Python NumPy Tutorial | NumPy Array | Python Tutorial For Beginners | Python Training | Edureka

hello everyone this is orders from ATO Rekha and in today's session we are going to focus on num py module available in Python so let us move forward and have a look at the agenda for today first we'll see what is numpy then we are going to compare it with list and we'll see why it is better than list then we are going to see various operations that we can perform with numpy arrays and there are certain special functions as well that we are going to focus on later in the session so are we all clear with the agenda timely give me a quick confirmation by writing down in the chat box Devon says e square so dusty on Johnny says move forward bases go on Lavinia Jason are you see Dorothy near or at fine guys so I've got a confirmation from almost everyone so let us move forward and understand what exactly is an umpire so what is numpy number is basically a module or you can say a library that is available in python for scientific computing now it contains a lot of things it contains a powerful n dimensional array object then tools for integrating with C C++ it is also very useful in linear algebra Fourier transform and random number capabilities now let me tell you guys numpy can also be used as an efficient multi-dimensional container for data for generic data now let me tell you what exactly is multi-dimensional array now over here this picture actually depicts multi-dimensional array so we have various elements that are stored in their respective memory locations so we have one two threes in their own memory locations now why is it two dimensional it is two dimensional because it has rows as well as columns so you can see we have three columns and we have four rows available so that is the reason why it becomes a two dimensional array so if I would have had only one row then I would have said that it is a one dimensional array but since it contains rows as well as columns that is it is represented in a matrix form that is why we call this as a two dimensional array so I hope we are clear with what exactly two dimensional arrays if you have any questions about you can ask me any questions and oh are you she geography Nia Lavinia Jason Theon Dave or a fine guy so we have no questions so let me open my pycharm and I will tell you practically how to actually create an umpire Eric so this is my Python guys over here the first thing that you need to do is first installed the num py module and how you're going to do that click on file go to settings tab and you see the project interpreter option and on the right hand side you'll see a plus symbols go on and type the module that you wanna install so I'm going to install numpy wises go there click on it and install package I've already done that so I'm not going to repeat it so this is my PI charms the first thing that I need to do is import numpy Y as NP now after that I need to create a num py RA so for that I'm going to define a variable that it be a and I'm going to type in here NP dot array and certain elements inside it so I'm going to put in 1 2 3 and a print hit that's all this will actually print a single dimensional arrays so 1 2 3 has appeared now if I wanna convert this to a 2d array so for that I'll keep this in parentheses and after a comma I'll add one more element and I'm going to give certain values inside that so I can give say 4 5 and 6 now go ahead and print this so as you can see that it is now a two-dimensional array so this is ru a quickly at our edge using num py module if you have any questions or doubts you can ask me they say these clear so dusty on Allen though I usually geography fine alright so we have no questions so now I'm going to open my slides and we'll move forward and see what is our next topic now let us see why I will be using numpy instead of a list all right so many of you might be thinking why are we using numpy why when we have lists all right so basically we use them py because of three main reasons the first thing is it occupies less memory when compared to lists then it is actually pretty fast when you are compared with list and at the same time it is very convenient to work with numpy Y so these are the three major advantages that num py has over list and that is the reason why we use numpy why instead of list now don't worry I'm actually going to prove it to you practically by bring my pycharm so why this is my pycharm again the first thing that I need to do is import numpy why as empty and now what I'm going to do is I'm going to import a couple of more modules I'm going to import time I'm going to import sis all right done so our first step is to actually define a list and the name that I'm going to give to my list is say s and I'll type in here range thousand so what this will actually do it will actually take all the integer values between 0 to thousand and it will give it to a variable s so this list will contain the integer values between 0 to 2000 but 8 more in 2000 it will be only there till triple night that is 999 and now I'm going to print the space occupied with this particular listed are what I need to do it in to type and print sis dot get size off any one element okay so you can give 3 4 5 anything I'm going to give it as 5 any one element and multiply that with the length of my list that's all so this will actually give me the space that has been occupied by the list because sis dot get size off will actually give me the memory occupied by one element and when I multiply that with length