matplotlib/1-4.Figure_Axes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1-4. Figure과 Axes\n",
    "\n",
    "matplotlib를 시작하고 가장 어려운 부분 중 하나는 Figure와 Axes입니다.\n",
    "우선 기본적인 문법으로 틀만 출력해보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mpl \n",
    "import matplotlib.pyplot as plt\n",
    "fig, axes = plt.subplots(1, 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`plt.subplots()`라는 함수를 사용해 우리는 2가지 객체를 받을 수 있습니다. \n",
    "(`plt.subplots()` 대신 `plt.subplot()` 함수를 사용하면 Axes만 받을 수도 있습니다.)\n",
    "\n",
    "1. **Figure** : plot의 최상위 레이어\n",
    "2. **Axes** : Figure의 일부분\n",
    "\n",
    "이렇게만 보면 왜 굳이 나누었는지 궁금합니다. \n",
    "\n",
    "좀 더 디테일하게 살펴봅시다.\n",
    "\n",
    "- **Figure**는 **subplots, subaxis, subtitiles, legends** 등 plot의 모든 것을 다룹니다.\n",
    "- **Axes**는 **x-axis limit, y-axis limit, label, graph 종류** 등 subplot의 모든 것을 다룹니다.\n",
    "\n",
    "Figure는 나타나는 모든 것을 다루는 반면, Axes는 서브플롯 안에 나타나는 모든 것을 다룬다고 생각하면 됩니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그래서 똑같은 사각형 2개를 그리더라도 다음과 같이 2가지 방법이 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# subplots 사용 (1)\n",
    "\n",
    "fig, axes = plt.subplots(1, 2) # 1 by 2\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python 문법을 조금 활용하여 axes 반환을 개별적으로 받을 수도 있습비다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# subplots 사용 (2)\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2) # 1 by 2\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "subplot을 사용하면 1개의 Ax를 반환하니 다음과 같이 할 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# subplot으로 개별적 그리기\n",
    "\n",
    "ax1 = plt.subplot(1, 2, 1) # 1 by 2의 1번째\n",
    "ax2 = plt.subplot(1, 2, 2) # 1 by 2의 2번째\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`plt.subplots`은 한번에 동일한 크기의 subplot을 한 번에 그리기 쉽습니다.\n",
    "`plt.subplot`은 개별적으로 그려 서로 다른 크기의 subplot을 그릴 수 있다는 장점이 있습니다. \n",
    "\n",
    "각각의 장점을 이용한 그래프를 그려보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# subplots로 12개 그리기\n",
    "\n",
    "fig, axes = plt.subplots(3, 4)\n",
    "axes[1][2].plot([1, 2, 3, 4]) # subplots으로 반환한 axes는 배열처럼 접근가능\n",
    "axes[2][1].bar([1, 2, 3, 4], [1, 2, 3, 4])\n",
