Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated:
1
00:00:00,000 --> 00:00:01,000
Instructor: I want to talk about a common error
2
00:00:01,000 --> 00:00:04,000
that you may experience when importing tables
3
00:00:04,000 --> 00:00:07,000
that contain a date or value from countries
4
00:00:07,000 --> 00:00:09,000
that use different standards.
5
00:00:09,000 --> 00:00:11,000
So, for example, I'm in the United States
6
00:00:11,000 --> 00:00:15,000
and the default date standard here is month, day, year,
7
00:00:15,000 --> 00:00:20,000
but in the UK for example, the standard is day, month, year.
8
00:00:20,000 --> 00:00:23,000
So, what would happen if I imported a table
9
00:00:23,000 --> 00:00:27,000
into my Power BI data model that contained UK dates?
10
00:00:27,000 --> 00:00:30,000
Well, I'll show you exactly what happens in a moment,
11
00:00:30,000 --> 00:00:33,000
but the short answer is that I'd get an error
12
00:00:33,000 --> 00:00:35,000
starting on the 13th of the month.
13
00:00:35,000 --> 00:00:38,000
The good news is that there's an easy process
14
00:00:38,000 --> 00:00:40,000
that we can use to update the locale
15
00:00:40,000 --> 00:00:43,000
used to recognize the date format, and remedy the error.
16
00:00:44,000 --> 00:00:46,000
All right, so our first step in the process here,
17
00:00:46,000 --> 00:00:50,000
we need to make sure that all of the data types are the same
18
00:00:50,000 --> 00:00:51,000
for the values within the column.
19
00:00:51,000 --> 00:00:54,000
And then, we're gonna left click the data type icon
20
00:00:54,000 --> 00:00:57,000
in the header and select using locale.
21
00:00:57,000 --> 00:01:00,000
After that, and this is the most important piece
22
00:01:00,000 --> 00:01:04,000
to get right, you need to select date as the data type
23
00:01:04,000 --> 00:01:08,000
and then select the location that the file originates from.
24
00:01:08,000 --> 00:01:11,000
So, in this course, all of the data types are based
25
00:01:11,000 --> 00:01:14,000
in English, United States.
26
00:01:14,000 --> 00:01:15,000
So, if you're running into this issue,
27
00:01:15,000 --> 00:01:19,000
select that option regardless of your actual location.
28
00:01:19,000 --> 00:01:21,000
If you select your own location
29
00:01:21,000 --> 00:01:23,000
the records won't update accurately.
30
00:01:23,000 --> 00:01:26,000
And then third, you really just want to confirm
31
00:01:26,000 --> 00:01:27,000
that the updates have been applied
32
00:01:27,000 --> 00:01:30,000
and that the data type is correctly recognized
33
00:01:30,000 --> 00:01:33,000
and without any error or issues.
34
00:01:33,000 --> 00:01:35,000
All right, so I'm gonna jump into Power BI
35
00:01:35,000 --> 00:01:36,000
and show you what this looks like.
36
00:01:36,000 --> 00:01:39,000
There isn't a data set to follow along with for this demo,
37
00:01:39,000 --> 00:01:42,000
so just sit back, watch and enjoy.
38
00:01:42,000 --> 00:01:46,000
All right, so we're gonna connect to an Excel file here
39
00:01:46,000 --> 00:01:48,000
that's located on my desktop.
40
00:01:49,000 --> 00:01:54,000
I go back to desktop and we have this date error demo.
41
00:01:54,000 --> 00:01:56,000
And we're gonna load this into the query editor
42
00:01:56,000 --> 00:01:58,000
to take a look at there
43
00:01:58,000 --> 00:02:00,000
and make some of these transformation steps
44
00:02:00,000 --> 00:02:01,000
that we talked about.
45
00:02:03,000 --> 00:02:06,000
And with Excel, because there's multiple sheets
46
00:02:06,000 --> 00:02:08,000
that are possible within an Excel document,
47
00:02:08,000 --> 00:02:09,000
you're first wanna select the sheet
48
00:02:09,000 --> 00:02:11,000
that you want to connect to.
49
00:02:11,000 --> 00:02:14,000
And we have our same kind of data preview window here.
50
00:02:14,000 --> 00:02:17,000
And immediately off the bat we kind of see
51
00:02:17,000 --> 00:02:19,000
that something's going on here, right?
