I am fairly new to redshift and I have the following postcodes in my table
B13 7GB
BA43 87F
BR8 H4D
B4H HFT
I would like to only extract the rows where there is a number FROM 0-9 after the first letter.
Expected output
B13 7GB
B4H HFT
Thank you
Perhaps someone can provide an "simpler" answer, but I like using REGEX functions to be as precise as possible.
select code
from tbl
where regexp_count(code,'^[A-Z]{1}[0-9]{1}')>0
^ -> Only check the start of the string (our code).
[A-Z]{1} -> Search for ONE Capital Letter.
[0-9]{1} -> Search for ONE number from 0-9.
All together:
At the start of the string, search for ONE capital letter that is followed by ONE number from 0-9.
Regexp is very flexible but in Redshift it is not very fast. If you dataset is very large you will likely be better off for this simple case with SIMILAR TO.
select *
from tbl
where code SIMILAR TO '_[0-9]%';
LIKE and SIMILAR TO are less flexible than regexp but compile and run faster. In general the more flexible the syntax the harder it is to execute. This (and backward compatibility) is why LIKE and SIMILAR TO still exists.
Related
Got an interesting one, and can't come up with any solid ideas, so thought maybe someone else may have done something similar.
I want to be able to identify strings of letters in a longer sentence that are not words and remove them. Essentially things like kuashdixbkjshakd
Everything annoyingly is in lowercase which makes it more difficult, but since I only care about English, I'm essentially looking for the opposite of consonant clusters, groups of them that don't make phonetically pronounceable sounds.
Has anyone heard of/done something like this before?
EDIT: this is what ChatGpt tells me
It is difficult to provide a comprehensive list of combinations of consonants that have never appeared in a word in the English language. The English language is a dynamic and evolving language, and new words are being created all the time. Additionally, there are many regional and dialectal variations of the language, which can result in different sets of words being used in different parts of the world.
It is also worth noting that the frequency of use of a particular combination of consonants in the English language is difficult to quantify, as the existing literature on the subject is limited. The best way to determine the frequency of use of a particular combination of consonants would be to analyze a large corpus of written or spoken English.
In general, most combinations of consonants are used in some words in the English language, but some combinations of consonants may be relatively rare. Some examples of relatively rare combinations of consonants in English include "xh", "xw", "ckq", and "cqu". However, it is still possible that some words with these combinations of consonants exist.
You could try to pass every single word inside the sentence to a function that checks wether the word is listed inside a dictionary. There is a good number of dictionary text files on GitHub. To speed up the process: use a hash map :)
You could also use an auto-corretion API or a library.
Algorithm to combine both methods:
Run sentence through auto correction
Run every word through dictionary
Delete words that aren't listed in the dictionary
This could remove typos and words that are non-existent.
You could train a simple model on sequences of characters which are permitted in the language(s) you want to support, and then flag any which contain sequences which are not in the training data.
The LangId language detector in SpamAssassin implements the Cavnar & Trenkle language-identification algorithm which basically uses a sliding window over the text and examines the adjacent 1 to 5 characters at each position. So from the training data "abracadabra" you would get
a 5
ab 2
abr 2
abra 2
abrac 1
b 2
br 2
bra 2
brac 1
braca 1
:
With enough data, you could build a model which identifies unusual patterns (my suggestion would be to try a window size of 3 or smaller for a start, and train it on several human languages from, say, Wikipedia) but it's hard to predict how precise exactly this will be.
SpamAssassin is written in Perl and it should not be hard to extract the language identification module.
As an alternative, there is a library called libtextcat which you can run standalone from C code if you like. The language identification in LibreOffice uses a fork which they adapted to use Unicode specifically, I believe (though it's been a while since I last looked at that).
Following Cavnar & Trenkle, all of these truncate the collected data to a few hundred patterns; you would probably want to extend this to cover up to all the 3-grams you find in your training data at least.
Perhaps see also Gertjan van Noord's link collection: https://www.let.rug.nl/vannoord/TextCat/
Depending on your test data, you could still get false positives e.g. on peculiar Internet domain names and long abbreviations. Tweak the limits for what you want to flag - I would think that GmbH should be okay even if you didn't train on German, but something like 7 or more letters long should probably be flagged and manually inspected.
