1 00:00:00,030 --> 00:00:04,500 Hello! In the previous lecture you were able to build this map with multiple 2 00:00:04,500 --> 00:00:09,690 markers. This map happens to have only two markers, but you get the idea and the 3 00:00:09,690 --> 00:00:15,870 script was this one here, so you can add as many coordinate pairs as you want in 4 00:00:15,870 --> 00:00:20,880 here, but in this lecture I want to show you how to add pairs of coordinates out 5 00:00:20,880 --> 00:00:31,320 of a text file which is this one here. Volcanoes.txt. If you like you can 6 00:00:31,320 --> 00:00:36,809 just rename the name of this from Volcanoes.txt to Volcanoes.CSV and 7 00:00:36,809 --> 00:00:42,809 then you can open it in a program such as Excel. If you like you can show it on 8 00:00:42,809 --> 00:00:48,840 a plain text file as I'm doing it being here. Python can read both CSV and text 9 00:00:48,840 --> 00:00:54,570 files, so what we have here are some data about volcanoes basically we have the 10 00:00:54,570 --> 00:01:02,789 number of the volcano which is some sort of ID, so volcano 020. We have yet 11 00:01:02,789 --> 00:01:10,680 another number then, and as you see every column, so we have columns in this data 12 00:01:10,680 --> 00:01:15,810 file and every column is separated by a comma. This is the name of the first 13 00:01:15,810 --> 00:01:20,100 column which is, which for the first value of this first column is this one 14 00:01:20,100 --> 00:01:24,900 here. Then there is the name of the second column, the first value of the second 15 00:01:24,900 --> 00:01:29,939 column is this one here, then the second value of the second column is this and so 16 00:01:29,939 --> 00:01:37,710 on, and then we have the name column, the name or the volcano in here. Sorry the 17 00:01:37,710 --> 00:01:46,380 name is here, Baker and the comma starts here which means that location is this 18 00:01:46,380 --> 00:01:53,299 here, US Washington. Then you have the status, status historical for the first volcano, 19 00:01:53,299 --> 00:01:59,399 and then we have the elevation which is this one in here. I'm not sure whether 20 00:01:59,399 --> 00:02:04,469 these are in feet or meters, but you can find out. If you've curious you can do 21 00:02:04,469 --> 00:02:11,009 research for this particular volcano and compare the elevation. Anyway, and then we 22 00:02:11,009 --> 00:02:13,640 have the type of the volcano. Then you have the time frame 23 00:02:18,310 --> 00:02:27,610 which is D3. I'm not sure what this means. D4 and so, then 24 00:02:27,610 --> 00:02:32,970 lastly this is what we are interested about, we have the lat and long columns. 25 00:02:32,970 --> 00:02:40,810 So this is a lat which means latitude, and this is the longitude of the volcano. 26 00:02:40,810 --> 00:02:46,690 So you need these two numeric values in order to map the features, so the 27 00:02:46,690 --> 00:02:54,420 volcanoes into a map, in this case into a Folium map, so this has around 63, 28 00:02:54,420 --> 00:03:04,000 actually 62. 63 including the header of the data and so how do we load this file 29 00:03:04,000 --> 00:03:12,780 into Python? Well, to load that file into Python, let me clear the terminal and 30 00:03:12,780 --> 00:03:19,720 you'd first need to install a very useful library called Pandas. We're going 31 00:03:19,720 --> 00:03:25,060 to use this library later on, so I'm not going to explain Pandas in detail for 32 00:03:25,060 --> 00:03:30,700 now because we were just using one or two of its functions which I'll explain 33 00:03:30,700 --> 00:03:33,640 in this lecture. So go ahead and install Pandas if you don't 34 00:03:33,640 --> 00:03:39,400 have Pandas. Pip install Pandas or pip3 install Pandas depending on how you 35 00:03:39,400 --> 00:03:46,799 have configured Python, and then you can go ahead and open a Python session. 