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numpy.histogram2d numpy.histogram2d(x, y, bins=10, range=None, normed=None, weights=None, density=None)[source]
Compute the bi-dimensional histogram of two data samples. Parameters
xarray_like, shape (N,)
An array containing the x coordinates of the points to be histogrammed.
yarray_like, shape (N,)
An ar... | numpy.reference.generated.numpy.histogram2d |
numpy.histogram_bin_edges numpy.histogram_bin_edges(a, bins=10, range=None, weights=None)[source]
Function to calculate only the edges of the bins used by the histogram function. Parameters
aarray_like
Input data. The histogram is computed over the flattened array.
binsint or sequence of scalars or str, opt... | numpy.reference.generated.numpy.histogram_bin_edges |
numpy.histogramdd numpy.histogramdd(sample, bins=10, range=None, normed=None, weights=None, density=None)[source]
Compute the multidimensional histogram of some data. Parameters
sample(N, D) array, or (D, N) array_like
The data to be histogrammed. Note the unusual interpretation of sample when an array_like: ... | numpy.reference.generated.numpy.histogramdd |
numpy.hsplit numpy.hsplit(ary, indices_or_sections)[source]
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 regardless of the array dimension. See also split
Spl... | numpy.reference.generated.numpy.hsplit |
numpy.hstack numpy.hstack(tup)[source]
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 dimension... | numpy.reference.generated.numpy.hstack |
numpy.hypot numpy.hypot(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'hypot'>
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 cas... | numpy.reference.generated.numpy.hypot |
numpy.i0 numpy.i0(x)[source]
Modified Bessel function of the first kind, order 0. Usually denoted \(I_0\). Parameters
xarray_like of float
Argument of the Bessel function. Returns
outndarray, shape = x.shape, dtype = float
The modified Bessel function evaluated at each of the elements of x. See a... | numpy.reference.generated.numpy.i0 |
numpy.identity numpy.identity(n, dtype=None, *, like=None)[source]
Return the identity array. The identity array is a square array with ones on the main diagonal. Parameters
nint
Number of rows (and columns) in n x n output.
dtypedata-type, optional
Data-type of the output. Defaults to float.
likearray_... | numpy.reference.generated.numpy.identity |
numpy.iinfo class numpy.iinfo(type)[source]
Machine limits for integer types. Parameters
int_typeinteger type, dtype, or instance
The kind of integer data type to get information about. See also finfo
The equivalent for floating point data types. Examples With types: >>> ii16 = np.iinfo(np.int16)
>>... | numpy.reference.generated.numpy.iinfo |
numpy.imag numpy.imag(val)[source]
Return the imaginary part of the complex argument. Parameters
valarray_like
Input array. Returns
outndarray or scalar
The imaginary component of the complex argument. If val is real, the type of val is used for the output. If val has complex elements, the returned ty... | numpy.reference.generated.numpy.imag |
numpy.in1d numpy.in1d(ar1, ar2, assume_unique=False, invert=False)[source]
Test whether each element of a 1-D array is also present in a second array. Returns a boolean array the same length as ar1 that is True where an element of ar1 is in ar2 and False otherwise. We recommend using isin instead of in1d for new co... | numpy.reference.generated.numpy.in1d |
numpy.indices numpy.indices(dimensions, dtype=<class 'int'>, sparse=False)[source]
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. Parameters
dimensionssequence of ints
The shape of the grid.
dty... | numpy.reference.generated.numpy.indices |
Constants NumPy includes several constants: numpy.Inf
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.Infinity
IEEE 754 floating point representation of (positive) infinity. Use inf ... | numpy.reference.constants |
numpy.inf
IEEE 754 floating point representation of (positive) infinity. Returns yfloat
A floating point representation of positive 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 ... | numpy.reference.constants#numpy.inf |
numpy.Infinity
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.Infinity |
numpy.info numpy.info(object=None, maxwidth=76, output=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>, toplevel='numpy')[source]
Get help information for a function, class, or module. Parameters
objectobject or str, optional
Input object or name to get information about. If object is a numpy ob... | numpy.reference.generated.numpy.info |
numpy.infty
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.infty |
numpy.inner numpy.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. Parameters
a, barray_like
If a and b are nonscalar, their last dimensions must match. Returns
outndarray
... | numpy.reference.generated.numpy.inner |
numpy.insert numpy.insert(arr, obj, values, axis=None)[source]
Insert values along the given axis before the given indices. Parameters
arrarray_like
Input array.