of my list I get the entire memory that I will be occupied by my list now the same I'm going to do with my num py array as well let me give a name to that I'm gonna type in D NP taught a range and the range will be thousand now a range function is pretty much similar to the range which is there so the same thing will happen here the integer values between 0 to thousand but it won't in few thousand will be present in my variable T so we have created an um py array now let us print the space occupied by it so the first thing is I'm going to type in here d dot size so this will actually give me the space occupied by one single element and when I multiply that with the length of my num py RA I get the entire memory that is occupied by the numpy Y array so I'm going to type in here d dot item size that's all now go ahead and print this so this actually shows the memory that has been occupied by my list and this shows the memory that has been occupied by my num py addict so as you can see there is a quite a lot of difference between both of them so we have proved the first point that it actually occupies less memory now when I talk about num py array is faster and more convenient than the list so the next step is I'm going to prove it to you that num p/yr a is actually faster and more convenient than list so I remove all of this and so now I'm going to show you that num py arrays are faster than lists and at the same time it is easier and more convenient in order to work with num py r is when compared to lists let me show you practically so first what I'm going to do is I'm going to define our variable say size which is equal to say 1000 and then I'm going to define two lists now what I'm going to do is I'm going to add those two lists as well as I'm going to add two numbers I added which I'm going to define now and then I'm going to compare the time taken in order to find the sum for list and the sample numpy y RS so first let me define two lists and to arrays for my first list will be equal to range size same goes for my second list as well just change the name to l2 and now I'm going to define two numpy virus a1 equal to NP dot a range size go ahead and do the same for the second numpy wide array as well and change the name as a two so we have two lists and two arrays and we need to compute the sum of both of these lists as well as address now before that I'm going to define a variable say start which is equals to time dot time and now I'm going to calculate the sum so I'm going to save that in result and first I'm going to calculate the sum of Lists that is L 1 and L 2 so for that what I need to do is I need to run a for loop because if I directly write L 1 plus L 2 it is going to give me a result which is nothing but the concatenation of both the lists so in order to calculate the sum I need to use for loop let me show you how to do that first I'm going to type in X comma Y and we have already studied loops and detail for X comma Y in zip and the name of the tool list that is l1 comma l2 that's all so what will happen here it will first take the first element of list l1 and then it'll take the first element of list l2 it will go in it will calculate some and store in result and then you keep on repeating until the range has been exceeded now this is how you calculate sum in list but when you talk about arrays what you need to do is you need to just write in a 1 plus a 2 that's all that's why I'm saying that it is more convenient in order to work with num py arrays and compared to lists now our next step is now our next step is to define the same variable start and initialize it with time dot time and now I'm going to find the sum of my - num py RS which is nothing but a1 plus a2 that's all and now print the time taken so print time dot time - start and then multiply it with thousand because by default it will take it in seconds and I need to convert it into milliseconds now I forgot to actually print the same thing for my list so I'm going to do it over here so this will actually give me the time taken by my list in order to compute the sum and this statement will give me the time taken by my num py array in order to compute the sum so of your clear till here if you have any doubts or questions you can ask me any questions guys alright so we have no questions so let us go ahead and execute this and still see what happens so it gives me 0 milliseconds because the size is small let me star a couple more zeros let's make it a million now go ahead and execute this now you can notice the difference that there is a significant change lists took 208 milliseconds whereas num p/yr it took almost 67 milliseconds so there is a huge difference between the compute time of a list as well as num py array that's why I say that num py array are faster convenient and at the same time they occupy less space compared to lists so that is the reason why we choose mpy a days over list if you have any questions doubts you can ask me any questions guys any questions alright so we have no questions so let us go ahead and move forward towards the next topic that is num py operations so let me go back to my slides so now is the time to see various operations that you can perform with the num py Alice so you can find at the dimension of your array whether it is a two-dimensional or a single dimensional array then you can even calculate the bite size of each element it is pretty easy I'm going to tell you that practically you don't need to worry about that and you can even find the data types of the elements that