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NumPy VS Hetzner Object Storage

Compare NumPy VS Hetzner Object Storage and see what are their differences

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NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

Hetzner Object Storage logo Hetzner Object Storage

Scalable object storage, S3-compatible and ideal for growing data volumes. Secure and flexible for efficient data storage.
  • NumPy Landing page
    Landing page //
    2023-05-13
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Object Storage is the S3-compatible storage solution that grows with any data requirements – secure, flexible and accessible via API. Object Storage offers the ideal solution for managing data-intensive workloads e.g. for backups, multimedia or big data. In contrast to conventional storage solutions, data is organized as objects consisting of the data itself, the metadata and a unique ID. This structure makes it easy to manage the data, search through it and organize it, and is particularly suitable for large, unstructured amounts of data. As a data center operator and hosting provider headquartered in Germany, data protection is a top priority at Hetzner. Thanks to strict compliance with the General Data Protection Regulation (GDPR), customers benefit from the highest security standards and best practices.

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.

Hetzner Object Storage features and specs

No features have been listed yet.

NumPy videos

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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

Hetzner Object Storage videos

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Category Popularity

0-100% (relative to NumPy and Hetzner Object Storage)

NumPy

Hetzner Object Storage

Data Science And Machine Learning
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Data Science And Machine Learning
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Cloud Storage
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Cloud Storage
100% 100
Data Science Tools
100 100%
Data Science Tools
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Object Storage
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Object Storage
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User comments

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Reviews

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

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

Hetzner Object Storage Reviews

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Social recommendations and mentions

Based on our record, NumPy seems to be more popular. It has been mentiond 119 times since March 2021. 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 / 4 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 / 8 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 / 8 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 / 9 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 / 9 months ago
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Hetzner Object Storage mentions (0)

We have not tracked any mentions of Hetzner Object Storage yet. Tracking of Hetzner Object Storage recommendations started around Apr 2025.

What are some alternatives?

When comparing NumPy and Hetzner Object Storage, 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.

Wasabi Cloud Object Storage - Storage made simple. Faster than Amazon's S3. Less expensive than Glacier.

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

Contabo Object Storage - S3-compatible cloud object storage with unlimited, free transfer at a fraction of what others charge. Easy migration & predictable billing. Sign up now & save.

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

IONOS Object Storage - IONOS Object Storage is a convenient, affordable and compliant way to store any amount of static company data. Pay only for what you use.

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