CSV (Comma-Separated Values) files are widely used for storing tabular data. MATLAB provides robust tools for working with CSV files, enabling efficient data import, export, and manipulation.

Reading CSV Files

MATLAB offers several functions to read CSV files. The most commonly used is readtable(), which imports data as a table object.

% Read a CSV file into a table
data = readtable('example.csv');

% Display the first few rows
head(data)

For more control over the import process, you can use readmatrix() to read numeric data directly into a matrix:

% Read numeric data from a CSV file
numericData = readmatrix('numeric_data.csv');

Writing CSV Files

To export data to a CSV file, MATLAB provides the writetable() function for table objects and writematrix() for numeric arrays.

% Create a sample table
T = table([1;2;3], ['A';'B';'C'], [10.5;20.7;30.2], 'VariableNames', {'ID', 'Category', 'Value'});

% Write the table to a CSV file
writetable(T, 'output.csv');

% Write a numeric matrix to a CSV file
M = [1 2 3; 4 5 6; 7 8 9];
writematrix(M, 'matrix_output.csv');

Manipulating CSV Data

Once you've imported CSV data, you can manipulate it using MATLAB's powerful array operations and matrix manipulation functions.

Filtering Data

Use logical indexing to filter rows based on specific conditions:

% Filter rows where 'Value' is greater than 20
filteredData = data(data.Value > 20, :);

Adding or Modifying Columns

You can easily add or modify columns in a table:

% Add a new column
data.NewColumn = data.Value * 2;

% Modify an existing column
data.Category = upper(data.Category);

Best Practices

Always check the imported data structure using functions like head(), size(), and class() to ensure correct import.
Use readtable() with the 'Format' option to specify column data types for more control over data import.
When writing CSV files, consider using the 'Delimiter' option in writetable() to specify a custom delimiter if needed.
For large CSV files, consider using datastore to efficiently process data that doesn't fit in memory.

Advanced CSV Operations

For more complex CSV operations, MATLAB offers additional functionality:

Handling Missing Data

CSV files often contain missing values. MATLAB represents these as NaN for numeric data or empty cells for string data. Use functions like ismissing() and fillmissing() to handle missing values effectively.

Merging CSV Files

To combine data from multiple CSV files, you can use matrix manipulation techniques or the join() function for table objects:

% Read two CSV files
data1 = readtable('file1.csv');
data2 = readtable('file2.csv');

% Merge tables based on a common key
mergedData = join(data1, data2, 'Keys', 'ID');

By mastering these CSV file operations, you'll be well-equipped to handle data import, export, and manipulation tasks in MATLAB, enhancing your data analysis capabilities.