WEBVTT 00:02.040 --> 00:04.890 Hello, everyone, and welcome to this by tend to go. 00:05.520 --> 00:09.900 Here we will understand some useful operations in data frames. 00:10.110 --> 00:11.130 Let us begin. 00:11.880 --> 00:16.380 First, we will import these two labor added numpty and Bendat. 00:17.960 --> 00:18.530 Great. 00:19.250 --> 00:24.980 After that, we have to define a data frame B F to define this data frame. 00:25.130 --> 00:26.930 First, define a dictionary. 00:27.320 --> 00:27.820 B. 00:29.770 --> 00:34.780 No defined data, frame B F and take the same grade. 00:35.380 --> 00:37.090 So this is our data frame. 00:37.180 --> 00:37.480 B. 00:37.540 --> 00:42.130 F in this data frame, they're out of food rules and three columns. 00:44.850 --> 00:45.660 No, baby. 00:48.840 --> 00:54.270 Select Golomb to dot unique. 00:57.640 --> 00:58.350 Execute. 00:59.290 --> 01:07.300 So did that the unit values in column to this matter, unique returns, unique values in a column like 01:07.300 --> 01:07.780 these. 01:08.530 --> 01:11.050 Similarly, we can take lente also. 01:14.750 --> 01:16.200 Type 80 and. 01:18.650 --> 01:19.220 Three. 01:19.910 --> 01:22.790 So there are three unique values in this column. 01:22.970 --> 01:31.700 Column two, to get the unique values, we can use one more method that is and unique. 01:34.390 --> 01:34.900 Three. 01:35.350 --> 01:37.450 So this method, any unique returns? 01:37.630 --> 01:39.250 Number of unique values. 01:39.850 --> 01:43.820 So this is all about getting unique values and unique value count. 01:45.590 --> 01:47.240 Let's see one more method. 01:50.760 --> 01:54.960 Column to dot value cound. 02:02.610 --> 02:04.410 It returns number of values. 02:05.010 --> 02:07.550 So there are two values for fortify you. 02:08.040 --> 02:11.910 One value for fifty for you and one value for 60 for you. 02:12.480 --> 02:15.720 So this method retains value cound like these. 02:17.700 --> 02:19.590 Doesn't this stand a blind method? 02:25.420 --> 02:34.410 To understand the Uplay method, we have to define a function name of the function ese times for you, 02:35.530 --> 02:40.630 specify argument as X. then return. 02:43.020 --> 02:46.050 X into five, you execute. 02:47.880 --> 02:49.350 No, call this function. 02:53.070 --> 02:53.900 13. 02:55.640 --> 02:56.320 Fifty. 02:56.790 --> 02:59.040 So this function is working fine. 02:59.310 --> 03:03.420 Let us understand a planned method, type D f. 03:06.100 --> 03:07.940 Column one dog. 03:08.940 --> 03:09.570 Some. 03:12.470 --> 03:13.040 Ben. 03:14.800 --> 03:16.010 US take B f. 03:19.020 --> 03:25.470 So some of the column VUN is in here, we have applied a method, some on this column. 03:27.730 --> 03:29.970 Notes, electic, welcome once again. 03:33.620 --> 03:36.810 Dot metal apply. 03:38.030 --> 03:42.330 And in parentheses aims for you. 03:43.430 --> 03:44.180 Execute. 03:45.600 --> 03:51.510 So this is detailed here, we how applied this function times for you to column one. 03:51.930 --> 03:53.220 So this is the result. 03:53.580 --> 03:54.900 One in two for you. 03:55.260 --> 03:56.460 Two in two for you. 03:56.850 --> 03:58.080 Three in two for you. 03:58.200 --> 03:59.580 And four in two for you. 04:00.330 --> 04:05.210 So this way we can use apply function or apply method here. 04:05.730 --> 04:08.340 We have specified a user defined function. 04:08.610 --> 04:10.440 This one aims for you. 04:10.860 --> 04:18.690 Similarly, we can use built-In function B F enter column three. 04:21.730 --> 04:22.790 Don't apply. 04:25.060 --> 04:27.160 No integrity built in function. 04:27.680 --> 04:28.330 Lynn. 04:30.980 --> 04:32.340 So this is the output. 