doc_content stringlengths 1 386k | doc_id stringlengths 5 188 |
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class numpy.longlong[source]
Signed integer type, compatible with C long long. Character code
'q' | numpy.reference.arrays.scalars#numpy.longlong |
numpy.lookfor numpy.lookfor(what, module=None, import_modules=True, regenerate=False, output=None)[source]
Do a keyword search on docstrings. A list of objects that matched the search is displayed, sorted by relevance. All given keywords need to be found in the docstring for it to be returned as a result, but the o... | numpy.reference.generated.numpy.lookfor |
Constants of the numpy.ma module In addition to the MaskedArray class, the numpy.ma module defines several constants. numpy.ma.masked
The masked constant is a special case of MaskedArray, with a float datatype and a null shape. It is used to test whether a specific entry of a masked array is masked, or to mask one ... | numpy.reference.maskedarray.baseclass |
numpy.ma.masked_array numpy.ma.masked_array[source]
alias of numpy.ma.core.MaskedArray | numpy.reference.generated.numpy.ma.masked_array |
numpy.ma.masked_print_options
String used in lieu of missing data when a masked array is printed. By default, this string is '--'. | numpy.reference.maskedarray.baseclass#numpy.ma.masked_print_options |
numpy.ma.MaskType numpy.ma.MaskType[source]
alias of numpy.bool_ | numpy.reference.generated.numpy.ma.masktype |
numpy.ma.nomask
Value indicating that a masked array has no invalid entry. nomask is used internally to speed up computations when the mask is not needed. It is represented internally as np.False_. | numpy.reference.maskedarray.baseclass#numpy.ma.nomask |
numpy.MachAr class numpy.MachAr(float_conv=<class 'float'>, int_conv=<class 'int'>, float_to_float=<class 'float'>, float_to_str=<function MachAr.<lambda>>, title='Python floating point number')[source]
Diagnosing machine parameters. Parameters
float_convfunction, optional
Function that converts an integer or... | numpy.reference.generated.numpy.machar |
numpy.mask_indices numpy.mask_indices(n, mask_func, k=0)[source]
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 ... | numpy.reference.generated.numpy.mask_indices |
numpy.mat numpy.mat(data, dtype=None)[source]
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). Parameters
dataarray_like
Input data.
dtypedata-type
Data-type of the output matrix. Return... | numpy.reference.generated.numpy.mat |
numpy.matmul numpy.matmul(x1, x2, /, out=None, *, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj, axes, axis]) = <ufunc 'matmul'>
Matrix product of two arrays. Parameters
x1, x2array_like
Input arrays, scalars not allowed.
outndarray, optional
A location into which the result i... | numpy.reference.generated.numpy.matmul |
numpy.matrix class numpy.matrix(data, dtype=None, copy=True)[source]
Note It is no longer recommended to use this class, even for linear algebra. Instead use regular arrays. The class may be removed in the future. Returns a matrix from an array-like object, or from a string of data. A matrix is a specialized 2-D ... | numpy.reference.generated.numpy.matrix |
numpy.maximum numpy.maximum(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'maximum'>
Element-wise maximum of array elements. Compare two arrays and returns a new array containing the element-wise maxima. If one of the elements being compare... | numpy.reference.generated.numpy.maximum |
numpy.maximum_sctype numpy.maximum_sctype(t)[source]
Return the scalar type of highest precision of the same kind as the input. Parameters
tdtype or dtype specifier
The input data type. This can be a dtype object or an object that is convertible to a dtype. Returns
outdtype
The highest precision data ... | numpy.reference.generated.numpy.maximum_sctype |
numpy.may_share_memory numpy.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. Only the memory bounds of a and b are checked by default. Parameters
a, bndarray
Inp... | numpy.reference.generated.numpy.may_share_memory |
numpy.mean numpy.mean(a, axis=None, dtype=None, out=None, keepdims=<no value>, *, where=<no value>)[source]
Compute the arithmetic mean along the specified axis. Returns the average of the array elements. The average is taken over the flattened array by default, otherwise over the specified axis. float64 intermedia... | numpy.reference.generated.numpy.mean |
numpy.median numpy.median(a, axis=None, out=None, overwrite_input=False, keepdims=False)[source]
Compute the median along the specified axis. Returns the median of the array elements. Parameters
aarray_like
Input array or object that can be converted to an array.
