how to dynamically access class Instance Attribute python [duplicate] - python-3.x

How do I call a function, using a string with the function's name? For example:
import foo
func_name = "bar"
call(foo, func_name) # calls foo.bar()

Given a module foo with method bar:
import foo
bar = getattr(foo, 'bar')
result = bar()
getattr can similarly be used on class instance bound methods, module-level methods, class methods... the list goes on.

Using locals(), which returns a dictionary with the current local symbol table:
locals()["myfunction"]()
Using globals(), which returns a dictionary with the global symbol table:
globals()["myfunction"]()

Based on Patrick's solution, to get the module dynamically as well, import it using:
module = __import__('foo')
func = getattr(module, 'bar')
func()

Just a simple contribution. If the class that we need to instance is in the same file, we can use something like this:
# Get class from globals and create an instance
m = globals()['our_class']()
# Get the function (from the instance) that we need to call
func = getattr(m, 'function_name')
# Call it
func()
For example:
class A:
def __init__(self):
pass
def sampleFunc(self, arg):
print('you called sampleFunc({})'.format(arg))
m = globals()['A']()
func = getattr(m, 'sampleFunc')
func('sample arg')
# Sample, all on one line
getattr(globals()['A'](), 'sampleFunc')('sample arg')
And, if not a class:
def sampleFunc(arg):
print('you called sampleFunc({})'.format(arg))
globals()['sampleFunc']('sample arg')

Given a string, with a complete python path to a function, this is how I went about getting the result of said function:
import importlib
function_string = 'mypackage.mymodule.myfunc'
mod_name, func_name = function_string.rsplit('.',1)
mod = importlib.import_module(mod_name)
func = getattr(mod, func_name)
result = func()

The best answer according to the Python programming FAQ would be:
functions = {'myfoo': foo.bar}
mystring = 'myfoo'
if mystring in functions:
functions[mystring]()
The primary advantage of this technique is that the strings do not need to match the names of the functions. This is also the primary technique used to emulate a case construct

The answer (I hope) no one ever wanted
Eval like behavior
getattr(locals().get("foo") or globals().get("foo"), "bar")()
Why not add auto-importing
getattr(
locals().get("foo") or
globals().get("foo") or
__import__("foo"),
"bar")()
In case we have extra dictionaries we want to check
getattr(next((x for x in (f("foo") for f in
[locals().get, globals().get,
self.__dict__.get, __import__])
if x)),
"bar")()
We need to go deeper
getattr(next((x for x in (f("foo") for f in
([locals().get, globals().get, self.__dict__.get] +
[d.get for d in (list(dd.values()) for dd in
[locals(),globals(),self.__dict__]
if isinstance(dd,dict))
if isinstance(d,dict)] +
[__import__]))
if x)),
"bar")()

For what it's worth, if you needed to pass the function (or class) name and app name as a string, then you could do this:
myFnName = "MyFn"
myAppName = "MyApp"
app = sys.modules[myAppName]
fn = getattr(app,myFnName)

Try this. While this still uses eval, it only uses it to summon the function from the current context. Then, you have the real function to use as you wish.
The main benefit for me from this is that you will get any eval-related errors at the point of summoning the function. Then you will get only the function-related errors when you call.
def say_hello(name):
print 'Hello {}!'.format(name)
# get the function by name
method_name = 'say_hello'
method = eval(method_name)
# call it like a regular function later
args = ['friend']
kwargs = {}
method(*args, **kwargs)

As this question How to dynamically call methods within a class using method-name assignment to a variable [duplicate] marked as a duplicate as this one, I am posting a related answer here:
The scenario is, a method in a class want to call another method on the same class dynamically, I have added some details to original example which offers some wider scenario and clarity:
class MyClass:
def __init__(self, i):
self.i = i
def get(self):
func = getattr(MyClass, 'function{}'.format(self.i))
func(self, 12) # This one will work
# self.func(12) # But this does NOT work.
def function1(self, p1):
print('function1: {}'.format(p1))
# do other stuff
def function2(self, p1):
print('function2: {}'.format(p1))
# do other stuff
if __name__ == "__main__":
class1 = MyClass(1)
class1.get()
class2 = MyClass(2)
class2.get()
Output (Python 3.7.x)
function1: 12
function2: 12

none of what was suggested helped me. I did discover this though.
<object>.__getattribute__(<string name>)(<params>)
I am using python 2.66
Hope this helps

Although getattr() is elegant (and about 7x faster) method, you can get return value from the function (local, class method, module) with eval as elegant as x = eval('foo.bar')(). And when you implement some error handling then quite securely (the same principle can be used for getattr). Example with module import and class:
# import module, call module function, pass parameters and print retured value with eval():
import random
bar = 'random.randint'
randint = eval(bar)(0,100)
print(randint) # will print random int from <0;100)
# also class method returning (or not) value(s) can be used with eval:
class Say:
def say(something='nothing'):
return something
bar = 'Say.say'
print(eval(bar)('nice to meet you too')) # will print 'nice to meet you'
When module or class does not exist (typo or anything better) then NameError is raised. When function does not exist, then AttributeError is raised. This can be used to handle errors:
# try/except block can be used to catch both errors
try:
eval('Say.talk')() # raises AttributeError because function does not exist
eval('Says.say')() # raises NameError because the class does not exist
# or the same with getattr:
getattr(Say, 'talk')() # raises AttributeError
getattr(Says, 'say')() # raises NameError
except AttributeError:
# do domething or just...
print('Function does not exist')
except NameError:
# do domething or just...
print('Module does not exist')

In python3, you can use the __getattribute__ method. See following example with a list method name string:
func_name = 'reverse'
l = [1, 2, 3, 4]
print(l)
>> [1, 2, 3, 4]
l.__getattribute__(func_name)()
print(l)
>> [4, 3, 2, 1]

Nobody mentioned operator.attrgetter yet:
>>> from operator import attrgetter
>>> l = [1, 2, 3]
>>> attrgetter('reverse')(l)()
>>> l
[3, 2, 1]
>>>

getattr calls method by name from an object.
But this object should be parent of calling class.
The parent class can be got by super(self.__class__, self)
class Base:
def call_base(func):
"""This does not work"""
def new_func(self, *args, **kwargs):
name = func.__name__
getattr(super(self.__class__, self), name)(*args, **kwargs)
return new_func
def f(self, *args):
print(f"BASE method invoked.")
def g(self, *args):
print(f"BASE method invoked.")
class Inherit(Base):
#Base.call_base
def f(self, *args):
"""function body will be ignored by the decorator."""
pass
#Base.call_base
def g(self, *args):
"""function body will be ignored by the decorator."""
pass
Inherit().f() # The goal is to print "BASE method invoked."

i'm facing the similar problem before, which is to convert a string to a function. but i can't use eval() or ast.literal_eval(), because i don't want to execute this code immediately.
e.g. i have a string "foo.bar", and i want to assign it to x as a function name instead of a string, which means i can call the function by x() ON DEMAND.
here's my code:
str_to_convert = "foo.bar"
exec(f"x = {str_to_convert}")
x()
as for your question, you only need to add your module name foo and . before {} as follows:
str_to_convert = "bar"
exec(f"x = foo.{str_to_convert}")
x()
WARNING!!! either eval() or exec() is a dangerous method, you should confirm the safety.
WARNING!!! either eval() or exec() is a dangerous method, you should confirm the safety.
WARNING!!! either eval() or exec() is a dangerous method, you should confirm the safety.

