exercise-scatter-plots.ipynb

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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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      "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",
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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",
       "    </tr>\n",
       "  </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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   "source": [
    "# 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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    "## 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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",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot showing the relationship btw 'sugarpercent' and 'winpercent'\n",
    "plt.figure(figsize=(7,7))\n",
    "plt.title('plot showing the relationship btw sugarpercent and winpercent')\n",
    "sns.scatterplot(x=candy_data['sugarpercent'], y=candy_data['winpercent']) # Your code here\n",
    "\n",
    "# Check your answer\n",
    "step_3.a.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "56f78f41",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-15T09:31:29.044439Z",
     "iopub.status.busy": "2023-01-15T09:31:29.044021Z",
     "iopub.status.idle": "2023-01-15T09:31:29.048850Z",
     "shell.execute_reply": "2023-01-15T09:31:29.047682Z"
    },
    "papermill": {
     "duration": 0.018738,
     "end_time": "2023-01-15T09:31:29.051271",
     "exception": false,
     "start_time": "2023-01-15T09:31:29.032533",
     "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": "b556abfa",
   "metadata": {
    "papermill": {
     "duration": 0.009747,
     "end_time": "2023-01-15T09:31:29.071644",
     "exception": false,
     "start_time": "2023-01-15T09:31:29.061897",
     "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": "40ccfdda",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-15T09:31:29.093834Z",
     "iopub.status.busy": "2023-01-15T09:31:29.093156Z",
     "iopub.status.idle": "2023-01-15T09:31:29.098424Z",
     "shell.execute_reply": "2023-01-15T09:31:29.097251Z"
    },
    "papermill": {
     "duration": 0.019033,
     "end_time": "2023-01-15T09:31:29.100851",
     "exception": false,
     "start_time": "2023-01-15T09:31:29.081818",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#step_3.b.hint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "113339be",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-15T09:31:29.123170Z",
     "iopub.status.busy": "2023-01-15T09:31:29.122744Z",
     "iopub.status.idle": "2023-01-15T09:31:29.131612Z",
     "shell.execute_reply": "2023-01-15T09:31:29.130479Z"
    },
    "papermill": {
     "duration": 0.022548,
     "end_time": "2023-01-15T09:31:29.133706",
     "exception": false,
     "start_time": "2023-01-15T09:31:29.111158",
     "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": "3aded252",
   "metadata": {
    "papermill": {
     "duration": 0.010162,
     "end_time": "2023-01-15T09:31:29.154450",
     "exception": false,
     "start_time": "2023-01-15T09:31:29.144288",
     "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": "d5d3b0f0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-15T09:31:29.177487Z",
     "iopub.status.busy": "2023-01-15T09:31:29.176842Z",
     "iopub.status.idle": "2023-01-15T09:31:29.495600Z",
     "shell.execute_reply": "2023-01-15T09:31:29.494428Z"
    },
    "papermill": {
     "duration": 0.333296,
     "end_time": "2023-01-15T09:31:29.498253",
     "exception": false,
     "start_time": "2023-01-15T09:31:29.164957",
     "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 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot w/ regression line showing the relationship between 'sugarpercent' and 'winpercent'\n",
    "# Your code here\n",
    "sns.regplot(x=candy_data['sugarpercent'], y=candy_data['winpercent'])\n",
    "# Check your answer\n",
    "step_4.a.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1907178a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-15T09:31:29.522962Z",
