semvec/pypi-packages huggingface dataset:
trainingsetai 0.0.2
The official Python SDK for Trainingset.ai.

# Trainingset.ai Python SDK

## Install

```sh
pip install trainingsetai
```

## Import and Initialize a client session

```python
from trainingsetai import TrainingsetClient

client = TrainingsetClient("api_key")
```

# Methods

You can use any parameters from the documentation available [here](https://documenter.getpostman.com/view/10426338/Szf9V75M?version=latest).

## Tasks

### Get tasks

You can load the tasks with `get_tasks` method:

```python
client.get_tasks({
    "limit": 1
})
```

## Create tasks

There is a method for each task type and it's very straightforward:

```python
annotation_task = {
    "attachment_url": "http://placekitten.com/1920/1080",
    "instructions": "Lorem ipsum dolor sit amet, consectetur adipiscing elit.",
    "objects_to_annotate": [{"label": "test", "color": "rgba(0, 0, 0, 0)"}]
}

client.create_box_annotation_task(annotation_task)
client.create_line_annotation_task(annotation_task)
client.create_point_annotation_task(annotation_task)
client.create_polygon_annotation_task(annotation_task)
client.create_segmentation_task(annotation_task)
client.create_point_cloud_annotation_task(annotation_task)

categorization_task = {
    "attachment_url": "http://placekitten.com/1920/1080",
    "instructions": "Lorem ipsum dolor sit amet, consectetur adipiscing elit.",
    "categories": ["category1", "category2"]
}

client.create_image_categorization_task(categorization_task)
```

## Projects

Managing projects is also a straightforward process:

```python
client.get_projects()
client.create_project("test_project")
client.delete_project("project_id")
```
truncate-and-write-to-sql 0.1.1
Truncate existing table and write to the table with new entries
Requires Python: >=3.8

# A small package for truncating a table before writing to it

## Install

```powershell
pip install truncate-and-write-to-sql
```

## Example usage

```python
import pandas as pd
from sqlalchemy import create_engine

from truncat_and_write_to_sql.methods import truncate_and_write_sql

# Establish a connection to a SQL database
connection_string = 'a connection string to a database'
engine = create_engine(connection_string)

# Create a dataframe to write to the database
data = {'First Column Name':  ['First value', 'Second value'],
        'Second Column Name': ['First value', 'Second value']}
df = pd.DataFrame(data)

# Truncate existing values and write in new ones
truncate_and_write_sql(
    con=engine,
    df=df,
    table='name of database table',
    schema='database schema e.g. stg',
    if_exists='append',
    mulit=True
)
```
celluloid-clust 1.2.5
Effective Clustering for Single Cell Sequencing Cancer Data
Requires Python: >=3.6

# celluloid
Effective Clustering for Single Cell Sequencing Cancer Data.
```
usage: celluloid [-h] {convert,cluster} ...

optional arguments:
  -h, --help         show this help message and exit

subcommands:
  valid subcommands

  {convert,cluster}
```
A detailed description of the module is available on our [github repo](https://github.com/AlgoLab/celluloid).