are stored in your array so if you want to know what is the data type of the elements you can do that as well so let me show you these three operations first and then we'll move forward to the other operations I'm going to open my Python ones for guys let me remove all of this so we have imported the num py module now what I'm going to do is I'm going to define a numpy Y array I'm going to name it as a and I'll write here n P dot array 1 comma 2 comma 3 put that in parenthesis now add one more element say 2 comma 3 comma 4 all right so it's a two dimensional array now if I want to know whether it's a two dimensional or a single dimensional array so I'm just going to type and print it dot end them and it'll give me the dimension so let me show you that and I'm going to run this so it says 2 that means it is a two dimensional array so what if I move this part and make it as a single dimensional array it should give us the result as one let's see if it does that or not and yep it gives us one as a result so this is how you actually calculate the dimension of Ferrari now if you want to find a bite size of each of the elements so what you need to do is instead of end him you can call a function called item size go ahead execute this and you'll get so each element occupies 4 bytes after if you want to know the datatype that is stored in the array so you can just type in d-type go ahead execute this it should give us integers integers 32-bit alright so this is how you can actually perform the b3 function that I've told you in my slides it's pretty basic if you have any questions or doubts you can ask me alright so we have no questions so let us proceed with the presentation now let us move forward and see one of the other operations that you can perform with num py module so by using num PV array you can actually find the size of your addict how you can do that that I will show you practically you don't need to worry about that so when I say size of the array that means the total number of elements are present at the Attic so if this is an array of the total number of elements become 4 1 2 3 & 4 now you can even find the shape of your array now what do you mean by shape so basically the total number of columns and rows now over here we have 3 columns and four rows so our shape is actually three columns and four rows now let me show you practically how you can do that again I'm going to open my pycharm and show you let me remove this print statement from here now if I want to find the size of my num py array I just need to type in print a dot size that's all you have to do and it'll give the size of your honor so there are three elements if I go on and add some more elements say 4 5 6 7 then if I execute this you'll see that seven elements have appeared that means the total number of elements in my array is 7 then comes the shape part that I was talking about so in order to find the shape what you can do is you can just type in here a dot shape and it will give you the shape so let us see what happens so it has 7 columns but there are no rows so it has given 7 comma black so what I can do is I can close this in parenthesis and I can define one more element say 8 9 10 11 12 13 14 go ahead and execute this and you can see 2 comma 7 because we have two rows and seven columns available with us so this is how you can actually find the size of your array as well as you can find the shape of your array now let us move forward and see what are the other operations that you can perform with num py module so we saw how to find the size and the shape of an array so we can perform reshape as well as slicing operation using numpy why now when I talk about reshape what do you actually mean by reship can I get some answers guys anyone all right so Devon says when you change the number of rows and columns that is called reshaping all right fine that is absolutely corrective on and I've got correct answers from other people as well our nails will say the same so does Jason Janice jaggedy all right fine so if that is absolutely correct guys now over here there is an example so we have three columns and two rows which we have converted to two columns and 3 rows now let me show you that practically how you can do it in a variable and I'm going to start a num Pira and I'll have two elements in that my first element will be 1 comma 2 comma 3 comma 4 and my other element will be say 3 comma 4 comma 5 comma 6 so I have a two-dimensional array that contains 2 rows and 4 columns now I can convert that to 4 rows into columns let me show you how to do that you're just going to type in a is equal to 8 dot reshape and I'm going to convert it to say 4 rows and 2 columns go ahead and trim this so it has converted that to 4 rows and 2 columns as you can see in front of your screen now let me show you that earlier this is not the case I'm going to type in a print statement here as well in order to show you that how it has reshaped so earlier we had four columns as well as two rows but now we have two columns are four rows so this is how you can perform the reshape operation now let us talk about slicing so slicing is basically extracting particular set of elements from your array and the slicing operation that happens there is pretty much similar to the one which is there in lists as well so suppose if I want only this particular element that is 3 so for that what I need to print I'll show you print a and the index value of 3 which is present at 0 comma and the index is 2 let me tell you how indexing happens so this element