MusicClustering-kmeans.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "from sklearn import preprocessing\n",
    "from sklearn import cluster\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "import IPython.display as ipd\n",
    "\n",
    "# Setup Seaborn\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_context(\"poster\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "musicdf = pd.read_csv('./musicdf30.csv', index_col=0, encoding='latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['composer', 'description', 'symphony', 'file', 'part', 'tempo', 'bpm',\n",
       "       'duration', 'zero_crossings', 'spectral_centroid_avg',\n",
       "       'spectral_bw_avg', 'Chroma_A', 'Chroma_A#', 'Chroma_B', 'Chroma_C',\n",
       "       'Chroma_C#', 'Chroma_D', 'Chroma_D#', 'Chroma_E', 'Chroma_F',\n",
       "       'Chroma_F#', 'Chroma_G', 'Chroma_G#', 'MFCC_0', 'MFCC_1', 'MFCC_10',\n",
       "       'MFCC_11', 'MFCC_2', 'MFCC_3', 'MFCC_4', 'MFCC_5', 'MFCC_6', 'MFCC_7',\n",
       "       'MFCC_8', 'MFCC_9', 'MFCC_SD_0', 'MFCC_SD_1', 'MFCC_SD_10',\n",
       "       'MFCC_SD_11', 'MFCC_SD_2', 'MFCC_SD_3', 'MFCC_SD_4', 'MFCC_SD_5',\n",
       "       'MFCC_SD_6', 'MFCC_SD_7', 'MFCC_SD_8', 'MFCC_SD_9', 'Chroma_SD_A',\n",
       "       'Chroma_SD_A#', 'Chroma_SD_B', 'Chroma_SD_C', 'Chroma_SD_C#',\n",
       "       'Chroma_SD_D', 'Chroma_SD_D#', 'Chroma_SD_E', 'Chroma_SD_F',\n",
       "       'Chroma_SD_F#', 'Chroma_SD_G', 'Chroma_SD_G#'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "musicdf.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Not using the Tempo labels. I am not sure if it adds any value\n",
    "#musicdf = pd.concat([musicdf, pd.get_dummies(musicdf['tempo'],  drop_first=True)], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Subset of columns that would be used for clustering\n",
    "feature_cols = musicdf.columns.drop(['composer', 'description', 'symphony', 'tempo', 'file', 'part', 'duration']\n",
    "#                                    + ['zero_crossings']\n",
    "+ ['MFCC_0', 'MFCC_1', 'MFCC_10', 'MFCC_11', 'MFCC_2', 'MFCC_3', 'MFCC_4',\n",
    "       'MFCC_5', 'MFCC_6', 'MFCC_7', 'MFCC_8', 'MFCC_9', 'MFCC_SD_0',\n",
    "       'MFCC_SD_1', 'MFCC_SD_10', 'MFCC_SD_11', 'MFCC_SD_2', 'MFCC_SD_3',\n",
    "       'MFCC_SD_4', 'MFCC_SD_5', 'MFCC_SD_6', 'MFCC_SD_7', 'MFCC_SD_8',\n",
    "       'MFCC_SD_9']);\n",
    "#+ ['Chroma_A', 'Chroma_A#', 'Chroma_B', 'Chroma_C', 'Chroma_C#',\n",
    "#       'Chroma_D', 'Chroma_D#', 'Chroma_E', 'Chroma_F', 'Chroma_F#',\n",
    "#       'Chroma_G', 'Chroma_G#', 'Chroma_SD_A', 'Chroma_SD_A#', 'Chroma_SD_B',\n",
    "#       'Chroma_SD_C', 'Chroma_SD_C#', 'Chroma_SD_D', 'Chroma_SD_D#',\n",
    "#       'Chroma_SD_E', 'Chroma_SD_F', 'Chroma_SD_F#', 'Chroma_SD_G',\n",
    "#       'Chroma_SD_G#'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['bpm', 'zero_crossings', 'spectral_centroid_avg', 'spectral_bw_avg',\n",
       "       'Chroma_A', 'Chroma_A#', 'Chroma_B', 'Chroma_C', 'Chroma_C#',\n",
       "       'Chroma_D', 'Chroma_D#', 'Chroma_E', 'Chroma_F', 'Chroma_F#',\n",
       "       'Chroma_G', 'Chroma_G#', 'Chroma_SD_A', 'Chroma_SD_A#', 'Chroma_SD_B',\n",
       "       'Chroma_SD_C', 'Chroma_SD_C#', 'Chroma_SD_D', 'Chroma_SD_D#',\n",
       "       'Chroma_SD_E', 'Chroma_SD_F', 'Chroma_SD_F#', 'Chroma_SD_G',\n",
       "       'Chroma_SD_G#'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61, 59)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "musicdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = musicdf[feature_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61, 28)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## https://stackoverflow.com/questions/20625582/how-to-deal-with-settingwithcopywarning-in-pandas\n",
    "df.is_copy = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Scalng for k-nearest neighbours\n",
    "## Scale the features (using the scale function) from -1 to 1.\n",
    "#min_max_scaler = preprocessing.MinMaxScaler(feature_range=(-1, 1))\n",
    "#df[feature_cols] = min_max_scaler.fit_transform(df)\n",
    "#http://sebastianraschka.com/Articles/2014_about_feature_scaling.html\n",
    "df[feature_cols] = preprocessing.StandardScaler().fit_transform(df[feature_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bpm</th>\n",
       "      <th>zero_crossings</th>\n",
       "      <th>spectral_centroid_avg</th>\n",
       "      <th>spectral_bw_avg</th>\n",
       "      <th>Chroma_A</th>\n",
       "      <th>Chroma_A#</th>\n",
       "      <th>Chroma_B</th>\n",
       "      <th>Chroma_C</th>\n",
       "      <th>Chroma_C#</th>\n",
       "      <th>Chroma_D</th>\n",
       "      <th>...</th>\n",
       "      <th>Chroma_SD_B</th>\n",
       "      <th>Chroma_SD_C</th>\n",
       "      <th>Chroma_SD_C#</th>\n",
       "      <th>Chroma_SD_D</th>\n",
       "      <th>Chroma_SD_D#</th>\n",
       "      <th>Chroma_SD_E</th>\n",
       "      <th>Chroma_SD_F</th>\n",
       "      <th>Chroma_SD_F#</th>\n",
       "      <th>Chroma_SD_G</th>\n",
       "      <th>Chroma_SD_G#</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.293897</td>\n",
       "      <td>-1.180141</td>\n",
       "      <td>0.068880</td>\n",
       "      <td>0.887257</td>\n",
       "      <td>1.341396</td>\n",
       "      <td>0.442128</td>\n",
       "      <td>-1.103446</td>\n",
       "      <td>-0.983774</td>\n",
       "      <td>0.158718</td>\n",
       "      <td>-0.596200</td>\n",
       "      <td>...</td>\n",
       "      <td>1.262277</td>\n",
       "      <td>1.743599</td>\n",
       "      <td>1.776464</td>\n",
       "      <td>2.120272</td>\n",
       "      <td>0.210404</td>\n",
       "      <td>1.876112</td>\n",
       "      <td>0.571282</td>\n",
       "      <td>-0.877557</td>\n",
       "      <td>-2.074283</td>\n",
       "      <td>1.042359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.293897</td>\n",
       "      <td>-1.118018</td>\n",
       "      <td>-0.873084</td>\n",
       "      <td>-0.008647</td>\n",
       "      <td>0.751337</td>\n",
       "      <td>0.204818</td>\n",
       "      <td>-0.810637</td>\n",
       "      <td>-1.188572</td>\n",
       "      <td>1.149090</td>\n",
       "      <td>1.563927</td>\n",
       "      <td>...</td>\n",
       "      <td>1.993880</td>\n",
       "      <td>0.259685</td>\n",
       "      <td>0.330945</td>\n",
       "      <td>1.470322</td>\n",
       "      <td>-0.241651</td>\n",
       "      <td>0.740272</td>\n",
       "      <td>0.271956</td>\n",
       "      <td>1.332379</td>\n",
       "      <td>-0.470211</td>\n",
       "      <td>1.151025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.416703</td>\n",
       "      <td>-0.581697</td>\n",
       "      <td>1.011198</td>\n",
       "      <td>2.038851</td>\n",
       "      <td>-1.312636</td>\n",
       "      <td>-0.064678</td>\n",
       "      <td>1.843617</td>\n",
       "      <td>0.643285</td>\n",
       "      <td>-1.005027</td>\n",
       "      <td>-0.862369</td>\n",
       "      <td>...</td>\n",
       "      <td>1.062708</td>\n",
       "      <td>-0.283381</td>\n",
       "      <td>-0.492948</td>\n",
       "      <td>0.347979</td>\n",
       "      <td>0.400544</td>\n",
       "      <td>0.282459</td>\n",
       "      <td>-0.823221</td>\n",
       "      <td>0.294371</td>\n",
       "      <td>-0.831604</td>\n",
       "      <td>-0.523833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.646297</td>\n",
       "      <td>-0.639386</td>\n",
       "      <td>0.181621</td>\n",
       "      <td>0.066358</td>\n",
       "      <td>-0.455925</td>\n",
       "      <td>0.018834</td>\n",
       "      <td>0.175453</td>\n",
       "      <td>-0.213685</td>\n",
       "      <td>1.089417</td>\n",
       "      <td>2.014804</td>\n",
       "      <td>...</td>\n",
       "      <td>0.953879</td>\n",
       "      <td>1.150526</td>\n",
       "      <td>0.177549</td>\n",
       "      <td>1.287415</td>\n",
       "      <td>0.415480</td>\n",
       "      <td>-0.294859</td>\n",
       "      <td>-0.354581</td>\n",
       "      <td>0.610085</td>\n",
       "      <td>0.598412</td>\n",
       "      <td>0.137836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.293897</td>\n",
       "      <td>-1.073563</td>\n",
       "      <td>-0.538791</td>\n",
       "      <td>-0.469352</td>\n",
       "      <td>1.014053</td>\n",
       "      <td>2.376781</td>\n",
       "      <td>0.895647</td>\n",
       "      <td>-2.596445</td>\n",
       "      <td>-1.213412</td>\n",
       "      <td>1.262394</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.645527</td>\n",
       "      <td>-1.520113</td>\n",
       "      <td>-0.986839</td>\n",
       "      <td>-1.357758</td>\n",
       "      <td>-2.036364</td>\n",
       "      <td>-1.223419</td>\n",
       "      <td>-0.470787</td>\n",
       "      <td>-0.304585</td>\n",
       "      <td>0.585820</td>\n",
       "      <td>-0.239144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        bpm  zero_crossings  spectral_centroid_avg  spectral_bw_avg  Chroma_A  \\\n",
       "0 -0.293897       -1.180141               0.068880         0.887257  1.341396   \n",
       "1 -0.293897       -1.118018              -0.873084        -0.008647  0.751337   \n",
       "2 -0.416703       -0.581697               1.011198         2.038851 -1.312636   \n",
       "3 -0.646297       -0.639386               0.181621         0.066358 -0.455925   \n",
       "4 -0.293897       -1.073563              -0.538791        -0.469352  1.014053   \n",
       "\n",
       "   Chroma_A#  Chroma_B  Chroma_C  Chroma_C#  Chroma_D      ...       \\\n",
       "0   0.442128 -1.103446 -0.983774   0.158718 -0.596200      ...        \n",
       "1   0.204818 -0.810637 -1.188572   1.149090  1.563927      ...        \n",
       "2  -0.064678  1.843617  0.643285  -1.005027 -0.862369      ...        \n",
       "3   0.018834  0.175453 -0.213685   1.089417  2.014804      ...        \n",
       "4   2.376781  0.895647 -2.596445  -1.213412  1.262394      ...        \n",
       "\n",
       "   Chroma_SD_B  Chroma_SD_C  Chroma_SD_C#  Chroma_SD_D  Chroma_SD_D#  \\\n",
       "0     1.262277     1.743599      1.776464     2.120272      0.210404   \n",
       "1     1.993880     0.259685      0.330945     1.470322     -0.241651   \n",
       "2     1.062708    -0.283381     -0.492948     0.347979      0.400544   \n",
       "3     0.953879     1.150526      0.177549     1.287415      0.415480   \n",
       "4    -0.645527    -1.520113     -0.986839    -1.357758     -2.036364   \n",
       "\n",
       "   Chroma_SD_E  Chroma_SD_F  Chroma_SD_F#  Chroma_SD_G  Chroma_SD_G#  \n",
       "0     1.876112     0.571282     -0.877557    -2.074283      1.042359  \n",
       "1     0.740272     0.271956      1.332379    -0.470211      1.151025  \n",
       "2     0.282459    -0.823221      0.294371    -0.831604     -0.523833  \n",
       "3    -0.294859    -0.354581      0.610085     0.598412      0.137836  \n",
       "4    -1.223419    -0.470787     -0.304585     0.585820     -0.239144  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "X = pca.fit_transform(df)[:,0]\n",
    "Y = pca.fit_transform(df)[:,1]\n",
    "#Z = pca.fit_transform(df)[:,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['x'] = X\n",
    "df['y'] = Y\n",
    "#df['z'] = Z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Elbow curve to determine the right number of clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2445243ed30>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iIiKCkSNHEhoamuEfwkVEJPfTmg4REREREXEohQ4REREREXGoXBM6IiMjb3qm9v/+7//w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\n8wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x244520802e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k_range = range(2,11)\n",
    "k_means_var = [sklearn.cluster.KMeans(n_clusters=k, random_state=10).fit(df[feature_cols]) for k in k_range]\n",
    "centroids = [X.cluster_centers_ for X in k_means_var]\n",
    "from scipy.spatial import distance\n",
    "k_euclid = [distance.cdist(df[feature_cols], cent, 'euclidean') for cent in centroids]\n",
    "dist = [np.min(ke, axis=1) for ke in k_euclid]\n",
    "wcss = [sum(d**2) for d in dist]\n",
    "# elbow curve\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(k_range, wcss, 'b*-')\n",
    "#ax.set_ylim((0,100))\n",
    "plt.grid(True)\n",
    "plt.xlabel('n_clusters')\n",
    "plt.ylabel('Sum of squares')\n",
    "plt.title('Sum of squares vs. k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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URNNevRgzbx7+gYFeqlBERORfCllSbfgHBXHl3LnsXryYv15+mdSDB6GwkLCoKHpcfz1d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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2445252f358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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xEoNwqnNWRESChEKWNCkOHCTQmwR6B7oUERGROulrv4iIiIgNFLJEREREbKCQJSIiImID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\nXyqyvMA0zYP88eZxhbw38l3Aw0Ba3j13v7xzdsBhmqbeXNyguNfiUnm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      "text/plain": [
