{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>16. Introduction to Matplotlib</h1>\n",
    "<h2>11/15/2023</h2>\n",
    "\n",
    "<h2>16.0 Last Time...</h2>\n",
    "<ul>\n",
    "    <li>A masked array has a <b>mask</b> attribute that allows us to identify suspicious or unwanted data.</li>\n",
    "    <li>We can use direct assignment, assignment by condition, and filling to create a masked array.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>16.1 What is Matplotlib?</h2>\n",
    "\n",
    "There's another object-oriented programming language out there called Matlab that handles plotting especially well. Unfortunately, Matlab costs a lot of money, so Python's overtaken it in popularity for good reason!\n",
    "\n",
    "And there's no reason we can't make use of Matlab's helpful plotting interface in this lovely open-source language...\n",
    "\n",
    "The submodule we want is called <b>matplotlib.pyplot</b>. It's fast and efficient, and the plots look great! You'll see a lot of Matlab-style plots applied pretty much right out of the box in published scientific articles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'y = 1/2x')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's start by creating a straight line.\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot([1,2,3,4])\n",
    "plt.ylabel('ints')\n",
    "plt.xlabel(\"indeces\")\n",
    "plt.title('y = 1/2x')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, so far so good! But why does the y-axis go from 1-4 when the x-axis goes from 0-3? If you just provide a single list or array to the plot() command (rather than giving it both the x and the y values), it assumes you're dealing with a sequence of y values and automatically generates x values for you. And, of course, Python defaults to counting starting at 0, so it's created a default x vector that has the same length as y but starts at 0: [0,1,2,3].\n",
    "\n",
    "If you're not working with evenly spaced data, you can provide both x and y values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6sAOBfj5mx5JroDIjIiJeJ+10HkNnb+JsXiGt69iYO7Ijwf6+ZseSa6QyIyIiXuVoVj5DZ28iM8dB05phLBjZifBAP7NjyW9gaplZu3Yt/fv3Jzo6GovFwrJly0q+VlRUxFNPPUWrVq0ICQkhOjqaBx98kIyMDPMCi4iIRztpL2Do7E0cO3ee+tVDWDi6M1VD/M2OJb+RqWUmLy+P+Ph4ZsyY8Yuv5efnk5iYyHPPPUdiYiIfffQRBw4c4I477jAhqYiIeLozuQ6Gzt7E4TP5xEQEsWhMZ6qHBZgdS8qAqROE/fr1o1+/fpf8ms1m4+uvv75o2+uvv06nTp04cuQIdevqzqUiIlI62flFDJuzmUOZudSyBbJ4TBdq2YLMjiVlxKNWO2VnZ2OxWKhSpcpl93E4HDgcjpLndru9ApKJiIi7ynUUM3zeZvYct1Mt1J+3x3QmJiLY7FhShjxmAXBBQQFPP/00Q4YMITw8/LL7JSQkYLPZSh4xMTEVmFJERNzJ+UIno+ZvITn9HFWC/Xh7TGcaVA81O5aUMY8oM0VFRQwePBiXy8V//vOfK+47efJksrOzSx7p6ekVlFJERNyJo9jJ7xZuZXPqWcICfFk4qjNNa17+f4bFc7n9NFNRURH33XcfqampfPvtt1e8KgMQEBBAQIAWdImIeLMip4txi5JYd/A0wf4+zB/VkVZ1bGbHknLi1mXmpyJz8OBBVq1aRWRkpNmRRETEzTldBo+/m8zKvSfx97Uy+8EOtK8XYXYsKUemlpnc3FwOHTpU8jw1NZXk5GQiIiKIjo7mnnvuITExkU8//RSn08mJEycAiIiIwN9fnwsgIiIXc7kMnvpwB5/uOI6fj4X/PtCebg2rmR1LypnFMAzDrB++evVqevfu/Yvtw4cP5/nnnycuLu6Sx61atYrrr7++VD/Dbrdjs9nIzs7+1SkqERHxXIZh8OePd7Nw42F8rBZm3N+Wfq1qmR1LrtHVvH6bemXm+uuv50pdysSeJSIiHsQwDBK+2MfCjYexWOBf98aryHgRj3g3k4iIyJW8uvIgM9emAPDiXa24s21tkxNJRVKZERERj/bmmh947ZuDAPylf3Pu76RPiPc2KjMiIuKxFmxI46Uv9gHwxM1NGNn90mstpXJTmREREY/03pZ0/vLJbgDG39CQcb0bmpxIzKIyIyIiHufj5GM89dEOAEb3iGPSjY1NTiRmUpkRERGP8tXuE0x6bzuGAUM71+XZ25phsVjMjiUmUpkRERGPsXp/JuMXJ+F0GQxsV5u/DWipIiMqMyIi4hm+/+EMDy3cRqHTxW2tavGPu1tjtarIiMqMiIh4gMQjWYxesAVHsYs+TaN4ZVAbfH30EiYXaCSIiIhb23Usm+FzN5Nf6KRHw2r8e2g7/H318iX/o9EgIiJu68DJHIbN2UROQTEdY6sy88H2BPr5mB1L3IzKjIiIuKXU03kMnb2JrPwi4uvYmDuiI8H+pt5SUNyUyoyIiLido1n5DJ21kVM5DprWDGPBqE6EBfqZHUvclMqMiIi4lZP2AobM2kRGdgENqofw9pjOVAn2NzuWuDGVGRERcRuncx0MmbWRI2fzqRsRzKIxXagWGmB2LHFzKjMiIuIWzuUXMmzOZn44lUctWyCLxnSmpi3Q7FjiAVRmRETEdDkFRQyft4W9x+1UCw1g0ZjOxEQEmx1LPITKjIiImOp8oZPR87eyPf0cVYP9WDSmM/Wrh5odSzyIyoyIiJimoMjJ7xZuZXPaWcICfVk4ujNNaoaZHUs8jMqMiIiYosjp4tHFiaw7eJpgfx/mj+xEy9o2s2OJB1KZERGRCud0GTz2bjIr92YS4Gtl9vAOtK9X1exY4qFUZkREpEK5XAZPfrCDz3Ycx8/Hwn+Htadbg2pmxxIPpjIjIiIVxjAM/vzJLj5MPIqP1cLr97fj+iZRZscSD6cyIyIiFcIwDF78fC9vbzyCxQLT7ovnlpY1zY4llYDKjIiIVIhXVh5k1rpUAF4a2IoBbWqbnEgqC5UZEREpd2+s/oHp3xwE4Pn+zRnUsa7JiaQyUZkREZFyNf+7VKZ+uQ+Ap25pyojucSYnkspGZUZERMrNu1uO8PzyPQBMuKEhv7++gcmJpDJSmRERkXLxcfIxnv5oJwBje8bx+I2NTU4klZXKjIiIlLkvd51g0nvbMQx4oEtdnrm1GRaLxexYUkmpzIiISJlatT+T8e8k4nQZ3N2uDn+9o6WKjJQrlRkRESkz3/9whocXbqPIaXBb61r8457WWK0qMlK+VGZERKRMbDucxegFW3AUu+jbLIpXB7XBR0VGKoDKjIiI/Ga7jmUzYu5m8gud9GxUjRlD2uHno5cYqRimjrS1a9fSv39/oqOjsVgsLFu27KKvG4bB888/T3R0NEFBQVx//fXs3r3bnLAiInJJ+0/kMGzOJnIcxXSKjWDmsA4E+vmYHUu8iKllJi8vj/j4eGbMmHHJr//jH/9g2rRpzJgxgy1btlCzZk1uvPFGcnJyKjipiIhcSsqpXIbO3kRWfhHxMVWYM6IDQf4qMlKxfM384f369aNfv36X/JphGLz66qv86U9/YuDAgQAsWLCAGjVqsHjxYh566KFLHudwOHA4HCXP7XZ72QcXERHSz+YzdPYmTuc6aFYrnLdGdiIs0M/sWOKF3HZCMzU1lRMnTnDTTTeVbAsICKBXr15s2LDhssclJCRgs9lKHjExMRURV0TEq5zILmDo7E0czy6gQfUQFo7uhC1YRUbM4bZl5sSJEwDUqFHjou01atQo+dqlTJ48mezs7JJHenp6ueYUEfE2p3MdDJ29kSNn86kXGczisV2oFhpgdizxYqZOM5XGzz9oyTCMK374UkBAAAEB+kclIlIezuUX8sDsTfxwKo9oWyCLxnSmRnig2bHEy7ntlZmaNWsC/OIqTGZm5i+u1oiISPnLKShi+NzN7DuRQ/WwABaN7UKdqsFmxxJx3zITFxdHzZo1+frrr0u2FRYWsmbNGrp162ZiMhER75NfWMyo+VvYfjSbqsF+LBrTmbhqIWbHEgFMnmbKzc3l0KFDJc9TU1NJTk4mIiKCunXr8thjj/Hiiy/SqFEjGjVqxIsvvkhwcDBDhgwxMbWIiHcpKHLyu7e2sSUti7BAXxaO7kzjGmFmxxIpYWqZ2bp1K7179y55PmnSJACGDx/O/PnzefLJJzl//jyPPPIIWVlZdO7cmRUrVhAWpn9EIiIVobDYxbhFiaw/dJoQfx8WjOpEy9o2s2OJXMRiGIZhdojyZLfbsdlsZGdnEx4ebnYcERGPUex0MXFJMp/tPE6Ar5UFozrRpX6k2bHES1zN67fbrpkRERHzuFwGT364g892Hsffx8rMBzuoyIjbUpkREZGLGIbBcx/v4qPEY/hYLcwY0pZejaubHUvkslRmRESkhGEY/P2zvSzadASLBabdF89NLWqaHUvkilRmRESkxLSvDzBnfSoAUwe2ZkCb2iYnEvl1KjMiIgLAf1Yf4vVvL3xcxl8HtOC+jrq3nXgGlRkREWHed6n848v9AEzu15QHu8aaG0jkKqjMiIh4uSWbjzBl+R4AJvZpxEO9GpicSOTqqMyIiHixZUnHmLx0JwC/u64+j/VtZHIikaunMiMi4qW+3HWcP7y/HcOAYV3qMblfUywWi9mxRK6ayoyIiBdatS+T8e8k4XQZ3NO+DlPuaKEiIx5LZUZExMtsOHSah9/eRpHT4PbWtZh6d2usVhUZ8VwqMyIiXmTb4bOMeWsrjmIXNzavwSuD2uCjIiMeTmVGRMRL7DyazYi5W8gvdNKzUTVmDGmLn49eBsTzaRSLiHiB/SdyGDZ3EzmOYjrFRTBzWAcCfH3MjiVSJnzNDiAiIuXrsx3HefrDHeQ4imkTU4W5IzoS5K8iI5WHyoyISCVVUOTk75/t4e2NRwDoUK8qc4Z3JDRAf/qlctGIFhGphFJO5TJucRJ7j9sBeOT6Bjx+Y2OtkZFKSWVGRKSS+Tj5GM98tJO8QicRIf68MqgNvRpXNzuWSLlRmRERqSTOFzqZsnw3S7akA9A5LoLp97elRnigyclEypfKjIhIJXAoM4dxi5LYfzIHiwXG927IhD6N8NW0kngBlRkREQ/3wbajPLdsF+eLnFQLDeDVQW3o0aia2bFEKsxvKjPbtm1j7969WCwWmjVrRrt27coql4iI/Ir8wmKeW7abDxOPAtC9YSSvDGpDVJimlcS7XFOZyczMZPDgwaxevZoqVapgGAbZ2dn07t2bJUuWUL26FpqJiJSn/SdyGLc4kUOZuVgt8Fjfxozr3VC3JhCvdE2TqePHj8dut7N7927Onj1LVlYWu3btwm63M2HChLLOKCIiPzIMg3e3HOGOGes5lJlLVFgAi8d2YUKfRioy4rUshmEYV3uQzWZj5cqVdOzY8aLtmzdv5qabbuLcuXNlle83s9vt2Gw2srOzCQ8PNzuOiMg1y3UU8+zSnSxLzgDgusbVmXZfPNVCA0xOJlL2rub1+5qmmVwuF35+fr/Y7ufnh8vlupZvKSIiV7A7I5vxi5NIOZ2Hj9XCH25qzMPXNcCqqzEi1zbNdMMNNzBx4kQyMjJKth07dozHH3+cPn36lFk4ERFvZxgGCzce5q7/bCDldB61bIG8+7suPHJ9QxUZkR9d05WZGTNmMGDAAGJjY4mJicFisXD48GFat27NwoULyzqjiIhXshcUMfmjnXy24zgAfZpG8fK98VQN8Tc5mYh7uaYyExMTQ2JiIitXrmTv3r0YhkHz5s3p27dvWecTEfFKO49mM25xIkfO5uNrtfDULU0Z0zMOi0VXY0R+7poWAAN88803fPPNN2RmZv5inczcuXPLJFxZ0AJgEfEkhmGwYEMaL36+j0Kni9pVgnh9SFva1a1qdjSRClXuC4CnTJnCX//6Vzp06ECtWrX0fwoiImUgO7+IJz/czle7TwJwU/Ma/POeeGzBv3zDhYj8zzWVmTfffJP58+czbNiwss4jIuKVko5kMf6dJI5mncfPx8IztzZjRLdY/c+iSClcU5kpLCykW7duZZ1FRMTrGIbBnPWpvPTFPopdBnUjgpkxpC2t61QxO5qIx7imt2aPGTOGxYsXl3UWERGvkpVXyJgFW/n7Z3spdhnc2qomn07ooSIjcpWu6cpMQUEBM2fOZOXKlbRu3foXH6A3bdq0MglXXFzM888/z6JFizhx4gS1atVixIgRPPvss1ituq29iHiurWlnmfBOEhnZBfj7Wnnu9uY80LmuppVErsE1lZkdO3bQpk0bAHbt2nXR18ryH+LUqVN58803WbBgAS1atGDr1q2MHDkSm83GxIkTy+zniIhUFJfL4M21P/CvFQdwugziqoUwY0hbWkTbzI4m4rGuqcysWrWqrHNc0vfff8+AAQO47bbbAIiNjeWdd95h69atlz3G4XDgcDhKntvt9nLPKSJSGmdyHUx6bztrDpwCYECbaF64qxWhAdf0p1hEfuTWczU9evTgm2++4cCBAwBs376d9evXc+utt172mISEBGw2W8kjJiamouKKiFzWppQz3Dp9HWsOnCLA18pLA1vx6qA2KjIiZeCaPzSvIhiGwTPPPMPUqVPx8fHB6XTywgsvMHny5Msec6krMzExMfrQPBExhdNl8J9Vh3hl5QFcBjSoHsK/h7ajaU39PRK5knL/0LyK8u677/L222+zePFiWrRoQXJyMo899hjR0dEMHz78kscEBAQQEBBQwUlFRH7pVI6Dx95N4rtDZwC4u10d/nZnC4L93fpPr4jHcet/UU888QRPP/00gwcPBqBVq1YcPnyYhISEy5YZERF38N2h00xckszpXAdBfj787c6W3NO+jtmxRColty4z+fn5v3gLto+Pzy/uBSUi4i6cLoPXvjnI698exDCgcY1Q/j2kHY1qhJkdTaTScusy079/f1544QXq1q1LixYtSEpKYtq0aYwaNcrsaCIiv3DSXsDEJUlsTDkLwOCOMfylfwuC/H1MTiZSubn1AuCcnByee+45li5dSmZmJtHR0dx///38+c9/xt/fv1TfQ3fNFpGKsObAKSa9m8yZvEJC/H14cWArBrSpbXYsEY91Na/fbl1myoLKjIiUp2Kni2lfH+A/q38AoFmtcP49pC31q4eanEzEs1WadzOJiLizjHPnmfBOElsPZwEwtHNdnru9OYF+mlYSqUgqMyIi1+DbfSeZ9N52zuUXERrgy0t3t+L21tFmxxLxSiozIiJXocjp4p9f7Wfm2hQAWtW2MWNIW+pFhpicTMR7qcyIiJTS0ax8Hl2cRHL6OQBGdItl8q1NCfDVtJKImVRmRERK4avdJ3ji/e3YC4oJD/TlH/fEc0vLmmbHEhFUZkRErqiw2EXCF3uZ910aAPExVZhxf1tiIoLNDSYiJVRmREQu48iZfB59J5EdR7MBGNszjiduboq/r/VXjhSRiqQyIyJyCZ/vPM5TH+wgx1FMlWA/Xr4nnr7Na5gdS0QuQWVGROT/KShy8sJne1m48TAA7etVZfr9baldJcjkZCJyOSozIiI/Sj2dx7hFiew5bgfg4V4N+MNNjfHz0bSSiDtTmRERAT5OPsYzH+0kr9BJRIg/0+6L5/omUWbHEpFSUJkREa9WUORkyvLdvLM5HYBOcRFMH9yWmrZAk5OJSGmpzIiI1zqUmcu4RYnsP5mDxQKP9m7IxD6N8NW0kohHUZkREa/04bajPLtsF+eLnFQLDeDVQW3o0aia2bFE5BqozIiIV8kvLObPH+/mg21HAejWIJJXB7chKkzTSiKeSmVGRLzGgZM5jFuUyMHMXKwWmNinMY/e0BAfq8XsaCLyG6jMiEilZxgG721N5y+f7KagyEVUWACvDW5L1waRZkcTkTKgMiMilVquo5hnl+5kWXIGAD0bVeOVQW2oFhpgcjIRKSsqMyJSae3JsPPo4kRSTufhY7Uw6cbG/L5XA6yaVhKpVFRmRKTSMQyDxZuPMGX5HgqLXdQMD+T1IW3pGBthdjQRKQcqMyJSqeQUFPH0Rzv5bMdxAG5oGsXL98YTEeJvcjIRKS8qMyJSaew8ms2j7yRy+Ew+vlYLT97ShDE96mtaSaSSU5kREY9nGAYLNqTx4uf7KHS6qF0liNeHtKVd3apmRxORCqAyIyIeLft8EU99sIMvd58A4MbmNfjnPa2pEqxpJRFvoTIjIh4rOf0cjy5O5GjWefx8LEzu14yR3WOxWDStJOJNVGZExOMYhsGc9am89MU+il0GMRFBzLi/HfExVcyOJiImUJkREY9yLr+QP76/nZV7MwG4tVVNXrq7NeGBfiYnExGzqMyIiMfYdvgs4xcnkZFdgL+Pledub8YDXeppWknEy6nMiIjbc7kMZq5L4Z9f7cfpMoiNDGbGkHa0rG0zO5qIuAGVGRFxa2dyHfzh/e2s3n8KgDvio3lxYCtCA/TnS0Qu0F8DEXFbm1LOMGFJEiftDgJ8rTx/RwsGd4zRtJKIXERlRkTcjtNl8J9Vh3hl5QFcBtSvHsK/h7SjWa1ws6OJiBtSmRERt3Iqx8Hj7yaz/tBpAAa2rc3f7mxJiKaVROQyrGYH+DXHjh3jgQceIDIykuDgYNq0acO2bdvMjiUi5WDDodPcOn0d6w+dJtDPyj/vac20QW1UZETkitz6L0RWVhbdu3end+/efPHFF0RFRfHDDz9QpUoVs6OJSBlyugxe++Ygr397EMOAxjVC+feQdjSqEWZ2NBHxAG5dZqZOnUpMTAzz5s0r2RYbG2teIBEpcyftBUxcksTGlLMADOoQw/N3tCDI38fkZCLiKdx6mumTTz6hQ4cO3HvvvURFRdG2bVtmzZp1xWMcDgd2u/2ih4i4p7UHTnHra+vYmHKWYH8fXh3Uhqn3tFaREZGr4tZlJiUlhTfeeINGjRrx1Vdf8fDDDzNhwgTeeuutyx6TkJCAzWYrecTExFRgYhEpjWKni39+tY/h8zZzJq+QpjXDWD6+B3e2rW12NBHxQBbDMAyzQ1yOv78/HTp0YMOGDSXbJkyYwJYtW/j+++8veYzD4cDhcJQ8t9vtxMTEkJ2dTXi43tYpYrbj2eeZ8E4SW9KyABjauS7P3d6cQD9djRGR/7Hb7dhstlK9frv1mplatWrRvHnzi7Y1a9aMDz/88LLHBAQEEBAQUN7RROQarNqXyaT3ksnKLyI0wJeEga3oHx9tdiwR8XBuXWa6d+/O/v37L9p24MAB6tWrZ1IiEbkWRU4XL3+1n/+uTQGgZe1wZtzfjthqISYnE5HKwK3LzOOPP063bt148cUXue+++9i8eTMzZ85k5syZZkcTkVI6mpXP+HeSSDpyDoAR3WKZfGtTAnw1rSQiZcOt18wAfPrpp0yePJmDBw8SFxfHpEmTGDt2bKmPv5o5NxEpWyt2n+CJD3aQfb6IsEBf/nlPa25pWcvsWCLiAa7m9dvty8xvpTIjUvEKi10kfLGXed+lARBfx8aMIe2IiQg2N5iIeIxKswBYRDzPkTP5PPpOIjuOZgMwukccT93SFH9ft/4kCBHxYCozIlJmvth5nCc/2EGOoxhbkB8v3xvPjc1rmB1LRCo5lRkR+c0Kipy8+Ple3vr+MADt6lbh9SHtqF0lyORkIuINVGZE5DdJPZ3Ho4sT2Z1x4dYhD/Wqzx9vaoKfj6aVRKRiqMyIyDX7ZHsGz3y0k1xHMREh/vzrvnh6N4kyO5aIeBmVGRG5agVFTqYs38M7m48A0Ck2gun3t6WmLdDkZCLijVRmROSqHMrM5dHFiew7kYPFAo/2bsjEPo3w1bSSiJhEZUZESu2jxKM8u2wX+YVOqoX688qgNvRsVN3sWCLi5VRmRORX5RcW85ePd/P+tqMAdK0fyWuD2xAVrmklETGfyoyIXNGBkzmMW5TIwcxcLBaY2KcR429ohI/VYnY0ERFAZUZELsMwDN7fepQ/f7KLgiIX1cMCeG1wG7o1qGZ2NBGRi6jMiMgv5DmKeXbZLpYmHQOgZ6NqvDKoDdVCA0xOJiLySyozInKRvcftjFuUSMrpPKwW+MNNTfh9rwZYNa0kIm5KZUZEgAvTSos3H2HK8j0UFruoGR7I9Pvb0ikuwuxoIiJXpDIjIuQUFDH5o518uuM4AL2bVOdf97UhIsTf5GQiIr9OZUbEy+06ls2jixNJO5OPr9XCEzc3YWzP+ppWEhGPoTIj4qUMw+Ct7w/zwmd7KXS6qF0liOn3t6V9vapmRxMRuSoqMyJeKPt8EU99sIMvd58AoG+zGrx8b2uqBGtaSUQ8j8qMiJdJTj/Ho4sTOZp1Hj8fC0/3a8ao7rFYLJpWEhHPpDIj4iUMw2DO+lSmfrmPIqdBTEQQM+5vR3xMFbOjiYj8JiozIl7gXH4hf3x/Byv3ngSgX8uavHR3a2xBfiYnExH57VRmRCq5bYezGL84kYzsAvx9rDx7ezOGdamnaSURqTRUZkQqKZfLYOa6FP751X6cLoN6kcH8e0g7Wta2mR1NRKRMqcyIVEJn8wqZ9F4yq/efAuD21rVIGNiKsEBNK4lI5aMyI1LJbE49y4R3kjhhLyDA18pf+rfg/k4xmlYSkUpLZUakknC5DP6z+hDTvj6Ay4D61UP495B2NKsVbnY0EZFypTIjUgmcynEw6b1k1h08DcDAtrX5250tCQnQP3ERqfz0l07Ew204dJqJ7yZzKsdBoJ+Vvw5oyb3t62haSUS8hsqMiIdyugymf3OQ6d8exDCgUVQo/x7ajsY1wsyOJiJSoVRmRDxQpr2AiUuS+T7lDAD3dajDlDtaEuTvY3IyEZGKpzIj4mHWHTzF4+8mczq3kGB/H/5+Z0sGtqtjdiwREdOozIh4iGKni1dXHuTfqw9hGNC0ZhgzhrSjYVSo2dFEREylMiPiAY5nn2fiO8lsTjsLwJDOdfnz7c0J9NO0koiIyoyIm1u1L5NJ7yWTlV9EaIAvLw5sxR3x0WbHEhFxG1azA1yNhIQELBYLjz32mNlRRMpdkdNFwud7GTl/C1n5RbSIDmf5+B4qMiIiP+MxV2a2bNnCzJkzad26tdlRRMrdsXPnGb84kcQj5wAY3rUek29tpmklEZFL8IgrM7m5uQwdOpRZs2ZRtWpVs+OIlBvDMPhy1wlufW0diUfOERboyxtD2zFlQEsVGRGRy/CIKzPjxo3jtttuo2/fvvz973+/4r4OhwOHw1Hy3G63l3c8kd+ssNjFpzsymL0ulT3HL4zZ+Do2Xr+/HXUjg01OJyLi3ty+zCxZsoTExES2bNlSqv0TEhKYMmVKOacSKRvZ+UUs2nyYBRvSOGm/UMID/ayM6BbHpBsb4+/rERdPRURM5dZlJj09nYkTJ7JixQoCAwNLdczkyZOZNGlSyXO73U5MTEx5RRS5JofP5DF3fSrvbT3K+SInANXDAhjRLZYhnepSNcTf5IQiIp7DYhiGYXaIy1m2bBl33XUXPj7/WyvgdDqxWCxYrVYcDsdFX7sUu92OzWYjOzub8PDw8o4sclmGYbDtcBaz1qWwYs9JfvqX17RmGGN71uf2+FoE+GpdjIgIXN3rt1tfmenTpw87d+68aNvIkSNp2rQpTz311K8WGRF3UOx08eXuE8xal8r29HMl269vUp2xPevTrUGk7nAtIvIbuHWZCQsLo2XLlhdtCwkJITIy8hfbRdxNTkER725JZ953aRw7dx4Af18rA9vWZnSPOBrp7tYiImXCrcuMiCc6du4889ansmRLOrmOYgAiQvwZ1qUew7rWo1pogMkJRUQqF48rM6tXrzY7gsglbU8/x6x1KXyx6wRO14UFMQ2qhzCmZ33ualtbnxMjIlJOPK7MiLgTp8tg5d6TzF6Xwpa0rJLt3RtGMqZHfXo1ro7VqvUwIiLlSWVG5BrkFxbzwbajzF2fStqZfAD8fCz0j49mdI84WkTbTE4oIuI9VGZErsJJewHzN6SxeNMRss8XAWAL8mNo57oM7xZLjfDSfR6SiIiUHZUZkVLYnZHNnHWpLN+RQZHzwnqYepHBjO4Rxz3t6xDsr39KIiJm0V9gkctwuQzWHDjFrHUpbPjhTMn2TrERjO4ZR99mNfDRehgREdOpzIj8TEGRk6VJx5izPpVDmbkA+Fgt3NqqFmN6xBEfU8XcgCIichGVGZEfnc51sPD7w7y98TBn8goBCAvwZXCnGIZ3i6VOVd29WkTEHanMiNc7eDKH2etSWZp8jMJiFwC1qwQxsnssgzrGEBboZ3JCERG5EpUZ8UqGYfDdoTPMWpfCmgOnSrbHx1RhbM84bmlRE18fq4kJRUSktFRmxKs4ip0s336c2etS2HciBwCLBW5uXpMxPeNoX6+qbvooIuJhVGbEK2TlFbJ48xEWbEgjM8cBQLC/D/d1iGFk91jqRYaYnFBERK6VyoxUaqmn85i7PpX3t6VTUHRhPUyN8ABGdItjSKe62IK1HkZExNOpzEilYxgGm1PPMmtdKt/sO4lx4TPuaF4rnLHXxXFbq2j8fbUeRkSkslCZkUqjyOni853HmbM+lR1Hs0u292kaxeiecXStH6n1MCIilZDKjHg8e0ERSzYfYf53aWRkFwAQ4Gvl7vZ1GNU9joZRoSYnFBGR8qQyIx4r/Ww+875L490tR8grdAJQLdSfB7vGMrRzXSJDA0xOKCIiFUFlRjxO4pEs5qxL5Ytdx3H9uB6mcY1QxvSozx1togn08zE3oIiIVCiVGfEITpfBit0nmLUuhcQj50q292xUjTE963Ndo2paDyMi4qVUZsSt5TqKeX9rOnO/SyX97HkA/H2sDGgTzeiecTStGW5yQhERMZvKjLil49nnmb8hjcWbjpBTUAxA1WA/HuhSj2Fd6xEVFmhyQhERcRcqM+JWdh3LZva6FD7dcZziHxfE1K8Wwqgecdzdrg5B/loPIyIiF1OZEdO5XAbf7stk9voUNqacLdnepX4EY3rU54amUVitWg8jIiKXpjIjpjlf6OTDxKPMXZ9Kyuk8AHytFm5vXYvRPerTqo7N5IQiIuIJVGakwmXmFLDw+8O8vfEwWflFAIQF+jKkc12Gd40lukqQyQlFRMSTqMxIhdl3ws6cdal8nJxBofPCTR9jIoIY1T2OezvEEBqg4SgiIldPrx5SrgzDYO3B08xel8K6g6dLtrerW4WxPetzU4ua+Gg9jIiI/AYqM1IuCoqcfJKcwez1KRw4mQuA1QL9WtZiVI842teranJCERGpLFRmpEydzSvk7Y2Heev7NE7nFgIQ4u/DoI51Gdk9lpiIYJMTiohIZaMyI2XiUGYuc79L5cNtR3EUX1gPU8sWyMjusQzqWBdbkJ/JCUVEpLJSmZFrZhgG36ecYc66VL7Zl1myvVVtG2N6xnFrq1r4+VhNTCgiIt5AZUauWmGxi892ZjB7XSq7M+wAWCzQp2kNxvaMo1NchG76KCIiFUZlRkotO7+IxZuPsGBDGifsBQAE+lm5t30MI7vHUr96qMkJRUTEG6nMyK86fCaPed+l8d7WdPILnQBUDwtgRLdYhnSqS9UQf5MTioiIN1OZkUsyDINth7OYvS6Vr/acwLhwz0ea1gxjTM/69I+vRYCvbvooIiLmc+syk5CQwEcffcS+ffsICgqiW7duTJ06lSZNmpgdrdIqdrr4cvcJZq9LJTn9XMn2Xo2rM7Znfbo3jNR6GBERcStuXWbWrFnDuHHj6NixI8XFxfzpT3/ipptuYs+ePYSEhJgdr1LJKSji3S3pzPsujWPnzgPg72vlrja1Gd0zjsY1wkxOKCIicmkWw/hpAsH9nTp1iqioKNasWcN1111XqmPsdjs2m43s7GzCw8PLOaHnOXbuPPO/S2XJ5nRyHMUARIT480CXegzrUo/qYQEmJxQREW90Na/fbn1l5ueys7MBiIiIuOw+DocDh8NR8txut5d7Lk+0Pf0cs9en8vnO4zhdF/psg+ohjO5Rn4HtahPop/UwIiLiGTymzBiGwaRJk+jRowctW7a87H4JCQlMmTKlApN5DqfLYOXek8xZl8rmtLMl27s1iGRsz/r0alwdq276KCIiHsZjppnGjRvHZ599xvr166lTp85l97vUlZmYmBivnmbKLyzmg21Hmbs+lbQz+QD4Wi3cER/N6J5xtIi2mZxQRETkYpVummn8+PF88sknrF279opFBiAgIICAAK3zADhpL2DBhjQWbTpC9vkiAMIDfRnapR7Du8ZS0xZockIREZHfzq3LjGEYjB8/nqVLl7J69Wri4uLMjuQR9mTYmb0+heXbMyhyXrjwVi8ymFHd47infR1CAtz6P7uIiMhVcetXtXHjxrF48WI+/vhjwsLCOHHiBAA2m42goCCT07kXl8tgzYFTzF6fwneHzpRs7xhbldE96nNj8xr4aD2MiIhUQm69ZuZyH842b948RowYUarvUdnfml1Q5GRp0jHmrE/lUGYuAD5WC/1a1mRMz/q0ialibkAREZFrUGnWzLhxzzLd6VwHC78/zNsbD3MmrxCA0ABfBneMYUT3WOpUDTY5oYiISMVw6zIjv3TwZA5z1qfyUdIxCotdANSuEsTI7rEM6hhDWKCfyQlFREQqlsqMBzAMgw0/nGHWuhRW7z9Vsj2+jo0xPevTr2VNfH2sJiYUERExj8qMGyssdvHJ9gxmr0th34kcACwWuKl5Dcb0rE+HelV100cREfF6KjNu6Fx+IYs2HWHBhjQycy58AGCQnw/3dajDqB5x1IvUTTZFRER+ojLjRlJP5zF3fSofbDvK+SInADXCAxjeLZahnephC9Z6GBERkZ9TmTGZYRhsScti1roUVu49yU9v4GpeK5wxPeO4vXU0/r5aDyMiInI5KjMmKXK6+HznceasT2XH0eyS7Tc0jWJMjzi6NojUehgREZFSUJmpYPaCIpZsPsL879LIyC4AIMDXysB2dRjdI5aGUWEmJxQREfEsKjMVJP1sPvO+S+PdLUfIK7ywHqZaqD/DusTyQJe6RIbq5pgiIiLXQmWmnCUdyWL2ulS+2HUc14/rYRpFhTKmZxwD2tQm0M/H3IAiIiIeTmWmHDhdBl/vOcGsdalsO5xVsr1no2qM7hFHr8bVtR5GRESkjKjMlKE8RzHvbU1n7neppJ89D4Cfj4UBbWozukcczWpVvhtdioiImE1lpgwczz