Matplotlib Exercises .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a href='http://www.pieriandata.com'> <img src='../../Pierian_Data_Logo.png' /></a>\n",
    "___\n",
    "# Matplotlib Exercises \n",
    "\n",
    "Welcome to the exercises for reviewing matplotlib! Take your time with these, Matplotlib can be tricky to understand at first. These are relatively simple plots, but they can be hard if this is your first time with matplotlib, feel free to reference the solutions as you go along.\n",
    "\n",
    "Also don't worry if you find the matplotlib syntax frustrating, we actually won't be using it that often throughout the course, we will switch to using pandas built-in visualization capabilities. But, those are built-off of matplotlib, which is why it is still important to get exposure to it!\n",
    "\n",
    "** * NOTE: ALL THE COMMANDS FOR PLOTTING A FIGURE SHOULD ALL GO IN THE SAME CELL. SEPARATING THEM OUT INTO MULTIPLE CELLS MAY CAUSE NOTHING TO SHOW UP. * **\n",
    "\n",
    "# Exercises\n",
    "\n",
    "Follow the instructions to recreate the plots using this data:\n",
    "\n",
    "## Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "x = np.arange(0,100)\n",
    "y = x*2\n",
    "z = x**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Import matplotlib.pyplot as plt and set %matplotlib inline if you are using the jupyter notebook. What command do you use if you aren't using the jupyter notebook?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 1\n",
    "\n",
    "** Follow along with these steps: **\n",
    "* ** Create a figure object called fig using plt.figure() **\n",
    "* ** Use add_axes to add an axis to the figure canvas at [0,0,1,1]. Call this new axis ax. **\n",
    "* ** Plot (x,y) on that axes and set the labels and titles to match the plot below:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x115ad5110>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x115b8f050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.plot(x, y)\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x111534c50>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TrGxJTWdxqxPW1mBhAZaWrGxJzWZxq/U2K/vUKStbUvNZ3Gotz2VLaiOLW63kuWxJbWVx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f0b1710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 2\n",
    "** Create a figure object and put two axes on it, ax1 and ax2. Located at [0,0,1,1] and [0.2,0.5,.2,.2] respectively.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x112965050>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2gDyuVho73yGqmCuQZaqVxs+Cq1E5Vytwrlaqlu8WPSNTrTxfrVQbFlyNyONqBR5XK9WS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\nK0lSA1hwJUlqgP8PCn016okRlmAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115fff910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_axes([0,0,1,1])\n",
