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Pandas VS Checkmarx

Compare Pandas VS Checkmarx and see what are their differences

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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.

Checkmarx logo Checkmarx

The industry’s most comprehensive AppSec platform, Checkmarx One is fast, accurate, and accelerates your business.
  • Pandas Landing page
    Landing page //
    2023-05-12
  • Checkmarx Landing page
    Landing page //
    2022-07-29

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.

Checkmarx features and specs

  • Comprehensive Coverage
    Checkmarx provides extensive support for multiple programming languages and frameworks, allowing for a broad range of applications to be scanned.
  • Integration Capabilities
    The platform integrates well with various DevOps tools and CI/CD pipelines, making it easier to incorporate security checks into the software development lifecycle.
  • Customization
    Offers highly customizable rule sets that can be tailored to specific security requirements and coding standards of the organization.
  • User-Friendly Interface
    Features an intuitive and easy-to-navigate user interface that allows users to efficiently manage and analyze security vulnerabilities.
  • Scalability
    Designed to scale efficiently, Checkmarx can handle large codebases and multiple projects concurrently without significant performance degradation.
  • Strong Reporting Capabilities
    Provides detailed and actionable reports that help developers and security teams quickly understand and address vulnerabilities.
  • Automation
    Supports automated scanning, which helps reduce manual efforts and accelerates the vulnerability detection process.

Possible disadvantages of Checkmarx

  • Cost
    Checkmarx can be expensive, especially for small to medium-sized organizations with limited budgets for security tools.
  • False Positives
    Although comprehensive, the platform sometimes generates false positives, which can lead to unnecessary work and distractions for development teams.
  • Learning Curve
    New users may face a steep learning curve due to the wide range of features and customization options available.
  • Performance Overhead
    In some cases, scanning large and complex codebases can be time-consuming and resource-intensive, potentially impacting development timelines.
  • Customer Support
    Some users have reported that customer support can be slow to respond and may not always provide satisfactory solutions to issues.
  • Initial Setup
    The initial setup process can be complex and time-consuming, requiring considerable effort to properly configure all settings and integrations.

Analysis of Pandas

Overall verdict

  • Pandas is highly recommended for tasks involving data manipulation and analysis, especially for those working with tabular data. Its efficiency and ease of use make it a staple in the data science toolkit.

Why this product is good

  • Pandas is widely considered a good library for data manipulation and analysis due to its powerful data structures, like DataFrames and Series, which make it easy to work with structured data. It provides a wide array of functions for data cleaning, transformation, and aggregation, which are essential tasks in data analysis. Furthermore, Pandas seamlessly integrates with other libraries in the Python ecosystem, making it a versatile tool for data scientists and analysts. Its extensive documentation and strong community support also contribute to its reputation as a reliable tool for data analysis tasks.

Recommended for

    Pandas is particularly recommended for data scientists, analysts, and engineers who need to perform data cleaning, transformation, and analysis as part of their work. It is also suitable for academics and researchers dealing with data in various formats and needing powerful tools for their data-driven research.

Analysis of Checkmarx

Overall verdict

  • Yes, Checkmarx is generally regarded as a reliable and effective application security testing solution. Its robust features and proactive approach to identifying and mitigating security risks make it a valuable tool for organizations focused on maintaining a secure software development process.

Why this product is good

  • Checkmarx is considered a good choice for application security due to its comprehensive suite of tools designed to identify vulnerabilities early in the software development lifecycle. It offers features like Static Application Security Testing (SAST), Software Composition Analysis (SCA), and Interactive Application Security Testing (IAST). The platform provides a high level of accuracy, integrates well with various development environments, and supports numerous programming languages. Additionally, Checkmarx is known for its ease of use and detailed reporting capabilities, which help developers quickly address security issues.

Recommended for

  • Companies looking for a comprehensive application security platform.
  • Development teams that need seamless integration with CI/CD pipelines.
  • Organizations that require support for multiple programming languages.
  • Security professionals who prioritize early vulnerability detection and detailed reporting.

