WEBVTT 00:01.180 --> 00:07.720 In this video we will learn some very helpful daytime indexed methods and attributes and with these 00:07.720 --> 00:13.870 methods and attributes you can for example create additional features for what data frame containing 00:13.870 --> 00:18.520 some kind of customized the representation of date or time. 00:18.530 --> 00:24.100 So let's first of all import pandas and we import our stocks. 00:24.140 --> 00:30.960 CSC and we set the date column as being the date time index. 00:31.120 --> 00:39.130 So let's have a look here and then we only selected the opening prices for our six stocks and we are 00:39.130 --> 00:45.590 creating a copy and start a new data frame and the variable clothes. 00:45.790 --> 00:47.380 So let's have a look here. 00:47.590 --> 00:53.780 So this is our close data frame with daily closing prices for our six stocks. 00:54.430 --> 01:01.310 And it's also called here the info method and we can see here that we have a daytime index with actually 01:01.310 --> 01:09.870 a daily timestamps and with the index attribute we can also have a closer look at our day time index. 01:09.910 --> 01:13.890 So here we have the daytime index to the timestamps. 01:13.930 --> 01:18.970 So in total we have two thousand two hundred and ninety Daily Times stems. 01:19.120 --> 01:21.930 And here we can see for example for one time stamp. 01:21.940 --> 01:25.820 So here we have the fourth of January 2010. 01:25.960 --> 01:33.540 And it might be quite helpful to get you additional information or different representation of the date. 01:33.550 --> 01:37.750 So for example it might be useful to have the day off the week. 01:37.750 --> 01:41.910 So for example Tuesday or Wednesday or the quarter of the year. 01:41.920 --> 01:49.510 So here in this example it's the first quarter and we can do this with quite a head for attributes and 01:49.510 --> 01:51.600 methods on the index. 01:52.150 --> 02:00.900 So first of all we FCB they attribute and we get actually the day of the month. 02:00.910 --> 02:06.430 So for example in our first time stamp we have for the thirty first of December and here we get the 02:06.460 --> 02:07.510 thirty first. 02:07.540 --> 02:14.890 So that data types see an integer and consequently we get you for the other timestamps for example here 02:14.950 --> 02:23.440 for 10 we get 5 and so on and we can also get an integer representation of the month and we can do this 02:23.680 --> 02:26.060 for the month at tribute. 02:26.080 --> 02:33.550 So here we have the data type in the data and we get twelve here for the timestamp in December and one 02:33.550 --> 02:41.890 for the time stamps in January and so on Next we can also get the year with the year attribute. 02:42.640 --> 02:45.970 So here again we have integer data types. 02:45.970 --> 02:49.500 2009 2010 and so on. 02:49.870 --> 02:56.380 And for each row off each time stamp and our data frame we can also get to the day of the week or the 02:56.380 --> 03:07.230 day name with the method day name so here we have a Thursday Monday Tuesday and so on and we can actually 03:07.230 --> 03:16.770 do the very same with the attribute week name all week day name and typical actually the same week day 03:16.770 --> 03:21.660 names and also instead of getting an integer representation of the month. 03:21.690 --> 03:32.030 So here twelve one 2 Three you can get the month name so you have with the method the month name and 03:32.040 --> 03:32.780 t we get. 03:32.790 --> 03:35.340 December January February and so on. 03:35.880 --> 03:44.170 So next we have to attribute to weekday and instead of getting here the string representation first 03:44.170 --> 03:50.610 day Monday Tuesday we get an intense representation so zero stands for Monday. 03:50.710 --> 04:00.320 Then one stands for Tuesday and so on and we can also get the quarter with the quarter attribute and 04:00.320 --> 04:07.460 by default the first quarter is from January to March the second from April to June and so on. 04:07.460 --> 04:14.330 So we can see for the time stamp in December we get a far and for the time stamps and January a one 04:14.390 --> 04:15.750 as in the jar. 04:15.810 --> 04:24.380 Then next we have also the attributed days and month and TV get for each row off each time stamp the 04:24.380 --> 04:28.100 amount of days in the respective month. 04:28.100 --> 04:39.240 So in December we have 31 and January 31 and so on and next that s also the week at tribute and TV we 04:39.250 --> 04:45.850 get actually the calendar week saw the very last week in December as the week fifty. 04:45.880 --> 04:53.860 Then the very first week in January is the week 1 and so on and the same we can actually get with the 04:53.890 --> 04:57.810 attribute week off year. 04:57.920 --> 05:06.500 So this is the same and that's another attribute court is month and and year pen us checks for each 05:06.500 --> 05:12.950 row of each time stem whether the day is actually the very last day of the month. 05:13.550 --> 05:17.960 So here we have for the thirty first of December a true. 05:17.960 --> 05:22.070 And for the 1st of January or for the second a fourth. 05:22.850 --> 05:30.570 So these are some very helpful attributes and methods on the daytime index and that even more methods 05:30.580 --> 05:33.140 so feel free to have a closer look. 05:33.140 --> 05:38.300 And now we can also create new columns and new features for our data frame. 05:38.300 --> 05:45.920 So for example we want to have the day name as the new feature and we can create yet a new column called 05:45.920 --> 05:46.510 day. 05:46.580 --> 05:50.580 And we want to have the quarter as the new column on your feet. 05:51.020 --> 05:56.480 So let's do this here and let's have a look here at our new data frame. 05:56.480 --> 06:01.260 So here we have our adjusted close that frame where we have you on the right hand side. 06:01.340 --> 06:08.480 The two additional columns day and quarter and year for each row off each timestamp we have here the 06:08.480 --> 06:10.640 weekday day and the quarter. 06:11.060 --> 06:17.780 So creating additional features with different representation of date might be in some cases quite helpful. 06:18.320 --> 06:20.570 And with this we are finished now. 06:20.570 --> 06:23.510 And I hope to see you also in the next video by.