Comparison Table

Here is a list of NumPy APIs and its corresponding DPNP implementations.

- in DPNP column means that DPNP implementation is not provided yet.

NumPy(v1.21.2) / DPNP(v0.9.0) / CuPy(v8.3.0) APIs

Summary

Name	NumPy	DPNP	CuPy
Module-Level	401	201	286
Multi-Dimensional Array	56	26	47
Linear Algebra	20	12	16
Discrete Fourier Transform	21	18	18
Random Sampling	51	48	48
Total	549	305	415

Module-Level

NumPy	DPNP	CuPy
`numpy.abs`	`dpnp.abs`	`cupy.abs`
`numpy.absolute`	`dpnp.absolute`	`cupy.absolute`
`numpy.add`	`dpnp.add`	`cupy.add`
`numpy.add_docstring`	-	-
`numpy.add_newdoc`	-	-
`numpy.add_newdoc_ufunc`	-	-
`numpy.alen`	-	-
`numpy.all`	`dpnp.all`	`cupy.all`
`numpy.allclose`	`dpnp.allclose`	`cupy.allclose`
`numpy.alltrue`	-	-
`numpy.amax`	`dpnp.amax`	`cupy.amax`
`numpy.amin`	`dpnp.amin`	`cupy.amin`
`numpy.angle`	-	`cupy.angle`
`numpy.any`	`dpnp.any`	`cupy.any`
`numpy.append`	-	-
`numpy.apply_along_axis`	-	-
`numpy.apply_over_axes`	-	-
`numpy.arange`	`dpnp.arange`	`cupy.arange`
`numpy.arccos`	`dpnp.arccos`	`cupy.arccos`
`numpy.arccosh`	`dpnp.arccosh`	`cupy.arccosh`
`numpy.arcsin`	`dpnp.arcsin`	`cupy.arcsin`
`numpy.arcsinh`	`dpnp.arcsinh`	`cupy.arcsinh`
`numpy.arctan`	`dpnp.arctan`	`cupy.arctan`
`numpy.arctan2`	`dpnp.arctan2`	`cupy.arctan2`
`numpy.arctanh`	`dpnp.arctanh`	`cupy.arctanh`
`numpy.argmax`	`dpnp.argmax`	`cupy.argmax`
`numpy.argmin`	`dpnp.argmin`	`cupy.argmin`
`numpy.argpartition`	-	`cupy.argpartition`
`numpy.argsort`	`dpnp.argsort`	`cupy.argsort`
`numpy.argwhere`	-	`cupy.argwhere`
`numpy.around`	`dpnp.around`	`cupy.around`
`numpy.array`	`dpnp.array`	`cupy.array`
`numpy.array2string`	-	-
`numpy.array_equal`	`dpnp.array_equal`	`cupy.array_equal`
`numpy.array_equiv`	-	-
`numpy.array_repr`	-	`cupy.array_repr`
`numpy.array_split`	-	`cupy.array_split`
`numpy.array_str`	-	`cupy.array_str`
`numpy.asanyarray`	`dpnp.asanyarray`	`cupy.asanyarray`
`numpy.asarray`	`dpnp.asarray`	`cupy.asarray`
`numpy.asarray_chkfinite`	-	-
`numpy.ascontiguousarray`	`dpnp.ascontiguousarray`	`cupy.ascontiguousarray`
`numpy.asfarray`	`dpnp.asfarray`	-
`numpy.asfortranarray`	-	`cupy.asfortranarray`
`numpy.asmatrix`	-	-
`numpy.asscalar`	-	-
`numpy.atleast_1d`	`dpnp.atleast_1d`	`cupy.atleast_1d`
`numpy.atleast_2d`	`dpnp.atleast_2d`	`cupy.atleast_2d`
`numpy.atleast_3d`	`dpnp.atleast_3d`	`cupy.atleast_3d`
`numpy.average`	`dpnp.average`	`cupy.average`
`numpy.bartlett`	-	`cupy.bartlett`
`numpy.base_repr`	-	`cupy.base_repr`
`numpy.binary_repr`	-	`cupy.binary_repr`
`numpy.bincount`	`dpnp.bincount`	`cupy.bincount`
`numpy.bitwise_and`	`dpnp.bitwise_and`	`cupy.bitwise_and`
`numpy.bitwise_not`	`dpnp.bitwise_not`	`cupy.bitwise_not`
`numpy.bitwise_or`	`dpnp.bitwise_or`	`cupy.bitwise_or`
`numpy.bitwise_xor`	`dpnp.bitwise_xor`	`cupy.bitwise_xor`
`numpy.blackman`	-	`cupy.blackman`
`numpy.block`	-	-
`numpy.bmat`	-	-
`numpy.broadcast_arrays`	-	`cupy.broadcast_arrays`
`numpy.broadcast_shapes`	-	-
`numpy.broadcast_to`	-	`cupy.broadcast_to`
`numpy.busday_count`	-	-
`numpy.busday_offset`	-	-
`numpy.byte_bounds`	-	-
