Spotify-EDA-DecisionTree.ipynb

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   "source": [
    "import pandas as pd # Dataframe, series\n",
    "import numpy as np # Scientific computing packages - Array\n",
    "\n",
    "from sklearn import tree\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as snb\n",
    "\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "### Spotify Song Attributes EDA\n",
    "\n",
    "- Import Dataset\n",
    "- EDA (exploratery data analysis) to visualise data and observer structure\n",
    "- Train a classifier (Decision Tree)\n",
    "- Predict target using the trained classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('data/data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
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    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
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   "source": [
    "type(data)"
   ]
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    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>key</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>time_signature</th>\n",
       "      <th>valence</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2.017000e+03</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1008.000000</td>\n",
       "      <td>0.187590</td>\n",
       "      <td>0.618422</td>\n",
       "      <td>2.463062e+05</td>\n",
       "      <td>0.681577</td>\n",
       "      <td>0.133286</td>\n",
       "      <td>5.342588</td>\n",
       "      <td>0.190844</td>\n",
       "      <td>-7.085624</td>\n",
       "      <td>0.612295</td>\n",
       "      <td>0.092664</td>\n",
       "      <td>121.603272</td>\n",
       "      <td>3.968270</td>\n",
       "      <td>0.496815</td>\n",
       "      <td>0.505702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>582.402066</td>\n",
       "      <td>0.259989</td>\n",
       "      <td>0.161029</td>\n",
       "      <td>8.198181e+04</td>\n",
       "      <td>0.210273</td>\n",
       "      <td>0.273162</td>\n",
       "      <td>3.648240</td>\n",
       "      <td>0.155453</td>\n",
       "      <td>3.761684</td>\n",
       "      <td>0.487347</td>\n",
       "      <td>0.089931</td>\n",
       "      <td>26.685604</td>\n",
       "      <td>0.255853</td>\n",
       "      <td>0.247195</td>\n",
       "      <td>0.500091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.122000</td>\n",
       "      <td>1.604200e+04</td>\n",
       "      <td>0.014800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018800</td>\n",
       "      <td>-33.097000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.023100</td>\n",
       "      <td>47.859000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034800</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>504.000000</td>\n",
       "      <td>0.009630</td>\n",
       "      <td>0.514000</td>\n",
       "      <td>2.000150e+05</td>\n",
       "      <td>0.563000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.092300</td>\n",
       "      <td>-8.394000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.037500</td>\n",
       "      <td>100.189000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.295000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1008.000000</td>\n",
       "      <td>0.063300</td>\n",
       "      <td>0.631000</td>\n",
       "      <td>2.292610e+05</td>\n",
       "      <td>0.715000</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.127000</td>\n",
       "      <td>-6.248000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.054900</td>\n",
       "      <td>121.427000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.492000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1512.000000</td>\n",
       "      <td>0.265000</td>\n",
       "      <td>0.738000</td>\n",
       "      <td>2.703330e+05</td>\n",
       "      <td>0.846000</td>\n",
       "      <td>0.054000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.247000</td>\n",
       "      <td>-4.746000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.108000</td>\n",
       "      <td>137.849000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.691000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>0.995000</td>\n",
       "      <td>0.984000</td>\n",
       "      <td>1.004627e+06</td>\n",
       "      <td>0.998000</td>\n",
       "      <td>0.976000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.969000</td>\n",
       "      <td>-0.307000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.816000</td>\n",
       "      <td>219.331000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.992000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  acousticness  danceability   duration_ms       energy  \\\n",
