{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab\n",
    "\n",
    "- Use this assignment to practice the chapter's concepts and coding skills in runnable Python code.\n",
    "- Sign in from the account menu before you start working on the assignment.\n",
    "- Write and run your code directly in the coding cells.\n",
    "- Multiple submissions are allowed. The highest score is kept.\n",
    "\n",
    "<div data-lab-answers-release-at=\"2026-10-13T23:59-05:00\"></div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Question 1: Revenue Trend\n",
    "# A store manager wants to see how revenue moved across the year.\n",
    "#   1. Create a figure and axes with plt.subplots().\n",
    "#   2. Plot quarters vs revenue as a line with circle markers (marker='o').\n",
    "#   3. Set the title \"Quarterly Revenue\", the x-label \"Quarter\",\n",
    "#      and the y-label \"Revenue ($K)\".\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "quarters = [\"Q1\", \"Q2\", \"Q3\", \"Q4\"]\n",
    "revenue = [120, 135, 128, 150]\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "hide-input",
     "lab-answer"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Solution\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "quarters = [\"Q1\", \"Q2\", \"Q3\", \"Q4\"]\n",
    "revenue = [120, 135, 128, 150]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(quarters, revenue, marker='o')\n",
    "ax.set_title(\"Quarterly Revenue\")\n",
    "ax.set_xlabel(\"Quarter\")\n",
    "ax.set_ylabel(\"Revenue ($K)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Question 2: Actual vs Forecast\n",
    "# Finance wants actual revenue compared with the forecast on one chart.\n",
    "#   1. Create a figure and axes with plt.subplots().\n",
    "#   2. Plot actual as a solid line with the label \"Actual\".\n",
    "#   3. Plot forecast as a dashed gray line (ls='--', color='gray')\n",
    "#      with the label \"Forecast\".\n",
    "#   4. Set the title \"Actual vs Forecast\" and show the legend.\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "quarters = [\"Q1\", \"Q2\", \"Q3\", \"Q4\"]\n",
    "actual = [120, 135, 128, 150]\n",
    "forecast = [118, 130, 140, 145]\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "hide-input",
     "lab-answer"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Solution\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "quarters = [\"Q1\", \"Q2\", \"Q3\", \"Q4\"]\n",
    "actual = [120, 135, 128, 150]\n",
    "forecast = [118, 130, 140, 145]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(quarters, actual, label=\"Actual\")\n",
    "ax.plot(quarters, forecast, ls='--', color='gray', label=\"Forecast\")\n",
    "ax.set_title(\"Actual vs Forecast\")\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Question 3: Regional Dashboard\n",
    "# A regional director wants two views side by side.\n",
    "#   1. Create one row of two axes: fig, axes = plt.subplots(1, 2, figsize=(10, 4)).\n",
    "#   2. On axes[0], draw a bar chart of regions vs region_revenue\n",
    "#      with the title \"Revenue by Region\".\n",
    "#   3. On axes[1], draw a histogram of order_values with bins=5\n",
    "#      and the title \"Order Values\".\n",
    "#   4. Add the figure title \"Regional Dashboard\" with fig.suptitle().\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "regions = [\"North\", \"South\", \"East\", \"West\"]\n",
    "region_revenue = [150, 120, 180, 90]\n",
    "order_values = [45, 52, 38, 61, 75, 49, 58, 66, 40, 55, 70, 48]\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "hide-input",
     "lab-answer"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Solution\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "regions = [\"North\", \"South\", \"East\", \"West\"]\n",
    "region_revenue = [150, 120, 180, 90]\n",
    "order_values = [45, 52, 38, 61, 75, 49, 58, 66, 40, 55, 70, 48]\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n",
    "axes[0].bar(regions, region_revenue)\n",
    "axes[0].set_title(\"Revenue by Region\")\n",
    "axes[1].hist(order_values, bins=5)\n",
    "axes[1].set_title(\"Order Values\")\n",
    "fig.suptitle(\"Regional Dashboard\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Question 4: Ad Spend and Sales\n",
    "# Marketing asks whether higher ad spend goes with higher sales.\n",
    "#   1. Create a figure and axes with plt.subplots().\n",
    "#   2. Draw a scatter plot of ad_spend (x) vs sales (y).\n",
    "#   3. Label the axes \"Ad Spend ($K)\" and \"Sales ($K)\".\n",
    "#   4. Start both axes at zero: x from 0 to 50 and y from 0 to 250\n",
    "#      using set_xlim() and set_ylim().\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "ad_spend = [5, 10, 15, 20, 25, 30, 35, 40]\n",
    "sales = [60, 85, 110, 120, 150, 165, 190, 205]\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": [
     "hide-input",
     "lab-answer"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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O8uqrr5qgcejQITOu55tvvjE3/VwHQj/22GMya9Ys07Y0dAC1TrFv3Lix2e7Ro4fZfuGFF8y2VnIGDx4sY8aMMd1md955p0REREhqaqpL19jw4cNNMBs6dKhERUXJqlWrzKBs+/0CqHz6to6VNwa3NxUhZ7qt+/U4ADgLskoajexk6dKlZsCzO9vqt9bFGYsaJK23iqTdcjrgWitNOv0fQGDQafc6y0wHVesYIu0yo0IEBI6zbnz/LnUoUqdOnTKVmJJ88sknZnVqf0MoAgBAKvX7d5nKMffcc49cuHCh2OM6pqe01SQAAABfUqZQdPHiRRN6Ll++XOjY2rVrzWU2Ro0a5c7zAwAA8L1QpJWgffv2ycMPP+wyw2z9+vVy7733moHW06dP98R5AgAA+E4o0rWBPv30U7NG0ejRo80+XR/oV7/6lQwZMsTMTgMAAKgUizfGx8fL6tWrzfR4HV+0ZMkSc5FWvdo9AABApQhF58+fdwQjvfZYv379zDgi7TJzHoAdHh7u/jMFAADwoDJNyQ8KKt3qr2W4S5/BlHwAAKRSv3+XqVKkV6gHAAAIRGUKRQMHDvTcmQAAAHiR266l4a6LwAIAAPhFKNK+O72Qq51ejb579+4SEhIid9xxhxw9etTd5wgAAOB7oSglJUVOnDjh2J46dark5uaaa55FR0fLuHHj3H2OAAAAvjOmKCcnx3z84IMPZOPGjY7tVatWybRp06RLly7SuHFjs36R/VhYWJinzhsAAMA7oUhXrFYnT56UCRMmSNWqVeXSpUuye/dumTdvnrnpuCKtItnb6lpGAAAAARWK7AHn66+/Ntc46927t6kaaQiyH9u7d6988803hCEAABD4l/l44oknZMCAAdK+fXvZtm2bvPPOO45jK1asMCtcAwAABHwoGjlypBk7lJqaKs8++6z07dvXcezKlSvy9NNPu/scAQAAfOsyH4GMy3wAAFC5379LPSX/3XffLdUCjXl5eaYtAACAPyl1KJo/f74kJCTIK6+8Inv27HE5psWm9PR0efnll+WWW24xbQEAAAK2+2zp0qXy5z//WbZs2SIRERFmsUb98qysLDl//rx06tRJnnzySTMQ29/QfQYAgFTq9+9yjSk6fPiwbNq0STIyMiQoKEhuuukm6dy5s8TFxYm/IhQBAFC537/LPPtMNWzY0NwAAAAq7bXPAAAAAhGhCAAAgFAEAABgQ6UIAACgvKHoxx9/lNdff92xrdc/a9mypdxzzz2SmZnJEwsAACpHKNLrm+n0N6UhaMyYMfLggw+aNYt0nSIAAAB/U64p+R9//LHMmDHDfL569Wrp3r27pKSkyLFjx6Rdu3buPkcAAADfrBRdvnxZrly5Yj5fu3at9OzZ03weHh4uubm57j1DAAAAXw1FejmP0aNHm3FFeumPpKQksz81NVVuv/12d58jAACAb4ai1157zVzrbM6cOTJ16lRp1qyZ2f/qq6/Kc8895+5zBOAFefmWbNl3SpZ/ddR81G0ACGTluvZZIOLaZ8C/rU47LpNXpsvx7BzHvtjIMElJSpC+rWN5qgAE5Pv3da1TdOnSJdm9e/d1nQAA3wtEI9/b6RKIVGZ2jtmvxwEgEJUrFGnX2eDBg83A6oSEBMf+++67T3bs2OHO8wNQgbSLTCtERZWP7fv0OF1pAAJRuULR+PHj5ejRo7Jt2zaX/UOGDJHJkye769wAVLCtB04XqhAVDEZ6XNsBQKAp1zpFOuNsw4YN0rRpU5f9OvPs/vvvd9e5AahgWedy3NoOAAK+UnTy5EmJjo42nwcFBbmMMWLcNuC/oiPC3NoOAAI+FLVt29asal0wFM2aNUtuu+02950dgArVsUldM8vs37/VrnS/Htd2ABBoytV99tJLL0lycrJs3rzZbP/pT38yl/vYtGmTrFu3zt3nCKCCVK0SZKbd6ywzDUDOA67tQUmPazsACDTlqhTpZT3WrFkjBw4ckJiYGJk5c6bUqFFDPv/8c+ncubP7zxJAhdF1iN4Y3F5iIl27yHRb97NOEYBAxeKN/4/FGwFXOu1eZ5npoGodQ6RdZlSIAATy+3e5us8ABD4NQInxUd4+DQCoMKUORY0bNy71nR48eLC85wMAAODboYgLvQIAgEBW6lA0bNgwz54JAACAFzGmCHAzBigDQCULRenp6bJs2TI5fPiwXL161eXY22+/7Y5zA/yOXkFeL5jqfP0wXexQ1/ZhKjsABOA6RStWrJAOHTqY65+9+eab5rIfn376qcydO1eOHz/u/rME/CQQ6aKHBS+ompmdY/brcQBAgIWilJQUE4B0FWulFaN9+/aZcUe6mCNQGbvMtELkvAK0nX2fHtd2AIAACkXfffed9OvXz3weHBxsLgRbrVo1c/kPrSIBlY0ucliwQuRMo5Ae13YAgAAKRTk5OVKzZk3zeWxsrPzwww/m87y8PBOQgMpGV312ZzsAgB/OPktKSpJHH31Ufv3rX8uSJUukS5cu7jkzwI/oZTDc2Q4A4CeVopUrVzo+f/nll+XnP/+5zJ8/X+rXr8/MM1RKel0wnWVW3LXjdb8e13YAAN/EBWH/HxeEhbtmnynn4dT2oMQV5gHAt9+/y1UpcmZZlnz22Wfy4YcfSmZm5vXeHeC3dB0iDT4xka5dZLpNIAKAABtT9P3338sf/vAHWbBggWNfcnKymZKv6tatK+vWrZNbb73V/WcK+Ekw6pUQY2aZ6aBqHUOkXWZ6xXkAgG8rU6VIxw/16dPHsa0BaPny5bJo0SKzaKMee/HFFz1xnoDf0ACUGB8l/do2MB8JRAAQgKHoH//4h9x1112ObV28sWvXrjJw4ECzaKMGoi+//NIT5wkAAOA7oejUqVMSERHh2NYA1LlzZ8d2gwYNTBsAAICADkXx8fGOS3tkZWVJamqqdOvWzXH8wIED0qRJkzKfhC76+NNPPxW6sGxRi0ZeS2naAAAAXFcoGjFihDzyyCNy3333mQpRw4YNTfeZ3Zo1a+SXv/xlqe/v2LFjMmnSJGnUqJHUqVNHNm7cWOy11mrXri3h4eHSvHlzRzAraxsAAAC3hKJRo0bJ9OnT5dy5c/KLX/xCVq1aJSEhIY7jaWlp8sQTT5T6/t59910zpX/x4sXFtpk9e7bMnDlTPvroI7l48aIMHTpU+vfvL3v37i1TGwAAAJ9fvPHIkSMSFxcn69evd+mOs3fZacB55ZVXHPsaN25sBndPmzat1G2uhcUbAQDwPz61eKMnnTx5Uvbv31/oemraZafjmUrbBgAAwK9DkQ7mVvXq1XPZHx0d7ThWmjZFyc3NNenS+QYAACovnw5Fdvn5+S7bOkstKCiozG2cTZkyxZTb7DftvgMAAJWXT4ei+vXrm48nTpxw2a8VIPux0rQpyvjx403/o/2WkZHhgUcAAAD8hU+HIp1i37p1a7OStnNFSC8vYl80sjRtihIaGmoGZDnfAABA5eXVUHT58mWzaKN9PM/58+fNtvMCjBMmTJB58+bJ+++/bwZUjx492owHGjlyZJnaAAAAlCRYvEjXJ9K1j5SO6xk8eLD5fNy4ceamHnzwQbl06ZL86U9/Ml1kbdq0MVWg2NhYx/2Upg0AAIDPr1PkC1inCAAA/1Np1ikCAACoKIQiAAAAQhEAAIANlSIAAABCEQAAgA2VIgAAAEIRAACADZUiAAAAQhEAAIANlSIAAABCEQAAgA9cEBYoKC/fkq0HTkvWuRyJjgiTjk3qStUqQTxRAACPIxTBZ6xOOy6TV6bL8ewcx77YyDBJSUqQvq1jvXpuAIDAx5gi+EwgGvneTpdApDKzc8x+PQ4AgCcRiuATXWZaIbKKOGbfp8e1HQAAnkIogtfpGKKCFSJnGoX0uLYDAMBTCEXwOh1U7c52AACUB6EIXqezzNzZDgCA8iAUwet02r3OMitu4r3u1+PaDgAATyEUwet0HSKddq8KBiP7th5nvSIAgCcRiuATdB2iNwa3l5hI1y4y3db9rFMEAPA0Fm+Ez9Dg0yshhhWtAQBeQSiCT9EussT4KG+fBgCgEqL7DAAAgFAEAABgQ6UIAACAUAQAAGBDpQgAAIBQBAAAYEOlCAAAgFAEAABgQ6UIAACAUAQAAGBDpQgAAIBQBAAAYEOlCAAAgFAEAABgQ6UIAACAUAQAAGBDpQgAAIBQBAAAYEOlCAAAgFAEAABgQ6UIAACAUAQAAGBDpQgAAIBQBAAAYEOlCAAAQESCeRYCQ16+JVsPnJasczkSHREmHZvUlapVgrx9WgAA+A1CUQBYnXZcJq9Ml+PZOY59sZFhkpKUIH1bx3r13AAA8Bd0nwVAIBr53k6XQKQys3PMfj0OAACujVDk511mWiGyijhm36fHtR0AACgZociP6RiighUiZxqF9Li2AwAAJSMU+TEdVO3OdgAAVGaEIj+ms8zc2Q4AgMqMUOTHdNq9zjIrbuK97tfj2g4AAJSMUOTHdB0inXavCgYj+7YeZ70iAACujVDk53QdojcGt5eYSNcuMt3W/axTBABA6bB4YwDQ4NMrIYYVrQEAuA6EogChXWSJ8VHePg0AAPwW3WcAAACEIgAAAD/pPvvxxx8lOzvbZV9oaKjExcUVanvhwgU5c+aMxMbGStWqVSvwLAEAgL/z+VD0/PPPy3vvvScxMTGOfS1atJCPP/7YsZ2XlydPPPGEvP3221KzZk0TiGbPni0PPPCAl84aAAD4G58PRapv376yePHiYo9PnTpVPvzwQ/nqq6/klltukf/93/+VwYMHS+vWrc0NAAAgYAZaHzt2zHSPFeWNN96QYcOGmUCkhg8fLk2bNjXhCAAAIGBC0d///ndp27at1K1bV9q3by9ffvml49iJEyckIyNDEhMTXb6mU6dOsn37di+cLQAA8Ec+H4o07KSnp0tWVpYZRN2uXTu5++675ejRo+b4yZMnzceoKNc1eurVq+c4VpTc3Fw5e/asyw0AAFRePh+KHnnkEUe3WI0aNeT111+Xq1evypIlS8y+KlVsD+HKlSsuX3f58uUSZ6BNmTJFIiMjHbeiZrMBAIDKw+dDUUE6HV9noh06dMhs33TTTeZjZmamSzvdth8ryvjx481Uf/tNu+AAAEDl5dOhyLIsM93e2ZEjR+TgwYPSrFkzsx0RESEdOnSQNWvWuFSJ1q5dK926dSsxXNWqVcvlBgAAKi+fnpKvXWJ33HGHjB07Vlq1aiWHDx+W5557Tpo0aWKm3NulpKRIcnKyGYytY5CmT58uISEhMmLECK+ePwAA8B8+XSnSYLNgwQJT9RkyZIjMmDFD+vfvLzt27JDw8HBHu6SkJFm0aJEsX75chg4davZt2LCh0OBrAACA4gRZ2kcFM/tMB1zr+CK60gAAqHzv3z5dKQIAAKgohCIAAABCEQAAgA2VIgAAAEIRAACADZUiAAAAQhEAAIANlSIAAABCEQAAgA2VIgAAAEIRAACADZUiAAAAQhEAAIANlSIAAABCEQAAgA2VIgAAAEIRAACADZUiAAAAQhEAAIANlSIAAABCEQAAgA2VIgAAAEIRAACADZUiAAAAQhEAAIANlSIAAABCEQAAgA2VIgAAAEIRAACADZUiAAAAQhEAAIANlSIAAABCEQAAgA2VIgAAAEIRAACADZUiAAAAQhEAAIANlSIAAABCEQAAgA2VIgAAAEIRAACADZUiAAAAQhEAAIANlSIAAABCEQAAgA2VIgAAAEIRAACADZUiAAAAQhEAAIANlSIAAABCEQAAgA2VIgAAAEIRAACADZUiAAAAQhEAAIANlSIAAABCEQAAgA2VIgAAAEIRAACADZUiAAAAQhEAAIANlSIAAIBAC0UZGRmyfft2OXv2rLdPBQAA+JmACEU5OTnyH//xH3LzzTfLww8/LDExMTJ79mxvnxYAAPAjwRIAJk+eLFu3bpV9+/ZJbGysLFu2TAYMGCAdO3aU2267zdunBwAA/EBAVIrmzZsnw4YNM4FI9e/fX1q3bm32AwAAVIpK0bFjx+TEiRPSoUMHl/1aJdq1a1exX5ebm2tudtnZ2eYj45EAAPAf9vdty7Ku+778PhSdPn3afIyKinLZr9v2Y0WZMmWK6XYrKC4uzgNnCQAAPOnUqVMSGRlZuUNRtWrVHIOtnV26dElCQkKK/brx48fL2LFjHds//fSTNGrUSA4fPnzdTyquP/VrONXZhLVq1eLp9CJeC9/Ba+E7eC18i/b0NGzYUOrWrXvd9+X3oUjfPKtUqSJHjx512a/b+iQVJzQ01NwK0kDEG7Fv0NeB18I38Fr4Dl4L38Fr4Vs0C1z3fYifq1Gjhtxxxx2yYsUKx74LFy7I2rVrpVevXl49NwAA4D/8vlKkXnrpJROAtEssMTHRrFEUHR0tw4cP9/apAQAAP+H3lSJ15513yvr16+XQoUMya9YsadWqlWzcuFHCw8NLfR/alZaSklJklxoqFq+F7+C18B28Fr6D1yJwX48gyx1z2AAAAPxcQFSKAAAArhehCAAAgFAEAAAQQLPP3LEK5v79+82aRzExMd4+nUpFF8vUW/v27c3yCkU5cOCAWZ28ZcuWUrNmzQo/x8pAhxbq74Be+iY+Pr7YAYt6SR19vZo0aSL16tWr8POsLPTn/eDBg9KgQQO58cYbi11AcM+ePWambUlrssE9vv32Wzlz5oy5yLh90WC7y5cvm+PVq1c3f6fgftu3by+0SPNNN90kjRs3LvS3LD09Xa5evWomXQUHlzHmWJXcxIkTrdDQUCshIcF8fPTRR628vDxvn1bA27Bhg3XPPfdYUVFROtDf+te//lWoTXZ2ttWzZ08rPDzcatGihfm4YMECr5xvIHvzzTetRo0aWfHx8dbNN99s1alTx5o7d65Lm/z8fGvUqFEuvyvPPPOM1845UO3du9dKSkqyoqOjrfbt21s1a9a0evfubZ08edKl3WuvvWZVr17datmypVWjRg2rX79+1sWLF7123oFu+/btVlhYmPlbdfz4cZdja9eutW644QarSZMm5u/Zrbfeah0+fNhr5xqoGjVqZDVr1szq1KmT4zZnzhyXNt9//735ndDfn7i4OKtBgwbW5s2by/R9KnUoWrZsmVWtWjVr06ZNZvu7776zIiMjrVmzZnn71AKe/lFfuXKltWPHjmJD0bBhw8yb9OnTp832W2+9ZQUHB5sffLjPCy+8YGVkZDi2582bZ1WpUsXauXOnY9///M//WBEREVZaWprZTk1NtUJCQqyFCxfyUriRvsGuW7fOsX3q1CnzRvD444879m3bts0KCgqylixZYrb1Tfqmm26ynn76aV4LDzh79qzVvHlz8/wWDEVnzpwx/4mYMGGC2c7NzbW6dOlide/endfCA6FI3wNK0q5dO/OfiqtXr5pt/b2pX7++denSpVJ/n0odiu69916rb9++hd6INemjYuzatavIUJSTk2P+B+z8PwGtVugP+LPPPsvL42FahdDgatexY0dryJAhLm1+9atfWX369OG18LDk5GTzXNtpxa5169YubSZNmmTVq1fP/I7AvR566CHrqaeesj755JNCoeidd94x/znQqrbdRx99ZNodOHCAl8LNoejll182/ynIzMwsdFz/E6fP+5dffunYp//Z0/9ALF26tNTfp1LPPtu1a5d06NDBZV/Hjh0lLS1Nrly54rXzgsj3338vFy9edHl9goKC5Oc//7l53eA5+vOvF1Ru1qyZ2db/PH399ddF/q7wWnjGli1bZN26dTJlyhTZsGGDPPPMM9f8u3Xy5Ek5cuSIh86ocpo3b54Zn/KHP/yhyOP6WjRv3tzlGo36WtiPwb30dRg2bJgZ99i1a1czDtLO/nzr+FTnMUexsbFlei2qVPbBjFFRUS77dDsvL88MYoR3Xxv76+FMt+3H4H4ahoYOHWoul9OzZ0/HtQR1ADavRcXR1XmffvppcwmjAQMGSNu2ba/5d8t+DO77j9nvf/97ef/99yUkJKTINkW9FvYrtfNauP93QidFffXVV+bqFWrQoEHm/dr+fGs4LTgIvqzvGZU6FOmTV3A0u74pqOJ+CVAx7D/YRb0+vDaeoTNoBg4cKD/99JP8/e9/d1xxmtei4n366aeyY8cO88d/69atJqja8XerYuhznpSUZN5Q9bJROrtM6euhMwOLey3s2/ydcv/rYf9bpEHnj3/8o+zcudPMwCzutSjPe0alDkWNGjWSo0ePuuzT7dq1a0tERITXzgu218b+ejjTbaYfey4Q6f+O9TqCWnK20+n5Oi2c16Li6bIH+mbwySefXPPvlnYv67IicA9dnkXfcMeNG2du2pVm78JZtWpVia+F4u+UZ9mXqrA/3/pa6N8x7Ua20yqSLiNSlteiUoeiXr16yccff+wov6nly5eb/fAu7QvW9T5WrFjh2JeVlWXGWvD6uJeOn9MytI6d+Oc//2me+4L0OV+5cqVjW39nPvroI14LN9OuyoL27t3r0kWjr4WON3Juq3+3br/99jJdBBslW7JkiakQ2W/Tpk1zPNejR492vBb6pqwVC+fXQrtxdD0jeO73QqupWs2+5ZZbzLaOMdKKkPN7hv6enDt3rmx/p6xK7NixY2Y9g4EDB1orVqww0/d0xlNR08PhXkeOHLG++OILM/1bfwx1/SHd1tfETmcMVK1a1XrxxRfN54mJiWZm4OXLl3k53GjQoEFmPZy//vWv5jWw3w4dOuRoo8tV6JR8XcdLf1ceeOABsyYL67G418MPP2w9+eST5ud91apV5nP9HdBZTnbnz583U8R79OhhlhUZP368WarCeSo/3K+o2WdKp4DrOmq6PIV9/ajp06fzErjRZ599ZnXr1s2sn7Z69Woz21Kf54LLUDz//PNW7dq1zdT9999/3yxV8Zvf/KZM3ytI/5FKTFdLnjp1quk20BLbf//3f8utt97q7dMKeH/7299kzpw5hfaPHTtWkpOTHdufffaZvPXWW6ZfX2ee6SycOnXqVPDZBrbevXubmX4FPfLII/LYY485tnVMxSuvvGLGuejsDx2Eap+hBvdV7d555x3zc69jIZo2bSqPPvqoy0Bre9X05ZdfNrMCdUXrUaNGSZcuXXgZPCg1NVWefPJJU4mwD6ZWOo5lxowZpttZV7R+4IEH5MEHH+S1cDMdyzV37lwznku7ie+///5CFSCNM/PnzzdjInVF6z59+siYMWMKDb4uSaUPRQAAAFLZxxQBAADYEYoAAAAIRQAAADZUigAAAAhFAAAANlSKAAAACEUAAAA2VIoAeIwusa8LdRZ1ocZAewy6qJxeJqWg3bt3m6t7l8exY8fM5QwAVAxCEYBSy8jIMAFBV4IvDb0ulK7u+9NPP5XY7vDhw7JmzRqzKvCZM2d86hUp7WP4zW9+47h6ujO9FtYPP/xQaP/58+fNc+l8AUulz63u37dvn7k49ZAhQ2T79u1ueCQAroVQBKDU9ArhGhAmTpzolmctPz/fXMZCL+qoF9zUS1f87Gc/k//8z//0q1dFL46rgUiDkbP9+/ebSwht27bNhCBnmZmZ5rn87rvvHPt27dplLuyqQUovO1SjRg1zmYJnn322wh4LUJkRigCUil4f7a9//as8/fTT5tpCxVVOvvnmG1m5cmWR1ZGCFixYYO5Tr+Gl1/vSapFWSNq0aeNynS+tnGiA+te//mUCg145vihadVm1apX84x//KFRxst+PXh8pPT3dBJk9e/Zc92NQeh2/hx9+2Fy1227EiBHSvn172bRpk8ybN08SEhLMldeL8/nnn0u3bt1k0KBB8v777zuu16T3q89Nac8FwHVw55VsAQSuefPmWQ0bNrSuXr1qrtI+Z86cQm1GjRpl1axZ0+rdu7fVtGlT6+677y7yyuJ2ehX4m2++ucTvu379enMfel9t2rSx7rrrLis0NNSaNm2aS7vZs2dbkZGRVs+ePU2bOnXqWIsXLy50P3pV8w4dOlh9+/Yt8n7K+hguXLhghYSEWGvXrnXs27x5s7m6/f79+60pU6ZYW7ZssU6dOmWufG/3ww8/mPv94osvrOXLl1thYWHWxIkTi/wezZo1s2bMmFHi8wTg+hGKAJRK586drcmTJ5vPp06darVr187l+Lp166zg4GBr165dZjs3N9fq2rVriYFi0aJF5riGgT179hTZxh5mhgwZYuXn5zu+rlq1ata+ffvM9qZNm6xatWpZ6enpjq9btmyZ2adhxPl+Jk2a5Ggzf/58q3r16uZcy/sYUlNTzfFjx4459n300Ufmfi9duuQIRQXZQ9Hw4cNNqNJQV5wBAwZYDz30ULHHAbgH3WcArknHxWzZskV++9vfmm0d/Pvtt9/Kzp07HW0WLlwoPXv2lLZt25rtkJAQ+a//+q8S73fgwIHy6quvmm60Fi1aSFRUlNx3332yY8eOQm2feuopCQoKcnxdXFycLF261GzPnz/ffL2e06JFi+TDDz80s8X0VvC+Ro4c6fhcu6suXbpkBnqX9zHYB0rXqVPH5X6bNGkid955pxk8rl1fV69eLfLr586da7oLhw8fXuz30PsuOCAbgPsRigBck75xx8fHy8aNG824HB2zc/PNN5v9dhosGjdu7PJ1GgyuRQdV64wrHUs0a9YsM9srMTHRDDp2VtR9Hzp0yHyug5x1DNHixYvNeCcdu6OBacCAAVK9enWXr6tbt67j89DQUPPRPt2+PI8hPDzcfLxw4YJjX82aNU0Ye/zxxyU7O9uMw4qJiZF333230NdrKNTvm5ycLLm5uUV+D73viIiIEs8DwPULdsN9AAhgV65cMZWcdu3aybJlyxz7NTx88MEHZtaYBg+t8hQc3FyW6fVNmzY1N60C1a9f31R89Hs635eGDeftevXqmc9r1aplKkUa2K5HeR6Dfl+lwU6/3i4sLMxU1nSAt1aOdCaazk7r06ePCUh2OttOq0k9evSQ/v37mzCnX+tM71srWAA8i0oRgBLpLCztYtJZXxo67Dfd1jdvrc6ozp07y9q1a10qJiXNtlJaIdFZZc7y8vJMENM1epw5BzKtKumMtU6dOpntvn37mupVwRlaGkj0vkqrPI9BA47OLNNZZiV93y5dupjHVlTIatWqlVn4UR/Tvffea55v51l/X331FaEIqABUigCUSLvI7r77bjO+xpmO79E3cD2u08aHDh1qur/uuusu87mO79ExOiXRYPX666+bCknz5s3l7NmzpotJu7gKrvmjaxhp2IiOjpaZM2eakKDVFfsYJ62waEjS7rjY2Fgzrf6TTz4xQcM+vf1ayvMY1GOPPWaWFvjd735ntvV76ufaJabrEOl5azVIK0ba7ViUli1bmmCk3zspKUlWrFhh1inSx6VrFunXAvAsKkUAiqVjXHSQb3GDgDU4aADRS2FoaNIxRxqgtm7dat7IN2zYIPfff3+hcT12GmB0XSH9Hlpp0QqQru+Tlpbm0sWkNm/ebMKNjtXRwdLOlaPg4GBT0dKApZfG0O+vIUPHJWmwUBqm9Fyc1xLS89J99qpUeR6DGjZsmFntOzU11Wz36tVLVq9ebcYBaTXsyJEjZsC2XrLD/v31mN7vDTfc4NIVp+sV3XjjjabL0r4G0oQJExyDzAF4TpBOQfPg/QPAddHqSffu3U13lIYfX6WhTAd8F1yNWytcWuXRlarLShep1KrY7NmzCUVABfDdvzAA4Ee0y6soOt7IPiC8rJo1a2YqRQAqBqEIgE8rqtvLn+i4KwD+ge4zAAAABloDAADY+Gc9GgAAwM0IRQAAAIQiAAAAGypFAAAAhCIAAAAbKkUAAACEIgAAABsqRQAAQCDyf19Hc+dVOihUAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Solution\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "ad_spend = [5, 10, 15, 20, 25, 30, 35, 40]\n",
    "sales = [60, 85, 110, 120, 150, 165, 190, 205]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(ad_spend, sales)\n",
    "ax.set_xlabel(\"Ad Spend ($K)\")\n",
    "ax.set_ylabel(\"Sales ($K)\")\n",
    "ax.set_xlim(0, 50)\n",
    "ax.set_ylim(0, 250)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Question 5: Report-Ready Figure\n",
    "# The margin chart goes into a printed quarterly report.\n",
    "#   1. Create a figure and axes with plt.subplots(figsize=(8, 4)).\n",
