1
00:00:00,270 --> 00:00:06,720
We loaded several types of files in Python with pandas in the previous lectures and all these files

2
00:00:06,720 --> 00:00:11,520
that we loaded had a similar thing between them.

3
00:00:11,640 --> 00:00:19,110
That was they all had a header row so that first row there is referred to as a header because it's not

4
00:00:19,110 --> 00:00:24,390
part of the values but it's it's a row that gives names to all columns.

5
00:00:24,990 --> 00:00:31,590
So it says that the first column is the id column, address for the second column and so on.

6
00:00:34,210 --> 00:00:37,300
And magically Python pandas understood that.

7
00:00:37,340 --> 00:00:43,560
And so it gives you this first row here which is a special row.

8
00:00:43,940 --> 00:00:47,750
So it gives it a special meaning which is a header.

9
00:00:47,750 --> 00:00:56,450
Now sometimes you might have data that 
don't have a header and just like those in here so

10
00:00:56,630 --> 00:00:58,950
data dot txt, if you

11
00:00:58,960 --> 00:00:59,340
load that

12
00:01:06,980 --> 00:01:15,440
and print it out, you'll see that the first value of those data is being assigned as a header and we don't

13
00:01:15,440 --> 00:01:15,850
know on that.

14
00:01:15,850 --> 00:01:23,200
So in that case if you know, if you know that the data don't have a header then you should say header

15
00:01:25,160 --> 00:01:26,170
equals to none.

16
00:01:31,110 --> 00:01:34,390
and in that case python will give you

17
00:01:34,390 --> 00:01:39,640
again it will always give you a header,
but in this case it will give you just a default header which

18
00:01:39,640 --> 00:01:47,800
is made of of numbers so all your rows
will be assigned as values.

19
00:01:47,800 --> 00:01:51,850
This is very important because when you do for example let's say later we'll learn all to do things,

20
00:01:52,420 --> 00:02:00,220
let's say if you do the sum of columns 6 you want 
in that sum all the values of all the

21
00:02:00,220 --> 00:02:01,620
rows to be included.

22
00:02:01,660 --> 00:02:11,530
So these values have to be in your rows which are normal rows not header rows such as in this case

23
00:02:12,230 --> 00:02:12,700
here.

24
00:02:12,790 --> 00:02:18,490
So number 8 would not be included in the sum that we'll do for this last column.

25
00:02:18,730 --> 00:02:26,350
Therefore in this case you have to say header none and then you'd ask how do we assign columns to those

26
00:02:26,350 --> 00:02:29,020
data because we didn't have the columns already in the file.

27
00:02:29,020 --> 00:02:31,830
So we need to find a way to assign them in Python.

28
00:02:32,380 --> 00:02:34,120
I'll show you how to do that in the next lecture.