I was trying to make variables inside a loop. i.e. I pass a pattern of variables, and the pattern of their values and the variables are accordingly created and stored in a text file.
But, I tried something off topic and did this:
a = lambda a: a
for i in ["a", "b"]:
b = eval(i)(a)
print(i)
the output was:
a
b
Can anyone please explain what has happened here?
Edit:
I have analysed its answer.
I will paste it below.
Please verify if it is correct.
Thank you!
Lets first break the problem in parts.
def a(n):
return n
b = eval("a")(a)
print("a")
b = eval("b")(a)
print("b")
We can clearly see that the output is due to the two print statements.
print("a")
print("b")
Thus the rest of the statements play no part in the output.
def a(n):
return n
b = eval("a")(a)
b = eval("b")(a)
These statements can simply be put across like this:
def a(n):
return n
b = a(a)
b = b(a)
The statement
b = a(a)
makes the same effect as
def b(n):
return n
Thus the entire code can be put across like this:
def a(n):
return n
def b(n):
return n
print("a")
print("b")
Thus there is no ambiguity in this question now.
Here's how you can deconstruct your loop to understand for yourself, and please don't do that as pointed out in the comment.
a = lambda a: a
# First iteration
i = "a"
b = eval(i)(a)
print(i) # a
# Second iteration
i = "b"
b = eval(i)(a) # eval("b") is now <function __main__.<lambda>(a)>
print(i) # b
You are printing the variable i (which takes the values "a" and "b" since you loop over ["a", "b"]). If you want to see which values the variable b takes, print b instead:
a = lambda a: a
for i in ["a", "b"]:
b = eval(i)(a)
print(b)
Lets first break the problem in parts.
def a(n):
return n
b = eval("a")(a)
print("a")
b = eval("b")(a)
print("b")
We can clearly see that the output is due to the two print statements.
print("a")
print("b")
Thus the rest of the statements play no part in the output.
def a(n):
return n
b = eval("a")(a)
b = eval("b")(a)
These statements can simply be put across like this:
def a(n):
return n
b = a(a)
b = b(a)
The statement
b = a(a)
makes the same effect as
def b(n):
return n
Thus the entire code can be put across like this:
def a(n):
return n
def b(n):
return n
print("a")
print("b")
Thus there is no ambiguity in this question now.
I have multiple expensive functions that return results. I want to return a tuple of the results of all the checks if all the checks succeed. However, if one check fails I don't want to call the later checks, like the short-circuiting behavior of and. I could nest if statements, but that will get out of hand if there are a lot of checks. How can I get the short-circuit behavior of and while also storing the results for later use?
def check_a():
# do something and return the result,
# for simplicity, just make it "A"
return "A"
def check_b():
# do something and return the result,
# for simplicity, just make it "B"
return "B"
...
This doesn't short-circuit:
a = check_a()
b = check_b()
c = check_c()
if a and b and c:
return a, b, c
This is messy if there are many checks:
if a:
b = check_b()
if b:
c = check_c()
if c:
return a, b, c
Is there a shorter way to do this?
Just use a plain old for loop:
results = {}
for function in [check_a, check_b, ...]:
results[function.__name__] = result = function()
if not result:
break
The results will be a mapping of the function name to their return values, and you can do what you want with the values after the loop breaks.
Use an else clause on the for loop if you want special handling for the case where all of the functions have returned truthy results.
Write a function that takes an iterable of functions to run. Call each one and append the result to a list, or return None if the result is False. Either the function will stop calling further checks after one fails, or it will return the results of all the checks.
def all_or_none(checks, *args, **kwargs):
out = []
for check in checks:
rv = check(*args, **kwargs)
if not rv:
return None
out.append(rv)
return out
rv = all_or_none((check_a, check_b, check_c))
# rv is a list if all checks passed, otherwise None
if rv is not None:
return rv
def check_a(obj):
...
def check_b(obj):
...
