# Numpy array dimensions

## Question:

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

``````a = np.array([[1,2],[3,4]])
``````

Use `.shape` to obtain a tuple of array dimensions:

``````>>> a.shape
(2, 2)
``````
``````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)
``````

## First:

By convention, in Python world, the shortcut for `numpy` is `np`, so:

``````In : import numpy as np

In : 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 : a.ndim  # num of dimensions/axes, *Mathematics definition of dimension*
Out: 2
``````

### axis/axes

the nth coordinate to index an `array` in Numpy. And multidimensional arrays can have one index per axis.

``````In : a[1,0]  # to index `a`, we specific 1 at the first axis and 0 at the second axis.
Out: 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 : a.shape
Out: (2, 2)  # both the first and second axis have 2 (columns/rows/pages/blocks/...) data
``````

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]])
``````

Use `.shape`:

``````In: a = np.array([[1,2,3],[4,5,6]])
In: a.shape
Out: (2, 3)
In: a.shape # x axis
Out: 2
In: a.shape # y axis
Out: 3
``````

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
2

>>> varshape
(2, 6)
``````

You can change the dimension using `.reshape` function:

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

>>> var_.ndim
2

>>> var_.shape
(3, 4)
``````

`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
``````
``````rows = a.shape # 2
cols = a.shape # 2
a.shape #(2,2)
a.size # rows * cols = 4
``````

Execute below code block in python notebook.

``````import numpy as np
a = np.array([[1,2],[1,2]])
print(a.shape)
print(type(a.shape))
print(a.shape)
``````

output

(2, 2)

<class ‘tuple’>

2

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

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