“is” operation returns false even though two objects have same id The 2019 Stack Overflow Developer Survey Results Are In Unicorn Meta Zoo #1: Why another podcast? Announcing the arrival of Valued Associate #679: Cesar Manara The Ask Question Wizard is Live! Data science time! April 2019 and salary with experienceHow to return multiple values from a function?Does Python have a ternary conditional operator?Relationship between SciPy and NumPyCreating a singleton in PythonPython: two objects are the sameWhy is [] faster than list()?Why does “not(True) in [False, True]” return False?is comparison returns False with strings using same idHow can two Python objects have same id but 'is' operator returns False?Comparing Objects - Why is == returning 'False' in the following example?
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"is" operation returns false even though two objects have same id
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“is” operation returns false even though two objects have same id
The 2019 Stack Overflow Developer Survey Results Are In
Unicorn Meta Zoo #1: Why another podcast?
Announcing the arrival of Valued Associate #679: Cesar Manara
The Ask Question Wizard is Live!
Data science time! April 2019 and salary with experienceHow to return multiple values from a function?Does Python have a ternary conditional operator?Relationship between SciPy and NumPyCreating a singleton in PythonPython: two objects are the sameWhy is [] faster than list()?Why does “not(True) in [False, True]” return False?is comparison returns False with strings using same idHow can two Python objects have same id but 'is' operator returns False?Comparing Objects - Why is == returning 'False' in the following example?
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Two python objects have the same id but "is" operation returns false as shown below:
a = np.arange(12).reshape(2, -1)
c = a.reshape(12, 1)
print("id(c.data)", id(c.data))
print("id(a.data)", id(a.data))
print(c.data is a.data)
print(id(c.data) == id(a.data))
Here is the actual output:
id(c.data) 241233112
id(a.data) 241233112
False
True
My question is... why "c.data is a.data" returns false even though they point to the same ID, thus pointing to the same object? I thought that they point to the same object if they have same ID or am I wrong? Thank you!
python numpy
|
show 9 more comments
Two python objects have the same id but "is" operation returns false as shown below:
a = np.arange(12).reshape(2, -1)
c = a.reshape(12, 1)
print("id(c.data)", id(c.data))
print("id(a.data)", id(a.data))
print(c.data is a.data)
print(id(c.data) == id(a.data))
Here is the actual output:
id(c.data) 241233112
id(a.data) 241233112
False
True
My question is... why "c.data is a.data" returns false even though they point to the same ID, thus pointing to the same object? I thought that they point to the same object if they have same ID or am I wrong? Thank you!
python numpy
1
@C.Nivs They don't even necessarily have different memory addresses (something which Python doesn't expose). Whatever memory was used for the first may have been reused for the second.
– chepner
4 hours ago
2
@C.Nivs Don't think of it in terms of memory addresses. How memory is managed is completely implementation dependent. All you know for sure is that two objects that overlap in time will not have the same id.
– chepner
4 hours ago
1
@Aran-Fey, that's okay a good question(though asked before) can sometimes be resurrected for a fruitful discussion
– amanb
4 hours ago
1
@C.Nivs no, ids do not belong to variables. They belong to objects. Many variables can reference the same object.
– juanpa.arrivillaga
3 hours ago
2
@juanpa.arrivillaga fair enough. Thanks for the explanation
– C.Nivs
2 hours ago
|
show 9 more comments
Two python objects have the same id but "is" operation returns false as shown below:
a = np.arange(12).reshape(2, -1)
c = a.reshape(12, 1)
print("id(c.data)", id(c.data))
print("id(a.data)", id(a.data))
print(c.data is a.data)
print(id(c.data) == id(a.data))
Here is the actual output:
id(c.data) 241233112
id(a.data) 241233112
False
True
My question is... why "c.data is a.data" returns false even though they point to the same ID, thus pointing to the same object? I thought that they point to the same object if they have same ID or am I wrong? Thank you!
python numpy
Two python objects have the same id but "is" operation returns false as shown below:
a = np.arange(12).reshape(2, -1)
c = a.reshape(12, 1)
print("id(c.data)", id(c.data))
print("id(a.data)", id(a.data))
print(c.data is a.data)
print(id(c.data) == id(a.data))
Here is the actual output:
id(c.data) 241233112
id(a.data) 241233112
False
True
My question is... why "c.data is a.data" returns false even though they point to the same ID, thus pointing to the same object? I thought that they point to the same object if they have same ID or am I wrong? Thank you!
python numpy
python numpy
edited 3 hours ago
ribitskiyb
376
376
asked 4 hours ago
drminixdrminix
513
513
1
@C.Nivs They don't even necessarily have different memory addresses (something which Python doesn't expose). Whatever memory was used for the first may have been reused for the second.
