Created on Cognitive Class Labs

Annotate.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x7fd7a298cef0>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a298c780>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a2981668>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a2944d68>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a294a320>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a294a898>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a294ae10>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a29513c8>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a294a3c8>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a29447b8>],\n",
       " <a list of 10 Text yticklabel objects>)"
      ]
     },
     "execution_count": 22,
     "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": [
    "xs = np.arange(0,10,1)\n",
    "ys = np.random.normal(loc=2.0,scale=0.8,size=10)\n",
    "plt.plot(xs,ys)\n",
    "plt.text(2,4,'This text starts at point (2,4)')\n",
    "plt.text(8,3,'This text ends at point (8,3)',horizontalalignment='right')\n",
    "plt.xticks(np.arange(0,10,1))\n",
    "plt.yticks(np.arange(0,5,0.5))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd7a291b390>]"
      ]
     },
     "execution_count": 23,
     "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": [
    "xs = np.arange(0,10,1)\n",
    "ys = np.random.normal(loc=3, scale=0.4, size=10)\n",
    "plt.plot(xs,ys,'bo-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x7fd7a27bcc50>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a27bc588>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a27db5f8>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a25d26d8>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a25cb5f8>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a25c2630>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a25d2dd8>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a25d9278>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a25d97f0>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a25d9d68>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a25df320>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a25df898>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a25dfe10>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a25e73c8>],\n",
       " <a list of 14 Text yticklabel objects>)"
      ]
     },
     "execution_count": 44,
     "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": [
    "xs = np.arange(0,10,1)\n",
    "ys = np.random.normal(loc=3,scale=0.4,size=10)\n",
    "plt.plot(xs,ys,'bo-')\n",
    "for x,y in zip(xs,ys):\n",
    "    label = \"{:.2f}\".format(y)\n",
    "    plt.annotate(label,#this is the text\n",
    "                 (x,y),#this is the point to label\n",
    "                 textcoords = \"offset points\",#how to position\n",
    "                 xytext = (0,10),#distance from text to points\n",
    "                 ha='center'\n",
    "                )\n",
    "plt.xticks(np.arange(0,10,1))\n",