objint, slice or sequence of ints
Object that defines the index or indices before which values is inserted. New in version 1.8.0. Support for ... | numpy.reference.generated.numpy.insert |
numpy.int8[source]
numpy.int16
numpy.int32
numpy.int64
Aliases for the signed integer types (one of numpy.byte, numpy.short, numpy.intc, numpy.int_ and numpy.longlong) with the specified number of bits. Compatible with the C99 int8_t, int16_t, int32_t, and int64_t, respectively. | numpy.reference.arrays.scalars#numpy.int16 |
numpy.int8[source]
numpy.int16
numpy.int32
numpy.int64
Aliases for the signed integer types (one of numpy.byte, numpy.short, numpy.intc, numpy.int_ and numpy.longlong) with the specified number of bits. Compatible with the C99 int8_t, int16_t, int32_t, and int64_t, respectively. | numpy.reference.arrays.scalars#numpy.int32 |
numpy.int8[source]
numpy.int16
numpy.int32
numpy.int64
Aliases for the signed integer types (one of numpy.byte, numpy.short, numpy.intc, numpy.int_ and numpy.longlong) with the specified number of bits. Compatible with the C99 int8_t, int16_t, int32_t, and int64_t, respectively. | numpy.reference.arrays.scalars#numpy.int64 |
numpy.int8[source]
numpy.int16
numpy.int32
numpy.int64
Aliases for the signed integer types (one of numpy.byte, numpy.short, numpy.intc, numpy.int_ and numpy.longlong) with the specified number of bits. Compatible with the C99 int8_t, int16_t, int32_t, and int64_t, respectively. | numpy.reference.arrays.scalars#numpy.int8 |
class numpy.int_[source]
Signed integer type, compatible with Python int and C long. Character code
'l' 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 enoug... | numpy.reference.arrays.scalars#numpy.int_ |
class numpy.intc[source]
Signed integer type, compatible with C int. Character code
'i' Alias on this platform (Linux x86_64)
numpy.int32: 32-bit signed integer (-2_147_483_648 to 2_147_483_647). | numpy.reference.arrays.scalars#numpy.intc |
numpy.interp numpy.interp(x, xp, fp, left=None, right=None, period=None)[source]
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. Parameters
xarray_lik... | numpy.reference.generated.numpy.interp |
numpy.intersect1d numpy.intersect1d(ar1, ar2, assume_unique=False, return_indices=False)[source]
Find the intersection of two arrays. Return the sorted, unique values that are in both of the input arrays. Parameters
ar1, ar2array_like
Input arrays. Will be flattened if not already 1D.
assume_uniquebool
If... | numpy.reference.generated.numpy.intersect1d |
numpy.intp[source]
Alias for the signed integer type (one of numpy.byte, numpy.short, numpy.intc, numpy.int_ and np.longlong) that is the same size as a pointer. Compatible with the C intptr_t. Character code
'p' | numpy.reference.arrays.scalars#numpy.intp |
numpy.invert numpy.invert(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'invert'>
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. T... | numpy.reference.generated.numpy.invert |
numpy.is_busday numpy.is_busday(dates, weekmask='1111100', holidays=None, busdaycal=None, out=None)
Calculates which of the given dates are valid days, and which are not. New in version 1.7.0. Parameters
datesarray_like of datetime64[D]
The array of dates to process.
