{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Introduction_Matplotlib.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/riskydav/Belajar-Python/blob/main/Introduction_Matplotlib.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BjU4kizMje0B"
      },
      "source": [
        "### Import Module"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ShFpgWHjZVF"
      },
      "source": [
        "import matplotlib.pyplot as plt # module utama ploting data\n",
        "import numpy as np"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AKhsYadXkBaf"
      },
      "source": [
        "### Membuat Plotting Sederhana"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O_jC-hUzkst3"
      },
      "source": [
        "Membuat sebuah figure yang memiliki sebuah axes; melakukan plotting data pada axes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "eViYpeGhj8Wz",
        "outputId": "77c9e65c-2294-4df3-d839-ac3fd82628a4"
      },
      "source": [
        "# persiapan sample data\n",
        "x = [1, 2, 3, 4]\n",
        "y = [1, 4, 2, 3]\n",
        "\n",
        "fig, ax = plt.subplots() # membuat sebuah figure dan sebuah axes (default)\n",
        "ax.plot(x, y) # melakukan plotting data pada axes"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f4ef81df0d0>]"
            ]
          },
          "metadata": {},
          "execution_count": 2
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0sbsB-xokgC4"
      },
      "source": [
        "Alternatif lain, kita bisa langsung memanfaatkan method plot() pada pyplot untuk melakukan plotting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "qeUAImSbkcOs",
        "outputId": "b7655426-293f-4e2a-ced2-e273b6ccf131"
      },
      "source": [
        "# persiapan sample data\n",
        "x = [1, 2, 3, 4]\n",
        "y = [1, 4, 2, 3]\n",
        "\n",
        "plt.plot(x, y)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f4efdd6dfd0>]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rIyk3ZIzhm+1H+cvSHZVnUQ3vxKThnWjo67yLzdRY6CISBpTayjwQGMWFL3ouBe4FfgTGAiuNvp/aqf61dh8FxWVMGx1jdRSPNH1UDFsO5vKnz7bTo21TurVpanUk5Uay8ot45vMdLNt1jF7hwbz7wAC6t3X+3yF7Drm0AVaJyDbgZyqPoX8lIs+LyBjbNm8CzUUkFZgGPOmcuArgREExb63fz/W92tC1tRaNM/j6NGDOuFiCA/2YuCiRfB3ipexgjOGjnzMYMeN7vk/O5qlru/LZo4PrpczBjhW6MWYbEFvF158953YRcJtjo6nqvL5mH0Wl5UwdqatzZwoLasi8u+IY968NPPHxNhaMj9Pz/FW1MnIKeWrJdtalHqd/h2a8eEsvosOa1GsGfaeom8nKL+LdH9O5OTacTi3r9y+LN+rfoRlPXtOV/+48yhtrdYiXulB5heGtdfsZPXMNWzJy+fvNPfngdwPrvcyhli+KKuu9ujqN0nLDlBGdrY7iNR66ogOJB07y4n/30CcihP4dmlkdSbmIlGOneOLTbWw+mMtVXcL4x2960TYk0LI8ukJ3I4dzz/DexoPcHt+O9s0bWx3Ha4gI/7ytNxGhgUx+L4msU0VWR1IWKymrYM6KFK6fs47046eZdUdf3rrvMkvLHLTQ3crclakATB6uq/P61jTAjwXj+5FfVDnEq6y8wupIyiLbDuUyZt46ZiQkc3XP1iRMu5KbY8Nd4vUVLXQ3cfBEIR9vymBc/wjCLV4FeKtubZry95t7sWFfDq8kJFsdR9WzotJyXvhmNzfPX8/JwhL+9dt45o6LpUWThlZH+4UeQ3cTc1am4NNAmHSVc95hpuwztl87Eg/ksGB1GnGRoYzq3srqSKoebNh3gic/3Ub6iULG9Y/gyWu7ERzoZ3WsC+gK3Q2kZRewJOkQ9wxsT8umAVbH8Xp/ubEHPcObMu2jLRw8oUO8PNmpolL+9Nl27ly4gQoD7z00gBdu6e2SZQ5a6G5h9vIUAvx8eGRYR6ujKCqHeC24ux8NRHSIlwdbuecYo2eu4f2fDvLQ5R34bupQBndy7QvIaKG7uL1HT/HltsPcNzjKpY7VebuIZo2YeUcfdh7O569Ld1odRzlQzukSpn6wmQfe3kRQgC+fThzMMzd0J9DfeTNYHEWPobu4mQnJNPH3ZcLQaKujqPMM79qKSVd1ZP6qNOLah3J7fITVkdQlMMbw5bYj/HXpTk4VlTJlRGcmXdUJf1/3WfdqobuwHZl5/HfnUaaO7ExII3+r46gqTBvVhc0Hc/nz5zvo0bYpPdrq5Et3dDSvcpjW8t3H6NMumJfGDnDLOUnu86PHC81ISCY40I8HLu9gdRRVDZ8GwpxxsYQ08uPRxUnkndEhXu7EGMP7Px1k1IzvWZeazTPXd2PJo0PcssxBC91lJR08yco9WTx8ZTRNA1zzFXVVqUWThsy/K47Mk2f4w8db0cnR7uHAidPc9a+NPLVkOz3Dg/lu6lAeuiIanwbWv0GorrTQXdSMZck0b+zPvYOirI6i7BAf1Ywnr+3Ksl3HWLhmn9Vx1EWUVxjeWLuPq2etYUdmHi/c0ov3fjfAI8Zp6DF0F7Rx3wnWpR7nmeu70bihPkXu4sHLO5B08CT//G4vfSNCGBB9wWV1lcX2Hq0cprU1I5eR3Vry95t70TrYc97boSt0F2OM4ZVlybQMasj4ge2tjqNqQUR46dbetG/WiMnvbyYrX4d4uYqSsgpmLU/mhrlrycgpZM64WP7123iPKnPQQnc561KP81N6DpOHdyLAz/XPe1W/FhTgx6vj4zhVVMpkHeLlErZk5HLj3HXMWp7Cdb3asHzalYzp09Ylhmk5mha6Czm7Og8PCeSOy/ScZnfVtXVTXrilFz/tz+HlZXutjuO1zpSU84+vd3HLq+vJO1PKm/fGM/vOWJo19txTgPUArQtZtTeLLRm5vHhLL6deGVw5329i27Ep/SSvf7+PfpGhjO7R2upIXuWHtOM8+el2DuYUcveASJ68titBXnC2mBa6izi7Oo9s1ohb+7WzOo5ygGdv7M72zDymf7yVr1oHecRZFK4uv6iUF77Zzfs/ZRDVvBEfTBjIQC96cVoPubiI73YeZefhfKaO7Iyfjz4tnqChrw/z74qjgQiPLErSIV5OtnzXMUbN+J4Pf87g4aHRfDtlqFeVOWihu4TyCsOMhGQ6hjXmpr7hVsdRDhTRrBGz7ujL7iP5/PnzHVbH8UgnCor5/fubeejdTYQ28ufzSUN46rpubjFMy9H0kIsL+GrbYZKPFTDvrli3fpeaqtpVXVvy++GdmLsylfioUO64LNLqSB7BGMPSrYf569KdFBSXMW1UDI9c2dGthmk5mha6xcrKK5i9PIWurYO4rmcbq+MoJ5k6MqZyiNcXO+nRNpie4TrE61Iczj3DM5/vYOWeLGIjQ3jp1t7EtAqyOpblavxRJiIRIrJKRHaJyE4RmVLFNsNEJE9Ettg+nnVOXM/z2eZM9h0/zeOjYmigq3OP5dNAmH1nX5o18tchXpegosKwaMMBRs9cw49pJ3j2hu588shgLXMbe343KQOmG2O6AwOBSSLSvYrt1hpj+to+nndoSg9VUlbBnJUp9AoPZrRem9LjNW/SkPl3x3E49wzTP9pKRYUO8aqN/cdPM+5fG3jm8x30iagcpvXA5R30MOU5aix0Y8wRY0yS7fYpYDegr9w5wMeJGWTknGHa6BiPfNeaulC/9qE8fV03lu8+xus6xMsuZeUVvP59GtfMWsOuI/n889beLHpwAJHNG1kdzeXU6hi6iEQBscDGKu4eJCJbgcPA/xhjLrgul4hMACYAREZ69wtDRaXlzFuZSr/2oQyLCbM6jqpH9w+JIvHgSV7+bg99I0IY1NG7Tq2rjd1H8vnjp9vYdiiPUd1b8febe9JKL5ReLbtfDhaRJsCnwFRjTP55dycB7Y0xfYC5wOdVfQ9jzEJjTLwxJj4szLtL7IOfDnIkr4jpo3R17m3ODvGKatGY3+sQryoVl5UzY9lebpy7jsO5Z5h/VxwL7+mnZV4DuwpdRPyoLPPFxpgl599vjMk3xhTYbn8D+ImIa18e20JnSsqZtyqNgdHNXP4q4so5mjT05bXx/ThdXMbk9zZTqkO8fpF44CTXz1nHnJWpjOnTloTHr+T63m104WMHe85yEeBNYLcxZkY127S2bYeI9Ld93xOODOpJ/rMhneMFxUwf3cXqKMpCMa2CePHWXvyUnsPL3+kQr8KSMp77cidjX/uBwuIy/n3/Zcy4oy+hHjxMy9HsOYY+BLgH2C4iW2xfexqIBDDGvAaMBSaKSBlwBrjT6HW4qlRQXMaC1WkMjQnjsqhmVsdRFrupbzib0k+ycM0+4iJDuaandw7xWpdynCeXbOPQyTP8dlB7nrimK0304i61VuP/MWPMOuCiv+sYY+YB8xwVypO9vX4/JwtLmT4qxuooykU8c0M3tmXm8YePt9KldRAdWnjPEK+8M6X84+tdfLTpEB1aNOajhwfRv4MudOrKe98ja4G8M6UsXLOPkd1a0ScixOo4ykVUDvGKxcdHmLgokTMl3jHE67udRxk143s+Tcpk4rCOfDvlCi3zS6SFXo/eXLuP/KLKmRNKnatdaOUQr73HTvHM5zvw5COW2aeKmbQ4iYf/k0jzJg35/NEh/PGarnqFLgfQg1T1JOd0CW+tT+f6Xm3o3rap1XGUCxrWpSW/H96ZOStSiI8KZVx/z3qvhjGGJUmZPP/VLs6UlPOHq7swYWi0jot2IC30evL6mjROl5QxdWRnq6MoFzZlRGc2HzzJX5bupFe45wzxysw9w9NLtvN9cjb92ofy0q296dSyidWxPI7+aKwH2aeKefeHA9zUpy2ddYiQuojKIV6xNG/szyOLEskrdO8hXhUVhnd/TGf0jO/5OT2Hv97YnY8fHqRl7iRa6PVgweo0SsormDJSj52rmjVr7M/8u+M4ll/EtI+2uO0Qr7TsAu5Y+CPPfrGTuPahfDd1KPcN6aBTRZ1IC93JjuSdYdHGA9waF+5Vp6OpSxMXGcqfruvGij1ZLPg+zeo4tVJWXsGrq1O5dvZa9h49xctje/PuA/2JaKbDtJxNj6E72fxVqRhj+P1wPXauaufewVEkHszllWV7iY0MYXBH1x8TsfNwHn/8dBs7MvO5tmdrnrupBy2DdP5KfdEVuhNl5BTy4c8Z3HFZhK5OVK2JCC/e0ovosCY89v5mjua57hCvotJyXv5uD2PmredoXjEL7o5jwfh+Wub1TAvdieauTEFEmHyVrs5V3TRu6Mtr4+MoLCln8ntJLjnEa1N6DtfNWcv8VWn8Jjac5dOGcm0vvZyiFbTQnWT/8dN8mpTJ+AHtaR2sqxRVd51aBvHirb3ZdOAkL327x+o4vzhdXMZfl+7kttd/pLi0gncf6M//3daHkEY6TMsqegzdSWYvT8bfpwETh3W0OoryAGP6tCUxPYc31u2nX/tQy1fAa5KzeWrJdg7nneHeQVH84eouNNZhWpbTZ8AJUo6d4outh3l4aEfCghpaHUd5iD9d352th/L4wyfb6NI6iOiw+j+XO7ewhL9/vZtPEg8RHdaYjx8eRLxODXUZesjFCWYtT6Gxvy8PD422OoryIP6+DZh/dxx+PsLERUkUlpTV6+N/u/0II2es4bPNmUy+qhPfPHaFlrmL0UJ3sJ2H8/h6+xEeGBKlg/mVw4WHBDL7zliSs07xzGf1M8QrK7+IR/6TyMTFSbRq2pClk4fwP1d30WFaLkgPuTjYzIQUmgb48uAVujpXzjE0JowpIzoza3kK/aJCuXtAe6c8jjGGTxIP8bevdlFUVsEfr+nK767ogK8O03JZWugOtCUjl+W7j/E/o2MIDvSzOo7yYI8N70zSwVyeW7qLXuHB9G7n2Pn6GTmFPP3ZdtamHOeyqFBevLU3HS04Zq9qR3/UOtCMhGSaNfbnviEdrI6iPFyDBsKsO/rSook/ExclkVtY4pDvW1FheHv9fq6etYakAyf52009+HDCIC1zN6GF7iA/p+ewJjmbR66M1mshqnrRrLE/r47vR9apIh7/8NKHeKVmneK213/kr1/u4rKoZnz3+FDuGRSlw7TciBa6g7yybC9hQQ25Z2CU1VGUF+kbEcKfb+jOqr3ZvLo6tU7fo7S8gnkrU7hu9jrSsguYcXsf3r7/MtqF6rgKd6NLSQf4IfU4G/ZVznoO9NdX/lX9umdgexIPnGRGQjKxkaEM6WT/EK8dmZXnte8+ks/1vdvw1xt76Hsn