{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Homework\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",
    "Write the interpretation responses below as Python comments in the same answer cells. They are for reflection and class discussion; the automatic score checks the existing coding tasks, not the written explanations. Keep the specified printed output and plots unchanged.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "::::{div} assignment-question-card\n",
    "\n",
    "**1. From Wide to Long**\n",
    "\n",
    "A sales table has the columns `quarter`, `North`, and `South`, with one revenue column per region. To color bars by region with `hue='region'`, the analyst first needs to reshape the table into long format, for example with `melt()`.\n",
    "\n",
    "<div class=\"tf-options\" role=\"radiogroup\" aria-label=\"Question 1 answer\">\n",
    "  <label><input type=\"radio\" name=\"ch07-hw-q1\" value=\"true\"> True</label>\n",
    "  <label><input type=\"radio\" name=\"ch07-hw-q1\" value=\"false\"> False</label>\n",
    "</div>\n",
    "\n",
    "::::\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-10T18:42:51.479701Z",
     "iopub.status.busy": "2026-10-10T18:42:51.479476Z",
     "iopub.status.idle": "2026-10-10T18:42:51.485233Z",
     "shell.execute_reply": "2026-10-10T18:42:51.484463Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "### Solution\n",
    "# True\n",
    "# Seaborn's `hue` needs a single `region` column. In wide format, region names are column headers, so the table must be melted into one row per quarter and region.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "::::{div} assignment-question-card\n",
    "\n",
    "**2. Hue Mapping**\n",
    "\n",
    "In `sns.scatterplot(data=campaigns, x='ad_spend', y='revenue', hue='region')`, the analyst must choose a color for each region before Seaborn can draw the plot.\n",
    "\n",
    "<div class=\"tf-options\" role=\"radiogroup\" aria-label=\"Question 2 answer\">\n",
    "  <label><input type=\"radio\" name=\"ch07-hw-q2\" value=\"true\"> True</label>\n",
    "  <label><input type=\"radio\" name=\"ch07-hw-q2\" value=\"false\"> False</label>\n",
    "</div>\n",
    "\n",
    "::::\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-10T18:42:51.487963Z",
     "iopub.status.busy": "2026-10-10T18:42:51.487765Z",
     "iopub.status.idle": "2026-10-10T18:42:51.490362Z",
     "shell.execute_reply": "2026-10-10T18:42:51.489685Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "### Solution\n",
    "# False\n",
    "# Seaborn maps each region to a color from the current palette automatically and adds a legend.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "::::{div} assignment-question-card\n",
    "\n",
    "**3. Figure-Level Functions**\n",
    "\n",
    "A manager wants a Seaborn scatter plot in the right panel of `fig, axes = plt.subplots(1, 2)`. Calling `sns.relplot(data=df, x='discount', y='units', ax=axes[1])` puts the plot in that panel.\n",
    "\n",
    "<div class=\"tf-options\" role=\"radiogroup\" aria-label=\"Question 3 answer\">\n",
    "  <label><input type=\"radio\" name=\"ch07-hw-q3\" value=\"true\"> True</label>\n",
    "  <label><input type=\"radio\" name=\"ch07-hw-q3\" value=\"false\"> False</label>\n",
    "</div>\n",
    "\n",
    "::::\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-10T18:42:51.492396Z",
     "iopub.status.busy": "2026-10-10T18:42:51.492169Z",
     "iopub.status.idle": "2026-10-10T18:42:51.494760Z",
     "shell.execute_reply": "2026-10-10T18:42:51.494098Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "### Solution\n",
    "# False\n",
    "# `relplot()` is figure-level and creates its own figure. Use the axes-level `sns.scatterplot(..., ax=axes[1])` instead.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "::::{div} assignment-question-card\n",
    "\n",
    "**4. Reading a Box Plot**\n",
    "\n",
    "A box plot of delivery days by shipping method shows each method's median and spread, so it can reveal that one method is faster on a typical order but less consistent than another.\n",
    "\n",
    "<div class=\"tf-options\" role=\"radiogroup\" aria-label=\"Question 4 answer\">\n",
    "  <label><input type=\"radio\" name=\"ch07-hw-q4\" value=\"true\"> True</label>\n",
    "  <label><input type=\"radio\" name=\"ch07-hw-q4\" value=\"false\"> False</label>\n",
    "</div>\n",
    "\n",
    "::::\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-10T18:42:51.496634Z",
     "iopub.status.busy": "2026-10-10T18:42:51.496506Z",
     "iopub.status.idle": "2026-10-10T18:42:51.498407Z",
     "shell.execute_reply": "2026-10-10T18:42:51.497919Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "### Solution\n",
    "# True\n",
    "# The line inside the box is the median, and the box and whiskers show the spread, so typical speed and consistency can be compared across methods.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "::::{div} assignment-question-card\n",
    "\n",
    "**5. Themes**\n",
    "\n",
    "Running `sns.set_theme(style='darkgrid')` before drawing a box plot changes the delivery-day values that the box plot summarizes.\n",
    "\n",
    "<div class=\"tf-options\" role=\"radiogroup\" aria-label=\"Question 5 answer\">\n",
    "  <label><input type=\"radio\" name=\"ch07-hw-q5\" value=\"true\"> True</label>\n",
    "  <label><input type=\"radio\" name=\"ch07-hw-q5\" value=\"false\"> False</label>\n",
    "</div>\n",
    "\n",
    "::::\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-10T18:42:51.500001Z",
     "iopub.status.busy": "2026-10-10T18:42:51.499863Z",
     "iopub.status.idle": "2026-10-10T18:42:51.501950Z",