weekmaskstr or array_like of bool, opt... | numpy.reference.generated.numpy.is_busday |
numpy.isclose numpy.isclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False)[source]
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 t... | numpy.reference.generated.numpy.isclose |
numpy.iscomplex numpy.iscomplex(x)[source]
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. Parameters
xarray_like
Input array. Returns
outndarray of bools
Output array. See also isr... | numpy.reference.generated.numpy.iscomplex |
numpy.iscomplexobj numpy.iscomplexobj(x)[source]
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. Parameters
xany
The input can be of any type and shape. Returns
... | numpy.reference.generated.numpy.iscomplexobj |
numpy.isfinite numpy.isfinite(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'isfinite'>
Test element-wise for finiteness (not infinity and not Not a Number). The result is returned as a boolean array. Parameters
xarray_like
Input values. ... | numpy.reference.generated.numpy.isfinite |
numpy.isfortran numpy.isfortran(a)[source]
Check if the array is Fortran contiguous but not C contiguous. This function is obsolete and, because of changes due to relaxed stride checking, its return value for the same array may differ for versions of NumPy >= 1.10.0 and previous versions. If you only want to check ... | numpy.reference.generated.numpy.isfortran |
numpy.isin numpy.isin(element, test_elements, assume_unique=False, invert=False)[source]
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. Parameters
elementa... | numpy.reference.generated.numpy.isin |
numpy.isinf numpy.isinf(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'isinf'>
Test element-wise for positive or negative infinity. Returns a boolean array of the same shape as x, True where x ==
+/-inf, otherwise False. Parameters
xarray_li... | numpy.reference.generated.numpy.isinf |
numpy.isnan numpy.isnan(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'isnan'>
Test element-wise for NaN and return result as a boolean array. Parameters
xarray_like
Input array.
outndarray, None, or tuple of ndarray and None, optional
... | numpy.reference.generated.numpy.isnan |
numpy.isnat numpy.isnat(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'isnat'>
Test element-wise for NaT (not a time) and return result as a boolean array. New in version 1.13.0. Parameters
xarray_like
Input array with datetime or timede... | numpy.reference.generated.numpy.isnat |
numpy.isneginf numpy.isneginf(x, out=None)[source]
Test element-wise for negative infinity, return result as bool array. Parameters
xarray_like
The input array.
outarray_like, optional
A location into which the result is stored. If provided, it must have a shape that the input broadcasts to. If not provid... | numpy.reference.generated.numpy.isneginf |
numpy.isposinf numpy.isposinf(x, out=None)[source]
Test element-wise for positive infinity, return result as bool array. Parameters
xarray_like
The input array.
outarray_like, optional
A location into which the result is stored. If provided, it must have a shape that the input broadcasts to. If not provid... | numpy.reference.generated.numpy.isposinf |
numpy.isreal numpy.isreal(x)[source]
Returns a bool array, where True if input element is real. If element has complex type with zero complex part, the return value for that element is True. Parameters
xarray_like
Input array. Returns
outndarray, bool
Boolean array of same shape as x. See also i... | numpy.reference.generated.numpy.isreal |
numpy.isrealobj numpy.isrealobj(x)[source]
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
xany
The input can b... | numpy.reference.generated.numpy.isrealobj |
numpy.isscalar numpy.isscalar(element)[source]
Returns True if the type of element is a scalar type. Parameters
elementany
Input argument, can be of any type and shape. Returns
valbool
True if element is a scalar type, False if it is not. See also ndim
Get the number of dimensions of an array ... | numpy.reference.generated.numpy.isscalar |
numpy.issctype numpy.issctype(rep)[source]
Determines whether the given object represents a scalar data-type. Parameters
repany
If rep is an instance of a scalar dtype, True is returned. If not, False is returned. Returns
outbool
Boolean result of check whether rep is a scalar dtype. See also
i... | numpy.reference.generated.numpy.issctype |
numpy.issubclass_ numpy.issubclass_(arg1, arg2)[source]
Determine if a class is a subclass of a second class. issubclass_ is equivalent to the Python built-in issubclass, except that it returns False instead of raising a TypeError if one of the arguments is not a class. Parameters
arg1class
Input class. True ... | numpy.reference.generated.numpy.issubclass_ |
numpy.issubdtype numpy.issubdtype(arg1, arg2)[source]
Returns True if first argument is a typecode lower/equal in type hierarchy. This is like the builtin issubclass, but for dtypes. Parameters
arg1, arg2dtype_like
dtype or object coercible to one Returns
outbool
See also Scalars
Overview of the ... | numpy.reference.generated.numpy.issubdtype |
numpy.issubsctype numpy.issubsctype(arg1, arg2)[source]
Determine if the first argument is a subclass of the second argument. Parameters
arg1, arg2dtype or dtype specifier
Data-types. Returns
outbool
The result. See also
issctype, issubdtype, obj2sctype
Examples >>> np.issubsctype('S8', str)... | numpy.reference.generated.numpy.issubsctype |
numpy.ix_ numpy.ix_(*args)[source]
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. Using ix_ one can qu... | numpy.reference.generated.numpy.ix_ |
numpy.kaiser numpy.kaiser(M, beta)[source]
Return the Kaiser window. The Kaiser window is a taper formed by using a Bessel function. Parameters
Mint
Number of points in the output window. If zero or less, an empty array is returned.