Pandas videos

+ Add

Ozzy Man Reviews: Pandas

More videos:

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

Checkmarx videos

+ Add

Viewing results and understanding security issues via Checkmarx online scanner

More videos:

  • Demo - Checkmarx CxSAST Demonstration
  • Review - Meetups at Checkmarx: An Introduction to API Security
  • Review - Source code review with Checkmarx
  • Review - Checkmarx Results Review

Category Popularity

0-100% (relative to Pandas and Checkmarx)

Pandas

Checkmarx

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Code Analysis
0 0%
Code Analysis
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Web Application Security
0 0%
Web Application Security
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 Pandas and Checkmarx

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

Checkmarx Reviews

The Top 11 Static Application Security Testing (SAST) Tools
Why We Picked Checkmarx SAST: We like Checkmarx SAST for its early detection of vulnerabilities, which enables faster and safer code development. Its AI-assisted prioritization of vulnerabilities according to severity and risk helps reduce false positives.
Source: expertinsights.com
Top 11 SonarQube Alternatives in 2024
Checkmarx is a developer-centric security tool that specializes in secure coding practices and compliance. It helps developers identify and fix security vulnerabilities in their code, ensuring that their applications are secure and compliant with regulatory standards. Checkmarx offers a range of tools and features to help developers build secure applications, including...
Source: www.codeant.ai
The 5 Best SonarQube Alternatives in 2024
While SonarQube offers some security features, Checkmarx provides a more holistic approach to application security, covering a more comprehensive range of security aspects throughout the SDLC. Checkmarx One's consolidation of multiple AppSec tools into a single platform could simplify management and reduce the total cost of ownership compared to using SonarQube alongside...
Source: blog.codacy.com
Ten Best SonarQube alternatives in 2021
CheckMarx has been used to test the programs to rectify vulnerability in the code and try the security lapses. Checkmarx is the software program exposure Platform for the enterprise. It has an impressive Codebashing characteristic that has the threshold over SonarQube. The software tracking-reporting function is good too. The "delta-experiment" function is it's far genuinely...
Source: duecode.io

Social recommendations and mentions

Based on our record, Pandas seems to be a lot more popular than Checkmarx. While we know about 219 links to Pandas, we've tracked only 3 mentions of Checkmarx. 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 (219)

  • Top Programming Languages for AI Development in 2025
    Libraries for data science and deep learning that are always changing. - Source: dev.to / about 1 month 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 2 months 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 / 2 months 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 / 10 months ago
View more

Checkmarx mentions (3)

  • Penetration Testing for API Security: Protecting Digital Gateways
    Tools like SonarQube, Checkmarx, or Snyk can automate parts of this process by scanning for known vulnerability patterns. While white box testing may not reflect real-world attack scenarios (as attackers rarely access source code), it provides the most thorough assessment of security posture. - Source: dev.to / about 2 months ago
  • A Guide to DevSecOps with API Gateway
    Automate security testing: Use tools such as OWASP ZAP, SonarQube, or Checkmarx to automate security testing. This will help you identify security issues early in the development process and reduce the risk of vulnerabilities being introduced into your code. - Source: dev.to / about 2 years ago
  • 11 Top DevSecOps Tools
    Application Security (AppSec) is the forte of Checkmarx, which is an award-winning AppSec Testing tool that integrates security policies into the DevOps workflow and ensures security across the application lifecycle. Checkmarx scans all your code and provides actionable insights for critical vulnerabilities. Checkmarx also offers developer-friendly AppSec training that makes the transition to DevSecOps more... - Source: dev.to / over 3 years ago

What are some alternatives?

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

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

SonarQube - SonarQube, a core component of the Sonar solution, is an open source, self-managed tool that systematically helps developers and organizations deliver Clean Code.

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

Coverity Scan - Find and fix defects in your Java, C/C++ or C# open source project for free

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

Veracode - Veracode's application security software products are simpler and more scalable to increase the resiliency of your application infrastructure.

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