ifcfill 0.3.4

Transformer of tabular data into Integer, Float and Categorical (IFC) variables with missing data imputation.

ifcfill

ifcfill is a Python library for preparing tabular data for synthetic-data generation. It transforms real tabular data into Integer, Float, and Categorical (IFC) variables with fast NumPy-powered missing data imputation, then applies the same learned inverse transformation to generated synthetic data so it can be mapped back to the original table structure.

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Purpose

ifcfill is designed to sit around tabular synthetic-data generators:

1. Fit IFCTransformer on real data.
2. Transform the real data into generator-friendly IFC variables.
3. Train or run any tabular generator on the transformed data.
4. Apply inverse_transform() to the synthetic output using the mappings learned from the real data.

If inverse transformation happens later or on another machine, save the fitted transformation state with save() and load it back with IFCTransformer.load().

The package is intentionally unsupervised: it does not require or model a target variable.

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Features

• Flexible input — accepts a pandas.DataFrame or a path to a CSV file
• Automatic type inference — detects integer, float, categorical, and datetime columns automatically; user overrides supported per column
• Configurable imputation — choose fill strategy independently for each type:
  • Integer: mean, median, mode, zero
  • Float: mean, median, mode, zero
  • Categorical: constant (default), mode
• Categorical missingness as a category — missing categorical values are transformed into a learnable category and converted back to missing values during inverse_transform()
• Namespaced missing sentinel — the default categorical missing category is __ifcfill_missing__ to reduce collisions with real values
• Optional categorical label encoding — fill categorical values first, then encode categories as integer codes through a separate label-encoding layer with inverse mappings
• Datetime → integer conversion — converts date/time columns to integers relative to a configurable anchor date and time unit (days, seconds, ms, …)
• Constant column removal — automatically drops true constant columns while preserving categorical missing categories when they are learnable
• Missing value tracking — records the count and fraction of missing values per column at fit time, accessible via missing_report_
• Full transformation bookkeeping — inverse_transform() restores dropped constants, original column order, and optionally re-introduces missing values at the original rate
• Portable fitted state — save all learned transformations to JSON and load them later for consistent transform/inverse-transform workflows on another machine
• Synthetic-data workflow support — apply one inverse transformation consistently to both transformed real data and generated synthetic data

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Installation

pip install ifcfill

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Quick Start

import pandas as pd
from ifcfill import IFCTransformer

df = pd.DataFrame({
    "age":    [25, 30, None, 40],
    "salary": [50_000.5, None, 75_000.0, 90_000.25],
    "city":   ["London", None, "Paris", "London"],
    "joined": pd.to_datetime(["2020-01-01", "2021-06-15", None, "2023-03-10"]),
    "flag":   ["yes", "yes", "yes", "yes"],   # constant → will be dropped
})

tf = IFCTransformer(
    int_fill="median",
    float_fill="mean",
    datetime_anchor="1970-01-01",
    datetime_unit="D",
)

transformed = tf.fit_transform(df)
print(transformed)

# Inspect missing-value distribution captured at fit time
print(tf.missing_report_)

# Restore original structure.
# Categorical missing categories are converted back to missing values.
restored = tf.inverse_transform(transformed, restore_missing=True, random_state=42)
print(restored)

# Save everything needed to transform or inverse-transform later.
tf.save("ifcfill-state.json")

# On another machine or in another process:
loaded_tf = IFCTransformer.load("ifcfill-state.json")
restored_again = loaded_tf.inverse_transform(transformed)

From a CSV file

transformed = IFCTransformer().fit_transform("data.csv")

Parallel column processing

# Use all available CPUs for per-column fit/transform work
tf = IFCTransformer(n_jobs=-1)
transformed = tf.fit_transform(df)

Override column types

tf = IFCTransformer(col_types={"age": "categorical", "score": "float"})
transformed = tf.fit_transform(df)

Label encoding for generators

tf = IFCTransformer(cat_encoding="label")
transformed = tf.fit_transform(df)

# Safe copies of learned category/code dictionaries
print(tf.get_category_mappings())

restored = tf.inverse_transform(transformed)

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Documentation

Full documentation including the API reference is available at: https://eulerlettersai.github.io/ifcfill

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License

Apache 2.0 — see LICENSE.