numpy_help

……………………… False_ ……………………… Help on bool object:

class bool(generic)

Boolean type (True or False), stored as a byte.

.. warning::

The bool type is not a subclass of the int_ type

(the bool is not even a number type). This is different

than Python’s default implementation of bool as a

……………………… ScalarType ……………………… Help on tuple object:

class tuple(object)

tuple(iterable=(), /)

Built-in immutable sequence.

If no argument is given, the constructor returns an empty tuple.

If iterable is specified the tuple is initialized from iterable’s items.

……………………… True_ ……………………… Help on bool object:

class bool(generic)

Boolean type (True or False), stored as a byte.

.. warning::

The bool type is not a subclass of the int_ type

(the bool is not even a number type). This is different

than Python’s default implementation of bool as a

……………………… abs ……………………… Help on ufunc in module numpy:

absolute = <ufunc ‘absolute’>

absolute(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Calculate the absolute value element-wise.

np.abs is a shorthand for this function.

Parameters

……………………… absolute ……………………… Help on ufunc in module numpy:

absolute = <ufunc ‘absolute’>

absolute(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Calculate the absolute value element-wise.

np.abs is a shorthand for this function.

Parameters

……………………… acos ……………………… Help on ufunc in module numpy:

arccos = <ufunc ‘arccos’>

arccos(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Trigonometric inverse cosine, element-wise.

The inverse of cos so that, if y = cos(x), then x = arccos(y).

Parameters

……………………… acosh ……………………… Help on ufunc in module numpy:

arccosh = <ufunc ‘arccosh’>

arccosh(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Inverse hyperbolic cosine, element-wise.

x : array_like

……………………… add ……………………… Help on ufunc in module numpy:

add = <ufunc ‘add’>

add(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Add arguments element-wise.

x1, x2 : array_like

……………………… all ……………………… Help on _ArrayFunctionDispatcher in module numpy:

all(a, axis=None, out=None, keepdims=<no value>, *, where=<no value>)

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

aarray_like

Input array or object that can be converted to an array.

axis : None or int or tuple of ints, optional

……………………… allclose ……………………… Help on _ArrayFunctionDispatcher in module numpy:

allclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False)

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

The tolerance values are positive, typically very small numbers. The relative difference (rtol * abs(b)) and the absolute difference atol are added together to compare against the absolute difference between a and b.

……………………… amax ……………………… Help on _ArrayFunctionDispatcher in module numpy:

amax(

a, axis=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>

) ……………………… amin ……………………… Help on _ArrayFunctionDispatcher in module numpy:

amin(

a, axis=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>

) ……………………… angle ……………………… Help on _ArrayFunctionDispatcher in module numpy:

angle(z, deg=False)

Return the angle of the complex argument.

zarray_like

A complex number or sequence of complex numbers.

deg : bool, optional

……………………… any ……………………… Help on _ArrayFunctionDispatcher in module numpy:

any(a, axis=None, out=None, keepdims=<no value>, *, where=<no value>)

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

Returns single boolean if axis is None

a : array_like

……………………… append ……………………… Help on _ArrayFunctionDispatcher in module numpy:

append(arr, values, axis=None)

Append values to the end of an array.

arrarray_like

Values are appended to a copy of this array.

values : array_like

……………………… apply_along_axis ……………………… Help on _ArrayFunctionDispatcher in module numpy:

apply_along_axis(func1d, axis, arr, *args, **kwargs)

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

Execute func1d(a, *args, **kwargs) where func1d operates on 1-D arrays and a is a 1-D slice of arr along axis.

This is equivalent to (but faster than) the following use of ndindex and s_, which sets each of ii, jj, and kk to a tuple of indices:

……………………… apply_over_axes ……………………… Help on _ArrayFunctionDispatcher in module numpy:

apply_over_axes(func, a, axes)

Apply a function repeatedly over multiple axes.

func is called as res = func(a, axis), where axis is the first element of axes. The result res of the function call must have either the same dimensions as a or one less dimension. If res has one less dimension than a, a dimension is inserted before axis. The call to func is then repeated for each axis in axes,

……………………… arange ……………………… Help on built-in function arange in module numpy:

arange(…)

arange([start,] stop[, step,], dtype=None, *, device=None, like=None)

Return evenly spaced values within a given interval.

arange can be called with a varying number of positional arguments:

• arange(stop): Values are generated within the half-open interval

……………………… arccos ……………………… Help on ufunc in module numpy:

arccos = <ufunc ‘arccos’>

arccos(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Trigonometric inverse cosine, element-wise.

The inverse of cos so that, if y = cos(x), then x = arccos(y).

Parameters

……………………… arccosh ……………………… Help on ufunc in module numpy:

arccosh = <ufunc ‘arccosh’>

arccosh(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Inverse hyperbolic cosine, element-wise.

x : array_like

……………………… arcsin ……………………… Help on ufunc in module numpy:

arcsin = <ufunc ‘arcsin’>

arcsin(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Inverse sine, element-wise.

x : array_like

……………………… arcsinh ……………………… Help on ufunc in module numpy:

arcsinh = <ufunc ‘arcsinh’>

arcsinh(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Inverse hyperbolic sine element-wise.

x : array_like

……………………… arctan ……………………… Help on ufunc in module numpy:

arctan = <ufunc ‘arctan’>

arctan(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Trigonometric inverse tangent, element-wise.

The inverse of tan, so that if y = tan(x) then x = arctan(y).

Parameters

……………………… arctan2 ……………………… Help on ufunc in module numpy:

arctan2 = <ufunc ‘arctan2’>

arctan2(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Element-wise arc tangent of x1/x2 choosing the quadrant correctly.

The quadrant (i.e., branch) is chosen so that arctan2(x1, x2) is the signed angle in radians between the ray ending at the origin and passing through the point (1,0), and the ray ending at the origin and …… …… ……………. x1 x2 arctan2(x1,x2) ====== ====== ================ +/- 0 +0 +/- 0 +/- 0 -0 +/- pi

> 0 +/-inf +0 / +pi < 0 +/-inf -0 / -pi

+/-inf +inf +/- (pi/4) +/-inf -inf +/- (3*pi/4) ====== ====== ================

……………………… arctanh ……………………… Help on ufunc in module numpy:

arctanh = <ufunc ‘arctanh’>

arctanh(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Inverse hyperbolic tangent element-wise.

x : array_like

……………………… argmax ……………………… Help on _ArrayFunctionDispatcher in module numpy:

argmax(a, axis=None, out=None, *, keepdims=<no value>)

Returns the indices of the maximum values along an axis.

aarray_like

Input array.

axis : int, optional

……………………… argmin ……………………… Help on _ArrayFunctionDispatcher in module numpy:

argmin(a, axis=None, out=None, *, keepdims=<no value>)

Returns the indices of the minimum values along an axis.

aarray_like

Input array.

axis : int, optional

……………………… argpartition ……………………… Help on _ArrayFunctionDispatcher in module numpy:

argpartition(a, kth, axis=-1, kind=’introselect’, order=None)

Perform an indirect partition along the given axis using the algorithm specified by the kind keyword. It returns an array of indices of the same shape as a that index data along the given axis in partitioned order.

……………………… argsort ……………………… Help on _ArrayFunctionDispatcher in module numpy:

argsort(a, axis=-1, kind=None, order=None, *, stable=None)

Returns the indices that would sort an array.

Perform an indirect sort along the given axis using the algorithm specified by the kind keyword. It returns an array of indices of the same shape as a that index data along the given axis in sorted order.

Parameters

……………………… argwhere ……………………… Help on _ArrayFunctionDispatcher in module numpy:

argwhere(a)

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

aarray_like

Input data.

……………………… around ……………………… Help on _ArrayFunctionDispatcher in module numpy:

around(a, decimals=0, out=None)

Round an array to the given number of decimals.

around is an alias of ~numpy.round.

ndarray.round : equivalent method

……………………… array ……………………… Help on built-in function array in module numpy:

array(…)

array(object, dtype=None, *, copy=True, order=’K’, subok=False, ndmin=0,

like=None)

Create an array.

….. ……… …………………………………………… order no copy copy=True ===== ========= =================================================== ‘K’ unchanged F & C order preserved, otherwise most similar order ‘A’ unchanged F order if input is F and not C, otherwise C order ‘C’ C order C order ‘F’ F order F order ===== ========= ===================================================

When copy=None and a copy is made for other reasons, the result is the same as if copy=True, with some exceptions for ‘A’, see the

……………………… array2string ……………………… Help on _ArrayFunctionDispatcher in module numpy:

array2string(

a, max_line_width=None, precision=None, suppress_small=None, separator=’ ‘, prefix=’’, style=<no value>,

……………………… array_equal ……………………… Help on _ArrayFunctionDispatcher in module numpy:

array_equal(a1, a2, equal_nan=False)

True if two arrays have the same shape and elements, False otherwise.

a1, a2array_like

Input arrays.

equal_nan : bool

……………………… array_equiv ……………………… Help on _ArrayFunctionDispatcher in module numpy:

array_equiv(a1, a2)

Returns True if input arrays are shape consistent and all elements equal.

Shape consistent means they are either the same shape, or one input array can be broadcasted to create the same shape as the other one.

……………………… array_repr ……………………… Help on _ArrayFunctionDispatcher in module numpy:

array_repr(arr, max_line_width=None, precision=None, suppress_small=None)

Return the string representation of an array.

arrndarray

Input array.

max_line_width : int, optional

……………………… array_split ……………………… Help on _ArrayFunctionDispatcher in module numpy:

array_split(ary, indices_or_sections, axis=0)

Split an array into multiple sub-arrays.

Please refer to the split documentation. The only difference between these functions is that array_split allows indices_or_sections to be an integer that does not equally divide the axis. For an array of length l that should be split into n sections, it returns l % n sub-arrays of size l//n + 1

……………………… array_str ……………………… Help on _ArrayFunctionDispatcher in module numpy:

array_str(a, max_line_width=None, precision=None, suppress_small=None)

Return a string representation of the data in an array.

The data in the array is returned as a single string. This function is similar to array_repr, the difference being that array_repr also returns information on the kind of array and its data type.

Parameters

……………………… asanyarray ……………………… Help on built-in function asanyarray in module numpy:

asanyarray(…)

asanyarray(a, dtype=None, order=None, *, device=None, copy=None, like=None)

Convert the input to an ndarray, but pass ndarray subclasses through.

a : array_like

……………………… asarray ……………………… Help on built-in function asarray in module numpy:

asarray(…)

asarray(a, dtype=None, order=None, *, device=None, copy=None, like=None)

Convert the input to an array.

a : array_like

……………………… asarray_chkfinite ……………………… Help on function asarray_chkfinite in module numpy:

asarray_chkfinite(a, dtype=None, order=None)

Convert the input to an array, checking for NaNs or Infs.

aarray_like

Input data, in any form that can be converted to an array. This includes lists, lists of tuples, tuples, tuples of tuples, tuples

……………………… ascontiguousarray ……………………… Help on built-in function ascontiguousarray in module numpy:

ascontiguousarray(…)

ascontiguousarray(a, dtype=None, *, like=None)

Return a contiguous array (ndim >= 1) in memory (C order).

a : array_like

……………………… asfortranarray ……………………… Help on built-in function asfortranarray in module numpy:

asfortranarray(…)

asfortranarray(a, dtype=None, *, like=None)

Return an array (ndim >= 1) laid out in Fortran order in memory.

a : array_like

……………………… asin ……………………… Help on ufunc in module numpy:

arcsin = <ufunc ‘arcsin’>

arcsin(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Inverse sine, element-wise.

x : array_like

……………………… asinh ……………………… Help on ufunc in module numpy:

arcsinh = <ufunc ‘arcsinh’>

arcsinh(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Inverse hyperbolic sine element-wise.

x : array_like

……………………… asmatrix ……………………… Help on function asmatrix in module numpy:

asmatrix(data, dtype=None)

Interpret the input as a matrix.

Unlike matrix, asmatrix does not make a copy if the input is already a matrix or an ndarray. Equivalent to matrix(data, copy=False).

……………………… astype ……………………… Help on _ArrayFunctionDispatcher in module numpy:

astype(x, dtype, /, *, copy=True, device=None)

Copies an array to a specified data type.

This function is an Array API compatible alternative to numpy.ndarray.astype.

……………………… atan ……………………… Help on ufunc in module numpy:

arctan = <ufunc ‘arctan’>

arctan(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Trigonometric inverse tangent, element-wise.

The inverse of tan, so that if y = tan(x) then x = arctan(y).

Parameters

……………………… atan2 ……………………… Help on ufunc in module numpy:

arctan2 = <ufunc ‘arctan2’>

arctan2(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Element-wise arc tangent of x1/x2 choosing the quadrant correctly.

The quadrant (i.e., branch) is chosen so that arctan2(x1, x2) is the signed angle in radians between the ray ending at the origin and passing through the point (1,0), and the ray ending at the origin and …… …… ……………. x1 x2 arctan2(x1,x2) ====== ====== ================ +/- 0 +0 +/- 0 +/- 0 -0 +/- pi

> 0 +/-inf +0 / +pi < 0 +/-inf -0 / -pi

+/-inf +inf +/- (pi/4) +/-inf -inf +/- (3*pi/4) ====== ====== ================

……………………… atanh ……………………… Help on ufunc in module numpy:

arctanh = <ufunc ‘arctanh’>

arctanh(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Inverse hyperbolic tangent element-wise.

x : array_like

……………………… atleast_1d ……………………… Help on _ArrayFunctionDispatcher in module numpy:

atleast_1d(*arys)

Convert inputs to arrays with at least one dimension.

Scalar inputs are converted to 1-dimensional arrays, whilst higher-dimensional inputs are preserved.

……………………… atleast_2d ……………………… Help on _ArrayFunctionDispatcher in module numpy:

atleast_2d(*arys)

View inputs as arrays with at least two dimensions.

arys1, arys2, …array_like

One or more array-like sequences. Non-array inputs are converted to arrays. Arrays that already have two or more dimensions are

……………………… atleast_3d ……………………… Help on _ArrayFunctionDispatcher in module numpy:

atleast_3d(*arys)

View inputs as arrays with at least three dimensions.

arys1, arys2, …array_like

One or more array-like sequences. Non-array inputs are converted to arrays. Arrays that already have three or more dimensions are

……………………… average ……………………… Help on _ArrayFunctionDispatcher in module numpy:

average(a, axis=None, weights=None, returned=False, *, keepdims=<no value>)

Compute the weighted average along the specified axis.

aarray_like

Array containing data to be averaged. If a is not an array, a conversion is attempted.

……………………… bartlett ……………………… Help on function bartlett in module numpy:

bartlett(M)

Return the Bartlett window.

The Bartlett window is very similar to a triangular window, except that the end points are at zero. It is often used in signal processing for tapering a signal, without generating too much ripple in the frequency domain.

……………………… base_repr ……………………… Help on function base_repr in module numpy:

base_repr(number, base=2, padding=0)

Return a string representation of a number in the given base system.

numberint

The value to convert. Positive and negative values are handled.

base : int, optional

……………………… binary_repr ……………………… Help on function binary_repr in module numpy:

binary_repr(num, width=None)

Return the binary representation of the input number as a string.

For negative numbers, if width is not given, a minus sign is added to the front. If width is given, the two’s complement of the number is returned, with respect to that width.

In a two’s-complement system negative numbers are represented by the two’s

……………………… bincount ……………………… Help on _ArrayFunctionDispatcher in module numpy:

bincount(…)

bincount(x, /, weights=None, minlength=0)

Count number of occurrences of each value in array of non-negative ints.

The number of bins (of size 1) is one larger than the largest value in x. If minlength is specified, there will be at least this number of bins in the output array (though it will be longer if necessary,

……………………… bitwise_and ……………………… Help on ufunc in module numpy:

bitwise_and = <ufunc ‘bitwise_and’>

bitwise_and(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Compute the bit-wise AND of two arrays element-wise.

Computes the bit-wise AND of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator &.

……………………… bitwise_count ……………………… Help on ufunc in module numpy:

bitwise_count = <ufunc ‘bitwise_count’>

bitwise_count(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Computes the number of 1-bits in the absolute value of x. Analogous to the builtin int.bit_count or popcount in C++.

……………………… bitwise_invert ……………………… Help on ufunc in module numpy:

invert = <ufunc ‘invert’>

invert(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Compute bit-wise inversion, or bit-wise NOT, element-wise.

Computes the bit-wise NOT of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator ~.

……………………… bitwise_left_shift ……………………… Help on ufunc in module numpy:

left_shift = <ufunc ‘left_shift’>

left_shift(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Shift the bits of an integer to the left.

Bits are shifted to the left by appending x2 0s at the right of x1. Since the internal representation of numbers is in binary format, this operation is equivalent to multiplying x1 by 2**x2.

……………………… bitwise_not ……………………… Help on ufunc in module numpy:

invert = <ufunc ‘invert’>

invert(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Compute bit-wise inversion, or bit-wise NOT, element-wise.

Computes the bit-wise NOT of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator ~.

……………………… bitwise_or ……………………… Help on ufunc in module numpy:

bitwise_or = <ufunc ‘bitwise_or’>

bitwise_or(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Compute the bit-wise OR of two arrays element-wise.

Computes the bit-wise OR of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator |.

……………………… bitwise_right_shift ……………………… Help on ufunc in module numpy:

right_shift = <ufunc ‘right_shift’>

right_shift(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Shift the bits of an integer to the right.

Bits are shifted to the right x2. Because the internal representation of numbers is in binary format, this operation is equivalent to dividing x1 by 2**x2.

