{ "metadata": { "name": "", "signature": "sha256:54c24f2df1c671684c8f9a8b0c95376a49cd4af976efe095208713eaf34f3135" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Example 3) Mining Conference Websites" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Idea:\n", "* Find out which topics are really hot\n", "* Identify sessions you just have to attend\n", "* Put conference in context\n", "* Identify trends" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "CAVEAT: Make sure the page owner allows crawling the content for scientific purpost (Terms, robots.txt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Strata Conference, one of the most important conferences for all things Big Data, Hadoop, Data Science." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.display import HTML\n", "HTML('')" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "" ], "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "" ] } ], "prompt_number": 5 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Crawl conference page to find abstracts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " from bs4 import BeautifulSoup\n", " import urllib2\n", "\n", " # List of conference schedule pages\n", " urls = {2011 : \"http://strataconf.com/strata2011/public/schedule/full\",\n", " 2012 : \"http://strataconf.com/strata2012/public/schedule/full/public\", \n", " 2013 : \"http://strataconf.com/strata2013/public/schedule/full/public\", \n", " 2014 : \"http://strataconf.com/strata2014/public/schedule/full/public\"}\n", "\n", " links = {}\n", "\n", " # Collecting the links to the talk abstracts\n", " for u in urls:\n", " raw = urllib2.urlopen(urls[u]).read()\n", " soup = BeautifulSoup(raw)\n", " yearlinks = set([l.get(\"href\") for l in soup.find_all(\"a\")])\n", " yearlinks = [l for l in yearlinks if not (l is None)]\n", " yearlinks = [l for l in yearlinks if '/detail' in l]\n", " yearlinks = [l.replace(\"http://strataconf.com\", \"\") for l in yearlinks]\n", " links[u] = yearlinks\n" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Crawl links to find abstracts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " abstracts = {}\n", "\n", " for year in links:\n", "\n", " for l in links[year]:\n", " raw = urllib2.urlopen(\"http://www.strataconf.com\" + l).read()\n", " soup = BeautifulSoup(raw)\n", " desc = soup.find(\"div\", class_=\"en_session_description description\")\n", " if year in abstracts:\n", " abstracts[year].append(desc.get_text())\n", " else:\n", " abstracts[year] = [desc.get_text()]\n", " " ] }, { "cell_type": "code", "collapsed": false, "input": [ "import json\n", "# Load Data (if you don't want to crawl the data)\n", "with open('strata_abstracts.json') as f:\n", " abstracts = json.load(f)\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "import nltk\n", "bigram_measures = nltk.collocations.BigramAssocMeasures()\n", "stop = nltk.corpus.stopwords.words('english')\n", "\n", "text = {}\n", "words = {}\n", "\n", "for year in abstracts:\n", " raw = \" \".join(abstracts[year])\n", " tokens = nltk.WordPunctTokenizer().tokenize(raw)\n", " text[year] = nltk.Text(tokens)\n", " words[year] = [w.lower() for w in text[year]]\n", " words[year] = [w for w in words[year] if w not in stop]\n", " words[year] = filter(lambda word: word not in u'%,-:()$\\/;?.