`numpy.can_cast`	-	`cupy.can_cast`
`numpy.cbrt`	`dpnp.cbrt`	`cupy.cbrt`
`numpy.ceil`	`dpnp.ceil`	`cupy.ceil`
`numpy.choose`	`dpnp.choose`	`cupy.choose`
`numpy.clip`	-	`cupy.clip`
`numpy.column_stack`	-	`cupy.column_stack`
`numpy.common_type`	-	`cupy.common_type`
`numpy.compare_chararrays`	-	-
`numpy.compress`	-	`cupy.compress`
`numpy.concatenate`	`dpnp.concatenate`	`cupy.concatenate`
`numpy.conj`	`dpnp.conj`	`cupy.conj`
`numpy.conjugate`	`dpnp.conjugate`	`cupy.conjugate`
`numpy.convolve`	`dpnp.convolve`	`cupy.convolve`
`numpy.copy`	`dpnp.copy`	`cupy.copy`
`numpy.copysign`	`dpnp.copysign`	`cupy.copysign`
`numpy.copyto`	`dpnp.copyto`	`cupy.copyto`
`numpy.corrcoef`	-	`cupy.corrcoef`
`numpy.correlate`	`dpnp.correlate`	`cupy.correlate`
`numpy.cos`	`dpnp.cos`	`cupy.cos`
`numpy.cosh`	`dpnp.cosh`	`cupy.cosh`
`numpy.count_nonzero`	`dpnp.count_nonzero`	`cupy.count_nonzero`
`numpy.cov`	`dpnp.cov`	`cupy.cov`
`numpy.cross`	`dpnp.cross`	`cupy.cross`
`numpy.cumprod`	`dpnp.cumprod`	`cupy.cumprod`
`numpy.cumproduct`	-	-
`numpy.cumsum`	`dpnp.cumsum`	`cupy.cumsum`
`numpy.datetime_as_string`	-	-
`numpy.datetime_data`	-	-
`numpy.deg2rad`	`dpnp.deg2rad`	`cupy.deg2rad`
`numpy.degrees`	`dpnp.degrees`	`cupy.degrees`
`numpy.delete`	-	-
`numpy.deprecate`	-	-
`numpy.deprecate_with_doc`	-	-
`numpy.diag`	`dpnp.diag`	`cupy.diag`
`numpy.diag_indices`	`dpnp.diag_indices`	`cupy.diag_indices`
`numpy.diag_indices_from`	`dpnp.diag_indices_from`	`cupy.diag_indices_from`
`numpy.diagflat`	`dpnp.diagflat`	`cupy.diagflat`
`numpy.diagonal`	`dpnp.diagonal`	`cupy.diagonal`
`numpy.diff`	`dpnp.diff`	`cupy.diff`
`numpy.digitize`	-	`cupy.digitize`
`numpy.disp`	-	-
`numpy.divide`	`dpnp.divide`	`cupy.divide`
`numpy.divmod`	-	`cupy.divmod`
`numpy.dot`	`dpnp.dot`	`cupy.dot`
`numpy.dsplit`	-	`cupy.dsplit`
`numpy.dstack`	-	`cupy.dstack`
`numpy.ediff1d`	`dpnp.ediff1d`	-
`numpy.einsum`	`dpnp.einsum`	`cupy.einsum`
`numpy.einsum_path`	`dpnp.einsum_path`	-
`numpy.empty`	`dpnp.empty`	`cupy.empty`
`numpy.empty_like`	`dpnp.empty_like`	`cupy.empty_like`
`numpy.equal`	`dpnp.equal`	`cupy.equal`
`numpy.exp`	`dpnp.exp`	`cupy.exp`
`numpy.exp2`	`dpnp.exp2`	`cupy.exp2`
`numpy.expand_dims`	`dpnp.expand_dims`	`cupy.expand_dims`
`numpy.expm1`	`dpnp.expm1`	`cupy.expm1`
`numpy.extract`	-	`cupy.extract`
`numpy.eye`	`dpnp.eye`	`cupy.eye`
`numpy.fabs`	`dpnp.fabs`	-
`numpy.fastCopyAndTranspose`	-	-
`numpy.fill_diagonal`	`dpnp.fill_diagonal`	`cupy.fill_diagonal`
`numpy.find_common_type`	-	`cupy.find_common_type`
`numpy.fix`	-	`cupy.fix`
`numpy.flatnonzero`	-	`cupy.flatnonzero`
`numpy.flip`	-	`cupy.flip`
`numpy.fliplr`	-	`cupy.fliplr`
`numpy.flipud`	-	`cupy.flipud`
`numpy.float_power`	-	-
`numpy.floor`	`dpnp.floor`	`cupy.floor`
`numpy.floor_divide`	`dpnp.floor_divide`	`cupy.floor_divide`
`numpy.fmax`	`dpnp.fmax`	`cupy.fmax`
`numpy.fmin`	`dpnp.fmin`	`cupy.fmin`
`numpy.fmod`	`dpnp.fmod`	`cupy.fmod`
`numpy.format_float_positional`	-	-
`numpy.format_float_scientific`	-	-
`numpy.frexp`	-	`cupy.frexp`
`numpy.frombuffer`	`dpnp.frombuffer`	-
`numpy.fromfile`	`dpnp.fromfile`	`cupy.fromfile`
