notebooks/forecasting_with_monte_carlo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Monte Carlo Simulation for Forecasting Cycle Times"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example uses a Monte Carlo simulation to predict the cycle times from previous work items. With the simulation we try to answer  how many work items might be done in the next few days.\n",
    "\n",
    "This Jupyter Notebook is inspired by previous publication from the agile / lean community where some ALM / issue trackers tools allow you to do some forecasts based on a Monte Carlo simulation. For more details have a look at the reference section in the [README](https://github.com/rueedlinger/monte-carlo-simulation)  of this GitHub project.\n",
    "\n",
    "So my goal was to build a Notebook which uses Monte Carlo simulation to forecast when the next  work items are finished or how many work items might be done in the next few days.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.mlab as mlab\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import scipy.stats as stats\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's first look at previous data (cycle times)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First let's load some cycle times from the CSV file [data.csv](data.csv)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data.csv', parse_dates=['created_date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data contains the following attributes:\n",
    "- __grp__ - the project to which the work item belongs \n",
    "- __cycle_time_days__ - the cycle time in days from stage _todo_ to the _done_ stage. A cycle time -1 means the ticket was not done for example rejected, won't fix, etc.)\n",
    "- __created_date__ - the date when the work item was created"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>grp</th>\n",
       "      <th>cycle_time_days</th>\n",
       "      <th>created_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5052</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2015-02-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>326</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2013-12-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5056</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2015-10-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>324</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>2013-08-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>325</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>2013-12-25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id  grp  cycle_time_days created_date\n",
       "0  5052    0               -1   2015-02-05\n",