……………………… bitwise_xor ……………………… Help on ufunc in module numpy:

bitwise_xor = <ufunc ‘bitwise_xor’>

bitwise_xor(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Compute the bit-wise XOR of two arrays element-wise.

Computes the bit-wise XOR of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator ^.

……………………… blackman ……………………… Help on function blackman in module numpy:

blackman(M)

Return the Blackman window.

The Blackman window is a taper formed by using the first three terms of a summation of cosines. It was designed to have close to the minimal leakage possible. It is close to optimal, only slightly worse than a Kaiser window.

……………………… block ……………………… Help on _ArrayFunctionDispatcher in module numpy:

block(arrays)

Assemble an nd-array from nested lists of blocks.

Blocks in the innermost lists are concatenated (see concatenate) along the last dimension (-1), then these are concatenated along the second-last dimension (-2), and so on until the outermost list is reached.

Blocks can be of any dimension, but will not be broadcasted using

……………………… bmat ……………………… Help on function bmat in module numpy:

bmat(obj, ldict=None, gdict=None)

Build a matrix object from a string, nested sequence, or array.

objstr or array_like

Input data. If a string, variables in the current scope may be referenced by name.

……………………… bool ……………………… Help on class bool in module numpy:

class bool(generic)

Boolean type (True or False), stored as a byte.

.. warning::

The bool type is not a subclass of the int_ type

(the bool is not even a number type). This is different

than Python’s default implementation of bool as a

……………………… bool_ ……………………… Help on class bool in module numpy:

class bool(generic)

Boolean type (True or False), stored as a byte.

.. warning::

The bool type is not a subclass of the int_ type

(the bool is not even a number type). This is different

than Python’s default implementation of bool as a

……………………… broadcast ……………………… Help on class broadcast in module numpy:

class broadcast(builtins.object)

Produce an object that mimics broadcasting.

Parameters

———-

in1, in2, … : array_like

Input parameters.

……………………… broadcast_arrays ……………………… Help on _ArrayFunctionDispatcher in module numpy:

broadcast_arrays(*args, subok=False)

Broadcast any number of arrays against each other.

*argsarray_likes

The arrays to broadcast.

……………………… broadcast_shapes ……………………… Help on function broadcast_shapes in module numpy:

broadcast_shapes(*args)

Broadcast the input shapes into a single shape.

Learn more about broadcasting here.

New in version 1.20.0.

Parameters

……………………… broadcast_to ……………………… Help on _ArrayFunctionDispatcher in module numpy:

broadcast_to(array, shape, subok=False)

Broadcast an array to a new shape.

arrayarray_like

The array to broadcast.

shape : tuple or int

……………………… busday_count ……………………… Help on _ArrayFunctionDispatcher in module numpy:

busday_count(…)

busday_count(

begindates, enddates, weekmask=’1111100’, holidays=[], busdaycal=None, out=None

……………………… busday_offset ……………………… Help on _ArrayFunctionDispatcher in module numpy:

busday_offset(…)

busday_offset(

dates, offsets, roll=’raise’, weekmask=’1111100’, holidays=None, busdaycal=None,

……………………… busdaycalendar ……………………… Help on class busdaycalendar in module numpy:

class busdaycalendar(builtins.object)

busdaycalendar(weekmask=’1111100’, holidays=None)

A business day calendar object that efficiently stores information

defining valid days for the busday family of functions.

The default valid days are Monday through Friday (“business days”).

A busdaycalendar object can be specified with any set of weekly

……………………… byte ……………………… Help on class int8 in module numpy:

class int8(signedinteger)

Signed integer type, compatible with C char.

:Character code: 'b'

:Canonical name: numpy.byte

:Alias on this platform (Linux x86_64): numpy.int8: 8-bit signed integer (-128 to 127).

Method resolution order:

……………………… bytes_ ……………………… Help on class bytes_ in module numpy:

class bytes_(builtins.bytes, character)

A byte string.

When used in arrays, this type strips trailing null bytes.

:Character code: 'S'

Method resolution order:

……………………… c_ ……………………… Help on CClass in module numpy.lib._index_tricks_impl object:

class CClass(AxisConcatenator)

Translates slice objects to concatenation along the second axis.

This is short-hand for np.r_['-1,2,0', index expression], which is

useful because of its common occurrence. In particular, arrays will be

stacked along their last axis after being upgraded to at least 2-D with

1’s post-pended to the shape (column vectors made out of 1-D arrays).

……………………… can_cast ……………………… Help on _ArrayFunctionDispatcher in module numpy:

can_cast(…)

can_cast(from_, to, casting=’safe’)

Returns True if cast between data types can occur according to the casting rule.

……………………… cbrt ……………………… Help on ufunc in module numpy:

cbrt = <ufunc ‘cbrt’>

cbrt(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Return the cube-root of an array, element-wise.

x : array_like

……………………… cdouble ……………………… Help on class complex128 in module numpy:

class complex128(complexfloating, builtins.complex)

complex128(real=0, imag=0)

Complex number type composed of two double-precision floating-point

numbers, compatible with Python complex.

:Character code: 'D'

:Canonical name: numpy.cdouble

……………………… ceil ……………………… Help on ufunc in module numpy:

ceil = <ufunc ‘ceil’>

ceil(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Return the ceiling of the input, element-wise.

The ceil of the scalar x is the smallest integer i, such that i >= x. It is often denoted as \(\lceil x \rceil\).

……………………… char ……………………… Help on package numpy.char in numpy:

NAME

numpy.char

DESCRIPTION

This module contains a set of functions for vectorized string operations and methods.

……………………… character ……………………… Help on class character in module numpy:

class character(flexible)

Abstract base class of all character string scalar types.

Method resolution order:

character

flexible

generic

builtins.object

……………………… choose ……………………… Help on _ArrayFunctionDispatcher in module numpy:

choose(a, choices, out=None, mode=’raise’)

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

First of all, if confused or uncertain, definitely look at the Examples - in its full generality, this function is less simple than it might seem from the following code description:

np.choose(a,c) == np.array([c[a[I]][I] for I in np.ndindex(a.shape)])

……………………… clip ……………………… Help on _ArrayFunctionDispatcher in module numpy:

clip(

a, a_min=<no value>, a_max=<no value>, out=None, *, min=<no value>, max=<no value>,

……………………… clongdouble ……………………… Help on class clongdouble in module numpy:

class clongdouble(complexfloating)

Complex number type composed of two extended-precision floating-point

numbers.

:Character code: 'G'

:Alias on this platform (Linux x86_64): numpy.complex256: Complex number type composed of 2 128-bit extended-precision floating-point numbers.

Method resolution order:

……………………… column_stack ……………………… Help on _ArrayFunctionDispatcher in module numpy:

column_stack(tup)

Stack 1-D arrays as columns into a 2-D array.

Take a sequence of 1-D arrays and stack them as columns to make a single 2-D array. 2-D arrays are stacked as-is, just like with hstack. 1-D arrays are turned into 2-D columns first.

……………………… common_type ……………………… Help on _ArrayFunctionDispatcher in module numpy:

common_type(*arrays)

Return a scalar type which is common to the input arrays.

The return type will always be an inexact (i.e. floating point) scalar type, even if all the arrays are integer arrays. If one of the inputs is an integer array, the minimum precision type that is returned is a 64-bit floating point dtype.

……………………… complex128 ……………………… Help on class complex128 in module numpy:

class complex128(complexfloating, builtins.complex)

complex128(real=0, imag=0)

Complex number type composed of two double-precision floating-point

numbers, compatible with Python complex.

:Character code: 'D'

:Canonical name: numpy.cdouble

……………………… complex256 ……………………… Help on class clongdouble in module numpy:

class clongdouble(complexfloating)

Complex number type composed of two extended-precision floating-point

numbers.

:Character code: 'G'

:Alias on this platform (Linux x86_64): numpy.complex256: Complex number type composed of 2 128-bit extended-precision floating-point numbers.

Method resolution order:

……………………… complex64 ……………………… Help on class complex64 in module numpy:

class complex64(complexfloating)

Complex number type composed of two single-precision floating-point

numbers.

:Character code: 'F'

:Canonical name: numpy.csingle

:Alias on this platform (Linux x86_64): numpy.complex64: Complex number type composed of 2 32-bit-precision floating-point numbers.

……………………… complexfloating ……………………… Help on class complexfloating in module numpy:

class complexfloating(inexact)

Abstract base class of all complex number scalar types that are made up of

floating-point numbers.

Method resolution order:

complexfloating

inexact

number

……………………… compress ……………………… Help on _ArrayFunctionDispatcher in module numpy:

compress(condition, a, axis=None, out=None)

Return selected slices of an array along given axis.

When working along a given axis, a slice along that axis is returned in output for each index where condition evaluates to True. When working on a 1-D array, compress is equivalent to extract.

Parameters

……………………… concat ……………………… Help on _ArrayFunctionDispatcher in module numpy:

concatenate(…)

concatenate(

(a1, a2, …), axis=0, out=None, dtype=None, casting=”same_kind”

)

……………………… concatenate ……………………… Help on _ArrayFunctionDispatcher in module numpy:

concatenate(…)

concatenate(

(a1, a2, …), axis=0, out=None, dtype=None, casting=”same_kind”

)

……………………… conj ……………………… Help on ufunc in module numpy:

conjugate = <ufunc ‘conjugate’>

conjugate(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Return the complex conjugate, element-wise.

The complex conjugate of a complex number is obtained by changing the sign of its imaginary part.

……………………… conjugate ……………………… Help on ufunc in module numpy:

conjugate = <ufunc ‘conjugate’>

conjugate(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Return the complex conjugate, element-wise.

The complex conjugate of a complex number is obtained by changing the sign of its imaginary part.

……………………… convolve ……………………… Help on _ArrayFunctionDispatcher in module numpy:

convolve(a, v, mode=’full’)

Returns the discrete, linear convolution of two one-dimensional sequences.

The convolution operator is often seen in signal processing, where it models the effect of a linear time-invariant system on a signal [1]_. In probability theory, the sum of two independent random variables is distributed according to the convolution of their individual distributions.

……………………… copy ……………………… Help on _ArrayFunctionDispatcher in module numpy:

copy(a, order=’K’, subok=False)

Return an array copy of the given object.

aarray_like

Input data.

order : {‘C’, ‘F’, ‘A’, ‘K’}, optional

……………………… copysign ……………………… Help on ufunc in module numpy:

copysign = <ufunc ‘copysign’>

copysign(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Change the sign of x1 to that of x2, element-wise.

If x2 is a scalar, its sign will be copied to all elements of x1.

Parameters

……………………… copyto ……………………… Help on _ArrayFunctionDispatcher in module numpy:

copyto(…)

copyto(dst, src, casting=’same_kind’, where=True)

Copies values from one array to another, broadcasting as necessary.

Raises a TypeError if the casting rule is violated, and if where is provided, it selects which elements to copy.

……………………… core ……………………… Help on package numpy.core in numpy:

NAME

numpy.core

DESCRIPTION

The numpy.core submodule exists solely for backward compatibility purposes. The original core was renamed to _core and made private. numpy.core will be removed in the future.

……………………… corrcoef ……………………… Help on _ArrayFunctionDispatcher in module numpy:

corrcoef(

x, y=None, rowvar=True, bias=<no value>, ddof=<no value>, *, dtype=None

……………………… correlate ……………………… Help on _ArrayFunctionDispatcher in module numpy:

correlate(a, v, mode=’valid’)

Cross-correlation of two 1-dimensional sequences.

This function computes the correlation as generally defined in signal processing texts [1]_:

\[c_k = \sum_n a_{n+k} \cdot \overline{v}_n\]

……………………… cos ……………………… Help on ufunc in module numpy:

cos = <ufunc ‘cos’>

cos(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Cosine element-wise.

x : array_like

……………………… cosh ……………………… Help on ufunc in module numpy:

cosh = <ufunc ‘cosh’>

cosh(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Hyperbolic cosine, element-wise.

Equivalent to 1/2 * (np.exp(x) + np.exp(-x)) and np.cos(1j*x).

Parameters

……………………… count_nonzero ……………………… Help on _ArrayFunctionDispatcher in module numpy:

count_nonzero(a, axis=None, *, keepdims=False)

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

The word “non-zero” is in reference to the Python 2.x built-in method __nonzero__() (renamed __bool__() in Python 3.x) of Python objects that tests an object’s “truthfulness”. For example, any number is considered truthful if it is nonzero, whereas any string is considered

……………………… cov ……………………… Help on _ArrayFunctionDispatcher in module numpy:

cov(

m, y=None, rowvar=True, bias=False, ddof=None, fweights=None, aweights=None,

……………………… cross ……………………… Help on _ArrayFunctionDispatcher in module numpy:

cross(a, b, axisa=-1, axisb=-1, axisc=-1, axis=None)

Return the cross product of two (arrays of) vectors.

The cross product of a and b in \(R^3\) is a vector perpendicular to both a and b. If a and b are arrays of vectors, the vectors are defined by the last axis of a and b by default, and these axes can have dimensions 2 or 3. Where the dimension of either a or b is 2, the third component of the input vector is assumed to be zero and the

……………………… csingle ……………………… Help on class complex64 in module numpy:

class complex64(complexfloating)

Complex number type composed of two single-precision floating-point

numbers.

:Character code: 'F'

:Canonical name: numpy.csingle

:Alias on this platform (Linux x86_64): numpy.complex64: Complex number type composed of 2 32-bit-precision floating-point numbers.

……………………… ctypeslib ……………………… Help on module numpy.ctypeslib in numpy:

NAME

numpy.ctypeslib

DESCRIPTION

……………………… cumprod ……………………… Help on _ArrayFunctionDispatcher in module numpy:

cumprod(a, axis=None, dtype=None, out=None)

Return the cumulative product of elements along a given axis.

aarray_like

Input array.

axis : int, optional

……………………… cumsum ……………………… Help on _ArrayFunctionDispatcher in module numpy:

cumsum(a, axis=None, dtype=None, out=None)

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

aarray_like

Input array.

axis : int, optional

……………………… cumulative_prod ……………………… Help on _ArrayFunctionDispatcher in module numpy:

cumulative_prod(x, /, *, axis=None, dtype=None, out=None, include_initial=False)

Return the cumulative product of elements along a given axis.

This function is an Array API compatible alternative to numpy.cumprod.

x : array_like

……………………… cumulative_sum ……………………… Help on _ArrayFunctionDispatcher in module numpy:

cumulative_sum(x, /, *, axis=None, dtype=None, out=None, include_initial=False)

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

This function is an Array API compatible alternative to numpy.cumsum.

x : array_like

……………………… datetime64 ……………………… Help on class datetime64 in module numpy:

class datetime64(generic)

If created from a 64-bit integer, it represents an offset from

1970-01-01T00:00:00.

If created from string, the string can be in ISO 8601 date

or datetime format.

When parsing a string to create a datetime object, if the string contains

a trailing timezone (A ‘Z’ or a timezone offset), the timezone will be

……………………… datetime_as_string ……………………… Help on _ArrayFunctionDispatcher in module numpy:

datetime_as_string(…)

datetime_as_string(arr, unit=None, timezone=’naive’, casting=’same_kind’)

Convert an array of datetimes into an array of strings.

arr : array_like of datetime64

……………………… datetime_data ……………………… Help on built-in function datetime_data in module numpy:

datetime_data(…)

datetime_data(dtype, /)

Get information about the step size of a date or time type.

The returned tuple can be passed as the second argument of numpy.datetime64 and numpy.timedelta64.

……………………… deg2rad ……………………… Help on ufunc in module numpy:

deg2rad = <ufunc ‘deg2rad’>

deg2rad(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Convert angles from degrees to radians.

x : array_like

……………………… degrees ……………………… Help on ufunc in module numpy:

degrees = <ufunc ‘degrees’>

degrees(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Convert angles from radians to degrees.

x : array_like

……………………… delete ……………………… Help on _ArrayFunctionDispatcher in module numpy:

delete(arr, obj, axis=None)

Return a new array with sub-arrays along an axis deleted. For a one dimensional array, this returns those entries not returned by arr[obj].

arr : array_like

……………………… diag ……………………… Help on _ArrayFunctionDispatcher in module numpy:

diag(v, k=0)

Extract a diagonal or construct a diagonal array.

See the more detailed documentation for numpy.diagonal if you use this function to extract a diagonal and wish to write to the resulting array; whether it returns a copy or a view depends on what version of numpy you are using.

……………………… diag_indices ……………………… Help on function diag_indices in module numpy:

diag_indices(n, ndim=2)

Return the indices to access the main diagonal of an array.

This returns a tuple of indices that can be used to access the main diagonal of an array a with a.ndim >= 2 dimensions and shape (n, n, …, n). For a.ndim = 2 this is the usual diagonal, for a.ndim > 2 this is the set of indices to access a[i, i, ..., i] for i = [0..n-1].

……………………… diag_indices_from ……………………… Help on _ArrayFunctionDispatcher in module numpy:

diag_indices_from(arr)

Return the indices to access the main diagonal of an n-dimensional array.

See diag_indices for full details.

arr : array, at least 2-D

……………………… diagflat ……………………… Help on _ArrayFunctionDispatcher in module numpy:

diagflat(v, k=0)

Create a two-dimensional array with the flattened input as a diagonal.

varray_like

Input data, which is flattened and set as the k-th diagonal of the output.

……………………… diagonal ……………………… Help on _ArrayFunctionDispatcher in module numpy:

diagonal(a, offset=0, axis1=0, axis2=1)

Return specified diagonals.