       "<matplotlib.figure.Figure at 0x244539eefd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SERERsYVCloiIiIgNFLJEREREbKCQJSIiImIDhSwRERERG9hygWgRaXy2VcDb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\nImG6554POfnkJ5k2bSmLFq1nyZJNzJv3H8aNe5s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99gwrVhx424+MDA/Dh/emT59eUakx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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24453ab0128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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A06Zplt0ld75hGInAnShkiYiISCPjrzFZTYH/ATMrLDeBRMMwYvx0HBEREZF6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\n9u7NqQ8/XO1pwDaDB3PhBx/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//xPZ2YU1tktLy+HRR78LQEUitaPThSJSZ1m7d5OenIzT\n7aZV//6and/PzmIcmWSwmU2VnjZ046YXvRnFKYEvLgB27cryue22berJktCjkCUitZb63XcseuIJ\n9q9ZQ/a+feBw0CwpidYDBzLm6adp3rVrsEtsEFy4uJwrWcA8Ukghg3SKKCKccBJJpBd9GMmoBtmL\nBVR7mrCi0tJaNPZ1n5SynnVsYQvgIYlO9KM/Llw1bisCClkiUktrp09n7l13kb1nz28LPR4OpaZy\nKDWVtLVrufjjj2ndv3/wimxAnDg5g7GcxhnsJ41ccokllkRaNthwVaZFiyif2yYmxvj12L+wlJ/5\nmXQO/DoH2SpW8gOLGcggRnCyX48nDZPGZImIz7L37WP+X/5ydMCq4ODmzXx29dWUlmhyTH9y4qQ1\nbehCV1rSqsEHLICrr+5PWFjNH1NxcRHcdddwvx33BxYxl69JY99Rk7yWUsp+0ljIAhYw32/Hk4ZL\nIUtEfPb9449zePv2GtulJyez/sMPA1CRNGSXXtqXQYPa1Nhu+PD2DB3a3i/HzCGHH/mRfPKrbFNA\nActYetQNu0Uqo5AlIj7b9dNPPrUrKSxk7fTpNlcjDZ3b7WTOnEsZNqwdbvexPXfh4S5OO60TH310\nsd+O+T0LyeJwje1yyGYh3/rtuNIwaUyWiPisONf3WcWLatFWpCqJiTEsWnQNb7+9mvfeW8OBA9b7\nqk2bWG64YQjnn9/Lr7O976HqU+EV7We/344rDVOjClkZmzez6NFH2bdqFUV5eYRFRdGyb19GPfgg\nCYYR7PJEQp4rIsLntu5atBWpTliYiylTBjFlyiDbj1VKaS3aBmfcYX5+Mf/970pWrNhHWJiToUNj\nGDq0ZVBqkeo1mpD104sv8sO0adbl5uWkrVnD1nnzOOnOOznpzjuDVF3D4PF42DJ3Lkv/8Q8O79yJ\np7SUqPh4el1wAUNuvBF3ZGSwS5Tj1Lp/f/atXFljO4fLRfcJEwJQkYh/ReD7l4PwWrT1B4/Hw/33\nL+DTT5NJScmg1JsH33rLTdeuTfj738MYN657QGuS6jWKkLVh5kwWPfYYuenpla7PSUtj8bRpNG3f\nnhMuuSTA1TUMJUVFfHTRRWz95ptjThPtWLKEVW+/zaSZM4nv3DlIFYo/jHzgATZ99RU5Fb6sVNSi\nRw8GTZkSoKpE/Kcv/djG1qOuKqxKT3oFoKLfTJ06h/feW0t+fvFRy/Pyilm37iBTpszmlVcm8Pvf\n9wxoXVK1RjHw/ecXXqgyYJXJy8hg6UsvBaiihmfW5MmYs2dXPg6ntJS0VauYcd55FObkBL448Zvm\nXbsy/E9/IrJ58yrbNG3fnrHPPqvThVIv9aM/iSTW2C6RlpzI0ABUZJk3bysffrj+mIBV3p492dx/\n/wKKijR9Sqho8CHrwMaN7F+3zqe2+9evZ++qVTZX1PAc3rGDrfPn1zg9c9rq1fz8j38EqCqxy4i7\n7+as55+n3bBhhMfG/ro8OiGBpFNO4fzp0+k+blwQKxSpOxcuLmISCdUErRa04AIuJIww2+vJySnk\nyScXc+WVn3LkSM33cTTNDN59d43tdYlvGvzpwn0rV5J/0Le5TAoOH2bv8uW0GTDA5qoalkVPPEFO\nWppPbVPmzGHkfffZXJHYrf9VV9HvyivZuWQJO5cswRUWRtczzySxV2BPn4jYIZGWXMsU5vMNO9hO\nNjmAhxhiaE8HTmcMccTZWoPH4+Fvf1vI9Onr2Lw50+ftiotLmTMnhWuuGWhjdeKrBh+yanM1FKDB\n2eWUFBZSWlKCOzISh6PqS6Szdu3yeZ81nbaV+sPhcNBxxAg6jhgR7FJE/C6WWH7PeZRQQhZZgIcm\nNMUdoI/Ne++dx8svLyM3t6jW2xYV+X6FpNirwYespJNPpmmHDmTt3Flj2yZt29L51FMDUFXoKi4o\nYOlLL5E8c6YVnjweIuPjSRo9mtEPPkhMy2MvE3Y4fT/rXF1YE7FDKaWsZQ2bSMGDh5a0ZBjDiURf\nqKRmLlzEEx/QY+7alcX//remTgELrNsMSWho8CErpmVLWvXr51PIatmvH03atg1AVaGpICuL98aP\nZ+eSJUeNr8ratYv9a9eyde5czn/vPdoOGXLUdi1POIGUOXN8OkZjfn4l8FaygiUsJp30o64WW8EK\netCDcUzA2fCHpko98+ij37FvX3adtm3WLILbb/+dnyuSumoUf13GPvss8V27VtumWefOjH3mmQBV\nFJo+vOgidv7wQ5UD2DNSUvj0yivJP3ToqOUn3XUXcUlJNe7fGRZG/6uv9kutIjVZzjK+5kvSSDvm\ncvyDZPILy5jJx3io/oINkUBLTq77sIqBA9swZIi+zIaKRhGyEgyDiz76iFYDBhwzRssVHk6r/v25\ncMYMWvbpE6QKg2/vypXsWbasxnbpGzey5Nlnj1oWFR9P38suIyw6utptO44YQb8rrjiuOkV8UUgh\ni++1mfAAACAASURBVFhELlXf2qeEEjaSzBa2BLAykZqVlNRtTNXgwW2YMeNCP1cjx6PBny4s02bg\nQG5Yvpzkzz5jzTvvUJyfjzsighMuu4zeF1yA0+UKdolB9eOzz/p8FeaWuXM57dFHj1p22uOPU1pS\nwtrp0zlSYSB8ZLNmtB8+nIs+/BCnu9G85SSIlvITmWTU2K6QQn7iB7rRLQBVifimWTPfxwu63Q66\ndGnKsGEtefnlC2jaVOOxQkmj+sRzOJ30Pv98ep9/frBLCTl5GTV/IJUpqHC6EKwB7WOeeoqT/vxn\nFj35JAc2bMBTUkJ0YiIj7rqLNoPsv+eYSJlUUn1um4lvXy5EAuW66wayYME28vOrn1TU7Xbw+OOn\nM358M1wupwJWCGpUIUuq5qhFT151bWNatuSs55/3R0kidVabm/x6/r+9O4+Pqr73P/6aZLIvLCGE\nEHaRA8gqEBDZRBErYimoqNQqrWul11pLfdR6qdaf1dtWa9tbfy51bd3FHcUqCgKCQCBgIRwiyBYg\nJCxZCGSZmfvHCUhClkkyZ9b300cekTPfzHzIkMx7vmsL2or4w4wZAxgypAtr1hQ02W7w4AzuvHMM\n33yzzU+VSUtFxJwsaV63Md6vRmnfq5d9hYj4QAIJXrdtyYHAgeZyuTl2rApPM6crSGiLinLw9ttX\nMXx4l0bbDB7cmYULryImJrKnugQ79WQJAGN+/nNyn3+eI9ubngTsTEwk+2c/81NVIq0zlvPZhkkl\nlc227UPTK48DzePx8Mor/+GZZzawY8cRampcJCfHMWJEJgsWTKRfv7RAlyg26No1lS++mMuf/vQl\nixZto7DQOvc1PT2RqVP7cvfd59OunfZ6C3YKWQJAbHIy2T/7Gcvuv7/RCfCO6Gj6TZtG30su8XN1\nIi2TRTeyyGIHO5ps15E0xjHBT1W1nMvlZvbsN/ngg21UVp4+P6eMrVuLWbp0J//zPxcxZ86QgNUo\n9klOjuW++yZx332TqKysweOB+Hi9bIcSDRfKKWPuuIMLfvc7Og0YcMa8q9QePRg+dy6zXnlFu7ZL\nSJjNtWTRrdHbO9CB7zODRJreeiSQ7rhjMe++u7VewPpOQUEZd9/9Kbm5B/xcmfhbXJxTASsE6RmT\nOrLnzePcm25i/T/+we4vvsDjdtPhrLMYO38+iWkalpDQkUACc/kJK1mOiUkZpXjwkEAC3enBJCbT\nnvaBLrNR5eVVLF78DTU1Tc+/Kigo44EHlrFw4Ww/VSYi3lLIkjM44+LIvv12sm+/PdCliLRJLLFc\nwIVMYjInOIEbN/HEE03wTxZ+/PG1bN/u3fYSubkHqKioJjExxuaqRKQlNFwoImHPgYMEEkgiKSQC\nFkBeXpHXbY8eraSwsHVn3YmIfRSyRESCUEuW5judUcTFaWBCJNgoZImIBKFrrx1McnKsV227d08l\nMzPZ5opEpKX01kckSHjwsJNv+ZKVlFBSO0k7nnMYzAhG4tSPK27cbMPkKEdIIpmz6Ucs3gWRUDNx\nYk8GDOjE2rX7mm37ve/11apfkSCk39oiQcCFizd5nXy2UUVVndt2sYsc1nE119KRjgGqMLDcuNnQ\nOYf9Sfsoo+zUsTlppNGDnkxjetiFLYfDwe9/fyE33PAOBQVljbYbO7Yb99wz3o+ViYi3NFwoEgTe\n5R22sPmMgAVWD9cB9vMKLzV4e7hz4+Zl/sXWjnmUxJfUOZfwEIfYwHpe4Lmw/N5cdFEfnnvu+wwd\nmkF8fN05Wp07J3LZZf1YvPiHJCRoVaFIMFJPlkiAlVLKdvLx0PR+SIUcYBUrmcgFfqosOCzlM74h\nH5oYDdvDbt7jHa7gKv8V5idTppzF+vV9eOedPN54YwvV1W7S0xOZP38sffpEZs+mSKhQyBIJsOUs\no4zGh4NOZ2JGVMjy4MHErNN71Zg97KaSypA68NlbUVEOZs4cyMyZAxu8vcYNUQ7rQ0SCh0KWSIAd\n5ajXbY9xzMZKgk8hByjGu/2ijnCEr9nESEbZXFVgHDwGD6+Ab4+CwwEDOkF5FSzfDYcqrIDVNQWu\nHAg/HQWxobEdmEhYU8gSCbCWdD44WtTat1y4qKSSWGL9ttLxGMeoptrr9t72CIYSlxtuXQSLt8Pe\n0u+uv91A250lsHovLMyDRddCavh16omEFIUskQDLoAtb2epV22T8vxfSbnaznGUc5CDVVBNNNB3p\nyCiyOYdBtga/JJKJJdbrSe2ppNpWSyB4PDDnbVi4BZo5wvAUN7BiD1z5Bnz8Q1vLE/GJsrJKHnts\nNR999A0lJZU4nQ769OnAL385lvPP7xHo8tpEIUskwMYyjo1s5ChNn1MXRRRDGeqnqizLWcZKVlBB\nRZ3rJRxlj2cPn5eadNozi4GdHAzJsIaxfCmDDDrRiX00v1dURzoyiMG+LSDAvtgNH+Z7H7BOt6YA\ncvbDiEzf1yXiKzk5+7juurfJyyuuc33TpoN89tlOZs7szzPPfJ+oEJ1wqC0cRFqgdO9ePrj1Vp4d\nN46nR4/mxYsuIuepp3BVez+kVV8CCQxhCDE0vQy/G905l5GtfpyWymMLKxoIWCfVOKrZl/g1Txxa\nwvjnYeyz8FSOb2tw4KA/A706b7AHvcJu0vujq6CslTtTHK2EP33p23pEfKmwsJxrr33rjIB1Umlp\nJS+//B/uuutjP1fmOwpZIl5aev/9PJ2dTc6TT7Jn5Ur2rVnDt0uWsOj223l65EiK8vJafd8XMoVs\nRpPSwHBXHHH04Sx+yI/8erjxalZxvJGAdVJMTA39+2+hvNrF6gKY/ync/alv65jARAz643A3/k62\nBz2ZzuW+feAgsKukbV9/6Lhv6hCxw/33L2PbtkNNtqmqcvHeeyalpZV+qsq3FLJEvPDlI4+w+tFH\nKd+//4zbPDU1FG7axOszZ3Ls4MFW3b8DB1P5HrfyU7IZQx/60IveDOQcruN6buDHxBPf1r+G10o4\nykEKvWqbllZE//5bACithCdz4OPtvqsliiiu4moGHhpEh+MdTgVNBw7SSWcU2VzP3GZ7AkORq/md\nK5rk1G94CVJut4fly3d71XbHjqM89thqmyuyh+ZkiTTDVV1N7rPPUlla2mS74q1b+XzBAi574olW\nP1YKKVzG9FZ/va8cpaTRYcL6nE43GRkHyMuz5kOVVMJfvoKpZ/muniiiGFI8lMHFQ0gakEgJJSSR\nRE96hfWZjh3amKuHd/FNHSK+VlpayeHD3ne15uV5t5VLsAnf304iDcg/BGv3gcNdzcrn7uHwwQKq\nqqq47bbbGDZsGPfeey+lpaW4XC5uvvlmMjMz2fjiixRv2+bV/e9esQJ3TQ1RztD+0YrBSTTR1FDj\nVfuq6rq9SHlFUFkDcT7+Njhw0JNevr3TIDajv7VSsBXz3unVDuaP9XlJIj4RHe1o0UKZqKjQ7JYN\n7VcCES99mA9//BK+PmjNU0nd/h4dy9tjzPwj9448yr23zGDMmDFMnz6dSy+9lNWrV5Ofn09mZia7\nly/HU+Nd2Dh28CDHDh4kpWtXm/9G9upMBu3p4NVGoOVlyWz+z5A6147XWEOH6foN0ya3jYQXNsKm\nFo5Cp8bBTedCe/+NMIu0SHJyLJmZKU0efn6S0+lgwoTQ3MohNKOhSAs8lws/fg+W7vpuInBZj0vY\nNfAOPt4O173jodoTzfr16yksLOSGG27g/fffZ9CgQYEtPICcOOlJT6/aHizKoKSk7hl68U5ICa+F\nfgGREANvXAmDOnvX3gH06wj3jIN7xttamkibOBwOpk0726u2/fp1Yu7c4TZXZA+FLAlrB8rhvqVQ\nWO80Gk9MEp6YZBzV5dQs+i8OD/05BQUFpKam8vzzz5OZmclbb70FQM8JE3B4OfyX1LkzSZ29fEUM\nclP5Hpk0vcnS4cMd+eijy864bqRZQasx1S54YzM88AX8bQ0Ulre12vDVLw1W3AD3joeRmdCzHfRu\nD2O7wQOT4I5smNkfZg2AP1wEG26Bu88PdNUizfvVr85n1Kime/3bt4/ntttGEhui50SpM1/C2oPL\nYXcj89Wdx/bT9YvbOdrvWg6kT2dQ0sNMnjwZgMmTJ/PAAw8AMOS661j16KMUbd7c7OP1GDcu5Odj\nnRRPPNfzY97gNfazn4rTzk08cSKW4uLOfPD+Dzh8qG6oTImF2xs5PtDtgV8vgUX5sLUYXLWTjf7w\nJQzLgCenQdfw2rTdJ9rFwwMXWB+u2sOgfb3xq4i/JSbG8NFHc5g163XWr99PWb1N4Xr3bs+8ednM\nm5cdoArbLjxeDUQasaag4evRx4vJ+uzHHBy1gONdzgMPOLqOYNmyZcyYMYO1a9fSo4c1ByA6Jobh\nc+ey7He/a3KFYaf+/bmgNpiFi0QSuZ65FFPMl6zgUNUJPt/h5PNVI9m7p9cZ7ZNj4fqhcLlx5n15\nPHD1Qnh7K9TU25pgb6n1MeUl+PccyFLQalS0xh8kjKSlJfL559ezfPlu/vrXrzh69AQxMVGMGpXF\nXXedR7t2oT2xUCFLwtrxRuard9z8BNFVpaR9/Th8/TgASVc+zLvv3surr75KcnIyt9xyy6n25911\nF1Xl5ax78skz9spyOJ10Puccrnj1VZLS0237uwRSJzpxOTMgFi7uCvMSIbrdd5tlJjhhYDrMGQx3\njmn4Pv6+Ft41zwxYp9tSBDe8C59c5/u/g7SOy61gJ/ZyOBxMmNCTCRO8mwcaShSyJKwlNPIvvGjk\nvRSNvLfOtUFp8Nxzz536c169Hdwn/va3DL/xRpY/+CCFmzbhrqkhNjmZQVdfzdDrryc6Jjg3wyyu\ngIdXWtsquD2QlQK/HgdndWz+axuSlQpvz4ZDFbB4u7WK8Jx0GN+j6SGsV/4DVa7m739jIWw/3Pr6\n2qLaBc9vtGo9fNwaluueCr84DyY08/cLJ2sKrH8zm4vgRLU1AX9IhvXvRntviXhPIUvC2siusKb5\ns4WJdjQ8xFVfalYW0x5/vO2F+YHHA7/6FF7bDHvqjXK+vw0u6A0vzoDWzidNS7R6rrxxoBy2N33+\n9SlFFfD4Onjk4tbV1VoFpfD912DjgboHMm84AJ/vhCl94NVZEBOa82+9tuBz+Ps6K2SezjxkfR9+\nMQZmpAWkNJGQo05gCWu/GQ/dUppv1y8NfhKaK4QbNf8Ta4iufsACOFgBr2+GK96wwpjdSk7ACe+2\nGgPgyHHw4GEn3/Ie7/AWC8nruJlqR+sP4m7K8Wq4/FXI2V83YJ1UVgXvbLW2AglnT6+Hv645M2Cd\nVFxhHTq9aJcXP1QiopAl4a1rCiyYCJ2TGm/TIxX+93ut79EJRgfK4bUtjc9JA2sX8SXf+vacwcZ0\nSoSkWO/bJ6Qe5ime4F+8yDrWkst6cjtvYHHvD/mA93DTxkP96vn/66weq6a4gU93wJ42HtocrDwe\n+Md661ikphw+Aa/s6OCfokRCnIYLJezddC5kJMEjq6wd34+csK53SbLmmTw42RpW9BcXLjaxkVw2\nUI6123EiSQxhKMM51ydn8T20wlqt15yKavjrV3BJ3zY/ZJPSEqF/GuxrfnNnuqRW02nUSxRQL/U4\noDyujHWspZxyZnMNDnwzSWrhVu+OrjlwDP7fcnjyzK3BQt5XBbCl2Lu2+SVxfFMSywB7SxIJeQpZ\nEhEuN6yPLUXWpN6YKBjfE3q0828dJzjBS/yTvezBxemzwIvYw242kMMcfkQSTXS9eSHPyxdLAC9O\ntfCJ20bCuv3WRPmmdMnahSe58W4lN25MtrKJjQxlmE9qK/buLGzAu/AainIPQHlV8+0Ayqqd5Jdo\nS3+R5mi4UCLKwHS4YRjMGeL/gOXBwyu8xC521gtYFjdu9rKXl/lXm4fD3C2YZ+Xyw5wsgCsGwo3D\nrc1KGzM0q4JLL3+t2fty4WI9OT6rrSX9YeG6wjCxhYtj451++ocjEsLUkxUiPG432z74gJynnqKy\ntJSo6Gg6Dx7M+N/8huSMjECXJ174lh0UsLfZdvsoYAubGYSXS/ca0DnR+7ZpCa1+mBZ75GIr6D6X\na/UqHjkBsVFwdpq1RcJ5Fy8iP8a7bqUjHMaFi2jaPpkuM9laPecNI0xX1l18lrW9hzc9m1mJVZzb\nqQXdfyIRSiErBJTu28frs2ZRmJtLzYkTp67vXLqUvLfe4twbb2TSffcFrkDxympWUUXz4zFWL826\nNoWs+edbe1gdOdF0u2gHXDmw1Q/TKj8Zbn1sO2QNvSXHWnsvxUTDq3i/etCFmxpqfBKyfjQUVuxp\neqNUsBZJ/Hpcmx8uKHVJhqEZ3oWsAR1O0C7Wt4sPRMKRhguDXGVZGa9Mm0bB6tV1AtZJZQUFrHr0\nUVY8/HAAqpOWOP3sv+bbNrKG3kvDu1gHCDdnaIa1MCAQ+qXB5N6QnfXd3lOJeN8FF0cssbRgyWIT\nrhvS/PcrLto6hLlTC3oJQ81fpkLfZhYOGmnw66GF/ilIJMT5LGQZhjHWMIzPDcM4ahjGPsMwXjQM\nQ+NYbbTy4Yc5kJvbZJuqsjJyX3ihwRAmwaMlK+GifLBq7rUr4MJeDW9N4cAKYm/Phrgg6s8ex3iv\nJ/1n0tVnqwudUfDBNdaGo6kNzOfOTIabz/X/Bqn+1jfN+jcxMvPMOVrJsTA6C96/GjKTWrDpmUgE\n88mvV8MwBgBLgE+Aa4AOwAPAx4ZhjDJN054dBMOcx+Mhf/Fir9oe2raNdU8+yZg77rC5Kmmt9nRg\nF7u8aptK22flJ8XCxz+E17fAMxus7RPcHqsnZmZ/a7VfQpCdBNSRNLLoxjbMJtulkMIkJvv0sVPi\n4N8/hFV74NHV1orDKIc1X+y/x0fOodWDOsOaG+GTHfBsrrVRa2IM3HIuTOxlTfzPOxjoKkVCg6/e\nw84D9gOzTgYqwzDygTXAFOBDHz1ORKk+doxjhV52y7vdFHz1lb0FSZtMYBL55Dc7bBhHHOOZ4JPH\njI6CawZZH6HiCq7iRZ5nL3savD2FFKYwlXTsOYz7vO7wRndb7jpkOBzWRPiLzwp0JSKhzVchazOw\npV6P1cm3or199BgRx9PC805a2l78K510+tOfjeQ2uIUDWEOKfTmbLLyYUBWm4olnLj9hGZ+TzzZK\nKMWNi+iqaNqf6MBlqZeTRVagyxQRaZZPQpZpmg2dmDu99vPW1txnXl5e6wsKYcePWxOe8/Ly8Hg8\nRCUne/21ji5dIvb7ZofTnwtf6Ud/SruUUpC8l+MxdSe3x1fH0+VYJoP2DyEPPY9ZdKcr3TjurMDl\ncEM5ON1OShNKKSVMdwQNEXb8bEjr6fkIXs2GLMMwYoCmOo0LTdM8Uu9rugN/AtYBn7WpwgjmcDjo\nPHo0JWbT81MAknv04Ow5c/xQlbSFAwejDozmHOdgtqT9h/LYMjxAUnUS5xQPJqmmbTu9hxsHDhJr\nvyfH3W1bcSki4m/e9GRlQZNvq+8EHjv5h9qAtQRr5eLVpmm2agxrwIDIPBXr5DuRk3//Xn/+M8+u\nW0fxli2Nfk10fDxDr7mGISNH+qXGSFH/ufC1EYyoe0Fn7jbJ7udDvKfnIrjo+Qi8nJyGT6BoNmSZ\nprkTL0+dMAxjEPAREANMMU1zu/clSkMSOnZk9ltv8cZVV1G0eTMeV925PAlpaQyaPZsLH3ooQBWK\niIhIQ3y2Q45hGKOBxUAJMNk0zXxf3Xek62QY3LRmDeuffpotCxdSWVJClNNJx759GXfPPWQMCqGl\nY+JXLlyn5i+lkIJThzyIiPiNr/bJ6o3Vg3UAuNA0zX2+uF/5jjMujux588ieNy/QpUgIKKOMJXzC\nHnZzrHbLiCSS6E4PLmQKKaQEuEIRkfDnq7e1jwGpwO1AD8Mwepx22y7TNPf76HFEpBmFFPIaL1NM\ncZ3rFVRQRBG72cVsriUDHcggImKnNoes2tWHlwLRwMsNNJmPtdJQRGxWQw1v8toZAet0xRTzBq9x\nKz/V8KEPHaSQ5XxBGWU4cJBGGhOYRCoRslW8iJyhzb9hazcgDbLDOUQi0wZyKKKo2XZFHGQDOYxi\ntB+qCm9WsH2dnXxLBRWnrm/nG/LYwgAGMo3pPjtnUURCh97GioSRzWzGjbvZdh48bGazQlYbefDw\nKi83etZiGWXksA4PMJ3L/VtcEFq5G/62FsoqISYapveDHw2x/l8kHClkiYSRSiptaSsNy2cb37Kj\nyTYuXOSxmYlM9MnB36HoQDlc+SZsOgClVd9dX5RvHcb98GSYbgSuPhG7RAW6ABHxnegW/EhH6ce/\nzb5iNdVUN9uunHKWstT+goLQkeNwyUuwYnfdgAVQ44YtRXDrh/Bv7aooYUi/ZUXCSAZdvG7bpQVt\npWEtOUPxMIdsrCR43fMZbCxsus2+MliwFHTGvYQbhSyRMDKJC7zaAyuFFCZygR8qCnfepwJPC9qG\ni2oXLN/tXdstRbByj731iPibQpZIGEkhlWzGEEdco23iiGMU2dpawAcS8f5A76QWtA0XO47AXi87\n+8qqYGFTp+SKhCBNfBcJMxOZRByxrGMdhyjGhXXeZTTRpNGJEYzkPMYGuMrwMISh7GJnsys6E0hg\nPBP8VFXwqHaDqwUdeJU19tUiEggKWSJhaAxjGcVovmYTO/kWD9Cb3gxmCNFovbyvDGM4a1nDPgqa\nbNeDnmTS1U9VBY8e7aBTApRXNd/W6YDsLPtrEvEnhSyRMBVNNMMYzjCGB7qUsBVNNHO4jpf5J/vZ\nf0aPlhMnPenFlcwOUIWBlRoHQ7vAzpLm2/ZLgzmD7a9JxJ8UskRE2iCFFH7CzWxgPV+ziQqO4cBB\nCimMYjQG/SN6u4wF4yFnH+wta7xNghOuOkebkkr4UcgSEWkjJ05Gkc0osgNdStA5tyv8eSr88hPY\n1UCPVrs4uHYQLIi8KWsSARSyRETEVlcMhPO6wQPL4au91krC2GhriPCuMTC+Z6ArFLGHQpaIiNgu\nKxWemBboKkT8K3InCoiIiIjYSCFLRERExAYKWSIiIiI2UMgSERERsYFCloiIiIgNFLJEREREbKCQ\nJSIiImIDhSwRERERGyhkiYiIiNhAIUtERETEBgpZIiIiIjZQyBIRERGxgUKWiIiIiA0UskRERERs\noJAlIiIiYgOFLBEREREbKGSJiIiI2EAhS0RERMQGClkiIiIiNlDIEhEREbGBQpaIiIiIDRSyRERE\nRGygkCUiIiJiA4UsERERERsoZImIiIjYQCFLRERExAYKWSIiIiI2UMgSERERsYFCloiIiIgNFLJE\nREREbKCQJSIiImIDhSwRERERGyhkiYiIiNhAIUtERETEBgpZIiIiIjZQyBIRERGxgUKWiIiIiA0U\nskRERERs4Ax0ASLSNgc5yGq+pJJKYoklmzFkkhnoskREIp5ClkiIKqecN3mdA+yngopT17ewmc5k\nMJMr6ECHAFYoIhLZNFwoEoIqqOAFnmUH2+sELIDjHGcXO/knL1BCSYAqFBERhSyRELSI9ymksMk2\nxRSxiPf9VJGIiNSnkCUSYiqppIC9XrXdz36OcczmikREpCEKWSIhZg+7Ocxhr9qWcJRtmDZXJCIi\nDVHIEgkxJzjRovZVVNpUiYiINEUhSyTEpNOZOOK8ahtDDOlk2FyRiIg0RCFLJMRkkEFnOnvVthPp\n9Ka3zRWJiEhDFLJEQtBwRjTbmxVDDIMZggOHn6oSEZHTKWSJhKCRjGIU2cQT3+DtscQyjOGczzg/\nVyYiIidpx3eREHUxl9CbPqziS4opooYaonHSiTSyGcMABga6RBGRiKaQJRLCzqYfZ9OPaqqppJI4\n4oghJtBliYgIClkiYSGm9j8REQkempMlIiIiYgOFLBEREREbKGSJiIiI2EAhS0RERMQGClkiIiIi\nNlDIEhEREbGBQpaIiIiIDRSyRERERGygkCUiIiJiA4UsERERERsoZImIiIjYQCFLRERExAYKWSIi\nIiI2cHg8nkDXcIacnJzgK0pERESkESNGjHDUvxaUIUtEREQk1Gm4UERERMQGClkiIiIiNlDIEhER\nEbGBQpaIiIiIDRSyRERERGygkCUiIiJiA4UsERERERsoZImIiIjYQCFLRERExAbOQBcg3jEM47fA\nfaZpnrFtv9jPMIyxwIPAcKAC+BSYb5pmYUALixCGYdwE/AroBuQCvzBNc1Vgq4pMhmFEA3cANwE9\ngF3A48DfTdPUESIBYhhGHNbPxlemad4Q4HKklnqyQoBhGIOAewJdR6QyDGMAsAQoA64BfgmcD3xs\nGEZMIGuLBIZhXA88AfwLmAUcxfre9w5oYZHrv4HfYz0flwOvA48B8wNZlPBboH+gi5C61JMV5Grf\nNT4LFAFZAS4nUs0D9gOzTNOsBjAMIx9YA0wBPgxgbWHNMAwHcD/wlGma99de+wQwgTuB/wpgeRGn\n9vfRL4A/mqb5YO3lJYZhpGO9+fhDwIqLYIZhDMf6WSgOdC1Sl3qygt+dQArwt0AXEsE2A4+cDFi1\nzNrP6k2xV1+gJ/DeyQu1z8Mi4JJAFRXBUoEXgbfqXTeBdMMwkvxfUmQzDMOJ9Ub8j0BBgMuRetST\nFcQMw+iL9S5+KjAywOVELNM0H2/g8vTaz1v9WUsE6lf7+Zt613cAZxmGEW2apsvPNUUs0zSPYPXs\n1jcd2Gua5jE/lyRwNxALPAT8IMC1SD0KWQFQO4/nrCaaFGLNO/kH8KJpmisMw1DIsoE3z0XtC8vp\nX9Md+BOwDvjMxvLE6jkBaz7c6cqweuKTgFK/ViR1GIZxI3ARGrr1u9r5or8BLjRNs8owjECXJPUo\nZAVGFpDXxO13Aiewhkou90tFkcub5+Kxk3+oDVhLsF7gr9ZqKtudXE3b2PfZ7a9C5EyGYczBWpTw\nJvC/AS4nohiGEYX1RvwZrbQNXgpZAWCa5k6+e/E4Q+0L+WZgLlBRO+YeVXubE3CbpqkXFx9o7rk4\nXe0qz4+AGGCKaZrbbSxNLCW1n1Oweng57c8u0zTL/V+SABiG8QusHt33gDl6w+F3P8PaQmNa6Wuo\n3AAAAZ1JREFU7evCSQ7DMJymadYEqC45jSa+B6cLsV5E3gSqaz8eqb2tGlgQoLoilmEYo4HlgAsY\nb5rmpgCXFCnyaz/3qXe9D7DNz7VILcMwfo/1O+mfwBWmaVYFuKRI9AOsfeOO8N3rxFDgR0C1YRi9\nAleanKSerOD0PjCq3rVrsJZOjwL2+b2iCFa7H9NHwAGsuQ/6/vtPPrAHmAH8G07No5uGtcJQ/Mww\njDuAXwN/Ae5UD1bA3IL1Zvx0L2G9+bgfvU4EBYfHo5+PUGAYxs+BP2vHd/8zDONdrBf164Bv6928\nyzTN/f6vKnIYhvFTrPk+DwErsVa3jQOGmaa5I5C1RRrDMDKxfga2ATc30GSdhqkCxzCMXCBXO74H\nD/VkiTShttfkUiAaeLmBJvOx5qWITUzTfNwwjASso1zuxDo6ZKoCVkBMBeKAwUBDk63T0YaYIqeo\nJ0tERETEBpr4LiIiImIDhSwRERERGyhkiYiIiNhAIUtERETEBgpZIiIiIjZQyBIRERGxgUKWiIiI\niA0UskRERERsoJAlIiIiYoP/A/6SXyIEtp3YAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24453b3ba20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RERERByhkiYiIiDhAIUtERETEAQpZIiIiIg5QyBIRERFxgEKWiIiIiAMUskREREQcoHmy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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2445410c8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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G/BO4BngQOMOyrOpIbVtERESktYnUPFm7AHcBPwGvAiONMbWb/E+hS0RERNqS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24453d91cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/fiOLF28nPj6K007r36grJpsrt9vFiSf2ZdGihkPWAQekMHRopwBUJdJ0Clki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\nBWmz27ly4ULSpk2jw4kn4qiZdG9zOEhOS2P4TTc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mzSy84goK6i2YKC8oID8jgz0rV3LV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\nBRSyRERERCzw/xyWq207Ir9gAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24453e4f780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Z5+LBF+4yRUREAIUsaWA8eIhlVGDoUEREJHJpuFBERETEBQpZIiIiIi5QyBIRERFxgUKW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\n28Z/y1vPQQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x244542d7828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PjlxAtyRZGFc6NoUsERFJKKlksQfXUskGLG9SxSb8pDGI/ejBtkk7P1A6HoUs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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24453d47be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "k_range = range(2,11)\n",
    "for n_clusters in k_range:\n",
    "    kmeanscluster = sklearn.cluster.KMeans(n_clusters=n_clusters, random_state=10)\n",
    "    df['cluster'] = kmeanscluster.fit_predict(df[feature_cols])\n",
    "    \n",
    "    fig = plt.figure()\n",
    "    ax1 = fig.add_subplot(111)\n",
    "    fig.set_size_inches(10, 8)\n",
    "    ax1.scatter(df['x'],df['y'],c=df['cluster'], cmap='jet')\n",
    "    ax1.set_title(\"Scatter plot for %d clusters\"%n_clusters)\n",
    "    for i in [60,26,23]:\n",
    "        ax1.annotate(i, (df['x'][i],df['y'][i]))\n",
    "    plt.show()\n",
    "    del df['cluster']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kmeanscluster = sklearn.cluster.KMeans(n_clusters=5, random_state=10)\n",
    "df['cluster'] = kmeanscluster.fit_predict(df[feature_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#kmeanscluster.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 4, 2, 4, 1, 2, 2, 1, 0, 3, 3, 0, 3, 1, 2, 0, 2, 1, 0, 4, 4, 2, 4,\n",
