I'm looking for some clarification regarding mutability and class objects. From what I understand, variables in Python are about assigning a variable name to an object.
If that object is immutable then when we set two variables to the same object, it'll be two separate copies (e.g. a = b = 3 so a changing to 4 will not affect b because 3 is a number, an example of an immutable object).
However, if an object is mutable, then changing the value in one variable assignment will naturally change the value in the other (e.g. a = b = [] -> a.append(1) so now both a and b will refer to "[1]")
Working with classes, it seems even more fluid than I believed. I wrote a quick example below to show the differences. The first class is a typical Node class with a next pointer and a value. Setting two variables, "slow" and "fast", to the same instance of the Node object ("head"), and then changing the values of both "slow" and "fast" won't affect the other. That is, "slow", "fast", and "head" all refer to different objects (verified by checking their id() as well).
The second example class doesn't have a next pointer and only has a self.val attribute. This time changing one of the two variables, "p1" and "p2", both of which are set to the same instance, "start", will affect the other. This is despite that self.val in the "start" instance is an immutable number.
'''
The below will have two variable names (slow, fast) assigned to a head Node.
Changing one of them will NOT change the other reference as well.
'''
class Node:
def __init__(self, x, next=None):
self.x = x
self.next = next
def __str__(self):
return str(self.x)
n3 = Node(3)
n2 = Node(2, n3)
n1 = Node(1, n2)
head = n1
slow = fast = head
print(f"Printing before moving...{head}, {slow}, {fast}") # 1, 1, 1
while fast and fast.next:
fast = fast.next.next
slow = slow.next
print(f"Printing after moving...{head}, {slow}, {fast}") # 1, 2, 3
print(f"Checking the ids of each variable {id(head)}, {id(slow)}, {id(fast)}") # all different
'''
The below will have two variable names (p1, p2) assigned to a start Dummy.
Changing one of them will change the other reference as well.
'''
class Dummy:
def __init__(self, val):
self.val = val
def __str__(self):
return str(self.val)
start = Dummy(100)
p1 = p2 = start
print(f"Printing before changing {p1}, {p2}") # 100, 100
p1.val = 42
print(f"Printing after changing {p1}, {p2}") # 42, 42
This is a bit murky for me to understand what is actually going on under the hood and I'm seeking clarification so I can feel confident in setting multiple variable assignments to the same object expecting a true copy (without resorting to "import copy; copy.deepcopy(x);")
Thank you for your help
This isn't a matter of immutability vs mutability. This is a matter of mutating an object vs reassigning a reference.
If that object is immutable then when we set two variables to the same object, it'll be two separate copies
This isn't true. A copy won't be made. If you have:
a = 1
b = a
You have two references to the same object, not a copy of the object. This is fine though because integers are immutable. You can't mutate 1, so the fact that a and b are pointing to the same object won't hurt anything.
Python will never make implicit copies for you. If you want a copy, you need to copy it yourself explicitly (using copy.copy, or some other method like slicing on lists). If you write this:
a = b = some_obj
a and b will point to the same object, regardless of the type of some_obj and whether or not it's mutable.
So what's the difference between your examples?
In your first Node example, you never actually alter any Node objects. They may as well be immutable.
slow = fast = head
That initial assignment makes both slow an fast point to the same object: head. Right after that though, you do:
fast = fast.next.next
This reassigns the fast reference, but never actually mutates the object fast is looking at. All you've done is change what object the fast reference is looking at.
In your second example however, you directly mutate the object:
p1.val = 42
While this looks like reassignment, it isn't. This is actually:
p1.__setattr__("val", 42)
And __setattr__ alters the internal state of the object.
So, reassignment changes what object is being looked at. It will always take the form:
a = b # Maybe chained as well.
Contrast with these that look like reassignment, but are actually calls to mutating methods of the object:
l = [0]
l[0] = 5 # Actually l.__setitem__(0, 5)
d = Dummy()
d.val = 42 # Actually d.__setattr__("val", 42)
You overcomplicate things. The fundamental, simple rule is: each time you use = to assign an object to a variable, you make the variable name refer to that object, that's all. The object being mutable or not makes no difference.
With a = b = 3, you make the names a and b refer to the object 3. If you then make a = 4, you make the name a refer to the object 4, and the name b still refers to 3.
With a = b = [], you've created two names a and b that refer to the same list object. When doing a.append(1), you append 1 to this list. You haven't assigned anything to a or b in the process (you didn't write any a = ... or b = ...). So, whether you access the list through the name a or b, it's still the same list that you manipulate. It can just be called by two different names.
