{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Data-driven Approach to CFP - Mining PyData Conferences" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Data-driven approach is good in the most cases. Most of us have seen people use this approach for some of the business decisions, or something you will care much more than a minor daily decision, but what if your toolkit is so fast and powerful that you can use it easily even for daily tasks.\n", "\n", "In this article, I'm going to explain one of such use-cases, and introduce you one of the tools I use for some of my tasks. \n", "\n", "So starting with the problem: I was interested to visit a conference, in my case it was one of the local PyData conferences. If you visit a conference you most likely focus on content, so I wanted to have an analysis of the content of the conference. From another side, I was also interested to see, how the focus of the conference was changing over the time and for the very last point, try to find out would it be the kind of conference where I can share my knowledge and experience of using python for data-related tasks." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import HTML\n", "HTML('')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loading data for nyc2014 conference\n", "loading data for berlin2014 conference\n", "loading data for sv2014 conference\n", "loading data for ldn2014 conference\n", "loading data for nyc2013 conference\n" ] } ], "source": [ "from bs4 import BeautifulSoup\n", "import urllib2\n", "\n", "# past events\n", "# TODO: fetch this data from past events page\n", "conferences = ['nyc2014', 'berlin2014', 'sv2014', 'ldn2014', 'nyc2013']\n", "\n", "abstract_url = \"http://pydata.org/%s/abstracts/\"\n", "\n", "conf_data = {}\n", "\n", "# Collecting data about abstracts\n", "for conference in conferences:\n", " print \"loading data for %s conference\" % conference\n", " raw = urllib2.urlopen(abstract_url % conference).read()\n", " soup = BeautifulSoup(raw)\n", " abstracts = [abstract.get_text().strip() for abstract in soup.find_all(class_=\"accordion-inner\")]\n", " titles = [title.get_text().strip() for title in soup.find_all(class_=\"accordion-toggle\")]\n", "# speakers = [speaker.get_text().strip() for speaker in soup.select(\".accordion-heading h5 a\")]\n", " \n", " conf_data[conference] = {}\n", " conf_data[conference]['abstracts'] = abstracts\n", " conf_data[conference]['titles'] = titles\n", "# conf_data[conference]['speakers'] = speakers" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[u'(Easy), High Performance Text Processing with Rosetta',\n", " u'A Machine Learning Pipeline with Scikit-Learn',\n", " u'Advanced IPython Notebook Widgets',\n", " u'Advanced scikit-learn',\n", " u'Analyzing Satellite Images With Python Scientific Stack',\n", " u'Beautiful Interactive Visualizations in the Browser with Bokeh',\n", " u'Biological Data Science',\n", " u'Blaze Foundations: Part 1',\n", " u'Data Community/Meetup Organizers',\n", " u\"Data Science: It's Easy as Py\\u01c3\",\n", " u'Data warehouse and conceptual modelling with Cubes 1.0',\n", " u'Data-driven conversations about biology',\n", " u'Decreasing Uncertainty with Weakly Informative Priors and Penalized Regression',\n", " u'Disco: Distributed Multi-Stage Data Pipelines',\n", " u'Driving Blaze in the Real World of Data Land Mines',\n", " u'Evaluating skills in educational and other settings: An overview',\n", " u'From DataFrame to Web Application in 10 minutes',\n", " u'Get To Know Your Data',\n", " u'Grids, Streets & Pipelines: Making a linguistic streetmap with scikit-learn',\n", " u'Healthcare Analytics']" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "conf_data['nyc2014']['titles'][:20]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### I use Python Pandas to structure all parsed data into