Module 3 Section 2 Lab 1 - Visualization with Matplotlib.ipynb

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    "# Module 3 Section 2 Lab 1 - Data Visualization with Matplotlib\n",
    "\n",
    "# Introduction\n",
    "\n",
    "- Membuat plot dan visualisasi statis atau interaktif adalah salah satu tugas terpenting dalam analisis data. Ini mungkin merupakan bagian dari proses eksplorasi; misalnya, membantu mengidentifikasi outlier, transformasi data yang diperlukan, atau menghasilkan ide untuk model.\n",
    "\n",
    "- Matplotlib (https://matplotlib.org/) adalah pustaka python yang paling banyak digunakan untuk visualisasi data karena fleksibilitasnya yang tinggi dan fungsionalitas luas yang disediakannya.\n",
    "\n",
    "# Agenda\n",
    "\n",
    "1. Setting up\n",
    "    - Importing matplotlib\n",
    "    - Matplotlib for Jupyter notebook\n",
    "    - Dataset \n",
    "    - Documentation\n",
    "2. Matplotlib basics\n",
    "    - Make a simple plot\n",
    "    - Labels, and Legends\n",
    "    - Size, Colors, Markers, and Line Styles\n",
    "    - Figures and subplots\n",
    "3. Line Chart\n",
    "4. Bar Chart\n",
    "5. Histogram\n",
    "6. Box plot\n",
    "7. Violin plot\n",
    "8. Scatter plot\n",
    "9. Bubble plot"
   ]
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    "# 1. Setting up\n",
    "\n",
    "## Importing matplotlib\n",
    "\n",
    "Sama seperti kita menggunakan singkatan `np` untuk NumPy dan singkatan `pd` untuk Pandas, kita akan menggunakan singkatan standar untuk impor Matplotlib:\n",
    "\n",
    "```python\n",
    "import matplotlib.pyplot as plt\n",
    "```\n",
    "Kita mengimpor  **pyplot** dari matplotlib dengan singkatan `plt` dan kita akan menggunakannya di seluruh notebook.\n",
    "\n",
    "## Matplotlib for Jupyter notebook\n",
    "\n",
    "Anda dapat langsung menggunakan matplotlib dengan notebook ini untuk membuat visualisasi yang berbeda di notebook itu sendiri. Untuk melakukan itu, perintah berikut digunakan:\n",
    "\n",
    "```python\n",
    "%matplotlib inline\n",
    "```\n",
    "\n",
    "## Documentation\n",
    "Semua fungsi yang tercakup dalam notebook ini dan deskripsi detailnya dapat ditemukan di [official matplotlib documentation](https://matplotlib.org/3.1.0/api/_as_gen/matplotlib.pyplot.html)."
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   "source": [
    "# importing required libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# importing matplotlib-\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# display plots in the notebook itself\n",
    "%matplotlib inline"
   ]
  },
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   "source": [
    "# 2. Matplotlib basics\n",
    "\n",
    "## Make a simple plot\n",
    "\n",
    "Mari buat plot dasar untuk mulai!"
   ]
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "height = [150,160,165,185]\n",
    "weight = [70, 80, 90, 100]\n",
    "\n",
    "# draw the plot\n",
    "# plt.plot(x, y)\n",
    "plt.plot(height, weight)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kita meneruskan dua array sebagai argumen input ke metode **plot()** untuk menampilkan plot yang diperlukan. Di sini perhatikan bahwa larik pertama muncul pada sumbu x dan larik kedua muncul pada sumbu y plot.\n",
    "\n",
    "#### Title, Labels, and Legends\n",
    "- Sekarang plot pertama kita sudah siap, mari kita tambahkan judul, dan beri nama sumbu x dan sumbu y masing-masing menggunakan metode title(), xlabel() dan ylabel().\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Weight')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw the plot\n",
    "plt.plot(height,weight)\n",
    "\n",
    "# add title\n",
    "plt.title(\"Relationship between height and weight\", pad=10)\n",
    "\n",
    "# label x axis\n",
    "plt.xlabel(\"Height\", labelpad=20)\n",
    "\n",
    "# label y axis\n",
    "plt.ylabel(\"Weight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x24df445c2e0>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "calories_burnt = [65, 75, 95, 99]\n",
    "\n",
    "# draw the plot for calories burnt\n",
    "plt.plot(calories_burnt)\n",
    "\n",
    "# draw the plot for weight\n",
    "plt.plot(weight)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Menambahkan **legends** juga sederhana di matplotlib, Anda dapat menggunakan `legend()` yang menggunakan **labels** dan **loc** sebagai nama label dan lokasi legenda pada gambar sebagai paremeter.\n",
    "\n",
    "https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x24df43db7f0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw the plot for calories burnt\n",
    "plt.plot(calories_burnt)\n",
    "\n",
    "# draw the plot for weight\n",
    "plt.plot(weight)\n",
    "\n",
    "# add legend in the lower right part of the figure\n",
    "plt.legend(labels=['Calories Burnt', 'Weight'], loc='best')\n",
    "\n",
    "# https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Perhatikan bahwa pada plot sebelumnya, kita tidak dapat memahami bahwa masing-masing nilai ini milik orang yang berbeda.\n",
    "- Lihat sumbu X, dapatkah kita menambahkan label untuk menunjukkan bahwa masing-masing milik orang yang berbeda?\n",
    "- Nilai berlabel pada sumbu apa pun dikenal sebagai **tick**.\n",