    "plt.yticks(np.arange(0,7,0.5))\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x7fd7a1b17f60>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a1b17898>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a1b0aac8>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a1ad3898>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a1ad3e10>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a1ad3ef0>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a1acc1d0>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a1adb3c8>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a1adb940>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a1adbeb8>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a1ae2470>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a1ae29e8>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a1ae2f60>,\n",
       "  <matplotlib.axis.YTick at 0x7fd7a1ae9518>],\n",
       " <a list of 14 Text yticklabel objects>)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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//PLLSmVuueUW3n///STJVatW8fzzzydJrlixgikpKdy1axeLiorYr18/fvvtt8H7FzVC2dlknz4kQA4YQP7rX2SzZu5x2e3ww8nmzd3yZ54hPQwLETNeeqn6+9GsmVsuEkvg15WiJLfBjbY4oMry7SR3Bu7PApBgZq3D/XFp164devfuDQA48sgjkZycjI0bN1Yq89VXX6Ffv34AgKSkJKxduxY///xzpRH14uPjy0fUq4v6HinMnj0b3bt3R9euXfHoo4/WKYYyO3YAN90EZGa6ppbXXgNmzQJuvhmYNMm1mZu5v889B3z7LXD66cAf/gAMHw788ku9dt9o3H33gRPEZXbvdstFDhYNfhQZKtPzQO27DYDEwP2mAOYBGFylTFscOMGaCWB92eNQt9ouLPrhhx/YoUMHFhYWVlp+55138uabbyZJLlq0iHFxcczOzvZ1RL36HCkUFxezc+fO/P7777lv3z6mpqZWe64XpaXkm2+SJ5xAmpF/+hO5dau355aUkH//Oxkf754/d27Yu29USksr18wr3syiHZ2I49dRJOpZQ28HYK6ZLQfwBVwb+owqE1wMB7DSzHIBPA5gRGDHdbJz504MGzYM48ePR8uWLSutu+OOO7B161akp6fjiSeeQK9evRAfH+/riHr1OVJYvHgxunbtis6dO+Owww7DiBEjMH369LD2v24dMGQIMGwY0Lo1sGABMGECkJjo7flNmgC33gosXAg0awacf76rqRYVhRVGo7BkCXD22aHXn3hi5GIRqcmtt0bgKDJUpm/oW6ga+v79+3nBBRfwn//8Z62/VKWlpezYsWO1Wjzp34h64R4pvPHGG7zmmmvKy02ZMoVjxozxtK/9+13Nulkzd/vnP8miovrFv2MHec01rjaQmUl+9139tnew+Pln97rMyDZt3P1g5xTUhh5dL71Eduzo/k8dOzbc/2P9+vXs27cvk5KS2KNHD44fP75amW3btnHw4MFMTU1ljx49OHnyZJLknj17eOqpp5Yvv++++3yN7YcfyJEj/TuKRA019IMqoZeWlvKKK67g2LFjQ76YrVu3ct++fSTJSZMm8Yorrihf9/PPP5Mk161bx+7du4ccC8GrHTt2sHfv3nzzzTerrSssLORVV13FtLQ0Xn755czIyGBOTg5ff/31agndS9PPggVkaqr7jwwZQq5bV6/Qq3n9dTIxkWzRgnzhBe8nTOvzRSHJq6++mm3atGFKSoovr2PfPvdD17Kla1L6858PNEVVTB4JCe4E8YYNvuxW6iCSJ6q9NJM+/PDD5ROjbNq0iUcffTT37dvnaQjgusjPJ8eMcZ/FI45wn9lgCb1jx/C222gS+rx58wiAPXv2ZFpaGtPS0jhz5sxKg9vMnz+fXbt2Zffu3Tl06NBKSfuss85icnIyU1NTOWfOnPDepSrqeqQwf/58XnDBBeXrHnnkET7yyCMhn7t1Kzl6tEtC7duTb79dr7BrtG4dec457r8+YoS3Nvn6fFFIN+bzkiVLfEnos2aR3buzvKfPqlWhy379tUvo55xT/6McqZuOHYMnsBNPbPh9DxkyhO+//36lZY888givu+46lpaWcs2aNezSpQtLSkoqlfEy01BtfvmFvOMOsmlTV+n44x/JvLzItKEfVAn9YFGfI4WioiKedNJJXLNmTflJ0ZUrVwbZB/nyy+Rxx5FNmpA330xu394wr6ei4mLyoYfIuDj3hfvss/CeX5cvyg8//FCvhP7NN+SFF7pPa9eu5H/+4+0IY8oU9xyfj6DFg337gifzstsLL5B79zbMvkM1k27fvp19+/Zl27Zt2bx5c86YMaN8nZfpDWuzYwf58MPkUUe5Ctrvf1+9idOPJigl9DDV90hh5syZ7NatGzt37syHHnqo2vZXryYvuMC9+xkZZKACHFELFpAnneR+TMaN81aLrcsXpex5dUnohYXkLbe4Q9Yjj3TnF8JNAlde6b48H30U9u6ljr76iuzdO3Qyj493f4891v3Y5uf7t++amknfeOMN3nTTTSwtLeV3333HTp06Vfsse5lpqKq9e8nHH3evp6zJNDe33i8lJCX0g8S+fa52fMQRLkE98YSrMUdLYSF5xRXuU3DGGe7kTSj1+aKEm9BLSsjJk93RC0BefXXdv/Q7drhmmnbt3IlUaTglJS6xHXEE2aoVOXZs6CaGDz4gBw92yxIS3OcwO7t++6+tmXTQoEH89NNPyx+fd955XLRoUbVyXqY3JF0l6PnnDzQt9e1LBuYlb1BK6AeBTz4hk5PdO/7b35IbN0Y7ogNeesn9wLRsSb7ySvX19f2ihJPQ5893Ry0AmZVFLl4c3msJJjfX9Xj51a9c0hH/bdx44Khz4MADP8C1NTF8+y15ww3uZD1Annkm+cYb4Z/38NJMOnr0aI4bN44k+dNPP/H4449nQUEBN23axK2BE0q7d+/mWWedxf/85z817MvFmJR04Cj7/fcjd2W2EnoUbd7sapgA2akTOXNmtCMKbs0a8vTTXZwjRx5oz6/PF6WMl4Sel0defrnbf7t25Isv+pt8J0502/7b3/zbpjhvvEEec4w7