project_argument/argument.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "42901bdc-1637-4bad-b7fd-d901aaa7488b",
   "metadata": {},
   "source": [
    "## **OVERARCHING QUESTION:** *DO PEOPLE WHO EXIBIT RISKY BEHAVIORS (SMOKING, GAMBLING, DRINKING) ORDER THEIR STEAK IN THE SAME RISKY MANNER AS THEIR BEAHVIORS (RARE). IS THERE A RELATIONSHIP BETWEEN THE TWO.*\n",
    "![alt text](risk.jpg)\n",
    "\n",
    "**Why this question**\n",
    "\n",
    "THE CENTRAL QUESTION WOULD ASK DO PEOPLE (males and females) WHO HAVE AN AFFINITY FOR RISKY BEHAVIORS, SPECIFICALLY (SMOKING, DRINKING, GAMBLING) ALSO HAVE A RISKY BEHAVIOR WHEN IT COMES TO EATING THEIR FOOD. SPECIFICALLY IN THIS STUDY, IT IS ABOUT ORDERING STEAK.\n",
    "I AM INTERESTED IN THIS QUESTION BECASUE IT IS INTERESTING TO SEE IF FOOD RISK BEHAVIORS ARE ASSOCIATED WITH ACTIVITY RISK BEHAVIORS. I ALSO AM INTERESTED TO SEE IN THIS STUDY HOW INCOME LEVELS EFFECT THESE DECISIONS AS WELL AS EDUCATION LEVELS, WHICH IS ALSO IN THE STUDY.\n",
    "\n",
    "![alt text](r.jpg)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "technical-evans",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Include any import statements you will need\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_theme()\n",
    "steak = pd.read_csv(\"steak.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "overhead-sigma",
   "metadata": {
    "scrolled": true
   },
   "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>smoke</th>\n",
       "      <th>drink</th>\n",
       "      <th>gamble</th>\n",
       "      <th>cooked</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Household Income</th>\n",
       "      <th>Education</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Male</td>\n",
       "      <td>&gt; 60</td>\n",
       "      <td>$50,000 - $99,999</td>\n",
       "      <td>Bachelor degree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Male</td>\n",
       "      <td>&gt; 60</td>\n",
       "      <td>$50,000 - $99,999</td>\n",
       "      <td>Graduate degree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Male</td>\n",
       "      <td>18-29</td>\n",
       "      <td>$50,000 - $99,999</td>\n",
       "      <td>Bachelor degree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Male</td>\n",
       "      <td>18-29</td>\n",
       "      <td>$50,000 - $99,999</td>\n",
       "      <td>Bachelor degree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Male</td>\n",
       "      <td>18-29</td>\n",
       "      <td>$50,000 - $99,999</td>\n",
       "      <td>Bachelor degree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Well</td>\n",
       "      <td>Female</td>\n",
       "      <td>45-60</td>\n",
       "      <td>$150,000+</td>\n",
       "      <td>High school degree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Well</td>\n",
       "      <td>Male</td>\n",
       "      <td>45-60</td>\n",
       "      <td>$50,000 - $99,999</td>\n",
       "      <td>Bachelor degree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Well</td>\n",
       "      <td>Male</td>\n",
       "      <td>30-44</td>\n",
       "      <td>$0 - $24,999</td>\n",
       "      <td>Some college or Associate degree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Well</td>\n",
       "      <td>Female</td>\n",
       "      <td>30-44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Some college or Associate degree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Well</td>\n",
       "      <td>Female</td>\n",
       "      <td>&gt; 60</td>\n",
       "      <td>$100,000 - $149,999</td>\n",
       "      <td>Bachelor degree</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>432 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    smoke drink gamble  cooked  Gender    Age     Household Income  \\\n",
       "0     Yes   Yes    Yes  Medium    Male   > 60    $50,000 - $99,999   \n",
       "1      No   Yes     No  Medium    Male   > 60    $50,000 - $99,999   \n",
       "2     Yes   Yes    Yes  Medium    Male  18-29    $50,000 - $99,999   \n",
       "3      No   Yes    Yes  Medium    Male  18-29    $50,000 - $99,999   \n",
       "4      No   Yes    Yes  Medium    Male  18-29    $50,000 - $99,999   \n",
       "..    ...   ...    ...     ...     ...    ...                  ...   \n",
       "427    No   Yes    Yes    Well  Female  45-60            $150,000+   \n",
       "428   NaN    No     No    Well    Male  45-60    $50,000 - $99,999   \n",
       "429    No   Yes     No    Well    Male  30-44         $0 - $24,999   \n",
       "430    No    No    Yes    Well  Female  30-44                  NaN   \n",
       "431    No   Yes    Yes    Well  Female   > 60  $100,000 - $149,999   \n",
       "\n",
       "                            Education  \n",
       "0                     Bachelor degree  \n",
       "1                     Graduate degree  \n",
       "2                     Bachelor degree  \n",
       "3                     Bachelor degree  \n",
       "4                     Bachelor degree  \n",
       "..                                ...  \n",
       "427                High school degree  \n",
       "428                   Bachelor degree  \n",
       "429  Some college or Associate degree  \n",
       "430  Some college or Associate degree  \n",
       "431                   Bachelor degree  \n",
       "\n",
       "[432 rows x 8 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "steak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8d7c325c-ef3f-4072-a51c-1e87325a3c96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "Male      212\n",