       "       4, 1, 1, 1, 1, 3, 3, 3, 0, 4, 0, 4, 3, 4, 4, 4, 1, 2, 2, 2, 1, 1, 4,\n",
       "       4, 3, 2, 0, 4, 4, 3, 4, 0, 4, 2, 0, 2, 2, 3])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeanscluster.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.concat([df, pd.DataFrame(kmeanscluster.fit_transform(df[feature_cols]).tolist(),  columns=['cluster_a','cluster_b', 'cluster_c', 'cluster_d', 'cluster_e'])], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.rename(columns={0: 'cluster'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Tried the Affinity propogation method but then decided not to use the eight clusters suggested by it.\n",
    "#model = sklearn.cluster.AffinityPropagation()\n",
    "#labels = model.fit_predict(df[feature_cols])\n",
    "#print(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['cluster_dist'] = df.loc[:, ['cluster_a', 'cluster_b', 'cluster_c', 'cluster_d', 'cluster_e']].min(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">cluster_dist</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.336545</td>\n",
       "      <td>4.764158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.256792</td>\n",
       "      <td>4.964188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.445539</td>\n",
       "      <td>6.309407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.180233</td>\n",
       "      <td>7.614865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.224881</td>\n",
       "      <td>6.561071</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        cluster_dist          \n",
       "                 min       max\n",
       "cluster                       \n",
       "0           3.336545  4.764158\n",
       "1           2.256792  4.964188\n",
       "2           2.445539  6.309407\n",
       "3           3.180233  7.614865\n",
       "4           3.224881  6.561071"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['cluster']).agg({\"cluster_dist\": [min, max]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clusters = df.groupby('cluster')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "albeniz alb_se3.mp3\n",
      "albeniz alb_se6.mp3\n",
      "bach bach_847.mp3\n",
      "beethoven beethoven_opus10_1.mp3\n",
      "beethoven waldstein_1.mp3\n",
      "beethoven waldstein_3.mp3\n",
      "borodin bor_ps3.mp3\n",
      "brahms brahms_opus1_1.mp3\n",
      "brahms brahms_opus1_4.mp3\n",
      "\n",
      "1\n",
      "albeniz alb_esp5.mp3\n",
      "albeniz alb_se2.mp3\n",
      "albeniz alb_se8.mp3\n",
      "balak islamei.mp3\n",
      "beethoven beethoven_opus22_1.mp3\n",
      "beethoven beethoven_opus22_2.mp3\n",
      "beethoven beethoven_opus22_3.mp3\n",
      "beethoven beethoven_opus22_4.mp3\n",
      "beethoven beethoven_les_adieux_3.mp3\n",
      "beethoven beethoven_hammerklavier_1.mp3\n",
      "beethoven beethoven_hammerklavier_2.mp3\n",
      "\n",
      "2\n",
      "albeniz alb_esp3.mp3\n",
      "albeniz alb_esp6.mp3\n",
      "albeniz alb_se1.mp3\n",
      "bach bach_846.mp3\n",
      "bach bach_850.mp3\n",
      "beethoven pathetique_1.mp3\n",
      "beethoven elise.mp3\n",
      "beethoven beethoven_opus90_1.mp3\n",
      "beethoven beethoven_opus90_2.mp3\n",
      "borodin bor_ps2.mp3\n",
      "brahms brahms_opus1_3.mp3\n",
      "brahms br_im2.mp3\n",
      "brahms br_im5.mp3\n",
      "\n",
      "3\n",
      "albeniz alb_se4.mp3\n",
      "albeniz alb_se5.mp3\n",
      "albeniz alb_se7.mp3\n",
      "beethoven mond_1.mp3\n",
      "beethoven mond_2.mp3\n",
      "beethoven mond_3.mp3\n",
      "beethoven appass_2.mp3\n",
      "borodin bor_ps1.mp3\n",
      "borodin bor_ps6.mp3\n",
      "brahms br_im6.mp3\n",
      "\n",
      "4\n",
      "albeniz alb_esp1.mp3\n",
      "albeniz alb_esp2.mp3\n",
      "albeniz alb_esp4.mp3\n",
      "beethoven beethoven_opus10_2.mp3\n",
      "beethoven beethoven_opus10_3.mp3\n",
      "beethoven pathetique_2.mp3\n",
      "beethoven pathetique_3.mp3\n",
      "beethoven waldstein_2.mp3\n",
      "beethoven appass_1.mp3\n",
      "beethoven appass_3.mp3\n",
      "beethoven beethoven_les_adieux_1.mp3\n",
      "beethoven beethoven_les_adieux_2.mp3\n",
      "beethoven beethoven_hammerklavier_3.mp3\n",
      "beethoven beethoven_hammerklavier_4.mp3\n",
      "borodin bor_ps4.mp3\n",
      "borodin bor_ps5.mp3\n",
      "borodin bor_ps7.mp3\n",
      "brahms brahms_opus1_2.mp3\n",
      "\n"
     ]
    }
   ],
   "source": [
    "finalfive = pd.DataFrame(columns=musicdf.columns)\n",
    "for name, group in clusters: \n",
    "    print(name)\n",
    "    songs = musicdf.iloc[clusters.get_group(name).index]\n",
    "    for index, row in songs.iterrows():\n",
    "        print(row['composer']+' '+row['file'])\n",
    "    print('')\n",
    "    #finalfive = pd.concat([finalfive, musicdf.iloc[group.nsmallest(2, 'cluster_dist').index]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29                       mond_2.mp3\n",
       "9                       alb_se4.mp3\n",
       "2                      alb_esp3.mp3\n",
       "16                     bach_850.mp3\n",
       "48                      bor_ps2.mp3\n",
       "50                      bor_ps4.mp3\n",
       "19           beethoven_opus10_2.mp3\n",
       "42           beethoven_opus90_2.mp3\n",
       "23                 pathetique_3.mp3\n",
       "44    beethoven_hammerklavier_2.mp3\n",
       "Name: file, dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalfive.file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>composer</th>\n",
       "      <th>description</th>\n",
       "      <th>symphony</th>\n",
       "      <th>file</th>\n",
       "      <th>part</th>\n",
       "      <th>tempo</th>\n",
       "      <th>bpm</th>\n",
       "      <th>duration</th>\n",
       "      <th>zero_crossings</th>\n",
       "      <th>spectral_centroid_avg</th>\n",
       "      <th>...</th>\n",
       "      <th>Chroma_SD_B</th>\n",
       "      <th>Chroma_SD_C</th>\n",
       "      <th>Chroma_SD_C#</th>\n",
       "      <th>Chroma_SD_D</th>\n",
       "      <th>Chroma_SD_D#</th>\n",
       "      <th>Chroma_SD_E</th>\n",
       "      <th>Chroma_SD_F</th>\n",
       "      <th>Chroma_SD_F#</th>\n",
       "      <th>Chroma_SD_G</th>\n",
       "      <th>Chroma_SD_G#</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>albeniz</td>\n",
       "      <td>Albeniz  Isaac(1860-1909)</td>\n",
       "      <td>Espana Opus 165</td>\n",
       "      <td>alb_esp1.mp3</td>\n",
       "      <td>Prelude</td>\n",
       "      <td>Andantino</td>\n",
       "      <td>120.185320</td>\n",
       "      <td>93.570612</td>\n",
       "      <td>98094</td>\n",
       "      <td>621.574748</td>\n",
       "      <td>...</td>\n",
       "      <td>0.175779</td>\n",
       "      <td>0.193223</td>\n",
       "      <td>0.195864</td>\n",
       "      <td>0.210012</td>\n",
       "      <td>0.152202</td>\n",
       "      <td>0.207500</td>\n",
       "      <td>0.167383</td>\n",
       "      <td>0.110378</td>\n",
       "      <td>0.088626</td>\n",
       "      <td>0.167561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>albeniz</td>\n",
       "      <td>Albeniz  Isaac(1860-1909)</td>\n",
       "      <td>Espana Opus 165</td>\n",
       "      <td>alb_esp2.mp3</td>\n",
       "      <td>Tango</td>\n",
       "      <td>Andantino</td>\n",
       "      <td>120.185320</td>\n",
       "      <td>128.705306</td>\n",
       "      <td>110224</td>\n",
       "      <td>542.039355</td>\n",
       "      <td>...</td>\n",
       "      <td>0.197480</td>\n",
       "      <td>0.148901</td>\n",
       "      <td>0.147744</td>\n",
       "      <td>0.189628</td>\n",
       "      <td>0.138035</td>\n",
       "      <td>0.170995</td>\n",
       "      <td>0.157763</td>\n",
       "      <td>0.167537</td>\n",
       "      <td>0.135463</td>\n",
       "      <td>0.170640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>albeniz</td>\n",
       "      <td>Albeniz  Isaac(1860-1909)</td>\n",
       "      <td>Espana Opus 165</td>\n",
       "      <td>alb_esp3.mp3</td>\n",
       "      <td>Malaguena</td>\n",
       "      <td>Allegretto</td>\n",
       "      <td>117.453835</td>\n",
       "      <td>176.039184</td>\n",
       "      <td>214944</td>\n",
       "      <td>701.140112</td>\n",
       "      <td>...</td>\n",
       "      <td>0.169860</td>\n",
       "      <td>0.132680</td>\n",
       "      <td>0.120317</td>\n",
       "      <td>0.154430</td>\n",
       "      <td>0.158160</td>\n",
       "      <td>0.156281</td>\n",
       "      <td>0.122567</td>\n",
       "      <td>0.140689</td>\n",
       "      <td>0.124910</td>\n",
       "      <td>0.123183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>albeniz</td>\n",
       "      <td>Albeniz  Isaac(1860-1909)</td>\n",
       "      <td>Espana Opus 165</td>\n",
       "      <td>alb_esp4.mp3</td>\n",
       "      <td>Serenata</td>\n",
       "      <td>Allegretto</td>\n",
       "      <td>112.347147</td>\n",
       "      <td>195.422041</td>\n",
       "      <td>203680</td>\n",
       "      <td>631.094119</td>\n",
       "      <td>...</td>\n",
       "      <td>0.166632</td>\n",
       "      <td>0.175509</td>\n",
       "      <td>0.142637</td>\n",
       "      <td>0.183892</td>\n",
       "      <td>0.158628</td>\n",
       "      <td>0.137726</td>\n",
       "      <td>0.137628</td>\n",
       "      <td>0.148855</td>\n",
       "      <td>0.166665</td>\n",
       "      <td>0.141932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>albeniz</td>\n",
       "      <td>Albeniz  Isaac(1860-1909)</td>\n",
       "      <td>Espana Opus 165</td>\n",
       "      <td>alb_esp5.mp3</td>\n",
       "      <td>Capricho Catalan</td>\n",
       "      <td>Allegretto</td>\n",
       "      <td>120.185320</td>\n",
       "      <td>124.499592</td>\n",
       "      <td>118904</td>\n",
       "      <td>570.265597</td>\n",
       "      <td>...</td>\n",
       "      <td>0.119192</td>\n",
       "      <td>0.095741</td>\n",
       "      <td>0.103875</td>\n",
       "      <td>0.100934</td>\n",
       "      <td>0.081791</td>\n",
       "      <td>0.107883</td>\n",
       "      <td>0.133893</td>\n",
       "      <td>0.125197</td>\n",
       "      <td>0.166297</td>\n",
       "      <td>0.131250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>albeniz</td>\n",
       "      <td>Albeniz  Isaac(1860-1909)</td>\n",
       "      <td>Espana Opus 165</td>\n",
       "      <td>alb_esp6.mp3</td>\n",
       "      <td>Zortzico</td>\n",
       "      <td>Allegretto</td>\n",
       "      <td>112.347147</td>\n",
       "      <td>154.827755</td>\n",
       "      <td>164246</td>\n",
       "      <td>631.638421</td>\n",
       "      <td>...</td>\n",
       "      <td>0.197129</td>\n",
       "      <td>0.133656</td>\n",
       "      <td>0.159364</td>\n",
       "      <td>0.140870</td>\n",
       "      <td>0.157394</td>\n",
       "      <td>0.164212</td>\n",
       "      <td>0.120355</td>\n",
       "      <td>0.146004</td>\n",
       "      <td>0.136968</td>\n",
       "      <td>0.167715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>albeniz</td>\n",
       "      <td>Albeniz  Isaac(1860-1909)</td>\n",
       "      <td>Suite espanola</td>\n",
       "      <td>alb_se1.mp3</td>\n",
       "      <td>Granada</td>\n",
       "      <td>Allegretto</td>\n",
       "      <td>132.512019</td>\n",
       "      <td>241.658776</td>\n",
       "      <td>317856</td>\n",
       "      <td>643.616803</td>\n",
       "      <td>...</td>\n",
       "      <td>0.081097</td>\n",
       "      <td>0.083933</td>\n",
       "      <td>0.060221</td>\n",
       "      <td>0.138567</td>\n",
       "      <td>0.167003</td>\n",
       "      <td>0.109643</td>\n",
       "      <td>0.113225</td>\n",
       "      <td>0.096489</td>\n",
       "      <td>0.100674</td>\n",
       "      <td>0.107103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>albeniz</td>\n",
       "      <td>Albeniz  Isaac(1860-1909)</td>\n",
       "      <td>Suite espanola</td>\n",
       "      <td>alb_se2.mp3</td>\n",
       "      <td>Cataluna (Curranda)</td>\n",
       "      <td>Allegro</td>\n",
       "      <td>156.605114</td>\n",
       "      <td>240.979592</td>\n",
       "      <td>314238</td>\n",
       "      <td>727.952305</td>\n",
       "      <td>...</td>\n",
       "      <td>0.114011</td>\n",
       "      <td>0.146534</td>\n",
       "      <td>0.151022</td>\n",
       "      <td>0.143592</td>\n",
       "      <td>0.144085</td>\n",
       "      <td>0.139092</td>\n",
       "      <td>0.167707</td>\n",
       "      <td>0.138745</td>\n",
       "      <td>0.209784</td>\n",
       "      <td>0.137540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>albeniz</td>\n",
       "      <td>Albeniz  Isaac(1860-1909)</td>\n",
       "      <td>Suite espanola</td>\n",
       "      <td>alb_se3.mp3</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>Allegretto</td>\n",
       "      <td>109.956782</td>\n",
       "      <td>251.872653</td>\n",
       "      <td>349920</td>\n",
       "      <td>743.385163</td>\n",
       "      <td>...</td>\n",
       "      <td>0.143624</td>\n",
       "      <td>0.090090</td>\n",
       "      <td>0.088240</td>\n",
       "      <td>0.113518</td>\n",
       "      <td>0.104565</td>\n",
       "      <td>0.149267</td>\n",
       "      <td>0.169314</td>\n",
       "      <td>0.075805</td>\n",
       "      <td>0.162057</td>\n",
       "      <td>0.103545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>albeniz</td>\n",
       "      <td>Albeniz  Isaac(1860-1909)</td>\n",
       "      <td>Suite espanola</td>\n",
       "      <td>alb_se4.mp3</td>\n",
       "      <td>Cádiz (Saeta)</td>\n",
       "      <td>Allegretto</td>\n",
       "      <td>99.384014</td>\n",
       "      <td>221.100408</td>\n",
       "      <td>284980</td>\n",
       "      <td>689.619629</td>\n",
       "      <td>...</td>\n",
       "      <td>0.130778</td>\n",
       "      <td>0.122177</td>\n",
       "      <td>0.155902</td>\n",
       "      <td>0.112533</td>\n",
       "      <td>0.161665</td>\n",
       "      <td>0.118075</td>\n",
       "      <td>0.138783</td>\n",
       "      <td>0.123560</td>\n",
       "      <td>0.138791</td>\n",
       "      <td>0.193307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>albeniz</td>\n",
       "      <td>Albeniz  Isaac(1860-1909)</td>\n",
       "      <td>Suite espanola</td>\n",
       "      <td>alb_se5.mp3</td>\n",
       "      <td>Asturias (Leyenda)</td>\n",
       "      <td>Allegro</td>\n",
       "      <td>132.512019</td>\n",
       "      <td>305.371429</td>\n",
       "      <td>421968</td>\n",
       "      <td>724.764828</td>\n",
       "      <td>...</td>\n",
       "      <td>0.105325</td>\n",
       "      <td>0.108144</td>\n",
       "      <td>0.059177</td>\n",
       "      <td>0.077075</td>\n",
       "      <td>0.094876</td>\n",
       "      <td>0.095828</td>\n",
       "      <td>0.067225</td>\n",
       "      <td>0.101229</td>\n",
       "      <td>0.117781</td>\n",
       "      <td>0.083586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>albeniz</td>\n",
