ferray-stats

NumPy-equivalent statistics, reductions, sorting, searching, and histograms for the ferray scientific computing library.

Part of the ferray workspace — a Rust-native, drop-in NumPy replacement. All functions operate on ferray-core's NdArray<T, D>, return FerrayResult<T>, and never panic.

Overview

Reductions — sum, prod, min, max, argmin, argmax, ptp (peak-to-peak), cumsum, cumprod, plus pre-allocated _into and reusable _with variants. Summation uses pairwise SIMD accumulation for accuracy and speed. NaN-propagating by default.
Descriptive statistics — mean, var, std_, average (weighted), median, with ddof control and axis support. scipy-parity helpers: skew, kurtosis, zscore, mode, iqr, sem, gmean, hmean.
Quantiles — quantile, percentile, and median, with quantile_with_method / percentile_with_method exposing all nine Hyndman–Fan interpolation methods plus the selection styles (Linear, Lower, Higher, Nearest, Midpoint) via the QuantileMethod enum.
NaN-aware variants — nansum, nanprod, nanmin, nanmax, nanargmin, nanargmax, nanmean, nanvar, nanstd, nancumsum, nancumprod, nanmedian, nanquantile, nanpercentile.
Sorting — sort, argsort, partition, argpartition, lexsort, sort_complex, and searchsorted / searchsorted_with_sorter (with SortKind and Side enums).
Searching — nonzero, where_ / where_condition / where_broadcast, count_nonzero, unique (and unique_values, unique_counts, unique_inverse, unique_all, unique_axis), plus set ops (union1d, intersect1d, setdiff1d, setxor1d, in1d, isin).
Histograms — histogram, histogram2d, histogramdd, histogram_bin_edges, bincount, digitize (with the Bins strategy enum).
Correlation & hypothesis tests — corrcoef, cov, correlate, plus pearsonr, spearmanr, ttest_1samp, ttest_ind, ks_2samp, chi2_contingency.

Large arrays dispatch to a Rayon-parallel path above an element-count threshold (parallel module).

NumPy correspondence

NumPy	ferray-stats
`np.sum` / `np.prod` / `np.ptp`	`sum`, `prod`, `ptp`
`np.mean` / `np.std` / `np.var`	`mean`, `std_`, `var`
`np.median` / `np.average`	`median`, `average`
`np.quantile` / `np.percentile`	`quantile`, `percentile` (+ `*_with_method`)
`np.argmin` / `np.argmax`	`argmin`, `argmax`
`np.cumsum` / `np.cumprod`	`cumsum`, `cumprod`
`np.sort` / `np.argsort`	`sort`, `argsort`
`np.partition` / `np.argpartition`	`partition`, `argpartition`
`np.lexsort` / `np.searchsorted`	`lexsort`, `searchsorted`
`np.nonzero` / `np.flatnonzero`	`nonzero`
`np.where` / `np.count_nonzero`	`where_`, `count_nonzero`
`np.unique`	`unique` (+ `unique_counts`, `unique_inverse`, …)
`np.histogram` / `np.bincount` / `np.digitize`	`histogram`, `bincount`, `digitize`
`np.nan*` (e.g. `np.nanmean`)	`nanmean`, `nanstd`, `nanquantile`, …

Feature flags

This crate defines no Cargo features. It always builds against ferray-core, ferray-ufunc, rayon, num-traits, num-complex, and pulp on the workspace MSRV with no optional runtime dependencies.

Example

use ferray_stats::{mean, std_, quantile, QuantileMethod, quantile_with_method};
use ferray_core::prelude::*;

let a = Array1::<f64>::linspace(0.0, 1.0, 1000)?;

// Descriptive statistics (axis = None reduces the whole array).
let m = mean(&a, None)?;
let s = std_(&a, None, 0)?;          // ddof = 0

// Median via the 0.5 quantile (NumPy-default linear interpolation).
let med = quantile(&a, 0.5, None)?;

// Explicit interpolation method.
let p90 = quantile_with_method(&a, 0.9, None, QuantileMethod::Hazen)?;
# Ok::<(), ferray_core::FerrayError>(())

This crate is re-exported through the umbrella ferray crate.

MSRV & edition