# NumPy Creating Arrays

## Create a NumPy ndarray Object

NumPy is used to work with arrays. The array object in NumPy is called
`ndarray`

.

We can create a NumPy
`ndarray`

object by using the `array()`

function.

### Example

```
import numpy as np
```

arr = np.array([1, 2, 3, 4, 5])

print(arr)

print(type(arr))

Try it Yourself »
**type():** This built-in Python function tells us the type of the object passed to it. Like in above code
it shows that `arr`

is
`numpy.ndarray`

type.

To create an `ndarray`

,
we can pass a list, tuple or any array-like object into the `array()`

method, and it will be converted into an
`ndarray`

:

### Example

Use a tuple to create a NumPy array:

```
import numpy as np
```

arr = np.array((1, 2, 3, 4, 5))

print(arr)

Try it Yourself »
## Dimensions in Arrays

A dimension in arrays is one level of array depth (nested arrays).

**nested array:** are arrays that have arrays as their elements.

## 0-D Arrays

0-D arrays, or Scalars, are the elements in an array. Each value in an array is a 0-D array.

### Example

Create a 0-D array with value 42

```
import numpy as np
```

arr = np.array(42)

print(arr)

Try it Yourself »
## 1-D Arrays

An array that has 0-D arrays as its elements is called uni-dimensional or 1-D array.

These are the most common and basic arrays.

### Example

Create a 1-D array containing the values 1,2,3,4,5:

```
import numpy as np
```

arr = np.array([1, 2, 3, 4, 5])

print(arr)

Try it Yourself »
## 2-D Arrays

An array that has 1-D arrays as its elements is called a 2-D array.

These are often used to represent matrix or 2nd order tensors.

NumPy has a whole sub module dedicated towards matrix operations called
`numpy.mat`

### Example

Create a 2-D array containing two arrays with the values 1,2,3 and 4,5,6:

```
import numpy as np
```

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

print(arr)

Try it Yourself »
## 3-D arrays

An array that has 2-D arrays (matrices) as its elements is called 3-D array.

These are often used to represent a 3rd order tensor.

### Example

Create a 3-D array with two 2-D arrays, both containing two arrays with the values 1,2,3 and 4,5,6:

```
import numpy as np
```

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

print(arr)

Try it Yourself »
## Check Number of Dimensions?

NumPy Arrays provides the `ndim`

attribute that returns an integer that tells us how many dimensions the array have.

### Example

Check how many dimensions the arrays have:

```
import numpy as np
```

a = np.array(42)

b = np.array([1, 2, 3, 4, 5])

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

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

print(a.ndim)

print(b.ndim)

print(c.ndim)

print(d.ndim)

Try it Yourself »
## Higher Dimensional Arrays

An array can have any number of dimensions.

When the array is created, you can define the number of dimensions by using
the `ndmin`

argument.

### Example

Create an array with 5 dimensions and verify that it has 5 dimensions:

```
import numpy as np
```

arr = np.array([1, 2, 3, 4], ndmin=5)

print(arr)

print('number of dimensions :', arr.ndim)

Try it Yourself »
In this array the innermost dimension (5th dim) has 4 elements, the 4th dim has 1 element that is the vector, the 3rd dim has 1 element that is the matrix with the vector, the 2nd dim has 1 element that is 3D array and 1st dim has 1 element that is a 4D array.