experiment-datavisualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from ggplot import *\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('bach-chordsequence.txt', 'r') as f:\n",
    "    BACH = [piece.strip('[]\\n').split(', ') for piece in f.readlines()]\n",
    "with open('beethoven-chordsequence.txt', 'r') as f:\n",
    "    BEETHOVEN = [piece.strip('[]\\n').split(', ') for piece in f.readlines()]\n",
    "with open('debussy-chordsequence.txt', 'r') as f:\n",
    "    DEBUSSY = [piece.strip('[]\\n').split(', ') for piece in f.readlines()]\n",
    "with open('scarlatti-chordsequence.txt', 'r') as f:\n",
    "    SCARLATTI = [piece.strip('[]\\n').split(', ') for piece in f.readlines()]\n",
    "with open('victoria-chordsequence.txt', 'r') as f:\n",
    "    VICTORIA = [piece.strip('[]\\n').split(', ') for piece in f.readlines()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4042.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>435.916378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>590.176691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10%</th>\n",
       "      <td>66.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>140.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>264.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>482.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90%</th>\n",
       "      <td>937.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8966.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            length\n",
       "count  4042.000000\n",
       "mean    435.916378\n",
       "std     590.176691\n",
       "min       4.000000\n",
       "10%      66.000000\n",
       "25%     140.000000\n",
       "50%     264.000000\n",
       "75%     482.250000\n",
       "90%     937.800000\n",
       "max    8966.000000"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = ['composer','length']\n",
    "df = pd.DataFrame.from_records([('bach', len(piece)) for piece in BACH] +\n",
    "                               [('beethoven', len(piece)) for piece in BEETHOVEN] +\n",
    "                               [('debussy', len(piece)) for piece in DEBUSSY] +\n",
    "                               [('scarlatti', len(piece)) for piece in SCARLATTI] +\n",
    "                               [('victoria', len(piece)) for piece in VICTORIA], columns = labels)\n",
    "df.describe(percentiles=[.1, .25, .5, .75, .90])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "flatten = lambda l: [item for sublist in l for item in sublist]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count      179.000000\n",
      "mean      4282.296089\n",
      "std       8649.457527\n",
      "min          4.000000\n",
      "25%        365.500000\n",
      "50%       1400.000000\n",
      "75%       4667.500000\n",
      "max      83691.000000\n",
      "Name: 1, dtype: float64\n",
      "count      173.000000\n",
      "mean      3653.150289\n",
      "std       5626.847864\n",
      "min         17.000000\n",
      "25%        820.000000\n",
      "50%       1837.000000\n",
      "75%       4581.000000\n",
      "max      52747.000000\n",
      "Name: 1, dtype: float64\n",
      "count     172.000000\n",
      "mean      478.848837\n",
      "std       532.832803\n",
      "min         5.000000\n",
      "25%       146.750000\n",
      "50%       331.000000\n",
      "75%       569.250000\n",
      "max      3002.000000\n",
      "Name: 1, dtype: float64\n",
      "count     173.000000\n",
      "mean      965.930636\n",
      "std      1406.654668\n",
      "min         1.000000\n",
      "25%       165.000000\n",
      "50%       446.000000\n",
      "75%      1210.000000\n",
      "max      9483.000000\n",
