WEBVTT 00:00.960 --> 00:06.400 In this video we are going to create our very first seaborne plot a so-called current plot. 00:06.510 --> 00:13.380 But before we need to import pandas and also seaborne and by convention the standard appreciation is 00:13.410 --> 00:20.500 s and s so we import seaborne S S and S and we also import met plot lip. 00:20.580 --> 00:24.090 And in this video we are working with the Titanic data frame. 00:24.090 --> 00:26.030 So let's also import this year. 00:26.190 --> 00:32.980 So we have the first five rows and now we want to create a seaborne Kong plot and early on this costs 00:33.000 --> 00:38.450 that we have seen that we can split our data into different groups by using categorical data. 00:38.910 --> 00:44.820 And we did this for example with the group by a method and with the group by method we were able to 00:44.820 --> 00:46.540 group our data out by a flight sample. 00:46.550 --> 00:49.830 The sex column into a female and male. 00:50.150 --> 00:56.550 All we also created the six groups by the category is sex and PE class and width of the group by method. 00:56.550 --> 01:00.600 We were also able to count the number of elements in each group. 01:00.630 --> 01:07.260 So for example the number of first class male passengers on board of the Titanic and exactly this we 01:07.260 --> 01:10.380 can do also with the the count plotted graphically. 01:11.220 --> 01:14.720 So first of 5 we set here the figure size 212 aid. 01:15.000 --> 01:18.830 And the final statement here with purity don't show. 01:18.840 --> 01:21.240 So this is nothing new. 01:21.340 --> 01:26.180 And now we want to create a conflict and we can do this with the S and S stopped count. 01:26.190 --> 01:30.000 Plot and let's have a look here inside the brackets. 01:30.300 --> 01:36.330 So the count plus shows of the Council of observations in each category they've been using boss and 01:36.330 --> 01:41.260 we have yes several pyramid does and the section of the seaborne plots are quite similar. 01:41.460 --> 01:44.170 So we always have fear the pyramid that data. 01:44.700 --> 01:48.180 And here we have to pass our data frame that we want to plot. 01:48.210 --> 01:51.030 So in this example Titanic. 01:51.030 --> 01:54.380 And then in addition we have 50 does X and Y. 01:54.580 --> 01:56.930 And here we have to pass of on column. 01:57.330 --> 02:03.470 So for example let's assume that we want to count the amount of female and the amount of males on board 02:03.480 --> 02:04.530 of the Titanic. 02:04.530 --> 02:10.990 Then we pass it to the parameter X our column Sex here in quotation marks. 02:11.010 --> 02:18.250 So let's say running in the center and we can actually see a bar chart maybe a few on the left hand 02:18.250 --> 02:20.230 side the amount of males. 02:20.260 --> 02:26.680 So it's about five hundred seventy and the number of females on board of the Titanic. 02:26.680 --> 02:29.230 So about the three hundred twenty something. 02:29.650 --> 02:31.780 So this is the vertical column plot. 02:31.810 --> 02:36.270 But we can also pass the sixth column to the Via pairing meter. 02:36.850 --> 02:40.930 And now we have the same chart horizontally. 02:40.930 --> 02:48.760 So let's go back to the vertical graph and actually the very powerful thing with the seaborne this that 02:48.850 --> 02:52.870 we can further split here our data into subgroups. 02:52.870 --> 02:58.990 So for example here we can split up the bin father made peasant just into a first class second class 02:58.990 --> 03:06.370 and third class bin and we can do the same with the female had been here and this the very powerful 03:06.370 --> 03:07.730 pyramid the hue. 03:07.870 --> 03:14.310 And we can find this parameter in many seaborne plots so let's have a look here. 03:14.350 --> 03:17.020 So here we have the parameter hue. 03:17.290 --> 03:20.530 And here we can pass a column with the categorical data. 03:20.860 --> 03:25.510 So in our example we could pass here for example the peak class column 03:28.960 --> 03:29.740 and the. 03:29.800 --> 03:30.520 Let's have a look. 03:30.520 --> 03:31.510 What do we get here. 03:33.220 --> 03:39.900 So here we actually split up our male been in our female bin in two uh three separate bins. 03:39.970 --> 03:47.410 So we have in total about 120 first class male peasants us then we have an orange for male and female. 03:47.410 --> 03:53.240 The second class peasant passengers and in green the third class passengers. 03:53.410 --> 03:55.060 So here we have our sixth group. 03:55.060 --> 04:01.600 And for each group we have the number of passengers which belong to their respective group and we can 04:01.600 --> 04:06.370 also customize and the change the style of symbol on plots. 04:06.700 --> 04:07.880 And we can do this here. 04:07.900 --> 04:11.140 This is an S thought to set. 04:12.190 --> 04:13.410 So let's have a look here. 04:14.940 --> 04:18.630 And the Tessier said a static pair meet us in one step. 04:18.630 --> 04:22.850 So for example here we can change the fun skater. 04:22.890 --> 04:24.240 Let's try this out here. 04:24.420 --> 04:31.410 So with this actually we can make the font size here on the x and y axis and on the let's and larger. 04:31.440 --> 04:39.940 So for example let's pass here to and now we have a significantly larger font size. 04:39.940 --> 04:47.420 And for example we can also have some other changes here so we can change the fund itself. 04:47.530 --> 04:52.440 Well we can also change the color map so when that plot slip we know this as color map. 04:52.480 --> 04:54.730 Here we have the pyramid palette. 04:54.790 --> 05:03.680 And for example let's call this perimeter here and that's pass the color map of it yes. 05:04.030 --> 05:07.060 And by doing so we can change here the color style. 05:07.930 --> 05:10.740 And actually many more options to customize the yellow graph. 05:10.760 --> 05:17.750 So feel free to have a look at the map plot lib and also that the Seabourn documentation and. 05:17.910 --> 05:23.320 For the time being while we are finished with this the first video and out to see you also in the next 05:23.320 --> 05:24.370 videos by.