3Jiu0C+RMYb/W7aXNsEB3OlhlwxT7kFEeOGWXnSsxRCvotJyXvx2DzfNX8/xgmJev6cf8++K0zJ3c1rol2h1cjZJB3OZPLyTnperLNPI35cF4/tRVFrOpBqGeP20P4frZq/lte/TGBvXjuWPX8nVPVrXY1rlLFrol8AYw4xlyUQ0C+S2fhFWx1FerlPLJrw0tjeJB07ywjcXDvEqKC7jz5/v4PbXf6SkvIJFDw7gpbG9CW6kp9h6ihqPoYtIBPAu0AowwEJjzOzzthFgNnAdUAjcZ4xJcnxc17Js1zG2Z+bx8tje+Pvqz0ZlvRt6t2VT+kneWl85xOv63pVDvFbtzeJPS7ZzJL+IB4Z04H+ujqGRv76E5mnseUbLgOnGmCQRCQISRSTBGLPrnG2uBTrbPgYAC2z/9VgVFYaZCclEt2jMb2LDrY6j1C+evq4b2w7l8sQnW2kd3JDFGw6yZHMmnVs24ZNHBtOvfajVEZWT1LisNMYcObvaNsacAnYD5zfYTcC7ptIGIEREPHrC/dfbj7Dn6CmmjOysb4VWLuXsEK+Gfj7cuuBHlm49zGPDO/HVY5drmXu4Wv3OJSJRQCyw8by7woGMcz4/ZPvakfP+/ARgAkBkpPueEVJeYZi1PJmYVk24sXdbq+ModYE2wYEsuDuOf69PZ8rIznRr09TqSKoe2F3oItIE+BSYaozJr8uDGWMWAgsB4uPjnT8mzkm+2JJJWvZpXhsfp++iUy5rQHRzBkQ3tzqGqkd2HSsQET8qy3yxMWZJFZtkAuee5tHO9jWPU1pewazlKfRo21RP9VJKuZQaC912BsubwG5jzF4pICgAAArvSURBVIxqNlsK/FYqDQTyjDFHqtnWrX2aeIiDOYVMHx1D5f8apZRyDfYcchkC3ANsF5Ettq89DUQCGGNeA76h8pTFVCpPW7zf8VGtV1xWztyVqfSNCOGqLi2tjqOUUr9SY6EbY9YBF12KmsrLpkxyVChX9eHPGWTmnuHFW3vp6lwp5XL0fDs7FZWWM29lKv07NOPyWgw/Ukqp+qKFbqdFGw6QdaqY6aP02LlSyjVpodvhdHEZC1ancUXnFnoamFLKZWmh2+GdH9M5cbqEaaNirI6ilFLV0kKvQX5RKa9/v4/hXVsSG6lvm1ZKuS4t9Bq8tW4/eWdKdXWulHJ5WugXkVtYwptr93NNj9b0DA+2Oo5SSl2UFvpFLFyzj4KSMh7X1blSyg1ooVfjeEExb/+Qzo2929KldZDVcZRSqkZa6NV4bXUaRaXlTBnZ2eooSillFy30KhzLL+I/Gw5wS1w7OoY1sTqOUkrZRQu9CvNXpVJeYZgyQlfnSin3oYV+nszcM3zwUwa3xUcQ0ayR1XGUUspuWujnmbcyBYDfD+9kcRKllKodLfRzHDhxmo82HeKuAZG0DQm0Oo5SStWKFvo5Zq9Iwc9HeHRYR6ujKKVUrWmh26RmFfD55kx+OyiKlk0DrI6jlFK1poVuM2t5MgF+Pjw8NNrqKEopVSda6MDuI/l8te0IDwzpQPMmDa2Oo5RSdaKFDsxMSCYowJffXaGrc6WU+/L6Qt9+KI9lu47xuyuiCW7kZ3UcpZSqM68v9BkJewlp5Mf9Q6KsjqKUUpfEqws98cBJVu3N5uGhHQkK0NW5Usq9eXWhz0jYS4sm/tw7uL3VUZRS6pLVWOgi8paIZInIjmruHyYieSKyxfbxrONjOt6PaSdYn3qCicM60cjf1+o4Sil1yexpsreBecC7F9lmrTHmBockqgfGGGYk7KV10wDuHhBpdRyllHKIGlfoxpg1QE49ZKk3a1OO83P6SSYN70SAn4/VcZRSyiEcdQx9kIhsFZFvRaRHdRuJyAQR2SQim7Kzsx300LVjjOGVZXsJDwnkjvgISzIopZQzOKLQk4D2xpg+wFzg8+o2NMYsNMbEG2Piw8LCHPDQtbdidxZbD+UxZURn/H29+jVhpZSHueRGM8bkG2MKbLe/AfxEpMUlJ3OCigrDKwnJRDVvxC1x4VbHUUoph7rkQheR1iIittv9bd/zxKV+X2f4786j7D6Sz9SRMfj66OpcKeVZajzLRUTeB4YBLUTkEPAXwA/AGPMaMBaYKCJlwBngTmOMcVriOiqvMMxMSKZzyybc2Ket1XGUUsrhaix0Y8y4Gu6fR+VpjS7ty62HSckqYP5dcfg0EKvjKKWUw3nFcYey8gpmLU+mW5umXNuztdVxlFLKKbyi0JckZZJ+opBpo2JooKtzpZSH8vhCLymrYPaKFPq0C2Zkt5ZWx1FKKafx+EL/aFMGmblnmDa6C7aTcZRSyiN5dKEXlZYzb2Uq8e1DGdrZJU+NV0oph/HoQn9v40GO5hcxXVfnSikv4LGFXlhSxqur0xjcsTmDOja3Oo5SSjmdxxb6uz8e4HhBMdNHx1gdRSml6oVHFvqpolJe/z6NYV3C6Ne+mdVxlFKqXnhkof97fTonC0uZNkpX50op7+FxhZ5XWMq/1u5jdPdW9G4XYnUcpZSqNx5X6G+s28epojIe19W5UsrLeFSh55wu4a11+7m+dxu6tWlqdRyllKpXHlXor3+fxpnSch4f2dnqKEopVe88ptCzThXxzo/p3Nw3nE4tg6yOo5RS9c5jCv3VVWmUlhum6OpcKeWlPKLQD+ee4b2NB7mtXzvaN29sdRyllLKERxT6vFWpGAyTh3eyOopSSlnG7Qs9I6eQj37OYFz/SNqFNrI6jlJKWcbtC332ihR8GgiTrtLVuVLKu7l1oe/LLmBJ0iHuGdieVk0DrI6jlFKWcutCn70ihQA/Hx4Z1tHqKEopZTm3LfS9R0+xdOth7h0cRYsmDa2Oo5RSlnPbQp+1PJkm/r48PDTa6ihKKeUSaix0EXlLRLJEZEc194uIzBGRVBHZJiJxjo/5azsy8/h2x1EeuLwDIY38nf1wSinlFuxZob8NXHOR+68FOts+JgALLj3Wxc1MSCY40I8Hr+jg7IdSSim3UWOhG2PWADkX2eQm4F1TaQMQIiJtHBXwfJsPnmTFniwmDI2maYCfsx5GKaXcjiOOoYcDGed8fsj2tQuIyAQR2SQim7Kzs+v0YAYYGhPGfYOj6vTnlVLKU9Xri6LGmIXGmHhjTHxYWFidvkdcZCjvPtCfxg19HZxOKaXcmyMKPROIOOfzdravKaWUqkeOKPSlwG9tZ7sMBPKMMUcc8H2VUkrVQo3HLUTkfWAY0EJEDgF/AfwAjDGvAd8A1wGpQCFwv7PCKqWUql6NhW6MGVfD/QaY5LBESiml6sRt3ymqlFLq17TQlVLKQ2ihK6WUh9BCV0opDyGVr2la8MAi2cCBOv7xFsBxB8axku6La/KUffGU/QDdl7PaG2OqfGemZYV+KURkkzEm3uocjqD74po8ZV88ZT9A98UeeshFKaU8hBa6Ukp5CHct9IVWB3Ag3RfX5Cn74in7AbovNXLLY+hKKaUu5K4rdKWUUufRQldKKQ/h0oXuiheorgs79mOYiOSJyBbbx7P1ndFeIhIhIqtEZJeI7BSRKVVs4/LPi5374RbPi4gEiMhPIrLVti/PVbFNQxH50PacbBSRqPpPWjM79+U+Eck+53l5yIqs9hARHxHZLCJfVXGf458TY4zLfgBDgThgRzX3Xwd8CwgwENhodeY67scw4Curc9q5L22AONvtICAZ6O5uz4ud++EWz4vt/3MT220/YCMw8LxtHgVes92+E/jQ6tyXsC/3AfOszmrn/kwD3qvq75EznhOXXqEbF7tAdV3ZsR9uwxhzxBiTZLt9CtjNhdeQdfnnxc79cAu2/88Ftk/9bB/nn+1wE/CO7fYnwAgRkXqKaDc798UtiEg74HrgjWo2cfhz4tKFbge7L1DtBgbZfs38VkR6WB3GHrZfEWOpXEWdy62el4vsB7jJ82L71X4LkAUkGGOqfU6MMWVAHtC8flPax459AbjVdjjvExGJqOJ+VzALeAKoqOZ+hz8n7l7oniKJyvkMfYC5wOcW56mRiDQBPgWmGmPyrc5TVzXsh9s8L8aYcmNMXyqv6dtfRHpanamu7NiXL4EoY0xvIIH/v8p1GSJyA5BljEmsz8d190L3iAtUG2Pyz/6aaYz5BvATkRYWx6qWiPhRWYKLjTFLqtjELZ6XmvbD3Z4XAGNMLrAKuOa8u355TkTEFwgGTtRvutqpbl+MMSeMMcW2T98A+tV3NjsMAcaISDrwATBcRBadt43DnxN3L3SPuEC1iLQ+e+xMRPpT+by45D82W843gd3GmBnVbObyz4s9++Euz4uIhIlIiO12IDAK2HPeZkuBe223xwIrje3VOFdiz76c93rMGCpf/3ApxpinjDHtjDFRVL7gudIYM/68zRz+nNR4TVEriYdcoNqO/RgLTBSRMuAMcKcr/mOzGQLcA2y3HecEeBqIBLd6XuzZD3d5XtoA74iID5U/dD4yxnwlIs8Dm4wxS6n84fUfEUml8gX6O62Le1H27MtjIjIGKKNyX+6zLG0tOfs50bf+K6WUh3D3Qy5KKaVstNCVUspDaKErpZSH0EJXSikPoYWulFIeQgtdKaU8hBa6Ukp5iP8HvyV/3uJ+yz4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gBfKDqKemSWG"
      },
      "source": [
        "Catatan :\n",
        "* Figure adalah container besar\n",
        "* Axes area gambar\n",
        "* Figure dapat terdiri dari beberapa axes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YaJzhf2bmsw0"