betafloat
Shape parameter for window. Returns
outarray
The window... | numpy.reference.generated.numpy.kaiser |
numpy.kron numpy.kron(a, b)[source]
Kronecker product of two arrays. Computes the Kronecker product, a composite array made of blocks of the second array scaled by the first. Parameters
a, barray_like
Returns
outndarray
See also outer
The outer product Notes The function assumes that the number ... | numpy.reference.generated.numpy.kron |
numpy.lcm numpy.lcm(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'lcm'>
Returns the lowest common multiple of |x1| and |x2| Parameters
x1, x2array_like, int
Arrays of values. If x1.shape != x2.shape, they must be broadcastable to a c... | numpy.reference.generated.numpy.lcm |
numpy.ldexp numpy.ldexp(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'ldexp'>
Returns x1 * 2**x2, element-wise. The mantissas x1 and twos exponents x2 are used to construct floating point numbers x1 * 2**x2. Parameters
x1array_like
A... | numpy.reference.generated.numpy.ldexp |
numpy.left_shift numpy.left_shift(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'left_shift'>
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... | numpy.reference.generated.numpy.left_shift |
numpy.less numpy.less(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'less'>
Return the truth value of (x1 < x2) element-wise. Parameters
x1, x2array_like
Input arrays. If x1.shape != x2.shape, they must be broadcastable to a common sh... | numpy.reference.generated.numpy.less |
numpy.less_equal numpy.less_equal(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'less_equal'>
Return the truth value of (x1 <= x2) element-wise. Parameters
x1, x2array_like
Input arrays. If x1.shape != x2.shape, they must be broadcast... | numpy.reference.generated.numpy.less_equal |
numpy.lexsort numpy.lexsort(keys, axis=- 1)
Perform an indirect stable sort using a sequence of keys. Given multiple sorting keys, which can be interpreted as columns in a spreadsheet, lexsort returns an array of integer indices that describes the sort order by multiple columns. The last key in the sequence is used... | numpy.reference.generated.numpy.lexsort |
numpy.lib.arraysetops Set operations for arrays based on sorting. Notes For floating point arrays, inaccurate results may appear due to usual round-off and floating point comparison issues. Speed could be gained in some operations by an implementation of numpy.sort, that can provide directly the permutation vectors, t... | numpy.reference.generated.numpy.lib.arraysetops |
numpy.lib.Arrayterator class numpy.lib.Arrayterator(var, buf_size=None)[source]
Buffered iterator for big arrays. Arrayterator creates a buffered iterator for reading big arrays in small contiguous blocks. The class is useful for objects stored in the file system. It allows iteration over the object without reading... | numpy.reference.generated.numpy.lib.arrayterator |
numpy.lib.mixins.NDArrayOperatorsMixin class numpy.lib.mixins.NDArrayOperatorsMixin[source]
Mixin defining all operator special methods using __array_ufunc__. This class implements the special methods for almost all of Python’s builtin operators defined in the operator module, including comparisons (==, >, etc.) an... | numpy.reference.generated.numpy.lib.mixins.ndarrayoperatorsmixin |
numpy.lib.NumpyVersion class numpy.lib.NumpyVersion(vstring)[source]
Parse and compare numpy version strings. NumPy has the following versioning scheme (numbers given are examples; they can be > 9 in principle): Released version: ‘1.8.0’, ‘1.8.1’, etc. Alpha: ‘1.8.0a1’, ‘1.8.0a2’, etc. Beta: ‘1.8.0b1’, ‘1.8.0b2’, ... | numpy.reference.generated.numpy.lib.numpyversion |
Structured arrays Introduction Structured arrays are ndarrays whose datatype is a composition of simpler datatypes organized as a sequence of named fields. For example, >>> x = np.array([('Rex', 9, 81.0), ('Fido', 3, 27.0)],
