metabase-api 3.6

A Python Wrapper for Metabase API

We built DataBot utilizing this repository as one of its core packages. DataBot is a self-hosted AI assistant for data analytics. It enables users to explore, visualize, analyze, and forecast data using natural language (with Metabase integration).

We've also built Sealed Intelligence, a zero-data-exposure conversational analytics app for enteprises that can't compromise on data privacy.

Installation

pip install metabase-api

Initializing

Synchronous

from metabase_api import Metabase_API

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

# authentication using API key
mb = Metabase_API('https://...', api_key='YOUR_API_KEY')

Async

from metabase_api import Metabase_API_Async

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

# authentication using API key
mb = Metabase_API_Async('https://...', api_key='YOUR_API_KEY')

Functions

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/').

For async operations, use await mb_async.get('/api/database/').

Helper Functions

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

Custom Functions

• create_card
• create_collection
• create_segment
• copy_card
• copy_pulse
• copy_dashboard
• copy_collection
• clone_card
• update_column
• search
• get_card_data
• move_to_archive
• delete_item
• make_json

For a complete list of functions parameters see the functions definitions using the above links. Here we provide a short description:

• create_card

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)

q = '''
  select *
  from my_table 
  where city = '{}'
'''

for city in city_list:

  query = q.format(city)
  
  # here I included the minimum keys required. You can add more.
  my_custom_json = {
    'name': 'test_card',
    'display': 'table',
    'dataset_query': {
      'database': db_id,
      'native': { 'query': query },
      'type': 'native' 
    }
  }
       
  # See the function definition for other parameters of the function (e.g. in which collection to save the card)
  mb.create_card(custom_json=my_custom_json)

• create_collection

Create an empty collection. Provide the name of the collection, and the name or id of the parent collection (i.e. where you want the created collection to reside). If you want to create the collection in the root, you need to provide parent_collection_name='Root'.

mb.create_collection(collection_name='test_collection', parent_collection_id=123)

• create_segment

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])

• copy_card

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)

• copy_pulse

Similar to copy_card but for pulses.

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

• copy_dashboard

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)

• copy_collection

Copies the given collection and its contents to 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.

• clone_card

Similar to copy_card but a different table is used as the source for filters of the card.
This comes in handy when you want to create similar cards with the same filters that differ only on the source of the filters (e.g. cards for 50 US states).

mb.clone_card(card_id=123, source_table_id=456, target_table_id=789, new_card_name='test clone', new_card_collection_id=1)

• update_column

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={'semantic_type':'type/Category'}  # (For Metabase versions before v.39, use: params={'special_type':'type/Category'}))

• search

Searches for Metabase objects and returns basic info.
Provide the search term and optionally item_type to limit the results.

mb.search(q='test', item_type='card')

• get_card_data

Returns the rows.
Provide the card name/id and the data format of the output (csv or json). You can also provide filter values.

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

• make_json

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']})

• move_to_archive

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

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

• delete_item

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

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