       "Female    200\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "steak.Gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2899053c-9446-4772-8cc2-113a238b967a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Gender', ylabel='Count'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=steak, x=\"Gender\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "continental-franklin",
   "metadata": {},
   "source": [
    "**What is the data about and where is it from?**\n",
    "\n",
    "![alt text](538.jpg)\n",
    "\n",
    "This is a data set from the website 538. It was a data set collected by Walt Hickey. It is a steak risky survey. The data examines how people value and interpret risk in life, and does this have a correlation to how they order their steak. 550 people were surveyed. It seeks to interpret if people who do risky behaviors (smoke, gamble, drink) order their steak in the same manner (rare) and risk food borne illness."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "infinite-instrument",
   "metadata": {},
   "source": [
    "# Methods and Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "recognized-positive",
   "metadata": {},
   "source": [
    "## First Research Question: *Do people (males and females) who drink also order their steak rare?*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "graduate-palmer",
   "metadata": {},
   "source": [
    "### Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "endless-variation",
   "metadata": {},
   "source": [
    "*Explain how you will approach this research question below. Consider the following:* \n",
    "  - *Which aspects of the dataset will you use?* \n",
    "  - *How will you reorganize/store the data?* \n",
    "  - *What data science tools/functions will you use and why?*\n",
    "\n",
    "\n",
    "**I will use the columns Drink/Cooked to answer this question.\n",
    "I will...\n",
    "\n",
    "-get the value of people who drink\n",
    "-get the value of people who order steak rare\n",
    "-get the value of people who do both drinking and order their steak rare\n",
    "\n",
    "I will use pandas to handle my data (as data frames), seaborne to create visualizations\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "portuguese-japan",
   "metadata": {},
   "source": [
    "# Results "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68678a65-6dd4-4fff-bb4e-70492249e021",
   "metadata": {},
   "source": [
    "### Number of people who Drink"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "negative-highlight",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "drink\n",
       "Yes    339\n",
       "No      93\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "steak.drink.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "victorian-burning",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='drink', ylabel='Count'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=steak, x=\"drink\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0ec8c49-e490-4bac-927a-0f3c50c72c8d",
   "metadata": {},
   "source": [
    "### Number of people who like their steak rare\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "46f2704d-6336-4b76-a7db-8cde388f7d55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cooked\n",
       "Medium rare    166\n",
       "Medium         132\n",
       "Medium Well     75\n",
       "Well            36\n",
       "Rare            23\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "steak.cooked.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "45451f54-ebc8-4acc-aec7-64ecd9842709",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='cooked', ylabel='Count'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=steak, x=\"cooked\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec996656-8880-4077-bc4f-01e7d600dece",
   "metadata": {},
   "source": [
    "### Number of people who like their steak rare, and who drink"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5d1555da-3c4f-4105-b7b7-83a1bfb3f833",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "drink  cooked     \n",
      "No     Medium          23\n",
      "       Medium Well     17\n",
      "       Medium rare     38\n",
      "       Rare             3\n",
      "       Well            12\n",
      "Yes    Medium         109\n",
      "       Medium Well     58\n",
      "       Medium rare    128\n",
      "       Rare            20\n",
      "       Well            24\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "counts = steak.groupby([\"drink\", 'cooked']).size()\n",
    "print(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "32ca9aa2-e1ad-4563-b2de-55326f187e1f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='drink', ylabel='count'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='drink', hue='cooked', data=steak)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a72f700e-4d9b-46d9-a216-69b754fd409f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of people who drink and order their steak rare: 20\n"
     ]
    }
   ],
   "source": [
    "df = steak.dropna(subset=['drink', 'cooked'])\n",
    "drink_rare = df[(df['drink'].str.lower() == 'yes') & (df['cooked'].str.lower() == 'rare')]\n",
    "count_drink_rare = len(drink_rare)\n",
    "print(f\"Number of people who drink and order their steak rare: {count_drink_rare}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "collectible-puppy",
   "metadata": {},
   "source": [
    "## Second Research Question: *Do people (males and females) who gamble also order their steak rare?*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "demographic-future",
   "metadata": {},
   "source": [
    "### Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "incorporate-roller",
   "metadata": {},
   "source": [
    "*Explain how you will approach this research question below. Consider the following:* \n",
    "  - *Which aspects of the dataset will you use?* \n",
    "  - *How will you reorganize/store the data?* \n",
