saved_songs_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "\n",
    "sp_df = pd.read_csv('spotify_analysis.csv')\n",
    "sp_df=sp_df[['name', 'added_at', 'danceability', 'energy', 'key', 'mode', 'acousticness','instrumentalness', 'liveness','valence','tempo','time_signature']]\n",
    "sp_df = sp_df.sort_values(by='added_at')\n",
    "sp2015_df = sp_df[(sp_df['added_at'] < '2017-01-01') & (sp_df['added_at'] >= '2016-01-01')]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sp_df['added_at'] = pd.to_datetime(sp_df['added_at']).dt.strftime('%Y-%m-%d').astype('datetime64[ns]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                        object\n",
       "added_at            datetime64[ns]\n",
       "danceability               float64\n",
       "energy                     float64\n",
       "key                          int64\n",
       "mode                         int64\n",
       "acousticness               float64\n",
       "instrumentalness           float64\n",
       "liveness                   float64\n",
       "valence                    float64\n",
       "tempo                      float64\n",
       "time_signature               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>added_at</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>mode</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>time_signature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>Lines</td>\n",
       "      <td>2014-11-02</td>\n",
       "      <td>0.535</td>\n",
       "      <td>0.394</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7670</td>\n",
       "      <td>0.00178</td>\n",
       "      <td>0.1040</td>\n",
       "      <td>0.2680</td>\n",
       "      <td>127.212</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>Tangle</td>\n",
       "      <td>2014-11-02</td>\n",
       "      <td>0.558</td>\n",
       "      <td>0.472</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6140</td>\n",
       "      <td>0.07950</td>\n",
       "      <td>0.0954</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>97.614</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>Russian Dolls</td>\n",
       "      <td>2014-11-02</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.417</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3700</td>\n",
       "      <td>0.05180</td>\n",
       "      <td>0.4010</td>\n",
       "      <td>0.0513</td>\n",
       "      <td>134.757</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>Cold Air</td>\n",
       "      <td>2014-11-02</td>\n",
       "      <td>0.555</td>\n",
       "      <td>0.465</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0678</td>\n",
       "      <td>0.09620</td>\n",
       "      <td>0.2450</td>\n",
       "      <td>0.1730</td>\n",
       "      <td>115.002</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>Lucky To Be Me</td>\n",
       "      <td>2014-11-11</td>\n",
       "      <td>0.484</td>\n",
       "      <td>0.021</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>0.90200</td>\n",
       "      <td>0.0914</td>\n",
       "      <td>0.1170</td>\n",
       "      <td>101.392</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               name   added_at  danceability  energy  key  mode  acousticness  \\\n",
       "756           Lines 2014-11-02         0.535   0.394    2     0        0.7670   \n",
       "753          Tangle 2014-11-02         0.558   0.472    5     0        0.6140   \n",
       "755   Russian Dolls 2014-11-02         0.361   0.417   11     0        0.3700   \n",
       "754        Cold Air 2014-11-02         0.555   0.465    8     1        0.0678   \n",
       "752  Lucky To Be Me 2014-11-11         0.484   0.021    4     1        0.9940   \n",
       "\n",
       "     instrumentalness  liveness  valence    tempo  time_signature  \n",
       "756           0.00178    0.1040   0.2680  127.212               4  \n",
       "753           0.07950    0.0954   0.1250   97.614               3  \n",
       "755           0.05180    0.4010   0.0513  134.757               4  \n",
       "754           0.09620    0.2450   0.1730  115.002               4  \n",
       "752           0.90200    0.0914   0.1170  101.392               3  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "danceability          0.546122\n",
       "energy                0.447847\n",
       "acousticness          0.489877\n",
       "instrumentalness      0.329873\n",
       "liveness              0.166421\n",
       "tempo               114.528908\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp_df[['danceability', 'energy', 'acousticness', 'instrumentalness', 'liveness','tempo']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "danceability          0.5430\n",
       "energy                0.4490\n",
       "acousticness          0.4890\n",
       "instrumentalness      0.0766\n",
       "liveness              0.1150\n",
       "tempo               111.7990\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp_df[['danceability', 'energy', 'acousticness', 'instrumentalness', 'liveness','tempo']].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp_df['key'].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp_df['key'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2015-01-25\n",
       "dtype: datetime64[ns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp_df['added_at'].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,10))\n",
    "ax = sp_df['valence'].plot.hist(bins=10, alpha=0.5, color='green',title='Valence')\n",
    "ax.set_xlabel('Valence')\n",
    "ax.set_ylabel('Songs')\n",
    "\n",
    "fig2 = plt.figure(figsize=(20,10))\n",
    "ax2 = sp_df['danceability'].plot.hist(bins=10,title='Danceability')\n",
    "ax2.set_xlabel('Danceability')\n",
    "ax2.set_ylabel('Songs')\n",
    "\n",
    "fig3 = plt.figure(figsize=(20,10))\n",
    "ax3 = sp_df['energy'].plot.hist(bins=10,color='purple', title='Energy')\n",
    "ax3.set_xlabel('Energy')\n",
    "ax3.set_ylabel('Songs')\n",
    "\n",
    "fig4 = plt.figure(figsize=(20,10))\n",
    "ax4 = sp_df['instrumentalness'].plot.hist(bins=10,color='orange',alpha=0.3, title='Instrumentalness')\n",
    "ax4.set_xlabel('Instrumentalness')\n",
    "ax4.set_ylabel('Songs')\n",
    "\n",
    "fig5 = plt.figure()\n",
    "ax5 = sp2015_df.plot.scatter(x='added_at',y='valence', color='red',alpha=0.7, title='Valence over Time',figsize=(20,10))"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}