diff --git a/tools/datalab/database_exploration.ipynb b/tools/datalab/database_exploration.ipynb
new file mode 100644
index 0000000..4c120dd
--- /dev/null
+++ b/tools/datalab/database_exploration.ipynb
@@ -0,0 +1,2092 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pylab inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function\n",
+    "from __future__ import division\n",
+    "from IPython.display import display, HTML"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "import MySQLdb as mdb\n",
+    "import bs4\n",
+    "import datetime\n",
+    "import time\n",
+    "from collections import defaultdict"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Some Helper Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import webbrowser\n",
+    "\n",
+    "def issue_id_to_local_id(issue_id):\n",
+    "    return chrome_issue[chrome_issue[\"issue_id\"] == issue_id][\"local_id\"].values[0]\n",
+    "\n",
+    "def open_issue_webpage(issue_id):\n",
+    "    '''This Function opens the webpage of the issue corresponding to issue_id.S'''\n",
+    "    chromium_path = \"https://bugs.chromium.org/p/chromium/issues/detail?id=\"\n",
+    "    local_id = issue_id_to_local_id(issue_id)\n",
+    "    webbrowser.open_new(chromium_path + str(local_id))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def table_to_dataframe(name, connection):\n",
+    "    return pd.read_sql(\"SELECT * FROM {};\".format(name) , con=connection)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def histrogram_plot(count_data, title, x_label, y_label, kde=False, hist=True):\n",
+    "    sns.distplot(count_data, kde=kde, hist=hist)\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel(x_label)\n",
+    "    plt.ylabel(y_label)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "connection = mdb.connect(host=\"173.194.230.234\", user=\"noblek\", db=\"monorail\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "cursor = connection.cursor()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The Names of the tables in the Database"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Attachment\n",
+      "AutocompleteExclusion\n",
+      "Comment\n",
+      "Component2Admin\n",
+      "Component2Cc\n",
+      "Component2Label\n",
+      "ComponentDef\n",
+      "DanglingIssueRelation\n",
+      "DismissedCues\n",
+      "ExtraPerm\n",
+      "FieldDef\n",
+      "FieldDef2Admin\n",
+      "FilterRule\n",
+      "Group2Project\n",
+      "Invalidate\n",
+      "Issue\n",
+      "Issue2Cc\n",
+      "Issue2Component\n",
+      "Issue2FieldValue\n",
+      "Issue2Label\n",
+      "Issue2Notify\n",
+      "IssueFormerLocations\n",
+      "IssueRelation\n",
+      "IssueStar\n",
+      "IssueSummary\n",
+      "IssueUpdate\n",
+      "LabelDef\n",
+      "LocalIDCounter\n",
+      "MemberNotes\n",
+      "Project\n",
+      "Project2SavedQuery\n",
+      "ProjectIssueConfig\n",
+      "ProjectStar\n",
+      "QuickEditHistory\n",
+      "QuickEditMostRecent\n",
+      "ReindexQueue\n",
+      "SavedQuery\n",
+      "SavedQueryExecutesInProject\n",
+      "SpamReport\n",
+      "SpamVerdict\n",
+      "StatusDef\n",
+      "Template\n",
+      "Template2Admin\n",
+      "Template2Component\n",
+      "Template2FieldValue\n",
+      "Template2Label\n",
+      "User\n",
+      "User2Project\n",
+      "User2SavedQuery\n",
+      "UserGroup\n",
+      "UserGroupSettings\n",
+      "UserStar\n",
+      "_Comment_old\n",
+      "dsns\n"
+     ]
+    }
+   ],
+   "source": [
+    "cursor.execute(\"SHOW  TABLES\")\n",
+    "for (table_name,) in cursor:\n",
+    "    print(table_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load the tables that will be used for analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "issue = table_to_dataframe(\"Issue\", connection)\n",
+    "comment = table_to_dataframe(\"Comment\", connection)\n",
+    "issue_summarny = table_to_dataframe(\"IssueSummary\", connection)\n",
+    "issue_label = table_to_dataframe(\"Issue2Label\", connection)\n",
+    "issue_component = table_to_dataframe(\"Issue2Component\", connection)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "issue.rename(columns={\"id\":\"issue_id\"}, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Select for only Chromium Projects"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "chrome_issue = issue[issue[\"project_id\"] == 16].copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "chrome_comment = comment[comment[\"project_id\"] == 16]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of Chromium Issues 611859\n",
+      "Number of Chromium Comments 5712584\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Number of Chromium Issues\", chrome_issue.shape[0])\n",
+    "print(\"Number of Chromium Comments\", chrome_comment.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "unique_chrome_issues = set(chrome_issue[\"issue_id\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Associate comments with their Issues"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1000000\n",
+      "2000000\n",
+      "3000000\n",
+      "4000000\n",
+      "5000000\n"
+     ]
+    }
+   ],
+   "source": [
+    "comments_by_issue = defaultdict(list)\n",
+    "i = 0\n",
+    "for index, row in chrome_comment.iterrows():\n",
+    "    comments_by_issue[row[\"issue_id\"]].append((index, row.created))\n",
+    "    if i % 1000000 == 0:\n",
+    "        print(i)\n",
+    "    i += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Make sure the list of Comments for each issue are sorted by time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "chrome_issue[\"comments\"] = chrome_issue[\"issue_id\"].apply(lambda i_id: [tup[0] for tup in sorted(comments_by_issue[i_id], key=lambda x: x[1])])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Explore the number of comments per issue"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "chrome_issue[\"num_comments\"] = chrome_issue[\"comments\"].apply(lambda comments: