{"id":5066,"date":"2021-02-19T00:16:03","date_gmt":"2021-02-18T23:16:03","guid":{"rendered":"https:\/\/blogs.mathworks.com\/student-lounge\/?p=5066"},"modified":"2021-04-06T21:38:35","modified_gmt":"2021-04-06T19:38:35","slug":"introduction-to-matlab-for-python-users","status":"publish","type":"post","link":"https:\/\/blogs.mathworks.com\/student-lounge\/2021\/02\/19\/introduction-to-matlab-for-python-users\/","title":{"rendered":"Introduction to MATLAB for Python Users"},"content":{"rendered":"
Today\u2019s blog is written by Jennifer Rebbin<\/a>, Jennifer is an Application Support Engineer with the Engineering Development Group at MathWorks. In this blog, she shares some important concepts that will help get you up to speed with using MATLAB as a Python user. Jennifer, over to you..<\/em><\/div>\n

—<\/p>\n

You have some Python experience \u2013 maybe from a programming or data science class, or maybe from fun side projects. And now you are digging into your engineering\u00a0<\/span>career or<\/span>\u00a0classes and need to use MATLAB.<\/span>\u00a0<\/span>There are a lot of similarities between MATLAB and Python, so your Python experience should\u00a0<\/span>be<\/span>\u00a0a good head start for learning MATLAB<\/span>.<\/span>\u00a0<\/span>This blog post examines the similarities and differences between MATLAB and Python and provides syntax examples for applying general programming concepts in MATLAB.<\/span>\u00a0<\/span><\/p>\n

Learn the basics of MATLAB\u00a0programming<\/span>\u00a0<\/span><\/h1>\n

The basic data structure in MATLAB is a matrix<\/span>, while Python treats everything as<\/span>\u00a0<\/span>a general object.<\/span>\u00a0<\/span>In fact,\u00a0<\/span>\u201c<\/span>MATLAB<\/span>\u201d<\/span>\u00a0is an abbreviation for \u201cmatrix laboratory\u201d<\/span>–\u00a0<\/span>this difference is an integral part of what defines MATLAB.<\/span>\u00a0<\/span>\u00a0<\/span><\/p>\n

Let\u2019s\u00a0<\/span>learn about MATLAB programming\u00a0<\/span>with an example\u00a0<\/span>u<\/span>sing<\/span>\u00a0<\/span>a<\/span>\u00a0<\/span>MATLAB dataset\u00a0<\/span>popcorn<\/span><\/i>.\u00a0<\/span>The data is from a study of popcorn brands and popper types (Hogg 1987). The columns of the matrix\u202fare<\/span>\u00a0popcorn<\/span>\u00a0<\/span>kernel\u00a0<\/span>brands, Gourmet, National, and Generic, respectivel<\/span>y.\u00a0<\/span>The first three rows correspond\u00a0<\/span>to\u00a0<\/span>batches using\u00a0<\/span>an<\/span>\u00a0oil popper, and the last three rows correspond to\u00a0<\/span>batches using\u00a0<\/span>an<\/span>\u00a0air popper. The response values are the yield in cups of popped popcorn<\/span>.<\/span>\u00a0<\/span><\/p>\n

We can<\/span>\u00a0load the data in the\u00a0<\/span>Command Window<\/span><\/a>\u00a0panel\u00a0<\/span>of the MATLAB Desktop<\/span>.<\/span>\u00a0<\/span>\"\"<\/p>\n

load 'popcorn'<\/span>;\r\npopcorn<\/pre>\n
 popcorn = \r\n   5.5\u00a0 \u00a0 \u00a04.5\u00a0\u00a0\u00a0 3.5\r\n   5.5\u00a0 \u00a0 \u00a04.5\u00a0 \u00a0 \u00a04\r\n    6\u00a0 \u00a0 \u00a0\u00a0 4\u00a0 \u00a0 \u00a0 3\r\n   6.5\u00a0\u00a0\u00a0\u00a0\u00a0\u00a05\u00a0 \u00a0 \u00a0 4\r\n    7\u00a0 \u00a0\u00a0 \u00a0 5\u00a0 \u00a0 \u00a0 5\r\n    7\u00a0 \u00a0 \u00a0 \u00a05\u00a0 \u00a0 \u00a04.5\r\n<\/pre>\n