axis{int, sequence of int, None}, optional
... | numpy.reference.generated.numpy.median |
numpy.memmap class numpy.memmap(filename, dtype=<class 'numpy.ubyte'>, mode='r+', offset=0, shape=None, order='C')[source]
Create a memory-map to an array stored in a binary file on disk. Memory-mapped files are used for accessing small segments of large files on disk, without reading the entire file into memory. N... | numpy.reference.generated.numpy.memmap |
numpy.meshgrid numpy.meshgrid(*xi, copy=True, sparse=False, indexing='xy')[source]
Return 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. Changed in version 1.9: 1-D ... | numpy.reference.generated.numpy.meshgrid |
numpy.mgrid numpy.mgrid = <numpy.lib.index_tricks.MGridClass object>
nd_grid instance which returns a dense multi-dimensional “meshgrid”. An instance of numpy.lib.index_tricks.nd_grid which returns an dense (or fleshed out) mesh-grid when indexed, so that each returned argument has the same shape. The dimensions an... | numpy.reference.generated.numpy.mgrid |
numpy.min_scalar_type numpy.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, and complex values are not demoted to floa... | numpy.reference.generated.numpy.min_scalar_type |
numpy.minimum numpy.minimum(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'minimum'>
Element-wise minimum of array elements. Compare two arrays and returns a new array containing the element-wise minima. If one of the elements being compare... | numpy.reference.generated.numpy.minimum |
numpy.mintypecode numpy.mintypecode(typechars, typeset='GDFgdf', default='d')[source]
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 o... | numpy.reference.generated.numpy.mintypecode |
numpy.mod numpy.mod(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'remainder'>
Returns the element-wise remainder of division. Computes the remainder complementary to the floor_divide function. It is equivalent to the Python modulus operato... | numpy.reference.generated.numpy.mod |
numpy.modf numpy.modf(x, [out1, out2, ]/, [out=(None, None), ]*, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'modf'>
Return the fractional and integral parts of an array, element-wise. The fractional and integral parts are negative if the given number is negativ... | numpy.reference.generated.numpy.modf |
numpy.moveaxis numpy.moveaxis(a, source, destination)[source]
Move axes of an array to new positions. Other axes remain in their original order. New in version 1.11.0. Parameters
anp.ndarray
The array whose axes should be reordered.
sourceint or sequence of int
Original positions of the axes to move. Th... | numpy.reference.generated.numpy.moveaxis |
numpy.msort numpy.msort(a)[source]
Return a copy of an array sorted along the first axis. Parameters
aarray_like
Array to be sorted. Returns
sorted_arrayndarray
Array of the same type and shape as a. See also sort
Notes np.msort(a) is equivalent to np.sort(a, axis=0). | numpy.reference.generated.numpy.msort |
numpy.multiply numpy.multiply(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'multiply'>
Multiply arguments element-wise. Parameters
x1, x2array_like
Input arrays to be multiplied. If x1.shape != x2.shape, they must be broadcastable to... | numpy.reference.generated.numpy.multiply |
numpy.NAN
IEEE 754 floating point representation of Not a Number (NaN). NaN and NAN are equivalent definitions of nan. Please use nan instead of NAN. See Also nan | numpy.reference.constants#numpy.NAN |
numpy.NaN
IEEE 754 floating point representation of Not a Number (NaN). NaN and NAN are equivalent definitions of nan. Please use nan instead of NaN. See Also nan | numpy.reference.constants#numpy.NaN |
numpy.nan
IEEE 754 floating point representation of Not a Number (NaN). Returns y : A floating point representation of Not a Number. See Also isnan : Shows which elements are Not a Number. isfinite : Shows which elements are finite (not one of Not a Number, positive infinity and negative infinity) Notes NumPy uses th... | numpy.reference.constants#numpy.nan |
numpy.nan_to_num numpy.nan_to_num(x, copy=True, nan=0.0, posinf=None, neginf=None)[source]
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 de... | numpy.reference.generated.numpy.nan_to_num |
numpy.nanargmax numpy.nanargmax(a, axis=None, out=None, *, keepdims=<no value>)[source]
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. Parameters
aarray_like
... | numpy.reference.generated.numpy.nanargmax |
numpy.nanargmin numpy.nanargmin(a, axis=None, out=None, *, keepdims=<no value>)[source]
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. Parameters
aarray_like
I... | numpy.reference.generated.numpy.nanargmin |
numpy.nancumprod numpy.nancumprod(a, axis=None, dtype=None, out=None)[source]
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 t... | numpy.reference.generated.numpy.nancumprod |
numpy.nancumsum numpy.nancumsum(a, axis=None, dtype=None, out=None)[source]