     "iopub.status.busy": "2023-01-15T09:31:29.522199Z",
     "iopub.status.idle": "2023-01-15T09:31:29.527395Z",
     "shell.execute_reply": "2023-01-15T09:31:29.526409Z"
    },
    "papermill": {
     "duration": 0.020311,
     "end_time": "2023-01-15T09:31:29.529825",
     "exception": false,
     "start_time": "2023-01-15T09:31:29.509514",
     "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": "819d3f62",
   "metadata": {
    "papermill": {
     "duration": 0.010816,
     "end_time": "2023-01-15T09:31:29.552005",
     "exception": false,
     "start_time": "2023-01-15T09:31:29.541189",
     "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": "ad3b6965",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-15T09:31:29.577396Z",
     "iopub.status.busy": "2023-01-15T09:31:29.576960Z",
     "iopub.status.idle": "2023-01-15T09:31:29.581849Z",
     "shell.execute_reply": "2023-01-15T09:31:29.580697Z"
    },
    "papermill": {
     "duration": 0.020631,
     "end_time": "2023-01-15T09:31:29.584637",
     "exception": false,
     "start_time": "2023-01-15T09:31:29.564006",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#step_4.b.hint()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8a17be6d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-15T09:31:29.608825Z",
     "iopub.status.busy": "2023-01-15T09:31:29.608407Z",
     "iopub.status.idle": "2023-01-15T09:31:29.617736Z",
     "shell.execute_reply": "2023-01-15T09:31:29.616651Z"
    },
    "papermill": {
     "duration": 0.023847,
     "end_time": "2023-01-15T09:31:29.619906",
     "exception": false,
     "start_time": "2023-01-15T09:31:29.596059",
     "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": "656336d5",
   "metadata": {
    "papermill": {
     "duration": 0.011116,
     "end_time": "2023-01-15T09:31:29.643307",
     "exception": false,
     "start_time": "2023-01-15T09:31:29.632191",
     "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": "dda03ca8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-15T09:31:29.668396Z",
     "iopub.status.busy": "2023-01-15T09:31:29.667705Z",
     "iopub.status.idle": "2023-01-15T09:31:29.894537Z",
     "shell.execute_reply": "2023-01-15T09:31:29.893295Z"
    },
    "papermill": {
     "duration": 0.242116,
     "end_time": "2023-01-15T09:31:29.896882",
     "exception": false,
     "start_time": "2023-01-15T09:31:29.654766",
     "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 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot showing the relationship between 'pricepercent', 'winpercent', and 'chocolate'\n",
    "sns.scatterplot(x=candy_data['pricepercent'], y=candy_data['winpercent']) \n",
    "# Check your answer\n",
    "step_5.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7df69688",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-15T09:31:29.923253Z",
     "iopub.status.busy": "2023-01-15T09:31:29.922840Z",
     "iopub.status.idle": "2023-01-15T09:31:29.927889Z",
     "shell.execute_reply": "2023-01-15T09:31:29.926812Z"
    },
    "papermill": {
     "duration": 0.021287,
     "end_time": "2023-01-15T09:31:29.930312",
     "exception": false,
     "start_time": "2023-01-15T09:31:29.909025",
     "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": "5b670e10",
   "metadata": {
    "papermill": {
     "duration": 0.012523,
     "end_time": "2023-01-15T09:31:29.955211",
     "exception": false,
     "start_time": "2023-01-15T09:31:29.942688",
     "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": "e2cfaf39",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-15T09:31:29.982003Z",
     "iopub.status.busy": "2023-01-15T09:31:29.981543Z",
     "iopub.status.idle": "2023-01-15T09:31:30.652707Z",
     "shell.execute_reply": "2023-01-15T09:31:30.651293Z"
    },
    "papermill": {
     "duration": 0.687798,
     "end_time": "2023-01-15T09:31:30.655499",
     "exception": false,
     "start_time": "2023-01-15T09:31:29.967701",