      "Name: 1, dtype: float64\n",
      "count     146.000000\n",
      "mean      780.684932\n",
      "std      1532.897818\n",
      "min         1.000000\n",
      "25%        19.750000\n",
      "50%       191.500000\n",
      "75%       814.000000\n",
      "max      9265.000000\n",
      "Name: 1, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "bach_chords = flatten(BACH)\n",
    "beethoven_chords = flatten(BEETHOVEN)\n",
    "debussy_chords = flatten(DEBUSSY)\n",
    "scarlatti_chords = flatten(SCARLATTI)\n",
    "victoria_chords = flatten(VICTORIA)\n",
    "\n",
    "for cnt in [Counter(bach_chords),Counter(beethoven_chords),Counter(debussy_chords),Counter(scarlatti_chords),Counter(victoria_chords)]:\n",
    "    print(pd.DataFrame.from_dict(cnt.most_common())[1].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('mxl-list.txt', 'r') as f:\n",
    "    files = [line.replace('-', '_').split('_')[0].strip('()') for line in f.readlines()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('bach', 2328),\n",
       " ('mozart', 767),\n",
       " ('haydn', 746),\n",
       " ('beethoven', 674),\n",
       " ('scarlatti', 598),\n",
       " ('handel', 536),\n",
       " ('polish', 394),\n",
       " ('hymns', 362),\n",
       " ('victoria', 333),\n",
       " ('schubert', 289),\n",
       " ('chopin', 277),\n",
       " ('tchaikovsky', 243),\n",
       " ('alkan', 238),\n",
       " ('dandrieu', 211),\n",
       " ('debussy', 199),\n",
       " ('pachelbel', 185),\n",
       " ('liszt', 174),\n",
       " ('brahms', 162),\n",
       " ('couperin', 159),\n",
       " ('canadian', 156),\n",
       " ('dvorak', 149),\n",
       " ('scottish', 140),\n",
       " ('irish', 140),\n",
       " ('lully', 119),\n",
       " ('schumann', 118),\n",
       " ('frescobaldi', 115),\n",
       " ('byrd', 113),\n",
       " ('saint', 105),\n",
       " ('cocquiel', 104),\n",
       " ('shostakovich', 104),\n",
       " ('faure', 100),\n",
       " ('english', 95),\n",
       " ('brade', 95),\n",
       " ('buxtehude', 94),\n",
       " ('schuetz', 92),\n",
       " ('morales', 89),\n",
       " ('scriabin', 87),\n",
       " ('guerrero', 87),\n",
       " ('lasso', 84),\n",
       " ('schein', 83),\n",
       " ('rosenmueller', 79),\n",
       " ('anonymous', 78),\n",
       " ('palestrina', 72),\n",
       " ('holborne', 69),\n",
       " ('mendelssohn', 68),\n",
       " ('anglebert', 67),\n",
       " ('franck', 65),\n",
       " ('soler', 65),\n",
       " ('raff', 63),\n",
       " ('albeniz', 63),\n",
       " ('cima', 62),\n",
       " ('markull', 62),\n",
       " ('froberger', 60),\n",
       " ('telemann', 60),\n",
       " ('godowsky', 58),\n",
       " ('corelli', 55),\n",
       " ('raehs', 55),\n",
       " ('eccard', 53),\n",
       " ('maier', 52),\n",
       " ('albinoni', 49),\n",
       " ('fuellsack', 48),\n",
       " ('various', 48),\n",
       " ('boyvin', 48),\n",
       " ('chaumont', 47),\n",
       " ('fischer', 47),\n",
       " ('satie', 47),\n",
       " ('ravenscroft', 47),\n",
       " ('clementi', 46),\n",
       " ('american', 45),\n",
       " ('grieg', 44),\n",
       " ('downland', 42),\n",
       " ('busoni', 42),\n",
       " ('hammerschmidt', 42),\n",
       " ('krebs', 41),\n",
       " ('welsh', 41),\n",
       " ('lefebure', 41),\n",
       " ('vivaldi', 40),\n",
       " ('gottschalk', 40),\n",
       " ('uccellini', 40),\n",
       " ('ravel', 40),\n",
       " ('stravinsky', 39),\n",
       " ('corrette', 38),\n",
       " ('praetorius', 38),\n",
       " ('joplin', 37),\n",
       " ('gesualdo', 37),\n",
       " ('danzi', 36),\n",
       " ('lemmens', 35),\n",
       " ('becker', 34),\n",