If a is 2-D, returns the diagonal of a with the given offset, i.e., the collection of elements of the form a[i, i+offset]. If a has more than two dimensions, then the axes specified by axis1 and axis2 are used to determine the 2-D sub-array whose diagonal is returned. The shape of the resulting array can be determined by

……………………… diff ……………………… Help on _ArrayFunctionDispatcher in module numpy:

diff(a, n=1, axis=-1, prepend=<no value>, append=<no value>)

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

The first difference is given by out[i] = a[i+1] - a[i] along the given axis, higher differences are calculated by using diff recursively.

Parameters

……………………… digitize ……………………… Help on _ArrayFunctionDispatcher in module numpy:

digitize(x, bins, right=False)

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

False increasing bins[i-1] <= x < bins[i] True increasing bins[i-1] < x <= bins[i] ……… …………. ……………………….

If values in x are beyond the bounds of bins, 0 or len(bins) is returned as appropriate.

xarray_like

Input array to be binned. Prior to NumPy 1.10.0, this array had to be 1-dimensional, but can now have any shape.

bins : array_like

……………………… divide ……………………… Help on ufunc in module numpy:

divide = <ufunc ‘divide’>

divide(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Divide arguments element-wise.

x1 : array_like

……………………… divmod ……………………… Help on ufunc in module numpy:

divmod = <ufunc ‘divmod’>

divmod(x1, x2[, out1, out2], / [, out=(None, None)], *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Return element-wise quotient and remainder simultaneously.

np.divmod(x, y) is equivalent to (x // y, x % y), but faster because it avoids redundant work. It is used to implement the Python built-in function divmod on NumPy arrays.

……………………… dot ……………………… Help on _ArrayFunctionDispatcher in module numpy:

dot(…)

dot(a, b, out=None)

Dot product of two arrays. Specifically,

• If both a and b are 1-D arrays, it is inner product of vectors (without complex conjugation).

……………………… double ……………………… Help on class float64 in module numpy:

class float64(floating, builtins.float)

float64(x=0, /)

Double-precision floating-point number type, compatible with Python

float and C double.

:Character code: 'd'

:Canonical name: numpy.double

……………………… dsplit ……………………… Help on _ArrayFunctionDispatcher in module numpy:

dsplit(ary, indices_or_sections)

Split array into multiple sub-arrays along the 3rd axis (depth).

Please refer to the split documentation. dsplit is equivalent to split with axis=2, the array is always split along the third axis provided the array dimension is greater than or equal to 3.

See Also

……………………… dstack ……………………… Help on _ArrayFunctionDispatcher in module numpy:

dstack(tup)

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

This is equivalent to concatenation along the third axis after 2-D arrays of shape (M,N) have been reshaped to (M,N,1) and 1-D arrays of shape (N,) have been reshaped to (1,N,1). Rebuilds arrays divided by dsplit.

……………………… dtype ……………………… Help on class dtype in module numpy:

class dtype(builtins.object)

dtype(dtype, align=False, copy=False, [metadata])

Create a data type object.

A numpy array is homogeneous, and contains elements described by a

dtype object. A dtype object can be constructed from different

combinations of fundamental numeric types.

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

……………………… dtypes ……………………… Help on module numpy.dtypes in numpy:

NAME

numpy.dtypes

DESCRIPTION

This module is home to specific dtypes related functionality and their classes. For more general information about dtypes, also see numpy.dtype and arrays.dtypes.

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

. ………………………………………………………………

0 if this is a structured array type, with fields

1 if this is a dtype compiled into numpy (such as ints, floats etc)

2 if the dtype is for a user-defined numpy type

A user-defined type uses the numpy C-API machinery to extend

numpy to handle a new array type. See

user.user-defined-data-types in the NumPy manual.

= ========================================================================

Examples

——–

. ………………….

b boolean

i signed integer

u unsigned integer

f floating-point

c complex floating-point

m timedelta

M datetime

O object

S (byte-)string

U Unicode

. ………………….

Examples

——–

>>> import numpy as np

>>> dt = np.dtype(‘i4’)

>>> dt.kind

‘i’

>>> dt = np.dtype(‘f8’)

>>> dt.kind

……………………… e ……………………… Help on float object:

class float(object)

float(x=0, /)

Convert a string or number to a floating-point number, if possible.

Methods defined here:

__abs__(self, /)

……………………… ediff1d ……………………… Help on _ArrayFunctionDispatcher in module numpy:

ediff1d(ary, to_end=None, to_begin=None)

The differences between consecutive elements of an array.

aryarray_like

If necessary, will be flattened before the differences are taken.

to_end : array_like, optional

……………………… einsum ……………………… Help on _ArrayFunctionDispatcher in module numpy:

einsum(*operands, out=None, optimize=False, **kwargs)

einsum(subscripts, *operands, out=None, dtype=None, order=’K’,

casting=’safe’, optimize=False)

Evaluates the Einstein summation convention on the operands.

Using the Einstein summation convention, many common multi-dimensional, linear algebraic array operations can be represented in a simple fashion.

……………………… einsum_path ……………………… Help on _ArrayFunctionDispatcher in module numpy:

einsum_path(*operands, optimize=’greedy’, einsum_call=False)

einsum_path(subscripts, *operands, optimize=’greedy’)

Evaluates the lowest cost contraction order for an einsum expression by considering the creation of intermediate arrays.

……………………… emath ……………………… Help on module numpy.lib.scimath in numpy.lib:

NAME

numpy.lib.scimath

DESCRIPTION

Wrapper functions to more user-friendly calling of certain math functions whose output data-type is different than the input data-type in certain domains of the input.

……………………… empty ……………………… Help on built-in function empty in module numpy:

empty(…)

empty(shape, dtype=float, order=’C’, *, device=None, like=None)

Return a new array of given shape and type, without initializing entries.

shape : int or tuple of int

……………………… empty_like ……………………… Help on _ArrayFunctionDispatcher in module numpy:

empty_like(…)

empty_like(prototype, dtype=None, order=’K’, subok=True, shape=None, *,

device=None)

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

……………………… equal ……………………… Help on ufunc in module numpy:

equal = <ufunc ‘equal’>

equal(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Return (x1 == x2) element-wise.

x1, x2 : array_like

……………………… errstate ……………………… Help on class errstate in module numpy:

class errstate(builtins.object)

errstate(

*,

call=<numpy._core._ufunc_config._unspecified object at 0x73733c208590>,

all=None,

divide=None,

over=None,

under=None,

……………………… euler_gamma ……………………… Help on float object:

class float(object)

float(x=0, /)

Convert a string or number to a floating-point number, if possible.

Methods defined here:

__abs__(self, /)

……………………… exceptions ……………………… Help on module numpy.exceptions in numpy:

NAME

numpy.exceptions

DESCRIPTION

General exceptions used by NumPy. Note that some exceptions may be module

……………………… exp ……………………… Help on ufunc in module numpy:

exp = <ufunc ‘exp’>

exp(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Calculate the exponential of all elements in the input array.

x : array_like

……………………… exp2 ……………………… Help on ufunc in module numpy:

exp2 = <ufunc ‘exp2’>

exp2(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Calculate 2**p for all p in the input array.

x : array_like

……………………… expand_dims ……………………… Help on _ArrayFunctionDispatcher in module numpy:

expand_dims(a, axis)

Expand the shape of an array.

Insert a new axis that will appear at the axis position in the expanded array shape.

……………………… expm1 ……………………… Help on ufunc in module numpy:

expm1 = <ufunc ‘expm1’>

expm1(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Calculate exp(x) - 1 for all elements in the array.

x : array_like

……………………… extract ……………………… Help on _ArrayFunctionDispatcher in module numpy:

extract(condition, arr)

Return the elements of an array that satisfy some condition.

This is equivalent to np.compress(ravel(condition), ravel(arr)). If condition is boolean np.extract is equivalent to arr[condition].

Note that place does the exact opposite of extract.

……………………… eye ……………………… Help on function eye in module numpy:

eye(N, M=None, k=0, dtype=<class ‘float’>, order=’C’, *, device=None, like=None)

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

Nint

Number of rows in the output.

M : int, optional

……………………… f2py ……………………… Help on package numpy.f2py in numpy:

NAME

numpy.f2py - Fortran to Python Interface Generator.

DESCRIPTION

Copyright 1999 – 2011 Pearu Peterson all rights reserved. Copyright 2011 – present NumPy Developers. Permission to use, modify, and distribute this software is given under the terms of the NumPy License.

……………………… fabs ……………………… Help on ufunc in module numpy:

fabs = <ufunc ‘fabs’>

fabs(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Compute the absolute values element-wise.

This function returns the absolute values (positive magnitude) of the data in x. Complex values are not handled, use absolute to find the absolute values of complex data.

……………………… fft ……………………… Help on package numpy.fft in numpy:

NAME

numpy.fft

DESCRIPTION

……………………… fill_diagonal ……………………… Help on _ArrayFunctionDispatcher in module numpy:

fill_diagonal(a, val, wrap=False)

Fill the main diagonal of the given array of any dimensionality.

For an array a with a.ndim >= 2, the diagonal is the list of values a[i, ..., i] with indices i all identical. This function modifies the input array in-place without returning a value.

Parameters

……………………… finfo ……………………… Help on class finfo in module numpy:

class finfo(builtins.object)

finfo(dtype)

finfo(dtype)

Machine limits for floating point types.

Attributes

……………………… fix ……………………… Help on _ArrayFunctionDispatcher in module numpy:

fix(x, out=None)

Round to nearest integer towards zero.

Round an array of floats element-wise to nearest integer towards zero. The rounded values have the same data-type as the input.

……………………… flatiter ……………………… Help on class flatiter in module numpy:

class flatiter(builtins.object)

Flat iterator object to iterate over arrays.

A flatiter iterator is returned by x.flat for any array x.

It allows iterating over the array as if it were a 1-D array,

either in a for-loop or by calling its next method.

Iteration is done in row-major, C-style order (the last

……………………… flatnonzero ……………………… Help on _ArrayFunctionDispatcher in module numpy:

flatnonzero(a)

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

This is equivalent to np.nonzero(np.ravel(a))[0].

a : array_like

……………………… flexible ……………………… Help on class flexible in module numpy:

class flexible(generic)

Abstract base class of all scalar types without predefined length.

The actual size of these types depends on the specific numpy.dtype

instantiation.

Method resolution order:

flexible

generic

……………………… flip ……………………… Help on _ArrayFunctionDispatcher in module numpy:

flip(m, axis=None)

Reverse the order of elements in an array along the given axis.

The shape of the array is preserved, but the elements are reordered.

m : array_like

……………………… fliplr ……………………… Help on _ArrayFunctionDispatcher in module numpy:

fliplr(m)

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

For a 2-D array, this flips the entries in each row in the left/right direction. Columns are preserved, but appear in a different order than before.

Parameters

……………………… flipud ……………………… Help on _ArrayFunctionDispatcher in module numpy:

flipud(m)

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

For a 2-D array, this flips the entries in each column in the up/down direction. Rows are preserved, but appear in a different order than before.

……………………… float128 ……………………… Help on class longdouble in module numpy:

class longdouble(floating)

Extended-precision floating-point number type, compatible with C

long double but not necessarily with IEEE 754 quadruple-precision.

:Character code: 'g'

:Alias on this platform (Linux x86_64): numpy.float128: 128-bit extended-precision floating-point number type.

Method resolution order:

……………………… float16 ……………………… Help on class float16 in module numpy:

class float16(floating)

Half-precision floating-point number type.

:Character code: 'e'

:Canonical name: numpy.half

:Alias on this platform (Linux x86_64): numpy.float16: 16-bit-precision floating-point number type: sign bit, 5 bits exponent, 10 bits mantissa.

Method resolution order:

……………………… float32 ……………………… Help on class float32 in module numpy:

class float32(floating)

Single-precision floating-point number type, compatible with C float.

:Character code: 'f'

:Canonical name: numpy.single

:Alias on this platform (Linux x86_64): numpy.float32: 32-bit-precision floating-point number type: sign bit, 8 bits exponent, 23 bits mantissa.

Method resolution order:

……………………… float64 ……………………… Help on class float64 in module numpy:

class float64(floating, builtins.float)

float64(x=0, /)

Double-precision floating-point number type, compatible with Python

float and C double.

:Character code: 'd'

:Canonical name: numpy.double

……………………… float_power ……………………… Help on ufunc in module numpy:

float_power = <ufunc ‘float_power’>

float_power(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

First array elements raised to powers from second array, element-wise.

Raise each base in x1 to the positionally-corresponding power in x2. x1 and x2 must be broadcastable to the same shape. This differs from the power function in that integers, float16, and float32 are promoted to

……………………… floating ……………………… Help on class floating in module numpy:

class floating(inexact)

Abstract base class of all floating-point scalar types.

Method resolution order:

floating

inexact

number

generic

……………………… floor ……………………… Help on ufunc in module numpy:

floor = <ufunc ‘floor’>

floor(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Return the floor of the input, element-wise.

The floor of the scalar x is the largest integer i, such that i <= x. It is often denoted as \(\lfloor x \rfloor\).

……………………… floor_divide ……………………… Help on ufunc in module numpy:

floor_divide = <ufunc ‘floor_divide’>

floor_divide(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Return the largest integer smaller or equal to the division of the inputs. It is equivalent to the Python // operator and pairs with the Python % (remainder), function so that a = a % b + b * (a // b) up to roundoff.

……………………… fmax ……………………… Help on ufunc in module numpy:

fmax = <ufunc ‘fmax’>

fmax(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Element-wise maximum of array elements.

Compare two arrays and return a new array containing the element-wise maxima. If one of the elements being compared is a NaN, then the non-nan element is returned. If both elements are NaNs then the first

……………………… fmin ……………………… Help on ufunc in module numpy:

fmin = <ufunc ‘fmin’>

fmin(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Element-wise minimum of array elements.

Compare two arrays and return a new array containing the element-wise minima. If one of the elements being compared is a NaN, then the non-nan element is returned. If both elements are NaNs then the first

……………………… fmod ……………………… Help on ufunc in module numpy:

fmod = <ufunc ‘fmod’>

fmod(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Returns the element-wise remainder of division.

This is the NumPy implementation of the C library function fmod, the remainder has the same sign as the dividend x1. It is equivalent to the Matlab(TM) rem function and should not be confused with the

……………………… format_float_positional ……………………… Help on function format_float_positional in module numpy:

format_float_positional(

x, precision=None, unique=True, fractional=True, trim=’k’, sign=False, pad_left=None,

……………………… format_float_scientific ……………………… Help on function format_float_scientific in module numpy:

format_float_scientific(

x, precision=None, unique=True, trim=’k’, sign=False, pad_left=None, exp_digits=None,

……………………… frexp ……………………… Help on ufunc in module numpy:

frexp = <ufunc ‘frexp’>

frexp(x[, out1, out2], / [, out=(None, None)], *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Decompose the elements of x into mantissa and twos exponent.

Returns (mantissa, exponent), where x = mantissa * 2**exponent. The mantissa lies in the open interval(-1, 1), while the twos exponent is a signed integer.

……………………… from_dlpack ……………………… Help on built-in function from_dlpack in module numpy:

from_dlpack(…)

from_dlpack(x, /, *, device=None, copy=None)

Create a NumPy array from an object implementing the __dlpack__ protocol. Generally, the returned NumPy array is a view of the input object. See [1]_ and [2]_ for more details.

Parameters

……………………… frombuffer ……………………… Help on built-in function frombuffer in module numpy:

frombuffer(…)

frombuffer(buffer, dtype=float, count=-1, offset=0, *, like=None)

Interpret a buffer as a 1-dimensional array.

buffer : buffer_like

……………………… fromfile ……………………… Help on built-in function fromfile in module numpy:

fromfile(…)

fromfile(file, dtype=float, count=-1, sep=’’, offset=0, *, like=None)

Construct an array from data in a text or binary file.

A highly efficient way of reading binary data with a known data-type, as well as parsing simply formatted text files. Data written using the tofile method can be read using this function.

……………………… fromfunction ……………………… Help on function fromfunction in module numpy:

fromfunction(function, shape, *, dtype=<class ‘float’>, like=None, **kwargs)

Construct an array by executing a function over each coordinate.

The resulting array therefore has a value fn(x, y, z) at coordinate (x, y, z).

……………………… fromiter ……………………… Help on built-in function fromiter in module numpy:

fromiter(…)

fromiter(iter, dtype, count=-1, *, like=None)

Create a new 1-dimensional array from an iterable object.

iter : iterable object

……………………… frompyfunc ……………………… Help on built-in function frompyfunc in module numpy:

frompyfunc(…)

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

Takes an arbitrary Python function and returns a NumPy ufunc.

Can be used, for example, to add broadcasting to a built-in Python function (see Examples section).

……………………… fromregex ……………………… Help on function fromregex in module numpy:

fromregex(file, regexp, dtype, encoding=None)

Construct an array from a text file, using regular expression parsing.

The returned array is always a structured array, and is constructed from all matches of the regular expression in the file. Groups in the regular expression are converted to fields of the structured array.

