Clustering_Spotify_Songs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 288x72 with 1 Axes>"
      ]
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     "metadata": {},
     "output_type": "display_data"
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   "source": [
    "# To support both python 2 and python 3\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "# To make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "\n",
    "import seaborn as sns\n",
    "#set style of plots\n",
    "sns.set_style('white')\n",
    "\n",
    "#define a custom palette\n",
    "customPalette = ['#630C3A', '#39C8C6', '#D3500C', '#FFB139']\n",
    "sns.set_palette(customPalette)\n",
    "sns.palplot(customPalette)\n",
    "\n",
    "\n",
    "# Ignore useless warnings (see SciPy issue #5998)\n",
    "import warnings\n",
    "warnings.filterwarnings(action=\"ignore\", message=\"^internal gelsd\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 822 entries, 0 to 821\n",
      "Data columns (total 11 columns):\n",
      "name            822 non-null object\n",
      "artist          822 non-null object\n",
      "id              822 non-null object\n",
      "danceability    822 non-null float64\n",
      "energy          822 non-null float64\n",
      "loudness        822 non-null float64\n",
      "speechiness     822 non-null float64\n",
      "acousticness    822 non-null float64\n",
      "tempo           822 non-null float64\n",
      "liveness        822 non-null float64\n",
      "valence         822 non-null float64\n",
      "dtypes: float64(8), object(3)\n",
      "memory usage: 70.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# Import my spotify data\n",
    "songs = pd.read_csv(\"../datasets/mySongsDataset.csv\")\n",
    "\n",
    "songs.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>artist</th>\n",
       "      <th>id</th>\n",
       "      <th>danceability</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>liveness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SPEEDBOAT</td>\n",
       "      <td>Denzel Curry</td>\n",
       "      <td>2FTOLKjQUswhpdMFq15Raf</td>\n",
       "      <td>0.925</td>\n",
       "      <td>-7.587</td>\n",
       "      <td>0.1310</td>\n",
       "      <td>0.0604</td>\n",
       "      <td>0.358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rodeo</td>\n",
       "      <td>Lil Nas X</td>\n",
       "      <td>4ak7xjvBeBOcJGWFDX9w5n</td>\n",
       "      <td>0.706</td>\n",
       "      <td>-5.614</td>\n",
       "      <td>0.0324</td>\n",
       "      <td>0.1390</td>\n",
       "      <td>0.465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Summertime Magic</td>\n",
       "      <td>Childish Gambino</td>\n",
       "      <td>4j3GWI86JvSaF0BLdmgcfF</td>\n",
       "      <td>0.760</td>\n",
       "      <td>-7.374</td>\n",
       "      <td>0.0569</td>\n",
       "      <td>0.0129</td>\n",
       "      <td>0.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rwm [run With Me]</td>\n",
       "      <td>House of Pharaohs</td>\n",
       "      <td>03RZdoidDQPpVGnSxPW2YO</td>\n",
       "      <td>0.763</td>\n",
       "      <td>-6.094</td>\n",
       "      <td>0.2570</td>\n",
       "      <td>0.1160</td>\n",
       "      <td>0.340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Price On My Head (feat. The Weeknd)</td>\n",
       "      <td>NAV</td>\n",
       "      <td>4hqOmrCCJKyiuwDexd8c2t</td>\n",
       "      <td>0.768</td>\n",
       "      <td>-6.104</td>\n",
       "      <td>0.1360</td>\n",
       "      <td>0.3840</td>\n",
       "      <td>0.336</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  name             artist  \\\n",
       "0                            SPEEDBOAT       Denzel Curry   \n",
       "1                                Rodeo          Lil Nas X   \n",
       "2                     Summertime Magic   Childish Gambino   \n",
       "3                    Rwm [run With Me]  House of Pharaohs   \n",
       "4  Price On My Head (feat. The Weeknd)                NAV   \n",
       "\n",
       "                       id  danceability  loudness  speechiness  acousticness  \\\n",
       "0  2FTOLKjQUswhpdMFq15Raf         0.925    -7.587       0.1310        0.0604   \n",
       "1  4ak7xjvBeBOcJGWFDX9w5n         0.706    -5.614       0.0324        0.1390   \n",
       "2  4j3GWI86JvSaF0BLdmgcfF         0.760    -7.374       0.0569        0.0129   \n",
       "3  03RZdoidDQPpVGnSxPW2YO         0.763    -6.094       0.2570        0.1160   \n",
       "4  4hqOmrCCJKyiuwDexd8c2t         0.768    -6.104       0.1360        0.3840   \n",
       "\n",
       "   liveness  \n",
       "0     0.358  \n",
       "1     0.465  \n",
       "2     0.106  \n",
       "3     0.340  \n",
       "4     0.336  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "songs = songs.drop(['energy','valence','tempo'],axis=1)\n",
    "songs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>danceability</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>liveness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>822.000000</td>\n",
       "      <td>822.000000</td>\n",
       "      <td>822.000000</td>\n",
       "      <td>822.000000</td>\n",
       "      <td>822.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.714193</td>\n",
       "      <td>-7.101165</td>\n",
       "      <td>0.156120</td>\n",
       "      <td>0.234338</td>\n",
       "      <td>0.184698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.131895</td>\n",
       "      <td>3.188877</td>\n",
       "      <td>0.121395</td>\n",
       "      <td>0.230739</td>\n",
       "      <td>0.145412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.141000</td>\n",
       "      <td>-35.422000</td>\n",
       "      <td>0.025100</td>\n",
       "      <td>0.000136</td>\n",
       "      <td>0.023300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.635250</td>\n",
       "      <td>-8.150750</td>\n",
       "      <td>0.056600</td>\n",
       "      <td>0.053275</td>\n",
       "      <td>0.095550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.735000</td>\n",
       "      <td>-6.656500</td>\n",
       "      <td>0.113500</td>\n",
       "      <td>0.172000</td>\n",
       "      <td>0.123500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.812750</td>\n",
       "      <td>-5.183000</td>\n",
       "      <td>0.234500</td>\n",
       "      <td>0.341250</td>\n",
       "      <td>0.232750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.964000</td>\n",
       "      <td>-0.155000</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.995000</td>\n",
       "      <td>0.894000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       danceability    loudness  speechiness  acousticness    liveness\n",
       "count    822.000000  822.000000   822.000000    822.000000  822.000000\n",
       "mean       0.714193   -7.101165     0.156120      0.234338    0.184698\n",
       "std        0.131895    3.188877     0.121395      0.230739    0.145412\n",
       "min        0.141000  -35.422000     0.025100      0.000136    0.023300\n",
       "25%        0.635250   -8.150750     0.056600      0.053275    0.095550\n",
       "50%        0.735000   -6.656500     0.113500      0.172000    0.123500\n",
       "75%        0.812750   -5.183000     0.234500      0.341250    0.232750\n",
       "max        0.964000   -0.155000     0.920000      0.995000    0.894000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to describe numerical features we can use describe()\n",
    "songs.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Loudness feature is from -60db to 0 so we will normalise this column between 0 and 1\n",
    "# Tempo feature is in BPM so we will scale between 0 and 1 too\n",
    "from sklearn import preprocessing\n",
    "\n",
    "loudness = songs[['loudness']].values\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "loudness_scaled = min_max_scaler.fit_transform(loudness)\n",
    "songs['loudness'] = pd.DataFrame(loudness_scaled)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "songs.hist(bins=50,figsize=(20,15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most features are skewed to the left or the right\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from pandas.plotting import scatter_matrix\n",
    "# # attributes should contain the features we want to compare\n",
    "# attr = ['danceability','energy','loudness','speechiness','acousticness','tempo','liveness','valence']\n",
    "\n",
    "# scatter_matrix(songs[attr],figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove song names, artist and id before clustering\n",
    "songs_features = songs.copy()\n",
    "songs_features = songs_features.drop(['name','artist','id'],axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "Sum_of_squared_distances = []\n",
    "K = range(1,15)\n",
    "for k in K:\n",
    "    km = KMeans(n_clusters=k)\n",
    "    km = km.fit(songs_features)\n",
    "    Sum_of_squared_distances.append(km.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters = 2, silhouette score is 0.38838474636235926)\n",
      "For n_clusters = 3, silhouette score is 0.29432771680771025)\n",
      "For n_clusters = 4, silhouette score is 0.28357050442816784)\n",
      "For n_clusters = 5, silhouette score is 0.23669850729527186)\n",
      "For n_clusters = 6, silhouette score is 0.21548849387943386)\n",
      "For n_clusters = 7, silhouette score is 0.21703545305045974)\n",
      "For n_clusters = 8, silhouette score is 0.21134781268673553)\n",
      "For n_clusters = 9, silhouette score is 0.21780161214910143)\n",
      "For n_clusters = 10, silhouette score is 0.2178225470446984)\n",
      "For n_clusters = 11, silhouette score is 0.21842892845390355)\n",
      "For n_clusters = 12, silhouette score is 0.21658228036211927)\n",
      "For n_clusters = 13, silhouette score is 0.22110157736485705)\n",
      "For n_clusters = 14, silhouette score is 0.23542565787442324)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import silhouette_score\n",
    "for n_clusters in range(2,15):\n",
    "    clusterer = KMeans (n_clusters=n_clusters)\n",
    "    preds = clusterer.fit_predict(songs_features)\n",
    "    centers = clusterer.cluster_centers_\n",
    "\n",
    "    score = silhouette_score (songs_features, preds, metric='euclidean')\n",
    "    print (\"For n_clusters = {}, silhouette score is {})\".format(n_clusters, score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(K, Sum_of_squared_distances, 'gx-')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Sum_of_squared_distances')\n",
    "plt.title('Elbow Method For Optimal k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=4, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=4)\n",
    "kmeans.fit(songs_features)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PCA to reduce our data to 2 dimensions for visualisation\n",
    "from sklearn.decomposition import PCA\n",
    "y_kmeans = kmeans.predict(songs_features)\n",
    "pca = PCA(n_components=2)\n",
    "principal_components = pca.fit_transform(songs_features)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 401.625x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pc = pd.DataFrame(principal_components)\n",
    "pc['label'] = y_kmeans\n",
    "pc.columns = ['x', 'y','label']\n",
    "\n",
    "#plot data with seaborn\n",
    "cluster = sns.lmplot(data=pc, x='x', y='y', hue='label', \n",
    "                   fit_reg=False, legend=True, legend_out=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE #T-Distributed Stochastic Neighbor Embedding\n",
    "#T-SNE with two dimensions\n",
    "tsne = TSNE(n_components=2, perplexity=50)\n",
    "\n",
    "tsne_components = tsne.fit_transform(songs_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 401.625x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts = pd.DataFrame(tsne_components)\n",
    "ts['label'] = y_kmeans\n",
    "ts.columns = ['x', 'y','label']\n",
    "\n",
    "#plot data with seaborn\n",
    "cluster = sns.lmplot(data=ts, x='x', y='y', hue='label', \n",
