API

Top level functions

add(x1, x2, /[, out, where, casting, order, …])

This docstring was copied from numpy.add.

all(a[, axis, keepdims, split_every, out])

Test whether all array elements along a given axis evaluate to True.

allclose(arr1, arr2[, rtol, atol, equal_nan])

Returns True if two arrays are element-wise equal within a tolerance.

angle(x[, deg])

Return the angle of the complex argument.

any(a[, axis, keepdims, split_every, out])

Test whether any array element along a given axis evaluates to True.

append(arr, values[, axis])

Append values to the end of an array.

apply_along_axis(func1d, axis, arr, *args[, …])

Apply a function to 1-D slices along the given axis.

apply_over_axes(func, a, axes)

Apply a function repeatedly over multiple axes.

arange(*args, **kwargs)

Return evenly spaced values from start to stop with step size step.

arccos(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.arccos.

arccosh(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.arccosh.

arcsin(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.arcsin.

arcsinh(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.arcsinh.

arctan(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.arctan.

arctan2(x1, x2, /[, out, where, casting, …])

This docstring was copied from numpy.arctan2.

arctanh(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.arctanh.

argmax(x[, axis, split_every, out])

Return the maximum of an array or maximum along an axis.

argmin(x[, axis, split_every, out])

Return the minimum of an array or minimum along an axis.

argtopk(a, k[, axis, split_every])

Extract the indices of the k largest elements from a on the given axis, and return them sorted from largest to smallest.

argwhere(a)

Find the indices of array elements that are non-zero, grouped by element.

around(x[, decimals])

Evenly round to the given number of decimals.

array(object[, dtype, copy, order, subok, …])

This docstring was copied from numpy.array.

asanyarray(a)

Convert the input to a dask array.

asarray(a[, allow_unknown_chunksizes])

Convert the input to a dask array.

atleast_1d(*arys)

Convert inputs to arrays with at least one dimension.

atleast_2d(*arys)

View inputs as arrays with at least two dimensions.

atleast_3d(*arys)

View inputs as arrays with at least three dimensions.

average(a[, axis, weights, returned])

Compute the weighted average along the specified axis.

bincount(x[, weights, minlength])

This docstring was copied from numpy.bincount.

bitwise_and(x1, x2, /[, out, where, …])

This docstring was copied from numpy.bitwise_and.

bitwise_not(x, /[, out, where, casting, …])

This docstring was copied from numpy.invert.

bitwise_or(x1, x2, /[, out, where, casting, …])

This docstring was copied from numpy.bitwise_or.

bitwise_xor(x1, x2, /[, out, where, …])

This docstring was copied from numpy.bitwise_xor.

block(arrays[, allow_unknown_chunksizes])

Assemble an nd-array from nested lists of blocks.

blockwise(func, out_ind, *args[, name, …])

Tensor operation: Generalized inner and outer products

broadcast_arrays(*args, **kwargs)

Broadcast any number of arrays against each other.

broadcast_to(x, shape[, chunks, meta])

Broadcast an array to a new shape.

cbrt(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.cbrt.

coarsen(reduction, x, axes[, trim_excess])

Coarsen array by applying reduction to fixed size neighborhoods

ceil(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.ceil.

choose(a, choices)

Construct an array from an index array and a list of arrays to choose from.

clip(*args, **kwargs)

Clip (limit) the values in an array.

compress(condition, a[, axis])

Return selected slices of an array along given axis.

concatenate(seq[, axis, …])

Concatenate arrays along an existing axis

conj(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.conjugate.

copysign(x1, x2, /[, out, where, casting, …])

This docstring was copied from numpy.copysign.

corrcoef(x[, y, rowvar])

Return Pearson product-moment correlation coefficients.

cos(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.cos.

cosh(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.cosh.

count_nonzero(a[, axis])

Counts the number of non-zero values in the array a.

cov(m[, y, rowvar, bias, ddof])

Estimate a covariance matrix, given data and weights.

cumprod(x[, axis, dtype, out, method])

Return the cumulative product of elements along a given axis.

cumsum(x[, axis, dtype, out, method])