     "shell.execute_reply": "2026-10-10T18:42:51.501360Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "### Solution\n",
    "# False\n",
    "# Themes change only the look of the chart (background, grid, fonts), not the data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-10T18:42:51.503188Z",
     "iopub.status.busy": "2026-10-10T18:42:51.503084Z",
     "iopub.status.idle": "2026-10-10T18:42:52.276259Z",
     "shell.execute_reply": "2026-10-10T18:42:52.275909Z"
    },
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### 6. Orders by Channel\n",
    "# The sales team wants to see which channel brings in the most orders.\n",
    "#   1. Create a figure and axes with plt.subplots().\n",
    "#   2. Use sns.countplot() to count orders by channel (x='channel').\n",
    "#   3. Set the title \"Orders by Channel\".\n",
    "# Reflection (answer in comments): Which channel has the most recorded orders? Does the chart identify the channel with the most revenue?\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "orders = pd.DataFrame({\n",
    "    \"channel\": [\"Online\", \"Retail\", \"Online\", \"Phone\", \"Online\",\n",
    "                \"Retail\", \"Online\", \"Phone\", \"Retail\", \"Online\"],\n",
    "})\n",
    "\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-10T18:42:52.277673Z",
     "iopub.status.busy": "2026-10-10T18:42:52.277552Z",
     "iopub.status.idle": "2026-10-10T18:42:52.325838Z",
     "shell.execute_reply": "2026-10-10T18:42:52.325432Z"
    },
    "tags": [
     "hide-input",
     "homework-answer"
    ]
   },
   "outputs": [
    {
     "data": {
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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",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "orders = pd.DataFrame({\n",
    "    \"channel\": [\"Online\", \"Retail\", \"Online\", \"Phone\", \"Online\",\n",
    "                \"Retail\", \"Online\", \"Phone\", \"Retail\", \"Online\"],\n",
    "})\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "sns.countplot(data=orders, x='channel', ax=ax)\n",
    "ax.set_title(\"Orders by Channel\")\n",
    "plt.show()\n",
    "# Sample reflection: Online has five orders, Retail three, and Phone two. Order counts do not determine revenue without order values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-10T18:42:52.326840Z",
     "iopub.status.busy": "2026-10-10T18:42:52.326777Z",
     "iopub.status.idle": "2026-10-10T18:42:52.328781Z",
     "shell.execute_reply": "2026-10-10T18:42:52.328454Z"
    },
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### 7. Spending by Customer Segment\n",
    "# Marketing wants to compare how much new and returning customers spend.\n",
    "#   1. Create a figure and axes with plt.subplots().\n",
    "#   2. Use sns.kdeplot() to draw the distribution of spend from customers,\n",
    "#      with one curve per segment (hue='segment').\n",
    "#   3. Set the title \"Spend by Segment\".\n",
    "# Reflection (answer in comments): Which segment tends to spend more in this sample? Why should you be cautious about the smooth curves?\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "customers = pd.DataFrame({\n",
    "    \"spend\": [22, 35, 28, 40, 31, 26, 55, 62, 48, 70, 58, 66],\n",
    "    \"segment\": [\"New\"] * 6 + [\"Returning\"] * 6,\n",
    "})\n",
    "\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-10T18:42:52.329527Z",
     "iopub.status.busy": "2026-10-10T18:42:52.329465Z",
     "iopub.status.idle": "2026-10-10T18:42:52.370941Z",
     "shell.execute_reply": "2026-10-10T18:42:52.370607Z"
    },
    "tags": [
     "hide-input",
     "homework-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",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "customers = pd.DataFrame({\n",
    "    \"spend\": [22, 35, 28, 40, 31, 26, 55, 62, 48, 70, 58, 66],\n",
    "    \"segment\": [\"New\"] * 6 + [\"Returning\"] * 6,\n",
    "})\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "sns.kdeplot(data=customers, x='spend', hue='segment', ax=ax)\n",
    "ax.set_title(\"Spend by Segment\")\n",
    "plt.show()\n",
    "# Sample reflection: Returning customers spend more in this sample. Each group has only six observations, so the smooth curves should not be treated as precise population distributions.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-10T18:42:52.371962Z",
     "iopub.status.busy": "2026-10-10T18:42:52.371901Z",
     "iopub.status.idle": "2026-10-10T18:42:52.373870Z",
     "shell.execute_reply": "2026-10-10T18:42:52.373577Z"
    },
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### 8. Monthly Revenue Trend\n",
    "# The regional managers want to compare revenue trends over six months.\n",
    "#   1. Create a figure and axes with plt.subplots().\n",
    "#   2. Use sns.lineplot() to draw revenue (y) by month (x) from monthly,\n",
    "#      with one line per region (hue='region') and circle markers (marker='o').\n",
    "#   3. Set the title \"Monthly Revenue\".\n",
    "# Reflection (answer in comments): Describe the two revenue trends. Can six months of observations guarantee the next month's revenue?\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "monthly = pd.DataFrame({\n",
    "    \"month\": [1, 2, 3, 4, 5, 6] * 2,\n",
    "    \"revenue\": [120, 128, 135, 131, 142, 150, 98, 104, 101, 110, 118, 125],\n",
    "    \"region\": [\"North\"] * 6 + [\"South\"] * 6,\n",
    "})\n",
    "\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-10T18:42:52.374620Z",
     "iopub.status.busy": "2026-10-10T18:42:52.374564Z",
     "iopub.status.idle": "2026-10-10T18:42:52.412939Z",
     "shell.execute_reply": "2026-10-10T18:42:52.412528Z"
