fantastic-matplotlib/第一回：Matplotlib初相识.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第一回：Matplotlib初相识"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一、认识matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib是一个Python 2D绘图库，能够以多种硬拷贝格式和跨平台的交互式环境生成出版物质量的图形，用来绘制各种静态，动态，交互式的图表。\n",
    "\n",
    "Matplotlib可用于Python脚本，Python和IPython Shell、Jupyter notebook，Web应用程序服务器和各种图形用户界面工具包等。\n",
    "\n",
    "Matplotlib是Python数据可视化库中的泰斗，它已经成为python中公认的数据可视化工具，我们所熟知的pandas和seaborn的绘图接口其实也是基于matplotlib所作的高级封装。\n",
    "\n",
    "为了对matplotlib有更好的理解，让我们从一些最基本的概念开始认识它，再逐渐过渡到一些高级技巧中。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 二、一个最简单的绘图例子"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib的图像是画在figure（如windows，jupyter窗体）上的，每一个figure又包含了一个或多个axes（一个可以指定坐标系的子区域）。最简单的创建figure以及axes的方式是通过`pyplot.subplots`命令，创建axes以后，可以使用`Axes.plot`绘制最简易的折线图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x29936f8d588>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nUQ3vxKThnWjo67yLzdRY6CISBpTayjwQGMWFL3ouBe4FfgTGAiuNvp/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/o2MIDvSzOo7yYI8N70zSwVyeW7qLXuHB9G4X4tDvn5FTyNOfbWdtynEuiwrlxVt709GCY/aqdvRHrQPNSEimWWN/7hvSweooysM1aCDMuqMvLZr4M3FRErmFJQ75vhUVhrfX7+fqWWtIOnCSv93Ugw8nDNIydxNa6A7yc3oOa5KzeeTKaL0WoqoXzRr78+r4fmSdKuLxDy99iFdq1ilue/1H/vrlLi6LasZ3jw/lnkFROkzLjWihO8gry/YSFtSQewZGWR1FeZG+ESH8+YburNqbzaurU+v0PUrLK5i3MoXrZq8jLbuAGbf34e37L6NdqI6rcDe6lHSAH1KPs2Ff5aznQH995V/Vr3sGtifxwElmJCQTGxnKkE72D/HakVl5XvvuI/lc37sNf72xh753wo3pCv0SGWP4v2V7aRMcwJ0edskw5R5EhBdu6UXHWgzxKiot58Vv93DT/PUcLyjm9Xv6Mf+uOC1zN6eFfolWJ2eTdDCXycM76Xm5yjKN/H1ZML4fRaXlTKphiNdP+3O4bvZaXvs+jbFx7Vj++JVc3aN1PaZVzqKFfgmMMcxYlkxEs0Bu6xdhdRzl5Tq1bMJLY3uTeOAkL3xz4RCvguIy/vz5Dm5//UdKyitY9OAAXhrbm+BGeoqtp6jxGLqIRADvAq0AAyw0xsw+bxsBZgPXAYXAfcaYJMfHdS3Ldh1je2YeL4/tjb+v/mxU1ruhd1s2pZ/krfWVQ7yu7105xGvV3iz+tGQ7R/KLeGBIB/7n6hga+etLaJ7Gnme0DJhujEkSkSAgUUQSjDG7ztnmWqCz7WMAsMD2X49VUWGYmZBMdIvG/CY23Oo4Sv3i6eu6se1QLk98spXWwQ1ZvOEgSzZn0rllEz55ZDD92odaHVE5SY3LSmPMkbOrbWPMKWA3cH6D3QS8ayptAEJExKMn3H+9/Qh7jp5iysjO+lZo5VLODvFq6OfDrQt+ZOnWwzw2vBNfPXa5lrmHq9XvXCISBcQCG8+7KxzIOOfzQ7avHTnvz08AJgBERrrvGSHlFYZZy5OJadWEG3u3tTqOUhdoExzIgrvj+Pf6dKaM7Ey3Nk2tjqTqgd2FLiJNgE+BqcaY/