A Python library to parse and analyze PBIX files used with Microsoft Power BI.
Project description
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)
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)
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)
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)
Project details
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