For the case when you know how many columns of data there will be in your CSV file, one simple call to textscan like Amro suggests will be your best solution.
However, if you don’t know a priori how many columns are in your file, you can use a more general approach like I did in the following function. I first used the function fgetl to read each line of the file into a cell array. Then I used the function textscan to parse each line into separate strings using a predefined field delimiter and treating the integer fields as strings for now (they can be converted to numeric values later). Here is the resulting code, placed in a function read_mixed_csv:
function lineArray = read_mixed_csv(fileName, delimiter)
fid = fopen(fileName, 'r'); % Open the file
lineArray = cell(100, 1); % Preallocate a cell array (ideally slightly
% larger than is needed)
lineIndex = 1; % Index of cell to place the next line in
nextLine = fgetl(fid); % Read the first line from the file
while ~isequal(nextLine, -1) % Loop while not at the end of the file
lineArray{lineIndex} = nextLine; % Add the line to the cell array
lineIndex = lineIndex+1; % Increment the line index
nextLine = fgetl(fid); % Read the next line from the file
end
fclose(fid); % Close the file
lineArray = lineArray(1:lineIndex-1); % Remove empty cells, if needed
for iLine = 1:lineIndex-1 % Loop over lines
lineData = textscan(lineArray{iLine}, '%s', ... % Read strings
'Delimiter', delimiter);
lineData = lineData{1}; % Remove cell encapsulation
if strcmp(lineArray{iLine}(end), delimiter) % Account for when the line
lineData{end+1} = ''; % ends with a delimiter
end
lineArray(iLine, 1:numel(lineData)) = lineData; % Overwrite line data
end
end
Running this function on the sample file content from the question gives this result:
>> data = read_mixed_csv('myfile.csv', ';')
data =
Columns 1 through 7
'04' 'abc' 'def' 'ghj' 'klm' '' ''
'' '' '' '' '' 'Test' 'text'
'' '' '' '' '' 'asdfhsdf' 'dsafdsag'
Columns 8 through 10
'' '' ''
'0xFF' '' ''
'0x0F0F' '' ''
The result is a 3-by-10 cell array with one field per cell where missing fields are represented by the empty string ''. Now you can access each cell or a combination of cells to format them as you like. For example, if you wanted to change the fields in the first column from strings to integer values, you could use the function str2double as follows:
>> data(:, 1) = cellfun(@(s) {str2double(s)}, data(:, 1))
data =
Columns 1 through 7
[ 4] 'abc' 'def' 'ghj' 'klm' '' ''
[NaN] '' '' '' '' 'Test' 'text'
[NaN] '' '' '' '' 'asdfhsdf' 'dsafdsag'
Columns 8 through 10
'' '' ''
'0xFF' '' ''
'0x0F0F' '' ''
Note that the empty fields results in NaN values.