numpy

np.ndarray(shape=(2,2), dtype=float, order='F')
array([[0.0e+000, 0.0e+000], # random
[ nan, 2.5e-323]])
Second mode:
np.ndarray((2,), buffer=np.array([1,2,3]),
... offset=np.int_().itemsize,
... dtype=int) # offset = 1itemsize, i.e. skip first element
array([2, 3])
Methods defined here:
__abs __(self, /)
abs(self)
__add __(self, value, /)
Return self+value.
__and __(self, value, /)
Return self&value.
__array __(...)
a.__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.
__array_function __(...)
__array_prepare __(...)
a.__array_prepare __(obj) -> Object of same type as ndarray object obj.
__array_ufunc __(...)
__array_wrap __(...)
a.__array_wrap __(obj) -> Object of same type as ndarray object a.
__bool __(self, /)
self != 0
__complex __(...)
__contains __(self, key, /)
Return key in self.
__copy __(...)
a.__copy __()
Used if :func:copy.copy is called on an array. Returns a copy of the array.
Equivalent to a.copy(order='K').
__deepcopy __(...)
a.__deepcopy __(memo, /) -> Deep copy of array.
Used if :func:copy.deepcopy is called on an array.
__delitem __(self, key, /)
Delete self[key].
__divmod __(self, value, /)
Return divmod(self, value).
__eq __(self, value, /)
Return self==value.
__float __(self, /)
float(self)
__floordiv __(self, value, /)
Return self//value.
__format __(...)
Default object formatter.
__ge __(self, value, /)
Return self>=value.
__getitem __(self, key, /)
Return self[key].
__gt __(self, value, /)
Return self>value.
__iadd __(self, value, /)
Return self+=value.
__iand __(self, value, /)
Return self&=value.
__ifloordiv __(self, value, /)
Return self//=value.
__ilshift __(self, value, /)
Return self<<=value.
__imatmul __(self, value, /)
Return self@=value.
__imod __(self, value, /)
Return self%=value.
__imul __(self, value, /)
Return self=value.
__index __(self, /)
Return self converted to an integer, if self is suitable for use as an index into a list.
__int __(self, /)
int(self)
__invert __(self, /)
~self
__ior __(self, value, /)
Return self|=value.
__ipow __(self, value, /)
Return self=value.
__irshift __(self, value, /)
Return self>>=value.
__isub __(self, value, /)
Return self-=value.
__iter __(self, /)
Implement iter(self).
__itruediv __(self, value, /)
Return self/=value.
__ixor __(self, value, /)
Return self^=value.
__le __(self, value, /)
Return self<=value.
__len __(self, /)
Return len(self).
__lshift __(self, value, /)
Return self< __lt __(self, value, /)
Return self __matmul __(self, value, /)
Return self@value.
__mod __(self, value, /)
Return self%value.
__mul __(self, value, /)
Return selfvalue.
__ne __(self, value, /)
Return self!=value.
__neg __(self, /)
-self
__or __(self, value, /)
Return self|value.
__pos __(self, /)
+self
__pow __(self, value, mod=None, /)
Return pow(self, value, mod).
__radd __(self, value, /)
Return value+self.
__rand __(self, value, /)
Return value&self.
__rdivmod __(self, value, /)
Return divmod(value, self).
__reduce __(...)
a.__reduce __()
For pickling.
__reduce_ex __(...)
Helper for pickle.
__repr __(self, /)
Return repr(self).
__rfloordiv __(self, value, /)
Return value//self.
__rlshift __(self, value, /)
Return value<>self.
__rshift __(self, value, /)
Return self>>value.
__rsub __(self, value, /)
Return value-self.
__rtruediv __(self, value, /)
Return value/self.
__rxor __(self, value, /)
Return value^self.
__setitem __(self, key, value, /)
Set self[key] to value.
__setstate __(...)
a.__setstate __(state, /)
For unpickling.
The state argument must be a sequence that contains the following
elements:
Parameters
----------
version : int
optional pickle version. If omitted defaults to 0.
shape : tuple
dtype : data-type
isFortran : bool
rawdata : string or list
a binary string with the data (or a list if 'a' is an object array)
__sizeof __(...)
Size of object in memory, in bytes.
