email               cc  nr_of_transactions  last_transaction_date   timebucket  total_basket
email1@email.com    us  2                   datetime value          1           20.29
email2@email.com    gb  3                   datetime value          2           50.84
email3@email.com    ca  5                   datetime value          3           119.12
...                 ... ...                 ...                     ...         ...

import csv
import scipy.stats as stats
import itertools
from operator import itemgetter


def add_rankperc(filename):
    '''
    Function that calculates percentile rank of total basket value of a user (i.e. email) within a country. Next, it assigns the user to a rankbucket based on its percentile rank, using the following rules:
     Percentage rank between 75 and 100 -> top25
     Percentage rank between 25 and 74  -> mid50
     Percentage rank between 0 and 24   -> bottom25
    '''

    # Defining headers for ease of use/DictReader
    headers = ['email', 'cc', 'nr_transactions', 'last_transaction_date', 'timebucket', 'total_basket']
    groups = []

    with open(filename, encoding='utf-8', mode='r') as f_in:
        # Input file is tab-separated, hence dialect='excel-tab'
        r = csv.DictReader(f_in, dialect='excel-tab', fieldnames=headers)
        # DictReader reads all dict values as strings, converting total_basket to a float
        dict_list = []
        for row in r:
            row['total_basket'] = float(row['total_basket'])
            # Append row to a list (of dictionaries) for further processing
            dict_list.append(row)

    # Groupby function on cc and total_basket
    for key, group in itertools.groupby(sorted(dict_list, key=itemgetter('cc', 'total_basket')), key=itemgetter('cc')):
        rows = list(group)
        for row in rows:
            # Calculates the percentile rank for each value for each country
            row['rankperc'] = stats.percentileofscore([row['total_basket'] for row in rows], row['total_basket'])
            # Percentage rank between 75 and 100 -> top25
            if 75 <= row['rankperc'] <= 100:
                row['rankbucket'] = 'top25'
            # Percentage rank between 25 and 74 -> mid50
            elif 25 <= row['rankperc'] < 75:
                row['rankbucket'] = 'mid50'
            # Percentage rank between 0 and 24 -> bottom25
            else:
                row['rankbucket'] = 'bottom25'
            # Appending all rows to a list to be able to return it and use it in another function
            groups.append(row)
    return groups


def filter_n_write(data):
    '''
    Function takes input data, groups by specified keys and outputs only the e-mail addresses to csv files as per the respective grouping.
    '''

    # Creating group iterator based on keys
    for key, group in itertools.groupby(sorted(data, key=itemgetter('timebucket', 'rankbucket')), key=itemgetter('timebucket', 'rankbucket')):
        # List comprehension to create a list of lists of email addresses. One row corresponds to the respective combination of grouping keys.
        emails = list([row['email'] for row in group])
        # Dynamically naming output file based on grouping keys
        f_out = 'output-{}-{}.csv'.format(key[0], key[1])
        with open(f_out, encoding='utf-8', mode='w') as fout:
            w = csv.writer(fout, dialect='excel', lineterminator='
')
            # Writerows using list comprehension to write each email in emails iterator (i.e. one address per row). Wrapping email in brackets to write full address in one cell.
            w.writerows([email] for email in emails)

filter_n_write(add_rankperc('infile.tsv'))

import csv
from scipy.stats import percentileofscore
from operator import itemgetter

# Run through the whole file once, saving each row to a file corresponding to
# its 'cc' column
cc_dict = {}
with open(input_path, encoding="utf-8", mode='r') as infile:
  csv_reader = csv.reader(infile, dialect="excel-tab")
  for row in csv_reader:
    cc = row[1]
    if cc not in cc_dict:
      intermediate_path ="intermediate_cc_{}.txt".format(cc)
      outfile = open(intermediate_path, mode='w', newline='')
      csv_writer = csv.writer(outfile)
      cc_dict[cc] = (intermediate_path, outfile, csv_writer)
    _ = cc_dict[cc][2].writerow(row)

# Close the output files
for cc in cc_dict.keys():
  cc_dict[cc][1].close()

# Run through the whole file once for each 'cc' value
for cc in cc_dict.keys():
  intermediate_path = cc_dict[cc][0]
  with open(intermediate_path, mode='r', newline='') as infile:
    csv_reader = csv.reader(infile)
    # Pick out all of the rows with the 'cc' value under consideration
    group = [row for row in csv_reader if row[1] == cc]
    # Get the 'total_basket' values for the group
    A_scores = [float(row[5]) for row in group]
    for row in group:
      # Compute this row's 'total_basket' score based on the rest of the
      # group's
      p = percentileofscore(A_scores, float(row[5]))
      row.append(p)
      # Categorize the score
      bucket = ("bottom25" if p < 25 else ("mid50" if p < 75 else"top100"))
      row.append(bucket)
  # Save the augmented rows to an intermediate file
  with open(output_path, mode='a', newline='') as outfile:
    csv_writer = csv.writer(outfile)
    csv_writer.writerows(group)