exercise-scatter-plots.ipynb

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    "**This notebook is an exercise in the [Data Visualization](https://www.kaggle.com/learn/data-visualization) course.  You can reference the tutorial at [this link](https://www.kaggle.com/alexisbcook/scatter-plots).**\n",
    "\n",
    "---\n"
   ]
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   "source": [
    "In this exercise, you will use your new knowledge to propose a solution to a real-world scenario.  To succeed, you will need to import data into Python, answer questions using the data, and generate **scatter plots** to understand patterns in the data.\n",
    "\n",
    "## Scenario\n",
    "\n",
    "You work for a major candy producer, and your goal is to write a report that your company can use to guide the design of its next product.  Soon after starting your research, you stumble across this [very interesting dataset](https://fivethirtyeight.com/features/the-ultimate-halloween-candy-power-ranking/) containing results from a fun survey to crowdsource favorite candies.\n",
    "\n",
    "## Setup\n",
    "\n",
    "Run the next cell to import and configure the Python libraries that you need to complete the exercise."
   ]
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     "text": [
      "Setup Complete\n"
     ]
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   ],
   "source": [
    "import pandas as pd\n",
    "pd.plotting.register_matplotlib_converters()\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "print(\"Setup Complete\")"
   ]
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   "source": [
    "The questions below will give you feedback on your work. Run the following cell to set up our feedback system."
   ]
  },
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     "text": [
      "Setup Complete\n"
     ]
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   ],
   "source": [
    "# Set up code checking\n",
    "import os\n",
    "if not os.path.exists(\"../input/candy.csv\"):\n",
    "    os.symlink(\"../input/data-for-datavis/candy.csv\", \"../input/candy.csv\") \n",
    "from learntools.core import binder\n",
    "binder.bind(globals())\n",
    "from learntools.data_viz_to_coder.ex4 import *\n",
    "print(\"Setup Complete\")"
   ]
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   "source": [
    "## Step 1: Load the Data\n",
    "\n",
    "Read the candy data file into `candy_data`.  Use the `\"id\"` column to label the rows."
   ]
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       "Correct"
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    "# Path of the file to read\n",
    "candy_filepath = \"../input/candy.csv\"\n",
    "\n",
    "# Fill in the line below to read the file into a variable candy_data\n",
    "candy_data = pd.read_csv(candy_filepath, index_col = 'id')\n",
    "\n",
    "# Run the line below with no changes to check that you've loaded the data correctly\n",
    "step_1.check()"
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    "# Lines below will give you a hint or solution code\n",
    "#step_1.hint()\n",
    "#step_1.solution()"
   ]
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   "source": [
    "## Step 2: Review the data\n",
    "\n",
    "Use a Python command to print the first five rows of the data."
   ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>competitorname</th>\n",
       "      <th>chocolate</th>\n",
       "      <th>fruity</th>\n",
       "      <th>caramel</th>\n",
       "      <th>peanutyalmondy</th>\n",
       "      <th>nougat</th>\n",
       "      <th>crispedricewafer</th>\n",
       "      <th>hard</th>\n",
       "      <th>bar</th>\n",
       "      <th>pluribus</th>\n",
       "      <th>sugarpercent</th>\n",
       "      <th>pricepercent</th>\n",
       "      <th>winpercent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100 Grand</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.860</td>\n",
       "      <td>66.971725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3 Musketeers</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0.604</td>\n",
       "      <td>0.511</td>\n",
       "      <td>67.602936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Air Heads</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.906</td>\n",
       "      <td>0.511</td>\n",
       "      <td>52.341465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Almond Joy</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.767</td>\n",
       "      <td>50.347546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Baby Ruth</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0.604</td>\n",
       "      <td>0.767</td>\n",
       "      <td>56.914547</td>\n",
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       "  </tbody>\n",
