genre_stat_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": "### Holiday Statistics Analysis\n#### Sharan Sokhi (ss1148)",
   "metadata": {
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    "source_hash": "644549be",
    "execution_start": 1634512206835,
    "cell_id": "00000-f69e135f-9800-43a3-a37c-6f184ff791ad",
    "deepnote_cell_type": "markdown"
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  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
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    "source_hash": "ddde4279",
    "execution_start": 1638672408651,
    "execution_millis": 1382,
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    "cell_id": "00001-567ad11b-dbfd-4580-aa71-1536ca89fb6d",
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   "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\nfrom scipy import stats\nfrom statsmodels.stats.proportion import proportions_ztest",
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": "#### Data loading and cleaning\n\nNotes:\n* No null values found\n* No duplicate values found",
   "metadata": {
    "tags": [],
    "cell_id": "00002-5f19121f-42e9-476e-8372-5466a5a5a9d1",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "f7f94194",
    "execution_start": 1638672410044,
    "execution_millis": 451,
    "is_code_hidden": false,
    "cell_id": "00003-ef20c01c-4e2b-4174-81d7-197716eddaf3",
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     228.65625
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   "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')\n\n# 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)\n\n# 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\n\n# 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()\n#data.tail()\n\n# 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\"]\n",
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 2,
     "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": {}
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "aceab1b",
    "execution_start": 1638672410325,
    "execution_millis": 3621,
    "is_code_hidden": false,
    "is_output_hidden": false,
    "cell_id": "00007-c76d405a-b2d9-473a-bf87-57a42134c9d7",
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   "source": "data.to_csv(\"./data.csv\")\n\n#For a clean copy for genre analysis\ncData = data.copy()\nrData = data.copy()\n\n# Load the Genre Songs from the csv into respective dataframes\nchristmas_songs = pd.read_csv('./datasets/christmas_hits.csv')\ncountry_songs = pd.read_csv('./datasets/country_hits.csv')\nrb_songs = pd.read_csv('./datasets/rb_hits.csv')\nmetal_songs = pd.read_csv('./datasets/metal_hits.csv')\nhiphop_songs = pd.read_csv('./datasets/hiphop_hits.csv')\n\n# Add a boolean value column indicating whether the song is in the playlist \"Christmas Hits\" by Spotify (https://open.spotify.com/playlist/37i9dQZF1DX0Yxoavh5qJV?si=96f9678a2982444d)\ndata['christmas'] = data.apply(lambda row: True if (row[\"track\"] in christmas_songs[\"Track name\"].tolist()) and (row[\"artist\"] in christmas_songs[\" Artist name\"].tolist()) else False, axis=1)\ndata['country'] = data.apply(lambda row: True if (row[\"track\"] in country_songs[\"Track name\"].tolist()) and (row[\"artist\"] in country_songs[\" Artist name\"].tolist()) else False, axis=1)\ndata['rb'] = data.apply(lambda row: True if (row[\"track\"] in rb_songs[\"Track name\"].tolist()) and (row[\"artist\"] in rb_songs[\" Artist name\"].tolist()) else False, axis=1)\ndata['metal'] = data.apply(lambda row: True if (row[\"track\"] in metal_songs[\"Track name\"].tolist()) and (row[\"artist\"] in metal_songs[\" Artist name\"].tolist()) else False, axis=1)\ndata['hiphop'] = data.apply(lambda row: True if (row[\"track\"] in hiphop_songs[\"Track name\"].tolist()) and (row[\"artist\"] in hiphop_songs[\" Artist name\"].tolist()) else False, axis=1)\n\nprint(\"Christmas: \", len(data.loc[data['christmas'] == True]))\nprint(\"Country: \", len(data.loc[data['country'] == True]))\nprint(\"RB: \", len(data.loc[data['rb'] == True]))\nprint(\"Metal: \", len(data.loc[data['metal'] == True]))\nprint(\"Hip-Hop: \", len(data.loc[data['hiphop'] == True]))\n",
   "outputs": [
    {
     "name": "stdout",
     "text": "Christmas:  22\nCountry:  27\nRB:  10\nMetal:  9\nHip-Hop:  8\n",
     "output_type": "stream"
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00008-25315139-ac1d-441b-b64c-235f817f6081",
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    "source_hash": "cece60bd",
    "execution_start": 1638672413953,
    "execution_millis": 20,
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   "source": "mask_christmas = data[\"christmas\"] == True\nmask_non_christmas = data[\"christmas\"] == False\n\ncommon_christmas_songs = data[mask_christmas]\ncommon_non_christmas_songs = data[mask_non_christmas]\n\nchristmas_hits = common_christmas_songs[common_christmas_songs[\"target\"] == 1]\nnon_christmas_hits =common_non_christmas_songs[common_non_christmas_songs[\"target\"] == 1]\n\nmask_christmas_mode = christmas_hits[\"mode\"] == 1\nmask_non_christmas_mode = non_christmas_hits[\"mode\"] == 1\n#non_christmas_sample = common_non_christmas_songs.sample(n=len(common_christmas_songs[\"mode\"]))\n\nproportion_happy_christmas = len(christmas_hits[mask_christmas_mode])/len(christmas_hits[\"mode\"])\n#proportion_happy_non_christmas = len(non_christmas_sample[mask_non_christmas_mode])/len(non_christmas_sample[\"mode\"])\n\nnum_bootstrap = 10000\nbootstrap_samples = np.random.choice(non_christmas_hits[\"mode\"], size=(num_bootstrap, len(christmas_hits[\"mode\"])), replace=True)\nsampling_proportions = np.average(bootstrap_samples, axis=1)\nnon_christmas_proportion_avg = np.average(sampling_proportions)\n\n# 51% percent men rank star wars\n\nsd_christmas = np.sqrt(proportion_happy_christmas*(1-proportion_happy_christmas))\nsd_non_christmas = np.sqrt(non_christmas_proportion_avg*(1-non_christmas_proportion_avg))\n\nnewpval = stats.ttest_ind_from_stats(mean1=proportion_happy_christmas, std1=sd_christmas, nobs1=len(christmas_hits[\"mode\"]), mean2=non_christmas_proportion_avg, std2=sd_non_christmas, nobs2=len(christmas_hits[\"mode\"]))\np_value_bootstrapping = newpval[1]/2\nprint(p_value_bootstrapping)\n\n# Null hypothesis: True Proportion of happy in Christmas = True Proportion of happy in non-christmas\n# P(christmas) = P(non-christmas), p = proportions\n# P(christmas) - P(non-christmas) = 0\n# P(non-christmas) = 0.90\n\n# Alternative hypothesis: True Proportion of happy in Christmas > True Proportion of happy in non-christmas\n# P(christmas) > P(non-christmas), p = proportion\n# P(christmas) - P(non-christmas) > 0\n\n# Find the SD using BRV = P(1-P), \n# SD - Christmas: P = #happy/total# = 20/22\n\n#sd_christmas = np.std(common_christmas_songs[mask_christmas_mode][\"mode\"])\n#sd_non_christmas = np.std(non_christmas_sample[mask_non_christmas_mode][\"mode\"])\n#standard_error = np.sqrt((sd_christmas/len(common_christmas_songs[\"mode\"]))+(sd_non_christmas/len(non_christmas_sample[\"mode\"])))\n#p_val = 1-stats.t.cdf((proportion_happy_christmas-proportion_happy_non_christmas)/standard_error, df=len(common_christmas_songs[\"mode\"])+len(non_christmas_sample[\"mode\"])-2)\n#print(p_val)\n\n#newpval = stats.ttest_ind_from_stats(mean1=proportion_happy_christmas, std1=sd_christmas, nobs1=len(common_christmas_songs[\"mode\"]), mean2=proportion_happy_non_christmas, std2=sd_non_christmas, nobs2=len(non_christmas_sample[\"mode\"]))\n\n\n# 1. SDs are 0 cause it's a boolean value. is this ok?\n# 2. P-value is 0? \n# 3. Could I use bootstrapping? \n# 4. what about (christmas) - P(non-christmas) > 0.4(?). Why not (christmas) - P(non-christmas) > 0\n\n# https://stattrek.com/hypothesis-test/difference-in-proportions.aspx\n#p = (proportion_happy_christmas * len(common_christmas_songs[\"mode\"]) + proportion_happy_non_christmas * len(non_christmas_sample[\"mode\"])) / (len(common_christmas_songs[\"mode\"]) + len(non_christmas_sample[\"mode\"]))\n#se = np.sqrt(p*(1-p)*((1/len(common_christmas_songs[\"mode\"]) + (1/len(non_christmas_sample[\"mode\"])))))\n#z = (proportion_happy_christmas-proportion_happy_non_christmas)/se\n#pval = 0.997\n\n#pval < 0.05 alpha level means reject null in favour of alterantive\n#pval > 0.05 alpha level means fail to reject null",
   "outputs": [
    {
     "name": "stdout",
     "text": "0.1123859767639733\n",
     "output_type": "stream"
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00009-f3884b29-8013-4e07-81b0-b82315c8900d",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "3a55a402",
    "execution_start": 1638672413977,
    "execution_millis": 167,
    "is_code_hidden": false,
    "deepnote_cell_type": "code"
   },
   "source": "from statsmodels.stats.proportion import proportions_ztest\nimport numpy as np\n\n# Alpha Level\nsignificance = 0.05\n\n# c = christmas\n# nc = non-christmas\nsample_success_c, sample_size_c = (len(common_christmas_songs[mask_christmas_mode]), len(common_christmas_songs[\"mode\"]))\nsample_success_nc, sample_size_nc = (len(non_christmas_sample[mask_non_christmas_mode]), len(non_christmas_sample[\"mode\"]))\n\n# check our sample against Ho for Ha != Ho\nsuccesses = np.array([sample_success_c, sample_success_nc])\nsamples = np.array([sample_size_c, sample_size_nc])\n\n# note, no need for a Ho value here - it's derived from the other parameters\nstat, p_value = proportions_ztest(count=successes, nobs=samples, alternative='two-sided')\n\n# report\nprint('z_stat: %0.3f, p_value: %0.3f' % (stat, p_value))\n\nif p_value > significance:\n   print (\"Fail to reject the null hypothesis - we have nothing else to say\")\nelse:\n   print (\"Reject the null hypothesis - suggest the alternative hypothesis is true\")",
   "outputs": [
    {
     "name": "stderr",
     "text": "/shared-libs/python3.7/py-core/lib/python3.7/site-packages/ipykernel_launcher.py:9: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n  if __name__ == '__main__':\n",
     "output_type": "stream"
    },
    {
     "output_type": "error",
     "ename": "IndexingError",
     "evalue": "Unalignable boolean Series provided as indexer (index of the boolean Series and of the indexed object do not match).",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexingError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-d75be0c43c25>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# c = christmas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m# nc = non-christmas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0msample_success_c\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_size_c\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcommon_christmas_songs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask_christmas_mode\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcommon_christmas_songs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"mode\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0msample_success_nc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_size_nc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnon_christmas_sample\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask_non_christmas_mode\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnon_christmas_sample\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"mode\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/shared-libs/python3.7/py/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3013\u001b[0m         \u001b[0;31m# Do we have a (boolean) 1d indexer?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3014\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3015\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_bool_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3016\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3017\u001b[0m         \u001b[0;31m# We are left with two options: a single key, and a collection of keys,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/shared-libs/python3.7/py/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_bool_array\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3066\u001b[0m         \u001b[0;31m# check_bool_indexer will throw exception if Series key cannot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3067\u001b[0m         \u001b[0;31m# be reindexed to match DataFrame rows\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3068\u001b[0;31m         \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3069\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3070\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_take_with_is_copy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/shared-libs/python3.7/py/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36mcheck_bool_indexer\u001b[0;34m(index, key)\u001b[0m\n\u001b[1;32m   2268\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2269\u001b[0m             raise IndexingError(\n\u001b[0;32m-> 2270\u001b[0;31m                 \u001b[0;34m\"Unalignable boolean Series provided as \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2271\u001b[0m                 \u001b[0;34m\"indexer (index of the boolean Series and of \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2272\u001b[0m                 \u001b[0;34m\"the indexed object do not match).\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexingError\u001b[0m: Unalignable boolean Series provided as indexer (index of the boolean Series and of the indexed object do not match)."
     ]
