WEBVTT 00:00.870 --> 00:06.020 Might it come to the solution for coding exercise one in this and the next exercises. 00:06.020 --> 00:13.510 We will deeply analyze the listings dataset that contains all current stock listings on the three major 00:13.510 --> 00:15.040 US stock exchanges. 00:15.050 --> 00:19.950 The New York Stock Exchange Nasdaq and American Stock Exchange. 00:20.120 --> 00:27.550 And let's start with the question one import penned us and by convention we import pandas SPDR. 00:28.220 --> 00:33.700 So let's run the sell here and then as a next step we want to import. 00:33.720 --> 00:39.700 Uh the listing state asset from the U.S. as we file listings that to see a three. 00:40.070 --> 00:46.880 And we can do this by passing the file name listing stud CSP strings or within quotation marks here 00:47.510 --> 00:50.420 to the read CSP method. 00:50.420 --> 00:56.600 And by doing so we creating a panel state of frame and we save the data frame and the variable listings 00:59.060 --> 01:03.340 and then as a very first step it is always useful to inspect. 01:03.360 --> 01:09.500 Uh the first uh 5 4 10 rows and we can do this with uh the head method. 01:09.500 --> 01:15.370 So here we pass uh tend to the head method to inspect the first 10 rows. 01:15.470 --> 01:19.640 The default setting is here the first five rows. 01:19.740 --> 01:21.250 Let's have a look here. 01:21.420 --> 01:24.630 And the question is uh what is the company name of. 01:24.680 --> 01:28.330 The very first listing in the first row. 01:28.400 --> 01:29.660 So let's have a look at the. 01:29.670 --> 01:30.730 The very first row. 01:30.740 --> 01:34.790 And the company name and that's Agilent Technologies. 01:35.240 --> 01:35.940 And uh. 01:35.960 --> 01:37.270 We are right here. 01:37.520 --> 01:39.640 Then here we have the fourth question. 01:39.650 --> 01:41.870 Also inspect the last five rows. 01:42.110 --> 01:45.930 And we can do this of course with the tail method. 01:46.010 --> 01:50.000 So the default setting is that we have the last five rows. 01:50.390 --> 01:54.730 So here we do not have to pass an argument with the empire in meta. 01:54.800 --> 01:57.010 And uh that's run the sale here. 01:57.770 --> 02:02.070 And the question here is how many listings are those do we have in the data set. 02:02.390 --> 02:06.770 And we should mind that we have a range and next starting from zero. 02:07.250 --> 02:13.280 So in total we have uh six thousand eight hundred fifty two listings. 02:13.280 --> 02:19.700 Next that might be useful to get some meta information in our data frame and that we can do this with 02:19.790 --> 02:27.770 the info method and the question is and which columns do we have a missing or any end value. 02:28.050 --> 02:32.010 So here we can see the number of none other values. 02:32.010 --> 02:36.160 So in total we have six thousand eight hundred fifty two rows. 02:36.270 --> 02:44.520 And obviously we have the missing values in the column last price the market cap ADR T S O IP year sector 02:44.550 --> 02:45.800 and industry. 02:46.470 --> 02:53.040 And finally we also get some summary statistics in our data frame and we can do this with the you describe 02:53.110 --> 03:00.080 mass at and the question here is what is the maximum value in the column last price. 03:00.140 --> 03:09.110 So let's go to the column last price and the maximum is here about four thousand three hundred so that 03:09.110 --> 03:11.800 was a very first inspection of the list things. 03:11.800 --> 03:17.960 The dataset and we can ask for more closer look here to the columns. 03:18.590 --> 03:25.060 So we have the column symbol and a stock ticker symbol is an abbreviation used to a uniquely identify 03:25.100 --> 03:27.320 publicly traded shares. 03:27.560 --> 03:30.860 And then we have for the next column exchange. 03:31.040 --> 03:35.150 And that's simply the marketplace in which the shares are traded. 03:35.330 --> 03:41.140 And here in our example we have the New York Stock Exchange and the Nasdaq and Amex. 03:41.300 --> 03:44.600 Then next we have the legal company name. 03:44.600 --> 03:49.220 Then the last trading price as of April 2000 and 19. 03:49.730 --> 03:54.170 So I extracted the data set here in April 2019. 03:54.680 --> 04:02.480 And also the market capitalization sets of April 2019 and the market cap is simply the dollar value 04:02.480 --> 04:09.340 of outstanding shares and can be actually computed as shares times the current price then we have for 04:09.350 --> 04:14.720 the additional column ADR TSR though and this includes some additional information on foreign stocks 04:14.720 --> 04:16.210 trading in the US. 04:16.430 --> 04:21.230 And then we have the IPO year so the year of the initial public offering. 04:21.230 --> 04:27.590 And then finally we have some information on the sector and the industry of the main business activity 04:27.590 --> 04:31.790 of the company is so that's the listing State asset. 04:31.810 --> 04:34.720 And I hope to see you also in the next exercises by.