When calling a Python ® function, MATLAB ® converts MATLAB data into types that best represent the data in the Python language. For information about using Python data in MATLAB, see Handle Data Returned from Python Function.
MATLAB Input Argument Type —
Scalar Values Only
Resulting Python py. Type
double (real)
single (real)
double (complex)
single (complex)
z = complex(1,2); py.cmath.polar(z)
ans = Python tuple with no properties. (2.23606797749979, 1.1071487177940904)
int8
uint8
int16
uint16
int32
uint32
int64
uint64
string scalar
char vector
value in string
py.list('Value'>)
ans = Python list with no properties. [None, 'Value']
Python object — py . type
function handle @ py . module . function , to Python functions only
MATLAB Input Argument Type —
1 -by- N Vector
Resulting Python Type
double (complex)
single (complex)
char vector
string scalar
The Python language provides a protocol for accessing memory buffers like the data stored in a MATLAB array. MATLAB implements this Python buffer protocol for MATLAB arrays so that you can read MATLAB arrays directly from Python code, running in the same process as MATLAB, without copying data.
Many Python functions directly use the MATLAB array from Python without converting it to a native Python type. Some functions might require a specific type, such as numpy.ndarray , or might modify data in the array. These functions might accept the MATLAB array and copy the data into the required type. Other functions might display an error if you do not pass the required type. To pass data to these functions, first create the required Python type from the MATLAB data, then pass it to the Python function. For example, to create array p to pass to a Python function that requires data of type numpy.array , type:
p = py.numpy.array(magic(3))
p = Python ndarray: 8 1 6 3 5 7 4 9 2 Use details function to view the properties of the Python object. Use double function to convert to a MATLAB array.
MATLAB sparse arrays are not supported in Python. See Unsupported MATLAB Types .
If a Python function expects a specific Python multidimensional array type such as numpy.ndarray , then MATLAB displays a message with tips about how to proceed. If the problem might be due to passing a matrix or a multidimensional array as an argument, then do the following.
For example, suppose that the following code returns an error.
a = [1 2; 3 4]; py.pyfunc(a)
If the documentation of pyfunc specifies that the expected type is numpy.ndarray , then try this conversion:
py.pyfunc(numpy.ndarray(a))
If the error persists, then determine the root cause by checking for additional information in the Python exception.
These MATLAB types are not supported in Python.