    }
   ],
   "execution_count": null
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00007-e69b7af8-eb06-4d1b-8743-b85f3cc4d81d",
    "deepnote_to_be_reexecuted": true,
    "source_hash": "b1a3fc",
    "execution_start": 1636348374848,
    "execution_millis": 4,
    "deepnote_cell_type": "code"
   },
   "source": "# Country Songs Vs. Instrumentalness\n\n# Hypothesis: \n# Null: Difference of means = 0\n# Alternative: Mean instrumentalness of Country > Mean instrumentalness of non-country\n\nmask_country = data[\"country\"] == True\nmask_non_country = data[\"country\"] == False\n\ncommon_country_songs = data[mask_country]\ncommon_non_country_songs = data[mask_non_country]\n\ncountry_hits = common_country_songs[common_country_songs[\"target\"]==1]\nnon_country_hits = common_non_country_songs[common_non_country_songs[\"target\"]==1]\n\nmean_instrumental_country = np.average(country_hits[\"instrumentalness\"])\n\nnum_bootstrap = 10000\nbootstrap_samples_country = np.random.choice(non_country_hits[\"instrumentalness\"], size=(num_bootstrap, len(country_hits[\"instrumentalness\"])), replace=True)\nsampling_proportions_country = np.average(bootstrap_samples_country, axis=1)\nmean_instrumental_non_country = np.average(sampling_proportions_country)\n\ncountry_sd = np.std(country_hits[\"instrumentalness\"])\nnon_country_sd = np.std(non_country_hits[\"instrumentalness\"])\n\nnewpval_country = stats.ttest_ind_from_stats(mean1=mean_instrumental_country, std1=country_sd, nobs1=len(country_hits[\"instrumentalness\"]), mean2=mean_instrumental_non_country, std2=non_country_sd, nobs2=len(country_hits[\"instrumentalness\"]))\np_value_bootstrapping_country = newpval_country[1]/2\nprint(p_value_bootstrapping_country)\n\n#p-val is more than 0.05, so we fail to reject the null",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "text": "0.11331717189302797\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00008-86859786-390f-4b8d-adec-1fc0c838a6e1",
    "deepnote_to_be_reexecuted": true,
    "source_hash": "c93893bf",
    "execution_start": 1636349627351,
    "execution_millis": 540,
    "deepnote_cell_type": "code"
   },
   "source": "# Instrumental Vs. Tempo \ndata['isInstrumental'] = data.apply(lambda row: True if (row[\"instrumentalness\"]>0.5) else False, axis=1)\n\n# Mean tempo of instrumental songs = Mean tempo of non instrumental songs\n# Mean tempo of instrumental songs not equal to mean tempo of non-instrumental songs\n\nmask_instrumental = data[\"isInstrumental\"] == True\nmask_non_instrumental = data[\"isInstrumental\"] == False\n\ncommon_instrumental_songs = data[mask_instrumental]\ncommon_non_instrumental_songs = data[mask_non_instrumental]\n\ninstrumental_hits = common_instrumental_songs[common_instrumental_songs[\"target\"]==1]\nnon_instrumental_hits = common_non_instrumental_songs[common_non_instrumental_songs[\"target\"]==1]\n\nmean_tempo_instrumental = np.average(instrumental_hits[\"tempo\"])\n\nnum_bootstrap = 10000\nbootstrap_samples_instrumental = np.random.choice(non_instrumental_hits[\"tempo\"], size=(num_bootstrap, len(instrumental_hits[\"tempo\"])), replace=True)\nsampling_proportions_instrumental = np.average(bootstrap_samples_instrumental, axis=1)\nmean_tempo_non_instrumental = np.average(sampling_proportions_instrumental)\n\ninstrumental_sd = np.std(instrumental_hits[\"tempo\"])\nnon_instrumental_sd = np.std(non_instrumental_hits[\"tempo\"])\n\nnewpval_instrumental = stats.ttest_ind_from_stats(mean1=mean_tempo_instrumental, std1=instrumental_sd, nobs1=len(instrumental_hits[\"tempo\"]), mean2=mean_tempo_non_instrumental, std2=non_instrumental_sd, nobs2=len(instrumental_hits[\"tempo\"]))\np_value_bootstrapping_instrumental = newpval_instrumental[1] #No need to divide by 2 as it is a 2 tailed-test\nprint(p_value_bootstrapping_instrumental)\n\n# BECAUSE WHEN U HAVE A SINGER (LESS INSTRU) - SINGING, CAN'T GO TO FAST CAUSE OF BREATHING \n\n#p-val is more than 0.05, so we fail to reject the null",
   "execution_count": null,
   "outputs": [
    {
     "name": "stdout",
     "text": "0.5190127994561866\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "### Country Data Analysis",
   "metadata": {
    "tags": [],
    "cell_id": "00009-eba2ec31-0b65-46a2-b747-924e868769e6",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00010-d47534c1-fc1c-4bdd-a757-b64cf9c1922e",
    "deepnote_to_be_reexecuted": true,
    "source_hash": "400487c0",
    "execution_start": 1636345699140,
    "execution_millis": 114,
    "is_code_hidden": false,
    "is_output_hidden": false,
    "deepnote_cell_type": "code"
   },
   "source": "\ncountry = country.rename(columns = {\"Track name\": \"track\"})\n\ncountryData = pd.merge(cData, country, on = \"track\", how = 'inner')\n\ncountryData = countryData.drop([\" Artist name\", \" Album\", \" Playlist name\", \" Type\", \" ISRC\"], axis = 1)\ncountryData['country'] = True\ncountryData",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "error",
     "ename": "NameError",
     "evalue": "name 'country' is not defined",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-2973a300f94e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcountry\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcountry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"Track name\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"track\"\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mcountryData\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcData\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcountry\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mon\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"track\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'inner'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mcountryData\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcountryData\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\" Artist name\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\" Album\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\" Playlist name\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\" Type\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\" ISRC\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'country' is not defined"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00010-5d4fa5ed-06c3-4b73-bdb6-f42037f5223c",
    "deepnote_to_be_reexecuted": true,
    "source_hash": "be796021",
    "execution_start": 1636234061614,
    "execution_millis": 772,
    "is_output_hidden": false,
    "is_code_hidden": false,
    "deepnote_cell_type": "code"
   },
   "source": "np.random.seed(101)\n\n\ncData['country'] = cData.apply(lambda row: True if (row[\"track\"] in country[\"track\"].tolist()) and (row[\"artist\"] in country[\" Artist name\"].tolist()) else False, axis=1)\ncData = cData.loc[cData['target'] == 1].reset_index(drop=True)\ncData = cData.loc[cData['country'] == False].reset_index(drop=True)\ncData\nnCountryRowIndices = np.random.randint(len(cData), size= 200).tolist()\n\nnCountry = cData.iloc[nCountryRowIndices , :].reset_index(drop = True)\n\nsampleData = pd.concat([nCountry, countryData]).reset_index(drop = True)\nsampleData\n",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 162,
     "data": {
      "application/vnd.deepnote.dataframe.v2+json": {
       "row_count": 258,
       "column_count": 24,
       "columns": [
        {
         "name": "track",
         "dtype": "object",
         "stats": {
          "unique_count": 237,
          "nan_count": 0,
          "categories": [
           {
            "name": "Home",
            "count": 8
           },
           {
            "name": "Trouble",
            "count": 5
           },
           {
            "name": "235 others",
            "count": 245
           }
          ]
         }
        },
        {
         "name": "artist",
         "dtype": "object",
         "stats": {
          "unique_count": 240,
          "nan_count": 0,
          "categories": [
           {
            "name": "Kenny Chesney",
            "count": 6
           },
           {
            "name": "Rascal Flatts",
            "count": 3
           },
           {
            "name": "238 others",
            "count": 249
           }
          ]
         }
        },
        {
         "name": "uri",
         "dtype": "object",
         "stats": {
          "unique_count": 257,
          "nan_count": 0,
          "categories": [
           {
            "name": "spotify:track:3MPZOtMvBoxAIRUkgux87d",
            "count": 2
           },
           {
            "name": "spotify:track:7tCim0aGAMujZuMicXAh5d",
            "count": 1
           },
           {
            "name": "255 others",
            "count": 255
           }
          ]
         }
        },
        {
         "name": "danceability",
         "dtype": "float64",
         "stats": {
          "unique_count": 208,
          "nan_count": 0,
          "min": "0.155",
          "max": "0.96",
          "histogram": [
           {
            "bin_start": 0.155,
            "bin_end": 0.2355,
            "count": 3
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           {
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        },
        {
         "name": "energy",
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         "stats": {
          "unique_count": 221,
          "nan_count": 0,
          "min": "0.152",
          "max": "0.995",
          "histogram": [
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          ]
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        },
        {
         "name": "key",
         "dtype": "int64",
         "stats": {
          "unique_count": 12,
          "nan_count": 0,
          "min": "0",
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          "histogram": [
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 <td>Commodores</td>\n      <td>spotify:track:7tCim0aGAMujZuMicXAh5d</td>\n      <td>0.518</td>\n      <td>0.574</td>\n      <td>6</td>\n      <td>-8.352</td>\n      <td>1</td>\n      <td>0.0570</td>\n      <td>0.19300</td>\n      <td>...</td>\n      <td>229307</td>\n      <td>4</td>\n      <td>28.17783</td>\n      <td>11</td>\n      <td>1</td>\n      <td>1970</td>\n      <td>229.0</td>\n      <td>-8.0</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Frankie And Johnny</td>\n      <td>Sam Cooke</td>\n      <td>spotify:track:4wJw1D8jb09iHuawQawYda</td>\n      <td>0.501</td>\n      <td>0.487</td>\n      <td>0</td>\n      <td>-9.799</td>\n      <td>1</td>\n      <td>0.1920</td>\n      <td>0.39900</td>\n      <td>...</td>\n      <td>180147</td>\n      <td>4</td>\n      <td>30.05113</td>\n      <td>8</td>\n      <td>1</td>\n      <td>1960</td>\n      <td>180.0</td>\n      <td>-10.0</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Don't Say Nothin' Bad (About My Baby)</td>\n      <td>The Cookies</td>\n      <td>spotify:track:5QV1ERhFeYKZfK1Y99gYG6</td>\n      <td>0.611</td>\n      <td>0.591</td>\n      <td>2</td>\n      <td>-7.138</td>\n      <td>1</td>\n      <td>0.0352</td>\n      <td>0.71700</td>\n      <td>...</td>\n      <td>165840</td>\n      <td>4</td>\n      <td>31.19564</td>\n      <td>9</td>\n      <td>1</td>\n      <td>1960</td>\n      <td>166.0</td>\n      <td>-7.0</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Do You Miss Me</td>\n      <td>Jocelyn Enriquez</td>\n      <td>spotify:track:6hpkEh9z1tuhOQPlCunMaj</td>\n      <td>0.720</td>\n      <td>0.872</td>\n      <td>11</td>\n      <td>-8.705</td>\n      <td>0</td>\n      <td>0.0631</td>\n      <td>0.00265</td>\n      <td>...</td>\n      <td>218253</td>\n      <td>4</td>\n      <td>34.17159</td>\n      <td>9</td>\n      <td>1</td>\n      <td>1990</td>\n      <td>218.0</td>\n      <td>-9.0</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Out Last Night</td>\n      <td>Kenny Chesney</td>\n      <td>spotify:track:4QoZGQ8woaho94v629kVl1</td>\n      <td>0.640</td>\n      <td>0.822</td>\n      <td>9</td>\n      <td>-4.954</td>\n      <td>1</td>\n      <td>0.0410</td>\n      <td>0.42000</td>\n      <td>...</td>\n      <td>199773</td>\n      <td>4</td>\n      <td>28.30111</td>\n      <td>10</td>\n      <td>1</td>\n      <td>2000</td>\n      <td>200.0</td>\n      <td>-5.0</td>\n      <td>False</td>\n      <td>False</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>253</th>\n      <td>All The Way</td>\n      <td>Calloway</td>\n      <td>spotify:track:2qx8wHW2Npc2wD1ONfR8Al</td>\n      <td>0.751</td>\n      <td>0.675</td>\n      <td>4</td>\n      <td>-13.077</td>\n      <td>0</td>\n      <td>0.0686</td>\n      <td>0.00230</td>\n      <td>...</td>\n      <td>409667</td>\n      <td>4</td>\n      <td>25.38221</td>\n      <td>16</td>\n      <td>1</td>\n      <td>1990</td>\n      <td>410.0</td>\n      <td>-13.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>254</th>\n      <td>Say You Will</td>\n      <td>Foreigner</td>\n      <td>spotify:track:5nW7RbN0mBLPts9CURNwdy</td>\n      <td>0.720</td>\n      <td>0.720</td>\n      <td>3</td>\n      <td>-7.383</td>\n      <td>0</td>\n      <td>0.0264</td>\n      <td>0.19500</td>\n      <td>...</td>\n      <td>255653</td>\n      <td>4</td>\n      <td>49.18574</td>\n      <td>12</td>\n      <td>1</td>\n      <td>1980</td>\n      <td>256.0</td>\n      <td>-7.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>255</th>\n      <td>Gentle On My Mind</td>\n      <td>Aretha Franklin</td>\n      <td>spotify:track:3r6EzxHkcX3JLzuV4a4waK</td>\n      <td>0.506</td>\n      <td>0.501</td>\n      <td>7</td>\n      <td>-12.986</td>\n      <td>0</td>\n      <td>0.0556</td>\n      <td>0.74100</td>\n      <td>...</td>\n      <td>148907</td>\n      <td>4</td>\n      <td>93.99515</td>\n      <td>6</td>\n      <td>1</td>\n      <td>1960</td>\n      <td>149.0</td>\n      <td>-13.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>256</th>\n      <td>Gentle On My Mind</td>\n      <td>Glen Campbell</td>\n      <td>spotify:track:0mLoTgCB8oU0sJGojRtvDu</td>\n      <td>0.597</td>\n      <td>0.415</td>\n      <td>3</td>\n      <td>-13.799</td>\n      <td>1</td>\n      <td>0.0323</td>\n      <td>0.18900</td>\n      <td>...</td>\n      <td>178627</td>\n      <td>4</td>\n      <td>21.93463</td>\n      <td>14</td>\n      <td>1</td>\n      <td>1960</td>\n      <td>179.0</td>\n      <td>-14.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>257</th>\n      <td>Gentle On My Mind</td>\n      <td>Patti Page</td>\n      <td>spotify:track:5n4EBkvyzXz3B7lGrWhXOO</td>\n      <td>0.652</td>\n      <td>0.471</td>\n      <td>7</td>\n      <td>-11.221</td>\n      <td>1</td>\n      <td>0.0323</td>\n      <td>0.83000</td>\n      <td>...</td>\n      <td>177200</td>\n      <td>4</td>\n      <td>22.94398</td>\n      <td>10</td>\n      <td>1</td>\n      <td>1960</td>\n      <td>177.0</td>\n      <td>-11.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>\n<p>258 rows × 24 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00011-4bd98ab8-136f-4095-b36e-99f93aafff0c",
