# numpy Array (1D and 2’D) creation and operation

## 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.

` ````
```import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(arr)
print(type(arr))

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`

:

` ````
```import numpy as np
arr = np.array((1, 2, 3, 4, 5))
print(arr)

## 0-D Arrays

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

` ````
```import numpy as np
arr = np.array(42)
print(arr)

## 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)

## 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.

### 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)

## 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)

## 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)

## 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)