Skip to main content

A Python Wrapper for Metabase API

Project description

PyPI version HitCount contributions welcome GitHub license


pip install metabase-api


from metabase_api import Metabase_API

mb = Metabase_API('https://...', 'username', 'password')  # if password is not given, it will prompt for password


REST functions (get, post, put, delete)

Calling Metabase API endpoints (documented here) can be done using the corresponding REST function in the wrapper.
E.g. to call the endpoint GET /api/database/, use mb.get('/api/database/').

Auxilliary Functions

You usually don't need to deal with these functions directly (e.g. get_item_id, get_item_name)

Custom Functions


Specify the name to be used for the card, which table (name/id) to use as the source of data and where (i.e. which collection (name/id)) to save the card (default is the root collection).

mb.create_card(card_name='test_card', table_name='mySourceTable')  # Setting `verbose=True` will print extra information while creating the card.

Using the column_order parameter we can specify how the order of columns should be in the created card. Accepted values are 'alphabetical', 'db_table_order' (default), or a list of column names.

mb.create_card(card_name='test_card', table_name='mySourceTable', column_order=['myCol5', 'myCol3', 'myCol8'])

All or part of the function parameters and many more information (e.g. visualisation settings) can be provided to the function in a dictionary, using the custom_json parameter. (also see the make_json function below)



Provide the name to be used for creating the segment, the name or id of the table you want to create the segment on, the column of that table to filter on and the filter values.

mb.create_segment(segment_name='test_segment', table_name='user_table', column_name='user_id', column_values=[123, 456, 789])


At the minimum you need to provide the name/id of the card to copy and the name/id of the collection to copy the card to.

mb.copy_card(source_card_name='test_card', destination_collection_id=123)


Similar to copy_card but for pulses.

mb.copy_pulse(source_pulse_name='test_pulse', destination_collection_id=123)


You can determine whether you want to deepcopy the dashboard or not (default False).
If you don't deepcopy, the duplicated dashboard will use the same cards as the original dashboard.
When you deepcopy a dashboard, the cards of the original dashboard are duplicated and these cards are used in the duplicate dashboard.
If the destination_dashboard_name parameter is not provided, the destination dashboard name will be the same as the source dashboard name (plus any postfix if provided).
The duplicated cards (in case of deepcopying) are saved in a collection called [destination_dashboard_name]'s cards and placed in the same collection as the duplicated dashboard.

mb.copy_dashboard(source_dashboard_id=123, destination_collection_id=456, deepcopy=True)


Copies all the items in the given collection (name/id) into the given destination_parent_collection (name/id). You can determine whether to deepcopy the dashboards.

mb.copy_collection(source_collection_id=123, destination_parent_collection_id=456, deepcopy_dashboards=True, verbose=True)

You can also specify a postfix to be added to the names of the child items that get copied.


Update the column in Data Model by providing the relevant parameter (list of all parameters can be found here).
For example to change the column type to 'Category', we can use:

mb.update_column(column_name='myCol', table_name='myTable', params={'special_type':'type/Category'})


Searches for Metabase objects and returns basic info.
Provide the search term and optionally item_type to limit the results.'test', item_type='card')


Returns the rows.
Provide the card name/id and the data format of the output (csv or json)

results = mb.get_card_data(card_id=123, data_format='csv')


It's very helpful to use the Inspect tool of the browser (network tab) to see what Metabase is doing. You can then use the generated json code to build your automation. To turn the generated json in the browser into a Python dictionary, you can copy the code, paste it into triple quotes (''' ''') and apply the function make_json:

raw_json = ''' {"name":"test","dataset_query":{"database":165,"query":{"fields":[["field-id",35839],["field-id",35813],["field-id",35829],["field-id",35858],["field-id",35835],["field-id",35803],["field-id",35843],["field-id",35810],["field-id",35826],["field-id",35815],["field-id",35831],["field-id",35827],["field-id",35852],["field-id",35832],["field-id",35863],["field-id",35851],["field-id",35850],["field-id",35864],["field-id",35854],["field-id",35846],["field-id",35811],["field-id",35933],["field-id",35862],["field-id",35833],["field-id",35816]],"source-table":2154},"type":"query"},"display":"table","description":null,"visualization_settings":{"table.column_formatting":[{"columns":["Diff"],"type":"range","colors":["#ED6E6E","white","#84BB4C"],"min_type":"custom","max_type":"custom","min_value":-30,"max_value":30,"operator":"=","value":"","color":"#509EE3","highlight_row":false}],"table.pivot_column":"Sale_Date","table.cell_column":"SKUID"},"archived":false,"enable_embedding":false,"embedding_params":null,"collection_id":183,"collection_position":null,"result_metadata":[{"name":"Sale_Date","display_name":"Sale_Date","base_type":"type/DateTime","fingerprint":{"global":{"distinct-count":1,"nil%":0},"type":{"type/DateTime":{"earliest":"2019-12-28T00:00:00","latest":"2019-12-28T00:00:00"}}},"special_type":null},{"name":"Account_ID","display_name":"Account_ID","base_type":"type/Text","fingerprint":{"global":{"distinct-count":411,"nil%":0},"type":{"type/Text":{"percent-json":0,"percent-url":0,"percent-email":0,"average-length":9}}},"special_type":null},{"name":"Account_Name","display_name":"Account_Name","base_type":"type/Text","fingerprint":{"global":{"distinct-count":410,"nil%":0.0015},"type":{"type/Text":{"percent-json":0,"percent-url":0,"percent-email":0,"average-length":21.2916}}},"special_type":null},{"name":"Account_Type","display_name":"Account_Type","base_type":"type/Text","special_type":"type/Category","fingerprint":{"global":{"distinct-count":5,"nil%":0.0015},"type":{"type/Text":{"percent-json":0,"percent-url":0,"percent-email":0,"average-length":3.7594}}}}],"metadata_checksum":"7XP8bmR1h5f662CFE87tjQ=="} '''
myJson = mb.make_json(raw_json)  # setting 'prettyprint=True' will print the output in a structured format.
mb.create_card('test_card2', table_name='mySourceTable', custom_json={'visualization_settings':myJson['visualization_settings']})


Moves the item (Card, Dashboard, Collection, Pulse, Segment) to the Archive section.

mb.move_to_archive('card', item_id=123)


Deletes the item (Card, Dashboard, Pulse). Currently Collections and Segments cannot be deleted using the Metabase API.

mb.delete_item('card', item_id=123)


There are also two other Python wrappers for Metabase API here and here.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

metabase-api-0.2.11.tar.gz (15.9 kB view hashes)

Uploaded source

Built Distribution

metabase_api-0.2.11-py3-none-any.whl (14.0 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page