read csv file with a lot of columns.

30 Mar 2022
2 Answers
Updated 31 Mar 2022
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Hello guys, i have the uploaded .csv file and i would like to only read the numbers that start with 0. how do i that? i tried 'readtable' and it reads but it puts everything in one cell and for textscan it has way too many columns.
I would appreciate some help thanks!

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Answers (2)

Stephen23
Stephen23 on 31 Mar 2022
Edited: Stephen23 on 31 Mar 2022
Ran in:

1 vote

Ran in:
A much better approach would use READTABLE to correctly import that numeric data (with decimal-commas in what is actually a semicolon-separated values file, not a CSV file). Do not import numeric data as text, as you are doing now. Fiddling around with text like that is fragile and inefficient.
T = readtable('rawdata.csv','FileType','text', 'Delimiter',';', 'DecimalSeparator',',',...
'ReadVariableNames',true, 'VariableNamingRule','preserve')
T = 92×130 table
Var1 908,1 914,294 920,489 926,683 932,877 939,072 945,266 951,46 957,655 963,849 970,044 976,238 982,432 988,627 994,821 1001,015 1007,21 1013,404 1019,598 1025,793 1031,987 1038,181 1044,376 1050,57 1056,764 1062,959 1069,153 1075,348 1081,542 1087,736 1093,931 1100,125 1106,319 1112,514 1118,708 1124,902 1131,097 1137,291 1143,485 1149,68 1155,874 1162,069 1168,263 1174,457 1180,652 1186,846 1193,04 1199,235 1205,429 1211,623 1217,818 1224,012 1230,206 1236,401 1242,595 1248,789 1254,984 1261,178 1267,373 1273,567 1279,761 1285,956 1292,15 1298,344 1304,539 1310,733 1316,927 1323,122 1329,316 1335,51 1341,705 1347,899 1354,094 1360,288 1366,482 1372,677 1378,871 1385,065 1391,26 1397,454 1403,648 1409,843 1416,037 1422,231 1428,426 1434,62 1440,814 1447,009 1453,203 1459,398 1465,592 1471,786 1477,981 1484,175 1490,369 1496,564 1502,758 1508,952 1515,147 1521,341 1527,535 1533,73 1539,924 1546,119 1552,313 1558,507 1564,702 1570,896 1577,09 1583,285 1589,479 1595,673 1601,868 1608,062 1614,256 1620,451 1626,645 1632,839 1639,034 1645,228 1651,423 1657,617 1663,811 1670,006 1676,2 Instrument Serial Number Temperature Notes Timestamp ____________________________ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ _______ ________ _______ ________ ________ ________ ________ ________ ________ _______ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ _______ ________ ________ ________ ________ ________ ________ _______ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ _______ ________ ________ ________ ________ ________ ________ _______ ________ ________ ________ ________ ________ ________ ________ ________ _______ ________ ________ ________ ________ ________ ________ _______ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ _______ ________ ________ ________ ________ ________ ________ _______ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ ________ _______ ________________________ ___________ _____ ___________________ {'Test-1.sam' } 0.83221 0.84018 0.84497 0.85111 0.85665 0.86114 0.86451 0.8665 0.86738 0.86788 0.86838 0.86873 0.86887 0.86825 0.8671 0.86564 0.86432 0.86318 0.86272 0.86209 0.86029 0.85956 0.85867 0.85694 0.85604 0.85543 0.85439 0.85327 0.85263 0.85151 0.85052 0.84977 0.84874 0.84828 0.84749 0.84644 0.84566 0.84442 0.84383 0.84308 0.84211 0.84122 0.84035 0.83933 0.83824 0.83746 0.83671 0.83557 0.83477 0.83396 0.833 0.83175 0.831 0.82983 0.829 0.82813 0.82721 0.82628 0.82546 0.82438 0.82337 0.82269 0.82179 0.82078 0.82033 0.81935 0.81845 0.81798 0.8172 0.81616 0.81544 0.81456 0.81345 0.81294 0.81228 