matplotlib/1-6.Text.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1-6 Text의 종류와 위치\n",
    "\n",
    "시각화의 핵심은 시각요소(색, 크기, 위치 등)들이지만, 정보를 시각요소만으로 전달하기는 어렵기에 각 시각요소가 나타내는 의미를 text로 명시해야 합니다.\n",
    "\n",
    "matplotlib에서 Text를 적을 수 있는 내용은 크게 다음과 같습니다.\n",
    "\n",
    "|pyplot API|객체지향 API|설명|\n",
    "|-|-|-|\n",
    "|`suptitle`|`suptitle`|Figure의 제목|\n",
    "|`title`|`set_title`|Axes의 제목|\n",
    "|`xlabel`|`set_xlabel`|x축의 라벨|\n",
    "|`ylabel`|`set_ylabel`|y축의 라벨|\n",
    "|`figtext`|`text`|Figure 임의의 위치에 텍스트 추가|\n",
    "|`text`|`text`|Axes 임의의 위치에 텍스트 추가|\n",
    "|`annoatate`|`annotate`|Axes 임의의 위치에 화살표와 함께 주석 추가|\n",
    "\n",
    "이렇게만 봐서는 이해하기 어려울 수 있습니다. 각각을 예시를 통해 살펴봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as fm\n",
    "\n",
    "font_list = fm.findSystemFonts(fontpaths = None, fontext = 'ttf')\n",
    "# print([i for i in font_list if 'kr' in i])\n",
    "\n",
    "font_location = '/Users/subinium/Library/Fonts/NotoSansCJKkr-Regular.otf' \n",
    "font_name = fm.FontProperties(fname=font_location).get_name()\n",
    "matplotlib.rc('font', family=font_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1-6-1. 제목 (suptitle, title) \n",
    "\n",
    "plot의 주제를 텍스트로 표현하여, 독자가 plot을 볼 때 가장 먼저 얻을 수 있는 정보입니다.\n",
    "\n",
    "이런 제목도 figure의 제목과 axes의 제목으로 나뉠 수 있습니다.\n",
    "각각의 함수는 suptitle과 title입니다. \n",
    "\n",
    "차이를 코드와 함께 살펴보겠습니다.\n",
    "\n",
    "figure의 경우는 figure 객체에 `fig`에 `fig.suptitle('figure 제목')` 와 같이 코드를 작성하여 제목을 추가할 수 있으며, ax의 경우에는 ax 객체 `ax`에 `ax.set_title('ax 제목')`으로 제목을 추가할 수 있습니다."
   ]
  },
  {
   "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": [
    "fig, ax = plt.subplots(1,1)\n",
    "fig.suptitle('figure suptitle')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": [
    "fig, ax = plt.subplots(1,1)\n",
    "ax.set_title('ax title')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "약간의 높낮이가 있다는 차이를 제외하고는 크게 차이가 없어보입니다. 그럼 두 개의 함수를 한번 같이 사용해보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "fig, ax = plt.subplots(1,1)\n",
    "fig.suptitle('figure title')\n",
    "ax.set_title('ax title')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "앞 절에서 이야기했듯이 figure는 ax들을 다루는 더 큰 단위입니다. 그렇기에 한 figure 내의 ax 전체적인 내용을 담는 큰 제목으로 볼 수 있습니다.\n",
    "즉 ax가 여러 개라면 다음과 같이 내용을 구성할 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": [
