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copied!<p>Having such a monolithic function that takes a filename instead of an open file is by itself not very Pythonic. You are trying to implement a stream processor here (<code>file stream -> line stream -> CSV record stream -> [transformator ->] data stream</code>), so using a generator is actually a good idea. I'd slightly refactor this to be a bit more modular:</p> <pre><code>import csv from collections import namedtuple def csv_rows(infile, header): reader = csv.reader(infile, delimiter="\t") if header: next(reader) return reader def data_sets(infile, header): gene_data = namedtuple("Data", 'id, name, q, start, end, sym') for row in csv_rows(infile, header): yield gene_data(*row) def read_file_type1(infile, header=True): # for this file type, we only need to pass the caller the raw # data objects return data_sets(infile, header) def read_file_type2(infile, header=True): # for this file type, we have to pre-process the data sets # before yielding them. A good way to express this is using a # generator expression (we could also add a filtering condition here) return (transform_data_set(x) for x in data_sets(infile, header)) # Usage sample: with open("...", "r") as f: for obj in read_file_type1(f): print obj </code></pre> <p>As you can see, we have to pass the <code>header</code> argument all the way through the function chain. This is a strong hint that an object-oriented approach would be appropriate here. The fact that we obviously face a hierarchical type structure here (basic data file, type1, type2) supports this.</p>

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