@@ -51,35 +51,46 @@ import dataworkbench
5151
5252To use it on your local machine, it requires you to set a set of variables to connect to the Veracity Dataworkbench API.
5353
54- ### Basic Example
5554
55+
56+ ## Examples
57+
58+ ### Saving a Spark DataFrame to the Data Catalogue
5659``` python
5760from dataworkbench import DataCatalogue
5861
5962df = spark.createDataFrame([(" a" , 1 ), (" b" , 2 ), (" c" , 3 )], [" letter" , " number" ])
6063
61- datacatalogue = DataCatalogue() # Naming subject to change
62- datacatalogue.save(df, " Dataset Name" , " Description" , tags = {" environment" : [" test" ]})
64+ datacatalogue = DataCatalogue()
65+ datacatalogue.save(
66+ df,
67+ " Dataset Name" ,
68+ " Description" ,
69+ tags = {" environment" : [" test" ]}
70+ ) # schema_id is optional - if not provided, schema will be inferred from the dataframe
6371```
64-
65- ## Examples
66-
67- ### Saving a Spark DataFrame to the Data Catalogue
68-
72+ #### Using an existing schema
73+ When you have an existing schema that you want to reuse:
6974``` python
7075from dataworkbench import DataCatalogue
7176
7277df = spark.createDataFrame([(" a" , 1 ), (" b" , 2 ), (" c" , 3 )], [" letter" , " number" ])
7378
74- datacatalogue = DataCatalogue() # Naming subject to change
75- datacatalogue.save(df, " Dataset Name" , " Description" , tags = {" environment" : [" test" ]})
79+ datacatalogue = DataCatalogue()
80+ datacatalogue.save(
81+ df,
82+ " Dataset Name" ,
83+ " Description" ,
84+ tags = {" environment" : [" test" ]},
85+ schema_id = " abada0f7-acb4-43cf-8f54-b51abd7ba8b1" # Using an existing schema ID
86+ )
7687```
7788
7889## API Reference
7990
8091### DataCatalogue
8192
82- - ` save(df, name, description=None, tags=None) ` : Save a Spark DataFrame to the Data Workbench Data Catalogue
93+ - ` save(df, name, description, schema_id =None, tags=None) ` : Save a Spark DataFrame to the Data Workbench Data Catalogue
8394
8495
8596## License
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