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... | numpy.reference.generated.numpy.nancumsum |
numpy.nanmax numpy.nanmax(a, axis=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)[source]
Return the maximum of an array or maximum along an axis, ignoring any NaNs. When all-NaN slices are encountered a RuntimeWarning is raised and NaN is returned for that slice. Parameters
aarray_li... | numpy.reference.generated.numpy.nanmax |
numpy.nanmean numpy.nanmean(a, axis=None, dtype=None, out=None, keepdims=<no value>, *, where=<no value>)[source]
Compute the arithmetic mean along the specified axis, ignoring NaNs. Returns the average of the array elements. The average is taken over the flattened array by default, otherwise over the specified axi... | numpy.reference.generated.numpy.nanmean |
numpy.nanmedian numpy.nanmedian(a, axis=None, out=None, overwrite_input=False, keepdims=<no value>)[source]
Compute the median along the specified axis, while ignoring NaNs. Returns the median of the array elements. New in version 1.9.0. Parameters
aarray_like
Input array or object that can be converted to ... | numpy.reference.generated.numpy.nanmedian |
numpy.nanmin numpy.nanmin(a, axis=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)[source]
Return minimum of an array or minimum along an axis, ignoring any NaNs. When all-NaN slices are encountered a RuntimeWarning is raised and Nan is returned for that slice. Parameters
aarray_like
... | numpy.reference.generated.numpy.nanmin |
numpy.nanpercentile numpy.nanpercentile(a, q, axis=None, out=None, overwrite_input=False, method='linear', keepdims=<no value>, *, interpolation=None)[source]
Compute the qth percentile of the data along the specified axis, while ignoring nan values. Returns the qth percentile(s) of the array elements. New in vers... | numpy.reference.generated.numpy.nanpercentile |
numpy.nanprod numpy.nanprod(a, axis=None, dtype=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)[source]
Return the product of array elements over a given axis treating Not a Numbers (NaNs) as ones. One is returned for slices that are all-NaN or empty. New in version 1.10.0. Parameters ... | numpy.reference.generated.numpy.nanprod |
numpy.nanquantile numpy.nanquantile(a, q, axis=None, out=None, overwrite_input=False, method='linear', keepdims=<no value>, *, interpolation=None)[source]
Compute the qth quantile of the data along the specified axis, while ignoring nan values. Returns the qth quantile(s) of the array elements. New in version 1.15... | numpy.reference.generated.numpy.nanquantile |
numpy.nanstd numpy.nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=<no value>, *, where=<no value>)[source]
Compute the standard deviation along the specified axis, while ignoring NaNs. Returns the standard deviation, a measure of the spread of a distribution, of the non-NaN array elements. The standard... | numpy.reference.generated.numpy.nanstd |
numpy.nansum numpy.nansum(a, axis=None, dtype=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)[source]
Return the sum of array elements over a given axis treating Not a Numbers (NaNs) as zero. In NumPy versions <= 1.9.0 Nan is returned for slices that are all-NaN or empty. In later version... | numpy.reference.generated.numpy.nansum |
numpy.nanvar numpy.nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=<no value>, *, where=<no value>)[source]
Compute the variance along the specified axis, while ignoring NaNs. Returns the variance of the array elements, a measure of the spread of a distribution. The variance is computed for the flattene... | numpy.reference.generated.numpy.nanvar |
numpy.ndarray class numpy.ndarray(shape, dtype=float, buffer=None, offset=0, strides=None, order=None)[source]
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 occ... | numpy.reference.generated.numpy.ndarray |
numpy.ndenumerate class numpy.ndenumerate(arr)[source]
Multidimensional index iterator. Return an iterator yielding pairs of array coordinates and values. Parameters
arrndarray
Input array. See also
ndindex, flatiter
Examples >>> a = np.array([[1, 2], [3, 4]])
>>> for index, x in np.ndenumerate(a):
... | numpy.reference.generated.numpy.ndenumerate |
numpy.ndindex class numpy.ndindex(*shape)[source]
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. Parameters
shapein... | numpy.reference.generated.numpy.ndindex |
numpy.nditer class numpy.nditer(op, flags=None, op_flags=None, op_dtypes=None, order='K', casting='safe', op_axes=None, itershape=None, buffersize=0)[source]
Efficient multi-dimensional iterator object to iterate over arrays. To get started using this object, see the introductory guide to array iteration. Paramete... | numpy.reference.generated.numpy.nditer |
numpy.negative numpy.negative(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'negative'>
Numerical negative, element-wise. Parameters
xarray_like or scalar
Input array.
