pbixray Python Library

pbixray is an open source Python library for parsing Power BI .pbix files directly from Python, without Power BI Desktop or a live Analysis Services connection.

It is designed for developers who need to inspect semantic models, extract metadata, read Power Query logic, and work with the internals of PBIX files programmatically. The same API also supports Excel .xlsx files that contain embedded PowerPivot models.

Install

pip install pbixray

Quick Start

from pbixray import PBIXRay

model = PBIXRay("path/to/your_report.pbix")

print(model.tables)
print(model.metadata)
print(model.power_query)
print(model.dax_measures)
print(model.relationships)

Supported Inputs

Power BI .pbix files
Excel .xlsx files with embedded PowerPivot models

Core Properties

These properties expose the most useful parts of a model as Python values or pandas DataFrames.

Property	Returns
`model.tables`	List of table names in the model
`model.metadata`	Metadata about the Power BI configuration used to create the model
`model.power_query`	DataFrame of Power Query / M expressions with `TableName` and `Expression`
`model.m_parameters`	DataFrame of M parameter values and expressions
`model.size`	Model size in bytes
`model.dax_tables`	DataFrame of calculated tables and their DAX expressions
`model.dax_measures`	DataFrame of measures including names, expressions, folders, and descriptions
`model.dax_columns`	DataFrame of calculated columns and their expressions
`model.schema`	DataFrame of table and column schema information with pandas types
`model.relationships`	DataFrame of relationship definitions and cardinality details
`model.rls`	DataFrame of row-level security roles and filter expressions
`model.statistics`	DataFrame of column cardinality and storage statistics

Common Examples

List tables

print(model.tables)

Read Power Query / M code

power_query = model.power_query
print(power_query[["TableName", "Expression"]])

Inspect measures

measures = model.dax_measures
print(measures[["TableName", "Name", "Expression"]])

Inspect calculated columns

columns = model.dax_columns
print(columns[["TableName", "ColumnName", "Expression"]])

Inspect relationships

relationships = model.relationships
print(
    relationships[
        [
            "FromTableName",
            "FromColumnName",
            "ToTableName",
            "ToColumnName",
            "Cardinality",
            "IsActive",
        ]
    ]
)

Inspect row-level security

rls = model.rls
print(rls[["RoleName", "TableName", "FilterExpression"]])

Read a table’s contents

sales = model.get_table("Sales")
print(sales.head())

Data Model Details

pbixray can be used both as a quick inspection tool and as a lower-level metadata extraction library.

Use it to:

enumerate tables and columns in a semantic model
inspect DAX calculated tables, measures, and calculated columns
extract Power Query and M parameter definitions
audit relationships, referential settings, and cross-filtering behavior
review row-level security rules
measure model size and storage statistics

Tabular Model Schema Endpoints

pbixray also exposes direct equivalents of many Analysis Services $System.TMSCHEMA_* DMVs by reading the embedded SQLite metadata database inside the PBIX.

Property	DMV equivalent
`model.tmschema_model`	`TMSCHEMA_MODEL`
`model.tmschema_tables`	`TMSCHEMA_TABLES`
`model.tmschema_columns`	`TMSCHEMA_COLUMNS`
`model.tmschema_partitions`	`TMSCHEMA_PARTITIONS`
`model.tmschema_hierarchies`	`TMSCHEMA_HIERARCHIES`
`model.tmschema_levels`	`TMSCHEMA_LEVELS`
`model.tmschema_datasources`	`TMSCHEMA_DATASOURCES`
`model.tmschema_perspectives`	`TMSCHEMA_PERSPECTIVES`
`model.tmschema_perspective_tables`	`TMSCHEMA_PERSPECTIVE_TABLES`
`model.tmschema_perspective_columns`	`TMSCHEMA_PERSPECTIVE_COLUMNS`
`model.tmschema_perspective_hierarchies`	`TMSCHEMA_PERSPECTIVE_HIERARCHIES`
`model.tmschema_perspective_measures`	`TMSCHEMA_PERSPECTIVE_MEASURES`
`model.tmschema_kpis`	`TMSCHEMA_KPIS`
`model.tmschema_annotations`	`TMSCHEMA_ANNOTATIONS`
`model.tmschema_extended_properties`	`TMSCHEMA_EXTENDED_PROPERTIES`
`model.tmschema_cultures`	`TMSCHEMA_CULTURES`
`model.tmschema_translations`	`TMSCHEMA_OBJECT_TRANSLATIONS`
`model.tmschema_linguistic_metadata`	`TMSCHEMA_LINGUISTIC_METADATA`
`model.tmschema_query_groups`	`TMSCHEMA_QUERY_GROUPS`
`model.tmschema_calculation_groups`	`TMSCHEMA_CALCULATION_GROUPS`
`model.tmschema_calculation_items`	`TMSCHEMA_CALCULATION_ITEMS`
`model.tmschema_calculation_expressions`	`TMSCHEMA_CALCULATION_EXPRESSIONS`
`model.tmschema_variations`	`TMSCHEMA_VARIATIONS`
`model.tmschema_attribute_hierarchies`	`TMSCHEMA_ATTRIBUTE_HIERARCHIES`
`model.tmschema_sets`	`TMSCHEMA_SETS`
`model.tmschema_refresh_policies`	`TMSCHEMA_REFRESH_POLICIES`
`model.tmschema_detail_rows_definitions`	`TMSCHEMA_DETAIL_ROWS_DEFINITIONS`
`model.tmschema_format_string_definitions`	`TMSCHEMA_FORMAT_STRING_DEFINITIONS`
`model.tmschema_functions`	`TMSCHEMA_FUNCTIONS`
`model.tmschema_calendars`	`TMSCHEMA_CALENDARS`
`model.tmschema_calendar_column_groups`	`TMSCHEMA_CALENDAR_COLUMN_GROUPS`
`model.tmschema_calendar_column_refs`	`TMSCHEMA_CALENDAR_COLUMN_REFERENCES`
`model.tmschema_alternate_of`	`TMSCHEMA_ALTERNATE_OF`
`model.tmschema_related_column_details`	`TMSCHEMA_RELATED_COLUMN_DETAILS`
`model.tmschema_group_by_columns`	`TMSCHEMA_GROUP_BY_COLUMNS`
`model.tmschema_binding_info`	`TMSCHEMA_BINDING_INFO`
`model.tmschema_analytics_ai_metadata`	`TMSCHEMA_ANALYTICS_AI_METADATA`
`model.tmschema_data_coverage_definitions`	`TMSCHEMA_DATA_COVERAGE_DEFINITIONS`
`model.tmschema_role_memberships`	`TMSCHEMA_ROLE_MEMBERSHIPS`

TMSCHEMA examples

# List all columns with table names and hidden flags
print(model.tmschema_columns[["TableName", "Name", "DataType", "IsHidden"]])

# Inspect incremental refresh policies
print(model.tmschema_refresh_policies)

# Inspect security role memberships
print(model.tmschema_role_memberships)

Requirements

Python 3.8+
No Power BI Desktop required
Works on macOS, Linux, and Windows