`numpy.fromfunction`	`dpnp.fromfunction`	-
`numpy.fromiter`	`dpnp.fromiter`	-
`numpy.frompyfunc`	-	-
`numpy.fromregex`	-	-
`numpy.fromstring`	`dpnp.fromstring`	-
`numpy.full`	`dpnp.full`	`cupy.full`
`numpy.full_like`	`dpnp.full_like`	`cupy.full_like`
`numpy.gcd`	-	`cupy.gcd`
`numpy.genfromtxt`	-	-
`numpy.geomspace`	`dpnp.geomspace`	-
`numpy.get_array_wrap`	-	-
`numpy.get_include`	`dpnp.get_include`	-
`numpy.get_printoptions`	-	-
`numpy.getbufsize`	-	-
`numpy.geterr`	-	-
`numpy.geterrcall`	-	-
`numpy.geterrobj`	-	-
`numpy.gradient`	`dpnp.gradient`	-
`numpy.greater`	`dpnp.greater`	`cupy.greater`
`numpy.greater_equal`	`dpnp.greater_equal`	`cupy.greater_equal`
`numpy.hamming`	-	`cupy.hamming`
`numpy.hanning`	-	`cupy.hanning`
`numpy.heaviside`	-	-
`numpy.histogram`	`dpnp.histogram`	`cupy.histogram`
`numpy.histogram2d`	-	-
`numpy.histogram_bin_edges`	-	-
`numpy.histogramdd`	-	-
`numpy.hsplit`	-	`cupy.hsplit`
`numpy.hstack`	`dpnp.hstack`	`cupy.hstack`
`numpy.hypot`	`dpnp.hypot`	`cupy.hypot`
`numpy.i0`	-	`cupy.i0`
`numpy.identity`	`dpnp.identity`	`cupy.identity`
`numpy.imag`	-	`cupy.imag`
`numpy.in1d`	-	`cupy.in1d`
`numpy.indices`	`dpnp.indices`	`cupy.indices`
`numpy.info`	-	-
`numpy.inner`	`dpnp.inner`	`cupy.inner`
`numpy.insert`	-	-
`numpy.interp`	-	-
`numpy.intersect1d`	-	-
`numpy.invert`	`dpnp.invert`	`cupy.invert`
`numpy.is_busday`	-	-
`numpy.isclose`	`dpnp.isclose`	`cupy.isclose`
`numpy.iscomplex`	-	`cupy.iscomplex`
`numpy.iscomplexobj`	-	`cupy.iscomplexobj`
`numpy.isfinite`	`dpnp.isfinite`	`cupy.isfinite`
`numpy.isfortran`	-	`cupy.isfortran`
`numpy.isin`	-	`cupy.isin`
`numpy.isinf`	`dpnp.isinf`	`cupy.isinf`
`numpy.isnan`	`dpnp.isnan`	`cupy.isnan`
`numpy.isnat`	-	-
`numpy.isneginf`	-	-
`numpy.isposinf`	-	-
`numpy.isreal`	-	`cupy.isreal`
`numpy.isrealobj`	-	`cupy.isrealobj`
`numpy.isscalar`	`dpnp.isscalar`	`cupy.isscalar`
`numpy.issctype`	-	`cupy.issctype`
`numpy.issubclass_`	-	`cupy.issubclass_`
`numpy.issubdtype`	-	`cupy.issubdtype`
`numpy.issubsctype`	-	`cupy.issubsctype`
`numpy.iterable`	-	-
`numpy.ix_`	-	`cupy.ix_`
`numpy.kaiser`	-	`cupy.kaiser`
`numpy.kron`	`dpnp.kron`	`cupy.kron`
`numpy.lcm`	-	`cupy.lcm`
`numpy.ldexp`	-	`cupy.ldexp`
`numpy.left_shift`	`dpnp.left_shift`	`cupy.left_shift`
`numpy.less`	`dpnp.less`	`cupy.less`
`numpy.less_equal`	`dpnp.less_equal`	`cupy.less_equal`
`numpy.lexsort`	-	`cupy.lexsort`
`numpy.linspace`	`dpnp.linspace`	`cupy.linspace`
`numpy.load`	-	`cupy.load`
`numpy.loads`	-	-
`numpy.loadtxt`	`dpnp.loadtxt`	-
`numpy.log`	`dpnp.log`	`cupy.log`
`numpy.log10`	`dpnp.log10`	`cupy.log10`
`numpy.log1p`	`dpnp.log1p`	`cupy.log1p`
`numpy.log2`	`dpnp.log2`	`cupy.log2`
`numpy.logaddexp`	-	`cupy.logaddexp`
`numpy.logaddexp2`	-	`cupy.logaddexp2`
`numpy.logical_and`	`dpnp.logical_and`	`cupy.logical_and`
`numpy.logical_not`	`dpnp.logical_not`	`cupy.logical_not`
`numpy.logical_or`	`dpnp.logical_or`	`cupy.logical_or`
`numpy.logical_xor`	`dpnp.logical_xor`	`cupy.logical_xor`
`numpy.logspace`	`dpnp.logspace`	`cupy.logspace`
`numpy.lookfor`	-	-
`numpy.mafromtxt`	-	-
`numpy.mask_indices`	-	-
