EDD Per Download License Calculations

license-calculations.py

# Script: EDD Per Download License Calculations
# Author: Frank Corso
# Date Created: 04/05/2020
# Last Modified: 07/16/2020
# Python Version: 3.6.5

# Takes exports of licenses from EDD and turns it into a CSV of totals and averages.
# To use, have CSVs of licenses for each download in EDD in a 'licenses' directory with each
# saved as edd-export-licenses-(download-slug).csv
# Put this script above that directory. In terminal, run python license_calculations.py

from os import listdir
from os import path

import pandas as pd
import re


def calculate_license_stats():
    """Loads all license exports and generates simplified aggregated license CSV."""

    # Edit this if you name your CSV files something different.
    p = re.compile(r'edd-export-licenses-(.+).csv')

    # Edit this to change input and output directories
    in_dir = 'licenses'
    out_dir = 'data-output'

    # Begins creating our new dataframe for our new CSV.
    totals = pd.DataFrame(columns=['Extension', 'Total Licences', 'Active/(Active+Inactive)', 'Active Licenses',
                                   'Inactive Licenses', 'Expired Licenses', 'Total Activated Sites',
                                   'AVG Site Per Licence', 'Total Active Activated Sites',
                                   'AVG Active Site Per Active License'])

    # Loop over all files in directory.
    for file in listdir(in_dir):
        print('Loading...', file)

        # Checks filename for REGEX defined above. If matches, uses that as download name.
        # If not matching, skips this file.
        extension_search = p.findall(file)
        if len(extension_search) > 0:
            extension_name = extension_search[0]
        else:
            print('Not using this file...')
            continue
        df = pd.read_csv(path.join(in_dir, file))

        # Make our calculations.
        total_activated = df['Activation Count'].sum()
        total_avg_activated = round(df['Activation Count'].mean(), 2)
        active = df[df['License Status'] == 'active']
        inactive = df[df['License Status'] == 'inactive']
        expired = df[df['License Status'] == 'expired']
        active_activated = active['Activation Count'].sum()
        active_avg_activated = round(active['Activation Count'].mean(), 2)

        active_percent = round(len(active) / (len(active) + len(inactive)), 3)

        # Adds new row to dataframe.
        totals.loc[len(totals)] = [extension_name, len(df), active_percent, len(active), len(inactive), len(expired),
                                   total_activated, total_avg_activated, active_activated, active_avg_activated]

    # Puts dataframe into our new CSV.
    print('Writing results to file...')
    totals.to_csv(path.join(out_dir, 'license-data.csv'))


if __name__ == '__main__':
    print('Calculating license stats...')
    calculate_license_stats()