# pass arguments to each check, useful for writing reusable checks
rv = all_or_none((check_a, check_b), obj=my_object)
In other languages that did have assignments as expressions you would be able to use
if (a = check_a()) and (b = check_b()) and (c = check_c()):
but Python is no such language. Still, we can circumvent the restriction and emulate that behaviour:
result = []
def put(value):
result.append(value)
return value
if put(check_a()) and put(check_b()) and put(check_c()):
# if you need them as variables, you could do
# (a, b, c) = result
# but you just want
return tuple(result)
This might loosen the connection between the variables and function calls a bit too much, so if you want to do lots of separate things with the variables, instead of using the result elements in the order they were put in the list, I would rather avoid this approach. Still, it might be quicker and shorter than some loop.
You could use either a list or an OrderedDict, using a for loop would serve the purpose of emulating short circuiting.
from collections import OrderedDict
def check_a():
return "A"
def check_b():
return "B"
def check_c():
return "C"
def check_d():
return False
def method1(*args):
results = []
for i, f in enumerate(args):
value = f()
results.append(value)
if not value:
return None
return results
def method2(*args):
results = OrderedDict()
for f in args:
results[f.__name__] = result = f()
if not result:
return None
return results
# Case 1, it should return check_a, check_b, check_c
for m in [method1, method2]:
print(m(check_a, check_b, check_c))
# Case 1, it should return None
for m in [method1, method2]:
print(m(check_a, check_b, check_d, check_c))
There are lots of ways to do this! Here's another.
You can use a generator expression to defer the execution of the functions. Then you can use itertools.takewhile to implement the short-circuiting logic by consuming items from the generator until one of them is false.
from itertools import takewhile
functions = (check_a, check_b, check_c)
generator = (f() for f in functions)
results = tuple(takewhile(bool, generator))
if len(results) == len(functions):
return results
Another way to tackle this is using a generator, since generators use lazy evaluation. First put all checks into a generator:
def checks():
yield check_a()
yield check_b()
yield check_c()
Now you could force evaluation of everything by converting it to a list:
list(checks())
But the standard all function does proper short cut evaluation on the iterator returned from checks(), and returns whether all elements are truthy:
all(checks())
Last, if you want the results of succeeding checks up to the failure you can use itertools.takewhile to take the first run of truthy values only. Since the result of takewhile is lazy itself you'll need to convert it to a list to see the result in a REPL:
from itertools import takewhile
takewhile(lambda x: x, checks())
list(takewhile(lambda x: x, checks()))
main logic:
results = list(takewhile(lambda x: x, map(lambda x: x(), function_list)))
if len(results) == len(function_list):
return results
you can learn a lot about collection transformations if you look at all methods of an api like http://www.scala-lang.org/api/2.11.7/#scala.collection.immutable.List and search/implement python equivalents
logic with setup and alternatives:
import sys
if sys.version_info.major == 2:
from collections import imap
map = imap
def test(bool):
def inner():
print(bool)
return bool
return inner
def function_for_return():
function_list = [test(True),test(True),test(False),test(True)]
from itertools import takewhile
print("results:")
results = list(takewhile(lambda x:x,map(lambda x:x(),function_list)))
if len(results) == len(function_list):
return results
print(results)
#personally i prefer another syntax:
class Iterator(object):
def __init__(self,iterable):
self.iterator = iter(iterable)
def __next__(self):
return next(self.iterator)
def __iter__(self):
return self
def map(self,f):
return Iterator(map(f,self.iterator))
def takewhile(self,f):
return Iterator(takewhile(f,self.iterator))
print("results2:")
results2 = list(
Iterator(function_list)
.map(lambda x:x())
.takewhile(lambda x:x)
)
print(results2)
print("with additional information")
function_list2 = [(test(True),"a"),(test(True),"b"),(test(False),"c"),(test(True),"d")]
results3 = list(
Iterator(function_list2)
.map(lambda x:(x[0](),x[1]))
.takewhile(lambda x:x[0])
)
print(results3)
function_for_return()
If you don't need to take an arbitrary number of expressions at runtime (possibly wrapped in lambdas), you can expand your code directly into this pattern:
def f ():
try:
return (<a> or jump(),
<b> or jump(),
<c> or jump())
except NonLocalExit:
return None
Where those definitions apply:
class NonLocalExit(Exception):
pass
def jump():
raise NonLocalExit()
Flexible short circuiting is really best done with Exceptions. For a very simple prototype you could even just assert each check result:
try:
a = check_a()
assert a
b = check_b()
assert b
c = check_c()
assert c
return a, b, c
except AssertionException as e:
return None
You should probably raise a custom Exception instead. You could change your check_X functions to raise Exceptions themself, in an arbitrary nested way. Or you could wrap or decorate your check_X functions to raise errors on falsy return values.
