cmrivers/journal_title_length

## journal_title_length
{
 "metadata": {
  "name": "words_in_title"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import pandas as pd\nimport matplotlib.pyplot as plt\nfrom scipy import stats\nimport d3py",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 134
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "impact = pd.read_csv('SNIP_SJR_complete_1999_2011new_SNIP_and_SJR_v1_Oct_2012.csv')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "def count_words(title):\n    wordCount = sum([1 for word in title.split(' ')])\n        \n    return wordCount\n\ndef impact_regression(df):\n    clean = df[['Source Title', '2011 SNIP2', 'title_wordcnt']].dropna(how='any')\n    slope, intercept, r_value, p_value, std_err = stats.linregress(clean['title_wordcnt'], clean['2011 SNIP2'])\n    print 'Rvalue: {0}\\nPvalue: {1}\\n\\n'.format(r_value, p_value)\n    \n    plt.scatter(clean['title_wordcnt'], clean['2011 SNIP2']);",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 191
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "impact['title_wordcnt'] = impact['Source Title'].map(count_words)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 182
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "impact.to_csv('impact_titlelength.csv')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 180
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "big4.title_wordcnt.describe()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 206,
       "text": "count    20698.000000\nmean         4.280075\nstd          2.504240\nmin          1.000000\n25%          3.000000\n50%          4.000000\n75%          5.000000\nmax         31.000000"
      }
     ],
     "prompt_number": 206
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "big4['2011 SNIP2'].describe()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 208,
       "text": "count    12581.000000\nmean         0.992255\nstd          1.067147\nmin          0.000000\n25%          0.427000\n50%          0.842000\n75%          1.271000\nmax         41.082000"
      }
     ],
     "prompt_number": 208
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "big4[big4['2011 SNIP2'] >=30][['2011 SNIP2', 'Source Title']]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>2011 SNIP2</th>\n      <th>Source Title</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4874 </th>\n      <td> 41.082</td>\n      <td>            CA - A Cancer Journal for Clinicians</td>\n    </tr>\n    <tr>\n      <th>10365</th>\n      <td> 32.028</td>\n      <td> Foundations and Trends in Information Retrieval</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 211,
       "text": "       2011 SNIP2                                     Source Title\n4874       41.082             CA - A Cancer Journal for Clinicians\n10365      32.028  Foundations and Trends in Information Retrieval"
      }
     ],
     "prompt_number": 211
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "impact['2011 SJR2'].clip(0, 5).hist(bins=50, color='MediumAquamarine');\nplt.title('Histogram of 2011 SJR scores');\nplt.xlabel('SJR score');\nplt.ylabel('Counts');",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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//geAsrIyIiMj6d+/P1FRUVRUVJj7LFmyhODgYAYMGMD69evN5ZmZmQwePJjg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\nl/4POwwfYstMf8QAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 185
