# 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]])
```

## Answers:

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 [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 `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[0] # x axis
Out: 2
In: a.shape[1] # 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[0] # 2
cols = a.shape[1] # 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[0])
```

**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]`