I have a dataset generated through a Python script that is giving me this error ("Object reference not set to an instance of an object") whenever I attempt to refresh the latest information from its source.
There doesn't appear to be any issues with establishing the connection or editing it in the Power Query Editor. I've ensured that there are no null values in any of the records and have changed privacy settings but no luck so far.
My report has one semantic model (Direct Query import) that is used for all of the other tables and has a relationship with the Python dataset that is imported into the file.
What are your recommendations on resolving this issue? I do not have a Microsoft Fabric license and would like to avoid having to upgrade or use a trial to utilize a Notebook.
Below is an example of the script I'm running in my workbook.
from simple_salesforce import Salesforce
import pandas as pd
sf = Salesforce(
username="xxx",
password="xxx",
security_token="xxx",
)
# Query Salesforce
soql = """
SELECT
Name,
xxx,
Task
FROM Project
"""
results = sf.query_all(soql)
# Extract records and normalize
records = results["records"]
project_tasks = pd.json_normalize(records)
project_tasks = project_tasks.drop(columns=[col for col in project_tasks.columns if 'attribute' in col.lower()], errors="ignore")
# List the columns not aggregated by the unpivot
id_cols = ['Name', 'xxx']
# Perform the unpivot (melt)
melted_df = pd.melt(
project_tasks,
id_vars=id_cols,
value_vars=[col for col in project_tasks.columns if col not in id_cols],
var_name='Task',
value_name='xxx'
)
# Clean up the task names by removing the _Hours suffix and __c
melted_df['Task'] = melted_df['Task'].str.replace('xxx', '')
# Capitalize each word in the task name
melted_df['Task'] = melted_df['Task'].str.title()
# Drop rows where Hours is null
melted_df = melted_df.dropna(subset=['xxx'])
# Fill in melted_df column 'Project Name' blank records with 'No Project Associated'
melted_df['xxx'] = melted_df['xxx'].fillna('No Project Associated')
# Sort by identifier columns and task
melted_df = melted_df.sort_values(by=id_cols + ['Task'])
# melted_df.to_csv('project_tasks.csv', index=False)
# return dataframe
melted_df = melted_df.drop(columns=['xxx'])
print(melted_df)
Please let me know if you need any further information.
Thanks!