    "fig, ax = plt.subplots(1,2)\n",
    "fig.suptitle('figure title')\n",
    "ax[0].set_title('ax[0] title')\n",
    "ax[1].set_title('ax[1] title')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> suptitle의 sup은 super title의 sup을 의미합니다. 여기서 super은 above, 즉 상위라는 의미를 담고 있어 상위 제목이라 생각하면 쉽게 외울 수 있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1-6-2. 축과 레이블\n",
    "\n",
    "축(axis)은 스케일을 알 수 있는 가장 중요한 정보입니다. \n",
    "\n",
    "대다수의 정적인 시각화는 2차원 데카르트 좌표계를 활용하여 데이터를 매핑시킵니다. \n",
    "그리고 가로축을 x축(x-axis), 세로축을 y(y-axis)축이라 부릅니다.\n",
    "\n",
    "그리고 축에는 여러 정보가 담길 수 있습니다. 크게 다음과 같은 정보가 담깁니다.\n",
    "\n",
    "- 축의 내용(라벨)\n",
    "- 축의 단위\n",
    "- 축의 스케일\n",
    "\n",
    "이번에는 이 중에서도 축의 내용인 라벨(label) 정보를 추가해보겠습니다.\n",
    "\n",
    "`ax` 객체의 x축 라벨은 `ax.set_xlabel('x축 라벨')`을 사용하여 추가할 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": [
    "fig, ax = plt.subplots(1,1)\n",
    "ax.set_xlabel('xlabel')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이미 예상하셨겠지만 y축은 `ax.set_ylabel('y축 라벨')`로 추가할 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": [
    "fig, ax = plt.subplots(1,1)\n",
    "ax.set_ylabel('ylabel')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.6.3 원하는 위치에 텍스트(text, annotation)\n",
    "\n",
    "이제 plot에서 핵심적인 정보들을 제외한 추가적인 정보를 주는 여러가지 방법을 살펴보겠습니다.\n",
    "우선 단순하게 임의의 위치에 텍스트를 추가하는 방법부터 알아보겠습니다.\n",
    "\n",
    "제목과 유사하게 이번에도 figure에 따른 텍스트와 ax에 따른 텍스트가 다릅니다.\n",
    "figure의 텍스트부터 살펴보겠습니다.\n",
    "\n",
    "`fig.text(x, y, 텍스트)`는 좌측 하단을 `(0, 0)`, 우측 상단을 `(1, 1)`로 두었을 때 `(x, y)`에 텍스트를 추가합니다.\n",
    "\n",
    "\n",
    "기본적인 크기 1x1 figure와 ax의 경우, x축과 y축의 범위가 각각 0~1.0으로 설정됩니다.\n",
    "그래서 figtext에 대한 좌표 개념이 헷갈릴 수 있습니다.\n",
    "\n",
    "이해를 돕기 위해 x축과 y축의 범위를 `-10`부터 `10`으로 범위를 변경하여 살펴보겠습니다.\n",
    "\n",
    "축 스케일은 `ax.set_xlim()`과 `ax.set_ylim()`으로 변경할 수 있습니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": [
    "fig, ax = plt.subplots(1,1)\n",
    "\n",
    "ax.set_xlim(-10, 10) #x축 스케일 변경\n",
    "ax.set_ylim(-10, 10) #y축 스케일 변경\n",
    "\n",
    "fig.text(1.0, 1.0, 'fig text (1, 1)')\n",
    "fig.text(0.5, 0.5, 'fig text (0.5, 0.5)')\n",
    "fig.text(0, 0, 'fig text (0, 0)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "비교를 위해 3개의 text를 추가해보았는데 축과 figure의 사이즈가 가늠이 안되어 어떻게 위치를 설정하는지 혼란이 올 수 있습니다.\n",
    "\n",
