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NumPy VS Google Scholar

Compare NumPy VS Google Scholar and see what are their differences

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

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

Google Scholar logo Google Scholar

Google Scholar is a freely accessible web search engine that indexes the full text of scholarly...
  • NumPy Landing page
    Landing page //
    2023-05-13
  • Google Scholar Landing page
    Landing page //
    2023-02-07

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.

Google Scholar features and specs

  • Accessibility
    Google Scholar is freely accessible to anyone with an internet connection, removing barriers to accessing academic research.
  • Wide Range of Sources
    It indexes scholarly articles from a broad range of disciplines and sources, including academic publishers, universities, and other scholarly websites.
  • Citation Tracking
    Google Scholar provides citation information, allowing users to see how often a paper has been cited and to track the influence of research over time.
  • Ease of Use
    The interface is user-friendly and familiar to anyone who has used Google, making it easy to search for and find scholarly papers.
  • Advanced Search Options
    Google Scholar offers advanced search capabilities, including the ability to search by author, date range, and specific journals.

Possible disadvantages of Google Scholar

  • Quality Control
    The inclusion criteria for sources indexed are not transparent, leading to variability in the quality of the materials available.
  • Coverage
    Although extensive, Google Scholar's coverage is not comprehensive, and some important journals and articles might be missing.
  • Duplicate Entries
    There can be multiple entries for the same document, making it difficult to determine the most authoritative version.
  • Limited Full-Text Availability
    Many articles listed in Google Scholar are behind paywalls, meaning full access often requires a subscription or purchase.
  • Inconsistent Metadata
    The metadata (author names, publication dates, etc.) can sometimes be inaccurate or incomplete, affecting search results and citation tracking.

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.

Analysis of Google Scholar

Overall verdict

  • Overall, Google Scholar is considered a good resource for academic research. It is user-friendly, provides comprehensive search results, and includes useful features such as citation analysis and linking to full-text articles when available. However, it may not have access to all subscription-only content available through university libraries or specialized databases.

Why this product is good

  • Google Scholar is a valuable tool because it provides free access to a vast range of scholarly articles, theses, books, conference papers, and patents across various disciplines. It indexes content from academic publishers, research institutions, and other scholarly websites, making it a convenient resource for researchers, students, and academics. Its citation tracking feature is particularly useful for understanding the impact and relevance of specific works.

Recommended for

  • Students looking for scholarly articles for their assignments.
  • Researchers who want to track citations and research trends.
  • Academics needing access to a wide range of publications.
  • Anyone interested in finding reliable, peer-reviewed sources for information.

NumPy videos

+ Add

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

Google Scholar videos

+ Add

How to do a literature review using Google Scholar

More videos:

  • Tutorial - How To Use Google Scholar | Writing A Literature Review
  • Tutorial - How to use Google Scholar to find journal articles | Essay Tips

Category Popularity

0-100% (relative to NumPy and Google Scholar)

NumPy

Google Scholar

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Digital Whiteboard
0 0%
Digital Whiteboard
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Research Tools
0 0%
Research 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 NumPy and Google Scholar

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

Google Scholar Reviews

We have no reviews of Google Scholar yet.
Be the first one to post

Social recommendations and mentions

Based on our record, Google Scholar should be more popular than NumPy. It has been mentiond 1004 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 (122)

View more

Google Scholar mentions (1004)

  • Who discovered grokking and why is the name hard to find?
    Https://arxiv.org/abs/2201.02177 This paper is not hard to find; it's the first result when you search for "grokking" with https://scholar.google.com. - Source: Hacker News / 5 months ago
  • AI generated font using nano banana
    Definitely not the first AI generated font. One can find an enormous amount of research in AI font generation on https://scholar.google.com/ going back many years. This could possibly be the first one that used Nano Banana though. - Source: Hacker News / 7 months ago
  • ChatGPT Search
    > Has google completely stopped working for anyone else? Yes. However, I found that https://scholar.google.com still works perfectly well. It feels just as the old Google without all the crap they've been adding in the last years. - Source: Hacker News / over 1 year ago
  • Is Psychology Going to Cincinnati?
    He links to a meta analysis* that says CBT does cure depression well enough and does so consistently for many decades without any declines in effectiveness. Later for some reason, he says no single mental illness was ever cured. It seems the main point of the article is to say that nothing except "nudges" ever worked in psychology - this is nonsense that he himself contradicts as I mentioned above. Just use... - Source: Hacker News / almost 2 years ago
  • Ask HN: Where do you subscribe to published journal topics?
    If you mean articles: No, it would be unfeasible. According to Science [https://www.science.org/content/article/scienceadviser-scientists-are-publishing-too-many-papers-and-s-bad-science] there are about 2.82 million articles coming out every year. That's 5.3 papers every minute, 24/7. If you mean a list of titles, your best bet would probably be something like https://www.ncbi.nlm.nih.gov/pmc/ [PMC, life... - Source: Hacker News / almost 2 years ago
View more

What are some alternatives?

When comparing NumPy and Google Scholar, 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.

PubMed.gov - PubMed comprises more than 29 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

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

SCI-HUB - It provides mass and public access to tens of millions of research papers

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

Forge - Static web hosting made simple

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