NumPy for Matlab users¶
Introduction¶
MATLAB® and NumPy/SciPy have a lot in common. But there are many differences. NumPy and SciPy were created to do numerical and scientific computing in the most natural way with Python, not to be MATLAB® clones. This page is intended to be a place to collect wisdom about the differences, mostly for the purpose of helping proficient MATLAB® users become proficient NumPy and SciPy users.
Some Key Differences¶
In MATLAB®, the basic data type is a multidimensional array of double precision floating point numbers. Most expressions take such arrays and return such arrays. Operations on the 2D instances of these arrays are designed to act more or less like matrix operations in linear algebra.  In NumPy the basic type is a multidimensional array . Operations
on these arrays in all dimensionalities including 2D are elementwise
operations. One needs to use specific functions for linear algebra
(though for matrix multiplication, one can use the @ operator
in python 3.5 and above). 
MATLAB® uses 1 (one) based indexing. The initial element of a sequence is found using a(1). See note INDEXING  Python uses 0 (zero) based indexing. The initial element of a sequence is found using a[0]. 
MATLAB®’s scripting language was created for doing linear algebra. The syntax for basic matrix operations is nice and clean, but the API for adding GUIs and making fullfledged applications is more or less an afterthought.  NumPy is based on Python, which was designed from the outset to be an excellent generalpurpose programming language. While Matlab’s syntax for some array manipulations is more compact than NumPy’s, NumPy (by virtue of being an addon to Python) can do many things that Matlab just cannot, for instance dealing properly with stacks of matrices. 
In MATLAB®, arrays have passbyvalue semantics, with a lazy copyonwrite scheme to prevent actually creating copies until they are actually needed. Slice operations copy parts of the array.  In NumPy arrays have passbyreference semantics. Slice operations are views into an array. 
‘array’ or ‘matrix’? Which should I use?¶
Historically, NumPy has provided a special matrix type, np.matrix, which is a subclass of ndarray which makes binary operations linear algebra operations. You may see it used in some existing code instead of np.array. So, which one to use?
Short answer¶
Use arrays.
 They are the standard vector/matrix/tensor type of numpy. Many numpy functions return arrays, not matrices.
 There is a clear distinction between elementwise operations and linear algebra operations.
 You can have standard vectors or row/column vectors if you like.
Until Python 3.5 the only disadvantage of using the array type was that you
had to use dot
instead of *
to multiply (reduce) two tensors
(scalar product, matrix vector multiplication etc.). Since Python 3.5 you
can use the matrix multiplication @
operator.
Given the above, we intend to deprecate matrix
eventually.
Long answer¶
NumPy contains both an array
class and a matrix
class. The
array
class is intended to be a generalpurpose ndimensional array
for many kinds of numerical computing, while matrix
is intended to
facilitate linear algebra computations specifically. In practice there
are only a handful of key differences between the two.
 Operators
*
and@
, functionsdot()
, andmultiply()
: For
array
, ``*`` means elementwise multiplication, while ``@`` means matrix multiplication; they have associated functionsmultiply()
anddot()
. (Before python 3.5,@
did not exist and one had to usedot()
for matrix multiplication).  For
matrix
, ``*`` means matrix multiplication, and for elementwise multiplication one has to use themultiply()
function.
 For
 Handling of vectors (onedimensional arrays)
 For
array
, the vector shapes 1xN, Nx1, and N are all different things. Operations likeA[:,1]
return a onedimensional array of shape N, not a twodimensional array of shape Nx1. Transpose on a onedimensionalarray
does nothing.  For
matrix
, onedimensional arrays are always upconverted to 1xN or Nx1 matrices (row or column vectors).A[:,1]
returns a twodimensional matrix of shape Nx1.
 For
 Handling of higherdimensional arrays (ndim > 2)
array
objects can have number of dimensions > 2;matrix
objects always have exactly two dimensions.
 Convenience attributes
array
has a .T attribute, which returns the transpose of the data.matrix
also has .H, .I, and .A attributes, which return the conjugate transpose, inverse, andasarray()
of the matrix, respectively.
