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These are the user uploaded subtitles that are being translated: 1 00:00:00,420 --> 00:00:05,250 Hello everyone and welcome to the lecture on data frame's selection and indexing. 2 00:00:05,530 --> 00:00:08,910 And this lecture we're going to learn how to grab data out of our data frame. 3 00:00:09,120 --> 00:00:12,410 Let's go ahead and jump to our studio to see how to do this. 4 00:00:12,450 --> 00:00:12,810 All right. 5 00:00:12,810 --> 00:00:19,230 So here our studio and I'm going to go ahead and just move the environment tabs and the file tabs to 6 00:00:19,230 --> 00:00:22,880 the side so we can focus on the scripting and console. 7 00:00:23,090 --> 00:00:27,200 Notice I'm going to be using the same data we used in a previous lecture. 8 00:00:27,240 --> 00:00:33,300 This weather data of the days the temperature and the rain to go ahead and pass this as ADF going to 9 00:00:33,300 --> 00:00:35,400 go ahead and run the source. 10 00:00:35,440 --> 00:00:39,750 And now on a call if it's going to reveal that data frame. 11 00:00:39,750 --> 00:00:45,030 Let's go ahead and learn how we can use this bracket notation we're familiar with to actually grab information 12 00:00:45,390 --> 00:00:52,050 from a data frame when we were learning about Matrix indexing and selection of elements from a matrix 13 00:00:52,060 --> 00:00:52,260 . 14 00:00:52,350 --> 00:00:55,350 We learned that we could just say something like Matt. 15 00:00:55,650 --> 00:01:00,750 And then the Rose who want it and the columns he wanted were actually going to be able to do the same 16 00:01:00,750 --> 00:01:02,550 thing with data frames. 17 00:01:02,550 --> 00:01:05,680 So imagine that we wanted everything from the first row. 18 00:01:05,790 --> 00:01:10,870 We would just input row number one comma and then blank in the killing. 19 00:01:11,010 --> 00:01:14,980 All the columns from the first row and that would actually return. 20 00:01:15,000 --> 00:01:20,330 Likewise the first row of our data frame and notice how the labels of the columns are kept in the results 21 00:01:20,330 --> 00:01:20,920 . 22 00:01:20,970 --> 00:01:24,900 They can also do the same thing to get everything from the first column. 23 00:01:25,080 --> 00:01:32,700 So I can say something like blank comma 1 instead of that square bracket notation and slow return the 24 00:01:32,700 --> 00:01:36,590 days notice that the levels are included here. 25 00:01:37,620 --> 00:01:42,600 Now something to note here is that one of the main points of using data frames is actually being able 26 00:01:42,600 --> 00:01:49,050 to label your columns and your rows by some sort of character or string names such as days temp or rain 27 00:01:49,050 --> 00:01:49,940 . 28 00:01:50,040 --> 00:01:55,230 We're going to be able to actually index using those names instead of having to remember which column 29 00:01:55,230 --> 00:01:57,270 numbers are which row numbers you want. 30 00:01:57,480 --> 00:01:59,220 Let's go ahead and show how we can do this. 31 00:01:59,340 --> 00:02:01,530 Going to go ahead and clear this. 32 00:02:01,530 --> 00:02:03,170 We have our data frame. 33 00:02:03,210 --> 00:02:07,240 Let's go ahead and select all range values from the rain column. 34 00:02:07,380 --> 00:02:15,330 I can say Blinkx comma and then input the string rain instead of inputting the number three for the 35 00:02:15,330 --> 00:02:21,230 third index column and that's going to return all the values of the rain call. 36 00:02:21,300 --> 00:02:26,770 For example let's say I wanted to get the first five rows for days and temps. 37 00:02:27,030 --> 00:02:33,600 Well I can say D-F if I want the first five rows That's actually all the rows in this case going to 38 00:02:33,600 --> 00:02:36,260 go ahead in the use slicing notation here. 39 00:02:36,300 --> 00:02:41,710 One colon five comma and then I want the columns days and temp. 40 00:02:41,730 --> 00:02:45,640 So I want all five row values for the columns days and temp. 41 00:02:45,780 --> 00:02:54,540 I can go ahead and pass and a vector is in the combined function with days and temp inside of it. 42 00:02:55,260 --> 00:03:00,350 And there you have that subsection of the data frame with just those two columns. 