fixed python sdk docs

Author: GuyEshdat

docs/python-sdk/api-reference/overview.mdx

@@ -39,7 +39,7 @@ client = ElementaryCloudClient(project_id, api_key, url)
 Where:
 - `project_id` is your Python project identifier (chosen by you, used to deduplicate and identify reported assets)
 - `api_key` is your API token (generated from the steps above)
-- `url` is the full SDK ingest endpoint URL: `{base_url}/sdk-ingest/{env_id}/batch`
+- `url` is the full SDK ingest endpoint URL (the Elementary team will provide you with this URL): `{base_url}/sdk-ingest/{env_id}/batch`
   - Example: `https://app.elementary-data.com/sdk-ingest/a6b2425d-36e2-4e13-8458-9825688ca1f2/batch`
 
 ## Test Context
@@ -75,15 +75,17 @@ You can also use context managers for inline tests:
 with elementary_test_context(asset=asset) as ctx:
     # Using context managers
     with ctx.boolean_test(name="my_test", description="Inline test") as my_bool_test:
-        my_bool_test.assert_value(False)
+        my_bool_test.assert_value(my_test_function())
 
     with ctx.expected_values_test(
         name="country_count",
         expected=[2, 3],
         allow_none=True,
         metadata={"my_metadata_field": "my_metadata_value"},
     ) as my_expected_values_test:
+        # This will fail
         my_expected_values_test.assert_value(5)
+        # This will pass
         my_expected_values_test.assert_value(3)
 
     with ctx.expected_range_test(
@@ -143,7 +145,6 @@ except Exception as e:
 - **Run multiple tests in one context** - All tests in a single `elementary_test_context` are automatically batched
 - **Use descriptive test names** - Clear names help identify tests in the Elementary UI
 - **Include asset metadata** - Add descriptions, owners, tags, and dependencies to assets
-- **Handle errors gracefully** - Wrap `send_to_cloud` calls in try-except blocks
 
 <Tip>
 All tests run within a single `elementary_test_context` are automatically batched and sent together.

docs/python-sdk/api-reference/table-assets.mdx

@@ -17,7 +17,7 @@ asset = TableAsset(
     description="string",           # Optional: Table description
     owners=["string"],              # Optional: List of owners (emails or usernames)
     tags=["string"],               # Optional: List of tags
-    depends_on=["string"]           # Optional: List of upstream asset IDs
+    depends_on=["string"]           # Optional: List of upstream fully qualified table names
 )
 ```
 
@@ -37,7 +37,7 @@ asset = TableAsset(
 | `description` | string | Human-readable description of the table |
 | `owners` | list[string] | List of owners (email addresses or usernames) |
 | `tags` | list[string] | List of tags for categorization |
-| `depends_on` | list[string] | List of upstream asset IDs (e.g., `["prod.public.customers", "prod.public.orders"]`) for lineage tracking |
+| `depends_on` | list[string] | List of upstream fully qualified table names (e.g., `["prod.public.customers", "prod.public.orders"]`) for lineage tracking |
 
 ## Example
 

docs/python-sdk/api-reference/test-decorators.mdx

@@ -49,8 +49,8 @@ def test_function(df: pd.DataFrame) -> bool:
 | `tags` | list[str] | No | `None` | List of tags |
 | `owners` | list[str] | No | `None` | List of owners |
 | `metadata` | dict | No | `None` | Additional metadata |
-| `quality_dimension` | QualityDimension | No | `None` | Quality dimension (defaults to VALIDITY if column_name is set) |
-| `skip` | bool | No | `False` | Whether to skip this test |
+| `quality_dimension` | QualityDimension | No | `None` | Quality dimension (defaults to VALIDITY) |
+| `skip` | bool | No | `False` | Whether to skip this test. Useful if you want the test to appear in Elementary Cloud, but you don't want to execute it in this run. |
 
 ### Example
 
@@ -68,7 +68,7 @@ def test_unique_ids(df: pd.DataFrame) -> bool:
 
 ## @expected_range
 
-Tests that return a numeric value that should fall within a range.
+Tests that return a numeric value that should fall within a range. They can also return a list of numeric values or a pandas Series.
 
 ### Signature
 
@@ -86,9 +86,11 @@ Tests that return a numeric value that should fall within a range.
     quality_dimension: QualityDimension | None = None,
     skip: bool = False,
 )
-def test_function(df: pd.DataFrame) -> float:
+def test_function(df: pd.DataFrame) -> float | list[float] | pd.Series:
     # Your test logic
-    return 25.5  # Numeric value
+    return df["age"].mean()  # Numeric value
+    # return [1, 2, 3]  # Numeric values
+    # return df["age"]  # pandas Series
 ```
 