    "  - *What data science tools/functions will you use and why?* \n",
    "\n",
    "✏️ *Write your answer below:*\n",
    "\n",
    "**I will use the columns Gamble/Cooked to answer this question.\n",
    "I will...\n",
    "\n",
    "-get the value of people who gamble\n",
    "-get the value of people who order steak rare\n",
    "-get the value of people who do both gamble and order their steak rare\n",
    "\n",
    "I will use pandas to handle my data (as data frames), seaborne to create visualizations\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "juvenile-creation",
   "metadata": {},
   "source": [
    "# Results "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d239cc7-d973-452d-a462-7317ab8fe5f2",
   "metadata": {},
   "source": [
    "### Number of people who gamble\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "pursuant-surrey",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gamble\n",
       "Yes    216\n",
       "No     213\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "steak.gamble.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "located-night",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='gamble', ylabel='Count'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=steak, x=\"gamble\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e213a7eb-6a17-4db7-bda6-200200421688",
   "metadata": {},
   "source": [
    "### Number of people who like their steak rare\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c520afe4-e348-4573-a6f3-9968cc8b594b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cooked\n",
       "Medium rare    166\n",
       "Medium         132\n",
       "Medium Well     75\n",
       "Well            36\n",
       "Rare            23\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "steak.cooked.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b7fd5c7a-a8cb-4253-912b-d062f88caf25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='cooked', ylabel='Count'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=steak, x=\"cooked\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "996d3496-8057-4c0f-b083-ead62e394fde",
   "metadata": {},
   "source": [
    "### Number of people who like their steak rare, and who gamble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f75ee0b2-8ed2-40d2-9a6e-dd839663f364",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gamble  cooked     \n",
      "No      Medium         59\n",
      "        Medium Well    40\n",
      "        Medium rare    85\n",
      "        Rare           11\n",
      "        Well           18\n",
      "Yes     Medium         73\n",
      "        Medium Well    32\n",
      "        Medium rare    81\n",
      "        Rare           12\n",
      "        Well           18\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "counts = df.groupby([\"gamble\", 'cooked']).size()\n",
    "print(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8082a773-1acd-4da5-beef-e78b82e4bca7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='gamble', ylabel='count'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='gamble', hue='cooked', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "506a2001-d2dc-4e66-b330-699d5194db2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of people who gamble and order their steak rare: 12\n"
     ]
    }
   ],
   "source": [
    "df = df.dropna(subset=['gamble', 'cooked'])\n",
    "gamble_rare = df[(df['gamble'].str.lower() == 'yes') & (df['cooked'].str.lower() == 'rare')]\n",
    "count_gamble_rare = len(gamble_rare)\n",
    "print(f\"Number of people who gamble and order their steak rare: {count_gamble_rare}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87df3b25-3df9-42a8-884f-2a9c1a362bf4",
   "metadata": {},
   "source": [
    "## Third Research Question: *Do people (males and females) who smoke also order their steak rare?*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f20e80e3-f582-4422-ba20-5ff133730a79",
   "metadata": {},
   "source": [
    "### Methods\n",
    "*Explain how you will approach this research question below. Consider the following:* \n",
    "  - *Which aspects of the dataset will you use?* \n",
    "  - *How will you reorganize/store the data?* \n",
    "  - *What data science tools/functions will you use and why?* \n",
    "\n",
    "✏️ *Write your answer below:*\n",
    "\n",
    "**I will use the columns Smoke/Cooked to answer this question.\n",
    "I will...\n",
    "\n",
    "-get the value of people who smoke\n",
    "-get the value of people who order steak rare\n",
    "-get the value of people who do both smoke and order their steak rare\n",
    "\n",
    "I will use pandas to handle my data (as data frames), seaborne to create visualizations\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3a6ce78-a232-440d-be07-d5263b922bd7",
   "metadata": {},
   "source": [
    "# Results "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdf13331-0905-49a9-9da6-97124c967248",
   "metadata": {},
   "source": [
    "### Number of people who smoke\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "22f25f57-c47a-4b73-9fc6-12a5dcd85d2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "smoke\n",
       "No     356\n",
       "Yes     72\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "steak.smoke.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2377f2bc-3aa3-4d92-a128-c4cc9eaf7d35",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='smoke', ylabel='Count'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=steak, x=\"smoke\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baefe767-40fb-4afe-ae12-2374ce33ece4",
   "metadata": {},
   "source": [
    "### Number of people who like their steak rare\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2e62dead-0ed2-422b-abdb-c0d359732689",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cooked\n",