Return the cumulative sum of the elements along a given axis.

deg2rad(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.deg2rad.

degrees(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.degrees.

diag(v)

Extract a diagonal or construct a diagonal array.

diagonal(a[, offset, axis1, axis2])

Return specified diagonals.

diff(a[, n, axis])

Calculate the n-th discrete difference along the given axis.

divmod(x1, x2[, out1, out2], / [[, out, …])

This docstring was copied from numpy.divmod.

digitize(a, bins[, right])

Return the indices of the bins to which each value in input array belongs.

dot(a, b[, out])

This docstring was copied from numpy.dot.

dstack(tup[, allow_unknown_chunksizes])

Stack arrays in sequence depth wise (along third axis).

ediff1d(ary[, to_end, to_begin])

The differences between consecutive elements of an array.

einsum(subscripts, *operands[, out, dtype, …])

This docstring was copied from numpy.einsum.

empty(*args, **kwargs)

Blocked variant of empty

empty_like(a[, dtype, order, chunks, name, …])

Return a new array with the same shape and type as a given array.

equal(x1, x2, /[, out, where, casting, …])

This docstring was copied from numpy.equal.

exp(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.exp.

exp2(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.exp2.

expm1(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.expm1.

eye(N[, chunks, M, k, dtype])

Return a 2-D Array with ones on the diagonal and zeros elsewhere.

fabs(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.fabs.

fix(*args, **kwargs)

Round to nearest integer towards zero.

flatnonzero(a)

Return indices that are non-zero in the flattened version of a.

flip(m[, axis])

Reverse element order along axis.

flipud(m)

Reverse the order of elements along axis 0 (up/down).

fliplr(m)

Reverse the order of elements along axis 1 (left/right).

float_power(x1, x2, /[, out, where, …])

This docstring was copied from numpy.float_power.

floor(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.floor.

floor_divide(x1, x2, /[, out, where, …])

This docstring was copied from numpy.floor_divide.

fmax(x1, x2, /[, out, where, casting, …])

This docstring was copied from numpy.fmax.

fmin(x1, x2, /[, out, where, casting, …])

This docstring was copied from numpy.fmin.

fmod(x1, x2, /[, out, where, casting, …])

This docstring was copied from numpy.fmod.

frexp(x[, out1, out2], / [[, out, where, …])

This docstring was copied from numpy.frexp.

fromfunction(func[, chunks, shape, dtype])

Construct an array by executing a function over each coordinate.

frompyfunc(func, nin, nout, *[, identity])

This docstring was copied from numpy.frompyfunc.

full(shape, fill_value, *args, **kwargs)

Blocked variant of full

full_like(a, fill_value[, order, dtype, …])

Return a full array with the same shape and type as a given array.

gradient(f, *varargs, **kwargs)

Return the gradient of an N-dimensional array.

greater(x1, x2, /[, out, where, casting, …])

This docstring was copied from numpy.greater.

greater_equal(x1, x2, /[, out, where, …])

This docstring was copied from numpy.greater_equal.

histogram(a[, bins, range, normed, weights, …])

Blocked variant of numpy.histogram().

histogram2d(x, y[, bins, range, normed, …])

Blocked variant of numpy.histogram2d().

histogramdd(sample, bins[, range, normed, …])

Blocked variant of numpy.histogramdd().

hstack(tup[, allow_unknown_chunksizes])

Stack arrays in sequence horizontally (column wise).

hypot(x1, x2, /[, out, where, casting, …])

This docstring was copied from numpy.hypot.

imag(*args, **kwargs)

Return the imaginary part of the complex argument.

indices(dimensions[, dtype, chunks])

Implements NumPy’s indices for Dask Arrays.

insert(arr, obj, values, axis)

Insert values along the given axis before the given indices.

invert(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.invert.

isclose(arr1, arr2[, rtol, atol, equal_nan])

Returns a boolean array where two arrays are element-wise equal within a tolerance.

iscomplex(*args, **kwargs)

Returns a bool array, where True if input element is complex.

isfinite(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.isfinite.

isin(element, test_elements[, …])

Calculates element in test_elements, broadcasting over element only.