Now that we have our data, we want to perform\u00a0<\/span>a simple calculation<\/span>\u00a0to<\/span>\u00a0determine the ratio of\u00a0<\/span>popped\u00a0<\/span>popcorn\u00a0<\/span>between the\u00a0<\/span>Gourmet and National brand<\/span>s<\/span>.<\/span>\u00a0Since we don\u2019t specify an\u00a0 output variable, MATLAB uses the v<\/span>ariable\u00a0<\/span>ans<\/span><\/i>\u00a0to store<\/span>\u00a0our calculation results.<\/span>\u00a0<\/span><\/p>\n
popcorn(:,1).\/popcorn(:,2)<\/pre>\n
 ans\u00a0= \r\n   1.22\r\n   1.22\r\n   1.5\r\n   1.3\r\n   1.27\r\n   1.4\r\n<\/pre>\n
MATLAB was created for linear algebra so operators such as the\u00a0<\/span>division<\/span>\u00a0operator \u201c<\/span>\/<\/span>\u201d produce conventional matrix operations<\/span>, while some Python libraries perform operations element-by-element<\/span>. To perform eleme<\/span>nt-wise\u00a0<\/span>operations in MATLA<\/span>B,\u00a0<\/span>a period is placed before the operator, as in\u00a0<\/span>\u201c.<\/span>\/<\/span>\u201d<\/span>.<\/span>\u00a0<\/span><\/p>\n
To learn more about<\/span>\u00a0common<\/span>\u00a0MATLAB syntax and its functionality, reference<\/span>\u00a0the table below<\/span>\u00a0<\/span>or<\/span>\u00a0<\/span>this comprehensive guide<\/span><\/a>.<\/span>\u00a0<\/span><\/p>\n
\"\"<\/p>\n
Index data and function outputs<\/span>\u00a0<\/span><\/h1>\n
Next, let\u2019s learn about indexing into data and function outputs. In MATLAB, the beginning element of an array has an index of 1, unlike in Python where the beginning element has an index of 0.<\/span>\u00a0<\/span><\/p>\n
M<\/span>ATLAB\u00a0<\/span>uses\u00a0<\/span>N-D\u00a0<\/span>indexing, or<\/span>\u00a0providing a specific index value for each array dimension (such as a row number and a column number). It\u2019s important to note that indices in Python are left inclusive and right exclusive but MATLAB is inclusive on both ends.<\/span>\u00a0<\/span><\/p>\n
popcorn<\/pre>\n
 popcorn =\r\n     5.5    4.5    3.5\r\n     5.5    4.5     4 \r\n      6      4      3 \r\n     6.5     5      4 \r\n      7      5      5 \r\n      7      5     4.5 \r\n<\/pre>\n
 % View batches with popper type\u00a0air\u00a0with N-D indexing<\/span>\r\npopcorn(4:end,:)<\/pre>\n
 ans =\r\n     6.5     5     4\r\n      7     5.5    5\r\n      7      5    4.5\r\n<\/pre>\n
 % View batches with\u00a0popcorn\u00a0brands Gourmet and\u00a0Generic\u00a0with N-D indexing<\/span>\r\npopcorn(:,[1 3])<\/pre>\n
 ans =\r\n     5.5    3.5    \r\n     5.5     4 \r\n      6      3 \r\n     6.5     4 \r\n      7      5 \r\n      7     4.5    \r\n<\/pre>\n
MATLAB also uses linear indexing where a single subscript traverses down each column of an array in order, or logical indexing where MATLAB extracts matrix elements corresponding to non-zero elements of a logical array\u00a0<\/span>produced by using an operator like \u201c>\u201d in indexing. Find more information about these indexing\u00a0strategies\u00a0<\/span>in this article<\/span><\/a>.<\/span>\u00a0<\/span><\/p>\n
 % View the ninth batch with linear indexing<\/span>\r\n   popcorn(9)<\/pre>\n
 ans = 4<\/pre>\n
 % View batches producing more than 6 cups of popcorn with logical indexing<\/span>\r\npopcorn(popcorn>6)<\/pre>\n