    },
    "tags": [
     "hide-input",
     "homework-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",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "monthly = pd.DataFrame({\n",
    "    \"month\": [1, 2, 3, 4, 5, 6] * 2,\n",
    "    \"revenue\": [120, 128, 135, 131, 142, 150, 98, 104, 101, 110, 118, 125],\n",
    "    \"region\": [\"North\"] * 6 + [\"South\"] * 6,\n",
    "})\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "sns.lineplot(data=monthly, x='month', y='revenue', hue='region', marker='o', ax=ax)\n",
    "ax.set_title(\"Monthly Revenue\")\n",
    "plt.show()\n",
    "# Sample reflection: Both regions finish above their starting revenue, with some month-to-month declines. Six observed months do not guarantee a future value.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-10T18:42:52.413827Z",
     "iopub.status.busy": "2026-10-10T18:42:52.413757Z",
     "iopub.status.idle": "2026-10-10T18:42:52.415746Z",
     "shell.execute_reply": "2026-10-10T18:42:52.415426Z"
    },
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### 9. Correlation Heatmap\n",
    "# An analyst wants to see which store metrics move together.\n",
    "#   1. Compute the correlation matrix with metrics.corr() and store it in corr.\n",
    "#   2. Create a figure and axes with plt.subplots().\n",
    "#   3. Use sns.heatmap() to draw corr with the values shown in each cell\n",
    "#      (annot=True), cmap='Blues', vmin=-1, and vmax=1.\n",
    "#   4. Set the title \"Metric Correlations\".\n",
    "# Reflection (answer in comments): Describe one relationship shown by the heatmap. Does it establish what would happen if ad spend were increased?\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "metrics = pd.DataFrame({\n",
    "    \"ad_spend\": [5, 10, 15, 20, 25, 30],\n",
    "    \"visits\": [210, 300, 250, 380, 330, 450],\n",
    "    \"returns\": [12, 9, 11, 8, 10, 9],\n",
    "})\n",
    "\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-10T18:42:52.416590Z",
     "iopub.status.busy": "2026-10-10T18:42:52.416532Z",
     "iopub.status.idle": "2026-10-10T18:42:52.461928Z",
     "shell.execute_reply": "2026-10-10T18:42:52.461563Z"
    },
    "tags": [
     "hide-input",
     "homework-answer"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bKVeunHdnAABAUkSAkfgBxvfffy8rV66UihUrevfsAAAgRYpXgKHDgRcpUsT+swEAIKnwYZHn6dOnzVK0aFEzPcfdbN682QwXEVXOnDmddZJhYWHRShoyZcokpUqVkiQVYDRv3lymT58uPXv2tP+MAABIpU0kERER0rVrV/nuu+8kV65ccunSJTNVh07REZPu3bvLzZs3nfevX78uO3bsMGNVDRkyxKzT2/Pnz5dChQo5tytfvrzMmDEjaQUYV65ckRdeeMHMmlqiRIlo3VPHjx9v1/kBAJBqDBo0SFavXi3//vuvFChQQKZMmWICjipVqpiAwIoOdOlKEwA6IGbnzp0tkwOJJV7h2Y0bN0yBZ7Zs2eTMmTPOVI5jAQAg2dMvz3YsHtJmjkmTJkmPHj1McKGefvppk3XQQMNTkydPlgYNGpgEgCvNcug8YqdOnZLEEK8MhmYuAABI0RK5ieTAgQPmS3vNmjXd1teuXVv+/vtvj46hmQ/thGHV9DF79mxZv369nDhxwgQtn3/+udSpU0cSSuI3MAEAkIpERETIxYsX3RZdF5UGF0qn4nCl9z1tHdDshbYuPPbYY27rmzRpIocOHTIBiD6PBhYtW7Z0jsadZAIMTeNonYVOdOb6RoSGhsqRI0fsPD8AAJJ1E8nw4cMlODjYbdF1VtNwqKjBh953PHY3t27dki+++MLUX0TtedKxY0dnjxJ9bMyYMWbQzAULFkiSCjA+/vhjeffdd00BiWu3l5IlS8rQoUPtPD8AAHxCOzDYsYSGhpqJQV0XXRdVwYIFzf/Hjh1zW69ZBsdjd7N48WKz7TPPPBPrthpkaIJAsxpJKsAYN26czJkzR15++eVoKZi5c+fadW4AACT7AMPf31+yZMnitliNbZE9e3apVKmS/Pjjj269NnX6jUaNGjnX6ZgWu3fvtmweue+++6L1Nrl9+7ZbN1al+2swUqZMGUlSRZ4HDx6Ue+65x9x27aIaGBjonL4dAADEzf/+9z9p3bq1GQCrWrVq8sEHH5jxMLQ3icOAAQNk586dbt1TtXBz4cKFMnHixGjH1CClXr16ZpqPsmXLmmJSnRFdu74+/vjjkqQyGFp9unHjxmgBhg4hnpDREAAAiSaNTUscaOGl1kWsWrXKBBJ6vf3tt9/MF3iHwoULS+nSpd320yxH1apVpV27dtGOGRQUZFodNm3aJP379ze9SXSgzLVr18Y6Sqg30kRajS8ai88++0xGjRolI0aMkCeeeMK8EUuWLJEPP/zQPKaDgsRVQJUX47wPUrBseX19BkhiVk3mMwL/qVE0OMHfjsB202w5zuXZcb8mpgTxaiLRUTy1qlUjIG3b0e4u2i1GCz/jE1wAAICUJV4Bhnr11Veld+/eph5Dgwyd/Cxt2rT2nh0AAD4SdRoMJFKA4aBBhS78ILwTkDG9PNqosuTOnkXGzlwhN2/e9vZHgyQmQ/q08mCtEpI/Z5DsPHhaVvx9INZ9smcJkEbVikpIcCY5cuqiLFu/T65d/68avPvDVSQoU4Zo++0+dFZ+/H2P7a8BCePShfOyaf0auRYeLqXK3yOFi919hsvff10qZ0+fdFuXr2BhqVKrruX2u7dtlj07tsg91WpLwaLuw0cjZlzXfFDkqTO1DRw4ULJmzWqKTXTR2zpbm85Tgrjp93RT2TZ/sPR8or4M79Na/NN7HfchickWlFFWf9ZNBj9dX+4pnlsmvdFKZv/vcfHzi/kb0gPVisrumS9Kl4cqSelCIRL6VF3ZPv0FKZ4/m3ObHFkzSe7sgc6lSN5sMrxHY6lZ9s6AOkj69u/ZIa91byurli6UPds3yzt9npV530y96z7L5s+RDWtWyIVzZ5zL1StXLLc9c+qEfDpioHwzeYzs3bk1gV4FEF26+DaPzJs3zxR66mieSsc3124v586dMyOEwXPb9h6VKo8NlXrVS8rsj57jrUuBBnapJxkzpJPaz0+Wq9duSJG8WeXvKc/Jk00qyvSf/rHcZ0DnOrL0z3/lySHfmftp/dLI31Ofl16P1ZA+Y5aade9NX+O2T8emFeXZllVl2uJNifCqYIfJHw+XClVryksD3jX3/1i1XMaOGCg17n/AZCViUqFqLWnT+e6fF7dv3ZLP3ntLHn2yu0wd8x4/sDgig+GDDIZO96oDauloYTooiC56WydB++qrr7w8pdTnx1Vb5cLlcF+fBhJQ6/pl5Zuft5ngQh04dl6Wb9gvbRqUjXGfiBu35Mr/b69u3Y40zSPhEe4D5rjq2ryyrPh7vzk+kr4TRw/Lgb07peFDjzrX1ajzgAQFBcv6Nb/cdd8jYftl6bxZ8udvP5sMhpW50ydKYJZgt+MjaXdTldQeYAQEBJhBQKLSfrn6GID/BGf2l7whgbLnkPtFYHfYaSlTKEeMb1XfsUulbOEc8vnrD8vrHe+X7959Qg6fvCgfzFxrub02ndS5p5BMXUT2Irk4emi/+T9vgf8yFX5+fpI7f0E5GnaXGp00ImdOHpfDB/fJku+/kb7d28q6VcvcNtm++S9ZtWyhPNP7zYR7AYDdTSRNmzY1Y17oiGOOFJIOp6Hr9DEA/3EUYZ6/fM3tbTl/OUICLQo0HbRJRBXKHSxXI25KnpBAOXr6kqT184sxe3Hq/BWZv2YXb38yEX71qvk/c2CQ2/pMmYPkWrh1TYV6qudrUqTEfwMtzflivEz6aJiUrVhVgrOFmKLRcSMHyXOvDpSg4KwJ+ApSNppIfBBgaJHnsGHDzMhg1atXN8HFX3/9ZcY2b9++vfTo0cO5rc66GpWOoRF1trjI27ckjR/dXJHyaHCgsmRyHzEvONDf2WRiZebgNrJqc5i8OOrOvAQay6/4pIuMeqmpPDX0h2jBSMcmFWXG0i1ygx5ISdLWv/+Ug/v+mz+i8cNtxT9jRnM7/OoVyRiQyflY+JXLkiN3nhiP5RpcqAdbt5d5M6eYIs5q99aX+bOmib9/Rgnbv9csKlIiZcvff4hf2rRSv2nLBHiFKQ8Bhg8CDJ00pU2bNua2I1CoWLGiWbQXSWzz1us0tUOGDHFblzZ3DUmft2Z8TgdI0s5eDJfTF65K0Xz/9f5QxfJlk72H/5uN2FVgQAYpWTBEhkxZ6VynY+6u2nxQ2jQoF237B2uXkLw5gmgeScKuXr3sVitx+/YtZ9PIiWOHJVtITnNbv7CdOnFUyle5U0DvibRp73yUh/9/T5KS5e4xF0e32ozISBO4XLpwzq6XlOIRYPggwNBiTm/oNLV9+vRxW5erbn+vjgkkZQvX7JbHHygno2evk5u3bkuubJmlSY1i8tbEFc5ttH5Cu6NOXrhRLodflxNnL0uNsvlk7sodzm2ql8kn/x6JHpR0eaiyrP4nTHZHqfNA0lGzzgNmcRWQKbMJMlYv/1HKVKhi1m35