if exist( 'grain.xls', 'file') ~= 2 % if file doesn't exist It is a typical messy dataset that requires some cleaning. School of Data hosts GRAIN landgrab data collected by an NGO. Tic, replace(namesString, 'Joey', 'Joe') toc, % time replace operation Elapsed time is 0.807283 seconds. Notice the performance difference: tic, strrep(namesCellAr, 'Joey', 'Joe') toc, % time strrep operation replace is a new string method which you can often use in place of strrep for replacing substrings of text. In most cases, you can also achieve better performance when you use string arrays with new string manipulation methods. The memory savings depends on the array data and size and is pronounced for arrays with many elements like this one. The string array uses about half the memory of the cell array of character vectors in this case. Whos( 'namesString', 'namesCellAr') % check size and type Name Size Bytes Class Attributes NamesCellAr = cellstr(namesString) % convert to cellstr namesString = names.name % this is string Let's consider the impact string arrays have on memory usage. Legend( 'Jack', 'Emily', 'Location', 'NorthWest') % add legend Memory Use and Performance of String Arrays vs. Xlabel( 'year') ylabel( 'births') % add axis labels Title( 'Baby Name Popularity') % add title Plot(Emily.year, Emily.births) % plot Emily
Jack = names(names.name = 'Jack', :) % rows named 'Jack' onlyĮmily = names(names.name = 'Emily', :) % rows named 'Emily' onlyĮmily = Emily(x = 'F', :) % just girls We can observe that Emily has seen a surge of popularity in recent years and Jack is staging a comeback.
#String split for matlab 2009 code
This makes our code clearer as compared to using strcmp. With string arrays, you can simply use the = operator for comparison. Let's plot how the popularity of the names 'Jack' and 'Emily' have changed over time. Vars = ) % concatenate tablesĭisp(names(1:5,:)) % preview 5 rows name sex births year Tbl1880 = readtable( 'names/yob1880.txt') % read the first file
#String split for matlab 2009 download
Unzip(url, 'names') % download and unzip data into |names| folder end if ~isdir( 'names') % if |names| folder doesn't exist The data is stored in separate text files by year of birth from 1880 to 2015.
Let's play with strings using the baby names dataset from Social Security Administration.
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