Software Alternatives, Accelerators & Startups
Register | Login

IRI Voracity VS NumPy

Compare IRI Voracity VS NumPy and see what are their differences

Frontegg
Elegant user management, tailor-made for B2B SaaS featured
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

IRI Voracity logo IRI Voracity

IRI Voracity is an automated data management platform that helps you extract, transform and load (ETL) your data lake to any data warehouse or cloud.

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • IRI Voracity Landing page
    Landing page //
    2022-02-15
  • NumPy Landing page
    Landing page //
    2023-05-13

IRI Voracity videos

+ Add

IRI Voracity Platform Introduction

More videos:

  • Demo - Sample Live Demo of IRI Voracity
  • Review - 2018 02 23 IRI Voracity Trailer

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

Category Popularity

0-100% (relative to IRI Voracity and NumPy)

IRI Voracity

NumPy

Business & Commerce
100 100%
Business & Commerce
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Online Services
100 100%
Online Services
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

Share your experience with using IRI Voracity and NumPy. 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 IRI Voracity and NumPy

IRI Voracity Reviews

We have no reviews of IRI Voracity yet.
Be the first one to post

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 more popular. It has been mentiond 109 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.

IRI Voracity mentions (0)

We have not tracked any mentions of IRI Voracity yet. Tracking of IRI Voracity recommendations started around Feb 2022.

NumPy mentions (109)

  • NumPy for Beginners: A Basic Guide to Get You Started
    This guide covers the basics of NumPy, and there's much more to explore. Visit numpy.org for more information and examples. - Source: dev.to / about 19 hours ago
  • 2 Minutes to JupyterLab Notebook on Docker Desktop
    Below is an example of a code cell. We'll visualize some simple data using two popular packages in Python. We'll use NumPy to create some random data, and Matplotlib to visualize it. - Source: dev.to / 9 months ago
  • Element-wise vs Matrix vs Dot multiplication
    In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication. - Source: dev.to / 3 months ago
  • JSON in data science projects: tips & tricks
    Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:. - Source: dev.to / 3 months ago
  • Introducing Flama for Robust Machine Learning APIs
    Numpy: A library for scientific computing in Python. - Source: dev.to / 6 months ago
View more

What are some alternatives?

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

Kylo - Kylo is an end-to-end data lake management software that provides data from many sources in an automated fashion and optimizes it.

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

Upsolver - Upsolver is a robust Data Lake Platform that simplifies big & streaming data integration, management and preparation on premise (HDFS) or in the cloud (AWS, Azure, GCP).

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

Lyftrondata - Lyftrondata is an end-to-end data delivery platform, providing the right digital transformation having rapid automation and optimizations in place, so one can do better management of their inventory.

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

Loading...