WEBVTT 00:00.740 --> 00:01.630 Hi everyone. 00:01.640 --> 00:07.670 So in this video we will have our very first look at the data type or class pendency serious and pendency 00:07.730 --> 00:15.440 areas are one dimensional labeled arrays that have specific attributes and methods to deal with readable 00:15.500 --> 00:15.950 data. 00:15.950 --> 00:20.210 So for sample data with the missing values and so on. 00:20.210 --> 00:22.670 So but first of all let's impart pandas. 00:22.670 --> 00:31.940 And we also import umpires and P and we import our Titanic data set and we will have a look at the first 00:31.940 --> 00:34.130 five froze. 00:34.460 --> 00:36.450 So each row is a peasant. 00:36.490 --> 00:40.310 And each column is a characteristic of the passenger. 00:40.310 --> 00:49.000 And we can also have a deeper look with the info method so here we can see that we have eight hundred 00:49.000 --> 00:50.000 ninety one rows. 00:50.000 --> 00:56.240 So we have eight hundred ninety one passengers and we have nine columns survived passenger class six 00:56.290 --> 01:05.610 H is the best P parts fair embarked and deck and in the H column we have some missing values and also 01:05.620 --> 01:12.810 in that that column we have many missing values and actually we can create a panda serious from a data 01:12.810 --> 01:20.840 frame by selecting one column of a data frame and we can do this by passing the label of the column. 01:20.840 --> 01:25.930 So for example H am within the spritz BAP brackets here. 01:26.290 --> 01:33.750 So now we should get here to the H column of our Titanic data frame so here we can see we have a one 01:33.750 --> 01:39.970 dimensional array so here we have only one array with the values so one array of one column. 01:40.170 --> 01:48.040 And still we have them the labels here on the left side of our data frame and the order of the rosiest 01:48.100 --> 01:55.570 actually is still the same as in our original data frame and we can also check here that the data type 01:55.570 --> 02:01.350 of our sliced columns so we check here with the function type the data type of our slice. 02:01.350 --> 02:09.460 The column here and here it's a pen that serious and we already learned to turn into so to select a 02:09.460 --> 02:10.030 column. 02:10.030 --> 02:15.790 So the first alternative is by passing the column labeled within this paper brackets. 02:15.790 --> 02:22.750 So like here I'll we use using the column label as the attribute of the data frame so titanic thought 02:22.800 --> 02:23.220 H. 02:23.250 --> 02:26.340 So that's the attribute uh for for each column. 02:26.350 --> 02:27.790 Also for H. 02:27.820 --> 02:34.450 And we can get them the column essay penned as serious by using the attribute notation here and we can 02:34.450 --> 02:40.420 check give them both alternatives are the same with the method equal so we check if getting the H column 02:40.420 --> 02:46.180 with them this notation isn't the same as that with this notation and it should be a true. 02:46.180 --> 02:47.620 Exactly. 02:47.680 --> 02:57.460 And now we can assign a variable to our column and safe our column in the variable H so h is now our 02:57.460 --> 03:01.710 panacea is and penned our series and data friends share. 03:01.720 --> 03:07.510 Actually most of the attributes and methods however there are some attributes and some methods some 03:07.510 --> 03:11.170 that only work with the data frames and the other way around. 03:11.380 --> 03:16.310 There are some attributes and methods that only exists for pandas serious. 03:16.480 --> 03:22.690 And first of all we can check if the method Hap also works for a panda serious here our serious age 03:24.280 --> 03:32.650 and this is true or had also gifts last year the first five values are to say Rose and by default it's 03:32.740 --> 03:33.670 in the first five. 03:33.700 --> 03:43.390 But let's take your shift tab so we can change and here to let's say uh 2 and then we get the first 03:43.660 --> 03:52.760 two values here and the total method consequently works also with pandas series so we get by default 03:52.760 --> 04:00.020 the last five values are rows and with the D type attribute we can also check them the data type of 04:00.020 --> 04:06.320 our values here in our pandas serious so it should be here um in a float object actually because we 04:06.420 --> 04:16.730 fear one peasant was twenty seven point 0 years so it should be a float and this is true and let's try. 04:16.730 --> 04:22.400 Also the info method and TV get an error message. 04:22.400 --> 04:25.060 A serious object has no attribute info. 04:25.190 --> 04:32.440 OK and we can also try the shape attribute and we get that. 04:32.660 --> 04:39.740 We have here a one dimensional array with eight hundred ninety one elements and we can also pass a panda 04:39.740 --> 04:47.750 serious to a Python build and function like the length function and the length function returns us here 04:47.780 --> 04:54.350 the number or the amount of elements in our pandas serious are the amount of rows in our original data 04:54.350 --> 05:00.010 frame actually and we can also check for the index. 05:00.010 --> 05:06.630 So we have a range in next starting from zero to eight hundred ninety one excluding with step 1. 05:06.640 --> 05:09.250 So we can check also here on the left side. 05:09.400 --> 05:12.340 So we have a range index starting from zero 05:15.400 --> 05:19.030 and last but not least we can also try the describe method 05:21.870 --> 05:27.870 and here we get for our pandas series very interesting and head for summary statistics so we get here 05:27.870 --> 05:28.710 count. 05:28.710 --> 05:33.960 So this is the amount of number our values and ours spend serious. 05:33.990 --> 05:41.220 So obviously we have here one hundred seventy seven missing values in our series we have for the mean 05:41.220 --> 05:42.060 of our values. 05:42.060 --> 05:42.930 So twenty nine. 05:42.930 --> 05:50.520 So the average age on the Titanic was twenty nine years old and the standard deviation of our age and 05:50.580 --> 05:51.570 the minimum age. 05:51.570 --> 05:56.820 So the youngest passenger on board was only 0 point forty two years old. 05:56.820 --> 06:01.340 So a baby and we get to the 25th person tire. 06:01.350 --> 06:02.940 The fifth person tied. 06:03.300 --> 06:08.160 So the fifth person title is actually the median and it's 28 years. 06:08.190 --> 06:15.270 So actually 50 percent on board of the Titanic was younger than twenty eight and 50 percent of the passengers 06:15.270 --> 06:17.900 were older than 28 years old. 06:18.120 --> 06:23.370 And also we have here the seventy fifth person tile and the max. 06:23.370 --> 06:29.010 So the oldest passenger on board of the Titanic was 80 years old. 06:29.030 --> 06:29.380 All right. 06:29.390 --> 06:35.270 So this uh the first steps with pen that serious and the next session we will have a look at the numerical 06:35.270 --> 06:42.130 serious them that contain either into just off load objects and we try to analyze them these serious 06:42.320 --> 06:44.390 so hope to see you there by.