doc_content stringlengths 1 386k | doc_id stringlengths 5 188 |
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numpy.matrix.sum method matrix.sum(axis=None, dtype=None, out=None)[source]
Returns the sum of the matrix elements, along the given axis. Refer to numpy.sum for full documentation. See also numpy.sum
Notes This is the same as ndarray.sum, except that where an ndarray would be returned, a matrix object is return... | numpy.reference.generated.numpy.matrix.sum |
numpy.matrix.swapaxes method matrix.swapaxes(axis1, axis2)
Return a view of the array with axis1 and axis2 interchanged. Refer to numpy.swapaxes for full documentation. See also numpy.swapaxes
equivalent function | numpy.reference.generated.numpy.matrix.swapaxes |
numpy.matrix.take method matrix.take(indices, axis=None, out=None, mode='raise')
Return an array formed from the elements of a at the given indices. Refer to numpy.take for full documentation. See also numpy.take
equivalent function | numpy.reference.generated.numpy.matrix.take |
numpy.matrix.tobytes method matrix.tobytes(order='C')
Construct Python bytes containing the raw data bytes in the array. Constructs Python bytes showing a copy of the raw contents of data memory. The bytes object is produced in C-order by default. This behavior is controlled by the order parameter. New in version ... | numpy.reference.generated.numpy.matrix.tobytes |
numpy.matrix.tofile method matrix.tofile(fid, sep='', format='%s')
Write array to a file as text or binary (default). Data is always written in ‘C’ order, independent of the order of a. The data produced by this method can be recovered using the function fromfile(). Parameters
fidfile or str or Path
An open f... | numpy.reference.generated.numpy.matrix.tofile |
numpy.matrix.tolist method matrix.tolist()[source]
Return the matrix as a (possibly nested) list. See ndarray.tolist for full documentation. See also ndarray.tolist
Examples >>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.to... | numpy.reference.generated.numpy.matrix.tolist |
numpy.matrix.tostring method matrix.tostring(order='C')
A compatibility alias for tobytes, with exactly the same behavior. Despite its name, it returns bytes not strs. Deprecated since version 1.19.0. | numpy.reference.generated.numpy.matrix.tostring |
numpy.matrix.trace method matrix.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)
Return the sum along diagonals of the array. Refer to numpy.trace for full documentation. See also numpy.trace
equivalent function | numpy.reference.generated.numpy.matrix.trace |
numpy.matrix.transpose method matrix.transpose(*axes)
Returns a view of the array with axes transposed. For a 1-D array this has no effect, as a transposed vector is simply the same vector. To convert a 1-D array into a 2D column vector, an additional dimension must be added. np.atleast2d(a).T achieves this, as doe... | numpy.reference.generated.numpy.matrix.transpose |
numpy.matrix.var method matrix.var(axis=None, dtype=None, out=None, ddof=0)[source]
Returns the variance of the matrix elements, along the given axis. Refer to numpy.var for full documentation. See also numpy.var
Notes This is the same as ndarray.var, except that where an ndarray would be returned, a matrix obj... | numpy.reference.generated.numpy.matrix.var |
numpy.matrix.view method matrix.view([dtype][, type])
New view of array with the same data. Note Passing None for dtype is different from omitting the parameter, since the former invokes dtype(None) which is an alias for dtype('float_'). Parameters
dtypedata-type or ndarray sub-class, optional
Data-type des... | numpy.reference.generated.numpy.matrix.view |
numpy.memmap.flush method memmap.flush()[source]
Write any changes in the array to the file on disk. For further information, see memmap. Parameters
None
See also memmap | numpy.reference.generated.numpy.memmap.flush |
Miscellaneous IEEE 754 Floating Point Special Values Special values defined in numpy: nan, inf, NaNs can be used as a poor-man’s mask (if you don’t care what the original value was) Note: cannot use equality to test NaNs. E.g.: >>> myarr = np.array([1., 0., np.nan, 3.])
