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@@ -17,7 +17,7 @@
from IPython.display import display, HTML
import requests
import sempy_labs._authentication as auth

from pyspark.sql import DataFrame
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this import should be within the function itself. otherwise if SLL is opened in a pure python notebook, SLL will fail

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Good to know. Removed, and will place this within the functions from now on.

Parameters
----------
path : str
The path or name of the delta table.
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I think we should have it say 'The abfss path or name of the delta table'.

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Updated accordingly

Defaults to None which leaves the description blank.
Returns
-------
Tuple[str, UUID]
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Tuple[str, uuid.UUID]

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Thx. Updated.


def _get_or_create_workspace(
workspace: Optional[str | UUID] = None,
capacity_id: Optional[UUID] = None,
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capacity: Optional[str | UUID] = None,

We can resolve a capacity name or ID to an ID using resolve_capacity_id() (see #533)

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Thanks. Code updated and parameter description updated.

The Fabric workspace name or ID.
Defaults to None which resolves to the workspace of the attached lakehouse
or if no lakehouse attached, resolves to the workspace of the notebook.
capacity_id : uuid.UUID, default=None
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capacity : str | uuid.UUID

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Fixed

# Otherwise create a new workspace.
try:
# But only if a human-friendly name was provided. If it's a Guid, raise an exception.
UUID(workspace)
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can also use if _is_valid_uuid(workspace):

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Thanks. Code simplified


except WorkspaceNotFoundException:
# Otherwise create a new workspace.
try:
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I prefer to avoid use try/except when possible because they can mask other issues. We could do something like

filter_condition = urllib.parse.quote(workspace)
dfW = fabric.list_workspaces(filter=f"name eq '{filter_condition}'")
if dfW.empty:

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No worries. Refactored.

Defaults to None which leaves the description blank.
Returns
-------
Tuple[str, UUID]
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Tuple[str, uuid.UUID]

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fixed

return (lakehouse, lakehouse_id)


def _save_as_delta_table(
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we already have this function

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OK, updated the description of your function to make clear that it accepts both, Pandas and PySpark dataframes.

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Removed the _save_as_delta_table version

dataframe.write.mode("overwrite").format("delta").save(filePath)
print(f"{icons.green_dot} Delta table '{delta_table_name}' created and {dataframe.count()} rows inserted.")

def _insert_into_delta_table(
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we already have this function as save_as_delta_table(write_mode='append')

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Cool. Removed.

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is this still relevant? I've made many changes to these functions since which have made the functions work for both pyspark and pure python.

Tuple[str, uuid.UUID]
A tuple holding the name and ID of the workspace.
"""
import urllib.parse
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this import already exists at the top of the file

# Otherwise create a new workspace.
if _is_valid_uuid(workspace):
# But only if a human-friendly name was provided. If it's a Guid, raise an exception.
raise ValueError("For new workspaces, the workspace parameter must be string, not a Guid. Please provide a workspace name.")
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please add the icons.red_dot

raise ValueError("For new workspaces, the workspace parameter must be string, not a Guid. Please provide a workspace name.")
elif not workspace:
# And also make sure the workspace parameter isn't empty.
raise ValueError("For new workspaces, the workspace parameter cannot be None or empty. Please provide a workspace name.")
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please add the icons.red_dot

# And also make sure the workspace parameter isn't empty.
raise ValueError("For new workspaces, the workspace parameter cannot be None or empty. Please provide a workspace name.")

if _is_valid_uuid(capacity):
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please use the resolve_capacity_id() logic. it handles this.

# Make sure the workspace exists. Raises WorkspaceNotFoundException otherwise.
(workspace_name, workspace_id) = resolve_workspace_name_and_id(workspace)

try:
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instead of using try can we do something like

dfI = fabric.list_items(type='Lakehouse', workspace=workspace)

and then filter that to see if the lakehouse exists. I prefer not to use try/except if we don't have to

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