few_hit_wonders_versus_consistent.ipynb

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  {
   "cell_type": "markdown",
   "source": "### Few Hit Wonders versus Consistent Hitters\n##### Joseph Scarpa (jcs140)",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "644549be",
    "execution_start": 1634512206835,
    "cell_id": "00000-62779dcc-1747-4972-a966-3bb9137308d0",
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   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "90e514b2",
    "execution_start": 1638671159513,
    "execution_millis": 1433,
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    "cell_id": "00001-a23f3dc2-ef4e-45a9-9dfc-cc210f3a6e3e",
    "deepnote_cell_type": "code"
   },
   "source": "# Imports (update throughout as necessary)\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport math\nimport matplotlib.pyplot as plt\npd.options.mode.chained_assignment = None\n\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics import plot_confusion_matrix\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.metrics import r2_score\nimport matplotlib.pyplot as plt # just to format confusion matrix\n\nfrom sklearn.feature_selection import SequentialFeatureSelector\nfrom sklearn.neighbors import KNeighborsClassifier\n\nimport re",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "#### Data loading and cleaning\n\nNotes:\n* No null values found\n* No duplicate values found",
   "metadata": {
    "tags": [],
    "cell_id": "00002-f6cdbfb6-4c7e-4b1d-8f4b-4620e52f2a8a",
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  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "1796852b",
    "execution_start": 1638671167927,
    "execution_millis": 160,
    "is_code_hidden": false,
    "cell_id": "00003-8b4abb4e-7b3a-4408-b1bb-b7d578fb38e8",
    "deepnote_cell_type": "code"
   },
   "source": "# Data loading\ndata10 = pd.read_csv('./datasets/dataset-of-10s.csv')\ndata00 = pd.read_csv('./datasets/dataset-of-00s.csv')\ndata90 = pd.read_csv('./datasets/dataset-of-90s.csv')\ndata80 = pd.read_csv('./datasets/dataset-of-80s.csv')\ndata70 = pd.read_csv('./datasets/dataset-of-70s.csv')\ndata60 = pd.read_csv('./datasets/dataset-of-60s.csv')",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "d1ca17dc",
    "execution_start": 1638671169787,
    "execution_millis": 40,
    "is_code_hidden": false,
    "cell_id": "00004-b3f0f212-52c1-4e53-a05b-d5e1ed2e0e1c",
    "deepnote_cell_type": "code"
   },
   "source": "# Data cleaning\n\n# Drop all na values\ndata10 = data10.dropna()\ndata00 = data00.dropna()\ndata90 = data90.dropna()\ndata80 = data80.dropna()\ndata70 = data70.dropna()\ndata60 = data60.dropna()\n\n\n# Add a decades column to each dataset (will be helpful when creating cumulative dataset)\ndata10['decade'] = [2010]*len(data10)\ndata00['decade'] = [2000]*len(data00)\ndata90['decade'] = [1990]*len(data90)\ndata80['decade'] = [1980]*len(data80)\ndata70['decade'] = [1970]*len(data70)\ndata60['decade'] = [1960]*len(data60)",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "b755cb78",
    "execution_start": 1638671172277,
    "execution_millis": 223,
    "is_code_hidden": false,
    "is_output_hidden": true,
    "cell_id": "00005-c13d7732-c24a-409d-9ddb-9e67e88e1066",
    "deepnote_cell_type": "code"
   },
   "source": "# Check for duplicates in dataset\nmerged = data10.merge(data00,on=['track','artist'])\nmerged = merged.merge(data90,on=['track','artist'])\nmerged = merged.merge(data80,on=['track','artist'])\nmerged = merged.merge(data70,on=['track','artist'])\nmerged = merged.merge(data60,on=['track','artist'])\nmerged\n# NOTE: No duplicates found",
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      "text/plain": "Empty DataFrame\nColumns: [uri_x, danceability_x, energy_x, key_x, loudness_x, mode_x, speechiness_x, acousticness_x, instrumentalness_x, liveness_x, valence_x, tempo_x, duration_ms_x, time_signature_x, chorus_hit_x, sections_x, target_x, decade_x, uri_y, danceability_y, energy_y, key_y, loudness_y, mode_y, speechiness_y, acousticness_y, instrumentalness_y, liveness_y, valence_y, tempo_y, duration_ms_y, time_signature_y, chorus_hit_y, sections_y, target_y, decade_y, uri_x, danceability_x, energy_x, key_x, loudness_x, mode_x, speechiness_x, acousticness_x, instrumentalness_x, liveness_x, valence_x, tempo_x, duration_ms_x, time_signature_x, chorus_hit_x, sections_x, target_x, decade_x, uri_y, danceability_y, energy_y, key_y, loudness_y, mode_y, speechiness_y, acousticness_y, instrumentalness_y, liveness_y, valence_y, tempo_y, duration_ms_y, time_signature_y, chorus_hit_y, sections_y, target_y, decade_y, uri_x, danceability_x, energy_x, key_x, loudness_x, mode_x, speechiness_x, acousticness_x, instrumentalness_x, liveness_x, valence_x, tempo_x, duration_ms_x, time_signature_x, chorus_hit_x, sections_x, target_x, decade_x, track, artist, uri_y, danceability_y, energy_y, key_y, loudness_y, mode_y, speechiness_y, acousticness_y, ...]