matlab 'unique' is skipping rows with data

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Theresa Bradley
Theresa Bradley on 2 Jan 2024
Commented: Star Strider on 2 Jan 2024
I'm trying to put parse a large file to only contain data that has changed. First column is continuous time so I exclude that column. Next 3 columns are what I need to parse...with unique values. When reviewing final file (I called 12Dec2023Events.csv) with a file I parsed by hand, I noticed not all data was included.
346:17:37:18.6839209 9 23 3E
346:17:37:18.6939210 2 E 3E (data not identified by unique)
unique did not pick up the change until
346:17:37:52.2139008 2 E 3E
346:17:37:52.2239008 2 0 3E
t=readtable('Myfile.csv');
[~,ind]=unique(t(:,2:4),'stable');
t2=t(ind,:);
Please help the files are just to massive to parse by hand and having a file with data missing isn't a solution either

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Answers (2)

Stephen23
Stephen23 on 2 Jan 2024
Edited: Stephen23 on 2 Jan 2024
Ran in:
The basic problem is that your file is large, and by default READTABLE checks a limited number of rows** before deciding what data type each column has***. Your data file has very different data at the start of those columns than it does further down those columns, e.g. some of them contain mostly numeric data at the start... but in fact you want columns 2, 3, & 4 imported as text (because they all contain alphanumeric characters****).
So you need to tell READMATRIX that, e.g.:
unzip Myfile.zip
fnm = 'Myfile.csv';
obj = detectImportOptions(fnm);
obj = setvartype(obj,2:4,'string');
tbl = readtable(fnm,obj)
Warning: Column headers from the file were modified to make them valid MATLAB identifiers before creating variable names for the table. The original column headers are saved in the VariableDescriptions property.
Set 'VariableNamingRule' to 'preserve' to use the original column headers as table variable names.
tbl = 520657×4 table
UTC MRC_TXID MRC_CommandID MRC_User_Defined ____________________ ________ _____________ ________________ 8321:16:37.994597698 "1A" "0" "8" 8321:16:38.004597698 "1A" "0" "8" 8321:16:38.014597698 "1A" "0" "8" 8321:16:38.024597698 "1A" "0" "8" 8321:16:38.034597698 "1A" "0" "8" 8321:16:38.044597698 "1A" "0" "8" 8321:16:38.054597599 "1A" "0" "8" 8321:16:38.064597698 "1A" "0" "8" 8321:16:38.074597698 "1A" "0" "8" 8321:16:38.084597599 "1A" "0" "8" 8321:16:38.094597599 "1A" "0" "8" 8321:16:38.104597599 "1A" "0" "8" 8321:16:38.114597599 "1A" "0" "8" 8321:16:38.124597599 "1A" "0" "8" 8321:16:38.134597599 "1A" "0" "8" 8321:16:38.144597599 "1A" "0" "8"
[~,ind] = unique(tbl(:,2:4),'stable');
t2 = tbl(ind,:)
t2 = 235×4 table
UTC MRC_TXID MRC_CommandID MRC_User_Defined ____________________ ________ _____________ ________________ 8321:16:37.994597698 "1A" "0" "8" 8321:29:57.394167701 "1" "E" "3E" 8321:29:58.474167098 "1" "23" "3E" 8321:30:52.554138698 "2" "E" "3E" 8321:30:53.634138099 "2" "23" "3E" 8321:31:30.414119201 "5" "23" "3E" 8321:34:20.234026298 "6" "23" "3E" 8321:34:41.864013999 "A" "23" "3E" 8321:35:00.254003200 "9" "23" "3E" 8321:37:52.223900798 "2" "0" "3E" 8321:38:07.373891498 "2" "24" "3E" 8321:38:11.693888801 "2" "12" "3E" 8321:38:17.103885501 "2" "31" "3E" 8321:38:20.353883499 "2" "1C" "3E" 8321:38:30.083877399 "2" "E" "30" 8321:38:35.493874000 "2" "E" "34"
And there is your "missing" data:
idx = all(t2{:,2:4}==["2","E","3E"],2);
t2(idx,:)
ans = 1×4 table
UTC MRC_TXID MRC_CommandID MRC_User_Defined ____________________ ________ _____________ ________________ 8321:30:52.554138698 "2" "E" "3E"
Just to confirm, lets check its location in the imported table:
idy = ind(idx)
idy = 85331
tbl(idy,:)
ans = 1×4 table
UTC MRC_TXID MRC_CommandID MRC_User_Defined ____________________ ________ _____________ ________________ 8321:30:52.554138698 "2" "E" "3E"
And checking that line in the original file (don't forget the header is also one line):
So far everything looks as expected.
"matlab 'unique' is skipping rows with data"
So far I don't see any problem with UNIQUE.
** Apparently fewer than 79825:
*** Because otherwise people complain that file importing takes too long. This is a good example of John Lydgate's aphorism about pleasing all people all of the time.
**** Look at your table t: numeric columns cannot contain alphabetic characters. That should be the big clue for you, that you need to modify the file importing.
  1 Comment
Stephen23
Stephen23 on 2 Jan 2024
Ran in:
In comparison, look at the columns of your table, what classes do columns 3 & 4 have? (hint: numeric).
When debugging always look at your data!
unzip Myfile.zip
t = readtable('Myfile.csv')
Warning: Column headers from the file were modified to make them valid MATLAB identifiers before creating variable names for the table. The original column headers are saved in the VariableDescriptions property.
Set 'VariableNamingRule' to 'preserve' to use the original column headers as table variable names.
t = 520657×4 table
UTC MRC_TXID MRC_CommandID MRC_User_Defined ____________________ ________ _____________ ________________ 8321:16:37.994597698 {'1A'} 0 8 8321:16:38.004597698 {'1A'} 0 8 8321:16:38.014597698 {'1A'} 0 8 8321:16:38.024597698 {'1A'} 0 8 8321:16:38.034597698 {'1A'} 0 8 8321:16:38.044597698 {'1A'} 0 8 8321:16:38.054597599 {'1A'} 0 8 8321:16:38.064597698 {'1A'} 0 8 8321:16:38.074597698 {'1A'} 0 8 8321:16:38.084597599 {'1A'} 0 8 8321:16:38.094597599 {'1A'} 0 8 8321:16:38.104597599 {'1A'} 0 8 8321:16:38.114597599 {'1A'} 0 8 8321:16:38.124597599 {'1A'} 0 8 8321:16:38.134597599 {'1A'} 0 8 8321:16:38.144597599 {'1A'} 0 8