a52cO3NKqt/XwLnd2hVLzLY6zoU2f2hwok0hDlprkcbPT0qVrxTjc2mtRs26jSj4RNIOMDRLsW7dOqlbt66zi+qECROkePHi0q9fP0mX7u6H1clVok6wkpqbRxrULCWVSxeUssXvzL/xQocGcuPGLfnhl01y4AgXjJTgnakrZeXYrrLkw46y+p9D8mi90rJ130mZ+uN/BZkt7ispjzcsZwIMFTrhZxnXt4Wpvdh58IzUuaegVCudT5q/NsPt2LmzZZYHaxWX50cuTPTXBe91e6m/jHzrVTPIVvYcOWXV0gXS7NH2Urj4f4X0P82bLTlz5zUBRsS1cHn/rVekeOnykitPPjm0f69s/GO1PPnMy5IrL+Of2IkMhg8CDK2/0CBCA4zLly9L8+bNpXLlyvLzzz/LxYsXTRMIPBeUKaPkzpFFzl64IqO//FmyB2c26/0zMOBWSnHszGWp8cxEeaJRBcmXI0jem7FG5vyy3WQzHFZtOuhWCDpz2Vb5c/sRaVazuOTImlm+X7VTug6bZ5pbXBXIlUXGzv1Tvl+5M1FfE+xRrlJ1GTF+pvy5+mcTZPR6Y6hUrFbbbZv7GzaTzEF3JvfKkTuvDB3zhaxf86scPxJmmlLad38p1uCieZsnpVCxkvzY4iIVdzH12WyqRYsWNdPHFihQwEzRrpOcaRZj69at0qJFCzM/SVwxmyrcMJsqomA2VST2bKohXWbacpwzX3SQ1CheX5FPnjxphgZXK1asMEGF0iaS2Ao8AQBIDmgi8cFAW+XLl5dPPvlE/vnnH/nmm2/kwQcfNOu3b98u5cpFr3AHACA5Bhh2LKlVvAKM9957T0aOHGmGCNeBtWrXvtNeOHr0aDOFOwAASN3i1UTSsGFD0xRy4cIFyZ49u1v307Jl/5tbYcOGDWYgLgAAkpvUnH2wQ7y7KaRNm9YtuFBRm0d0ptV41JACAOB7xBeJ30QCAABwNwy0AACABZpIvEOAAQCABQIM7xBgAABggQDDO9RgAAAA25HBAADAAhmMJBxgnDp1KiEPDwBAwqGbauIEGG3btvX4oN9++635P0eOHPE7KwAAkDpqMPLkyeNc/P39Ze7cubJt2zYJCAgwi97WdRkzZkzYMwYAIBEwF0kiZTDGjh3rvN2pUycZOHCgvPPOO842Kh2x8+2335YDBw54eUoAAPgeNRg+qMFYtmyZ7Nq1y+3N19t9+vRxm4sEAACkTvHqpnrt2jXZuXNntPW6Th8DACC5o4nEBxmMp556yhR9apOIY0IznTlVm0y6dOni5SkBAJAE0Isk8QOMUaNGSUhIiLz++utmynaVNWtWeeWVV+TNN9/07owAAEDqDDDSp08vgwcPlkGDBsnRo0fNunz58lEQAwBIMSjy9OFAW/rm58+f38tTAAAg6SHA8NFcJFrMuWrVKpk+fbpMmzbNbQEAILnzVZHnhx9+KMWKFZPAwECpU6eOqXG8m+7du0u6dOncltq1a3t9XJ9kMLZs2SItW7aUs2fPyqVLl0w9xpkzZ5xNJV27drX7PAEASPHGjRtnyg9mzZol1atXl6FDh0rjxo1NL00d6NLKrVu35IknnpAvvvjCuS5qYBOf4/okg/Hqq69Ku3btnAWep0+flr1798p9990nvXr1svscAQBIFRmMkSNHSo8ePaRFixaSO3duGT16tKl7/Pzzz2M9V9cMRtq0aW05bqIHGH/99Zf079/f+eZdv35dihcvLpMnT5YJEybYf5YAACS2NDYtHjp27Jjs379fGjZs6FyngUL9+vVl7dq1d913/vz5EhQUJIULFzZDSRw5csSW4yZ6gHH+/HnTLKJy5szp7EmiBZ8nTpyw9wwBAEjGIiIi5OLFi26LrotKAwGVK1cut/V6nT1+/HiMxy9ZsqSphwwLCzOBxsGDB03wcOXKFa+O67MiTwdtFtF2HS0WCQ0NZahwAECKYFcTyfDhwyU4ONht0XWe8vPzMwNaxkTHn2rVqpVky5ZNKlWqZGY013nBdAJSb47rkyJPbR5xeP/99+Wxxx4zI3oWKFDAFJAAAJDc2dVNNTQ01MzV5UpnJY9KayPUqVOn3NafPHnS+ZgnNDOhHS727Nlj63ETJYMxYsQIt9SM9irR3iSHDh0yGQ0AAPBfMJElSxa3xSrA0DKDQoUKycqVK916iOiQEHG5tmoPT20WyZs3r63HTfQmEgftVwsAQEqhCQw7lrjo27ev6VL6yy+/mJ6a2mIQHh4uzz77rHMbnfPLMc6FbvPkk0/Kpk2bTF2HznSuXVa1TrJ9+/ZxOm6SGskTAICUyhcjeb788sumCFSDBh0CQmsqFi9e7DZqtmYfbt68aW5rPUebNm1MF9TNmzdL9uzZTYHnunXrzO24HNduaSITssIjDgKqvOjrU0BSku1Oag9wWDWZzwj8p0bR4AR/O0r2W2LLcfaMfFDsdPv2bVOcGXWsi6SGDAYAABZ8kMDwiPb+SA4IMAAAsMBkZ94hwAAAIBllMJKL5JFnAQAAyQoZDAAALPj5kcLwBgEGAAAWaCLxDk0kAADAdmQwAACwQC8S7xBgAABggSYS79BEAgAAbEcGAwAACzSReIcAAwAACwQY3qGJBAAA2I4MBgAAFijy9A4BBgAAFmgi8Q4BBgAAFshgeIcaDAAAYDsyGAAAWKCJxDsEGAAAWKCJxDs0kQAAANuRwQAAwAJNJN4hwAAAwAJNJN6hiQQAANiODAYAABZoIvEOAQYAABZoIkkpAUa2vL4+AyQl5475+gyQxOTLFuDrUwCQLAMMAACSEJpIvEOAAQCABZpIvEOAAQCABTIY3qGbKgAAsB0ZDAAALNBE4h0CDAAALNBE4h2aSAAAgO3IYAAAYIEMhncIMAAAsEANhncIMAAASEJ++eUXGT9+vJw8eVIqV64sAwYMkFy5csW4/alTp2TcuHGybt06yZAhg9SvX1969uwpGTNmdG7zv//9TxYtWuS2X9myZWXq1KkJ9joIMAAASCJNJD/99JM8/PDDMnDgQOnSpYuMGjVK6tSpI5s2bZJMmTJF2z48PFxq1qxptn3xxRfl8uXL8tZbb8nChQtl6dKlkjZtWrPdv//+K1mzZpXBgwc79w0MDEzQ10KAAQBAEmkiGTBggHTs2FEGDRpk7tetW1fy5s1rMg29evWKtr2/v79s27bNLfgoUqSI1KpVSzZv3ixVq1Z1rs+RI4fUrl07kV4JvUgAAIgxg2HH4qlz587J33//LS1btnSuy5IlizRo0ECWL19uuY+fn1+0zIYjM3H9+nW39WvWrJGGDRtKmzZt5JNPPpEbN25IQiKDAQBAAoqIiDBL1MyDLq7CwsLM//ny5XNbr/f/+usvj59v2LBhUrBgQalSpYpb0NG5c2eTEdHn0ZqM77//Xn7++WcTpCQEAgwAABKwiWT48OEyZMgQt3XaBOJaD6EcGYWogUdAQIDH2Yb3339f5s6dazIersf58MMP3e7fd999Uq5cOZk/f748+uijkhAIMAAAsOBnU4QRGhoqffr0cVsXNYhQ2bNnN/+fPXvWbf2ZM2ckJCQk1ucZM2aMvP322/Ldd9+ZwtC7PZ/2IMmfP7+p00ioAIORPAEASED+/v6mlsJ1sQowtDhTg4z169e7rf/zzz/dmjusfPrpp9KvXz+TvWjevHms56T1GRrIZM6cWRIKAQYAABY0gWHH4imthejatatMmDBBTpw4YdbNmDFD9u3bJ926dXNup00rrvd1DIy+ffua4KJFixbRjqtdV7W7q6OZRf/X7W/fvi2tW7eWhEITCQAASWQcjKFDh5oxK4oWLWqaMI4dO2YCjnvuuce5zd69e2Xnzp3mtgYi2n1VMx9auKmLg9Z9NGvWzNRw6GBcefLkMQWjR44ckZw5c5qxMooXL55gryVNZGRkpCQBAQ8M8/UpICk5d8zXZ4AkZu+KUb4+BSQh+bNmSPDnaPbZH7Yc56cXasV5n8OHD5ugoFSpUtGaMTQA0QG2KlSoYJo6tGurlRIlSpixLxx02927d5t6Dh1bI6GRwQAAwIKfDwbacihQoIBZrLhmHXRocE8Hz9JtNShJLAQYAABYYDZV71DkCQAAbEcGAwAAC0zX7h0CDAAALKQRHxZhpAAEGAAAJLEiz5SAGgwAAGA7MhgAAFigF4l3CDAAALBAkad3aCIBAAC2I4MBAEACTteeWhFgAABggfjCOzSRAAAA25HBAADAAr1IvEOAAQCABZpIvEMTCQAAsB0ZDAAALNCLxDsEGAAAWKCTqncIMAAAsECRp3eowQAAALYjgwEAgAWma/cOAQYAABZoIvEOTSQAACBpBBhHjx6VDz74wHl/9OjRkidPHrn//vslLCzMzvMDAMBnA23ZsaRW8Qow+vTpI0WKFDG3Dx8+LKGhoWbJnz+/eQwAgJTQRGLHklrFqwZj+fLlMmHCBHN7yZIl0qxZM+ndu7e0b99eKlSoYPc5AgCA1BBg3L59W65cuSLBwcGybNkyadSokVnv7+8vt27dsvscAQBIdPQi8UGA0aBBA+nevbvUrVtXFixYIO+//75Zv2bNGlOHAQBAcpeamzd8VoPx6aefStasWWXhwoWmqaRw4cJm/RdffCGDBg2y5cQAAEAqy2BogDFz5sxo62fPni0DBw6U6tWr23FuAAD4DPkLH2Qwhg0bFq/HAABITrOp2rGkVraO5Ll161bJmTOnnYcEAMAnUnFskPgBRo4cOSxvO3qWnD9/Xl566SV7zgwAAKSOAGPs2LHm/w4dOjhvO6RPn94MvlWtWjV7zzCZK1skh2TOmEG2/HtCIm7E3oU3Q/q0UiJ/dsmYIZ3sO3pOzl++5nwswD+dVC+Tz3K/vYfPyrEzl209d/hW7pAgKVUkj2z/96icOX+FH0cKd2DfvxIeflWKFS8p/hkzxrr9jRs35PChgxJx7Zrky19QsgQHW24XdnC/nD19WipUqizp0qVPgDNPuehFkogBhg6k5cheNG7c2MunTtkK5AySue8+IXmyB8qFy9cke5YA6fbuPFm2fl+M+7SuV0Y+fuVBOX/pmly8el3KFckhE+b9JaHjfzaP5wjOJAO71HPbJyQ4QMoXzSVPvP2tzF+9K8FfFxJehZL55I1nHpT7qhSXvDmDpd2rn8uCX//hrU+hzpw+JQP69JITx49JluCscv7cGen/9jC5v17DGPdZs2qFjBo+RDJlyiyBQUGyf99eafFIG3mxzxvOi+La336VWdOnStiB/XLh/Dn5/qdVEpw1WyK+suSPJhIf1GAQXMTu8/4tTaBwf48pcvPWbRMYfPVWaynX6TM5ezE8+g8irZ9M7N9Sxs5dL4On/GrW1a1USJZ+1FmW/LFXVm48KIdOXpRmfaa77fdez8aSK1tmWbxuT3x+lEiCShXOLT/8vEleHDpTjq0a6evTQQL7YNggSZ8hg8xe+LNkyJBBvp42SYa93V+mz/1Rsoe4N0U7mqNHDHlTmrdqLT179zPrtm/ZLC8+00lq1L5fat9/50vI4bCD8vTzL0pERIS88UpPfo7JyOHDh+Xrr7+WkydPSuXKlU2rQdq0ab3eJz7HTZReJDruhS6ut2NaUruCubJIw6pFZdSs301woUbPXmeaPx6pW9pyn0wZ00uAf3r5a9dR57oNO4/K7duREpIlwHKf9On8pEOTCjJj6Ra5cfPO8yD5+275Rvl26d9y4yaj4qZ0Z8+clj9/Xy3tOnY1wYVq076TyUL8+vNPlvvcuH7dNKWULvvftAylypaTtGnTycULF5zr2nXsIpWq1kiEV5Fy+aIXybZt26RixYqyevVqCQoKMmNLNW/e3ASW3uwTn+MmWgZj0qRJlrcRXaUSecz/G3cfd667HH7d1Ek4Hovq4pUIGTF9tQx6ur6pv9DtuzWvLKs2H5SFa62zEy3vLyU5s2aWqYs28WMAkqF/9+ySyMhIKVWmnHOd1l8UKlJM9u7aabmPPt712Rfki0mfSWTkbcmcOUiWLPpBylW4R+o/0CQRzz7l80UTyWuvvSY1a9aU+fPnm/tPPfWUlCxZUr799ltp165dvPeJz3ETLcBo27at5W1ElzXoToFW1KaQMxfDJdv/P2Zl3m+7pFnN4ibIuHT1uuTOHij9P1sm12MoDu3yUGX5bfNB2X3oDD8GIBm6dPGi+T9LFvcCzeDgrHLp0p3HrGh9xrrVK2XK+DGSOSiLnDl1Up5/qa9HxaFIuq5evWrm95oyZYpzXdGiRc0UHPPmzbMMBDzZJz7H9VkNxtGjR007jkZEavTo0TJixAgpXry4GeGzUKFCd91f2wR1cRV5+6ak8bN1WA6fufH/AYH2+tBAwSGTf/oYgwVtBvnpo07y8ew/TCZD1S6fX34a1dkcQ+swohaRNqpWVJ55b0GCvhYA9jgUdkDOnDrlvF/+nsqSLv2dXh0REdckU+bMzsei3nd1+dJFebVHN2nZpp107/GyWbdrxzZ5+dnOkjFjRqlHFiPJ9SKJsLjm6eSgurjav3+/mTBUL/6uihUrZpo4rHiyT3yO67ORPPv06WO6pDqKRkJDQ82SP39+81hshg8fbmZidV1uHlwpKUXYiTvtoPlyBLmt1/uOx6K6r2JByRqYUSYt+Nu5bt22I7J1/0l56N4S0bbv/GAluXg1Qr5fucP28wdgP804aLOGYwm/elXy5MlrHjt96qTbtqdPnpTceay7pG/f+o9cvHhBWrb+71tn6bLlpVTZ8vL76pTzOZoU+Nm0DLe45um6qMLD72S9tUbCVZYsWUwWwoon+8TnuHaIV8pg+fLlZpIztWTJEmnWrJn07t3bdGOtUOG/wqOYaDASNRDJ1eojSSk27Doq5y6Fy8P3lZJdYb+bdVVK5pECubLIL3/td25XtVReU2uhTRyn/n+cA93m9IWrzp4lebMHyqlz0X8BnnqwksxcttWjsTUA+N7jT3Yxi6vMgYGSNVt206W0ZOmyZp12OT1yOEyq1azt3G73zu2SIYO/FClW3GyvTp44Lrly36np0m+nGqRUrFQlUV8TPBNqcc2Lmr1QgYGB5n8dtNLVuXPnTDBgxZN94nNcnwUYWnV65coVE4Vpu06jRo2cb5j+osfGKjWUUppHlPboGDJlpQzv0UgibtyUk+eumG6qi37fI2u2HHJu99lrLeSfvcflufcXyp87jsq6bYdl2puPyIjpa+TS1QhTY6G9S75cstnt+A2rFpEiebPK1B8p7kyJQrJmlnLF80lAxjvp83Il8sr5S+Fy5OQ52XfotK9PDzbSLoLPvNBbPn5/qAQEZJIcOXPJV5PHS7Wa90r1Wvc5t/v4/WGSJ28+eWvYSClRqoxUrlpD3hvypnTu/rwEBmWRpYvmm7Eumrdq49znyOFDcurEcdm3d7e5v3XzRskcGOQWpCBxmkj8La55VrTJQpu5duzYIQ0aNHCu1/uVKlWK9z7xOa4d0kRqCXMcPfbYYyblUrduXRk6dKg5SZ2yfdGiRTJ+/HhZsCDudQEBD6S8SdIerVta2j1Q3lwoVm8Ok7Fz/3TLOHzWt4XsO3pWPpj5u7Nm47lW1aRmufzinyGd7A47LeN/2CBhJ9yLvV5pV0vKFclpApMU69wxSa3uq1xMBvVqGW3998s3yvhZqyS12rtilKRUOnDWssUL5Fr4ValYuZq0bd/ZrWBz9PtDJXv2EHnqmTvjWejonQu+nyPbtmwytwsUKiyPPt7BjOjpMG/uLPl1+ZJoz6U9UFJC99X8We90601Ir8yz7skTV6MfKePxtk8++aTs2bPHdCfVoOSPP/6Q2rVry9KlS6VJkzu9hL788ks5ceKE9OvXz+N9PNkmSQQYx44dM+megwcPSs+ePaVz585mvVaivv766/Garj0lBhjwQioOMJD6AgwkzQCjz3x7AoxRrcrEqRNF/fr1TcZBSw4WL14sHTt2lE8//dS5TadOnWTnzp2yYcMGj/fxZJskEWAkBAIMuCHAQBQEGEgNAYbSFoKffvpJTp06ZUbcrFHDPeOkpQlaP+HavTS2fTzdxk4EGEiaCDAQBQEGEjvA6LvAnvmdPmxpPYJzSudxZaVjCHCtQo1tOPColaoAACQ3fj4YyTMlYahwAACQNIYKb9iwoYSEhNh/NgAAJBFM1+6DkTzz5s0rjzzyiMyZM0euXbvm5SkAAJD0+GI2VUntAcYPP/xgRgbr1q2b5MmTR7p37y4rVqxI0GlfAQBACg8wdA75GTNmmIE+tA+tjovRtGlTM9hW//797T9LAACS6VwkqZVXrz1z5sxmoI4ff/xRNm/ebOoy3n//ffvODgAAH9HWDTuW1MqrAEPnI9Fp21u0aGHGMz9z5oxz6FIAAJB6xWuGMR1iVJtItBYjXbp00qZNGzOeuQ5D6ueXmhNCAICUIjUXaPoswHj00UdNHca0adOkZcuWHs0SBwBAckJ84YMA4/jx45ItWzYvnxoAgKSLkTy9E6/2DIILAABgewYDAICUjhoM7xBgAABggRoM79DlAwAA2I4MBgAAFijy9A4BBgAAFtII42B4gyYSAABgOzIYAABYoInEOwQYAABYIMDwDk0kAADAdmQwAACwkIaBMLxCgAEAgAWaSLxDgAEAgAUSGN6hBgMAANiODAYAABaY7Mw7BBgAAFigBsM7NJEAAADbkcEAAMACRZ7eIcAAAMCCH5OdeYUmEgAAYDsyGAAAWKCJxDsEGAAAWKAXiXdoIgEAALYjgwEAQDIeaOvGjRvy22+/ycmTJ6VSpUpStmzZWPc5duyYrF+/XjJkyCDVq1eXHDlyuD2+evVq2bt3r9s63ebhhx/2+LwIMAAAsJAc4otTp05Jo0aN5MqVK1KuXDl59tlnpVevXjJixAjL7W/evCndunWTVatWmWDk8uXL8ueff8rHH38s3bt3d243adIkE7TUrVvXua5o0aIEGAAApIYMRr9+/cy08lu2bJFMmTKZwKF+/fry0EMPmf+junXrljRr1kymTZsmadOmNes+/fRT6dGjh9knX758zm3vvfdes118UYMBAEAydOPGDZkzZ47JWmhwoerVqyeVK1eWr7/+2nIff39/6dSpkzO4UC1btjSZjZ07d7ptq00us2bNkmXLlsmZM2fifH40kQAAYMGuBEZERIRZol7odfHG/v375erVq1K+fHm39RUqVJCtW7d6fJwlS5aYgCPqcbZt2yazZ8+WsLAwE3x88sknpnnFUwQYAAAkYIp/+PDhMmTIELd1gwYNksGDB0fbduXKlSZwuJt27dqZjMWFCxfM/axZs7o9nj17dtm4caNH57Zjxw55/fXXTVNL7ty5neu1HmPixImSPn16c/+jjz6S559/XmrVqmVqPTxBgAEAQAIKDQ2VPn36uK2LKXuxfft2+eOPP+56vEceecQEGAEBAea+Fni6unTpkvOxu9m3b580adJEmjdvLsOGDXN7zLW4U73yyismKFq6dCkBBgAA3tDiSTv4x6E5pGfPnmbxRJEiRcTPz08OHDggderUca7X+8WLF7/rvpoladCggdx///3y1VdfmePE9l5o0BKXWgyKPAEAsLqo2rQklMDAQBMkaCGmw+HDh033UtfxKvT+woUL3QIQ3U97icyYMcOt4NNRPHr8+HG3ddo7RYs+a9eu7fH50UQCAEAyNXLkSNNz5Mknn5Rq1arJ5MmTTeDQoUMH5zYTJkwwRZoadOi4Fw0bNjS9Rpo2bSrTp093bqfHKVasmAkwHnjgAbPooF0akIwfP948hzaneIoAAwCAZDoORtWqVWXz5s0ydepU2b17t7z00kvy9NNPu2UlNHAoWbKkua3Bg2N8DM1suCpdurQJMLS+Y8OGDaar66ZNmyQkJETmz59vApO4SBMZGRkpSUDAA+4FJkjlzh3z9Rkgidm7YpSvTwFJSP6sGRL8OWb8ddiW43SsVkBSI2owAACA7WgiAQDAQjJoIUnSCDAAAEjAbqqpFQEGAAAWqCHwDu8fAACwHRkMAAAs0ETiHQIMAAAsUIHhHZpIAACA7chgAABggSaSFBJgrJr8oq9PAUlIvmyxTzWM1KVEQ/fprpG6hW8cm+DPQYrfO7x/AAAg5WYwAABISmgi8Q4BBgAAFuhF4h2aSAAAgO3IYAAAYIGpSLxDgAEAgAU/Gkm8QoABAIAFMhjeoQYDAADYjgwGAAAW0tBE4hUCDAAALNBE4h2aSAAAgO3IYAAAYIFeJN4hwAAAwAJNJN6hiQQAANiODAYAABbIYHiHAAMAAAt0U/UOTSQAAMB2ZDAAALDgx3ztXiHAAADAAk0k3iHAAADAAkWe3qEGAwAA2I4MBgAAFmgi8Q4BBgAAFijy9A5NJAAAwHZkMAAASOZNJKdPnzZL0aJFxd/f/67bhoWFydmzZ93WZcqUSUqVKuXVcaMiwAAAIJn2IomIiJCuXbvKd999J7ly5ZJLly7J2LFjpVOnTjHu8/bbb8v8+fOlUKFCznXly5eXGTNmeHXcqAgwAABIpgYNGiSrV6+Wf//9VwoUKCBTpkwxgUGVKlVM0BCT5s2by/Tp020/ritqMAAAsJDGpiWhREZGyqRJk6RHjx4mCFBPP/20yUxoQHA3N2/elF27dsmpU6dsPa4rAgwAAKwukGnS2LIklAMHDsiZM2ekZs2abutr164tf//99133nT17tsliaG1FuXLlTLbCjuO6ookEAIAEFBERYRZXWjBpVTR58OBBOXfu3F2Pp00U6dOnN0GACgkJcXtc72/ZsiXG/Zs0aSLDhg2T/Pnzm/N66aWXpGXLlrJt2zbJly9fvI8bFQEGAAAW7Mo9DB8+XIYMGRKtxmHw4MHRth03bpwsWbLkrsdbunSpKbzUIENFDV70vuMxKx07dnTe1iBnzJgx8tVXX8mCBQvk+eefj/dxoyLAAAAgASOM0NBQ6dOnj9u6mLp8jhgxwiyeKFiwoPn/2LFjbuuPHj3qfMwTei6anTh06JCtx6UGAwCAGMbBsOOfv7+/ZMmSxW2J65gSVrJnzy6VKlWSH3/80bnuypUr8uuvv0qjRo3cxr3YvXu3uX379m1T4OlKH9PgoUyZMnE6bmzIYAAAkEz973//k9atW5tBsqpVqyYffPCBaT7RXh8OAwYMkJ07d8qGDRtMoFCvXj3p1auXlC1b1hR0alONdj99/PHH43Tc2JDBAADAgnYAsWNJSFqcqbUTq1atMoGEdiX97bffJDAw0LlN4cKFpXTp0uZ2UFCQzJkzRzZt2iT9+/c3vUl69uwpa9eudcuqeHLc2KSJ1A6v8aBVpo4K0xMnTpgTLl68uDz00EPxOZys338hXvshZcqXLcDXp4AkpkRD9zZspG7hG8cm+HOs32fPdalGsWBJjeLVRKIDbfz+++8yceJEuXHjhtSvX18uXrwo58+flw8//NBEQwAAIPWKVxOJBhH9+vUzt1euXCm3bt0yfXcXLlwon3zyid3nCABA4kvqQ3mmxAzGvn37nJOkrFixQh555BHTN/a+++4zgQYAAMldcppNNcVkMLRgRAcCCQ8PNwUijRs3Nuu1GrVIkSJ2nyMAAEgNAYYOGtK2bVvJkSOHBAcHOwOMadOmmdnWAABI7pJDL5IU10TSpUsX0xyio37p/+nS3TlMjRo1pEWLFnafIwAAiS4Vxwa2iPdAWyVLljSLqzZt2thxTgAAILUGGD///LOsWbNGzp49G+2x0aNHe3teAAD4FimMxA8w3nnnHbPUqlVLsmXL5t0ZAACQBNGLxAcBhk4nq5OgNG3a1MunBwAgaUrNBZo+60Vy/fp1qVOnji0nAAAAUp54BRj333+/GWALAICUioE8fdBEUq5cOenQoYM8++yzUqJECUkTJY/Uo0cPL08LAAAfo4kk8QOMb7/91swLP2/ePMvHCTAAAEjd4hVg7N271/4zAQAgCaEXiQ9qMIoWLerl0wIAkLQxVLgPAowLFy7I+fPnvXxqAACQUsUrwHjiiSfk448/tv9sUribN296tN3tW7fkWvjVaEtkZGSM+0Rcu2YWJB+3bt266880vvtoN3J+F1KmgIzpJX26tL4+jVSDXiQ+qME4fPiwjB8/Xr755hspW7asZMiQwe1xXY///LPhd/lq/Cg5fvSQZA4Mkodad5BHOjwd41u0ecNa+XBQX/HPGOC2/r3PZ0mOXHkstv9dPnjrFcmZJ7+Mmvodb30Sd+zoYfnw3cGy+e+/xC+tn9R/oIn07jdQMgcGxrjPqhXL5YuJn8rhQ2Gm11bpMuWlR+/XpGz5is5tNm74U+Z/N0vWrPxFChctLhOnf5tIrwgJSQOKxxpXkWcfryP3Vy0h479ZKa++N4c3PTHQiyTxA4zcuXNL9+7dvXvmVOLY4YPy0ZB+0rrTs/Jg6/aye9tm+WjwaxIUnE0eaN46xv3Sp88gk39YGevxz589LVM+flcq16wjR8L223z2sNutmzdlwKu9JH/BQjJ38a9y9cplCe3TSz54d5AMevdDy30OhR2Qd958Tbr3eEkef/IpuR5xXT754F0JfaWnfLt4haRLl95kNr6aPF5aPtZOQkJyyuaNG/jhpRBVyxWS5vUryODPFsqwlx/x9ekACRtgTJo0KT67pUo/L5or2XPmllZPdDH3K1SpKXUaN5cl38+8a4ChNBUeefu2+KW1Tonevn1bxo8cLA+27iCXL14gwEgG1q9bKwcP7JPho8dJluBgs3R7rpcMGdBXTp04LjlzR89Qhe3fZ5rNWrZ+3AQTujRr8Ygs/XG+nDl9WnLnyStp06aVUeOmmO13bt/qg1eGhPLHP/vNgsRHLxIf1GDAc3t2bJUyFSq7rStbqZocPXTAfHuNyY0b1+WZR+vL04/WkwE9n5T1a6KPnLpg9hemzFkDDCQP27Zuklx58kqevPmc6ypXq2mCyR3btljuU6laDcmbL7/MmDZJzpw+JUePHJLvZk2X6rXuk1wWAQkAe9CLxAcZjMaNG9/18eXLl8f3fFKci+fPStA9Vd3WZQnO5nwsU+bo7e4ZAzJL994DpFa9xuLn5yfLF86VT4a+If3+N1ruqX6v2WbP9n9k6bxZMnTsV9FGUkXSdf7sWcma1X0G4qAsWSRt2nRy7txZy30CA4Nk4NCR8vbrvWXO11+YzFXxkqXl3VGf8rMHEhCfrD4IMKpXr+52Xz/w9uzZI/Pnz5enn465eNEhIiLCLK6uR0RIBn9/Sa70G2jEtXDnfZPKTp/+zmO3b0d7v4wYAoOy91Q1i8PDj3eW7Zs3yE/zZpsAQ3sIfDriLenQ/WUJyJTZ9DC5dUt7qESa2xky+MfYrILEc+1auETevtPjQwNF/4wZze3bUXqBmKYwiYwxWDiw71/p26u7PNW9p7Rp38n8rXz60XvS+7kuMvnr7yQgU6ZEeDUAkAgBxogRIyzXT548WX799ddY9x8+fLgMGTLEbd0zL/eX514JleTqzKkT0v+5J5z3m7RsK+27vyTZsueUi+fPuW2r9/VikjVbiMfHz1ewsPyzYZ25feniebNMHfueWdTNmzdMO32vDg/Ji6FDpUqtura9NsTPi907meYMpb06xk2dKSE5c8n5tb+5bXfxwnnzswsJyWF5nOVLFkpw1mzSvnM3cz99+vTywiv9pFXj++WPtaukQeMH+REBCYEURuIHGDFp166dDBgwINbtQkNDpU+fPm7rthxN3mM4aPdRq14fpSpUkt9X/GS+pTq+oW7d+KcUKlZSMgbc+eapF5fr1yNMt9QYv8Xu3W2KRWN6rjnTxsnvK5fRTTUJmTRjbrR1FStVkS8njZOwg/ulUOE7I+L+9efvJsNRrmIl53bhVzUTlUHSpksn6TNkMGOouP4O3bhxw/yfPkPyzfoBSR1FnkmoyHPHjh0eDRzk7+8vWbJkcVuSc/PI3TRp+bhcuXJZZnw+Wk6fPC5rflkia35ZLC3b3elVov7+4zd5pnUDOX3imLk/+eN3ZfXPi839E0cPm313bdskLdp09OErgR2qVK8lpctVkFHDh0jYgX2yY+s/MvGzj+XBlq0lW/YQZ3DRomEtWbLozmSCdeo/YLJe4z/50PQ0ORx20OwfkiOn3FP5v6Y0bTrTfbUrbGTkbXNbFyR/mQMymMXPL42kS5fW3M7of6cJFkhRGYwXX3wx2rpz587JwoULpWvXrnacV4oRkjO3hA4fKzMnfSIDe3WS4Gwh0u3F/lK7fhPnNtrF0GQv/O7Ee48+2V1++HqyzP1qgqnXKFC4uLz1wedSqtw9MT5PuvQZJOP/t/Ej6dJMxbAPxsi4j0fKqz26mexE/UZNpXvP3v9tlEYLfQMkXbo7f55a0Dli9HiZMe1zeeHpJ01tjw609cHYSRKUJdi5W98Xn5F9e3c777dp3sD8P3vhz6ZQFMlTpowZ5MDy4c77ZYrllfbNa8jOfcekbucPfHpuKR31895JExnXsYr1Avjoo9HWZcuWTerWrStdunQxF8y4Wr//Qpz3QcqVL5v7KKZAiYbuzapI3cI3jk3w59h93J4MYKk8qbMQO14ZjB49esiDD1oXli1ZsiTGxwAAQOoQrwDjoYceirHW4m6PAQCQbNCLJOn0Ijlz5owp2AQAILmjF0kiBhiutRdR6zC0GHH79u1Sp04dL08JAACkqgCjQIEClrcdg/80atTIFHkCAJDc0YskEQOMsWPvVO3myJFDBg8e7OVTAwCQdFGC4YOBtgguAACpIsKwY0lgH374oRQrVkwCAwNNmcKGDRvuur2OmaTj7ERd+vfv79yme/fu0R6vXbt2wgcYt27dMvORlCxZ0m3Mi969e8v+/fvjc0gAABBH48aNk0GDBsmYMWPk33//lSpVqpgZz48fPx7jPleuXJFr1645l3nz5pnr+mOPPebcRu8/8cQTbtutWbMm4QOM9957T6ZMmSJvvfXWfzODikjNmjXlnXfeic8hAQBIcr1I7PiXkEaOHGnGpmrRooXkzp1bRo8ebWoiP//88xj30cSAa2Zi2rRpUrFiRalVq5bbdjr3ket2cR1EM14Bhs6aOmvWLHnqqafc1jds2NBM2Q4AQEoo8rRjSSjHjh0zrQZ67XXQIKB+/fqydu1aj45x+vRpc91+9tlnoz2m64OCgqRw4cLmen/kyJGEDzAOHz4sZcqUMbddZ//UScw09QIAAO6IiIiQixcvui26zo4AQ+XKlcttfc6cOe/aROLqq6++MnMkderUyW29lkBMnz5dwsLCTKBx8OBBE7jE5RofrwCjRIkS8scff0QLMGbOnCkVKlSIzyEBAEiRNZ7Dhw+X4OBgt0XXWenYsaNlAabrElsmQQMGT0fU1haJtm3bmvnEXL355pvSqlUrs75SpUry7bffyoEDB2Tu3LkJO5Ln66+/bqIdR2+SRYsWmTlIJkyYYCIeAACSPZuaN0JDQ6VPH/fJ+jTjH1NG4Ysvvrjr8RwzLWvNhTp16pTb4ydPnnQ+djfr1q2Tbdu2yWeffRbrtpoVyZcvn+zZsyfWbZ3nKfGgg2lpcefQoUPN/w8//LAUKlTIFJW0a9cuPocEACBF8vf3jzGgsMo+6OKJ/Pnzm2vvypUrpXnz5s7eH6tWrZKePXt6lL0oVaqU1KtXL9Ztz549a5pk8ubNKwnaRLJp0ybp1q2b6RKjT6pFIto+07Vr1/gcDgCAJCc59CLp27ev6ar6yy+/yIULF8xYFuHh4W5Fm5oUiDqGhdZSaGcNq+JOPc6TTz5prvVaK7Jr1y7TZTUkJETat2+fsBmMatWqmShJRW23AQAgJUgOQ4W//PLLpmhUAwL9sq/1EosXLzbZDQe9Xt+8edNtv9mzZ5uxLaym99AakTZt2pjur5s3b5bs2bObAk9tUtHbnkoTGY+51YsWLWqiJf3fLuv3X7DtWEj+8mUL8PUpIIkp0dC9DRupW/jGO1NXJKSws9739FCFsnvWPJJQtJRBL/Wu41hYrbNbvJpIBg4cKM8884zs3LnTbaAtAABSimQyUnistKYjaiBhtc5u8Woi6dWrl2mXKVu2rHNEMFeadgEAIDlLDk0kSVm8AgztDwsAQMpGhJHoAYZ2SwUAALA1wAAAIKWjicQ7BBgAAFiggcQHvUgAAADuhgwGAAAWaCLxDgEGAAAWEnqY75SOJhIAAGA7MhgAAFghgeEVAgwAACwQX3iHJhIAAGA7MhgAAFigF4l3CDAAALBALxLvEGAAAGCFIgyvUIMBAABsRwYDAAALJDC8Q4ABAIAFijy9QxMJAACwHRkMAAAs0IvEOwQYAABYoInEOzSRAAAA2xFgAAAA29FEAgCABZpIvEMGAwAA2I4MBgAAFuhF4h0CDAAALNBE4h0CDAAALDBUuHeowQAAALYjgwEAgBVSGF4hwAAAwAJFnt6hiQQAANiODAYAABboReIdAgwAACxQguEdmkgAAEjGVq9eLZ07d5batWvLtm3bPNrnl19+kXbt2kmDBg3klVdekZMnT8Zrm7shwAAAIKYUhh1LAnrttdekf//+UrZsWfnjjz/k0qVLse7z008/SbNmzaR8+fLSr18/2bJli9SpU0euXr0ap21ikyYyMjJSkoD1+y/4+hSQhOTLFuDrU0ASU6JhH1+fApKQ8I1jE/45bthznID0kmAuXLggwcHBsnfvXilZsqT8/vvvJpNxN9WqVZOKFSvKtGnTzP2LFy9K3rx55f3335devXp5vE1syGAAABBDkacdS0LS4CIuzp07J3///be0bNnSuS5LliymGWT58uUeb+MJijwBAEhAERERZnHl7+9vlsQWFhZm/s+XL5/ber3/119/ebxNsgowahSNWxSWEukv4PDhwyU0NNQnv3hIWvh9SPyUeFLH70TiymjTFXLw0OEyZMgQt3WDBg2SwYMHR9v2rbfekmXLlt31eAsWLJCcOXPG61xu3LjT7hP1GhMQEOB8zJNtklUNBu60cWm6S9vUNB2F1I3fB/A7kfoyGHv37pXTp0/f9XhVq1aVDBkyRNvPkxqMffv2SfHixU0Q07hxY+f6jh07ypEjR+TXX3/1aJtklcEAACAl8o9Dc0iJEiXMklCKFCki2bNnl/Xr17sFD3/++ac8/PDDHm/jCYo8AQBIwQYPHizdunUzt/38/KRr164yYcIEOXHihFk3Y8YMk7WIyzaeIIMBAEAy9f3338t7773nbIJ55plnJDAw0HQl1cG3HM0nO3fudO4zdOhQ+ffff6Vo0aKSP39+OXbsmAkm7rnnnjhtExsCjCREU2ha+EOBJ/h9AJ8R8MT9998vo0ePjra+UKFCzttaYBoeHu5WrPnDDz/I4cOH5dSpU1KqVCnJnDmz2/6ebBMbijwBAIDtqMEAAAC2I8AAAAC2I8Cwwa5du2Tp0qWS3KWU15GcaH/yf/75x/ZtAcDXCDBsoKOqDRgwQJK7lPI6khOt1P7666/jta1OpawzHCJl0Ar9F154wdenAdiGXiSADzVs2FCKFSsWr23feecdM32yzniI5O/QoUOyfft2X58GYBsCDBfa51dHLlNBQUFSvnx5M6JZVDdv3pTffvvN/F+5cuU4v+lHjx6VTZs2meHAdUpc7Q6kdJ0OE16zZk3ZuHGjnDlzRurWrStZs2aNdgzt06yLTp+rw8amT//ffMCO4+hwsXr77NmzUqNGDcmRI4etrwN3pz9Dfe8bNWrktl5nKdTZCnW9djHTEfNc7dixQ/bs2WO6mWmfcx30Rrluu2bNGjl58qS5IH3zzTdm3SOPPGJ+l2LaHwlPm7Cee+45k40YOXKkHDhwQD755BPzN7p7924ZNWqU8++2U6dO0qJFC7PfrFmzzLTYOjy8Y5hnzVjpsM+3bt1ym7Piq6++MtmrqVOnmvtjxowxAyDdd9998uWXX5rxELSpUwdE0s8P/V3T5rW0adOawZMeffRRt9+1jz76yIyTULhwYenZs6f5/AHsQIDhYv/+/abfr9I/dL346mAlI0aMcG6jf6wPPPCA6Res3xz1A0XHf/fUZ599Jv379zcfBjppzPHjx2XmzJlSqVIlmT59uixcuFB0epgCBQqY8ei1D/LixYudf/T64fHUU0/JypUrTdCgH2C3b982zRuOb7d6nEWLFkm6dOnMB9n58+fNh5t+6DiO4+3rQOz0Q79Lly5mJDzX/uP6wa8XFg0w9CJSvXp15+A1ur3+7DSY0EA0U6ZMMn/+fDNHjeu2Ggjr74fWzTh+Z5s2bSo9evSIcX8kPP3c+OOPP+Sxxx4zk1aVLl3aDPusgb7+vb344ovy+OOPm88a/T3QyQ27d+8u9evXlyZNmsjmzZudYxro36P+LesXAFc6F4Rr09jBgwdl/Pjx5m/41VdflTx58pj1uo0GLvo70bt3bzMLpp6XBqf33nuvXLp0SerVqyft27c356rH7du3r2mGK1iwIL8u8J5OdgZre/bsicyUKVPkhg0bnOteffXVyAoVKkRevHjR3N+3b19kUFBQZLVq1Tx6G3PkyBH59ddfO++HhYVFrl+/3tzu27evTjwXOWfOHOfjXbt2jaxcuXLk7du3zf0BAwZE3nvvvZFXrlxxbvPss89GNmvWzHlfj5M2bdrIdevWOde1bds2slWrVra9DsTu2rVrkVmzZo2cPn26c92WLVvMz3jbtm3mfqNGjSL79+9vbu/cudM8dvDgQef2a9eujTx27Fi0bVX9+vUj33zzTef92PZHwvvtt9/Mz2D+/Plu65s2bRrZr18/t3VTp06NLFy4sPO+/iz1Z+qqS5cukR07dnRbN3z4cLe/U/17Dw4Odv4tO+g2LVu2dFtXr169yDfeeMPc1s+dNGnSREZERDgfv3Xrltt9wBtkMKK4evWqSWFrZkG/OeTOndtM8KJNGWr27NlmOnVtQlE6jKp+I9FvHp7QFPa2bdvMsTXDoN8UXL8taJNM27Ztnfdff/11KVeunMlA6LchTYu2atVKfvzxR5Pp0CVXrlwmbaqZDEc6XFOytWrVch6nQYMGJpXq4O3rQOx0RNY2bdqYMfx1FkKlt6tUqWJ+plFlzJhR0qRJY76JOkbh02+anvJ2f9hHM5QO+jeqTRSaaVi7dq3z71azHbpOR1h0NJPGl2YhHX/LrjTL6Uozo/rZpsqUKWOyHZrV0CyKZjNCQkKizdIJxBcBhovly5ebdGG+fPlMc4N+YGsaUdu6lQYFmnaOWpehF2dPL8zazqpp0k8//dT8QWswoRcfR2BgdWylH0R6W8eD13ZTbfZwpe3v165dMylxFbVdXy92+rhdrwOe0Z+tNl1oU5TWwGhz2Msvv2y5rbaBaxCoH/b689KiTm0z1/894e3+sI9rk5g2hV6/ft3MCxG1HkfZcUGPaQhn19ospQGoBjdK56vQL1OTJk2Sjz/+2PyuPvjgg/LFF19YBitAXBFguOjTp4+pudBx2x00ynf8QWrGQduytX7BVdT7d6PtsFqYp7UTP/30k/Tr188EDO+++67lsRz39eKkHxYa9DzxxBNedWez43XAM5o50m+J2haudTZaU9OhQ4cYt9ffPy200/Zzra3Q4ET/dxQDxsbb/WE/DSA0S6lfChwFnFb0S4bjs8Y1cNAvFa4cGQg76O/mwIEDzaK1QppdmzhxovksBLxFeXmUP1xthnDQVLNW47vSboGOojpHNkCL6Dyh1eCOqW81e/D888+bbw3r1q1zbqMXBu3N4vDdd9+ZbIQGOvrto1mzZuYbhx7LlRZoxYU3rwOe05+ZBhTaNKKLBphaeGtFew1plkkvNBqM6MR32tSlRYNW9BuoIysVn/2ReDRrqQXeq1atcq4LCwszmQMHbY7VzGLUpg8tNtcMmNLeHlr4aQctFNYeSI6gRrOf+uUjatYDiC8yGC60+5YONKXfNK5cuWK6b+mHuCsde0DbV7XaXyv1tZZBt/ekSl9TpXph1yBBvyloLwCtqRg2bJhzGz3OQw89JC+99JJ5XKfh/eCDD5xNH9rNTSvO9Ry0m5t+OOiHln5L8nTAJm9fB+JGf07aZXHr1q2maSwm2rNAA06thdEeBBrgasA5duxYy+21R4l2S9R6Dv390PZ1bR7xdH8kntdee83UXOjftmYNHBd1/b1w0FoI/XvX5lmtq9JeQ9rEpX+b2hNF62o0gNSupzpmhre0SU0/47SXiX7h0ayqNqfpdN+AHZhN1YW2k37++eemO5eOUdGuXTtZtmyZVKhQwa3wUusUHFkEvTjrRVn7q7sGCjHRwEXbOLXtU9s5H374YWe7rH4I6UVIu4xpdkHHUGjZsqVbv3WlgYAeQ7u+6RgZGnBoDYZ+W1b6TVk/gN544w3nPitWrDDZENdCT29eB+L+DVYzDJp+dg1a9b3WC4qj2USbUDRo0G+qjrESypYta7mtFgdq90QNIrQ4Wb8h6/8x7Y+EpzVbWsStBdaOv0dX+jPTgm39u9ULvNVnkGYwNRjRoEKLLpVe/DXLqD9/zbTqZ4N+LjkyIfpz1yynK/0s0eyn1pQ5OLKjxYsXdztn7VKt2+XMmdPGdwOpHQFGEuIIMJYsWeLrUwEAwCs0kdhs7ty5pinEijaPaBobAICUjgDDZjqipmvhXdRuoHcLMLQuI+pw3gAAJEc0kQAAANvRTRUAANiOAAMAANiOAAMAANiOAAMAANiOAAMAANiOAAMAANiOAAMAANiOAAMAANiOAAMAAIjd/g+wpNqfWOz6CAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Solution\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "metrics = pd.DataFrame({\n",
    "    \"ad_spend\": [5, 10, 15, 20, 25, 30],\n",
    "    \"visits\": [210, 300, 250, 380, 330, 450],\n",
    "    \"returns\": [12, 9, 11, 8, 10, 9],\n",
    "})\n",
    "\n",
    "corr = metrics.corr()\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "sns.heatmap(corr, annot=True, cmap='Blues', vmin=-1, vmax=1, ax=ax)\n",
    "ax.set_title(\"Metric Correlations\")\n",
    "plt.show()\n",
    "# Sample reflection: Ad spend and visits have a positive association. Correlations from six observations do not establish a causal response to a change in ad spend.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-10T18:42:52.462914Z",
     "iopub.status.busy": "2026-10-10T18:42:52.462846Z",
     "iopub.status.idle": "2026-10-10T18:42:52.464761Z",
     "shell.execute_reply": "2026-10-10T18:42:52.464490Z"
    },
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### 10. Order Values by Channel\n",
    "# The store manager wants separate order-value histograms for each channel.\n",
    "#   1. Use the figure-level function sns.displot() to draw histograms of\n",
    "#      order_value from orders with bins=4.\n",
    "#   2. Put each channel in its own column (col='channel') and use height=3.\n",
    "# Reflection (answer in comments): Compare the channel distributions and explain one limitation of the sample. Then choose an alternative chart for a manager who wants to compare typical order value and variability by channel; justify your choice in comments without drawing another plot.\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "orders = pd.DataFrame({\n",
    "    \"order_value\": [45, 52, 38, 61, 75, 49, 58, 66, 40, 55, 70, 48],\n",
    "    \"channel\": [\"Online\", \"Retail\"] * 6,\n",
    "})\n",
    "\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-10T18:42:52.465507Z",
     "iopub.status.busy": "2026-10-10T18:42:52.465453Z",
     "iopub.status.idle": "2026-10-10T18:42:52.529045Z",
     "shell.execute_reply": "2026-10-10T18:42:52.528636Z"
    },
    "tags": [
     "hide-input",