       "      <td>Albeniz  Isaac(1860-1909)</td>\n",
       "      <td>Suite espanola</td>\n",
       "      <td>alb_se6.mp3</td>\n",
       "      <td>Aragon (Fantasia)</td>\n",
       "      <td>Allegro</td>\n",
       "      <td>105.468750</td>\n",
       "      <td>243.513469</td>\n",
       "      <td>366638</td>\n",
       "      <td>799.040670</td>\n",
       "      <td>...</td>\n",
       "      <td>0.124915</td>\n",
       "      <td>0.121395</td>\n",
       "      <td>0.084969</td>\n",
       "      <td>0.141055</td>\n",
       "      <td>0.116771</td>\n",
       "      <td>0.153677</td>\n",
       "      <td>0.107955</td>\n",
       "      <td>0.123052</td>\n",
       "      <td>0.171489</td>\n",
       "      <td>0.114191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>albeniz</td>\n",
       "      <td>Albeniz  Isaac(1860-1909)</td>\n",
       "      <td>Suite espanola</td>\n",
       "      <td>alb_se7.mp3</td>\n",
       "      <td>Castilla (Seguidillas)</td>\n",
       "      <td>Allegro</td>\n",
       "      <td>166.708669</td>\n",
       "      <td>156.395102</td>\n",
       "      <td>229950</td>\n",
       "      <td>779.097070</td>\n",
       "      <td>...</td>\n",
       "      <td>0.119271</td>\n",
       "      <td>0.104646</td>\n",
       "      <td>0.137708</td>\n",
       "      <td>0.084906</td>\n",
       "      <td>0.145295</td>\n",
       "      <td>0.089313</td>\n",
       "      <td>0.086392</td>\n",
       "      <td>0.159794</td>\n",
       "      <td>0.084004</td>\n",
       "      <td>0.161897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>albeniz</td>\n",
       "      <td>Albeniz  Isaac(1860-1909)</td>\n",
       "      <td>Suite espanola</td>\n",
       "      <td>alb_se8.mp3</td>\n",
       "      <td>Cuba (Notturno)</td>\n",
       "      <td>Allegretto</td>\n",
       "      <td>135.999178</td>\n",
       "      <td>272.222041</td>\n",
       "      <td>326088</td>\n",
       "      <td>641.144951</td>\n",
       "      <td>...</td>\n",
       "      <td>0.107563</td>\n",
       "      <td>0.132366</td>\n",
       "      <td>0.131447</td>\n",
       "      <td>0.126307</td>\n",
       "      <td>0.163392</td>\n",
       "      <td>0.090098</td>\n",
       "      <td>0.171666</td>\n",
       "      <td>0.094812</td>\n",
       "      <td>0.153058</td>\n",
       "      <td>0.138831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>bach</td>\n",
       "      <td>Bach  Johann Sebastian(1685-1750)</td>\n",
       "      <td>The Well-Tempered Clavier Part I BWV 846-869</td>\n",
       "      <td>bach_846.mp3</td>\n",
       "      <td>Prelude and Fugue in C major BWV 846</td>\n",
       "      <td>NaN</td>\n",
       "      <td>126.048018</td>\n",
       "      <td>227.317551</td>\n",
       "      <td>225662</td>\n",
       "      <td>647.489429</td>\n",
       "      <td>...</td>\n",
       "      <td>0.161284</td>\n",
       "      <td>0.132739</td>\n",
       "      <td>0.112274</td>\n",
       "      <td>0.221261</td>\n",
       "      <td>0.088599</td>\n",
       "      <td>0.224013</td>\n",
       "      <td>0.135668</td>\n",
       "      <td>0.122892</td>\n",
       "      <td>0.151393</td>\n",
       "      <td>0.107751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>bach</td>\n",
       "      <td>Bach  Johann Sebastian(1685-1750)</td>\n",
       "      <td>The Well-Tempered Clavier Part I BWV 846-869</td>\n",
       "      <td>bach_847.mp3</td>\n",
       "      <td>Prelude and Fugue in C minor BWV 847</td>\n",
       "      <td>NaN</td>\n",
       "      <td>143.554688</td>\n",
       "      <td>191.529796</td>\n",
       "      <td>210332</td>\n",
       "      <td>780.659917</td>\n",
       "      <td>...</td>\n",
       "      <td>0.104775</td>\n",
       "      <td>0.138016</td>\n",
       "      <td>0.117120</td>\n",
       "      <td>0.148757</td>\n",
       "      <td>0.115832</td>\n",
       "      <td>0.095209</td>\n",
       "      <td>0.126109</td>\n",
       "      <td>0.076569</td>\n",
       "      <td>0.122057</td>\n",
       "      <td>0.123116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>bach</td>\n",
       "      <td>Bach  Johann Sebastian(1685-1750)</td>\n",
       "      <td>The Well-Tempered Clavier Part I BWV 846-869</td>\n",
       "      <td>bach_850.mp3</td>\n",
       "      <td>Prelude and Fugue in D major BWV 850</td>\n",
       "      <td>NaN</td>\n",
       "      <td>117.453835</td>\n",
       "      <td>176.195918</td>\n",
       "      <td>191426</td>\n",
       "      <td>736.341559</td>\n",
       "      <td>...</td>\n",
       "      <td>0.156684</td>\n",
       "      <td>0.140587</td>\n",
       "      <td>0.146742</td>\n",
       "      <td>0.163448</td>\n",
       "      <td>0.133275</td>\n",
       "      <td>0.151187</td>\n",
       "      <td>0.138327</td>\n",
       "      <td>0.155704</td>\n",
       "      <td>0.148592</td>\n",
       "      <td>0.136464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>balak</td>\n",
       "      <td>Balakirew  Mili Alexejewitsch(1837-1910)</td>\n",
       "      <td>Islamej(Oriental fantasia)</td>\n",
       "      <td>islamei.mp3</td>\n",
       "      <td>Islamej</td>\n",
       "      <td>Presto con fuoco</td>\n",
       "      <td>126.048018</td>\n",
       "      <td>498.485986</td>\n",
       "      <td>575308</td>\n",
       "      <td>680.998049</td>\n",
       "      <td>...</td>\n",
       "      <td>0.127714</td>\n",
       "      <td>0.140272</td>\n",
       "      <td>0.168282</td>\n",
       "      <td>0.130241</td>\n",
       "      <td>0.154759</td>\n",
       "      <td>0.125400</td>\n",
       "      <td>0.164967</td>\n",
       "      <td>0.138473</td>\n",
       "      <td>0.152864</td>\n",
       "      <td>0.185501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>beethoven</td>\n",
       "      <td>Beethoven  Ludwig van(1770-1827)</td>\n",
       "      <td>Sonata No. 5 C minor Opus 10/1</td>\n",
       "      <td>beethoven_opus10_1.mp3</td>\n",
       "      <td>1. Movement</td>\n",
       "      <td>Allegro molto e con brio</td>\n",
       "      <td>178.205819</td>\n",
       "      <td>394.057143</td>\n",
       "      <td>409118</td>\n",
       "      <td>603.723822</td>\n",
       "      <td>...</td>\n",
       "      <td>0.132041</td>\n",
       "      <td>0.117183</td>\n",
       "      <td>0.097864</td>\n",
       "      <td>0.107088</td>\n",
       "      <td>0.122157</td>\n",
       "      <td>0.124517</td>\n",
       "      <td>0.186754</td>\n",
       "      <td>0.109863</td>\n",
       "      <td>0.164265</td>\n",
       "      <td>0.104576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>beethoven</td>\n",
       "      <td>Beethoven  Ludwig van(1770-1827)</td>\n",
       "      <td>Sonata No. 5 C minor Opus 10/1</td>\n",
       "      <td>beethoven_opus10_2.mp3</td>\n",
       "      <td>2. Movement</td>\n",
       "      <td>Adagio molto</td>\n",
       "      <td>139.674831</td>\n",
       "      <td>393.639184</td>\n",
       "      <td>353624</td>\n",
       "      <td>475.342641</td>\n",
       "      <td>...</td>\n",
       "      <td>0.152306</td>\n",
       "      <td>0.151944</td>\n",
       "      <td>0.135379</td>\n",
       "      <td>0.152754</td>\n",
       "      <td>0.217126</td>\n",
       "      <td>0.146462</td>\n",
       "      <td>0.156430</td>\n",
       "      <td>0.150890</td>\n",
       "      <td>0.157358</td>\n",
       "      <td>0.187417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>beethoven</td>\n",
       "      <td>Beethoven  Ludwig van(1770-1827)</td>\n",
       "      <td>Sonata No. 5 C minor Opus 10/1</td>\n",
       "      <td>beethoven_opus10_3.mp3</td>\n",
       "      <td>3. Movement</td>\n",
       "      <td>Prestissimo</td>\n",
       "      <td>99.384014</td>\n",
       "      <td>219.193469</td>\n",
       "      <td>218308</td>\n",
       "      <td>585.544664</td>\n",
       "      <td>...</td>\n",
       "      <td>0.141795</td>\n",
       "      <td>0.192293</td>\n",
       "      <td>0.135512</td>\n",
       "      <td>0.153703</td>\n",
       "      <td>0.158208</td>\n",
       "      <td>0.120557</td>\n",
       "      <td>0.150871</td>\n",
       "      <td>0.123061</td>\n",
       "      <td>0.172785</td>\n",
       "      <td>0.141157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>beethoven</td>\n",
       "      <td>Beethoven  Ludwig van(1770-1827)</td>\n",
       "      <td>Sonata No. 8 C minor(Pathetique) Opus 13</td>\n",
       "      <td>pathetique_1.mp3</td>\n",
       "      <td>1. Movement</td>\n",
       "      <td>Grave - Allegro di molto e con brio</td>\n",
       "      <td>151.999081</td>\n",
       "      <td>562.677551</td>\n",
       "      <td>571466</td>\n",
       "      <td>562.675867</td>\n",
       "      <td>...</td>\n",
       "      <td>0.147401</td>\n",
       "      <td>0.111301</td>\n",
       "      <td>0.106388</td>\n",
       "      <td>0.139456</td>\n",
       "      <td>0.154623</td>\n",
       "      <td>0.116710</td>\n",
       "      <td>0.109585</td>\n",
       "      <td>0.135425</td>\n",
       "      <td>0.163141</td>\n",
       "      <td>0.095910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>beethoven</td>\n",
       "      <td>Beethoven  Ludwig van(1770-1827)</td>\n",
       "      <td>Sonata No. 8 C minor(Pathetique) Opus 13</td>\n",
       "      <td>pathetique_2.mp3</td>\n",
       "      <td>2. Movement</td>\n",
       "      <td>Adagio cantabile</td>\n",
       "      <td>109.956782</td>\n",
       "      <td>309.733878</td>\n",
       "      <td>199136</td>\n",
       "      <td>379.836122</td>\n",
       "      <td>...</td>\n",
       "      <td>0.122226</td>\n",
       "      <td>0.152574</td>\n",
       "      <td>0.158630</td>\n",
       "      <td>0.132891</td>\n",
       "      <td>0.234525</td>\n",
       "      <td>0.143175</td>\n",
       "      <td>0.214932</td>\n",
       "      <td>0.151870</td>\n",
       "      <td>0.175659</td>\n",
       "      <td>0.155899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>beethoven</td>\n",
       "      <td>Beethoven  Ludwig van(1770-1827)</td>\n",
       "      <td>Sonata No. 8 C minor(Pathetique) Opus 13</td>\n",
       "      <td>pathetique_3.mp3</td>\n",
       "      <td>3. Movement</td>\n",
       "      <td>Rondo allegro</td>\n",
       "      <td>103.359375</td>\n",
       "      <td>260.048980</td>\n",
       "      <td>287380</td>\n",
       "      <td>604.357711</td>\n",
       "      <td>...</td>\n",
       "      <td>0.135771</td>\n",
       "      <td>0.186967</td>\n",
       "      <td>0.137967</td>\n",
       "      <td>0.158510</td>\n",
       "      <td>0.152708</td>\n",
       "      <td>0.134058</td>\n",
       "      <td>0.175962</td>\n",
       "      <td>0.125300</td>\n",
       "      <td>0.169176</td>\n",
       "      <td>0.159236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>beethoven</td>\n",
       "      <td>Beethoven  Ludwig van(1770-1827)</td>\n",
       "      <td>Sonata No. 11 Bb major Opus 22</td>\n",
       "      <td>beethoven_opus22_1.mp3</td>\n",
       "      <td>1. Movement</td>\n",
       "      <td>Allegro con brio</td>\n",
       "      <td>161.499023</td>\n",
       "      <td>402.991020</td>\n",
       "      <td>387972</td>\n",
       "      <td>588.797263</td>\n",
       "      <td>...</td>\n",
       "      <td>0.120557</td>\n",
       "      <td>0.183013</td>\n",
       "      <td>0.139378</td>\n",
       "      <td>0.134819</td>\n",
       "      <td>0.133918</td>\n",
       "      <td>0.123872</td>\n",
       "      <td>0.176456</td>\n",
       "      <td>0.119616</td>\n",
       "      <td>0.167173</td>\n",
       "      <td>0.128282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>beethoven</td>\n",
       "      <td>Beethoven  Ludwig van(1770-1827)</td>\n",
       "      <td>Sonata No. 11 Bb major Opus 22</td>\n",
       "      <td>beethoven_opus22_2.mp3</td>\n",
       "      <td>2. Movement</td>\n",
       "      <td>Adagio con molta espressione</td>\n",
       "      <td>129.199219</td>\n",
       "      <td>322.690612</td>\n",
       "      <td>281874</td>\n",
       "      <td>470.234412</td>\n",
       "      <td>...</td>\n",
       "      <td>0.098503</td>\n",
       "      <td>0.153847</td>\n",
       "      <td>0.099911</td>\n",
       "      <td>0.130549</td>\n",
       "      <td>0.169744</td>\n",
       "      <td>0.133609</td>\n",
       "      <td>0.169713</td>\n",
       "      <td>0.119224</td>\n",
       "      <td>0.141926</td>\n",
       "      <td>0.108736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>beethoven</td>\n",
       "      <td>Beethoven  Ludwig van(1770-1827)</td>\n",
       "      <td>Sonata No. 11 Bb major Opus 22</td>\n",
       "      <td>beethoven_opus22_3.mp3</td>\n",
       "      <td>3. Movement</td>\n",
       "      <td>Minuetto</td>\n",
       "      <td>123.046875</td>\n",
       "      <td>184.999184</td>\n",
       "      <td>183292</td>\n",
       "      <td>552.620512</td>\n",
       "      <td>...</td>\n",
       "      <td>0.148465</td>\n",
       "      <td>0.129310</td>\n",
       "      <td>0.129408</td>\n",
       "      <td>0.175399</td>\n",
       "      <td>0.150455</td>\n",
       "      <td>0.169945</td>\n",
       "      <td>0.196738</td>\n",
       "      <td>0.146167</td>\n",
       "      <td>0.161436</td>\n",
       "      <td>0.139485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>beethoven</td>\n",
       "      <td>Beethoven  Ludwig van(1770-1827)</td>\n",
       "      <td>Sonata No. 11 Bb major Opus 22</td>\n",
       "      <td>beethoven_opus22_4.mp3</td>\n",
       "      <td>4. Movement</td>\n",
       "      <td>Rondo-Allegretto</td>\n",
       "      <td>114.843750</td>\n",
       "      <td>286.145306</td>\n",
       "      <td>301332</td>\n",
       "      <td>591.557574</td>\n",
       "      <td>...</td>\n",
       "      <td>0.131816</td>\n",
       "      <td>0.143192</td>\n",
       "      <td>0.126775</td>\n",
       "      <td>0.149664</td>\n",
       "      <td>0.145004</td>\n",
       "      <td>0.136648</td>\n",
       "      <td>0.161246</td>\n",
       "      <td>0.145788</td>\n",
       "      <td>0.174377</td>\n",
       "      <td>0.136785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>beethoven</td>\n",
       "      <td>Beethoven  Ludwig van(1770-1827)</td>\n",
       "      <td>Sonata No. 14 C# minor(Moonlight) Opus 27/2</td>\n",
       "      <td>mond_1.mp3</td>\n",
       "      <td>1. Movement</td>\n",
       "      <td>Adagio sostenuto</td>\n",
       "      <td>143.554688</td>\n",
       "      <td>368.169796</td>\n",
       "      <td>312122</td>\n",
       "      <td>494.042753</td>\n",
       "      <td>...</td>\n",
       "      <td>0.133752</td>\n",
       "      <td>0.148160</td>\n",
       "      <td>0.179279</td>\n",
       "      <td>0.132102</td>\n",
       "      <td>0.134835</td>\n",
       "      <td>0.175077</td>\n",
       "      <td>0.132190</td>\n",
       "      <td>0.176021</td>\n",
       "      <td>0.133202</td>\n",
       "      <td>0.136353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>beethoven</td>\n",
       "      <td>Beethoven  Ludwig van(1770-1827)</td>\n",
