I have a situation with some code where eval() came up as a possible solution. Now I have never had to use eval() before but, I have come across plenty of information about the potential danger it can cause. That said, I'm very wary about using it.
My situation is that I have input being given by a user:
datamap = input('Provide some data here: ')
Where datamap needs to be a dictionary. I searched around and found that eval() could work this out. I thought that I might be able to check the type of the input before trying to use the data and that would be a viable security precaution.
datamap = eval(input('Provide some data here: ')
if not isinstance(datamap, dict):
return
I read through the docs and I am still unclear if this would be safe or not. Does eval evaluate the data as soon as its entered or after the datamap variable is called?
Is the ast module's .literal_eval() the only safe option?
datamap = eval(input('Provide some data here: ')) means that you actually evaluate the code before you deem it to be unsafe or not. It evaluates the code as soon as the function is called. See also the dangers of eval.
ast.literal_eval raises an exception if the input isn't a valid Python datatype, so the code won't be executed if it's not.
Use ast.literal_eval whenever you need eval. You shouldn't usually evaluate literal Python statements.
ast.literal_eval() only considers a small subset of Python's syntax to be valid:
The string or node provided may only consist of the following Python literal structures: strings, bytes, numbers, tuples, lists, dicts, sets, booleans, and None.
Passing __import__('os').system('rm -rf /a-path-you-really-care-about') into ast.literal_eval() will raise an error, but eval() will happily delete your files.
Since it looks like you're only letting the user input a plain dictionary, use ast.literal_eval(). It safely does what you want and nothing more.
eval:
This is very powerful, but is also very dangerous if you accept strings to evaluate from untrusted input. Suppose the string being evaluated is "os.system('rm -rf /')" ? It will really start deleting all the files on your computer.
ast.literal_eval:
Safely evaluate an expression node or a string containing a Python literal or container display. The string or node provided may only consist of the following Python literal structures: strings, bytes, numbers, tuples, lists, dicts, sets, booleans, None, bytes and sets.
Syntax:
eval(expression, globals=None, locals=None)
import ast
ast.literal_eval(node_or_string)
Example:
# python 2.x - doesn't accept operators in string format
import ast
ast.literal_eval('[1, 2, 3]') # output: [1, 2, 3]
ast.literal_eval('1+1') # output: ValueError: malformed string
# python 3.0 -3.6
import ast
ast.literal_eval("1+1") # output : 2
ast.literal_eval("{'a': 2, 'b': 3, 3:'xyz'}") # output : {'a': 2, 'b': 3, 3:'xyz'}
# type dictionary
ast.literal_eval("",{}) # output : Syntax Error required only one parameter
ast.literal_eval("__import__('os').system('rm -rf /')") # output : error
eval("__import__('os').system('rm -rf /')")
# output : start deleting all the files on your computer.
# restricting using global and local variables
eval("__import__('os').system('rm -rf /')",{'__builtins__':{}},{})
# output : Error due to blocked imports by passing '__builtins__':{} in global
# But still eval is not safe. we can access and break the code as given below
s = """
(lambda fc=(
lambda n: [
c for c in
().__class__.__bases__[0].__subclasses__()
if c.__name__ == n
][0]
):
fc("function")(
fc("code")(
0,0,0,0,"KABOOM",(),(),(),"","",0,""
),{}
)()
)()
"""
eval(s, {'__builtins__':{}})
In the above code ().__class__.__bases__[0] nothing but object itself.
Now we instantiated all the subclasses, here our main enter code hereobjective is to find one class named n from it.
We need to code object and function object from instantiated subclasses. This is an alternative way from CPython to access subclasses of object and attach the system.
From python 3.7 ast.literal_eval() is now stricter. Addition and subtraction of arbitrary numbers are no longer allowed. link
Python's eager in its evaluation, so eval(input(...)) (Python 3) will evaluate the user's input as soon as it hits the eval, regardless of what you do with the data afterwards. Therefore, this is not safe, especially when you eval user input.
Use ast.literal_eval.
