Pandas groupby nunique输出到列表

Question

I have as input a dataset like the following:

labels = ['chrom', 'start', 'end', 'read']
my_data = [['chr1', 784344, 800125, 'read1'],
           ['chr1', 784344, 800124, 'read2'],
           ['chr1', 784344, 800124, 'read3']]    

Which I convert to a pandas dataframe using:

my_data_pd = pd.DataFrame.from_records(my_data, columns=labels)

and that looks like this:

  chrom   start     end   read
0  chr1  784344  800125  read1
1  chr1  784344  800124  read2
2  chr1  784344  800124  read3

What I want to do is the following: I wan't merge the rows that have indentical chrom,start,end values, and count the number of disntinct occurences of the values in the 'read' column for those rows that were merged. Finally, I want to convert convert that output to a list/tuple, as in this example (note that the last column has the count information):

[('chr1', 784344, 800125,1), ('chr1', 784344, 800124,2)]

What I have been able to do:

Unsing Pandas Groupby and the nunique() with the command:

my_data_pd.groupby(['chrom','start','end'],sort=False).read.nunique()

I arrive to a Pandas.Series object that looks to what I want:

chrom  start   end   
chr1   784344  800125    1
               800124    2
Name: read, dtype: int64

However, when I convert it to a list/tuple using:

 sortedd.index.tolist()

the last column gets excluded, leading to the resulting output:

[('chr1', 784344, 800125), ('chr1', 784344, 800124)]

Any idea about how can I get around trough this problem?

For all those that might come up with a solution, I am doing this inside a big program thousands of times, so speed is a big issue. Thats the reason I am avoiding other tools like BedTools and pybedtools

Thanks!

Answer 1

You can set_index

sortedd.to_frame('val').set_index('val',append=True).index.tolist()
Out[277]: [('chr1', 784344, 800125, 1), ('chr1', 784344, 800124, 2)]

Answer 2

First reset_index and then in list comprehension convert to tuples :

L = [tuple(x) for x in sortedd.reset_index().values.tolist()]
print (L)
[('chr1', 784344, 800125, 1), ('chr1', 784344, 800124, 2)]

Answer 3

You can use multi index ie

idx = pd.MultiIndex.from_arrays(sortedd.reset_index().values.T)

idx.tolist()
[('chr1', 784344, 800125, 1), ('chr1', 784344, 800124, 2)]