WEBVTT 00:01.280 --> 00:07.400 In this video we learn how to sort pen out serious so let's assume we have the H column of our Titanic 00:07.400 --> 00:14.570 data set and we want to sort the age from young to old or from 0 to young and we can do this with the 00:14.660 --> 00:22.990 sort values method so that's option number one we can sort a panda series by values but we can also 00:22.990 --> 00:26.590 swap a panda serious by the index. 00:26.590 --> 00:33.850 So let's go into our examples here and we import pandas and then we create a dictionary with the daily 00:33.850 --> 00:35.920 sales that we have on Monday. 00:35.920 --> 00:38.890 So one sense for Monday we have 10 sets. 00:39.190 --> 00:43.780 Then we have Wednesday 25 cents and then we have Tuesday success. 00:43.810 --> 00:46.590 So our dictionary has not really sorted. 00:46.620 --> 00:52.470 So Wednesday comes before Tuesday then we have a first day 36 sales. 00:52.480 --> 01:01.300 Then Friday to say is Saturday cereals layers because we do not work on Saturday and Sunday as we do 01:01.300 --> 01:02.920 not work on Sundays as well. 01:02.920 --> 01:08.920 We have also cereal sales but instead of having a zero we have just a month so we can do this and let's 01:08.920 --> 01:11.120 see what we get here. 01:11.470 --> 01:18.740 We get here and dictionary with um weekdays we'll use Paris and you can see here on Sunday it's still 01:18.820 --> 01:29.070 the value none included so let's pass our dictionary to PD dot series and let's create a pan that serious. 01:29.170 --> 01:35.230 So now you can see here our panel serious with on the left side our keys of our dictionary and in the 01:35.230 --> 01:36.800 same saw that as we passed it. 01:36.820 --> 01:43.210 So beginning with the day one and then they three they four five six seven. 01:43.210 --> 01:44.460 So here again. 01:44.800 --> 01:53.410 And we also have on the right side the values and you can see on Sunday we AFE and they end on missing 01:53.410 --> 01:53.970 value. 01:54.700 --> 02:00.340 So the key about num actually transforms and append us serious and the PAN US data frame to a missing 02:00.340 --> 02:07.750 value and and and as we do not have here the right sequence in our index so starting from Monday but 02:07.810 --> 02:15.940 then having Wednesday and then Tuesday we can sort our index with the site index method. 02:15.940 --> 02:23.260 So now you can see we have our index sorted starting from 1 and then we have 2 3 4 5 6 and 7 and we 02:23.260 --> 02:27.650 can also determine whether we want an ascending or descending order. 02:28.480 --> 02:32.920 So by default here we have an ascending order starting with them the lowest index. 02:33.400 --> 02:41.110 So here 1 so here is the salt index method we have a parameter ascending so let's press shift tap here 02:41.110 --> 02:49.340 and have a look so here we have the ascending parameter and by default it is set to true. 02:49.510 --> 02:50.670 So let's run again here. 02:51.460 --> 02:57.760 So we have here the ascending order but we can change this to the descending order by setting the parameter 02:57.760 --> 03:00.710 to force. 03:00.740 --> 03:06.800 So now we have from the descending order starting from Sunday or from seven seven six five four three 03:06.800 --> 03:08.000 two one. 03:08.000 --> 03:08.360 All right. 03:08.360 --> 03:15.010 So now let's change it back to ascending order. 03:15.010 --> 03:15.380 All right. 03:15.380 --> 03:17.900 So now we have changed to our that serious. 03:17.990 --> 03:23.810 Say it's by sorting the index and let's print again our panel series. 03:23.810 --> 03:29.300 And now you can see here that our change so sorting the index is not stored in memory so we haven't 03:29.300 --> 03:36.840 actually changed our series here and what we did here actually we just created a copy of our pandas 03:36.840 --> 03:42.030 series says and we uh sorted the index of the copy only. 03:42.210 --> 03:43.600 But uh yeah what. 03:43.680 --> 03:48.100 We want to change our original series and that's a solution for it. 03:48.900 --> 03:53.150 So let's go inside this site and next method and check with Chef tab. 03:53.160 --> 04:00.760 And here is the parameter in place and in place is by a defined set of files. 04:00.770 --> 04:04.540 And if it's true it performs the operation in place. 04:05.030 --> 04:10.460 So what in place is doing if it's set to fires all changes we made. 04:10.490 --> 04:18.290 So for example sorting the index are performed only on a copy and the original serious has not changed. 