Parameters

……………………… fromstring ……………………… Help on built-in function fromstring in module numpy:

fromstring(…)

fromstring(string, dtype=float, count=-1, *, sep, like=None)

A new 1-D array initialized from text data in a string.

string : str

……………………… full ……………………… Help on function full in module numpy:

full(shape, fill_value, dtype=None, order=’C’, *, device=None, like=None)

Return a new array of given shape and type, filled with fill_value.

shapeint or sequence of ints

Shape of the new array, e.g., (2, 3) or 2.

fill_value : scalar or array_like

……………………… full_like ……………………… Help on _ArrayFunctionDispatcher in module numpy:

full_like(

a, fill_value, dtype=None, order=’K’, subok=True, shape=None, *,

……………………… gcd ……………………… Help on ufunc in module numpy:

gcd = <ufunc ‘gcd’>

gcd(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Returns the greatest common divisor of |x1| and |x2|

x1, x2 : array_like, int

……………………… generic ……………………… Help on class generic in module numpy:

class generic(builtins.object)

Base class for numpy scalar types.

Class from which most (all?) numpy scalar types are derived. For

consistency, exposes the same API as ndarray, despite many

consequent attributes being either “get-only,” or completely irrelevant.

This is the class from which it is strongly suggested users should derive

custom scalar types.

……………………… genfromtxt ……………………… Help on function genfromtxt in module numpy:

genfromtxt(

fname, dtype=<class ‘float’>, comments=’#’, delimiter=None, skip_header=0, skip_footer=0, converters=None,

……………………… geomspace ……………………… Help on _ArrayFunctionDispatcher in module numpy:

geomspace(start, stop, num=50, endpoint=True, dtype=None, axis=0)

Return numbers spaced evenly on a log scale (a geometric progression).

This is similar to logspace, but with endpoints specified directly. Each output sample is a constant multiple of the previous.

……………………… get_include ……………………… Help on function get_include in module numpy:

get_include()

Return the directory that contains the NumPy *.h header files.

Extension modules that need to compile against NumPy may need to use this function to locate the appropriate include directory.

……………………… get_printoptions ……………………… Help on function get_printoptions in module numpy:

get_printoptions()

Return the current print options.

print_optsdict

Dictionary of current print options with keys

……………………… getbufsize ……………………… Help on function getbufsize in module numpy:

getbufsize()

Return the size of the buffer used in ufuncs.

getbufsizeint

Size of ufunc buffer in bytes.

……………………… geterr ……………………… Help on function geterr in module numpy:

geterr()

Get the current way of handling floating-point errors.

resdict

A dictionary with keys “divide”, “over”, “under”, and “invalid”, whose values are from the strings “ignore”, “print”, “log”, “warn”,

……………………… geterrcall ……………………… Help on function geterrcall in module numpy:

geterrcall()

Return the current callback function used on floating-point errors.

When the error handling for a floating-point error (one of “divide”, “over”, “under”, or “invalid”) is set to ‘call’ or ‘log’, the function that is called or the log instance that is written to is returned by geterrcall. This function or log instance has been set with seterrcall.

……………………… gradient ……………………… Help on _ArrayFunctionDispatcher in module numpy:

gradient(f, *varargs, axis=None, edge_order=1)

Return the gradient of an N-dimensional array.

The gradient is computed using second order accurate central differences in the interior points and either first or second order accurate one-sides (forward or backwards) differences at the boundaries. The returned gradient hence has the same shape as the input array.

……………………… greater ……………………… Help on ufunc in module numpy:

greater = <ufunc ‘greater’>

greater(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Return the truth value of (x1 > x2) element-wise.

x1, x2 : array_like

……………………… greater_equal ……………………… Help on ufunc in module numpy:

greater_equal = <ufunc ‘greater_equal’>

greater_equal(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Return the truth value of (x1 >= x2) element-wise.

x1, x2 : array_like

……………………… half ……………………… Help on class float16 in module numpy:

class float16(floating)

Half-precision floating-point number type.

:Character code: 'e'

:Canonical name: numpy.half

:Alias on this platform (Linux x86_64): numpy.float16: 16-bit-precision floating-point number type: sign bit, 5 bits exponent, 10 bits mantissa.

Method resolution order:

……………………… hamming ……………………… Help on function hamming in module numpy:

hamming(M)

Return the Hamming window.

The Hamming window is a taper formed by using a weighted cosine.

M : int

……………………… hanning ……………………… Help on function hanning in module numpy:

hanning(M)

Return the Hanning window.

The Hanning window is a taper formed by using a weighted cosine.

M : int

……………………… heaviside ……………………… Help on ufunc in module numpy:

heaviside = <ufunc ‘heaviside’>

heaviside(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Compute the Heaviside step function.

The Heaviside step function [1]_ is defined as:

0   if x1 < 0

……………………… histogram ……………………… Help on _ArrayFunctionDispatcher in module numpy:

histogram(a, bins=10, range=None, density=None, weights=None)

Compute the histogram of a dataset.

aarray_like

Input data. The histogram is computed over the flattened array.

bins : int or sequence of scalars or str, optional

……………………… histogram2d ……………………… Help on _ArrayFunctionDispatcher in module numpy:

histogram2d(x, y, bins=10, range=None, density=None, weights=None)

Compute the bi-dimensional histogram of two data samples.

xarray_like, shape (N,)

An array containing the x coordinates of the points to be histogrammed.

……………………… histogram_bin_edges ……………………… Help on _ArrayFunctionDispatcher in module numpy:

histogram_bin_edges(a, bins=10, range=None, weights=None)

Function to calculate only the edges of the bins used by the histogram function.

aarray_like

Input data. The histogram is computed over the flattened array.

……………………… histogramdd ……………………… Help on _ArrayFunctionDispatcher in module numpy:

histogramdd(sample, bins=10, range=None, density=None, weights=None)

Compute the multidimensional histogram of some data.

sample(N, D) array, or (N, D) array_like

The data to be histogrammed.

……………………… hsplit ……………………… Help on _ArrayFunctionDispatcher in module numpy:

hsplit(ary, indices_or_sections)

Split an array into multiple sub-arrays horizontally (column-wise).

Please refer to the split documentation. hsplit is equivalent to split with axis=1, the array is always split along the second axis except for 1-D arrays, where it is split at axis=0.

See Also

……………………… hstack ……………………… Help on _ArrayFunctionDispatcher in module numpy:

hstack(tup, *, dtype=None, casting=’same_kind’)

Stack arrays in sequence horizontally (column wise).

This is equivalent to concatenation along the second axis, except for 1-D arrays where it concatenates along the first axis. Rebuilds arrays divided by hsplit.

This function makes most sense for arrays with up to 3 dimensions. For

……………………… hypot ……………………… Help on ufunc in module numpy:

hypot = <ufunc ‘hypot’>

hypot(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Given the “legs” of a right triangle, return its hypotenuse.

Equivalent to sqrt(x1**2 + x2**2), element-wise. If x1 or x2 is scalar_like (i.e., unambiguously cast-able to a scalar type), it is broadcast for use with each element of the other argument.

……………………… i0 ……………………… Help on _ArrayFunctionDispatcher in module numpy:

i0(x)

Modified Bessel function of the first kind, order 0.

Usually denoted \(I_0\).

x : array_like of float

……………………… identity ……………………… Help on function identity in module numpy:

identity(n, dtype=None, *, like=None)

Return the identity array.

The identity array is a square array with ones on the main diagonal.

……………………… iinfo ……………………… Help on class iinfo in module numpy:

class iinfo(builtins.object)

iinfo(int_type)

iinfo(type)

Machine limits for integer types.

Attributes

……………………… imag ……………………… Help on _ArrayFunctionDispatcher in module numpy:

imag(val)

Return the imaginary part of the complex argument.

valarray_like

Input array.

……………………… in1d ……………………… Help on _ArrayFunctionDispatcher in module numpy:

in1d(ar1, ar2, assume_unique=False, invert=False, *, kind=None)

Test whether each element of a 1-D array is also present in a second array.

Deprecated since version 2.0: Use isin() instead of in1d for new code.

Returns a boolean array the same length as ar1 that is True where an element of ar1 is in ar2 and False otherwise.

……………………… index_exp ……………………… Help on IndexExpression in module numpy.lib._index_tricks_impl object:

class IndexExpression(builtins.object)

IndexExpression(maketuple)

A nicer way to build up index tuples for arrays.

.. note::

Use one of the two predefined instances index_exp or s_

rather than directly using IndexExpression.

……………………… indices ……………………… Help on function indices in module numpy:

indices(dimensions, dtype=<class ‘int’>, sparse=False)

Return an array representing the indices of a grid.

Compute an array where the subarrays contain index values 0, 1, … varying only along the corresponding axis.

……………………… inexact ……………………… Help on class inexact in module numpy:

class inexact(number)

Abstract base class of all numeric scalar types with a (potentially)

inexact representation of the values in its range, such as

floating-point numbers.

Method resolution order:

inexact

number

……………………… inf ……………………… Help on float object:

class float(object)

float(x=0, /)

Convert a string or number to a floating-point number, if possible.

Methods defined here:

__abs__(self, /)

……………………… info ……………………… Help on function info in module numpy:

info(object=None, maxwidth=76, output=None, toplevel=’numpy’)

Get help information for an array, function, class, or module.

objectobject or str, optional

Input object or name to get information about. If object is an ndarray instance, information about the array is printed.

……………………… inner ……………………… Help on _ArrayFunctionDispatcher in module numpy:

inner(…)

inner(a, b, /)

Inner product of two arrays.

Ordinary inner product of vectors for 1-D arrays (without complex conjugation), in higher dimensions a sum product over the last axes.

……………………… insert ……………………… Help on _ArrayFunctionDispatcher in module numpy:

insert(arr, obj, values, axis=None)

Insert values along the given axis before the given indices.

arrarray_like

Input array.

obj : slice, int, array-like of ints or bools

……………………… int16 ……………………… Help on class int16 in module numpy:

class int16(signedinteger)

Signed integer type, compatible with C short.

:Character code: 'h'

:Canonical name: numpy.short

:Alias on this platform (Linux x86_64): numpy.int16: 16-bit signed integer (-32_768 to 32_767).

Method resolution order:

……………………… int32 ……………………… Help on class int32 in module numpy:

class int32(signedinteger)

Signed integer type, compatible with C int.

:Character code: 'i'

:Canonical name: numpy.intc

:Alias on this platform (Linux x86_64): numpy.int32: 32-bit signed integer (-2_147_483_648 to 2_147_483_647).

Method resolution order:

……………………… int64 ……………………… Help on class int64 in module numpy:

class int64(signedinteger)

Default signed integer type, 64bit on 64bit systems and 32bit on 32bit

systems.

:Character code: 'l'

:Canonical name: numpy.int_

:Alias on this platform (Linux x86_64): numpy.int64: 64-bit signed integer (-9_223_372_036_854_775_808 to 9_223_372_036_854_775_807).

:Alias on this platform (Linux x86_64): numpy.intp: Signed integer large enough to fit pointer, compatible with C intptr_t.

……………………… int8 ……………………… Help on class int8 in module numpy:

class int8(signedinteger)

Signed integer type, compatible with C char.

:Character code: 'b'

:Canonical name: numpy.byte

:Alias on this platform (Linux x86_64): numpy.int8: 8-bit signed integer (-128 to 127).

Method resolution order:

……………………… int_ ……………………… Help on class int64 in module numpy:

class int64(signedinteger)

Default signed integer type, 64bit on 64bit systems and 32bit on 32bit

systems.

:Character code: 'l'

:Canonical name: numpy.int_

:Alias on this platform (Linux x86_64): numpy.int64: 64-bit signed integer (-9_223_372_036_854_775_808 to 9_223_372_036_854_775_807).

:Alias on this platform (Linux x86_64): numpy.intp: Signed integer large enough to fit pointer, compatible with C intptr_t.

……………………… intc ……………………… Help on class int32 in module numpy:

class int32(signedinteger)

Signed integer type, compatible with C int.

:Character code: 'i'

:Canonical name: numpy.intc

:Alias on this platform (Linux x86_64): numpy.int32: 32-bit signed integer (-2_147_483_648 to 2_147_483_647).

Method resolution order:

……………………… integer ……………………… Help on class integer in module numpy:

class integer(number)

Abstract base class of all integer scalar types.

Method resolution order:

integer

number

generic

builtins.object

……………………… interp ……………………… Help on _ArrayFunctionDispatcher in module numpy:

interp(x, xp, fp, left=None, right=None, period=None)

One-dimensional linear interpolation for monotonically increasing sample points.

Returns the one-dimensional piecewise linear interpolant to a function with given discrete data points (xp, fp), evaluated at x.

……………………… intersect1d ……………………… Help on _ArrayFunctionDispatcher in module numpy:

intersect1d(ar1, ar2, assume_unique=False, return_indices=False)

Find the intersection of two arrays.

Return the sorted, unique values that are in both of the input arrays.

ar1, ar2 : array_like

……………………… intp ……………………… Help on class int64 in module numpy:

class int64(signedinteger)

Default signed integer type, 64bit on 64bit systems and 32bit on 32bit

systems.

:Character code: 'l'

:Canonical name: numpy.int_

:Alias on this platform (Linux x86_64): numpy.int64: 64-bit signed integer (-9_223_372_036_854_775_808 to 9_223_372_036_854_775_807).

:Alias on this platform (Linux x86_64): numpy.intp: Signed integer large enough to fit pointer, compatible with C intptr_t.

……………………… invert ……………………… Help on ufunc in module numpy:

invert = <ufunc ‘invert’>

invert(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Compute bit-wise inversion, or bit-wise NOT, element-wise.

Computes the bit-wise NOT of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator ~.

……………………… is_busday ……………………… Help on _ArrayFunctionDispatcher in module numpy:

is_busday(…)

is_busday(

dates, weekmask=’1111100’, holidays=None, busdaycal=None, out=None

)

……………………… isclose ……………………… Help on _ArrayFunctionDispatcher in module numpy:

isclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False)

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

The tolerance values are positive, typically very small numbers. The relative difference (rtol * abs(b)) and the absolute difference atol are added together to compare against the absolute difference between a and b.

……………………… iscomplex ……………………… Help on _ArrayFunctionDispatcher in module numpy:

iscomplex(x)

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

What is tested is whether the input has a non-zero imaginary part, not if the input type is complex.

……………………… iscomplexobj ……………………… Help on _ArrayFunctionDispatcher in module numpy:

iscomplexobj(x)

Check for a complex type or an array of complex numbers.

The type of the input is checked, not the value. Even if the input has an imaginary part equal to zero, iscomplexobj evaluates to True.

……………………… isdtype ……………………… Help on function isdtype in module numpy:

isdtype(dtype, kind)

Determine if a provided dtype is of a specified data type kind.

This function only supports built-in NumPy’s data types. Third-party dtypes are not yet supported.

……………………… isfinite ……………………… Help on ufunc in module numpy:

isfinite = <ufunc ‘isfinite’>

isfinite(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Test element-wise for finiteness (not infinity and not Not a Number).

The result is returned as a boolean array.

Parameters

……………………… isfortran ……………………… Help on function isfortran in module numpy:

isfortran(a)

Check if the array is Fortran contiguous but not C contiguous.

This function is obsolete. If you only want to check if an array is Fortran contiguous use a.flags.f_contiguous instead.

……………………… isin ……………………… Help on _ArrayFunctionDispatcher in module numpy:

isin(element, test_elements, assume_unique=False, invert=False, *, kind=None)

Calculates element in test_elements, broadcasting over element only. Returns a boolean array of the same shape as element that is True where an element of element is in test_elements and False otherwise.

element : array_like

……………………… isinf ……………………… Help on ufunc in module numpy:

isinf = <ufunc ‘isinf’>

isinf(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Test element-wise for positive or negative infinity.

Returns a boolean array of the same shape as x, True where x == +/-inf, otherwise False.

……………………… isnan ……………………… Help on ufunc in module numpy:

isnan = <ufunc ‘isnan’>

isnan(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Test element-wise for NaN and return result as a boolean array.

x : array_like

……………………… isnat ……………………… Help on ufunc in module numpy:

isnat = <ufunc ‘isnat’>

isnat(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Test element-wise for NaT (not a time) and return result as a boolean array.

x : array_like

……………………… isneginf ……………………… Help on _ArrayFunctionDispatcher in module numpy:

isneginf(x, out=None)

Test element-wise for negative infinity, return result as bool array.

xarray_like

The input array.

out : array_like, optional

……………………… isposinf ……………………… Help on _ArrayFunctionDispatcher in module numpy:

isposinf(x, out=None)

Test element-wise for positive infinity, return result as bool array.

xarray_like

The input array.

out : array_like, optional

……………………… isreal ……………………… Help on _ArrayFunctionDispatcher in module numpy:

isreal(x)

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

If element has complex type with zero imaginary part, the return value for that element is True.

……………………… isrealobj ……………………… Help on _ArrayFunctionDispatcher in module numpy:

isrealobj(x)

Return True if x is a not complex type or an array of complex numbers.

The type of the input is checked, not the value. So even if the input has an imaginary part equal to zero, isrealobj evaluates to False if the data type is complex.

Parameters

……………………… isscalar ……………………… Help on function isscalar in module numpy:

isscalar(element)

Returns True if the type of element is a scalar type.

elementany

Input argument, can be of any type and shape.

+………………………………+……………+……………….+ | PEP 3141 numeric objects | True | True | | (including builtins) | | | +————————————+—————+——————-+ | builtin string and buffer objects | True | True | +————————————+—————+——————-+ | other builtin objects, like | False | True | | pathlib.Path, Exception, | | | | the result of re.compile | | | +————————————+—————+——————-+ | third-party objects like | False | True |

……………………… issubdtype ……………………… Help on function issubdtype in module numpy:

issubdtype(arg1, arg2)

Returns True if first argument is a typecode lower/equal in type hierarchy.