      },
      "source": [
        "Cara untuk membuat figure dan axes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "8HXsCLUFl9AS",
        "outputId": "4f48e8fb-785c-4ec7-ebc6-2038eae0bda1"
      },
      "source": [
        "fig = plt.figure() # hanya membuat satu figure tanpa axes, \n",
        "# tidak bisa melakukan visualisasi data"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "bxTIHYgil9zn",
        "outputId": "30c639f9-772b-4558-fc33-d466f4459453"
      },
      "source": [
        "fig, ax = plt.subplots() # sebuah figure dengan sebuah axes"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "kKniT9yBl-In",
        "outputId": "e5678b83-fa5f-4b4c-a689-feeb7074a916"
      },
      "source": [
        "fig, axs = plt.subplots(2, 3) # sebuah figure dengan 2x3 grid axes\n",
        "# dengan 6 buah object axes"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 6 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kb3JLGtVnt9c"
      },
      "source": [
        "Dua cara dalam menggunakan Matplotlib\n",
        "* OO Style (Object Oriented)\n",
        "* pyplot Style"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wSOYZxa1oBmk"
      },
      "source": [
        "### Object Oriented Style untuk Plotting"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iZ1juGtTpBm9"
      },
      "source": [
        "secara explisit membuat figures beserta axes, dan memanggil methods dari keduanya."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5hACwZ4noIRg",
        "outputId": "937d2c48-95f8-4681-ff4f-ac5a465c6e04"
      },
      "source": [
        "x = np.linspace(0, 2, 100) # total jumlah data poin 100\n",
        "x"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.        , 0.02020202, 0.04040404, 0.06060606, 0.08080808,\n",
              "       0.1010101 , 0.12121212, 0.14141414, 0.16161616, 0.18181818,\n",
              "       0.2020202 , 0.22222222, 0.24242424, 0.26262626, 0.28282828,\n",
              "       0.3030303 , 0.32323232, 0.34343434, 0.36363636, 0.38383838,\n",
              "       0.4040404 , 0.42424242, 0.44444444, 0.46464646, 0.48484848,\n",
              "       0.50505051, 0.52525253, 0.54545455, 0.56565657, 0.58585859,\n",
              "       0.60606061, 0.62626263, 0.64646465, 0.66666667, 0.68686869,\n",
              "       0.70707071, 0.72727273, 0.74747475, 0.76767677, 0.78787879,\n",