`numpy.mat`	-	-
`numpy.matmul`	`dpnp.matmul`	`cupy.matmul`
`numpy.max`	`dpnp.max`	`cupy.max`
`numpy.maximum`	`dpnp.maximum`	`cupy.maximum`
`numpy.maximum_sctype`	-	-
`numpy.may_share_memory`	-	`cupy.may_share_memory`
`numpy.mean`	`dpnp.mean`	`cupy.mean`
`numpy.median`	`dpnp.median`	`cupy.median`
`numpy.meshgrid`	`dpnp.meshgrid`	`cupy.meshgrid`
`numpy.min`	`dpnp.min`	`cupy.min`
`numpy.min_scalar_type`	-	`cupy.min_scalar_type`
`numpy.minimum`	`dpnp.minimum`	`cupy.minimum`
`numpy.mintypecode`	-	`cupy.mintypecode`
`numpy.mod`	`dpnp.mod`	`cupy.mod`
`numpy.modf`	`dpnp.modf`	`cupy.modf`
`numpy.moveaxis`	`dpnp.moveaxis`	`cupy.moveaxis`
`numpy.msort`	-	`cupy.msort`
`numpy.multiply`	`dpnp.multiply`	`cupy.multiply`
`numpy.nan_to_num`	-	`cupy.nan_to_num`
`numpy.nanargmax`	-	`cupy.nanargmax`
`numpy.nanargmin`	-	`cupy.nanargmin`
`numpy.nancumprod`	`dpnp.nancumprod`	-
`numpy.nancumsum`	`dpnp.nancumsum`	-
`numpy.nanmax`	-	`cupy.nanmax`
`numpy.nanmean`	-	`cupy.nanmean`
`numpy.nanmedian`	-	-
`numpy.nanmin`	-	`cupy.nanmin`
`numpy.nanpercentile`	-	-
`numpy.nanprod`	`dpnp.nanprod`	`cupy.nanprod`
`numpy.nanquantile`	-	-
`numpy.nanstd`	-	`cupy.nanstd`
`numpy.nansum`	`dpnp.nansum`	`cupy.nansum`
`numpy.nanvar`	`dpnp.nanvar`	`cupy.nanvar`
`numpy.ndfromtxt`	-	-
`numpy.ndim`	-	`cupy.ndim`
`numpy.negative`	`dpnp.negative`	`cupy.negative`
`numpy.nested_iters`	-	-
`numpy.nextafter`	-	`cupy.nextafter`
`numpy.nonzero`	`dpnp.nonzero`	`cupy.nonzero`
`numpy.not_equal`	`dpnp.not_equal`	`cupy.not_equal`
`numpy.obj2sctype`	-	`cupy.obj2sctype`
`numpy.ones`	`dpnp.ones`	`cupy.ones`
`numpy.ones_like`	`dpnp.ones_like`	`cupy.ones_like`
`numpy.outer`	`dpnp.outer`	`cupy.outer`
`numpy.packbits`	-	`cupy.packbits`
`numpy.pad`	-	`cupy.pad`
`numpy.partition`	`dpnp.partition`	`cupy.partition`
`numpy.percentile`	-	`cupy.percentile`
`numpy.piecewise`	-	`cupy.piecewise`
`numpy.place`	`dpnp.place`	`cupy.place`
`numpy.poly`	-	-
`numpy.polyadd`	-	`cupy.polyadd`
`numpy.polyder`	-	-
`numpy.polydiv`	-	-
`numpy.polyfit`	-	-
`numpy.polyint`	-	-
`numpy.polymul`	-	`cupy.polymul`
`numpy.polysub`	-	`cupy.polysub`
`numpy.polyval`	-	`cupy.polyval`
`numpy.positive`	-	-
`numpy.power`	`dpnp.power`	`cupy.power`
`numpy.printoptions`	-	-
`numpy.prod`	`dpnp.prod`	`cupy.prod`
`numpy.product`	-	-
`numpy.promote_types`	-	`cupy.promote_types`
`numpy.ptp`	`dpnp.ptp`	`cupy.ptp`
`numpy.put`	`dpnp.put`	`cupy.put`
`numpy.put_along_axis`	`dpnp.put_along_axis`	-
`numpy.putmask`	`dpnp.putmask`	`cupy.putmask`
`numpy.quantile`	-	-
`numpy.rad2deg`	`dpnp.rad2deg`	`cupy.rad2deg`
`numpy.radians`	`dpnp.radians`	`cupy.radians`
`numpy.ravel`	`dpnp.ravel`	`cupy.ravel`
`numpy.ravel_multi_index`	-	`cupy.ravel_multi_index`
`numpy.real`	-	`cupy.real`
`numpy.real_if_close`	-	-
`numpy.recfromcsv`	-	-
`numpy.recfromtxt`	-	-
`numpy.reciprocal`	`dpnp.reciprocal`	`cupy.reciprocal`
`numpy.remainder`	`dpnp.remainder`	`cupy.remainder`
`numpy.repeat`	`dpnp.repeat`	`cupy.repeat`
`numpy.require`	-	`cupy.require`
`numpy.reshape`	`dpnp.reshape`	`cupy.reshape`
`numpy.resize`	-	-
`numpy.result_type`	-	`cupy.result_type`