       "      <td>Sonata No. 14 C# minor(Moonlight) Opus 27/2</td>\n",
       "      <td>mond_2.mp3</td>\n",
       "      <td>2. Movement</td>\n",
       "      <td>Allegretto</td>\n",
       "      <td>105.468750</td>\n",
       "      <td>126.171429</td>\n",
       "      <td>132338</td>\n",
       "      <td>621.451924</td>\n",
       "      <td>...</td>\n",
       "      <td>0.116196</td>\n",
       "      <td>0.107412</td>\n",
       "      <td>0.136405</td>\n",
       "      <td>0.097876</td>\n",
       "      <td>0.135593</td>\n",
       "      <td>0.096862</td>\n",
       "      <td>0.145431</td>\n",
       "      <td>0.145959</td>\n",
       "      <td>0.109440</td>\n",
       "      <td>0.156911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>beethoven</td>\n",
       "      <td>Beethoven  Ludwig van(1770-1827)</td>\n",
       "      <td>Sonata No. 21 C major(Waldstein) Opus 53</td>\n",
       "      <td>waldstein_1.mp3</td>\n",
       "      <td>1. Movement</td>\n",
       "      <td>Allegro con brio</td>\n",
       "      <td>105.468750</td>\n",
       "      <td>621.688163</td>\n",
       "      <td>702612</td>\n",
       "      <td>636.247847</td>\n",
       "      <td>...</td>\n",
       "      <td>0.131345</td>\n",
       "      <td>0.148408</td>\n",
       "      <td>0.108017</td>\n",
       "      <td>0.141555</td>\n",
       "      <td>0.105382</td>\n",
       "      <td>0.176480</td>\n",
       "      <td>0.172282</td>\n",
       "      <td>0.128237</td>\n",
       "      <td>0.200779</td>\n",
       "      <td>0.105336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>beethoven</td>\n",
       "      <td>Beethoven  Ludwig van(1770-1827)</td>\n",
       "      <td>Sonata No. 21 C major(Waldstein) Opus 53</td>\n",
       "      <td>waldstein_2.mp3</td>\n",
       "      <td>2. Movement</td>\n",
       "      <td>Adagio molto</td>\n",
       "      <td>87.592691</td>\n",
       "      <td>247.666939</td>\n",
       "      <td>192828</td>\n",
       "      <td>450.723972</td>\n",
       "      <td>...</td>\n",
       "      <td>0.173177</td>\n",
       "      <td>0.163273</td>\n",
       "      <td>0.152775</td>\n",
       "      <td>0.128672</td>\n",
       "      <td>0.189503</td>\n",
       "      <td>0.176843</td>\n",
       "      <td>0.176057</td>\n",
       "      <td>0.166450</td>\n",
       "      <td>0.152504</td>\n",
       "      <td>0.151681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>beethoven</td>\n",
       "      <td>Beethoven  Ludwig van(1770-1827)</td>\n",
       "      <td>Sonata No. 21 C major(Waldstein) Opus 53</td>\n",
       "      <td>waldstein_3.mp3</td>\n",
       "      <td>3. Movement</td>\n",
       "      <td>Allegro moderato</td>\n",
       "      <td>105.468750</td>\n",
       "      <td>581.955918</td>\n",
       "      <td>610616</td>\n",
       "      <td>580.741632</td>\n",
       "      <td>...</td>\n",
       "      <td>0.121536</td>\n",
       "      <td>0.132552</td>\n",
       "      <td>0.104888</td>\n",
       "      <td>0.135578</td>\n",
       "      <td>0.123498</td>\n",
       "      <td>0.198634</td>\n",
       "      <td>0.162182</td>\n",
       "      <td>0.099428</td>\n",
       "      <td>0.128543</td>\n",
       "      <td>0.102507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>beethoven</td>\n",
       "      <td>Beethoven  Ludwig van(1770-1827)</td>\n",
       "      <td>Sonata No. 23 F minor(Appassionata) Opus 57</td>\n",
       "      <td>appass_1.mp3</td>\n",
       "      <td>1. Movement</td>\n",
       "      <td>Allegro assai</td>\n",
       "      <td>120.185320</td>\n",
       "      <td>562.546939</td>\n",
       "      <td>588102</td>\n",
       "      <td>605.638087</td>\n",
       "      <td>...</td>\n",
       "      <td>0.143950</td>\n",
       "      <td>0.183551</td>\n",
       "      <td>0.185654</td>\n",
       "      <td>0.167580</td>\n",
       "      <td>0.157621</td>\n",
       "      <td>0.170062</td>\n",
       "      <td>0.177732</td>\n",
       "      <td>0.154572</td>\n",
       "      <td>0.156599</td>\n",
       "      <td>0.149271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>beethoven</td>\n",
       "      <td>Beethoven  Ludwig van(1770-1827)</td>\n",
       "      <td>Sonata No. 23 F minor(Appassionata) Opus 57</td>\n",
       "      <td>appass_2.mp3</td>\n",
       "      <td>2. Movement</td>\n",
       "      <td>Andante con moto</td>\n",
       "      <td>103.359375</td>\n",
       "      <td>371.774694</td>\n",
       "      <td>333476</td>\n",
       "      <td>533.579553</td>\n",
       "      <td>...</td>\n",
       "      <td>0.093595</td>\n",
       "      <td>0.104033</td>\n",
       "      <td>0.159964</td>\n",
       "      <td>0.098220</td>\n",
       "      <td>0.225842</td>\n",
       "      <td>0.131910</td>\n",
       "      <td>0.188316</td>\n",
       "      <td>0.142919</td>\n",
       "      <td>0.113406</td>\n",
       "      <td>0.153785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>beethoven</td>\n",
       "      <td>Beethoven  Ludwig van(1770-1827)</td>\n",
       "      <td>Sonata No. 23 F minor(Appassionata) Opus 57</td>\n",
       "      <td>appass_3.mp3</td>\n",
       "      <td>3. Movement</td>\n",
       "      <td>Allegro ma non troppo</td>\n",
       "      <td>139.674831</td>\n",
       "      <td>468.767347</td>\n",
       "      <td>508932</td>\n",
       "      <td>622.679336</td>\n",
       "      <td>...</td>\n",
       "      <td>0.128914</td>\n",
       "      <td>0.169054</td>\n",
       "      <td>0.154957</td>\n",
       "      <td>0.092752</td>\n",
       "      <td>0.113583</td>\n",
       "      <td>0.175141</td>\n",
       "      <td>0.178648</td>\n",
       "      <td>0.128323</td>\n",
       "      <td>0.169087</td>\n",
       "      <td>0.141750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>beethoven</td>\n",
       "      <td>Beethoven  Ludwig van(1770-1827)</td>\n",
       "      <td>Sonata No. 26 Eb major(Les Adieux) Opus 81a</td>\n",
       "      <td>beethoven_les_adieux_1.mp3</td>\n",
       "      <td>1. Movement</td>\n",
       "      <td>Adagio</td>\n",
       "      <td>123.046875</td>\n",
       "      <td>345.182041</td>\n",
       "      <td>363008</td>\n",
       "      <td>585.345616</td>\n",
       "      <td>...</td>\n",
       "      <td>0.169361</td>\n",
       "      <td>0.180378</td>\n",
       "      <td>0.146605</td>\n",
       "      <td>0.137997</td>\n",
       "      <td>0.141499</td>\n",
       "      <td>0.154004</td>\n",
       "      <td>0.170220</td>\n",
       "      <td>0.139611</td>\n",
       "      <td>0.188848</td>\n",
       "      <td>0.154492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>beethoven</td>\n",
       "      <td>Beethoven  Ludwig van(1770-1827)</td>\n",
       "      <td>Sonata No. 26 Eb major(Les Adieux) Opus 81a</td>\n",
       "      <td>beethoven_les_adieux_2.mp3</td>\n",
       "      <td>2. Movement</td>\n",
       "      <td>Andante espressivo</td>\n",
       "      <td>123.046875</td>\n",
       "      <td>176.248163</td>\n",
       "      <td>187066</td>\n",
       "      <td>547.044611</td>\n",
       "      <td>...</td>\n",
       "      <td>0.192799</td>\n",
       "      <td>0.168409</td>\n",
       "      <td>0.139489</td>\n",
       "      <td>0.200261</td>\n",
       "      <td>0.177890</td>\n",
       "      <td>0.139943</td>\n",
       "      <td>0.187929</td>\n",
       "      <td>0.131064</td>\n",
       "      <td>0.206620</td>\n",
       "      <td>0.156995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>beethoven</td>\n",
       "      <td>Beethoven  Ludwig van(1770-1827)</td>\n",
       "      <td>Sonata No. 26 Eb major(Les Adieux) Opus 81a</td>\n",
       "      <td>beethoven_les_adieux_3.mp3</td>\n",
       "      <td>3. Movement</td>\n",
       "      <td>Vivacissimamente</td>\n",
       "      <td>109.956782</td>\n",
       "      <td>304.274286</td>\n",
       "      <td>345640</td>\n",
       "      <td>660.312470</td>\n",
       "      <td>...</td>\n",
       "      <td>0.102362</td>\n",
       "      <td>0.157916</td>\n",
       "      <td>0.115170</td>\n",
       "      <td>0.154029</td>\n",
       "      <td>0.120230</td>\n",
       "      <td>0.115631</td>\n",
       "      <td>0.157457</td>\n",
       "      <td>0.092486</td>\n",
       "      <td>0.148769</td>\n",
       "      <td>0.110015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>beethoven</td>\n",
       "      <td>Beethoven  Ludwig van(1770-1827)</td>\n",
       "      <td>For Elise</td>\n",
       "      <td>elise.mp3</td>\n",
       "      <td>For Elise</td>\n",
       "      <td>Poco moto</td>\n",
       "      <td>135.999178</td>\n",
       "      <td>167.601633</td>\n",
       "      <td>168700</td>\n",
       "      <td>545.958378</td>\n",
       "      <td>...</td>\n",
       "      <td>0.142600</td>\n",
       "      <td>0.132634</td>\n",
       "      <td>0.132222</td>\n",
       "      <td>0.194048</td>\n",
       "      <td>0.132569</td>\n",
       "      <td>0.175152</td>\n",
       "      <td>0.121340</td>\n",
       "      <td>0.112003</td>\n",
       "      <td>0.154373</td>\n",
       "      <td>0.151393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>beethoven</td>\n",
       "      <td>Beethoven  Ludwig van(1770-1827)</td>\n",
       "      <td>Sonata No. 27 E minor Opus 90</td>\n",
       "      <td>beethoven_opus90_1.mp3</td>\n",
       "      <td>1. Movement</td>\n",
       "      <td>Mit Lebhaftigkeit und durchaus mit Empfindung ...</td>\n",
       "      <td>151.999081</td>\n",
       "      <td>295.079184</td>\n",
       "      <td>293070</td>\n",
       "      <td>540.946245</td>\n",
       "      <td>...</td>\n",
       "      <td>0.149050</td>\n",
       "      <td>0.136343</td>\n",
       "      <td>0.145192</td>\n",
       "      <td>0.152806</td>\n",
       "      <td>0.166919</td>\n",
       "      <td>0.235471</td>\n",
       "      <td>0.142020</td>\n",
       "      <td>0.131507</td>\n",
       "      <td>0.165348</td>\n",
       "      <td>0.137968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>beethoven</td>\n",
       "      <td>Beethoven  Ludwig van(1770-1827)</td>\n",
       "      <td>Sonata No. 27 E minor Opus 90</td>\n",
       "      <td>beethoven_opus90_2.mp3</td>\n",
       "      <td>2. Movement</td>\n",
       "      <td>Nicht zu geschwind und sehr singbar vorgetragen</td>\n",
       "      <td>139.674831</td>\n",
       "      <td>496.666122</td>\n",
       "      <td>496902</td>\n",
       "      <td>530.535298</td>\n",
       "      <td>...</td>\n",
       "      <td>0.146659</td>\n",
       "      <td>0.100206</td>\n",
       "      <td>0.146107</td>\n",
       "      <td>0.137619</td>\n",
       "      <td>0.148320</td>\n",
       "      <td>0.189121</td>\n",
       "      <td>0.129771</td>\n",
       "      <td>0.168571</td>\n",
       "      <td>0.120082</td>\n",
       "      <td>0.165217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>beethoven</td>\n",
       "      <td>Beethoven  Ludwig van(1770-1827)</td>\n",
       "      <td>Sonata No. 29 Bb major(Hammerklavier) Opus 106</td>\n",
       "      <td>beethoven_hammerklavier_1.mp3</td>\n",
       "      <td>1. Movement</td>\n",
       "      <td>Allegro</td>\n",
       "      <td>112.347147</td>\n",
       "      <td>595.330612</td>\n",
       "      <td>774374</td>\n",
       "      <td>681.895016</td>\n",
       "      <td>...</td>\n",
       "      <td>0.096649</td>\n",
       "      <td>0.144549</td>\n",
       "      <td>0.123033</td>\n",
       "      <td>0.132664</td>\n",
       "      <td>0.129792</td>\n",
       "      <td>0.111455</td>\n",
       "      <td>0.140494</td>\n",
       "      <td>0.093406</td>\n",
       "      <td>0.164675</td>\n",
       "      <td>0.135279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>beethoven</td>\n",
       "      <td>Beethoven  Ludwig van(1770-1827)</td>\n",
       "      <td>Sonata No. 29 Bb major(Hammerklavier) Opus 106</td>\n",
       "      <td>beethoven_hammerklavier_2.mp3</td>\n",
       "      <td>2. Movement</td>\n",
       "      <td>Scherzo</td>\n",
       "      <td>114.843750</td>\n",
       "      <td>138.004898</td>\n",
       "      <td>154132</td>\n",
       "      <td>588.215204</td>\n",
       "      <td>...</td>\n",
       "      <td>0.115932</td>\n",
       "      <td>0.143798</td>\n",
       "      <td>0.114542</td>\n",
       "      <td>0.147561</td>\n",
       "      <td>0.127767</td>\n",
       "      <td>0.148698</td>\n",
       "      <td>0.204054</td>\n",
       "      <td>0.101446</td>\n",
       "      <td>0.177308</td>\n",
       "      <td>0.120155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>beethoven</td>\n",
       "      <td>Beethoven  Ludwig van(1770-1827)</td>\n",
       "      <td>Sonata No. 29 Bb major(Hammerklavier) Opus 106</td>\n",
       "      <td>beethoven_hammerklavier_3.mp3</td>\n",
       "      <td>3. Movement</td>\n",
       "      <td>Adagio sostenuto</td>\n",
       "      <td>90.666118</td>\n",
       "      <td>765.727347</td>\n",
       "      <td>725502</td>\n",
       "      <td>476.077863</td>\n",
       "      <td>...</td>\n",
       "      <td>0.200215</td>\n",
       "      <td>0.147396</td>\n",
       "      <td>0.195893</td>\n",
       "      <td>0.162455</td>\n",
       "      <td>0.156692</td>\n",
       "      <td>0.177100</td>\n",
       "      <td>0.199258</td>\n",
       "      <td>0.165242</td>\n",
       "      <td>0.114056</td>\n",
       "      <td>0.139364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>beethoven</td>\n",
       "      <td>Beethoven  Ludwig van(1770-1827)</td>\n",
       "      <td>Sonata No. 29 Bb major(Hammerklavier) Opus 106</td>\n",
       "      <td>beethoven_hammerklavier_4.mp3</td>\n",
       "      <td>4. Movement</td>\n",
       "      <td>Largo - Allegro</td>\n",
       "      <td>132.512019</td>\n",
       "      <td>645.642449</td>\n",
       "      <td>692146</td>\n",
       "      <td>616.518193</td>\n",
       "      <td>...</td>\n",
       "      <td>0.184551</td>\n",
       "      <td>0.134429</td>\n",
       "      <td>0.193379</td>\n",
       "      <td>0.155816</td>\n",
       "      <td>0.172381</td>\n",
       "      <td>0.192749</td>\n",
       "      <td>0.200937</td>\n",
       "      <td>0.166324</td>\n",
       "      <td>0.164770</td>\n",
       "      <td>0.155315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>borodin</td>\n",
       "      <td>Borodin  Alexander(1833-1887)</td>\n",
       "      <td>Petite Suite</td>\n",
       "      <td>bor_ps1.mp3</td>\n",
       "      <td>In the monastery</td>\n",
       "      <td>Andante religioso</td>\n",
       "      <td>83.354335</td>\n",
       "      <td>261.328980</td>\n",
       "      <td>235808</td>\n",
       "      <td>554.005008</td>\n",
       "      <td>...</td>\n",
       "      <td>0.147801</td>\n",
       "      <td>0.201036</td>\n",
       "      <td>0.195617</td>\n",
       "      <td>0.154318</td>\n",
       "      <td>0.193853</td>\n",
       "      <td>0.172541</td>\n",
       "      <td>0.132465</td>\n",
       "      <td>0.188293</td>\n",
       "      <td>0.130467</td>\n",
       "      <td>0.154484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>borodin</td>\n",