will be 0 this element will be 1 now if I want 3 from here the from the zeroth element I want the index to indexing starts from 0 1 & 2 so that's why I've written 0 comma 2 and it should print 3 for me let us see if it does that or not and yep it prints 3 now say if I want to print 4 & 6 now for that what I need to do is I need to remove this 2 here and I'm going to put a colon that says all the rules including 0 and in that row I want only index 3 so we have only 2 rows so if I would have written 0 colon 1 then it wouldn't have included this particular row so if I have one more element here so I can actually write here too so that would actually include the element which is present of the second index so when I say 0 colon this actually means all the rows that infused 0 as well so we have only 2 rows it will include both the rows and at the same time it will actually going to print the third index from both of these rows so let me show you if that happens or not and yep it happens we have 4 & 6 available with us now just to remove confusion what I'm going to do is I'm going to add one more element and I'm going to give values to it say seven eight nine and ten so now if I want four and six I can't just write 0 colon because if I do that it'll print 10 as well let me show you that yep it has printed 10 now in order to avoid that what I can do is I can write in here too so I've told you this is the 0th element first element and the second element so when I write 0 colon - it won't include this second element he'll only infused 0 as well as the first element now inside that we have index 3 from both of these rows that is I will actually get 4 and 6 never see if that happens or not and you can see 4 & 6 is now available so this is all you can perform slicing when we in numpy by RS now what I'm going to do is I'm going to type in a equal to NP dot line space and now over here first I write 1 comma say 3 and I want say 5 values between 1 comma 3 what this will do is it'll actually print the five values which are equally spaced between one two three so let me print this first I'm going to type in print a and go ahead exit you this and you can see that we have one then we have 1.5 then we have to 2.5 as well as 3 so if I would have written here 10 it'll actually give me 10 values between 1 2 3 and yep you can see we have 2 10 values between 1 2 3 so this is how you can perform line spacing as well so any questions or any doubts till now guys you can ask me so there are no questions will be open my slides and we'll see what other operations that you can perform with numpy why so we saw how to perform reshaping and slicing now let us see what are the other operations so we are now going to find out the minimum maximum as well as the sum of our numpy by arrays so let us go ahead and execute that practically let me remove all of this and now I'm going to find one more num py ra n P dot array and I want elements in it say 1 2 3 now if I want to find the element which has the maximum value so what I can do is I can just type in here friend a dot max that's all and it will give me the maximum value in my a numpy yr a which is 3 obviously if I want to find minimum value so I'm just going to type in here min and we print the same for me which is 1 now if I want to calculate sum it is pretty easy just go on and type sum and it will give you this sum that's all guys it start simple I pretty sure there won't be any doubts here if there are you can just ask me fine so there are no doubt so Johnny's have the question she's asking me is it that simple to work with non py already on it so let me tell you that whatever I'm telling you these are all the basics that you require after that whatever your requirement is there on that basis you need to use those basic knowledge that you have and implement it now the best way to do that now the best way to understand any programming language is to play around with it so you know the basics with the help of those basics just install Python first and try out new things like how should I get that how should I get this and if I'm not getting it what is the reason behind it so go on and try to discover new new things so the conclusion is you need to actually perform things practically you need to make sure that you are not only getting the theoretical knowledge you need to perform it practically that's why I always say the type I said when I'm executing these particles you need to do that as well although you might find it pretty basic but with the help of this knowledge you can perform a lot bigger tasks as well all right the Janus looks happy now so let's move forward to the slides so now comes the axis concept here guys it is pretty similar we have an umpire array which looks like this and zeros are called axis one and columns are call axes zero now you must be thinking what is the use of this axis suppose if you want to calculate the sum of all the rows then you can actually use the axis and you can do that now let me show you practically how it happens I'm going to open my pie charm again and I'm going to show it to let me remove this and let me add one more element here 3 comma 4 comma 5 all right so if I want to find the sum of axis 0 it's very very easy just go on and type print a dot sum and type in axis equal to 0 go ahead and print this and you can see 4 6 & 8 1 plus 3 is 4 2 plus 4 is 6 similarly three plus five is eight if I make this as axis one and print this so it gives me 6 and 12 because 3 plus 2 plus 1 is 6 similarly 4 plus 5 plus 3 is 12 so this