43 00:03:01,140 --> 00:03:05,030 We also have the ability to just grab all the values of a particular column. 44 00:03:05,130 --> 00:03:08,250 Now we can do this in a couple of ways. 45 00:03:08,280 --> 00:03:11,940 One way we can do this is by using the dollar sign notation. 46 00:03:11,940 --> 00:03:17,480 So you put the name of your data frame a dollar sign and then the name of the column. 47 00:03:17,670 --> 00:03:22,950 And you'll notice that our studio actually provides little clips for the column names. 48 00:03:23,160 --> 00:03:27,430 So I can just go ahead and say days will return all the values for days. 49 00:03:27,660 --> 00:03:32,630 The dollar sign and I begin to get the other column name option's temp etc.. 50 00:03:32,960 --> 00:03:36,240 We can also use bracket notation to do the same thing. 51 00:03:36,270 --> 00:03:42,140 I can put India off and then just pass in a string of the column I want. 52 00:03:42,150 --> 00:03:44,050 Notice how this is different though. 53 00:03:44,400 --> 00:03:50,040 If I use the bracket notation I'm actually getting the data returns in a data frame format. 54 00:03:50,040 --> 00:03:58,230 So I keep this index notation with the column days and the row number values if I use the dollar sign 55 00:03:58,230 --> 00:04:01,130 format actually getting back a vector. 56 00:04:01,170 --> 00:04:07,050 So keep that in mind when you're using this notation to callback columns completely. 57 00:04:07,050 --> 00:04:07,550 All right. 58 00:04:07,620 --> 00:04:13,020 Let's go ahead and learn how we can use the subset function to grab a subset of values from our data 59 00:04:13,030 --> 00:04:15,280 Freyne based off some condition. 60 00:04:15,610 --> 00:04:19,120 For example imagine I wanted to grab the days where it rained. 61 00:04:19,170 --> 00:04:24,540 That means we want to grab the days where rain is equal to true but use a subset condition to do that 62 00:04:25,440 --> 00:04:29,450 you put in subset you pass in your data frame source. 63 00:04:29,450 --> 00:04:31,890 So in our case it's día. 64 00:04:32,910 --> 00:04:39,350 Then you pass in the subset argument and you go ahead and pasand some sort of condition statement that 65 00:04:39,380 --> 00:04:43,220 these conditions statements pretty much always use comparison operators. 66 00:04:43,440 --> 00:04:47,580 And we want our condition to be rain is equal to true. 67 00:04:47,580 --> 00:04:51,780 Notice that rain doesn't actually have to be a character string. 68 00:04:51,870 --> 00:04:56,190 You can just put in range without any quotes and that's because subset knows that you're going to be 69 00:04:56,190 --> 00:05:01,580 referring to column names in the court and subset. 70 00:05:01,580 --> 00:05:08,090 If we go ahead and do that we get back a data frame where the values of rain was equal to true again 71 00:05:08,110 --> 00:05:08,190 . 72 00:05:08,220 --> 00:05:12,960 Notice how the condition uses some sort of comparison operator in the above case it was the equality 73 00:05:12,960 --> 00:05:13,700 operator. 74 00:05:13,800 --> 00:05:15,130 Equals equals. 75 00:05:15,480 --> 00:05:20,310 Let's go ahead and show another example where we grab days where the temperature was greater than 23 76 00:05:20,310 --> 00:05:21,960 degrees. 77 00:05:22,170 --> 00:05:30,120 Again I say subset pass in my data frame source put in subset as my argument and then put in some sort 78 00:05:30,120 --> 00:05:31,040 of condition. 79 00:05:31,170 --> 00:05:39,370 This case I want temp column to be greater than 23 so return all instances where that's true. 80 00:05:39,660 --> 00:05:40,470 And there you have it. 81 00:05:40,470 --> 00:05:44,320 It's rows 4 and 5 and it's returned as a data frame. 