I'm currently trying to learn Numpy and Python. Given the following array:
import numpy as N a = N.array([[1,2],[1,2]]) Is there a function that returns the dimensions of a (e.g.a is a 2 by 2 array)?
size() returns 4 and that doesn't help very much.
import numpy as np >>> np.shape(a) (2,2) Also works if the input is not a numpy array but a list of lists
>>> a = [[1,2],[1,2]] >>> np.shape(a) (2,2) By convention, in Python world, the shortcut for numpy is np, so:
In [1]: import numpy as np In [2]: a = np.array([[1,2],[3,4]]) In Numpy, dimension, axis/axes, shape are related and sometimes similar concepts:
In Mathematics/Physics, dimension or dimensionality is informally defined as the minimum number of coordinates needed to specify any point within a space. But in Numpy, according to the numpy doc, it's the same as axis/axes:
In Numpy dimensions are called axes. The number of axes is rank.
In [3]: a.ndim # num of dimensions/axes, *Mathematics definition of dimension* Out[3]: 2 the nth coordinate to index an array in Numpy. And multidimensional arrays can have one index per axis.
In [4]: a[1,0] # to index `a`, we specific 1 at the first axis and 0 at the second axis. Out[4]: 3 # which results in 3 (locate at the row 1 and column 0, 0-based index) describes how many data (or the range) along each available axis.
In [5]: a.shape Out[5]: (2, 2) # both the first and second axis have 2 (columns/rows/pages/blocks/...) data You can use .shape
In: a = np.array([[1,2,3],[4,5,6]]) In: a.shape Out: (2, 3) In: a.shape[0] # x axis Out: 2 In: a.shape[1] # y axis Out: 3 The shape method requires that a be a Numpy ndarray. But Numpy can also calculate the shape of iterables of pure python objects:
np.shape([[1,2],[1,2]])