 Convenience constructor
 The
array
constructor takes (nested) Python sequences as initializers. As in,array([[1,2,3],[4,5,6]])
.  The
matrix
constructor additionally takes a convenient string initializer. As inmatrix("[1 2 3; 4 5 6]")
.
 The
There are pros and cons to using both:
array
:)
Elementwise multiplication is easy:A*B
.:(
You have to remember that matrix multiplication has its own operator,@
.:)
You can treat onedimensional arrays as either row or column vectors.A @ v
treatsv
as a column vector, whilev @ A
treatsv
as a row vector. This can save you having to type a lot of transposes.:)
array
is the “default” NumPy type, so it gets the most testing, and is the type most likely to be returned by 3rd party code that uses NumPy.:)
Is quite at home handling data of any number of dimensions.:)
Closer in semantics to tensor algebra, if you are familiar with that.:)
All operations (*
,/
,+
,
etc.) are elementwise.:(
Sparse matrices fromscipy.sparse
do not interact as well with arrays.
matrix
:\\
Behavior is more like that of MATLAB® matrices.<:(
Maximum of twodimensional. To hold threedimensional data you needarray
or perhaps a Python list ofmatrix
.<:(
Minimum of twodimensional. You cannot have vectors. They must be cast as singlecolumn or singlerow matrices.<:(
Sincearray
is the default in NumPy, some functions may return anarray
even if you give them amatrix
as an argument. This shouldn’t happen with NumPy functions (if it does it’s a bug), but 3rd party code based on NumPy may not honor type preservation like NumPy does.:)
A*B
is matrix multiplication, so it looks just like you write it in linear algebra (For Python >= 3.5 plain arrays have the same convenience with the@
operator).<:(
Elementwise multiplication requires calling a function,multiply(A,B)
.<:(
The use of operator overloading is a bit illogical:*
does not work elementwise but/
does. Interaction with
scipy.sparse
is a bit cleaner.
The array
is thus much more advisable to use. Indeed, we intend to
deprecate matrix
eventually.
Table of Rough MATLABNumPy Equivalents¶
The table below gives rough equivalents for some common MATLAB® expressions. These are not exact equivalents, but rather should be taken as hints to get you going in the right direction. For more detail read the builtin documentation on the NumPy functions.
In the table below, it is assumed that you have executed the following commands in Python:
from numpy import *
import scipy.linalg
Also assume below that if the Notes talk about “matrix” that the arguments are twodimensional entities.
General Purpose Equivalents¶
MATLAB  numpy  Notes 

help func 
info(func) or help(func) or func? (in Ipython) 
get help on the function func 
which func 
see note HELP  find out where func is defined 
type func 
source(func) or func?? (in Ipython) 
print source for func (if not a native function) 
a && b 
a and b 
shortcircuiting logical AND operator (Python native operator); scalar arguments only 
a  b 
a or b 
shortcircuiting logical OR operator (Python native operator); scalar arguments only 
1*i , 1*j , 1i , 1j 
1j 
complex numbers 
eps 
np.spacing(1) 
Distance between 1 and the nearest floating point number. 
ode45 
scipy.integrate.solve_ivp(f) 
integrate an ODE with RungeKutta 4,5 
ode15s 
scipy.integrate.solve_ivp(f, method='BDF') 
integrate an ODE with BDF method 
Linear Algebra Equivalents¶
MATLAB  NumPy  Notes 

ndims(a) 
ndim(a) or a.ndim 
get the number of dimensions of an array 
numel(a) 
size(a) or a.size 
get the number of elements of an array 
size(a) 
shape(a) or a.shape 
get the “size” of the matrix 
size(a,n) 
a.shape[n1] 
get the number of elements of the nth dimension of array a . (Note
that MATLAB® uses 1 based indexing while Python uses 0 based indexing,
See note INDEXING) 
[ 1 2 3; 4 5 6 ] 
array([[1.,2.,3.], [4.,5.,6.]]) 
