pipolars 0.1.3

High-performance PI System data extraction library with Polars DataFrames

PIPolars

High-performance PI System data extraction library with Polars DataFrames.

PIPolars is a modern Python library for extracting data from OSIsoft PI System and converting it to Polars DataFrames for efficient data science workflows. It's designed to be faster, more memory-efficient, and more Pythonic than existing alternatives.

Features

• Polars DataFrames: 10-100x faster than pandas for many operations
• Modern Python: Full type hints, Pydantic configuration, Python 3.10+
• Efficient Bulk Operations: Native bulk API support for extracting multiple tags
• Lazy Evaluation: Polars LazyFrame support for query optimization
• Caching: SQLite and Arrow IPC caching for reduced server load
• Fluent Query API: Method chaining for readable, declarative queries
• uv Compatible: Modern package management with Astral's uv

Requirements

• Python 3.10+
• Windows (for PI AF SDK)
• OSIsoft PI AF SDK 2.x (.NET 4.8)

Installation

Using uv (recommended)

uv add pipolars

Using pip

pip install pipolars

Development Installation

git clone https://github.com/pipolars/pipolars.git
cd pipolars
uv sync

Quick Start

Basic Usage

from pipolars import PIClient

# Connect to PI Server
with PIClient("my-pi-server") as client:
    # Get current value
    df = client.snapshot("SINUSOID")
    print(df)

    # Get last 24 hours of data
    df = client.recorded_values("SINUSOID", start="*-1d", end="*")
    print(f"Retrieved {len(df)} values")

    # Get multiple tags at once
    df = client.recorded_values(
        ["TAG1", "TAG2", "TAG3"],
        start="*-1h",
        end="*",
    )

Query Builder (Fluent API)

from pipolars import PIClient, SummaryType

with PIClient("my-pi-server") as client:
    # Fluent query building
    df = (
        client.query("SINUSOID")
        .last(hours=24)
        .interpolated(interval="15m")
        .with_quality()
        .to_dataframe()
    )

    # Multi-tag query with pivot
    df = (
        client.query(["TAG1", "TAG2", "TAG3"])
        .time_range("*-1d", "*")
        .interpolated(interval="1h")
        .pivot()  # Tags become columns
        .to_dataframe()
    )

    # Summary statistics
    df = (
        client.query("SINUSOID")
        .last(days=7)
        .summary(SummaryType.AVERAGE, SummaryType.MAXIMUM, SummaryType.MINIMUM)
        .to_dataframe()
    )

Data Processing with Polars

import polars as pl
from pipolars import PIClient

with PIClient("my-pi-server") as client:
    # Get data
    df = client.interpolated_values("SINUSOID", "*-1d", "*", interval="5m")

    # Process with Polars
    result = (
        df.with_columns([
            pl.col("value").rolling_mean(window_size=12).alias("rolling_avg"),
            pl.col("value").diff().alias("change"),
        ])
        .filter(pl.col("value") > 50)
    )

Caching

from pipolars import PIClient, PIConfig
from pipolars.core.config import CacheBackend, CacheConfig, PIServerConfig

# Configure with SQLite cache
config = PIConfig(
    server=PIServerConfig(host="my-pi-server"),
    cache=CacheConfig(
        backend=CacheBackend.SQLITE,
        ttl_hours=24,
    ),
)

with PIClient(config=config) as client:
    # First query - fetches from PI
    df1 = client.recorded_values("SINUSOID", "*-1h", "*")

    # Second query - from cache (faster)
    df2 = client.recorded_values("SINUSOID", "*-1h", "*")

    # Check cache stats
    print(client.cache_stats())

Standalone Script with uv

You can run PIPolars scripts with uv's inline dependencies:

#!/usr/bin/env python3
# /// script
# requires-python = ">=3.10"
# dependencies = ["pipolars"]
# ///

from pipolars import PIClient

with PIClient("my-pi-server") as client:
    df = client.last("SINUSOID", hours=24)
    print(df.describe())

Run with:

uv run my_script.py

Configuration

Environment Variables

# PI Server configuration
PI_SERVER_HOST=my-pi-server
PI_SERVER_PORT=5450
PI_SERVER_TIMEOUT=30

# Cache configuration
PIPOLARS_CACHE_BACKEND=sqlite
PIPOLARS_CACHE_TTL_HOURS=24

# Polars configuration
PIPOLARS_POLARS_TIMEZONE=America/New_York

Configuration File

from pipolars import PIConfig

config = PIConfig.from_file("config.toml")

API Reference

PIClient

Main client for PI data extraction.

Method	Description
snapshot(tag)	Get current value
snapshots(tags)	Get current values for multiple tags
recorded_values(tags, start, end)	Get recorded values
interpolated_values(tags, start, end, interval)	Get interpolated values
plot_values(tag, start, end, intervals)	Get plot-optimized values
summary(tags, start, end, summary_types)	Get summary statistics
summaries(tag, start, end, interval, summary_types)	Get interval summaries
query(tags)	Start building a query
last(tags, hours, days, minutes)	Convenience method for recent data
today(tags)	Get today's data

Time Expressions

PIPolars supports PI time expressions:

Expression	Description
*	Now
*-1h	1 hour ago
*-1d	1 day ago
t	Today at midnight
y	Yesterday at midnight
2024-01-15	Absolute date
2024-01-15T10:00:00	Absolute datetime

Summary Types

from pipolars import SummaryType

SummaryType.TOTAL      # Sum of values
SummaryType.AVERAGE    # Time-weighted average
SummaryType.MINIMUM    # Minimum value
SummaryType.MAXIMUM    # Maximum value
SummaryType.RANGE      # Max - Min
SummaryType.STD_DEV    # Standard deviation
SummaryType.COUNT      # Number of values
SummaryType.PERCENT_GOOD  # Percentage of good values

Comparison with PIconnect

Feature	PIconnect	PIPolars
DataFrame output	pandas	Polars (faster)
Bulk operations	Limited	Native bulk API
Lazy evaluation	No	Yes (LazyFrame)
Caching	No	SQLite + Arrow
Type hints	Partial	Full coverage
Async support	No	Optional async
Package manager	pip	uv compatible
Memory efficiency	Medium	High (zero-copy)
Query builder	Basic	Fluent API

Development

Setup

# Clone the repository
git clone https://github.com/pipolars/pipolars.git
cd pipolars

# Install with dev dependencies
uv sync --all-extras

# Run tests
uv run pytest

# Run type checking
uv run mypy src

# Run linting
uv run ruff check src

Running Tests

# Unit tests only
uv run pytest tests/unit

# Integration tests (requires PI connection)
PI_SERVER=my-server uv run pytest -m integration

# With coverage
uv run pytest --cov=pipolars --cov-report=html

License

MIT License - see LICENSE for details.

Contributing

Contributions are welcome! Please see our Contributing Guide for details.

Acknowledgments

• OSIsoft/AVEVA for PI System
• Polars for the amazing DataFrame library
• Astral for uv and modern Python tooling
• PIconnect for inspiration