data_visualization/04-scatter-plots.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[Data Visualization: From Non-Coder to Coder Home Page](https://www.kaggle.com/learn/data-visualization-from-non-coder-to-coder)**\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup Complete\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "print(\"Setup Complete\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The questions below will give you feedback on your work. Run the following cell to set up our feedback system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup Complete\n"
     ]
    }
   ],
   "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\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Load the Data\n",
    "\n",
    "Read the candy data file into `candy_data`.  Use the `\"id\"` column to label the rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.3333333333333333, \"interactionType\": 1, \"questionType\": 1, \"questionId\": \"1_LoadData\", \"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"
    }
   ],
   "source": [
    "# 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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lines below will give you a hint or solution code\n",
    "# step_1.hint()\n",
    "# step_1.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Review the data\n",
    "\n",
    "Use a Python command to print the first five rows of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </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",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the first five rows of the data\n",
    "candy_data.head() # Your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.3333333333333333, \"interactionType\": 1, \"questionType\": 1, \"questionId\": \"2_ReviewData\", \"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"
    }
   ],
   "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lines below will give you a hint or solution code\n",
    "# step_2.hint()\n",
    "# step_2.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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!_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.3333333333333333, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"3.1_PlotBlueScatter\", \"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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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot showing the relationship between '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,
   "metadata": {},
   "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",
   "metadata": {},
   "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?\n",
    "\n",
    "- **My answer:** there's a **weak correlation** between these attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# step_3.b.hint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "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": [
    "step_3.b.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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,
   "metadata": {},
   "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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\n",
      "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",
    "sns.regplot(x=candy_data['sugarpercent'], y=candy_data['winpercent']) # Your code here\n",
    "\n",
    "# Check your answer\n",
    "step_4.a.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "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",
   "metadata": {},
   "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?\n",
    "\n",
    "- **My answer:** It means that people tend to prefer candies with more sugar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# step_4.b.hint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "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": [
    "step_4.b.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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,
   "metadata": {},
   "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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\n",
      "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='pricepercent', y='winpercent', hue='chocolate', data=candy_data) # Your code here\n",
    "\n",
    "# Check your answer\n",
    "step_5.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lines below will give you a hint or solution code\n",
    "# step_5.hint()\n",
    "# step_5.solution_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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,
   "metadata": {},
   "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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\n",
      "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,
   "metadata": {},
   "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",
   "metadata": {},
   "source": [
    "#### Part B\n",
    "\n",
    "Using the regression lines, what conclusions can you draw about the effects of chocolate and price on candy popularity?\n",
    "\n",
    "- **My answer:** This chart shows that when candies contain chocolate, they tend to be more popular the more it costs. In cotrast with candies that do not contain chocolate, these are more popular the cheaper they are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# step_6.b.hint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "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": [
    "step_6.b.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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,
   "metadata": {},
   "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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\n",
      "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=\"chocolate\", y=\"winpercent\", data=candy_data) # Your code here\n",
    "\n",
    "# Check your answer\n",
    "step_7.a.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "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",
   "metadata": {},
   "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**?\n",
    "\n",
    "- **My answer:** The plot from the **Step 7** is more appropiate, It contains less noising information to tell this story."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# step_7.b.hint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "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": [
    "step_7.b.solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Keep going\n",
    "\n",
    "Explore **[histograms and density plots](https://www.kaggle.com/alexisbcook/distributions)**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "**[Data Visualization: From Non-Coder to Coder Home Page](https://www.kaggle.com/learn/data-visualization-from-non-coder-to-coder)**\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "*Have questions or comments? Visit the [Learn Discussion forum](https://www.kaggle.com/learn-forum) to chat with other Learners.*"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}