       "1   326    0               -1   2013-12-25\n",
       "2  5056    0               -1   2015-10-16\n",
       "3   324    0               18   2013-08-13\n",
       "4   325    0               -1   2013-12-25"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have to clean the data:\n",
    "\n",
    "- remove cycle times < 0\n",
    "- remove cycle times befor 1. Jan 2018\n",
    "- group cycle times by project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaned = df[df['created_date'] > '2017-12-31']\n",
    "cleaned = cleaned[cleaned['cycle_time_days'] > -1]\n",
    "\n",
    "\n",
    "project_0_cycle_times = cleaned[cleaned['grp'] == 0].cycle_time_days\n",
    "project_1_cycle_times = cleaned[cleaned['grp'] == 1].cycle_time_days\n",
    "\n",
    "project_0_cycle_times = project_0_cycle_times.values\n",
    "project_1_cycle_times = project_1_cycle_times.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have cycle times for two projects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "fig.suptitle('Histogram Cycle Times (Days)', fontsize=14, fontweight='bold')\n",
    "plt.hist([project_0_cycle_times, project_1_cycle_times], label=['Project 0', 'Project 1'])\n",
    "ax.set_xlabel('Cycle Times')\n",
    "ax.set_ylabel('Frequency')\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ftD1zjOeeaXvQ9uDw8HCZZQIAAGRRavCKiM0RMU/SwZKOsd0q6VxJR0p6m6Q5khaP8dzlEdEWEW3Nzc1llgkAAJBFlrMaI2KDpH5JJ0TEo1HYJOnLko7JUQMAAEDVyjyrsdn27PT4FZLmS/qR7QNTmyWdImlNWTUAAAA0kjLPajxQ0uW2m1QEvK9HxPW2v2O7WZIl3S1pUYk1AAAANIwyz2pcLenoUdqPL+uYAAAAjYwr1wMAAGRC8AIAAMiE4AUAAJAJwQsAACATghcAAEAmBC8AAIBMCF4AAACZELwAAAAyIXgBAABkQvACAADIhOAFAACQCcELAAAgE4IXAABAJgQvAACATAheAAAAmRC8AAAAMiF4AQAAZELwAgAAyITgBQAAkAnBCwAAIBOCFwAAQCYELwAAgEwIXgAAAJmUFrxsz7J9h+0f2L7X9qdS+6G2b7e91vZVtvcpqwYAAIBGUmaP1yZJx0fEUZLmSTrB9rGSPifpixFxuKT1kjpLrAEAAKBhlBa8ovBcWpyRbiHpeElXp/bLJZ1SVg0AAACNpNQ5XrabbN8t6QlJKyXdL2lDRLyYNnlY0kFl1gAAANAoSg1eEbE5IuZJOljSMZKOnOhzbZ9pe9D24PDwcFklAgAAZJPlrMaI2CCpX9LbJc22vXdadbCkR8Z4zvKIaIuItubm5hxlAgAAlKrMsxqbbc9Oj18hab6kIRUB7LfSZqdLurasGgAAABpJmT1eB0rqt71a0p2SVkbE9ZIWSzrH9lpJr5XUW2INAADssr6+PrW2tqqpqUmtra3q6+uruiRMcXvveJNdExGrJR09SvsDKuZ7AQDQsPr6+tTd3a3e3l61t7drYGBAnZ3FFZAWLlxYcXWYqhwRVdewQ21tbTE4OFh1GQCAPUhra6uWLl2qjo6OLW39/f3q6urSmjVrKqwMjc72qohoG3UdwQsAgJdramrSxo0bNWPGjC1tIyMjmjVrljZv3lxhZWh04wUvvqsRAIBRtLS0aGBgYJu2gYEBtbS0VFQRpgOCFwAAo+ju7lZnZ6f6+/s1MjKi/v5+dXZ2qru7u+rSMIWVNrkeAICprDaBvqurS0NDQ2ppaVFPTw8T67FbmOMFAAAwiZjjBQAA0AAIXgAAAJkQvAAAADIheAEAAGRC8AIAAMiE4AUAAJAJwQsAACATghcAAEAmBC8AAIBMCF4AAACZELwAAAAyIXgBAABkQvACAADIhOAFAACQCcELAAAgE4IXAABAJgQvAACATAheAAAAmZQWvGy/zna/7R/avtf2n6T2C2w/YvvudPuNsmoAAABoJHuXuO8XJX0sIu6yva+kVbZXpnVfjIjPl3hsAACAhlNa8IqIRyU9mh4/a3tI0kFlHQ8AAKDRZZnjZXuupKMl3Z6aPmJ7te1Lbe83xnPOtD1oe3B4eDhHmQAAAKUqPXjZfpWkb0r6aEQ8I+kiSYdJmqeiR+wLoz0vIpZHRFtEtDU3N5ddJgAAQOlKDV62Z6gIXVdExLckKSIej4jNEfGSpEskHVNmDQAAAI2izLMaLalX0lBELKlrP7Bus1MlrSmrBgAAgEayw+Bl+zDbM9Pj42yfbXv2BPb9Dkm/J+n47S4d8Ve277G9WlKHpD/djfoBAChNX1+fWltb1dTUpNbWVvX19VVdEqa4iZzV+E1JbbYPl7Rc0rWSviZp3OtvRcSAJI+y6oadLRIAgNz6+vrU3d2t3t5etbe3a2BgQJ2dnZKkhQsXVlwdpqqJDDW+FBEvqhgWXBoRH5d04A6eAwDAlNbT06Pe3l51dHRoxowZ6ujoUG9vr3p6eqouDVPYRILXiO2Fkk6XdH1qm1FeSQAAVG9oaEjt7e3btLW3t2toaKiiijAdTCR4/aGkt0vqiYgHbR8q6avllgUAQLVaWlo0MDCwTdvAwIBaWloqqgjTwQ6DV0T8UNJiSXel5Qcj4nNlFwYAQJW6u7vV2dmp/v5+jYyMqL+/X52dneru7q66NExhO5xcb/vdkj4vaR9Jh9qeJ+kvIuLkkmsDAKAytQn0XV1dGhoaUktLi3p6ephYj93iiBh/A3uVpOMl3RoRR6e2NRHRmqE+SVJbW1sMDg7mOhwAAMAus70qItpGWzehyfUR8fR2bS/tflkAAAB7lolcx+te2++X1GT7CElnS/rXcssCAACYfibS49Ul6U2SNknqk/SMpI+WWBMAAMC0tMMer4h4XlJ3ugEAAGAXTeSsxjZJ50maW799RLylvLIAAACmn4nM8bpC0scl3SMm1QMAAOyyiQSv4Yi4rvRKAAAAprmJBK/zba+QdIuKCfaSpIj4VmlVAQAATEMTCV5/KOlIFV+MXRtqDEkELwAAgJ0wkeD1toj45dIrAQAAmOYmch2vf7X9xtIrAQAAmOYmEryOlXS37R/bXm37Hturyy4M2Bl9fX1qbW1VU1OTWltb1dfXV3VJAAC8zESGGk8ovQpgN/T19am7u1u9vb1qb2/XwMCAOjs7JUkLFy6suDoAALZyRIy+wn51RDxje85o6yPiqVIrq9PW1haDg4O5DocpprW1VUuXLlVHR8eWtv7+fnV1dWnNmjUVVgYA2BPZXhURbaOuGyd4XR8RJ9l+UMVZjK5bHRHxhskvdXQEL4ynqalJGzdu1IwZM7a0jYyMaNasWdq8eXOFlQEA9kTjBa/x5nh9WZIi4tCIeEO6r92yhS5gR1paWjQwMLBN28DAgFpaWiqqCACA0Y0XvPhSbEwJ3d3d6uzsVH9/v0ZGRtTf36/Ozk51d/MrDABoLBOZXL9LbL9O0lckHaBiqHJ5RPxtmjN2lYov3V4n6XciYn1ZdWD6q02g7+rq0tDQkFpaWtTT08PEegBAwxlvjtfzktaOtkrFHK+3jLtj+0BJB0bEXbb3lbRK0imS/kDSUxHxWduflLRfRCweb1/M8QIAVKGrq0uXXHKJNm3apJkzZ+qMM87Q0qVLqy4LDW68OV7j9Xg9KOndu3rQiHhU0qPp8bO2hyQdJOk9ko5Lm10u6VZJ4wYvAABy6+rq0rJly/S5z31OixYt0rJly7R4cfHnivCFXTVej9f3I+LoSTmIPVfS9yS1SvpJRMxO7Za0vrY8Fnq8AAC5zZo1S5/5zGd0zjnnbGlbsmSJzjvvPG3cuLHCytDodvWsxn+ZpIO/StI3JX00Ip6pXxdF6hs1+dk+0/ag7cHh4eHJKAUAgAnbtGmTFi1atE3bokWLtGnTpooqwnQwZvCKiI/s7s5tz1ARuq6IiG+l5sfT/K/aPLAnxjj+8ohoi4i25ubm3S0FAICdMnPmTC1btmybtmXLlmnmzJkVVYTpYCLf1bhL0jBir6ShiFhSt+o6Saenx6dLurasGgAA2FVnnHGGFi9erCVLluj555/XkiVLtHjxYp1xxhlVl4YpbMw5Xru9Y7td0m2S7pH0Umo+T9Ltkr4u6RBJD6m4nMS4Xz/EHC8AQBUWLFiglStXKiJkW/Pnz9dNN91UdVlocLs6x6v25J+z/ee2L0nLR9g+aUfPi4iBiHBEvCUi5qXbDRHx04h4Z0QcERG/nvM7HwEAmKi+vj7dd999uuWWW/TCCy/olltu0X333ae+vr6qS8MUNpGhxi9L2iTp7Wn5EUl/WVpFAAA0gJ6eHvX29qqjo0MzZsxQR0eHent71dPTU3VpmMImErwOi4i/kjQiSRHxvLb9wmwAAKadoaEhtbe3b9PW3t6uoaGhiirCdDCR4PWC7VcoXfbB9mEqesAAAJi2WlpaNDAwsE3bwMCAWlpaKqoI08FEgtf5km6U9DrbV0i6RdInSq0KAICKdXd3q7OzU/39/RoZGVF/f786OzvV3d1ddWmYwnb4JdkRsdL2XZKOVTHE+CcR8WTplQEAUKGFCxdKKr46aGhoSC0tLerp6dnSDuyK8b4y6FfGe2JE3FVKRaPgchIAAGCq2NUvyf7COOtC0vG7VRUAAMAeZszgFREdOQsBAACY7iZyAdU/tj27bnk/22eVWhUAAMA0NJGzGs+IiA21hYhYL4kvqgIAANhJEwleTekLryVJtpsk7VNeSQAAANPTDi8nIekmSVfZvjgtf0jFdb0AAACwEyYSvD6hImx9OC2vlLSitIoAAACmqYkEr9+UdHFEXFR2MQAAANPZROZ4/a6k+2z/le0jyy4IAIBcbJdyA8Yyka8M+oDtV0taKOky2yHpy5L6IuLZsgsEAKAsY317y/ZsT3hbYDwT6fFSRDwj6WpJV0o6UNKpku6y3VVibQAAANPKRC6gerLtayTdKmmGpGMi4kRJR0n6WLnlAQAATB8TmVz/XklfjIjv1TdGxPO2O8spCwAAYPoZM3jZPlzSARFx+nbt75D0WETcHxG3lF0gAADAdDHeUOPfSHpmlPZn0joAAADshPGC1wERcc/2jaltbmkVAQAATFPjBa/Z46x7xSTXAQAAMO2NF7wGbZ+xfaPtP5K0qrySAAAApqfxzmr8qKRrbJ+mrUGrTdI+Kq7jNS7bl0o6SdITEdGa2i6QdIak4bTZeRFxwy5VDgAAMMWMGbwi4nFJ/8N2h6TW1PyPEfGdCe77Mkl/L+kr27V/MSI+v7OFAgAATHUT+cqgfkn9O7vjiPie7bm7UhQAAMB0NKGvDJpkH7G92valtvcbayPbZ9oetD04PDw81mYAAABTRu7gdZGkwyTNk/SopC+MtWFELI+Itohoa25uzlQeAABAebIGr4h4PCI2R8RLki6RdEzO4wMAAFQpa/CyfWDd4qmS1uQ8PgAAQJUm8iXZu8R2n6TjJO1v+2FJ50s6zvY8SSFpnaQPlXV8AACARlNa8IqIhaM095Z1PAAAgEZXxVmNAAAAeySCFwAAQCYELwAAgEwIXgAAAJkQvDAtLFiwQHvttZdsa6+99tKCBQuqLgkAgJcheGHKW7BggW6++WYtWrRIGzZs0KJFi3TzzTcTvgAADae0y0kAuaxcuVIf/vCH9aUvfUmSttwvW7asyrIAAHgZR0TVNexQW1tbDA4OVl0GGpRtbdiwQa95zWu2tD399NOaPXu2psLvN4DGZ5vPE0yY7VUR0TbaOoYaMeXZ1rnnnrtN27nnnivbFVUEAMDoCF6Y8ubPn6+LLrpIZ511lp5++mmdddZZuuiiizR//vyqSwMAYBsMNWJaWLBggVauXKmIkG3Nnz9fN910U9VlAZgmGGrEzhhvqJHJ9ZgWCFkAgKmAoUYAAIBMCF4AAACZELwAAAAyIXgBAABkQvACAADIhOAFAACQCcELAAAgE4IXAABAJlxAFVNCWd+7yJWoAQA5EbwwJUw0IPG1HgCARsZQIwAAQCalBS/bl9p+wvaaurY5tlfavi/d71fW8QEAABpNmT1el0k6Ybu2T0q6JSKOkHRLWgYAANgjlBa8IuJ7kp7arvk9ki5Pjy+XdEpZxwcAAGg0ued4HRARj6bHj0k6IPPxAQAAKlPZ5PooTj0b8/Qz22faHrQ9ODw8nLEyAACAcuQOXo/bPlCS0v0TY20YEcsjoi0i2pqbm7MVCAAAUJbcwes6Saenx6dLujbz8QEAACpT5uUk+iT9m6Rftv2w7U5Jn5U03/Z9kn49LQMAAOwRSrtyfUQsHGPVO8s6JgAAQCPjyvUAAACZELwAAAAyIXgBAABkQvACAADIhOAFAACQCcELAAAgE4IXAABAJgQvAACATEq7gCoAAFWZM2eO1q9fP6n7tD2p+9tvv/301FNPTeo+0fgIXgCAaWf9+vWKiKrLGNdkBzlMDQw1AgAAZELwAgAAyITgBQAAkAnBCwAAIBOCFwAAQCYELwAAgEwIXgAAAJkQvAAAADIheAEAAGRC8AIAAMiE4AUAAJAJwQsAACATghcAAEAmBC8AAIBM9q7ioLbXSXpW0mZJL0ZEWxV1AAAA5FRJ8Eo6IuLJCo8PAACQFUONAAAAmVQVvELSzbZX2T5ztA1sn2l70Pbg8PBw5vKQy5w5c2R70m6SJnV/tjVnzpyKf0oAgOmiqqHG9oh4xPbPS1pp+0cR8b36DSJiuaTlktTW1hZVFInyrV+/XhGN/fbWAh0AALurkh6viHgk3T8h6RpJx1RRBwAAQE7Zg5ftV9ret/ZY0rskrcldBwAAQG5VDDUeIOmaNHyzt6SvRcSNFdQBAACQVfbgFREPSDoq93EBAACqxuUkAAAAMiF4AQAAZELwAgAAyITgBQAAkAnBCwAAIBOCFwAAQCYELwAAgEyq+q5GAABKE+e/WrrgNVWXMa44/9VVl4AKELwAANOOP/WMIqLqMsZlW3FB1VUgN4YaAQAAMiF4AQAAZELwAgAAyITgBQAAkAnBCwAAIBPOakSlOOUbALAnIXihUpzyDQDYkzDUCAAAkAnBCwAAIBOCFwAAQCYELwAAgEyYXI/K2a66hHHtt99+VZcAYBfw2YJGRPBCpRr9jEYAUxOfLWhUDDUCAABkUknwsn2C7R/bXmv7k1XUAAAAkFv24GW7SdKFkk6U9EZJC22/MXcdAAAAuVXR43WMpLUR8UBEvCDpSknvqaAOAACArKoIXgdJ+s+65YdTGwAAwLTWsJPrbZ9pe9D24PDwcNXlAAAA7LYqgtcjkl5Xt3xwattGRCyPiLaIaGtubs5WHAAAQFmqCF53SjrC9qG295H0PknXVVAHAABAVtkvoBoRL9r+iKSbJDVJujQi7s1dBwAAQG6VXLk+Im6QdEMVxwYAAKhKw06uBwAAmG48Fb7PyvawpIeqrgNTwv6Snqy6CADTDp8t2Bmvj4hRzwycEsELmCjbgxHRVnUdAKYXPlswWRhqBAAAyITgBQAAkAnBC9PN8qoLADAt8dmCScEcLwAAgEzo8QIAAMiE4IVK2d5s+27ba2x/w/bP7cRz22z/3S4e96NjHSt9ndXtttfavip9tRWAKaRBP1s+kj5Xwvb+u7J/TH0MNaJStp+LiFelx1dIWhURS+rW7x0RL5Zw3HWS2iLiZdflsf11Sd+KiCttL5P0g4i4aLJrAFCeBv1sOVrSekm3jrUNpj96vNBIbpN0uO3jbN9m+zpJP7Q9y/aXbd9j+/u2OyQpbXd9evxK25faviNt857U3mT78+l/vattd9k+W9IvSuq33V9fgG1LOl7S1anpckmnZHn1AMpS+WeLJEXE9yNiXbZXjYZUyXc1AtuzvbekEyXdmJp+RVJrRDxo+2OSIiLebPtISTfb/qXtdtEt6TsR8UHbsyXdYfufJf2+pLmS5qUvaJ8TEU/ZPkdSxyj/43ytpA11/xN+WNJBk/xyAWTSQJ8tgCR6vFC9V9i+W9KgpJ9I6k3td0TEg+lxu6R/kKSI+JGKr4/a/sPxXZI+mfZ1q6RZkg6R9OuSLq4FqYh4qqwXAqCh8NmChkSPF6r2s4iYV99QjPbpv3dyP5b03oj48Sj72hk/lTS7bv7HwZIe2dmdAKhco322AJLo8cLUcJuk0yQpDQMcIunH221zk6SuNEerNolVklZK+lAabpDtOan9WUn7bn+gKM426Zf0W6npdEnXTtorAdBIsn22ADUEL0wFX5K0l+17JF0l6Q8iYlNaVzst99OSZkhabfvetCxJK1QMM6y2/QNJ70/tyyXdONoEWEmLJZ1je62KOV+9o2wDYOrL+tli+2zbD6voSV9te0UZLwqNjctJYMqy/V5JJ0fE6VXXAmD64LMFZWKOF6Yk2ydL6pH0waprATB98NmCstHjBQAAkAlzvAAAADIheAEAAGRC8AIAAMiE4AUAAJAJwQsAACATghcAAEAm/x8yH3ioUBX4ZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "fig.suptitle('Boxplot Cycle Times (Day)', fontsize=14, fontweight='bold')\n",
    "\n",
    "plt.boxplot([project_0_cycle_times, project_1_cycle_times])\n",
    "\n",
    "ax.set_xticklabels(['Project 0', 'Project 1'])\n",
    "ax.set_ylabel('Cycle Times')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's print out some descriptive statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DescribeResult(nobs=51, minmax=(1, 21), mean=3.7058823529411766, variance=17.93176470588235, skewness=2.0789642149745884, kurtosis=4.474217402617895)\n",
      "DescribeResult(nobs=58, minmax=(1, 39), mean=6.327586206896552, variance=61.2416817906836, skewness=2.146885333051502, kurtosis=4.8044881900831315)\n"
     ]
    }
   ],
   "source": [
    "print(stats.describe(project_0_cycle_times))\n",
    "print(stats.describe(project_1_cycle_times))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also show the coefficient of variation (CV) for the different projects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.131409450013304\n",
      "1.2260520692931383\n"
     ]
    }
   ],
   "source": [
    "print(stats.variation(project_0_cycle_times))\n",
    "print(stats.variation(project_1_cycle_times))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Monte Carlo Simulation\n",
    "\n",
    "\n",
    "### Utility Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cumulative_sum(total_work_items, cycle_times, selector):\n",
    "    total = [0]\n",
    "    for i, e in enumerate(range(total_work_items)):\n",
    "            total.append(selector(cycle_times) + total[i])\n",
    "    return total    \n",
    "\n",
    "\n",
    "def simulate_cycle_times(num_of_iterations, total_work_items, cycle_times):\n",
