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   "source": [
    "# Data Disaggregation\n",
    "\n",
    "**Disaggregation** means describing or plotting data separately for different categories of individuals.\n",
    "\n",
    "As we saw in the first lecture of the series, data in a single dataset can arise from different causal processes, for example:\n",
    "* The distribution of age at death in 1840 includes a set of deaths caused by infant/child mortality, and a set caused by old age\n",
    "* The distribution of reaction times in a psychological experiment may include a mixture of 'true' responses, false starts, and missed trials\n",
    "\n",
    "*Disaggregating* data so that we are reporting statistics separately for these different groups is an important part of describing and analyzing data. For example:\n",
    "* We would like to report the mean reaction time for each condition of our psychological experiment based on 'true' responses, not including missed trials, which contribute a lot of noise to our estimate of the mean.\n",
    "\n",
    "\n",
    "Disaggregation becomes even more important when we think about making predictions based on data. For example:\n",
    "* If a patient presents with chest pain, is it more likely to be indigestion or a heart attack? The answer to this question partly depends on the age of the patient (heart attacks are much less likely in young patients), BUT that is different again for men and women.\n",
    "\n",
    "\n",
    "#### Equality\n",
    "\n",
    "If a dataset includes a majority and minority group (for example, if the dataset consistes of more men than women, or more white people than black people), then failure to disaggregate data results in findings being biased towards the majority group\n",
    "\n",
    "* For example, shockingly, <a href=\"https://www.theguardian.com/society/2021/nov/11/black-women-uk-maternal-mortality-rates\">black women are four times more likely to die in childbirth</a> than white women in the UK, a statistic that was long un-remarked because data on maternal outcomes were not routinely disaggregated by race\n",
    "\n",
    "    \n",
    "#### Disaggregation skills\n",
    "    \n",
    "Working out  which categories of data should be presented in disaggretgated form is a skill that you will learn through practice. Too little disaggregation can obscure important group differences or retains noise that could be removed; but too much disaggregation can result in an ocean of graphs and statistics that makes it hard to see the big picture.\n",
    "\n",
    "In this section we will look at disaggregation in the context of the heart attack dataset.    \n",
    "    \n",
    "### Set up Python Libraries\n",
    "\n",
    "As usual you will need to run this code block to import the relevant Python libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "be5b5d06",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set-up Python libraries - you need to run this but you don't need to change it\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set_theme(style='white')\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7fee4fc",
   "metadata": {},
   "source": [
    "### Import a dataset to work with\n",
    "\n",
    "Let's continue with the NYC heart attack dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ac9c9d93",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CHARGES</th>\n",
       "      <th>LOS</th>\n",
       "      <th>AGE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>DRG</th>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>M</td>\n",
       "      <td>121.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>12843</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>81.0</td>\n",
       "      <td>M</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1.0</td>\n",
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       "  </tbody>\n",
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       "<p>12844 rows × 6 columns</p>\n",
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      ],
      "text/plain": [
       "        CHARGES  LOS   AGE SEX    DRG  DIED\n",
       "0       4752.00   10  79.0   F  122.0   0.0\n",
       "1       3941.00    6  34.0   F  122.0   0.0\n",
       "2       3657.00    5  76.0   F  122.0   0.0\n",
       "3       1481.00    2  80.0   F  122.0   0.0\n",
       "4       1681.00    1  55.0   M  122.0   0.0\n",
       "...         ...  ...   ...  ..    ...   ...\n",
       "12839  22603.57   14  79.0   F  121.0   0.0\n",
       "12840       NaN    7  91.0   F  121.0   0.0\n",
       "12841  14359.14    9  79.0   F  121.0   0.0\n",
       "12842  12986.00    5  70.0   M  121.0   0.0\n",
       "12843       NaN    1  81.0   M  123.0   1.0\n",
       "\n",
       "[12844 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hospital=pd.read_csv('https://raw.githubusercontent.com/jillxoreilly/StatsCourseBook_2024/main/data/heartAttack.csv')\n",
    "display(hospital)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4846c77",
   "metadata": {},
   "source": [
    "#### Clean the data\n",
    "\n",
