Read data from lakehouse table from azure function

[AslakJonhaugen]

Hi,

I must read data from a lakehouse table in Fabric with an Azure Function (Python). However, I cannot find any code examples. Can anyone provide an example, or how to achieve this? 

Regards from Norway!

[AslakJonhaugen · Accepted solution]

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.")

[collinq]

Hi @AslakJonhaugen ,

To do this you must first connect to your Lake - for Python I believe that DirectLake mode is the best but I am not an expert in this area.

Then, install your python scripts from here -   File > Options and settings > Options > Python scripting

Then, use your script to connect and do your operations.