WEBVTT 00:02.090 --> 00:05.780 Hello and welcome to the second patented Odille Undetailed Frames. 00:06.230 --> 00:07.160 Let us begin. 00:07.580 --> 00:09.830 First, we have to import the library reads. 00:11.670 --> 00:12.240 Great. 00:12.840 --> 00:14.430 Then create the data frame. 00:16.240 --> 00:18.130 No dignity, delphinium the F. 00:21.990 --> 00:26.130 So this is the same day timeframe that we had created in previous ordeal. 00:26.610 --> 00:34.250 These are the column names W, X, Y, Z, and these are the indexes, A, B, C, D, E, no. 00:34.410 --> 00:37.860 Verville, understand conditional selection in data frames. 00:40.070 --> 00:41.040 No, down here. 00:47.200 --> 00:48.610 Conditional election. 00:49.700 --> 00:56.270 Babe, the name of the doorframe, the F specified can be John be F is greater than zero. 00:58.720 --> 01:00.370 So this is our output. 01:00.940 --> 01:03.490 This output is in the form of booleans. 01:03.790 --> 01:06.780 These are the boolean values, true and false. 01:07.480 --> 01:12.160 When a value in data frame is greater than zero, then it is true. 01:12.280 --> 01:13.840 Otherwise, it is false. 01:14.870 --> 01:22.940 You can see here minus two falls, four plus two point seven two four zero point six for you. 01:24.230 --> 01:26.690 We can store this result in a variable. 01:26.780 --> 01:28.880 Also, let us do it. 01:29.450 --> 01:30.220 Copy this. 01:31.850 --> 01:33.020 Create a variable. 01:33.020 --> 01:35.030 B, execute. 01:36.260 --> 01:37.910 Now, take variable be. 01:39.200 --> 01:42.330 He had also be how same result Willian values. 01:43.560 --> 01:47.760 No tape did offer him b f specify this variable. 01:50.240 --> 01:51.890 Now we have different output. 01:52.130 --> 01:56.210 Let us understand this, Verdie, billion values are true. 01:56.420 --> 01:58.400 Only those values are shown. 01:59.120 --> 02:01.040 Otherwise, there are Adnen values. 02:01.670 --> 02:04.220 You can see for true values. 02:05.760 --> 02:09.210 Photo values for falls, then value. 02:09.810 --> 02:16.950 So this is how we can select the values from our data frame on the basis of a condition instead of that 02:16.950 --> 02:20.540 variable b we can specify can be shared directly. 02:22.390 --> 02:25.690 Delete this variable be specified the condition. 02:26.530 --> 02:30.490 This one data frame, the F is greater than zero. 02:32.260 --> 02:32.950 Execute. 02:34.580 --> 02:35.480 Same result. 02:36.270 --> 02:37.470 No, no, down here. 02:37.660 --> 02:41.000 We have specified this condition for interrogate our frame. 02:41.640 --> 02:44.750 We can specify this condition for a specific column. 02:44.850 --> 02:45.480 Also. 02:46.400 --> 02:51.990 Let us see how the name of the data from the F select the column W. 02:52.830 --> 02:54.030 Greater than zero. 02:54.340 --> 02:55.070 Execute. 02:56.400 --> 02:59.210 So these are the billion values for Colomb W.. 02:59.490 --> 03:02.580 Drew Drew falls through to. 03:04.150 --> 03:06.670 Now store this result in a variable. 03:13.520 --> 03:14.720 Take this video while. 03:17.000 --> 03:17.960 Same result. 03:18.910 --> 03:21.850 No bussing, this can be shown in detail from the F. 03:28.060 --> 03:29.200 No, no, down here. 03:29.350 --> 03:33.770 Third row is missing, and this is because condition is false. 03:33.880 --> 03:36.040 For the third, Drew, you can see. 03:36.950 --> 03:37.640 Falls. 03:38.210 --> 03:45.830 So here we have selected this data frame on the basis of a column that is conditions in a single column. 03:46.550 --> 03:52.310 Instead of that variable, we can post in condition basically copy this condition. 03:55.480 --> 03:56.780 Data from the EFF. 03:57.760 --> 03:59.810 Then the condition execute. 04:00.640 --> 04:01.600 Same result. 04:02.260 --> 04:06.370 So this is how we can apply conditional selection in a data frame. 04:09.050 --> 04:12.810 Let us understand how to apply multiple can be at a time. 04:19.570 --> 04:20.950 Multiple conditions. 04:22.130 --> 04:23.780 Take the day delphinium, D.F.. 04:26.950 --> 04:31.420 No, Louisville specified the multiple conditions together, laid out for him D. 04:31.510 --> 04:31.940 F. 04:32.510 --> 04:35.020 He said we will specify multiple conditions. 04:36.100 --> 04:37.750 This is for first condition. 04:38.410 --> 04:40.210 And this is for second condition. 04:40.690 --> 04:43.930 First, we will select Golomb W is greater than zero. 04:44.140 --> 04:50.920 This one baby F column name and this column W is greater than zero. 04:52.120 --> 04:55.690 And along this condition we how to add one more condition. 