PBIXRay is a Python library designed to parse and analyze PBIX files, which are used with Microsoft Power BI. This library provides a straightforward way to extract valuable information from PBIX files, including tables, metadata, Power Query code, and more.
This library is the Python implementation of the logic embedded in the DuckDB extension duckdb-pbix-extension.
Before using PBIXRay, ensure you have the following Python modules installed: apsw
, kaitaistruct
, and pbixray
. You can install them using pip:
pip install pbixray
To start using PBIXRay, import the module and initialize it with the path to your PBIX file:
from pbixray import PBIXRay
model = PBIXRay('path/to/your/file.pbix')
To list all tables in the model:
tables = model.tables
print(tables)
To get metadata about the Power BI configuration used during model creation:
metadata = model.metadata
print(metadata)
To display all M/Power Query code used for data transformation, in a dataframe with TableName
and Expression
columns:
power_query = model.power_query
print(power_query)
To find out the model size in bytes:
size = model.size
print(f"Model size: {size} bytes")
To view DAX calculated tables in a dataframe with TableName
and Expression
columns:
dax_tables = model.dax_tables
print(dax_tables)
To access DAX measures in a dataframe with TableName
, Name
, Expression
, DisplayFolder
, and Description
columns:
dax_measures = model.dax_measures
print(dax_measures)
To get details about the data model schema and column types in a dataframe with TableName
, ColumnName
, and PandasDataType
columns:
schema = model.schema
print(schema)
To get the details about the data model relationships in a dataframe with FromTableName
, FromColumnName
, ToTableName
, ToColumnName
, IsActive
, Cardinality
, CrossFilteringBehavior
, FromKeyCount
, ToKeyCount
and RelyOnReferentialIntegrity
columns:
relationships = model.relationships
print(relationships)
To retrieve the contents of a specified table:
table_name = 'YourTableName'
table_contents = model.get_table(table_name)
print(table_contents)
To get statistics about the model, including column cardinality and byte sizes of dictionary, hash index, and data components, in a dataframe with columns TableName
, ColumnName
, Cardinality
, Dictionary
, HashIndex
, and DataSize
:
statistics = model.statistics
print(statistics)