WEBVTT 00:01.350 --> 00:06.830 In this video we learn how to use some more advanced filtering techniques service to the mess that's 00:06.840 --> 00:10.610 uh between IS and the to the operator. 00:10.650 --> 00:16.980 So first of all let's import pandas and we are working with our Olympic Games medal status said 00:20.480 --> 00:25.710 And now let's assume that we want to have all medals from these Summer Games in nineteen hundred eighty 00:25.710 --> 00:34.640 eight and so and we can filter our data frame by checking in the column if the value for each row in 00:34.640 --> 00:36.380 the year columnist 1988 00:38.880 --> 00:44.860 and by doing so you'll be creating a really serious and bypassing the boolean serious here to the Logue 00:44.880 --> 00:45.390 operator. 00:45.500 --> 00:51.560 We are creating a brand new data frame with the only rows them that fulfill the condition here. 00:51.570 --> 00:59.120 So let's try it out and star the new data frame and the variable Olympic Games nineteen eighty eight. 01:00.150 --> 01:02.100 And let's check head and tail 01:05.460 --> 01:11.680 so you can see here that we only have medals that were awarded in the year nineteen eighty eight and 01:11.680 --> 01:16.470 so let's check also the info method. 01:16.470 --> 01:20.540 So we have in total one thousand five hundred forty six medals. 01:20.860 --> 01:29.490 And now what if you want to have not only the medals for one year but for example all medals since 1992 01:30.710 --> 01:35.960 so from the addition in 1992 in Barcelona including until today. 01:36.260 --> 01:41.270 And what we could actually do is we could create a boolean serious for each additional so let's say 01:41.630 --> 01:49.670 we create a boolean serious for 1992 then a boolean serious for 1996 with the equals sign and so on 01:49.670 --> 01:57.550 and then we could combine all boolean series with an R operator or what we could do we could simply 01:57.550 --> 02:07.260 use that the Creator equals sign and that we are filtering all rows that the year is 1992 or later so 02:07.260 --> 02:13.140 we don't have to use your many conditions and combine them with an operator we can just use here the 02:13.440 --> 02:20.880 greater or equal sign and let's do this and save the new data frame and the variable Olympic Games since 02:20.880 --> 02:22.080 the 1992 02:24.740 --> 02:29.460 and let's have a look at the first and last throws. 02:29.720 --> 02:35.920 So here we can see we have a data frame starting from 1992 until 2012. 02:35.990 --> 02:41.140 So the dataset ends here in 2012 and the 2016 edition. 02:41.150 --> 02:43.580 So now that a frame. 02:43.620 --> 02:44.040 All right. 02:44.050 --> 02:49.420 So now let's assume that you want to have all medals that were awarded in the 1960s. 02:49.420 --> 02:56.030 So we want to include all games in the 1960s and we can do this. 02:56.100 --> 03:03.890 So the between mass it so we can first of all select the year column and then use that between method 03:03.950 --> 03:10.730 and check if the values in the year series between 1960 and 1969. 03:10.730 --> 03:13.060 So let's check this here. 03:13.490 --> 03:20.130 So we get here a boolean serious and obviously here for the very first rows we get a false because some 03:20.130 --> 03:23.950 of that data frame is starting with eighteen hundred ninety six. 03:24.260 --> 03:31.910 And now again we can pass here our boolean serious to our complete data frame with the local operator 03:32.330 --> 03:33.110 and with the. 03:33.130 --> 03:36.830 Between method that is one additional parameter here. 03:37.220 --> 03:44.000 So this the between method we have to define the left boundary it's 1960 and the right boundary it's 03:44.090 --> 03:50.660 1969 and we have to define if the boundaries are inclusive or exclusive. 03:51.530 --> 03:59.830 So since we want to f all editions in the 60s we pass here through and here we are creating a brand 03:59.830 --> 04:04.040 new data frame and we are storing the data frame and the variable Olympic Games. 