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Spring Framework VS Pandas

Compare Spring Framework VS Pandas and see what are their differences

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Spring Framework logo Spring Framework

The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.

Pandas logo Pandas

Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
  • Spring Framework Landing page
    Landing page //
    2023-08-18
  • Pandas Landing page
    Landing page //
    2023-05-12

Spring Framework features and specs

  • Comprehensive Ecosystem
    Spring Framework provides a vast array of tools and modules which address various aspects of application development such as security, data access, and messaging. This helps in building robust enterprise applications.
  • Inversion of Control (IoC) Container
    Spring's IoC container promotes loose coupling by managing object lifecycles and dependencies, making the code more modular and testable.
  • Aspect-Oriented Programming (AOP)
    Spring's AOP module allows for separating cross-cutting concerns like logging, transaction management, and security, making the code cleaner and more maintainable.
  • Spring Boot
    Spring Boot streamlines the setup and development of new Spring applications with built-in configurations and convention over configuration, reducing boilerplate code and speeding up development time.
  • Large Community and Support
    Spring has a large and active community, extensive documentation, and a wide selection of online resources which make it easier to find support and solutions to common problems.
  • Integration Capabilities
    Spring Framework offers seamless integration with various other technologies and frameworks, including Hibernate for ORM, Apache Kafka for messaging, and more.

Possible disadvantages of Spring Framework

  • Complexity
    Spring Framework can be complex and have a steep learning curve, especially for newcomers who are not familiar with its extensive set of features and configurations.
  • Configuration Overhead
    Although Spring Boot reduces the configuration burden, traditional Spring applications may still require extensive XML or annotation-based configurations, which can be cumbersome.
  • Performance Overhead
    The flexibility and the modular nature of Spring can introduce some performance overhead compared to more lightweight solutions, which could be a concern in highly performance-sensitive applications.
  • Version Incompatibility
    Upgrading between different versions of the Spring Framework and its associated projects can sometimes lead to compatibility issues and necessitate significant code changes.
  • Dependency Management
    Managing dependencies in a large Spring application can become complicated, particularly when dealing with multiple modules and third-party libraries, potentially leading to dependency conflicts.

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.

Spring Framework videos

+ Add

What is the Spring framework really all about?

More videos:

  • Tutorial - Spring Framework Tutorial | Full Course

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 Spring Framework and Pandas)

Spring Framework

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 Spring Framework and Pandas

Spring Framework Reviews

Top 9 best Frameworks for web development
Spring offers a wide range of frameworks, such as an MVC framework, a data access framework and a transaction management framework. With its focus on scalability and security, Spring is an excellent choice.
Source: www.kiwop.com
17 Popular Java Frameworks for 2023: Pros, cons, and more
Therefore, the configuration, setup, build, and deployment processes all require multiple steps you might not want to deal with, especially if you’re working on a smaller project. Spring Boot (a micro framework that runs on top of the Spring Framework) is a solution for this problem, as it allows you to set up your Spring application faster, with much less configuration.
Source: raygun.com
Top 10 Phoenix Framework Alternatives
Spring Framework is an open-source app framework and inversion of control container for the Java platform, providing the infrastructure required to develop Java and web apps on top of the Java EE platform.
Source: blog.back4app.com
10 Best Java Frameworks You Should Know
Spring Framework is one of the most extensively used, top-notch, lightweight software application frameworks built for software design, development, and deployment in Java.
Source: www.simplilearn.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

Based on our record, Pandas seems to be a lot more popular than Spring Framework. While we know about 219 links to Pandas, we've tracked only 13 mentions of Spring Framework. 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.

Spring Framework mentions (13)

  • March 2025 Java Key Updates in Boot, Security, and More
    The release of Spring Framework 6.2.5 includes:. - Source: dev.to / about 2 months ago
  • Getting Started with Spring Boot 3 for .NET Developers
    Spring Framework 6: https://spring.io/projects/spring-framework. - Source: dev.to / 4 months ago
  • Want to Get Better at Java? Go Old School.
    We had to write our own frameworks (uphill, both ways) but most current frameworks will have similar documentation pages as well. Both Apache and Spring are especially good at that. - Source: dev.to / over 2 years ago
  • Best Frameworks For Web Development
    Framework link: https://spring.io/projects/spring-framework Github Link: https://github.com/spring-projects/spring-framework. - Source: dev.to / over 2 years ago
  • What to you do now?
    A common used Java framework is Spring framework (ie https://spring.io/projects/spring-framework and short tutorials at https://www.baeldung.com/spring-intro). Source: almost 3 years ago
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 / 10 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 / 26 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 / 30 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
View more

What are some alternatives?

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

Grails - An Open Source, full stack, web application framework for the JVM

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

Django - The Web framework for perfectionists with deadlines

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

Laravel - A PHP Framework For Web Artisans

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

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