    "ax2 = fig.add_axes([0.2, 0.5, .2, .2])\n",
    "\n",
    "ax1.plot(x, y)\n",
    "ax2.plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1144d8160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now plot (x,y) on both axes. And call your figure object to show it.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4ETqpKfvZ65dpwaplQTcJAEKDxI1Qafssm6INAKcicSMUSNkAkB4SNwJHygaA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\nraoRPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1144d8160>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 3\n",
    "\n",
    "** Create the plot below by adding two axes to a figure object at [0,0,1,1] and [0.2,0.5,.4,.4]**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 50)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XVo15L/5Cwpo0cLok8TBnG7EvMsZ8aYyJPMVrT9ZEQVJ7jDEEB5dPxCkuLqa4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\niIiIF1Gwi4iIeBEFu4iIiBf5/ybgyp/77OXAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11634f1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax2 = fig.add_axes([.2,.5,.4,.4])\n",
    "\n",
    "ax.plot(x, z)\n",
    "ax.set_xlabel('X')\n",
    "ax.set_ylabel('Z')\n",
    "\n",
    "ax2.plot(x, y)\n",
    "ax2.set_title('Zoom')\n",
    "ax2.set_xlabel('X')\n",
    "ax2.set_ylabel('Y')\n",
    "ax2.set_xlim([20, 22])\n",
    "ax2.set_ylim([30, 50])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now use x,y, and z arrays to recreate the plot below. Notice the xlimits and y limits on the inserted plot:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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X9L49nClToFMnGDVKIS9hEvS5shI8J+ek8NasWePmzJnjnHNu69atrnbt2m7h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\nH51ua+eImT++AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c212cf8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X9L49nClToFMnGDVKIS9hEvS5shI8J+ek8NasWePmzJnjnHNu69atrnbt2m7h\nwoWuX79+bujQoc4554YMGeL69+9/yLZ79+51Z5xxhluxYoXbvXu3a9SokVu4cGFU1uqcczVr1nQb\nNmyIWH2FqXXRokVuyZIl7ve//7379ttvC9y2pPdrcet1Ljr27SeffOL27t3rnHOuf//+bsCAAYds\nG8S+LcjEic5Vrerc9Okl/qslioXyq8j5pwlzpEApKSk0btwYgPLly1OvXj2ys7MLNZFOVlYWZ511\nFjVq1KB06dJ0796d9957LyprBf/Hbl5eXsTqO1qtq1evpk6dOpx11llHHChW0vu1uPVCdOzb1q1b\nk5Tkv+patGhBdnb2IdsGsW8P9uKL8D//41v0F15Yor9a4pyCXo5qxYoVzJ07lxYtWhRqIp3Vq1dz\n2mmn7X+dmprK6tWro7JW8Oe/9k0