how to convert items of array into array themselves Python

Question

My problem is that I've got this array:

np.array([0.0, 0.0, -1.2, -1.2, -3.4, -3.4, -4.5, -4.5])

and I want to convert the elements to array like this:

np.array([[0.0], [0.0], [-1.2], [-1.2], [-3.4], [-3.4], [-4.5], [-4.5]])

So is there a loop or a numpy function that I could use to do this task?

akuiper · Accepted Answer · 2016-07-11 20:00:29Z

13

Or simply:

arr[:,None]

# array([[ 0. ],
#        [ 0. ],
#        [-1.2],
#        [-1.2],
#        [-3.4],
#        [-3.4],
#        [-4.5],
#        [-4.5]])

edited Jul 11, 2016 at 20:00

answered Jul 11, 2016 at 19:38

akuiper

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4 Comments

Moses Koledoye Over a year ago

Same as a1[:,np.newaxis]. None in a np slice is synonymous to np.newaxis

akuiper Over a year ago

@utkarsh13 Honestly, I can't explain this better than the doc docs.scipy.org/doc/numpy/reference/…. Basically, None means creating a newaxis here.

akuiper Over a year ago

@OrB Works on Python 2 as well.

Or B Over a year ago

@Psidom OK, I thought it should work for any list. Turns out it works for numpy arrays only.

Bahrom · Accepted Answer · 2016-07-11 19:37:27Z

7

You can use a list comprehension:

>>> a1 = np.array([0.0, 0.0, -1.2, -1.2, -3.4, -3.4, -4.5, -4.5])
>>> np.array([[x] for x in a1])
array([[ 0. ],
       [ 0. ],
       [-1.2],
       [-1.2],
       [-3.4],
       [-3.4],
       [-4.5],
       [-4.5]])
>>>

answered Jul 11, 2016 at 19:37

Bahrom

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Comments

zwol · Accepted Answer · 2016-07-11 19:39:24Z

6

Isn't this just a reshape operation from row to column vector?

In [1]: import numpy as np
In [2]: x = np.array([0.0, 0.0, -1.2, -1.2, -3.4, -3.4, -4.5, -4.5])
In [3]: np.reshape(x, (-1,1))
Out[3]: 
array([[ 0. ],
       [ 0. ],
       [-1.2],
       [-1.2],
       [-3.4],
       [-3.4],
       [-4.5],
       [-4.5]])

answered Jul 11, 2016 at 19:39

zwol

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Comments

MSeifert · Accepted Answer · 2016-12-13 11:42:14Z

There are several ways to achieve this with numpy functions:

np.expand_dims - the explicit option

>>> import numpy as np
>>> a = np.array([0.0, 0.0, -1.2, -1.2, -3.4, -3.4, -4.5, -4.5])

>>> np.expand_dims(a, axis=1)
array([[ 0. ], [ 0. ], [-1.2], [-1.2], [-3.4], [-3.4], [-4.5], [-4.5]])

slicing with np.newaxis (which is an alias for None)

>>> np.array(a)[:, np.newaxis]
array([[ 0. ], [ 0. ], [-1.2], [-1.2], [-3.4], [-3.4], [-4.5], [-4.5]])
>>> np.array(a)[:, None]
array([[ 0. ], [ 0. ], [-1.2], [-1.2], [-3.4], [-3.4], [-4.5], [-4.5]])

Instead of adding an axis manually you can also use some default ways to create a multidimensional array and then swap the axis, for example with np.transpose but you could also use np.swapaxes or np.reshape:

np.array with ndmin=2

>>> np.array([0.0, 0.0, -1.2, -1.2, -3.4, -3.4, -4.5, -4.5], ndmin=2).T
array([[ 0. ], [ 0. ], [-1.2], [-1.2], [-3.4], [-3.4], [-4.5], [-4.5]])

np.atleast_2d and

>>> np.atleast_2d(a).swapaxes(1, 0)
array([[ 0. ], [ 0. ], [-1.2], [-1.2], [-3.4], [-3.4], [-4.5], [-4.5]])

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