Improve readme (#19)

* Update README.md

* Update README.md

* Update README.md

* Update df_checker.py

* Update input_helpers.py

* Update pyproject.toml

README.md

@@ -31,6 +31,18 @@ dfs = [df]
 results, brontabel_df, bronattribute_df, dqRegel_df = dq_suite.df_check(dfs, dq_rules, "showcase")
 ```
 
+
+# Export the schema from Unity Catalog to the Input Form
+In order to output the schema from Unity Catalog, use the following commands (using the required schema name):
+
+```
+schema_output = dq_suite.export_schema('schema_name', spark)
+print(schema_output)
+```
+
+Copy the string to the Input Form to quickly ingest the schema in Excel.
+
+
 # Validate the schema of a table
 It is possible to validate the schema of an entire table to a schema definition from Amsterdam Schema in one go. This is done by adding two fields to the "dq_rules" JSON when describing the table (See: https://github.com/Amsterdam/dq-suite-amsterdam/blob/main/dq_rules_example.json). 
 
@@ -40,6 +52,7 @@ You will need:
 
 The schema definition is converted into column level expectations (expect_column_values_to_be_of_type) on run time.
 
+
 # Known exceptions
 The functions can run on Databricks using a Personal Compute Cluster or using a Job Cluster. Using a Shared Compute Cluster will results in an error, as it does not have the permissions that Great Expectations requires.
 

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "dq-suite-amsterdam"
-version = "0.5.1"
+version = "0.5.2"
 authors = [
   { name="Arthur Kordes", email="a.kordes@amsterdam.nl" },
   { name="Aysegul Cayir Aydar", email="a.cayiraydar@amsterdam.nl" }

src/dq_suite/df_checker.py

@@ -7,7 +7,7 @@
 import great_expectations as gx
 from great_expectations.checkpoint import Checkpoint
 
-from dq_suite.input_validator import validate_dqrules, expand_input, generate_dq_rules_from_schema, fetch_schema_from_github
+from dq_suite.input_helpers import validate_dqrules, expand_input, export_schema, generate_dq_rules_from_schema, fetch_schema_from_github
 from dq_suite.output_transformations import extract_dq_validatie_data, extract_dq_afwijking_data, create_brontabel, create_bronattribute, create_dqRegel
 
 def df_check(dfs: list, dq_rules: str, check_name: str) -> Tuple[Dict[str, Any], Dict[str, Tuple[Any, Any]], pd.DataFrame, pd.DataFrame, pd.DataFrame]:

src/dq_suite/input_helpers.py

@@ -41,7 +41,7 @@ def expand_input(rule_json):
     :rtype: dict
     """
 
-    for table in rule_json["dataframe_parameters"]:
+    for table in rule_json["tables"]:
         for rule in table["rules"]:
             for parameter in rule["parameters"]:
                 if "row_condition" in parameter: