Here's a pattern that might work;
- unpack the adoadapi results with a function to get it into a pandas frame
- If you have some upstream process that handles this part using a conn object that read_sql can use, then you might only need to use the date() function on your DATESBETWEEN inputs; hard to see without the rest of your implementation code
- sample code:
`
import adodbapi
import pandas as pd
import numpy as np
workspace = 'your published workspace'
report = 'report_name'
sec = 'Integrated Security=SSPI'
conn = adodbapi.connect(f"Provider=MSOLAP.8;Data Source='powerbi://api.powerbi.com/v1.0/myorg/{workspace}';Initial Catalog={report};{sec};")
cur = conn.cursor()
def get_df(data):
ar = np.array(data.ado_results) # turn ado results into a numpy array
df = pd.DataFrame(ar).transpose() # create a dataframe from the array
df.columns = data.columnNames.keys() # set column names
return df
your_table_name = "Data"
your_column_name = "date_created"
DAX_Query = f'''
DEFINE
VAR FilteredTable =
FILTER(
'{your_table_name}',
AND(
'{your_table_name}'[{your_column_name}] >= DATE(2021, 1, 2),
'{your_table_name}'[{your_column_name}] < DATE(2022, 4, 2)
)
)
EVALUATE
FilteredTable
'''
cur.execute(DAX_Query)
data=cur.fetchall()
demo_df = get_df(data)
`