uid	api-index

NumSharp API Reference

NumSharp is a .NET port of Python's NumPy library. This API reference is organized by functionality, matching NumPy's documentation structure.

Core Types

The essential types for working with NumSharp.

Type	Description
@NumSharp.NDArray	The main n-dimensional array type
@NumSharp.np	Static API class (like `import numpy as np` in Python)
@NumSharp.Shape	Array dimensions and strides
@NumSharp.Slice	Slice specification for array indexing
@NumSharp.Generic.NDArray`1	Generic typed wrapper for type-safe access

Quick Example

using NumSharp;

// Create arrays
var a = np.array(new[] { 1, 2, 3, 4, 5 });
var b = np.zeros((3, 4));
var c = np.arange(10);

// Operations
var sum = np.sum(a);
var reshaped = a.reshape(5, 1);
var sliced = a["1:4"];  // Elements 1, 2, 3

Array Creation

Functions for creating new arrays.

Function	Description
`np.array(data)`	Create array from existing data
`np.zeros(shape)`	Array filled with zeros
`np.zeros_like(a)`	Array of zeros with same shape as `a`
`np.ones(shape)`	Array filled with ones
`np.ones_like(a)`	Array of ones with same shape as `a`
`np.empty(shape)`	Uninitialized array
`np.full(shape, value)`	Array filled with a constant value
`np.eye(N)`	Identity matrix
`np.arange(start, stop, step)`	Evenly spaced values within interval
`np.linspace(start, stop, num)`	Evenly spaced values (specify count)
`np.meshgrid(x, y)`	Coordinate matrices from vectors
`np.copy(a)`	Return a copy of the array
`np.asarray(a)`	Convert input to array
`np.frombuffer(buffer)`	Create array from buffer

Stacking & Joining

Functions for combining multiple arrays.

Function	Description
`np.concatenate(arrays, axis)`	Join arrays along an existing axis
`np.stack(arrays, axis)`	Join arrays along a new axis
`np.vstack(arrays)`	Stack arrays vertically (row-wise)
`np.hstack(arrays)`	Stack arrays horizontally (column-wise)
`np.dstack(arrays)`	Stack arrays depth-wise (along 3rd axis)

Math Operations

Arithmetic and mathematical functions.

Arithmetic Operators

Operator	Description
`a + b`	Element-wise addition
`a - b`	Element-wise subtraction
`a * b`	Element-wise multiplication
`a / b`	Element-wise division
`a % b`	Element-wise modulo
`-a`	Element-wise negation

Math Functions

Function	Description
`np.sum(a, axis)`	Sum of array elements
`np.prod(a)`	Product of array elements
`np.cumsum(a)`	Cumulative sum
`np.sqrt(a)`	Element-wise square root
`np.power(a, n)`	Element-wise power
`np.abs(a)`	Element-wise absolute value
`np.sign(a)`	Element-wise sign
`np.floor(a)`	Element-wise floor
`np.ceil(a)`	Element-wise ceiling
`np.round(a)`	Element-wise rounding
`np.clip(a, min, max)`	Clip values to range
`np.maximum(a, b)`	Element-wise maximum
`np.minimum(a, b)`	Element-wise minimum

Exponentials & Logarithms

Function	Description
`np.exp(a)`	Element-wise exponential
`np.exp2(a)`	Element-wise 2^x
`np.expm1(a)`	exp(x) - 1
`np.log(a)`	Natural logarithm
`np.log2(a)`	Base-2 logarithm
`np.log10(a)`	Base-10 logarithm
`np.log1p(a)`	log(1 + x)

Trigonometric Functions

Function	Description
`np.sin(a)`	Element-wise sine
`np.cos(a)`	Element-wise cosine
`np.tan(a)`	Element-wise tangent

Statistics

Statistical functions.

Function	Description
`np.mean(a, axis)`	Arithmetic mean
`np.std(a, axis)`	Standard deviation
`np.var(a, axis)`	Variance
`np.amax(a, axis)`	Maximum value
`np.amin(a, axis)`	Minimum value

Sorting & Searching

Functions for sorting arrays and finding elements.