isinf(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.isinf.

isneginf

This docstring was copied from numpy.equal.

isnan(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.isnan.

isnull(values)

pandas.isnull for dask arrays

isposinf

This docstring was copied from numpy.equal.

isreal(*args, **kwargs)

Returns a bool array, where True if input element is real.

ldexp(x1, x2, /[, out, where, casting, …])

This docstring was copied from numpy.ldexp.

less(x1, x2, /[, out, where, casting, …])

This docstring was copied from numpy.less.

linspace(start, stop[, num, endpoint, …])

Return num evenly spaced values over the closed interval [start, stop].

log(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.log.

log10(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.log10.

log1p(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.log1p.

log2(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.log2.

logaddexp(x1, x2, /[, out, where, casting, …])

This docstring was copied from numpy.logaddexp.

logaddexp2(x1, x2, /[, out, where, casting, …])

This docstring was copied from numpy.logaddexp2.

logical_and(x1, x2, /[, out, where, …])

This docstring was copied from numpy.logical_and.

logical_not(x, /[, out, where, casting, …])

This docstring was copied from numpy.logical_not.

logical_or(x1, x2, /[, out, where, casting, …])

This docstring was copied from numpy.logical_or.

logical_xor(x1, x2, /[, out, where, …])

This docstring was copied from numpy.logical_xor.

map_overlap(func, *args[, depth, boundary, …])

Map a function over blocks of arrays with some overlap

map_blocks(func, *args[, name, token, …])

Map a function across all blocks of a dask array.

matmul(x1, x2, /[, out, casting, order, …])

This docstring was copied from numpy.matmul.

max(a[, axis, keepdims, split_every, out])

Return the maximum of an array or maximum along an axis.

maximum(x1, x2, /[, out, where, casting, …])

This docstring was copied from numpy.maximum.

mean(a[, axis, dtype, keepdims, …])

Compute the arithmetic mean along the specified axis.

median(a[, axis, keepdims, out])

Compute the median along the specified axis.

meshgrid(*xi, **kwargs)

Return coordinate matrices from coordinate vectors.

min(a[, axis, keepdims, split_every, out])

Return the minimum of an array or minimum along an axis.

minimum(x1, x2, /[, out, where, casting, …])

This docstring was copied from numpy.minimum.

mod(x1, x2, /[, out, where, casting, order, …])

This docstring was copied from numpy.remainder.

modf(x[, out1, out2], / [[, out, where, …])

This docstring was copied from numpy.modf.

moment(a, order[, axis, dtype, keepdims, …])

moveaxis(a, source, destination)

Move axes of an array to new positions.

multiply(x1, x2, /[, out, where, casting, …])

This docstring was copied from numpy.multiply.

nanargmax(x[, axis, split_every, out])

Return the maximum of an array or maximum along an axis, ignoring any NaNs.

nanargmin(x[, axis, split_every, out])

Return minimum of an array or minimum along an axis, ignoring any NaNs.

nancumprod(x, axis[, dtype, out, method])

Return the cumulative product of array elements over a given axis treating Not a Numbers (NaNs) as one.

nancumsum(x, axis[, dtype, out, method])

Return the cumulative sum of array elements over a given axis treating Not a Numbers (NaNs) as zero.

nanmax(a[, axis, keepdims, split_every, out])

Return the maximum of an array or maximum along an axis, ignoring any NaNs.

nanmean(a[, axis, dtype, keepdims, …])

Compute the arithmetic mean along the specified axis, ignoring NaNs.

nanmedian(a[, axis, keepdims, out])

Compute the median along the specified axis, while ignoring NaNs.

nanmin(a[, axis, keepdims, split_every, out])

Return minimum of an array or minimum along an axis, ignoring any NaNs.

nanprod(a[, axis, dtype, keepdims, …])

Return the product of array elements over a given axis treating Not a Numbers (NaNs) as ones.

nanstd(a[, axis, dtype, keepdims, ddof, …])

Compute the standard deviation along the specified axis, while ignoring NaNs.

nansum(a[, axis, dtype, keepdims, …])

