Hi, I found that the easiest way to solve this was to write the data I need in the Azure Function as a csv file from a dataframe in a Python notebook and and read the data in Python code in the Azure with a Pandas dataframe:
This is the Azure Python Function code:
from azure.storage.filedatalake import DataLakeServiceClient
from azure.identity import DefaultAzureCredential
import pandas as pd
from io import StringIO
# Set your account and workspace details
ACCOUNT_NAME = "onelake"
WORKSPACE_NAME = "GUID" # Workspace GUID
DIRECTORY_PATH = "GUID/Files/FILENAME" # Directory path containing the CSV file
def main():
# Create a service client using the default Azure credential
account_url = f"https://{ACCOUNT_NAME}.dfs.fabric.microsoft.com"
token_credential = DefaultAzureCredential()
service_client = DataLakeServiceClient(account_url, credential=token_credential)
# Create a file system client for the workspace
file_system_client = service_client.get_file_system_client(WORKSPACE_NAME)
# List all files in the specified directory
paths = file_system_client.get_paths(path=DIRECTORY_PATH)
# Find the CSV file in the directory
csv_file_path = None
for path in paths:
if path.name.endswith('.csv', csv_file_path = path.name
break # Stop after finding the first CSV file
if csv_file_path:
print(f"Found CSV file: {csv_file_path}")
# Create a file client for the specific CSV file
file_client = file_system_client.get_file_client(csv_file_path)
# Download the file content
download = file_client.download_file()
downloaded_bytes = download.readall()
# Convert the downloaded bytes to a StringIO object for use with pandas
csv_data = StringIO(downloaded_bytes.decode('utf-8'))
# Load the CSV data into a pandas DataFrame
df = pd.read_csv(csv_data)
# Print or process the DataFrame
print(df)
else:
print("No CSV file found in the directory.")