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       "   competitorname chocolate fruity caramel peanutyalmondy nougat  \\\n",
       "id                                                                 \n",
       "0       100 Grand       Yes     No     Yes             No     No   \n",
       "1    3 Musketeers       Yes     No      No             No    Yes   \n",
       "2       Air Heads        No    Yes      No             No     No   \n",
       "3      Almond Joy       Yes     No      No            Yes     No   \n",
       "4       Baby Ruth       Yes     No     Yes            Yes    Yes   \n",
       "\n",
       "   crispedricewafer hard  bar pluribus  sugarpercent  pricepercent  winpercent  \n",
       "id                                                                              \n",
       "0               Yes   No  Yes       No         0.732         0.860   66.971725  \n",
       "1                No   No  Yes       No         0.604         0.511   67.602936  \n",
       "2                No   No   No       No         0.906         0.511   52.341465  \n",
       "3                No   No  Yes       No         0.465         0.767   50.347546  \n",
       "4                No   No  Yes       No         0.604         0.767   56.914547  "
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   "source": [
    "# Print the first five rows of the data\n",
    "candy_data.head() # Your code here"
   ]
  },
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   "source": [
    "The dataset contains 83 rows, where each corresponds to a different candy bar.  There are 13 columns:\n",
    "- `'competitorname'` contains the name of the candy bar. \n",
    "- the next **9** columns (from `'chocolate'` to `'pluribus'`) describe the candy.  For instance, rows with chocolate candies have `\"Yes\"` in the `'chocolate'` column (and candies without chocolate have `\"No\"` in the same column).\n",
    "- `'sugarpercent'` provides some indication of the amount of sugar, where higher values signify higher sugar content.\n",
    "- `'pricepercent'` shows the price per unit, relative to the other candies in the dataset.\n",
    "- `'winpercent'` is calculated from the survey results; higher values indicate that the candy was more popular with survey respondents.\n",
    "\n",
    "Use the first five rows of the data to answer the questions below."
   ]
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    "# Fill in the line below: Which candy was more popular with survey respondents:\n",
    "# '3 Musketeers' or 'Almond Joy'?  (Please enclose your answer in single quotes.)\n",
    "more_popular = '3 Musketeers'\n",
    "\n",
    "# Fill in the line below: Which candy has higher sugar content: 'Air Heads'\n",
    "# or 'Baby Ruth'? (Please enclose your answer in single quotes.)\n",
    "more_sugar = 'Air Heads'\n",
    "\n",
    "# Check your answers\n",
    "step_2.check()"
   ]
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    "# Lines below will give you a hint or solution code\n",
    "#step_2.hint()\n",
    "#step_2.solution()"
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   "source": [
    "## Step 3: The role of sugar\n",
    "\n",
    "Do people tend to prefer candies with higher sugar content?  \n",
    "\n",
    "#### Part A\n",
    "\n",
    "Create a scatter plot that shows the relationship between `'sugarpercent'` (on the horizontal x-axis) and `'winpercent'` (on the vertical y-axis).  _Don't add a regression line just yet -- you'll do that in the next step!_"
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A5kbTEoBmhUL7OryD+YfgKyQyAE4r2NvX4XnMPwRfomkJAOAxzD8EXyORAQB4DPMPwddIZAAAHsP8Q/A1vycy3333nX73u98pISFB0dHR6tOnjz766CPnfsMwNH36dKWmpio6Olo5OTnavXu3HyP2DFulQ0WHKlRYfERF31dQbgUQFJh/CL7m186+R44c0eDBg3XZZZfpzTffVJcuXbR792516tTJ+Zx58+bpqaee0vPPP6/u3bvroYce0ogRI/T5558rKirKj9G7j45wAIJV/fxDG5toXmL+IXhDmGEYhr9++dSpU7V582a99957Te43DENpaWm69957dd9990mSbDabkpOTtWzZMt1www0t/g673S6r1Sqbzaa4uDiPxu8OW6VDE14qbLINeUhmohaM7scIESAIhdJw5APlxzV11Y4GyUz9/EOp3KzBRa5ev/1akXn99dc1YsQIXXvttdqwYYPOOOMMjR8/Xrfddpskad++fSopKVFOTo7zNVarVQMHDtSWLVuaTGSqq6tVXV3t/Nlut3v/P9IKrnSEC9YvNyBUhVoVlvmH4Etu9ZE566yzdPjw4Ubby8vLddZZZ7l8nL1792rx4sXKzMzU2rVrdeedd+quu+7S888/L0kqKSmRJCUnJzd4XXJysnPfqfLz82W1Wp2P9PR0l+PxBTrCwWzoz9U2oToc2dohUj2SYtQ3o5N6JMWQxMBr3KrIfP3116qtrW20vbq6Wt99953Lx6mrq9OAAQM0e/ZsSVK/fv20c+dOPfPMMxo7dqw7oWnatGmaPHmy82e73R5QyQwd4WAmoVZJ8AaqsIB3tSqRef31153/Xrt2raxWq/Pn2tpaFRQUqFu3bi4fLzU1Veeee26Dbb1799aqVaskSSkpKZKk0tJSpaamOp9TWlqqvn37NnlMi8Uii8Xicgy+Rkc4mEVLlQT6c7mGKizgXa1KZEaOHClJCgsLa1QxiYiIULdu3fT444+7fLzBgwdr165dDbZ99dVX6tq1qySpe/fuSklJUUFBgTNxsdvt+uCDD3TnnXe2JvSAUb8QX3Md4bgwIFBQSfAMqrCAd7Uqkamrq5N0MsH48MMPlZiY2MIrTm/SpEkaNGiQZs+ereuuu07/+c9/9Oyzz+rZZ5+VdDJhuueeezRr1ixlZmY6h1+npaU5kyozoiMczIBKgmdQhQW8y60+Mvv27fPIL//Zz36mV199VdOmTdMjjzyi7t2768knn9SYMWOcz5kyZYqOHTum22+/XeXl5br44ov11ltvmXYOmXosxIdARyXBM6jCAt7l9jwyBQUFKigo0KFDh5yVmnp/+9vfPBKcJwTaPDKAWdgqHZr4UmGzlQT6yLRO/TwyVGEB13h1HpmHH35YjzzyiAYMGKDU1FSFhYW5HSiAwEQlwbOowgLe4VZFJjU1VfPmzdONN97ojZg8iooM0DZUEgD4g1crMg6HQ4MGDXI7OADmQSUBQCBza2bfW2+9VcuXL/d0LAAAAK3iVkWmqqpKzz77rNatW6esrCxFRDQcvfDEE094JDgEj1BaMA8N8bcH4E1uJTI7duxwTlC3c+fOBvvo+ItTMc196OJv/yMSOsA73B5+bRZ09vUvW6VDE14qbHKGWIbwBjf+9j8ioQNaz9Xrt1t9ZOrt2bNHa9eu1fHjxyVJQZ4TwQ2uTHOP4MTf/qRQXf0a8BW3EpnDhw9r+PDhOvvss/XLX/5SBw8elCSNGzdO9957r0cDhLkxzb352SodKjpUocLiIyr6vsLlCy9/+5NI6ADvciuRmTRpkiIiIlRcXKwOHTo4t19//fV66623PBYczI9p7s3tQPlxTXipUMOf2KCrF72v4Y9v0MSXCnWg/HiLr+VvfxIJHeBdbiUy//73vzV37lydeeaZDbZnZmbqm2++8UhgCA71C+Y1hQXzAltbm0T4259EQgd4l1uJzLFjxxpUYur98MMPslgsbQ4KwaN+mvtTL2hMcx/42tokwt/+JBI6wLvcGn59ySWX6O9//7tmzpwp6eSQ67q6Os2bN0+XXXaZRwOE+aXFR2vB6H5Mc28ynmgS4W/PmlWAt7mVyMybN0/Dhw/XRx99JIfDoSlTpuizzz7TDz/8oM2bN3s6RgQBprk3H081ifC3J6EDvMmtROb888/XV199paefflqxsbGqqKjQqFGjlJeXp9TUVE/HiDZiIi64o75JZGMz88DQJNI6JHTBg+/UwMKEeEGOibg8K9S+wA6UH2+2SSSV9w9CkBm/U836veXq9dutRGbp0qWKiYnRtdde22D7ypUrVVlZqbFjx7Y+Yi8J5USGmVU9y4xfYJ5Q/yVIk0jbmPVigh+Z8TvVzN9bXp3ZNz8/X4mJjXvhJyUlafbs2e4cEl7ARFyeE8qzs1o7RKpHUoz6ZnRSj6SYgPuiNoO2zMeDwGG279RQ+d5yK5EpLi5W9+7dG23v2rWriouL2xwUPIOJuDynuS+wDpHtlJUer4O2qlbPfIvQECoXk1Bgtu9UsyVe7nKrs29SUpJ27Nihbt26Ndj+ySefKCEhwRNxwQOYiMtzmvoC6xDZTk+N7qelm/fp6Xf2OLebpWwL33DlYkKVyxzM9p1qtsTLXW5VZEaPHq277rpL7777rmpra1VbW6t33nlHd999t2644QZPxwg3MRGX5zT1BXbLxd21dPM+bd5zuMF27rTxU6FyMQkFZvtONVvi5S63EpmZM2dq4MCBGj58uKKjoxUdHa1f/OIXGjZsGH1kAggzq3pOU19g/dLjGyUx9YKpbIu2CZWLSSgw23eq2RIvd7W6ackwDJWUlGjZsmWaNWuWtm/frujoaPXp00ddu3b1RoxoAybi8oymZmetPlF32tdwpw2J+XiCjZm+U0NlVulWD7+uq6tTVFSUPvvsM2VmZnorLo8J5eHX8LyfDkWOimin//rLe80+t2DyUPVIivFhdAhUzMcDfzLrFAquXr9bXZEJDw9XZmamDh8+bIpEBvCkn87Oaqt0cKcNl5jpLh7BJ9hnlXarj8ycOXP0xz/+UTt37vR0PIBpmK29HP7FfDyAd7g1s2+nTp1UWVmpEydOKDIyUtHRDUujP/zwg8cCbCualuBtZi3bAkAg81rTkiQ9+eST7sYF+JU3pokP9rItAAQytxKZQFpLyaxYd8X3zLzmCACgaW4lMpJUVFSkpUuXqqioSH/5y1+UlJSkN998UxkZGTrvvPM8GWPQ4YLqey1NEx+Ii70BAFrmVmffDRs2qE+fPvrggw+0evVqVVRUSDq5RMGMGTM8GmCwYd0V/wiVNUcABB9bpUNFhypYz60ZblVkpk6dqlmzZmny5MmKjY11bh82bJiefvppjwUXjFh3xT+YJh6AGVHBb5lbFZlPP/1UV199daPtSUlJKitr+iKNk7ig+gfTxAMwGyr4rnErkYmPj9fBgwcbbS8sLNQZZ5zR5qCCGRdU/wiVNUcABA+axF3jViJzww036P7771dJSYnCwsJUV1enzZs367777tNNN93k6RiDChdU/2DyOgBmQwXfNW71kZk9e7by8vKUnp6u2tpanXvuuaqtrdVvf/tb/elPf/J0jEElVBbxCkRME+8fTDUAuIcKvmvcmtm3XnFxsXbu3KmKigr169cvINdeCtSZfZkNFqGAjoqA+2yVDk18qbDZ9dyCfdoIV6/fbUpkJKn+5WFhYW05jNcEaiIDBDtbpUMTXipsso0/FL6EAU8I5ZXTvbpEgSQtWbJE8+fP1+7duyVJmZmZuueee3Trrbe6e0gAQYSpBoC2o0m8ZW4lMtOnT9cTTzyhiRMnKjs7W5K0ZcsWTZo0ScXFxXrkkUc8GiQA86GjIuAZrOd2em4lMosXL9Zzzz2n0aNHO7f95je/UVZWliZOnEgiA4COigB8wq3h1zU1NRowYECj7f3799eJEyfaHBQA82OqAQC+4FYic+ONN2rx4sWNtj/77LMaM2ZMm4MCYH7WDpGaNfJ8XdwzocH2i3smaNbI8ymVA/AItxIZ6WRn3/PPP1+33nqrbr31VvXp00fPPfecwsPDNXnyZOfjdP785z8rLCyswaNXr17O/VVVVcrLy1NCQoJiYmKUm5ur0tJSd0MG4EO2Soce+dfn6pvRSUvGDtCiMRdqydgB6pvRSTP/9TnTqwPwCLf6yOzcuVMXXnihJKmoqEiSlJiYqMTERO3cudP5PFeGZJ933nlat27djwG1/zGkSZMmac2aNVq5cqWsVqsmTJigUaNGafPmze6EDcCHyiocWvfFIa374lCz+6nKAGgrtxKZd99913MBtG+vlJSURtttNpuWLFmi5cuXa9iwYZKkpUuXqnfv3tq6dasuuugij8UAwPPsVTXqENlOt1zcXf3S41V9ok5REe30cfER/W3TPkYtAfAIt+eR8ZTdu3crLS1NUVFRys7OVn5+vjIyMrRt2zbV1NQoJyfH+dxevXopIyNDW7ZsaTaRqa6uVnV1tfNnu93u9f8DgMas0RF6anQ/Ld28T0+/s8e5fXDPBD01up/iohm1hNDAMh3e5VYic+zYMc2ZM0cFBQU6dOiQ6urqGuzfu3evS8cZOHCgli1bpnPOOUcHDx7Uww8/rEsuuUQ7d+5USUmJIiMjFR8f3+A1ycnJKikpafaY+fn5evjhh1v9fwLgWR0t7bV08z5t3nO4wfbNew4rTNLj1/X1S1yAL7FMh/e5lcjceuut2rBhg2688Ualpqa6vTzBFVdc4fx3VlaWBg4cqK5du+rll19WdLR7f+Bp06Y16GRst9uVnp7u1rEAuK+i6kSjJKbepj2HVVF1QskhtGoId+Whx1bpaJTESCdntp66agfLdHiIW4nMm2++qTVr1mjw4MEeDSY+Pl5nn3229uzZo8svv1wOh0Pl5eUNqjKlpaVN9qmpZ7FYZLFYPBoXgNZjZt8fcVcemlimwzfcGn7dqVMnde7c2dOxqKKiQkVFRUpNTVX//v0VERGhgoIC5/5du3apuLjYuSwCgMDFzL4ntXRXzjD04EUy7xtuJTIzZ87U9OnTVVlZ2aZfft9992nDhg36+uuv9f777+vqq69Wu3btNHr0aFmtVo0bN06TJ0/Wu+++q23btun3v/+9srOzGbEEmAAz+57kyl05ghPJvG+41bT0+OOPq6ioSMnJyerWrZsiIhr+MT7++GOXjvPtt99q9OjROnz4sLp06aKLL75YW7duVZcuXSRJ8+fPV3h4uHJzc1VdXa0RI0Zo0aJF7oQMwMesHSI1JzdLU1ft0MZTmlTm5maFTEmdu/LQVZ/Mb2wikQ2lZN7b3EpkRo4c6ZFfvmLFitPuj4qK0sKFC7Vw4UKP/D4AvpUWH60Fo/uprMKho1U1io2KUGJMaHVy5a48dJHM+4ZbicyMGTM8HQeAIGXtEFqJy6m4Kw9tJPPe5/cJ8QCzY1gtToe7coR6Mu9tLicynTt31ldffaXExER16tTptHPH/PDDDx4JDgh0DKuFK7grB7zH5URm/vz5io2NlSQ9+eST3ooHMA0mu0JrcFcOeIfLiczYsWOd/y4oKNCll16qoUOHqkePHl4JDAh0ZRUObfvmiCYM69nkoohMdgUA3udWHxmLxaI5c+botttuU1pamoYOHepMbDIzMz0dIxCQKqprTrso4rFqhtUCgLeFGYZhuPvi7777Ths3btSGDRu0YcMGffXVV0pNTdW3337ryRjbxG63y2q1ymazKS4uhBZ2gdd9U3ZMD7z2aZPrCQ3umaDZI/uoa2JHP0QGIJiE6oACV6/fbRq11KlTJyUkJKhTp06Kj49X+/btnZPZAcHOUVvX7KKIm/cclqO2rsl9AOAqBhS0zK0lCh544AENGjRICQkJmjp1qqqqqjR16lSVlJSosLDQ0zECAami+sRp9x9rYT+A0GCrdKjoUIUKi4+o6PsKl9fXYp0u17hVkZkzZ466dOmiGTNmaNSoUTr77LM9HRcQ8Jix1TWhWhYHpLZVVFg92zVuJTKFhYXasGGD1q9fr8cff1yRkZHODr+XXnopiQ1CAjO2toyyOEJZW6doYJ0u17jVtHTBBRforrvu0urVq/X999