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python xlrd VS Keras

Compare python xlrd VS Keras and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

python xlrd logo python xlrd

Please use openpyxl where you can... Contribute to python-excel/xlrd development by creating an account on GitHub.

Keras logo Keras

Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
  • python xlrd Landing page
    Landing page //
    2023-08-18
  • Keras Landing page
    Landing page //
    2023-10-16

python xlrd features and specs

  • 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 of python xlrd

  • 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.

Keras features and specs

  • User-Friendly
    Keras provides a simple and intuitive interface, making it easy for beginners to start building and training models without needing extensive experience in deep learning.
  • Modularity
    Keras follows a modular design, allowing users to easily plug in different neural network components, such as layers, activation functions, and optimizers, to create complex models.
  • Pre-trained Models
    Keras includes a wide range of pre-trained models and offers easy integration with transfer learning techniques, reducing the time required to achieve good results on new tasks.
  • Integration with TensorFlow
    As part of TensorFlowโ€™s ecosystem, Keras provides deep integration with TensorFlow functionalities, enabling users to leverage TensorFlow's powerful features and performance optimizations.
  • Extensive Documentation
    Keras has comprehensive and well-organized documentation, along with numerous tutorials and code examples, making it easier for developers to learn and use the framework.
  • Community Support
    Keras benefits from a large and active community, which provides support through forums, GitHub, and specialized user groups, facilitating the resolution of issues and sharing of best practices.

Possible disadvantages of Keras

  • Performance Limitations
    Due to its high-level abstraction, Keras may incur performance overheads, making it less suitable for scenarios requiring extremely fast execution and low-level optimizations.
  • Limited Low-Level Control
    The simplicity and abstraction of Keras can be a downside for advanced users who need fine-grained control over model components and custom operations, which may require them to resort to lower-level frameworks.
  • Scalability Issues
    In some complex applications and large-scale deployments, Keras might face scalability challenges, where more specialized or low-level frameworks could handle such tasks more efficiently.
  • Dependency on TensorFlow
    While the integration with TensorFlow is generally an advantage, it also means that the performance and features of Keras are closely tied to the development and updates of TensorFlow.
  • Lagging Behind Latest Research
    Keras, being a user-friendly high-level API, might not always incorporate the latest cutting-edge research advancements in deep learning as quickly as more research-oriented frameworks.

Analysis of Keras

Overall verdict

  • Keras is a solid choice for deep learning projects, offering simplicity and flexibility without sacrificing performance. It is well-suited for educational purposes, research, and even deploying models in production environments.

Why this product is good

  • Keras is widely regarded as a good deep learning library because it provides a user-friendly API that allows for easy and fast prototyping of neural networks. It is built on top of other libraries like TensorFlow, making it robust and efficient for both beginners and experienced developers. Its modularity, extensibility, and compatibility with other tools and libraries make it a popular choice for developing deep learning models.

Recommended for

  • Beginners who are new to deep learning
  • Researchers looking for an easy-to-use platform for prototyping models
  • Developers working on projects that require quick experimentation and development
  • Individuals and companies deploying models into production environments

python xlrd videos

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Keras videos

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3. Deep Learning Tutorial (Tensorflow2.0, Keras & Python) - Movie Review Classification

More videos:

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Category Popularity

0-100% (relative to python xlrd and Keras)

python xlrd

Keras

Application Builder
100 100%
Application Builder
0% 0
Data Science And Machine Learning
13 13%
Data Science And Machine Learning
87% 87
Development Tools
100 100%
Development Tools
0% 0
OCR
0 0%
OCR
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare python xlrd and Keras

python xlrd Reviews

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Keras Reviews

10 Python Libraries for Computer Vision
TensorFlow and Keras are widely used libraries for machine learning, but they also offer excellent support for computer vision tasks. TensorFlow provides pre-trained models like Inception and ResNet for image classification, while Keras simplifies the process of building, training, and evaluating deep learning models.
Source: clouddevs.com
25 Python Frameworks to Master
Keras is a high-level deep-learning framework capable of running on top of TensorFlow, Theano, and CNTK. It was developed by Franรงois Chollet in 2015 and is designed to provide a simple and user-friendly interface for building and training deep learning models.
Source: kinsta.com
15 data science tools to consider using in 2021
Keras is a programming interface that enables data scientists to more easily access and use the TensorFlow machine learning platform. It's an open source deep learning API and framework written in Python that runs on top of TensorFlow and is now integrated into that platform. Keras previously supported multiple back ends but was tied exclusively to TensorFlow starting with...
Source: searchbusinessanalytics.techtarget.com

Social recommendations and mentions

Based on our record, Keras seems to be a lot more popular than python xlrd. While we know about 35 links to Keras, we've tracked only 2 mentions of python xlrd. 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.

python xlrd mentions (2)

  • 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: about 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

Keras mentions (35)

  • Top Programming Languages for AI Development in 2025
    The unchallenged leader in AI development is still Python. And Keras, and robust community support. - Source: dev.to / about 1 year ago
  • Top 8 OpenSource Tools for AI Startups
    If you need simplicity, Keras is a great high-level API built on top of TensorFlow. It lets you quickly prototype neural networks without worrying about low-level implementations. Keras is perfect for getting those first models up and runningโ€”an essential part of the startup hustle. - Source: dev.to / over 1 year ago
  • Top 5 Production-Ready Open Source AI Libraries for Engineering Teams
    At its heart is TensorFlow Core, which provides low-level APIs for building custom models and performing computations using tensors (multi-dimensional arrays). It has a high-level API, Keras, which simplifies the process of building machine learning models. It also has a large community, where you can share ideas, contribute, and get help if you are stuck. - Source: dev.to / almost 2 years ago
  • Using Google Magika to build an AI-powered file type detector
    The core model architecture for Magika was implemented using Keras, a popular open source deep learning framework that enables Google researchers to experiment quickly with new models. - Source: dev.to / about 2 years ago
  • My Favorite DevTools to Build AI/ML Applications!
    As a beginner, I was looking for something simple and flexible for developing deep learning models and that is when I found Keras. Many AI/ML professionals appreciate Keras for its simplicity and efficiency in prototyping and developing deep learning models, making it a preferred choice, especially for beginners and for projects requiring rapid development. - Source: dev.to / about 2 years ago
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What are some alternatives?

When comparing python xlrd and Keras, you can also consider the following products

Ionic - Ionic is a cross-platform mobile development stack for building performant apps on all platforms with open web technologies.

TensorFlow - 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.

Flutter - Build beautiful native apps in record time ๐Ÿš€

PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...

python pillow - The friendly PIL fork (Python Imaging Library). Contribute to python-pillow/Pillow development by creating an account on GitHub.

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

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