patito.Relation.__str__
- Relation.__str__()
Return string representation of Relation object.
Includes an expression tree, the result columns, and a result preview.
Example
>>> import patito as pt >>> products = pt.Relation( ... pt.DataFrame( ... { ... "product_name": ["apple", "red_apple", "banana", "oranges"], ... "supplier_id": [2, 2, 1, 3], ... } ... ) ... ).set_alias("products") >>> print(str(products)) # xdoctest: +SKIP --------------------- --- Relation Tree --- --------------------- arrow_scan(94609350519648, 140317161740928, 140317161731168, 1000000) --------------------- -- Result Columns -- --------------------- - product_name (VARCHAR) - supplier_id (BIGINT) --------------------- -- Result Preview -- --------------------- product_name supplier_id VARCHAR BIGINT [ Rows: 4] apple 2 red_apple 2 banana 1 oranges 3
>>> suppliers = pt.Relation( ... pt.DataFrame( ... { ... "id": [1, 2], ... "supplier_name": ["Banana Republic", "Applies Inc."], ... } ... ) ... ).set_alias("suppliers") >>> relation = ( ... products.set_alias("p") ... .inner_join( ... suppliers.set_alias("s"), ... on="p.supplier_id = s.id", ... ) ... .aggregate( ... "supplier_name", ... num_products="count(product_name)", ... group_by=["supplier_id", "supplier_name"], ... ) ... ) >>> print(str(relation)) # xdoctest: +SKIP --------------------- --- Relation Tree --- --------------------- Aggregate [supplier_name, count(product_name)] Join INNER p.supplier_id = s.id arrow_scan(94609350519648, 140317161740928, 140317161731168, 1000000) arrow_scan(94609436221024, 140317161740928, 140317161731168, 1000000) --------------------- -- Result Columns -- --------------------- - supplier_name (VARCHAR) - num_products (BIGINT) --------------------- -- Result Preview -- --------------------- supplier_name num_products VARCHAR BIGINT [ Rows: 2] Applies Inc. 2 Banana Republic 1
- Return type
str