WEBVTT 00:00.920 --> 00:07.590 In the last video we have created a histogram for our each column and we did this with the panda's method 00:07.590 --> 00:16.160 to adopt plot where we pass the Hist to the parameter kind so we can see this and not only for histogram 00:16.190 --> 00:18.040 but also for other plot types. 00:18.050 --> 00:24.380 There is more than one way to plot data not data frame and in this video I will show you two other ways 00:24.410 --> 00:31.070 how to create here the same histogram and we will see that there are slightly differences and the pros 00:31.070 --> 00:32.870 and cons for each way. 00:33.140 --> 00:38.430 So in my opinion there is no optimal solution but let's have a look now. 00:38.510 --> 00:44.350 So the second alternative would be to use the direct method the shortest. 00:44.540 --> 00:51.020 So instead of having the other method plot and re pass hist to the perimeter there is also a direct 00:51.020 --> 00:58.300 method called hist and also here we can define the fixed size twelve eight and let's have a look here. 00:58.310 --> 01:03.560 So here we have some different parameter us in the documentation. 01:03.620 --> 01:08.550 So here you have explicitly the bins parameter and by default it sets of 10. 01:08.550 --> 01:10.720 But he said it to 80. 01:11.510 --> 01:17.080 And we can also set the X label size and the Y label size here. 01:17.120 --> 01:20.650 So we set it to 15 and we can see that. 01:20.710 --> 01:23.690 This is the last line difference here to the plot method. 01:23.960 --> 01:30.980 Slowly in the plot method we can actually determine the x level size and violated the size with the 01:31.010 --> 01:40.300 font size parameter and we've also seen this the direct plot method that there is no explicit have been 01:40.300 --> 01:41.690 parameter here. 01:41.710 --> 01:48.190 So here we do not see the bins parameter but if he passes his to the kind parameter then we can nevertheless 01:48.190 --> 01:50.010 see a use of the bins per meter. 01:50.170 --> 01:55.750 Even if it's not explicitly listed here all right. 01:55.810 --> 02:03.940 So now we come running our cell and we actually create the very same histogram so we FBA the same amount 02:03.940 --> 02:04.670 of bins. 02:04.690 --> 02:12.700 Then the same size of the labels here and we can see here by default the tier with the plot method we 02:12.700 --> 02:15.640 have here at the Y label frequency. 02:15.640 --> 02:17.670 And here we do not have the VI label. 02:17.680 --> 02:24.190 So this is a minor the friends and all let's go to the third alternative where we use them the Hist 02:24.190 --> 02:31.750 method directly on Mad plot flipped up type plots or purity not hist and we have to pass then the sequence 02:31.900 --> 02:34.390 from it so we want to have the histogram. 02:34.390 --> 02:41.080 So here in this case that we want to have the pen series the Titanic not age and also here we can set 02:41.380 --> 02:45.670 the bins parameter to 80 and let's try to run here. 02:45.720 --> 02:49.180 So and again error message. 02:49.180 --> 02:56.380 And in the end it's because the direct met plot Lapis method cannot by default handle here are missing 02:56.380 --> 02:56.950 values. 02:57.220 --> 03:04.740 So yeah our H column was missing values and if you pass the hour each column to the direct mat properly 03:04.860 --> 03:06.430 the method tests the. 03:06.460 --> 03:08.660 Then we have a problem. 03:08.710 --> 03:14.690 So before we can plot here our H column we have to drop them the missing values and we can do this with 03:14.710 --> 03:16.320 the method the drop and a. 03:16.360 --> 03:19.920 So we will have a closer look at later in this class on this here. 03:19.990 --> 03:21.470 So drop an A. 03:22.870 --> 03:28.540 And by doing so we are dropping all missing or add a values and now actually our code should work. 03:28.540 --> 03:30.710 So let's try this out here. 03:30.790 --> 03:38.530 So this looks quite similar to the other graphs like this the Rams and let's have a look here at the 03:38.530 --> 03:44.300 additional pair meet us and we can see here that here we have some more parameters. 03:44.440 --> 03:51.250 So for example we have the density meter and by default it set to false but we can also set this to 03:51.250 --> 03:59.020 true and then instead of getting here the absolute quantity for all of the beans here we get the relative 03:59.020 --> 03:59.680 quantity. 03:59.680 --> 04:01.120 So let's try this out here 04:03.880 --> 04:10.000 so here we have the relative quantity for all beans and we can also transforming our histogram into 04:10.000 --> 04:12.090 a cumulative histogram. 04:12.160 --> 04:19.960 So by passing here true to the parameter accumulated and here we are actually adding up all beans starting 04:19.960 --> 04:21.600 from the left hand side. 04:21.670 --> 04:26.830 So in the very end here we end up with the 1 0 100 percent. 04:26.830 --> 04:34.060 So still I feel normalized quantities and in the end if you sum over all beans then we have 100 percent 04:34.100 --> 04:35.800 of the data here included. 04:35.830 --> 04:43.270 So yeah we are ending at the 100 percent or 1 and actually in the other 2 direct Pena's methods that 04:43.300 --> 04:50.400 are explicitly know apparently does density and accumulative but still we can use them. 04:50.410 --> 04:53.320 So that several look here. 04:53.320 --> 04:59.340 So here we do not see any density are cumulative but still we can use these parameters here. 04:59.350 --> 05:00.220 So let's try. 05:00.250 --> 05:02.050 The cumulative parameter. 05:03.870 --> 05:05.310 And let's set it to true. 05:06.580 --> 05:15.880 And we get the absolute accumulated histogram so starting from zero here at the left hand side to over 05:15.880 --> 05:24.070 700 peasant just at the in total at the very end and also here for our plot method for example we can 05:24.280 --> 05:26.850 determine the parameter density 05:30.630 --> 05:32.640 and we can set it to true. 05:32.760 --> 05:38.600 And now we should get on the left hand side and set off absolute quantities the relative quantities. 05:38.730 --> 05:40.780 And this is true here. 05:40.830 --> 05:48.050 So actually the direct Panda's methods have the advantage that by default they can handle missing values 05:48.510 --> 05:52.760 but if we have a look at the pyramid us not all pyramid does that. 05:52.830 --> 06:00.510 The direct aim at plot lib method provides explicitly here listed in the parameters and if you do not 06:00.510 --> 06:02.780 know that these parameters exists. 06:02.910 --> 06:06.500 So then you could get the limited here and the functionalities. 06:07.050 --> 06:15.060 So in the end it's best to at least know all three alternatives and no all parameters that you can actually 06:15.060 --> 06:15.770 use. 06:15.930 --> 06:23.100 And then in the end it's actually a matter of taste which one of these alternatives you use all right. 06:23.100 --> 06:27.440 With this we are finished here and I hope to see you also in the next video by.