04:56.050 --> 05:01.640 So specify and operator values in column VI is greater than one. 05:01.810 --> 05:03.250 This column A. 05:03.250 --> 05:03.550 B. 05:03.610 --> 05:04.000 F. 05:05.080 --> 05:06.040 Column VI. 05:07.750 --> 05:08.980 Greater than one. 05:09.550 --> 05:11.960 Let us understand these two conditions again. 05:12.670 --> 05:16.450 First, we are selecting this data frame on the basis of this column. 05:16.690 --> 05:17.680 Column W. 05:17.920 --> 05:22.420 Here we are going to select only those values which are greater than zero. 05:22.960 --> 05:28.140 And after that, we are selecting data from the F on the basis of this column VI. 05:28.510 --> 05:33.510 Here we will select only those values in column VI, which are greater than one. 05:34.000 --> 05:37.330 And between these two conditions we are using and operator. 05:38.110 --> 05:43.330 So here we are selecting only those values which satisfies these two conditions. 05:43.960 --> 05:45.190 I will not done here. 05:46.150 --> 05:47.230 And operator 05:49.930 --> 05:50.650 execute. 05:52.140 --> 05:53.460 So this is the result. 05:54.090 --> 05:57.270 Only a single row satisfies these two conditions. 05:57.600 --> 05:58.960 This one rule. 06:00.270 --> 06:01.140 Let us understand. 06:01.230 --> 06:04.010 Our operator copy this line. 06:06.180 --> 06:06.780 Based. 06:08.140 --> 06:09.750 Maintain or operate the. 06:14.420 --> 06:17.590 And this is the symbol for auto operator by. 06:18.980 --> 06:21.050 Let us understand these two conditions. 06:21.740 --> 06:27.290 First, we are selecting only those values from column W, which are a greater than zero. 06:27.620 --> 06:33.830 And after that, we are selecting only those value from column VI, which had a greater than one and 06:33.890 --> 06:35.540 in-between V health specified. 06:35.890 --> 06:36.490 Operator. 06:36.860 --> 06:37.490 This one. 06:38.120 --> 06:44.890 So here we will get only those values which satisfies at least one can be can no execute. 06:47.030 --> 06:48.410 So this is the output. 06:48.740 --> 06:50.450 Only one drawer is missing. 06:50.990 --> 06:54.940 So this fold roll satisfies at least one of these two condition. 06:55.700 --> 06:59.490 So this is how we can do multiple conditioning, the digital frames. 07:01.970 --> 07:02.840 Let us understand. 07:02.870 --> 07:04.100 Index in detail. 07:08.750 --> 07:10.460 Take the data out from the EV. 07:12.680 --> 07:13.310 Great. 07:13.970 --> 07:16.040 Now tape the F dot. 07:17.280 --> 07:18.360 Reset index. 07:19.780 --> 07:20.460 Execute. 07:22.070 --> 07:25.010 Here you can see one index is added. 07:25.220 --> 07:26.750 This one, this column. 07:27.230 --> 07:30.170 So using this method, we can they say the index. 07:31.390 --> 07:36.880 And to permanently save this, you have to make this parameter in place as glue. 07:37.480 --> 07:39.620 As of now, I'm not going to do that. 07:41.850 --> 07:43.230 Now create a list. 07:45.560 --> 07:46.580 New index. 07:49.340 --> 07:51.230 R g. 07:52.630 --> 07:53.250 B. 07:55.270 --> 07:55.800 All. 07:58.990 --> 07:59.540 Vij. 08:04.070 --> 08:04.940 Take the least. 08:07.490 --> 08:08.020 Great. 08:08.760 --> 08:12.750 No, add this list as a column in this data frame from the F. 08:13.620 --> 08:20.610 To do that, they D.F. name off the column glove's. 08:21.800 --> 08:26.330 Then the name of the least new index executed. 08:28.850 --> 08:30.820 No, Jake Beatty, Delphinium, D.F.. 08:32.900 --> 08:37.370 Great column is added, name of the column is Gullahs. 08:38.510 --> 08:47.090 And these are the values, no, they say the values of this new column as index to do that type name 08:47.090 --> 08:56.390 of the data frame dot method, set index and in parentheses, name of the column. 08:57.510 --> 08:58.200 Gullahs. 08:59.810 --> 09:00.740 Take the cube. 09:02.780 --> 09:06.500 So here we have successfully edited this column as index. 09:07.710 --> 09:12.650 To permanently store this make parameter in place is equal to true. 09:13.290 --> 09:16.080 So this is all about the state index method. 09:18.320 --> 09:19.910 And they it index method. 09:21.300 --> 09:24.210 Let us revise what we have learned in this tutorial. 09:25.890 --> 09:26.120 Flushed. 09:26.370 --> 09:27.220 Well understood. 09:27.270 --> 09:29.570 Conditional selection in the adult frames. 09:32.670 --> 09:39.390 After that, we all understood how to select the values from a data frame on the basis of multiple conditions. 09:40.140 --> 09:44.270 And to do that, we use these two operators and or. 09:45.000 --> 09:47.280 And at the end, we help understood index. 09:47.970 --> 09:53.400 These two methods that he said index method and state index method. 09:54.330 --> 09:57.120 So this second by 10 did all deal on data frames. 09:57.150 --> 10:00.180 And here I will see you in the next one. 10:00.590 --> 10:02.310 Then happy learning.