Connect to azure datalake store using python

# Import the required modules
from azure.datalake.store import core, lib

# Define the parameters needed to authenticate using client secret
token = lib.auth(tenant_id = 'TENANT',
                 client_secret = 'SECRET',
                 client_id = 'ID')

# Create a filesystem client object for the Azure Data Lake Store name (ADLS)
adl = core.AzureDLFileSystem(token, store_name='ADLS Account Name')

'''
Please visit here to check the list of operations ADLS filesystem client can perform -
(https://azure-datalake-store.readthedocs.io/en/latest/api.html#azure.datalake.store.core.AzureDLFileSystem)
'''

# Import the required modules
import pandas as pd

# Read a file stored in csv format as input_file
with adl.open('Path-to-Data-Lake-Store-File-stored-in-csv-format', 'rb') as f:
    input_file = pd.read_csv(f)

# convert the output_file dataframe to csv format
output_str = output_file.to_csv(mode = 'w', index=False)

# save the converted object to a particular location with the file name with which to be saved
with adl.open('Path-to-Data-Lake-Store-File-as-to-save', 'wb') as o:
    o.write(str.encode(output_str))
    o.close()

'''
The both path, input path and output path to data lake store starts with the root folder and access to the same depends on the level of access provided to Azure AD Web App from which client secret is generated
'''