dataframes" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " abstracts \\\n", "0 \n", "1 The The Greater Plains Collaborative (GPC) is ... \n", "2 To a lot of people, Facebook is a website for ... \n", "3 The ad targeting team at Yelp is tasked with p... \n", "4 \n", "\n", " titles conference city year \n", "0 sv2014 sv 2014 \n", "1 Using Python and Paver to Control a Large Medi... sv2014 sv 2014 \n", "2 A Full Stack Approach to Data Visualization: T... sv2014 sv 2014 \n", "3 Ad Targeting at Yelp sv2014 sv 2014 \n", "4 Analyzing Satellite Images With Python Scienti... sv2014 sv 2014 \n", " abstracts \\\n", "0 The Python data ecosystem has grown beyond the... \n", "1 In this talk I will give an overview of Random... \n", "2 Clustering data is a fundamental technique in ... \n", "3 At Conversocial we use machine learning to fil... \n", "4 At WIDE IO, we are specialists in image proces... \n", "\n", " titles conference city year \n", "0 Massively Parallel Processing with Procedural ... ldn2014 ldn 2014 \n", "1 A Beginner's guide to Random Forests - R vs Py... ldn2014 ldn 2014 \n", "2 A Full Stack Approach to Data Visualization: T... ldn2014 ldn 2014 \n", "3 Adaptive Filtering of Tweets with Machine Lear... ldn2014 ldn 2014 \n", "4 An introduction to video action recognition ldn2014 ldn 2014 \n", " abstracts \\\n", "0 I'll walk you through Python's best tools for ... \n", "1 An increasing amount of information is being c... \n", "2 Scikit-learn is one of the most well-known mac... \n", "3 Attendees will be given a practical introducti... \n", "4 Coming soon. \n", "\n", " titles conference city year \n", "0 A practical introduction to IPython Notebook &... nyc2013 nyc 2013 \n", "1 Image Features in Python nyc2013 nyc 2013 \n", "2 A Beginner’s Guide to Machine Learning with Sc... nyc2013 nyc 2013 \n", "3 A practical introduction to Pandas with Citibi... nyc2013 nyc 2013 \n", "4 An Intro to FOSS Licenses and Copyrights in Da... nyc2013 nyc 2013 \n", " abstracts \\\n", "0 ABBY is a Django app that helps you manage you... \n", "1 Python is quickly becoming the glue language w... \n", "2 \n", "3 Coming Soon \n", "4 Learn how to program and utilize the parallel ... \n", "\n", " titles conference city year \n", "0 ABBY - A Django app to document your A/B tests berlin2014 berlin 2014 \n", "1 Algorithmic Trading with Zipline berlin2014 berlin 2014 \n", "2 Blaze berlin2014 berlin 2014 \n", "3 Building the PyData Community berlin2014 berlin 2014 \n", "4 CUDA 6 Tutorial berlin2014 berlin 2014 \n", " abstracts \\\n", "0 This talk covers rapid prototyping of a high p... \n", "1 Scikit-Learn is one of the most popular machin... \n", "2 IPython recently introduced a new framework fo... \n", "3 Coming soon. \n", "4 Python has a rich ecosystem of open source geo... \n", "\n", " titles conference city year \n", "0 (Easy), High Performance Text Processing with ... nyc2014 nyc 2014 \n", "1 A Machine Learning Pipeline with Scikit-Learn nyc2014 nyc 2014 \n", "2 Advanced IPython Notebook Widgets nyc2014 nyc 2014 \n", "3 Advanced scikit-learn nyc2014 nyc 2014 \n", "4 Analyzing Satellite Images With Python Scienti... nyc2014 nyc 2014 \n" ] } ], "source": [ "import pandas as pd\n", "\n", "pydata = pd.DataFrame()\n", "\n", "for conf in conf_data:\n", " conf_dataframe = pd.DataFrame.from_dict(conf_data[conf])\n", " conf_dataframe['conference'] = conf\n", " conf_dataframe['city'] = conf[:-4]\n", " conf_dataframe['year'] = int(conf[-4:])\n", " \n", " print pd.DataFrame.head(conf_dataframe)\n", " \n", " pydata = pydata.append(conf_dataframe)\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Interesting to see how many talks we had from year to year, also that's another point to check that data looks close to what we expect" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "records in dataframe 233\n" ] }, { "data": { "text/html": [ "
