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A Python Wrapper for Metabase API

Project description

PyPI version contributions welcome GitHub license

Installation

pip install metabase-api

Initializing

from metabase_api import Metabase_API

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

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/').

Auxilliary Functions

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

Custom Functions

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)

mb.create_card(custom_json=myCustomJson)

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 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.

clone_card

Similar to copy_card but a different table for filters of the card is used.
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, destination_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)

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)

Notes

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

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