     "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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PDy/qfZcaCzDGmEhqVhbZyVze9UbqeiUjnB6bOZB4Amf3Zdm4opuNK7vYuKKbi9b18by1fQv5KEuWBRhjTCTNN4tseKzA3mO5oCfigsm+4yMM5QozvocAa/oyLpCs6GLTym42ruhm3UDXlOKsK7rTkS/YGjUWYIwxkTRTFtnrLzqbnxw8Ve2N7AuCyfGR/Kyvt2Z5hg0ruoJeiQso6we6yNQEMNNcFmA6wHx2tDQmLi5c28uvXnQO//rw0xw8OebWGwnc9JVHZn3eYE+6GkA2ruhm08pu1q/oqusJmdawABNzzdjR0ph2Gi2UeOp4/dDW3mMjHBken/V5K5el6uZINq7oZsOKLrrT9rUWFfYvEXNh7mhpTDONF0o8dSLHk9UJ9xH2Hc/xzKkxZls+OtCdcr2RYH6k0jNZlrGvr6izf6GYa8eOlsbMJl8ss/9EkK1Vk7116NQosxUi6MsmJ3ojNUNcvZP2wJmv+/ec4I4H9nPo9ChrQqydZqayABNzUd3R0nS+QqnMgaHRam+kEkwOnpw9kPRmEvW9kSCY9HWlmt7G+/ec4JZ7niDhCb2ZBMdHxrnlnie4jubvwGmmsgATc3Ha0dLEU7FU5uDJ0fq1JMdGOHBydNbaaMvSiboAUumZ9HclW1aR2kr+t5cFmJjr1B0tTeuVyhoEkhH2HZsY4tp/IkdxlkDSlfKrAWRDTTBZuSzV9i2treR/e1mA6QBLeUdLM3+lsivcWJlkfzIY4nrqRI5CaeZAkkl6bFhRn/67cUUXq3rSbQ8kM7GS/+1lAcaYDlVW5fDpserq9r1BMNl/Isd4cebyzumEN7EgsTLEtbKbwZ40XkQDyUys5H97WYAxJuZUlSPD49XCjdUCjidGGCvMHEiSvrB+YKI8SiWD66zlmdgFkpm0cgdOM5UFGBNrSykFVVU5diY/Kf3XDXPlZtkELekL6/q7XK+kJnvr7L4svtcZgWQ2rdqB00xlAcbEVqemoFZLyR8b4cnaXsnxEUbGZw4kvies7c+yadLq9nP6l0YgMdFjAcbEViekoJ7M5SfKo9TsTTJXKfm1/V01qb9dbFjRzdr+LEnfqv2a6LAAY2IrTimop0YLdXMk+4LbJ0dnLiVf2ZNkYsLdBZN1/VNLyRsTRaEGGBHpA/4WuBC30fjbgMeAzwIbgb3Am1R1KMx2tIpVNW6tKKagnhkrVoez9h7LBUNcs+9JAq6UfH3hRldKPm0VgE2Mhd2DuQW4S1XfICIpoAv4A+CbqnqziNwA3ABcH3I7QmdVjVuvnSmoI+NF9h2fmBt5MuiZHD8z+54kq3vTk0qkWCl507lCCzAishzYCrwFQFXzQF5EXgdsCx52O7CDDggwVtW49VqRgjqaL9Vlbe0Lbs9VSn7VsnRdb6QSTLKpxQWSpZQ1Z+IvzB7MJuAo8EkReT7wA+A6YLWqHgoe8wyweroni8g1wDUA69evD7GZzWFVjdujWSmoY0Ep+b2TJtyfOT026/NWTC4lH0y4LwthT5JOzZoLmwXl9gkzwCSAFwDvUtX7ROQW3HBYlaqqiExbm0JVbwNuA9iyZcts20VEglU1jod8sewCSTA3UhnammtPkv6u5JT9SDau7KIns7hS8vPRCVlzrWZBub3CDDAHgAOqel9w/wu4AHNYRNao6iERWQMcCbENLWNVjaMlXyxzYChX7Y08ecwtSHx6jlLyy7PJugBSyd5a3tW6QDKTOGXNRYUF5fYKLcCo6jMisl9Enq2qjwGvAh4JLlcBNwfXXw6rDa1kVY3bo1gqc+DkqEv/rdmX5MBQbtZA0pNJMGWXxJXd9IewJ0mzRDFrLuosKLdX2Flk7wI+HWSQ7QHeCnjA50TkamAf8KaQ29AyVtU4PKWycnBotC5ra9/xEQ4Mjc5aSr475bsKwCtram6t6GKgu/2l5OfLCjfOnwXl9moowIjIy1X1u3Mdm0xVfwhsmeZHr2q4hWZJKZWVQ6dG6yoA7z3uKgDPVkq+K+VPrQAckT1JmsUKN86fBeX2EtW5589F5EFVfcFcx8KyZcsW3bVr17yec2a8yJE5MoBM+5TV7UlSmRupZG09NZQjP0sp+UzCq/ZIaudJBiO8J4lpr0oW2WKD8oru9ELn4pbsL+asPRgReSnwMmCViLy35ke9gK0MM3MqV0rJH6uvAPzU8RxjswSSVMJj/UB9va1NK7tZ3ds5peRNa1g15faZa4gsBSwLHtdTc/w08IawGmXiR1U5Ojxet4akUkp+tDB7KXkXSOqzts5anrEKwEtY0vdIJzx8T0gEBTxLZaVUVsqq1duF0swnKab9Zg0wqvpt4Nsi8ilV3deiNpkIU1WOj+SnLEjcd3yEkVn2JElUSskHcyMbgmByTsT2JOnURXmeCAlfSPnuS1tEkOC457lS/54IIiCTRnS0ZoWQqruIuOf4Iiiup1rW+seBe5wq1aDgjrl3KKlSDgKF7wm+JyR9j5Tv4TX4O1EqK+PFEuOFMoVSmXypTKmsrp2431fTPo1mkaVF5DZcgcrqc1TVUqY6VHVPkskVgI/nGJ6jlPyUza1WdrG2L1s9E22V+RYf7YRFeemkTyr4kk4mgi9tr/Ev7IXy2zTN4HtCVyrBTNnl5bKSL7ngo5OOl3WiV1RSpVRy1xaUmqfRAPN54BO4ysgzn6aaWDoVBJL6fUnm3pPk7L4sG1d0s6k64R6dPUkWUnw0DovyRIRKrPBESCU8kr5HJumRSfihBxKAHY8e4dade9g/lGNdfxfXbt3MtvMHQ3/fhfA8IeP5ZOZRTLRcVgrlMsWSUiwp46UShZIi7f+1jp1GA0xRVf861JaY0A2PFepKyO897oa2ZislL8Cavkw1/bcyxLVuINp7kiyk+GjUFuW5wOGTSbogkgyGt9ppx6NHuPHO3SR9oS+b5MjwGDfeuZvtENkgM1+eJ6Q9n4lycu2v4hBXjQaYfxWR3wW+BFTLyKrqiVBaZRblzHixbo5kX3D7+MjspeTXLM9MrCVZObEnyXzO/qJiIcVH27EoL5XwSCU8fJHqPMl85yFa6dade0j6blgKoCuVIJcvcuvOPZENMHHqcXWaRgPMVcH1+2uOKbC5uc0x85HLB3uSTJpwP3pm9lLygz3pagCpVgAeWHwp+ShZSPHRsBfliQjphEc26YZs0oloBpHZ7B/K0ZetP6PPJn0ODOXa1KLZLYUeV5Q1FGBUdVPYDTEzGy2UeOp4LpgjmVhPMteeJCuXpaak/25Y0UV3CKXko2YhxUfDWCnviZBN+XSnE3QlWzNHEqZ1/V0cGR6r9mDA/X6u7e+a5VntE8ceVydptFRMF/BeYL2qXiMi5wLPVtWvhNq6JWa8sifJpArAc5WSH6jsSTIpmCzLdH4gmclCi48udlGe70kwb+J6KOmE11EVBq7dupkb79xNLl+sJkEUSsq1W6M5mBG3HlenafQb6JO4DcNeFtw/iMssswCzAPlimf0ncnW9kb1BKfm59iTZUFdrywWT3qxNQk6nFcVHqwEl4ZNJeaQTnTPMOJ1t5w+yHdczODCUY23E5zTi1uPqNI0GmF9Q1TeLyG8AqGpOOum0bB7msxCvUCpzYGi0roz8k8dG5tyTpDeTYMOK7iBjayKY9EWslPx815l0gtoeSjbpRzqTLizbzh+MbECZLG49rk7TaIDJi0gWN7GPiPwCNdlkS8VMC/HeWX4WZ/dnqr2RJ4+PsO9YjgMnR6url6ezLJ2Y0htxe5Ik5zWs0o4v+oWsM4mD2hOIs5dn+e2XbmDreatIBsNdnd5D6TRx63F1mkarKV8K/C/gOcA3gJcDb1HVHaG2LhCVasrvueOHHB4eQ0TIF8vki2XGCiWKZZ11aKsr5dcFkEpQWdGEPUlqv+hrJ7PD/qI/61/eODVLq5Cj2D3IM6//fGjvG6b795zgY/c8QTLh0Z30GS+VKZSU7VdcYF9Ij98N37sFTu6Dvg3wsuvgvEvb3aq4WJKjPdB4FtndIvIg8BLc/6zrVPVYqC1ro7Iqh06NTQxtBaVSfn50ZNbnZZLexBxJi0rJL2RBYTMsZJ1JVCV9j+50gi89dJBsyp/IOPI9yzgCF1y+9j7wUpDph+HD7j4fsSBjZtVoFtmvAveo6leD+30i8npV/ZcwGxe2siqHT4/Vb251bISnTuQYn6WUvOAWyLkMIRjoSvPBX72Qwd50y0vJt+uLfiHrTKIk6XssSyfoTieq8ygHT41axtF0vneLCy6p4N861QX54HgMAowttGyfRudg/lBVv1S5o6onReQPgX8JpVVNppU9SWq22t17LMe+