    "deepnote_to_be_reexecuted": true,
    "source_hash": "c4564f70",
    "execution_start": 1636233558967,
    "execution_millis": 5717,
    "is_code_hidden": false,
    "is_output_hidden": true,
    "deepnote_cell_type": "code"
   },
   "source": "# loudness vs target (Line Plot)\nsns.relplot(x=\"decade\", y=\"loudness\", hue=\"country\", kind=\"line\", data = sampleData)\nwhy are we plotting so many things lol\n# energy vs target (Line Plot)\nsns.relplot(x=\"decade\", y=\"energy\",  hue=\"country\", kind=\"line\", data = sampleData)\n\n# speechiness vs target (Line Plot)\nsns.relplot(x=\"decade\", y=\"speechiness\",  hue=\"country\", kind=\"line\", data = sampleData)\n\n# speechiness vs target (Line Plot)\nsns.relplot(x=\"decade\", y=\"danceability\", hue=\"country\", kind=\"line\", data = sampleData)\n\n# valence vs target (Line Plot)\nsns.relplot(x=\"decade\", y=\"valence\",  hue=\"country\", kind=\"line\", data = sampleData)\n\n# tempo vs target (Line Plot)\nsns.relplot(x=\"decade\", y=\"tempo\",  hue=\"country\", kind=\"line\", data = sampleData)\n\n# instrumental vs target (Line Plot)\nsns.relplot(x=\"decade\", y=\"instrumentalness\",  hue=\"country\", kind=\"line\", data = sampleData)\n\n# duration over time (Line Plot)\nsns.relplot(x=\"decade\", y=\"duration_sec\", hue=\"country\", kind=\"line\", data = sampleData)",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 148,
     "data": {
      "text/plain": "<seaborn.axisgrid.FacetGrid at 0x7f6ce73b09d0>"
     },
     "metadata": {}
    },
    {
     "data": {
      "text/plain": "<Figure size 421.25x360 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 410,
       "height": 352
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 421.25x360 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 410,
       "height": 352
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 421.25x360 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 409,
       "height": 352
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 421.25x360 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 410,
       "height": 352
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 421.25x360 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 411,
       "height": 352
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 421.25x360 with 1 Axes>",
      "image/png": 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ymaMR8S6hTQ3CHV+A5l7oW12Z85iNeJvXxqZlEdjHOCdVFhps1MKz0Ld/Nq63wHE6uOx+1LtUBtB5Ipz2Cli6BhafObc2PlYUrvws/Pw9cNvH4ZXf9rKcWlLaxqb31GqPZkEILNiIyA3Ay4AhY8zpB33vQ8CXgR5jzIiICPBPwNVAGnibMWZ9UGNTC9xC2/7ZGK9dy871/rzLBq/TNUDbMjjpRX5wOdtr7VIOsXa46u/hF++BWz8K13wLoi3leexyibfD+HZo64eo9sYLWpCZzfeAbwI3lh4UkWXAlcD2ksMvBVb6H+cD3/H/Vaq8prd/jrdVeyTBSu7ZP+ey6w+QHvGON/XCcRfBkjWw5GzvMldQ2pfBlZ+DX34Ifv1peOnf19aEvITAisDYM7D4rGqPpuEFFmyMMXeJyIrDfOsfgb8GflFy7BrgRmOMAR4QkXYRWWyM2R3U+NQC1ajbP5euddn5B0ju8o7HO7z5liVrvEn9liWVvXy4+Cy47K+9+Zu7vwqX/3VtXb6MtkJyENrHy5fVqcOq6JyNiFwD7DTGPCIH/sItBXaUfD3gHzsk2IjIdcB1AMuXLw9usKoxpYYh3ABTlbnk/uBSutYl0uRdDjvjNV6Q6VhR/Rf3k6/0KtTWf9+7ZFVLJdGlbWz6n1f9/6sGVrG/OhFJAH+Ddwltzowx1wPXA6xdu1abHKmZ27f9cx1eny+kYc9jXtaya/3R17rU4kZwB5REL4YTX1DtEe0XaYKpYa9Csbmn2qNpWJV8i3cicDwwndX0A+tF5DxgJ7Cs5Lb9/jGlyief9Mqe62H75/msdalFIt4ltKlBuOPvoLmvtkqip9vYJDprM1g3gIr91RljHgP2zUaKyFZgrV+NdgvwPhH5EV5hwITO16iyS43U7gtJOde61KpwxC+Jfi/c9jfwyu/UTkm0FYPssN/GZmm1R9OQgix9vhm4AugWkQHg08aY7x7h5v+DV/a8Ba/0+e1BjUstULW2/bNxYezZ/eXIpWtduk6EVdd4wWWua11qVawdrvoi/OK9tVcSHW/3Lk8299ZHtlhnxNTx3g5r164169atq/YwVD3IJWH7A9BUA403dzwIv/vC/sWlbcu8wFLutS61bPcjXkn0ojPgpV+qnRf31Ch0nVTONjZaceCrg4vXSpVBrWz/nE/Bnf/gvcO/4L3Br3WpMGNmWNC1+CxvDud3NVYSrW1sAqPBRi0MtbL98++/662JufKz0HtatUdTVvmiy9ODU1ghoa81RkvMJny0+L7ySq8Z5vrve/Mk57y5YmM9opAFhLSNTQA02KjGN739c7UvoQ0/AZt+5s3HNFigKbrw3EiavOPihoUtw1OERehtjdLZFCFuH6EwY19J9P/zu0TXQEl0vE3b2ARAg41qfLWw/bPreJeL4h1w3v+p9mjKyhjYvjdFMlugLe7NvcTsMK4LQ5M5do1naInZ9LXGaD042zm4JLqp16u4q6bpNjajW7zLnKosauAitlIBS+7yVolX06afe6vUL3pfY1WXAXsms4wk87TFDpzkD4WgOWbRnohQKLo8MzzFowPj7BzPkMkX998wHPF6qDX1wq8+7lUNVluszeuhlxmv9kgahgYb1dicnPeCYVcx2EwNwbrveu1QTqjBzcTmYW86z469aVpj9lHrrmJ2mLa4TSJiMTSZY9PuSTbvSbI3XcBxjffi/tK/99Kk//2IVz1YbdNtbOq4YreWaLBRja0W3pne/02v2/Qlf1EbFVdlMpV32DKUoiVqE5rhK8l0ttMWt3H8bOexgQl2jGXIxBd7hRPJ3fDrT3ndHqop0uT9/qRGqjuOBqHBRjW2am//vP1+eO4uWPOW2lktXwY5x6s8S0TCWOG5BdDSbGdkKsfGXZNslhOYuuAvvU4Kd3+l+lnFdBsbt3js26qj0mCjGlex4HV5rtYltEIG7vma13n5zNdXZwwBKLiGZ4amCCNErPm/hOyf27EpFg1PtFzI7hNfD0/dSv7hfyvDiOfBink/x+Se6o6jAWg1mmpc1d7+ef2NXpXVy79eOyvk58l1YetIiqzj0hIr/8tH1A4RtUNMrnoz0cweOtffwADdJE57Ea1xGytUhZ9lvA1Gn4aWxcz4eqE6hAYb1biquf3z2LPw6E/glKu9/mYNYud4honM/hLnoITCwti5HySeG2HJhn/kSauDbZ2n0d0cpas5QiJSwYaqYRtyjtfPTi8GzZn+z6nGNL39czUuoRnXm2+INsP5f1r55w/I0GSOPROZQ0qcg2LCNrvO/zhOvJuVG/6OtsIwI1M5Nu2a5PHdk4yl8l4lm6oLGmxUY5re/rkaWwo88UsY3AQXvNsr6W0AE+kC20ZTtMYjFW0t6UZa2XXhZxBj6H/wb2kNZWlP2BgDz46keHRggh1jaVJ5p3KDUnMyo2AjIn0i8jL/o3G6BqrGVa3tn9Nj8OA/e92bV76k8s8fgHS+yJbhKZpnUeJcToXmpew6/+PYqT0sfujz4BaIWCHa4jZNEYuRqTyP70ry+C4v2ylotlOTjvmrIyKvAx4CXgu8DnhQRF4T9MCUmrNqbv/8wLfBycKljbGmJl902TKUJGKFsKzqnU+2+3QG13yAxMhj9G341r6S6NJKNoOX7Tym2U5Nmslbv48DzzPGDAGISA/wG+A/ghyYUnNWre2fB9bBlt94a2raj6vscwfA8UucXQNNlZyQP4LksudjT+2i68mbyTctZu8pB5aTR6wQESuE68LIVJ49k1maIhaL2mK0xG3salSyqX1m8tcYmg40vlF0rkfVstRo5feucXJw79egdSmc/abKPncAjIEdY2nS+SKtAVeezcbYqW/ETu2he/O/UWhaxFT/5YfcZjrbAcg7Ls8MpwiFoKc5SmdzhCbbaoSks+7MJNjcKiK3ATf7X78ebxtnpWrP9PbPlW4Nv+EmmBiAq79cvXLrMtozkWVkKkd7PFLtoRxIhKFz3o+dGaJv/ddw4r1ku468XUNptjM6lWdwMksiEmZRa4zWRESznQo65ts/Y8xfAf8MnOl/XG+M+UjQA1NqTvIpcDJeJ+FKGd/uBZsTXwj9ayv3vAEZnfKaa7ZVuPJspvaXRPew+MHPYqd2H/M+pR2oQXhuNM2jA+NsG0szlXeq3hVnIZjptYb7gDuB3wH3BzccpeYpXeFLaMZ4+9RYEbjwPZV73oAkcw7PjaZoidk1fanJK4n+NGIMS+7/W0L5qRnfd7qSrSVqMzaVZ/OuJI/vnmB0KkehqFEnKDOpRvs/eNVorwJeAzwgIu8IemBKzUmlt39++lewewOcdx0kuir3vAHIFly2DE4Rt+feXLOSpkuirfT+kujZENlfySYl2c7W0ZRmOwGYyVvAvwLOMca8zRjzVuBcQC+jqdozvf1zpeZMspNeqXPvKjjt5ZV5zoAUioYtw0nCofI016yUbPfpDJ3jl0T/4Ztz7hJtT2c7MZvxVIHNu5Js3DXBcFKznXKZyW/VKFC6k1HSP6ZUbcmOU9FJhof+2dvk69IPVb76rYxcF7aOpig4hngNlDjPVnLZ8xk99Y207ridjqd+Mq/HEoEmP9sJi7BtzMt2du7NYGiMoCMiHxSRivdxmkk12ha8hZy/AAxwDfCoiPwlgDHmqwGOT6mZm9wJkQrtXbPnUa8tzZmvh64TK/OcATAGdoynGE8XaE/UTonzbI2d8gbsqd1HLYmeLdsK0WaFMAZGxvP0uQar/mLx4XwQ+AGQPvgbIhI2xgSyec9M3o49A/wc9oX1XwDPAS3+h1LVN739s1WBYFMseEUBzX1w7tuCf74ADSWzDE3maK+htTRz4pdEZ7pW07f+a8RGHy/nQ1e8KE9E3iIij4rIIyLybyKyQkR+6x+7XUSW+7f7XmlHFxGZ8v+9QkTuEJH/EJEnROSH4nk/sAT4nYj8bvo+IvIVEXkE+LiI/Lzk8V4sIj8rxzkdM7MxxvxtOZ5IqUBVcu+ax34Ce7fCS75Q3V1A52k8XWD7WIbWWG2WOM/WdEn0sjs/zOIHP8fA5V+h0LS42sOaNRFZDXwCuMgYMyIincD3ge8bY77vF2h9HXjlMR7qHGA1sAu4F7jYGPN1/6rU840x0/tdNwEPGmM+JCICbBaRHmPMMPB24IZynNdMqtHWisjPRGS9H1UfFZFHy/HkSpXN5C6wYxV4nt3w8I2w4lI47qLgny8gqbzDMyMpmqNWQ+0HdmhJdPLYd6o9LwD+fToYGGPGgAuBm/zv/xtwyQwe5yFjzIAxxgU2ACuOcLsi8FP/uYz/+G8WkXb/ef93TmdxkJn8mv0Q+Ffgj4GXl3woVRuKBUiNgB1wybMxXkuaUAgu+vNgnytAOcdly9AU0XCoLkqcZ+vAkugvzLokus44+K/jIhICSlcz50o+L3LkK1nZg+Zp/hV4M/AGvKBXlo6mMwk2w8aYW4wxzxljtk1/HOtOInKDiAyJyMaSY5/1M6MNIvIrEVniHxcR+bqIbPG/v2Ye56QWmuwkYIK/hPbcnbDjQVj7Dmiuz502HNfw7PAUxvW2YG5U5SqJrpLfAq8VkS4A/zLafcC1/vffBNztf74VbzkKwCuAmUy+JTnKfLsxZhfepbdP4AWespjJb9unReT/icgbROTV0x8zuN/3gKsOOvYPxpgzjTFnA/8NfMo//lJgpf9xHfCdGY1eKYCpCmz/nJ+C+74BXSth9auCfa6AGAPbR1Ok8kWaYo2/I/yBJdE/rvZwZswYswn4PHCnP2n/VeDPgbf7Uxh/AnzAv/m/AJf7t7sQSM3gKa7H63n5u6Pc5ofADmPM5jmexiFm8hv3duBUvIjp+scM8J9Hu5Mx5i4RWXHQscmSL5vYX+F2DXCjf73wARFpF5HFxphjNz1SC5tbhOQgxFqDfZ7ff9fbGO3Kz1d+64Iy2TmeYTSdr73mmgHaXxL9AwpNi8tSEl0Jxpjv4xUFlHrBYW43CFxQcugj/vE7gDtKbve+ks+/AXyj5OvDda29BC+Qlc1M/mqeZ4w5pVxPKCKfB94CTADP9w8vBXaU3GzAP3ZIsBGR6/CyH5YvX16uYal6VYntn4eegE0/h9WvhN5Tg3ueAI0kc+yeyHrNNRcSvyTaygzTt/4fceI9ZLtWVXtUNU1EHsbLkD5UzsedyWW0+0SkbD8dY8zHjTHL8NK09x3r9oe5//XGmLXGmLU9PT3lGpaqV0Fv/+w6cPdXINEJz3tncM8ToGTW4bnRNC3RhbmPiwnb7D7/b3DivSx58HPYU7uqPaSaZow51xhzmTEmd+xbz9xMgs0FwAYRedKfvH+sTKXPP8SrcAPYCSwr+V6/f0ypI6vE9s+bfgajT8OF76vONtPzlCkU2TI0RVMkTLgBK89marokGmNY8kDdlkTXtZkEm6vwJu6vxCt5fhlzLH0WkZUlX14DPOF/fgvwFr8q7QJgQudr1DEFvf3z1BCsuwGWnQcnXBHMcwSoUDRsGZrCCgl2HTXXDMr+kuhBFj84+y7Ran5msnnaNrys4wX+5+mZ3E9Ebsbb++YUERkQkXcCXxSRjX5mdCX7Kyr+B3gWrw/bvwD1vzGICl5qLNgGmPd9wytAuPiDlelMUEZFF54bSeEUDbE6bK4ZlH0l0aMb6fvDN+qtJLquHfMtoYh8GlgLnIJXc23jNXG7+Gj3M8a84TCHv3uE2xrgvccai2psxhhyjku+6JIruKTzDlNZh9a4zaK2GHY4VHpjr/FmUNs/b7sPtt4Nz3sXtC4J5jkCYgzs2JtiMlugrd57ngUguez52KnddD1xE/nmJew95dpj30nN20yuP7wKr8fOevAW/IiINuBUc5Z3XHJOkbzjks4XSeUcpnIOqbyzrxjeAFYohB0WhqdybBtNcUJ3M31tMcIh2b/9czSArgGFDNz7T9CxAs58XfkfP2CDySzDyXz9N9cMUL2WRM+ViBSBx0oOvdIYs/UIt506Qjn0vMwk2OSNMUZEjD+QCm6DqOqVU3S9LMVxyeSLTOX9gJJzKLpeRDEGb7OucAg7HKIjHkEOc7kqEbEoFF2eHEyydSzFST3NdDujhILqHvnw92FqEF7xdQjX1wv23nSe7aNpr8S5vq78VdYhJdHdZLtWV3tUQcr4i+mrZibB5ici8s9Au4i8C3gHZV7so+pT0TX7spRcoUgy55DKFZnKOeSLLiG8DEXwAooVFlpjNqE5zH/Y4RDdzVHyjsum3RP0jD3J0o4o7eXuUjP6jNfV+ZSrYdGZZXzg4E3lHZ4ZTtESsxuquWZQpkuil935YZY8+Hl2XPZlCs31dcl0rkSkGW+7mA68qZFPGGN+cdBtFgM/BlrxYsW7jTF3i8iVwN8CUbwtaN5ujJk61nPOJNj0AP8BTOLN23wKeNFMT0rVt+l5lJwfVNK56aDikC0UD3jzbIVCRKwQTRGL1lAwb6sjVogeIOIk2bLXomlqkv6OBC2xMqwhMa63pibaAuf/aTmGWzE5x+XpwSliVrghm2sGZboketmdH2bJA3/Ljsu+jBsJbpZgxUd/+TXg7DI/7IatX/yjDx7jNnER2eB//hzwWuBVxphJEenG69xyiz9/Pu2NwG3GmM+LSBhI+Lf9BPAiY0xKRD4C/CXw/x1rkDMJNi82xnwE+PX0ARH5Cn5bBFX/jDHki66fpXiXvZK5AqlskXTBASPgb4o7PY8SCXtBpRrC2XFs26I9ZpMruDw5OElzzKa/PU7LfHp+bf5vGHocrvgYxNrKN+CAFVzDM0NThGjs5ppBmS6JXnrfJ1j84OfZefFnIVRfl09n4IDLaCJiA18Qkcvw2pAtBfqAPSX3+T1wg3/bnxtjNojI5cAq4F7/kncEr+r4mI74lyki78YrQT7hoEWcLXgb8ag6UzhgHsUhlS+SzDikC/vnUQDCIthhL0s50jxKNdnp3RT9HTmjdoioHSFbKPLEnknaExGWtMdmHwjTY/DQ9bDkHFh5ZQCjDobrwvaRFFnHnV+gXeCmS6IXPfwV+v7wDQbX/EUg5e4zyEAq5U14V63ONcYURGQrcMCGUH5/y8uAPwK+JyJfBfYCvz5CtfFRHe238ya8TXP+DvhoyfGkv5mPqkFF1+yr9MoWiiSzXpXXVNbBKQkoIbyAYltzn0epBinmCGfHceKdBxyP2WFidphMrsjju5J0NdksbosTn+kakwe+7W0tfUkwLzJB2TmeYSxdoD1R3Xfixhh+sCnPg7sKrFlkcUm/zcqOUM29UTkaryR6D11P/JBC02LGTp3162k9aQOG/EDzfOC4g28gIscBA8aYfxGRKLAGrxv1t0TkJGPMFr9gbKkx5qljPeERg40xZgKvWWZD/483glTOYc9ElqFklpzjHjCPYvuVXs1R2ysZrnPh3KS/KfzhzyUeDRM3YSYzDqOpSXpaIixqjRM72uWlgXWw5Tew5q3QXj/NXYeTOfZMZqreXLPoGr72+yy3PlfguNYQP96c5+bH8/QmhEv6bS7ut1jdHa6L37+xU67FTu3aF3CSy66o9pCC8kPgv0TkMWAd+7u5lLoC+CsRKQBTwFuMMcMi8jbgZj8AgTeHM/dgo2qb6xrGMwW2j6YYzxQIi9ActWiONty15gNY6UHcY+1dI3j7tRjYmyownMyzqDVGb2uU6MFtW5wc3POP0NYPZ78xuIGX2WTGYetIitZ4pKqJWM4xfOH+DPftdHjz6ghvOT3KZN5w/06Hewcc/mtLnv98Kk97VLhoqcUly2zO7g1j12oRgwhDZ78fKz1M7x++RiHR0xAl0Qevm/G3nL7waLc9wjYHGGN+CzxvtmMQU8ftGtauXWvWrVtX7WFUVM4pMjSZY8feNHnHJW6HSVRpor7i3AJNu+6jGG2bVZsaY7zszzWGxW1xelqi+1/s1t0A62+Eq78M/WsDGnh5ZfJFNu+ZJG5ZWFb1XrRTecMn706zcbjIe9bEeOXJh2ZY6YLh97sd7hko8OAuh4wDCRsuWOJdalu72CJexXM4klB+kmV3fphwYYodl32ZyYLFqktejmXPOousvZOrkgXyKlXfjDFMZh12T2TYPZFFgNaYTUuDZzEHC+eTiDGz7ocmAs0xC9eFXRMZBiezLG6P0e0MYm24CU56Ud0EmnzRZctQEjscqmqgGcu4fOzONNsmXD56YZwXHHf438WELVy+3Oby5Tb5ouEPgw737HC4b6fDb7c5RMKwdpHFJf0W5y+xaY3WxmuzVxL9GZbd5ZVEp87U4tv50symhjlFl7GUtyI8mXOIhEM0x6y6mcwvt+jYk4SzI/NeB1EsGqZyBU5Z90maktswr72RcHPnse9YZUUXnh5Kki0UaYpW733irqTLR+9IsTdr+NQlCZ63ePZjKbqGjcNF7hnwsp6RjCEscFZvmEv6bS7qt+iKV7+MOza6iaX3fpx0ywnE3n0HVmzWDVQW5h/rYWhmU4PSeYfBySwDezMUXUNTxKK7+RjzFI3OLWJlBimWYcFdOCz0j9xNy9gmtq76MybGQywTb7vkWl15bwxsH0sxlXOq2lzzmb1FPnZnGseFL70gwWldc3sJCYeEs/oszuqzePeaKE+Nudw7UODuAYevP5zlGw/DaV1hLllmcfFSmyUt1fnBZLtWM3TOBzF7txGr0+3Aa4X+79UI1zVMZArs2JtmdCqPFRJaYo1RQVYOoUISKRZB5t8uP5SfpHvjd8l0nkph5dVEisIzI97q+/6OOO1VnnQ/nD0TWUamqttc89Ehh0/enabJEv7hhQmOayvP1gUhEU7tCnNqV5h3nGnYNulyr5/xXL8hx/UbcpzQHuKSfptL+i1WtFW2pDq57AoyzUP01NovRZ3RYFNlecdlJJlj21iKbMGb8O9qqr2FlNVmZUZxy7T9c/em7xEuTLHzrPeChLAsaLci5B2XLcMpmuxs+VrglMFYKs+O8TRtseo117xvoMDn7suwqCnEF69I0NsUTKYhIqxoC7OiLcybVkfZPeVlPPcOOPzbxhw3bsyxpDnEJf0WF/dbnNoVXrCXleuNBpsqSWYL7BrPsGciC0BLzG74suU5MwY7tZuiPf+G47GRjbRt+xVjJ72afNvxB3wvYnldE8raAmeekjm/uWa0es01b302zz/+PsvKjhCfvzxBW7RyA1ncHOI1p0Z5zalRxjIu9+10uHegwE+fzPOTJ/J0xYWL+71LbWf2hrH0SsAhRKQLuN3/chFQBIb9r88zxuQrMQ4NNhVUdA1jqRzbxtIkMw52OER7IqLvzI4hVEh6W/jO95q5W6D3kW9TiPcyduqR19SUrQXOPGULLlsGp0hEqtdc88ebc/y/R3Ks6QvzmUsSxO3q/a52xkO87KQILzspwlTe8MAuL+O57dkCtzxdoCUiXLjUy3jO7bOI1mBJdTUYY0bxm3+KyGeAKWPMl6e/LyKWMcYJehwabCogWygyOJFlx940jmtI2DrhPxvh7N6ybP/cseVnRJPb2XXBpzBW7Ji3n3cLnHkouIZnhpOERYgcvBC1Aowx/MsjOf79iTxXLLf46/PjNbUQszkivGhFhBetiJB1DOv2eItI7x0o8KvnCsQsOG+xt5bnvCUWTVUMkrVIRL4HZPE2xrxXRCYpCUIishF4mTFmq4i8GXg/XtPNB4H3GGOKs31ODTYBMcab8N85nmFoMkc4JLRELaxwjZY71SpjsFO7cO35bRxopfbQ+cSPmFp8EalF583qvnNqgTMPrgtbR1LkHUNzFS7hFV3DVx7K8uutBV6x0uY958RqulAlZolfPGBTKMZ4ZKjIPQMF7tvpcNcOBzsE5/R5a3kuXGrRHqvi3+Bn2r5GAFsM8JmJD87hfv3ARcaYop/xHEJETgNeD1zs91H7Nl4Tzxtn+2QabMqsUPQm/LePpckUHKJhSyf85yHkpAk5OZz4POZrjKH3ke9AKMzwmdfN7TFm0wJnnnaOpxmvUnPNnGP43H0ZHtjl8JbTo7x5dX397tphYe1ii7WLLf78XMPmUW8tz70DBb76e4fQOji9e39JdVCFDnXi32eQobwQOBf4vf97EAeG5vJkGmzKZCrnsGciw87xDMZAc9Siq+nYl2rU4YmTJZyfxE7txszzElrzrntoGnqY4TPehRPvnufAvG4ExsDIVI6hZJYlbXG6S1vgzMPgZJY9k1naq9Bccypv+ORdaTaNFPnzc2O8YmV1G3zOVzgknN5jcXqPxZ+eHeWZcZd7/Mq2b6/P8e31OU7u3F9Svaw1+Mujc8xAgpIq+dwBSv/Qpl+8BPi+MeZj830yDTbzUHQN4+k828e8d6JWSGiLRWr6kkPNcouECknCuXHs1CDipBEE14p6vdDmKFRI0fPo9WTbTmT8hJeVbbilLXB2TmTYM90Cpzk654qo8XSB7aNpWuOVL3Eezbh87I40O5IuH78ozuXLG6syUkQ4qSPMSR1h3nYGDEwWuWenw707CtzwaI4bHs2xvNUrqb6k3+akOtseoQy2Ai8DEJE1wHSp5u3AL0TkH40xQyLSCbQYY7bN9gk02MxBtlBkOJlj22iaQtElEQnrhP8ciJMhnJvEygwTzo4hbhETDuNaTbjxrrI8R9fjNxLOTbDrgk+VZUHowUIhaIvbFIuGgb1e77r+9jidTVFmMz2XzhfZMjxFcxVKnHf67WfGc4bPXZbg3EWN/7LQ3xrm2tYw154WZTjt7isu+NHmPDc9nqcvIVzsZzyruiuQ8VTfT4G3iMgmvCKApwCMMY+LyCeAX4lICCgA7wVmHWy0N9oMTTfD3Lk3w1DSa4bZErOxdcJ/5lyHUGGKcHYvdnoQcbz/R9eK4VqxslSclYrufZJld36YiRNexvCZf1rWxz4Sp2hI5R3ssLCsIzGjFjg5x+WJPZOERIjZlX1h27K3yMfuSOMa+MLlCU7pWhAvrEc0kXP3bY/w8B6Hgou3PUJvgX9611XYkVm/qVxQ6dHRNP5bmHmaboa5bTTFVK5I1ArRmaivSdNqkkKacH4SKz1EODeOmCImZOFaCdx4Irgndov0bvgWxVgHo6f9SXDPcxArLLTFbRzHzKgFjuManh2ewhiIVaCkutQjgw6fujtNU0T44hUJls9zzkKKOX9zuzBFOzGj8vJa0xYNcdUJEa46IXLA9gjjOUf/5udJg80RpPMOu8ez7JrQZpiz4jqE81OEs6NY6UFCxRyI4IZjFKOtZc9ejqT92f8iNvEsu5/3UVw7wKB2BJYl+1vgDKVoihzaAscY2D6aIl0o0hqr7BzJPQMFvnBfhsXNXvuZnsT8fi5SSBN2cmR6zwG3SHTiOcLpEVw7UZX//3Io3R4hs3eq2sOpexpsSkzvfrljb5qxqRxWOERLg2ynHBhjSuZeBv3sxcUNWd4LTWR+62PmwkoP07X5B6T61jK15OKKP3+pg1vgtMRslnbEaYla7JrIMJrK056obNXX/zyT55/WZTmlM8znLovTOs/2M6H8FGJc0n1r9m3/kI53E86NE5nc6ve1i3hrpTQ7WLA02ODtfjmczLFjLE3WcYlbYbqb6+8SQMW4BT97GcFKDxMq5jF4cy/FaHvVX1B6HvtnwDB05p9VfSzT9rXAyRd5YrfXdy2ZKVQ00Bhj+PHmPN99NMfaRWE+dUli3rtkhnMTmFCUdO8ZGCu+/xsiFGMdZGIdhPKT2Mkd2KlBTNiiGKlchqtqR2DBRkRuwCulGzLGnO4f+wfg5UAeeAZ4uzFm3P/ex4B34jWJe78x5ragxjZtMltg9/j+3S+1GeYRGOMtrsxNYKUHsXITALihMK7dVJXs5Uiadj9I8+4HGFn1VpymRdUeziFikTCxSJhs3rt0VqlY6BrD9Rty/PTJPM9fbvFXZWg/Y2XHKEbayHatxoSPHDTdSCu5rtXkW48nMjWAPbULIyFvb6LQwi5IWEiCzGy+B3yTA9sa/Br4mDHGEZG/Bz4GfEREVgHXAquBJcBvROTkufTfORZjDMP+Cv9ktkAkHKZDm2Eeyi0QzicJZ/zsxc1jEFw7jhPrCDxjSOYNGwYddky6PP84m8XNx34nLE6Wnkf/L7mW5ew96VWBjm++KlkM4LiGLz+Y5fZtBV65MsK710Tn9/tuDFZmjEJTH7mOk2fcINXYCXIdJ5NvWY6V3kNkcjtiXJxoC4T0TV6jCyzYGGPuEpEVBx37VcmXDwCv8T+/BviRMSYHPCciW4DzgPvLPa7JrMNjOydojdl6qayUMYQKKe+SR3rQuzwCmLA/9xKa/w6ZR5MvGjaPFHl40GH9Hoen97q4flX+9zfmeMFxNteeFjnqhl1dT9yEnRlmx6Vfmn+H6AaRdQyfvTfDQ7sd3nZGlDeummclpSl6gaZlObn2E+d0OcxYMQqtKyg0L8VKDRKd3AbuJG6kGRPWIpxGVc2/yHcAP/Y/X4oXfKYN+McOISLXAdcBLF++fPbPasAOVX49Qy2SYp5QPomVGcXKDCGu48292InAsxdjDFsnXB7e47B+0OGxoSLZIoTE2w74zaujnNMXpjcR4j+fyvPLLXlu31rgkmUWb1wV5aSOA39+kYlnaX/m50wcdyXZrlWBjbueTOYMn7w7zeaRIh9YG+NlJ81zfsh1sLJ7ybWfTKGlf/6/HyEbp6Ufp2kxVmaEyORWwvkRilYCU6cVbOrIqhJsROTjeL14fjjb+xpjrgeuB29RZ5mH1tiMIVSYIpSb8LOXSYyACUe8jckCvn4+knZZP+iwfk+R9YMOe7Pej29Zq7e24ZxFYc7qPbQd/J+dE+Pa0yL87Kk8P386z907HM5fYvHGVRFWdVtgXG9Njd3CyOq3B3oO9WIk7fKxO9PsTLp84uI4ly2b32Uqbw1NkmzX6vLPhYXCOE19OIker4Jt/Fms9CiuFcW1m2qmyEPNT8WDjYi8Da9w4IVmf/uCncCykpv1+8fUPEkxRyg/hZUZ2Z+9SAjXiuMkytMS5kjSBcOjQw4PDxZZv8dh+6QLeCuy1ywKc06fxZo+a0add9tjId5+ZozXnhrlF0/n+c8n83zgN2nO7g3zya7fEd/7JHvW/OW+0tuFbGCyyEfvSDOZN3z+8gTn9M3vz1ycDOFClkzvGoqx9vIM8rBPFKIY6yTT51WwRZLbsdIjuGELN9KqQafOVTTYiMhVwF8Dlxtj0iXfugW4SUS+ilcgsBJ4qJJjaxjG9VvCTGCl9xAupMAYr6Gl3Rxo9lJ0DU+OFXl4jxdcNo8WKRqIhuGMnjBXnRBlTZ/F8e2hOU9QN0eEN62O8uqTI/zymTy/fWKIlRM3siF8OhvDl3CeMQt6pfdTY0U+fqf3p/XlFzRxcuf8ft6HW0MTOBHcaBvZ6BmE8lPYUzuxU34FW7Q1kB53KnhBlj7fDFwBdIvIAPBpvOqzKPBr/wXhAWPMnxljNonIT4DH8S6vvTeISrRGJU6WUGEKKzOMlRkB14Hp7CXeGdjzGmPYmXT3ZS4bhhzSBa8Z1MrOEK89NcKaRRaru8NEyrzLY9wWXnNqlHcnbyaxK8/nzNtZd3eWE9vzvHF1lEv6rQVXYfiHQYdP352m1W8/0z/P9jNHXENTQW6kmVznKeRbj/OCztQAgsGJtGoRSJ0JshrtDYc5/N2j3P7zwOeDGk9D2Ze97MVODxEqTAGCG45QjDQH+s5vPOvyBz+4rB90GEp7V0IXNQlXLLNZs8jinL7wvFelz0RiaD3tO+9i9JQ38NlTTub2rQVu3pzns/dmWNYa4g2nRXjBcQujA8RdOwp88f4MS1tC/N3lCbrn2X5mpmtoKsVYMfLtJ1JoWYaVGiSS3Iq4DsVIS02MTx2bvjWoE95mYknCfvYipujNvdgJnDK14z+cnGPYOOIFl4f3ODwz7s27NNveVrvXrrI4t89iSUtlV4RLMUfPI98h37SEvSe/FiskvOSECC9aYXP3gMNNm3J86cEs/7Yxx+tPi/Li4+2yZ1e14r+35Pn6uiyndYf57KUJWqOVX0NTKSYcodC6jEKzX8E28SzhXJJipKkuG38uJLX1m6T2c4te9uJvJhZyUuzLXqItgWUvrjE8s9fdt95l43CRggtWCFZ3h3nbGVHOXWSxsiNU1Yyh48mfEEntZuDizx/wzjYcEq5YbnPZMosHdjncvCnH19Zl+cGmHK89NcLVJ0aIzbNFS60wxnDT43m+91iO8xZbfPLi+PzOrQxraComZOE0LcJJ9BLOjhGZeNYrJrDjXgWbqjkabGqIOFnCuQlvM7HMKGJcTCiEazcFmr0Mpvz1Lnsc/jBYZDLvXRo7vi3EK1ZGWNMX5oxea959tMrFTu6g8+mfMtn/fDI9Zx32NiERLlpqc+ESi/WDRW7alOM7f8hx8+N5/viUCC9fGTmkxLqeuMbwnT/k+PlTeV54nM2Hz4/NeYdQ7wGn19CspNCyrH4qvyREMd5NJtZFODdR0vjTxrVb6uc8FgANNtV0tK2QY22BvbOcyhs2DDn+vEuRnUnv0lhXXDh/icWaRWHW9Fl0xmvwna0x9G74Fq4VY+SMdx7z5iLCuYsszl1ksXHY4abHc3z30Rw/3pzjlSdHeNXJkYrML5VToWj48kMZfrvN4Y9PiXDd2fNrPyPFPOHcRDBraCpFhGKsnUzsbEL5pNf4Mz2ICVleD7ZaztIWCA02FXbIVsjT2UsZt0I+WKFo2Dxa3Bdcnhwr4hqIWXBWr8UrTrI5d5HF8tba33e9ZfvtJEY3Mnj2+7wO07Nweo/FFy63eHKsyM2P5/jBpjz/8WSeV5wU4Y9PidRmcD1IxjF89t40v99d5J1nRnn9afNrP7N/Dc25wa6hqSA30kKuaxX51hXYqZ1EkjsxiFc2rY0/q0aDTdD8zcRCuUO3Qg5qMzFjDNsm3X3B5ZEhh6zjtYI5pTPMG1ZFOLfP4tSu8Lw7/1ZSKD9Jz6YbyHSuYvK4K+f8OKd0hvnMJQmeGy/yo805/uNJrzPBS0+wed2p0RktMq2GyZzLx+/K8NRYkb94XoyrT5xfFVZV1tBUkLET5NtXUmhZjpXaQ2RyG2Jcv4JNG39WmgabABy4FfJeP3sJdivksYw37/KHQa8VzGjGm3fpbwnx4hVe5nJWr0VzpH6Cy8G6N/4roUKKobPeU5YgfXx7mI9dmOAtp7v8aHOOXz5T4JfPFHjRCptrT4uytMIVdkcznHb56B1pdk+5fPLiOJf0z+/FshbW0FSKCUcptB5HoXkJVnrY78E2SdHWCrZK0mBTDofbChkJfDOx3VMud+8ocPeAwxOj3hrYtqhwTp8357JmkUVfjb5Ln63YyEbatv+asZV/TL5tRVkfe2lLiA+dF+dPVkf5yRM5/vfZAr96rsAVyy3esCrKiqN0mq6E7X77mVTe8HeXJzhrnu1nam0NTcWEbJzmJTiJPqzsKJGJrXW/dXU90WAzF/s2E0t6LWHyExXbCnnbRJF7Bhzu3lHYt+ZlZUeIt50R5fwlFifMoxVMzXIL9D7yLQrxXsZOOdxa4fLobQrxvnPjvHFVlJ8+meeWLXl+u83h4n6v0/R8W7/MxZOjRf7mzjQhga+8sOmQbtezUuNraComFMZJ9OLE/cafk895ZdNWTBt/BmiB/rbNQZW2QjbG8Mz4/gxmh9/MclV3mD89O8ol/TaLZrCxWD3rePpnRJM72HnBpyty2aMzHuJdZ8d4/XSn6afy3Dvg8LzFYd64KsrpPZX5s3l4j8Nn7knTHhW+eEXT/C7rVWgNTSrnEK6XLTxKt67OTXgVbJkhTMjWCrYAaLA5kipuhewawxOjRe7e4XDPQIE9KUNI4MyeMK9cGeOifovuOqicKgc7tZvOJ39EcslFpBc9r6LP3RoN8dYzYrzm1Cj/9XSenz6Z5y9uT3NmT5g3ro6ypi8cWPXendsLfPGBDMtaQvzdFQm65vPzrsAamkLRZSJToKMpQrFoGE3lAIiGw8Qj4ZpvGeRG28hF2ygUUtjJAezUboyINv4sIw02JaSY91btV2Er5KJreGy4yD0DBe4dcBjJGKyQ1xLmjassLuq3aKuz9SDzZgw9j3wHEwozfMZ1VRtGky1cuyrKK0+O8D/P5Pn3J/J89I40p3aGeMPqKBcuscoadP7r6TzfeDjL6u4wn70sMa+ijkqsoZnMFnBdw2mLWuhriyEi5JwiyazDSDLH8FSOomsIixCPhIlatfvi7dpN+xp/WqndRJI7EFzArfbQ6t7CDjb7tkKewE4PVXwr5ELRsGGoyN07Cty302EiZ4iGYe1ii//Tb3PBEoumOq4emysp5mnedS9tW28lPrqJoTOuoxjvrvawiFnCq0+J8rKTIvz6uQI/2pzj03dnOKE9xBtWRbm035rXO3hjDD/YlOfGjTkuWGLx8Yvm137mwH1oOub8OEfiZTN5elqinNTbcsCls6gVJtocprs5ysmuYSrvMJEuMDiZZWQqh4h3m7hdm1mPsWIU2o7HaV5KOD1EobhTL6vNk+zfv6z+rF271qxbt25W95lI5Xh8y7N0yZS/mVjBz14S3v7nAU8O5hzDuj0Od+9weGBXgVQBEhacv8Ti0mU2axfXTluYSrOTO2jbeiut239LuJAk37SYieOvZvzEV9TkpYyia/jtNq/T9I5Jl/6WENeeFuGFK+xZt45xjeFb67Pc8nSBF6+w+cvz5td+ZnoNTabnrEDW0ExkvDnLU/pa6GmJziqzyxaKTOUchpM5RpI5isYQEiFRw1nPWCrHxSd1Y4VnHXAW5h/zYSy8zCY3ScvoI4SbWyuyFTJ4O1Y+tNurIHtot7fAsiUCF/fbXNrvlSg3akfiY5nOYlq33kZidCNGLKaWXMjEipeQ6T6zpt9NhkPCi4/3gss9fqfpLz+0v9P0S06YWafpQtHwpQcz3LHd4bWnRnjXWbN78T5kXAGuock7LpPZPH2tMU7oaZ5TIUDMDhOzvazH7fOynvFUnsHJLKOpHMZ4t0lEwo1XWbmALbxgA16JcsArpidzhgd2Fbh7h9eav+BCR0x40QqbS/ttzuwNz69xYp07XBYzsvptTC5/0azb0FRbSITLlnlvHB7a7XDTpjxff3h/p+k/OilyxGw1UzB85p406weLvOusKK87LTqvsQS1hsYYw0S2gACnL22ju3l+AXFaKCS0xmxaYzbLu5rIFry5nuFklpGpPK6f9TRFLCJW7b7xUMe2IINNUPZmXe7118A8MuRtidyTEF5+UoRLl1mc1lWb16cr5dAsJszU4guZOP6qms9iZkJEOH+JzXmLLR4ZKvLDTTn+eYPXafrVp0R45crIAXNwEzmXT9yZ5qm9Lh86L8ZVJ8wjOOxbQ9NLruOUsq6hyTsuE9k8i9vinNDTFOilrumsp6clStE1TOVKs56CdxvLq3DTrKe+aLCZp6GUy707vQxm43ARAyxpDvGaUyNc2m9zcmftN7cMmpfF3Ebrjt8Szk96WcyqtzG5/IWBTFxXm4hwdp/F2X0Wj484+/ac+fcnclyzMsKrT4mQc+Cjd6TZk3L59CVxLlo6j/YzAa2hMcYwkSkgIThzaRvdLZVt7RIOCW1xm7a4zXHdXtYzmSkwMpXbl/WERWiKWtizn0tRFabBZg52JV3uHihwz44CT4x5JZEr2kK8eXWES5fZrGjTAONlMffRuvXWhsxiZmpVt8XnLrPYsrfITY97Wc5/PpknZgl51/DFKxKc2TuPP8N9a2hOotCyvGwFLjmnyESmwNKOOCd0N9fEJazprKe3NbYv69nrZz2TmQKIZj21TIPNDEx3UZ5eZPms3ybm5M4Q7zwzyiX9Fv2ttVlFU2mHZDGJRQ2dxczUSR1hPnVxgm0TRX60Oc9TY0X+5sIEJ86j/UwQa2iMMexN57HCIc5Z3kFnU232TivNelaUZD1DyRxjKc16apEGmyMwxvD0Xpd7Bgrcs8NhR9JF8LZG/rNzvDYxjdLkcr6OmMWsuIpMz8LJYmbiuLYwH7lg/hVi3hqaTFnX0GQLRZK5Av0dcY7vbq6rF+lDsp6sw1g6x+BkjsmsN9cTt711PQv9qkO1aLAp4Rpvk7G7dzjcW9Im5qzeMK862WsTM6+2IQ0m6CzGGMjmi0iI+ui1VSH796E5tyxVla6fzUStEGuWd9CeqM1sZqbCIaEtYdOWsDm+u5lMvshkJs9gMsd4uoBrDFYoRCISrquAWu8WfLApuoZHh71V/PcOOIxlDXYI1iyyeNNqi4uWWnW3bXCQgs5ijPHeYeeKRcISoiNhk3VcxtN57FCIRNRa0E15y72GxstmHJZ3xDmuu6khX3zjkTDxSJy+tjhO0WUq5zCWyjOkWU9FLchgU3Dh0V0F7hlwuL+kTcx5iy0u6bc5f6lFk62/dKUCzWIMZApF8kUXEeiI2yxvStAcs/atRZrKeyvOR6fyCNAUsQgvsIWw4exe3EhrWdbQuMYwns4TtcOcu7yDtsTC2LnSCodoT0RoT0Q4oaeZdN4hmSkwmMyxN53HGDTrCciCCjZDk1k+89/PcMdTMdJOhoQNFyzxAszzFlvz6kPViKRY8NfFBJDF7MtgvGKLjoQXYJpiFvZh1iI1RyyauyyWtMUZS+XZM5GlmDfE7XBNVEoFanoNTaKHXOep815Dk8kXmcoVWN7VxIquxFxasDSMRMQiEbEOyHpGp/IMJfdXuMXtMPXb1Kt2LKhg0xq32TCQ5KK+Ilec2MI5fQu3TczR2MkBb3V/ubMYA1mnSM7xAkx73Ka/2ctgDhdgDidqhVjcFqOnJcpkpsCuiYz3Dt1v6thwnajKuIZmem4mboc5d0UnbfGFkc3M1IFZTxMZv8JtcDKLAb3ENk8LrhHn+OgQ2x+7i3h7X0Cjqk9SLNC0+z7anvvfkizmAiZWvHR+WYwfYLIF7xJZW9ymuzlKc9TCLkOgNwamcg57JrOMpwtYISERsQg1wpv1Mq6hSecd0nmHFd1NLOtY2NnMXBhj5hpsNEL5AstsROQG4GXAkDHmdP/Ya4HPAKcB5xlj1pXc/mPAO4Ei8H5jzG0BjSuIh61bgWQxJRmMAdpiFkvb47TE7LIEmFIi0BKzaIk1kykUGUnmGZrKYow3r2PVaeZarjU0RdcwnsnTFPGymdaYZjNzoa8b8xfkZbTvAd8Ebiw5thF4NfDPpTcUkVXAtcBqYAnwGxE52RhTDHB8C1ZQWUyu4JJ1ihgDbXEvwDTHLCIVehcdt8Ms64yzqC3GeCrP7skMU3lDzArVVel0udbQeNlMkRO6m+jvTCzovnyq+gILNsaYu0RkxUHHNsNh3yVcA/zIGJMDnhORLcB5wP1BjW8hspMDtG27jdbtt5dkMW/1Oi3P8UUt77hkCl6AaYlZHN+WoDlmE63ipL0dFnpao3Q1R0nmHH9ex7vE1lTjpdPlWEMznc00Ry3WruigRbMZVQNqpUBgKfBAydcD/rFDiMh1wHUAy5cvD35kdW5fFrP1VhIjj+3LYiZXXEW656w5ZTF5xyVb8JqONkUsju+qfoA5nFDIy7Da4i2k/NLp4ak8IWqzdNpbQxMh3XvOnNfQpHIOmUKRE3uaWNqh2YyqHbUSbGbMGHM9cD14BQJVHk7NKncWM53BYCARDbO8M0FrvPYCzJE0RSyauiwWl5ROOzlDIlIbpdP719Cs8naMnaWi61WatcYtTu/vpDlad3/aqsHVym/kTmBZydf9/jE1C+XOYgqOS3o6wETCHNeRoCVuE7Or/+I8V9Ol070tMSYyeXb7l9ii4RDxSBVKp8uwhmYq65B1ipzU28zS9jghzWZUDaqVYHMLcJOIfBWvQGAl8FB1h1Q/7Kmd/q6X889iCtNzMEDcDrG8I05rPFLXAeZwwiHobIrQkYgwlXMYSmYZS1W4dHqea2iKrmFvJkd7PMKZy9po0mxG1bAgS59vBq4AukVkAPg0MAZ8A+gBfikiG4wxLzHGbBKRnwCPAw7wXq1EO7pyZjGOY8g4RVzXEI+E6O+I0xq3vUWSDa60dHpJu1c6PTyVwzWGJtvCCqqrxDzX0CSzBQqu4eTeVha3xTSbUTVvwS3qnBgbZtujd9btos7DZTGTK14y6yzGKRqyhSJF1xC1Q/S0RGmL2d6lpAWu4BqvdHoiQ65YntJpKRaQYpZQ0Wv8aCREruPkWa+hcYou45kCnU0RVvY1k4hoNlPj9F2AT39T60C5sph9AcYYolaIRW0x2uMaYA5mh4SelihdTV7p9J7Zlk4bFynmCDlZxBgwBtdO4MR7cKMdFO0mr9psltnMZKaA47qcuqiFRW0xXWio6ooGmxrmZTG30bL9N1j5SQqJvlnPxRyQwfgBps2/RKavVUdXWjqdzhcZSmYZmcoDXmPQ6dLpw2UtxWgb+cQSitEWjJWYV5fmgp/NdDVFOLmvRd8cqLqkwabGHD6LOZ/JFS+dcRZTLBoyfoCJWEJfa4y2hEXCru0FjbUsEQmzoquJxa1RxieTDI2NkHNdYnYIK9q8L2tx7QSuFS/b7qQTmQKucVm1qIU+zWZUHdNgUyMOn8W8hcnlL55RFlN6icwKCb2tUdoTtgaY+SoWoJCBopfRRCVMX2c73UtOZKwYZfskTDgh4laYpmj5Nt/yspk8PS1RVva21FW7HaUOR4NNFc0nizEGciX7wUTCQndzlI5mmyYNMHNjXHCyUMh6nwNYcWjug0Qn2AnvIxQijFdS2d1jmMw6DOxNM5zMERahJWbPa+X+ZLaA6xpOX9JGT0tUsxnVEDTYVMFcs5i845J3XIrGEBLxuik3xUlErIZbB1MRB2Qt4k3Yx9qhtR+iLRBpBuvocy0iQlvcpi3eRiZfZM9khh1jGVxjaInas+pOUCh621/3tcY4sbdZsxnVUDTYVMj+LOY2EiOPlmQxV5HuOfuwWUyx5NLYdB+yxe0xmmMWCatB9myplCNmLb0Q74RI076sZa7ikTDHdzfT35FgJJlj22iayWyBRCR81BJlYwwTWa+44Iz+NrqbNZtRjUeDTcBmk8UYAzmnSN7fydIOC13NEVrjEX9PdH0BmrFiwQsuTo7DZy1NYM2+B9lM2OEQi9vj9LXGmMgU2DaaYmQqhx0O0RKzCJUEkrzjMpHNs8jPZqKWZjOqMWmwCcBsspiDL421xiyWtMVIRC1ilpYnz8gBWYu3rgU7Dk09Zcta5iIUEjqaInQ0eS1xdo1n2DWeAaA1ZpPKOUgIzlzaRndLrKJjU6rSNNiU0UyyGO/SmIPjXxprtvdfGotbFrpb7wwcNmtpg5alEGsNNGuZq+aoxcl9LRzXlWBoMsf2sTQ9rVFO6G6uia7TSgVNg808eVnM/X5FmZfFpBadz8TxXhZjCHmXxjIFjAErLHQ2R2jTS2MzY4yftWSOkLUkwG6qeNYyV1ErzLLOBP0dcZ2XUQuKBps5OnIW8yKyVjtZx8XNFgmJq5fG5qJYgNykF1ziHdCypGazlrnQQKMWGg02s3CkLGZs+UvY23EGBSPe3i/A4ja/aszWS2Ozkk9BIQ3hGHSfDE29YOt8hlL1ToPNDBycxeQTfew55U8YXHwFhUgnVtibCG6L2SSiYSIaXWbHdSCbBONAvAt6TvOymTq5NKaUOjYNNkdwaBYTYrz3PIb7X0Ky6yxaExH6E7ZeGpsPJwu5KZAwtC2DtiXeZTKlVMPRYHMQe2onrVtvo9XPYnLxXgZOehPpFS+mpWsRi2MWJ+qlsbkzLuSS3pxMpAkWnQGJbgjrr6JSjUz/wvGymMTu+2l97laaR70sZqL3PHIr/4jocefRG7P10th8lU74ty6B1qVeubKmhEotCAs62Mj4AC3bbqVz4LfYBa+iLHXW2widcjVtbb36OlgOpRP+XSu9ppY64a/UgrPwgk0xT8ueh+h69D5axx7DSAhn2UUUV70Ce9la7DLtQ7Kg6YS/UuogCyvYjD1H6w0vpC0zimleBM/7P8gpL8VOdFV7ZI1BJ/yVUkewsIJN+3HIqS+F9uORk55ftt0UFzSd8FdKzcDCekUIheDKz8HAeg0086UT/kqpWVhYwUbNn074K6XmQIONOja3CNlJnfBXSs2ZBht1ZAdP+LcuhmhztUellKpDGmzUgXTCXykVAH0FUR6d8FdKBUiDzUKnE/5KqQoIbIZXRG4QkSER2VhyrFNEfi0iT/v/dvjHRUS+LiJbRORREVkT1LgU3oR/ei+khsGKw5JzYcUl0HGcBhqlVCCCLCf6HnDVQcc+CtxujFkJ3O5/DfBSYKX/cR3wnQDHtXA5WUiNQHbCm/BffhH0nwtNXVpZppQKVGCX0Ywxd4nIioMOXwNc4X/+feAO4CP+8RuNMQZ4QETaRWSxMWZ3UONbMIzxJ/zzOuGvlKqaSr/i9JUEkD1An//5UmBHye0G/GOHBBsRuQ4v+2H58uXBjbTe6YS/UqqGVO3trTHGiIiZw/2uB64HWLt27azv3/DyKShkIBzVCX+lVM2odLAZnL48JiKLgSH/+E5gWcnt+v1jaiYOWOHfqSv8lVI1p9KvRrcAb/U/fyvwi5Ljb/Gr0i4AJnS+ZgYOO+G/Vif8lVI1J7DMRkRuxisG6BaRAeDTwBeBn4jIO4FtwOv8m/8PcDWwBUgDbw9qXHVPJ/yVUnUoyGq0NxzhWy88zG0N8N6gxtIQSif8WxZDW79O+Cul6oa+Ha51OuGvlGoAGmxqkU74K6UajAabWrKvpX8I2pZrS3+lVMPQYFNth53w74KwXe2RKaVU2WiwqRad8FdKLSAabCpNJ/yVUguQBptK0Al/pdQCp8EmSDrhr5RSgAab8tMJf6WUOoQGm3LRCX+llDoiDTbzpRP+Sil1TBps5kIn/JVSalY02MyGTvgrpdScaLA5loMn/PtOh6ZunfBXSqlZ0GBzJDrhr5RSZaPB5mA64a+UUmWnwQa8Cf9cEtyCTvgrpVQAFnaw0Ql/pZSqiIUZbIo5SI3ohL9SSlXIwgs24Sh0ngAti3TCXymlKmThBZtIAnpPrfYolFJqQdEZcKWUUoHTYKOUUipwGmyUUkoFToONUkqpwGmwUUopFTgNNkoppQJXlWAjIh8QkY0isklEPugf6xSRX4vI0/6/HdUYm1JKqfKreLARkdOBdwHnAWcBLxORk4CPArcbY1YCt/tfK6WUagDVyGxOAx40xqSNMQ5wJ/Bq4Brg+/5tvg+8sgpjU0opFYBqBJuNwKUi0iUiCeBqYBnQZ4zZ7d9mD9BXhbEppZQKQMXb1RhjNovI3wO/AlLABqB40G2MiJjD3V9ErgOuA1i+fHmwg1VKKVUWVSkQMMZ81xhzrjHmMmAv8BQwKCKLAfx/h45w3+uNMWuNMWt7