    "                   fit_reg=False, legend=True, legend_out=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.49127267, 0.19507364])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The explained variance tells you how much information (variance) can be attributed to each of the principal components. By looking at the explained_variance_ratio_ we can see the 1st component contains 87% and the 2nd 5% which implies we've retained 92% of the original variance in the data which is awesome! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      danceability  loudness  speechiness  acousticness  liveness\n",
      "PC-1     -0.160294 -0.202982    -0.013570      0.965367 -0.031398\n",
      "PC-2     -0.357319 -0.005390     0.228586     -0.027812  0.905135\n"
     ]
    }
   ],
   "source": [
    "# Dump components relations with features:\n",
    "print (pd.DataFrame(pca.components_,columns=songs_features.columns,index = ['PC-1','PC-2']))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Next step lets explore each of the 4 clusters and see what songs they contain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    471\n",
       "3    186\n",
       "2    111\n",
       "0     54\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "songs['label'] = y_kmeans\n",
    "\n",
    "# shuffle dataset\n",
    "\n",
    "songs = songs.sample(frac=1)\n",
    "songs['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>artist</th>\n",
       "      <th>id</th>\n",
       "      <th>danceability</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>마담 (Madame)</td>\n",
       "      <td>Zion.T</td>\n",
       "      <td>1zZqfWosLsfirzQuY2TPQQ</td>\n",
       "      <td>0.514</td>\n",
       "      <td>0.579012</td>\n",
       "      <td>0.0414</td>\n",
       "      <td>0.976</td>\n",
       "      <td>0.0923</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>Freddie Freeloader</td>\n",
       "      <td>Miles Davis</td>\n",
       "      <td>2x91iJc0UkFcjRMEZ2CoWB</td>\n",
       "      <td>0.638</td>\n",
       "      <td>0.555505</td>\n",
       "      <td>0.0347</td>\n",
       "      <td>0.703</td>\n",
       "      <td>0.1180</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>Hope</td>\n",
       "      <td>XXXTENTACION</td>\n",
       "      <td>2ZRo7axmMPeSVUvDbGkJah</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.831968</td>\n",
       "      <td>0.0603</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.1460</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>the remedy for a broken heart (why am I so in ...</td>\n",
       "      <td>XXXTENTACION</td>\n",
       "      <td>4UKCKdYiLN6IMA5ZESUTL7</td>\n",
       "      <td>0.699</td>\n",
       "      <td>0.687045</td>\n",
       "      <td>0.0993</td>\n",
       "      <td>0.779</td>\n",
       "      <td>0.1370</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Poem (Shhh)</td>\n",
       "      <td>Colde</td>\n",
       "      <td>4UyV2ZMo5NpCcO0u3xX8uB</td>\n",
       "      <td>0.821</td>\n",
       "      <td>0.688207</td>\n",
       "      <td>0.0424</td>\n",
       "      <td>0.751</td>\n",
       "      <td>0.1560</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>Première Gymnopédie</td>\n",
       "      <td>Erik Satie</td>\n",
       "      <td>4jep6dMPP0z7HrN375InoL</td>\n",
       "      <td>0.593</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0699</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.0814</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>changes</td>\n",
       "      <td>XXXTENTACION</td>\n",
       "      <td>7AFASza1mXqntmGtbxXprO</td>\n",
       "      <td>0.669</td>\n",
       "      <td>0.718916</td>\n",
       "      <td>0.0290</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.0984</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>Passion (From \"Kingdom Hearts II\") [Piano Solo]</td>\n",
       "      <td>Kyle Landry</td>\n",
       "      <td>2xFBrGSZ4mSURd8p5IU4ed</td>\n",
       "      <td>0.406</td>\n",
       "      <td>0.428219</td>\n",
       "      <td>0.0515</td>\n",
       "      <td>0.964</td>\n",
       "      <td>0.0964</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>love yourself (interlude)</td>\n",
       "      <td>XXXTENTACION</td>\n",
       "      <td>1Znlc3UZAbSodFaMBjdpsX</td>\n",
       "      <td>0.746</td>\n",
       "      <td>0.622395</td>\n",
       "      <td>0.1090</td>\n",
       "      <td>0.674</td>\n",
       "      <td>0.5290</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Petite fleur</td>\n",
       "      <td>Jill Barber</td>\n",
       "      <td>0oMp06EjwhYFvDB33VCiW3</td>\n",
       "      <td>0.616</td>\n",
       "      <td>0.731222</td>\n",
       "      <td>0.0329</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.0945</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>Beyond the Sea</td>\n",
       "      <td>Bobby Darin</td>\n",
       "      <td>2EIaO5knwUA3F2qnf9LV3S</td>\n",
       "      <td>0.521</td>\n",
       "      <td>0.792979</td>\n",
       "      <td>0.0369</td>\n",
       "      <td>0.723</td>\n",
       "      <td>0.2570</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>Sinister Shadows</td>\n",
       "      <td>Moisés Nieto</td>\n",
       "      <td>2ipiuwavFHIz5NNiIqAVhz</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.644625</td>\n",
       "      <td>0.0373</td>\n",
       "      <td>0.970</td>\n",
       "      <td>0.1750</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>German</td>\n",
       "      <td>EO</td>\n",
       "      <td>7cwLbWHs42c8JmrW9BJZos</td>\n",
       "      <td>0.862</td>\n",
       "      <td>0.826892</td>\n",
       "      <td>0.0654</td>\n",
       "      <td>0.811</td>\n",
       "      <td>0.1910</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Solo for so Long</td>\n",
       "      <td>Shiloh Dynasty</td>\n",
       "      <td>2sdeZROoz7qPsKs2ivqbiv</td>\n",
       "      <td>0.823</td>\n",
       "      <td>0.471999</td>\n",
       "      <td>0.1040</td>\n",
       "      <td>0.937</td>\n",
       "      <td>0.7070</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>German</td>\n",
       "      <td>EO</td>\n",
       "      <td>7cwLbWHs42c8JmrW9BJZos</td>\n",
       "      <td>0.862</td>\n",
       "      <td>0.826892</td>\n",
       "      <td>0.0654</td>\n",
       "      <td>0.811</td>\n",
       "      <td>0.1910</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>Spirited Away - One Summer's Day</td>\n",
       "      <td>Joe Hisaishi</td>\n",
       "      <td>5LQqc2rfmxKJY1kuCDM2ml</td>\n",
       "      <td>0.318</td>\n",
       "      <td>0.399864</td>\n",
       "      <td>0.0379</td>\n",
       "      <td>0.951</td>\n",
       "      <td>0.0732</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>String</td>\n",
       "      <td>Colde</td>\n",
       "      <td>6V0cdiNRgprv11NGheX87j</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.764085</td>\n",
       "      <td>0.0470</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.1310</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>Take Five</td>\n",
       "      <td>Dave Brubeck</td>\n",
       "      <td>5p6me2mwQrGfH30eExHn6v</td>\n",
       "      <td>0.457</td>\n",
       "      <td>0.569853</td>\n",
       "      <td>0.0385</td>\n",
       "      <td>0.545</td>\n",
       "      <td>0.0681</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>Dream A Little Dream Of Me</td>\n",
       "      <td>Ella Fitzgerald</td>\n",
       "      <td>26dt6n5cyVRdX7vD8rmUWw</td>\n",
       "      <td>0.515</td>\n",
       "      <td>0.620892</td>\n",
       "      <td>0.0752</td>\n",
       "      <td>0.916</td>\n",
       "      <td>0.1680</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Origami</td>\n",
       "      <td>Samwise</td>\n",
       "      <td>7kwrZ8s3Ji0oPfDCaJxwCL</td>\n",
       "      <td>0.769</td>\n",
       "      <td>0.621261</td>\n",
       "      <td>0.0454</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.1730</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Vscrl_</td>\n",
       "      <td>invention_</td>\n",
       "      <td>2I4DlVnoNzTiH2k7rVjYOK</td>\n",
       "      <td>0.489</td>\n",
       "      <td>0.719681</td>\n",
       "      <td>0.0486</td>\n",
       "      <td>0.923</td>\n",
       "      <td>0.3620</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Poem (Shhh)</td>\n",
       "      <td>Colde</td>\n",
       "      <td>4UyV2ZMo5NpCcO0u3xX8uB</td>\n",
       "      <td>0.821</td>\n",
       "      <td>0.688207</td>\n",
       "      <td>0.0424</td>\n",
       "      <td>0.751</td>\n",
       "      <td>0.1560</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>ALONE, PART 3</td>\n",
       "      <td>XXXTENTACION</td>\n",
       "      <td>2oe6dDthgthqUkfL3czpEd</td>\n",
       "      <td>0.610</td>\n",
       "      <td>0.642640</td>\n",
       "      <td>0.0331</td>\n",
       "      <td>0.883</td>\n",
       "      <td>0.1030</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Log Cabin</td>\n",
       "      <td>Abe M Beats</td>\n",
       "      <td>5dYGOt9Suc5Nq2poIibZFx</td>\n",
       "      <td>0.525</td>\n",
       "      <td>0.575496</td>\n",
       "      <td>0.0522</td>\n",
       "      <td>0.956</td>\n",
       "      <td>0.4710</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>World Without You</td>\n",
       "      <td>Blai$y</td>\n",
       "      <td>0Qj2Mf1p69Brx9GVFNRnIT</td>\n",
       "      <td>0.703</td>\n",
       "      <td>0.598662</td>\n",
       "      <td>0.2090</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.1060</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>Eso Se Baila Asi</td>\n",
       "      <td>Willie Colón</td>\n",
       "      <td>71pNDcd8132efh1p56PwUP</td>\n",
       "      <td>0.636</td>\n",
       "      <td>0.764681</td>\n",
       "      <td>0.0501</td>\n",
       "      <td>0.679</td>\n",
       "      <td>0.0702</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>I Got a Woman</td>\n",
       "      <td>Ray Charles</td>\n",
       "      <td>43ldBB2KlpK7BiJSxiNYHj</td>\n",
       "      <td>0.609</td>\n",
       "      <td>0.653330</td>\n",
       "      <td>0.3820</td>\n",
       "      <td>0.643</td>\n",
       "      <td>0.1990</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651</th>\n",
       "      <td>Fate of the Unknown</td>\n",
       "      <td>Moisés Nieto</td>\n",
       "      <td>7ktyaYV77Xk6o2mf6LHDk1</td>\n",
       "      <td>0.378</td>\n",
       "      <td>0.596847</td>\n",
       "      <td>0.0510</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.0846</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>Musique Pour la Tristesse de Xion</td>\n",
       "      <td>Moisés Nieto</td>\n",
       "      <td>2KuqJA2dvzArMClsKBCdXJ</td>\n",
       "      <td>0.480</td>\n",
       "      <td>0.593132</td>\n",
       "      <td>0.0434</td>\n",
       "      <td>0.982</td>\n",
       "      <td>0.1120</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>I Know You Know</td>\n",
       "      <td>Esperanza Spalding</td>\n",
       "      <td>6LRRTTt97J9LnK1D3YcNGB</td>\n",
       "      <td>0.578</td>\n",
       "      <td>0.678793</td>\n",
       "      <td>0.0696</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.0965</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Car Radio</td>\n",
       "      <td>Flovry</td>\n",
       "      <td>3yM1SaSngFZCla4gOOUn2a</td>\n",
       "      <td>0.479</td>\n",
       "      <td>0.588425</td>\n",
       "      <td>0.1490</td>\n",
       "      <td>0.650</td>\n",
       "      <td>0.3790</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>Dance of the Daring</td>\n",
       "      <td>Moisés Nieto</td>\n",
       "      <td>4ceSUzOKodKwAXF2ZiOVq5</td>\n",
       "      <td>0.537</td>\n",
       "      <td>0.631213</td>\n",
       "      <td>0.0916</td>\n",
       "      <td>0.961</td>\n",
       "      <td>0.0851</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>So What</td>\n",
       "      <td>Miles Davis</td>\n",
       "      <td>4vLYewWIvqHfKtJDk8c8tq</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.514816</td>\n",
       "      <td>0.0373</td>\n",
