python-intro/solutions/ex08_plotting.ipynb

{
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    "slideshow": {
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    "tags": []
   },
   "source": [
    "# Exercise 8: Plotting with `matplotlib`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc7ae3ea-49ef-46a7-8db5-3249f25b3b9b",
   "metadata": {},
   "source": [
    "## Aim: Use `pyplot` to create time series plots."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ca667c7-2ff3-4828-8a7b-147d6cdfe2be",
   "metadata": {},
   "source": [
    "### Issues covered:\n",
    "- Import `pyplot` from `matplotlib`\n",
    "- Create a basic time series plot from lists\n",
    "- Create a plot from a `pandas` DataFrame\n",
    "- Plot multiple series on one graph\n",
    "- Create different kinds of plots - including scatter and bar graphs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "359977f2-af21-4ee1-b957-66d3e09fcca7",
   "metadata": {},
   "source": [
    "## 1. Create a simple time series from a list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1387a54b-2cf4-41a2-8497-334b4989a5da",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Import `matplotlib.pyplot` as `plt`, then create two datasets - one for the time values `[0, 10, 20, 30]`, and one for the positions with the value `[0, 200, 400, 600]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e045729e-e211-4c7f-bbfa-4de6e0d97b4a",
   "metadata": {
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    "execution": {
     "iopub.execute_input": "2023-10-23T10:34:24.690219Z",
     "iopub.status.busy": "2023-10-23T10:34:24.689481Z",
     "iopub.status.idle": "2023-10-23T10:34:25.576326Z",
     "shell.execute_reply": "2023-10-23T10:34:25.574763Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "clear_answer_cell"
    ]
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "time = [0, 10, 20, 30]\n",
    "position = [0, 200, 400, 600]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e15c4d73-bb75-4581-abcb-5a4264d6280f",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Plot the data using `plt.plot()` and add axis labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ef059b01-478f-4696-982a-c36fddcbf9b0",
   "metadata": {
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    "execution": {
     "iopub.execute_input": "2023-10-23T10:34:25.584075Z",
     "iopub.status.busy": "2023-10-23T10:34:25.583162Z",
     "iopub.status.idle": "2023-10-23T10:34:25.721056Z",
     "shell.execute_reply": "2023-10-23T10:34:25.720418Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "clear_answer_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Position (km)')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TGtjJcYUbCh8BM4PPKxQS+qCZu7t65YrEqI07D5KSmsbHG3czrFtLHhvVn8SWDaJdlsS4cEPhN8BQIP3Ll6aKSGwpKi7h+Q828uSCNdSpVYPHb+jPTckd1KJCwhJuKKwFMhQIIrFtZfZ+xqemkZa1j8v6tOXR6/rRtkm9aJclcSTcUMgGFprZPKDg6KAuSRWJDQVFxTz9zjqeWbieZg1q8/Q3B3JV0mnaO5ATFm4obAy+6gRfUEazOhGpfP/dvIfx09NYm3uA6we058Fr+tBcDezkJIUbClPdfVPpATMbVPHliEi4Dh0p4tfz1/DihxtJaFKPF+8YxEU925S/oMjXCDcUUs3sWnffCmBm5wNPA0kRq0xEjuuDtTuZMDONLbsPc/vQ0xk3vBeN6uqW63Lqwv1f9H1glpmNAAYCvwSuilhVIlKmfYcLmTR3BdOWZtG5VUOmfW8ogzu3iHZZUoWEFQruvsTM7gHeJNTu4jJ33xHRykTkC+ZnbufBWRnsOniE/7mwK/de0p16tdXATipWefdTeI0vnlBuAOwDnjcz3P3aSBYnIrAjr4CJczKZm55N74QmPD9mEEkdmka7LKmiyttT+HWlVCEiX+HuzPx0Kw//ewWHCor56RU9GXt+F2rXVAM7iZzyQmFReR9YMzPTh9pEKtbWvYe5f2Y6C1fvYGBiMx6/sT/d2qiBnUReeaHwrpmlArPdffPRQTOrA5xL6Haa7wJ/jViFItVISYnzj48/Z/K8VTgwcUQfvjVUDeyk8pQXCsOBO4FXzKwzsJfQPZprEjrp/Ft3/yySBYpUF+t3HGBCajqfbNrNed1b8ctRSXRsoQZ2Urm+NhTcPR94BnjGzGoDrYDD7r63EmoTqRaKikuY8v4GfvfWWurVqsETN/bnxrPUwE6iI+xPu7h7IaEeSCJSQTK37WN8ahoZW/czvO9pPHxdX9o0VgM7iR59BFIkCvILi/nDO2t59r0NNG9Qhz/dOpArkxKiXZaIQkGksi37fDfjpqexfsdBbhjYgQev6U2zBmpgJ7FBoSBSSQ4WFPHE/NVMXbyJdk3rM/XOwVzQo3W0yxL5grBCwcyuB34FtCF0K07djlPkBCxas4MJM9LZtu8wY4Z24idX9FQDO4lJ4f6vfBwY4e4rw31hM6sHLALqBu8z3d0fMrMWwD+BTsAm4GZ33xMsMwG4CygG7nH3+eG+n0gs2nvoCI/OXcn0ZVl0ad2Qf31vKMmd1MBOYle4oZBzIoEQKAAudvcDweWsHwR3brseeNvdJ5tZCpACjDezPsBooC/QDnjLzHq4e/EJvq9ITJiXns2DszPZc+gId1/UlR9erAZ2EvvCDYWlZvZPYBZfvB3njOMtELS+OBA8rB18OTASuDAYnwosBMYH46+6ewGw0czWAYOBxWHWKBITcvPyeWh2JvMyttO3XROm3jmIvu3UwE7iQ7ih0AQ4BFxeasyB44YCgJnVBJYB3YCn3f1jM2vr7tkA7p5tZkdvFdUe+KjU4lnB2JdfcywwFiAxMTHM8kUiz92ZviyLR+eu5HBhMeOG9+S756mBncSXcO+ncMfJvHhw6OdMM2sGzDSzfl8ze1kf3/xKoz13nwJMAUhOTlYjPokJW3Yf4mcz03l/7U4GdWrO5Bv607V1o2iXJXLCwr36qAPwB2AYoV/UHwD3untWOMu7+14zW0iol1KOmSUEewkJQG4wWxbQsdRiHYBtYa2FSJSUlDgvLd7E4/NXY8DDI/ty25DTqaEGdhKnwt2vfRGYQ+gEcHvgtWDsuMysdbCHgJnVBy4FVgWvMyaYbQwwO5ieA4w2s7pB873uwCdhr4lIJVuXe4Cb/7yYia+tYFCnFsz/0fncPrSTAkHiWrjnFFq7e+kQ+KuZ3VfOMgnA1OC8Qg1gmrv/28wWA9PM7C5gM3ATgLtnmtk0YAVQBNytK48kFhUWlzBl0QaeemstDerW5Mmbz2DUgPZqYCdVQrihsNPMbgNeCR7fAuz6ugXcPQ0YUMb4LuCS4ywzCZgUZk0ilS5j6z7GTU9jRfZ+rk5KYOK1fWnduG60yxKpMOGGwp3AH4HfEjqn8GEwJlIt5BcW89Tba5myaAMtGtbh2dvOYni/06JdlkiFC/fqo83AtRGuRSQmLdm0m/HT09iw8yA3J3fg/qv60LRB7WiXJRIRXxsKZjbO3R83sz9Q9uWh90SsMpEoO1BQxONvrOKlxZ/ToXl9/n7XEM7t3iraZYlEVHl7CkdbWyyNdCEisWTh6lzun5nBtn2HuXNYZ/7v8h40VAM7qQbKux3na8HkIXf/V+nnzOymiFUlEiV7Dh7hkbkrmPHfrXRr04jp3z+Hs05vHu2yRCpNuH/6TAD+FcaYSFxyd15P385DczLYe6iQey7uxt0Xd6NuLTWwk+qlvHMKVwJXAe3N7PelnmpC6LMEInEvd38+D87OYH5mDkntm/LSnUPo0063CpHqqbw9hW2EzidcS6ix3VF5wI8iVZRIZXB3/rU0i0fmruBIUQkTruzFXed2ppYa2Ek1Vt45heXAcjP7h7trz0CqjC27DzFhRjofrNvJ4M4tmHx9El3UwE6k3MNH09z9ZuBTMyt9SerR23H2j2h1IhWsuMSZ+uEmnpi/mpo1jEev68c3ByeqX5FIoLzDR/cG36+JdCEikbY2J49xqWl8unkvF/VszaRRSbRrVj/aZYnElPIOH2UHkzuBw+5eYmY9gF7AvEgXJ1IRjhSV8Ox76/njO+toWLcmv/vGmYw8s50a2ImUIdxLUhcB55lZc+BtQiefvwHcGqnCRCpCWtZexk1PY9X2PEac0Y6HRvShVSM1sBM5nnBDwdz9UNDu+g9B64tPI1mYyKnILyzmtwvW8Nz7G2jduC7P3Z7MZX3aRrsskZgXdiiY2VBCewZ3neCyIpXqow27SElNY9OuQ9wyuCMpV/amaX01sBMJR7i/2O8j9AnmmcHNcLoA70asKpGTkJdfyOR5q/jHx5tJbNGAl78zhHO6qYGdyIkIt3X2e8B7ZtbYzBq5+wZAHVIlZryzKof7Z2aQsz+f75zbmR9f3oMGdbQzK3KiwvqpMbMk4CWgReih7QBud/fMSBYnUp7dB4/w8GuZzPpsGz3aNuKZW89hQKIa2ImcrHD/lPoz8GN3fxfAzC4EngPOiUxZIl/P3XktLZuJczLJyy/k3ku6c/dF3ahTSy0qRE5FuKHQ8GggALj7QjNrGKGaRL7W9n35PDArg7dW5nBGh6b86sYh9DpNDexEKkK4obDBzB4E/hY8vg3YGJmSRMrm7ry6ZAu/nLuSwpIS7r+qN3ee25maalEhUmHCDYU7gV8AM4LHi4A7IlKRSBk+33WQlNR0Fm/YxdldWjD5+v50aqWdVZGKVl5DvHrA94FuQDrwf+5eWBmFiUCogd2L/9nIr99cTe0aNfjlqCRGD+qoBnYiEVLensJUoBB4H7gS6E3oMwsiEbd6e6iB3fIte7mkVxseHdWPhKZqYCcSSeWFQh93TwIws+eBT8J9YTPrSOgy1tOAEmCKuz9lZi2AfwKdgE3Aze6+J1hmAqFPTBcD97j7/BNaG6kSjhSV8MzCdTz97joa16vN728ZwIj+CWpgJ1IJyguFY4eK3L3oBH8oiwgdbvqvmTUGlpnZAuDbwNvuPtnMUoAUYLyZ9QFGA32BdsBbZtbD3YtP5E0lvn22ZS/jp6exOiePkWe246ERfWnRsE60yxKpNsoLhTPMbH8wbUD94PHRm+wc9zrAoO12djCdZ2YrgfbASODCYLapwEJgfDD+qrsXABvNbB0wGFh8EuslcebwkWKeXLCa5z/YSJvG9Xh+TDKX9FYDO5HKVt79FGpWxJuYWSdgAPAx0PbofRrcPdvM2gSztQc+KrVYVjD25dcaC4wFSExMrIjyJMo+XL+TlNR0Nu8+xC2DE5lwVS+a1FMDO5FoiHhzGDNrBKQC97n7/q85BFXWE/6VAfcpwBSA5OTkrzwv8WN/fiGPvb6SVz7ZwuktG/DKd89maNeW0S5LpFqLaCiYWW1CgfAPdz/6GYccM0sI9hISgNxgPAvoWGrxDsC2SNYn0fPWihzun5XOjrwCxp7fhR9d2oP6dSpkx1RETkHEGsVYaJfgeWCluz9Z6qk5wJhgegwwu9T4aDOra2adge6cwNVOEh92HSjgh698yndeWkrzBnWY+YNh/Oyq3goEkRgRyT2FYcC3gHQz+ywY+xkwGZgW3MVtM3ATQHCfhmnACkJXLt2tK4+qDndnzvJtTJyTyYGCIn58WQ++f0FXNbATiTERCwV3/4CyzxMAXHKcZSYBkyJVk0THtr2HeWBWBu+syuXMjs14/Mb+9GjbONpliUgZdBcSiZiSEuflTzYzed4qikucB6/pw7fP6aQGdiIxTKEgEbFx50FSUtP4eONuhnVryWOj+pPYskG0yxKRcigUpEIVFZfw/AcbeXLBGurUqsGvbkji5uSOalEhEicUClJhVmbvZ3xqGmlZ+7isT1seva4fbZvUi3ZZInICFApyygqKinn6nXU8s3A9zRrU5ulvDuSqpNO0dyAShxQKckqWfb6H8alprMs9wPUD2vPgNX1orgZ2InFLoSAn5dCRIp6Yv5q/friJhCb1ePGOQVzUs035C4pITFMoyAn7YO1OUmakkbXnMN86+3TGDe9JYzWwE6kSFAoStn2HC5k0dwXTlmbRuVVD/jn2bIZ0UQM7kapEoSBhmZ+5nQdnZbDr4BH+58Ku3HtJd+rVVr8ikapGoSBfa0deARPnZDI3PZveCU14fswgkjo0jXZZIhIhCgUpk7sz479befjfKzh8pJifXtGTsed3oXZNNbATqcoUCvIVW/ce5mcz0nlvzQ4GJoYa2HVrowZ2ItWBQkGOKSlx/v7x5/xq3iocmDiiD98aqgZ2ItWJQkEAWL/jACmpaSzZtIfzurfil6OS6NhCDexEqhuFQjVXWFzCc+9v4HdvraVerRo8cWN/bjyrg1pUiFRTCoVqLGPrPsanppG5bT/D+57Gw9f1pU1jNbATqc4UCtVQfmExf3hnLc++t4HmDerwp1sHcmVSQrTLEpEYoFCoZpZu2s241DQ27DjIDQM78OA1vWnWQA3sRCREoVBNHCwINbCbungT7ZrWZ+qdg7mgR+tolyUiMUahUA0sWrODCTPS2bbvMGOGduKnV/SkYV1tehH5Kv1mqML2HjrCo3NXMn1ZFl1aN+Rf3xtKcqcW0S5LRGKYQqGKmpeezYOzM9lz6Ah3X9SVH16sBnYiUj6FQhWTuz+fn8/O5I3M7fRt14Spdw6ibzs1sBOR8ESsu5mZvWBmuWaWUWqshZktMLO1wffmpZ6bYGbrzGy1mV0RqbqqKnfnX0u3cOmT7/HO6lzGDe/JrLuHKRBE5IREsuXlX4HhXxpLAd529+7A28FjzKwPMBroGyzzjJnpWEeYtuw+xO0vfMJPp6fR87TGzLv3PH5wYTd1NBWRExaxw0fuvsjMOn1peCRwYTA9FVgIjA/GX3X3AmCjma0DBgOLI1VfVVBc4vxt8SYen78aAx4Z2Zdbh5xODTWwE5GTVNnnFNq6ezaAu2eb2dE7vbcHPio1X1Yw9hVmNhYYC5CYmBjBUmPbutw8xqems+zzPVzQozWTRvWjQ3M1sBORUxMrJ5rL+tPWy5rR3acAUwCSk5PLnKcqKywu4c/vref3b6+jQd2aPHnzGYwa0F4N7ESkQlR2KOSYWUKwl5AA5AbjWUDHUvN1ALZVcm0xL2PrPn46PY2V2fu5OimBidf2pXXjutEuS0SqkMo+EzkHGBNMjwFmlxofbWZ1zawz0B34pJJri1n5hcVMnreKkU//h50HCnj2trN4+taBCgQRqXAR21Mws1cInVRuZWZZwEPAZGCamd0FbAZuAnD3TDObBqwAioC73b04UrXFk0827iYlNY0NOw/yjeSO/Oyq3jRtUDvaZYlIFRXJq49uOc5Tlxxn/knApEjVE2/y8gt5/I3V/O2jz+nQvD5/v2sI53ZvFe2yRKSKi5UTzVLKu6tzuX9GOtn787lzWGd+ckUPGtTRphKRyNNvmhiy5+ARHvn3CmZ8upVubRox/fvncNbpzctfUESkgigUYoC7Mzc9m4dmZ7LvcCH3XNyNuy/uRt1a+lC3iFQuhUKU5ezP58FZGby5Ioek9k35+3eG0DuhSbTLEpFqSqEQJe7OtKVbeHTuSo4UlTDhyl7cdW5naqlfkYhEkUIhCjbvOkTKjDQ+XL+LwZ1b8Ksb+tO5VcNolyUiolCoTMUlzl8/3MSv56+mZg3j0ev68c3BiWpgJyIxQ6FQSdbk5DFuehqfbdnLRT1bM2lUEu2a1Y92WSIiX6BQiLAjRSU8+956/vDOWhrVrcVTo8/k2jPaqYGdiMQkhUIELd+yl/GpaazanseIM9oxcUQfWjZSvyIRiV0KhQg4fKSY3761hr+8v4HWjevy3O3JXNanbbTLEhEpl0Khgi1ev4sJM9LYtOsQtwzuyISretOknhrYiUh8UChUkP35hUyet4qXP95MYosGvPydIZzTTQ3sRCS+KBQqwDurcvjZjAxy8/L5zrmd+b/Le1K/jlpUiEj8USicgl0HCnj43yuY/dk2erRtxJ9uO4cBiWpgJyLxS6FwEtydOcu38YvXVpCXX8h9l3bnBxd2o04ttagQkfimUDhB2fsO88DMDN5elcsZHZvx+A396Xla42iXJSJSIRQKYSopcV5dsoXHXl9JYUkJD1zdmzuGdaamWlSISBWiUAjDpp0HSZmRxkcbdjO0S0sm35DE6S3VwE5Eqh6FwtcoLnFe+GAjv1mwmto1avDY9UmMHtRRLSpEpMpSKBzH6u15jJu+nOVZ+7i0dxsevS6J05rWi3ZZIiIRpVD4koKiYp55dz3PLFxHk3q1+cMtA7imf4L2DkSkWlAolPLp5j2MT01jTc4BrjuzHT8f0ZcWDetEuywRkUqjUAAOHSniN2+u4YX/bOS0JvV44dvJXNxLDexEpPqJuVAws+HAU0BN4C/uPjmS7/fhup2kzEhn8+5D3DokkZQre9FYDexEpJqKqVAws5rA08BlQBawxMzmuPuKin6vfYcLeez1lby6ZAudWjbg1bFnc3aXlhX9NiIicSWmQgEYDKxz9w0AZvYqMBKo0FBIy9rLd19ayo68Ar53QRd+dGkP6tVWAzsRkVgLhfbAllKPs4AhpWcws7HAWIDExMSTepPEFg3o0bYxz92eTP8OzU6uUhGRKijWQqGs6z79Cw/cpwBTAJKTk72M+cvVrEEd/nbXkPJnFBGpZmKtrWcW0LHU4w7AtijVIiJS7cRaKCwBuptZZzOrA4wG5kS5JhGRaiOmDh+5e5GZ/S8wn9AlqS+4e2aUyxIRqTZiKhQA3P114PVo1yEiUh3F2uEjERGJIoWCiIgco1AQEZFjFAoiInKMuZ/U579igpntAD4/hZdoBeysoHKiqaqsB2hdYlFVWQ/Quhx1uru3LuuJuA6FU2VmS909Odp1nKqqsh6gdYlFVWU9QOsSDh0+EhGRYxQKIiJyTHUPhSnRLqCCVJX1AK1LLKoq6wFal3JV63MKIiLyRdV9T0FEREpRKIiIyDHVMhTMbLiZrTazdWaWEu16ToWZbTKzdDP7zMyWRrueE2FmL5hZrplllBprYWYLzGxt8L15NGsMx3HWY6KZbQ22y2dmdlU0awyXmXU0s3fNbKWZZZrZvcF4PG6X461LXG0bM6tnZp+Y2fJgPX4RjEdkm1S7cwpmVhNYA1xG6KY+S4Bb3L1C7wNdWcxsE5Ds7nH3gRwzOx84ALzk7v2CsceB3e4+OQjs5u4+Ppp1luc46zEROODuv45mbSfKzBKABHf/r5k1BpYB1wHfJv62y/HW5WbiaNuYmQEN3f2AmdUGPgDuBa4nAtukOu4pDAbWufsGdz8CvAqMjHJN1ZK7LwJ2f2l4JDA1mJ5K6Ic4ph1nPeKSu2e7+3+D6TxgJaF7p8fjdjneusQVDzkQPKwdfDkR2ibVMRTaA1tKPc4iDv+jlOLAm2a2zMzGRruYCtDW3bMh9EMNtIlyPafif80sLTi8FPOHW77MzDoBA4CPifPt8qV1gTjbNmZW08w+A3KBBe4esW1SHUPByhiL52Now9x9IHAlcHdwKEOi709AV+BMIBv4TVSrOUFm1ghIBe5z9/3RrudUlLEucbdt3L3Y3c8kdN/6wWbWL1LvVR1DIQvoWOpxB2BblGo5Ze6+LfieC8wkdHgsnuUEx4KPHhPOjXI9J8Xdc4If5BLgOeJouwTHrVOBf7j7jGA4LrdLWesSz9vG3fcCC4HhRGibVMdQWAJ0N7POZlYHGA3MiXJNJ8XMGgYn0DCzhsDlQMbXLxXz5gBjgukxwOwo1nLSjv6wBkYRJ9slOKn5PLDS3Z8s9VTcbZfjrUu8bRsza21mzYLp+sClwCoitE2q3dVHAMElaL8DagIvuPuk6FZ0csysC6G9Awjdb/vleFoXM3sFuJBQC+Ac4CFgFjANSAQ2Aze5e0yfxD3OelxI6PCEA5uA7x09/hvLzOxc4H0gHSgJhn9G6Fh8vG2X463LLcTRtjGz/oROJNck9If8NHd/2MxaEoFtUi1DQUREylYdDx+JiMhxKBREROQYhYKIiByjUBARkWMUCiIicoxCQao1M2tZqlvm9lLdMw+Y2TMRes/7zOz2YHqhmZV78/XgWvU3IlGPSGm1ol2ASDS5+y5C16xXSmdTM6sF3AkMPJFl3H2HmWWb2TB3/0+k6hPRnoJIGczsQjP7dzA90cymmtmbFrp/xfVm9riF7mPxRtBKATM7y8zeC5oTzv/SJ2ePuhj4r7sXlRq7KeiXv8bMzgte69tm9i8zew14M5hvFnBrxFZaBIWCSLi6AlcTalf8d+Bdd08CDgNXB8HwB+BGdz8LeAEo69Plwwj19S+tlrsPBu4j9Gnoo4YCY9z94uDxUuC8ilkdkbLp8JFIeOa5e6GZpRNqN3D0+H460AnoCfQDFoRa7lCTUAfOL0sg1Ne/tKNN55YFr3XUgi+1LcgF2p38KoiUT6EgEp4CAHcvMbNC///9YUoI/RwZkOnuQ8t5ncNAvbJeGyjmiz+TB780X71geZGI0eEjkYqxGmhtZkMh1LLZzPqWMd9KoNtJvkcPYryjp8Q/hYJIBQhu7Xoj8CszWw58BpxTxqzzgJO9EdJFwNyTXFYkLOqSKlLJzGwmMM7d157gcouAke6+JzKViSgURCqdmfUkdH/dRSewTGtCt16dFbHCRFAoiIhIKTqnICIixygURETkGIWCiIgco1AQEZFjFAoiInLM/wNop/bG1RWN3wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(time, position)\n",
    "plt.xlabel('Time (hr)')\n",
    "plt.ylabel('Position (km)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b38ecb0-1397-417b-a708-db45181ab8a0",
   "metadata": {},
   "source": [
    "## 2. Create a plot from a DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49e01685-b6dc-46b2-ba4e-e6a5763b75d4",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Create a dataframe that we will use to make a plot. Use the file `data/oceania_gdp` and index it by country. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3c4198de-2ae8-4222-86ed-b82788cf9831",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2023-10-23T10:34:25.727911Z",
     "iopub.status.busy": "2023-10-23T10:34:25.727522Z",
     "iopub.status.idle": "2023-10-23T10:34:25.944915Z",
     "shell.execute_reply": "2023-10-23T10:34:25.944095Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "clear_answer_cell"
    ]
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('../data/oceania_gdp.csv', index_col='country')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5b1b76a-0be3-4080-bd1f-9a363c85c922",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Extract the year from the last 4 characters of the column names using:\n",
    "```\n",
    "data.columns.str.replace('gdpPercap_', '')\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "12ce0da5-d7a9-459c-b5d3-4697646b4a1e",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2023-10-23T10:34:25.950487Z",
     "iopub.status.busy": "2023-10-23T10:34:25.950098Z",
     "iopub.status.idle": "2023-10-23T10:34:25.954655Z",
     "shell.execute_reply": "2023-10-23T10:34:25.954028Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "clear_answer_cell"
    ]
   },
   "outputs": [],
   "source": [
    "years = data.columns.str.replace('gdpPercap_', '')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2146833-4ac7-45d7-aa30-2d2a10e7da7d",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Convert the years values to integers using `astype()`and save the results back to the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2797c7ce-dbe8-4cc7-930c-165f24812223",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2023-10-23T10:34:25.959998Z",
     "iopub.status.busy": "2023-10-23T10:34:25.959671Z",
     "iopub.status.idle": "2023-10-23T10:34:25.966014Z",
     "shell.execute_reply": "2023-10-23T10:34:25.965355Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "clear_answer_cell"
    ]
   },
   "outputs": [],
   "source": [
    "data.columns = years.astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90f7c956-ab79-4d50-a9a4-11f079bd7462",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Plot the resulting data for 'Australia'. Set the x and y axis labels to `Year` and `GDP per capita` respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c44cbad7-b04a-48ae-9f3d-51f0944cbd1c",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2023-10-23T10:34:25.970042Z",
     "iopub.status.busy": "2023-10-23T10:34:25.969690Z",
     "iopub.status.idle": "2023-10-23T10:34:26.104122Z",
     "shell.execute_reply": "2023-10-23T10:34:26.103402Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "clear_answer_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'GDP per capita')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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m48yseVCWDGwsUS0rKEsOto8uP6KOuxcAu4GWpXz/HWaWZmZp27Ztq0joIhIDtu45xE0vzePA4UImjRlEl9aNIx1SjVKeHsl5wCXuPt7dxwOXAeeX9wuC5VWmAPe6+x5Cl6lOIbReVzbwx+JDS6nuZZSXVefIAvcX3D3V3VMTExPLG7qIxIDt+/K46aV5bN+Xx8TRA/Wc9QgoTyJZDbQv8bkdkH6MY49gZnUIJZFX3P0tAHff6u6F7l4EvAgMDA7PCs5dLAXYHJSnlFJ+RB0zqw00AzSbTKSG2H0gn++PnU/WzgOMu2UAfdudFOmQaqTyJJKWwEoz+9zMPgdWAIlmNs3Mph2rUjBWMRZY6e5PlyhPKnHY1YQulwFMA0YEM7E6AV2B+e6eDew1s8HBOUcCU0vUGRVsXwt8GoyjiEic25dXwKjx8/kmZx9/vzmVQZ2/dVVbqkl5bvP8zQme+2zgZmCpmS0Oyn4J3GBmfQldgloH3Ang7svNbDKhRFUA3B3M2AK4C5gANCA0W6v4wVpjgUlmlkGoJzLiBGMVkRhy8HAhYyYsYOmm3Tx/0xmc102XrCPJatof8KmpqZ6WdtzbYEQkSuUVFHL7ywuZuWYbfxrel6F9y5oMKlXFzBa6e2pp+8oza2uwmS0ws31mdtjMCs1sT9WHKSJStvzCIn706ld88fU2nrimt5JIlCjPGMlzwA3AGkKXlm4LykREqk1hkfPzN5bw0YqtPHJlD64f0O74laRalGspTHfPMLOEYMxivJnNDnNcIiL/5u7899tLmbp4Mw8MOZVbzj7RBTckHMqTSA6YWV1gsZk9Sejej0bhDUtEJMTd+e07K3htwUZ+dGEXfnh+l0iHJEcpz6Wtm4Pj7gH2E7pvY1g4gxIRKfbUR6uZMHsdo8/uxE+/2y3S4UgpytMjyQUOu/sh4LdmlgBoOU0RCauiIue5zzL4y2ffcMPA9vz6itPQUnrRqTyJZAZwMbAv+NwA+Ag4K1xBiUjNdSi/kCmLshg7ay2Z2/Zzdb9kHruqp5JIFCtPIqnv7sVJBHffZ2ZaVlNEqlTuvjwmzVnPpLnr2bH/MD2Tm/K/I/pyRe+21KqlJBLNypNI9pvZGcVLwptZf+BgeMMSkZoiI2cfY2dlMmXRJg4XFHFR99bcdm5nBnduoV5IjChPIrkXeMPMihdKTEIPjxKRSnB35mRu56WZa/l0VQ71atdi2BkpjDmnk5aAj0HHTSTuvsDMugOnElq2fZW754c9MhGJO/mFRby/NJsXZ2aybNMeWjaqy70Xd+XmwR1o2VhzeGJVeW9IzOc/q/SKiFTInkP5vDZ/A+O/XEf27kOcktiIP1zTi6v7JVO/TkKkw5NKKlciERE5EVk7DzD+y3W8vmAj+/IKGNy5BY9d3ZPzu7XWAHocKTORBM//SHH3jWUdJyJS0pKNu3hxZibTl20B4IreSdx+bmd6JjeLcGQSDmUmEnd3M/s/oH/1hCMisaqoyJmxKocXZ2Yyf+0OmtSrzZhzOnHLWR1pe1KDSIcnYVSeS1tzzWyAuy8IezQiEnMOHg7dQDhu1loyc/eTfFIDfnX5aQwf0I4m9etEOjypBuVJJBcAPzCzdYTW2jJCnZXe4QxMRKLbtr15TJqzjklz17PzQD69U5rx7A39uKznydROKM8yfhIvypNILg17FCISM9Zs3ctLM9fy9uJN5BcWcVH3Ntx+bicGdtINhDVVee4jWW9m5wBd3X28mSUCumNIpIbZuf8wv566