2x3 matrix literal 
[ a b; c d ] 
block([[a,b], [c,d]]) 
construct a matrix from blocks a , b , c , and d 
a(end) 
a[1] 
access last element in the 1xn matrix a 
a(2,5) 
a[1,4] 
access element in second row, fifth column 
a(2,:) 
a[1] or a[1,:] 
entire second row of a 
a(1:5,:) 
a[0:5] or a[:5] or a[0:5,:] 
the first five rows of a 
a(end4:end,:) 
a[5:] 
the last five rows of a 
a(1:3,5:9) 
a[0:3][:,4:9] 
rows one to three and columns five to nine of a . This gives
readonly access. 
a([2,4,5],[1,3]) 
a[ix_([1,3,4],[0,2])] 
rows 2,4 and 5 and columns 1 and 3. This allows the matrix to be modified, and doesn’t require a regular slice. 
a(3:2:21,:) 
a[ 2:21:2,:] 
every other row of a , starting with the third and going to the
twentyfirst 
a(1:2:end,:) 
a[ ::2,:] 
every other row of a , starting with the first 
a(end:1:1,:) or flipud(a) 
a[ ::1,:] 
a with rows in reverse order 
a([1:end 1],:) 
a[r_[:len(a),0]] 
a with copy of the first row appended to the end 
a.' 
a.transpose() or a.T 
transpose of a 
a' 
a.conj().transpose() or a.conj().T 
conjugate transpose of a 
a * b 
a @ b 
matrix multiply 
a .* b 
a * b 
elementwise multiply 
a./b 
a/b 
elementwise divide 
a.^3 
a**3 
elementwise exponentiation 
(a>0.5) 
(a>0.5) 
matrix whose i,jth element is (a_ij > 0.5). The Matlab result is an
array of 0s and 1s. The NumPy result is an array of the boolean
values False and True . 
find(a>0.5) 
nonzero(a>0.5) 
find the indices where (a > 0.5) 
a(:,find(v>0.5)) 
a[:,nonzero(v>0.5)[0]] 
extract the columms of a where vector v > 0.5 
a(:,find(v>0.5)) 
a[:,v.T>0.5] 
extract the columms of a where column vector v > 0.5 
a(a<0.5)=0 
a[a<0.5]=0 
a with elements less than 0.5 zeroed out 
a .* (a>0.5) 
a * (a>0.5) 
a with elements less than 0.5 zeroed out 
a(:) = 3 
a[:] = 3 
set all values to the same scalar value 
y=x 
y = x.copy() 
numpy assigns by reference 
y=x(2,:) 
y = x[1,:].copy() 
numpy slices are by reference 
y=x(:) 
y = x.flatten() 
turn array into vector (note that this forces a copy) 
1:10 
arange(1.,11.) or r_[1.:11.] or r_[1:10:10j] 
create an increasing vector (see note RANGES) 
0:9 
arange(10.) or r_[:10.] or r_[:9:10j] 
create an increasing vector (see note RANGES) 
[1:10]' 
arange(1.,11.)[:, newaxis] 
create a column vector 
zeros(3,4) 
zeros((3,4)) 
3x4 twodimensional array full of 64bit floating point zeros 
zeros(3,4,5) 
zeros((3,4,5)) 
3x4x5 threedimensional array full of 64bit floating point zeros 
ones(3,4) 
ones((3,4)) 
3x4 twodimensional array full of 64bit floating point ones 
eye(3) 
eye(3) 
3x3 identity matrix 
diag(a) 
diag(a) 
vector of diagonal elements of a 
diag(a,0) 
diag(a,0) 
square diagonal matrix whose nonzero values are the elements of
a 
rand(3,4) 
random.rand(3,4) or random.random_sample((3, 4)) 
random 3x4 matrix 
linspace(1,3,4) 
linspace(1,3,4) 
4 equally spaced samples between 1 and 3, inclusive 
[x,y]=meshgrid(0:8,0:5) 
mgrid[0:9.,0:6.] or meshgrid(r_[0:9.],r_[0:6.] 
two 2D arrays: one of x values, the other of y values 
ogrid[0:9.,0:6.] or ix_(r_[0:9.],r_[0:6.] 
