semvec/pypi-packages huggingface dataset:
wowool-sdk-cpp 3.6.0
Wowool NLP Toolkit C++ SDK Python Bindings
Requires Python: >=3.11

# Wowool NLP Toolkit C++ SDK Python Bindings

## Introduction

This package contains the cpp bindings of the wowool-sdk and should not be install as a separated package, install the wowool-sdk instead which contains this package.

## License

In both cases you will need to acquirer a license file at https://www.wowool.com

### Non-Commercial

    This library is licensed under the GNU AGPLv3 for non-commercial use.  
    For commercial use, a separate license must be purchased.  

### Commercial license Terms

    1. Grants the right to use this library in proprietary software.  
    2. Requires a valid license key  
    3. Redistribution in SaaS requires a commercial license.
stackit-serviceaccount 0.5.0
Service Account API
Requires Python: <4.0,>=3.9

# stackit.serviceaccount
API to manage Service Accounts and their Access Tokens.

### System for Cross-domain Identity Management (SCIM)
Service Account Service offers SCIM APIs to query state. The SCIM protocol was created as standard for
 automating the exchange of user identity information between identity domains, or IT systems. Service accounts
 are be handled as indentites similar to SCIM users. A custom SCIM schema has been created: `/ServiceAccounts`

#### Syntax
##### Attribute operators
| OPERATOR | DESCRIPTION              |
|----------|--------------------------|
| eq       | equal                    |
| ne       | not equal                |
| co       | contains                 |
| sw       | starts with              |
| ew       | ends with                |

##### Logical operators
| OPERATOR | DESCRIPTION              |
|----------|--------------------------|
| and      | logical \"and\"            |
| or       | logical \"or\"             |

##### Grouping operators
| OPERATOR | DESCRIPTION              |
|----------|--------------------------|
| ()       | precending grouping      |

##### Example
```
filter=email eq \"my-service-account-aBc2defg@sa.stackit.cloud\"
filter=email ne \"my-service-account-aBc2defg@sa.stackit.cloud\"
filter=email co \"my-service-account\"
filter=name sw \"my\"
filter=name ew \"account\"
filter=email co \"my-service-account\" and name sw \"my\"
filter=email co \"my-service-account\" and (name sw \"my\" or name ew \"account\")
```

#### Sorting

> Sorting is optional

| PARAMETER | DESCRIPTION                          |
|-----------|--------------------------------------|
| sortBy    | attribute response is ordered by     |
| sortOrder | 'ASCENDING' (default) or 'DESCENDING'|

#### Pagination

| PARAMETER    | DESCRIPTION                                  |
|--------------|----------------------------------------------|
| startIndex   | index of first query result, default: 1      |
| count        | maximum number of query results, default: 100|


This package is part of the STACKIT Python SDK. For additional information, please visit the [GitHub repository](https://github.com/stackitcloud/stackit-sdk-python) of the SDK.


## Installation & Usage
### pip install

```sh
pip install stackit-serviceaccount
```

Then import the package:
```python
import stackit.serviceaccount
```

## Getting Started

[Examples](https://github.com/stackitcloud/stackit-sdk-python/tree/main/examples) for the usage of the package can be found in the [GitHub repository](https://github.com/stackitcloud/stackit-sdk-python) of the SDK.
pyrsm 2.3.1
Python package used in the MSBA program the Rady School of Management @ UCSD
Requires Python: <3.14,>=3.12

# PYRSM

Python functions and classes for Business Analytics at the Rady School of Management (RSM), University of California, San Diego (UCSD).

## Features

**Basics Module** - Statistical tests and analyses:
- `compare_means` - Compare means across groups (t-tests, ANOVA)
- `compare_props` - Compare proportions between groups
- `correlation` - Correlation analysis with significance tests
- `cross_tabs` - Cross-tabulation with chi-square tests
- `goodness` - Goodness of fit tests
- `single_mean` - Single sample mean tests
- `single_prop` - Single sample proportion tests
- `prob_calc` - Probability calculator for common distributions

**Model Module** - Regression and machine learning:
- `regress` - Linear regression with statsmodels
- `logistic` - Logistic regression with statsmodels
- `mlp` - Multi-layer perceptron (neural network) with sklearn
- `rforest` - Random forest with sklearn
- `xgboost` - XGBoost gradient boosting

**EDA Module** - Exploratory data analysis:
- `explore` - Data exploration and summary statistics
- `pivot` - Pivot tables
- `visualize` - Data visualization

All modules use [Polars](https://pola.rs/) DataFrames and [plotnine](https://plotnine.org/) for visualization.

## Installation

Requires Python 3.12+ and UV:

```bash
mkdir ~/project
cd ~/project
uv init .
uv venv --python 3.13
source .venv/bin/activate
uv add pyrsm
```

For machine learning models, install with extras:

```bash
uv add "pyrsm[ml]"
```

For all features:

```bash
uv add "pyrsm[all]"
```

## Quick Start

```python
import polars as pl
from pyrsm import basics, model

# Load data
df = pl.read_parquet("data.parquet")

# Statistical test
cm = basics.compare_means(df, var="price", byvar="category")
cm.summary()
cm.plot()

# Regression model
reg = model.regress(df, rvar="price", evar=["size", "age", "type"])
reg.summary()
reg.plot()
```

## Examples

Extensive example notebooks are available at: <https://github.com/radiant-ai-hub/pyrsm/tree/main/examples>

## License

This project is licensed under the [GNU Affero General Public License v3.0](LICENSE).