All language subtitles for 019 Change Type with Locale_en

af Afrikaans
ak Akan
sq Albanian
am Amharic
ar Arabic
hy Armenian
az Azerbaijani
eu Basque
be Belarusian
bem Bemba
bn Bengali
bh Bihari
bs Bosnian
br Breton
bg Bulgarian
km Cambodian
ca Catalan
ceb Cebuano
chr Cherokee
ny Chichewa
zh-CN Chinese (Simplified)
zh-TW Chinese (Traditional)
co Corsican
hr Croatian
cs Czech
da Danish
nl Dutch
en English
eo Esperanto
et Estonian
ee Ewe
fo Faroese
tl Filipino
fi Finnish
fr French Download
fy Frisian
gaa Ga
gl Galician
ka Georgian
de German
el Greek
gn Guarani
gu Gujarati
ht Haitian Creole
ha Hausa
haw Hawaiian
iw Hebrew
hi Hindi
hmn Hmong
hu Hungarian
is Icelandic
ig Igbo
id Indonesian
ia Interlingua
ga Irish
it Italian
ja Japanese
jw Javanese
kn Kannada
kk Kazakh
rw Kinyarwanda
rn Kirundi
kg Kongo
ko Korean
kri Krio (Sierra Leone)
ku Kurdish
ckb Kurdish (Soranî)
ky Kyrgyz
lo Laothian
la Latin
lv Latvian
ln Lingala
lt Lithuanian
loz Lozi
lg Luganda
ach Luo
lb Luxembourgish
mk Macedonian
mg Malagasy
ms Malay
ml Malayalam
mt Maltese
mi Maori
mr Marathi
mfe Mauritian Creole
mo Moldavian
mn Mongolian
my Myanmar (Burmese)
sr-ME Montenegrin
ne Nepali
pcm Nigerian Pidgin
nso Northern Sotho
no Norwegian
nn Norwegian (Nynorsk)
oc Occitan
or Oriya
om Oromo
ps Pashto
fa Persian
pl Polish
pt-BR Portuguese (Brazil)
pt Portuguese (Portugal)
pa Punjabi
qu Quechua
ro Romanian
rm Romansh
nyn Runyakitara
ru Russian
sm Samoan
gd Scots Gaelic
sr Serbian
sh Serbo-Croatian
st Sesotho
tn Setswana
crs Seychellois Creole
sn Shona
sd Sindhi
si Sinhalese
sk Slovak
sl Slovenian
so Somali
es Spanish
es-419 Spanish (Latin American)
su Sundanese
sw Swahili
sv Swedish
tg Tajik
ta Tamil
tt Tatar
te Telugu
th Thai
ti Tigrinya
to Tonga
lua Tshiluba
tum Tumbuka
tr Turkish
tk Turkmen
tw Twi
ug Uighur
uk Ukrainian
ur Urdu
uz Uzbek
vi Vietnamese
cy Welsh
wo Wolof
xh Xhosa
yi Yiddish
yo Yoruba
zu Zulu
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.