The following article explains how to convert numpy array to dictionary in Python. Array in Numpy is a table of elements (usually numbers), all of the same type, indexed by a tuple of positive integers. In Numpy, number of dimensions of the array is called rank of the array. A tuple of integers giving the size of the array along each dimension is known as shape of the array. An array class in Numpy is called as ndarray. Elements in Numpy arrays are accessed by using square brackets and can be initialized by using nested Python Lists.
Approach
To convert a numpy array to dictionary the following program uses dict(enumerate(array.flatten(), 1)) and this is what it exactly does:
- array.flatten: This function is used to get a copy of given array, collapsed into one dimension.
- enumerate: Enumerate method comes with an automatic counter/index for each of the items present in the list. The first index value will start from 0
- dict: this function is used to convert any object to dictionary.
Example 1:
Python3
# importing required librariesimport numpy as np# creating a numpy arrayarray = np.array([['a', 'b', 'c'], ['d', 'e', 'f'], ['g', 'h', 'i']])# convert numpy array to dictionaryd = dict(enumerate(array.flatten(), 1))# print numpy arrayprint(array)print(type(array))# print dictionaryprint(d)print(type(d)) |
Output:
[[‘a’ ‘b’ ‘c’]
[‘d’ ‘e’ ‘f’]
[‘g’ ‘h’ ‘i’]]
<class ‘numpy.ndarray’>
{1: ‘a’, 2: ‘b’, 3: ‘c’, 4: ‘d’, 5: ‘e’, 6: ‘f’, 7: ‘g’, 8: ‘h’, 9: ‘i’}
<class ‘dict’>
Time Complexity: The time complexity for converting a numpy array to a dictionary is O(n), where n is the number of elements in the numpy array.
Space Complexity: The space complexity for converting a numpy array to a dictionary is O(n), where n is the number of elements in the numpy array.
Example 2:
Python3
# importing required librariesimport numpy as np# creating a numpy arrayarray = np.array([['1', '2', '3','4','5'], ['6', '7', '8','9','10'], ['11', '12', '13','14','15']])# convert numpy array to dictionaryd = dict(enumerate(array.flatten(), 1))# print numpy arrayprint(array)print(type(array))# print dictionaryprint(d)print(type(d)) |
Output:
[[‘1’ ‘2’ ‘3’ ‘4’ ‘5’]
[‘6’ ‘7’ ‘8’ ‘9’ ’10’]
[’11’ ’12’ ’13’ ’14’ ’15’]]
<class ‘numpy.ndarray’>
{1: ‘1’, 2: ‘2’, 3: ‘3’, 4: ‘4’, 5: ‘5’, 6: ‘6’, 7: ‘7’, 8: ‘8’, 9: ‘9’, 10: ’10’, 11: ’11’, 12: ’12’, 13: ’13’, 14: ’14’, 15: ’15’}
<class ‘dict’>
Time complexity: O(n), where n is the number of elements in the numpy array.
Auxiliary space: O(n), where n is the number of elements in the numpy array, due to the creation of the dictionary.