       "      <td>Borodin  Alexander(1833-1887)</td>\n",
       "      <td>Petite Suite</td>\n",
       "      <td>bor_ps2.mp3</td>\n",
       "      <td>Intermezzo</td>\n",
       "      <td>Tempo di minuetto</td>\n",
       "      <td>129.199219</td>\n",
       "      <td>165.537959</td>\n",
       "      <td>173936</td>\n",
       "      <td>651.111392</td>\n",
       "      <td>...</td>\n",
       "      <td>0.125475</td>\n",
       "      <td>0.142821</td>\n",
       "      <td>0.147564</td>\n",
       "      <td>0.170337</td>\n",
       "      <td>0.171148</td>\n",
       "      <td>0.142108</td>\n",
       "      <td>0.161789</td>\n",
       "      <td>0.130092</td>\n",
       "      <td>0.188598</td>\n",
       "      <td>0.132631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>borodin</td>\n",
       "      <td>Borodin  Alexander(1833-1887)</td>\n",
       "      <td>Petite Suite</td>\n",
       "      <td>bor_ps3.mp3</td>\n",
       "      <td>Mazurka</td>\n",
       "      <td>Allegro</td>\n",
       "      <td>172.265625</td>\n",
       "      <td>160.208980</td>\n",
       "      <td>176982</td>\n",
       "      <td>684.618830</td>\n",
       "      <td>...</td>\n",
       "      <td>0.140364</td>\n",
       "      <td>0.154573</td>\n",
       "      <td>0.122219</td>\n",
       "      <td>0.145019</td>\n",
       "      <td>0.114124</td>\n",
       "      <td>0.140863</td>\n",
       "      <td>0.146510</td>\n",
       "      <td>0.114351</td>\n",
       "      <td>0.178900</td>\n",
       "      <td>0.135758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>borodin</td>\n",
       "      <td>Borodin  Alexander(1833-1887)</td>\n",
       "      <td>Petite Suite</td>\n",
       "      <td>bor_ps4.mp3</td>\n",
       "      <td>Mazurka</td>\n",
       "      <td>Allegretto</td>\n",
       "      <td>129.199219</td>\n",
       "      <td>191.921633</td>\n",
       "      <td>188254</td>\n",
       "      <td>632.641128</td>\n",
       "      <td>...</td>\n",
       "      <td>0.158683</td>\n",
       "      <td>0.203712</td>\n",
       "      <td>0.197448</td>\n",
       "      <td>0.175578</td>\n",
       "      <td>0.157561</td>\n",
       "      <td>0.164260</td>\n",
       "      <td>0.209342</td>\n",
       "      <td>0.156374</td>\n",
       "      <td>0.154712</td>\n",
       "      <td>0.169909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>borodin</td>\n",
       "      <td>Borodin  Alexander(1833-1887)</td>\n",
       "      <td>Petite Suite</td>\n",
       "      <td>bor_ps5.mp3</td>\n",
       "      <td>Reverie</td>\n",
       "      <td>Andante</td>\n",
       "      <td>143.554688</td>\n",
       "      <td>97.802449</td>\n",
       "      <td>88802</td>\n",
       "      <td>574.734702</td>\n",
       "      <td>...</td>\n",
       "      <td>0.129292</td>\n",
       "      <td>0.142757</td>\n",
       "      <td>0.178079</td>\n",
       "      <td>0.139173</td>\n",
       "      <td>0.160313</td>\n",
       "      <td>0.126322</td>\n",
       "      <td>0.160557</td>\n",
       "      <td>0.180701</td>\n",
       "      <td>0.143493</td>\n",
       "      <td>0.214164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>borodin</td>\n",
       "      <td>Borodin  Alexander(1833-1887)</td>\n",
       "      <td>Petite Suite</td>\n",
       "      <td>bor_ps6.mp3</td>\n",
       "      <td>Serenade</td>\n",
       "      <td>Allegretto</td>\n",
       "      <td>143.554688</td>\n",
       "      <td>114.442449</td>\n",
       "      <td>106482</td>\n",
       "      <td>601.668027</td>\n",
       "      <td>...</td>\n",
       "      <td>0.076352</td>\n",
       "      <td>0.091228</td>\n",
       "      <td>0.096272</td>\n",
       "      <td>0.102574</td>\n",
       "      <td>0.150193</td>\n",
       "      <td>0.095224</td>\n",
       "      <td>0.135566</td>\n",
       "      <td>0.121046</td>\n",
       "      <td>0.104194</td>\n",
       "      <td>0.106733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>borodin</td>\n",
       "      <td>Borodin  Alexander(1833-1887)</td>\n",
       "      <td>Petite Suite</td>\n",
       "      <td>bor_ps7.mp3</td>\n",
       "      <td>Nocturne</td>\n",
       "      <td>Andantino</td>\n",
       "      <td>166.708669</td>\n",
       "      <td>125.387755</td>\n",
       "      <td>118402</td>\n",
       "      <td>593.992851</td>\n",
       "      <td>...</td>\n",
       "      <td>0.132081</td>\n",
       "      <td>0.144864</td>\n",
       "      <td>0.194309</td>\n",
       "      <td>0.136356</td>\n",
       "      <td>0.148404</td>\n",
       "      <td>0.132709</td>\n",
       "      <td>0.131239</td>\n",
       "      <td>0.155766</td>\n",
       "      <td>0.106609</td>\n",
       "      <td>0.195507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>brahms</td>\n",
       "      <td>Brahms  Johannes(1833-1897)</td>\n",
       "      <td>Sonata C major Opus 1</td>\n",
       "      <td>brahms_opus1_1.mp3</td>\n",
       "      <td>1. Movement</td>\n",
       "      <td>Allegro</td>\n",
       "      <td>112.347147</td>\n",
       "      <td>692.088163</td>\n",
       "      <td>1031058</td>\n",
       "      <td>764.429737</td>\n",
       "      <td>...</td>\n",
       "      <td>0.105437</td>\n",
       "      <td>0.111414</td>\n",
       "      <td>0.081889</td>\n",
       "      <td>0.145014</td>\n",
       "      <td>0.097960</td>\n",
       "      <td>0.148631</td>\n",
       "      <td>0.117504</td>\n",
       "      <td>0.097057</td>\n",
       "      <td>0.156097</td>\n",
       "      <td>0.099269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>brahms</td>\n",
       "      <td>Brahms  Johannes(1833-1897)</td>\n",
       "      <td>Sonata C major Opus 1</td>\n",
       "      <td>brahms_opus1_2.mp3</td>\n",
       "      <td>2. Movement</td>\n",
       "      <td>Andante</td>\n",
       "      <td>117.453835</td>\n",
       "      <td>284.290612</td>\n",
       "      <td>314106</td>\n",
       "      <td>582.421503</td>\n",
       "      <td>...</td>\n",
       "      <td>0.177739</td>\n",
       "      <td>0.219122</td>\n",
       "      <td>0.143668</td>\n",
       "      <td>0.211349</td>\n",
       "      <td>0.201344</td>\n",
       "      <td>0.154176</td>\n",
       "      <td>0.096331</td>\n",
       "      <td>0.116597</td>\n",
       "      <td>0.200906</td>\n",
       "      <td>0.124421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>brahms</td>\n",
       "      <td>Brahms  Johannes(1833-1897)</td>\n",
       "      <td>Sonata C major Opus 1</td>\n",
       "      <td>brahms_opus1_3.mp3</td>\n",
       "      <td>3. Movement</td>\n",
       "      <td>Scherzo</td>\n",
       "      <td>178.205819</td>\n",
       "      <td>385.488980</td>\n",
       "      <td>519252</td>\n",
       "      <td>700.663724</td>\n",
       "      <td>...</td>\n",
       "      <td>0.200612</td>\n",
       "      <td>0.141636</td>\n",
       "      <td>0.131593</td>\n",
       "      <td>0.167637</td>\n",
       "      <td>0.147170</td>\n",
       "      <td>0.191785</td>\n",
       "      <td>0.113779</td>\n",
       "      <td>0.167865</td>\n",
       "      <td>0.165050</td>\n",
       "      <td>0.137565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>brahms</td>\n",
       "      <td>Brahms  Johannes(1833-1897)</td>\n",
       "      <td>Sonata C major Opus 1</td>\n",
       "      <td>brahms_opus1_4.mp3</td>\n",
       "      <td>4. Movement</td>\n",
       "      <td>Finale - Allegro con fuoco</td>\n",
       "      <td>132.512019</td>\n",
       "      <td>421.015510</td>\n",
       "      <td>590966</td>\n",
       "      <td>734.213189</td>\n",
       "      <td>...</td>\n",
       "      <td>0.103566</td>\n",
       "      <td>0.103910</td>\n",
       "      <td>0.088926</td>\n",
       "      <td>0.082075</td>\n",
       "      <td>0.092075</td>\n",
       "      <td>0.133124</td>\n",
       "      <td>0.091235</td>\n",
       "      <td>0.121015</td>\n",
       "      <td>0.134277</td>\n",
       "      <td>0.077988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>brahms</td>\n",
       "      <td>Brahms  Johannes(1833-1897)</td>\n",
       "      <td>7 Fantasia Opus 116</td>\n",
       "      <td>br_im2.mp3</td>\n",
       "      <td>No. 2 - Intermezzo</td>\n",
       "      <td>Andante</td>\n",
       "      <td>143.554688</td>\n",
       "      <td>163.186939</td>\n",
       "      <td>198250</td>\n",
       "      <td>633.863684</td>\n",
       "      <td>...</td>\n",
       "      <td>0.143528</td>\n",
       "      <td>0.130796</td>\n",
       "      <td>0.111139</td>\n",
       "      <td>0.160540</td>\n",
       "      <td>0.130326</td>\n",
       "      <td>0.149402</td>\n",
       "      <td>0.125759</td>\n",
       "      <td>0.114903</td>\n",
       "      <td>0.129063</td>\n",
       "      <td>0.090374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>brahms</td>\n",
       "      <td>Brahms  Johannes(1833-1897)</td>\n",
       "      <td>7 Fantasia Opus 116</td>\n",
       "      <td>br_im5.mp3</td>\n",
       "      <td>No. 5 - Intermezzo</td>\n",
       "      <td>Andante con grazia ed intimissomo sentimento</td>\n",
       "      <td>161.499023</td>\n",
       "      <td>177.293061</td>\n",
       "      <td>197878</td>\n",
       "      <td>589.114445</td>\n",
       "      <td>...</td>\n",
       "      <td>0.151926</td>\n",
       "      <td>0.123093</td>\n",
       "      <td>0.141053</td>\n",
       "      <td>0.157537</td>\n",
       "      <td>0.111362</td>\n",
       "      <td>0.164461</td>\n",
       "      <td>0.111019</td>\n",
       "      <td>0.137527</td>\n",
       "      <td>0.158093</td>\n",
       "      <td>0.138278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>brahms</td>\n",
       "      <td>Brahms  Johannes(1833-1897)</td>\n",
       "      <td>7 Fantasia Opus 116</td>\n",
       "      <td>br_im6.mp3</td>\n",
       "      <td>No. 6 - Intermezzo</td>\n",
       "      <td>Andantino teneramente</td>\n",
       "      <td>95.703125</td>\n",
       "      <td>144.013061</td>\n",
       "      <td>156344</td>\n",
       "      <td>609.424457</td>\n",
       "      <td>...</td>\n",
       "      <td>0.161649</td>\n",
       "      <td>0.106614</td>\n",
       "      <td>0.187461</td>\n",
       "      <td>0.125560</td>\n",
       "      <td>0.149242</td>\n",
       "      <td>0.161210</td>\n",
       "      <td>0.121903</td>\n",
       "      <td>0.171299</td>\n",
       "      <td>0.119107</td>\n",
       "      <td>0.153919</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     composer                               description  \\\n",
       "0     albeniz                 Albeniz  Isaac(1860-1909)   \n",
       "1     albeniz                 Albeniz  Isaac(1860-1909)   \n",
       "2     albeniz                 Albeniz  Isaac(1860-1909)   \n",
       "3     albeniz                 Albeniz  Isaac(1860-1909)   \n",
       "4     albeniz                 Albeniz  Isaac(1860-1909)   \n",
       "5     albeniz                 Albeniz  Isaac(1860-1909)   \n",
       "6     albeniz                 Albeniz  Isaac(1860-1909)   \n",
       "7     albeniz                 Albeniz  Isaac(1860-1909)   \n",
       "8     albeniz                 Albeniz  Isaac(1860-1909)   \n",
       "9     albeniz                 Albeniz  Isaac(1860-1909)   \n",
       "10    albeniz                 Albeniz  Isaac(1860-1909)   \n",
       "11    albeniz                 Albeniz  Isaac(1860-1909)   \n",
       "12    albeniz                 Albeniz  Isaac(1860-1909)   \n",
       "13    albeniz                 Albeniz  Isaac(1860-1909)   \n",
       "14       bach         Bach  Johann Sebastian(1685-1750)   \n",
       "15       bach         Bach  Johann Sebastian(1685-1750)   \n",
       "16       bach         Bach  Johann Sebastian(1685-1750)   \n",
       "17      balak  Balakirew  Mili Alexejewitsch(1837-1910)   \n",
       "18  beethoven          Beethoven  Ludwig van(1770-1827)   \n",
       "19  beethoven          Beethoven  Ludwig van(1770-1827)   \n",
       "20  beethoven          Beethoven  Ludwig van(1770-1827)   \n",
       "21  beethoven          Beethoven  Ludwig van(1770-1827)   \n",
       "22  beethoven          Beethoven  Ludwig van(1770-1827)   \n",
       "23  beethoven          Beethoven  Ludwig van(1770-1827)   \n",
       "24  beethoven          Beethoven  Ludwig van(1770-1827)   \n",
       "25  beethoven          Beethoven  Ludwig van(1770-1827)   \n",
       "26  beethoven          Beethoven  Ludwig van(1770-1827)   \n",
       "27  beethoven          Beethoven  Ludwig van(1770-1827)   \n",
       "28  beethoven          Beethoven  Ludwig van(1770-1827)   \n",
       "29  beethoven          Beethoven  Ludwig van(1770-1827)   \n",
       "..        ...                                       ...   \n",
       "31  beethoven          Beethoven  Ludwig van(1770-1827)   \n",
       "32  beethoven          Beethoven  Ludwig van(1770-1827)   \n",
       "33  beethoven          Beethoven  Ludwig van(1770-1827)   \n",
       "34  beethoven          Beethoven  Ludwig van(1770-1827)   \n",
       "35  beethoven          Beethoven  Ludwig van(1770-1827)   \n",
       "36  beethoven          Beethoven  Ludwig van(1770-1827)   \n",
       "37  beethoven          Beethoven  Ludwig van(1770-1827)   \n",
       "38  beethoven          Beethoven  Ludwig van(1770-1827)   \n",
       "39  beethoven          Beethoven  Ludwig van(1770-1827)   \n",
       "40  beethoven          Beethoven  Ludwig van(1770-1827)   \n",
       "41  beethoven          Beethoven  Ludwig van(1770-1827)   \n",
       "42  beethoven          Beethoven  Ludwig van(1770-1827)   \n",
       "43  beethoven          Beethoven  Ludwig van(1770-1827)   \n",
       "44  beethoven          Beethoven  Ludwig van(1770-1827)   \n",
       "45  beethoven          Beethoven  Ludwig van(1770-1827)   \n",
       "46  beethoven          Beethoven  Ludwig van(1770-1827)   \n",
       "47    borodin             Borodin  Alexander(1833-1887)   \n",
       "48    borodin             Borodin  Alexander(1833-1887)   \n",
       "49    borodin             Borodin  Alexander(1833-1887)   \n",
       "50    borodin             Borodin  Alexander(1833-1887)   \n",
       "51    borodin             Borodin  Alexander(1833-1887)   \n",
       "52    borodin             Borodin  Alexander(1833-1887)   \n",
       "53    borodin             Borodin  Alexander(1833-1887)   \n",
       "54     brahms               Brahms  Johannes(1833-1897)   \n",
       "55     brahms               Brahms  Johannes(1833-1897)   \n",
       "56     brahms               Brahms  Johannes(1833-1897)   \n",
       "57     brahms               Brahms  Johannes(1833-1897)   \n",
       "58     brahms               Brahms  Johannes(1833-1897)   \n",
       "59     brahms               Brahms  Johannes(1833-1897)   \n",
       "60     brahms               Brahms  Johannes(1833-1897)   \n",
       "\n",
       "                                          symphony  \\\n",
       "0                                  Espana Opus 165   \n",
       "1                                  Espana Opus 165   \n",
       "2                                  Espana Opus 165   \n",
       "3                                  Espana Opus 165   \n",