Cfjvf9ctsW3b5oazOOkklp84/dvfyC1bvD3fSzPpxo0b2b9/f55yyilMSUnhiy++SDL0TENVlZaSs2cfGIIjOdld/BfpITbqldABHAFgMYBcAF8CeCBIGYO7oGg1gOUAete23VhP6KWl7sRP69auzfD228ldu6IdVc2KilybZpMmZJcu5KJF9fuikOSIESPYtm1bxsfH84QTTuCzzz5baZ979rgTSc2bk4cdRt55p2sm8VtpKTl8uPtfROKw+FCwbduBJrtTT3U9i+qruJh85x3XfFHWRDN6tGuXj6bPPyfPPZfl3QxfeCF6zaX1TeiGAwNvJQBYBCCrSplBAN4LlM0CsKi27dYloUfqIoX6WrXqwAfyjDPI5cujHVF4Pv3U1ZDi48lHHmmYD25pqeui2bmze59+8xt3srghbd3qjpJOPNF1LWvMvFwfMHfuXLZs2bL8h7hizXPr1q0cNmwYu3fvzqSkJM4P81fuk0/c+xgX5yoB+/fX+yVVk5ND/t//65rLANekM2tWZJvNcnIO9LA67jh33quheud45VuTC4BmAJYCOK3K8qcBXFrh8TcA2tW0rXATemMYTW/PHvLee11NMzGRnDSp8bbZbt1KXnKJe5/PPZdcv96/ba9cSfbr57bdo4c7QRYpixa5H6qhQxv3aJRerg+YO3cuL7zwwqDPHzlyJJ955hmSbpLmrR4HCtq7l7z1Vlep6tLF9ZZqaJs2kX/5i2uKA8iTTyaffLJhjuTKfPsteemlbn+JieRf/0ru3Nlw+wtHvRM6gDgAOXATXfwtyPoZAM6q8PhDABlByo0CkA0g+8Qwry4IdZFC06bkqFHk3XeTjz3m+nbPmeNOhOXnR+6ikg8+cH2kAfKyy8hGPoE5SZfwnn/eNYccfTQ5bVr9trdlC3n99a5Wl5joekRE46Kff/zD/Z+efDLy+24owa4PCJXQCwsL2alTJ5aG+Yu2YgWZlubeu1GjGjahBrNvHzl1qmveAVx/7//6r5p7Z4VrwwbyD39wn9Fmzci77jr4jub8rKGHGg99ZpCE3qembYVbQzcLntDLDoWaNAm9/phjXNe1s88mL77Ytcnde687fHr1VddHecUK163NS/NCxaaf9u1ds0rZRS+RrG1GyrffHuh5cu214ddUiovdybJWrdz/afRossI504grKSEHDXJHUsuWRS8Ov4S6PmDu3Lk85phjmJqaygEDBpRf4LZs2TKeeuqpvPLKK5mens5rrrmGO2v4p5aUuOEWDjvM9bV+990GfTm1Ki1150F+9zuXeJs0cd/rTz6p+1HXpk1uGInDD3fdKG+4wd/+8X7ytZcLgHFw84dGtMklVA29bByEkhLXo+Srr9w/9o03XBK5/343nsJvf+vatXv0cIM5hfqBMHMnMpOTXVPD8OFu6Npx48gJE9w/uqxNr+Jt6FDX5BKr9u1zlzObuUNerxdDzZ17YIyac891bZIHg4IC8vjjyW7dInOFbkOpbbyhsjFKZs6cya5du5Ikv/jiC8bFxZVf3n7jjTfynnvuCbr99evJ8893/79f//rg68u/fr37XB5zjIsxPd0dVXr9LhYWuu92ixbuh+Gqq/yt8TeE+p4U9TIe+oVVToourm270W5DLy52v8orV7qk89prrsZ+332uBjlsmKvRJyUd+LDUdAt3gJ3G6qOP3BjrCQmu6SLUOYK1a92PYVkXtNdfP/jarD/+2H2JL7/84IvNi3DGGyLJjh07sqCggPn5+exY4QP76aefctCgQdXKv/yyaxpr3vzgnwFr1y7y6addhS3YVahVO1RMnuw+v61aufLDhkW/J41X9U3oqQCWBbojrgRwX2D5aACjA/cNwAQA3wNYEaz9vOqtsfVyKSpyH46aavaHis2bXa8UgOzf37VFl/1fOnRw6444wp3feOABcvfuaEcc2gMPuNfx/PPRjiQ8Xq4PyM/PL28nX7RoETt06FD++KyzzuLXgX6G48aN4y233FL+vF9+OXBCMCurcQ25XFpa/SrUM890n8eq31fAXWz2xRfRjjo8urDIR7U1/RwqSktdjSghIfj7kZXl/xDADaG4mDzvPHe011hqaKS36wOeeOIJ9ujRg6mpqTzttNP4+eeflz9/2bJl7NOnD3v27MmLLrqofCyiOXPceaG4ONezpDGPVFl2FWqoSthxx0U7wrqpKaGXTRsXcRkZGczOzo7Kvutj6lRg1KjKM480a+bm97zssujFFS3HHw/k51df3rEjsHZtxMOpkx9/BNLTgeOOAxYvBpo2jXZEkbd3L3DXXcD//A/QvTvw4ovAqadGOyp/NGniUnhVZkBpaeTjqS8zW0IyI9i6sCaJFpe0q07OfKgmcwD46afgy9evj2wc9XH88cCUKcDKlW7y7UNNbi6QkeGS+ZgxwNKlsZPMgdDTEMbi9IRK6HVw2WWu9lla6v4eqskciJ0vy4ABwG23AU8/Dbz+erSjiYySEuDvf3fJe8sW4L33gCefdEecseThh6u/pmbN3PJYo4Qu9RJLX5aHHgKysoA//AFYsyba0TSsdevc5OG33w78+tfAihXuRy0WHUpH1UroUi+x9GVJSABeecW1uf7ud8D+/dGOyH+ka15KTQWWLQOefx6YNg1o3TrakTWsQ+WoWgld6i2WviydOgHPPQdkZwN33hntaPy1ZQtwySXAlVe6hJ6bC1x1lfshltighC5SxcUXu5OD//oXMGNGtKOpm6lT3Y9Tkybu7x13AD17AtOnA3/9K/Dxx8BJJ0U5SPGdui2KBLF3L3D66a63Tm4u0L59tCPyLljXWsD15pkxA+jVKzpxiT/q1W3RzDqY2VwzW2VmX5rZ2CBl+ppZoZnlBG73+RG4SLQccQTw2mvAvn3A738PFBdHOyLv7r67ejIHgLg4JfNY56XJpRjAf5FMhhunZYyZ9QhSbh7J9MDtQV+jFImCk08GnnoKmDcPeLARfaJDXQOQlxfZOCTyak3oJPNJLg3c3wFgFYATGjowkYPB5Ze7E4cPPQR8+CGwYcMGnHfeeUhOTkZKSgoee+yxas8hiRtvvBFdu3ZFamoqli5dCgDYu3cvMjMzkZaWhpSUFIwbN87XWL/+Gvjtb4NfFQk0vmsDpA5CjQkQ7AagE4D1AFpWWd4XwBa4eUffA5AS4vl1nuBCJFp27nSjbrZtS+bm1j5T0MyZMzlgwACWlpZywYIFzMzMJOkG1Cobznb//v3MzMzkAh+m/Fm3zk3V1qSJGwb24ovdwGgH8+xeUneoYSwXz71czKwFgDcB3ERye5XVSwF0JJkG4AkA74T48ZhEMoNkRps2bbzuWiSqmjd3V49u2wbcems7pKf3BgAceeSRSE5OxsaNGyuVnz59OkaOHAkzQ1ZWFrZt24b8/HyYGVq0aAEAKCoqQlFREawefQY3bQJuugno1s2dCB071l0Q9eabwDPPxMa1ARIeTwndzBLgkvlUkm9VXU9yO8mdgfuzACSYWYxfqiCHkp49gfHjgfffd5fLA8DatWuxbNkynHbaaZXKbty4ER06dCh/3L59+xOub5wAAAxuSURBVPKkX1JSgvT0dBx77LHo379/ted6UVgI3Hcf0KUL8MQTwBVXAN9957pZltWTYunaAPHOSy8XA/AcgFUk/xWiTNtAOZhZZmC7W/wMVCTaRo1ybdT33APMmbMTw4YNw/jx49GyZctK5RikEbusJh4XF4ecnBzk5eVh8eLFWLlypef979kD/OMfQOfOwF/+AgwcCHz1FfDss0CF3w85hHmpoZ8J4AoA51foljjIzEab2ehAmeEAVppZLoDHAYxgsE+1SCNm5poyOnQowq9/PQxDh16Giy++uFq59u3bY8OGDeWP8/LycPzxx1cqk5iYiL59+2L27Nm17reoyDWZdOsG3HqrG0wrO9s1A3XvXv/XJbHDSy+Xz0gayVQe6JY4i+RTJJ8KlHmSZArJNJJZJOc3fOgikdeyJZGcfA3270/GkiV/DtqjZMiQIZgyZQpIYuHChTjqqKPQrl07FBQUYNu2bQCAPXv2YM6cOUhKSgq5r9JS4NVXgR49gD/+0fVS+fhjYPZsoE+fBnqB0qjFRzsAkcbk888/x3vvvYh27XrinXfS0aEDMGnSI1gf6Pw9evRoDBo0CLNmzULXrl3RrFkzPP/88wCA/Px8XHnllSgpKUFpaSkuueQSDB48uNo+SDeU7d13Azk5rv3+3XeBwYM17orUTJf+i9QBCQwZ4k6SLlgA9O7tz3Y/+8wNCvbZZ66t/MEHgUsvdWOyiACasUjEd2Zu6Nk2bdxQuzt21G97OTnAhRcCZ58NfP898O9/A6tWud4pSubilT4qInXUurUbP33NGmD06NBXaNbku+9cDbxXL1fTf/RRYPVq4LrrgMMO8z9miW1K6CL1cPbZwP33Ay+/7GrsXuXluW6Qycmuffyuu9wPw+23x94UcBI5Sugi9XTXXW46t+uvd/3Ca7JlC3DLLUDXrsALLwB/+pNrYnn4YSAxMSLhSgxTQhepp7g44KWXgBYtXHt6sKFrd+xwJzhPOsld0TliBPDtt8DjjwNt20Y+ZolN6rYo4oN27YAXX3QTLQ8e7JpP1q93E2Occ47rDVNQAAwd6q7yTEmJdsQSi/ya4MLM7HEzW21my83Mp05cIo3Hr37lkvncucC6de4k6YYNbuCsNm2ARYuAt95SMpeG46WGXjbBxVIzOxLAEjP7gGTF1sKBALoFbqcBmBj4K3JIWb48+PJdu4DMzMjGIocevya4uAjAlMBwvQsBJJpZO9+jFTnIVRjCpZJQswiJ+Cmsk6Jm1glALwCLqqw6AUDFj3IegsxqZGajzCzbzLILCgrCi1SkEQg1K5BmC5JI8GuCi2AjTFS7zEITXEise/jh6v3ImzVzy0Uami8TXMDVyCuOyNwewI/1D0+kcbnsMjfUrWYLkmio9aSolwkuALwL4HozexXuZGghyXz/whRpPC67TAlcosNLL5eyCS5WmFlOYNldAE4EgMCY6LMADAKwGsBuAFf7H6qIiNSk1oRO8jMEbyOvWIYAxvgVlIiIhE+X/ouIxAgldBGRGKGELiISI5TQRURihBK6iEiMUEIXEYkRSugiIjHCy3jok81sk5mtDLG+r5kVmllO4Haf/2GKiEhtvFwp+gKAJwFMqaHMPJKDfYlIRETqxMt46J8C+CUCsYiISD341YZ+upnlmtl7ZqYJtkREosCPSaKXAuhIcqeZDQLwDtxUdNWY2SgAowDgRI34LyLiq3rX0EluJ7kzcH8WgAQzax2irCa4EBFpIPVO6GbWNjBmOswsM7DNLfXdroiIhMfLBBevAOgLoLWZ5QEYByABKB8LfTiA68ysGMAeACMCw+mKiEgEeRkP/dJa1j8J161RRESiSFeKiojECCV0EZEYoYQuIhIjlNBFRGKEErqISIxQQhcRiRFK6CIiMUIJXUQkRvgxwYWZ2eNmttrMlptZb//DFBGR2nipob8AYEAN6wfCja7YDW4kxYn1D0tERMLlxwQXFwGYQmchgEQza+dXgCIi4o0fbegnANhQ4XFeYFk1ZjbKzLLNLLugoMCHXYuISBk/EroFWRZ0tEWNhy4i0nD8SOh5ADpUeNwewI8+bFdERMLgR0J/F8DIQG+XLACFJPN92K6IiITBjwkuZgEYBGA1gN0Arm6oYEVEJDQ/JrgggDG+RSQiInWiK0VFRGKEErqISIxQQhcRiRFK6CIiMUIJXUQkRiihi4jECCV0EZEYoYQuIhIjPCV0MxtgZt8EJrG4I8j6vmZWaGY5gdt9/ocqIiI18XLpfxyACQD6ww3E9YWZvUvyqypF55Ec3AAxioiIB15q6JkAVpNcQ3I/gFfhJrUQEZGDiJeE7nUCi9PNLNfM3jOzlGAb0gQXIiINx0tC9zKBxVIAHUmmAXgCwDvBNqQJLkREGo6XhF7rBBYkt5PcGbg/C0CCmbX2LUoREamVl4T+BYBuZnaSmR0GYATcpBblzKytmVngfmZgu1v8DlZERELzMh56sZldD+D/AYgDMJnkl2Y2OrD+KQDDAVxnZsUA9gAYERgnXUREIsSilXczMjKYnZ0dlX2LiDRWZraEZEawdbpSVEQkRiihi4jECCV0EZEYoYQuIhIjlNBFRGKEErqISIxQQhcRiRFK6CIiMcKvCS7MzB4PrF9uZr39D1VERGpSa0KvMMHFQAA9AFxqZj2qFBsIoFvgNgrARJ/jFBGRWvg1wcVFAKbQWQgg0cza+RyriIjUoNbBuRB8govTPJQ5AUB+xUJmNgquBg8AO83sm7CiPaA1gM11fK6fFEdliqOygyGOgyEGQHFUVZ84OoZa4SWhe5ngwksZkJwEYJKHfdYckFl2qMFpIklxKI6DPY6DIQbFEbk4fJngwmMZERFpQL5McBF4PDLQ2yULQCHJ/KobEhGRhuPXBBezAAwCsBrAbgBXN1zIAHxotvGJ4qhMcVR2MMRxMMQAKI6qGiSOqE1wISIi/tKVoiIiMUIJXUQkRjS6hF7bMAQRimGymW0ys5XR2H+FODqY2VwzW2VmX5rZ2CjEcISZLTaz3EAMD0Q6hirxxJnZMjObEcUY1prZCjPLMbOoTZxrZolmNs3Mvg58Rk6PQgzdA+9D2W27md0UhThuDnw+V5rZK2Z2RKRjCMQxNhDDlw3yPpBsNDe4k7LfA+gM4DAAuQB6RCGOcwD0BrAyyu9HOwC9A/ePBPBtpN8PuGsQWgTuJwBYBCAriu/JnwG8DGBGFGNYC6B1ND8bgTj+F8C1gfuHAUiMcjxxAH4C0DHC+z0BwA8AmgYevw7gqii8/lMArATQDK5DyhwA3fzcR2OroXsZhqDBkfwUwC+R3m+QOPJJLg3c3wFgFdyHN5IxkOTOwMOEwC0qZ9rNrD2ACwE8G439H0zMrCVcxeM5ACC5n+S26EaFfgC+J7kuCvuOB9DUzOLhEmo0rpNJBrCQ5G6SxQA+ATDUzx00toQeaoiBQ56ZdQLQC66GHOl9x5lZDoBNAD4gGfEYAsYDuA1AaZT2X4YA3jezJYHhLqKhM4ACAM8HmqCeNbPmUYqlzAgAr0R6pyQ3AvgHgPVww5EUknw/0nHA1c7PMbNWZtYMrqt3h1qeE5bGltA9DTFwqDGzFgDeBHATye2R3j/JEpLpcFcIZ5rZKZGOwcwGA9hEckmk9x3EmSR7w41COsbMzolCDPFwzYITSfYCsAtAVM45AUDgosQhAN6Iwr6PhjuSPwnA8QCam9nlkY6D5CoAfwPwAYDZcE3GxX7uo7EldA0xUIWZJcAl86kk34pmLIFD+o8BDIjC7s8EMMTM1sI1xZ1vZi9FIQ6Q/DHwdxOAt+GaCiMtD0BehaOlaXAJPloGAlhK8uco7Pv/APiBZAHJIgBvATgjCnGA5HMke5M8B67Z9js/t9/YErqXYQgOGWZmcG2kq0j+K0oxtDGzxMD9pnBfnq8jHQfJO0m2J9kJ7nPxEcmI18LMrLmZHVl2H8AFcIfaEUXyJwAbzKx7YFE/AF9FOo4KLkUUmlsC1gPIMrNmge9MP7jzTRFnZscG/p4I4GL4/J54GW3xoMEQwxBEOg4zewVAXwCtzSwPwDiSz0U6Drha6RUAVgTasAHgLpKzIhhDOwD/G5gIpQmA10lGrcvgQeA4AG+7vIF4AC+TnB2lWG4AMDVQ+VmDhh+SI6hAe3F/AH+Mxv5JLjKzaQCWwjVxLEP0hgB408xaASgCMIbkVj83rkv/RURiRGNrchERkRCU0EVEYoQSuohIjFBCFxGJEUroIiIxQgldRCRGKKGLiMSI/w8K4WmM9E4IOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = np.arange(0,10,1)\n",
    "ys = np.random.normal(loc=3,scale=0.4,size=10)\n",
    "plt.plot(xs,ys,'bo-')\n",
    "for x,y in zip(xs,ys):\n",
    "    label = \"{:.2f}\".format(y)\n",
    "    plt.annotate(label,(x,y),textcoords=\"offset points\",xytext=(0,10),ha='center')\n",
    "plt.xticks(np.arange(0,10,1))\n",
    "plt.yticks(np.arange(0,7,0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAfVklEQVR4nO3de3BV5fn28e9NAC0og0hQIKCgCIEAMQQMHhC1aKCOKNAWqqWKDK+KLbz9SbWdUUc7U7WnAQ8VKWirVZj+rFhEQCmKoK+AARIKBhQxSBQlIEfRQvB+/9ibNAkJewX2XiGL6zOzh3V49n7uLXhl5VnPWsvcHRERafga1XcBIiKSHAp0EZGIUKCLiESEAl1EJCIU6CIiEaFAFxGJiMCBbmZpZrbazObWsG+gme02s8L4677klikiIok0rkPbCUAx0KKW/Uvd/drjL0lERI5FoCN0M8sAvgdMT205IiJyrIIeoU8GfgGcfpQ2/c2sCPgMuMvd11VvYGbjgHEAzZs379OtW7c6lisicnJbuXLldndPr2lfwkA3s2uBbe6+0swG1tJsFXCOu+8zsyHAy0CX6o3cfRowDSA3N9cLCgoCfgUREQEws8217Qsy5HIJcJ2ZlQCzgCvN7G+VG7j7HnffF1+eBzQxs9bHXrKIiNRVwkB391+6e4a7nwuMBN5w95sqtzGzs83M4sv94p+7IwX1iohILeoyy6UKM7sNwN2nAiOA282sHPgaGOm6jaOISKisvnJXY+giInVnZivdPbemfbpSVEQkIhToIiIRoUAXEYkIBbqISEQo0EVEIkKBLiISEQp0EZGIUKCLiESEAl1EJCIU6CIiEaFAFxGJCAW6iEhEKNBFRCJCgS4iEhEKdBGRiAgc6GaWZmarzWxuDfvMzB41s41mtsbMcpJbpoiIJFKXI/QJQHEt+wYTeyh0F2Ac8ORx1iUiInUUKNDNLAP4HjC9liZDgWc9ZhnQ0szaJqnGWn3zzTf069eP3r1706NHD+6///4j2vzud78jOzub7OxssrKySEtL48svvwRgypQpZGVl0aNHDyZPnpzqckVEUiroEfpk4BfAt7Xsbw9sqbReGt+WUqeccgpvvPEGRUVFFBYWsmDBApYtW1alzaRJkygsLKSwsJCHHnqIyy+/nFatWrF27Vr+/Oc/s2LFCoqKipg7dy4ffvhhqksWEUmZhIFuZtcC29x95dGa1bDtiIeVmtk4Mysws4KysrI6lFlrbZx22mkAHDx4kIMHD2JWUykxM2fOZNSoUQAUFxeTl5dHs2bNaNy4MZdffjmzZ88+7ppEROpLkCP0S4DrzKwEmAVcaWZ/q9amFOhQaT0D+Kz6B7n7NHfPdffc9PT0Yyy5qkOHDpGdnU2bNm0YNGgQF110UY3t9u/fz4IFCxg+fDgAWVlZLFmyhB07drB//37mzZvHli1banyviEhDkDDQ3f2X7p7h7ucCI4E33P2mas3mAKPjs13ygN3uvjX55R4pLS2NwsJCSktLWbFiBWvXrq2x3SuvvMIll1xCq1atAMjMzOTuu+9m0KBB5Ofn07t3bxo3bhxGySIiKXHM89DN7DYzuy2+Og/YBGwE/gzckYTa6qRly5YMHDiQBQsW1Lh/1qxZFcMth916662sWrWKJUuW0KpVK7p06RJGqXIcgpwIB1i8eDHZ2dn06NGDyy+/HIANGzZUnCDPzs6mRYsWOhku0eLu9fLq06ePH69t27b5zp073d19//79fumll/orr7xyRLtdu3b5GWec4fv27auy/YsvvnB3982bN3vXrl39yy+/PO6aJLW+/fZb37t3r7u7HzhwwPv16+fvvvtulTY7d+70zMxM37x5s7v/9++5svLycj/rrLO8pKQk9UWLJBFQ4LXkaoMeY9i6dSs/+clPOHToEN9++y0/+MEPuPbaa5