>>> np.nonzero(myarr == np.nan)
(array([], dtype... | numpy.user.misc |
numpy.ndarray.__abs__ method ndarray.__abs__(self) | numpy.reference.generated.numpy.ndarray.__abs__ |
numpy.ndarray.__add__ method ndarray.__add__(value, /)
Return self+value. | numpy.reference.generated.numpy.ndarray.__add__ |
numpy.ndarray.__and__ method ndarray.__and__(value, /)
Return self&value. | numpy.reference.generated.numpy.ndarray.__and__ |
numpy.ndarray.__array__ method ndarray.__array__([dtype, ]/) → reference if type unchanged, copy otherwise.
Returns either a new reference to self if dtype is not given or a new array of provided data type if dtype is different from the current dtype of the array. | numpy.reference.generated.numpy.ndarray.__array__ |
numpy.ndarray.__array_wrap__ method ndarray.__array_wrap__(array, [context, ]/)
Returns a view of array with the same type as self. | numpy.reference.generated.numpy.ndarray.__array_wrap__ |
numpy.ndarray.__bool__ method ndarray.__bool__(/)
self != 0 | numpy.reference.generated.numpy.ndarray.__bool__ |
numpy.ndarray.__class_getitem__ method ndarray.__class_getitem__(item, /)
Return a parametrized wrapper around the ndarray type. New in version 1.22. Returns
aliastypes.GenericAlias
A parametrized ndarray type. See also PEP 585
Type hinting generics in standard collections. numpy.typing.NDArray
An ... | numpy.reference.generated.numpy.ndarray.__class_getitem__ |
numpy.ndarray.__complex__ method ndarray.__complex__() | numpy.reference.generated.numpy.ndarray.__complex__ |
numpy.ndarray.__contains__ method ndarray.__contains__(key, /)
Return key in self. | numpy.reference.generated.numpy.ndarray.__contains__ |
numpy.ndarray.__copy__ method ndarray.__copy__()
Used if copy.copy is called on an array. Returns a copy of the array. Equivalent to a.copy(order='K'). | numpy.reference.generated.numpy.ndarray.__copy__ |
numpy.ndarray.__deepcopy__ method ndarray.__deepcopy__(memo, /) → Deep copy of array.
Used if copy.deepcopy is called on an array. | numpy.reference.generated.numpy.ndarray.__deepcopy__ |
numpy.ndarray.__divmod__ method ndarray.__divmod__(value, /)
Return divmod(self, value). | numpy.reference.generated.numpy.ndarray.__divmod__ |
numpy.ndarray.__eq__ method ndarray.__eq__(value, /)
Return self==value. | numpy.reference.generated.numpy.ndarray.__eq__ |
numpy.ndarray.__float__ method ndarray.__float__(self) | numpy.reference.generated.numpy.ndarray.__float__ |
numpy.ndarray.__floordiv__ method ndarray.__floordiv__(value, /)
Return self//value. | numpy.reference.generated.numpy.ndarray.__floordiv__ |
numpy.ndarray.__ge__ method ndarray.__ge__(value, /)
Return self>=value. | numpy.reference.generated.numpy.ndarray.__ge__ |
numpy.ndarray.__getitem__ method ndarray.__getitem__(key, /)
Return self[key]. | numpy.reference.generated.numpy.ndarray.__getitem__ |
numpy.ndarray.__gt__ method ndarray.__gt__(value, /)
Return self>value. | numpy.reference.generated.numpy.ndarray.__gt__ |
numpy.ndarray.__iadd__ method ndarray.__iadd__(value, /)
Return self+=value. | numpy.reference.generated.numpy.ndarray.__iadd__ |
numpy.ndarray.__iand__ method ndarray.__iand__(value, /)
Return self&=value. | numpy.reference.generated.numpy.ndarray.__iand__ |
numpy.ndarray.__ifloordiv__ method ndarray.__ifloordiv__(value, /)
Return self//=value. | numpy.reference.generated.numpy.ndarray.__ifloordiv__ |
numpy.ndarray.__ilshift__ method ndarray.__ilshift__(value, /)
Return self<<=value. | numpy.reference.generated.numpy.ndarray.__ilshift__ |
numpy.ndarray.__imod__ method ndarray.__imod__(value, /)
