semvec/pypi-packages huggingface dataset:
pycounts_bzrudski 0.3.0
Calculate word counts in a text file!
License: MIT
Requires Python: >=3.9

# pycounts_bzrudski

Calculate word counts in a text file!

This repository is based on the tutorial presented in the [Py-Pkgs tutorial](https://py-pkgs.org/welcome). Due to slight differences in the versions of available packages, some of the code is a bit different.

## Installation

```bash
$ pip install pycounts_bzrudski
```

## Usage

`pycounts_bzrudski` can be used to count words in a text file and plot results
as follows:

```python
from pycounts_bzrudski.pycounts_bzrudski import count_words
from pycounts_bzrudski.plotting import plot_words
import matplotlib.pyplot as plt

file_path = "test.txt" # path to your file
counts = count_words(file_path)
fig = plot_words(counts, n=10)
plt.show()
```

## Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

## License

`pycounts_bzrudski` was created by bzrudski. It is licensed under the terms of the MIT license.

## Credits

`pycounts_bzrudski` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).
data-complexity-profiler 0.2.0
Data Complexity Profiler
License: MIT
Requires Python: <3.13,>=3.12

[![SQAaaS badge shields.io](https://github.com/EOSC-synergy/data-complexity-profiler.assess.sqaaas/raw/main/.badge/status_shields.svg)](https://sqaaas.eosc-synergy.eu/#/full-assessment/report/https://raw.githubusercontent.com/eosc-synergy/data-complexity-profiler.assess.sqaaas/main/.report/assessment_output.json)


# Data Complexity

The Data Complexity Measures in pure Python.

## Install

```bash
pip install data-complexity-profiler
```

## How it works

One can import the model and use the common _.fit()_ and
_.transform()_ functions (sklearn-like interface)

```python
import dcp
from sklearn import datasets

iris = datasets.load_iris()
X = iris.data
y = iris.target

model = dcp.ComplexityProfile()
model.fit(X, y)
model.transform()
```

Complexity profile takes different inputs from none to
specific measures to be obtained.

## References

[1] How Complex is your classification problem? A survey on measuring
classification complexity, [ArXiv](https://arxiv.org/abs/1808.03591)

[2] The Extended Complexity Library (ECoL),
[github repo](https://github.com/lpfgarcia/ECoL)