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Laravel VS Pandas

Compare Laravel VS Pandas and see what are their differences

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Laravel logo Laravel

A PHP Framework For Web Artisans

Pandas logo Pandas

Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
  • Laravel Landing page
    Landing page //
    2023-07-24
  • Pandas Landing page
    Landing page //
    2023-05-12

Laravel features and specs

  • Eloquent ORM
    Laravel includes Eloquent ORM, which provides a beautiful and simple ActiveRecord implementation for working with your database. It allows for easy interaction with your databases, offering an intuitive syntax.
  • Blade Templating Engine
    The Blade templating engine offers a clean and efficient syntax for writing templates. It provides features like template inheritance and sections, which makes template design more manageable and organized.
  • Artisan CLI
    Laravel's Artisan Command Line Interface (CLI) allows developers to perform repetitive tasks and manage their Laravel project more efficiently with built-in commands for database migration, seeding, and building tasks.
  • Strong Community and Ecosystem
    Laravel has a large and active community that provides an abundance of resources, including packages, tutorials, and screencasts on Laracasts. This ecosystem allows for quick problem-solving and an extensive library of reusable components.
  • Robust Security Features
    Laravel provides built-in security features such as salted and hashed passwords, encryption, and protection against common vulnerabilities like SQL injection and cross-site request forgery (CSRF).
  • Efficient Testing
    Laravel comes with PHPUnit integrated, along with convenient helper methods, making writing test cases and performing automated testing more straightforward. This leads to better code reliability and fewer bugs.
  • Comprehensive Documentation
    Laravel has thorough and well-organized documentation that covers all its features in detail. This makes it easier for new and experienced developers to understand and use the framework effectively.

Possible disadvantages of Laravel

  • Performance Overhead
    Since Laravel is a full-featured framework, it includes many built-in functions and layers that can create performance overhead. For very high-performance applications, fine-tuning may be necessary.
  • Steep Learning Curve for Beginners
    For those new to web development or coming from a different programming paradigm, Laravel can be challenging to grasp initially due to its extensive features and modern PHP practices.
  • Heavy Dependency on Composer
    Laravel heavily relies on Composer for dependency management. While this is beneficial for package management, it can be a downside if you are not familiar with Composer or have issues managing packages.
  • Frequent Updates
    Laravel receives frequent updates and changes in the new versions, which can sometimes lead to compatibility issues with existing projects. Keeping up with the updates can be time-consuming.
  • Hosting Requirements
    Laravel requires specific server configurations and dependencies, which may not be available on all shared hosting services. This can necessitate using a Virtual Private Server (VPS) or dedicated server, which might have higher costs.

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.

Laravel videos

+ Add

Laravel in 100 seconds

More videos:

  • Review - Why Laravel is Still Best in 2018

Pandas videos

+ Add

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 Laravel and Pandas)

Laravel

Pandas

Developer Tools
100 100%
Developer Tools
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Web Frameworks
100 100%
Web Frameworks
0% 0
Data Science Tools
0 0%
Data Science Tools
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 Laravel and Pandas

Laravel Reviews

Laravel vs. Symfony: A Comprehensive Comparison of PHP Frameworks
Laravel has a vibrant ecosystem with many first-party packages, such as Laravel Horizon for queue management, Laravel Echo for real-time events, and Laravel Sanctum for API authentication, that make it easy to extend functionality without much hassle.
Source: blog.radwebhosting.com
The 20 Best Laravel Alternatives for Web Development
Oh, you bet. When devs talk Symfony, they’re eyeing robustness and a modular vibe that Laravel fans might miss. Its reusable components could tempt even the most loyal Laravel artisans to at least take a peek.
Source: tms-outsource.com
Top 9 best Frameworks for web development
The best frameworks for web development include React, Angular, Vue.js, Django, Spring, Laravel, Ruby on Rails, Flask and Express.js. Each of these frameworks has its own advantages and distinctive features, so it is important to choose the framework that best suits the needs of your project.
Source: www.kiwop.com
Top 5 Laravel Alternatives
However, there are other excellent choices other than Laravel as well. So, let’s check out some excellent Laravel alternatives before you hire Laravel developers India for your web development project. This post provides you with a thorough understanding of the available web development framework choices and their benefits over Laravel. For that, let’s first discuss the...
Source: www.etatvasoft.com
Framework review: Laravel vs CodeIgniter
Let's start with CodeIgniter first. It focuses on performance and speed. It offers a simple, easy-to-learn syntax, making it ideal for beginners. CodeIgniter uses its own proprietary Active Record implementation for database operations, which provides a simple and intuitive way to interact with data. Unlike Laravel, CodeIgniter does not enforce a specific architectural...
Source: infinyhost.com

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

Laravel might be a bit more popular than Pandas. We know about 240 links to it since March 2021 and only 219 links to Pandas. 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.

Laravel mentions (240)

View more

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 / 6 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 / 23 days 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 / 26 days 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 / 3 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 / 8 months ago
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What are some alternatives?

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

Django - The Web framework for perfectionists with deadlines

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

Ruby on Rails - Ruby on Rails is an open source full-stack web application framework for the Ruby programming...

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

CodeIgniter - A Fully Baked PHP Framework

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

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