EnhancedPolars 0.0.2

Enhanced utilities for Polars DataFrames

EnhancedPolars

Enhanced utilities for Polars DataFrames, providing pandas-like convenience while maintaining Polars' blazing-fast performance.

Features

• Unified Namespace (epl) - Access all extensions through df.epl.*
• Pandas-like Indexing - loc, iloc, at, iat accessors
• Enhanced Merging - Automatic dtype resolution and asof joins
• ML Pipeline Tools - Standardization, encoding, and preprocessing
• SQL Integration - Direct DataFrame upload to databases
• Time Series Utilities - Interpolation, boundaries, and cohort analysis
• Statistical Functions - Hypothesis tests and descriptive statistics
• Full LazyFrame Support - All operations work with lazy evaluation

---

Installation

pip install EnhancedPolars

Or with uv:

uv add EnhancedPolars

---

Quick Start

import polars as pl
from enhancedpolars import epl
from enhancedpolars.register import *  # Register the 'epl' namespace

# Read data with automatic type optimization
df = epl.read_csv("data.csv", cleanup=True)

# Pandas-like indexing
subset = df.epl.loc[0:10, ["col1", "col2"]]
value = df.epl.at[0, "col1"]

# Enhanced merging with automatic dtype resolution
result = df.epl.merge(other_df, on="key", how="left")

# Time-aware asof join
trades = trades_df.epl.merge_asof(prices_df, on="timestamp", strategy="backward")

# GroupBy with pandas-style syntax
summary = df.epl.groupby(["category"]).agg({
    "value": ["mean", "sum", "max"],
    "count": "sum"
})

# ML preprocessing
df_ready, metadata = df.epl.make_ml_ready(
    target_col="label",
    default_numeric_scaler="StandardScaler"
)

# SQL upload
df.epl.to_sql(connection, "table_name", if_exists="replace")

---

Documentation

Document	Description
Getting Started	Installation and quick start guide
API Reference	Complete API documentation
Examples	Practical usage examples
Contributing	Contributing guidelines and style guide

---

Key Modules

Indexing

df.epl.loc[0]                      # First row
df.epl.loc[0:5, ["a", "b"]]        # Slice with columns
df.epl.iloc[0, 0]                  # Position-based access
df.epl.at[0, "column"]             # Single value access

Merging

df.epl.merge(right, on="key", how="left")
df.epl.merge_asof(right, on="timestamp", by="group")
df.epl.concat(df2, df3, how="vertical")

GroupBy

df.epl.groupby(["col"]).agg({"value": ["mean", "sum"]})
df.epl.groupby(["col"]).apply(custom_function)
df.epl.groupby(["col"]).ffill(columns=["value"])

Interpolation

df.epl.ffill(columns=["value"])
df.epl.bfill(columns=["value"])
df.epl.interpolate(columns=["value"], method="linear")
df.epl.fillna(columns=["value"], value=0)

ML Pipeline

df.epl.standardize(columns=["numeric_col"])
df.epl.clip_and_impute(columns=["value"], impute_strategy="median")
df.epl.make_ml_ready(target_col="label")

# Series-level operations
series.epl.scale_encode(path="scaler.joblib", scaler_type="StandardScaler")
series.epl.isnull()  # Handles both null and NaN

SQL

df.epl.to_sql(connection, "table_name", if_exists="replace", batch_size=10000)

---

Requirements

Core Dependencies

• Python 3.12+
• polars >= 1.33.0
• numpy >= 2.3.0
• pandas >= 2.3.0
• pyarrow >= 21.0.0
• Plus: tqdm, python-dateutil, joblib

Optional Dependencies

Install with extras for additional functionality:

# For scientific computing (interpolation, hypothesis tests)
pip install "EnhancedPolars[sci]"

# For ML preprocessing (scalers, encoders)
pip install "EnhancedPolars[ml]"

# Install all optional dependencies
pip install "EnhancedPolars[all]"

• [sci] - Scientific computing: scipy >= 1.16.0
• [ml] - Machine learning: scikit-learn >= 1.7.0

---

License

This project is licensed under the MIT License - see the LICENSE file for details.

---

Author

@Ruppert20

---

AI Authorship Disclaimer

This package was developed with the assistance of LLM-based coding tools (Claude Code by Anthropic). AI tools were used for the following activities:

• Code authorship - Implementation of utilities, functions, and classes
• Test development - Creation of comprehensive unit tests
• Documentation - Generation of NumPy-style docstrings and README content
• Code review - Identification of bugs, edge cases, and improvements

Users should evaluate the code for their specific use cases and report any issues through the GitHub issue tracker.

---

Contributing

See Contributing Guide for guidelines on:

• Development setup
• Code style
• Testing requirements
• Pull request process