Statistics

const a = np.array([[10, 7, 4], [3, 2, 1]]);
np.quantile(a, 0.5);                       // => 3.5
np.percentile(a, [25, 75], { axis: 1 });   // => [[5.5, 1.5], [8.5, 2.5]]
np.quantile(a, 0.5, { method: "lower" });  // => 3
np.nanquantile([1, NaN, 3], 0.5);          // => 2
np.quantile([1, 2, 3], 0.5, { weights: [1, 1, 4], method: "inverted_cdf" }); // => 3

np.median([[10, 7, 4], [3, 2, 1]], { axis: 0 }); // => [6.5, 4.5, 2.5]
Number.isNaN(np.median([1, NaN, 3]).item()); // => true
np.nanmedian([1, NaN, 3]); // => 2

np.cumulativeSum([1, 2, 3], { includeInitial: true }); // => [0, 1, 3, 6]
np.cumulativeProd([[1, 2], [3, 4]], { axis: 1 });      // => [[1, 2], [3, 12]]

np.diff([1, 4, 9, 16]);                 // => [3, 5, 7]
np.diff([1, 4, 9, 16], 2);              // => [2, 2]
np.diff([1, 2], { prepend: 0 });        // => [1, 1]
np.diff([[1, 2], [4, 8]], { axis: 0 }); // => [[3, 6]]

np.ptp([[1, 5], [2, 9]]);            // => 8
np.ptp([[1, 5], [2, 9]], { axis: 0 }); // => [1, 4]

const x = np.array([[1, NaN, 3], [4, 5, NaN]]);
np.nansum(x);                 // => 13
np.nanmean(x, { axis: 1 });   // => [2, 4.5]
np.nanmax(x, { axis: 0 });    // => [4, 5, 3]
np.nanvar([1, NaN, 2], { ddof: 1 }); // => 0.5

np.nanargmax([NaN, 2, 5, NaN]);                  // => 2
np.nanargmin([[NaN, 1], [2, 3]], { axis: 0 });   // => [1, 0]

np.average([1, 2, 3, 4]);                          // => 2.5
np.average([1, 2, 3], { weights: [3, 0, 1] });      // => 1.5
np.average([[1, 2], [3, 4]], { axis: 1, weights: [1, 3], returned: true }).map((r) => r.toArray()); // => [[1.75, 3.75], [4, 4]]

np.cov([1, 2, 3]);                          // => 1
np.cov([1, 2, 3], { y: [1, 5, 2] });        // => [[1, 0.5], [0.5, 4.333333333333334]]
np.corrcoef([[1, 2, 3], [3, 2, 1]]);        // => [[1, -1], [-1, 1]]

np.gradient([1, 2, 4, 7, 11]);                     // => [1, 1.5, 2.5, 3.5, 4]
np.gradient([1, 2, 4, 7, 11], 2);                  // => [0.5, 0.75, 1.25, 1.75, 2]
np.gradient([1, 2, 4, 7, 11], { edgeOrder: 2 });   // => [0.5, 1.5, 2.5, 3.5, 4.5]

np.trapezoid([1, 2, 3]);                    // => 4
np.trapezoid([1, 2, 3], { x: [0, 1, 3] });  // => 6.5

np.correlate([1, 2, 3], [0, 1, 0.5]);             // => [3.5]
np.correlate([1, 2, 3], [0, 1, 0.5], 'full');     // => [0.5, 2, 3.5, 3, 0]
np.correlate([1, 2, 3], [0, 1, 0.5], 'same');     // => [2, 3.5, 3]

np.convolve([1, 2, 3], [0, 1, 0.5]);             // => [0, 1, 2.5, 4, 1.5]
np.convolve([1, 2, 3], [0, 1, 0.5], 'same');     // => [1, 2.5, 4]
np.convolve([1, 2, 3], [0, 1, 0.5], 'valid');    // => [2.5]

const { hist, edges } = np.histogram([1, 2, 1, 3], 3);
hist.toArray();   // => [2, 1, 1]
edges.toArray();  // => [1, 1.6666666666666667, 2.333333333333333, 3]
np.histogramBinEdges([1, 2, 3, 4], 2).toArray(); // => [1, 2.5, 4]

const { hist, xedges, yedges } = np.histogram2d([0,1,2],[0,1,2], 3);
hist.shape;         // => [3, 3]
xedges.size;        // => 4

const { hist, edges } = np.histogramdd([[0,0],[1,1],[2,2]], 2);
hist.shape;       // => [2, 2]
edges.length;     // => 2

np.bincount([1, 0, 2, 0, 1]).toArray();           // => [2, 2, 1]
np.bincount([0, 1], { minlength: 5 }).toArray();   // => [1, 1, 0, 0, 0]

np.digitize([0.2, 6.4, 3.0, 1.6], [0, 1, 2.5, 4, 10]).toArray(); // => [1, 4, 3, 2]

np.interp([0, 1, 1.5, 2, 2.5, 3], [1, 2, 3], [3, 2, 0]).toArray(); // => [3, 3, 2.5, 2, 1, 0]
np.interp([-1, 5], [0, 1, 2], [0, 1, 2], { left: -99, right: 99 }).toArray(); // => [-99, 99]