Software Alternatives, Accelerators & Startups
Register | Login

NumPy VS Thonny

Compare NumPy VS Thonny and see what are their differences

LibHunt
LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives. featured
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

Thonny logo Thonny

Python IDE for beginners
  • NumPy Landing page
    Landing page //
    2023-05-13
  • Thonny Landing page
    Landing page //
    2023-07-15

NumPy features and specs

  • Performance
    NumPy operations are executed with highly optimized C and Fortran libraries, making them significantly faster than standard Python arithmetic operations, especially for large datasets.
  • Versatility
    NumPy supports a vast range of mathematical, logical, shape manipulation, sorting, selecting, I/O, and basic linear algebra operations, making it a versatile tool for scientific and numeric computing.
  • Ease of Use
    NumPy provides an intuitive, easy-to-understand syntax that extends Python's ability to handle arrays and matrices, lowering the barrier to performing complex scientific computations.
  • Community Support
    With a large and active community, NumPy offers extensive documentation, tutorials, and support for troubleshooting issues, as well as continuous updates and enhancements.
  • Integrations
    NumPy integrates seamlessly with other libraries in Python's scientific stack like SciPy, Matplotlib, and Pandas, facilitating a streamlined workflow for data science and analysis tasks.

Possible disadvantages of NumPy

  • Memory Consumption
    NumPy arrays can consume large amounts of memory, especially when working with very large datasets, which can become a limitation on systems with limited memory capacity.
  • Learning Curve
    For users new to scientific computing or coming from different programming backgrounds, understanding the intricacies of NumPy's operations and efficient usage can take time and effort.
  • Limited GPU Support
    NumPy primarily runs on the CPU and doesn't natively support GPU acceleration, which can be a disadvantage for extremely compute-intensive tasks that could benefit from parallel processing.
  • Dependency on Python
    Since NumPy is a Python library, it depends on the Python runtime environment. This can be a limitation in environments where Python is not the primary language or isn't supported.
  • Indexing Complexity
    Although NumPy's slicing and indexing capabilities are powerful, they can sometimes be complex or unintuitive, especially for multi-dimensional arrays, leading to potential errors and confusion.

Thonny features and specs

  • Beginner-Friendly Interface
    Thonny is designed with simplicity in mind, which makes it ideal for beginners. The interface is clean and intuitive, helping new users to focus on learning Python without being overwhelmed by unnecessary features.
  • Integrated Python Debugger
    Thonny comes with a built-in debugger that allows users to step through their code line by line. This feature helps in understanding the flow of the program and is especially useful for debugging and learning.
  • Simple Variable Inspector
    The variable inspector in Thonny provides a clear and straightforward view of variables and their values, helping users to better understand how data changes as the program runs.
  • Preinstalled with Python
    Thonny comes with Python preinstalled, which makes the setup process easier for beginners. Users don’t have to worry about separate Python installation and can start coding right away.
  • Support for MicroPython
    Thonny supports MicroPython, making it a suitable choice for programming microcontrollers and embedded systems. This expands its usability beyond just desktop applications.
  • Educational Focus
    Thonny includes features specifically designed for educational purposes, such as highlighting syntax errors and visualizing code execution, which helps students grasp programming concepts more effectively.

Possible disadvantages of Thonny

  • Limited Advanced Features
    While Thonny is great for beginners, it lacks some advanced features found in other IDEs like PyCharm or Visual Studio Code. This may hinder users as they advance and need more complex workflows and customizations.
  • Performance Issues with Large Projects
    Thonny may not perform efficiently with large-scale projects or extremely large files, potentially causing lag or crashes, which could be a limitation for more extensive development work.
  • Plugin Ecosystem
    Thonny's plugin ecosystem is not as robust as some other IDEs. Users might find fewer extensions and customizations available, which can limit its functionality for certain workflows.
  • Limited Language Support
    Thonny is designed primarily for Python and lacks support for other programming languages. This makes it less flexible for developers who work with multiple languages.
  • Basic Editor Features
    Essential features such as intelligent code completion, refactoring tools, and version control integration are present but not as sophisticated as in more professional IDEs, which could limit productivity for seasoned developers.
  • Less Frequent Updates
    Compared to more popular and commercially-backed IDEs, Thonny receives less frequent updates. This could mean slower adoption of new Python features and slower fixes for bugs.