len(comments))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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mzp2r+Pj4Lw4eGak2Edc0ud1ma7iszwUAAFeDkJ3jsGvXLv31r3/V+vXrVVRUpMrKSuXl\n5SkrK0tr166VMUZbt27VqVOnVFRUpPXr1+u5555TQUGB6uvrtW7dOvXq1Utr167VyJEjtXr1akmS\n2+1WQUGB1q1bp7179+rQoUM6cOCA9uzZo02bNmnFihVatGhRqGIBABDWQlYcSktL1atXL82YMUPT\npk3TkCFDdODAASUlJUmS0tPTVVZWpn379ikhIUEOh0Mul0vdu3fXkSNHVF5ervT0dElSWlqaduzY\nIY/HI5/Pp9jYWElSamqqysrKVF5erpSUFElSly5d5Pf7deZM0+cvAACAf03IXqqorq5WVVWV1qxZ\no8rKSk2bNk3GmOB2p9OpmpoaeTweRUdHNxr3eDzyeDxy/t/Jief39Xq9crlcjfatrKxUmzZt1L59\n+wvm6NChQ6jiAQAQlkJWHDp06KC4uDhFRkbqhhtuUJs2bXTy5Mngdo/Ho5iYGLlcLnm93uC41+tV\ndHR0o3Gv16uYmBg5nc5G+56fw+FwXHSO5kS72ja5rZ3Dr06dmp/jy+xqz9cc8odv/nDOLpE/3PNf\nqpAVh+9+97t6/vnnde+99+rEiROqra1V//79tXv3bvXr108lJSVKTk5WfHy8VqxYofr6etXV1eno\n0aPq2bOnEhISVFJSovj4eJWUlCgxMVEul0sOh0OVlZXq1q2bSktLlZmZKbvdrvz8fE2ePFlVVVUK\nBAKNjkA0pcZT2+Q2Y/tUp07VXM6npFXp1Cn6qs7XHPKHb/5wzi6Rn/yXXppCVhwGDRqkPXv26Ec/\n+pECgYDcbre6du2qBQsWyOfzKS4uTsOGDZPNZtOkSZM0btw4BQIBZWVlKSoqShkZGcrOzta4ceMU\nFRWlgoICSVJubq5mz54tv9+v1NTU4LsnEhMTNWbMmOBjAQCAy89mPn/iQRj5/dY39VnA1eR2l61G\niX17t+CKWhatm/zhmj+cs0vkJ/+lH3HgktMAAMAyigMAALCM4gAAACyjOAAAAMsoDgAAwDKKAwAA\nsIziAAAALKM4AAAAyygOAADAMooDAACwjOIAAAAsozgAAADLKA4AAMAyigMAALCM4gAAACyjOAAA\nAMsoDgAAwDKKAwAAsIziAAAALKM4AAAAyygOAADAMooDAACwjOIAAAAsozgAAADLKA4AAMAyigMA\nALCM4gAAACyjOAAAAMsoDgAAwDKKAwAAsIziAAAALKM4AAAAyygOAADAMooDAACwjOIAAAAsiwz1\nA9x1111yuVySpNjYWE2dOlU5OTmKiIhQjx495Ha7ZbPZtHHjRm3YsEGRkZGaPn26Bg0apNraWs2Z\nM0fV1dVyOp3Ky8tTx44dVVFRoaVLl8putyslJUWZmZmSpMLCQm3fvl12u11z585VfHx8qOMBABBW\nQloc6urqJElFRUXBsWnTpikrK0tJSUlyu93aunWr+vbtq6KiIhUXF6uurk4ZGRkaMGCA1q1bp169\neikzM1OvvvqqVq9erXnz5sntdquwsFCxsbGaMmWKDh06pEAgoD179mjTpk2qqqrSzJkz9eKLL4Yy\nHgAAYSekxeHw4cP67LPPNHnyZDU0NOiRRx7RwYMHlZSUJElKT09XaWmpIiIilJCQIIfDIYfDoe7d\nu+vIkSMqLy/XAw88IElKS0vT008/LY/HI5/Pp9jYWElSamqqysrKFBUVpZSUFElSly5d5Pf7debM\nGXXo0CGUEQEACCshLQ7XXHONJk+erNGjR+vYsWO6//77G213Op2qqamRx+NRdHR0o3GPxyOPxyOn\n09loX6/XG3zp4/x4ZWWl2rRpo/bt218wB8UBAIDLJ6TF4Rvf+Ia6d+8e/Lp9+/Y6dOhQcLvH41FM\nTIxcLpe8Xm9w3Ov1Kjo6utG41+tVTEyMnE5no33Pz+FwOC46BwAAuHxCWhyKi4t15MgRud1unThx\nQl6vVykpKdq9e7f69eunkpISJScnKz4+XitWrFB9fb3q6up09OhR9ezZUwkJCSopKVF8fLxKSkqU\nmJgol8slh8OhyspKdevWTaWlpcrMzJTdbld+fr4mT56sqqoqBQKBRkcgLiba1bbJbe0cfnXqdHUX\nj6s9X3PIH775wzm7RP5wz3+pbMYYE6rJGxoa9LOf/UzHjx+XJM2ZM0ft27fXggUL5PP5FBcXp8WL\nF8tms2nTpk3asGGDAoGApk+frttuu021tbXKzs7WqVOnFBUVpYKCAl133XV66623tHTpUvn9fqWm\npurhhx+WdO5dFSUlJQoEApo7d64SEhKaXNvvt76pzwKuJre7bDVK7Nv78j4hrUinTtE6darmSi/j\niiF/+OYP5+wS+cl/6aUppMWhNaM48I+H/OGZP5yzS+Qn/6UXBy4ABQAALKM4AAAAyygOAADAMooD\nAACwjOIAAAAsozgAAADLKA4AAMAyigMAALCM4gAAACyjOAAAAMsoDgAAwDKKAwAAsIziAAAALKM4\nAAAAyygOAADAMooDAACwjOIAAAAsozgAAADLKA4AAMAyigMAALCM4gAAACyjOAAAAMsoDgAAwDKK\nAwAAsIziAAAALKM4AAAAyygOAADAMooDAACwjOIAAAAsozgAAADLKA4AAMAyigMAALCM4gAAACyj\nOAAAAMsoDgAAwDKKAwAAsCzkxeGjjz7SwIED9d577+n9999XRkaGxo8fr4ULF8oYI0nauHGjRo0a\npTFjxmjbtm2SpNraWs2cOVPjx4/XlClTVF1dLUmqqKjQj3/8Y2VkZKiwsDD4OIWFhRo9erTGjh2r\nvXv3hjoWAABhKaTFwefz6dFHH9U111wjY4wee+wxZWVlae3atTLGaOvWrTp16pSKioq0fv16Pffc\ncyooKFB9fb3WrVunXr16ae3atRo5cqRWr14tSXK73SooKNC6deu0d+9eHTp0SAcOHNCePXu0adMm\nrVixQosWLQplLAAAwlZIi8Pjjz+ujIwMderUSZJ08OBBJSUlSZLS09NVVlamffv2KSEhQQ6HQy6X\nS927d9eRI0dUXl6u9PR0SVJaWpp27Nghj8cjn8+n2NhYSVJqaqrKyspUXl6ulJQUSVKXLl3k9/t1\n5syZUEYDACAshaw4FBcXq2PHjkpNTZUkGWOCL01IktPpVE1NjTwej6KjoxuNezweeTweOZ3ORvt6\nvV65XC7LcwAAgMsrMlQTFxcXy2azqaysTIcPH1ZOTk6jowAej0cxMTFyuVzyer3Bca/Xq+jo6Ebj\nXq9XMTExcjqdjfY9P4fD4bjoHM2JdrVtcls7h1+dOjU/x5fZ1Z6vOeQP3/zhnF0if7jnv1QhKw4v\nvPBC8OuJEycqNzdXjz/+uHbv3q1+/fqppKREycnJio+P14oVK1RfX6+6ujodPXpUPXv2VEJCgkpK\nShQfH6+SkhIlJibK5XLJ4XCosrJS3bp1U2lpqTIzM2W325Wfn6/JkyerqqpKgUBA7du3b3aNNZ7a\nJrcZ26c6darmsjwXrVGnTtFXdb7mkD9884dzdon85L/00hSy4vCPbDabcnJytGDBAvl8PsXFxWnY\nsGGy2WyaNGmSxo0bp0AgoKysLEVFRSkjI0PZ2dkaN26coqKiVFBQIEnKzc3V7Nmz5ff7lZqaqvj4\neElSYmKixowZo0AgILfb3VKxAAAIKzbz+RMPwsjvt76pzwKuJre7bDVK7Nu7BVfUsmjd5A/X/OGc\nXSI/+S/9iAMXgAIAAJZRHAAAgGUUBwAAYBnFAQAAWEZxAAAAljVbHN55550LxioqKkKyGAAA0Lo1\neR2HN998U4FAQAsWLNDixYtljJHNZlNDQ4Pcbrf+9Kc/teQ6AQBAK9BkcSgrK9OePXt08uRJrVy5\n8v/fITJSY8eObZHFAQCA1qXJ4vCTn/xEkvTyyy9r5MiRLbYgAADQejV7yenExEQtW7ZMH3/8caPx\nxx57LGSLAgAArVOzxeHhhx9WUlKSkpKSgmM2my2kiwIAAK1Ts8XB7/crOzu7JdYCAABauWbfjvnd\n735XW7duVX19fUusBwAAtGLNHnH44x//qBdeeKHRmM1m06FDh0K2KAAA0Do1Wxz+/Oc/t8Q6AADA\nl0CzxaGwsPCi45mZmZd9MQAAoHVr9hwHY0zwa5/Pp9dff10fffRRSBcFAABap2aPOMycObPR7Qcf\nfFD33ntvyBYEAABar3/60zE9Ho+qqqpCsRYAANDKNXvEYciQIY1unz17VpMnTw7ZggAAQOvVbHF4\n/vnng1eKtNlsiomJkcvlCvnCAABA69Nscfj617+udevWaefOnWpoaFD//v01ceJERUT8069yAACA\nL7lmi0N+fr7ef/99jRo1SsYYbd68WR988IHmzZvXEusDAACtiKULQL388suy2+2SpEGDBmn48OEh\nXxgAAGh9mn29IRAIyO/3B2/7/X5FRjbbNwAAwFWo2QYwYsQITZw4UcOHD5cxRn/4wx90xx13tMTa\nAABAK/OFxeHs2bP68Y9/rD59+mjnzp3auXOn7r77bo0cObKl1gcAAFqRJl+qOHjwoL7//e9r//79\nGjhwoLKzs5Wamqrly5fr8OHDLblGAADQSjRZHPLy8vTEE08oPT09ODZr1iw99thjysvLa5HFAQCA\n1qXJ4vDJJ5/olltuuWA8LS1N1dXVIV0UAABonZosDn6/X4FA4ILxQCCghoaGkC4KAAC0Tk0Wh8TE\nRBUWFl4w/vTTT+vmm28O6aIAAEDr1OS7KmbNmqUHHnhAv/vd7xQfH69AIKCDBw+qY8eOWr16dUuu\nEQAAtBJNFgeXy6W1a9dq165dOnjwoOx2uyZMmKDExMSWXB8AAGhFvvA6DhEREUpOTlZycnJLrQcA\nALRifMQlAACwLKQfOuH3+zV//nwdO3ZMNptNubm5ioqKUk5OjiIiItSjRw+53W7ZbDZt3LhRGzZs\nUGRkpKZPn65BgwaptrZWc+bMUXV1tZxOp/Ly8tSxY0dVVFRo6dKlstvtSklJUWZmpiSpsLBQ27dv\nl91u19y5cxUfHx/KeAAAhJ2QFoc33nhDERERWrdunXbv3q0nnnhCkpSVlaWkpCS53W5t3bpVffv2\nVVFRkYqLi1VXV6eMjAwNGDBA69atU69evZSZmalXX31Vq1ev1rx58+R2u1VYWKjY2FhNmTJFhw4d\nUiAQ0J49e7Rp0yZVVVVp5syZevHFF0MZDwCAsBPS4jB06FANHjxYkvThhx+qXbt2KisrU1JSkiQp\nPT1dpaWlioiIUEJCghwOhxwOh7p3764jR46ovLxcDzzwgKRzF556+umn5fF45PP5FBsbK0lKTU1V\nWVmZoqKilJKSIknq0qWL/H6/zpw5ow4dOoQyIgAAYSXk5zjY7Xbl5ORoyZIlGjFihIwxwW1Op1M1\nNTXyeDyKjo5uNO7xeOTxeOR0Ohvt6/V65XK5LM8BAAAun5AecTgvLy9Pp0+f1ujRo1VfXx8c93g8\niomJkcvlktfrDY57vV5FR0c3Gvd6vYqJiZHT6Wy07/k5HA7HRef4ItGutk1ua+fwq1OnL77/l93V\nnq855A/f/OGcXSJ/uOe/VCEtDi+//LJOnDihqVOnqm3btoqIiNDNN9+s3bt3q1+/fiopKVFycrLi\n4+O1YsUK1dfXq66uTkePHlXPnj2VkJCgkpISxcfHq6SkRImJiXK5XHI4HKqsrFS3bt1UWlqqzMxM\n2e125efna/LkyaqqqlIgEFD79u2/cH01ntomtxnbpzp1quZyPyWtRqdO0Vd1vuaQP3zzh3N2ifzk\nv/TSFNLiMGzYMOXk5GjChAlqaGjQvHnzdOONN2rBggXy+XyKi4vTsGHDZLPZNGnSJI0bN06BQEBZ\nWVmKiopSRkaGsrOzNW7cOEVFRamgoECSlJubq9mzZ8vv9ys1NTX47onExESNGTNGgUBAbrc7lNEA\nAAhLNvP5kw7CyO+3vqnPAq4mt7tsNUrs27sFV9SyaN3kD9f84ZxdIj/5L/2IAxeAAgAAllEcAACA\nZRQHAABgGcUBAABYRnEAAACWURwAAIBlFAcAAGAZxQEAAFhGcQAAAJZRHAAAgGUUBwAAYBnFAQAA\nWEZxAAAAllEcAACAZRQHAABgGcUBAABYRnEAAACWURwAAIBlFAcAAGAZxQEAAFhGcQAAAJZRHAAA\ngGUUBwCyHkuNAAAVbUlEQVQAYBnFAQAAWEZxAAAAllEcAACAZRQHAABgGcUBAABYRnEAAACWURwA\nAIBlFAcAAGAZxQEAAFhGcQAAAJZRHAAAgGUUBwAAYBnFAQAAWBYZqol9Pp/mzp2r48ePq76+XtOn\nT1dcXJxycnIUERGhHj16yO12y2azaePGjdqwYYMiIyM1ffp0DRo0SLW1tZozZ46qq6vldDqVl5en\njh07qqKiQkuXLpXdbldKSooyMzMlSYWFhdq+fbvsdrvmzp2r+Pj4UEUDACBshaw4vPLKK+rYsaPy\n8/N19uxZ/eAHP1CfPn2UlZWlpKQkud1ubd26VX379lVRUZGKi4tVV1enjIwMDRgwQOvWrVOvXr2U\nmZmpV199VatXr9a8efPkdrtVWFio2NhYTZkyRYcOHVIgENCePXu0adMmVVVVaebMmXrxxRdDFQ0A\ngLAVsuIwbNgwfe9735MkBQIBRUZG6uDBg0pKSpIkpaenq7S0VBEREUpISJDD4ZDD4VD37t115MgR\nlZeX64EHHpAkpaWl6emnn5bH45HP51NsbKwkKTU1VWVlZYqKilJKSookqUuXLvL7/Tpz5ow6dOgQ\nqngAAISlkJ3jcO2118rpdMrj8eihhx7Sww8/rEAgENzudDpVU1Mjj8ej6OjoRuMej0cej0dOp7PR\nvl6vVy6Xy/IcAADg8grZEQdJqqqqUmZmpsaPH6/hw4crPz8/uM3j8SgmJkYul0terzc47vV6FR0d\n3Wjc6/UqJiZGTqez0b7n53A4HBedoznRrrZNbmvn8KtTp+bn+DK72vM1h/zhmz+cs0vkD/f8lypk\nxeH06dO677775Ha71b9/f0lSnz59tHv3bvXr108lJSVKTk5WfHy8VqxYofr6etXV1eno0aPq2bOn\nEhISVFJSovj4eJWUlCgxMVEul0sOh0OVlZXq1q2bSktLlZmZKbvdrvz8fE2ePFlVVVUKBAJq3759\ns2us8dQ2uc3YPtWpUzWX7flobTp1ir6q8zWH/OGbP5yzS+Qn/6WXppAVh2eeeUY1NTV66qmn9NRT\nT0mS5s2bpyVLlsjn8ykuLk7Dhg2TzWbTpEmTNG7cOAUCAWVlZSkqKkoZGRnKzs7WuHHjFBUVpYKC\nAklSbm6uZs+eLb/fr9TU1OC7JxITEzVmzBgFAgG53e5QxQIAIKzZjDHmSi/iSvj91jf1WcDV5HaX\nrUaJfXu34IpaFq2b/OGaP5yzS+Qn/6UfceACUAAAwDKKAwAAsIziAAAALKM4AAAAyygOAADAMooD\nAACwjOIAAAAsozgAAADLKA4AAMAyigMAALCM4gAAACyjOAAAAMsoDgAAwDKKAwAAsIziAAAALKM4\nAAAAyygOAADAMooDAACwjOIAAAAsozgAAADLKA4AAMAyigMAALCM4gAAACyjOAAAAMsoDgAAwDKK\nAwAAsIziAAAALKM4AAAAyygOAADAMooDAACwjOIAAAAsozgAAADLKA4AAMAyigMAALCM4gAAACyj\nOAAAAMtCXhzeeustTZw4UZL