       "count  2017.000000   2017.000000   2017.000000  2.017000e+03  2017.000000   \n",
       "mean   1008.000000      0.187590      0.618422  2.463062e+05     0.681577   \n",
       "std     582.402066      0.259989      0.161029  8.198181e+04     0.210273   \n",
       "min       0.000000      0.000003      0.122000  1.604200e+04     0.014800   \n",
       "25%     504.000000      0.009630      0.514000  2.000150e+05     0.563000   \n",
       "50%    1008.000000      0.063300      0.631000  2.292610e+05     0.715000   \n",
       "75%    1512.000000      0.265000      0.738000  2.703330e+05     0.846000   \n",
       "max    2016.000000      0.995000      0.984000  1.004627e+06     0.998000   \n",
       "\n",
       "       instrumentalness          key     liveness     loudness         mode  \\\n",
       "count       2017.000000  2017.000000  2017.000000  2017.000000  2017.000000   \n",
       "mean           0.133286     5.342588     0.190844    -7.085624     0.612295   \n",
       "std            0.273162     3.648240     0.155453     3.761684     0.487347   \n",
       "min            0.000000     0.000000     0.018800   -33.097000     0.000000   \n",
       "25%            0.000000     2.000000     0.092300    -8.394000     0.000000   \n",
       "50%            0.000076     6.000000     0.127000    -6.248000     1.000000   \n",
       "75%            0.054000     9.000000     0.247000    -4.746000     1.000000   \n",
       "max            0.976000    11.000000     0.969000    -0.307000     1.000000   \n",
       "\n",
       "       speechiness        tempo  time_signature      valence       target  \n",
       "count  2017.000000  2017.000000     2017.000000  2017.000000  2017.000000  \n",
       "mean      0.092664   121.603272        3.968270     0.496815     0.505702  \n",
       "std       0.089931    26.685604        0.255853     0.247195     0.500091  \n",
       "min       0.023100    47.859000        1.000000     0.034800     0.000000  \n",
       "25%       0.037500   100.189000        4.000000     0.295000     0.000000  \n",
       "50%       0.054900   121.427000        4.000000     0.492000     1.000000  \n",
       "75%       0.108000   137.849000        4.000000     0.691000     1.000000  \n",
       "max       0.816000   219.331000        5.000000     0.992000     1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>key</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>time_signature</th>\n",
       "      <th>valence</th>\n",
       "      <th>target</th>\n",
       "      <th>song_title</th>\n",
       "      <th>artist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0102</td>\n",
       "      <td>0.833</td>\n",
       "      <td>204600</td>\n",
       "      <td>0.434</td>\n",
       "      <td>0.021900</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1650</td>\n",
       "      <td>-8.795</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>150.062</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.286</td>\n",
       "      <td>1</td>\n",
       "      <td>Mask Off</td>\n",
       "      <td>Future</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1990</td>\n",
       "      <td>0.743</td>\n",
       "      <td>326933</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.006110</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1370</td>\n",
       "      <td>-10.401</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0794</td>\n",
       "      <td>160.083</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.588</td>\n",
       "      <td>1</td>\n",
       "      <td>Redbone</td>\n",
       "      <td>Childish Gambino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0344</td>\n",
       "      <td>0.838</td>\n",
       "      <td>185707</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0.000234</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>-7.148</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2890</td>\n",
       "      <td>75.044</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.173</td>\n",
       "      <td>1</td>\n",
       "      <td>Xanny Family</td>\n",
       "      <td>Future</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.6040</td>\n",
       "      <td>0.494</td>\n",
       "      <td>199413</td>\n",
       "      <td>0.338</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0922</td>\n",
       "      <td>-15.236</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0261</td>\n",
       "      <td>86.468</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.230</td>\n",
       "      <td>1</td>\n",
