WEBVTT 00:00.730 --> 00:05.670 Welcome to the solution for coding exercise to and and Question 7. 00:05.670 --> 00:10.430 We are asked to import pandas and as always we import pandas Expedia. 00:10.710 --> 00:13.910 Then we should import the listings dataset. 00:14.130 --> 00:21.180 And of course we pass the listings of the CSP file name to the agreed CSB method. 00:21.660 --> 00:25.150 And finally it is always a good idea to inspect. 00:25.170 --> 00:32.230 The first 5 or 10 rows and we can do the service to the head method and then we should select the the 00:32.230 --> 00:38.470 column name with the attribute notation and the question is what is so the company name with index label 00:38.470 --> 00:39.360 4. 00:39.400 --> 00:43.580 So we are coding your listings start name and let's have a look. 00:43.600 --> 00:50.800 So that's in the name column and at the index label for we have here American Airlines Group. 00:50.800 --> 00:52.450 So that's correct. 00:52.510 --> 00:59.140 And uh the next question is apply also the second alternative to select the column name and we can do 00:59.140 --> 01:01.540 this with scrap records and half. 01:01.550 --> 01:08.820 Yeah the column label name so that's identical actually. 01:08.850 --> 01:14.630 Next we have to select the listing our row at the next position 100 with the ILO operator. 01:14.670 --> 01:17.350 So that's a position based indexing. 01:17.580 --> 01:25.050 And the question is what is the IPO year of that the respective listing service U.S.A. The ILO operator 01:25.080 --> 01:26.820 and we pass the 100 01:30.860 --> 01:37.470 and the IPO year of this respective listing is two thousand and fourteen. 01:37.540 --> 01:41.590 Here we have to select the listing all the row at index position 2000. 01:41.710 --> 01:46.970 Again with the ILO operator and we only have to select the column name. 01:47.080 --> 01:48.460 So this is position based. 01:48.460 --> 01:52.240 The next thing and the zero best and next thing applies also here. 01:52.240 --> 01:56.330 So we have to determine at which index position we have. 01:56.350 --> 01:58.150 Uh the column name. 01:58.300 --> 02:01.960 So let's have a look here at the beginning. 02:01.970 --> 02:07.610 So we have here the column name and the column is that the next position is 0 1 2 02:12.750 --> 02:19.120 and therefore we pass the other row and next position 2000 and the column and next position 2. 02:19.170 --> 02:22.560 And the question is about us the company name of this listing. 02:22.590 --> 02:25.430 So let's have a look and it's Eagle Bancorp. 02:26.040 --> 02:28.500 So let's go to the next question. 02:28.500 --> 02:35.640 Select the last 10 rows and the columns name last price sector and industry with the ILO operator. 02:35.640 --> 02:44.430 So still this is a position based indexing and the last 10 rows means from index position minus 10 to 02:44.460 --> 02:46.130 the very last row. 02:46.680 --> 02:52.430 And the for the columns we offer a list with the indexed positions 2 4. 02:52.440 --> 02:59.040 The name column then 3 7 and 8 for the columns the last price sector and industry. 02:59.040 --> 03:00.640 So you can check this. 03:01.110 --> 03:04.770 And the question is here what is the the sector of these same works. 03:04.780 --> 03:10.450 A company and a civil look here. 03:10.510 --> 03:18.510 Here we have signed works and the sector is healthcare here we have to report the listening state asset 03:18.960 --> 03:23.010 and that we have to set the column symbol s the index. 03:23.110 --> 03:24.380 And again here we can do this. 03:24.410 --> 03:27.960 The city agreed CSB method and b pass of the file name. 03:28.090 --> 03:34.600 And then we pass the the column Labor symbol to the index called the parameter and we save the data 03:34.600 --> 03:38.520 frame again in the variable listings so let's have a look here. 03:39.490 --> 03:41.260 Here we have on the left hand side. 03:41.290 --> 03:45.270 The former column symbol as the index of the data frame. 03:45.670 --> 03:49.930 And also we can check this with the info method. 03:50.020 --> 03:56.170 Here we have in the index six thousand eight hundred fifty two entries from a to z y x. 03:56.270 --> 03:56.880 I. 03:57.610 --> 03:59.170 And the next question is. 03:59.170 --> 04:03.070 Inspect the new and next left the data frame by calling the index attribute. 04:03.070 --> 04:04.510 So that's an easy one here. 04:06.330 --> 04:11.330 So here we have all stock tickers from a onto a set y x eye. 04:11.880 --> 04:15.480 Then the next question select the row with the index label. 04:15.480 --> 04:22.350 Apple AAPL and this is obviously a label based indexing with the loke operator. 04:22.890 --> 04:30.460 So we pass uh the stock ticker AAPL to the local operator within square brackets here. 04:30.600 --> 04:36.790 And the question is what is the last price and the last price of the Apple stock. 04:36.820 --> 04:37.230 Yes. 04:37.270 --> 04:40.290 Uh two hundred point six two. 04:40.410 --> 04:47.060 Then we have to select the row with the index label FP for Facebook and only the columns IPO year and 04:47.120 --> 04:48.290 industry. 04:48.470 --> 04:54.590 And again we are using here the local operator we have label based indexing and on the left hand side 04:54.590 --> 05:02.180 of the comma we pass the arrow label FP and on the right hand side a list with the column labels IPO 05:02.180 --> 05:03.960 year and industry. 05:04.010 --> 05:05.810 So let's have a look here. 05:05.840 --> 05:12.830 The question is what is the industry of Facebook and the industry is obviously computer software programming 05:12.830 --> 05:21.230 and data processing and the next question we have to select the columns and name and last price with 05:21.290 --> 05:23.980 the local operator and all rows. 05:24.080 --> 05:30.020 So we use a colon for all rows and then again here a list with the column labels 05:34.240 --> 05:40.570 and the final question select the last three columns IPO year sector and industry for Microsoft and 05:40.570 --> 05:48.310 Disney and we have to do this with the local operator and we shouldn't use the sector or industry in 05:48.310 --> 05:49.490 our code. 05:49.510 --> 05:54.960 So first of all we are only interested in the Rose are the listings Microsoft and Disney. 05:55.540 --> 06:02.200 And we pass here the list with those two stock takers and then we should select the last three columns 06:02.290 --> 06:10.780 starting from IPO year then sector and industry and we can simply do this by passing the IPO year and 06:10.780 --> 06:15.640 colon on that means until the very last column inclusive. 06:15.640 --> 06:22.460 So let's run this out here and the question is What is this in this industry and this nice industry 06:22.460 --> 06:27.710 as television services so that's the solution for exercise too. 06:27.710 --> 06:30.620 And I hope to see you also in the next exercises by.