Hacker News Data Analysis using Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hacker News Data Analysis using Python \n",
    "\n",
    "I'll be working with a dataset that has submissions to Hacker News from 2006 to 2015. Hacker News is a site where users can submit articles from across the internet (usually about technology and startups), and others can \"upvote\" the articles, signifying that they like them. The more upvotes a submission gets, the more popular it was in the community. Popular articles get to the \"front page\" of Hacker News, where they're more likely to be seen by others.\n",
    "\n",
    "You may download the dataset from [here](https://github.com/arnauddri/hn/blob/master/data/stories.7z). It worth mentioning that it has 1,553,934 entries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>created_at</th>\n",
       "      <th>created_at_i</th>\n",
       "      <th>author</th>\n",
       "      <th>points</th>\n",
       "      <th>url_hostname</th>\n",
       "      <th>num_comments</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9079983</td>\n",
       "      <td>2015-02-20T11:34:22.000Z</td>\n",
       "      <td>1424432062</td>\n",
       "      <td>Rutger24s</td>\n",
       "      <td>1</td>\n",
       "      <td>startupjuncture.com</td>\n",
       "      <td>0</td>\n",
       "      <td>24sessions: live business advice over video-chat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9079986</td>\n",
       "      <td>2015-02-20T11:35:32.000Z</td>\n",
       "      <td>1424432132</td>\n",
       "      <td>AndrewDucker</td>\n",
       "      <td>3</td>\n",
       "      <td>blog.erratasec.com</td>\n",
       "      <td>0</td>\n",
       "      <td>Some notes on SuperFish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9079988</td>\n",
       "      <td>2015-02-20T11:36:18.000Z</td>\n",
       "      <td>1424432178</td>\n",
       "      <td>davidiach</td>\n",
       "      <td>1</td>\n",
       "      <td>twitter.com</td>\n",
       "      <td>0</td>\n",
       "      <td>Apple Watch models could contain 29.16g of gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9080000</td>\n",
       "      <td>2015-02-20T11:41:06.000Z</td>\n",
       "      <td>1424432466</td>\n",
       "      <td>CiaranR</td>\n",
       "      <td>1</td>\n",
       "      <td>phpconference.co.uk</td>\n",
       "      <td>0</td>\n",
       "      <td>PHP UK Conference Diversity Scholarship Programme</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9080006</td>\n",
       "      <td>2015-02-20T11:43:04.000Z</td>\n",
       "      <td>1424432584</td>\n",
       "      <td>mstolpm</td>\n",
       "      <td>2</td>\n",
       "      <td>preview.onedrive.com</td>\n",
       "      <td>2</td>\n",
       "      <td>Microsoft giving away 100GB free OneDrive stor...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                created_at  created_at_i        author  points  \\\n",
       "0  9079983  2015-02-20T11:34:22.000Z    1424432062     Rutger24s       1   \n",
       "1  9079986  2015-02-20T11:35:32.000Z    1424432132  AndrewDucker       3   \n",
       "2  9079988  2015-02-20T11:36:18.000Z    1424432178     davidiach       1   \n",
       "3  9080000  2015-02-20T11:41:06.000Z    1424432466       CiaranR       1   \n",
       "4  9080006  2015-02-20T11:43:04.000Z    1424432584       mstolpm       2   \n",
       "\n",
       "           url_hostname  num_comments  \\\n",
       "0   startupjuncture.com             0   \n",
       "1    blog.erratasec.com             0   \n",
       "2           twitter.com             0   \n",
       "3   phpconference.co.uk             0   \n",
       "4  preview.onedrive.com             2   \n",
       "\n",
       "                                               title  \n",
       "0   24sessions: live business advice over video-chat  \n",
       "1                            Some notes on SuperFish  \n",
       "2    Apple Watch models could contain 29.16g of gold  \n",
       "3  PHP UK Conference Diversity Scholarship Programme  \n",
       "4  Microsoft giving away 100GB free OneDrive stor...  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv(\"stories.csv\")\n",
    "\n",
    "data.columns = [\"id\", \"created_at\", \"created_at_i\", \"author\", \"points\", \"url_hostname\", \"num_comments\", \"title\"]\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What words appear most often in the headlines?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('the', 331506),\n",
       " ('to', 263390),\n",
       " ('a', 208287),\n",
       " ('of', 182585),\n",
       " ('for', 177458),\n",
       " ('in', 155726),\n",
       " ('and', 146612),\n",
       " ('', 114528),\n",
       " ('on', 99864),\n",
       " ('is', 96621),\n",
       " ('with', 85871),\n",
       " ('how', 84466),\n",
       " ('hn:', 83996),\n",
       " ('your', 75014),\n",
       " ('-', 73723),\n",
       " ('you', 62178),\n",
       " ('ask', 58810),\n",
       " ('new', 51081),\n",
