aggdisagg 1.0.4

Temporal Aggregation & Disaggregation for Modern Python (Polars-first)

aggdisagg

Temporal Aggregation & Disaggregation for Modern Python

Install & try in 10 seconds:

pip install aggdisagg

import polars as pl
from aggdisagg import TemporalAligner
df = pl.DataFrame({"date": ["2020", "2021"], "y": [100.0, 120.0]})
print(TemporalAligner().fit_transform(df, datetime_col="date", target_col="y"))

aggdisagg is a clean, Polars-first Python library for converting time series between frequencies with perfect aggregation consistency.

• Disaggregate low → high frequency (with indicators)
• Aggregate high → low frequency (symmetric)
• Works with Polars (primary), pandas, and xarray

Installation + Try in 10 Seconds

pip install "aggdisagg[all]" && python -c "
import polars as pl
from datetime import date
from aggdisagg import TemporalAligner
df = pl.DataFrame({'date':[date(2020,1,1),date(2021,1,1)], 'y':[1000.,1200.]})
print(TemporalAligner(method='uniform').fit_transform(df, datetime_col='date', target_col='y'))
"

Quickstart with TemporalAligner

import polars as pl
from datetime import date
from aggdisagg import TemporalAligner

df = pl.DataFrame({
    "date": [date(2020, 1, 1), date(2021, 1, 1), date(2022, 1, 1)],
    "y": [1200.0, 1500.0, 1350.0],      # low-frequency target
    "indicator": [100.0, 125.0, 110.0], # high-frequency indicator
})

aligner = TemporalAligner(
    method="chow-lin-opt",
    target_freq="1mo",
    agg="sum",
    indicator_cols=["indicator"],
)

monthly = aligner.fit_transform(df, datetime_col="date", target_col="y")
print(monthly.head())

# Perfect symmetric aggregation
yearly_back = aligner.aggregate(monthly, freq="1y")
print("Roundtrip OK:", (yearly_back["y_1y"] - df["y"]).abs().sum() < 1e-8)

# Plot (requires plotly)
monthly.plot()  # or use .plot() on the result if extended

Supported Methods

• uniform
• linear
• denton / denton-cholette
• chow-lin, chow-lin-opt (auto ρ via maxlog/minrss)
• litterman, fernandez

All methods guarantee C @ y_high ≈ y_low exactly.

Why aggdisagg?

• Polars-native core (lazy-friendly)
• Perfect consistency by construction (C/D matrices)
• Sklearn-style + fluent API
• Real econometric methods (Denton quadratic, Chow-Lin GLS)
• Excellent pandas / xarray interop
• Production quality (typed, tested, documented)

v0.2 Highlights

• Hierarchical reconciliation (national → regional)
• Uncertainty (bootstrap + analytic std errors)
• Full Polars lazy + xarray DataArray I/O
• Negative post-correction + NNLS ensemble
• sktime / statsforecast compatible wrapper

# Hierarchical
rec = aligner.reconcile_hierarchical([nat_df, reg_df])

# Uncertainty
mean, std = aligner.predict_with_uncertainty()

# Lazy + xarray
lazy_high = aligner.fit_transform(lazy_df)
xa = aligner.to_xarray(high_df)

See examples/quickstart.py for complete gallery.

Development & Publishing

uv sync --all-extras
uv run pytest
uv run python examples/quickstart.py
uv build
# twine or uv publish

License

MIT

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Built for data scientists who want temporal frequency conversion that just works.