    "- Anda dapat menggunakan `xticks` untuk mengubah lokasi setiap centang dan labelnya. Mari kita lihat ini dalam contoh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BC7TW7ymlZpQcSyr52Ata6zUmRWnjpkyZYlctdWGfUrLyLrTId5w0RrDkFBj729f2dCEk0Jv7uhrJPNgSrfIrif3LKJbGR0KdYHjgf9Ckr6WjsrhyJ3elVFuMJN4FyAd+VUr9XHL6Xa31W6YGp7W2XEtAXBdr6NYTZfd3q3xn7D9dLCdT/mmVtw6owb2dG9CxZEGt+jUt2Cq/kpSjsHYGRP8Anv7GCJj2I8HBSn7oWJgpLfdWwF9a62wApdRGYJhZogJcXV1JSUnBx8fH+v5SCcBI7CkpKbi6Wv/yp1VdcbFm4+Ekth8/d2FceXa+0Sr39TBa5X8XPoPreeFWzYoTZPY52DQXIhaAYzXo8wL0eMJYkldcUO6CqlKqFbAa6A7kAOuASCAFGAtklHz/tNY6tZTrxwPjAQIDAzudPHnykvMFBQXExcWRm5tbrvhE5XB1daV+/fo4OztbOhRxBVprpv+wn2VbT+LooGjtX8NY4rZktqdVtspLU5hnJPRNb0JeJnR8wCiWeta1dGQWc7WCqkmjZZRSDwETgSzgAEaSnwMkAxp4DfDXWo+72n1KGy0jhDCP/1t/hLm/HWRcz0ZMHdTCulvlpdEaDvzP6IJJPQFN+8OAV6FOm2tcaP8qbLSM1nohsLDkIbOBOK31hfnzSqkFwE+mPEMIUX7fRJ5i7m8Hub1DAC8NaYWDgw200C92KsIolsZFgF8buP87aNrP0lHZBFNHy/hprc8qpQKB4UB3pZS/1jqh5CPDgH2mBimEKLv1MWd57ru93NDUl7l3tbetxH7uuDGscf/34FEHhn4IHe6TYmkZmDrO/VullA9QAEzUWqcqpT5XSnXA6JY5AcigbiEq2c7YVB5fvoNW/p588kCnq29SYU1yUo0JSBHzwcHJWK2xx5Pg4mHpyGyOqd0yvUo59oAp9xRCmOZYUhbjlmzH17Mai8Z2xsPFBuYqFuZD5ELY+AbkpEHH+6DvS1DD/5qXitLZwFsXQlyvsxm5jF4UgVKKZeO64udp5cNUtTbGqf8+HVKPQ+O+MHAm1G1r6chsniR3IexEZm4BYxZvJyUrn6/Gd6ORr5WP+46LMvYsjd0KtVvCfauMkTC2MCzTBkhyF8IO5BUWMeGLKA4nZvLZmFDaN/C2dEhXlnrSKJbu+xaq+8Gt7xlj1h0lHZmT/GkKYeOKizXPfLOHP4+k8Pbd7enTwkpXQMxJg81vw7ZPQDlC76nQ8z/g4mnpyOySJHchbJjWmpk/R/Pj7tM8e3NL7uxU39Ih/VtRgbEJ9YY5xmiYDqOg74vgVc/Skdk1Se5C2LAFm4+x6M/jjO0RxIQbG1s6nEtpDTE/w+/T4NxRaNTbKJb6t7d0ZFWCJHchbNT3O+OYvSaGIe38mXZra+taHyZ+B4S9BCf/BN8WMOprY411a4rRzklyF8IGbTqUxNRv9tCtcS3euceKZp+mxcK612Dv1+DuC0PeMXZDkmJppZM/cSFszJ64NCZ8EUVTPw/mjw7FxckKpuTnpsOWd2HrPKN13utp6DkZXGtYOrIqS5K7EDbkRPJ5Hly8nZru1Vg6rgs1XC281HJRAUQtgQ2vQ3YKtBsB/V4GLyss7FYxktyFsBFJmXmMWRxBsdYse6gLdWpYcPap1nDoV6NYmnwIGt4Ag2ZCQEfLxSQuIcldCBuQlVfIuCXbSczI5ctHutGktgUX0jq9yyiWntgMPs1gxApoMViKpVZGkrsQVi6/sJjHvojiQEIG8x/oREhgTcsEkh5nFEv3fAXuPnDLW9BpLDjKLlzWSJK7EFasuFjz31W72Xw4mTfvbEe/VnUqP4i8zJJi6f8Z3TE9J0Ovp8DVq/JjEddNkrsQVuyNX2P4367TPDOwOfd0blC5Dy8qhB1LjWLp+SQIvscolnoHVm4colwkuQthpT7bfIxPNx3jgW4Nmdi3aeU9WGs4HAZhL0PyQQjsAaNWQr1OlReDMJkkdyGs0A+7TzPz52hublOXGUPbVN7s04Q9RrH0+Eao1QTuXQ4th0ix1AZJchfCyoQfSebpr3fRJagW743ogGNlzD7NOA1/zIRdX4JbTRj8JoSOk2KpDZPkLoQV2X86nfGfR9HItzoLRofi6lzBs0/zsuDP9yH8Q9BFxn6lvZ4GN++Kfa6ocJLchbASp85lM3bxdjxdnVg6rgte7hXYai4ugp2fwx+z4PxZaHsn9JsGNYMq7pmiUpmU3JVS/wEeARSwQGv9nlKqFrASCAJOAPdorVNNjFMIu5aSlcfoRRHkFxbz5YTu+Hu5VdzDDq+F31+GswegQTcYuQLqh1bc84RFOJT3QqVUW4zE3gVoD9yqlGoGPAes01o3A9aVfC+EuILs/ELGLY3kdFoOC8eE0qxOBe1MdGYffD4Mlt8JBTlwzzIY96skdjtlSsu9FfCX1jobQCm1ERgG3A70KfnMUmAD8KwJzxHCbhUUFTNx+Q72xqXx8f2dCA2qZf6HZCTA+lmw8wtj4tGg16Hzw+BUzfzPElbDlOS+D5illPIBcoBbgEigjtY6AUBrnaCUKnVDR6XUeGA8QGCgTIoQVY/Wmue/28v6g0nMGtaWQW3qmvcB+efhzw8g/ANj9cbuE6H3M8ZoGGH3yp3ctdbRSqk3gN+BLGA3UFiG6+cD8wFCQ0N1eeMQwla9FXaQVVFx/KdfM+7r2tB8Ny4ugl3LjWJp1hloMwz6TYdajcz3DGH1TCqoaq0XAgsBlFKzgTggUSnlX9Jq9wfOmh6mEPZlafgJ/m/9UUZ2acDk/s3Md+OjfxgzSxP3Qf0ucO/n0KCL+e4vbIapo2X8tNZnlVKBwHCgO9AIGAPMKfm62uQohbAja/YmMOPH/fRvVYfXbm9rntmniQeMETBH1oJ3Q7h7CbS+Q2aWVmGmjnP/tqTPvQCYqLVOVUrNAb5WSj0ExAJ3mxqkEPZi69EUJn+1i5DAmnw4siNOjuUesGbITCwpln4OLp4wcBZ0eQScXMwTsLBZpnbL9CrlWArQz5T7CmGPohMyGL8skkAfdxaOCcWtmgmzT/OzYetHsOU9KMqHrhOg91Rwr4DRNsImyQxVISpBXGo2YxdH4O7iyNJxXfB2L+cwxOIi2P0V/PEaZCZAq6HQfwb4NDFrvML2SXIXooKlns9nzKIIsvOL+GZCd+p5l3P26bENxoqNZ/Yay+/etRgadjdrrMJ+SHIXogLl5Bfx0NLtnErNYdm4LrSsW6PsNzkbY2xEffg38AqEOxcaa8FIsVRchSR3ISpIYVExT67Yyc5TacwbFUK3xj5lu0HWWVg/29gNqZonDHgVujwKzq4VE7CwK5LchagAWmteXr2PtdGJvDK0DYOD/a//4oIcY7/SLe9CYS50fgRufBaql/GHg6jSJLkLUQHeW3uYFRGnmNi3CWN6BF3fRcXFsPdrWPcqZMRDy1uh/yvgW4lb7Am7IcldCDNbvu0k7687zF2d6vPMwBbXd9HxzRD2IiTshoCOMHw+BN1QsYEKuybJXQgz+m3/GV7+3z76tqjN68ODrz37NOmQUSw99At4NYDhnxnFUgcTJzeJKk+SuxBmsv3EOSat2ElwfW/+774QnK82+/R8Mmx4HSIXQ7Xqxlj1rhPAuQI36RBViiR3IczgUGImDy3ZTj1vNxaP7Yx7tSv80yrIgb8+hs3vQEG2sQl1n+egum/lBizsniR3IUyUkJ7DmEURuDgbs09rVS9l9mlxMexbZRRL009B88HG0MbazSs/YFElSHIXwgTp2QWMWRRBZm4hKx/tRoNa7v/+0Ik/jWLp6Z3g3x7umAeNeld+sKJKkeQuRDnlFhTxyLJIjiefZ+mDXWgT4HXpB5KPwNrpEPMT1KgHwz6F4HukWCoqhSR3IcqhqFjzn692EnHiHB+O7EiPphf1mZ9PgY1vQORCcHKFm16Gbo9DtVJa9UJUEEnuQpSR1pppq/fx2/5Ept3amtvaBxgnCnIh4lPY9DbkZ0KnsdDnefAodRthISqUJHchyuijP46wfFssj97YmHE3NAKtYd+3sO4VSIuFZoOMYqlfS0uHKqowSe5ClMFXEbG8/fshhnesx7ODWsLJrUaxND4K6gTD6NXQuI+lwxRCkrsQ12tddCIv/m8fvZvX5o2bPHD45gGI/hE8/eH2edB+BDiYsLuSEGYkyV2I67AjNpWJX+6gW11Y6LcK548XgWM16PsidJ9ozDIVwoqYlNyVUlOAhwEN7AUeBJ4DHgGSSj72gtZ6jSnPEcKSjpzN4tHF4TzpFsZj57/HISoTOj5gJHbPOpYOT4hSlTu5K6XqAZOA1lrrHKXU18CIktPvaq3fMkeAQlhSYnoOSxe8y/d6GfXzz0LT/jDgNajT2tKhCXFVpnbLOAFuSqkCwB04DQSZGpQQ1uD80XBSvpzCa0Ux5NRsCUM+gab9LB2WENel3FPltNbxwFtALJAApGutw0pOP6GU2qOUWqSUqlna9Uqp8UqpSKVUZFJSUmkfEcIyzh2naOVoqn8+GN/CMxzqOhu3J8MlsQubUu7kXpK0bwcaAQFAdaXU/cDHQBOgA0bSf7u067XW87XWoVrr0Nq1a5c3DCHMJycVfnsR/VFnCmN+473C4fx1axjNB0+UUTDC5pjSLdMfOK61TgJQSn0H9NBaf/H3B5RSC4CfTAtRiApWmA/bP4ONb6Bz09npM4QJ8YN5+JbuDO3cxNLRCVEupiT3WKCbUsodyAH6AZFKKX+tdULJZ4YB+0yMUYiKoTVE/wC/T4fU49C4L1/XepRntxTz0A2NeKRXY0tHKES5lTu5a623KaVWATuAQmAnMB/4TCnVAWN45AngUdPDFMLM4iLhtxfh1F9QuxXc9y2r0lvw7Ko93NY+gBdvaXXtLfKEsGImjZbRWk8Hpl92+AFT7ilEhUo9AWtfgf3fQXU/uO196HA/64+c49nvIunZ1Ie37m6Hg4MkdmHbZIaqqBpy0mDz27DtE1CO0Pu/0HMSuHiy61Qaj3+xgxZ1PPnk/k64OEnxVNg+Se7CvhUVQOQi2DDHGA3TYZQxs9SrHgDHk88zbsl2fD2rsWRcZzxdnS0csBDmIcld2CetIeZn+H0anDtqbGs3cKaxzV2Js5m5jF60DYClD3bBz9PVUtEKYXaS3IX9iY+CsJfh5J/g2wJGfQ3NBsJFBdLM3AIeXLyd5Mx8VozvRuPaHhYMWAjzk+Qu7EdaLKx7FfZ+A+6+MOQdCBkDjpf+Nc8vLGbCF1HEnMnkszGhdGjgbZl4hahAktyF7ctNh83vwF8fG63zXk9Dz8ngWuNfHy0u1kxdtZs/j6Tw1t3t6dtCtsAT9kmSu7BdRQUQtQQ2vA7ZKdBuBPR7GbzqX/GS13+JZvWu0/z35hbc1enKnxPC1klyF7ZHazj4i1EsTTkMQb2MYmlAh6tetmDTMRZsPs7YHkE8dqMsKyDsmyR3YVtO7zSKpSc2g08zGLECWgy+pFhamtW74pm1Jpohwf68fGtrmX0q7J4kd2Eb0uNg3Wuw5ytw94Fb3oJOY8Hx2uPSNx9O4plvdtO1US3evqc9jjL7VFQBktyFdcvNgD/fg63/Z3TH9JwMvZ4CV6/runxffDoTPo+iSW0P5o8OxdVZZp+KqkGSu7BORYWwY6lRLD2fBMH3GMVS78DrvsXJlPOMXRyBt3s1ljzYBS83mX0qqg5J7sK6aA2Hw4x+9eSDENgDRq2Eep3KdJvkrDzGLIqgsFjz1bjO1PWS2aeiapHkLqxHwh4IexGOb4JaTeDe5dByyDWLpZc7n1fIuCXbOZORy/KHu9HUz7OCAhbCeklyF5aXHg9/zITdK8CtJgx+Ezo9CE7VynyrgqJiHlu+g33x6cx/IJRODUvdwlcIuyfJXVhOXib8+T6EfwS6CHo8acwudfMu1+201jy7ag+bDiUxZ3gw/VvXMW+8QtgQSe6i8hUVwq4v4I9ZcP4stL0T+k2DmkEm3faNXw/y3c54nhrQnBFdrr/wKoQ9kuQuKo/WcGStUSxNioYG3WDkCqgfavKtF205zicbj3Jf10CevKmpGYIVwrZJcheV48xeI6kfWw81G8E9y6DV0DIXS0vz4+7TvPbzAQa2rsOrt7eV2adCYGJyV0pNAR7G2Ax7L/Ag4A6sBIIwNsi+R2udalKUwnZlJMD6mbBzuTHxaNDr0PnhchVLSxN+NJmnv95NaMOafDCyo8w+FaJEuZO7UqoeMAlorbXOUUp9DYwAWgPrtNZzlFLPAc8Bz5olWmE78rIg/EMI/8BYvbH7RKNY6l7LbI84cDqDR5dFEeTrzmejO8vsUyEuYmq3jBPgppQqwGixnwaeB/qUnF8KbECSe9VRXAS7lhvF0qwz0PoO6D8dajU262NOnctm7OIIPFydjNmn7jL7VIiLlTu5a63jlVJvAbFADhCmtQ5TStXRWieUfCZBKVXqbghKqfHAeIDAQBnZYBeOrDP61c/uh/qdjX71wK5mf8y58/mMWRxBbkERqx7rQYC3m9mfIYStM6VbpiZwO9AISAO+UUrdf73Xa63nA/MBQkNDdXnjEFYg8QCEvQRH14F3Q7h7idFir4DCZna+Mfs0PjWHLx7uSvM6MvtUiNKY0i3THziutU4CUEp9B/QAEpVS/iWtdn/grBniFNYoMxHWz4Kdn4OLJwycBV0eASeXCnlcYVExT3y5kz1xaXx8fyc6B5mv/14Ie2NKco8Fuiml3DG6ZfoBkcB5YAwwp+TralODFFYm/7yxBO+W96AoH7pOgN5TzVosvZzWmhe+38sfMWeZeUdbBrWpW2HPEsIemNLnvk0ptQrYARQCOzG6WTyAr5VSD2H8ALjbHIEKK1BcBLu/gj9eg8wEaHUb9H8FfCp+y7p3fj/E15FxTLqpKfd3a1jhzxPC1pk0WkZrPR2YftnhPIxWvLAnR9cbxdLEvcbyu3cthobdK+XRn289wYd/HOHe0AZMGdC8Up4phK2TGari6s5GGxtRHw4Dr0C4cyG0GQ4ODpXy+F/3JTDth/30a+nHrGEy+1SI6yXJXZQu6yysn23shlTNEwa8Cl0eBefK2/Ri27EUJn21iw4NvPloVAhOjpXzA0UIeyDJXVwqPxv+KimWFuZC50fgxmehuk+lhhFzJoOHl0VSv6Ybi8Z0xq2azD4VoiwkuQtDcTHsWWkUSzPioeWtRrHUt/JXWIxPy2Hsou24OTuybFwXalY3zzo0QlQlktyFsa3dby/CmT0Q0BGGz4egGywSSlp2PmMWRXA+r5CvJ3Snfk13i8QhhK2T5F6VJR0yiqWHfoEa9WH4Amh7V6UVSy+XW1DEQ0sjiU3JZum4LrTyr2GROISwB5Lcq6KsJNg4ByIXg7M79JsO3R4DZ8ut0VJYVMyTK3ayIzaVj0aG0L1J5fbxC2FvJLlXJQU58NfHsPkdKMiG0AfhxufAo7ZFw9Ja8/Lq/fx+IJEZt7VmSDt/i8YjhD2Q5F4VFBfDvlWw7lVIPwXNBxtDG2tbx4SgD9YdYUVELI/3acLYno0sHY4QdkGSu7078SeEvQind4J/e7hjHjTqbemoLlgREcu7aw9xZ0h9pg5qYelwhLAbktztVfIRWDsdYn6CGvVg2KcQfI/FiqWl+f1AIi9+v5c+LWoz585gmX0qhBlJcrc351NKiqWLwMkVbnoZuj0O1axrSGHUyXM88eUOgut5Me++EJxl9qkQZiXJ3V4U5MK2T2Dz25CfBSFjoO8L4FHqRlgWdeRsJuOWRBLg7caisZ1xryZ/DYUwN/lXZeu0hn3fwtpXID0Wmg2EAa+BX0tLR1aqM+m5jF4YQTUnB5aN64KPR8Vs7CFEVSfJ3Zad3GoUS+OjoE4wDP0fNOlr6aiuKD2ngDGLIsjILeSr8d1oUMu6uoqEsCeS3G1RylGjWBr9I3j6w+3zoP0IcLDexbVyC4p4ZFkkx5KzWDy2C23reVk6JCHsmiR3W5J9Dja+CdsXgKML9H0Ruk+EatUtHdlVFRVrpqzcRcTxc7w/ogM3NPO1dEhC2D1J7ragMA8i5sOmuZCXCR0fMBK7Zx1LR3ZNWmte+XE/v+w7w0tDWnF7h3qWDkmIKkGSuzXTGvZ/D2tnQNpJaNrfKJbWaW3pyK7bvA1HWbb1JON7N+bhXo0tHY4QVUa5k7tSqgWw8qJDjYFpgDfwCJBUcvwFrfWa8j6nyordZhRL47aDXxu4/ztoaltb034deYq5vx3kjg4BPHezdY7eEcJelTu5a60PAh0AlFKOQDzwPfAg8K7W+i1zBFjlnDtmDGs88D/wqANDP4QO91l1sbQ0f8Qk8vx3e+nVzJc372qPg4PMPhWiMpmrW6YfcFRrfVKmkJdT9jnY9JbRt+7obKzW2ONJcPGwdGRlFnXyHBOX76SVvycf39+Jak4y+1SIymau5D4CWHHR908opUYDkcDTWuvUyy9QSo0HxgMEBgaaKQwbVJhvjH7Z+CbkpkPH+6DvS1DD9pa9TcrM4721h/hq+ynqebuxeGwXPFykrCOEJSittWk3UKoacBpoo7VOVErVAZIBDbwG+Gutx13tHqGhoToyMtKkOGyO1hD9A/w+HVKPQ+O+MHAm1G1r6cjKLLegiIVbjvPxhqPkFhRxX9dA/tO/ObVk71MhKpRSKkprHVraOXM0qwYDO7TWiQB/fy158ALgJzM8w77ERRp7lp76C2q3gvu+NYqlNtalVVys+d+ueOb+dpCE9FwGtK7Dc4Nb0qS27XUlCWFvzJHcR3JRl4xSyl9rnVDy7TBgnxmeYR9STxjF0v3fQXU/uO196HA/ONpe18XWoynMWnOAffEZBNfz4t17O9CtsWyNJ4S1MCmrKKXcgQHAoxcdflMp1QGjW+bEZeeqppw02PwWbPsUlCP0/i/0nAQunpaOrMyOnM1izi/RrI0+S4CXK+/d24Gh7QNkNIwQVsak5K61zgZ8Ljv2gEkR2ZPCfGNd9Y1zjATfYZQxs9TL9mZppmTl8d7aw3wZEYubsyP/vbkF43o2wtXZtoZoClFV2F5/gC3Q2tgB6ffpcO6osa3dwJnGNnc2JregiEV/Hmfe+qPkFBQxqksg/+nfDF9ZqlcIqybJ3dzio+C3lyA2HHxbwKivjTXWbbBY+uOe07z560Hi03Lo38qP5wa3pKmf7XUlCVEVSXI3l7RYWPcq7P0G3H1hyDvGbkg2WCzddiyF2Wui2R2XTpuAGsy9qx09mspKjkLYEtvLPNYmNx02vwN/fWy0zns9DT0ng2sNS0dWZseSspjzSwxhBxKpW8OVt+9uz7CO9aRYKoQNkuReXkUFELUENrwO2SnQbgT0exm86ls6sjI7dz6fD9Yd5ou/TuLi5MAzA5vz0A2NcasmxVIhbJUk97LSGg7+Ar9Pg5TDENTLKJYGdLB0ZGWWW1DE0vATfLT+COfzChnRJZAp/ZtT21OKpULYOknuZXF6J4S9DCc2g08zGLECWgy2uWKp1pof9yTw5q8xxKXm0LdFbV64pRXN6kixVAh7Icn9eqTHGcXSPSvB3QdueQs6jTVWb7Qx20+cY+bP0ew+lUYr/xp88VA72fZOCDskyf1qcjNgy7vw1zyjO6bnZOj1FLja3ubOJ5LPM+eXGH7df4Y6NVyYe1c7hofUx1GKpULYJUnupSkqhB1LYP3rkJ0MwfcYxVJv21uaOC07nw/WHeHzv07g7OjAlP7NeaR3I9yryasXwp7Jv/CLaQ2Hw4x+9eSDENgDBn0N9TpZOrIyyyss4vOtJ/lg3WGy8gq5t3MDpvRvjl8NV0uHJoSoBJLc/5awx9iz9PgmqNUE7l0OLYfYZLF0zd4zvPFrDLHnsrmxeW2ev6UlLeva3rh7IUT5SXJPj4c/ZsLuFeBWEwa/CaHjbLJYGnUylVk/H2BHbBot63qybFwXejevbemwhBAWUHWTe14m/Pk+hH8EusjYr7TX0+DmbenIyiw2JZs3fo3h570J1PZ04Y07g7mrUwMplgpRhVW95F5UCDs/h/Wz4fxZaHsn9JsGNYMsHVmZpWcX8OEfh1m69QRODg78p18zxvduTHXZt1SIKq/qZAGt4chao1iaFA0NusHIFVC/1O0HrVp+YTGf/2UUSzNyC7i7U32eHtiCOlIsFUKUqBrJ/cxeCHsJjm2Amo3gnmXQaqhNFkt/3XeGOb/GcDIlmxua+vLCLa1oHSDFUiHEpew7uWckwPqZsHO5MfFo0OvQ+WFwqmbpyMpsZ2wqs36OJvJkKs38PFj8YGf6NK+NsrEfUEKIymGfyT0vC8I/hPAPjNUbu0+E3s8Yo2FszKlz2bz520F+3H0aXw8XZg8L5p7Q+jg5Olg6NCGEFbOv5F5cBLuWwx+zIOsMtL4D+s+AWo0sHVmZpecUMG/9ERb/eQIHB3jypqY8emMTPKRYKoS4DuXOFEqpFsDKiw41BqYBy0qOBwEngHu01qnlD/E6HVlnFEvP7of6nY1+9cCuFf5YcysoKmb5Xyd5f91h0nIKuDOkPk8PbI6/l5ulQxNC2JByJ3et9UGgA4BSyhGIB74HngPWaa3nKKWeK/n+