       "      <td>0.705</td>\n",
       "      <td>0.1160</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>Sometimes...</td>\n",
       "      <td>Tyler, The Creator</td>\n",
       "      <td>4KgTrzmfiWNcVneh2Dd1Ou</td>\n",
       "      <td>0.552</td>\n",
       "      <td>0.664247</td>\n",
       "      <td>0.0451</td>\n",
       "      <td>0.959</td>\n",
       "      <td>0.1640</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>768</th>\n",
       "      <td>Beyond the Sea</td>\n",
       "      <td>Bobby Darin</td>\n",
       "      <td>2EIaO5knwUA3F2qnf9LV3S</td>\n",
       "      <td>0.521</td>\n",
       "      <td>0.792979</td>\n",
       "      <td>0.0369</td>\n",
       "      <td>0.723</td>\n",
       "      <td>0.2570</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>Coming, Going? (feat. Take 6)</td>\n",
       "      <td>KYLE</td>\n",
       "      <td>5XfIFkmZfqdiBOHL5aOEjH</td>\n",
       "      <td>0.339</td>\n",
       "      <td>0.847875</td>\n",
       "      <td>0.0539</td>\n",
       "      <td>0.765</td>\n",
       "      <td>0.1230</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>Air</td>\n",
       "      <td>House of Pharaohs</td>\n",
       "      <td>7ySpnmzHr1ROlNca2baDtv</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.714407</td>\n",
       "      <td>0.2780</td>\n",
       "      <td>0.599</td>\n",
       "      <td>0.1030</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Nowhere Else</td>\n",
       "      <td>Kupla</td>\n",
       "      <td>7MPQwVGvLjelw7v6nhOPM2</td>\n",
       "      <td>0.690</td>\n",
       "      <td>0.736864</td>\n",
       "      <td>0.0374</td>\n",
       "      <td>0.728</td>\n",
       "      <td>0.6120</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Sinkerator</td>\n",
       "      <td>weird inside</td>\n",
       "      <td>7Bj1vOev2wuPhoOZXQbHAb</td>\n",
       "      <td>0.453</td>\n",
       "      <td>0.614314</td>\n",
       "      <td>0.4080</td>\n",
       "      <td>0.742</td>\n",
       "      <td>0.3240</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>The Book of Soul</td>\n",
       "      <td>Ab-Soul</td>\n",
       "      <td>1to4zsRWRjzbRGcBSvEQsm</td>\n",
       "      <td>0.522</td>\n",
       "      <td>0.801939</td>\n",
       "      <td>0.3220</td>\n",
       "      <td>0.701</td>\n",
       "      <td>0.2070</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>samba to massage your mate by</td>\n",
       "      <td>Wun Two</td>\n",
       "      <td>0SSYId9APdlTyWRwcXPky3</td>\n",
       "      <td>0.788</td>\n",
       "      <td>0.821476</td>\n",
       "      <td>0.3420</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.1090</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>Final Fantasy X (Piano Concerto): III. Kessen</td>\n",
       "      <td>Nobuo Uematsu</td>\n",
       "      <td>1IoOns7rs5wjSN89JYPEbU</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.535146</td>\n",
       "      <td>0.0328</td>\n",
       "      <td>0.891</td>\n",
       "      <td>0.0987</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>Kairi</td>\n",
       "      <td>Project Destati</td>\n",
       "      <td>01aqIxHsUWrPX4slQwkkp4</td>\n",
       "      <td>0.193</td>\n",
       "      <td>0.703916</td>\n",
       "      <td>0.0351</td>\n",
       "      <td>0.921</td>\n",
       "      <td>0.1190</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>809</th>\n",
       "      <td>Track 1</td>\n",
       "      <td>Skepta</td>\n",
       "      <td>6hSvW6MGOftQjo3p86O2rd</td>\n",
       "      <td>0.631</td>\n",
       "      <td>0.754757</td>\n",
       "      <td>0.2780</td>\n",
       "      <td>0.749</td>\n",
       "      <td>0.1320</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>Summer Time Ting</td>\n",
       "      <td>ROMderful</td>\n",
       "      <td>5KBguUZE23tccIQcn9HakB</td>\n",
       "      <td>0.554</td>\n",
       "      <td>0.762611</td>\n",
       "      <td>0.3240</td>\n",
       "      <td>0.677</td>\n",
       "      <td>0.0799</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>Final Fantasy X (Piano Concerto): I. Zanarkand</td>\n",
       "      <td>Nobuo Uematsu</td>\n",
       "      <td>3LeLEZCHjaKuCIOuz8XFcT</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.356707</td>\n",
       "      <td>0.0387</td>\n",
       "      <td>0.958</td>\n",
       "      <td>0.0522</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Hello Friend</td>\n",
       "      <td>Leavv</td>\n",
       "      <td>3mB3p3Dh014P4fdxvEb07J</td>\n",
       "      <td>0.691</td>\n",
       "      <td>0.628264</td>\n",
       "      <td>0.0593</td>\n",
       "      <td>0.665</td>\n",
       "      <td>0.0886</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Wall</td>\n",
       "      <td>Colde</td>\n",
       "      <td>0k4NtEeeoUFL4qnJG1Eedl</td>\n",
       "      <td>0.692</td>\n",
       "      <td>0.715230</td>\n",
       "      <td>0.0485</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.1040</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>before I close my eyes</td>\n",
       "      <td>XXXTENTACION</td>\n",
       "      <td>16x9bQHw8TBNaGDyULvwad</td>\n",
       "      <td>0.356</td>\n",
       "      <td>0.574872</td>\n",
       "      <td>0.0388</td>\n",
       "      <td>0.976</td>\n",
       "      <td>0.1110</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>Smile</td>\n",
       "      <td>Nat King Cole</td>\n",
       "      <td>1oDCK7PW72XEZ1pE5rh87A</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.459892</td>\n",
       "      <td>0.0327</td>\n",
       "      <td>0.862</td>\n",
       "      <td>0.3020</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  name              artist  \\\n",
       "641                                        마담 (Madame)              Zion.T   \n",
       "734                                 Freddie Freeloader         Miles Davis   \n",
       "354                                               Hope        XXXTENTACION   \n",
       "343  the remedy for a broken heart (why am I so in ...        XXXTENTACION   \n",
       "88                                         Poem (Shhh)               Colde   \n",
       "759                                Première Gymnopédie          Erik Satie   \n",
       "353                                            changes        XXXTENTACION   \n",
       "655    Passion (From \"Kingdom Hearts II\") [Piano Solo]         Kyle Landry   \n",
       "350                          love yourself (interlude)        XXXTENTACION   \n",
       "45                                        Petite fleur         Jill Barber   \n",
       "729                                     Beyond the Sea         Bobby Darin   \n",
       "648                                   Sinister Shadows        Moisés Nieto   \n",
       "338                                             German                  EO   \n",
       "82                                    Solo for so Long      Shiloh Dynasty   \n",
       "169                                             German                  EO   \n",
       "654                   Spirited Away - One Summer's Day        Joe Hisaishi   \n",
       "86                                              String               Colde   \n",
       "725                                          Take Five        Dave Brubeck   \n",
       "731                         Dream A Little Dream Of Me     Ella Fitzgerald   \n",
       "60                                             Origami             Samwise   \n",
       "67                                              Vscrl_          invention_   \n",
       "10                                         Poem (Shhh)               Colde   \n",
       "340                                      ALONE, PART 3        XXXTENTACION   \n",
       "57                                           Log Cabin         Abe M Beats   \n",
       "83                                   World Without You              Blai$y   \n",
       "763                                   Eso Se Baila Asi        Willie Colón   \n",
       "733                                      I Got a Woman         Ray Charles   \n",
       "651                                Fate of the Unknown        Moisés Nieto   \n",
       "650                  Musique Pour la Tristesse de Xion        Moisés Nieto   \n",
       "727                                    I Know You Know  Esperanza Spalding   \n",
       "64                                           Car Radio              Flovry   \n",
       "649                                Dance of the Daring        Moisés Nieto   \n",
       "726                                            So What         Miles Davis   \n",
       "473                                       Sometimes...  Tyler, The Creator   \n",
       "768                                     Beyond the Sea         Bobby Darin   \n",
       "309                      Coming, Going? (feat. Take 6)                KYLE   \n",
       "335                                                Air   House of Pharaohs   \n",
       "70                                        Nowhere Else               Kupla   \n",
       "65                                          Sinkerator        weird inside   \n",
       "256                                   The Book of Soul             Ab-Soul   \n",
       "56                       samba to massage your mate by             Wun Two   \n",
       "754      Final Fantasy X (Piano Concerto): III. Kessen       Nobuo Uematsu   \n",
       "653                                              Kairi     Project Destati   \n",
       "809                                            Track 1              Skepta   \n",
       "234                                   Summer Time Ting           ROMderful   \n",
       "752     Final Fantasy X (Piano Concerto): I. Zanarkand       Nobuo Uematsu   \n",
       "66                                        Hello Friend               Leavv   \n",
       "9                                                 Wall               Colde   \n",
       "356                             before I close my eyes        XXXTENTACION   \n",
       "732                                              Smile       Nat King Cole   \n",
       "\n",
       "                         id  danceability  loudness  speechiness  \\\n",
       "641  1zZqfWosLsfirzQuY2TPQQ         0.514  0.579012       0.0414   \n",
       "734  2x91iJc0UkFcjRMEZ2CoWB         0.638  0.555505       0.0347   \n",
       "354  2ZRo7axmMPeSVUvDbGkJah         0.592  0.831968       0.0603   \n",
       "343  4UKCKdYiLN6IMA5ZESUTL7         0.699  0.687045       0.0993   \n",
       "88   4UyV2ZMo5NpCcO0u3xX8uB         0.821  0.688207       0.0424   \n",
       "759  4jep6dMPP0z7HrN375InoL         0.593  0.000000       0.0699   \n",
       "353  7AFASza1mXqntmGtbxXprO         0.669  0.718916       0.0290   \n",
       "655  2xFBrGSZ4mSURd8p5IU4ed         0.406  0.428219       0.0515   \n",
       "350  1Znlc3UZAbSodFaMBjdpsX         0.746  0.622395       0.1090   \n",
       "45   0oMp06EjwhYFvDB33VCiW3         0.616  0.731222       0.0329   \n",
       "729  2EIaO5knwUA3F2qnf9LV3S         0.521  0.792979       0.0369   \n",
       "648  2ipiuwavFHIz5NNiIqAVhz         0.422  0.644625       0.0373   \n",
       "338  7cwLbWHs42c8JmrW9BJZos         0.862  0.826892       0.0654   \n",
       "82   2sdeZROoz7qPsKs2ivqbiv         0.823  0.471999       0.1040   \n",
       "169  7cwLbWHs42c8JmrW9BJZos         0.862  0.826892       0.0654   \n",
       "654  5LQqc2rfmxKJY1kuCDM2ml         0.318  0.399864       0.0379   \n",
       "86   6V0cdiNRgprv11NGheX87j         0.591  0.764085       0.0470   \n",
       "725  5p6me2mwQrGfH30eExHn6v         0.457  0.569853       0.0385   \n",
       "731  26dt6n5cyVRdX7vD8rmUWw         0.515  0.620892       0.0752   \n",
       "60   7kwrZ8s3Ji0oPfDCaJxwCL         0.769  0.621261       0.0454   \n",
       "67   2I4DlVnoNzTiH2k7rVjYOK         0.489  0.719681       0.0486   \n",
       "10   4UyV2ZMo5NpCcO0u3xX8uB         0.821  0.688207       0.0424   \n",
       "340  2oe6dDthgthqUkfL3czpEd         0.610  0.642640       0.0331   \n",
