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ScrapingBee VS NumPy

Compare ScrapingBee VS NumPy and see what are their differences

Piloterr
Piloterr web scraping API handles headless browsers, rotates proxies for you, and offers a library featured
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ScrapingBee logo ScrapingBee

ScrapingBee is a Web Scraping API that handles proxies and Headless browser for you, so you can focus on extracting the data you want, and nothing else.

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • ScrapingBee Landing page
    Landing page //
    2022-01-12

Web Scraping is hard, scraping at scale can be very challenging.

You have to handle:

  • Javascript rendering 💻
  • Chrome headless 🛠
  • Captcha 🤖
  • Proxy 🕵️‍♀️

ScrapingBee is a simple API that does all the above for you, and much more.

  • NumPy Landing page
    Landing page //
    2023-05-13

ScrapingBee

Website
scrapingbee.com
Pricing URL
Official ScrapingBee Pricing
$ Details
freemium $49.0 / Monthly (Freelance / 10,000 searches / 100,000 credits)
Platforms
REST API
Release Date
2019 July
Edit details

NumPy

Website
numpy.org
Pricing URL
-
$ Details
Platforms
-
Release Date
-
Edit details

ScrapingBee features and specs

  • Easy to Use
    ScrapingBee provides a simple API that allows developers to scrape web pages without worrying about handling proxies or web browser rendering.
  • JavaScript Rendering
    With built-in JavaScript rendering, ScrapingBee can handle complex web pages that rely heavily on JavaScript for content display, making it suitable for scraping modern websites.
  • Proxy Management
    ScrapingBee automatically manages proxies, meaning developers don't have to deal with proxy rotation, blacklisting, or bans.
  • Rate Limiting Control
    The service offers control over rate limits, making it possible to scrape at a custom speed that suits your needs and prevents being blocked by target websites.
  • Custom Headers Support
    ScrapingBee allows the use of custom headers, enabling users to mimic different browsers or add specific headers required by the target site.
  • Geolocation
    It provides geolocation-based scraping, which is useful for accessing content that is region-restricted.

Possible disadvantages of ScrapingBee

  • Cost
    ScrapingBee is a paid service, and costs can add up depending on the volume and complexity of your scraping needs.
  • Rate Limits
    Even though it offers control over rate limits, there are still predefined limits depending on your plan, which might not suit very high-volume scraping needs.
  • Dependency on External Service
    Relying on an external service means that you are dependent on ScrapingBee's uptime and performance, which may affect your operations if the service faces downtime.
  • Data Privacy
    Using a third-party service for web scraping means sharing your scraping activities with ScrapingBee, which could raise data privacy concerns.
  • Limited Customization
    While ScrapingBee handles many aspects of web scraping for you, it may not offer the level of customization that a self-built scraping solution could provide.

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.

Analysis of ScrapingBee

Overall verdict

  • ScrapingBee is generally considered a good choice for web scraping, especially for users who want to streamline the process and avoid the complexities of managing their own infrastructure. It is well-regarded for its ease of use, reliability, and comprehensive feature set.

Why this product is good

  • ScrapingBee is a popular web scraping service because it provides a simple and efficient way to scrape websites without the need to manage proxy servers or deal with headless browser setup. It offers features like rendering JavaScript, handling CAPTCHAs, and supporting various customization options, making it suitable for different scraping needs.

Recommended for

  • Developers looking to automate data extraction from websites
  • Businesses needing reliable web scraping solutions without investing in infrastructure
  • Users who require JavaScript rendering and CAPTCHA handling in their scraping tasks
  • Projects that require scalable and customizable web scraping options

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.

ScrapingBee videos

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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

Category Popularity

0-100% (relative to ScrapingBee and NumPy)

ScrapingBee

NumPy

Web Scraping
100 100%
Web Scraping
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Data Extraction
100 100%
Data Extraction
0% 0
Data Science Tools
0 0%
Data Science Tools
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 ScrapingBee and NumPy

ScrapingBee Reviews

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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

Social recommendations and mentions

Based on our record, NumPy seems to be a lot more popular than ScrapingBee. While we know about 119 links to NumPy, we've tracked only 3 mentions of ScrapingBee. 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.

ScrapingBee mentions (3)

  • Self-hosted, simple web browser service – send URL, get screenshots
    If you’re worried about the security risks, edge cases, maintenance pain and scaling challenges of self hosting there are various solid hosted alternatives: - https://browserless.io - low level browser control - https://scrapingbee.com - scraping specialists - https://urlbox.com - screenshot specialists* They’re all profitable and have been around for years so you can depend on the businesses and the tech. *... - Source: Hacker News / 4 months ago
  • Are there any APIs that maintain a database of subscriptions?
    If you really just need the data you can use something like https://scrapingbee.com to scrape the info from the various price pages to make sure your info is always up to date. Source: about 2 years ago
  • Our bootstrapped SaaS just turned 3 and reached $1.5m ARR: the lessons learned.
    Well done! And posting here was a great idea. Not sure I would have found scrapingbee.com otherwise. We will probably become a customer. Signed up for the trial account. Source: almost 3 years ago

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 / 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
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What are some alternatives?

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

Zyte - We're Zyte (formerly Scrapinghub), the central point of entry for all your web data needs.

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

Bright Data - World's largest proxy service with a residential proxy network of 72M IPs worldwide and proxy management interface for zero coding.

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

Apify - Apify is a web scraping and automation platform that can turn any website into an API.

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

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