EyOMFWYOJElf2DAw0RPJF8t87SeH3GK5lF9diHft1s2ctTzTwk80oV1f9AtZZ9JuCc+jK+2zLJ2YtjqBZRzN4OQ+13OplczCyafa0555sIWW7dVogJkuVSayiyx+cvAU3378KD85eKq6J0lullLySV+mVADetLKLNcunlpJ/zpreSG1Z264v+oWuM2m1pO/RFSx0nKvkjWUczaBvgxsWS9UE2sIo9K1vX5saZAst26vRILFLRP4M+Mvg/jtw62Ii6Ru7n+Fj9/xsynG/sifJpAWJ5/Q3vidJ1HbHa+cXfSvWmcxXOumTCep7pRPzSyO2jKMZvOw6N+eSx/VcCqNQzrvjEWcLLdur0QDzLuD/Az6LS1W+GxdkIukX1/SyaWU36/qzE7W2VkSnlHyzRfGLvpVEhGXpBL3ZxKLTiOO0xqNlzrsU+EiQRfaU67nEJIssbsOeIvIp4Cuq+oV2vb6IvAX4hqo+vdj3mzPAiIgfNOiXFvtmrfLa567hP523qulpyiY6RKQ69NUJNb4i77xLYxFQJrNhzwV5C/ATYNEBZs7TeVUtAWURWb7YNzNmMZK+R08myereDBtXdLG6N8OydMKCi5nRtvMH2X7FBQz2ZDg1WmCwJxOpdU0i8tsi8rCI/EhE/jE4vFVEvicie0TkDcHjREQ+LCI/EZEfi8iba17j+uDYj0Tk5uDYRSJyb/DaXxKR/mne+0YReSB4zduC93gDsAX4tIj8UESyIvJCEfm2iPxARL4uImsa/XyNDpGdAX4sIncD1cUgqvruRt9oKYhz6ZQotr2yHW532ied8Nu+2ZaJp6gOe4rIBbgF7C9T1WMiMgD8GbAGeAVwPnAn8AXg14CLgOcDK4EHRGRncOx1wIuDEl6VCeJ/AN6lqt8Wke3AHwK/N6kJf6Gq24O2/CNwuap+QUTeCbxPVXeJSBL4OPA6VT0aBLYPAm9r5DM2GmC+GFzMDOJcOiUqbfc9IZ0IqhAn3b4pS7TknVkaXgl8vrJoXVVPBL/v/6KqZeAREVkdPPYVwGeCEaXDIvJt4EXAfwY+qaq5mtdYDvSp6reD596OK0482S+JyO8DXcAAsBv410mPeTZwIXB30DYfONToB2x0Jf/tQS2y9ar6WKMvvpS0a0V9M7Sz7ZW5lGXpBF2pCAQUK4li2q+2zmMofxAikgH+CtiiqvtF5CZgusV8AuxW1Zcu5H0aSqkSkf8C/BC4K7h/kYjcuZA37FTJ0/vRRP3WunEpndLqtid9j76uFGuWZ6tzKd3pRDSCy9fe59Z81JZEefzu9rbLdKp7gDeKyAqAmuGt6XwHeLOI+CKyCtgK3I/L6H1rsGcXIjKgqqeAIRH5T8Fzfwv49qTXqwSTYyKyDHhDzc+GgZ7g9mPAKhF5afD6yWBoryGNDpHdBFwC7ABQ1R+KiKVh1Ihz6ZRWtD3heXSnfZZlFp9KHJqYl0Qx8aKqu0Xkg8C3RaQEPDTLw78EvBT4EW6pyO+r6jPAXSJyEW6tYh74N+APcNvcfyIIPHuAt05675Mi8je4bLFngAdqfvyp4LmjwXu+AfhYMPSWAD6KG06bU6PVlO9V1ZeIyEOqenFw7GFVfV4jb7JYUammPJt2VTVuhrDaLiJ0p1xQqV2HEFkffa7rudT2pFRh7CT83sNta5aJvSU7kdjoqsPdIvKbgC8i54rIx4Hvhdiu2KmsqC92D+KNn6TYPRiL4ALNbbubU0mwqifNhoEuBnsz8Qgu4OZcCqP1x2JSEsWYKGq0B9MFfAB4DS4afx34Y1VtSRchDj2YpazSU+lOJ8hGadHjfCfsa8vS15ZEea2VpTeLEpE/iNZrKMBUHyzSC6iqDofXpKkswERTOumzLJVgWSYRvTUqCw0W1aAUr5IoJtIi9sfROo3uB/Mi4O8JMgtE5BTwNlWNbMFL03yeCNmU24++O+WTiHJdt4VO2Me0JIoxUdTo4PjfAb+rqt8BEJFXAJ8EWjLJb9rH94TudGLGPVQiK8Z7mBjTKRoNMKVKcAFQ1f8QkWJIbTJtJCKkEx6ZpE826ZNJeu1fn7IQMd7DxJhO0WiA+baI3Ap8BpeD/WZgh4i8AEBVHwypfaZFUgmPnnQymvMpCxHjPUyizrYgjjdxZ4zfAT6oql8Ljr0RuFpVL2vqezWYRfatWX6sqjptPquI7MWtCi0BRVXdEqxW/SywEdgLvElVh2Z7f5vkD0dlH5WeTMyGvxplE/ZNV7sFcW35+yhVKI6gyJ2xiciFuPpkF+M6Gg8Bl6nqz5v6PvPJIpv3i7sAs6VSzC049ifACVW9WURuAPpV9frZXscCTHOlEq7sfY+Vujfz9Bu33TtlA69cvshgT4bPXPOSNrYs0hb1R7bxhq9eBrwf2AQ8CXx4782/cteiG+W+i0eA7uB6A66wZRK4SVW/HJSF+SSQwq2b/HVVfaLR92g0iywN/Dqu11F9TqXU8zy9DtgW3L4dV35m1gBjFk9E6E779GaSTeut2FDJ0tOKLYjt92pCEFz+ElcA8wSulP9fbrzhq+9oQpD5I+BB3EDyV4B7VPVtItIH3C8i/w78d+AWVf20iKRw1ZQb1mie6ZdxgaGIi3SVy1wU+EawUc01wbHVqlop9/wMsHq6J4rINSKyS0R2HT16tMFmmsmSvseK7jTrB7oY7Mk0NbjceOdujgyP0ZdNcmR4jBvv3M2OR4805fVNNK3r72K0UKo71swtiO33aor344JLJYLngvvvX+wLq+oIbrriH4FLgRtE5Ie4k/4MsB74PvAHInI9sEFVR6d/tek1Osm/doGTP69Q1YMiMojbT+DR2h+qqorItGN0qnobcBu4IbIFvPeS5YlLLQ5zbuXWnXtI+lIdKulKJcjli9y6c8+SPdtcCsLegth+r6bYhOu51MoFx5uhHFwEN/w1eTuWn4rIfcCvAP8mIteq6j2NvnijPZjvichzG33RClU9GFwfwVUDvQS3Wc4agOB6yZ6aNFs25TPYm2HDii5W9aRDnbjfP5SjWCqz5+gZHn3mNHuOnqFYKjd1qMRET9hbEO8fypGd9Hvb7CG4mHkStyFYra7geDN9HXhXkGGGiFSKGm8G9qjqx3AjWfNa+9hoD+YVwFtE5Elc90xwHZAZ30xEugFPVYeD268BtuO2AL0KuDm4/vJ8Gmzq+Z64CftMgmQLV9b3pBM8ceQMvif4nlAsKwdPjnHu4LKWtcG0R5hbEK/r75qSRNDMIbgY+jBuDgZcz6ULSAfHm+mPcWX4HxYRDxfALgfeBPyWiBRwUxr/Zz4v2miAee18XjSwGvhSEBATwD+r6l0i8gDwORG5GtiH+wCh80aPo14STaTBT7fiLUOVSfr0ZpN0t2kXyGr2YWXwUicdN2YBwh6Ci5u9N//KXRtv+Oo7CCGLDEBVb6q5e+00P78Z1xlYkFkDjIj0qupp3FqWeVHVPcDzpzl+HHjVfF9vsbz8GURd8QHFQxNpNJFB/Qzqp8GL/jqQSibY8myy7Zt2ncmXOKcvw7EzefKlMinf46xlaUbypbmfbMwMtp0/yHbcXMyBoRxrl3gWGbggQ7CbcNzM1YP5Z1w36Qe4c9TaU2UFYnlaIZSR4igUJxIiVBKon3KBx0ujfgr85Cyv0jpJ36Mnk6Ank4zMKvvKUMbmVRNDYpX1EMYsRphDcKa1Zg0wqnp5cPO7uD2dv6Oqj87ylNgSLSLFIhQnJhMVzwUdP+0Cj58GP9Wa9ojQlfLpiehukDaUYVrB1sTE23yqKf8n4OMi8gu4xTnfUdVbQmtZBAhlpDQGpTG3FAlQ8d3QWiJLOZFtesBJeJXeSiLS5fBtKMOErbYsTe2amO1gv2cx0XCpGBHxgRcBv4Rb3TmqqueH2LaqZpSKSZzaV52DaSaVBJrIUE5kF5VA0JVK0JuNZm/FmHbooLI00RjXboNGS8V8E1ev5vu4KpwvCta2LHmiRaRwBq9wBgBFgiG1bHDJwAxZXi3vrcx3C2Fj2qgVZWlMuBr9VnsYN0h0IW6hzYUikg2tVTEmKF5pDH98iMTI0yROPYk/fBAvdwzJD0MpT1cqwereDOsGsvR3p1oXXL72PrdHSqbfXX/tfe64MREUdlmapUxEVET+tOb++0Tkpma/T0PfbKr6HlXdCvwacBxXXfNksxvTiSoBJ1UYpr98knXeMc4qHqR77DCSOw5jp6E4Hn