## .T in NumPy: Meaning?

Here’s what the .T in NumPy means: .T is the returned transposed array with its axes permuted that is used in NumPy. It is the same as calling self.transpose(). When you use .T, you must have at least 2 arrays to work with. Having only 1 array is ineffective. This is a standard matrix transpose for a 2-D array. So if you want to learn all about the meaning of .T in NumPy in Python, then this article is for you. Let’s jump right into it! How Is .Transpose() Used In NumPy? When using .transpose() in NumPy, you’re essentially changing how you look at the array. Remember it needs to be a 2D array or bigger in order for transpose to work. Once called, transpose() will reverse or permute the array on its axes. Take a look at the image below. You have 2 stacks of blocks from 0-7 in front of you. Once you have called transpose(), the blocks have changed! No longer are they standing up but laying down on their side! You still have all 8 blocks(0-7) but their position has changed. It still reads 0-7 in order. Since multidimensional arrays in NumPy are fixed-sized, even transposed, the ndarray has the same number of items. All I did was change the group sizes. Instead of now having 4 groups of 2, I now have 2 groups of 4. If I didn’t have a 2D array, it would return the only ndarray available with no changes. The ndim or the number of array dimensions must be >= 2, else nothing happens. How Is a Transposed Array in NumPy Reshaped? (2 Methods) As you know with Python and so for NumPy, you can stack methods on top of one another to change the outcome of the output. In NumPy, you have 2 methods called .shape() and .reshape() respectively. Not to confuse the 2 of them together. You can use