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NumPy VS Upsolver

Compare NumPy VS Upsolver and see what are their differences

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

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

Upsolver logo 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).
  • NumPy Landing page
    Landing page //
    2023-05-13
  • Upsolver Landing page
    Landing page //
    2023-08-06

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

Upsolver videos

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Category Popularity

0-100% (relative to NumPy and Upsolver)

NumPy

Upsolver

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

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

Upsolver Reviews

Top 10 AWS ETL Tools and How to Choose the Best One | Visual Flow
In this way, Upsolver removes the complexity of Big Data and Real-Time projects and reduces their use time from several weeks or months to several hours. With the latest Volcano technology, this tool queries the entire data lake in less than a millisecond and stores 10x the amount of data in RAM.
Source: visual-flow.com

Social recommendations and mentions

Based on our record, NumPy seems to be a lot more popular than Upsolver. While we know about 111 links to NumPy, we've tracked only 1 mention of Upsolver. 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 (111)

  • Documenting my pin collection with Segment Anything: Part 3
    NumPy: This library is fundamental for handling arrays and matrices, such as for operations that involve image data. NumPy is used to manipulate image data and perform calculations for image transformations and mask operations. - Source: dev.to / about 12 hours ago
  • Awesome List
    NumPy - The fundamental package for scientific computing with Python. NumPy Documentation - Official documentation. - Source: dev.to / 6 days ago
  • 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 / 8 days 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
View more

Upsolver mentions (1)

  • Anyone Used Dremio?
    Most of the pains of using query engines over object storage are in the ongoing management of files (partitioning, compression, merging many small files into fewer larger files) Cloud data lakes are tremendously valuable when it comes to exploratory and ad-hoc data analysis. If you really require sub-second queries on structured data, you're better off with a data warehouse. I'm not totally clear on your use... Source: almost 3 years ago

What are some alternatives?

When comparing NumPy and Upsolver, 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.

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

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

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.

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

Mozart Data - The easiest way for teams to build a Modern Data Stack

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