82 00:05:45,030 --> 00:05:51,300 OK finally let's learn about ordering a data from we can sort the order of our data frame by using the 83 00:05:51,390 --> 00:05:56,250 order function you pass in the column you want to sort by into the order function. 84 00:05:56,430 --> 00:05:59,550 Then you use that vector to select from the data frame. 85 00:05:59,550 --> 00:06:03,000 Let's go ahead and see an example of this. 86 00:06:03,320 --> 00:06:09,430 I'm going to make a variable called sorted temp. 87 00:06:10,080 --> 00:06:17,960 I'm going to go ahead and call the order function and then Pessin ZF. 88 00:06:18,090 --> 00:06:28,740 Let's go ahead and pasand DFA temp in if I call sort of temp. 89 00:06:28,740 --> 00:06:29,490 All right. 90 00:06:29,490 --> 00:06:36,180 Notice how I get back the index notation for the temp vector. 91 00:06:36,180 --> 00:06:41,510 Basically what this means is it's the sorted temperatures by index. 92 00:06:41,550 --> 00:06:49,030 So temperature 21 should be sorted first if you're going to sort them from least to greatest. 93 00:06:49,050 --> 00:06:53,650 Then it goes twenty two point two and the remaining three were already in order. 94 00:06:53,670 --> 00:06:58,160 Let me go ahead and show you how you can use this sort of vector to actually sort your data frame. 95 00:06:58,340 --> 00:07:08,220 You would just go DMF pass in sort of temp comma and this will go ahead and sort your data frame by 96 00:07:08,220 --> 00:07:17,010 those temperature values and you can notice now how these rows values were connected to sort of temp 97 00:07:17,010 --> 00:07:17,970 . 98 00:07:18,300 --> 00:07:24,000 When we think of what's actually going on here we're essentially just asking for the index elements 99 00:07:24,090 --> 00:07:26,760 in that order and by default it's ascending. 100 00:07:26,760 --> 00:07:30,570 You can also pass a negative sign to do a descending order. 101 00:07:30,600 --> 00:07:33,060 Let me go ahead and show you how we can do that. 102 00:07:33,110 --> 00:07:45,150 All say sending 10 order that a negative term. 103 00:07:45,270 --> 00:07:48,010 Take a look at the ascending temp. 104 00:07:48,210 --> 00:07:50,290 Now it's 5 4 3 1 2. 105 00:07:50,430 --> 00:07:59,310 And when we pass that into our data frame the sending temp column or comics Scuse me because all the 106 00:07:59,310 --> 00:08:01,170 columns. 107 00:08:01,170 --> 00:08:05,310 Now we get a descending temperature again with this sort of notation. 108 00:08:05,310 --> 00:08:07,680 All you're doing is you're basically just asking. 109 00:08:07,800 --> 00:08:15,660 Give me back the rows of the data frame in this order 5 4 3 1 2 because you use the order function to 110 00:08:15,660 --> 00:08:17,900 sort by temperature. 111 00:08:17,910 --> 00:08:22,800 You also could have done this exact operation instead of using the bracket notation here by using the 112 00:08:22,800 --> 00:08:26,200 dollar sign notation to show you a quick example that. 113 00:08:26,250 --> 00:08:35,110 Let's go ahead and just say the sending temp order E-F. 114 00:08:35,890 --> 00:08:42,480 Tim I will go ahead and put a negative sign up front that is sending and then we'll actually get the 115 00:08:42,480 --> 00:08:47,170 same thing when we call for the sending as the data frame. 116 00:08:47,190 --> 00:08:52,650 All right that's it for the basics of selection and indexing of a data frame in the next lecture. 117 00:08:52,650 --> 00:08:58,200 We're going to be doing a complete overview of the various operations and capabilities of a data frame 118 00:08:58,200 --> 00:08:58,830 . 119 00:08:58,890 --> 00:09:04,200 Remember for the next lecture there's an entire notebook ready for you to reference almost as a cheat 120 00:09:04,200 --> 00:09:06,090 sheet for use in data frames. 121 00:09:06,150 --> 00:09:09,930 The next lecture is actually going to be one of the most fundamental lectures as far as learning how 122 00:09:09,930 --> 00:09:12,390 to use data frames with our. 123 00:09:12,390 --> 00:09:15,200 OK thanks everyone and I'll see you at the next lecture 13109