7P/A1pvLPpCPaCYgCqBPvxQOd6PNi1HlHhgSYnFBERqbxUZn6DXceymb0uhU93HKf4xwUxcdVCGNUjjnva1SHIX+thREREypvKzDX626d7mLM+teR557gIxvSsT5+mUVitWg8jIiJSUVRmrlGnuAjmb0jj9ta1GN0jjtZ1qpgdSURExCupzFyjvs1qsO7J3kRXCTI7ioiIiFfTTX+ukY/VoiIjIiLiBlRmRERExKOpzIiIiIhHU5kRERERj6YyIyIiIh5NZUZEREQ8msqMiIiIeDSVGREREfFoKjMiIiLi0VRmRERExKOpzIiIiIhHU5kRERERj6YyIyIiIh5NZUZEREQ8mq/ZAcqbYRgA2O12k5OIiIhIaf30uv3T6/iVVPoyk5OTA0BMTIzJSURERORq5eTkYLPZrriPxShN5fFgLpeLjIwMwsLCsFgsZfq97XY7MTExpKenEx4eXqbfu7LRuSo9navS07kqPZ2r0tO5Kr3yPFeGYZCTk0N0dDRW65VXxVT6KzNWq5U6deqU688IDw/XgC8lnavS07kqPZ2r0tO5Kj2dq9Irr3P1a1dkfqIFwCIiIuLRVGZERETEo6nM/AYBAQH85S9/ISAgwOwobk/nqvR0rkpP56r0dK5KT+eq9NzlXFX6BcAiIiJSuenKjIiIiHg0lRkRERHxaCozIiIi4tFUZkRERMSjqcxcxtq1a+nfvz/R0dFYLBaWLVv2q8esWbOG9u3bExgYSP369XnzzTfLP6gbuNpztXr1aiwWyy8e+/btq5jAJkpISKBjx46EhYURFRXFnXfeyf79+3/1OG8cW9dyrrx1bL3xxhu0bt265IPLunbtyhdffHHFY7xxTMHVnytvHVOXkpCQgMVi4bHHHrvifmaMLZWZy8jLyyM+Pp4ZM2aUav/U1FRuvfVWevbsSVJSEs888wwTJkzgww8/LOek5rvac/WT/fv3c/z48ZJHo0aNyimh+1izZg3jxo1j48aNfP311xQXF3PTTTeRl5d32WO8dWxdy7n6ibeNrTp16vDSSy+xdetWtm7dyg033MCAAQPYvXv3Jff31jEFV3+ufuJtY+rntmzZwsyZM2nduvUV9zNtbBnyqwBj6dKlV9znySefNJo2bXrRtoceesjo0qVLOSZzP6U5V6tWrTIAIysrq0IyubPMzEwDMNasWXPZfTS2LijNudLY+p+qVasas2fPvuTXNKYudqVzpTFlGDk5OUajRo2Mr7/+2ujVq5cxceLEy+5r1tjSlZky8v3333PTTTddtO3mm29m69atFBUVmZTKvbVt25ZatWrRp08fVq1aZXYcU2RnZwMQERFx2X00ti4ozbn6iTePLafTyZIlS8jLy6Nr166X3Edj6oLSnKufePOYGjduHLfddht9+/b91X3NGluV/kaTFeXEiRPUqFHjom01atSguLiY06dPU6tWLZOSuZ9atWoxc+ZM2rdvj8PhYOHChfTp04fVq1dz3XXXmR2vwhiGwaRJk+jRowctW7a87H4aW6U/V948tnbu3EnXrl0pKCggNDSUpUuX0rx580vu6+1j6mrOlTePKYAlS5aQmJjIli1bSrW/WWNLZaYMWSyWi54bP3648s+3e7smTZrQpEmTkuddu3YlPT2dl19+2Sv+OPzk0UcfZceOHaxfv/5X9/X2sVXac+XNY6tJkyYkJydz7tw5PvzwQ4YPH86aNWsu+yLtzWPqas6VN4+p9PR0Jk6cyIoVKwgMDCz1cWaMLU0zlZGaNWty4sSJi7ZlZmbi6+tLZGSkSak8R5cuXTh48KDZMSrM+PHj+eSTT1i1ahV16tS54r7ePrau5lxdireMLX9/fxo2bEiHDh1ISEggPj6e11577ZL7evuYuppzdSneMqa2bdtGZmYm7du3x9fXF19fX9asWcP06dPx9fXF6XT+4hizxpauzJSRrl27snz58ou2rVixgg4dOuDn52dSKs+RlJRU6S9tw4X/Qxk/fjxLly5l9erVxMXF/eox3jq2ruVcXYq3jK2fMwwDh8Nxya9565i6nCudq0vxljHVp08fdu7cedG2kSNH0rRpU5566il8fHx+cYxpY6tclxd7sJycHCMpKclISkoyAGPatGlGUlKScfjwYcMwDOPpp582hg0bVrJ/SkqKERwcbDz++OPGnj17jDlz5hh+fn7GBx98YNavUGGu9ly98sorxtKlS40DBw4Yu3btMp5++mkDMD788EOzfoUK8/vf/96w2WzG6tWrjePHj5c88vPzS/bR2LrgWs6Vt46tyZMnG2vXrjVSU1ONHTt2GM8884xhtVqNFStWGIahMfX/Xe258tYxdTk/fzeTu4wtlZnL+OnteD9/DB8+3DAMwxg+fLjRq1evi45ZvXq10bZtW8Pf39+IjY013njjjYoPboKrPVdTp041GjRoYAQGBhpVq1Y1evToYXz22WfmhK9glzpPgDFv3rySfTS2LriWc+WtY2vUqFFGvXr1DH9/f6N69epGnz59Sl6cDUNj6v+72nPlrWPqcn5eZtxlbFkM48eVOSIiIiIeSAuARURExKOpzIiIiIhHU5kRERERj6YyIyIiIh5NZUZEREQ8msqMiIiIeDSVGREREfFoKjMiIiLi0VRmRERExKOpzIiIiIhHU5kRERERj6YyIyJu5a233iIyMhKHw3HR9rvvvpsHH3wQgOXLl9O+fXsCAwOpX78+U6ZMobi4uGTfadOm0apVK0JCQoiJieGRRx4hNze3Qn8PEak4KjMi4lbuvfdenE4nn3zyScm206dP8+mnnzJy5Ei++uorHnjgASZMmMCePXv473//y/z583nhhRdK9rdarUyfPp1du3axYMECvv32W5588kkzfh0RqQC6a7aIuJ1HHnmEtLQ0Pv/8cwBee+01pk+fzqFDh+jVqxf9+vVj8uTJJfu//fbbPPnkk2RkZFzy+73//vv8/ve/5/Tp0xWSX0QqlsqMiLidpKQkOnbsyOHDh6lduzZt2rTh7rvv5rnnniMkJASXy4WPj0/J/k6nk4KCAvLy8ggODmbVqlW8+OKL7NmzB7vdTnFxMQUFBeTm5hISEmLibyYi5UFlRkTcUvv27bnnnnu4+eab6dixI2lpacTExBAUFMSUKVMYOHDgL46pX78+6enpNG3alIcffphBgwYRERHB+vXrGT16NFlZWVSpUqXifxkRKVe+ZgcQEbmUMWPG8Morr3Ds2DH69u1LTEwMAO3atWP//v00bNjwksdt3bqV4uJi/vWvf2G1XlgW+N5771VYbhGpeCozIuKWhg4dyh//+EdmzZrFW2+9VbL9z3/+M7fffjsxMTHce++9WK1WduzYwc6dO/n73/9OgwYNKC4u5vXXX6d///589913vPnmmyb+JiJS3vRuJhFxS+Hh4dx9992EhoZy5513lmy/+eab+fTTT/n666/p2LEjXbp0Ydq0adSrVw+ANm3aMG3aNKZOnUrLli1ZtGgRCQkJJv0WIlIRtGZGRNzWjTfeSLNmzZg+fbrZUUTEjanMiIjbOXv2LCtWrGDo0KHs2bOHJk2amB1JRNyY1syIiNtp164dWVlZTJ06VUVGRH6VrsyIiIiIR9MCYBEREfFoKjMiIiLi0VRmRERExKOpzIiIiIhHU5kRERERj6YyIyIiIh5NZUZEREQ8msqMiIiIeLT/A6l2KT4DXBEAAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1,2,3,4],[1,4,9,16])\n",
    "plt.xlabel('yea')\n",
    "plt.ylabel('no')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you wanted to plot x squared with more data, you can specify that as well..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1111ea8d0>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.arange(1,4,0.001)\n",
    "plt.plot(x,x**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>16.2 Formatting your Plot</h2>\n",
    "\n",
    "Every time there's an x,y pair of arguments, there's an optional third argument that is called the <b>format string</b> that will indicate the color and line type.\n",
    "\n",
    "Here's the list! You can also specify things outside this list, as we'll see, but these are the quick ones:\n",
    "\n",
    "<b>Line Style Specifiers</b>\n",
    "<table style=\"width:100%\">\n",
    "    <tr>\n",
    "        <th>Specifier</th>\n",
    "        <th>Line Style</th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>'-'</td>\n",
    "        <td>Solid line (default)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>'--'</td>\n",
    "        <td>Dashed line</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>':'</td>\n",
    "        <td>Dotted line</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>'-.'</td>\n",
    "        <td>Dash-dot line</td>\n",
    "    </tr>\n",
    "</table>\n",
    "\n",
    "<b>Marker Specifiers</b>\n",
    "<table style=\"width:100%\">\n",
    "    <tr>\n",
    "        <th>Specifier</th>\n",
    "        <th>Marker Type</th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>'+'</td>\n",
    "        <td>Plus sign</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>'o'</td>\n",
    "        <td>Circle</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>'*'</td>\n",
    "        <td>Asterisk</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>'.'</td>\n",
    "        <td>Point</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>'x'</td>\n",
    "        <td>Cross</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>'square' or 's'</td>\n",
    "        <td>Square</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>'diamond' or 'd'</td>\n",
    "        <td>Diamond</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>'^'</td>\n",
    "        <td>Upward-pointing triangle</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>'v'</td>\n",
    "        <td>Downward-pointing triangle</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>'&gt'</td>\n",
    "        <td>Right-pointing triangle</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>'&lt'</td>\n",
    "        <td>Left-pointing triangle</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>'pentagram' or 'p'</td>\n",