0.81149 0.81095 0.81022 0.80915 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{'Probe-67Prozent-rF-3.sam'} 0.82651 0.83412 0.84217 0.84876 0.85404 0.85938 0.8631 0.86514 0.86605 0.86714 0.86741 0.86758 0.86752 0.86686 0.86637 0.86484 0.86412 0.86385 0.86272 0.86191 0.86078 0.85959 0.85849 0.85745 0.85671 0.85583 0.85446 0.85345 0.85258 0.85168 0.85106 0.85027 0.84922 0.84869 0.84781 0.84707 0.84614 0.84525 0.84459 0.84364 0.8427 0.84198 0.84121 0.84011 0.83961 0.83854 0.83735 0.83652 0.83582 0.8347 0.83405 0.83316 0.8321 0.83077 0.82992 0.82903 0.82797 0.82701 0.82665 0.82555 0.82474 0.82382 0.82287 0.82196 0.82128 0.82035 0.81954 0.81896 0.81831 0.81736 0.81662 0.81579 0.81478 0.81409 0.81337 0.81265 0.81198 0.81118 0.81023 0.80978 0.80924 0.8085 0.80799 0.80731 0.80646 0.80585 0.805 0.80443 0.80411 0.80364 0.80303 0.80278 0.80237 0.80155 0.80111 0.80064 0.7998 0.79939 0.79897 0.79824 0.79785 0.79741 0.79679 0.79641 0.79614 0.79544 0.79491 0.79462 0.79381 0.79334 0.79312 0.79233 0.79172 0.79145 0.7907 0.79003 0.78984 0.78902 0.78853 0.78816 0.78724 0.78643 0.78579 0.78481 0.7836 {'S1-PAT50023'} 38.125 NaN 15.03.2022 11:54:05 {'Probe-67Prozent-rF-4.sam'} 0.82632 0.8343 0.84069 0.84755 0.85328 0.85799 0.86216 0.8644 0.86582 0.86717 0.86704 0.8677 0.86739 0.86662 0.8664 0.86524 0.86385 0.86305 0.86253 0.86179 0.86035 0.8594 0.85838 0.85713 0.85624 0.85541 0.85441 0.85316 0.85232 0.85131 0.85061 0.85008 0.84889 0.84832 0.8478 0.84683 0.84593 0.84505 0.84439 0.84349 0.84261 0.84148 0.8411 0.84007 0.83923 0.8381 0.83738 0.83648 0.83523 0.83472 0.83389 0.83287 0.83183 0.83077 0.82979 0.8289 0.82784 0.82689 0.82653 0.82531 0.82462 0.82371 0.82266 0.82174 0.82117 0.82016 0.81925 0.81866 0.8181 0.81717 0.81652 0.81563 0.8146 0.81392 0.81328 0.8125 0.81181 0.81114 0.8102 0.80951 0.80897 0.80819 0.80773 0.80709 0.80628 0.80575 0.80489 0.80429 0.80401 0.80358 0.80292 0.80265 0.80224 0.80148 0.80096 0.8004 0.79962 0.7992 0.79881 0.79812 0.79773 0.79728 0.79662 0.79624 0.79597 0.7953 0.79476 0.79439 0.79368 0.79321 0.79295 0.79217 0.79157 0.79135 0.79062 0.78994 0.78962 0.78891 0.78832 0.78796 0.78714 0.78631 0.78561 0.78464 0.78347 {'S1-PAT50023'} 38.188 NaN 15.03.2022 11:54:24 {'Probe-67Prozent-rF-5.sam'} 0.82713 0.83447 0.84136 0.84849 0.85406 0.85938 0.86319 0.86457 0.86631 0.86735 0.86741 0.86758 0.86752 0.86686 0.86637 0.86509 0.86434 0.86338 0.8621 0.86171 0.86082 0.85961 0.85831 0.85728 0.8565 0.85525 0.85428 0.85358 0.85272 0.85135 0.85096 0.8505 0.84896 0.84856 0.84798 0.84682 0.84593 0.84505 0.84439 0.84352 0.84283 0.84163 0.84108 0.84006 0.83925 0.83823 0.8372 0.83634 0.83537 0.83456 0.8339 0.83302 0.83196 0.83063 0.82965 0.82878 0.82784 0.82689 0.82641 0.82531 0.82462 0.8236 0.82265 0.82174 0.82107 0.82015 0.81934 0.81885 0.81813 0.81717 0.81644 0.81561 0.81459 0.814 0.81315 0.81247 0.81175 0.81106 0.81016 0.80964 0.80908 0.80828 0.80775 0.80714 0.80625 0.80567 0.80494 0.80421 0.80391 0.80356 0.80287 0.80262 0.80223 0.80137 0.80089 0.80044 0.79966 0.79917 0.7988 0.79816 0.79771 0.79724 0.79659 0.7962 0.79598 0.7953 0.79478 0.79444 0.79365 0.79317 0.79295 0.79211 0.79152 0.7913 0.79057 0.78995 0.78966 0.78881 0.78832 0.78795 0.78708 0.78631 0.78561 0.78457 0.78341 {'S1-PAT50023'} 38.188 NaN 15.03.2022 11:54:45 {'Probe-67Prozent-rF-6.sam'} 0.82632 0.83415 0.84236 0.84738 0.85294 0.85802 0.86216 0.86447 0.8654 0.86652 0.86682 0.86666 0.86668 0.86668 0.86594 0.86432 0.86389 0.86326 0.86165 0.86088 0.86021 0.85942 0.85838 0.85713 0.85627 0.85518 0.85382 0.8531 0.85257 0.8514 0.8508 0.84999 0.84854 0.84791 0.84765 0.84685 0.84589 0.84483 