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added properties to the export to align with model bpa #57

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merged 1 commit into from
Aug 6, 2024
Merged

added properties to the export to align with model bpa #57

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31 changes: 29 additions & 2 deletions src/sempy_labs/_vertipaq.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,7 @@
format_dax_object_name,
get_direct_lake_sql_endpoint,
resolve_lakehouse_name,
resolve_dataset_id,
)
from sempy_labs._list_functions import list_relationships
from sempy_labs.lakehouse._get_lakehouse_tables import get_lakehouse_tables
Expand Down Expand Up @@ -483,16 +484,42 @@ def vertipaq_analyzer(
f"{icons.in_progress} Saving Vertipaq Analyzer to delta tables in the lakehouse...\n"
)
now = datetime.datetime.now()
dfD = fabric.list_datasets(workspace=workspace, mode="rest")
dfD_filt = dfD[dfD["Dataset Name"] == dataset]
configured_by = dfD_filt["Configured By"].iloc[0]
dfW = fabric.list_workspaces(filter=f"name eq '{workspace}'")
capacity_id = dfW["Capacity Id"].iloc[0]
dfC = fabric.list_capacities()
dfC_filt = dfC[dfC["Id"] == capacity_id]
if len(dfC_filt) == 1:
capacity_name = dfC_filt["Display Name"].iloc[0]
else:
capacity_name = None
for key, (obj, df) in dfMap.items():
df["Timestamp"] = now
df["Capacity Name"] = capacity_name
df["Capacity Id"] = capacity_id
df["Configured By"] = configured_by
df["Workspace Name"] = workspace
df["Workspace Id"] = fabric.resolve_workspace_id(workspace)
df["Dataset Name"] = dataset
df["Dataset Id"] = resolve_dataset_id(dataset, workspace)
df["RunId"] = runId

colName = "Workspace Name"
colName = "Capacity Name"
df.insert(0, colName, df.pop(colName))
colName = "Dataset Name"
colName = "Capacity Id"
df.insert(1, colName, df.pop(colName))
colName = "Workspace Name"
df.insert(2, colName, df.pop(colName))
colName = "Workspace Id"
df.insert(3, colName, df.pop(colName))
colName = "Dataset Name"
df.insert(4, colName, df.pop(colName))
colName = "Dataset Id"
df.insert(5, colName, df.pop(colName))
colName = "Configured By"
df.insert(6, colName, df.pop(colName))

df.columns = df.columns.str.replace(" ", "_")

Expand Down
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