philler 0.1.1

representation-guided imputation for missing tabular data

Phil fills missing values in tabular data by comparing several possible completions. It describes each candidate's structure with the Euler Characteristic Transform (ECT), then selects a candidate close to the ensemble's center. You can also inspect the candidates and combine them with an ensemble collapse strategy.

Install philler, import phil. Requires Python 3.10 or newer. Phil is experimental (0.x); public APIs may change between minor releases.

Install

pip install philler

To use Phil from an AI assistant, install the optional MCP server:

pip install "philler[mcp]"

Quick start

import pandas as pd
from phil import Phil

df = pd.DataFrame({
    "x": [1.0, 2.0, None, 4.0, 5.0, 6.0],
    "y": [2.0, None, 6.0, 8.0, 10.0, 12.0],
})

imputer = Phil(samples=10, random_state=42)
completed = imputer.fit(df)
print(completed)

samples limits how many grid candidates are explored. The result keeps the original columns and units, and observed values stay unchanged. Reuse the selected fitted pipeline with imputer.transform(other_df), supplying the same columns.

Phil selects for structural representativeness. Assess the completed data against the needs of your downstream analysis.

Explore the ensemble

• Choose a grid: pass param_grid="default", "sampling", "finance", "healthcare", "marketing", or "engineering" to Phil.
• Understand missing columns: use profile_predictability(df) to examine empirical predictors and missingness patterns.
• Add domain knowledge: build_research_worksheet(df) helps organize research for cited predictor sets, bounds, and covariance priors.
• Compare collapse strategies: inspect the ensemble and choose a single candidate or pool several candidates without fitting them again.

After the quick start above:

comparison = imputer.collapse(strategy="auto", compare_all=True)
completed = comparison.collapsed_df
print(comparison.strategy)
print(comparison.column_variability)

The default Phil selector chooses the candidate closest to the mean ECT descriptor. collapse(strategy="auto") uses distribution diagnostics to choose among six selection and pooling strategies. ECT computation uses the trailed backend.

See the programmatic guide for domain priors and the advanced guide for collapse diagnostics.

Use with an AI assistant

Phil's MCP server lets Claude, Cursor, Gemini, and other MCP clients run the workflow from a file path. A typical request is:

Use Phil to fill missing values in /data/input.csv. Inspect the candidate ensemble and export the result to /data/completed.csv.

With uv installed, add this server to your MCP client's configuration:

{
  "mcpServers": {
    "phil": {
      "command": "uv",
      "args": ["tool", "run", "--from", "philler[mcp]", "phil-mcp"]
    }
  }
}

The client must be able to find uv, and the server must be able to read the dataset path. You can also launch an installed server with phil-mcp.

The tools cover ingest, profiling, research templates, grid configuration, imputation sweeps, ensemble inspection, collapse, and export. For Polars data, write a Parquet file and ingest its path.

See the MCP guide for tool details and the medical demo for a local walkthrough.

Use in a scikit-learn pipeline

For numeric features, PhilTransformer can be the imputation step in a predictive pipeline:

from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import Pipeline
from phil import PhilTransformer

pipeline = Pipeline([
    ("impute", PhilTransformer(samples=10, random_state=42)),
    ("model", RandomForestClassifier(random_state=42)),
])

pipeline.fit(df, [0, 0, 0, 1, 1, 1])

Phil returns categorical columns in their original form. Add an encoding step before models that require numeric inputs.

Documentation and development

• User guides — installation, configuration, and examples.
• Changelog — release notes and the reset to the experimental 0.x series.
• Agent guide — package layout and contributor conventions.

Install contributor dependencies and run the suite:

uv sync --group dev --group mcp --group docs
uv run pytest -v
uv run ruff check phil/
uv run ruff format --check phil/

Build the documentation:

uv run --group docs sphinx-build -M html docs/source docs/build