Skip to content

read-write-power-bi-python.md

Latest commit

 

History

History
224 lines (164 loc) · 11.2 KB

read-write-power-bi-python.md

File metadata and controls

224 lines (164 loc) · 11.2 KB
title Read data from semantic models and write data that semantic models can consume using python
description Learn how to read data, metadata, and evaluate measures from semantic models using Python's SemPy library in Microsoft Fabric.
ms.author scottpolly
author s-polly
ms.reviewer ruxu
reviewer ruixinxu
ms.topic how-to
ms.date 09/25/2025
ms.search.form Read write powerbi

Read from semantic models and write data consumable by Power BI using Python

In this article, you learn to read data, metadata, and evaluate measures in semantic models using the SemPy Python library in Microsoft Fabric. You also learn to write data that semantic models can consume.

Prerequisites

[!INCLUDE prerequisites]

Upload the semantic model into your workspace

This article uses the Customer Profitability Sample.pbix semantic model. The semantic model references a company that manufactures marketing materials. It includes product, customer, and revenue data for different business units.

  1. Open your workspace in Fabric Data Science.
  2. Select Import > Report, Paginated Report, or Workbook > From this computer, and select the Customer Profitability Sample.pbix semantic model.

:::image type="content" source="media/read-write-power-bi-python/upload-power-bi-data-to-workspace.png" alt-text="Screenshot of the interface for uploading a semantic model into the workspace." lightbox="media/read-write-power-bi-python/upload-power-bi-data-to-workspace.png":::

After the upload is complete, your workspace includes three new artifacts: a Power BI report, a dashboard, and a semantic model named Customer Profitability Sample. The steps in this article rely on this semantic model.

:::image type="content" source="media/read-write-power-bi-python/uploaded-artifacts-in-workspace.png" alt-text="Screenshot of the items from the Power BI file uploaded into the workspace." lightbox="media/read-write-power-bi-python/uploaded-artifacts-in-workspace.png":::

Use Python to read data from semantic models

The SemPy Python API can retrieve data and metadata from semantic models located in a Microsoft Fabric workspace. The API can also execute queries on them.

[!INCLUDE sempy-default-workspace]

To read data from semantic models, follow these steps:

  1. List the available semantic models in your workspace.

    import sempy.fabric as fabric

df_datasets = fabric.list_datasets()
df_datasets

  2. List the tables available in the Customer Profitability Sample semantic model.

    df_tables = fabric.list_tables("Customer Profitability Sample", include_columns=True)
df_tables

  3. List the measures defined in the Customer Profitability Sample semantic model.

    [!TIP] In the following code sample, we specified the workspace for SemPy to use for accessing the semantic model. You can replace <Your Workspace> with the name of the workspace where you uploaded the semantic model (from the Upload the semantic model into your workspace section).

    df_measures = fabric.list_measures("Customer Profitability Sample", workspace="<Your Workspace>")
df_measures

    Here, we determined that the Customer table is the table of interest.

  4. Read the Customer table from the Customer Profitability Sample semantic model.

    df_table = fabric.read_table("Customer Profitability Sample", "Customer")
df_table

    [!NOTE]

    • Data is retrieved using XMLA, which requires at least XMLA read-only to be enabled.
    • The amount of retrievable data is limited by: - The maximum memory per query of the capacity SKU that hosts the semantic model. - The Spark driver node (visit node sizes for more information) that runs the notebook.
    • All requests use low priority to minimize the impact on Microsoft Azure Analysis Services performance and are billed as interactive requests.

  5. Evaluate the Total Revenue measure for the state and date of each customer.

    df_measure = fabric.evaluate_measure(
    "Customer Profitability Sample",
    "Total Revenue",
    ["'Customer'[State]", "Calendar[Date]"])
df_measure

    [!NOTE]

    • By default, data is not retrieved using XMLA, so XMLA read-only doesn't need to be enabled.
    • The data isn't subject to Power BI backend limitations.
    • The amount of retrievable data is limited by: - The maximum memory per query of the capacity SKU hosting the semantic model. - The Spark driver node (visit node sizes for more information) that runs the notebook.
    • All requests are billed as interactive requests.
    • The evaluate_dax function doesn't auto-refresh the semantic model. Visit this page for more details.

