WEBVTT 00:01.020 --> 00:04.500 When I come to part one and Section One of our costs. 00:04.500 --> 00:07.410 So here you will learn Panda's coding from scratch. 00:07.590 --> 00:11.700 But before we move to our Jupiter environment and stop coding. 00:11.760 --> 00:19.290 First of all I want to introduce the concept of tabular data and some terms and vocabulary that I very 00:19.290 --> 00:21.660 frequently use throughout the costs. 00:22.200 --> 00:28.510 So in the whole costs we will work with data that is organized in a tabular manner and in Panda. 00:28.520 --> 00:32.100 So this is a so-called pandas data frame. 00:32.100 --> 00:38.850 So this the CIA Penn State frame and we can actually see here that we have here five famous football 00:38.850 --> 00:42.810 players and some characteristics of these players. 00:42.810 --> 00:51.030 So nationality the clap and then whether the player won the championship with the national team then 00:51.060 --> 00:56.740 the hype and the goats in the season two thousand and eighteen. 00:56.860 --> 01:00.900 So this is exactly the tabular structure that we are faced with. 01:01.030 --> 01:05.700 Throughout the course and let's have a look at the some vocabulary here. 01:05.840 --> 01:08.940 So first of all let's have a look here at the one play. 01:08.970 --> 01:13.390 It's Christiana Romano door and this is actually one row. 01:13.600 --> 01:17.890 So we have fewer rows and we have fewer columns. 01:17.890 --> 01:26.130 So for example here at the height of the football players is a column of our data frame and actually 01:26.130 --> 01:27.300 one single element. 01:27.300 --> 01:33.780 So for example at the height of Christiana remodelled also one point eighty seven meters is an element 01:33.840 --> 01:43.060 a value on entry of a data frame and next year we have a subset or a slice of our data frame. 01:43.080 --> 01:48.450 So we have to play us and only the columns nationality and club 01:51.240 --> 01:58.010 next we can see on the left hand side of the arrow index off our data frame are simply called the index. 01:58.020 --> 02:00.840 So in this example here we have our players. 02:00.840 --> 02:02.250 So the player names. 02:02.250 --> 02:09.540 So actually in the most left on part of our data frame with the boat let us that we can find the index 02:10.290 --> 02:15.380 and the elements in our next hour called index label score also Row Labels. 02:15.390 --> 02:18.540 So for example at this row has their own label. 02:18.540 --> 02:26.360 Cristiano Ronaldo and we can also give a name to our index soared to our role in Xander. 02:26.370 --> 02:28.620 This was Katia the row index name. 02:28.640 --> 02:35.780 So here in this example a player but it could also be name or player name and. 02:35.800 --> 02:41.890 And here we have the columns or the column index service all of the column labels. 02:41.910 --> 02:46.320 So here we have behind us are the column labels for each column. 02:46.320 --> 02:49.710 So nationality clapped world champion Hide and Go is 02:53.540 --> 02:59.020 and actually penned US is designed in a way that is an optimal Data Organization. 02:59.180 --> 03:07.690 So we FESA rose the observations of like sample the football players and each play us actually an observation 03:08.390 --> 03:10.160 and then we fill in the columns. 03:10.160 --> 03:16.850 The characteristics of the observations and you can also say that these are the variables are the features 03:16.940 --> 03:18.650 of our observations. 03:18.650 --> 03:25.220 So for example at the height of the football players it's actually a characteristic or a feature of 03:25.340 --> 03:26.350 the players here. 03:28.080 --> 03:35.850 And actually pandas and functionality of PANDAS is designed in a way that at the optimal way is that 03:35.850 --> 03:38.700 we have only one data type column. 03:38.790 --> 03:43.560 And typically we have and the columns that characteristics are the features. 03:43.560 --> 03:49.190 And for example here the nationality and the IVF here the same data type. 03:49.200 --> 03:51.660 So here we have strings them in the club column. 03:51.660 --> 03:59.210 We have also strings then you the bird championship column we have boolean values and you end the hate 03:59.220 --> 04:07.650 column we have to objects and in the goes 2018 column we have into us. 04:07.710 --> 04:14.130 So this is very important and essential in pandas that we have here and the rows the observations and 04:14.140 --> 04:21.000 here in the columns the features us and each feature each column should contain only one data type. 04:21.240 --> 04:27.120 And as long as we have this structure here and our pandas set of frames then we can be sure we have 04:27.390 --> 04:30.950 the optimal or the maximum functionality. 04:30.960 --> 04:37.530 And in contrast the fifth and one column more than one data type then this definitely restricts our 04:37.530 --> 04:44.610 functionality of the whole data frame and therefore we so definitely avoid this and Taff here the structure 04:44.640 --> 04:51.160 that here we have in the rows of the observations and in the columns see other characteristics of features 04:52.140 --> 04:59.010 ripeness so this we are finishing with the introduction on tabular data and in the next video we are 04:59.010 --> 05:00.420 starting to code. 05:00.600 --> 05:02.370 So to see an error by.