将具有特定格式的tsv解析为python

Question

I have a tsv file containing a network. Here's a snippet. Column 0 contains unique IDs, column 1 contains an alternative ID (not necessarily unique). Each pair of columns after that contains an 'interactor' and a score of interaction.

11746909_a_at A1CF             SHPRH    0.11081568      TRIM10    0.11914056   
11736238_a_at ABCA5           ANKS1A     0.1333185     CCDC90B    0.14495682   
11724734_at   ABCB8             HYKK    0.09577321        LDB3    0.09845833   
11723976_at   ABCC8          FAM161B    0.15087105         ID1    0.14801268   
11718612_a_at ABCD4            HOXC6    0.23559235       LCMT2    0.12867001   
11758217_s_at ABHD17C           FZD7    0.46334574      HIVEP3    0.24272481 

So for example, A1CF connects to SHPRH and TRIM10 with scores of 0.11081568 and 0.11914056 respectively. I'm trying to convert this data into a 'flat' format using pandas which would look like this:

11746909_a_at    A1CF    SHPRH   0.11081568
                         TRIM10  0.11914056 
11736238_a_at    ABCA5   ANKS1A  0.1333185
                         CCDC90B 0.14495682
...... and so on........ ........ ....

Note that each row can have an arbitrary number of (interactor, score) pairs.

I've tried setting columns 0 and 1 to indexes then giving the columns names df.colnames = ['Interactor', Weight']*int(df.shape[1]/2) then using pandas.groupby but so far my attempts have not been successful. Can anybody suggest a way to do this?

Answer 1

Producing an output dataframe like you specified above shouldn't be too hard

from collections import OrderedDict
import pandas as pd


def open_network_tsv(filepath):
    """
    Read the tsv file, returning every line split by tabs
    """
    with open(filepath) as network_file:
        for line in network_file.readlines():
            line_columns = line.strip().split('\t')
            yield line_columns

def get_connections(potential_conns):
    """
    Get the connections of a particular line, grouped
    in interactor:score pairs
    """
    for idx, val in enumerate(potential_conns):
        if not idx % 2:
            if len(potential_conns) >= idx + 2:
                yield val, potential_conns[idx+1]


def create_connections_df(filepath):
    """
    Build the desired dataframe
    """
    connections = OrderedDict({
        'uniq_id': [],
        'alias': [],
        'interactor': [],
        'score': []
    })
    for line in open_network_tsv(filepath):
        uniq_id, alias, *potential_conns = line
        for connection in get_connections(potential_conns):
            connections['uniq_id'].append(uniq_id)
            connections['alias'].append(alias)
            connections['interactor'].append(connection[0])
            connections['score'].append(connection[1])
    return pd.DataFrame(connections)

Maybe you can do a dataframe.set_index(['uniq_id', 'alias']) or dataframe.groupby(['uniq_id', 'alias']) on the output afterward