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NumPy VS Backlog

Compare NumPy VS Backlog and see what are their differences

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NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

Backlog logo Backlog

Built for teams that move fast โ€” Backlog is the all-in-one project management solution with exactly what you need, and nothing you donโ€™t.
Visit Website
  • NumPy Landing page
    Landing page //
    2023-05-13
  • Backlog Kanban Board
    Kanban Board //
    2025-08-25
  • Backlog Gantt Chart
    Gantt Chart //
    2025-08-25
  • Backlog Git
    Git //
    2025-08-25
  • Backlog Burndown Chart
    Burndown Chart //
    2025-08-25
  • Backlog Bug Tracking
    Bug Tracking //
    2025-08-25

Built for teams of all sizes, it helps you manage sprints, client projects, and internal requests without the clutter of disconnected tools.

Visualize work with Kanban boards or Gantt charts, break down complex projects with subtasks, and customize task fields to fit your teamโ€™s workflow. Built-in Git and SVN support let developers manage code right alongside their tasks, while wikis, file sharing, and real-time notifications keep everyone on the same page.

Whether youโ€™re replacing legacy tools or streamlining your tech stack, Backlog is quick to roll out, easy to use, and flexible enough to support cross-functional collaboration at any scale.

Backlog

Website
nulab.com
$ Details
freemium $35.0 / Monthly (For growing teams, up to 30 users, 5 projects, 1 GB storage.)
Release Date
2004 January
Startup details
Country
Japan
City
Fukuoka
Founder(s)
Masanori Hashimoto, Shinsuke Tabata
Employees
100 - 249

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.

Backlog features and specs

  • Comprehensive Project Management
    Backlog provides a wide range of project management features including task tracking, Gantt charts, and burndown charts which help teams to plan, execute, and monitor their projects efficiently.
  • Integrated Bug Tracking
    The platform includes robust bug tracking tools that allow for detailed tracking, reporting, and resolution of software bugs which is crucial for development teams.
  • Collaboration Tools
    Backlog offers several collaboration features such as wikis, file sharing, and comment threads to facilitate team communication and knowledge sharing.
  • Customizable Workflow
    Users can customize workflows to reflect their team's processes, which makes the tool adaptable to different project management styles.
  • Multiple Integrations
    Backlog integrates with numerous other tools including Git, SVN, and Slack, allowing teams to streamline their workflow and use their preferred tools.
  • User-Friendly Interface
    The platform offers an intuitive and easy-to-navigate user interface, making it accessible even for less tech-savvy users.
  • Mobile Application
    Backlog offers a mobile application for both iOS and Android, allowing team members to stay updated and manage tasks on the go.

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 Backlog

Overall verdict

  • Backlog is considered a good choice for teams and organizations, especially those that require a balanced mix of collaboration and project management features. It is particularly useful for teams that need an integrated approach to managing tasks and tracking project progress.

Why this product is good

  • Backlog (backlog.com) is a project management and collaboration tool that's praised for its user-friendly interface, robust features, and ability to seamlessly integrate with other tools. It offers functionalities like task management, bug tracking, version control, and file sharing, which make it versatile for teams looking to streamline their workflows. The platform is also visually intuitive, which helps in keeping all team members on the same page.

Recommended for

    Backlog is recommended for software development teams, marketing teams, and any organization looking for a tool that supports collaborative work while providing comprehensive project management features. It's particularly beneficial for small to medium-sized businesses or teams within larger organizations that need customizable workflows and have a need for integrated version control.

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

Backlog videos

Manage projects with Backlog

Category Popularity

0-100% (relative to NumPy and Backlog)
Data Science And Machine Learning
Project Management
0 0%
100% 100
Data Science Tools
100 100%
0% 0
Task Management
0 0%
100% 100

Questions and Answers

As answered by people managing NumPy and Backlog.

What makes your product unique?

Backlog's answer:

Backlog combines project management with issue tracking and version control in one simple, intuitive platform. Unlike tools that focus on just tasks or just code, Backlog bridges the gap between teams and developers, making collaboration seamless for everyone.

