NumPy doesn't recognize well array shape

Question

I have a code which is as follows:

data = np.array([[[i, j], i * j] for i in range(10) for j in range(10)])
print(data)

x = np.array(data[:,0])
x1 = x[:,0]
x2 = x[:,1]
print(x)

data correctly outputs [[[0,0],0],[[0,1],0],[[0,2],0],...,[[9,9],81]] which is, by the way, the multiplication table and it's results.

So, the first column of the data (which is x) must be separated into x1 and x2, which are the first and last column of it respectively. Which I think I did it right but it raises an error saying too many indices for array. What am I doing wrong?

np.array(data[:,0]) doesn't change the dtype (stays as object of 1D array). To fix : np.vstack(data[:,0]) could be used. — Divakar
– Divakar, Commented Mar 15, 2017 at 13:06
The error says so because in the line x1 = x[:,0] you call an index for an axis that does not exist as you can see when calling x.shape (this gives (100,)). Also you see when calling x that dtype=object. I guess those are the two things you expected differently. — Michael H.
– Michael H., Commented Mar 15, 2017 at 13:14

B. M. · Accepted Answer · 2017-03-15 18:02:12Z

1

data.dtype is object because the elements of [[i,j],k] are not homogeneous. A workaround for you :

data = np.array([(i, j, i * j) for i in range(10) for j in range(10)])
print(data)

x1 = data[:,:2]
x2 = data[:,2]

data.shape is now (100,3), data.dtype is int and x1 and x2 what you want.

edited Mar 15, 2017 at 18:02

answered Mar 15, 2017 at 15:58

B. M.

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Comments

hpaulj · Accepted Answer · 2017-03-15 16:33:07Z

Because of the mix of list lengths, this produces an object array:

In [97]: data = np.array([[[i, j], i * j] for i in range(3) for j in range(3)])
In [98]: data
Out[98]: 
array([[[0, 0], 0],
       [[0, 1], 0],
       [[0, 2], 0],
       [[1, 0], 0],
       [[1, 1], 1],
       [[1, 2], 2],
       [[2, 0], 0],
       [[2, 1], 2],
       [[2, 2], 4]], dtype=object)
In [99]: data.shape
Out[99]: (9, 2)

One column contains numbers (but is still object dtype), the other lists. Both have (9,) shape

In [100]: data[:,1]
Out[100]: array([0, 0, 0, 0, 1, 2, 0, 2, 4], dtype=object)
In [101]: data[:,0]
Out[101]: 
array([[0, 0], [0, 1], [0, 2], [1, 0], [1, 1], [1, 2], [2, 0], [2, 1],
       [2, 2]], dtype=object)

The easiest way of turning that column into a numeric arrays is via .tolist

In [104]: np.array(data[:,0].tolist())
Out[104]: 
array([[0, 0],
       [0, 1],
       [0, 2],
       [1, 0],
       [1, 1],
       [1, 2],
       [2, 0],
       [2, 1],
       [2, 2]])
In [105]: _.shape
Out[105]: (9, 2)

The [i, j, i * j] elements as suggested in the other answer are easier to work with.

A structured array approach to generating such a 'table':

In [113]: dt='(2)int,int'
In [114]: data = np.array([([i, j], i * j) for i in range(3) for j in range(3)],
     ...: dtype=dt)
In [115]: data
Out[115]: 
array([([0, 0], 0), ([0, 1], 0), ([0, 2], 0), ([1, 0], 0), ([1, 1], 1),
       ([1, 2], 2), ([2, 0], 0), ([2, 1], 2), ([2, 2], 4)], 
      dtype=[('f0', '<i4', (2,)), ('f1', '<i4')])
In [116]: data['f0']
Out[116]: 
array([[0, 0],
       [0, 1],
       [0, 2],
       [1, 0],
       [1, 1],
       [1, 2],
       [2, 0],
       [2, 1],
       [2, 2]])
In [117]: data['f1']
Out[117]: array([0, 0, 0, 0, 1, 2, 0, 2, 4])

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