WEBVTT 00:02.700 --> 00:07.410 In this session, we would begin with the dolphins and find those. 00:08.370 --> 00:14.940 So they do things on the very basic data structure which are used in case of. 00:16.350 --> 00:23.360 These are two dimensional labelled data structures with columns of potentially different shapes. 00:23.610 --> 00:29.280 So we can have a growing column structure, but just kind of a double structure to work with. 00:29.850 --> 00:37.390 Now, you can think of it like a spreadsheet or a school table or a normal Excel spreadsheet. 00:37.650 --> 00:45.210 Usually what we work with our papers from databases or we work with Excel files or CSC files. 00:46.780 --> 00:56.080 Now, these are all created from a dictionary or tabular data, so we will learn how we can work with 00:56.080 --> 00:59.900 these data structures and how we can create a definition. 01:00.370 --> 01:05.200 So let us first begin by creation of our data feme. 01:08.280 --> 01:15.120 Now, before starting with the first thing which we will have to do is we will have to import the Fondas 01:15.120 --> 01:18.990 library and the library so we will have thesea. 01:20.770 --> 01:21.610 Import. 01:23.710 --> 01:28.360 No, I as a. an import. 01:30.700 --> 01:33.060 Fondas, Aspy. 01:34.920 --> 01:43.950 Now, the next step would be to start creating different datasets now to create a dataset, let us create 01:43.950 --> 01:49.110 certain attributes which we will be looking for or different features which we will be looking for. 01:49.440 --> 01:51.630 OK, so let us create a feature. 01:51.630 --> 01:52.490 See Idee. 01:53.860 --> 01:58.420 And let this IDB and the run random. 02:00.170 --> 02:06.800 North London and I want this idea to range from. 02:07.900 --> 02:21.580 So let us create these IDs in a random manner and let's keep those values to be low from 20 and high 02:21.580 --> 02:28.100 value to visit 2000 and let us be 20 such values. 02:28.360 --> 02:31.390 So I give size equal to. 02:33.270 --> 02:35.490 RCMP say 20. 02:36.450 --> 02:38.810 So let me explain it for you. 02:42.260 --> 02:45.950 So we give size equal to. 02:49.370 --> 02:49.940 20. 02:51.510 --> 02:54.860 So here we have random ideas which have been created. 02:55.880 --> 02:58.580 Now, the next thing which we want is. 03:01.380 --> 03:09.900 Let us see the age of a person, so let us bridge each column and it should be in the dot random. 03:11.320 --> 03:12.040 Dorte. 03:14.130 --> 03:25.410 Run in leathered range from as low as 15, high to be, let's say. 03:27.160 --> 03:28.210 Sixty three. 03:29.690 --> 03:34.520 And size should again be 20. 03:36.830 --> 03:41.060 We are giving the citizens 20 because we want 20 rolls of data. 03:41.920 --> 03:45.340 So we have created each. 03:46.640 --> 03:53.780 Now, next thing what we can look for is now one more thing to take care of is that when we are done 03:53.780 --> 04:03.680 reading these ideas, these ideas of the dawn want them to be randomly generated, or we might keep 04:03.680 --> 04:07.200 them random or we can simply create them in a different way. 04:07.370 --> 04:11.440 It is completely your choice if you want to put place equal to Broadfoot. 04:13.950 --> 04:27.120 The next thing, let us create a column, see Sydney and in Sydney, we can put in the dot random dot 04:27.600 --> 04:28.560 choice. 04:29.480 --> 04:36.110 And the choice should be taken from these cities, say New York. 04:37.490 --> 04:38.600 And I want. 04:41.000 --> 04:41.720 Darlene. 04:42.910 --> 04:44.800 And I want to see. 04:46.320 --> 04:50.220 Iris and I won. 04:54.050 --> 04:58.330 OK, and I won 20 such occurrences of this again. 04:59.350 --> 05:07.750 So I give city, so we have these cities present now, the next thing what we can do is we can have 05:07.750 --> 05:11.110 if someone's a loan has been approved or not. 05:12.090 --> 05:13.710 So we can see approval. 05:14.670 --> 05:24.540 And we can give in or random choice, and in this we can give. 05:25.680 --> 05:33.180 Zero and one, and you will get 20 such values, so here we have approval. 05:35.560 --> 05:41.270 Similarly, we can have as many problems, we can create as many problems as we want, so I just keep 05:41.270 --> 05:42.520 it to dispatch only. 05:42.880 --> 05:53.050 And from this now there are different ways how we can convert data to data from the first way, which 05:53.050 --> 06:02.500 I would tell you is converting this entire data into a list and then converting that list into a data 06:02.500 --> 06:02.830 frame. 