I'm attempting to create a Regex that finds only 2-digit integers or numbers with a precision of 2 decimal points.
In the example string at the bottom, I want to find only the following:
21 and 10.50
Using this expression, 100% is getting captured, in addition to the strings I desire to capture:
(\d){1,2}(\.?)([0-9]?[0-9]?){1,2}
I know I need to use ^% somewhere, but I can't figure out where it goes. Any suggestions are greatly appreciated.
Here's my sample string:
Earn Up to $21 Per Hour - Deliver Food with !!
Delivery Drivers work when they want and make great money when they do.
All orders are prepaid, just pick them up and deliver them to hungry diners. No waiting in line or fumbling with receipts and prepaid cards.
It's fast and easy to start working. Get started today.
Apply Now
Why choose ?
More orders than any other takeout platform
100% of our restaurants are official partners
Competitive pay: Per order fee + mileage + tips
We guarantee an hourly minimum of $10.50/hour*
Create your own schedule & work the hours you want
Word boundaries in your regular expression will grant you a bit more control.
Since word boundaries are a bit strict, we need to introduce an OR condition to address both cases which will satisfy your regex.
(\b[\d]{2}\.[\d]{2}\b)|(\b[\d]{2}\b)
Edit: Try this one,
\b[\d]{2}\b(\.[\d]{2})?
The first example has a chance to fail as it is order dependent due to the way it short-circuits. This I believe should address multiple cases properly.
I think this should work:
(?<!\d)((\d+\.\d\d)|(\d\d))(?!%|\d)
Demo (and explanation)
EDIT:
Improved version:
(?<!\d)(\d{1,2}(?:\.\d{1,2})?)(?!%|\d)
Demo (and explanation)
You can try this variant: (\d{1,}|[\d.])\b(?!%)
It uses negative lookahead (?!%) to exclude digits following by % sign.
Details at regex101
Related
From text transcripts, I want to capture all names of speakers.
The target names start at the start of a line and should end at a ": " (ie. colon and space).
Optionally, for even finer control, it may be safe to assume the first colon and two spaces.
Example text:
Julian Z.: What's really exciting is the opportunity to be more intelligent about how you approach trying to reach your consumer. In a world where digital and the use of digital has exploded, to be able to have one-on-one conversations in the digital world, and to be able to eventually translate that into the TV space, whether that be addressable or data-driven, is really fantastic. Because at the end of the day, you want your brand, in our case, our networks, to be able to have a relationship with the consumer. Data is a proxy to allow for that to occur.
From an advertiser perspective, obviously now the ability to go to the broadcast networks and have a data-driven buy has absolutely blown up and proliferated. That's with us. That's with some of our competitors. Obviously, we think we're the best at it, but neither here nor there. I think it's a really wonderful foundational approach for advertisers to take. I think it's a great advancement in the market.
As a spender of money, and as somebody who is trying to get people to engage with our brands, the ability to use data to really have, again, these really one-on-one, unique conversations, and to be able to deliver creative content that's relevant for individual consumers, that's driven by what we know about the consumer, now, ultimately, where we can reach them effectively and in environments where we know they're engaged, is really a great, tremendous advancement. You'll see by our ratings numbers, which are on the upswing, that approach has really had a direct impact on what our linear ratings have resulted in.
Speaker 2: Great. Tell us a little bit about Viacom. It's a lot of fans, a lot of passion in people. How do you define the audience in broad strokes? How do they respond to advertising and what are some of the concerns that consumers have around ads?
Julian Z.: Well, I think, again, when you're talking about how we're reaching fans, it is using intelligence, and information, and data, not only to profile who our fans are, but ultimately where they're best reached. Our job is to deliver great, compelling content, which we believe we're really, really good at.
In order to do that, there's the linear side of the equation, but of course we want to make sure that we're reaching our fans in digital as well, and that there's a 360 kind of fan experience. We believe holistically that our fans are really the base of what we're trying to do. We're trying to please and create value for our fans. The more we engage with them, and the more we know about them, the better we're able to deliver customized content that fits their need.
Ultimately, as a content creator, what's more exciting than to delivery really great content to people that they really, really engage with and they build relationships with? That's all you can really hope for is, somebody that creates content, is to be able to develop compelling content and content that your audience really wants to engage with.
Speaker 2: When you look at targeting, is that a cross-platform? Where does that targeting happen?
Julian Z.: It absolutely is cross-platform. Of course, there is natural addressability in the digital market, because it is much more of a one-to-one. But now you see a lot of the MVPDs have obviously opened up addressable inventory. A lot of the MVPDs now have matured their addressable footprint, which allows you now to have a digital-like, not exactly the same obviously, but a digital-like experience in the linear space, to deliver content to the consumer or advertising to the consumer when it's relevant and when it's going to have the most impact for your message.