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "impact.title_wordcnt.clip(1, 10).hist(bins=10, color='m', alpha=.4);\nplt.title('Title length')\nplt.xlabel('Number of words in title')\nplt.ylabel('Count');",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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y2eEydp3dpbUY2j4QHLlsO2nTcnPG/i/mrP131bLtpI1d3+vg99Gz9T+O/vcq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\nEHZJsRBCCGHX/wNr5dgSFxqfswAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 186
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "impact_regression(impact)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Rvalue: -0.0196096947592\nPvalue: 0.00688449443127\n\n\n"
      },
      {
       "output_type": "display_data",
       "png": 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jKZnrX57LVQez5k+JA7Ro0dwnrrqICBdeOIp//et53nrre+6++2VSUobj9Xr/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\nIKeFXimlgpwWeqWUCnJa6JVSKsj9P/xbWgiW88QJAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 192
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "usa = impact[impact.Country == 'United States']",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 193
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "impact_regression(usa)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Rvalue: 0.000655392936585\nPvalue: 0.961306145523\n\n\n"
      },
      {
       "output_type": "display_data",
       "png": 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gSuA2IAU4yB133GGKOzV3/9forqULOHr0R0tbLFjwCTAE+A5wYLe3qpubHZ/B\nrl272LnzIOXlrwHXUFHxD/bvP8bWrVstsSUlJSxYsIB33nmHH3+05i5EoJBCXw8++OAjvN5mwO+A\nEbjddmbPfsMU06xZM3TXx4PoW/aNBlyWpQS++24H+gw92/hSbNu2xRSj++jL0TNqxgFZQLDPM2xY\nhC7gDwF3o4v/KV6vl7IyN/AK0Bfdl9/cMpd+y5YtQB/j9QHAByhlXcPm2283o9QW9IHqTUpLm5KV\ntcUSB7Bt2zaWLl3qcymIs6Uv8PJy6gCmUMpjufCroKCA5OTejB37KnfdtYCOHbtZDrJCBAop9PWg\noqIUPSAajT7TriAnx3wvWH1FahD6jHkMcBFgtwzG6qJbASQaX15cLnM3xKmz62/RZ8//BdyWC6b0\njcF/jR4YbYY+8zdfGVtSUmLsbzS6v/83wCV8+umnpjh9b9v/GjndgO5WsnaPeL0O4GogF/2p5TDF\nxcctcRMnPkGvXoO4+eaZdOrUg4ULF1lizkZCQgJduyYQFnYz8H+EhqbTsWNLunbtaoqbOvV5Dh/u\nS3HxZxQXf8CxYxO4996JNdqnEOc7KfT1wG4PBV5DX7g0BRiB02keZP3mm2/QM2mWoLtcVgBey4VJ\nuniGo8/Wj6APDuYuGb0ccSTwLBAKPAC0M2b2VPc68DTwAvAwembNKfpA4wSeQ58V7wD+Q0FBgSlO\n30AkD9iNPqj9ycizelt40QeWicDfgZuIiGhkitm0aRN/+9sblJZu5dix/+ByLePWW8f67Ff/6quv\nuPPO+7nnnvFG91f1/dn57LMPue++9vTvP5d77mnFqlVLLQfQ/ftzqKjoVfVYqV6n3StXiMAihb4e\nhIYGAY8DvYGLga20aWNej153EzRBd8uALtQhbNhQ/S5NIcB49EBqOLqIm2e36CJWgi7aT6CXK95F\nZaW5W0YfNBzATODf6AurfM26KUGvmxOB7p7pZLkhSnl5OfpTSCLQFL0OfrGlLYKDHcD96BurHAJe\nISLCfNDLzs6msrIzevpoGdATt9tujD2csnLlSgYMGMrs2W149dUYevdONbqQzJxOJ8899wwrVixi\nxowMY+DYLC3tcpzOV9EH0DLCwl7gV7+63BInRCCQQl8PXK4ydEGNAloBB/juO/NgoJ5dk40+c96K\nPqvHUtx0f/NnxvcKPXhrLs764FAK/BZdLK8FynxMKwwG/oyeu381MBd99n6Kx+NBH3QGA8vRM2p2\nWO5qpW2aKkFUAAAWOklEQVRCz+J5B30Qcloi8vOPo+fttwOuB65jxQrzVE2bzUZFxSr0wSASuAC3\n22Us7XDKE088j8s1A5iAUk9QUjKB6dNf8pHX/3bbbWNo3twFtASiCAr6kkceedASd/DgQS68MBG7\nPYaIiJZ88MEHZ9xmaWkpR48ePeOUWiH8RQp9PfB6i9H90fej7716hN2795lidLeEB3gRXXTnAZVV\nq1SaLUBf6NQH3dVjprtVIoEr0N0jw/BVdPWvey+6S2kUupCbByn1p4AT6KmXo4DhQDPLDVF0t1Bf\ndJH/PdCR6l1KAEq50YO+/4ceE1jPjh3WdXN0HpXoTyNFgNe43eIppaVl6APZSdG4XOVUl5uby/XX\nj6Jt22QGD/41hw8ftsTou221BY4BJ6io+DV/+tMTlrjOnXtz8OBxlCrD5YLhw29h27ZtlrgpU6bS\nuHFTWrfuQHJyH2PKqxDnByn09SICvbjY/cAzwO9xuazdGroLxonu674AcPtY1MyFvoLWhv51HTWe\nO0UPoCr0QWA4ulumOVYVxmud0Wfsr6C7aU7Rq2NGoA8Yn6GnaxZbpmrqwv8l8BjwH3Rxtq6sqccU\nXkIfrL4E2lB9SufHH39s/Gwq+tNGO8BhuabgzjtvJjT0XnS73kdo6GOMGzfSFON2u7niimv45JO2\nHDgwl+XLk7j88jTLmj4bN26ntHQQMBt4iYqKy9m82