The same happens in your example with classes: when you write fast = fast.next.next, you make the name fast refer to a new object.
When you do p1.val = 42, you don't make p1 refer to a new different instance, but you change the val attribute of this instance. p1 and p2are still two names for this unique instance, so using either name lets you refer to the same instance.
Mutable and Immutable Objects
When a program is run, data objects in the program are stored in the computer’s
memory for processing. While some of these objects can be modified at that memory
location, other data objects can’t be modified once they are stored in the memory. The
property of whether or not data objects can be modified in the same memory location
where they are stored is called mutability. We can check the mutability of an object by checking its memory location before and
after it is modified. If the memory location remains the same when the data object is
modified, it means it is mutable. To check the memory location of where a data object is stored, we use the function, id(). Consider the following example
a=[5, 10, 15]
id(a)
#1906292064
a[1]=20
id(a)
#1906292064
#Assigning values to the list a. The ID of the memory location where a is stored.
#Replacing the second item in the list,10 with a new item, 20.
#print(a) Using the print() function to verify the new value of a.# Using the function #id() to get the memory location of a.
#The ID of the memory location where a is stored.
the memory location has not changed as the ID remains (1906292064)
remains the same before and after the variable is modified. This indicates that the list
is mutable, i.e., it can be modified at the same memory location where it is stored
How can I pass an integer by reference in Python?
I want to modify the value of a variable that I am passing to the function. I have read that everything in Python is pass by value, but there has to be an easy trick. For example, in Java you could pass the reference types of Integer, Long, etc.
How can I pass an integer into a function by reference?
What are the best practices?
It doesn't quite work that way in Python. Python passes references to objects. Inside your function you have an object -- You're free to mutate that object (if possible). However, integers are immutable. One workaround is to pass the integer in a container which can be mutated:
def change(x):
x[0] = 3
x = [1]
change(x)
print x
This is ugly/clumsy at best, but you're not going to do any better in Python. The reason is because in Python, assignment (=) takes whatever object is the result of the right hand side and binds it to whatever is on the left hand side *(or passes it to the appropriate function).
Understanding this, we can see why there is no way to change the value of an immutable object inside a function -- you can't change any of its attributes because it's immutable, and you can't just assign the "variable" a new value because then you're actually creating a new object (which is distinct from the old one) and giving it the name that the old object had in the local namespace.
Usually the workaround is to simply return the object that you want:
def multiply_by_2(x):
return 2*x
x = 1
x = multiply_by_2(x)
*In the first example case above, 3 actually gets passed to x.__setitem__.
Most cases where you would need to pass by reference are where you need to return more than one value back to the caller. A "best practice" is to use multiple return values, which is much easier to do in Python than in languages like Java.
Here's a simple example:
def RectToPolar(x, y):
r = (x ** 2 + y ** 2) ** 0.5
theta = math.atan2(y, x)
return r, theta # return 2 things at once
r, theta = RectToPolar(3, 4) # assign 2 things at once
Not exactly passing a value directly, but using it as if it was passed.
x = 7
def my_method():
nonlocal x
x += 1
my_method()
print(x) # 8
Caveats:
nonlocal was introduced in python 3
If the enclosing scope is the global one, use global instead of nonlocal.
Maybe it's not pythonic way, but you can do this
import ctypes
def incr(a):
a += 1
x = ctypes.c_int(1) # create c-var
incr(ctypes.ctypes.byref(x)) # passing by ref
Really, the best practice is to step back and ask whether you really need to do this. Why do you want to modify the value of a variable that you're passing in to the function?
If you need to do it for a quick hack, the quickest way is to pass a list holding the integer, and stick a [0] around every use of it, as mgilson's answer demonstrates.
If you need to do it for something more significant, write a class that has an int as an attribute, so you can just set it. Of course this forces you to come up with a good name for the class, and for the attribute—if you can't think of anything, go back and read the sentence again a few times, and then use the list.
More generally, if you're trying to port some Java idiom directly to Python, you're doing it wrong. Even when there is something directly corresponding (as with static/#staticmethod), you still don't want to use it in most Python programs just because you'd use it in Java.