       "4                                  Espana Opus 165   \n",
       "5                                  Espana Opus 165   \n",
       "6                                   Suite espanola   \n",
       "7                                   Suite espanola   \n",
       "8                                   Suite espanola   \n",
       "9                                   Suite espanola   \n",
       "10                                  Suite espanola   \n",
       "11                                  Suite espanola   \n",
       "12                                  Suite espanola   \n",
       "13                                  Suite espanola   \n",
       "14    The Well-Tempered Clavier Part I BWV 846-869   \n",
       "15    The Well-Tempered Clavier Part I BWV 846-869   \n",
       "16    The Well-Tempered Clavier Part I BWV 846-869   \n",
       "17                      Islamej(Oriental fantasia)   \n",
       "18                  Sonata No. 5 C minor Opus 10/1   \n",
       "19                  Sonata No. 5 C minor Opus 10/1   \n",
       "20                  Sonata No. 5 C minor Opus 10/1   \n",
       "21        Sonata No. 8 C minor(Pathetique) Opus 13   \n",
       "22        Sonata No. 8 C minor(Pathetique) Opus 13   \n",
       "23        Sonata No. 8 C minor(Pathetique) Opus 13   \n",
       "24                  Sonata No. 11 Bb major Opus 22   \n",
       "25                  Sonata No. 11 Bb major Opus 22   \n",
       "26                  Sonata No. 11 Bb major Opus 22   \n",
       "27                  Sonata No. 11 Bb major Opus 22   \n",
       "28     Sonata No. 14 C# minor(Moonlight) Opus 27/2   \n",
       "29     Sonata No. 14 C# minor(Moonlight) Opus 27/2   \n",
       "..                                             ...   \n",
       "31        Sonata No. 21 C major(Waldstein) Opus 53   \n",
       "32        Sonata No. 21 C major(Waldstein) Opus 53   \n",
       "33        Sonata No. 21 C major(Waldstein) Opus 53   \n",
       "34     Sonata No. 23 F minor(Appassionata) Opus 57   \n",
       "35     Sonata No. 23 F minor(Appassionata) Opus 57   \n",
       "36     Sonata No. 23 F minor(Appassionata) Opus 57   \n",
       "37     Sonata No. 26 Eb major(Les Adieux) Opus 81a   \n",
       "38     Sonata No. 26 Eb major(Les Adieux) Opus 81a   \n",
       "39     Sonata No. 26 Eb major(Les Adieux) Opus 81a   \n",
       "40                                       For Elise   \n",
       "41                   Sonata No. 27 E minor Opus 90   \n",
       "42                   Sonata No. 27 E minor Opus 90   \n",
       "43  Sonata No. 29 Bb major(Hammerklavier) Opus 106   \n",
       "44  Sonata No. 29 Bb major(Hammerklavier) Opus 106   \n",
       "45  Sonata No. 29 Bb major(Hammerklavier) Opus 106   \n",
       "46  Sonata No. 29 Bb major(Hammerklavier) Opus 106   \n",
       "47                                    Petite Suite   \n",
       "48                                    Petite Suite   \n",
       "49                                    Petite Suite   \n",
       "50                                    Petite Suite   \n",
       "51                                    Petite Suite   \n",
       "52                                    Petite Suite   \n",
       "53                                    Petite Suite   \n",
       "54                           Sonata C major Opus 1   \n",
       "55                           Sonata C major Opus 1   \n",
       "56                           Sonata C major Opus 1   \n",
       "57                           Sonata C major Opus 1   \n",
       "58                             7 Fantasia Opus 116   \n",
       "59                             7 Fantasia Opus 116   \n",
       "60                             7 Fantasia Opus 116   \n",
       "\n",
       "                             file                                  part  \\\n",
       "0                    alb_esp1.mp3                               Prelude   \n",
       "1                    alb_esp2.mp3                                 Tango   \n",
       "2                    alb_esp3.mp3                             Malaguena   \n",
       "3                    alb_esp4.mp3                              Serenata   \n",
       "4                    alb_esp5.mp3                      Capricho Catalan   \n",
       "5                    alb_esp6.mp3                              Zortzico   \n",
       "6                     alb_se1.mp3                               Granada   \n",
       "7                     alb_se2.mp3                   Cataluna (Curranda)   \n",
       "8                     alb_se3.mp3                               Sevilla   \n",
       "9                     alb_se4.mp3                         Cádiz (Saeta)   \n",
       "10                    alb_se5.mp3                    Asturias (Leyenda)   \n",
       "11                    alb_se6.mp3                     Aragon (Fantasia)   \n",
       "12                    alb_se7.mp3                Castilla (Seguidillas)   \n",
       "13                    alb_se8.mp3                       Cuba (Notturno)   \n",
       "14                   bach_846.mp3  Prelude and Fugue in C major BWV 846   \n",
       "15                   bach_847.mp3  Prelude and Fugue in C minor BWV 847   \n",
       "16                   bach_850.mp3  Prelude and Fugue in D major BWV 850   \n",
       "17                    islamei.mp3                               Islamej   \n",
       "18         beethoven_opus10_1.mp3                           1. Movement   \n",
       "19         beethoven_opus10_2.mp3                           2. Movement   \n",
       "20         beethoven_opus10_3.mp3                           3. Movement   \n",
       "21               pathetique_1.mp3                           1. Movement   \n",
       "22               pathetique_2.mp3                           2. Movement   \n",
       "23               pathetique_3.mp3                           3. Movement   \n",
       "24         beethoven_opus22_1.mp3                           1. Movement   \n",
       "25         beethoven_opus22_2.mp3                           2. Movement   \n",
       "26         beethoven_opus22_3.mp3                           3. Movement   \n",
       "27         beethoven_opus22_4.mp3                           4. Movement   \n",
       "28                     mond_1.mp3                           1. Movement   \n",
       "29                     mond_2.mp3                           2. Movement   \n",
       "..                            ...                                   ...   \n",
       "31                waldstein_1.mp3                           1. Movement   \n",
       "32                waldstein_2.mp3                           2. Movement   \n",
       "33                waldstein_3.mp3                           3. Movement   \n",
       "34                   appass_1.mp3                           1. Movement   \n",
       "35                   appass_2.mp3                           2. Movement   \n",
       "36                   appass_3.mp3                           3. Movement   \n",
       "37     beethoven_les_adieux_1.mp3                           1. Movement   \n",
       "38     beethoven_les_adieux_2.mp3                           2. Movement   \n",
       "39     beethoven_les_adieux_3.mp3                           3. Movement   \n",
       "40                      elise.mp3                             For Elise   \n",
       "41         beethoven_opus90_1.mp3                           1. Movement   \n",
       "42         beethoven_opus90_2.mp3                           2. Movement   \n",
       "43  beethoven_hammerklavier_1.mp3                           1. Movement   \n",
       "44  beethoven_hammerklavier_2.mp3                           2. Movement   \n",
       "45  beethoven_hammerklavier_3.mp3                           3. Movement   \n",
       "46  beethoven_hammerklavier_4.mp3                           4. Movement   \n",
       "47                    bor_ps1.mp3                      In the monastery   \n",
       "48                    bor_ps2.mp3                            Intermezzo   \n",
       "49                    bor_ps3.mp3                               Mazurka   \n",
       "50                    bor_ps4.mp3                               Mazurka   \n",
       "51                    bor_ps5.mp3                               Reverie   \n",
       "52                    bor_ps6.mp3                              Serenade   \n",
       "53                    bor_ps7.mp3                              Nocturne   \n",
       "54             brahms_opus1_1.mp3                           1. Movement   \n",
       "55             brahms_opus1_2.mp3                           2. Movement   \n",
       "56             brahms_opus1_3.mp3                           3. Movement   \n",
       "57             brahms_opus1_4.mp3                           4. Movement   \n",
       "58                     br_im2.mp3                    No. 2 - Intermezzo   \n",
       "59                     br_im5.mp3                    No. 5 - Intermezzo   \n",
       "60                     br_im6.mp3                    No. 6 - Intermezzo   \n",
       "\n",
       "                                                tempo         bpm    duration  \\\n",
       "0                                           Andantino  120.185320   93.570612   \n",
       "1                                           Andantino  120.185320  128.705306   \n",
       "2                                          Allegretto  117.453835  176.039184   \n",
       "3                                          Allegretto  112.347147  195.422041   \n",
       "4                                          Allegretto  120.185320  124.499592   \n",
       "5                                          Allegretto  112.347147  154.827755   \n",
       "6                                          Allegretto  132.512019  241.658776   \n",
       "7                                             Allegro  156.605114  240.979592   \n",
       "8                                          Allegretto  109.956782  251.872653   \n",
       "9                                          Allegretto   99.384014  221.100408   \n",
       "10                                            Allegro  132.512019  305.371429   \n",
       "11                                            Allegro  105.468750  243.513469   \n",
       "12                                            Allegro  166.708669  156.395102   \n",
       "13                                         Allegretto  135.999178  272.222041   \n",
       "14                                                NaN  126.048018  227.317551   \n",
       "15                                                NaN  143.554688  191.529796   \n",
       "16                                                NaN  117.453835  176.195918   \n",
       "17                                   Presto con fuoco  126.048018  498.485986   \n",
       "18                           Allegro molto e con brio  178.205819  394.057143   \n",
       "19                                       Adagio molto  139.674831  393.639184   \n",
       "20                                        Prestissimo   99.384014  219.193469   \n",
       "21                Grave - Allegro di molto e con brio  151.999081  562.677551   \n",
       "22                                   Adagio cantabile  109.956782  309.733878   \n",
       "23                                      Rondo allegro  103.359375  260.048980   \n",
       "24                                   Allegro con brio  161.499023  402.991020   \n",
       "25                       Adagio con molta espressione  129.199219  322.690612   \n",
       "26                                           Minuetto  123.046875  184.999184   \n",
       "27                                   Rondo-Allegretto  114.843750  286.145306   \n",
       "28                                   Adagio sostenuto  143.554688  368.169796   \n",
       "29                                         Allegretto  105.468750  126.171429   \n",
       "..                                                ...         ...         ...   \n",
       "31                                   Allegro con brio  105.468750  621.688163   \n",
       "32                                       Adagio molto   87.592691  247.666939   \n",
       "33                                   Allegro moderato  105.468750  581.955918   \n",
       "34                                      Allegro assai  120.185320  562.546939   \n",
       "35                                   Andante con moto  103.359375  371.774694   \n",
       "36                              Allegro ma non troppo  139.674831  468.767347   \n",
       "37                                             Adagio  123.046875  345.182041   \n",
       "38                                 Andante espressivo  123.046875  176.248163   \n",
       "39                                   Vivacissimamente  109.956782  304.274286   \n",
       "40                                          Poco moto  135.999178  167.601633   \n",
       "41  Mit Lebhaftigkeit und durchaus mit Empfindung ...  151.999081  295.079184   \n",
       "42    Nicht zu geschwind und sehr singbar vorgetragen  139.674831  496.666122   \n",
       "43                                            Allegro  112.347147  595.330612   \n",
       "44                                            Scherzo  114.843750  138.004898   \n",
       "45                                   Adagio sostenuto   90.666118  765.727347   \n",
       "46                                    Largo - Allegro  132.512019  645.642449   \n",
       "47                                  Andante religioso   83.354335  261.328980   \n",
       "48                                  Tempo di minuetto  129.199219  165.537959   \n",
       "49                                            Allegro  172.265625  160.208980   \n",
       "50                                         Allegretto  129.199219  191.921633   \n",
       "51                                            Andante  143.554688   97.802449   \n",
       "52                                         Allegretto  143.554688  114.442449   \n",
       "53                                          Andantino  166.708669  125.387755   \n",
       "54                                            Allegro  112.347147  692.088163   \n",
       "55                                            Andante  117.453835  284.290612   \n",
       "56                                            Scherzo  178.205819  385.488980   \n",
       "57                         Finale - Allegro con fuoco  132.512019  421.015510   \n",
       "58                                            Andante  143.554688  163.186939   \n",
       "59       Andante con grazia ed intimissomo sentimento  161.499023  177.293061   \n",
       "60                              Andantino teneramente   95.703125  144.013061   \n",
       "\n",
       "    zero_crossings  spectral_centroid_avg      ...       Chroma_SD_B  \\\n",
       "0            98094             621.574748      ...          0.175779   \n",
       "1           110224             542.039355      ...          0.197480   \n",
       "2           214944             701.140112      ...          0.169860   \n",
       "3           203680             631.094119      ...          0.166632   \n",