0/vvvKyMjQ+PHj9fChQtljJEkbdy4UaNGjdKYMWO0bds2SVJtba1m\nzpyp8ePHa8qUKaqurpYkVVRU6Mc//rEyMjJUWFgYfJzCwkKNHj1aY8eO1d69e0MdCwCAsBTS4vDs\ns89q/vz58vl8kqTHHntMWVlZWrt2rYwx2rp1q06dOqWioiKtX79ezz33nAoKClRfX69169apV69e\nWrt2rUaOHKnVq1dLktxutwoKCrRu3Trt3btXhw4d0oEDB7Rnzx5t2rRJK1as0KJFi0IZCwCAsBXS\n4tC9e3cVFhYGjywcPHhQSUlJkqT09HSVlZVp3759SkhIkMPhkMvlUvfu3XXkyBGVl5crPT1dkpSW\nlqYdO3bI4/HI5/MpNjZWkpSamqqysjKVl5crJSVFktSlSxf5/X6dOXMmlNEAAAhLIS0Ot99+u+x2\ne/D2+QIhSU6nUzU1NfJ4PIqOjm407vF45PF45HQ6G+3r9XrlcrkszwEAAC6vyJZ8sIiI/99TPB6P\nYmJi5HK55PV6g+Ner1fR0dGNxr1er2JiYuR0Ohvte34Oh8Nx0TmaE+1q2+S2dg6/OnVqfo4vs6s9\nX3PIH775wzm7RP5wz3+pWrQ49OnTR7t371a/fv1UUlKi5ORkxcfHa8WKFaqvr1ddXZ2OHj2qnj17\nKiEhQSUlJYqPj1dJSYkSExPlcrnkcDhUWVmpbt26qbS0VJmZmbLb7crPz9fkyZNVVVWlQCCg9u3b\nN7ueGk9tk9uM7VOdOlVzOeO3Kp06RV/V+ZpD/vDNH87ZJfKT/9JLU4sUB5vNJknKycnRggUL5PP5\nFBcXp2HDhslms2nSpEkaN26cAoGAsrKyFBUVpYyMDGVnZ2vcuHGKiopSQUGBJCk3N1ezZ8+W3+9X\namqq4uPjJUmJiYkaM2aMAoGA3G53S8QCACDs2MznTzwII7/f+qY+C7ia3O6y1Sixb+8WXFHLonWT\nP1zzh3N2ifzkv/QjDlwACgAAWEZxAAAAllEcAACAZRQHAABgGcUBAABYRnEAAACWURwAAIBlFAcA\nAGAZxQEAAFhGcQAAAJZRHAAAgGUUBwAAYBnFAQAAWEZxAAAAllEcAACAZRQHAABgGcUBAABYRnEA\nAACWURwAAIBlFAcAAGAZxQEAAFhGcQAAAJZRHAAAgGUUBwAAYBnFAQAAWEZxAAAAllEcAACAZRQH\nAABgGcUBAABYRnEAAACWURwAAIBlFAcAAGAZxQEAAFhGcQAAAJZRHAAAgGUUBwAAYFnklV7A5RQI\nBLRw4UK9/fbbcjgcWrJkia6//vorvSwAAK4aV9URh9dee00+n0/r16/X7NmzlZeXd6WXBADAVeWq\nKg7l5eVKS0uTJPXt21f79++/wisCAODqclW9VOHxeORyuYK37Xa7AoGAIiIu7EcBX60+9Xza5FwR\nDp8++eRsSNbZGkRFBfTJJzVXehlXDPnDN384Z5fIH6r8MTHtLvucrdVVVRxcLpe8Xm/wdlOlQZLu\nHJbaUstqtdq1C58f9Ishf/jmD+fsEvnDPf+luqpeqkhISFBJSYkkqaKiQr169brCKwIA4OpiM8aY\nK72Iy8UYo4ULF+rIkSOSpMcee0w33HDDFV4VAABXj6uqOAAAgNC6ql6qAAAAoUVxAAAAllEcAACA\nZVfV2zGtCIfLUr/11ltavny5ioqK9P777ysnJ0cRERHq0aOH3G63bDabNm7cqA0bNigyMlLTp0/X\noEGDVFtbqzlz5qi6ulpOp1N5eXnq2LHjlY5jmc/n09y5c3X8+HHV19dr+vTpiouLC5v8fr9f8+fP\n17Fjx2Sz2ZSbm6uoqKiwyS9JH330kX74wx/qV7/6lSIiIsIq+1133RW8jk1sbKymTp0aVvnXrFmj\nN954Qz6fTxMmTFBCQkLY5H/ppZdUXFwsSaqrq9Phw4f1m9/8RkuWLAlNfhNm/vu//9vk5OQYY4yp\nqKgw06dPv8Irurz+8z//0wwfPtyMGTPGGGPM1KlTze7du40xxjz66KNmy5Yt5uTJk2b48OGmvr7e\n1NTUmOHDh5u6ujrzy1/+0qxatcoYY8wf/vAHs3jx4iuW41+xefNms3TpUmOMMR9//LEZOHCgmTZt\nWtjk37Jli5k7d64xxphdu3aZadOmhVX++vp6M2PGDPO9733PHD16NKx+9mtra83IkSMbjYVT/p07\nd5qpU6caY4zxer3mySefDKuf/c/Lzc01GzduDGn+sHup4mq/LHX37t1VWFgo839vljl48KCSkpIk\nSenp6SorK9O+ffuUkJAgh8Mhl8ul7t2768iRIyovL1d6erokKS0tTTt27LhiOf4Vw4YN009+8hNJ\n544sRUZGhlX+oUOHatGiRZKkDz/8UO3atdOBAwfCJv/jjz+ujIwMderUSVJ4/ewfPnxYn332mSZP\nnqy7775bFRUVYZW/tLRUvXr10owZMzRt2jQNGTIkrH72z9u3b5/+9re/afTo0SHNH3bFoanLUl8t\nbr/9dtnt9uBt87l32zqdTtXU1Mjj8Sg6OrrRuMfjkcfjkdPpbLTvl8m1114bzPLQQw/p4YcfbvS9\nvdrzS+d+nnNycrRkyRKNGDEibL7/xcXF6tixo1JTz10R1hgTNtkl6ZprrtHkyZP13HPPKTc3V7Nn\nz260/WrPX11drf3792vlypXKzc3VrFmzwur7f96aNWuUmZkpKbT/94fdOQ7/zGWprwafz+bxeBQT\nE3PBc+D1ehUdHd1o3Ov1KiYmpsXXe6mqqqqUmZmp8ePHa/jw4crPzw9uC4f8kpSXl6fTp09r9OjR\nqq+vD45fzfmLi4tls9lUVlamw4cPKycnR2fOnAluv5qzS9I3vvENde/ePfh1+/btdejQoeD2qz1/\nhw4dFBcXp8jISN1www1q06aNTp48Gdx+teeXpE8++UTHjh1Tv379JIX2//6r9zdmE8LtstR9+vTR\n7t27JUklJSVKTExUfHy83nzzTdXX16umpkZHjx5Vz549Gz035/f9Mjl9+rTuu+8+zZkzRz/84Q8l\nhVf+l19+WWvWrJEktW3bVhEREbr55pvDIv8LL7ygoqIiFRUVqXfv3lq2bJlSU1PDIrt0rjjl5eVJ\nkk6cOCGv16uUlJSwyf/d735X//M//yPpXP7a2lr1798/bPJL0p49e9S/f//g7VD+3xd2V440YXBZ\n6g8++ECzZ8/W+vXrdezYMS1YsEA+n09xcXFavHixbDabNm3apA0bNigQCGj69Om67bbbVFtbq+zs\nbJ06dUpRUVEqKCjQddddd6XjWLZ48WL98Y9/bPT9nDdvnpYsWRIW+Wtra5WTk6PTp0+roaFBU6ZM\n0Y033hg23//zJk6cqEWLFslms4VN9oaGBv3sZz/T8ePHJUlz5sxR+/btwya/JOXn52vXrl0KBAKa\nNWuWunbtGlb5n3vuOTkcDk2aNEmSQvp/f9gVBwAA8K8Lu5cqAADAv47iAAAALKM4AAAAyygOAADA\nMooDAACwjOIAAAAsozgArdgHH3yg3r17q6ysrNH4kCFDgu/ZvxRDhgzRxx9/fMnzfJHjx49r2LBh\nGjVqVKOr1knSu+++q2nTpmnEiBEaMWKEZs2a1eiKj63N66+/rl/96ldXehnAFUVxAFq5yMhIzZ8/\n/4JfupdLqC/lsnv3bn3rW9/S5s2bg9fDl85d4e/uu+/W2LFj9corr+iVV15Rz549g9fab40OHDgg\nj8dzpZcBXFFh91kVwJdN586dlZqaqmXLlgU//fK8Xbt2qbCwUEVFRZKknJwc3XLLLerXr59mzJih\n66+/Xm+//bZuvvlm9evXTy+99JLOnj2rwsJCxcXFSZKWL1+ugwcPqk2bNlq8eLFuuukmnT59Wm63\nW1VVVYqIiNCsWbOUnJysVatWqaKiQn//+981YcIEZWRkBNfy3nvv6dFHH9XZs2d17bXXat68eXI4\nHHryySf16aefauHChVq4cGFw/3Xr1ik1NVWDBg0Kjj3wwAOKjY2V3+9XfX295s+fr7fffls2m033\n3XefRo4cqeLiYm3btk0nT54Mlo/jx49r586dat++vX7xi1/o5MmTevDBB5vNv3fvXuXl5am2tlYd\nOnRQbm6uunXrpokTJyo+Pl5/+ctfVF1drfnz56tr165av369bDabunbtqq997WvKz8+XzWZTu3bt\nVFBQoA4dOoTuBwFoLULxeeAALo/KykozePBgU1NTYwYPHmxKS0uNMcYMHjzYfPjhh2bnzp1mwoQJ\nwf1zcnLMSy+9ZCorK03v3r3NoUOHTCAQMLfddpt54oknjDHGrFq1yixdujQ4z3/9138ZY4zZtm2b\nGTVqlDHGmIcffths3brVGGPMiRMnzNChQ43H4zErV640EydOvOhaR40aZbZs2WKMMaaiosIMHjzY\n1NXVmeLiYpOTk3PB/lOnTjXr1q1rMvuyZcvM4sWLjTHGVFdXm1tvvdUcPnzYbN682QwePNh4PB7z\n4Ycfml69epk///nPxhhjJk6caF577TVL+evr682IESNMVVWVMcaYkpISc8899xhjjJkwYULwOXr9\n9dfNXXfdFbzvqlWrgo+1b98+Y4wxzz//fHANwNWOIw7Al4DL5dJ//Md/aP78+XrllVcs3ecrX/mK\nevfuLUn66le/GvwAnK9//evas2dPcL8f/ehHkqSBAwfqpz/9qTwej8rKyvTee+9p5cqVkiS/36/K\nykrZbDb17dv3gsfyer2qrKzU0KFDJUl9+/ZVu3bt9N577zX5UojNZvvCj7TftWuXli5dKuncpx/e\neuut2r17t1wul77zne/I6XQGX/pITk6WJHXt2jX4kcBN5e/atat2796tY8eOqbKyUtOmTWuU47y0\ntDRJ0k033aSzZ89KOveyjs1mk3Tu/JAHH3xQQ4cO1a233qoBAwY0mQW4mlAcgC+JlJQUpaSkBD8F\nUVLwl9h5Pp8v+LXD4Wi0LTLy4v/c7Xb7BfsZY/T8888HP173xIkT6tSpk1577TW1adPmgjmMMRcU\nBGOMAoHABWs87+abb9b+/fsbjQUCAf3kJz9Rbm7uBXMGAgH5/X5JUlRUVKP7ff4jhM9rKv/5Of1+\nv2JjY/Xyyy8H5z916lRw//M5bTbbRcvPPffcoyFDhuiNN95Qfn6+vve97zUqIcDVipMjgS+R7Oxs\nlZaW6uTJk5LO/SVeWVmp+vp6ffzxx/rLX/7yT895/gjGli1bdOONN6pt27bq37+/1q5dK0l65513\ndOedd+qzzz5r8uiBy+VSbGystmzZIuncR9afPn1aPXr0aPI+Y8aM0fbt27V9+3ZJ536hP/300zpz\n5oyuu+463XLLLXrxxRclSdXV1dq6datuueWWZk/mbG77eTfeeKPOnj2rN998U5K0efNmzZ49+wvv\nExkZqYaGBknS2LFj5fV6dffdd+vuu+/WwYMHLT0u8GXHEQeglfv8X+znX7K4//77JUk9evTQwIED\ndccdd6hr165KTEwM3qepv/T/cfztt9/WyJEjFR0drWXLlkmS5s+fr0cffVR33nmnjDFavny5nE5n\nk3NK5z7W2O12a+XKlWrTpo0KCwsVGRnZ5H2+8pWv6Nlnn9Xjjz+u5cuXKxAI6Fvf+paeeuopSdKD\nDz6o3NxcjRgxIvgRwH369NHhw4e/MM/57M3lj4qK0pNPPqklS5aorq5O0dHRjY7mXOw+SUlJys7O\nVqdOnfTQQw8pJydHdrtd11xzjXJzc5t8boCrCR+rDQAALOOlCgAAYBnFAQAAWEZxAAAAllEcAACA\nZRQHAABgGcUBAABYRnEAAACWURwAAIBl/w8JqmLhhNLLPAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd68c77ad90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "histrogram_plot(chrome_issue[\"num_comments\"], \"Number of Comments per Issue\",\"Number of Comments\", \"Count\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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48chrrrlG69atk9frDcV4AABAM9LgisJf//pX/eEPf6jTZrPZtHPnzqANCgAA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\nI2uS1Z3HbaEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd64d25d810>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "histrogram_plot(chrome_issue[chrome_issue[\"num_comments\"] < 100][\"num_comments\"], \"Number of Comments(Less Than 100)\", \"Number of Comments\", \"Count\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Statistics of the number of comments per issue"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    611859.000000\n",
+       "mean          9.336439\n",
+       "std          27.077464\n",
+       "min           1.000000\n",
+       "25%           4.000000\n",