As an example, entering this at the prompt could be very bad for you:
__import__('os').system('rm -rf /a-path-you-really-care-about')
In recent Python3 ast.literal_eval() no longer parses simple strings, instead you are supposed to use the ast.parse() method to create an AST then interpret it.
This is a complete example of using ast.parse() correctly in Python 3.6+ to evaluate simple arithmetic expressions safely.
import ast, operator, math
import logging
logger = logging.getLogger(__file__)
def safe_eval(s):
def checkmath(x, *args):
if x not in [x for x in dir(math) if not "__" in x]:
raise SyntaxError(f"Unknown func {x}()")
fun = getattr(math, x)
return fun(*args)
binOps = {
ast.Add: operator.add,
ast.Sub: operator.sub,
ast.Mult: operator.mul,
ast.Div: operator.truediv,
ast.Mod: operator.mod,
ast.Pow: operator.pow,
ast.Call: checkmath,
ast.BinOp: ast.BinOp,
}
unOps = {
ast.USub: operator.neg,
ast.UAdd: operator.pos,
ast.UnaryOp: ast.UnaryOp,
}
ops = tuple(binOps) + tuple(unOps)
tree = ast.parse(s, mode='eval')
def _eval(node):
if isinstance(node, ast.Expression):
logger.debug("Expr")
return _eval(node.body)
elif isinstance(node, ast.Str):
logger.debug("Str")
return node.s
elif isinstance(node, ast.Num):
logger.debug("Num")
return node.value
elif isinstance(node, ast.Constant):
logger.info("Const")
return node.value
elif isinstance(node, ast.BinOp):
logger.debug("BinOp")
if isinstance(node.left, ops):
left = _eval(node.left)
else:
left = node.left.value
if isinstance(node.right, ops):
right = _eval(node.right)
else:
right = node.right.value
return binOps[type(node.op)](left, right)
elif isinstance(node, ast.UnaryOp):
logger.debug("UpOp")
if isinstance(node.operand, ops):
operand = _eval(node.operand)
else:
operand = node.operand.value
return unOps[type(node.op)](operand)
elif isinstance(node, ast.Call):
args = [_eval(x) for x in node.args]
r = checkmath(node.func.id, *args)
return r
else:
raise SyntaxError(f"Bad syntax, {type(node)}")
return _eval(tree)
if __name__ == "__main__":
logger.setLevel(logging.DEBUG)
ch = logging.StreamHandler()
logger.addHandler(ch)
assert safe_eval("1+1") == 2
assert safe_eval("1+-5") == -4
assert safe_eval("-1") == -1
assert safe_eval("-+1") == -1
assert safe_eval("(100*10)+6") == 1006
assert safe_eval("100*(10+6)") == 1600
assert safe_eval("2**4") == 2**4
assert safe_eval("sqrt(16)+1") == math.sqrt(16) + 1
assert safe_eval("1.2345 * 10") == 1.2345 * 10
print("Tests pass")
If all you need is a user provided dictionary, a possible better solution is json.loads. The main limitation is that JSON dicts ("objects") require string keys. Also you can only provide literal data, but that is also the case for ast.literal_eval.
Related
I have a situation with some code where eval() came up as a possible solution. Now I have never had to use eval() before but, I have come across plenty of information about the potential danger it can cause. That said, I'm very wary about using it.
My situation is that I have input being given by a user:
datamap = input('Provide some data here: ')
Where datamap needs to be a dictionary. I searched around and found that eval() could work this out. I thought that I might be able to check the type of the input before trying to use the data and that would be a viable security precaution.
datamap = eval(input('Provide some data here: ')
if not isinstance(datamap, dict):
return
I read through the docs and I am still unclear if this would be safe or not. Does eval evaluate the data as soon as its entered or after the datamap variable is called?
Is the ast module's .literal_eval() the only safe option?
datamap = eval(input('Provide some data here: ')) means that you actually evaluate the code before you deem it to be unsafe or not. It evaluates the code as soon as the function is called. See also the dangers of eval.
ast.literal_eval raises an exception if the input isn't a valid Python datatype, so the code won't be executed if it's not.
