Documentation Index
Fetch the complete documentation index at: https://numpyts.dev/llms.txt
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NaN-Safe Reductions
These functions are identical to their non-nan counterparts but ignore NaN values instead of propagating them through the computation. Use them when your data may contain missing or invalid values represented as NaN.
nansum
Return the sum of array elements, treating NaN as zero.
function nansum(
a: ArrayLike,
axis?: number,
keepdims?: boolean
): number | NDArray | Complex;
| Parameter | Type | Default | Description |
|---|
a | ArrayLike | — | Input array. |
axis | number | number[] | undefined | Axis or axes along which to sum. |
keepdims | boolean | false | If true, reduced axes are kept as dimensions with size 1. |
number when reducing all axes, NDArray when reducing along specific axes.
import { array, nansum } from 'numpy-ts';
nansum(array([1, NaN, 3])); // 4
nansum(array([[1, NaN], [3, 4]]), 0); // array([4, 4])
nanprod
Return the product of array elements, treating NaN as one.
function nanprod(
a: ArrayLike,
axis?: number,
keepdims?: boolean
): number | NDArray | Complex;
| Parameter | Type | Default | Description |
|---|
a | ArrayLike | — | Input array. |
axis | number | number[] | undefined | Axis or axes along which to compute the product. |
keepdims | boolean | false | If true, reduced axes are kept as dimensions with size 1. |
number when reducing all axes, NDArray when reducing along specific axes.
import { array, nanprod } from 'numpy-ts';
nanprod(array([1, NaN, 3])); // 3
nanprod(array([[2, NaN], [3, 4]]), 1); // array([2, 12])
nanmean
Compute the arithmetic mean, ignoring NaN values.
function nanmean(
a: ArrayLike,
axis?: number,
keepdims?: boolean
): number | NDArray | Complex;
| Parameter | Type | Default | Description |
|---|
a | ArrayLike | — | Input array. |
axis | number | number[] | undefined | Axis or axes along which to compute the mean. |
keepdims | boolean | false | If true, reduced axes are kept as dimensions with size 1. |
number when reducing all axes, NDArray when reducing along specific axes.
import { array, nanmean } from 'numpy-ts';
nanmean(array([1, NaN, 3])); // 2 (mean of [1, 3])
nanmean(array([[1, NaN], [3, 4]]), 0); // array([2, 4])
nanstd
Compute the standard deviation, ignoring NaN values.
function nanstd(
a: ArrayLike,
axis?: number,
ddof?: number,
keepdims?: boolean
): number | NDArray;
| Parameter | Type | Default | Description |
|---|
a | ArrayLike | — | Input array. |
axis | number | number[] | undefined | Axis or axes along which to compute the standard deviation. |
ddof | number | 0 | Delta degrees of freedom. |
keepdims | boolean | false | If true, reduced axes are kept as dimensions with size 1. |
number when reducing all axes, NDArray when reducing along specific axes.
import { array, nanstd } from 'numpy-ts';
nanstd(array([1, NaN, 3])); // 1 (std of [1, 3])
nanstd(array([1, NaN, 3]), undefined, 1); // 1.4142135623730951
nanvar
Compute the variance, ignoring NaN values.
function nanvar(
a: ArrayLike,
axis?: number,
ddof?: number,
keepdims?: boolean
): number | NDArray;
| Parameter | Type | Default | Description |
|---|
a | ArrayLike | — | Input array. |
axis | number | number[] | undefined | Axis or axes along which to compute the variance. |
ddof | number | 0 | Delta degrees of freedom. |
keepdims | boolean | false | If true, reduced axes are kept as dimensions with size 1. |
number when reducing all axes, NDArray when reducing along specific axes.
import { array, nanvar } from 'numpy-ts';
nanvar(array([1, NaN, 3])); // 1 (variance of [1, 3])
nanvar(array([[1, NaN], [3, 4]]), 0); // array([1, 0])
nanmin
Return the minimum, ignoring NaN values.
function nanmin(
a: ArrayLike,
axis?: number | number[],
keepdims?: boolean
): number | NDArray | Complex;
| Parameter | Type | Default | Description |
|---|
a | ArrayLike | — | Input array. |
axis | number | number[] | undefined | Axis or axes along which to find the minimum. |
keepdims | boolean | false | If true, reduced axes are kept as dimensions with size 1. |
number when reducing all axes, NDArray when reducing along specific axes.
import { array, nanmin } from 'numpy-ts';
nanmin(array([2, NaN, 1, NaN])); // 1
nanmin(array([[NaN, 3], [1, 2]]), 0); // array([1, 2])
nanmax
Return the maximum, ignoring NaN values.
function nanmax(
a: ArrayLike,
axis?: number | number[],
keepdims?: boolean
): number | NDArray | Complex;
| Parameter | Type | Default | Description |
|---|
a | ArrayLike | — | Input array. |
axis | number | number[] | undefined | Axis or axes along which to find the maximum. |
keepdims | boolean | false | If true, reduced axes are kept as dimensions with size 1. |
number when reducing all axes, NDArray when reducing along specific axes.