 ans =\r\n     6.5\r\n      7 \r\n      7 \r\n<\/pre>\n
To<\/span>\u00a0<\/span>expand an array<\/span><\/a>, in Python you could append the item. In MATLAB, array size increases automatically to accommodate new elements outside index bounds which allows us to quickly manipulate arrays. If information is<\/span>\u00a0missing, MATLAB pads the matrix with zeros to keep it rectangular.<\/span>\u00a0Let\u2019s add a new column for a fourth popper brand to be tested.<\/span>\u00a0<\/span><\/p>\n
popcorn(1,4) = 0<\/pre>\n
 popcorn =\r\n     5.5    4.5    3.5    0\r\n     5.5    4.5     4     0\r\n      6      4      3     0\r\n     6.5     5      4     0\r\n      7     5.5     5     0\r\n      7      5     4.5    0\r\n<\/pre>\n
Now that we know how to expand an array in MATLAB, let\u2019s revert to the original matrix. To remove the column we just added, simply execute the following.<\/span>\u00a0<\/span><\/p>\n
popcorn(:,4) = [];<\/pre>\n
MATLAB and Python incorporate slightly different referencing schemes. Check out the table below to observe additional uses of MATLAB referencing syntax.<\/span>\u00a0<\/span><\/p>\n
\"\"<\/p>\n
 <\/p>\n
Writ<\/span>e<\/span>\u00a0and us<\/span>e<\/span>\u00a0MATLAB\u00a0functions<\/span>\u00a0<\/span><\/h1>\n
Now that we\u00a0<\/span>understand<\/span>\u00a0basic MATLAB programming syntax<\/span>\u00a0and indexing<\/span>, let\u2019s<\/span>\u00a0explore\u00a0<\/span>a\u00a0<\/span>MATLAB\u00a0<\/span>built-in\u00a0<\/span>function<\/span>\u00a0<\/span>mean<\/span><\/i>\u00a0<\/span>and find the average\u00a0<\/span>popcorn yield<\/span>\u00a0for each popper brand.\u00a0<\/span>When calling built-in functions<\/span>\u00a0in MATLAB<\/span>, we won\u2019t need to reference a package nam<\/span>e.<\/span>\u00a0<\/span><\/p>\n
brandAvg = mean(popcorn)<\/pre>\n
 brandAvg = \r\n     6.25    4.75    4<\/pre>\n
Let\u2019s create a<\/span>\u00a0<\/span>user-defined function<\/span><\/a>\u00a0<\/span>popperTypeAvg<\/span><\/i>\u00a0<\/span>in a\u00a0<\/span>new Function file<\/span><\/a>\u00a0<\/span>to<\/span>\u00a0compare the yield of<\/span>\u00a0oil\u00a0<\/span>and<\/span>\u00a0air popper types.<\/span>\u00a0<\/span>MATLAB uses\u00a0<\/span>function<\/span><\/i>\u00a0and\u00a0<\/span>end<\/span><\/i>, while Python uses\u00a0<\/span>def<\/span><\/i>.\u00a0<\/span>We will use one input argument and two output arguments to\u00a0<\/span>bring variables from the function workspace to our MATLAB base workspace.<\/span>\u00a0<\/span><\/p>\n
 % Saved as\u00a0function\u00a0file on MATLAB path called\u00a0popperTypeAvg.m (file and function names must match)<\/span>\r\nfunction<\/span> [firstHalfAvg, secondHalfAvg] = popperTypeAvg(data)\r\n     rows1to3 = mean(data(1:3,:),'all') \r\n     rows4to6 = mean(data(4:6,:),'all')\r\nend<\/span> \r\n<\/pre>\n
Now let\u2019s call\u00a0<\/span>our<\/span>\u00a0function\u00a0<\/span>popperTypeAvg<\/span><\/i>, declaring the input as our dataset\u00a0<\/span>popcor<\/span><\/i>n<\/span><\/i>\u00a0and the output<\/span>s<\/span>\u00a0as variables\u00a0<\/span>oilAvg<\/span><\/i>\u00a0and\u00a0<\/span>airAvg<\/span><\/i>.<\/span>\u00a0<\/span><\/p>\n
[oilAvg,airAvg] = popperTypeAvg(popcorn)<\/pre>\n
