On 03/06/14 17:24, jarod...@libero.it wrote:
with open("prova.csv") as p:
If processing csv files its usually better to use the csv modulew, it
can handle many obscure quoting issues etc, especially if the data is
sourced from, say, Excel.
In particular I recommend using the dict-reader which returns a
list of dictionaries based on your column names. This makes
writing list comprehensions to extract data sets much easier.
['programs ', 'sample', 'gene', 'values']
['program1', 'sample1', 'TP53', '2']
['program1', 'sample1', 'TP53', '3']
['program1', 'sample2', 'PRNP', '4']
['program1', 'sample2', 'ATF3', '3']
['program2', 'sample1', 'TP53', '2']
['program2', 'sample1', 'PRNP', '5']
['program2', 'sample2', 'TRIM32', '4']
['program2', 'sample2', 'TLK1', '4']
I want to create a dictionary with set data with the names of the genes:
example:
dic = {}
dic['program1-sample1] = set(TP53)
You could do this using the dict reader of csv module like
tp53set = [sample for sample in data if datum['gene'] == 'TP53']
Obviously you can select which sample columns to keep
as you require.
You ask for a set but don't illustrate what that set
would actually contain so I'm not sure what aspects of
the data you are trying to get at...
So If I have a dictionary like that I can compare two set I will compare the
capacity of the programs in function of the gene show.
Sorry, that was just gobbledegook to me. No idea what you mean.
--
Alan G
Author of the Learn to Program web site
http://www.alan-g.me.uk/
http://www.flickr.com/photos/alangauldphotos
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