+       "50%           6.000000\n",
+       "75%          11.000000\n",
+       "max        6354.000000\n",
+       "Name: num_comments, dtype: float64"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chrome_issue[\"num_comments\"].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The Number of Deleted Comments: 49186\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"The Number of Deleted Comments:\", np.where(~np.isnan(chrome_comment[\"deleted_by\"]))[0].shape[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Coarse Plots of Comments over time\n",
+    "\n",
+    "It appears that it is not unusal for issues to be commented on the order of years"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def plot_comments_over_time(comments):\n",
+    "    num_comments = len(comments)\n",
+    "    plt.plot(map(datetime.datetime.fromtimestamp, comments[\"created\"]), xrange(1, num_comments+1))\n",
+    "    plt.xlabel(\"Time\")\n",
+    "    plt.ylabel(\"Number of Comments\")\n",
+    "    plt.title(\"time vs number of comments\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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2brPZ8POf/7zL8s2bN59/pURERCNc1NDOzc3F448/DpfLBUVRYLPZ4lEXERFR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\nrkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd64def9c90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for idx in xrange(0, len(chrome_issue), 10000):\n",
+    "    if len(chrome_issue.iloc[idx][\"comments\"]) > 500:\n",
+    "        continue\n",
+    "    plot_comments_over_time(chrome_comment.loc[chrome_issue.iloc[idx][\"comments\"]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Explore The timeframe from when an issue is opened to closed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "time_to_closure = (chrome_issue[\"closed\"] - chrome_issue[\"opened\"])\n",
+    "time_to_closure = time_to_closure[time_to_closure > 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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/9J8yZQoTJkxg+vTpOBwOvF4ve/bsYcmSJTz66KOBrFFEREQCpNFgMGDAAEJC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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd64e12f550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "histrogram_plot(time_to_closure / (365*24*3600), \"Number Closed over years\", \"Years\", \"Number Closed\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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lltvtdtntdlePxIVtHQ6HgoODy70PAABw6dzeA7ls2TIlJydXyMF8fP6bTex2\nu0JDQxUcHCyHw+Fa7nA4FBISUmq5w+FQaGiorFZrqW0v7MPf3/+i+3AnJLh2messRqHq1w9RaKj7\n/aBsDRrQft5AO3sebex5tHHV5TYsLFmypMLCwvXXX69t27apQ4cOysvLU8eOHRUZGal58+apqKhI\nhYWFOnDggFq0aKG2bdsqLy9PkZGRysvLU7t27RQcHCx/f38dPnxYTZs21ebNm5WamipfX1/NmTNH\nQ4YM0dGjR+V0OhUWFua2Hpu9oMx1DnuBTpywqbDQUiHnXhM1aBCi48dtlV1GtUc7ex5t7Hm0sedd\nShhzGxb+9re/aeDAgWrTpk2pSZou3IVQHhcedZ2WlqbJkyeruLhYzZo1U2xsrCwWiwYOHKiUlBQ5\nnU6NHj1aAQEBSk5O1rhx45SSkqKAgADNnTtXkjR16lSNGTNGJSUlio6Odt310K5dO/Xr109Op1Pp\n6el/qhEAAEDZLMZvBxJcxIVHVV/4g28YhiwWy58KC1XRqrWfy/AJLHO9w/arYv7RVCEhoV6sqnrh\nm4J30M6eRxt7Hm3seR7tWRg1apQcDocOHz6sFi1aKD8/3zXwEAAAVH9u74bYunWr4uPjNXLkSB0/\nflxdu3bVxo0bvVEbAACoAtyGhblz52rp0qUKDQ1Vo0aNtGTJEj311FPeqA0AAFQBbsOC0+lUw4YN\nXa+bN2/uGr8AAACqP7djFho3bqwNGzZIks6cOaOlS5eqSZMmHi8MAABUDW57FqZOnaq33npLR48e\nVbdu3bR3715NmzbNG7UBAIAqwG3PQv369TVv3jzZ7Xb5+fmpdu2yZz0EAADVj9uw8O233yotLU2H\nDx+WJF177bWaPXu2rrrqKo8XBwAAKp/byxCTJk3SqFGj9Omnn+rTTz/V4MGDNXHiRG/UBgAAqgC3\nYaGwsFCdO3d2vb799ttlszHLFgAANUWZYeH06dM6deqUbrjhBr366quy2+3Kz8/XihUr1K5dO2/W\nCAAAKlGZYxYSExNdP2/dulWvvfZaqfWTJk3yXFUAAKDKKDMsXJhbAQAA1Gxu74Y4cOCAVqxYoTNn\nzpRaPmvWLI8VBQAAqg63YSE1NVV33nmnWrZs6VrGdM8AANQcbsNCnTp1lJqa6o1aAABAFeQ2LCQk\nJGjevHm6+eab5ef3383bt2/v0cIAAEDV4DYsbNu2Tbt27dKOHTtKLc/KyvJYUQAAoOpwGxZ2796t\ntWvXMk4E2KySAAAQbElEQVQBAIAayu0Mji1atND+/fu9UQsAAKiC3PYsHDp0SAkJCapfv778/f0l\nnb8b4sMPP/R4cQAAoPK5DQvPP/+8DMMotYxLEgAA1BzlGuB4sXBw5ZVXeqQgAABQtbgNC59++qkr\nLBQXF+vf//632rVrp/j4eI8XBwAAKp/bsJCRkVHq9enTp/Xoo496rCAAAFC1uL0b4veCgoL0ww8/\neKIWAABQBbntWRgwYECp14cPH1bnzp09VhAAAKhayvUgqQssFovq1q2r5s2be7QoAABQdZQZFn78\n8UdJUnh4+EXXNWnSxHNVAQCAKqPMsHDvvfdedPnPP/+skpIS7d2712NFAQCAqqPMsLBhw4ZSrx0O\nhzIyMrR582ZNnz7d44UBAICqoVx3Q2zZskW9evWSJOXm5ioqKsqjRQEAgKrDdICjw+HQ7NmztWnT\nJk2fPp2QAABADVRmzwK9CQAAQDLpWRg8eLD8/Py0adMmbdq0qdQ6njoJAEDNUWZYWL9+vTfrAAAA\nVVSZYaFp06berAMAAFRRf/rZEAAAoGYhLAAAAFOEBQAAYIqwAAAATBEWAACAKcICAAAwRVgAAACm\nCAsAAMAUYQEAAJgiLAAAAFOEBQAAYIqwAAAATBEWAACAKcICAAAwRVgAAACmCAsAAMAUYQEAAJgi\nLAAAAFOEBQAAYIqwAAAATBEWAACAKcICAAAwRVgAAACmCAsAAMAUYQEAAJgiLAAAAFOEBQAAYIqw\nAAAATBEWAACAKcICAAAwRVgAAACmCAsAAMCUX2UcNCEhQcHBwZKk8PBwDRs2TGlpafLx8VHz5s2V\nnp4ui8WiFStWaPny5fLz89OIESPUpUsXFRQUaOzYsTp58qSsVqsyMjJUr1497dy5UzNnzpSvr6+i\noqKUmppaGacGAEC14/WwUFhYKEnKyspyLRs+fLhGjx6t9u3bKz09XR9++KHatGmjrKwsZWdnq7Cw\nUMnJybrlllu0bNkytWzZUqmpqXr33Xe1aNEiTZw4Uenp6crMzFR4eLiGDh2qvXv36vrrr/f26QEA\nUO14/TLEvn37lJ+fryFDhmjQoEHauXOn9uzZo/bt20uSYmJitGXLFu3atUtt27aVv7+/goODFRER\nof3792vHjh2KiYmRJHXq1Elbt26V3W5XcXGxwsPDJUnR0dHasmWLt08NAIBqyes9C4GBgRoyZIj6\n9u2r77//Xg888ECp9VarVTabTXa7XSEhIaWW2+122e12Wa3WUts6HA7XZY0Lyw8fPuydEwIAoJrz\neli4+uqrFRER4fo5LCxMe/fuda232+0KDQ1VcHCwHA6Ha7nD4VBISEip5Q6HQ6GhobJaraW2vbAP\nd0KCa5e5zmIUqn79EIWGhpS5Ddxr0ID28wba2fNoY8+jjasur4eF7Oxs7d+/X+np6Tp27JgcDoei\noqK0bds2dejQQXl5eerYsaMiIyM1b948FRUVqbCwUAcOHFCLFi3Utm1b5eXlKTIyUnl5eWrXrp2C\ng4Pl7++vw4cPq2nTptq8eXO5Bjja7AVlrnPYC3TihE2FhZaKPP0apUGDEB0/bqvsMqo92tnzaGPP\no40971LCmNfDwl133aXx48frnnvukSTNmjVLYWFhmjx5soqLi9WsWTPFxsbKYrFo4MCBSklJkdPp\n1OjRoxUQEKDk5GSNGzdOKSkpCggI0Ny5cyVJU6dO1ZgxY1RSUqLo6GhFRkZ6+9QAAKiWLIZhGJVd\nRGVYtfZzGT6BZa532H5VzD+aKiTE/eUMXBzfFLyDdvY82tjzaGPPu5SeBSZlAgAApggLAADAFGEB\nAACYIiwAAABThAUAAGCKsAAAAEwRFgAAgCnCAgAAMEVYAAAAprw+3fPlwjAM2Wxn5G6Cy5CQUFks\nPD8CAFB9ERbKkH/WoY93/Ko6deuZbnP7P69TaGgdL1YGAIB3ERZM1A4MUpCVR6YCAGo2xiwAAABT\nhAUAAGCKsAAAAEwRFgAAgCnCAgAAMEVYAAAApggLAADAFGEBAACYIiwAAABThAUAAGCK6Z4vwYWH\nTbnDw6YAAJczwsIlOP+wqZMKq3eF6TY8bAoAcDkjLFwiHjYFAKjuGLMAAABMERYAAIApwgIAADBF\nWAAAAKYICwAAwBRhAQAAmCIsAAAAU4QFAABgirAAAABMERYAAIApwgIAADBFWAAAAKYICwAAwBRh\nAQAAmCIsAAAAU4QFAABgirAAAABMERYAAIApwgIAADDlV9kFVHeGYchmO+N2u5CQUFksFi9UBADA\nn0NY8LD8sw59vOOkwupdYbrN7f+8TqGhdbxYGQAA5UNY8ILagUEKsoZUdhkAAPwljFkAAACmCAsA\nAMAUYQEAAJhizEIVwB0TAICqjLBQBXDHBACgKiMsVBHcMQEAqKoYswAAAEwRFgAAgCnCAgAAMMWY\nhcsEd0wAACoLYeEyUZ47Js467Or490YKCQk13ReBAgDwZxAWLiPu7pg467Dr4x2HLjlQECYAAL9F\nWKhmLjVQMJ8DAOD3CAs1kFmgYGwEAOD3CAsohdkkAQC/R1jAH7i7lFHe3of69YMrsiwAQCUhLOBP\nK2/vQ3L9EDGVBwBc/ggL+EvK0/vw66+/qri47LBgGIYkuR37wPgIAKhchAV4RP5Zh9ZuPaCAWmVf\nijh54ph8fPwu+VZPQgcAeBZhAR4TGGhVrUDz2zh9fHwvee6I8oQOBmUCwF9HWECVV565I9yFjvIM\nyixPDwW9GABqIsICaoTyDMosTw+FNy+dlDeYcNcJAE+rNmHB6XTqySef1Ndffy1/f3/NmDFDV111\nVWWXhSqkInoovHnppLzB5E5/5yUNJK2o4FJdt5HcB7KK6rmSvNMr5c2etvJu4+9fojNn7B4/luS+\njStqcrrqNMldtQkL69evV3Fxsd544w198cUXysjI0PPPP1/ZZaGG8mYwudSBpBUVXKrrNuUJZDbb\nGX3y1c8KtFov+Vje6JWqqHorcpvgEOslD4iuqDYuT/t4cz8VFZQaNCj7s8SdahMWduzYoU6dOkmS\n2rRpo927d1dyRYB3XOpA0ooMLtV1m/IEsiBr6GXTK1WR9VbUNhU1ILqi2rg87ePN/Vzqv3n+WYea\nNWta5u+7U23Cgt1uV3Dwf/8z+/r6