This will match words with more than 5 consonants (you probably want "y" to not be considered a consonant, but it's up to you):
\b[a-z]*[b-z&&[^aeiouy]]{6}[a-z]*\b
See live demo.
5 was chosen because I believe witchcraft has the longest chain of consonants of any English word. You could dial back "6" in the regex to say 5 or even 4 if you don't mind matching some outliers.
I have a string variable containing school names and I need to find all the possible combination of each word in this string variable in stata:
For example variation of a word "Academy" would be:
Academy,
Academy,
acdamey,
aacdemy,
dmcaamy,
aacedmy,
and so on.
I need this to standardize the raw data of school names, which has many typos of each word due to data entry issues, like the ones given above for "academy".
Depending whether your data is already in the Excel sheets or a file, you can either use regex trying to match all possible combinations (and probably fix them when found) or parse the strings first before bringing them into Excel. In either case you could make a file (or Excel list/table/area/etc.) that includes all the common typos and pick each typo as regex match to use when comparing to your actual input.
Making regexp that would actually find all possible cases is next to impossible, especially if there are cases where very similar (but correct) names for schools exist. In any case direct regexps would be very messy and complex, so I would advice you to parse the data by finding first the correct form, excluding it and then using (greedy) search/regex to find the typoed versions. You can then save the typos to use them as a filter/match/pattern.
To get some sort of starting ideas, check this links:
Regex: Search for verb roots
Read text file and extract string into Excel sheet using regex
P.s You should keep the count of all strings/school names and finally get a list of all names that did not match correct form or any of your regexp filters, so you can manually insert/correct them.
I have incoming input entries.
Like these
750
1500
1
100
25
55
And There is an lookup table like given below
25
7
5
75
So when I will receive my first entry, in this case its 750. So this will look up into lookup entry table will try to match with a string which having max match from left to right.
So for 750, max match case would be 75.
I was wondering, Is that possible if we could write a regex for this kind of scenario. Because if I choose using startsWith java function It can get me output of 7 as well.
As input entries will be coming from text file one by one and all lookup entries present in file different text file.
I'm using java language.
May I know how can I write a regex for this flavor..?
This doesn't seem like a regex problem at first, but you could actually solve it with a regex, and the result would be pretty efficient.
A regex for your example lookup table would be:
/^(75?|5|25)/
This will do what you want, and it will avoid the repeated searches of a naive "check every one" approach.
The regex would get complicated,though, as your lookup table grew. Adding a couple of terms to your lookup table:
25
7
5
75
750
72
We now have:
/^(7(50?|2)?|5|25)/
This is obviously going to get complicated quickly. The trick would be programmatically constructing the appropriate regex for arbitrary data--not a trivial problem, but not insurmountable either.
That said, this would be an..umm...unusual thing to implement in production code.
I would be hesitant to do so.
In most cases, I would simply do this:
Find all the strings that match.
Find the longest one.
(?: 25 | 5 | 75? )
There is free software that automatically makes full blown regex trie for you.
Just put the output regex into a text file and load it instead.
If your values don't change very much, this is a very fast way to do a lookup.
If it does change, generate another one.
Whats good about a full blown trie here, is that it never takes more than 8
steps to match.
The one I just did http://imgur.com/a/zwMhL
App screenshot
Even a 175,000 Word Dictionary takes no more than 8 steps.
Internally the app initially makes a ternary tree from the input
then converts it into a full blown regex trie.
My question is a continuation of this one. Basically, I have a table of words like so:
HAT18178_890909.098070313.1
HAT18178_890909.098070313.2
HAT18178_890909.143412462.1
HAT18178_890909.143412462.2
For my purposes, I do not need the terminal .1 or .2 for this set of names. I can manually write the following regex (using Python syntax):
r = re.compile('(.*\.\d+)\.\d+')
However, I cannot guarantee that my next set of names will have a similar structure where the final 2 characters will be discardable - it could be 3 characters (i.e. .12) and the separator could change as well (i.e. . to _).
What is the appropriate way to either explicitly learn a regex or to determine which characters are unnecessary?
It's an interesting problem.
