Seems like the wrong scope of a question for this particular mailing list.
Maybe you want a python data science group that is likely to use pandas in
their workflows?
On Sat, Feb 8, 2020, 2:50 AM <himanshu...@gmail.com> wrote:
> Hi all, I am a biologist trying to learn python. Please help me for the
> below code.
>
> Number of files can be more than 50
>
> >>> file1
> cNo gene year
> 1 A,B 2004
> 2 C,D 2008
> 3 K,L 2011
>
> >>> file2
> cNo gene year
> 1 a,e 2001
> 2 d,c,p 2003
> 3 x,y,x 2000
> 4 m,n 1988
>
> >>> file3
> cNo gene year
> 1 R,S 2002
> 2 X 2005
> 3 A,Q 2002
>
>
> Condition: compare gene of each file among all and find common/partial
> common 'genes' and respective 'name' and 'cNo'
>
> final_output
> name cNo genes
> file1,file2,file3 1,1,3 [A,B], [a,e], [A,Q]
> file1,file2 2,2 [C,D], [d,c,p]
> file2,file3 3,2 [x,y,z], [X]
>
>
> import pandas as pd
> import functools
>
> file1 = pd.DataFrame({'cNo':[1,2,3], 'gene':
> ['A,B','C,D','K,L'],'year':[2004,2008,2011]})
> file2 =
> pd.DataFrame({'cNo':[1,2,3,4],'gene':['a,e','d,c,p','x,y,x','m,n'],'year':[2001,2003,2000,1988]})
> file3 =
> pd.DataFrame({'cNo':[1,2,3],'gene':['R,S','X','A,Q'],'year':[2002,2005,2002]})
>
> files = [file1, file2, file3]
>
> #I don't know how to merge by partial matching of column
> df = functools.reduce(lambda left,right: pd.merge(left,right,on='genes'),
> files)
> print (df)
>
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