semvec/pypi-packages huggingface dataset:
dutch-text-analytics 0.1.2
Dutch Text Analytics is a versatile toolkit designed to facilitate the exploration, execution, and validation of a diverse range of Natural Language Processing (NLP) tasks specifically tailored for the Dutch language. This repository provides a comprehensive set of tools, including code examples, scripts, and resources, to enhance and streamline your Dutch NLP projects.

# Dutch Text Analytics
Dutch Text Analytics is a versatile toolkit designed to facilitate the exploration, execution, and validation of a diverse range of Natural Language Processing (NLP) tasks specifically tailored for the Dutch language. This repository provides a comprehensive set of tools, including code examples, scripts, and resources, to enhance and streamline your Dutch NLP projects.

## Getting Started

### Prerequisites

- Ensure you have Python version 3.8.10 installed on your system.

### Installation

- To install the *Dutch Text Analytics* package, open a command prompt and run:

```bash
pip install dutch_text_analytics
```
### Usage

- In your Python script or Jupyter Notebook, import the library as follows:

```python
import dutch_text_analytics.text_analytics as ta
```
- To access modules and leverage the functionalities provided by the toolkit, instantiate the TextProcessing class:

```python
text_processor = ta.TextProcessing()
```
- For detailed usage examples and demonstrations, refer to the demo scripts available in the [demos](https://github.com/allmin/dutch_text_analytics/tree/main/dutch_text_analytics/demos) folder.

## Modules Overview

## Text Processing

- The TextProcessing module provides powerful tools for working with Dutch text, including lemmatization, handling separable verbs, and displaying dependency trees.

```python
# Example: Instantiate TextProcessing
text_processor = ta.TextProcessing()

# Example: Lemmatize a sentence
lemmatized_sentence = text_processor.lemmatize("Your Dutch sentence here.")

# Example: Handle separable verbs in a sentence
processed_sentence = text_processor.handle_separable_verbs("Your Dutch sentence here.")

# Example: Display dependency tree of a sentence
text_processor.display_dependency("Your Dutch sentence here.")
```
- Explore the module for additional functionalities to enhance your Dutch text processing workflows.

- Tip: The TextProcessing takes in language argument. For english:

```python
# Example: Instantiate TextProcessing
text_processor = ta.TextProcessing(language='en')
```

## Demos

- Check out the [demos](https://github.com/allmin/dutch_text_analytics/tree/main/dutch_text_analytics/demos) folder for hands-on demonstrations and code examples showcasing the capabilities of Dutch Text Analytics across various NLP tasks.

Feel free to contribute, report issues, or suggest improvements. Happy coding with Dutch Text Analytics!
ascii-canvas 2.0.1
Treat strings like Items on a 2D Canvas.
Requires Python: >=3.8.1

[![Version](https://img.shields.io/pypi/v/ascii_canvas.svg)](https://pypi.org/project/ascii_canvas/)

# Treat Strings like Items on a 2D Canvas

With this primitive library you can do things like this:

```python
from ascii_canvas import canvas
from ascii_canvas import item

canvas_ = canvas.Canvas()

rect_a = item.Item('+-----+\n|Hello|\n+-----+', position=[0, 0])
rect_b = item.Item('+-----+\n|World|\n+-----+', position=[16, 5])
rect_c = item.Item('+-+\n|!|\n+-+', position=[32, 0])
line_a = item.Line(start=[7, 1], end=[15, 6])
line_b = item.Line(start=[23, 6], end=[31, 1])

canvas_.add_item(rect_a)
canvas_.add_item(rect_b)
canvas_.add_item(rect_c)
canvas_.add_item(line_a)
canvas_.add_item(line_b)
print(canvas_.render())
```

Which results in this output:

```
+-----+                         +-+
|Hello|----+               +----|!|
+-----+    |               |    +-+
           |               |
           |               |
           |    +-----+    |
           +----|World|----+
                +-----+
```