       "57   5dYGOt9Suc5Nq2poIibZFx         0.525  0.575496       0.0522   \n",
       "83   0Qj2Mf1p69Brx9GVFNRnIT         0.703  0.598662       0.2090   \n",
       "763  71pNDcd8132efh1p56PwUP         0.636  0.764681       0.0501   \n",
       "733  43ldBB2KlpK7BiJSxiNYHj         0.609  0.653330       0.3820   \n",
       "651  7ktyaYV77Xk6o2mf6LHDk1         0.378  0.596847       0.0510   \n",
       "650  2KuqJA2dvzArMClsKBCdXJ         0.480  0.593132       0.0434   \n",
       "727  6LRRTTt97J9LnK1D3YcNGB         0.578  0.678793       0.0696   \n",
       "64   3yM1SaSngFZCla4gOOUn2a         0.479  0.588425       0.1490   \n",
       "649  4ceSUzOKodKwAXF2ZiOVq5         0.537  0.631213       0.0916   \n",
       "726  4vLYewWIvqHfKtJDk8c8tq         0.517  0.514816       0.0373   \n",
       "473  4KgTrzmfiWNcVneh2Dd1Ou         0.552  0.664247       0.0451   \n",
       "768  2EIaO5knwUA3F2qnf9LV3S         0.521  0.792979       0.0369   \n",
       "309  5XfIFkmZfqdiBOHL5aOEjH         0.339  0.847875       0.0539   \n",
       "335  7ySpnmzHr1ROlNca2baDtv         0.505  0.714407       0.2780   \n",
       "70   7MPQwVGvLjelw7v6nhOPM2         0.690  0.736864       0.0374   \n",
       "65   7Bj1vOev2wuPhoOZXQbHAb         0.453  0.614314       0.4080   \n",
       "256  1to4zsRWRjzbRGcBSvEQsm         0.522  0.801939       0.3220   \n",
       "56   0SSYId9APdlTyWRwcXPky3         0.788  0.821476       0.3420   \n",
       "754  1IoOns7rs5wjSN89JYPEbU         0.350  0.535146       0.0328   \n",
       "653  01aqIxHsUWrPX4slQwkkp4         0.193  0.703916       0.0351   \n",
       "809  6hSvW6MGOftQjo3p86O2rd         0.631  0.754757       0.2780   \n",
       "234  5KBguUZE23tccIQcn9HakB         0.554  0.762611       0.3240   \n",
       "752  3LeLEZCHjaKuCIOuz8XFcT         0.141  0.356707       0.0387   \n",
       "66   3mB3p3Dh014P4fdxvEb07J         0.691  0.628264       0.0593   \n",
       "9    0k4NtEeeoUFL4qnJG1Eedl         0.692  0.715230       0.0485   \n",
       "356  16x9bQHw8TBNaGDyULvwad         0.356  0.574872       0.0388   \n",
       "732  1oDCK7PW72XEZ1pE5rh87A         0.226  0.459892       0.0327   \n",
       "\n",
       "     acousticness  liveness  label  \n",
       "641         0.976    0.0923      0  \n",
       "734         0.703    0.1180      0  \n",
       "354         0.661    0.1460      0  \n",
       "343         0.779    0.1370      0  \n",
       "88          0.751    0.1560      0  \n",
       "759         0.995    0.0814      0  \n",
       "353         0.883    0.0984      0  \n",
       "655         0.964    0.0964      0  \n",
       "350         0.674    0.5290      0  \n",
       "45          0.860    0.0945      0  \n",
       "729         0.723    0.2570      0  \n",
       "648         0.970    0.1750      0  \n",
       "338         0.811    0.1910      0  \n",
       "82          0.937    0.7070      0  \n",
       "169         0.811    0.1910      0  \n",
       "654         0.951    0.0732      0  \n",
       "86          0.870    0.1310      0  \n",
       "725         0.545    0.0681      0  \n",
       "731         0.916    0.1680      0  \n",
       "60          0.883    0.1730      0  \n",
       "67          0.923    0.3620      0  \n",
       "10          0.751    0.1560      0  \n",
       "340         0.883    0.1030      0  \n",
       "57          0.956    0.4710      0  \n",
       "83          0.645    0.1060      0  \n",
       "763         0.679    0.0702      0  \n",
       "733         0.643    0.1990      0  \n",
       "651         0.946    0.0846      0  \n",
       "650         0.982    0.1120      0  \n",
       "727         0.737    0.0965      0  \n",
       "64          0.650    0.3790      0  \n",
       "649         0.961    0.0851      0  \n",
       "726         0.705    0.1160      0  \n",
       "473         0.959    0.1640      0  \n",
       "768         0.723    0.2570      0  \n",
       "309         0.765    0.1230      0  \n",
       "335         0.599    0.1030      0  \n",
       "70          0.728    0.6120      0  \n",
       "65          0.742    0.3240      0  \n",
       "256         0.701    0.2070      0  \n",
       "56          0.848    0.1090      0  \n",
       "754         0.891    0.0987      0  \n",
       "653         0.921    0.1190      0  \n",
       "809         0.749    0.1320      0  \n",
       "234         0.677    0.0799      0  \n",
       "752         0.958    0.0522      0  \n",
       "66          0.665    0.0886      0  \n",
       "9           0.832    0.1040      0  \n",
       "356         0.976    0.1110      0  \n",
       "732         0.862    0.3020      0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "songs[songs['label'] == 0].tail(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>artist</th>\n",
       "      <th>id</th>\n",
       "      <th>danceability</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>No Problem (feat. Lil Wayne &amp; 2 Chainz)</td>\n",
       "      <td>Chance the Rapper</td>\n",
       "      <td>0v9Wz8o0BT8DU38R4ddjeH</td>\n",
       "      <td>0.652</td>\n",
       "      <td>0.857175</td>\n",
       "      <td>0.1740</td>\n",
       "      <td>0.15600</td>\n",
       "      <td>0.1230</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>A$AP Forever</td>\n",
       "      <td>A$AP Rocky</td>\n",
       "      <td>1YmF9PvLhIISIANoMLIYGq</td>\n",
       "      <td>0.469</td>\n",
       "      <td>0.820370</td>\n",
       "      <td>0.1220</td>\n",
       "      <td>0.22400</td>\n",
       "      <td>0.1300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>Θ. Macarena</td>\n",
       "      <td>Damso</td>\n",
       "      <td>2ewjMyCbNv2X1dB2qIDCwD</td>\n",
       "      <td>0.764</td>\n",
       "      <td>0.771770</td>\n",
       "      <td>0.0709</td>\n",
       "      <td>0.20900</td>\n",
       "      <td>0.0494</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>Big Shot (with Travis Scott)</td>\n",
       "      <td>Kendrick Lamar</td>\n",
       "      <td>5A4anUcHrgnqH9UorW9XbG</td>\n",
       "      <td>0.859</td>\n",
       "      <td>0.747214</td>\n",
       "      <td>0.2170</td>\n",
       "      <td>0.00699</td>\n",
       "      <td>0.0979</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>Cupido (feat. Quavo)</td>\n",
       "      <td>Sfera Ebbasta</td>\n",
       "      <td>7bklDXyWkNYQi6tBXvf9Wo</td>\n",
       "      <td>0.798</td>\n",
       "      <td>0.857629</td>\n",
       "      <td>0.0386</td>\n",
       "      <td>0.06730</td>\n",
       "      <td>0.1040</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>Emotions</td>\n",
       "      <td>Mariah Carey</td>\n",
       "      <td>0cELvuwJW1acISUHYB6suj</td>\n",
       "      <td>0.741</td>\n",
       "      <td>0.805654</td>\n",
       "      <td>0.0359</td>\n",
       "      <td>0.04110</td>\n",
       "      <td>0.0233</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Chanel</td>\n",
       "      <td>Di-Meh</td>\n",
       "      <td>4AoLo6CrJR7vYuSiiJeSGk</td>\n",
       "      <td>0.797</td>\n",
       "      <td>0.749454</td>\n",
       "      <td>0.1070</td>\n",
       "      <td>0.07920</td>\n",
       "      <td>0.1560</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>Relationship (feat. Future)</td>\n",
       "      <td>Young Thug</td>\n",
       "      <td>25oOaleife6E2MIKmFkPvg</td>\n",
       "      <td>0.842</td>\n",
       "      <td>0.824737</td>\n",
       "      <td>0.0627</td>\n",
       "      <td>0.00252</td>\n",
       "      <td>0.1240</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>Are You That Somebody</td>\n",
       "      <td>Aaliyah</td>\n",
       "      <td>4QDAMBiRAZXK20zDCQiiuE</td>\n",
       "      <td>0.852</td>\n",
       "      <td>0.802960</td>\n",
       "      <td>0.1720</td>\n",
       "      <td>0.16100</td>\n",
       "      <td>0.0999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Intro (Doug Funny)</td>\n",
       "      <td>Azizi Gibson</td>\n",
       "      <td>2NtpDCsfFuWc7mm2hFCw4H</td>\n",
       "      <td>0.716</td>\n",
       "      <td>0.784926</td>\n",
       "      <td>0.0385</td>\n",
       "      <td>0.02210</td>\n",
       "      <td>0.1440</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Met Gala (feat. Offset)</td>\n",
       "      <td>Gucci Mane</td>\n",
       "      <td>2XaInsPPUkZRkY4uFJ3AHK</td>\n",
       "      <td>0.817</td>\n",
       "      <td>0.820030</td>\n",
       "      <td>0.1080</td>\n",
       "      <td>0.05200</td>\n",
       "      <td>0.1850</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>Stainless</td>\n",
       "      <td>Logic</td>\n",
       "      <td>2gOYY2glUc6XMTnzBG59I4</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.862960</td>\n",
       "      <td>0.0364</td>\n",
       "      <td>0.07120</td>\n",
       "      <td>0.2710</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Herizen Guardiola</td>\n",
       "      <td>Manast LL'</td>\n",
       "      <td>1XzqDSbJZHRsTV9BDaOqPS</td>\n",
       "      <td>0.813</td>\n",
       "      <td>0.685995</td>\n",
       "      <td>0.0710</td>\n",
       "      <td>0.02540</td>\n",
       "      <td>0.0828</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662</th>\n",
       "      <td>Airplane Mode (feat. 이주한 혜원, Simo)</td>\n",
       "      <td>Dynamic Duo</td>\n",
       "      <td>29GxZ2LkrnE7p6YFZIKlYF</td>\n",
       "      <td>0.685</td>\n",
       "      <td>0.846712</td>\n",
       "      <td>0.0834</td>\n",
       "      <td>0.09040</td>\n",
       "      <td>0.1720</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>London</td>\n",
       "      <td>AJ x Deno</td>\n",
       "      <td>0lstGz2advSx5VqY8olvzt</td>\n",
       "      <td>0.743</td>\n",
       "      <td>0.878158</td>\n",
       "      <td>0.1760</td>\n",
       "      <td>0.03160</td>\n",
       "      <td>0.0942</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        name             artist  \\\n",
       "571  No Problem (feat. Lil Wayne & 2 Chainz)  Chance the Rapper   \n",
       "260                             A$AP Forever         A$AP Rocky   \n",
       "269                              Θ. Macarena              Damso   \n",
       "263             Big Shot (with Travis Scott)     Kendrick Lamar   \n",
       "384                     Cupido (feat. Quavo)      Sfera Ebbasta   \n",
       "689                                 Emotions       Mariah Carey   \n",
       "32                                    Chanel             Di-Meh   \n",
       "455              Relationship (feat. Future)         Young Thug   \n",
       "708                    Are You That Somebody            Aaliyah   \n",
       "112                       Intro (Doug Funny)       Azizi Gibson   \n",
       "152                  Met Gala (feat. Offset)         Gucci Mane   \n",
       "617                                Stainless              Logic   \n",
       "102                        Herizen Guardiola         Manast LL'   \n",
       "662       Airplane Mode (feat. 이주한 혜원, Simo)        Dynamic Duo   \n",
       "165                                   London          AJ x Deno   \n",
       "\n",
       "                         id  danceability  loudness  speechiness  \\\n",
       "571  0v9Wz8o0BT8DU38R4ddjeH         0.652  0.857175       0.1740   \n",
       "260  1YmF9PvLhIISIANoMLIYGq         0.469  0.820370       0.1220   \n",
       "269  2ewjMyCbNv2X1dB2qIDCwD         0.764  0.771770       0.0709   \n",
       "263  5A4anUcHrgnqH9UorW9XbG         0.859  0.747214       0.2170   \n",
       "384  7bklDXyWkNYQi6tBXvf9Wo         0.798  0.857629       0.0386   \n",
       "689  0cELvuwJW1acISUHYB6suj         0.741  0.805654       0.0359   \n",
       "32   4AoLo6CrJR7vYuSiiJeSGk         0.797  0.749454       0.1070   \n",
       "455  25oOaleife6E2MIKmFkPvg         0.842  0.824737       0.0627   \n",
       "708  4QDAMBiRAZXK20zDCQiiuE         0.852  0.802960       0.1720   \n",
       "112  2NtpDCsfFuWc7mm2hFCw4H         0.716  0.784926       0.0385   \n",
       "152  2XaInsPPUkZRkY4uFJ3AHK         0.817  0.820030       0.1080   \n",
       "617  2gOYY2glUc6XMTnzBG59I4         0.700  0.862960       0.0364   \n",
