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NumPy VS Assembla

Compare NumPy VS Assembla 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

Assembla logo Assembla

Integrated, on-demand tools to build software faster, with less stress. Get started for free and find out why over 800,000 users trust Assembla.
  • NumPy Landing page
    Landing page //
    2023-05-13
  • Assembla Landing page
    Landing page //
    2023-10-06

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.

Assembla features and specs

  • Comprehensive Project Management Tools
    Assembla offers a variety of tools for project management, including ticketing, milestone tracking, and issue management, which help teams stay organized and efficient.
  • Version Control Integration
    Supports multiple version control systems like Git, SVN, and Perforce, enabling teams to use their preferred version control systems without switching platforms.
  • Cloud-Based
    Being a cloud-based platform, Assembla allows team members to access project tools and files from anywhere, promoting flexibility and remote work.
  • Security
    Assembla provides strong security features such as IP whitelisting, 2-factor authentication, and audit logs, which help protect sensitive project data.
  • Customizable Workspaces
    Each workspace can be tailored to suit the specific needs of a project or team, making it adaptable to various workflows and projects.

Possible disadvantages of Assembla

  • Complexity
    The wide range of features can be overwhelming for new users, and there may be a steep learning curve for teams that are not familiar with such comprehensive tools.
  • Price
    Assembla's pricing can be higher compared to some other project management tools, which might be a concern for smaller teams or startups with limited budgets.
  • User Interface
    The user interface, while functional, is considered by some users to be less intuitive and visually appealing compared to competitors, potentially leading to slower user adoption.
  • Limited Offline Access
    Because Assembla is primarily a cloud-based service, it offers limited functionality without an active internet connection, which can be a drawback for users who need offline access.
  • Support
    Some users have reported that customer support can be slow to respond or less than satisfactory, which can lead to delays in resolving issues.

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.

Analysis of Assembla

Overall verdict

  • Assembla is a good option for teams that require strong version control and collaboration capabilities. Its extensive features and integrations make it a viable solution for software development project management. However, the user interface and experience may vary depending on individual preference, so it might not be ideal for teams seeking a more modern or simplified project management tool.

Why this product is good

  • Assembla is a project management and collaboration tool designed primarily for teams working in software development. It is known for its robust version control integrations, including Git, Perforce, and Subversion. Assembla provides features like ticketing systems, time tracking, and code repositories that are essential for managing and organizing complex software projects. Its ability to support distributed teams and integrate with various development tools makes it popular among development teams.

Recommended for

    Assembla is recommended for software development teams looking for a comprehensive project management platform with strong version control support. It is particularly suited for distributed teams and organizations that require integration with tools like Git, Perforce, and Subversion. It may also be a good fit for teams that need detailed tracking and reporting capabilities.

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

Assembla videos

+ Add

Assembla Review

More videos:

Category Popularity

0-100% (relative to NumPy and Assembla)

NumPy

Assembla

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Git
0 0%
Git
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Code Collaboration
0 0%
Code Collaboration
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 Assembla

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

Assembla Reviews

12 Best JIRA Alternatives in 2019
Assembla is a younger platform than JIRA but offers a broader range of functionality in its core product like git hosting, code deployment, agile tools, time tracking.
Source: www.guru99.com
6 JIRA Alternatives for Your Dev Team
Assembla offers many functions right out-of-the-box that JIRA requires as an add-on, including subversion and git hosting, code deployment, agile tools, time tracking, and social media-style collaboration (message boards, @mentions, activity stream). The greatest irony is that Assembla is actually less expensive.
Source: technologyadvice.com

Social recommendations and mentions

Based on our record, NumPy seems to be more popular. 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)

View more

Assembla mentions (0)

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

What are some alternatives?

When comparing NumPy and Assembla, 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.

GitHub - Originally founded as a project to simplify sharing code, GitHub has grown into an application used by over a million people to store over two million code repositories, making GitHub the largest code host in the world.

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

BitBucket - Bitbucket is a free code hosting site for Mercurial and Git. Manage your development with a hosted wiki, issue tracker and source code.

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

GitLab - Create, review and deploy code together with GitLab open source git repo management software | GitLab

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