    "We have reloaded the dataframe from the .csv file, so we need to re-implement the data cleaning steps we already decided were necessary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "93e6c314",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>CHARGES</th>\n",
       "      <th>LOS</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DRG</th>\n",
       "      <th>DIED</th>\n",
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       "      <th>count</th>\n",
       "      <td>12145.000000</td>\n",
       "      <td>12843.000000</td>\n",
       "      <td>12840.000000</td>\n",
       "      <td>12841.000000</td>\n",
       "      <td>12841.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9879.087615</td>\n",
       "      <td>7.567858</td>\n",
       "      <td>66.288162</td>\n",
       "      <td>121.690523</td>\n",
       "      <td>0.109805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6558.399650</td>\n",
       "      <td>5.114357</td>\n",
       "      <td>13.654237</td>\n",
       "      <td>0.658289</td>\n",
       "      <td>0.312658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5422.200000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8445.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12569.040000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
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       "      <td>38.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>1.000000</td>\n",
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      ],
      "text/plain": [
       "            CHARGES           LOS           AGE           DRG          DIED\n",
       "count  12145.000000  12843.000000  12840.000000  12841.000000  12841.000000\n",
       "mean    9879.087615      7.567858     66.288162    121.690523      0.109805\n",
       "std     6558.399650      5.114357     13.654237      0.658289      0.312658\n",
       "min        3.000000      0.000000     20.000000    121.000000      0.000000\n",
       "25%     5422.200000      4.000000     57.000000    121.000000      0.000000\n",
       "50%     8445.000000      7.000000     67.000000    122.000000      0.000000\n",
       "75%    12569.040000     10.000000     77.000000    122.000000      0.000000\n",
       "max    47910.120000     38.000000    103.000000    123.000000      1.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hospital.replace(9999, np.nan, inplace=True)\n",
    "hospital.AGE.replace(774, np.nan, inplace=True )\n",
    "hospital.describe() # check it worked"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3ddc6de",
   "metadata": {},
   "source": [
    "## Age vs Sex\n",
    "\n",
    "Is the age distribution of heart attack patients the same regardless of sex?\n",
    "\n",
    "Let's find out by plotting the age distribution separately for men and women:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "34255c9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data=hospital, x='AGE', hue='SEX', fill=True) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de9b2638",
   "metadata": {},
   "source": [
    "**Note-**\n",
    "* More men had heart attacks than women\n",
    "* The female patients tend to be older.\n",
    "\n",
    "**Think**\n",
    "\n",
    "A 40 year old patient presents with chest pain. It could be a heart attack or it could be indigestion. The doctor needs to decide the likely cause. Does it matter whether the patient is a man or a woman?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c57ea956",
   "metadata": {},
   "source": [
    "### Length of stay\n",
    "\n",
    "Let's plot the distribution of Length of Stay in hospital:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b3b236c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=hospital, x='LOS', bins=range(0,40))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5043d9c",
   "metadata": {},
   "source": [
    "**Note-**\n",
    "\n",
    "There is something unusual here - the dataset is bimodal, with a large number of people staying just one day in hospital.\n",
    "\n",
    "Often a bimodal distribution is a hint that data the data distribution is a mixture of data arising from two causes. In other words, we suspect the length of stay data could be meaningfully disaggregated.\n",
    "\n",
    "If we disaggregate these by the categorical variable <tt>DIED</tt>, we can get a clearer picture what happened:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "434075b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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22msuwUiSkpOTFR0drfLycudYY2OjqqqqNG7cOEmS3W7Xnj171N7e7qwpKytTUlIS640AAPCRc12A9XWVlZWKi4tzBiNJuvbaa2WxWLRnzx5vtxrYR45Wrlypzz77TBs3btSgQYN09OhR57ZBgwbJarUqOztbq1ev1qBBgzR48GCtWrVKNptNmZmZkqTJkydr48aNys/P19y5c7V3715t2bLF5V5JAADAu851AdbX1dXVdbrYymq1auDAgc4LrrwpYMORw+HQ22+/rTNnzmjGjBmdtu/atUuXXnqpcnNz1dbWpsWLF6u5uVl2u11FRUXORdixsbHauHGjVqxYoaysLMXFxWnBggXKysry9ZQAAIAbmpqaOl1MJblecOVNARWOnnrqKef3ISEh2rt3b7evCQ0NVV5envLy8r6xZvTo0SopKfFIjwAAwLu6uphKOntBlS8upgroNUcAAKD/6epiqtbWVn355Zc+uZiKcAQAAAKK3W5XbW2ty4PkOy6+Gjt2rNc/n3AEAAD8qr29XUePHlVzc7MkKSUlRWPHjtX8+fO1d+9e/elPf9KSJUt0xx13+OTIUUCtOQIAAO67aNCAoPjMmpoaTZo0SStXrtSdd94pi8WidevWadmyZZoxY4bCw8N18803a9GiRR7/7K4QjgAA6IMcDsd53an6fD87JKT3J5/MF2BJ0qWXXqqPP/7YZSw2NlYFBQW9/ozzwWk1AAD6oPMJJ335