04:18.470 --> 04:25.190 And the fifth set of the in-place parameter to true then all changes also apply for the original series 04:25.190 --> 04:33.030 so the changes in place and the in-place perimeter and pan US is a very important parameter and you 04:33.030 --> 04:40.860 can see it in many methods for panda serious and also for panda data frames and the tabs you a lot to 04:40.860 --> 04:49.410 understand if a change is actually made on a copy or on the origin panda series or data frame and depending 04:49.410 --> 04:54.660 on what you actually want to do you can set the in place to true or false. 04:55.170 --> 05:02.220 So let's explicitly here introduce the in-place parameter and by default is set of false 05:04.980 --> 05:08.770 so now here we created a copy of states and decided to index. 05:08.790 --> 05:14.330 And if we print our original sales it's still not in place of the change here. 05:14.340 --> 05:17.580 But if we change the in-place parameter to true 05:21.490 --> 05:28.490 and now you can see here we do not see any uh printed copy of the say so that's the first good indication 05:28.490 --> 05:32.990 that we changed the original say it's the panda series and let's print here. 05:33.260 --> 05:35.250 Oh pen serious. 05:35.360 --> 05:36.680 And now we can see that. 05:36.860 --> 05:37.120 Yeah. 05:37.130 --> 05:38.390 That changes in place. 05:38.380 --> 05:44.240 So here we saw that our index and we changed our original pen series say it's all right. 05:44.240 --> 05:46.760 So now let's head over to a sorting value. 05:46.760 --> 05:54.560 So so far we only saw that our pandas serious by the index and we can also sort our serious by the values 05:55.280 --> 05:57.970 and we set in place to force. 05:58.260 --> 06:03.690 And let's see what we get here so by default the place apparently does set to fall. 06:03.710 --> 06:08.780 So I only want to make this explicitly here and we can see here on the right side. 06:08.820 --> 06:13.280 We have sorted ourselves pandas serious by the values. 06:13.450 --> 06:15.230 And then ascending order. 06:15.270 --> 06:26.000 So starting from zero Zs on Saturday and ending with 36 sets on Thursday and the end value is at the 06:26.000 --> 06:28.760 very end and also for the site various method. 06:28.760 --> 06:32.240 There are some parameters so we have the ascending parameter. 06:32.300 --> 06:40.090 We can determine whether we want to sort an ascending or descending manner so by default that the ascending 06:40.090 --> 06:41.490 parameter is set to true. 06:41.520 --> 06:43.280 But we can set it to false. 06:43.750 --> 06:45.130 Let's check here. 06:46.630 --> 06:54.110 So now we sort out our values in descending order starting with so the highest value 36 on Thursday. 06:54.340 --> 07:00.550 And we can also determine here you can see that still our any end value is in the last position and 07:00.550 --> 07:04.300 we can also determine the position of um and a values. 07:04.690 --> 07:12.490 So here we have last but we can also change this for example to first. 07:12.500 --> 07:17.350 So now here we have our end end value at the first position but in the end. 07:17.360 --> 07:20.120 So it makes more sense to have it at the last position 07:24.290 --> 07:30.430 and sorting ourselves from high to low makes makes sense so we keep ascending equal scores. 07:30.680 --> 07:34.880 And once we are happy here we can set our in-place parameter to true 07:38.210 --> 07:42.610 and in this way we save our changes actually in our original panel series. 07:42.610 --> 07:52.160 So let's run here and that's print our series and we can see that the changes we make here are in place. 07:52.190 --> 07:57.920 All right so now let's again create our dictionary and now this Monday Tuesday Wednesday and so on the 07:57.920 --> 08:07.180 strings and we can pass also this dictionary to uh PD dot series and create a panel series. 08:07.180 --> 08:09.200 Let's have a look here. 08:10.290 --> 08:16.790 And now if we sought the index with the salt index method then let's see what we get here. 08:18.650 --> 08:22.090 So then our index is actually sorted in an alphabet equal manner. 08:22.100 --> 08:28.280 So Friday starts with an F and then we have Monday with M first day Tuesday and Wednesday. 08:28.790 --> 08:33.110 So if we have a numerical index uh then the numerical index is sorted. 08:33.130 --> 08:39.620 Yeah from high to low and this a string index that's sorted uh alphabetically and we can also change 08:39.620 --> 08:42.790 here the order from ascending to descending. 08:45.260 --> 08:52.970 So now it's starting with uh the Wednesday w all right so now we are finished with this video and the 08:52.970 --> 08:59.150 next video will have a look at the methods and largest and smallest so hope to see you there by.