0.8442 0.84326 0.84226 0.84127 0.84075 0.8399 0.83908 0.83807 0.83701 0.83588 0.83508 0.83442 0.83374 0.8326 0.83183 0.83076 0.82926 0.82865 0.82771 0.82677 0.82652 0.82532 0.8244 0.82348 0.82265 0.82164 0.82107 0.82005 0.81924 0.81867 0.81793 0.81707 0.81634 0.8156 0.81453 0.81383 0.8132 0.81235 0.81164 0.81098 0.81008 0.80956 0.80899 0.80828 0.80758 0.807 0.80618 0.80565 0.80485 0.80411 0.80384 0.80349 0.80283 0.80248 0.80207 0.80135 0.80086 0.80035 0.79957 0.79908 0.7987 0.79807 0.79761 0.79718 0.79656 0.79618 0.79589 0.79517 0.79471 0.79444 0.79353 0.79308 0.7929 0.79206 0.79145 0.7912 0.79055 0.78981 0.78957 0.78876 0.78826 0.78784 0.78702 0.7862 0.78549 0.78446 0.7833 {'S1-PAT50023'} 38.188 NaN 15.03.2022 11:55:04 {'Probe-67Prozent-rF-7.sam'} 0.82829 0.83539 0.84237 0.84861 0.85531 0.86042 0.86379 0.86582 0.86674 0.86725 0.86779 0.86818 0.86803 0.86767 0.86738 0.86622 0.86444 0.86372 0.8632 0.86212 0.86073 0.85977 0.85855 0.85725 0.85673 0.85585 0.85425 0.8535 0.85312 0.85165 0.85113 0.8505 0.84921 0.84879 0.84825 0.84728 0.84626 0.84508 0.84461 0.84367 0.84283 0.84185 0.84146 0.84039 0.83939 0.83824 0.83737 0.8365 0.83551 0.83457 0.83405 0.83302 0.8321 0.8309 0.82979 0.82903 0.82797 0.82701 0.82653 0.82543 0.82451 0.82359 0.82286 0.82185 0.82128 0.82006 0.81933 0.81885 0.81812 0.81725 0.81653 0.81578 0.81471 0.81393 0.81328 0.8125 0.8118 0.81121 0.81012 0.80962 0.80908 0.80835 0.80777 0.80719 0.80632 0.80579 0.80493 0.80424 0.80394 0.80359 0.80294 0.80268 0.8023 0.80139 0.80092 0.80049 0.79965 0.7992 0.7989 0.79823 0.79767 0.79719 0.79662 0.79626 0.79602 0.79529 0.79478 0.79446 0.7937 0.79315 0.7929 0.79215 0.79154 0.79136 0.79055 0.78992 0.78968 0.78887 0.78837 0.788 0.78713 0.78631 0.78561 0.78463 0.7834 {'S1-PAT50023'} 38.188 NaN 15.03.2022 11:55:24 {'Probe-33Prozent-rF-1.sam'} 0.82916 0.83714 0.84351 0.85021 0.85635 0.86165 0.8657 0.8684 0.86931 0.87024 0.87002 0.87056 0.87078 0.87022 0.87008 0.86932 0.86791 0.86659 0.86598 0.86548 0.86449 0.86363 0.86214 0.86073 0.86002 0.85948 0.85823 0.85711 0.85653 0.85537 0.85493 0.85454 0.85293 0.85245 0.85155 0.8511 0.85018 0.84914 0.8484 0.84757 0.84682 0.84576 0.84488 0.84444 0.84345 0.84234 0.84136 0.84042 0.83951 0.8389 0.83826 0.83714 0.83613 0.83497 0.83389 0.83305 0.83213 0.8312 0.83051 0.82954 0.82863 0.82795 0.82701 0.82611 0.82545 0.82433 0.82341 0.82292 0.82238 0.82145 0.82081 0.82 0.819 0.81832 0.81763 0.81686 0.81604 0.81546 0.81445 0.81394 0.8133 0.81258 0.81215 0.81154 0.81055 0.80998 0.80915 0.80841 0.80803 0.80769 0.80709 0.80679 0.8064 0.80558 0.80515 0.80474 0.80388 0.80339 0.80298 0.80232 0.80187 0.80133 0.80071 0.80034 0.80013 0.7994 0.79894 0.79868 0.79782 0.79734 0.79715 0.79629 0.79568 0.79554 0.79476 0.79411 0.79376 0.79291 0.79244 0.79211 0.79116 0.79032 0.78958 0.7884 0.78695 {'S1-PAT50023'} 38.438 NaN 15.03.2022 11:58:51
T.Timestamp % it even identifies the timestamp correctly.
ans = 92×1 datetime array
15.03.2022 11:44:55 15.03.2022 11:48:52 15.03.2022 11:49:10 15.03.2022 11:49:30 15.03.2022 11:49:50 15.03.2022 11:50:10 15.03.2022 11:50:30 15.03.2022 11:50:50 15.03.2022 11:53:25 15.03.2022 11:53:44 15.03.2022 11:54:05 15.03.2022 11:54:24 15.03.2022 11:54:45 15.03.2022 11:55:04 15.03.2022 11:55:24 15.03.2022 11:58:51 15.03.2022 11:59:12 15.03.2022 11:59:30 15.03.2022 11:59:50 15.03.2022 12:00:10 15.03.2022 12:00:30 15.03.2022 12:00:50 15.03.2022 12:02:06 15.03.2022 12:02:25 15.03.2022 12:02:45 15.03.2022 12:03:05 15.03.2022 12:03:25 15.03.2022 12:03:45 15.03.2022 12:04:05 15.03.2022 13:19:11
Arif Hoq
Arif Hoq on 30 Mar 2022
Edited: Arif Hoq on 30 Mar 2022