5DarcQFnHXXsod7xSP3w2fuhw++lx3bcEz1q7duplCScnli6i669ATSVShVITCGIwPw+gQjByHM0ch31E9p3Hg10RkZZhv0ugQ2Ttxk/wvxO3h8ve4oTIzK5cJ1pXyySZr/lerug2wCjV140QgkXFJA4mUm8tJpGccXpu3MLYQjtKQW6WH5qXqe2h8xIYBY6opiSSqUCpAuQDlImgZymXQEpRL7n7d7VnmpP0EU6u2tMBNy6eU6+emU4tdF1PE1Xp8D/CB2h+IyEbcd/xK4CjwVlVd0BdFozPKGeDPgB+ohjBT3mESnseydIKudAK/0QAxU9Dxk5DIumCQzC58QWiztxCO2hd6bQ8N3HU+OG4BprlaeGIx65qYciUwlFzwqASJctEFlVLeHYszF1ymlOvnpuXvaEKQ+UtcaZg/mXT848Dtqnq7iLwN+Bjw+oW8QUMBRlU/spAXX1qE3sP3s+yRf8Yffhp6z4aLr4KNL1/4S6pCMe8uY6fcMT/pejqJYE2OnwQvMXdPp9lbCEftCz2MHpqZKswTC9UgaBQnLlquDxyN9jY6x3Tl+ivHFxVgVPW0iPwD8G6gtgz/S3HTIeBK+U8OQA3ryJzYHY8e4a92/Jx9J0ZY05vlyhet42UrwnmvhOfRnU7Qfeg+/Pv/DLwkpJe7cdudHwKuX1yQmaxUcJfxSdV7PN8FGj/pAo+XqAlAfvDH/5HmbSEctS/0ZvfQzPQaPbEolyeGn7Q8KVCUJgJFXdBYEgFjvsIu1/9R3LrGTzbp9ep0XICpLM7yBXozCY6PjHPLPU/Q/ZIMF61tXqXfTDLBsnTN3MoP/8EFl2RQKiWZgQLw0O3NDTAzqQwXTJcw4HmubWueC2+8fSIIJRZR9DNqX+jN7qEtRdWgMMvlxB53AlUqBE9SdwJzfA+ceHIp9Sxa5UncsFhthkHTyvWr6gkR+RxwNW7eBeB7wJW43st/ZRHz7R0XYCobFqUTPsVSuVrG5CsPH+Kitecu6rV9CXoraZ+ENykB7/TTgA8jT7k/Pj8JmQE4fWja12qpchnK49MHHz8xkVBQWSjqN/BrEbUv9Gb30OJs8lBT7bBSbe+hLng0GBR6znK982RNzbnCGPSeFf/5jmhqRbn+PwXeWXP/XcAnReT9BJP8C33hjgswlcVZ5Zq/l0zS4+iZsZmfNIdMMkF3yp99lX2yG4aeBPHcfEipAGcOQX+zerIhKRXdJV+zA7aI+xyVnk7tsJv4LgBF8Qv9vEs7P6BMnqOYcr/oTijCcvFVbui3gJsLLI65DK2LrwrvPZeym07dxU3Lp5TrX+wEv6ouq7l9mJr0OFXdB7xyMa9f0XEBplLlt7ac/VihzOZl86vy67YdTtKd9klO7q1Mp26SXahukNKGvVoWTXXiLLcwTWAWcQHn7OfDlZ+eSDpYisrTzDPMZLqehHguaFeyA1UBrZ+ziNLE9saXA9e7od/Th6B3zeKTWczsXDDpyHL9sVOp8lsuF0n4wlihTLGsXP68NQ09P5306U4l6Er5yHxKCOXPQM8aGD0xMUSWHajvGXQKVTfcVjvkJhJcgi9LLzmRYFD9Ek0El+gW8QQmBYHKF3zQ05vce1iKNr7cAoppSMcFmMrirL/a8XOeOjHCWUEW2fNXDMMMS3gEoSudYFk6QWqhZVt6z3Zj07WT3IUx6A4pfS1qVIOz63IwATzLkKTnAcEwnHgTGXBSOT4psItMHK+ewdecydee1dfOMUxtJNW6g9XX0okeW2URnjGmKTouwIALMls2DdRVU+bU1E05E57HskyC7lQCb7FDWUthbHrvd4OhkUWu86l+iS/RHoAxS0RHBpjJ1v3jS1E/TSm7glLXKmTZalLLV5PsPQuWDUL3IKR7Fjdf0ulj03u/6wJo2Ot8jDEdo+MDjOSHSZ4OFv8NPTHzAxNpF2i6B2HZKli2GrpXBfcHg9sr3VDOTDp5bPqh29u7zscYEzsdHWBEhO5Mhvzr/obU8UfcwsCRI64y6shhGDk6MVZfHIdT+91lxhf03MT9stUuCHUHgWfZ4ERPqHuwfvFhpzj9tOu51EpkorHOxxgTSR0bYDIJj/UDXfheN6x4k1tlPHniV8uuHPeZI0HgOTJxe+ToxO1KJpiWIXfMXWbbbi3VXdPzGZwUgIKAlO0PJrVjopLEULvArjjmhgKNMWYaHRtgpt3Ea6ZJ6q4VwC/O/GL5nOvxnDk6EYhGjsKZwxPXuRNUM5vyI5B/0i28nImXqBmCWwXdNb2i6pDcquisL1kKSQzAfXuOc8eu/TxzapSzlme5css6Xrx5iWQCGtNkou1euNWALVu26K5duxb3Ig/+E+z4P24eofYLcmuTJqlLBcgdr+/5VIJPbWAqzXNzsUxffU+o0gOq7SEtNkEBGssQqz6mA5MYcMHllnueqJYaGi+WKJSU6155rgUZ45YcZPvnftxUMVxt3RxLJ8D8zatg5NjUGkrdK+BXb1vcazdKFcZP1w/J1QakynWlNH+jEpn63tDkBIVlg66XNlOCQm2GWBjBNybe87kfcmJknEzN5nBjhSID3Wn+/E0Xta9hJhoswMxbxw6RTXH6aUj31h9r9SS1CGSWu8vKWQpvFsdd4Kn2gCYNyVV6R9UEhbF5JChMMx/00D+50b1Eyv0pLNEMsWdOjdKTSdYdSyd8Dp8aneEZxpjZLJ0A03v21B5MVCepE2lYvtZdZjJdgsJ0vaFpExQemf41hwlW1gclXc4chnv/Kv4JCg06a3mWc04+yK+U/p2V5WMc81byVf/VHOx7QbubZkwsLZ0A88K3ujmYTpmkFs8NezWUoHBkauCp7RXljk88Xstuq9lS3t3f9XdTX9NLuDVB1cSESQkKlbkiP9XUjzxv86w8cM36QwwcuYMCPiPSRU9piN8q3cGJ9WuAi1rWbGM6xdIJMBtf7uYUOniSelqpLkhthP6N9ccrX75+Cgae5YJNMusCV3HMBZiVzwZ0Yu1QJUGhXIThZ9xlNtUEhcnBp8kJCtNZQOWBXzz0JXLdXRwf9ygVy/iJLD3pMqsPfQl4bfPbaEyHWzoBBjp7pf18TP7yLY65XklqGRRyMLBpavCtS1CoTdGeJUFh7KS7HHt85rYkMjWp2oPTB6TZEhRmspDKA6efpiu7nK7adbKKLSY1ZoGWVoAJW7OKQYZtui9fgOxy+M3PTv+chhMUxoLAM8OaoWkTFJ5yl5lMl6BQva5UVJhUQWEhlQdsMakxTWUBplniVAwyzLIviQwsX+cuM5mzgkKwdqgwjwQFcBUUKinapTwMH3LBwku4f5dyyW35O5Mlspg0kuJycmbmxQJMs8SpGGS7z9QbTlAYqakdN1OCwqQKCif2uEvF+KQ1RcNPw+2Xz1xB4YVXw2P/6urWLZV5unaL08mZmRcLMM0Sp2KQcTlTT3VDahP0b5r5MZUKCtUeUE1AOvYztzaotnqCllzPZniOf5dMH4wPw48/B0/uCK+CgonXyZmZFwswzdLuXsF8dNLeNX7SDXvNNvQ1XYLCdEVN552gULvFQxMTFJaaOJ2cmXmx3/xmiUuvoGIpZdSFlqDQ4BYPXSsmJSSsmrTlQ4du8dCoOJ2cmXkJPcCIiA/sAg6q6uUisgm4A1gB/AD4LVXNh92O0HVSr2Cpmm+CwpnD9UNyM23xUCn7s6gtHlZDtq8jKyjE7uTMNCz0Ypci8l5gC9AbBJjPAV9U1TtE5BPAj1T1r2d7jaYUu5xuPxhjwjJbgkLldm2CQiOmbPFQ2xuqKeXT7goKCxGHSt1W7HLeQg0wIrIWuB34IPBe4L8AR4GzVLUoIi8FblLV/2e217EAYzpS3RYPk3pDlV1XaysoNCrTN5GuPXmLh0pAsgSF+bMAM29hD5F9FPh9oCe4vwI4qarF4P4B4Jzpnigi1wDXAKxfv745rZlvrn3lD3CmIOz5bpK5UhxSvOA5UvPHG9wWz93W8vSXcsldT3lsqeb91d3Wcs3tNm63YGsXFqcuQeG50z9mIVs8VBMUHpv5vRPpaTa5m7TnkCUomEUK7bdHRC4HjqjqD0Rk23yfr6q3AbeB68EsukH77oXv/Al4aciugNFT8B9/6s7mznuN+2Kv/bL2Jo11V77YK70gPwlP/Dt87xY4uQ/6NsDLroPzLl10U+el0q5KcFMNglWp5roI5XLN7eBaywsPUHu/