Calculate the PPM score from Visitor's Appended Data

ppm_score.py

#! *-* coding: utf-8 *-*

import csv
import config
import requests
from datetime import datetime

today = datetime.now()


def calc_ppm(visitor):
    """
    Calculate the Visitor's Propensity to Purchase Score
    :param visitor:
    :return: ppm score int
    """
    score = 0
    max_score = 10

    try:
        ppm_fields = [visitor['ppm_segment'], visitor['ppm_indicator'], visitor['ppm_type']]

        if all(ppm_fields):
            score += 6
        else:
            if visitor['ppm_type'] != '':
                score += 2
            if visitor['ppm_segment'] != '':
                score += 2
            if visitor['ppm_indicator'] != '':
                score += 2

        # credit range
        if visitor['credit_range'] != '':
            credit_check = visitor['credit_range'].split('-')
            if len(credit_check) == 2:
                if credit_check[1] >= 650:
                    score += 1

        # auto transaction date
        if visitor['auto_trans_date'] != '':
            auto_date = datetime.strptime(visitor['auto_trans_date'], '%m/%d/%Y')
            auto_date_year = auto_date.year

            # score a point if the trans date is older than the delta
            if int(auto_date_year) < int(today.year - 3):
                score += 1

        # auto year
        if visitor['auto_year'] != '':
            auto_year = datetime.strptime(visitor['auto_year'], '%Y')
            auto_year = auto_year.year

            # score a point if the visitors car is older than the delta
            if int(auto_year) < int(today.year - 4):
                score += 1

        # auto_purchase type
        if str(visitor['ppm_type'].lower()) in str(visitor['auto_purchase_type'].lower()):
            score += 1

    # except out
    except ValueError as err:
        return str(err)

    # return the score
    return score


def get_test_data():
    """
    Get the test data to return to the main function for processing
    :param url: mockaroo url
    :param key: config.MOCK_API_KEY
    :param hdr: http headers
    :return: CSV data
    """

    api_method = 'GET'
    key = config.MOCK_API_KEY
    url = 'https://api.mockaroo.com/api/c97a7ea0?count=1000&key=' + key
    hdr = {'user-agent': 'SimplePythonFoo()', 'content-type': 'text/csv'}
    rows = []

    try:
        r = requests.request(api_method, url, headers=hdr)

        if r.status_code == 200:
            if len(r.content) >= 1:
                rows = r.content.split('\n')

    except requests.HTTPError as http_err:
        print('A communication error occurred: {}'.format(str(http_err)))


    return rows


def main():
    """
    Program Entry point.  Read data from Mockaroo file and calculate the PPM score for each visitor
    :return: none
    """

    # get the test data first
    f1 = get_test_data()

    # read the CSV file and convert to dict
    reader = csv.DictReader(f1, fieldnames=('id', 'first_name', 'last_name', 'auto_trans_date',
                                            'auto_purchase_type', 'ppm_segment', 'ppm_indicator',
                                            'ppm_type', 'credit_range', 'auto_year'))
    # skip the header row
    reader.next()

    # loop the CSV object and calculate the score
    for row in reader:
        print('The PPM score for {} {} {} was calculated as {}'.format(row['id'], row['first_name'],
                                                                       row['last_name'], str(calc_ppm(row))))


if __name__ == '__main__':
    main()

Getting the data remotely from Mockaroo now instead of a local data file.