You means get the pointer to an inner function from a module
import foo
method = foo.bar
executed = method(parameter)
This is not a better pythonic way indeed is possible for punctual cases

This is a simple answer, this will allow you to clear the screen for example. There are two examples below, with eval and exec, that will print 0 at the top after cleaning (if you're using Windows, change clear to cls, Linux and Mac users leave as is for example) or just execute it, respectively.
eval("os.system(\"clear\")")
exec("os.system(\"clear\")")

Related

Convert string to class function [duplicate]

How do I call a function, using a string with the function's name? For example:
import foo
func_name = "bar"
call(foo, func_name) # calls foo.bar()
Given a module foo with method bar:
import foo
bar = getattr(foo, 'bar')
result = bar()
getattr can similarly be used on class instance bound methods, module-level methods, class methods... the list goes on.
Using locals(), which returns a dictionary with the current local symbol table:
locals()["myfunction"]()
Using globals(), which returns a dictionary with the global symbol table:
globals()["myfunction"]()
Based on Patrick's solution, to get the module dynamically as well, import it using:
module = __import__('foo')
func = getattr(module, 'bar')
func()
Just a simple contribution. If the class that we need to instance is in the same file, we can use something like this:
# Get class from globals and create an instance
m = globals()['our_class']()
# Get the function (from the instance) that we need to call
func = getattr(m, 'function_name')
# Call it
func()
For example:
class A:
def __init__(self):
pass
def sampleFunc(self, arg):
print('you called sampleFunc({})'.format(arg))
m = globals()['A']()
func = getattr(m, 'sampleFunc')
func('sample arg')
# Sample, all on one line
getattr(globals()['A'](), 'sampleFunc')('sample arg')
And, if not a class:
def sampleFunc(arg):
print('you called sampleFunc({})'.format(arg))
globals()['sampleFunc']('sample arg')
Given a string, with a complete python path to a function, this is how I went about getting the result of said function:
import importlib
function_string = 'mypackage.mymodule.myfunc'
mod_name, func_name = function_string.rsplit('.',1)
mod = importlib.import_module(mod_name)
func = getattr(mod, func_name)
result = func()
The best answer according to the Python programming FAQ would be:
functions = {'myfoo': foo.bar}
mystring = 'myfoo'
if mystring in functions:
functions[mystring]()
The primary advantage of this technique is that the strings do not need to match the names of the functions. This is also the primary technique used to emulate a case construct
The answer (I hope) no one ever wanted
Eval like behavior
getattr(locals().get("foo") or globals().get("foo"), "bar")()
Why not add auto-importing
getattr(
locals().get("foo") or
globals().get("foo") or
__import__("foo"),
"bar")()
In case we have extra dictionaries we want to check
getattr(next((x for x in (f("foo") for f in
[locals().get, globals().get,
self.__dict__.get, __import__])
if x)),
"bar")()
We need to go deeper
getattr(next((x for x in (f("foo") for f in
([locals().get, globals().get, self.__dict__.get] +
[d.get for d in (list(dd.values()) for dd in
[locals(),globals(),self.__dict__]
if isinstance(dd,dict))
if isinstance(d,dict)] +
[__import__]))
if x)),
"bar")()
For what it's worth, if you needed to pass the function (or class) name and app name as a string, then you could do this:
myFnName = "MyFn"
myAppName = "MyApp"
app = sys.modules[myAppName]
fn = getattr(app,myFnName)
Try this. While this still uses eval, it only uses it to summon the function from the current context. Then, you have the real function to use as you wish.
The main benefit for me from this is that you will get any eval-related errors at the point of summoning the function. Then you will get only the function-related errors when you call.
def say_hello(name):
print 'Hello {}!'.format(name)
# get the function by name
method_name = 'say_hello'
method = eval(method_name)
# call it like a regular function later
args = ['friend']
kwargs = {}
method(*args, **kwargs)
As this question How to dynamically call methods within a class using method-name assignment to a variable [duplicate] marked as a duplicate as this one, I am posting a related answer here:
The scenario is, a method in a class want to call another method on the same class dynamically, I have added some details to original example which offers some wider scenario and clarity:
class MyClass:
def __init__(self, i):
self.i = i
def get(self):
func = getattr(MyClass, 'function{}'.format(self.i))
func(self, 12) # This one will work
# self.func(12) # But this does NOT work.
def function1(self, p1):
print('function1: {}'.format(p1))
# do other stuff
def function2(self, p1):
print('function2: {}'.format(p1))
# do other stuff
if __name__ == "__main__":
class1 = MyClass(1)
class1.get()
class2 = MyClass(2)
class2.get()
Output (Python 3.7.x)
function1: 12
function2: 12
none of what was suggested helped me. I did discover this though.
<object>.__getattribute__(<string name>)(<params>)
I am using python 2.66
Hope this helps
Although getattr() is elegant (and about 7x faster) method, you can get return value from the function (local, class method, module) with eval as elegant as x = eval('foo.bar')(). And when you implement some error handling then quite securely (the same principle can be used for getattr). Example with module import and class:
# import module, call module function, pass parameters and print retured value with eval():
import random
bar = 'random.randint'
randint = eval(bar)(0,100)
print(randint) # will print random int from <0;100)
# also class method returning (or not) value(s) can be used with eval:
class Say:
def say(something='nothing'):
return something
bar = 'Say.say'
print(eval(bar)('nice to meet you too')) # will print 'nice to meet you'
When module or class does not exist (typo or anything better) then NameError is raised. When function does not exist, then AttributeError is raised. This can be used to handle errors:
# try/except block can be used to catch both errors
try:
eval('Say.talk')() # raises AttributeError because function does not exist
eval('Says.say')() # raises NameError because the class does not exist
# or the same with getattr:
getattr(Say, 'talk')() # raises AttributeError
getattr(Says, 'say')() # raises NameError
except AttributeError:
# do domething or just...
print('Function does not exist')
except NameError:
# do domething or just...
print('Module does not exist')
In python3, you can use the __getattribute__ method. See following example with a list method name string:
func_name = 'reverse'
l = [1, 2, 3, 4]
print(l)
>> [1, 2, 3, 4]
l.__getattribute__(func_name)()
print(l)
>> [4, 3, 2, 1]
Nobody mentioned operator.attrgetter yet:
>>> from operator import attrgetter
>>> l = [1, 2, 3]
>>> attrgetter('reverse')(l)()
>>> l
[3, 2, 1]
>>>
getattr calls method by name from an object.
But this object should be parent of calling class.
The parent class can be got by super(self.__class__, self)
class Base:
def call_base(func):
"""This does not work"""
def new_func(self, *args, **kwargs):
name = func.__name__
getattr(super(self.__class__, self), name)(*args, **kwargs)
return new_func
def f(self, *args):
print(f"BASE method invoked.")
def g(self, *args):
print(f"BASE method invoked.")
class Inherit(Base):
#Base.call_base
def f(self, *args):
"""function body will be ignored by the decorator."""
pass
#Base.call_base
def g(self, *args):
"""function body will be ignored by the decorator."""
pass
Inherit().f() # The goal is to print "BASE method invoked."
i'm facing the similar problem before, which is to convert a string to a function. but i can't use eval() or ast.literal_eval(), because i don't want to execute this code immediately.
e.g. i have a string "foo.bar", and i want to assign it to x as a function name instead of a string, which means i can call the function by x() ON DEMAND.
here's my code:
str_to_convert = "foo.bar"
exec(f"x = {str_to_convert}")
x()
as for your question, you only need to add your module name foo and . before {} as follows:
str_to_convert = "bar"
exec(f"x = foo.{str_to_convert}")
x()
WARNING!!! either eval() or exec() is a dangerous method, you should confirm the safety.
WARNING!!! either eval() or exec() is a dangerous method, you should confirm the safety.
WARNING!!! either eval() or exec() is a dangerous method, you should confirm the safety.
You means get the pointer to an inner function from a module
import foo
method = foo.bar
executed = method(parameter)
This is not a better pythonic way indeed is possible for punctual cases
This is a simple answer, this will allow you to clear the screen for example. There are two examples below, with eval and exec, that will print 0 at the top after cleaning (if you're using Windows, change clear to cls, Linux and Mac users leave as is for example) or just execute it, respectively.
eval("os.system(\"clear\")")
exec("os.system(\"clear\")")