Analysis of NumPy

Overall verdict

  • Yes, NumPy is considered good. It is a foundational library in the Python ecosystem for numerical computing and is used globally by researchers, engineers, and data scientists.

Why this product is good

  • NumPy is widely regarded as a good library because it offers fast, flexible, and efficient array handling that is integral to scientific computing in Python. It provides tools for integrating C/C++ and Fortran code, useful linear algebra, random number capabilities, and a vast collection of mathematical functions. Its array broadcasting capabilities and versatility make complex mathematical computations straightforward.

Recommended for

  • Scientists and researchers working with large-scale scientific computations.
  • Data scientists engaged in data analysis and manipulation.
  • Engineers and developers needing performance-optimized mathematical computations.
  • Educators and students in STEM fields.

Analysis of Thonny

Overall verdict

  • Overall, Thonny is highly regarded as a good choice for beginner programmers, particularly those starting with Python. It effectively balances simplicity and functionality, making it an excellent tool for educational purposes.

Why this product is good

  • Thonny is a beginner-friendly Python IDE designed to make programming more accessible and understandable for newcomers. It offers an intuitive interface, a simple debugger, and features like variable tracing and expression evaluation that are particularly helpful for beginners. Its design minimizes distractions and helps in focusing on learning the basics of programming without being overwhelmed by complex features.

Recommended for

  • Beginners learning Python.
  • Educational environments and coding classrooms.
  • Users who prefer a simple and clean interface without extra complexity.
  • Those interested in a straightforward setup to start coding quickly.

NumPy videos

+ Add

Learn NUMPY in 5 minutes - BEST Python Library!

More videos:

  • Review - Python for Data Analysis by Wes McKinney: Review | Learn python, numpy, pandas and jupyter notebooks
  • Review - Effective Computation in Physics: Review | Learn python, numpy, regular expressions, install python

Thonny videos

+ Add

Thonny - a Python IDE for beginners

More videos:

  • Review - Step Through Python Scripts With Thonny
  • Review - Introducing Thonny

Category Popularity

0-100% (relative to NumPy and Thonny)

NumPy

Thonny

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Text Editors
0 0%
Text Editors
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
IDE
0 0%
IDE
100% 100

User comments

Share your experience with using NumPy and Thonny. For example, how are they different and which one is better?
Log in or Post with

Reviews

These are some of the external sources and on-site user reviews we've used to compare NumPy and Thonny

NumPy Reviews

25 Python Frameworks to Master
SciPy provides a collection of algorithms and functions built on top of the NumPy. It helps to perform common scientific and engineering tasks such as optimization, signal processing, integration, linear algebra, and more.
Source: kinsta.com
Top 8 Image-Processing Python Libraries Used in Machine Learning
Scipy is used for mathematical and scientific computations but can also perform multi-dimensional image processing using the submodule scipy.ndimage. It provides functions to operate on n-dimensional Numpy arrays and at the end of the day images are just that.
Source: neptune.ai
Top Python Libraries For Image Processing In 2021
Numpy It is an open-source python library that is used for numerical analysis. It contains a matrix and multi-dimensional arrays as data structures. But NumPy can also use for image processing tasks such as image cropping, manipulating pixels, and masking of pixel values.
Source: www.analyticsvidhya.com
4 open source alternatives to MATLAB
NumPy is the main package for scientific computing with Python (as its name suggests). It can process N-dimensional arrays, complex matrix transforms, linear algebra, Fourier transforms, and can act as a gateway for C and C++ integration. It's been used in the world of game and film visual effect development, and is the fundamental data-array structure for the SciPy Stack,...
Source: opensource.com

Thonny Reviews

  1. steve
    · retired ·
    I like Thonny

    I started an on-line python course that used Pycharm as its basis. I had previously used Thonny to look at code for various programs. I found Pycharm to be over-featured for a beginner like me. Thonny seems much more on my level so I am continuing the course using it instead. And successfully I might add.