dznf+TIEU6cOIH+pJSDHt8I4sUXXzTFffLJ\nJ0yf/jpu9x7KywvYuXMAI0eO89EWQviHFPp64UV3Q5wUQ/UzZ60c+Cu62+V2IMRH140TfRD4Dj0H\n/gKqn62f6ovvij54tKH6tEnNjh40/ROQzqkul1N27NiBLv4PoOfSd0ffxNxMfyIZhf70kAC8ia9Z\nN7otfgC6AVcBXbHZzAOjenmFKGAt+pNNPlBhme7o9bopL89Fdxl9Q3n5McuA886dO8nNdVFZ+SgQ\njMfzEAUFQZZ59Bdd1AJ4Cj0V9SBwJxdc0NQUo9u1HD143Rh9cLzSMjD99dfrKSn5DbobyEZl5QNs\n3Pi1j7YQwj+k0NeLSvTc8lB0IZ2C7yLYCb3GTQhwHxBqdGOcrhQ9yPmA8XXMeK46N3qwsxVQiP6U\nUJ0D80BuCNUPQLov3mm8VoQ++95v2ZLuh16PHogNQve/+7oXbBl6HOJX6IPUv8jPNw/s6jnuxehl\nnfeiB3ZDiI+PN8U98MBj6EHpTcBmIJxbb73TFBMWFobLlQvEo7u74nG5DlpulL5z5x50F9abwCzg\nr+zda76SuHnzk5+K9hn/VgI7GTFihCnO5SpBf/o5eXD9AqXOfEMXIX5uUujrRQX6P30Uuvj56tIA\nOIAucOXAYXzNWtEHCht6qYFvjG356n+3oeftX4su9mcqNM+hDxgPobskzAcg3Z99crB3AnA5vj+N\ngC6ACt0/vwXzjU9Oz78tuttjCdABpcyDyXqphh7oTwag5+Q7jCmop+hPEVeiD3b5QCcKC4ssMV6v\nFz119FvgMTweN8XF5rZds+Yb9Iwhj9EGHTh0yHytg9frNQ5CfdFtdhl2e7HloBEWFm60RYKR31hj\nnEb80m3bto1bbrmd66+/mQ8+WOi3PORWgvUiBF3gC9FF0tcVqqAHPVugz3qd+D7u2tAF+eSdoB4G\n/nmG7W1ET5ksQRfX6sWm3MjrPfSZ8Y9U7y/X3SgedNGabmwv1si1unDj+b3AheiDVXUKfcBra8Tv\ns0To2UFZ6Nk5RUa8m/37q3+SsHOqKysI6A+Y+9VnzJiBPuA9ajzzEPAq06ZNY+HCU//RXK4iI+YB\nI8fmeL3mgV23243dbsfjeQd9oO1DSMjblvGK7du3odvsUSOvC3C7zReYifqVmZnJ228vICrKyQMP\n3G1cLe5fO3fupE+f/rhcD6NULCtWPMiJEydIT7/tZ89FzujrxcniGY4u+grfZ8Unr1S9Fd2F4yvm\n5OyWk/ri+9fWCF2UQR9YLvQRY0MX+jHomTCRVD/W6xuDhKIHfU9+KjjTDBIvemqoCz27yFf+XvSn\nm9nosQFf0zRBF8rX0IU+A1B8/XX1fm43sBP4BFiI7joyD7LGxMSgz/hPDjKXAgWWqZrh4RHo9jpo\n7DPFMnYQERFBUFAk8Ef0p5FXKStbSdu25msi9Cyc7uiDxn3AXrxe3+/T6/WSk5Nj+YRR3wJ5yufC\nhQu59trRvPbaRcyaZad798vIzs72d1r885+v43LdhVITgN/icr3J00/P9EsuUujrxcn7t45AzyTx\n1aUBuiCXoadF5uK7u6UUmIo+Oz8BTMN3sSxBnxGXoqcy7vIRE4peZ/4F9Pz3Nyz71Msp2NADjweN\nmDN98PsN+oIwG/ps1jrVUR9Y3gJ6oQ9q9+L7gBCDXqr5OfTZs/IxXhFs5Hu1kV9Lql88pi+gUujl\nlR9CX7TmZvPmzaa4xo1j0DOTkoDWQAEOh/l9Hj9+nPJyF/pAdxjd1WazdCnt3bsXPVi+CziO/n1a\nl2w+fPgwiYk9adeuKzExcUye/IyPdqhbGzduJD6+M0FBwbRv383n1NCG7tFHMygtfQN4CK83gxMn\nxvDqq7P9nZaxTPnpfwehPi5i/JnU3b1PzJYtW6Y6duyo2rdvrzIyMkyv1eNuzwvQRME7p9196RYF\njmoxKLhIgceIKVEQbmkbsClorCDY+GqkwOZjW04FcQqCqu4cZd1WiILnTsvrawVNfGzLrsB9WtwI\nH9tCwSUKFimYoGC+gggfbRGlYM1p2/rdGbZ1pYIfFCxRkKfAqRo3blwtrpGRi9u4I1eqgnBTTJs2\nbYx2ilDQ3GiHYNWkifl9OhyhCmIUfKrgKwUdLXfbOnr0qPHzXRTMNn6PUWrYsGGmOLvdoWCSgu8U\nfK5gr4IwS1v07TtI2e2PKditYJuKiOigli1bZolbt26dSki4RDVu3FJdc81wdeTIEUvM2SgqKlJN\nmrRUMNdo23+qZs3aKJfLVaPtna/i47sqWH/a39hUdc894/2dltq0aZNyOpspeF3Bx8rpTFIzZsys\n1TZrWjvrpeJWVlaqiy++WGVnZ6uKigqVkpKiduzYcWqnAV/oGyvYetof3nMKQqvFYBSQkzGVRiGr\nXgSjFbyroExBufGfNsbHtoKMonqRsX9fhd5hvLZMwQYFyZbiprcVomCHkZfHiPNVnMON2Cilbzlo\nPgDpuFClb0v4ooL7jQLsa1shRmwTI/cQFRYWVi0uWsGK09rsbcuBKi0tzchrlRHzpYJwlZKSUm1b\njRT87bRtrbC06759+4w2yzdivAp6qEsvvdQU53A4FDQzDjBRRhubD0BKKRUSEmUcUC409t9PPfXU\nU6aYAwcOqIiIZgreV3BABQffr3r16m/Z1tlYu3atcjoTjPZvoSBWhYdfaLldZUP31FPPKqezp/G7\n/lA5nXFq9erV/k5LKaXU6tWrVf/+Q9Wllw5Uf//7q8rr9dZqezWtnfUyGLt+/Xrat29fNT1u1KhR\nfPjhhyQmJtbH7s5DHvQiXu+gpzm+gO957dnoC46GoC9Q8viIAd0dc/IjoAvfXUHNgHXoLoR26H74\n6uvmOIBr0H3OZegZNQd8bMuOnvM+Gj3A6ysG9KDpJvTg6FR0t1J1Yca2fjD235TqF2lpIegLqrqg\n59L/DuuabG7gY/QgrDK+N0811YvCxaJnv4C+mvhCy60Q9e/j9DXoj1B9YFqvMGpDj2VgfN8Ij8d8\nIVd4eDjFxWHANmPfj6MvEjOrqHCgl4h+FD1rqKflpuurV6/GZktFd4uB2/0imzZFUlJScoZlp8/M\n4XDgch1EXwF9OfAZpaVDfVyU17A99thEbDYbb755DxERTqZOfY1+/fr5Oy0A+vXrx4oV/s+lXgr9\n4cOHadOmTdXj1q1bWwbWJk+eXPV9amoqqamp9ZGKn9jQqzo2RxewaHwX5w7owcWP0TcXCcFaBE9e\nvFRibOPP+FpqQA9A/oCeXrkZvepkdXb0VawvGLn9ierTK2NiYigoKEKvW78W6IkeiJzvY3sj0FM5\nQQ9CTvYRU2nsL9543OwMcb3RRR5gJDCO6Ogm1WIc6Ct7l6PboIRTd9XS9NW0bnRbtEfP8jlEaan5\nfQYH23G7n0Mf8BqjD1TmMQZ9YVQo+qA5Hn0g2kR4eLIp7qKL2rF16xBODYbfh74rWHVlwMkbuDQF\nRlJZae7vb9SoEfrA6kX/vv4LUKPiXFlZSUhIAhUVJxdrG0hIyAWWq4QbOrvdzuOPT+Lxxyf5O5U6\nl5mZWTc36qnV54gzWLBggbr99turHs+dO1fdd999VY/rabfnDd310FhBvPGxuekZuiucCr4wugTm\nnqG7JUJBK+OjfiMFrVX1vnCquj7Cle6nDzO+fO2zvYLrFAxS0N+yrVP5LzK6K44aH/t9bauz0aWk\njPgoH9tqrOBfRoxbQT/LtkJDT3bvFBhxmxSEqFWrVvnY1p1K96t/puAG5XCY9/nEE08Y7z1awUCl\n++HD1dixY01xffpcYcSEGu3mVDEx8aYYt9tttOsoBZca7dZUTZkyxRR39913K+ipoMLI/x0VEtLc\n0hbh4S0UvGnElCpIVpMmTTLFVFRUqF69+iun82oFTyin82KVkfG8ZVtnY//+/SosrKmCg8Y+96iw\nsCY17vMX/lfT2lkvFffLL79UV199ddXjZ5991jQgG/iFHqPYdDSKtFMNGjTIR1ywEReifPVdn9pW\nhIIUpfv0I38izqF0H3ewgiBLTMuWLY1tOY0i91PbCleQqHSfc4TKzs42xaxatcp4rZWCK5SvgWSl\nlAoODjb219c48EWplStX+tin0yjKV/g84Cml1JtvvmnkfJGx3yi1du1aS5zDEW7EORREKJvNOjCa\nm5urQkObKWiroJOy2SLVmjVrLHH333+/kc/lCpqpCy/sZIkpLy9XcXHtle57v1zZbBFq3rx5lrjl\ny5cruz3K+F3Gqs6deymPx2OJKysrU6+88op67LHHfQ7Wnotp02ao8PA41ajREBUeHqtefvkftdqe\n8K/zqtC73W7Vrl07lZ2drcrLy39xg7FKKRUREWEUTNTTTz99xriTMT/VJkFBQVUxTqfzjHFns62R\nI0eeVdzpMfv37/cZowcrdUxQkPXActJNN92kAOVwONTu3bvPGBcbG6vsdrvq1avXGWO2bt2qBgwY\noK655hp1+PDhM8b98Y9/VO3atVP33nvvGWOKi4vVCy+8oJ5++ml16NChM8atWbNGPfLII2ru3Lln\njKmsrFRz585V06dPV3v27DljXEFBgfrwww/VV199VeuBubO1fft2tWjRIvX999//LPsT9aemtdNm\n/HCdW7ZsGePHj8fj8TBu3DgeeeSRqtdsNltAX8AhhBD1oaa1s94K/U/uVAq9EEKcs5rWTrkyVggh\nApwUeiGECHBS6IUQIsBJoRdCiAAnhV4IIQKcFHohhAhwUuiFECLASaEXQogAJ4VeCCECnBR6IYQI\ncFLohRAiwEmhF0KIACeFXgghApwUeiGECHBS6IUQIsBJoRdCiAAnhV4IIQKcFHohhAhwUuiFECLA\nSaEXQogAJ4VeCCECnBR6IYQIcFLohRAiwEmhF0KIACeFXgghApwUeiGECHBS6IUQIsBJoa+BzMxM\nf6dQK5K/f0n+/tOQc6+NGhf6+fPn07lzZxwOB5s2bTK9NnXqVDp06ECnTp349NNPa53k+aah/7FI\n/v4l+ftPQ869NoJq+oPJycksXLiQu+66y/T8jh07mDdvHjt27ODw4cMMHDiQXbt2YbfLhwchhPCH\nGlffTp06kZCQYHn+ww8/ZPTo0QQHBxMfH0/79u1Zv359rZIUQghRC6qWUlNT1caNG6se33fffert\nt9+uejxu3Di1YMEC088A8iVf8iVf8lWDr5r4ya6btLQ0cnNzLc8/++yzXH/99T/1oyY2m830WNd6\nIYQQP4efLPTLly8/5w22atWKgwcPVj0+dOgQrVq1OvfMhBBC1Ik6GSE9/Qx96NChvPfee1RUVJCd\nnc3u3bvp1atXXexGCCFEDdS40C9cuJA2bdrw1VdfMWTIEAYPHgxAUlISI0aMICkpicGDB/P3v//d\n0nUjhBDiZ1Sjnv068uSTT6pWrVqpbt26qW7duqlly5b5M52zsmzZMtWxY0fVvn17lZGR4e90zlnb\ntm1VcnKy6tatm7r00kv9nc7/NHbsWBUbG6u6dOlS9Vx+fr4aOHCg6tChg0pLS1OFhYV+zPCn+cq/\nofzdHzhwQKWmpqqkpCTVuXNnNXPmTKVUw2n/M+XfUNq/tLRU9erVS6WkpKjExEQ1adIkpVTN2t+v\nhX7y5MnqhRde8GcK56SyslJdfPHFKjs7W1VUVKiUlBS1Y8cOf6d1TuLj41V+fr6/0zhrX3zxhdq0\naZOpUD788MNq2rRpSimlMjIy1MSJE/2V3v/kK/+G8nefk5OjsrKylFJKnThxQiUkJKgdO3Y0mPY/\nU/4Npf2VUqqkpEQppZTb7Va9e/dWq1evrlH7+/0qJtWAZuCsX7+e9u3bEx8fT3BwMKNGjeLDDz/0\nd1rnrCG1+RVXXEF0dLTpucWLF5Oeng5Aeno6ixYt8kdqZ8VX/tAwfgctWrSgW7duAERGRpKYmMjh\nw4cbTPufKX9oGO0P4HQ6AaioqMDj8RAdHV2j9vd7oX/ppZdISUlh3LhxFBUV+Tudn3T48GHatGlT\n9bh169ZVfzgNhc1mY+DAgfTs2ZPZs2f7O50aycvLIy4uDoC4uDjy8vL8nNG5a0h/9wD79u0jKyuL\n3r17N8j2P5l/nz59gIbT/l6vl27duhEXF0f//v3p3Llzjdq/3gt9WloaycnJlq/Fixdz9913k52d\nzebNm2nZsiUPPfRQfadTK4EwqLx27VqysrJYtmwZL7/8MqtXr/Z3SrVis9ka3O+lof3dFxcXM3z4\ncGbOnElUVJTptYbQ/sXFxfz6179m5syZREZGNqj2t9vtbN68mUOHDvHFF1+wcuVK0+tn2/41Xuvm\nbJ3tXPzbb7/9nC7C8ofq1wgcPHiQ1q1b+zGjc9eyZUsAmjdvzo033sj69eu54oor/JzVuYmLiyM3\nN5cWLVqQk5NDbGysv1M6J6fne77/3bvdboYPH86YMWMYNmwY0LDa/2T+t956a1X+Dan9T2rcuDFD\nhgxh48aNNWp/v3bd5OTkVH2/cOFCkpOT/ZjN/9azZ092797Nvn37qKioYN68eQwdOtTfaZ01l8vF\niRMnACgpKeHTTz8979vcl6FDhzJnzhwA5syZU/UfuKFoKH/3SinGjRtHUlIS48ePr3q+obT/mfJv\nKO1/9OjRqm6l0tJSli9fTvfu3WvW/vU0WHxWxowZo5KTk1XXrl3VDTfcoHJzc/2ZzllZunSpSkhI\nUBdffLF69tln/Z3OOdm7d69KSUlRKSkpqnPnzg0i/1GjRqmWLVuq4OBg1bp1a/X666+r/Px8NWDA\ngPN+ep9S1vxfe+21BvN3v3r1amWz2VRKSoppKmJDaX9f+S9durTBtP+3336runfvrlJSUlRycrKa\nPn26UkrVqP1tSjWQ4WchhBA14vdZN0IIIeqXFHohhAhwUuiFECLASaEXQogAJ4VeCCECnBR6IYQI\ncP8PzuQnIhckXSYAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 194