       "102  1XzqDSbJZHRsTV9BDaOqPS         0.813  0.685995       0.0710   \n",
       "662  29GxZ2LkrnE7p6YFZIKlYF         0.685  0.846712       0.0834   \n",
       "165  0lstGz2advSx5VqY8olvzt         0.743  0.878158       0.1760   \n",
       "\n",
       "     acousticness  liveness  label  \n",
       "571       0.15600    0.1230      1  \n",
       "260       0.22400    0.1300      1  \n",
       "269       0.20900    0.0494      1  \n",
       "263       0.00699    0.0979      1  \n",
       "384       0.06730    0.1040      1  \n",
       "689       0.04110    0.0233      1  \n",
       "32        0.07920    0.1560      1  \n",
       "455       0.00252    0.1240      1  \n",
       "708       0.16100    0.0999      1  \n",
       "112       0.02210    0.1440      1  \n",
       "152       0.05200    0.1850      1  \n",
       "617       0.07120    0.2710      1  \n",
       "102       0.02540    0.0828      1  \n",
       "662       0.09040    0.1720      1  \n",
       "165       0.03160    0.0942      1  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "songs[songs['label']==1].head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>artist</th>\n",
       "      <th>id</th>\n",
       "      <th>danceability</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>Go Get It</td>\n",
       "      <td>T.I.</td>\n",
       "      <td>3ZfACixzMNAbRcpGwEkLRe</td>\n",
       "      <td>0.366</td>\n",
       "      <td>0.843253</td>\n",
       "      <td>0.2460</td>\n",
       "      <td>0.00212</td>\n",
       "      <td>0.398</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>Wicked</td>\n",
       "      <td>Future</td>\n",
       "      <td>6BbINUfGabVyiNFJpQXn3x</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.837128</td>\n",
       "      <td>0.0706</td>\n",
       "      <td>0.00191</td>\n",
       "      <td>0.385</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>Guap</td>\n",
       "      <td>Big Sean</td>\n",
       "      <td>5KliOPpfkFJzNXJpmjgTR7</td>\n",
       "      <td>0.650</td>\n",
       "      <td>0.839737</td>\n",
       "      <td>0.1720</td>\n",
       "      <td>0.31100</td>\n",
       "      <td>0.412</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>Shooters</td>\n",
       "      <td>Tory Lanez</td>\n",
       "      <td>09BhVtcw1sVubMNHLM5I4v</td>\n",
       "      <td>0.630</td>\n",
       "      <td>0.808603</td>\n",
       "      <td>0.0517</td>\n",
       "      <td>0.10600</td>\n",
       "      <td>0.714</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>Orso (off top)</td>\n",
       "      <td>¿Téo?</td>\n",
       "      <td>3noZqIAtdmzGPGZo211EjI</td>\n",
       "      <td>0.709</td>\n",
       "      <td>0.733519</td>\n",
       "      <td>0.0498</td>\n",
       "      <td>0.26300</td>\n",
       "      <td>0.361</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>instagram</td>\n",
       "      <td>DEAN</td>\n",
       "      <td>4LjlYndpXP76iDArlJaWXT</td>\n",
       "      <td>0.463</td>\n",
       "      <td>0.828735</td>\n",
       "      <td>0.1450</td>\n",
       "      <td>0.19600</td>\n",
       "      <td>0.284</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Photosynthesis</td>\n",
       "      <td>Saba</td>\n",
       "      <td>3DlgDXIYtnWtJKiB8bZQMv</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.766411</td>\n",
       "      <td>0.2700</td>\n",
       "      <td>0.53600</td>\n",
       "      <td>0.593</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>I Know There's Gonna Be (Good Times)</td>\n",
       "      <td>Jamie xx</td>\n",
       "      <td>6393Du1axGG1NvAMq0OcfU</td>\n",
       "      <td>0.458</td>\n",
       "      <td>0.723141</td>\n",
       "      <td>0.2200</td>\n",
       "      <td>0.12000</td>\n",
       "      <td>0.477</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>막을 올리며 Encore</td>\n",
       "      <td>Epik High</td>\n",
       "      <td>6r97KRZqC4MZioqRhXrNYJ</td>\n",
       "      <td>0.583</td>\n",
       "      <td>0.829642</td>\n",
       "      <td>0.2890</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.731</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>Father Stretch My Hands Pt. 1</td>\n",
       "      <td>Kanye West</td>\n",
       "      <td>1947qHiGxQpqleCxLphoOH</td>\n",
       "      <td>0.712</td>\n",
       "      <td>0.880029</td>\n",
       "      <td>0.0564</td>\n",
       "      <td>0.13500</td>\n",
       "      <td>0.544</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>Family Don't Matter (feat. Millie Go Lightly)</td>\n",
       "      <td>Young Thug</td>\n",
       "      <td>445YTBOjx9kYThBepGA9au</td>\n",
       "      <td>0.630</td>\n",
       "      <td>0.714662</td>\n",
       "      <td>0.4330</td>\n",
       "      <td>0.17200</td>\n",
       "      <td>0.429</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>RiRi</td>\n",
       "      <td>Young Thug</td>\n",
       "      <td>20rZiHL20MTJjfhLQZQsm4</td>\n",
       "      <td>0.677</td>\n",
       "      <td>0.794992</td>\n",
       "      <td>0.2770</td>\n",
       "      <td>0.01440</td>\n",
       "      <td>0.318</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>Wicked</td>\n",
       "      <td>Future</td>\n",
       "      <td>6BbINUfGabVyiNFJpQXn3x</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.837128</td>\n",
       "      <td>0.0706</td>\n",
       "      <td>0.00191</td>\n",
       "      <td>0.385</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Bebop</td>\n",
       "      <td>[bsd.u]</td>\n",
       "      <td>3U19GVybWYwG0wMXzrgL2h</td>\n",
       "      <td>0.726</td>\n",
       "      <td>0.663595</td>\n",
       "      <td>0.1050</td>\n",
       "      <td>0.03860</td>\n",
       "      <td>0.371</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>House Party (Remix) (Feat. Fabolous, Wale &amp; Ma...</td>\n",
       "      <td>Meek Mill</td>\n",
       "      <td>4nh1AZPCk7noadkKfRgRxY</td>\n",
       "      <td>0.581</td>\n",
       "      <td>0.694842</td>\n",
       "      <td>0.2950</td>\n",
       "      <td>0.06170</td>\n",
       "      <td>0.524</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Magnolia</td>\n",
       "      <td>Playboi Carti</td>\n",
       "      <td>1e1JKLEDKP7hEQzJfNAgPl</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.796751</td>\n",
       "      <td>0.2860</td>\n",
       "      <td>0.01130</td>\n",
       "      <td>0.350</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>911 / Mr. Lonely</td>\n",
       "      <td>Tyler, The Creator</td>\n",
       "      <td>4bEcoz1OcfMgUbp2ft8ieQ</td>\n",
       "      <td>0.480</td>\n",
       "      <td>0.804718</td>\n",
       "      <td>0.3050</td>\n",
       "      <td>0.04560</td>\n",
       "      <td>0.313</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>1Train</td>\n",
       "      <td>A$AP Rocky</td>\n",
       "      <td>7AijU6oTPGmG64uWf63Qvc</td>\n",
       "      <td>0.622</td>\n",
       "      <td>0.907903</td>\n",
       "      <td>0.3320</td>\n",
       "      <td>0.34900</td>\n",
       "      <td>0.695</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>Rodeo</td>\n",
       "      <td>Lil Nas X</td>\n",
       "      <td>4ak7xjvBeBOcJGWFDX9w5n</td>\n",
       "      <td>0.706</td>\n",
       "      <td>0.845209</td>\n",
       "      <td>0.0324</td>\n",
       "      <td>0.13900</td>\n",
       "      <td>0.465</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805</th>\n",
       "      <td>Ali Bomaye</td>\n",
       "      <td>The Game</td>\n",
       "      <td>6AcMShL9nO1oQHsmVhiKPn</td>\n",
       "      <td>0.381</td>\n",
       "      <td>0.855417</td>\n",
       "      <td>0.4300</td>\n",
       "      <td>0.02070</td>\n",
       "      <td>0.469</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>We'll Be Fine</td>\n",
       "      <td>Drake</td>\n",
       "      <td>7udsBKuqnJ5csWTAkR0vEI</td>\n",
       "      <td>0.386</td>\n",
       "      <td>0.797318</td>\n",
       "      <td>0.4220</td>\n",
       "      <td>0.00171</td>\n",
       "      <td>0.640</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>Fade Away</td>\n",
       "      <td>Logic</td>\n",
       "      <td>5veIeQ8tk07IlIp34NeI8s</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0.924263</td>\n",
       "      <td>0.2290</td>\n",
       "      <td>0.26100</td>\n",
       "      <td>0.296</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>pick up the phone</td>\n",
       "      <td>Young Thug</td>\n",
       "      <td>4GXl8l1MfZPf2GvpyRPJBf</td>\n",
       "      <td>0.706</td>\n",
       "      <td>0.883517</td>\n",
       "      <td>0.0768</td>\n",
       "      <td>0.11500</td>\n",
       "      <td>0.316</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>Check</td>\n",
       "      <td>Frenna</td>\n",
       "      <td>6CISA66GSRDq7rtkHnqjuI</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.860096</td>\n",
       "      <td>0.2480</td>\n",
       "      <td>0.33200</td>\n",
       "      <td>0.468</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>All The Way Up (Remix)</td>\n",
       "      <td>Fat Joe</td>\n",
       "      <td>2AYTGaclgKXACh13nJRVcV</td>\n",
       "      <td>0.564</td>\n",
       "      <td>0.822837</td>\n",
       "      <td>0.3970</td>\n",
       "      <td>0.09700</td>\n",
       "      <td>0.493</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>Day N Night</td>\n",
       "      <td>Peejay</td>\n",
       "      <td>2okuMoTCN92sukH42o2v8v</td>\n",
       "      <td>0.640</td>\n",
       "      <td>0.765985</td>\n",
       "      <td>0.0361</td>\n",
       "      <td>0.10600</td>\n",
       "      <td>0.435</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>November</td>\n",
       "      <td>Tyler, The Creator</td>\n",
       "      <td>4XDpeWqPADoWRKcUY3dC84</td>\n",
       "      <td>0.547</td>\n",
       "      <td>0.804803</td>\n",
       "      <td>0.4130</td>\n",
       "      <td>0.35400</td>\n",
       "      <td>0.676</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>Touch The Sky</td>\n",
       "      <td>Kanye West</td>\n",
       "      <td>2pRyjmTGkz5i8xemOtJ7pG</td>\n",
       "      <td>0.524</td>\n",
       "      <td>0.865115</td>\n",
       "      <td>0.3470</td>\n",
       "      <td>0.01140</td>\n",
       "      <td>0.309</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>Born Hater (feat. Beenzino, Verbal Jint, B.I, ...</td>\n",
       "      <td>Epik High</td>\n",
       "      <td>7bE0L65oh3vLNe9cI0QXCA</td>\n",
       "      <td>0.788</td>\n",
       "      <td>0.847988</td>\n",
       "      <td>0.3150</td>\n",
       "      <td>0.02710</td>\n",
       "      <td>0.313</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>Pow (Forward)</td>\n",
       "      <td>Lethal Bizzle</td>\n",
       "      <td>37uFAVNwaRF7XUdByyJyNN</td>\n",
       "      <td>0.535</td>\n",
       "      <td>0.773726</td>\n",
       "      <td>0.3930</td>\n",
       "      <td>0.00389</td>\n",
       "      <td>0.370</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  name              artist  \\\n",
       "772                                          Go Get It                T.I.   \n",
       "511                                             Wicked              Future   \n",
       "669                                               Guap            Big Sean   \n",
       "249                                           Shooters          Tory Lanez   \n",
       "216                                     Orso (off top)               ¿Téo?   \n",
       "444                                          instagram                DEAN   \n",
       "31                                      Photosynthesis                Saba   \n",
       "146               I Know There's Gonna Be (Good Times)            Jamie xx   \n",
       "738                                      막을 올리며 Encore           Epik High   \n",
       "671                      Father Stretch My Hands Pt. 1          Kanye West   \n",
       "453      Family Don't Matter (feat. Millie Go Lightly)          Young Thug   \n",
       "556                                               RiRi          Young Thug   \n",