`numpy.right_shift`	`dpnp.right_shift`	`cupy.right_shift`
`numpy.rint`	-	`cupy.rint`
`numpy.roll`	-	`cupy.roll`
`numpy.rollaxis`	`dpnp.rollaxis`	`cupy.rollaxis`
`numpy.roots`	-	`cupy.roots`
`numpy.rot90`	-	`cupy.rot90`
`numpy.round`	-	-
`numpy.round_`	`dpnp.round_`	`cupy.round_`
`numpy.row_stack`	-	-
`numpy.safe_eval`	-	-
`numpy.save`	-	`cupy.save`
`numpy.savetxt`	-	-
`numpy.savez`	-	`cupy.savez`
`numpy.savez_compressed`	-	`cupy.savez_compressed`
`numpy.sctype2char`	-	`cupy.sctype2char`
`numpy.searchsorted`	`dpnp.searchsorted`	`cupy.searchsorted`
`numpy.select`	`dpnp.select`	`cupy.select`
`numpy.set_numeric_ops`	-	-
`numpy.set_printoptions`	-	-
`numpy.set_string_function`	-	-
`numpy.setbufsize`	-	-
`numpy.setdiff1d`	-	-
`numpy.seterr`	-	-
`numpy.seterrcall`	-	-
`numpy.seterrobj`	-	-
`numpy.setxor1d`	-	-
`numpy.shape`	-	`cupy.shape`
`numpy.shares_memory`	-	`cupy.shares_memory`
`numpy.show_config`	-	`cupy.show_config`
`numpy.sign`	`dpnp.sign`	`cupy.sign`
`numpy.signbit`	-	`cupy.signbit`
`numpy.sin`	`dpnp.sin`	`cupy.sin`
`numpy.sinc`	-	`cupy.sinc`
`numpy.sinh`	`dpnp.sinh`	`cupy.sinh`
`numpy.size`	-	`cupy.size`
`numpy.sometrue`	-	-
`numpy.sort`	`dpnp.sort`	`cupy.sort`
`numpy.sort_complex`	-	`cupy.sort_complex`
`numpy.source`	-	-
`numpy.spacing`	-	-
`numpy.split`	-	`cupy.split`
`numpy.sqrt`	`dpnp.sqrt`	`cupy.sqrt`
`numpy.square`	`dpnp.square`	`cupy.square`
`numpy.squeeze`	`dpnp.squeeze`	`cupy.squeeze`
`numpy.stack`	`dpnp.stack`	`cupy.stack`
`numpy.std`	`dpnp.std`	`cupy.std`
`numpy.subtract`	`dpnp.subtract`	`cupy.subtract`
`numpy.sum`	`dpnp.sum`	`cupy.sum`
`numpy.swapaxes`	`dpnp.swapaxes`	`cupy.swapaxes`
`numpy.take`	`dpnp.take`	`cupy.take`
`numpy.take_along_axis`	`dpnp.take_along_axis`	`cupy.take_along_axis`
`numpy.tan`	`dpnp.tan`	`cupy.tan`
`numpy.tanh`	`dpnp.tanh`	`cupy.tanh`
`numpy.tensordot`	`dpnp.tensordot`	`cupy.tensordot`
`numpy.tile`	-	`cupy.tile`
`numpy.trace`	`dpnp.trace`	`cupy.trace`
`numpy.transpose`	`dpnp.transpose`	`cupy.transpose`
`numpy.trapz`	`dpnp.trapz`	-
`numpy.tri`	`dpnp.tri`	`cupy.tri`
`numpy.tril`	`dpnp.tril`	`cupy.tril`
`numpy.tril_indices`	`dpnp.tril_indices`	-
`numpy.tril_indices_from`	`dpnp.tril_indices_from`	-
`numpy.trim_zeros`	-	`cupy.trim_zeros`
`numpy.triu`	`dpnp.triu`	`cupy.triu`
`numpy.triu_indices`	`dpnp.triu_indices`	-
`numpy.triu_indices_from`	`dpnp.triu_indices_from`	-
`numpy.true_divide`	`dpnp.true_divide`	`cupy.true_divide`
`numpy.trunc`	`dpnp.trunc`	`cupy.trunc`
`numpy.typename`	-	`cupy.typename`
`numpy.union1d`	-	-
`numpy.unique`	`dpnp.unique`	`cupy.unique`
`numpy.unpackbits`	-	`cupy.unpackbits`
`numpy.unravel_index`	-	`cupy.unravel_index`
`numpy.unwrap`	`dpnp.unwrap`	`cupy.unwrap`
`numpy.vander`	`dpnp.vander`	-
`numpy.var`	`dpnp.var`	`cupy.var`
`numpy.vdot`	`dpnp.vdot`	`cupy.vdot`
`numpy.vsplit`	-	`cupy.vsplit`
`numpy.vstack`	`dpnp.vstack`	`cupy.vstack`
`numpy.where`	`dpnp.where`	`cupy.where`
`numpy.who`	-	`cupy.who`
`numpy.zeros`	`dpnp.zeros`	`cupy.zeros`
`numpy.zeros_like`	`dpnp.zeros_like`	`cupy.zeros_like`
NumPy Total	DPNP Total	CuPy Total
401	201	286