Ultimately, it's absolutely cross-platform because addressability is all about having that conversation, having that direct one-to-one with your audience. Our partners on the MVPD side have really matured over the last several years as of regard to addressable, and now you can have that 360 experience of having a conversation in linear and in digital that really is addressable.
Example strings to be captured are: Julian Z. and Speaker 2. Names will vary from text to text. I need all/multiple names present. As you see, names may include a mixture of alpha case, punctuation characters and numbers.
I will want to deduplicate names, which are repeated in the text, but believe I should shelve that for now, focusing this question on the capture.
I have tried plenty, for the last day or two.
eg. ^[^:]+\s* with /g comes close, but only captures the first, single Julian Z., whereas I want everything. For now, I am out of ideas and need to learn how to do this.
Regex to match any characters up until the first colon:
/^.*?(?=:)/gm
https://regex101.com/r/3uyXMM/3
^: match from beginning of line
.: match anything
*?: non-greedy search, so it stops at first colon (see next line)
(?=:): positive lookahead meaning next character should be colon but it doesn't capture
g: don't return after first match, returns all matches
m: run regex for each line
You can use this regex based on a negated character class:
/^\w[^:\n]*/mg
RegEx Demo 1
RegEx Demo 2
RegEx Breakup:
^\w: Match a word character at the start
[^:\n]*: Match zero or more of any character that is not a colon and not a newline.
Code:
var names = inputData.transcript.match(/^\w[^:\n]*/mg) || [];
I'm trying to extract regions around keywords from longer passages of text. They should include complete sentences, based on the following conditions:
n=250 Charactars before / after keyword should be included if existing (the keyword can be closer then this to the start / end of the text)
from there it should expand further to include the complete sentence (let's assume here we can define sentence borders with ".?! or :" knowing it's not completely accurate)
I already achieved the expanding to the end of the last sentence, but not to start of the first in the following example, where vitamin is the keyword and the italic is captured by the regex. However, it should capture from "An extra 24 hours..."
Apparently I don't get the corresponding group up front, neither using lazy nor using lookbehind.
((.{0,250}(vitamin)\b.{0,250})(.+?(\.|\!|\?|\:))?)/ig
Well, this year you’re getting an extra day to get ahead on your taxes or (finally) clean out the garage. (Hey, we’re not trying to tell you what do but you might as well be productive.) February 29 is back on the calendar this year because it’s a leap year. Whether you love or loathe the extra winter day, you’re probably wondering why it happens in the first place. An extra 24 hours — or day — is built into the calen dar every four years to ensure it aligns with the Earth’s movement around the sun. There’s 365 days in a calendar year, but it actually takes longer for the Earth’s annual journey — about 365.2421 days — around the star that gives us light, life and vitamin D. The difference may seem like no big deal to us, but over time, it adds up. “To ensure consistency with the true astronomical year, it is necessary to periodically add in an extra day to make up the lost time and get the calendar back in sync with the heavens,” according the history. com.
Acknowledgement of the need for a leap year happened around the time of Julius Caesar. In 46 B.C., Caesar enlisted the help of astronomer Sosigenes to update the calendar so that it had 12 months and 365 days, including a leap year every four years.,
You can try something like this:
(([.?!:][^.?!:]*.{250}\bvitamin\b.{250})[^.?!:]*[.?!:])
It works by consuming 250 characters of text before and after the keyword "vitamin". From that point it finds the first punctuation point (.?!:) before/after the 250 characters of text.
Here's a sample of it in action.
You can you use extra parentheses () to strategically group what exact output you want. For example, the above answer includes the ending period from the preceding sentence in the output. So you could use
(([.?!:]([^.?!:]*.{250}\bvitamin\b.{250})[^.?!:]*[.?!:]))
and use group 3 from the result set which doesn't have this ending period.
I do not see how the specification in the question can be matched by a regex. It boils down to the following logic problem:
to match as many characters as possible but no more than 250 before/after the keyword, .{0,250} needs to be greedy and can neither be lazy .{0,250}? nor possessive .{0,250}+
if this part is greedy, you will miss the occurrences of the keyword that start before the .{0,250} part is matched.
The same logic applies to my understanding to the 'match back to the start of the sentnence as well.
I played around with the following more or less meaningful regex:
[.?!:]?([^.?!:]*?(.{0,250}\byear\b.{0,250})[^.?!:]*[.?!:]?) misses first 'year'
[.?!:]?([^.?!:]*?(.{0,250}?\byear\b.{0,250})[^.?!:]*[.?!:]?) gets the first 'year' but fails on others.
I suggest you write your on extraction logic in a function, eihter using regex or not, to achieve the extraction you want.
You could for example find the index of the start of the keyword \bkeyword\b and the full stops (\.[^\d]|[.?!:]$) and then with this information extract the part of the text you want.
