Software Alternatives, Accelerators & Startups

Pandas VS Backlog

Compare Pandas VS Backlog and see what are their differences

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Pandas logo Pandas

Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the 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
  • Pandas Landing page
    Landing page //
    2023-05-12
  • 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

Pandas features and specs

  • Data Wrangling
    Pandas offers robust tools for manipulating, cleaning, and transforming data, making it easier to prepare data for analysis.
  • Flexible Data Structures
    Pandas provides two primary data structures: Series and DataFrame, which are flexible and offer powerful capabilities for handling various types of datasets.
  • Integration with Other Libraries
    Pandas integrates seamlessly with other Python libraries such as NumPy, Matplotlib, and SciPy, facilitating comprehensive data analysis workflows.
  • Performance with Data Size
    For data sizes that fit into memory, Pandas performs excellently with operations and computations being highly optimized.
  • Rich Feature Set
    Pandas provides a wide array of functionalities, including but not limited to group-by operations, merging and joining data sets, time-series functionality, and input/output tools.
  • Community and Documentation
    Pandas has a strong community and extensive documentation, offering a wealth of tutorials, examples, and support for new and experienced users alike.

Possible disadvantages of Pandas

  • Memory Consumption
    Pandas can become memory inefficient with very large datasets because it relies heavily on in-memory operations.
  • Single-threaded
    Many Pandas operations are single-threaded, which can lead to performance bottlenecks when handling very large datasets.
  • Steep Learning Curve
    For users who are new to data analysis or Pandas, there can be a steep learning curve due to its extensive capabilities and complex syntax at times.
  • Less Suitable for Real-time Analytics
    Pandas is not designed for real-time analytics and is better suited for batch processing due to its in-memory operations and single-threaded nature.
  • Error Handling
    Error messages in Pandas can sometimes be cryptic and hard to interpret, making debugging a challenge for users.

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 Pandas

Overall verdict

  • Pandas is highly recommended for tasks involving data manipulation and analysis, especially for those working with tabular data. Its efficiency and ease of use make it a staple in the data science toolkit.

Why this product is good

  • Pandas is widely considered a good library for data manipulation and analysis due to its powerful data structures, like DataFrames and Series, which make it easy to work with structured data. It provides a wide array of functions for data cleaning, transformation, and aggregation, which are essential tasks in data analysis. Furthermore, Pandas seamlessly integrates with other libraries in the Python ecosystem, making it a versatile tool for data scientists and analysts. Its extensive documentation and strong community support also contribute to its reputation as a reliable tool for data analysis tasks.

Recommended for

    Pandas is particularly recommended for data scientists, analysts, and engineers who need to perform data cleaning, transformation, and analysis as part of their work. It is also suitable for academics and researchers dealing with data in various formats and needing powerful tools for their data-driven research.

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.

Pandas videos

Ozzy Man Reviews: Pandas

More videos:

  • Review - Ozzy Man Reviews: PANDAS Part 2
  • Review - Trash Pandas Review with Sam Healey

Backlog videos

Manage projects with Backlog

Category Popularity

0-100% (relative to Pandas 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 Pandas 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 Pandas 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 Pandas and Backlog

Pandas Reviews

25 Python Frameworks to Master
Pandas is a powerful and flexible open-source library used to perform data analysis in Python. It provides high-performance data structures (i.e., the famous DataFrame) and data analysis tools that make it easy to work with structured data.
Source: kinsta.com
Python & ETL 2020: A List and Comparison of the Top Python ETL Tools
When it comes to ETL, you can do almost anything with Pandas if you're willing to put in the time. Plus, pandas is extraordinarily easy to run. You can set up a simple script to load data from a Postgre table, transform and clean that data, and then write that data to another Postgre table.
Source: www.xplenty.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, Pandas seems to be a lot more popular than Backlog. While we know about 220 links to Pandas, 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.

Pandas mentions (220)

  • 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
  • Top Programming Languages for AI Development in 2025
    Libraries for data science and deep learning that are always changing. - Source: dev.to / 5 months ago
  • How to import sample data into a Python notebook on watsonx.ai and other questionsโ€ฆ
    # Read the content of nda.txt Try: Import os, types Import pandas as pd From botocore.client import Config Import ibm_boto3 Def __iter__(self): return 0 # @hidden_cell # The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials. # You might want to remove those credentials before you share the notebook. Cos_client = ibm_boto3.client(service_name='s3', ... - Source: dev.to / 6 months ago
  • How I Hacked Uberโ€™s Hidden API to Download 4379 Rides
    As with any web scraping or data processing project, I had to write a fair amount of code to clean this up and shape it into a format I needed for further analysis. I used a combination of Pandas and regular expressions to clean it up (full code here). - Source: dev.to / 6 months ago
  • Must-Know 2025 Developerโ€™s Roadmap and Key Programming Trends
    Pythonโ€™s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether youโ€™re experienced or just starting, Pythonโ€™s clear style makes it a good choice for diving into machine learning. Actionable Tip: If youโ€™re new to Python,... - Source: dev.to / 8 months ago
View more

Backlog mentions (3)

What are some alternatives?

When comparing Pandas and Backlog, you can also consider the following products

NumPy - NumPy is the fundamental package for scientific computing with 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.

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

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.

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

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.