Use ast.literal_eval whenever you need eval. You shouldn't usually evaluate literal Python statements.
ast.literal_eval() only considers a small subset of Python's syntax to be valid:
The string or node provided may only consist of the following Python literal structures: strings, bytes, numbers, tuples, lists, dicts, sets, booleans, and None.
Passing __import__('os').system('rm -rf /a-path-you-really-care-about') into ast.literal_eval() will raise an error, but eval() will happily delete your files.
Since it looks like you're only letting the user input a plain dictionary, use ast.literal_eval(). It safely does what you want and nothing more.
eval:
This is very powerful, but is also very dangerous if you accept strings to evaluate from untrusted input. Suppose the string being evaluated is "os.system('rm -rf /')" ? It will really start deleting all the files on your computer.
ast.literal_eval:
Safely evaluate an expression node or a string containing a Python literal or container display. The string or node provided may only consist of the following Python literal structures: strings, bytes, numbers, tuples, lists, dicts, sets, booleans, None, bytes and sets.
Syntax:
eval(expression, globals=None, locals=None)
import ast
ast.literal_eval(node_or_string)
Example:
# python 2.x - doesn't accept operators in string format
import ast
ast.literal_eval('[1, 2, 3]') # output: [1, 2, 3]
ast.literal_eval('1+1') # output: ValueError: malformed string
# python 3.0 -3.6
import ast
ast.literal_eval("1+1") # output : 2
ast.literal_eval("{'a': 2, 'b': 3, 3:'xyz'}") # output : {'a': 2, 'b': 3, 3:'xyz'}
# type dictionary
ast.literal_eval("",{}) # output : Syntax Error required only one parameter
ast.literal_eval("__import__('os').system('rm -rf /')") # output : error
eval("__import__('os').system('rm -rf /')")
# output : start deleting all the files on your computer.
# restricting using global and local variables
eval("__import__('os').system('rm -rf /')",{'__builtins__':{}},{})
# output : Error due to blocked imports by passing '__builtins__':{} in global
# But still eval is not safe. we can access and break the code as given below
s = """
(lambda fc=(
lambda n: [
c for c in
().__class__.__bases__[0].__subclasses__()
if c.__name__ == n
][0]
):
fc("function")(
fc("code")(
0,0,0,0,"KABOOM",(),(),(),"","",0,""
),{}
)()
)()
"""
eval(s, {'__builtins__':{}})
In the above code ().__class__.__bases__[0] nothing but object itself.
Now we instantiated all the subclasses, here our main enter code hereobjective is to find one class named n from it.
We need to code object and function object from instantiated subclasses. This is an alternative way from CPython to access subclasses of object and attach the system.
From python 3.7 ast.literal_eval() is now stricter. Addition and subtraction of arbitrary numbers are no longer allowed. link
Python's eager in its evaluation, so eval(input(...)) (Python 3) will evaluate the user's input as soon as it hits the eval, regardless of what you do with the data afterwards. Therefore, this is not safe, especially when you eval user input.
Use ast.literal_eval.
As an example, entering this at the prompt could be very bad for you:
__import__('os').system('rm -rf /a-path-you-really-care-about')
In recent Python3 ast.literal_eval() no longer parses simple strings, instead you are supposed to use the ast.parse() method to create an AST then interpret it.
This is a complete example of using ast.parse() correctly in Python 3.6+ to evaluate simple arithmetic expressions safely.
