bootstrapx-lib 0.3.0

Production-grade bootstrap uncertainty estimation: 15+ methods, sklearn CV, pandas accessor.

bootstrapx

Production-grade bootstrap uncertainty estimation for Python.

15 bootstrap methods · sklearn-compatible · pandas accessor · memory-safe batching

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Why bootstrapx?

scipy.stats.bootstrap covers 3 CI types and only iid data.
The R boot package is comprehensive but not Pythonic.
bootstrapx bridges this gap.

Feature	scipy	arch	bootstrapx
BCa interval	✅	❌	✅
Studentized (bootstrap-t)	❌	❌	✅
Bayesian bootstrap	❌	❌	✅
Poisson / Bernoulli weights	❌	❌	✅
MBB / CBB / Stationary block	❌	✅	✅
Sieve (AR-based)	❌	❌	✅
Wild bootstrap	❌	✅	✅
Cluster / Stratified	❌	❌	✅
scikit-learn CV API	❌	❌	✅
pandas .bootstrap accessor	❌	❌	✅
Reproducible (seeded RNG)	✅	partial	✅
Constant memory (batched)	❌	❌	✅

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Installation

pip install bootstrapx-lib                  # core (numpy + scipy only)
pip install "bootstrapx-lib[pandas]"        # + pandas accessor
pip install "bootstrapx-lib[numba]"         # + Numba JIT acceleration
pip install "bootstrapx-lib[pandas,numba]"  # everything

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Quick Start

Basic usage

import numpy as np
from bootstrapx import bootstrap

data = np.random.default_rng(42).normal(5, 2, size=300)

result = bootstrap(data, np.mean)
print(result)
# BootstrapResult(method='bca', theta_hat=4.97, se=0.11, CI=[4.75, 5.19])

print(result.confidence_interval.low, result.confidence_interval.high)
print(5.0 in result.confidence_interval)  # True

pandas accessor

import pandas as pd
import numpy as np
import bootstrapx  # registers .bootstrap accessor

s = pd.Series(np.random.default_rng(0).exponential(scale=2, size=500))

# On a Series
r = s.bootstrap.bca(np.mean)
print(r)

# On a DataFrame — column-wise summary
df = pd.DataFrame({"control": s, "treatment": s * 1.1 + 0.3})
print(df.bootstrap.summary(np.mean))
#              theta_hat    ci_low   ci_high        se method
# column
# control       1.9973    1.8215    2.1862    0.0941    bca
# treatment     2.4970    2.3036    2.7048    0.1035    bca

scikit-learn cross-validation

from bootstrapx import BootstrapCV
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import cross_val_score
from sklearn.datasets import load_breast_cancer

X, y = load_breast_cancer(return_X_y=True)

cv = BootstrapCV(n_splits=200, random_state=42)
scores = cross_val_score(
    GradientBoostingClassifier(n_estimators=100),
    X, y, cv=cv, scoring="roc_auc"
)
print(f"AUC: {scores.mean():.4f} ± {scores.std():.4f}")
# AUC: 0.9921 ± 0.0071

Time-series bootstrap

import numpy as np
from bootstrapx import bootstrap

# AR(1) process
rng = np.random.default_rng(0)
y = np.zeros(500)
for t in range(1, 500):
    y[t] = 0.7 * y[t-1] + rng.normal()

# Moving Block Bootstrap — preserves serial correlation
result = bootstrap(y, np.mean, method="mbb", block_length=15, n_resamples=4999)
print(result)

# Sieve Bootstrap — fits AR(p) model to residuals (16x faster than 0.2.0)
result = bootstrap(y, np.mean, method="sieve", n_resamples=9999)
print(result)

A/B test with clustered data

import numpy as np
from bootstrapx import bootstrap

# Users clustered by session (standard iid bootstrap underestimates variance)
n_clusters = 50
cluster_ids = np.repeat(np.arange(n_clusters), 20)  # 1000 obs, 50 clusters
rng = np.random.default_rng(1)
data = rng.normal(loc=cluster_ids * 0.1, scale=1.0)

result = bootstrap(
    data, np.mean,
    method="cluster",
    cluster_ids=cluster_ids,
    n_resamples=4999,
)
print(result)
# Correctly wider CI that accounts for within-cluster correlation

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Performance

Measured on Intel Core i7-12700, Python 3.11, n_resamples=9 999.
See benchmarks/ for reproducible scripts.

N	Method	scipy	bootstrapx	Speedup
1 000	BCa	0.18s	0.09s	2.0×
5 000	BCa	0.80s	0.27s	3.0×
50 000	Percentile	7.29s	2.01s	3.6×
100 000	Percentile	54.34s	3.99s	13.6×
300	Sieve	—	0.19s	scipy N/A

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Documentation

📖 Full docs: artyerokhin.github.io/bootstrapx

• Getting Started
• Methods Guide — math behind each method
• API Reference
• Benchmarks

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All supported methods

Method	method=	Use case
BCa	"bca"	General purpose, best coverage accuracy
Percentile	"percentile"	Simple, fast
Basic (Hall)	"basic"	Symmetric distributions
Studentized	"studentized"	Known variance structure
Bayesian	"bayesian"	Bayesian UQ, non-parametric posterior
Poisson weights	"poisson"	Weighted bootstrap, survey data
Bernoulli weights	"bernoulli"	Subsampling variant
Subsampling	"subsampling"	Heavy tails, no finite variance
Moving Block (MBB)	"mbb"	Stationary time series
Circular Block (CBB)	"cbb"	Stationary TS, edge-effect free
Stationary	"stationary"	Politis & Romano (1994)
Tapered Block	"tapered"	Paparoditis & Politis (2001)
Sieve	"sieve"	AR(p) time series (Bühlmann 1997)
Wild	"wild"	Heteroscedastic residuals (Wu 1986)
Cluster	"cluster"	Multi-level / panel data
Stratified	"strata"	Stratified sampling designs

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Contributing

git clone https://github.com/artyerokhin/bootstrapx.git
cd bootstrapx
pip install -e ".[dev,pandas]"
pytest tests/ -v

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Citation

If you use bootstrapx in academic work:

@software{bootstrapx,
  author  = {Erokhin, Artem},
  title   = {bootstrapx: Production-grade bootstrap uncertainty estimation},
  url     = {https://github.com/artyerokhin/bootstrapx},
  version = {0.3.0},
  year    = {2026},
}

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License

MIT — see LICENSE.