outndarray, None, or tuple of ndarray and None, optional
A location... | numpy.reference.generated.numpy.negative |
numpy.nested_iters numpy.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 outermos... | numpy.reference.generated.numpy.nested_iters |
numpy.newaxis
A convenient alias for None, useful for indexing arrays. Examples >>> newaxis is None
True
>>> x = np.arange(3)
>>> x
array([0, 1, 2])
>>> x[:, newaxis]
array([[0],
[1],
[2]])
>>> x[:, newaxis, newaxis]
array([[[0]],
[[1]],
[[2]]])
>>> x[:, newaxis] * x
array([[0, 0, 0],
[0, 1, 2],
[0, 2, 4]])
Outer pr... | numpy.reference.constants#numpy.newaxis |
numpy.nextafter numpy.nextafter(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'nextafter'>
Return the next floating-point value after x1 towards x2, element-wise. Parameters
x1array_like
Values to find the next representable value of.... | numpy.reference.generated.numpy.nextafter |
numpy.NINF
IEEE 754 floating point representation of negative infinity. Returns yfloat
A floating point representation of negative infinity. See Also isinf : Shows which elements are positive or negative infinity isposinf : Shows which elements are positive infinity isneginf : Shows which elements are negative i... | numpy.reference.constants#numpy.NINF |
numpy.nonzero numpy.nonzero(a)[source]
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. To group the indices by ... | numpy.reference.generated.numpy.nonzero |
numpy.not_equal numpy.not_equal(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'not_equal'>
Return (x1 != x2) element-wise. Parameters
x1, x2array_like
Input arrays. If x1.shape != x2.shape, they must be broadcastable to a common shape... | numpy.reference.generated.numpy.not_equal |
class numpy.number[source]
Abstract base class of all numeric scalar types. | numpy.reference.arrays.scalars#numpy.number |
numpy.NZERO
IEEE 754 floating point representation of negative zero. Returns yfloat
A floating point representation of negative zero. See Also PZERO : Defines positive zero. isinf : Shows which elements are positive or negative infinity. isposinf : Shows which elements are positive infinity. isneginf : Shows whi... | numpy.reference.constants#numpy.NZERO |
numpy.obj2sctype numpy.obj2sctype(rep, default=None)[source]
Return the scalar dtype or NumPy equivalent of Python type of an object. Parameters
repany
The object of which the type is returned.
defaultany, optional
If given, this is returned for objects whose types can not be determined. If not given, Non... | numpy.reference.generated.numpy.obj2sctype |
class numpy.object_[source]
Any Python object. Character code
'O' | numpy.reference.arrays.scalars#numpy.object_ |
numpy.ogrid numpy.ogrid = <numpy.lib.index_tricks.OGridClass object>
nd_grid instance which returns an open multi-dimensional “meshgrid”. An instance of numpy.lib.index_tricks.nd_grid which returns an open (i.e. not fleshed out) mesh-grid when indexed, so that only one dimension of each returned array is greater th... | numpy.reference.generated.numpy.ogrid |
numpy.ones numpy.ones(shape, dtype=None, order='C', *, like=None)[source]
Return a new array of given shape and type, filled with ones. Parameters
shapeint or sequence of ints
Shape of the new array, e.g., (2, 3) or 2.
dtypedata-type, optional
The desired data-type for the array, e.g., numpy.int8. Default... | numpy.reference.generated.numpy.ones |
numpy.ones_like numpy.ones_like(a, dtype=None, order='K', subok=True, shape=None)[source]
Return an array of ones with the same shape and type as a given array. Parameters
aarray_like
The shape and data-type of a define these same attributes of the returned array.
dtypedata-type, optional
Overrides the da... | numpy.reference.generated.numpy.ones_like |
numpy.outer numpy.outer(a, b, out=None)[source]
Compute the outer product of two vectors. Given two vectors, a = [a0, a1, ..., aM] and b = [b0, b1, ..., bN], the outer product [1] is: [[a0*b0 a0*b1 ... a0*bN ]
[a1*b0 .