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "impact.Country.value_counts()[:10]",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 195,
       "text": "United States     9731\nUnited Kingdom    5961\nNetherlands       2617\nGermany           2389\nFrance             966\nChina              758\nJapan              722\nItaly              703\nSwitzerland        618\nSpain              511"
      }
     ],
     "prompt_number": 195
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "country_list = ['United States', 'United Kingdom', 'Netherlands', 'Germany']\nbig4 = impact[impact.Country.isin(country_list)]",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 196
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "impact_regression(big4)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Rvalue: -0.014060656878\nPvalue: 0.114787327644\n\n\n"
      },
      {
       "output_type": "display_data",
       "png": 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3Xren1IVOC309yM/P57rrJtKokROnM5K5c5/2ijEzZYox0xg7YG70Ucbp06er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\nC71SSgU4LfRKKRXg/j/gR6X0s+agJQAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 197
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "uk = impact[impact.Country == 'United Kingdom']\nimpact_regression(uk)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Rvalue: -0.0751363159685\nPvalue: 2.11846434762e-06\n\n\n"
      },
      {
       "output_type": "display_data",
       "png": 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ZCygaNWpUpC47cAh4BHgWyCIvzzmY16xZE9iHXk0KOo3AUUMO9XM12u3YbDaX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\n8Y7fZwIDyc8vbqFY2Th48CA9e97E0aP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      }
     ],
     "prompt_number": 198
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "neth = impact[impact.Country == 'Netherlands']\nimpact_regression(neth)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Rvalue: -0.0110826798416\nPvalue: 0.639559349463\n\n\n"
      },
      {
       "output_type": "display_data",
       "png": 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q6NA7yqjF4mzkT+yskGPm2dnZuN1B6LM7AZzk5YUbnXu5b98+9DBLOnqxzVwg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\nA8kiW7VcbbiwAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 199
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "germ = impact[impact.Country == 'Germany']\nimpact_regression(germ)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Rvalue: 0.00935943389908\nPvalue: 0.732414716492\n\n\n"
      },
      {
       "output_type": "display_data",
       "png": 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ly5Zx772P4vX+REFBFunpN3PNNTc5jlfE7/czd+5cJkyYcNaOtFDq95SYYr53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\nqhTQYq6UUqWAFnOllCoF/g9xm1yb06JoJgAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 200
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "simplified = big4[['Country', 'title_wordcnt', '2011 SNIP2']]\nsimpl = simplified.groupby(by=tmp.Country)\nsimplified.to_csv('big4_titlelength.csv')\n",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 203
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "big4",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 205,