WdNDvYLEA0ax9Og68G4Idy8xWuw21hettSbsQCJzfonhePJ5ejTx4YVbWtG2nu0tUiaEsDxz/R+/H3BUa31SKXU70Kfk+FJgAxWV3I9vgmW3g4snDJwFXR4BJ9vbaGL3qTRmrYkm4vg5mvp5sGhsKH1b+EmxVAhRbuZK7iOAFSW/rqO1TgDQWicopfxKu0ApNR4YDxAYWM7VFgO7Q5/njREw7rXKdw8LikvNZu5vB1m96zQ+1asx8462jOjcQIqlQgiTKa21aTdQqhpwGmijtU5USqVprb0vOp+qtb7qMJXQ0FAdGRlpUhy2JCO3gI83HGXhluMo4OFejZhwYxM8XW1vPRshhOUopaK01qXOxDRHy30wsENrnVjyfaJSyr+k1e4PnDXDM+xCQVExX0XE8u7aw5w7n8/wjvV4elAL6nlLsVQIYV7mSO4j+adLBuAHYAwwp+TrajM8w6ZprVkbfZbXf4nmWNJ5ujaqxUtDWhNcX4qlQoiKYVJyV0q5AwOARy86PAf4Win1EBAL3G3KM2zd3rh0Zq05wF/HztG4dnUWjA6lfysplgohKpZJyV1rnQ34XHYsBWP0TJV2Oi2Ht347yHc746lVvRqv3t6GkV0CcZZiqRCiEsh0RzPLzC3gk41H+WzzcTQw4cYmPN63CTWkWCqEqESS3M2ksKiYr7af4r21h0jOyuf2DgE8M7AFDWq5Wzo0IUQVJMndRFpr1h88y+w1MRw5m0WXoFosHNOK9g28LR2aEKIKk+Rugv2n05m9Jpo/j6TQyLc6nz7QiYGt60ixVAhhcZLcy+FMei5vhR3k2x1xeLs5M+O21ozq2pBqTlIsFUJYB0nuZZCVV8j8jUeZv/kYxcUwvldjHu/bFC83KZYKIayLJPfrUFhUzDdRcbwddojkrDxuax/AfwdJsVQIYb0kuV+F1poNh5J4fU00hxKz6NSwJgtGd6JjoO3t6CSEqFokuV/BgdMZvP5LNJsPJ9PQx52P7wvh5rZ1pVgqhLAJktwvk5iRy9thB/kmKo4ars68fGtrHugmxVIhhG2R5F4iO7+QTzceY/6mYxQWF/NQz0Y8cVNTvN2rWTo0IYQosyqf3IuKNauiTvF22CHOZuYxJNif/97cgoY+1S0dmhBClFuVTu6bDiUxe000MWcy6Rjozcf3h9Cpoe3t6CSEEJerksn94JlMZq+JZuOhJBrUcuP/RoVwS7AUS4UQ9qNKJfezmbm8+/shVm4/hYeLEy/e0orRPRri4uRo6dCEEMKsqkRyz84v5LPNx/lk41HyC4sZ0yOISTc1o2Z1KZYKIeyTXSf3omLNdzvieCvsIIkZedzcpi7PDm5JI18plgoh7JvdJvc/jyQz8+doohMyaN/Am49GhdA5SIqlQoiqwe6S++HETF7/JYY/Ys5Sz9uND0Z25NZgfxwcpFgqhKg6TN0g2xv4DGgLaGAcMAh4BEgq+dgLWus1pjzneiRl5vHu2kN8FRFLdRcnnh/ckjE9gnB1lmKpEKLqMbXl/j7wq9b6LqVUNcAdI7m/q7V+y+TorkNOfhELtxzj4w1HySssZnT3ICb1a0YtKZYKIaqwcid3pVQNoDcwFkBrnQ/kV+ZY8d2n0pjwRRQJ6bkMaF2H5we3pHFtj0p7vhBCWCtTWu6NMbpeFiul2gNRwH9Kzj2hlBoNRAJPa61TL79YKTUeGA8QGBhYrgCCfKrT1M+Dd+/tQLfGPuW6hxBC2COltS7fhUqFAn8BPbXW25RS7wMZwEdAMkYf/GuAv9Z63NXuFRoaqiMjI8sVhxBCVFVKqSitdWhp50xZxzYOiNNabyv5fhUQorVO1FoXaa2LgQVAFxOeIYQQohzKndy11meAU0qpFiWH+gEHlFL+F31sGLDPhPiEEEKUg6mjZZ4ElpeMlDkGPAh8oJTqgNEtcwJ41MRnCCGEKCOTkrvWehdweX/PA6bcUwghhOlk7zghhLBDktyFEMIOSXIXQgg7JMldCCHsULknMZk1CKWSgJMm3MIXY+KUsB7yTqyPvBPrZMp7aai1rl3aCatI7qZSSkVeaZaWsAx5J9ZH3ol1qqj3It0yQghhhyS5CyGEHbKX5D7f0gGIf5F3Yn3knVinCnkvdtHnLoQQ4lL20nIXQghxEUnuQghhh+wiuSulnlBKHVFKaaWUr6XjEQal1HKl1EGl1D6l1CKllLOlY6rqlFILlVK7lVJ7lFKrlFKyL6WVUEp9qJTKMtf97CK5A38C/TFtIpQwv+VASyAYcAMetmw4ApiitW6vtW4HxAJPWDogcWFnO29z3tOmkrtSKkgpFaOUWnpRy8Nda71Ta33C0vFVVVd5L2t0CSACqG/pWKuKq7yTjJLzCuMHroyoqCRXeidKKUdgLvBfcz7PppJ7iRbA/JKWRwbwuIXjEYYrvpeS7pgHgF8tFFtVVeo7UUotBs5g/K/qQ8uFVyWV9k6eAH7QWieY80G2mNxPaa3/LPn1F8ANlgxGXHC19zIP2KS13lz5YVVppb4TrfWDQAAQDdxrodiqqsvfyUDgbirgh6wtJvfL/xsp/620DqW+F6XUdKA28FSlRySu+G9Fa10ErATurNSIxOXvpDPQFDiilDoBuCuljpjjQbaY3AOVUt1Lfj0S2GLJYMQF/3ovSqmHgUHASK11seVCq7JKeydN4UKf+21AjKWCq6IufycztdZ1tdZBWusgIFtr3dQcD7LF5B4NjFFK7QFqAR8rpSYppeIwCnZ7lFKfWTTCqulf7wX4BKgDbFVK7VJKTbNkgFVQae9kqVJqL7AX8AdetWB8VVFp76RC2NTyA0qpIOAnrXVbS8ci/iHvxfrIO7E+lf1ObLHlLoQQ4hpsquUuhBDi+kjLXQgh7JAkdyGEsEOS3IUQwg5JchdCCDskyV0IIezQ/wPHG0ZDx9owXgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw the plot\n",
    "plt.plot(calories_burnt)\n",
    "plt.plot(weight)\n",
    "\n",
    "# add legend in the lower right part of the figure\n",
    "plt.legend(labels=['Calories Burnt', 'Weight'], loc='best')\n",
    "\n",
    "# set labels for each of these persons\n",
    "plt.xticks(ticks=[0,1,2,3], labels=['p1', 'p2', 'p3', 'p4']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Size, Colors, Markers and Line styles\n",
    "\n",
    "- Anda juga dapat menentukan ukuran gambar menggunakan metode `figure()` dan meneruskan nilai sebagai tupel dengan panjang baris dan kolom ke argumen figsize.\n",
    "- Nilai panjang dianggap dalam **inches**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# figure size in inches\n",
    "plt.figure(figsize=(10,5))\n",
    "\n",
    "# draw the plot\n",
    "plt.plot(calories_burnt)\n",
    "plt.plot(weight)\n",
    "\n",
    "# add legend in the lower right part of the figure\n",
    "plt.legend(labels=['Calories Burnt', 'Weight'], loc='lower right')\n",
    "\n",
    "# set labels for each of these persons\n",
    "plt.xticks(ticks=[0,1,2,3], labels=['p1', 'p2', 'p3', 'p4']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Dengan setiap argumen X dan Y, Anda juga dapat meneruskan argumen ketiga opsional dalam bentuk string yang menunjukkan warna dan jenis garis plot.\n",
    "- Format default adalah `b-` yang berarti **solid blue line**. Pada gambar di bawah ini kita menggunakan `go` yang artinya **green circles**. Demikian juga, kita dapat membuat banyak kombinasi seperti itu untuk memformat plot kita.\n",
    "\n",
    "https://matplotlib.org/stable/gallery/color/named_colors.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw the plot\n",
    "plt.plot(calories_burnt)\n",
    "plt.plot(weight, 'ro')\n",
    "\n",
    "# add legend in the lower right part of the figure\n",
    "plt.legend(labels=['Calories Burnt', 'Weight'], loc='best')\n",
    "\n",
    "# set labels for each of these persons\n",
    "plt.xticks(ticks=[0,1,2,3], labels=['p1', 'p2', 'p3', 'p4']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Kita juga dapat memplot beberapa set data dengan meneruskan beberapa set argumen sumbu X dan Y dalam metode `plot()` seperti yang ditunjukkan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Figure and subplots\n",
    "\n",
