What packages are needed to use a Azure URI with pandas

Use the azureml-fsspec package:

pip install azureml-fsspec

Note: The accepted URI format for the datastore URI is: azureml://subscriptions/([^/]+)/resourcegroups/([^/]+)/workspaces/([^/]+)/datastores/([^/]+)/paths/([^/]+)

This should technically work:

import azureml-fsspec
import pandas as pd

# credentials and variables
subscription = '<subscription_id>'
resource_group = '<resource_group>'
workspace = '<workspace>'
datastore_name = '<datastore>'
path_on_datastore '<path>'
file = '<myfile.csv>'

# generate uri:
uri = f'azureml://subscriptions/{subscription}/resourcegroups/{resource_group}/workspaces/{workspace}/datastores/{datastore_name}/paths/{path_on_datastore}/{file}'

# read via pandas
df = pd.read_csv(uri)

See Azure Machine Learning - Access Data from Azure Cloud Storage During Interactive Development for details.

or you could try the AzureMachineLearningFileSystem class from the package:

import pandas
from azureml.fsspec import AzureMachineLearningFileSystem

# instantiate file system using following URI
fs = AzureMachineLearningFileSystem('azureml://subscriptions/<subid>/resourcegroups/<rgname>/workspaces/<workspace_name>/datastore/datastorename')

fs.ls() # list folders/files in datastore 'datastorename'

# use an open context
with fs.open('./folder1/file1.csv') as f:
    # do some process
    df = pandas.read_csv(f)