I trying to create a regular expression to catch the following conditions, but totally failing to get my head around it (Friday) and need a bit of help please?
Trying to capture UK phone numbers starting with area code or no area code, but excluding mobiles.
example: 01316691234 or 6691234 but not any number starting with 07
got this so far ^[0-9]1?(\d{6,11}) but struggling to exclude the 07 numbers.
This is based on the supposition that UK area codes:
start with 0 and are followed by 1 (usual) or 2 (London);
run to 3-5 digits
are followed by a phone number 6-7 digits long
Whilst this seems sound to me, I'm no telecoms anorak so you'll need to modify accordingly if any part of this supposition is wrong:
/^(0[12345689]\d{1,3} ?)?\d{6,7}$/
Either way, it's a bit of a can of worms. Postscodes and phone numbers don't lend themselves well to REGEX; the more tightly you refine it, the more at risk you are from new rules being added tomorrow - e.g. if they launched a new area code starting 03.
Use a negative look ahead to prevent numbers starting with "07" matching:
^(?!07)([0-9]1)?(\d{6,11})
Ok what I'm trying to do is to check for the presence of
"TestItem-1"
a number greater then 1
one of the possible words in the list of "KG. Kg, kg, Kilo(s) or Kilogram(s)"
Where any of the items could be in any order and within a 6 word limit of each other.
Has to be done in regex as there is no access to the underlying scripting engine
This is what I've got as there a way of checking greater then I decided to use a range of 1-999 for the number check.
\b(?:[T|t]estItem-1\W+(?:\w+\W+){1,6}(^[0-9]|[1-9][0-9]|[1-9][0-9][0-9])$)\W+(?:\w+\W+){1,6}[K|k]il[o|os]|[K|k][[G|GS]|[g|gs]]|[|K|k]ilogra[m|ms]\b
Examples of what I need to find would be like -
"TestItem-1 is unstable in quanties above 12 Kilograms"
"1 Kilogram of TestItem-1"
While I wouldn't want to find -
"15 units of TestItem-1"
I know that what I got isn't working each section appears to work independently of each other but not together.
I pass this over to far greater minds then mine :)
You can try something like this:
\b(?:[2-9]|\d\d+)\b\s\b(?:KG.|Kg,|kg,|Kilos?|Kilograms?)\b(?:\S+\s){0,6}\bTestItem-1\b|\bTestItem-1\b(?:\S+\s){0,6}\b(?:[2-9]|\d\d+)\b\s\b(?:KG.|Kg,|kg,|Kilos?|Kilograms?)\b
Not ideal with the duplication but without lookarounds that's the best I could think of. I'll try and improve it in a bit.
I have some SQLCLR code for working with Regular Expresions. But now that it is getting migrated into Azure, which does not allow SQLCLR, that's out. I need to find a way to do regex in pure T-SQL.
Master Data Services are not available because the dev edition of MSSQL we have is not R2.
All ideas appreciated, thanks.
Regular expression match samples that need handling
(culled from regexlib and other places over the past few years)
email address
^[\w-]+(\.[\w-]+)*#([a-z0-9-]+(\.[a-z0-9-]+)*?\.[a-z]{2,6}|(\d{1,3}\.){3}\d{1,3})(:\d{4})?$
dollars
^(\$)?(([1-9]\d{0,2}(\,\d{3})*)|([1-9]\d*)|(0))(\.\d{2})?$
uri
^(http|https|ftp)\://([a-zA-Z0-9\.\-]+(\:[a-zA-Z0-9\.&%\$\-]+)*#)*((25[0-5]|2[0-4][0-9]|[0-1]{1}[0-9]{2}|[1-9]{1}[0-9]{1}|[1-9])\.(25[0-5]|2[0-4][0-9]|[0-1]{1}[0-9]{2}|[1-9]{1}[0-9]{1}|[1-9]|0)\.(25[0-5]|2[0-4][0-9]|[0-1]{1}[0-9]{2}|[1-9]{1}[0-9]{1}|[1-9]|0)\.(25[0-5]|2[0-4][0-9]|[0-1]{1}[0-9]{2}|[1-9]{1}[0-9]{1}|[0-9])|localhost|([a-zA-Z0-9\-]+\.)*[a-zA-Z0-9\-]+\.(com|edu|gov|int|mil|net|org|biz|arpa|info|name|pro|aero|coop|museum|[a-zA-Z]{2}))(\:[0-9]+)*(/($|[a-zA-Z0-9\.\,\?\'\\\+&%\$#\=~_\-]+))*$
one numeric digit
^\d$
percentage
^-?[0-9]{0,2}(\.[0-9]{1,2})?$|^-?(100)(\.[0]{1,2})?$
height notation
^\d?\d'(\d|1[01])"$
numbers between 1 1000
^([1-9]|[1-9]\d|1000)$
credit card numbers
^((4\d{3})|(5[1-5]\d{2})|(6011))-?\d{4}-?\d{4}-?\d{4}|3[4,7]\d{13}$
list of years
^([1-9]{1}[0-9]{3}[,]?)*([1-9]{1}[0-9]{3})$
days of the week
^(Sun|Mon|(T(ues|hurs))|Fri)(day|\.)?$|Wed(\.|nesday)?$|Sat(\.|urday)?$|T((ue?)|(hu?r?))\.?$
time on 12 hour clock
(?<Time>^(?:0?[1-9]:[0-5]|1(?=[012])\d:[0-5])\d(?:[ap]m)?)
