WEBVTT 00:01.320 --> 00:06.820 All right in this video we will learn how to create a histogram For our Titanic age column. 00:06.820 --> 00:13.170 So histogram says specifically useful to visualize the frequency distributions of values in the marriage 00:13.210 --> 00:14.420 columns. 00:14.620 --> 00:19.860 So fiscal 5 import pandas and we are also importing a map plot flip. 00:19.900 --> 00:21.070 S p a t. 00:21.070 --> 00:23.650 And we use the Seabourn style. 00:24.130 --> 00:32.520 And we are importing our Titanic dataset and before we also land that we can analyze the frequency of 00:32.520 --> 00:36.170 fair use with the value counts method. 00:36.180 --> 00:42.170 So here we are selecting the H column and we are analyzing the H column with the value counts method 00:44.230 --> 00:48.410 and the most frequent values actually at 24. 00:48.640 --> 00:56.350 So 30 passengers are twenty four years old and here at the end we have some outliers low point for two 00:56.350 --> 01:05.680 years for example and we can also organize our ages here in bins and let's have a look here so that 01:05.680 --> 01:08.960 I see the barometer bins and by default it set to None. 01:10.910 --> 01:17.480 But we can also set the bins to true and then rather than count better use bins that group them in two 01:17.480 --> 01:18.690 bins. 01:18.830 --> 01:21.740 So for example now let's set the bins to 10. 01:21.740 --> 01:24.510 So they're creating 10 bins. 01:24.510 --> 01:32.210 All right an S is missing years out of work and here we have ten equal width bins. 01:32.210 --> 01:40.840 So starting here from zero to eight then eight to 16 and so on and the bin are the in Taba. 01:40.880 --> 01:43.040 This the the most value use year. 01:43.040 --> 01:46.100 Actually the interval between 16 and 24. 01:46.520 --> 01:49.480 So here we have one hundred seventy seven passengers. 01:50.330 --> 01:57.980 And the least frequent business here between 72 and 80 of us only two passengers and the same we can 01:57.980 --> 02:01.780 actually do graphically with a histogram. 02:01.970 --> 02:08.240 So we are selecting here the H column and then we're using the plot method and the. 02:08.270 --> 02:09.860 So let's run the FSA far. 02:10.520 --> 02:18.560 So here we have our line plot and instead of passing lines or line plots actually here to Fort value 02:19.130 --> 02:27.600 and instead of passing line to the perimeter kind we can also pass a hist for histogram. 02:28.020 --> 02:30.570 And here we have the histogram. 02:30.570 --> 02:36.030 And by default we have 10 bins and these are actually the same bins says here above. 02:36.030 --> 02:43.680 So from zero today to then from eight to 16 and so on and the very last bin this farm seventy two to 02:43.770 --> 02:51.770 80 90 and this spend we have only two passengers and the largest our most frequent bin is actually a 02:51.840 --> 03:01.320 this bin and this is on the Internet 16 to 24 so having here 10 bins is actually the default setting 03:01.650 --> 03:05.710 but we can also change the amount of bins and the. 03:05.860 --> 03:08.420 There is here the parameter bins. 03:08.410 --> 03:16.200 However if you have here a look with this if tab then the parameter bins is not explicitly displayed 03:16.200 --> 03:21.240 here and this is actually a disadvantage if you see the plot method. 03:21.450 --> 03:27.990 So some specific parameters which are only available for some specific plot types of example here that 03:28.080 --> 03:36.060 inspire me to find the histogram are actually not listed here in the plot method however at least in 03:36.060 --> 03:39.780 this case for the bins parameter we can still use it here. 03:40.200 --> 03:48.750 So let's simply call here the bins parameter and for example let's assume we want to have uh 30 bins. 03:48.750 --> 03:49.790 That's roughly the same. 03:50.190 --> 03:57.300 And then we can see here that we have 30 separate bins and so if you get a range between 0 and 80 it 03:57.300 --> 03:59.640 could also make sense to FBA 80 bins. 04:00.210 --> 04:03.030 So for each year we have one bin. 04:03.840 --> 04:10.200 So for example here we have one 80 year old passenger and here a lot of spin and sexually I think it's 04:10.380 --> 04:12.040 24. 04:12.050 --> 04:18.690 All right so now we have seen that the plot method is actually not the optimal solution if we FBA other 04:18.690 --> 04:27.060 kinds than airline plots and in the next video we will also learn some other alternatives how to create 04:27.060 --> 04:28.380 here the histogram. 04:28.770 --> 04:31.980 But for the time being we are not finished and I hope to see you. 04:31.980 --> 04:33.450 And then next video by.