\nIndex: []\n\n[0 rows x 110 columns]",
      "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>uri_x</th>\n      <th>danceability_x</th>\n      <th>energy_x</th>\n      <th>key_x</th>\n      <th>loudness_x</th>\n      <th>mode_x</th>\n      <th>speechiness_x</th>\n      <th>acousticness_x</th>\n      <th>instrumentalness_x</th>\n      <th>liveness_x</th>\n      <th>...</th>\n      <th>instrumentalness_y</th>\n      <th>liveness_y</th>\n      <th>valence_y</th>\n      <th>tempo_y</th>\n      <th>duration_ms_y</th>\n      <th>time_signature_y</th>\n      <th>chorus_hit_y</th>\n      <th>sections_y</th>\n      <th>target_y</th>\n      <th>decade_y</th>\n    </tr>\n  </thead>\n  <tbody>\n  </tbody>\n</table>\n<p>0 rows × 110 columns</p>\n</div>"
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   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "fd8a8",
    "execution_start": 1638671174511,
    "execution_millis": 116,
    "is_code_hidden": false,
    "is_output_hidden": true,
    "cell_id": "00006-4782d5ce-4d04-4b2a-9223-d9c6b5cd66ff",
    "deepnote_cell_type": "code"
   },
   "source": "# Create cumulative dataset (from songs across all decades)\ndata = data10.append(data00,ignore_index=True).append(data90,ignore_index=True).append(data80,ignore_index=True).append(data70,ignore_index=True).append(data60,ignore_index=True)\ndata.head()\ndata.tail()",
   "execution_count": null,
   "outputs": [
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     "output_type": "execute_result",
     "execution_count": 5,
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      "text/plain": "                                track                       artist  \\\n41101                 Le ire di Ursus           Giampiero Boneschi   \n41102                           A-B-C                John Zacherle   \n41103               Useless Landscape         Antônio Carlos Jobim   \n41104               Vendedor De Assaí                     Ary Lobo   \n41105  Mambo Simphony - Remasterizado  Orquesta Casino De La Playa   \n\n                                        uri  danceability  energy  key  \\\n41101  spotify:track:3dVgolCVuNgecU5jovpc45         0.420  0.8730    3   \n41102  spotify:track:0zXhzKf4raEYstebL7L42C         0.716  0.7420   10   \n41103  spotify:track:2uC02PCRVM6BhAaK7dgxxp         0.361  0.0944   10   \n41104  spotify:track:0gpSUWZWNIRrEenaC1ZGk9         0.452  0.7590    4   \n41105  spotify:track:5j0G0nkCSPlmSCSgXchMn3         0.554  0.2780    5   \n\n       loudness  mode  speechiness  acousticness  instrumentalness  liveness  \\\n41101   -11.689     1       0.1000         0.808          0.879000     0.363   \n41102    -5.727     0       0.2420         0.663          0.000000     0.107   \n41103   -22.663     1       0.0327         0.940          0.000178     0.115   \n41104    -7.821     0       0.1750         0.658          0.000000     0.738   \n41105    -8.386     1       0.0349         0.928          0.922000     0.136   \n\n       valence    tempo  duration_ms  time_signature  chorus_hit  sections  \\\n41101    0.183  119.821        26244               4     0.00000         2   \n41102    0.834  135.104       142987               4    28.34316        10   \n41103    0.139   92.472       137200               4    56.63498         6   \n41104    0.853  137.976       159107               3    28.24372         9   \n41105    0.428  131.401       164227               4    37.39251         8   \n\n       target  decade  \n41101       0    1960  \n41102       0    1960  \n41103       0    1960  \n41104       0    1960  \n41105       0    1960  ",