    "#   2. Draw a bar chart of regions vs margin_pct.\n",
    "#   3. Set the title \"Q2 Margin by Region\" and the y-label \"Margin (%)\".\n",
    "#   4. Save the figure as \"regional_margin_q2.png\" with dpi=200.\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "regions = [\"North\", \"South\", \"East\", \"West\"]\n",
    "margin_pct = [12.5, 9.8, 14.2, 7.6]\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": [
     "hide-input",
     "lab-answer"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Solution\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "regions = [\"North\", \"South\", \"East\", \"West\"]\n",
    "margin_pct = [12.5, 9.8, 14.2, 7.6]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "ax.bar(regions, margin_pct)\n",
    "ax.set_title(\"Q2 Margin by Region\")\n",
    "ax.set_ylabel(\"Margin (%)\")\n",
    "fig.savefig(\"regional_margin_q2.png\", dpi=200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<style>\n",
    ".ch06-lab-grader { max-width: 900px; margin-top: 1rem; }\n",
    ".ch06-lab-feedback-list { display: grid; gap: 0.45rem; margin: 0 0 1rem; }\n",
    ".ch06-lab-feedback-list[hidden] { display: none; }\n",
    ".ch06-lab-feedback-row { border: 1px solid #d8dee4; border-radius: 6px; padding: 0.55rem 0.65rem; }\n",
    ".ch06-lab-feedback-row strong { margin-right: 0.35rem; }\n",
    ".ch06-lab-feedback-row.correct { border-color: #aceebb; background: #dafbe1; color: #116329; }\n",
    ".ch06-lab-feedback-row.incorrect { border-color: #ffcecb; background: #ffebe9; color: #cf222e; }\n",
    ".ch06-lab-actions { display: flex; gap: 0.75rem; align-items: center; margin: 1rem 0 0; flex-wrap: wrap; }\n",
    ".ch06-lab-actions button { border: 1px solid #176de8; border-radius: 6px; background: #176de8; color: white; padding: 0.55rem 0.9rem; cursor: pointer; font-weight: 600; }\n",
    ".ch06-lab-actions button.secondary { background: white; color: #176de8; }\n",
    ".ch06-lab-actions.is-locked { opacity: 0.55; }\n",
    ".ch06-lab-actions button:disabled { cursor: not-allowed; opacity: 0.65; }\n",
    ".ch06-lab-result { font-weight: 700; }\n",
    "</style>\n",
    "\n",
    "<div class='ch06-lab-grader' id='ch06-lab-grader'>\n",
    "  <div class='ch06-lab-feedback-list' aria-live='polite' hidden>\n",
    "    <div class='ch06-lab-feedback-row' data-question='q1'><strong>Question 1</strong><span></span></div>\n",
    "    <div class='ch06-lab-feedback-row' data-question='q2'><strong>Question 2</strong><span></span></div>\n",
    "    <div class='ch06-lab-feedback-row' data-question='q3'><strong>Question 3</strong><span></span></div>\n",
    "    <div class='ch06-lab-feedback-row' data-question='q4'><strong>Question 4</strong><span></span></div>\n",
    "    <div class='ch06-lab-feedback-row' data-question='q5'><strong>Question 5</strong><span></span></div>\n",
    "  </div>\n",
    "  <div class='ch06-lab-actions'>\n",
    "    <button type='button' id='ch06-lab-submit' disabled>Submit</button>\n",
    "    <button type='button' class='secondary' id='ch06-lab-reset'>Clear Results</button>\n",
    "    <span class='ch06-lab-result' id='ch06-lab-result' aria-live='polite'></span>\n",
    "  </div>\n",
    "</div>\n",
    "\n",
    "<script>\n",
    "(function () {\n",
    "  var labId = 'ch06-lab';\n",
    "\n",
    "  function getParams() {\n",
    "    var params = new URLSearchParams(window.location.search);\n",
    "    return {\n",