yul0ysfn4t8GivJPq/icrcz9Fef/qnPngnXWUfY2BfkO+fj4\nVattKvI4fn5SibPslFuVzvty3uZS27kqnlNV2yY4pOxvhv/d7myF1eMNFVlvVXgf/9lt3Clv+3hr\nP5Wt6ldYTsHBwXI4HK7XZkFBklLib/VGWQAAXPaqzVy8bdu2VV5eniRp586datmyZSVXBABA9WAx\nLoyKuMwZhqEnn3xS+/fvlyTNmjVL11xzTSVXBQDA5a/ahAUAAOAZ1eYyBAAA8AzCAgAAMEVYAAAA\npqrNrZPlxbTQFe+LL77Q008/raysLB08eFBpaWny8fFR8+bNlZ6eLovFohUrVmj58uXy8/PTiBEj\n1KVLl8ou+7JRXFysCRMm6Mcff1RRUZFGjBihZs2a0c4VqKSkRJMmTdL3338vi8WiqVOnKiAggDb2\ngF9++UWJiYl69dVX5ePjQxt7QEJCgmveofDwcA0bNuzS29moYdauXWukpaUZhmEYO3fuNEaMGFHJ\nFV3eXnzxRSMuLs7o16+fYRiGMWzYMGPbtm2GYRjGlClTjHXr1hk///yzERcXZxQVFRk2m82Ii4sz\nCgsLK7Psy8rq1auNmTNnGoZhGKdPnzY6d+5sDB8+nHauQOvWrTMmTJhgGIZhfPrpp8bw4cNpYw8o\nKioyRo4cafTo0cM4cOAAnxceUFBQYMTHx5daVhHtXOMuQzAtdMWKiIhQZmama17yPXv2qH379pKk\nmJgYbdmyRbt27VLbtm3l7++v4OBgRUREuG5xhXuxsbF6+OGHJZ3vGfPz86OdK1i3bt00bdo0SdIP\nP/ygOnXq6KuvvqKNK9hTTz2l5ORkNWjQQBKfF56wb98+5efna8iQIRo0aJB27txZIe1c48JCWdNC\n46/p3r27fH19Xa+N39yJa7VaZbPZZLfbFRISUmq53V720+VQWlBQkKvNHnnkET366KOl3rO0c8Xw\n9fVVWlqaZsyYoV69evFermDZ2dmqV6+eoqOjJZ3/rKCNK15gYKCGDBmil19+WVOnTtWYMWNKrf+r\n7Vzjxiz82Wmh8ef8ti3tdrtCQ0P/0OYOh0OhoWU/YQ1/dPToUaWmpuqee+5RXFyc5syZ41pHO1ec\njIwMnThxQn379lVRUZFrOW186bKzs2WxWLRlyxbt27dPaWlpOnXqlGs9bVwxrr76akVERLh+DgsL\n0969e13r/2o717i/kkwL7VnXX3+9tm3bJknKy8tTu3btFBkZqc8++0xFRUWy2Ww6cOCAmjdvXsmV\nXj5OnDihwYMHa+zYsUpMTJREO1e0nJwcvfDCC5Kk2rVry8fHRzfeeCNtXIGWLFmirKwsZWVlqVWr\nVpo9e7aio6Np4wqWnZ2tjIwMSdKxY8fkcDgUFRV1ye1c43oWbr/9dm3evFn9+/eXdH5aaFy6C89Q\nT0tL0+TJk1VcXKxmzZopNjZWFotFAwcOVEpKipxOp0aPHq2AgIBKrvjysXjxYtlsNi1cuFALFy6U\nJE2cOFEzZsygnStIbGys0tLSdO+99+rcuXOaOHGirr32Wt7LHmSxWPi88IC77rpL48eP1z333CPp\n/N+4sLCwS25npnsGAACmatxlCAAA8OcQFgAAgCnCAgAAMEVYAAAApggLAADAFGEBAACYqnHzLACo\nWEeOHFFsbKyuu+46SVJBQYFatmypKVOm6Iorrqjk6gBUBHoWAFyyhg0bKicnRzk5OXr//fcVERHh\nevgVgMsfPQsAKtyoUaMUFRWl/fv3KysrS99++61OnDiha665RpmZmVq0aJEMw9Bjjz0mSRo/frw6\ndeqkkpISvfzyy/Lx8VHTpk319NNPM3sfUAXQswCgwvn7+ysiIkLr169XrVq19MYbb2jdunUqKCjQ\nxx9/rKSkJL399tuSpLNnz+qTTz5Rt27dNH/+fL3yyivKzs7Wtddeq++++66SzwSARM8CAA+xWCz6\n+9//rqZNm2rp0qX67rvvdPDgQZ09e1bh4eG68sortX37dv3www/q0qWLAgICdOuttyo5OVm33Xab\nevTooVatWlX2aQAQPQsAPKCoqEj/+c9/dOjQIY0ZM0ZBQUFKSkpS+/btXdskJSXprbfe0jvvvKOE\nhARJ5x+Q9dxzzyksLExjx45Vbm5uZZ0CgN8gLACoUE6nUwsWLNA//vEPHTp0SHfccYcSEhJ0xRVX\naPv27Tp37pyk80963Lp1q3755RdFRkaqpKREPXr0UN26dTV06FD16dNHe/fureSzASBxGQJABfj5\n558VHx8vSSopKdHf//53zZ07Vz/99JMef/xxffDBB2rQoIFuu+02/fDDD5KkWrVq6aabblLLli0l\nSb6+vnr44Yd1//33q3bt2qpTp44yMjIq7ZwA/BePqAZQKex2u/r3769//etfzMcAVHFchgDgdV9+\n+aVuu+029evXj6AAXAboWQAAAKboWQAAAKYICwAAwBRhAQAAmCIsAAAAU4QFAABgirAAAABM/T8b\nHsGvYGz4kAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd6cc4702d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "histrogram_plot(time_to_closure[time_to_closure < (500*24*3600)] / (3600 * 24), \"Number Closed over Days (Less than 500)\", \"Days\", \"Number Closed\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Statistics on time to closure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average time to closure 177.271462655 Days\n",
+      "Median time to closure 21.178587963 Days\n",
+      "Standard Deviation of time to closure 335.366124974 Days\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average time to closure\", time_to_closure.mean() / (3600 * 24), \"Days\")\n",
+    "print(\"Median time to closure\", time_to_closure.median() / (3600 * 24), \"Days\")\n",
+    "print(\"Standard Deviation of time to closure\", time_to_closure.std() / (3600 * 24), \"Days\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Explore the length of a typical comment (Really Rough still need to remove markup, stop words, etc...) I'm also not differentiting between first comment and everything else.\n",
+    "..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "length_of_comments = chrome_comment[\"content\"].apply(lambda content: len(content.split()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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S3759tWHDBnm9XgUCAcXFxUmSkpOTlZ+fr82bNyspKUmS1KlTJ1VWVurQoUORPDUAAFqM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\nCAsAAMAUYQEAAJj6f+Jh/5/WfKhoAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd64d328950>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "histrogram_plot(length_of_comments[(length_of_comments > 0) & (length_of_comments < 200)], \"Length of Issue (<200)\", \"Number of Words\", \"Count\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Statistics on the lengh of Comments"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    5.712584e+06\n",
+       "mean     3.971911e+01\n",
+       "std      7.033108e+01\n",
+       "min      0.000000e+00\n",
+       "25%      0.000000e+00\n",
+       "50%      1.800000e+01\n",
+       "75%      5.600000e+01\n",
+       "max      7.504000e+03\n",
+       "Name: content, dtype: float64"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "length_of_comments.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore Labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "chrome_issue_id_set = set(chrome_issue[\"issue_id\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1000000\n",
+      "2000000\n",
+      "3000000\n"
+     ]
+    }
+   ],
+   "source": [
+    "labels_by_issue = defaultdict(list)\n",
+    "num_times_label_used = defaultdict(int)\n",
+    "i = 0\n",
+    "for index, row in issue_label.iterrows():\n",
+    "    if row[\"issue_id\"] in chrome_issue_id_set:\n",
+    "        labels_by_issue[row[\"issue_id\"]].append(row[\"label_id\"])\n",
+    "        num_times_label_used[row[\"label_id\"]] += 1\n",
+    "    if i % 1000000 == 0:\n",
+    "        print(i)\n",
+    "    i += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "chrome_issue[\"labels\"] = chrome_issue[\"issue_id\"].apply(lambda i_id: labels_by_issue[i_id])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "chrome_issue[\"num_labels\"] = chrome_issue[\"labels\"].apply(lambda labels_list: len(labels_list))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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XklRcXKwPP/xQVqtV06dPV1JSUqhaAwCg1QtZQHjjjTfUrl07zZs3T8ePH9fQoUPVs2dP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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd69d327e10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "histrogram_plot(chrome_issue[\"num_labels\"], \"Number of Labels per Issue\", \"Num Labels\", \"Count\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Statistics on Labels per Issue"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    611859.000000\n",
+       "mean          5.243520\n",
+       "std           2.379956\n",
+       "min           0.000000\n",
+       "25%           4.000000\n",
+       "50%           5.000000\n",
+       "75%           6.000000\n",
+       "max          70.000000\n",