X y
HAT18178_890909.098070313.1 HAT18178_890909.098070313
HAT18178_890909.098070313.2 HAT18178_890909.098070313
HAT18178_890909.143412462.1 HAT18178_890909.143412462
HAT18178_890909.143412462.2 HAT18178_890909.143412462
The problem is that there is not a single solution but many.
Even for a human it is not clear what the regex should be that you want.
Based on this data, I would think the possibilities to learn are:
Just match a fixed width of 25: .{25}
Fixed first part: HAT18178_890909.
Then:
There's only 2 varying numbers on each single spot (as you show 2 cases).
So e.g. [01] (either 0 or 1), [94] the next spot and so on would be a good solution.
The obvious one would be \d+
But it could also be \d{9}
You see, there are multiple correct answers.
These regexes would still work if the second point would be an underscore instead.
My conclusion:
The problem is that it is much more work to prepare the data for machine learning than it is to create a regex. If you want to be sure you cover everything, you need to have complete data, so then a regex is probably less effort.
You could split on non-alphanumeric characters;
[^a-zA-Z0-9']+
That would get you, in this case, few strings like this:
HAT18178
890909
098070313
1
From there on you can simply discard the last one if that's never necessary, and continue on processing the first sequences
Intro
I work in a facility where we have microscopes. These guys can be asked to generate 4D movies of a sample: they take e.g. 10 pictures at different Z position, then wait a certain amount of time (next timepoint) and take 10 slices again.
They can be asked to save a file for each slice, and they use an explicit naming pattern, something like 2009-11-03-experiment1-Z07-T42.tif. The file names are numbered to reflect the Z position and the time point
Question
Once you have all these file names, you can use a regex pattern to extract the Z and T value, if you know the backbone pattern of the file name. This I know how to do.
The question I have is: do you know a way to automatically generate regex pattern from the file name list? For instance, there is an awesome tool on the net that does similar thing: txt2re.
What algorithm would you use to parse all the file name list and generate a most likely regex pattern?
There is a Perl module called String::Diff which has the ability to generate a regular expression for two different strings. The example it gives is
my $diff = String::Diff::diff_regexp('this is Perl', 'this is Ruby');
print "$diff\n";
outputs:
this\ is\ (?:Perl|Ruby)
Maybe you could feed pairs of filenames into this kind of thing to get an initial regex. However, this wouldn't give you capturing of numbers etc. so it wouldn't be completely automatic. After getting the diff you would have to hand-edit or do some kind of substitution to get a working final regex.
First of all, you are trying to do this the hard way. I suspect that this may not be impossible but you would have to apply some artificial intelligence techniques and it would be far more complicated than it is worth. Either neural networks or a genetic algorithm system could be trained to recognize the Z numbers and T numbers, assuming that the format of Z[0-9]+ and T[0-9]+ is always used somewhere in the regex.
What I would do with this problem is to write a Python script to process all of the filenames. In this script, I would match twice against the filename, one time looking for Z[0-9]+ and one time looking for T[0-9]+. Each time I would count the matches for Z-numbers and T-numbers.
I would keep four other counters with running totals, two for Z-numbers and two for T-numbers. Each pair would represent the count of filenames with 1 match, and the ones with multiple matches. And I would count the total number of filenames processed.
At the end, I would report as follows:
nnnnnnnnnn filenames processed
Z-numbers matched only once in nnnnnnnnnn filenames.
Z-numbers matched multiple times in nnnnnn filenames.
T-numbers matched only once in nnnnnnnnnn filenames.
T-numbers matched multiple times in nnnnnn filenames.
If you are lucky, there will be no multiple matches at all, and you could use the regexes above to extract your numbers. However, if there are any significant number of multiple matches, you can run the script again with some print statements to show you example filenames that provoke a multiple match. This would tell you whether or not a simple adjustment to the regex might work.
For instance, if you have 23,768 multiple matches on T-numbers, then make the script print every 500th filename with multiple matches, which would give you 47 samples to examine.
Probably something like [ -/.=]T[0-9]+[ -/.=] would be enough to get the multiple matches down to zero, while also giving a one-time match for every filename. Or at worst, [0-9][ -/.=]T[0-9]+[ -/.=]
For Python, see this question about TemplateMaker.