    "      canvas_course_id: params.get('canvas_course_id') || params.get('course_id') || '',\n",
    "      canvas_assignment_id: params.get('canvas_assignment_id') || params.get('assignment_id') || '',\n",
    "      canvas_user_id: params.get('canvas_user_id') || params.get('user_id') || ''\n",
    "    };\n",
    "  }\n",
    "\n",
    "  function loadSessionIdentity(identity, actions, submit, result) {\n",
    "    submit.disabled = true;\n",
    "    actions.classList.add('is-locked');\n",
    "    result.textContent = 'Sign in from the account menu to submit this assignment.';\n",
    "\n",
    "    return fetch('/api/v1/session.php', { credentials: 'same-origin' })\n",
    "      .then(function (response) { return response.ok ? response.json() : null; })\n",
    "      .then(function (payload) {\n",
    "        if (!payload || !payload.authenticated || !payload.identity) return identity;\n",
    "        identity.canvas_user_id = payload.identity.canvas_user_id || identity.canvas_user_id || '';\n",
    "        identity.student_identifier = payload.identity.student_identifier || '';\n",
    "        submit.disabled = false;\n",
    "        actions.classList.remove('is-locked');\n",
    "        result.textContent = payload.identity.display_name ? 'Signed in as ' + payload.identity.display_name + '.' : 'Signed in.';\n",
    "        return identity;\n",
    "      })\n",
    "      .catch(function () { return identity; });\n",
    "  }\n",
    "\n",
    "  function codeFromCell(cell) {\n",
    "    var cm = cell.querySelector('.CodeMirror');\n",
    "    if (cm && cm.CodeMirror && typeof cm.CodeMirror.getValue === 'function') return cm.CodeMirror.getValue();\n",
    "    var cmContent = cell.querySelector('.cm-content');\n",
    "    if (cmContent && cmContent.cmView && cmContent.cmView.view) return cmContent.cmView.view.state.doc.toString();\n",
    "    var textarea = cell.querySelector('textarea');\n",
    "    if (textarea) return textarea.value;\n",
    "    var pre = cell.querySelector('.cell_input pre');\n",
    "    return pre ? pre.textContent : '';\n",
    "  }\n",
    "\n",
    "  function collectCodeCells() {\n",
    "    var cells = Array.prototype.slice.call(document.querySelectorAll('div.cell.tag_thebe-interactive')).slice(0, 5);\n",
    "    var code = {};\n",
    "    cells.forEach(function (cell, index) {\n",
    "      code['q' + (index + 1)] = codeFromCell(cell);\n",
    "    });\n",
    "    return code;\n",
    "  }\n",
    "\n",
    "  function applyFeedback(grader, feedback) {\n",
    "    var list = grader.querySelector('.ch06-lab-feedback-list');\n",
    "    list.hidden = false;\n",
    "    ['q1', 'q2', 'q3', 'q4', 'q5'].forEach(function (question) {\n",
    "      var row = grader.querySelector('[data-question=' + question + ']');\n",
    "      var message = row.querySelector('span');\n",
    "      var item = feedback[question];\n",
    "      row.className = 'ch06-lab-feedback-row';\n",
    "      if (!item) return;\n",
    "      message.textContent = item.message + ' (' + item.score + ' / ' + item.max_score + ')';\n",
    "      row.classList.add(item.correct ? 'correct' : 'incorrect');\n",
    "    });\n",
    "  }\n",
    "\n",
    "  function clearFeedback(grader) {\n",
    "    var list = grader.querySelector('.ch06-lab-feedback-list');\n",
    "    list.hidden = true;\n",
    "    grader.querySelectorAll('.ch06-lab-feedback-row').forEach(function (row) {\n",
    "      row.className = 'ch06-lab-feedback-row';\n",