  oilAvg = 4.5\r\n  airAvg = 5.5<\/pre>\n
In addition to creating a function definition file,<\/span>\u00a0there are other wa<\/span>y<\/span>s to define functions<\/span>\u00a0in MATLAB\u00a0including<\/span>\u00a0<\/span>local functions<\/span><\/a>\u00a0at the end of script file<\/span>s<\/span>,\u00a0<\/span>nested functions<\/span><\/a>\u00a0located within another function<\/span>, and<\/span>\u00a0<\/span>anonymous functions<\/span><\/a>\u00a0defined\u00a0<\/span>in an<\/span>\u00a0executable statement<\/span>.<\/span>\u00a0<\/span><\/p>\n
Visualize\u00a0data<\/span>\u00a0<\/span><\/h1>\n
Let\u2019s<\/span>\u00a0<\/span>visualize our data and get a better idea of which brand\u00a0<\/span>yields<\/span>\u00a0the most popcorn.\u00a0<\/span>We can adjust figure\u00a0<\/span>p<\/span>roperties<\/span>\u00a0<\/span>within the function call\u00a0<\/span>using name-value pairs,\u00a0<\/span>or with separate\u00a0<\/span>command<\/span>s.<\/span>\u00a0<\/span><\/p>\n
bar(popcorn,'BarWidth',.75) \r\nbrandNames = [\"Gourmet\",\"National\",\"Generic\"]; \r\nlegend(brandNames,'Location','bestoutside') \r\ntitle('Popcorn Yield') \r\nxlabel('Batch Number') \r\nylabel('Cups of Popped Popcorn')<\/pre>\n
\"\"<\/p>\n
If we want to create\u00a0<\/span>one figure with three plots (one for each popcorn popper brand)<\/span>, we can use<\/span>\u00a0<\/span>the<\/span>\u00a0MATLAB function\u00a0<\/span>tiledlayout<\/span>\u00a0<\/span>and<\/span>\u00a0specify the number of rows and colum<\/span>ns as input arguments.<\/span>\u00a0<\/span><\/p>\n
% To obtain the number of columns of an array, we can specify the second dimension\u00a0in size(A,dim)<\/span>\r\ntiledlayout(1,size(popcorn,2)) \r\nfor<\/span> i = 1:size(popcorn,2) \r\n    nexttile \r\n    bar(popcorn(:,i)) \r\n    title(brandNames(i)) \r\n    xlabel('Batch Number') \r\n    ylabel('Cups of Popped Popcorn') \r\nend<\/span><\/pre>\n
\"\"<\/p>\n
Understand data\u00a0types<\/span>\u00a0<\/span><\/h1>\n
Unlike some other languages, neither Python\u00a0<\/span>n<\/span>or MATLAB require declaring a variable<\/span>\u00a0or variable type<\/span>;\u00a0<\/span>a<\/span>\u00a0<\/span>variable\u00a0<\/span>is<\/span>\u00a0created the moment\u00a0<\/span>it\u00a0<\/span>is<\/span>\u00a0assigned\u00a0<\/span>a\u00a0<\/span>value<\/span>.<\/span>\u00a0<\/span><\/p>\n
While\u00a0<\/span>in Python\u00a0<\/span>it is easy to<\/span>\u00a0accidentally<\/span>\u00a0create\u00a0<\/span>ints<\/span><\/i>\u00a0and\u00a0<\/span>special constructors\u00a0<\/span>may be\u00a0<\/span>necessary<\/span>\u00a0<\/span>to create numeric arrays,\u00a0<\/span>MATLAB\u2019s default numeric class<\/span>\u00a0is double-precision floating point<\/span>.<\/span>\u00a0In MATLAB,\u00a0<\/span>\u201c1\u201d<\/span>\u00a0and\u00a0<\/span>\u201c1.<\/span>\u201d\u00a0<\/span>are always equal.<\/span>\u00a0Doubles can be complex\u00a0<\/span>and\u00a0<\/span>accept most mathematical operations.<\/span>\u00a0T<\/span>o view a variable\u2019s data\u00a0<\/span>type,<\/span>\u00a0you could use\u00a0<\/span>type<\/span><\/i>\u00a0in Python. In MATLAB we\u00a0<\/span>can<\/span>\u00a0use the\u00a0<\/span>whos<\/span><\/i>\u00a0<\/span>function<\/span>, or<\/span>\u00a0view the variable data type in the\u00a0<\/span>MATLAB Workspace Browser<\/span><\/a>.<\/span>\u00a0<\/span><\/p>\n
