pbixray 0.1.15

A Python library to parse and analyze PBIX files used with Microsoft Power BI.

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.

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)