import { array, nanmax } from 'numpy-ts';
nanmax(array([2, NaN, 5, NaN])); // 5
nanmax(array([[NaN, 3], [1, 2]]), 1); // array([3, 2])
Compute the median, ignoring NaN values.
function nanmedian(
a: ArrayLike,
axis?: number,
keepdims?: boolean
): number | NDArray;
| Parameter | Type | Default | Description |
|---|
a | ArrayLike | — | Input array. |
axis | number | number[] | undefined | Axis or axes along which to compute the median. |
keepdims | boolean | false | If true, reduced axes are kept as dimensions with size 1. |
number when reducing all axes, NDArray when reducing along specific axes.
import { array, nanmedian } from 'numpy-ts';
nanmedian(array([1, NaN, 3, 5])); // 3 (median of [1, 3, 5])
nanmedian(array([[NaN, 2], [3, 4]]), 0); // array([3, 3])
nancumsum
Return the cumulative sum, treating NaN as zero.
function nancumsum(
a: ArrayLike,
axis?: number
): NDArray;
| Parameter | Type | Default | Description |
|---|
a | ArrayLike | — | Input array. |
axis | number | undefined | Axis along which to compute. When undefined, the input is flattened. |
NDArray containing the cumulative sums with NaN replaced by zero.
import { array, nancumsum } from 'numpy-ts';
nancumsum(array([1, NaN, 3, NaN, 5])); // array([1, 1, 4, 4, 9])
nancumprod
Return the cumulative product, treating NaN as one.
function nancumprod(
a: ArrayLike,
axis?: number
): NDArray;
| Parameter | Type | Default | Description |
|---|
a | ArrayLike | — | Input array. |
axis | number | undefined | Axis along which to compute. When undefined, the input is flattened. |
NDArray containing the cumulative products with NaN replaced by one.
import { array, nancumprod } from 'numpy-ts';
nancumprod(array([1, NaN, 3, NaN, 2])); // array([1, 1, 3, 3, 6])
nanargmin
Return the index of the minimum value, ignoring NaN.
function nanargmin(
a: ArrayLike,
axis?: number
): number | NDArray;
| Parameter | Type | Default | Description |
|---|
a | ArrayLike | — | Input array. |
axis | number | undefined | Axis along which to search. When undefined, operates on the flattened array. |
number when no axis is specified, NDArray of indices when an axis is given.
import { array, nanargmin } from 'numpy-ts';
nanargmin(array([5, NaN, 2, NaN, 3])); // 2
nanargmin(array([[NaN, 3], [1, 2]]), 0); // array([1, 1])
nanargmax
Return the index of the maximum value, ignoring NaN.
function nanargmax(
a: ArrayLike,
axis?: number
): number | NDArray;
| Parameter | Type | Default | Description |
|---|
a | ArrayLike | — | Input array. |
axis | number | undefined | Axis along which to search. When undefined, operates on the flattened array. |
number when no axis is specified, NDArray of indices when an axis is given.
import { array, nanargmax } from 'numpy-ts';
nanargmax(array([5, NaN, 2, NaN, 3])); // 0
nanargmax(array([[NaN, 3], [1, 2]]), 1); // array([1, 1])
nanpercentile
Compute the q-th percentile of the data, ignoring NaN values.
function nanpercentile(
a: ArrayLike,
q: number,
axis?: number,
keepdims?: boolean
): number | NDArray;
| Parameter | Type | Default | Description |
|---|
a | ArrayLike | — | Input array. |
q | number | number[] | — | Percentile(s) to compute, in the range [0, 100]. |
axis | number | number[] | undefined | Axis or axes along which to compute. |
keepdims | boolean | false | If true, reduced axes are kept as dimensions with size 1. |
number for a single percentile over all axes, NDArray otherwise.
import { array, nanpercentile } from 'numpy-ts';
nanpercentile(array([1, NaN, 3, 4, 5]), 50); // 3.5
nanpercentile(array([1, NaN, 3, 4, 5]), [25, 75]); // array([2, 4.5])
nanquantile
Compute the q-th quantile of the data, ignoring NaN values.
function nanquantile(
a: ArrayLike,
q: number,
axis?: number,
keepdims?: boolean
): number | NDArray;
| Parameter | Type | Default | Description |
|---|
a | ArrayLike | — | Input array. |
q | number | number[] | — | Quantile(s) to compute, in the range [0, 1]. |
axis | number | number[] | undefined | Axis or axes along which to compute. |
keepdims | boolean | false | If true, reduced axes are kept as dimensions with size 1. |
number for a single quantile over all axes, NDArray otherwise.
import { array, nanquantile } from 'numpy-ts';
nanquantile(array([1, NaN, 3, 4, 5]), 0.5); // 3.5
nanquantile(array([1, NaN, 3, 4, 5]), [0.25, 0.75]); // array([2, 4.5])