Function	Description
`np.argsort(a)`	Indices that would sort an array
`np.argmax(a, axis)`	Index of maximum value
`np.argmin(a, axis)`	Index of minimum value
`np.searchsorted(a, v)`	Find indices for inserting values
`np.nonzero(a)`	Indices of non-zero elements

Linear Algebra

Matrix and vector operations.

Function	Description
`np.dot(a, b)`	Dot product / matrix multiplication
`np.matmul(a, b)`	Matrix product (@ operator)
`np.outer(a, b)`	Outer product of two vectors

Shape Manipulation

Functions for changing array shape and dimensions.

Function	Description
`np.reshape(a, shape)`	Reshape without changing data
`a.reshape(shape)`	Instance method for reshape
`np.transpose(a)`	Permute array dimensions
`a.T`	Transpose property
`np.ravel(a)`	Flatten to 1-D array (returns view)
`a.flatten()`	Flatten to 1-D array (returns copy)
`np.squeeze(a)`	Remove axes of length 1
`np.expand_dims(a, axis)`	Insert a new axis
`np.swapaxes(a, ax1, ax2)`	Swap two axes
`np.moveaxis(a, src, dst)`	Move axes to new positions
`np.rollaxis(a, axis)`	Roll axis backwards
`np.flip(a, axis)`	Reverse element order along axes (returns view)
`np.fliplr(a)`	Flip left/right — reverse axis 1 (returns view)
`np.flipud(a)`	Flip up/down — reverse axis 0 (returns view)
`np.atleast_1d(a)`	Convert to at least 1-D
`np.atleast_2d(a)`	Convert to at least 2-D
`np.atleast_3d(a)`	Convert to at least 3-D

Indexing & Slicing

NumSharp supports Python-style array indexing and slicing.

Slice Syntax

a[":"]           // All elements
a["1:5"]         // Elements 1-4 (stop exclusive)
a["::2"]         // Every 2nd element
a["-1"]          // Last element (reduces dimension)
a["::-1"]        // Reversed
a[":, 0"]        // All rows, first column
a["..., -1"]     // Ellipsis fills dimensions

Special Slice Constants

Constant	Description
`Slice.All`	All elements (`:`)
`Slice.Ellipsis`	Fill remaining dimensions (`...`)
`Slice.NewAxis`	Insert new dimension
`Slice.Index(n)`	Single element selection

Boolean Masking

var a = np.array(new[] { 1, 2, 3, 4, 5 });
var mask = a > 2;        // [false, false, true, true, true]
var filtered = a[mask];  // [3, 4, 5]

Logic Functions

Boolean operations and comparisons.

Comparison Operators

Operator	Description
`a == b`	Element-wise equality
`a != b`	Element-wise inequality
`a > b`	Element-wise greater than
`a >= b`	Element-wise greater or equal
`a < b`	Element-wise less than
`a <= b`	Element-wise less or equal
`!a`	Element-wise NOT (boolean arrays)

Logic Functions

Function	Description
`np.all(a, axis)`	Test if all elements are true
`np.any(a, axis)`	Test if any element is true
`np.array_equal(a, b)`	Test if arrays are equal
`np.isscalar(a)`	Test if input is scalar

Random Sampling

Random number generation. Access via @NumSharp.NumPyRandom.

Function	Description
`np.random.rand(d0, d1, ...)`	Random values in [0, 1)
`np.random.randn(d0, d1, ...)`	Standard normal distribution
`np.random.randint(low, high, size)`	Random integers
`np.random.uniform(low, high, size)`	Uniform distribution
`np.random.choice(a, size, replace)`	Random sample from array
`np.random.shuffle(a)`	Shuffle array in-place
`np.random.permutation(a)`	Random permutation

Distributions

Function	Description
`np.random.beta(a, b, size)`	Beta distribution
`np.random.binomial(n, p, size)`	Binomial distribution
`np.random.gamma(shape, scale, size)`	Gamma distribution
`np.random.poisson(lam, size)`	Poisson distribution
`np.random.exponential(scale, size)`	Exponential distribution
`np.random.geometric(p, size)`	Geometric distribution
`np.random.lognormal(mean, sigma, size)`	Log-normal distribution
`np.random.chisquare(df, size)`	Chi-square distribution
`np.random.bernoulli(p, size)`	Bernoulli distribution

Broadcasting

Functions for array broadcasting.