06:03.160 --> 06:11.320 So what we can do is we can simply say or did the my data or we can say the list. 06:12.360 --> 06:20.370 And in this, I want to have a list which has been generated from the zip of. 06:23.200 --> 06:24.130 Age. 06:26.190 --> 06:26.940 City. 06:29.630 --> 06:30.770 Approval. 06:32.410 --> 06:35.740 And I so I'm giving idee. 06:37.010 --> 06:38.890 Age, city answer. 06:40.870 --> 06:42.820 So I will simply. 06:43.900 --> 06:46.630 Run this and get the biggest. 06:48.000 --> 06:49.560 So this is my off list. 06:49.860 --> 06:57.540 Now, each of these values have been combined and created into couples and these couples are put into 06:57.540 --> 06:58.110 a list. 06:58.140 --> 06:59.110 So this is what we get. 06:59.460 --> 07:04.410 Now, the next thing which we will be doing is converting it into a data frame. 07:04.410 --> 07:10.380 So to convert anything to a data frame, what we simply do is we give the name data frame and we'll 07:10.380 --> 07:14.510 see DOT data then. 07:15.870 --> 07:26.250 And then we give the data, so the data is my D.F. list, so I will give data is equal to the list and 07:26.250 --> 07:32.700 I have to give the column names, because if I will not provide the column names, then what will happen 07:32.700 --> 07:36.360 is it will by default, consider the indexes to be the column names. 07:36.510 --> 07:42.600 And I don't want to work with the indexes because it will be really difficult to work with the indexes. 07:42.600 --> 07:45.840 And I will get to know that which index belongs to which columns. 07:45.840 --> 07:52.890 And when we are working with huge data set at that time, we can get confused and we can do something 07:52.890 --> 07:58.180 which is not actually the necessity or not required and we can just mess it up. 07:58.440 --> 08:03.870 So what we will be doing is we will create column names so we can see columns. 08:05.790 --> 08:12.750 And we will give the names, so let us give the column names like this will need these as my column 08:12.750 --> 08:13.260 names. 08:16.010 --> 08:18.290 I give this into a list from. 08:19.930 --> 08:23.050 And these have to be springform. 08:33.620 --> 08:42.740 Now, if I had done this and I will be able to view this data frame by typing vertically, so this is 08:42.740 --> 08:49.340 the lead offering, which has been creative, I can view eatin' and you can see the indexes from zero 08:49.340 --> 08:54.350 to 90 and the following names that I'd need to be in approval. 08:54.620 --> 08:58.130 And what I can do is I can view different data points. 08:58.130 --> 09:05.420 We can view different columns also by simply saying see the if not age. 09:08.020 --> 09:18.400 So this gives me the data from the Age column, I can do something like the thought ID, so I was able 09:18.400 --> 09:20.620 to see the values from the ID column. 09:20.890 --> 09:27.760 Now, this is possible only if we don't have any spaces in my column names in case I have any spaces 09:27.760 --> 09:30.020 in my column names, then this would not be possible. 09:30.400 --> 09:31.900 So what I can do is. 09:34.320 --> 09:41.820 Here, if I have something like Idi, Idi, something like this, then what we happen is now I try to 09:41.820 --> 09:44.700 retrieve these IDs as IDs face ID. 09:44.730 --> 09:48.360 Now, this would throw an error because it doesn't really know what this IDs. 09:48.630 --> 09:57.060 So it is always suggested to either replace any spaces present in the column names by underscore or 09:57.120 --> 09:59.460 just don't have any other source in the column names. 09:59.850 --> 10:07.780 Now, if you want to access these values instead of using this, we can also use this index. 10:08.310 --> 10:12.890 So in this way I can view the ID column into another thing. 