Maybe slightly more self-documenting than the list-of-length-1 trick is the old empty type trick:
def inc_i(v):
v.i += 1
x = type('', (), {})()
x.i = 7
inc_i(x)
print(x.i)
A numpy single-element array is mutable and yet for most purposes, it can be evaluated as if it was a numerical python variable. Therefore, it's a more convenient by-reference number container than a single-element list.
import numpy as np
def triple_var_by_ref(x):
x[0]=x[0]*3
a=np.array([2])
triple_var_by_ref(a)
print(a+1)
output:
7
The correct answer, is to use a class and put the value inside the class, this lets you pass by reference exactly as you desire.
class Thing:
def __init__(self,a):
self.a = a
def dosomething(ref)
ref.a += 1
t = Thing(3)
dosomething(t)
print("T is now",t.a)
In Python, every value is a reference (a pointer to an object), just like non-primitives in Java. Also, like Java, Python only has pass by value. So, semantically, they are pretty much the same.
Since you mention Java in your question, I would like to see how you achieve what you want in Java. If you can show it in Java, I can show you how to do it exactly equivalently in Python.
class PassByReference:
def Change(self, var):
self.a = var
print(self.a)
s=PassByReference()
s.Change(5)
class Obj:
def __init__(self,a):
self.value = a
def sum(self, a):
self.value += a
a = Obj(1)
b = a
a.sum(1)
print(a.value, b.value)// 2 2
In Python, everything is passed by value, but if you want to modify some state, you can change the value of an integer inside a list or object that's passed to a method.
integers are immutable in python and once they are created we cannot change their value by using assignment operator to a variable we are making it to point to some other address not the previous address.
In python a function can return multiple values we can make use of it:
def swap(a,b):
return b,a
a,b=22,55
a,b=swap(a,b)
print(a,b)
To change the reference a variable is pointing to we can wrap immutable data types(int, long, float, complex, str, bytes, truple, frozenset) inside of mutable data types (bytearray, list, set, dict).
#var is an instance of dictionary type
def change(var,key,new_value):
var[key]=new_value
var =dict()
var['a']=33
change(var,'a',2625)
print(var['a'])
I already read How to get a function name as a string?.
How can I do the same for a variable? As opposed to functions, Python variables do not have the __name__ attribute.
In other words, if I have a variable such as:
foo = dict()
foo['bar'] = 2
I am looking for a function/attribute, e.g. retrieve_name() in order to create a DataFrame in Pandas from this list, where the column names are given by the names of the actual dictionaries:
# List of dictionaries for my DataFrame
list_of_dicts = [n_jobs, users, queues, priorities]
columns = [retrieve_name(d) for d in list_of_dicts]
With Python 3.8 one can simply use f-string debugging feature:
>>> foo = dict()
>>> f'{foo=}'.split('=')[0]
'foo'
One drawback of this method is that in order to get 'foo' printed you have to add f'{foo=}' yourself. In other words, you already have to know the name of the variable. In other words, the above code snippet is exactly the same as just
>>> 'foo'
Even if variable values don't point back to the name, you have access to the list of every assigned variable and its value, so I'm astounded that only one person suggested looping through there to look for your var name.
Someone mentioned on that answer that you might have to walk the stack and check everyone's locals and globals to find foo, but if foo is assigned in the scope where you're calling this retrieve_name function, you can use inspect's current frame to get you all of those local variables.
My explanation might be a little bit too wordy (maybe I should've used a "foo" less words), but here's how it would look in code (Note that if there is more than one variable assigned to the same value, you will get both of those variable names):
import inspect
x, y, z = 1, 2, 3
def retrieve_name(var):
callers_local_vars = inspect.currentframe().f_back.f_locals.items()
return [var_name for var_name, var_val in callers_local_vars if var_val is var]
print(retrieve_name(y))
If you're calling this function from another function, something like:
def foo(bar):
return retrieve_name(bar)
foo(baz)
And you want the baz instead of bar, you'll just need to go back a scope further. This can be done by adding an extra .f_back in the caller_local_vars initialization.
See an example here: ideone
The only objects in Python that have canonical names are modules, functions, and classes, and of course there is no guarantee that this canonical name has any meaning in any namespace after the function or class has been defined or the module imported. These names can also be modified after the objects are created so they may not always be particularly trustworthy.
What you want to do is not possible without recursively walking the tree of named objects; a name is a one-way reference to an object. A common or garden-variety Python object contains no references to its names. Imagine if every integer, every dict, every list, every Boolean needed to maintain a list of strings that represented names that referred to it! It would be an implementation nightmare, with little benefit to the programmer.