    "    list_random_walks = []\n",
    "    totals = []\n",
    "    \n",
    "    for i in range(num_of_iterations):\n",
    "        list_random_walks.append(\n",
    "            cumulative_sum(total_work_items, cycle_times, random.choice))\n",
    "    \n",
    "    for i in list_random_walks:\n",
    "        totals.append(i[-1])\n",
    "    \n",
    "    return totals, list_random_walks\n",
    "\n",
    "\n",
    "def simulate_days(num_of_iterations, max_days, cycle_times):\n",
    "    totals = []\n",
    "    for i in range(num_of_iterations):\n",
    "        simulated_cycle_times = [0]\n",
    "             \n",
    "        while True:\n",
    "            n = random.choice(cycle_times) + simulated_cycle_times[-1] \n",
    "            if n > max_days:\n",
    "                break\n",
    "            else:\n",
    "                simulated_cycle_times.append(n)\n",
    "        \n",
    "        totals.append(len(simulated_cycle_times) - 1)\n",
    "            \n",
    "    return totals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### When are the next 30 work items finished?\n",
    "Let's simulate for 'project 0' how many days it might take to finish the next 30 work items.\n",
    "\n",
    "First we just simulate one run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_work_items = 30\n",
    "\n",
    "cumulative_sum_min = cumulative_sum(total_work_items, project_0_cycle_times, min)\n",
    "cumulative_sum_max = cumulative_sum(total_work_items, project_0_cycle_times, max)\n",
    "cumulative_random = cumulative_sum(total_work_items, project_0_cycle_times, random.choice)\n",
    "\n",
    "range_work_items = [x for x in range(total_work_items + 1)]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "\n",
    "\n",
    "fig.suptitle('Forecast with Min / Max', fontsize=14, fontweight='bold')\n",
    "\n",
    "plt.plot(cumulative_sum_min, range_work_items, label='min',  color='green')\n",
    "plt.plot(cumulative_sum_max, range_work_items, label='max', color='red')\n",
    "plt.plot(cumulative_random, range_work_items, label='random', color='gray')\n",
    "\n",
    "value_max = cumulative_sum_max[-1]\n",
    "value_min = cumulative_sum_min[-1]\n",
    "value_random =  cumulative_random[-1]\n",
    "\n",
    "plt.axvline(value_max, color='silver', linestyle='dashed', linewidth=2)\n",
    "plt.axvline(value_min, color='silver', linestyle='dashed', linewidth=2)\n",
    "plt.axvline(value_random, color='silver', linestyle='dashed', linewidth=2)\n",
    "\n",
    "t = ax.text(value_max, total_work_items, value_max, ha=\"right\", size=8)\n",
    "t = ax.text(value_min, total_work_items, value_min, ha=\"right\", size=8)\n",