Return the sum of array elements over a given axis treating Not a Numbers (NaNs) as zero.

nanvar(a[, axis, dtype, keepdims, ddof, …])

Compute the variance along the specified axis, while ignoring NaNs.

nan_to_num(*args, **kwargs)

Replace NaN with zero and infinity with large finite numbers (default behaviour) or with the numbers defined by the user using the nan, posinf and/or neginf keywords.

negative(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.negative.

nextafter(x1, x2, /[, out, where, casting, …])

This docstring was copied from numpy.nextafter.

nonzero(a)

Return the indices of the elements that are non-zero.

not_equal(x1, x2, /[, out, where, casting, …])

This docstring was copied from numpy.not_equal.

notnull(values)

pandas.notnull for dask arrays

ones(*args, **kwargs)

Blocked variant of ones

ones_like(a[, dtype, order, chunks, name, shape])

Return an array of ones with the same shape and type as a given array.

outer(a, b)

Compute the outer product of two vectors.

pad(array, pad_width[, mode])

Pad an array.

percentile(a, q[, interpolation, method])

Approximate percentile of 1-D array

PerformanceWarning

A warning given when bad chunking may cause poor performance

piecewise(x, condlist, funclist, *args, **kw)

Evaluate a piecewise-defined function.

power(x1, x2, /[, out, where, casting, …])

This docstring was copied from numpy.power.

prod(a[, axis, dtype, keepdims, …])

Return the product of array elements over a given axis.

ptp(a[, axis])

Range of values (maximum - minimum) along an axis.

rad2deg(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.rad2deg.

radians(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.radians.

ravel(array_like)

Return a contiguous flattened array.

real(*args, **kwargs)

Return the real part of the complex argument.

reciprocal(x, /[, out, where, casting, …])

This docstring was copied from numpy.reciprocal.

rechunk(x[, chunks, threshold, …])

Convert blocks in dask array x for new chunks.

reduction(x, chunk, aggregate[, axis, …])

General version of reductions

register_chunk_type(type)

Register the given type as a valid chunk and downcast array type

remainder(x1, x2, /[, out, where, casting, …])

This docstring was copied from numpy.remainder.

repeat(a, repeats[, axis])

Repeat elements of an array.

reshape(x, shape[, merge_chunks])

Reshape array to new shape

result_type(*arrays_and_dtypes)

This docstring was copied from numpy.result_type.

rint(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.rint.

roll(array, shift[, axis])

Roll array elements along a given axis.

rollaxis(a, axis[, start])

rot90(m[, k, axes])

Rotate an array by 90 degrees in the plane specified by axes.

round(a[, decimals])

Round an array to the given number of decimals.

searchsorted(a, v[, side, sorter])

Find indices where elements should be inserted to maintain order.

sign(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.sign.

signbit(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.signbit.

sin(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.sin.

sinc(*args, **kwargs)

Return the normalized sinc function.

sinh(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.sinh.

sqrt(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.sqrt.

square(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.square.

squeeze(a[, axis])

Remove axes of length one from a.

stack(seq[, axis, allow_unknown_chunksizes])

Stack arrays along a new axis

std(a[, axis, dtype, keepdims, ddof, …])

Compute the standard deviation along the specified axis.

subtract(x1, x2, /[, out, where, casting, …])

This docstring was copied from numpy.subtract.

sum(a[, axis, dtype, keepdims, split_every, out])

Sum of array elements over a given axis.

take(a, indices[, axis])

Take elements from an array along an axis.

tan(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.tan.

tanh(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.tanh.

tensordot(lhs, rhs[, axes])

Compute tensor dot product along specified axes.

tile(A, reps)

Construct an array by repeating A the number of times given by reps.

topk(a, k[, axis, split_every])

Extract the k largest elements from a on the given axis, and return them sorted from largest to smallest.

trace(a[, offset, axis1, axis2, dtype])

Return the sum along diagonals of the array.

transpose(a[, axes])

Reverse or permute the axes of an array; returns the modified array.

true_divide(x1, x2, /[, out, where, …])

This docstring was copied from numpy.true_divide.

tril(m[, k])

Lower triangle of an array.