TL;DR
Use the Wrapper helper from python-varname:
from varname.helpers import Wrapper
foo = Wrapper(dict())
# foo.name == 'foo'
# foo.value == {}
foo.value['bar'] = 2
For list comprehension part, you can do:
n_jobs = Wrapper(<original_value>)
users = Wrapper(<original_value>)
queues = Wrapper(<original_value>)
priorities = Wrapper(<original_value>)
list_of_dicts = [n_jobs, users, queues, priorities]
columns = [d.name for d in list_of_dicts]
# ['n_jobs', 'users', 'queues', 'priorities']
# REMEMBER that you have to access the <original_value> by d.value
I am the author of the python-varname package. Please let me know if you have any questions or you can submit issues on Github.
The long answer
Is it even possible?
Yes and No.
We are retrieving the variable names at runtime, so we need a function to be called to enable us to access the previous frames to retrieve the variable names. That's why we need a Wrapper there. In that function, at runtime, we are parsing the source code/AST nodes in the previous frames to get the exact variable name.
However, the source code/AST nodes in the previous frames are not always available, or they could be modified by other environments (e.g: pytest's assert statement). One simple example is that the codes run via exec(). Even though we are still able to retrieve some information from the bytecode, it needs too much effort and it is also error-prone.
How to do it?
First of all, we need to identify which frame the variable is given. It's not always simply the direct previous frame. For example, we may have another wrapper for the function:
from varname import varname
def func():
return varname()
def wrapped():
return func()
x = wrapped()
In the above example, we have to skip the frame inside wrapped to get to the right frame x = wrapped() so that we are able to locate x. The arguments frame and ignore of varname allow us to skip some of these intermediate frames. See more details in the README file and the API docs of the package.
Then we need to parse the AST node to locate where the variable is assigned value (function call) to. It's not always just a simple assignment. Sometimes there could be complex AST nodes, for example, x = [wrapped()]. We need to identify the correct assignment by traversing the AST tree.
How reliable is it?
Once we identify the assignment node, it is reliable.
varname is all depending on executing package to look for the node. The node executing detects is ensured to be the correct one (see also this).
It partially works with environments where other AST magics apply, including pytest, ipython, macropy, birdseye, reticulate with R, etc. Neither executing nor varname is 100% working with those environments.
Do we need a package to do it?
Well, yes and no, again.
If your scenario is simple, the code provided by #juan Isaza or #scohe001 probably is enough for you to work with the case where a variable is defined at the direct previous frame and the AST node is a simple assignment. You just need to go one frame back and retrieve the information there.
However, if the scenario becomes complicated, or we need to adopt different application scenarios, you probably need a package like python-varname, to handle them. These scenarios may include to:
present more friendly messages when the source code is not available or AST nodes are not accessible
skip intermediate frames (allows the function to be wrapped or called in other intermediate frames)
automatically ignores calls from built-in functions or libraries. For example: x = str(func())
retrieve multiple variable names on the left-hand side of the assignment
etc.
How about the f-string?
Like the answer provided by #Aivar Paalberg. It's definitely fast and reliable. However, it's not at runtime, meaning that you have to know it's foo before you print the name out. But with varname, you don't have to know that variable is coming:
from varname import varname
def func():
return varname()
# In external uses
x = func() # 'x'
y = func() # 'y'
Finally
python-varname is not only able to detect the variable name from an assignment, but also:
Retrieve variable names directly, using nameof
Detect next immediate attribute name, using will
Fetch argument names/sources passed to a function using argname
Read more from its documentation.
However, the final word I want to say is that, try to avoid using it whenever you can.
Because you can't make sure that the client code will run in an environment where the source node is available or AST node is accessible. And of course, it costs resources to parse the source code, identify the environment, retrieve the AST nodes and evaluate them when needed.
On python3, this function will get the outer most name in the stack:
import inspect
def retrieve_name(var):
"""
Gets the name of var. Does it from the out most frame inner-wards.
:param var: variable to get name from.
:return: string
"""
for fi in reversed(inspect.stack()):
names = [var_name for var_name, var_val in fi.frame.f_locals.items() if var_val is var]
if len(names) > 0:
return names[0]
It is useful anywhere on the code. Traverses the reversed stack looking for the first match.
