1 00:00:06,050 --> 00:00:07,596 Hi, welcome to this new video. 2 00:00:07,596 --> 00:00:12,890 In this video we're going to be learning how to read data from a CSV. 3 00:00:12,890 --> 00:00:14,879 We just learned what a CSV file is. 4 00:00:14,879 --> 00:00:18,917 It's a comma separated value file, and 5 00:00:18,917 --> 00:00:25,580 we have constructed data with our writer script that is a CSV file. 6 00:00:25,580 --> 00:00:27,072 So we actually have a CSV file. 7 00:00:27,072 --> 00:00:30,639 If you don't have one, go to the previous video where we construct one. 8 00:00:30,639 --> 00:00:32,709 You can also use something from the Internet, but 9 00:00:32,709 --> 00:00:35,364 it might not be formatted the same way in which we have it here. 10 00:00:35,364 --> 00:00:39,647 Let's cover a few ways, because you're going to realize that there's many ways in 11 00:00:39,647 --> 00:00:41,650 which you can read data. 12 00:00:41,650 --> 00:00:45,800 Often we're relying in Python, we're relying with functionality that is either 13 00:00:45,800 --> 00:00:49,654 inbuilt into Python or external libraries that might become a little bit more 14 00:00:49,654 --> 00:00:53,410 complicated, but certainly going to offer us more functionality, right? 15 00:00:53,410 --> 00:00:58,354 So with processing, right, we have an inbuilt reader function 16 00:00:58,354 --> 00:01:02,970 which we already covered in order to read a TXT file. 17 00:01:02,970 --> 00:01:07,830 As you can see here, we have a reader file that it's reading a CSV 18 00:01:07,830 --> 00:01:11,638 file we can go through with the read line method. 19 00:01:11,638 --> 00:01:14,405 We can skip the header, if you don't want to read the header, 20 00:01:14,405 --> 00:01:16,176 you would just want to read the data. 21 00:01:16,176 --> 00:01:20,031 You can basically use that to read each line. 22 00:01:20,031 --> 00:01:25,170 And we already saw how to use a while statement to loop through the data. 23 00:01:25,170 --> 00:01:28,906 The only difference here, as you can see in this example, 24 00:01:28,906 --> 00:01:33,330 is that we're using the split line dot split and saying separate this 25 00:01:33,330 --> 00:01:38,133 line wherever you see a comma, because we know that comma is going to be the key 26 00:01:38,133 --> 00:01:42,552 value that separates one piece of data from another piece of data. 27 00:01:42,552 --> 00:01:47,615 So the split line function is going to allow us to access multiple entries and 28 00:01:47,615 --> 00:01:50,748 we have to use the different index numbers. 29 00:01:50,748 --> 00:01:54,923 So data 0 is going to be the first entry, data 1 is going to the second, and so 30 00:01:54,923 --> 00:01:55,886 forth,right? 31 00:01:55,886 --> 00:02:00,747 So we have this file for you in the exercises examples, 32 00:02:00,747 --> 00:02:05,500 but we're going to learn a different version of how to do 33 00:02:05,500 --> 00:02:10,430 this using an inbuilt CSV library in Python. 34 00:02:10,430 --> 00:02:15,207 That introduces us to a new concept, which is the width statement, right? 35 00:02:15,207 --> 00:02:16,972 And this is something that you might come across so 36 00:02:16,972 --> 00:02:19,126 it's good that we discuss a little bit what this is, right? 37 00:02:19,126 --> 00:02:23,714 So a width statement is a specific statement that would allow us to open 38 00:02:23,714 --> 00:02:28,298 a file in this case and would give us a space in which we could a context, 39 00:02:28,298 --> 00:02:33,389 if you want, in which those resources are going to be available for us, right? 40 00:02:33,389 --> 00:02:37,491 So if we're trying to read the example text file, as you can see on the right, 41 00:02:37,491 --> 00:02:40,523 the width statement it's going to create a context in which 42 00:02:40,523 --> 00:02:42,636 that text file is in fact available. 