pytest VS Pandas

Pytest: is a third-party testing framework that supports fixtures, parameterized testing, and easy test discovery while having room to add plugins to extend its functionality.

Next, you need to install a testing framework that will be used for performing unit testing in your project. Several testing frameworks are available depending on the programming language used to create an application. For example, JUnit is commonly used for Java apps, pytest for Python apps, NUnit for .NET apps, Jest for JavaScript apps, and so on. We’ll use the Jest framework for this tutorial since we are using JavaScript.

Yea, read through the pytest docs.

Since ITK's inception in 1999, there has been a focus on engineering practices that result in high-quality software. High-quality scientific software is driven by regression testing. The ITK project supported the development of CTest and CDash unit testing and software quality dashboard tools for use with the CMake build system. In the Python programming language, the pytest test driver helps developers write small, readable scripts that ensure their software will continue to work as expected. However, pytest can only test Python scripts by default, and errors in untested computational notebooks are more common than well-tested Python code.

Inorder to have this project be as well-rounded as possible we'll code a series of tests to test the code using pytest. Create a new directory called tests and create a new file called test_db.py in the tests directory. Inside create a test_players.py file.

that doesn't make much sense and there are necessary uses for eval() /exec(), mostly for dynamic creation of code:

For example here's Python dataclasses in the standard library using exec() to create the `__init__` and other methods that go on your dataclass:

https://github.com/python/cpython/blob/main/Lib/dataclasses....

Here's Pydantic using it for a jupyter notebook check:

https://github.com/pydantic/pydantic/blob/594effa279668bd955...

here's Pytest using it to rewrite modules so that functions like assert etc. are instrumented by pytest:

https://github.com/pytest-dev/pytest/blob/eca93db05b6c5ec101...

Here's the decorator module using it (as is the only way to do this in Python) to create a signature matching decorator for an arbitrary function:

https://github.com/micheles/decorator/blob/ad013a2c1ad796996...

All of these libraries are completely secure as eval/exec are used with code fragments that are generated by the libraries, not based on untrusted input.

eval() /exec() are not running executable files, just Python code, the same way all the rest of the package is already doing.

For brevity we will test the numbers from 0 through 10, below are a collection of pytest tests covering these cases. Thus, in test_willans.py we have the following:

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.

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

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.

pandas: A library for data analysis in Python

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.

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:

Explore here.

AWS Data Wrangler is a Python library that simplifies the process of interacting with various AWS services, built on top of some useful data tools and open-source projects such as Pandas, Apache Arrow and Boto3. It offers streamlined functions to connect to, retrieve, transform, and load data from AWS services, with a strong focus on Amazon S3.

Pandas