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nyc2014 50 50 50 50
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ldn2014 44 44 44 44
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" ], "text/plain": [ " abstracts titles city year\n", "conference \n", "berlin2014 50 50 50 50\n", "nyc2014 50 50 50 50\n", "sv2014 49 49 49 49\n", "ldn2014 44 44 44 44\n", "nyc2013 40 40 40 40" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print 'records in dataframe %i' % len(pydata)\n", "pydata.groupby(['conference']).count(1).sort('year', ascending=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Seems like number of talks is slowly growing from 40 during nyc2013 and up to 50 during the last pydata of 2014**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Now we have all the data. Let's try to analyse it ##\n", "\n", "\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### So what size of proposal do they usually have?" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[961, 741, 326, 534, 534, 2089, 738, 306, 2222, 1331, 421, 402, 650, 490, 822, 415, 752, 188, 188, 553, 444, 1300, 663, 991, 536, 913, 911, 2362, 865, 1415, 918, 1145, 1198, 1023, 1311, 581, 1889, 277, 1015, 2281, 684, 1057, 783, 590, 902, 366, 657, 504, 1182, 266, 1390, 534, 877, 880, 398, 445, 733, 619, 1598, 668, 415, 1056, 764, 534, 618, 1148, 1688, 918, 616, 440, 172, 220, 982, 674, 1127, 896, 1093, 1036, 685, 707, 493, 901, 2022, 414, 221, 294, 1842, 548, 825, 827, 689, 690, 1531, 249, 1004, 242, 395, 727, 173, 896, 453, 108, 928, 762, 253, 238, 480, 2093, 503, 1195, 1571, 477, 1268, 408, 1721, 264, 1522, 1113, 578, 885, 650, 1893, 561, 559, 670, 722, 128, 1196, 1179, 1395, 742, 415, 188, 460, 1158, 534, 315, 908, 1805, 1093, 684, 788, 2347, 2347, 902, 756, 1234, 604, 909, 1048, 2270, 2000, 277, 793, 1129, 860, 698, 513, 1025, 919, 632, 829, 132, 637, 683, 1092, 568, 359, 456, 1024, 2757, 1013, 771, 66, 535, 893, 1115, 394, 499, 646, 875, 1542, 1055, 1255, 430, 1015, 545, 599, 421, 1253, 1084, 925, 871, 822, 824, 990, 615, 1568]\n", "\n" ] } ], "source": [ "abstract_lens = [len(abst) for abst in pydata['abstracts'] if len(abst) > 44]\n", "print abstract_lens\n", "print\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array([ 23., 46., 45., 43., 17., 7., 6., 4., 6., 1.]),\n", " array([ 66. , 335.1, 604.2, 873.3, 1142.4, 1411.5, 1680.6,\n", " 1949.7, 2218.8, 2487.9, 2757. ]),\n", " )" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ 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min 66.000000
25% 506.250000
50% 759.000000
75% 1056.750000
max 2757.000000
\n", "
" ], "text/plain": [ " 0\n", "count 198.000000\n", "mean 858.030303\n", "std 509.741916\n", "min 66.000000\n", "25% 506.250000\n", "50% 759.000000\n", "75% 1056.750000\n", "max 2757.000000" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame(abstract_lens).describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### What about word corpus from different years" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import nltk\n", "stop = nltk.corpus.stopwords.words('english')\n", "\n", "text = {}\n", "words = {}" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "stop_list = [\"ll\", \"II\", \"ll\", \"http\", \"://\", \"e\", \"g\", \"2\", \"0\"]\n", "\n", "for conference in conf_data:\n", " raw = \" \".join(conf_data[conference]['abstracts'])\n", " tokens = nltk.WordPunctTokenizer().tokenize(raw)\n", " text[conference] = nltk.Text(tokens)\n", " words[conference] = [w.lower() for w in text[conference] if w.lower() not in stop_list]\n", " words[conference] = [w for w in words[conference] if w not in stop]\n", " words[conference] = filter(lambda word: word not in u'%,-:()$\\/;?.’–“”*\\'[]', words[conference])\n", " words[conference] = [w for w in words[conference] if w not in stop_list]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Let's check collocations in the abstracts.