0 votes

try this:
A=readtable('rawdata.csv','ReadVariableName',false);
B=table2array(A(:,1));
C=split(B,';');
output=C(:,2:125);

6 Comments
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Alex Perrakis
Alex Perrakis on 30 Mar 2022
Can you explain the solution?
Arif Hoq
Arif Hoq on 30 Mar 2022
Edited: Arif Hoq on 30 Mar 2022
B=table2array(A(:,1)); % converts the table, T, to a homogeneous array, B
C=split(B,';'); % split the string with delimiter semicolon.
output=C(:,2:125); % use linear indexing to find out the value starts with 0
Alex Perrakis
Alex Perrakis on 30 Mar 2022
Cool thanks! one more question, i have other .csv files with more rows but the same number of columns and for some reason it always returns 125 do you have any idea why? i mean you are using all the rows.
Arif Hoq
Arif Hoq on 30 Mar 2022
please attach your csv file
Alex Perrakis
Alex Perrakis on 30 Mar 2022
For example this one , they have a varied amount of rows. They are named the same because the system names them like that.
Arif Hoq
Arif Hoq on 30 Mar 2022
try this:
A=readtable('rawdata2.csv','ReadVariableName',false);
B=table2array(A(:,1));
C=split(B,';');
output=C(:,2:126);

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Asked:

Alex Perrakis
Alex Perrakis
on 30 Mar 2022

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Alex Perrakis
Alex Perrakis
on 31 Mar 2022

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