10:12.900 --> 10:21.580 What I can do is I can say the dot head and it will show me the top five rows of the data. 10:22.320 --> 10:27.330 I can also define the number of values which I want, but by default it will give five. 10:27.540 --> 10:30.960 If I want to view mine, then it will give me nine rows of data. 10:31.140 --> 10:34.440 So it is completely up to me what all values I want to see. 10:34.920 --> 10:42.810 If I want to view the deal, then I can say the dot the and again, it will show the bottom five to 10:42.810 --> 10:43.020 me. 10:44.580 --> 10:47.820 Now, the next thing what I can do is. 10:49.720 --> 11:00.340 I can create a data frame using a dictionary format, so let us be downloaded of via and likely this 11:00.340 --> 11:01.570 will override the previous. 11:02.560 --> 11:05.080 So it's a fresh data from which I will be creating. 11:05.110 --> 11:09.200 So it's not a data frame. 11:09.220 --> 11:13.810 Please make sure that the F is capital in this. 11:15.060 --> 11:21.390 And then we give the wrong reasons and then we create the dictionary and in the dictionary we will simply 11:21.390 --> 11:23.440 give the column name, which we want to have. 11:23.760 --> 11:24.850 So I give it. 11:27.870 --> 11:38.700 Along with it, I will give the of the data which I have, so I give you, then I'll give each. 11:40.260 --> 11:41.610 And each. 11:43.120 --> 11:44.170 Then we have. 11:45.450 --> 11:46.440 Approver. 11:48.480 --> 11:54.030 And approval, and we also have to be so we give Cindy. 11:55.610 --> 11:57.460 And Sydney. 11:59.000 --> 12:04.140 So like this, we can think of another data name, and this is data from the 10th. 12:05.360 --> 12:07.430 So we have created this definition. 12:07.460 --> 12:13.700 Now, one thing you can notice here is that the sequence has also changed once I change the sequence 12:13.700 --> 12:14.810 in the dictionary. 12:15.980 --> 12:20.300 So this is one thing to take care of. 12:23.060 --> 12:32.780 The next thing is, let us see, I want to access a particular value so we have access. 12:40.520 --> 12:50.620 So we can access values, so let us say I want to access it again so I can say the and then I can definitely 12:50.690 --> 12:51.800 see it here. 12:52.940 --> 12:59.260 Or I can use a door that is completely up to me so I can do it this way now. 13:00.270 --> 13:03.900 What I can do is I can see the. 13:08.430 --> 13:09.120 Age. 13:10.130 --> 13:11.450 Greater than C.. 13:13.030 --> 13:13.540 30. 13:15.260 --> 13:22.710 So it will give me out the data, but when people have age greater than 30 or say greater than 50, 13:22.740 --> 13:24.050 you want greater than 50. 13:24.500 --> 13:28.140 So we get the data when people have age greater than 50. 13:28.670 --> 13:40.280 Now, another thing, what we can do is we can access the data from files also so we can use read CSFI 13:40.280 --> 13:40.990 for that. 13:43.280 --> 13:51.230 So we will simply read the part of the fighting, so for you to define is equal to the fighting name 13:51.230 --> 13:54.480 or by name in case it is in the same folder. 13:54.830 --> 13:56.770 So I have bang. 13:58.160 --> 13:58.660 Full. 14:00.130 --> 14:02.130 Dart County. 14:02.830 --> 14:09.880 So I have this file and I want to read this file into a data frame, so I will say data frame is equal 14:09.880 --> 14:15.760 to read orders for CSP and this has to be a battle field. 14:16.480 --> 14:24.160 So if we don't read CSFI and I give the file for you, so I will get the data frame and you can review 14:24.160 --> 14:26.930 the data frame again by using the updated. 14:29.850 --> 14:37.140 So this is the entire state of Maine now, please see that all this detail, which I have provided, 14:37.350 --> 14:39.570 has come in a single column. 14:40.970 --> 14:46.790 Here, this is the view which we get when we have the volume separator, but here all the details coming 14:46.790 --> 14:48.180 into simply single volume. 14:48.200 --> 14:52.820 So what we can do is we have to provide the limit, though, so we'll see. 14:56.510 --> 14:57.440 Is equal to. 14:58.810 --> 14:59.650 Semicolon. 15:01.080 --> 15:08.750 So now it is separated all the data in two different columns now. 15:09.790 --> 15:17.020 How we access these data and how we modify these data is something which we will know on in the next 15:17.020 --> 15:24.220 session, so we will learn how we will convert these columns into numeric form, which we have already 15:24.910 --> 15:29.320 taken a load off in the session, which we have discussed. 15:30.830 --> 15:36.120 So let us see how we can modify this done, how we can work with the state.