/rjTfeUGRkpPLy8tS7d29Pxwh4jLsl3qbUz9h66grPzNh6EmVxhLq2rnxO1dc1blVkDMNQYWGh1q9fr/Xr12vTpk2y2+3KysrS0KFDPR0jApiZmg28UR1gxtbmURZHqGtrRYWqr2vcSmQ6d+6siooKXXDBBRo6dKhuu+02XXLJJYqPj/dweAhkZmo28OYsvMzY2jTK4gh1ba2osE6Xa9xKZF544QVdcsklzMsSwryZGHijykN1wPcoiyPUeaKiQtW3ZW4lMldeeaWn44DJeCsx8FaVh+qA71EWR6jzVEWFqu/ptWlCPIQubyQG3qzyUB3wPcriABUVXyCRgVu8kRh4s/mH6oB/8CUOUFHxNreGXwP1iUFT3E0MvNn8w1Bp/7F2iFSPpBj1zeikHkkxnGsAHkVFBm7xRrOBt5t/qA4AQPAhkYHbPJ0Y+KL5xxslXjPNpQMAwYZEBm3iycTAjJ1DzTSXDgAEozDDMAx/B+FNdrtdVqtVNpuNeW9Mor7CEejNP7ZKhya8VNhkB+UhmYltGmUFAKHO1es3FRkEHLP08GeSPQDwP0YtAW5ikj0A8D8SGcBNTLIHAP5HIgO4yRtz6QAAWodEJgTYKh0qOlShwuIjKvq+QrZKh79DCgpMsgcA/kdn3yDH8GDvCpN0RZ9UjR3UTdUn6mRpH65DR6v9HRaAAMJcU95FIhPEvLkII06e3ylNnF+J4dcATuJm0vtoWgpirgwPhvs4vwBOp6WbSZr5PYOKTBBjeLB3cX6B5tGcwlxTvkIiE8QYHuxdnF/XcEELPTSnnMTNjm+QyAQxXyzC2JRQuXD56/yaCRe00EPfvB9xs+Mb9JEJYv4YHnyg/LgmvFSo4U9s0NWL3tfwxzdo4kuFOlB+3OO/y98Yfn169A8ITfQd+xFzTfkGFZkglxYfrQWj+/lkEcZQvBPz5fk1G/oHhCaaU35Uf7MzddWOBpVbbnY8i0QmBPhqEUZPXbjM1jRllkUufY0LWkNme1+7i+aUhrjZ8T4SGXiMJy5c9KkIHlzQfhRK72v6jjXGzY530UcGHtPWCxd9KoIL/QNOCrX3NX3H4GtUZOAxbb0To09FcKF/wEmh+L6mOQW+RCIDj2nrhYs+FcGHC1rovq9pToGvkMjAo9py4aJPRXAK9Qsa72vAu+gjA4+zdohUj6QY9c3opB5JMS5fxOhTgWDE+xrwLhIZBAw6CSIY8b4GvCvMMAzD30FI0pw5czRt2jTdfffdevLJJyVJVVVVuvfee7VixQpVV1drxIgRWrRokZKTk10+rt1ul9Vqlc1mU1xcnJeihyfVz7cRqn0qEJx4XwOt4+r1OyD6yHz44Yf6n//5H2VlZTXYPmnSJK1Zs0YrV66U1WrVhAkTNGrUKG3evNlPkcIXQr1PBYIT72vAO/yeyFRUVGjMmDF67rnnNGvWLOd2m82mJUuWaPny5Ro2bJgkaenSperdu7e2bt2qiy66qMnjVVdXq7q62vmz3W737n8AHhcqM6ACwYzPMXzF74lMXl6errzySuXk5DRIZLZt26aamhrl5OQ4t/Xq1UsZGRnasmVLs4lMfn6+Hn74Ya/HDe8IpRlQgWDF5xi+5NdEZsWKFfr444/14YcfNtpXUlKiyMhIxcfHN9ienJyskpKSZo85bdo0TZ482fmz3W5Xenq6x2I+He5A2iYUF50Ego2t0qHp/7dTF6TH6+ZB3VR9ok5REe30cfERzfi/nXrs2gv4HMOj/JbI7N+/X3fffbfefvttRUVFeey4FotFFovFY8dzlTfvQEIlQQrFGVAROkLlc3z4mEM3/DxDSzfv09Pv7HFuH9wzQb8f3F2Hj/E5hmf5LZHZtm2bDh06pAsvvNC5rba2Vhs3btTTTz+ttWvXyuFwqLy8vEFVprS0VCkpKX6IuHnerCSEUok2VGdARfALpc/xiTpDSzfv0+Y9hxtsr//5z78+zx9hIYj5bR6Z4cOH69NPP9X27dudjwEDBmjMmDHOf0dERKigoMD5ml27dqm4uFjZ2dn+CrtJrlQS3BFqi80xAyqCUah9juvqjEZJTL3New6rti4gZvxAEPFbRSY2Nlbnn39+g20dO3ZUQkKCc/u4ceM0efJkde7cWXFxcZo4caKys7Ob7ejrL96qJIRaU0tbF50EAlGofY4rHSda2F/ro0gQKgJ6Zt/58+frV7/6lXJzczVkyBClpKRo9erV/g6rEW9VEkKtqYUZUBGMQu5zHH36z6k1msoqPMvvw69/av369Q1+joqK0sKFC7Vw4UL/BOQib1USQrGphdWSEWxC7XNMZdXzQqWjuLsCKpExq/pKwtRVOxp8eNtaSQjVLwRvzoDKFwJ8LdQ+x976PgxVodRR3F0Bs9aSt/hyrSVvrKVyoPx4s18IqbyJW4UvBPhLKH6OWVuq7WyVDk14qbDJPlZDMhODfm4tU621FCy8UUkIk3RFn1SN/f8TS1nah+vQ0eoWX4eGmGwP/hSKTaasLdV2odZR3F0kMgHMVunQlCYuvlJoZOOexBcCvKE1TZVc2NFaodZR3F0kMgGMi6/n8IUAT6OpEt4Wah3F3RXQw69DHRdfz+ELAZ4UapPcwT/qO4o3JRg7iruLRCaAcfH1HL4Q4Enems0b+Cnm1nINTUsBLNSGbXoTQ0LhSVRL4Suh2FG8tUhkAhgXX8/iCwGeQrUUvkRH8dMjkQlwXHw9iy8EeALVUiBwkMiYABdfILBQLW0Zs2jDV0hkAMANVEubx9B0+BKjlgDATdYOkeqRFKO+GZ3UIymGJEYMTYfvkcgAADyGoenwNZqWAAAeY6+qUYfIdrrl4u7qlx6v6hN1iopop4+Lj+hvm/YxNB0eRyIDAPAYa3SEnhrdT0s379PT7+xxbh/cM0FPje6nuGiGpsOzaFoCAHhMR0t7Ld28T5v3HG6wffOew1q2eZ86Wrh/hmfxjkLAYdgmYF4VVScaJTH1Nu05rIqqE0qO83FQCGokMggoDNtEIPJEch0qCTrLN8DXSGQQMFoatrlgdL+g/OJHYPNEch1KCTrLN8DX6CODgMGwzeBkq3So6FCFCouPqOj7ClPNI+KJOVFCbV4VVpqHr1GRQcCgJB18zF6JcCW5bqlK6IljmAnLN8DXSGQQMChJB5dgaCr0RHIdigk6yzfAl0hkEDBYUTi4BEMlwhPJdagm6Cx2C1+hjwwCRn1J+tT2dUrS5hQMlQhP9PegzwjMxIx92qjIIKBQkg4ewVCJ8ER/D/qMwCzM2qctzDAMw99BeJPdbpfVapXNZlNcHLMwAb5iq3Ro4kuFzTYVmqGPTL36OWDaklx74hiAt9gqHZrwUmGTzcH++ry6ev2mIgPAK4KpEuGJ/h70GUEgM3OfNhIZAF5DUyFgDmbu00YiA8CrqEQAgc/MfdoYtQQAQIgz8+g6EhkAksw57BKAZ5h5+gualgCYdtglAM8xa582KjJAiAu1RQ0BNM/aIVI9kmLUN6OTeiTFBHwSI5HIACGPVccBmBmJDBDizDzsEgBIZIAQZ+ZhlwBAIgOEODMPuwQAEhkgxJl52CUAMPwagGmHXQIAiQwASSwlAMCcaFoCAACmRSIDAABMy6+JzOLFi5WVlaW4uDjFxcUpOztbb775pnN/VVWV8vLylJCQoJiYGOXm5qq0tNSPEQMAgEDi10TmzDPP1Jw5c7Rt2zZ99NFHGjZsmK666ip99tlnkqRJkybpn//8p1auXKkNGzbowIEDGjVqlD9DBgC4gEVI4SthhmEY/g7ipzp37qz//u//1jXXXKMuXbpo+fLluuaaayRJX375pXr37q0tW7booosucul4drtdVqtVNptNcXFx3gwdACAWIYVnuHr9Dpg+MrW1tVqxYoWOHTum7Oxsbdu2TTU1NcrJyXE+p1evXsrIyNCWLVuaPU51dbXsdnuDBwDAN1iEFL7m90Tm008/VUxMjCwWi+644w69+uqrOvfcc1VSUqLIyEjFx8c3eH5ycrJKSkqaPV5+fr6sVqvzkZ6e7uX/AQCgHouQwtf8nsicc8452r59uz744APdeeedGjt2rD7//HO3jzdt2jTZbDbnY//+/R6MFgBwOixCCl/z+4R4kZGR6tmzpySpf//++vDDD/WXv/xF119/vRwOh8rLyxtUZUpLS5WSktLs8SwWiywWi7fDBgA0gUVI4Wt+r8icqq6uTtXV1erfv78iIiJUUFDg3Ldr1y4VFxcrOzvbjxECAJrDIqTwNb9WZKZNm6YrrrhCGRkZOnr0qJYvX67169dr7dq1slqtGjdunCZPnqzOnTsrLi5OEydOVHZ2tssjlgAAvlW/COnUVTu08ZRRSyxCCm/wayJz6NAh3XTTTTp48KCsVquysrK0du1aXX755ZKk+fPnKzw8XLm5uaqurtaIESO0aNEif4YMAGgBi5DClwJuHhlPC4Z5ZGyVDpVVOGSvqlFcdIQSO/KFAAAIbq5ev/3e2Renx8RSAAA0L+A6++JHTCwFAMDpkcgEMCaWAgDg9GhaCmCBNLEU/XQAAIGIRCaABcrEUvTTAQAEKpqWAlggTCxFPx0AQCAjkQlg9RNLnZrM+HJiKfrpAAACGU1LAc7fE0sFUj8dAABORSJjAtYO/utYGyj9dAAAaApNSzitQOinAwBAc0hkcFqB0E8HAIDm0LSEFvm7nw4AAM0hkYFL/NlPBwCA5tC0BAAATItEBgAAmBaJDAAAMC0SGQAAYFokMgAAwLRIZAAAgGkx/NqDbJUOlVU4ZK+qUVx0hBI7MmQZAABvIpHxkAPlx3X/qh0NVooekpmoOblZSouP9mNkAAAEL5qWPMBW6WiUxEjSxt1lmrpqh2yVDj9FBgBAcCOR8YCyCkejJKbext1lKqsgkQEAwBtIZDzAXlVz2v1HW9gPAADcQyLjAXFREafdH9vCfgAA4B4SGQ9IjInUkMzEJvcNyUxUYgwjl4KZrdKhokMVKiw+oqLvK+gTBQA+xKglD7B2iNSc3CxNXbVDG08ZtTQ3N4sh2EGM0WoA4F9hhmEY/g7Cm+x2u6xWq2w2m+Li4rz6u+rnkTlaVaPYqAglxjCPTDCzVTo04aXCJjt6D8lM1ILR/fj7A4CbXL1+U5HxIGsHEpdQ4spoNd4PAOBd9JEB3MRoNQDwPyoy8IlgXL6B0WoA4H8kMvC6YO0QWz9abWMzfWQYrQYA3kfTErwqmJdvqB+tdurQe0arAYDvUJGBVwV7h9i0+GgtGN2P0WoA4CckMvCqUOgQy2g1APAfmpbgVXSIBQB4E4kMvIrlGwAA3kQiA6+iQywAwJvoIwOvo0MsAMBbSGTgE3SIBQB4A01LAADAtEhkAACAaZHIAAAA0yKRAQAApuXXRCY/P18/+9nPFBsbq6SkJI0cOVK7du1q8Jyqqirl5eUpISFBMTExys3NVWlpqZ8iBgAAgcSvicyGDRuUl5enrVu36u2331ZNTY1+8Ytf6NixY87nTJo0Sf/85z+1cuVKbdiwQQcOHNCoUaP8GDUAAAgUYYZhGP4Oot7333+vpKQkbdiwQUOGDJHNZlOXLl20fPlyXXPNNZKkL7/8Ur1799aWLVt00UUXNTpGdXW1qqurnT/b7Xalp6fLZrMpLi7OZ/8XAADgPrvdLqvV2uL1O6D6yNhsNklS586dJUnbtm1TTU2NcnJynM/p1auXMjIytGXLliaPkZ+fL6vV6nykp6d7P3AAAOAXAZPI1NXV6Z577tHgwYN1/vnnS5JKSkoUGRmp+Pj4Bs9NTk5WSUlJk8eZNm2abDab87F//35vhw4AAPwkYGb2zcvL086dO7Vp06Y2HcdischisXgoKgAAEMgCIpGZMGGC/vWvf2njxo0688wzndtTUlLkcDhUXl7eoCpTWlqqlJQUl45d3wXIbrd7NGYAAOA99dftFrvyGn5UV1dn5OXlGWlpacZXX33VaH95ebkRERFhvPLKK85tX375pSHJ2LJli0u/Y//+/YYkHjx48ODBg4cJH/v37z/tdd6vo5bGjx+v5cuX6//+7/90zjnnOLdbrVZFR0dLku6880698cYbWrZsmeLi4jRx4kRJ0vvvv+/S76irq9OBAwcUGxursLAw5/b60Uz79+9nNJOPce79g/PuP5x7/+C8+48nzr1hGDp69KjS0tIUHt58l16/Ni0tXrxYknTppZc22L506VLdfPPNkqT58+crPDxcubm5qq6u1ogRI7Ro0SKXf0d4eHiD5qpTxcXF8Qb3E869f3De/Ydz7x+cd/9p67m3Wq0tPseviYwrxaCoqCgtXLhQCxcu9EFEAADATAJm+DUAAEBrhWwiY7FYNGPGDIZq+wHn3j847/7DufcPzrv/+PLcB9QSBQAAAK0RshUZAABgfiQyAADAtEhkAACAaZHIAAAA0wrqRGbhwoXq1q2boqKiNHDgQP3nP/857fNXrlypXr16KSoqSn369NEbb7zho0iDT2vO/XPPPadLLrlEnTp1UqdOnZSTk9Pi3wpNa+17vt6KFSsUFhamkSNHejfAINbac19eXq68vDylpqbKYrHo7LPP5jvHDa09708++aTOOeccRUdHKz09XZMmTVJVVZWPog0eGzdu1K9//WulpaUpLCxMr732WouvWb9+vS688EJZLBb17NlTy5Yt80wwrq6LZDYrVqwwIiMjjb/97W/GZ599Ztx2221GfHy8UVpa2uTzN2/ebLRr186YN2+e8fnnnxt/+tOfjIiICOPTTz/1ceTm19pz/9vf/tZYuHChUVhYaHzxxRfGzTffbFitVuPbb7/1ceTm1trzXm/fvn3GGWecYVxyySXGVVdd5Ztgg0xrz311dbUxYMAA45e//KWxadMmY9++fcb69euN7du3+zhyc2vteX/xxRcNi8VivPjii8a+ffuMtWvXGqmpqcakSZN8HLn5vfHGG8aDDz5orF692pBkvPrqq6d9/t69e40OHToYkydPNj7//HNjwYIFRrt27Yy33nqrzbEEbSLz85//3MjLy3P+XFtba6SlpRn5+flNPv+6664zrrzyygbbBg4caPzhD3/wapzBqLXn/lQnTpwwYmNjjeeff95bIQYld877iRMnjEGDBhl//etfjbFjx5LIuKm1537x4sXGWWedZTgcDl+FGJRae97z8vKMYcOGNdg2efJkY/DgwV6NM9i5kshMmTLFOO+88xpsu/76640RI0a0+fcHZdOSw+HQtm3blJOT49wWHh6unJwcbdmypcnXbNmypcHzJWnEiBHNPh9Nc+fcn6qyslI1NTXq3Lmzt8IMOu6e90ceeURJSUkaN26cL8IMSu6c+9dff13Z2dnKy8tTcnKyzj//fM2ePVu1tbW+Ctv03DnvgwYN0rZt25zNT3v37tUbb7yhX/7ylz6JOZR58xrr17WWvKWsrEy1tbVKTk5usD05OVlffvllk68pKSlp8vklJSVeizMYuXPuT3X//fcrLS2t0ZsezXPnvG/atElLlizR9u3bfRBh8HLn3O/du1fvvPOOxowZozfeeEN79uzR+PHjVVNToxkzZvgibNNz57z/9re/VVlZmS6++GIZhqETJ07ojjvu0AMPPOCLkENac9dYu92u48ePKzo62u1jB2VFBuY1Z84crVixQq+++qqioqL8HU7QOnr0qG688UY999xzSkxM9Hc4Iaeurk5JSUl69tln1b9/f11//fV68MEH9cwzz/g7tKC2fv16zZ49W4sWLdLHH3+s1atXa82aNZo5c6a/Q0MbBGVFJjExUe3atVNpaWmD7aWlpUpJSWnyNSkpKa16Pprmzrmv99hjj2nOnDlat26dsrKyvBlm0GnteS8qKtLXX3+tX//6185tdXV1kqT27dtr165d6tGjh3eDDhLuvOdTU1MVERGhdu3aObf17t1bJSUlcjgcioyM9GrMwcCd8/7QQw/pxhtv1K233ipJ6tOnj44dO6bbb79dDz74oMLDubf3luausXFxcW2qxkhBWpGJjIxU//79VVBQ4NxWV1engoICZWdnN/ma7OzsBs+XpLfffrvZ56Np7px7SZo3b55mzpypt956SwMGDPBFqEGltee9V69e+vTTT7V9+3bn4ze/+Y0uu+wybd++Xenp6b4M39Tcec8PHjxYe/bscSaPkvTVV18pNTWVJMZF7pz3ysrKRslKfTJpsOygV3n1Gtvm7sIBasWKFYbFYjGWLVtmfP7558btt99uxMfHGyUlJYZhGMaNN95oTJ061fn8zZs3G+3btzcee+wx44svvjBmzJjB8Gs3tfbcz5kzx4iMjDReeeUV4+DBg87H0aNH/fVfMKXWnvdTMWrJfa0998XFxUZsbKwxYcIEY9euXca//vUvIykpyZg1a5a//gum1NrzPmPGDCM2NtZ46aWXjL179xr//ve/jR49ehjXXXedv/4LpnX06FGjsLDQKCwsNCQZTzzxhFFYWGh88803hmEYxtSpU40bb7zR+fz64dd//OMfjS+++MJYuHAhw69dsWDBAiMjI8OIjIw0fv7znxtbt2517hs6dKgxduzYBs9/+eWXjbPPPtuIjIw0zjvvPGPNmjU+jjh4tObcd+3a1ZDU6DFjxgzfB25yrX3P/xSJTNu09ty///77xsCBAw2LxWKcddZZxqOPPmqcOHHCx1GbX2vOe01NjfHnP//Z6NGjhxEVFWWkp6cb48ePN44cOeL7wE3u3XffbfJ7u/58jx071hg6dGij1/Tt29eIjIw0zjrrLGPp0qUeiSXMMKinAQAAcwrKPjIAACA0kMgAAADTIpEBAACmRSIDAABMi0QGAACYFokMAAAwLRIZAABgWiQyAADAtEhkAACAaZHIAICXdevWTU8++aS/wwCCEokMgIBVW1vbYIVoTzIMQydOnPDKsQH4DokMAKdXXnlFffr0UXR0tBISEpSTk6Njx47p0ksv1T333NPguSNHjtTNN9/s/PngwYO68sorFR0dre7du2v58uWNKhFPPPGE+vTpo44dOyo9PV3jx49XRUWFc/+yZcsUHx+v119/Xeeee64sFouKi4t18803a+TIkXr44YfVpUsXxcXF6Y477pDD4XC+tq6uTvn5+erevbuio6N1wQUX6JVXXnHuX79+vcLCwvTmm2+qf//+slgs2rRpk+rq6jRv3jz17NlTFotFGRkZevTRR52v279/v6677jrFx8erc+fOuuqqq/T1118799fH9thjjyk1NVUJCQnKy8tTTU2NJOnSSy/VN998o0mTJiksLExhYWFt/CsB+Kn2/g4AQGA4ePCgRo8erXnz5unqq6/W0aNH9d5778nVdWVvuukmlZWVaf369YqIiNDkyZN16NChBs8JDw/XU089pe7du2vv3r0aP368pkyZokWLFjmfU1lZqblz5+qvf/2rEhISlJSUJEkqKChQVFSU1q9fr6+//lq///3vlZCQ4Ew68vPz9cILL+iZZ55RZmamNm7cqN/97nfq0qWLhg4d6jz+1KlT9dhjj+mss85Sp06dNG3aND333HOaP3++Lr74Yh08eFBffvmlJKmmpkYjRoxQdna23nvvPbVv316zZs3Sf/3Xf2nHjh2KjIyUJL377rtKTU3Vu+++qz179uj6669X3759ddttt2n16tW64IILdPvtt+u2225z/w8EoGkeWUMbgOlt27bNkGR8/fXXjfYNHTrUuPvuuxtsu+qqq4yxY8cahmEYX3zxhSHJ+PDDD537d+/ebUgy5s+f3+zvXLlypZGQkOD8eenSpYYkY/v27Q2eN3bsWKNz587GsWPHnNsWL15sxMTEGLW1tUZVVZXRoUMH4/3332/wunHjxhmjR482DMMw3n33XUOS8dprrzn32+12w2KxGM8991yT8f3jH/8wzjnnHKOurs65rbq62oiOjjbWrl3rjK1r167GiRMnnM+59tprjeuvv975c9euXU97HgC4j4oMAEnSBRdcoOHDh6tPnz4aMWKEfvGLX+iaa65Rp06dWnztrl271L59e1144YXObT179mz02nXr1ik/P19ffvml7Ha7Tpw4oaqqKlVWVqpDhw6SpMjISGVlZTUZX/1zJCk7O1sVFRXav3+/KioqVFlZqcsvv7zBaxwOh/r169dg24ABA5z//uKLL1RdXa3hw4c3+f/65JNPtGfPHsXGxjbYXlVVpaKiIufP5513ntq1a+f8OTU1VZ9++mmTxwTgWSQyACRJ7dq109tvv633339f//73v7VgwQI9+OCD+uCDDxQeHt6oiam+D4irvv76a/3qV7/SnXfeqUcffVSdO3fWpk2bNG7cODkcDmeSEh0d3ep+JPX9bNasWaMzzjijwT6LxdLg544dOzr/HR0d3eJx+/fvrxdffLHRvi5dujj/HRER0WBfWFiY1zopA2iIRAaAU1hYmAYPHqzBgwdr+vTp6tq1q1599VV16dJFBw8edD6vtrZWO3fu1GWXXSZJOuecc3TixAkVFhaqf//+kqQ9e/boyJEjztds27ZNdXV1evzxxxUefnKcwcsvv+xybJ988omOHz/uTD62bt2qmJgYpaenq3Pnzs6OwT/tD9OSzMxMRUdHq6CgQLfeemuj/RdeeKH+93//V0lJSYqLi3P5uKeKjIxUbW2t268H0DxGLQGQJH3wwQeaPXu2PvroIxUXF2v16tX6/vvv1bt3bw0bNkxr1qzRmjVr9OWXX+rOO+9UeXm587W9evVSTk6Obr/9dv3nP/9RYWGhbr/99gbVlZ49e6qmpkYLFizQ3r179Y9//EPPPPOMy/E5HA6NGzdOn3/+ud544w3NmDFDEyZMUHh4uGJjY3Xfffdp0qRJev7551VUVKSPP/5YCxYs0PPPP9/sMaOionT//fdrypQp+vvf/66ioiJt3bpVS5YskSSNGTNGiYmJuuqqq/Tee+9p3759Wr9+ve666y59++23LsferVs3bdy4Ud99953Kyspcfh2AllGRASBJiouL08aNG/Xkk0/Kbrera9euevzxx3XFFVeopqZGn3zyiW666Sa1b99ekyZNclZj6v3973/XuHHjNGTIEKWkpCg/P1+fffaZoqKiJJ3s4/LEE09o7ty5mjZtmoYMGaL8/HzddNNNLsU3fPhwZWZmasiQIaqurtbo0aP15z//2bl/5syZ6tKli/Lz87V3717Fx8frwgsv1AMPPHDa4z700ENq3769pk+frgMHDig1NVV33HGHJKlDhw7auHGj7r//fo0aNUpHjx7VGWecoeHDh7eqQvPII4/oD3/4g3r06KHq6mqXR4IBaFmYwScKgBd8++23Sk9P17p165rtTOuqm2++WeXl5Xrttdc8ExyAoEFFBoBHvPPOO6qoqFCfPn108OBBTZkyRd26ddOQIUP8HRqAIEYiA8Ajampq9MADD2jv3r2KjY3VoEGD9OKLLzYa0QMAnkTTEgAAMC1GLQEAANMikQEAAKZFIgMAAEyLRAYAAJgWiQwAADAtEhkAAGBaJDIAAMC0SGQAAIBp/T+SGvzAafwpNQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot showing the relationship between 'sugarpercent' and 'winpercent'\n",
    "sns.scatterplot(x='sugarpercent', y='winpercent', data=candy_data)\n",
    "\n",
    "# Your code here\n",
    "\n",
    "# Check your answer\n",
    "step_3.a.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a0ac56e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-07T03:22:58.213260Z",
     "iopub.status.busy": "2023-12-07T03:22:58.212721Z",
     "iopub.status.idle": "2023-12-07T03:22:58.218300Z",
     "shell.execute_reply": "2023-12-07T03:22:58.217006Z"
    },
    "papermill": {
     "duration": 0.021508,
     "end_time": "2023-12-07T03:22:58.221009",
     "exception": false,
     "start_time": "2023-12-07T03:22:58.199501",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Lines below will give you a hint or solution code\n",
    "#step_3.a.hint()\n",
    "#step_3.a.solution_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1d12403",
   "metadata": {
    "papermill": {
     "duration": 0.011283,
     "end_time": "2023-12-07T03:22:58.244124",
     "exception": false,
     "start_time": "2023-12-07T03:22:58.232841",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Part B\n",
    "\n",
    "Does the scatter plot show a **strong** correlation between the two variables?  If so, are candies with more sugar relatively more or less popular with the survey respondents?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "098c7b87",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-07T03:22:58.269888Z",
     "iopub.status.busy": "2023-12-07T03:22:58.269217Z",
     "iopub.status.idle": "2023-12-07T03:22:58.275613Z",
     "shell.execute_reply": "2023-12-07T03:22:58.274655Z"
    },
    "papermill": {
     "duration": 0.0232,
     "end_time": "2023-12-07T03:22:58.278843",
     "exception": false,
     "start_time": "2023-12-07T03:22:58.255643",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#step_3.b.hint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "06c2cac6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-07T03:22:58.302422Z",
     "iopub.status.busy": "2023-12-07T03:22:58.301295Z",
     "iopub.status.idle": "2023-12-07T03:22:58.310508Z",
     "shell.execute_reply": "2023-12-07T03:22:58.309406Z"
    },
    "papermill": {
     "duration": 0.023242,
     "end_time": "2023-12-07T03:22:58.312906",
     "exception": false,
     "start_time": "2023-12-07T03:22:58.289664",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"interactionType\": 3, \"questionType\": 4, \"questionId\": \"3.2_ThinkBlueScatter\", \"learnToolsVersion\": \"0.3.4\", \"valueTowardsCompletion\": 0.0, \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\", \"outcomeType\": 4}}, \"*\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#33cc99\">Solution:</span> The scatter plot does not show a strong correlation between the two variables. Since there is no clear relationship between the two variables, this tells us that sugar content does not play a strong role in candy popularity."
