Sable is a multi-tenant compliance and vendor risk platform for MSPs and growing companies, backed by an in-house security team for pen testing, SOC monitoring and incident response. The platform and the people to act on it.
sponsored
Which is more popular?
Based on our record, PyTorch
seems to be a lot more popular than python xlrd.
While we know about 146 links to PyTorch,
we've tracked only 2 mentions of python xlrd.
social mentions
146 vs 2
Data Science And Machine Learning popularity
93% vs 7%
alternatives listed
151 vs 12
Base details
Website, pricing, platforms and company facts side by side.
Dynamic Computation Graph PyTorch uses a dynamic computation graph, which allows for interactive and flexible model building. This is particularly beneficial for researchers who need to modify the network architecture on-the-fly.
Pythonic Nature PyTorch is designed to be deeply integrated with Python, making it very intuitive for Python developers. The framework feels more 'native' to Python, which improves the ease of learning and use.
Strong Community Support PyTorch has a large, active, and growing community. This means abundant resources such as tutorials, forums, and third-party tools are available to help developers solve problems and share solutions.
Flexibility and Control PyTorch offers granular control over computations and provides extensive debugging capabilities. This level of control is beneficial for tasks that require precise tuning and custom implementations.
Support for GPU Acceleration PyTorch offers seamless integration with GPU hardware, which significantly accelerates the computation process. This makes it highly efficient for deep learning tasks.
Rich Ecosystem PyTorch has a rich ecosystem including libraries like torchvision, torchaudio, and torchtext, which are specialized for different data types and can significantly shorten development times.
Possible disadvantages
Limited Production Deployment Tools PyTorch is primarily designed for research rather than production. While deployment tools like TorchServe exist, they are not as mature or integrated as solutions offered by other frameworks like TensorFlow.
Lesser Adoption in Industry While PyTorch is popular among researchers, it has historically seen less adoption in industry compared to TensorFlow, which means there might be fewer resources for large-scale production deployments.
Inconsistent API Changes As PyTorch continues to evolve rapidly, occasionally there are breaking changes or inconsistent API updates. This can create maintenance challenges for existing codebases.
Steeper Learning Curve for Beginners Despite its Pythonic design, PyTorch's focus on flexibility and control can make it slightly harder for beginners to get started compared to some other high-level libraries and frameworks.
Less Mature Documentation Although the documentation is improving, it has been historically less comprehensive and mature compared to other frameworks like TensorFlow, which can make it difficult to find detailed, clear information.
Simplicity xlrd provides a straightforward and easy-to-use API for reading Excel files, making it accessible for beginners and quick implementations.
Widely Used xlrd has been a popular choice for handling Excel files in Python, which means there is a lot of available documentation and community support.
Efficient Reading It is optimized for reading data from Excel files without loading entire data into memory, which is beneficial for handling large files.
Possible disadvantages
No Write Support xlrd is designed solely for reading, and it does not support writing or modifying Excel files.
Limited to Older Excel Formats With version 2.0 and above, xlrd only supports the older .xls Excel file format and does not support .xlsx files.
Deprecated Features Due to changes in dependencies and updates to Excel formats, some features in xlrd have become deprecated or removed, which can limit its functionality.
Analysis
An editorial look at what each product does well and who it suits.
PyTorchpython xlrd
Overall verdict
Yes, PyTorch is considered a good deep learning framework.
Why this product is good
Ease of Use: PyTorch has an intuitive interface that makes it easier to learn and use, especially for beginners.
Dynamic Computation Graphs: PyTorch employs dynamic computation graphs, which provide more flexibility in building and modifying models on the fly.
Strong Community and Support: PyTorch has a large and active community, offering extensive resources, forums, and tutorials.
Research Adoption: PyTorch is widely adopted in the research community, making state-of-the-art models and techniques readily available.
Integration: PyTorch integrates well with other libraries and tools in the Python ecosystem, providing robust support for various applications.
Recommended for
Researchers and Academics: Ideal for those who need a flexible and dynamic tool for experimenting with new models and techniques.
Industry Practitioners: Suitable for developers and data scientists working on production-level machine learning solutions.
Educators and Learners: Great for educational purposes due to its easy-to-understand syntax and comprehensive documentation.
Similar to TensorFlow and Keras, PyTorch and torchvision offer powerful tools for computer vision tasks. PyTorch’s dynamic computation graph and torchvision’s datasets and pre-trained models make it easy to implement...
Along with TensorFlow, PyTorch (developed by Facebook’s AI research group) is one of the most used tools for building deep learning models. It can be used for a variety of tasks such as computer vision, natural...
PyTorch is another open-source machine learning framework that is widely used in academia and industry. PyTorch provides excellent support for building deep learning models, and it has several pre-trained models for...
Recommendations tracked on public social media and blogs since March 2021.
PyTorch146 mentionspython xlrd2 mentions
Train a Modern VLM on One GPU: Qwen3-VL with Unsloth and Transformers 5.12.1
Install PyTorch first with the selector on pytorch.org, then install the Python packages used by the examples:.
- Source: dev.to
/
8 days ago
Context Encoders: Learning to Inpaint and What It Teaches a Network
In this technical blog, we will deep dive into the core of context encoders and implement it using PyTorch and CelebA dataset from scratch.
- Source: dev.to
/
about 1 month ago
Developer Take On: A High-Resolution Neural Cellular Automata
PyTorch: A popular deep learning framework for Python.
- Source: dev.to
/
5 months ago
I need to read multiple excel files, extract a column from each and compose a new file
So to get this out of the way first, xlrd has less features than openpyxl and in addition only works with the old '.xls' format, not the newer '.xlsx' format. Even on the xlrd's Github repo it says: 'Please use openpyxl where you can... '.
Source:
over 4 years ago
Sending Bulk SMS using Africas Talking, Python and Excel
There are few alternative libraries for reading and writing excel files: Pandas,
Xlrd , openpyxl among others. In the end I settled for openpyxl as I had the most experience
Using it and it had support for .xlsx files.
- Source: dev.to
/
over 5 years ago
Alternatives to PyTorch and python xlrd
When comparing PyTorch and python xlrd, you can also consider the following products.
TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.
TensorFlow
Keras
Scikit-learn
NumPy
CUDA Toolkit
Pandas
MLKit
Ionic
Flutter
python pillow
Telerik
python docx
Material UI
bokeh python
