Write a Pandas DataFrame to Google Cloud Storage or BigQuery

Question

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

Answer 1

I spent a lot of time to find the easiest way to solve this:

import pandas as pd

df = pd.DataFrame(...)

df.to_csv('gs://bucket/path')

Answer 2

Uploading to Google Cloud Storage without writing a temporary file and only using the standard GCS module

from google.cloud import storage
import os
import pandas as pd

# Only need this if you're running this code locally.
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = r'/your_GCP_creds/credentials.json'

df = pd.DataFrame(data=[{1,2,3},{4,5,6}],columns=['a','b','c'])

client = storage.Client()
bucket = client.get_bucket('my-bucket-name')
    
bucket.blob('upload_test/test.csv').upload_from_string(df.to_csv(), 'text/csv')

Answer 3

I think you need to load it into a plain bytes variable and use a %%storage write --variable $sample_bucketpath(see the doc) in a separate cell... I'm still figuring it out... But That is roughly the inverse of what I needed to do to read a CSV file in, I don't know if it makes a difference on write but I had to use BytesIO to read the buffer created by the %% storage read command... Hope it helps, let me know!

Answer 4

Since 2017, Pandas has a Dataframe to BigQuery function pandas.DataFrame.to_gbq

The documentation has an example:

import pandas_gbq as gbq gbq.to_gbq(df, 'my_dataset.my_table', projectid, if_exists='fail')

Parameter if_exists can be set to 'fail', 'replace' or 'append'

See also this example.

Answer 5

Using the Google Cloud Datalab documentation

import datalab.storage as gcs
gcs.Bucket('bucket-name').item('to/data.csv').write_to(simple_dataframe.to_csv(),'text/csv')

Answer 6

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.