How to pass self to function instance when it gets assigned in a decorator?

I am trying to assign dictionary keys to object functions but for some reason it won't work inside of decorators. When I try to call a.run(), self doesn't seem to be passed into the dictionary func. I also don't have access to f.self in decorator so I know it has to be something wrong in there. I have written a simple example of my code. I want it to be something similar to app.route in flask being that it init the mapping between endpoints and functions.
ERROR:
Traceback (most recent call last):
File "main.py", line 27, in <module>
a.run()
File "main.py", line 14, in run
self.rmap[k](data)
TypeError: one_way() missing 1 required positional argument: 'data'
CODE:
class A (object):
def __init__(self):
self.rmap = {}
def route(self, r):
def decorator(f):
self.rmap[r] = f
return f
return decorator
def run(self):
data = [1,2,3]
for k in self.rmap.keys():
self.rmap[k](data)
a = A()
class B (object):
def __init__(self):
pass
#a.route('/one/way')
def one_way (self, data):
print('A WAY:{}'.format(self))
b = B()
a.run()
At the time it's being decorated, one_way() is a plain function, not a method - it only becomes a method when looked up on a B instance. IOW, you need to explicitely provide a B instance when calling it from A().run() (the fact you have a global b instance in your code is irrelevant - the function object stored in a.rmap knows absolutely nothing about it, nor even about the B class FWIW.
To make a long story short, your current design cannot work as is. If you only ever intend to decorate methods (well, functions) from one single class and call them on one single instance of this class, you could pass an instance of this class to a.run() ie:
class A():
# ...
def run(self, obj):
data = [1,2,3]
for k in self.rmap.keys():
self.rmap[k](obj, data)
b = B()
a.run(b)
but this would be of very limited use.
Or you could just use the decorator to "mark" functions to be used for routing (together with the effective route), add some register() methdo to A and explicitely pass B or whatever else instance to this method ie
def route(r):
def decorator(f):
f._A_route = r
return f
return decorator
class A (object):
def __init__(self):
self.rmap = {}
def register(self, *objects):
for obj in objects:
self._register(obj)
def _register(self, obj):
for name in dir(obj):
if name.startswith("_"):
continue
attr = getattr(obj, name)
if callable(attr) and hasattr(attr, "_A_route"):
self.rmap[attr._A_route] = attr
def run(self):
data = [1,2,3]
for k in self.rmap.keys():
self.rmap[k](data)
class B (object):
def __init__(self):
pass
#route('/one/way')
def one_way (self, data):
print('A WAY:{}'.format(self))
if __name__ == "__main__":
a = A()
b = B()
a.register(b)
a.run()
Now there might be better solutions for your concrete use case, but it's impossible to tell without knowing about the whole context etc.
When calling self.rmap[k](data) you are not passing in the self parameter. This has to be an instance of class B in order to work.
Normally you'd just pass on the parameters with which the decorated function was called, but you seem to want to use your decorated function differently. In your case what would work is:
def run(self):
data = [1,2,3]
b = B()
for k in self.rmap.keys():
self.rmap[k](b, data)
You could of course also instantiate the B instance somewhere else if you want to reuse it between calls.

Redefining a function for a different type [duplicate]