\u2019\u2013\u201c\u201d', words[year])\n", " words[year] = [w for w in words[year] if w not in [\"ll\", \"II\", \"ll\", \"http\", \"://\", \"e\", \"g\", \"2\", \"0\"]]\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "text[\"2012\"]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "for year in text:\n", " print year\n", " text[year].collocations()\n", " print\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2014\n", "Building collocations list\n", "big data; machine learning; Big Data; open source; data science; case\n", "studies; data scientists; best practices; http ://; Machine Learning;\n", "every day; Energy Project; time series; use cases; data center; Apache\n", "Hadoop; Industrial Internet; Clean Energy; take advantage; software\n", "engineering" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\n", "2011\n", "Building collocations list\n", "Big Data; Executive Summit; open source; big data; machine learning;\n", "data science; Riak Core; Bob Page; time series; witch doctors; data\n", "sets; reserved table; best practices; Apache Mahout; Science Fair;\n", "dark underbelly; darkly humorous; http ://; lays bare; litmus tests" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\n", "2013\n", "Building collocations list\n", "big data; Big Data; open source; machine learning; use cases; http\n", "://; Data Science; Rest Devices; social media; data sets; Strata\n", "Conference; relational database; Expo Hall; Stitch Fix; https ://;\n", "lessons learned; data science; command line; data collection; :// www" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\n", "2012\n", "Building collocations list\n", "Big Data; big data; open source; machine learning; data sets; http\n", "://; Alistair Croll; social media; Climate Corporation; use cases;\n", "variable importance; Tableau Public; social contagion; Apache Hadoop;\n", "supply chain; Avinash Kaushik; Opening remarks; case study; real\n", "world; natural language" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "\n" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "numwords = {}\n", "uniwords = {}\n", "\n", "for year in text:\n", " numwords[year] = len(text[year])\n", " uniwords[year] = len(set(text[year]))\n", "\n", "print numwords\n", "print uniwords" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "{u'2014': 24326, u'2011': 19869, u'2013': 24963, u'2012': 30902}\n", "{u'2014': 4149, u'2011': 3860, u'2013': 4436, u'2012': 4895}\n" ] } ], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "\n", "freq_table = pd.DataFrame()\n", "\n", "for year in words:\n", " fd = nltk.FreqDist(words[year])\n", " if (len(freq_table) == 0):\n", " freq_table = pd.DataFrame(fd.items(), columns=[\"Word\", \"Freq_\" + str(year)])\n", " else:\n", " freq_table = freq_table.merge(pd.DataFrame(fd.items(), columns=[\"Word\", \"Freq_\" + str(year)]))\n", "\n", "print freq_table[:10]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " Word Freq_2014 Freq_2011 Freq_2013 Freq_2012\n", "0 data 555 448 568 723\n", "1 hadoop 101 21 95 102\n", "2 big 92 58 122 144\n", "3 time 68 51 63 72\n", "4 real 64 42 59 59\n", "5 analytics 62 56 60 52\n", "6 new 61 61 56 75\n", "7 talk 58 32 50 49\n", "8 using 57 24 53 61\n", "9 use 56 44 68 79\n" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "for year in numwords:\n", " freq_table[\"Perc_\" + year] = 100.0 * freq_table[\"Freq_\" + year] / numwords[year]\n", "\n", "for year in [\"2012\", \"2013\", \"2014\"]:\n", " print year\n", " freq_table[\"Growth_\" + year] = 100.0 * freq_table[\"Perc_\" + year] / freq_table[\"Perc_\" + str(int(year)-1)]\n", " tb = freq_table[freq_table['Perc_' + str(year)] >= 0.08].sort(columns=\"Growth_\" + str(year), ascending=False)[[\"Word\", \"Freq_\" + str(year), \"Perc_\" + str(year), \"Growth_\" + str(year)]]\n", " tb.columns = [\"Word\", \"Freq\", \"Percent\", \"Index\"]\n", " tb.Index = tb['Index'].round(1)\n", " tb.Percent = tb['Percent'].round(4)\n", " print