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Ingram Micro VS NumPy

Compare Ingram Micro VS NumPy and see what are their differences

Flagsmith
Flagsmith lets you manage feature flags and remote config across web, mobile and server side applications. Deliver true Continuous Integration. Get builds out faster. Control who has access to new features. We're Open Source. featured
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Ingram Micro logo Ingram Micro

Delivering global technology and supply chain services to support cloud aggregation, data center management, logistics, technology distribution, mobility device life-cycle and training.

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Ingram Micro Landing page
    Landing page //
    2021-09-28
  • NumPy Landing page
    Landing page //
    2023-05-13

Ingram Micro features and specs

  • Global Reach
    Ingram Micro has a vast global network, offering access to markets and customers around the world.
  • Comprehensive Product Portfolio
    The company offers a wide range of products and services, from hardware and software to cloud solutions, making it a one-stop-shop for many IT needs.
  • Strong Vendor Relationships
    Ingram Micro has established strong relationships with key vendors, ensuring reliable access to products and preferential pricing.
  • Advanced Logistics
    The company possesses sophisticated logistics capabilities, which help ensure timely and efficient delivery of products.
  • Financial Stability
    As a large, financially stable company, Ingram Micro can offer credit terms and financing options that smaller distributors might not.

Possible disadvantages of Ingram Micro

  • Complexity
    The sheer size and scope of Ingram Micro's operations can sometimes lead to complexity and inefficiencies.
  • Customer Service Issues
    Some customers have reported inconsistent customer service experiences, which can impact satisfaction and loyalty.
  • Higher Prices
    While Ingram Micro offers a wide range of products, their prices may be higher compared to smaller, more niche distributors.
  • Limited Customization
    Given their broad focus, Ingram Micro may not be able to provide the same level of customization and specialized services as smaller, more focused competitors.
  • Dependency on Vendors
    The company's success is closely tied to its vendor relationships. Any disruptions or changes in these relationships could impact product availability and pricing.

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.

Ingram Micro videos

+ Add

2019 Year in Review | Ingram Micro Commerce & Lifecycle Services

More videos:

  • Review - Trust X Alliance 2019 Year in Review | Ingram Micro
  • Review - Platinum Equity Buys HNA’s Ingram Micro for $7.2 Million

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 Ingram Micro and NumPy)

Ingram Micro

NumPy

CRM
100 100%
CRM
0% 0
Data Science And Machine Learning
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Data Science And Machine Learning
100% 100
Developer Tools
100 100%
Developer Tools
0% 0
Data Science Tools
0 0%
Data Science Tools
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 Ingram Micro and NumPy

Ingram Micro Reviews

We have no reviews of Ingram Micro yet.
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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 119 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.

Ingram Micro mentions (0)

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

NumPy mentions (119)

  • Building an AI-powered Financial Data Analyzer with NodeJS, Python, SvelteKit, and TailwindCSS - Part 0
    The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis. - Source: dev.to / 3 months ago
  • F1 FollowLine + HSV filter + PID Controller
    This library provides functions for working in domain of linear algebra, fourier transform, matrices and arrays. - Source: dev.to / 7 months ago
  • Intro to Ray on GKE
    The Python Library components of Ray could be considered analogous to solutions like numpy, scipy, and pandas (which is most analogous to the Ray Data library specifically). As a framework and distributed computing solution, Ray could be used in place of a tool like Apache Spark or Python Dask. It’s also worthwhile to note that Ray Clusters can be used as a distributed computing solution within Kubernetes, as... - Source: dev.to / 8 months ago
  • Streamlit 101: The fundamentals of a Python data app
    It's compatible with a wide range of data libraries, including Pandas, NumPy, and Altair. Streamlit integrates with all the latest tools in generative AI, such as any LLM, vector database, or various AI frameworks like LangChain, LlamaIndex, or Weights & Biases. Streamlit’s chat elements make it especially easy to interact with AI so you can build chatbots that “talk to your data.”. - Source: dev.to / 9 months ago
  • A simple way to extract all detected objects from image and save them as separate images using YOLOv8.2 and OpenCV
    The OpenCV image is a regular NumPy array. You can see it shape:. - Source: dev.to / 9 months ago
View more

What are some alternatives?

When comparing Ingram Micro and NumPy, you can also consider the following products

Cdw - cdw: ncurses interface for GNU/Linux command line CD/DVD tools

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

CompuCom - Technology Solutions for the Digital Workplace

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

Sirius - An open-source clone of Siri from UMICH

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

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