I know that Python does not support method overloading, but I've run into a problem that I can't seem to solve in a nice Pythonic way.
I am making a game where a character needs to shoot a variety of bullets, but how do I write different functions for creating these bullets? For example suppose I have a function that creates a bullet travelling from point A to B with a given speed. I would write a function like this:
def add_bullet(sprite, start, headto, speed):
# Code ...
But I want to write other functions for creating bullets like:
def add_bullet(sprite, start, direction, speed):
def add_bullet(sprite, start, headto, spead, acceleration):
def add_bullet(sprite, script): # For bullets that are controlled by a script
def add_bullet(sprite, curve, speed): # for bullets with curved paths
# And so on ...
And so on with many variations. Is there a better way to do it without using so many keyword arguments cause its getting kinda ugly fast. Renaming each function is pretty bad too because you get either add_bullet1, add_bullet2, or add_bullet_with_really_long_name.
To address some answers:
No I can't create a Bullet class hierarchy because thats too slow. The actual code for managing bullets is in C and my functions are wrappers around C API.
I know about the keyword arguments but checking for all sorts of combinations of parameters is getting annoying, but default arguments help allot like acceleration=0
What you are asking for is called multiple dispatch. See Julia language examples which demonstrates different types of dispatches.
However, before looking at that, we'll first tackle why overloading is not really what you want in Python.
Why Not Overloading?
First, one needs to understand the concept of overloading and why it's not applicable to Python.
When working with languages that can discriminate data types at
compile-time, selecting among the alternatives can occur at
compile-time. The act of creating such alternative functions for
compile-time selection is usually referred to as overloading a
function. (Wikipedia)
Python is a dynamically typed language, so the concept of overloading simply does not apply to it. However, all is not lost, since we can create such alternative functions at run-time:
In programming languages that defer data type identification until
run-time the selection among alternative
functions must occur at run-time, based on the dynamically determined
types of function arguments. Functions whose alternative
implementations are selected in this manner are referred to most
generally as multimethods. (Wikipedia)
So we should be able to do multimethods in Python—or, as it is alternatively called: multiple dispatch.
Multiple dispatch
The multimethods are also called multiple dispatch:
Multiple dispatch or multimethods is the feature of some
object-oriented programming languages in which a function or method
can be dynamically dispatched based on the run time (dynamic) type of
more than one of its arguments. (Wikipedia)
Python does not support this out of the box1, but, as it happens, there is an excellent Python package called multipledispatch that does exactly that.
Solution
Here is how we might use multipledispatch2 package to implement your methods:
>>> from multipledispatch import dispatch
>>> from collections import namedtuple
>>> from types import * # we can test for lambda type, e.g.:
>>> type(lambda a: 1) == LambdaType
True
>>> Sprite = namedtuple('Sprite', ['name'])
>>> Point = namedtuple('Point', ['x', 'y'])
>>> Curve = namedtuple('Curve', ['x', 'y', 'z'])
>>> Vector = namedtuple('Vector', ['x','y','z'])
>>> #dispatch(Sprite, Point, Vector, int)
... def add_bullet(sprite, start, direction, speed):
... print("Called Version 1")
...
>>> #dispatch(Sprite, Point, Point, int, float)
... def add_bullet(sprite, start, headto, speed, acceleration):
... print("Called version 2")
...
>>> #dispatch(Sprite, LambdaType)
... def add_bullet(sprite, script):
... print("Called version 3")
...
>>> #dispatch(Sprite, Curve, int)
... def add_bullet(sprite, curve, speed):
... print("Called version 4")
...
>>> sprite = Sprite('Turtle')
>>> start = Point(1,2)
>>> direction = Vector(1,1,1)
>>> speed = 100 #km/h
>>> acceleration = 5.0 #m/s**2
>>> script = lambda sprite: sprite.x * 2
>>> curve = Curve(3, 1, 4)
>>> headto = Point(100, 100) # somewhere far away
>>> add_bullet(sprite, start, direction, speed)
Called Version 1
>>> add_bullet(sprite, start, headto, speed, acceleration)
Called version 2
>>> add_bullet(sprite, script)
Called version 3
>>> add_bullet(sprite, curve, speed)
Called version 4
1. Python 3 currently supports single dispatch
2. Take care not to use multipledispatch in a multi-threaded environment or you will get weird behavior.
Python does support "method overloading" as you present it. In fact, what you just describe is trivial to implement in Python, in so many different ways, but I would go with:
class Character(object):
# your character __init__ and other methods go here
def add_bullet(self, sprite=default, start=default,
direction=default, speed=default, accel=default,
curve=default):
# do stuff with your arguments
In the above code, default is a plausible default value for those arguments, or None. You can then call the method with only the arguments you are interested in, and Python will use the default values.
You could also do something like this:
class Character(object):
# your character __init__ and other methods go here
def add_bullet(self, **kwargs):
# here you can unpack kwargs as (key, values) and
# do stuff with them, and use some global dictionary
# to provide default values and ensure that ``key``
# is a valid argument...
# do stuff with your arguments
Another alternative is to directly hook the desired function directly to the class or instance:
def some_implementation(self, arg1, arg2, arg3):
# implementation
my_class.add_bullet = some_implementation_of_add_bullet
Yet another way is to use an abstract factory pattern:
class Character(object):
def __init__(self, bfactory, *args, **kwargs):
self.bfactory = bfactory
def add_bullet(self):
sprite = self.bfactory.sprite()
speed = self.bfactory.speed()
# do stuff with your sprite and speed
class pretty_and_fast_factory(object):
def sprite(self):
return pretty_sprite
def speed(self):
return 10000000000.0
my_character = Character(pretty_and_fast_factory(), a1, a2, kw1=v1, kw2=v2)
my_character.add_bullet() # uses pretty_and_fast_factory
# now, if you have another factory called "ugly_and_slow_factory"
# you can change it at runtime in python by issuing
my_character.bfactory = ugly_and_slow_factory()
# In the last example you can see abstract factory and "method
# overloading" (as you call it) in action
You can use "roll-your-own" solution for function overloading. This one is copied from Guido van Rossum's article about multimethods (because there is little difference between multimethods and overloading in Python):
registry = {}
class MultiMethod(object):
def __init__(self, name):
self.name = name
self.typemap = {}
def __call__(self, *args):
types = tuple(arg.__class__ for arg in args) # a generator expression!
function = self.typemap.get(types)
if function is None:
raise TypeError("no match")
return function(*args)
def register(self, types, function):
if types in self.typemap:
raise TypeError("duplicate registration")
self.typemap[types] = function
def multimethod(*types):
def register(function):
name = function.__name__
mm = registry.get(name)
if mm is None:
mm = registry[name] = MultiMethod(name)
mm.register(types, function)
return mm
return register
The usage would be
from multimethods import multimethod
import unittest
# 'overload' makes more sense in this case
overload = multimethod
class Sprite(object):
pass
class Point(object):
pass
class Curve(object):
pass
#overload(Sprite, Point, Direction, int)
def add_bullet(sprite, start, direction, speed):
# ...
#overload(Sprite, Point, Point, int, int)
