numpy tutorial - basic array operations

hello everybody we are going to dive deep into numpy by module today if you have watched my previous tutorial you have initial idea on what number your module is and how to install it in this tutorial we are going to look take a detailed look at number wise or a object which is the main feature of this module so let's begin by importing num py as NP now as you all know you can create multi dimensional array in numpy by using NP dot array now this is how you create let's say one dimensional array and you can access it just like your list but to create two dimensional array you can do again this it the syntax looks pretty similar to how you would do it with our normal Python list so you create your through that two dimensions and you initialize it with these list of elements so this is my two dimensional array now there is this property called ending that you can use to print the dimensions okay so if you have let's say this array and if you print and them it will be one but now I have this two-dimensional array for which the dimension would be two okay there is item size property which will print the byte size of each of these elements now these are integer elements so that's why the item size is four bytes as you know that integers occupy four bytes if you have float number so let's let's initialize this array with float is a data type now before I do that let me print the current data type of this array so here it is saying it is integer 32 now if I want to initialize the same array with a different data type and I need to use d-type argument so here you can say D type is equal to NP dot here you can say float64 so float64 is one of the types now 64 means it occupies 8 bytes so now when you print the item size instead of 4 it's gonna be 8 because now each of these elements they are floor numbers and they occupy 8 bytes so if you print a here you can see 8 1 point something so this is now float okay so let me just clear this alright another important property that an array has is okay so it is sighs so size is basically the size of your array total total number of elements so here you can see 1 2 3 4 5 6 so it has like total six elements then you have shape so shape represents the information on dimension so short of like width and height so here it is it has 3 rows so 1 2 3 so 3 rows and 2 columns so 1 & 2 alright now we already covered the D type D type arguments were here I covered the d-type argument let me just copy it from here so you can use the type to initialize your array with a specific data type ok ok so now instead of float64 you can also specify your type to be the complex number so if you say complex what is gonna do is it's gonna create an array with complex numbers now sometimes you want to initialize your array with some placeholder numbers let's say you want to initialize your array with all zeroes if if you want to do that you will use n P dot zeroes function and just mention your shape here shape means the information on your dimension so this is creating an array of three by four three rows four columns 1 2 3 4 all initialized with zeros you can do same thing but with once so instead of zeros you will say once and it will initialize all the elements with one number now sometimes you want to use a function similar to range so you know that in Python that is this range function that we use for list right so what this function does is it's gonna create a list of numbers from 0 to 4 so that's what it did now nan py also has similar function it is called a range so you if you do NP here if you do NP dot a range or a range basically 1 to 5 then this is gonna create array of 1 to 4 number so Phi is not included by default that's how the even range function behaves so this is very similar to pythons native range function ok now sometimes you want to do the same thing so let's say initialize array which from 1 to 4 but then you want to have like steps of two numbers so this means that you start at one then you take a step of 2 so 1 plus 2 is 3 3 plus 2 is 5 but then this is your end so this is your start this is your end and these are like number of steps so once you reach 5 you will stop so that's why it is 1 and 3 here you can also use a linspace function so let me demo and peel dot linspace - now here you will specify your start number and n number first okay so you will say ok my start number is let's say 1 and my stop number is say 5 and in between these two numbers I want to generate let's say 10 numbers okay so what this will do is it will generate 10 numbers between 1 and 5 which are linearly spaced so you can see that you got this nice range of 1 and 5 and then these numbers are linearly spaced now if you do the same thing with let's save 5 then they are spaced by number 1 ok you can do whatever you can do even 20 this is pretty useful if you want to create like this linear sequence of numbers you can also reshape your arrays with reshape functions so for example if you have this array and then the shape of this array is 3 by 2 it has 3 rows and 2 columns now let's say you want to reshape this to be 2 by 3 so you can say reshape 2 by 3 basically now you want to rows and 3 columns and it would work so you can reshape it to any dimensions that you like and the dimension should be compatible with your initial dimension so you can even do a dot reshape to be let's say you want let's say 6 rows and 1 column so you see 6 rows one column you can also use revell function to flatten your array so this will just flatten it make it one dimension so you have n dimension array when you call it or travel it will flatten it make it one dimension all right now when I print a after flattering it I see that it still an original array because a dot revel will not touch the original array it will return a new array so you can capture the output into new variable and have access to flattened structure so that applies to all of these functions just remember that it's not gonna alter your original array so that's something you have to keep it in mind now let's look at some of the mathematical functions that numpy array cover so you have management so let's say I had this array okay if you do a dot minutes gonna print your minimum element which is one a dot max will print your maximum element which is six okay now you can also do a dot sum and it's gonna sum all the numbers together now there is a concept of excess in number array axis means your dimensions to my X's 0 will be this columns okay and my axis 1 will be these rows right here so when I do a dot sum and when I say X is equal to 0 it's gonna look at each of this column so it added these numbers together 5 3 8 + 1 9 so it printed 9 6 4 10 + 2 is 12 so it's added these together and printed cloth if you want to sum all the elements in rows together then you use X s 1 so here 2 N 1 is 3 3 & 4 is 7 5 6 is 11 so that's what it did so that's what the axis means here you can also do a square root so if you do SQ RT u dot s 2 RT let's see so if you do so a dot s square root is not a function of individual array element its generic functions so you have to do NP dot s RNP is your non-pure module so it's gonna compute the square root of each of these numbers so for example for square root you know is 2 then 2 square root you obviously you don't remember but if you open your calculator and find the square root is gonna be this okay you can also do standard deviation so n P dot standard deviation so standard deviation of all these numbers is here again no one is gonna remember this but if you do your math then you will find that it will be this number okay we are going to now look at some basic mathematical operations and for this I'm going to have these two arrays why I just copy pasted it from my notepad but it didn't work anyways I will just create it here so I have two dimensional array here so one two three four okay and I have this second array okay this is my first dimension my second dimension is five six seven eight okay okay so I have these two array a a and B to be dimensional array now num py supports very basic operations such as let's see if you want to add them together it you can do it using plus operator this is something you can't do with Python native list so it's very convenient with num py arrays so this added these together fine one is six six and two is eight is basically adding the individual elements you can do multiplication you can do division you can do all sorts of operation you can also do a matrix product so if you do a I thought be it's gonna do matrix products of these two individual matrices all right so that was all about our number by array it wasn't all about nampara errors actually we still need to cover few topics such as indexing slicing hydrating stacking these number of errors together but all those remaining things we are going to cover in our next tutorial until then thank you for watching and good luck with your Python and Numbi by learning thanks again

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Keywords: numpy tutorial, numpy tutorial python, numpy tutorial for beginners, python numpy tutorial, python numpy, numpy array, numpy array tutorial, numpy array python, numpy linspace, numpy matrix multiplication, python tutorial, python 3 tutorial, numpy, codebasics numpy tutorial, codebasics numpy, numpy python, numpy arrays, python numpy arrays, numpy arrays python

Id: a8aDcLk4vRc

Length: 13min 48sec (828 seconds)

Published: Mon Dec 26 2016