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NumPy VS Steampipe

Compare NumPy VS Steampipe and see what are their differences

LibHunt
LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives. featured
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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

Steampipe logo Steampipe

Steampipe: select * from cloud; The extensible SQL interface to your favorite cloud APIs select * from AWS, Azure, GCP, Github, Slack etc.
  • NumPy Landing page
    Landing page //
    2023-05-13
  • Steampipe Landing page
    Landing page //
    2023-09-30

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.

Steampipe features and specs

  • Unified Interface
    Steampipe provides a unified SQL-based interface to query data from various cloud services and APIs, simplifying data access.
  • Open Source
    Being open source, Steampipe allows for community contributions, transparency, and flexibility in adapting the tool to specific needs.
  • Plugin Ecosystem
    Steampipe has a growing ecosystem of plugins that enable easy integration with numerous services, enhancing its versatility.
  • Real-Time Data Access
    It facilitates real-time querying of data from live APIs, which is beneficial for up-to-date insights and monitoring.
  • Cross-Platform Compatibility
    Steampipe is designed to work on multiple platforms, including Windows, MacOS, and Linux, making it accessible to a wide range of users.

Possible disadvantages of Steampipe

  • Complex Setup
    Initial setup and configuration can be complex, requiring a good understanding of SQL and the specific APIs being used.
  • Performance Overhead
    Query performance may be impacted due to the abstraction layer and real-time consolidation of data from multiple sources.
  • Limited Community Support
    As a relatively new tool, Steampipe may have limited community support and fewer resources compared to more established alternatives.
  • Resource Intensive
    Running multiple queries against APIs and cloud services can become resource intensive, potentially increasing costs and load on systems.
  • Learning Curve
    Users unfamiliar with SQL may face a learning curve in effectively utilizing Steampipe for querying different data sources.

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

Steampipe videos

+ Add

Superbooth 2023: Erica Synths - Steampipe

More videos:

  • Review - BEST SYNTHS @ SUPERBOOTH23: PWM Mantis, UDO Super Gemini, Erica Synths STEAMPIPEโ€ฆ and more
  • Review - Erica Synths STEAMPIPE The Synth with no oscillators!

Category Popularity

0-100% (relative to NumPy and Steampipe)

NumPy

Steampipe

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Big Data
0 0%
Big Data
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Cloud Infrastructure
0 0%
Cloud Infrastructure
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 Steampipe

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

Steampipe Reviews

We have no reviews of Steampipe yet.
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Social recommendations and mentions

Based on our record, NumPy should be more popular than Steampipe. 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)

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Steampipe mentions (42)

  • Build API integrations with SQL and YAML โ€“ no SaaS lock-in, no drag-and-drop UIs
    The request / data fetching is interesting in how "easy" it is to write. I did basic perusal of the examples, but I'd be interested to see what it looks like with rate-limited endpoints and concurrent requests. Another tangentially related project is https://steampipe.io/ though it is for exposing APIs via Postgres tables and the clients are written using Go code and shared through a marketplace. - Source: Hacker News / about 1 year ago
  • Cyphernetes: A Query Language for Kubernetes
    I really really like Steampipe to do this kind of query: https://steampipe.io, which is essentially PostgreSQL (literally) to query many different kind of APIs, which means you have access to all PostgreSQL's SQL language can offer to request data. They have a Kubernetes plugin at https://hub.steampipe.io/plugins/turbot/kubernetes and there are a couple of things I really like: * it's super easy to request... - Source: Hacker News / over 1 year ago
  • DuckDB Doesn't Need Data to Be a Database
    Https://steampipe.io/ showcases some really interesting scenarios for using FDWs in place of regular ETL and API integrations. - Source: Hacker News / about 2 years ago
  • Cloud Tools You Probably Haven't Heard Of
    Steampipe is a tool for querying cloud APIs and other data sources using SQL in a zero-ETL manner. - Source: dev.to / over 2 years ago
  • Osquery: An sqlite3 virtual table exposing operating system data to SQL
    Few projects in the same realm that you should also checkout - [1] Steampipe (https://steampipe.io/) [2] InfraSQL (https://iasql.com/). - Source: Hacker News / over 2 years ago
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What are some alternatives?

When comparing NumPy and Steampipe, 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.

CloudQuery - CloudQuery enables you to assess, audit, and evaluate the configurations of your cloud assets.

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

StackQL.io - Query, provision, secure & operate cloud resources using SQL

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

Turbot - Turbot's guardrails deliver automated operational, cloud security and cloud compliance controls of AWS deployments and other cloud enterprise infrastructure. Learn more.

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