      "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>track</th>\n      <th>artist</th>\n      <th>uri</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>duration_ms</th>\n      <th>time_signature</th>\n      <th>chorus_hit</th>\n      <th>sections</th>\n      <th>target</th>\n      <th>decade</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>41101</th>\n      <td>Le ire di Ursus</td>\n      <td>Giampiero Boneschi</td>\n      <td>spotify:track:3dVgolCVuNgecU5jovpc45</td>\n      <td>0.420</td>\n      <td>0.8730</td>\n      <td>3</td>\n      <td>-11.689</td>\n      <td>1</td>\n      <td>0.1000</td>\n      <td>0.808</td>\n      <td>0.879000</td>\n      <td>0.363</td>\n      <td>0.183</td>\n      <td>119.821</td>\n      <td>26244</td>\n      <td>4</td>\n      <td>0.00000</td>\n      <td>2</td>\n      <td>0</td>\n      <td>1960</td>\n    </tr>\n    <tr>\n      <th>41102</th>\n      <td>A-B-C</td>\n      <td>John Zacherle</td>\n      <td>spotify:track:0zXhzKf4raEYstebL7L42C</td>\n      <td>0.716</td>\n      <td>0.7420</td>\n      <td>10</td>\n      <td>-5.727</td>\n      <td>0</td>\n      <td>0.2420</td>\n      <td>0.663</td>\n      <td>0.000000</td>\n      <td>0.107</td>\n      <td>0.834</td>\n      <td>135.104</td>\n      <td>142987</td>\n      <td>4</td>\n      <td>28.34316</td>\n      <td>10</td>\n      <td>0</td>\n      <td>1960</td>\n    </tr>\n    <tr>\n      <th>41103</th>\n      <td>Useless Landscape</td>\n      <td>Antônio Carlos Jobim</td>\n      <td>spotify:track:2uC02PCRVM6BhAaK7dgxxp</td>\n      <td>0.361</td>\n      <td>0.0944</td>\n      <td>10</td>\n      <td>-22.663</td>\n      <td>1</td>\n      <td>0.0327</td>\n      <td>0.940</td>\n      <td>0.000178</td>\n      <td>0.115</td>\n      <td>0.139</td>\n      <td>92.472</td>\n      <td>137200</td>\n      <td>4</td>\n      <td>56.63498</td>\n      <td>6</td>\n      <td>0</td>\n      <td>1960</td>\n    </tr>\n    <tr>\n      <th>41104</th>\n      <td>Vendedor De Assaí</td>\n      <td>Ary Lobo</td>\n      <td>spotify:track:0gpSUWZWNIRrEenaC1ZGk9</td>\n      <td>0.452</td>\n      <td>0.7590</td>\n      <td>4</td>\n      <td>-7.821</td>\n      <td>0</td>\n      <td>0.1750</td>\n      <td>0.658</td>\n      <td>0.000000</td>\n      <td>0.738</td>\n      <td>0.853</td>\n      <td>137.976</td>\n      <td>159107</td>\n      <td>3</td>\n      <td>28.24372</td>\n      <td>9</td>\n      <td>0</td>\n      <td>1960</td>\n    </tr>\n    <tr>\n      <th>41105</th>\n      <td>Mambo Simphony - Remasterizado</td>\n      <td>Orquesta Casino De La Playa</td>\n      <td>spotify:track:5j0G0nkCSPlmSCSgXchMn3</td>\n      <td>0.554</td>\n      <td>0.2780</td>\n      <td>5</td>\n      <td>-8.386</td>\n      <td>1</td>\n      <td>0.0349</td>\n      <td>0.928</td>\n      <td>0.922000</td>\n      <td>0.136</td>\n      <td>0.428</td>\n      <td>131.401</td>\n      <td>164227</td>\n      <td>4</td>\n      <td>37.39251</td>\n      <td>8</td>\n      <td>0</td>\n      <td>1960</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "29a140c6",
    "execution_start": 1638671176994,
    "execution_millis": 11,
    "is_code_hidden": false,
    "is_output_hidden": true,
    "cell_id": "00007-0ea0ebc1-5bb7-466a-9bab-c4b0a1d44892",
    "deepnote_cell_type": "code"
   },
   "source": "# Add custom columns that will be useful later\n\n# Duration of song in seconds\ndata[\"duration_sec\"] = np.round(data[\"duration_ms\"]*(1/1000))\ndata[\"duration_sec\"]\n\n# Loudness of sound (rounded)\ndata[\"loudness_rounded\"] = np.round(data[\"loudness\"])\ndata[\"loudness_rounded\"]",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 6,
     "data": {
      "text/plain": "0        -5.0\n1       -15.0\n2        -7.0\n3        -6.0\n4        -4.0\n         ... \n41101   -12.0\n41102    -6.0\n41103   -23.0\n41104    -8.0\n41105    -8.0\nName: loudness_rounded, Length: 41106, dtype: float64"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "350c8684",
    "execution_start": 1638671233167,
    "execution_millis": 23,
    "cell_id": "00046-0f59f1c3-8c60-40dc-a018-e7ea4312022b",
    "deepnote_cell_type": "code"
   },
   "source": "#Clone dataset\nartistsModData = data.copy()\nartistsModData = artistsModData.loc[artistsModData['target'] == 1].reset_index(drop=True)\nartistsModData.head()\nprint(len(artistsModData))",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "text": "20553\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "9e1d1afd",
