Exercise 4-scatter-plots.ipynb

{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.7.9","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"**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","metadata":{}},{"cell_type":"markdown","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\nYou 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\nRun the next cell to import and configure the Python libraries that you need to complete the exercise.","metadata":{}},{"cell_type":"code","source":"import pandas as pd\npd.plotting.register_matplotlib_converters()\nimport matplotlib.pyplot as plt\n%matplotlib inline\nimport seaborn as sns\nprint(\"Setup Complete\")","metadata":{"execution":{"iopub.status.busy":"2021-06-18T08:22:19.188528Z","iopub.execute_input":"2021-06-18T08:22:19.188853Z","iopub.status.idle":"2021-06-18T08:22:19.195536Z","shell.execute_reply.started":"2021-06-18T08:22:19.188826Z","shell.execute_reply":"2021-06-18T08:22:19.194557Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stdout","text":"Setup Complete\n","output_type":"stream"}]},{"cell_type":"markdown","source":"The questions below will give you feedback on your work. Run the following cell to set up our feedback system.","metadata":{}},{"cell_type":"code","source":"# Set up code checking\nimport os\nif not os.path.exists(\"../input/candy.csv\"):\n    os.symlink(\"../input/data-for-datavis/candy.csv\", \"../input/candy.csv\") \nfrom learntools.core import binder\nbinder.bind(globals())\nfrom learntools.data_viz_to_coder.ex4 import *\nprint(\"Setup Complete\")","metadata":{"execution":{"iopub.status.busy":"2021-06-18T08:22:19.200966Z","iopub.execute_input":"2021-06-18T08:22:19.201300Z","iopub.status.idle":"2021-06-18T08:22:19.219161Z","shell.execute_reply.started":"2021-06-18T08:22:19.201270Z","shell.execute_reply":"2021-06-18T08:22:19.218294Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"Setup Complete\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Step 1: Load the Data\n\nRead the candy data file into `candy_data`.  Use the `\"id\"` column to label the rows.","metadata":{}},{"cell_type":"code","source":"# Path of the file to read\ncandy_filepath = \"../input/candy.csv\"\n\n# Fill in the line below to read the file into a variable candy_data\ncandy_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\nstep_1.check()","metadata":{"execution":{"iopub.status.busy":"2021-06-18T08:22:19.220730Z","iopub.execute_input":"2021-06-18T08:22:19.221008Z","iopub.status.idle":"2021-06-18T08:22:19.237160Z","shell.execute_reply.started":"2021-06-18T08:22:19.220982Z","shell.execute_reply":"2021-06-18T08:22:19.236295Z"},"trusted":true},"execution_count":21,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","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\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct","text/markdown":"<span style=\"color:#33cc33\">Correct</span>"},"metadata":{}}]},{"cell_type":"code","source":"# Lines below will give you a hint or solution code\n#step_1.hint()\n#step_1.solution()","metadata":{"execution":{"iopub.status.busy":"2021-06-18T08:22:19.238110Z","iopub.execute_input":"2021-06-18T08:22:19.238381Z","iopub.status.idle":"2021-06-18T08:22:19.242277Z","shell.execute_reply.started":"2021-06-18T08:22:19.238353Z","shell.execute_reply":"2021-06-18T08:22:19.241367Z"},"trusted":true},"execution_count":22,"outputs":[]},{"cell_type":"markdown","source":"## Step 2: Review the data\n\nUse a Python command to print the first five rows of the data.","metadata":{}},{"cell_type":"code","source":"# Print the first five rows of the data\ncandy_data.head() # Your code here","metadata":{"execution":{"iopub.status.busy":"2021-06-18T08:22:19.243750Z","iopub.execute_input":"2021-06-18T08:22:19.244140Z","iopub.status.idle":"2021-06-18T08:22:19.266075Z","shell.execute_reply.started":"2021-06-18T08:22:19.244103Z","shell.execute_reply":"2021-06-18T08:22:19.265307Z"},"trusted":true},"execution_count":23,"outputs":[{"execution_count":23,"output_type":"execute_result","data":{"text/plain":"   