s30huGcHAADQQ4QjAAAAE8IRAACACeEIAADAhHAEAECAMwzD3y30CZ76PRGOAAAIUBdccIEk6fTp037upG/o+D11/N56i/scAQAQoEJDQzVw4EDnc8aioqJksVj83FXgMQxDp0+fVn19vQYOHKjQ0NDzej/CEQAAAcxms0lSpwexorOBAwc6f1/ng3AEAEAAs1gsSkxMVHx8vM6cOePvdgLWBRdccN5HjDoQjgAA6ANCQ0M99scf58aCbAAAABPCEQAAgAnhCAAAwIRwBAAAYEI4AgAAMCEcAQAAmBCOAAAATAhHAAAAJoQjAAAAE8IRAACACeEIAADAhHAEAABgQjgCAAAwIRwBAACYEI4AAABMCEcAAAAmhCMAAAATwhEAAIAJ4QgAAMCEcAQAAGBCOAIAADAhHAEAAJgQjgAAAEwIRwAAACYBFY4KCws1ffp0l7F9+/YpOztbqampmjhxooqKily2OxwOFRQUKD09XSkpKZo9e7YOHTrUo/cAAADoEDDhaPPmzSooKHAZO378uGbNmqWhQ4eqtLRUOTk5Wrt2rUpLS501hYWFKi4u1vLly1VSUiKLxaJ58+aptbXV7fcAAADoEObvBurq6pSfn689e/YoKSnJZdvrr78uq9WqpUuXKiwsTMOGDdOhQ4e0YcMGTZ48Wa2trdq0aZPy8vI0YcIESdKaNWuUnp6unTt36tZbb+32PQD3WWQYhiwWi9uv6Gk9AMD//H7k6KOPPtKAAQP05ptvKiUlxWVbZWWl7Ha7wsK+ynBpaWk6cOCAGhoaVF1drVOnTiktLc25PSYmRiNHjlRFRYVb7wG4zSJZLBY1Ha3T6bqabr+ajtYRjACgD/L7kaOMjAxlZGR0ua22tlYjRoxwGYuPj5ckHTlyRLW1tZKkxMTETjU1NTVuvUdsbOz5TwL9iuFwSIbDjTofNAMA8Di/Hzk6l+bmZlmtVpex8PBwSVJLS4uampokqcualpYWt94DAADALKDDUUREhHNhdYeOQBMVFaWIiAhJ6rImMjLSrfcAAAAwC+hwZLPZVF9f7zLW8XNCQoLzdFpXNTabza33AAAAMAvocGS327Vnzx61t7c7x8rKypSUlKTY2FglJycrOjpa5eXlzu2NjY2qqqrSuHHj3HoPAAAAs4AOR5MnT9bJkyeVn5+v/fv364033tCWLVt03333STq71ig7O1urV6/Wrl27VF1drfnz58tmsykzM9Ot9wAAADDz+9Vq5xIbG6uNGzdqxYoVysrKUlxcnBYsWKCsrCxnTW5urtra2rR48WI1NzfLbrerqKjIuQjbnfcAAADoYDEMw/B3E33NpEmTJEm7du3ycyf9y+m6mu6vjw8JVVS8zb3antb39L0tIYpKSOy+DgDgE+7+/Q7o02oAAAC+RjgCAAAwIRwBAACYEI4AAABMCEcAAAAmhCMAAAATwhEAAIAJ4QgAAMCEcAQAAGBCOAIAADAhHAEAAJgQjgAAAEwIRwAAACaEIwAAABPCEQAAgAnhCAAAwIRwBAAAYEI4AgAAMCEcAQAAmBCO4BeGYfi7BQAAuhTm7wbQP1ksFjUdrZPhcHRfGxqqyIvjfdAVAACEI/iR4XBIRvfhyHBYfNANAABncVoNAADAhHAEAABgQjgCAAAwIRwBAACYEI4AAABMCEcAAAAmhCMAAAATwhEAAIAJ4QgAAMCEcAQAAGBCOAIAADAhHAEAAJgQjgAAAEwIRwAAACaEIwAAABPCEQAAgAnhCAAAwIRwBAAAYNInwtGZM2e0Zs0aTZw4UWPGjNHUqVP15z//2bl93759ys7OVmpqqiZOnKiioiKX1zscDhUUFCg9PV0pKSmaPXu2Dh065OtpAACAPqBPhKP169ertLRUy5cv17Zt23TFFVdo3rx5qqur0/HjxzVr1iwNHTpUpaWlysnJ0dq1a1VaWup8fWFhoYqLi7V8+XKVlJTIYrFo3rx5am1t9eOsAABAIOoT4WjXrl36/ve/rxtuuEFDhgzRT37yE508eVIffPCBXn/9dVmtVi1dulTDhg3T5MmTNXPmTG3YsEGS1Nraqk2bNiknJ0cTJkxQcnKy1qxZo7q6Ou3cudPPMwMAAIGmT4SjgQMH6re//a0OHz6s9vZ2lZSUyGq16sorr1RlZaXsdrvCwsKc9WlpaTpw4IAaGhpUXV2tU6dOKS0tzbk9JiZGI0eOVEVFhT+mAwAAAlhY9yX+l5+fr/nz52vSpEkKDQ1VSEiI1q5dq8svv1y1tbUaMWKES318fLwk6ciRI6qtrZUkJSYmdqqpqanxzQQAAECf0SfC0aeffqqYmBi98MILSkhI0NatW7Vw4UK9+uqram5ultVqdakPDw+XJLW0tKipqUmSuqw5ceKEbyYAAAD6jIAPR59//rny8vK0efNmjRs3TpI0atQo7d+/X88//7wiIiI6LaxuaWmRJEVFRSkiIkLS2bVHHd931ERGRvpoFgAAoK/o1ZqjiooKnTp1qsttjY2Neuutt86rKbO9e/fqzJkzGjVqlMt4SkqKDh48KJvNpvr6epdtHT8nJCQ4T6d1VWOz2TzWJwAACA69Ckf33HOPPv300y63VVVVadGiRefVlFlHuPn4449dxj/55BMNGTJEdrtde/bsUXt7u3NbWVmZkpKSFBsbq+TkZEVHR6u8vNy5vbGxUVVVVc4jUQAAAB3cPq22cOFC5wJmwzC0dOlSRUdHd6o7ePCgLr74Yo81OHr0aI0bN04LFy7UkiVLZLPZtG3bNpWVlem1117TZZddpo0bNyo/P19z587V3r17tWXLFi1btkzS2bVG2dnZWr16tQYNGqTBgwdr1apVstlsyszM9FifAAAgOLgdjm666Sa9/PLLLmOGYbj8HBoaqtTUVE2bNs0z3UkKCQlRYWGhnnvuOS1atEgnTpzQiBEjtHnzZqWmpkqSNm7cqBUrVigrK0txcXFasGCBsrKynO+Rm5urtrY2LV68WM3NzbLb7SoqKuq0SBsAAMBifD3huGH69OnOmy72R5MmTZJ09uaU6L3TdTWS4ei+MCRUUfE29+p7Uuvt97aEKCohsfs6AIBPuPv3u1dXq73yyiu9eRkAAEDA61U4ampq0osvvqjf/va3ampqksPh+r+iLRaL3n33XY80CAAA4Eu9CkcrVqxQaWmprr32Wl155ZUKCekTTyEBAADoVq/C0TvvvKP58+fr3nvv9XQ/AAAAftWrQz5tbW0aPXq0p3sBAADwu16FoxtuuEF/+MMfPN0LAACA3/XqtNott9yiJUuW6NixY0pJSenyGWV33HHH+fYGAADgc70KRw899JAkadu2bdq2bVun7RaLhXDUDxmGIYvF4u82AAA4L70KR9z8EF2xWCxqOlonw3HuGyRaQkMVeXG8j7oCAKBnehWOBg8e7Ok+ECQMh6Pbu0cbDo4uAQACV6/C0bp167qtefDBB3vz1gAAAH7l8XAUHR2t+Ph4whEAAOiTehWOqqurO42dPn1ae/bs0dKlS/XYY4+dd2MAAAD+4LHnfkRFRSk9PV0PPPCAfvazn3nqbYE+zCLDMNyu7kktAMB7enXk6FwSExP16aefevptgb7H0oMr+EJCFBmX4KPGAADn4rFwZBiGampqtGHDBq5mA0zcu4LPR80AALrVq3CUnJz8jTf7MwyD02oAAKDP6lU4euCBB7oMR9HR0Zo4caKGDh16vn0BAAD4Ra/CUU5Ojqf7AAAACAi9XnPU2tqqN954Q+Xl5WpsbNRFF12kcePGKSsrS+Hh4Z7sEQAAwGd6FY4aGxt1zz33qLq6Wpdcconi4uJ04MABbd++Xb/4xS/02muv6cILL/R0rwAAAF7Xq/scPfPMM6qtrdWrr76q9957TyUlJXrvvff06quvqqGhQWvXrvV0nwAAAD7Rq3C0a9cuPfTQQxo3bpzL+Lhx45Sbm6t33nnHI80BAAD4Wq/C0alTp3TZZZd1ue2yyy7Tl19+eT49AQAA+E2vwtEVV1yh3/72t11u27Vrl4YMGXJeTQEAAPhLrxZkz5kzRw8//LBaW1t122236eKLL9YXX3yhX//619q6dauWLl3q4TYBAAB8o1fh6JZbbtHBgwf14osvauvWrc7xCy64QA888ICmTJnisQYBAAB8qVfh6PTp07r//vuVnZ2tDz74QCdOnFBNTY2mTJmiAQMGeLpHAAAAn+nRmqN9+/bpjjvu0ObNmyVJMTExGj9+vMaPH6/nnntOU6dO1aeffuqNPgEAAHzC7XD02WefaebMmTpx4oSGDx/uss1qterRRx/VqVOnNHXqVNXW1nq8UQAAAF9wOxy99NJLuuiii/Sf//mfuvHGG122RUZGKjs7W6WlpYqKitKLL77o8UYBAAB8we1wVFZWprlz52rgwIHfWBMbG6tZs2aprKzME70BAAD4nNvh6OjRo27dv2jEiBGcVgMAAH2W2+Fo0KBBqq+v77bu2LFj5zy6BAAAEMjcDkd2u11vvPFGt3Xbtm3TlVdeeV5NAQAA+Ivb4Wj69OkqLy/XU089pZaWlk7bW1tb9fTTT+u///u/NW3aNI82CQAA4Ctu3wRy1KhRWrRokZ588kn96le/0nXXXadLL71U7e3tOnLkiMrLy3X8+HH9v//3/5Senu7NnuEjhmHIYrH4uw0AAHyqR3fInjZtmpKTk1VUVKRdu3Y5jyB961vf0g033KDZs2crJSXFK43C9ywWi5qO1slwOLqvDQ1V5MXxPugKAADv6vHjQ6655hpdc801kqTjx48rJCSER4YEMcPhkIzuw5Hh4AgTACA49OjxIV930UUX+SwYbdu2TbfccotGjRqlW2+9VTt27HBu27dvn7Kzs5WamqqJEyeqqKjI5bUOh0MFBQVKT09XSkqKZs+erUOHDvmkbwAA0LecVzjylV/96ld69NFHNWXKFG3fvl233HKLHn74Yf3lL3/R8ePHNWvWLA0dOlSlpaXKycnR2rVrVVpa6nx9YWGhiouLtXz5cpWUlMhisWjevHlqbW3146w8wzAMr9QCANBf9fi0mq8ZhqG1a9dqxowZmjFjhiTpgQce0J///Gft3r1bu3fvltVq1dKlSxUWFqZhw4bp0KFD2rBhgyZPnqzW1lZt2rRJeXl5mjBhgiRpzZo1Sk9P186dO3Xrrbf6c3rnzd11QZaQEEXGJfioKwAA+q6AP3L0j3/8Q59//rluu+02l/GioiLdd999qqyslN1uV1jYVzkvLS1NBw4cUENDg6qrq3Xq1CmlpaU5t8fExGjkyJGqqKjw2Ty8ybku6Bxf7iyqBgAAfSAcHTx4UJJ0+vRpzZkzR9ddd53uuusuvffee5Kk2tpa2Ww2l9fEx5+9aurIkSPOR5kkJiZ2qqmpqfFy9wAAoK8J+HB08uRJSdLChQv1/e9/X5s2bdL111+v+++/X2VlZWpubpbVanV5TXh4uCSppaVFTU1NktRlTVc3swQAAP1bwK85uuCCCyRJc+bMUVZWliTpyiuvVFVVlV5++WVFRER0WljdEXqioqIUEREh6ewdvDu+76iJjIz0xRQAAEAfEvBHjjpOmY0YMcJlfPjw4Tp8+LBsNlunB+J2/JyQkOA8ndZVzddPxwEAAAR8OBo5cqS+9a1v6cMPP3QZ/+STT3T55ZfLbrdrz549am9vd24rKytTUlKSYmNjlZycrOjoaJWXlzu3NzY2qqqqSuPGjfPZPAAAQN8Q8KfVIiIiNHfuXL3wwgtKSEjQ6NGj9dZbb+mPf/yjNm/erOHDh2vjxo3Kz8/X3LlztXfvXm3ZskXLli2TdHatUXZ2tlavXq1BgwZp8ODBWrVqlWw2mzIzM/08OwAAEGgCPhxJ0v3336/IyEitWbNGdXV1GjZsmJ5//nl997vflSRt3LhRK1asUFZWluLi4rRgwQLn+iRJys3NVVtbmxYvXqzm5mbZ7XYVFRV1WqQNAADQJ8KRJM2aNUuzZs3qctvo0aNVUlLyja8NDQ1VXl6e8vLyvNUeAAAIEn0mHOGbVX/0iZpPN5+zJiIqQmMTEs9ZAwAACEdBoe1Mu9ra2rqtAQAA3Qv4q9XgGSGhITx4FgAAN3DkqJ8ICQl1+yG1kmQJDVXkxfE+6AwAgMBCOOpnnA+p7bbO4oNu8BWLDMOQxeL+772n9QAA9xCOgEBgUc+O7IWEKDIuwQeNAUD/QzgCAoj7R/Z80AwA9FMsyAYAADAhHAEAAJgQjgAAAEwIRwAAACaEIwAAABPCEQAAgAnhCAAAwIRwBAAAYEI4AgAAMCEcAQAAmBCOAAAATAhHAAAAJoQjAAAAE8IRAACACeEIAADAhHAEAABgQjgCAAAwIRwBAACYEI4AAABMCEcAAAAmhCMAAAATwhEAAIAJ4QgAAMCEcAQAAGBCOAIAADAhHAEAAJgQjgAAAEwIRwAAACaEIwAAABPCEQAAgAnhCAAAwIRwBAAAYEI4AgAAMOlT4ejAgQMaM2aM3njjDefYvn37lJ2drdTUVE2cOFFFRUUur3E4HCooKFB6erpSUlI0e/ZsHTp0yNetAwCAPqLPhKMzZ87oxz/+sU6fPu0cO378uGbNmqWhQ4eqtLRUOTk5Wrt2rUpLS501hYWFKi4u1vLly1VSUiKLxaJ58+aptbXVH9MAAAABrs+Eo+eff17f+ta3XMZef/11Wa1WLV26VMOGDdPkyZM1c+ZMbdiwQZLU2tqqTZs2KScnRxMmTFBycrLWrFmjuro67dy50x/TAAAAAa5PhKOKigqVlJTo6aefdhmvrKyU3W5XWFiYcywtLU0HDhxQQ0ODqqurderUKaWlpTm3x8TEaOTIkaqoqPBZ/wAAoO8I+HDU2NioBQsWaPHixUpMTHTZVltbK5vN5jIWHx8vSTpy5Ihqa2slqdPr4uPjVVNT48WuAQBAXxXw4Wjp0qVKTU3Vbbfd1mlbc3OzrFary1h4eLgkqaWlRU1NTZLUZU1LS4uXOgYAAH1ZWPcl/rNt2zZVVlbq17/+dZfbIyIiOi2s7gg9UVFRioiIkHR27VHH9x01kZGRXuoaAAD0ZQEdjkpLS9XQ0KCJEye6jC9ZskRFRUW65JJLVF9f77Kt4+eEhAS1tbU5xy6//HKXmuTkZO82DwAA+qSADkerV69Wc3Ozy9iNN96o3Nxc3XLLLXrrrbdUXFys9vZ2hYaGSpLKysqUlJSk2NhYXXjhhYqOjlZ5ebkzHDU2NqqqqkrZ2dk+nw8AAAh8AR2OEhISuhyPjY3V4MGDNXnyZG3cuFH5+fmaO3eu9u7dqy1btmjZsmWSzq41ys7O1urVqzVo0CANHjxYq1atks1mU2Zmpi+nAgAA+oiADkfdiY2N1caNG7VixQplZWUpLi5OCxYsUFZWlrMmNzdXbW1tWrx4sZqbm2W321VUVNRpkTbQt1hkGIYsFovbr+hpPQD0V30uHH388ccuP48ePVolJSXfWB8aGqq8vDzl5eV5uzXAdyySxWJR09E6GQ5H9+UhIYqM6/pILADAVZ8LRwC+YjgcktF9OHKjBADwfwL+PkcAAAC+RDgCAAAwIRwBAACYEI4AAABMCEcAAAAmhCMAAAATwhEAAIAJ4QgAAMCEcAQAAGBCOAIAADAhHAEAAJgQjgAAAEwIRwAAACaEIwAAABPCEdAvWGQYhtvVPakFgGAT5u8GAPiARbJYLGo6WifD4Th3aUiIIuMSfNQYAAQewhHQjxgOh2ScOxx1sxkAgh6n1QAAAEwIRwAAACaEIwAAABPCEQAAgAnhCAAAwIRwBAAAYEI4AgAAMCEcAQAAmBCOAAAATAhHAAAAJoQjAAAAE8IRAACACeEIAADAhHAEAABgQjgCAAAwIRwBAACYEI4AAABMCEcAAAAmhCMAX2ORYRg9ekVP6wEgkIX5uwEAAcYiWSwWNR2tk+FwdF8eEqLIuAQfNAYAvkE4AtAlw+GQjO7DkRslANCncFoNAADAJODD0ZdffqnHH39c48eP19ixY/WDH/xAlZWVzu379u1Tdna2UlNTNXHiRBUVFbm83uFwqKCgQOnp6UpJSdHs2bN16NAhX08DAAD0EQEfjh5++GF9+OGHevbZZ/XLX/5SV111lebMmaNPP/1Ux48f16xZszR06FCVlpYqJydHa9euVWlpqfP1hYWFKi4u1vLly1VSUiKLxaJ58+aptbXVj7MCAACBKqDXHB06dEh//OMf9R//8R8aO3asJCk/P19/+MMftH37dkVERMhqtWrp0qUKCwvTsGHDdOjQIW3YsEGTJ09Wa2urNm3apLy8PE2YMEGStGbNGqWnp2vnzp269dZb/Tk9AAAQgAL6yNFFF12kl156SVdffbVzzGI5e5nxiRMnVFlZKbvdrrCwrzJeWlqaDhw4oIaGBlVXV+vUqVNKS0tzbo+JidHIkSNVUVHh07kAAIC+IaDDUUxMjCZMmCCr1eoc27Fjh/75z3/qhhtuUG1trWw2m8tr4uPjJUlHjhxRbW2tJCkxMbFTTU1NjZe7BwAAfVFAh6Ov27Nnjx599FFNmjRJGRkZam5udglOkhQeHi5JamlpUVNTkyR1WdPS0uKbpgEAQJ/SZ8LRu+++qzlz5mj06NF69tlnJUkRERGdFlZ3hJ6oqChFRERIUpc1kZGRPugaAAD0NX0iHL366qvKycnR+PHjtWHDBmfosdlsqq+vd6nt+DkhIcF5Oq2rmq+fjgPQWz173AiPGgEQ6AL6ajVJeu211/TEE09o+vTpevTRRxUS8lWes9vtKi4uVnt7u0JDQyVJZWVlSkpKUmxsrC688EJFR0ervLxcl19+uSSpsbFRVVVVys7O9st8gKDTg8eN8KgRAH1BQB85OnDggJ588kllZmbqvvvuU0NDg44ePaqjR4/qf//3fzV58mSdPHlS+fn52r9/v9544w1t2bJF9913n6Sza42ys7O1evVq7dq1S9XV1Zo/f75sNpsyMzP9PDsguDgfN3KOL3ee1QYA/hbQR45+85vf6MyZM9q5c6d27tzpsi0rK0tPPfWUNm7cqBUrVigrK0txcXFasGCBsrKynHW5ublqa2vT4sWL1dzcLLvdrqKiok6LtAEAAKQAD0c//OEP9cMf/vCcNaNHj1ZJSck3bg8NDVVeXp7y8vI83R4AAAhCAX1aDQAAwNcIRwAAACaEo34iJMTi7xYAAOgTAnrNETzo/8LRJ9Wf6vT/nuq2/FsDopUSz72gAAD9D+Gon2lva1dbW1v3dWe45BoA0D9xWg0AAMCEcAQAAGBCOAIAADAhHAEAAJgQjgAAAEwIRwAAACaEIwA+ZJFhGD16RU/rAeB8cZ8jAL5jkSwWi5qO1slwdH8vLUtIiCLjEnzQGAB8hXAEwOcMh0Myug9HbpQAgMdxWg0AAMCEcIQu8aBaAEB/xWk1dI0H1QIA+inCEc6JB9UCAPobTqsBAACYEI4AAABMCEcAAAAmhCMAAAATwhGAoOHtR5PwKBOgf+BqNQAB7Oyz2CwW9+671aNHk4SGKvLieB5lAqATwhE8gptGwit68Cy2jrDj/qNJLP/3f3mUCQBXhCN4Rg9uGnnhRQM0Kt7GDSbhNncCTEfYAYDzRTiCR7lz08j29na3ayVuMAkA8C0WZCPgccoOAOBLHDlC4OvBKTtOwQEAzhfhCH2GO6fhjHYutYa39OzKuZ7UAggshCMElx4cZZI40oQe6MmVc1z2D/RphCMEJRZ7w1vcu3LOR80A8AoWZAMAAJgQjgAAAEwIR+jXuE0AAODrWHOE/o3bBCBAcCUcEDgIR4DcvLM3i7fhtp5d9i9xJRwQSAhHgJs4BQe39eCyf6lnD801HD0PXhyVAnqGcAS4i3sooYfcCTtn63oQRnoZvNw9KhVxcbz7vYgwheBEOAJ6iHsoIRD0NHi5dVTKsPQseHGKD0Gq34Qjh8OhdevWaevWrWpsbNQ111yjJUuWaMiQIf5uDUHKF6fhOIoFb3A/eHm5Dy+ePgTOpd+Eo8LCQhUXF2vlypVKSEjQqlWrNG/ePG3fvl1Wq9Xf7SEY9eA03IUXDdCoeJvbYaejnqNY8C/vPm+Oo1jwl34RjlpbW7Vp0ybl5eVpwoQJkqQ1a9YoPT1dO3fu1K233urnDhHM3LoSrr3d7Vpzvbt6cxSLo1Lolg+eN9eT04feDGqBsqjd20fTOPp2Vr8IR9XV1Tp16pTS0tKcYzExMRo5cqQqKioIRwh+vTiK5W5QM9oNj7SIvstbV9n1iJeDWk/e25uL2r21GF/i6JtZvwhHtbW1kqTExESX8fj4eNXU1PT4/err69Xe3q5JkyZ5pL/z1drcIqObf5hCQkJ0QXi4Wlta3PqPxJv1vLdv39tXvZxpaZHhOHdQsoSE6IJwq1u1Pa335nub6w13jtpZJEtIqHu1Pa3v6+/taJfcydMWiywhId7pRWeDQ095Z55n6z3eh7mXHhxp7s3vpS+pqalRqBtz7BfhqKmpSZI6rS0KDw/XiRMnevx+4eHham1t9UhvnmCNCHe/Ntz