tb[:10]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2012\n", " Word Freq Percent Index\n", "84 models 39 0.1262 417.9\n", "1 hadoop 102 0.3301 312.3\n", "100 value 29 0.0938 310.8\n", "135 experience 29 0.0938 310.8\n", "130 social 52 0.1683 278.6\n", "169 simple 28 0.0906 257.2\n", "388 r 30 0.0971 241.1\n", "342 support 25 0.0809 229.6\n", "73 problems 25 0.0809 229.6\n", "24 platform 33 0.1068 212.2" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2013\n", " Word Freq Percent Index\n", "584 engine 20 0.0801 2475.8\n", "317 google 28 0.1122 693.2\n", "85 queries 20 0.0801 275.1\n", "54 human 21 0.0841 260.0\n", "216 strata 20 0.0801 247.6\n", "41 science 31 0.1242 225.7\n", "11 scale 41 0.1642 181.3\n", "138 hive 25 0.1001 162.9\n", "134 database 25 0.1001 162.9\n", "112 two 22 0.0881 160.2" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "2014\n", " Word Freq Percent Index\n", "60 components 23 0.0945 393.4\n", "38 cluster 30 0.1233 342.1\n", "33 building 31 0.1274 289.2\n", "23 processing 37 0.1521 253.1\n", "61 graph 23 0.0945 214.6\n", "76 organizations 20 0.0822 205.2\n", "39 high 30 0.1233 205.2\n", "56 storage 25 0.1028 197.3\n", "77 project 20 0.0822 186.6\n", "53 build 25 0.1028 183.2" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "from nltk.collocations import *\n", "\n", "bigram_measures = nltk.collocations.BigramAssocMeasures()\n", "trigram_measures = nltk.collocations.TrigramAssocMeasures()\n", "\n", "for year in [\"2011\", \"2012\", \"2013\", \"2014\"]: \n", " print \"Bigrams \" + str(year)\n", " finder = BigramCollocationFinder.from_words(words[year])\n", " scored = finder.score_ngrams(bigram_measures.raw_freq)\n", " print pd.DataFrame(scored[:10])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Bigrams 2011\n", " 0 1\n", "0 (big, data) 0.004392\n", "1 (data, science) 0.002100\n", "2 (real, time) 0.001909\n", "3 (open, source) 0.001528\n", "4 (data, driven) 0.001241\n", "5 (executive, summit) 0.001146\n", "6 (machine, learning) 0.001146\n", "7 (data, sets) 0.001050\n", "8 (open, data) 0.001050\n", "9 (real, world) 0.001050" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Bigrams 2012\n", " 0 1\n", "0 (big, data) 0.007987\n", "1 (data, driven) 0.001474\n", "2 (real, time) 0.001413\n", "3 (data, sets) 0.001352\n", "4 (real, world) 0.001229\n", "5 (session, sponsored) 0.001167\n", "6 (machine, learning) 0.001044\n", "7 (open, source) 0.001044\n", "8 (data, analysis) 0.000983\n", "9 (data, analytics) 0.000922" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Bigrams 2013\n", " 0 1\n", "0 (big, data) 0.008412\n", "1 (real, time) 0.002577\n", "2 (data, science) 0.001970\n", "3 (open, source) 0.001591\n", "4 (machine, learning) 0.001516\n", "5 (session, sponsored) 0.001516\n", "6 (use, cases) 0.001212\n", "7 (data, scientists) 0.000985\n", "8 (data, sets) 0.000985\n", "9 (real, world) 0.000985" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Bigrams 2014\n", " 0 1\n", "0 (big, data) 0.006557\n", "1 (machine, learning) 0.002931\n", "2 (real, time) 0.002854\n", "3 (data, science) 0.001928\n", "4 (open, source) 0.001851\n", "5 (session, sponsored) 0.001697\n", "6 (real, world) 0.001388\n", "7 (data, scientists) 0.001311\n", "8 (data, analysis) 0.001080\n", "9 (data, analytics) 0.001003" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "for year in abstracts:\n", " print \"Trigrams \" + str(year)\n", " finder = TrigramCollocationFinder.from_words(text[year])\n", " scored = finder.score_ngrams(trigram_measures.raw_freq)\n", "\n", " print pd.DataFrame(scored[:10])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Trigrams 