competitorname chocolate fruity caramel peanutyalmondy nougat  \\\nid                                                                 \n0       100 Grand       Yes     No     Yes             No     No   \n1    3 Musketeers       Yes     No      No             No    Yes   \n2       Air Heads        No    Yes      No             No     No   \n3      Almond Joy       Yes     No      No            Yes     No   \n4       Baby Ruth       Yes     No     Yes            Yes    Yes   \n\n   crispedricewafer hard  bar pluribus  sugarpercent  pricepercent  winpercent  \nid                                                                              \n0               Yes   No  Yes       No         0.732         0.860   66.971725  \n1                No   No  Yes       No         0.604         0.511   67.602936  \n2                No   No   No       No         0.906         0.511   52.341465  \n3                No   No  Yes       No         0.465         0.767   50.347546  \n4                No   No  Yes       No         0.604         0.767   56.914547  ","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>"},"metadata":{}}]},{"cell_type":"markdown","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\nUse the first five rows of the data to answer the questions below.","metadata":{}},{"cell_type":"code","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.)\nmore_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.)\nmore_sugar = 'Air Heads'\n\n# Check your answers\nstep_2.check()","metadata":{"execution":{"iopub.status.busy":"2021-06-18T08:22:19.267711Z","iopub.execute_input":"2021-06-18T08:22:19.268046Z","iopub.status.idle":"2021-06-18T08:22:19.284075Z","shell.execute_reply.started":"2021-06-18T08:22:19.268014Z","shell.execute_reply":"2021-06-18T08:22:19.282879Z"},"trusted":true},"execution_count":24,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","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\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct","text/markdown":"<span style=\"color:#33cc33\">Correct</span>"},"metadata":{}}]},{"cell_type":"code","source":"# Lines below will give you a hint or solution code\n#step_2.hint()\n#step_2.solution()","metadata":{"execution":{"iopub.status.busy":"2021-06-18T08:22:19.286160Z","iopub.execute_input":"2021-06-18T08:22:19.286555Z","iopub.status.idle":"2021-06-18T08:22:19.291050Z","shell.execute_reply.started":"2021-06-18T08:22:19.286516Z","shell.execute_reply":"2021-06-18T08:22:19.290112Z"},"trusted":true},"execution_count":25,"outputs":[]},{"cell_type":"markdown","source":"## Step 3: The role of sugar\n\nDo people tend to prefer candies with higher sugar content?  \n\n#### Part A\n\nCreate 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!_","metadata":{}},{"cell_type":"code","source":"# Scatter plot showing the relationship between 'sugarpercent' and 'winpercent'\nsns.scatterplot(x=candy_data['sugarpercent'], y=candy_data['winpercent']) # Your code here\n\n# Check your answer\nstep_3.a.check()","metadata":{"execution":{"iopub.status.busy":"2021-06-18T08:22:19.292623Z","iopub.execute_input":"2021-06-18T08:22:19.292994Z","iopub.status.idle":"2021-06-18T08:22:19.453402Z","shell.execute_reply.started":"2021-06-18T08:22:19.292957Z","shell.execute_reply":"2021-06-18T08:22:19.452442Z"},"trusted":true},"execution_count":26,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","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\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct","text/markdown":"<span