OneWayneG-100DaysOfCode.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3e25b461",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Track Number                  Track Name  Popularity  Duration\n",
      "1             36         20191009 I Like Her          73    136888\n",
      "2             24                    20190724          70     70097\n",
      "3              1                    20180512          64    247663\n",
      "4             39     20191012 Fooled By Love          62    197719\n",
      "5              3                    20180701          61    125614\n",
      "6             37  20191010 No Doubt About It          61    182602\n",
      "7              5                  20180702 2          60    157507\n",
      "8             38   20191011 You Made The Bed          60    133731\n",
      "9              2                    20180529          60    151258\n",
      "10             6                    20180816          59    135097\n",
      "11             9                    20180927          59     79162\n",
      "12            11                    20181002          59     32945\n",
      "13             4                    20180702          59    140515\n",
      "14            10                    20180930          59    188559\n",
      "15             7                    20180913          58    131427\n",
      "16             8                    20180924          58    123406\n",
      "17            14                    20181212          58    166413\n",
      "18            12                    20181019          57    175990\n",
      "19            46                    20191229          57    375654\n",
      "20            13                    20181108          56    183114\n"
     ]
    },
    {
     "data": {
      "image/png": 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5csSY9v3336tOnTp699135eXlpfLly+vzzz9Xvnz5HOoKb1euXFFcXJycnZ2N/dP2b/369VWwYEEtXrxYUvy++fvvvys6Olr16tWzW80p5enpqVKlSun33383Tgpr1qwpb29vSfGDiErxXRgLFCigAwcO2K3Wx1G8eHHt3bvX+NElc+bMxnHk8uXLypIli1q0aKG33npLv/32myQ5TOgWFRVl7KNt2rRRly5dNHr0aH3//fcqVKiQhgwZotq1a2vUqFG6dOmS9u7dq3Xr1ql69er2Lj3ZLBaL4uLiVL58eb344ov6+OOPjW05b948lShRQpMmTdK3336rrl27OtxFMkqXLq1Lly5p27ZtkuL3T9s6x8XFKVOmTGrQoIEmT56sf/75RydPnrRzxcm3evVq3b17N8EFWWJjY1WoUCFj/LqhQ4ca6+5I38sk6fnnn9ehQ4d0/PhxSfGf9+PGjVO3bt1UvXp1ubi4yMXFRVWrVpWPj4/Onj1r34IfQ0Y9P7p9+7YuX76sYsWKJWjpHBcXp5o1aypfvnz69ddfjel79+7V1KlTE7RmTO8aN24sFxcXdejQQdu2bZO7u7suXLggZ2dnRUdHG5/tlSpV0vXr1xUTE2PnipPv1q1bunTpkipWrChXV1fju8sPP/yg2rVr6+uvv1bx4sXVunVrTZgwQVeuXDF+eEvvHOPbhx3ZvsB1795dc+fO1eDBgxUTE6Nz584ZAUBUVJTxIVqwYEGH2fg2th36/fff14ULF4xB0yRp4cKFeuutt/TXX3+pRYsWypo1q8M04ZSkbNmyadCgQRo3bpyk//slrlChQvrmm28UFBRk9PMfMGCApk+froCAADtXnXylS5dWpkyZjEHAY2JiZLFYdP36dS1atEgvv/yyJKlEiRKKiYnRtWvX7FxxyuTLl0+9e/dOMB7P+fPnVbFiRfn7+yc4IHt4eChnzpyKjIy0V7kpcurUKTVs2FDlypUzptnWpUmTJho8eLDOnDmjIUOG6OLFiw5zsmhTpEgR/fLLLypXrpzGjh2r+vXry9nZWXXq1JGLS/w1NmzH17x58ypnzpy6dOmSPUtOsZIlS+qZZ57R559/rp49e+r111/X9u3bVbt2bXl6eio2NlZxcXG6e/eu8ufPr9DQUHuXnGzZsmWTl5eXMRbIvaGoh4eHWrZsqc2bN+vq1au6c+eOpkyZou7du9ur3Mfi6uqqjh07asOGDeratatu3ryprVu3GsdJ235qaxHgaIPZ2k4I3377bc2bN09Xr16Vs7Ozli5dqi1bthjjhnh7eytTpkwOdeJosVhUsWJFvfXWW3Jzc5MUf8zp2LGjRowYobFjx8rPz08TJ05U/fr19fHHH6tOnTp69tln7Vx5yjg5OSlz5swaNGiQrFar5s6dq1u3bmnu3Lnq0qWLfH191aJFCw0ZMsTepabY888/rw4dOuh///ufpk+fbrzvnJyc5OTkpJiYGFmtVuXOnVvPPvusQ4XCBQoU0Ndff52gS6Wzs7MsFotq166tmTNnKkuWLOrRo4dWr15tx0ofT7Vq1VSxYkV17NhR5cqV04cffqjIyEgVKFBATk5Oxkm/q6urnJ2dH7jQiaPIiOdHMTExioiI0Llz5xJcCdLJyUlZs2ZV48aN9fvvv+vGjRu6e/eufvrpJ1WvXt0hjp1Wq9Vo9WX7sXPs2LG6dOmSfv/9d928eVOZM2eWi4uLbty4oV27dsnHx8e4WrkjiImJUdWqVfXss88ax5TQ0FBt2bJFb731lnLlymXst3FxcSpSpIjDfLYzplQy3bhxQ59//rlWrFihQoUK6fLlyxoyZIhef/11o+nx9u3b1alTJy1btizB6Pfpne2Kbc7OzhoxYoSmTZumdevW6ciRI+rZs6cWL16sihUrSnLsAcHvd++6hIaGqnfv3tq2bZuOHj3qEJdEtQ1qOnnyZH333XfKkyePatasqaioKO3fv19Wq1Xr1q2Tm5ubVq5cqcGDB2vx4sUqXLiwvUtPtdDQ0Ae+5MyfP1+DBw/WgQMHHGL72Tzskt4LFizQwIEDVahQIY0ePdohB3uNiorSwYMHtXDhQl2/fl0DBgzQ888/n+D9N2fOHA0bNkx79+51mG1ne/8tX75c48aNk5OTk5o2baqjR4/KyclJEydONObds2ePOnfubIw95Sji4uIUHR1tnPRL/3fc3L9/vzp06KDBgwcrd+7cevvtt/XPP/841HiKNj/99JO+//57xcTEyMnJSd27d9ebb76p3Llz6+bNm5o7d65Gjx6tI0eOOFwwtW/fPr399tu6fv26vL29FRUVpdu3b6tJkyYaM2aMrFarZsyYYVxh0RbEOYqoqCi5uroqLi4uwaWwJenatWvas2eP0XV//fr1KlmypB2rfTy2Y82CBQs0bdo