whos 'popcorn'<\/span><\/pre>\n
Name     Size   Bytes   Class   Attributes\r\npopcorn  6x3     144    double \r\n<\/pre>\n
MATLAB has built-in functions<\/span>\u00a0for converting data types. Let\u2019s explore\u00a0<\/span>one<\/span>\u00a0of these functions\u00a0<\/span>array2table<\/span><\/i>\u00a0to\u00a0<\/span>create a table and label the<\/span>\u00a0<\/span>columns of\u00a0<\/span>our popcorn dataset.<\/span>\u00a0<\/span><\/p>\n
brandNames = [\"Gourmet\",\"National\",\"Generic\"]; \r\narray2table(popcorn,'VariableNames',brandNames)<\/pre>\n
 ans = \r\n   6x3 table<\/a>\r\n      Gourmet    National    Generic\r\n      _______    ________    ________<\/strong>\r\n        5.5        4.5         3.5  \r\n        5.5        4.5          4  \r\n         6          4           3  \r\n        6.5         5           4  \r\n         7         5.5          5  \r\n         7          5          4.5 \r\n<\/pre>\n
I<\/span>n MATLAB,\u00a0<\/span>the\u00a0<\/span>logical data<\/span>\u00a0<\/span>type<\/span><\/a>\u00a0can\u00a0<\/span>have value \u201c0\u201d<\/span>\u00a0for false<\/span>\u00a0or \u201c1<\/span>\u201d<\/span>\u00a0for true<\/span>.<\/span>\u00a0Let\u2019s explore this behavior\u00a0<\/span>by<\/span>\u00a0utilizing logical indexing\u00a0<\/span>and a conditional statement<\/span>\u00a0to determine if the Gourmet brand\u00a0<\/span>kernels<\/span>\u00a0produced the largest average batch<\/span>.<\/span>\u00a0<\/span><\/p>\n
brandAvg = mean(popcorn)\r\n(brandAvg(1)>brandAvg(2))&&(brandAvg(1)>brandAvg(3))<\/pre>\n
brandAvg = \r\n    6.25   4.75   4\r\n\r\nans = \r\n   logical<\/a>\r\n      1<\/pre>\n
Other data type options such as character, date, categorical array, structure, and cell array can be\u00a0<\/span>explored<\/span>\u00a0<\/span><\/span>in the MATLAB data types documentation<\/span><\/span><\/a>\u00a0or in the\u00a0<\/span><\/span>table below<\/span><\/span>.<\/span><\/span>\u00a0<\/span><\/p>\n
\"\"<\/p>\n
Implement looping\u00a0behavior<\/span>\u00a0<\/span><\/h1>\n
In programming, loops repeat a sequence until a specified condition is met. Often, loops resize arrays and require MATLAB to continuously look<\/span>\u00a0for larger contiguous blocks of memor<\/span>y.\u00a0<\/span>One trick\u00a0<\/span>to improve code execution time\u00a0<\/span>when using loops and conditional statements in MATLAB\u00a0is\u00a0<\/span>pre-allocation<\/span><\/a>, or establishing the maximum amount of space required for the array<\/span>. To pre-allocate an array, create an array with the intended resulting<\/span>\u00a0size and then replace the elements, rather than changing the array size with each iteration.<\/span>\u00a0<\/span> Let\u2019s<\/span>\u00a0find the\u00a0<\/span>kernel\u00a0<\/span>brand w<\/span>ith the highest yield f<\/span>or\u00a0<\/span>each\u00a0<\/span>batc<\/span>h.<\/span>\u00a0<\/span><\/p>\n