style=\"color:#33cc33\">Correct</span>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAX4AAAEGCAYAAABiq/5QAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8+yak3AAAACXBIWXMAAAsTAAALEwEAmpwYAAAg7ElEQVR4nO3dfbRddX3n8fcnBAjhMZBLSMFrQKkM4IBwtUGlRVAXzTiCyqBYFVtWs+yDodJRqXYNdErXAluxxHbQKGrGqQJFnpZlECagxAfQRHlGG4yEghACQoCEAOF+54+9b7hJ7sM+55599v7t/Xmtddc959zz8Nvn7Ps9v/37fX/frYjAzMzaY1rVDTAzs/5y4DczaxkHfjOzlnHgNzNrGQd+M7OWmV51A4qYPXt2zJs3r+pmmJklZeXKlY9HxMC2tycR+OfNm8eKFSuqboaZWVIkrRnrdg/1mJm1jAO/mVnLOPCbmbWMA7+ZWcs48JuZtUwSWT1mVl/Dw8EDT2xg7dObmLPHDObtsyvTpqnqZtkEHPjNrGvDw8H19zzKWZffzqYXh5mx4zQuPPVITjxsPwf/GvNQj5l17YEnNmwJ+gCbXhzmrMtv54EnNlTcMpuIA7+ZdW3t05u2BP0Rm14c5rFnNlXUIivCgd/MujZnjxnM2HHrMDJjx2nsu/uMilpkRTjwm1nX5u2zKxeeeuSW4D8yxj9vn10rbplNxJO7Zta1adPEiYftxyGLjuWxZzax7+7O6kmBA7+ZTcm0aeKggd04aGC3qptiBTU28Du32OrC+6LVTSMDv3OLrS68L1odNXJy17nFVhfeF62OGhn4nVtsdeF90eqokYHfucVWF94XrY5KDfySPibpHkl3S/qmpBmSDpR0m6T7JV0maadev65zi60uvC9aHSkiynliaX/g+8ChEfGcpMuB64AFwJURcamkLwB3RMTFEz3X0NBQdHrO3ZFMCucWW9W8L1pVJK2MiKFtby87q2c6sIukF4GZwCPA8cD7878vBc4FJgz83XBusdWF90Wrm9KGeiLiYeAfgAfJAv56YCXwVERszu/2ELB/WW0wM7PtlRb4Jc0CTgIOBH4L2BU4sYPHL5S0QtKKdevWldRKM7P2KXNy963AryJiXUS8CFwJvAnYS9LIENMBwMNjPTgilkTEUEQMDQwMlNhMM7N2KTPwPwjMlzRTkoATgHuBm4FT8vucDlxTYhvMzGwbZY7x3wZcAfwUuCt/rSXAJ4GzJN0P7ANcUlYbzMxse6Vm9UTEOcA529y8GnhDma9rZmbja+TKXTMzG58Dv5lZyzSyLLP1h+vMF+P3yerGgd+64jrzxfh9sjryUI91xXXmi/H7ZHXkwG9dcZ35Yvw+WR058FtXXGe+GL9PVkcO/NYV15kvxu+T1VFp9fh7qZt6/FY+15kvxu+TVaWqevzWYK4zX4zfJ6sbD/WYmbWMA7+ZWcs48JuZtYzH+C05LoFgNjUO/JaUtpdA8JdeO5T9OTvwW1LGK4FwyKJjG5810/Yvvbbox+fsMX5LSptLILjuTzv043N24LektLkEQpu/9NqkH5+zA78lpc0lENr8pdcm/ficXbLBktPWEgge4x9fkya9e/k5j1eywYHfLCFt/dKbSBO/EHv1OY8X+D3UY5agBPprfdPESe+R+k7zD5rNQQO79fwLzOmcZoloYs+2FyaaDG16im+3SuvxS3qNpNtH/Twt6S8k7S3pRkmr8t+zymqDWZM0sWfbC5707lxpgT8ifhERR0bEkcDRwEbgKuBsYFlEHAwsy6+b2SSczjm2Nmd6datfQz0nAL+MiDWSTgKOy29fCnwX+GSf2mGWlNHZKjN3ms4r99mFNU88t+Xv7tlm4+EnHrYfhyw61pPeBfUr8L8P+GZ+eU5EPJJffhSY06c2mCVlrDH9804+nM/ftIo1Tzznnu0oPtlNZ0pP55S0E/Br4LCIWCvpqYjYa9Tfn4yI7cb5JS0EFgIMDg4evWbNmlLbaVY3q9c9y4LFy7ca3pmx4zQuWzif5158yT1bm1SV6Zy/D/w0Itbm19dKmps3ai7w2FgPioglETEUEUMDAwN9aKZZvYw3pv/ciy+VluZn7dCPwH8aLw/zAFwLnJ5fPh24pg9tMEuOs1WsLKUGfkm7Am8Drhx18/nA2yStAt6aXzezbThbxcpS6uRuRGwA9tnmtifIsnzMbAJjZasMzprZmJo0Vh2v3DWrsdHZKl65a73iWj19NDwcrF73LD/65eOsXvcsw8MuuGLFpbJy1/t5/bnH3yfurdlUpVCTxvt5Gtzj75NUemudcM+uv1LI8qlqP/e+2Bn3+Pskhd5aJ9yz67+RLJ9t3/M6ZflUsZ97X+ycA3+fjPTWtl2FWafeWifG69kdsujYJL/IUpBCTZoq9vNu9sUmnbGrGx7q6ZOm5WS7UmQ1yj5Bx1RVsZ93ui+OHCEsWLyc0750GwsWL+f6ex5t1fCQe/x9kkJvrRNNO4Kx3qhiP+90X/TRqnv8fVX33lonmnYEY73T7/28033RR6vu8VuXmnYEY+nqdF/00aoDv02Ba6BbXXSyL9YlO6rKCWYHfjNrlTocrVadguoxfjNrnarn26pe0OnAb2bWZ1VPMHuox8ysBBON4Vc9wewev5lZj022SKzqdOjST7beC0NDQ7FixYqqm2EJaPtSfKuH1eueZcHi5dv16K8btUhsZF8tc4J5vJOte6jHGqPqTAmzEUWK1VWZDu2hHmuMqjMlzEbUvYS2A781RtWZEmYjqh7Dn4yHeqwxqs6UMBtRh0ViE3GP3xqj7r2stvDZsDJVLxKbiHv81hh172W1gSfY01Coxy/pTUVuM6tanXtZbeAJ9jQUHer5fMHbtiJpL0lXSPq5pPskHSNpb0k3SlqV/57VWZN7z4emZr3hCfY0TDjUI+kY4I3AgKSzRv1pD2CHAs9/EXB9RJwiaSdgJvApYFlEnC/pbOBs4JNdtb4HfGhqKan7AjVPsKdhsh7/TsBuZF8Qu4/6eRo4ZaIHStoT+F3gEoCIeCEingJOApbmd1sKnNxd03vDh6bN0uSjtxTOFesJ9jRM2OOPiO8B35P0tYhY0+FzHwisA74q6QhgJXAmMCciHsnv8ygwZ6wHS1oILAQYHBzs8KWLK7LCztLQ9KO3FM4V6wn2NBQd499Z0hJJN0i6aeRnksdMB44CLo6I1wEbyIZ1toisUNCY3ZWIWBIRQxExNDAwULCZnav7CjsrrulHb6mMn3uCvf6KBv5/BX4G/DXw8VE/E3kIeCgibsuvX0H2RbBW0lyA/PdjnTa6l3xo2hypBMZuuZNivVI0j39zRFzcyRNHxKOS/kPSayLiF8AJwL35z+nA+fnvazp53l7zoWlzNH1isS7nirX0FSrLLOlcsp75VcDzI7dHxG8medyRwJfJJolXA39IdpRxOTAIrAFOnex5XJbZimj6GD/0p5SvNcd4ZZmLBv5fjXFzRMRBvWjcZBz4rSgHRrOXTakef0Qc2PsmWSfqnr9dF53WOPf7am1UKPBLmgmcBQxGxEJJBwOviYhvl9o6A9oxhFGFFN9Xf1FZLxTN6vkq8ALZKl6Ah4HzSmmRbafpaYq91MkCrtTe1xQWcFkaigb+V0XEZ4AXASJiI+BuRp80PU2xVzoNjKm9r6l9UVl9FQ38L0jahXyxlaRXMSq7x8rl/O1iOg2Mqb2vqX1RWX0VDfznANcDr5D0L8Ay4BOltSphZdSK8SKzYjoNjKm9r6l9UVl9Fc3quVHST4H5ZEM8Z0bE46W2LEFlTRbWaZFZnScXO13AVaf3tQgv4OpeFfttnf9Xiubxvwu4KSLW59f3Ao6LiKtLbV0ulTz+1eueZcHi5dsFnutqVERrKuqeBVP39vXC5s3D3PPIeh5Zv4m5e+7CYXP3YPp0n0F1IlXsF3XZF6eUxw+cExFXjVyJiKcknQNc3aP2NULTK