Multi-Dimensional Array

NumPy	DPNP	CuPy
`numpy.ndarray.all()`	`dpnp.ndarray.all()`	`cupy.ndarray.all()`
`numpy.ndarray.any()`	`dpnp.ndarray.any()`	`cupy.ndarray.any()`
`numpy.ndarray.argmax()`	`dpnp.ndarray.argmax()`	`cupy.ndarray.argmax()`
`numpy.ndarray.argmin()`	`dpnp.ndarray.argmin()`	`cupy.ndarray.argmin()`
`numpy.ndarray.argpartition()`	-	`cupy.ndarray.argpartition()`
`numpy.ndarray.argsort()`	`dpnp.ndarray.argsort()`	`cupy.ndarray.argsort()`
`numpy.ndarray.astype()`	`dpnp.ndarray.astype()`	`cupy.ndarray.astype()`
`numpy.ndarray.byteswap()`	-	-
`numpy.ndarray.choose()`	`dpnp.ndarray.choose()`	`cupy.ndarray.choose()`
`numpy.ndarray.clip()`	-	`cupy.ndarray.clip()`
`numpy.ndarray.compress()`	-	`cupy.ndarray.compress()`
`numpy.ndarray.conj()`	`dpnp.ndarray.conj()`	`cupy.ndarray.conj()`
`numpy.ndarray.conjugate()`	`dpnp.ndarray.conjugate()`	`cupy.ndarray.conjugate()`
`numpy.ndarray.copy()`	-	`cupy.ndarray.copy()`
`numpy.ndarray.cumprod()`	-	`cupy.ndarray.cumprod()`
`numpy.ndarray.cumsum()`	`dpnp.ndarray.cumsum()`	`cupy.ndarray.cumsum()`
`numpy.ndarray.diagonal()`	`dpnp.ndarray.diagonal()`	`cupy.ndarray.diagonal()`
`numpy.ndarray.dot()`	-	`cupy.ndarray.dot()`
`numpy.ndarray.dump()`	-	`cupy.ndarray.dump()`
`numpy.ndarray.dumps()`	-	`cupy.ndarray.dumps()`
`numpy.ndarray.fill()`	`dpnp.ndarray.fill()`	`cupy.ndarray.fill()`
`numpy.ndarray.flatten()`	`dpnp.ndarray.flatten()`	`cupy.ndarray.flatten()`
`numpy.ndarray.getfield()`	-	-
`numpy.ndarray.item()`	`dpnp.ndarray.item()`	`cupy.ndarray.item()`
`numpy.ndarray.itemset()`	-	-
`numpy.ndarray.max()`	`dpnp.ndarray.max()`	`cupy.ndarray.max()`
`numpy.ndarray.mean()`	`dpnp.ndarray.mean()`	`cupy.ndarray.mean()`
`numpy.ndarray.min()`	`dpnp.ndarray.min()`	`cupy.ndarray.min()`
`numpy.ndarray.newbyteorder()`	-	-
`numpy.ndarray.nonzero()`	-	`cupy.ndarray.nonzero()`
`numpy.ndarray.partition()`	-	`cupy.ndarray.partition()`
`numpy.ndarray.prod()`	`dpnp.ndarray.prod()`	`cupy.ndarray.prod()`
`numpy.ndarray.ptp()`	-	`cupy.ndarray.ptp()`
`numpy.ndarray.put()`	-	`cupy.ndarray.put()`
`numpy.ndarray.ravel()`	-	`cupy.ndarray.ravel()`
`numpy.ndarray.repeat()`	-	`cupy.ndarray.repeat()`
`numpy.ndarray.reshape()`	`dpnp.ndarray.reshape()`	`cupy.ndarray.reshape()`
`numpy.ndarray.resize()`	-	-
`numpy.ndarray.round()`	`dpnp.ndarray.round()`	`cupy.ndarray.round()`
`numpy.ndarray.searchsorted()`	-	-
`numpy.ndarray.setfield()`	-	-
`numpy.ndarray.setflags()`	-	-
`numpy.ndarray.sort()`	-	`cupy.ndarray.sort()`
`numpy.ndarray.squeeze()`	`dpnp.ndarray.squeeze()`	`cupy.ndarray.squeeze()`
`numpy.ndarray.std()`	`dpnp.ndarray.std()`	`cupy.ndarray.std()`
`numpy.ndarray.sum()`	`dpnp.ndarray.sum()`	`cupy.ndarray.sum()`
`numpy.ndarray.swapaxes()`	-	`cupy.ndarray.swapaxes()`
`numpy.ndarray.take()`	`dpnp.ndarray.take()`	`cupy.ndarray.take()`
`numpy.ndarray.tobytes()`	-	`cupy.ndarray.tobytes()`
`numpy.ndarray.tofile()`	-	`cupy.ndarray.tofile()`
`numpy.ndarray.tolist()`	-	`cupy.ndarray.tolist()`
`numpy.ndarray.tostring()`	-	-
`numpy.ndarray.trace()`	-	`cupy.ndarray.trace()`
`numpy.ndarray.transpose()`	`dpnp.ndarray.transpose()`	`cupy.ndarray.transpose()`
`numpy.ndarray.var()`	`dpnp.ndarray.var()`	`cupy.ndarray.var()`
`numpy.ndarray.view()`	-	`cupy.ndarray.view()`
NumPy Total	DPNP Total	CuPy Total
56	26	47