       "Medium rare    166\n",
       "Medium         132\n",
       "Medium Well     75\n",
       "Well            36\n",
       "Rare            23\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "steak.cooked.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "09aec01a-465f-49e7-8b2f-f6e3c74720da",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='cooked', ylabel='Count'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=steak, x=\"cooked\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee1b058c-71de-4141-b9e6-c1e66ebb6033",
   "metadata": {},
   "source": [
    "### Number of people who like their steak rare, and who smoke"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6abbe66a-565d-47a1-a6b0-b676c0362261",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "smoke  cooked     \n",
      "No     Medium         111\n",
      "       Medium Well     61\n",
      "       Medium rare    136\n",
      "       Rare            16\n",
      "       Well            30\n",
      "Yes    Medium          20\n",
      "       Medium Well     11\n",
      "       Medium rare     30\n",
      "       Rare             6\n",
      "       Well             5\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "counts = df.groupby([\"smoke\", 'cooked']).size()\n",
    "print(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4751cff3-a23a-449d-b8f3-c52072d02768",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='smoke', ylabel='count'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='smoke', hue='cooked', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81274425-b884-4604-a2d4-7f2cc6b4a1c2",
   "metadata": {},
   "source": [
    "### Number of people who like their steak rare, and who do all 3 risky behaviors(smoke, drink, gamble)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8817c157-0528-47c7-8680-c886dd5499f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of people who smoke, drink, and gamble who order their steak rare: 5\n"
     ]
    }
   ],
   "source": [
    "df = df.dropna(subset=['smoke', 'drink', 'gamble', 'cooked'])\n",
    "smoke_drink_gamble_rare = df[\n",
    "    (df['smoke'].str.lower() == 'yes') &\n",
    "    (df['drink'].str.lower() == 'yes') &\n",
    "    (df['gamble'].str.lower() == 'yes') &\n",
    "    (df['cooked'].str.lower() == 'rare')\n",
    "]\n",
    "count_smoke_drink_gamble_rare = len(smoke_drink_gamble_rare)\n",
    "print(f\"Number of people who smoke, drink, and gamble who order their steak rare: {count_smoke_drink_gamble_rare}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beneficial-invasion",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "about-raise",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "**OVERARCHING QUESTION**\n",
    "*DO PEOPLE WHO EXIBIT RISKY BEHAVIORS (SMOKING, GAMBLING, DRINKING) ORDER THEIR STEAK IN THE SAME RISKY MANNER AS THEIR BEAHVIORS (RARE). IS THERE A RELATIONSHIP BETWEEN THE TWO?*\n",
    "\n",
    "## *Findings \n",
    "-The number of total people in the survey was **432** \n",
    "\n",
    "-The number of people who smoke was **72** or **17%** of people in survey\n",
    "\n",
    "-The number of people who drink was **339** or **78%** of people in survey\n",
    "\n",
    "-The number of people who gamble was **216** or **50%** of people in survey\n",
    "\n",
    "-The number of people that order their steak rare was **23** or **5%** \n",
    "\n",
    "-The number of people who smoke and order steak rare was **6** or **1%** of people in survey\n",
    "\n",
    "-The number of people who drink and order steak rare was **19** or **4%** of people in survey\n",
    "\n",
    "-The number of people who Gamble and order steak rare was **12** or **3%** of people in survey\n",
    "\n",
    "# The most surprising stat was that the Number of people who do all 3 risky behaviors (smoke, drink, gamble) and order steak rare was **5** or **1%*\n",
    "\n",
    "\n",
    "\n",
    "**What did I learn about my data?**\n",
    "I learned that you can extract a bunch of different data about a survey. This was kind of a silly survey, but fun to see the results. What I learned was that out of small sample size of 432 people, doing risky behaviors such as smoking, drinking, and gambling, have nothing to do with food choices, specifically risky food choices such as ordering steak rare.\n",
    "\n",
    "**Did the results make sense?**\n",
    "Yes. The results made sense to me in the fact that I came in with a preconceived notion that the two wouldnt be related. What would have been interesting would be if you did a study between people who do risky behaviors who also live shorter lives.\n",
    "\n",
    "**What was most surprising?**\n",
    "# The most surprising stat was that the Number of people who do all 3 risky behaviors (smoke, drink, gamble) and order steak rare was **5** or **1%*\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "infectious-symbol",
   "metadata": {},
   "source": [
    "# Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bearing-stadium",
   "metadata": {},
   "source": [
    "*It's important to recognize the limitations of our research.\n",
    "Consider the following:*\n",
    "\n",
    "**Do the results give an accurate depiction of your research question? Why or why not?**\n",
    "Yes becasue it answers the question.\n",
    "  \n",
    "**What were limitations of your datset?**\n",
    "I only had a small sample size.\n",
    "\n",
    "**Are there any known biases in the data?**\n",
    "I dont believe so.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78e2f56b-6b99-4d03-b4ca-5ef0a78979d6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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