Why?

Python notebook access

This package was created to enable reading data from Metabase into a python notebook for further analysis.

It's a way to provide business users with access to data in complex analyses.

For example, a product manager user may refresh an experiment evaluation or a sankey diagram by using only their metabase credentials.

Or, a marketer could use a notebook that their data team made, to get user segmentation data and push it to Mailchimp or Facebook audiences.

Possible uses:

• Adding notebook-based analyses such as a/b testing significance calculators or data based attribution models
• Adding complex visualisations such as sankey diagrams
• Programmatic access to data for other parties (via metabase access management) means you can easily manage access in metabase rather than database.

Credential management, easy authentication and querying

The advantage over going directly to the db are :

• Unified query method: You can query in the same way a card that reads data from Google Analytics or a database
• Unified access control: Access to data can be managed in metabase for sources or even individual questions. If metabase reads from multiple source systems, this also reduces #nr of credentials.

Typical use cases are:

• Simple authentication and querying: Metabase is likely to be publicly accessible (as opposed to data warehouses that might be VPC only). Authentication is handled and does not require any complex flows.
• Possiblity to consume "drag and drop" queries created in Metabase: A non tech user could create and maintain a data feed.

Examples:

pip install metabase-data-api see the examples folder

code snippet:

import pandas as pd
from metabase_data_api import MetabaseApi as M
import json

session_params = dict(user='name@company.ai',
                      password='demopass1',
                      url='https://yourteam.metabaseapp.com/'
                      )

mb_api = M(**session_params)

query = 'SELECT 12 as col'

#get raw file data via export
d = mb_api.export_from_query(query, database_id=4)

json.loads(d.decode("utf-8"))

df = pd.DataFrame.from_records(d)


print(df)
#   col
#0   12

Limits

The export method uses the file download functionality which is limited to 1m rows.

The get_ methods are calling the same endpoints that are used by metabase for its own charting and are limited to 2k rows of results