triu(m[, k])

Upper triangle of an array.

trunc(x, /[, out, where, casting, order, …])

This docstring was copied from numpy.trunc.

unique(ar[, return_index, return_inverse, …])

Find the unique elements of an array.

unravel_index(indices, shape[, order])

This docstring was copied from numpy.unravel_index.

var(a[, axis, dtype, keepdims, ddof, …])

Compute the variance along the specified axis.

vdot(a, b)

This docstring was copied from numpy.vdot.

vstack(tup[, allow_unknown_chunksizes])

Stack arrays in sequence vertically (row wise).

where(condition, [x, y])

This docstring was copied from numpy.where.

zeros(*args, **kwargs)

Blocked variant of zeros

zeros_like(a[, dtype, order, chunks, name, …])

Return an array of zeros with the same shape and type as a given array.

Array Methods

Array(dask, name, chunks[, dtype, meta, shape])

Parallel Dask Array

Array.all([axis, out, keepdims, where])

This docstring was copied from numpy.ndarray.all.

Array.any([axis, out, keepdims, where])

This docstring was copied from numpy.ndarray.any.

Array.argmax([axis, out])

This docstring was copied from numpy.ndarray.argmax.

Array.argmin([axis, out])

This docstring was copied from numpy.ndarray.argmin.

Array.argtopk(k[, axis, split_every])

The indices of the top k elements of an array.

Array.astype(dtype, **kwargs)

Copy of the array, cast to a specified type.

Array.choose(choices[, out, mode])

This docstring was copied from numpy.ndarray.choose.

Array.clip([min, max, out])

This docstring was copied from numpy.ndarray.clip.

Array.compute(**kwargs)

Compute this dask collection

Array.compute_chunk_sizes()

Compute the chunk sizes for a Dask array.

Array.conj()

Array.copy()

Copy array.

Array.cumprod([axis, dtype, out])

This docstring was copied from numpy.ndarray.cumprod.

Array.cumsum([axis, dtype, out])

This docstring was copied from numpy.ndarray.cumsum.

Array.dot(b[, out])

This docstring was copied from numpy.ndarray.dot.

Array.flatten([order])

This docstring was copied from numpy.ndarray.ravel.

Array.map_blocks(*args[, name, token, …])

Map a function across all blocks of a dask array.

Array.map_overlap(func, depth[, boundary, trim])

Map a function over blocks of the array with some overlap

Array.max([axis, out, keepdims, initial, where])

This docstring was copied from numpy.ndarray.max.

Array.mean([axis, dtype, out, keepdims, where])

This docstring was copied from numpy.ndarray.mean.

Array.min([axis, out, keepdims, initial, where])

This docstring was copied from numpy.ndarray.min.

Array.moment(order[, axis, dtype, keepdims, …])

Calculate the nth centralized moment.

Array.nonzero()

This docstring was copied from numpy.ndarray.nonzero.

Array.persist(**kwargs)

Persist this dask collection into memory

Array.prod([axis, dtype, out, keepdims, …])

This docstring was copied from numpy.ndarray.prod.

Array.ravel([order])

This docstring was copied from numpy.ndarray.ravel.

Array.rechunk([chunks, threshold, …])

See da.rechunk for docstring

Array.repeat(repeats[, axis])

This docstring was copied from numpy.ndarray.repeat.

Array.reshape(shape[, order])

This docstring was copied from numpy.ndarray.reshape.

Array.round([decimals, out])

This docstring was copied from numpy.ndarray.round.

Array.squeeze([axis])

This docstring was copied from numpy.ndarray.squeeze.

Array.std([axis, dtype, out, ddof, …])

This docstring was copied from numpy.ndarray.std.

Array.store(targets[, lock, regions, …])

Store dask arrays in array-like objects, overwrite data in target

Array.sum([axis, dtype, out, keepdims, …])

This docstring was copied from numpy.ndarray.sum.

Array.swapaxes(axis1, axis2)

This docstring was copied from numpy.ndarray.swapaxes.

Array.to_dask_dataframe([columns, index, meta])

Convert dask Array to dask Dataframe

Array.to_delayed([optimize_graph])

Convert into an array of dask.delayed.Delayed objects, one per chunk.

