music.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import re\n",
    "\n",
    "def contains_non_english_chars(text):\n",
    "    pattern = re.compile(r'[^\\x00-\\x7F]')\n",
    "    return bool(pattern.search(text))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['id', 'track_id', 'artists', 'album_name', 'track_name', 'popularity',\n",
      "       'duration_ms', 'explicit', 'danceability', 'energy', 'key', 'loudness',\n",
      "       'mode', 'speechiness', 'acousticness', 'instrumentalness', 'liveness',\n",
      "       'valence', 'tempo', 'time_signature', 'track_genre'],\n",
      "      dtype='object')\n",
      "7318\n",
      "10029\n",
      "12725\n",
      "14204\n",
      "['acoustic' 'afrobeat' 'alt-rock' 'alternative' 'ambient' 'anime'\n",
      " 'black-metal' 'bluegrass' 'blues' 'brazil' 'breakbeat' 'british'\n",
      " 'cantopop' 'chicago-house' 'children' 'chill' 'classical' 'club' 'comedy'\n",
      " 'country' 'dance' 'dancehall' 'death-metal' 'deep-house' 'detroit-techno'\n",
      " 'disco' 'disney' 'drum-and-bass' 'dub' 'dubstep' 'edm' 'electro'\n",
      " 'electronic' 'emo' 'folk' 'forro' 'french' 'funk' 'garage' 'german'\n",
      " 'gospel' 'goth' 'groove' 'grunge' 'guitar' 'happy' 'hard-rock' 'hardcore'\n",
      " 'hardstyle' 'heavy-metal' 'hip-hop' 'honky-tonk' 'house' 'idm' 'indian'\n",
      " 'indie-pop' 'indie' 'industrial' 'j-dance' 'j-idol' 'j-pop' 'j-rock'\n",
      " 'jazz' 'k-pop' 'kids' 'latin' 'latino' 'malay' 'mandopop' 'metal'\n",
      " 'metalcore' 'minimal-techno' 'mpb' 'new-age' 'opera' 'pagode' 'party'\n",
      " 'piano' 'pop-film' 'pop' 'power-pop' 'progressive-house' 'psych-rock'\n",
      " 'punk-rock' 'punk' 'r-n-b' 'reggae' 'reggaeton' 'rock-n-roll' 'rock'\n",
      " 'rockabilly' 'sad' 'salsa' 'samba' 'sertanejo' 'show-tunes'\n",
      " 'singer-songwriter' 'ska' 'sleep' 'songwriter' 'soul' 'spanish' 'swedish'\n",
      " 'synth-pop' 'techno' 'trance' 'trip-hop' 'turkish' 'world-music']\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
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     "text": [
      "20545\n"
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       "      <td>Kina Grannis</td>\n",
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       "      <td>Can't Help Falling In Love</td>\n",
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       "      <td>201933</td>\n",
       "      <td>False</td>\n",
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       "      <td>0.9050</td>\n",
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       "      <td>0.1320</td>\n",
       "      <td>0.1430</td>\n",
       "      <td>181.740</td>\n",
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       "    <tr>\n",
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       "      <td>4</td>\n",
       "      <td>4</td>\n",
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       "      <td>Chord Overstreet</td>\n",
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       "      <td>198853</td>\n",
       "      <td>False</td>\n",
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       "      <td>58</td>\n",
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       "      <td>0.1050</td>\n",
       "      <td>0.2890</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1890</td>\n",
       "      <td>0.6660</td>\n",
       "      <td>98.017</td>\n",
       "      <td>4</td>\n",
       "      <td>acoustic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20540</th>\n",
       "      <td>113414</td>\n",
       "      <td>113414</td>\n",
       "      <td>0mqDhUDKwpnDvIFoQYR524</td>\n",
       "      <td>Cory Asbury</td>\n",
       "      <td>Reckless Love</td>\n",
       "      <td>Endless Alleluia</td>\n",
       "      <td>56</td>\n",
       "      <td>265377</td>\n",
       "      <td>False</td>\n",
       "      <td>0.261</td>\n",
       "      <td>...</td>\n",
       "      <td>-10.423</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0368</td>\n",
       "      <td>0.8480</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1130</td>\n",
       "      <td>0.2290</td>\n",
       "      <td>77.473</td>\n",
       "      <td>4</td>\n",
       "      <td>world-music</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20541</th>\n",
       "      <td>113454</td>\n",
       "      <td>113454</td>\n",
       "      <td>1d6BkMki9M6K0yp9OLBzOM</td>\n",
       "      <td>Actress</td>\n",
       "      <td>Karma &amp; Desire</td>\n",
       "      <td>Fire and Light</td>\n",
       "      <td>57</td>\n",
       "      <td>75969</td>\n",
       "      <td>False</td>\n",
       "      <td>0.337</td>\n",
       "      <td>...</td>\n",
       "      <td>-18.646</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0384</td>\n",
       "      <td>0.9870</td>\n",
       "      <td>0.882000</td>\n",
       "      <td>0.0851</td>\n",
       "      <td>0.0824</td>\n",
       "      <td>140.004</td>\n",
       "      <td>4</td>\n",
       "      <td>world-music</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20542</th>\n",
       "      <td>113489</td>\n",
       "      <td>113489</td>\n",
       "      <td>0uryoZxnSimQVSFnGhyqxU</td>\n",
       "      <td>Rend Collective</td>\n",
       "      <td>Whosoever</td>\n",
       "      <td>Hallelujah Anyway</td>\n",
       "      <td>57</td>\n",
       "      <td>225755</td>\n",
       "      <td>False</td>\n",