enoqN2illFJzJsYcNoEI9klFeo0xQyKyHC/DuQD4ODBqjPmiiHwU6DTG/PUxHmcY2DaHIXQDI3O4X61rxPNqxHMCPa96M9fzGjHGXFXuwdSjagWbu4EuoAD8pTHmdhHpAn4CLMcLIK8zxowF9PzrjDFrg3jsamrE82rEcwI9r3rTqOdVSVXZYsAYc+lhjo0CL6zCcJRSSgVMOwgopZQK3EINNtdXewABacTzasRzAj2vetOo51UxVZmzUUoptbAs1MxGKaVUBWmwUUopFbiGCDYicoOIDInIxpJjZ4nI/SLymIj8l4i0lnzvTP97m/zvx/zj5/pfbxGRr4uIVON8SsY54/MSkTeJyIaSD1dEzva/V8/nZYvI9/3jm0XkYyX3uUpEnvTPq+qNW2d5XhER+Vf/+CMickXJfWrm5yUiy0TkdyLyuP/38gH/+GG7tIvn6/7YHxWRNSWP9Vb/9k+LyFurdU7+WGZ7Xqf6P8eciHz4oMeqqd/DmmWMqfsP4DJgDbCx5Njvgcv9z98BfNb/3AIeBc7yv+4Cwv7nD+EtMBXgf4GX1st5HXS/M4BnSr6u2/MC3gj8yP88AWwFVgBh4BngBCACPAKsqqPzei/wr/7nvcDDQKjWfl7AYmCN/3kLXrePVcCXgI/6xz8K/L3/+dX+mMU/hwf9453As/6/Hf7nHXV0Xr3A84DPAx8ueZya+z2s1Y+GyGyMMXcBBy8APRm4y//818Af+59fCTxqjHnEv++oMaYoXoucVmPMA8b7LbqRKneenuV5lXoD8CPY1/qnns/LAE0iYgFxIA9M4m1RscUY86wxJo93vtcEPfajmeV5rQJ+699vCBgH1tbaz8sYs9sYs97/PAlsBpZy5C7t1wA3Gs8DQLt/Ti8Bfm2MGTPG7MX7v6jayvrZnpcxZsgY83u8heilau73sFY1RLA5gk3s/6G/Fq+zNHh//EZEbhOR9SIy3RJnKTBQcv8B/1itOdJ5lXo9cLP/eb2f13/gNWzdDWwHvmy8zhJLgR0l96+383oEeIWIWCJyPHCu/72a/XmJyArgHOBBjtyl/Ug/l5r9ec3wvI6kZs+r1jRysHkH8B4ReRgvTc77xy3gEuBN/r+vEpF66lxwpPMCQETOB9LGmI2Hu3MNO9J5nYfXFXwJcDzwIRE5oTpDnJMjndcNeC9M64CvAfdxUPfzWiIizcBPgQ8aYyZLv+dnYHW5hqJRz6sWVaVdTSUYY57Au2SGiJwM/JH/rQHgLmPMiP+9/8G7zv4DoL/kIfqBnRUb8Awd5bymXcv+rAa8c6jn83ojcKsxpgAMici9wFq8d5OlWV1dnZfxNg78i+nbich9ePMGe6mxn5eI2HgvyD80xvynf3hQRBYbY3bLgV3ad3L4n8tO4IqDjt8R5LiPZZbndSRHOl91kIbNbESk1/83BHwC+L/+t24DzhCRhD8PcDnwuJ86T4rIBX71z1uAX1Rh6Ed1lPOaPvY6/Pka8K5NU9/ntR14gf+9JrxJ5yfwJt5XisjxIhLBC7K3VHrcx3Kk8/J//5r8z18MOMaYmvs99MfwXWCzMearJd+6BZiuKHsr+8d4C/AWvyrtAmDCP6fbgCtFpMOv8LrSP1YVczivI6mL38OaUO0KhXJ84L2T3403eTcAvBP4AN47xaeAL+J3S/Bv/2a8a+kbgS+VHF/rH3sG+GbpferkvK4AHjjM49TteQHNwL/7P6/Hgb8qeZyr/ds/A3y8nn4P8SrqnsSbmP4NcFwt/rzwLjUbvArODf7H1XhVnLcDT/vj7/RvL8C3/LE/Bqwteax3AFv8j7dX+Wc12/Na5P9MJ/GKOQbwCjlq7vewVj+0XY1SSqnANexlNKWUUrVDg41SSqnAabBRSikVOA02SimlAqfBRimlVOA02KiGJCKfObg7bxkf+20i8s0gHlupRqXBRimlVOA02KiGISIfF5GnROQe4BT/2IkicquIPCwid4vIqf7xPhH5mXh7yTwiIhf5x3/u33aTiFxX8thv9x/7IeDikuM9IvJTEfm9/3ExSqlD6KJO1RBE5Fzge8D5eD3/1uO1hnkp8GfGmKf9JqV/Z4x5gYj8GLjfGPM1EQkDzcaYCRHpNMaMiUgcfy8avH1KHsTrzDwB/A74gzHmfSJyE/BtY8w9IrIcuM0Yc1pFT16pOtCwjTjVgnMp8DNjTBpARG4BYsBFwL/L/s0uo/6/L8DrO4YxpogXRADeLyKv8j9fBqzEa1VyhzFm2H/sH+NtVQHwImBVyeO3ikizMWaq7GeoVB3TYKMaWQgYN8acPZMbi7c184uAC40xaRG5Ay9gHes5LjDGZOc+TKUan87ZqEZxF/BKEYmLSAvwciANPCcirwWv06+InOXf/nbg3f7xsIi0AW3AXj/QnIrXYRq8S2iXi0iX35b+tSXP+yvgz6e/EJGzAztDpeqYBhvVEIy3xe+P8XbA/F+8+RbwNsl7p4g8woG7Zn4AeL6IPAY8jLdN862AJSKb8To0P+A/9m7gM8D9wL14nZqnvR9vO+dHReRx4M+COkel6pkWCCillAqcZjZKKaUCp8FGKaVU4DTYKKWUCpwGG6WUUoHTYKOUUipwGmyUUkoFToONUkqpwP3/xwlL0yZacOAAAAAASUVORK5CYII=\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 411,
       "height": 352
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 421.25x360 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 411,
       "height": 352
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 421.25x360 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 412,
       "height": 352
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "### RB Data Analysis",
   "metadata": {
    "tags": [],
    "cell_id": "00010-6152c0f1-b1dd-49a0-9fce-66e9bca182a7",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00011-d437a5cf-2d2f-446d-90cb-817ac57e1dd5",
    "deepnote_to_be_reexecuted": true,
    "source_hash": "adea8911",
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Sure!   \n10                Adorn                                             Miguel   \n11         Get Her Back                                       Robin Thicke   \n12             Promises                                               NERO   \n13             Promises                          Calvin Harris & Sam Smith   \n14             Promises                                   Barbra Streisand   \n15             Promises                                       Trevor Rabin   \n16             Promises                                             Fugazi   \n17          Slow Motion                                         Trey Songz   \n18          Slow Motion                     Juvenile Featuring Soulja Slim   \n19          Slow Motion                                      Color Me Badd   \n20          Slow Motion                                       David Lindup   \n21            New Flame            Chris Brown Featuring Usher & Rick Ross   \n22       Baby Don't Lie                                       Gwen Stefani   \n23                 Hope                                   Bugge Wesseltoft   \n24                 Hope                                       XXXTENTACION   \n25     L.A.LOVE (la la)                                             Fergie   \n26         Losing Sleep                                        Chris Young   \n27          Hey Porsche                                              Nelly   \n28                Maybe                                       Sick Puppies   \n29                Maybe                                  The Three Degrees   \n30                Maybe                                    The Shangri-Las   \n31          Nobody Love                                         Tori Kelly   \n32                I Bet                                              Ciara   \n33       Ain't My Fault                                       Zara Larsson   \n34            Like This                                               Mims   \n35            Like This                        Kelly Rowland Featuring Eve   \n36                  Run                                      George Strait   \n37                  Run                                      George Strait   \n38                  Run                                    Collective Soul   \n39                  Run                                    Collective Soul   \n40        Baby Don't Go                  Fabolous Featuring Jermaine Dupri   \n41        Baby Don't Go                                       Karla Bonoff   \n42        Baby Don't Go                                       Sonny & Cher   \n43               Nobody                                       Wonder Girls   \n44               Nobody                  Keith Sweat Featuring Athena Cage   \n45               Nobody                                             Sylvia   \n46               Nobody                                The Doobie Brothers   \n47         Call My Name                                             Prince   \n48           First Time                                          Lifehouse   \n49           First Time                                     Matt Nathanson   \n50           All I Want                                      Susanna Hoffs   \n51           All I Want                              Toad The Wet Sprocket   \n52           All I Want                                       Howard Jones   \n53           All I Want                                           Tim Maia   \n54               Rumble                                  The Swiv-O-Matics   \n55  Dancing In The Dark                                  Bruce Springsteen   \n56  Behind Closed Doors                                       Charlie Rich   \n57      It's Your World                                      Marty Robbins   \n\n                                     uri  danceability  energy  key  loudness  \\\n0   spotify:track:7ioiB40H9xKs04QtIso2I3         0.830  0.5200    0    -8.714   \n1   spotify:track:735RSTb2xbtWbDtKD9XDYi         0.630  0.6710    6    -6.659   \n2   spotify:track:4m3VFxVwSPk8VGoA9qcLRC         0.544  0.1690    1   -17.224   \n3   spotify:track:3KTxIOIZIE7UM6vwCX4Ofv         0.545  0.7490    9    -5.485   \n4   spotify:track:2stPxcgjdSImK7Gizl8ZUN         0.308  0.7690   11    -7.256   \n5   spotify:track:282L6SR4Y8Rs0VUgtEy1Zw         0.766  0.7390    1   -10.364   \n6   spotify:track:7seTcUFOhn5caSDbiSfsp0         0.706  0.5720    8    -5.799   \n7   spotify:track:28OX5qT45GZBuu8CNKBOj6         0.668  0.6400    3    -5.208   \n8   spotify:track:6N1sNzq3CArC1pMSaZTxaT         0.529  0.6620    5   -11.745   \n9   spotify:track:6oPnKLqVBDI0SoAzxoMlKN         0.635  0.5220    2   -10.498   \n10  spotify:track:25cUhiAod71TIQSNicOaW3         0.625  0.5760   11    -5.693   \n11  spotify:track:231O3DltLcwtgIKoNbMkrt         0.579  0.2870    4   -13.642   \n12  spotify:track:4A7qcJp2Sg867JST1pSPJz         0.489  0.9520    9    -4.372   \n13  spotify:track:5N5k9nd479b1xpDZ4usjrg         0.781  0.7680   11    -5.991   \n14  spotify:track:2zj2oanLv5B1RwlqIQYrOD         0.679  0.3880   11   -15.870   \n15  spotify:track:2sZ0WTc3Sq2rzoxrcDlXCB         0.496  0.4200    7   -12.558   \n16  spotify:track:4VMMmrVvOlb2FEXJEWktNg         0.459  0.5250   11    -7.254   \n17  spotify:track:4NYwy0R3NdvORX2B6OZXBT         0.733  0.4080    7    -7.834   \n18  spotify:track:6ihObRBTB8xdSH2mlERtOX         0.713  0.7340    0    -4.894   \n19  spotify:track:03GzYByXsXHcoEAxjsWv9G         0.783  0.8850    6    -7.747   \n20  spotify:track:7goghYQcWoCUsj0r5iQ0FZ         0.483  0.4760    7   -11.901   \n21  spotify:track:3fBbfrWZUuWWrMQXFISr7N         0.702  0.6290    1    -4.292   \n22  spotify:track:5t54WbqXtMwT5yF1nQSuKz         0.688  0.5510   10    -5.496   \n23  spotify:track:1P7Dth7R72rUlLEEWPizFg         0.421  0.0412    9   -19.097   \n24  spotify:track:2ZRo7axmMPeSVUvDbGkJah         0.592  0.4570    9    -6.081   \n25  spotify:track:5h2nC1QYVpK0AJra70Ja3p         0.509  0.4820    7    -6.965   \n26  spotify:track:6waqLPcT6ruMhmd36OhmFO         0.584  0.6210    1    -5.703   \n27  spotify:track:6QoxM9xgqazNPGF026CjpS         0.729  0.7930    9    -4.716   \n28  spotify:track:0nbaoA1FPNt1tRnbXB6FFa         0.437  0.9130    7    -4.315   \n29  spotify:track:4PpUJ2gJYbSa6F7EQlSTiM         0.483  0.2090    8   -13.799   \n30  spotify:track:1asba4YipSYUk4WY6A2O1g         0.361  0.5740    7    -8.674   \n31  spotify:track:7fpU0bRJt0SDC8jIgVpbT6         0.753  0.7090    1    -5.132   \n32  spotify:track:20lNo44eUUCnyNNInzwkv9         0.743  0.6180    8    -7.227   \n33  spotify:track:0ADG9OgdVTL7fgREP75BrZ         0.576  0.7820    6    -4.825   \n34  spotify:track:2Z2JnZNB7lbUdKR5GynUlV         0.771  0.6930    7    -7.483   \n35  spotify:track:3sfydNh0QQAdnIAtDVG16H         0.676  0.7300    1    -5.963   \n36  spotify:track:1EXMjx4nCIvYTaY5CZMUSl         0.544  0.5700    2   -10.192   \n37  spotify:track:1EXMjx4nCIvYTaY5CZMUSl         0.544  0.5700    2   -10.192   \n38  spotify:track:5C9OFRqE2KHK7bOk2Z2z49         0.552  0.7270   11    -7.015   \n39  spotify:track:5C9OFRqE2KHK7bOk2Z2z49         0.552  0.7270   11    -7.015   \n40  spotify:track:2FSQYGTyk8GTIaiRyAWu74         0.834  0.6870    1    -6.473   \n41  spotify:track:6WearzOTuR0pEewHf1NR8E         0.743  0.7060    0    -8.495   \n42  spotify:track:1DM97JJjBJShdMuWI0B5L9         0.540  0.4920    0    -9.087   \n43  spotify:track:0TBNg82Zqi0k1VslEe2ooq         0.901  0.7400   11    -5.108   \n44  spotify:track:4SF1747p541umnykBp352Q         0.699  0.4920    9    -9.449   \n45  spotify:track:1IyktkUQ3ktjlc6eDXG967         0.764  0.4480    0   -12.371   \n46  spotify:track:1w6uOq9M9XOqr5ftBZHfcN         0.634  0.8920    2    -5.374   \n47  spotify:track:1NtMkOgf5U9YMaXyuEIRn2         0.622  0.5740    5    -5.068   \n48  spotify:track:6zg7srP3ORV0V8sUPk7zjh         0.442  0.9520    3    -2.234   \n49  spotify:track:2EZFzm4VZtbxJXBDhHRmwu         0.540  0.4560    6    -6.133   \n50  spotify:track:53m1rGnPJVtI0zUryVyL9N         0.617  0.5590    4    -5.738   \n51  spotify:track:2GHYJ0dhVds3PODKnTdiGE         0.600  0.8110    5    -7.693   \n52  spotify:track:70nB7hTC7FDiYoJp0TVwnp         0.611  0.8680    3    -7.982   \n53  spotify:track:1Fxoj2yydAJV8wDWlglgAo         0.527  0.6180    7    -6.450   \n54  spotify:track:3tLkw752BPYiYYqartTVju         0.206  0.7850    9    -4.236   \n55  spotify:track:7FwBtcecmlpc1sLySPXeGE         0.527  0.9420    1    -5.640   \n56  spotify:track:6d7Jz2np6jtNaZv1DiWfJs         0.492  0.3290    2   -11.788   \n57  spotify:track:4YOujaYI7YhWtjHQg7ekDx         0.624  0.3230    5   -11.342   \n\n    mode  speechiness  acousticness  ...  duration_ms  time_signature  \\\n0      1       0.0376      0.000792  ...       189907               4   \n1      0       0.2560      0.363000  ...       174333               4   \n2      1       0.0283      0.272000  ...       213560               4   \n3      0       0.0273      0.002450  ...       283387               4   \n4      0       0.0650      0.033100  ...       254880               4   \n5      1       0.0805      0.019200  ...       296520               4   \n6      0       0.0326      0.026200  ...       214726               4   \n7      0       0.0888      0.049000  ...       181627               4   \n8      1       0.0273      0.153000  ...       320867               4   \n9      1       0.0536      0.306000  ...       354960               4   \n10     0       0.1750      0.054300  ...       193147               4   \n11     0       0.0828      0.915000  ...       213173               4   \n12     0       0.0616      0.000045  ...       257360               4   \n13     1       0.0394      0.011900  ...       213309               4   \n14     1       0.0369      0.037200  ...       261040               4   \n15     1       0.0279      0.233000  ...       355133               4   \n16     0       0.0330      0.004900  ...       242120               4   \n17     0       0.0388      0.679000  ...       197854               4   \n18     0       0.3250      0.549000  ...       