– chepner
4 hours ago
2
@C.Nivs Don't think of it in terms of memory addresses. How memory is managed is completely implementation dependent. All you know for sure is that two objects that overlap in time will not have the same id.
– chepner
4 hours ago
1
@Aran-Fey, that's okay a good question(though asked before) can sometimes be resurrected for a fruitful discussion
– amanb
4 hours ago
1
@C.Nivs no, ids do not belong to variables. They belong to objects. Many variables can reference the same object.
– juanpa.arrivillaga
3 hours ago
2
@juanpa.arrivillaga fair enough. Thanks for the explanation
– C.Nivs
2 hours ago
|
show 9 more comments
1
@C.Nivs They don't even necessarily have different memory addresses (something which Python doesn't expose). Whatever memory was used for the first may have been reused for the second.
– chepner
4 hours ago
2
@C.Nivs Don't think of it in terms of memory addresses. How memory is managed is completely implementation dependent. All you know for sure is that two objects that overlap in time will not have the same id.
– chepner
4 hours ago
1
@Aran-Fey, that's okay a good question(though asked before) can sometimes be resurrected for a fruitful discussion
– amanb
4 hours ago
1
@C.Nivs no, ids do not belong to variables. They belong to objects. Many variables can reference the same object.
– juanpa.arrivillaga
3 hours ago
2
@juanpa.arrivillaga fair enough. Thanks for the explanation
– C.Nivs
2 hours ago
1
1
@C.Nivs They don't even necessarily have different memory addresses (something which Python doesn't expose). Whatever memory was used for the first may have been reused for the second.
– chepner
4 hours ago
@C.Nivs They don't even necessarily have different memory addresses (something which Python doesn't expose). Whatever memory was used for the first may have been reused for the second.
– chepner
4 hours ago
2
2
@C.Nivs Don't think of it in terms of memory addresses. How memory is managed is completely implementation dependent. All you know for sure is that two objects that overlap in time will not have the same id.
– chepner
4 hours ago
@C.Nivs Don't think of it in terms of memory addresses. How memory is managed is completely implementation dependent. All you know for sure is that two objects that overlap in time will not have the same id.
– chepner
4 hours ago
1
1
@Aran-Fey, that's okay a good question(though asked before) can sometimes be resurrected for a fruitful discussion
– amanb
4 hours ago
@Aran-Fey, that's okay a good question(though asked before) can sometimes be resurrected for a fruitful discussion
– amanb
4 hours ago
1
1
@C.Nivs no, ids do not belong to variables. They belong to objects. Many variables can reference the same object.
– juanpa.arrivillaga
3 hours ago
@C.Nivs no, ids do not belong to variables. They belong to objects. Many variables can reference the same object.
– juanpa.arrivillaga
3 hours ago
2
2
@juanpa.arrivillaga fair enough. Thanks for the explanation
– C.Nivs
2 hours ago
@juanpa.arrivillaga fair enough. Thanks for the explanation
– C.Nivs
2 hours ago
|
show 9 more comments
2 Answers
2
active
oldest
votes
a.data
and c.data
both produce a transient object, with no reference to it. As such, both are immediately garbage-collected. The same id can be used for both.
In your first if
statement, the objects have to co-exist while is
checks if they are identical, which they are not.
In the second if
statement, each object is released as soon as id
returns its id.
If you save references to both objects, keeping them alive, you can see they are not the same object.
r0 = a.data
r1 = c.data
assert r0 is not r1
4
what is confusing is the fact thatdata
looks like an attribute, but is probably a property
– Jean-François Fabre♦
4 hours ago
In my tests, the id's are different in the first run, but change to become the same on subsequent runs.
– amanb
4 hours ago
@Jean-FrançoisFabre so would that mean that the object itself is only returned when a getter is called, and the property is not actually stored in the class? I'm not quite familiar with the differences between a property vs attribute
– C.Nivs
4 hours ago
5
a property is a method disguised as an attribute. So it can return a discardable integer, object, whatever.