    "- Kita dapat menggunakan metode `subplots()` untuk menambahkan lebih dari satu plot dalam satu gambar.\n",
    "- Metode `subplots()` membutuhkan dua argumen: mereka adalah **nrows, ncols**. Mereka menunjukkan jumlah baris, jumlah kolom masing-masing.\n",
    "- Metode ini membuat dua objek: **figure** dan **axes** yang kita simpan dalam variabel `fig` dan `ax`.\n",
    "- Anda memplot setiap gambar dengan menentukan posisinya menggunakan indeks baris dan indeks kolom. Mari kita lihat contoh di bawah ini:\n",
    "\n",
    "https://matplotlib.org/stable/gallery/subplots_axes_and_figures/subplots_demo.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create 2 plots\n",
    "fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(10,10))\n",
    "\n",
    "# plot on 0 row and 0 column\n",
    "ax[0,0].plot(calories_burnt,'go')\n",
    "\n",
    "# plot on 0 row and 1 column\n",
    "ax[0,1].plot(weight)\n",
    "\n",
    "# set titles for subplots\n",
    "ax[0,0].set_title(\"Calories Burnt\")\n",
    "ax[0,1].set_title(\"Weight\")\n",
    "\n",
    "# set ticks for each of these persons\n",
    "ax[0,0].set_xticks(ticks=[0,1,2,3]);\n",
    "ax[0,1].set_xticks(ticks=[0,1,2,3]);\n",
    "\n",
    "# set labels for each of these persons\n",
    "ax[0,0].set_xticklabels(labels=['p1', 'p2', 'p3', 'p4']);\n",
    "ax[0,1].set_xticklabels(labels=['p1', 'p2', 'p3', 'p4']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Perhatikan bahwa pada gambar di atas kita memiliki dua plot kosong, itu karena kita membuat 4 subplot (2 baris dan 2 kolom).\n",
    "- Sebagai data scientist, akan ada saatnya Anda harus memiliki sumbu yang sama untuk semua subplot Anda. Anda dapat melakukannya dengan menggunakan parameter **sharex** dan **sharey** dari `subplot()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create 2 plots\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(6,6), sharex=True, sharey=True)\n",
    "\n",
    "# plot on 0 row and 0 column\n",
    "ax[0].plot(calories_burnt,'go')\n",
    "\n",
    "# plot on 0 row and 1 column\n",
    "ax[1].plot(weight)\n",
    "\n",
    "# set titles for subplots\n",
    "ax[0].set_title(\"Calories Burnt\")\n",
    "ax[1].set_title(\"Weight\")\n",
    "\n",
    "# set ticks for each of these persons\n",
    "ax[0].set_xticks(ticks=[0,1,2,3]);\n",
    "ax[1].set_xticks(ticks=[0,1,2,3]);\n",
    "\n",
    "# set labels for each of these persons\n",
    "ax[0].set_xticklabels(labels=['p1', 'p2', 'p3', 'p4']);\n",
    "ax[1].set_xticklabels(labels=['p1', 'p2', 'p3', 'p4']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Perhatikan pada plot di atas, sekarang sumbu x dan y hanya diberi label satu kali untuk masing-masing plot luar. Ini karena plot bagian dalam \"berbagi\" kedua sumbu.\n",
    "- Selain itu, hanya ada **dua plot** karena kita mengurangi jumlah baris menjadi 1 dan kolom menjadi 2 di `subplot()`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "untuk jupyter notebook (local) perlu install module wget\n",
    "pip install wget\n",
    "\n",
    "google colab gunakan perintah:\n",
    "!wget https://raw.githubusercontent.com/sharmaroshan/Big-Mart-Sales-Prediction/master/bm_Train.csv\n",
    "\n",
    "''' \n",
    "import wget\n",
    "\n",
    "url = 'https://raw.githubusercontent.com/sharmaroshan/Big-Mart-Sales-Prediction/master/bm_Train.csv'\n",
    "\n",
    "wget.download(url,'bigmart_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.300</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.920</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>17.500</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>Meat</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.930</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Household</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FDP36</td>\n",
       "      <td>10.395</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>51.4008</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>556.6088</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0           FDA15        9.300          Low Fat         0.016047   \n",
       "1           DRC01        5.920          Regular         0.019278   \n",
       "2           FDN15       17.500          Low Fat         0.016760   \n",
       "4           NCD19        8.930          Low Fat         0.000000   \n",
       "5           FDP36       10.395          Regular         0.000000   \n",
       "\n",
       "      Item_Type  Item_MRP Outlet_Identifier  Outlet_Establishment_Year  \\\n",
       "0         Dairy  249.8092            OUT049                       1999   \n",
       "1   Soft Drinks   48.2692            OUT018                       2009   \n",
       "2          Meat  141.6180            OUT049                       1999   \n",
       "4     Household   53.8614            OUT013                       1987   \n",
       "5  Baking Goods   51.4008            OUT018                       2009   \n",
       "\n",
       "  Outlet_Size Outlet_Location_Type        Outlet_Type  Item_Outlet_Sales  \n",
       "0      Medium               Tier 1  Supermarket Type1          3735.1380  \n",
       "1      Medium               Tier 3  Supermarket Type2           443.4228  \n",
       "2      Medium               Tier 1  Supermarket Type1          2097.2700  \n",