      ],
      "text/plain": [
       "Solution: The scatter plot does not show a strong correlation between the two variables. Since there is no clear relationship between the two variables, this tells us that sugar content does not play a strong role in candy popularity."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check your answer (Run this code cell to receive credit!)\n",
    "step_3.b.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a80d236c",
   "metadata": {
    "papermill": {
     "duration": 0.010002,
     "end_time": "2023-12-07T03:22:58.333311",
     "exception": false,
     "start_time": "2023-12-07T03:22:58.323309",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Step 4: Take a closer look\n",
    "\n",
    "#### Part A\n",
    "\n",
    "Create the same scatter plot you created in **Step 3**, but now with a regression line!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0013fb1b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-07T03:22:58.358282Z",
     "iopub.status.busy": "2023-12-07T03:22:58.357364Z",
     "iopub.status.idle": "2023-12-07T03:22:58.752391Z",
     "shell.execute_reply": "2023-12-07T03:22:58.750665Z"
    },
    "papermill": {
     "duration": 0.410409,
     "end_time": "2023-12-07T03:22:58.754789",
     "exception": false,
     "start_time": "2023-12-07T03:22:58.344380",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.3333333333333333, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"4.1_PlotBlueReg\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#33cc33\">Correct</span>"
      ],
      "text/plain": [
       "Correct"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot w/ regression line showing the relationship between 'sugarpercent' and 'winpercent'\n",
    "sns.regplot(x='sugarpercent', y='winpercent', data=candy_data) # Your code here\n",
    "\n",
    "# Check your answer\n",
    "step_4.a.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "add862f6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-07T03:22:58.780226Z",
     "iopub.status.busy": "2023-12-07T03:22:58.779023Z",
     "iopub.status.idle": "2023-12-07T03:22:58.785026Z",
     "shell.execute_reply": "2023-12-07T03:22:58.783838Z"
    },
    "papermill": {
     "duration": 0.021732,
     "end_time": "2023-12-07T03:22:58.787789",
     "exception": false,
     "start_time": "2023-12-07T03:22:58.766057",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Lines below will give you a hint or solution code\n",
    "#step_4.a.hint()\n",
    "#step_4.a.solution_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0af014fe",
   "metadata": {
    "papermill": {
     "duration": 0.011121,
     "end_time": "2023-12-07T03:22:58.811012",
     "exception": false,
     "start_time": "2023-12-07T03:22:58.799891",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Part B\n",
    "\n",
    "According to the plot above, is there a **slight** correlation between `'winpercent'` and `'sugarpercent'`?  What does this tell you about the candy that people tend to prefer?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e7cc60cf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-07T03:22:58.837019Z",
     "iopub.status.busy": "2023-12-07T03:22:58.836579Z",
     "iopub.status.idle": "2023-12-07T03:22:58.841340Z",
     "shell.execute_reply": "2023-12-07T03:22:58.840216Z"
    },
    "papermill": {
     "duration": 0.020097,
     "end_time": "2023-12-07T03:22:58.843760",
     "exception": false,
     "start_time": "2023-12-07T03:22:58.823663",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#step_4.b.hint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e36372b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-07T03:22:58.931105Z",
     "iopub.status.busy": "2023-12-07T03:22:58.930731Z",
     "iopub.status.idle": "2023-12-07T03:22:58.940999Z",
     "shell.execute_reply": "2023-12-07T03:22:58.939753Z"
    },
    "papermill": {
     "duration": 0.025949,
     "end_time": "2023-12-07T03:22:58.943639",
     "exception": false,
     "start_time": "2023-12-07T03:22:58.917690",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"interactionType\": 3, \"questionType\": 4, \"questionId\": \"4.2_ThinkBlueReg\", \"learnToolsVersion\": \"0.3.4\", \"valueTowardsCompletion\": 0.0, \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\", \"outcomeType\": 4}}, \"*\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#33cc99\">Solution:</span> Since the regression line has a slightly positive slope, this tells us that there is a slightly positive correlation between `'winpercent'` and `'sugarpercent'`. Thus, people have a slight preference for candies containing relatively more sugar."
      ],
      "text/plain": [
       "Solution: Since the regression line has a slightly positive slope, this tells us that there is a slightly positive correlation between `'winpercent'` and `'sugarpercent'`. Thus, people have a slight preference for candies containing relatively more sugar."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check your answer (Run this code cell to receive credit!)\n",
    "step_4.b.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd6ca3b6",
   "metadata": {
    "papermill": {
     "duration": 0.012094,
     "end_time": "2023-12-07T03:22:58.967099",
     "exception": false,
     "start_time": "2023-12-07T03:22:58.955005",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Step 5: Chocolate!\n",
    "\n",
    "In the code cell below, create a scatter plot to show the relationship between `'pricepercent'` (on the horizontal x-axis) and `'winpercent'` (on the vertical y-axis). Use the `'chocolate'` column to color-code the points.  _Don't add any regression lines just yet -- you'll do that in the next step!_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bfb08f40",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-07T03:22:58.991948Z",
     "iopub.status.busy": "2023-12-07T03:22:58.991285Z",
     "iopub.status.idle": "2023-12-07T03:22:59.293884Z",
     "shell.execute_reply": "2023-12-07T03:22:59.292782Z"
    },
    "papermill": {
     "duration": 0.317845,
     "end_time": "2023-12-07T03:22:59.296492",
     "exception": false,
     "start_time": "2023-12-07T03:22:58.978647",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.3333333333333333, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"5_ColorScatter\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#33cc33\">Correct</span>"
      ],
      "text/plain": [
       "Correct"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot showing the relationship between 'pricepercent', 'winpercent', and 'chocolate'\n",
    "sns.scatterplot(x='pricepercent', y='winpercent', color='chocolate', data=candy_data) # Your code here\n",
    "\n",
    "# Check your answer\n",
    "step_5.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7b7ee43c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-07T03:22:59.323034Z",
     "iopub.status.busy": "2023-12-07T03:22:59.322069Z",
     "iopub.status.idle": "2023-12-07T03:22:59.326938Z",
     "shell.execute_reply": "2023-12-07T03:22:59.325812Z"
    },
    "papermill": {
     "duration": 0.021799,
     "end_time": "2023-12-07T03:22:59.330521",
     "exception": false,
     "start_time": "2023-12-07T03:22:59.308722",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Lines below will give you a hint or solution code\n",
    "#step_5.hint()\n",
    "#step_5.solution_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8674b2c2",
   "metadata": {
    "papermill": {
     "duration": 0.011725,
     "end_time": "2023-12-07T03:22:59.354391",
     "exception": false,
     "start_time": "2023-12-07T03:22:59.342666",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Can you see any interesting patterns in the scatter plot?  We'll investigate this plot further  by adding regression lines in the next step!\n",
    "\n",
    "## Step 6: Investigate chocolate\n",
    "\n",
    "#### Part A\n",
    "\n",
    "Create the same scatter plot you created in **Step 5**, but now with two regression lines, corresponding to (1) chocolate candies and (2) candies without chocolate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5e663607",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-07T03:22:59.382138Z",
     "iopub.status.busy": "2023-12-07T03:22:59.381749Z",
     "iopub.status.idle": "2023-12-07T03:23:00.213162Z",
     "shell.execute_reply": "2023-12-07T03:23:00.212018Z"
    },
    "papermill": {
     "duration": 0.848691,
     "end_time": "2023-12-07T03:23:00.215666",
     "exception": false,
     "start_time": "2023-12-07T03:22:59.366975",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.3333333333333333, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"6.1_PlotColorReg\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#33cc33\">Correct</span>"
      ],
      "text/plain": [
       "Correct"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 578.611x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Color-coded scatter plot w/ regression lines\n",
    "sns.lmplot(x='pricepercent',y='winpercent', hue='chocolate', data=candy_data) # Your code here\n",
    "\n",
    "# Check your answer\n",
    "step_6.a.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d1ead48a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-07T03:23:00.244883Z",
     "iopub.status.busy": "2023-12-07T03:23:00.244485Z",
     "iopub.status.idle": "2023-12-07T03:23:00.249740Z",