[ ... .
[aM*b0 aM*bN ]]
Parameters
a(M,) array_like
First input... | numpy.reference.generated.numpy.outer |
numpy.packbits numpy.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
aarray_like
An array of integers or booleans whose elements should be packed to bits.
axisin... | numpy.reference.generated.numpy.packbits |
numpy.pad numpy.pad(array, pad_width, mode='constant', **kwargs)[source]
Pad an array. Parameters
arrayarray_like of rank N
The array to pad.
pad_width{sequence, array_like, int}
Number of values padded to the edges of each axis. ((before_1, after_1), … (before_N, after_N)) unique pad widths for each axis... | numpy.reference.generated.numpy.pad |
numpy.partition numpy.partition(a, kth, axis=- 1, kind='introselect', order=None)[source]
Return a partitioned copy of an array. Creates a copy of the array with its elements rearranged 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. All elements smalle... | numpy.reference.generated.numpy.partition |
numpy.percentile numpy.percentile(a, q, axis=None, out=None, overwrite_input=False, method='linear', keepdims=False, *, interpolation=None)[source]
Compute the q-th percentile of the data along the specified axis. Returns the q-th percentile(s) of the array elements. Parameters
aarray_like
Input array or obje... | numpy.reference.generated.numpy.percentile |
numpy.pi
pi = 3.1415926535897932384626433... References https://en.wikipedia.org/wiki/Pi | numpy.reference.constants#numpy.pi |
numpy.piecewise numpy.piecewise(x, condlist, funclist, *args, **kw)[source]
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. Parameters
xndarray or scalar
The input domain.
condlistlist of b... | numpy.reference.generated.numpy.piecewise |
numpy.PINF
IEEE 754 floating point representation of (positive) infinity. Use inf because Inf, Infinity, PINF and infty are aliases for inf. For more details, see inf. See Also inf | numpy.reference.constants#numpy.PINF |
numpy.place numpy.place(arr, mask, vals)[source]
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 ... | numpy.reference.generated.numpy.place |
numpy.poly numpy.poly(seq_of_zeros)[source]
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. ... | numpy.reference.generated.numpy.poly |
numpy.poly1d class numpy.poly1d(c_or_r, r=False, variable=None)[source]
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. A summary of the differences can be found in the transition guide. A conve... | numpy.reference.generated.numpy.poly1d |
numpy.polyadd numpy.polyadd(a1, a2)[source]
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. Returns the polynomial resulting from ... | numpy.reference.generated.numpy.polyadd |
numpy.polyder numpy.polyder(p, m=1)[source]
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. Paramete... | numpy.reference.generated.numpy.polyder |
numpy.polydiv numpy.polydiv(u, v)[source]
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. The input arra... | numpy.reference.generated.numpy.polydiv |
numpy.polyfit numpy.polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False)[source]
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 gui... | numpy.reference.generated.numpy.polyfit |
numpy.polyint numpy.polyint(p, m=1, k=None)[source]
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. ... | numpy.reference.generated.numpy.polyint |
numpy.polymul numpy.polymul(a1, a2)[source]
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. Finds the polynomial resulting fro... | numpy.reference.generated.numpy.polymul |
numpy.polynomial.chebyshev.Chebyshev class numpy.polynomial.chebyshev.Chebyshev(coef, domain=None, window=None)[source]
A Chebyshev series class. The Chebyshev class provides the standard Python numerical methods ‘+’, ‘-’, ‘*’, ‘//’, ‘%’, ‘divmod’, ‘**’, and ‘()’ as well as the methods listed below. Parameters
... | numpy.reference.generated.numpy.polynomial.chebyshev.chebyshev |
numpy.polynomial.hermite.Hermite class numpy.polynomial.hermite.Hermite(coef, domain=None, window=None)[source]
An Hermite series class. The Hermite class provides the standard Python numerical methods ‘+’, ‘-’, ‘*’, ‘//’, ‘%’, ‘divmod’, ‘**’, and ‘()’ as well as the attributes and methods listed in the ABCPolyBase... | numpy.reference.generated.numpy.polynomial.hermite.hermite |
numpy.polynomial.hermite_e.HermiteE class numpy.polynomial.hermite_e.HermiteE(coef, domain=None, window=None)[source]
An HermiteE series class. The HermiteE class provides the standard Python numerical methods ‘+’, ‘-’, ‘*’, ‘//’, ‘%’, ‘divmod’, ‘**’, and ‘()’ as well as the attributes and methods listed in the ABC... | numpy.reference.generated.numpy.polynomial.hermite_e.hermitee |