       "4           118904             570.265597      ...          0.119192   \n",
       "5           164246             631.638421      ...          0.197129   \n",
       "6           317856             643.616803      ...          0.081097   \n",
       "7           314238             727.952305      ...          0.114011   \n",
       "8           349920             743.385163      ...          0.143624   \n",
       "9           284980             689.619629      ...          0.130778   \n",
       "10          421968             724.764828      ...          0.105325   \n",
       "11          366638             799.040670      ...          0.124915   \n",
       "12          229950             779.097070      ...          0.119271   \n",
       "13          326088             641.144951      ...          0.107563   \n",
       "14          225662             647.489429      ...          0.161284   \n",
       "15          210332             780.659917      ...          0.104775   \n",
       "16          191426             736.341559      ...          0.156684   \n",
       "17          575308             680.998049      ...          0.127714   \n",
       "18          409118             603.723822      ...          0.132041   \n",
       "19          353624             475.342641      ...          0.152306   \n",
       "20          218308             585.544664      ...          0.141795   \n",
       "21          571466             562.675867      ...          0.147401   \n",
       "22          199136             379.836122      ...          0.122226   \n",
       "23          287380             604.357711      ...          0.135771   \n",
       "24          387972             588.797263      ...          0.120557   \n",
       "25          281874             470.234412      ...          0.098503   \n",
       "26          183292             552.620512      ...          0.148465   \n",
       "27          301332             591.557574      ...          0.131816   \n",
       "28          312122             494.042753      ...          0.133752   \n",
       "29          132338             621.451924      ...          0.116196   \n",
       "..             ...                    ...      ...               ...   \n",
       "31          702612             636.247847      ...          0.131345   \n",
       "32          192828             450.723972      ...          0.173177   \n",
       "33          610616             580.741632      ...          0.121536   \n",
       "34          588102             605.638087      ...          0.143950   \n",
       "35          333476             533.579553      ...          0.093595   \n",
       "36          508932             622.679336      ...          0.128914   \n",
       "37          363008             585.345616      ...          0.169361   \n",
       "38          187066             547.044611      ...          0.192799   \n",
       "39          345640             660.312470      ...          0.102362   \n",
       "40          168700             545.958378      ...          0.142600   \n",
       "41          293070             540.946245      ...          0.149050   \n",
       "42          496902             530.535298      ...          0.146659   \n",
       "43          774374             681.895016      ...          0.096649   \n",
       "44          154132             588.215204      ...          0.115932   \n",
       "45          725502             476.077863      ...          0.200215   \n",
       "46          692146             616.518193      ...          0.184551   \n",
       "47          235808             554.005008      ...          0.147801   \n",
       "48          173936             651.111392      ...          0.125475   \n",
       "49          176982             684.618830      ...          0.140364   \n",
       "50          188254             632.641128      ...          0.158683   \n",
       "51           88802             574.734702      ...          0.129292   \n",
       "52          106482             601.668027      ...          0.076352   \n",
       "53          118402             593.992851      ...          0.132081   \n",
       "54         1031058             764.429737      ...          0.105437   \n",
       "55          314106             582.421503      ...          0.177739   \n",
       "56          519252             700.663724      ...          0.200612   \n",
       "57          590966             734.213189      ...          0.103566   \n",
       "58          198250             633.863684      ...          0.143528   \n",
       "59          197878             589.114445      ...          0.151926   \n",
       "60          156344             609.424457      ...          0.161649   \n",
       "\n",
       "    Chroma_SD_C  Chroma_SD_C#  Chroma_SD_D  Chroma_SD_D#  Chroma_SD_E  \\\n",
       "0      0.193223      0.195864     0.210012      0.152202     0.207500   \n",
       "1      0.148901      0.147744     0.189628      0.138035     0.170995   \n",
       "2      0.132680      0.120317     0.154430      0.158160     0.156281   \n",
       "3      0.175509      0.142637     0.183892      0.158628     0.137726   \n",
       "4      0.095741      0.103875     0.100934      0.081791     0.107883   \n",
       "5      0.133656      0.159364     0.140870      0.157394     0.164212   \n",
       "6      0.083933      0.060221     0.138567      0.167003     0.109643   \n",
       "7      0.146534      0.151022     0.143592      0.144085     0.139092   \n",
       "8      0.090090      0.088240     0.113518      0.104565     0.149267   \n",
       "9      0.122177      0.155902     0.112533      0.161665     0.118075   \n",
       "10     0.108144      0.059177     0.077075      0.094876     0.095828   \n",
       "11     0.121395      0.084969     0.141055      0.116771     0.153677   \n",
       "12     0.104646      0.137708     0.084906      0.145295     0.089313   \n",
       "13     0.132366      0.131447     0.126307      0.163392     0.090098   \n",
       "14     0.132739      0.112274     0.221261      0.088599     0.224013   \n",
       "15     0.138016      0.117120     0.148757      0.115832     0.095209   \n",
       "16     0.140587      0.146742     0.163448      0.133275     0.151187   \n",
       "17     0.140272      0.168282     0.130241      0.154759     0.125400   \n",
       "18     0.117183      0.097864     0.107088      0.122157     0.124517   \n",
       "19     0.151944      0.135379     0.152754      0.217126     0.146462   \n",
       "20     0.192293      0.135512     0.153703      0.158208     0.120557   \n",
       "21     0.111301      0.106388     0.139456      0.154623     0.116710   \n",
       "22     0.152574      0.158630     0.132891      0.234525     0.143175   \n",
       "23     0.186967      0.137967     0.158510      0.152708     0.134058   \n",
       "24     0.183013      0.139378     0.134819      0.133918     0.123872   \n",
       "25     0.153847      0.099911     0.130549      0.169744     0.133609   \n",
       "26     0.129310      0.129408     0.175399      0.150455     0.169945   \n",
       "27     0.143192      0.126775     0.149664      0.145004     0.136648   \n",
       "28     0.148160      0.179279     0.132102      0.134835     0.175077   \n",
       "29     0.107412      0.136405     0.097876      0.135593     0.096862   \n",
       "..          ...           ...          ...           ...          ...   \n",
       "31     0.148408      0.108017     0.141555      0.105382     0.176480   \n",
       "32     0.163273      0.152775     0.128672      0.189503     0.176843   \n",
       "33     0.132552      0.104888     0.135578      0.123498     0.198634   \n",
       "34     0.183551      0.185654     0.167580      0.157621     0.170062   \n",
       "35     0.104033      0.159964     0.098220      0.225842     0.131910   \n",
       "36     0.169054      0.154957     0.092752      0.113583     0.175141   \n",
       "37     0.180378      0.146605     0.137997      0.141499     0.154004   \n",
       "38     0.168409      0.139489     0.200261      0.177890     0.139943   \n",
       "39     0.157916      0.115170     0.154029      0.120230     0.115631   \n",
       "40     0.132634      0.132222     0.194048      0.132569     0.175152   \n",
       "41     0.136343      0.145192     0.152806      0.166919     0.235471   \n",
       "42     0.100206      0.146107     0.137619      0.148320     0.189121   \n",
       "43     0.144549      0.123033     0.132664      0.129792     0.111455   \n",
       "44     0.143798      0.114542     0.147561      0.127767     0.148698   \n",
       "45     0.147396      0.195893     0.162455      0.156692     0.177100   \n",
       "46     0.134429      0.193379     0.155816      0.172381     0.192749   \n",
       "47     0.201036      0.195617     0.154318      0.193853     0.172541   \n",
       "48     0.142821      0.147564     0.170337      0.171148     0.142108   \n",
       "49     0.154573      0.122219     0.145019      0.114124     0.140863   \n",
       "50     0.203712      0.197448     0.175578      0.157561     0.164260   \n",
       "51     0.142757      0.178079     0.139173      0.160313     0.126322   \n",
       "52     0.091228      0.096272     0.102574      0.150193     0.095224   \n",
       "53     0.144864      0.194309     0.136356      0.148404     0.132709   \n",
       "54     0.111414      0.081889     0.145014      0.097960     0.148631   \n",
       "55     0.219122      0.143668     0.211349      0.201344     0.154176   \n",
       "56     0.141636      0.131593     0.167637      0.147170     0.191785   \n",
       "57     0.103910      0.088926     0.082075      0.092075     0.133124   \n",
       "58     0.130796      0.111139     0.160540      0.130326     0.149402   \n",
       "59     0.123093      0.141053     0.157537      0.111362     0.164461   \n",
       "60     0.106614      0.187461     0.125560      0.149242     0.161210   \n",
       "\n",
       "    Chroma_SD_F  Chroma_SD_F#  Chroma_SD_G  Chroma_SD_G#  \n",
       "0      0.167383      0.110378     0.088626      0.167561  \n",
       "1      0.157763      0.167537     0.135463      0.170640  \n",
       "2      0.122567      0.140689     0.124910      0.123183  \n",
       "3      0.137628      0.148855     0.166665      0.141932  \n",
       "4      0.133893      0.125197     0.166297      0.131250  \n",
       "5      0.120355      0.146004     0.136968      0.167715  \n",
       "6      0.113225      0.096489     0.100674      0.107103  \n",
       "7      0.167707      0.138745     0.209784      0.137540  \n",
       "8      0.169314      0.075805     0.162057      0.103545  \n",
       "9      0.138783      0.123560     0.138791      0.193307  \n",
       "10     0.067225      0.101229     0.117781      0.083586  \n",
       "11     0.107955      0.123052     0.171489      0.114191  \n",
       "12     0.086392      0.159794     0.084004      0.161897  \n",
       "13     0.171666      0.094812     0.153058      0.138831  \n",
       "14     0.135668      0.122892     0.151393      0.107751  \n",
       "15     0.126109      0.076569     0.122057      0.123116  \n",
       "16     0.138327      0.155704     0.148592      0.136464  \n",
       "17     0.164967      0.138473     0.152864      0.185501  \n",
       "18     0.186754      0.109863     0.164265      0.104576  \n",
       "19     0.156430      0.150890     0.157358      0.187417  \n",
       "20     0.150871      0.123061     0.172785      0.141157  \n",
       "21     0.109585      0.135425     0.163141      0.095910  \n",
       "22     0.214932      0.151870     0.175659      0.155899  \n",
       "23     0.175962      0.125300     0.169176      0.159236  \n",
       "24     0.176456      0.119616     0.167173      0.128282  \n",
       "25     0.169713      0.119224     0.141926      0.108736  \n",
       "26     0.196738      0.146167     0.161436      0.139485  \n",
       "27     0.161246      0.145788     0.174377      0.136785  \n",
       "28     0.132190      0.176021     0.133202      0.136353  \n",
       "29     0.145431      0.145959     0.109440      0.156911  \n",
       "..          ...           ...          ...           ...  \n",
       "31     0.172282      0.128237     0.200779      0.105336  \n",
       "32     0.176057      0.166450     0.152504      0.151681  \n",
       "33     0.162182      0.099428     0.128543      0.102507  \n",
       "34     0.177732      0.154572     0.156599      0.149271  \n",
       "35     0.188316      0.142919     0.113406      0.153785  \n",
       "36     0.178648      0.128323     0.169087      0.141750  \n",
       "37     0.170220      0.139611     0.188848      0.154492  \n",
       "38     0.187929      0.131064     0.206620      0.156995  \n",
       "39     0.157457      0.092486     0.148769      0.110015  \n",
       "40     0.121340      0.112003     0.154373      0.151393  \n",
       "41     0.142020      0.131507     0.165348      0.137968  \n",
       "42     0.129771      0.168571     0.120082      0.165217  \n",
       "43     0.140494      0.093406     0.164675      0.135279  \n",
       "44     0.204054      0.101446     0.177308      0.120155  \n",
       "45     0.199258      0.165242     0.114056      0.139364  \n",
       "46     0.200937      0.166324     0.164770      0.155315  \n",
       "47     0.132465      0.188293     0.130467      0.154484  \n",
       "48     0.161789      0.130092     0.188598      0.132631  \n",
       "49     0.146510      0.114351     0.178900      0.135758  \n",
       "50     0.209342      0.156374     0.154712      0.169909  \n",
       "51     0.160557      0.180701     0.143493      0.214164  \n",
       "52     0.135566      0.121046     0.104194      0.106733  \n",
       "53     0.131239      0.155766     0.106609      0.195507  \n",
       "54     0.117504      0.097057     0.156097      0.099269  \n",
       "55     0.096331      0.116597     0.200906      0.124421  \n",
       "56     0.113779      0.167865     0.165050      0.137565  \n",
       "57     0.091235      0.121015     0.134277      0.077988  \n",
       "58     0.125759      0.114903     0.129063      0.090374  \n",
       "59     0.111019      0.137527     0.158093      0.138278  \n",
       "60     0.121903      0.171299     0.119107      0.153919  \n",
       "\n",
       "[61 rows x 59 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "musicdf.iloc[[29,2,48,19,23]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.iloc[finalfive.index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The <b> final 15 </b> (five from each of the 3 clusters) thirty second clip that are chosen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "threedee = plt.figure(figsize=(12, 10)).gca(projection='3d')\n",
    "threedee.scatter(df.x, df.y, df.z,c=df['cluster'], cmap='jet')\n",
    "threedee.set_xlabel('x')\n",
    "threedee.set_ylabel('y')\n",
    "threedee.set_zlabel('z')\n",
    "j = 0;\n",
    "for i in [25,48,41]:\n",
    "    threedee.text(df.x[i],df.y[i], df.z[i],  'Cluster: %s' % (str(j)), size=20, zorder=1, color='k')\n",
    "    j+=1\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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