248200               4   \n19     0       0.0648      0.005310  ...       265667               4   \n20     0       0.0306      0.515000  ...       181867               4   \n21     1       0.0410      0.050600  ...       244227               4   \n22     0       0.0310      0.012500  ...       202533               4   \n23     0       0.0482      0.961000  ...       520960               5   \n24     0       0.0603      0.661000  ...       110886               4   \n25     1       0.1760      0.018600  ...       211512               4   \n26     1       0.0443      0.071500  ...       178253               4   \n27     1       0.0293      0.151000  ...       209147               4   \n28     1       0.0972      0.017100  ...       206120               4   \n29     1       0.0800      0.154000  ...       339053               3   \n30     1       0.0343      0.552000  ...       151373               3   \n31     0       0.0347      0.006340  ...       203453               4   \n32     1       0.0732      0.076000  ...       287933               4   \n33     0       0.0296      0.007780  ...       224031               4   \n34     1       0.2930      0.031700  ...       203627               4   \n35     1       0.2670      0.001930  ...       215853               4   \n36     1       0.0326      0.462000  ...       245427               4   \n37     1       0.0326      0.462000  ...       245427               4   \n38     1       0.0242      0.027500  ...       273453               4   \n39     1       0.0242      0.027500  ...       273453               4   \n40     1       0.1050      0.088500  ...       216653               4   \n41     1       0.0589      0.240000  ...       195493               4   \n42     1       0.0286      0.473000  ...       194400               4   \n43     1       0.0751      0.161000  ...       214773               4   \n44     0       0.0318      0.484000  ...       264773               4   \n45     1       0.0336      0.249000  ...       199200               4   \n46     1       0.0309      0.013400  ...       260027               4   \n47     0       0.1550      0.685000  ...       315773               4   \n48     1       0.0585      0.000024  ...       202707               4   \n49     1       0.0274      0.201000  ...       239947               4   \n50     1       0.0269      0.035900  ...       198373               4   \n51     1       0.0788      0.055800  ...       196116               4   \n52     1       0.0294      0.039700  ...       275733               4   \n53     0       0.1750      0.359000  ...       204733               4   \n54     1       0.0490      0.000612  ...       110507               4   \n55     0       0.0366      0.011500  ...       241307               4   \n56     1       0.0274      0.961000  ...       174667               4   \n57     1       0.0263      0.750000  ...       164267               4   \n\n    chorus_hit  sections  target  decade  duration_sec  loudness_rounded  \\\n0     19.54918         8       1    2010         190.0              -9.0   \n1     49.36604         9       1    2010         174.0              -7.0   \n2     42.21306        10       1    2000         214.0             -17.0   \n3     33.37022        10       1    1980         283.0              -5.0   \n4     29.07343        11       1    2010         255.0              -7.0   \n5     33.08374        10       1    2010         297.0             -10.0   \n6     34.82205         9       1    2010         215.0              -6.0   \n7     31.07414         9       1    2010         182.0              -5.0   \n8     37.27743         9       1    1990         321.0             -12.0   \n9     23.05400        15       1    1990         355.0             -10.0   \n10    28.83480         8       1    2010         193.0              -6.0   \n11    17.10822        12       1    2010         213.0             -14.0   \n12    52.93212         8       1    2010         257.0              -4.0   \n13    34.16174         9       1    2010         213.0              -6.0   \n14    18.55017        14       1    1980         261.0             -16.0   \n15    28.05539        13       0    1980         355.0             -13.0   \n16    26.77761         8       0    1980         242.0              -7.0   \n17    30.37535        11       1    2010         198.0              -8.0   \n18    34.10403        14       1    2000         248.0              -5.0   \n19    49.84596        12       1    1990         266.0              -8.0   \n20    43.13040         8       0    1970         182.0             -12.0   \n21    40.68701        11       1    2010         244.0              -4.0   \n22    20.10120         6       1    2010         203.0              -5.0   \n23    32.58080        20       0    2010         521.0             -19.0   \n24    30.34884         5       1    2010         111.0              -6.0   \n25    40.50528        11       1    2010         212.0              -7.0   \n26    24.94152        10       1    2010         178.0              -6.0   \n27    21.47027        11       1    2010         209.0              -5.0   \n28    35.41809        11       1    2010         206.0              -4.0   \n29    30.20125        14       1    1970         339.0             -14.0   \n30    41.21433         6       1    1960         151.0              -9.0   \n31    49.10386         8       1    2010         203.0              -5.0   \n32    71.20857        13       1    2010         288.0              -7.0   \n33    42.98133        11       1    2010         224.0              -5.0   \n34    71.22774         7       1    2000         204.0              -7.0   \n35    25.28362        11       1    2000         216.0              -6.0   \n36    18.93509        12       1    2000         245.0             -10.0   \n37    18.93509        12       1    2000         245.0             -10.0   \n38    71.02886        10       1    1990         273.0              -7.0   \n39    71.02886        10       1    1990         273.0              -7.0   \n40    27.79978         8       1    2000         217.0              -6.0   \n41    46.92040         8       1    1980         195.0              -8.0   \n42    49.56247         9       1    1960         194.0              -9.0   \n43    30.78755        13       1    2000         215.0              -5.0   \n44    32.47790        12       1    1990         265.0              -9.0   \n45    24.42111         9       1    1980         199.0             -12.0   \n46    27.17715        12       1    1970         260.0              -5.0   \n47    59.07282         8       1    2000         316.0              -5.0   \n48    37.09826         7       1    2000         203.0              -2.0   \n49    38.41405        12       0    1990         240.0              -6.0   \n50    28.05580        11       1    1990         198.0              -6.0   \n51    23.32175        11       1    1990         196.0              -8.0   \n52    35.62317        12       1    1980         276.0              -8.0   \n53    32.06329        11       0    1970         205.0              -6.0   \n54    23.47467         5       0    1990         111.0              -4.0   \n55    47.47864        10       1    1980         241.0              -6.0   \n56    25.75736         9       1    1970         175.0             -12.0   \n57    36.15282         8       1    1960         164.0             -11.0   \n\n    christmas    rb  \n0       False  True  \n1       False  True  \n2       False  True  \n3       False  True  \n4       False  True  \n5       False  True  \n6       False  True  \n7       False  True  \n8       False  True  \n9       False  True  \n10      False  True  \n11      False  True  \n12      False  True  \n13      False  True  \n14      False  True  \n15      False  True  \n16      False  True  \n17      False  True  \n18      False  True  \n19      False  True  \n20      False  True  \n21      False  True  \n22      False  True  \n23      False  True  \n24      False  True  \n25      False  True  \n26      False  True  \n27      False  True  \n28      False  True  \n29      False  True  \n30      False  True  \n31      False  True  \n32      False  True  \n33      False  True  \n34      False  True  \n35      False  True  \n36      False  True  \n37      False  True  \n38      False  True  \n39      False  True  \n40      False  True  \n41      False  True  \n42      False  True  \n43      False  True  \n44      False  True  \n45      False  True  \n46      False  True  \n47      False  True  \n48      False  True  \n49      False  True  \n50      False  True  \n51      False  True  \n52      False  True  \n53      False  True  \n54      False  True  \n55      False  True  \n56      False  True  \n57      False  True  \n\n[58 rows x 24 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>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>...</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      <th>duration_sec</th>\n      <th>loudness_rounded</th>\n      <th>christmas</th>\n      <th>rb</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Somebody</td>\n      <td>Natalie La Rose Featuring Jeremih</td>\n      <td>spotify:track:7ioiB40H9xKs04QtIso2I3</td>\n      <td>0.830</td>\n      <td>0.5200</td>\n      <td>0</td>\n      <td>-8.714</td>\n      <td>1</td>\n      <td>0.0376</td>\n      <td>0.000792</td>\n      <td>...</td>\n      <td>189907</td>\n      <td>4</td>\n      <td>19.54918</td>\n      <td>8</td>\n      <td>1</td>\n      <td>2010</td>\n      <td>190.0</td>\n      <td>-9.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Somebody</td>\n      <td>Internet Money, Lil Tecca &amp; A Boogie Wit da Ho...</td>\n      <td>spotify:track:735RSTb2xbtWbDtKD9XDYi</td>\n      <td>0.630</td>\n      <td>0.6710</td>\n      <td>6</td>\n      <td>-6.659</td>\n      <td>0</td>\n      <td>0.2560</td>\n      <td>0.363000</td>\n      <td>...</td>\n      <td>174333</td>\n      <td>4</td>\n      <td>49.36604</td>\n      <td>9</td>\n      <td>1</td>\n      <td>2010</td>\n      <td>174.0</td>\n      <td>-7.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Somebody</td>\n      <td>Reba McEntire</td>\n      <td>spotify:track:4m3VFxVwSPk8VGoA9qcLRC</td>\n      <td>0.544</td>\n      <td>0.1690</td>\n      <td>1</td>\n      <td>-17.224</td>\n      <td>1</td>\n      <td>0.0283</td>\n      <td>0.272000</td>\n      <td>...</td>\n      <td>213560</td>\n      <td>4</td>\n      <td>42.21306</td>\n      <td>10</td>\n      <td>1</td>\n      <td>2000</td>\n      <td>214.0</td>\n      <td>-17.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Somebody</td>\n      <td>Bryan Adams</td>\n      <td>spotify:track:3KTxIOIZIE7UM6vwCX4Ofv</td>\n      <td>0.545</td>\n      <td>0.7490</td>\n      <td>9</td>\n      <td>-5.485</td>\n      <td>0</td>\n      <td>0.0273</td>\n      <td>0.002450</td>\n      <td>...</td>\n      <td>283387</td>\n      <td>4</td>\n      <td>33.37022</td>\n      <td>10</td>\n      <td>1</td>\n      <td>1980</td>\n      <td>283.0</td>\n      <td>-5.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>The Man</td>\n      <td>Aloe Blacc</td>\n      <td>spotify:track:2stPxcgjdSImK7Gizl8ZUN</td>\n      <td>0.308</td>\n      <td>0.7690</td>\n      <td>11</td>\n      <td>-7.256</td>\n      <td>0</td>\n      <td>0.0650</td>\n      <td>0.033100</td>\n      <td>...</td>\n      <td>254880</td>\n      <td>4</td>\n      <td>29.07343</td>\n      <td>11</td>\n      <td>1</td>\n      <td>2010</td>\n      <td>255.0</td>\n      <td>-7.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>She Knows</td>\n      <td>J. Cole Featuring Amber Coffman &amp; The Cults</td>\n      <td>spotify:track:282L6SR4Y8Rs0VUgtEy1Zw</td>\n      <td>0.766</td>\n      <td>0.7390</td>\n      <td>1</td>\n      <td>-10.364</td>\n      <td>1</td>\n      <td>0.0805</td>\n      <td>0.019200</td>\n      <td>...</td>\n      <td>296520</td>\n      <td>4</td>\n      <td>33.08374</td>\n      <td>10</td>\n      <td>1</td>\n      <td>2010</td>\n      <td>297.0</td>\n      <td>-10.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>She Knows</td>\n      <td>Ne-Yo Featuring Juicy J</td>\n      <td>spotify:track:7seTcUFOhn5caSDbiSfsp0</td>\n      <td>0.706</td>\n      <td>0.5720</td>\n      <td>8</td>\n      <td>-5.799</td>\n      <td>0</td>\n      <td>0.0326</td>\n      <td>0.026200</td>\n      <td>...</td>\n      <td>214726</td>\n      <td>4</td>\n      <td>34.82205</td>\n      <td>9</td>\n      <td>1</td>\n      <td>2010</td>\n      <td>215.0</td>\n      <td>-6.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Right Now</td>\n      <td>Rihanna Featuring David Guetta</td>\n      <td>spotify:track:28OX5qT45GZBuu8CNKBOj6</td>\n      <td>0.668</td>\n      <td>0.6400</td>\n      <td>3</td>\n      <td>-5.208</td>\n      <td>0</td>\n      <td>0.0888</td>\n      <td>0.049000</td>\n      <td>...</td>\n      <td>181627</td>\n      <td>4</td>\n      <td>31.07414</td>\n      <td>9</td>\n      <td>1</td>\n      <td>2010</td>\n      <td>182.0</td>\n      <td>-5.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Right Now</td>\n      <td>Van Halen</td>\n      <td>spotify:track:6N1sNzq3CArC1pMSaZTxaT</td>\n      <td>0.529</td>\n      <td>0.6620</td>\n      <td>5</td>\n      <td>-11.745</td>\n      <td>1</td>\n      <td>0.0273</td>\n      <td>0.153000</td>\n      <td>...</td>\n      <td>320867</td>\n      <td>4</td>\n      <td>37.27743</td>\n      <td>9</td>\n      <td>1</td>\n      <td>1990</td>\n      <td>321.0</td>\n      <td>-12.