04:32.930 --> 04:34.700 These are the length of the column. 04:34.730 --> 04:37.400 Three, three, three three three. 04:38.030 --> 04:41.540 You can see three, three, three and three. 04:42.680 --> 04:45.140 So this is all about the apply method. 04:46.170 --> 04:52.460 Let us understand, dropped method APF dot drop. 04:55.240 --> 04:57.820 Tape labels is equal to column one. 05:00.850 --> 05:02.950 And Axis is equal to one. 05:04.870 --> 05:05.410 Great. 05:06.030 --> 05:13.860 So column one is the little here to save this change permanently specify in place is equal do true. 05:16.740 --> 05:20.220 This one, by default, in place is equal to falls. 05:25.540 --> 05:26.980 We can take Golomb names. 05:27.100 --> 05:30.940 Also A, B, F dot columns. 05:33.660 --> 05:34.500 Execute. 05:35.780 --> 05:40.150 So these are the column names called One Call to end call. 05:40.210 --> 05:40.720 Three. 05:41.210 --> 05:41.660 No, no. 05:41.660 --> 05:42.470 Down here. 05:42.710 --> 05:44.180 This is not a method. 05:44.300 --> 05:45.710 It is an attribute. 05:47.900 --> 05:52.670 Similarly, we can do four indexes, A, B, F dot. 05:54.340 --> 05:55.060 Index. 05:55.900 --> 05:56.650 Execute. 05:57.430 --> 06:01.780 So these are the details about index stock at zero. 06:01.840 --> 06:03.050 Stop at Ford. 06:03.190 --> 06:04.030 And stay safe. 06:04.070 --> 06:05.260 It equal to one. 06:05.620 --> 06:07.570 That means zero. 06:07.720 --> 06:08.920 One, two, three. 06:09.730 --> 06:10.360 This one. 06:10.680 --> 06:11.080 Zero. 06:11.200 --> 06:12.280 One, two, three. 06:12.580 --> 06:14.590 And upper bound is excluded. 06:15.580 --> 06:17.650 Let us understand one more method. 06:19.530 --> 06:25.390 A, b, f dort sort underscore values. 06:28.230 --> 06:29.280 Gholam Van. 06:31.830 --> 06:34.680 So here values are sorted along the column. 06:34.720 --> 06:38.100 One, we can't specify any column name. 06:39.520 --> 06:41.680 Bilyk van and specified to. 06:44.030 --> 06:47.510 Now the values are assaulted on the basis of column two. 06:47.660 --> 06:52.040 That is along the column to other values are changed. 06:52.250 --> 06:57.340 You can see index zero, then three, then one and then two. 06:57.890 --> 07:05.510 This is along the column to let us understand one more method, A, B, F, dot. 07:07.170 --> 07:07.950 Is null. 07:11.760 --> 07:17.410 This method returns boolean output, all the values are faulty or false. 07:17.450 --> 07:22.350 Means no null value and true means there is a null value. 07:22.920 --> 07:25.530 So there is no null value in this data frame. 07:25.660 --> 07:26.370 The F. 07:27.090 --> 07:31.100 So these are the some basic operations or methods that we help. 07:31.170 --> 07:31.740 Understood. 07:31.830 --> 07:32.880 Enlisted Odille. 07:35.180 --> 07:36.080 Operations. 07:36.470 --> 07:36.780 Foot. 07:36.860 --> 07:39.210 We have defined a data frame, the F. 07:39.950 --> 07:42.490 After that, we help understood the unique method. 07:43.040 --> 07:45.890 Unique Len and N unique. 07:46.450 --> 07:48.700 Then VLT understood a method. 07:50.650 --> 07:56.350 In applying method, we can specify user defined functions as well as built-In functions. 07:57.920 --> 08:02.470 Then be out understood these two attributes, columns and index. 08:03.320 --> 08:05.900 Then we have understood sort values matured. 08:06.710 --> 08:10.040 And at the end we understood is Nele mattered. 08:10.910 --> 08:14.840 So despite tend to do deal on data frame operations entier. 08:15.140 --> 08:17.060 I will see you in the next one. 08:17.360 --> 08:19.190 buildOn, happy learning.