In short, exception handling is very flexible and exactly what you are looking for, don't be afraid to use it. If you learned somewhere that exception handling is not to be used for your own flow control, this does not apply to python. Liberal use of exception handling is considered pythonic, as in EAFP.
You mentioned 'short-circuiting' in your answer, which can be done with the 'or' statement. Top answer basically does the same thing, but in case someone wants to know more about this behaviour you could do this;
class Container(object):
def __init__(self):
self.values = []
def check_and_cache(self, value, checking_function):
value_true = checking_function(value)
if value_true:
self.values.append(value)
return True
c = Container()
if not c.check_and_cache(a, check_a) or not c.check_and_cache(b, check_b) or not c.check_and_cache(c, check_c):
print 'done'
return tuple(c.values)
The 'not .. or' setup of the if statements will result in a 'True' if the check fails, so the overall if statement passes without evaluating the remaining values.
Since I can not comment "wim":s answer as guest, I'll just add an extra answer.
Since you want a tuple, you should collect the results in a list and then cast to tuple.
def short_eval(*checks):
result = []
for check in checks:
checked = check()
if not checked:
break
result.append(checked)
return tuple(result)
# Example
wished = short_eval(check_a, check_b, check_c)
You can try use #lazy_function decorator from lazy_python
package. Example of usage:
from lazy import lazy_function, strict
#lazy_function
def check(a, b):
strict(print('Call: {} {}'.format(a, b)))
if a + b > a * b:
return '{}, {}'.format(a, b)
a = check(-1, -2)
b = check(1, 2)
c = check(-1, 2)
print('First condition')
if c and a and b: print('Ok: {}'.format((a, b)))
print('Second condition')
if c and b: print('Ok: {}'.format((c, b)))
# Output:
# First condition
# Call: -1 2
# Call: -1 -2
# Second condition
# Call: 1 2
# Ok: ('-1, 2', '1, 2')
This is similar to Bergi's answer but I think that answer misses the point of wanting separate functions (check_a, check_b, check_c):
list1 = []
def check_a():
condition = True
a = 1
if (condition):
list1.append(a)
print ("checking a")
return True
else:
return False
def check_b():
condition = False
b = 2
if (condition):
list1.append(b)
print ("checking b")
return True
else:
return False
def check_c():
condition = True
c = 3
if (condition):
list1.append(c)
print ("checking c")
return True
else:
return False
if check_a() and check_b() and check_c():
# won't get here
tuple1 = tuple(list1)
print (tuple1)
# output is:
# checking a
# (1,)
Or, if you don't want to use the global list, pass a reference of a local list to each of the functions.
If the main objection is
This is messy if there are many checks:
if a:
b = check_b()
if b:
c = check_c()
if c:
return a, b, c
A fairly nice pattern is to reverse the condition and return early
if not a:
return # None, or some value, or however you want to handle this
b = check_b()
if not b:
return
c = check_c()
if not c:
return
# ok, they were all truthy
return a, b, c
Is there a way to have
statements = [statement1, statement2, statement3, ...]
in Python?
