presentation_nb.ipynb

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       "                var nbb_formatted_code = \"%reload_ext nb_black\\nfrom matplotlib import pyplot as plt\\nimport plotly\\nimport seaborn as sns\\nfrom statsmodels.graphics.gofplots import qqplot\\nfrom statsmodels.stats.multitest import multipletests\\n\\n%matplotlib inline\\nimport pandas as pd\\nimport numpy as np\\nfrom scipy import stats\\n\\nfrom tqdm.notebook import tqdm as status\";\n",
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   "source": [
    "%reload_ext nb_black\n",
    "from matplotlib import pyplot as plt\n",
    "import plotly\n",
    "import seaborn as sns\n",
    "from statsmodels.graphics.gofplots import qqplot\n",
    "from statsmodels.stats.multitest import multipletests\n",
    "\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "\n",
    "from tqdm.notebook import tqdm as status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>case_id</th>\n",
       "      <th>Hospital_code</th>\n",
       "      <th>Hospital_type_code</th>\n",
       "      <th>City_Code_Hospital</th>\n",
       "      <th>Hospital_region_code</th>\n",
       "      <th>Available Extra Rooms in Hospital</th>\n",
       "      <th>Department</th>\n",
       "      <th>Ward_Type</th>\n",
       "      <th>Ward_Facility_Code</th>\n",
       "      <th>Bed Grade</th>\n",
       "      <th>patientid</th>\n",
       "      <th>City_Code_Patient</th>\n",
       "      <th>Type of Admission</th>\n",
       "      <th>Severity of Illness</th>\n",
       "      <th>Visitors with Patient</th>\n",
       "      <th>Age</th>\n",
       "      <th>Admission_Deposit</th>\n",
       "      <th>Stay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>c</td>\n",
       "      <td>3</td>\n",
       "      <td>Z</td>\n",
       "      <td>3</td>\n",
       "      <td>radiotherapy</td>\n",
       "      <td>R</td>\n",
       "      <td>F</td>\n",
       "      <td>2.0</td>\n",
       "      <td>31397</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Emergency</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>2</td>\n",
       "      <td>51-60</td>\n",
       "      <td>4911.0</td>\n",
       "      <td>0-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>c</td>\n",
       "      <td>5</td>\n",
       "      <td>Z</td>\n",
       "      <td>2</td>\n",
       "      <td>radiotherapy</td>\n",
       "      <td>S</td>\n",
       "      <td>F</td>\n",
       "      <td>2.0</td>\n",
       "      <td>31397</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Trauma</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>2</td>\n",
       "      <td>51-60</td>\n",
       "      <td>5954.0</td>\n",
       "      <td>41-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>e</td>\n",
       "      <td>1</td>\n",
       "      <td>X</td>\n",
       "      <td>2</td>\n",
       "      <td>anesthesia</td>\n",
       "      <td>S</td>\n",
       "      <td>E</td>\n",
       "      <td>2.0</td>\n",
       "      <td>31397</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Trauma</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>2</td>\n",
       "      <td>51-60</td>\n",
       "      <td>4745.0</td>\n",
       "      <td>31-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "      <td>Y</td>\n",
       "      <td>2</td>\n",
       "      <td>radiotherapy</td>\n",
       "      <td>R</td>\n",
       "      <td>D</td>\n",
       "      <td>2.0</td>\n",
       "      <td>31397</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Trauma</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>2</td>\n",
       "      <td>51-60</td>\n",
       "      <td>7272.0</td>\n",
       "      <td>41-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "      <td>Y</td>\n",
       "      <td>2</td>\n",
       "      <td>radiotherapy</td>\n",
       "      <td>S</td>\n",
       "      <td>D</td>\n",
       "      <td>2.0</td>\n",
       "      <td>31397</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Trauma</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>2</td>\n",
       "      <td>51-60</td>\n",
       "      <td>5558.0</td>\n",
       "      <td>41-50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   case_id  Hospital_code Hospital_type_code  City_Code_Hospital  \\\n",
       "0        1              8                  c                   3   \n",
       "1        2              2                  c                   5   \n",
       "2        3             10                  e                   1   \n",
       "3        4             26                  b                   2   \n",
       "4        5             26                  b                   2   \n",
       "\n",
