PBIXRay
Overview
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.
Installation
Before using PBIXRay, ensure you have the following Python modules installed: apsw, kaitaistruct, and pbixray. You can install them using pip:
pip install pbixray
Getting Started
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')
Features and Usage
Tables
To list all tables in the model:
tables = model.tables
print(tables)
Metadata
To get metadata about the Power BI configuration used during model creation:
metadata = model.metadata
print(metadata)
Power Query
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)
M Parameters
To display all M Parameters values in a dataframe with ParameterName, Description, Expression and ModifiedTime columns:
m_parameters = model.m_parameters
print(m_parameters)
Model Size
To find out the model size in bytes:
size = model.size
print(f"Model size: {size} bytes")
DAX Calculated Tables
To view DAX calculated tables in a dataframe with TableName and Expression columns:
dax_tables = model.dax_tables
print(dax_tables)
DAX Measures
To access DAX measures in a dataframe with TableName, Name, Expression, DisplayFolder, and Description columns:
dax_measures = model.dax_measures
print(dax_measures)
Calculated Columns
To access calculated column DAX expressions in a dataframe with TableName,ColumnName and Expression columns:
dax_columns = model.dax_columns
print(dax_columns)
Schema
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)
Relationships
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)
Get Table Contents
To retrieve the contents of a specified table:
table_name = 'YourTableName'
table_contents = model.get_table(table_name)
print(table_contents)
Statistics
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)
1 maintainer
Metadata
1 maintainer
Release files for pbixray 0.3.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pbixray-0.3.3.tar.gz | 20.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pbixray-0.3.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 45.4 kB
Release files / pbixray-0.3.3.tar.gz
| Download URL | pbixray-0.3.3.tar.gz |
|---|---|
| Size | 20.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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|
|
BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.9
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Transparency logRelease files / pbixray-0.3.3-py3-none-any.whl
| Download URL | pbixray-0.3.3-py3-none-any.whl |
|---|---|
| Size | 24.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
25ca79a1926a2abc3277881c1db175839b002f9edb25fd64f70cac599266f6cd
|
|
BLAKE2b-256 checksum How to use checksums |
4553bf8b7f9ce6cf955b394811658bea465822319ffd57ae061bb1bfef780b5c
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.9
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on May 1, 2025.
Transparency log
