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API

Top level functions

abs(x, /[, out, where, casting, order, ...])	This docstring was copied from numpy.absolute.
absolute(x, /[, out, where, casting, order, ...])	This docstring was copied from numpy.absolute.
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[, chunks, like, dtype])	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(a[, axis, keepdims, split_every, out])	Returns the indices of the maximum values along an axis.
argmin(a[, axis, keepdims, split_every, out])	Returns the indices of the minimum values 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])	Round an array to the given number of decimals.
array(object[, dtype, copy, order, subok, ...])	This docstring was copied from numpy.array.
asanyarray(a[, dtype, order, like, inline_array])	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, keepdims])	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[, subok])	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.
delete(arr, obj, axis)	Return a new array with sub-arrays along an axis deleted.
diag(v[, k])	Extract a diagonal or construct a diagonal array.
diagonal(a[, offset, axis1, axis2])	Return specified diagonals.
diff(a[, n, axis, prepend, append])	Calculate the n-th discrete difference along the given axis.
divide(x1, x2, /[, out, where, casting, ...])	This docstring was copied from numpy.divide.
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_like
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.
expand_dims(a, axis)	Expand the shape of an array.
expm1(x, /[, out, where, casting, order, ...])	This docstring was copied from numpy.expm1.
extract(condition, arr)	Return the elements of an array that satisfy some condition.
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_like
full_like(a, fill_value[, order, dtype, ...])	Return a full array with the same shape and type as a given array.
gradient(f, *varargs[, axis])	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.
i0(*args, **kwargs)	Modified Bessel function of the first kind, order 0.
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.
left_shift(x1, x2, /[, out, where, casting, ...])	This docstring was copied from numpy.left_shift.
less(x1, x2, /[, out, where, casting, ...])	This docstring was copied from numpy.less.
less_equal(x1, x2, /[, out, where, casting, ...])	This docstring was copied from numpy.less_equal.
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[, sparse, indexing])	Return a tuple of 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, ...])	Calculate the nth centralized moment.
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(a[, axis, keepdims, split_every, out])	Return the indices of the maximum values in the specified axis ignoring NaNs.
nanargmin(a[, axis, keepdims, split_every, out])	Return the indices of the minimum values in the specified axis ignoring 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_like
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[, method, internal_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.
positive(x, /[, out, where, casting, order, ...])	This docstring was copied from numpy.positive.
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.
ravel_multi_index(multi_index, dims[, mode, ...])	Converts a tuple of index arrays into an array of flat indices, applying boundary modes to the multi-index.
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 each element of an array after themselves
reshape(x, shape[, merge_chunks, limit])	Reshape array to new shape
reshape_blockwise(x, shape[, chunks])	Blockwise-reshape into a new shape.
result_type(*arrays_and_dtypes)	This docstring was copied from numpy.result_type.
right_shift(x1, x2, /[, out, where, ...])	This docstring was copied from numpy.right_shift.
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])	Evenly round to the given number of decimals.
searchsorted(a, v[, side, sorter])	Find indices where elements should be inserted to maintain order.
select(condlist, choicelist[, default])	Return an array drawn from elements in choicelist, depending on conditions.
shape(array)	Return the shape of an array.
shuffle(x, indexer, axis[, chunks])	Reorders one dimensions of a Dask Array based on an indexer.
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.
spacing(x, /[, out, where, casting, order, ...])	This docstring was copied from numpy.spacing.
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.
swapaxes(a, axis1, axis2)	Interchange two axes of an array.
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])	Returns an array with axes transposed.
tri(N[, M, k, dtype, chunks, like])	An array with ones at and below the given diagonal and zeros elsewhere.
tril(m[, k])	Lower triangle of an array.
tril_indices(n[, k, m, chunks])	Return the indices for the lower-triangle of an (n, m) array.
tril_indices_from(arr[, k])	Return the indices for the lower-triangle of arr.
triu(m[, k])	Upper triangle of an array.
triu_indices(n[, k, m, chunks])	Return the indices for the upper-triangle of an (n, m) array.
triu_indices_from(arr[, k])	Return the indices for the upper-triangle of arr.
true_divide(x1, x2, /[, out, where, ...])	This docstring was copied from numpy.divide.
trunc(x, /[, out, where, casting, order, ...])	This docstring was copied from numpy.trunc.
union1d(ar1, ar2)	Find the union of two arrays.
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_like
zeros_like(a[, dtype, order, chunks, name, ...])	Return an array of zeros with the same shape and type as a given array.