       "624                                             Wicked              Future   \n",
       "62                                               Bebop             [bsd.u]   \n",
       "534  House Party (Remix) (Feat. Fabolous, Wale & Ma...           Meek Mill   \n",
       "189                                           Magnolia       Playboi Carti   \n",
       "468                                   911 / Mr. Lonely  Tyler, The Creator   \n",
       "776                                             1Train          A$AP Rocky   \n",
       "134                                              Rodeo           Lil Nas X   \n",
       "805                                         Ali Bomaye            The Game   \n",
       "576                                      We'll Be Fine               Drake   \n",
       "582                                          Fade Away               Logic   \n",
       "610                                  pick up the phone          Young Thug   \n",
       "157                                              Check              Frenna   \n",
       "514                             All The Way Up (Remix)             Fat Joe   \n",
       "584                                        Day N Night              Peejay   \n",
       "490                                           November  Tyler, The Creator   \n",
       "542                                      Touch The Sky          Kanye West   \n",
       "745  Born Hater (feat. Beenzino, Verbal Jint, B.I, ...           Epik High   \n",
       "532                                      Pow (Forward)       Lethal Bizzle   \n",
       "\n",
       "                         id  danceability  loudness  speechiness  \\\n",
       "772  3ZfACixzMNAbRcpGwEkLRe         0.366  0.843253       0.2460   \n",
       "511  6BbINUfGabVyiNFJpQXn3x         0.718  0.837128       0.0706   \n",
       "669  5KliOPpfkFJzNXJpmjgTR7         0.650  0.839737       0.1720   \n",
       "249  09BhVtcw1sVubMNHLM5I4v         0.630  0.808603       0.0517   \n",
       "216  3noZqIAtdmzGPGZo211EjI         0.709  0.733519       0.0498   \n",
       "444  4LjlYndpXP76iDArlJaWXT         0.463  0.828735       0.1450   \n",
       "31   3DlgDXIYtnWtJKiB8bZQMv         0.777  0.766411       0.2700   \n",
       "146  6393Du1axGG1NvAMq0OcfU         0.458  0.723141       0.2200   \n",
       "738  6r97KRZqC4MZioqRhXrNYJ         0.583  0.829642       0.2890   \n",
       "671  1947qHiGxQpqleCxLphoOH         0.712  0.880029       0.0564   \n",
       "453  445YTBOjx9kYThBepGA9au         0.630  0.714662       0.4330   \n",
       "556  20rZiHL20MTJjfhLQZQsm4         0.677  0.794992       0.2770   \n",
       "624  6BbINUfGabVyiNFJpQXn3x         0.718  0.837128       0.0706   \n",
       "62   3U19GVybWYwG0wMXzrgL2h         0.726  0.663595       0.1050   \n",
       "534  4nh1AZPCk7noadkKfRgRxY         0.581  0.694842       0.2950   \n",
       "189  1e1JKLEDKP7hEQzJfNAgPl         0.791  0.796751       0.2860   \n",
       "468  4bEcoz1OcfMgUbp2ft8ieQ         0.480  0.804718       0.3050   \n",
       "776  7AijU6oTPGmG64uWf63Qvc         0.622  0.907903       0.3320   \n",
       "134  4ak7xjvBeBOcJGWFDX9w5n         0.706  0.845209       0.0324   \n",
       "805  6AcMShL9nO1oQHsmVhiKPn         0.381  0.855417       0.4300   \n",
       "576  7udsBKuqnJ5csWTAkR0vEI         0.386  0.797318       0.4220   \n",
       "582  5veIeQ8tk07IlIp34NeI8s         0.698  0.924263       0.2290   \n",
       "610  4GXl8l1MfZPf2GvpyRPJBf         0.706  0.883517       0.0768   \n",
       "157  6CISA66GSRDq7rtkHnqjuI         0.760  0.860096       0.2480   \n",
       "514  2AYTGaclgKXACh13nJRVcV         0.564  0.822837       0.3970   \n",
       "584  2okuMoTCN92sukH42o2v8v         0.640  0.765985       0.0361   \n",
       "490  4XDpeWqPADoWRKcUY3dC84         0.547  0.804803       0.4130   \n",
       "542  2pRyjmTGkz5i8xemOtJ7pG         0.524  0.865115       0.3470   \n",
       "745  7bE0L65oh3vLNe9cI0QXCA         0.788  0.847988       0.3150   \n",
       "532  37uFAVNwaRF7XUdByyJyNN         0.535  0.773726       0.3930   \n",
       "\n",
       "     acousticness  liveness  label  \n",
       "772       0.00212     0.398      2  \n",
       "511       0.00191     0.385      2  \n",
       "669       0.31100     0.412      2  \n",
       "249       0.10600     0.714      2  \n",
       "216       0.26300     0.361      2  \n",
       "444       0.19600     0.284      2  \n",
       "31        0.53600     0.593      2  \n",
       "146       0.12000     0.477      2  \n",
       "738       0.14100     0.731      2  \n",
       "671       0.13500     0.544      2  \n",
       "453       0.17200     0.429      2  \n",
       "556       0.01440     0.318      2  \n",
       "624       0.00191     0.385      2  \n",
       "62        0.03860     0.371      2  \n",
       "534       0.06170     0.524      2  \n",
       "189       0.01130     0.350      2  \n",
       "468       0.04560     0.313      2  \n",
       "776       0.34900     0.695      2  \n",
       "134       0.13900     0.465      2  \n",
       "805       0.02070     0.469      2  \n",
       "576       0.00171     0.640      2  \n",
       "582       0.26100     0.296      2  \n",
       "610       0.11500     0.316      2  \n",
       "157       0.33200     0.468      2  \n",
       "514       0.09700     0.493      2  \n",
       "584       0.10600     0.435      2  \n",
       "490       0.35400     0.676      2  \n",
       "542       0.01140     0.309      2  \n",
       "745       0.02710     0.313      2  \n",
       "532       0.00389     0.370      2  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "songs[songs['label']==2].head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>artist</th>\n",
       "      <th>id</th>\n",
       "      <th>danceability</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>Yo Darlin'</td>\n",
       "      <td>NSG</td>\n",
       "      <td>7Lh7cJzYuo4U5Uq687rLSm</td>\n",
       "      <td>0.674</td>\n",
       "      <td>0.861797</td>\n",
       "      <td>0.2460</td>\n",
       "      <td>0.303</td>\n",
       "      <td>0.1400</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>SWEET</td>\n",
       "      <td>BROCKHAMPTON</td>\n",
       "      <td>2DgMxFMUQRPthW4ROhjen1</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.834066</td>\n",
       "      <td>0.2230</td>\n",
       "      <td>0.543</td>\n",
       "      <td>0.1160</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>I Wish I Missed My Ex</td>\n",
       "      <td>Mahalia</td>\n",
       "      <td>4jwP0pxx7su04FYc6J12EQ</td>\n",
       "      <td>0.680</td>\n",
       "      <td>0.859302</td>\n",
       "      <td>0.0607</td>\n",
       "      <td>0.319</td>\n",
       "      <td>0.1580</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>Corazón</td>\n",
       "      <td>Maluma</td>\n",
       "      <td>4lESS6vuruP6a79KWRaQou</td>\n",
       "      <td>0.871</td>\n",
       "      <td>0.851249</td>\n",
       "      <td>0.1220</td>\n",
       "      <td>0.283</td>\n",
       "      <td>0.1620</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>pay stub</td>\n",
       "      <td>gianni &amp; kyle</td>\n",
       "      <td>6UlzoN4Vv5YM1oLJlsZcci</td>\n",
       "      <td>0.820</td>\n",
       "      <td>0.775172</td>\n",
       "      <td>0.1280</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.3230</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>Big Dusty</td>\n",
       "      <td>Joey Bada$$</td>\n",
       "      <td>5YHWLmV91wCzgfeKW6rH8X</td>\n",
       "      <td>0.677</td>\n",
       "      <td>0.799189</td>\n",
       "      <td>0.4940</td>\n",
       "      <td>0.577</td>\n",
       "      <td>0.1380</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>Bemba Colora</td>\n",
       "      <td>Reyes De Cancion</td>\n",
       "      <td>4tS5HJSVRyutrYAnVC74Py</td>\n",
       "      <td>0.802</td>\n",
       "      <td>0.604134</td>\n",
       "      <td>0.0410</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.1040</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Nightfall</td>\n",
       "      <td>Lester Nowhere</td>\n",
       "      <td>6hJIhtBrsJB7ywzzvkhFFA</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.819094</td>\n",
       "      <td>0.0309</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.0756</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>do u even miss me at all?</td>\n",
       "      <td>gianni &amp; kyle</td>\n",
       "      <td>7yGUFMHTVa71SBBhDWjZ46</td>\n",
       "      <td>0.819</td>\n",
       "      <td>0.801372</td>\n",
       "      <td>0.0768</td>\n",
       "      <td>0.384</td>\n",
       "      <td>0.0899</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>The Way</td>\n",
       "      <td>Ariana Grande</td>\n",
       "      <td>0S4RKPbRDA72tvKwVdXQqe</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.913432</td>\n",
       "      <td>0.1130</td>\n",
       "      <td>0.294</td>\n",
       "      <td>0.0759</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>Check-In</td>\n",
       "      <td>Reddy</td>\n",
       "      <td>2q2079txZy57DJQ37PGmQp</td>\n",
       "      <td>0.851</td>\n",
       "      <td>0.846599</td>\n",
       "      <td>0.1920</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0.1070</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Dance</td>\n",
       "      <td>offonoff</td>\n",
       "      <td>3rQmTAyMDDZ73rMyhLkn98</td>\n",
       "      <td>0.805</td>\n",
       "      <td>0.806590</td>\n",
       "      <td>0.0339</td>\n",
       "      <td>0.717</td>\n",
       "      <td>0.1050</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>Fa - Laay - Fanaan</td>\n",
       "      <td>Baaba Maal</td>\n",
       "      <td>5J4qCKoXwuIBE9iG6KYiOb</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.718292</td>\n",
       "      <td>0.0367</td>\n",
       "      <td>0.683</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>State of Elevation</td>\n",
       "      <td>Dillon Cooper</td>\n",
       "      <td>7CquDZal3tGvTJQuJdHGzw</td>\n",
       "      <td>0.755</td>\n",
       "      <td>0.812714</td>\n",
       "      <td>0.2870</td>\n",
       "      <td>0.380</td>\n",
       "      <td>0.0854</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>CAN'T GET OVER YOU (feat. Clams Casino)</td>\n",
       "      <td>Joji</td>\n",
       "      <td>7ewESHEg3P3JN66IcWwDho</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.754955</td>\n",
       "      <td>0.3320</td>\n",
       "      <td>0.684</td>\n",
       "      <td>0.0853</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>Under Control</td>\n",
       "      <td>The Internet</td>\n",
       "      <td>2kBHv0gFA2iheTUXmPB9e1</td>\n",
       "      <td>0.706</td>\n",
       "      <td>0.830465</td>\n",
       "      <td>0.0610</td>\n",
       "      <td>0.327</td>\n",
       "      <td>0.1850</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>Shhhlide</td>\n",
       "      <td>WeAreYou</td>\n",
       "      <td>62tFJ5q7zzRGPc1SQitYFv</td>\n",
       "      <td>0.834</td>\n",
       "      <td>0.758131</td>\n",
       "      <td>0.1320</td>\n",
       "      <td>0.434</td>\n",
       "      <td>0.3700</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>Frontin' (feat. Jay-Z) - Club Mix</td>\n",
       "      <td>Pharrell Williams</td>\n",
       "      <td>0iFOG4Ki9aDmJUYUFHQlPG</td>\n",
       "      <td>0.894</td>\n",
       "      <td>0.836731</td>\n",
       "      <td>0.1300</td>\n",
       "      <td>0.453</td>\n",
       "      <td>0.0801</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Vice</td>\n",
       "      <td>Jay Prince</td>\n",
       "      <td>6n4g9rQE65ZgPQo32mWixM</td>\n",
       "      <td>0.659</td>\n",
       "      <td>0.828338</td>\n",
       "      <td>0.1110</td>\n",
       "      <td>0.576</td>\n",
       "      <td>0.1110</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>Nayhoo</td>\n",
       "      <td>Chon</td>\n",
       "      <td>4WCgIZ1n7I6yRaAQ9GjS07</td>\n",
       "      <td>0.812</td>\n",