       "      <td>Master Of None</td>\n",
       "      <td>Beach House</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.1800</td>\n",
       "      <td>0.678</td>\n",
       "      <td>392893</td>\n",
       "      <td>0.561</td>\n",
       "      <td>0.512000</td>\n",
       "      <td>5</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>-11.648</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0694</td>\n",
       "      <td>174.004</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.904</td>\n",
       "      <td>1</td>\n",
       "      <td>Parallel Lines</td>\n",
       "      <td>Junior Boys</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  acousticness  danceability  duration_ms  energy  \\\n",
       "0           0        0.0102         0.833       204600   0.434   \n",
       "1           1        0.1990         0.743       326933   0.359   \n",
       "2           2        0.0344         0.838       185707   0.412   \n",
       "3           3        0.6040         0.494       199413   0.338   \n",
       "4           4        0.1800         0.678       392893   0.561   \n",
       "\n",
       "   instrumentalness  key  liveness  loudness  mode  speechiness    tempo  \\\n",
       "0          0.021900    2    0.1650    -8.795     1       0.4310  150.062   \n",
       "1          0.006110    1    0.1370   -10.401     1       0.0794  160.083   \n",
       "2          0.000234    2    0.1590    -7.148     1       0.2890   75.044   \n",
       "3          0.510000    5    0.0922   -15.236     1       0.0261   86.468   \n",
       "4          0.512000    5    0.4390   -11.648     0       0.0694  174.004   \n",
       "\n",
       "   time_signature  valence  target      song_title            artist  \n",
       "0             4.0    0.286       1        Mask Off            Future  \n",
       "1             4.0    0.588       1         Redbone  Childish Gambino  \n",
       "2             4.0    0.173       1    Xanny Family            Future  \n",
       "3             4.0    0.230       1  Master Of None       Beach House  \n",
       "4             4.0    0.904       1  Parallel Lines       Junior Boys  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2017 entries, 0 to 2016\n",
      "Data columns (total 17 columns):\n",
      "Unnamed: 0          2017 non-null int64\n",
      "acousticness        2017 non-null float64\n",
      "danceability        2017 non-null float64\n",
      "duration_ms         2017 non-null int64\n",
      "energy              2017 non-null float64\n",
      "instrumentalness    2017 non-null float64\n",
      "key                 2017 non-null int64\n",
      "liveness            2017 non-null float64\n",
      "loudness            2017 non-null float64\n",
      "mode                2017 non-null int64\n",
      "speechiness         2017 non-null float64\n",
      "tempo               2017 non-null float64\n",
      "time_signature      2017 non-null float64\n",
      "valence             2017 non-null float64\n",
      "target              2017 non-null int64\n",
      "song_title          2017 non-null object\n",
      "artist              2017 non-null object\n",
      "dtypes: float64(10), int64(5), object(2)\n",
      "memory usage: 268.0+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "train, test = train_test_split(data, test_size = 0.15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training size: 1714; Test size: 303\n"
     ]
    }
   ],
   "source": [
    "print(\"Training size: {}; Test size: {}\".format(len(train), len(test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1714, 17)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Custom palette\n",
    "red_blue = [\"#19b5fe\", \"#ef4836\"]\n",
    "palette = snb.color_palette(red_blue)\n",
    "snb.set_palette(palette)\n",
    "snb.set_style(\"white\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "pos_tempo = data[data['target'] == 1]['tempo']\n",
    "neg_tempo = data[data['target'] == 0]['tempo']\n",
    "\n",
    "pos_dance = data[data['target'] == 1]['danceability']\n",
    "neg_dance = data[data['target'] == 0]['danceability']\n",
    "\n",
    "pos_duration = data[data['target'] == 1]['duration_ms']\n",
    "neg_duration = data[data['target'] == 0]['duration_ms']\n",
    "\n",
    "pos_loudness = data[data['target'] == 1]['loudness']\n",
    "neg_loudness = data[data['target'] == 0]['loudness']\n",
    "\n",
    "pos_speechiness = data[data['target'] == 1]['speechiness']\n",
    "neg_speechiness = data[data['target'] == 0]['speechiness']\n",
    "\n",
    "pos_valence = data[data['target'] == 1]['valence']\n",
    "neg_valence = data[data['target'] == 0]['valence']\n",
    "\n",