Array

Array(dask, name, chunks[, dtype, meta, shape])	Parallel Dask Array
Array.all([axis, keepdims, split_every, out])	Returns True if all elements evaluate to True.
Array.any([axis, keepdims, split_every, out])	Returns True if any of the elements evaluate to True.
Array.argmax([axis, keepdims, split_every, out])	Return indices of the maximum values along the given axis.
Array.argmin([axis, keepdims, split_every, out])	Return indices of the minimum values along the given axis.
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.blocks	An array-like interface to the blocks of an array.
Array.choose(choices)	Use an index array to construct a new array from a set of choices.
Array.chunks	Chunks property.
Array.chunksize
Array.clip([min, max])	Return an array whose values are limited to [min, max].
Array.compute(**kwargs)	Compute this dask collection
Array.compute_chunk_sizes()	Compute the chunk sizes for a Dask array.
Array.conj()	Complex-conjugate all elements.
Array.copy()	Copy array.
Array.cumprod(axis[, dtype, out, method])	Return the cumulative product of the elements along the given axis.
Array.cumsum(axis[, dtype, out, method])	Return the cumulative sum of the elements along the given axis.
Array.dask
Array.dot(other)	Dot product of self and other.
Array.dtype
Array.flatten()	Return a flattened array.
Array.imag
Array.itemsize	Length of one array element in bytes
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, keepdims, split_every, out])	Return the maximum along a given axis.
Array.mean([axis, dtype, keepdims, ...])	Returns the average of the array elements along given axis.
Array.min([axis, keepdims, split_every, out])	Return the minimum along a given axis.
Array.moment(order[, axis, dtype, keepdims, ...])	Calculate the nth centralized moment.
Array.name
Array.nbytes	Number of bytes in array
Array.ndim
Array.nonzero()	Return the indices of the elements that are non-zero.
Array.npartitions
Array.numblocks
Array.partitions	Slice an array by partitions.
Array.persist(**kwargs)	Persist this dask collection into memory
Array.prod([axis, dtype, keepdims, ...])	Return the product of the array elements over the given axis
Array.ravel()	Return a flattened array.
Array.real
Array.rechunk([chunks, threshold, ...])	Convert blocks in dask array x for new chunks.
Array.repeat(repeats[, axis])	Repeat elements of an array.
Array.reshape(*shape[, merge_chunks, limit])	Reshape array to new shape
Array.round([decimals])	Return array with each element rounded to the given number of decimals.
Array.shape
Array.shuffle(indexer, axis[, chunks])	Reorders one dimensions of a Dask Array based on an indexer.
Array.size	Number of elements in array
Array.squeeze([axis])	Remove axes of length one from array.
Array.std([axis, dtype, keepdims, ddof, ...])	Returns the standard deviation of the array elements along given axis.
Array.store(targets[, lock, regions, ...])	Store dask arrays in array-like objects, overwrite data in target
Array.sum([axis, dtype, keepdims, ...])	Return the sum of the array elements over the given axis.
Array.swapaxes(axis1, axis2)	Return a view of the array with axis1 and axis2 interchanged.
Array.to_backend([backend])	Move to a new Array backend
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])	Return the sum along diagonals of the array.
Array.transpose(*axes)	Reverse or permute the axes of an array.
Array.var([axis, dtype, keepdims, ddof, ...])	Returns the variance of the array elements, along given axis.
Array.view([dtype, order])	Get a view of the array as a new data type
Array.vindex	Vectorized indexing with broadcasting.
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, norm])	Wrapping of numpy.fft.fft
fft.fft2(a[, s, axes, norm])	Wrapping of numpy.fft.fft2
fft.fftn(a[, s, axes, norm])	Wrapping of numpy.fft.fftn
fft.ifft(a[, n, axis, norm])	Wrapping of numpy.fft.ifft
fft.ifft2(a[, s, axes, norm])	Wrapping of numpy.fft.ifft2
fft.ifftn(a[, s, axes, norm])	Wrapping of numpy.fft.ifftn
fft.rfft(a[, n, axis, norm])	Wrapping of numpy.fft.rfft
fft.rfft2(a[, s, axes, norm])	Wrapping of numpy.fft.rfft2
fft.rfftn(a[, s, axes, norm])	Wrapping of numpy.fft.rfftn
fft.irfft(a[, n, axis, norm])	Wrapping of numpy.fft.irfft
fft.irfft2(a[, s, axes, norm])	Wrapping of numpy.fft.irfft2
fft.irfftn(a[, s, axes, norm])	Wrapping of numpy.fft.irfftn
fft.hfft(a[, n, axis, norm])	Wrapping of numpy.fft.hfft
fft.ihfft(a[, n, axis, norm])	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, assume_a])	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.empty_like(prototype[, dtype, order, ...])	This docstring was copied from numpy.ma.core.empty_like.
ma.filled(a[, fill_value])	Return input as an ~numpy.ndarray, with masked values replaced by 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.nonzero(self)	This docstring was copied from numpy.ma.core.nonzero.
ma.ones_like(a, **kwargs)	Return an array of ones with the same shape and type as a given array.
ma.set_fill_value(a, fill_value)	Set the filling value of a, if a is a masked array.
ma.where(condition[, x, y])	Return a masked array with elements from x or y, depending on condition.
ma.zeros_like(a, **kwargs)	Return an array of zeros with the same shape and type as a given array.