import ast, operator, math
import logging
logger = logging.getLogger(__file__)
def safe_eval(s):
def checkmath(x, *args):
if x not in [x for x in dir(math) if not "__" in x]:
raise SyntaxError(f"Unknown func {x}()")
fun = getattr(math, x)
return fun(*args)
binOps = {
ast.Add: operator.add,
ast.Sub: operator.sub,
ast.Mult: operator.mul,
ast.Div: operator.truediv,
ast.Mod: operator.mod,
ast.Pow: operator.pow,
ast.Call: checkmath,
ast.BinOp: ast.BinOp,
}
unOps = {
ast.USub: operator.neg,
ast.UAdd: operator.pos,
ast.UnaryOp: ast.UnaryOp,
}
ops = tuple(binOps) + tuple(unOps)
tree = ast.parse(s, mode='eval')
def _eval(node):
if isinstance(node, ast.Expression):
logger.debug("Expr")
return _eval(node.body)
elif isinstance(node, ast.Str):
logger.debug("Str")
return node.s
elif isinstance(node, ast.Num):
logger.debug("Num")
return node.value
elif isinstance(node, ast.Constant):
logger.info("Const")
return node.value
elif isinstance(node, ast.BinOp):
logger.debug("BinOp")
if isinstance(node.left, ops):
left = _eval(node.left)
else:
left = node.left.value
if isinstance(node.right, ops):
right = _eval(node.right)
else:
right = node.right.value
return binOps[type(node.op)](left, right)
elif isinstance(node, ast.UnaryOp):
logger.debug("UpOp")
if isinstance(node.operand, ops):
operand = _eval(node.operand)
else:
operand = node.operand.value
return unOps[type(node.op)](operand)
elif isinstance(node, ast.Call):
args = [_eval(x) for x in node.args]
r = checkmath(node.func.id, *args)
return r
else:
raise SyntaxError(f"Bad syntax, {type(node)}")
return _eval(tree)
if __name__ == "__main__":
logger.setLevel(logging.DEBUG)
ch = logging.StreamHandler()
logger.addHandler(ch)
assert safe_eval("1+1") == 2
assert safe_eval("1+-5") == -4
assert safe_eval("-1") == -1
assert safe_eval("-+1") == -1
assert safe_eval("(100*10)+6") == 1006
assert safe_eval("100*(10+6)") == 1600
assert safe_eval("2**4") == 2**4
assert safe_eval("sqrt(16)+1") == math.sqrt(16) + 1
assert safe_eval("1.2345 * 10") == 1.2345 * 10
print("Tests pass")
If all you need is a user provided dictionary, a possible better solution is json.loads. The main limitation is that JSON dicts ("objects") require string keys. Also you can only provide literal data, but that is also the case for ast.literal_eval.
I have an issue checking variable types in ruby. Here is a sample code of python that I would like to replicate in ruby. I would like to check the input type: whether string, int, or list, and proceed to a specific printing action.
def printing (input):
if type(input) == type(""):
pass
elif type(input) == type(1):
pass
elif type(input) == type([]):
pass
elif type(input) == type({}):
pass
elif type(input) == type(()):
pass
I cannot find a method that will do this in ruby. The code below is what I want it to look like. I am assuming that I have to check the type at the case stage.
def printing (element)
case element
when element.type("")
puts element
when element.type(2)
puts element
when element.type({})
element.each_pair { |name, val| print "#{name} : #{value}"}
when element.type([])
element.each {|x| print x}
end
end
I think you are looking for Object#class. Here:
element = {}
element.class
# => Hash
a = []
a.class
# => Array
This will make your switch case as follows:
case element
when String
# do something
when Fixnum
# do something
when Hash
# do something
when Array
# do something
end
Note:
As mentioned by #ndn in comments below, case statement should not have .class in it (which I had initially in my answer). You can find the explanation here.
This is not the "correct answer", I would simply like to point out that you should not use type in python you should use isinstance instead
isinstance(input, list) # test for list
isinstance(inpit, [float, int]) # test for number
if you are using python 3 you can check for abstract base classes
import collections
isinstance(input, collections.abs.Sequence) # sequence = tuple, list and a lot of other stuff that behaves that way
I would like to output a user input expression to a string.
The reason is that the input expression is user defined. I want to output the result of the expression, and print the statement which lead to this result.
import sys
import shutil
expression1 = sys.path
expression2 = shutil.which
def get_expression_str(expression):
if callable(expression):
return expression.__module__ +'.'+ expression.__name__
else:
raise TypeError('Could not convert expression to string')
#print(get_expression_str(expression1))
# returns : builtins.TypeError: Could not convert expression to string
#print(get_expression_str(expression2))
# returns : shutil.which
#print(str(expression1))
#results in a list like ['/home/bernard/clones/it-should-work/unit_test', ... ,'/usr/lib/python3/dist-packages']
#print(repr(expression1))
#results in a list like ['/home/bernard/clones/it-should-work/unit_test', ... ,'/usr/lib/python3/dist-packages']
I looked into the Python inspect module but even
inspect.iscode(sys.path)
returns False
For those who wonder why it is the reverse of a string parsed to an expression using functools.partial see parse statement string
Background.