the best way to eval functions on a grid  
[x,y]=meshgrid([1,2,4],[2,4,5]) 
meshgrid([1,2,4],[2,4,5]) 

ix_([1,2,4],[2,4,5]) 
the best way to eval functions on a grid  
repmat(a, m, n) 
tile(a, (m, n)) 
create m by n copies of a 
[a b] 
concatenate((a,b),1) or hstack((a,b)) or
column_stack((a,b)) or c_[a,b] 
concatenate columns of a and b 
[a; b] 
concatenate((a,b)) or vstack((a,b)) or r_[a,b] 
concatenate rows of a and b 
max(max(a)) 
a.max() 
maximum element of a (with ndims(a)<=2 for matlab) 
max(a) 
a.max(0) 
maximum element of each column of matrix a 
max(a,[],2) 
a.max(1) 
maximum element of each row of matrix a 
max(a,b) 
maximum(a, b) 
compares a and b elementwise, and returns the maximum value
from each pair 
norm(v) 
sqrt(v @ v) or np.linalg.norm(v) 
L2 norm of vector v 
a & b 
logical_and(a,b) 
elementbyelement AND operator (NumPy ufunc) See note LOGICOPS 
a  b 
logical_or(a,b) 
elementbyelement OR operator (NumPy ufunc) See note LOGICOPS 
bitand(a,b) 
a & b 
bitwise AND operator (Python native and NumPy ufunc) 
bitor(a,b) 
a  b 
bitwise OR operator (Python native and NumPy ufunc) 
inv(a) 
linalg.inv(a) 
inverse of square matrix a 
pinv(a) 
linalg.pinv(a) 
pseudoinverse of matrix a 
rank(a) 
linalg.matrix_rank(a) 
matrix rank of a 2D array / matrix a 
a\b 
linalg.solve(a,b) if a is square; linalg.lstsq(a,b)
otherwise 
solution of a x = b for x 
b/a 
Solve a.T x.T = b.T instead  solution of x a = b for x 
[U,S,V]=svd(a) 
U, S, Vh = linalg.svd(a), V = Vh.T 
singular value decomposition of a 
chol(a) 
linalg.cholesky(a).T 
cholesky factorization of a matrix (chol(a) in matlab returns an
upper triangular matrix, but linalg.cholesky(a) returns a lower
triangular matrix) 
[V,D]=eig(a) 
D,V = linalg.eig(a) 
eigenvalues and eigenvectors of a 
[V,D]=eig(a,b) 
D,V = scipy.linalg.eig(a,b) 
eigenvalues and eigenvectors of a , b 
[V,D]=eigs(a,k) 
find the k largest eigenvalues and eigenvectors of a 

[Q,R,P]=qr(a,0) 
Q,R = scipy.linalg.qr(a) 
QR decomposition 
[L,U,P]=lu(a) 
L,U = scipy.linalg.lu(a) or LU,P=scipy.linalg.lu_factor(a) 
LU decomposition (note: P(Matlab) == transpose(P(numpy)) ) 
conjgrad 
scipy.sparse.linalg.cg 
Conjugate gradients solver 
fft(a) 
fft(a) 
Fourier transform of a 
ifft(a) 
ifft(a) 
inverse Fourier transform of a 
sort(a) 
sort(a) or a.sort() 
sort the matrix 
[b,I] = sortrows(a,i) 
I = argsort(a[:,i]), b=a[I,:] 
sort the rows of the matrix 
regress(y,X) 
linalg.lstsq(X,y) 
multilinear regression 
decimate(x, q) 
scipy.signal.resample(x, len(x)/q) 
downsample with lowpass filtering 
unique(a) 
unique(a) 

squeeze(a) 
a.squeeze() 
Notes¶
Submatrix: Assignment to a submatrix can be done with lists of
indexes using the ix_
command. E.g., for 2d array a
, one might
do: ind=[1,3]; a[np.ix_(ind,ind)]+=100
.
HELP: There is no direct equivalent of MATLAB’s which
command,
but the commands help
and source
will usually list the filename
where the function is located. Python also has an inspect
module (do
import inspect
) which provides a getfile
that often works.
