Numpy Hstack's Weird Behavior
Solution 1:
Transposing 1-d arrays such as arr3
and arr4
returns a 1-d array, not a 2-d array.
np.repeat(True,5)
# returns:array([ True, True, True, True, True])
np.repeat(True,5).T
# returns:array([ True, True, True, True, True])
It does not produce a new axis. You need to do that before transposing.
To increase the number of axes, you can use np.newaxis
.
a = np.repeat(True, 5)
a[:, np.newaxis]
# returns:
array([[ True],
[ True],
[ True],
[ True],
[ True]])
a[:, np.newaxis].T
# returns:
array([[ True, True, True, True, True]])
Solution 2:
Your problem is Even with T but your arr is one dimension (n,)
, which mean you can not simple T
to make it become (n,1)
dimension
How to fix it : with numpy
broadcast to get (n,1)
Y = np.hstack((arr3[:,None],
arr4[:,None]
))
Y
Out[14]:
array([[1, 0],
[1, 0],
[1, 0],
[1, 0],
[1, 1],
[1, 1],
[1, 1],
[1, 1],
[1, 2],
[1, 2],
[1, 2],
[1, 2],
[1, 3],
[1, 3],
[1, 3],
[1, 3],
[1, 4],
[1, 4],
[1, 4],
[1, 4]])
Solution 3:
In [92]: arr1 = np.repeat(True,10)
...: arr2 = np.repeat(np.arange(5),2)
In [93]: arr1.shape
Out[93]: (10,)
In [94]: arr2.shape
Out[94]: (10,)
Transpose switches axes, but does not add any.
In[95]: arr1.T.shapeOut[95]: (10,)
vstack (vertical) makes sure the inputs are atleast 2d, and joins them on the 1st axis
In [96]: np.vstack((arr1,arr2))
Out[96]:
array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[0, 0, 1, 1, 2, 2, 3, 3, 4, 4]])
In [97]: _.shape
Out[97]: (2, 10)
Effectively it does:
In [99]: np.concatenate((arr1.reshape(1,-1),arr2.reshape(1,-1)), axis=0)
Out[99]:
array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[0, 0, 1, 1, 2, 2, 3, 3, 4, 4]])
Note that the boolean True
has been changed to numeric 1
so it has the same dtype as arr2
.
hstack
makes sure the inputs have at least 1 dimension, and joins on the last. [source]
In[100]: np.hstack((arr1,arr2))
Out[100]: array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 2, 2, 3, 3, 4, 4])
In[101]: _.shapeOut[101]: (20,)
Again transpose doesn't change the 1d shape.
Another convenience function:
In [102]: np.column_stack((arr1,arr2)).shape
Out[102]: (10, 2)
this makes the inputs 2d, and joins on the last axis (look at its code for details)
yet another convenience:
In[103]: np.stack((arr1,arr2),axis=1).shapeOut[103]: (10, 2)
In[104]: np.stack((arr1,arr2),axis=0).shapeOut[104]: (2, 10)
All of these just tweak the dimensions and then use concatenate
.
structured array
In [110]: arr = np.zeros((10,), dtype='bool,i')
In [111]: arr['f0']=arr1
In [112]: arr['f1']=arr2
In [113]: arr
Out[113]:
array([( True, 0), ( True, 0), ( True, 1), ( True, 1), ( True, 2),
( True, 2), ( True, 3), ( True, 3), ( True, 4), ( True, 4)],
dtype=[('f0', '?'), ('f1', '<i4')])
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