This is like the builtin issubclass(), but for dtypes.

arg1, arg2 : dtype_like

……………………… iterable ……………………… Help on function iterable in module numpy:

iterable(y)

Check whether or not an object can be iterated over.

yobject

Input object.

……………………… ix_ ……………………… Help on _ArrayFunctionDispatcher in module numpy:

ix_(*args)

Construct an open mesh from multiple sequences.

This function takes N 1-D sequences and returns N outputs with N dimensions each, such that the shape is 1 in all but one dimension and the dimension with the non-unit shape value cycles through all N dimensions.

……………………… kaiser ……………………… Help on function kaiser in module numpy:

kaiser(M, beta)

Return the Kaiser window.

The Kaiser window is a taper formed by using a Bessel function.

M : int …. ………………….. beta Window shape ==== ======================= 0 Rectangular 5 Similar to a Hamming 6 Similar to a Hanning 8.6 Similar to a Blackman ==== =======================

A beta value of 14 is probably a good starting point. Note that as beta gets large, the window narrows, and so the number of samples needs to be

……………………… kron ……………………… Help on _ArrayFunctionDispatcher in module numpy:

kron(a, b)

Kronecker product of two arrays.

Computes the Kronecker product, a composite array made of blocks of the second array scaled by the first.

……………………… lcm ……………………… Help on ufunc in module numpy:

lcm = <ufunc ‘lcm’>

lcm(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Returns the lowest common multiple of |x1| and |x2|

x1, x2 : array_like, int

……………………… ldexp ……………………… Help on ufunc in module numpy:

ldexp = <ufunc ‘ldexp’>

ldexp(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Returns x1 * 2**x2, element-wise.

The mantissas x1 and twos exponents x2 are used to construct floating point numbers x1 * 2**x2.

……………………… left_shift ……………………… Help on ufunc in module numpy:

left_shift = <ufunc ‘left_shift’>

left_shift(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Shift the bits of an integer to the left.

Bits are shifted to the left by appending x2 0s at the right of x1. Since the internal representation of numbers is in binary format, this operation is equivalent to multiplying x1 by 2**x2.

……………………… less ……………………… Help on ufunc in module numpy:

less = <ufunc ‘less’>

less(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Return the truth value of (x1 < x2) element-wise.

x1, x2 : array_like

……………………… less_equal ……………………… Help on ufunc in module numpy:

less_equal = <ufunc ‘less_equal’>

less_equal(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Return the truth value of (x1 <= x2) element-wise.

x1, x2 : array_like

……………………… lexsort ……………………… Help on _ArrayFunctionDispatcher in module numpy:

lexsort(…)

lexsort(keys, axis=-1)

Perform an indirect stable sort using a sequence of keys.

Given multiple sorting keys, lexsort returns an array of integer indices that describes the sort order by multiple keys. The last key in the sequence is used for the primary sort order, ties are broken by the

……………………… lib ……………………… Help on package numpy.lib in numpy:

NAME

numpy.lib

DESCRIPTION

numpy.lib is mostly a space for implementing functions that don’t belong in core or in another NumPy submodule with a clear purpose (e.g. random, fft, linalg, ma).

……………………… linalg ……………………… Help on package numpy.linalg in numpy:

NAME

numpy.linalg

DESCRIPTION

The NumPy linear algebra functions rely on BLAS and LAPACK to provide efficient

….. ………………………. p norm for matrices ===== ============================ None 2-norm, computed directly using the SVD ‘fro’ Frobenius norm inf max(sum(abs(x), axis=1)) -inf min(sum(abs(x), axis=1)) 1 max(sum(abs(x), axis=0)) -1 min(sum(abs(x), axis=0)) 2 2-norm (largest sing. value) -2 smallest singular value ….. ……………………….

inf means the numpy.inf object, and the Frobenius norm is the root-of-sum-of-squares norm.

c{float, inf}

The condition number of the matrix. May be infinite.

See Also ….. ………………………. …………………….. ord norm for matrices norm for vectors ===== ============================ ========================== None Frobenius norm 2-norm ‘fro’ Frobenius norm – ‘nuc’ nuclear norm – inf max(sum(abs(x), axis=1)) max(abs(x)) -inf min(sum(abs(x), axis=1)) min(abs(x)) 0 – sum(x != 0) 1 max(sum(abs(x), axis=0)) as below -1 min(sum(abs(x), axis=0)) as below ….. ………………………. ……………………..

The Frobenius norm is given by [1]_:

\(||A||_F = [\sum_{i,j} abs(a_{i,j})^2]^{1/2}\)

The nuclear norm is the sum of the singular values.

Both the Frobenius and nuclear norm orders are only defined for matrices and raise a ValueError when x.ndim != 2.

……………………… linspace ……………………… Help on _ArrayFunctionDispatcher in module numpy:

linspace(

start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0,

……………………… little_endian ……………………… Help on bool object:

class bool(int)

bool(object=False, /)

Returns True when the argument is true, False otherwise.

The builtins True and False are the only two instances of the class bool.

The class bool is a subclass of the class int, and cannot be subclassed.

Method resolution order:

……………………… load ……………………… Help on function load in module numpy:

load(

file, mmap_mode=None, allow_pickle=False, fix_imports=True, encoding=’ASCII’, *, max_header_size=10000

……………………… loadtxt ……………………… Help on function loadtxt in module numpy:

loadtxt(

fname, dtype=<class ‘float’>, comments=’#’, delimiter=None, converters=None, skiprows=0, usecols=None,

……………………… log ……………………… Help on ufunc in module numpy:

log = <ufunc ‘log’>

log(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Natural logarithm, element-wise.

The natural logarithm log is the inverse of the exponential function, so that log(exp(x)) = x. The natural logarithm is logarithm in base e.

……………………… log10 ……………………… Help on ufunc in module numpy:

log10 = <ufunc ‘log10’>

log10(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Return the base 10 logarithm of the input array, element-wise.

x : array_like

……………………… log1p ……………………… Help on ufunc in module numpy:

log1p = <ufunc ‘log1p’>

log1p(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Return the natural logarithm of one plus the input array, element-wise.

Calculates log(1 + x).

Parameters

……………………… log2 ……………………… Help on ufunc in module numpy:

log2 = <ufunc ‘log2’>

log2(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Base-2 logarithm of x.

x : array_like

……………………… logaddexp ……………………… Help on ufunc in module numpy:

logaddexp = <ufunc ‘logaddexp’>

logaddexp(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Logarithm of the sum of exponentiations of the inputs.

Calculates log(exp(x1) + exp(x2)). This function is useful in statistics where the calculated probabilities of events may be so small as to exceed the range of normal floating point numbers. In such cases

……………………… logaddexp2 ……………………… Help on ufunc in module numpy:

logaddexp2 = <ufunc ‘logaddexp2’>

logaddexp2(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Logarithm of the sum of exponentiations of the inputs in base-2.

Calculates log2(2**x1 + 2**x2). This function is useful in machine learning when the calculated probabilities of events may be so small as to exceed the range of normal floating point numbers. In such cases

……………………… logical_and ……………………… Help on ufunc in module numpy:

logical_and = <ufunc ‘logical_and’>

logical_and(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Compute the truth value of x1 AND x2 element-wise.

x1, x2 : array_like

……………………… logical_not ……………………… Help on ufunc in module numpy:

logical_not = <ufunc ‘logical_not’>

logical_not(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Compute the truth value of NOT x element-wise.

x : array_like

……………………… logical_or ……………………… Help on ufunc in module numpy:

logical_or = <ufunc ‘logical_or’>

logical_or(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Compute the truth value of x1 OR x2 element-wise.

x1, x2 : array_like

……………………… logical_xor ……………………… Help on ufunc in module numpy:

logical_xor = <ufunc ‘logical_xor’>

logical_xor(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Compute the truth value of x1 XOR x2, element-wise.

x1, x2 : array_like

……………………… logspace ……………………… Help on _ArrayFunctionDispatcher in module numpy:

logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None, axis=0)

Return numbers spaced evenly on a log scale.

In linear space, the sequence starts at base ** start (base to the power of start) and ends with base ** stop (see endpoint below).

Changed in version 1.25.0.

……………………… long ……………………… Help on class int64 in module numpy:

class int64(signedinteger)

Default signed integer type, 64bit on 64bit systems and 32bit on 32bit

systems.

:Character code: 'l'

:Canonical name: numpy.int_

:Alias on this platform (Linux x86_64): numpy.int64: 64-bit signed integer (-9_223_372_036_854_775_808 to 9_223_372_036_854_775_807).

:Alias on this platform (Linux x86_64): numpy.intp: Signed integer large enough to fit pointer, compatible with C intptr_t.

……………………… longdouble ……………………… Help on class longdouble in module numpy:

class longdouble(floating)

Extended-precision floating-point number type, compatible with C

long double but not necessarily with IEEE 754 quadruple-precision.

:Character code: 'g'

:Alias on this platform (Linux x86_64): numpy.float128: 128-bit extended-precision floating-point number type.

Method resolution order:

……………………… longlong ……………………… Help on class longlong in module numpy:

class longlong(signedinteger)

Signed integer type, compatible with C long long.

:Character code: 'q'

Method resolution order:

longlong

signedinteger

……………………… ma ……………………… Help on package numpy.ma in numpy:

NAME

numpy.ma

DESCRIPTION

…… …… ……………. x1 x2 arctan2(x1,x2) ====== ====== ================ +/- 0 +0 +/- 0 +/- 0 -0 +/- pi

> 0 +/-inf +0 / +pi < 0 +/-inf -0 / -pi

+/-inf +inf +/- (pi/4) +/-inf -inf +/- (3*pi/4) ====== ====== ================

……………………… mask_indices ……………………… Help on function mask_indices in module numpy:

mask_indices(n, mask_func, k=0)

Return the indices to access (n, n) arrays, given a masking function.

Assume mask_func is a function that, for a square array a of size (n, n) with a possible offset argument k, when called as mask_func(a, k) returns a new array with zeros in certain locations (functions like triu or tril do precisely this). Then this function returns the indices where the non-zero values would be located.

……………………… matmul ……………………… Help on ufunc in module numpy:

matmul = <ufunc ‘matmul’>

matmul(x1, x2, /, out=None, *, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature, axes, axis])

Matrix product of two arrays.

x1, x2 : array_like

……………………… matrix ……………………… Help on class matrix in module numpy:

class matrix(ndarray)

matrix(data, dtype=None, copy=True)

matrix(data, dtype=None, copy=True)

Returns a matrix from an array-like object, or from a string of data.

A matrix is a specialized 2-D array that retains its 2-D nature

……………………… matrix_transpose ……………………… Help on _ArrayFunctionDispatcher in module numpy:

matrix_transpose(x, /)

Transposes a matrix (or a stack of matrices) x.

This function is Array API compatible.

x : array_like

……………………… matvec ……………………… Help on ufunc in module numpy:

matvec = <ufunc ‘matvec’>

matvec(x1, x2, /, out=None, *, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature, axes, axis])

Matrix-vector dot product of two arrays.

Given a matrix (or stack of matrices) \(\mathbf{A}\) in x1 and a vector (or stack of vectors) \(\mathbf{v}\) in x2, the matrix-vector product is defined as:

……………………… max ……………………… Help on _ArrayFunctionDispatcher in module numpy:

max(

a, axis=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>

) ……………………… maximum ……………………… Help on ufunc in module numpy:

maximum = <ufunc ‘maximum’>

maximum(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Element-wise maximum of array elements.

Compare two arrays and return a new array containing the element-wise maxima. If one of the elements being compared is a NaN, then that element is returned. If both elements are NaNs then the first is

……………………… may_share_memory ……………………… Help on _ArrayFunctionDispatcher in module numpy:

may_share_memory(…)

may_share_memory(a, b, /, max_work=None)

Determine if two arrays might share memory

A return of True does not necessarily mean that the two arrays share any element. It just means that they might.

……………………… mean ……………………… Help on _ArrayFunctionDispatcher in module numpy:

mean(

a, axis=None, dtype=None, out=None, keepdims=<no value>, *, where=<no value>

……………………… median ……………………… Help on _ArrayFunctionDispatcher in module numpy:

median(a, axis=None, out=None, overwrite_input=False, keepdims=False)

Compute the median along the specified axis.

Returns the median of the array elements.

a : array_like

……………………… memmap ……………………… Help on class memmap in module numpy:

class memmap(ndarray)

memmap(

filename,

dtype=<class ‘numpy.uint8’>,

mode=’r+’,

offset=0,

shape=None,

order=’C’

……………………… meshgrid ……………………… Help on _ArrayFunctionDispatcher in module numpy:

meshgrid(*xi, copy=True, sparse=False, indexing=’xy’)

Return a tuple of coordinate matrices from coordinate vectors.

Make N-D coordinate arrays for vectorized evaluations of N-D scalar/vector fields over N-D grids, given one-dimensional coordinate arrays x1, x2,…, xn.

Parameters

……………………… mgrid ……………………… Help on MGridClass in module numpy.lib._index_tricks_impl object:

class MGridClass(nd_grid)

An instance which returns a dense multi-dimensional “meshgrid”.

An instance which returns a dense (or fleshed out) mesh-grid

when indexed, so that each returned argument has the same shape.

The dimensions and number of the output arrays are equal to the

number of indexing dimensions. If the step length is not a complex

number, then the stop is not inclusive.

……………………… min ……………………… Help on _ArrayFunctionDispatcher in module numpy:

min(

a, axis=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>

) ……………………… min_scalar_type ……………………… Help on _ArrayFunctionDispatcher in module numpy:

min_scalar_type(…)

min_scalar_type(a, /)

For scalar a, returns the data type with the smallest size and smallest scalar kind which can hold its value. For non-scalar array a, returns the vector’s dtype unmodified.

Floating point values are not demoted to integers,

……………………… minimum ……………………… Help on ufunc in module numpy:

minimum = <ufunc ‘minimum’>

minimum(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Element-wise minimum of array elements.

Compare two arrays and return a new array containing the element-wise minima. If one of the elements being compared is a NaN, then that element is returned. If both elements are NaNs then the first is

……………………… mintypecode ……………………… Help on function mintypecode in module numpy:

mintypecode(typechars, typeset=’GDFgdf’, default=’d’)

Return the character for the minimum-size type to which given types can be safely cast.

The returned type character must represent the smallest size dtype such that an array of the returned type can handle the data from an array of all types in typechars (or if typechars is an array, then its dtype.char).

……………………… mod ……………………… Help on ufunc in module numpy:

remainder = <ufunc ‘remainder’>

remainder(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Returns the element-wise remainder of division.

Computes the remainder complementary to the floor_divide function. It is equivalent to the Python modulus operator x1 % x2 and has the same sign as the divisor x2. The MATLAB function equivalent to np.remainder

……………………… modf ……………………… Help on ufunc in module numpy:

modf = <ufunc ‘modf’>

modf(x[, out1, out2], / [, out=(None, None)], *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Return the fractional and integral parts of an array, element-wise.

The fractional and integral parts are negative if the given number is negative.

……………………… moveaxis ……………………… Help on _ArrayFunctionDispatcher in module numpy:

moveaxis(a, source, destination)

Move axes of an array to new positions.

Other axes remain in their original order.

a : np.ndarray

……………………… multiply ……………………… Help on ufunc in module numpy:

multiply = <ufunc ‘multiply’>

multiply(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Multiply arguments element-wise.

x1, x2 : array_like

……………………… nan ……………………… Help on float object:

class float(object)

float(x=0, /)

Convert a string or number to a floating-point number, if possible.

Methods defined here:

__abs__(self, /)

……………………… nan_to_num ……………………… Help on _ArrayFunctionDispatcher in module numpy:

nan_to_num(x, copy=True, nan=0.0, posinf=None, neginf=None)

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.

If x is inexact, NaN is replaced by zero or by the user defined value in nan keyword, infinity is replaced by the largest finite floating point values representable by x.dtype or by the user defined value in

……………………… nanargmax ……………………… Help on _ArrayFunctionDispatcher in module numpy:

nanargmax(a, axis=None, out=None, *, keepdims=<no value>)

Return the indices of the maximum values in the specified axis ignoring NaNs. For all-NaN slices ValueError is raised. Warning: the results cannot be trusted if a slice contains only NaNs and -Infs.

……………………… nanargmin ……………………… Help on _ArrayFunctionDispatcher in module numpy:

nanargmin(a, axis=None, out=None, *, keepdims=<no value>)

Return the indices of the minimum values in the specified axis ignoring NaNs. For all-NaN slices ValueError is raised. Warning: the results cannot be trusted if a slice contains only NaNs and Infs.

a : array_like

……………………… nancumprod ……………………… Help on _ArrayFunctionDispatcher in module numpy:

nancumprod(a, axis=None, dtype=None, out=None)

Return the cumulative product of array elements over a given axis treating Not a Numbers (NaNs) as one. The cumulative product does not change when NaNs are encountered and leading NaNs are replaced by ones.

Ones are returned for slices that are all-NaN or empty.

Parameters

……………………… nancumsum ……………………… Help on _ArrayFunctionDispatcher in module numpy:

nancumsum(a, axis=None, dtype=None, out=None)

Return the cumulative sum of array elements over a given axis treating Not a Numbers (NaNs) as zero. The cumulative sum does not change when NaNs are encountered and leading NaNs are replaced by zeros.

Zeros are returned for slices that are all-NaN or empty.