2014\n", " 0 1\n", "0 (., In, this) 0.002097\n", "1 (., This, session) 0.001151\n", "2 (In, this, talk) 0.001151\n", "3 (is, sponsored, by) 0.001069\n", "4 (real, -, time) 0.001069\n", "5 (., We, will) 0.001028\n", "6 (This, session, is) 0.000904\n", "7 (session, is, sponsored) 0.000904\n", "8 (We, \u2019, ll) 0.000863\n", "9 (this, talk, ,) 0.000781" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Trigrams 2011\n", " 0 1\n", "0 (., In, this) 0.001107\n", "1 (real, -, time) 0.000755\n", "2 (the, Executive, Summit) 0.000604\n", "3 (we, \u2019, ll) 0.000604\n", "4 (don, \u2019, t) 0.000554\n", "5 (it, \u2019, s) 0.000554\n", "6 (., This, session) 0.000503\n", "7 (part, of, the) 0.000503\n", "8 (,, we, \u2019) 0.000453\n", "9 (., It, \u2019) 0.000453" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Trigrams 2013\n", " 0 1\n", "0 (., This, session) 0.001202\n", "1 (We, \u2019, ll) 0.001162\n", "2 (., In, this) 0.001082\n", "3 (is, sponsored, by) 0.001001\n", "4 (., We, \u2019) 0.000961\n", "5 (real, -, time) 0.000961\n", "6 (., We, will) 0.000881\n", "7 (This, session, is) 0.000841\n", "8 (session, is, sponsored) 0.000801\n", "9 (., This, talk) 0.000601" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Trigrams 2012\n", " 0 1\n", "0 (., This, session) 0.001003\n", "1 (., In, this) 0.000938\n", "2 (We, \u2019, ll) 0.000938\n", "3 (., We, \u2019) 0.000809\n", "4 (., We, will) 0.000777\n", "5 (is, sponsored, by) 0.000680\n", "6 (some, of, the) 0.000680\n", "7 (This, session, is) 0.000647\n", "8 (it, \u2019, s) 0.000647\n", "9 (real, -, time) 0.000615" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "from collections import Counter\n", "import pandas as pd\n", "\n", "trending_words = pd.DataFrame()\n", "\n", "for year in words:\n", " fdist = nltk.FreqDist(words[year])\n", " if len(trending_words) == 0:\n", " trending_words = pd.DataFrame(fdist.items(), columns=[\"word\", str(year)])\n", " trending_words[str(year)] = trending_words[str(year)] / float(trending_words[str(year)].sum())\n", " else:\n", " trending_words = trending_words.merge(pd.DataFrame(fdist.items(), columns=[\"word\", str(year)]), how=\"outer\")\n", " trending_words[str(year)] = trending_words[str(year)] / float(trending_words[str(year)].sum())\n", " \n", "print trending_words[:10]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " word 2014 2011 2013 2012\n", "0 data 0.042811 0.042773 0.043043 0.044419\n", "1 hadoop 0.007791 0.002005 0.007199 0.006267\n", "2 big 0.007097 0.005538 0.009245 0.008847\n", "3 time 0.005245 0.004869 0.004774 0.004423\n", "4 real 0.004937 0.004010 0.004471 0.003625\n", "5 analytics 0.004782 0.005347 0.004547 0.003195\n", "6 new 0.004705 0.005824 0.004244 0.004608\n", "7 talk 0.004474 0.003055 0.003789 0.003010\n", "8 using 0.004397 0.002291 0.004016 0.003748\n", "9 use 0.004320 0.004201 0.005153 0.004853\n" ] } ], "prompt_number": 33 }, { "cell_type": "code", "collapsed": false, "input": [ "trending_words[\"plus12\"] = trending_words[\"2012\"] / trending_words[\"2011\"]\n", "trending_words[\"plus13\"] = trending_words[\"2013\"] / trending_words[\"2012\"]\n", "trending_words[\"plus14\"] = trending_words[\"2014\"] / trending_words[\"2013\"]\n", "trending_words = trending_words.fillna(0)\n", "\n", "print trending_words[(trending_words[\"2012\"] > 0.001) & (trending_words[\"2011\"] > 0)].sort(\"plus12\", ascending=False)[:10]\n", "print\n", "print trending_words[(trending_words[\"2013\"] > 0.0005) & (trending_words[\"2011\"] > 0)].sort(\"plus13\", ascending=False)[:10]\n", "print\n", "print trending_words[(trending_words[\"2014\"] > 