... dtype=[('name', 'U10'), ('age', 'i4'), ('weight', 'f4')])
>>> x
array([('Rex'... | numpy.user.basics.rec |
numpy.lib.recfunctions.apply_along_fields(func, arr)[source]
Apply function ‘func’ as a reduction across fields of a structured array. This is similar to apply_along_axis, but treats the fields of a structured array as an extra axis. The fields are all first cast to a common type following the type-promotion rules fr... | numpy.user.basics.rec#numpy.lib.recfunctions.apply_along_fields |
numpy.lib.recfunctions.assign_fields_by_name(dst, src, zero_unassigned=True)[source]
Assigns values from one structured array to another by field name. Normally in numpy >= 1.14, assignment of one structured array to another copies fields “by position”, meaning that the first field from the src is copied to the first... | numpy.user.basics.rec#numpy.lib.recfunctions.assign_fields_by_name |
numpy.lib.recfunctions.drop_fields(base, drop_names, usemask=True, asrecarray=False)[source]
Return a new array with fields in drop_names dropped. Nested fields are supported. Changed in version 1.18.0: drop_fields returns an array with 0 fields if all fields are dropped, rather than returning None as it did previou... | numpy.user.basics.rec#numpy.lib.recfunctions.drop_fields |
numpy.lib.recfunctions.find_duplicates(a, key=None, ignoremask=True, return_index=False)[source]
Find the duplicates in a structured array along a given key Parameters
aarray-like
Input array
key{string, None}, optional
Name of the fields along which to check the duplicates. If None, the search is performed... | numpy.user.basics.rec#numpy.lib.recfunctions.find_duplicates |
numpy.lib.recfunctions.flatten_descr(ndtype)[source]
Flatten a structured data-type description. Examples >>> from numpy.lib import recfunctions as rfn
>>> ndtype = np.dtype([('a', '<i4'), ('b', [('ba', '<f8'), ('bb', '<i4')])])
>>> rfn.flatten_descr(ndtype)
(('a', dtype('int32')), ('ba', dtype('float64')), ('bb', dt... | numpy.user.basics.rec#numpy.lib.recfunctions.flatten_descr |
numpy.lib.recfunctions.get_fieldstructure(adtype, lastname=None, parents=None)[source]
Returns a dictionary with fields indexing lists of their parent fields. This function is used to simplify access to fields nested in other fields. Parameters
adtypenp.dtype
Input datatype
lastnameoptional
Last processed f... | numpy.user.basics.rec#numpy.lib.recfunctions.get_fieldstructure |
numpy.lib.recfunctions.get_names(adtype)[source]
Returns the field names of the input datatype as a tuple. Parameters
adtypedtype
Input datatype Examples >>> from numpy.lib import recfunctions as rfn
>>> rfn.get_names(np.empty((1,), dtype=int))
Traceback (most recent call last):
...
AttributeError: 'num... | numpy.user.basics.rec#numpy.lib.recfunctions.get_names |
numpy.lib.recfunctions.get_names_flat(adtype)[source]
Returns the field names of the input datatype as a tuple. Nested structure are flattened beforehand. Parameters
adtypedtype
Input datatype Examples >>> from numpy.lib import recfunctions as rfn
>>> rfn.get_names_flat(np.empty((1,), dtype=int)) is None
Tr... | numpy.user.basics.rec#numpy.lib.recfunctions.get_names_flat |
numpy.lib.recfunctions.join_by(key, r1, r2, jointype='inner', r1postfix='1', r2postfix='2', defaults=None, usemask=True, asrecarray=False)[source]
Join arrays r1 and r2 on key key. The key should be either a string or a sequence of string corresponding to the fields used to join the array. An exception is raised if t... | numpy.user.basics.rec#numpy.lib.recfunctions.join_by |
numpy.lib.recfunctions.merge_arrays(seqarrays, fill_value=- 1, flatten=False, usemask=False, asrecarray=False)[source]
Merge arrays field by field. Parameters
seqarrayssequence of ndarrays
Sequence of arrays
fill_value{float}, optional
Filling value used to pad missing data on the shorter arrays.