    "        <td>Five-pointed star (pentagram)</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>'hexagram' or 'h'</td>\n",
    "        <td>Six-pointed star (hexagram)</td>\n",
    "    </tr>\n",
    "</table>\n",
    "\n",
    "<b>Color Specifiers</b>\n",
    "<table style=\"width:100%\">\n",
    "    <tr>\n",
    "        <th>Specifier</th>\n",
    "        <th>Color</th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>r</td>\n",
    "        <td>Red</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>g</td>\n",
    "        <td>Green</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>b</td>\n",
    "        <td>Blue</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>c</td>\n",
    "        <td>Cyan</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>m</td>\n",
    "        <td>Magenta</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>y</td>\n",
    "        <td>Yellow</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>k</td>\n",
    "        <td>Black</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>w</td>\n",
    "        <td>White</td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1111f4090>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How about plotting some red circles separated by dotted lines?\n",
    "plt.plot([1,2,3,4],[1,4,9,16],\"ro:\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also specify the axes that we want to see using the <b>matplotlib.pyplot.axis()</b> module, which takes as input a list of [xmin, xmax, ymin, ymax]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1,2,3,4],[1,4,9,16],\"g-.^\")\n",
    "plt.axis([0,10,0,30])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot using arrays! We're not just limited to lists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = np.arange(0,5,0.2)\n",
    "\n",
    "# Create an array that counts from 0 to 5 at 0.2 intervals.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>1. Create a plot where the x values are given by t and the y values are given by t. Use red circles separated by dashes.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1105bcf90>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t,t,\"r--o\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>2. Create a plot where the x values are given by t and the y values are given by t squared. Use blue squares separated by dots.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14aa2b990>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t,t**2,\"bs:\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>3. Create a plot where the x values are given by t and the y values are given by t cubed. Use green upward-pointing triangles.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14ab5b990>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t,t**3,\"g^\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>16.3 Take-Home Points</h2>\n",
    "<ul>\n",
    "    <li><b>matplotlib</b>'s <b>pyplot</b> module lets us use Matlab's powerful plotting tools in Python.</li>\n",
    "    <li>The <b>matplotlib.pyplot.plot()</b> function is a simple way to plot 2-D data.</li>\n",
    "    <li>We can specify axis limits as well as line style and color.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}