       "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 20698 entries, 2 to 32053\nData columns:\nSource Title                                         20698  non-null values\nPrint ISSN                                           20162  non-null values\nE-ISSN                                               5574  non-null values\nPublisher's Name                                     20606  non-null values\nPublisher imprints grouped to main Publisher         20605  non-null values\nCountry                                              20698  non-null values\n1999 SNIP2                                           7738  non-null values\n1999 SJR2                                            12805  non-null values\n2000 SNIP2                                           7939  non-null values\n2000 SJR2                                            12805  non-null values\n2001 SNIP2                                           8575  non-null values\n2001 SJR2                                            12805  non-null values\n2002 SNIP2                                           8929  non-null values\n2002 SJR2                                            12805  non-null values\n2003 SNIP2                                           9533  non-null values\n2003 SJR2                                            12805  non-null values\n2004 SNIP2                                           9792  non-null values\n2004 SJR2                                            12805  non-null values\n2005 SNIP2                                           9993  non-null values\n2005 SJR2                                            12805  non-null values\n2006 SNIP2                                           10598  non-null values\n2006 SJR2                                            12805  non-null values\n2007 SNIP2                                           11079  non-null values\n2007 SJR2                                            12805  non-null values\n2008 SNIP2                                           11435  non-null values\n2008 SJR2                                            12805  non-null values\n2009 SNIP2                                           11772  non-null values\n2009 SJR2                                            12805  non-null values\n2010 SNIP2                                           12267  non-null values\n2010 SJR2                                            12805  non-null values\n2011 SNIP2                                           12581  non-null values\n2011 SJR2                                            12805  non-null values\nLife sciences                                        3616  non-null values\nSocial sciences                                      5873  non-null values\nPhysical sciences                                    7219  non-null values\nTop level:\nHealth Sciences                           7439  non-null values\n1000 \nGeneral                                        36  non-null values\n1100\nAgricultural and Biological Sciences            1179  non-null values\n1200\nArts and Humanities                             1614  non-null values\n1300\nBiochemistry, Genetics and Molecular Biology    1625  non-null values\n1400\nBusiness, Management and Accounting             1013  non-null values\n1500\nChemical Engineering                            564  non-null values\n1600\nChemistry                                       677  non-null values\n1700\nComputer Science                                1153  non-null values\n1800\nDecision Sciences                               233  non-null values\n1900\nEarth and Planetary Sciences                    994  non-null