04:04.060 --> 04:15.440 60s so let's run the cell and let's have a look at the first rose and the last rose and you can see 04:15.440 --> 04:19.300 here we are starting with the rose where the years are 1960. 04:19.310 --> 04:25.750 So we have here the addition in Rome and we are ending with the 1968 in Mexico. 04:26.390 --> 04:32.240 So and this is actually the good thing with the between method that this works even if I'm not sure 04:32.240 --> 04:35.540 and which yes some Olympic Games took place. 04:35.540 --> 04:40.240 So I do not have to pass a specific year where I know that there was Olympic Summer Games. 04:40.240 --> 04:44.630 So for example in 1964 I simply pass an interval. 04:44.810 --> 04:54.260 So between 1960 and 1969 and I get here the medals for the additions 1960 1964 and 1968. 04:54.330 --> 04:59.130 All right so so far we selected editions that were on a sequential order. 04:59.130 --> 05:06.200 So for example here we selected all games from 1992 until today. 05:06.300 --> 05:12.560 Here we selected all games between 1960 and 1968. 05:12.550 --> 05:18.450 But but if you want to select the Olympic Games that are not sequentially ordered so let's assume that 05:18.450 --> 05:24.390 our favorite games were the additions in 1972 and 1996. 05:24.390 --> 05:30.650 So they are obviously not sequential because they are games between those two for example in nineteen 05:30.850 --> 05:34.470 seventy six or 1992 and 05:37.920 --> 05:43.790 that's create here a list of my favorite games so in 1972 and 1996 05:46.730 --> 05:53.110 and now if you want to filter the data frame we could use two conditions so we feel that for 1972 and 05:53.110 --> 06:00.970 for nineteen ninety six and we combine those two conditions with an R operator all we could use the 06:01.030 --> 06:09.910 s in method so we select first of all the column and then we use in the S1 method and then we pass our 06:09.910 --> 06:18.380 list my favorite games with the two values 1972 and 1996 and then pan as checks for each value in the 06:18.380 --> 06:23.640 year column if the value is either 1972 or 1996. 06:23.650 --> 06:31.180 So let's run near the set and I would expect to get a brilliant serious correct. 06:31.570 --> 06:38.380 And now we can pass here our brilliant series to our summer data frame and the load operator and then 06:38.380 --> 06:48.020 we create a brand new data frame was the medals that were awarded in 1972 or 1996 and we call the data 06:48.020 --> 06:56.610 frame Olympic Games 70 to 96 so let's run here and let's have a look at the first five rows. 06:56.960 --> 07:07.540 And the last five rows so we are starting here with 1972 and ending with the 1996 and let's assume that 07:07.660 --> 07:16.000 we deeply analyze here our data frame and now we want to analyze all other years that are not 1972 or 07:16.000 --> 07:23.830 1996 and we can do this by passing simply here again now append us seriously with all rows with the 07:23.840 --> 07:33.070 1972 on 1996 and then we can use the 2 December which gives us the opposite and then we can pass here 07:33.100 --> 07:39.310 the whole statement to our summer data frame and the log operator and we should get a brand new data 07:39.310 --> 07:48.820 frame with all matters in all year except the additions 1972 and 1996 and we saw the new data frame 07:48.820 --> 07:53.910 and the venerable Olympic summer games not seventy two in 1960. 07:54.040 --> 07:57.060 So let's check this out here. 07:57.370 --> 08:06.180 Let's have a look. 08:06.720 --> 08:13.710 So starting from hundred nineteen six and we can check give us the unique method which uh unique value 08:13.700 --> 08:23.430 is we actually have in our year column so let's check this and we can see that we have all yes except 08:23.640 --> 08:27.980 the years 1972 and 1996. 08:27.990 --> 08:30.740 So our code worked here. 08:30.760 --> 08:35.070 All right so now we are finished with this session and I hope to see you on the next one by.