43 00:02:42,636 --> 00:02:46,936 The great thing about the width statement in Python is that it will actually take 44 00:02:46,936 --> 00:02:51,366 care of the cleanup automatically, would actually do the cleanup or the sequence 45 00:02:51,366 --> 00:02:55,693 that might be required to kind of close that resource once we finish reading it. 46 00:02:55,693 --> 00:02:57,857 So if we move forward with a code. 47 00:02:57,857 --> 00:03:02,918 It's a very clean way of operating within a space in which we need those resources, 48 00:03:02,918 --> 00:03:05,665 access those resources and then we move on and 49 00:03:05,665 --> 00:03:10,210 the programming language will take care of the cleanup state. 50 00:03:10,210 --> 00:03:12,392 So let's see how that would actually look. 51 00:03:12,392 --> 00:03:15,910 It would look slightly different if we use this kind of CSV. 52 00:03:15,910 --> 00:03:20,913 We have to import CSV in our code and this would give us 53 00:03:20,913 --> 00:03:25,938 access to a different kind of syntax csv.reader. 54 00:03:25,938 --> 00:03:29,710 We can actually access the same CSV file here and 55 00:03:29,710 --> 00:03:35,772 we use next to move to the next line similarly to what we were doing before. 56 00:03:35,772 --> 00:03:40,516 But in this case the way we kind of skip to the next line is 57 00:03:40,516 --> 00:03:43,310 with the next keyword, right? 58 00:03:43,310 --> 00:03:48,187 And then the for loop becomes a little bit easier because we do a four row, right? 59 00:03:48,187 --> 00:03:52,258 So each row could be represented as an entity within 60 00:03:52,258 --> 00:03:56,435 the data that was obtained from the reader, right? 61 00:03:56,435 --> 00:04:01,386 So similarly we have to access entries like the index 0 entry will 62 00:04:01,386 --> 00:04:07,110 represent the x, index 1, it will represent the y, and so forth. 63 00:04:07,110 --> 00:04:13,130 So we're going to see how to access the data using this index information. 64 00:04:13,130 --> 00:04:18,018 So let's jump into processing and put this together and 65 00:04:18,018 --> 00:04:21,500 think through it as we write the code. 66 00:04:21,500 --> 00:04:24,520 So here we are in processing, right? 67 00:04:24,520 --> 00:04:29,392 And the first thing that we want to do 68 00:04:29,392 --> 00:04:34,368 is import our CSV module, right? 69 00:04:34,368 --> 00:04:38,558 That's going to give us access to the CSV reading 70 00:04:38,558 --> 00:04:42,596 functionality built into Python, right? 71 00:04:42,596 --> 00:04:43,248 And again, 72 00:04:43,248 --> 00:04:48,530 there's even more powerful versions of this using other external libraries. 73 00:04:48,530 --> 00:04:52,648 But again, it's good to know that there's multiple ways of doing the same thing. 74 00:04:52,648 --> 00:04:55,961 So let's start by creating our with statement, right? 75 00:04:55,961 --> 00:05:00,072 So with open, right? 76 00:05:00,072 --> 00:05:04,950 So we're going to open the data.csv. 77 00:05:07,850 --> 00:05:13,275 So that's going to open that and we're going to say us, 78 00:05:13,275 --> 00:05:18,449 this is basically saying when we open this file, 79 00:05:18,449 --> 00:05:22,112 let's call it CSV file, right? 80 00:05:22,112 --> 00:05:26,378 So we're actually creating a variable within this 81 00:05:26,378 --> 00:05:30,549 statement that is the CSV file variable, right? 82 00:05:30,549 --> 00:05:37,845 And here data, that is the data that is contained within that file, 83 00:05:37,845 --> 00:05:43,092 it's going to be using the csv.reader, right? 