Social recommendations and mentions

NumPy might be a bit more popular than Thonny. We know about 119 links to it since March 2021 and only 111 links to Thonny. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

NumPy mentions (119)

  • Building an AI-powered Financial Data Analyzer with NodeJS, Python, SvelteKit, and TailwindCSS - Part 0
    The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis. - Source: dev.to / 5 months ago
  • F1 FollowLine + HSV filter + PID Controller
    This library provides functions for working in domain of linear algebra, fourier transform, matrices and arrays. - Source: dev.to / 9 months ago
  • Intro to Ray on GKE
    The Python Library components of Ray could be considered analogous to solutions like numpy, scipy, and pandas (which is most analogous to the Ray Data library specifically). As a framework and distributed computing solution, Ray could be used in place of a tool like Apache Spark or Python Dask. It’s also worthwhile to note that Ray Clusters can be used as a distributed computing solution within Kubernetes, as... - Source: dev.to / 9 months ago
  • Streamlit 101: The fundamentals of a Python data app
    It's compatible with a wide range of data libraries, including Pandas, NumPy, and Altair. Streamlit integrates with all the latest tools in generative AI, such as any LLM, vector database, or various AI frameworks like LangChain, LlamaIndex, or Weights & Biases. Streamlit’s chat elements make it especially easy to interact with AI so you can build chatbots that “talk to your data.”. - Source: dev.to / 10 months ago
  • A simple way to extract all detected objects from image and save them as separate images using YOLOv8.2 and OpenCV
    The OpenCV image is a regular NumPy array. You can see it shape:. - Source: dev.to / 10 months ago
View more

Thonny mentions (111)

  • Essential Software for Hardware Hackers: Your First IDE & Tools
    Raspberry Pi Pico (MicroPython): The Thonny IDE is highly recommended and often pre-installed in Raspberry Pi OS. - Source: dev.to / 2 months ago
  • Antirez/load81: SDL based Lua programming environment for kids similar to Codea
    The nice thing about Python is that because it's so pervasive as a teaching language, there are many tools for it that specifically cater to this niche. In particular, https://thonny.org is very nice for explaining basic programming concepts like expression evaluation and function calls. And yeah, turtle graphics is the time-honored method of teaching things like loops, recursion, and parametrization via functions... - Source: Hacker News / 8 months ago
  • Python: From Beginners to Pro in 30 Mins (Part 1)
    Thonny or IDLE are great starting points for beginners due to their simplicity. As you become more comfortable, VS Code or PyCharm Community Edition offers more features to help you develop your skills. - Source: dev.to / 11 months ago
  • FeedMyFurBabies – Send and Receive MQTT messages between AWS IoT Core and your micro-controller
    Install Thonny and run it. Then go to Tools -> Options, to configure the ESP32C3 device in Thonny to match the settings shown in the screenshot below. - Source: dev.to / over 1 year ago
  • Raspberry Pico Badger: Hardware Features and OS
    The recommended way to programm MicroPython on the Raspberry Pico is to use the Thonny IDE. Accessing the Badger with reveals the following file structure:. - Source: dev.to / over 1 year ago
View more

What are some alternatives?

When comparing NumPy and Thonny, you can also consider the following products

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

PyCharm - Python & Django IDE with intelligent code completion, on-the-fly error checking, quick-fixes, and much more...

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

VS Code - Build and debug modern web and cloud applications, by Microsoft

OpenCV - OpenCV is the world's biggest computer vision library

IDLE - Default IDE which come installed with the Python programming language.

Loading...