     "homework-answer"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Solution\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "orders = pd.DataFrame({\n",
    "    \"order_value\": [45, 52, 38, 61, 75, 49, 58, 66, 40, 55, 70, 48],\n",
    "    \"channel\": [\"Online\", \"Retail\"] * 6,\n",
    "})\n",
    "\n",
    "sns.displot(data=orders, x='order_value', col='channel', bins=4, height=3)\n",
    "plt.show()\n",
    "# Sample reflection: The channel distributions overlap, and each contains only six observations. A box plot would compare the median and middle-half spread by channel; a mean bar chart alone would hide much of that variation.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<style>\n",
    ".ch07-hw-grader { max-width: 900px; margin-top: 1rem; }\n",
    ".ch07-hw-feedback-list { display: grid; gap: 0.45rem; margin: 0 0 1rem; }\n",
    ".ch07-hw-feedback-list[hidden] { display: none; }\n",
    ".ch07-hw-feedback-row { border: 1px solid #d8dee4; border-radius: 6px; padding: 0.55rem 0.65rem; }\n",
    ".ch07-hw-feedback-row strong { margin-right: 0.35rem; }\n",
    ".ch07-hw-feedback-row.correct { border-color: #aceebb; background: #dafbe1; color: #116329; }\n",
    ".ch07-hw-feedback-row.incorrect { border-color: #ffcecb; background: #ffebe9; color: #cf222e; }\n",
    ".ch07-hw-actions { display: flex; gap: 0.75rem; align-items: center; margin: 1rem 0 0; flex-wrap: wrap; }\n",
    ".ch07-hw-actions button { border: 1px solid #176de8; border-radius: 6px; background: #176de8; color: white; padding: 0.55rem 0.9rem; cursor: pointer; font-weight: 600; }\n",
    ".ch07-hw-actions button.secondary { background: white; color: #176de8; }\n",
    ".ch07-hw-actions.is-locked { opacity: 0.55; }\n",
    ".ch07-hw-actions button:disabled { cursor: not-allowed; opacity: 0.65; }\n",
    ".ch07-hw-result { font-weight: 700; }\n",
    "</style>\n",
    "\n",
    "<div class='ch07-hw-grader' id='ch07-hw-grader'>\n",
    "  <div class='ch07-hw-feedback-list' aria-live='polite' hidden>\n",
    "    <div class='ch07-hw-feedback-row' data-question='q1'><strong>Question 1</strong><span></span></div>\n",
    "    <div class='ch07-hw-feedback-row' data-question='q2'><strong>Question 2</strong><span></span></div>\n",
    "    <div class='ch07-hw-feedback-row' data-question='q3'><strong>Question 3</strong><span></span></div>\n",
    "    <div class='ch07-hw-feedback-row' data-question='q4'><strong>Question 4</strong><span></span></div>\n",
    "    <div class='ch07-hw-feedback-row' data-question='q5'><strong>Question 5</strong><span></span></div>\n",
    "    <div class='ch07-hw-feedback-row' data-question='q6'><strong>Question 6</strong><span></span></div>\n",
    "    <div class='ch07-hw-feedback-row' data-question='q7'><strong>Question 7</strong><span></span></div>\n",
    "    <div class='ch07-hw-feedback-row' data-question='q8'><strong>Question 8</strong><span></span></div>\n",
    "    <div class='ch07-hw-feedback-row' data-question='q9'><strong>Question 9</strong><span></span></div>\n",
    "    <div class='ch07-hw-feedback-row' data-question='q10'><strong>Question 10</strong><span></span></div>\n",
    "  </div>\n",
    "  <div class='ch07-hw-actions'>\n",
    "    <button type='button' id='ch07-hw-submit' disabled>Submit</button>\n",
    "    <button type='button' class='secondary' id='ch07-hw-reset'>Clear Results</button>\n",
    "    <span class='ch07-hw-result' id='ch07-hw-result' aria-live='polite'></span>\n",
    "  </div>\n",
    "</div>\n",
    "\n",
    "<script>\n",
    "(function () {\n",
    "  var homeworkId = 'ch07-homework';\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 + 6)] = codeFromCell(cell);\n",
    "    });\n",
    "    return code;\n",
    "  }\n",
    "\n",
    "  function collectAnswers() {\n",
    "    var answers = {};\n",
    "    ['q1', 'q2', 'q3', 'q4', 'q5'].forEach(function (question) {\n",
    "      var checked = document.querySelector('input[name=\"ch07-hw-' + question + '\"]:checked');\n",
    "      answers[question] = checked ? checked.value : '';\n",
    "    });\n",
    "    return answers;\n",
    "  }\n",
    "\n",
    "  function applyFeedback(grader, feedback) {\n",
    "    var list = grader.querySelector('.ch07-hw-feedback-list');\n",
    "    list.hidden = false;\n",
    "    ['q1', 'q2', 'q3', 'q4', 'q5', 'q6', 'q7', 'q8', 'q9', 'q10'].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 = 'ch07-hw-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('.ch07-hw-feedback-list');\n",
    "    list.hidden = true;\n",
    "    grader.querySelectorAll('.ch07-hw-feedback-row').forEach(function (row) {\n",
    "      row.className = 'ch07-hw-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/homework-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 initHomeworkGrader() {\n",
    "    var grader = document.getElementById('ch07-hw-grader');\n",
    "    if (!grader || grader.dataset.ready === 'true') return;\n",
    "    grader.dataset.ready = 'true';\n",
    "\n",
    "    var identity = getParams();\n",
    "    var actions = grader.querySelector('.ch07-hw-actions');\n",
    "    var result = document.getElementById('ch07-hw-result');\n",
    "    var submit = document.getElementById('ch07-hw-submit');\n",
    "    var reset = document.getElementById('ch07-hw-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 homework.';\n",
    "\n",
    "      saveAttempt({\n",
    "        homework_id: homeworkId,\n",
    "        answers: collectAnswers(),\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', initHomeworkGrader);\n",
    "  } else {\n",
    "    initHomeworkGrader();\n",
    "  }\n",
    "})();\n",
    "</script>\n"
   ]
  }
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