31uO4Y+a+ZO/PzRp5mx4zTm7bNr5T2a1HrwnRoeDm64b23lwSQ1VVQ1rXsl1aJdhbHu5/P1bqPpY7Cjv9jm7jmDD85/JUtuWc0ffW1FbVILm1wZ0lk93aliUrzuE/FFA/8KSRdKelX+cyFZfX0bJbXJwk6N/mJ791EHsPimVQ5CfVT3YFJXVXTI6t4JLBr4P0q2gOsy4FJgE/BnZTUqVSNDDdctOpZLF/4O1y06tlGH4aO/2CQchPqs7sGkrqrokNW9Ezjp5K6kHYD/FxFv6U+TtpfK5G4bjGQqrHv2eU7/yo8bO5FdR3WZMExRFcX76lAwcKrVOZcB7x7J6uk3B/7ydJty5iBUjToEE0vHVLN6ngXuknQj2SkUAYiIRT1qX6XqnG9bpqkE76Zn0NRVp9VHzcZSNPBfmf80Tpt7rlNNOXMQsrZpSiex6MrdpXmtnsH8NIqNUfd82zI1fd2BWS81qZNYKKtH0n8Fbier14OkIyVdW2K7+qbNKXLOEjErrknrKIqmc54LvAF4CiAibgf6ctrFsrU5+NU95awfyiiqZ83UpE5i0TH+FyNivbTV4czweHdOSZsLX7V9grZJh+5Wvk6LANZZ0XTOS8hKMZ8NvAdYBOwYER8pt3mZstM5nSLXTk0vqme9lWJHYarpnB8FPk128pVvAt8B/rZ3zauWs1PayZPb1okmHSEXzerZCHxa0gXZ1Xim3GaZla9Jh+7WH03pJBbN6nm9pLuAO8kWct0h6egCj3tA0l2Sbpe0Ir9tb0k3SlqV/541tU0w687grJmcd/LhW01un3fy4QzOmllxy8zKVXSo5xLgTyNiOYCkNwNfBf5zgce+ZZuzdZ0NLIuI8yWdnV//ZAdtNuuJB5/cyKU/XsNnTjmC557fzMydp7P0h6s5anBW8j06K64pi7I6UTTwvzQS9AEi4vuSNnf5micBx+WXlwLfxYHfKvDEhuc5/pD9+MQVd2yZrFt0/MH8ZsPzDvwtkeKEbS8UzeP/nqQvSjpO0u9J+l/AdyUdJemoCR4XwA2SVkpamN82JyIeyS8/CswZ64GSFkpaIWnFunXrCjbTrLiddpi23TkFFt+0ih13qO+pDL3uoLeatCirE0V7/Efkv8/Z5vbXkQX348d53Jsj4mFJ+wI3Svr56D9GREgac8+NiCXAEsjSOQu206ywjS+8NGZWz8YXXqqoRRNra++0TG3N7Cqa1dNVLf6IeDj//Zikq8hW/66VNDciHpE0F3ism+c2m6rxsnrm7FHPrJ4215UqS1szu4pm9ews6f2SPiXpf4z8TPKYXSXtPnIZeDtwN3AtcHp+t9OBa7pvvln3UitZ0aSSAXWR2j7QK0WHeq4B1pOdZ/f5go+ZA1yVl3mYDnwjIq6X9BPgcklnAGuAUztrstVF6tkQqS3IaWvvtEyp7QO9UrRkw90RcXgf2jMmn4Grfjze3H9+z61TUy3Z8ENJr42Iu3rcLkvUA09s4ILr7+OMNx/ESO2+C66/j0P2293jzSVpa+/Ueq9o4H8z8GFJvyIb6hFZUk6RBVzWQE9seJ73Dg1uSYd0Dnx/NKVkQKpSH94cUTTw/36prbDkjJcDf9nC+RW3zKwzRYN5k4baJgz8kvaIiKcBF2WzraSWAz+epvTgrDudBPMmpdNO1uP/BvAOsmyeIBviGRE05CxcdVFFEOr2NVPLgR9Lk3pw1p1OgnmTFntNGPgj4h35xR8A3wOWR8TPJ3iIdWkqQajb4D2V12zCmcua1IOz7nQSzJuUTlu0KMklwFzg85JWS7pC0pkltqt1uq0ZMhK8Fyxezmlfuo0Fi5dz/T2PFqrhMpU6JSMZJtctOpZLF/4O1y06NrmeshdEWSfn3G7SYq+iJRtulnQL8HrgLcBHgMOBi0psW6t0exg5lV7rVA9dU88waVIPzrrTyZFrk9JpCwV+ScuAXYEfAcuB10eEa+z0ULdBaCrBu+2BrwnDVTY1nQbz1Ds7I4qmc94JHE3Wy18PPCXpRxHxXGkta5lug9BUgvdUA1/qGTFN6sFZ95oSzDtRqGTDljtnRdc+DPx3YL+I2Lmkdm2lLSUbRgJpJ0Foqpkp3bxmL17XzMo3XsmGorV6/hw4lqzX/wDZcM/yiLipx+0cU1sCf7e6Dd5TsXrdsyxYvHy7I43rnBFjVhtTrdUzA7gQWBkR3Z5y0UpSxaFqk3KazdqmaFbPP5TdkKZIfdy7qLZPDJulrL4nF03QVHLqUzM4aybnnXz4VjnN5518OIOzZlbcMrPOtPE8xkWHeqyANq0EffDJjXz+plVbyjJHwOdvWsVRg7Mat63WmZSOetuapODA30NtGvde+/Qm1jzxHP988/1b3Z7atqYUpFKQWiBtU2dtNA/19FAny7+LqPMhaK+3tQptGprrl6mUAalCW8t2OPD3UC9reXQSlKr4gmhC3ZLUglQKUgukTejAdMNDPT3Uy5WgRQ9Bqzq0bsKq1xSH5uo+NJVatldby3Y48PdYr3LqiwalKscoU1/qnlqQSmH8PLVA2oQOTDcc+GuqaFBKsddaF6kFqRQmIlMMpKl3YLrhwF9TRYNSar3WOkktSKXyJd/GQJqa0gO/pB2AFcDDEfEOSQcClwL7kJ3S8YMR8ULZ7UhN0aCUWq+1blIKUv6St17pqDpnVy8gnQUMAXvkgf9y4MqIuFTSF4A7IuLiiZ7DRdomtnnzMPc8sp5H1m9i7p67cNjcPZg+3QlbTZPCGD/UfwK6TaZapK3bFz0A+C/A3wFnSRJwPPD+/C5LgXOBCQN/LzR1ZxweDm64b23tg4FNXQpDU8PDwU2/WMudD61nOGAHwWsP2JPjXzOnVu1su7KHev4R+ASwe359H+CpURU+HwL2H+uBkhYCCwEGBwen1Ihe1ayv45dGChN+1nslH6h37cHfbGDV2mdZcsvqLf9rZ55wMK8e2I15s70/1kVp4wGS3gE8FhEru3l8RCyJiKGIGBoYGJhSW6ayUKfuqztTWzBj3av7vgiw9unnuWjZqq3+1y5atoq1Tz9fcctstDIHgt8EvFPSA2STuceTnZx9L0kjRxoHAA+X2AZgasGx7qs727rysI3qvi8CbHhh85j/axtf8Gk86qS0wB8RfxURB0TEPOB9wE0R8QfAzcAp+d1OB64pqw0jphIc696jnmrphDrXA7Kt1X1fBHjl3ruO+b82uHczssya8v9SRR7/J4FLJZ0H/Ay4pOwXnErKY91T6KYy4ZdKlohl6r4vAhw4e+z/tQNnpx/4m/T/Uno6Zy/0Ip2z2/PSbt48zNV3PMxfX333lg/7vJMP5+Qj9k8+ZdLnza2n8ZIJUgk8VZwDuh9S/H+pJJ2zTrpdqNPkE46kshK0TSYL7nVP54S0FsV1okn/L60J/N1qyglHxpLC0EHbTJae29SgmoIm/b+kPVbRB03OmmlCTf2mSWECt62a9P/iHv8kmlwLJ5WhgzZpQq+yzgsep6JJ/y+tmdydiqZOVln9pDKBO57U2980403uOvCb1UzKHY0UM1+arPVZPWapSHkCd+3Tm5g1cyfefdQBKP+u+tbKhxqRDNEkDvxm1jNz95zBh4555ZZ6PSNF2vbbI505ijZwVk8DNGUZeVt18vnV/bN+aZgxi7S9NDzJA62v3ONPnCfTpqbqDJROPr8UPuvHnhk7HXXds5t41b4e6qkL9/gTl0LFxrqqQ5njTj6/FD7rJq97aRIH/sR5wU/36hBIO/n8Uvism7TIqck81JO4Jiz4qUodaq908vml8Fk3aZFT2aocZnSPP3HuYXWvDsMSnXx+qXzWI+mo8w+avaW+kG2t6mFGL+BqgJQX/FSpLpOlnXx+/qyboV8L3byAq8FSXvBTpboMS3Ty+fmzboaqhxkd+C15UxkrdSC1KlQ9X+Mxfkta1WOlZt2oer7GY/yWNBcFs1T1Y77GY/zWSFWPlZp1q8phRg/1WNLqkJJplhoHfkta1WOlZinyUI8lrS4pmWYpKS3wS5oB3ALsnL/OFRFxjqQDgUuBfYCVwAcj4oWy2mHN55RMs86UOdTzPHB8RBwBHAmcKGk+cAHwuYh4NfAkcEaJbTCzPqv7OQOsxB5/ZHmiz+ZXd8x/AjgeeH9++1LgXODistphZv1TlzIYNrFSJ3cl7SDpduAx4Ebgl8BTEbE5v8tDwP7jPHahpBWSVqxbt67MZppZj9Sh1LVNrtTAHxEvRcSRwAHAG4BDOnjskogYioihgYGBsppoZj2UwjkDrE/pnBHxFHAzcAywl6SRIaYDgIf70QYzK5/XVaShtMAvaUDSXvnlXYC3AfeRfQGckt/tdOCastrQK56sMivG6yrSUGYe/1xgqaQdyL5gLo+Ib0u6F7hU0nnAz4BLSmzDlHmyyqw4r6tIg4u0TaLMImBVnnrNzJrPRdq6VFYRMB9JmFlVXKtnEmVNVjntzcyq4sA/ibImq5z2ZmZV8VDPJMqarKr61Gtm1l7u8RcwUgRs/kGzOWhgt56MwTvtzcyq4h5/RZz2ZmZVceCvkMsJm1kVPNRjZtYyrenxe7GUmVmmFYHfi6XMzF7WiqEeL5YyM3tZKwK/F0uZmb2sFUM9XixVDs+bmKWpFT1+L5bqvZF5kwWLl3Pal25jweLlXH/Poz5XgVkCWlOWeaR36sVSvVFmuWoz643Wl2VOfbFU3YZVyipXbWbla03gT1kd01E9b2KWrlaM8aeujumonjcxS5d7/Amo47CKi8yZpcuBPwF1HVZJfd7ErK081JMAD6uYWS+5x58AD6uYWS858CfCwypm1iulDfVIeoWkmyXdK+keSWfmt+8t6UZJq/Lfs8pqg5mZba/MMf7NwF9GxKHAfODPJB0KnA0si4iDgWX5dTMz65PSAn9EPBIRP80vPwPcB+wPnAQsze+2FDi5rDaYmdn2+pLVI2ke8DrgNmBORDyS/+lRYM44j1koaYWkFevWretHM83MWqH0wC9pN+BbwF9ExNOj/xZZhbgxq8RFxJKIGIqIoYGBgbKbaWbWGqVm9UjakSzo/0tEXJnfvFbS3Ih4RNJc4LHJnmflypWPS1oz6qbZwOO9b3EyvP3e/rZuf5u3HTrf/leOdWNpgV+SgEuA+yLiwlF/uhY4HTg//33NZM8VEVt1+SWtGKvUaFt4+739bd3+Nm879G77y+zxvwn4IHCXpNvz2z5FFvAvl3QGsAY4tcQ2mJnZNkoL/BHxfWC8paUnlPW6ZmY2sVRr9SypugEV8/a3W5u3v83bDj3a/iROvWhmZr2Tao/fzMy65MBvZtYytQ78kk6U9AtJ90varqaPpJ0lXZb//bZ8hXBjFNj+s/IieHdKWiZpzJzdVE22/aPu9x5JIakxaX5Ftl3SqaOKIH6j320sU4F9fzAvAvmzfP9fUEU7yyDpK5Iek3T3OH+XpMX5e3OnpKM6fpGIqOUPsAPwS+AgYCfgDuDQbe7zp8AX8svvAy6rut193v63ADPzy3/Stu3P77c7cAtwKzBUdbv7+NkfDPwMmJVf37fqdvd5+5cAf5JfPhR4oOp293D7fxc4Crh7nL8vAP4vWdbkfOC2Tl+jzj3+NwD3R8TqiHgBuJSswNtoowu+XQGckC8ca4JJtz8ibo6IjfnVW4ED+tzGMhX5/AH+FrgA2NTPxpWsyLb/MfDPEfEkQERMugI+IUW2P4A98st7Ar/uY/tKFRG3AL+Z4C4nAf87MrcCe+VVEAqrc+DfH/iPUdcfym8b8z4RsRlYD+zTl9aVr8j2j3YGWS+gKSbd/vwQ9xUR8W/9bFgfFPnsfxv4bUk/kHSrpBP71rryFdn+c4EPSHoIuA74aH+aVgudxobt+AxcDSDpA8AQ8HtVt6VfJE0DLgQ+XHFTqjKdbLjnOLIjvVskvTYinqqyUX10GvC1iPispGOAr0s6PCKGq25YCurc438YeMWo6wfkt415H0nTyQ75nuhL68pXZPuR9Fbg08A7I+L5PrWtHybb/t2Bw4HvSnqAbKzz2oZM8Bb57B8Cro2IFyPiV8C/k30RNEGR7T8DuBwgIn4EzCArYNYGhWLDROoc+H8CHCzpQEk7kU3eXrvNfUYKvgGcAtwU+exHA0y6/ZJeB3yRLOg3aYwXJtn+iFgfEbMjYl5EzCOb43hnRKyoprk9VWTfv5qst4+k2WRDP6v72MYyFdn+B8lLv0j6T2SBvy0n7rgW+FCe3TMfWB8vn+OkkNoO9UTEZkl/DnyHbJb/KxFxj6T/CayIiGvJqn9+XdL9ZJMh76uuxb1VcPv/HtgN+Nd8TvvBiHhnZY3uoYLb30gFt/07wNsl3Qu8BHw8IhpxtFtw+/8S+JKkj5FN9H64KZ0+Sd8k+1Kfnc9hnAPsCBARXyCb01gA3A9sBP6w49doyHtlZmYF1Xmox8zMSuDAb2bWMg78ZmYt48BvZtYyDvxmZi3jwG+WIEknSzq06nZYmhz4zQqStMMUH9/LdTMnk1WlNOuYA78lTdKukv5N0h2S7pb0XkkP5KtZkTQk6bv55QFJN+b1678sac2o+10taWX+t4Wjnv9ZSZ+VdAdwTP7cn5F0l6QfS3r1qOf+lqSf5D9vym8/V9LXJf2AbLHhHElX5e29Q9Ib8/t9IH++2yV9ceRLJn/9v8vve2v++DcC7wT+Pr//q/r2hlsjOPBb6k4Efh0RR0TE4cD1E9z3HLKyHoeRlfEeHPW3P4qIo8mK3S2SNFLldVeyeudHRMT389vWR8RrgX8C/jG/7SLgcxHxeuA9wJdHPfehwFsj4jRgMfC9iDiCrOb6PXnJgfcCb4qII8lW4v7BqNe/Nb//LcAfR8QPyZbtfzwijoyIXxZ7q8wytS3ZYFbQXcBnJV0AfDsilk9wSoY3A+8CiIjrJT056m+LJL0rv/wKsoJnT5AF4W9t8zzfHPX7c/nltwKHjnrtPSTtll++NiKeyy8fD3wob8NLwHpJHwSOBn6SP34XYKT20gvAt/PLK4G3jbdxZkU58FvSIuLf87r8C4DzJC0DNvPy0eyMyZ5D0nFkgfuYiNiYDw2NPG5THqC3etkxLk8D5kfEVieEyQP5hsmaACyNiL8a428vjqpB8xL+n7Ue8FCPJU3SbwEbI+L/kBWtOwp4gKwHDdmwy4gfAKfmj3s7MCu/fU/gyTzoH0JW4nki7x31+0f55RsYdTIQSUeO89hlZKfJRNIOkvbMbztF0r757Xtr8vMnP0NWmtqsYw78lrrXAj+WdDvZGP55wN8AF0laQdZLHvE3ZBUt7wb+G/AoWQC9Hpgu6T7gfLISzxOZJelO4EzgY/lti4AhZSe/vhf4yDiPPRN4i6S7yIZuDo2Ie4G/Bm7In/dGYLJT6V0KfFzZycY9uWsdcXVOaw1JOwMv5WV/jwEuzidTO3mOB8hO6v54CU006wuPF1qbDAKXKztt4wtkJyw3ax33+M3MWsZj/GZmLePAb2bWMg78ZmYt48BvZtYyDvxmZi3z/wFcsNNzfn5tYgAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# Lines below will give you a hint or solution