def add_bullet(sprite, start, headto, speed, acceleration):
# ...
#overload(Sprite, str)
def add_bullet(sprite, script):
# ...
#overload(Sprite, Curve, speed)
def add_bullet(sprite, curve, speed):
# ...
Most restrictive limitations at the moment are:
methods are not supported, only functions that are not class members;
inheritance is not handled;
kwargs are not supported;
registering new functions should be done at import time thing is not thread-safe
A possible option is to use the multipledispatch module as detailed here:
http://matthewrocklin.com/blog/work/2014/02/25/Multiple-Dispatch
Instead of doing this:
def add(self, other):
if isinstance(other, Foo):
...
elif isinstance(other, Bar):
...
else:
raise NotImplementedError()
You can do this:
from multipledispatch import dispatch
#dispatch(int, int)
def add(x, y):
return x + y
#dispatch(object, object)
def add(x, y):
return "%s + %s" % (x, y)
With the resulting usage:
>>> add(1, 2)
3
>>> add(1, 'hello')
'1 + hello'
In Python 3.4 PEP-0443. Single-dispatch generic functions was added.
Here is a short API description from PEP.
To define a generic function, decorate it with the #singledispatch decorator. Note that the dispatch happens on the type of the first argument. Create your function accordingly:
from functools import singledispatch
#singledispatch
def fun(arg, verbose=False):
if verbose:
print("Let me just say,", end=" ")
print(arg)
To add overloaded implementations to the function, use the register() attribute of the generic function. This is a decorator, taking a type parameter and decorating a function implementing the operation for that type:
#fun.register(int)
def _(arg, verbose=False):
if verbose:
print("Strength in numbers, eh?", end=" ")
print(arg)
#fun.register(list)
def _(arg, verbose=False):
if verbose:
print("Enumerate this:")
for i, elem in enumerate(arg):
print(i, elem)
The #overload decorator was added with type hints (PEP 484).
While this doesn't change the behaviour of Python, it does make it easier to understand what is going on, and for mypy to detect errors.
See: Type hints and PEP 484
This type of behaviour is typically solved (in OOP languages) using polymorphism. Each type of bullet would be responsible for knowing how it travels. For instance:
class Bullet(object):
def __init__(self):
self.curve = None
self.speed = None
self.acceleration = None
self.sprite_image = None
class RegularBullet(Bullet):
def __init__(self):
super(RegularBullet, self).__init__()
self.speed = 10
class Grenade(Bullet):
def __init__(self):
super(Grenade, self).__init__()
self.speed = 4
self.curve = 3.5
add_bullet(Grendade())
def add_bullet(bullet):
c_function(bullet.speed, bullet.curve, bullet.acceleration, bullet.sprite, bullet.x, bullet.y)
void c_function(double speed, double curve, double accel, char[] sprite, ...) {
if (speed != null && ...) regular_bullet(...)
else if (...) curved_bullet(...)
//..etc..
}
Pass as many arguments to the c_function that exist, and then do the job of determining which c function to call based on the values in the initial c function. So, Python should only ever be calling the one c function. That one c function looks at the arguments, and then can delegate to other c functions appropriately.
You're essentially just using each subclass as a different data container, but by defining all the potential arguments on the base class, the subclasses are free to ignore the ones they do nothing with.
When a new type of bullet comes along, you can simply define one more property on the base, change the one python function so that it passes the extra property, and the one c_function that examines the arguments and delegates appropriately. It doesn't sound too bad I guess.
It is impossible by definition to overload a function in python (read on for details), but you can achieve something similar with a simple decorator
class overload:
def __init__(self, f):
self.cases = {}
def args(self, *args):
def store_function(f):
self.cases[tuple(args)] = f
return self
return store_function
def __call__(self, *args):
function = self.cases[tuple(type(arg) for arg in args)]
return function(*args)
You can use it like this
#overload
def f():
pass
#f.args(int, int)
def f(x, y):
print('two integers')
#f.args(float)
def f(x):
print('one float')
f(5.5)
f(1, 2)
Modify it to adapt it to your use case.
A clarification of concepts
function dispatch: there are multiple functions with the same name. Which one should be called? two strategies
static/compile-time dispatch (aka. "overloading"). decide which function to call based on the compile-time type of the arguments. In all dynamic languages, there is no compile-time type, so overloading is impossible by definition
dynamic/run-time dispatch: decide which function to call based on the runtime type of the arguments. This is what all OOP languages do: multiple classes have the same methods, and the language decides which one to call based on the type of self/this argument. However, most languages only do it for the this argument only. The above decorator extends the idea to multiple parameters.
To clear up, assume that we define, in a hypothetical static language, the functions
void f(Integer x):
print('integer called')
void f(Float x):
print('float called')
void f(Number x):
print('number called')
Number x = new Integer('5')
f(x)
x = new Number('3.14')
f(x)
With static dispatch (overloading) you will see "number called" twice, because x has been declared as Number, and that's all overloading cares about. With dynamic dispatch you will see "integer called, float called", because those are the actual types of x at the time the function is called.
By passing keyword args.
def add_bullet(**kwargs):
#check for the arguments listed above and do the proper things
Python 3.8 added functools.singledispatchmethod
Transform a method into a single-dispatch generic function.
To define a generic method, decorate it with the #singledispatchmethod
decorator. Note that the dispatch happens on the type of the first
non-self or non-cls argument, create your function accordingly:
from functools import singledispatchmethod
class Negator:
#singledispatchmethod
def neg(self, arg):
raise NotImplementedError("Cannot negate a")
#neg.register
def _(self, arg: int):
return -arg
#neg.register
def _(self, arg: bool):
return not arg
negator = Negator()
for v in [42, True, "Overloading"]:
neg = negator.neg(v)
print(f"{v=}, {neg=}")
Output
v=42, neg=-42
v=True, neg=False
NotImplementedError: Cannot negate a
#singledispatchmethod supports nesting with other decorators such as
#classmethod. Note that to allow for dispatcher.register,
singledispatchmethod must be the outer most decorator. Here is the
Negator class with the neg methods being class bound:
from functools import singledispatchmethod
class Negator:
#singledispatchmethod
#staticmethod
def neg(arg):
raise NotImplementedError("Cannot negate a")
#neg.register
def _(arg: int) -> int:
return -arg
#neg.register
def _(arg: bool) -> bool:
return not arg
for v in [42, True, "Overloading"]:
neg = Negator.neg(v)
print(f"{v=}, {neg=}")
Output:
v=42, neg=-42
v=True, neg=False
NotImplementedError: Cannot negate a
The same pattern can be used for other similar decorators:
staticmethod, abstractmethod, and others.
I think your basic requirement is to have a C/C++-like syntax in Python with the least headache possible. Although I liked Alexander Poluektov's answer it doesn't work for classes.
The following should work for classes. It works by distinguishing by the number of non-keyword arguments (but it doesn't support distinguishing by type):
class TestOverloading(object):
def overloaded_function(self, *args, **kwargs):
# Call the function that has the same number of non-keyword arguments.
getattr(self, "_overloaded_function_impl_" + str(len(args)))(*args, **kwargs)
def _overloaded_function_impl_3(self, sprite, start, direction, **kwargs):
print "This is overload 3"
