Transposing a 1D NumPy array
PythonNumpyTransposePython Problem Overview
I use Python and NumPy and have some problems with "transpose":
import numpy as np
a = np.array([5,4])
print(a)
print(a.T)
Invoking a.T
is not transposing the array. If a
is for example [[],[]]
then it transposes correctly, but I need the transpose of [...,...,...]
.
Python Solutions
Solution 1 - Python
It's working exactly as it's supposed to. The transpose of a 1D array is still a 1D array! (If you're used to matlab, it fundamentally doesn't have a concept of a 1D array. Matlab's "1D" arrays are 2D.)
If you want to turn your 1D vector into a 2D array and then transpose it, just slice it with np.newaxis
(or None
, they're the same, newaxis
is just more readable).
import numpy as np
a = np.array([5,4])[np.newaxis]
print(a)
print(a.T)
Generally speaking though, you don't ever need to worry about this. Adding the extra dimension is usually not what you want, if you're just doing it out of habit. Numpy will automatically broadcast a 1D array when doing various calculations. There's usually no need to distinguish between a row vector and a column vector (neither of which are vectors. They're both 2D!) when you just want a vector.
Solution 2 - Python
Use two bracket pairs instead of one. This creates a 2D array, which can be transposed, unlike the 1D array you create if you use one bracket pair.
import numpy as np
a = np.array([[5, 4]])
a.T
More thorough example:
>>> a = [3,6,9]
>>> b = np.array(a)
>>> b.T
array([3, 6, 9]) #Here it didn't transpose because 'a' is 1 dimensional
>>> b = np.array([a])
>>> b.T
array([[3], #Here it did transpose because a is 2 dimensional
[6],
[9]])
Use numpy's shape
method to see what is going on here:
>>> b = np.array([10,20,30])
>>> b.shape
(3,)
>>> b = np.array([[10,20,30]])
>>> b.shape
(1, 3)
Solution 3 - Python
For 1D arrays:
a = np.array([1, 2, 3, 4])
a = a.reshape((-1, 1)) # <--- THIS IS IT
print a
array([[1],
[2],
[3],
[4]])
Once you understand that -1 here means "as many rows as needed", I find this to be the most readable way of "transposing" an array. If your array is of higher dimensionality simply use a.T
.
Solution 4 - Python
You can convert an existing vector into a matrix by wrapping it in an extra set of square brackets...
from numpy import *
v=array([5,4]) ## create a numpy vector
array([v]).T ## transpose a vector into a matrix
numpy also has a matrix
class (see array vs. matrix)...
matrix(v).T ## transpose a vector into a matrix
Solution 5 - Python
numpy 1D array --> column/row matrix:
>>> a=np.array([1,2,4])
>>> a[:, None] # col
array([[1],
[2],
[4]])
>>> a[None, :] # row, or faster `a[None]`
array([[1, 2, 4]])
And as @joe-kington said, you can replace None
with np.newaxis
for readability.
Solution 6 - Python
To 'transpose' a 1d array to a 2d column, you can use numpy.vstack
:
>>> numpy.vstack(numpy.array([1,2,3]))
array([[1],
[2],
[3]])
It also works for vanilla lists:
>>> numpy.vstack([1,2,3])
array([[1],
[2],
[3]])
Solution 7 - Python
You can only transpose a 2D array. You can use numpy.matrix
to create a 2D array. This is three years late, but I am just adding to the possible set of solutions:
import numpy as np
m = np.matrix([2, 3])
m.T
Solution 8 - Python
instead use arr[:,None]
to create column vector
Solution 9 - Python
Basically what the transpose function does is to swap the shape and strides of the array:
>>> a = np.ones((1,2,3))
>>> a.shape
(1, 2, 3)
>>> a.T.shape
(3, 2, 1)
>>> a.strides
(48, 24, 8)
>>> a.T.strides
(8, 24, 48)
In case of 1D numpy array (rank-1 array) the shape and strides are 1-element tuples and cannot be swapped, and the transpose of such an 1D array returns it unchanged. Instead, you can transpose a "row-vector" (numpy array of shape (1, n)
) into a "column-vector" (numpy array of shape (n, 1)
). To achieve this you have to first convert your 1D numpy array into row-vector and then swap the shape and strides (transpose it). Below is a function that does it:
from numpy.lib.stride_tricks import as_strided
def transpose(a):
a = np.atleast_2d(a)
return as_strided(a, shape=a.shape[::-1], strides=a.strides[::-1])
Example:
>>> a = np.arange(3)
>>> a
array([0, 1, 2])
>>> transpose(a)
array([[0],
[1],
[2]])
>>> a = np.arange(1, 7).reshape(2,3)
>>> a
array([[1, 2, 3],
[4, 5, 6]])
>>> transpose(a)
array([[1, 4],
[2, 5],
[3, 6]])
Of course you don't have to do it this way since you have a 1D array and you can directly reshape it into (n, 1)
array by a.reshape((-1, 1))
or a[:, None]
. I just wanted to demonstrate how transposing an array works.
Solution 10 - Python
Another solution.... :-)
import numpy as np
a = [1,2,4]
> [1, 2, 4]
b = np.array([a]).T
> array([[1], > [2], > [4]])
Solution 11 - Python
The transpose of
x = [[0 1],
[2 3]]
is
xT = [[0 2],
[1 3]]
well the code is:
import numpy as np
a = [[0, 1],[2, 3]]
x = np.array(a);
np.transpose(x)
Or the simple way:
x.T
this a link for more information: <http://docs.scipy.org/doc/numpy/reference/generated/numpy.transpose.html>
** Transposing a 1-D array returns an unchanged view of the original array. Try this for 1D array:
b = np.array([a])
Solution 12 - Python
I am just consolidating the above post, hope it will help others to save some time:
The below array has (2, )
dimension, it's a 1-D array,
b_new = np.array([2j, 3j])
There are two ways to transpose a 1-D array:
slice it with "np.newaxis" or none.!
print(b_new[np.newaxis].T.shape)
print(b_new[None].T.shape)
other way of writing, the above without T
operation.!
print(b_new[:, np.newaxis].shape)
print(b_new[:, None].shape)
Wrapping [ ] or using np.matrix, means adding a new dimension.!
print(np.array([b_new]).T.shape)
print(np.matrix(b_new).T.shape)
Solution 13 - Python
There is a method not described in the answers but described in the documentation for the numpy.ndarray.transpose
method:
> For a 1-D array this has no effect, as a transposed vector is simply the same vector. To convert a 1-D array into a 2D column vector, an additional dimension must be added. np.atleast2d(a).T achieves this, as does a[:, np.newaxis].
One can do:
import numpy as np
a = np.array([5,4])
print(a)
print(np.atleast_2d(a).T)
Which (imo) is nicer than using newaxis
.
Solution 14 - Python
As some of the comments above mentioned, the transpose of 1D arrays are 1D arrays, so one way to transpose a 1D array would be to convert the array to a matrix like so:
np.transpose(a.reshape(len(a), 1))
Solution 15 - Python
The name of the function in numpy
is column_stack.
>>>a=np.array([5,4])
>>>np.column_stack(a)
array([[5, 4]])
Solution 16 - Python
To transpose a 1-D array (flat array) as you have in your example, you can use the np.expand_dims()
function:
>>> a = np.expand_dims(np.array([5, 4]), axis=1)
array([[5],
[4]])
np.expand_dims()
will add a dimension to the chosen axis. In this case, we use axis=1
, which adds a column dimension, effectively transposing your original flat array.