Connect to MicroStrategy

The Connection object manages your connection to MicroStrategy. Connect to your MicroStrategy environment by providing the URL to the MicroStrategy REST API server, your username, password and the ID of the Project to connect to. When a Connection object is created the user will be automatically logged-in. Connection object automatically renews the connection or reconnects, if session becomes inactive. Reconnection doesn’t work if authenticated with identity token.

from mstrio.connection import Connection
from getpass import getpass

base_url = ""
mstr_username = "Username"
mstr_password = getpass("Password: ")
project_id = "PROJECT_ID"
conn = Connection(base_url, mstr_username, mstr_password, project_id=project_id)

The URL for the REST API server typically follows this format:

Validate that the REST API server is running by accessing in your web browser.

To manage the connection the following methods are made available:


Authentication Methods

Currently, supported authentication modes are Standard (the default) and LDAP. To use LDAP, add login_mode=16 when creating your Connection object:

conn = Connection(base_url, mstr_username, mstr_password, project_id=project_id, login_mode=16)

Optionally, the Connection object can be created by passing the identity_token parameter, which will create a delegated session. The identity token can be obtained by sending a request to MicroStrategy REST API /auth/identityToken endpoint.

conn = Connection(base_url, identity_token=identity_token, project_id=project_id)

SSL Self-signed Certificate

By default, SSL certificates are validated with each API request. To turn this off, use ssl_verify flag:

conn = Connection(base_url, mstr_username, mstr_password, project_id=project_id, ssl_verify=False)

If you are using a SSL with a self-signed certificate you will need to perform an additional step to configure your connection. There are 2 ways to set it up:

  1. The easiest way to configure the SSL is to move your certificate file to your working directory. Just make sure the ssl_verify parameter is set to True when creating the Connection object in mstrio-py (it is True by default):

conn = Connection(base_url, mstr_username, mstr_password, project_id=project_id, ssl_verify=True)
  1. The second way is to pass the certificate_path parameter to your connection object in mstrio. It has to be the absolute path to your certificate file:

conn = Connection(base_url, mstr_username, mstr_password, project_id=project_id, certificate_path="C:/path/to/your/certificate.pem")


Optionally, proxy settings can be set for the MicroStrategy Connection object.

proxies = {'http': '', '': ''}
conn = Connection(base_url, mstr_username, mstr_password, project_id=project_id, proxies=proxies)

User can also specify username and password in proxies parameter to use HTTP Basic Auth:

proxies = {'http': 'http://<username>:<password>@<ip_address>:<port>/'}
conn = Connection(base_url, mstr_username, mstr_password, project_id=project_id, proxies=proxies)

Import Data from Cubes and Reports

Better fetching performance can be achieved by utilizing the parallel download of data chunks. This feature is controlled by the parallel flag and is enabled by default. Disabling this setting will lower the peak I-Server load. To import the contents of a published Cube into a DataFrame for analysis in Python, use the OlapCube SuperCube class. If you are not sure which type of cube you want to import use load_cube function.

from mstrio.project_objects import load_cube, OlapCube
my_cube = OlapCube(connection=conn, id=id)
my_cube = load_cube(connection=conn, cube_id=cube_id)
df = my_cube.to_dataframe()

To import Reports into a DataFrame for analysis in Python use the appropriate Report class:

from mstrio.project_objects import Report
my_report = Report(connection=conn, report_id=report_id, parallel=False)
df = my_report.to_dataframe()

By default, all rows are imported when my_cube.to_dataframe() or my_report.to_dataframe() are called. Filter the contents of a Cube / Report by passing the selected object IDs for the metrics, attributes, and attribute elements to the apply_filters() method.

To get the list of object IDs of the metrics, attributes, or attribute elements that are available within the Cube / Report MicroStrategy objects, use the following Cube / Report class properties:


Then, choose those elements by passing their IDs to the my_cube.apply_filters() method. To see the chosen elements, call my_cube.selected_attributes, my_cube.selected_metrics, my_cube.selected_attr_elements. To clear any active filters, call my_cube.clear_filters().

    attributes=["A598372E11E9910D1CBF0080EFD54D63", "A59855D811E9910D1CC50080EFD54D63"],
    attr_elements=["A598372E11E9910D1CBF0080EFD54D63:Los Angeles", "A598372E11E9910D1CBF0080EFD54D63:Seattle"])

df = my_cube.to_dataframe()

