WEBVTT 00:00.700 --> 00:04.920 HEY EVERYBODY IS SOME this video we make our very first steps with pandas. 00:04.960 --> 00:07.130 And then you work with data. 00:07.200 --> 00:13.400 We first have to import the data into our place of environment and then make a very first inspection. 00:13.420 --> 00:18.520 So typically when we've asked this and new data we do not really know how the data looks like and therefore 00:18.520 --> 00:20.910 we have to get a very first impression. 00:21.040 --> 00:27.310 And after that then we can determine further steps like that we need additional data or we need to clean 00:27.310 --> 00:31.350 that data out handle missing values or rearrange the data. 00:31.350 --> 00:37.480 But the effect this pandas pendency first have to import pandas so it's not by default imparted to our 00:37.480 --> 00:38.450 Python Ryan and 00:44.540 --> 00:46.720 and we import pandas as PD. 00:46.730 --> 00:54.960 So that's spy convention here and in this video and the next video Seberg heavily with the Titanic data 00:54.960 --> 00:58.420 set where we get many information about each passenger. 00:58.410 --> 01:03.050 So for example age or whether the passenger has survived or not. 01:03.050 --> 01:06.590 And in our folder we already have from the CSB fire. 01:06.620 --> 01:14.240 So CSP stands for camera separated value and the Titanic CSB fire that we have to impart here and our 01:14.240 --> 01:21.950 paisan and Ryan Hammond and this independence method read sea as we where we can impart them CSP fires 01:22.700 --> 01:26.690 and we already get a kind of Tabula organization of our data. 01:26.720 --> 01:37.690 So let's have feared the CSP method and then in brackets we have to pass in the file name so it's Titanic 01:37.700 --> 01:38.810 dot see a suite. 01:39.160 --> 01:48.970 And we need this in quotation marks and let's run the set here and this already looks quite good. 01:48.970 --> 01:54.620 So we have here the Titanic data set where we have firm rows here and each row stands for one passenger 01:54.630 --> 02:01.210 and we have here nine columns and each column is actually a characteristic of the passengers of Flight 02:01.220 --> 02:08.860 sample Passenger 1 has not survived them the disaster so zero sense form that survived and one stands 02:08.860 --> 02:11.940 for survived and we have the passenger class. 02:11.950 --> 02:18.580 So we have three Peasants are classes and then we have the sex or gender of the passengers in this example 02:18.580 --> 02:28.520 it's a male then we have the h here and sip S P stands for the amount of sibling saw policies of the 02:28.520 --> 02:37.160 peasants are onboard and Podge stands for the amount of to run or parents of the peasants aboard and 02:37.160 --> 02:43.730 ferries and actually the ticket price and the peasants are paid for the trip then we have here the part 02:43.730 --> 02:50.870 of embarkation So for example S stands for South Hampton and last but not least here we have the deck 02:50.870 --> 02:53.210 where the cabin of the peasant sir was located. 02:53.210 --> 02:56.640 So for example a taxi or E. 02:56.720 --> 03:01.220 All right so this looks pretty good and I think we are already happy with them the importing of the 03:01.220 --> 03:09.920 data also a fair press here just tab we can see that the method read CSP has many options here and this 03:09.920 --> 03:15.860 is very helpful when we have a messy data to clean the data directly when we impart it but that's not 03:15.860 --> 03:25.340 necessary here so then we can save our data set and assign a variable so let's say we have type panic. 03:25.500 --> 03:32.430 So now we have saved our data set into our terminal notebook and just all the variables we can just 03:32.430 --> 03:40.880 print Titanic here now we can have a deeper look here so we are starting with the first peasant at an 03:40.880 --> 03:46.100 exposition zero and we end up with. 03:46.100 --> 03:51.530 They are actually eight hundred ninety one rows so each row stencil on passenger and nine columns and 03:51.530 --> 04:00.630 each column stands for a characteristic of the passenger and we can also see here that by default pace 04:00.630 --> 04:08.950 and prints out them the first three rows and the last 30 rows so when we have a very large data set 04:08.950 --> 04:13.840 with let's say ten thousand rows it definitely does not make sense to print out all the rows. 04:13.840 --> 04:23.300 So that's by default here we have six zeros however we can change this default setting with the PD dot 04:23.330 --> 04:30.290 options that this play thought Max row is. 04:30.380 --> 04:31.390 So we this. 04:31.400 --> 04:32.470 And here we can change. 04:32.470 --> 04:34.610 So by default at 62 times 30. 04:34.610 --> 04:36.870 And by default we can change this too. 04:36.890 --> 04:38.560 For example 100. 04:38.810 --> 04:43.470 So then because let's see here the first 50 rows and the last 50 rows. 04:43.940 --> 04:44.840 Let's check here 04:50.200 --> 04:51.390 and here we can see. 04:51.390 --> 04:51.840 All right. 04:51.870 --> 04:53.480 So we have the first 50 rows. 04:53.490 --> 05:01.420 And the last 50 rows and when we are not working in Jupiter we when we want to print the data. 05:01.450 --> 05:03.640 So we have to use them that print function near 05:08.600 --> 05:13.110 and then we use in the print function you can see here that the other data is not not really formatted. 05:13.110 --> 05:19.770 So the graphical rendering of took it a notebook here is much better and yeah if you want to print a 05:19.770 --> 05:22.100 data set a data frame here interpret a notebook. 05:22.110 --> 05:23.610 I would always use it. 05:23.700 --> 05:25.830 Without the print function. 05:25.830 --> 05:28.230 So here's the graphical rendering is quite pretty here. 05:32.670 --> 05:33.000 All right. 05:33.000 --> 05:38.430 So now we are finished with this video and in the next video we continue our first inspection.