U9NJLL5VInflrPe+88wq1fpD7FY69Xoi+fTty5Eg6dOhwyPpB\n7lvn4KGHYOhQf5vZRo1K5NdKAtG0C3JE27Zto2vXrgwfPpzy5ctH9UQ6Ra31iy++4NRTT+Xnn3+m\nTZs21KtXjwsj3KQ6uNZoV9R6o2nfPvzww5QuXZprrrkmor//WOTl+dvMTp8OM2bAqacGXZHEI7Xo\n5bByc3Pp2rUrPXv23H8dfXJyMjk5OQCsXbuWqlWrHrJd9erV+fHHH/e/zs7Opnr1giZXDL5WgFND\n366nnHIKl19+OVlZWSVea2EEsV+h6PVC9Ozb0aNHM2nSJMaPH1/gdkHs29274dprYd48yMxUyEvk\nKOjlsG644QbOPvts7rrrrv3LCjORTvPmzVm2bBkrV65k9+7dTJgwgU6dOkVlrdu3b2fbtm0A/Prr\nr0yZMoUGDRqUeK35He68dxD7FYpeb7Ts28mTJ/PYY4+RkZHB8ccfX+B2Jb1vt26FP/4RduyAyZN1\nm1mJsOKM5IulBxp1f0xmzJjhkpKSXKNGjVzjxo1dkyZN3EcffeR++eUXd+mll7ratWu7Nm3auI0b\nNzrnnPvpp5/cH/7wh/3bf/TRR6527druzDPPdIMHD47aWpcvX75/uwYNGgRW6zvvvONSU1PdCSec\n4FJSUlz79u0PqdW5kt2vxa03GvbtpEmT3JlnnulOP/1016RJE9ekSRN36623HlKrcyW3b9eude6c\nc5y7+Wbn9uyJ2K+ROEIxR90fccKceKIJc0QkaD/8AO3awZ/+BPffryltpXAiPWGOiIiEwezZft76\nv/4VBg1r9aaEAAAWOElEQVRSyEvJ0ah7EZEImzLFt+JfeAEuvzzoaiTRqEUvIhJBo0dDr17wzjsK\neQmGWvQiIhHgnL+17IgR/vK5unWDrkgSlYJeRCTMcnOhTx/IyoIvv9Q18hIsBb2ISBht2wbdu8Oe\nPX5K2woxczNviVc6Ry8SRbKzs6lVqxabNm0C/K12a9WqdcCsbRK91q6FSy6B5GT44AOFvEQHBb1I\nFElNTeW2226jf//+AAwYMIBbbrmF008/PeDK5GgWLICWLaFLF39evnTpoCsS8TRhjkiUyc3NpVmz\nZlx//fWMGDGCuXPnctxxxwVdlhzB559Dt27w2GN+hL1IOBV3whydoxeJMqVKleLRRx+lffv2TJ06\nVSEf5caN85PgjB8Pl14adDUih1LXvUgUmjRpEtWqVWP+/PlBlyKH4Zyfxva+++CzzxTyEr0CDXoz\nSzKz2WaWEXp9splNMbPFZvaxmVXKt+49ZrbUzBaaWdt8y5ua2TwzW2JmTwbxOUTCae7cuXz66afM\nnDmTJ554Yv+tdiV67NoFPXv6O8/NnAlnnx10RSKHF3SL/i5gQb7XA4Cpzrk6wDTgHgAzOxvoBtQD\nOgDPme2fKfp54EbnXG2gtpm1K6niRSLhtttuY/jw4aSmptKvXz/+8pe/BF2S5LN+PbRpAzt3+pZ8\ncnLQFYkcWWBBb2apQEdgRL7FnYExoedjgC6h552ACc65XOfcCmApcK6ZpQAVnHOzQuuNzbeNSMx5\n6aWXqFGjBq1atQLg1ltvZdGiRUyfPj3gygRg8WJo0cKPrp84EcqWDboikaMLcjDeMOBvQKV8y5Kd\nczkAzrm1ZlY1tLw68FW+9VaHluUC2fmWZ4eWi8Skm2++mZtvvnn/66SkJL755psAK5J9pk2DHj1g\n8GC44YagqxEpvEBa9Gb2ByDHOTcXONIlA7oeTkQCN2KED/nXX1fIS+wJqkV/AdDJzDoCJwIVzGwc\nsNbMkp1zOaFu+XWh9VcDp+XbPjW07HDLCzRo0KD9z9PT00lPTy/+JxGRuLV3L/TrB++/D9OnQ+3a\nQVckiSAzM5PMzMywvV/gE+aY2SXAX5xznczsUeAX59xQM+sPnOycGxAajPcqcB6+a/4T4CznnDOz\nmcCdwCzgQ+Ap59zkAn6PJswRkULbssW34nft8ufjK1cOuiJJVMWdMCfoUfcHGwK0MbPFwKWh1zjn\nFgAT8SP0JwG35Uvt24GXgSXA0oJCXkTkWCxfDuefDzVqwEcfKeQltgXeoi8patGLSGFMn+6ns/37\n3+H224OuRiT+WvQiIoF56SXo2hXGjFHIS/zQXPcikvD27IG+fWHqVJgxA846K+iKRMJHQS8iCe2X\nX+Cqq+DEE/10tpUqHX0bkViirnsRSVjz58O550Lz5pCRoZCX+KQWvYgkpLfegltugSefhGuvDboa\nkchR0ItIQsnL87eWHTfO333unHOCrkgkshT0IpIwNm/2rfetW2HWLKha9ejbiMQ6naMXkYSwYIE/\nF5+W5kfXK+QlUSjoRSTuvfkmXHIJ3HsvPPMMlC4ddEUiJUdd9yISt/buhYED4bXX/FS2zZoFXZFI\nyVPQi0hcWr8errkGcnPhm2/glFOCrkgkGOq6F5G4k5XlR9M3aQJTpijkJbGpRS8iccM5P1/9wIHw\nwgtwxRVBVyQSPAW9iMSFHTv8jWi+/trPV1+nTtAViUQHdd2LSMxbtgxatPBh//XXCnmR/BT0IhLT\n3nkHzj8f/vu/Yfx4KF8+6IpEoou67kUkJu3ZA/fc46+R/+ADf3MaETmUgl5EYs6qVdC9O5x0Enz7\nLVSpEnRFItFLXfciElMmTfJT2XbqBO+/r5AXORq16EUkJuTmwt//Dq++6rvrL7ww6IpEYoOCXkSi\n3qpVfpa7cuVg9mxNgCNyLNR1LyJR7f33/Rz1f/iD77ZXyIscG7XoRSQq7d4N/fv7y+f2XUInIsdO\nQS8iUWfZMujRA6pX9131lSsHXZFI7FLXvYhElVdegZYtoVcv35JXyIsUj1r0IhIVtm71c9XPmgVT\np0KjRkFXJBIf1KIXkcDNmgVNm8Lxx/t7xyvkRcJHLXoRCczevTB0KDz5JDz7LFx1VdAVicQfBb2I\nBOLHH6FnTzDz09iedlrQFYnEJ3Xdi0iJe/11f218hw7w6acKeZFIUoteRErMpk3Qp48/Jz9pkg97\nEYkstehFpERkZvpBdhUr+mvjFfIiJUMtehGJqF274H//118fP2IEdOwYdEUiiUVBLyIRM3euH3B3\n1lnwf/+neepFgqCuexEJu9xcePhhaNsW+vWDt95SyIsERS16EQmrxYvhuuv8LWV12ZxI8NSiF5Gw\n2LsXhg2DCy6Aa6+FKVMU8iLRQC16ESm2Zcvg+uv985kz4cwzg61HRH6jFr2IFFleHjz9NLRoAVde\n6S+hU8iLRBe16EWkSJYuhRtv9F32X3wBdeoEXZGIFEQtehE5Jnv3whNP+HvGX3kl/PvfCnmRaKYW\nvYgU2oIFcNNNULq0zsWLxAq16EXkqHbvhgcfhIsv9hPgfPaZQl4kVqhFLyJHlJXlz8XXqAFz5uiS\nOZFYoxa9iBRo2za4+27o1AnuuQfef18hLxKLFPQicogPP4T69WHDBvjuO7jmGjALuioRKQp13YvI\nfmvXwl13+alrX34ZWrcOuiIRKS616EWEvDx4/nlo2BBq1YL58xXyIvFCLXqRBDd3LtxyC5Qq5UfT\nN2gQdEUiEk6BtOjNLNXMppnZ92Y238zuDC0/2cymmNliM/vYzCrl2+YeM1tqZgvNrG2+5U3NbJ6Z\nLTGzJ4P4PCKxaOtW+MtfoF07uPlmP/GNQl4k/gTVdZ8L9HXO1QdaArebWV1gADDVOVcHmAbcA2Bm\nZwPdgHpAB+A5s/1Dg54HbnTO1QZqm1m7kv0oIrHFOZg4EerV+22w3Y03QpJO5InEpUC67p1za4G1\noefbzGwhkAp0Bi4JrTYGyMSHfydggnMuF1hhZkuBc81sJVDBOTcrtM1YoAvwcUl9FpFYsngx9OkD\nOTkwYQJceGHQFYlIpAX+N7yZpQGNgZlAsnMuB/b/MVA1tFp1YFW+zVaHllUHsvMtzw4tE5F8tm3z\n18JfcAF07AizZyvkRRJFoIPxzKw88CZwV6hl7w5a5eDXxTJo0KD9z9PT00lPTw/n24tEnX3d9H/9\nK6Snw7x5UK1a0FWJyJFkZmaSmZkZtvcz58KapYX/xWalgA+Aj5xzw0PLFgLpzrkcM0sBPnPO1TOz\nAYBzzg0NrTcZuB9YuW+d0PLuwCXOuVsL+H0uqM8qEoTvvoM77/Tn4Z9+Gi66KOiKRKQozAznXJGn\nrAqy634ksGBfyIdkANeFnvcG3su3vLuZlTGzmsCZQFaoe3+zmZ0bGpzXK982IglpwwZ/Hr5VK38b\n2W++UciLJLKgLq+7ALgWaGVmc8xstpm1B4YCbcxsMXApMATAObcAmAgsACYBt+Vrnt8OvAwsAZY6\n5yaX7KcRiQ65ufDss1C3rn+9cCHcfru/Pl5EEldgXfclTV33Es8+/thfE1+1Kgwf7me4E5H4UNyu\ne/2tLxLDFi70Ab90KTz2GHTurJvPiMiBAr+8TkSO3fr1cMcdcPHFfk7677+HLl0U8iJyKAW9SAzZ\nuROGDvWz2oFv0fftC2XKBFuXiEQvdd2LxIC8PHjtNbj3XjjnHPjiC6hdO+iqRCQWKOhFotwnn0D/\n/lC6NLz6qma0E5Fjo6AXiVKzZ8OAAbBiBQweDFdcoXPwInLsdI5eJMosXQo9esAf/+jD/fvv/cQ3\nCnkRKQoFvUiUyM6GP/8ZWrb094VfsgRuucV32YuIFJWCXiRgP//sbzrTqBFUruwDfuBAKF8+6MpE\nJB4o6EUCsnGjD/S6dWH7dpg/H4YM8WEvIhIuCnqRErZlCzz0EJx1FuTk+EF3zz2n28eKSGQo6EVK\nyJYt8PDDcMYZvnt+5kwYMQJq1Ai6MhGJZwp6kQjLH/CLFsGMGTBuHJx5ZtCViUgi0HX0IhGycSM8\n9RQ88wy0a+cDvk6doKsSkUSjFr1ImP38s5+q9swz4ccf4csv4ZVXFPIiEgwFvUiYrFoFd93lA33j\nRvj2W3j5ZT/oTkQkKAp6kWJatAiuv95fB1+6NHz3HTz/PKSlBV2ZiIjO0YsU2cyZ8NhjMH069OkD\ny5bpGngRiT4KepFjkJcHkybBo4/6rvq+fWHsWChXLujKREQKpqAXKYQdO/yAumHD4IQToF8/6NoV\nSun/IBGJcvqaEjmCdev8rHXPPw/Nm8Ozz0J6uu4kJyKxQ4PxRAowd64fYFenDqxZA59/Dh98AL//\nvUJeRGKLWvQiIbm5kJEBw4fDDz/A7bf7e8P/7ndBVyYiUnQKekl469b5Oef/9S9ITfXXwl9xhe4D\nLyLxQV33kpCc85fH9ewJtWv7Fvy77/pZ7K6+WiEvIvHDnHNB11AizMwlymeVw9u6FV591bfet22D\n//5vuOEGqFIl6MpERApmZjjnijw6SEEvCeHbb+Gll+D116FVK7jlFrj0UkhSn5aIRLniBr3O0Uvc\n2rzZt95HjPBzz994I3z/PVSrFnRlIiIlRy16iSt5ef5SuFGj/Aj6tm3hppugdWu13kUkNqnrvpAU\n9PFtxQo/Fe3o0VChgr8G/tpr4ZRTgq5MRKR41HUvCWvLFnjzTR/w33/vR8u/+SY0aaJJbURE9lGL\nXmLK7t0wZYo/9/7RR35gXa9e0LEjlCkTdHUiIuGnrvtCUtDHrrw8+OILGD/et9jr1oVrroFu3XRZ\nnIjEP3XdS1xyDr7+2l8O98YbcPLJ/pz7rFmQlhZ0dSIisUNBL1HDOcjK8q32N97wt4O9+mrfVX/2\n2UFXJyISmxT0Eqi9e/20s2+95R/ly/v7vL/3HvzXf2lQnYhIcSnopcTt3AlTp/q55TMy4NRT4cor\n4eOP1XIXEQk3DcaTErFuHXz4Ibz/Pnz6KTRuDF26+EfNmkFXJyISvTTqvpAU9CUrLw/mzPGXwH3w\nASxa5Gep++Mf/aVwuse7iEjhKOgLSUEfeevX+y75yZP946STfKh37AgXX6zr3EVEikJBX0gK+vDb\ntQu++go++cSPjF+yxAd6+/bQoQPUqhV0hSIisU9BX0gK+uLLzfXd8Z9+CtOm+ZCvV893ybdtCy1a\nqNUuIhJuCvpCUtAfu9xcmD0bMjP9HeG++AJSU/193Fu1gksu8d3zIiISOQr6QlLQH93WrTBzJsyY\n4UM9K8uPiL/kEv+4+GLdDU5EpKQp6AtJQX+gvDx/Tn3mTP/46itYtgzOOQcuvNA/Wrb0U8+KiEhw\nFPSFlMhB7xz8+CN8842fK37WLPj2W9/t3rKlP7fesiU0agTHHx90tSIikp+CvpASJehzc31L/f/+\nzw+cmz3b/1umDDRrBs2b+3+bNYOqVYOuVkREjkZBX0jxFvT7WukLFsD338N338G8eX5imtRUaNgQ\nmjb1jyZNICUl6IpFRKQoFPSAmbUHngSSgJedc0MLWCcmg/7XX+GHH2DxYv9YtOi3fytW9HPD16/v\nH40a+X/LlQu6ahERCZeED3ozSwKWAJcCPwGzgO7OuUUHrReVQZ+XB2vWwH/+AytW+H//8x8/MG7Z\nMti0yY98r1PnwEfdutExUC4zM5P09PSgy4hL2reRof0aOdq3kVHcoI+Hu9edCyx1zq0EMLMJQGdg\n0RG3KgG//gpr1/og/+mn3/5dteq3x5o1ULmyD/O0NP/v+edDr15w5plQrRokJQX9SQ5P/2NHjvZt\nZGi/Ro72bXSKh6CvDqzK9zobH/7F5hzs3g3btvlrzLduhc2bf3ts2gQbNsAvv/jHhg3w88/+Tm3r\n1vnWenIyVK/ub8VarZr/t2FDfx79tNP8z044IRzVioiIHCoegr7QOnb04ZuXB3v3wp49Bz527YId\nO/z90nfuhO3bfWu6fPnfHiedBJUq/fZv5co+sBs1gipV/F3ZkpP9iPZy5cCK3NkiIiJSfPFwjr4F\nMMg51z70egDgDh6QZ2ax/UFFRCRhJfpgvOOAxfjBeGuALKCHc25hoIWJiIhEgZjvunfO7TWzPsAU\nfru8TiEvIiJCHLToRURE5PCi+MKt8DGz9ma2yMyWmFn/oOuJVWaWambTzOx7M5tvZneGlp9sZlPM\nbLGZfWxmlYKuNRaZWZKZzTazjNBr7dcwMLNKZvaGmS0MHbvnad8Wn5ndbWbfmdk8M3vVzMpovxaN\nmb1sZjlmNi/fssPuSzO7x8yWho7ptkd7/7gP+tCEOs8A7YD6QA8zqxtsVTErF+jrnKsPtARuD+3L\nAcBU51wdYBpwT4A1xrK7gAX5Xmu/hsdwYJJzrh7QCD/HhvZtMZhZNeAOoKlz7r/wp4F7oP1aVKPw\nGZVfgfvSzM4GugH1gA7Ac2ZHvr4r7oOefBPqOOf2APsm1JFj5Jxb65ybG3q+DVgIpOL355jQamOA\nLsFUGLvMLBXoCIzIt1j7tZjMrCJwkXNuFIBzLtc5txnt23A4DihnZqWAE4HVaL8WiXNuBrDxoMWH\n25edgAmhY3kFsJSjzB2TCEFf0IQ61QOqJW6YWRrQGJgJJDvncsD/MQDovnjHbhjwNyD/oBnt1+Kr\nCaw3s1Gh0yIvmllZtG+LxTn3E/A48CM+4Dc756ai/RpOVQ+zLw/OtNUcJdMSIeglzMysPPAmcFeo\nZX/wiE6N8DwGZvYHICfUW3KkLjjt12NXCmgKPOucawr8iu8S1TFbDGZ2Er7FWQOohm/ZX4v2ayQV\neV8mQtCvBk7P9zo1tEyKINRN9yYwzjn3Xmhxjpklh36eAqwLqr4YdQHQycyWA68BrcxsHLBW+7XY\nsoFVzrlvQq/fwge/jtniaQ0sd85tcM7tBd4Bzkf7NZwOty9XA6flW++omZYIQT8LONPMaphZGaA7\nkBFwTbFsJLDAOTc837IM4LrQ897AewdvJIfnnLvXOXe6c64W/vic5pzrCbyP9muxhLo+V5lZ7dCi\nS4Hv0TFbXD8CLczshNBAsEvxA0m1X4vOOLBH73D7MgPoHrrKoSZwJn6iuMO/cSJcRx+6X/1wfptQ\nZ0jAJcUkM7sA+DcwH9+N5IB78QfZRPxfmSuBbs65TUHVGcvM7BLgL865TmZWGe3XYjOzRvhBjqWB\n5cD1+IFk2rfFYGb34/8w3QPMAW4CKqD9eszMbDyQDlQBcoD7gXeBNyhgX5rZPcCN+H1/l3NuyhHf\nPxGCXkREJFElQte9iIhIwlLQi4iIxDEFvYiISBxT0IuIiMQxBb2IiEgcU9CLiIjEMQW9iBRJ6LbF\ny0PToe67reZyMzv9aNuKSMlR0ItIkTjnsoHngKGhRUOAfznnfgyuKhE5mCbMEZEiC9374Bv8/bRv\nAhqH5j4XkShRKugCRCR2OedyzawfMBlorZAXiT7quheR4uoI/AQ0DLoQETmUgl5EiszMGuPvXNYC\n6LvvtpoiEj0U9CJSHM/h756VDTwKPB5wPSJyEAW9iBSJmd0MrHTOTQsteh6oa2YXBViWiBxEo+5F\nRETimFr0IiIicUxBLyIiEscU9CIiInFMQS8iIhLHFPQiIiJxTEEvIiISxxT0IiIicUxBLyIiEsf+\nH51ua+eImT++AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c212cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 4\n",
    "\n",
    "** Use plt.subplots(nrows=1, ncols=2) to create the plot below.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x116aa6610>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EEc8zZ0ZiPH06zJsX+xdD7DF61llRLd6rFxx0kGZuRGqpwSfJlRXgFRTAL39Z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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1169b5e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize = (12, 2))\n",
    "axes[0].plot(x, y, color='blue', ls='--', lw=3)\n",
    "axes[1].plot(x, z, color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now plot (x,y) and (x,z) on the axes. Play around with the linewidth and style**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SCR/guuuii6WR1NKX2FJLX0LTvz+89ZY/btEiMYMxA9SnLyKSSdu3JxI+pI5zzgFK+nlq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\nyP8DG1xU47jRvuwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114be5e48>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** See if you can resize the plot by adding the figsize() argument in plt.subplots() are copying and pasting your previous code.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1141b4ba8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Yxm0iBUxpJHkknbLp7dvXlU3v31952CIikuf+7d+SB9X//GfY+a9jsUQArWxn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\nJEsUbIuIiIiIZMn/A0B/LV+00DLqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11400a908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Great Job!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:anaconda]",
   "language": "python",
   "name": "conda-env-anaconda-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}