I don't believe this is possible. Consider the following example:
>>> a = []
>>> b = a
>>> id(a)
140031712435664
>>> id(b)
140031712435664
The a and b point to the same object, but the object can't know what variables point to it.
def name(**variables):
return [x for x in variables]
It's used like this:
name(variable=variable)
>> my_var = 5
>> my_var_name = [ k for k,v in locals().items() if v == my_var][0]
>> my_var_name
'my_var'
In case you get an error if myvar points to another variable, try this (suggested by #mherzog)-
>> my_var = 5
>> my_var_name = [ k for k,v in locals().items() if v is my_var][0]
>> my_var_name
'my_var'
locals() - Return a dictionary containing the current scope's local variables.
by iterating through this dictionary we can check the key which has a value equal to the defined variable, just extracting the key will give us the text of variable in string format.
from (after a bit changes)
https://www.tutorialspoint.com/How-to-get-a-variable-name-as-a-string-in-Python
I wrote the package sorcery to do this kind of magic robustly. You can write:
from sorcery import dict_of
columns = dict_of(n_jobs, users, queues, priorities)
and pass that to the dataframe constructor. It's equivalent to:
columns = dict(n_jobs=n_jobs, users=users, queues=queues, priorities=priorities)
Here's one approach. I wouldn't recommend this for anything important, because it'll be quite brittle. But it can be done.
Create a function that uses the inspect module to find the source code that called it. Then you can parse the source code to identify the variable names that you want to retrieve. For example, here's a function called autodict that takes a list of variables and returns a dictionary mapping variable names to their values. E.g.:
x = 'foo'
y = 'bar'
d = autodict(x, y)
print d
Would give:
{'x': 'foo', 'y': 'bar'}
Inspecting the source code itself is better than searching through the locals() or globals() because the latter approach doesn't tell you which of the variables are the ones you want.
At any rate, here's the code:
def autodict(*args):
get_rid_of = ['autodict(', ',', ')', '\n']
calling_code = inspect.getouterframes(inspect.currentframe())[1][4][0]
calling_code = calling_code[calling_code.index('autodict'):]
for garbage in get_rid_of:
calling_code = calling_code.replace(garbage, '')
var_names, var_values = calling_code.split(), args
dyn_dict = {var_name: var_value for var_name, var_value in
zip(var_names, var_values)}
return dyn_dict
The action happens in the line with inspect.getouterframes, which returns the string within the code that called autodict.
The obvious downside to this sort of magic is that it makes assumptions about how the source code is structured. And of course, it won't work at all if it's run inside the interpreter.
This function will print variable name with its value:
import inspect
def print_this(var):
callers_local_vars = inspect.currentframe().f_back.f_locals.items()
print(str([k for k, v in callers_local_vars if v is var][0])+': '+str(var))
***Input & Function call:***
my_var = 10
print_this(my_var)
***Output**:*
my_var: 10
>>> locals()['foo']
{}
>>> globals()['foo']
{}
If you wanted to write your own function, it could be done such that you could check for a variable defined in locals then check globals. If nothing is found you could compare on id() to see if the variable points to the same location in memory.
If your variable is in a class, you could use className.dict.keys() or vars(self) to see if your variable has been defined.
I have a method, and while not the most efficient...it works! (and it doesn't involve any fancy modules).
Basically it compares your Variable's ID to globals() Variables' IDs, then returns the match's name.
def getVariableName(variable, globalVariables=globals().copy()):
""" Get Variable Name as String by comparing its ID to globals() Variables' IDs
args:
variable(var): Variable to find name for (Obviously this variable has to exist)
kwargs:
globalVariables(dict): Copy of the globals() dict (Adding to Kwargs allows this function to work properly when imported from another .py)
"""
for globalVariable in globalVariables:
if id(variable) == id(globalVariables[globalVariable]): # If our Variable's ID matches this Global Variable's ID...
return globalVariable # Return its name from the Globals() dict
In Python, the def and class keywords will bind a specific name to the object they define (function or class). Similarly, modules are given a name by virtue of being called something specific in the filesystem. In all three cases, there's an obvious way to assign a "canonical" name to the object in question.
However, for other kinds of objects, such a canonical name may simply not exist. For example, consider the elements of a list. The elements in the list are not individually named, and it is entirely possible that the only way to refer to them in a program is by using list indices on the containing list. If such a list of objects was passed into your function, you could not possibly assign meaningful identifiers to the values.
Python doesn't save the name on the left hand side of an assignment into the assigned object because:
It would require figuring out which name was "canonical" among multiple conflicting objects,
It would make no sense for objects which are never assigned to an explicit variable name,
It would be extremely inefficient,
Literally no other language in existence does that.
So, for example, functions defined using lambda will always have the "name" <lambda>, rather than a specific function name.
The best approach would be simply to ask the caller to pass in an (optional) list of names. If typing the '...','...' is too cumbersome, you could accept e.g. a single string containing a comma-separated list of names (like namedtuple does).
