semvec/pypi-packages huggingface dataset:
document-tracking-resources 1.0.1.202209211711
Package to load the resources required by the document_tracking package.

# `document_tracking_resources`

This package is part of the `news_tracking` project and aims at providing the necessary resources for algorithms to run and data preparation.

## Installation

```bash
pip install document_tracking_resources
```

## Data format

Document tracking algorithms use two classes, `NewsCorpus` and `TwitterCorpus` respectively for news articles data (with title and content) and tweets. A corpus is to be seen as a table of elements that comprises, at least, three categories of elements:

  * Document **characteristics**: the columns that correspond to the document itself: date of publication, title, text, source, etc. See the `DOCUMENT_COLUMNS` property.
  * Document **features**: the different computed features (either TF-IDF weightings or dense representation for instance) that represent the multiple dimensions of the documents (title, text or both) of the original corpus. See the `FEATURE_COLUMNS` property.
  * Document **cluster**: the ground truth cluster id of each document, in order to train and test the corpus. See the `GROUND_TRUTH_COLUMNS` property.

This data format relies on the `pandas` library, especially the `DataFrame` data structure. It is generally saved in `.pickle` format in order to make it easy to load and save the structure. The `Corpus` API provides functions to load `DataFrame` in `.pickle` format as a `Corpus`.

### Features format

The features of the `Corpus` are column named after the `FEATURES_COLUMNS` list. This package allows manipulating two kind of data different features:

* **Sparse**: generally produced via a TF-IDF weighting model, the sparse representation is saved as a mapping, for each dimension (title, text or both) between the feature to weight (the tokens for instance) and its weight. Each dimension is then a dictionary of all the terms and their weightings.
* **Dense**: vectors of equal size that provide a representation, as a vector of number of each dimension of the original document (title, text or both).
num-prueba-2 1.2
Librería para Data Scientists

# NUMERITOS

#### Este es una librería enfocada a todo el campo de Data Science para poder ahorrar tiempo de desarrollo de código para tareas comunes.

#### Esta librería cuenta con más de 40 funciones documentadas de visualización, transformación de datos y entrenamiento de modelos.

-----

## Equipo:

#### [Laura Barreda](https://github.com/lauragreemko) / [Javier Tenorio](https://github.com/75Engel) / [Antonio Morales](https://github.com/Toni2Morales) / [Mario Sabrás](https://github.com/Masara00)
#### [Christian Jerome Bader](https://github.com/jeromebader) / [Sara Devera](saradevera) / [Enrique Rubio](https://github.com/EnriRuRu) / [Xinmeng Ye](xyaimao)
#### [Qinhua Liang](https://github.com/qinghua03) / [Irene Glez](https://github.com/irene-glez) / [Luis Miguel Valverde](https://github.com/lumivalsa) / [Tarik El Hannach](https://github.com/tarikelhannach)

----

### [Enlace a Paquete Pypi]()

----
## Visualización
* feature_visual
* grafico_goscatter
* gen_diagram_caja
* pieplot_one_column
* joyplot_one_column
* subplots
* graf_displot


## Transformación de datos
* time_now
* sustituye_texto
* extraer_con_regex
* eliminar_entre_parentesis_en_df
* where_contains
* drop_con_condicion
* data_report
* number_of_outliers
* radical_dropping
* read_data_bw
* read_data_color
* read_data
* sustituir_outliers
* muestra_nan
* acotar_valores_clase
* wrap_perspective_cv2
* train_sampler
* string_replacer
* basic_encoding
* clean_emoji
* MinMaxCorr
* DfTransType
* sustituye_nan_moda
* train_regression
* clean_edad


## Modelos de ML
* funcion_lineal_regression
* funcion_metricas_error
* funcion_ridge
* funcion_lassoV
* error_modelo
* feature_importance
* nine_Regressor_Models
* PruebaModelos
* root_mean_squared_error

----

## Dependencias principales

* **Pandas**
* **Numpy**
* **CV2**
* **Sklearn**
* **Skimage**
* **time**
* **pygame**
* **joypy**
* **plotly**
* **matplotlib**
* **Seaborn**
* **re**
* **os**
num-prueba-1 1
Librería para Data Scientists

# NUMERITOS

#### Este es una librería enfocada a todo el campo de Data Science para poder ahorrar tiempo de desarrollo de código para tareas comunes.

#### Esta librería cuenta con más de 40 funciones documentadas de visualización, transformación de datos y entrenamiento de modelos.

-----

## Equipo:

#### [Laura Barreda](https://github.com/lauragreemko) / [Javier Tenorio](https://github.com/75Engel) / [Antonio Morales](https://github.com/Toni2Morales) / [Mario Sabrás](https://github.com/Masara00)
#### [Christian Jerome Bader](https://github.com/jeromebader) / [Sara Devera](saradevera) / [Enrique Rubio](https://github.com/EnriRuRu) / [Xinmeng Ye](xyaimao)
#### [Qinhua Liang](https://github.com/qinghua03) / [Irene Glez](https://github.com/irene-glez) / [Luis Miguel Valverde](https://github.com/lumivalsa) / [Tarik El Hannach](https://github.com/tarikelhannach)

----

### [Enlace a Paquete Pypi]()

----
## Visualización
* feature_visual
* grafico_goscatter
* gen_diagram_caja
* pieplot_one_column
* joyplot_one_column
* subplots
* graf_displot


## Transformación de datos
* time_now
* sustituye_texto
* extraer_con_regex
* eliminar_entre_parentesis_en_df
* where_contains
* drop_con_condicion
* data_report
* number_of_outliers
* radical_dropping
* read_data_bw
* read_data_color
* read_data
* sustituir_outliers
* muestra_nan
* acotar_valores_clase
* wrap_perspective_cv2
* train_sampler
* string_replacer
* basic_encoding
* clean_emoji
* MinMaxCorr
* DfTransType
* sustituye_nan_moda
* train_regression
* clean_edad


## Modelos de ML
* funcion_lineal_regression
* funcion_metricas_error
* funcion_ridge
* funcion_lassoV
* error_modelo
* feature_importance
* nine_Regressor_Models
* PruebaModelos
* root_mean_squared_error

----

## Dependencias principales

* **Pandas**
* **Numpy**
* **CV2**
* **Sklearn**
* **Skimage**
* **time**
* **pygame**
* **joypy**
* **plotly**
* **matplotlib**
* **Seaborn**
* **re**
* **os**
ogcmaps 0.1.1
Python client and wrapper for OGC Maps API
Requires Python: >=3.6

# API Maps Client
Python client for OGC API - Maps.

## Installation
```bash
pip install ogcmaps
```

## Usage
```python
from ogcmaps import landing
data = landing.metadata()

print(data)
```

See the [docs](https://52north.github.io/api-maps-client/) for complete API documentation.

## For developers

Install
```bash
virtualenv env && source env/bin/activate
```
```bash
pip3 install -r requirements.txt
```
Install pre-commit and pytest
```bash
pip3 install pre-commit pytest
```
Build from source
```bash
pip3 install .
```

### Run tests
```bash
cd tests && pytest
```

### Generate docs
```bash
sphinx-apidoc -fo docs/source ogcmaps ogcmaps/utils/*
```