WEBVTT 00:02.020 --> 00:09.270 Hello and welcome to this tutorial here, we will understand merging, joining and concatenating the 00:09.270 --> 00:10.210 data frames. 00:10.450 --> 00:11.350 Let us begin. 00:11.980 --> 00:13.420 First, import reliability. 00:17.160 --> 00:17.770 Great. 00:18.110 --> 00:18.400 --. 00:18.510 --> 00:26.340 We will understand data frames, concatenation, the concatenation, glue's data frames together. 00:28.070 --> 00:33.020 To understand the data frame concatenation first, to be able to define three data streams. 00:33.470 --> 00:33.710 B. 00:33.740 --> 00:34.550 F one. 00:34.790 --> 00:35.750 B, F two. 00:35.960 --> 00:37.160 And the F three. 00:38.470 --> 00:39.190 Execute. 00:42.270 --> 00:42.830 Great. 00:44.650 --> 00:46.570 No, take these three data frames. 00:48.240 --> 00:49.470 B, f one. 00:55.400 --> 00:56.750 Then D.F. to. 01:02.010 --> 01:03.330 And the EF three. 01:07.600 --> 01:11.470 So these are the three data frames in these three data frames. 01:11.500 --> 01:13.870 There are four rows and four columns. 01:14.020 --> 01:19.290 You can see column names are A, B, C, D, same here. 01:19.750 --> 01:21.540 And same in data frame three. 01:22.150 --> 01:30.100 But the indexes are different in first data frame zero, two, three, then four, five, six, seven. 01:30.370 --> 01:33.190 And after that, eight, nine, ten, eleven. 01:34.090 --> 01:35.650 Let us do concatenation. 01:39.960 --> 01:44.010 PD dot concat. 01:47.170 --> 01:50.250 So these are the parameters that we can enter here. 01:50.560 --> 01:52.570 We will enter only one parameter. 01:52.780 --> 01:53.410 This one. 01:57.760 --> 02:07.970 Type objects enter the data frames, the F one, B, F two and B F three. 02:09.220 --> 02:11.830 And by default, access is equal to zero. 02:12.580 --> 02:13.740 No execute. 02:15.970 --> 02:16.570 Great. 02:17.170 --> 02:18.730 So this is our output. 02:19.330 --> 02:25.210 We have successfully concatenated all these three data streams from zero to three. 02:25.300 --> 02:27.900 B, F one, then B, F two. 02:28.030 --> 02:30.350 And at the end, B, F three. 02:31.050 --> 02:34.540 Here we have concatenated all these data frames along. 02:34.540 --> 02:36.500 The axis is equal to zero. 02:40.600 --> 02:48.250 Let us do concatenation along the axis is equal to one, the dot concat. 02:51.830 --> 02:54.290 Specified barometer OBD as. 02:58.040 --> 03:03.020 Data from one, data from two and data from three. 03:05.800 --> 03:11.620 Then specify access is equal to one execute. 03:13.600 --> 03:14.950 Now this is the output. 03:15.130 --> 03:17.110 Let us understand here. 03:17.140 --> 03:21.190 V.L. applied concatenation along the axis is equal to one. 03:21.820 --> 03:23.380 This is data frame one. 03:23.920 --> 03:25.370 This is data frame two. 03:25.540 --> 03:27.190 And this is data stream three. 03:27.590 --> 03:34.660 In simple words, we can say that access is equal to zero means along the road and access is equal to 03:34.660 --> 03:37.840 one means along the columns here. 03:37.840 --> 03:45.780 First four columns for D.F. one, then second four columns for the F two and last four columns for the 03:45.840 --> 03:46.600 F3. 03:47.110 --> 03:49.830 And all other values are none to your. 03:51.070 --> 03:55.390 While applying access is equal to zero or access is equal to one. 03:55.660 --> 03:58.150 We help to take our information first. 03:58.480 --> 04:03.490 Otherwise, the output will be like this, many nail values in the output. 04:04.180 --> 04:07.280 So this is all about the data frames, concatenation. 04:08.050 --> 04:08.860 Let us understand. 04:08.860 --> 04:10.300 Data frames margin. 04:13.090 --> 04:16.030 He had also v, how to define these two data frames. 04:16.240 --> 04:18.640 B, A, Ford and DFI U. 04:19.960 --> 04:20.680 Execute. 