code\n#step_3.a.hint()\n#step_3.a.solution_plot()","metadata":{"execution":{"iopub.status.busy":"2021-06-18T08:22:19.454934Z","iopub.execute_input":"2021-06-18T08:22:19.455319Z","iopub.status.idle":"2021-06-18T08:22:19.460138Z","shell.execute_reply.started":"2021-06-18T08:22:19.455282Z","shell.execute_reply":"2021-06-18T08:22:19.458916Z"},"trusted":true},"execution_count":27,"outputs":[]},{"cell_type":"markdown","source":"#### Part B\n\nDoes 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?","metadata":{}},{"cell_type":"code","source":"#step_3.b.hint()","metadata":{"execution":{"iopub.status.busy":"2021-06-18T08:22:19.463827Z","iopub.execute_input":"2021-06-18T08:22:19.464207Z","iopub.status.idle":"2021-06-18T08:22:19.475185Z","shell.execute_reply.started":"2021-06-18T08:22:19.464170Z","shell.execute_reply":"2021-06-18T08:22:19.474123Z"},"trusted":true},"execution_count":28,"outputs":[]},{"cell_type":"code","source":"# Check your answer (Run this code cell to receive credit!)\nstep_3.b.solution()","metadata":{"execution":{"iopub.status.busy":"2021-06-18T08:22:19.477998Z","iopub.execute_input":"2021-06-18T08:22:19.478316Z","iopub.status.idle":"2021-06-18T08:22:19.491594Z","shell.execute_reply.started":"2021-06-18T08:22:19.478288Z","shell.execute_reply":"2021-06-18T08:22:19.490366Z"},"trusted":true},"execution_count":29,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","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}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"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.","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."},"metadata":{}}]},{"cell_type":"markdown","source":"## Step 4: Take a closer look\n\n#### Part A\n\nCreate the same scatter plot you created in **Step 3**, but now with a regression line!","metadata":{}},{"cell_type":"code","source":"# Scatter plot w/ regression line showing the relationship between 'sugarpercent' and 'winpercent'\nsns.regplot(x=candy_data['sugarpercent'], y=candy_data['winpercent'])  # Your code here\n\n# Check your answer\nstep_4.a.check()","metadata":{"execution":{"iopub.status.busy":"2021-06-18T08:22:19.493488Z","iopub.execute_input":"2021-06-18T08:22:19.493949Z","iopub.status.idle":"2021-06-18T08:22:19.724203Z","shell.execute_reply.started":"2021-06-18T08:22:19.493907Z","shell.execute_reply":"2021-06-18T08:22:19.723358Z"},"trusted":true},"execution_count":30,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","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\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct","text/markdown":"<span style=\"color:#33cc33\">Correct</span>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# Lines below will give you a hint or solution code\n#step_4.a.hint()\n#step_4.a.solution_plot()","metadata":{"execution":{"iopub.status.busy":"2021-06-18T08:22:19.725387Z","iopub.execute_input":"2021-06-18T08:22:19.725946Z","iopub.status.idle":"2021-06-18T08:22:19.729358Z","shell.execute_reply.started":"2021-06-18T08:22:19.725893Z","shell.execute_reply":"2021-06-18T08:22:19.728547Z"},"trusted":true},"execution_count":31,"outputs":[]},{"cell_type":"markdown","source":"#### Part B\n\nAccording 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?","metadata":{}},{"cell_type":"code","source":"#step_4.b.hint()","metadata":{"execution":{"iopub.status.busy":"2021-06-18T08:22:19.731615Z","iopub.execute_input":"2021-06-18T08:22:19.732016Z","iopub.status.idle":"2021-06-18T08:22:19.742971Z","shell.execute_reply.started":"2021-06-18T08:22:19.731988Z","shell.execute_reply":"2021-06-18T08:22:19.741938Z"},"trusted":true},"execution_count":32,"outputs":[]},{"cell_type":"code","source":"# Check your answer (Run this code cell to receive credit!)\nstep_4.b.solution()","metadata":{"execution":{"iopub.status.busy":"2021-06-18T08:22:19.744879Z","iopub.execute_input":"2021-06-18T08:22:19.745324Z","iopub.status.idle":"2021-06-18T08:22:19.757575Z","shell.execute_reply.started":"2021-06-18T08:22:19.745273Z","shell.execute_reply":"2021-06-18T08:22:19.756448Z"},"trusted":true},"execution_count":33,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","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}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"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.","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."},"metadata":{}}]},{"cell_type":"markdown","source":"## Step 5: Chocolate!\n\nIn 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!_","metadata":{}},{"cell_type":"code","source":"# Scatter plot showing the relationship between 'pricepercent', 'winpercent', and 'chocolate'\nsns.scatterplot(x=candy_data['pricepercent'], y=candy_data['winpercent'], hue=candy_data['chocolate']) # Your code here\n\n# Check your answer\nstep_5.check()","metadata":{"execution":{"iopub.status.busy":"2021-06-18T08:22:19.759200Z","iopub.execute_input":"2021-06-18T08:22:19.759619Z","iopub.status.idle":"2021-06-18T08:22:19.963007Z","shell.execute_reply.started":"2021-06-18T08:22:19.759577Z","shell.execute_reply":"2021-06-18T08:22:19.962168Z"},"trusted":true},"execution_count":34,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","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\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct","text/markdown":"<span style=\"color:#33cc33\">Correct</span>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# Lines below will give you a hint or solution code\n#step_5.hint()\n#step_5.solution_plot()","metadata":{"execution":{"iopub.status.busy":"2021-06-18T08:22:19.964212Z","iopub.execute_input":"2021-06-18T08:22:19.964487Z","iopub.status.idle":"2021-06-18T08:22:19.969223Z","shell.execute_reply.started":"2021-06-18T08:22:19.964460Z","shell.execute_reply":"2021-06-18T08:22:19.967763Z"},"trusted":true},"execution_count":35,"outputs":[]},{"cell_type":"markdown","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\nCreate 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.","metadata":{}},{"cell_type":"code","source":"# Color-coded scatter plot w/ regression lines\nsns.lmplot(x=\"pricepercent\", y=\"winpercent\", hue=\"chocolate\", data=candy_data) # Your code here\n\n# Check your answer\nstep_6.a.check()","metadata":{"execution":{"iopub.status.busy":"2021-06-18T08:22:19.970352Z","iopub.execute_input":"2021-06-18T08:22:19.970663Z","iopub.status.idle":"2021-06-18T08:22:20.458191Z","shell.execute_reply.started":"2021-06-18T08:22:19.970635Z","shell.execute_reply":"2021-06-18T08:22:20.457028Z"},"trusted":true},"execution_count":36,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","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\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct","text/markdown":"<span style=\"color:#33cc33\">Correct</span>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 416.375x360 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# Lines below will give you a hint or solution code\n#step_6.a.hint()\n#step_6.a.solution_plot()","metadata":{"execution":{"iopub.status.busy":"2021-06-18T08:22:20.459563Z","iopub.execute_input":"2021-06-18T08:22:20.459841Z","iopub.status.idle":"2021-06-18T08:22:20.463761Z","shell.execute_reply.started":"2021-06-18T08:22:20.459814Z","shell.execute_reply":"2021-06-18T08:22:20.462770Z"},"trusted":true},"execution_count":37,"outputs":[]},{"cell_type":"markdown","source":"#### Part B\n\nUsing the regression lines, what conclusions can you draw about the effects of chocolate and price on candy popularity?","metadata":{}},{"cell_type":"code","source":"#step_6.b.hint()","metadata":{"execution":{"iopub.status.busy":"2021-06-18T08:22:20.465252Z","iopub.execute_input":"2021-06-18T08:22:20.465636Z","iopub.status.idle":"2021-06-18T08:22:20.479267Z","shell.execute_reply.started":"2021-06-18T08:22:20.465597Z","shell.execute_reply":"2021-06-18T08:22:20.478486Z"},"trusted":true},"execution_count":38,"outputs":[]},{"cell_type":"code","source":"# Check your answer (Run this code cell to receive credit!)