36 00:03:46,799 --> 00:03:54,489 Import Pandas and the way to load a file with Pandas is by pointing to pandas.read 37 00:03:54,489 --> 00:04:03,340 CSV volcanoes.txt. Make sure that your Python session has 38 00:04:03,340 --> 00:04:10,000 been opened in the directory where your volcanoes.txt file is, so for 39 00:04:10,000 --> 00:04:15,280 recess my session is inside mapping folder which is this one here, so I can 40 00:04:15,280 --> 00:04:18,209 just pass the file name. Otherwise you may have to pass the 41 00:04:20,379 --> 00:04:27,159 absolute path of the file which starts from like C slash slash and so on depending 42 00:04:27,159 --> 00:04:34,330 on what operating system you are in. Execute that and call data and you get 43 00:04:34,330 --> 00:04:39,539 the data printed out. Just like that. So Pandas is able, actually the read 44 00:04:43,000 --> 00:04:50,560 method is able to distinguish between these comas where the commas are and so 45 00:04:50,560 --> 00:04:59,490 it creates a well-structured data format with columns so if you go down here, 46 00:04:59,490 --> 00:05:07,270 so you have some columns here, column, column, column and then up to the end, but 47 00:05:07,270 --> 00:05:12,370 then you have other columns because Python cannot print out, actually the 48 00:05:12,370 --> 00:05:17,740 terminal doesn't allow you to have all the columns in one, inside this 49 00:05:17,740 --> 00:05:22,419 area here because it would be too much. Anyway you have status, elevation, type, 50 00:05:22,419 --> 00:05:29,940 timeframe, lat and lon, so that's about the data. 51 00:05:29,940 --> 00:05:38,050 Now you need to figure out a way to iterate through that data frame, so this 52 00:05:38,050 --> 00:05:45,430 is called a data frame, so the data object is called a data frame. You can 53 00:05:45,430 --> 00:05:56,020 check its type actually. Pandas.core. frame.DataFrame. So what I'm thinking of 54 00:05:56,020 --> 00:06:05,650 is to actually create two list out of these data frame columns, so to put the 55 00:06:05,650 --> 00:06:11,069 latitude column in a Python list, and the longitude column in another Python list. 56 00:06:11,069 --> 00:06:15,550 And so that we have a native Python object which is a list and then we can 57 00:06:15,550 --> 00:06:25,919 iterate through that list using the for loop, so let me try to do that. 58 00:06:26,399 --> 00:06:34,490 Import Pandas, then create the data frame object, 59 00:06:34,490 --> 00:06:39,880 so loading the data read CSV Volcanoes.txt. 60 00:06:39,880 --> 00:06:47,600 And yeah, that's it. So how do we convert a data frame column 61 00:06:47,600 --> 00:06:54,890 into list? Well, the way to do that is by just doing let's say L or lat equals to 62 00:06:54,890 --> 00:07:04,010 data and then the name of the column which is lat, so you can get the list of 63 00:07:04,010 --> 00:07:09,310 columns by the way by doing data columns. Here you see you have a lat and a lon 64 00:07:13,490 --> 00:07:26,270 column and then you do lat equals to list, data Lon in square 65 00:07:26,270 --> 00:07:33,890 brackets, close it. And so what you did here is data with the lon attribute 66 00:07:33,890 --> 00:07:40,160 there will turn a series object, but that series object we are converting it 67 00:07:40,160 --> 00:07:50,240 into a native list, so that will be a Python list. This would also show up as 68 00:07:50,240 --> 00:07:59,030 a similar object. So this is a series object and this is a list, but I prefer 69 00:07:59,030 --> 00:08:04,520 to work on a list because it working on lists is faster then working on data 70 00:08:04,520 --> 00:08:15,580 frame series, so the idea is to have two lists here. That equals to list data lon. 71 00:08:15,580 --> 00:08:25,130 Copy that, paste it here, and the same thing goes for longitude, so lon, 72 00:08:25,130 --> 00:08:28,300 lat and lon lists. So now we need to be careful here on how 73 00:08:33,750 --> 00:08:40,050 we iterate through these two lists because you know what we have is