Array.to_hdf5(filename, datapath, **kwargs)

Store array in HDF5 file

Array.to_svg([size])

Convert chunks from Dask Array into an SVG Image

Array.to_tiledb(uri, *args, **kwargs)

Save array to the TileDB storage manager

Array.to_zarr(*args, **kwargs)

Save array to the zarr storage format

Array.topk(k[, axis, split_every])

The top k elements of an array.

Array.trace([offset, axis1, axis2, dtype, out])

This docstring was copied from numpy.ndarray.trace.

Array.transpose(*axes)

This docstring was copied from numpy.ndarray.transpose.

Array.var([axis, dtype, out, ddof, …])

This docstring was copied from numpy.ndarray.var.

Array.view([dtype, order])

Get a view of the array as a new data type

Array.visualize([filename, format, …])

Render the computation of this object’s task graph using graphviz.

Fast Fourier Transforms

fft.fft_wrap(fft_func[, kind, dtype])

Wrap 1D, 2D, and ND real and complex FFT functions

fft.fft(a[, n, axis])

Wrapping of numpy.fft.fft

fft.fft2(a[, s, axes])

Wrapping of numpy.fft.fft2

fft.fftn(a[, s, axes])

Wrapping of numpy.fft.fftn

fft.ifft(a[, n, axis])

Wrapping of numpy.fft.ifft

fft.ifft2(a[, s, axes])

Wrapping of numpy.fft.ifft2

fft.ifftn(a[, s, axes])

Wrapping of numpy.fft.ifftn

fft.rfft(a[, n, axis])

Wrapping of numpy.fft.rfft

fft.rfft2(a[, s, axes])

Wrapping of numpy.fft.rfft2

fft.rfftn(a[, s, axes])

Wrapping of numpy.fft.rfftn

fft.irfft(a[, n, axis])

Wrapping of numpy.fft.irfft

fft.irfft2(a[, s, axes])

Wrapping of numpy.fft.irfft2

fft.irfftn(a[, s, axes])

Wrapping of numpy.fft.irfftn

fft.hfft(a[, n, axis])

Wrapping of numpy.fft.hfft

fft.ihfft(a[, n, axis])

Wrapping of numpy.fft.ihfft

fft.fftfreq(n[, d, chunks])

Return the Discrete Fourier Transform sample frequencies.

fft.rfftfreq(n[, d, chunks])

Return the Discrete Fourier Transform sample frequencies (for usage with rfft, irfft).

fft.fftshift(x[, axes])

Shift the zero-frequency component to the center of the spectrum.

fft.ifftshift(x[, axes])

The inverse of fftshift.

Linear Algebra

linalg.cholesky(a[, lower])

Returns the Cholesky decomposition, \(A = L L^*\) or \(A = U^* U\) of a Hermitian positive-definite matrix A.

linalg.inv(a)

Compute the inverse of a matrix with LU decomposition and forward / backward substitutions.

linalg.lstsq(a, b)

Return the least-squares solution to a linear matrix equation using QR decomposition.

linalg.lu(a)

Compute the lu decomposition of a matrix.

linalg.norm(x[, ord, axis, keepdims])

Matrix or vector norm.

linalg.qr(a)

Compute the qr factorization of a matrix.

linalg.solve(a, b[, sym_pos])

Solve the equation a x = b for x.

linalg.solve_triangular(a, b[, lower])

Solve the equation a x = b for x, assuming a is a triangular matrix.

linalg.svd(a[, coerce_signs])

Compute the singular value decomposition of a matrix.

linalg.svd_compressed(a, k[, iterator, …])

Randomly compressed rank-k thin Singular Value Decomposition.

linalg.sfqr(data[, name])

Direct Short-and-Fat QR

linalg.tsqr(data[, compute_svd, …])

Direct Tall-and-Skinny QR algorithm

Masked Arrays

ma.average(a[, axis, weights, returned])

Return the weighted average of array over the given axis.

ma.filled(a[, fill_value])

Return input as an array with masked data replaced by a fill value.