Lo8mDFmIbAQID4+3vlj4pzkiy2ZpGWf5rXxcfouOuWyBkQ3Z0B0c6tjqHpk17ECEfGjsswXG2OWVLFJJnDuaR7tbF/zOKXlFcxankKPtk31VC+llEupsdBtZ7C8Cew2xsyoZrOlwG+l0kAgzxhzpJpt3dqniYc4mFPI9NExVP6vUUop12DPIZchwD3AdhHZYvva00AkgDHmNeAbKk9ZTKXytMX7HZ7UBRSXlTN3ZSp9I0K4qktLq+MopdSv1Fjoxph1wEWXoqbysimTHBXKVX34cwaZuWd48dZeujpXSrkcPd/OTkWl5cxbmUr/Ds24vBbDj5RSqr5oodtp0YYDZJ0qZvooPXaulHJNWuh2OF1cxoLVaVzRuYWeBqaUclla6HZ458d0TpwuYdqoGKujKKVUtbTQa5BfVMrr3+9jeNeWxEbq26aVUq5LC70Gb63bT96ZUl2dK6Vcnhb6ReQWlvDm2v1c06M1PcODrY6jlFIXpYV+EQvX7KOgpIzHdXWulHIDWujVOF5QzNs/pHNj77Z0aR1kdRyllKqRFno1XludRlFpOVNGdrY6ilJK2UULvQrH8ov4z4YD3BLXjo5hTayOo5RSdtFCr8L8VamUVximjNDVuVLKfWihnycz9wwf/JTBbfERRDRrZHUcpZSymxb6eeatTAHg98M7WZxEKaVqRwv9HAdOnOajTYe4a0AkbUMCrY6jlFK1ooV+jtkrUvDzER4d1tHqKEopVWta6DapWQV8vjmT3w6KomXTAKvjKKVUrWmh28xankyAnw8PD422OopSStWJFjqw+0g+X207wgNDOtC8SUOr4yilVJ1ooQMzE5IJCvDld1fo6lwp5b68vtC3H8pj2a5j/O6KaIIb+VkdRyml6szrC31Gwl5CGvlx/5Aoq6MopdQl8epCTzxwklV7s3l4aEeCAnR1rpRyb15d6DMS9tKiiT/3Dm5vdRSllLpkNRa6iLwlIlkisqOa+4eJSJ6IbLF9POv4mI73Y9oJ1qeeYOKwTjTy97U6jlJKXTJ7muxtYB7w7kW2WWuMucEhieqBMYYZCXtp3TSAuwdEWh1HKaUcosYVujFmDZBTD1nqzdqU4/ycfpJJwzsR4OdjdRyllHIIRx1DHyQiW0XkWxHpUd1GIjJBRDaJyKbs7GwHPXTtGGN4ZdlewkMCuSM+wpIMSinlDI4o9CSgvTGmDzAX+Ly6DY0xC40x8caY+LCwMAc8dO2t2J3F1kN5TBnRGX9fr35NWCnlYS650Ywx+caYAtvtbwA/EWlxycmcoKLC8EpCMlHNG3FLXLjVcZRSyqEuudBFpLWIiO12f9v3PHGp39cZ/rvzKLuP5DN1ZAy+Pro6V0p5lhrPchGR94FhQAsROQT8BfADMMa8BowFJopIGXAGuNMYY5yWuI7KKwwzE5Lp3LIJN/Zpa3UcpZRyuBoL3Rgzrob751F5WqNL+3LrYVKyCph/Vxw+DcTqOEop5XBecdyhrLyCWcuT6damKdf2bG11HKWUcgqvKPQlSZmknyhk2qgYGujqXCnloTy+0EvKKpi9IoU+7YIZ2a2l1XGUUsppPL7QP9qUQWbuGaaN7oLtZByllPJIHl3oRaXlzFuZSnz7UIZ2dslT45VSymE8utDf23iQo/lFTNfVuVLKC3hsoReWlPHq6jQGd2zOoI7NrY6jlFJO57GF/u6PBzheUMz00TFWR1FKqXrhkYV+qqiU179PY1iXMPq1b2Z1HKWUqhceWej/Xp/OycJSpo3S1blSynt4XKHnFZbyr7X7GN29Fb3bhVgdRyml6o3HFfob6/ZxqqiMx3V1rpTyMh5V6DmnS3hr3X6u792Gbm2aWh1HKaXqlUcV+uvfp3GmtJzHR3a2OopSStU7jyn0rFNFvPNjOjf3DadTyyCr4yilVL3zmEJ/dVUapeWGKbo6V0p5KY8o9MO5Z3hv40Fu69eO9s0bWx1HKaUs4RGFPm9VKgbD5OGdrI6ilFKWcftCz8gp5KOfMxjXP5J2oY2sjqOUUpZx+0KfvSIFnwbCpKt0da6U8m5uXej7sgtYknSIewa2p1XTAKvjKKWUpdy60GevSCHAz4dHhnW0OopSSlnObQt979FTLN16mHsHR9GiSUOr4yillOXcttBnLU+mib8vDw+NtjqKUkq5hBoLXUTeEpEsEdlRzf0iInNEJFVEtolInONj/tqOzDy+3XGUBy7vQEgjf2c/nFJKuQV7VuhvA9dc5P5rgc62jwnAgkuPdXEzE5IJDvTjwSs6OPuhlFLKbdRY6MaYNUDORTa5CXjXVNoAhIhIG0cFPN/mgydZsSeLCUOjaRrg56yHUUopt+OIY+jhQMY5nx+yfe0CIjJBRDaJyKbs7Ow6PZgBhsaEcd/gqDr9eaWU8lT1+qKoMWahMSbeGBMfFhZWp+8RFxnKuw/0p3FDXwenU0op9+aIQs8EIs75vJ3ta0oppeqRIwp9KfBb29kuA4E8Y8wRB3xfpZRStVDjcQsReR8YBrQQkUPAXwA/AGPMa8A3wHVAKlAI3O+ssEoppapXY6EbY8bVcL8BJjkskVJKqTpx23eKKqWU+jUtdKWU8hBa6Eop5SG00JVSykNI5WuaFjywSDZwoI5/vAVw3IFxrKT74po8ZV88ZT9A9+Ws9saYKt+ZaVmhXwoR2WSMibc6hyPovrgmT9kXT9kP0H2xhx5yUUopD6GFrpRSHsJdC32h1QEcSPfFNXnKvnjKfoDuS43c8hi6UkqpC7nrCl0ppdR5tNCVUspDuHShu+IFquvCjv0YJiJ5IrLF9vFsfWe0l4hEiMgqEdklIjtFZEoV27j882LnfrjF8yIiASLyk4hste3Lc1Vs01BEPrQ9JxtFJMqCqDWyc1/uE5Hsc56Xh6zIag8R8RGRzSLyVRX3Of45Mca47AcwFIgDdlRz/3XAt4AAA4GNVmeu434MA76yOqed+9IGiLPdDgKSge7u9rzYuR9u8bzY/j83sd32AzYCA8/b5lHgNdvtO4EPrc59CftyHzDP6qx27s804L2q/h454zlx6RW6cbELVNeVHfvhNowxR4wxSbbbp4DdXHgNWZd/XuzcD7dg+/9cYPvUz/Zx/tkONwHv2G5/AowQEamniHazc1/cgoi0A64H3qhmE4c/Jy5d6Haw+wLVbmCQ7dfMb0Wkh9Vh7GH7FTGWylXUudzqebnIfoCbPC+2X+23AFlAgjGm2ufEGFMG5AHN6zWknezYF4BbbYfzPhGRiCrudwWzgCeAimrud/hz4u6F7imSqJzP0AeYC3xubZyaiUgT4FNgqjEm3+o8dVXDfrjN82KMKTfG9KXymr79RaSnxZHqzI59+RKIMsb0BhL4/6tclyEiNwBZxpjE+nxcdy90j7hAtTEm/+yvmcaYbwA/EWlhcaxqiYgflSW42BizpIpN3OJ5qWk/3O15ATDG5AKrgGvOu+uX50REfIFg4ES9hqul6vbFGHPCGFNs+/QNoF89R7PHEGCMiKQDHwDDRWTReds4/Dlx90L3iAtUi0jrs8fORKQ/lc+LS/5js+V8E9htjJlRzWYu/7zYsx/u8ryISJiIhNhuBwKjgD3nbbYUuNd2eyyw0thejXMl9uzLea/HjKHy9Q+XYox5yhjTzhgTReULniuNMePP28zhz0mN1xS1knjIBart2I+xwEQRKQPOAHe64j82myHAPcB223FOgKeBSHCr58We/XCX56UN8I6I+FD5Q+cjY8xXIvI8sMkYs5TKH17/EZFUKl+gv9O6uBdlz748JiJjgDIq9+U+y9LWkrOfE33rv1JKeQh3P+SilFLKRgtdKaU8hBa6Ukp5CC10pZTyEFroSinlIbTQlVLKQ2ihK6WUh/h/vyV/3iC5QPwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()  # 创建一个包含一个axes的figure\n",
    "ax.plot([1, 2, 3, 4], [1, 4, 2, 3])  # 绘制图像"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "和MATLAB命令类似，你还可以通过一种更简单的方式绘制图像，`matplotlib.pyplot`方法能够直接在当前axes上绘制图像，如果用户未指定axes，matplotlib会帮你自动创建一个。所以上面的例子也可以简化为以下这一行代码。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x29937023948>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot([1, 2, 3, 4], [1, 4, 2, 3])  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 三、Figure的组成"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在我们来深入看一下figure的组成。通过一张figure解剖图，我们可以看到一个完整的matplotlib图像通常会包括以下四个层级，这些层级也被称为容器（container），下一节会详细介绍。在matplotlib的世界中，我们将通过各种命令方法来操纵图像中的每一个部分，从而达到数据可视化的最终效果，一副完整的图像实际上是各类子元素的集合。\n",
