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Track changes inside a class using a dict [duplicate]

I read an article about the getattr function, but I still can't understand what it's for.
The only thing I understand about getattr() is that getattr(li, "pop") is the same as calling li.pop.
When and how do I use this exactly? The book said something about using it to get a reference to a function whose name isn't known until runtime, but when and why would I use this?

Objects in Python can have attributes -- data attributes and functions to work with those (methods). Actually, every object has built-in attributes (try dir(None), dir(True), dir(...), dir(dir) in Python console).
For example you have an object person, that has several attributes: name, gender, etc.
You access these attributes (be it methods or data objects) usually writing: person.name, person.gender, person.the_method(), etc.
But what if you don't know the attribute's name at the time you write the program? For example you have attribute's name stored in a variable called attr_name.
if
attr_name = 'gender'
then, instead of writing
gender = person.gender
you can write
gender = getattr(person, attr_name)
Some practice:
Python 3.4.0 (default, Apr 11 2014, 13:05:11)
>>> class Person():
... name = 'Victor'
... def say(self, what):
... print(self.name, what)
...
>>> getattr(Person, 'name')
'Victor'
>>> attr_name = 'name'
>>> person = Person()
>>> getattr(person, attr_name)
'Victor'
>>> getattr(person, 'say')('Hello')
Victor Hello
getattr will raise AttributeError if attribute with the given name does not exist in the object:
>>> getattr(person, 'age')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'Person' object has no attribute 'age'
But you can pass a default value as the third argument, which will be returned if such attribute does not exist:
>>> getattr(person, 'age', 0)
0
You can use getattr along with dir to iterate over all attribute names and get their values:
>>> dir(1000)
['__abs__', '__add__', ..., '__trunc__', '__xor__', 'bit_length', 'conjugate', 'denominator', 'from_bytes', 'imag', 'numerator', 'real', 'to_bytes']
>>> obj = 1000
>>> for attr_name in dir(obj):
... attr_value = getattr(obj, attr_name)
... print(attr_name, attr_value, callable(attr_value))
...
__abs__ <method-wrapper '__abs__' of int object at 0x7f4e927c2f90> True
...
bit_length <built-in method bit_length of int object at 0x7f4e927c2f90> True
...
>>> getattr(1000, 'bit_length')()
10
A practical use for this would be to find all methods whose names start with test and call them.
Similar to getattr there is setattr which allows you to set an attribute of an object having its name:
>>> setattr(person, 'name', 'Andrew')
>>> person.name # accessing instance attribute
'Andrew'
>>> Person.name # accessing class attribute
'Victor'
>>>

getattr(object, 'x') is completely equivalent to object.x.
There are only two cases where getattr can be useful.
you can't write object.x, because you don't know in advance which attribute you want (it comes from a string). Very useful for meta-programming.
you want to provide a default value. object.y will raise an AttributeError if there's no y. But getattr(object, 'y', 5) will return 5.

For me, getattr is easiest to explain this way:
It allows you to call methods based on the contents of a string instead of typing the method name.
For example, you cannot do this:
obj = MyObject()
for x in ['foo', 'bar']:
obj.x()
because x is not of the type builtin, but str. However, you CAN do this:
obj = MyObject()
for x in ['foo', 'bar']:
getattr(obj, x)()
It allows you to dynamically connect with objects based on your input. I've found it useful when dealing with custom objects and modules.

A pretty common use case for getattr is mapping data to functions.
For instance, in a web framework like Django or Pylons, getattr makes it straightforward to map a web request's URL to the function that's going to handle it. If you look under the hood of Pylons's routing, for instance, you'll see that (by default, at least) it chops up a request's URL, like:
http://www.example.com/customers/list
into "customers" and "list". Then it searches for a controller class named CustomerController. Assuming it finds the class, it creates an instance of the class and then uses getattr to get its list method. It then calls that method, passing it the request as an argument.
Once you grasp this idea, it becomes really easy to extend the functionality of a web application: just add new methods to the controller classes, and then create links in your pages that use the appropriate URLs for those methods. All of this is made possible by getattr.

Here's a quick and dirty example of how a class could fire different versions of a save method depending on which operating system it's being executed on using getattr().
import os
class Log(object):
def __init__(self):
self.os = os.name
def __getattr__(self, name):
""" look for a 'save' attribute, or just
return whatever attribute was specified """
if name == 'save':
try:
# try to dynamically return a save
# method appropriate for the user's system
return getattr(self, self.os)
except:
# bail and try to return
# a default save method
return getattr(self, '_save')
else:
return getattr(self, name)
# each of these methods could have save logic specific to
# the system on which the script is executed
def posix(self): print 'saving on a posix machine'
def nt(self): print 'saving on an nt machine'
def os2(self): print 'saving on an os2 machine'
def ce(self): print 'saving on a ce machine'
def java(self): print 'saving on a java machine'
def riscos(self): print 'saving on a riscos machine'
def _save(self): print 'saving on an unknown operating system'
def which_os(self): print os.name
Now let's use this class in an example:
logger = Log()
# Now you can do one of two things:
save_func = logger.save
# and execute it, or pass it along
# somewhere else as 1st class:
save_func()
# or you can just call it directly:
logger.save()
# other attributes will hit the else
# statement and still work as expected
logger.which_os()