  6. To add filters to the measure calculation, specify a list of permissible values for a particular column.

    filters = {
    "State[Region]": ["East", "Central"],
    "State[State]": ["FLORIDA", "NEW YORK"]
}
df_measure = fabric.evaluate_measure(
    "Customer Profitability Sample",
    "Total Revenue",
    ["Customer[State]", "Calendar[Date]"],
    filters=filters)
df_measure

  7. Evaluate the Total Revenue measure per customer's state and date with a DAX query.

    df_dax = fabric.evaluate_dax(
    "Customer Profitability Sample",
    """
    EVALUATE SUMMARIZECOLUMNS(
        'State'[Region],
        'Calendar'[Date].[Year],
        'Calendar'[Date].[Month],
        "Total Revenue",
        CALCULATE([Total Revenue]))
    """)

    [!NOTE]

    • Data is retrieved using XMLA and therefore requires at least XMLA read-only to be enabled
    • The amount of retrievable data is limited by the available memory in Microsoft Azure Analysis Services and the Spark driver node (visit node sizes for more information)
    • All requests use low priority to minimize the impact on Analysis Services performance and are billed as interactive requests

  8. Use the %%dax cell magic to evaluate the same DAX query, without the need to import the library. Run this cell to load %%dax cell magic:

    %load_ext sempy

    The workspace parameter is optional. It follows the same rules as the workspace parameter of the evaluate_dax function.

    The cell magic also supports access of Python variables with the {variable_name} syntax. To use a curly brace in the DAX query, escape it with another curly brace (example: EVALUATE {{1}}).

    %%dax "Customer Profitability Sample" -w "<Your Workspace>"
EVALUATE SUMMARIZECOLUMNS(
    'State'[Region],
    'Calendar'[Date].[Year],
    'Calendar'[Date].[Month],
    "Total Revenue",
    CALCULATE([Total Revenue]))

    The resulting FabricDataFrame is available via the _ variable. That variable captures the output of the last executed cell.

    df_dax = _

df_dax.head()

  9. You can add measures to data retrieved from external sources. This approach combines three tasks:

    • It resolves column names to Power BI dimensions
    • It defines group by columns
    • It filters the measure Any column names that can't be resolved within the given semantic model are ignored (visit the supported DAX syntax resource for more information).
    from sempy.fabric import FabricDataFrame

df = FabricDataFrame({
        "Sales Agent": ["Agent 1", "Agent 1", "Agent 2"],
        "Customer[Country/Region]": ["US", "GB", "US"],
        "Industry[Industry]": ["Services", "CPG", "Manufacturing"],
    }
)

joined_df = df.add_measure("Total Revenue", dataset="Customer Profitability Sample")
joined_df

Special parameters

The SemPy read_table and evaluate_measure methods have more parameters that are useful for manipulating the output. These parameters include:

  • pandas_convert_dtypes: If set to True, pandas casts the resulting DataFrame columns to the best possible dtype. Learn more in convert_dtypes. If this parameter is turned off, type incompatibility issues between columns of related tables might occur. The Power BI model might not detect these issues due to DAX implicit type conversion.

SemPy read_table also uses the model information that Power BI provides.

  • multiindex_hierarchies: If set to True, it converts Power BI hierarchies to a pandas MultiIndex structure.

Write data consumable by semantic models

Spark tables added to a Lakehouse are automatically added to the corresponding default semantic model. This article demonstrates how to write data to the attached Lakehouse. The FabricDataFrame accepts the same input data as Pandas dataframes.

from sempy.fabric import FabricDataFrame

df_forecast = FabricDataFrame({'ForecastedRevenue': [1, 2, 3]})

df_forecast.to_lakehouse_table("ForecastTable")

With Power BI, the ForecastTable table can be added to a composite semantic model that includes the Lakehouse semantic model.

Related content