__str __(self, /)
Return str(self).
__sub __(self, value, /)
Return self-value.
__truediv __(self, value, /)
Return self/value.
__xor __(self, value, /)
Return self^value.
all(...)
a.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
any(...)
a.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
argmax(...)
a.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
argmin(...)
a.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
argpartition(...)
a.argpartition(kth, axis=-1, kind='introselect', order=None)
Returns the indices that would partition this array.
Refer to numpy.argpartition for full documentation.
.. versionadded:: 1.8.0
See Also
--------
numpy.argpartition : equivalent function
argsort(...)
a.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
astype(...)
a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)
Copy of the array, cast to a specified type.
Parameters
----------
dtype : str 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 result.
'C' means C order, 'F' means Fortran order, 'A'
means 'F' order if all the arrays are Fortran contiguous,
'C' order otherwise, and 'K' means as close to the
order the array elements appear in memory as possible.
Default is 'K'.
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
Controls what kind of data casting may occur. Defaults to 'unsafe'
for backwards compatibility.
* 'no' means the data types should not be cast at all.
* 'equiv' means only byte-order changes are allowed.
* 'safe' means only casts which can preserve values are allowed.
* 'same_kind' means only safe casts or casts within a kind,
like float64 to float32, are allowed.
* 'unsafe' means any data conversions may be done.
subok : bool, optional
If True, then sub-classes will be passed-through (default), otherwise
the returned array will be forced to be a base-class array.
copy : bool, optional
By default, astype always returns a newly allocated array. If this
is set to false, and the dtype, order, and subok
requirements are satisfied, the input array is returned instead
of a copy.
Returns
-------
arr_t : ndarray
Unless copy is False and the other conditions for returning the input
array are satisfied (see description for copy input parameter), arr_t
is a new array of the same shape as the input array, with dtype, order
given by dtype, order.
Notes
-----
.. versionchanged:: 1.17.0
Casting between a simple data type and a structured one is possible only
for "unsafe" casting. Casting to multiple fields is allowed, but
casting from multiple fields is not.
.. versionchanged:: 1.9.0
Casting from numeric to string types in 'safe' casting mode requires
that the string dtype length is long enough to store the max
integer/float value converted.
Raises
------
ComplexWarning
When casting from complex to float or int. To avoid this,
one should use a.real.astype(t).
Examples
--------
>>> x = np.array([1, 2, 2.5])
>>> x
array([1. , 2. , 2.5])
>>> x.astype(int)
array([1, 2, 2])
byteswap(...)
a.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 swapped individually.
Parameters
----------
inplace : bool, optional
If True, swap bytes in-place, default is False.
Returns
-------
out : ndarray
The byteswapped array. If inplace is True, this is
a view to self.
Examples
--------
>>> A = np.array([1, 256, 8755], dtype=np.int16)
>>> list(map(hex, A))
['0x1', '0x100', '0x2233']
>>> A.byteswap(inplace=True)
array([ 256, 1, 13090], dtype=int16)
>>> list(map(hex, A))
['0x100', '0x1', '0x3322']
Arrays of byte-strings are not swapped
>>> A = np.array([b'ceg', b'fac'])
>>> A.byteswap()
array([b'ceg', b'fac'], dtype='|S3')
A.newbyteorder().byteswap() produces an array with the same values
but different representation in memory
>>> A = np.array([1, 2, 3])
>>> A.view(np.uint8)
array([1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0,
0, 0], dtype=uint8)
>>> A.newbyteorder().byteswap(inplace=True)
array([1, 2, 3])
>>> A.view(np.uint8)
array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0,
0, 3], dtype=uint8)
choose(...)
a.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
clip(...)
a.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
compress(...)
a.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
conj(...)
a.conj()
Complex-conjugate all elements.
Refer to numpy.conjugate for full documentation.
See Also
--------
numpy.conjugate : equivalent function
conjugate(...)
a.conjugate()
Return the complex conjugate, element-wise.
Refer to numpy.conjugate for full documentation.
See Also
--------
numpy.conjugate : equivalent function
copy(...)
a.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 possible. (Note that this function and :func:numpy.copy are very
similar but have different default values for their order=
arguments, and this function always passes sub-classes through.)
