In this tutorial, we will cover the concept of `array()`

function in the NumPy library.

The `array()`

function in the NumPy library is mainly used to create an array. Just like the Numpy arange() function.

- In the NumPy library the
**homogeneous multidimensional array**is the main object. Homogeneous multidimensional array is basically a table of elements and these elements are all of the same type and**indexed by a tuple**of positive integers. The dimensions of an array are called as**axis**in NumPy. - The
`array`

class in the Numpy library is mainly known as**ndarray**or an**alias array**. - The
`numpy.array()`

is not same as the standard Python library class`array.array`

because in python`array.array`

is used to handle only one-dimensional arrays and thus it provides less functionality.

### Syntax of `numpy.array()`

:

The syntax required to use this method is as follows:

`numpy.array(object, dtype=None, copy=True, order='K', subok=False, ndmin=0) `

**Parameters:**

Let us now take a look at the parameters of this function:

**object**This parameter indicates an array or it can be any object exposing the array interface mainly it can be an object whose`__array__`

method returns an array, or any nested sequence.**dtype**This parameter defines the desired data-type for the array. In case if this parameter is not given, then the type will be determined as the minimum type required to hold the objects in the sequence.**copy**If value of this parameter is set to**True**, the object will be copied else the copy will be made only when an object is a nested sequence, or if a copy is just needed to satisfy any of the other requirements such as dtype, order, etc.**order**This prameter is used to specify the memory layout of the array. In case if the object is not an array then the newly created array will be in**C order**that is row major unless**‘F’**is specified, in that case it will be in**Fortran order**that is column order. If the object is mainly an array then the following holds:

Order | No Copy | copy=true |

‘K’ | unchanged | If copy is true in this case then F & C order preserved otherwise most similar order |

‘A’ | unchanged | In this case if copy is true and input is F not C then the order is F otherwise the order is C |

‘C’ | C order | In this case the order is C |

‘F’ | F order | In this case the order is F. |

**subok**This is an**optional**parameter, when the value of this parameter is**True**, then sub-classes will pass-through otherwise by default the returned array will force to be a base-class array.**ndmin**It is an**optional**parameter that is used to specify the minimum number of dimensions that the resulting array should have.

**Returned Values:**

The `array()`

function in the numpy library is used to return an array object that satisfies the specified requirements.

## Example 1: Basic example of `array()`

function

Below we have a basic example where we create an array having only one dimension with the help of `array()`

function:

```
import numpy as np
a = np.array([1,4,9])
print("The Array is:")
print(a)
```

Output:

The Array is: [1 4 9]

## Example 2: Multi-dimension array

Now we will create an array having more than one dimension and the code for the same is as follows:

```
import numpy as np
a = np.array([[1, 7], [6, 4]])
print("The Array with more than one dimension:")
print(a)
```

Output:

The Array with more than one dimension: [[1 7] [6 4]]

## Example 3: Using the `dtype`

Parameter

Below we have an example where we will use the `dtype`

parameter:

```
import numpy as np
a = np.array([1, 7,9], dtype='complex')
print("The Array with more than one dimension:")
print(a)
```

Output:

The Array with more than one dimension: [1.+0.j 7.+0.j 9.+0.j]

**Note:** The output of the above code snippet indicates the values of the array elements in the form of complex numbers.