9Wm/X896+fW9v93JBD+p7UhtI7y317I9HT//Q9Iv37mEQ8GYvPeXNeXqrj97UB7OwsDC31hn3i3AUEREh6ezao47vJamlpUWRkZE9fr/KykqP9QYAAAJLv3jwbMfptPr6epfx+vp62WwsJAUAAF/pF+EoOTlZ0dHRKi8vd441NjaqqqpK48aN82NnAAAg0PSL02pWq1XZ2dlavXq1Bg0apMGDB2vVqlWy2WzKzMz0d3sAACCA9ItwJEm5ublqa2vT4sWL1dzcLLvdrqKiIm4ACQAAXFgMw+AmJQAAAP+nX6w5AgAAcBfhCAAAwIRwBAAAYEI4AgAAMCEcAQAAmBCOAAAATAhHAAAAJoSjAOFwOFRQUKD09HSlpKRo9uzZOnTokL/b8rjPP/9c3/nOdzp9bd261d+teURhYaGmT5/uMrZv3z5lZ2crNTVVEydOVFFRkZ+685yu5rlo0aJO+3X8+PF+6rB3vvzySz3++OMaP368xo4dqx/84AcuD5oOln3Z3TyDYV9KUkNDg/Ly8pSWlqYxY8bo3nvv1f79+53bg2V/djfPYNmfZgcOHNCYMWP0xhtvOMc8uj8NBITnn3/euO6664zf/e53xr59+4zZs2cbmZmZRktLi79b86hdu3YZo0aNMurq6oz6+nrnV1NTk79bO28vv/yy8Z3vfMfIzs52jh07dsz47ne/a+Tn5xv79+83fvnLXxqjRo0yfvnLX/qx0/PT1TwNwzCysrKMZ5991mW/NjQ0+KnL3pk1a5Zx++23GxUVFcann35qPPHEE8bo0aON/fv3B9W+PNc8DSM49qVhGMZdd91lTJkyxdi7d6+xf/9+Iycnx7j++uuN06dPB9X+PNc8DSN49meH1tZW48477zRGjBhhlJaWGobh+X9rCUcBoKWlxRgzZozx2muvOcdOnDhhjB492ti+fbsfO/O89evXG7fffru/2/Co2tpaY86cOUZqaqpx8803u4SGF1980UhPTzfOnDnjHHvmmWeMm266yR+tnpdzzbOtrc0YNWqUsXPnTj92eH4OHjxojBgxwtizZ49zzOFwGJmZmcZzzz0XNPuyu3kGw740jLN/LOfPn2988sknzrF9+/YZI0aMMD788MOg2Z/dzTNY9qfZM888Y0yfPt0lHHl6f3JaLQBUV1fr1KlTSktLc47FxMRo5MiRqqio8GNnnvfxxx9r+PDh/m7Doz766CMNGDBAb775plJSUly2VVZWym63Kyzsq8cYpqWl6cCBA2poaPB1q+flXPM8ePCgWlpaNGzYMD91d/4uuugivfTSS7r66qudYxaLRYZh6MSJE0GzL7ubZzDsS+nsPJ999ll9+9vfliR98cUXKioqks1m0/Dhw4Nqf55rnsGyPztUVFSopKRETz/9tMu4p/dnv3nwbCCrra2VJCUmJrqMx8fHq6amxh8tec0nn3yiuLg4TZ06VQcPHtSQIUN0//33Kz093d+t9VpGRoYyMjK63FZbW6sRI0a4jMXHx0uSjhw5otjYWK/35ynnmucnn3wii8WiLVu26A9/+INCQkI0YcIEPfTQQ7rwwgt93GnvxMTEaMKECS5jO3bs0D//+U/dcMMNWrNmTVDsy+7mGQz78usee+wxvf7667JarVq/fr2ioqKC6r/NDl3NM5j2Z2NjoxYsWKDFixd3+nvp6f3JkaMA0NTUJEmyWq0u4+Hh4WppafFHS17R2tqqgwcP6uTJk3rooYf00ksvadSoUZo3b57Kysr83Z5XNDc3d7lfJQXVvv373/+ukJAQDR48WC+++KIWLlyo3//+97r//vvlcDj83V6v7NmzR48++qgmTZqkjIyMoN2XX59nMO7LGTNmqLS0VLfffrseeOABffTRR0G5P7uaZzDtz6VLlyo1NVW33XZbp22e3p8cOQoAERERks6Gh47vpbM7NDIy0l9teZzValVFRYXCwsKc/0989dVX69NPP1VRUZGuu+46P3foeREREWptbXUZ6/gPNSoqyh8teUVOTo5mzpypmJgYSdKIESMUFxenKVOm6K9//Wun03CB7t1339WPf/xjpaSk6Nlnn5UUnPuyq3kG276U5DyV/8QTT+iDDz7Qq6++GpT7s6t5Pvnkk0GxP7dt26bKykr9+te/7nK7p/cnR44CQMfhwfr6epfx+vp62Ww2f7TkNVFRUZ3S/YgRI1RXV+enjrzLZrN1uV8lKSEhwR8teYXFYnH+49uh4xB3x2njvuLVV19VTk6Oxo8frw0bNjj/B0uw7ctvmmew7MuGhgZt375d7e3tzrGQkBANGzbM+W9rMOzP7uYZLPuztLRUDQ0NmjhxosaMGaMxY8ZIkpYsWaJbb73V4/uTcBQAkpOTFR0drfLycudYY2OjqqqqNG7cOD925lnV1dUaM2aMy/1UJOlvf/tb0C3S7mC327Vnzx6Xf7jKysqUlJTUJ9c0fJNHHnlEc+bMcRn761//Kkl9at++9tpreuKJJzRt2jQ999xzLkE+mPblueYZLPuyvr5ejzzyiHbv3u0cO3PmjKqqqjRs2LCg2Z/dzTNY9ufq1av19ttva9u2bc4vScrNzdVLL73k+f3pgavq4AHPPvusce211xrvvvuu8z5HN954Y1Dd56i9vd246667jO9///tGRUWFsX//fuPJJ580rr76aqO6utrf7XnEwoULXS5x/+KLLwy73W4sXLjQ+Pvf/26UlpYao0aNMt544w0/dnn+vj7P9957z/jOd75jFBYWGocOHTJ+97vfGRkZGcbDDz/sxy575h//+Idx1VVXGQ888IDL/WDq6+uNxsbGoNmX3c0zGPalYZy9PcHs2bONm266yaioqDA+/vhjY/78+Ybdbjc+//zzoNmf3c0zWPZnV8yX8nt6fxKOAkRbW5vxs5/9zEhLSzNSU1ONefPmGZ999pm/2/K4hoYGY9GiRcb1119vjBo1ypgyZYpRUVHh77Y85uuhwTAM48MPPzTuvvtu4+qrrzb+5V/+xXjllVf81J3ndDXP//qv/zLuuOMOY/To0cb1119vPPXUU0Zzc7OfOuy59evXGyNGjOjya+HChYZhBMe+dGeefX1fdmhsbDSWLFliXH/99cbo0aON2bNnu9wPKBj2p2F0P89g2Z9fZw5HhuHZ/WkxDMPw0FEvAACAPo81RwAAACaEIwAAABPCEQAAgAnhCAAAwIRwBAAAYEI4AgAAMCEcAQAAmPDgWQBBZ/r06ZKkV1555Zx1hw8f1oYNG/T++++rvr5eAwYM0NVXX63s7GzdcMMNneo///xzFRYW6v3331dDQ4Oio6OVkpKiWbNmKS0tzStzAeB7hCMA/VJZWZkeeOABJSQkaM6cORo+fLiOHTum7du3a86cOZoxY4YeffRRZ/3Ro0c1ZcoUxcfHa/78+brkkkt07Ngxbd26VTNnzlRBQYFuvPFGP84IgKcQjgD0O3V1dcrNzdWYMWNUWFio8PBw57abb75Zmzdv1sqVK/Xtb39bd911lyTp9ddfV2Njo3bs2KELL7zQWZ+Zmam77rpLa9euJRwBQYI1RwD6nc2bN+vUqVNavny5SzDqMHPmTKWmpmr9+vXqeMLSF198IYvFIofD4VIbGhqqRx55RHfffbdPegfgfYQjAP3O+++/ryuvvFKJiYnfWPO9731Pn3/+ufbt2ydJmjhxopqbm3X33XerqKhIVVVVam9vlyRdf/31mjFjhk96B+B9hCMA/c7hw4d16aWXnrNmyJAhks4uwpakCRMm6PHHH9cXX3yhn/3sZ8rKytK1116rBx98UO+//77XewbgO4QjAP2OYRgKCzv3ksvQ0FBnbYdp06bp/fff17p16zRt2jQlJiZq586dmjNnjp566imv9gzAd1iQDaDfGTx4sPOI0Df57LPPJEmXXHKJy3hkZKQyMzOVmZkpSTp06JDy8/P18ssv684779SIESO80zQAn+HIEYB+JyMjQ3/9619VU1PzjTW/+c1vlJiYqJEjR6q9vV0ZGRkqKCjoVDdkyBDl5+dLkvbv3++1ngH4DuEIQL8zffp0RUdHa9GiRWppaem0/bXXXlN5ebnuu+8+hYSEKDQ0VPHx8SotLdXx48c71R84cECSOGoEBAmLYT6hDgBBYPr06aqtrdW0adM6bRs+fLhuuOEG/elPf1Jubq7i4uJ0zz336IorrtCJEye0Y8cOvfXWW5o6daoef/xx5+v+9re/afr06Ro4cKDuueceXXnllXI4HKqoqNDmzZt1++23a9myZb6cJgAvIRwBCDrTp0/X7t27u9yWlZXlXDx95MgRbd68Wb///e9VU1OjmJgYjRo1SlOnTlV6enqn1x46dEg///nPVV5erqNHjyo0NFTDhw/X3XffrX//93+XxWLx6rwA+AbhCAAAwIQ1RwAAACaEIwAAABPCEQAAgAnhCAAAwIRwBAAAYEI4AgAAMCEcAQAAmBCOAAAATAhHAAAAJoQjAAAAE8IRAACACeEIAADA5P8D4B5N6Utlm6gAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=hospital, x='LOS', hue='DIED', bins=range(0,40))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a38f4b2",
   "metadata": {},
   "source": [
    "**Note-**\n",
    "\n",
    "People who sadly died from their heart attack tended to have short stays in hosiptal, with many dying on the same day they were brought to hospital.\n",
    "\n",
    "For those who eventually survived, it was more typical to stay in hospital for 7-10 days."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2aaad28d",
   "metadata": {},
   "source": [
    "## Mortality by sex\n",
    "\n",
    "At first glance, female patients are much more likely to die than males:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "18fe4e26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=hospital, y='DIED', x='SEX')\n",