       "4        High               Tier 3  Supermarket Type1           994.7052  \n",
       "5      Medium               Tier 3  Supermarket Type2           556.6088  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "Jika excel, sesuaikan dengan pandas: pd.read_excel()\n",
    "'''\n",
    "\n",
    "# read the dataset\n",
    "data_BM = pd.read_csv('bigmart_data.csv')\n",
    "\n",
    "# drop the null values\n",
    "data_BM = data_BM.dropna(how=\"any\")\n",
    "\n",
    "# view the top results\n",
    "data_BM.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 3. Line Chart\n",
    "\n",
    "- Kita akan membuat diagram garis untuk menunjukkan **harga rata-rata per item**.\n",
    "- Dengan beberapa kumpulan data, Anda mungkin ingin memahami perubahan dalam satu variabel sebagai fungsi waktu, atau variabel kontinu yang serupa.\n",
    "- Di matplotlib, **line chart** adalah plot default saat menggunakan `plot()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Type\n",
       "Baking Goods             125.795653\n",
       "Breads                   141.300639\n",
       "Breakfast                134.090683\n",
       "Canned                   138.551179\n",
       "Dairy                    149.481471\n",
       "Frozen Foods             140.095830\n",
       "Fruits and Vegetables    145.418257\n",
       "Hard Drinks              140.102908\n",
       "Health and Hygiene       131.437324\n",
       "Household                149.884244\n",
       "Meat                     140.279344\n",
       "Others                   137.640870\n",
       "Seafood                  146.595782\n",
       "Snack Foods              147.569955\n",
       "Soft Drinks              130.910182\n",
       "Starchy Foods            151.256747\n",
       "Name: Item_MRP, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_by_item = data_BM.groupby('Item_Type').Item_MRP.mean()\n",
    "\n",
    "price_by_item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x24df43574f0>]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mean price based on item type\n",
    "price_by_item = data_BM.groupby('Item_Type').Item_MRP.mean()[:10]\n",
    "\n",
    "x = price_by_item.index.tolist()\n",
    "y = price_by_item.values.tolist()\n",
    "\n",
    "# set figure size\n",
    "plt.figure(figsize=(14, 8))\n",
    "\n",
    "# set title\n",
    "plt.title('Mean price for each item type')\n",
    "\n",
    "# set axis labels\n",
    "plt.xlabel('Item Type')\n",
    "plt.ylabel('Mean Price')\n",
    "\n",
    "# set xticks \n",
    "plt.xticks(labels=x, ticks=np.arange(len(x)), rotation=45)\n",
    "\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#  4. Bar Chart\n",
    "\n",
    "- Misalkan kita ingin melihat **berapa rata-rata penjualan untuk setiap jenis outlet?**\n",
    "- Bagan batang adalah jenis visualisasi sederhana lainnya yang digunakan untuk variabel kategoris.\n",
    "- Anda dapat menggunakan `plt.bar()` sebagai ganti `plt.plot()` untuk membuat diagram batang."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 3 artists>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VfamqPjY39pyq+nxVnTN9PHRu2TOr6qKqurCqHjI3fnBVnTcte1lV1UrNGQAAIFnZI0onJTliifGXdvdB08dpSVJV+yc5KskB0zYnVNV20/qvSHJskv2mj6X2CQAAsMmsWCh19/uSXL3M1Y9M8ubuvq67L05yUZJDq2qPJLfv7rO6u5O8NskjVmTCAAAAk0W8R+lpVXXudGrertPYnkkunVvnsmlsz+n2OL6kqjq2qtZW1dorr7xyU88bAADYRmzuUHpFknskOSjJFUleMo0v9b6j3sD4krr7xO4+pLsPWbNmzS2cKgAAsK3arKHU3V/s7hu6+3tJ/ibJodOiy5LsPbfqXkkun8b3WmIcAABgxWzWUJrec7TOI5OsuyLeqUmOqqpbV9XdM7tow4e6+4ok11TVYdPV7p6Y5O2bc84AAMC2Z/uV2nFVvSnJ4Ul2q6rLkvxRksOr6qDMTp+7JMlvJEl3n19VpyS5IMn1SZ7a3TdMu3pKZlfQ2ynJu6YPAACAFbNiodTdj1ti+FUbWP/4JMcvMb42yYGbcGoAAAAbtIir3gEAAGzRhBIAAMBAKAEAAAyEEgAAwEAoAQAADIQSAADAQCgBAAAMhBIAAMBAKAEAAAyEEgAAwEAoAQAADIQSAADAQCgBAAAMhBIAAMBAKAEAAAyEEgAAwEAoAQAADIQSAADAQCgBAAAMhBIAAMBAKAEAAAyEEgAAwEAoAQAADIQSAADAQCgBAAAMhBIAAMBAKAEAAAyEEgAAwEAoAQAADG5yKFXVrarq9isxGQAAgC3BskKpqt5YVbevqp2TXJDkwqp6xspODQAAYDGWe0Rp/+7+epJHJDktyd2SPGGlJgUAALBIyw2lHapqh8xC6e3d/d0kvWKzAgAAWKDlhtJfJ7kkyc5J3ldV+yT5+kpNCgAAYJG2X85K3f2yJC+bG/psVT1wZaYEAACwWMu9mMNdqupVVfWu6f7+SY5e0ZkBAAAsyHJPvTspybuT3HW6/8kkv7MC8wEAAFi45YbSbt19SpLvJUl3X5/khhWbFQAAwAItN5S+UVV3znSlu6o6LMnXVmxWAAAAC7Ssizkk+b0kpya5R1V9IMmaJI9asVkBAAAs0HKveveRqnpAknsnqSQXTn9LCQAAYKuzwVCqql9az6J7VVW6+20rMCcAAICF2tgRpYdvYFknEUoAAMBWZ4Oh1N2/trkmAgAAsKVY7sUcUlUPS3JAktusG+vu563EpAAAABZpWZcHr6pXJnlskt/K7GIOj06yzwrOCwAAYGGW+3eUfrK7n5jkK9393CT3S7L3yk0LAABgcZYbSt+aPn+zqu6a5Pokd1+ZKQEAACzWct+j9I6qumOSP01y9jT2tysyIwAAgAXb2N9Ruk+SS7v7+dP9XZKcl+QTSV668tMDAADY/DZ26t1fJ/lOklTV/ZO8cBr7WpITV3ZqAAAAi7GxU++26+6rp9uPTXJid781yVur6pwVnRkAAMCCbOyI0nZVtS6mHpTkn+eWLftvMAEAAKwmG4udNyV5b1VdldmV796fJFV1z8xOvwMAANjqbDCUuvv4qjojyR5J3tPdPS26VWZ/fBYAAGCrs9HT57r735cY++TKTAcAAGDxlvsHZwEAALYZQgkAAGAglAAAAAZCCQAAYCCUAAAABkIJAABgIJQAAAAGQgkAAGAglAAAAAZCCQAAYCCUAAAABkIJAABgIJQAAAAGQgkAAGAglAAAAAZCCQAAYCCUAAAABkIJAABgIJQAAAAGQgkAAGAglAAAAAZCCQAAYLBioVRVr66qL1XVx+bG7lRVp1fVp6bPu84te2ZVXVRVF1bVQ+bGD66q86ZlL6uqWqk5AwAAJCt7ROmkJEcMY8clOaO790tyxnQ/VbV/kqOSHDBtc0JVbTdt84okxybZb/oY9wkAALBJrVgodff7klw9DB+Z5OTp9slJHjE3/ubuvq67L05yUZJDq2qPJLfv7rO6u5O8dm4bAACAFbG536N0l+6+Ikmmz7tP43smuXRuvcumsT2n2+M4AADAitlSLuaw1PuOegPjS++k6tiqWltVa6+88spNNjkAAGDbsrlD6YvT6XSZPn9pGr8syd5z6+2V5PJpfK8lxpfU3Sd29yHdfciaNWs26cQBAIBtx+YOpVOTHD3dPjrJ2+fGj6qqW1fV3TO7aMOHptPzrqmqw6ar3T1xbhsAAIAVsf1K7biq3pTk8CS7VdVlSf4oyQuTnFJVT0ryuSSPTpLuPr+qTklyQZLrkzy1u2+YdvWUzK6gt1OSd00fAAAAK2bFQqm7H7eeRQ9az/rHJzl+ifG1SQ7chFMDAADYoC3lYg4AAABbDKEEAAAwEEoAAAADoQQAADAQSgAAAAOhBAAAMBBKAAAAA6EEAAAwEEoAAAADoQQAADAQSgAAAAOhBAAAMBBKAAAAA6EEAAAwEEoAAAADoQQAADAQSgAAAAOhBAAAMBBKAAAAA6EEAAAwEEoAAAADoQQAADAQSgAAAAOhBAAAMBBKAAAAA6EEAAAwEEoAAAADoQQAADAQSgAAAAOhBAAAMBBKAAAAA6EEAAAwEEoAAAADoQQAADAQSgAAAAOhBAAAMBBKAAAAA6EEAAAwEEoAAAADoQQAADAQSgAAAAOhBAAAMBBKAAAAA6EEAAAwEEoAAAADoQQAADAQSgAAAAOhBAAAMBBKAAAAA6EEAAAwEEoAAAADoQQAADAQSgAAAAOhBAAAMBBKAAAAA6EEAAAwEEoAAAADoQQAADAQSgAAAAOhBAAAMBBKAAAAA6EEAAAwEEoAAAADoQQAADAQSgAAAAOhBAAAMBBKAAAAA6EEAAAwEEoAAAADoQQAADAQSgAAAAOhBAAAMBBKAAAAA6EEAAAwEEoAAAADoQQAADAQSgAAAAOhBAAAMBBKAAAAg4WEUlVdUlXnVdU5VbV2GrtTVZ1eVZ+aPu86t/4zq+qiqrqwqh6yiDkDAADbjkUeUXpgdx/U3YdM949LckZ375fkjOl+qmr/JEclOSDJEUlOqKrtFjFhAABg27AlnXp3ZJKTp9snJ3nE3Pibu/u67r44yUVJDt380wMAALYViwqlTvKeqjq7qo6dxu7S3VckyfR592l8zySXzm172TQGAACwIrZf0OP+VHdfXlW7Jzm9qj6xgXVribFecsVZdB2bJHe7291u+SwBAIBt0kKOKHX35dPnLyX5h8xOpftiVe2RJNPnL02rX5Zk77nN90py+Xr2e2J3H9Ldh6xZs2alpg8AAGzlNnsoVdXOVXW7dbeTPDjJx5KcmuToabWjk7x9un1qkqOq6tZVdfck+yX50OadNQAAsC1ZxKl3d0nyD1W17vHf2N3/WFUfTnJKVT0pyeeSPDpJuvv8qjolyQVJrk/y1O6+YQHzBgAAthGbPZS6+zNJfmyJ8S8nedB6tjk+yfErPDUAAIAkW9blwQEAALYIQgkAAGAglAAAAAZCCQAAYCCUAAAABkIJAABgIJQAAAAGQgkAAGAglAAAAAZCCQAAYCCUAAAABkIJAABgIJQAAAAGQgkAAGAglAAAAAZCCQAAYCCUAAAABkIJAABgIJQAAAAGQgkAAGAglAAAAAZCCQAAYCCUAAAABkIJAABgIJQAAAAGQgkAAGAglAAAAAZCCQAAYCCUAAAABkIJAABgIJQAAAAGQgkAAGAglAAAAAZCCQAAYCCUAAAABkIJAABgIJQAAAAGQgkAAGAglAAAAAZCCQAAYCCUAAAABkIJAABgIJQAAAAGQgkAAGAglAAAAAZCCQAAYCCUAAAABkIJAABgIJQAAAAGQgkAAGAglAAAAAZCCQAAYCCUAAAABkIJAABgIJQAAAAGQgkAAGAglAAAAAZCCQAAYCCUAAAABkIJAABgIJQAAAAGQgkAAGAglAAAAAZCCQAAYCCUAAAABkIJAABgIJQAAAAGQgkAAGAglAAAAAZCCQAAYCCUAAAABkIJAABgIJQAAAAGQgkAAGAglAAAAAZCCQAAYCCUAAAABkIJAABgIJQAAAAGqyaUquqIqrqwqi6qquMWPR8AAGDrtSpCqaq2S/JXSX4+yf5JHldV+y92VgAAwNZqVYRSkkOTXNTdn+nu7yR5c5IjFzwnAABgK7VaQmnPJJfO3b9sGgMAANjktl/0BJaplhjrG61UdWySY6e711bVhSs6K1a73ZJctehJMKeWeqnDJuM1vyV6vNc9K8rrfkuz5b3k91nfgtUSSpcl2Xvu/l5JLh9X6u4Tk5y4uSbF6lZVa7v7kEXPA9g8vOZh2+N1zy2xWk69+3CS/arq7lW1Y5Kjkpy64DkBAABbqVVxRKm7r6+qpyV5d5Ltkry6u89f8LQAAICt1KoIpSTp7tOSnLboebBVcZombFu85mHb43XPzVbdN7omAgAAwDZttbxHCQAAYLMRSqwqVdVV9bq5+9tX1ZVV9Y6buJ8zq+qQ6fZpVXXHTTxVYDOpqmuH+8dU1cun20+uqiduZPv/XB/YslXVs6vq/Ko6t6rOqar7boJ9Xjt93reqPnbLZ8nWYtW8Rwkm30hyYFXt1N3fSvJzST5/S3bY3Q/dJDMDtjjd/cpFzwHYNKrqfkl+IclPdPd1VbVbkh0XPC22Yo4osRq9K8nDptuPS/KmdQuqaueqenVVfbiqPlpVR07jO1XVm6ffQL0lyU5z21xSVbuNv0mqqt+vqudMt8+sqpdW1fuq6uNVdZ+qeltVfaqqXrAZnjNwM1TVc6rq96fb95n+H3BWVb1o+M3xXavqH6fX9J8uaLrAhu2R5Kruvi5Juvuq7r58+nf8j6fX9tqq+omqendVfbqqnpwkVbVLVZ1RVR+pqvPW/XwAGyKUWI3enOSoqrpNkh9N8sG5Zc9O8s/dfZ8kD0zyoqraOclTknyzu380yfFJDr4Zj/ud7r5/klcmeXuSpyY5MMkxVXXnm/1sgFtqp+kUnHOq6pwkz1vPeq9J8uTuvl+SG4ZlByV5bJIfSfLYqto7wJbmPUn2rqpPVtUJVfWAuWWXTq/t9yc5KcmjkhyW7///4NtJHtndP5HZzwcvqarafFNnNXLqHatOd59bVftmdjRpvGT8g5P84rrfICe5TZK7Jbl/kpfNbX/uzXjodX/k+Lwk53f3FUlSVZ9JsneSL9+MfQK33Le6+6B1d6rqmCSHzK8wvQ/xdt39b9PQGzM7hWedM7r7a9O6FyTZJ8mlKzdl4Kbq7mur6uAkP51Z7Lylqo6bFs//G71Ld1+T5Jqq+vb0+v9Gkj+uqvsn+V6SPZPcJckXNudzYHURSqxWpyZ5cZLDk8wfzakkv9zdF86vPP3SaGPXwr8+P3iU9TbD8uumz9+bu73uvtcSbNk29pvj+df0DfGahi1Sd9+Q5MwkZ1bVeUmOnhZt7N/oxydZk+Tg7v5uVV2SG/87Dz/AqXesVq9O8rzuPm8Yf3eS31p3OL2qfnwaf19m/5NMVR2Y2Sl7oy8m2b2q7lxVt84P/rYZWMW6+yuZ/Xb5sGnoqEXOB7jpqureVbXf3NBBST67zM3vkORLUyQ9MLOjxrBBQolVqbsv6+6/WGLR85PskOTc6Y3az5/GX5Fkl+mUuz9I8qEl9vndzM5l/mCSdyT5xErMHViYJyU5sarOyuwI09cWPB/gptklyclVdcH07/n+SZ6zzG3fkOSQqlqb2S9O/RvPRlX3xs5GAoDVr6p26e51fy/luCR7dPdvL3haAGyhnIMNwLbiYVX1zMz+7ftskmMWOx0AtmSOKAEAAAy8RwkAAGAglAAAAAZCCQAAYCCUAFioqtqrqt5eVZ+qqk9X1V9U1Y7L2O5Zw/1rN7L+HavqNzew/NlVdX5VnVtV51TVfafxv62q/Zf7fADYOriYAwALM/1x6A8meUV3v6aqtktyYpKru/sZG9n22u7eZX33l1h/3yTv6O4Dl1h2vyR/luTw7r6uqnZLsmN3X36znhgAq54jSgAs0s8k+XZ3vyZJuvuGJL+b5L9X1W2r6piqevm6lavqHVV1eFW9MMlO05GfN4w7rapnVNWHp6NDz52GX5jkHtM2Lxo22SPJVd193TSPq9ZFUlWdWVWHVNUvTtueU1UXVtXF0/KDq+q9VXV2Vb27qvbYtF8iABZBKAGwSAckOXt+oLu/nuRzSe65vo26+7gk3+rug7r78fPLqurBSfZLcmiSg5IcXFX3T3Jckk9P24xHq96TZO+q+mRVnVBVD1jiMU+dtj0oyX8keXFV7ZDkL5M8qrsPTvLqJMcv/+kDsKXyB2cBWKRKstQ54OsbX44HTx8fne7vklk4fW59G3T3tVV1cJKfTvLAJG+pquO6+6QbTazqDzKLtL+qqgOTHJjk9NlZhNkuyRU3c94AbEGEEgCLdH6SX54fqKrbJ9k7yaeT/Fh+8OyH2yxjn5Xk/3T3Xw/73XdDG02n/Z2Z5MyqOi/J0UlOGvbxoCSPTnL/ucc6v7vvt4x5AbCKOPUOgEU6I8ltq+qJSTJdzOElSU7q7m8muSTJQVV1q6raO7PT6db57nTq2+jdmb3HaZdpn3tW1e5Jrklyu6UmUVX3rqr95oYOSvLZYZ19kpyQ5DHd/a1p+MIka6aLQaSqdqiqA5b97AHYYgklABamZ5defWSSR1fVp5J8Msm3k6y79PcHklyc5LwkL07ykbnNT0xy7ngxh+5+T5I3JjlrOjL090lu191fTvKBqvrYEhdz2CXJyVV1QVWdm2T/JM8Z1jkmyZ2T/MN0QYfTuvs7SR6V5E+q6j+SnJPkJ2/WFwOALYrLgwMAAAwcUQIAABgIJQAAgIFQAgAAGAglAACAgVACAAAYCCUAAICBUAIAABgIJQAAgMH/B2k9rx3F3FWPAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sales by outlet size\n",
    "sales_by_outlet_size = data_BM.groupby('Outlet_Size').Item_Outlet_Sales.mean()\n",
    "\n",
    "# sort by sales\n",
    "sales_by_outlet_size.sort_values(inplace=True)\n",
    "\n",
    "x = sales_by_outlet_size.index.tolist()\n",
    "y = sales_by_outlet_size.values.tolist()\n",
    "\n",
    "# set figure size\n",
    "plt.figure(figsize=(14, 8))\n",
    "\n",
    "# set axis labels\n",
    "plt.xlabel('Outlet Size')\n",
    "plt.ylabel('Sales')\n",
    "\n",
    "# set title\n",
    "plt.title('Mean sales for each outlet type')\n",
    "\n",
    "# set xticks \n",
    "plt.xticks(labels=x, ticks=np.arange(len(x)))\n",
    "\n",