is pretty easy I know now let us go back to our slide and see what are the other operations so there are many mathematical functions that you can perform with numpy why that is to find the square root of each element you can even find the standard deviation all right so we have a question popped on my screen and dave is asking me what exactly you mean by standard deviation or it's a standard deviation basically means that whatever the elements that are there in your npy are a do you find the mean of that and you actually find out how much each element deviates from that means that exactly is standard deviation I hope this answers a question all right so these two operations can be performed with the help of numpy why you can find the square root you can find the standard deviation now let me show you that how you can do it this is my pie charm again so now I'm going to remove this print statement here I want to print the square root of each of the elements that are there in Magnum PU IRA which is actually assigned to a variable a so I'm going to type in her print n P dot s QR T that is square root of mine mpy array a go ahead and execute this and it is actually printed the square root of each of the elements of the square root of 1 is 1 for 2 it is 1 point 4 1 4 4 3 it is 1.73 I gave a 3 it is 1.73 then for 4 it is 2 then for five it is 2 point 2 3 this is how you can find the square root of each of the element now when I talk about standard deviation so you can find that by typing here so now if I want to find it started deviation what I need to do is instead of sq RT I will just type in an STD and it will give you the standard deviation that is how much each element varies from the mean value of mynum py re and this is the standard deviation guys it starts simple so this is how you find standard deviation now let's go back to our slides and see what are the other operations that are still left now these are the basic mathematical functions that you can perform with mum py RS like addition multiplication subtraction and division and that will actually happen element wise so basically you are performing matrix addition matrix multiplication matrix division as well as matrix subtraction let me go ahead and show it to you practically it is very very simple guys so similarly I'm going to define one more array and let me name it as D let me remove this print statement now if I want to calculate the sum so what I need to do is I need to type and print a plus B that's all you need to do but when I talk about lists again I'm telling you that when I talk about lists if I do that it will concatenate both lists so if I want to print list that is the addition of two lists I need to use for loop so that is where numpy wire is stands apart and it is pretty convenient go ahead and execute this and you see that element wise addition has happened 1 plus 1 is 2 2 plus 2 is 4 3 plus 3 is 6 similarly 3 plus 3 is 6 then 4 plus 4 is 8 pi plus 5 is 10 all right so this is how you can perform addition you can perform subtraction by using the subtraction operator go ahead execute this and you'll find all zeros because 1 minus 1 2 minus 2 3 minus 3 3 minus 3 4 minus 4 PI minus 5 will be 0 only right no rocket st. now go ahead and multiply it as well and see what happens so you have 1 into 1 is 1 2 into 2 is 4 3 into 3 is 9 again 3 into 3 is 9 4 4 16 5 into 5 is 25 if I go ahead and divide this it will give me all ones and yep it does so this is how you can actually perform addition subtraction multiplication and division using a numpy y RS now let me go back to my slides and see what are the other operations present so now guys let me tell you one more thing if I actually want to concatenate two arrays I don't just want to add those two arrays you can say that if my one adder is a box then I want another array on top of it so let me show you how you can do that actually there are two ways to do that one is called vertical stacking and another is called horizontal stacking let me show it to you one by one first I'm going to show you vertical stacking without what I need to do is print n P dot V stack and a comma B let us see what happens when I run this so we have 1 2 3 3 4 5 then again we have 1 2 3 & 3 4 5 so this is called vertical stacking if I want that horizontally I'll just write in here H stack and I'm going to run this now and you can see that we have 1 2 3 then again 1 2 3 that means these two are present added horizontally and we have 3 4 5 again we have 3 4 5 so this is how you can perform stacking as well now there's one more thing that I want to show you if I actually want to convert this particular in numpy y array that is a to say a single column so how I can do that thus type in here print a dot revell that's all you have to do go ahead and execute this so now 1 2 3 3 4 5 so you have 1 2 3 3 4 5 let me go back to my slides and see what are the other topics that we are going to cover now come certain numpy Y special functions now I'm going to talk about sine function and cosine function first now I hope all of us know what is a sine function and what is the cosine function if you have any doubt with respect to what these two are you can ask me although I'm pretty much expecting that you guys know this all right so I've got answers all right so I've got answers from almost everyone and they say that they know it fine guys so what I'm going to do is I'm going to use this cosine and sine function I'm going to plot sine and a cosine graph so for that I'm going to import a module called matplotlib