See also
--------
numpy.copy : Similar function with different default behavior
numpy.copyto
Notes
-----
This function is the preferred method for creating an array copy. The
function :func:numpy.copy is similar, but it defaults to using order 'K',
and will not pass sub-classes through by default.
Examples
--------
>>> x = np.array([[1,2,3],[4,5,6]], order='F')
>>> y = x.copy()
>>> x.fill(0)
>>> x
array([[0, 0, 0],
[0, 0, 0]])
>>> y
array([[1, 2, 3],
[4, 5, 6]])
>>> y.flags['C_CONTIGUOUS']
True
cumprod(...)
a.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
cumsum(...)
a.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
diagonal(...)
a.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 :func:numpy.diagonal for full documentation.
See Also
--------
numpy.diagonal : equivalent function
dot(...)
a.dot(b, out=None)
Dot product of two arrays.
Refer to numpy.dot for full documentation.
See Also
--------
numpy.dot : equivalent function
Examples
--------
>>> a = np.eye(2)
>>> b = np.ones((2, 2)) * 2
>>> a.dot(b)
array([[2., 2.],
[2., 2.]])
This array method can be conveniently chained:
>>> a.dot(b).dot(b)
array([[8., 8.],
[8., 8.]])
dump(...)
a.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
----------
file : str or Path
A string naming the dump file.
.. versionchanged:: 1.17.0
pathlib.Path objects are now accepted.
dumps(...)
a.dumps()
Returns the pickle of the array as a string.
pickle.loads or numpy.loads will convert the string back to an array.
Parameters
----------
None
fill(...)
a.fill(value)
Fill the array with a scalar value.
Parameters
----------
value : scalar
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.])
flatten(...)
a.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
order if a is Fortran contiguous in memory,
row-major order otherwise. 'K' means to flatten
a in the order the elements occur in memory.
The default is 'C'.
Returns
-------
y : ndarray
A copy of the input array, flattened to one dimension.
See Also
--------
ravel : Return a flattened array.
flat : A 1-D flat iterator over the array.
Examples
--------
>>> a = np.array([[1,2], [3,4]])
>>> a.flatten()
array([1, 2, 3, 4])
>>> a.flatten('F')
array([1, 3, 2, 4])
getfield(...)
a.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 the view dtype fits in the
array dtype; for example an array of dtype complex128 has 16-byte elements.
If taking a view with a 32-bit integer (4 bytes), the offset needs to be
between 0 and 12 bytes.
Parameters
----------
dtype : str or dtype
The data type of the view. The dtype size of the view can not be larger
than that of the array itself.
offset : int
Number of bytes to skip before beginning the element view.
Examples
--------
>>> x = np.diag([1.+1.j]2)
>>> x[1, 1] = 2 + 4.j
>>> x
array([[1.+1.j, 0.+0.j],
[0.+0.j, 2.+4.j]])
>>> x.getfield(np.float64)
array([[1., 0.],
[0., 2.]])
By choosing an offset of 8 bytes we can select the complex part of the
array for our view:
>>> x.getfield(np.float64, offset=8)
array([[1., 0.],
[0., 4.]])
item(...)
a.item(args)
Copy an element of an array to a standard Python scalar and return it.
Parameters
----------
*args : Arguments (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 object and returned.
* int_type: this argument is interpreted as a flat index into
the array, specifying which element to copy and return.
* tuple of int_types: functions as does a single int_type argument,
except that the argument is interpreted as an nd-index into the
array.
Returns
-------
z : Standard Python scalar object
A copy of the specified element of the array as a suitable
Python scalar
Notes
-----
When the data type of a is longdouble or clongdouble, item() returns
a scalar array object because there is no available Python scalar that
would not lose information. Void arrays return a buffer object for item(),
unless fields are defined, in which case a tuple is returned.
item is very similar to a[args], except, instead of an array scalar,
a standard Python scalar is returned. This can be useful for speeding up
access to elements of the array and doing arithmetic on elements of the
array using Python's optimized math.
Examples
--------
>>> np.random.seed(123)
>>> x = np.random.randint(9, size=(3, 3))
>>> x
array([[2, 2, 6],
[1, 3, 6],
[1, 0, 1]])
>>> x.item(3)
1
>>> x.item(7)
0
>>> x.item((0, 1))
2
>>> x.item((2, 2))
1
itemset(...)
a.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
must select a single item in the array a.