– Jean-François Fabre♦
4 hours ago
add a comment |
In [62]: a = np.arange(12).reshape(2,-1)
...: c = a.reshape(12,1)
.data
returns a memoryview
object. id
just gives the id of that object; it's not the value of the object, or any indication of where a
databuffer is located.
In [63]: a.data
Out[63]: <memory at 0x7f672d1101f8>
In [64]: c.data
Out[64]: <memory at 0x7f672d1103a8>
In [65]: type(a.data)
Out[65]: memoryview
https://docs.python.org/3/library/stdtypes.html#memoryview
If you want to verify that a
and c
share a data buffer, I find the __array_interface__
to be a better tool.
In [66]: a.__array_interface__['data']
Out[66]: (50988640, False)
In [67]: c.__array_interface__['data']
Out[67]: (50988640, False)
It even shows the offset produced by slicing - here 24 bytes, 3*8
In [68]: c[3:].__array_interface__['data']
Out[68]: (50988664, False)
I haven't seen much use of a.data
. It can be used as the buffer
object when creating a new array with ndarray
:
In [70]: d = np.ndarray((2,6), dtype=a.dtype, buffer=a.data)
In [71]: d
Out[71]:
array([[ 0, 1, 2, 3, 4, 5],
[ 6, 7, 8, 9, 10, 11]])
In [72]: d.__array_interface__['data']
Out[72]: (50988640, False)
But normally we create new arrays with shared memory with slicing or np.array
(copy=False).
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
a.data
and c.data
both produce a transient object, with no reference to it. As such, both are immediately garbage-collected. The same id can be used for both.
In your first if
statement, the objects have to co-exist while is
checks if they are identical, which they are not.
In the second if
statement, each object is released as soon as id
returns its id.
If you save references to both objects, keeping them alive, you can see they are not the same object.
r0 = a.data
r1 = c.data
assert r0 is not r1
4
what is confusing is the fact thatdata
looks like an attribute, but is probably a property
– Jean-François Fabre♦
4 hours ago
In my tests, the id's are different in the first run, but change to become the same on subsequent runs.
– amanb
4 hours ago
@Jean-FrançoisFabre so would that mean that the object itself is only returned when a getter is called, and the property is not actually stored in the class? I'm not quite familiar with the differences between a property vs attribute
– C.Nivs
4 hours ago
5
a property is a method disguised as an attribute. So it can return a discardable integer, object, whatever.
– Jean-François Fabre♦
4 hours ago
add a comment |
a.data
and c.data
both produce a transient object, with no reference to it. As such, both are immediately garbage-collected. The same id can be used for both.
In your first if
statement, the objects have to co-exist while is
checks if they are identical, which they are not.
In the second if
statement, each object is released as soon as id
returns its id.
If you save references to both objects, keeping them alive, you can see they are not the same object.
r0 = a.data
r1 = c.data
assert r0 is not r1
4
what is confusing is the fact thatdata
looks like an attribute, but is probably a property
– Jean-François Fabre♦
4 hours ago
In my tests, the id's are different in the first run, but change to become the same on subsequent runs.
– amanb
4 hours ago
@Jean-FrançoisFabre so would that mean that the object itself is only returned when a getter is called, and the property is not actually stored in the class? I'm not quite familiar with the differences between a property vs attribute
– C.Nivs
4 hours ago
5
a property is a method disguised as an attribute. So it can return a discardable integer, object, whatever.
– Jean-François Fabre♦
4 hours ago
add a comment |
a.data
and c.data
both produce a transient object, with no reference to it. As such, both are immediately garbage-collected. The same id can be used for both.
In your first if
statement, the objects have to co-exist while is
checks if they are identical, which they are not.
In the second if
statement, each object is released as soon as id
returns its id.
If you save references to both objects, keeping them alive, you can see they are not the same object.
r0 = a.data
r1 = c.data
assert r0 is not r1
a.data
and c.data
both produce a transient object, with no reference to it. As such, both are immediately garbage-collected. The same id can be used for both.
In your first if
statement, the objects have to co-exist while is
checks if they are identical, which they are not.
In the second if
statement, each object is released as soon as id
returns its id.
If you save references to both objects, keeping them alive, you can see they are not the same object.
r0 = a.data
r1 = c.data
assert r0 is not r1
edited 4 hours ago
answered 4 hours ago
chepnerchepner
262k35251345
262k35251345
4
what is confusing is the fact thatdata
looks like an attribute, but is probably a property
– Jean-François Fabre♦
4 hours ago
In my tests, the id's are different in the first run, but change to become the same on subsequent runs.