ma.fix_invalid(a[, fill_value])

Return input with invalid data masked and replaced by a fill value.

ma.getdata(a)

Return the data of a masked array as an ndarray.

ma.getmaskarray(a)

Return the mask of a masked array, or full boolean array of False.

ma.masked_array(data[, mask, fill_value])

An array class with possibly masked values.

ma.masked_equal(a, value)

Mask an array where equal to a given value.

ma.masked_greater(x, value[, copy])

Mask an array where greater than a given value.

ma.masked_greater_equal(x, value[, copy])

Mask an array where greater than or equal to a given value.

ma.masked_inside(x, v1, v2)

Mask an array inside a given interval.

ma.masked_invalid(a)

Mask an array where invalid values occur (NaNs or infs).

ma.masked_less(x, value[, copy])

Mask an array where less than a given value.

ma.masked_less_equal(x, value[, copy])

Mask an array where less than or equal to a given value.

ma.masked_not_equal(x, value[, copy])

Mask an array where not equal to a given value.

ma.masked_outside(x, v1, v2)

Mask an array outside a given interval.

ma.masked_values(x, value[, rtol, atol, shrink])

Mask using floating point equality.

ma.masked_where(condition, a)

Mask an array where a condition is met.

ma.set_fill_value(a, fill_value)

Set the filling value of a, if a is a masked array.

Random

random.beta(a, b[, size, chunks])

Draw samples from a Beta distribution.

random.binomial(n, p[, size, chunks])

Draw samples from a binomial distribution.

random.chisquare(df[, size, chunks])

Draw samples from a chi-square distribution.

random.choice(a[, size, replace, p, chunks])

Generates a random sample from a given 1-D array

random.exponential([scale, size, chunks])

Draw samples from an exponential distribution.

random.f(dfnum, dfden[, size, chunks])

Draw samples from an F distribution.

random.gamma(shape[, scale, size, chunks])

Draw samples from a Gamma distribution.

random.geometric(p[, size, chunks])

Draw samples from the geometric distribution.

random.gumbel([loc, scale, size, chunks])

Draw samples from a Gumbel distribution.

random.hypergeometric(ngood, nbad, nsample)

Draw samples from a Hypergeometric distribution.

random.laplace([loc, scale, size, chunks])

Draw samples from the Laplace or double exponential distribution with specified location (or mean) and scale (decay).

random.logistic([loc, scale, size, chunks])

Draw samples from a logistic distribution.

random.lognormal([mean, sigma, size, chunks])

Draw samples from a log-normal distribution.

random.logseries(p[, size, chunks])

Draw samples from a logarithmic series distribution.

random.negative_binomial(n, p[, size, chunks])

Draw samples from a negative binomial distribution.

random.noncentral_chisquare(df, nonc[, …])

Draw samples from a noncentral chi-square distribution.

random.noncentral_f(dfnum, dfden, nonc[, …])

Draw samples from the noncentral F distribution.

random.normal([loc, scale, size, chunks])

Draw random samples from a normal (Gaussian) distribution.

random.pareto(a[, size, chunks])

Draw samples from a Pareto II or Lomax distribution with specified shape.

random.permutation(x)

Randomly permute a sequence, or return a permuted range.

random.poisson([lam, size, chunks])

Draw samples from a Poisson distribution.

random.power(a[, size, chunks])

Draws samples in [0, 1] from a power distribution with positive exponent a - 1.

random.randint(low[, high, size, chunks, dtype])

Return random integers from low (inclusive) to high (exclusive).

random.random([size, chunks])

Return random floats in the half-open interval [0.0, 1.0).

random.random_sample([size, chunks])

Return random floats in the half-open interval [0.0, 1.0).

random.rayleigh([scale, size, chunks])

Draw samples from a Rayleigh distribution.

random.standard_cauchy([size, chunks])

Draw samples from a standard Cauchy distribution with mode = 0.

random.standard_exponential([size, chunks])

Draw samples from the standard exponential distribution.

random.standard_gamma(shape[, size, chunks])

Draw samples from a standard Gamma distribution.

random.standard_normal([size, chunks])