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Right Now</td>\n      <td>Al B. Sure!</td>\n      <td>spotify:track:6oPnKLqVBDI0SoAzxoMlKN</td>\n      <td>0.635</td>\n      <td>0.5220</td>\n      <td>2</td>\n      <td>-10.498</td>\n      <td>1</td>\n      <td>0.0536</td>\n      <td>0.306000</td>\n      <td>...</td>\n      <td>354960</td>\n      <td>4</td>\n      <td>23.05400</td>\n      <td>15</td>\n      <td>1</td>\n      <td>1990</td>\n      <td>355.0</td>\n      <td>-10.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Adorn</td>\n      <td>Miguel</td>\n      <td>spotify:track:25cUhiAod71TIQSNicOaW3</td>\n      <td>0.625</td>\n      <td>0.5760</td>\n      <td>11</td>\n      <td>-5.693</td>\n      <td>0</td>\n      <td>0.1750</td>\n      <td>0.054300</td>\n      <td>...</td>\n      <td>193147</td>\n      <td>4</td>\n      <td>28.83480</td>\n      <td>8</td>\n      <td>1</td>\n      <td>2010</td>\n      <td>193.0</td>\n      <td>-6.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Get Her Back</td>\n      <td>Robin Thicke</td>\n      <td>spotify:track:231O3DltLcwtgIKoNbMkrt</td>\n      <td>0.579</td>\n      <td>0.2870</td>\n      <td>4</td>\n      <td>-13.642</td>\n      <td>0</td>\n      <td>0.0828</td>\n      <td>0.915000</td>\n      <td>...</td>\n      <td>213173</td>\n      <td>4</td>\n      <td>17.10822</td>\n      <td>12</td>\n      <td>1</td>\n      <td>2010</td>\n      <td>213.0</td>\n      <td>-14.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Promises</td>\n      <td>NERO</td>\n      <td>spotify:track:4A7qcJp2Sg867JST1pSPJz</td>\n      <td>0.489</td>\n      <td>0.9520</td>\n      <td>9</td>\n      <td>-4.372</td>\n      <td>0</td>\n      <td>0.0616</td>\n      <td>0.000045</td>\n      <td>...</td>\n      <td>257360</td>\n      <td>4</td>\n      <td>52.93212</td>\n      <td>8</td>\n      <td>1</td>\n      <td>2010</td>\n      <td>257.0</td>\n      <td>-4.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Promises</td>\n      <td>Calvin Harris &amp; Sam Smith</td>\n      <td>spotify:track:5N5k9nd479b1xpDZ4usjrg</td>\n      <td>0.781</td>\n      <td>0.7680</td>\n      <td>11</td>\n      <td>-5.991</td>\n      <td>1</td>\n      <td>0.0394</td>\n      <td>0.011900</td>\n      <td>...</td>\n      <td>213309</td>\n      <td>4</td>\n      <td>34.16174</td>\n      <td>9</td>\n      <td>1</td>\n      <td>2010</td>\n      <td>213.0</td>\n      <td>-6.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Promises</td>\n      <td>Barbra Streisand</td>\n      <td>spotify:track:2zj2oanLv5B1RwlqIQYrOD</td>\n      <td>0.679</td>\n      <td>0.3880</td>\n      <td>11</td>\n      <td>-15.870</td>\n      <td>1</td>\n      <td>0.0369</td>\n      <td>0.037200</td>\n      <td>...</td>\n      <td>261040</td>\n      <td>4</td>\n      <td>18.55017</td>\n      <td>14</td>\n      <td>1</td>\n      <td>1980</td>\n      <td>261.0</td>\n      <td>-16.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Promises</td>\n      <td>Trevor Rabin</td>\n      <td>spotify:track:2sZ0WTc3Sq2rzoxrcDlXCB</td>\n      <td>0.496</td>\n      <td>0.4200</td>\n      <td>7</td>\n      <td>-12.558</td>\n      <td>1</td>\n      <td>0.0279</td>\n      <td>0.233000</td>\n      <td>...</td>\n      <td>355133</td>\n      <td>4</td>\n      <td>28.05539</td>\n      <td>13</td>\n      <td>0</td>\n      <td>1980</td>\n      <td>355.0</td>\n      <td>-13.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Promises</td>\n      <td>Fugazi</td>\n      <td>spotify:track:4VMMmrVvOlb2FEXJEWktNg</td>\n      <td>0.459</td>\n      <td>0.5250</td>\n      <td>11</td>\n      <td>-7.254</td>\n      <td>0</td>\n      <td>0.0330</td>\n      <td>0.004900</td>\n      <td>...</td>\n      <td>242120</td>\n      <td>4</td>\n      <td>26.77761</td>\n      <td>8</td>\n      <td>0</td>\n      <td>1980</td>\n      <td>242.0</td>\n      <td>-7.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Slow Motion</td>\n      <td>Trey Songz</td>\n      <td>spotify:track:4NYwy0R3NdvORX2B6OZXBT</td>\n      <td>0.733</td>\n      <td>0.4080</td>\n      <td>7</td>\n      <td>-7.834</td>\n      <td>0</td>\n      <td>0.0388</td>\n      <td>0.679000</td>\n      <td>...</td>\n      <td>197854</td>\n      <td>4</td>\n      <td>30.37535</td>\n      <td>11</td>\n      <td>1</td>\n      <td>2010</td>\n      <td>198.0</td>\n      <td>-8.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Slow Motion</td>\n      <td>Juvenile Featuring Soulja Slim</td>\n      <td>spotify:track:6ihObRBTB8xdSH2mlERtOX</td>\n      <td>0.713</td>\n      <td>0.7340</td>\n      <td>0</td>\n      <td>-4.894</td>\n      <td>0</td>\n      <td>0.3250</td>\n      <td>0.549000</td>\n      <td>...</td>\n      <td>248200</td>\n      <td>4</td>\n      <td>34.10403</td>\n      <td>14</td>\n      <td>1</td>\n      <td>2000</td>\n      <td>248.0</td>\n      <td>-5.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Slow Motion</td>\n      <td>Color Me Badd</td>\n      <td>spotify:track:03GzYByXsXHcoEAxjsWv9G</td>\n      <td>0.783</td>\n      <td>0.8850</td>\n      <td>6</td>\n      <td>-7.747</td>\n      <td>0</td>\n      <td>0.0648</td>\n      <td>0.005310</td>\n      <td>...</td>\n      <td>265667</td>\n      <td>4</td>\n      <td>49.84596</td>\n      <td>12</td>\n      <td>1</td>\n      <td>1990</td>\n      <td>266.0</td>\n      <td>-8.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Slow Motion</td>\n      <td>David Lindup</td>\n      <td>spotify:track:7goghYQcWoCUsj0r5iQ0FZ</td>\n      <td>0.483</td>\n      <td>0.4760</td>\n      <td>7</td>\n      <td>-11.901</td>\n      <td>0</td>\n      <td>0.0306</td>\n      <td>0.515000</td>\n      <td>...</td>\n      <td>181867</td>\n      <td>4</td>\n      <td>43.13040</td>\n      <td>8</td>\n      <td>0</td>\n      <td>1970</td>\n      <td>182.0</td>\n      <td>-12.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>New Flame</td>\n      <td>Chris Brown Featuring Usher &amp; Rick Ross</td>\n      <td>spotify:track:3fBbfrWZUuWWrMQXFISr7N</td>\n      <td>0.702</td>\n      <td>0.6290</td>\n      <td>1</td>\n      <td>-4.292</td>\n      <td>1</td>\n      <td>0.0410</td>\n      <td>0.050600</td>\n      <td>...</td>\n      <td>244227</td>\n      <td>4</td>\n      <td>40.68701</td>\n      <td>11</td>\n      <td>1</td>\n      <td>2010</td>\n      <td>244.0</td>\n      <td>-4.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Baby Don't Lie</td>\n      <td>Gwen Stefani</td>\n      <td>spotify:track:5t54WbqXtMwT5yF1nQSuKz</td>\n      <td>0.688</td>\n      <td>0.5510</td>\n      <td>10</td>\n      <td>-5.496</td>\n      <td>0</td>\n      <td>0.0310</td>\n      <td>0.012500</td>\n      <td>...</td>\n      <td>202533</td>\n      <td>4</td>\n      <td>20.10120</td>\n      <td>6</td>\n      <td>1</td>\n      <td>2010</td>\n      <td>203.0</td>\n      <td>-5.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Hope</td>\n      <td>Bugge Wesseltoft</td>\n      <td>spotify:track:1P7Dth7R72rUlLEEWPizFg</td>\n      <td>0.421</td>\n      <td>0.0412</td>\n      <td>9</td>\n      <td>-19.097</td>\n      <td>0</td>\n      <td>0.0482</td>\n      <td>0.961000</td>\n      <td>...</td>\n      <td>520960</td>\n      <td>5</td>\n      <td>32.58080</td>\n      <td>20</td>\n      <td>0</td>\n      <td>2010</td>\n      <td>521.0</td>\n      <td>-19.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Hope</td>\n      <td>XXXTENTACION</td>\n      <td>spotify:track:2ZRo7axmMPeSVUvDbGkJah</td>\n      <td>0.592</td>\n      <td>0.4570</td>\n      <td>9</td>\n      <td>-6.081</td>\n      <td>0</td>\n      <td>0.0603</td>\n      <td>0.661000</td>\n      <td>...</td>\n      <td>110886</td>\n      <td>4</td>\n      <td>30.34884</td>\n      <td>5</td>\n      <td>1</td>\n      <td>2010</td>\n      <td>111.0</td>\n      <td>-6.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>L.A.LOVE (la la)</td>\n      <td>Fergie</td>\n      <td>spotify:track:5h2nC1QYVpK0AJra70Ja3p</td>\n      <td>0.509</td>\n      <td>0.4820</td>\n      <td>7</td>\n      <td>-6.965</td>\n      <td>1</td>\n      <td>0.1760</td>\n      <td>0.018600</td>\n      <td>...</td>\n      <td>211512</td>\n      <td>4</td>\n      <td>40.50528</td>\n      <td>11</td>\n      <td>1</td>\n      <td>2010</td>\n      <td>212.0</td>\n      <td>-7.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Losing Sleep</td>\n      <td>Chris Young</td>\n      <td>spotify:track:6waqLPcT6ruMhmd36OhmFO</td>\n      <td>0.584</td>\n      <td>0.6210</td>\n      <td>1</td>\n      <td>-5.703</td>\n      <td>1</td>\n      <td>0.0443</td>\n      <td>0.071500</td>\n      <td>...</td>\n      <td>178253</td>\n      <td>4</td>\n      <td>24.94152</td>\n      <td>10</td>\n      <td>1</td>\n      <td>2010</td>\n      <td>178.0</td>\n      <td>-6.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Hey Porsche</td>\n      <td>Nelly</td>\n      <td>spotify:track:6QoxM9xgqazNPGF026CjpS</td>\n      <td>0.729</td>\n      <td>0.7930</td>\n      <td>9</td>\n      <td>-4.716</td>\n      <td>1</td>\n      <td>0.0293</td>\n      <td>0.151000</td>\n      <td>...</td>\n      <td>209147</td>\n      <td>4</td>\n      <td>21.47027</td>\n      <td>11</td>\n      <td>1</td>\n      <td>2010</td>\n      <td>209.0</td>\n      <td>-5.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>Maybe</td>\n      <td>Sick Puppies</td>\n      <td>spotify:track:0nbaoA1FPNt1tRnbXB6FFa</td>\n      <td>0.437</td>\n      <td>0.9130</td>\n      <td>7</td>\n      <td>-4.315</td>\n      <td>1</td>\n      <td>0.0972</td>\n      <td>0.017100</td>\n      <td>...</td>\n      <td>206120</td>\n      <td>4</td>\n      <td>35.41809</td>\n      <td>11</td>\n      <td>1</td>\n      <td>2010</td>\n      <td>206.0</td>\n      <td>-4.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>Maybe</td>\n      <td>The Three Degrees</td>\n      <td>spotify:track:4PpUJ2gJYbSa6F7EQlSTiM</td>\n      <td>0.483</td>\n      <td>0.2090</td>\n      <td>8</td>\n      <td>-13.799</td>\n      <td>1</td>\n      <td>0.0800</td>\n      <td>0.154000</td>\n      <td>...</td>\n      <td>339053</td>\n      <td>3</td>\n      <td>30.20125</td>\n      <td>14</td>\n      <td>1</td>\n      <td>1970</td>\n      <td>339.0</td>\n      <td>-14.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>Maybe</td>\n      <td>The Shangri-Las</td>\n      <td>spotify:track:1asba4YipSYUk4WY6A2O1g</td>\n      <td>0.361</td>\n      <td>0.5740</td>\n      <td>7</td>\n      <td>-8.674</td>\n      <td>1</td>\n      <td>0.0343</td>\n      <td>0.552000</td>\n      <td>...</td>\n      <td>151373</td>\n      <td>3</td>\n      <td>41.21433</td>\n      <td>6</td>\n      <td>1</td>\n      <td>1960</td>\n      <td>151.0</td>\n      <td>-9.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>Nobody Love</td>\n      <td>Tori Kelly</td>\n      <td>spotify:track:7fpU0bRJt0SDC8jIgVpbT6</td>\n      <td>0.753</td>\n      <td>0.7090</td>\n      <td>1</td>\n      <td>-5.132</td>\n      <td>0</td>\n      <td>0.0347</td>\n      <td>0.006340</td>\n      <td>...</td>\n      <td>203453</td>\n      <td>4</td>\n      <td>49.10386</td>\n      <td>8</td>\n      <td>1</td>\n      <td>2010</td>\n      <td>203.0</td>\n      <td>-5.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>I Bet</td>\n      <td>Ciara</td>\n      <td>spotify:track:20lNo44eUUCnyNNInzwkv9</td>\n      <td>0.743</td>\n      <td>0.6180</td>\n      <td>8</td>\n      <td>-7.227</td>\n      <td>1</td>\n      <td>0.0732</td>\n      <td>0.076000</td>\n      <td>...</td>\n      <td>287933</td>\n      <td>4</td>\n      <td>71.20857</td>\n      <td>13</td>\n      <td>1</td>\n      <td>2010</td>\n      <td>288.0</td>\n      <td>-7.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>Ain't My Fault</td>\n      <td>Zara Larsson</td>\n      <td>spotify:track:0ADG9OgdVTL7fgREP75BrZ</td>\n      <td>0.576</td>\n      <td>0.7820</td>\n      <td>6</td>\n      <td>-4.825</td>\n      <td>0</td>\n      <td>0.0296</td>\n      <td>0.007780</td>\n      <td>...</td>\n      <td>224031</td>\n      <td>4</td>\n      <td>42.98133</td>\n      <td>11</td>\n      <td>1</td>\n      <td>2010</td>\n      <td>224.0</td>\n      <td>-5.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>Like This</td>\n      <td>Mims</td>\n      <td>spotify:track:2Z2JnZNB7lbUdKR5GynUlV</td>\n      <td>0.771</td>\n      <td>0.6930</td>\n      <td>7</td>\n      <td>-7.483</td>\n      <td>1</td>\n      <td>0.2930</td>\n      <td>0.031700</td>\n      <td>...</td>\n      <td>203627</td>\n      <td>4</td>\n      <td>71.22774</td>\n      <td>7</td>\n      <td>1</td>\n      <td>2000</td>\n      <td>204.0</td>\n      <td>-7.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>Like This</td>\n      <td>Kelly Rowland Featuring Eve</td>\n      <td>spotify:track:3sfydNh0QQAdnIAtDVG16H</td>\n      <td>0.676</td>\n      <td>0.7300</td>\n      <td>1</td>\n      <td>-5.963</td>\n      <td>1</td>\n      <td>0.2670</td>\n      <td>0.001930</td>\n      <td>...</td>\n      <td>215853</td>\n      <td>4</td>\n      <td>25.28362</td>\n      <td>11</td>\n      <td>1</td>\n      <td>2000</td>\n      <td>216.0</td>\n      <td>-6.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>Run</td>\n      <td>George Strait</td>\n      <td>spotify:track:1EXMjx4nCIvYTaY5CZMUSl</td>\n      <td>0.544</td>\n      <td>0.5700</td>\n      <td>2</td>\n      <td>-10.192</td>\n      <td>1</td>\n      <td>0.0326</td>\n      <td>0.462000</td>\n      <td>...</td>\n      <td>245427</td>\n      <td>4</td>\n      <td>18.93509</td>\n      <td>12</td>\n      <td>1</td>\n      <td>2000</td>\n      <td>245.0</td>\n      <td>-10.