Software Alternatives, Accelerators & Startups

PartKeepr VS NumPy

Compare PartKeepr VS NumPy and see what are their differences

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PartKeepr logo PartKeepr

PartKeepr is an open source inventory management system that you can alter according to the particular requirements of your business or of the area from where you are operating.

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • PartKeepr Landing page
    Landing page //
    2021-07-24
  • NumPy Landing page
    Landing page //
    2023-05-13

PartKeepr features and specs

  • Open Source
    PartKeepr is open source, meaning it's free to use and modify, which provides flexibility to customize it according to user needs.
  • Community Support
    With an active community of users and developers, PartKeepr benefits from collaborative development and shared knowledge.
  • Feature-Rich
    The software offers various features for managing electronic parts inventory effectively, such as stock tracking, supplier management, and parts search.
  • Web-Based
    Being web-based allows users to access the inventory system from any device with internet access, providing convenience and flexibility.
  • Custom Field Support
    PartKeepr allows users to add custom fields to parts, offering personalized data fields to fit specific needs.

Possible disadvantages of PartKeepr

  • Installation Complexity
    The initial setup and installation can be complex, requiring some technical skills and familiarity with server environments.
  • User Interface
    The user interface can feel outdated and may not be as intuitive or modern as some other inventory management systems.
  • Limited Documentation
    Sparse or outdated documentation may pose challenges for new users trying to learn how to effectively use the software.
  • Performance
    Users with large databases might experience performance issues, such as slow query times or lag, especially if the server setup is not optimal.
  • Dependency on Community
    As an open-source project, its development pace can vary based on community involvement, leading to potential delays in updates or feature additions.

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.

PartKeepr videos

Partkeepr (inventory application) installation on debian 9

More videos:

  • Review - PartKeepr: Customize Columns

NumPy videos

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 PartKeepr and NumPy)
Inventory Management
100 100%
0% 0
Data Science And Machine Learning
ERP
100 100%
0% 0
Data Science Tools
0 0%
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 PartKeepr and NumPy

PartKeepr Reviews

  1. Disappointed

    We installed the software and started to populate it. After a while we experienced login problems. For some mysterious reason, some users can only login to it from one computer but when they try it from a different computer using the same credentials then it fails. Documentation is poor - when we go to the Wiki, the FAQ pages are not available? The discussion page is also empty. We could not find a user manual. We are looking for other options.

  2. Exceptionally good considering it is completely free

    Running on Ubuntu 16.04LTS server. It works very well. We wrote some aditional php web-page functions to directly access the MYSQL database to implement a few additional functions. Because it is open source it is relatively easy to modify for specific extra functions. Fast FREE and not encumbered by proprietary stuff, adverts or forcing you to divulge company data to an online service. Easy to do backups and clone to other machines. A really useful tool. It is a good idea if the administrator has a basic competance in Linux and web/php admin. Overall very good.

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.
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 a lot more popular than PartKeepr. While we know about 119 links to NumPy, we've tracked only 10 mentions of PartKeepr. 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.

PartKeepr mentions (10)

  • What do you use to keep track of parts inventory?
    Thanks for the links. I am now looking at PartKeepr, which doesn't seem too daunting. Source: about 2 years ago
  • Which language would you use for a stock control app?
    You should look at any open source apps that do something like this already. There are many. https://partkeepr.org/ is one I found after 2 minutes of googling. Source: about 2 years ago
  • recommend me a self-hosted open-source inventory management solution!
    Partkeepr may solve some of that problem for you - https://partkeepr.org/. Source: about 3 years ago
  • Suggestion for a software to collate all the stuff I own [Home Inventory?]
    For your use case it sounds that snipe-it might be the perfect fit. It can be self hosted, open source, has a login system, has a user system incase you let people borrow items, supports barcodes, supports putting an item out of service incase its broken, supports keeping track of serial numbers and much more. Another one ive tried is partkeepr which is mainly focused on individual electronic components but can... Source: over 3 years ago
  • Simple inventory management software?
    Regarding other softwares for manual entry you might want to take a look at: https://partkeepr.org/. Source: over 3 years ago
View more

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 PartKeepr and NumPy, you can also consider the following products

Oracle Warehouse Management Cloud - See how Oracle Warehouse Management solutions provide a unified platform to optimize resource usage and process flows across your global supply chain.

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

DSI Cloud Inventory WMS - DSI Cloud Inventory WMS is a cloud-based warehouse management system that allows you to automate your warehouse inventory.

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

Snipe-IT - Managing assets with a Google doc or a shared Excel spreadsheet is more common than you think.

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