A program should work. Should, but it not always does. Because a program need specific resources, OS, OS version, other packages, files, etc. Every program needs different requirements (resources) to function properly.
Which specific requirement are needed can not be predicted. The system knows best which resources are and are not available. So instead of manually checking all settings and configurations let a help program do this for you.
So the user, or developer of a program, specify his requirements together with statements how to to retrieve this information : expressions. Which could be executed using eval. Could. Like mentioned on StackOverflow eval is evil.
Use of eval is hard to make secure using a blacklist, see : http://nedbatchelder.com/blog/201206/eval_really_is_dangerous.html
Using multiple tips of SO I use a namedtuple, with a string, to compare with the user input string, and a function.
A white-list is better then a blacklist. Only if the parsed expression string match a "bare_expression" then an expression is returned.
This white-list contains more information how to process f.e. the "unit_of_measurement" . It goes to far to explain what and why, but this is needed. The list of the namedtuples is much more then just a white-list and is defined :
Expr_UOfM = collections.namedtuple('Expr_UOfM', ['bare_expression', 'keylist', 'function', 'unit_of_measurement', 'attrlist'])
The namedtuple which match a (very limited) list:
Exp_list = [Expr_UOfM('sys.path', '' , sys.path, un.STR, []),
Expr_UOfM('shutil.which', '', shutil.which, None, [])]
This list may be very long and the content is crucial for further correct processing. Note the first and third field are very similar. There should be a single point of reference, but for me, this is on this moment not possible. Note the string : 'sys.path' is equal to (a part of) the user input, and the expression : sys.path is part of the namedtuple list. A good separation, limiting possible abuse.
If the string and the expression are not 100% identical weird behavior may occur which is very hard to debug.
So it want using the get_expression_str function check if the first and third field are identical. Just for total robustness of
the program.
I use Python 3.4
You could use inspect.getsource() and wrap your expression in a lambda. Then you can get an expression with this function:
def lambda_to_expr_str(lambda_fn):
"""c.f. https://stackoverflow.com/a/52615415/134077"""
if not lambda_fn.__name__ == "<lambda>":
raise ValueError('Tried to convert non-lambda expression to string')
else:
lambda_str = inspect.getsource(lambda_fn).strip()
expression_start = lambda_str.index(':') + 1
expression_str = lambda_str[expression_start:].strip()
if expression_str.endswith(')') and '(' not in expression_str:
# i.e. l = lambda_to_expr_str(lambda x: x + 1) => x + 1)
expression_str = expression_str[:-1]
return expression_str
Usage:
$ lambda_to_expr_str(lambda: sys.executable)
> 'sys.executable'
OR
$ f = lambda: sys.executable
$ lambda_to_expr_str(f)
> 'sys.executable'
And then eval with
$ eval(lambda_to_expr_str(lambda: sys.executable))
> '/usr/bin/python3.5'
Note that you can take parameters with this approach and pass them with the locals param of eval.
$ l = lambda_to_expr_str(lambda x: x + 1) # now l == 'x + 1'
$ eval(l, None, {'x': 1})
> 2
Here be Dragons. There are many pathological cases with this approach:
$ l, z = lambda_to_expr_str(lambda x: x + 1), 1234
$ l
> 'x + 1), 1234'
This is because inspect.getsource gets the entire line of code the lambda was declared on. Getting source of functions declared with def would avoid this problem, however passing a function body to eval is not possible as there could be side effects, i.e. setting variables, etc... Lambda's can produce side effects as well in Python 2, so even more dragons lie in pre-Python-3 land.
Why not use eval?