    "pos_energy = data[data['target'] == 1]['energy']\n",
    "neg_energy = data[data['target'] == 0]['energy']\n",
    "\n",
    "pos_acousticness = data[data['target'] == 1]['acousticness']\n",
    "neg_acousticness = data[data['target'] == 0]['acousticness']\n",
    "\n",
    "pos_key = data[data['target'] == 1]['key']\n",
    "neg_key = data[data['target'] == 0]['key']\n",
    "\n",
    "pos_instrumentalness = data[data['target'] == 1]['instrumentalness']\n",
    "neg_instrumentalness = data[data['target'] == 0]['instrumentalness']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x110cee898>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AlNjZs2f1t7/9TSdOnFDnzp1ls9m0dOlS3X777fr73/+uOXPmqEWLFgoKCtLbb7+tffv2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\nAABj/T9AuvpPVaz36QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110a27588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "plt.title(\"Song Tempo Like / Dislike Distribution\")\n",
    "pos_tempo.hist(alpha=0.7, bins=30, label='positive')\n",
    "neg_tempo.hist(alpha=0.7, bins=30, label='negetive')\n",
    "plt.legend(loc=\"upper right\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114c73cc0>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VOnXqVOE2isazZs0a/vrXv9KrVy/atm2LyWQiJCSEV155xfI6o9Foc9eH/fv3ExAQgKen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\ncccdTJo0iTp16jBu3Dj8/Pw4c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+If//gHd955Z4H1unbtWmzcHTp0oF+/fixZsqTIz8hT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113cecda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 = plt.figure(figsize=(15, 15))\n",
    "\n",
    "# Danceability\n",
    "ax3 = fig2.add_subplot(331)\n",
    "ax3.set_xlabel(\"Danceability\")\n",
    "ax3.set_ylabel(\"Count\")\n",
    "ax3.set_title(\"Song Danceability Like Distribution\")\n",
    "pos_dance.hist(alpha=0.5, bins=30)\n",
    "ax4 = fig2.add_subplot(331)\n",
    "neg_dance.hist(alpha=0.5, bins=30)\n",
    "\n",
    "# Duration\n",
    "ax5 = fig2.add_subplot(332)\n",
    "ax5.set_xlabel(\"Duration (ms)\")\n",
    "ax5.set_ylabel(\"Count\")\n",
    "ax5.set_title(\"Song Duration Like Distribution\")\n",
    "pos_duration.hist(alpha=0.5, bins=30)\n",
    "ax6 = fig2.add_subplot(332)\n",
    "neg_duration.hist(alpha=0.5, bins=30)\n",
    "\n",
    "# Loudness\n",
    "ax7 = fig2.add_subplot(333)\n",
    "ax7.set_xlabel(\"Loudness\")\n",
    "ax7.set_ylabel(\"Count\")\n",
    "ax7.set_title(\"Song Loudness Like Distribution\")\n",
    "pos_loudness.hist(alpha=0.5, bins=30)\n",
    "ax8 = fig2.add_subplot(333)\n",
    "neg_loudness.hist(alpha=0.5, bins=30)\n",
    "\n",
    "# Speechiness\n",
    "ax7 = fig2.add_subplot(334)\n",
    "ax7.set_xlabel(\"Speechiness\")\n",
    "ax7.set_ylabel(\"Count\")\n",
    "ax7.set_title(\"Song Speechiness Like Distribution\")\n",
    "pos_speechiness.hist(alpha=0.5, bins=30)\n",
    "ax8 = fig2.add_subplot(334)\n",
    "neg_speechiness.hist(alpha=0.5, bins=30)\n",
    "\n",
    "# Valence\n",
    "ax7 = fig2.add_subplot(335)\n",
    "ax7.set_xlabel(\"Valence\")\n",
    "ax7.set_ylabel(\"Count\")\n",
    "ax7.set_title(\"Song Valence Like Distribution\")\n",
    "pos_valence.hist(alpha=0.5, bins=30)\n",
    "ax8 = fig2.add_subplot(335)\n",
    "neg_valence.hist(alpha=0.5, bins=30)\n",
    "\n",
    "# Energy\n",
    "ax7 = fig2.add_subplot(336)\n",
    "ax7.set_xlabel(\"Energy\")\n",
    "ax7.set_ylabel(\"Count\")\n",
    "ax7.set_title(\"Song Energy Like Distribution\")\n",
    "pos_energy.hist(alpha=0.5, bins=30)\n",
    "ax8 = fig2.add_subplot(336)\n",
    "neg_energy.hist(alpha=0.5, bins=30)\n",
    "\n",
    "# Acousticness\n",
    "ax7 = fig2.add_subplot(337)\n",
    "ax7.set_xlabel(\"Acousticness\")\n",
    "ax7.set_ylabel(\"Count\")\n",
    "ax7.set_title(\"Song Acousticness Like Distribution\")\n",
    "pos_acousticness.hist(alpha=0.5, bins=30)\n",
    "ax8 = fig2.add_subplot(337)\n",
    "neg_acousticness.hist(alpha=0.5, bins=30)\n",
    "\n",
    "# Key\n",
    "ax7 = fig2.add_subplot(338)\n",
    "ax7.set_xlabel(\"Key\")\n",
    "ax7.set_ylabel(\"Count\")\n",
    "ax7.set_title(\"Song Key Like Distribution\")\n",
    "pos_key.hist(alpha=0.5, bins=30)\n",
    "ax8 = fig2.add_subplot(338)\n",
    "neg_key.hist(alpha=0.5, bins=30)\n",
    "\n",
    "# Instrumentalness\n",
    "ax7 = fig2.add_subplot(339)\n",
    "ax7.set_xlabel(\"Instrumentalness\")\n",
    "ax7.set_ylabel(\"Count\")\n",
    "ax7.set_title(\"Song Instrumentalness Like Distribution\")\n",
    "pos_instrumentalness.hist(alpha=0.5, bins=30)\n",
    "ax8 = fig2.add_subplot(339)\n",
    "neg_instrumentalness.hist(alpha=0.5, bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}