C9/8I8ifca+XO+Huv+oPLcg0U2hbPIzPo4LC6klDcpPStZdygoKZVZinJy8HrhCRXwYyQC9wC9AnIomgF7MWOBhiGyb86NOQ6Jr4Y/CXuUrD9/4FnH+ZOzbbkIEIiO96LQCP3w1fex94Kcj0u4V5X/5d90c3frp1AafSrtr7foJ5/dNODkq18wLVIBQ8phKc7v0Ld5Ysnvt/omV3//sfj3WAie2WyY1u8ZA7MRFwandbnSlBoVpBYZb3Ti2buahph2/xYGYXWoBR1f8J/E+AoAfzPlX9ryLyeeANuEyyq4Avh9WGOif3uUBQK5mFk7Ocwc3me7e44FIJWOUCjJ5wi/NWnucCztfeB3yk9b2a+VpIUBraFwTc4EtDfCgFx1PdQcn/QhitDU3tlsk9mSQnRsa55Z4nuA7iEWTmIl6wzcJK5qygMHlIbtYKCmfgxJn6CgqTTUlQiOgWD6ap2jHAej1wh4j8b+AhYJoNR0LQt8F96dd25wuj0LfA+Z3JAWvkqPuS1ZL7wk51QR4XiKIeYBZCgv/U9vo8zx2vrF9QdUGmXKgfmisXg+PF9s4hTXLHrv0kfalumeyui9yxa39nBJhGpbpdRe2BBiooTE5QqB2Sm7LFQ4MVFKrzQKunH5KzBIXYaEmAUdUdwI7g9h7gkla8b52XXed6FHlcz6UwCuW8O74QkwNWKT+x7XDFYnpIUTfwLDeJXGZi7kdLsOLZE48RCf5/zHJWWi65dQ/FcXcpF9yXVxsCj22ZPA+hJygsYIuH2oBkCQqRsHT+Bc67FPhIMCn/lOu5LGaOZHLAEh+04H65KxbTQ4q6V/8R/N+rIT88kWSQ6nHH58Pzg5pj3fXHK/NC1R5Pvv7+YpMUpnHW8iwnRsarPRiA8WKJ1cuzTXuPJWUhFRTqekBHZkhQmM8WD5N3Xa1N2bYEhbAtnQADLpg0a7hqcsAa2Awjx0AS7ktvsT2kOEikgy/+ojtbTKSb99qVeSF/jl/Rcsn1eCq7cFaH5ErzDj5XblnHLfc8ARRJJ3zGiyUKJeXKLbOs7DeL12gFhboEhcnVtY/MssXDLO9du8XD5N5QEIjuO1Tijh8c5NHTKfoGBrl262a2nT84y4uaitBX8jfDohZaPn5361KJq+/VhB5S1H3q8qlzWvkc9KyGt3ylfe2qNbkHVMxPDMHNsOi2kkV2+NQoq+OURWac2goK0/WGzhxxc0fzqKBQxGdIlnPCW8k3E6/gDv8Ktl9xwXyCzJKdMOrsHsx0qcQtyeyKftBetGZn5YXBq6SVp4FphuCK4xPzP6VxKBV48eYVFlDibD5bPFTngw7PmqCQoMQqPcGq0gkeTf4iSV+4dece68U0oLMDzORU4jAzu9oWzNqk2Vl5rSbiqvfWVvBVDYbZgqG2SgCyEkOdZR5bPPz+7XdzTvIM/XqSrI7y4+TzySZ9DgzlWtfeGOvsANPKs+xWBrMoaHZWXhSIuHmkyXNJ5VJdL6cahMrl9rTThC9IUCj0P4sHR8bJpBKckl5GvB5G80XW9ltyQCM6e2lt3wb3xVcrrLPsk/vcF22tqA0ZNdN5l8JrP+LmXMZOuuvXdmhvzfPdCUO23038Ll/rkjr6N7o1P10D7ueeP+dLmXi5css6CiVlrFBEFXL5IoWScu3Wze1uWix0dg+mlWfZcR8yWohmZuXFUSXLrTbFulwKEgrG669jkExjpnrx5hVch1uE++jpAoMD/ZZFNg+dHWCavfZlNp04ZGTmz/PBy07tzRbzboitmA8SC8Ys6MRENfFj4eX6l6zODjDQurPsVgYzE56w0toTKXepnd6pBJ3KvE5h1BIKTEfp/HUwxjSqNhOwthfayrmlYh6Ko+69CzlLJIgS23Bs3jq/B2NMo6KQCVjp6WSWu/vFvAs0xTELOCZ2LMAYUxHFxaOVgFNRHHe9m9q06RiMQpilyQKMMRVxyAScbp1OqaYcTmHUDbGVitM/3zRmuu3AL7ii3a2Knc5eB2PMfLzsOjfnks+5XkE+F49MQD/hKhJket16pP6N0L/BrdlJ99j6nPna+13Y+SEYOQ7p5e5654fg5zva3bLYsR5MA3Y8eoRbd+5h/1COdf1dlgffqTopE9BPukum190vFdwcTmHUstXm8tDt4CUnygglM1AA7vsEXPirbW1a3FiAqZghPXXHo0e48c7dJH2hL5vkyPAYN965m+1gQaaVlapbpVMXj/pJ8Jdb8kAjTj/tei61Ehk4daA97YkxGyKDifTU4cP1hSofv5tbd+4h6QtdqQQi7rpSTXVJm+X/mYmBRAqyfa7g48Bm11vrXumG1PzknE/vaL1nu8BbqzjmSgSZebEAA/XpqSJBXakUfO8W9g/lyCbrx7Ctmiqz/j+LjMfvdvvWfPS57tqC38yqAWe1m78Z2BTM4SwDb4l9TVx8lds3qDDmdt4ojLn7L/7v7W5Z7Cyx35wZzFKocl1/F6OF+vHq0ULJqqlGvbin9bAWx/ODpIGaHs6yVe5Yp/dwNr4ctl7vFlaOn3bXW6+HX9jW7pbFjs3BwKzpqde+ZDM33rmbXL5INukzWihZNVWIfkpvFBZNdpLJ63HK5YmaaqWgvlonpUZvfLm7mEWxHgzMmp667fxBtl9xAYM9GU6NFhjsycx3u9TOFPWU3qj3sOLs8bvhH66Av3oxfO634dCPXWr0wGZYfo474091L72hNTOF9WBgzvTUbecPWkCZLOopvVHvYcXVXDu3VipJV2J7XW01S49eaqzYpelMUShc2Yk+dfnUwJ3PueSAt3xl7ueXCkG1gTF3XSqE19Zms2KX82Y9GNOZot7DiqvF1murLAAlWABaLgVzOeMTgScGJ72mMRZgTOfq1EWT7dTsoUfPd/M1tbuCFsYmqg5YwIk1m4UzxjSuFckdyQx0DbiEgYHNbuFjtq8+i83EgvVgzMJ1YqkYM7tWDz1WFvFWekzlUtC7GXPJA8V8OO9rmsIm+c3C2CS6iYJqwAl2AA1zLY5N8s+b9WDMwthCRhMFnu/qp6V73P1q1ehKEU9Li24nCzBmYaK4+6Mxk6tGW8BpKwswZmFsIaOJgynbFIxDfiTYqmDcMtRCZllkZmGiXirGmOkk0kGG2lro3+QWiNqun6GxHoxZGFvIaOLO8+rnb6x303QWYMzC2UJG00kSaXdhIKgWPRrM3YxYOvQCWYAxxpjJPK+mwsCKIP3ZejTzFdocjIhkROR+EfmRiOwWkT8Kjm8SkftE5Gci8lkRseW5xpho8xOdv9FaCMKc5B8HXqmqzwcuAi4TkZcAHwL+XFWfBQwBV4fYBmOMMW0SWoBR50xwNxlcFHgl8IXg+O3A68Nqw4LZXu7GGLNooaYpi4gvIj8EjgB3Az8HTqpqpZ7DAeCcMNswb7aXuzHGNEWoAUZVS6p6EbAWuAQ4v9Hnisg1IrJLRHYdPXo0rCZOVVsCpVJoz0u548YYYxrWkoWWqnoS+BbwUqBPRCrZa2uBgzM85zZV3aKqW1atWtWKZjq2l7sxxjRFmFlkq0SkL7idBS4FfooLNG8IHnYV8OWw2rAgfRtcyZNaVgLFGGPmLcwezBrgWyLyMPAAcLeqfgW4HniviPwMWAH8XYhtmD8rgWKMMU0R2kJLVX0YuHia43tw8zHRZCVQjDGmKWwl/3SsBMrSZbt0GtM0Vk3ZmApLUTemqSzAGFNhKerGNJUFGGMqLEXdmKayOZhmsvH7eLNdOo1pKuvBNIuN38efpagb01QWYJrFxu/j77xL4bUfcdvojp1016/9iPVCjVkgGyJrlpP7XM+llo3fx4+lqBvTNNaDaRYrMWOMMXUswDSLjd8bY0wdCzDNYuP3xhhTx+ZgmsnG740xpsp6MMYYY0JhAcYYY0woLMAYY4wJhQUYY4wxobBJfrNwVnvNGDML68GYhbHaa8aYOViAMQtjtdeMMXOwAGMWxvZOMcbMwQKMWRirvWaMmYMFGLMwnVp77fG74VOXw0ef665tTsmYBbMAYxamE2uvWeKCMU1lacpm4Tqt9lpt4gK463xwvJM+pzEtYj0YYyosccGYprIAY0yFJS4Y01QWYIyp6NTEBbM4lvixYBZgjKnoxMQFsziW+LEoNslvTK1OS1wwi2OJH4tiPRhjjJmJJX4sigUYY4yZiSV+LIoFGGOMmYklfiyKBRhjjJmJJX4sik3yG