LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives.
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Which is more popular?
Based on our record, NumPy
seems to be a lot more popular than PyQt.
While we know about 122 links to NumPy,
we've tracked only 4 mentions of PyQt.
social mentions
4 vs 122
Rapid Application Development popularity
100% vs 0%
alternatives listed
102 vs 240+
Base details
Website, pricing, platforms and company facts side by side.
Comprehensive UI library PyQt provides a wide range of UI components, from basic widgets to advanced tools. This allows for the creation of highly sophisticated interfaces.
Cross-platform Applications built with PyQt can run on different operating systems such as Windows, macOS, and Linux without requiring significant changes in the code.
Integration with Qt Designer Developers can use Qt Designer to design and implement their UIs visually, which can then be seamlessly integrated with Python code in PyQt.
Powerful event handling PyQt includes a highly efficient event handling system that makes it easy to manage user interactions and system events.
Good documentation and community support PyQt is well-documented, and there's a large community of developers who can provide support and share resources.
Python-specific advantages Leveraging Python's simplicity and readability, PyQt allows for rapid development and easy maintenance of applications.
Possible disadvantages
License considerations PyQt is available under the GPL and a commercial license. If you want to create proprietary software without open-sourcing your code, you need to purchase a commercial license.
Steep learning curve While PyQt is powerful, it can have a steep learning curve for newcomers, particularly those who are not familiar with Qt and its paradigms.
Performance overhead Being a binding for Qt, some operations may have extra overhead compared to native Qt applications written in C++.
Dependency on external libraries PyQt relies on the Qt library, which means that you have to manage and distribute these dependencies along with your application.
Large binary sizes Applications created with PyQt can result in relatively large binary sizes because of the included Qt binaries.
Fragmentation of tools There can be fragmentation concerns, as PyQt must stay in sync with Qt, and different versions of Qt may introduce changes that are not immediately reflected in PyQt.
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
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.
Analysis
An editorial look at what each product does well and who it suits.
PyQtNumPy
Overall verdict
PyQt is considered a good choice for developers looking to create robust, high-performance desktop applications with Python. Its ability to leverage the powerful Qt framework makes it a reliable option for both beginners and experienced developers.
Why this product is good
PyQt is a set of Python bindings for the Qt libraries, allowing developers to create cross-platform applications with native look and feel. It provides comprehensive support for building GUI applications and includes an extensive range of modules and functions, making it suitable for both simple and complex projects. Additionally, it benefits from a large and active community, extensive documentation, and commercial support from Riverbank Computing.
Recommended for
Developers looking for cross-platform GUI toolkits
Projects that require a modern, native look and feel
Development teams requiring robust commercial support
Python developers interested in leveraging a well-documented and extensive framework
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.
PyQt is one of the most popular sets of Python bindings for the Qt cross-platform application framework. This framework perfectly combines the simplicity of Python as a general-purpose language and the powerful Qt...
Before the Qt Company (under Nokia) released the officially supported PySide library in 2009, Riverbank Computing had released PyQt in 1998. The main difference between these two libraries is in licensing. The...
Developed by Riverbank Computing, PyQt5 is one of the most popular Python frameworks for GUI. The PyQt package is built around the Qt framework, which is a cross-platform framework used for creating various...
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...
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...
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...
JavaScript is a clear winner in the category of mobile development. There are some niche frameworks to do mobile development with Python—like Kivy and PyQT—but pretty much nobody uses them.
- Source: dev.to
/
over 4 years ago
what would be the best looking GUI framework to develop a desktop python application? (other than Tkinter)
If none of those are to your liking, you can use PyQT (or Pyside) but the learning curve is much steeper.
Source:
over 4 years ago
Is there a "Windows Forms" GUI designer for Python?
Also, there is the PyQt module which is a comprehensive set of Python bindings for the Qt GUI. It has Qt Designer.
Source:
about 5 years ago
The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages
Familiarity with Python as a language is assumed; if you need a quick...
- Source: dev.to
/
12 months ago
AI starts with math and coding. You don’t need a PhD—just high school math like algebra and some geometry. Linear algebra (think matrices) and calculus (like slopes) help understand how AI models work. Python is the main language for AI,...
- Source: dev.to
/
about 1 year ago
Tkinter
PySimpleGUI
GTK
wxWidgets
MD Python Designer
BeeWare
Kivy
Pandas
Scikit-learn
OpenCV
Dataiku
Exploratory
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Figure Eight