       "      <td>0.802535</td>\n",
       "      <td>0.0830</td>\n",
       "      <td>0.539</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>Joints</td>\n",
       "      <td>House of Pharaohs</td>\n",
       "      <td>0ZSX8HJ5oWPG3NFmg8bCeB</td>\n",
       "      <td>0.744</td>\n",
       "      <td>0.740522</td>\n",
       "      <td>0.2140</td>\n",
       "      <td>0.459</td>\n",
       "      <td>0.1440</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>Say You Love Me (feat. WizKid)</td>\n",
       "      <td>LeriQ</td>\n",
       "      <td>2D1nJ8Q2rhJM3716dVVBxb</td>\n",
       "      <td>0.620</td>\n",
       "      <td>0.801146</td>\n",
       "      <td>0.2920</td>\n",
       "      <td>0.332</td>\n",
       "      <td>0.0844</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Laputa</td>\n",
       "      <td>DPR LIVE</td>\n",
       "      <td>2Hhvi0VT7CaRzeKPwIH8ja</td>\n",
       "      <td>0.813</td>\n",
       "      <td>0.803017</td>\n",
       "      <td>0.2690</td>\n",
       "      <td>0.316</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>On My Mind (Jorja Smith X Preditah)</td>\n",
       "      <td>Jorja Smith</td>\n",
       "      <td>1OxL1FWSEd7QmJoWLRP2NO</td>\n",
       "      <td>0.710</td>\n",
       "      <td>0.835767</td>\n",
       "      <td>0.1750</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.0723</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>I Am You, You Are Me</td>\n",
       "      <td>Zico</td>\n",
       "      <td>75ai0ibymmqyCyaxwZuhnu</td>\n",
       "      <td>0.769</td>\n",
       "      <td>0.807951</td>\n",
       "      <td>0.0531</td>\n",
       "      <td>0.317</td>\n",
       "      <td>0.3050</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>7 Girls</td>\n",
       "      <td>House of Pharaohs</td>\n",
       "      <td>4p8pS6hViqvT9aH2hhOl07</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.682791</td>\n",
       "      <td>0.3440</td>\n",
       "      <td>0.634</td>\n",
       "      <td>0.0990</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>693</th>\n",
       "      <td>In Love with the Glove</td>\n",
       "      <td>Helper</td>\n",
       "      <td>0yxnko5RpHDhbseVGU7tno</td>\n",
       "      <td>0.765</td>\n",
       "      <td>0.580911</td>\n",
       "      <td>0.0756</td>\n",
       "      <td>0.547</td>\n",
       "      <td>0.1280</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>Big Poppa - 2005 Remaster</td>\n",
       "      <td>The Notorious B.I.G.</td>\n",
       "      <td>2g8HN35AnVGIk7B8yMucww</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.799671</td>\n",
       "      <td>0.2740</td>\n",
       "      <td>0.432</td>\n",
       "      <td>0.1380</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Alkaline</td>\n",
       "      <td>Kota the Friend</td>\n",
       "      <td>0ror2i7yC3O4iqS1Fmvhtb</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0.808773</td>\n",
       "      <td>0.2970</td>\n",
       "      <td>0.679</td>\n",
       "      <td>0.1730</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>dayfly</td>\n",
       "      <td>DEAN</td>\n",
       "      <td>2AA1f7utyvXpmtKsEvp5lz</td>\n",
       "      <td>0.726</td>\n",
       "      <td>0.776250</td>\n",
       "      <td>0.0794</td>\n",
       "      <td>0.566</td>\n",
       "      <td>0.2570</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>Little</td>\n",
       "      <td>Octavian</td>\n",
       "      <td>3nQpvXVQ0xkCFJXonA1kFX</td>\n",
       "      <td>0.731</td>\n",
       "      <td>0.694077</td>\n",
       "      <td>0.2120</td>\n",
       "      <td>0.449</td>\n",
       "      <td>0.1070</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>Most Wanted</td>\n",
       "      <td>House of Pharaohs</td>\n",
       "      <td>1elaAUz6JjiNKgOEMpaXjc</td>\n",
       "      <td>0.802</td>\n",
       "      <td>0.844075</td>\n",
       "      <td>0.1420</td>\n",
       "      <td>0.327</td>\n",
       "      <td>0.1060</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>Champion (feat. Travis Scott)</td>\n",
       "      <td>NAV</td>\n",
       "      <td>6nO3tr47nr2P7f3hXb8JIo</td>\n",
       "      <td>0.642</td>\n",
       "      <td>0.908129</td>\n",
       "      <td>0.0477</td>\n",
       "      <td>0.381</td>\n",
       "      <td>0.1080</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>Normally [interlude]</td>\n",
       "      <td>House of Pharaohs</td>\n",
       "      <td>6koKX4N7LexY0rXXG0UTPo</td>\n",
       "      <td>0.742</td>\n",
       "      <td>0.659030</td>\n",
       "      <td>0.0498</td>\n",
       "      <td>0.575</td>\n",
       "      <td>0.0879</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Na Hora (feat. Faktiss &amp; Chris MC Clenney)</td>\n",
       "      <td>Sango</td>\n",
       "      <td>4xzUN1LCcqPwLoPaVwxXjV</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.721354</td>\n",
       "      <td>0.3650</td>\n",
       "      <td>0.314</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Feel</td>\n",
       "      <td>Misha</td>\n",
       "      <td>7uDOBBPGLWdbrpOlfQ8rxU</td>\n",
       "      <td>0.887</td>\n",
       "      <td>0.777327</td>\n",
       "      <td>0.0462</td>\n",
       "      <td>0.436</td>\n",
       "      <td>0.0921</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Get You (feat. Kali Uchis)</td>\n",
       "      <td>Daniel Caesar</td>\n",
       "      <td>7zFXmv6vqI4qOt4yGf3jYZ</td>\n",
       "      <td>0.658</td>\n",
       "      <td>0.762441</td>\n",
       "      <td>0.0321</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.0749</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>Pothole</td>\n",
       "      <td>Tyler, The Creator</td>\n",
       "      <td>5SxlUF7J8tyFIEF22EomeP</td>\n",
       "      <td>0.543</td>\n",
       "      <td>0.749936</td>\n",
       "      <td>0.3380</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.2800</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>Look What U Started</td>\n",
       "      <td>The Internet</td>\n",
       "      <td>0sSNa2XDu7dxbnjK0lKnDH</td>\n",
       "      <td>0.817</td>\n",
       "      <td>0.840389</td>\n",
       "      <td>0.0570</td>\n",
       "      <td>0.322</td>\n",
       "      <td>0.1090</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>Peach</td>\n",
       "      <td>Reddy</td>\n",
       "      <td>0FEofi94EjNE256GIcTanq</td>\n",
       "      <td>0.826</td>\n",
       "      <td>0.830635</td>\n",
       "      <td>0.1710</td>\n",
       "      <td>0.425</td>\n",
       "      <td>0.1110</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           name                artist  \\\n",
       "176                                  Yo Darlin'                   NSG   \n",
       "266                                       SWEET          BROCKHAMPTON   \n",
       "21                        I Wish I Missed My Ex               Mahalia   \n",
       "420                                     Corazón                Maluma   \n",
       "375                                    pay stub         gianni & kyle   \n",
       "818                                   Big Dusty           Joey Bada$$   \n",
       "761                                Bemba Colora      Reyes De Cancion   \n",
       "55                                    Nightfall        Lester Nowhere   \n",
       "202                   do u even miss me at all?         gianni & kyle   \n",
       "181                                     The Way         Ariana Grande   \n",
       "363                                    Check-In                 Reddy   \n",
       "97                                        Dance              offonoff   \n",
       "730                          Fa - Laay - Fanaan            Baaba Maal   \n",
       "255                          State of Elevation         Dillon Cooper   \n",
       "91      CAN'T GET OVER YOU (feat. Clams Casino)                  Joji   \n",
       "297                               Under Control          The Internet   \n",
       "447                                    Shhhlide              WeAreYou   \n",
       "682           Frontin' (feat. Jay-Z) - Club Mix     Pharrell Williams   \n",
       "27                                         Vice            Jay Prince   \n",
       "482                                      Nayhoo                  Chon   \n",
       "327                                      Joints     House of Pharaohs   \n",
       "533              Say You Love Me (feat. WizKid)                 LeriQ   \n",
       "33                                       Laputa              DPR LIVE   \n",
       "228         On My Mind (Jorja Smith X Preditah)           Jorja Smith   \n",
       "585                        I Am You, You Are Me                  Zico   \n",
       "334                                     7 Girls     House of Pharaohs   \n",
       "693                      In Love with the Glove                Helper   \n",
       "451                   Big Poppa - 2005 Remaster  The Notorious B.I.G.   \n",
       "124                                    Alkaline       Kota the Friend   \n",
       "98                                       dayfly                  DEAN   \n",
       "223                                      Little              Octavian   \n",
       "329                                 Most Wanted     House of Pharaohs   \n",
       "199               Champion (feat. Travis Scott)                   NAV   \n",
       "330                        Normally [interlude]     House of Pharaohs   \n",
       "37   Na Hora (feat. Faktiss & Chris MC Clenney)                 Sango   \n",
       "71                                         Feel                 Misha   \n",
       "79                   Get You (feat. Kali Uchis)         Daniel Caesar   \n",
       "472                                     Pothole    Tyler, The Creator   \n",
       "230                         Look What U Started          The Internet   \n",
       "222                                       Peach                 Reddy   \n",
       "\n",
       "                         id  danceability  loudness  speechiness  \\\n",
       "176  7Lh7cJzYuo4U5Uq687rLSm         0.674  0.861797       0.2460   \n",
       "266  2DgMxFMUQRPthW4ROhjen1         0.718  0.834066       0.2230   \n",
       "21   4jwP0pxx7su04FYc6J12EQ         0.680  0.859302       0.0607   \n",
       "420  4lESS6vuruP6a79KWRaQou         0.871  0.851249       0.1220   \n",
       "375  6UlzoN4Vv5YM1oLJlsZcci         0.820  0.775172       0.1280   \n",
       "818  5YHWLmV91wCzgfeKW6rH8X         0.677  0.799189       0.4940   \n",
       "761  4tS5HJSVRyutrYAnVC74Py         0.802  0.604134       0.0410   \n",
       "55   6hJIhtBrsJB7ywzzvkhFFA         0.517  0.819094       0.0309   \n",
       "202  7yGUFMHTVa71SBBhDWjZ46         0.819  0.801372       0.0768   \n",
       "181  0S4RKPbRDA72tvKwVdXQqe         0.645  0.913432       0.1130   \n",
       "363  2q2079txZy57DJQ37PGmQp         0.851  0.846599       0.1920   \n",
       "97   3rQmTAyMDDZ73rMyhLkn98         0.805  0.806590       0.0339   \n",
       "730  5J4qCKoXwuIBE9iG6KYiOb         0.825  0.718292       0.0367   \n",
       "255  7CquDZal3tGvTJQuJdHGzw         0.755  0.812714       0.2870   \n",
       "91   7ewESHEg3P3JN66IcWwDho         0.900  0.754955       0.3320   \n",
       "297  2kBHv0gFA2iheTUXmPB9e1         0.706  0.830465       0.0610   \n",
       "447  62tFJ5q7zzRGPc1SQitYFv         0.834  0.758131       0.1320   \n",
       "682  0iFOG4Ki9aDmJUYUFHQlPG         0.894  0.836731       0.1300   \n",
       "27   6n4g9rQE65ZgPQo32mWixM         0.659  0.828338       0.1110   \n",
       "482  4WCgIZ1n7I6yRaAQ9GjS07         0.812  0.802535       0.0830   \n",
       "327  0ZSX8HJ5oWPG3NFmg8bCeB         0.744  0.740522       0.2140   \n",
       "533  2D1nJ8Q2rhJM3716dVVBxb         0.620  0.801146       0.2920   \n",
       "33   2Hhvi0VT7CaRzeKPwIH8ja         0.813  0.803017       0.2690   \n",
       "228  1OxL1FWSEd7QmJoWLRP2NO         0.710  0.835767       0.1750   \n",
       "585  75ai0ibymmqyCyaxwZuhnu         0.769  0.807951       0.0531   \n",
