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Building a prediction model. Data sets used in Kaggle notebooks are not loaded into python environment. I was trying to use below method to load dataset. But it gives me error. df = pd.read_csv('') ValueError: You must supply a value for parsing A: I found the answer. We need to create google-cloud-storage-client with below arguments. From BQ Quickstart: You can now use bigquery directly from Cloud Storage files, even if the file is not in the same Google Cloud project as the code that reads it. You can use this package on the client side, but you must configure your application to know about it. To use the package, add google.cloud.storage to the list of Google dependencies in your application’s package.json file. In the application code, get the Cloud Storage client for a bucket, as follows: import google.cloud.storage as storage_client client = storage_client.Client.from_service_account_json( '/path/to/json/private_key.json') You can now use the bigquery API client to access data in a bucket. From the client, create a bucket: client.create_bucket(name='my-bucket') From the client, create a table in the bucket: table_ref = client.table( project=PROJECT_ID, dataset=DATASET_ID, table_id='my_table') Read the data and return it as a Pandas dataframe: data = client.read_table( table_ref, schema='my_table.schema', skiprows='1') return data In the below script, I can access data from google-cloud-storage using google-cloud-storage-client. from google.cloud import storage from google.cloud import bigquery client = storage.Client.from_service_account

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