%First, pre-allocate the bestBrand array and change the data type to categorical<\/span>\r\nbestBrand = zeros(6,1); \r\nbestBrand = categorical(bestBrand); \r\n% For each row, check which column contains the maximum yield <\/span>\r\nfor<\/span> i = 1:6 \r\n    if<\/span> popcorn(i,1) == max(popcorn(i,:)) \r\n        bestBrand(i) = 'Gourmet'; \r\n    end<\/span> \r\n    if<\/span> popcorn(i,2) == max(popcorn(i,:)) \r\n        bestBrand(i) = 'National'; \r\n    end<\/span> \r\n    if<\/span> popcorn(i,3) == max(popcorn(i,:)) \r\n        bestBrand(i) = 'Generic'; \r\n    end<\/span> \r\nend<\/span> \r\nbestBrand<\/pre>\n
bestBrand = \r\n  6x1 categorical <\/a>array\r\n\r\n    Gourmet\r\n    Gourmet\r\n    Gourmet\r\n    Gourmet\r\n    Gourmet\r\n    Gourmet<\/pre>\n
From this analysis, we see the Gourmet brand produced the most popcorn in\u00a0<\/span>all six<\/span>\u00a0<\/span>batch<\/span>es<\/span>.\u00a0<\/span>While this method\u00a0<\/span>provided the<\/span>\u00a0desired result<\/span>,\u00a0<\/span>the code is lengthy and takes a long time to execute. How could we find the best brand more efficiently?<\/span>\u00a0<\/span><\/p>\n
M<\/span>ATLAB often\u00a0<\/span>doesn\u2019t require writing loops as it is \u201cvectorized\u201d.<\/span>\u00a0MATLAB is optimized for matri<\/span>x\u00a0<\/span>and vector<\/span>\u00a0operations<\/span>,\u00a0<\/span>so\u00a0<\/span>writing vectorized code<\/span><\/a>\u00a0can improve code readability, speed, and memory<\/span>.<\/span>\u00a0<\/span>We can<\/span>\u00a0<\/span>vectorize our loop by calling\u00a0<\/span>max<\/span><\/i>\u00a0<\/span>on the entire\u00a0<\/span>popcorn<\/span><\/i>\u00a0<\/span>array\u00a0<\/span>rather than<\/span>\u00a0one row at a time.<\/span>\u00a0The\u00a0<\/span>max<\/span><\/i>\u00a0function returns multiple outputs,\u00a0<\/span>so we will capture these in square brackets.<\/span>\u00a0<\/span><\/p>\n
% To return the max of each row as a column vector, we specify the second dimension in max(A,[],dim) <\/span>\r\n[maxVal,index] = max(popcorn,[],2); \r\nbrandNames(index)<\/pre>\n
ans = \r\n   1x6 string <\/a>array\r\n     \"Gourmet\"  \"Gourmet\"  \"Gourmet\"  \"Gourmet\"  \"Gourmet\"  \"Gourmet\"<\/pre>\n
Check out the table below\u00a0<\/span><\/span>for<\/span><\/span>\u00a0more looping behaviors<\/span><\/span>\u00a0and conditional statements<\/span><\/span>\u00a0t<\/span><\/span>o<\/span><\/span>\u00a0incorporate in MATLAB<\/span><\/span>.<\/span><\/span>\u00a0<\/span><\/p>\n
\"\"<\/p>\n
Define a custom\u00a0class<\/span>\u00a0<\/span><\/h1>\n
W<\/span>e\u00a0<\/span>can\u00a0use<\/span>\u00a0<\/span>object-oriented programming<\/span><\/a>\u00a0<\/span>when building an application to group\u00a0<\/span>parameters (properties) and functions (methods) into one class.<\/span>\u00a0<\/span>While Python classes can be defined anywhere, MATLAB class<\/span>es are created<\/span>\u00a0in a class definitio<\/span>n<\/span>\u00a0file<\/span>.<\/span>\u00a0MATLAB classes can be useful to have more control over what can and cannot be done to an object, such as defining a property as \u201cconstant\u201d to disable modification.<\/span>\u00a0<\/span>Check<\/span>\u00a0out<\/span>\u00a0a<\/span>n<\/span>\u00a0<\/span>example utilizing sensor array data<\/span><\/a>\u00a0<\/span>or follow the class framework below.<\/span>\u00a0<\/span><\/p>\n
 <\/p>\n
 % Saved as file MyClass.m, class name must match file name <\/span>\r\nclassdef<\/span> MyClass \r\n      properties<\/span>\r\n            prop \r\n      end<\/span> \r\n      methods<\/span> \r\n            function<\/span> obj = MyClass(val) \r\n            end<\/span> \r\n      end<\/span> \r\nend <\/span><\/pre>\n
