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The 13 Different MATLAB Data Types: Ranked and Reviewed

Overview of MATLAB Data Types

MATLAB offers 13 different data types to represent a wide variety of data for scientific computing and analysis. Choosing the right data type is crucial for optimizing your code‘s speed, memory usage, and data processing capabilities.

The 13 MATLAB data types are:

  • Numeric (double, single, int8 etc.)
  • Characters and strings
  • Date and time (datetime, duration etc.)
  • Categorical arrays
  • Tables
  • Timetables
  • Structures
  • Cell arrays
  • Function handles
  • Maps
  • Time series
  • Tall arrays
  • Datastores

Let‘s explore each of these data types in more detail.

Numeric Data Types

Numeric data types are used to represent numeric data and perform mathematical operations. The key numeric types in MATLAB are:

Double – Double precision floating point number with 15-16 decimal digit precision. Best for mathematical computations.

Single – Single precision floating point number with 7 decimal digit precision. Uses less memory than double.

Integers – int8, int16, int32 etc. Whole numbers without decimal points. Different sizes support larger integer ranges.

When performing math-heavy computations, double is usually the best choice for precision. But for large arrays, single may be preferred to conserve memory. Integers are most efficient for counting and indexing.

Characters and Strings

Text data is represented in MATLAB using characters and strings:

Characters – Single text characters delimited by single quotes e.g. ‘A‘.

Strings – Sequence of characters delimited by double quotes e.g. "Hello world".

Strings in MATLAB support many convenient functions for concatenation, comparison, conversion between formats and more.

For storing and processing textual data, MATLAB strings and chars are very useful. They have less overhead than cell arrays. But for binary data and advanced text processing, other programs may be better suited.

Date and Time

Date and time data types are used for working with temporal data:

Datetime – Date and time values as numeric, string, vector, array etc. Many functions for manipulation.

Duration – Differences between dates and times. Allows easy date math.

CalendarDuration – Differences in calendar time units independent of clock time.

Date and time processing in MATLAB is very convenient with a large set of functions for conversion, extracting date parts, time zone adjustments, duration calculations etc. The syntax can be somewhat verbose for complex tasks however.

Categorical Arrays

Categorical arrays represent data with discrete categories or levels. Some key features:

  • Categories can be strings or numbers
  • Powerful grouping, sorting and filtering functionality
  • Save memory compared to cell arrays
  • Missing data support

Categoricals in MATLAB make working with discrete, tabular data very efficient. They have less overhead than structures while keeping the same convenience of accessing data by category names instead of indices.

Tables

Tables are two-dimensional arrays with metadata for storing tabular data. Their key characteristics are:

  • Column names and types defined
  • Supports heterogeneous column types
  • Row and column manipulation functions
  • Import/export from Excel, CSV etc.
  • Filtering, sorting and summary stats functionality

Tables are great for working with tabular data from files/databases because of their labeled columns and built in functions for slicing, indexing, transforming and analyzing data. They have more overhead than regular arrays however.

Timetables

Timetables represent tabular data with rows indexed by time. Features include:

  • Rows automatically indexed by dates and times
  • Fast aggregation over time intervals
  • Join, aggregate and resample operations based on time values
  • Visualization tools tailored for time-based patterns

Timetables excel at handling time series data, especially with irregular time sampling or gaps. All the usual tabular data functionality is augmented with time-based processing and visualization specifically for temporal data analysis.

Structures

Structures group together heterogeneous data types under labeled fields. These behave like objects in OOP. Benefits include:

  • Group related data of different types together
  • Access data via field names instead of indices
  • Nested structures allow complex hierarchy
  • Useful for interfacing with object-oriented languages

Structures are ideal for organizing complex, real-world data for analysis and processing in MATLAB. But they have more overhead compared to arrays for simple numeric computations.

Cell Arrays

Cell arrays are indexed arrays that can hold any type of data. Key features:

  • Insert any MATLAB data types in cells
  • Access cells quickly using indexing
  • Nested cell arrays
  • Dynamically sized
  • No memory preallocation needed

Cell arrays provide flexibility to store mixed data together. But cell indexing is slower than arrays. Also inserting or accessing nested cells repeatedly can become slow.