values\n2000\nEconomics, Econometrics and Finance             637  non-null values\n2100\nEnergy                                          300  non-null values\n2200\nEngineering                                     2931  non-null values\n2300\nEnvironmental Science                           1190  non-null values\n2400\nImmunology and Microbiology                     462  non-null values\n2500\nMaterials Science                               942  non-null values\n2600\nMathematics                                     861  non-null values\n2700\nMedicine                                        6941  non-null values\n2800\nNeuroscience                                    405  non-null values\n2900\nNursing                                         483  non-null values\n3000\nPharmacology, Toxicology and Pharmaceutics      597  non-null values\n3100\nPhysics and Astronomy                           805  non-null values\n3200\nPsychology                                      873  non-null values\n3300\nSocial Sciences                                 3457  non-null values\n3400\nVeterinary                                      124  non-null values\n3500\nDentistry                                       104  non-null values\n3600\nHealth Professions                              315  non-null values\nAll Science Classification Codes (ASJC)              20500  non-null values\nSourcerecord id                                      20698  non-null values\ntitle_wordcnt                                        20698  non-null values\ndtypes: float64(26), int64(2), object(38)"
      }
     ],
     "prompt_number": 205
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "simpl.describe()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>title_wordcnt</th>\n      <th>2011 SNIP2</th>\n    </tr>\n    <tr>\n      <th>Country</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"8\" valign=\"top\">Germany</th>\n      <th>count</th>\n      <td> 2389.000000</td>\n      <td> 1337.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>    3.860611</td>\n      <td>    0.692680</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>    2.423934</td>\n      <td>    0.617181</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>    1.000000</td>\n      <td>    0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>    2.000000</td>\n      <td>    0.238000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>    3.000000</td>\n      <td>    0.608000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>    5.000000</td>\n      <td>    1.018000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>   23.000000</td>\n      <td>    6.801000</td>\n    </tr>\n    <tr>\n      <th rowspan=\"8\" valign=\"top\">Netherlands</th>\n      <th>count</th>\n      <td> 2617.000000</td>\n      <td> 1788.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>    3.993122</td>\n      <td>    1.118182</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>    2.167832</td>\n      <td>    0.868283</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>    1.000000</td>\n      <td>    0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>    2.000000</td>\n      <td>    0.597000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>    4.000000</td>\n      <td>    1.019000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>    5.000000</td>\n      <td>    1.451500</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>   21.000000</td>\n      <td>   11.363000</td>\n    </tr>\n    <tr>\n      <th rowspan=\"8\" valign=\"top\">United Kingdom</th>\n      <th>count</th>\n      <td> 5961.000000</td>\n      <td> 3974.