52
00:02:19,000 --> 00:02:22,000
When we get from the 12th or the 13th,
53
00:02:22,000 --> 00:02:23,000
there's some sort of issue.
54
00:02:23,000 --> 00:02:27,000
So, let's click transform data, and we're gonna work
55
00:02:27,000 --> 00:02:30,000
through the process that we outlined in the slides.
56
00:02:30,000 --> 00:02:33,000
First things first here, I wanna kind of demonstrate the way
57
00:02:33,000 --> 00:02:37,000
to not update this so you can kind of see what happens here.
58
00:02:37,000 --> 00:02:39,000
Obviously, we've got something going on.
59
00:02:39,000 --> 00:02:42,000
We're not seeing errors here on the 13th of the month,
60
00:02:42,000 --> 00:02:45,000
but we do have our values split out to the left
61
00:02:45,000 --> 00:02:47,000
and to the right side.
62
00:02:47,000 --> 00:02:49,000
From the data type here, we can see that it's mixed
63
00:02:49,000 --> 00:02:53,000
between text and integer values or whole numbers.
64
00:02:53,000 --> 00:02:56,000
So, what if we clicked here and just clicked update date?
65
00:02:56,000 --> 00:02:59,000
All right, now we get all of these errors here, right?
66
00:02:59,000 --> 00:03:01,000
We can click into this error.
67
00:03:01,000 --> 00:03:04,000
Power Query is saying we couldn't parse the input provided
68
00:03:04,000 --> 00:03:08,000
as a date value, saying 13/1/2023, right?
69
00:03:08,000 --> 00:03:11,000
It doesn't recognize that as a date value.
70
00:03:11,000 --> 00:03:13,000
So, if we close out here
71
00:03:13,000 --> 00:03:16,000
and then we'll also remove that change type step.
72
00:03:16,000 --> 00:03:18,000
What we want to do first is
73
00:03:18,000 --> 00:03:21,000
because this is actually a mixed data type in the column
74
00:03:21,000 --> 00:03:24,000
we have both text and whole numbers,
75
00:03:24,000 --> 00:03:28,000
we actually want to update this all to text.
76
00:03:28,000 --> 00:03:29,000
We wanna make sure that we're working
77
00:03:29,000 --> 00:03:32,000
with the exact same data type here.
78
00:03:32,000 --> 00:03:36,000
Then we can come back in, we'll update using locale.
79
00:03:36,000 --> 00:03:38,000
And for the purpose of this demo,
80
00:03:38,000 --> 00:03:41,000
this file has come from a friend in the UK, right?
81
00:03:41,000 --> 00:03:43,000
So, what we want to do is we want to update
82
00:03:43,000 --> 00:03:47,000
this data type to date and the locale that we want
83
00:03:47,000 --> 00:03:49,000
to base the transformation off of
84
00:03:49,000 --> 00:03:52,000
is English, United Kingdom, right?
85
00:03:52,000 --> 00:03:55,000
And we can see a sample of the input values, right?
86
00:03:55,000 --> 00:03:58,000
So, we have the day, the month, and then the year,
87
00:03:58,000 --> 00:04:01,000
which aligns with the way that this file is set up.
88
00:04:01,000 --> 00:04:04,000
So, we'll click okay, and now look what happens.
89
00:04:04,000 --> 00:04:06,000
Because I'm based in the US
90
00:04:06,000 --> 00:04:10,000
and I want my date set up as month, day, year,
91
00:04:10,000 --> 00:04:12,000
I now have that appropriately configured
92
00:04:12,000 --> 00:04:15,000
for all of the values within the column.
93
00:04:15,000 --> 00:04:19,000
We've got January 19th, 2023, January 20th, 2023,
94
00:04:19,000 --> 00:04:22,000
so on and so forth.
95
00:04:22,000 --> 00:04:24,000
And from here you can continue
96
00:04:24,000 --> 00:04:27,000
and just use the add column tools to build out
97
00:04:27,000 --> 00:04:30,000
additional columns based on this date field.
98
00:04:30,000 --> 00:04:34,000
So, if you ever run into a situation where your date decimal
99
00:04:34,000 --> 00:04:37,000
or currency columns are returning errors,
100
00:04:37,000 --> 00:04:39,000
I'd suggest using this process to update
101
00:04:39,000 --> 00:04:42,000
the data types based on the locale of the source.
8296
Can't find what you're looking for?
Get subtitles in any language from opensubtitles.com, and translate them here.