84 00:05:43,092 --> 00:05:48,474 So notice that we're using, similar to how we have been using random in the past, 85 00:05:48,474 --> 00:05:53,077 we are using CSV now, that's not a variable that's the module that we 86 00:05:53,077 --> 00:05:58,114 imported that contains the reader function and we can read this CSV file. 87 00:05:58,114 --> 00:06:01,948 The CSV file, remember, is going to be what we opened here, right? 88 00:06:01,948 --> 00:06:05,538 And because we're doing it within the width statement, 89 00:06:05,538 --> 00:06:10,174 this resource of the data containing the CSV file will only be available in 90 00:06:10,174 --> 00:06:12,895 the block defined by the width statement. 91 00:06:12,895 --> 00:06:15,882 Anything that is indented here, right? 92 00:06:15,882 --> 00:06:20,332 So at this point, you see we are operating within the loop or 93 00:06:20,332 --> 00:06:23,542 within this block, the width statement. 94 00:06:23,542 --> 00:06:25,227 This is not necessarily a loop, right? 95 00:06:25,227 --> 00:06:29,788 This is just an area in which, similar to an if statement, but 96 00:06:29,788 --> 00:06:34,550 it's a condition in which the resources are available. 97 00:06:34,550 --> 00:06:39,316 What kind of functionality can we have we can use next, right? 98 00:06:39,316 --> 00:06:44,797 That's in the case that we basically moving the head the same way in which we 99 00:06:44,797 --> 00:06:50,890 are starting to read the first line, the second line, and so on, right? 100 00:06:50,890 --> 00:06:56,818 At this point, if you want, we could print, 101 00:06:56,818 --> 00:07:04,782 let's print the data just to see what we have so far, right? 102 00:07:04,782 --> 00:07:09,117 So right now what we have is not data itself, we cannot see it. 103 00:07:09,117 --> 00:07:14,720 It's actually giving reference to this specific kind of object, right, this data. 104 00:07:15,810 --> 00:07:18,240 So, how do we access the actual data? 105 00:07:19,250 --> 00:07:24,150 So let's do a loop. 106 00:07:24,150 --> 00:07:28,260 So for a row in data. 107 00:07:31,350 --> 00:07:35,153 So, we need to understand that this data structure, 108 00:07:35,153 --> 00:07:40,330 which is in fact an object, contains a series of rows, right? 109 00:07:40,330 --> 00:07:48,173 So let's just print now, Now, 110 00:07:48,173 --> 00:07:54,209 right, So let's see what we're getting. 111 00:07:54,209 --> 00:08:01,476 So here we are starting to see the data that it's inside the object, right? 112 00:08:01,476 --> 00:08:06,669 And because this CSV file structure or this kind of module 113 00:08:06,669 --> 00:08:12,612 is doing a little bit of content, it's content aware in a way. 114 00:08:12,612 --> 00:08:16,279 It's going to to structure that data already in a way that it's easier 115 00:08:16,279 --> 00:08:17,381 to access, right? 116 00:08:17,381 --> 00:08:21,665 And here we're going to start addressing one of the topics that 117 00:08:21,665 --> 00:08:24,775 we haven't really covered in this course. 118 00:08:24,775 --> 00:08:28,267 But it's going to be the central focus of course 2, 119 00:08:28,267 --> 00:08:33,441 which is collections and data structures and in this case a list, right? 120 00:08:33,441 --> 00:08:35,964 But for now we want to make sure that we access, 121 00:08:35,964 --> 00:08:38,438 similar to what we have done with tuples. 122 00:08:38,438 --> 00:08:41,361 How do we access an entry in a collection, right? 123 00:08:41,361 --> 00:08:46,546 So let's imagine, or let's just remember what we wrote last lesson. 124 00:08:46,546 --> 00:08:54,130 So X equals, it's a float, we know it's a float from the row. 125 00:08:54,130 --> 00:08:58,118 And because we printed all the rows, right? 126 00:08:58,118 --> 00:09:05,368 We want to access the first entity within that row, right? 127 00:09:05,368 --> 00:09:10,055 So that should represent the x variable. 128 00:09:10,055 --> 00:09:13,731 Let's print it and see if in fact that's working right? 