guide to Getting Started with NumPy

NumPy is similar to python list and can perform most of the operations python lists perform but NumPy has its own advantages for which it is the preferred library for handling large numerical data

Whenever someone decides to enter into the world of data science, the one library he needs to learn is NumPy. NumPy Stands for Numerical Python and this article will be like a crash course in NumPy.

So Why Numpy?

NumPy is similar to python list and can perform most of the operations python lists perform but NumPy has its own advantages for which it is the preferred library for handling large numerical data. Some of these are:

• NumPy, unlike python lists, has homogenous data type because of which it can be densely packed into memory and it also frees memory faster. This makes it 10 to 100 times faster than python lists.
• Numpy breaks down a task into smaller fragments and can process those fragments parallelly.
• Numpy internally uses languages like Fortran, C, and C++ which have much faster execution time compared to python.

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