Parameters
----------
*args : Arguments
If one argument: a scalar, only used in case a is of size 1.
If two arguments: the last argument is the value to be set
and must be a scalar, the first argument specifies a single array
element location. It is either an int or a tuple.
Notes
-----
Compared to indexing syntax, itemset provides some speed increase
for placing a scalar into a particular location in an ndarray,
if you must do this. However, generally this is discouraged:
among other problems, it complicates the appearance of the code.
Also, when using itemset (and item) inside a loop, be sure
to assign the methods to a local variable to avoid the attribute
look-up at each loop iteration.
Examples
--------
>>> np.random.seed(123)
>>> x = np.random.randint(9, size=(3, 3))
>>> x
array([[2, 2, 6],
[1, 3, 6],
[1, 0, 1]])
>>> x.itemset(4, 0)
>>> x.itemset((2, 2), 9)
>>> x
array([[2, 2, 6],
[1, 0, 6],
[1, 0, 9]])
max(...)
a.max(axis=None, out=None, keepdims=False, initial=, where=True)
Return the maximum along a given axis.
Refer to numpy.amax for full documentation.
See Also
--------
numpy.amax : equivalent function
mean(...)
a.mean(axis=None, dtype=None, out=None, keepdims=False, , where=True)
Returns the average of the array elements along given axis.
Refer to numpy.mean for full documentation.
See Also
--------
numpy.mean : equivalent function
min(...)
a.min(axis=None, out=None, keepdims=False, initial=, where=True)
Return the minimum along a given axis.
Refer to numpy.amin for full documentation.
See Also
--------
numpy.amin : equivalent function
newbyteorder(...)
arr.newbyteorder(new_order='S', /)
Return the array with the same data viewed with a different byte order.
Equivalent to::
arr.view(arr.dtype.newbytorder(new_order))
Changes are also made in all fields and sub-arrays of the array data
type.
Parameters
----------
new_order : string, optional
Byte order to force; a value from the byte order specifications
below. new_order codes can be any of:
* 'S' - swap dtype from current to opposite endian
* {'<', 'little'} - little endian
* {'>', 'big'} - big endian
* '=' - native order, equivalent to sys.byteorder
* {'|', 'I'} - ignore (no change to byte order)
The default value ('S') results in swapping the current
byte order.
Returns
-------
new_arr : array
New array object with the dtype reflecting given change to the
byte order.
nonzero(...)
a.nonzero()
Return the indices of the elements that are non-zero.
Refer to numpy.nonzero for full documentation.
See Also
--------
numpy.nonzero : equivalent function
partition(...)
a.partition(kth, axis=-1, kind='introselect', order=None)
Rearranges the elements in the array in such a way that the value of the
element in kth position is in the position it would be in a sorted array.
All elements smaller than the kth element are moved before this element and
all equal or greater are moved behind it. The ordering of the elements in
the two partitions is undefined.
.. versionadded:: 1.8.0
Parameters
----------
kth : int or sequence of ints
Element index to partition by. The kth element value will be in its
final sorted position and all smaller elements will be moved before it
and all equal or greater elements behind it.
The order of all elements in the partitions is undefined.
If provided with a sequence of kth it will partition all elements
indexed by kth of them into their sorted position at once.
axis : int, optional
Axis along which to sort. Default is -1, which means sort along the
last axis.
kind : {'introselect'}, optional
Selection algorithm. Default is 'introselect'.
order : str or list of str, optional
When a is an array with fields defined, this argument specifies
which fields to compare first, second, etc. A single field can
be specified as a string, and not all fields need to be specified,
but unspecified fields will still be used, in the order in which
they come up in the dtype, to break ties.
See Also
--------
numpy.partition : Return a parititioned copy of an array.
argpartition : Indirect partition.
sort : Full sort.
Notes
-----
See np.partition for notes on the different algorithms.