print "Sprite: %s" % str(sprite)
print "Start: %s" % str(start)
print "Direction: %s" % str(direction)
def _overloaded_function_impl_2(self, sprite, script):
print "This is overload 2"
print "Sprite: %s" % str(sprite)
print "Script: "
print script
And it can be used simply like this:
test = TestOverloading()
test.overloaded_function("I'm a Sprite", 0, "Right")
print
test.overloaded_function("I'm another Sprite", "while x == True: print 'hi'")
Output:
This is overload 3
Sprite: I'm a Sprite
Start: 0
Direction: Right
This is overload 2
Sprite: I'm another Sprite
Script:
while x == True: print 'hi'
You can achieve this with the following Python code:
#overload
def test(message: str):
return message
#overload
def test(number: int):
return number + 1
Either use multiple keyword arguments in the definition, or create a Bullet hierarchy whose instances are passed to the function.
I think a Bullet class hierarchy with the associated polymorphism is the way to go. You can effectively overload the base class constructor by using a metaclass so that calling the base class results in the creation of the appropriate subclass object. Below is some sample code to illustrate the essence of what I mean.
Updated
The code has been modified to run under both Python 2 and 3 to keep it relevant. This was done in a way that avoids the use Python's explicit metaclass syntax, which varies between the two versions.
To accomplish that objective, a BulletMetaBase instance of the BulletMeta class is created by explicitly calling the metaclass when creating the Bullet baseclass (rather than using the __metaclass__= class attribute or via a metaclass keyword argument depending on the Python version).
class BulletMeta(type):
def __new__(cls, classname, bases, classdict):
""" Create Bullet class or a subclass of it. """
classobj = type.__new__(cls, classname, bases, classdict)
if classname != 'BulletMetaBase':
if classname == 'Bullet': # Base class definition?
classobj.registry = {} # Initialize subclass registry.
else:
try:
alias = classdict['alias']
except KeyError:
raise TypeError("Bullet subclass %s has no 'alias'" %
classname)
if alias in Bullet.registry: # unique?
raise TypeError("Bullet subclass %s's alias attribute "
"%r already in use" % (classname, alias))
# Register subclass under the specified alias.
classobj.registry[alias] = classobj
return classobj
def __call__(cls, alias, *args, **kwargs):
""" Bullet subclasses instance factory.
Subclasses should only be instantiated by calls to the base
class with their subclass' alias as the first arg.
"""
if cls != Bullet:
raise TypeError("Bullet subclass %r objects should not to "
"be explicitly constructed." % cls.__name__)
elif alias not in cls.registry: # Bullet subclass?
raise NotImplementedError("Unknown Bullet subclass %r" %
str(alias))
# Create designated subclass object (call its __init__ method).
subclass = cls.registry[alias]
return type.__call__(subclass, *args, **kwargs)
class Bullet(BulletMeta('BulletMetaBase', (object,), {})):
# Presumably you'd define some abstract methods that all here
# that would be supported by all subclasses.
# These definitions could just raise NotImplementedError() or
# implement the functionality is some sub-optimal generic way.
# For example:
def fire(self, *args, **kwargs):
raise NotImplementedError(self.__class__.__name__ + ".fire() method")
# Abstract base class's __init__ should never be called.
# If subclasses need to call super class's __init__() for some
# reason then it would need to be implemented.
def __init__(self, *args, **kwargs):
raise NotImplementedError("Bullet is an abstract base class")
# Subclass definitions.
class Bullet1(Bullet):
alias = 'B1'
def __init__(self, sprite, start, direction, speed):
print('creating %s object' % self.__class__.__name__)
def fire(self, trajectory):
print('Bullet1 object fired with %s trajectory' % trajectory)
class Bullet2(Bullet):
alias = 'B2'
def __init__(self, sprite, start, headto, spead, acceleration):
print('creating %s object' % self.__class__.__name__)
class Bullet3(Bullet):
alias = 'B3'
def __init__(self, sprite, script): # script controlled bullets
print('creating %s object' % self.__class__.__name__)
class Bullet4(Bullet):
alias = 'B4'
def __init__(self, sprite, curve, speed): # for bullets with curved paths
print('creating %s object' % self.__class__.__name__)
class Sprite: pass
class Curve: pass
b1 = Bullet('B1', Sprite(), (10,20,30), 90, 600)
b2 = Bullet('B2', Sprite(), (-30,17,94), (1,-1,-1), 600, 10)
b3 = Bullet('B3', Sprite(), 'bullet42.script')
b4 = Bullet('B4', Sprite(), Curve(), 720)
b1.fire('uniform gravity')
b2.fire('uniform gravity')
Output:
creating Bullet1 object
creating Bullet2 object
creating Bullet3 object
creating Bullet4 object
Bullet1 object fired with uniform gravity trajectory
Traceback (most recent call last):
File "python-function-overloading.py", line 93, in <module>
b2.fire('uniform gravity') # NotImplementedError: Bullet2.fire() method
File "python-function-overloading.py", line 49, in fire
raise NotImplementedError(self.__class__.__name__ + ".fire() method")
NotImplementedError: Bullet2.fire() method
You can easily implement function overloading in Python. Here is an example using floats and integers:
class OverloadedFunction:
def __init__(self):
self.router = {int : self.f_int ,
float: self.f_float}
def __call__(self, x):
return self.router[type(x)](x)
def f_int(self, x):
print('Integer Function')
return x**2
def f_float(self, x):
print('Float Function (Overloaded)')
return x**3
# f is our overloaded function
f = OverloadedFunction()
print(f(3 ))
print(f(3.))
# Output:
# Integer Function
# 9
# Float Function (Overloaded)
# 27.0
The main idea behind the code is that a class holds the different (overloaded) functions that you would like to implement, and a Dictionary works as a router, directing your code towards the right function depending on the input type(x).
PS1. In case of custom classes, like Bullet1, you can initialize the internal dictionary following a similar pattern, such as self.D = {Bullet1: self.f_Bullet1, ...}. The rest of the code is the same.
PS2. The time/space complexity of the proposed solution is fairly good as well, with an average cost of O(1) per operation.
Use keyword arguments with defaults. E.g.
def add_bullet(sprite, start=default, direction=default, script=default, speed=default):
In the case of a straight bullet versus a curved bullet, I'd add two functions: add_bullet_straight and add_bullet_curved.
Overloading methods is tricky in Python. However, there could be usage of passing the dict, list or primitive variables.
I have tried something for my use cases, and this could help here to understand people to overload the methods.
Let's take your example:
A class overload method with call the methods from different class.
def add_bullet(sprite=None, start=None, headto=None, spead=None, acceleration=None):
Pass the arguments from the remote class:
add_bullet(sprite = 'test', start=Yes,headto={'lat':10.6666,'long':10.6666},accelaration=10.6}
Or
add_bullet(sprite = 'test', start=Yes, headto={'lat':10.6666,'long':10.6666},speed=['10','20,'30']}
So, handling is being achieved for list, Dictionary or primitive variables from method overloading.
Try it out for your code.
Plum supports it in a straightforward pythonic way. Copying an example from the README below.
from plum import dispatch
#dispatch
def f(x: str):
return "This is a string!"
#dispatch
def f(x: int):
return "This is an integer!"
>>> f("1")
'This is a string!'
>>> f(1)
'This is an integer!'
You can also try this code. We can try any number of arguments
# Finding the average of given number of arguments
def avg(*args): # args is the argument name we give
sum = 0
for i in args:
sum += i
average = sum/len(args) # Will find length of arguments we given
print("Avg: ", average)
# call function with different number of arguments
avg(1,2)
avg(5,6,4,7)
avg(11,23,54,111,76)

