WEBVTT 00:00.780 --> 00:01.930 Hi everybody. 00:02.010 --> 00:08.340 This video is a sworn to X causes to object oriented programming and I promise you that it's only a 00:08.340 --> 00:09.460 short x cause. 00:09.460 --> 00:19.630 So first off all we import pandas SPDC and we import our Titanic file and save it in the available Titanic. 00:19.950 --> 00:22.320 And let's have a look at the first five froze. 00:22.350 --> 00:27.750 So we already know the data frame here and in Piceance everything is not checked. 00:27.750 --> 00:36.250 So also our Titanic tabular data or data frame is an object and we can also check the data type of followed 00:36.630 --> 00:37.460 tabular data. 00:37.470 --> 00:40.760 So let's check the type of Titanic 00:43.470 --> 00:50.370 so what Titanic tabular data is of the data type data frame or in other words it's an instance of the 00:50.370 --> 00:58.470 class pinned us data frame and with all objects that object specific attributes and methods specifically 00:58.470 --> 01:00.250 designed for that data type. 01:00.450 --> 01:07.950 So over many years more than 800 developers contributed to the design methods and attributes especially 01:07.950 --> 01:12.030 for lots and messy data organized in a tabular manner. 01:12.090 --> 01:15.900 And this effort makes panda so a powerful tool. 01:16.170 --> 01:22.590 But before we move on to methods and attributes of our pendants data frame we know that they are also 01:22.590 --> 01:26.940 built and functions in Python that can be applied to many data types. 01:26.940 --> 01:33.330 So for example RF and the length function which we can apply to lists to put saw also an umpire race 01:33.810 --> 01:39.480 and the links function returns to the amount of elements in a list or why it took root. 01:40.200 --> 01:47.250 And we can also pass a data frame object to pass input and functions and sometimes we get a head full 01:47.270 --> 01:48.520 resolve and sometimes not. 01:48.520 --> 01:56.610 And sometimes it's not even defined to pass a data frame to a specific Python bit and function. 01:56.610 --> 02:02.930 All right so let's start and then let's pass to the Python boot and function lengths from our data frame 02:02.940 --> 02:03.650 Titanic 02:06.890 --> 02:09.030 and Python returns us 891. 02:09.150 --> 02:15.110 So so from the last session we know that our Titanic data frame has eight hundred ninety one rows so 02:15.170 --> 02:20.450 the links function returns us and the amount of growth of our data frame. 02:20.840 --> 02:24.350 And we already know also the around function. 02:24.350 --> 02:30.860 So we can pass our Titanic data frame and let's say you want to to 0 digits. 02:30.930 --> 02:38.600 Let's see what we get here and we can see here that by using the around function each float object and 02:38.600 --> 02:46.500 our data frame is around the 2 0 digits so if we can see here the faster our first passenger paid seven 02:46.500 --> 02:53.760 point two five pound or dollar and this was from the tour seven point zero and also here the second 02:53.760 --> 03:00.420 passenger paid seventy one point two eight dollars so pounds and this was all surrounded 271 so the 03:00.420 --> 03:04.560 round function round of float objects in our data frame by default 03:07.690 --> 03:14.590 and we can also try to transform our data frame into an integer by using the int function. 03:15.400 --> 03:22.270 So let's see if this works here and now so we're getting an error message into the argument must be 03:22.270 --> 03:26.400 a string a bytes like object or a number not a data frame. 03:26.440 --> 03:35.090 So the integer function is not defined for a data frame and we can also use the main function for example 03:35.090 --> 03:39.140 which calculates in that list the minimum element in the list. 03:39.140 --> 03:44.850 So let's see what we get here and we get to your age so why do we get here. 03:44.860 --> 03:48.580 Age so if we have a look at our data frame in our columns here. 03:48.710 --> 03:54.120 So alphabetically speaking HS and the very first elements starting with an A. 03:54.170 --> 03:59.990 So when passing a data frame to the main function some returns as the minimum column later. 04:00.020 --> 04:06.290 So in this case it's sphere H because it's starting with an A and I think this was not what we intended. 