– amanb
4 hours ago
@Jean-FrançoisFabre so would that mean that the object itself is only returned when a getter is called, and the property is not actually stored in the class? I'm not quite familiar with the differences between a property vs attribute
– C.Nivs
4 hours ago
5
a property is a method disguised as an attribute. So it can return a discardable integer, object, whatever.
– Jean-François Fabre♦
4 hours ago
add a comment |
4
what is confusing is the fact thatdata
looks like an attribute, but is probably a property
– Jean-François Fabre♦
4 hours ago
In my tests, the id's are different in the first run, but change to become the same on subsequent runs.
– amanb
4 hours ago
@Jean-FrançoisFabre so would that mean that the object itself is only returned when a getter is called, and the property is not actually stored in the class? I'm not quite familiar with the differences between a property vs attribute
– C.Nivs
4 hours ago
5
a property is a method disguised as an attribute. So it can return a discardable integer, object, whatever.
– Jean-François Fabre♦
4 hours ago
4
4
what is confusing is the fact that
data
looks like an attribute, but is probably a property– Jean-François Fabre♦
4 hours ago
what is confusing is the fact that
data
looks like an attribute, but is probably a property– Jean-François Fabre♦
4 hours ago
In my tests, the id's are different in the first run, but change to become the same on subsequent runs.
– amanb
4 hours ago
In my tests, the id's are different in the first run, but change to become the same on subsequent runs.
– amanb
4 hours ago
@Jean-FrançoisFabre so would that mean that the object itself is only returned when a getter is called, and the property is not actually stored in the class? I'm not quite familiar with the differences between a property vs attribute
– C.Nivs
4 hours ago
@Jean-FrançoisFabre so would that mean that the object itself is only returned when a getter is called, and the property is not actually stored in the class? I'm not quite familiar with the differences between a property vs attribute
– C.Nivs
4 hours ago
5
5
a property is a method disguised as an attribute. So it can return a discardable integer, object, whatever.
– Jean-François Fabre♦
4 hours ago
a property is a method disguised as an attribute. So it can return a discardable integer, object, whatever.
– Jean-François Fabre♦
4 hours ago
add a comment |
In [62]: a = np.arange(12).reshape(2,-1)
...: c = a.reshape(12,1)
.data
returns a memoryview
object. id
just gives the id of that object; it's not the value of the object, or any indication of where a
databuffer is located.
In [63]: a.data
Out[63]: <memory at 0x7f672d1101f8>
In [64]: c.data
Out[64]: <memory at 0x7f672d1103a8>
In [65]: type(a.data)
Out[65]: memoryview
https://docs.python.org/3/library/stdtypes.html#memoryview
If you want to verify that a
and c
share a data buffer, I find the __array_interface__
to be a better tool.
In [66]: a.__array_interface__['data']
Out[66]: (50988640, False)
In [67]: c.__array_interface__['data']
Out[67]: (50988640, False)
It even shows the offset produced by slicing - here 24 bytes, 3*8
In [68]: c[3:].__array_interface__['data']
Out[68]: (50988664, False)
I haven't seen much use of a.data
. It can be used as the buffer
object when creating a new array with ndarray
:
In [70]: d = np.ndarray((2,6), dtype=a.dtype, buffer=a.data)
In [71]: d
Out[71]:
array([[ 0, 1, 2, 3, 4, 5],
[ 6, 7, 8, 9, 10, 11]])
In [72]: d.__array_interface__['data']
Out[72]: (50988640, False)
But normally we create new arrays with shared memory with slicing or np.array
(copy=False).
add a comment |
In [62]: a = np.arange(12).reshape(2,-1)
...: c = a.reshape(12,1)
.data
returns a memoryview
object. id
just gives the id of that object; it's not the value of the object, or any indication of where a
databuffer is located.
In [63]: a.data
Out[63]: <memory at 0x7f672d1101f8>
In [64]: c.data
Out[64]: <memory at 0x7f672d1103a8>
In [65]: type(a.data)
Out[65]: memoryview
https://docs.python.org/3/library/stdtypes.html#memoryview
If you want to verify that a
and c
share a data buffer, I find the __array_interface__
to be a better tool.
