Different ways to convert a Python dictionary to a NumPy array

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 librariess
import numpy as np
from ast import literal_eval

# creating class of string
name_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 dictionary
t = literal_eval(name_list)

# printing the original dictionary
print("\nPrinting the original Name_list dictionary:\n",
      t)

print("Type of original dictionary:\n",
      type(t))

# converting dictionary to numpy array
result_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 array
print("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 library
import numpy as np

# creating dictionary as key as
# a number and value as its cube
dict_created = {0: 0, 1: 1, 2: 8, 3: 27,
                4: 64, 5: 125, 6: 216}

# printing type of dictionary created
print(type(dict_created))

# converting dictionary to
# numpy array
res_array = np.array(list(dict_created.items()))

# printing the converted array
print(res_array)

# printing type of converted array
print(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).