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Star Strider
Star Strider on 2 Jan 2024
Ran in:
I am not certain what the problem is, however if you want to test for more than one value in a row, specify that with the 'rows' argument. It will consider all the elements in a row (columns 2 to 4 in this instance) in its determination.
Try this —
Uz = unzip('Myfile.zip')
Uz = 1×2 cell array
{'12Dec2023Events.csv'} {'Myfile.csv'}
t=readtable(Uz{2}, 'VariableNamingRule','preserve')
t = 520657×4 table
UTC MRC-TXID MRC-CommandID MRC-User-Defined ____________________ ________ _____________ ________________ 8321:16:37.994597698 {'1A'} 0 8 8321:16:38.004597698 {'1A'} 0 8 8321:16:38.014597698 {'1A'} 0 8 8321:16:38.024597698 {'1A'} 0 8 8321:16:38.034597698 {'1A'} 0 8 8321:16:38.044597698 {'1A'} 0 8 8321:16:38.054597599 {'1A'} 0 8 8321:16:38.064597698 {'1A'} 0 8 8321:16:38.074597698 {'1A'} 0 8 8321:16:38.084597599 {'1A'} 0 8 8321:16:38.094597599 {'1A'} 0 8 8321:16:38.104597599 {'1A'} 0 8 8321:16:38.114597599 {'1A'} 0 8 8321:16:38.124597599 {'1A'} 0 8 8321:16:38.134597599 {'1A'} 0 8 8321:16:38.144597599 {'1A'} 0 8
[~,ind]=unique(t(:,2:4),'stable','rows');
t2=t(ind,:)
t2 = 156874×4 table
UTC MRC-TXID MRC-CommandID MRC-User-Defined ____________________ ________ _____________ ________________ 8321:16:37.994597698 {'1A'} 0 8 8321:29:57.394167701 {'1' } NaN NaN 8321:29:57.404167701 {'1' } NaN NaN 8321:29:57.414167701 {'1' } NaN NaN 8321:29:57.424167701 {'1' } NaN NaN 8321:29:57.434167701 {'1' } NaN NaN 8321:29:57.444167701 {'1' } NaN NaN 8321:29:57.454167598 {'1' } NaN NaN 8321:29:57.464167598 {'1' } NaN NaN 8321:29:57.474167598 {'1' } NaN NaN 8321:29:57.484167598 {'1' } NaN NaN 8321:29:57.494167598 {'1' } NaN NaN 8321:29:57.504167598 {'1' } NaN NaN 8321:29:57.514167598 {'1' } NaN NaN 8321:29:57.524167598 {'1' } NaN NaN 8321:29:57.534167598 {'1' } NaN NaN
If you intend something else or want a different result, please provide more details.
.
  2 Comments
Dyuman Joshi
Dyuman Joshi on 2 Jan 2024
The output for a table without the 'rows' option is the same with the 'rows' option specified (see the description - https://in.mathworks.com/help/releases/R2019b/matlab/ref/unique.html?s_tid=doc_ta ), so that would not make a difference.
Star Strider
Star Strider on 2 Jan 2024
That’s not how I read the section on Unique Rows in Matrix, although in this instance it is considering everything in the last three columns.

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