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NumPy VS Checkmarx

Compare NumPy VS Checkmarx and see what are their differences

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NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

Checkmarx logo Checkmarx

The industry’s most comprehensive AppSec platform, Checkmarx One is fast, accurate, and accelerates your business.
  • NumPy Landing page
    Landing page //
    2023-05-13
  • Checkmarx Landing page
    Landing page //
    2022-07-29

NumPy features and specs

  • Performance
    NumPy operations are executed with highly optimized C and Fortran libraries, making them significantly faster than standard Python arithmetic operations, especially for large datasets.
  • Versatility
    NumPy supports a vast range of mathematical, logical, shape manipulation, sorting, selecting, I/O, and basic linear algebra operations, making it a versatile tool for scientific and numeric computing.
  • Ease of Use
    NumPy provides an intuitive, easy-to-understand syntax that extends Python's ability to handle arrays and matrices, lowering the barrier to performing complex scientific computations.
  • Community Support
    With a large and active community, NumPy offers extensive documentation, tutorials, and support for troubleshooting issues, as well as continuous updates and enhancements.
  • Integrations
    NumPy integrates seamlessly with other libraries in Python's scientific stack like SciPy, Matplotlib, and Pandas, facilitating a streamlined workflow for data science and analysis tasks.

Possible disadvantages of NumPy

  • Memory Consumption
    NumPy arrays can consume large amounts of memory, especially when working with very large datasets, which can become a limitation on systems with limited memory capacity.
  • Learning Curve
    For users new to scientific computing or coming from different programming backgrounds, understanding the intricacies of NumPy's operations and efficient usage can take time and effort.
  • Limited GPU Support
    NumPy primarily runs on the CPU and doesn't natively support GPU acceleration, which can be a disadvantage for extremely compute-intensive tasks that could benefit from parallel processing.
  • Dependency on Python
    Since NumPy is a Python library, it depends on the Python runtime environment. This can be a limitation in environments where Python is not the primary language or isn't supported.
  • Indexing Complexity
    Although NumPy's slicing and indexing capabilities are powerful, they can sometimes be complex or unintuitive, especially for multi-dimensional arrays, leading to potential errors and confusion.

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 NumPy

Overall verdict

  • Yes, NumPy is considered good. It is a foundational library in the Python ecosystem for numerical computing and is used globally by researchers, engineers, and data scientists.

Why this product is good

  • NumPy is widely regarded as a good library because it offers fast, flexible, and efficient array handling that is integral to scientific computing in Python. It provides tools for integrating C/C++ and Fortran code, useful linear algebra, random number capabilities, and a vast collection of mathematical functions. Its array broadcasting capabilities and versatility make complex mathematical computations straightforward.

Recommended for

  • Scientists and researchers working with large-scale scientific computations.
  • Data scientists engaged in data analysis and manipulation.
  • Engineers and developers needing performance-optimized mathematical computations.
  • Educators and students in STEM fields.

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.

NumPy videos

+ Add

Learn NUMPY in 5 minutes - BEST Python Library!

More videos:

  • Review - Python for Data Analysis by Wes McKinney: Review | Learn python, numpy, pandas and jupyter notebooks
  • Review - Effective Computation in Physics: Review | Learn python, numpy, regular expressions, install python

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 NumPy and Checkmarx)

NumPy

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 NumPy and Checkmarx

NumPy Reviews

25 Python Frameworks to Master
SciPy provides a collection of algorithms and functions built on top of the NumPy. It helps to perform common scientific and engineering tasks such as optimization, signal processing, integration, linear algebra, and more.
Source: kinsta.com
Top 8 Image-Processing Python Libraries Used in Machine Learning
Scipy is used for mathematical and scientific computations but can also perform multi-dimensional image processing using the submodule scipy.ndimage. It provides functions to operate on n-dimensional Numpy arrays and at the end of the day images are just that.
Source: neptune.ai
Top Python Libraries For Image Processing In 2021
Numpy It is an open-source python library that is used for numerical analysis. It contains a matrix and multi-dimensional arrays as data structures. But NumPy can also use for image processing tasks such as image cropping, manipulating pixels, and masking of pixel values.
Source: www.analyticsvidhya.com
4 open source alternatives to MATLAB
NumPy is the main package for scientific computing with Python (as its name suggests). It can process N-dimensional arrays, complex matrix transforms, linear algebra, Fourier transforms, and can act as a gateway for C and C++ integration. It's been used in the world of game and film visual effect development, and is the fundamental data-array structure for the SciPy Stack,...
Source: opensource.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, NumPy seems to be a lot more popular than Checkmarx. While we know about 119 links to NumPy, 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.

NumPy mentions (119)

  • Building an AI-powered Financial Data Analyzer with NodeJS, Python, SvelteKit, and TailwindCSS - Part 0
    The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis. - Source: dev.to / 4 months ago
  • F1 FollowLine + HSV filter + PID Controller
    This library provides functions for working in domain of linear algebra, fourier transform, matrices and arrays. - Source: dev.to / 9 months ago
  • Intro to Ray on GKE
    The Python Library components of Ray could be considered analogous to solutions like numpy, scipy, and pandas (which is most analogous to the Ray Data library specifically). As a framework and distributed computing solution, Ray could be used in place of a tool like Apache Spark or Python Dask. It’s also worthwhile to note that Ray Clusters can be used as a distributed computing solution within Kubernetes, as... - Source: dev.to / 9 months ago
  • Streamlit 101: The fundamentals of a Python data app
    It's compatible with a wide range of data libraries, including Pandas, NumPy, and Altair. Streamlit integrates with all the latest tools in generative AI, such as any LLM, vector database, or various AI frameworks like LangChain, LlamaIndex, or Weights & Biases. Streamlit’s chat elements make it especially easy to interact with AI so you can build chatbots that “talk to your data.”. - Source: dev.to / 10 months ago
  • A simple way to extract all detected objects from image and save them as separate images using YOLOv8.2 and OpenCV
    The OpenCV image is a regular NumPy array. You can see it shape:. - 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 / over 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 NumPy and Checkmarx, you can also consider the following products

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the 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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