jHfTs6lXuxbX9Q/dQNg5Ub8OarrjJhIzexhIJXRD4nigDvAP4OzwhiYi0WLmmm38bPISdh44zI8v7MKoszrqBkL5t/Jc2roa6AcsAnD3zWbWJKxRiUhUOJRfyJMfrGbcl2vp2rox428dwOltNYVXjlSeRHI4mAbsAGampyOK1ACrtuzhJ/9czOqte7nlrI48eGl33YUupSpPIplsZn8HTjKz24HRwIvhDUtEIqWoyBn35Vqe/GA1TRvUYfytA7jg1NaRDkuiWHkG258ys+8Ce4BuwG/c/eOwRyYi1W7rnkP8bPISZmXkcvFpbXhiWC+NhchxlXetraWEnozowbaIxJnpS7N56O2l5OUX8YdrejFiQDtN55VyKc+srduA3wCfEroZ8c9m9qi7jwt3cCISfvvyCvjttOW8sTCL3inN+NPwvprSKxVSnh7J/UA/d98OYGYtgdmAEolIjFu0YSf3vb6YjTsOcM8FXfjJxV2po7vSpYLKk0iygL0lPu8FtBqwSAwrKCziz59m8NxnGZzctD6v33kmAzq2iHRYEqPKk0g2AfPMbCqhMZKhwHwz+ymAuz8dxvhEpIqt376fe19fzFcbdnFNv2QeGXo6TbW4olRCeRLJN8Gr2NTgXTclisQQd+eNhVn8dtpyEmoZf76hH1f2aRvpsCQOlGf672+rIxARCZ+d+w/zy7eXMn3ZFgZ3bsHT1/fVM0KkyoRtVM3M2pnZZ2a20syWm9lPgvIWZvaxma0J3puXqPOQmWWY2Wozu6REeX8zWxrsezZ4ciNmVs/MXg/K55lZx3C1RyRWzVqTy5D//YJPVm7loUu78+ptg5VEpEqFc3pGAfAzdz8NGAzcbWY9gAeBGe7eFZgRfCbYNwI4HRgC/NXMitdjeB64A+gavIYE5WOAne7eBXgGeCKM7RGJKYfyC/nduyv4/th5NKlfh7d/eDZ3nneKnpUuVe64icTMWp3Iid09292LF3rcC6wEkgkN1k8MDpsIXBVsDwVec/c8d18LZAADzSwJaOruc9zdgZePqlN8rjeBi4p7KyI12eote7nqL18ydtZaRp7ZgXfuOUfPS5ewOeYYiZldSehekQIzKwSud/fZJ/IlwSWnfsA8oI27Z0Mo2ZhZ8SI+ycDcEtWygrL8YPvo8uI6G4NzFZjZbqAlkHvU999BqEdD+/btT6QJIjGhqMiZMHsdj3+wiqb1azP+lgFc0F3rZEl4lTXY/hhwrruvMrNBwJPAeRX9AjNrDEwB7nX3PWV0GErb4WWUl1XnyAL3F4AXAFJTU7+1XyQebN1ziJ+/sYSZa3K5+LTWPD6sN620TpZUg7ISSYG7rwJw93kn8gwSM6tDKIm84u5vBcVbzSwp6I0kATlBeRbQrkT1FGBzUJ5SSnnJOllmVhtoBuyoaJwise6DZVt46K10DuYX8tjVPblxYHutkyXVpqxE0rr4psPSPh/vRsRgrGIssPKoY6cBo4DHg/epJcpfNbOngbaEBtXnu3uhme01s8GELo2NBP581LnmANcCnwbjKCI1wra9eTz14WpeT9tIr+Rm/GlEX07ROllSzcpKJC9y5E2HR38+nrOBm4GlZrY4KPsloQQy2czGABuA6wDcfbmZTQZWEJrxdbe7Fwb17gImEFqBeHrwglCimmRmGYR6IiMqEJ9ITMrdl8cHy7bwXno289Zux4Efnn8K917cjbq1tU6WVD+raX/Ap6amelpaWqTDEKmQ7fvy+GB5KHnMzdxOkUPnxEZc0SuJK/u0pWsbLTQh4WVmC909tbR9Zd7ZbmYXAPcA3YOilcBz7v55lUYoIt+yfV8eHy7fyntLNzPnmyB5tGrE3Rd04bJeSXQ/uYnGQSQqlDX993LgOeDR4GXAGcA4M7vH3d+vnhBFao4d+w/zYdDzmJO5ncIip1OrRvzw/FDyOC1JyUOiT1k9kvuBq9x9SYmyxWaWRmiwW4lEpArsLE4eS7OZ/U0oeXRs2ZAfnNeZy3ol0SOpqZKHRLWyEsnJRyURANw93czahDEmkbi3c/9hPlqxhXfT/5M8OrRsyJ3fCSWP09sqeUjsKCuR7D/BfSJSil0HDvPR8q28uzSb2Rm5FBQ57Vs05I7vdOZyJQ+JYWUlklPMbFop5QZ0DlM8InFl94F8PlwRGvP4Mkge7Vo04LZzQ8mjZ7KSh8S+shLJ0DL2PVXVgYjEi90H8vloRWjM48uMXPILnZTmDRhzbieu6NVWyUPizjETibv/qzoDEYl1GTn7GDsrkymLNnG4oIjkkxow+uxOXN47iV7JzZQ8JG6VNf13KJDi7n8JPs8DEoPdD7j7m9UQn0hUc3fmrd3Bi19kMmNVDvVq1+La/ikMT21H7xQlD6kZyrq09QBHLjlSDxgANALGE3r+h0iNVFBYxPRlW3hxZibpWbtp0agu917clZsHd6ClVtyVGqasRFLX3TeW+DzL3bcD282sUZjjEolK+/IKmLxgI2NnrWXTroN0atWIx67uybAzUqhfJ+H4JxCJQ2UlkuYlP7j7PSU+JiJSg2zZfYgJs9fxyrz17D1UwICOzXn4yh5cfFobPbpWaryyEsk8M7vd3V8sWWhmdwLzwxuWSHRYtWUPL36xlmlLNlFY5FzaM4nbzu1Ev/bNj19ZpIYoK5HcB/yfmd0ILArK+hMaK7kqzHGJRIy782XGdl6YmckXX2+jQZ0EbhrUgdFnd6J9y4aRDk8k6pQ1/TcHOMvMLgROD4rfc/dPqyUykWp2uKCId9M388IXmazaspdWjetx/yWnctOg9pzUsG6kwxOJWmUuIw8QJA4lD4lbew7l8895Gxj/5Tq27DlE19aNeXJYb4b2a0u92hpAFzme4yYSkXi1addBxs9ay2sLNrIvr4CzTmnJH4b14ryuiRpAF6kAJRKpcZZt2s2LMzN5Nz0bgCt6J3H7uZ3pmdwswpGJxCYlEqkRioqcf329jRe+yGRO5nYa1U3g1rM6cus5nUg+qUGkwxOJaUokEtfcnXfSs/nzjDWsydnHyU3r89Cl3RkxsD3NGtSJdHgicUGJROLW1j2H+OVbS5mxKofuJzfhmeF9uLxXW+rWrhXp0ETiihKJxB13582FWfzu3RXkFRTxq8tP49azO5GgAXSRsFAikbiSvfsgD721lM9Xb2NAx+Y8eW0fOrXS0nAi4aREInHB3ZmctpHfv7uSgiLn4St7MOrMjprGK1INlEgk5m3adZAHp6Qzc00ugzq14Mlre9OhpXohItVFiURilrvzz/kb+Z/3V1LkzqNDT+f7gzqoFyJSzZRIJCZt3HGAh95ayqyMXM7s3JInr+1NuxZaUFEkEpRIJKYUFTmvzN/A4++vBOD3V/XkxoHt1QsRiSAlEokZG7Yf4IEpS5ibuYNzurTi8WG9SGmuXohIpCmRSNQrKnImzV3P49NXkVDLePyaXgwf0A4z9UJEooESiUS1dbn7eWBKOvPX7uA73RL5wzW9tDaWSJRRIpGoVFTkTJi9jic/XEWdhFo8eW1vruufol6ISBQK26JDZjbOzHLMbFmJskfMbJOZLQ5el5XY95CZZZjZajO7pER5fzNbGux71oLfJGZWz8xeD8rnmVnHcLVFqlfmtn1c//c5PPruCs46pRUf33ce16fqUpZItArn6nUTgCGllD/j7n2D1/sAZtYDGEHokb5DgL+aWfGj6Z4H7gC6Bq/ic44Bdrp7F+AZ4IlwNUSqR2GR8+IXmVz6vzP5eute/nhdH8aOSuXkZvUjHZqIlCFsl7bc/YsK9BKGAq+5ex6w1swygIFmtg5o6u5zAMzsZeAqYHpQ55Gg/pvAc2Zm7u5V1gipNhk5+7j/zSV8tWEXF5/Wmseu7kWbpkogIrEgEmMk95jZSCAN+Jm77wSSgbkljskKyvKD7aPLCd43Arh7gZntBloCuUd/oZndQahXQ/v27au0MVI5hUXOSzMz+ePHX9OwbgJ/Gt6XoX3b6jKWSAyp7gczPA+cAvQFsoE/BuWl/dbwMsrLqvPtQvcX3D3V3VMTExMrFLCEz5qte7nm+dn8Yfoqzu+WyEf3fYer+iUriYjEmGrtkbj71uJtM3sReDf4mAW0K3FoCrA5KE8ppbxknSwzqw00A3aEJ3KpSgWFRbwwM5M/fbyGRvUSePaGflzZO0kJRCRGVWuPxMySSny8Giie0TUNGBHMxOpEaFB9vrtnA3vNbHAwW2skMLVEnVHB9rXApxofiX4ZOXsZ9vxsnvxgNRf3aM3HPz2P7/XRpSyRWBa2HomZ/RM4H2hlZlnAw8D5ZtaX0CWodcCdAO6+3MwmAyuAAuBudy8MTnUXoRlgDQgNsk8PyscCk4KB+R2EZn1JlCoscsbOyuSpj76mUd0EnruxH1f0bhvpsESkClhN+yM+NTXV09LSIh1GjbI2dz/3v7GEtPU7+W6PNvzP1b1IbFIv0mGJSAWY2UJ3Ty1tn+5sl7ApXiPrD9NXUiehFk9f34erNZguEneUSCQsNu44wANvpjMnczvndUvkiWG9dWOhSJxSIpEq5e68tmAjv393BWZaqVekJlAikSqTvfsgD05Zyr++3sZZp4SeWqjnhYjEPyUSqTR3561Fm3jkneUUFOrZ6SI1jRKJVErO3kP88q1lfLJyKwM6Nuf/XduHjq0aRTosEalGSiRywt5ZspnfTF3G/sOF/Ory07j17E4kqBciUuMokUiF7dh/mF9PXcZ76dn0SWnGH6/vQ5fWTSIdlohEiBKJVMhHy7fwy7eXsvtgPvdfcip3fqcztROqe+1PEYkmSiRSLrsP5PPbd5bz1leb6JHUlEljBnFaUtNIhyUiUUCJRI7r89U5/GJKOrn7DvPji7pyzwVdqFtbvRARCVEikWPaeyifx95byWsLNtKtTWNeGjmAXinNIh2WiEQZJRIp1eyMXO5/M53s3Qf5wXmncO/FXalfJyHSYYlIFFIikSMcOFzA49NX8fKc9XRu1Yg3fnAW/Ts0j3RYIhLFlEjk3xas28HP31jC+u0HGH12J+6/5FQa1FUvRETKpkQibNp1kLEz1zJ+9lpSmjfgtTsGM7hzy0iHJSIxQomkhtqfV8AHy7YwZVEWczK34w43DWrPLy87jUb19M9CRMpPvzFqkKIiZ27mdt5clMUHy7Zw4HAh7Vs05N6LunHNGcm0a6GVekWk4pRIaoC1uft5a1EWby3axKZdB2lSrzbf69OWYf1TSO3QXM8KEZFKUSKJU7sP5vNeejZTFmWxcP1Oahmc0zWRB4acyn/1OFmD6CJSZZRI4khBYREzM3KZsjCLj1Zs5XBBEV1bN+bBS7tzVd9kPepWRMJCiSQOrN6ylymLsnj7q01s25vHSQ3rcMOAdgzrn0Kv5Ga6dCUiYaVEEqO278tj2pLNTFmUxbJNe6hdy7ige2uGnZHChd1bay0sEak2SiQx5HBBEZ+uymHKoiw+W5VDQZHTM7kpD1/Zg+/1aUvLxvUiHaKI1EBKJFHO3Vm6aTdTFmYxbclmdh7IJ7FJPUaf04lrzkim+8layl1EIkuJJEpt2nWQd5ZsZsrCLNbk7KNu7Vr8V482DOufwrldWulhUiISNZRIosTug/nMzdzOlxm5zFqTS2bufgD6d2jO/1zdi8t7J9GsQZ0IRyki8m1KJBGSV1DIVxt28WVGLjPX5JKetYsih4Z1ExjcuSU3De7Ahd1b06lVo0iHKiJSJiWSalJU5KzeuvffiWP+2h0czC8koZbRJ6UZ91zYlXO6tKJvu5M040pEYooSSRht2nWQL9fkMisjl9nf5JK77zAApyQ2YviAdpzdpRWDOregaX1dshKR2KVEUoV2H8xnzjehcY4vM/4zzpHYpB7ndk3k7C6tOLtLS5KaNYhwpCIiVSdsicTMxgFXADnu3jMoawG8DnQE1gHXu/vOYN9DwBigEPixu38YlPcHJgANgPeBn7i7m1k94GWgP7AdGO7u68LVntLkFRSyaH1onGNWRunjHOd0aUW3No11d7mIxK1w9kgmAM8R+mVf7EFghrs/bmYPBp9/YWY9gBHA6UBb4BMz6+buhcDzwB3AXEKJZAgwnVDS2enuXcxsBPAEMDyM7aGoyFm1Ze+/E0fJcY6+7U7SOIeI1EhhSyTu/oWZdTyqeChwfrA9Efgc+EVQ/pq75wFrzSwDGGhm64Cm7j4HwMxeBq4ilEiGAo8E53oTeM7MzN09HO15fcEG/t+Hq/89ztGldWONc4iIUP1jJG3cPRvA3bPNrHVQnkyox1EsKyjLD7aPLi+uszE4V4GZ7QZaArlHf6mZ3UGoV0P79u1PKPDWTeprnENEpBTRMthe2gCCl1FeVp1vF7q/ALwAkJqaekI9lgu6t+aC7q2Pf6CISA1T3Rfyt5pZEkDwnhOUZwHtShyXAmwOylNKKT+ijpnVBpoBO8IWuYiIlKq6E8k0YFSwPQqYWqJ8hJnVM7NOQFdgfnAZbK+ZDbbQtKeRR9UpPte1wKfhGh8REZFjC+f0338SGlhvZWZZwMPA48BkMxsDbACuA3D35WY2GVgBFAB3BzO2AO7iP9N/pwcvgLHApGBgfgehWV8iIlLNrKb9EZ+amuppaWmRDkNEJKaY2UJ3Ty1tn252EBGRSlEiERGRSlEiERGRSlEiERGRSqlxg+1mtg1YX6KoFaXcDR9H4rl9alvsiuf2xWvbOrh7Ymk7alwiOZqZpR1rJkI8iOf2qW2xK57bF89tOxZd2hIRkUpRIhERkUpRIgkWc4xj8dw+tS12xXP74rltparxYyQiIlI56pGIiEilKJGIiEilxGUiMbNxZpZjZstKlPUxszlmttTM3jGzpkF5RzM7aGaLg9ffStTpHxyfYWbPBkvZR1RF2hbs6x3sWx7srx+Ux3TbzOymEj+zxWZWZGZ9g31R1zaocPvqmNnEoHylmT1Uok7Uta+CbatrZuOD8iVmdn6JOtHYtnZm9lnwc1huZj8JyluY2cdmtiZ4b16izkNBG1ab2SUlyqOufVXC3ePuBXwHOANYVqJsAXBesD0a+F2w3bHkcUedZz5wJqGnMU4HLo2xttUG0oE+weeWQEI8tO2oer2AzGj+uZ3Az+5G4LVguyGwDugYre2rYNvuBsYH262BhUCtKG5bEnBGsN0E+BroATwJPBiUPwg8EWz3AJYA9YBOwDfR/P9dVbziskfi7l/w7aclngp8EWx/DAwr6xwWeoJjU3ef46F/AS8DV1VxqBVWwbb9F5Du7kuCutvdvTBO2lbSDcA/IXp/blDh9jnQyEJP/2wAHAb2RGv7Kti2HsCMoF4OsAtIjeK2Zbv7omB7L7ASSAaGAhODwybyn1iHEvojIM/d1wIZwMBobV9ViMtEcgzLgO8F29dx5KN9O5nZV2b2LzM7NyhLJvQ432JZQVk0OlbbugFuZh+a2SIzeyAoj4e2lTScIJEQW22DY7fvTWA/kE3oIXBPufsOYqt9x2rbEmComdW20BNR+wf7or5tZtYR6AfMA9p46CmuBO+tg8OSgY0lqhW3I+rbd6JqUiIZDdxtZgsJdU8PB+XZQHt37wf8FHg1uJZb2rXLaJ0rfay21QbOAW4K3q82s4uIj7YBYGaDgAPuXnxtPpbaBsdu30CgEGhL6PLIz8ysM7HVvmO1bRyhX6JpwJ+A2YSejBrVbTOzxsAU4F5331PWoaWUeRnlMS9sj9qNNu6+itClHsysG3B5UJ4H5AXbC83sG0J/yWcBKSVOkQJsrs6Yy+tYbSPUhn+5e26w731C17H/Qey3rdgI/tMbgRj6uUGZ7bsR+MDd84EcM/sSSAVmEiPtK+P/uQLgvuLjzGw2sAbYSZS2zczqEEoir7j7W0HxVjNLcvfs4LJVTlCexZE95+J2xNS/zYqoMT0SM2sdvNcCfgX8LficaGYJwXZnoCuhgdtsYK+ZDQ5mVowEpkYk+OM4VtuAD4HeZtYwuNZ+HrAiTtpWXHYd8FpxWSy1Dcps3wbgQgtpBAwGVsVS+8r4f65h0CbM7LtAgbtH7b/LIJaxwEp3f7rErmnAqGB7FP+JdRowwszqBZfuugLzo7V9VSLSo/3heBH6CzUbyCf0V8AY4CeEZlt8DTzOf+7qHwYsJ3TddhFwZYnzpBK6zvsN8FxxnVhpW3D894P2LQOejLO2nQ/MLeU8Ude2E/h32Rh4I/jZrQDuj+b2VbBtHYHVhAatPyG0PHk0t+0cQpeg0oHFwesyQrMgZxDqTc0AWpSo899BG1ZTYmZWNLavKl5aIkVERCqlxlzaEhGR8FAiERGRSlEiERGRSlEiERGRSlEiERGRSlEiEQmz4F6QWWZ2aYmy683sg0jGJVJVNP1XpBqYWU9C94X0AxII3YswxN2/OYFzJbh7YdVGKHLilEhEqomZPUloIcZGwXsHQkvg1wYecfepwaKAk4JjAO5x99nBMzseJnTTX19371G90YscmxKJSDUJlgVZRGjxwneB5e7+DzM7idBzKvoRuoO6yN0PmVlX4J/unhokkveAnh5amlwkatSYRRtFIs3d95vZ68A+4HrgSjP7ebC7PtCe0CJ+z1noaY+FhBYQLTZfSUSikRKJSPUqCl4GDHP31SV3mtkjwFagD6HJMIdK7N5fTTGKVIhmbYlExofAj4qf2W1m/YLyZkC2uxcBNxMamBeJakokIpHxO6AOkG5my4LPAH8FRpnZXEKXtdQLkainwXYREakU9UhERKRSlEhERKRSlEhERKRSlEhERKRSlEhERKRSlEhERKRSlEhERKRS/j+OUY1TOa3AQAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.loc['Australia'].plot()\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('GDP per capita')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc0a3be8-8675-40e7-8632-c4d64c5a4d79",
