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NumPy VS Embeddable

Compare NumPy VS Embeddable and see what are their differences

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NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

Embeddable logo Embeddable

The toolkit for building fast, interactive, fully-custom analytics experiences into your app.
  • NumPy Landing page
    Landing page //
    2023-05-13
  • Embeddable Headless Embedded Analytics
    Headless Embedded Analytics //
    2025-03-18

Build Remarkable Analytics Experiences. No more 'Build vs. Buy'. Embeddable is the embedded analytics tool where you own the front-end code and we handle everything else. Now you can build fully-bespoke, fast-loading charts and dashboards in your app without the engineering costs. Delight your customers, reduce engineering overheads, and deliver your dream experience, fast. Compatible with all major databases. Cloud & Self-hosted. Multi-tenancy. Open source component library + more

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.

Embeddable features and specs

  • Cloud-Hosted Option
  • Self-Hosted Option
  • Frontend SDK
  • No-code Dashboard Builder
  • Performant Embedding
  • Row-Level Security
  • Configurable Cache
  • Compatible with Major Databases
  • Compatible with Charting Libraries
  • Template Charting Components Provided
    Included
  • Dedicated Account Management
  • Version Control
  • Audit Logs
  • Documentation

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.

NumPy videos

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

Embeddable videos

No Embeddable videos yet. You could help us improve this page by suggesting one.

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Category Popularity

0-100% (relative to NumPy and Embeddable)
Data Science And Machine Learning
Business Intelligence
0 0%
100% 100
Data Science Tools
100 100%
0% 0
Data Dashboard
73 73%
27% 27

Questions & Answers

As answered by people managing NumPy and Embeddable.

How would you describe the primary audience of your product?

Embeddable's answer:

Software companies who care about the UX and loading speed of their customer-facing analytics.

What makes your product unique?

Embeddable's answer:

Get the best of 'Build vs. Buy' in one stack-agnostic solution. Embeddable gives you full control over the frontend of your analytics experience, and handles the backend for you. No longer do you have to choose between a limited out-of-the-box solution, or building everything from scratch.

What's the story behind your product?

Embeddable's answer:

Embeddable is from the team behind Trevor.io -- a popular internal BI tool which also allows you to embed dashboards into your app. We realised embedding dashboards from a BI tool into your app wasn't the 'dream solution', and building analytics from scratch was super expensive... so we built Embeddable from the ground up to enable teams to deliver fully-bespoke, highly-performant analytics in their apps for their customers in 10% of the time.

Who are some of the biggest customers of your product?

Embeddable's answer:

  • Scalapay
  • Adthena
  • Irwin
  • EtonX
  • Resident Advisor
  • Facilities Solutions Group (FSG)
  • Multibrain
  • Raydiant
  • ThinkCERCA
  • Tixly
  • Softools
  • Faheem App
  • Just Move In
  • Any Creek

Why should a person choose your product over its competitors?

Embeddable's answer:

If you want full control over the UX of your customer-facing analytics experience, but don't want to invest months of developer time on building and maintaining a fully-custom build -- OR -- if you're using an embedded analytics too already that loads slowly and doesn't look and feel like the rest of your platform.

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 Embeddable

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.
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

Embeddable Reviews

6 Best Looker alternatives
After a successful, oversubscribed Private Beta, Embeddable is now publicly available. More information on how to work with Embeddable can be found on their homepage at embeddable.com. Get in touch with the Embeddable team for pricing.
Source: trevor.io
Power BI Embedded vs Looker Embedded: Everything you need to know
The main differences between Power BI Embedded and Embeddable are performance, price, and customizability. Embeddable gives you full control over your charting components and data models. Itโ€™s also built from the ground up to enable companies to deliver fully bespoke, highly-performant analytics experiences to their customers, without requiring an expensive in-house build....
Source: embeddable.com
Embedded analytics in B2B SaaS: A comparison
Iโ€™m happy to say that weโ€™ve enrolled in the beta program of Embeddable. After learning all the above it seems like this is the option weโ€™d want to invest in. Weโ€™ll keep you posted on how this pans out, but weโ€™re excited about what Embeddable is building and is going to offer.
Source: medium.com

Social recommendations and mentions

Based on our record, NumPy seems to be a lot more popular than Embeddable. While we know about 122 links to NumPy, we've tracked only 2 mentions of Embeddable. 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

Embeddable mentions (2)

  • AI in BI tools: why we're not there yet
    Then comes data modeling. BI tools such as Embeddable need to know how different tables and fields relate to each other. Someone has to define what terms like โ€œtop customerโ€ or โ€œQ3 revenueโ€ actually mean. Without this, the AI won't know where to look or how to answer even basic questions. - Source: dev.to / about 1 year ago
  • Apache Superset
    Itโ€™s still pretty new but build by an experienced team. Itโ€™s commercial software though. https://embeddable.com/. - Source: Hacker News / over 2 years ago

What are some alternatives?

When comparing NumPy and Embeddable, 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.

Luzmo - From data to decisions, damn fast. Embed beautiful, easy-to-use dashboards in your SaaS product in days, not months.

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

Metabase - Metabase is the easy, open source way for everyone in your company to ask questions and learn from...

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

Looker - Looker makes it easy for analysts to create and curate custom data experiencesโ€”so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.