# 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. 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. 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. 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. Return indices that are non-zero in the flattened version of a. flip(m[, axis]) Reverse element order along axis. Reverse the order of elements along axis 0 (up/down). 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. 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 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 Compute the chunk sizes for a Dask array. 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. 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. 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. Compute the lu decomposition of a matrix. linalg.norm(x[, ord, axis, keepdims]) Matrix or vector norm. 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

 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. Return the data of a masked array as an ndarray. 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. 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. 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 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