numpy.polynomial.laguerre.Laguerre class numpy.polynomial.laguerre.Laguerre(coef, domain=None, window=None)[source]
A Laguerre series class. The Laguerre class provides the standard Python numerical methods ‘+’, ‘-’, ‘*’, ‘//’, ‘%’, ‘divmod’, ‘**’, and ‘()’ as well as the attributes and methods listed in the ABCPol... | numpy.reference.generated.numpy.polynomial.laguerre.laguerre |
numpy.polynomial.legendre.Legendre class numpy.polynomial.legendre.Legendre(coef, domain=None, window=None)[source]
A Legendre series class. The Legendre class provides the standard Python numerical methods ‘+’, ‘-’, ‘*’, ‘//’, ‘%’, ‘divmod’, ‘**’, and ‘()’ as well as the attributes and methods listed in the ABCPol... | numpy.reference.generated.numpy.polynomial.legendre.legendre |
numpy.polynomial.polynomial.Polynomial class numpy.polynomial.polynomial.Polynomial(coef, domain=None, window=None)[source]
A power series class. The Polynomial class provides the standard Python numerical methods ‘+’, ‘-’, ‘*’, ‘//’, ‘%’, ‘divmod’, ‘**’, and ‘()’ as well as the attributes and methods listed in the... | numpy.reference.generated.numpy.polynomial.polynomial.polynomial |
numpy.polysub numpy.polysub(a1, a2)[source]
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. Given two polynomials a1 a... | numpy.reference.generated.numpy.polysub |
numpy.polyval numpy.polyval(p, x)[source]
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. If p is of length N, this funct... | numpy.reference.generated.numpy.polyval |
numpy.positive numpy.positive(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'positive'>
Numerical positive, element-wise. New in version 1.13.0. Parameters
xarray_like or scalar
Input array. Returns
yndarray or scalar
Returned ar... | numpy.reference.generated.numpy.positive |
numpy.power numpy.power(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'power'>
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 bro... | numpy.reference.generated.numpy.power |
numpy.printoptions numpy.printoptions(*args, **kwargs)[source]
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. See also
set_printoptions, get_printoptions
E... | numpy.reference.generated.numpy.printoptions |
numpy.prod numpy.prod(a, axis=None, dtype=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)[source]
Return the product of array elements over a given axis. Parameters
aarray_like
Input data.
axisNone or int or tuple of ints, optional
Axis or axes along which a product is performed... | numpy.reference.generated.numpy.prod |
numpy.promote_types numpy.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 in native byte order. This function is symmetric, but rarely associative. Parameters
type1dtype or dtype ... | numpy.reference.generated.numpy.promote_types |
numpy.ptp numpy.ptp(a, axis=None, out=None, keepdims=<no value>)[source]
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 (... | numpy.reference.generated.numpy.ptp |
numpy.put numpy.put(a, ind, v, mode='raise')[source]
Replaces specified elements of an array with given values. The indexing works on the flattened target array. put is roughly equivalent to: a.flat[ind] = v
Parameters
andarray
Target array.
indarray_like
Target indices, interpreted as integers.
varray... | numpy.reference.generated.numpy.put |
numpy.put_along_axis numpy.put_along_axis(arr, indices, values, axis)[source]
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 s... | numpy.reference.generated.numpy.put_along_axis |
numpy.putmask numpy.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. This gives behavior different from a[mask] = values. Parameters
andar... | numpy.reference.generated.numpy.putmask |
numpy.PZERO
IEEE 754 floating point representation of positive zero. Returns yfloat
A floating point representation of positive zero. See Also NZERO : Defines negative zero. isinf : Shows which elements are positive or negative infinity. isposinf : Shows which elements are positive infinity. isneginf : Shows whi... | numpy.reference.constants#numpy.PZERO |
numpy.quantile numpy.quantile(a, q, axis=None, out=None, overwrite_input=False, method='linear', keepdims=False, *, interpolation=None)[source]
Compute the q-th quantile of the data along the specified axis. New in version 1.15.0. Parameters
aarray_like
Input array or object that can be converted to an arra... | numpy.reference.generated.numpy.quantile |
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