I think it's so difficult to do this in Python because of the simple fact that you never will not know the name of the variable you're using. So, in his example, you could do:
Instead of:
list_of_dicts = [n_jobs, users, queues, priorities]
dict_of_dicts = {"n_jobs" : n_jobs, "users" : users, "queues" : queues, "priorities" : priorities}
Many of the answers return just one variable name. But that won't work well if more than one variable have the same value. Here's a variation of Amr Sharaki's answer which returns multiple results if more variables have the same value.
def getVariableNames(variable):
results = []
globalVariables=globals().copy()
for globalVariable in globalVariables:
if id(variable) == id(globalVariables[globalVariable]):
results.append(globalVariable)
return results
a = 1
b = 1
getVariableNames(a)
# ['a', 'b']
just another way to do this based on the content of input variable:
(it returns the name of the first variable that matches to the input variable, otherwise None. One can modify it to get all variable names which are having the same content as input variable)
def retrieve_name(x, Vars=vars()):
for k in Vars:
if isinstance(x, type(Vars[k])):
if x is Vars[k]:
return k
return None
If the goal is to help you keep track of your variables, you can write a simple function that labels the variable and returns its value and type. For example, suppose i_f=3.01 and you round it to an integer called i_n to use in a code, and then need a string i_s that will go into a report.
def whatis(string, x):
print(string+' value=',repr(x),type(x))
return string+' value='+repr(x)+repr(type(x))
i_f=3.01
i_n=int(i_f)
i_s=str(i_n)
i_l=[i_f, i_n, i_s]
i_u=(i_f, i_n, i_s)
## make report that identifies all types
report='\n'+20*'#'+'\nThis is the report:\n'
report+= whatis('i_f ',i_f)+'\n'
report+=whatis('i_n ',i_n)+'\n'
report+=whatis('i_s ',i_s)+'\n'
report+=whatis('i_l ',i_l)+'\n'
report+=whatis('i_u ',i_u)+'\n'
print(report)
This prints to the window at each call for debugging purposes and also yields a string for the written report. The only downside is that you have to type the variable twice each time you call the function.
I am a Python newbie and found this very useful way to log my efforts as I program and try to cope with all the objects in Python. One flaw is that whatis() fails if it calls a function described outside the procedure where it is used. For example, int(i_f) was a valid function call only because the int function is known to Python. You could call whatis() using int(i_f**2), but if for some strange reason you choose to define a function called int_squared it must be declared inside the procedure where whatis() is used.
Maybe this could be useful:
def Retriever(bar):
return (list(globals().keys()))[list(map(lambda x: id(x), list(globals().values()))).index(id(bar))]
The function goes through the list of IDs of values from the global scope (the namespace could be edited), finds the index of the wanted/required var or function based on its ID, and then returns the name from the list of global names based on the acquired index.
Whenever I have to do it, mostly while communicating json schema and constants with the frontend I define a class as follows
class Param:
def __init__(self, name, value):
self.name = name
self.value = value
Then define the variable with name and value.
frame_folder_count = Param({'name':'frame_folder_count', 'value':10})
Now you can access the name and value using the object.
>>> frame_folder_count.name
'frame_folder_count'
>>> def varname(v, scope=None):
d = globals() if not scope else vars(scope); return [k for k in d if d[k] == v]
...
>>> d1 = {'a': 'ape'}; d2 = {'b': 'bear'}; d3 = {'c': 'cat'}
>>> ld = [d1, d2, d3]
>>> [varname(d) for d in ld]
[['d1'], ['d2'], ['d3']]
>>> d5 = d3
>>> [varname(d) for d in ld]
[['d1'], ['d2'], ['d3', 'd5']]
>>> def varname(v, scope=None):
d = globals() if not scope else vars(scope); return [k for k in d if d[k] is v]
...
>>> [varname(d) for d in ld]
[['d1'], ['d2'], ['d3', 'd5']]
As you see and is noted here, there can be multiple variables with the same value or even address, so using a wrapper to keep the names with the data is best.
Following method will not return the name of variable but using this method you can create data frame easily if variable is available in global scope.
class CustomDict(dict):
def __add__(self, other):
return CustomDict({**self, **other})
class GlobalBase(type):
def __getattr__(cls, key):
return CustomDict({key: globals()[key]})
def __getitem__(cls, keys):
return CustomDict({key: globals()[key] for key in keys})
class G(metaclass=GlobalBase):
pass
x, y, z = 0, 1, 2
print('method 1:', G['x', 'y', 'z']) # Outcome: method 1: {'x': 0, 'y': 1, 'z': 2}
print('method 2:', G.x + G.y + G.z) # Outcome: method 2: {'x': 0, 'y': 1, 'z': 2}
A = [0, 1]
B = [1, 2]
pd.DataFrame(G.A + G.B) # It will return a data frame with A and B columns
Some of the previous cases would fail if there are two variables with the same value. So it is convenient to alert it:
Defining function:
# Variable to string of variable name
def var_name(variable,i=0):
results = []
for name in globals():
if eval(name) == variable:
results.append(name)
if len(results) > 1:
print('Warning:' )
print(' var_name() has found',len(results), 'possible outcomes.')
print(' Please choose the suitable parameter "i". Where "i" is the index')
print(' that matches your choice from the list below.')
print(' ',results) ; print('')
return results[i]
Use:
var_1 = 10
var_name(var_1) # Output will be "var_1"
If you have 2 variables with the same value like var_1 = 8 and var_2 = 8, then a warning will appear.
var_1 = 8
var_2 = 8
var_name(var_2) # Output will be "var_1" too but Warning will appear
You can get your variable as kwargs and return it as string:
var=2
def getVarName(**kwargs):
return list(kwargs.keys())[0]
print (getVarName(var = var))
Note: variable name must be equal to itself.
I try to get name from inspect locals, but it cann't process var likes a[1], b.val.
After it, I got a new idea --- get var name from the code, and I try it succ!
code like below:
#direct get from called function code
def retrieve_name_ex(var):
stacks = inspect.stack()
try:
func = stacks[0].function
code = stacks[1].code_context[0]
s = code.index(func)
s = code.index("(", s + len(func)) + 1
e = code.index(")", s)
return code[s:e].strip()
except:
return ""
You can try the following to retrieve the name of a function you defined (does not work for built-in functions though):
import re
def retrieve_name(func):
return re.match("<function\s+(\w+)\s+at.*", str(func)).group(1)
def foo(x):
return x**2
print(retrieve_name(foo))
# foo
When finding the name of a variable from its value,
you may have several variables equal to the same value,
for example var1 = 'hello' and var2 = 'hello'.
My solution:
def find_var_name(val):
dict_list = []
global_dict = dict(globals())
for k, v in global_dict.items():
dict_list.append([k, v])
return [item[0] for item in dict_list if item[1] == val]
var1 = 'hello'
var2 = 'hello'
find_var_name('hello')
Outputs
['var1', 'var2']
Compressed version of iDilip's answer:
import inspect
def varname(x):
return [k for k,v in inspect.currentframe().f_back.f_locals.items() if v is x][0]
hi = 123
print(varname(hi))
It's totally possible to get the name of an instance variable, so long as it is the property of a class.
I got this from Effective Python by Brett Slatkin. Hope it helps someone:
The class must implement the get, set, and set_name dunder methods, which are part of the "Descriptor Protocol"
This worked when I ran it:
class FieldThatKnowsItsName():
def __init__(self):
self.name = None
self._value= None
self.owner = None
def __set_name__(self, owner, name):
self.name = name
self.owner = owner
self.owner.fields[self.name] = self
def __get__(self, instance, instance_type):
return self
def __set__(self, instance, value):
self = value
class SuperTable:
fields = {}
field_1=FieldThatKnowsItsName()
field_2=FieldThatKnowsItsName()
table = SuperTable()
print(table.field_1.name)
print(table.field_2.name)
You can then add methods and or extend your datatype as you like.
As a bonus, the set_name(self, owner, name) dunder also passes the parent instance, so the Field class instance can register itself with the parent.
I got this from Effective Python by Brett Slatkin. It took a while to figure out how to implement.
How can I do the same for a variable? As opposed to functions, Python variables do not have the __name__ attribute.
The problem comes up because you are confused about terminology, semantics or both.
"variables" don't belong in the same category as "functions". A "variable" is not a thing that takes up space in memory while the code is running. It is just a name that exists in your source code - so that when you're writing the code, you can explain which thing you're talking about. Python uses names in the source code to refer to (i.e., give a name to) values. (In many languages, a variable is more like a name for a specific location in memory where the value will be stored. But Python's names actually name the thing in question.)