       "334  4p8pS6hViqvT9aH2hhOl07         0.908  0.682791       0.3440   \n",
       "693  0yxnko5RpHDhbseVGU7tno         0.765  0.580911       0.0756   \n",
       "451  2g8HN35AnVGIk7B8yMucww         0.778  0.799671       0.2740   \n",
       "124  0ror2i7yC3O4iqS1Fmvhtb         0.774  0.808773       0.2970   \n",
       "98   2AA1f7utyvXpmtKsEvp5lz         0.726  0.776250       0.0794   \n",
       "223  3nQpvXVQ0xkCFJXonA1kFX         0.731  0.694077       0.2120   \n",
       "329  1elaAUz6JjiNKgOEMpaXjc         0.802  0.844075       0.1420   \n",
       "199  6nO3tr47nr2P7f3hXb8JIo         0.642  0.908129       0.0477   \n",
       "330  6koKX4N7LexY0rXXG0UTPo         0.742  0.659030       0.0498   \n",
       "37   4xzUN1LCcqPwLoPaVwxXjV         0.789  0.721354       0.3650   \n",
       "71   7uDOBBPGLWdbrpOlfQ8rxU         0.887  0.777327       0.0462   \n",
       "79   7zFXmv6vqI4qOt4yGf3jYZ         0.658  0.762441       0.0321   \n",
       "472  5SxlUF7J8tyFIEF22EomeP         0.543  0.749936       0.3380   \n",
       "230  0sSNa2XDu7dxbnjK0lKnDH         0.817  0.840389       0.0570   \n",
       "222  0FEofi94EjNE256GIcTanq         0.826  0.830635       0.1710   \n",
       "\n",
       "     acousticness  liveness  label  \n",
       "176         0.303    0.1400      3  \n",
       "266         0.543    0.1160      3  \n",
       "21          0.319    0.1580      3  \n",
       "420         0.283    0.1620      3  \n",
       "375         0.350    0.3230      3  \n",
       "818         0.577    0.1380      3  \n",
       "761         0.572    0.1040      3  \n",
       "55          0.536    0.0756      3  \n",
       "202         0.384    0.0899      3  \n",
       "181         0.294    0.0759      3  \n",
       "363         0.412    0.1070      3  \n",
       "97          0.717    0.1050      3  \n",
       "730         0.683    0.0700      3  \n",
       "255         0.380    0.0854      3  \n",
       "91          0.684    0.0853      3  \n",
       "297         0.327    0.1850      3  \n",
       "447         0.434    0.3700      3  \n",
       "682         0.453    0.0801      3  \n",
       "27          0.576    0.1110      3  \n",
       "482         0.539    0.1100      3  \n",
       "327         0.459    0.1440      3  \n",
       "533         0.332    0.0844      3  \n",
       "33          0.316    0.2430      3  \n",
       "228         0.420    0.0723      3  \n",
       "585         0.317    0.3050      3  \n",
       "334         0.634    0.0990      3  \n",
       "693         0.547    0.1280      3  \n",
       "451         0.432    0.1380      3  \n",
       "124         0.679    0.1730      3  \n",
       "98          0.566    0.2570      3  \n",
       "223         0.449    0.1070      3  \n",
       "329         0.327    0.1060      3  \n",
       "199         0.381    0.1080      3  \n",
       "330         0.575    0.0879      3  \n",
       "37          0.314    0.1140      3  \n",
       "71          0.436    0.0921      3  \n",
       "79          0.422    0.0749      3  \n",
       "472         0.510    0.2800      3  \n",
       "230         0.322    0.1090      3  \n",
       "222         0.425    0.1110      3  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "songs[songs['label']==3].head(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x11d947630>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11d989f98>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11d9b4630>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11d9d8c88>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11da07320>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11da2b978>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "songs[songs['label']==2].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "danceability    0.740957\n",
       "loudness        0.797812\n",
       "speechiness     0.174659\n",
       "acousticness    0.441355\n",
       "liveness        0.144303\n",
       "label           3.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "songs[songs['label']==3].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.utils.multiclass import unique_labels\n",
    "\n",
    "X = songs_features\n",
    "y = y_kmeans\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)\n",
    "\n",
    "rfc = RandomForestClassifier(n_estimators=100,criterion='gini')\n",
    "rfc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the Test set results\n",
    "y_pred = rfc.predict(X_test)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def plot_confusion_matrix(y_true, y_pred, classes,\n",
    "                          normalize=False,\n",
    "                          title=None,\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if not title:\n",
    "        if normalize:\n",
    "            title = 'Normalized confusion matrix'\n",
    "        else:\n",
    "            title = 'Confusion matrix'\n",
    "\n",
    "    # Compute confusion matrix\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    # Only use the labels that appear in the data\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    ax.figure.colorbar(im, ax=ax)\n",
    "    # We want to show all ticks...\n",
    "    ax.set(xticks=np.arange(cm.shape[1]),\n",
    "           yticks=np.arange(cm.shape[0]),\n",
    "           # ... and label them with the respective list entries\n",
    "           xticklabels=classes, yticklabels=classes,\n",
    "           title=title,\n",
    "           ylabel='True label',\n",
    "           xlabel='Predicted label')\n",
    "\n",
    "    # Rotate the tick labels and set their alignment.\n",
    "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "             rotation_mode=\"anchor\")\n",
    "\n",
    "    # Loop over data dimensions and create text annotations.\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i in range(cm.shape[0]):\n",
    "        for j in range(cm.shape[1]):\n",
    "            ax.text(j, i, format(cm[i, j], fmt),\n",
    "                    ha=\"center\", va=\"center\",\n",
    "                    color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "    fig.tight_layout()\n",
    "    return ax\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[ 14   0   0   4]\n",
      " [  0 164   0   0]\n",
      " [  0   1  34   1]\n",
      " [  1   0   1  52]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11dcbe860>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion matrix\n",
    "definitions = ['Chill','Energetic','Cheerful','Romantic']\n",
    "# reversefactor = dict(zip(range(4),definitions))\n",
    "# actual = np.vectorize(reversefactor.get)(y_test)\n",
    "# pred = np.vectorize(reversefactor.get)(y_pred)\n",
    "# print(pd.crosstab(actual, pred, rownames=['Actual Mood'], colnames=['Predicted Mood']))\n",
    "\n",
    "plot_confusion_matrix(y_test, y_pred, classes=definitions,\n",
    "                      title='Confusion matrix for Random Forest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('danceability', 0.06703729205210224),\n",
       " ('loudness', 0.06320214769610095),\n",
       " ('speechiness', 0.03769455303598156),\n",
       " ('acousticness', 0.5476709899131079),\n",
       " ('liveness', 0.2843950173027072)]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View a list of the features and their importance scores\n",
    "features = songs_features.columns\n",
    "list(zip(songs_features[features], rfc.feature_importances_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=3, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "# Train the model using the training sets\n",
    "knn.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_pred =knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[ 11   0   0   7]\n",
      " [  0 160   3   1]\n",
      " [  0   4  31   1]\n",
      " [  0   5   1  48]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11dda8d30>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, knn_pred, classes=definitions,\n",
    "                      title='Confusion matrix for KNN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import svm model\n",
    "from sklearn import svm\n",
    "#Create a svm Classifier\n",
    "svm = svm.SVC(kernel=\"linear\") \n",
    "\n",
    "#Train the model using the training sets\n",
    "svm.fit(X_train, y_train)\n",
    "\n",
    "#Predict the response for test dataset\n",
    "svm_pred = svm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[  7   0   0  11]\n",
      " [  0 164   0   0]\n",
      " [  0  11  24   1]\n",
      " [  0   7   0  47]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x120d66ac8>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, svm_pred, classes=definitions,\n",
    "                      title='Confusion matrix for SVM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/SylvesterCardorelle/ml/env/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(100,), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n",
       "       random_state=None, shuffle=True, solver='adam', tol=0.0001,\n",
       "       validation_fraction=0.1, verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp = MLPClassifier()\n",
    "mlp.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp_pred = mlp.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[  9   0   0   9]\n",
      " [  0 164   0   0]\n",
      " [  0   5  30   1]\n",
      " [  1   4   0  49]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x122a40240>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, mlp_pred, classes=definitions,\n",
    "                      title='Confusion matrix for MLP')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       Chill       0.90      0.50      0.64        18\n",
      "   Energetic       0.95      1.00      0.97       164\n",
      "    Cheerful       1.00      0.83      0.91        36\n",
      "    Romantic       0.83      0.91      0.87        54\n",
      "\n",
      "   micro avg       0.93      0.93      0.93       272\n",
      "   macro avg       0.92      0.81      0.85       272\n",
      "weighted avg       0.93      0.93      0.92       272\n",
      "MLP\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,mlp_pred,target_names=definitions)+'MLP')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       Chill       1.00      0.39      0.56        18\n",
      "   Energetic       0.90      1.00      0.95       164\n",
      "    Cheerful       1.00      0.67      0.80        36\n",
      "    Romantic       0.80      0.87      0.83        54\n",
      "\n",
      "   micro avg       0.89      0.89      0.89       272\n",
      "   macro avg       0.92      0.73      0.78       272\n",
      "weighted avg       0.90      0.89      0.88       272\n",
      "SVM\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,svm_pred,target_names=definitions)+'SVM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       Chill       1.00      0.61      0.76        18\n",
      "   Energetic       0.95      0.98      0.96       164\n",
      "    Cheerful       0.89      0.86      0.87        36\n",
      "    Romantic       0.84      0.89      0.86        54\n",
      "\n",
      "   micro avg       0.92      0.92      0.92       272\n",
      "   macro avg       0.92      0.83      0.86       272\n",
      "weighted avg       0.92      0.92      0.92       272\n",
      "KNN\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,knn_pred,target_names=definitions)+'KNN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       Chill       0.93      0.78      0.85        18\n",
      "   Energetic       0.99      1.00      1.00       164\n",
      "    Cheerful       0.97      0.94      0.96        36\n",
      "    Romantic       0.91      0.96      0.94        54\n",
      "\n",
      "   micro avg       0.97      0.97      0.97       272\n",
      "   macro avg       0.95      0.92      0.94       272\n",
      "weighted avg       0.97      0.97      0.97       272\n",
      "forest\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred,target_names=definitions)+'forest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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