pytutorial/numpy/dimensions

Dimensions and shape

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The goal

Questions to David Rotermund

numpy.ndarray.shape

ndarray.shape

Tuple of array dimensions.

The shape property is usually used to get the current shape of an array, but may also be used to reshape the array in-place by assigning a tuple of array dimensions to it. As with numpy.reshape, one of the new shape dimensions can be -1, in which case its value is inferred from the size of the array and the remaining dimensions. Reshaping an array in-place will fail if a copy is required.

import numpy as np

data = np.zeros((2, 4, 2, 7, 2))
print(data.shape)  # -> (2, 4, 2, 7, 2)

Vanishing dimensions

import numpy as np

data = np.zeros((5, 3, 2))

# All the same dimensionwise
print(data.shape)  # -> (5, 3, 2)
print(data[:].shape)  # ->  (5, 3, 2)
print(data[:, :, :].shape)  # ->  (5, 3, 2)
print(data[...].shape)  # ->  (5, 3, 2)


print(data[0, :, :].shape)  # ->  (3, 2)
print(data[:, 0, :].shape)  # ->  (5, 2)
print(data[:, :, 0].shape)  # ->  (5, 3)

print(data[:, 0, 0].shape)  # ->  (5,)
print(data[0, :, 0].shape)  # ->  (3,)
print(data[0, 0, :].shape)  # ->  (2,)

print(data[0, 0, 0].shape)  # ->  ()
print(type(data[0, 0, 0]))  # ->  <class 'numpy.float64'>

keepdims

There are functions like

ndarray.sum(axis=None, dtype=None, out=None, keepdims=False, initial=0, where=True)
ndarray.mean(axis=None, dtype=None, out=None, keepdims=False, *, where=True)
ndarray.prod(axis=None, dtype=None, out=None, keepdims=False, initial=1, where=True)
ndarray.max(axis=None, out=None, keepdims=False, initial=<no value>, where=True)
ndarray.min(axis=None, out=None, keepdims=False, initial=<no value>, where=True)
ndarray.argmax(axis=None, out=None, *, keepdims=False)
ndarray.argmin(axis=None, out=None, *, keepdims=False)

that normally make one dimension vanish. However, often this type of functions have an argument keepdims that keeps this dimension alive.

import numpy as np

data = np.zeros((5, 3, 2))

# All the same dimensionwise
print(data.shape)  # -> (5, 3, 2)
print(data.sum().shape)  # -> ()
print(data.sum(axis=0).shape)  # -> (3, 2)
print(data.sum(axis=1).shape)  # -> (5, 2)
print(data.sum(axis=2).shape)  # -> (5, 3)

# You can use keepdims:

print(data.sum(axis=0, keepdims=True).shape)  # -> (1, 3, 2)
print(data.sum(axis=1, keepdims=True).shape)  # -> (5, 1, 2)
print(data.sum(axis=2, keepdims=True).shape)  # -> (5, 3, 1)

As as reminder, shape is only availabe for np.ndarray and torch.Tensor matrices:

z = int(7)
print(np.array(z).shape)  # -> ()
print(type(np.array(z)))  # -> <class 'numpy.ndarray'>
print(type(z))  # -> <class 'int'>
print(z.shape)  # -> AttributeError: 'int' object has no attribute 'shape'