       "      <td>0.494</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.042</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0379</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.1320</td>\n",
       "      <td>0.4670</td>\n",
       "      <td>129.994</td>\n",
       "      <td>4</td>\n",
       "      <td>world-music</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20543</th>\n",
       "      <td>113598</td>\n",
       "      <td>113598</td>\n",
       "      <td>2t8f07knCNSY19avq19l1R</td>\n",
       "      <td>Mosaic MSC;Cody Carnes;Amanda Cook;Joe L Barnes</td>\n",
       "      <td>Thanks</td>\n",
       "      <td>This Is How I Thank The Lord (feat. Cody Carne...</td>\n",
       "      <td>56</td>\n",
       "      <td>221274</td>\n",
       "      <td>False</td>\n",
       "      <td>0.455</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.221</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0388</td>\n",
       "      <td>0.5550</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.3270</td>\n",
       "      <td>0.4340</td>\n",
       "      <td>125.858</td>\n",
       "      <td>4</td>\n",
       "      <td>world-music</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20544</th>\n",
       "      <td>113872</td>\n",
       "      <td>113872</td>\n",
       "      <td>7xzMrUmlooPa1Fmp88hlYc</td>\n",
       "      <td>Cesária Evora</td>\n",
       "      <td>Miss Perfumado</td>\n",
       "      <td>Sodade</td>\n",
       "      <td>59</td>\n",
       "      <td>293640</td>\n",
       "      <td>False</td>\n",
       "      <td>0.575</td>\n",
       "      <td>...</td>\n",
       "      <td>-13.168</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0363</td>\n",
       "      <td>0.8220</td>\n",
       "      <td>0.000661</td>\n",
       "      <td>0.1150</td>\n",
       "      <td>0.4270</td>\n",
       "      <td>82.691</td>\n",
       "      <td>4</td>\n",
       "      <td>world-music</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20545 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        index      id                track_id  \\\n",
       "0           0       0  5SuOikwiRyPMVoIQDJUgSV   \n",
       "1           2       2  1iJBSr7s7jYXzM8EGcbK5b   \n",
       "2           3       3  6lfxq3CG4xtTiEg7opyCyx   \n",
       "3           4       4  5vjLSffimiIP26QG5WcN2K   \n",
       "4           5       5  01MVOl9KtVTNfFiBU9I7dc   \n",
       "...       ...     ...                     ...   \n",
       "20540  113414  113414  0mqDhUDKwpnDvIFoQYR524   \n",
       "20541  113454  113454  1d6BkMki9M6K0yp9OLBzOM   \n",
       "20542  113489  113489  0uryoZxnSimQVSFnGhyqxU   \n",
       "20543  113598  113598  2t8f07knCNSY19avq19l1R   \n",
       "20544  113872  113872  7xzMrUmlooPa1Fmp88hlYc   \n",
       "\n",
       "                                               artists  \\\n",
       "0                                          Gen Hoshino   \n",
       "1                               Ingrid Michaelson;ZAYN   \n",
       "2                                         Kina Grannis   \n",
       "3                                     Chord Overstreet   \n",
       "4                                         Tyrone Wells   \n",
       "...                                                ...   \n",
       "20540                                      Cory Asbury   \n",
       "20541                                          Actress   \n",
       "20542                                  Rend Collective   \n",
       "20543  Mosaic MSC;Cody Carnes;Amanda Cook;Joe L Barnes   \n",
       "20544                                    Cesária Evora   \n",
       "\n",
       "                                              album_name  \\\n",
       "0                                                 Comedy   \n",
       "1                                         To Begin Again   \n",
       "2      Crazy Rich Asians (Original Motion Picture Sou...   \n",
       "3                                                Hold On   \n",
       "4                                   Days I Will Remember   \n",
       "...                                                  ...   \n",
       "20540                                      Reckless Love   \n",
       "20541                                     Karma & Desire   \n",
       "20542                                          Whosoever   \n",
       "20543                                             Thanks   \n",
       "20544                                     Miss Perfumado   \n",
       "\n",
       "                                              track_name  popularity  \\\n",
       "0                                                 Comedy          73   \n",
       "1                                         To Begin Again          57   \n",
       "2                             Can't Help Falling In Love          71   \n",
       "3                                                Hold On          82   \n",
       "4                                   Days I Will Remember          58   \n",
       "...                                                  ...         ...   \n",
       "20540                                   Endless Alleluia          56   \n",
       "20541                                     Fire and Light          57   \n",
       "20542                                  Hallelujah Anyway          57   \n",
       "20543  This Is How I Thank The Lord (feat. Cody Carne...          56   \n",
       "20544                                             Sodade          59   \n",
       "\n",
       "       duration_ms  explicit  danceability  ...  loudness  mode  speechiness  \\\n",