04:25.730 --> 04:30.200 Now, take these two data frames, B F for. 04:34.630 --> 04:35.950 And D.F., you. 04:41.550 --> 04:44.970 So these are the two data frames, the Ford. 04:48.970 --> 04:54.650 And DFI you in these two data frames, there are three columns and Ford rows. 04:55.360 --> 05:01.760 These are the columns, key A and B here, also same key C. 05:03.010 --> 05:04.480 And these are the indexes. 05:05.200 --> 05:07.540 Zero two three zero two three. 05:12.670 --> 05:19.270 To merge these two data frames, IPD dot merge. 05:22.470 --> 05:26.560 He added, We will specify three parameters left, right. 05:26.920 --> 05:31.600 And this one on the left is equal to. 05:33.910 --> 05:35.070 D.F. Ford. 05:38.040 --> 05:39.510 And right is equal to. 05:42.330 --> 05:43.500 B F for you. 05:45.470 --> 05:48.410 And we're merging these two data frames on. 05:50.540 --> 05:51.470 Gholam Key. 05:54.630 --> 05:55.370 Execute. 05:58.180 --> 05:58.750 Great. 05:59.290 --> 06:07.710 So this is the output column A and B from data from the F Food and column C and D from the F for you. 06:08.020 --> 06:12.490 And this is the common column G on the basis of column key. 06:12.700 --> 06:14.530 We merged these two data frames. 06:15.240 --> 06:16.970 Let us see one more example. 06:18.710 --> 06:22.630 Defined to date, DuFresne's, B, F six and B, F seven. 06:24.530 --> 06:25.130 Great. 06:25.880 --> 06:27.650 No, take these two data frames. 06:37.130 --> 06:38.330 The F7. 06:43.090 --> 06:43.690 Great. 06:43.930 --> 06:44.770 No mud. 06:48.420 --> 06:50.160 B, b dot. 06:51.470 --> 06:52.090 Marge. 06:55.440 --> 06:56.600 Interdependency does. 06:58.370 --> 07:01.940 Left is equal to be EF six. 07:04.790 --> 07:06.260 And right is equal to. 07:08.680 --> 07:11.680 B f7 on. 07:15.590 --> 07:19.940 Key vun and key to. 07:24.710 --> 07:31.970 So this is the output column A and B from the F six, column C and D from the F seven. 07:32.300 --> 07:36.230 And these two are the common columns given and keep two. 07:37.010 --> 07:39.650 So this is all about demerging data frames. 07:40.700 --> 07:41.530 Let us understand. 07:41.540 --> 07:42.890 Joining the data frames. 07:47.210 --> 07:48.570 Define to date doorframes. 07:49.380 --> 07:49.650 B. 07:49.710 --> 07:51.700 F eight and B, F nine. 07:55.580 --> 07:57.320 Now, take these two data frames. 08:14.670 --> 08:15.210 Great. 08:15.930 --> 08:18.380 So there are two columns and Ford rules. 08:18.540 --> 08:19.290 Same here. 08:19.590 --> 08:21.450 Two columns and four rows. 08:26.230 --> 08:33.030 Draining the data frames is a convenient method for combining the columns of different data frames into 08:33.070 --> 08:36.520 single one to join these two data streams. 08:36.830 --> 08:47.620 They first data frame B F eight dot join and in parentheses, specify second data. 08:47.620 --> 08:50.650 Frame the F nine. 08:55.590 --> 08:56.160 Great. 08:56.430 --> 08:57.720 So this is the output. 08:58.440 --> 09:01.800 This is the data from the F eight first two columns. 09:01.920 --> 09:06.060 A and B, and after that we joined the F nine. 09:06.210 --> 09:07.320 These two columns. 09:07.500 --> 09:09.290 Column C and column D.. 09:09.930 --> 09:13.920 And this is the index for these two data frames, common index. 09:14.790 --> 09:17.580 Let us revise what we had learned in this tutorial. 09:24.050 --> 09:31.620 Merging, dining and concatenating, the data frames first well understood, concatenation of data frames. 09:34.050 --> 09:37.350 After that, we help understood data frames emerging. 09:39.090 --> 09:42.570 And at the end, we all understood joining the data frames. 09:43.700 --> 09:46.000 So this biting ordeal ends here. 09:46.310 --> 09:49.040 I will see you in the next one, buildOn. 09:49.250 --> 09:50.240 Happy learning.