Return self%=value. | numpy.reference.generated.numpy.ndarray.__imod__ |
numpy.ndarray.__imul__ method ndarray.__imul__(value, /)
Return self*=value. | numpy.reference.generated.numpy.ndarray.__imul__ |
numpy.ndarray.__int__ method ndarray.__int__(self) | numpy.reference.generated.numpy.ndarray.__int__ |
numpy.ndarray.__invert__ method ndarray.__invert__(/)
~self | numpy.reference.generated.numpy.ndarray.__invert__ |
numpy.ndarray.__ior__ method ndarray.__ior__(value, /)
Return self|=value. | numpy.reference.generated.numpy.ndarray.__ior__ |
numpy.ndarray.__ipow__ method ndarray.__ipow__(value, /)
Return self**=value. | numpy.reference.generated.numpy.ndarray.__ipow__ |
numpy.ndarray.__irshift__ method ndarray.__irshift__(value, /)
Return self>>=value. | numpy.reference.generated.numpy.ndarray.__irshift__ |
numpy.ndarray.__isub__ method ndarray.__isub__(value, /)
Return self-=value. | numpy.reference.generated.numpy.ndarray.__isub__ |
numpy.ndarray.__itruediv__ method ndarray.__itruediv__(value, /)
Return self/=value. | numpy.reference.generated.numpy.ndarray.__itruediv__ |
numpy.ndarray.__ixor__ method ndarray.__ixor__(value, /)
Return self^=value. | numpy.reference.generated.numpy.ndarray.__ixor__ |
numpy.ndarray.__le__ method ndarray.__le__(value, /)
Return self<=value. | numpy.reference.generated.numpy.ndarray.__le__ |
numpy.ndarray.__len__ method ndarray.__len__(/)
Return len(self). | numpy.reference.generated.numpy.ndarray.__len__ |
numpy.ndarray.__lshift__ method ndarray.__lshift__(value, /)
Return self<<value. | numpy.reference.generated.numpy.ndarray.__lshift__ |
numpy.ndarray.__lt__ method ndarray.__lt__(value, /)
Return self<value. | numpy.reference.generated.numpy.ndarray.__lt__ |
numpy.ndarray.__matmul__ method ndarray.__matmul__(value, /)
Return self@value. | numpy.reference.generated.numpy.ndarray.__matmul__ |
numpy.ndarray.__mod__ method ndarray.__mod__(value, /)
Return self%value. | numpy.reference.generated.numpy.ndarray.__mod__ |
numpy.ndarray.__mul__ method ndarray.__mul__(value, /)
Return self*value. | numpy.reference.generated.numpy.ndarray.__mul__ |
numpy.ndarray.__ne__ method ndarray.__ne__(value, /)
Return self!=value. | numpy.reference.generated.numpy.ndarray.__ne__ |
numpy.ndarray.__neg__ method ndarray.__neg__(/)
-self | numpy.reference.generated.numpy.ndarray.__neg__ |
numpy.ndarray.__new__ method ndarray.__new__(*args, **kwargs) | numpy.reference.generated.numpy.ndarray.__new__ |
numpy.ndarray.__or__ method ndarray.__or__(value, /)
Return self|value. | numpy.reference.generated.numpy.ndarray.__or__ |
numpy.ndarray.__pos__ method ndarray.__pos__(/)
+self | numpy.reference.generated.numpy.ndarray.__pos__ |
numpy.ndarray.__pow__ method ndarray.__pow__(value, mod=None, /)
Return pow(self, value, mod). | numpy.reference.generated.numpy.ndarray.__pow__ |
numpy.ndarray.__reduce__ method ndarray.__reduce__()
For pickling. | numpy.reference.generated.numpy.ndarray.__reduce__ |
numpy.ndarray.__repr__ method ndarray.__repr__(/)
Return repr(self). | numpy.reference.generated.numpy.ndarray.__repr__ |
numpy.ndarray.__rshift__ method ndarray.__rshift__(value, /)
Return self>>value. | numpy.reference.generated.numpy.ndarray.__rshift__ |
numpy.ndarray.__setitem__ method ndarray.__setitem__(key, value, /)
Set self[key] to value. | numpy.reference.generated.numpy.ndarray.__setitem__ |
numpy.ndarray.__setstate__ method ndarray.__setstate__(state, /)
For unpickling. The state argument must be a sequence that contains the following elements: Parameters
versionint
optional pickle version. If omitted defaults to 0.