>>> exp1 = "sys.path"
>>> exp2 = "[x*x for x in [1,2,3]]"
>>> eval(exp1)
['', 'C:\\Python27\\lib\\site-packages\\setuptools-0.6c11-py2.7.egg', 'C:\\Pytho
n27\\lib\\site-packages\\pip-1.1-py2.7.egg', 'C:\\Python27\\lib\\site-packages\\
django_celery-3.1.1-py2.7.egg', 'C:\\Python27\\lib\\site-packages\\south-0.8.4-p
y2.7.egg', 'C:\\Windows\\system32\\python27.zip', 'C:\\Python27\\DLLs', 'C:\\Pyt
hon27\\lib', 'C:\\Python27\\lib\\plat-win', 'C:\\Python27\\lib\\lib-tk', 'C:\\Py
thon27', 'C:\\Python27\\lib\\site-packages', 'C:\\Python27\\lib\\site-packages\\
PIL']
>>> eval(exp2)
[1, 4, 9]
I have some inkling of how this might work, but I do not know the exact syntax of how to do it.
An example:
def function(string):
string = string + "A"
How will the function know that what I am inputting is a string?
Is it something along the lines of...
string = type.str
There are some ways to check the type of a variable. First, you could check if the parameter is an instance of str:
def isstring(parameter):
return isinstance(parameter, str)
Another way could be to try to concatenate a string (in this case "") with the parameter and catch eventually exceptions (TypeError exception):
def isstring(parameter):
try:
parameter += ""
return True
except TypeError:
return False
Another way could be to use type (similar to isinstance, but difference mostly when polymorphism is involved):
def isstr(parameter):
return type(parameter) == str
You could also use assert, but if you want just to check if a parameter is of a certain type, I would adopt the first case.
No, you could NOT, python is dynamic but not static, so it could not know the argument type at "compile time", instead, you could check the input type by assert.
def fun(string):
assert isinstance(string, basestring) # we usually check if it type is basestring as this works fine for both ASCII string and Unicode.
string = string + "A"
return string
if __name__ == '__main__':
fun(2)
This question already has answers here:
Replacements for switch statement in Python?
(44 answers)
Closed 27 days ago.
I have tried making a switch like statement in python, instead of having a lot of if statements.
The code looks like this:
def findStuff(cds):
L=[]
c=0
for i in range(0, len(cds), 3):
a=differencesTo(cds[i:i+3])
result = {
a[2][0]==1: c=i+1,
a[2][1]==1: c=i+2,
a[2][2]==1: c=i+3,
a[1]==1: L.append((cds[i:i+3], a[0], c))
}
return L
My problem is, that this does not work. (Works with if statements, but this would in my opinion be more pretty).
I have found some examples of switches in Python, and they follow this structure. Can anyone help me?
(a) I fail to see what is wrong with if...elif...else
(b) I assume that python does not have a switch statement for the same reason that Smalltalk doesn't: it's almost completely redundant, and in the case where you want to switch on types, you can add an appropriate method to your classes; and likewise switching on values should be largely redundant.
Note: I am informed in the comments that whatever Guido's reason for not creating a switch in the first place, PEPs to have it added were rejected on the basis that support for adding such a statement is extremely limited. See: http://www.python.org/dev/peps/pep-3103/
(c) If you really need switching behaviour, use a hashtable (dict) to store callables. The structure is:
switch_dict = {
Foo: self.doFoo,
Bar: self.doBar,
}
func = switch_dict[switch_var]
result = func() # or if they take args, pass args
There's nothing wrong with a long if:
if switch == 'case0':
do_case0()
elif switch == 'case1':
do_case1()
elif switch == 'case2':
do_case2()
...
If that's too long winded, or if you have a lot of cases, put them in a dictionary:
switch = {'case0': do_case0, 'case1': do_case1, 'case2': do_case2, ...}
switch[case_variable]()
// Alternative:
(switch[case_variable]).__call__()
If your conditions are a bit more complex, you need to think a little about your data structures. e.g.:
switch = {
(0,21): 'never have a pension',
(21,50): 'might have a pension',
(50,65): 'definitely have a pension',
(65, 200): 'already collecting pension'
}
for key, value in switch.items():
if key[0] <= case_var < key[1]:
print(value)
Other ans are suitable for older version of python. For python v3.10+ you can use match/case which is more powerful than general switch/case construct.
def something(val):
match val:
case "A":
return "A"
case "B":
return "B"
case "C":
return "C"
case _:
return "Default"
something("A")
Assignment in Python is a statement, and cannot be a part of expression. Also, using literal in this way evaluates everything at once, which is probably not what you want. Just use ifs, you won't gain any readability by using this.