Why should a person choose your product over its competitors?

Backlog's answer:

Backlog is affordable, built for all skill levels, and designed to be powerful without being overwhelming. Teams can manage projects, track bugs, review code, and collaborate visually (with Gantt charts, Kanban boards, and wikis) all in one place. Itโ€™s easier to get started with than many enterprise tools, but it still scales to handle complex projects.

How would you describe your primary audience?

Backlog's answer:

Our core audience includes software development teams, IT departments, and project managers who need both project visibility and technical depth. At the same time, marketing, design, and business teams also rely on Backlog to stay connected with developers in a shared workspace.

What's the story behind your product?

Backlog's answer:

Backlog was created by Nulab, a Japan-based software company, to make teamwork easier and more enjoyable. We needed a tool where developers and non-developers could work together without friction. Since its launch, Backlog has grown into a global product that helps thousands of teams deliver projects more efficiently.

Who are some of the biggest customers of your product?

Backlog's answer:

Backlog is trusted by teams at Buzzfeed, Rakuten, and SoftBank, along with thousands of startups, agencies, and enterprises worldwide. Whether itโ€™s a small consultancy or a large global brand, Backlog helps teams of all sizes manage projects with clarity and collaboration.

Which are the primary technologies used for building your product?

Backlog's answer:

Backlog's backend is written using the programming language Scala. This robust foundation helps Backlog run smoothly, even when managing large projects or complex tasks.

User comments

Share your experience with using NumPy and Backlog. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare NumPy and Backlog

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

Backlog Reviews

  1. dolcemediterranea
    Good system

    I'm on the free plan, all the basic options to run a project are there and the mail notification/comment system works very well. The user interface is good. My only complaint is that you cannot have more than one person assigned to a task. It's their philosophy and they say that if you want to have more assignees you should either duplicate the task or create subtask (only for paid plans).

    ๐Ÿ Competitors: Jira, Trello
    ๐Ÿ‘ Pros:    User friendly interface|Free basic plan
    ๐Ÿ‘Ž Cons:    Cannot have more people assigned to a task

12 Best JIRA Alternatives in 2019
Backlog is an all-in-one project management tool built for developers. It's a popular alternative to Jira, with a much simpler and intuitive interface. Development teams use Backlog to work with other teams for enhanced team collaboration and high-quality project delivery.
Source: www.guru99.com

Social recommendations and mentions

Based on our record, NumPy seems to be a lot more popular than Backlog. While we know about 121 links to NumPy, we've tracked only 3 mentions of Backlog. 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 (121)

  • Top 5 GitHub Repositories for Data Science in 2026
    The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, Aโ€ฆ. - Source: dev.to / 14 days ago
  • Your 2025 Roadmap to Becoming an AI Engineer for Free for Vue.js Developers
    AI starts with math and coding. You donโ€™t need a PhDโ€”just high school math like algebra and some geometry. Linear algebra (think matrices) and calculus (like slopes) help understand how AI models work. Python is the main language for AI, thanks to tools like TensorFlow and NumPy. If you know JavaScript from Vue.js, Pythonโ€™s syntax is straightforward. - Source: dev.to / about 2 months ago
  • 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 / 8 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 / about 1 year 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. Itโ€™s also worthwhile to note that Ray Clusters can be used as a distributed computing solution within Kubernetes, as... - Source: dev.to / about 1 year ago
View more

Backlog mentions (3)

What are some alternatives?

When comparing NumPy and Backlog, 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.

Jira - The #1 software development tool used by agile teams. Jira Software is built for every member of your software team to plan, track, and release great software.

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

Wrike - Wrike is a flexible, scalable, and easy-to-use collaborative work management software that helps high-performance teams organize and accomplish their work. Try it now.

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

Asana - Asana project management is an effort to re-imagine how we work together, through modern productivity software. Fast and versatile, Asana helps individuals and groups get more done.