time on 24 hour clock
^(?:(?:(?:0?[13578]|1[02])(\/|-|\.)31)\1|(?:(?:0?[13-9]|1[0-2])(\/|-|\.)(?:29|30)\2))(?:(?:1[6-9]|[2-9]\d)?\d{2})$|^(?:0?2(\/|-|\.)29\3(?:(?:(?:1[6-9]|[2-9]\d)?(?:0[48]|[2468][048]|[13579][26])|(?:(?:16|[2468][048]|[3579][26])00))))$|^(?:(?:0?[1-9])|(?:1[0-2]))(\/|-|\.)(?:0?[1-9]|1\d|2[0-8])\4(?:(?:1[6-9]|[2-9]\d)?\d{2})$
usa phone numbers
^\(?[\d]{3}\)?[\s-]?[\d]{3}[\s-]?[\d]{4}$
Unfortunately, you will not be able to move your CLR function(s) to SQL Azure. You will need to either use the normal string functions (PATINDEX, CHARINDEX, LIKE, and so on) or perform these operations outside of the database.
EDIT Adding some information for the examples added to the question.
Email address
This one is always controversial because people disagree about which version of the RFC they want to support. The original didn't support apostrophes, for example (or at least people insist that it didn't - I haven't dug it up from the archives and read it myself, admittedly), and it has to be expanded quite often for new TLDs (once for 4-letter TLDs like .info, then again for 6-letter TLDs like .museum). I've often heard quite knowledgeable people state that perfect e-mail validation is impossible, and having previously worked for an e-mail service provider, I can tell you that it was a constantly moving target. But for the simplest approaches, see the question TSQL Email Validation (without regex).
One numeric digit
Probably the easiest one of the bunch:
WHERE #s LIKE '[0-9]';
Credit card numbers
Assuming you strip out dashes and spaces, which you should do in any case. Note that this isn't an actual check of the credit card number algorithm to ensure that the number itself is actually valid, just that it conforms to the general format (AmEx = 15 digits starting with a 3, the rest are 16 digits - Visa starts with a 4, MasterCard starts with a 5, Discover starts with 6 and I think there's one that starts with a 7 (though that may just be gift cards of some kind)):
WHERE #s + ' ' LIKE '[3-7]'+ REPLICATE('[0-9]', 14) + '[0-9 ]';
If you want to be a little more precise at the cost of being long-winded, you can say:
WHERE (LEN(#s) = 15 AND #s LIKE '3' + REPLICATE('[0-9]', 14))
OR (LEN(#s) = 16 AND #s LIKE '[4-7]' + REPLICATE('[0-9]', 15));
USA phone numbers
Again, assuming you're going to strip out parentheses, dashes and spaces first. Pretty sure a US area code can't start with a 1; if there are other rules, I am not aware of them.
WHERE #s LIKE '[2-9]' + REPLICATE('[0-9]', 9);
-----
I'm not going to go further, because a lot of the other expressions you've defined can be extrapolated from the above. Hopefully this gives you a start. You should be able to Google for some of the others to see how other people have replicated the patterns with T-SQL. Some of them (like days of the week) can probably just be checked against a table - seems overkill to do an invasie pattern matching for a set of 7 possible values. Similarly with a list of 1000 numbers or years, these are things that will be much easier (and probably more efficient) to check if the numeric value is in a table rather than convert it to a string and see if it matches some pattern.
I'll state again that a lot of this will be much better if you can cleanse and validate the data before it gets into the database in the first place. You should strive to do this wherever possible, because without CLR, you just can't do powerful RegEx inside SQL Server.
Ken Henderson wrote about ways to replicate RegEx without CLR, but they require sp_OA* procedures, which are even less likely to ever see the light of day in Azure than CLR. Most of the other articles you'll find online use an approach similar to Ken's or use complex use of built-in string functions.
Which portions of RegEx specifically are you trying to replicate? Can you show an example of the input/output of one of your functions? Perhaps it will be easy to convert to get similar results using the built-in string functions like PATINDEX.