we have two 74 00:08:40,050 --> 00:08:46,200 lists and each of them has 62 items, you can check that using the len here, down 75 00:08:46,200 --> 00:08:54,710 here and lat. So it has 62 items and the same goes for the longitude list. 76 00:08:54,710 --> 00:09:00,810 That means that the first item of the latitude list corresponds to the second 77 00:09:00,810 --> 00:09:05,460 item on the latitude list, sorry of the longitude list. So the first two items 78 00:09:05,460 --> 00:09:12,720 make up the first location of the first marker. That means that in the first 79 00:09:12,720 --> 00:09:18,240 iteration we need to extract both locations, so actually this sort of 80 00:09:18,240 --> 00:09:23,490 structure won't be appropriate anymore. What you need to do is you need to go 81 00:09:23,490 --> 00:09:30,780 through let's say lat and lon variables. In, when you iterate through two lists 82 00:09:30,780 --> 00:09:36,930 and at the same time you need to use the zip function and that goes lot and 83 00:09:36,930 --> 00:09:44,640 lon, so basically what this function does is like this. 84 00:09:44,640 --> 00:09:50,570 Let's say for I, J in zip. Let's say the first list is 1, 2, 3 and 85 00:09:52,250 --> 00:10:12,950 the second is 4, 5, 6. Print I and J, so this is what happens. This loop will go 86 00:10:12,950 --> 00:10:17,790 through these two lists at the same time, so what happens is that I will go 87 00:10:17,790 --> 00:10:23,400 through the first item of the first list while J will go through the first item of 88 00:10:23,400 --> 00:10:31,260 the second list. And so in the first iteration 1 and 4 are extracted, so 1 89 00:10:31,260 --> 00:10:39,510 here and 4 here. And you can do such an extraction by using the zip function. 90 00:10:39,510 --> 00:10:45,150 Otherwise you won't be able to do that if you don't use the zip function, so zip 91 00:10:45,150 --> 00:10:50,660 sort of distributes the items one by one. And so the same goes here. 92 00:10:50,660 --> 00:10:57,030 Lt will get the first item of this list, l1 will get the first item of this list, 93 00:10:57,030 --> 00:11:05,880 and then here what you need to do is you need to construct a list with lt and ln, yeah. 94 00:11:05,880 --> 00:11:14,610 That's it. Save the script, go to the terminal to execute. It's not here the 95 00:11:14,610 --> 00:11:26,760 terminal. I'll just exit that. Python3 map1.py execute. No errors. 96 00:11:26,760 --> 00:11:29,450 Go to Firefox and reload. I've got no markers for some reason, but 97 00:11:36,090 --> 00:11:40,830 I didn't get an error so this is one of those scenarios where you don't get an 98 00:11:40,830 --> 00:11:45,320 error in the terminal so in that case you need to double check your code 99 00:11:45,320 --> 00:11:56,100 carefully. So data pandas.read.csv. Lat list data. Lon, here the error, so what's 100 00:11:56,100 --> 00:12:05,220 happening with that I'm assigning the wrong column to the latitude list, so 101 00:12:05,220 --> 00:12:13,110 let me change the order and try again. So don't get intimidated by errors. Errors 102 00:12:13,110 --> 00:12:19,140 happen all the time you just needed to be cold-blooded and read the errors if 103 00:12:19,140 --> 00:12:27,090 you get any error here and also look at the code carefully. Let me reload. I've 104 00:12:27,090 --> 00:12:35,640 got some markers now. So these are the locations of volcanoes in North, in US 105 00:12:35,640 --> 00:12:46,380 actually, United States. Okay. I also have this pop-up which is working well, but I 106 00:12:46,380 --> 00:12:50,220 assume you don't like it very much because it's not showing any information. 107 00:12:50,220 --> 00:12:55,390 So what we need to do here is to actually make this pop-up 108 00:12:55,390 --> 00:13:01,740 dynamic so that it shows some actual information, and I'll show you how to 109 00:13:01,740 --> 00:13:06,000 create dynamic pop-ups in the next video. Talk to you there!