       "0           230666     False         0.676  ...    -6.746     0       0.1430   \n",
       "1           210826     False         0.438  ...    -9.734     1       0.0557   \n",
       "2           201933     False         0.266  ...   -18.515     1       0.0363   \n",
       "3           198853     False         0.618  ...    -9.681     1       0.0526   \n",
       "4           214240     False         0.688  ...    -8.807     1       0.1050   \n",
       "...            ...       ...           ...  ...       ...   ...          ...   \n",
       "20540       265377     False         0.261  ...   -10.423     1       0.0368   \n",
       "20541        75969     False         0.337  ...   -18.646     0       0.0384   \n",
       "20542       225755     False         0.494  ...    -4.042     1       0.0379   \n",
       "20543       221274     False         0.455  ...    -8.221     1       0.0388   \n",
       "20544       293640     False         0.575  ...   -13.168     1       0.0363   \n",
       "\n",
       "       acousticness  instrumentalness  liveness  valence    tempo  \\\n",
       "0            0.0322          0.000001    0.3580   0.7150   87.917   \n",
       "1            0.2100          0.000000    0.1170   0.1200   76.332   \n",
       "2            0.9050          0.000071    0.1320   0.1430  181.740   \n",
       "3            0.4690          0.000000    0.0829   0.1670  119.949   \n",
       "4            0.2890          0.000000    0.1890   0.6660   98.017   \n",
       "...             ...               ...       ...      ...      ...   \n",
       "20540        0.8480          0.000000    0.1130   0.2290   77.473   \n",
       "20541        0.9870          0.882000    0.0851   0.0824  140.004   \n",
       "20542        0.0019          0.000008    0.1320   0.4670  129.994   \n",
       "20543        0.5550          0.000000    0.3270   0.4340  125.858   \n",
       "20544        0.8220          0.000661    0.1150   0.4270   82.691   \n",
       "\n",
       "       time_signature  track_genre  \n",
       "0                   4     acoustic  \n",
       "1                   4     acoustic  \n",
       "2                   3     acoustic  \n",
       "3                   4     acoustic  \n",
       "4                   4     acoustic  \n",
       "...               ...          ...  \n",
       "20540               4  world-music  \n",
       "20541               4  world-music  \n",
       "20542               4  world-music  \n",
       "20543               4  world-music  \n",
       "20544               4  world-music  \n",
       "\n",
       "[20545 rows x 22 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('dataset.csv')\n",
    "df=df[df['popularity']>55]\n",
    "df['artists']=df['artists'].str.replace(r'\\W',\"\")\n",
    "print(df.columns)\n",
    "print(len(pd.unique(df['artists'])))\n",
    "print(len(pd.unique(df['album_name'])))\n",
    "print(len(pd.unique(df['track_name'])))\n",
    "print(len(pd.unique(df['track_id'])))\n",
    "genre=pd.unique(df['track_genre'])\n",
    "print(genre)\n",
    "sns.countplot(x='track_genre',data=df)\n",
    "plt.show()\n",
    "print(len(df))\n",
    "df.reset_index(inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='popularity',data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<bound method Series.unique of 0           acoustic\n",
      "1           acoustic\n",
      "2           acoustic\n",
      "3           acoustic\n",
      "4           acoustic\n",
      "            ...     \n",
      "20540    world-music\n",
      "20541    world-music\n",
      "20542    world-music\n",
      "20543    world-music\n",
      "20544    world-music\n",
      "Name: track_genre, Length: 20545, dtype: object>\n"
     ]
    }
   ],
   "source": [
    "print(df['track_genre'].unique)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_23392\\2040840116.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_1.sort_values('popularity',inplace=True,ascending=False)\n"
     ]
    }
   ],
   "source": [
    "df_1=df[df['track_genre']=='acoustic']\n",
    "df_1.sort_values('popularity',inplace=True,ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_23392\\1599024828.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_2.sort_values('popularity',inplace=True,ascending=False)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acoustic': 0.5700673076923077, 'afrobeat': 0.6877499999999999, 'alt-rock': 0.5246996904024768, 'alternative': 0.5584147157190635, 'ambient': 0.38010729613733907, 'anime': 0.5357496527777778, 'black-metal': 0.20900000000000002, 'bluegrass': 0.5443999999999999, 'blues': 0.5283514492753623, 'brazil': 0.6408235294117646, 'breakbeat': 0.6068333333333333, 'british': 0.5368387654320987, 'cantopop': 0.5472127659574468, 'chicago-house': 0.78525, 'children': 0.7615204081632652, 'chill': 0.6763164335664336, 'classical': 0.3483125, 'club': 0.6307037037037039, 'comedy': 0.7361764705882352, 'country': 0.5591217948717948, 'dance': 0.6792526315789473, 'dancehall': 0.7445297297297298, 'death-metal': 0.3904838709677419, 'deep-house': 0.7075342019543974, 'detroit-techno': 0.716, 'disco': 0.6844329501915709, 'disney': 0.48807407407407416, 'drum-and-bass': 0.5268510638297872, 'dub': 0.5506279069767441, 'dubstep': 0.48681538461538465, 'edm': 0.6398960739030023, 'electro': 0.6514628099173554, 'electronic': 0.6568444444444445, 'emo': 0.6063937007874016, 'folk': 0.5363399209486166, 'forro': 0.5726666666666667, 'french': 0.6628000000000001, 'funk': 0.6938030303030303, 'garage': 