Collocations are expressions of multiple words which commonly co-occur. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sv2014\n", "http ://; nearest neighbor; machine learning; Reference Model;\n", "neighbor algorithm; IPython Notebook; big data; open source; make\n", "predictions; data analysis; Big Data; github repository; current\n", "state; means clustering; visualization libraries; https ://; compiler\n", "optimizations; accepting payments; block fraud; concise construction\n", "None\n", "\n", "ldn2014\n", "http ://; machine learning; :// www; data processing; open source;\n", "Matrix Factorisation; certain types; public clouds; rent ratios;\n", "financial industry; PyData Boston; blocking technique; cloud\n", "computing; exact solution; includes two; presentation focuses; drug\n", "development; graphical plotting; quantum chemistry; wide range\n", "None\n", "\n", "berlin2014\n", "http ://; machine learning; Big Data; Quantified Self; self tracking;\n", "Semantic Web; Coming Soon; among others; open source; data analysis;\n", "case study; Hadoop jobs; :// www; working knowledge; predictive model;\n", "time permits; Add tranformations; Machine Learning; Operating System;\n", "Pythonista interested\n", "None\n", "\n", "nyc2013\n", "machine learning; Coming Soon; open source; IPython Notebook; Coming\n", "soon; chip design; broad range; face detection; file descriptor; https\n", "://; hue binning; resources needed; image features; talk covers;\n", "computer vision; oriented computations; future computation; CPython\n", "interpreter; analytics capabilities; sampling algorithms\n", "None\n", "\n", "nyc2014\n", "machine learning; open source; data science; Cloud Foundry; command\n", "line; Coming soon; time series; financial statements; New York; Time\n", "permitting; confidence pool; crafted artisanal; dimensionality\n", "reduction; gene expression; keep track; rapid prototyping; big data;\n", "http ://; style buildpack; web application\n", "None\n", "\n" ] } ], "source": [ "for conference in text:\n", " print conference\n", " print text[conference].collocations()\n", " print \n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Words used in abstracts" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "nyc2013: \tnumwords - 4329, unique - 1337\n", "ldn2014: \tnumwords - 5860, unique - 1738\n", "sv2014: \tnumwords - 7462, unique - 1906\n", "berlin2014: \tnumwords - 7633, unique - 1877\n", "nyc2014: \tnumwords - 6293, unique - 1824\n" ] } ], "source": [ "numwords = {}\n", "uniwords = {}\n", "\n", "for conference in text:\n", " numwords[conference] = len(text[conference])\n", " uniwords[conference] = len(set(text[conference]))\n", "\n", "for conference in reversed(conferences):\n", " print \"%s: \\tnumwords - %i, unique - %i\" % \\\n", " (conference, numwords[conference], uniwords[conference])" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEACAYAAABWLgY0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAEodJREFUeJzt3X+QpVdd5/H3B0LWAJHIovk1AVI6UQbRhGCCsguDLHH2\n", "VxIWJOgSUpiyqBoRtaRW4laR2V1kwVIwwCalKz+SFSKzq8REYsgYbGUVGBMSMzLEJLXMbqZJBquM\n", "BoR1J/DdP57T5EnTv3K7b3fPmferqmvOc55f59x7n0+fe+59elJVSJL69LiNboAkaXoMeUnqmCEv\n", "SR0z5CWpY4a8JHXMkJekji0Z8klOS/JHST6b5C+TvKHVPzXJniR3J7k5yQmjfS5Lck+Su5KcN6o/\n", "O8m+tu6K6XVJkjRnuZH8YeDnqurZwPOBn0ryLOBNwJ6qOgO4pS2TZBtwEbAN2AFcmSTtWFcBl1bV\n", "VmBrkh1r3htJ0qMsGfJV9UBV3dHKXwY+B5wKnA9c3Ta7GriwlS8Arq2qw1V1ALgXODfJycDxVbW3\n", "bXfNaB9J0pSseE4+yTOBs4BPAydW1aG26hBwYiufAhwc7XaQ4ZfC/PrZVi9JmqIVhXySJwO/A/xM\n", "VX1pvK6Gv4vg30aQpE3omOU2SPIEhoD/b1V1Xas+lOSkqnqgTcV8sdXPAqeNdt/CMIKfbeVx/ewC\n", "5/KXhSRNoKqyUP2SId8+NH0vsL+qfm206nrgEuDt7d/rRvUfSvIOhumYrcDeqqokDyU5F9gLXAy8\n", 