WEBVTT 00:00.800 --> 00:06.260 Racking and handling time loans is considered one of the most unpleasant parts when working with time 00:06.260 --> 00:07.250 series data. 00:07.820 --> 00:13.580 If you have time serious data with daytime information first of all you never know which time zones. 00:13.610 --> 00:20.840 The daytime information is referring to and therefore many users choose to work with the Coordinated 00:20.960 --> 00:29.180 Universal Time or short UTC but as currently the international standard and among others the United 00:29.180 --> 00:34.100 Kingdom Portugal and Iceland are located in the UTC time zone. 00:34.100 --> 00:35.900 So let's have a look at an example. 00:35.900 --> 00:43.350 And we import pandas and then we import historical stock prices for General Electric. 00:45.020 --> 00:45.860 Let's have a look here. 00:46.280 --> 00:53.570 So here we are on the left hand side a daytime index and we have actually time increments of 30 minutes 00:54.200 --> 01:01.030 and we actually open high low close and volume data so let's have a more detailed look here. 01:01.320 --> 01:03.920 And uh we uh. 01:04.030 --> 01:07.190 Salmon the first uh 30 timestamps. 01:07.280 --> 01:15.380 So actually here we are starting at nine thirty eight uh 10 a.m. Then we have for the next period from 01:15.500 --> 01:18.680 10 a.m. to 10 30 and so on. 01:18.680 --> 01:27.870 And actually the very last the timestamp on a day is actually here from 15 30 to 4:00 p.m. So these 01:27.880 --> 01:32.670 are actually the irregular trading hours on the New York Stock Exchanges. 01:32.730 --> 01:40.440 So this daytime information is they are actually based on New York time are actually Eastern time and 01:40.440 --> 01:48.270 actually New York Time or Eastern time is four hours behind UTC and we can also get some meta information 01:48.270 --> 01:58.260 here so that's a daytime index and let's have a closer look on the index and apparently here we cannot 01:58.260 --> 02:06.870 see any information on the time zone and we can explicitly get information on the time so nervous. 02:06.900 --> 02:08.300 The index method. 02:08.340 --> 02:16.800 T said and obviously here we have no information on the time so and so we have actually no time zone 02:16.850 --> 02:20.550 addressed here to our data frame or to our daytime index. 02:20.550 --> 02:27.000 So to say our data frame and our daytime index here time less and that's actually our first step here 02:27.000 --> 02:35.190 we have to localize our daytime index and we can actually do this with the math that tease that localize 02:35.790 --> 02:40.830 and by doing so we actually set as safe a time zone on our daytime index. 02:40.860 --> 02:43.390 And for example we can choose a UTC. 02:43.830 --> 02:48.650 So we know that here our daytime index is based on is done on New York time. 02:48.660 --> 02:53.090 But first of all we can for example localize that to UTC 02:56.030 --> 03:02.420 and still be a fear the day and the time and then we have fear and additional information plus the zero 03:02.420 --> 03:03.750 hours and minutes. 03:03.920 --> 03:10.130 And actually once we have localized our daytime index then here we can see the difference the time offset 03:10.220 --> 03:12.070 to the UTC time. 03:12.260 --> 03:19.880 And if we localize what it takes to UTC then of course the time difference offset is zero. 03:19.880 --> 03:26.840 So now let's localize our daytime index to a New York time and we can do this by passing America New 03:26.840 --> 03:27.290 York 03:30.820 --> 03:33.980 now and still of course we have the same daytime information. 03:33.990 --> 03:40.710 So the twenty ninth of July and the first time the embassy or 10:00 but here we have the additional 03:40.710 --> 03:45.540 information that we are four hours behind the UTC time. 03:45.600 --> 03:46.830 So that's the New York. 03:46.860 --> 03:56.200 Eastern time and do we have 10 a.m. and the corresponding UTC time is actually at 2:00 p.m. or 14 p.m.. 03:56.250 --> 04:02.740 All right and actually to save our time zone information we have uh 2 0 0 0 right here. 04:02.760 --> 04:04.210 Our data frame. 04:04.380 --> 04:11.920 So we have to reassign it here to the variability. 04:12.120 --> 04:18.780 So here we have on the left hand side the localized daytime index and in the next video we will see 04:18.780 --> 04:26.220 how to convert a localized the daytime index to another time slow and soft like cyber to L.A.. 04:26.550 --> 04:31.710 So for example we can get to the L.A. Time corresponding to the New York time. 04:31.800 --> 04:35.530 So thanks for watching and I'll see you also on the next video. 04:35.600 --> 04:35.880 By.