{"id":18029,"date":"2017-07-28T16:15:32","date_gmt":"2017-07-28T13:15:32","guid":{"rendered":"http:\/\/www.webcodegeeks.com\/?p=18029"},"modified":"2018-01-09T09:30:18","modified_gmt":"2018-01-09T07:30:18","slug":"python-numpy-array-tutorial","status":"publish","type":"post","link":"https:\/\/www.webcodegeeks.com\/python\/python-numpy-array-tutorial\/","title":{"rendered":"Python NumPy Array Tutorial"},"content":{"rendered":"

Today we will be talking about one useful library that is used in data science. Now, why do we need a library and can’t use Python itself? Well, we can actually use Python itself, but once you get familiar with working in NumPy<\/code>, you will see the difference.
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\n[ulp id=’R7QVpFMZmjosABLC’]<\/p>\n

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Table Of Contents<\/h3>\n
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1. What is NumPy? <\/a><\/dt>\n
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1.1. Installing NumPy<\/a><\/dt>\n<\/dl>\n<\/dd>\n
2. Working with Arrays<\/a><\/dt>\n
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2.1. Arrays manipulation<\/a><\/dt>\n<\/dl>\n<\/dd>\n
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2.2. NumPy arrays<\/a><\/dt>\n<\/dl>\n<\/dd>\n
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2.3. Indexing<\/a><\/dt>\n<\/dl>\n<\/dd>\n
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2.4. Datatypes<\/a><\/dt>\n<\/dl>\n<\/dd>\n
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2.5. NumPy Math<\/a><\/dt>\n<\/dl>\n<\/dd>\n
3. Broadcasting<\/a><\/dt>\n
4. Summary<\/a><\/dt>\n
5.Homework<\/a><\/dt>\n
6.Download the Source Code<\/a><\/dt>\n<\/dl>\n<\/div>\n

<\/a>1. What is NumPy?<\/h2>\n

NumPy is the core library and foundation for data science. It works great with multidimensional array objects. The library’s name is short for “Numerical Python”<\/i>. So basically what we do with it is solve on a computer mathematical models of problems in Science and Engineering. It’s far more efficient just to use this library instead of writing your own in Python, because it’s already got all features you need. So let’s get started, shall we?<\/p>\n

<\/a>1.1 Installing NumPy<\/h3>\n

You need to install NumPy before working with it. It’s not a pre-installed library in your computer. By now, you need to have at least Python installed in your computer. We will do the most common way of installing which is pip<\/code>.
\nIf you are Windows user, try this:<\/p>\n

Windows installation<\/em><\/span><\/p>\n

pip3 install numpy\r\n<\/pre>\n
Note that it may now work, so there are 2 solutions: either add Python to the PATH environment, or check if you have pip<\/code> installed. If you don’t, then try this:<\/p>\n
Windows installation<\/em><\/span><\/p>\n
pip install pip --upgrade\r\npip --version\r\n<\/pre>\n
Then you need to download wheel from the Internet and install it. Then you will be ready to go. Now, if you are a Linux user, this one will help: Go to command line by thisCtrl<\/code>+Alt<\/code>+T<\/code> or any other combination you have in your distribution. Then type:<\/p>\n
Linux installation<\/em><\/span><\/p>\n
sudo pip3 install numpy\r\n<\/pre>\n
That’s it! Now you are ready to code!<\/p>\n
<\/a>2. Working with arrays<\/h2>\n
<\/a>2.1. Arrays manipulation<\/h3>\n
We already know what an array is. It’s a grid of values of the same type. An array represents any regular data in a structured way. For example, you may have an array of strings, integers, booleans, etc. Let’s create one:<\/p>\n
Python Shell<\/em><\/span><\/p>\n
>>> my_array = [1,2,3]\r\n>>> print(my_array)\r\n[1, 2, 3]\r\n>>> my_2d_array = [4,[5,6],7]\r\n>>> print(my_2d_array)\r\n[4, [5, 6], 7]\r\n>>> my_3d_array = [8,[9,[10,11],12],13]\r\n>>> print(my_3d_array)\r\n[8, [9, [10, 11], 12], 13]\r\n<\/pre>\n
As we can see, we have got couple arrays representing integers. However, if we dive into Computer Science, an array actually contains more than just elements. It contains information about the raw data, where and how to locate elements in the array and how to interpret them. Let’s talk about 4 basic aspects of manipulating arrays:<\/p>\n