Other than all the amazing answers here, there is a way to use getattr to save copious lines of code and keeping it snug. This thought came following the dreadful representation of code that sometimes might be a necessity.
Scenario
Suppose your directory structure is as follows:
- superheroes.py
- properties.py
And, you have functions for getting information about Thor, Iron Man, Doctor Strange in superheroes.py. You very smartly write down the properties of all of them in properties.py in a compact dict and then access them.
properties.py
thor = {
'about': 'Asgardian god of thunder',
'weapon': 'Mjolnir',
'powers': ['invulnerability', 'keen senses', 'vortex breath'], # and many more
}
iron_man = {
'about': 'A wealthy American business magnate, playboy, and ingenious scientist',
'weapon': 'Armor',
'powers': ['intellect', 'armor suit', 'interface with wireless connections', 'money'],
}
doctor_strange = {
'about': ' primary protector of Earth against magical and mystical threats',
'weapon': 'Magic',
'powers': ['magic', 'intellect', 'martial arts'],
}
Now, let's say you want to return capabilities of each of them on demand in superheroes.py. So, there are functions like
from .properties import thor, iron_man, doctor_strange
def get_thor_weapon():
return thor['weapon']
def get_iron_man_bio():
return iron_man['about']
def get_thor_powers():
return thor['powers']
...and more functions returning different values based on the keys and superhero.
With the help of getattr, you could do something like:
from . import properties
def get_superhero_weapon(hero):
superhero = getattr(properties, hero)
return superhero['weapon']
def get_superhero_powers(hero):
superhero = getattr(properties, hero)
return superhero['powers']
You considerably reduced the number of lines of code, functions and repetition!
Oh and of course, if you have bad names like properties_of_thor for variables , they can be made and accessed by simply doing
def get_superhero_weapon(hero):
superhero = 'properties_of_{}'.format(hero)
all_properties = getattr(properties, superhero)
return all_properties['weapon']
NOTE: For this particular problem, there can be smarter ways to deal with the situation, but the idea is to give an insight about using getattr in right places to write cleaner code.

# getattr
class hithere():
def french(self):
print 'bonjour'
def english(self):
print 'hello'
def german(self):
print 'hallo'
def czech(self):
print 'ahoj'
def noidea(self):
print 'unknown language'
def dispatch(language):
try:
getattr(hithere(),language)()
except:
getattr(hithere(),'noidea')()
# note, do better error handling than this
dispatch('french')
dispatch('english')
dispatch('german')
dispatch('czech')
dispatch('spanish')

I sometimes use getattr(..) to lazily initialise attributes of secondary importance just before they are used in the code.
Compare the following:
class Graph(object):
def __init__(self):
self.n_calls_to_plot = 0
#...
#A lot of code here
#...
def plot(self):
self.n_calls_to_plot += 1
To this:
class Graph(object):
def plot(self):
self.n_calls_to_plot = 1 + getattr(self, "n_calls_to_plot", 0)
The advantage of the second way is that n_calls_to_plot only appears around the place in the code where it is used. This is good for readability, because (1) you can immediately see what value it starts with when reading how it's used, (2) it doesn't introduce a distraction into the __init__(..) method, which ideally should be about the conceptual state of the class, rather than some utility counter that is only used by one of the function's methods for technical reasons, such as optimisation, and has nothing to do with the meaning of the object.

Quite frequently when I am creating an XML file from data stored in a class I would frequently receive errors if the attribute didn't exist or was of type None. In this case, my issue wasn't not knowing what the attribute name was, as stated in your question, but rather was data ever stored in that attribute.
class Pet:
def __init__(self):
self.hair = None
self.color = None
If I used hasattr to do this, it would return True even if the attribute value was of type None and this would cause my ElementTree set command to fail.
hasattr(temp, 'hair')
>>True
If the attribute value was of type None, getattr would also return it which would cause my ElementTree set command to fail.
c = getattr(temp, 'hair')
type(c)
>> NoneType
I use the following method to take care of these cases now:
def getRealAttr(class_obj, class_attr, default = ''):
temp = getattr(class_obj, class_attr, default)
if temp is None:
temp = default
elif type(temp) != str:
temp = str(temp)
return temp
This is when and how I use getattr.

Another use of getattr() in implementing a switch statement in Python. It uses both reflection to get the case type.
import sys
class SwitchStatement(object):
""" a class to implement switch statement and a way to show how to use gettattr in Pythion"""
def case_1(self):
return "value for case_1"
def case_2(self):
return "value for case_2"
def case_3(self):
return "value for case_3"
def case_4(self):
return "value for case_4"
def case_value(self, case_type=1):
"""This is the main dispatchmethod, that uses gettattr"""
case_method = 'case_' + str(case_type)
# fetch the relevant method name
# Get the method from 'self'. Default to a lambda.
method = getattr(self, case_method, lambda: "Invalid case type")
# Call the method as we return it
return method()
def main(_):
switch = SwitchStatement()
print swtich.case_value(_)
if __name__ == '__main__':
main(int(sys.argv[1]))

setattr()
We use setattr to add an attribute to our class instance. We pass the class instance, the attribute name, and the value.
getattr()
With getattr we retrive these values
For example
Employee = type("Employee", (object,), dict())
employee = Employee()
# Set salary to 1000
setattr(employee,"salary", 1000 )
# Get the Salary
value = getattr(employee, "salary")
print(value)