 ### Parameters
@@ -120,7 +122,7 @@ def test_average_age(df: pd.DataFrame) -> float:
 
 ## @expected_values
 
-Tests that return a value that should match one of a list of expected values.
+Tests that return a value (or values) that should match one of a list of expected values.
 
 ### Signature
 

docs/python-sdk/guides/authentication.mdx

@@ -116,6 +116,7 @@ If you receive a 404 error:
 - Verify your URL is correct and includes the correct environment ID
 - Check that the environment exists in your Elementary Cloud account
 - Ensure you have access to the environment
+- Contact the Elementary team for help
 
 ## Related Documentation
 

docs/python-sdk/introduction.mdx

@@ -32,7 +32,7 @@ The SDK allows you to:
 - **Framework-agnostic** - Works with any Python testing framework (Great Expectations, DQX, custom code)
 - **Decorator-based API** - Simple decorators to define tests (`@boolean_test`, `@expected_range`, etc.)
 - **Context management** - Use `elementary_test_context` to automatically capture test results
-- **Unified observability** - Python tests appear alongside dbt tests and cloud tests in Elementary
+- **Unified observability** - Python tests appear alongside dbt tests and cloud tests in Elementary Cloud
 - **Full lineage** - Connect Python assets to dbt models, warehouse tables, and ML outputs
 
 ## Use Cases

docs/python-sdk/quickstart.mdx

@@ -19,6 +19,8 @@ from elementary_python_sdk.core.tests import (
     boolean_test,
     elementary_test_context,
     expected_range,
+    expected_values,
+    row_count,
 )
 from elementary_python_sdk.core.types.asset import TableAsset
 ```
@@ -59,20 +61,20 @@ def test_average_age(df: pd.DataFrame) -> float:
     severity="WARNING",
     description="Validate user count is within expected range",
 )
-def get_users_df(df: pd.DataFrame) -> pd.DataFrame:
-    """Return the dataframe - decorator calls len() on it."""
+def test_users_row_count(df: pd.DataFrame) -> pd.DataFrame:
+    """Return the DataFrame; the decorator calls len() on it."""
     return df
 
 # Define an expected values test
 @expected_values(
-    name="country_count",
-    expected=2,
+    name="only_valid_countries",
+    expected=["Germany", "France", "Italy"],
     severity="ERROR",
-    description="Should have exactly 2 countries",
+    description="Should contain only valid countries",
     column_name="country",
 )
-def count_unique_countries(df: pd.DataFrame) -> int:
-    return df["country"].nunique()
+def test_only_valid_countries(df: pd.DataFrame) -> pd.Series:
+    return df["country"]
 ```
 
 ## Step 4: Create Your Data Asset
@@ -109,6 +111,8 @@ def main():
         # Run tests - results are automatically captured
         test_average_age(users_df)
         test_unique_ids(users_df)
+        test_users_row_count(users_df)
+        test_only_valid_countries(users_df)
 
         # Send results to Elementary Cloud
         PROJECT_ID = "my-python-project"  # Your Python project identifier (used to deduplicate and identify assets)
@@ -191,15 +195,15 @@ def main():
 
         client = ElementaryCloudClient(PROJECT_ID, API_KEY, URL)
         client.send_to_cloud(ctx)
+
+if __name__ == "__main__":
+    main()
 ```
 
 **Note:** 
 - Replace `API_KEY` and `URL` with your actual credentials. The `URL` should be the full SDK ingest endpoint including your environment ID.
 - `PROJECT_ID` is your Python project identifier - choose any string to identify your code project. This will appear in the metadata of assets you report and is used for deduplication.
 
-if __name__ == "__main__":
-    main()
-```
 
 ## What Happens Next?