Examples
--------
>>> a = np.array([3, 4, 2, 1])
>>> a.partition(3)
>>> a
array([2, 1, 3, 4])
>>> a.partition((1, 3))
>>> a
array([1, 2, 3, 4])
prod(...)
a.prod(axis=None, dtype=None, out=None, keepdims=False, initial=1, where=True)
Return the product of the array elements over the given axis
Refer to numpy.prod for full documentation.
See Also
--------
numpy.prod : equivalent function
ptp(...)
a.ptp(axis=None, out=None, keepdims=False)
Peak to peak (maximum - minimum) value along a given axis.
Refer to numpy.ptp for full documentation.
See Also
--------
numpy.ptp : equivalent function
put(...)
a.put(indices, values, mode='raise')
Set a.flat[n] = values[n] for all n in indices.
Refer to numpy.put for full documentation.
See Also
--------
numpy.put : equivalent function
ravel(...)
a.ravel([order])
Return a flattened array.
Refer to numpy.ravel for full documentation.
See Also
--------
numpy.ravel : equivalent function
ndarray.flat : a flat iterator on the array.
repeat(...)
a.repeat(repeats, axis=None)
Repeat elements of an array.
Refer to numpy.repeat for full documentation.
See Also
--------
numpy.repeat : equivalent function
reshape(...)
a.reshape(shape, order='C')
Returns an array containing the same data with a new shape.
Refer to numpy.reshape for full documentation.
See Also
--------
numpy.reshape : equivalent function
Notes
-----
Unlike the free function numpy.reshape, this method on ndarray allows
the elements of the shape parameter to be passed in as separate arguments.
For example, a.reshape(10, 11) is equivalent to
a.reshape((10, 11)).
resize(...)
a.resize(new_shape, refcheck=True)
Change shape and size of array in-place.
Parameters
----------
new_shape : tuple of ints, or n ints
Shape of resized array.
refcheck : bool, optional
If False, reference count will not be checked. Default is True.
Returns
-------
None
Raises
------
ValueError
If a does not own its own data or references or views to it exist,
and the data memory must be changed.
PyPy only: will always raise if the data memory must be changed, since
there is no reliable way to determine if references or views to it
exist.
SystemError
If the order keyword argument is specified. This behaviour is a
bug in NumPy.
See Also
--------
resize : Return a new array with the specified shape.
Notes
-----
This reallocates space for the data area if necessary.
Only contiguous arrays (data elements consecutive in memory) can be
resized.
The purpose of the reference count check is to make sure you
do not use this array as a buffer for another Python object and then
reallocate the memory. However, reference counts can increase in
other ways so if you are sure that you have not shared the memory
for this array with another Python object, then you may safely set
refcheck to False.
Examples
--------
Shrinking an array: array is flattened (in the order that the data are
stored in memory), resized, and reshaped:
>>> a = np.array([[0, 1], [2, 3]], order='C')
>>> a.resize((2, 1))
>>> a
array([[0],
[1]])
>>> a = np.array([[0, 1], [2, 3]], order='F')
>>> a.resize((2, 1))
>>> a
array([[0],
[2]])
Enlarging an array: as above, but missing entries are filled with zeros:
>>> b = np.array([[0, 1], [2, 3]])
>>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple
>>> b
array([[0, 1, 2],
[3, 0, 0]])
Referencing an array prevents resizing...
>>> c = a
>>> a.resize((1, 1))
Traceback (most recent call last):
...
ValueError: cannot resize an array that references or is referenced ...
Unless refcheck is False:
>>> a.resize((1, 1), refcheck=False)
>>> a
array([[0]])
>>> c
array([[0]])
round(...)
a.round(decimals=0, out=None)
Return a with each element rounded to the given number of decimals.
Refer to numpy.around for full documentation.
See Also
--------
numpy.around : equivalent function
searchsorted(...)
a.searchsorted(v, side='left', sorter=None)
Find indices where elements of v should be inserted in a to maintain order.
For full documentation, see numpy.searchsorted
See Also
--------
numpy.searchsorted : equivalent function
setfield(...)
a.setfield(val, dtype, offset=0)
Put a value into a specified place in a field defined by a data-type.
Place val into a's field defined by dtype and beginning offset
bytes into the field.
Parameters
----------
val : object
Value to be placed in field.
dtype : dtype object
Data-type of the field in which to place val.
offset : int, optional
The number of bytes into the field at which to place val.