shapetuple
dtypedata-type
isFortranbool
rawdatastring or list
a binary... | numpy.reference.generated.numpy.ndarray.__setstate__ |
numpy.ndarray.__str__ method ndarray.__str__(/)
Return str(self). | numpy.reference.generated.numpy.ndarray.__str__ |
numpy.ndarray.__sub__ method ndarray.__sub__(value, /)
Return self-value. | numpy.reference.generated.numpy.ndarray.__sub__ |
numpy.ndarray.__truediv__ method ndarray.__truediv__(value, /)
Return self/value. | numpy.reference.generated.numpy.ndarray.__truediv__ |
numpy.ndarray.__xor__ method ndarray.__xor__(value, /)
Return self^value. | numpy.reference.generated.numpy.ndarray.__xor__ |
numpy.ndarray.all method ndarray.all(axis=None, out=None, keepdims=False, *, where=True)
Returns True if all elements evaluate to True. Refer to numpy.all for full documentation. See also numpy.all
equivalent function | numpy.reference.generated.numpy.ndarray.all |
numpy.ndarray.any method ndarray.any(axis=None, out=None, keepdims=False, *, where=True)
Returns True if any of the elements of a evaluate to True. Refer to numpy.any for full documentation. See also numpy.any
equivalent function | numpy.reference.generated.numpy.ndarray.any |
numpy.ndarray.argmax method ndarray.argmax(axis=None, out=None)
Return indices of the maximum values along the given axis. Refer to numpy.argmax for full documentation. See also numpy.argmax
equivalent function | numpy.reference.generated.numpy.ndarray.argmax |
numpy.ndarray.argmin method ndarray.argmin(axis=None, out=None)
Return indices of the minimum values along the given axis. Refer to numpy.argmin for detailed documentation. See also numpy.argmin
equivalent function | numpy.reference.generated.numpy.ndarray.argmin |
numpy.ndarray.argpartition method ndarray.argpartition(kth, axis=- 1, kind='introselect', order=None)
Returns the indices that would partition this array. Refer to numpy.argpartition for full documentation. New in version 1.8.0. See also numpy.argpartition
equivalent function | numpy.reference.generated.numpy.ndarray.argpartition |
numpy.ndarray.argsort method ndarray.argsort(axis=- 1, kind=None, order=None)
Returns the indices that would sort this array. Refer to numpy.argsort for full documentation. See also numpy.argsort
equivalent function | numpy.reference.generated.numpy.ndarray.argsort |
numpy.ndarray.astype method ndarray.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)
Copy of the array, cast to a specified type. Parameters
dtypestr or dtype
Typecode or data-type to which the array is cast.
