{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "        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>Unnamed: 0</th>\n",
       "      <th>ID</th>\n",
       "      <th>Survive</th>\n",
       "      <th>Age</th>\n",
       "      <th>AgeGroup</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Infection</th>\n",
       "      <th>SysBP</th>\n",
       "      <th>Pulse</th>\n",
       "      <th>Emergency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>142</td>\n",
       "      <td>88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>77</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>128</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  ID  Survive  Age  AgeGroup  Sex  Infection  SysBP  Pulse  \\\n",
       "0           1   4        0   87         3    1          1     80     96   \n",
       "1           2   8        1   27         1    1          1    142     88   \n",
       "2           3  12        1   59         2    0          0    112     80   \n",
       "3           4  14        1   77         3    0          0    100     70   \n",
       "4           5  27        0   76         3    1          1    128     90   \n",
       "\n",
       "   Emergency  \n",
       "0          1  \n",
       "1          1  \n",
       "2          1  \n",
       "3          0  \n",
       "4          1  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "health = pd.read_csv('ICU.csv.txt')\n",
    "health .head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dữ liệu xuất phát từ một nghiên cứu mô tả cắt ngang, khảo sát huyết áp và mạch của 200 bệnh nhân shock nhiễm khuẩn trong khoa ICU. Câu hỏi nghiên cứu giả định của chúng ta là : So sánh huyết áp tâm thu (SysBP) giữa 2 phân nhóm bệnh nhân Tử vong và Sống sót.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>AgeGroup</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Infection</th>\n",
       "      <th>SysBP</th>\n",
       "      <th>Pulse</th>\n",
       "      <th>Emergency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survive</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>90.4500</td>\n",
       "      <td>395.9500</td>\n",
       "      <td>65.125</td>\n",
       "      <td>2.325</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.600</td>\n",
       "      <td>118.82500</td>\n",
       "      <td>100.625</td>\n",
       "      <td>0.95000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>103.0125</td>\n",
       "      <td>457.0375</td>\n",
       "      <td>55.650</td>\n",
       "      <td>1.950</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.375</td>\n",
       "      <td>135.64375</td>\n",
       "      <td>98.500</td>\n",
       "      <td>0.68125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0        ID     Age  AgeGroup    Sex  Infection      SysBP  \\\n",
       "Survive                                                                        \n",
       "0           90.4500  395.9500  65.125     2.325  0.400      0.600  118.82500   \n",
       "1          103.0125  457.0375  55.650     1.950  0.375      0.375  135.64375   \n",
       "\n",
       "           Pulse  Emergency  \n",
       "Survive                      \n",
       "0        100.625    0.95000  \n",
       "1         98.500    0.68125  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "health.groupby('Survive').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The SysBP of people who did not survive is smaller than those who have survived.\n",
    "## Next step: Hypothesis testing to see if this difference is statistically significant (later)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bargraph dead vs survive regarding SysBP\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "health.set_index('Survive')['SysBP'].plot.bar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x291fb037400>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dead = health[(health['Survive'] == 0)]\n",
    "dead.set_index('Survive')['SysBP'].plot.bar()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     40.000000\n",
       "mean     118.825000\n",
       "std       41.080838\n",
       "min       36.000000\n",
       "25%       89.000000\n",
       "50%      126.000000\n",
       "75%      140.000000\n",
       "max      256.000000\n",
       "Name: SysBP, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dead['SysBP'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x291fb131208>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alive = health[(health['Survive'] == 1)]\n",
    "alive.set_index('Survive')['SysBP'].plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    160.000000\n",
       "mean     135.643750\n",
       "std       29.801513\n",
       "min       48.000000\n",
       "25%      112.000000\n",
       "50%      132.000000\n",
       "75%      154.000000\n",
       "max      224.000000\n",
       "Name: SysBP, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alive['SysBP'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_plot = dead.set_index('ID')['SysBP'].plot.line()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_plot = alive.set_index('ID')['SysBP'].plot.line()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "alive = health[(health['Survive'] == 1)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  2,   8,   1, ..., 142,  88,   1],\n",
       "       [  3,  12,   1, ..., 112,  80,   1],\n",
       "       [  4,  14,   1, ..., 100,  70,   0],\n",
       "       ...,\n",
       "       [198, 924,   1, ..., 162, 100,   1],\n",
       "       [199, 925,   1, ..., 100,  88,   1],\n",
       "       [200, 929,   1, ..., 122,  84,   1]], dtype=int64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(alive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  2,   8,   1, ..., 142,  88,   1],\n",
       "       [  3,  12,   1, ..., 112,  80,   1],\n",
       "       [  4,  14,   1, ..., 100,  70,   0],\n",
       "       ...,\n",
       "       [198, 924,   1, ..., 162, 100,   1],\n",
       "       [199, 925,   1, ..., 100,  88,   1],\n",
       "       [200, 929,   1, ..., 122,  84,   1]], dtype=int64)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(health[(health['Survive'] == 1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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