# How to convert a list and tuple into NumPy arrays?

In this article, let’s discuss how to convert a list and tuple into arrays using NumPy. NumPy provides various methods to do the same. Let’s discuss them

Method 1: Using numpy.asarray()

It converts the input to an array. The input may be lists of tuples, tuples, tuples of tuples, tuples of lists and ndarray.

Syntax:

`numpy.asarray(  a, type = None, order = None ) `

Example:

## Python3

 `import` `numpy as np ` ` `  ` `  `# list ` `list1 ``=` `[``3``, ``4``, ``5``, ``6``] ` `print``(``type``(list1)) ` `print``(list1) ` `print``() ` ` `  `# conversion ` `array1 ``=` `np.asarray(list1) ` `print``(``type``(array1)) ` `print``(array1) ` `print``() ` ` `  `# tuple ` `tuple1 ``=` `([``8``, ``4``, ``6``], [``1``, ``2``, ``3``]) ` `print``(``type``(tuple1)) ` `print``(tuple1) ` `print``() ` ` `  `# conversion ` `array2 ``=` `np.asarray(tuple1) ` `print``(``type``(array2)) ` `print``(array2)`

Output:

```<class 'list'>
[3, 4, 5, 6]

<class 'numpy.ndarray'>
[3 4 5 6]

<class 'tuple'>
([8, 4, 6], [1, 2, 3])

<class 'numpy.ndarray'>
[[8 4 6]
[1 2 3]]
```

Method 2: Using numpy.array()

It creates an array.

Syntax: numpy.array( object, dtype  = None, *, copy = True, order = ‘K’, subok = False, ndmin = 0 )

Parameters:

1. object: array-like
2. dtype: data-type, optional ( The desired data-type for the array. If not given, then the type will be determined as the minimum type required to hold the objects in the sequence. )
3. copy: bool, optional ( If true (default), then the object is copied. Otherwise, a copy will only be made if __array__ returns a copy, if obj is a nested sequence, or if a copy is needed to satisfy any of the other requirements (dtype, order, etc.). )
4. order: {‘K’, ‘A’, ‘C’, ‘F’}, optional ( same as above )
5. subok: bool, optional ( If True, then sub-classes will be passed-through, otherwise the returned array will be forced to be a base-class array (default). )
6. ndmin: int, optional ( Specifies the minimum number of dimensions that the resulting array should have. Ones will be pre-pended to the shape as needed to meet this requirement. )

Returns: ndarray ( An array object satisfying the specified requirements. )

Example:

## Python3

 `import` `numpy as np ` ` `  ` `  `# list ` `list1 ``=` `[``1``, ``2``, ``3``] ` `print``(``type``(list1)) ` `print``(list1) ` `print``() ` ` `  `# conversion ` `array1 ``=` `np.array(list1) ` `print``(``type``(array1)) ` `print``(array1) ` `print``() ` ` `  `# tuple ` `tuple1 ``=` `((``1``, ``2``, ``3``)) ` `print``(``type``(tuple1)) ` `print``(tuple1) ` `print``() ` ` `  `# conversion ` `array2 ``=` `np.array(tuple1) ` `print``(``type``(array2)) ` `print``(array2) ` `print``() ` ` `  `# list, array and tuple ` `array3 ``=` `np.array([tuple1, list1, array2]) ` `print``(``type``(array3)) ` `print``(array3)`

Output:

```<class 'list'>
[1, 2, 3]

<class 'numpy.ndarray'>
[1 2 3]

<class 'tuple'>
(1, 2, 3)

<class 'numpy.ndarray'>
[1 2 3]

<class 'numpy.ndarray'>
[[1 2 3]
[1 2 3]
[1 2 3]]```

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