Models

The DataScribe API uses Pydantic models to provide type-safe data structures for all API responses. All models include:

• Full type validation: Automatic validation of data types and structure
• IDE support: Auto-completion and type hints in your editor
• Serialization: Easy conversion to JSON, dicts, and DataFrames
• Documentation: Built-in field descriptions and examples

Available Models

Data Table Models

• DataTable: Represents a single data table with metadata
• DataTables: Collection of data tables
• DataTableRows: Collection of rows from a table query
• DataTableColumns: Column definitions for a table
• DataTableMetadata: Complete metadata for a table
• DataTableRowsCount: Row count information

Materials Models

• MaterialSearchResults: Results from a materials search
• MaterialByIdResults: Results from a material ID lookup

Common Methods

All collection models (DataTables, DataTableRows, DataTableColumns, MaterialSearchResults, MaterialByIdResults) support these methods:

to_dataframe()

Convert the model data to a pandas DataFrame

from datascribe_api import DataScribeClient

client = DataScribeClient()
tables = client.get_data_tables_for_user()

# Convert to DataFrame
df = tables.to_dataframe()
print(df.head())

to_list()

Convert the model data to a list of dictionaries

rows = client.get_data_table(tableName="my_table")

# Convert to list
data = rows.to_list()
for row_dict in data:
    print(row_dict)

Model Serialization

All models support standard Pydantic serialization:

To dictionary

data_dict = model.model_dump()

To JSON string

json_str = model.model_dump_json()

To JSON with indentation

json_pretty = model.model_dump_json(indent=2)

Exclude certain fields

data_dict = model.model_dump(exclude={"user_id"})

Include only certain fields

data_dict = model.model_dump(include={"table_name", "display_name"})