02-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 seaborn and 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": 7,
   "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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A+1o9KDMngAmA8fHx7lJe6hFbq6Re6UXAXkzpx8MRsSgztxdX3wk80YPnkCpna5XUS11tPSLiNcBvAx8oDf9xRCxn+kfEW2fcJtWOrVVSFboK2Mz8B+CNM8Yu7WpGUh/ZWiVVxS2JRpKtVVLVDFiNHFurpH5wq6KRYWuV1E8GrEaCrVVSv7mFUaP5O4QlDYoBq8aytUoaJLc2ahz3tUqqAwNWjWJrlVQXbnnUCLZWSXVjwGro2Vol1ZFbIQ0tjxCWVGcGrIaSrVVS3blF0lApt9bjXmdrlVRfBqyGRrm1XrJyjLWrlrHA1iqpptw6qfZsrZKGkQGrWrO1ShpWbqlUS7ZWScPOgFXt2FolNYFbLdWGrVVSkxiwqgVbq6SmcQumgZrZWm+/fAVn21olNYABq4GxtUpqMrdm6jv3tUoaBQas+srWKmlUdLVli4itwC+AKWBfZo5HxBuALwFLgK3AuzPzZ91NU8PO1ipp1PSiOvxGZj5fur4WeCgzPxYRa4vr1/TgeTSkbK2SRlEVW7kLgHOLy7cBf40BO5JsrZJGWbcBm8ADEZHATZk5ARybmdsBMnN7RBzT7SQ1fB7fupuP2FoljbBut3hnZ+ZzRYg+GBE/bPeBEbEGWAMwNjbW5TRUF3v2TvHx+zfxhW/ZWiWNtq4CNjOfK/7fGRFfBc4EdkTEoqK9LgJ2zvLYCWACYHx8PLuZh+rBfa2S9EuHdPrAiDgyIl67/zLwr4AngHuBy4q7XQZ8vdtJqt727J3io3+xgYtuepS9+17mjstX8F8u/KeGq6SR1s0W8FjgqxGx/+PckZl/GRGPA3dHxPuBZ4GLup+m6sp9rZLUWsdbwsx8Gvi1FuM/Bc7rZlKqP48QlqSDs2po3mytkjQ3t4pqW/kI4cVH2Vol6WAMWLWl3FovXXkia1edwpG2VkmalVtIHZStVZI6Y8BqVuXzWm2tkjQ/bi11AFurJHXPgNUruK9VknrDLacAW6sk9ZoBqwPOa123apmtVZK65FZ0hNlaJak6BuyImrmv9ZpVp/jbmCSph9yijhhbqyT1hwE7QjyvVZL6x63rCLC1SlL/GbAN53mtkjQYbmkbqvz3Wm2tktR/BmwDeV6rJA2eW90GcV+rJNWHAdsQ7muVpHpxCzzkbK2SVE8G7BCztUpSfbk1HkK2VkmqPwN2yNhaJWk4uGUeErZWSRouHQdsRJwAfBH4J8DLwERmfjoi/gi4AthV3PXazPxGtxMdZZ7XKknDp5ut9D7g6sz8bkS8FlgfEQ8Wt30qM2/sfnqjzd/GJEnDq+OAzcztwPbi8i8iYiOwuFcTG3W2Vkkabj3ZYkfEEuAM4NvA2cCHIuI9wCTTLfdnvXieUeC+VklqhkO6/QARsQC4B7gqM18APgu8CVjOdMP9xCyPWxMRkxExuWvXrlZ3GTmPb93N2//0EW75my1csuJE7r/qHMNVkoZUVw02Il7FdLjenplfAcjMHaXbPwfc1+qxmTkBTACMj49nN/MYdrZWSWqebo4iDuBmYGNmfrI0vqjYPwvwTuCJ7qbYbOuf2c3v/XfPa5WkpulmS342cCnwg4j4fjF2LXBxRCwHEtgKfKCL52isPXun+MQDm7jZI4QlqZG6OYr4m0C0uMlzXucwuXU3v1/6bUzXrDqFBbZWSWoUt+p9ZGuVpNFhwPZJubV6XqskNZ9b+Ir525gkaTQZsBWaua/VI4QlaXS4ta+ArVWSZMD2mK1VkgQGbM/YWiVJZQZsD3heqyRpJlOgC7ZWSdJsDNgOeV6rJOlgTIR5srVKktphwM6DRwhLktplOrTB1ipJmi8Ddg62VklSJ0yKWdhaJUndMGBbsLVKkrplapTYWiVJvWLAFjyvVZLUSyOfILZWSVIVRjpg3dcqSarKSKaJrVWSVLWRC1hbqySpH0YmWWytkqR+GomAnXmE8NpVy/x7rZKkSlWWMhFxPvBp4FDg85n5saqeazbl1nrc62ytkqT+qSRgI+JQ4M+B3wa2AY9HxL2ZuaGK52vF1ipJGqSqEudMYHNmPg0QEXcBFwCVB6ytVZJUB1UF7GLgx6Xr24AVFT3X//fyy8m7/uu3ePK5F2ytkqSBqip9osVYvuIOEWuANQBjY2M9edJDDgmu+PWTOea1R9haJUkDVVXAbgNOKF0/HniufIfMnAAmAMbHx18Rvt248IzFvfpQkiR17JCKPu7jwNKIOCkiDgdWA/dW9FySJNVOJQ02M/dFxIeA+5k+TeeWzHyyiueSJKmOKjsCKDO/AXyjqo8vSVKdVfUjYkmSRpoBK0lSBQxYSZIqYMBKklQBA1aSpAoYsJIkVcCAlSSpAgasJEkViMye/RrgzicRsQt4pocfciHwfA8/3ihx7brj+nXOteuca9edbtbvxMw8utUNtQjYXouIycwcH/Q8hpFr1x3Xr3OuXedcu+5UtX7+iFiSpAoYsJIkVaCpATsx6AkMMdeuO65f51y7zrl23alk/Rq5D1aSpEFraoOVJGmgGhWwEXF+RGyKiM0RsXbQ86m7iDghIv4qIjZGxJMR8eFi/A0R8WBE/Kj4//WDnmtdRcShEfG9iLivuO7atSEijoqIL0fED4vX3z937doXEf+xeM8+ERF3RsQ/cv1ai4hbImJnRDxRGpt1rSJiXZEhmyLibd08d2MCNiIOBf4cWAWcClwcEacOdla1tw+4OjOXASuBK4s1Wws8lJlLgYeK62rtw8DG0nXXrj2fBv4yM08Bfo3pNXTt2hARi4H/AIxn5unAocBqXL/Z3AqcP2Os5VoV27/VwGnFYz5TZEtHGhOwwJnA5sx8OjP3AncBFwx4TrWWmdsz87vF5V8wvZFbzPS63Vbc7TbgwoFMsOYi4njgd4DPl4ZduzlExD8GzgFuBsjMvZn5c1y7+TgMeHVEHAa8BngO16+lzHwY2D1jeLa1ugC4KzNfzMwtwGams6UjTQrYxcCPS9e3FWNqQ0QsAc4Avg0cm5nbYTqEgWMGOLU6+xPgI8DLpTHXbm4nA7uALxQ/Xv98RByJa9eWzPwJcCPwLLAd+LvMfADXbz5mW6ue5kiTAjZajHmIdBsiYgFwD3BVZr4w6PkMg4h4B7AzM9cPei5D6DDgnwGfzcwzgL/HH2e2rdhfeAFwEnAccGREXDLYWTVGT3OkSQG7DTihdP14pn9sooOIiFcxHa63Z+ZXiuEdEbGouH0RsHNQ86uxs4F/ExFbmd4d8ZsR8d9w7dqxDdiWmd8urn+Z6cB17drzW8CWzNyVmS8BXwHOwvWbj9nWqqc50qSAfRxYGhEnRcThTO+ovnfAc6q1iAim94NtzMxPlm66F7isuHwZ8PV+z63uMnNdZh6fmUuYfq3978y8BNduTpn5f4EfR8RbiqHzgA24du16FlgZEa8p3sPnMX38hOvXvtnW6l5gdUQcEREnAUuB73T6JI36RRMR8Xam94sdCtySmTcMdkb1FhH/AngE+AG/3I94LdP7Ye8Gxph+M1+UmTMPElAhIs4Ffi8z3xERb8S1m1NELGf64LDDgaeB9zL9Db9r14aI+M/A7zJ9JsD3gMuBBbh+B4iIO4Fzmf6LOTuA64CvMctaRcQfAO9jem2vysz/2fFzNylgJUmqiyb9iFiSpNowYCVJqoABK0lSBQxYSZIqYMBKklQBA1aSpAoYsJIkVcCAlSSpAv8PJu9Z9FQ7gPgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure=plt.figure()\n",