I don't know which article you've found to do something like this, but this is really messy: the whole result diction will be always evaluated, and instead of doing only part of the work (as a switch / if do), you'll do the whole work everytime. (even if you use only a part of the result).
Really, a fast switch statement in Python is using "if":
if case == 1:
pass
elif case == 2:
pass
elif case == 3:
pass
else:
# default case
pass
With "get" method, you can have the same effect as "switch..case" in C.
Marcin example :
switch_dict = {
Foo: self.doFoo,
Bar: self.doBar,
}
func = switch_dict.get(switch_var, self.dodefault)
result = func() # or if they take args, pass args
You can do something like what you want, but you shouldn't. That said, here's how; you can see how it does not improve things.
The biggest problem with the way you have it is that Python will evaluate your tests and results once, at the time you declare the dictionary. What you'd have to do instead is make all conditions and the resulting statements functions; this way, evaluation is deferred until you call them. Fortunately there is a way to do this inline for simple functions using the lambda keyword. Secondly, the assignment statement can't be used as a value in Python, so our action functions (which are executed if the corresponding condition function returns a truthy value) have to return a value that will be used to increment c; they can't assign to c themselves.
Also, the items in a dictionary aren't ordered, so your tests won't necessarily be performed in the order you define them, meaning you probably should use something other than a dictionary that preserves order, such as a tuple or a list. I am assuming you want only ever one case to execute.
So, here we go:
def findStuff(cds):
cases = [ (lambda: a[2][0] == 1, lambda: i + 1),
(lambda: a[2][1] == 1, lambda: i + 2),
(lambda: a[2][2] == 1, lambda: i + 3),
(lambda: a[1] == 1, lambda: L.append(cds[i:i+3], a[0], c) or 0)
]
L=[]
c=0
for i in range(0, len(cds), 3):
a=differencesTo(cds[i:i+3])
for condition, action in cases:
if condition():
c += action()
break
return L
Is this more readable than a sequence of if/elif statements? Nooooooooooooo. In particular, the fourth case is far less comprehensible than it should be because we are having to rely on a function that returns the increment for c to modify a completely different variable, and then we have to figure out how to get it to return a 0 so that c won't actually be modified. Uuuuuugly.
Don't do this. In fact this code probably won't even run as-is, as I deemed it too ugly to test.
While there is nothing wrong with if..else, I find "switch in Python" still an intriguing problem statement. On that, I think Marcin's (deprecated) option (c) and/or Snim2's second variant can be written in a more readable way.
For this we can declare a switch class, and exploit the __init__() to declare the case we want to switch, while __call__() helps to hand over a dict listing the (case, function) pairs:
class switch(object):
def __init__(self, case):
self._case = case
def __call__(self, dict_):
try:
return dict_[self._case]()
except KeyError:
if 'else' in dict_:
return dict_['else']()
raise Exception('Given case wasn\'t found.')
Or, respectively, since a class with only two methods, of which one is __init__(), isn't really a class:
def switch(case):
def cases(dict_):
try:
return dict_[case]()
except KeyError:
if 'else' in dict_:
return dict_['else']()
raise Exception('Given case wasn\'t found.')
return cases
(note: choose something smarter than Exception)
With for example
def case_a():
print('hello world')
def case_b():
print('sth other than hello')
def default():
print('last resort')
you can call
switch('c') ({
'a': case_a,
'b': case_b,
'else': default
})
which, for this particular example would print
last resort
This doesn't behave like a C switch in that there is no break for the different cases, because each case executes only the function declared for the particular case (i.e. break is implicitly always called). Secondly, each case can list exactly only one function that will be executed upon a found case.