+       "Name: num_labels, dtype: float64"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chrome_issue[\"num_labels\"].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "number_times_label_used = pd.DataFrame(num_times_label_used.items(), columns=[\"label_id\", \"number_time_used\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of Unique Labels 20493\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Number of Unique Labels\", number_times_label_used.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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2bdummpqaYB0CAAD4kaCFw5AhQ3TfffdJkvx+v8LDw1VdXa3k5GRJUnp6usrLy/Xxxx8r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\nRjgAAABjhAMAADBGOAAAAGP/H525Lub2Dn6cAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd64a4fc450>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "histrogram_plot(number_times_label_used[\"number_time_used\"], \"Number of Times Label Used\", \"Number Used\", \"Count\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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brVu31pYtWxQbGytJio+PV1ZWli9LAgDAaD49NHD48GHt27dPr732mvbs2aPe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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd69d321450>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "histrogram_plot(number_times_label_used[number_times_label_used[\"number_time_used\"] > 1000][\"number_time_used\"], \"Number of Times Label Used(Less Than 100)\", \"Number Used\", \"Count\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Statistics on how often labels have been used"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count     20493.000000\n",
+       "mean        156.555653\n",
+       "std        4947.912097\n",
+       "min           1.000000\n",
+       "25%           1.000000\n",
+       "50%           1.000000\n",
+       "75%           5.000000\n",
+       "max      469002.000000\n",
+       "Name: number_time_used, dtype: float64"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "number_times_label_used[\"number_time_used\"].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>label_id</th>\n",
+       "      <th>number_time_used</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>17030</th>\n",
+       "      <td>18246154</td>\n",
+       "      <td>469002</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       label_id  number_time_used\n",
+       "17030  18246154            469002"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "number_times_label_used[number_times_label_used[\"number_time_used\"] > 450000]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "label_def = table_to_dataframe(\"LabelDef\", connection)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>project_id</th>\n",
+       "      <th>label</th>\n",
+       "      <th>rank</th>\n",
+       "      <th>docstring</th>\n",
+       "      <th>deprecated</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5566</th>\n",
+       "      <td>18246154</td>\n",
+       "      <td>16</td>\n",
+       "      <td>Type-Bug</td>\n",
+       "      <td>215.0</td>\n",
+       "      <td>Software not working correctly</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            id  project_id     label   rank                       docstring  \\\n",
+       "5566  18246154          16  Type-Bug  215.0  Software not working correctly   \n",
+       "\n",
+       "      deprecated  \n",
+       "5566         0.0  "
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "label_def[label_def[\"id\"] == 18246154]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore Components"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "100000\n",
+      "200000\n",
+      "300000\n",
+      "400000\n",
+      "500000\n"
+     ]
+    }
+   ],
+   "source": [
+    "components_by_issue = defaultdict(list)\n",
+    "num_times_comp_used = defaultdict(int)\n",
+    "i = 0\n",
+    "for index, row in issue_component.iterrows():\n",
+    "    if row[\"issue_id\"] in chrome_issue_id_set:\n",
+    "        components_by_issue[row[\"issue_id\"]].append(row[\"component_id\"])\n",
+    "        num_times_comp_used[row[\"component_id\"]] += 1\n",
+    "    if i % 100000 == 0:\n",
+    "        print(i)\n",
+    "    i += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "chrome_issue[\"components\"] = chrome_issue[\"issue_id\"].apply(lambda i_id: components_by_issue[i_id])\n",
+    "chrome_issue[\"num_components\"] = chrome_issue[\"components\"].apply(lambda comp_list: len(comp_list))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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SkpIUHh6uuXPn2jkmTZqkqqoqJSQk2LMP4uLiNGzYMDmOo/T09AuqDwAAnOAy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\nAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd63079db90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "histrogram_plot(chrome_issue[\"num_components\"], \"Number of Components per Issue\", \"Number of Components\", \"Count\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Statistics on Components per issue"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    611859.000000\n",
+       "mean          0.870696\n",
+       "std           0.843908\n",
+       "min           0.000000\n",
+       "25%           0.000000\n",
+       "50%           1.000000\n",
+       "75%           1.000000\n",
+       "max           8.000000\n",
+       "Name: num_components, dtype: float64"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chrome_issue[\"num_components\"].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "number_comp_used = pd.DataFrame(num_times_comp_used.items(), columns=[\"component_id\", \"number_time_used\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of Unique Components 657\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Number of Unique Components\", number_comp_used.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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kAFiWJUnKz8/X5MmT5ff7lZiYqKysLFmWpaFDh2rQoEEKBALKy8tTVFRUg3NW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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd64be42710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "histrogram_plot(number_comp_used[\"number_time_used\"], \"Number of Times Comp Used\", \"Number Used\", \"Count\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Statistics on Component Frequency"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count      657.000000\n",
+       "mean       810.872146\n",
+       "std       3318.952857\n",
+       "min          1.000000\n",
+       "25%         15.000000\n",
+       "50%        121.000000\n",
+       "75%        557.000000\n",
+       "max      59116.000000\n",
+       "Name: number_time_used, dtype: float64"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "number_comp_used[\"number_time_used\"].describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Use Issue Updates to determine time to correct component assignment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "component_def = table_to_dataframe(\"ComponentDef\", connection)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "chrome_components = component_def[component_def[\"project_id\"] == 16]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "issue_update = table_to_dataframe(\"IssueUpdate\", connection)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "chrome_issue_update = issue_update[issue_update[\"issue_id\"].isin(unique_chrome_issues)]\n",