   "metadata": {},
   "source": [
    "## 3. Plot multiple series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e763190f-386b-4c90-96d5-e768e6fa04c7",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Plot multiple series using `.T.plot()` and set the axis labels to `Year` and `GDP per capita` again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "443ec95b-6ccd-42e8-a0ec-279299bcf5b0",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2023-10-23T10:34:26.110531Z",
     "iopub.status.busy": "2023-10-23T10:34:26.110308Z",
     "iopub.status.idle": "2023-10-23T10:34:26.252988Z",
     "shell.execute_reply": "2023-10-23T10:34:26.251856Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "clear_answer_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'GDP per capita')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.T.plot()\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('GDP per capita')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f37d0942-8269-4f1e-8524-3cff1d113eb6",
   "metadata": {},
   "source": [
    "## 4. Plot some different types of graphs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e15c35b3-d498-4b54-a376-90e9296a5922",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Plot the data in the form of a bar chart, including axis labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b6dd18d0-6d3d-48e3-84fe-2f6479f531a9",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2023-10-23T10:34:26.258928Z",
     "iopub.status.busy": "2023-10-23T10:34:26.258498Z",
     "iopub.status.idle": "2023-10-23T10:34:26.466592Z",
     "shell.execute_reply": "2023-10-23T10:34:26.465384Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "clear_answer_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Year')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('ggplot')\n",
    "data.T.plot(kind='bar')\n",
    "plt.ylabel('GDP per capita')\n",
    "plt.xlabel('Year')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1609d38-c8e5-4711-a577-c8c4ef741684",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Create a variable called `years`, setting the value as the columns from your dataframe. Create a variable called `gdp_australia` which uses `loc()` to get the data for Australia. Try making a plot by calling the `plt.plot(x,y)` function directly for `years` on the x axis and `gdp_australia` on the y axis. Plot the data using a blue dashed line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "362a52f5-b853-4335-a8ec-8dbaf8b3213c",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2023-10-23T10:34:26.472433Z",
     "iopub.status.busy": "2023-10-23T10:34:26.472074Z",
     "iopub.status.idle": "2023-10-23T10:34:26.585330Z",
     "shell.execute_reply": "2023-10-23T10:34:26.584159Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "clear_answer_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7f349de410>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aefLJJ3n22WdRVZUNGzYwZ84c4uPjWbFiBfv27SMpKYnCwkLCwsJYt24du3btYvPmzSxatKhnKhZCeL3ych2//rWJZ5+1csUVDgmDXtJpl5HRaGTo0KEA6PV6YmNjsVgsHb6/tLSUUaNGERgYSExMDAMGDKCsrAyr1UpLSwvDhg1DURRGjx5NaWkpAHv37mXMmDEApKSksH//fjS5zZEQfVJFhY477zRz8qRKiCxP1KvO68K0qqoqDh8+TFxcHF988QXbtm2juLiYoUOHcu+992IwGLBYLMTHx7v3MZlMWCwWdDodZrPZvd1sNruDxWKxuF/T6XSEhobS0NDg7o46paCggIKCAgBWrlxJVFTU94UEBJz23N/4c31Sm+/q7vqOHYO77gqkuRk+/NDBlVf267avfb78/bM7my4HQmtrK9nZ2cyYMYPQ0FAmTJjA7bffDsBrr73Gpk2bmD9/fodH9uc64j/ba8pZFiZJT08nPT3d/by6utr9OCoq6rTn/saf65PafFd31mexqPzyl2aqqjRefbWGgQPtePJb56+f3cCBAzt8rUuzjBwOB9nZ2Vx//fVce+21APTr1w9VVVFVlfHjx3Po0CGg/ci/pqbGva/FYsFkMp2xvaamBpPJdMY+TqeT5uZmDAbDeZYphPBler2Ln/zEwaZNFq66yu7p5vRJnQaCpmmsX7+e2NhYbr75Zvd2q9Xqfrxnzx4GDRoEQHJyMiUlJdjtdqqqqqisrCQuLg6j0Yher+fgwYNomkZxcTHJyckAjBw5kh07dgCwe/duEhISznqGIITwP42NCnV1Cno9PP+8lZQUm6eb1Gd12mX05ZdfUlxczODBg3nooYeA9immu3bt4siRIyiKQnR0NLNnzwZg0KBBpKamkpWVhaqqzJw5E/W7ywpnzZpFfn4+NpuNxMREkpKSABg3bhy5ubksXLgQg8FAZmZmD5UrhPAmLS0KM2aYaGlReOedalmozsMUzYen8xw7dsz92F/7+07x5/qkNt91MfW1tcHMmSZ27AgmN7eWW29t6ebWXRx//ezONYYgy18LIXqdwwH332/ko49CyM62el0Y9FWydIUQotf94Q8RfPCBnt//vpa77pIw8BZyhiCE6HUzZzYxdKiDe+5p9nRTxA/IGYIQoldoGrzzTghOJ8TGOiUMvJAEghCiVzz1VDjz5pl4+229p5siOiBdRkKIHuVywbPPGli3Lpxp05q47TYZM/BWEghCiB7z6adBPPRQJIcOBfLLXzazYkUdcs2p95JAEEJ0q+pqleZmhcGDnURFOTEYNHJzrUya1CIXnnk5CQQhRLcoKwvghRfCePPNUMaNa+XFF638+MdO/vIX/7u4y19JIAghLsqePUHk5hrYvj2EkBCN229vZvbsJk83S1wACQQhxHmz23F3/xQVBbNvXyAPPljPffc1Yza7PNs4ccFk2qkQosvq6xXWrw8jNbU///u/7bczmzevkU8/PUFWVqOEgY+TMwQhRKcqKnS8+GIYf/pTKI2NKqmpbe4//nK/Y/8hgSCEOCdNg6lTzXz9tY5Jk1qYM6eJESPkBjb+SAJBCHEalwsKCoJ59dVQ8vKs6PWQnV1LbKyD2FjpEvJnEghCCKD9ZjVvvKFnwwYD5eUBxMY6+PrrAH76UwfXXCN3MesLJBCEEBw7pnLDDdFYLDp+9jMb+fkWJk5sJUD+QvQp8nEL0Ud99VUABw4EcuutLVxyiYs77mhhwoRWrr3WJstL9FESCEL0MRaLwpIl/XjnHT1Go5Mbb2whOBiWLq33dNOEh8l1CEL0IcXFwaSnx/DBByFkZjZQVHSS4GBPt0p4CzlDEKKPOHpUZfp0Ez/6kYNNm2q44gqHp5skvIwEghB+rqpKJSbGRWysi5despCa2oZe7lEjzkK6jITwUy4XPP98GCkp/dmxo71faNw4CQPRMTlDEMIPVVaqLFpkZOfOYCZMaJEri0WXSCAI4Wc++CCE3/ymH21t8NRTtUyb1izTSEWXSCAI4WdOnlS57DIH69a136BGiK6SQBDCD/zf/wVSVaXjxhtbmT69malTmwkM9HSrhK+RQWUhfJjDAdnZ4dx2WxTZ2eG4XKAoSBiICyKBIISPOnJEx223RbF6dTiTJ7fw1lvVqPIbLS6CdBkJ4YMqK1UmTIhGp4P8fAuTJ7d6uknCD0ggCOFDHA4ICIBLLnHx8MMN3HhjK7GxMnAsukengVBdXU1eXh61tbUoikJ6ejo33XQTjY2N5OTkcPLkSaKjo1m0aBEGgwGALVu2UFhYiKqqZGRkkJiYCEB5eTl5eXnYbDaSkpLIyMhAURTsdju5ubmUl5cTHh5OZmYmMTExPVq4EL6muDiIRx7px4YNFq64wsGsWU2ebpLwM532OOp0OqZPn05OTg7Lly9n27ZtVFRUsHXrVkaMGMHatWsZMWIEW7duBaCiooKSkhJWr17NkiVL2LhxIy5X+12WNmzYwJw5c1i7di3Hjx9n3759ABQWFhIWFsa6deuYOHEimzdv7rGChfA1ra3wn/8ZwdSpUQQFaXJNgegxnQaC0Whk6NChAOj1emJjY7FYLJSWlpKWlgZAWloapaWlAJSWljJq1CgCAwOJiYlhwIABlJWVYbVaaWlpYdiwYSiKwujRo9377N27lzFjxgCQkpLC/v370TS5cbcQX3wRwM03R7Nhg4EZM5r461+rSUiQRelEzzivMYSqqioOHz5MXFwcdXV1GI1GoD006uvb11K3WCzEx8e79zGZTFgsFnQ6HWaz2b3dbDZjsVjc+5x6TafTERoaSkNDAxEREaf9/wUFBRQUFACwcuVKoqKivi8kIOC05/7Gn+uT2jpWWKijpkZl61Y7N94YCJg73ac3yWfnX7ocCK2trWRnZzNjxgxCQ0M7fF9HR/bnOuI/22vKWc6L09PTSU9Pdz+vrq52P46Kijrtub/x5/qkttMdP65y/LiOxEQ7v/413Hmnitnswhu/RfLZ+Z6BAwd2+FqXZi07HA6ys7O5/vrrufbaawGIjIzEarUCYLVa3UfzZrOZmpoa974WiwWTyXTG9pqaGkwm0xn7OJ1Ompub3QPUQvQlH3wQQnp6NAsWGHE62y8wM5tdnm6W6CM6DQRN01i/fj2xsbHcfPPN7u3JyckUFRUBUFRUxNVXX+3eXlJSgt1up6qqisrKSuLi4jAajej1eg4ePIimaRQXF5OcnAzAyJEj2bFjBwC7d+8mISHhrGcIQvirkydVfvObSGbNMjFokJM//rEGnc7TrRJ9TaddRl9++SXFxcUMHjyYhx56CICpU6dy6623kpOTQ2FhIVFRUWRlZQEwaNAgUlNTycrKQlVVZs6cifrd5ZOzZs0iPz8fm81GYmIiSUlJAIwbN47c3FwWLlyIwWAgMzOzh8oVwntoWvsyE+XlOtLSYtA0WLCggQcfbCAoyNOtE32RovnwdJ5jx465H/trf98p/lxfX6qtpkblL38J4b339Fx2mYOnn65D02D9+jDGj29j2DDfmkHUlz47f3GuMQS5UlmIXvD22yG88koYJSVBuFwKQ4c6GDOmfbkJRYF58+QiM+F5EghC9ACLReXDD4OZN6/9eWlpEEeP6liwoJGbb25h+HCHXGAmvI4EghDdxGJR+Otf9bz7bgi7dgXjdCokJ9v58Y9hyZJ6nnyyXkJAeDUJBCG6wd/+FsjkyVE4nQpDhjiYN6+RW25p4Zpr+lFTg9zYXvgECQQhzpPVqrBtWwjvvqvnqqvsPPhgAwkJdhYsaOSmm1pISPi+O0jOCIQvkUAQoou2bNHz5z/r2bkzGIdD4bLLHIwd2wZAUBA8/HCDh1soxMWRQBCiA7W1Cp98EsyNN7bPBvrggxAOHQpg9uxGbrmllREj7HIGIPyKBIIQ/6asLIAXXgjjzTdDaWtT2LXrBEOGOMnOrsVgkOWnhf+SQBDiO99+q+PxxyMpKAghJETjjjuamTq1mcsua78jWXi4z17DKUSXSCCIPs3hgBMndMTGOomIcHHwYAAPPljPffc1y6Jyos+RQBB9UmOjwp/+FMqLL4YRGamxbdtJIiM1du2qQu3SGsBC+B8JBNGnHD+u8l//FcbLL4dRX69yzTVtzJ37/bIREgaiL5NAEH3CqZVFi4qCee45Azfd1MqcOY1cdZXd000TwmtIIAi/pWmwc2cQzz9vYMyYNn796yZuvbWFlBSbe6BYCPE9OUEWfsdmgzff1DNhQjRTp0Zx4EAgISHtM4SCg5EwEKIDcoYg/M4DDxh59109w4bZWb3ayq23thAc7OlWCeH9JBCEzzt6VMfGjWHMnt3IgAEuZs1qZMqUZsaObZOLyIQ4DxIIwmf985+BPP98GO+8076U6M9+ZmPy5FaSk2WgWIgLIYEgfI7DAdOnmyguDsFgcDFzZhOzZjURGytjA0JcDAkE4RM0DT79NIiUFBsBARAf7yAtrY67724mIkKWlBCiO0ggCK93/LjKI4/0o6AghKKiE8TFOXniiXpPN0sIvyPTToXX0jR47TU948bF8PHHwSxbVsePfiTdQkL0FDlDEF5J02D2bCN/+Yuea65pIzu7lqFDJQyE6EkSCMKrnFpiQlFg1Kg2UlJsZGQ0yRpDQvQC+TUTXuPoUR3Tppl4990QADIympk5U8JAiN4iv2rC4zQNNm8OZdy4aEpLg2hulqvJhPAE6TISHlVRoWP69AAKC/tx3XVtPPNMLYMHy1iBEJ4ggSA86v/+L5A9exRWrqzlnnuaZakJITxIAkH0uq+/1rF/fyATJ7YyaVIrt9xiR1WbPd0sIfo8GUMQvcblgpdeCmX8+GiWLImkpaV9NlFMjKdbJoQACQTRS44c0XHHHWYef7wfKSk23n//JHq9p1slhPgh6TISPa66WuXnP48mIABWr7YyZUqLjBUI4YU6DYT8/Hw+++wzIiMjyc7OBuD1119n+/btREREADB16lSuuuoqALZs2UJhYSGqqpKRkUFiYiIA5eXl5OXlYbPZSEpKIiMjA0VRsNvt5ObmUl5eTnh4OJmZmcRIH4JfsFgUTCaNqCgXTzxRz5gxrVxyicvTzRJCdKDTLqMxY8bw2GOPnbF94sSJrFq1ilWrVrnDoKKigpKSElavXs2SJUvYuHEjLlf7H4ANGzYwZ84c1q5dy/Hjx9m3bx8AhYWFhIWFsW7dOiZOnMjmzZu7sTzhCU4nrF8fxjXX9GfPniAApk5tljAQwst1GgjDhw/HYDB06YuVlpYyatQoAgMDiYmJYcCAAZSVlWG1WmlpaWHYsGEoisLo0aMpLS0FYO/evYwZMwaAlJQU9u/fj6bJcsa+qqwsgFtvjeLJJyO5/vo2LrvM4ekmCSG66ILHELZt20ZxcTFDhw7l3nvvxWAwYLFYiI+Pd7/HZDJhsVjQ6XSYzWb3drPZjMViAcBisbhf0+l0hIaG0tDQ4O6O+qGCggIKCgoAWLlyJVFRUd8XEhBw2nN/4wv15eaqPPaYjrAw+O//dnDnnToUxdTpfr5Q24Xy59rAv+vz59o6ckGBMGHCBG6//XYAXnvtNTZt2sT8+fM7PLI/1xH/2V5TOhhxTE9PJz093f28urra/TgqKuq05/7GF+qrrQ1j7NggVqyoIybGRU1N1/bzhdoulD/XBv5dn7/WNnDgwA5fu6Bpp/369UNVVVRVZfz48Rw6dAhoP/Kv+cFfAYvFgslkOmN7TU0NJpPpjH2cTifNzc1d7qISnuVwQG6ugffea1+MbvbsJl580UpMjIwVCOGLLigQrFar+/GePXsYNGgQAMnJyZSUlGC326mqqqKyspK4uDiMRiN6vZ6DBw+iaRrFxcUkJycDMHLkSHbs2AHA7t27SUhI6PAMQXiPr74KYPLkKFasiGDnzmAAVBWZTiqED+u0y2jNmjV8/vnnNDQ0MHfuXKZMmcKBAwc4cuQIiqIQHR3N7NmzARg0aBCpqalkZWWhqiozZ85E/W7t4lmzZpGfn4/NZiMxMZGkpCQAxo0bR25uLgsXLsRgMJCZmdlz1YqL5nTChg1hPP10BKGhLp57zsKkSa2ebpYQohsomg9P6Tl27Jj7sb/2953iLfV99FEw99xj5oYbWnjqqTqioy++e8hbausJ/lwb+Hd9/lrbucYQ5Epl0SmXCz7/PIArrnAwZkwbb7xRTWqqTbqHhPAzspaROKdvv9Vx551mJk+OpqJC992tLSUMhPBHEgjirDQN/ud/2lcm/cc/Avn97+uIjZUb1wjhz6TLSJzB6YQZM0wUFoZw3XVtrF5dy6WXShgI4e8kEMQZdDpISLAzfnwr997bLDe5F6KPkF91AUBVlcrMmUb3YnSLFzcwY4aEgRB9ify6C95+O4SxY2P46KMQjhzRebo5QggPkS6jPsxiUXn00Ujee09PUpKNNWtqiYuT1UmF6KskEPqwrVv1bNsWwuLF9cyb10iA/DQI0afJn4A+prZW4dChAEaOtHPffU2MHt1KXJzMIBJCyBhCn1JYGMz48THMmmWitbV9NpGEgRDiFAmEPqChQeGhhyKZPt1MZKSL//5vCyEhnm6VEMLbSJeRn6upUbnxxigqK3Xcf38DDz7YQHCwp1slhPBGEgh+StPa701gMrmYNKmVG29sYeRIu6ebJYTwYtJl5If27AkiPT2asrL2xegef7xewkAI0SkJBD9SUaFj2bIIfvlLM83NCg0N8vEKIbpOuoz8xOzZRt5/Xw/A9OlN/Pa39YSF+ey9j4QQHiCB4INcLigpCWL79hCWLq1HUeCKK+xcfrmd//iPFgYPlqmkQojzJ4HgQ8rLdbzxRih//rOeo0cDCA93cd99TQwZ4uSBBxo93TwhhI+TQPARH32k8Itf9EdVNUaPbuOxxxq44YYW9HpPt0wI4S8kELyQwwHFxcG8/nooI0bYuf/+Rq67TmPp0jomT25hwICLv7G9EEL8OwkEL/LFFwG88UYob72lp6pKh9HoJCGhfbpoUBDMmdPk4RYKIfyZBIKH1dcrRES0zwZ6+ulwtm8PYfz4Vu64o4Xx41sJCvJwA4UQfYYEggfYbFBQEMIbb+j56KMQioqquOwyJ7/9bT2rVtVhNkuXkBCi90kg9KITJ1TWrTOwdaseq1VHTIyTmTObCApqP0P40Y9kuqgQwnMkEHrY0aM66uoUhg93EBgIb74ZytixbdxxRzOjR7fJTWmEEF5D/hx1s7o6hU8+CWbnzmCKi4MpLw/gmmva2LKlBpPJxd/+dlymigohvJIEwkVqa4MvvwzkyivbZwPNnm3i44+DCQ11kZJi4957mxg/vtX9fgkDIYS3kkA4Ty5X+/TQ4uJgPv44mN27g2hrU9i//ziRkRqLFjWQldVAUpJNZggJIXyKBEIXHD2qIzLShcGg8fLLoTz2WD8A4uLs3HVX+1hAcHD7wHBKis2DLRVCiAsngXAWdXUKJSXt4wA7d7aPA+TmWrntthbGj28jJ8fK//t/bQwcKNNDhRD+o9NAyM/P57PPPiMyMpLs7GwAGhsbycnJ4eTJk0RHR7No0SIMBgMAW7ZsobCwEFVVycjIIDExEYDy8nLy8vKw2WwkJSWRkZGBoijY7XZyc3MpLy8nPDyczMxMYmJieq7is2hrg/p6lehoFydOqCQn98flUggNdZGa2j4OcPXV7Uf+l17qZMqUll5tnxBC9IZO76AyZswYHnvssdO2bd26lREjRrB27VpGjBjB1q1bAaioqKCkpITVq1ezZMkSNm7ciMvVfhS9YcMG5syZw9q1azl+/Dj79u0DoLCwkLCwMNatW8fEiRPZvHlz91Z4Fi4XHDgQwPr1YUybZmL48AEsXRoJQP/+LpYsqeett6o5cOA4mzZZ+PWvm7j0UrlGQAjh3zoNhOHDh7uP/k8pLS0lLS0NgLS0NEpLS93bR40aRWBgIDExMQwYMICysjKsVistLS0MGzYMRVEYPXq0e5+9e/cyZswYAFJSUti/fz+a1rM3dpkyxcyECTE8+WQkR4/quPvuZqZObXa/PnduE9deK4PCQoi+5YLGEOrq6jAajQAYjUbq6+sBsFgsxMfHu99nMpmwWCzodDrMZrN7u9lsxmKxuPc59ZpOpyM0NJSGhgYiIiLO+H8LCgooKCgAYOXKlURFRX1fSEDAac/PZe5clVmzHIwd6yI2FiDou3/hXf0W9Lrzqc/XSG2+y5/r8+faOtKtg8odHdmf64j/bK8pinLW96anp5Oenu5+Xl1d7X4cFRV12vNz+cGXoIu7eNz51OdrpDbf5c/1+WttAwcO7PC1C7oLe2RkJFarFQCr1eo+mjebzdTU1LjfZ7FYMJlMZ2yvqanBZDKdsY/T6aS5ufmMLiohhBA974ICITk5maKiIgCKioq4+uqr3dtLSkqw2+1UVVVRWVlJXFwcRqMRvV7PwYMH0TSN4uJikpOTARg5ciQ7duwAYPfu3SQkJHR4hiCEEKLndNpltGbNGj7//HMaGhqYO3cuU6ZM4dZbbyUnJ4fCwkKioqLIysoCYNCgQaSmppKVlYWqqsycORNVbc+cWbNmkZ+fj81mIzExkaSkJADGjRtHbm4uCxcuxGAwkJmZ2XPVCiGE6JCi9fSUnh507Ngx92N/7e87xZ/rk9p8lz/X56+1dfsYghBCCP8jgSCEEAKQQBBCCPEdCQQhhBCAjw8qCyGE6D5+c4awePFiTzehR/lzfVKb7/Ln+vy5to74TSAIIYS4OBIIQgghAD8KhB8ueueP/Lk+qc13+XN9/lxbR2RQWQghBOBHZwhCCCEujgSCEEIIoJtvkNPd8vPz+eyzz4iMjCQ7OxuAI0eOsGHDBlpbW4mOjuaBBx4gNDSUqqoqFi1a5F64KT4+ntmzZwNQXl5OXl4eNpuNpKQkMjIyPL7E9vnUBvD111/zwgsv0NLSgqIorFixgqCgIJ+vbefOnbzzzjvufb/55hueeuophgwZ4pW1wfnV53A4WL9+PYcPH8blcjF69Ghuu+02wPd/Lh0OBy+88AKHDh1CVVVmzJhBQkIC4J21VVdXk5eXR21tLYqikJ6ezk033URjYyM5OTmcPHmS6OhoFi1a5L4ny5YtWygsLERVVTIyMkhMTAS8s75uoXmxAwcOaIcOHdKysrLc2xYvXqwdOHBA0zRN2759u/anP/1J0zRNO3HixGnv+6HFixdrX375peZyubTly5drn332Wc83vhPnU5vD4dAefPBB7fDhw5qmaVp9fb3mdDrd+/hybT/09ddfa/fff/9p+3hbbZp2fvXt3LlTy8nJ0TRN01pbW7X58+drJ06ccO/jbfWdT20ffPCBlpeXp2maptXW1moPP/ywV/9cWiwW7dChQ5qmaVpzc7P2wAMPaN9++6328ssva1u2bNE0TdO2bNmivfzyy5qmadq3336r/eY3v9FsNpt24sQJbcGCBV5dX3fw6i6j4cOHn3H3tGPHjnH55ZcDcOWVV/Lpp5+e82tYrVZaWloYNmwYiqIwevRoSktLe6zNXXU+tf39739n8ODBDBkyBIDw8HBUVfWL2n7o448/5rrrrgO893OD86+vtbUVp9OJzWYjICCA0NBQr63vfGqrqKjgiiuuANrvohgWFkZ5ebnX1mY0Ghk6dCgAer2e2NhYLBYLpaWlpKWlAZCWluZua2lpKaNGjSIwMJCYmBgGDBhAWVmZ19bXHbw6EM5m0KBB7N27F2i/w9oPb81ZVVXFww8/zLJly/jXv/4FtN/G02w2u99jNpuxWCy92+gu6qi2yspKFEVh+fLlPPLII7z99tuAf9T2Q5988ok7EHypNui4vpSUFEJCQpg9ezbz58/nlltuwWAw+FR9HdU2ZMgQ9u7di9PppKqqivLycqqrq32itqqqKg4fPkxcXBx1dXUYjUagPTTq6+uBM38GTSYTFovFJ+q7UF49hnA28+bN46WXXuLNN98kOTmZgID2EoxGI/n5+YSHh1NeXs6qVavIzs5G86FZtR3V5nQ6+eKLL1ixYgXBwcE88cQTDB06FL1e7+EWd11HtZ3y1VdfERQUxODBgwF86nODjusrKytDVVWef/55mpqaWLp0KSNGjPCp+jqqbezYsVRUVLB48WKio6P5yU9+gk6n8/raWltbyc7OZsaMGe4xurPpqA5vr+9i+FwgxMbG8vjjjwPtp7KfffYZAIGBgQQGBgIwdOhQ+vfvT2VlJWaz+bSj0ZqaGkwmU+83vAs6qs1sNjN8+HAiIiIASEpK4vDhw1x//fU+X9spu3btcp8dAD71uUHH9X388cckJiYSEBBAZGQkP/nJTzh06BCXX365z9TXUW06nY4ZM2a43/f4449zySWXEBYW5rW1ORwOsrOzuf7667n22muB9u4uq9WK0WjEarW6f8/+/WfQYrFgMpl87mfzfPhcl1FdXR0ALpeLt956i5///OcA1NfX43K5ADhx4gSVlZX0798fo9GIXq/n4MGDaJpGcXExycnJHmv/uXRU289+9jO++eYb2tracDqd/Otf/+LSSy/1i9pObdu9e/dpgeBLtUHH9UVFRbF//340TaO1tZWvvvqK2NhYn6qvo9ra2tpobW0F4B//+Ac6nc6rfy41TWP9+vXExsZy8803u7cnJydTVFQEQFFREVdffbV7e0lJCXa7naqqKiorK4mLi/Pa+rqDV1+pvGbNGj7//HMaGhqIjIxkypQptLa2sm3bNgCuueYa7r77bhRFYffu3bz++uvodDpUVeWOO+5wf0iHDh0iPz8fm81GYmIiv/rVrzw+Rex8agMoLi5m69atKIpCUlIS99xzD+AftR04cIBXXnmF5cuXn/Z1vLE2OL/6Wltbyc/Pp6KiAk3TGDt2LJMmTQK8s77zqa2qqorly5ejqiomk4m5c+cSHR0NeGdtX3zxBUuXLmXw4MHutkydOpX4+HhycnKorq4mKiqKrKws98D6W2+9xUcffeSeVpuUlAR4Z33dwasDQQghRO/xuS4jIYQQPUMCQQghBCCBIIQQ4jsSCEIIIQAJBCGEEN+RQBBCCAFIIAghhPjO/wfsW2XaySXSKwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "years = data.columns\n",
    "gdp_australia = data.loc['Australia']\n",
    "plt.plot(years, gdp_australia, 'b--')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "572b2994-09d3-436f-9f8f-6deada5ce7f7",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Plot multiple datasets on the same graph by following the steps above for both Australia and New Zealand. Include axis labels and a legend. Plot New Zealand with a green dashed line and Australia a red dashed line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "94fa587e-a8e2-427b-a403-ebe613b21e46",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2023-10-23T10:34:26.590850Z",
     "iopub.status.busy": "2023-10-23T10:34:26.590461Z",
     "iopub.status.idle": "2023-10-23T10:34:26.724950Z",
     "shell.execute_reply": "2023-10-23T10:34:26.723549Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "clear_answer_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'GDP per capita ($)')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the dataset for New Zealand. \n",
    "gdp_nz = data.loc['New Zealand']\n",
    "\n",
    "# Plot the lines with differently-coloured markers.\n",
    "plt.plot(years, gdp_australia, 'r--', label='Australia')\n",
    "plt.plot(years, gdp_nz, 'g--', label='New Zealand')\n",
    "\n",
    "# Create the legend and add labels\n",
    "plt.legend(loc='upper left')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('GDP per capita ($)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8de7333-337c-4791-9667-df008c97d9b7",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Create a scatter plot using `plt.scatter()` to compare the GDP of Australia and New Zealand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1094947a-0858-4283-a54a-5203321950f9",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2023-10-23T10:34:26.731669Z",
     "iopub.status.busy": "2023-10-23T10:34:26.731103Z",
     "iopub.status.idle": "2023-10-23T10:34:26.912236Z",
     "shell.execute_reply": "2023-10-23T10:34:26.911224Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "clear_answer_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'GDP New Zealand')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(gdp_australia, gdp_nz)\n",
    "plt.xlabel('Australia')\n",
    "plt.ylabel('GDP New Zealand')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0b7be4f-86b0-4537-aaa1-ca1dc0e9e826",
   "metadata": {},
   "source": [
    "## 5. Extension activity: Customise a plot and save it."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "023a55ae-e5f9-4a2a-823f-44fd5d0c8e4e",
   "metadata": {},
   "source": [
    "1. Create an x axis using `x = np.arange(0, 5, 0.1)`. Print it out to see what this line of code does."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "011ef001-1c61-4cf9-89a8-958b68bc7d06",
   "metadata": {},