129 00:09:13,731 --> 00:09:17,145 So you can see now that we're only printing the x, 130 00:09:17,145 --> 00:09:21,074 which is the first coordinate that we store in the file. 131 00:09:21,074 --> 00:09:24,787 We're going through all the rows and 132 00:09:24,787 --> 00:09:29,335 we are obtaining the value of the x variable by 133 00:09:29,335 --> 00:09:34,830 accessing the first entry within the row list. 134 00:09:34,830 --> 00:09:38,174 For now, we need to understand that a list is very similar to a tuple. 135 00:09:38,174 --> 00:09:42,548 It has some differences that we're going to be covering in course 2. 136 00:09:42,548 --> 00:09:48,404 But for now we can basically use the same syntax to access entities within that. 137 00:09:48,404 --> 00:09:55,689 So if we have row, the x variable, we can probably access all of them, right? 138 00:09:55,689 --> 00:09:59,830 Reconstruct what we have done for y. 139 00:09:59,830 --> 00:10:03,090 And in this case would be the second entry. 140 00:10:05,690 --> 00:10:13,882 And here maybe you want to give a little bit of space for r, g and b, right? 141 00:10:13,882 --> 00:10:21,703 So this is r, this is g, And this is b, right? 142 00:10:21,703 --> 00:10:28,230 And here the numbers would be the second, sorry, the third or fifth element. 143 00:10:28,230 --> 00:10:32,017 So knowing what we know the data, just by looking at the data and 144 00:10:32,017 --> 00:10:34,671 understanding how many columns do we have. 145 00:10:34,671 --> 00:10:38,743 We have x and y representing first and second entries, and 146 00:10:38,743 --> 00:10:42,827 the next three entries represent the color data, right? 147 00:10:42,827 --> 00:10:47,742 So we no longer need the print. 148 00:10:47,742 --> 00:10:53,161 If we wanted to visualize this, we can do what we did before. 149 00:10:53,161 --> 00:10:58,822 So r, g, b and ellipse, 150 00:10:58,822 --> 00:11:03,788 x, y, 20, 20. 151 00:11:03,788 --> 00:11:08,030 And this is exactly what we done in the past. 152 00:11:08,030 --> 00:11:11,248 Let's see if this actually works, there we go. 153 00:11:11,248 --> 00:11:12,745 So we reconstructed the drawing. 154 00:11:12,745 --> 00:11:18,383 This drawing was originally made, created using random functions, right? 155 00:11:18,383 --> 00:11:22,564 So every time that you press play it would be slightly different and 156 00:11:22,564 --> 00:11:26,244 you can actually save it to a data file as we did last week. 157 00:11:26,244 --> 00:11:31,020 And now we can read it and reconstruct it with the exact coordinates that 158 00:11:31,020 --> 00:11:34,454 the circles had and the exact colors that they had. 159 00:11:34,454 --> 00:11:36,764 Even if they were random initially, 160 00:11:36,764 --> 00:11:41,704 we can reconstruct the data by saving it into a CSV and loading it once again. 161 00:11:41,704 --> 00:11:44,924 So this is kind of a different technique of doing it. 162 00:11:44,924 --> 00:11:47,319 We certainly wanted to introduce the width statement. 163 00:11:47,319 --> 00:11:52,383 Which is something that you might see as you kind of Google and kind of try to find 164 00:11:52,383 --> 00:11:57,913 examples of data visualization or any kind of example that uses resources, external 165 00:11:57,913 --> 00:12:02,940 resources such as data files, CSV files or JSON files or other kind of files. 166 00:12:02,940 --> 00:12:05,727 You probably will see this structure, the width statement. 167 00:12:05,727 --> 00:12:09,236 So this is how we could use it. 168 00:12:09,236 --> 00:12:10,764 Again, the notion of lists, 169 00:12:10,764 --> 00:12:15,114 it's a very important concept that we're going to be covering in future lessons. 170 00:12:15,114 --> 00:12:19,930 So I look forward to sharing that with you in future videos, so I'll see you then.