Returns
-------
None
See Also
--------
getfield
Examples
--------
>>> x = np.eye(3)
>>> x.getfield(np.float64)
array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]])
>>> x.setfield(3, np.int32)
>>> x.getfield(np.int32)
array([[3, 3, 3],
[3, 3, 3],
[3, 3, 3]], dtype=int32)
>>> x
array([[1.0e+000, 1.5e-323, 1.5e-323],
[1.5e-323, 1.0e+000, 1.5e-323],
[1.5e-323, 1.5e-323, 1.0e+000]])
>>> x.setfield(np.eye(3), np.int32)
>>> x
array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]])
setflags(...)
a.setflags(write=None, align=None, uic=None)
Set array flags WRITEABLE, ALIGNED, (WRITEBACKIFCOPY and UPDATEIFCOPY),
respectively.
These Boolean-valued flags affect how numpy interprets the memory
area used by a (see Notes below). The ALIGNED flag can only
be set to True if the data is actually aligned according to the type.
The WRITEBACKIFCOPY and (deprecated) UPDATEIFCOPY flags can never be set
to True. The flag WRITEABLE can only be set to True if the array owns its
own memory, or the ultimate owner of the memory exposes a writeable buffer
interface, or is a string. (The exception for string is made so that
unpickling can be done without copying memory.)
Parameters
----------
write : bool, optional
Describes whether or not a can be written to.
align : bool, optional
Describes whether or not a is aligned properly for its type.
uic : bool, optional
Describes whether or not a is a copy of another "base" array.
Notes
-----
Array flags provide information about how the memory area used
for the array is to be interpreted. There are 7 Boolean flags
in use, only four of which can be changed by the user:
WRITEBACKIFCOPY, UPDATEIFCOPY, WRITEABLE, and ALIGNED.
WRITEABLE (W) the data area can be written to;
ALIGNED (A) the data and strides are aligned appropriately for the hardware
(as determined by the compiler);
UPDATEIFCOPY (U) (deprecated), replaced by WRITEBACKIFCOPY;
WRITEBACKIFCOPY (X) this array is a copy of some other array (referenced
by .base). When the C-API function PyArray_ResolveWritebackIfCopy is
called, the base array will be updated with the contents of this array.
All flags can be accessed using the single (upper case) letter as well
as the full name.
Examples
--------
>>> y = np.array([[3, 1, 7],
... [2, 0, 0],
... [8, 5, 9]])
>>> y
array([[3, 1, 7],
[2, 0, 0],
[8, 5, 9]])
>>> y.flags
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : True
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
>>> y.setflags(write=0, align=0)
>>> y.flags
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : True
WRITEABLE : False
ALIGNED : False
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
>>> y.setflags(uic=1)
Traceback (most recent call last):
File "", line 1, in
ValueError: cannot set WRITEBACKIFCOPY flag to True
sort(...)
a.sort(axis=-1, kind=None, order=None)
Sort an array in-place. Refer to numpy.sort for full documentation.
Parameters
----------
axis : int, optional
Axis along which to sort. Default is -1, which means sort along the
last axis.
kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional
Sorting algorithm. The default is 'quicksort'. Note that both 'stable'
and 'mergesort' use timsort under the covers and, in general, the
actual implementation will vary with datatype. The 'mergesort' option
is retained for backwards compatibility.
.. versionchanged:: 1.15.0
The 'stable' option was added.
order : str or list of str, optional
When a is an array with fields defined, this argument specifies
which fields to compare first, second, etc. A single field can
be specified as a string, and not all fields need be specified,
but unspecified fields will still be used, in the order in which
they come up in the dtype, to break ties.
See Also
--------
numpy.sort : Return a sorted copy of an array.
numpy.argsort : Indirect sort.
numpy.lexsort : Indirect stable sort on multiple keys.
numpy.searchsorted : Find elements in sorted array.
numpy.partition: Partial sort.
Notes
-----
See numpy.sort for notes on the different sorting algorithms.