I think this example is self explanatory. It runs the method of first parameter, whose name is given in the second parameter.
class MyClass:
def __init__(self):
pass
def MyMethod(self):
print("Method ran")
# Create an object
object = MyClass()
# Get all the methods of a class
method_list = [func for func in dir(MyClass) if callable(getattr(MyClass, func))]
# You can use any of the methods in method_list
# "MyMethod" is the one we want to use right now
# This is the same as running "object.MyMethod()"
getattr(object,'MyMethod')()

It is also clarifying from https://www.programiz.com/python-programming/methods/built-in/getattr
class Person:
age = 23
name = "Adam"
person = Person()
print('The age is:', getattr(person, "age"))
print('The age is:', person.age)
The age is: 23
The age is: 23
class Person:
age = 23
name = "Adam"
person = Person()
# when default value is provided
print('The sex is:', getattr(person, 'sex', 'Male'))
# when no default value is provided
print('The sex is:', getattr(person, 'sex'))
The sex is: Male
AttributeError: 'Person' object has no attribute 'sex'

I have tried in Python2.7.17
Some of the fellow folks already answered. However I have tried to call
getattr(obj, 'set_value') and this didn't execute the set_value method, So i changed to getattr(obj, 'set_value')() --> This helps to invoke the same.
Example Code:
Example 1:
class GETATT_VERIFY():
name = "siva"
def __init__(self):
print "Ok"
def set_value(self):
self.value = "myself"
print "oooh"
obj = GETATT_VERIFY()
print getattr(GETATT_VERIFY, 'name')
getattr(obj, 'set_value')()
print obj.value

Dynamically setting class attributes which point to descriptors

I have a function which gets data from a spreadsheet and processes the data into a dictionary
def get_employee_data(path=None):
# load excel file from 'path'
# return processed data as dictionary
return {
'bob':{'id':'12039123','age':90,'occupation':'manager'},
'john':{'id':'43434433','age':66,'occupation':'janitor'},
'hannah':{'id':'48484839','age':1,'occupation':'proffesional hamster'},
}
the init method iterates over the dictionary to set class attributes where the 'attribute name' is set based on the employee name - I want this to store my descriptor class, 'Employee'
class Employee:
def __set_name__(self, owner, name):
self.public_name = name
self.private_name = '_' + name
def __get__(self, obj, objType=None):
print('GET...')
return getattr(obj, self.private_name)
def __set__(self, obj, value):
print('SET...')
setattr(obj, self.private_name, value)
class ManagementSystem:
employee_data = get_employee_data()
william = Employee() # test works as expected
def __init__(self):
for employee in self.employee_data:
setattr(self, employee, Employee())
self.william = 'william' # test works as expected
With the same design, I aim to create another descriptor to handle access to employ details, where said descriptor will be assigned within the 'Employee' class
class EmployeeDetail:
# example implementation:
# ms.bob.id
pass
For testing I have created a class attribute for ManagementSystem 'william' which works as expected, but accessing an attribute 'bob' instantiated using setattr() behaves differently
>>> ms = ManagementSystem()
SET...
>>> ms.william
GET...
'william'
>>> ms.william = 'walliamson'
SET...
>>> ms.bob
<__main__.Employee object at 0x7f7f01a54e20>
>>> ms.bob = 'bob'
>>> ms.bob
'bob'
I have some understanding as to why this may be, but have not been able to find a solution to this problem.
Thank you for help as always :)

Ok, this makes me feel silly, but I have already found a solution as I've gone back to test some things based on this answer:
Setting a class attribute with a given name in python while defining the class
class ManagementSystem:
employee_data = get_employee_data()
for employee in employee_data:
vars()[employee] = Employee()
def __init__(self):
for employee in self.employee_data:
setattr(self, employee, employee)
I believe that I was previously attempting to set attributes outside what is actually a class attribute (I am creating instance attributes):
'william' is a class attribute so storing the descriptor instance will handle behavior - bob cannot follow the same behavior and is just an instance of the 'Employee' class, therefore when I attempt to set bob it is overwritten
Still, if there is a more efficient way to do this (or my understanding seems incomplete), I would still very much appreciate improvements upon my given answer/explaination,
Thank you :)

Polluting a class's environment

I have an object that holds lots of ids that are accessed statically. I want to split that up into another object which holds only those ids without the need of making modifications to the already existen code base. Take for example:
class _CarType(object):
DIESEL_CAR_ENGINE = 0
GAS_CAR_ENGINE = 1 # lots of these ids
class Car(object):
types = _CarType
I want to be able to access _CarType.DIESEL_CAR_ENGINE either by calling Car.types.DIESEL_CAR_ENGINE, either by Car.DIESEL_CAR_ENGINE for backwards compatibility with the existent code. It's clear that I cannot use __getattr__ so I am trying to find a way of making this work (maybe metaclasses ? )

