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About

Coverage.py is a tool for measuring code coverage of Python programs. It monitors your program, noting which parts of the code have been executed, then analyzes the source to identify code that could have been executed but was not. Coverage measurement is typically used to gauge the effectiveness of tests. It can show which parts of your code are being exercised by tests, and which are not. Use coverage run to run your test suite and gather data. However you normally run your test suite, and you can run your test runner under coverage. If your test runner command starts with “python”, just replace the initial “python” with “coverage run”. To limit coverage measurement to code in the current directory, and also find files that weren’t executed at all, add the source argument to your coverage command line. By default, it will measure line (statement) coverage. It can also measure branch coverage. It can tell you what tests ran which lines.

About

Powerful and affordable RPA platform. Use the flexibility of Python, the convenience of low code, and the potential of AI for intelligent automation. Python RPA is an easy-to-use platform for developing and managing bots in Python. The capabilities of Python make the platform an effective and powerful tool for automating business processes. Enterprise-grade orchestrator for managing Python scripts and low-code projects. Basic Python knowledge is enough to start your automation journey. Stay ahead with instant notifications and a status management board. Uninterrupted flow of process execution, keeping things running smoothly. Ensure secured and managed user access. Keep your credentials secured and ensure activities are being logged. Use any library or framework for creating your project. Develop your Python automation in any open-source Python development environment.

About

Scikit-learn provides simple and efficient tools for predictive data analysis. Scikit-learn is a robust, open source machine learning library for the Python programming language, designed to provide simple and efficient tools for data analysis and modeling. Built on the foundations of popular scientific libraries like NumPy, SciPy, and Matplotlib, scikit-learn offers a wide range of supervised and unsupervised learning algorithms, making it an essential toolkit for data scientists, machine learning engineers, and researchers. The library is organized into a consistent and flexible framework, where various components can be combined and customized to suit specific needs. This modularity makes it easy for users to build complex pipelines, automate repetitive tasks, and integrate scikit-learn into larger machine-learning workflows. Additionally, the library’s emphasis on interoperability ensures that it works seamlessly with other Python libraries, facilitating smooth data processing.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Any user looking for a solution to measure line and branch coverage to produce test reports

Audience

Enterprises interested in a tool to manage their business processes and automate routine tasks

Audience

Engineers and data scientists requiring a solution to manage and improve their machine learning research

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Free Version
Free Trial

Pricing

$275 per month
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Coverage.py
United States
coverage.readthedocs.io/en/7.0.0/

Company Information

Python RPA
Founded: 2019
Kazakhstan
pythonrpa.org

Company Information

scikit-learn
United States
scikit-learn.org/stable/

Alternatives

Alternatives

Alternatives

Gensim

Gensim

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JCov

JCov

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ML.NET

ML.NET

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MLlib

MLlib

Apache Software Foundation
blanket.js

blanket.js

Blanket.js
Devel::Cover

Devel::Cover

metacpan
Keepsake

Keepsake

Replicate

Categories

Categories

Categories

Integrations

Python
C
Codecov
Django
Docker
GPT-4
HTML
Intel Tiber AI Studio
Keepsake
MLJAR Studio
Mako
ModelOp
NumPy
OpenAI
SAP Cloud Platform
SQLite
Train in Data
Visual Studio
XML
pytest

Integrations

Python
C
Codecov
Django
Docker
GPT-4
HTML
Intel Tiber AI Studio
Keepsake
MLJAR Studio
Mako
ModelOp
NumPy
OpenAI
SAP Cloud Platform
SQLite
Train in Data
Visual Studio
XML
pytest

Integrations

Python
C
Codecov
Django
Docker
GPT-4
HTML
Intel Tiber AI Studio
Keepsake
MLJAR Studio
Mako
ModelOp
NumPy
OpenAI
SAP Cloud Platform
SQLite
Train in Data
Visual Studio
XML
pytest
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