In this article, we will see Different ways to convert a python dictionary into a Numpy array using NumPy library. It’s sometimes required to convert a dictionary in Python into a NumPy array and Python provides an efficient method to perform this operation. Converting a dictionary to NumPy array results in an array holding the key-value pairs of the dictionary.
Let’s see the different methods:
Method 1: Using numpy.array() and List Comprehension together.
Syntax: numpy.array(object, dtype = None, *, copy = True, order = ‘K’, subok = False, ndmin = 0)
Return: An array object satisfying the specified requirements.
We have used np.array() to convert a dictionary to nd array. And to get each and every value of dictionary as a list for the input to the np.array(), concept of List comprehension is used.
Example:
Python3
# importing required librariessimport numpy as npfrom ast import literal_eval# creating class of stringname_list = """{ "column0": {"First_Name": "Akash", "Second_Name": "kumar", "Interest": "Coding"}, "column1": {"First_Name": "Ayush", "Second_Name": "Sharma", "Interest": "Cricket"}, "column2": {"First_Name": "Diksha", "Second_Name": "Sharma","Interest": "Reading"}, "column3": {"First_Name":" Priyanka", "Second_Name": "Kumari", "Interest": "Dancing"} }"""print("Type of name_list created:\n", type(name_list))# converting string type to dictionaryt = literal_eval(name_list)# printing the original dictionaryprint("\nPrinting the original Name_list dictionary:\n", t)print("Type of original dictionary:\n", type(t))# converting dictionary to numpy arrayresult_nparra = np.array([[v[j] for j in ['First_Name', 'Second_Name', 'Interest']] for k, v in t.items()])print("\nConverted ndarray from the Original dictionary:\n", result_nparra)# printing the type of converted arrayprint("Type:\n", type(result_nparra)) |
Output:
Type of name_list created: <class ‘str’> Printing the original Name_list dictionary: {‘column0’: {‘First_Name’: ‘Akash’, ‘Second_Name’: ‘kumar’, ‘Interest’: ‘Coding’}, ‘column1’: {‘First_Name’: ‘Ayush’, ‘Second_Name’: ‘Sharma’, ‘Interest’: ‘Cricket’}, ‘column2’: {‘First_Name’: ‘Diksha’, ‘Second_Name’: ‘Sharma’, ‘Interest’: ‘Reading’}, ‘column3’: {‘First_Name’: ‘ Priyanka’, ‘Second_Name’: ‘Kumari’, ‘Interest’: ‘Dancing’}} Type of original dictionary: <class ‘dict’> Converted ndarray from the Original dictionary: [[‘Akash’ ‘kumar’ ‘Coding’] [‘Ayush’ ‘Sharma’ ‘Cricket’] [‘Diksha’ ‘Sharma’ ‘Reading’] [‘ Priyanka’ ‘Kumari’ ‘Dancing’]] Type: <class ‘numpy.ndarray’>
Time complexity: The time complexity of the above code is O(n) as the code iterates through a single loop (literal_eval) to convert the string to a dictionary.
Space complexity: The space complexity of the above code is O(n) as the size of the data increases, the space needed to store the data also increases.
Method 2: Using numpy.array() and dictionary_obj.items().
We have used np.array() to convert a dictionary to nd array. And to get each and every value of dictionary as a list for the input to the np.array() method, dictionary_obj.items() is used.
Example:
Python3
# importing libraryimport numpy as np# creating dictionary as key as# a number and value as its cubedict_created = {0: 0, 1: 1, 2: 8, 3: 27, 4: 64, 5: 125, 6: 216}# printing type of dictionary createdprint(type(dict_created))# converting dictionary to# numpy arrayres_array = np.array(list(dict_created.items()))# printing the converted arrayprint(res_array)# printing type of converted arrayprint(type(res_array)) |
Output:
<class 'dict'> [[ 0 0] [ 1 1] [ 2 8] [ 3 27] [ 4 64] [ 5 125] [ 6 216]] <class 'numpy.ndarray'>
Time complexity: O(n) where n is the size of the dictionary.
Auxiliary space: O(n) for the dictionary and O(n) for the numpy array, so the total auxiliary space is O(n).