so you don't need to worry about that module because I'm going to discuss about matplotlib in the upcoming sessions so there'll be a detailed session especially on matplotlib so you don't need to worry about what exactly matplotlib is and how it works and all those things because that'll be covered in the upcoming session for now what we are going to do we are just going to use sine and cosine function in order to print their graph so that I'll open white pycharm and let me remove this and import my plot lip dot pie plot as PLT now we are going to define two coordinates that is x and y first x is equal to NP dot arrange 0 comma 3 n 2 NP dot pi comma 0 point 1 now I'm going to find y so for that I'll type y NP dot sine X now I'm going to use PLT in order to plot the graph X comma Y now finally show the plot for that I'll type plot dot show you must plot that show to make a graphics up here so plot dot show and here we go go ahead and execute this and you might be able to see a graph and yep it is here similarly for if I change the sign to cause and should give me the cosine graph go ahead and run this and you can see we have a cosine graph as well similarly if I write in air tan that is any other trigonometric function and I print this so I get the graph for tan as well so any doubt still here guys no doubts fine so open my presentation once more and we are going to see what are the other special functions that we can use with num py now num py comes with two very good functionality that would say that is called exponential function and in logarithmic functions now exponential this e value is somewhere equal to point seven and we all know load so when I talk about log it is actually log base 10 and when I'm talking about natural log that is log base E I will write it as Ellen so instead of that I've written log that means log base 10 so you can perform these operations with the help of num py let me show you how you can do that or before that if you have any questions with what exactly these two things are you can ask me this is pretty basic alright so no questions fine I suppose everyone know about this so I'll open my pycharm let me remove this and I'm going to define a non py array let it be a are equal to NP dot array 1 comma 2 comma 3 now I want to calculate the exponential value I'm going to throw in a print statement and I'm going to write in here n P dot exp AR and this will calculate the exponential value for me and let us see if a does that or not so up as I've told you earlier as well value of e is 2 point 7 1 so e to the power of 1 is actually equal to e so it has it returned the e value but e to the power of 2 will be somewhere equal to seven point three eight e to the power of three will be some very equal to 20 point zero eight five five now in order to calculate log what you can do is you can just type in here log now this will give you natural log so when I talk about natural log it is nothing but Ln or you can say log base E but if I want to calculate log base 10 so I need to type in here ten first let me show you how you can find the natural log just go ahead and execute this alright so when I talk about one so e to the power of zero will be equal to one right so log or Ln a are equal to zero similarly the other values as well now if I want log base ten instead of Ln or you can say natural log I can just write in ten and go ahead execute this and you'll find log base 10 values so obviously when answer is one that means anything to the power of zero is equal to one so your answer will be zero here and similarly we have other answers as well if you are pretty if you're unsure about it you can open your calculators and do that or it's 11 essays I trust you thank you love and err for those kind words let me open my slides once more and by this we come to the end of today's session if you have any questions or doubts you can ask me right now John is say the amazing session thank you John is for those kind words Jessica say is very informative Thank You Jessica all right fine guys so let me give you a brief summary of what all things we have discussed first we saw what exactly is lump I then we compared umpire with list and we understood why we use numpy instead of lists then we saw various numpy operations like slicing stacking addition subtraction multiplication all those things then we focus on certain special functions like sine and cosine functions thank you guys for attending today's session this video will be uploaded into your LMS so you can go through it if you have any questions or doubts you can ask our 24/7 support team or you can even bring your doubts in the next class as well thank you and have a great day I hope you enjoyed listening to this video please be kind enough to like it and you can comment any of your doubts and queries and we will reply to them at the earliest to look out for more videos in our playlist and subscribe to our at Eureka channel to learn more happy learning

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Keywords: yt:cc=on, python numpy, python numpy tutorial, numpy python, numpy python tutorial, numpy tutorial, numpy tutorial for beginners, python tutorial, python tutorial for beginners, python numpy install, python numpy array, numpy array, numpy basics, introduction to numpy, numpy beginner, python numpy module, python programming, python training, numpy ndarray, numpy histogram, numpy where, numpy install windows, numpy, edureka, python edureka

Id: 8JfDAm9y_7s

Length: 34min 54sec (2094 seconds)

Published: Tue Apr 11 2017