## The Question :

*390 people think this question is useful*

I’m currently trying to learn Numpy and Python. Given the following array:

import numpy as np
a = np.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.

*The Question Comments :*

## The Answer 1

*527 people think this answer is useful*

It is `.shape`

:

ndarray.**shape**

Tuple of array dimensions.

Thus:

>>> a.shape
(2, 2)

## The Answer 2

*71 people think this answer is useful*

## First:

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]])

## Second：

In Numpy, **dimension**, **axis/axes**, **shape** are related and sometimes similar concepts:

### dimension

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

### axis/axes

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)

### shape

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

## The Answer 3

*47 people think this answer is useful*

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)

Or a tuple of tuples

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

## The Answer 4

*18 people think this answer is useful*

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 Answer 5

*10 people think this answer is useful*

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
# displays 2
var.shape
# display 6, 2

You can change the dimension using `.reshape`

function

var = np.array([[1,2,3,4,5,6], [1,2,3,4,5,6]]).reshape(3,4)
var.ndim
#display 2
var.shape
#display 3, 4

## The Answer 6

*7 people think this answer is useful*

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]])

## The Answer 7

*2 people think this answer is useful*

`a.shape`

is just a limited version of `np.info()`

. Check this out:

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

Out

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

## The Answer 8

*0 people think this answer is useful*

rows = a.shape[0] # 2
cols = a.shape[1] # 2
a.shape #(2,2)
a.size # rows * cols = 4