Although this is not exactly what subclassing is made for, it accomplishes what you describe:
class _CarType(object):
DIESEL_CAR_ENGINE = 0
GAS_CAR_ENGINE = 1 # lots of these ids
class Car(_CarType):
types = _CarType

Something like:
class Car(object):
for attr, value in _CarType.__dict__.items():
it not attr.startswith('_'):
locals()[attr] = value
del attr, value
Or you can do it out of the class declaration:
class Car(object):
# snip
for attr, value in _CarType.__dict__.items():
it not attr.startswith('_'):
setattr(Car, attr, value)
del attr, value

This is how you could do this with a metaclass:
class _CarType(type):
DIESEL_CAR_ENGINE = 0
GAS_CAR_ENGINE = 1 # lots of these ids
def __init__(self,name,bases,dct):
for key in dir(_CarType):
if key.isupper():
setattr(self,key,getattr(_CarType,key))
class Car(object):
__metaclass__=_CarType
print(Car.DIESEL_CAR_ENGINE)
print(Car.GAS_CAR_ENGINE)

Your options fall into two substantial categories: you either copy the attributes from _CarType into Car, or set Car's metaclass to a custom one with a __getattr__ method that delegates to _CarType (so it isn't exactly true that you can't use __getattr__: you can, you just need to put in in Car's metaclass rather than in Car itself;-).
The second choice has implications that you might find peculiar (unless they are specifically desired): the attributes don't show up on dir(Car), and they can't be accessed on an instance of Car, only on Car itself. I.e.:
>>> class MetaGetattr(type):
... def __getattr__(cls, nm):
... return getattr(cls.types, nm)
...
>>> class Car:
... __metaclass__ = MetaGetattr
... types = _CarType
...
>>> Car.GAS_CAR_ENGINE
1
>>> Car().GAS_CAR_ENGINE
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'Car' object has no attribute 'GAS_CAR_ENGINE'
You could fix the "not from an instance" issue by also adding a __getattr__ to Car:
>>> class Car:
... __metaclass__ = MetaGetattr
... types = _CarType
... def __getattr__(self, nm):
... return getattr(self.types, nm)
...
to make both kinds of lookup work, as is probably expected:
>>> Car.GAS_CAR_ENGINE
1
>>> Car().GAS_CAR_ENGINE
1
However, defining two, essentially-equal __getattr__s, doesn't seem elegant.
So I suspect that the simpler approach, "copy all attributes", is preferable. In Python 2.6 or better, this is an obvious candidate for a class decorator:
def typesfrom(typesclass):
def decorate(cls):
cls.types = typesclass
for n in dir(typesclass):
if n[0] == '_': continue
v = getattr(typesclass, n)
setattr(cls, n, v)
return cls
return decorate
#typesfrom(_CarType)
class Car(object):
pass
In general, it's worth defining a decorator if you're using it more than once; if you only need to perform this task for one class ever, then expanding the code inline instead (after the class statement) may be better.
If you're stuck with Python 2.5 (or even 2.4), you can still define typesfrom the same way, you just apply it in a slightly less elegant matter, i.e., the Car definition becomes:
class Car(object):
pass
Car = typesfrom(_CarType)(Car)
Do remember decorator syntax (introduced in 2.2 for functions, in 2.6 for classes) is just a handy way to wrap these important and frequently recurring semantics.

class _CarType(object):
DIESEL_CAR_ENGINE = 0
GAS_CAR_ENGINE = 1 # lots of these ids
class Car:
types = _CarType
def __getattr__(self, name):
return getattr(self.types, name)
If an attribute of an object is not found, and it defines that magic method __getattr__, that gets called to try to find it.
Only works on a Car instance, not on the class.

Getting an instance name inside class __init__() [duplicate]

This question already has answers here:
Getting the name of a variable as a string
(32 answers)
Closed 3 years ago.
While building a new class object in python, I want to be able to create a default value based on the instance name of the class without passing in an extra argument. How can I accomplish this? Here's the basic pseudo-code I'm trying for:
class SomeObject():
defined_name = u""
def __init__(self, def_name=None):
if def_name == None:
def_name = u"%s" % (<INSTANCE NAME>)
self.defined_name = def_name
ThisObject = SomeObject()
print ThisObject.defined_name # Should print "ThisObject"

Well, there is almost a way to do it:
#!/usr/bin/env python
import traceback
class SomeObject():
def __init__(self, def_name=None):
if def_name == None:
(filename,line_number,function_name,text)=traceback.extract_stack()[-2]
def_name = text[:text.find('=')].strip()
self.defined_name = def_name
ThisObject = SomeObject()
print ThisObject.defined_name
# ThisObject
The traceback module allows you to peek at the code used to call SomeObject().
With a little string wrangling, text[:text.find('=')].strip() you can
guess what the def_name should be.
However, this hack is brittle. For example, this doesn't work so well:
ThisObject,ThatObject = SomeObject(),SomeObject()
print ThisObject.defined_name
# ThisObject,ThatObject
print ThatObject.defined_name
# ThisObject,ThatObject
So if you were to use this hack, you have to bear in mind that you must call SomeObject()
using simple python statement:
ThisObject = SomeObject()
By the way, as a further example of using traceback, if you define
def pv(var):
# stack is a list of 4-tuples: (filename, line number, function name, text)
# see http://docs.python.org/library/traceback.html#module-traceback
#
(filename,line_number,function_name,text)=traceback.extract_stack()[-2]
# ('x_traceback.py', 18, 'f', 'print_var(y)')
print('%s: %s'%(text[text.find('(')+1:-1],var))
then you can call
x=3.14
pv(x)
# x: 3.14
to print both the variable name and its value.

