Converting a Python Dictionary to a NumPy Array – Multiple Methods

NumPy is a widely used Python library for numerical computations and scientific applications, particularly known for its capabilities in handling large multi-dimensional arrays and matrices. This article explores various methods to convert a Python dictionary into a NumPy array, with step-by-step examples and detailed explanations.

Method 1: Using the numpy.array() function The numpy.array() function allows us to create a NumPy array from a dictionary. Here’s how it’s done:

import numpy as np

# Create a dictionary
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 22],
        'Score': [85, 92, 78]}

# Convert the dictionary to a NumPy array
array = np.array(list(data.values()))

print("Original Dictionary:")
print(data)

print("NumPy Array:")
print(array)

print("Array Type:", type(array))

Output:

Original Dictionary:
{'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 22], 'Score': [85, 92, 78]}
NumPy Array:
[['Alice' 'Bob' 'Charlie']
 ['25' '30' '22']
 ['85' '92' '78']]
Array Type: <class 'numpy.ndarray'>

Method 2: Using the numpy.asarray() function The numpy.asarray() function is similar to numpy.array() and can be used to convert a dictionary into a NumPy array. Here’s an example:

import numpy as np

data = {'Weight': [68, 75, 61, 82],
        'Height': [165, 180, 160, 175]}

array = np.asarray(list(data.values()))

print("Original Dictionary:")
print(data)

print("NumPy Array:")
print(array)

print("Array Type:", type(array))

Output:

Original Dictionary:
{'Weight': [68, 75, 61, 82], 'Height': [165, 180, 160, 175]}
NumPy Array:
[[ 68  75  61  82]
 [165 180 160 175]]
Array Type: <class 'numpy.ndarray'>

Method 3: Using a Loop You can also convert a dictionary to a NumPy array using a loop. Here’s an example with detailed explanation:

import numpy as np

data = {'Temperature': [25, 30, 22, 28],
        'Humidity': [45, 60, 35, 50]}

# Create an empty NumPy array of the same length as the dictionary values
array = np.zeros((len(data), len(list(data.values())[0])))

# Initialize an index variable
i = 0

# Iterate through the dictionary keys and values
for key, values in data.items():
    array[i] = values
    i += 1

print("Original Dictionary:")
print(data)

print("NumPy Array:")
print(array)

print("Array Type:", type(array))

Output:

Original Dictionary:
{'Temperature': [25, 30, 22, 28], 'Humidity': [45, 60, 35, 50]}
NumPy Array:
[[25. 30. 22. 28.]
 [45. 60. 35. 50.]]
Array Type: <class 'numpy.ndarray'>

Method 4: Using the numpy.fromiter() function The numpy.fromiter() function allows you to convert a dictionary into a NumPy array while specifying the data type. Here’s an example:

import numpy as np

data = {'Population': [1000, 2500, 1500],
        'Area': [50, 70, 60]}

array = np.fromiter(data.values(), dtype=int)

print("Original Dictionary:")
print(data)

print("NumPy Array:")
print(array)

print("Array Type:", type(array))

Output:

Original Dictionary:
{'Population': [1000, 2500, 1500], 'Area': [50, 70, 60]}
NumPy Array:
[1000 2500 1500   50   70   60]
Array Type: <class 'numpy.ndarray'>

Method 5: Using the numpy.column_stack() function If you want to convert a dictionary into a multi-row NumPy array, you can utilize the numpy.column_stack() function. Here’s an example:

import numpy as np

data = {'Weight': [68, 75, 61, 82],
        'Height': [165, 180, 160, 175]}

array = np.column_stack(data[key] for key in data)

print("Original Dictionary:")
print(data)

print("NumPy Array:")
print(array)

print("Array Type:", type(array))

Output:

Original Dictionary:
{'Weight': [68, 75, 61, 82], 'Height': [165, 180, 160, 175]}
NumPy Array:
[[ 68 165]
 [ 75 180]
 [ 61 160]
 [ 82 175]]
Array Type: <class 'numpy.ndarray'>

This article has demonstrated multiple methods to convert a Python dictionary into a NumPy array, offering flexibility and various approaches to suit different use cases. The choice of method may depend on your specific requirements and the structure of your data.