    "execution_start": 1638671358579,
    "execution_millis": 9355,
    "is_output_hidden": false,
    "cell_id": "00047-53144625-28a3-4d14-a334-c6044c776f15",
    "deepnote_cell_type": "code"
   },
   "source": "# One-hit wonders versus Staying Power\n\n#Cleaning data\n# Removes Parentheses\nartistsModData['artist'] = artistsModData['artist'].str.replace(r\"\\(.*\\)\",\"\")\nprint(len(artistsModData))\nfor i in range(len(artistsModData)):\n    artistsModData['artist'][i] = re.split(\" [fF][eE][aA][tT]\", artistsModData['artist'][i])[0]\n\n",
   "execution_count": null,
   "outputs": [
    {
     "name": "stderr",
     "text": "/shared-libs/python3.7/py-core/lib/python3.7/site-packages/ipykernel_launcher.py:5: FutureWarning: The default value of regex will change from True to False in a future version.\n  \"\"\"\n20553\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00010-b93b7b1f-72e8-419f-afd3-e1d9023d0329",
    "deepnote_cell_type": "code"
   },
   "source": "\n\n# Count Data\nartistsCountData = artistsModData.groupby('artist').count().sort_values(by = 'uri' ,ascending=False)\nartistsCountData = artistsCountData[\"track\"].reset_index()\nartistsCountData = artistsCountData.rename(columns = {'track' : 'counts'})",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "79e7538",
    "execution_start": 1638668828044,
    "execution_millis": 40,
    "cell_id": "00048-bf1f81bb-ed84-46ae-96c9-a73c2722b8d0",
    "deepnote_cell_type": "code"
   },
   "source": "# Get percentiles\n#https://www.datasciencemadesimple.com/percentile-rank-column-pandas-python-2/\n\n#Explore\nthree_or_less = artistsCountData['counts'].quantile(.65912)\nprint(three_or_less)\nprint(artistsCountData['counts'].mean())\n\n#Make percentile column\nartistsCountData['percentile'] = artistsCountData['counts'].rank(pct=True)\n\n#Is Consistent Hitter Versus One Time Hitter\nartistsCountData['Few-Hit Wonder'] = (artistsCountData['percentile'] < .65912).astype(int)\n\nmerger = artistsCountData[['artist', 'Few-Hit Wonder']]\n\nprint(merger.columns)",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "text": "3.0\n3.751916757940854\nIndex(['artist', 'Few-Hit Wonder'], dtype='object')\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "8d901f5f",
    "execution_start": 1638668828084,
    "execution_millis": 3609,
    "output_cleared": false,
    "cell_id": "00049-dd0f1de4-c621-46ce-a90d-c6a0623ba7cc",
    "deepnote_output_heights": [
     21.1875,
     30.46875,
     337.375,
     337.375,
     337.375,
     337.375
    ],
    "deepnote_cell_type": "code"
   },
   "source": "# Merge with hits \n#to analyze difference in hits between 3 or less timers and consistent hitters\n\nartistsModDataR = pd.merge(merger, artistsModData, on = \"artist\", how = 'inner')\n\nartistsModDataR['Few-Hit Wonder'] = artistsModDataR['Few-Hit Wonder'].astype('bool')\nsns.displot(artistsModDataR , x=\"energy\", hue='Few-Hit Wonder', kind=\"kde\", fill=True)\nsns.displot(artistsModDataR , x=\"danceability\", hue='Few-Hit Wonder', kind=\"kde\", fill=True)\nsns.displot(artistsModDataR , x=\"instrumentalness\", hue='Few-Hit Wonder', kind=\"kde\", fill=True)\n# Non picked factors: \nsns.displot(artistsModDataR , x=\"loudness\", hue='Few-Hit Wonder', kind=\"kde\", fill=True)\nsns.displot(artistsModDataR , x=\"valence\", hue='Few-Hit Wonder', kind=\"kde\", fill=True)\n",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 10,
     "data": {
      "text/plain": "<seaborn.axisgrid.FacetGrid at 0x7fbd1d01dd50>"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 446.75x360 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 445,
       "height": 352
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 446.75x360 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 445,
       "height": 352
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 446.75x360 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 445,
       "height": 352
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 446.75x360 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 445,
       "height": 352
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 446.75x360 with 1 Axes>",
      "image/png": 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\n"
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