Instances don't have names. By the time the global name ThisObject gets bound to the instance created by evaluating the SomeObject constructor, the constructor has finished running.
If you want an object to have a name, just pass the name along in the constructor.
def __init__(self, name):
self.name = name

You can create a method inside your class that check all variables in the current frame and use hash() to look for the self variable.
The solution proposed here will return all the variables pointing to the instance object.
In the class below, isinstance() is used to avoid problems when applying hash(), since some objects like a numpy.array or a list, for example, are unhashable.
import inspect
class A(object):
def get_my_name(self):
ans = []
frame = inspect.currentframe().f_back
tmp = dict(frame.f_globals.items() + frame.f_locals.items())
for k, var in tmp.items():
if isinstance(var, self.__class__):
if hash(self) == hash(var):
ans.append(k)
return ans
The following test has been done:
def test():
a = A()
b = a
c = b
print c.get_my_name()
The result is:
test()
#['a', 'c', 'b']

This cannot work, just imagine this: a = b = TheMagicObjet(). Names have no effect on Values, they just point to them.

One horrible, horrible way to accomplish this is to reverse the responsibilities:
class SomeObject():
def __init__(self, def_name):
self.defined_name = def_name
globals()[def_name] = self
SomeObject("ThisObject")
print ThisObject.defined_name
If you wanted to support something other than global scope, you'd have to do something even more awful.

In Python, all data is stored in objects. Additionally, a name can be bound with an object, after which that name can be used to look up that object.
It makes no difference to the object what names, if any, it might be bound to. It might be bound to dozens of different names, or none. Also, Python does not have any "back links" that point from an object to a name.
Consider this example:
foo = 1
bar = foo
baz = foo
Now, suppose you have the integer object with value 1, and you want to work backwards and find its name. What would you print? Three different names have that object bound to them, and all are equally valid.
print(bar is foo) # prints True
print(baz is foo) # prints True
In Python, a name is a way to access an object, so there is no way to work with names directly. You could search through various name spaces until you find a name that is bound with the object of interest, but I don't recommend this.
How do I get the string representation of a variable in python?
There is a famous presentation called "Code Like a Pythonista" that summarizes this situation as "Other languages have 'variables'" and "Python has 'names'"
http://python.net/~goodger/projects/pycon/2007/idiomatic/handout.html#other-languages-have-variables

If you want an unique instance name for a class, try __repr__() or id(self)
class Some:
def __init__(self):
print(self.__repr__()) # = hex(id(self))
print(id(self))
It will print the memory address of the instance, which is unique.

Inspired by the answers of unutbu and Saullo Castro, I have created a more sophisticated class that can even be subclassed. It solves what was asked for in the question.
"create a default value based on the instance name of the class
without passing in an extra argument."
Here's what it does, when an instance of this class or a subclass is created:
Go up in the frame stack until the first frame which does not belong to a method of the current instance.
Inspect this frame to get the attributes self.creation_(name/file/module/function/line/text).
Perform an an additional check whether an object with name self.creation_name was actually defined in the frame's locals() namespace to make 100% sure the found creation_name is correct or raise an error otherwise.
The Code:
import traceback, threading, time
class InstanceCreationError(Exception):
pass
class RememberInstanceCreationInfo:
def __init__(self):
for frame, line in traceback.walk_stack(None):
varnames = frame.f_code.co_varnames
if varnames is ():
break
if frame.f_locals[varnames[0]] not in (self, self.__class__):
break
# if the frame is inside a method of this instance,
# the first argument usually contains either the instance or
# its class
# we want to find the first frame, where this is not the case
else:
raise InstanceCreationError("No suitable outer frame found.")
self._outer_frame = frame
self.creation_module = frame.f_globals["__name__"]
self.creation_file, self.creation_line, self.creation_function, \
self.creation_text = \
traceback.extract_stack(frame, 1)[0]
self.creation_name = self.creation_text.split("=")[0].strip()
super().__init__()
threading.Thread(target=self._check_existence_after_creation).start()
def _check_existence_after_creation(self):
while self._outer_frame.f_lineno == self.creation_line:
time.sleep(0.01)
# this is executed as soon as the line number changes
# now we can be sure the instance was actually created
error = InstanceCreationError(
"\nCreation name not found in creation frame.\ncreation_file: "
"%s \ncreation_line: %s \ncreation_text: %s\ncreation_name ("
"might be wrong): %s" % (
self.creation_file, self.creation_line, self.creation_text,
self.creation_name))
nameparts = self.creation_name.split(".")
try:
var = self._outer_frame.f_locals[nameparts[0]]
except KeyError:
raise error
finally:
del self._outer_frame
# make sure we have no permament inter frame reference
# which could hinder garbage collection
try:
for name in nameparts[1:]: var = getattr(var, name)
except AttributeError:
raise error
if var is not self: raise error
def __repr__(self):
return super().__repr__()[
:-1] + " with creation_name '%s'>" % self.creation_name
A simple example:
class MySubclass(RememberInstanceCreationInfo):
def __init__(self):
super().__init__()
def print_creation_info(self):
print(self.creation_name, self.creation_module, self.creation_function,
self.creation_line, self.creation_text, sep=", ")
instance = MySubclass()
instance.print_creation_info()
#out: instance, __main__, <module>, 68, instance = MySubclass()
If the creation name cannot be determined properly an error is raised:
variable, another_instance = 2, MySubclass()
# InstanceCreationError:
# Creation name not found in creation frame.
# creation_file: /.../myfile.py
# creation_line: 71
# creation_text: variable, another_instance = 2, MySubclass()
# creation_name (might be wrong): variable, another_instance