05cuXT6dOndKCBQuUP39+hxzg3ObYsWP64IMPdPjwYbVs2VLt2rUzwn7bOtnGT1m2bJlDfgbey3bSLMV3P/3444+VI0cO/fDDDw513iDF/0C4aNEi/fvvv2rbtq0mTZokDw8PjR49WlL8j8BLlizRoEGDdPDgQYc7dtrY3l8Z6fzozp07unnzpry9vR+o+/Dhw2rQoIF++eUXFShQQA0bNtTy5cuNrn6OZOPGjfr+++918OBBSfGthxs3bqzSpUtr9uzZ2r9/vwIDAx3uvScl/OxzcnJSeHi43N3dE5xPBAUF6Y033tDSpUsdYsBzQqkk2N6k958w/vDDD5o1a5aROlapUkW1atXSjRs39Pvvv6tYsWKaOXOmnapOG+3bt1epUqW0a9cuFSpUSGPGjJGbm9tDT54dlW07X7hwQW+88YaqVKmib7/91t5lPZSt5rt37xonEH/88YcWLlyonTt3KjIyUg0aNNCbb76pcuXKKTw8XF988YVOnz6d4FeejOTMmTMKCAhQ+fLlHWb72T5Q7p9+//9nzJihAQMG6M0339TQoUPtUnNaiIqKMi5xLv3fOp47d049e/ZUhQoVNGzYMDtX+WiJvf8k6fr168qdO7emT5+u8ePH64svvlDFihV1+vRp/fjjj4qNjdW8efPsWHnasbWaatmypVxcXPTff/+pUqVK+v777+1c2eM7fPiwvvrqK6M7jbe3t8qUKaN//vlH0dHR6tq1q9577z07V/l4rFarduzYoZkzZ8rNzU01atRQw4YN5enpqeDgYPXq1Utly5Z1qOPLw76P3Hv8DA8PV69evRQWFqalS5eaWGHaCw8P11tvvaVt27apf//+6tWrl8N+J7v/RHjs2LH6+eefFR4erpdfflkvvvii8uXLp5CQEC1ZskReXl7GuGcZyb59+9S0aVO999576t+/v73LSZXZs2dr4MCBatu2rV599VUFBQVp+fLlatGihT755BN7l5ciSQVMGeH86P7vLondV79+fRUuXFi5cuXS/v37HWKc3Xnz5ilbtmwPXDDn8uXL2rZtm5YuXao//vjDmF66dGm98847at26tdmlpkpi+9q902z7bmhoqAYOHKjTp08nuCJ7ekYo9RDHjh3TvHnzdP78edWvX18dOnSQFD8WzKZNm7R06VLt37/f6Dvds2dPdezY0SGugBIaGqr169frzJkzKlWqVII38aZNm+Tv76/Y2FjNnj3b4cYpSCmr1aoFCxbogw8+0MGDBx3i6jz37ptNmzZVq1atFBERIRcXF127di3BlZaWLVumL774QhMnTnS4q9c8jO3Ae+PGDY0ZM0br16/XqlWrHOIKKLbtd+HCBTVu3DjBuBOJfRkaM2aM8UU9I7D9svPvv/9q/PjxWrZsmTZs2OAQ2076v+137tw5NWjQwPhskOJ/Pe7WrZv++ecf5cqVSzdu3DAua+5IYzI8jG0f/fXXX/XZZ59Jkg4dOuQw2+9hzpw5o5kzZ+rw4cP6999/5eXlpR49eqh69eoO04rvYe49voSFhWnUqFEO9/6zeVTLBKvVqm3btum1117TrFmzVKdOHROrezKsVqvWr1+vUqVKJficd0S2rs22FjSnTp3SqlWrtHjxYp08eVLu7u6Kjo6Wv7+/unTp4hDdh5LL1mIqLi5OS5cuVaNGjeTp6WnvslIlIiLCaJF/584dOTk5GWMtOUK3tvvZWrTd20Aho5wfJXXstE2fNGmSvvvuO8XGxmrOnDmqVauWHapMvrt37xrHh+LFi6tHjx4JvpdJ8fvn3bt3FRQUJB8fH+XKlcvhPvNsktMqLzAwUCNHjtSYMWMcZixFQqkkLF261BinJ0eOHLpz545GjBihpk2bJpjv9OnTunXrlp599lk988wzdqo2Zf744w+jqb6rq6tcXFzUqlUrDRkyxGgKuHjxYkVERKhVq1YOdYnvx3X+/Hlt3779gYNYenTvvpk9e3ZFRERozJgxxvgg97p48aLmz5+v6Ohoffrpp3ao9snbtGmTZs+erfr166t9+/b2LueRkntskR7+i1ZGsGHDBk2aNElNmzZ1mEvWJmf7hYWF6bffftO2bdtUtGhRdezYUXny5LFj1U9GcHCw2rRpo/r16+ubb76xdzlpytaS0fYedKSuGckVFhamrVu3KjY2NtHjT3q0Z88e3blzR5UqVUowSG9S2+fQoUNatGiRBg0aZGaZSEJS2+9+J0+eVGhoqAoWLJghj50Z2ZUrV3T48GHlzp1bZcuWdajj5qOOL1arVYsWLXLI86OUHDtPnDih2rVry8fHR1u3bjW71BS7ePGi2rZtqyxZsihnzpzavn27ChQooLfeektvvvmm3NzcNGDAAFWpUkXNmze3d7mPJSXbb/v27erbt69q166t4cOHm13qYyOUSkLVqlVVvXp1BQQEyMXFRT169FCRIkXk7++vxYsX67///lPTpk0d6kpDNnXr1pWvr68CAgJksVj0448/aunSpZowYYJatWplzGf7Un7vrwWwv4ftm4sWLdKtW7fUvHlz1a1bV1L8rwNxcXEO/yvcw0RFRcnZ2dkhApzkHFvu3X4Z3Z07d+Ti4uIwrVAe9f7777//1LJlywzRKiM5bt26ZQyQCjxpL730kjJnzqzGjRurTp06Klu2bILPtvu/oFutVsXExDjseDYZzaO2X2JjgwFmedT+aXP/8AuOIKXHzm3btsnDw8MhxpKKi4vT5MmTFRgYqK+++kqnTp3S8uXLtX//fuXIkUMNGzZUYGCgfvvtN1WsWNEhx+FL6fY7d+6csmTJoty5c9uj3MdCKJWImTNnavz48VqyZInRFW/9+vUaNmyY/vvvP2XKlEkxMTG6cuWKevXqpU8//VQuLi4O8SH666+/auLEiVqwYIEKFSpkTG/ZsqXi4uI0d+7cBM1sw8LCONlIR1KybwYEBKhfv358GU9HUrr9Pv74Y4c5tjwNUvrZYNt+jjLWBJCeLV++XL169VKJEiV07Ngx5c6dW40bN1b9+vVVoUKFBK3V79y5o6tXr+q5556zX8FIICXbLyIiQiEhISpcuLDDnTzCMaVk/4yMjNSVK1f07LPPOsT3s5S+965cuSJfX187Vpxyd+7c0Wuvvaby5ctryJAhOn36tDZu3KhNmzZpy5Ytslqtat++vfr27esQw+zcK6WffZcvX3a47SdJHOkTsWjRIrVs2VJ58+Y1BnQ9d+6cTp06pX79+un333/XrFmz1LJlSwUGBio4ONghDkpS/NU+GjdurLx580qKvzqGJHXr1k0HDx7UX3/9Zcx79epVValSRevXr7dLrXhQSvbN2bNn6+LFi3auGPdK6fZzpGPL0yClnw2XLl0ikALSSFBQkMqVK2dcTa969eqaM2eOAgIC9OWXX2rFihW6cuWKpPj35SuvvKI1a9bYuWrYpGT7nT9/Xq+88orWrVtn56rxtEjJ/nn27Fm98sorDjEAuJTy9179+vUd6thptVrl7u6ugQMHav369QoKCpKvr6/8/f318ccfKzY2VqVKldLq1atVuXJlbd++3d4lp0hKP/scbfvZpP++LiY7d+6cnJyclCtXrgTdSSZPnqy33npLrVu3lqurq7Jnz66OHTtq06ZNOn/+vHx8fOxYdfJcuXJFcXFxcnZ2NlrP2P6tX7++ChYsqMWLF6t27dqKi4vT2rVrFR0d7ZBdFDOijLxvPg3Yfo6N7QfYT2hoqIKDg3Xjxg3jCp4TJ07UZ599pilTpmju3LlatWqVateurfr16+vw4cNycXFRo0aN7Fw5pMfffg0bNrRz5XgaZOT982k4dlosFsXFxal8+fJ68cUX9fHHH2v+/PnKly+f5s2bpxIlSmjSpEn6+++/tXPnTocaoP5p2H42hFL3yZcvn3r37q18+fIZ086fP6+KFSvK39/fGGPJYrHIw8NDOXPmVGRkpB0rTr5s2bLJy8vLOEm6t0+th4eHWrZsqVmzZunq1avy9PTUlClT1L17d3uWjHtk5H3zacD2c2xsP8B+smXLpkGDBunatWuS4lt5Z8qUSYUKFdI333yjDz74QNOnT9eMGTP0xx//r717j6m6/uM4/gIEJEFlCuFRk8G0hoRWJo2JrvJwCSWXCl5y1sSVZollicsGy7RZVFsZgpHlZZMDC5dipCCmFORds7KMcsZFlxiJ18Dw94fj1PmdY+Ht4PnyfPyj+3w+5/v9fM77ywavfb+f71ZDv9zDFVE/3MqMfH0aeW3/5O7uLi8vL6Wnp+vRRx9VXl6ennzySeXl5Sk9PV0hISEKCQlx+FKoW1lHqZ/EnlJt1tjYqK5du9q05efnKyMjQwcOHHCZTXpbWlrU1NSkzp07W9ta/5Dav3+/JkyYoIyMDPXs2VPTpk3T4cOHXW4zv47GKNdmR0X9XBv1A9rXPzd4bWxs1LPPPquvvvpKhw4d4ufPBVA/3MqMfH0abW2tN1sUFBQoNzdXQUFB+vnnn1VQUKBevXq55Abn/8Zo9TNOZW6y//+j48iRI/r44481ZswYlyq8u7u7TSAl/X3bY0REhAYMGKD8/HwtXrxYSUlJBFIuwCjXZkdF/Vwb9QPaV+ur2qXLb4Osrq5WUlISP38ugvrhVmbk69Noa2sNnOLj49WtWzeVlpZqwoQJCgwMtOk3CqPVj8f3rkJrInny5EmtWLFCDQ0NevHFF9t7WjdE6yt4x48fr7S0NEmXN/WFazDytdkRUD/XRv2A9tX6y3llZaV+/PFHFRQUtPeUcBWoH25lRr4+jbg2X19fWSwWlZaWKiwszNAvnDFS/QilrkLrLXIHDx7U8ePHNWfOHPn7+7fzrG6M1rU9+OCD6tOnj8xms2HW1hEY+drsCKifa6N+QPtzc3PTAw88oLfeeks9evRo7+ngKlE/3MqMfH0acW1ubm4ym83tPQ2nMEr92FPqGv3555/y8PBQp07Gy/VOnTold3d3+fn5tfdUcA2MfG12BNTPtVE/AAAAoO0IpQAAAAAAAOB0xtrxCwAAAAAAAC6BUAoAAAAAAABORygFAAAAAAAApyOUAgAAAAAAgNMRSgEAAAAAAMDpCKUAAACukcVikclkksViae+pOF1HXjsAALgxOrX3BAAAABwxmUxXNb6uru4mzeTmGzp0qGpqahQQEKDKykrddtttdmNMJpNCQ0NVXl7eDjMEAAC48QilAADALen555+3a3v77bfVtWtXpaSktMOMbr4TJ04oOzvb4doBAACMhlAKAADckubOnWvX1hpKOepzdZ6engoMDFR2dramTp2qHj16tPeUAAAAbir2lAIAAC6turpaJpNJqamp+umnnzRt2jQNHDhQJpNJ1dXVkqTi4mLNmDFDUVFRCgkJ0Z133qkxY8Zo48aNVzzu999/r1mzZum+++5TcHCwBg8erEmTJmnz5s3/Oafa2loNHz5cISEhKikpadM63N3dNXfuXJ05c0bvvPNOmz6Tmppqs85/yszMlMlkUkVFhbWtoqJCJpNJmZmZ2rVrl8aNG6f+/fsrPDxc8+fP1/nz5yVJW7duVWJiokJDQxUREaHXXntNf/311xXnUVxcrPj4eIWEhGjQoEFKS0vTH3/84XDsr7/+qhdeeEFDhgyxfq+pqamqqamxG2symTR27FgdO3ZMqampGjRokHr37m2zJgAA4Lq4UwoAABjCkSNHNHr0aA0YMEBJSUlqaGiQp6enJGnx4sXy8vLS0KFDFRgYqJMnT2rz5s2aPn26Fi5cqGnTptkcq7i4WDNnzlRLS4vMZrNCQ0NVX1+vffv2ae3atYqJibniPA4fPqyJEyfq/PnzysvL09ChQ9u8hvHjxysnJ0dr1qzR9OnT1a9fv2v7Mv7Dvn37lJWVpREjRujxxx9XRUWFVq5cqdOnTys2NlazZ89WTEyM7r33XpWWliorK0t+fn6aPXu23bE2btyo7du3a9SoUYqOjlZlZaVWrVql3bt3a8OGDfLx8bGO3bt3ryZNmqRz587JbDYrODhYNTU1KiwsVFlZmTZs2GC35oaGBo0ePVrdu3dXYmKimpub5evre1O+FwAA4FyEUgAAwBB27dql1NRUvfTSS3Z9a9assQs7zp49q8TERL355puaOHGidXPx+vp6Pffcc/Lw8ND69et1991323zu3zZU3717t6ZOnSpvb28VFhbqrrvuuqo1uLu7Ky0tTU888YSWLFmirKysq/p8W23dulUrVqxQXFycJKm5uVnx8fFat26dvvjiCxUWFmrw4MGSLj9GGRUVpeXLl+uZZ55Rp062vz6WlpbKYrEoOjra2jZnzhxZLBZlZ2drzpw51nPMmDFDLS0tKi4u1sCBA63jd+zYoXHjxumVV17RqlWrbI7/ww8/KDk5WZmZmfLw8LgZXwcAAGgnPL4HAAAMITAwUKmpqQ77HN1x1KVLFyUlJamxsVEHDhywtufn5+vs2bN66qmn7AIp6cpvBdyyZYuSk5Pl7++v9evXX3Ug1SomJkaRkZH69NNPdfDgwWs6xn+JioqyBlLS5f2sEhISdOnSJZnNZmsgJUm+vr4aOXKkGhoadOzYMbtjDR8+3CaQkqR58+bJ09NTBQUF1rbS0lJVV1dr5syZNoGUJEVGRio2NlZlZWU6ffq0TZ+Xl5cWLFhAIAUAgAFxpxQAADCEsLAweXl5Oeyrr6/X0qVLVVZWppqaGl24cMGm//jx49b/79+/X5I0YsSINp+7qKhI27ZtU3h4uFavXn3dm5S//PLLSkxM1KJFi5SXl3ddx3IkPDzcri0wMFCS7AIjSbr99tslSceOHVPfvn1t+iIjI+3GBwUFqV+/fqqqqtKZM2fk6+urPXv2SJKqqqqUmZlp95nffvtNLS0t+uWXXzRo0CBre9++fdn0HQAAgyKUAgAAhhAQEOCwvaGhQfHx8aqtrdX999+v6OhodevWTe7u7vruu++0adMmNTU1WcefOnVKktSrV682n3vPnj26ePGiIiMjb0iAMmTIEMXFxenzzz9XeXm53Z1I18vRnkytj+X5+fnZ9bXepXTx4kW7vp49ezo8R0BAgKqqqnT69Gn5+vpaNz4vLCz817mdO3fO7jgAAMCYCKUAAIAhuLm5OWxfu3atamtrNW/ePLuNut977z1t2rTJpq1bt26SHN8VdCVpaWnatGmTcnJy5OHhoQULFlzDCmzNnz9fJSUlWrRokYqLix2OaV2zo7Do/x+Du1nq6+sdtp84cULS3yFX678rV66U2Wxu8/GvVFcAAOD62FMKAAAY2tGjRyXJ4Rvzdu7cadfWup/Stm3b2nwOb29vrVixQg899JCysrK0cOHCa5vsP/Tv31/Jycn65ptvtH79eodjunfvLsn28cNW33777XXPoS127Nhh13b8+HEdPXpUwcHB1ruy7rnnHkmyPsYHAABAKAUAAAytT58+kuwDqMLCQm3ZssVufFJSkrp06aKcnByHwY6jzb6ly8HUhx9+qJEjR2rZsmV69dVXr3vuc+fOVefOnfXGG2847I+IiJAkWSwWm/aioiJVVlZe9/nbYvv27SovL7dpW7JkiZqbmzV+/HhrW2xsrHr37q3ly5fr66+/tjtOc3Ozw4ALAAAYF4/vAQAAQxs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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "from dotenv import load_dotenv\n",
    "import pandas as pd\n",
    "import spotipy\n",
    "from spotipy.oauth2 import SpotifyClientCredentials\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load environment variables from .env file\n",
    "load_dotenv()\n",
    "\n",
    "# Set up the Spotify API credentials\n",
    "client_id = os.environ.get('SPOTIFY_CLIENT_ID')\n",
    "client_secret = os.environ.get('SPOTIFY_CLIENT_SECRET')\n",
    "\n",
    "if not client_id or not client_secret:\n",
    "    print(\"Missing Spotify API credentials\")\n",
    "    exit()\n",
    "\n",
    "client_credentials_manager = SpotifyClientCredentials(client_id=client_id, client_secret=client_secret)\n",
    "sp = spotipy.Spotify(client_credentials_manager=client_credentials_manager)\n",
    "\n",
    "# Search for the album by Mac DeMarco\n",
