search
HomeBackend DevelopmentPython TutorialHow to convert dictionary to matrix or nArray in Python?

How to convert dictionary to matrix or nArray in Python?

Aug 27, 2023 pm 09:33 PM
dictionary transformation matrix

How to convert dictionary to matrix or nArray in Python?

In this article, we will show you how to convert a dictionary to a matrix or a NumPy array using the array() function from Python’s NumPy library.

Sometimes you need to convert a dictionary in Python to a NumPy array, and Python provides an efficient way to achieve this. Converting a dictionary to a NumPy array results in an array containing the key-value pairs in the dictionary.

In this section, we will look at some examples of converting various types of dictionaries to NumPy arrays in Python

  • Convert dictionary to Numpy array
  • Convert nested dictionary to Numpy array
  • Convert dictionary with mixed keys to Numpy array

numpy.array() function

It returns an ndarray. ndarray is an array object that meets the given requirements.

To convert a dictionary into a NumPy array, Python provides the numpy.array() method, but we must do some preparation work first. Follow these three basic steps as a preliminary task.

  • First, use dict.items() to obtain a set of key-value pairs in the dictionary.
  • Then, taking this group as an object, use list(obj) to convert it to a list.
  • Finally, using this list as data, call numpy.array(data) to convert it to an array.

grammar

numpy.array(object, dtype = None, *, copy = True, order = ‘K’, subok = False, ndmin = 0)

parameter

  • object − This is an array or any object that exposes the array interface.

  • dtype − The preferred data type for arrays.

  • copy − If true (default), the item is copied. Otherwise, a copy will only be made if __array__ returns a copy

  • order − It represents the memory layout of the array

  • subok − If true, the subclass will be passed; otherwise, the returned array will be cast to a base class array (default)

  • ndmin − Indicates the minimum dimension of the result array.

  • Return Value − Returns an ndarray (it is an array object that meets the specified requirements)

Convert dictionary to Numpy array

Algorithm (steps)

The following are the algorithms/steps to perform the required task:

  • Use the import keyword to import the numpy module with an alias (np).

  • Create a variable to store the input dictionary.

  • Apply the items() function (which returns the key-value pairs in the dictionary) to the input dictionary to get all the key-value pairs in the dictionary and create a variable to store it .

  • Use the list() function (returns a list of iterable objects) to convert all key-value pairs of the dictionary to the list data type.

  • Use the array() function of the NumPy module (returns an ndarray. ndarray is an array object that meets the given requirements) to convert the above data list into a NumPy array.

  • Print the NumPy array converted from the input dictionary.

Example

The following program uses the array() function to convert the input dictionary into a NumPy array and returns it -

# importing numpy module with an alias name
import numpy as np

# creating a dictionary
inputDict = {1: 'Hello',
2: 'Tutorialspoint',
3: 'python'}

# getting all the key-value pairs in the dictionary
result_keyvalpairs = inputDict.items()

# converting an object to a list
list_data = list(result_keyvalpairs)

# converting list to an numpy array using numpy array() function
numpy_array = np.array(list_data)
print("Input Dictionary =",inputDict)

# printing the resultant numpy array
print("The resultant numpy array:\n", numpy_array)

Output

When executed, the above program will generate the following output

Input Dictionary = {1: 'Hello', 2: 'Tutorialspoint', 3: 'python'}
The resultant numpy array:
 [['1' 'Hello']
 ['2' 'Tutorialspoint']
 ['3' 'python']]

Convert nested dictionary to Numpy array

Algorithm (steps)

The following are the algorithms/steps to perform the required task:

  • Create a variable to store an input nested dictionary (a dictionary within another dictionary).

  • Convert all nested key-value pairs of the dictionary to the list data type using the list() function (which returns a list of iterable objects).

  • Use the array() function of the NumPy module to convert the above data list into a NumPy array.

  • Print the NumPy array converted from the input dictionary.

Example

The following program uses the array() function to convert a nested input dictionary into a NumPy array and returns it

# importing NumPy module with an alias name
import numpy as np

# creating a nested dictionary
nestedDictionary = {1: 'Hello',
                    2: 'Tutorialspoint',
                    3: {'X': 'This is',
                        'Y': 'python',
                        'Z': 'code'}}

# getting all the key-value pairs in the dictionary
result_keyvalpairs = nestedDictionary.items()

# converting an object to a list
list_data = list(result_keyvalpairs)

# converting list to an array using numpy array() function
numpy_array = np.array(list_data)
print("Input nested Dictionary = ",nestedDictionary)

# printing the resultant numpy array
print("\nThe resultant numpy array:\n", numpy_array)

Output

When executed, the above program will generate the following output

Input nested Dictionary =  {1: 'Hello', 2: 'Tutorialspoint', 3: {'X': 'This is', 'Y': 'python', 'Z': 'code'}}

The resultant numpy array:
 [[1 'Hello']
   [2 'Tutorialspoint']
   [3 {'X': 'This is', 'Y': 'python', 'Z': 'code'}]]

Convert dictionary with mixed keys to Numpy array

Create an input dictionary with mixed keys such as strings, integers, floats, lists, etc. and fill it with random values.