04:06.380 --> 04:14.300 So maybe our intention was to get the let's say the youngest pessimism or data frame or the lowest fare 04:14.330 --> 04:15.610 paid by a peasant shop. 04:15.640 --> 04:21.830 But our intention was not to get the column label which starts wasn't any actually. 04:21.830 --> 04:22.220 All right. 04:22.220 --> 04:29.240 So now let's move to data frame attributes and the first attribute we already know 04:31.880 --> 04:41.780 so we can check the shape of our data frame and it's 891 rose and nine columns and that's also the attribute 04:41.780 --> 04:42.440 size 04:48.980 --> 04:54.710 so we get to eight thousand nineteen and uh what uh pandas return census actually year. 04:54.860 --> 04:56.980 The amount of all elements in our data frame. 04:56.990 --> 04:59.670 So we have eight hundred ninety one rows and nine columns. 04:59.690 --> 05:05.310 So eight hundred ninety one times nine gives us eight thousand ninety and elements and our data frame. 05:05.750 --> 05:12.950 And we can also specifically return the index labels. 05:12.950 --> 05:19.140 So here we have a range index starting from zero and ending at eight hundred ninety or so eight hundred 05:19.140 --> 05:24.620 ninety one excluding and we can also explicitly check the column labels 05:31.010 --> 05:36.410 so here our column labels survive peasant class sex and so on. 05:36.410 --> 05:44.970 Now let's move to data frame methods and we already learned one of the most important methods so we 05:44.970 --> 05:52.140 have the method thought had which returns us and the other very first rows of our data frame and by 05:52.140 --> 05:55.760 default the first five rows some in imparting a data frame. 05:55.770 --> 06:01.470 The method is actually the very first method we will use in order to get the very first impression of 06:01.470 --> 06:03.600 our data frame. 06:03.850 --> 06:11.860 And also we already learned about the infill method which returns us meta information on our data frame 06:13.210 --> 06:15.240 so we have eight hundred ninety one rows. 06:15.250 --> 06:21.850 We have nine columns and for each column we have a specific amount of nominal values and we have found 06:21.850 --> 06:28.540 the data type in each column and we affirm the memory usage of the total data frame. 06:28.660 --> 06:32.020 And now let's use another method call it mean 06:36.450 --> 06:42.930 so in year we get for each column the minimum value is so we know that in the survived column there 06:42.930 --> 06:49.580 are the values 0 and 1 and the minimum value is 0 for the peasants are class we have from the values 06:49.770 --> 06:50.490 1 2 3. 06:50.490 --> 06:58.170 So the minimum value is 1 for the sixth column we have female and male and female it comes alphabetically 06:58.170 --> 06:58.990 reform males. 06:59.000 --> 07:05.820 So we have female and the youngest passenger on the Titanic was only 0 point for 2 years old and the 07:05.830 --> 07:10.860 passengers who didn't have any brothers sisters or spouses aboard. 07:10.890 --> 07:18.270 And also the passengers who didn't have any terminal parents onboard and lots of passengers who actually 07:18.510 --> 07:22.030 didn't pay any fares so the ferries 0. 07:22.180 --> 07:28.810 And if you compare here the min method with our bid and function men here then we see the power of the 07:28.810 --> 07:34.750 pen data frame class cost by applying the min method them Panda's returns less than the minimum for 07:34.750 --> 07:35.440 each column. 07:35.440 --> 07:36.520 So that's quite cool. 07:37.300 --> 07:43.150 So the main method is specifically defined for a data frame and gives us much more functionality and 07:43.150 --> 07:47.630 tells us much more than the pacing function. 07:47.740 --> 07:54.340 So this is what I wanted to show you that out of the data frame class is very powerful and we can also 07:54.340 --> 08:01.360 have a look here at the documentation so you can see a description of pen that Ida frames so it's a 08:01.360 --> 08:07.740 two dimensional size mutable a potentially heterogeneous tabular data structure mislabeled axis and 08:07.740 --> 08:11.920 t you can find actually all attributes and all methods. 08:11.950 --> 08:14.050 So it's quite a long list here 08:16.910 --> 08:19.310 then in the next the lessons and in our. 08:19.340 --> 08:24.950 We will have a look at many of these methods and learn a lot of functionalities so hope to see on the 08:24.950 --> 08:26.030 next lessons by.