order{‘C’, ‘F’, ‘A’, ‘K’}, optional
Controls the memory layout order of the resu... | numpy.reference.generated.numpy.ndarray.astype |
numpy.ndarray.base attribute ndarray.base
Base object if memory is from some other object. Examples The base of an array that owns its memory is None: >>> x = np.array([1,2,3,4])
>>> x.base is None
True
Slicing creates a view, whose memory is shared with x: >>> y = x[2:]
>>> y.base is x
True | numpy.reference.generated.numpy.ndarray.base |
numpy.ndarray.byteswap method ndarray.byteswap(inplace=False)
Swap the bytes of the array elements Toggle between low-endian and big-endian data representation by returning a byteswapped array, optionally swapped in-place. Arrays of byte-strings are not swapped. The real and imaginary parts of a complex number are ... | numpy.reference.generated.numpy.ndarray.byteswap |
numpy.ndarray.choose method ndarray.choose(choices, out=None, mode='raise')
Use an index array to construct a new array from a set of choices. Refer to numpy.choose for full documentation. See also numpy.choose
equivalent function | numpy.reference.generated.numpy.ndarray.choose |
numpy.ndarray.clip method ndarray.clip(min=None, max=None, out=None, **kwargs)
Return an array whose values are limited to [min, max]. One of max or min must be given. Refer to numpy.clip for full documentation. See also numpy.clip
equivalent function | numpy.reference.generated.numpy.ndarray.clip |
numpy.ndarray.compress method ndarray.compress(condition, axis=None, out=None)
Return selected slices of this array along given axis. Refer to numpy.compress for full documentation. See also numpy.compress
equivalent function | numpy.reference.generated.numpy.ndarray.compress |
numpy.ndarray.conj method ndarray.conj()
Complex-conjugate all elements. Refer to numpy.conjugate for full documentation. See also numpy.conjugate
equivalent function | numpy.reference.generated.numpy.ndarray.conj |
numpy.ndarray.conjugate method ndarray.conjugate()
Return the complex conjugate, element-wise. Refer to numpy.conjugate for full documentation. See also numpy.conjugate
equivalent function | numpy.reference.generated.numpy.ndarray.conjugate |
numpy.ndarray.copy method ndarray.copy(order='C')
Return a copy of the array. Parameters
order{‘C’, ‘F’, ‘A’, ‘K’}, optional
Controls the memory layout of the copy. ‘C’ means C-order, ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout of a as closely as poss... | numpy.reference.generated.numpy.ndarray.copy |
numpy.ndarray.ctypes attribute ndarray.ctypes
An object to simplify the interaction of the array with the ctypes module. This attribute creates an object that makes it easier to use arrays when calling shared libraries with the ctypes module. The returned object has, among others, data, shape, and strides attribute... | numpy.reference.generated.numpy.ndarray.ctypes |
numpy.ndarray.cumprod method ndarray.cumprod(axis=None, dtype=None, out=None)
Return the cumulative product of the elements along the given axis. Refer to numpy.cumprod for full documentation. See also numpy.cumprod
equivalent function | numpy.reference.generated.numpy.ndarray.cumprod |
numpy.ndarray.cumsum method ndarray.cumsum(axis=None, dtype=None, out=None)
Return the cumulative sum of the elements along the given axis. Refer to numpy.cumsum for full documentation. See also numpy.cumsum
equivalent function | numpy.reference.generated.numpy.ndarray.cumsum |
numpy.ndarray.data attribute ndarray.data
Python buffer object pointing to the start of the array’s data. | numpy.reference.generated.numpy.ndarray.data |
numpy.ndarray.diagonal method ndarray.diagonal(offset=0, axis1=0, axis2=1)
Return specified diagonals. In NumPy 1.9 the returned array is a read-only view instead of a copy as in previous NumPy versions. In a future version the read-only restriction will be removed. Refer to numpy.diagonal for full documentation. ... | numpy.reference.generated.numpy.ndarray.diagonal |
numpy.ndarray.dtype attribute ndarray.dtype
Data-type of the array’s elements. Parameters
None
Returns
dnumpy dtype object