Example

The following program uses the array() function to convert a dictionary with mixed keys into a NumPy array and returns it −

# importing numpy module with an alias name
import numpy as np

# creating a dictionary with mixed keys(like string and numbers as keys)
nestedDictionary = {'website': 'Tutorialspoint', 10: [2, 5, 8]}

# getting all the key-value pairs in the dictionary
result_keyvalpairs = nestedDictionary.items()

# converting an object to a list
list_data = list(result_keyvalpairs)

# converting list to an array using numpy array() function
numpy_array = np.array(list_data, dtype=object)

# printing the resultant numpy array
print("The resultant numpy array:\n", numpy_array)

Output

When executed, the above program will generate the following output

The resultant numpy array:
 [['website' 'Tutorialspoint']
   [10 list([2, 5, 8])]]

in conclusion

In this article, we learned about the various types of key-value pairs in a dictionary and how to convert them into a matrix or Numpy array.

The above is the detailed content of How to convert dictionary to matrix or nArray in Python?. For more information, please follow other related articles on the PHP Chinese website!

Statement
This article is reproduced at:tutorialspoint. If there is any infringement, please contact admin@php.cn delete
Python vs. C  : Learning Curves and Ease of UsePython vs. C : Learning Curves and Ease of UseApr 19, 2025 am 12:20 AM

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

Python vs. C  : Memory Management and ControlPython vs. C : Memory Management and ControlApr 19, 2025 am 12:17 AM

Python and C have significant differences in memory management and control. 1. Python uses automatic memory management, based on reference counting and garbage collection, simplifying the work of programmers. 2.C requires manual management of memory, providing more control but increasing complexity and error risk. Which language to choose should be based on project requirements and team technology stack.

Python for Scientific Computing: A Detailed LookPython for Scientific Computing: A Detailed LookApr 19, 2025 am 12:15 AM

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.

Python and C  : Finding the Right ToolPython and C : Finding the Right ToolApr 19, 2025 am 12:04 AM

Whether to choose Python or C depends on project requirements: 1) Python is suitable for rapid development, data science, and scripting because of its concise syntax and rich libraries; 2) C is suitable for scenarios that require high performance and underlying control, such as system programming and game development, because of its compilation and manual memory management.

Python for Data Science and Machine LearningPython for Data Science and Machine LearningApr 19, 2025 am 12:02 AM

Python is widely used in data science and machine learning, mainly relying on its simplicity and a powerful library ecosystem. 1) Pandas is used for data processing and analysis, 2) Numpy provides efficient numerical calculations, and 3) Scikit-learn is used for machine learning model construction and optimization, these libraries make Python an ideal tool for data science and machine learning.

Learning Python: Is 2 Hours of Daily Study Sufficient?Learning Python: Is 2 Hours of Daily Study Sufficient?Apr 18, 2025 am 12:22 AM

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Python for Web Development: Key ApplicationsPython for Web Development: Key ApplicationsApr 18, 2025 am 12:20 AM

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python vs. C  : Exploring Performance and EfficiencyPython vs. C : Exploring Performance and EfficiencyApr 18, 2025 am 12:20 AM

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.

See all articles

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

AI Hentai Generator

AI Hentai Generator

Generate AI Hentai for free.

Hot Tools

mPDF

mPDF

mPDF is a PHP library that can generate PDF files from UTF-8 encoded HTML. The original author, Ian Back, wrote mPDF to output PDF files "on the fly" from his website and handle different languages. It is slower than original scripts like HTML2FPDF and produces larger files when using Unicode fonts, but supports CSS styles etc. and has a lot of enhancements. Supports almost all languages, including RTL (Arabic and Hebrew) and CJK (Chinese, Japanese and Korean). Supports nested block-level elements (such as P, DIV),

VSCode Windows 64-bit Download

VSCode Windows 64-bit Download

A free and powerful IDE editor launched by Microsoft

EditPlus Chinese cracked version

EditPlus Chinese cracked version

Small size, syntax highlighting, does not support code prompt function

MantisBT

MantisBT

Mantis is an easy-to-deploy web-based defect tracking tool designed to aid in product defect tracking. It requires PHP, MySQL and a web server. Check out our demo and hosting services.

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use