How to typecast a variable with the type stored in another variable? [duplicate]

How do I call a function, using a string with the function's name? For example:
import foo
func_name = "bar"
call(foo, func_name) # calls foo.bar()
Given a module foo with method bar:
import foo
bar = getattr(foo, 'bar')
result = bar()
getattr can similarly be used on class instance bound methods, module-level methods, class methods... the list goes on.
Using locals(), which returns a dictionary with the current local symbol table:
locals()["myfunction"]()
Using globals(), which returns a dictionary with the global symbol table:
globals()["myfunction"]()
Based on Patrick's solution, to get the module dynamically as well, import it using:
module = __import__('foo')
func = getattr(module, 'bar')
func()
Just a simple contribution. If the class that we need to instance is in the same file, we can use something like this:
# Get class from globals and create an instance
m = globals()['our_class']()
# Get the function (from the instance) that we need to call
func = getattr(m, 'function_name')
# Call it
func()
For example:
class A:
def __init__(self):
pass
def sampleFunc(self, arg):
print('you called sampleFunc({})'.format(arg))
m = globals()['A']()
func = getattr(m, 'sampleFunc')
func('sample arg')
# Sample, all on one line
getattr(globals()['A'](), 'sampleFunc')('sample arg')
And, if not a class:
def sampleFunc(arg):
print('you called sampleFunc({})'.format(arg))
globals()['sampleFunc']('sample arg')
Given a string, with a complete python path to a function, this is how I went about getting the result of said function:
import importlib
function_string = 'mypackage.mymodule.myfunc'
mod_name, func_name = function_string.rsplit('.',1)
mod = importlib.import_module(mod_name)
func = getattr(mod, func_name)
result = func()
The best answer according to the Python programming FAQ would be:
functions = {'myfoo': foo.bar}
mystring = 'myfoo'
if mystring in functions:
functions[mystring]()
The primary advantage of this technique is that the strings do not need to match the names of the functions. This is also the primary technique used to emulate a case construct
The answer (I hope) no one ever wanted
Eval like behavior
getattr(locals().get("foo") or globals().get("foo"), "bar")()
Why not add auto-importing
getattr(
locals().get("foo") or
globals().get("foo") or
__import__("foo"),
"bar")()
In case we have extra dictionaries we want to check
getattr(next((x for x in (f("foo") for f in
[locals().get, globals().get,
self.__dict__.get, __import__])
if x)),
"bar")()
We need to go deeper
getattr(next((x for x in (f("foo") for f in
([locals().get, globals().get, self.__dict__.get] +
[d.get for d in (list(dd.values()) for dd in
[locals(),globals(),self.__dict__]
if isinstance(dd,dict))
if isinstance(d,dict)] +
[__import__]))
if x)),
"bar")()
For what it's worth, if you needed to pass the function (or class) name and app name as a string, then you could do this:
myFnName = "MyFn"
myAppName = "MyApp"
app = sys.modules[myAppName]
fn = getattr(app,myFnName)
Try this. While this still uses eval, it only uses it to summon the function from the current context. Then, you have the real function to use as you wish.
The main benefit for me from this is that you will get any eval-related errors at the point of summoning the function. Then you will get only the function-related errors when you call.
def say_hello(name):
print 'Hello {}!'.format(name)
# get the function by name
method_name = 'say_hello'
method = eval(method_name)
# call it like a regular function later
args = ['friend']
kwargs = {}
method(*args, **kwargs)
As this question How to dynamically call methods within a class using method-name assignment to a variable [duplicate] marked as a duplicate as this one, I am posting a related answer here:
The scenario is, a method in a class want to call another method on the same class dynamically, I have added some details to original example which offers some wider scenario and clarity:
class MyClass:
def __init__(self, i):
self.i = i
def get(self):
func = getattr(MyClass, 'function{}'.format(self.i))
func(self, 12) # This one will work
# self.func(12) # But this does NOT work.
def function1(self, p1):
print('function1: {}'.format(p1))
# do other stuff
def function2(self, p1):
print('function2: {}'.format(p1))
# do other stuff
if __name__ == "__main__":
class1 = MyClass(1)
class1.get()
class2 = MyClass(2)
class2.get()
Output (Python 3.7.x)
function1: 12
function2: 12
none of what was suggested helped me. I did discover this though.
<object>.__getattribute__(<string name>)(<params>)
I am using python 2.66
Hope this helps
Although getattr() is elegant (and about 7x faster) method, you can get return value from the function (local, class method, module) with eval as elegant as x = eval('foo.bar')(). And when you implement some error handling then quite securely (the same principle can be used for getattr). Example with module import and class:
# import module, call module function, pass parameters and print retured value with eval():
import random
bar = 'random.randint'
randint = eval(bar)(0,100)
print(randint) # will print random int from <0;100)
# also class method returning (or not) value(s) can be used with eval:
class Say:
def say(something='nothing'):
return something
bar = 'Say.say'
print(eval(bar)('nice to meet you too')) # will print 'nice to meet you'
When module or class does not exist (typo or anything better) then NameError is raised. When function does not exist, then AttributeError is raised. This can be used to handle errors:
# try/except block can be used to catch both errors
try:
eval('Say.talk')() # raises AttributeError because function does not exist
eval('Says.say')() # raises NameError because the class does not exist
# or the same with getattr:
getattr(Say, 'talk')() # raises AttributeError
getattr(Says, 'say')() # raises NameError
except AttributeError:
# do domething or just...
print('Function does not exist')
except NameError:
# do domething or just...
print('Module does not exist')
In python3, you can use the __getattribute__ method. See following example with a list method name string:
func_name = 'reverse'
l = [1, 2, 3, 4]
print(l)
>> [1, 2, 3, 4]
l.__getattribute__(func_name)()
print(l)
>> [4, 3, 2, 1]
Nobody mentioned operator.attrgetter yet:
>>> from operator import attrgetter
>>> l = [1, 2, 3]
>>> attrgetter('reverse')(l)()
>>> l
[3, 2, 1]
>>>
getattr calls method by name from an object.
But this object should be parent of calling class.
The parent class can be got by super(self.__class__, self)
class Base:
def call_base(func):
"""This does not work"""
def new_func(self, *args, **kwargs):
name = func.__name__
getattr(super(self.__class__, self), name)(*args, **kwargs)
return new_func
def f(self, *args):
print(f"BASE method invoked.")
def g(self, *args):
print(f"BASE method invoked.")
class Inherit(Base):
#Base.call_base
def f(self, *args):
"""function body will be ignored by the decorator."""
pass
#Base.call_base
def g(self, *args):
"""function body will be ignored by the decorator."""
pass
Inherit().f() # The goal is to print "BASE method invoked."
i'm facing the similar problem before, which is to convert a string to a function. but i can't use eval() or ast.literal_eval(), because i don't want to execute this code immediately.
e.g. i have a string "foo.bar", and i want to assign it to x as a function name instead of a string, which means i can call the function by x() ON DEMAND.
here's my code:
str_to_convert = "foo.bar"
exec(f"x = {str_to_convert}")
x()
as for your question, you only need to add your module name foo and . before {} as follows:
str_to_convert = "bar"
exec(f"x = foo.{str_to_convert}")
x()
WARNING!!! either eval() or exec() is a dangerous method, you should confirm the safety.
WARNING!!! either eval() or exec() is a dangerous method, you should confirm the safety.
WARNING!!! either eval() or exec() is a dangerous method, you should confirm the safety.
You means get the pointer to an inner function from a module
import foo
method = foo.bar
executed = method(parameter)
This is not a better pythonic way indeed is possible for punctual cases
This is a simple answer, this will allow you to clear the screen for example. There are two examples below, with eval and exec, that will print 0 at the top after cleaning (if you're using Windows, change clear to cls, Linux and Mac users leave as is for example) or just execute it, respectively.
eval("os.system(\"clear\")")
exec("os.system(\"clear\")")

