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rubular VS Pandas

Compare rubular VS Pandas and see what are their differences

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rubular logo rubular

A ruby based regular expression editor

Pandas logo Pandas

Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
  • rubular Landing page
    Landing page //
    2023-10-19
  • Pandas Landing page
    Landing page //
    2023-05-12

rubular features and specs

  • Immediate Feedback
    Rubular provides instantaneous feedback on your regex patterns, making it easier to see if your regular expressions are correct and behaving as expected.
  • Simple Interface
    The website has a straightforward and easy-to-use interface that allows users to paste text and immediately test their regex.
  • Ruby-Based
    Rubular utilizes Ruby for regex testing, which is beneficial for Ruby developers who want to ensure their regex works correctly in that specific language context.
  • Offline Syntax Help
    The site offers a built-in regex syntax help section, which can be invaluable for both beginners and experienced users who need a quick reference.

Possible disadvantages of rubular

  • Limited Language Support
    Rubular is designed for Ruby, which means that regular expressions tested on Rubular may not behave the same way in other programming languages.
  • Lack of Advanced Features
    Advanced functionalities such as detailed debugging, regex optimization tips, and visual explanations are missing, which can be a disadvantage for more complex regex tasks.
  • No Saved Sessions
    The site doesn't offer a way to save your session or patterns, making it harder to manage and share multiple regex tests over time.
  • No Collaboration Tools
    Rubular lacks collaboration features such as shared workspace or real-time editing, which would benefit teams working on regex patterns together.

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.

rubular videos

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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 rubular and Pandas)
Regular Expressions
100 100%
0% 0
Data Science And Machine Learning
Programming Tools
100 100%
0% 0
Data Science 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 rubular and Pandas

rubular Reviews

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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 should be more popular than rubular. It has been mentiond 219 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.

rubular mentions (36)

  • Ask HN: How did you learn Regex?
    I read a lot on https://www.regular-expressions.info and experimented on https://rubular.com since I was also learning Ruby at the time. https://regexr.com is another good tool that breaks down your regex and matches. One of the things I remember being difficult at the beginning was the subtle differences between implementations, like `^` meaning "beginning of line" in Ruby (and others) but meaning "beginning of... - Source: Hacker News / 10 months ago
  • Building a syntax highlighting extension for VS Code
    As a ruby developer, I was happy to find that VS Code / TextMate grammar files use the same regular expression engine called Oniguruma as ruby itself. Thus, I could be sure that when trying my regular expressions in my favorite online regex tool, rubular.com, there would be no inconsistencies due to the engine inner workings. - Source: dev.to / over 1 year ago
  • Data cleaning problem
    In my testing on a couple of regex testers (https://rubular.com/ & https://regex101.com/) this seems to select the postcode correctly each time. Source: almost 2 years ago
  • Anchor
    Copied from Rubular ( a nice tool to test regexes ):. Source: over 2 years ago
  • Advice on preparing for the Alteryx Advanced Exam?
    To add on to this from a regex perspective - I find regex to be invaluable in my workflows. Once you learn the basics I always test and debug my strings using https://rubular.com because it has string hints at the bottom that are readily available. Source: over 2 years ago
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Pandas mentions (219)

  • Top Programming Languages for AI Development in 2025
    Libraries for data science and deep learning that are always changing. - Source: dev.to / 23 days ago
  • How to import sample data into a Python notebook on watsonx.ai and other questions…
    # 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 / about 1 month ago
  • How I Hacked Uber’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 / about 1 month ago
  • Must-Know 2025 Developer’s Roadmap and Key Programming Trends
    Python’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 you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 4 months ago
  • Sample Super Store Analysis Using Python & Pandas
    This tutorial provides a concise and foundational guide to exploring a dataset, specifically the Sample SuperStore dataset. This dataset, which appears to originate from a fictional e-commerce or online marketplace company's annual sales data, serves as an excellent example for learning and how to work with real-world data. The dataset includes a variety of data types, which demonstrate the full range of... - Source: dev.to / 9 months ago
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What are some alternatives?

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

RegExr - RegExr.com is an online tool to learn, build, and test Regular Expressions.

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

Expresso - The award-winning Expresso editor is equally suitable as a teaching tool for the beginning user of regular expressions or as a full-featured development environment for the experienced programmer with an extensive knowledge of regular expressions.

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

RegEx Generator - RegEx Generator is a simple-to-use application that comes with the brilliance of intuitive regex and is also helping you out to test the regex.

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