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"Y7wX+MdJ7gF+FnjT3MHbu6rdwEuS3JfkpQu0YdIbUoohyH4qyX7gKbT5eODt7fVwO8O3YpY6xkrO\n", "A5vned1M195ybdvUvBlqAkneyPAJ/eUb3RbpaOK199g5J/8YJfkIcDrDd6MlrROvvck4kpekjjkn\n", "L0kdM+QlqWOGvCR1zJCXpI4Z8pLUMUNekjr2/wEaUX1amq3kawAAAABJRU5ErkJggg==\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.bar(range(len(uniwords)), [uniwords[conference] for conference in reversed(conferences)], align='center', )\n", "plt.xticks(range(len(uniwords)), [conference for conference in reversed(conferences)])\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Seems like the number of unique words had its peak during the sv2014 and right now is pretty stable and even slowly decreasing\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Bigrams" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Bigrams nyc2013\n", " 0 1\n", "0 (machine, learning) 0.008158\n", "1 (scikit, learn) 0.005152\n", "2 (coming, soon) 0.003435\n", "3 (data, analysis) 0.002147\n", "4 (chip, design) 0.001717\n", "5 (data, science) 0.001717\n", "6 (image, features) 0.001717\n", "7 (ipython, notebook) 0.001717\n", "8 (open, source) 0.001717\n", "9 (scidb, py) 0.001717\n", "10 (data, scientists) 0.001288\n", "11 (learning, algorithms) 0.001288\n", "12 (learning, tasks) 0.001288\n", "13 (models, like) 0.001288\n", "14 (python, data) 0.001288\n", "15 (talk, covers) 0.001288\n", "16 (across, organization) 0.000859\n", "17 (analytics, capabilities) 0.000859\n", "18 (array, based) 0.000859\n", "19 (array, oriented) 0.000859\n", "20 (asynchronous, o) 0.000859\n", "21 (asynchronous, programming) 0.000859\n", "22 (broad, range) 0.000859\n", "23 (c, ++) 0.000859\n", "24 (challenge, scaling) 0.000859\n", "\n", "\n", "\n", "Bigrams ldn2014\n", " 0 1\n", "0 (data, processing) 0.001969\n", "1 (machine, learning) 0.001969\n", "2 (data, science) 0.001312\n", "3 (data, set) 0.001312\n", "4 (open, source) 0.001312\n", "5 (python, r) 0.001312\n", "6 (python, used) 0.001312\n", "7 (scikit, learn) 0.001312\n", "8 (use, python) 0.001312\n", "9 (com, vstoxx) 0.000984\n", "10 (data, mining) 0.000984\n", "11 (derivatives, analytics) 0.000984\n", "12 (eurexchange, com) 0.000984\n", "13 (financial, analytics) 0.000984\n", "14 (financial, industry) 0.000984\n", "15 (high, performance) 0.000984\n", "16 (information, visualization) 0.000984\n", "17 (ipython, notebook) 0.000984\n", "18 (python, based) 0.000984\n", "19 (scientific, data) 0.000984\n", "20 (using, bokeh) 0.000984\n", "21 (www, eurexchange) 0.000984\n", "22 (!, wish) 0.000656\n", "23 (.