NumPy (Numerical Python) is a Python library that is used for working with arrays. NumPy provides an array object which is known as “ndarray” and the fun fact is, it’s 50x faster than lists. NumPy is a Python library and is written partially in Python, but most of the parts that require fast computation are written in C or C++. To know more about this interesting library with amazing examples, watch this tutorial till the end.

NumPy is a python library that is used for working with arrays. NumPy was developed by Travis Oliphant in 2005. It is an open source project and you can use it freely. NumPy stands for Numerical Python. It is helpful in solving problems related to linear algebra, fourier transform, and matrices.

One of the most common Python data structures is lists that serve the purpose of arrays, but they are slow to process. NumPy provides an array object which is known as “ndarray” and the fun fact is, it’s 50x faster than lists.

NumPy arrays are stored at one continuous place in memory unlike lists, so processes can access and manipulate them very efficiently. NumPy is a Python library and is written partially in Python, but most of the parts that require fast computation are written in C or C++. To know more about this interesting library with amazing examples, watch this tutorial till the end.

Following pointers will be covered in this video:

- 00:00:00 - Introduction
- 00:00:41 Python Numpy
- 00:01:07 Creating NumPy Array
- 00:11:44 NumPy-Shape
- 00:14:02 Joining NumPy Arrays
- 00:17:34 NumPy Intersection & Difference
- 00:21:08 Numpy Array Mathematics
- 00:28:35 NumPy Matrix
- 00:31:47 NumPy Matrix Transpose
- 00:32:42 NumPy Matrix Multiplication
- 00:35:05 NumPy Save & Load

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