decade_stat_analysis.ipynb

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  {
   "cell_type": "markdown",
   "source": "### Decsde by Decade Statistics Analysis\n##### Cameron Jarnot (cmj36)",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "644549be",
    "execution_start": 1634512206835,
    "cell_id": "00000-dec04ee8-8e04-4487-aaa5-6613e07cda9d",
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   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "90e514b2",
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    "cell_id": "00001-f144bbee-8934-4a87-b75b-1009c3b00df2",
    "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": "00002-6344e7c2-65c3-4ab3-a428-f261966fee1b",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "1796852b",
    "execution_start": 1636137025202,
    "execution_millis": 150,
    "is_code_hidden": true,
    "cell_id": "00003-b9bb4ce9-7ffb-4158-a344-7573c898aba9",
    "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": 1636137025364,
    "execution_millis": 36,
    "is_code_hidden": true,
    "cell_id": "00004-aa81682a-171b-47cb-99f5-e4b6191f5605",
    "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": 1636137025409,
    "execution_millis": 230,
    "is_code_hidden": true,
    "is_output_hidden": true,
    "cell_id": "00005-e5cba57d-5734-44e5-accc-f0539f24a91b",
    "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": 1636137025683,
    "execution_millis": 67,
    "is_code_hidden": true,
    "is_output_hidden": true,
    "cell_id": "00006-b16724d8-1194-4638-9c2d-f966ec243a64",
    "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": 1636137025751,
    "execution_millis": 7,
    "is_code_hidden": true,
    "is_output_hidden": true,
    "cell_id": "00007-6b51b452-d3e9-4744-a4ed-8db7f13dbe0e",
    "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": "markdown",
   "source": "#### Decade by decade statistical analysis\nAnalysis of relationships between each column and 'hits' vs 'flops' for each decade\n\nNotes:\n* Results are stored in dataframe named \"decade_by_decade\"",
   "metadata": {
    "tags": [],
    "cell_id": "00013-d43d8e00-db90-4391-9b1f-aef277b669a7",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "5c972ef0",
    "execution_start": 1636137025796,
    "execution_millis": 0,
    "cell_id": "00014-235da060-0fef-4e47-832c-fd6065b7d181",
    "deepnote_cell_type": "code"
   },
   "source": "# Target proportion versus duration in seconds.\ndecades = [data60,data70,data80,data90,data00,data10]\nyears = ['1960','1970','1980','1990','2000','2010']",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "5d49968d",
    "execution_start": 1636137025797,
    "execution_millis": 384,
    "cell_id": "00015-1176c8b4-25fb-4fed-96d6-dc4b9e4bd507",
    "deepnote_cell_type": "code"
   },
   "source": "# Decade by decade stat analysis (relationships between each decade and each column)\n# Cameron\ndurations = []\nfor decade in [data60,data70,data80,data90,data00,data10]:\n    decade['duration_sec'] = data60['duration_ms']//1000\n    relevant = decade['duration_sec']\n    bin_max = decade[\"duration_sec\"].max()\n    i = 1\n    bin_arr = []\n    while i * 10 < bin_max+10:\n        bin_arr.append(i*10)\n        i+=1\n    bins = pd.cut(decade['duration_sec'], bin_arr)\n    hitsDuration = decade.groupby(bins)['target'].agg([\"mean\", \"count\"]).reset_index().dropna()\n\n    hitsDurationSliced = hitsDuration[:82]\n    hitsDurationSliced.insert(0, 'ds_int', range(1, 1+ len(hitsDurationSliced)))\n    hitsDurationSliced[\"ds_int\"] = hitsDurationSliced[\"ds_int\"] *10\n    durations += [hitsDurationSliced]\nmaxDurs = []\n# print(durations)\nfor dur in durations:\n    maxDurs += [dur['ds_int'][dur['mean'].idxmax()]]\n# print(maxDurs)\nplt.plot(years,maxDurs)\nplt.xlabel('Decade')\nplt.ylabel('Duration')\nplt.title('Duration Resulting in Highest Proportion Hits Across Decades 1960-2010')",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 8,
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Duration Resulting in Highest Proportion Hits Across Decades 1960-2010')"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 452,
       "height": 278
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "14ecf293",
    "execution_start": 1636137026176,
    "execution_millis": 300,
    "cell_id": "00016-ba7666c6-4e5b-4364-a88f-14303c8c867e",
    "deepnote_cell_type": "code"
   },
   "source": "louds = []\nfor decade in [data60,data70,data80,data90,data00,data10]:\n    decade['loudness_rounded'] = round(decade['loudness'])\n    hitsLoudness = decade.groupby(\"loudness_rounded\")['target'].agg([\"mean\", \"count\"]).reset_index().dropna()\n    hitsLoudness = hitsLoudness.loc[hitsLoudness['count'] > 10]\n    louds += [hitsLoudness]\nmaxLoud = []\nfor dur in louds:\n    maxLoud += [dur['loudness_rounded'][dur['mean'].idxmax()]]\nplt.plot(years,maxLoud)\nplt.xlabel('Decade')\nplt.ylabel('Loudness')\nplt.title('Loudness Resulting in Highest Proportion Hits Across Decades 1960-2010')",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 9,
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Loudness Resulting in Highest Proportion Hits Across Decades 1960-2010')"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 456,
       "height": 278
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "a85e5964",
    "execution_start": 1636137045214,
    "execution_millis": 341,
    "cell_id": "00017-f1693f45-2080-4817-91c2-e9c04839bb51",
    "deepnote_cell_type": "code"
   },
