How to convert a list and tuple into NumPy arrays?

29 Aug, 2020
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    # listlist1 = [3, 4, 5, 6]print(type(list1))print(list1)print()  # conversionarray1 = np.asarray(list1)print(type(array1))print(array1)print()  # tupletuple1 = ([8, 4, 6], [1, 2, 3])print(type(tuple1))print(tuple1)print()  # conversionarray2 = 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:
object: array-likedtype: 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. )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.). )order: {‘K’, ‘A’, ‘C’, ‘F’}, optional ( same as above )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). )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. )
object: array-like
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. )
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.). )
order: {‘K’, ‘A’, ‘C’, ‘F’}, optional ( same as above )
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). )
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    # listlist1 = [1, 2, 3]print(type(list1))print(list1)print()  # conversionarray1 = np.array(list1)print(type(array1))print(array1)print()  # tupletuple1 = ((1, 2, 3))print(type(tuple1))print(tuple1)print()  # conversionarray2 = np.array(tuple1)print(type(array2))print(array2)print()  # list, array and tuplearray3 = 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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