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>    4.160208</td>\n      <td>    0.987923</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>    2.282337</td>\n      <td>    0.848802</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>    1.000000</td>\n      <td>    0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>    2.000000</td>\n      <td>    0.502000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>    4.000000</td>\n      <td>    0.876000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>    5.000000</td>\n      <td>    1.255000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>   28.000000</td>\n      <td>   13.535000</td>\n    </tr>\n    <tr>\n      <th rowspan=\"8\" valign=\"top\">United States</th>\n      <th>count</th>\n      <td> 9731.000000</td>\n      <td> 5482.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>    4.533655</td>\n      <td>    1.027386</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>    2.702778</td>\n      <td>    1.313232</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>    1.000000</td>\n      <td>    0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>    3.000000</td>\n      <td>    0.401000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>    4.000000</td>\n      <td>    0.813500</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>    6.000000</td>\n      <td>    1.286000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>   31.000000</td>\n      <td>   41.082000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
       "output_type": "pyout",
       "prompt_number": 204,
       "text": "                      title_wordcnt   2011 SNIP2\nCountry                                         \nGermany        count    2389.000000  1337.000000\n               mean        3.860611     0.692680\n               std         2.423934     0.617181\n               min         1.000000     0.000000\n               25%         2.000000     0.238000\n               50%         3.000000     0.608000\n               75%         5.000000     1.018000\n               max        23.000000     6.801000\nNetherlands    count    2617.000000  1788.000000\n               mean        3.993122     1.118182\n               std         2.167832     0.868283\n               min         1.000000     0.000000\n               25%         2.000000     0.597000\n               50%         4.000000     1.019000\n               75%         5.000000     1.451500\n               max        21.000000    11.363000\nUnited Kingdom count    5961.000000  3974.000000\n               mean        4.160208     0.987923\n               std         2.282337     0.848802\n               min         1.000000     0.000000\n               25%         2.000000     0.502000\n               50%         4.000000     0.876000\n               75%         5.000000     1.255000\n               max        28.000000    13.535000\nUnited States  count    9731.000000  5482.000000\n               mean        4.533655     1.027386\n               std         2.702778     1.313232\n               min         1.000000     0.000000\n               25%         3.000000     0.401000\n               50%         4.000000     0.813500\n               75%         6.000000     1.286000\n               max        31.000000    41.082000"
      }
     ],
     "prompt_number": 204
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}