   "source": "energies = []\nfor decade in [data60,data70,data80,data90,data00,data10]:\n    i = 0\n    bin_arr_energy = []\n    while i <= 100:\n        bin_arr_energy.append(i/100)\n        i+=1\n    bins_energy = pd.cut(decade['energy'], bin_arr_energy)\n    hitsEnergy = decade.groupby(bins_energy)['target'].agg([\"mean\", \"count\"]).reset_index().dropna()\n    hitsEnergy.insert(0, 'en_int', range(0, 100))\n    hitsEnergy[\"en_int\"] = hitsEnergy[\"en_int\"] / 100\n    energies += [hitsEnergy]\nmaxEnergies = []\nfor en in energies:\n    maxEnergies += [en['mean'].idxmax()]\n# print(maxEnergies)\nplt.plot(years,maxEnergies)\nplt.xlabel('Decade')\nplt.ylabel('Energy')\nplt.title('Energy Resulting in Highest Proportion Hits Across Decades 1960-2010')",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 16,
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Energy Resulting in Highest Proportion Hits Across Decades 1960-2010')"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 441,
       "height": 278
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "e464be84",
    "execution_start": 1636137026816,
    "execution_millis": 231,
    "cell_id": "00018-72218617-b4f7-4af0-bf5a-63cc55799502",
    "deepnote_cell_type": "code"
   },
   "source": "dances = []\nfor decade in [data60,data70,data80,data90,data00,data10]:\n    # print(decade)\n    i = 5\n    bin_arr_danceability = []\n    while i <= 98:\n        bin_arr_danceability.append(i/100)\n        i+=1\n\n    bins_danceability = pd.cut(decade['danceability'], bin_arr_danceability)\n    target_prop_by_danceability = decade.groupby(bins_danceability)['target'].agg([\"mean\", \"count\"]).reset_index().dropna()\n\n    minDance = decade['danceability'].min()*100\n    maxDance = decade['danceability'].max()*100\n    target_prop_by_danceability.insert(0, 'da_int', decade['danceability']*100)\n    target_prop_by_danceability['da_int'] = target_prop_by_danceability['da_int']/100\n\n    dances += [target_prop_by_danceability]\nmaxDances = []\nfor dan in dances:\n    maxDances += [dan['mean'].idxmax()]\nplt.plot(years,maxDances)\nplt.xlabel('Decade')\nplt.ylabel('Danceability')\nplt.title('Danceability Resulting in Highest Proportion Hits Across Decades 1960-2010')",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 11,
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Danceability Resulting in Highest Proportion Hits Across Decades 1960-2010')"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 475,
       "height": 278
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "66d7ece9",
    "execution_start": 1636137027095,
    "execution_millis": 219,
    "cell_id": "00019-d1b25f6f-e874-4135-bd88-1c2d114132b1",
    "deepnote_cell_type": "code"
   },
   "source": "vals = []\nfor decade in [data60,data70,data80,data90,data00,data10]:\n    target_prop_by_valence = decade.groupby(\"valence\")['target'].agg([\"mean\", \"count\"]).reset_index().dropna()\n    target_prop_by_valence = target_prop_by_valence.loc[target_prop_by_valence['count'] > 10]\n    vals += [target_prop_by_valence]\nmaxVals = []\nfor val in vals:\n    maxVals += [val['mean'].idxmax()]\nplt.plot(years,maxVals)\nplt.xlabel('Decade')\nplt.ylabel('Valence')\nplt.title('Valence Resulting in Highest Proportion Hits Across Decades 1960-2010')",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 12,
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Valence Resulting in Highest Proportion Hits Across Decades 1960-2010')"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 447,
       "height": 278
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "cc52529f",
    "execution_start": 1636137027359,
    "execution_millis": 201,
    "cell_id": "00020-cb04aa2e-be42-487b-9f69-2b36472494fb",
    "deepnote_cell_type": "code"
   },
   "source": "modes = []\nfor decade in [data60,data70,data80,data90,data00,data10]:\n    target_prop_by_mode = data.groupby(\"mode\")['target'].agg([\"mean\", \"count\"]).reset_index().dropna()\n    modes += [target_prop_by_mode]\nmaxModes = []\nfor mode in modes:\n    maxModes += [mode['mean'].idxmax()]\nplt.plot(years,maxModes)\nplt.xlabel('Decade')\nplt.ylabel('Mode')\nplt.title('Mode Resulting in Highest Proportion Hits Across Decades 1960-2010')",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 13,
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Mode Resulting in Highest Proportion Hits Across Decades 1960-2010')"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 434,
       "height": 278
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "d39872e6",
    "execution_start": 1636137027607,
    "execution_millis": 232,
    "cell_id": "00021-988c7b9b-4f8c-4acd-ba6d-51ef0be95c66",
    "deepnote_cell_type": "code"
   },
   "source": "timeSigs = []\nfor decade in [data60,data70,data80,data90,data00,data10]:\n    target_prop_by_time_signature = data.groupby(\"time_signature\")['target'].agg([\"count\"]).sort_values(by='count',ascending=False).reset_index().dropna()\n    timeSigs += [target_prop_by_time_signature.loc[0,'time_signature']]\nplt.scatter(years,timeSigs)\nplt.xlabel('Decade')\nplt.ylabel('Time Signature')\nplt.title('Time Signature Resulting in Highest Proportion Hits Across Decades 1960-2010')",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 14,
     "data": {
      "text/plain": "Text(0.5, 1.0, 'Time Signature Resulting in Highest Proportion Hits Across Decades 1960-2010')"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
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   "source": "# Add plots with data from decade by decade stat analysis\n# Cameron\n\n# Plot maximum hit proportion for each variable across the decades\n\n# pd.crosstab(data['decade'],data['target']).plot.bar(stacked = True)\"\"",
   "execution_count": null,
   "outputs": []
  },
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