Parameters

……………………… nanmax ……………………… Help on _ArrayFunctionDispatcher in module numpy:

nanmax(

a, axis=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>

) ……………………… nanmean ……………………… Help on _ArrayFunctionDispatcher in module numpy:

nanmean(

a, axis=None, dtype=None, out=None, keepdims=<no value>, *, where=<no value>

……………………… nanmedian ……………………… Help on _ArrayFunctionDispatcher in module numpy:

nanmedian(a, axis=None, out=None, overwrite_input=False, keepdims=<no value>)

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

Returns the median of the array elements.

a : array_like

……………………… nanmin ……………………… Help on _ArrayFunctionDispatcher in module numpy:

nanmin(

a, axis=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>

) ……………………… nanpercentile ……………………… Help on _ArrayFunctionDispatcher in module numpy:

nanpercentile(

a, q, axis=None, out=None, overwrite_input=False, method=’linear’, keepdims=<no value>,

……………………… nanprod ……………………… Help on _ArrayFunctionDispatcher in module numpy:

nanprod(

a, axis=None, dtype=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>

……………………… nanquantile ……………………… Help on _ArrayFunctionDispatcher in module numpy:

nanquantile(

a, q, axis=None, out=None, overwrite_input=False, method=’linear’, keepdims=<no value>,

……………………… nanstd ……………………… Help on _ArrayFunctionDispatcher in module numpy:

nanstd(

a, axis=None, dtype=None, out=None, ddof=0, keepdims=<no value>, *,

……………………… nansum ……………………… Help on _ArrayFunctionDispatcher in module numpy:

nansum(

a, axis=None, dtype=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>

……………………… nanvar ……………………… Help on _ArrayFunctionDispatcher in module numpy:

nanvar(

a, axis=None, dtype=None, out=None, ddof=0, keepdims=<no value>, *,

……………………… ndarray ……………………… Help on class ndarray in module numpy:

class ndarray(builtins.object)

ndarray(shape, dtype=float, buffer=None, offset=0,

strides=None, order=None)

An array object represents a multidimensional, homogeneous array

of fixed-size items. An associated data-type object describes the

format of each element in the array (its byte-order, how many bytes it

occupies in memory, whether it is an integer, a floating point number,

……………………… ndenumerate ……………………… Help on class ndenumerate in module numpy:

class ndenumerate(builtins.object)

ndenumerate(arr)

Multidimensional index iterator.

Return an iterator yielding pairs of array coordinates and values.

Parameters

……………………… ndim ……………………… Help on _ArrayFunctionDispatcher in module numpy:

ndim(a)

Return the number of dimensions of an array.

aarray_like

Input array. If it is not already an ndarray, a conversion is attempted.

……………………… ndindex ……………………… Help on class ndindex in module numpy:

class ndindex(builtins.object)

ndindex(*shape)

An N-dimensional iterator object to index arrays.

Given the shape of an array, an ndindex instance iterates over

the N-dimensional index of the array. At each iteration a tuple

of indices is returned, the last dimension is iterated over first.

……………………… nditer ……………………… Help on class nditer in module numpy:

class nditer(builtins.object)

nditer(op, flags=None, op_flags=None, op_dtypes=None, order=’K’,

casting=’safe’, op_axes=None, itershape=None, buffersize=0)

Efficient multi-dimensional iterator object to iterate over arrays.

To get started using this object, see the

introductory guide to array iteration.

……………………… negative ……………………… Help on ufunc in module numpy:

negative = <ufunc ‘negative’>

negative(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Numerical negative, element-wise.

x : array_like or scalar

……………………… nested_iters ……………………… Help on built-in function nested_iters in module numpy:

nested_iters(…)

nested_iters(op, axes, flags=None, op_flags=None, op_dtypes=None, order=”K”, casting=”safe”, buffersize=0)

Create nditers for use in nested loops

Create a tuple of nditer objects which iterate in nested loops over different axes of the op argument. The first iterator is used in the outermost loop, the last in the innermost loop. Advancing one will change

……………………… newaxis ……………………… Help on NoneType object:

class NoneType(object)

The type of the None singleton.

Methods defined here:

__bool__(self, /)

True if self else False

……………………… nextafter ……………………… Help on ufunc in module numpy:

nextafter = <ufunc ‘nextafter’>

nextafter(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Return the next floating-point value after x1 towards x2, element-wise.

x1 : array_like

……………………… nonzero ……………………… Help on _ArrayFunctionDispatcher in module numpy:

nonzero(a)

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

Returns a tuple of arrays, one for each dimension of a, containing the indices of the non-zero elements in that dimension. The values in a are always tested and returned in row-major, C-style order.

……………………… not_equal ……………………… Help on ufunc in module numpy:

not_equal = <ufunc ‘not_equal’>

not_equal(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Return (x1 != x2) element-wise.

x1, x2 : array_like

……………………… number ……………………… Help on class number in module numpy:

class number(generic)

Abstract base class of all numeric scalar types.

Method resolution order:

number

generic

builtins.object

……………………… object_ ……………………… Help on class object_ in module numpy:

class object_(generic)

Any Python object.

:Character code: 'O'

Method resolution order:

object_

generic

……………………… ogrid ……………………… Help on OGridClass in module numpy.lib._index_tricks_impl object:

class OGridClass(nd_grid)

An instance which returns an open multi-dimensional “meshgrid”.

An instance which returns an open (i.e. not fleshed out) mesh-grid

when indexed, so that only one dimension of each returned array is

greater than 1. The dimension and number of the output arrays are

equal to the number of indexing dimensions. If the step length is

not a complex number, then the stop is not inclusive.

……………………… ones ……………………… Help on function ones in module numpy:

ones(shape, dtype=None, order=’C’, *, device=None, like=None)

Return a new array of given shape and type, filled with ones.

shapeint or sequence of ints

Shape of the new array, e.g., (2, 3) or 2.

dtype : data-type, optional

……………………… ones_like ……………………… Help on _ArrayFunctionDispatcher in module numpy:

ones_like(a, dtype=None, order=’K’, subok=True, shape=None, *, device=None)

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

aarray_like

The shape and data-type of a define these same attributes of the returned array.

……………………… outer ……………………… Help on _ArrayFunctionDispatcher in module numpy:

outer(a, b, out=None)

Compute the outer product of two vectors.

Given two vectors a and b of length M and N, respectively, the outer product [1]_ is:

[[a_0*b_0  a_0*b_1 ... a_0*b_{N-1} ]
 [a_1*b_0    .

……………………… packbits ……………………… Help on _ArrayFunctionDispatcher in module numpy:

packbits(…)

packbits(a, /, axis=None, bitorder=’big’)

Packs the elements of a binary-valued array into bits in a uint8 array.

The result is padded to full bytes by inserting zero bits at the end.

Parameters

……………………… pad ……………………… Help on _ArrayFunctionDispatcher in module numpy:

pad(array, pad_width, mode=’constant’, **kwargs)

Pad an array.

arrayarray_like of rank N

The array to pad.

pad_width : {sequence, array_like, int}

……………………… partition ……………………… Help on _ArrayFunctionDispatcher in module numpy:

partition(a, kth, axis=-1, kind=’introselect’, order=None)

Return a partitioned copy of an array.

Creates a copy of the array and partially sorts it in such a way that the value of the element in k-th position is in the position it would be in a sorted array. In the output array, all elements smaller than the k-th element are located to the left of this element and all equal or greater are located to its right. The ordering of the elements in the two …………….. ……. …………. ………… …….

kind speed worst case work space stable

‘introselect’

1

O(n)

0

no

All the partition algorithms make temporary copies of the data when partitioning along any but the last axis. Consequently, partitioning along the last axis is faster and uses less space than partitioning along any other axis.

……………………… percentile ……………………… Help on _ArrayFunctionDispatcher in module numpy:

percentile(

a, q, axis=None, out=None, overwrite_input=False, method=’linear’, keepdims=False,

……………………… permute_dims ……………………… Help on _ArrayFunctionDispatcher in module numpy:

transpose(a, axes=None)

Returns an array with axes transposed.

For a 1-D array, this returns an unchanged view of the original array, as a transposed vector is simply the same vector. To convert a 1-D array into a 2-D column vector, an additional dimension must be added, e.g., np.atleast_2d(a).T achieves this, as does a[:, np.newaxis].

……………………… pi ……………………… Help on float object:

class float(object)

float(x=0, /)

Convert a string or number to a floating-point number, if possible.

Methods defined here:

__abs__(self, /)

……………………… piecewise ……………………… Help on _ArrayFunctionDispatcher in module numpy:

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

Evaluate a piecewise-defined function.

Given a set of conditions and corresponding functions, evaluate each function on the input data wherever its condition is true.

……………………… place ……………………… Help on _ArrayFunctionDispatcher in module numpy:

place(arr, mask, vals)

Change elements of an array based on conditional and input values.

Similar to np.copyto(arr, vals, where=mask), the difference is that place uses the first N elements of vals, where N is the number of True values in mask, while copyto uses the elements where mask is True.

……………………… poly ……………………… Help on _ArrayFunctionDispatcher in module numpy:

poly(seq_of_zeros)

Find the coefficients of a polynomial with the given sequence of roots.

Note

This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in numpy.polynomial is preferred. A summary of the differences can be found in the transition guide.

……………………… poly1d ……………………… Help on class poly1d in module numpy:

class poly1d(builtins.object)

poly1d(c_or_r, r=False, variable=None)

A one-dimensional polynomial class.

.. note::

This forms part of the old polynomial API. Since version 1.4, the

new polynomial API defined in numpy.polynomial is preferred.

……………………… polyadd ……………………… Help on _ArrayFunctionDispatcher in module numpy:

polyadd(a1, a2)

Find the sum of two polynomials.

Note

This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in numpy.polynomial is preferred. A summary of the differences can be found in the transition guide.

……………………… polyder ……………………… Help on _ArrayFunctionDispatcher in module numpy:

polyder(p, m=1)

Return the derivative of the specified order of a polynomial.

Note

This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in numpy.polynomial is preferred. A summary of the differences can be found in the transition guide.

……………………… polydiv ……………………… Help on _ArrayFunctionDispatcher in module numpy:

polydiv(u, v)

Returns the quotient and remainder of polynomial division.

Note

This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in numpy.polynomial is preferred. A summary of the differences can be found in the transition guide.

……………………… polyfit ……………………… Help on _ArrayFunctionDispatcher in module numpy:

polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False)

Least squares polynomial fit.

Note

This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in numpy.polynomial is preferred. A summary of the differences can be found in the transition guide.

……………………… polyint ……………………… Help on _ArrayFunctionDispatcher in module numpy:

polyint(p, m=1, k=None)

Return an antiderivative (indefinite integral) of a polynomial.

Note

This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in numpy.polynomial is preferred. A summary of the differences can be found in the transition guide.

……………………… polymul ……………………… Help on _ArrayFunctionDispatcher in module numpy:

polymul(a1, a2)

Find the product of two polynomials.

Note

This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in numpy.polynomial is preferred. A summary of the differences can be found in the transition guide.

……………………… polynomial ……………………… Help on package numpy.polynomial in numpy:

NAME

numpy.polynomial - A sub-package for efficiently dealing with polynomials.

DESCRIPTION

Within the documentation for this sub-package, a “finite power series,” i.e., a polynomial (also referred to simply as a “series”) is represented by a 1-D numpy array of the polynomial’s coefficients, ordered from lowest order term to highest. For example, array([1,2,3]) represents …………………… ……………. Name Provides ======================== ================ ~polynomial.Polynomial Power series ~chebyshev.Chebyshev Chebyshev series ~legendre.Legendre Legendre series ~laguerre.Laguerre Laguerre series ~hermite.Hermite Hermite series ~hermite_e.HermiteE HermiteE series ======================== ================

The following lists the various constants and methods common to all of the classes representing the various kinds of polynomials. In the following, the term Poly represents any one of the convenience classes (e.g. ~polynomial.Polynomial, ~chebyshev.Chebyshev, ~hermite.Hermite, etc.) while the lowercase p represents an instance of a polynomial class.

……………………… polysub ……………………… Help on _ArrayFunctionDispatcher in module numpy:

polysub(a1, a2)

Difference (subtraction) of two polynomials.

Note

This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in numpy.polynomial is preferred. A summary of the differences can be found in the transition guide.

……………………… polyval ……………………… Help on _ArrayFunctionDispatcher in module numpy:

polyval(p, x)

Evaluate a polynomial at specific values.

Note

This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in numpy.polynomial is preferred. A summary of the differences can be found in the transition guide.

……………………… positive ……………………… Help on ufunc in module numpy:

positive = <ufunc ‘positive’>

positive(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Numerical positive, element-wise.

x : array_like or scalar

……………………… pow ……………………… Help on ufunc in module numpy:

power = <ufunc ‘power’>

power(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

First array elements raised to powers from second array, element-wise.

Raise each base in x1 to the positionally-corresponding power in x2. x1 and x2 must be broadcastable to the same shape.

……………………… power ……………………… Help on ufunc in module numpy:

power = <ufunc ‘power’>

power(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

First array elements raised to powers from second array, element-wise.

Raise each base in x1 to the positionally-corresponding power in x2. x1 and x2 must be broadcastable to the same shape.

……………………… printoptions ……………………… Help on function printoptions in module numpy:

printoptions(*args, **kwargs)

Context manager for setting print options.

Set print options for the scope of the with block, and restore the old options at the end. See set_printoptions for the full description of available options.

Examples

……………………… prod ……………………… Help on _ArrayFunctionDispatcher in module numpy:

prod(

a, axis=None, dtype=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>

……………………… promote_types ……………………… Help on built-in function promote_types in module numpy:

promote_types(…)

promote_types(type1, type2)

Returns the data type with the smallest size and smallest scalar kind to which both type1 and type2 may be safely cast. The returned data type is always considered “canonical”, this mainly means that the promoted dtype will always be in native byte order.

……………………… ptp ……………………… Help on _ArrayFunctionDispatcher in module numpy:

ptp(a, axis=None, out=None, keepdims=<no value>)

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

The name of the function comes from the acronym for ‘peak to peak’.

Warning

ptp preserves the data type of the array. This means the return value for an input of signed integers with n bits

……………………… put ……………………… Help on _ArrayFunctionDispatcher in module numpy:

put(a, ind, v, mode=’raise’)

Replaces specified elements of an array with given values.

The indexing works on the flattened target array. put is roughly equivalent to:

……………………… put_along_axis ……………………… Help on _ArrayFunctionDispatcher in module numpy:

put_along_axis(arr, indices, values, axis)

Put values into the destination array by matching 1d index and data slices.

This iterates over matching 1d slices oriented along the specified axis in the index and data arrays, and uses the former to place values into the latter. These slices can be different lengths.

Functions returning an index along an axis, like argsort and

……………………… putmask ……………………… Help on _ArrayFunctionDispatcher in module numpy:

putmask(…)

putmask(a, mask, values)

Changes elements of an array based on conditional and input values.

Sets a.flat[n] = values[n] for each n where mask.flat[n]==True.

If values is not the same size as a and mask then it will repeat.

……………………… quantile ……………………… Help on _ArrayFunctionDispatcher in module numpy:

quantile(

a, q, axis=None, out=None, overwrite_input=False, method=’linear’, keepdims=False, …………………………. …………… …………… method number in H&F m =============================== =============== =============== interpolated_inverted_cdf 4 0 hazen 5 1/2 weibull 6 q linear (default) 7 1 - q median_unbiased 8 q/3 + 1/3 normal_unbiased 9 q/4 + 3/8 =============================== =============== ===============

……………………… r_ ……………………… Help on RClass in module numpy.lib._index_tricks_impl object:

class RClass(AxisConcatenator)

Translates slice objects to concatenation along the first axis.

This is a simple way to build up arrays quickly. There are two use cases.

1. If the index expression contains comma separated arrays, then stack

them along their first axis.

2. If the index expression contains slice notation or scalars then create

……………………… rad2deg ……………………… Help on ufunc in module numpy:

rad2deg = <ufunc ‘rad2deg’>

rad2deg(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Convert angles from radians to degrees.

x : array_like

……………………… radians ……………………… Help on ufunc in module numpy:

radians = <ufunc ‘radians’>

radians(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Convert angles from degrees to radians.

x : array_like

……………………… random ……………………… Help on package numpy.random in numpy:

NAME

numpy.random

DESCRIPTION

…………… ………………………………………………… Generator ————— ——————————————————— Generator Class implementing all of the random number distributions default_rng Default constructor for Generator =============== =========================================================

BitGenerator Streams that work with Generator
MT19937
	…
Getting entropy to initialize a BitGenerator
SeedSequence

……………….. ………………………………………………… Utility functions ——————– ——————————————————— random Uniformly distributed floats over [0, 1) bytes Uniformly distributed random bytes. permutation Randomly permute a sequence / generate a random sequence. shuffle Randomly permute a sequence in place. choice Random sample from 1-D array. ==================== =========================================================

Multivariate distributions ——————– ———————————————————- dirichlet Multivariate generalization of Beta distribution. multinomial Multivariate generalization of the binomial distribution. multivariate_normal Multivariate generalization of the normal distribution. ==================== ==========================================================

……………….. ………………………………………………… Standard distributions ——————– ——————————————————— standard_cauchy Standard Cauchy-Lorentz distribution. standard_exponential Standard exponential distribution. standard_gamma Standard Gamma distribution. standard_normal Standard normal distribution. standard_t Standard Student’s t-distribution. ==================== =========================================================

……………….. ………………………………………………… Internal functions ——————– ——————————————————— get_state Get tuple representing internal state of generator. set_state Set state of generator. ==================== =========================================================

PACKAGE CONTENTS

_bounded_integers _common _generator

……………………… ravel ……………………… Help on _ArrayFunctionDispatcher in module numpy:

ravel(a, order=’C’)

Return a contiguous flattened array.