       " ('google', 47700),\n",
       " ('from', 46876)]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import collections as cl\n",
    "\n",
    "all_titles = ''\n",
    "\n",
    "for line in data[\"title\"]:\n",
    "    all_titles+=str(line) + ' '\n",
    "\n",
    "words = all_titles.lower().split(' ')\n",
    "\n",
    "c = cl.Counter(words)\n",
    "\n",
    "c.most_common(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What domains were submitted most often to Hacker News?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('', 94735),\n",
       " ('blogspot.com', 36831),\n",
       " ('github.com', 30318),\n",
       " ('techcrunch.com', 26609),\n",
       " ('nytimes.com', 24125),\n",
       " ('youtube.com', 22030),\n",
       " ('google.com', 16307),\n",
       " ('wordpress.com', 15537),\n",
       " ('medium.com', 12992),\n",
       " ('arstechnica.com', 12336)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tldextract as tld\n",
    "\n",
    "def only_domain(domain):\n",
    "    if type(domain) != str:\n",
    "        return ''\n",
    "    tld_obj = tld.extract(domain)\n",
    "    return tld_obj.domain + \".\" + tld_obj.suffix\n",
    "\n",
    "data[\"url_hostname\"] = data[\"url_hostname\"].apply(only_domain)\n",
    "\n",
    "urls = cl.Counter(data[\"url_hostname\"])\n",
    "\n",
    "urls.most_common(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What authors with no domains submitted most often to Hacker News?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('amichail', 525),\n",
       " ('anujkk', 143),\n",
       " ('rokhayakebe', 137),\n",
       " ('marcamillion', 124),\n",
       " ('hoodoof', 114),\n",
       " ('andrewstuart', 114),\n",
       " ('ColinWright', 110),\n",
       " ('whoishiring', 110),\n",
       " ('petervandijck', 109),\n",
       " ('diminium', 105)]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "authors = cl.Counter(data[\"author\"][data[\"url_hostname\"] == ''])\n",
    "\n",
    "authors.most_common(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### At what day of week, month and hour are the most articles submitted?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import dateutil\n",
    "\n",
    "data[\"created_at\"] = data[\"created_at\"].apply(dateutil.parser.parse)\n",
    "\n",
    "data[\"hour\"] = data[\"created_at\"].dt.hour\n",
    "data[\"dayofweek\"] = data[\"created_at\"].dt.dayofweek\n",
    "data[\"dayofmonth\"] = data[\"created_at\"].dt.day\n",
    "\n",
    "days = {0:'Mon',1:'Tues',2:'Weds',3:'Thurs',4:'Fri',5:'Sat',6:'Sun'}\n",
    "data['dayofweek'] = data['dayofweek'].apply(lambda x: days[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best day of month:  [19, 9, 18, 10, 20, 17, 3, 12, 11, 16]\n",
      "Best day of week:  ['Tues', 'Weds', 'Thurs']\n",
      "Best hour of day:  [16, 17, 15, 18, 14]\n"
     ]
    }
   ],
   "source": [
    "print(\"Best day of month: \",[x for x,y in data[\"dayofmonth\"].value_counts()[:10].iteritems()])\n",
    "print(\"Best day of week: \",[x for x,y in data[\"dayofweek\"].value_counts()[:3].iteritems()])\n",
    "print(\"Best hour of day: \",[x for x,y in data[\"hour\"].value_counts()[:5].iteritems()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What headline length leads to the most upvotes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best length mean based on top 1000 voted posts:  38.307\n"
     ]
    }
   ],
   "source": [
    "def tit_len(inp):\n",
    "    return len(str(inp))\n",
    "\n",
    "data[\"tit_len\"] = data[\"title\"].apply(tit_len)\n",
    "\n",
    "best_len = data[[\"points\",\"tit_len\"]].sort_values(\"points\",ascending=False)[:1000][\"tit_len\"].mean()\n",
    "\n",
    "print(\"Best length mean based on top 1000 voted posts: \", best_len)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What submission hour leads to the most upvotes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best posting hour mean based on top 1000 voted posts:  14.273\n"
     ]
    }
   ],
   "source": [
    "best_hour = data[[\"points\",\"hour\"]].sort_values(\"points\",ascending=False)[:1000][\"hour\"].mean()\n",
    "\n",
    "print(\"Best posting hour mean based on top 1000 voted posts: \", best_hour)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### How are the total numbers of upvotes changing over time? (trend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KygCoZUnTxnK6OqZjcQ0ZMgQ1atSI2de3b1/07Wvti0pERPEE3UTCPhje/PILcPTRqeUx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11517aeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data[\"date\"] = data[\"created_at\"].dt.date\n",
    "\n",
    "df = data[[\"date\",\"points\"]].groupby(\"date\").sum()\n",
    "\n",
    "df.plot()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "---\n",
    "### That's it!\n",
    "\n",
    "Prepared by Issam Hijazi\n",
    "\n",
    "https://ae.linkedin.com/in/ihijazi\n",
    "\n",
    "@iHijazi"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}