       "  Hospital_region_code  Available Extra Rooms in Hospital    Department  \\\n",
       "0                    Z                                  3  radiotherapy   \n",
       "1                    Z                                  2  radiotherapy   \n",
       "2                    X                                  2    anesthesia   \n",
       "3                    Y                                  2  radiotherapy   \n",
       "4                    Y                                  2  radiotherapy   \n",
       "\n",
       "  Ward_Type Ward_Facility_Code  Bed Grade  patientid  City_Code_Patient  \\\n",
       "0         R                  F        2.0      31397                7.0   \n",
       "1         S                  F        2.0      31397                7.0   \n",
       "2         S                  E        2.0      31397                7.0   \n",
       "3         R                  D        2.0      31397                7.0   \n",
       "4         S                  D        2.0      31397                7.0   \n",
       "\n",
       "  Type of Admission Severity of Illness  Visitors with Patient    Age  \\\n",
       "0         Emergency             Extreme                      2  51-60   \n",
       "1            Trauma             Extreme                      2  51-60   \n",
       "2            Trauma             Extreme                      2  51-60   \n",
       "3            Trauma             Extreme                      2  51-60   \n",
       "4            Trauma             Extreme                      2  51-60   \n",
       "\n",
       "   Admission_Deposit   Stay  \n",
       "0             4911.0   0-10  \n",
       "1             5954.0  41-50  \n",
       "2             4745.0  31-40  \n",
       "3             7272.0  41-50  \n",
       "4             5558.0  41-50  "
      ]
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     "execution_count": 2,
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       "                var nbb_unformatted_code = \"# import dataset and dictionary for the data\\ndf = pd.read_csv(\\\"data/train_data.csv\\\")\\ndf_dict = pd.read_csv(\\\"data/train_data_dictionary.csv\\\")\\ndf.head()\";\n",
       "                var nbb_formatted_code = \"# import dataset and dictionary for the data\\ndf = pd.read_csv(\\\"data/train_data.csv\\\")\\ndf_dict = pd.read_csv(\\\"data/train_data_dictionary.csv\\\")\\ndf.head()\";\n",
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   "source": [
    "# import dataset and dictionary for the data\n",
    "df = pd.read_csv(\"data/train_data.csv\")\n",
    "df_dict = pd.read_csv(\"data/train_data_dictionary.csv\")\n",
    "df.head()"
   ]
  },
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   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bed_grade</th>\n",
       "      <th>patientid</th>\n",
       "      <th>admission_type</th>\n",
       "      <th>severity</th>\n",
       "      <th>visitors</th>\n",
       "      <th>age</th>\n",
       "      <th>deposit</th>\n",
       "      <th>stay</th>\n",
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       "      <th>case_id</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>31397</td>\n",
       "      <td>Emergency</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>2</td>\n",
       "      <td>51-60</td>\n",
       "      <td>4911.0</td>\n",
       "      <td>0-10</td>\n",
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       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>31397</td>\n",
       "      <td>Trauma</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>2</td>\n",
       "      <td>51-60</td>\n",
       "      <td>5954.0</td>\n",
       "      <td>41-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>31397</td>\n",
       "      <td>Trauma</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>2</td>\n",
       "      <td>51-60</td>\n",
       "      <td>4745.0</td>\n",
       "      <td>31-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>31397</td>\n",
       "      <td>Trauma</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>2</td>\n",
       "      <td>51-60</td>\n",
       "      <td>7272.0</td>\n",
       "      <td>41-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>31397</td>\n",
       "      <td>Trauma</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>2</td>\n",
       "      <td>51-60</td>\n",
       "      <td>5558.0</td>\n",
       "      <td>41-50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         bed_grade  patientid admission_type severity  visitors    age  \\\n",
       "case_id                                                                  \n",
       "1              2.0      31397      Emergency  Extreme         2  51-60   \n",