k6dSoAt90W+wVi9uzZXH311TRv3rzK+4cPH86OHTto0qQJTzzxBGeccUbo30HqJsiJ8BdeeIFhw4bRsWNHANq0aXPE5yxatIjzzjuPc845J/VFi4TEYoEfvtzcXC8oKKiXvqVhO3ToEH369GHjxo2MHz+eRx55pMr+iRMncvDgQdatW8fevXuZMGECo0ePrtJmzJgx5OTkcOedd4ZZushxM7OV7p5b0z7dy0UanEQnwsvLy1m5ciWvvvoqr732Gr/+9a/54IMPKvYfOHCAOXPm8P3vfz/s0kVSSoEuDVZtJ8IzMjLIz8+nefPmtG7dmgEDBlBUVFSxf/78+eTk5HDWWWeFXbJISinQpUEpKytj165dAHz99df861//olu3blXaDB06lKVLl1JeXs7+/ftZvnw5mZmZFfsrX2AmEiUN8qToufe8mvI+Sh7+Xsr7kLoLciI8MzOT/Px8evXqRaNGjRg7dixZWVlA7AKzhQsX8tRTT9Xn16izb775hgEDBvCf//yH8vJyRowYwQMPPFClzeLFixk6dCidOnUCYNiwYdx3330V+w8dOkRubi7t27dn7twjbpoqEdAgA11OXr169WL16tVHbD88o+mwSZMmMWnSpCPaNWvWjB07dqSsvlQ5fN+i0047jYMHD3LppZcyePBg8vLyqrS77LLLag3rKVOmkJmZyZ49e8IoWeqBhlxEGoC63reoutLSUl599VXGjh2bqhLlBKBAF2kggty36N1336V3794MHjyYdevWVWyfOHEiv/3tb2nUqO7/ywe9OhfgvffeIy0tjRdffLFi265duxgxYgTdunUjMzOTd999t841SDAKdJEGItF0zZycHDZv3kxRURE//elPuf766wGYO3cubdq0oU+fPsfUb5DbVEPsB87dd9/NNddcU2X7hAkTyM/PZ/369RQVFVU5QS3JpTF0aRBSfSK8IZ0Erzxd8/DJXoAWLVpULA8ZMoQ77riD7du388477zBnzhzmzZvHN998w549e7jpppv429+q3zS1ZkGHex577DGGDx/Oe++9V7Ftz549LFmyhL/85S8ANG3alKZNmx7L15YAdIQu0gAEma75+eefc/jK7xUrVvDtt99y5pln8tBDD1FaWkpJSQmzZs3iyiuvDBzmhyUa7vn000+ZPXv2ESenN23aRHp6OrfccgsXXnghY8eO5auvvqrr15eAFOgiDcDWrVu54oor6NWrF3379mXQoEEV0zUPT9l88cUXycrKonfv3vzsZz9j1qxZdTpxejSJhnsmTpzII488QlpaWpXt5eXlrFq1ittvv53Vq1fTvHlzHn744aTUJEdqkPdy0Tz0k4+GXE4cDzzwAM2bN+euu+6q2NapU6eK3w62b99Os2bNmDZtGnl5eeTl5VFSUgLA0qVLefjhh3n11dT/PxxVupeLiByzIMM9H3/8MSUlJZSUlDBixAj+9Kc/cf3113P22WfToUMHNmzYAMTuctm9e/fQv8PJQidFReSogt6mujaPPfYYN954IwcOHKBz584888wzYZR9UkoY6GZ2KrAEOCXe/kV3v79am4HAP4GP45tecvcHk1uqiNSHoFfnHnZ4Rsth2dnZ6FbZ4QhyhP4f4Ep332dmTYC3zWy+u1efiLrU3a9NfokiJy+dL5K6SBjo8Uce7YuvNom/6udMqoiI1CrQSVEzSzOzQmAbsNDdl9fQrL+ZFZnZfDPrUcvnjDOzAjMrKCsrO46yRUSkukCB7u6H3D0byAD6mVlWtSargHPcvTfwGPByLZ8zzd1z3T03PT39eOoWEZFq6jTLxd13mdliIB9YW2n7nkrL88zsT2bW2t23J61SEQmVxu8bnoRH6GaWbmYt48vfAb4LrK/W5myLX5JmZv3in9vwbjotItKABTlCbwv81czSiAX13919rpndBuDuU4ERwO1mVg58DYz0+roEVUTkJJXwCN3d17j7he7ey92zDs8vd/ep8TDH3R939x7u3tvd89z9/6W68JNdkHtUr1+/nv79+3PKKafw+9//vk7vPVH7FpHa6UrRBirII8latWrFo48+yssvv1zn956ofYtI7XQvlwYqyD2q27RpQ9++fWnSpEmd33ui9i0itVOgN2BBHkmWivfWd98iUjMFegOW6B7VqXpvffctIjVToEdA5UeShfne+u5bRKpSoDdQQe5RnYr31nffIlI7zXJpoILco/rzzz8nNzeXPXv20KhRIyZPnsz7779f63sbQt8iUjsFegMV5B7VZ599NqWlpYHf2xD6FgnLli1bGD16NJ9//jmNGjVi3LhxTJgwoUqbnTt3MmbMGD766CNOPfVUnn76abKystiwYQM//OEPK9pt2rSJBx98kIkTJ6a0ZgW6iEgNGjduzB/+8AdycnLYu3cvffr0YdCgQVUeofeb3/yG7OxsZs+ezfr16xk/fjyLFi2ia9euFBYWArFZXe3bt+eGG25Iec0aQxcRqUHbtm3JyckB4PTTTyczM5NPP/20Spv333+fq666CoBu3bpRUlLCF198UaXNokWLOO+88zjnnHNSXrMCXUQkgZKSElavXn3ENRO9e/fmpZdeAmDFihVs3rz5iKHGWbNmMWrUqFDq1JBLA5PqW5oe7Xam9dm3SH3Zt28fw4cPZ/LkybRo0aLKvnvuuYcJEyaQnZ1Nz549ufDCC2nc+L+xeuDAAebMmcNDDz0USq0KdBGRWhw8eJDhw4dz4403MmzYsCP2t2jRgmeeeQYAd6dTp0506tSpYv/8+fPJycnhrLPOCqVeDbmIiNTA3bn11lvJzMzk5z//eY1tdu3axYEDBwCYPn06AwYMqHIUP3PmzNCGW0BH6CIiNXrnnXd47rnn6NmzJ9nZ2UBsVssnn3wCxKbpFhcXM3r0aNLS0ujevTszZsyoeP/+/ftZuHAhTz31VGg1Jwx0MzsVWAKcEm//orvfX62NAVOAIcB+4GZ3X5X8ckXqT5B5ybt37+amm27ik08+oby8nLvuuotbbrkFgAULFjBhwgQOHTrE2LFjueeee+rja0hAl156KYme09O/f38+/PDDGvc1a9aMHTvCfXBbkCP0/wBXuvs+M2sCvG1m8919WaU2g4Eu8ddFwJPxP0UiI8i85CeeeILu3bvzyiuvUFZWRteuXbnxxhtJS0tj/PjxLFy4kIyMDPr27ct1111X5b0ixyvIE4vc3ffFV5vEX9V/bA0Fno23XQa0NLO2yS1VpH4FmZdsZuzduxd3Z9++fbRq1YrGjRuzYsUKzj//fDp37kzTpk0ZOXIk//znP+vja0iEBTopamZpZlYIbAMWuvvyak3aA1sqrZfGt1X/nHFmVmBmBWVlZcdas0i9q21e8p133klxcTHt2rWjZ8+eTJkyhUaNGvHpp5/SoUOHinYZGRlH/DAQOV6BToq6+yEg28xaArPNLMvdK9/EuqZHzhwx+OTu04BpALm5uXqItDRIR5uX/Nprr5Gdnc0bb7zBRx99xKBBg7jssstqHIvVk5pOTKm+3gJSd81FnaYtuvsuYDGQX21XKdCh0noG8NlxVSZyAko0L/mZZ55h2LBhmBnnn38+nTp1Yv369WRkZLBly39/iS0tLaVdu3Zhli4ngYSBbmbp8SNzzOw7wHeB9dWazQFGW0wesNvdtya9WpF6FGRecseOHVm0aBEAX3zxBRs2bKBz58707duXDz/8kI8//pgDBw4wa9YsrrvuujDLl5NAkCGXtsBfzSyN2A+Av7v7XDO7DcDdpwLziE1Z3Ehs2uItKapXpN4EmZd87733cvPNN9OzZ0/cnUceeYTWrVsD8Pjjj3PNNddw6NAhxowZQ48ePertu0g0JQx0d18DXFjD9qmVlh0Yn9zSRE4sQeYlt2vXjtdff73GfUOGDGHIkCGpKC2ygsz9B1i8eDETJ07k4MGDtG7dmrfeeguAc889l9NPP520tDQaN25MQUFB2F8hVLpSVEROWEHm/u/atYs77riDBQsW0LFjR7Zt21blM958882K35KiTvdyEZETVpC5/y+88ALDhg2jY8eOALRp0yb0Ok8UCnQRaRBqm/v/wQcfsHPnTgYOHEifPn149tlnK/aZGVdffTV9+vRh2rRpYZccOg25iCTQkOclR8XR5v6Xl5ezcuVKFi1axNdff03//v3Jy8vjggsu4J133qFdu3Zs27aNQYMG0a1bNwYMGFBP3yL1dIQuIie0RHP/MzIyyM/Pp3nz5rRu3ZoBAwZQVFQEUDHXv02bNtxwww2sWLEi1NrDpkAXkRNWkLn/Q4cOZenSpZSXl7N//36WL19OZmYmX331FXv37gXgq6++4vXXXycrKyvM8kOnIRcROWEFmfufmZlJfn4+vXr1olGjRowdO5asrCw2bdrEDTfcAMSGZX70ox+Rn1/9IvdoUaCLyAkryNx/gEmTJjFp0qQq2zp37lwx9HKy0JCLiEhEKNBFRCJCgS4iEhEaQxeRE47m/h8bHaGLiESEAl1EJCIU6CIiERHkiUUdzOxNMys2s3VmdsTNiM1soJntNrPC+Ou+1JQrIiK1CXJStBz4H3dfZWanAyvNbKG7v1+t3VJ3vzb5JYqISBAJj9Ddfau7r4ov7wWKgfapLkxEROqmTmPoZnYuscfRLa9hd38zKzKz+WZW48MSzWycmRWYWUFZWVmdixURkdoFDnQzOw34BzDR3fdU270KOMfdewOPAS/X9BnuPs3dc909Nz09/VhrFhGRGgQKdDNrQizMn3f3l6rvd/c97r4vvjwPaGJmkX6I35YtW7jiiivIzMykR48eTJky5Yg2zz//PL169aJXr15cfPHFVW4UNGbMGNq0aRP523mKSHiCzHIxYAZQ7O5/rKXN2fF2mFm/+OfuSGahJ5rDD68tLi5m2bJlPPHEE7z/ftXzxJ06deKtt95izZo13HvvvYwbN65i380338yCBQvCLltEIizILJdLgB8D/zazwvi2XwEdAdx9KjACuN3MyoGvgZEe5J6XDVjbtm1p27YtUPXhtZWfRn7xxRdXLOfl5VFaWlqxPmDAAEpKSkKrV0SiL2Ggu/vbgCVo8zjweLKKamhqe3htZTNmzGDw4MEhViUiJxvdnOs4He3htYe9+eabzJgxg7fffjvk6kTkZKJAPw6JHl4LsGbNGsaOHcv8+fM588wzQ65QRE4mupfLMQry8NpPPvmEYcOG8dxzz3HBBReEXKGInGx0hH6Mgjy89sEHH2THjh3ccccdQGxmTEFBAQCjRo1i8eLFbN++nYyMDB544AFuvfXW+vkyIhIJCvRjFOThtdOnT2f69Ok17ps5c2YqyhKRk5iGXEREIkKBLiISEQp0EZGI0Bh6HenhtSJyotIRuohIRCjQRUQiQoEuIhIRCnQRkYhQoIuIRIQCXUQkIoI8saiDmb1pZsVmts7MJtTQxszsUTPbaGZrzCwnNeWKiEhtgsxDLwf+x91XmdnpwEozW+julZ+3NhjoEn9dBDwZ/1NEREKS8Ajd3be6+6r48l6gGGhfrdlQ4FmPWQa0NLO2Sa9WRERqVacxdDM7F7gQWF5tV3tgS6X1Uo4MfcxsnJkVmFlBWVlZ3SoVEZGjChzoZnYa8A9gorvvqb67hrcccW9Zd5/m7rnunpuenl63SkVE5KgCBbqZNSEW5s+7+0s1NCkFOlRazwA+O/7yREQkqCCzXAyYARS7+x9raTYHGB2f7ZIH7Hb3rUmsU0REEggyy+US4MfAv82sML7tV0BHAHefCswDhgAbgf3ALckvVUREjiZhoLv729Q8Rl65jQPjk1WUiIjUna4UFRGJCAW6iEhEKNBFRCJCgS4iEhEKdBGRiFCgi4hEhAJdRCQiFOgiIhGhQBcRiQgFuohIRCjQRUQiQoEuIhIRCnQRkYhQoIuIRIQCXUQkIoI8sehpM9tmZmtr2T/QzHabWWH8dV/yyxQRkUSCPLHoL8DjwLNHabPU3a9NSkUiInJMEh6hu/sS4MsQahERkeOQrDH0/mZWZGbzzaxHbY3MbJyZFZhZQVlZWZK6FhERSE6grwLOcffewGPAy7U1dPdp7p7r7rnp6elJ6FpERA477kB39z3uvi++PA9oYmatj7syERGpk+MOdDM728wsvtwv/pk7jvdzRUSkbhLOcjGzmcBAoLWZlQL3A00A3H0qMAK43czKga+Bke7uKatYRERqlDDQ3X1Ugv2PE5vWKCIi9UhXioqIRIQCXUQkIhToIiIRoUAXEYkIBbqISEQo0EVEIkKBLiISEQp0EZGIUKCLiESEAl1EJCIU6CIiEaFAFxGJCAW6iEhEKNBFRCJCgS4iEhEJA93MnjazbWa2tpb9ZmaPmtlGM1tjZjnJL1NERBIJcoT+FyD/KPsHA13ir3HAk8dfloiI1FXCQHf3JcCXR2kyFHjWY5YBLc2sbbIKFBGRYJIxht4e2FJpvTS+7QhmNs7MCsysoKysLAldi4jIYckIdKthW40PiXb3ae6e6+656enpSehaREQOS0aglwIdKq1nAJ8l4XNFRKQOkhHoc4DR8dkuecBud9+ahM8VEZE6aJyogZnNBAYCrc2sFLgfaALg7lOBecAQYCOwH7glVcWKiEjtEga6u49KsN+B8UmrSEREjomuFBURiQgFuohIRCjQRUQiQoEuIhIRCnQRkYhQoIuIRIQCXUQkIhToIiIRoUAXEYkIBbqISEQo0