aggdisagg 1.9.2

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

aggdisagg

Temporal Aggregation & Disaggregation for Modern Python

Install & try in 10 seconds:

pip install "aggdisagg[all]"

Note: Requires Python ≥ 3.10. For Excel support (read_excel etc.) use the excel extra or install fastexcel / openpyxl separately (or read with pandas + pl.from_pandas).

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.

Real-World Example: Disaggregating Multiple Quarterly Series to Monthly

When you have a DataFrame with several low-frequency series (e.g. quarterly revenue for multiple companies) and want to convert them all to monthly while preserving the aggregation constraint, use the disaggregate_columns helper:

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

# Synthetic quarterly data (mimics real company revenue files)
df_q = pl.DataFrame({
    "date": [date(2018, 3, 1), date(2018, 6, 1), date(2018, 9, 1), date(2018, 12, 1)],
    "Krones": [1_020_000_000, 1_028_000_000, 1_032_000_000, 1_328_000_000],
    "JBT":     [409_200_000,   491_300_000,   481_900_000,   537_300_000],
    "GEA":     [1_189_000_000, 1_403_000_000, 1_360_000_000, 1_570_000_000],
})

aligner = TemporalAligner(method="linear", target_freq="1mo", agg="sum")

monthly = aligner.disaggregate_columns(
    df_q,
    datetime_col="date",
    include_dates=True,   # automatically generates proper monthly dates
)

print(monthly.head(6))
# date        Krones        JBT           GEA
# 2018-01-01  ~339.1m      ~131.1m      ~374.3m
# ...
# Round-trip check
reagg = aligner.aggregate(monthly.drop("date"), freq="1q")
print("Sums match original quarters:", 
      (reagg["y_1q"] - df_q["Krones"]).abs().sum() < 1e-6)

Notes

• The helper automatically detects numeric columns as targets (or pass target_cols=[...]).
• Date inference now correctly chooses a ratio of 3 for quarterly → monthly (instead of assuming annual).
• Use include_dates=True for a ready-to-use monthly date column, or call expand_high_freq_dates yourself for custom alignment.
• All series are disaggregated independently but share the same frequency mapping.
• New in 1.4.1: extrapolate ("nan" default) controls NaN low-freq input handling. "nan" (default) and "drop" never fabricate values from missing inputs; "hold"/"linear" fill using last anchor when requested. Pass on fit_transform(..., extrapolate=...) or disaggregate_columns(...).

See examples/quickstart.py for more patterns.

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)

First-User Tips & Current Limitations

Recommended starting point

aligner = TemporalAligner(method="chow-lin-opt", target_freq="1mo", agg="sum", indicator_cols=[...])
high = aligner.fit_transform(low_df, datetime_col="date", target_col="y")
back = aligner.aggregate(high, freq="1y")   # should match original low almost exactly

Output shape The returned DataFrame contains y_disaggregated (and _std / _lower / _upper bands only when with_uncertainty=True was passed to disaggregate_columns). Original context columns are not automatically repeated. You can expand dates yourself:

# Example: attach proper high-freq dates (fit_transform itself returns only values)
low_dates = low_df["date"]
high = aligner.fit_transform(low_df, datetime_col="date", target_col="y")
high = high.with_columns(aligner.expand_high_freq_dates(low_dates).alias("date"))

Limitations (as of 1.1.0)

• Date expansion in the output is basic (low-freq dates are not auto-expanded).
• Uncertainty is a simple bootstrap and can be noisy or near-zero.
• All listed methods are fully implemented, produce distinct results, respond to indicators/ρ where applicable, and satisfy the aggregation constraint. (Denton uses quadratic penalty on first/second differences; Chow-Lin family uses GLS with AR(1) or IAR(1) errors.)
• Only regular frequency ratios are supported.

See the CHANGELOG for the full list of recent robustness and correctness fixes.

• 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()  # real std when uncertainty was requested in disagg (otherwise near-zero)

# 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.