Matrix Transpose Calculator
Welcome to the matrix transpose calculator, where you'll have the opportunity to learn all about transposing matrices. Apart from the determinant and the inverse, transposition is one of the basic matrix operations. Therefore, we've all come here not just to see how to transpose a matrix, but also to get to get the lowdown on all the matrix transpose properties. But, don't worry, we'll go through all the information you need together, before we even come to what is, say, the transpose of a 2x2 matrix.
Grab your morning/afternoon tea, and let's get going, shall we?
What is a matrix?
What does "two" mean? What does it represent? Believe it or not, there is an school of thought called the philosophy of mathematics, where a couple of elder scientists ask these fundamental questions. Fortunately, we can leave them sitting there with their heads in the clouds while we deal with the problem in a more downtoearth manner.
Numbers, as we know, describe lengths, dimensions, weighs, etc. For example, a fraction can be a pizza slice. A negative number can be a bank debt. Our point is that all these numbers, which we call real numbers, are... well, real. They appear all around us. Even the square root of a number describes the diagonal of a rectangle, and the famous π is present in all circle calculations. So it seems like we know all there is to know about numbers, doesn't it?
Wrong. In fact, the real numbers are only where the fun starts. Fortunately, we're not here to go into the crazy (but very useful and exciting) extension called complex numbers. We'd like to go in a different direction: to try to understand objects that we can't accurately describe with a single number.
A matrix is an array of elements (usually numbers) that has a set number of rows and columns. An example of a matrix would be
A  = 

Moreover, we say that a matrix has cells, or boxes, into which we write the elements of our array. For example, the above matrix A
has the value 2
in the cell that is in the second row and the second column.
As you can see, matrices are a tool used to write a few numbers concisely and operate on the whole lot as a single object. As such, they are extremely useful when dealing with:
 systems of equations, especially when trying to find the reduced row echelon form of a system;
 vectors and vector spaces;
 3dimensional geometry (e.g., the dot product and the cross product);
 eigenvalues and eigenvectors; and
 graph theory and discrete mathematics.
To understand matrices better, mathematicians define several operations. For example, we can add the arrays together. Heck, we can even multiply them, although this can get a little tricky. And when you
want to find the inverse of a matrix via the socalled adjoint matrix, you need to know how to transpose a matrix (the cofactor matrix in this case), which brings us to why we're here right now.
How to transpose a matrix?
To transpose a matrix, we just flip its cells so that what was a row before will now be a column and vice versa. To help visualize it, imagine that your matrix is written on a piece of paper. To find its transpose, it's enough to flip the sheet to the other side and rotate it 90 degrees counterclockwise.
To those who like formulas, let's say that we have a matrix A
whose cells are indexed by aᵢⱼ
, where i
denotes the number of the row and j
is the number of the column. For example, the element a₂₃
is in the second row of the third column. Then, the transpose of A
, denoted Aᵀ
, will have elements aⱼᵢ
, i.e., the second index is now the number of the row, and the first is the number of the column. In particular, the a₂₃
from before will now be in the third row of the second column.
Easy enough, isn't it? Well, it's not rocket science, so let's stop at that. And even better  there are several interesting and useful matrix transpose properties!
Matrix transpose properties
Now that we know how to transpose a matrix, let's take a closer look at
and try to find some matrix transpose properties that can simplify our calculations.
In general, the shape of the transpose is different from that of the original matrix. Say that we start with a matrix that has
n
rows andm
columns. After transposing, the first row (which hasm
elements) will become the first column, which means the matrix transpose will havem
rows. Similarly, it will haven
columns (as opposed tom
in the initial matrix). Of course, the shape will not change only if our array was a square, for example, if we wanted to find the transpose of a 2x2 matrix. 
The transpose of a transpose is the initial matrix. By definition, transposing means exchanging rows with columns. Therefore, if we do this flip a second time, we'll change them back and obtain what we started with. Symbolically, this property can be written as
(Aᵀ)ᵀ = A
, whereA
is any matrix. 
The transpose of a sum is the sum of the transposes. In other words, if we find ourselves adding (or subtracting) some matrices, then it doesn't matter if we add them together first and transpose the result, or transpose each summand and then add them together after. Symbolically, this means that
(A + B)ᵀ = Aᵀ + Bᵀ
, whereA
andB
are arbitrary matrices of the same size. 
The transpose of a product is the product of the transposes reversed. This means that if we want to multiply two matrices and transpose the result, then we can alternatively begin by transposing the factors and multiplying them but in reverse order. In other words,
(A * B)ᵀ = Bᵀ * Aᵀ
for any two matricesA
andB
, for whichA * B
exists. 
The determinant stays the same after transposition. If we have a square matrix and we'd like to find its determinant, then we automatically know that its transpose will have the same determinant. In symbolic notation, this means that
A = Aᵀ
for any square matrixA
(here 
denote the determinant, not the absolute value of a number).
All this time spent reading through the theory should be rewarded with a nice example, wouldn't you say?
Example: using the matrix transpose calculator
Recall the matrix A
that we met :
A  = 

Let's use the matrix transpose calculator to find Aᵀ
.
The first thing we have to do is figure out the size. Our A
has three rows and two columns, so we need to tell that to our calculator by choosing the correct values under "number of rows" and "number of columns". This will show us a symbolic picture of a matrix similar to ours with its cells denoted by a₁
, a₂
, b₁
, and so on. We then have to input the data from A
, i.e., the numbers in its boxes, as the symbols corresponding to the cells in the picture. Since our matrix has elements 3
and 1
in its first row, and the picture tells us that the first row has symbols a₁
and a₂
, we should write
a₁ = 3
and a₂ = 1
.
Similarly, we fill out the other rows:
b₁ = 0
, b₂ = 2
,
c₁ = 1
, and c₂ = 1
.
This will make the matrix transpose calculator spit out the result. Nevertheless, let's try to also calculate the answer by hand.
Matrix A
has three rows and two columns, so its transpose Aᵀ
will have a different shape: two rows and three columns. (Recall that for square matrices, the shape doesn't change, say, for the transpose of a 2x2 matrix.) Also, A
's first row has elements 3
and 1
, so we copy them into the first column of Aᵀ
:
Aᵀ  = 

Similarly, we put the second row of A
into the second column of Aᵀ
, and the third row into the third column.
Aᵀ  = 

That wasn't so bad, was it? No need to lose any more sleep on all those crucial matrix transpose questions. Omni Calculator saved the day yet again!
A  = 

⌈  ⌉ᵀ  =  ⌈  ⌉  
⌊  ⌋  ⌊  ⌋ 