A 1-D array, containing the elements of the input, is returned. A copy is made only if needed.

As of NumPy 1.10, the returned array will have the same type as the input array. (for example, a masked array will be returned for a masked array

……………………… ravel_multi_index ……………………… Help on _ArrayFunctionDispatcher in module numpy:

ravel_multi_index(…)

ravel_multi_index(multi_index, dims, mode=’raise’, order=’C’)

Converts a tuple of index arrays into an array of flat indices, applying boundary modes to the multi-index.

……………………… real ……………………… Help on _ArrayFunctionDispatcher in module numpy:

real(val)

Return the real part of the complex argument.

valarray_like

Input array.

……………………… real_if_close ……………………… Help on _ArrayFunctionDispatcher in module numpy:

real_if_close(a, tol=100)

If input is complex with all imaginary parts close to zero, return real parts.

“Close to zero” is defined as tol * (machine epsilon of the type for a).

Parameters

……………………… rec ……………………… Help on package numpy.rec in numpy:

NAME

numpy.rec - This module contains a set of functions for record arrays.

PACKAGE CONTENTS

CLASSES

builtins.object

……………………… recarray ……………………… Help on class recarray in module numpy.rec:

class recarray(numpy.ndarray)

recarray(

shape,

dtype=None,

buf=None,

offset=0,

strides=None,

formats=None,

……………………… reciprocal ……………………… Help on ufunc in module numpy:

reciprocal = <ufunc ‘reciprocal’>

reciprocal(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Return the reciprocal of the argument, element-wise.

Calculates 1/x.

Parameters

……………………… record ……………………… Help on class record in module numpy:

class record(void)

A data-type scalar that allows field access as attribute lookup.

Method resolution order:

record

void

flexible

generic

……………………… remainder ……………………… Help on ufunc in module numpy:

remainder = <ufunc ‘remainder’>

remainder(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Returns the element-wise remainder of division.

Computes the remainder complementary to the floor_divide function. It is equivalent to the Python modulus operator x1 % x2 and has the same sign as the divisor x2. The MATLAB function equivalent to np.remainder

……………………… repeat ……………………… Help on _ArrayFunctionDispatcher in module numpy:

repeat(a, repeats, axis=None)

Repeat each element of an array after themselves

aarray_like

Input array.

repeats : int or array of ints

……………………… require ……………………… Help on function require in module numpy:

require(a, dtype=None, requirements=None, *, like=None)

Return an ndarray of the provided type that satisfies requirements.

This function is useful to be sure that an array with the correct flags is returned for passing to compiled code (perhaps through ctypes).

……………………… reshape ……………………… Help on _ArrayFunctionDispatcher in module numpy:

reshape(a, /, shape=None, order=’C’, *, newshape=None, copy=None)

Gives a new shape to an array without changing its data.

aarray_like

Array to be reshaped.

shape : int or tuple of ints

……………………… resize ……………………… Help on _ArrayFunctionDispatcher in module numpy:

resize(a, new_shape)

Return a new array with the specified shape.

If the new array is larger than the original array, then the new array is filled with repeated copies of a. Note that this behavior is different from a.resize(new_shape) which fills with zeros instead of repeated copies of a.

……………………… result_type ……………………… Help on _ArrayFunctionDispatcher in module numpy:

result_type(…)

result_type(*arrays_and_dtypes)

Returns the type that results from applying the NumPy type promotion rules to the arguments.

Type promotion in NumPy works similarly to the rules in languages like C++, with some slight differences. When both scalars and

……………………… right_shift ……………………… Help on ufunc in module numpy:

right_shift = <ufunc ‘right_shift’>

right_shift(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Shift the bits of an integer to the right.

Bits are shifted to the right x2. Because the internal representation of numbers is in binary format, this operation is equivalent to dividing x1 by 2**x2.

……………………… rint ……………………… Help on ufunc in module numpy:

rint = <ufunc ‘rint’>

rint(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Round elements of the array to the nearest integer.

x : array_like

……………………… roll ……………………… Help on _ArrayFunctionDispatcher in module numpy:

roll(a, shift, axis=None)

Roll array elements along a given axis.

Elements that roll beyond the last position are re-introduced at the first.

……………………… rollaxis ……………………… Help on _ArrayFunctionDispatcher in module numpy:

rollaxis(a, axis, start=0)

Roll the specified axis backwards, until it lies in a given position.

This function continues to be supported for backward compatibility, but you should prefer moveaxis. The moveaxis function was added in NumPy 1.11.

Parameters

+……………….+………………….+ | -(arr.ndim+1) | raise AxisError | +——————-+———————-+ | -arr.ndim | 0 | +——————-+———————-+ | |vdots| | |vdots| | +——————-+———————-+ | -1 | arr.ndim-1 | +——————-+———————-+ | 0 | 0 | +——————-+———————-+

……………………… roots ……………………… Help on _ArrayFunctionDispatcher in module numpy:

roots(p)

Return the roots of a polynomial with coefficients given in p.

Note

This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in numpy.polynomial is preferred. A summary of the differences can be found in the transition guide.

……………………… rot90 ……………………… Help on _ArrayFunctionDispatcher in module numpy:

rot90(m, k=1, axes=(0, 1))

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

Rotation direction is from the first towards the second axis. This means for a 2D array with the default k and axes, the rotation will be counterclockwise.

Parameters

……………………… round ……………………… Help on _ArrayFunctionDispatcher in module numpy:

round(a, decimals=0, out=None)

Evenly round to the given number of decimals.

aarray_like

Input data.

decimals : int, optional

……………………… row_stack ……………………… Help on function row_stack in module numpy:

row_stack(tup, *, dtype=None, casting=’same_kind’)

Stack arrays in sequence vertically (row wise).

This is equivalent to concatenation along the first axis after 1-D arrays of shape (N,) have been reshaped to (1,N). Rebuilds arrays divided by vsplit.

This function makes most sense for arrays with up to 3 dimensions. For

……………………… s_ ……………………… Help on IndexExpression in module numpy.lib._index_tricks_impl object:

class IndexExpression(builtins.object)

IndexExpression(maketuple)

A nicer way to build up index tuples for arrays.

.. note::

Use one of the two predefined instances index_exp or s_

rather than directly using IndexExpression.

……………………… save ……………………… Help on _ArrayFunctionDispatcher in module numpy:

save(file, arr, allow_pickle=True, fix_imports=<no value>)

Save an array to a binary file in NumPy .npy format.

filefile, str, or pathlib.Path

File or filename to which the data is saved. If file is a file-object, then the filename is unchanged. If file is a string or Path,

……………………… savetxt ……………………… Help on _ArrayFunctionDispatcher in module numpy:

savetxt(

fname, X, fmt=’%.18e’, delimiter=’ ‘, newline=’n’, header=’’, footer=’’,

……………………… savez ……………………… Help on _ArrayFunctionDispatcher in module numpy:

savez(file, *args, allow_pickle=True, **kwds)

Save several arrays into a single file in uncompressed .npz format.

Provide arrays as keyword arguments to store them under the corresponding name in the output file: savez(fn, x=x, y=y).

If arrays are specified as positional arguments, i.e., savez(fn, x, y), their names will be arr_0, arr_1, etc.

……………………… savez_compressed ……………………… Help on _ArrayFunctionDispatcher in module numpy:

savez_compressed(file, *args, allow_pickle=True, **kwds)

Save several arrays into a single file in compressed .npz format.

Provide arrays as keyword arguments to store them under the corresponding name in the output file: savez_compressed(fn, x=x, y=y).

If arrays are specified as positional arguments, i.e., savez_compressed(fn, x, y), their names will be arr_0, arr_1, etc.

……………………… sctypeDict ……………………… Help on dict object:

class dict(object)

dict() -> new empty dictionary

dict(mapping) -> new dictionary initialized from a mapping object’s

(key, value) pairs

dict(iterable) -> new dictionary initialized as if via:

d = {}

for k, v in iterable:

d[k] = v

……………………… searchsorted ……………………… Help on _ArrayFunctionDispatcher in module numpy:

searchsorted(a, v, side=’left’, sorter=None)

Find indices where elements should be inserted to maintain order.

Find the indices into a sorted array a such that, if the corresponding elements in v were inserted before the indices, the order of a would be preserved.

Assuming that a is sorted: …… ………………………. side returned index i satisfies ====== ============================ left a[i-1] < v <= a[i] right a[i-1] <= v < a[i] ====== ============================

a1-D array_like

Input array. If sorter is None, then it must be sorted in

……………………… select ……………………… Help on _ArrayFunctionDispatcher in module numpy:

select(condlist, choicelist, default=0)

Return an array drawn from elements in choicelist, depending on conditions.

condlistlist of bool ndarrays

The list of conditions which determine from which array in choicelist the output elements are taken. When multiple conditions are satisfied,

……………………… set_printoptions ……………………… Help on function set_printoptions in module numpy:

set_printoptions(

precision=None, threshold=None, edgeitems=None, linewidth=None, suppress=None, nanstr=None, infstr=None,

……………………… setbufsize ……………………… Help on function setbufsize in module numpy:

setbufsize(size)

Set the size of the buffer used in ufuncs.

Changed in version 2.0: The scope of setting the buffer is tied to the numpy.errstate context. Exiting a with errstate(): will also restore the bufsize.

Parameters

……………………… setdiff1d ……………………… Help on _ArrayFunctionDispatcher in module numpy:

setdiff1d(ar1, ar2, assume_unique=False)

Find the set difference of two arrays.

Return the unique values in ar1 that are not in ar2.

ar1 : array_like

……………………… seterr ……………………… Help on function seterr in module numpy:

seterr(all=None, divide=None, over=None, under=None, invalid=None)

Set how floating-point errors are handled.

Note that operations on integer scalar types (such as int16) are handled like floating point, and are affected by these settings.

……………………… seterrcall ……………………… Help on function seterrcall in module numpy:

seterrcall(func)

Set the floating-point error callback function or log object.

There are two ways to capture floating-point error messages. The first is to set the error-handler to ‘call’, using seterr. Then, set the function to call using this function.

The second is to set the error-handler to ‘log’, using seterr.

……………………… setxor1d ……………………… Help on _ArrayFunctionDispatcher in module numpy:

setxor1d(ar1, ar2, assume_unique=False)

Find the set exclusive-or of two arrays.

Return the sorted, unique values that are in only one (not both) of the input arrays.

……………………… shape ……………………… Help on _ArrayFunctionDispatcher in module numpy:

shape(a)

Return the shape of an array.

aarray_like

Input array.

……………………… shares_memory ……………………… Help on _ArrayFunctionDispatcher in module numpy:

shares_memory(…)

shares_memory(a, b, /, max_work=None)

Determine if two arrays share memory.

Warning

This function can be exponentially slow for some inputs, unless

……………………… short ……………………… Help on class int16 in module numpy:

class int16(signedinteger)

Signed integer type, compatible with C short.

:Character code: 'h'

:Canonical name: numpy.short

:Alias on this platform (Linux x86_64): numpy.int16: 16-bit signed integer (-32_768 to 32_767).

Method resolution order:

……………………… show_config ……………………… Help on function show_config in module numpy:

show_config(mode=’stdout’)

Show libraries and system information on which NumPy was built and is being used

mode{‘stdout’, ‘dicts’}, optional.

Indicates how to display the config information.

……………………… show_runtime ……………………… Help on function show_runtime in module numpy:

show_runtime()

Print information about various resources in the system including available intrinsic support and BLAS/LAPACK library in use

New in version 1.24.0.

See Also

……………………… sign ……………………… Help on ufunc in module numpy:

sign = <ufunc ‘sign’>

sign(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Returns an element-wise indication of the sign of a number.

The sign function returns -1 if x < 0, 0 if x==0, 1 if x > 0. nan is returned for nan inputs.

……………………… signbit ……………………… Help on ufunc in module numpy:

signbit = <ufunc ‘signbit’>

signbit(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Returns element-wise True where signbit is set (less than zero).

x : array_like

……………………… signedinteger ……………………… Help on class signedinteger in module numpy:

class signedinteger(integer)

Abstract base class of all signed integer scalar types.

Method resolution order:

signedinteger

integer

number

generic

……………………… sin ……………………… Help on ufunc in module numpy:

sin = <ufunc ‘sin’>

sin(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Trigonometric sine, element-wise.

x : array_like

……………………… sinc ……………………… Help on _ArrayFunctionDispatcher in module numpy:

sinc(x)

Return the normalized sinc function.

The sinc function is equal to \(\sin(\pi x)/(\pi x)\) for any argument \(x\ne 0\). sinc(0) takes the limit value 1, making sinc not only everywhere continuous but also infinitely differentiable.

……………………… single ……………………… Help on class float32 in module numpy:

class float32(floating)

Single-precision floating-point number type, compatible with C float.

:Character code: 'f'

:Canonical name: numpy.single

:Alias on this platform (Linux x86_64): numpy.float32: 32-bit-precision floating-point number type: sign bit, 8 bits exponent, 23 bits mantissa.

Method resolution order:

……………………… sinh ……………………… Help on ufunc in module numpy:

sinh = <ufunc ‘sinh’>

sinh(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Hyperbolic sine, element-wise.

Equivalent to 1/2 * (np.exp(x) - np.exp(-x)) or -1j * np.sin(1j*x).

……………………… size ……………………… Help on _ArrayFunctionDispatcher in module numpy:

size(a, axis=None)

Return the number of elements along a given axis.

aarray_like

Input data.

axis : int, optional

……………………… sort ……………………… Help on _ArrayFunctionDispatcher in module numpy:

sort(a, axis=-1, kind=None, order=None, *, stable=None)

Return a sorted copy of an array.

aarray_like

Array to be sorted.

axis : int or None, optional

……………………… sort_complex ……………………… Help on _ArrayFunctionDispatcher in module numpy:

sort_complex(a)

Sort a complex array using the real part first, then the imaginary part.

aarray_like

Input array

……………………… spacing ……………………… Help on ufunc in module numpy:

spacing = <ufunc ‘spacing’>

spacing(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Return the distance between x and the nearest adjacent number.

x : array_like

……………………… split ……………………… Help on _ArrayFunctionDispatcher in module numpy:

split(ary, indices_or_sections, axis=0)

Split an array into multiple sub-arrays as views into ary.

aryndarray

Array to be divided into sub-arrays.

indices_or_sections : int or 1-D array

……………………… sqrt ……………………… Help on ufunc in module numpy:

sqrt = <ufunc ‘sqrt’>

sqrt(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Return the non-negative square-root of an array, element-wise.

x : array_like

……………………… square ……………………… Help on ufunc in module numpy:

square = <ufunc ‘square’>

square(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Return the element-wise square of the input.

x : array_like

……………………… squeeze ……………………… Help on _ArrayFunctionDispatcher in module numpy:

squeeze(a, axis=None)

Remove axes of length one from a.

aarray_like

Input data.

axis : None or int or tuple of ints, optional

……………………… stack ……………………… Help on _ArrayFunctionDispatcher in module numpy:

stack(arrays, axis=0, out=None, *, dtype=None, casting=’same_kind’)

Join a sequence of arrays along a new axis.

The axis parameter specifies the index of the new axis in the dimensions of the result. For example, if axis=0 it will be the first dimension and if axis=-1 it will be the last dimension.

Parameters

……………………… std ……………………… Help on _ArrayFunctionDispatcher in module numpy:

std(

a, axis=None, dtype=None, out=None, ddof=0, keepdims=<no value>, *,

……………………… str_ ……………………… Help on class str_ in module numpy:

class str_(builtins.str, character)

A unicode string.

This type strips trailing null codepoints.

>>> s = np.str_(“abcx00”)

>>> s

‘abc’

……………………… strings ……………………… Help on package numpy.strings in numpy:

NAME

numpy.strings

DESCRIPTION

This module contains a set of functions for vectorized string operations.

PACKAGE CONTENTS ……………………… subtract ……………………… Help on ufunc in module numpy:

subtract = <ufunc ‘subtract’>

subtract(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Subtract arguments, element-wise.

x1, x2 : array_like

……………………… sum ……………………… Help on _ArrayFunctionDispatcher in module numpy:

sum(

a, axis=None, dtype=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>

……………………… swapaxes ……………………… Help on _ArrayFunctionDispatcher in module numpy:

swapaxes(a, axis1, axis2)

Interchange two axes of an array.

aarray_like

Input array.

axis1 : int

……………………… take ……………………… Help on _ArrayFunctionDispatcher in module numpy:

take(a, indices, axis=None, out=None, mode=’raise’)

Take elements from an array along an axis.

When axis is not None, this function does the same thing as “fancy” indexing (indexing arrays using arrays); however, it can be easier to use if you need elements along a given axis. A call such as np.take(arr, indices, axis=3) is equivalent to arr[:,:,:,indices,...].

……………………… take_along_axis ……………………… Help on _ArrayFunctionDispatcher in module numpy:

take_along_axis(arr, indices, axis)

Take values from the input array by matching 1d index and data slices.

This iterates over matching 1d slices oriented along the specified axis in the index and data arrays, and uses the former to look up values in the latter. These slices can be different lengths.

Functions returning an index along an axis, like argsort and

……………………… tan ……………………… Help on ufunc in module numpy:

tan = <ufunc ‘tan’>

tan(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Compute tangent element-wise.

Equivalent to np.sin(x)/np.cos(x) element-wise.

Parameters

……………………… tanh ……………………… Help on ufunc in module numpy:

tanh = <ufunc ‘tanh’>

tanh(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Compute hyperbolic tangent element-wise.

