Software Alternatives, Accelerators & Startups
Register | Login

NumPy VS Ninox

Compare NumPy VS Ninox and see what are their differences

Insightful.io
The precision Work Intelligence for actionable bottom line impact and #1 workforce analytics software that shows how work actually happens: get real-time and historical data, understand productivity at every level, track AI adoption across your org. featured
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.
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

Ninox logo Ninox

Ninox is a human-friendly database. Create your own Business Application with Ninox that matches your workflow. Ninox lets you integrate CRM, ERP, HR and many more...
  • NumPy Landing page
    Landing page //
    2023-05-13
  • Ninox Landing page
    Landing page //
    2023-05-22

Create your own Business Application with Ninox that matches your workflow. Ninox lets you integrate and customize applications from different departments such as CRM, ERP, HR, Accounting, Sales and PM to streamline your operations most efficiently. Additionally Ninox promotes team collaboration functionalities and the integration of the most common used services e.g. Google (Sheets, Drive, Calendar, Forms) and many more.

NumPy

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

Ninox

Website
ninox.com
Pricing URL
Official Ninox Pricing
$ Details
paid Free Trial โ‚ฌ10.0 / Monthly (2 GB per user)
Platforms
Browser Android iOS Web Windows Mac OSX Google Chrome
Release Date
2013 January
Startup details
Country
Germany
State
Berlin
City
Berlin
Founder(s)
Alexander Koenig
Employees
10 - 19
Edit details

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.

Ninox features and specs

  • Versatility
    Ninox offers a robust set of features that cater to various business needs, including database management, workflow automation, and integration capabilities, making it suitable for many different types of users and industries.
  • User-Friendly Interface
    Ninox has an intuitive and user-friendly interface, which makes it relatively easy for users to create and manage databases without requiring deep technical knowledge.
  • Cloud and On-Premises Options
    Ninox offers both cloud-based and on-premises solutions, giving organizations the flexibility to choose the deployment method that best suits their security and access needs.
  • Cross-Platform Support
    Ninox is available on various platforms including iOS, macOS, and Windows, in addition to a web-based version. This makes it accessible from almost any device.
  • Customization
    Users can customize tables, forms, fields, and reports to tailor the application to their specific needs, enhancing its flexibility and usability.

Possible disadvantages of Ninox

  • Pricing
    Compared to some other database management and automation tools, Ninox can be relatively expensive, especially for smaller organizations with limited budgets.
  • Learning Curve
    While Ninox is user-friendly, there is still a learning curve associated with mastering its full suite of features, especially for users who are not familiar with database management concepts.
  • Limited Public API
    Ninox has a limited public API, which can restrict its integration capabilities with some third-party applications and services, posing a challenge for users with complex integration needs.
  • Complexity with Advanced Features
    The more advanced features of Ninox, such as scripting for automation and complex database relationships, may require more technical knowledge and could be daunting for less experienced users.
  • Mobile App Limitations
    Although the mobile app is functional, it can sometimes lack the full feature set of the desktop and web versions, leading to a less optimal experience on mobile devices.

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

+ 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

Ninox videos

+ Add

Ninox Database iPad App Demo

More videos:

Category Popularity

0-100% (relative to NumPy and Ninox)

NumPy

Ninox

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Project Management
0 0%
Project Management
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Databases
0 0%
Databases
100% 100

User comments

Share your experience with using NumPy and Ninox. For example, how are they different and which one is better?
Log in or Post with

Reviews

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

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

Ninox Reviews

Top 10 Microsoft Power Apps Alternatives and Competitors 2024
Ninox Pricing: Ninox offers a freemium plan with limited features for a single user. Paid plans start at โ‚ฌ8 per user per month for additional features and functionalities. Enterprise plans with custom pricing cater to large-scale deployments.
Source: medium.com
10 Best Low-Code Development Platforms in 2020
A free trial of 30 days is available for the product. Ninox has two pricing plans i.e. Ninox Cloud and Private Cloud. Ninox Cloud price starts at $8.33 per user per month. Private cloud price starts at $16.66 per user per month.
Source: www.softwaretestinghelp.com

Social recommendations and mentions

Based on our record, NumPy seems to be more popular. It has been mentiond 122 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

Ninox mentions (0)

We have not tracked any mentions of Ninox yet. Tracking of Ninox recommendations started around Mar 2021.

What are some alternatives?

When comparing NumPy and Ninox, 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.

Nintex - Cloud-based digital workflow management automation platform

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

QuickBase - Quickbase provides a no-code operational agility platform that enables organizations to improve operations through real time insights and automation across complex processes and disparate systems. โ€‹โ€‹

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

Airtable - Airtable works like a spreadsheet but gives you the power of a database to organize anything. Sign up for free.

Loading...