0.5258291814946618, 'german': 0.5326435714285714, 'gospel': 0.4661666666666667, 'goth': 0.47438461538461546, 'groove': 0.5985954545454546, 'grunge': 0.4597327586206897, 'guitar': 0.49474999999999997, 'happy': 0.5052592592592592, 'hard-rock': 0.4560547263681592, 'hardcore': 0.6899264705882353, 'hardstyle': 0.46459999999999996, 'heavy-metal': 0.48979999999999996, 'hip-hop': 0.7046408450704226, 'honky-tonk': 0.47433333333333333, 'house': 0.6602632743362832, 'idm': 0.623, 'indian': 0.59227969348659, 'indie-pop': 0.603997461928934, 'indie': 0.5922117647058823, 'industrial': 0.5412847222222221, 'j-dance': 0.7078048780487805, 'j-idol': 0.5183333333333333, 'j-pop': 0.5667539432176656, 'j-rock': 0.5009709302325581, 'jazz': 0.5513365384615385, 'k-pop': 0.6535, 'kids': 0.7606666666666667, 'latin': 0.7446521739130434, 'latino': 0.7626493506493508, 'malay': 0.5656153846153846, 'mandopop': 0.5390400000000001, 'metal': 0.467044921875, 'metalcore': 0.4457133956386292, 'minimal-techno': 0.7250263157894737, 'mpb': 0.7424999999999999, 'new-age': 0.427515, 'opera': 0.3937878787878788, 'pagode': 0.622, 'party': 0.6570769230769231, 'piano': 0.4740742782152231, 'pop-film': 0.6000414394765541, 'pop': 0.6316242236024845, 'power-pop': 0.552375, 'progressive-house': 0.6439042253521127, 'psych-rock': 0.5150344827586207, 'punk-rock': 0.5142242424242425, 'punk': 0.49267647058823527, 'r-n-b': 0.6527882352941176, 'reggae': 0.7570239520958084, 'reggaeton': 0.7639735682819384, 'rock-n-roll': 0.539562091503268, 'rock': 0.5590580357142857, 'rockabilly': 0.5402533333333334, 'sad': 0.706966265060241, 'salsa': 0.6889090909090909, 'samba': 0.6484, 'sertanejo': 0.5747142857142857, 'show-tunes': 0.5259485294117647, 'singer-songwriter': 0.5699068493150685, 'ska': 0.5009333333333333, 'sleep': 0.14329198113207547, 'songwriter': 0.5699068493150685, 'soul': 0.6310887096774194, 'spanish': 0.6406190476190476, 'swedish': 0.6220837696335079, 'synth-pop': 0.6409579831932773, 'techno': 0.6855395683453237, 'trance': 0.5979903846153846, 'trip-hop': 0.6283333333333333, 'turkish': 0.6826551724137931, 'world-music': 0.42974157303370797}\n"
     ]
    }
   ],
   "source": [
    "artist=pd.unique(df['artists'])\n",
    "dance={}\n",
    "for i in genre:\n",
    "    df_2=df[df['track_genre']==i]\n",
    "    df_2.sort_values('popularity',inplace=True,ascending=False)\n",
    "    p=df_2['danceability'].mean()\n",
    "    dance.update({i:p})\n",
    "    df_2.to_csv('genre/'+i+'.csv')\n",
    "print(dance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#printing avg dancebility of song\n",
    "g=list(dance.keys())\n",
    "val=list(dance.values())\n",
    "sns.lineplot(x=g,y=val)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>popularity</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</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>0</th>\n",
       "      <td>73</td>\n",
       "      <td>230666</td>\n",
       "      <td>0.676</td>\n",
       "      <td>0.4610</td>\n",
       "      <td>1</td>\n",
       "      <td>-6.746</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1430</td>\n",
       "      <td>0.0322</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.3580</td>\n",
       "      <td>0.7150</td>\n",
       "      <td>87.917</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>210826</td>\n",
       "      <td>0.438</td>\n",
       "      <td>0.3590</td>\n",
       "      <td>0</td>\n",
       "      <td>-9.734</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0557</td>\n",
       "      <td>0.2100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1170</td>\n",
       "      <td>0.1200</td>\n",
       "      <td>76.332</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>71</td>\n",
       "      <td>201933</td>\n",
       "      <td>0.266</td>\n",
       "      <td>0.0596</td>\n",
       "      <td>0</td>\n",
       "      <td>-18.515</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0363</td>\n",
       "      <td>0.9050</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.1320</td>\n",
       "      <td>0.1430</td>\n",
       "      <td>181.740</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>82</td>\n",
       "      <td>198853</td>\n",
       "      <td>0.618</td>\n",
       "      <td>0.4430</td>\n",
       "      <td>2</td>\n",
       "      <td>-9.681</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0526</td>\n",
       "      <td>0.4690</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0829</td>\n",
       "      <td>0.1670</td>\n",
       "      <td>119.949</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58</td>\n",
       "      <td>214240</td>\n",
       "      <td>0.688</td>\n",
       "      <td>0.4810</td>\n",
       "      <td>6</td>\n",
       "      <td>-8.807</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1050</td>\n",
       "      <td>0.2890</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1890</td>\n",
       "      <td>0.6660</td>\n",
       "      <td>98.017</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20540</th>\n",
       "      <td>56</td>\n",
       "      <td>265377</td>\n",
       "      <td>0.261</td>\n",
       "      <td>0.1790</td>\n",
       "      <td>9</td>\n",
       "      <td>-10.423</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0368</td>\n",
       "      <td>0.8480</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1130</td>\n",
       "      <td>0.2290</td>\n",
       "      <td>77.473</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20541</th>\n",
       "      <td>57</td>\n",
