semvec/pypi-packages huggingface dataset:
pulsefire 2.0.30
A modern and flexible Riot Games Python SDK.
License: MIT
Keywords: Riot Games, League of Legends, Teamfight Tactics, Valorant, Legends of Runeterra, API, SDK, asyncio

# Pulsefire

Pulsefire is a modern and flexible Riot Games Python SDK. It was built to be simple to use, highly configurable, and extendable. Objects and client responses are fully typed to speed up coding efficiency. With support for community resources such as CDragon.

This package is a modern replacement of [Pyot](https://github.com/ianhco/Pyot). By comparison, code clarity has greatly improved and speed performance is more than 10x faster.

- Get Started: [Documentation](https://pulsefire.ianhco.dev)
- Consider starring this repository!

For developers who want to use pulsefire for typing purposes only, [alternative packages](https://pulsefire.ianhco.dev/usage/basic/installation/) containing only the `pulsefire.schemas` module are available in both python and typescript.

##

![banner](https://raw.communitydragon.org/pbe/plugins/rcp-be-lol-game-data/global/default/assets/characters/twistedfate/skins/skin11/images/twistedfate_splash_uncentered_11.jpg)
pulsefire-types 2.0.30
A modern and flexible Riot Games Python SDK.
License: MIT
Keywords: Riot Games, League of Legends, Teamfight Tactics, Valorant, Legends of Runeterra, API, SDK, asyncio

# Pulsefire
> **⚠: This package contains schema typings only!**

Pulsefire is a modern and flexible Riot Games Python SDK. It was built to be simple to use, highly configurable, and extendable. Objects and client responses are fully typed to speed up coding efficiency. With support for community resources such as CDragon.

This package is a modern replacement of [Pyot](https://github.com/ianhco/Pyot). By comparison, code clarity has greatly improved and speed performance is more than 10x faster.

- Get Started: [Documentation](https://pulsefire.ianhco.dev)
- Consider starring this repository!

For developers who want to use pulsefire for typing purposes only, [alternative packages](https://pulsefire.ianhco.dev/usage/basic/installation/) containing only the `pulsefire.schemas` module are available in both python and typescript.

##

![banner](https://raw.communitydragon.org/pbe/plugins/rcp-be-lol-game-data/global/default/assets/characters/twistedfate/skins/skin11/images/twistedfate_splash_uncentered_11.jpg)
ngram-polars 0.1.1
High-performance n-gram generation for Polars
License: MIT
Requires Python: >=3.9
Keywords: ngram, polars, text-processing, nlp, data-analysis

# ngram_polars - N-Gram Generation for Polars

A high-performance Polars plugin for generating n-grams from text data in Python.

## Installation

```bash
pip install ngram-polars
```

# Basic example
```python
import polars as pl
from ngram_polars import ngrams

df = pl.DataFrame({
    "id": [1, 2],
    "words": [
        ["the", "quick", "brown", "fox"],
        ["hello", "world"]
    ]
})

# Generate bigrams
result = df.with_columns(
    bigrams=ngrams(pl.col("words"), n_range=[2])
)
```

# more advanced examples

```python
# Multiple n-gram sizes
df.with_columns(
    multi_ngrams=ngrams(pl.col("words"), n_range=[1, 2, 3])
)

# Custom delimiter
df.with_columns(
    underscored=ngrams(pl.col("words"), n_range=[2], delimiter="_")
)

# Lazy evaluation
(df.lazy()
   .with_columns(
       ngrams=ngrams(pl.col("words"), n_range=[2, 3])
   )
   .collect()
)
```

## API Reference

`ngrams(expr, n_range, delimiter)`
Generate n-grams from a list of strings.

### Parameters:
- `expr: IntoExpr` - Polars expression representing a list of strings
- `n_range: list[int]` - List of n-gram sizes to generate (default: [1])
- `delimiter: str` - String delimiter between words (default: " ")
### Returns:
- `pl.Expr` - Expression that generates lists of n-gram strings

### Behavior:
- Returns a new list column containing all generated n-grams
- Works element-wise on list columns
- Changes the length of the output (each input list produces a new list of n-grams)
- Supports both eager and lazy evaluation

### Performance Tips
- Use Lazy Evaluation: For large datasets, use lazy evaluation to optimize query planning
- Batch N-Gram Sizes: Generate multiple n-gram sizes in one call when possible
- Choose Appropriate N-Range: Only generate the n-gram sizes you actually need

### Requirements
- Python 3.9 -> 3.13
- Polars requirement are not fully tested, tested on the latest version.
- Compatible with both eager and lazy Polars APIs
weasel 0.4.3
Weasel: A small and easy workflow system
License: MIT
Requires Python: >=3.7

<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a>

# Weasel: A small and easy workflow system

Weasel lets you manage and share **end-to-end workflows** for
different **use cases and domains**, and orchestrate training, packaging and
serving your custom pipelines. You can start off by cloning a pre-defined
project template, adjust it to fit your needs, load in your data, train a
pipeline, export it as a Python package, upload your outputs to a remote storage
and share your results with your team. Weasel can be used via the
[`weasel`](https://github.com/explosion/weasel/blob/main/docs/cli.md) command and we provide templates in our
[`projects`](https://github.com/explosion/projects) repo.

![Illustration of project workflow and commands](https://raw.githubusercontent.com/explosion/weasel/main/docs/assets/images/projects.svg)

## 💡 Example: Get started with a project template

The easiest way to get started is to clone a project template and run it – for
example, this [end-to-end template](https://github.com/explosion/projects/tree/v3/pipelines/tagger_parser_ud)
that lets you train a spaCy **part-of-speech
tagger** and **dependency parser** on a Universal Dependencies treebank.

```shell
python -m weasel clone pipelines/tagger_parser_ud
```

> **Note**
>
> Our [`projects`](https://github.com/explosion/projects) repo includes various
> project templates for different NLP tasks, models, workflows and integrations
> that you can clone and run. The easiest way to get started is to pick a
> template, clone it and start modifying it!

## 📕 Documentation

Get started with the documentation:

- [Learn how to create a Weasel workflow](https://github.com/explosion/weasel/blob/main/docs/tutorial/workflow.md)
- [Working with directory and assets](https://github.com/explosion/weasel/blob/main/docs/tutorial/directory-and-assets.md)
- [Running custom scripts](https://github.com/explosion/weasel/blob/main/docs/tutorial/custom-scripts.md)
- [Using remote storage](https://github.com/explosion/weasel/blob/main/docs/tutorial/remote-storage.md)
- [Weasel integrations](https://github.com/explosion/weasel/blob/main/docs/tutorial/integrations.md)
- [Command line interface description](https://github.com/explosion/weasel/blob/main/docs/cli.md)

## Migrating from spaCy Projects

Weasel is a standalone replacement for spaCy Projects.
There are a few backward incompatibilities that you should be aware of:

- The `SPACY_CONFIG_OVERRIDES` environment variable is no longer checked.
  You can set configuration overrides using `WEASEL_CONFIG_OVERRIDES`.
- Support for the `spacy_version` configuration key has been dropped.
- Support for the `check_requirements` configuration key has been dropped.
- Support for `SPACY_PROJECT_USE_GIT_VERSION` environment variable has been dropped.
- Error codes are now Weasel-specific, and do not follow spaCy error codes.

Weasel checks for the first three incompatibilities and will issue a
warning if you're using it with spaCy-specific configuration options.