flatten{... | numpy.user.basics.rec#numpy.lib.recfunctions.merge_arrays |
numpy.lib.recfunctions.rec_append_fields(base, names, data, dtypes=None)[source]
Add new fields to an existing array. The names of the fields are given with the names arguments, the corresponding values with the data arguments. If a single field is appended, names, data and dtypes do not have to be lists but just val... | numpy.user.basics.rec#numpy.lib.recfunctions.rec_append_fields |
numpy.lib.recfunctions.rec_drop_fields(base, drop_names)[source]
Returns a new numpy.recarray with fields in drop_names dropped. | numpy.user.basics.rec#numpy.lib.recfunctions.rec_drop_fields |
numpy.lib.recfunctions.rec_join(key, r1, r2, jointype='inner', r1postfix='1', r2postfix='2', defaults=None)[source]
Join arrays r1 and r2 on keys. Alternative to join_by, that always returns a np.recarray. See also join_by
equivalent function | numpy.user.basics.rec#numpy.lib.recfunctions.rec_join |
numpy.lib.recfunctions.recursive_fill_fields(input, output)[source]
Fills fields from output with fields from input, with support for nested structures. Parameters
inputndarray
Input array.
outputndarray
Output array. Notes
output should be at least the same size as input
Examples >>> from numpy.lib... | numpy.user.basics.rec#numpy.lib.recfunctions.recursive_fill_fields |
numpy.lib.recfunctions.rename_fields(base, namemapper)[source]
Rename the fields from a flexible-datatype ndarray or recarray. Nested fields are supported. Parameters
basendarray
Input array whose fields must be modified.
namemapperdictionary
Dictionary mapping old field names to their new version. Exam... | numpy.user.basics.rec#numpy.lib.recfunctions.rename_fields |
numpy.lib.recfunctions.repack_fields(a, align=False, recurse=False)[source]
Re-pack the fields of a structured array or dtype in memory. The memory layout of structured datatypes allows fields at arbitrary byte offsets. This means the fields can be separated by padding bytes, their offsets can be non-monotonically in... | numpy.user.basics.rec#numpy.lib.recfunctions.repack_fields |
numpy.lib.recfunctions.require_fields(array, required_dtype)[source]
Casts a structured array to a new dtype using assignment by field-name. This function assigns from the old to the new array by name, so the value of a field in the output array is the value of the field with the same name in the source array. This h... | numpy.user.basics.rec#numpy.lib.recfunctions.require_fields |
numpy.lib.recfunctions.stack_arrays(arrays, defaults=None, usemask=True, asrecarray=False, autoconvert=False)[source]
Superposes arrays fields by fields Parameters
arraysarray or sequence
Sequence of input arrays.
