Features

• Search and retrieve data tables and metadata from the DataScribe API
• Simple Python interface for querying endpoints
• Automatic model mapping for API responses
• Context manager support for resource management

Installation

You can use pip to install the datascribe_api package directly from PyPI:

pip install datascribe_api

Quick Start

Python Client Usage

To get started with the datascribe_api, you can use the following example to retrieve and print the names of data tables available to the user:

from datascribe_api import DataScribeClient

with DataScribeClient(api_key="YOUR_API_TOKEN") as client:
    tables = client.get_data_tables_for_user()
    for table in tables:
        print(f"Table Name: {table.display_name}")

Make sure to replace the DataScribeClient initialization with your actual API key or store it in an environment variable named DATASCRIBE_API_TOKEN for authentication.

API Endpoints

Below is a list of all available endpoints in the DataScribe API Python client:

Filtering Data with the Filter Class

The datascribe_api package provides a powerful and expressive Filter class for building complex queries in a Pythonic way. Filters can be passed to API methods as dictionaries, single Filter objects, or lists of Filter objects (for AND logic).

Basic Usage

from datascribe_api import DataScribeClient
from datascribe_api.filter import Filter

with DataScribeClient(api_key="YOUR_API_TOKEN") as client:
    filters = Filter("age") > 30
    rows = client.get_data_table_rows(tableName="users", columns=["age", "name"], filters=filters)
    for row in rows:
        print(row)

Supported Filter Operations

• Equality and Comparison

  Filter("age") == 25
  Filter("score") != 100
  Filter("height") > 170
  Filter("height") >= 180
  Filter("height") < 200
  Filter("height") <= 160
  

• IN and NOT IN

  Filter("status").in_(["active", "pending"])
  Filter("role").not_in(["guest", "banned"])
  

• LIKE and ILIKE (case-insensitive LIKE)

  Filter("name").like("%John%")
  Filter("email").ilike("%@gmail.com")
  

• IS NULL and IS NOT NULL

  Filter("deleted_at").is_null()
  Filter("deleted_at").is_not_null()
  

Combining Multiple Filters (AND logic)

You can pass a list of filters to combine them with AND logic:

filters = [
    Filter("age") > 18,
    Filter("status") == "active",
    Filter("country").in_(["US", "CA"])
]
rows = client.get_data_table_rows(tableName="users", columns=["age", "status", "country"], filters=filters)

Passing Filters as Dictionaries

You can also pass filters as plain dictionaries if you prefer:

filters = {"column": "age", "operator": ">", "value": 21}
rows = client.get_data_table_rows(tableName="users", columns=["age"], filters=filters)

CLI Usage

You can also use the command-line interface to interact with the DataScribe API. Here are some examples:

# List all data tables for the authenticated user
datascribe_cli data-tables-for-user

# Retrieve rows from the data table named m3gnet_mpf
datascribe_cli data-table --table-name m3gnet_mpf

See the CLI documentation for more commands and options.

License

This project is licensed under the GNU GPLv3 License. See the LICENSE file for details.