I think that names matters if they are the pointers to any object..
no matters if:
foo = 1
bar = foo
I know that foo points to 1 and bar points to the same value 1 into the same memory space.
but supose that I want to create a class with a function that adds a object to it.
Class Bag(object):
def __init__(self):
some code here...
def addItem(self,item):
self.__dict__[somewaytogetItemName] = item
So, when I instantiate the class bag like below:
newObj1 = Bag()
newObj2 = Bag()
newObj1.addItem(newObj2)I can do this to get an attribute of newObj1:
newObj1.newObj2

The best way is really to pass the name to the constructor as in the chosen answer. However, if you REALLY want to avoid asking the user to pass the name to the constructor, you can do the following hack:
If you are creating the instance with 'ThisObject = SomeObject()' from the command line, you can get the object name from the command string in command history:
import readline
import re
class SomeObject():
def __init__(self):
cmd = readline.get_history_item(readline.get_current_history_length())
self.name = re.split('=| ',cmd)[0]
If you are creating the instance using 'exec' command, you can handle this with:
if cmd[0:4] == 'exec': self.name = re.split('\'|=| ',cmd)[1] # if command performed using 'exec'
else: self.name = re.split('=| ',cmd)[0]

How do I get list of methods in a Python class?

I want to iterate through the methods in a class, or handle class or instance objects differently based on the methods present. How do I get a list of class methods?
Also see:
How can I list the methods in a
Python 2.5 module?
Looping over
a Python / IronPython Object
Methods
Finding the methods an
object has
How do I look inside
a Python object?
How Do I
Perform Introspection on an Object in
Python 2.x?
How to get a
complete list of object’s methods and
attributes?
Finding out which
functions are available from a class
instance in python?

An example (listing the methods of the optparse.OptionParser class):
>>> from optparse import OptionParser
>>> import inspect
#python2
>>> inspect.getmembers(OptionParser, predicate=inspect.ismethod)
[([('__init__', <unbound method OptionParser.__init__>),
...
('add_option', <unbound method OptionParser.add_option>),
('add_option_group', <unbound method OptionParser.add_option_group>),
('add_options', <unbound method OptionParser.add_options>),
('check_values', <unbound method OptionParser.check_values>),
('destroy', <unbound method OptionParser.destroy>),
('disable_interspersed_args',
<unbound method OptionParser.disable_interspersed_args>),
('enable_interspersed_args',
<unbound method OptionParser.enable_interspersed_args>),
('error', <unbound method OptionParser.error>),
('exit', <unbound method OptionParser.exit>),
('expand_prog_name', <unbound method OptionParser.expand_prog_name>),
...
]
# python3
>>> inspect.getmembers(OptionParser, predicate=inspect.isfunction)
...
Notice that getmembers returns a list of 2-tuples. The first item is the name of the member, the second item is the value.
You can also pass an instance to getmembers:
>>> parser = OptionParser()
>>> inspect.getmembers(parser, predicate=inspect.ismethod)
...

There is the dir(theobject) method to list all the fields and methods of your object (as a tuple) and the inspect module (as codeape write) to list the fields and methods with their doc (in """).
Because everything (even fields) might be called in Python, I'm not sure there is a built-in function to list only methods. You might want to try if the object you get through dir is callable or not.

Python 3.x answer without external libraries
method_list = [func for func in dir(Foo) if callable(getattr(Foo, func))]
dunder-excluded result:
method_list = [func for func in dir(Foo) if callable(getattr(Foo, func)) and not func.startswith("__")]

Say you want to know all methods associated with list class
Just Type The following
print (dir(list))
Above will give you all methods of list class

Try the property __dict__.

you can also import the FunctionType from types and test it with the class.__dict__:
from types import FunctionType
class Foo:
def bar(self): pass
def baz(self): pass
def methods(cls):
return [x for x, y in cls.__dict__.items() if type(y) == FunctionType]
methods(Foo) # ['bar', 'baz']

You can list all methods in a python class by using the following code
dir(className)
This will return a list of all the names of the methods in the class

Note that you need to consider whether you want methods from base classes which are inherited (but not overridden) included in the result. The dir() and inspect.getmembers() operations do include base class methods, but use of the __dict__ attribute does not.

If your method is a "regular" method and not a staticmethod, classmethod etc.
There is a little hack I came up with -
for k, v in your_class.__dict__.items():
if "function" in str(v):
print(k)
This can be extended to other type of methods by changing "function" in the if condition correspondingly.
Tested in Python 2.7 and Python 3.5.