Examples
--------
>>> a = np.array([[1,4], [3,1]])
>>> a.sort(axis=1)
>>> a
array([[1, 4],
[1, 3]])
>>> a.sort(axis=0)
>>> a
array([[1, 3],
[1, 4]])
Use the order keyword to specify a field to use when sorting a
structured array:
>>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])
>>> a.sort(order='y')
>>> a
array([(b'c', 1), (b'a', 2)],
dtype=[('x', 'S1'), ('y', 'a. Refer to numpy.squeeze for full documentation. See Also -------- numpy.squeeze : equivalent function std(...) a.std(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True) Returns the standard deviation of the array elements along given axis. Refer to numpy.std for full documentation. See Also -------- numpy.std : equivalent function sum(...) a.sum(axis=None, dtype=None, out=None, keepdims=False, initial=0, where=True) Return the sum of the array elements over the given axis. Refer to numpy.sum for full documentation. See Also -------- numpy.sum : equivalent function swapaxes(...) a.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 take(...) a.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 tobytes(...) a.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. .. versionadded:: 1.9.0 Parameters ---------- order : {'C', 'F', 'A'}, optional Controls the memory layout of the bytes object. 'C' means C-order, 'F' means F-order, 'A' (short for *Any*) means 'F' if a is Fortran contiguous, 'C' otherwise. Default is 'C'. Returns ------- s : bytes Python bytes exhibiting a copy of a's raw data. Examples -------- >>> x = np.array([[0, 1], [2, 3]], dtype='>> x.tobytes()
b'\x00\x00\x01\x00\x02\x00\x03\x00'
>>> x.tobytes('C') == x.tobytes()
True
>>> x.tobytes('F')
b'\x00\x00\x02\x00\x01\x00\x03\x00'
tofile(...)
a.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
----------
fid : file or str or Path
An open file object, or a string containing a filename.
.. versionchanged:: 1.17.0
pathlib.Path objects are now accepted.
sep : str
Separator between array items for text output.
If "" (empty), a binary file is written, equivalent to
file.write(a.tobytes()).
format : str
Format string for text file output.
Each entry in the array is formatted to text by first converting
it to the closest Python type, and then using "format" % item.
Notes
-----
This is a convenience function for quick storage of array data.
Information on endianness and precision is lost, so this method is not a
good choice for files intended to archive data or transport data between
machines with different endianness. Some of these problems can be overcome
by outputting the data as text files, at the expense of speed and file
size.
When fid is a file object, array contents are directly written to the
file, bypassing the file object's write method. As a result, tofile
cannot be used with files objects supporting compression (e.g., GzipFile)
or file-like objects that do not support fileno() (e.g., BytesIO).
tolist(...)
a.tolist()
Return the array as an a.ndim-levels deep nested list of Python scalars.
Return a copy of the array data as a (nested) Python list.
Data items are converted to the nearest compatible builtin Python type, via
the ~numpy.ndarray.item function.
If a.ndim is 0, then since the depth of the nested list is 0, it will
not be a list at all, but a simple Python scalar.
Parameters
----------
none
Returns
-------
y : object, or list of object, or list of list of object, or ...
The possibly nested list of array elements.
Notes
-----
The array may be recreated via a = np.array(a.tolist()), although this
may sometimes lose precision.
Examples
--------
For a 1D array, a.tolist() is almost the same as list(a),
except that tolist changes numpy scalars to Python scalars:
>>> a = np.uint32([1, 2])
>>> a_list = list(a)
>>> a_list
[1, 2]
>>> type(a_list[0])

>>> a_tolist = a.tolist()
>>> a_tolist
[1, 2]
>>> type(a_tolist[0])

Additionally, for a 2D array, tolist applies recursively:
>>> a = np.array([[1, 2], [3, 4]])
>>> list(a)
[array([1, 2]), array([3, 4])]
>>> a.tolist()
[[1, 2], [3, 4]]
The base case for this recursion is a 0D array:
>>> a = np.array(1)
>>> list(a)
Traceback (most recent call last):
...
TypeError: iteration over a 0-d array
>>> a.tolist()
1
tostring(...)
a.tostring(order='C')
A compatibility alias for tobytes, with exactly the same behavior.
Despite its name, it returns bytes not str\ s.
.. deprecated:: 1.19.0
trace(...)
a.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
transpose(...)
a.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 does
a[:, np.newaxis].