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>Run</td>\n      <td>George Strait</td>\n      <td>spotify:track:1EXMjx4nCIvYTaY5CZMUSl</td>\n      <td>0.544</td>\n      <td>0.5700</td>\n      <td>2</td>\n      <td>-10.192</td>\n      <td>1</td>\n      <td>0.0326</td>\n      <td>0.462000</td>\n      <td>...</td>\n      <td>245427</td>\n      <td>4</td>\n      <td>18.93509</td>\n      <td>12</td>\n      <td>1</td>\n      <td>2000</td>\n      <td>245.0</td>\n      <td>-10.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>Run</td>\n      <td>Collective Soul</td>\n      <td>spotify:track:5C9OFRqE2KHK7bOk2Z2z49</td>\n      <td>0.552</td>\n      <td>0.7270</td>\n      <td>11</td>\n      <td>-7.015</td>\n      <td>1</td>\n      <td>0.0242</td>\n      <td>0.027500</td>\n      <td>...</td>\n      <td>273453</td>\n      <td>4</td>\n      <td>71.02886</td>\n      <td>10</td>\n      <td>1</td>\n      <td>1990</td>\n      <td>273.0</td>\n      <td>-7.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>Run</td>\n      <td>Collective Soul</td>\n      <td>spotify:track:5C9OFRqE2KHK7bOk2Z2z49</td>\n      <td>0.552</td>\n      <td>0.7270</td>\n      <td>11</td>\n      <td>-7.015</td>\n      <td>1</td>\n      <td>0.0242</td>\n      <td>0.027500</td>\n      <td>...</td>\n      <td>273453</td>\n      <td>4</td>\n      <td>71.02886</td>\n      <td>10</td>\n      <td>1</td>\n      <td>1990</td>\n      <td>273.0</td>\n      <td>-7.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>Baby Don't Go</td>\n      <td>Fabolous Featuring Jermaine Dupri</td>\n      <td>spotify:track:2FSQYGTyk8GTIaiRyAWu74</td>\n      <td>0.834</td>\n      <td>0.6870</td>\n      <td>1</td>\n      <td>-6.473</td>\n      <td>1</td>\n      <td>0.1050</td>\n      <td>0.088500</td>\n      <td>...</td>\n      <td>216653</td>\n      <td>4</td>\n      <td>27.79978</td>\n      <td>8</td>\n      <td>1</td>\n      <td>2000</td>\n      <td>217.0</td>\n      <td>-6.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>Baby Don't Go</td>\n      <td>Karla Bonoff</td>\n      <td>spotify:track:6WearzOTuR0pEewHf1NR8E</td>\n      <td>0.743</td>\n      <td>0.7060</td>\n      <td>0</td>\n      <td>-8.495</td>\n      <td>1</td>\n      <td>0.0589</td>\n      <td>0.240000</td>\n      <td>...</td>\n      <td>195493</td>\n      <td>4</td>\n      <td>46.92040</td>\n      <td>8</td>\n      <td>1</td>\n      <td>1980</td>\n      <td>195.0</td>\n      <td>-8.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>Baby Don't Go</td>\n      <td>Sonny &amp; Cher</td>\n      <td>spotify:track:1DM97JJjBJShdMuWI0B5L9</td>\n      <td>0.540</td>\n      <td>0.4920</td>\n      <td>0</td>\n      <td>-9.087</td>\n      <td>1</td>\n      <td>0.0286</td>\n      <td>0.473000</td>\n      <td>...</td>\n      <td>194400</td>\n      <td>4</td>\n      <td>49.56247</td>\n      <td>9</td>\n      <td>1</td>\n      <td>1960</td>\n      <td>194.0</td>\n      <td>-9.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>Nobody</td>\n      <td>Wonder Girls</td>\n      <td>spotify:track:0TBNg82Zqi0k1VslEe2ooq</td>\n      <td>0.901</td>\n      <td>0.7400</td>\n      <td>11</td>\n      <td>-5.108</td>\n      <td>1</td>\n      <td>0.0751</td>\n      <td>0.161000</td>\n      <td>...</td>\n      <td>214773</td>\n      <td>4</td>\n      <td>30.78755</td>\n      <td>13</td>\n      <td>1</td>\n      <td>2000</td>\n      <td>215.0</td>\n      <td>-5.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>Nobody</td>\n      <td>Keith Sweat Featuring Athena Cage</td>\n      <td>spotify:track:4SF1747p541umnykBp352Q</td>\n      <td>0.699</td>\n      <td>0.4920</td>\n      <td>9</td>\n      <td>-9.449</td>\n      <td>0</td>\n      <td>0.0318</td>\n      <td>0.484000</td>\n      <td>...</td>\n      <td>264773</td>\n      <td>4</td>\n      <td>32.47790</td>\n      <td>12</td>\n      <td>1</td>\n      <td>1990</td>\n      <td>265.0</td>\n      <td>-9.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>Nobody</td>\n      <td>Sylvia</td>\n      <td>spotify:track:1IyktkUQ3ktjlc6eDXG967</td>\n      <td>0.764</td>\n      <td>0.4480</td>\n      <td>0</td>\n      <td>-12.371</td>\n      <td>1</td>\n      <td>0.0336</td>\n      <td>0.249000</td>\n      <td>...</td>\n      <td>199200</td>\n      <td>4</td>\n      <td>24.42111</td>\n      <td>9</td>\n      <td>1</td>\n      <td>1980</td>\n      <td>199.0</td>\n      <td>-12.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>Nobody</td>\n      <td>The Doobie Brothers</td>\n      <td>spotify:track:1w6uOq9M9XOqr5ftBZHfcN</td>\n      <td>0.634</td>\n      <td>0.8920</td>\n      <td>2</td>\n      <td>-5.374</td>\n      <td>1</td>\n      <td>0.0309</td>\n      <td>0.013400</td>\n      <td>...</td>\n      <td>260027</td>\n      <td>4</td>\n      <td>27.17715</td>\n      <td>12</td>\n      <td>1</td>\n      <td>1970</td>\n      <td>260.0</td>\n      <td>-5.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>Call My Name</td>\n      <td>Prince</td>\n      <td>spotify:track:1NtMkOgf5U9YMaXyuEIRn2</td>\n      <td>0.622</td>\n      <td>0.5740</td>\n      <td>5</td>\n      <td>-5.068</td>\n      <td>0</td>\n      <td>0.1550</td>\n      <td>0.685000</td>\n      <td>...</td>\n      <td>315773</td>\n      <td>4</td>\n      <td>59.07282</td>\n      <td>8</td>\n      <td>1</td>\n      <td>2000</td>\n      <td>316.0</td>\n      <td>-5.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>First Time</td>\n      <td>Lifehouse</td>\n      <td>spotify:track:6zg7srP3ORV0V8sUPk7zjh</td>\n      <td>0.442</td>\n      <td>0.9520</td>\n      <td>3</td>\n      <td>-2.234</td>\n      <td>1</td>\n      <td>0.0585</td>\n      <td>0.000024</td>\n      <td>...</td>\n      <td>202707</td>\n      <td>4</td>\n      <td>37.09826</td>\n      <td>7</td>\n      <td>1</td>\n      <td>2000</td>\n      <td>203.0</td>\n      <td>-2.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>First Time</td>\n      <td>Matt Nathanson</td>\n      <td>spotify:track:2EZFzm4VZtbxJXBDhHRmwu</td>\n      <td>0.540</td>\n      <td>0.4560</td>\n      <td>6</td>\n      <td>-6.133</td>\n      <td>1</td>\n      <td>0.0274</td>\n      <td>0.201000</td>\n      <td>...</td>\n      <td>239947</td>\n      <td>4</td>\n      <td>38.41405</td>\n      <td>12</td>\n      <td>0</td>\n      <td>1990</td>\n      <td>240.0</td>\n      <td>-6.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>All I Want</td>\n      <td>Susanna Hoffs</td>\n      <td>spotify:track:53m1rGnPJVtI0zUryVyL9N</td>\n      <td>0.617</td>\n      <td>0.5590</td>\n      <td>4</td>\n      <td>-5.738</td>\n      <td>1</td>\n      <td>0.0269</td>\n      <td>0.035900</td>\n      <td>...</td>\n      <td>198373</td>\n      <td>4</td>\n      <td>28.05580</td>\n      <td>11</td>\n      <td>1</td>\n      <td>1990</td>\n      <td>198.0</td>\n      <td>-6.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>All I Want</td>\n      <td>Toad The Wet Sprocket</td>\n      <td>spotify:track:2GHYJ0dhVds3PODKnTdiGE</td>\n      <td>0.600</td>\n      <td>0.8110</td>\n      <td>5</td>\n      <td>-7.693</td>\n      <td>1</td>\n      <td>0.0788</td>\n      <td>0.055800</td>\n      <td>...</td>\n      <td>196116</td>\n      <td>4</td>\n      <td>23.32175</td>\n      <td>11</td>\n      <td>1</td>\n      <td>1990</td>\n      <td>196.0</td>\n      <td>-8.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>All I Want</td>\n      <td>Howard Jones</td>\n      <td>spotify:track:70nB7hTC7FDiYoJp0TVwnp</td>\n      <td>0.611</td>\n      <td>0.8680</td>\n      <td>3</td>\n      <td>-7.982</td>\n      <td>1</td>\n      <td>0.0294</td>\n      <td>0.039700</td>\n      <td>...</td>\n      <td>275733</td>\n      <td>4</td>\n      <td>35.62317</td>\n      <td>12</td>\n      <td>1</td>\n      <td>1980</td>\n      <td>276.0</td>\n      <td>-8.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>All I Want</td>\n      <td>Tim Maia</td>\n      <td>spotify:track:1Fxoj2yydAJV8wDWlglgAo</td>\n      <td>0.527</td>\n      <td>0.6180</td>\n      <td>7</td>\n      <td>-6.450</td>\n      <td>0</td>\n      <td>0.1750</td>\n      <td>0.359000</td>\n      <td>...</td>\n      <td>204733</td>\n      <td>4</td>\n      <td>32.06329</td>\n      <td>11</td>\n      <td>0</td>\n      <td>1970</td>\n      <td>205.0</td>\n      <td>-6.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>Rumble</td>\n      <td>The Swiv-O-Matics</td>\n      <td>spotify:track:3tLkw752BPYiYYqartTVju</td>\n      <td>0.206</td>\n      <td>0.7850</td>\n      <td>9</td>\n      <td>-4.236</td>\n      <td>1</td>\n      <td>0.0490</td>\n      <td>0.000612</td>\n      <td>...</td>\n      <td>110507</td>\n      <td>4</td>\n      <td>23.47467</td>\n      <td>5</td>\n      <td>0</td>\n      <td>1990</td>\n      <td>111.0</td>\n      <td>-4.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>Dancing In The Dark</td>\n      <td>Bruce Springsteen</td>\n      <td>spotify:track:7FwBtcecmlpc1sLySPXeGE</td>\n      <td>0.527</td>\n      <td>0.9420</td>\n      <td>1</td>\n      <td>-5.640</td>\n      <td>0</td>\n      <td>0.0366</td>\n      <td>0.011500</td>\n      <td>...</td>\n      <td>241307</td>\n      <td>4</td>\n      <td>47.47864</td>\n      <td>10</td>\n      <td>1</td>\n      <td>1980</td>\n      <td>241.0</td>\n      <td>-6.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>Behind Closed Doors</td>\n      <td>Charlie Rich</td>\n      <td>spotify:track:6d7Jz2np6jtNaZv1DiWfJs</td>\n      <td>0.492</td>\n      <td>0.3290</td>\n      <td>2</td>\n      <td>-11.788</td>\n      <td>1</td>\n      <td>0.0274</td>\n      <td>0.961000</td>\n      <td>...</td>\n      <td>174667</td>\n      <td>4</td>\n      <td>25.75736</td>\n      <td>9</td>\n      <td>1</td>\n      <td>1970</td>\n      <td>175.0</td>\n      <td>-12.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>57</th>\n      <td>It's Your World</td>\n      <td>Marty Robbins</td>\n      <td>spotify:track:4YOujaYI7YhWtjHQg7ekDx</td>\n      <td>0.624</td>\n      <td>0.3230</td>\n      <td>5</td>\n      <td>-11.342</td>\n      <td>1</td>\n      <td>0.0263</td>\n      <td>0.750000</td>\n      <td>...</td>\n      <td>164267</td>\n      <td>4</td>\n      <td>36.15282</td>\n      <td>8</td>\n      <td>1</td>\n      <td>1960</td>\n      <td>164.0</td>\n      <td>-11.0</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>\n<p>58 rows × 24 columns</p>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00012-727c1322-67cd-48a2-b447-61ace2eb758b",
    "deepnote_to_be_reexecuted": true,
    "source_hash": "7b118be1",
    "is_output_hidden": false,
    "is_code_hidden": false,
    "allow_embed": false,
    "deepnote_cell_type": "code"
   },
   "source": "np.random.seed(101)\n\n\nrData['rb'] = rData.apply(lambda row: True if (row[\"track\"] in rb[\"track\"].tolist()) and (row[\"artist\"] in rb[\" Artist name\"].tolist()) else False, axis=1)\nrData = rData.loc[rData['target'] == 1].reset_index(drop=True)\nrData = cData.loc[rData['country'] == False].reset_index(drop=True)\nrData\nnRBRowIndices = np.random.randint(len(rData), size= 200).tolist()\n\nnCountry = cData.iloc[nCountryRowIndices , :].reset_index(drop = True)\n\nsampleData = pd.concat([nCountry, countryData]).reset_index(drop = True)\nsampleData\n",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00013-fd8ae290-84be-447d-9740-8554b19a2479",
    "deepnote_to_be_reexecuted": true,
    "source_hash": "c4564f70",
    "is_code_hidden": false,
    "is_output_hidden": false,
    "allow_embed": false,
    "deepnote_cell_type": "code"
   },
   "source": "# loudness vs target (Line Plot)\nsns.relplot(x=\"decade\", y=\"loudness\", hue=\"country\", kind=\"line\", data = sampleData)\nwhy are we plotting so many things lol\n# energy vs target (Line Plot)\nsns.relplot(x=\"decade\", y=\"energy\",  hue=\"country\", kind=\"line\", data = sampleData)\n\n# speechiness vs target (Line Plot)\nsns.relplot(x=\"decade\", y=\"speechiness\",  hue=\"country\", kind=\"line\", data = sampleData)\n\n# speechiness vs target (Line Plot)\nsns.relplot(x=\"decade\", y=\"danceability\", hue=\"country\", kind=\"line\", data = sampleData)\n\n# valence vs target (Line Plot)\nsns.relplot(x=\"decade\", y=\"valence\",  hue=\"country\", kind=\"line\", data = sampleData)\n\n# tempo vs target (Line Plot)\nsns.relplot(x=\"decade\", y=\"tempo\",  hue=\"country\", kind=\"line\", data = sampleData)\n\n# instrumental vs target (Line Plot)\nsns.relplot(x=\"decade\", y=\"instrumentalness\",  hue=\"country\", kind=\"line\", data = sampleData)\n\n# duration over time (Line Plot)\nsns.relplot(x=\"decade\", y=\"duration_sec\", hue=\"country\", kind=\"line\", data = sampleData)",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "<a style='text-decoration:none;line-height:16px;display:flex;color:#5B5B62;padding:10px;justify-content:end;' href='https://deepnote.com?utm_source=created-in-deepnote-cell&projectId=162d76af-48a2-4f75-9aef-34643a48aa39' target=\"_blank\">\n<img alt='Created in deepnote.com' style='display:inline;max-height:16px;margin:0px;margin-right:7.5px;' src='data:image/svg+xml;base64,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' > </img>\nCreated in <span style='font-weight:600;margin-left:4px;'>Deepnote</span></a>",
   "metadata": {
    "tags": [],
    "created_in_deepnote_cell": true,
    "deepnote_cell_type": "markdown"
   }
  }
 ],
 "nbformat": 4,
 "nbformat_minor": 2,
 "metadata": {
  "orig_nbformat": 2,
  "deepnote": {
   "is_reactive": false
  },
  "deepnote_notebook_id": "551b303d-5b6c-44c7-9511-f40a3d6a1b41",
  "deepnote_execution_queue": []
 }
}