    "축 정보를 숨기고, tight_layout을 이용하여 ax를 figure에 꽉 맞춰서 시각화해보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": [
    "fig, ax = plt.subplots(1,1)\n",
    "\n",
    "ax.set_xlim(-10, 10)\n",
    "ax.set_ylim(-10, 10)\n",
    "\n",
    "fig.text(1.0, 1.0, 'fig text (1, 1)')\n",
    "fig.text(0.5, 0.5, 'fig text (0.5, 0.5)')\n",
    "fig.text(0, 0, 'fig text (0, 0)')\n",
    "\n",
    "ax.xaxis.set_visible(False) #x축 숨기기\n",
    "ax.yaxis.set_visible(False) #y축 숨기기\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure는 언제나 ax들의 상위 개념이라 ax의 개수가 변경되더라도 text의 위치는 변함이 없습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "fig, ax = plt.subplots(1,3)\n",
    "fig.text(1.0, 1.0, 'fig text (1, 1)')\n",
    "fig.text(0.5, 0.5, 'fig text (0.5, 0.5)')\n",
    "fig.text(0, 0, 'fig text (0, 0)')\n",
    "\n",
    "for i in range(3):\n",
    "    ax[i].xaxis.set_visible(False) \n",
    "    ax[i].yaxis.set_visible(False) \n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이번에는 ax에서 text를 살펴보겠습니다.\n",
    "\n",
    "figure와 거의 유사한 코드로 텍스트를 추가해보겠습니다.\n",
    "\n",
    "위의 `fig.text` 대신에 `ax.text`로 ax에 직접적으로 추가합니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "fig, ax = plt.subplots(1,1)\n",
    "\n",
    "ax.set_xlim(-10, 10)\n",
    "ax.set_ylim(-10, 10)\n",
    "ax.text(1.0, 1.0, 'ax text (1, 1)')\n",
    "ax.text(0.5, 0.5, 'ax text (0.5, 0.5)')\n",
    "ax.text(0, 0, 'ax text (0, 0)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그리고 볼 수 있듯이 여기서는 전체 크기의 비율이 아닌 좌표를 기준으로 하는 것을 알 수 있습니다.\n",
    "\n",
    "그렇기에 ax가 여러개라면 따로 좌표로 관리할 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "fig, ax = plt.subplots(1, 2)\n",
    "\n",
    "for i in range(2):\n",
    "    ax[i].text(1.0, 1.0, 'ax text (1, 1)')\n",
    "    ax[i].text(0.5, 0.5, 'ax text (0.5, 0.5)')\n",
    "    ax[i].text(0, 0, 'ax text (0, 0)')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그렇다면 좌표축이 범주형이라면 어떨까요? 한 번 살펴보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAUt0lEQVR4nO3dfZBV5Z3g8e+PBrSRYMe1AWniIiUSzTCG2CarMQYSExyZWGhwjNmKISGjo5vUWFZYdbHKTdaUU0tKNxHxJa6jO74su5RSmZiCmEGT1WTWtNEaNwkgETSBBFvRIEwjb7/9oy9t093ycu/Ffpr+fqq6uOe55+WROsmXc+7hEpmJJEmlGdLfE5AkqS8GSpJUJAMlSSqSgZIkFclASZKKNLS/DnzsscfmhAkT+uvwkqRCPPPMM69mZnPP8X4L1IQJE2hra+uvw0uSChERL/U17i0+SVKRDJQkqUgGSpJUJAMlSSqSgZIkFclASZKKZKAkSUU6oL8HFRHnAN/MzDMry58G3gc0Afdk5usR0Qp8GBgOLM3MdYdmytLAt/TZ9SxYvooNb3QwrqmReTMmM2tqS39PSyrKAQUqM38cEeMBIiKASZl5W0QMBy4F7gY+UhkL4G+A2w/VpKWBbOmz67nu4efp2LELgPVvdHDdw88DGCmpm2pu8Z0A/AYgM7cDGRHDgC2VsQS2122G0mFmwfJVXXHao2PHLhYsX9VPM5LKVE2gmoE/dFveBRwDvNJjrJeIuCwi2iKirb29vYpDSwPfhjc6DmpcGqyqfUgiDnBsL5l5V2a2ZmZrc3Ov7wWUBoVxTY0HNS4NVtUE6hXguG7LDcAmYHSPMUl9mDdjMo3D9v6fSOOwBubNmNxPM5LKVE2g1gHvB6g8JBGZuQMYWRkLOp/kk9SHWVNbuOnCKbQ0NRJAS1MjN104xQckpB72+xRf5QGIi4EzI2InnQ9IvBARX6HymHll1X+OiCuBI4BHDtF8pcPCrKktBknaj/0GqnJ1dH/lZ1/rtQH+A0+SpLrwmyQkSUUyUJKkIhkoSVKRDJQkqUgGSpJUJAMlSSqSgZIkFclASZKKZKAkSUUyUJKkIhkoSVKRDJQkqUgGSpJUJAMlSSqSgZIkFclASZKKZKAkSUUyUJKkIhkoSVKRDJQkqUgGSpJUJAMlSSqSgZIkFclASZKKZKAkSUUyUJKkIhkoSVKRDJQkqUhDq9koIoYCfw1soTNy/wBcC2yorPIvmfnLusxQkjQoVRUo4LPAssxcGxEtwMeAtZn5UP2mJkkazKoN1DHAOoDMXB8RZwFTIgJgBHB/Zr5VlxlKkgalaj+DWgOcBhARfwZMBBZWrqAeBr7U10YRcVlEtEVEW3t7e5WHliQNBtUG6sfAhIi4BGgCXs7MDQCZ+Tqwra+NMvOuzGzNzNbm5uYqDy1JGgyqusWXmQksga4HJsb3XKXGeUmSBrmqrqCi8mFTxSeBtRHRUHlvCDC8DnOTJA1i1T4kMT4iPgNsBjYBvwUuj4gtwHuAxXWanyRpkKr2Ft/vgEU9hnsuS5JUNb9JQpJUJAMlSSqSgZIkFclASZKKZKAkSUUyUJKkIhkoSVKRDJQkqUgGSpJUJAMlSSqSgZIkFclASZKKZKAkSUUyUJKkIhkoSVKRDJQkqUgGSpJUJAMlSSqSgZIkFclASZKKZKAkSUUyUJKkIhkoSVKRDJQkqUgGSpJUJAMlSSqSgZIkFclASZKKZKAkSUUaWs1GETEU+GtgC52R+wfgHOB9QBNwT2a+Xq9JSpIGn6oCBXwWWJaZayOiBfgYMCkzb4uI4cClwN31mqQkafCp9hbfMcA6gMxcD/w74DeV5e1A1mNykqTBq9pArQFOA4iIPwOOBP7Q7f1dfW0UEZdFRFtEtLW3t1d5aEnSYFBtoH4MTIiIS+j8zAkg9rdRZt6Vma2Z2drc3FzloSVJg0FVn0FlZgJLoOuBiY8BxwG/rqzSUJfZSZIGraquoCKi+9XSJ4F7gPdX3hvOAVxNSZK0L9U+xTc+Ij4DbAY2ZebGiHghIr5C5THzus1QkjQoVXuL73fAoh5jP6rLjCRJwm+SkCQVykBJkopkoCRJRTJQkqQiGShJUpEMlCSpSAZKklQkAyVJKpKBkiQVyUBJkopkoCRJRTJQkqQiGShJUpEMlCSpSAZKklQkAyVJKpKBkiQVyUBJkopkoCRJRTJQkqQiGShJUpEMlCSpSAZKklQkAyVJKpKBkiQVyUBJkopkoCRJRTJQkqQiDa12w4iYA4wEjgAWA5cCGypv/0tm/rLm2UmSBq2qAhURRwNHZubCiBgO/C2wNjMfquvsJEmDVrVXUFuBVwAyc3tEvAycGhEAI4D7M/Ot+kxRkjQYVRWozNwZER0R8QXgZeBJ4P9k5oaIeC/wJeCOnttFxGXAZQDHH3989bOWJB32qnpIonKL7zhgGXA2cFRmbgDIzNeBbX1tl5l3ZWZrZrY2NzdXOWVJ0mBQ7VN85wCLM7MduBE4vcf7WdOsJEmDXrWBGsbetwfHR0QDQEQMAYbXOjFJ0uBW7UMSDwOXR8TrdD5q/lRleQvwHjofO5ckqWrVPiSxHbi1x/CTtU9HkqROfpOEJKlIBkqSVCQDJUkqkoGSJBXJQEmSimSgJElFMlCSpCIZKElSkQyUJKlIBkqSVCQDJUkqkoGSJBXJQEmSimSgJElFMlCSpCIZKElSkQyUJKlIBkqSVCQDJUkqkoGSJBXJQEmSimSgJElFMlCSpCIZKElSkQZ8oLZt28bXv/51LrnkElauXNk1vmTJkq7Xt99+O9OmTev6eeqpp3qt093NN9+81/rTpk3jzDPP5Gc/+1nXOmvWrOHzn/888+bNY9u2bX0et6elS5futbxu3Tra2toAePTRR3sd8+c//3mvfX7/+99n+vTpTJ8+nTvvvLPP47a3t/d5/O7r3HvvvXsd6zvf+c5+5y9J76ah/T2BWt1yyy3MmjWLiRMncsMNN3DHHXfQ0NCw1zpXXHEFV1xxBQBr165l9+7d+9zn1VdfzdVXX73X2MqVK7v2m5nccsstLFiwgN/+9rfceuutzJs3b79zffnll9/xvZkzZzJz5kyefvppxo4dy/HHH99rnZ07d/Lcc8+xYsUKIoI777yTjRs3MmbMmL3W+8lPfsLs2bP3OZeRI0fyxBNP7HfOktRfBvwV1OjRoznrrLMYN24cV155Jc8999w+19+4cSMPPfQQ06ZN44Ybbjjg46xbt47jjjsOgBdffJGLL76YlpYWzj77bJqbm/e7fWbygx/8gJUrV7Js2TKmTZvG5z73uV7rvfXWW7z22mts37696+rmj3/8IwCbNm3iox/9KBEBwIwZM/YZPUkayAb8FdSoUaO6Xk+ePJkVK1a847rPPPMM48eP5/rrr+f666/v83bWt771LR577LFe46+88gqjR4/m2muvZdSoUUyaNKnrvV27djFt2jQATjzxxD6vXh555BFuuukmvve97/HNb36Tc889l3Xr1vHqq692rbN161ZGjRrFzp07aW9v77rC2TPP5uZmli5dyqRJkxgxYgS33nor3/jGN/b9G3SQnnvuORYuXMi1117LueeeW9d9S9LBGPCB2rp1a9frF198kdGjR/daZ82aNfzqV7/iIx/5CGPHjt3n/ubPn8/8+fO7lpcsWdIrOKtXr+all17quqJqaGjoFZPuHnvsMY4++mhOO+00Jk2axOLFi7n00kv3WueFF17g97//PR//+McZMmQIy5YtY8eOHUyYMKFrnYjga1/7GvPnz+/67G3kyJEAvW7zVeuDH/wgN954Y132JUm1qDpQETEHGAkcASwGTgHeBzQB92Tm6/WYYF+WPrueBctXseGNDnb98qdsPXoicz59GosWLer6sL+7HTt28KlPfYoRI0bU5fgnnngiV111FVOmTOGll17a7222c845h4jgwQcf5IEHHmDz5s0sW7aMuXPncsYZZwBwwgkn8Oqrr/KJT3yi1/bnn39+1+uTTjqJiy66aK8xgDfffHOv5Z07d7J161Zee+01Vq9e3WeYd+/ezebNm9m0aRObNm1i3LhxB/x7IEmHWlWBioijgSMzc2FEDAf+FvjXzLytsnwpcHcd59ll6bPrue7h5+nYsatz4AN/wX/6z/+F//7tndz53/4rw4YN67XNySef3OeV0MSJE/s8xuWXX86qVau6lhcuXAjAV7/6VWbPns2QIUOYO3cuF110Ecceeyzf/e539znniGDFihXs2rWLu+++mzFjxrB27VoeeOABpkyZwqhRoxg6dChnnHFGnw8u9LwqW7x4MTfffPNeY+PHj+e8884DYMuWLcycOZMxY8YwadIkZs6cyamnnsqaNWu61v/Tn/7E9OnTOeqooxg7diznnXceJ5988j7/OyTp3VTtFdRW4BWAzNweEUOA33RbzjrNr5cFy1e9HSdgyBEjeO9fXEVjUyOnn376Qe3rQx/6UJ/j3R/f7q57KE499VR++MMfHvCx3nzzTaZOndp1W3DixIlMmjSJLVu2HMSMO11wwQW9Ytv9EfY5c+YwZ86cfe5j7ty5zJ07d5/r3HfffUyZMuUdf58k6VCqKlCZuTMiOiLiC8DLwD9TCVbFrr62i4jLgMuAPh+jPhAb3ug4qPE9Ojo6uh5k6G7RokWccsopVc3lYMycOZNrrrmGxx9/nMbGRrZt28aXv/xlTjrppIPe1yOPPNJ1VbfH+PHjmTVrVr2mC8AXv/jFuu5Pkg5GZB78xU7lFt9ngX8E/gb4EfBmZv668v6czLx3X/tobW3NPX9J9WB89O9WsL6PGLU0NfLUtb0/v5EklS0insnM1p7j1f49qHOAxZnZDtxYWT6u2/sNfW5VB/NmTKZx2N67bxzWwLwZkw/VISVJ/aDaQA1j79uDO4H3A1Qekoga5/WOZk1t4aYLp9DS1EjQeeV004VTmDW15VAdUpLUD6q9xTccuBx4nc5HzZ8Ajq/87HnMfNO+9lHtLT5J0uHlnW7xVfuQxHbg1h7DK/taV5Kkagz47+KTJB2eDJQkqUgGSpJUJAMlSSqSgZIkFclASZKKZKAkSUUyUJKkIhkoSVKRDJQkqUgGSpJUJAMlSSqSgZIkFclASZKKZKAkSUUyUJKkIhkoSVKRDJQkqUgGSpJUJAMlSSqSgZIkFclASZKKZKAkSUUyUJKkIhkoSVKRDJQkqUgGSpJUJAMlSSrS0Go2iojTgQ9UFocDzcBRwOrK2M8yc3Vf20qSdCCqClRm/gL4BUBEzAYeBD6TmQ/VcW6SpEGsqkDtERFDK/voAFojogFI4KHM3F2H+UmSBqlaP4OaBjwO7Aa+nZn3A08Cn+tr5Yi4LCLaIqKtvb29xkNLkg5ntQaqOTM3Zqc/AGTmS3R+LtVLZt6Vma2Z2drc3FzjoSVJh7NaA/VOtwizxv1Kkga5ujxmHhEnRURUXh8F7KrHfiVJg1dND0nwduB2AJdHxL8C7wHuqXG/kqRBrqZAZeZ9lV/XAnfUZUaSJOE3SUiSCmWgJElFMlCSpCIZKElSkQyUJKlIBkqSVCQDJUkqkoGSJBXJQEmSimSgJElFMlCSpCIZKElSkQyUJKlIBkqSVCQDJUkqkoGSJBXJQEmSimSgJElFMlCSpCIZKElSkQyUJKlIBkqSVCQDJUkqkoGSJBXJQEmSimSgJElFMlCSpCIZKElSkQyUJKlIQ6vZKCJOBz5QWRwONAOrgPcATcCizHyrLjOUJBVl6bPrWbB8FRve6GBcUyPzZkxm1tSWuh+nqkBl5i+AXwBExGzgQWB2Zv59RBwDXAD8z7rNUpJUhKXPrue6h5+nY8cuANa/0cF1Dz8PUPdI1XSLLyKG0hm5DwM/BcjMTcBRtU9NklSaBctXdcVpj44du1iwfFXdj1XrZ1DTgMfpvMX3h27ju/paOSIui4i2iGhrb2+v8dCSpHfbhjc6Dmq8FrUGqjkzN1Zex/5Wzsy7MrM1M1ubm5trPLQk6d02rqnxoMZrUWug9nyG9QpwXLfxhhr3K0kq0LwZk2kctvf/xTcOa2DejMl1P1a9HjN/GjgbICL+DbC1TvuVJBVk1tQWbrpwCi1NjQTQ0tTITRdOKecpvm6GAGTm1ojYEhFfAt4L3FbzzCRJRZo1teWQBKmnmgKVmfd1e/2/a5+OJEmd/CYJSVKRDJQkqUgGSpJUJAMlSSqSgZIkFSkys38OHNEOvFTjbo4FXq3DdKT+4jmsgaxe5++/zcxeXy/Ub4Gqh4hoy8zW/p6HVC3PYQ1kh/r89RafJKlIBkqSVKSBHqi7+nsCUo08hzWQHdLzd0B/BiVJOnwN9CsoSdJhykBJkopkoCRJRSo+UBHxiYh4oo+fqyrvj4iI70TE/RFxfLftZvffrKVOEfGViPhpRDwWEX/ZbXx25VfPXw0IlX/vr/vykIhoqvzM7fZ6SL3O31r/wcJDLjNXACt6jkfEZyovrwIeoPOfnb8G+A/v3uykdxYRY4HjgI8DAXw7Iv4jsBtYAyzB81cDQEQcCZwSERMz88XK8FBgAtAMHAmcDrQDv67XcYu/gupLRDQCuyqLGzLz6cxcB/woIo7pv5lJe3kfcH922g3cC3wxM6cByyrreP6qaBFxGnAJcD3w5xHx6YhoyMztdIbpLeB/VH4dWhm/oHKnq6GWYxd9BRURM4F5fbx1BHBkRPw5sLrb+GpgeURsBUbS+SdUqb/8GpgOrK0sfwz4q4hI3r6C2txtfc9fFSUiZgC/z8y/rwwtjYgxwGcjYgnwYTr/EPZmRPwK+CugDXgkM2s+f4sOVGY+Cjy6ZzkiZvf8j46IL3RbnAD8ZWZu9B6++ltmbo2IVRFxPp23+P4pM2+DvT5jOqrbJhPw/FVBMnN5H2Mbgf8FEBH/SGe0hgPbgS/0XL8WRQfqAE2IiBOA14HzefvWidTvMvOFiNgJ3ABMigiA/wvcXlnF81dFiohzgWu7DTXT+RnTHt/PzJuBs/vYvC6fQxX/TRIRcQVwcR9vPZmZ10fEe4GFQBNwTWb+v8p2va62pP4QETfQ+SDEi3T+oXAKcHZm3uL5q4Hinc7JiPgnoOdnTU9m5vW1HrP4K6jMvJ23/7TZZc8tkMx8Hfj37/a8pIPQAfyu8qDE9oh4ETgLPH818GXmJ3uODZrHzKXDwOPAssrTpw3ABuDK/p2SVL7ib/FJkganAfn3oCRJhz8DJUkqkoGSJBXJQEmSimSgJElFMlCSpCIZKElSkf4/02iG35RpXV0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "ax.scatter(['국어', '영어', '수학'], [80, 100, 70])\n",
    "ax.text('국어', 80, '이건 국어 성적입니다.')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이런 식으로 범주형의 경우에는 다음과 같이 범주로 매핑하여 텍스트를 추가할 수 있습니다."
   ]
  }
 ],
 "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
}