Software Alternatives & Reviews

KEEL VS Pandas

Compare KEEL VS Pandas and see what are their differences

KEEL logo KEEL

KEEL contains classical knowledge extraction algorithms, preprocessing techniques, Computational Intelligence based learning algorithms, evolutionary rule learning algorithms, genetic fuzzy systems, evolutionary neural networks, etc.

Pandas logo Pandas

Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
  • KEEL Landing page
    Landing page //
    2021-09-20
  • Pandas Landing page
    Landing page //
    2023-05-12

KEEL

Categories
  • Data Science And Machine Learning
  • Data Science Tools
  • Development
  • Data Integration
Website sci2s.ugr.es
Details $-

Pandas

Categories
  • Data Science And Machine Learning
  • Data Science Tools
  • Python Tools
  • Software Libraries
Website pandas.pydata.org
Details $

KEEL videos

Creating a "High Performance Twin Fin Keel" Design Vlog by Noel Salas

More videos:

  • Review - 2019 Twin and keel fin guide / Rob Machado Seaside Quad fins
  • Review - FCS II Rob Machado "FW Glazer" Tri-Keel

Pandas videos

Ozzy Man Reviews: Pandas

More videos:

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

Category Popularity

0-100% (relative to KEEL and Pandas)
Data Science And Machine Learning
Data Science Tools
3 3%
97% 97
Development
100 100%
0% 0
Python 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 KEEL and Pandas

KEEL Reviews

We have no reviews of KEEL yet.
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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

Social recommendations and mentions

Based on our record, Pandas seems to be more popular. It has been mentiond 196 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.

KEEL mentions (0)

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

Pandas mentions (196)

  • Deploying a Serverless Dash App with AWS SAM and Lambda
    Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail.... - Source: dev.to / 27 days ago
  • Stuff I Learned during Hanukkah of Data 2023
    Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts. - Source: dev.to / 3 months ago
  • Exploring Open-Source Alternatives to Landing AI for Robust MLOps
    Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks. - Source: dev.to / 4 months ago
  • Mastering Pandas read_csv() with Examples - A Tutorial by Codes With Pankaj
    Pandas, a powerful data manipulation library in Python, has become an essential tool for data scientists and analysts. One of its key functions is read_csv(), which allows users to read data from CSV (Comma-Separated Values) files into a Pandas DataFrame. In this tutorial, brought to you by CodesWithPankaj.com, we will explore the intricacies of read_csv() with clear examples to help you harness its full potential. - Source: dev.to / 4 months ago
  • What Would Go in Your Dream Documentation Solution?
    So, what I'd like to do is write a documentation package in Python to recreate what I've lost. I plan to build upon the fantastic python-docx and docxtpl packages, and I'll probably rely on pandas from much of the tabular stuff. Here are the features I intend to include:. Source: 4 months ago
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What are some alternatives?

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

Apache Mahout - Distributed Linear Algebra

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

KNIME - KNIME, the open platform for your data.

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

WEKA - WEKA is a set of powerful data mining tools that run on Java.

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