0.0005) & (trending_words[\"2011\"] > 0)].sort(\"plus14\", ascending=False)[:10]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " word 2014 2011 2013 2012 plus12 plus13 \\\n", "1729 variable 0.000154 0.000095 0.000076 0.001044 10.939239 0.072558 \n", "52 sponsored 0.002006 0.000191 0.002122 0.001413 7.400074 1.501628 \n", "142 hive 0.001080 0.000191 0.001895 0.001167 6.113104 1.623000 \n", "354 solution 0.000617 0.000191 0.001364 0.001167 6.113104 1.168560 \n", "86 models 0.001466 0.000573 0.000758 0.002396 4.182650 0.316277 \n", "604 nosql 0.000386 0.000382 0.000985 0.001474 3.860908 0.668135 \n", "299 set 0.000694 0.000382 0.000758 0.001413 3.700037 0.536296 \n", "39 cluster 0.002314 0.000382 0.000682 0.001352 3.539166 0.504605 \n", "1 hadoop 0.007791 0.002005 0.007199 0.006267 3.125497 1.148829 \n", "139 experience 0.001080 0.000573 0.001516 0.001782 3.110176 0.850676 \n", "\n", " plus14 \n", "1729 2.035791 \n", "52 0.945189 \n", "142 0.570022 \n", "354 0.452398 \n", "86 1.934002 \n", "604 0.391498 \n", "299 0.916106 \n", "39 3.392986 \n", "1 1.082184 \n", "139 0.712527 \n", "\n", " word 2014 2011 2013 2012 plus12 \\\n", "701 engine 0.000309 0.000668 0.001516 0.000061 0.091926 \n", "161 energy 0.001003 0.000095 0.001364 0.000061 0.643485 \n", "334 languages 0.000617 0.000191 0.001061 0.000061 0.321742 \n", "1391 fraud 0.000154 0.000095 0.000834 0.000061 0.643485 \n", "679 computations 0.000309 0.000095 0.000834 0.000061 0.643485 \n", "930 efficiency 0.000231 0.000382 0.000758 0.000061 0.160871 \n", "3648 openstack 0.000000 0.001241 0.000682 0.000061 0.049499 \n", "1227 centric 0.000154 0.000095 0.000606 0.000061 0.643485 \n", "3700 forecasting 0.000000 0.000382 0.001137 0.000123 0.321742 \n", "4691 location 0.000000 0.000095 0.001061 0.000123 1.286969 \n", "\n", " plus13 plus14 \n", "701 24.669597 0.203579 \n", "161 22.202637 0.735147 \n", "334 17.268718 0.581655 \n", "1391 13.568278 0.185072 \n", "679 13.568278 0.370144 \n", "930 12.334798 0.305369 \n", "3648 11.101319 0.000000 \n", "1227 9.867839 0.254474 \n", "3700 9.251099 0.000000 \n", "4691 8.634359 0.000000 \n", "\n", " word 2014 2011 2013 2012 plus12 \\\n", "181 deployment 0.000926 0.000191 0.000076 0.000307 1.608712 \n", "209 crowd 0.000849 0.000095 0.000076 0.000184 1.930454 \n", "238 highly 0.000771 0.000764 0.000076 0.000553 0.723920 \n", "239 humans 0.000771 0.000095 0.000076 0.000184 1.930454 \n", "267 clean 0.000694 0.000095 0.000076 0.000000 0.000000 \n", "101 traffic 0.001311 0.000191 0.000152 0.000123 0.643485 \n", "317 computational 0.000617 0.000573 0.000076 0.000184 0.321742 \n", "437 workflows 0.000540 0.000095 0.000076 0.000123 1.286969 \n", "418 reports 0.000540 0.000095 0.000076 0.000000 0.000000 \n", "407 options 0.000540 0.000095 0.000076 0.000553 5.791362 \n", "\n", " plus13 plus14 \n", "181 0.246696 12.214749 \n", "209 0.411160 11.196853 \n", "238 0.137053 10.178957 \n", "239 0.411160 10.178957 \n", "267 0.000000 9.161061 \n", "101 1.233480 8.652114 \n", "317 0.411160 8.143166 \n", "437 0.616740 7.125270 \n", "418 0.000000 7.125270 \n", "407 0.137053 7.125270 \n" ] } ], "prompt_number": 35 }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "\n", "result = pd.DataFrame()\n", "\n", "for year in words:\n", " finder = BigramCollocationFinder.from_words(words[year], window_size = 2)\n", " #finder.apply_freq_filter(2)\n", " ignored_words = nltk.corpus.stopwords.words('english')\n", " finder.apply_word_filter(lambda w: len(w) < 3 or w.lower() in ignored_words)\n", " scores = finder.score_ngrams(bigram_measures.raw_freq)\n", " \n", " if len(result) == 0:\n", " result = pd.DataFrame(scores, columns=[\"ngram\", str(year)])\n", " else:\n", " result = result.merge(pd.DataFrame(scores, columns=[\"ngram\", str(year)]))\n", " \n", "print result[:10]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " ngram 2014 2011 2013 2012\n", "0 (big, data) 0.006557 0.004392 0.008412 0.007987\n", "1 (machine, learning) 0.002931 0.001146 0.001516 0.001044\n", "2 (real, time) 0.002854 0.001909 0.002577 0.001413\n", "3 (data, science) 0.001928 0.002100 0.001970 0.000737\n", "4 (open, source) 0.001851 0.001528 0.001591 0.001044\n", "5 (real, world) 0.001388 0.001050 0.000985 0.001229\n", "6 (data, scientists) 0.001311 0.000764 0.000985 0.000737\n", "7 (data, analysis) 0.001080 0.000191 0.000834 0.000983\n", "8 (data, analytics) 0.001003 0.000955 0.000758 0.000922\n", "9 (large, scale) 0.000926 0.000191 0.000758 0.000307\n" ] } ], "prompt_number": 37 }, { "cell_type": "code", "collapsed": false, "input": [ "result[\"plus12\"] = result[\"2012\"] / result[\"2011\"]\n", "result[\"plus13\"] = result[\"2013\"] / result[\"2012\"]\n", "result[\"plus14\"] = result[\"2014\"] / result[\"2013\"]\n", "\n", "print result[result[\"2014\"] > 0.0005].sort(\"plus14\", ascending=False)[:10]\n", "print\n", "print result[result[\"2013\"] > 0.0005].sort(\"plus13\", ascending=False)[:10]\n", "print\n", "print result[result[\"2012\"] > 0.0005].sort(\"plus12\", ascending=False)[:10]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " ngram 2014 2011 2013 2012 plus12 \\\n", "16 (data, solutions) 0.000540 0.000095 0.000076 0.000061 0.643485 \n", "11 (time, series) 0.000771 0.000859 0.000152 0.000369 0.428990 \n", "19 (enterprise, data) 0.000540 0.000191 0.000152 0.000061 0.321742 \n", "21 (world, examples) 0.000540 0.000286 0.000152 0.000246 0.857980 \n", "14 (case, studies) 0.000617 0.000191 0.000227 0.000307 1.608712 \n", "17 (data, sources) 0.000540 0.000191 0.000227 0.000737 3.860908 \n", "1 (machine, learning) 0.002931 0.001146 0.001516 0.001044 0.911603 \n", "18 (decision, making) 0.000540 0.000764 0.000303 0.000246 0.321742 \n", "13 (data, driven) 0.000694 0.001241 0.000455 0.001474 1.187972 \n", "20 (new, data) 0.000540 0.000095 0.000379 0.000369 3.860908 \n", "\n", " plus13 plus14 \n", "16 1.233480 7.125270 \n", "11 0.411160 5.089479 \n", "19 2.466960 3.562635 \n", "21 0.616740 3.562635 \n", "14 0.740088 2.714389 \n", "17 0.308370 2.375090 \n", "1 1.451153 1.934002 \n", "18 1.233480 1.781317 \n", "13 0.308370 1.526844 \n", "20 1.027900 1.425054 \n", "\n", " ngram 2014 2011 2013 2012 plus12 \\\n", "157 (relational, database) 0.000077 0.000191 0.000530 0.000123 0.643485 \n", "12 (best, practices) 0.000694 0.000573 0.000530 0.000184 0.321742 \n", "3 (data, science) 0.001928 0.002100 0.001970 0.000737 0.350992 \n", "9 (large, scale) 0.000926 0.000191 0.000758 0.000307 1.608712 \n", "2 (real, time) 0.002854 0.001909 0.002577 0.001413 0.740007 \n", "52 (open, data) 0.000231 0.001050 0.000758 0.000430 0.409490 \n", "36 (data, collection) 0.000309 0.000382 0.000909 0.000553 1.447841 \n", "4 (open, source) 0.001851 0.001528 0.001591 0.001044 0.683702 \n", "15 (use, cases) 0.000617 0.000191 0.001212 0.000799 4.182650 \n", "1 (machine, learning) 0.002931 0.001146 0.001516 0.001044 0.911603 \n", "\n", " plus13 plus14 \n", "157 4.317179 0.145414 \n", "12 2.878120 1.308723 \n", "3 2.672540 0.978746 \n", "9 2.466960 1.221475 \n", "2 1.823405 1.107710 \n", "52 1.762114 0.305369 \n", "36 1.644640 0.339299 \n", "4 1.523710 1.163309 \n", "15 1.518129 0.508948 \n", "1 1.451153 1.934002 \n", "\n", " ngram 2014 2011 2013 2012 plus12 \\\n", "10 (apache, hadoop) 0.000849 0.000095 0.000606 