       "2              2.0      31397         Trauma  Extreme         2  51-60   \n",
       "3              2.0      31397         Trauma  Extreme         2  51-60   \n",
       "4              2.0      31397         Trauma  Extreme         2  51-60   \n",
       "5              2.0      31397         Trauma  Extreme         2  51-60   \n",
       "\n",
       "         deposit   stay  \n",
       "case_id                  \n",
       "1         4911.0   0-10  \n",
       "2         5954.0  41-50  \n",
       "3         4745.0  31-40  \n",
       "4         7272.0  41-50  \n",
       "5         5558.0  41-50  "
      ]
     },
     "execution_count": 3,
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       "            setTimeout(function() {\n",
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       "                var nbb_formatted_code = \"#  save the df with only the fields of interest\\ndf = df[\\n    [\\n        \\\"case_id\\\",\\n        \\\"Bed Grade\\\",\\n        \\\"patientid\\\",\\n        \\\"Type of Admission\\\",\\n        \\\"Severity of Illness\\\",\\n        \\\"Visitors with Patient\\\",\\n        \\\"Age\\\",\\n        \\\"Admission_Deposit\\\",\\n        \\\"Stay\\\",\\n    ]\\n]\\n\\n# set index to case_id\\ndf = df.set_index(\\\"case_id\\\", inplace=False)\\n\\n# rename columns so they're easier to work with\\ndf = df.rename(\\n    columns={\\n        \\\"Bed Grade\\\": \\\"bed_grade\\\",\\n        \\\"Type of Admission\\\": \\\"admission_type\\\",\\n        \\\"Severity of Illness\\\": \\\"severity\\\",\\n        \\\"Visitors with Patient\\\": \\\"visitors\\\",\\n        \\\"Age\\\": \\\"age\\\",\\n        \\\"Admission_Deposit\\\": \\\"deposit\\\",\\n        \\\"Stay\\\": \\\"stay\\\",\\n    },\\n    inplace=False,\\n)\\n\\ndf.head()\";\n",
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   "source": [
    "#  save the df with only the fields of interest\n",
    "df = df[\n",
    "    [\n",
    "        \"case_id\",\n",
    "        \"Bed Grade\",\n",
    "        \"patientid\",\n",
    "        \"Type of Admission\",\n",
    "        \"Severity of Illness\",\n",
    "        \"Visitors with Patient\",\n",
    "        \"Age\",\n",
    "        \"Admission_Deposit\",\n",
    "        \"Stay\",\n",
    "    ]\n",
    "]\n",
    "\n",
    "# set index to case_id\n",
    "df = df.set_index(\"case_id\", inplace=False)\n",
    "\n",
    "# rename columns so they're easier to work with\n",
    "df = df.rename(\n",
    "    columns={\n",
    "        \"Bed Grade\": \"bed_grade\",\n",
    "        \"Type of Admission\": \"admission_type\",\n",
    "        \"Severity of Illness\": \"severity\",\n",
    "        \"Visitors with Patient\": \"visitors\",\n",
    "        \"Age\": \"age\",\n",
    "        \"Admission_Deposit\": \"deposit\",\n",
    "        \"Stay\": \"stay\",\n",
    "    },\n",
    "    inplace=False,\n",
    ")\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>31397</td>\n",
       "      <td>Trauma</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>5954.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>31397</td>\n",
       "      <td>Trauma</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>4745.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>31397</td>\n",
       "      <td>Trauma</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>7272.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>31397</td>\n",
       "      <td>Trauma</td>\n",
       "      <td>Extreme</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>5558.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         bed_grade  patientid admission_type severity  visitors  age  deposit  \\\n",
       "case_id                                                                         \n",
       "1              2.0      31397      Emergency  Extreme         2   55   4911.0   \n",
       "2              2.0      31397         Trauma  Extreme         2   55   5954.0   \n",
       "3              2.0      31397         Trauma  Extreme         2   55   4745.0   \n",
       "4              2.0      31397         Trauma  Extreme         2   55   7272.0   \n",
       "5              2.0      31397         Trauma  Extreme         2   55   5558.0   \n",
       "\n",
       "         stay  \n",
       "case_id        \n",
       "1         5.0  \n",
       "2        45.0  \n",
       "3        35.0  \n",
       "4        45.0  \n",
       "5        45.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 4;\n",