     "shell.execute_reply": "2023-12-07T03:23:00.248590Z"
    },
    "papermill": {
     "duration": 0.022055,
     "end_time": "2023-12-07T03:23:00.251998",
     "exception": false,
     "start_time": "2023-12-07T03:23:00.229943",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Lines below will give you a hint or solution code\n",
    "#step_6.a.hint()\n",
    "#step_6.a.solution_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0629427d",
   "metadata": {
    "papermill": {
     "duration": 0.012682,
     "end_time": "2023-12-07T03:23:00.277884",
     "exception": false,
     "start_time": "2023-12-07T03:23:00.265202",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Part B\n",
    "\n",
    "Using the regression lines, what conclusions can you draw about the effects of chocolate and price on candy popularity?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4a98c0eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-07T03:23:00.306756Z",
     "iopub.status.busy": "2023-12-07T03:23:00.306367Z",
     "iopub.status.idle": "2023-12-07T03:23:00.310865Z",
     "shell.execute_reply": "2023-12-07T03:23:00.309688Z"
    },
    "papermill": {
     "duration": 0.021805,
     "end_time": "2023-12-07T03:23:00.313121",
     "exception": false,
     "start_time": "2023-12-07T03:23:00.291316",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#step_6.b.hint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6166574b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-07T03:23:00.341136Z",
     "iopub.status.busy": "2023-12-07T03:23:00.340763Z",
     "iopub.status.idle": "2023-12-07T03:23:00.349662Z",
     "shell.execute_reply": "2023-12-07T03:23:00.348428Z"
    },
    "papermill": {
     "duration": 0.02595,
     "end_time": "2023-12-07T03:23:00.352260",
     "exception": false,
     "start_time": "2023-12-07T03:23:00.326310",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"interactionType\": 3, \"questionType\": 4, \"questionId\": \"6.2_ThinkColorReg\", \"learnToolsVersion\": \"0.3.4\", \"valueTowardsCompletion\": 0.0, \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\", \"outcomeType\": 4}}, \"*\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#33cc99\">Solution:</span> We'll begin with the regression line for chocolate candies. Since this line has a slightly positive slope, we can say that more expensive chocolate candies tend to be more popular (than relatively cheaper chocolate candies).  Likewise, since the regression line for candies without chocolate has a negative slope, we can say that if candies don't contain chocolate, they tend to be more popular when they are cheaper.  One important note, however, is that the dataset is quite small -- so we shouldn't invest too much trust in these patterns!  To inspire more confidence in the results, we should add more candies to the dataset."
      ],
      "text/plain": [
       "Solution: We'll begin with the regression line for chocolate candies. Since this line has a slightly positive slope, we can say that more expensive chocolate candies tend to be more popular (than relatively cheaper chocolate candies).  Likewise, since the regression line for candies without chocolate has a negative slope, we can say that if candies don't contain chocolate, they tend to be more popular when they are cheaper.  One important note, however, is that the dataset is quite small -- so we shouldn't invest too much trust in these patterns!  To inspire more confidence in the results, we should add more candies to the dataset."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check your answer (Run this code cell to receive credit!)\n",
    "step_6.b.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95bd53c5",
   "metadata": {
    "papermill": {
     "duration": 0.012992,
     "end_time": "2023-12-07T03:23:00.378434",
     "exception": false,
     "start_time": "2023-12-07T03:23:00.365442",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Step 7: Everybody loves chocolate.\n",
    "\n",
    "#### Part A\n",
    "\n",
    "Create a categorical scatter plot to highlight the relationship between `'chocolate'` and `'winpercent'`.  Put `'chocolate'` on the (horizontal) x-axis, and `'winpercent'` on the (vertical) y-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0d77f12a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-07T03:23:00.406521Z",
     "iopub.status.busy": "2023-12-07T03:23:00.406119Z",
     "iopub.status.idle": "2023-12-07T03:23:00.763700Z",
     "shell.execute_reply": "2023-12-07T03:23:00.762401Z"
    },
    "papermill": {
     "duration": 0.374772,
     "end_time": "2023-12-07T03:23:00.766307",
     "exception": false,
     "start_time": "2023-12-07T03:23:00.391535",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.3333333333333333, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"7.1_PlotSwarm\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#33cc33\">Correct</span>"
      ],
      "text/plain": [
       "Correct"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot showing the relationship between 'chocolate' and 'winpercent'\n",
    "sns.swarmplot(data=candy_data, x='chocolate', y='winpercent') # Your code here\n",
    "\n",
    "# Check your answer\n",
    "step_7.a.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ee058f16",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-07T03:23:00.799642Z",
     "iopub.status.busy": "2023-12-07T03:23:00.799227Z",
     "iopub.status.idle": "2023-12-07T03:23:00.807840Z",
     "shell.execute_reply": "2023-12-07T03:23:00.806787Z"
    },
    "papermill": {
     "duration": 0.028806,
     "end_time": "2023-12-07T03:23:00.810286",
     "exception": false,
     "start_time": "2023-12-07T03:23:00.781480",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"interactionType\": 2, \"questionType\": 2, \"questionId\": \"7.1_PlotSwarm\", \"learnToolsVersion\": \"0.3.4\", \"valueTowardsCompletion\": 0.0, \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\", \"outcomeType\": 4}}, \"*\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#3366cc\">Hint:</span> Use `sns.swarmplot`, and set the variables for the x-axis and y-axis by using `x=` and `y=`, respectively."
      ],
      "text/plain": [
       "Hint: Use `sns.swarmplot`, and set the variables for the x-axis and y-axis by using `x=` and `y=`, respectively."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lines below will give you a hint or solution code\n",
    "step_7.a.hint()\n",
    "#step_7.a.solution_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d50cd71b",
   "metadata": {
    "papermill": {
     "duration": 0.014994,
     "end_time": "2023-12-07T03:23:00.839409",
     "exception": false,
     "start_time": "2023-12-07T03:23:00.824415",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Part B\n",
    "\n",
    "You decide to dedicate a section of your report to the fact that chocolate candies tend to be more popular than candies without chocolate.  Which plot is more appropriate to tell this story: the plot from **Step 6**, or the plot from **Step 7**?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9ae34af0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-07T03:23:00.870435Z",
     "iopub.status.busy": "2023-12-07T03:23:00.869610Z",
     "iopub.status.idle": "2023-12-07T03:23:00.874487Z",
     "shell.execute_reply": "2023-12-07T03:23:00.873573Z"
    },
    "papermill": {
     "duration": 0.024226,
     "end_time": "2023-12-07T03:23:00.878102",
     "exception": false,
     "start_time": "2023-12-07T03:23:00.853876",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#step_7.b.hint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "838aec5d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-07T03:23:00.908707Z",
     "iopub.status.busy": "2023-12-07T03:23:00.907921Z",
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    "tags": []
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   "outputs": [
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      "text/markdown": [
       "<span style=\"color:#33cc99\">Solution:</span> In this case, the categorical scatter plot from **Step 7** is the more appropriate plot. While both plots tell the desired story, the plot from **Step 6** conveys far more information that could distract from the main point."
      ],
      "text/plain": [
       "Solution: In this case, the categorical scatter plot from **Step 7** is the more appropriate plot. While both plots tell the desired story, the plot from **Step 6** conveys far more information that could distract from the main point."
      ]
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   "source": [
    "# Check your answer (Run this code cell to receive credit!)\n",
    "step_7.b.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ae428c4",
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    "tags": []
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   "source": [
    "## Keep going\n",
    "\n",
    "Explore **[histograms and density plots](https://www.kaggle.com/alexisbcook/distributions)**."
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "---\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "*Have questions or comments? Visit the [course discussion forum](https://www.kaggle.com/learn/data-visualization/discussion) to chat with other learners.*"
   ]
  }
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