semvec/pypi-packages huggingface dataset:
mmm-proto-schema 1.1.1
A Meridian submodule for MMM model representation in protobuf form.
Requires Python: >=3.10
Keywords: mmm, protobuf

# About MMM Proto Schema

The MMM Proto Schema is a language-agnostic data standard for representing
trained Marketing Mix Models (MMMs) and their analysis artifacts in a
consistent, serializable format. It establishes a common language for MMM
outputs, allowing results from different tools and methodologies to be uniformly
represented, stored, shared, and compared. This standardized representation
enhances interoperability and facilitates downstream applications like scenario
planning, optimization, and reporting.

## Install Meridian with MMM Proto Schema

Using PIP:

```sh
pip install --upgrade mmm-proto-schema
```

Alternatively, to install this package from source code:

```sh
git clone https://github.com/google/meridian.git
cd meridian
pip install .[schema]
```
flatspark 0.1.0
Add your description here
Requires Python: >=3.13

# flatspark
An opinionated pyspark package to handle deeply nested DataFrames with ease. 

## Key Features
- Flatten deeply nested DataFrames with arrays and structs
- Automatic generation of technical IDs for joins
- Customizable explode strategies for arrays
- Support for incremental loads with existing technical IDs
- Apply additional transformations during flattening
- Select standard columns across all flattened tables

## Getting Started

`pip install flatspark`

```
from pyspark.sql import SparkSession
from flatspark import get_flattened_dataframes

spark = SparkSession.builder.getOrCreate()

# inital DataFrame contains one array
df = spark.createDataFrame(
    [
        ("Alice", ["reading", "hiking"]),
        ("Bob", ["cooking"]),
        ("Charlie", []),
    ],
    ["name", "hobbies"],
)

flat_dfs = get_flattened_dataframes(df=df, root_name="friends")
```

A primary key column has been added to the root DataFrame `friends`: 

```
flat_dfs["friends"].show()

+-------+--------------------+                                                  
|name   |friends_technical_id|
+-------+--------------------+
|Alice  |0                   |
|Bob    |1                   |
|Charlie|2                   |
+-------+--------------------+
```

The array `hobbies` has been seperated in its own DataFrame with a primary key and a foreign key column to enable joins: 

```
+--------------------+-------+--------------------+
|friends_technical_id|hobbies|hobbies_technical_id|
+--------------------+-------+--------------------+
|0                   |reading|0                   |
|0                   |hiking |1                   |
|1                   |cooking|2                   |
|2                   |NULL   |3                   |
+--------------------+-------+--------------------+
```

### Additional Examples
Additional examples showing the features of the package can be found here: 
- [simple example](examples/simple.py)
- [advanced example](examples/advanced.py)
safecomms 0.1.1
Official Python SDK for SafeComms API
Requires Python: >=3.7

# SafeComms Python SDK

Official Python client for the SafeComms API.

SafeComms is a powerful content moderation platform designed to keep your digital communities safe. It provides real-time analysis of text to detect and filter harmful content, including hate speech, harassment, and spam.

**Get Started for Free:**
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## Documentation

For full API documentation and integration guides, visit [https://safecomms.dev/docs](https://safecomms.dev/docs).

## Installation

```bash
pip install safecomms
```

## Usage

```python
from safecomms import SafeCommsClient

client = SafeCommsClient(api_key="your-api-key")

# Moderate text
result = client.moderate_text(
    content="Some text to check",
    language="en",
    replace=True
)
print(result)

# Get usage
usage = client.get_usage()
print(usage)
```