Check whether a sequence of characters is present in the string C++

Credit: Jürgen Hermann, Horst Hansen

You need to check for the occurrence of any of a set of characters in a string.

The solution generalizes to any sequence (not just a string), and any set (any object in which membership can be tested with the in operator, not just one of characters):

def containsAny(str, set): """ Check whether sequence str contains ANY of the items in set. """ return 1 in [c in str for c in set] def containsAll(str, set): """ Check whether sequence str contains ALL of the items in set. """ return 0 not in [c in str for c in set]

While the find and count string methods can check for substring occurrences, there is no ready-made function to check for the occurrence in a string of a set of characters.

While working on a condition to check whether a string contained the special characters used in the glob.glob standard library function, I came up with the above code (with help from the OpenProjects IRC channel #python). Written this way, it really is compatible with human thinking, even though you might not come up with such code intuitively. That is often the case with list comprehensions.

The following code creates a list of 1/0 values, one for each item in the set:

[c in str for c in set]

Then this code checks whether there is at least one true value in that list:

1 in [c in str for c in set]

Similarly, this checks that no false values are in the list:

0 not in [c in str for c in set]

Usage examples are best cast in the form of unit tests to be appended to the .py source file of this module, with the usual idiom to ensure that the tests execute if the module runs as a main script:

if _ _name_ _ == "_ _main_ _": # unit tests, must print "OK!" when run assert containsAny('*.py', '*?[]') assert not containsAny('file.txt', '*?[]') assert containsAll('43221', '123') assert not containsAll('134', '123') print "OK!"

Of course, while the previous idioms are neat, there are alternatives (aren’t there always?). Here are the most elementary—and thus, in a sense, the most Pythonic—alternatives:

def containsAny(str, set): for c in set: if c in str: return 1 return 0 def containsAll(str, set): for c in set: if c not in str: return 0 return 1

Here are some alternatives that ensure minimal looping (earliest possible return). These are the most concise and thus, in a sense, the most powerful:

from operator import and_, or_, contains def containsAny(str, set): return reduce(or_, map(contains, len(set)*[str], set)) def containsAll(str, set): return reduce(and_, map(contains, len(set)*[str], set))

Here are some even slimmer variants of the latter that rely on a special method that string objects supply only in Python 2.2 and later:

from operator import and_, or_ def containsAny(str, set): return reduce(or_, map(str._ _contains_ _, set)) def containsAll(str, set): return reduce(and_, map(str._ _contains_ _, set))

And here is a tricky variant that relies on functionality also available in 2.0:

def containsAll(str, set): try: map(str.index, set) except ValueError: return 0 else: return 1

Fortunately, this rather tricky approach lacks an immediately obvious variant applicable to implement containsAny. However, one last tricky scheme, based on string.translate’s ability to delete all characters in a set, does apply to both functions:

import string notrans = string.maketrans('', '') # identity "translation" def containsAny(str, set): return len(set)!=len(set.translate(notrans, str)) def containsAll(str, set): return 0==len(set.translate(notrans, str))

This trick at least has some depth—it relies on set.translate(notrans, str) being the subsequence of set that is made of characters not in str. If that subsequence has the same length as set, no characters have been removed by set.translate, so no characters of set are in str. Conversely, if that subsequence has length 0, all characters have been removed, so all characters of set are in str. The translate method of string objects keeps coming up naturally when one wants to treat strings as sets of characters, partly because it’s so speedy and partly because it’s so handy and flexible. See Recipe 3.8 for another similar application.

One last observation is that these different ways to approach the task have very different levels of generality. At one extreme, the earliest approaches, relying only on in (for looping on str and for membership in set) are the most general; they are not at all limited to string processing, and they make truly minimal demands on the representations of str and set. At the other extreme, the last approach, relying on the translate method, works only when both str and set are strings or closely mimic string objects’ functionality.

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Credit: Jürgen Hermann, Nick Perkins

Given a set of characters to keep, you need to build a filtering functor (a function-like, callable object). The specific functor you need to build is one that, applied to any string s, returns a copy of s that contains only characters in the set.

The string.maketrans function and translate method of string objects are fast and handy for all tasks of this ilk:

import string # Make a reusable string of all characters _allchars = string.maketrans('', '') def makefilter(keep): """ Return a functor that takes a string and returns a partial copy of that string consisting of only the characters in 'keep'. """ # Make a string of all characters that are not in 'keep' delchars = _allchars.translate(_allchars, keep) # Return the functor, binding the two strings as default args return lambda s, a=_allchars, d=delchars: s.translate(a, d) def canonicform(keep): """ Given a string, considered as a set of characters, return the string's characters as a canonic-form string: alphabetized and without duplicates. """ return makefilter(keep)(_allchars) if _ _name_ _ == '_ _main_ _': identifier = makefilter(string.letters + string.digits + '_') print identifier(_allchars)

The key to understanding this recipe lies in the definitions of the translate and maketrans functions in the string module. translate takes a string and replaces each character in it with the corresponding character in the translation table passed in as the second argument, deleting the characters specified in the third argument. maketrans is a utility routine that helps create the translation tables.

Efficiency is vastly improved by splitting the filtering task into preparation and execution phases. The string of all characters is clearly reusable, so we build it once and for all when this module is imported. That way, we ensure that each filtering functor has a reference to the same string of all characters, not wasting any memory. The string of characters to delete depends on the set of characters to keep, so we build it in the makefilter factory function. This is done quite rapidly using the translate method to delete the characters to keep from the string of all characters. The translate method is very fast, as are the construction and execution of these useful little functors. The solution also supplies an extremely simple function to put any set of characters, originally an arbitrary string, into canonic-string form (alphabetically sorted, without duplicates). The same trick encapsulated in the canonicform function is also explicitly used in the test code that is executed when this runs as a script.

Of course, you don’t have to use lambda (here or anywhere else). A named function local to the factory function will do just as well. In other words, this recipe works fine if you change makefilter’s return statement into the following two statements:

def filter(s, a=_allchars, d=delchars): return s.translate(a, d) return filter

Many Pythonistas would consider this clearer and more readable.

This isn’t a big issue, but remember that lambda is never necessary. In any case in which you find yourself straining to fit code into a lambda’s limitations (i.e., just an expression, with no statements allowed), you can and should always use a local named function instead, to avoid all the limitations and problems.

With Python 2.2, or Python 2.1 and a from _ _future_ _ import nested_scopes, you get lexically nested scopes, so that if you want to, you can avoid binding _allchars and delchars as default values for arguments in the returned functor. However, it is (marginally) faster to use this binding anyway: local variables are the fastest kind to access, and arguments are nothing but prebound local variables. Globals and names from nested scopes require a little more effort from the interpreter (and sometimes, perhaps more significantly, from a human being who is reading the code). This is why we bind _allchars as argument a here despite the fact that, in any release of Python, we could have just accessed it as a global variable.

Documentation for the maketrans function in the string module in the Library Reference.

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