Draw samples from a standard Normal distribution (mean=0, stdev=1).

random.standard_t(df[, size, chunks])

Draw samples from a standard Student’s t distribution with df degrees of freedom.

random.triangular(left, mode, right[, size, …])

Draw samples from the triangular distribution over the interval [left, right].

random.uniform([low, high, size, chunks])

Draw samples from a uniform distribution.

random.vonmises(mu, kappa[, size, chunks])

Draw samples from a von Mises distribution.

random.wald(mean, scale[, size, chunks])

Draw samples from a Wald, or inverse Gaussian, distribution.

random.weibull(a[, size, chunks])

Draw samples from a Weibull distribution.

random.zipf(a[, size, chunks])

Standard distributions

Stats

stats.ttest_ind(a, b[, axis, equal_var])

Calculate the T-test for the means of two independent samples of scores.

stats.ttest_1samp(a, popmean[, axis, nan_policy])

Calculate the T-test for the mean of ONE group of scores.

stats.ttest_rel(a, b[, axis, nan_policy])

Calculate the t-test on TWO RELATED samples of scores, a and b.

stats.chisquare(f_obs[, f_exp, ddof, axis])

Calculate a one-way chi-square test.

stats.power_divergence(f_obs[, f_exp, ddof, …])

Cressie-Read power divergence statistic and goodness of fit test.

stats.skew(a[, axis, bias, nan_policy])

Compute the sample skewness of a data set.

stats.skewtest(a[, axis, nan_policy])

Test whether the skew is different from the normal distribution.

stats.kurtosis(a[, axis, fisher, bias, …])

Compute the kurtosis (Fisher or Pearson) of a dataset.

stats.kurtosistest(a[, axis, nan_policy])

Test whether a dataset has normal kurtosis.

stats.normaltest(a[, axis, nan_policy])

Test whether a sample differs from a normal distribution.

stats.f_oneway(*args)

Perform one-way ANOVA.

stats.moment(a[, moment, axis, nan_policy])

Calculate the nth moment about the mean for a sample.

Image Support

image.imread(filename[, imread, preprocess])

Read a stack of images into a dask array

Slightly Overlapping Computations

overlap.overlap(x, depth, boundary)

Share boundaries between neighboring blocks

overlap.map_overlap(func, *args[, depth, …])

Map a function over blocks of arrays with some overlap

lib.stride_tricks.sliding_window_view(x, …)

Create a sliding window view into the array with the given window shape.

overlap.trim_internal(x, axes[, boundary])

Trim sides from each block

overlap.trim_overlap(x, depth[, boundary])

Trim sides from each block.

Create and Store Arrays

from_array(x[, chunks, name, lock, asarray, …])

Create dask array from something that looks like an array.

from_delayed(value, shape[, dtype, meta, name])

Create a dask array from a dask delayed value

from_npy_stack(dirname[, mmap_mode])

Load dask array from stack of npy files

from_zarr(url[, component, storage_options, …])

Load array from the zarr storage format

from_tiledb(uri[, attribute, chunks, …])

Load array from the TileDB storage format

store(sources, targets[, lock, regions, …])

Store dask arrays in array-like objects, overwrite data in target

to_hdf5(filename, *args, **kwargs)

Store arrays in HDF5 file

to_zarr(arr, url[, component, …])

Save array to the zarr storage format

to_npy_stack(dirname, x[, axis])

Write dask array to a stack of .npy files

to_tiledb(darray, uri[, compute, …])

Save array to the TileDB storage format

Generalized Ufuncs

apply_gufunc(func, signature, *args[, axes, …])

Apply a generalized ufunc or similar python function to arrays.

as_gufunc([signature])

Decorator for dask.array.gufunc.

gufunc(pyfunc, **kwargs)

Binds pyfunc into dask.array.apply_gufunc when called.

Internal functions

blockwise(func, out_ind, *args[, name, …])

Tensor operation: Generalized inner and outer products

normalize_chunks(chunks[, shape, limit, …])

Normalize chunks to tuple of tuples

unify_chunks(*args, **kwargs)

Unify chunks across a sequence of arrays