0.000614 6.434847 \n", "130 (data, set) 0.000077 0.000095 0.000227 0.000614 6.434847 \n", "7 (data, analysis) 0.001080 0.000191 0.000834 0.000983 5.147877 \n", "15 (use, cases) 0.000617 0.000191 0.001212 0.000799 4.182650 \n", "17 (data, sources) 0.000540 0.000191 0.000227 0.000737 3.860908 \n", "91 (social, media) 0.000154 0.000286 0.000606 0.000799 2.788434 \n", "0 (big, data) 0.006557 0.004392 0.008412 0.007987 1.818544 \n", "29 (data, visualization) 0.000386 0.000477 0.000455 0.000737 1.544363 \n", "36 (data, collection) 0.000309 0.000382 0.000909 0.000553 1.447841 \n", "28 (data, sets) 0.000386 0.001050 0.000985 0.001352 1.286969 \n", "\n", " plus13 plus14 \n", "10 0.986784 1.399607 \n", "130 0.370044 0.339299 \n", "7 0.848017 1.295504 \n", "15 1.518129 0.508948 \n", "17 0.308370 2.375090 \n", "91 0.759065 0.254474 \n", "0 1.053202 0.779470 \n", "29 0.616740 0.848246 \n", "36 1.644640 0.339299 \n", "28 0.728874 0.391498 \n" ] } ], "prompt_number": 38 }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline \n", "import matplotlib.pyplot as plt\n", "\n", "query = [(\"big\", \"data\"), (\"data\", \"science\"), (\"real\", \"time\"), (\"machine\", \"learning\"), (\"social\", \"media\"), (\"open\", \"source\")]\n", "\n", "query_results = result[result['ngram'].isin(query)][[\"2011\", \"2012\", \"2013\", \"2014\"]].transpose()\n", "query_results.columns = [\" \".join(q) for q in query]\n", "\n", "print query_results.plot(figsize=(10,5), title=\"Strata topics\")\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Axes(0.125,0.125;0.775x0.775)\n" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAmcAAAFCCAYAAAC0O3oJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n", "text": [ "" ] } ], "prompt_number": 39 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Latent Dirichlet Analysis from https://github.com/shuyo/iir/blob/master/lda/lda.py" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%run lda.py -f strata_abstracts.txt -s --stopwords -k 7" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "IOError", "evalue": "[Errno 2] No such file or directory: 'strata_abstracts.txt'", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mIOError\u001b[0m Traceback (most recent call last)", "\u001b[1;32mC:\\Users\\Koehler\\Documents\\IPython Notebooks\\PyData_Berlin2014\\lda.py\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 145\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 146\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0m__name__\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"__main__\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 147\u001b[1;33m \u001b[0mmain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32mC:\\Users\\Koehler\\Documents\\IPython Notebooks\\PyData_Berlin2014\\lda.py\u001b[0m in \u001b[0;36mmain\u001b[1;34m()\u001b[0m\n\u001b[0;32m 126\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 127\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 128\u001b[1;33m \u001b[0mcorpus\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvocabulary\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_file\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 129\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 130\u001b[0m \u001b[0mcorpus\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvocabulary\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_corpus\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcorpus\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mC:\\Users\\Koehler\\Documents\\IPython Notebooks\\PyData_Berlin2014\\vocabulary.py\u001b[0m in \u001b[0;36mload_file\u001b[1;34m(filename)\u001b[0m\n\u001b[0;32m 17\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mload_file\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[0mcorpus\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'r'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 20\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 21\u001b[0m \u001b[0mdoc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mre\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfindall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mr'\\w+(?:\\'\\w+)?'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mIOError\u001b[0m: [Errno 2] No such file or directory: 'strata_abstracts.txt'" ] } ], "prompt_number": 47 }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline \n", "import matplotlib.pyplot as plt\n", "\n", "query = [\"hadoop\", \"yarn\", \"storm\"]\n", "query = [\"python\", \"julia\", \"r\", \"sas\", \"stata\", \"excel\"]\n", "query_results = trending_words[trending_words['word'].isin(query)][[\"word\", \"2011\", \"2012\", \"2013\", \"2014\"]]\n", "query_results = query_results.set_index(query_results['word']).drop(\"word\", 1).transpose()\n", "print query_results.plot(figsize=(10,6), title=\"Programming Langugages @ Strata Conferences 2011-2014\")\n", "\n", "query = [\"business\", \"energy\", \"advertising\", \"banking\", \"health\", \"politics\", \"government\", \"finance\", \"automotive\"]\n", "query_results = trending_words[trending_words['word'].isin(query)][[\"word\", \"2011\", \"2012\", \"2013\", \"2014\"]]\n", "query_results = query_results.set_index(query_results['word']).drop(\"word\", 1).transpose()\n", "print query_results.plot(figsize=(10,6), title=\"Strata topics\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Axes(0.125,0.125;0.775x0.775)\n", "Axes(0.125,0.125;0.775x0.775)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAm4AAAF6CAYAAACgB9QDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCCGEEIn4f75RJ40wh8KOAAAAAElFTkSuQmCC\n", "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAmcAAAF6CAYAAABcEv/JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text": [ "" ] } ], "prompt_number": 103 }, { "cell_type": "code", "collapsed": false, "input": [ "query = [\"google\", \"facebook\", \"yahoo\", \"linkedin\", \"microsoft\"]\n", "query_results = trending_words[trending_words['word'].isin(query)][[\"word\", \"2011\", \"2012\", \"2013\", \"2014\"]]\n", "query_results = query_results.set_index(query_results['word']).drop(\"word\", 1).transpose()\n", "print query_results.plot(figsize=(10,6), title=\"Strata topics\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Axes(0.125,0.125;0.775x0.775)\n" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAm4AAAF6CAYAAACgB9QDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n", "text": [ "" ] } ], "prompt_number": 51 }, { "cell_type": "code", "collapsed": false, "input": [ "query = [\"modern\", \"machine\", \"learning\"]\n", "query_results = trending_words[trending_words['word'].isin(query)][[\"word\", \"2011\", \"2012\", \"2013\", \"2014\"]]\n", "query_results = query_results.set_index(query_results['word']).drop(\"word\", 1).transpose()\n", "print query_results.plot(figsize=(10,6), title=\"Topics at Strata Conferences 2011-14\")\n", "plt.savefig(\"Strata_ModernMachineLearning.png\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Axes(0.125,0.125;0.775x0.775)\n" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAm4AAAF6CAYAAACgB9QDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAACIEgQ3AACAKPH/cmyh7K8BCa0AAAAASUVORK5CYII=\n", "text": [ "" ] } ], "prompt_number": 55 } ], "metadata": {} } ] }