2PMbCzxY8GsB2OMMSYUFmCMMcaEwgKMMcaYUIQWYERknYh8S0QeEZHdInJdcHxARO4WkSeC6/6w2mCMMaZ9wuzBFIH/oarPAV4CvENEngPcAHxTVc8FvhncN8YY02FCCzCqekhVHwxuDwM/Bc4BXgfcHjzsduD1YbXBGGNM+7RkDkZENgIXA/cBq1X1UPCjZ4DVMzznGhHZJSK7jh492opmGmOMaaLQA4yILAP+L/B7qnq69meqqoBO9zxVvU1Vt6jqllWrVoXdTGOMMU0WaoARkSQuuHxaVb8YHD4sImuCn68BjoTZBmOMMe0hrhMRwguLCG6O5YSq/l7N8Q8Dx1X1ZhG5ARhQ1d+f47WOAvsafOuVwLGFtTo27DN2jqXwOZf6Zzymqpe1sjFREWaAeQXwHeDHQDk4/Ae4eZjPAetxQeNNqnqiie+7S1W3NOv1osg+Y+dYCp/TPuPSFVotMlX9D0Bm+PGrwnpfY4wx0WAr+Y0xxoSiEwPMbe1uQAvYZ+wcS+Fz2mdcokKbgzHGGLO0dWIPxhhjTARYgDHGGBOKWAYYEblMRB4TkZ8Fa2km/zwtIp8Nfn5fUKomdhr4nO8NqlU/LCLfFJEN7WjnYsz1GWse9+sioiISu1TQRj6jiLyppvL4P7e6jc3QwO/r+qDC+kPB7+wvt6OdiyEify8iR0TkJzP8XETkY8H/g4dF5AWtbmOkqGqsLoAP/BzYDKSAHwHPmfSY3wU+Edy+Evhsu9sd0uf8JaAruP32uH3ORj5j8LgeYCdwL7Cl3e0O4d/xXOAhoD+4P9judof0OW8D3h7cfg6wt93tXsDn3Aq8APjJDD//ZeBruCUaLwHua3eb23mJYw/mEuBnqrpHVfPAHbgKzbVqKzZ/AXhVUFkgTub8nKr6LVXNBXfvBda2uI2L1ci/JcAfAx8CxlrZuCZp5DP+v8BfquoQgKrGsXxSI59Tgd7g9nLg6Ra2rylUdScw28Lw1wH/oM69QF+lNNZSFMcAcw6wv+b+geDYtI9R1SJwCljRktY1TyOfs9bVuDOnOJnzMwZDDOtU9autbFgTNfLveB5wnoh8V0TuFZE4lhVp5HPeBPw3ETkA/BvwrtY0raXm+3fb0UJbyW9aR0T+G7AF+M/tbksziYgH/BnwljY3JWwJ3DDZNlwvdKeIPFdVT7azUSH4DeBTqvqnIvJS4B9F5EJVLc/1RBNPcezBHATW1dxfGxyb9jEiksB1x4+3pHXN08jnREReDXwAuEJVx1vUtmaZ6zP2ABcCO0RkL25M+86YTfQ38u94ALhTVQuq+iTwOC7gxEkjn/NqXB1CVPX7QAZXJLKTNPR3u1TEMcA8AJwrIptEJIWbxL9z0mPuBK4Kbr8BuEeDGbgYmfNzisjFwK244BLHcftZP6OqnlLVlaq6UVU34uaZrlDVXe1p7oI08vv6L7jeCyKyEjdktqeFbWyGRj7nUwR1CEXkF3EBptN2E7wT+O0gm+wlwCmd2GBxyYndEJmqFkXkncDXcZkrf6+qu0VkO7BLVe8E/g7X/f4ZbkLuyva1eGEa/JwfBpYBnw9yGJ5S1Sva1uh5avAzxlqDn/HrwGtE5BGgBLxfVWPV427wc/4P4G9E5D24Cf+3xO3ET0Q+gzsZWBnMJf0hkARQ1U/g5pZ+GfgZkAPe2p6WRoOVijHGGBOKOA6RGWOMiQELMMYYY0JhAcYYY0woLMAYY4wJhQUYY4wxobAAY2JFRLYHi0tjS0T+oN1tMKYVLE3ZxIaI+Kpaivt7i8gZVV3WjNcyJsqsB2MiQUQ2isijIvJpEfmpiHxBRLpEZK+IfEhEHgTeKCKfEpE3BM95kYh8T0R+JCL3i0iPiPgi8mEReSDYj+Pa4LHbRGSniHw12LPkE0GtM0TkNSLyfRF5UEQ+LyLLguOT3/uy4DE/EpFvBo/pDvYIuT/Y5+R1wfG3iMgXReQuEXlCRP4kOH4zkBWRH4rIp1v+P9qYFordSn7T0Z4NXK2q3xWRv8ft6wNwXFVfAG5Tq+A6BXwWeLOqPiAivcAort7VKVV9kYikge+KyDeC17kEtw/JPuAu4NdEZAfwv4BXq+qIiFwPvBfYXvveIrIKeBDYqqpPishA8PMP4EoRvU1E+oD7ReTfg59dBFwMjAOPicjHVfUGEXmnql7UvP9txkSTBRgTJftV9bvB7X8C3h3c/uw0j302cEhVHwBQ1dPgeiPA8yq9HFyh03OBPHC/qu4JHvcZ4BW4PWaegwtE4DbL+n7N+1Te+yXAzqAYJapa2RPkNcAVIvK+4H4GWB/c/qaqngre7xFgA/Wl3I3paBZgTJRMnhCs3B+Zx2sI8C5V/XrdQZFtM7y+AHer6m/M8HpzvbcAv66qj016vxfjei4VJezvzSwxNgdjomR9sE8IwG8C/zHLYx8D1ojIiwCC+ZcErtji20UkGRw/T0S6g+dcElT79YA3B69/L/ByEXlW8PhuETlvmve7F9gqIpuCx1WGyL4OvEuC7o+4CtdzKVTaZ0wnswBjouQx4B0i8lOgH/jrmR4YbMv7ZuDjIvIj4G7c8NTfAo8AD4rIT3DbGVR6Dg8AfwH8FHgS+JKqHsVtaPYZEXkYNzx2/jTvdxS4Bvhi8H6VobM/xlXTfVhEdgf353Jb8Hib5DcdzdKUTSSIyEbgK6p6YUivvw14n6peHsbrG2Omsh6MMcaYUFgPxhhjTCisB2OMMSYUFmCMMcaEwgKMMcaYUFiAMcYYEwoLMMYYY0Lx/wMtcwsavSkGKwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 416.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": "11bddc88",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-15T09:31:30.683295Z",
     "iopub.status.busy": "2023-01-15T09:31:30.682886Z",
     "iopub.status.idle": "2023-01-15T09:31:30.688084Z",
     "shell.execute_reply": "2023-01-15T09:31:30.687027Z"
    },
    "papermill": {
     "duration": 0.021742,
     "end_time": "2023-01-15T09:31:30.690458",
     "exception": false,
     "start_time": "2023-01-15T09:31:30.668716",
     "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": "9fb0b8dd",
   "metadata": {
    "papermill": {
     "duration": 0.012174,
     "end_time": "2023-01-15T09:31:30.715077",
     "exception": false,
     "start_time": "2023-01-15T09:31:30.702903",
     "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": "40272145",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-15T09:31:30.742110Z",
     "iopub.status.busy": "2023-01-15T09:31:30.741657Z",
     "iopub.status.idle": "2023-01-15T09:31:30.747343Z",
     "shell.execute_reply": "2023-01-15T09:31:30.745940Z"
    },
    "papermill": {
     "duration": 0.022295,
     "end_time": "2023-01-15T09:31:30.749985",
     "exception": false,
     "start_time": "2023-01-15T09:31:30.727690",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#step_6.b.hint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b9f5e969",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-15T09:31:30.776985Z",