See also numpy.dtype
Examples >>> x
array([[0, 1],
[2, 3]])
>>> x.dtype
dtype('int32')
>>> type(x.dtype)
<type 'numpy.dtype'> | numpy.reference.generated.numpy.ndarray.dtype |
numpy.ndarray.dump method ndarray.dump(file)
Dump a pickle of the array to the specified file. The array can be read back with pickle.load or numpy.load. Parameters
filestr or Path
A string naming the dump file. Changed in version 1.17.0: pathlib.Path objects are now accepted. | numpy.reference.generated.numpy.ndarray.dump |
numpy.ndarray.dumps method ndarray.dumps()
Returns the pickle of the array as a string. pickle.loads will convert the string back to an array. Parameters
None | numpy.reference.generated.numpy.ndarray.dumps |
numpy.ndarray.fill method ndarray.fill(value)
Fill the array with a scalar value. Parameters
valuescalar
All elements of a will be assigned this value. Examples >>> a = np.array([1, 2])
>>> a.fill(0)
>>> a
array([0, 0])
>>> a = np.empty(2)
>>> a.fill(1)
>>> a
array([1., 1.]) | numpy.reference.generated.numpy.ndarray.fill |
numpy.ndarray.flags attribute ndarray.flags
Information about the memory layout of the array. Notes The flags object can be accessed dictionary-like (as in a.flags['WRITEABLE']), or by using lowercased attribute names (as in a.flags.writeable). Short flag names are only supported in dictionary access. Only the WRIT... | numpy.reference.generated.numpy.ndarray.flags |
numpy.ndarray.flat attribute ndarray.flat
A 1-D iterator over the array. This is a numpy.flatiter instance, which acts similarly to, but is not a subclass of, Python’s built-in iterator object. See also flatten
Return a copy of the array collapsed into one dimension. flatiter
Examples >>> x = np.arange(1, 7).... | numpy.reference.generated.numpy.ndarray.flat |
numpy.ndarray.flatten method ndarray.flatten(order='C')
Return a copy of the array collapsed into one dimension. Parameters
order{‘C’, ‘F’, ‘A’, ‘K’}, optional
‘C’ means to flatten in row-major (C-style) order. ‘F’ means to flatten in column-major (Fortran- style) order. ‘A’ means to flatten in column-major o... | numpy.reference.generated.numpy.ndarray.flatten |
numpy.ndarray.getfield method ndarray.getfield(dtype, offset=0)
Returns a field of the given array as a certain type. A field is a view of the array data with a given data-type. The values in the view are determined by the given type and the offset into the current array in bytes. The offset needs to be such that t... | numpy.reference.generated.numpy.ndarray.getfield |
numpy.ndarray.imag attribute ndarray.imag
The imaginary part of the array. Examples >>> x = np.sqrt([1+0j, 0+1j])
>>> x.imag
array([ 0. , 0.70710678])
>>> x.imag.dtype
dtype('float64') | numpy.reference.generated.numpy.ndarray.imag |
numpy.ndarray.item method ndarray.item(*args)
Copy an element of an array to a standard Python scalar and return it. Parameters
*argsArguments (variable number and type)
none: in this case, the method only works for arrays with one element (a.size == 1), which element is copied into a standard Python scalar ... | numpy.reference.generated.numpy.ndarray.item |
numpy.ndarray.itemset method ndarray.itemset(*args)
Insert scalar into an array (scalar is cast to array’s dtype, if possible) There must be at least 1 argument, and define the last argument as item. Then, a.itemset(*args) is equivalent to but faster than a[args] = item. The item should be a scalar value and args m... | numpy.reference.generated.numpy.ndarray.itemset |
numpy.ndarray.itemsize attribute ndarray.itemsize
Length of one array element in bytes. Examples >>> x = np.array([1,2,3], dtype=np.float64)
>>> x.itemsize
8
>>> x = np.array([1,2,3], dtype=np.complex128)
>>> x.itemsize
16 | numpy.reference.generated.numpy.ndarray.itemsize |
numpy.ndarray.max method ndarray.max(axis=None, out=None, keepdims=False, initial=<no value>, where=True)
Return the maximum along a given axis. Refer to numpy.amax for full documentation. See also numpy.amax
equivalent function | numpy.reference.generated.numpy.ndarray.max |
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