In Python, a function is a value. (In some languages, this is not the case; although there are bytes of memory used to represent the actual executable code, it isn't a discrete chunk of memory that your program logic gets to interact with directly.) In Python, every value is an object, meaning that you can assign names to it freely, pass it as an argument, return it from a function, etc. (In many languages, this is not the case.) Objects in Python have attributes, which are the things you access using the . syntax. Functions in Python have a __name__ attribute, which is assigned when the function is created. Specifically, when a def statement is executed (in most languages, creation of a function works quite differently), the name that appears after def is used as a value for the __name__ attribute, and also, independently, as a variable name that will get the function object assigned to it.
But most objects don't have an attribute like that.
In other words, if I have a variable such as:
That's the thing: you don't "have" the variable in the sense that you're thinking of. You have the object that is named by that variable. Anything else depends on the information incidentally being stored in some other object - such as the locals() of the enclosing function. But it would be better to store the information yourself. Instead of relying on a variable name to carry information for you, explicitly build the mapping between the string name you want to use for the object, and the object itself.
I understand that in python every thing, be it a number, string, dict or anything is an object. The variable name simply points to the object in the memory. Now according to this question,
>> a_dict = b_dict = c_dict = {}
This creates an empty dictionary and all the variables point to this dict object. So, changing any one would be reflected in the other variables.
>> a_dict["key"] = "value" #say
>> print a_dict
>> print b_dict
>> print c_dict
would give
{'key': value}
{'key': value}
{'key': value}
I had understood the concept of variables pointing to objects, so this seems fair enough.
Now even though it might be weird, since its such a basic statement, why does this happen ?
>> a = b = c = 1
>> a += 1
>> print a, b, c
2, 1, 1 # and not 2, 2, 2
First part of question: Why isn't the same concept applied here ?
Actually this doubt came up when I was trying to search for a solution for this:
>> a_dict = {}
>> some_var = "old_value"
>> a_dict['key'] = some_var
>> some_var = "new_value"
>> print a_dict
{'key': 'old_value'} # and not {'key': 'new_value'}
This seemed counter-intuitive since I had always assumed that I am telling the dictionary to hold the variable, and changing the object that the variable was pointing to would obviously reflect in the dictionary. But this seems to me as if the value is being copied, not referenced. This was the second thing I didn't understand.
Moving on, i tried something else
>> class some_class(object):
.. def __init__(self):
.. self.var = "old_value"
>> some_object = some_class()
>> a_dict = {}
>> a_dict['key'] = some_object
>> some_object.var = "new_value"
>> print a_dict['key'].var
"new_value" # even though this was what i wanted and expected, it conflicts with the output in the previous code
Now, over here, obviously it was being referenced. These contradictions has left me squacking at the unpredictable nature of python, even though I still love it, owing to the fact I don't know any other language well enough :p . Even though I'd always imagined that assignments lead to reference of the object, however these 2 cases are conflicting. So this is my final doubt . I understand that it might be one those python gotcha's . Please educate me.
You're wrestling with 2 different things here. The first is the idea of mutability vs. immutability. In python, str, int, tuple are some of the builtin immutable types compared to list, dict (and others) which are mutable types. immutable objects are ones which cannot be changed once they are created. So, in your example:
a = b = c = 1
After that line, all a, b and c refer to the same integer in memory (you can check by printing their respecitve id's and noting that they are the same). However, when you do:
a += 1
a now refers to a new (different) integer at a different memory location. Note that as a convention, += should return a new instance of something if the type is immutable. If the type is mutable, it should change the object in place and return it. I explain some of the more gory detail in this answer.
For the second part, you're trying to figure out how python's identifiers work. The way that I think of it is this... when you write a statement:
name = something
The right hand side is evaluated into some object (an integer, string, ...). That object is then given the name on the left hand side1. When a name is on the right hand side, the corresponding object is automatically "looked up" and substituted for the name in the calculation. Note that in this framework, assignment doesn't care if anything had that name before -- it simply overwrites the old value with the new one. Objects which were previously constructed using that name don't see any changes -- either. They've already been created -- keeping references to the objects themselves, not the names. So:
a = "foo" # `a` is the name of the string "foo"
b = {"bar": a} # evaluate the new dictionary and name it `b`. `a` is looked up and returns "foo" in this calculation
a = "bar" # give the object "bar" the name `a` irrespecitve of what previously had that name
1I'm glossing over a few details here for simplicity -- e.g. what happens when you assign to a list element: lst[idx] = some_value * some_other_value.
This is because += can be interpreted as a = a + 1, which rebinds the variable a to the value a + 1, that is, 2.
Similarly, some_var = "new_value" rebinds the variable and the object is not changed, so the key, value pair in the dictionary still points to that object.
In your last example, you are not rebinding, but mutating the object, so the value is changed in the dictionary.