       " ('mahler', 33),\n",
       " ('reger', 33),\n",
       " ('anonimo', 32),\n",
       " ('vecchi', 32),\n",
       " ('merula', 31),\n",
       " ('dornel', 31),\n",
       " ('simpson', 31),\n",
       " ('groh', 30),\n",
       " ('gabrieli', 30),\n",
       " ('pederson', 30),\n",
       " ('hummel', 30),\n",
       " ('bruckner', 30),\n",
       " ('young', 30),\n",
       " ('fuehrer', 29),\n",
       " ('cramer', 29),\n",
       " ('castelnuovo', 29),\n",
       " ('clerambault', 28),\n",
       " ('carols', 27),\n",
       " ('well', 27),\n",
       " ('rameau', 26),\n",
       " ('delibes', 26),\n",
       " ('paganini', 26),\n",
       " ('visee', 26),\n",
       " ('jacquest', 25),\n",
       " ('cazzati', 25),\n",
       " ('pozzoli', 24),\n",
       " ('offenbach', 24),\n",
       " ('donizetti', 24),\n",
       " ('reicha', 24),\n",
       " ('hindemith', 23),\n",
       " ('marcello', 23),\n",
       " ('buehler', 23),\n",
       " ('lecuona', 22),\n",
       " ('henry', 22),\n",
       " ('dowland', 22),\n",
       " ('janacek', 22),\n",
       " ('bizet', 21),\n",
       " ('roman', 21),\n",
       " ('gervaise', 21),\n",
       " ('adson', 21),\n",
       " ('monteverdi', 21),\n",
       " ('mainerio', 21),\n",
       " ('brachrogge', 21),\n",
       " ('lotti', 20),\n",
       " ('altenburg', 20),\n",
       " ('marini', 19),\n",
       " ('cannabich', 19),\n",
       " ('glazunov', 19),\n",
       " ('hassler', 19),\n",
       " ('lemko', 18),\n",
       " ('german', 18),\n",
       " ('morley', 18),\n",
       " ('senfl', 18),\n",
       " ('bartok', 18),\n",
       " ('daquin', 17),\n",
       " ('festa', 17),\n",
       " ('gershwin', 17),\n",
       " ('rubinstein', 17),\n",
       " ('kleber', 17),\n",
       " ('desprez', 17),\n",
       " ('castello', 16),\n",
       " ('balbastre', 16),\n",
       " ('jollage', 16),\n",
       " ('bertini', 16),\n",
       " ('beckwith', 15),\n",
       " ('jacchini', 15),\n",
       " ('carpentier', 15),\n",
       " ('cabanilles', 15),\n",
       " ('abel', 15),\n",
       " ('tchajkowsky', 14),\n",
       " ('mattheson', 14),\n",
       " ('campra', 14),\n",
       " ('mussorgsky', 14),\n",
       " ('walther', 14),\n",
       " ('dupre', 14),\n",
       " ('gade', 13),\n",
       " ('rachmaninov', 13),\n",
       " ('galilei', 13),\n",
       " ('dufay', 13),\n",
       " ('encina', 13),\n",
       " ('karg', 13),\n",
       " ('glinka', 13),\n",
       " ('isaac', 13),\n",
       " ('albrechtsberger', 13),\n",
       " ('wagner', 13),\n",
       " ('sousa', 13),\n",
       " ('strauss', 13),\n",
       " ('onslow', 12),\n",
       " ('respighi', 12),\n",
       " ('horak', 12),\n",
       " ('alexandra', 12),\n",
       " ('hertel', 12),\n",
       " ('chabrier', 12),\n",
       " ('pres', 11),\n",
       " ('poulenc', 11),\n",
       " ('sibelius', 11),\n",
       " ('tartini', 11),\n",
       " ('guilmant', 11),\n",
       " ('vitali', 11),\n",
       " ('brosig', 11),\n",
       " ('vierne', 11),\n",
       " ('cherubini', 10),\n",
       " ('rachmaninoff', 10),\n",
       " ('arcadelt', 10),\n",
       " ('asola', 10),\n",
       " ('weelkes', 10),\n",
       " ('gounod', 10),\n",
       " ('britten', 10),\n",
       " ('dunstable', 10),\n",
       " ('schoenberg', 10),\n",
       " ('laub', 10),\n",
       " ('sanz', 9),\n",
       " ('cambini', 9),\n",
       " ('zipoli', 9),\n",
       " ('messiaen', 9),\n",
       " ('morton', 9),\n",
       " ('ockeghem', 9),\n",
       " ('bustijin', 9),\n",
       " ('aiblinger', 9),\n",
       " ('mussorgskij', 9),\n",
       " ('fuherer', 9),\n",
       " ('agricola', 9),\n",
       " ('verdi', 8),\n",
       " ('spohr', 8),\n",