In [66]: a.__array_interface__['data']
Out[66]: (50988640, False)
In [67]: c.__array_interface__['data']
Out[67]: (50988640, False)
It even shows the offset produced by slicing - here 24 bytes, 3*8
In [68]: c[3:].__array_interface__['data']
Out[68]: (50988664, False)
I haven't seen much use of a.data
. It can be used as the buffer
object when creating a new array with ndarray
:
In [70]: d = np.ndarray((2,6), dtype=a.dtype, buffer=a.data)
In [71]: d
Out[71]:
array([[ 0, 1, 2, 3, 4, 5],
[ 6, 7, 8, 9, 10, 11]])
In [72]: d.__array_interface__['data']
Out[72]: (50988640, False)
But normally we create new arrays with shared memory with slicing or np.array
(copy=False).
add a comment |
In [62]: a = np.arange(12).reshape(2,-1)
...: c = a.reshape(12,1)
.data
returns a memoryview
object. id
just gives the id of that object; it's not the value of the object, or any indication of where a
databuffer is located.
In [63]: a.data
Out[63]: <memory at 0x7f672d1101f8>
In [64]: c.data
Out[64]: <memory at 0x7f672d1103a8>
In [65]: type(a.data)
Out[65]: memoryview
https://docs.python.org/3/library/stdtypes.html#memoryview
If you want to verify that a
and c
share a data buffer, I find the __array_interface__
to be a better tool.
In [66]: a.__array_interface__['data']
Out[66]: (50988640, False)
In [67]: c.__array_interface__['data']
Out[67]: (50988640, False)
It even shows the offset produced by slicing - here 24 bytes, 3*8
In [68]: c[3:].__array_interface__['data']
Out[68]: (50988664, False)
I haven't seen much use of a.data
. It can be used as the buffer
object when creating a new array with ndarray
:
In [70]: d = np.ndarray((2,6), dtype=a.dtype, buffer=a.data)
In [71]: d
Out[71]:
array([[ 0, 1, 2, 3, 4, 5],
[ 6, 7, 8, 9, 10, 11]])
In [72]: d.__array_interface__['data']
Out[72]: (50988640, False)
But normally we create new arrays with shared memory with slicing or np.array
(copy=False).
In [62]: a = np.arange(12).reshape(2,-1)
...: c = a.reshape(12,1)
.data
returns a memoryview
object. id
just gives the id of that object; it's not the value of the object, or any indication of where a
databuffer is located.
In [63]: a.data
Out[63]: <memory at 0x7f672d1101f8>
In [64]: c.data
Out[64]: <memory at 0x7f672d1103a8>
In [65]: type(a.data)
Out[65]: memoryview
https://docs.python.org/3/library/stdtypes.html#memoryview
If you want to verify that a
and c
share a data buffer, I find the __array_interface__
to be a better tool.
In [66]: a.__array_interface__['data']
Out[66]: (50988640, False)
In [67]: c.__array_interface__['data']
Out[67]: (50988640, False)
It even shows the offset produced by slicing - here 24 bytes, 3*8
In [68]: c[3:].__array_interface__['data']
Out[68]: (50988664, False)
I haven't seen much use of a.data
. It can be used as the buffer
object when creating a new array with ndarray
:
In [70]: d = np.ndarray((2,6), dtype=a.dtype, buffer=a.data)
In [71]: d
Out[71]:
array([[ 0, 1, 2, 3, 4, 5],
[ 6, 7, 8, 9, 10, 11]])
In [72]: d.__array_interface__['data']
Out[72]: (50988640, False)
But normally we create new arrays with shared memory with slicing or np.array
(copy=False).
edited 3 hours ago
answered 3 hours ago
hpauljhpaulj
118k787160
118k787160
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1
@C.Nivs They don't even necessarily have different memory addresses (something which Python doesn't expose). Whatever memory was used for the first may have been reused for the second.
– chepner
4 hours ago
2
@C.Nivs Don't think of it in terms of memory addresses. How memory is managed is completely implementation dependent. All you know for sure is that two objects that overlap in time will not have the same id.
– chepner
4 hours ago
1
@Aran-Fey, that's okay a good question(though asked before) can sometimes be resurrected for a fruitful discussion
– amanb
4 hours ago
1
@C.Nivs no, ids do not belong to variables. They belong to objects. Many variables can reference the same object.
– juanpa.arrivillaga
3 hours ago
2
@juanpa.arrivillaga fair enough. Thanks for the explanation
– C.Nivs
2 hours ago