I want to be able to do:
run statements[i]
or:
f = statements[j] (where f is a function)
P.S. I want to have a list of assignment statements (lambda would not work) and I rather not create functions. For example:
switch = [output = input, output = 2 * input, output = input ** 2]
Is there any other way than defining a function for each entry?
Thank you everyone who answered my question.
Yes. Functions are first-class-citizens in python: i.e. you could pass them as parameters or even store them in an array.
It is not uncommon to have a list of functions:
You could build a simple registry in python like this:
#!/usr/bin/env python
processing_pipeline = []
def step(function):
processing_pipeline.append(function);
return function
#step
def step_1(data):
print("processing step1")
#step
def step_2(data):
print("processing step2")
#step
def step_3(data):
print("processing step3")
def main():
data = {}
for process in processing_pipeline:
process(data)
if __name__ == '__main__':
main()
Here the processing_pipeline is just a list with functions.
step is a so called decorator-function, which works like a closure.
The python interpreter adds while parsing the file every decorated #step to the pipeline.
And you are able to access the function with an iterator, or via processing_pipeline[i]: try adding processing_pipeline[2](data).
I want to be able to do: run statements[i]
well, you can do that by exec:
statements = ["result=max(1,2)","print(result)"]
for idx in range(len(statements)):
exec(statements[idx])
print(result)
Hope it helps!
This is perfectly fine:
def addbla(item):
return item + ' bla'
items = ['Trump', 'Donald', 'tax declaration']
new_items = [addbla(item) for item in items]
print(new_items)
It adds a political statement to every item in items :)
If you want to run a block of statements, use a function.
def run_statements():
func()
for i in range(3):
if i > 1:
break
extra_func()
run_statements()
If you want to choose specific statements from a list, wrap each one in a function:
def looper():
for i in range(3):
if i>1:
break
def func():
print('hello')
statements = [looper, func]
statements[-1]()
If your statements are simply function calls, you can put them directly into a list without creating wrapper functions.
You can do:
funcs = [min, max, sum]
f = funcs[0]
funcs[1](1,2,3) # out 3
funcs[2]([1,2,3]) # out 6
Since we've had every other way, I thought I'd toss this out:
def a(input):
return pow(input, 3)
def b(input):
return abs(input)
def c(input):
return "I have {0} chickens".format(str(input))
#make an array of functions
foo = [a,b,c]
#make a list comprehension of the list of functions
dop = [x(3) for x in foo]
print dop
please keep in mind that while I showcase my code, that I am fairly new to programming. So please forgive any problems. I am writing a piece of python code that uses the output of one function and then averages it in another function. I am having troubling proceeding on how to do that, this is what I have so far:
def avg(A):
if not A:
return 0
return sum(A) / len(A)
Using the function above, I have to use it to calculate the average of the function produced below:
def SampleFunction(): # Example Function
A = list(range(300))
for i in range(300):
if i%2:
A[i] = 3.1*(i+1)**1.2 - 7.9*i
else:
A[i] = 4.2*(i+2)**.8 - 6.8*i
return A
Below this is a function I have trying to tie the two together.
def average(SampleFunction):
if len(SampleFunction) == 0: return 0
return sum(SampleFunction) / len(SampleFunction)
def avg(A):
if not A:
return 0
return sum(A) / len(A)
def SampleFunction(): # Example Function
A = list(range(300))
for i in range(300):
if i%2:
A[i] = 3.1*(i+1)**1.2 - 7.9*i
else:
A[i] = 4.2*(i+2)**.8 - 6.8*i
return avg(A) #Return the avg of A instead of just A
You are right at the moment of passing SampleFunction as parameter, but it's a function, you have to call invoke it inside average():
def average(some_function):
result = some_function() # invoke
return avg(result) # use the already defined function 'avg'
When you call it, pass the function you want to average():
print average(SampleFunction)
Note:
I would recommend you to follow Python naming conventions. Names like SomeName are used for classes, whereas names like some_name are used for functions.