    "ax=figure.add_axes([0,0,1,1])\n",
    "ax.plot(x,y)\n",
    "plt.xlabel=(\"x\")\n",
    "plt.ylabel=(\"y\")\n",
    "plt.title=(\"title\")\n",
    "plt.show()"
   ]
  },
  {
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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])"
   ]
  },
  {
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax1.plot(x,y,color='red')\n",
    "ax1.set_label(\"x\")\n",
    "ax1.set_ylabel(\"y\")\n",
    "ax2.plot(x,y,color='blue')\n",
    "ax2.set_xlabel(\"x\")\n",
    "ax2.set_ylabel(\"y\")\n",
    "fig"
   ]
  },
  {
   "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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAd0AAAFDCAYAAAB/UdRdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAb1klEQVR4nO3df2xVdZ7/8debdklWdscBgY3lliydKwiVSoZbtpKvqDOZLTRjcXH+KLNZBrukqcC6JpOMZhPX6GQSyP6zrjDb3AximChNxtWtO9OWMY44oyPCxQhCRYt0tK2OFEXHXxFa398/Wpm2FHto7/2c3vJ8JDfpOedzb1/5eMPLc+7p55q7CwAA5N6UuAMAAHCpoHQBAAiE0gUAIBBKFwCAQChdAAACoXQBAAhk1NI1s4fM7KSZHbnAcTOz/zKz42Z22My+mf2YAADkvyhnug9LWvkVx1dJumrgUSfpv8cfCwCAyWfU0nX330p6/yuGrJa0y/vtk/R1M7syWwEBAJgssvGZ7hxJnYO2uwb2AQCAQQqz8Bo2wr4R15Y0szr1X4LWtGnTll599dVZ+PUAAIRz8ODBU+4+ayzPzUbpdkkqHrSdkPT2SAPdPS0pLUmpVMozmUwWfj0AAOGY2ZtjfW42Li8/KWndwF3MFZI+dPd3svC6AABMKqOe6ZrZbkk3SpppZl2S7pX0F5Lk7g2SmiVVSTou6VNJt+UqLAAA+WzU0nX3taMcd0mbspYIAIBJihWpAAAIhNIFACAQShcAgEAoXQAAAqF0AQAIhNIFACAQShcAgEAoXQAAAqF0AQAIhNIFACAQShcAgEAoXQAAAqF0AQAIhNIFACAQShcAgEAoXQAAAqF0AQAIhNIFACAQShcAgEAoXQAAAqF0Y1RbW6vZs2frmmuuGfG4u+uOO+5QMplUWVmZXnrppcAJAQDZROnGaP369Wptbb3g8ZaWFrW3t6u9vV3pdFq33357wHQAgGyjdGO0YsUKzZgx44LHm5qatG7dOpmZKioq9MEHH+idd94JmBAAkE2U7gTW3d2t4uLic9uJRELd3d0xJgIAjEdh3AFwYe5+3j4zG3FsOp1WOp2WJB07dkxXX311TrMB2fKHP/xBp06dijsGEASlO4ElEgl1dnae2+7q6lJRUdGIY+vq6lRXVydJSqVSymQyQTIC45VKpeKOAATD5eUJrLq6Wrt27ZK7a9++fbr88st15ZVXxh0LADBGnOnGaO3atdq7d69OnTqlRCKh++67T2fPnpUk1dfXq6qqSs3NzUomk7rsssu0c+fOmBMDAMaD0o3R7t27v/K4mWn79u2B0gAAco3LywAABELpAgAQCKULAEAglC4AAIFQugAABELpAgAQCKULAEAglC4AAIFQugAABELpAgAQCKULAEAglC4AAIFQugAABELpAgAQCKUbo9bWVi1YsEDJZFJbtmw57/iHH36om2++Wddee61KS0v5Pl0AyHOUbkz6+vq0adMmtbS0qK2tTbt371ZbW9uQMdu3b9eiRYt06NAh7d27Vz/84Q915syZmBIDAMaL0o3J/v37lUwmVVJSoqlTp6qmpkZNTU1DxpiZPvroI7m7Pv74Y82YMUOFhYUxJQYAjBelG5Pu7m4VFxef204kEuru7h4yZvPmzXr11VdVVFSkxYsX64EHHtCUKfwnA4B8xb/gMXH38/aZ2ZDtPXv2aMmSJXr77bf18ssva/PmzfrTn/404uul02mlUimlUin19PTkJDMAYHwo3ZgkEgl1dnae2+7q6lJRUdGQMTt37tSaNWtkZkomk5o3b56OHTs24uvV1dUpk8kok8lo1qxZOc0OABibSKVrZivN7DUzO25md49w/HIz+z8zO2RmR83stuxHnVzKy8vV3t6ujo4OnTlzRo2Njaqurh4yZu7cuXr66aclSe+++65ee+01lZSUxBEXAJAFo96VY2YFkrZL+o6kLkkHzOxJdx98q+0mSW3ufrOZzZL0mpk94u7cansBhYWF2rZtmyorK9XX16fa2lqVlpaqoaFBklRfX6977rlH69ev1+LFi+Xu2rp1q2bOnBlzcgDAWEW5FXaZpOPufkKSzKxR0mpJg0vXJf219X8o+VeS3pfUm+Wsk05VVZWqqqqG7Kuvrz/3c1FRkX7961+HjgUAyJEol5fnSOoctN01sG+wbZIWSnpb0iuS/tXdvxj+QmZWZ2YZM8twsw8A4FITpXRthH3Db72tlPSypCJJSyRtM7Ovnfck97S7p9w9xc0+AIBLTZTS7ZJUPGg7of4z2sFuk/S49zsuqUPS1dmJCADA5BCldA9IusrM5pnZVEk1kp4cNuYtSd+WJDP7G0kLJJ3IZlAAAPLdqDdSuXuvmW2WtEdSgaSH3P2omdUPHG+Q9GNJD5vZK+q/HH2Xu5/KYW4AAPJOpIV83b1ZUvOwfQ2Dfn5b0t9nNxoAAJMLK1IBABAIpQsAQCCULgAAgVC6AAAEQukCABAIpQsAQCCULgAAgVC6AAAEQunGqLW1VQsWLFAymdSWLVtGHLN3714tWbJEpaWluuGGGwInBABkU6QVqZB9fX192rRpk5566iklEgmVl5erurpaixYtOjfmgw8+0MaNG9Xa2qq5c+fq5MmTMSYGAIwXZ7ox2b9/v5LJpEpKSjR16lTV1NSoqalpyJhHH31Ua9as0dy5cyVJs2fPjiMqACBLKN2YdHd3q7j4z9+YmEgk1N3dPWTM66+/rtOnT+vGG2/U0qVLtWvXrtAxAQBZxOXlmLj7efvMbMh2b2+vDh48qKefflqfffaZrrvuOlVUVGj+/PnnPTedTiudTkuSenp6chMaADAunOnGJJFIqLOz89x2V1eXioqKzhuzcuVKTZs2TTNnztSKFSt06NChEV+vrq5OmUxGmUxGs2bNyml2AMDYULoxKS8vV3t7uzo6OnTmzBk1Njaqurp6yJjVq1frd7/7nXp7e/Xpp5/qxRdf1MKFC2NKDAAYLy4vx6SwsFDbtm1TZWWl+vr6VFtbq9LSUjU09H9NcX19vRYuXKiVK1eqrKxMU6ZM0YYNG3TNNdfEnBwAMFY20meLIaRSKc9kMrH87skulUqJuUW+4P2KfGNmB909NZbncnkZAIBAKF0AAAKhdAEACITSBQAgEEoXAIBAKF0AAAKhdAEACITSBQAgEEoXAIBAKF0AAAKhdAEACITSBQAgEEoXAIBAKF0AAAKhdGPU2tqqBQsWKJlMasuWLRccd+DAARUUFOixxx4LmA4AkG2Ubkz6+vq0adMmtbS0qK2tTbt371ZbW9uI4+666y5VVlbGkBIAkE2Ubkz279+vZDKpkpISTZ06VTU1NWpqajpv3IMPPqhbb71Vs2fPjiElACCbKN2YdHd3q7i4+Nx2IpFQd3f3eWOeeOIJ1dfXh44HAMiBwrgDXKrc/bx9ZjZk+84779TWrVtVUFAw6uul02ml02lJUk9PT3ZCAgCyitKNSSKRUGdn57ntrq4uFRUVDRmTyWRUU1MjSTp16pSam5tVWFioW2655bzXq6urU11dnSQplUrlLjgAYMwo3ZiUl5ervb1dHR0dmjNnjhobG/Xoo48OGdPR0XHu5/Xr1+u73/3uiIULAMgPlG5MCgsLtW3bNlVWVqqvr0+1tbUqLS1VQ0ODJPE5LgBMQpRujKqqqlRVVTVk34XK9uGHHw6QCACQS9y9DABAIJQuAACBULoAAARC6QIAEAilCwBAIJQuAACBULoAAARC6QIAEEik0jWzlWb2mpkdN7O7LzDmRjN72cyOmtmz2Y0JAED+G3VFKjMrkLRd0nckdUk6YGZPunvboDFfl/RTSSvd/S0z48tfAQAYJsqZ7jJJx939hLufkdQoafWwMd+X9Li7vyVJ7n4yuzEBAMh/UUp3jqTOQdtdA/sGmy9pupntNbODZrYuWwEBAJgsonzhgY2wb/g3sBdKWirp25L+UtILZrbP3V8f8kJmdZLqJGnu3LkXnxYAgDwW5Uy3S1LxoO2EpLdHGNPq7p+4+ylJv5V07fAXcve0u6fcPTVr1qyxZgYAIC9FKd0Dkq4ys3lmNlVSjaQnh41pknS9mRWa2WWS/k7Sq9mNOvm0trZqwYIFSiaT2rJly3nHH3nkEZWVlamsrEzLly/XoUOHYkgJAMiWUS8vu3uvmW2WtEdSgaSH3P2omdUPHG9w91fNrFXSYUlfSPqZux/JZfB819fXp02bNumpp55SIpFQeXm5qqurtWjRonNj5s2bp2effVbTp09XS0uL6urq9OKLL8aYGgAwHpG+xN7dmyU1D9vXMGz7PyT9R/aiTW779+9XMplUSUmJJKmmpkZNTU1DSnf58uXnfq6oqFBXV1fwnACA7GFFqph0d3eruPjPH5UnEgl1d3dfcPyOHTu0atWqENEAADkS6UwX2ec+/AZwyWykG8WlZ555Rjt27NBzzz13wddLp9NKp9OSpJ6enuyEBABkFWe6MUkkEurs/POfP3d1damoqOi8cYcPH9aGDRvU1NSkK6644oKvV1dXp0wmo0wmI+4MB4CJidKNSXl5udrb29XR0aEzZ86osbFR1dXVQ8a89dZbWrNmjX7+859r/vz5MSUFAGQLl5djUlhYqG3btqmyslJ9fX2qra1VaWmpGhr670+rr6/X/fffr/fee08bN24895xMJhNnbADAONhIny2GkEqlnALJjVQqRTkjb/B+Rb4xs4PunhrLc7m8DABAIJQuAACBULoAAARC6QIAEAilCwBAIJQuAACBULoAAARC6QIAEAilCwBAIJQuAACBULoAAARC6QIAEAilCwBAIJQuAACBULoxam1t1YIFC5RMJrVly5bzjru77rjjDiWTSZWVlemll16KISUAIFso3Zj09fVp06ZNamlpUVtbm3bv3q22trYhY1paWtTe3q729nal02ndfvvtMaUFAGQDpRuT/fv3K5lMqqSkRFOnTlVNTY2ampqGjGlqatK6detkZqqoqNAHH3ygd955J6bEAIDxonRj0t3dreLi4nPbiURC3d3dFz0GAJA/CuMOcKly9/P2mdlFj/lSOp1WOp2WJB05ckSpVCoLKcenp6dHs2bNijuGpImTZaLkkCZOlmPHjsUdAQiG0o1JIpFQZ2fnue2uri4VFRVd9Jgv1dXVqa6uTpKUSqWUyWRykPriTJQc0sTJMlFySBMny0T4H0QgFC4vx6S8vFzt7e3q6OjQmTNn1NjYqOrq6iFjqqurtWvXLrm79u3bp8svv1xXXnllTIkBAOPFmW5MCgsLtW3bNlVWVqqvr0+1tbUqLS1VQ0ODJKm+vl5VVVVqbm5WMpnUZZddpp07d8acGgAwHpRujKqqqlRVVTVkX319/bmfzUzbt2+/6Nf98jJz3CZKDmniZJkoOaSJk2Wi5ABCsJFu1gkhlUr5RPg8CQCAi2FmB919TDcj8JkuAACBULp5aiItITlalkceeURlZWUqKyvT8uXLdejQoVhyfOnAgQMqKCjQY489lpMcUbPs3btXS5YsUWlpqW644YZYcnz44Ye6+eabde2116q0tDSn9w3U1tZq9uzZuuaaa0Y8zrKnuCS4eyyPpUuXOsamt7fXS0pK/I033vDPP//cy8rK/OjRo0PG/OpXv/KVK1f6F1984S+88IIvW7YstizPP/+8v//+++7u3tzcnJMsUXJ8Oe6mm27yVatW+S9+8Yus54ia5fTp075w4UJ/88033d393XffjSXHT37yE//Rj37k7u4nT5706dOn++eff571LO7uzz77rB88eNBLS0tHPB7qPQuMl6SMj7H7ONPNQxNpCckoWZYvX67p06dLkioqKtTV1RVLDkl68MEHdeutt2r27NlZz3AxWR599FGtWbNGc+fOlaSc5ImSw8z00Ucfyd318ccfa8aMGSoszM39lStWrNCMGTMueJxlT3EpoHTz0ERaQvJif8+OHTu0atWqWHJ0d3friSeeGHKHeC5EyfL666/r9OnTuvHGG7V06VLt2rUrlhybN2/Wq6++qqKiIi1evFgPPPCApkyJ558Flj3FpYA/GcpDnuUlJHOd5UvPPPOMduzYoeeeey6WHHfeeae2bt2qgoKCrP/+i83S29urgwcP6umnn9Znn32m6667ThUVFZo/f37QHHv27NGSJUv0m9/8Rm+88Ya+853v6Prrr9fXvva1rOWIKtR7FogTpZuHsr2EZK6zSNLhw4e1YcMGtbS06IorroglRyaTUU1NjSTp1KlTam5uVmFhoW655ZbgWRKJhGbOnKlp06Zp2rRpWrFihQ4dOpTV0o2SY+fOnbr77rtlZkomk5o3b56OHTumZcuWZS1HVKHes0Csxvph8Hgf3Eg1dmfPnvV58+b5iRMnzt0gc+TIkSFjfvnLXw65KaW8vDy2LG+++aZ/4xvf8Oeffz4nGaLmGOwHP/hBzm6kipKlra3Nv/Wtb/nZs2f9k08+8dLSUn/llVeC56ivr/d7773X3d3/+Mc/elFRkff09GQ1x2AdHR0XvJEq1HsWGC+N40YqznTz0ERaQjJKlvvvv1/vvfeeNm7ceO452V4YJUqOUKJkWbhwoVauXKmysjJNmTJFGzZsuOCf0uQyxz333KP169dr8eLFcndt3bpVM2fOzGqOL61du1Z79+7VqVOnlEgkdN999+ns2bPnsrDsKS4FrEgFAMBFYEUqAADyAKULAEAglC4AAIFQugAABELpAgAQCKULAEAglC4AAIFQugAABELpAgAQSKTSNbOVZvaamR03s7u/Yly5mfWZ2feyFxEAgMlh1NI1swJJ2yWtkrRI0lozW3SBcVsl7cl2SAAAJoMoZ7rLJB139xPufkZSo6TVI4z7F0n/I+lkFvMBADBpRCndOZI6B213Dew7x8zmSPoHSQ3ZiwYAwOQSpXRthH3Dv5roPyXd5e59X/lCZnVmljGzTE9PT8SIAABMDlG+T7dLUvGg7YSkt4eNSUlqNDNJmimpysx63f1/Bw9y97SktNT/1X5jzAwAQF6KUroHJF1lZvMkdUuqkfT9wQPcfd6XP5vZw5J+ObxwAQC41I1auu7ea2ab1X9XcoGkh9z9qJnVDxznc1wAACKIcqYrd2+W1Dxs34hl6+7rxx8LAIDJhxWpAAAIhNIFACAQShcAgEAoXQAAAqF0AQAIhNIFACAQShcAgEAoXQAAAqF0AQAIhNIFACAQShcAgEAoXQAAAqF0AQAIhNIFACAQShcAgEAoXQAAAqF0AQAIhNIFACAQShcAgEAoXQAAAqF0AQAIhNIFACAQShcAgEAoXQAAAqF0AQAIhNIFACAQShcAgEAoXQAAAqF0AQAIhNIFACAQShcAgEAoXQAAAqF0AQAIhNIFACAQShcAgEAoXQAAAqF0AQAIhNIFACAQShcAgEAoXQAAAqF0AQAIhNIFACAQShcAgEAoXQAAAqF0AQAIhNIFACAQShcAgEAoXQAAAqF0AQAIJFLpmtlKM3vNzI6b2d0jHP9HMzs88Pi9mV2b/agAAOS3UUvXzAokbZe0StIiSWvNbNGwYR2SbnD3Mkk/lpTOdlAAAPJdlDPdZZKOu/sJdz8jqVHS6sED3P337n56YHOfpER2YwIAkP+ilO4cSZ2DtrsG9l3IP0tqGemAmdWZWcbMMj09PdFTAgAwCUQpXRthn4840Owm9ZfuXSMdd/e0u6fcPTVr1qzoKQEAmAQKI4zpklQ8aDsh6e3hg8ysTNLPJK1y9/eyEw8AgMkjypnuAUlXmdk8M5sqqUbSk4MHmNlcSY9L+id3fz37MQEAyH+jnum6e6+ZbZa0R1KBpIfc/aiZ1Q8cb5D075KukPRTM5OkXndP5S42AAD5x9xH/Hg251KplGcymVh+NwAAY2VmB8d6YsmKVAAABELpAgAQCKULAEAglC4AAIFQugAABELpAgAQCKULAEAglC4AAIFQugAABELpAgAQCKULAEAglC4AAIFQugAABELpAgAQCKULAEAglC4AAIFQugAABELpAgAQCKULAEAglC4AAIFQugAABELpAgAQCKULAEAglC4AAIFQugAABELpAgAQCKULAEAglC4AAIFQugAABELpAgAQCKULAEAglC4AAIFQugAABELpAgAQCKULAEAglC4AAIFQugAABELpAgAQCKULAEAglC4AAIFQugAABELpAgAQCKULAEAglC4AAIFQugAABELpAgAQCKULAEAglC4AAIFEKl0zW2lmr5nZcTO7e4TjZmb/NXD8sJl9M/tRAQDIb6OWrpkVSNouaZWkRZLWmtmiYcNWSbpq4FEn6b+znBMAgLwX5Ux3maTj7n7C3c9IapS0etiY1ZJ2eb99kr5uZldmOSsAAHktSunOkdQ5aLtrYN/FjgEA4JJWGGGMjbDPxzBGZlan/svPkvS5mR2J8Ptx8WZKOhV3iEmIec0N5jV3mNvcWDDWJ0Yp3S5JxYO2E5LeHsMYuXtaUlqSzCzj7qmLSotImNvcYF5zg3nNHeY2N8wsM9bnRrm8fEDSVWY2z8ymSqqR9OSwMU9KWjdwF3OFpA/d/Z2xhgIAYDIa9UzX3XvNbLOkPZIKJD3k7kfNrH7geIOkZklVko5L+lTSbbmLDABAfopyeVnu3qz+Yh28r2HQzy5p00X+7vRFjkd0zG1uMK+5wbzmDnObG2OeV+vvSwAAkGssAwkAQCA5L12WkMyNCPP6jwPzedjMfm9m18aRMx+NNreDxpWbWZ+ZfS9kvnwVZV7N7EYze9nMjprZs6Ez5qMI/xZcbmb/Z2aHBuaVe24iMLOHzOzkhf60dczd5e45e6j/xqs3JJVImirpkKRFw8ZUSWpR/9/6Vkh6MZeZJsMj4rwulzR94OdVzGv25nbQuN+o/16H78Wde6I/Ir5nvy6pTdLcge3Zceee6I+I8/pvkrYO/DxL0vuSpsadfaI/JK2Q9E1JRy5wfEzdleszXZaQzI1R59Xdf+/upwc296n/b6cxuijvWUn6F0n/I+lkyHB5LMq8fl/S4+7+liS5O3M7uijz6pL+2sxM0l+pv3R7w8bMP+7+W/XP1YWMqbtyXbosIZkbFztn/6z+/yPD6EadWzObI+kfJDUIUUV5z86XNN3M9prZQTNbFyxd/ooyr9skLVT/gkWvSPpXd/8iTLxJbUzdFelPhsYha0tIYojIc2ZmN6m/dP9fThNNHlHm9j8l3eXuff0nD4ggyrwWSloq6duS/lLSC2a2z91fz3W4PBZlXislvSzpW5K+IekpM/udu/8px9kmuzF1V65LN2tLSGKISHNmZmWSfiZplbu/FyhbvosytylJjQOFO1NSlZn1uvv/BkmYn6L+W3DK3T+R9ImZ/VbStZIo3QuLMq+3Sdri/R9EHjezDklXS9ofJuKkNabuyvXlZZaQzI1R59XM5kp6XNI/caZwUUadW3ef5+5/6+5/K+kxSRsp3FFF+begSdL1ZlZoZpdJ+jtJrwbOmW+izOtb6r96IDP7G/Uv1n8iaMrJaUzdldMzXWcJyZyIOK//LukKST8dOCPrdRY+H1XEucVFijKv7v6qmbVKOizpC0k/c3e+iewrRHy//ljSw2b2ivovid7l7nzz0CjMbLekGyXNNLMuSfdK+gtpfN3FilQAAATCilQAAARC6QIAEAilCwBAIJQuAACBULoAAARC6QIAEAilCwBAIJQuAACB/H+/unHaIRVkSgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure=plt.figure()\n",
    "ax=figure.add_axes([0,0,1,1])\n",
    "ax2=figure.add_axes([0.2,0.5,.4,.4])"
   ]
  },
  {
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.plot(x,z)\n",
    "ax.set_label(\"x\")\n",
    "ax.set_ylabel(\"y\")\n",
    "ax2.plot(x,y)\n",
    "ax2.set_xlabel(\"Z\")\n",
    "ax2.set_ylabel(\"Y\")\n",
    "ax2.set_title(\"zoom\")\n",
    "ax2.set_xlim(20,22)\n",
    "ax2.set_ylim(30,50)\n",
    "figure"
   ]
  },
  {
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kuV9g8uuK/4vJVc9HrpB8zWXuIrO9qFwvMts7Ldf+agVJasJ32kpSExa+JDVh4UtSExa+JDVh4UtSExa+JDVh4UtSE/8NbVa/mhVCdpUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes=plt.subplots(nrows=1,ncols=2)"
   ]
  },
  {
   "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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x144 with 2 Axes>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axes[0].plot(x,y,color=\"red\",lw=3,ls=\"--\")\n",
    "axes[1].plot(x,z,color=\"yellow\",lw=3,ls=\"-\")\n",
    "fig"
   ]
  },
  {
   "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'z')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes=plt.subplots(nrows=1,ncols=2,figsize=(12,2))\n",
    "axes[0].plot(x,y,color=\"blue\",lw=5)\n",
    "axes[0].set_xlabel(\"x\")\n",
    "axes[0].set_ylabel(\"y\")\n",
    "axes[1].plot(x,z,color=\"red\",lw=3,ls=\"--\")\n",
    "axes[1].set_xlabel(\"x\")\n",
    "axes[1].set_ylabel(\"z\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Great Job!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}