Function Handles

Function handles represent functions as data. This allows functions to be:

  • Assigned to variables
  • Passed as arguments
  • Returned from other functions

Benefits include:

  • Avoid rewriting similar functions
  • Parameterize and generalize functions
  • Improve code efficiency for iterative operations

By representing functions as data instead of names, they can be easily reused and passed around, enabling efficient, modular code.

Maps

Maps, also known as containers or dictionaries, are data structures that store key-value associations. Their pros and cons are:

Pros

  • Fetch values quickly based on keys
  • Variable length, dynamically resized
  • Flexible key and value types

Cons

  • Memory intensive for large data
  • Slower than arrays for vectorized ops
  • Only accessed sequentially

Overall, maps provide fast lookup times and flexibility at the cost of lower iteration speeds and higher memory usage compared to arrays. They shine when fast keyed access is critical.

Time Series

Time series matrices and datasets represent data changing over time. Features include:

  • Time and value metadata tracking
  • Built-in temporal functions
  • Resampling and imputing missing values
  • Advanced forecasting and modeling algorithms

Time series analysis in MATLAB is very accessible through time series objects and the Econometrics and Financial Toolboxes. They are memory intensive however, and may require understanding complex statistical concepts for advanced analysis.

Tall Arrays

Tall arrays store data too large to fit in memory by streaming from disk. Benefits include:

  • Analyze and process out-of-memory data
  • Operate on data as arrays without loading fully
  • Parallelize algorithms without memory bottleneck

For extremely large datasets, tall arrays remove memory constraints and utilize efficient streaming-based techniques for seamless large data analysis. But algorithms have to be optimized for sequential access when using tall arrays.

Datastores

Datastores provide abstraction when working with large datasets across files. Key features are:

  • Analyze data too large for memory across files
  • Access file data using familiar array syntax
  • Supports tall arrays for out-of-memory analysis

By allowing large file-based data to be treated as arrays, datastores simplify working with data scattered across files. But custom functions may be needed for operations not supported by datastores.

How to Choose the Right Data Type

When choosing MATLAB data types, consider factors like:

Data complexity – Are relationships between data elements important? Choose structures, tables etc. For purely numeric data, arrays.

Data size – Larger or smaller than memory? Use tall arrays, datastores for larger. Avoid cell arrays.

Access pattern – Frequent lookups by key? Use maps. Mostly sequential access? Arrays.

Memory usage – Tight on memory? Prefer singles over doubles. Table over cell arrays.

Computation speed – Numeric computations? Prefer arrays over cell arrays.

Also consider compatibility with other data sources and software you need to integrate with when choosing data types in MATLAB.

Identifying Data Types in MATLAB

To check a variable‘s data type in MATLAB, use the class function:

a = 5; 
class(a)

ans =

  double

This returns the variable a‘s double precision floating point numeric type.

We can also use the whos command to show all current variables and their types:

>> whos
  Name      Size            Bytes  Class     Attributes

  a         1x1                 8  double

Converting Between Data Types

Data types can be explicitly converted using various type casting functions:

int32(5.3) % double to int32

ans = 

   5

string(5.3) % double to string 

ans =

  5.3000  

categorical(cellstr(dates)) % strings in cell array to categorical

Care must be taken when converting data to avoid losing precision and causing errors.

Frequently Asked Questions

How to find data types in MATLAB?

Use the class and whos commands to reveal variables‘ data types.

What is the best data type for storing strings?

Strings are ideal for storing textual data in MATLAB. For non-ASCII strings, unicode strings provide better support.

How can I tell if my data will fit in memory?

Use the whos command or check the number of elements and data type‘s byte sizes. Data larger than your available RAM will need tall arrays or datastores.

What data type uses the least memory in MATLAB?

For numeric data, single (float32) uses half the memory of double (float64). For strings, the shorter the average string length, the less memory used.

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