       "      <td>75969</td>\n",
       "      <td>0.337</td>\n",
       "      <td>0.2800</td>\n",
       "      <td>0</td>\n",
       "      <td>-18.646</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0384</td>\n",
       "      <td>0.9870</td>\n",
       "      <td>0.882000</td>\n",
       "      <td>0.0851</td>\n",
       "      <td>0.0824</td>\n",
       "      <td>140.004</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20542</th>\n",
       "      <td>57</td>\n",
       "      <td>225755</td>\n",
       "      <td>0.494</td>\n",
       "      <td>0.8570</td>\n",
       "      <td>8</td>\n",
       "      <td>-4.042</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0379</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.1320</td>\n",
       "      <td>0.4670</td>\n",
       "      <td>129.994</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20543</th>\n",
       "      <td>56</td>\n",
       "      <td>221274</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.4030</td>\n",
       "      <td>4</td>\n",
       "      <td>-8.221</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0388</td>\n",
       "      <td>0.5550</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.3270</td>\n",
       "      <td>0.4340</td>\n",
       "      <td>125.858</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20544</th>\n",
       "      <td>59</td>\n",
       "      <td>293640</td>\n",
       "      <td>0.575</td>\n",
       "      <td>0.4300</td>\n",
       "      <td>8</td>\n",
       "      <td>-13.168</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0363</td>\n",
       "      <td>0.8220</td>\n",
       "      <td>0.000661</td>\n",
       "      <td>0.1150</td>\n",
       "      <td>0.4270</td>\n",
       "      <td>82.691</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20545 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       popularity  duration_ms  danceability  energy  key  loudness  mode  \\\n",
       "0              73       230666         0.676  0.4610    1    -6.746     0   \n",
       "1              57       210826         0.438  0.3590    0    -9.734     1   \n",
       "2              71       201933         0.266  0.0596    0   -18.515     1   \n",
       "3              82       198853         0.618  0.4430    2    -9.681     1   \n",
       "4              58       214240         0.688  0.4810    6    -8.807     1   \n",
       "...           ...          ...           ...     ...  ...       ...   ...   \n",
       "20540          56       265377         0.261  0.1790    9   -10.423     1   \n",
       "20541          57        75969         0.337  0.2800    0   -18.646     0   \n",
       "20542          57       225755         0.494  0.8570    8    -4.042     1   \n",
       "20543          56       221274         0.455  0.4030    4    -8.221     1   \n",
       "20544          59       293640         0.575  0.4300    8   -13.168     1   \n",
       "\n",
       "       speechiness  acousticness  instrumentalness  liveness  valence  \\\n",
       "0           0.1430        0.0322          0.000001    0.3580   0.7150   \n",
       "1           0.0557        0.2100          0.000000    0.1170   0.1200   \n",
       "2           0.0363        0.9050          0.000071    0.1320   0.1430   \n",
       "3           0.0526        0.4690          0.000000    0.0829   0.1670   \n",
       "4           0.1050        0.2890          0.000000    0.1890   0.6660   \n",
       "...            ...           ...               ...       ...      ...   \n",
       "20540       0.0368        0.8480          0.000000    0.1130   0.2290   \n",
       "20541       0.0384        0.9870          0.882000    0.0851   0.0824   \n",
       "20542       0.0379        0.0019          0.000008    0.1320   0.4670   \n",
       "20543       0.0388        0.5550          0.000000    0.3270   0.4340   \n",
       "20544       0.0363        0.8220          0.000661    0.1150   0.4270   \n",
       "\n",
       "         tempo  time_signature  \n",
       "0       87.917               4  \n",
       "1       76.332               4  \n",
       "2      181.740               3  \n",
       "3      119.949               4  \n",
       "4       98.017               4  \n",
       "...        ...             ...  \n",
       "20540   77.473               4  \n",
       "20541  140.004               4  \n",
       "20542  129.994               4  \n",
       "20543  125.858               4  \n",
       "20544   82.691               4  \n",
       "\n",
       "[20545 rows x 14 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#taking only needed parameters\n",
    "col_to_m=['popularity','duration_ms','danceability','energy','key',\t'loudness','mode','speechiness','acousticness','instrumentalness','liveness','valence','tempo','time_signature']\n",
    "new_df=df[col_to_m]\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_23392\\3071270059.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df[i]=new_df[i].astype(float)\n",
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_23392\\3071270059.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df[i]=(new_df[i]-m)/(m1-m)\n",
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_23392\\3071270059.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df[i]=new_df[i].astype(float)\n",
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_23392\\3071270059.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df[i]=(new_df[i]-m)/(m1-m)\n",
