python numpy tutorial for beginnersLearn programming in NumPy, a Python Library, 100 seconds at a time in this video for beginners. This is video number 1: Introduction to NumPy
We won’t jump into the code just yet (except for a few examples).
This is just to give a (very) brief introduction to NumPy.
In the next video we will get hands-on with the code.

I know im going fast in this video, please use timestamps below to navigate the video.
Timestamps:
00:00 - Start
00:16 - Purpose of NumPy
00:28 - how to install NumPy with pip3
00:33 - how to use NumPy in your code editor
00:50 - NumPy array’s
01:01 - types of array’s
01:10 - Why NumPy array's are faster than Python lists

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Introduction to NumPy
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NumPy is a Python library and is written partially in Python, but most of the parts that require fast computation are executed by pre-compiled C code.

C is a different programming language that is, simply put, faster than Python.

NumPy is the fundamental package for scientific computing in Python.
The most popular uses for Numpy are for mathematical reasons, supporting the Pandas Python library and use cases for machine learning.

You can install numpy via your terminal using pip3 or pip

Use numpy by importing it in your program

Standard practice is to import numpy as np, but technically speaking you can choose any name instead of ‘np’ , though this is discouraged as you might end up choosing a name which is a built in function and could cause errors in the program.

Numpy stores data in so called ‘arrays’, which is the `numpy version of a python list.

numpy is called a multiple dimensional array library
Because you can create an array with more than one dimension

Numpy can store data in one dimensional arrays, also called a vector
Two dimension arrays also called a matrix

A NumPy array could also be referred to as a ndarray, which stands for N-Dimensional Array, where N represents the number of dimension

Numpy arrays are different from standard python lists.
Numpy arrays operate much faster, for multiple reasons.

First numpy is partially written in python and partially makes use of pre-compiled C code. Remember C is a programming language faster than Python.
Second Numpy arrays uses less memory

Third all elements in a numpy array are the same type of data

For example, python needs to check in a list whether an element is an integer or a string.

NumPy doesn’t have to check what type of data the object in an array is and thus has less steps in the execution.

NumPy arrays are between 10 to 100 times faster than regular Python Lists.
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