\", \") 0.000656\n", "24 (analysis, python) 0.000656\n", "\n", "\n", "\n", "Bigrams sv2014\n", " 0 1\n", "0 (big, data) 0.002805\n", "1 (machine, learning) 0.002550\n", "2 (open, source) 0.002295\n", "3 (scikit, learn) 0.002295\n", "4 (ipython, notebook) 0.002040\n", "5 (high, performance) 0.001785\n", "6 (data, analysis) 0.001530\n", "7 (data, science) 0.001530\n", "8 (high, level) 0.001530\n", "9 (k, nearest) 0.001530\n", "10 (nearest, neighbor) 0.001530\n", "11 (neighbor, algorithm) 0.001530\n", "12 (real, time) 0.001530\n", "13 (reference, model) 0.001530\n", "14 (using, bokeh) 0.001530\n", "15 (large, data) 0.001275\n", "16 (python, code) 0.001275\n", "17 (data, processing) 0.001020\n", "18 (k, means) 0.001020\n", "19 (make, predictions) 0.001020\n", "20 (talk, walk) 0.001020\n", "21 (time, series) 0.001020\n", "22 (training, data) 0.001020\n", "23 (clinical, trial) 0.000765\n", "24 (compiler, optimizations) 0.000765\n", "\n", "\n", "\n", "Bigrams berlin2014\n", " 0 1\n", "0 (machine, learning) 0.002329\n", "1 (data, analysis) 0.002070\n", "2 (big, data) 0.001811\n", "3 (data, processing) 0.001811\n", "4 (real, time) 0.001552\n", "5 (ipython, notebook) 0.001294\n", "6 (knowledge, python) 0.001294\n", "7 (open, source) 0.001294\n", "8 (processing, large) 0.001294\n", "9 (scikit, learn) 0.001294\n", "10 (coming, soon) 0.001035\n", "11 (csv, json) 0.001035\n", "12 (data, driven) 0.001035\n", "13 (data, science) 0.001035\n", "14 (data, source) 0.001035\n", "15 (hadoop, jobs) 0.001035\n", "16 (jobs, python) 0.001035\n", "17 (knowledge, mapreduce) 0.001035\n", "18 (large, data) 0.001035\n", "19 (need, process) 0.001035\n", "20 (quantified, self) 0.001035\n", "21 (self, tracking) 0.001035\n", "22 (semantic, web) 0.001035\n", "23 (signal, processing) 0.001035\n", "24 (state, art) 0.001035\n", "\n", "\n", "\n", "Bigrams nyc2014\n", " 0 1\n", "0 (machine, learning) 0.004188\n", "1 (data, science) 0.003290\n", "2 (scikit, learn) 0.003290\n", "3 (open, source) 0.002692\n", "4 (big, data) 0.001496\n", "5 (coming, soon) 0.001197\n", "6 (data, processing) 0.001197\n", "7 (data, sets) 0.001197\n", "8 (python, ecosystem) 0.001197\n", "9 (cloud, foundry) 0.000897\n", "10 (command, line) 0.000897\n", "11 (data, analysis) 0.000897\n", "12 (deep, learning) 0.000897\n", "13 (financial, statements) 0.000897\n", "14 (languages, like) 0.000897\n", "15 (learning, techniques) 0.000897\n", "16 (light, weight) 0.000897\n", "17 (one, might) 0.000897\n", "18 (selective, search) 0.000897\n", "19 (show, one) 0.000897\n", "20 (talk, go) 0.000897\n", "21 (time, series) 0.000897\n", "22 (web, application) 0.000897\n", "23 (3d, graphics) 0.000598\n", "24 (analysis, use) 0.000598\n", "\n", "\n", "\n" ] } ], "source": [ "from nltk.collocations import *\n", "\n", "bigram_measures = nltk.collocations.BigramAssocMeasures()\n", "\n", "for conference in reversed(conferences):\n", " print \"Bigrams \" + str(conference)\n", " finder = BigramCollocationFinder.from_words(words[conference])\n", " scored = finder.score_ngrams(bigram_measures.raw_freq)\n", " print pd.DataFrame(scored[:25])\n", " print \"\\n\\n\"" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "# Year over Year" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ngram nyc2013 ldn2014 sv2014 berlin2014 nyc2014\n", "0 (machine, learning) 0.008158 0.001969 0.002550 0.002329 0.004188\n", "1 (scikit, learn) 0.005152 0.001312 0.002295 0.001294 0.003290\n", "2 (data, analysis) 0.002147 0.000656 0.001530 0.002070 0.000897\n", "3 (data, science) 0.001717 0.001312 0.001530 0.001035 0.003290\n", "4 (ipython, notebook) 0.001717 0.000984 0.002040 0.001294 0.000598\n", "5 (open, source) 0.001717 0.001312 0.002295 0.001294 0.002692\n", "6 (python, data) 