bootstrapx-lib 0.3.1

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

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
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

n_clusters = 50
cluster_ids = np.repeat(np.arange(n_clusters), 20)
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 Apple M1, Python 3.12, n_resamples=4 999, median of 5 runs.
Run yourself: python benchmarks/bench_speed.py --quick

BCa (bias-corrected and accelerated):

n	scipy (ms)	bootstrapx (ms)	Speedup
200	9.6	5.8	1.7×
2 000	69	58	1.2×
5 000	433	156	2.8×
10 000	1 015	289	3.5×

At n < 1 000, scipy and bootstrapx are comparable; bootstrapx applies a vectorised fast path for numpy built-ins (mean, median, std, etc.) at n < 500. Speedup grows with sample size due to O(n) vectorised jackknife vs O(n²) in scipy.

Coverage accuracy

BCa empirical coverage at nominal 95%, 1 000 Monte Carlo simulations across normal, log-normal, exponential and t(3) distributions: bootstrapx matches scipy to within simulation noise (< 0.01) for mean and median.

Note: BCa coverage for np.std on heavy-tailed distributions (exponential) is ~91–93% at n = 200 — identical behaviour in both bootstrapx and scipy. This reflects known instability of jackknife acceleration for scale statistics, not a library-specific issue. Use n_resamples ≥ 9 999 or method="studentized" for better coverage when estimating variance.

Run yourself: python benchmarks/bench_coverage_accuracy.py --fast

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Documentation

📖 Full docs: artyerokhin.github.io/bootstrapx

• Getting Started
• Methods Guide
• 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 = {0.3.1},
  year    = {2026},
}

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License

MIT — see LICENSE.