Linear Algebra

NumPy	DPNP	CuPy
`numpy.linalg.cholesky`	`dpnp.linalg.cholesky`	`cupy.linalg.cholesky`
`numpy.linalg.cond`	`dpnp.linalg.cond`	-
`numpy.linalg.det`	`dpnp.linalg.det`	`cupy.linalg.det`
`numpy.linalg.eig`	`dpnp.linalg.eig`	-
`numpy.linalg.eigh`	-	`cupy.linalg.eigh`
`numpy.linalg.eigvals`	`dpnp.linalg.eigvals`	-
`numpy.linalg.eigvalsh`	-	`cupy.linalg.eigvalsh`
`numpy.linalg.inv`	`dpnp.linalg.inv`	`cupy.linalg.inv`
`numpy.linalg.lstsq`	-	`cupy.linalg.lstsq`
`numpy.linalg.matrix_power`	`dpnp.linalg.matrix_power`	`cupy.linalg.matrix_power`
`numpy.linalg.matrix_rank`	`dpnp.linalg.matrix_rank`	`cupy.linalg.matrix_rank`
`numpy.linalg.multi_dot`	`dpnp.linalg.multi_dot`	-
`numpy.linalg.norm`	`dpnp.linalg.norm`	`cupy.linalg.norm`
`numpy.linalg.pinv`	-	`cupy.linalg.pinv`
`numpy.linalg.qr`	`dpnp.linalg.qr`	`cupy.linalg.qr`
`numpy.linalg.slogdet`	-	`cupy.linalg.slogdet`
`numpy.linalg.solve`	-	`cupy.linalg.solve`
`numpy.linalg.svd`	`dpnp.linalg.svd`	`cupy.linalg.svd`
`numpy.linalg.tensorinv`	-	`cupy.linalg.tensorinv`
`numpy.linalg.tensorsolve`	-	`cupy.linalg.tensorsolve`
NumPy Total	DPNP Total	CuPy Total
20	12	16

Discrete Fourier Transform

NumPy	DPNP	CuPy
`numpy.fft.fft`	`dpnp.fft.fft`	`cupy.fft.fft`
`numpy.fft.fft2`	`dpnp.fft.fft2`	`cupy.fft.fft2`
`numpy.fft.fftfreq`	`dpnp.fft.fftfreq`	`cupy.fft.fftfreq`
`numpy.fft.fftn`	`dpnp.fft.fftn`	`cupy.fft.fftn`
`numpy.fft.fftshift`	`dpnp.fft.fftshift`	`cupy.fft.fftshift`
`numpy.fft.hfft`	`dpnp.fft.hfft`	`cupy.fft.hfft`
`numpy.fft.ifft`	`dpnp.fft.ifft`	`cupy.fft.ifft`
`numpy.fft.ifft2`	`dpnp.fft.ifft2`	`cupy.fft.ifft2`
`numpy.fft.ifftn`	`dpnp.fft.ifftn`	`cupy.fft.ifftn`
`numpy.fft.ifftshift`	`dpnp.fft.ifftshift`	`cupy.fft.ifftshift`
`numpy.fft.ihfft`	`dpnp.fft.ihfft`	`cupy.fft.ihfft`
`numpy.fft.irfft`	`dpnp.fft.irfft`	`cupy.fft.irfft`
`numpy.fft.irfft2`	`dpnp.fft.irfft2`	`cupy.fft.irfft2`
`numpy.fft.irfftn`	`dpnp.fft.irfftn`	`cupy.fft.irfftn`
`numpy.fft.register_func`	-	-
`numpy.fft.restore_all`	-	-
`numpy.fft.restore_func`	-	-
`numpy.fft.rfft`	`dpnp.fft.rfft`	`cupy.fft.rfft`
`numpy.fft.rfft2`	`dpnp.fft.rfft2`	`cupy.fft.rfft2`
`numpy.fft.rfftfreq`	`dpnp.fft.rfftfreq`	`cupy.fft.rfftfreq`
`numpy.fft.rfftn`	`dpnp.fft.rfftn`	`cupy.fft.rfftn`
NumPy Total	DPNP Total	CuPy Total
21	18	18

