chr     start       stop        geneID
0 chr13   32889584    32889814    BRCA2
1 chr13   32890536    32890737    BRCA2
2 chr13   32893194    32893307    BRCA2
3 chr13   32893282    32893400    BRCA2
4 chr13   32893363    32893466    BRCA2
5 chr13   32899127    32899242    BRCA2

import pandas as pd
import numpy as np

columns = ['chr','start','stop','geneID']
bed = pd.read_table('bedfile.txt',sep='s',names=['chr','start','stop','geneID'],engine='python')

def bed_prepare(inp_bed):
    inp_bed['next_start'] = inp_bed['start'].shift(periods=-1)
    inp_bed['distance_to_next'] = inp_bed['next_start'] - inp_bed['stop']
    inp_bed['next_region_overlap'] = inp_bed['next_start'] < inp_bed['stop']
    intermediate_bed = inp_bed
    return intermediate_bed

print bed_prepare(bed)

chr     start      stop geneID  next_start  distance_to_next  next_region_overlap
0    chr13  32889584  32889814  BRCA2    32890536               722  False
1    chr13  32890536  32890737  BRCA2    32893194              2457  False
2    chr13  32893194  32893307  BRCA2    32893282               -25  True
3    chr13  32893282  32893400  BRCA2    32893363               -37  True
4    chr13  32893363  32893466  BRCA2    32899127              5661  False

new_bed = pd.DataFrame(data=np.zeros((0,len(columns))),columns=columns)

def bed_collapse(intermediate_bed, new_bed):
    for row in bed.itertuples():
        if row[7] == False:
            output_row = list(row[1:5])
            #print output_row
        if row[7] == True:
            output_row = list(row[1:3])
            bed.itertuples().next()
            print row
            output_row.append(row[3])
            output_row.append(row[4])
            #print output_row
        #print output_row
        new_bed = new_bed.append({columns[0]:output_row[0],columns[1]:output_row[1],columns[2]:output_row[2],columns[
3]:output_row[3]},ignore_index=True)
    output_bed = new_bed
    return output_bed


int_bed = bed_prepare(bed)
print bed_collapse(int_bed,new_bed)

chr     start       stop        geneID
0 chr13   32889584    32889814    BRCA2
1 chr13   32890536    32890737    BRCA2
2 chr13   32893194    32893466    BRCA2
5 chr13   32899127    32899242    BRCA2