INDEXING: MATLAB® uses one based indexing, so the initial element of a sequence has index 1. Python uses zero based indexing, so the initial element of a sequence has index 0. Confusion and flamewars arise because each has advantages and disadvantages. One based indexing is consistent with common human language usage, where the “first” element of a sequence has index 1. Zero based indexing simplifies indexing. See also a text by prof.dr. Edsger W. Dijkstra.
RANGES: In MATLAB®, 0:5
can be used as both a range literal
and a ‘slice’ index (inside parentheses); however, in Python, constructs
like 0:5
can only be used as a slice index (inside square
brackets). Thus the somewhat quirky r_
object was created to allow
numpy to have a similarly terse range construction mechanism. Note that
r_
is not called like a function or a constructor, but rather
indexed using square brackets, which allows the use of Python’s slice
syntax in the arguments.
LOGICOPS: & or  in NumPy is bitwise AND/OR, while in Matlab & and  are logical AND/OR. The difference should be clear to anyone with significant programming experience. The two can appear to work the same, but there are important differences. If you would have used Matlab’s & or  operators, you should use the NumPy ufuncs logical_and/logical_or. The notable differences between Matlab’s and NumPy’s & and  operators are:
 Nonlogical {0,1} inputs: NumPy’s output is the bitwise AND of the inputs. Matlab treats any nonzero value as 1 and returns the logical AND. For example (3 & 4) in NumPy is 0, while in Matlab both 3 and 4 are considered logical true and (3 & 4) returns 1.
 Precedence: NumPy’s & operator is higher precedence than logical operators like < and >; Matlab’s is the reverse.
If you know you have boolean arguments, you can get away with using NumPy’s bitwise operators, but be careful with parentheses, like this: z = (x > 1) & (x < 2). The absence of NumPy operator forms of logical_and and logical_or is an unfortunate consequence of Python’s design.
RESHAPE and LINEAR INDEXING: Matlab always allows multidimensional arrays to be accessed using scalar or linear indices, NumPy does not. Linear indices are common in Matlab programs, e.g. find() on a matrix returns them, whereas NumPy’s find behaves differently. When converting Matlab code it might be necessary to first reshape a matrix to a linear sequence, perform some indexing operations and then reshape back. As reshape (usually) produces views onto the same storage, it should be possible to do this fairly efficiently. Note that the scan order used by reshape in NumPy defaults to the ‘C’ order, whereas Matlab uses the Fortran order. If you are simply converting to a linear sequence and back this doesn’t matter. But if you are converting reshapes from Matlab code which relies on the scan order, then this Matlab code: z = reshape(x,3,4); should become z = x.reshape(3,4,order=’F’).copy() in NumPy.
Customizing Your Environment¶
In MATLAB® the main tool available to you for customizing the environment is to modify the search path with the locations of your favorite functions. You can put such customizations into a startup script that MATLAB will run on startup.
NumPy, or rather Python, has similar facilities.
 To modify your Python search path to include the locations of your
own modules, define the
PYTHONPATH
environment variable.  To have a particular script file executed when the interactive Python
interpreter is started, define the
PYTHONSTARTUP
environment variable to contain the name of your startup script.
Unlike MATLAB®, where anything on your path can be called immediately, with Python you need to first do an ‘import’ statement to make functions in a particular file accessible.
For example you might make a startup script that looks like this (Note: this is just an example, not a statement of “best practices”):
# Make all numpy available via shorter 'np' prefix
import numpy as np
# Make all matlib functions accessible at the top level via M.func()
import numpy.matlib as M
# Make some matlib functions accessible directly at the top level via, e.g. rand(3,3)
from numpy.matlib import rand,zeros,ones,empty,eye
# Define a Hermitian function
def hermitian(A, **kwargs):
return np.transpose(A,**kwargs).conj()
# Make some shortcuts for transpose,hermitian:
# np.transpose(A) > T(A)
# hermitian(A) > H(A)
T = np.transpose
H = hermitian
Links¶
See http://mathesaurus.sf.net/ for another MATLAB®/NumPy crossreference.
An extensive list of tools for scientific work with python can be found in the topical software page.
MATLAB® and SimuLink® are registered trademarks of The MathWorks.