Try
print(help(ClassName))
It prints out methods of the class

I just keep this there, because top rated answers are not clear.
This is simple test with not usual class based on Enum.
# -*- coding: utf-8 -*-
import sys, inspect
from enum import Enum
class my_enum(Enum):
"""Enum base class my_enum"""
M_ONE = -1
ZERO = 0
ONE = 1
TWO = 2
THREE = 3
def is_natural(self):
return (self.value > 0)
def is_negative(self):
return (self.value < 0)
def is_clean_name(name):
return not name.startswith('_') and not name.endswith('_')
def clean_names(lst):
return [ n for n in lst if is_clean_name(n) ]
def get_items(cls,lst):
try:
res = [ getattr(cls,n) for n in lst ]
except Exception as e:
res = (Exception, type(e), e)
pass
return res
print( sys.version )
dir_res = clean_names( dir(my_enum) )
inspect_res = clean_names( [ x[0] for x in inspect.getmembers(my_enum) ] )
dict_res = clean_names( my_enum.__dict__.keys() )
print( '## names ##' )
print( dir_res )
print( inspect_res )
print( dict_res )
print( '## items ##' )
print( get_items(my_enum,dir_res) )
print( get_items(my_enum,inspect_res) )
print( get_items(my_enum,dict_res) )
And this is output results.
3.7.7 (default, Mar 10 2020, 13:18:53)
[GCC 9.2.1 20200306]
## names ##
['M_ONE', 'ONE', 'THREE', 'TWO', 'ZERO']
['M_ONE', 'ONE', 'THREE', 'TWO', 'ZERO', 'name', 'value']
['is_natural', 'is_negative', 'M_ONE', 'ZERO', 'ONE', 'TWO', 'THREE']
## items ##
[<my_enum.M_ONE: -1>, <my_enum.ONE: 1>, <my_enum.THREE: 3>, <my_enum.TWO: 2>, <my_enum.ZERO: 0>]
(<class 'Exception'>, <class 'AttributeError'>, AttributeError('name'))
[<function my_enum.is_natural at 0xb78a1fa4>, <function my_enum.is_negative at 0xb78ae854>, <my_enum.M_ONE: -1>, <my_enum.ZERO: 0>, <my_enum.ONE: 1>, <my_enum.TWO: 2>, <my_enum.THREE: 3>]
So what we have:
dir provide not complete data
inspect.getmembers provide not complete data and provide internal keys that are not accessible with getattr()
__dict__.keys() provide complete and reliable result
Why are votes so erroneous? And where i'm wrong? And where wrong other people which answers have so low votes?

There's this approach:
[getattr(obj, m) for m in dir(obj) if not m.startswith('__')]
When dealing with a class instance, perhaps it'd be better to return a list with the method references instead of just names¹. If that's your goal, as well as
Using no import
Excluding private methods (e.g. __init__) from the list
It may be of use. You might also want to assure it's callable(getattr(obj, m)), since dir returns all attributes within obj, not just methods.
In a nutshell, for a class like
class Ghost:
def boo(self, who):
return f'Who you gonna call? {who}'
We could check instance retrieval with
>>> g = Ghost()
>>> methods = [getattr(g, m) for m in dir(g) if not m.startswith('__')]
>>> print(methods)
[<bound method Ghost.boo of <__main__.Ghost object at ...>>]
So you can call it right away:
>>> for method in methods:
... print(method('GHOSTBUSTERS'))
...
Who you gonna call? GHOSTBUSTERS
¹ An use case:
I used this for unit testing. Had a class where all methods performed variations of the same process - which led to lengthy tests, each only a tweak away from the others. DRY was a far away dream.
Thought I should have a single test for all methods, so I made the above iteration.
Although I realized I should instead refactor the code itself to be DRY-compliant anyway... this may still serve a random nitpicky soul in the future.

This also works:
In mymodule.py:
def foo(x):
return 'foo'
def bar():
return 'bar'
In another file:
import inspect
import mymodule
method_list = [ func[0] for func in inspect.getmembers(mymodule, predicate=inspect.isroutine) if callable(getattr(mymodule, func[0])) ]
Output:
['foo', 'bar']
From the Python docs:
inspect.isroutine(object)
Return true if the object is a user-defined or built-in function or method.

def find_defining_class(obj, meth_name):
for ty in type(obj).mro():
if meth_name in ty.__dict__:
return ty
So
print find_defining_class(car, 'speedometer')
Think Python page 210

You can use a function which I have created.
def method_finder(classname):
non_magic_class = []
class_methods = dir(classname)
for m in class_methods:
if m.startswith('__'):
continue
else:
non_magic_class.append(m)
return non_magic_class
method_finder(list)
Output:
['append',
'clear',
'copy',
'count',
'extend',
'index',
'insert',
'pop',
'remove',
'reverse',
'sort']

methods = [(func, getattr(o, func)) for func in dir(o) if callable(getattr(o, func))]
gives an identical list as
methods = inspect.getmembers(o, predicate=inspect.ismethod)
does.