defaultsdictionary, optional
Dictionary mapping field names to the corresponding default value... | numpy.user.basics.rec#numpy.lib.recfunctions.stack_arrays |
numpy.lib.recfunctions.structured_to_unstructured(arr, dtype=None, copy=False, casting='unsafe')[source]
Converts an n-D structured array into an (n+1)-D unstructured array. The new array will have a new last dimension equal in size to the number of field-elements of the input array. If not supplied, the output datat... | numpy.user.basics.rec#numpy.lib.recfunctions.structured_to_unstructured |
numpy.lib.recfunctions.unstructured_to_structured(arr, dtype=None, names=None, align=False, copy=False, casting='unsafe')[source]
Converts an n-D unstructured array into an (n-1)-D structured array. The last dimension of the input array is converted into a structure, with number of field-elements equal to the size of... | numpy.user.basics.rec#numpy.lib.recfunctions.unstructured_to_structured |
numpy.lib.user_array.container class numpy.lib.user_array.container(data, dtype=None, copy=True)[source]
Standard container-class for easy multiple-inheritance. Methods
copy
tostring
byteswap
astype | numpy.reference.generated.numpy.lib.user_array.container |
numpy.linspace numpy.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0)[source]
Return evenly spaced numbers over a specified interval. Returns num evenly spaced samples, calculated over the interval [start, stop]. The endpoint of the interval can optionally be excluded. Changed in ver... | numpy.reference.generated.numpy.linspace |
numpy.load numpy.load(file, mmap_mode=None, allow_pickle=False, fix_imports=True, encoding='ASCII')[source]
Load arrays or pickled objects from .npy, .npz or pickled files. Warning Loading files that contain object arrays uses the pickle module, which is not secure against erroneous or maliciously constructed data... | numpy.reference.generated.numpy.load |
numpy.loadtxt numpy.loadtxt(fname, dtype=<class 'float'>, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0, encoding='bytes', max_rows=None, *, like=None)[source]
Load data from a text file. Each row in the text file must have the same number of values. Parameters
f... | numpy.reference.generated.numpy.loadtxt |
numpy.log numpy.log(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'log'>
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... | numpy.reference.generated.numpy.log |
numpy.log10 numpy.log10(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'log10'>
Return the base 10 logarithm of the input array, element-wise. Parameters
xarray_like
Input values.
outndarray, None, or tuple of ndarray and None, optional
... | numpy.reference.generated.numpy.log10 |
numpy.log1p numpy.log1p(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'log1p'>
Return the natural logarithm of one plus the input array, element-wise. Calculates log(1 + x). Parameters
xarray_like
Input values.
outndarray, None, or tuple... | numpy.reference.generated.numpy.log1p |
numpy.log2 numpy.log2(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'log2'>
Base-2 logarithm of x. Parameters
xarray_like
Input values.
outndarray, None, or tuple of ndarray and None, optional
A location into which the result is stored... | numpy.reference.generated.numpy.log2 |
numpy.logaddexp numpy.logaddexp(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'logaddexp'>
Logarithm of the sum of exponentiations of the inputs. Calculates log(exp(x1) + exp(x2)). This function is useful in statistics where the calculated ... | numpy.reference.generated.numpy.logaddexp |
numpy.logaddexp2 numpy.logaddexp2(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'logaddexp2'>
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 ... | numpy.reference.generated.numpy.logaddexp2 |
numpy.logical_and numpy.logical_and(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'logical_and'>
Compute the truth value of x1 AND x2 element-wise. Parameters
x1, x2array_like
Input arrays. If x1.shape != x2.shape, they must be broadc... | numpy.reference.generated.numpy.logical_and |
numpy.logical_not numpy.logical_not(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'logical_not'>
Compute the truth value of NOT x element-wise. Parameters
xarray_like
Logical NOT is applied to the elements of x.
outndarray, None, or tupl... | numpy.reference.generated.numpy.logical_not |
numpy.logical_or numpy.logical_or(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'logical_or'>
Compute the truth value of x1 OR x2 element-wise. Parameters
x1, x2array_like
Logical OR is applied to the elements of x1 and x2. If x1.shap... | numpy.reference.generated.numpy.logical_or |
numpy.logical_xor numpy.logical_xor(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'logical_xor'>
Compute the truth value of x1 XOR x2, element-wise. Parameters
x1, x2array_like
Logical XOR is applied to the elements of x1 and x2. If x... | numpy.reference.generated.numpy.logical_xor |
numpy.logspace numpy.logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None, axis=0)[source]
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 1.16.0: Non-sc... | numpy.reference.generated.numpy.logspace |
numpy.longcomplex[source]
alias of numpy.clongdouble | numpy.reference.arrays.scalars#numpy.longcomplex |
class numpy.longdouble[source]
Extended-precision floating-point number type, compatible with C long double but not necessarily with IEEE 754 quadruple-precision. Character code
'g' Alias
numpy.longfloat Alias on this platform (Linux x86_64)
numpy.float128: 128-bit extended-precision floating-point number type. | numpy.reference.arrays.scalars#numpy.longdouble |
numpy.longfloat[source]
alias of numpy.longdouble | numpy.reference.arrays.scalars#numpy.longfloat |
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