The<\/span>\u00a0above<\/span>\u00a0<\/span>class framework\u00a0<\/span>shows the\u00a0<\/span>MATLAB default<\/span>\u00a0copy<\/span>–<\/span>by<\/span>–<\/span>write<\/span>\u00a0value class<\/span>, as opposed to a copy-by-reference class.\u00a0<\/span>Check\u00a0out\u00a0<\/span>this page<\/span><\/a>\u00a0outlining the differences between these two class types<\/span>.<\/span>\u00a0<\/span><\/p>\n
Closing thoughts<\/span>\u00a0<\/span><\/h1>\n
We hope\u00a0<\/span>this information paints a<\/span>\u00a0picture of the functional\u00a0<\/span>similarities and\u00a0<\/span>differences<\/span>\u00a0<\/span>between<\/span>\u00a0<\/span>the\u00a0<\/span>Python and MATLAB<\/span>\u00a0languages.\u00a0<\/span>Hopef<\/span>u<\/span>lly you are finding MATLAB easy to learn and can<\/span>\u00a0us<\/span>e this information to get starte<\/span>d<\/span>.<\/span>\u00a0For more\u00a0<\/span>introductory resources,<\/span>\u00a0check out our two\u00a0hour\u00a0<\/span>MATLAB Onramp<\/span><\/a>\u00a0<\/span>tutorial, read\u00a0<\/span>\u201c<\/span>Get Started with MATLAB<\/span><\/a>“<\/span>,<\/span>\u00a0or dive into the MathWorks<\/span>\u00a0documentation and<\/span>\u00a0articles linked throughout this blog.\u00a0<\/span>Be sure to comment below\u00a0<\/span>or use your preferred social media platform to share what you\u2019re working on and ask any question<\/span>s<\/span>.<\/span>\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"
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Today\u2019s blog is written by Jennifer Rebbin, Jennifer is an Application Support Engineer with the Engineering Development Group at MathWorks. In this blog, she shares some important concepts that will… read more >><\/a><\/p>\n","protected":false},"author":165,"featured_media":5180,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[365,6,10],"tags":[88,470,431],"_links":{"self":[{"href":"https:\/\/blogs.mathworks.com\/student-lounge\/wp-json\/wp\/v2\/posts\/5066"}],"collection":[{"href":"https:\/\/blogs.mathworks.com\/student-lounge\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.mathworks.com\/student-lounge\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/student-lounge\/wp-json\/wp\/v2\/users\/165"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/student-lounge\/wp-json\/wp\/v2\/comments?post=5066"}],"version-history":[{"count":62,"href":"https:\/\/blogs.mathworks.com\/student-lounge\/wp-json\/wp\/v2\/posts\/5066\/revisions"}],"predecessor-version":[{"id":5236,"href":"https:\/\/blogs.mathworks.com\/student-lounge\/wp-json\/wp\/v2\/posts\/5066\/revisions\/5236"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/student-lounge\/wp-json\/wp\/v2\/media\/5180"}],"wp:attachment":[{"href":"https:\/\/blogs.mathworks.com\/student-lounge\/wp-json\/wp\/v2\/media?parent=5066"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.mathworks.com\/student-lounge\/wp-json\/wp\/v2\/categories?post=5066"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.mathworks.com\/student-lounge\/wp-json\/wp\/v2\/tags?post=5066"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}