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Pandas VS FeedBear

Compare Pandas VS FeedBear 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
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Pandas logo Pandas

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

FeedBear logo FeedBear

The smartest way to manage feedback from your customers
  • Pandas Landing page
    Landing page //
    2023-05-12
  • FeedBear Landing page
    Landing page //
    2023-09-28

FeedBear helps you make informed product decisions and keep users in the loop.

  • Feedback boards allow you to collect feedback from your customers in one, central place.
  • Roadmap fills up automatically as you make progress.
  • Changelog allows you to make bigger announcements in a beautiful way.

Check out our website to learn more.

Pandas

Website
pandas.pydata.org
Pricing URL
-
$ Details
Platforms
-
Release Date
-
Edit details

FeedBear

Website
feedbear.com
Pricing URL
Official FeedBear Pricing
$ Details
paid Free Trial $59.0 / Monthly (All-inclusive)
Platforms
Web
Release Date
2018 August
Startup details
Country
Slovakia
City
Bratislava
Employees
1 - 9
Edit details

Pandas features and specs

No features have been listed yet.

FeedBear features and specs

  • Integrations: Intercom, Slack
  • Public roadmap: Show your progress
  • Feedback boards: Let your users post feedback & requests
  • Changelog: Announce important updates
  • Free Trial: 14-day free trial, no credit card required
  • Easy to Set-up and use: less than 5 min setup
  • Clean UI: Clean and Smooth UI, No technical jargon and just works

Pandas videos

+ Add

Ozzy Man Reviews: Pandas

More videos:

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

FeedBear videos

No FeedBear videos yet. You could help us improve this page by suggesting one.

Add video

Category Popularity

0-100% (relative to Pandas and FeedBear)

Pandas

FeedBear

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Customer Feedback
0 0%
Customer Feedback
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
User Feedback
0 0%
User Feedback
100% 100

User comments

Share your experience with using Pandas and FeedBear. 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 FeedBear

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

FeedBear Reviews

We have no reviews of FeedBear yet.
Be the first one to post

Social recommendations and mentions

Based on our record, Pandas seems to be more popular. It has been mentiond 201 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.

Pandas mentions (201)

  • Essential Deep Learning Checklist: Best Practices Unveiled
    How to Accomplish: Use statistical analysis tools and libraries (e.g., Pandas for tabular data) to calculate and visualize these characteristics. For image datasets, custom scripts to analyze object sizes or mask distributions can be useful. Tools like OpenCV can assist in analyzing image properties, while libraries like Pandas and NumPy are excellent for tabular and numerical analysis. To address class... - Source: dev.to / 20 days ago
  • Awesome List
    Pandas - A powerful data analysis and manipulation library for Python. Pandas Documentation - Official documentation. - Source: dev.to / 25 days ago
  • The ultimate guide to creating a secure Python package
    It's also possible for you to give a package an alias by using the as keyword. For instance, you could use the pandas package as pd like this:. - Source: dev.to / about 2 months ago
  • AWS Serverless Diversity: Multi-Language Strategies for Optimal Solutions
    Python is a natural fit for serverless development. It boasts a vast array of libraries, including Powertools for AWS and robust libraries for data engineers. Its versatility and excellent developer experience make it a top choice for serverless projects, offering a seamless and enjoyable development experience. - Source: dev.to / 2 months ago
  • Pandas reset_index(): How To Reset Indexes in Pandas
    In data analysis, managing the structure and layout of data before analyzing them is crucial. Python offers versatile tools to manipulate data, including the often-used Pandas reset_index() method. - Source: dev.to / 2 months ago
View more

FeedBear mentions (0)

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

What are some alternatives?

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

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

Canny - Canny helps you collect and organize feature requests to better understand customer needs and prioritize your roadmap.

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

Upvoty - User feedback in 1 simple overview 🔥

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

Nolt - A fast & beautiful way to collect user feedback

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