              "       0.80808081, 0.82828283, 0.84848485, 0.86868687, 0.88888889,\n",
              "       0.90909091, 0.92929293, 0.94949495, 0.96969697, 0.98989899,\n",
              "       1.01010101, 1.03030303, 1.05050505, 1.07070707, 1.09090909,\n",
              "       1.11111111, 1.13131313, 1.15151515, 1.17171717, 1.19191919,\n",
              "       1.21212121, 1.23232323, 1.25252525, 1.27272727, 1.29292929,\n",
              "       1.31313131, 1.33333333, 1.35353535, 1.37373737, 1.39393939,\n",
              "       1.41414141, 1.43434343, 1.45454545, 1.47474747, 1.49494949,\n",
              "       1.51515152, 1.53535354, 1.55555556, 1.57575758, 1.5959596 ,\n",
              "       1.61616162, 1.63636364, 1.65656566, 1.67676768, 1.6969697 ,\n",
              "       1.71717172, 1.73737374, 1.75757576, 1.77777778, 1.7979798 ,\n",
              "       1.81818182, 1.83838384, 1.85858586, 1.87878788, 1.8989899 ,\n",
              "       1.91919192, 1.93939394, 1.95959596, 1.97979798, 2.        ])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "uMxjeG7VnZok",
        "outputId": "d402b274-1dca-4f76-e04b-a77ad4efcbbb"
      },
      "source": [
        "fig, ax = plt.subplots() # membuat sebuah figure dan sebuah axes\n",
        "\n",
        "# plotting tiga varian data pada axes\n",
        "ax.plot(x, x, label='linear')\n",
        "ax.plot(x, x**2, label='quadratic')\n",
        "ax.plot(x, x**3, label='cubic')\n",
        "\n",
        "ax.set_xlabel('x label') # menyertakan x-label pada axes\n",
        "ax.set_ylabel('y label') # menyertakan y-label pada axes\n",
        "ax.set_title(\"Simple Plot\") # menyertakan title pada axes\n",
        "ax.legend() # menyertakan legend atau garis"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f4ef7a5dc90>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kf-otycIq90j"
      },
      "source": [
        "### pyplot Style untuk Plotting"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zq4W5n8XrDoo"
      },
      "source": [
        "Mengandalkan pyplot untuk membuat dan mengelola figures dan axes, serta menggunakan fungsi pada pyplot untuk melakukan plotting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D5ShPSS7rPKh",
        "outputId": "d5b4a62b-e7b6-415d-b042-c92ce098b723"
      },
      "source": [
        "x = np.linspace(0, 2, 100)\n",
        "x"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.        , 0.02020202, 0.04040404, 0.06060606, 0.08080808,\n",
              "       0.1010101 , 0.12121212, 0.14141414, 0.16161616, 0.18181818,\n",
              "       0.2020202 , 0.22222222, 0.24242424, 0.26262626, 0.28282828,\n",
              "       0.3030303 , 0.32323232, 0.34343434, 0.36363636, 0.38383838,\n",
              "       0.4040404 , 0.42424242, 0.44444444, 0.46464646, 0.48484848,\n",
              "       0.50505051, 0.52525253, 0.54545455, 0.56565657, 0.58585859,\n",
              "       0.60606061, 0.62626263, 0.64646465, 0.66666667, 0.68686869,\n",
              "       0.70707071, 0.72727273, 0.74747475, 0.76767677, 0.78787879,\n",
              "       0.80808081, 0.82828283, 0.84848485, 0.86868687, 0.88888889,\n",
              "       0.90909091, 0.92929293, 0.94949495, 0.96969697, 0.98989899,\n",
              "       1.01010101, 1.03030303, 1.05050505, 1.07070707, 1.09090909,\n",
              "       1.11111111, 1.13131313, 1.15151515, 1.17171717, 1.19191919,\n",
              "       1.21212121, 1.23232323, 1.25252525, 1.27272727, 1.29292929,\n",
              "       1.31313131, 1.33333333, 1.35353535, 1.37373737, 1.39393939,\n",
              "       1.41414141, 1.43434343, 1.45454545, 1.47474747, 1.49494949,\n",
              "       1.51515152, 1.53535354, 1.55555556, 1.57575758, 1.5959596 ,\n",
              "       1.61616162, 1.63636364, 1.65656566, 1.67676768, 1.6969697 ,\n",
              "       1.71717172, 1.73737374, 1.75757576, 1.77777778, 1.7979798 ,\n",
              "       1.81818182, 1.83838384, 1.85858586, 1.87878788, 1.8989899 ,\n",
              "       1.91919192, 1.93939394, 1.95959596, 1.97979798, 2.        ])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "AFurAcwarUyI",
        "outputId": "ff2e3b55-f28c-436d-b080-4f214c31b1fb"
      },
      "source": [
        "# plotting tiga varian data pada axes\n",
        "plt.plot(x, x, label='linear')\n",
        "plt.plot(x, x**2, label='quadratic')\n",
        "plt.plot(x, x**3, label='cubic')\n",
        "\n",
        "plt.xlabel('x label') # menyertakan x-label pada axes\n",
        "plt.ylabel('y label') # menyertakan y-label pada axes\n",
        "plt.title(\"Simple Plot\") # menyertakan title pada axes\n",
        "plt.legend() # menyertakan legend atau garis"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f4ef7989dd0>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}