Numpy array dimensions


How do I get the dimensions of an array? For instance, this is 2×2:

a = np.array([[1,2],[3,4]])
Asked By: morgan freeman



Use .shape to obtain a tuple of array dimensions:

>>> a.shape
(2, 2)
Answered By: Felix Kling
import numpy as np   
>>> np.shape(a)

Also works if the input is not a numpy array but a list of lists

>>> a = [[1,2],[1,2]]
>>> np.shape(a)

Or a tuple of tuples

>>> a = ((1,2),(1,2))
>>> np.shape(a)
Answered By: OMRY VOLK


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
Answered By: YaOzI

The shape method requires that a be a Numpy ndarray. But Numpy can also calculate the shape of iterables of pure python objects:

Answered By: aph

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
Answered By: Rhuan Caetano

You can use .ndim for dimension and .shape to know the exact dimension:

>>> var = np.array([[1,2,3,4,5,6], [1,2,3,4,5,6]])

>>> var.ndim

>>> varshape
(2, 6) 

You can change the dimension using .reshape function:

>>> var_ = var.reshape(3, 4)

>>> var_.ndim

>>> var_.shape
(3, 4)
Answered By: Deepak Kr Gupta

a.shape is just a limited version of Check this out:

import numpy as np
a = np.array([[1,2],[1,2]])


class:  ndarray
shape:  (2, 2)
strides:  (8, 4)
itemsize:  4
aligned:  True
contiguous:  True
fortran:  False
data pointer: 0x27509cf0560
byteorder:  little
byteswap:  False
type: int32
Answered By: prosti
rows = a.shape[0] # 2 
cols = a.shape[1] # 2
a.shape #(2,2)
a.size # rows * cols = 4
Answered By: Ashish Madkaikar

Execute below code block in python notebook.

import numpy as np
a = np.array([[1,2],[1,2]])


(2, 2)

<class ‘tuple’>


then you realized that a.shape is a tuple.
so you can get any dimension’s size by a.shape[index of dimention]

Answered By: Pasindu Perera
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