\nstep_6.b.solution()","metadata":{"execution":{"iopub.status.busy":"2021-06-18T08:22:20.480558Z","iopub.execute_input":"2021-06-18T08:22:20.480847Z","iopub.status.idle":"2021-06-18T08:22:20.492337Z","shell.execute_reply.started":"2021-06-18T08:22:20.480822Z","shell.execute_reply":"2021-06-18T08:22:20.491389Z"},"trusted":true},"execution_count":39,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","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}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"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.","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."},"metadata":{}}]},{"cell_type":"markdown","source":"## Step 7: Everybody loves chocolate.\n\n#### Part A\n\nCreate 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.","metadata":{}},{"cell_type":"code","source":"# Scatter plot showing the relationship between 'chocolate' and 'winpercent'\nsns.swarmplot(x=candy_data['chocolate'], y=candy_data['winpercent'])  # Your code here\n\n# Check your answer\nstep_7.a.check()","metadata":{"execution":{"iopub.status.busy":"2021-06-18T08:22:20.493589Z","iopub.execute_input":"2021-06-18T08:22:20.493978Z","iopub.status.idle":"2021-06-18T08:22:20.625259Z","shell.execute_reply.started":"2021-06-18T08:22:20.493940Z","shell.execute_reply":"2021-06-18T08:22:20.624453Z"},"trusted":true},"execution_count":40,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","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\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Correct","text/markdown":"<span style=\"color:#33cc33\">Correct</span>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# Lines below will give you a hint or solution code\n#step_7.a.hint()\n#step_7.a.solution_plot()","metadata":{"execution":{"iopub.status.busy":"2021-06-18T08:22:20.626462Z","iopub.execute_input":"2021-06-18T08:22:20.627002Z","iopub.status.idle":"2021-06-18T08:22:20.630593Z","shell.execute_reply.started":"2021-06-18T08:22:20.626960Z","shell.execute_reply":"2021-06-18T08:22:20.629642Z"},"trusted":true},"execution_count":41,"outputs":[]},{"cell_type":"markdown","source":"#### Part B\n\nYou 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**?","metadata":{}},{"cell_type":"code","source":"#step_7.b.hint()","metadata":{"execution":{"iopub.status.busy":"2021-06-18T08:22:20.631863Z","iopub.execute_input":"2021-06-18T08:22:20.632390Z","iopub.status.idle":"2021-06-18T08:22:20.642938Z","shell.execute_reply.started":"2021-06-18T08:22:20.632352Z","shell.execute_reply":"2021-06-18T08:22:20.641965Z"},"trusted":true},"execution_count":42,"outputs":[]},{"cell_type":"code","source":"# Check your answer (Run this code cell to receive credit!)\nstep_7.b.solution()","metadata":{"execution":{"iopub.status.busy":"2021-06-18T08:22:20.644232Z","iopub.execute_input":"2021-06-18T08:22:20.644869Z","iopub.status.idle":"2021-06-18T08:22:20.658777Z","shell.execute_reply.started":"2021-06-18T08:22:20.644822Z","shell.execute_reply":"2021-06-18T08:22:20.657630Z"},"trusted":true},"execution_count":43,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","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}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"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.","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."},"metadata":{}}]},{"cell_type":"markdown","source":"## Keep going\n\nExplore **[histograms and density plots](https://www.kaggle.com/alexisbcook/distributions)**.","metadata":{}},{"cell_type":"markdown","source":"---\n\n\n\n\n*Have questions or comments? Visit the [Learn Discussion forum](https://www.kaggle.com/learn-forum/161291) to chat with other Learners.*","metadata":{}}]}