I know this is an old post, but just wrote this function and will leave it here is case someone stumbles looking for an answer:
def classMethods(the_class,class_only=False,instance_only=False,exclude_internal=True):
def acceptMethod(tup):
#internal function that analyzes the tuples returned by getmembers tup[1] is the
#actual member object
is_method = inspect.ismethod(tup[1])
if is_method:
bound_to = tup[1].im_self
internal = tup[1].im_func.func_name[:2] == '__' and tup[1].im_func.func_name[-2:] == '__'
if internal and exclude_internal:
include = False
else:
include = (bound_to == the_class and not instance_only) or (bound_to == None and not class_only)
else:
include = False
return include
#uses filter to return results according to internal function and arguments
return filter(acceptMethod,inspect.getmembers(the_class))

use inspect.ismethod and dir and getattr
import inspect
class ClassWithMethods:
def method1(self):
print('method1')
def method2(self):
print('method2')
obj=ClassWithMethods()
method_names = [attr for attr in dir(obj) if inspect.ismethod(getattr(obj,attr))
print(method_names)
output:
[[('method1', <bound method ClassWithMethods.method1 of <__main__.ClassWithMethods object at 0x00000266779AF388>>), ('method2', <bound method ClassWithMethods.method2 of <__main__.ClassWithMethods object at 0x00000266779AF388>>)]]

None of the above worked for me.
I've encountered this problem while writing pytests.
The only work-around I found was to:
1- create another directory and place all my .py files there
2- create a separate directory for my pytests and then importing the classes I'm interested in
This allowed me to get up-to-dated methods within the class - you can change the method names and then use print(dir(class)) to confirm it.

For my use case, I needed to distinguish between class methods, static methods, properties, and instance methods. The inspect module confuses the issue a bit (particularly with class methods and instance methods), so I used vars based on a comment on this SO question. The basic gist is to use vars to get the __dict__ attribute of the class, then filter based on various isinstance checks. For instance methods, I check that it is callable and not a class method. One caveat: this approach of using vars (or __dict__ for that matter) won't work with __slots__. Using Python 3.6.9 (because it's what the Docker image I'm using as my interpreter has):
class MethodAnalyzer:
class_under_test = None
#classmethod
def get_static_methods(cls):
if cls.class_under_test:
return {
k for k, v in vars(cls.class_under_test).items()
if isinstance(v, staticmethod)
}
return {}
#classmethod
def get_class_methods(cls):
if cls.class_under_test:
return {
k for k, v in vars(cls.class_under_test).items()
if isinstance(v, classmethod)
}
return {}
#classmethod
def get_instance_methods(cls):
if cls.class_under_test:
return {
k for k, v in vars(cls.class_under_test).items()
if callable(v) and not isinstance(v, classmethod)
}
return {}
#classmethod
def get_properties(cls):
if cls.class_under_test:
return {
k for k, v in vars(cls.class_under_test).items()
if isinstance(v, property)
}
return {}
To see it in action, I created this little test class:
class Foo:
#staticmethod
def bar(baz):
print(baz)
#property
def bleep(self):
return 'bloop'
#classmethod
def bork(cls):
return cls.__name__
def flank(self):
return 'on your six'
then did:
MethodAnalyzer.class_under_test = Foo
print(MethodAnalyzer.get_instance_methods())
print(MethodAnalyzer.get_class_methods())
print(MethodAnalyzer.get_static_methods())
print(MethodAnalyzer.get_properties())
which output
{'flank'}
{'bork'}
{'bar'}
{'bleep'}
In this example I'm discarding the actual methods, but if you needed to keep them you could just use a dict comprehension instead of a set comprehension:
{
k, v for k, v in vars(cls.class_under_test).items()
if callable(v) and not isinstance(v, classmethod)
}

This is just an observation. "encode" seems to be a method for string objects
str_1 = 'a'
str_1.encode('utf-8')
>>> b'a'
However, if str1 is inspected for methods, an empty list is returned
inspect.getmember(str_1, predicate=inspect.ismethod)
>>> []
So, maybe I am wrong, but the issue seems to be not simple.

To produce a list of methods put the name of the method in a list without the usual parenthesis. Remove the name and attach the parenthesis and that calls the method.
def methodA():
print("# MethodA")
def methodB():
print("# methodB")
a = []
a.append(methodA)
a.append(methodB)
for item in a:
item()

Just like this
pprint.pprint([x for x in dir(list) if not x.startswith("_")])

class CPerson:
def __init__(self, age):
self._age = age
def run(self):
pass
#property
def age(self): return self._age
#staticmethod
def my_static_method(): print("Life is short, you need Python")
#classmethod
def say(cls, msg): return msg
test_class = CPerson
# print(dir(test_class)) # list all the fields and methods of your object
print([(name, t) for name, t in test_class.__dict__.items() if type(t).__name__ == 'function' and not name.startswith('__')])
print([(name, t) for name, t in test_class.__dict__.items() if type(t).__name__ != 'function' and not name.startswith('__')])
output
[('run', <function CPerson.run at 0x0000000002AD3268>)]
[('age', <property object at 0x0000000002368688>), ('my_static_method', <staticmethod object at 0x0000000002ACBD68>), ('say', <classmethod object at 0x0000000002ACF0B8>)]

If you want to list only methods of a python class
import numpy as np
print(np.random.__all__)