Random

random.beta(*args, **kwargs)	Draw samples from a Beta distribution.
random.binomial(*args, **kwargs)	Draw samples from a binomial distribution.
random.chisquare(*args, **kwargs)	Draw samples from a chi-square distribution.
random.choice(*args, **kwargs)	Generates a random sample from a given 1-D array
random.default_rng([seed])	Construct a new Generator with the default BitGenerator (PCG64).
random.exponential(*args, **kwargs)	Draw samples from an exponential distribution.
random.f(*args, **kwargs)	Draw samples from an F distribution.
random.gamma(*args, **kwargs)	Draw samples from a Gamma distribution.
random.geometric(*args, **kwargs)	Draw samples from the geometric distribution.
random.gumbel(*args, **kwargs)	Draw samples from a Gumbel distribution.
random.hypergeometric(*args, **kwargs)	Draw samples from a Hypergeometric distribution.
random.laplace(*args, **kwargs)	Draw samples from the Laplace or double exponential distribution with specified location (or mean) and scale (decay).
random.logistic(*args, **kwargs)	Draw samples from a logistic distribution.
random.lognormal(*args, **kwargs)	Draw samples from a log-normal distribution.
random.logseries(*args, **kwargs)	Draw samples from a logarithmic series distribution.
random.multinomial(*args, **kwargs)	Draw samples from a multinomial distribution.
random.negative_binomial(*args, **kwargs)	Draw samples from a negative binomial distribution.
random.noncentral_chisquare(*args, **kwargs)	Draw samples from a noncentral chi-square distribution.
random.noncentral_f(*args, **kwargs)	Draw samples from the noncentral F distribution.
random.normal(*args, **kwargs)	Draw random samples from a normal (Gaussian) distribution.
random.pareto(*args, **kwargs)	Draw samples from a Pareto II or Lomax distribution with specified shape.
random.permutation(*args, **kwargs)	Randomly permute a sequence, or return a permuted range.
random.poisson(*args, **kwargs)	Draw samples from a Poisson distribution.
random.power(*args, **kwargs)	Draws samples in [0, 1] from a power distribution with positive exponent a - 1.
random.randint(*args, **kwargs)	Return random integers from low (inclusive) to high (exclusive).
random.random(*args, **kwargs)	Return random floats in the half-open interval [0.0, 1.0).
random.random_integers(*args, **kwargs)	Random integers of type numpy.int_ between low and high, inclusive.
random.random_sample(*args, **kwargs)	Return random floats in the half-open interval [0.0, 1.0).
random.rayleigh(*args, **kwargs)	Draw samples from a Rayleigh distribution.
random.standard_cauchy(*args, **kwargs)	Draw samples from a standard Cauchy distribution with mode = 0.
random.standard_exponential(*args, **kwargs)	Draw samples from the standard exponential distribution.
random.standard_gamma(*args, **kwargs)	Draw samples from a standard Gamma distribution.
random.standard_normal(*args, **kwargs)	Draw samples from a standard Normal distribution (mean=0, stdev=1).
random.standard_t(*args, **kwargs)	Draw samples from a standard Student's t distribution with df degrees of freedom.
random.triangular(*args, **kwargs)	Draw samples from the triangular distribution over the interval [left, right].
random.uniform(*args, **kwargs)	Draw samples from a uniform distribution.
random.vonmises(*args, **kwargs)	Draw samples from a von Mises distribution.
random.wald(*args, **kwargs)	Draw samples from a Wald, or inverse Gaussian, distribution.
random.weibull(*args, **kwargs)	Draw samples from a Weibull distribution.
random.zipf(*args, **kwargs)	Draw samples from a Zipf distribution.

Stats

stats.ttest_ind(a, b[, axis, equal_var])	This docstring was copied from scipy.stats.ttest_ind.
stats.ttest_1samp(a, popmean[, axis, nan_policy])	This docstring was copied from scipy.stats.ttest_1samp.
stats.ttest_rel(a, b[, axis, nan_policy])	This docstring was copied from scipy.stats.ttest_rel.
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])	This docstring was copied from scipy.stats.skew.
stats.skewtest(a[, axis, nan_policy])	This docstring was copied from scipy.stats.skewtest.
stats.kurtosis(a[, axis, fisher, bias, ...])	This docstring was copied from scipy.stats.kurtosis.
stats.kurtosistest(a[, axis, nan_policy])	This docstring was copied from scipy.stats.kurtosistest.
stats.normaltest(a[, axis, nan_policy])	This docstring was copied from scipy.stats.normaltest.
stats.f_oneway(*args)	This docstring was copied from scipy.stats.f_oneway.
stats.moment(a[, moment, axis, nan_policy])	This docstring was copied from scipy.stats.moment.

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[, chunks])	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, *[, signature, vectorize, ...])	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

Dask Metadata

meta_from_array(x[, ndim, dtype])

Normalize an array to appropriate meta object

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