Codacy VS NumPy
Compare Codacy VS NumPy and see what are their differences
LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives.
featured
Codacy
SonarQube
CodeClimate
CodeFactor.io
ESLint
Source Insight
Coveralls
Checkmarx
NumPy
Pandas
OpenCV
Scikit-learn
Dataiku
Exploratory
htm.java
WEKA
Codacy automates code reviews and monitors code quality on every commit and pull request reporting back the impact of every commit or pull request, issues concerning code style, best practices, security, and many others. It monitors changes in code coverage, code duplication and code complexity. Saving developers time in code reviews thus efficiently tackling technical debt. JavaScript, Java, Ruby, Scala, PHP, Python, CoffeeScript and CSS are currently supported. Codacy is static analysis without the hassle.
CodacyBased on our record, NumPy seems to be a lot more popular than Codacy. While we know about 109 links to NumPy, we've tracked only 4 mentions of Codacy. 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.
I'm trying to use Codacy to review my code. One of the issues is regarding the use of the "setcookie" function. Source: over 2 years ago
Does anyone have an example on how to get this conversion done on github actions where I can convert the *.coverage file into a *.xml file for uploading to codacy.com. Source: almost 3 years ago
Online analysisFinally, if you want a simple way to analyze your code without having to manually configure everything locally, you can use an online code review service such as Codacy (shameless plug here). We already integrate some of the mentioned detection tools in this article and we are working every day to improve the service. The other main benefit of using automated code review tools is to allow you to... - Source: dev.to / about 3 years ago
Because you care and because you always want to be better, automation is a great way to optimize your review workflow process. Go ahead and do a quick search on Google for automated code reviews and see who better fits your workflow. You'll find Codacy on your Google search and we hope you like what we do. - Source: dev.to / about 3 years ago
This guide covers the basics of NumPy, and there's much more to explore. Visit numpy.org for more information and examples. - Source: dev.to / about 24 hours ago
Below is an example of a code cell. We'll visualize some simple data using two popular packages in Python. We'll use NumPy to create some random data, and Matplotlib to visualize it. - Source: dev.to / 9 months ago
In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication. - Source: dev.to / 3 months ago
Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:. - Source: dev.to / 3 months ago
Numpy: A library for scientific computing in Python. - Source: dev.to / 6 months ago
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.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
CodeClimate - Code Climate provides automated code review for your apps, letting you fix quality and security issues before they hit production. We check every commit, branch and pull request for changes in quality and potential vulnerabilities.
OpenCV - OpenCV is the world's biggest computer vision library
CodeFactor.io - Automated Code Review for GitHub & BitBucket
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.