ESeufert

$(document).ready(function() {

    var data = getData();

    data = createAverages(data);

    buildLineChart(data, "Retention",
        ['d1_retention', 'd7_retention', 'd30_retention'],
        1000, 300, "Date", "Retention (percentage)");
        

ESeufert

function getData() {

	var data = [];

	var metrics =
	{
	    "countries":
	    [
	        {
	            "country": "USA",

ESeufert

function buildConsole(data) {
	var html = "";
	
	html += "<div id=\"country-selector\" class=\"console-element\">";
	for (var i = 0; i < data.length; i++) {
		html += "<div style=\"width: 150px; float: left;\">";
		html += "<input name=\"countries\" id=\"country-selector-" + data[i][0] + "\" type=\"checkbox\" ";
		if (i == 0) {
			html += "checked"; // set the first country to checked to provide some default data for the graphs
		}

ESeufert

function cloneData(data) {
	//create a "deep copy" of the objects within the metrics array
	//ensures that objects are not copied by reference
	var metrics = [];
	for (var i = 0; i < data.length; i++) {
		metrics.push(cloneMetric(data[i]));
	}
	return metrics;
}
 

ESeufert

function aggregateMetric(metrics, countries) {
	var metric = []; 			 	//the single metric array of dates that we'll return,
					 			 	//aggregated over the selected countries
	var count = 0;   			 	// a count of countries being aggregated over
 
	for (var i = 0; i < metrics.length; i++) {
		if (jQuery.inArray(metrics[i][0], countries) > -1) { //this is a country we should aggregate for
			if (count == 0) {
				metric = cloneMetric(metrics[i]); // since metric is empty, set metric to the first set of metrics we find
				count++;

ESeufert

function resetCharts(metrics) {
	var countries = getCountries(); 					// get checked items
	var metric = aggregateMetric(metrics, countries);   // build one metric item out of the selected countries and the full dataset
	$('#metrics').html(''); 							// reset the metrics div html
 
	buildLineChart(metric, "Retention", ['d1_retention_pct', 'd7_retention_pct', 'd30_retention_pct'], 1000, 300, "Date", "Retention (percentage)");
	buildLineChart(metric, "Daily New Users", ['DNU'], 500, 300, "Date", "New Users");
	buildLineChart(metric, "Daily Active Users", ['DAU'], 500, 300, "Date", "Users");
	buildLineChart(metric, "Average Session Length", ['average_session_length'], 500, 300, "Date", "Session Length (minutes)");
	buildLineChart(metric, "Average Sessions per User", ['average_sessions_per_user'], 500, 300, "Date", "Sessions per User");

ESeufert

$(document).ready(function() {

	var data = getData();

	buildConsole(data);

	resetCharts(data);

	$('#country-selector').change(function() {
		var metrics = cloneData(data);

ESeufert

import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import pandas as pd
import numpy as np
import random

def build_userbase( n, payer_percentage ):
    users = pd.DataFrame( columns=[ "user", "payer", "payment_probability", "payment" ] )
    for x in range( 1, n + 1 ):
        payer = True if random.randint( 1, 100 ) <= ( payer_percentage * 100 ) else 0

ESeufert

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import Ridge
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import make_pipeline
from scipy.optimize import curve_fit
import statsmodels.stats.api as sms
import math
import operator

ESeufert

import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import matplotlib.colors as pltcolors
import pandas as pd
import numpy as np
import random
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import colors as mcolors
from matplotlib import cm
from beautifultable import BeautifulTable