       " ('susato', 8),\n",
       " ('finger', 8),\n",
       " ('selma', 8),\n",
       " ('lalande', 8),\n",
       " ('boyce', 8),\n",
       " ('bernhard', 8),\n",
       " ('dumage', 8),\n",
       " ('medtner', 8),\n",
       " ('fontana', 8),\n",
       " ('sommer', 8),\n",
       " ('locke', 8),\n",
       " ('rossini', 8),\n",
       " ('widor', 8),\n",
       " ('scheidt', 8),\n",
       " ('russian', 8),\n",
       " ('berlioz', 8),\n",
       " ('falconieri', 7),\n",
       " ('marenzio', 7),\n",
       " ('chelleri', 7),\n",
       " ('arbeau', 7),\n",
       " ('machaut', 7),\n",
       " ('pezel', 7),\n",
       " ('foucquet', 7),\n",
       " ('gallus', 7),\n",
       " ('lemaigre', 7),\n",
       " ('leclair', 7),\n",
       " ('pasquini', 7),\n",
       " ('gigout', 7),\n",
       " ('rimsky', 7),\n",
       " ('soriano', 7),\n",
       " ('dutch', 7),\n",
       " ('josquin', 7),\n",
       " ('boismortier', 7),\n",
       " ('lebegue', 7),\n",
       " ('arriaga', 6),\n",
       " ('grigny', 6),\n",
       " ('drobisch', 6),\n",
       " ('merulo', 6),\n",
       " ('dussek', 6),\n",
       " ('reiche', 6),\n",
       " ('gibbons', 6),\n",
       " ('vazquez', 6),\n",
       " ('penalosa', 6),\n",
       " ('kreisler', 6),\n",
       " ('dieupart', 6),\n",
       " ('stehle', 6),\n",
       " ('puccini', 6),\n",
       " ('barber', 6),\n",
       " ('lechner', 6),\n",
       " ('hainlein', 6),\n",
       " ('reimann', 6),\n",
       " ('dubois', 6),\n",
       " ('kempter', 6),\n",
       " ('porpora', 6),\n",
       " ('garcia', 6),\n",
       " ('grossi', 6),\n",
       " ('viadana', 6),\n",
       " ('chausson', 6),\n",
       " ('fantini', 6),\n",
       " ('landini', 6),\n",
       " ('roberti', 6),\n",
       " ('prokofiev', 6),\n",
       " ('campion', 6),\n",
       " ('leo', 6),\n",
       " ('tallard', 5),\n",
       " ('geminiani', 5),\n",
       " ('forqueray', 5),\n",
       " ('scott', 5),\n",
       " ('mechelaere', 5),\n",
       " ('zangl', 5),\n",
       " ('rue', 5),\n",
       " ('gastoldi', 5),\n",
       " ('galuppi', 5),\n",
       " ('aguilera', 5),\n",
       " ('hofmann', 5),\n",
       " ('moskowsky', 5),\n",
       " ('lindeman', 5),\n",
       " ('miaskowskij', 5),\n",
       " ('flecha', 5),\n",
       " ('lazzari', 5),\n",
       " ('obrecht', 5),\n",
       " ('geoffry', 5),\n",
       " ('shakespearean', 5),\n",
       " ('italian', 5),\n",
       " ('french', 5),\n",
       " ('cavazzoni', 5),\n",
       " ('cabezon', 5),\n",
       " ('escobar', 5),\n",
       " ('skraup', 5),\n",
       " ('tchajkowski', 5),\n",
       " ('shostakovitch', 5),\n",
       " ('schiedermayr', 5),\n",
       " ('grapner', 5),\n",
       " ('ives', 5),\n",
       " ('tchaikowsky', 5),\n",
       " ('moussorgskij', 5),\n",
       " ('biber', 5),\n",
       " ('delaland', 5),\n",
       " ('sor', 5),\n",
       " ('ysaye', 5),\n",
       " ('lehar', 5),\n",
       " ('oltra', 5),\n",
       " ('gumpelzhaimer', 5),\n",
       " ('stolzer', 5),\n",
       " ('gombert', 4),\n",
       " ('redford', 4),\n",
       " ('eybler', 4),\n",
       " ('godard', 4),\n",
       " ('campos', 4),\n",
       " ('roeder', 4),\n",
       " ('arbo', 4),\n",
       " ('franáaix', 4),\n",
       " ('fosse', 4),\n",
       " ('lidner', 4),\n",
       " ('kittel', 4),\n",
       " ('herzogenberg', 4),\n",
       " ('loeillet', 4),\n",
       " ('othmayr', 4),\n",
       " ('goudimel', 4),\n",
       " ('sacchini', 4),\n",
       " ('cornysh', 4),\n",
       " ('schiedermaier', 4),\n",
       " ('muffat', 4),\n",
       " ('bloch', 4),\n",
       " ('borodin', 4),\n",
       " ('tallis', 4),\n",
       " ('hilton', 4),\n",
       " ('bruna', 4),\n",
