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AslakJonhaugen's avatar
AslakJonhaugen
Helper I
1 year ago
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Read data from lakehouse table from azure function

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 fr...
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  • AslakJonhaugen's avatar
    AslakJonhaugen
    1 year ago

    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.")
     
AslakJonhaugen's avatar
AslakJonhaugen
Helper I
1 year ago

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.")