I have a number of large csv (tab delimited) data stored as azure blobs, and I want to create a pandas dataframe from these. I can do this locally as follows:
I think you want to use get_blob_to_bytes
, or get_blob_to_text
; these should output a string which you can use to create a dataframe as
from io import StringIO
blobstring = blob_service.get_blob_to_text(CONTAINERNAME,BLOBNAME)
df = pd.read_csv(StringIO(blobstring))
Working as on 20th Sep 2020
STEP 1: First generate a SAS token & URL for the target CSV(blob) file on Azure-storage by right-clicking the blob/storage CSV file(blob file).
STEP 2: Copy the Blob SAS URL
that appears below the button used for generating SAS token and URL.
STEP 3: Use the below line of code in your Jupyter notbook to import the desired CSV. Replace url
value with your Blob SAS URL
copied in the above step.
import pandas as pd
url ='Your Blob SAS URL'
df = pd.read_csv(url)
df.head()
Thanks for the answer, I think some correction is needed. You need to get content from the blob object and in the get_blob_to_text there's no need for the local file name.
from io import StringIO
blobstring = blob_service.get_blob_to_text(CONTAINERNAME,BLOBNAME).content
df = pd.read_csv(StringIO(blobstring))
The accepted answer will not work in the latest Azure Storage SDK. MS has rewritten the SDK completely. It's kind of annoying if you are using the old version and update it. The below code should work in the new version.
from azure.storage.blob import ContainerClient
from io import StringIO
import pandas as pd
conn_str = ""
container = ""
blob_name = ""
container_client = ContainerClient.from_connection_string(
conn_str=conn_str,
container_name=container
)
# Download blob as StorageStreamDownloader object (stored in memory)
downloaded_blob = container_client.download_blob(blob_name)
df = pd.read_csv(StringIO(downloaded_blob.content_as_text()))