    "      var message = row.querySelector('span');\n",
    "      if (message) message.textContent = '';\n",
    "    });\n",
    "  }\n",
    "\n",
    "  function saveAttempt(payload) {\n",
    "    return fetch('/api/v1/lab-attempts.php', {\n",
    "      method: 'POST',\n",
    "      headers: { 'Content-Type': 'application/json' },\n",
    "      credentials: 'same-origin',\n",
    "      body: JSON.stringify(payload)\n",
    "    }).then(function (response) {\n",
    "      return response.text().then(function (text) {\n",
    "        var payload = text ? JSON.parse(text) : null;\n",
    "        if (!payload || !response.ok || !payload.ok) {\n",
    "          throw new Error((payload && (payload.message || payload.error)) || 'Submission API is unavailable.');\n",
    "        }\n",
    "        return payload;\n",
    "      });\n",
    "    });\n",
    "  }\n",
    "\n",
    "  function initLabGrader() {\n",
    "    var grader = document.getElementById('ch06-lab-grader');\n",
    "    if (!grader || grader.dataset.ready === 'true') return;\n",
    "    grader.dataset.ready = 'true';\n",
    "\n",
    "    var identity = getParams();\n",
    "    var actions = grader.querySelector('.ch06-lab-actions');\n",
    "    var result = document.getElementById('ch06-lab-result');\n",
    "    var submit = document.getElementById('ch06-lab-submit');\n",
    "    var reset = document.getElementById('ch06-lab-reset');\n",
    "\n",
    "    loadSessionIdentity(identity, actions, submit, result);\n",
    "\n",
    "    submit.addEventListener('click', function () {\n",
    "      clearFeedback(grader);\n",
    "      submit.disabled = true;\n",
    "      result.textContent = 'Submitting code.';\n",
    "\n",
    "      saveAttempt({\n",
    "        lab_id: labId,\n",
    "        code: collectCodeCells(),\n",
    "        canvas_course_id: identity.canvas_course_id,\n",
    "        canvas_assignment_id: identity.canvas_assignment_id,\n",
    "        canvas_user_id: identity.canvas_user_id\n",
    "      })\n",
    "        .then(function (payload) {\n",
    "          var best = (payload.best_score === null || typeof payload.best_score === 'undefined') ? payload.score : payload.best_score;\n",
    "          var attempt = (payload.attempt_count === null || typeof payload.attempt_count === 'undefined') ? '' : ' Attempt ' + payload.attempt_count + '.';\n",
    "          applyFeedback(grader, payload.feedback || {});\n",
    "          result.textContent = 'Submission saved.' + attempt + ' Score: ' + payload.score + ' / ' + payload.max_score + '. Best score: ' + best + ' / ' + payload.max_score + '. Canvas sync: ' + payload.canvas_sync_status + '.';\n",
    "        })\n",
    "        .catch(function (error) {\n",
    "          result.textContent = 'Could not save this attempt: ' + error.message;\n",
    "        })\n",
    "        .finally(function () {\n",
    "          submit.disabled = false;\n",
    "        });\n",
    "    });\n",
    "\n",
    "    reset.addEventListener('click', function () {\n",
    "      clearFeedback(grader);\n",
    "      result.textContent = '';\n",
    "    });\n",
    "  }\n",
    "\n",
    "  if (document.readyState === 'loading') {\n",
    "    document.addEventListener('DOMContentLoaded', initLabGrader);\n",
    "  } else {\n",
    "    initLabGrader();\n",
    "  }\n",
    "})();\n",
    "</script>\n"
   ]
  }
 ],
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