     "iopub.status.busy": "2023-01-15T09:31:30.776600Z",
     "iopub.status.idle": "2023-01-15T09:31:30.785761Z",
     "shell.execute_reply": "2023-01-15T09:31:30.784383Z"
    },
    "papermill": {
     "duration": 0.02545,
     "end_time": "2023-01-15T09:31:30.788047",
     "exception": false,
     "start_time": "2023-01-15T09:31:30.762597",
     "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": "f945ac4e",
   "metadata": {
    "papermill": {
     "duration": 0.012248,
     "end_time": "2023-01-15T09:31:30.812983",
     "exception": false,
     "start_time": "2023-01-15T09:31:30.800735",
     "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": "6b122405",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-15T09:31:30.839931Z",
     "iopub.status.busy": "2023-01-15T09:31:30.839478Z",
     "iopub.status.idle": "2023-01-15T09:31:31.076081Z",
     "shell.execute_reply": "2023-01-15T09:31:31.074783Z"
    },
    "papermill": {
     "duration": 0.252958,
     "end_time": "2023-01-15T09:31:31.078646",
     "exception": false,
     "start_time": "2023-01-15T09:31:30.825688",
     "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 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot showing the relationship between 'chocolate' and 'winpercent'\n",
    "sns.swarmplot(x=candy_data['chocolate'], y=candy_data['winpercent'])\n",
    " # Your code here\n",
    "\n",
    "# Check your answer\n",
    "step_7.a.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "256a0e4f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-15T09:31:31.107683Z",
     "iopub.status.busy": "2023-01-15T09:31:31.107232Z",
     "iopub.status.idle": "2023-01-15T09:31:31.112102Z",
     "shell.execute_reply": "2023-01-15T09:31:31.110846Z"
    },
    "papermill": {
     "duration": 0.021693,
     "end_time": "2023-01-15T09:31:31.114308",
     "exception": false,
     "start_time": "2023-01-15T09:31:31.092615",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "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": "19d33903",
   "metadata": {
    "papermill": {
     "duration": 0.013525,
     "end_time": "2023-01-15T09:31:31.141244",
     "exception": false,
     "start_time": "2023-01-15T09:31:31.127719",
     "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": "ac5ae27a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-15T09:31:31.169676Z",
     "iopub.status.busy": "2023-01-15T09:31:31.169236Z",
     "iopub.status.idle": "2023-01-15T09:31:31.174344Z",
     "shell.execute_reply": "2023-01-15T09:31:31.173079Z"
    },
    "papermill": {
     "duration": 0.022059,
     "end_time": "2023-01-15T09:31:31.176758",
     "exception": false,
     "start_time": "2023-01-15T09:31:31.154699",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#step_7.b.hint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5c394eb1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-01-15T09:31:31.205107Z",
     "iopub.status.busy": "2023-01-15T09:31:31.204676Z",
     "iopub.status.idle": "2023-01-15T09:31:31.213319Z",
     "shell.execute_reply": "2023-01-15T09:31:31.212218Z"
    },
    "papermill": {
     "duration": 0.02557,
     "end_time": "2023-01-15T09:31:31.215621",
     "exception": false,
     "start_time": "2023-01-15T09:31:31.190051",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/javascript": "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"interactionType\": 3, \"questionType\": 4, \"questionId\": \"7.2_ThinkSwarm\", \"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> 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."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check your answer (Run this code cell to receive credit!)\n",
    "step_7.b.solution()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
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   "default_parameters": {},
   "duration": 13.788,
   "end_time": "2023-01-15T09:31:32.006692",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2023-01-15T09:31:18.218692",
   "version": "2.3.4"
  }
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