If you need to exclude specific attribute elements, pass the operator="NotIn" parameter to the apply_filters() method.

    attributes=["A598372E11E9910D1CBF0080EFD54D63", "A59855D811E9910D1CC50080EFD54D63"],
    attr_elements=["A598372E11E9910D1CBF0080EFD54D63:Los Angeles", "A598372E11E9910D1CBF0080EFD54D63:Seattle"],
df = my_cube.to_dataframe()

Export Data into MicroStrategy with Datasets

Create a New SuperCube

With mstrio-py you can create and publish single or multi-table Datasets. This is done by passing Pandas DataFrames to the SuperCube constructor which translates the data into the format needed by MicroStrategy.

import pandas as pd
stores = {"store_id": [1, 2, 3],
          "location": ["New York", "Seattle", "Los Angeles"]}
stores_df = pd.DataFrame(stores, columns=["store_id", "location"])

sales = {"store_id": [1, 2, 3],
         "category": ["TV", "Books", "Accessories"],
         "sales": [400, 200, 100],
         "sales_fmt": ["$400", "$200", "$100"]}
sales_df = pd.DataFrame(sales, columns=["store_id", "category", "sales", "sales_fmt"])

from mstrio.project_objects import SuperCube
ds = SuperCube(connection=conn, name="Store Analysis")
ds.add_table(name="Stores", data_frame=stores_df, update_policy="add")
ds.add_table(name="Sales", data_frame=sales_df, update_policy="add")

By default SuperCube.create() will create a SuperCube, upload the data to the Intelligence Server and publish it. If you just want to create the SuperCube and upload the row-level data but leave it unpublished, use SuperCube.create(auto_publish=False). If you want to create an empty SuperCube, use SuperCube.create(auto_upload=False, auto_publish=False). Skipped actions can be performed later using SuperCube.update() and SuperCube.publish() methods.

When using SuperCube.add_table(), Pandas data types are mapped to MicroStrategy data types. By default, numeric data (integers and floats) are modeled as MicroStrategy Metrics and non-numeric data are modeled as MicroStrategy Attributes. This can be problematic if your data contains columns with integers that should behave as Attributes (e.g. a row ID), or if your data contains string-based, numeric-looking data which should be Metrics (e.g. formatted sales data: ["$450", "$325"]). To control this behavior, provide a list of columns that you want to convert from one type to another.

ds.add_table(name="Stores", data_frame=stores_df, update_policy="add",

ds.add_table(name="Sales", data_frame=sales_df, update_policy="add",

It is also possible to specify where the SuperCube should be created by providing a folder ID in SuperCube.create(folder_id=folder_id).

After creating the SuperCube, you can obtain its ID using This ID is needed for updating the data later.

Update a SuperCube

When the source data changes and users need the latest data for analysis and reporting in MicroStrategy, mstrio-py allows you to update the previously created SuperCube.

from mstrio.project_objects import SuperCube
ds = SuperCube(connection=conn, id=dataset_id)
ds.add_table(name="Stores", data_frame=stores_df, update_policy="update")
ds.add_table(name="Sales", data_frame=sales_df, update_policy="upsert")

The update_policy parameter controls how the data in the SuperCube gets updated. Currently supported update operations are add (inserts entirely new data), update (updates existing data), upsert (simultaneously updates existing data and inserts new data), and replace (truncates and replaces the data). Using the update and upsert update policies, it’s only possible to update metric values. It’s not possible to update values of attributes, because values of attributes are used to identify rows, which metric values will be updated.

By default SuperCube.update() will upload the data to the Intelligence Server and publish the SuperCube. If you just want to update the SuperCube but not publish the row-level data, use SuperCube.update(auto_publish=False). To publish it later, use SuperCube.publish().

By default, the raw data is transmitted to the server in increments of 100,000 rows. For very large datasets (>1 GB) it is beneficial to increase the number of rows transmitted to the Intelligence Server with each request. Do this with the chunksize parameter:


Certify a super cube

Use SuperCube.certify() to certify / decertify an existing super cube.


Updating Datasets that were not created using the MicroStrategy REST API is not possible. This applies for example to Cubes created via MicroStrategy Web client.

Using mstrio as a MicroStrategy Intelligence Server administration tool