+    "chrome_comp_updates = chrome_issue_update[chrome_issue_update[\"field\"] == \"components\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "100000\n",
+      "200000\n",
+      "300000\n",
+      "400000\n",
+      "500000\n",
+      "600000\n"
+     ]
+    }
+   ],
+   "source": [
+    "updates_by_issue_id = dict()\n",
+    "i = 0\n",
+    "for index, row in chrome_issue.iterrows():\n",
+    "    issue_id = row[\"issue_id\"]\n",
+    "    updates_by_issue_id[issue_id] = []\n",
+    "    for comp_index, comp_row in chrome_comp_updates[chrome_comp_updates[\"issue_id\"] == issue_id].iterrows():\n",
+    "        updates_by_issue_id[issue_id].append(comp_index)\n",
+    "    \n",
+    "    if i % 100000 == 0:\n",
+    "        print(i)\n",
+    "    i += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "chrome_issue[\"comp_updates\"] = chrome_issue[\"issue_id\"].apply(lambda i_id: updates_by_issue_id[i_id])\n",
+    "chrome_issue[\"num_comp_updates\"] = chrome_issue[\"issue_id\"].apply(lambda i_id: len(updates_by_issue_id[i_id]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Work only with Closed Issues to determine time to correct component assignment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "closed_chrome_issue = chrome_issue[chrome_issue[\"closed\"] != 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The Number of closed chrome issues 538161\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"The Number of closed chrome issues\", closed_chrome_issue.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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w3HPPaerUqSorK5MkzZo1S5MmTdKLL74oY4zWr1+vo0ePasWKFXrppZf0xz/+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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd64ceb8c10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "histrogram_plot(closed_chrome_issue[\"num_components\"], \"Number of Components (Closed)\", \"Number of Components\", \"Count\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Statistics on Component assignment for closed chrome issues"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    538161.000000\n",
+       "mean          0.853496\n",
+       "std           0.850581\n",
+       "min           0.000000\n",
+       "25%           0.000000\n",
+       "50%           1.000000\n",
+       "75%           1.000000\n",
+       "max           8.000000\n",
+       "Name: num_components, dtype: float64"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "closed_chrome_issue[\"num_components\"].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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WLNHixYsVFBSkZ599Vh07dlRJSYlGjBihoqIiORwOZWVlKSwsTJs3b9arr76q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\nYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd616727450>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "histrogram_plot(closed_chrome_issue[\"num_comp_updates\"], \"Number of Component Updates (Closed Issues)\", \"Number of Updates\", \"Count\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Only include issues that were created after issue tracking was transorted to Monorail"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import_date = \"02/18/2016\"\n",
+    "import_date = time.mktime(datetime.datetime.strptime(import_date, \"%m/%d/%Y\").timetuple())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "closed_chrome_issues_monorail = closed_chrome_issue[closed_chrome_issue[\"opened\"] > import_date]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(12381, 24)"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "closed_chrome_issues_monorail.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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o0SLV1dXJ5/Np0aJF6t69u+6//37l5ORo1KhRCg0NVXR0tP/tjsrKSlVVVen2229v1fOL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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd6a66983d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "histrogram_plot(closed_chrome_issues_monorail[\"num_components\"],  \"Number of Component Per Issue(Closed Issues)\", \"Number of Comps\", \"Count\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    12381.000000\n",
+       "mean         0.725305\n",
+       "std          0.663298\n",
+       "min          0.000000\n",
+       "25%          0.000000\n",
+       "50%          1.000000\n",
+       "75%          1.000000\n",
+       "max          5.000000\n",
+       "Name: num_components, dtype: float64"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "closed_chrome_issues_monorail[\"num_components\"].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "closed_chrome_with_comps = closed_chrome_issues_monorail[closed_chrome_issues_monorail[\"num_components\"] > 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of Closed Issues with Components (opened after 2/18/2016) 7672\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Number of Closed Issues with Components (opened after 2/18/2016)\", closed_chrome_with_comps.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of Closed Issues with no Components (opened after 2/18/2016) 4709\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Number of Closed Issues with no Components (opened after 2/18/2016)\", np.sum(closed_chrome_issues_monorail[\"num_components\"] == 0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "no_comps_with_updates = closed_chrome_issues_monorail[(closed_chrome_issues_monorail[\"num_comp_updates\"] > 0) & (closed_chrome_issues_monorail[\"num_components\"] == 0)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of Issues that were asigned a componet but were closed with no componet: 27\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Number of Issues that were asigned a componet but were closed with no componet:\", no_comps_with_updates.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes.AxesSubplot at 0x7fd684fae6d0>"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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H45FlWbIsSx6PJ7UtGv30Ix0vx+fN6nhne5YCAZ/cbrej9+ot3O6EvJ5meS7ozaV9SlNc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\nwxHWAAAYjrAGAMBw/w+tq+AIQS6RMgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd64c67d910>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(closed_chrome_with_comps[\"num_comp_updates\"], kde=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def time_of_last_component_1(updates):\n",
+    "    # If there are no comp updates then all comp assignments \n",
+    "    # were assigned during issue creation\n",
+    "    if len(updates) == 0:\n",
+    "        return 0\n",
+    "    # If there is more than one update the time to correct component assignment\n",