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_23392\\3071270059.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df[i]=new_df[i].astype(float)\n",
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_23392\\3071270059.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df[i]=(new_df[i]-m)/(m1-m)\n",
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_23392\\3071270059.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df[i]=new_df[i].astype(float)\n",
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_23392\\3071270059.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df[i]=(new_df[i]-m)/(m1-m)\n",
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_23392\\3071270059.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df[i]=new_df[i].astype(float)\n",
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_23392\\3071270059.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df[i]=(new_df[i]-m)/(m1-m)\n",
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_23392\\3071270059.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df[i]=new_df[i].astype(float)\n",
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_23392\\3071270059.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df[i]=(new_df[i]-m)/(m1-m)\n",
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_23392\\3071270059.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df[i]=new_df[i].astype(float)\n",
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_23392\\3071270059.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df[i]=(new_df[i]-m)/(m1-m)\n",
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_23392\\3071270059.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df[i]=new_df[i].astype(float)\n",
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_23392\\3071270059.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df[i]=(new_df[i]-m)/(m1-m)\n",
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_23392\\3071270059.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df[i]=new_df[i].astype(float)\n",
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_23392\\3071270059.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df[i]=(new_df[i]-m)/(m1-m)\n",
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_23392\\3071270059.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df[i]=new_df[i].astype(float)\n",
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_23392\\3071270059.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df[i]=(new_df[i]-m)/(m1-m)\n",
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_23392\\3071270059.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df[i]=new_df[i].astype(float)\n",
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_23392\\3071270059.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df[i]=(new_df[i]-m)/(m1-m)\n",
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_23392\\3071270059.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df[i]=new_df[i].astype(float)\n",
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_23392\\3071270059.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df[i]=(new_df[i]-m)/(m1-m)\n",
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_23392\\3071270059.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df[i]=new_df[i].astype(float)\n",
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_23392\\3071270059.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df[i]=(new_df[i]-m)/(m1-m)\n",
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_23392\\3071270059.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df[i]=new_df[i].astype(float)\n",
      "C:\\Users\\LENOVO\\AppData\\Local\\Temp\\ipykernel_23392\\3071270059.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df[i]=(new_df[i]-m)/(m1-m)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>popularity</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</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",
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       "      <td>0.689796</td>\n",
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       "      <td>0.356731</td>\n",
       "      <td>0.720766</td>\n",
       "      <td>0.361245</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.022727</td>\n",
       "      <td>0.129279</td>\n",
       "      <td>0.446939</td>\n",
       "      <td>0.359706</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.760849</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.060941</td>\n",
       "      <td>0.210842</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.108687</td>\n",
       "      <td>0.120968</td>\n",
       "      <td>0.313643</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.340909</td>\n",
       "      <td>0.122835</td>\n",
       "      <td>0.271429</td>\n",
       "      <td>0.059700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.565629</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.039716</td>\n",
       "      <td>0.908634</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.124125</td>\n",
       "      <td>0.144153</td>\n",
       "      <td>0.746758</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.120603</td>\n",
       "      <td>0.630612</td>\n",
       "      <td>0.443876</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.762028</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.057549</td>\n",
       "      <td>0.470883</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.073590</td>\n",
       "      <td>0.168347</td>\n",
       "      <td>0.492863</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.131753</td>\n",