    "t = ax.text(value_random, total_work_items, value_random, ha=\"right\", size=8)\n",
    "\n",
    "ax.set_xlabel(\"Days\")\n",
    "ax.set_ylabel(\"Work Items\")\n",
    "\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's run the same simulation 5000 times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_of_iterations = 5000\n",
    "total_work_items = 30\n",
    "\n",
    "\n",
    "simulated_total, simulated_work_done = simulate_cycle_times(num_of_iterations, total_work_items, project_0_cycle_times)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "\n",
    "fig.suptitle('Simulated Durations (Days)', fontsize=14, fontweight='bold')\n",
    "\n",
    "plt.plot(cumulative_sum_min, range_work_items, label='min',  color='green')\n",
    "plt.plot(cumulative_sum_max, range_work_items, label='max', color='red')\n",
    "plt.plot(0, 0, linestyle='dotted', color='gray', label='random')\n",
    "\n",
    "\n",
    "for r in simulated_work_done:\n",
    "    plt.plot(r, range_work_items, linestyle='dotted', color='gray')\n",
    "\n",
    "ax.set_xlabel(\"Days\")\n",
    "ax.set_ylabel(\"Work Items\")\n",
    "\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "fig.suptitle('Histogram Simulated Durations (Days)', fontsize=14, fontweight='bold')\n",
    "ax.set_xlabel('Days')\n",
    "ax.set_ylabel('Frequency')\n",
    "plt.hist(simulated_total, bins=50);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We might end up with the 30 work items in approximately **140 days** with a probability of **90%**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([142.])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.quantile(simulated_total, q=[0.90])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many work items might be finished in 30 days?\n",
    "\n",
    "No we want to answer the question how many work items we can finish in the next 300 days with a probability of 85%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_of_iterations = 5000\n",
    "max_work_days = 142\n",
    "\n",
    "\n",
    "simulated_work_items_total = simulate_days(num_of_iterations, max_work_days, project_0_cycle_times)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "\n",
    "plt.hist(simulated_work_items_total);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With a probability of **90%**  we might end up in the next 140 days with approximately **30** work items.\n",
    "\n",
    "> **Note:** Here you have to flip the x-axis for the 90% quantile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([30.])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.quantile(simulated_work_items_total, q=[0.1])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}