SeqTools extends the functionalities of itertools to indexable (list-like) objects. Some of the provided functionalities include: element-wise function mapping, reordering, reindexing, concatenation, joining, slicing, minibatching, etc.
SeqTools functions implement on-demand evaluation under the hood: operations and transformations are only applied to individual items when they are actually accessed. A simple but powerful prefetch function is also provided to quickly evaluate elements.
SeqTools originally targets data science, more precisely the data preprocessing stages. Being aware of the experimental nature of this usage, on-demand execution is made as transparent as possible by providing fault-tolerant functions and insightful error message.
>>> def f1(x): ... return x + 1 ... >>> def f2(x): # slow and memory heavy transformation ... time.sleep(.01) ... return [x for _ in range(500)] ... >>> def f3(x): ... return sum(x) / len(x) ... >>> data = list(range(1000))
Without delayed evaluation, defining the pipeline and reading values looks like so:
>>> tmp1 = [f1(x) for x in data] >>> tmp2 = [f2(x) for x in tmp1] # takes 10 seconds and a lot of memory >>> res = [f3(x) for x in tmp2] >>> print(res) 3.0 >>> print(max(tmp2)) # requires to store 499 500 useless values along 3
>>> tmp1 = seqtools.smap(f1, data) >>> tmp2 = seqtools.smap(f2, tmp1) >>> res = seqtools.smap(f3, tmp2) # no computations so far >>> print(res) # takes 0.01 seconds 3.0 >>> print(max(tmp2)) # easy access to intermediate results 3
The library comes with a set of functions to manipulate sequences:
and others (suggestions are also welcome).