Random Sampling

NumPy	DPNP	CuPy
`numpy.random.beta`	`dpnp.random.beta`	`cupy.random.beta`
`numpy.random.binomial`	`dpnp.random.binomial`	`cupy.random.binomial`
`numpy.random.bytes`	`dpnp.random.bytes`	`cupy.random.bytes`
`numpy.random.chisquare`	`dpnp.random.chisquare`	`cupy.random.chisquare`
`numpy.random.choice`	`dpnp.random.choice`	`cupy.random.choice`
`numpy.random.default_rng`	-	-
`numpy.random.dirichlet`	`dpnp.random.dirichlet`	`cupy.random.dirichlet`
`numpy.random.exponential`	`dpnp.random.exponential`	`cupy.random.exponential`
`numpy.random.f`	`dpnp.random.f`	`cupy.random.f`
`numpy.random.gamma`	`dpnp.random.gamma`	`cupy.random.gamma`
`numpy.random.geometric`	`dpnp.random.geometric`	`cupy.random.geometric`
`numpy.random.get_state`	-	-
`numpy.random.gumbel`	`dpnp.random.gumbel`	`cupy.random.gumbel`
`numpy.random.hypergeometric`	`dpnp.random.hypergeometric`	`cupy.random.hypergeometric`
`numpy.random.laplace`	`dpnp.random.laplace`	`cupy.random.laplace`
`numpy.random.logistic`	`dpnp.random.logistic`	`cupy.random.logistic`
`numpy.random.lognormal`	`dpnp.random.lognormal`	`cupy.random.lognormal`
`numpy.random.logseries`	`dpnp.random.logseries`	`cupy.random.logseries`
`numpy.random.multinomial`	`dpnp.random.multinomial`	`cupy.random.multinomial`
`numpy.random.multivariate_normal`	`dpnp.random.multivariate_normal`	`cupy.random.multivariate_normal`
`numpy.random.negative_binomial`	`dpnp.random.negative_binomial`	`cupy.random.negative_binomial`
`numpy.random.noncentral_chisquare`	`dpnp.random.noncentral_chisquare`	`cupy.random.noncentral_chisquare`
`numpy.random.noncentral_f`	`dpnp.random.noncentral_f`	`cupy.random.noncentral_f`
`numpy.random.normal`	`dpnp.random.normal`	`cupy.random.normal`
`numpy.random.pareto`	`dpnp.random.pareto`	`cupy.random.pareto`
`numpy.random.permutation`	`dpnp.random.permutation`	`cupy.random.permutation`
`numpy.random.poisson`	`dpnp.random.poisson`	`cupy.random.poisson`
`numpy.random.power`	`dpnp.random.power`	`cupy.random.power`
`numpy.random.rand`	`dpnp.random.rand`	`cupy.random.rand`
`numpy.random.randint`	`dpnp.random.randint`	`cupy.random.randint`
`numpy.random.randn`	`dpnp.random.randn`	`cupy.random.randn`
`numpy.random.random`	`dpnp.random.random`	`cupy.random.random`
`numpy.random.random_integers`	`dpnp.random.random_integers`	`cupy.random.random_integers`
`numpy.random.random_sample`	`dpnp.random.random_sample`	`cupy.random.random_sample`
`numpy.random.ranf`	`dpnp.random.ranf`	`cupy.random.ranf`
`numpy.random.rayleigh`	`dpnp.random.rayleigh`	`cupy.random.rayleigh`
`numpy.random.sample`	`dpnp.random.sample`	`cupy.random.sample`
`numpy.random.seed`	`dpnp.random.seed`	`cupy.random.seed`
`numpy.random.set_state`	-	-
`numpy.random.shuffle`	`dpnp.random.shuffle`	`cupy.random.shuffle`
`numpy.random.standard_cauchy`	`dpnp.random.standard_cauchy`	`cupy.random.standard_cauchy`
`numpy.random.standard_exponential`	`dpnp.random.standard_exponential`	`cupy.random.standard_exponential`
`numpy.random.standard_gamma`	`dpnp.random.standard_gamma`	`cupy.random.standard_gamma`
`numpy.random.standard_normal`	`dpnp.random.standard_normal`	`cupy.random.standard_normal`
`numpy.random.standard_t`	`dpnp.random.standard_t`	`cupy.random.standard_t`
`numpy.random.triangular`	`dpnp.random.triangular`	`cupy.random.triangular`
`numpy.random.uniform`	`dpnp.random.uniform`	`cupy.random.uniform`
`numpy.random.vonmises`	`dpnp.random.vonmises`	`cupy.random.vonmises`
`numpy.random.wald`	`dpnp.random.wald`	`cupy.random.wald`
`numpy.random.weibull`	`dpnp.random.weibull`	`cupy.random.weibull`
`numpy.random.zipf`	`dpnp.random.zipf`	`cupy.random.zipf`
NumPy Total	DPNP Total	CuPy Total
51	48	48