       "      <td>0.702041</td>\n",
       "      <td>0.481953</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.781458</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.114880</td>\n",
       "      <td>0.290160</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.182791</td>\n",
       "      <td>0.671371</td>\n",
       "      <td>0.402746</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20540</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.168807</td>\n",
       "      <td>0.266327</td>\n",
       "      <td>0.179342</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.745531</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.040263</td>\n",
       "      <td>0.851405</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.104570</td>\n",
       "      <td>0.230847</td>\n",
       "      <td>0.318332</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20541</th>\n",
       "      <td>0.022727</td>\n",
       "      <td>0.031559</td>\n",
       "      <td>0.343878</td>\n",
       "      <td>0.280546</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.562717</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.042013</td>\n",
       "      <td>0.990964</td>\n",
       "      <td>0.882000</td>\n",
       "      <td>0.075854</td>\n",
       "      <td>0.083065</td>\n",
       "      <td>0.575267</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20542</th>\n",
       "      <td>0.022727</td>\n",
       "      <td>0.140097</td>\n",
       "      <td>0.504082</td>\n",
       "      <td>0.858715</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.887394</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.041466</td>\n",
       "      <td>0.001907</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.124125</td>\n",
       "      <td>0.470766</td>\n",
       "      <td>0.534137</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20543</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.136850</td>\n",
       "      <td>0.464286</td>\n",
       "      <td>0.403795</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.794486</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.042451</td>\n",
       "      <td>0.557228</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.324825</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.517142</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20544</th>\n",
       "      <td>0.068182</td>\n",
       "      <td>0.189287</td>\n",
       "      <td>0.586735</td>\n",
       "      <td>0.430850</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.684504</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.039716</td>\n",
       "      <td>0.825301</td>\n",
       "      <td>0.000661</td>\n",
       "      <td>0.106628</td>\n",
       "      <td>0.430444</td>\n",
       "      <td>0.339772</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20545 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       popularity  duration_ms  danceability    energy       key  loudness  \\\n",
       "0        0.386364     0.143655      0.689796  0.461913  0.090909  0.827279   \n",
       "1        0.022727     0.129279      0.446939  0.359706  0.000000  0.760849   \n",
       "2        0.340909     0.122835      0.271429  0.059700  0.000000  0.565629   \n",
       "3        0.590909     0.120603      0.630612  0.443876  0.181818  0.762028   \n",
       "4        0.045455     0.131753      0.702041  0.481953  0.545455  0.781458   \n",
       "...           ...          ...           ...       ...       ...       ...   \n",
       "20540    0.000000     0.168807      0.266327  0.179342  0.818182  0.745531   \n",
       "20541    0.022727     0.031559      0.343878  0.280546  0.000000  0.562717   \n",
       "20542    0.022727     0.140097      0.504082  0.858715  0.727273  0.887394   \n",
       "20543    0.000000     0.136850      0.464286  0.403795  0.363636  0.794486   \n",
       "20544    0.068182     0.189287      0.586735  0.430850  0.727273  0.684504   \n",
       "\n",
       "       mode  speechiness  acousticness  instrumentalness  liveness   valence  \\\n",
       "0       0.0     0.156455      0.032328          0.000001  0.356731  0.720766   \n",
       "1       1.0     0.060941      0.210842          0.000000  0.108687  0.120968   \n",
       "2       1.0     0.039716      0.908634          0.000071  0.124125  0.144153   \n",
       "3       1.0     0.057549      0.470883          0.000000  0.073590  0.168347   \n",
       "4       1.0     0.114880      0.290160          0.000000  0.182791  0.671371   \n",
       "...     ...          ...           ...               ...       ...       ...   \n",
       "20540   1.0     0.040263      0.851405          0.000000  0.104570  0.230847   \n",
       "20541   0.0     0.042013      0.990964          0.882000  0.075854  0.083065   \n",
       "20542   1.0     0.041466      0.001907          0.000008  0.124125  0.470766   \n",
       "20543   1.0     0.042451      0.557228          0.000000  0.324825  0.437500   \n",
       "20544   1.0     0.039716      0.825301          0.000661  0.106628  0.430444   \n",
       "\n",
       "          tempo  time_signature  \n",
       "0      0.361245             0.8  \n",
       "1      0.313643             0.8  \n",
       "2      0.746758             0.6  \n",
       "3      0.492863             0.8  \n",
       "4      0.402746             0.8  \n",
       "...         ...             ...  \n",
       "20540  0.318332             0.8  \n",
       "20541  0.575267             0.8  \n",
