WEBVTT 00:01.340 --> 00:01.950 All right. 00:02.010 --> 00:06.560 Come to the first inspection and visualization of our stock price data. 00:06.920 --> 00:14.540 So first of all we import pandas and we import our mighty index CSP fire stocks see a. 00:14.840 --> 00:22.070 And as you want to f for the first rose as mighty index we pass the the index positions zero and one 00:22.070 --> 00:23.680 to the perimeter header. 00:24.170 --> 00:29.950 And also we want to have the very first column at index Position ZERO as a daytime index. 00:30.320 --> 00:38.690 And therefore we pass here as a list the index positions 0 with the parameters index call and past dates 00:39.140 --> 00:43.930 and be safe our employer the data frame in the various stocks. 00:43.940 --> 00:47.920 So let's do this here and let's have a look at the first five rows. 00:47.930 --> 00:49.480 And the last five rows. 00:50.180 --> 00:58.870 So here we have actually a daily stock prices for Apple Boeing Disney IBM Coca-Cola and Microsoft. 00:58.970 --> 01:07.940 And the very first day is that the thirty first of December 2009 and the very last day is the 6th of 01:08.030 --> 01:10.710 February 2010 19. 01:10.730 --> 01:19.390 So let's also call it here the Inform method and here we have on the left hand side our columns. 01:19.510 --> 01:24.420 So these are all possible combinations of the two mighty index levels. 01:24.460 --> 01:32.320 So we have here the opening prices for all stocks than the daily highs for stocks daily lows for stocks 01:32.650 --> 01:38.830 and also the closing prices and actually also the adjusted housing prices. 01:39.340 --> 01:47.020 So the adjusted closing prices also include the dividends and the stock splits and are therefore a better 01:47.020 --> 01:53.370 metric to determine the total return and not only the price return of the stocks. 01:53.410 --> 02:00.580 And then finally we have also here the daily trading volume in terms of stock units. 02:00.580 --> 02:05.070 So in total we have FBA two thousand two hundred ninety euros or days. 02:05.350 --> 02:13.450 And actually we do not have any missing value here and the data type off and the stock prices are actually 02:13.450 --> 02:14.500 floating. 02:15.070 --> 02:22.390 And here for the volume we have in that or because there are no fractions of traded units of stocks. 02:22.390 --> 02:30.060 And the total memory usage is the six hundred sixty two kilobytes and we can also further analyze our 02:30.060 --> 02:39.380 data with the described method so here we can see that for all combinations of open high close and yeah 02:39.450 --> 02:45.460 the stock take us we have two thousand two hundred ninety nine low values. 02:45.460 --> 02:52.890 And here we have them in stock prices the minimum the maximum and so on and actually in this and the 02:52.890 --> 02:56.910 next videos we only want to back with the closing price. 02:57.300 --> 03:02.930 So this is the very last trading price of a stock in a particular day. 03:03.060 --> 03:10.210 And therefore we select here all rows and in the outer index be labeled close. 03:10.440 --> 03:16.050 And actually we are creating a copy here and we save the new data frame and the available clothes. 03:17.910 --> 03:24.360 And that's also if I look at the first five rows of our new data frame close. 03:24.700 --> 03:34.320 So here we have the daily closing prices for Apple Boeing Disney IBM Coca-Cola and Microsoft from 2000 03:34.340 --> 03:38.520 and 10 until February 2000 and 19. 03:38.530 --> 03:44.710 And as always this time serious that makes a lot of sense to also visualize them. 03:44.710 --> 03:50.000 And therefore we import here met plot slip and we use the Seabourn style. 03:50.470 --> 03:56.730 So we do not meet this year and then we want to plot all six columns the time series. 03:56.800 --> 04:03.760 Within one plot and therefore we are calling here the plot method on our close state frame and the fixed 04:03.760 --> 04:10.200 size should be 15 aid and we have a font size of 13 so let's do this here 04:15.640 --> 04:23.580 and now you're actually BFA the price charts for all six stocks from 2010 until 2019. 04:23.890 --> 04:27.100 And apparently they're all starting at a different level. 04:27.460 --> 04:30.310 So for example a fearful look here. 04:30.520 --> 04:37.300 So the IBM stock is starting in 2010 at a price of one hundred and thirty. 04:37.690 --> 04:43.000 And for example the Apple stock prices starting at 30 dollars. 04:43.030 --> 04:47.240 So all stocks are starting at a different level. 04:47.260 --> 04:50.230 And of course also ending at a different level. 04:50.920 --> 04:58.240 However it's pretty hard to see and compare the performance of the six stocks as the they do not start 04:58.240 --> 04:59.950 here at the same level. 05:00.250 --> 05:02.330 But at least we can say here. 05:02.530 --> 05:10.060 The green chart so this is Boeing and starting from here in the middle they actually end up here at 05:10.330 --> 05:12.380 the very top of the stocks. 05:12.580 --> 05:20.780 And what we also can say that here Coca-Cola seems to be quite stable over the time but obviously not 05:20.980 --> 05:24.170 with the very best performance. 05:24.190 --> 05:29.500 So this is our very first price chart and in the next videos we are going to improve this. 05:29.500 --> 05:31.750 So I hope to see also there by.