For a 2-D array, this is a standard matrix transpose.
For an n-D array, if axes are given, their order indicates how the
axes are permuted (see Examples). If axes are not provided and
a.shape = (i[0], i[1], ... i[n-2], i[n-1]), then
a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0]).
Parameters
----------
axes : None, tuple of ints, or n ints
* None or no argument: reverses the order of the axes.
* tuple of ints: i in the j-th place in the tuple means a's
i-th axis becomes a.transpose()'s j-th axis.
* n ints: same as an n-tuple of the same ints (this form is
intended simply as a "convenience" alternative to the tuple form)
Returns
-------
out : ndarray
View of a, with axes suitably permuted.
See Also
--------
transpose : Equivalent function
ndarray.T : Array property returning the array transposed.
ndarray.reshape : Give a new shape to an array without changing its data.
Examples
--------
>>> a = np.array([[1, 2], [3, 4]])
>>> a
array([[1, 2],
[3, 4]])
>>> a.transpose()
array([[1, 3],
[2, 4]])
>>> a.transpose((1, 0))
array([[1, 3],
[2, 4]])
>>> a.transpose(1, 0)
array([[1, 3],
[2, 4]])
var(...)
a.var(axis=None, dtype=None, out=None, ddof=0, keepdims=False, , where=True)
Returns the variance of the array elements, along given axis.
Refer to numpy.var for full documentation.
See Also
--------
numpy.var : equivalent function
view(...)
a.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
----------
dtype : data-type or ndarray sub-class, optional
Data-type descriptor of the returned view, e.g., float32 or int16.
Omitting it results in the view having the same data-type as a.
This argument can also be specified as an ndarray sub-class, which
then specifies the type of the returned object (this is equivalent to
setting the type parameter).
type : Python type, optional
Type of the returned view, e.g., ndarray or matrix. Again, omission
of the parameter results in type preservation.
Notes
-----
a.view() is used two different ways:
a.view(some_dtype) or a.view(dtype=some_dtype) constructs a view
of the array's memory with a different data-type. This can cause a
reinterpretation of the bytes of memory.
a.view(ndarray_subclass) or a.view(type=ndarray_subclass) just
returns an instance of ndarray_subclass that looks at the same array
(same shape, dtype, etc.) This does not cause a reinterpretation of the
memory.
For a.view(some_dtype), if some_dtype has a different number of
bytes per entry than the previous dtype (for example, converting a
regular array to a structured array), then the behavior of the view
cannot be predicted just from the superficial appearance of a (shown
by print(a)). It also depends on exactly how a is stored in
memory. Therefore if a is C-ordered versus fortran-ordered, versus
defined as a slice or transpose, etc., the view may give different
results.
Examples
--------
>>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])
Viewing array data using a different type and dtype:
>>> y = x.view(dtype=np.int16, type=np.matrix)
>>> y
matrix([[513]], dtype=int16)
>>> print(type(y))

Creating a view on a structured array so it can be used in calculations
>>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])
>>> xv = x.view(dtype=np.int8).reshape(-1,2)
>>> xv
array([[1, 2],
[3, 4]], dtype=int8)
>>> xv.mean(0)
array([2., 3.])
Making changes to the view changes the underlying array
>>> xv[0,1] = 20
>>> x
array([(1, 20), (3, 4)], dtype=[('a', 'i1'), ('b', 'i1')])
Using a view to convert an array to a recarray:
>>> z = x.view(np.recarray)
>>> z.a
array([1, 3], dtype=int8)
Views share data:
>>> x[0] = (9, 10)
>>> z[0]
(9, 10)
Views that change the dtype size (bytes per entry) should normally be
avoided on arrays defined by slices, transposes, fortran-ordering, etc.:
>>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16)
>>> y = x[:, 0:2]
>>> y
array([[1, 2],
[4, 5]], dtype=int16)
>>> y.view(dtype=[('width', np.int16), ('length', np.int16)])
Traceback (most recent call last):
...
ValueError: To change to a dtype of a different size, the array must be C-contiguous
>>> z = y.copy()
>>> z.view(dtype=[('width', np.int16), ('length', np.int16)])
array([[(1, 2)],
[(4, 5)]], dtype=[('width', '