       "20542  0.534137             0.8  \n",
       "20543  0.517142             0.8  \n",
       "20544  0.339772             0.8  \n",
       "\n",
       "[20545 rows x 14 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#normalize\n",
    "for i in new_df.columns:\n",
    "    new_df[i]=new_df[i].astype(float)\n",
    "    m=new_df[i].min()\n",
    "    m1=new_df[i].max()\n",
    "    new_df[i]=(new_df[i]-m)/(m1-m)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LENOVO\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\sklearn\\utils\\extmath.py:193: RuntimeWarning: invalid value encountered in matmul\n",
      "  ret = a @ b\n"
     ]
    }
   ],
   "source": [
    "#cosine simmilarity\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "cps=cosine_similarity(new_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.70781647, 0.56085902, ..., 0.76250525, 0.75319184,\n",
       "        0.68599701],\n",
       "       [0.70781647, 1.        , 0.84239634, ..., 0.88642713, 0.94080097,\n",
       "        0.86981464],\n",
       "       [0.56085902, 0.84239634, 1.        , ..., 0.6952008 , 0.88685044,\n",
       "        0.852445  ],\n",
       "       ...,\n",
       "       [0.76250525, 0.88642713, 0.6952008 , ..., 1.        , 0.91889746,\n",
       "        0.89085918],\n",
       "       [0.75319184, 0.94080097, 0.88685044, ..., 0.91889746, 1.        ,\n",
       "        0.9617273 ],\n",
       "       [0.68599701, 0.86981464, 0.852445  , ..., 0.89085918, 0.9617273 ,\n",
       "        1.        ]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recommend(song):\n",
    "    song_index=new_df[df['track_name']==song].index[0]\n",
    "    dis=cps[song_index]\n",
    "    m_l=sorted(list(enumerate(dis)),reverse=True,key=lambda x:x[1])[2:40]\n",
    "    return m_l\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['On the Low',\n",
       " 'Thank You',\n",
       " \"I'm On Fire\",\n",
       " \"California Dreamin' - Single Version\",\n",
       " 'Jolene',\n",
       " 'Some',\n",
       " '_WORLD',\n",
       " 'Schedule',\n",
       " 'Breakfast In America - 2010 Remastered',\n",
       " 'Sway',\n",
       " 'Showed Me (How I Fell In Love With You)',\n",
       " 'Sweet Dreams (Are Made of This) - Remastered',\n",
       " 'Beast Of Burden - Remastered 1994',\n",
       " 'Manike (From  Thank God )',\n",
       " 'Listen to the Music',\n",
       " 'Go Crazy',\n",
       " 'Bijlee Bijlee',\n",
       " 'Gbona',\n",
       " 'La Vida Es Un Carnaval',\n",
       " 'Eyes Off You',\n",
       " 'Desesperados',\n",
       " 'River',\n",
       " 'How Long',\n",
       " 'Like',\n",
       " 'Brother Louie',\n",
       " 'Summer High',\n",
       " 'Gimme Shelter',\n",
       " 'Whenever, Wherever']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l=recommend('Shape of You')\n",
    "name=[]\n",
    "for i in l:\n",
    " name.append(df.iloc[i[0]].track_name)\n",
    "name\n",
    "s_n=[]\n",
    "m=len(name)\n",
    "for i in range(0,m):\n",
    "    flag=0\n",
    "    for j in range(i+1,m):\n",
    "        if name[i]==name[j]:\n",
    "            flag=1\n",
    "    if flag==0:\n",
    "        s_n.append(name[i])\n",
    "s_n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle as plp\n",
    "plp.dump(new_df,open('music1.pkl','wb'))\n",
    "plp.dump(df,open('music2.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "plp.dump(new_df.to_dict(),open('movie1_dict.pkl','wb'))\n",
    "plp.dump(df.to_dict(),open('movie12_dict.pkl','wb'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "plp.dump(cps,open('cps.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "genre1=pd.unique(df['track_genre'])\n",
    "plp.dump(genre1,open('genree.pkl','wb'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['acoustic', 'afrobeat', 'alt-rock', 'alternative', 'ambient',\n",
       "       'anime', 'black-metal', 'bluegrass', 'blues', 'brazil',\n",
       "       'breakbeat', 'british', 'cantopop', 'chicago-house', 'children',\n",
       "       'chill', 'classical', 'club', 'comedy', 'country', 'dance',\n",
       "       'dancehall', 'death-metal', 'deep-house', 'detroit-techno',\n",
       "       'disco', 'disney', 'drum-and-bass', 'dub', 'dubstep', 'edm',\n",
       "       'electro', 'electronic', 'emo', 'folk', 'forro', 'french', 'funk',\n",
       "       'garage', 'german', 'gospel', 'goth', 'groove', 'grunge', 'guitar',\n",
       "       'happy', 'hard-rock', 'hardcore', 'hardstyle', 'heavy-metal',\n",
       "       'hip-hop', 'honky-tonk', 'house', 'idm', 'indian', 'indie-pop',\n",
       "       'indie', 'industrial', 'j-dance', 'j-idol', 'j-pop', 'j-rock',\n",
       "       'jazz', 'k-pop', 'kids', 'latin', 'latino', 'malay', 'mandopop',\n",
       "       'metal', 'metalcore', 'minimal-techno', 'mpb', 'new-age', 'opera',\n",
       "       'pagode', 'party', 'piano', 'pop-film', 'pop', 'power-pop',\n",
       "       'progressive-house', 'psych-rock', 'punk-rock', 'punk', 'r-n-b',\n",
       "       'reggae', 'reggaeton', 'rock-n-roll', 'rock', 'rockabilly', 'sad',\n",
       "       'salsa', 'samba', 'sertanejo', 'show-tunes', 'singer-songwriter',\n",
       "       'ska', 'sleep', 'songwriter', 'soul', 'spanish', 'swedish',\n",
       "       'synth-pop', 'techno', 'trance', 'trip-hop', 'turkish',\n",
       "       'world-music'], dtype=object)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genre1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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