+    "    # is the time of the last assignment. Note that this interpretation assumes\n",
+    "    # a correct component assignment as being not just assinging the correct one\n",
+    "    # but also removing any incorrect ones.\n",
+    "    comment_id = updates.iloc[-1][\"comment_id\"]\n",
+    "    return chrome_comment[chrome_comment[\"id\"] == comment_id][\"created\"].values[0]\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1000\n",
+      "2000\n",
+      "3000\n",
+      "4000\n",
+      "5000\n",
+      "6000\n",
+      "7000\n"
+     ]
+    }
+   ],
+   "source": [
+    "i = 0\n",
+    "time_to_comp_assignment = dict()\n",
+    "for index, row in closed_chrome_with_comps.iterrows():\n",
+    "    ish_id = row[\"issue_id\"]\n",
+    "    updates = chrome_comp_updates.loc[row[\"comp_updates\"]]\n",
+    "    time = time_of_last_component_1(updates)\n",
+    "    if time == 0:\n",
+    "        time_to_comp_assignment[ish_id] = time\n",
+    "    else:\n",
+    "        time = time - row[\"opened\"]\n",
+    "        time_to_comp_assignment[ish_id] = time\n",
+    "    \n",
+    "    if (i % 1000) == 0:\n",
+    "        print(i)\n",
+    "    i += 1\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Histogram Showing time to correct component assignment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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fVVlZmaZMmaLCwkKFhIQoKytLP/nJT666zpzN+1VpVL8d4yg+p1/E3+PToR8R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\ngg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd69d32d110>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "histrogram_plot(pd.DataFrame(time_to_comp_assignment.items())[1] / (3600*24), \"Time to assignment\", \"Time (Days)\", \"Number of Issues\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    7672.000000\n",
+       "mean        5.716000\n",
+       "std        14.322087\n",
+       "min         0.000000\n",
+       "25%         0.000000\n",
+       "50%         0.000000\n",
+       "75%         1.160851\n",
+       "max        71.252500\n",
+       "Name: 1, dtype: float64"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(pd.DataFrame(time_to_comp_assignment.items())[1] / (3600*24)).describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "no_comp = closed_chrome_issues_monorail[closed_chrome_issues_monorail[\"num_components\"] == 0]\n",
+    "with_comp = closed_chrome_issues_monorail[closed_chrome_issues_monorail[\"num_components\"] != 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fd617f5dd90>"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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Bgwejbdu2rvePGjUKDz74IF5//XWcO3cOAPDKK6/gnnvuKfNzjMdOwtPxyslL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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd6167363d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot((no_comp[\"closed\"] - no_comp[\"opened\"]) / (3600 * 24), kde=False, label=\"NO Component\", norm_hist=True, color=\"pink\")\n",
+    "sns.distplot((with_comp[\"closed\"] - with_comp[\"opened\"]) / (3600 * 24), kde=False, label=\"With Component\", norm_hist=True, color=\"Blue\")\n",
+    "plt.xlabel(\"Days\")\n",
+    "plt.ylabel(\"Numbe of Issues (normalized)\")\n",
+    "plt.title(\"Time to closure (normalized)\")\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    4709.000000\n",
+       "mean        7.838234\n",
+       "std        13.047354\n",
+       "min         0.000000\n",
+       "25%         0.254734\n",
+       "50%         1.507581\n",
+       "75%         8.759780\n",
+       "max        76.061817\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "((no_comp[\"closed\"] - no_comp[\"opened\"]) / (3600 * 24)).describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    7672.000000\n",
+       "mean        9.754343\n",
+       "std        13.647165\n",
+       "min         0.000000\n",
+       "25%         0.735463\n",
+       "50%         4.048987\n",
+       "75%        13.455067\n",
+       "max        84.907685\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "((with_comp[\"closed\"] - with_comp[\"opened\"]) / (3600 * 24)).describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Perform the same analysis as above but now excluding issues marked as \"WontFix\" or \"Duplicate\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "status_def = table_to_dataframe(\"StatusDef\", connection)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "chrome_not_valid = set(chrome_issue_update[(chrome_issue_update[\"new_value\"] == \"WontFix\") | \n",
+    "                                  (chrome_issue_update[\"new_value\"] == \"Duplicate\")][\"issue_id\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "closed_chrome_valid = closed_chrome_issue[closed_chrome_issue[\"issue_id\"].apply(lambda i_id: i_id not in chrome_not_valid)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "no_comp_valid = closed_chrome_valid[closed_chrome_valid[\"num_components\"] == 0]\n",
+    "with_comp_valid = closed_chrome_valid[closed_chrome_valid[\"num_components\"] != 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fd6161809d0>"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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OLygePXC2DeC0X8+2v8vr3zVr1jBo0CBuvvlmBg8eTK9evczfn/JqK6stIyODHTt2MHz4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\naAaLAiIiIgIA/AeooPI3e/wIxwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fd6627d47d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot((no_comp_valid[\"closed\"] - no_comp_valid[\"opened\"]) / (3600 * 24), kde=False, label=\"NO Component\", norm_hist=True, color=\"pink\")\n",
+    "sns.distplot((with_comp_valid[\"closed\"] - with_comp_valid[\"opened\"]) / (3600 * 24), kde=False, label=\"With Component\", norm_hist=True, color=\"Blue\")\n",
+    "plt.xlabel(\"Days\")\n",
+    "plt.ylabel(\"Numbe of Issues (normalized)\")\n",
+    "plt.title(\"Time to issue closure(Excluding Issues marked WontFix or Duplicate)\")\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    94498.000000\n",
+       "mean       246.740942\n",
+       "std        408.972934\n",
+       "min          0.000000\n",
+       "25%          0.000000\n",
+       "50%         11.163003\n",
+       "75%        411.270145\n",
+       "max       2656.412662\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "((no_comp_valid[\"closed\"] - no_comp_valid[\"opened\"]) / (3600 * 24)).describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    177877.000000\n",
+       "mean        153.512277\n",
+       "std         311.320375\n",
+       "min           0.000000\n",
+       "25%           3.199873\n",
+       "50%          19.683600\n",
+       "75%         120.228102\n",
+       "max        2798.843438\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "((with_comp_valid[\"closed\"] - with_comp_valid[\"opened\"]) / (3600 * 24)).describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}