7_Matplotlib 특강 2 - 객체 지향 방식.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib 특강 2 - 객체 지향 방식"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline \n",
    "# ipython magic method / 셀 안에 그림 그려주는 명령어"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 객체 지향 방식\n",
    "\n",
    "객체 지향 좋아하는 사람들은 이걸 많이들 사용합니다.\n",
    "\n",
    "장점은 동시에 여러 그래프(subgraph)를 그릴 수 있다는 점입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) plt.subplots\n",
    "\n",
    "https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplots.html?highlight=subplots#matplotlib.pyplot.subplots\n",
    "\n",
    "### 0> shift+tab\n",
    "\n",
    "- nrows와 ncols로 subplot 배치할 행과 열을 생성\n",
    "\n",
    "- return을 fig와 ax로 2개한다 (fix와 ax라는 객체를 만들어서 진행 => 객체 지향)\n",
    "\n",
    "- fig => figure\n",
    "\n",
    "- ax => axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/iJ8BVoGvA6/cprr/Avw38I3pn+Xteszr1o75i7HZYw7wt8Bp4FvAoW38Oe8DvjL9pfkG8Acj1PwU8D3gZ0zOeO4E3ga8bebxHp329K2x/p0XOdeLnO1FzfUiZ3sRcz3P2farFSSpCa+0laQmDHxJasLAl6QmDHxJasLAl6QmDHxJasLAl6Qm/g9yAKZwvIhC+gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1> 각 그래프 설정 (ax)\n",
    "\n",
    "- nd array indexing 하듯 각 그래프를 지칭하면 됩니다.\n",
    "\n",
    "- set을 앞에 붙여서 옵션을 추가합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'title 2')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,2)\n",
    "ax[0,0].set_title('title 1')\n",
    "ax[0,0].plot([1,2,3])\n",
    "ax[0,1].set_title('title 2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2> 이미지 사이즈 조절\n",
    "\n",
    "- figsize로 figure 크기 확보"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'title 2')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,2, figsize = (10,10))\n",
    "ax[0,0].set_title('title 1')\n",
    "ax[0,1].set_title('title 2')"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}