Function	Description
`np.broadcast_to(a, shape)`	Broadcast array to new shape
`np.broadcast_arrays(a, b, ...)`	Broadcast arrays against each other

Broadcasting automatically aligns array shapes for operations:

Shapes align from the right
Dimensions must be equal OR one must be 1
Dimension of 1 "stretches" to match

File I/O

Functions for saving and loading arrays.

Function	Description
`np.save(file, arr)`	Save array to `.npy` file
`np.load(file)`	Load `.npy` or `.npz` file
`np.fromfile(file, dtype)`	Load array from binary file
`arr.tofile(file)`	Write array to binary file

Unique & Set Operations

Function	Description
`np.unique(a)`	Find unique elements
`np.repeat(a, repeats)`	Repeat elements of array

Internals & Advanced

These types are for advanced users extending NumSharp or understanding its internals.

Storage & Backends

Type	Description
@NumSharp.Backends.UnmanagedStorage	Raw unmanaged memory management
@NumSharp.TensorEngine	Abstract computation backend interface
@NumSharp.Backends.DefaultEngine	Pure C# implementation of TensorEngine

Iteration

Type	Description
@NumSharp.Backends.Iteration.NDIterRef	NumPy-aligned multi-operand iterator (broadcasting, buffering, casting, reductions); drives every kernel
@NumSharp.Backends.Iteration.NDFlatIterator	Flat C-order element iterator; backs `np.broadcast(...).iters`
@NumSharp.np.Broadcast	Broadcast result object — N operands, iterable, with `.iters` / `.index` / `.reset()`

Memory Management

Type	Description
@NumSharp.Backends.Unmanaged.ArraySlice`1	Typed memory slice
@NumSharp.Backends.Unmanaged.IMemoryBlock	Memory block interface

Utilities

Type	Description
@NumSharp.Utilities.InfoOf`1	Static type information cache
@NumSharp.NPTypeCode	Enum of supported data types

Supported Data Types

NumSharp supports 15 public array dtypes:

NPTypeCode	C# Type	NumPy Equivalent
Boolean	`bool`	`np.bool_`
Byte	`byte`	`np.uint8`
SByte	`sbyte`	`np.int8`
Int16	`short`	`np.int16`
UInt16	`ushort`	`np.uint16`
Int32	`int`	`np.int32`
UInt32	`uint`	`np.uint32`
Int64	`long`	`np.int64`
UInt64	`ulong`	`np.uint64`
Char	`char`	(no equivalent)
Half	`System.Half`	`np.float16`
Single	`float`	`np.float32`
Double	`double`	`np.float64`
Decimal	`decimal`	(no equivalent)
Complex	`System.Numerics.Complex`	`np.complex128`

NPTypeCode.Empty, NPTypeCode.String, and the NPTypeCode.Float alias are enum/compatibility values, not additional public array dtypes. Float resolves to Single.

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NumSharp API Reference

Core Types

Quick Example

Array Creation

Stacking & Joining

Math Operations

Arithmetic Operators

Math Functions

Exponentials & Logarithms

Trigonometric Functions

Statistics

Sorting & Searching

Linear Algebra

Shape Manipulation

Indexing & Slicing

Slice Syntax

Special Slice Constants

Boolean Masking

Logic Functions

Comparison Operators

Logic Functions

Random Sampling

Distributions

Broadcasting

File I/O

Unique & Set Operations

Internals & Advanced

Storage & Backends

Iteration

Memory Management

Utilities

Supported Data Types

See Also

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NumSharp API Reference

Core Types

Quick Example

Array Creation

Stacking & Joining

Math Operations

Arithmetic Operators

Math Functions

Exponentials & Logarithms

Trigonometric Functions

Statistics

Sorting & Searching

Linear Algebra

Shape Manipulation

Indexing & Slicing

Slice Syntax

Special Slice Constants

Boolean Masking

Logic Functions

Comparison Operators

Logic Functions

Random Sampling

Distributions

Broadcasting

File I/O

Unique & Set Operations

Internals & Advanced

Storage & Backends

Iteration

Memory Management

Utilities

Supported Data Types

See Also