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:
- 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 # 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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