Hello and thanks for your time and consideration. I am developing a Jupyter Notebook in the Google Cloud Platform / Datalab. I have created a Pandas DataFrame and would like to write this DataFrame to both Google Cloud Storage(GCS) and/or BigQuery. I have a bucket in GCS and have, via the following code, created the following objects:
import gcp
import gcp.storage as storage
project = gcp.Context.default().project_id
bucket_name = 'steve-temp'
bucket_path = bucket_name
bucket = storage.Bucket(bucket_path)
bucket.exists()
I have tried various approaches based on Google Datalab documentation but continue to fail. Thanks
Try the following working example:
from datalab.context import Context
import google.datalab.storage as storage
import google.datalab.bigquery as bq
import pandas as pd
# Dataframe to write
simple_dataframe = pd.DataFrame(data=[{1,2,3},{4,5,6}],columns=['a','b','c'])
sample_bucket_name = Context.default().project_id + '-datalab-example'
sample_bucket_path = 'gs://' + sample_bucket_name
sample_bucket_object = sample_bucket_path + '/Hello.txt'
bigquery_dataset_name = 'TestDataSet'
bigquery_table_name = 'TestTable'
# Define storage bucket
sample_bucket = storage.Bucket(sample_bucket_name)
# Create storage bucket if it does not exist
if not sample_bucket.exists():
sample_bucket.create()
# Define BigQuery dataset and table
dataset = bq.Dataset(bigquery_dataset_name)
table = bq.Table(bigquery_dataset_name + '.' + bigquery_table_name)
# Create BigQuery dataset
if not dataset.exists():
dataset.create()
# Create or overwrite the existing table if it exists
table_schema = bq.Schema.from_data(simple_dataframe)
table.create(schema = table_schema, overwrite = True)
# Write the DataFrame to GCS (Google Cloud Storage)
%storage write --variable simple_dataframe --object $sample_bucket_object
# Write the DataFrame to a BigQuery table
table.insert(simple_dataframe)
I used this example, and the _table.py file from the datalab github site as a reference. You can find other datalab
source code files at this link.