    "plt.bar(x, y, color=['red', 'orange', 'magenta'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 5. Histogram\n",
    "\n",
    "- **Distribusi Harga Barang**\n",
    "- Histogram adalah jenis plot yang sangat umum ketika kita melihat data seperti tinggi dan berat badan, harga saham, waktu tunggu untuk pelanggan, dll yang sifatnya kontinu.\n",
    "- Data histogram diplot dalam rentang terhadap frekuensinya.\n",
    "- Histogram adalah grafik yang sangat umum muncul dalam probabilitas dan statistik dan membentuk dasar untuk berbagai distribusi seperti distribusi -normal, distribusi t, dll."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set figure size\n",
    "plt.figure(figsize=(14, 8))\n",
    "\n",
    "# title\n",
    "plt.title('Item MRP (price) distribution')\n",
    "\n",
    "# xlabel\n",
    "plt.xlabel('Item_MRP')\n",
    "\n",
    "# ylabel\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "# plot histogram\n",
    "plt.hist(data_BM['Item_MRP'], bins=20, color='lightblue');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 6. Box Plots\n",
    "\n",
    "- **Distribusi penjualan**\n",
    "- Box plots menunjukkan tiga nilai kuartil dari distribusi bersama dengan nilai ekstrim (outlier).\n",
    "- 'Whisker' meluas ke titik-titik yang terletak dalam 1,5 IQR dari kuartil bawah dan atas, dan kemudian pengamatan yang berada di luar kisaran ini ditampilkan secara independen.\n",
    "- Ini berarti bahwa setiap nilai dalam boxplot sesuai dengan pengamatan sebenarnya dalam data.\n",
    "- Mari kita coba untuk memvisualisasikan distribusi Item_Outlet_Sales item."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = data_BM[['Item_Outlet_Sales']]\n",
    "\n",
    "# set figure size\n",
    "plt.figure(figsize=(14, 8))\n",
    "\n",
    "# create outlier point shape\n",
    "red_diamond = dict(markerfacecolor='r', marker='D')\n",
    "\n",
    "# set title\n",
    "plt.title('Item Sales distribution')\n",
    "\n",
    "# make the boxplot\n",
    "plt.boxplot(data.values, labels=['Item Sales'], flierprops=red_diamond);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Kita juga dapat membuat  multiple boxplots untuk kolom yang berbeda dari kumpulan data Anda.\n",
    "- Untuk memplot beberapa boxplot, Anda dapat menggunakan `subplots()` yang sama seperti yang kita lihat sebelumnya.\n",
    "- Mari kita lihat distribusi Item_Weight, Item_MRP bersama"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = data_BM[['Item_Weight', 'Item_MRP']]\n",
    "\n",
    "# create outlier point shape\n",
    "red_diamond = dict(markerfacecolor='r', marker='D')\n",
    "\n",
    "# generate subplots\n",
    "fig, ax = plt.subplots(figsize=(14, 8))\n",
    "\n",
    "# make the boxplot\n",
    "plt.boxplot(data.values, labels=['Item Weight', 'Item MRP (price)'], flierprops=red_diamond);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 7. Violin Plots\n",
    "\n",
    "- Distribusi kepadatan berat Item dan harga Item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = data_BM[['Item_Weight', 'Item_MRP']]\n",
    "\n",
    "# generate subplots\n",
    "fig, ax = plt.subplots(figsize=(14, 8))\n",
    "\n",
    "# add labels to x axis\n",
    "plt.xticks(ticks=[1,2], labels=['Item Weight', 'Item MRP'])\n",
    "\n",
    "# make the violinplot\n",
    "plt.violinplot(data.values);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 8. Scatter Plots\n",
    "\n",
    "- **Distribusi relatif berat item dan visibilitasnya**\n",
    "- Ini menggambarkan distribusi dua variabel menggunakan awan titik, di mana setiap titik mewakili pengamatan dalam kumpulan data.\n",
    "- Penggambaran ini memungkinkan mata untuk menyimpulkan sejumlah besar informasi tentang apakah ada hubungan yang berarti di antara mereka.\n",
    "\n",
    "**CATATAN : Di sini, kita hanya akan menggunakan sebagian data untuk plot.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x24df5b6a4c0>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set figure size\n",
    "plt.figure(figsize=(14, 8))\n",
    "\n",
    "# set label of axes \n",
    "plt.xlabel('Item_Weight')\n",
    "plt.ylabel('Item_Visibility')\n",
    "\n",
    "# plot\n",
    "plt.scatter(data_BM[\"Item_Weight\"][:200], data_BM[\"Item_Visibility\"][:200])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 9. Bubble Plots\n",
    "\n",
    "- **Distribusi penjualan relatif, harga barang, dan visibilitas barang**\n",
    "- Mari kita buat scatter plot Item_Outlet_Sales dan Item_MRP dan buat **size** gelembung dengan kolom Item_Visibility.\n",
    "- Plot ini memungkinkan kita memahami hubungan saling ketergantungan antara 3 variabel.\n",
    "\n",
    "**CATATAN: Perhatikan bahwa kita hanya menggunakan subset data untuk plot.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x24df702dfd0>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set figure size\n",
    "plt.figure(figsize=(14, 8))\n",
    "\n",
    "# set label of axes \n",
    "plt.xlabel('Item_MRP')\n",
    "plt.ylabel('Item_Outlet_Sales')\n",
    "\n",
    "# set title\n",
    "plt.title('Item Outlet Sales vs Item MRP (price)')\n",
    "\n",
    "# plot\n",
    "plt.scatter(data_BM[\"Item_MRP\"][:100], data_BM[\"Item_Outlet_Sales\"][:100], s=data_BM[\"Item_Visibility\"][:100]*1000, c='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}