saving some time summarizing individual excel files

Bosque_summarize.py

#!/usr/bin/env python
from glob import glob
import pandas as pd
import re

#keep a running list of results
results_list = []

#for each excel file, which we will call xlsx
for xlsx in glob("*.xlsx"):
    #see if we can extract the ID and isolation number with a regular expression
    #regular expression: http://regexr.com/3glnf
    match = re.match(r'(\d+)_isolation(\d+)\.xlsx', xlsx)
    if match:
        #trick to convert the strings into numbers called "list comprehension"
        sample_id, isolation = [int(x) for x in match.groups()]
    else:
        print("File {} doesn't seem to be named like I thought it should be.".format(xlsx))
        continue
        
    #read in the data from excel into a pandas data frame
    df = pd.read_excel(xlsx)
    
    #select all rows (:) and columns from mult_steps to noise_dist ('mult_steps':'noise_dist')
    # sum them, then divide by the number of rows, which is the first number in df.shape
    summary = df.loc[:,'mult_steps':'noise_dist'].sum() / df.shape[0]
    
    #add the sample ID and isolation number into the result
    summary.name = sample_id
    summary['isolation'] = isolation
    
    #add our results to the list
    results_list.append(summary)

#finally, concatenate our results and save to 'sumary.csv'
results = pd.concat(results_list, axis=1)
results.to_csv('summary.csv')