Equivalent to np.sinh(x)/np.cosh(x) or -1j * np.tan(1j*x).

Parameters

……………………… tensordot ……………………… Help on _ArrayFunctionDispatcher in module numpy:

tensordot(a, b, axes=2)

Compute tensor dot product along specified axes.

Given two tensors, a and b, and an array_like object containing two array_like objects, (a_axes, b_axes), sum the products of a’s and b’s elements (components) over the axes specified by a_axes and b_axes. The third argument can be a single non-negative integer_like scalar, N; if it is such, then the last N dimensions

……………………… test ……………………… Help on PytestTester in module numpy object:

class PytestTester(builtins.object)

PytestTester(module_name)

Pytest test runner.

A test function is typically added to a package’s __init__.py like so::

from numpy._pytesttester import PytestTester

……………………… testing ……………………… Help on package numpy.testing in numpy:

NAME

numpy.testing - Common test support for all numpy test scripts.

DESCRIPTION

This single module should provide all the common functionality for numpy tests in a single location, so that test scripts can just import it and work right away.

……………………… tile ……………………… Help on _ArrayFunctionDispatcher in module numpy:

tile(A, reps)

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

If reps has length d, the result will have dimension of max(d, A.ndim).

If A.ndim < d, A is promoted to be d-dimensional by prepending new axes. So a shape (3,) array is promoted to (1, 3) for 2-D replication,

……………………… timedelta64 ……………………… Help on class timedelta64 in module numpy:

class timedelta64(signedinteger)

A timedelta stored as a 64-bit integer.

See arrays.datetime for more information.

:Character code: 'm'

Method resolution order:

……………………… trace ……………………… Help on _ArrayFunctionDispatcher in module numpy:

trace(a, offset=0, axis1=0, axis2=1, dtype=None, out=None)

Return the sum along diagonals of the array.

If a is 2-D, the sum along its diagonal with the given offset is returned, i.e., the sum of elements a[i,i+offset] for all i.

If a has more than two dimensions, then the axes specified by axis1 and axis2 are used to determine the 2-D sub-arrays whose traces are returned.

……………………… transpose ……………………… Help on _ArrayFunctionDispatcher in module numpy:

transpose(a, axes=None)

Returns an array with axes transposed.

For a 1-D array, this returns an unchanged view of the original array, as a transposed vector is simply the same vector. To convert a 1-D array into a 2-D column vector, an additional dimension must be added, e.g., np.atleast_2d(a).T achieves this, as does a[:, np.newaxis].

……………………… trapezoid ……………………… Help on _ArrayFunctionDispatcher in module numpy:

trapezoid(y, x=None, dx=1.0, axis=-1)

Integrate along the given axis using the composite trapezoidal rule.

If x is provided, the integration happens in sequence along its elements - they are not sorted.

Integrate y (x) along each 1d slice on the given axis, compute \(\int y(x) dx\).

……………………… trapz ……………………… Help on function trapz in module numpy:

trapz(y, x=None, dx=1.0, axis=-1)

trapz is deprecated in NumPy 2.0.

Please use trapezoid instead, or one of the numerical integration functions in scipy.integrate.

=========================== tri =========================== Help on function tri in module numpy: ……………………… tril ……………………… Help on _ArrayFunctionDispatcher in module numpy:

tril(m, k=0)

Lower triangle of an array.

Return a copy of an array with elements above the k-th diagonal zeroed. For arrays with ndim exceeding 2, tril will apply to the final two axes.

Parameters

……………………… tril_indices ……………………… Help on function tril_indices in module numpy:

tril_indices(n, k=0, m=None)

Return the indices for the lower-triangle of an (n, m) array.

nint

The row dimension of the arrays for which the returned indices will be valid.

……………………… tril_indices_from ……………………… Help on _ArrayFunctionDispatcher in module numpy:

tril_indices_from(arr, k=0)

Return the indices for the lower-triangle of arr.

See tril_indices for full details.

arr : array_like

……………………… trim_zeros ……………………… Help on _ArrayFunctionDispatcher in module numpy:

trim_zeros(filt, trim=’fb’, axis=None)

Remove values along a dimension which are zero along all other.

filtarray_like

Input array.

trim : {“fb”, “f”, “b”}, optional

……………………… triu ……………………… Help on _ArrayFunctionDispatcher in module numpy:

triu(m, k=0)

Upper triangle of an array.

Return a copy of an array with the elements below the k-th diagonal zeroed. For arrays with ndim exceeding 2, triu will apply to the final two axes.

Please refer to the documentation for tril for further details.

……………………… triu_indices ……………………… Help on function triu_indices in module numpy:

triu_indices(n, k=0, m=None)

Return the indices for the upper-triangle of an (n, m) array.

nint

The size of the arrays for which the returned indices will be valid.

……………………… triu_indices_from ……………………… Help on _ArrayFunctionDispatcher in module numpy:

triu_indices_from(arr, k=0)

Return the indices for the upper-triangle of arr.

See triu_indices for full details.

arr : ndarray, shape(N, N)

……………………… true_divide ……………………… Help on ufunc in module numpy:

divide = <ufunc ‘divide’>

divide(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Divide arguments element-wise.

x1 : array_like

……………………… trunc ……………………… Help on ufunc in module numpy:

trunc = <ufunc ‘trunc’>

trunc(x, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature])

Return the truncated value of the input, element-wise.

The truncated value of the scalar x is the nearest integer i which is closer to zero than x is. In short, the fractional part of the signed number x is discarded.

……………………… typecodes ……………………… Help on dict object:

class dict(object)

dict() -> new empty dictionary

dict(mapping) -> new dictionary initialized from a mapping object’s

(key, value) pairs

dict(iterable) -> new dictionary initialized as if via:

d = {}

for k, v in iterable:

d[k] = v

……………………… typename ……………………… Help on function typename in module numpy:

typename(char)

Return a description for the given data type code.

charstr

Data type code.

……………………… typing ……………………… Help on package numpy.typing in numpy:

NAME

numpy.typing

DESCRIPTION

……………………… ubyte ……………………… Help on class uint8 in module numpy:

class uint8(unsignedinteger)

Unsigned integer type, compatible with C unsigned char.

:Character code: 'B'

:Canonical name: numpy.ubyte

:Alias on this platform (Linux x86_64): numpy.uint8: 8-bit unsigned integer (0 to 255).

Method resolution order:

……………………… ufunc ……………………… Help on class ufunc in module numpy:

class ufunc(builtins.object)

Functions that operate element by element on whole arrays.

To see the documentation for a specific ufunc, use info. For

example, np.info(np.sin). Because ufuncs are written in C

(for speed) and linked into Python with NumPy’s ufunc facility,

Python’s help() function finds this page whenever help() is called

on a ufunc.

……………………… uint ……………………… Help on class uint64 in module numpy:

class uint64(unsignedinteger)

Unsigned signed integer type, 64bit on 64bit systems and 32bit on 32bit

systems.

:Character code: 'L'

:Canonical name: numpy.uint

:Alias on this platform (Linux x86_64): numpy.uint64: 64-bit unsigned integer (0 to 18_446_744_073_709_551_615).

:Alias on this platform (Linux x86_64): numpy.uintp: Unsigned integer large enough to fit pointer, compatible with C uintptr_t.

……………………… uint16 ……………………… Help on class uint16 in module numpy:

class uint16(unsignedinteger)

Unsigned integer type, compatible with C unsigned short.

:Character code: 'H'

:Canonical name: numpy.ushort

:Alias on this platform (Linux x86_64): numpy.uint16: 16-bit unsigned integer (0 to 65_535).

Method resolution order:

……………………… uint32 ……………………… Help on class uint32 in module numpy:

class uint32(unsignedinteger)

Unsigned integer type, compatible with C unsigned int.

:Character code: 'I'

:Canonical name: numpy.uintc

:Alias on this platform (Linux x86_64): numpy.uint32: 32-bit unsigned integer (0 to 4_294_967_295).

Method resolution order:

……………………… uint64 ……………………… Help on class uint64 in module numpy:

class uint64(unsignedinteger)

Unsigned signed integer type, 64bit on 64bit systems and 32bit on 32bit

systems.

:Character code: 'L'

:Canonical name: numpy.uint

:Alias on this platform (Linux x86_64): numpy.uint64: 64-bit unsigned integer (0 to 18_446_744_073_709_551_615).

:Alias on this platform (Linux x86_64): numpy.uintp: Unsigned integer large enough to fit pointer, compatible with C uintptr_t.

……………………… uint8 ……………………… Help on class uint8 in module numpy:

class uint8(unsignedinteger)

Unsigned integer type, compatible with C unsigned char.

:Character code: 'B'

:Canonical name: numpy.ubyte

:Alias on this platform (Linux x86_64): numpy.uint8: 8-bit unsigned integer (0 to 255).

Method resolution order:

……………………… uintc ……………………… Help on class uint32 in module numpy:

class uint32(unsignedinteger)

Unsigned integer type, compatible with C unsigned int.

:Character code: 'I'

:Canonical name: numpy.uintc

:Alias on this platform (Linux x86_64): numpy.uint32: 32-bit unsigned integer (0 to 4_294_967_295).

Method resolution order:

……………………… uintp ……………………… Help on class uint64 in module numpy:

class uint64(unsignedinteger)

Unsigned signed integer type, 64bit on 64bit systems and 32bit on 32bit

systems.

:Character code: 'L'

:Canonical name: numpy.uint

:Alias on this platform (Linux x86_64): numpy.uint64: 64-bit unsigned integer (0 to 18_446_744_073_709_551_615).

:Alias on this platform (Linux x86_64): numpy.uintp: Unsigned integer large enough to fit pointer, compatible with C uintptr_t.

……………………… ulong ……………………… Help on class uint64 in module numpy:

class uint64(unsignedinteger)

Unsigned signed integer type, 64bit on 64bit systems and 32bit on 32bit

systems.

:Character code: 'L'

:Canonical name: numpy.uint

:Alias on this platform (Linux x86_64): numpy.uint64: 64-bit unsigned integer (0 to 18_446_744_073_709_551_615).

:Alias on this platform (Linux x86_64): numpy.uintp: Unsigned integer large enough to fit pointer, compatible with C uintptr_t.

……………………… ulonglong ……………………… Help on class ulonglong in module numpy:

class ulonglong(unsignedinteger)

Signed integer type, compatible with C unsigned long long.

:Character code: 'Q'

Method resolution order:

ulonglong

unsignedinteger

……………………… union1d ……………………… Help on _ArrayFunctionDispatcher in module numpy:

union1d(ar1, ar2)

Find the union of two arrays.

Return the unique, sorted array of values that are in either of the two input arrays.

……………………… unique ……………………… Help on _ArrayFunctionDispatcher in module numpy:

unique(

ar, return_index=False, return_inverse=False, return_counts=False, axis=None, *, equal_nan=True

……………………… unique_all ……………………… Help on _ArrayFunctionDispatcher in module numpy:

unique_all(x)

Find the unique elements of an array, and counts, inverse, and indices.

This function is an Array API compatible alternative to:

np.unique(x, return_index=True, return_inverse=True,
          return_counts=True, equal_nan=False)

……………………… unique_counts ……………………… Help on _ArrayFunctionDispatcher in module numpy:

unique_counts(x)

Find the unique elements and counts of an input array x.

This function is an Array API compatible alternative to:

np.unique(x, return_counts=True, equal_nan=False)

but returns a namedtuple for easier access to each output.

……………………… unique_inverse ……………………… Help on _ArrayFunctionDispatcher in module numpy:

unique_inverse(x)

Find the unique elements of x and indices to reconstruct x.

This function is an Array API compatible alternative to:

np.unique(x, return_inverse=True, equal_nan=False)

but returns a namedtuple for easier access to each output.

……………………… unique_values ……………………… Help on _ArrayFunctionDispatcher in module numpy:

unique_values(x)

Returns the unique elements of an input array x.

This function is an Array API compatible alternative to:

np.unique(x, equal_nan=False)

Parameters

……………………… unpackbits ……………………… Help on _ArrayFunctionDispatcher in module numpy:

unpackbits(…)

unpackbits(a, /, axis=None, count=None, bitorder=’big’)

Unpacks elements of a uint8 array into a binary-valued output array.

Each element of a represents a bit-field that should be unpacked into a binary-valued output array. The shape of the output array is either 1-D (if axis is None) or the same shape as the input

……………………… unravel_index ……………………… Help on _ArrayFunctionDispatcher in module numpy:

unravel_index(…)

unravel_index(indices, shape, order=’C’)

Converts a flat index or array of flat indices into a tuple of coordinate arrays.

……………………… unsignedinteger ……………………… Help on class unsignedinteger in module numpy:

class unsignedinteger(integer)

Abstract base class of all unsigned integer scalar types.

Method resolution order:

unsignedinteger

integer

number

generic

……………………… unstack ……………………… Help on _ArrayFunctionDispatcher in module numpy:

unstack(x, /, *, axis=0)

Split an array into a sequence of arrays along the given axis.

The axis parameter specifies the dimension along which the array will be split. For example, if axis=0 (the default) it will be the first dimension and if axis=-1 it will be the last dimension.

The result is a tuple of arrays split along axis.

……………………… unwrap ……………………… Help on _ArrayFunctionDispatcher in module numpy:

unwrap(p, discont=None, axis=-1, *, period=6.283185307179586)

Unwrap by taking the complement of large deltas with respect to the period.

This unwraps a signal p by changing elements which have an absolute difference from their predecessor of more than max(discont, period/2) to their period-complementary values.

For the default case where period is \(2\pi\) and discont is

……………………… ushort ……………………… Help on class uint16 in module numpy:

class uint16(unsignedinteger)

Unsigned integer type, compatible with C unsigned short.

:Character code: 'H'

:Canonical name: numpy.ushort

:Alias on this platform (Linux x86_64): numpy.uint16: 16-bit unsigned integer (0 to 65_535).

Method resolution order:

……………………… vander ……………………… Help on _ArrayFunctionDispatcher in module numpy:

vander(x, N=None, increasing=False)

Generate a Vandermonde matrix.

The columns of the output matrix are powers of the input vector. The order of the powers is determined by the increasing boolean argument. Specifically, when increasing is False, the i-th output column is the input vector raised element-wise to the power of N - i - 1. Such a matrix with a geometric progression in each row is named for Alexandre-

……………………… var ……………………… Help on _ArrayFunctionDispatcher in module numpy:

var(

a, axis=None, dtype=None, out=None, ddof=0, keepdims=<no value>, *,

……………………… vdot ……………………… Help on _ArrayFunctionDispatcher in module numpy:

vdot(…)

vdot(a, b, /)

Return the dot product of two vectors.

The vdot function handles complex numbers differently than dot: if the first argument is complex, it is replaced by its complex conjugate in the dot product calculation. vdot also handles multidimensional

……………………… vecdot ……………………… Help on ufunc in module numpy:

vecdot = <ufunc ‘vecdot’>

vecdot(x1, x2, /, out=None, *, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature, axes, axis])

Vector dot product of two arrays.

Let \(\mathbf{a}\) be a vector in x1 and \(\mathbf{b}\) be a corresponding vector in x2. The dot product is defined as:

……………………… vecmat ……………………… Help on ufunc in module numpy:

vecmat = <ufunc ‘vecmat’>

vecmat(x1, x2, /, out=None, *, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature, axes, axis])

Vector-matrix dot product of two arrays.

Given a vector (or stack of vector) \(\mathbf{v}\) in x1 and a matrix (or stack of matrices) \(\mathbf{A}\) in x2, the vector-matrix product is defined as:

……………………… vectorize ……………………… Help on class vectorize in module numpy:

class vectorize(builtins.object)

vectorize(

pyfunc=<no value>,

otypes=None,

doc=None,

excluded=None,

cache=False,

signature=None

……………………… void ……………………… Help on class void in module numpy:

class void(flexible)

np.void(length_or_data, /, dtype=None)

Create a new structured or unstructured void scalar.

Parameters

———-

length_or_data : int, array-like, bytes-like, object

……………………… vsplit ……………………… Help on _ArrayFunctionDispatcher in module numpy:

vsplit(ary, indices_or_sections)

Split an array into multiple sub-arrays vertically (row-wise).

Please refer to the split documentation. vsplit is equivalent to split with axis=0 (default), the array is always split along the first axis regardless of the array dimension.

See Also

……………………… vstack ……………………… Help on _ArrayFunctionDispatcher in module numpy:

vstack(tup, *, dtype=None, casting=’same_kind’)

Stack arrays in sequence vertically (row wise).

This is equivalent to concatenation along the first axis after 1-D arrays of shape (N,) have been reshaped to (1,N). Rebuilds arrays divided by vsplit.

This function makes most sense for arrays with up to 3 dimensions. For

……………………… where ……………………… Help on _ArrayFunctionDispatcher in module numpy:

where(…)

where(condition, [x, y], /)

Return elements chosen from x or y depending on condition.

Note

When only condition is provided, this function is a shorthand for np.asarray(condition).nonzero(). Using nonzero directly should be

……………………… zeros ……………………… Help on built-in function zeros in module numpy:

zeros(…)

zeros(shape, dtype=float, order=’C’, *, like=None)

Return a new array of given shape and type, filled with zeros.

shape : int or tuple of ints

……………………… zeros_like ……………………… Help on _ArrayFunctionDispatcher in module numpy:

zeros_like(a, dtype=None, order=’K’, subok=True, shape=None, *, device=None)

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

aarray_like

The shape and data-type of a define these same attributes of the returned array.