EVEIkKBLiISEQp0EZGIUKCLiESEAl1EJCICBbqZ5ZvZBjPbaGb31LB/oJntNrPC+Ou+5JcqIiJHE+SJRWnAE8AgYs8Pfc/M5rj7+9WaLnX3a1NQo4iIBBDkCL0fsNHdN7n7AWAWMDS1ZYmISF0FCfT2wJZK66XxbdX1N7MiM5tvZj2SUp2IiASWcMgFsBq2ebX1VcA57r7PzIYALwNdjvggs3HAOICOHTvWsVQRETmaIEfopUCHSusZwGeVG7j7HnffF1+eBzQxs9bVP8jdp7l7rrvnpqenH0fZIiJSXZBAfw/oYmadzKwpMBKYU7mBmZ1tZhZf7hf/3B3JLlZERGqXcMjF3cvN7E7gNSANeNrd15nZbfH9U4ERwO1mVg58DYx09+rDMiIikkJBxtAPD6PMq7ZtaqXlx4HHk1uaiIjUha4UFRGJCAW6iEhEKNBFRCJCgS4iEhEKdBGRiFCgi4hEhAJdRCQiFOgiIhGhQBcRiQgFuohIRCjQRUQiQoEuIhIRCnQRkYhQoIuIRIQCXUQkIhToIiIRESjQzSzfzDaY2UYzu6eG/WZmj8b3rzGznOSXKiIiR5Mw0M0sDXgCGAx0B0aZWfdqzQYDXeKvccCTSa5TREQSCHKE3g/Y6O6b3P0AMAsYWq3NUOBZj1kGtDSztkmuVUREjsISPcvZzEYA+e4+Nr7+Y+Aid7+zUpu5wMPu/nZ8fRFwt7sXVPusccSO4AG6AhuS9UUCaA1sD7E/9a2+1bf6ToVz3D29ph1BHhJtNWyr/lMgSBvcfRowLUCfSWdmBe6eq77Vt/pW31Hpu7ogQy6lQIdK6xnAZ8fQRkREUihIoL8HdDGzTmbWFBgJzKnWZg4wOj7bJQ/Y7e5bk1yriIgcRcIhF3cvN7M7gdeANOBpd19nZrfF908F5gFDgI3AfuCW1JV8zOplqEd9q2/1rb7DkvCkqIiINAy6UlREJCIU6CIiERH5QE9024IU9/20mW0zs7Vh9hvvu4OZvWlmxWa2zswmhNj3qWa2wsyK4n0/EFbf8f7TzGx1/PqIUJlZiZn928wKzawg8TuS2ndLM3vRzNbH/977h9Rv1/j3PfzaY2YTw+g73v//jf87W2tmM83s1BD7nhDvd12Y37lW7h7ZF7GTuB8BnYGmQBHQPcT+BwA5wNp6+O5tgZz48unAB2F9d2LXJZwWX24CLAfyQvzuPwdeAObWw3/3EqB12P3G+/4rMDa+3BRoWQ81pAGfE7v4JYz+2gMfA9+Jr/8duDmkvrOAtUAzYhNM/gV0qY+/+8OvqB+hB7ltQcq4+xLgy7D6q9b3VndfFV/eCxQT+8cfRt/u7vviq03ir1DOvptZBvA9YHoY/Z0ozKwFsQOIGQDufsDdd9VDKVcBH7n75hD7bAx8x8waEwvXsK6ByQSWuft+dy8H3gJuCKnvGkU90NsDWyqtlxJSqJ1IzOxc4EJiR8ph9ZlmZoXANmChu4fV92TgF8C3IfVXnQOvm9nK+K0uwtIZKAOeiQ83TTez5iH2f9hIYGZYnbn7p8DvgU+ArcSugXk9pO7XAgPM7Ewza0Zs6naHBO9JqagHeqBbEkSZmZ0G/AOY6O57wurX3Q+5ezaxq4b7mVlWqvs0s2uBbe6+MtV9HcUl7p5D7A6k481sQEj9NiY2vPeku18IfAWEfc6oKXAd8L8h9nkGsd+6OwHtgOZmdlMYfbt7MfAIsBBYQGxItzyMvmsT9UA/qW9JYGZNiIX58+7+Un3UEP+1fzGQH0J3lwDXmVkJseG1K83sbyH0W8HdP4v/uQ2YTWzYLwylQGml34ReJBbwYRoMrHL3L0Ls87vAx+5e5u4HgZeAi8Pq3N1nuHuOuw8gNrz6YVh91yTqgR7ktgWRZGZGbDy12N3/GHLf6WbWMr78HWL/061Pdb/u/kt3z3D3c4n9Xb/h7qEcrQGYWXMzO/3wMnA1sV/LU87dPwe2mFnX+KargPfD6LuSUYQ43BL3CZBnZs3i/+avIna+KBRm1ib+Z0dgGOF//yqC3G2xwfJablsQVv9mNhMYCLQ2s1LgfnefEVL3lwA/Bv4dH8sG+JW7zwuh77bAX+MPR2kE/N3dQ59CWA/OAmbHcoXGwAvuviDE/n8KPB8/eNlEiLfgiI8hDwL+T1h9Arj7cjN7EVhFbLhjNeFeiv8PMzsTOAiMd/edIfZ9BF36LyISEVEfchEROWko0EVEIkKBLiISEQp0EZGIUKCLiESEAl1EJCIU6CIiEfH/AR4IGzuz/qLHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = np.arange(0,10,1)\n",
    "ys = np.random.normal(loc=3,scale=0.4,size=10)\n",
    "plt.bar(xs,ys)\n",
    "for x,y in zip(xs,ys):\n",
    "    label = \"{:.2f}\".format(y)\n",
    "    plt.annotate(label,(x,y),textcoords = \"offset points\",xytext=(0,5),ha='center')\n",
    "plt.xticks(np.arange(0,10,1))\n",
    "plt.yticks(np.arange(0,5,0.5))\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fd7a18f97b8>"
      ]
     },
     "execution_count": 51,
     "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": [
    "xs = np.random.normal(loc=4, scale=2.0, size=10)\n",
    "ys = np.random.normal(loc=2.0, scale=0.8, size=10)\n",
    "\n",
    "plt.scatter(xs,ys)\n",
    "for x,y in zip(xs,ys):\n",
    "    label = \"{:.}\".format(y)\n",
    "    plt.annotate(label,(x,y),textcoords=\"offset points\",xytext=(0,10),ha=\"center\")\n",
    "plt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python",
   "language": "python",
   "name": "conda-env-python-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}