Spock
Pandas
Spock | Pandas | |
---|---|---|
11 | 395 | |
3,489 | 41,983 | |
0.1% | 0.6% | |
9.4 | 10.0 | |
8 days ago | 3 days ago | |
Java | Python | |
Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Spock
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Mastering Spring Cloud Gateway Testing: Predicates (part 1)
I love using the Spock framework for its simplicity, readability, and maintainability. That's why we use Spock to drive our integration tests.
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Helidon Níma is the first Java microservices framework based on virtual threads
Well I care a lot that it exists. And many other people I know do as well. Just because you don't seem to like it, you shouldn't imagine everyone else is like you.
Maybe Grails is no longer used as much (like Rails itself), but Groovy found other usages since then, like https://spockframework.org/ and Jenkins pipelines (https://www.jenkins.io/doc/book/pipeline/syntax/). It's not going anywhere, and I see no reason for anyone to be upset about it.
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Ask HN: What's your favorite software testing framework and why?
In my opinion it is Spock for Java/Groovy [1]. The amount of functionality and readability you can squeeze from Groovy's DSLesque is absurd. Is basically a full fledged new test language with Java sprinkled as the test contents code
[1]: https://spockframework.org/
- 7 Awesome Libraries for Java Unit & Integration Testing
- There is framework for everything.
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Are there languages that allow to extend its syntax ?
Groovy allows you to perform transforms on it's AST. If you look at the Spock framework, they used AST transforms to pull off a lot of the DSL.
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Using Cucumber and Spock for API test Automation — What Benefits Can You Expect?
Spock and Cucumber exemplify the philosophy of behavior-driven development (BDD). The principle behind BDD is that you must first define the desired result of the added feature in a subject-oriented language before writing any tests. The developers are then given the final documentation.
- A linguagem de programação Groovy - Radar da itexto
- Gradle 7.0 Released
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HTTPS Client Certificate Authentication With Java
As a quick demonstration, the following (Spock) test asserts that the client JVM code fails to create an SSL connection with the service. Note that I chose to use Vert.x Web Client to handle interacting with the service, but don't let this decision distract from the core content of this post. Nevertheless, if you haven't used Vert.x, I encourage you to try it out -- especially for building server-side network applications.
Pandas
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AWS Serverless Diversity: Multi-Language Strategies for Optimal Solutions
Python is a natural fit for serverless development. It boasts a vast array of libraries, including Powertools for AWS and robust libraries for data engineers. Its versatility and excellent developer experience make it a top choice for serverless projects, offering a seamless and enjoyable development experience.
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Pandas reset_index(): How To Reset Indexes in Pandas
In data analysis, managing the structure and layout of data before analyzing them is crucial. Python offers versatile tools to manipulate data, including the often-used Pandas reset_index() method.
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Deploying a Serverless Dash App with AWS SAM and Lambda
Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail. Instead, we'll focus on what's necessary to make it run serverless.
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Help Us Build Our Roadmap – Pydantic
there is pull request to integrate in both pydantic extra types and into pandas cose [1]
[1]: https://github.com/pandas-dev/pandas/issues/53999
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Stuff I Learned during Hanukkah of Data 2023
Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts.
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Introducing Flama for Robust Machine Learning APIs
pandas: A library for data analysis in Python
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.
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Mastering Pandas read_csv() with Examples - A Tutorial by Codes With Pankaj
Pandas, a powerful data manipulation library in Python, has become an essential tool for data scientists and analysts. One of its key functions is read_csv(), which allows users to read data from CSV (Comma-Separated Values) files into a Pandas DataFrame. In this tutorial, brought to you by CodesWithPankaj.com, we will explore the intricacies of read_csv() with clear examples to help you harness its full potential.
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What Would Go in Your Dream Documentation Solution?
So, what I'd like to do is write a documentation package in Python to recreate what I've lost. I plan to build upon the fantastic python-docx and docxtpl packages, and I'll probably rely on pandas from much of the tabular stuff. Here are the features I intend to include:
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How do people know when to use what programming language?
Weirdly most of my time spent with data analysis was in the C layers in pandas.
What are some alternatives?
Cucumber - Cucumber for the JVM
Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
REST Assured - Java DSL for easy testing of REST services
tensorflow - An Open Source Machine Learning Framework for Everyone
AssertJ - AssertJ is a library providing easy to use rich typed assertions
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
Awaitility - Awaitility is a small Java DSL for synchronizing asynchronous operations
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Mockito - Most popular Mocking framework for unit tests written in Java
Keras - Deep Learning for humans
ArchUnit - A Java architecture test library, to specify and assert architecture rules in plain Java
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration