pbixray 0.5.0
A Python library to parse and analyze PBIX files used with Microsoft Power BI and Excel PowerPivot.
License: MIT
Requires Python: >=3.8

# PBIXRay
[![Downloads](https://static.pepy.tech/badge/pbixray)](https://pepy.tech/project/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](https://github.com/Hugoberry/duckdb-pbix-extension/).

> **Note:** PBIXRay also supports Excel (XLSX) files with embedded PowerPivot models. You can use the same API to extract and analyze data models from XLSX files that contain PowerPivot data.

## Installation

Before using PBIXRay, ensure you have the following Python modules installed: `apsw`, `kaitaistruct`, and `pbixray`. You can install them using pip:

```bash
pip install pbixray
```

## Getting Started
To start using PBIXRay, import the module and initialize it with the path to your PBIX file:
```python
from pbixray import PBIXRay

model = PBIXRay('path/to/your/file.pbix')
```

## Features and Usage
### Tables
To list all tables in the model:
```python
tables = model.tables
print(tables)
```
### Metadata
To get metadata about the Power BI configuration used during model creation:
```python
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:
```python
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:
```python
m_parameters = model.m_parameters
print(m_parameters)
```
### Model Size
To find out the model size in bytes:
```python
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:
```python
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:
```python
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:
```python
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:
```python
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:
```python
relationships = model.relationships
print(relationships)
```
### Row-Level Security (RLS)
To get the details about Row-Level Security roles and permissions in a dataframe with `TableName`, `RoleName`, `RoleDescription`, `FilterExpression`, `State` and `MetadataPermission` columns:
```python
rls = model.rls
print(rls)
```
### Get Table Contents
To retrieve the contents of a specified table:
```python
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`:
```python
statistics = model.statistics
print(statistics)
```