       " ('bacewitz', 4),\n",
       " ('paderewsky', 4),\n",
       " ('charpentier', 4),\n",
       " ('lupo', 4),\n",
       " ('ghiselin', 4),\n",
       " ('ohnewald', 4),\n",
       " ('goedicke', 4),\n",
       " ('fasch', 4),\n",
       " ('rore', 4),\n",
       " ('guammi', 4),\n",
       " ('croce', 4),\n",
       " ('barsanti', 4),\n",
       " ('waldteufel', 4),\n",
       " ('fux', 3),\n",
       " ('busnois', 3),\n",
       " ('sammartini', 3),\n",
       " ('paradisi', 3),\n",
       " ('kuhlau', 3),\n",
       " ('bernstein', 3),\n",
       " ('poglietti', 3),\n",
       " ('tye', 3),\n",
       " ('madrid', 3),\n",
       " ('vulpius', 3),\n",
       " ('tejeda', 3),\n",
       " ('andreu', 3),\n",
       " ('cornelius', 3),\n",
       " ('hartmann', 3),\n",
       " ('indy', 3),\n",
       " ('happy', 3),\n",
       " ('distler', 3),\n",
       " ('falla', 3),\n",
       " ('est', 3),\n",
       " ('enrique', 3),\n",
       " ('bellasio', 3),\n",
       " ('bononcini', 3),\n",
       " ('ingegneri', 3),\n",
       " ('cato', 3),\n",
       " ('pinto', 3),\n",
       " ('haussmann', 3),\n",
       " ('haendel', 3),\n",
       " ('breton', 3),\n",
       " ('albert', 3),\n",
       " ('herzog', 3),\n",
       " ('bruhns', 3),\n",
       " ('boellmann', 3),\n",
       " ('beatles', 3),\n",
       " ('compere', 3),\n",
       " ('arne', 3),\n",
       " ('hickin', 3),\n",
       " ('casella', 3),\n",
       " ('beck', 3),\n",
       " ('alexandrov', 3),\n",
       " ('friml', 3),\n",
       " ('galynin', 3),\n",
       " ('neukomm', 3),\n",
       " ('purcell', 3),\n",
       " ('kabalevski', 3),\n",
       " ('bull', 3),\n",
       " ('bennet', 3),\n",
       " ('malvezzi', 3),\n",
       " ('kirnberger', 3),\n",
       " ('iglo', 3),\n",
       " ('reinecke', 3),\n",
       " ('sermisy', 3),\n",
       " ('cimarosa', 3),\n",
       " ('seixas', 3),\n",
       " ('lamb', 3),\n",
       " ('forster', 3),\n",
       " ('gheyn', 3),\n",
       " ('suppe', 3),\n",
       " ('piazzola', 3),\n",
       " ('casanovas', 3),\n",
       " ('rossi', 3),\n",
       " ('grecianinov', 3),\n",
       " ('emmerig', 3),\n",
       " ('prieto', 3),\n",
       " ('leybach', 3),\n",
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      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(files).most_common()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Below is just poking around the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plot = ggplot(aes(x='length', color='composer'), data=df) + geom_density() + ylab('density')\n",
    "plot.save('density.png', width=8, height=6, dpi=180)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plot = ggplot(aes(x='length', fill='composer', color='composer'), data=df) + geom_histogram(binwidth=100, alpha=0.5) + \\\n",
    "ylab('# pieces')\n",
    "plot.save('histogram.png', width=6, height=6, dpi=180)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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yyy9r6dKlWrhwYUwC293dLcMwtGDBAqvM6/WquLhY7e3tWr58uTo7OxWJRGLq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\nv3+/KioqlJGRMdpDGZbxfO5hRIBNS0uTy+UakPI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wRY/saWtrkyR1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\nP6kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12d15cf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223372036542203041)>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(df, aes(x='length')) + \\\n",
    "    geom_histogram(binwidth=100, alpha=0.6) + \\\n",
    "    facet_wrap(\"composer\", scales=\"free\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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