    "plt.tight_layout() # subplot간 margin(여백으로 이해하시면 됩니다.)이 겹치지 않게 하는 함수\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# subplot으로 다른 크기 그리기\n",
    "\n",
    "ax221 = plt.subplot(2, 2, 1)\n",
    "ax222 = plt.subplot(2, 2, 2)\n",
    "ax212 = plt.subplot(2, 1, 2)\n",
    "\n",
    "ax221.scatter([1, 2, 3], [3, 2, 1])\n",
    "ax212.plot([1,2,3,4,5]) # subplot으로 만든 ax는 각각을 개별적으로 사용할 수 있음\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "물론 subplot을 여러개 그리는 더 고급진 방법이 있으나 이는 후에 소개하도록 하겠습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그리고 ax를 받으면 이제 이 객체에는 여러 개의 그래프를 함께 그릴 수도 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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u7pvOm9+7OHQkkZSlEbikhPKKKn7/zkYe/8tG2jZvwu/+ZTAjBmboqvAiJ6ACl+CW5+/hvjkrWP9pCdcN6sr/vaY/HVo1DR1LJOWpwCWYQ4creWzBBv701810btOcqWOyuOzsLqFjiUSGClyC+HDzTn44dwV5Ow8walgP7r/qbNo2bxI6lkikqMAlqfaXVfDT19Yx84Mt9OjQgmfuOJ8vnNEpdCyRSFKBS9K8u6GYB+at5JO9B7ntwkz+42tn0rKpfgVFakt/PZJwew8c5pH5a5iTW8Dp6a2Yc+dwhvTqEDqWSOSpwCWhXl+1nQdfXMWu0nLuuuR0vn1pH5o3SQsdS6ReUIFLQuzYX8aPXlrN/BWF9Mtoy7SxQxnQrV3oWCL1igpc4srdeXHZJzz88mpKyyr5weV9mXDx6TRJ06RfkXhTgUvcFO49yH++sIq31xUxuGd7/t8N59CnS5vQsUTqrToVuJnlASVAJVDh7lnxCCXR4u7Mzs7n0flrOVxVxYNX92PsFzJJa6Rp8CKJFI8R+CXuviMOnyMRtHXnASbOW8GiTTsZ3rsjk24YSK+OrULHEmkQtAtFaqWyypm+KI+fv7GetEbGo9cPZNSwHjr5lEgS1bXAHfgfM3Pgj+4+5egXmNl4YDxAz54967g6SQUbi0q4b84Klmzdw6Vndea/rh9ARrsWoWOJNDh1LfCL3H2bmXUGFpjZOndf+NkXxEp9CkBWVpaujxVhhyurmLJwM79+8yNaNkvjVzcNYuSgrhp1iwRSpwJ3922xn0Vm9gIwDFh44ndJFK3atpf75qxgTeE+RgzM4KFr+5PeplnoWCINWq0L3MxaAY3cvSR2/3Lgx3FLJinh0OFKfvv2R0x+dzMdWjVl8i1DuGLAqaFjiQh1G4F3AV6I/e9zY+AZd389LqkkJeRu2c19c5azqbiUbwzpzoMj+tGupU75KpIqal3g7r4ZODeOWSRFHCiv4OdvbGDaoo/p2q4FM24fxsV900PHEpGj6DBC+SeLNu5g4ryVbN11gNEX9OKHV55F62b6NRFJRfrLFAD2HTrMf7+6jlmLt5LZsSXPjr+A83t3DB1LRE5ABd7Arf5kL89m5/PC0m2UllUw4Uu9+e5X++qUryIRoAKPiMyJ85Oynj8u3MwfF25O2OfnTRqRsM8WaWh0jk8RkYhSgYuIRJQKXEQkolTgIiIRpQIXEYkoFbiISESpwEVEIkoFHgHrt5eEjiAiKUgTeVJUaVkFr6z4hFmL81mWvyd0HBFJQSrwFOLuLC/Yy+zFW3l5+SeUllfSp3NrHry6H4+8siZ0PBFJMSrwFLDnQDkvLN3Gs9n5rNteQosmaVxzbgY3De3JeT3bY2YqcBH5HBV4IO7O+5t38mx2Pq+t2k55RRXndm/Ho9cP5JpzM2jTXBdOEJETU4EnWdG+Q8xZUsBz2fnk7TxA2+aNGTW0BzcN7Um/rm1DxxORCFGBJ0FFZRULPypm1uJ83l5XRGWVc/5pHbjnK324ckCGTt0qIrWiAk+g/F0HeD4nn+dyCti+7xCdWjflji+exk1ZPeid3jp0PBGJOBV4nJVVVPLmmiJmZ2/lvY07ALi4bzoPXdufy87uTJM0HXovIvGhAo+TjUUlzF6cz7yl29hVWk639i2497K+fDOrO13btwgdT0TqIRV4HRwor2D+ikKezc4nZ8tuGjcyLu/fhZuG9uSiMzqR1shCRxSRekwFXgsrC/YyO3srLy37hJKyCnqnt+KBq87i6+d1p1PrZqHjiUgDoQKvob0HD/PSsm3Mzs5n9Sf7aN6kEVcNzODmoT0ZmnkKZhpti0hyqcBPwN3JztvN7OytvLqykEOHq+iX0ZZHRvbn2kHdaNdCk21EJBwV+DHs2F/GvCUFzM7OZ3NxKW2aNeaG87ozalhPBnRrFzqeiAigAv+HyirnvY07mL14KwvWfEpFlZPV6xT+/ZtncNXAU2nZVP9UIpJa1Eoxf9u4gzFPLqZDq6bcdmEmNw3twRmd24SOJSJyXJEp8MyJ85Oynl2l5Tzx14954q8fJ2wdeZNGJOyzRaTh0LRAEZGIUoGLiESUClxEJKJU4CIiEVWnAjezK8xsvZltNLOJ8QolIiLVq3WBm1ka8HvgSqAfMMrM+sUrmIiInFhdRuDDgI3uvtndy4HZwMj4xBIRkeqYu9fujWbfAK5w9ztij0cD57v73Ue9bjwwPvbwTGB97eMmRSdgR+gQgWjbG66GvP1R2PZe7p5+9MKET+Rx9ynAlESvJ17MLMfds0LnCEHb3jC3HRr29kd52+uyC2Ub0OMzj7vHlomISBLUpcCzgT5mdpqZNQVuBl6KTywREalOrXehuHuFmd0NvAGkAU+6++q4JQsnMrt7EkDb3nA15O2P7LbX+ktMEREJSzMxRUQiSgUuIhJRKnDAzJ40syIzWxU6Swhm1sPM3jGzNWa22szuCZ0pWcysuZktNrPlsW1/OHSmZDOzNDNbamavhM6SbGaWZ2YrzWyZmeWEznOytA8cMLMvAfuBP7v7gNB5ks3MMoAMd19iZm2AXOA6d18TOFrCmZkBrdx9v5k1Ad4D7nH3DwJHSxoz+x6QBbR196tD50kmM8sDstw91SfyHJNG4IC7LwR2hc4RirsXuvuS2P0SYC3QLWyq5PAj9sceNondGsyoxsy6AyOAP4XOIidPBS7/xMwygcHAh2GTJE9sF8IyoAhY4O4NZtuBXwH3AVWhgwTiwP+YWW7stB+RogKXfzCz1sBc4F533xc6T7K4e6W7D+LIbOJhZtYgdqOZ2dVAkbvnhs4S0EXufh5Hzqp6V2x3amSowAWA2P7fucDT7j4vdJ4Q3H0P8A5wRegsSXIhcG1sP/Bs4FIzeypspORy922xn0XACxw5y2pkqMDl71/kTQXWuvtjofMkk5mlm1n72P0WwFeBdWFTJYe73+/u3d09kyOnwnjb3W8JHCtpzKxV7Et7zKwVcDkQqSPRVOCAmc0C3gfONLMCMxsXOlOSXQiM5sgIbFnsdlXoUEmSAbxjZis4cn6fBe7e4A6na6C6AO+Z2XJgMTDf3V8PnOmk6DBCEZGI0ghcRCSiVOAiIhGlAhcRiSgVuIhIRKnARUQiSgUuIhJRKnARkYj6X9CPw6E/4zT3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot()\n",
    "\n",
    "x = np.array([1, 2, 3, 4, 5])\n",
    "y = x**2\n",
    "\n",
    "ax.bar(x, y)\n",
    "ax.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이렇게 한 plot에는 다양한 종류의 graph를 그려 내용을 추가할 수 있습니다. \n",
    "\n",
    "하지만 지금처럼 아에 색이 같으면 그래프가 서로 중첩되며 가독성이 떨어집니다. \n",
    "이런 상황을 방지하기 위해 색, 투명도 등을 조절할 수 있습니다.\n",
    "\n",
    "다음 장에서 좀 더 자세하게 다뤄보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}