Pandas VS Codacy
Compare Pandas VS Codacy 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.
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Pandas
NumPy
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Exploratory
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WEKA
Codacy
SonarQube
CodeClimate
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Coveralls
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.
Pandas CodacyPandas 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.
Based on our record, Pandas seems to be a lot more popular than Codacy. While we know about 219 links to Pandas, 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.
Libraries for data science and deep learning that are always changing. - Source: dev.to / about 1 month ago
# 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
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
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
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 / 9 months ago
I'm trying to use Codacy to review my code. One of the issues is regarding the use of the "setcookie" function. Source: over 3 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 4 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 4 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 4 years ago
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.
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