0.001288 0.000328 0.000765 0.000259 0.000299\n", "7 (user, interface) 0.000859 0.000328 0.000765 0.000259 0.000299\n", "8 (big, data) 0.000429 0.000656 0.002805 0.001811 0.001496\n", "9 (data, driven) 0.000429 0.000656 0.000510 0.001035 0.000598\n", "10 (data, processing) 0.000429 0.001969 0.001020 0.001811 0.001197\n", "11 (data, sets) 0.000429 0.000656 0.000765 0.000259 0.001197\n", "12 (every, day) 0.000429 0.000328 0.000510 0.000259 0.000299\n", "13 (high, performance) 0.000429 0.000984 0.001785 0.000259 0.000598\n", "14 (large, datasets) 0.000429 0.000656 0.000765 0.000517 0.000299\n" ] } ], "source": [ "result = pd.DataFrame()\n", "\n", "for conference in reversed(conferences):\n", " finder = BigramCollocationFinder.from_words(words[conference], window_size = 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(conference)])\n", " else:\n", " result = result.merge(pd.DataFrame(scores, columns=[\"ngram\", str(conference)]))\n", " \n", "print result[:15]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[(u'machine', u'learning') (u'scikit', u'learn') (u'data', u'analysis')\n", " (u'data', u'science') (u'ipython', u'notebook') (u'open', u'source')\n", " (u'python', u'data') (u'user', u'interface') (u'big', u'data')\n", " (u'data', u'driven')]]\n" ] } ], "source": [ "transposed = result[:10].transpose()\n", "headers = transposed[0:1:].values\n", "print headers" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAA78AAAK+CAYAAABqy6mLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XmcHVWZ//HPl0BASEhYAiQQ1gQBZVURZwZpddSYIOqM\n", "oKwGHEFR4OewKLIISkBncBTRAUF2kQgjKKugQNiRwWERQ4CQhCSEhC0hYZEtz++POhcql+7btzvd\n", "Xfd0f9+vV79yq+qcqqfu0+h9+pxTVxGBmZmZmZmZWX+2QtUBmJmZmZmZmfU2F79mZmZmZmbW77n4\n", 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nyc2014\n", "0 (machine, learning) 0.008158 0.001969 0.002550 0.002329 0.004188\n", "1 (scikit, learn) 0.005152 0.001312 0.002295 0.001294 0.003290\n", "3 (data, science) 0.001717 0.001312 0.001530 0.001035 0.003290\n", "5 (open, source) 0.001717 0.001312 0.002295 0.001294 0.002692\n", "8 (big, data) 0.000429 0.000656 0.002805 0.001811 0.001496\n", "10 (data, processing) 0.000429 0.001969 0.001020 0.001811 0.001197\n", "11 (data, sets) 0.000429 0.000656 0.000765 0.000259 0.001197\n", "2 (data, analysis) 0.002147 0.000656 0.001530 0.002070 0.000897\n", "4 (ipython, notebook) 0.001717 0.000984 0.002040 0.001294 0.000598\n", "9 (data, driven) 0.000429 0.000656 0.000510 0.001035 0.000598\n", "13 (high, performance) 0.000429 0.000984 0.001785 0.000259 0.000598\n", "6 (python, data) 0.001288 0.000328 0.000765 0.000259 0.000299\n", "7 (user, interface) 0.000859 0.000328 0.000765 0.000259 0.000299\n", "12 (every, day) 0.000429 0.000328 0.000510 0.000259 0.000299\n", "14 (large, datasets) 0.000429 0.000656 0.000765 0.000517 0.000299\n" ] } ], "source": [ "print result[:15].sort(['nyc2014'], ascending=[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## We have very few data points, but based on data we can see, that *(machine, learning)* and *(scikit, learn)* is still on top. *(open, source)* and *(big, data)* is also picking up, that means, that we already have some experience and starting to play bigger." ] } ], "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": "2.7.5" } }, "nbformat": 4, "nbformat_minor": 0 }