    "\n",
    "- `Figure`：顶层级，用来容纳所有绘图元素 \n",
    "\n",
    "- `Axes`：matplotlib宇宙的核心，容纳了大量元素用来构造一幅幅子图，一个figure可以由一个或多个子图组成\n",
    "\n",
    "- `Axis`：axes的下属层级，用于处理所有和坐标轴，网格有关的元素\n",
    "\n",
    "- `Tick`：axis的下属层级，用来处理所有和刻度有关的元素\n",
    "\n",
    "  ![](https://matplotlib.org/_images/anatomy.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 四、两种绘图接口"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "matplotlib提供了两种最常用的绘图接口\n",
    "\n",
    "1. 显式创建figure和axes，在上面调用绘图方法，也被称为OO模式（object-oriented style)\n",
    "\n",
    "2. 依赖pyplot自动创建figure和axes，并绘图\n",
    "\n",
    "使用第一种绘图接口，是这样的："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x299370a6d88>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "x = np.linspace(0, 2, 100)\n",
    "\n",
    "fig, ax = plt.subplots()  \n",
    "ax.plot(x, x, label='linear')  \n",
    "ax.plot(x, x**2, label='quadratic')  \n",
    "ax.plot(x, x**3, label='cubic')  \n",
    "ax.set_xlabel('x label') \n",
    "ax.set_ylabel('y label') \n",
    "ax.set_title(\"Simple Plot\")  \n",
    "ax.legend() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "而如果采用第二种绘图接口，绘制同样的图，代码是这样的："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x29937137e48>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "x = np.linspace(0, 2, 100)\n",
    "\n",
    "plt.plot(x, x, label='linear') \n",
    "plt.plot(x, x**2, label='quadratic')  \n",
    "plt.plot(x, x**3, label='cubic')\n",
    "plt.xlabel('x label')\n",
    "plt.ylabel('y label')\n",
    "plt.title(\"Simple Plot\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 参考资料\n",
    "\n",
    "[1.matplotlib官网用户指南](https://matplotlib.org/tutorials/introductory/usage.html#sphx-glr-tutorials-introductory-usage-py)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 作业\n",
    "你在工作或学习中通常何时会用到数据可视化，希望通过可视化达到什么目的？"
   ]
  }
 ],
 "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": "3.7.9"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}