    "results = sp.search(q='artist:Mac DeMarco album:One Wayne G', type='album')\n",
    "if not results['albums']['items']:\n",
    "    print(\"Album not found\")\n",
    "    exit()\n",
    "album_id = results['albums']['items'][0]['id']\n",
    "\n",
    "# Get the tracks from the album\n",
    "tracks = sp.album_tracks(album_id)\n",
    "\n",
    "# Create a list of lists containing the track name, popularity, and duration\n",
    "track_list = []\n",
    "for index, track in enumerate(tracks['items'], start=1):\n",
    "    if not track['name']:\n",
    "        continue\n",
    "    track_info = sp.track(track['id'])\n",
    "    popularity = track_info['popularity'] if 'popularity' in track_info else 0\n",
    "    track_list.append([index, track['name'], popularity, track['duration_ms']])\n",
    "\n",
    "# Create a pandas DataFrame from the track list\n",
    "df = pd.DataFrame(track_list, columns=['Track Number', 'Track Name', 'Popularity', 'Duration'])\n",
    "\n",
    "# Sort the DataFrame by popularity in descending order\n",
    "df = df.sort_values(by='Popularity', ascending=False)\n",
    "\n",
    "# Reset the index to start from 1\n",
    "df.reset_index(drop=True, inplace=True)\n",
    "df.index = df.index + 1\n",
    "\n",
    "# Set the gray color palette\n",
    "sns.set_palette(\"gray\")\n",
    "\n",
    "# Create the bar plot with a gray background\n",
    "plt.figure(figsize=(12, 6), facecolor='0.9')\n",
    "ax = sns.barplot(x=\"Track Number\", y=\"Popularity\", data=df.head(20))\n",
    "ax.set_title(\"Most Played Songs from One Wayne G\", fontsize=18)\n",
    "ax.set_xlabel(\"Track Number\", fontsize=14)\n",
    "ax.set_ylabel(\"Popularity\", fontsize=14)\n",
    "ax.set_xticklabels(df.head(20)['Track Number'], rotation=65, ha='right', fontsize=12)\n",
    "plt.subplots_adjust(bottom=0.4)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"Top_20_tracks.png\")\n",
    "\n",
    "df_top20 = df.head(20)\n",
    "print(df_top20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba86376d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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