class instance from nowhere [duplicate]

If I have a class ...
class MyClass:
def method(arg):
print(arg)
... which I use to create an object ...
my_object = MyClass()
... on which I call method("foo") like so ...
>>> my_object.method("foo")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: method() takes exactly 1 positional argument (2 given)
... why does Python tell me I gave it two arguments, when I only gave one?
In Python, this:
my_object.method("foo")
... is syntactic sugar, which the interpreter translates behind the scenes into:
MyClass.method(my_object, "foo")
... which, as you can see, does indeed have two arguments - it's just that the first one is implicit, from the point of view of the caller.
This is because most methods do some work with the object they're called on, so there needs to be some way for that object to be referred to inside the method. By convention, this first argument is called self inside the method definition:
class MyNewClass:
def method(self, arg):
print(self)
print(arg)
If you call method("foo") on an instance of MyNewClass, it works as expected:
>>> my_new_object = MyNewClass()
>>> my_new_object.method("foo")
<__main__.MyNewClass object at 0x29045d0>
foo
Occasionally (but not often), you really don't care about the object that your method is bound to, and in that circumstance, you can decorate the method with the builtin staticmethod() function to say so:
class MyOtherClass:
#staticmethod
def method(arg):
print(arg)
... in which case you don't need to add a self argument to the method definition, and it still works:
>>> my_other_object = MyOtherClass()
>>> my_other_object.method("foo")
foo
In simple words
In Python you should add self as the first parameter to all defined methods in classes:
class MyClass:
def method(self, arg):
print(arg)
Then you can use your method according to your intuition:
>>> my_object = MyClass()
>>> my_object.method("foo")
foo
For a better understanding, you can also read the answers to this question: What is the purpose of self?
Something else to consider when this type of error is encountered:
I was running into this error message and found this post helpful. Turns out in my case I had overridden an __init__() where there was object inheritance.
The inherited example is rather long, so I'll skip to a more simple example that doesn't use inheritance:
class MyBadInitClass:
def ___init__(self, name):
self.name = name
def name_foo(self, arg):
print(self)
print(arg)
print("My name is", self.name)
class MyNewClass:
def new_foo(self, arg):
print(self)
print(arg)
my_new_object = MyNewClass()
my_new_object.new_foo("NewFoo")
my_bad_init_object = MyBadInitClass(name="Test Name")
my_bad_init_object.name_foo("name foo")
Result is:
<__main__.MyNewClass object at 0x033C48D0>
NewFoo
Traceback (most recent call last):
File "C:/Users/Orange/PycharmProjects/Chapter9/bad_init_example.py", line 41, in <module>
my_bad_init_object = MyBadInitClass(name="Test Name")
TypeError: object() takes no parameters
PyCharm didn't catch this typo. Nor did Notepad++ (other editors/IDE's might).
Granted, this is a "takes no parameters" TypeError, it isn't much different than "got two" when expecting one, in terms of object initialization in Python.
Addressing the topic: An overloading initializer will be used if syntactically correct, but if not it will be ignored and the built-in used instead. The object won't expect/handle this and the error is thrown.
In the case of the sytax error: The fix is simple, just edit the custom init statement:
def __init__(self, name):
self.name = name
Newcomer to Python, I had this issue when I was using the Python's ** feature in a wrong way. Trying to call this definition from somewhere:
def create_properties_frame(self, parent, **kwargs):
using a call without a double star was causing the problem:
self.create_properties_frame(frame, kw_gsp)
TypeError: create_properties_frame() takes 2 positional arguments but 3 were given
The solution is to add ** to the argument:
self.create_properties_frame(frame, **kw_gsp)
As mentioned in other answers - when you use an instance method you need to pass self as the first argument - this is the source of the error.
With addition to that,it is important to understand that only instance methods take self as the first argument in order to refer to the instance.
In case the method is Static you don't pass self, but a cls argument instead (or class_).
Please see an example below.
class City:
country = "USA" # This is a class level attribute which will be shared across all instances (and not created PER instance)
def __init__(self, name, location, population):
self.name = name
self.location = location
self.population = population
# This is an instance method which takes self as the first argument to refer to the instance
def print_population(self, some_nice_sentence_prefix):
print(some_nice_sentence_prefix +" In " +self.name + " lives " +self.population + " people!")
# This is a static (class) method which is marked with the #classmethod attribute
# All class methods must take a class argument as first param. The convention is to name is "cls" but class_ is also ok
#classmethod
def change_country(cls, new_country):
cls.country = new_country
Some tests just to make things more clear:
# Populate objects
city1 = City("New York", "East", "18,804,000")
city2 = City("Los Angeles", "West", "10,118,800")
#1) Use the instance method: No need to pass "self" - it is passed as the city1 instance
city1.print_population("Did You Know?") # Prints: Did You Know? In New York lives 18,804,000 people!
#2.A) Use the static method in the object
city2.change_country("Canada")
#2.B) Will be reflected in all objects
print("city1.country=",city1.country) # Prints Canada
print("city2.country=",city2.country) # Prints Canada
It occurs when you don't specify the no of parameters the __init__() or any other method looking for.
For example:
class Dog:
def __init__(self):
print("IN INIT METHOD")
def __unicode__(self,):
print("IN UNICODE METHOD")
def __str__(self):
print("IN STR METHOD")
obj = Dog("JIMMY", 1, 2, 3, "WOOF")
When you run the above programme, it gives you an error like that:
TypeError: __init__() takes 1 positional argument but 6 were given
How we can get rid of this thing?
Just pass the parameters, what __init__() method looking for
class Dog:
def __init__(self, dogname, dob_d, dob_m, dob_y, dogSpeakText):
self.name_of_dog = dogname
self.date_of_birth = dob_d
self.month_of_birth = dob_m
self.year_of_birth = dob_y
self.sound_it_make = dogSpeakText
def __unicode__(self, ):
print("IN UNICODE METHOD")
def __str__(self):
print("IN STR METHOD")
obj = Dog("JIMMY", 1, 2, 3, "WOOF")
print(id(obj))
If you want to call method without creating object, you can change method to static method.
class MyClass:
#staticmethod
def method(arg):
print(arg)
MyClass.method("i am a static method")
I get this error when I'm sleep-deprived, and create a class using def instead of class:
def MyClass():
def __init__(self, x):
self.x = x
a = MyClass(3)
-> TypeError: MyClass() takes 0 positional arguments but 1 was given
You should actually create a class:
class accum:
def __init__(self):
self.acc = 0
def accumulator(self, var2add, end):
if not end:
self.acc+=var2add
return self.acc
In my case, I forgot to add the ()
I was calling the method like this
obj = className.myMethod
But it should be is like this
obj = className.myMethod()

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