   "source": [
    "_Hint: Remember to import `numpy` as `np`._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e3fabef6-1199-43a0-892a-cbd1d9b5a5ae",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2023-10-23T10:34:26.918281Z",
     "iopub.status.busy": "2023-10-23T10:34:26.917922Z",
     "iopub.status.idle": "2023-10-23T10:34:26.924073Z",
     "shell.execute_reply": "2023-10-23T10:34:26.923209Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "clear_answer_cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.  0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.  1.1 1.2 1.3 1.4 1.5 1.6 1.7\n",
      " 1.8 1.9 2.  2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.  3.1 3.2 3.3 3.4 3.5\n",
      " 3.6 3.7 3.8 3.9 4.  4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.arange(0, 5, 0.1)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb0ebb62-eea4-4baa-af87-9741eaacebcd",
   "metadata": {},
   "source": [
    "2. Create a y axis with values $sin(x)$ using a relevant function from `numpy` with the x values from the previous question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4bcdb740-210f-4304-b551-67471bf98e0d",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2023-10-23T10:34:26.929114Z",
     "iopub.status.busy": "2023-10-23T10:34:26.928891Z",
     "iopub.status.idle": "2023-10-23T10:34:26.934265Z",
     "shell.execute_reply": "2023-10-23T10:34:26.932913Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "clear_answer_cell"
    ]
   },
   "outputs": [],
   "source": [
    "y = np.sin(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b8a3d70-097d-4b5c-9ec5-df4088e3aef4",
   "metadata": {},
   "source": [
    "3. Use `pyplot` to plot these values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1b9a20ad-17e3-425f-a105-9bb1a4b9dafb",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2023-10-23T10:34:26.937452Z",
     "iopub.status.busy": "2023-10-23T10:34:26.937111Z",
     "iopub.status.idle": "2023-10-23T10:34:27.053409Z",
     "shell.execute_reply": "2023-10-23T10:34:27.052570Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "clear_answer_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7f3493a170>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d48c4111-b92e-4c90-a9f7-40a4c8ddb4d5",
   "metadata": {},
   "source": [
    "4. Plot a cos graph on the same graph as the sin curve. Add a legend and axis labels. Customise the colours too if you like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5bcb653e-bbc4-4f1c-af03-5e5961b55cb6",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2023-10-23T10:34:27.058912Z",
     "iopub.status.busy": "2023-10-23T10:34:27.058691Z",
     "iopub.status.idle": "2023-10-23T10:34:27.306970Z",
     "shell.execute_reply": "2023-10-23T10:34:27.306081Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "clear_answer_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'y')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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w8ECpVNK0aVNiYmKeu/zhw4dp1qxZKSY0TQPrDGTmWzP5+ebPTD48+ZmXDeflwejRKvbutSU0NOW5hUQomv/t8urXTy3G8xLMllF0cyUmJqLRaPIfazSa/HmK/y07O5szZ84waNCgJ54PCQkBoF27dvj6+j5z3ejoaKKjowEICwvDza1ol7oqlcoir1vaJreZTJY8i7AjYXipvAhuHZz/mk4HQ4cq+PFHBWFhWsaOtQee/83KlNpdkorbbjc3iI7W06EDDByoITpaS4MGkncIFIjY5wX34MEDlEqj+Egtlv9tg42NTYF/DkbR8md9Y37eFQknT56kZs2aODo65j8XHByMWq0mJSWFOXPmULZsWWrXrv3Uuv/cyPOPoh6+m9qh/6jao7ideJt5R+dho7Nh2OvD0OshKMiFL76wZvz4VPr2TedlTTK1dpeUkmi3XA4bN8rp0sWNzp3lbNsWT6VKxjvh2T/EPi+4rKwsFCY+WN2/u7mysrKe+jkYdTeXRqMhISEh/3FCQgIqleqZyx4+fJjmzZs/8dw/11S7uLjQsGFDYmNjDRfWBMlkMuY2m0unyp0IPhbM5mubCQtzYv16B4YNS2fs2HSpI1oEDw8dX32VgFb7+Kq5+Hij+PMTSohcLn/ig9jUabVa5IW4wcwojkyqVq3KvXv3iIuLQ61Wc+TIEfz9/Z9aLiMjg0uXLjF69Oj857KystDr9djZ2ZGVlcW5c+fo0aNHacY3CQq5gog2EaTmpDLhQAC6X6rQp48vQUGpFPNmeaEQqlXLY8OGBD74QEP//mq+/TYBBwfT6PISXszW1pasrCyys7OLPQKFVGxsbMjOzkav1yOXy7G1tS3wukZRTBQKBQMHDiQkJASdTkebNm0oX748e/bsAaB9+/YAHD9+nDfeeOOJBqakpLBgwQIA8vLyaN68OfXq1Sv1NpgCG4UNfa2/4ODtD5B/8AGdO36FTNZE6lgWx9s7l88+S2LQIDXDhqlYty4RKzG9vMmTyWTY2dlJHaNYitOtaRSXBkvF3C8N/rdLl5T4+blRrkYc2r4tSch+yI6uO6jiUqVA65tqu4vLUO3+6it7Jk505f33M1i8ONkojxDFPrcsBWm3UZ8zEQzv/n05/fppcHLS89Xn8OU7G5DL5AzYPUCSGRsF+OijDMaNS+O77+wJD3eSOo4gFIsoJhYgI0PGgAFqUlJkbNiQwCuv6KjoXJE1vmv4O+1vhu0dVqhhV4SSM25cGh999Ihly5z4/nvT7iIRLJsoJmYuLw9GjnTl4sXHw6TUqfPfq00av9KY8BbhHLpziBlHZ0iY0nLJZBASkkKTJtlMnOjK6dPi5IlgmkQxMXOzZzuzZ48dwcEp+PpmP/X6BzU+YPjrw9lwaYNBZ2sUns/KClavTqJMmTwGD1aL2RoFkyR+a83Ypk12rFnjyKBB6QwYkPHc5SY3nEz7iu2ZfnQ6B24fKMWEwj/Uah3r1iWSmvp4CoAsMc6mYGJEMTFTZ89aMWWKKy1aZDN9euoLl1XIFSxrvYwaqhoM2zuM2GRx06cUatfWsnRpMqdPWxMY6IrlXmcpmCJRTMxQQoKcwYNVuLvnsWJFEgUZLsjR2pH17ddjrbCm/+7+pOa8uAAJhvHuu1n5V3h9/rmD1HEEocBEMTEzWi0MH64iMVHBmjVJqNUFn763nFM5Pvf9nFtptxh3YNwzx0wTDG/s2DTeeSeT4GBnDhx48Tw0gmAsRDExM2Fhzhw+bENoaDJ16xb+ct9Gno0IahzEzj93sur8KgMkFF5GLoelS5OpWVPL8OEq/vzTtAcPFCyDKCZmZMcOWz77zJH+/R/Rs2fR5yX5pM4ndKzckbnH53L03tESTCgUlIODnnXrHk+9OmyYSpyQF4yeKCZm4soVJePGufLmmznMnJlSrPeSyWQsbLmQSs6VGL53OA8yHpRQSqEwKlTIY8mSJM6ft2b2bBep4wjCC4liYgZSU2UMHqzGwUHP6tWJWJfAZH5O1k587vs56bnpDN87XNwhL5H27bMZPjydDRsc2Lat4CO4CkJpE8XExOn1EBDgyt9/K1i1KglPz4KfcH+ZmuqazGsxj2P3jxEWE1Zi7ysUzqRJqTRsmE1AgCuxseL8iWCcRDExcRs32vPjj3ZMnJhG48Y5Jf7+3ap1Y0DtAaw8t5KtV7eW+PsLL2dlBStWJGFjo2fYMDWZmUY4vLBg8UQxMWGXLyuZOdOFVq2yGDHCcLMlTn9rOvXL1Gfwj4P5M/VPg21HeL6yZXVERCRz5YqSoCBnqeMIwlNEMTFRGRkyhg9X4eysY+nSZAoxu2ah2ShsWOWzCoVcwah9o8T5E4m0aZONv386mzY5sHmzGGFYMC5GMdMiwJkzZ4iKikKn0+Hj44Ofn98Tr1+8eJF58+ZRpkwZABo3bpw/Pe/L1jVH06Y5Exur5JtvEnB3L7nzJM/j5ejFZ+98Ru8ferPgxAImN5ps8G0KTxs/Po2YGGumTHHhjTdyefVV85lzXDBtRlFMdDoda9euJSgoCI1Gw+TJk/H29qZcuXJPLFerVi0CAwOLtK45+eEHOzZtcsDfP40WLUr+PMnzdHu1Gx+9+hGRZyNp7tWcFl4tSm3bwmMKBURGJtGunTsjR6r46aeHFGKabkEwGKPo5oqNjcXT0xMPDw+USiVNmzYlJibG4Ouaops3FUya5EKjRtmMH59W6tuf1WQW1Vyr8en+T0nITCj17QtQpoyOxYuTuXLFirlzxfkTwTgYxZFJYmIiGo0m/7FGo+H69etPLXft2jUCAgJQqVT07duX8uXLF3hdgOjoaKKjowEICwvDzc2tSHmVSmWR1y2O7GwYPVqJjY2Mr7+W4elZuhmUSiXlPcvzdbevabahGYG/B7KlxxZkxjh5eQmSan+/SM+e8PvveURGOtKliw1vv22YcdSMse2lQbS7COuWcJYiedaAgv/+gKpcuTIrVqzA1taWU6dOMX/+fCIiIgq07j98fX3x9fXNfxwfH1+kvG5ubkVetzhmzXLm9GlroqISsLPLprQj/NPusoqyTGs0jWlHpzH/wHwG1hlYukFKmVT7+2XGjYO9e90ZPFhOdPRD3NxK/tyZsbbd0ES7n69s2bLPfN4ourk0Gg0JCf/tMklISEClUj2xjL29Pbb/3zncoEED8vLySE1NLdC65uC336xZvfrxuFvt2z89Y2Jp+/i1j/Gt4Muc43O4mHBR6jgWydYWli9PIjVVzrhxYv4TQVpGUUyqVq3KvXv3iIuLQ6vVcuTIEby9vZ9YJjk5Of8oJDY2Fp1Oh5OTU4HWNXXJyTLGjFFRtWou06YZxzwjMpmMRS0X4Wrjyoh9I8jUFn1gSaHoatXSMnVqKnv32rJhg73UcQQLZhTdXAqFgoEDBxISEoJOp6NNmzaUL1+ePXv2ANC+fXt+//139uzZg0KhwNramjFjxiCTyZ67rjmZOtWFhw/lbN+eiJ2d8Xz91NhpWNp6Kb1+7kXIsRDmNJsjdSSLNHDgI3791YbgYBeaNMmhZk1xubBQ+mR6C54B6e7du0VarzT7U7dutWPkSBUBAamMGWO4u9wL4nntnnF0BmsurOGbd76hZbmWEiQzLFPoP3/4UI6Pjztlyuj48ceSu1zYFNpuCKLdz2fU50yEZ7tzR86UKS68+WYOo0ZJW0heJLBhINVdqzP24FiSs5OljmOR3N11LFqUzOXLVoSFicuFhdIniomR0ulg7FgVubkQEVGwedylYqe0I6J1BPEZ8Uw7Mk3qOBbL1zebfv0esWaNA0ePlsA8BIJQCKKYGKk1axw4fNiGWbNSqVQpT+o4L/W6++uMaTCGLbFb2PHHDqnjWKygoFQqVsxj3DhX0tPN+/4fwbiIYmKErlxREhbmTPv2mfTunSF1nAIbXW809d3rE/hboJidUSIODnoWL07m1i0FwcGiu0soPaKYGJncXPD3V+HkpGP+/BRM6eZypVzJktZLyNJmMeHghGfeUCoYXqNGOQwd+oiNGx3Yv99G6jiChRDFxMgsW+bIxYtWzJuXYpA7mg2tmms1ghoHse/WPr668pXUcSxWQEAq1avnMn68K8nJJvSNRDBZopgYkQsXlCxd6kS3bhl06JAldZwi61+7Py28WjDr91liMi2J2NrC0qXJPHwoZ/p0F6njCBZAFBMjkZMDY8aoUKt1zJ6dInWcYpHL5CxsuRClXMn4A+PR6U3vCMscvPFGLv7+6fznP/bs3CnGqRcMSxQTI7F0qROXL1sRHp6MSmX65xq8HL2Y+dZMfr//OxsubZA6jsXy90+jTp0cJk1yISFB/LkLhiN+u4zA+fNWLFvmSPfuGUYxiGNJ6VmjJ23KtSHkeAh/pf4ldRyLZG0NS5Ykk5YmJzDQRQwGKRiMKCYSy86GsWNdcXMz/e6tf5PJZIS3CEchUzD+oOjukkqtWlrGj0/j55/t+PFH0d0lGIYoJhJbsuS/3Vuurub3tdHL0YsZb83g6L2jfHn5S6njWKxhw9J5/fUcpk51ITFR/NkLJU/8Vkno7FkrIiMdef/9DNq1M5/urX/rXbM3Lb1aEnI8hFtpt6SOY5GUSli0KJnUVDnTp4ubGYWSJ4qJRP7p3nJ31zFrlnl1b/2bTCZjfov5AOJmRgnVqqXF3z+NH36wZ88ecTOjULJEMZHI8uVOXL36uHvLxcX8P1zLOZUjqFEQv939TdzMKKFRo9KpVSuXwEBXUlLEzYxCyTGasWjPnDlDVFQUOp0OHx8f/Pz8nnj90KFDbNu2DQBbW1sGDx5MpUqVABg5ciS2trbI5XIUCgVhYWGlnL5wLl9WsmyZI926ZeDra77dW//Wp1Yffrz5I8HHgmlTvg1ejl5SR7I41taPu7s6dXJj9mxnFi4076NiofQYxZGJTqdj7dq1TJkyhcWLF3P48GFu3779xDJlypRh5syZLFiwgO7du7N69eonXp8xYwbz5883+kKSlwcTJrji5KRj1izjmIK3tMhlcha0WIBOr2PioYmiu0sir7+ey/Dh6Wza5MCBA6K7SygZRlFMYmNj8fT0xMPDA6VSSdOmTYmJiXlimZo1a+Lo6AhA9erVSUhIkCJqsa1Z48CZM9bMmZOCWm15l8pWcK5AYMNA9t/ez39i/yN1HIs1dmwa1arlEhDgIoaqF0qEUXRzJSYmotFo8h9rNBquX7/+3OX37dtH/fr1n3guJCQEgHbt2uHr6/vM9aKjo4mOjgYgLCwMNze3IuVVKpVFWvfGDZg/34qOHXUMHOiITOZYpO1Lpajt/rcJLSfw018/Mev3WXR7vRtlHMqUQDrDKal2G5u1a6F1awWLFpUhIuLZc+aYa9tfRrS7COuWcJYieVZ3h+w5Y69fuHCBX3/9ldmzZ+c/FxwcjFqtJiUlhTlz5lC2bFlq16791Lq+vr5PFJqizvFclPmh9XoYPFiDUqln5sw4EhJM76ikJOfFDm0SSocfOjDqp1GsaLuiRN7TUMx1PvBq1WDwYGdWrXKkQ4ckGjfOeWoZc237y4h2P59RzwGv0Wie6LZKSEhApVI9tdxff/3FqlWrCAgIwMnJKf95tVoNgIuLCw0bNiQ2NtbwoQvpm2/sOXLEhqCgVMqWNb1CUtJqqmviX8+fbTe28ctfv0gdx2JNnJhG+fJaAgJcyDLdgaoFI2AUxaRq1arcu3ePuLg4tFotR44cwdvb+4ll4uPjWbBgAaNGjXqiMmZlZZGZmZn/73PnzlGhQoVSzf8y9+7JmT3bmaZNs/noI9OZOdHQRtUbRU1VTSYfnkxaTprUcSySvb2e8PAUbtywIiLC6eUrCMJzGEU3l0KhYODAgYSEhKDT6WjTpg3ly5dnz549ALRv357vv/+e9PR01qxZk79OWFgYKSkpLFiwAIC8vDyaN29OvXr1pGrKU/R6mDLFhdxcmDcv2aRmTjQ0a4U181vMp+v2rsw9PpfQ5qFSR7JIrVpl06NHBpGRjnTunEmtWlqpIwkmSKa34Osz7969W6T1CtOfumOHLcOGqZk2LYVhwx4VaXvGwlD9yNOPTmfthbVs6bSFxq80LvH3Ly5L6D9PTJTRunUZKlTIY9u2eBSKx89bQtufxZzbfTXxKjXVNZ/5msmfMzFXyckypk1z4fXXcxg82LQLiSFN8p5EOcdyTDg0gSyt6LiXglqtZ/bsVE6ftiYqykHqOIKBHLx9kLb/actPN38q8fcWxcSAQkKcSUyUM39+Mkqj6FA0Tg5WDsxrMY8/Uv5gyeklUsexWF27ZtK2bRbh4U7cvq2QOo5QwjK1mQT+FkgVlyr4lPcp8fcXxcRAjh615uuvHRgy5BF16og+6JdpVa4VPar34LOzn3E58bLUcSySTAahoY+HV5k8WUykZW4WnVzEX2l/Ma/FPGyVJT+vjSgmBpCVBRMnulKx4uNJiYSCmfHWDJxtnAk4FECe7tk30QmGVa5cHoGBaezbZ8vWrXZSxxFKyIWEC6w6v4reNXvT5JUmBtmGKCYGEBHhxB9/KAkLS8HOTny9Kyi1rZqZb83kdNxpvrj8hdRxLNaAAY+oXz+H6dOdMdNz0BYlT5fHxIMTUduqCWocZLDtiGJSwq5cURIZ6UiPHhm0bGk5IwKXlG7VutHKqxWhMaHcSb8jdRyLpFDAggWPJ9IKDBTnTkzduovrOBt/lllNZuFq42qw7YhiUoLy8iAgwBVnZx0zZljWiMAlRSaTEdo8lDxdHkFHgsTIwhJ59VUtI0ak8+WXCg4dspY6jlBEt9NuM+/EPHzK+9ClSheDbksUkxL0xRf2nDplzcyZqRY5InBJqehckQlvTmDPX3vY+edOqeNYLH//NKpW1RMY6Mr/DzIhmBC9Xs/kw5MBCG0e+tzxDkuKKCYl5O5dOaGhzrRunUW3buIvr7g+qfsJr2leI+hIEKk54ihPCnZ2EBmp5c8/lSxZIoZaMTXb/9jOvlv7mNRwUqlMRCeKSQkJCnIhL+/xpZViyJTiU8qVzGsxj4eZDwk9LoZZkUqbNnp69sxg5UpHLl8WN0uZiqSsJKYfnU4993p8XPvjUtmmKCYlYOdOW3bvtmPChDQqVBCXtJaUeu71GPjaQL64/AUx92NevoJgENOmpeDsrCMgwJU88ettEkKOh5CUlcS8FvNQyEvnIgpRTIopLU1GUJALtWvniiFTDGCi90S8HL2YeGgiOXlPz7chGJ5arWfmzMdDrXz5pb3UcYSX+P3e73xz9RuG1h3Ka5rXSm27opgUU1iYMw8ePB4yxcpK6jTmx8HKgbnN5nIt+Rorzhr3JFrmrFu3TFq2zCI01Jl798THhrHKzstm4qGJVHCqwLg3x5XqtsVvRTGcPGnFhg32DBz4iHr1cqWOY7Z8K/jSqXInIs5EcCP5htRxLNI/Q61otY8HLxWMU+SZSG6k3CC0WSh2ytIdwUAUkyLKzYVJk1zx9NQxcaIYMsXQZjedjY3ChsDfAsW9JxKpVCmPcePS2LnTjl27Sn5sJ6F4YpNjWXZmGX5V/WhdvnWpb18UkyJavdqRy5etCAlJwdFRfLgZmoe9B5MbTubIvSN8d/07qeNYrCFD0qlVK5epU11ISxOXLRoLnV7HpEOTsFPaMfOtmZJkMJpicubMGT799FNGjx7N1q1bn3pdr9ezbt06Ro8ezYQJE/jjjz8KvG5Ju3EDFi1y5J13MunQQcy/UVr61OqDt4c3s3+fTWJWotRxLJKV1eMZQx88kDNvnrj3xFhsvrqZ3+//TlDjINzt3SXJUOBismHDBv7880+DhNDpdKxdu5YpU6awePFiDh8+zO3bt59Y5vTp09y/f5+IiAiGDBmSP31vQdYtSXo9jB6tRKmE4OAUg21HeJpcJie8eThpOWnM+n2W1HEsVoMGufTvn0FUlAOnT4urTqQWnxnPnONzaOzZmF41e0mWo8DFJC8vj5CQEMaPH8/WrVtJSEgosRCxsbF4enri4eGBUqmkadOmxMQ8eV/BiRMnaNmyJTKZjBo1avDo0SOSkpIKtG5J+uEHO/bulRMYmMorr4ghU0rbq+pXGf7GcL6//j2H7hySOo7FCgxMxcNDx8SJruSKa08kNfPoTDJyMwhvHo5cJl1nU4FvaR04cCADBgzg9OnTHDp0iC1btlC9enVatmxJ48aNsbUt+gm5xMRENBpN/mONRsP169efWsbNze2JZRITEwu07j+io6OJjo4GICws7In3K6iEBDnNmukZN84ehcKyrrlXKpVF+pmVtGDfYH7+62emHp3KyUEnsbMy7FUrxtJuKTyv7W5usHSpjg8+sOLrr8swfrx5fbEylX3+yx+/8MONHwhqHkST6sWfp6Q47S7U+AhyuZw333yTN998k1u3bhEREcGKFStYs2YNzZo1o2fPnqjV6kKHeNbVOf8elOx5yxRk3X/4+vri6+ub/zi+CJM1fPIJBAS4kZRkeRM9uLm5FelnZgghTULo9XMvpkdPZ1LDSQbdljG1u7S9qO3NmkH79ipmz7ahTZsEsxr9wRT2eaY2kxE/j6CqS1UG1hhYInkL0u6yZcs+8/lCHRNlZGSwb98+Zs2axYwZM6hWrRqzZs1i8eLF2NraMnfu3MK8XT6NRvNEt1lCQgIqleqpZf63kf8sU5B1S5qYz116Lbxa0L1ad1acXcHVxKtSx7FIMhnMmZOCQgFTpohpfkvb4lOL+Tvtb8JbhGOjsJE6TsGLycKFCxk2bBjHjh2jXbt2rFq1iqFDh/Lqq6/i5uZGv379iIuLK1KIqlWrcu/ePeLi4tBqtRw5cgRvb+8nlvH29ubgwYPo9XquXbuGvb09KpWqQOsK5mnGWzNwsnZi4m8T0enNq5vFVHh5Pb7P6tdfbdm+Xdx7UlouJVxi5bmV9KrRy2DT8BaWTF/AO8C2b99Oy5YtcXV1fe4y2dnZ2NgUrUKeOnWKDRs2oNPpaNOmDd26dWPPnj0AtG/fHr1ez9q1azl79izW1taMGDGCqlWrPnfdgrh7926RsprCIbAhGGO7N1/bzLgD4whrHkbfWn0Nsg1jbHdpKUjb8/Kgc2c37txRcOBAHK6upn+IYsz7PE+XR9cdXfk79W8OvH8AlW3J9cQUp5urwMXEHIliUjjG2G69Xk/Pn3pyIeEC+9/fj4e9R4lvwxjbXVoK2vYLF5S8+647vXplMG+e6V8yb8z7POpiFEFHgljeZjnvVXuvRN+71M6ZCIKxkclkhLcIJzsvm+lHpksdx2LVqaNl8OBHfPWVA8eOiWl+DeXeo3uExYTRyqsVflX9pI7zBFFMBJNXxaUKn9b/lB9v/sgvf/0idRyLNWFCGuXKaZk40YXsbKnTmKdpR6ah1WlLZRrewhLFRDALw18fTk1VTaYcnsKjXDGvjBTs7fWEhqYQG2tFZKSj1HHMzu4/d7Pzz52MazCOis4VpY7zFFFMBLNgrbAmvHk4dx/dZd6JeVLHsVht22bj55fBsmVOXL8urqEvKek56Uw9MpVa6loMeX2I1HGeSRQTwWw09GxI31p9WXdxHWcfnpU6jsWaOTMVe3s9kya5oBNXbJeIeSfmcf/Rfea1mIeV3DjHQxPFRDArkxtOxs3WjYBDAWh1WqnjWCR3dx3TpqVw7JgN33xjWUMOGcKpuFOsu7iOAbUH0KBMA6njPJcoJoJZcbFxIbhpMBcTLrLmwhqp41isDz7IpEmTbObMcSYuTnzMFFVOXg4TD03E08GTwIaBUsd5IbGXBbPTsXJH2lVox/wT8/kr9S+p41gkmQzCw5PJzpYxY4aY5reoPjv3GZcTLzO32VwcrY37ogZRTASzI5PJCGkWgkKuENP8Sqhq1Tz8/dPYvt2O6Gjpx44yNTeSb7D09FI6Ve5E+4rtpY7zUqKYCGbJy9GLKQ2ncPDOQb6//r3UcSzWiBHp1KiRy+TJLqSnG9d9EcZMp9cx6bdJ2CpsCW4aLHWcAhHFRDBb/Wr3480ybzLz95nEZxrn0Bjmztoa5s9P5t49BeHhYprfgtp0dRNH7x0lqHEQZezLSB2nQEQxEcyWXCZnfov5PMp9xMyjM6WOY7G8vXMZMOARUVEOnDhhnJe1GpMHGQ+Yc2wOTV5pQu+avaWOU2CimAhmraa6JqPrjeaHGz+w79Y+qeNYrMDANF55JY+AAFdycqROY9ymHZlGVl4W4c3DjW7IlBcRxUQwe6PqjaK6a3UCfwsUQ61IxNFRT1hYCteuWbF8uXFflSSlPX/t4aebPzGm/hiqulaVOk6hiGIimD0bhQ3zW8znTvodwk+ESx3HYvn4ZPPeexlERDhx7ZoYauXf0nLSmHx4MrXUtRj+xnCp4xSaKCaCRWjo2ZB+tfqx7sI6TsWdkjqOxZo1KxVHRx0TJriSZz5TxpeIOcfmEJcRZ9RDpryI5F8P0tPTWbx4MQ8fPsTd3Z2xY8fi6PjkYXB8fDyRkZEkJycjk8nw9fXl3XffBeDbb79l7969ODs7A9C7d28aNDDeIQcE6UxpNIU9f+8h4GAAO9/bibVCzLtR2jQaHbNmpeLvr+KLL+z5+OMMqSMZhSN3j7DxykaG1B1i1EOmvIjkRyZbt26lbt26REREULduXbZu3frUMgqFgr59+7J48WJCQkLYvXs3t2/fzn+9Y8eOzJ8/n/nz54tCIjyXk7UTYc3CuJJ0hWVnlkkdx2J165ZJ69ZZhIY6c+eOQuo4ksvUZhJwKICKThWZ6D1R6jhFJnkxiYmJoVWrVgC0atWKmJiYp5ZRqVRUqVIFADs7O7y8vEhMTCzVnIJ5aFexHe9VfY+I0xFcSrgkdRyL9HiolRT0eggMdMHSByhYcHIBf6b+yfyW87FT2kkdp8gk7+ZKSUlBpVIBj4tGamrqC5ePi4vj5s2bVKtWLf+53bt3c/DgQapUqUK/fv2e6ib7R3R0NNHR0QCEhYXh5uZWpMxKpbLI65oyc2n38k7L+e3z35h0ZBKH+h9CKX/xn4G5tLsoDNV2NzeYPVvHhAm27NlTho8+Mq6x6ktrn5+4e4LV51czqN4gur7e1eDbe5nitFumL4WBi4KDg0lOTn7q+V69ehEZGcn69evzn/v444+Jiop65vtkZWUxY8YMunXrRuPGjQFITk7OP1+yefNmkpKSGDFiRIFy3b17t3AN+X9ubm7Ex1veHdXm1O7tN7YzfN9wpjaayog3Xvz7Yk7tLixDtj0vD7p1cyM2Vsm+fXF4eBhPQSmNfZ6Tl8M7P7xDck4yv/b4FWdrZ4NuryAK0u6yZcs+8/lSOTKZNm3ac19zcXEhKSkJlUpFUlJSfmH4N61Wy8KFC2nRokV+IQFwdXXN/7ePjw/h4eLST+HlOlfpzPY/trPg5ALaV2xPNddqL19JKFEKBSxcmESHDmWYMsWFNWuSMKF79Ipt+ZnlXEm6wvr2642ikBSX5OdMvL29OXDgAAAHDhygYcOGTy2j1+tZuXIlXl5edOrU6YnXkpKS8v99/Phxypcvb9jAgln4Z2RhW4UtEw5OQKc3nm/FlqRatTwmTEhl1y47tm+3lTpOqbmSeIWIMxG8V/U92lVsJ3WcEiF5MfHz8+PcuXP4+/tz7tw5/Pz8AEhMTCQ0NBSAq1evcvDgQS5cuEBAQAABAQGcOvX4XoGNGzcyfvx4JkyYwMWLF+nfv79UTRFMjIe9BzObzCTmQQzrL66XOo7FGjLkEfXr5xAU5EJCguQfSQan1WkZf3A8TtZOzGoyS+o4JaZUzpkYK3HOpHDMsd16vZ6+u/py7P4x9vXYR3mnp49szbHdBVVabb92TUmHDu506JDFypVJL1/BwAzZ7hVnVxByPITINpH4VfMzyDaKqjjnTMz/a4AgvIBMJiO8xeMB9QIOBYiJtCRSo4aWsWPT2LHDjp9/Nt/urmtJ15h/Yj7vVHqHrlWlv3qrJIliIlg8L0cvghoFcejOIb68/KXUcSzW8OHp1K2bw+TJLiQmmt+ZeK1Oy5j9Y3CwciC0WahJjQhcEKKYCALQt1ZfWni1IPhYsJg3XiJWVrBoUTLJyXKznDd+xdkVnI0/y9xmc3G3d5c6TokTxUQQeNzdtbDlQhQyBeMOjBNXd0mkdm0t/v7pbNliz+7d5tPddTnxMotOLaJT5U50qdpF6jgGIYqJIPw/L0cvZjWZxe/3f2fthbVSx7FYo0en8dpruUycaB5Xd+XqchmzfwzO1s7MbTZX6jgGY/p7ShBKUM8aPfGt4EtYTBixybFSx7FI1tYQEZFEaqrcLMbuWn5mORcSLhDePByNnUbqOAYjiokg/A+ZTMa8FvOwVdoy9sBY8nRi0g0pvPqqloCANH7+2Y4tW0x38MMLCRdYcmoJ71V9j3cqvyN1HIMSxUQQ/sXD3oM5TedwKu4UK8+tlDqOxRo6NJ2GDbMJCnLh7l3T+6jKycthzP4xqG3VzG46W+o4Bmd6e0gQSoFfVT/erfwuC04u4OLDi1LHsUgKBSxZkoxWC+PHu5pcd9eiU4u4nHiZ8BbhqG3VUscxOFFMBOEZZDIZYc3CcLJ2YuCOgeTqcqWOZJEqVcpj2rRUDh60ZcMGe6njFNjx+8eJPBtJzxo9aV+xvdRxSoUoJoLwHBo7DeHNwznz4AwLTy6UOo7F6ts3g9ats5gzx5k//jD+mRnTctLw/9Wf8o7lCW4SLHWcUiOKiSC8wDuV36H/6/1ZfmY5v9/7Xeo4FkkmgwULkrG2hjFjVOQZ+TUR045M486jOyxtsxRH62dP1GeORDERhJdY6LuQis4V8d/vT0p2itRxLNIrr+gICUnh5ElrIiON9wN6xx87+O76d/jX86ehx9PTaZgzUUwE4SWcbJxY1mYZ9x/dZ+rhqVLHsVh+fpl06ZLJggVOnDplJXWcp9x7dI/A3wKp516PMQ3GSB2n1IliIggF0KBMA8Y2GMsPN35gS+wWqeNYJJkMwsKS8fTMY9QoFWlpxjNQok6vY+yBsWTnZRPROgIrufEVO0MrlWl7XyQ9PZ3Fixfz8OFD3N3dGTt2LI6OTx/Gjhw5EltbW+RyOQqFgrCwsEKtLwjFNbreaPbf3s+U36bQ0KPhM+c+EQzLxUVPZGQy3bppmDrVhYiIZKkjAbDu4joO3TlEWPMwqrpWlTqOJCQ/Mtm6dSt169YlIiKCunXrsnXr1ucuO2PGDObPn59fSAq7viAUh1KuZFnrZejR8+n+T8Xd8RJp2DCHsWPT+M9/7I3i7vgriVeYe3wuvhV86fNqH6njSEbyYhITE0OrVq0AaNWqFTExMaW6viAURgXnCoQ0C+HY/WNEno2UOo7F8vd/fHf85Mku/PWXdJcLZ2ozGblvJI5WjixsudDs5igpDMmLSUpKCiqVCgCVSkVqaupzlw0JCWHSpElER0cXaX1BKAndq3WnS5UuLDy5kFNxp6SOY5GUSli+PBm5HEaOVJEr0T2lM47O4ErSFZa2XoqbnZs0IYxEqZwzCQ4OJjk5+anne/XqVaj3UKvVpKSkMGfOHMqWLUvt2rULlSM6Ojq/EIWFheHmVrSdr1Qqi7yuKRPt/q/VXVbTOKoxo/aP4tjHx1DZqSRKZ1jGvM/d3GDFCh19+lizapUHM2eWXLdjQdq9+eJmvrryFQFNAni//vsltm0pFWd/l0oxmTZt2nNfc3FxISkpCZVKRVJSEs7Ozs9cTq1W5y/fsGFDYmNjqV27doHXB/D19cXX1zf/cXx8fJHa4+bmVuR1TZlo95OWt17Oe9vfo/8P/Vnbbq1ZdnEY+z5v0wY++MCVsDA73nwziSZNckrkfV/W7j9S/mD4zuF4e3gzsvZIo/4ZFUZB9nfZsmWf+bzk3Vze3t4cOHAAgAMHDtCw4dM3+mRlZZGZmZn/73PnzlGhQoUCry8IhtCgTAOmNp7K7r92s+bCGqnjWKzg4BQqVXp8uXB8vOE/0rLzshm+dzhWcitWtF1hkZcBP4vkxcTPz49z587h7+/PuXPn8PPzAyAxMZHQ0FDg8XmR6dOnExAQwJQpU2jQoAH16tV74fqCUBo+qfMJHSp2YM6xOeL8iUQcHPSsXJlIcrKckSMNP9zKnGNzuJBwgcWtFuPl6GXYjZkQmV5vagM7l5y7d+8WaT1jP/Q3FNHuZ0vOTubtLW+jR8+u93ahsjWf8yemtM83bbJj/HgVY8akERCQVqz3el67d97cyeDowXxS5xNmNplZrG0YI5Pu5hIEU+dq48pnPp/xIOMB4w6Ow4K/n0mqV69MPvgggyVLnNi3z6bE3/9W2i3GHxxPPfd6TGk0pcTf39SJYiIIJaB+mfoENQ5iz197WH1+tdRxLFZISAq1auUyerSKO3dK7v6Tf86T6NHzWdvPsFZYl9h7mwtRTAShhAx6bRDvVHqHucfncuLBCanjWCQ7Oz2rVyei1cLQoSpySubiLqYdmcbph6dZ2HIhFZwrlMybmhlRTAShhMhkMha2XEhZx7IMjR7Kg4wHUkeySFWq5LFoUTKnT1sTHPz8WwUKauPljXx15StG1xvNu5XfLYGE5kkUE0EoQS42Lqxtt5aUnBQ++eUTsvOypY5kkTp2zOKTT9JZt86Rbdtsi/w+Jx6cIOhIEG3KtSHgzYASTGh+RDERhBJWW1Obxa0WczLuJEGHg8QJeYlMnZqKt3cOEya4culS4e/PfpDxgCHRQyjrUJblbZejkBv/lMFSEsVEEAygc5XOjK43mq+vfs0Xl7+QOo5FsrKCVasScXbW8/HH6kLd0JiTl8OQ6CGk5qSytv1aXG1cDRfUTIhiIggGMtF7Ij7lfZh+ZLqYP14inp461q1LJD5ewSefqMguYK/j+F/Gc+LBCRa1XEQtdS3DhjQTopgIgoHIZXKWt11OReeKDIkewp30O1JHskhvvJHLokVJHD9uw+TJrrys1/GbK9+w+vRqRrw+gi5Vu5ROSDMgiokgGJCztTPr2q8jJy+HQb8MIlObKXUki9S1axZjxqSxebM9n3/u8Nzljt8/zpTDU/Cp5ENgw8BSTGj6RDERBAOr5lqNZW2WcSH+AuMPjken10kdySKNH5/Gu+9mEhzs/Mw75GOTY/l4z8eUcyrHRr+N4oR7IYliIgiloF3FdkxuOJltN7YRHhMudRyLJJfD0qXJvPqqlhEjVFy//t8rvBIyE+i3qx8KmYKNb29EbaeWMKlpEsVEEErJiDdG0LdWX5afXc76S+uljmOR7O31rF+fiI2NngED1CQmysnUZjJgzwAeZDwgqn0UFZ0rSh3TJIliIgilRCaTMafpHNpVaEfQ4SB2/blL6kgWycsrj7VrE7l/X0Hffq6MiPbndNxplrVZxpseb0odz2SJYiIIpUgpV7Ki7Qrquddj5L6RYgwviXh75/LZZ4mcKTOFPbd+Zqr3dDFUSjGJYiIIpczeyp4NHTbg6eDJgN0DuJF8Q+pIFumO10poshiOjebC2gB04rqIYimVOeBfJD09ncWLF/Pw4UPc3d0ZO3Ysjo6OTyxz9+5dFi9enP84Li6Onj170rFjR7799lv27t2bP/d77969adCgQam2QRAKS2OnYePbG+myvQt9d/Vle9ftuNm5SR3LYvx08yemH51O+4rtqZ89i/BQB9w0embOTEUmkzqdaZK8mGzdupW6devi5+fH1q1b2bp1K3369HlimbJlyzJ//nwAdDodQ4cOpVGjRvmvd+zYkS5dxM1Fgmmp7FKZDR028P6P79NvVz82ddyEs3XxR7kVXuyXv35hxN4R1HevT2SbSOzaZZLw0Io1axxxd9cxalS61BFNkuTdXDExMbRq1QqAVq1aERMT88Llz58/j6enJ+7u7qURTxAMqkGZBqz0WcnFhIv02dmH9BzxQWZIB24fYEj0EF7TvMbGdzZib2WPTAYzZqTSrVsGoaHObNpkJ3VMkyT5kUlKSgoq1eM5s1UqFampqS9c/vDhwzRr1uyJ53bv3s3BgwepUqUK/fr1e6qb7B/R0dFER0cDEBYWhptb0boVlEplkdc1ZaLdhtHbrTd2jnZ8+MOHDNw7kO0fbMfR+tm/w6XNnPb5wb8PMuiXQdR0q8muD3c9dS/Jhg2QlqYjIMCVcuX0+PmZR7sLozj7W6YvhfGxg4ODSU5Ofur5Xr16ERkZyfr16/Of+/jjj4mKinrm+2i1WoYOHcrChQtxdXUFIDk5Of98yebNm0lKSmLEiBEFynX37t1CteMfbm5uxMfHF2ldUybabVg7/tjByH0jaejRkC/f/hJ7K3uDb/NlzGWfn3hwgt4/98bL0Yv/dPoPGjvNM5fLyJDx4YdqTp2yZtmyJLp2zSrlpNIqyP4uW7bsM58vlSOTadOmPfc1FxcXkpKSUKlUJCUl5ReGZzl9+jSVK1fOLyTAE//28fEhPFzcXSyYps5VOqPT6xj16yj67+7PF29/gZ1SdLkU19mHZ+mzsw9l7MuwuePm5xYSeHxT41dfJTJ4sAejRqnIzU2mRw8xnlpBSH7OxNvbmwMHDgBw4MABGjZs+Nxln9XFlZSUlP/v48ePU758ecMEFYRS0LVqV5a2XsrRe0cZsHuAGBiymC4mXOTDnR/iauPKtx2/xcPe46XrODjo2bZNS9OmOYwZ4yrOoRSQ5MXEz8+Pc+fO4e/vz7lz5/Dz8wMgMTGR0NDQ/OWys7M5d+4cjRs3fmL9jRs3Mn78eCZMmMDFixfp379/acYXhBLXrVo3FrVaxOG7hxm0R4w0XFS/3/udHj/2wE5px7cdv8XL0avA69rbw/r1CbRqlc348Sq+/FL6LkdjVyrnTIyVOGdSOKLdpWvT1U1MODiBBmUasL7DetS2pT/4oKnu811/7mLEvhGUdyrP1+98XahCAv9td1YWDB2qJjraljlzkvn44wwDJTYOxTlnIvmRiSAIz9arZi9W+a7iQsIFum7vyt+pf0sdySR8feVrPon+hNqa2vzQ+YdCF5L/ZWsLn3+eyDvvZBIU5MqyZY4vnVzLUoliIghGrGPljmx6dxOJWYl02d6F8/HnpY5ktPR6PRGnIwg4FEArr1Z8++63JXI0Z20Nn32WxHvvZRAW5syECS7k5JRAYDMjiokgGLlGno3Y2nkrNgobuv/Ynf239ksdyejo9DpmHJ1B+IlwulXrRlSHqBK9tNrKCpYtS2bs2DQ2bXLgww81JCWJcVf+lygmgmACqquqs73rdio5V6L/7v5svrZZ6khG41HuI4btHcbai2sZUncIS1svxUpuVeLbkclgwoQ0IiKSOHnSms6d3fnjDzEb4z9EMREEE+Fh78F/Ov2HpmWbMu7AOEKOhZCry5U6lqSuJ12n49aO7PxzJ9MaT2N64+nIZYb9WOvePZPNmxNISZHRubM7R49aG3R7pkIUE0EwIU7WTmzosIG+tfqy4twK3v/xfe6k35E6liS239jOu1vfJSk7iU3vbmLY68OQldKQv40a5bBjRzxubnn07q3h66/tLf7EvCgmgmBirBXWhDUPY0XbFVxKvESHLR3Y+/deqWOVmpy8HKYfnc7wfcOpranNrvd20axss5evWMIqVcpj+/Z43norh4AAV4YNU5GcbLnnUUQxEQQT1bVqV3b67eQVh1fot7sfc4/PNftur3uP7vH+T++z9sJaBtcZzPedvucVh1cky+PiouerrxKYPDmVXbts8fUtw+HDltntJYqJIJiwqq5V2d51O31e7UPk2Uiz7fbS6/X85/p/aL+lPZcSLrGi7QpmNZllkBPthaVQwKhR6WzfHo+dnZ4PPtAwd66TxV0+LIqJIJg4O6Ud4S3CiWwTyaXES7T+rjUrz600m6OUP1P/5MOdH+K/359KzpX42e9nulbtKnWsp7zxRi67dz/kww8ziIx0oksXN2JjJZ/lo9SIYiIIZsKvmh/R3aJpWrYpwceC6bClA7/f+13qWEWWq8sl8kwkPt/7cCruFCHNQtjaeSvVVdWljvZc9vZ65s1LYe3aRG7fVtC+vTuhoU6kpZn/uRRRTATBjFRwrsCGDhuIah/Fo9xHdP+xO5/u/5SHGQ+ljlYop+NO884P7zA3Zi5tyrdh//v7GVB7AAq5adzX8fbbWezd+5BOnTJZvtyJ5s3LsHGjPVqt1MkMRxQTQTBD7Su2Z//7+xldbzTbbmyj5XctWX1+NRm5xj1Q4fn483zyyyd02taJpOwk1rZby5p2ayQ9yV5UHh46IiKS+fnnh1SpomXSJFc6dHDn4EEbqaMZhCgmgmCm7JR2BDYMJLp7NG+4v8Gs32fR8JuGzDsxj/hM4xoJOOZBDH139eXtH97mt7u/8Wn9T9nfYz9vV3pb6mjF9sYbuWzZksDq1YlkZsro3VtD375qjhyxNqt7U8QQ9EVgqsNyF5dot2mLeRDDyrMr2f3XbqwV1vSo3oOhdYdS1bXqc9cxZNv1ej2/3f2NiNMRHLl3BLWtmk/qfMKA1wbgbP38GVdLg6HanZ0NUVEOLFvmRHKynOrVc+nXL4MePTJwdpb+o7g4Q9CLYlIE5vLhUlii3eYhNjmW1edX8/3178nJy8G3gi8dK3fEp4LPU6PslnTb9Xo9Zx6e4aebP/HTzZ/4O+1vPO09Gfr6UPq82sco5r0Hw+/zzEzYscOOL75w4PRpa+zsdHTrlkm/fo+oU0e6EysmXUyOHj3Kd999x507d5g7dy5Vqz77W9KZM2eIiopCp9Ph4+OTPyNjeno6ixcv5uHDh7i7uzN27FgcHR0LtG1RTApHtNu8PMx4SNSlKDZf3cz9jPvIZXK8y3jTrmI72lVoRzXXari7uxe77Tq9jlNxp/ILyJ30OyhlSlp4taBz1c74VfXDRmFc5xFKc5+fO2fFF1/Y88MPdmRlyalYUUvTptk0a5ZD06bZeHjoir0NvR7++kvBqVPWnDplxZAhj6hQIe+p5Uy6mNy+fRu5XM7q1avp27fvM4uJTqfj008/JSgoCI1Gw+TJk/n0008pV64cGzduxNHRET8/P7Zu3Up6ejp9+vQp0LZFMSkc0W7zpNfrOR9/nl/+/oU9f+3hQsIFACo6VeSNV97Ay9aLyi6VqeJShcrOlfGw93jmGFh6vZ77Gfe5mniVK0lXuJp0lauJV7mWfI1MbSbWcmtalmtJx8odaV+xPa42rqXc0oKTYp8nJ8vYutWOQ4dsOHrUhpSUx6e0q1fPpVmzHGrVysXDIw8PDx1lyuTh5qZD+T+3sej1kJUlIyVFRlqanHv35Jw+bZ1fQBITH18JZ2+vY/XqJNq0yX4qQ3GKieR31JQrV+6ly8TGxuLp6YmHhwcATZs2JSYmhnLlyhETE8PMmTMBaNWqFTNnzixwMREEAWQyGa+7v87r7q8z/s3x3Em/Q/Tf0Ry6c4hrCdfYlbSLHN1/b+e2U9pho7AhT5dHnj4PnV5Hnj4PrU6Lnv9+Ny1jV4aa6pp89OpH1HOvh08FH8nPhRgzV1c9AwZkMGBABnl5cPGiFYcPW3P4sA3ffmtHRobDE8vL5Xrc3HTY2elJTX1cQLTap4t89eq5tGuXTYMGOTRokEPNmloUBrjCWvJiUhCJiYloNJr8xxqNhuvXrwOQkpKCSqUCQKVSkZqa+tz3iY6OJjo6GoCwsDDc3NyKlEepVBZ5XVMm2m0Z3NzceKPSG4xnPEqlkuycbG6l3uJ64nVik2K5kXSD3LxclHIlCrni8f9lChQyBWUcylCnTB1qu9XGzd50f2bGsM89PKBt28f/1mq13LsH9+/LuHv38f/v3ZNx/z5kZspwcQEXFx0uLvr//ze4u+upX1+Pqys8/qhXAi8+J1WcdpdKMQkODiY5Ofmp53v16kXDhg1fuv6zeuKKMtS0r68vvr6++Y+Lehhr7t0ezyPabXnc3NxISkzCEUfqO9envnN9qFiAFTMgPsN0f2bGuM/t7KBy5cf/FZRWC4VphtF3c02bNq1Y62s0GhISEvIfJyQk5B+NuLi4kJSUhEqlIikpCWdncRgtCIJQ2kzipsWqVaty79494uLi0Gq1HDlyBG9vbwC8vb05cOAAAAcOHCjQkY4gCIJQsiQvJsePH2fYsGFcu3aNsLAwQkJCgMfnSUJDQwFQKBQMHDiQkJAQxo4dS5MmTShfvjwAfn5+nDt3Dn9/f86dO5d/ybAgCIJQeiS/NFhK4tLgwhHttjyW2nbR7ud73jkTyY9MBEEQBNMniokgCIJQbKKYCIIgCMUmiokgCIJQbBZ9Al4QBEEoGeLIpAgCAwOljiAJ0W7LY6ltF+0uPFFMBEEQhGITxUQQBEEoNlFMiuB/B4u0JKLdlsdS2y7aXXjiBLwgCIJQbOLIRBAEQSg2UUwEQRCEYjOJmRaNyZkzZ4iKikKn0+Hj42MRoxSvWLGCU6dO4eLiwsKFC6WOU2ri4+OJjIwkOTkZmUyGr68v7777rtSxDC4nJ4cZM2ag1WrJy8vjrbfeomfPnlLHKjU6nY7AwEDUarXFXCI8cuRIbG1tkcvlKBQKwsLCCv0eopgUgk6nY+3atQQFBaHRaJg8eTLe3t4FmsfelLVu3Zq3336byMhIqaOUKoVCQd++falSpQqZmZkEBgby+uuvm/3+trKyYsaMGdja2qLVapk+fTr16tWjRo0aUkcrFT///DNeXl5kZmZKHaVUzZgxo1iTC4purkKIjY3F09MTDw8PlEolTZs2JSYmRupYBle7dm0cHR2ljlHqVCoVVapUAcDOzg4vLy8SExMlTmV4MpkMW1tbAPLy8sjLyyvSNNmmKCEhgVOnTuHj4yN1FJMjjkwKITExEY1Gk/9Yo9Fw/fp1CRMJpSUuLo6bN29SrVo1qaOUCp1Ox6RJk7h//z4dOnSgevXqUkcqFevXr6dPnz4Wd1QC5E9M2K5duyJdIiyKSSE86ypqS/nGZsmysrJYuHAhAwYMwN7eXuo4pUIulzN//nwePXrEggUL+Pvvv6lQoYLUsQzq5MmTuLi4UKVKFS5evCh1nFIVHByMWq0mJSWFOXPmULZsWWrXrl2o9xDFpBA0Gg0JCQn5jxMSElCpVBImEgxNq9WycOFCWrRoQePGjaWOU+ocHByoXbs2Z86cMfticvXqVU6cOMHp06fJyckhMzOTiIgI/P39pY5mcGq1GgAXFxcaNmxIbGxsoYuJOGdSCFWrVuXevXvExcWh1Wo5cuQI3t7eUscSDESv17Ny5Uq8vLzo1KmT1HFKTWpqKo8ePQIeX9l1/vx5vLy8JE5leB9++CErV64kMjKSMWPGUKdOHYsoJFlZWfndellZWZw7d65IXxzEkUkhKBQKBg4cSEhICDqdjjZt2lC+fHmpYxnckiVLuHTpEmlpaQwbNoyePXvStm1bqWMZ3NWrVzl48CAVKlQgICAAgN69e9OgQQOJkxlWUlISkZGR6HQ69Ho9TZo04c0335Q6lmAgKSkpLFiwAHh8wUXz5s2pV69eod9HDKciCIIgFJvo5hIEQRCKTRQTQRAEodhEMREEQRCKTRQTQRAEodhEMREEQRCKTRQTQRAEodhEMREEQRCKTRQTQRAEodhEMREEI3D//n0+/vhj/vjjD+DxCNWDBg2yuAEHBdMliokgGAFPT08++ugjli1bRnZ2Np999hmtWrXitddekzqaIBSIGE5FEIxIeHg4cXFxyGQyQkNDsbKykjqSIBSIODIRBCPi4+PDrVu3ePvtt0UhEUyKKCaCYCSysrLYsGEDbdu25bvvviM9PV3qSIJQYKKYCIKRiIqKonLlygwbNowGDRqwevVqqSMJQoGJYiIIRiAmJoYzZ84wZMgQAPr378/Nmzc5dOiQxMkEoWDECXhBEASh2MSRiSAIglBsopgIgiAIxSaKiSAIglBsopgIgiAIxSaKiSAIglBsopgIgiAIxSaKiSAIglBsopgIgiAIxfZ/YFPAB0RT4kQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "z = np.cos(x)\n",
    "plt.plot(x, y, 'b', label='sin')\n",
    "plt.plot(x, z, 'g', label='cos')\n",
    "plt.legend(loc='upper right')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0458ddf6-5e75-4778-8e2d-0768a350811a",
   "metadata": {},
   "source": [
    "5. Save your plot to a file in the `images` folder using `plt.savefig('filename')`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "89fe847f-891d-46d1-b369-5059128a95bb",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2023-10-23T10:34:27.312939Z",
     "iopub.status.busy": "2023-10-23T10:34:27.312584Z",
     "iopub.status.idle": "2023-10-23T10:34:27.328308Z",
     "shell.execute_reply": "2023-10-23T10:34:27.327521Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "clear_answer_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.savefig('../images/sin_cos.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 + Jaspy",
   "language": "python",
   "name": "jaspy"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}