    "plt.ylabel('proportion who died')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c64a0e6",
   "metadata": {},
   "source": [
    "Is this due to difference in severity of heart attacks by sex, or perhaps differences int he effectiveness of treatment?\n",
    "\n",
    "Probably not. We noticed earlier that the female patients were older than the males, and it is reasonable to wonder whether younger patients are more likely to survive.\n",
    "\n",
    "It turns out this is true, younger patients are more likely to survive. We can see this clearly by plotting the proportion who died at each age\n",
    "\n",
    "* This is a plot of mortality **conditional** upon age\n",
    "    * If you are interested you can have a look at the syntax to produce this graph, which is actually a form of KDE plot\n",
    "    * remember that is you google `sns.kdeplot()` you will find the manual page for KDE plot which explains this form\n",
    "    * you won't be required to reproduce this type of plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a5377f97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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D1bZtW61cufKM/W+//fZ67bz60vDc3FxddNFFofbc3NzTjoTXDNeSlJCQoNTUVOXk5NRrnwAAAACAc4u9Q0eZrDa99docORxlsS6nweoVsCdMmKAlS5aobdu2dT6DuibDMOodsHv06KEWLVooLS0tFLAdDof27NmjMWPG1Oq/cOFCvf766/rmm29CI95lZWVKT0/XL3/5y3rtEwAAAABw7rC0aClbqzZavzZN2zfvinU5Z6VeAXvNmjVKSkoKLUeLzWbTmDFj9Oqrr6pdu3bq0qWLpkyZopSUFN10003y+XwqLCxUy5YtZbfb9ZOf/ETTp0/XU089pXHjxsnpdGrq1Klq166dRo0aFbW6AAAAAACNz2S1KT4pRXk5BXrv7fPrkVx1qdckZ126dAlNRLZ582YlJCSoS5cutV42m02rVq2KqIDx48frl7/8pZ577jndeeedMpvNmjNnjmw2m7KysnTdddeFttmpUye9//77Ki8v15133qlf//rXatmypebPnx92DzcAAAAA4NxmmC1K6Jwqr9enl5+bGutyosIIBAKBSL7Qs2dPLV68WH369Km1bt26dXr00Ue1a9f5M6w/fPhwSdInC+bJV1kR42oAAAAA4AJgMimxy0UyzBb9z7NTdezI8VhXdFrbD2xQ23at63U1d70uER87dqwOHjwoSQoEAnr00UdrPVpLkgoKCsImKwMAAAAAIIxhKCGli0wWq96Y8u45Ha4jVe+AvXTpUknSRx99pF69eqldu3ZhfUwmk1q1aqXRo0dHv0oAAAAAOF8ZhkxWm0xWq0xWmwyzWYZhkmEySSaTDMNQIOBXwO9XwOeX/D75fV753W753C7J74/1EURVfMcUme3x+mDOMu3ctjvW5URVvQJ2//791b9/f0lSRUWFnnzySXXt2rVRCwMAAACA847JJHOcPfiyx8scZ5fJcjJ2BQIB+Xw++X1+uT1eedwe+Xx+Wa0WWW1xstjNMpuC4dswDEmS3+uVz+WUz1khb0WF/G5XrI7urNk7dJQlsaU+/3iN1n65PtblRF29AnZNO3bs0NatWwnYAAAAAGAyyRKfIEt8gszxCcERasNQIBBQeVmFsg9n6ERGttKPHNOBPYeUlZlTr83a7TZd1benelx1hS66JFUpnZKU2C5J9vaG/D6vvBXl8paXy1tRJkU2rVZsGIbiO6bIkthS3/5ri5Z/+I9YV9QoIg7YPp9Pbdu2bYxaAAAAAODcZhgy2+ODoTohUSZbnAzDkM/rVW5OgQ7sPaRtm3dqz8798p/Fpd1Op1tb077T1rTvQm32BLuGjvixBg7up9SLOiuhZWsF/H55ykvlKXWcs5M2Gyaz4jt1kTnOrv/9ZI2WLWye4VpqQMD+3e9+p0mTJik/P19XXHGFOnToUKtP586do1IcAAAAAMSayWqTJSFR5qqRasNkkt/vV0Feofb+33atX/tvHTqQ3uh1OCuc+ucnX+mfn3wlSep51RUaOfqnuqLHpUps2Vp+n1eeUoc8jhL5Pe5Gr6c+DItViZ1TJbNF8/62SOu//nesS2pUET+m66qrrpLP5wt+ueqegFPt3bv37CtrIjymCwAAAEBNhtkcDNMJibLEJ8pksYQu+T70fbo2rE0LG1k+Fwy6tr9u/tkwXXRxFxkmk7zOSnkcxfKUlcbsEnJLQqLsHTvJHwho6stva//ugzGp42xF/TFdNU2aNKlBRQEAAADAuSgUqO0JMickyGwNPpLY7XbraHqmtm3aqW/WbFR52bk7ILdpwzZt2rBNdrtNP///btGPbxikFh07yd4hOTiqXVoin8vZJLUYZovsHTrK2qKlykrLNOmZqcrLLWiSfcdaxAF71KhRjVEHAAAAADQBQ+a4uNAM3+b4eJksVkmS1+NVdnaedu/cp3999W+dOJ4d41oj53S6tXj+Si2ev1JX9LhUo3/1M13e/WLZWreRz+2Sx1EiT5lDgaqrkqPN1rqt4tp1UEDS6s/X6cN5yxtlP+eqiAO2JBUWFuq9995TWlqaHA6H2rZtqwEDBujXv/612rdvH+0aAQAAACBihtksky1OZlucTHFxwUdm1Zjl2+l0KfNolvbvPaQNa9OUmZEV65Kj6vt9h/XKi2/IZDLp5luH6cafXqt27ZMU1z5JPpdT3rJSecpLFfB6z2o/htksa4tWsrZuI5PFqoz0TL3x13dUVFgcnQM5j0R8D3Z2drbuuOMOFRYWql+/fkpKSlJeXp62b9+utm3batmyZUpOTm6seqOOe7ABAACA85dhNstktclkscqwWmWyWoOB2mqTYTJJCj572u1yqyC/SBlHM7X7u33a/O0Oud3nxkRgTal9UjuNvP0m9RtwtVq1bhmcAd3llM9ZKZ/TKZ+rUn6P54zbMcwWWeITZG3ZSub4BElSUWGxlv79E23asK2xD6NJNeo92FOmTJHFYtGqVavCnoWdkZGh+++/X9OmTdNf/vKXSDcLAAAAAGEMk0mGxSqTxSLDbAm+W4Ihunq55sTLfr9fbpdbhQUO5ebk6Vh6pg7sPaS9uw6c1SOzmpOCvELNf2ex5r+zWK3btNLNtw1TvwG91a59G9laBx/HHPD75Pd4FPD5FPD7FfD7pYBfhsUS/McMq1WGEfzHi4rySm1ft0krF3+uwoKiWB7aOSHigL1+/Xo988wzYeFakrp27apHH31Uf/3rX6NWHAAAAIDm62RYttYRni2hECcFR6EDfr/cbo9KyyvlKMlXQV6hso5n62j6cR3af0QOR1kMj+b8U1LsCN2vLQVHtwf/uL969u6uNm1bK84eJ5vNKqstTmaTWU6nU8X5+crLyVfmsSzt2L5b6QePxfYgzjERB2yfz6e2bdvWua5du3YqK+OkBgAAABBkWKwy24KjniarTSZbXHAE1GwJjT6HwrPHo9KyYHguKihSbna+TmTmKONopk5kZMt7lvcK44cV5BVq1certerj1bEu5bwVccC+8sor9fHHH+uGG26otW7lypXq3r17VAoDAAAAcP4wTObgRGK2uODEYnG174P2erxylJapqCBXOdl5OpGRpcOHjurI98cuyPuh0fxEHLAfeeQRPfDAAyouLtatt96qDh06KD8/X//4xz+0ceNGvfHGG41RJwAAAIBzhGEyy2y3yxRnl9kWF1yuetRVIBCQ2+1WfqFDJ45n6/DBo9q/53sdOXiM+6DR7EUcsK+99lq98sormjJlijZs2BBq79Chg15++WXddNNNUS0QAAAAQAwZRlWIrnputD1eJmtVmPb7VV5eqdz0Ezr8fbp2bt/DhGK4oDXoOdg///nPddttt+nw4cMqKSlR69atdemll4bN4AcAAADg/GNYrDLb7TLHxcsSHy+TLe7kc6MrXcrMyNbB/Ye1Ne07Hdh7KNblAueUBgVsSVq3bp22bt2qkpIStW/fXj/+8Y81YMCAaNYGAAAAoDGZTMFR6aqRaYs9XobZLEnyeb0qKChW+qE92r5ll3Zs3im3m0nGgB8SccAuLi7WQw89pF27dslisahNmzYqLi7W22+/reuvv14zZsyQzWZrjFoBAAAANJRhVIXpOJni4oOj1Nbg7+3BS70rdHj/Ee3ZtV9pG7YpLyc/xgUD55+IA/bLL7+sY8eOacaMGRo+fLgMw5Df79fq1av1/PPPa9q0aXr66acbo1YAAAAA9WCYLcEgbaua1btqErLqS71dLreyj+fq4IEj2r5ll/b93/fcNw1EQcQB+5tvvtGTTz6pESNGhNpMJpN++tOfqrCwUDNmzCBgAwAAAE3g5DOmbTJVvZvj4mSYgpd5V983nZeZqyMHj2nXjr3a/d0eLvUGGkmD7sHu0KFDne2dOnVSRUXFWRUEAAAAoIrJJJPFGnxZrTKq3s02m4yqEWkpGKQ9bo+KS0qVfeCYDn+frv/7bq8OHUiPbf3ABSbigD1q1Ci9/fbbGjRokBITE0PtXq9XH3zwgUaNGhXVAgEAAIDmyDBbZFgsMpnNVe+WYIC21Hg3mUL9A4GAvF6vKsorVZSTo9zsPGWkZ2rf7oNKP8wzpoFzQcQB2263Kz09XcOGDdOwYcPUsWNHFRUVaf369crOzlbr1q31xz/+UZJkGIZefvnlqBcNAAAAnIsMszkYnM3mqsBc/Tk8SMtkCnvEbSAQUMDvl9vjUVm5U6WOIhUXFis7K0+Zx07oyKFjOnE8O4ZHBqA+Ig7Yn3zyiVq0aCFJSktLC1uXkpKibdu2hT7zXGwAAACc90ymqrBcIzhXheeT7cHR5lN//w0EAvL7fHK7PSqrdKq8rFiOklIVFZYoP7dAOVm5On4sS9knchmBBpqBiAP2V1991Rh1AAAAAE3LMIIjyxZLjUu0qz6bLcFZt83msMu0pVNDs0vlZcUqdZSpuLBEBQVFyssJBucTGdmqqKiM0cEBiIUGTXImSQ6HQzt27FBpaanatWun3r17h0a2AQAAgJgyjGBAPjUwWywyWa1Vo9DmsK8EAgH5vD65XG5VOCqCobmoRAX5RcrJylN2Zo4yM7LkcJTF6KAAnOsaFLBnz56tmTNnyul0htqsVqt++9vf6tFHH41acQAAAEAtJlPt0FwdpEOTg4WHZ7/fL6/Hq/KKSpUVOFRc5FB+XoFys/KVeTxLGemZKil2xOiAADQXEQfs5cuXa+rUqfrlL3+p2267TR06dFBeXp4+/vhjzZgxQ507d2YmcQAAAETMMJlDk4LVuue5xkj0qZds+/1+eaonBystUXFhifJyC5R9IlcZRzN17GimnBXO0+wVAKIn4oA9b9483XnnnZo4cWKo7dJLL9XgwYNlt9s1f/58AjYAAMCFzjCCgdlsqnqvepnMJ2fUrppVO9RexwRhoUu2SytV5ihTUWGx8nILlX0ieLl2RvoJud3uGB0kAISLOGAfPXpUEyZMqHPd8OHDtXz58rMuCgAAAOeO2gG5Rmiuua76s8lUa5RZqnoUVSAgn88nj9urygqnKsqDE4Q5SspUkFeo3Jx8ZZ/IYYIwAOeliAN2cnKyjh8/Xue6jIwMJjoDAAA41xkmmWo8n9kwm6suv64OyjVGmE+5l7laIBCQ3+8PjjC7PXKVO+WsdKmivEKlpeVylJSqpMihwoJiFRYEJwkrKijmUVQAmrWIA/awYcP0xhtv6Morr1S/fv1C7du3b9ebb76pYcOGRbM+AAAARCB42bU1fNKvM9zDXB2WvV6vXC63KkvLVV5eobLScjlKHCoudKiosFgF+UXKy8lXQV6B3G5vjI4QAM5dEQfscePGaePGjbrzzjvVuXNnJSUlKS8vTydOnNBll12mJ554ojHqBAAAgBQcXbZag7NnW6uDtFUmq1WGxRp2H3P185pdLo8qSoOPnSoqKlFBXqHycgqUXfWs5qLC4tgdEAA0IxEH7BYtWmjZsmVavny5Nm/erJKSEvXp00cPPPCARo8eLbvd3hh1AgAAXBhMplBgDr1bbXUGaL/fL4/bI0d5hUqyClSQV6jsrFxlZmTp6OHjysnKjeGBAMCFJ+KA/dvf/lb33nuv7rrrLt11112NURMAAEDzZRhhI87hQdoads9zIBCQ1+NVWVmFirMLlZebrxMZ2Tp25LgOHjiistLyGB4IAOBUEQfszZs36ze/+U1j1AIAAHDeq/MeaItFJqut6n7o8ADt9/nldDrlyC9RQX6hsjJzlJGeqYMHjignKy+GRwIAiFTEAfvaa6/V0qVL1bdvXy4HBwAAF4zas23XmDSs6j5ow2yu8x5op9Ot0gKHCguKlJsTHIVOP3xMRw8fl9fLZGEA0FxEHLDj4uL0+eef68svv1Rqaqrat28ftt4wDL3//vtRKxAAACCqDOM0z3I2nfJM5x9+VJXf75fP55PL6VZ5UZkcJQ4VFZYoNydfWcezdeRQBvdAA8AFJuKAnZ2drWuuuSb0ORAIhK0/9TMAAEDUGIYMUzAIy2SqWj75ksl8SltVOK4OzyZT2AhzTdWXa3u9XjldbjlLy1VRXiGHo1TFhcHR57zcAmVn5ujE8SweUwUAqCXigL1gwYLGqAMAADRHJpMMozr8BsPtyeUa7XV9PrWfYZw2HEvBgFz9PGefzye32yt3pUcuV5lclS5VVFSqrLS86tnOpSopdqggv0j5uQUqyCviUm0AwFmLKGDv3LlTmZmZ6tatm3r16tVYNQEAgCZnSCYjFHR/MAwbJhkmo+6QfMr3f0j1VW8Bf0D+gF9+n18er09er0dut1tuV/DldLpUWeFUZUVleEh2lMpRXCpHSamKCkrkdrub4gcFAMBp1StgOxwOjR07Vjt27FAgEJBhGOrXr5+mTp2qTp06NXaNAABc2KpHbo2qUVyTEQyx1QHYqPk52M8wTgZgVfUP9a0eHa4OysaZw7B0coQ44PfL5wuOEnu9XnmcbrndHrldbrlcbrlcLjkrg6G4oqJS5WXlqiivVJmjXI7SUpWWlKmk2KHysoom+OEBANB06hWwp0+frj179mjcuHG6+uqrdfjwYc2aNUvPP/+83n333bMqwO/3a8aMGVq6dKkcDod+9KMfaeLEierWrVud/YuKijRp0iStW7dOknTzzTfrj3/8oxISEs6qDgDAeaw6HBqGDBlSKCtWLxtVXU5+Ptm/xvdrLBs1tll723Vt1wjfnmGcXB9aV6OfUXPZFNYWvu7Mwbem6hCsQEB+/8nLpb1en7xujzwerzxuTygQO50uOSudqqx0ylnhVHlZhcrKylVeXhEcKXaUq6TYoYrySvn9/ohqAQDgQlOvgP3111/r8ccf13333SdJuuGGG5ScnKwnn3xSFRUVZxVuZ86cqUWLFmny5MlKTk7WlClT9NBDD+nTTz+VzWar1X/8+PFyuVyaN2+eHA6Hnn32Wb300kt65ZVXGlwDgCYQFjCk2gGlqi3MyUkTg1eSBqqaAlX/C4YIXeiTK576sw2FstOFPtVYr/oFRyl8OzXf6wqv1duoo79hGGdsC6/HCC2eGpAjDZ/RVGtSz0Cg6vQMBE9LVY/2nrwv2O8PXgbt8/uq3v3yeX3BAOzxyuv1yePxyOv1yu32yFP1cjqrL5V2yuV0B8NwZXB0uLK8UuXllSovLVdFRWUsfhQAAKBKvQJ2Xl6errrqqrC2wYMHy+fzKSsrS5dddlmDdu52uzV37lz94Q9/0NChQyVJ06ZN0/XXX68vv/xSI0eODOu/fft2bdq0SatWrQrt809/+pMefPBBPf7440pOTm5QHYBqhI26R79OIxD6T+hzoPpz4JT3plQzqNQIWrVGxU7bZqpjXfVlpjUuJ63RXmubNffZRKpH7YJBp8byKe9hywpUvQWXq8NReJivuk/05I5OX0R1GKxarloID621zrdT/iyqt1OrveafiU5+jrLTBseay1X/qQ6SoVBZfQlx6LO/KlwGFAgEA2bAX+Nz1azN1eEz4A/IVxVC/X6ffFXrfD6//L5gEK1e7/P65Pf75fX5Qtvwen1hAdbr9VUt+0KXNPu8Ve9Vr+pteavbqpb93uCor8/nk8ftldfjCY4Ce4OXRTOaCwAATlWvgO31emuNJrdu3VqS5HK5Grzzffv2qby8XEOGDAm1tWrVSr169dLmzZtrBewtW7YoKSkpLNAPGjRIhmFo69atuuWWWxpcC84xYYHulMl2at4zaKp5n6GhWvcTmk4Jgmd56eXZCgsuYSEmoFNyep3CB3pr1H7qJa3RqLFGUAqGoEAo8ARH3Pzyeb2hsOH1+uT1BC8/9Xq9wUDi9YY+ez3Bl8frrRV8/L5gIA6O9J08JMMUHMk0DENms1lms0lmi1lmk0lmi0UWq1kWiyX4slpksZhlsVpktVhktphltVpktlhkNpuD6yxmmUwmmczBbZjMJpnNwcf2mEzBe1dNVeeFUWNUNmyEvT4/4urwWXO55ohm2KjmyZHN6jB5akD0+2r8jL1VI52+qp+x5+TP2OOpGvH0Bi8B9rq9cns8Ve0n11d/x+32yOf1ye12y+vxyO0mNAIAAJyNiB/Tdaqzee51dna2JNWaKK1jx47Kysqq1T8nJ6dWX5vNpjZt2tTZvz5yc3Pl8/l0611jLthneBt1LP1ApzM1npoCw9qN07TXL7X8sJp/fqGZaQMnLyUO1Ag6dV3KWTMIVW3lzIPPpx5Tzc91LqvGqGbdbWHtpx5jrYZAeFuN0de6jq/myKKqgnP1eyj0nTbeAwAAABcet8cpT279BpbPOmCfzYhZZWXwXrFTR8fj4uJUUlJSZ/+67suOi4tr8Eh6XFyc3G63DLMlChEPTemH/kHEiOKILgAAAIALV15enuLi4urVt94B+8UXX1SLFi1Cn6vDzfPPP6/ExMRQu2EYev/99+u1TbvdLil4L3b1shS87Dw+Pr7O/nU949LlcjV4orUtW7Y06HsAAAAAANRkqk+ngQMHKjExMWzymur2hISEsPZI7t+rvtw7Nzc3rD03N1cpKSm1+qekpNTq63a7VVxczARnAAAAAICYqtcI9oIFCxpl5z169FCLFi2Ulpamiy66SJLkcDi0Z88ejRkzplb/gQMH6tVXX9XRo0dDz8lOS0uTJPXv379RagQAAAAAoD7O+h7ss2Gz2TRmzBi9+uqrateunbp06aIpU6YoJSVFN910k3w+nwoLC9WyZUvZ7Xb17dtX/fv312OPPaYXX3xRFRUVmjhxom6//XZGsAEAAAAAMWUEYjx1ts/n09SpU7VixQo5nU4NHDhQL7zwglJTU3X8+HENHz5ckydP1ujRoyVJBQUFeumll/Svf/1LcXFxuvnmm/XHP/6x3jedAwAAAADQGGIesAEAAAAAaA7qNckZAAAAAAD4YQRsAAAAAACigIANAAAAAEAUELABAAAAAIgCAjYAAAAAAFFAwAYAAAAAIAoI2M1QcXGxXnjhBd1www3q37+/7rzzTm3ZsiW0fu/evRozZoz69eunG2+8UXPmzIlhtUD0HTlyRNdcc41WrFgRauO8R3O2cuVK3XLLLerdu7dGjhypzz//PLSOcx/Nlcfj0bRp03TjjTfqmmuu0V133aVt27aF1nPuo7mZOXOm7rnnnrC2M53nfr9fb7zxhq6//nr17dtX999/v44ePdqUZV9wCNjN0OOPP67vvvtOU6dO1bJly3TVVVfpgQce0KFDh1RUVKTf/OY3uvjii7V8+XKNGzdOr7/+upYvXx7rsoGo8Hg8evLJJ1VRURFq47xHc/bxxx/rmWee0R133KFPP/1Ut9xyix5//HFt376dcx/N2ttvv63ly5dr0qRJWrlypS699FI99NBDysnJ4dxHszNv3jy98cYbYW31Oc9nzpypRYsWadKkSVq8eLEMw9BDDz0kt9vd1IdwwbDEugBE19GjR7VhwwZ9+OGH6t+/vyTp2Wef1bp16/Tpp5/KbrfLZrPpxRdflMVi0WWXXaajR4/qnXfe0S9+8YsYVw+cvTfffFOJiYlhbUuWLOG8R7MUCAT0+uuv67777tN9990nSXr00Ue1bds2bdq0SZs2beLcR7O1Zs0a/exnP9N1110nSZowYYKWLl2qHTt2KD09nXMfzUJOTo6effZZbd26VZdccknYujP9fuN2uzV37lz94Q9/0NChQyVJ06ZN0/XXX68vv/xSI0eOjMUhNXuMYDczbdu21ezZs3X11VeH2gzDUCAQUElJibZs2aKBAwfKYjn5bytDhgzRkSNHVFBQEIuSgajZvHmzFi9erFdeeSWsnfMezdXhw4eVmZmpW2+9Nax9zpw5Gjt2LOc+mrU2bdro66+/1vHjx+Xz+bR48WLZbDb17NmTcx/Nxu7du9W6dWt98skn6tu3b9i6M53n+/btU3l5uYYMGRJa36pVK/Xq1UubN29usmO40BCwm5lWrVpp6NChstlsobbPP/9cx44d03XXXafs7GylpKSEfadjx46SpBMnTjRprUA0ORwOPfXUU3ruuefUqVOnsHWc92iu0tPTJUkVFRV64IEH9P/+3//Tf/7nf+qrr76SxLmP5u3ZZ5+VxWLR8OHD1bt3b02bNk3Tp0/XRRddxLmPZmPYsGF67bXX1LVr11rrznSeZ2dnS1Kt34s6duyorKysRqoYBOxmbuvWrXrmmWc0fPhwDRs2TE6nMyx8S1JcXJwkyeVyxaJEICpefPFF9evXr9ZIniTOezRbZWVlkqSnn35aP/vZzzR37lxde+21euSRR/Ttt99y7qNZO3TokFq1aqW33npLixcv1ujRo/X0009r3759nPu4IJzpPK+srJSkOvvw96DxcA92M7Z69Wo9+eST6tu3r6ZOnSpJstvttSY1qP4LlpCQ0OQ1AtGwcuVKbdmyRf/4xz/qXM95j+bKarVKkh544AGNGjVKktSzZ0/t2bNH7733Huc+mq3MzEz94Q9/0Lx58zRgwABJUu/evXXw4EG9+eabnPu4IJzpPLfb7ZIkt9sdWq7uEx8f33SFXmAYwW6mPvjgA40bN0433HCD3nnnndBfqpSUFOXm5ob1rf6cnJzc5HUC0bB8+XIVFBSEHtVyzTXXSJImTpyokSNHct6j2aq+NLB79+5h7ZdffrmOHz/OuY9ma+fOnfJ4POrdu3dYe9++fZWens65jwvCmc7z6kvD6+pz6qXliB4CdjO0cOFC/c///I/uvvtuTZ8+PeyykIEDB2rr1q3y+Xyhtm+//VaXXHKJ2rdvH4tygbP26quvatWqVVq5cmXoJUnjx4/X7NmzOe/RbPXq1UuJiYn67rvvwtoPHDigiy66iHMfzVZ1cNi/f39Y+4EDB9StWzfOfVwQznSe9+jRQy1atFBaWlpovcPh0J49e0JXfiD6CNjNzJEjR/Tyyy/rpptu0tixY1VQUKC8vDzl5eWptLRUv/jFL1RWVqZnn31WBw8e1IoVK/T+++9r7NixsS4daLDk5GR169Yt7CVJ7du3V5cuXTjv0WzZ7XY9+OCDeuutt/Tpp5/q2LFjevvtt7Vhwwb95je/4dxHs9WnTx8NGDBATz/9tP79738rPT1d06dP17fffquHH36Ycx8XhDOd5zabTWPGjNGrr76qNWvWaN++fXrssceUkpKim266KcbVN19GIBAIxLoIRM+sWbM0bdq0OteNGjVKf/nLX7Rz5079+c9/1p49e5SUlKT7779fY8aMaeJKgcZ15ZVXavLkyRo9erQkcd6jWXvvvff0wQcfKCcnR5dddpnGjRunESNGSOLcR/NVUlKi6dOna+3atSopKVH37t31+OOPa9CgQZI499H8TJgwQZmZmVqwYEGo7Uznuc/n09SpU7VixQo5nU4NHDhQL7zwglJTU2NxCBcEAjYAAAAAAFHAJeIAAAAAAEQBARsAAAAAgCggYAMAAAAAEAUEbAAAAAAAooCADQAAAABAFBCwAQAAAACIAgI2AAAAAABRQMAGAAAAACAKLLEuAAAANK6nnnpKH3/8sZ544gk9/PDDdfbJzs7WBx98oLVr1yozM1OSdPHFF+vmm2/WPffco4SEhFDfCRMm6KOPPjrt/tq0aaO0tLToHgQAAOcBAjYAAM1YWVmZvvjiC3Xv3l1LlizRQw89JMMwwvqkpaVp/PjxatWqle6++25deeWV8vv9SktL06xZs/TPf/5TCxculN1uD30nKSlJM2bMqHOfFgu/XgAALkz8PyAAAM3YZ599Jp/Pp+eee0733nuv1q9fr+uvvz60vrCwUI899pi6du2q+fPnh41UX3vttRoxYoR+9atf6f3339fYsWND62w2m/r169eUhwIAwDmPe7ABAGjGli9frsGDB2vw4MG65JJLtGjRorD1CxcuVEFBgSZPnhwWrqv16dNH9913nxITE5uqZAAAzluMYAMA0EwdOnRI3333naZOnSpJGj16tKZPn66cnBwlJydLktasWaMrr7xSV1xxxWm38/TTT9fZ7vV662w3m821LkMHAOBCwAg2AADN1LJly9SqVSuNGDFCknT77bdLkpYuXRrqc+zYMV188cW1vuv1emu9asrMzNRVV11V5+vtt99utGMCAOBcxgg2AADNkNfr1SeffKIRI0bI5XLJ5XLJbrdr8ODBWrp0qf7rv/5LZrNZfr+/zu9eddVVtdr3798fWk5KSjptkK4eHQcA4EJDwAYAoBlau3at8vPztWLFCq1YsaLW+q+//lojRoxQampq6LFc1SwWi5YtWxb6vGTJEi1ZsiSsj81mU+/evRuneAAAzlMEbAAAmqFly5apS5cumjx5cq1148eP16JFizRixAgNGzZMf/vb35SRkaGuXbuG+tQMz2vXrm2KkgEAOO9xDzYAAM1Mfn6+/vWvf2nkyJGhGcRrvm655RZt2LBBGRkZGjNmjNq1a6enn35aZWVltbbl8/l0+PDhGBwFAADnH0awAQBoZj766CN5vV6NHDmyzvWjRo3SwoULtWTJEj3xxBN688039bvf/U633nqr7rjjDl199dUymUzavXu3li1bpvT0dN16661h23C73dqxY8dpa+jevXudj/0CAKA5MwKBQCDWRQAAgOi55ZZbZDKZ9Omnn562z3/8x3+opKREa9eulc1mU2FhoT788EOtXr1aGRkZcrvd6tSpk4YMGaI77rhDvXr1Cn13woQJ+uijj36whmXLlnGPNgDggkPABgAAAAAgCrgHGwAAAACAKCBgAwAAAAAQBQRsAAAAAACigIANAAAAAEAUELABAAAAAIgCAjYAAAAAAFFAwAYAAAAAIAoI2AAAAAAARAEBGwAAAACAKCBgAwAAAAAQBQRsAAAAAACigIANAAAAAEAU/P+4vJg1KLWeQwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,2))\n",
    "plt.ylabel('Proportion Died')\n",
    "sns.kdeplot(data=hospital, x='AGE', hue='DIED', multiple='fill', legend=False)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a92e3d7f",
   "metadata": {},
   "source": [
    "If we plot mortality conditional upon age disaggregated by men and women, we can see that *across all ages* men are actually more likely to die than women; the higher overall mortality for women is explained by the presence of more older women in the sample (which in turn probably reflects the fact that women have longer life expectany than men)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "08448d87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "\n",
    "plt.subplot(2,1,1)\n",
    "plt.title('Men')\n",
    "plt.ylabel('Proportion Died')\n",
    "sns.kdeplot(data=hospital.query('SEX == \"M\"'), x='AGE', hue='DIED', multiple='fill', legend=False)\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.title('Women')\n",
    "plt.ylabel('Proportion Died')\n",
    "sns.kdeplot(data=hospital.query('SEX == \"F\"'), x='AGE', hue='DIED', multiple='fill', legend=False)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c66e8db",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "You can learn a lot by disaggregating data!\n",
    "\n",
    "The process of breaking data down to find evidence of different underlying distributions and relationships between variables is at the core of what a good data scientist, or indeed a research scientist, does.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75a11199",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}