       "                var nbb_unformatted_code = \"# set all stay values of \\\"More than 100 Days\\\" to 100 numeric\\n# over_100 = df.stay == \\\"More than 100 Days\\\"\\ndf.loc[df.stay == \\\"More than 100 Days\\\", \\\"stay\\\"] = 100\\n# df[df.stay == 100]\\n\\n\\n# stay column converted to number value\\nstay_slice = (pd.to_numeric(df.stay.str.slice(0, 1)) * 10) + 5\\ndf.stay = stay_slice\\n\\n# age column converted to number value\\nage_slice = (pd.to_numeric(df.age.str.slice(0, 1)) * 10) + 5\\ndf.age = age_slice\\n\\ndf.head()\";\n",
       "                var nbb_formatted_code = \"# set all stay values of \\\"More than 100 Days\\\" to 100 numeric\\n# over_100 = df.stay == \\\"More than 100 Days\\\"\\ndf.loc[df.stay == \\\"More than 100 Days\\\", \\\"stay\\\"] = 100\\n# df[df.stay == 100]\\n\\n\\n# stay column converted to number value\\nstay_slice = (pd.to_numeric(df.stay.str.slice(0, 1)) * 10) + 5\\ndf.stay = stay_slice\\n\\n# age column converted to number value\\nage_slice = (pd.to_numeric(df.age.str.slice(0, 1)) * 10) + 5\\ndf.age = age_slice\\n\\ndf.head()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set all stay values of \"More than 100 Days\" to 100 numeric\n",
    "# over_100 = df.stay == \"More than 100 Days\"\n",
    "df.loc[df.stay == \"More than 100 Days\", \"stay\"] = 100\n",
    "# df[df.stay == 100]\n",
    "\n",
    "\n",
    "# stay column converted to number value\n",
    "stay_slice = (pd.to_numeric(df.stay.str.slice(0, 1)) * 10) + 5\n",
    "df.stay = stay_slice\n",
    "\n",
    "# age column converted to number value\n",
    "age_slice = (pd.to_numeric(df.age.str.slice(0, 1)) * 10) + 5\n",
    "df.age = age_slice\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 5;\n",
       "                var nbb_unformatted_code = \"df = df.fillna(100, inplace=False)\";\n",
       "                var nbb_formatted_code = \"df = df.fillna(100, inplace=False)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df.fillna(100, inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 6;\n",
       "                var nbb_unformatted_code = \"plt.figure(figsize=[15, 7])\\nplt.title(\\\"Stay Distribution\\\")\\nsns.distplot(df.stay)\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"plt.figure(figsize=[15, 7])\\nplt.title(\\\"Stay Distribution\\\")\\nsns.distplot(df.stay)\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[15, 7])\n",
    "plt.title(\"Stay Distribution\")\n",
    "sns.distplot(df.stay)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([55, 75, 35, 45, 85, 65, 25, 15,  5, 95], dtype=int64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 7;\n",
       "                var nbb_unformatted_code = \"df.age.unique()\";\n",
       "                var nbb_formatted_code = \"df.age.unique()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.age.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 8;\n",
       "                var nbb_unformatted_code = \"plt.figure(figsize=[15, 7])\\nplt.title(\\\"Age Distribution\\\")\\n# plt.axvline(df.age.mean(), color=\\\"r\\\")\\nsns.distplot(df.age)\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"plt.figure(figsize=[15, 7])\\nplt.title(\\\"Age Distribution\\\")\\n# plt.axvline(df.age.mean(), color=\\\"r\\\")\\nsns.distplot(df.age)\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[15, 7])\n",
    "plt.title(\"Age Distribution\")\n",
    "# plt.axvline(df.age.mean(), color=\"r\")\n",
    "sns.distplot(df.age)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Moderate    175843\n",
       "Minor        85872\n",
       "Extreme      56723\n",
       "Name: severity, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 9;\n",
       "                var nbb_unformatted_code = \"df.severity.value_counts()\";\n",
       "                var nbb_formatted_code = \"df.severity.value_counts()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.severity.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Trauma       152261\n",
       "Emergency    117676\n",
       "Urgent        48501\n",
       "Name: admission_type, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 10;\n",
       "                var nbb_unformatted_code = \"df.admission_type.value_counts()\";\n",
       "                var nbb_formatted_code = \"df.admission_type.value_counts()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.admission_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 11;\n",
       "                var nbb_unformatted_code = \"admission_type = df.groupby(\\\"admission_type\\\").count()\\nseverity = df.groupby(\\\"severity\\\").count()\\nbed_grade = df.groupby(\\\"bed_grade\\\").count()\";\n",
       "                var nbb_formatted_code = \"admission_type = df.groupby(\\\"admission_type\\\").count()\\nseverity = df.groupby(\\\"severity\\\").count()\\nbed_grade = df.groupby(\\\"bed_grade\\\").count()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "admission_type = df.groupby(\"admission_type\").count()\n",
    "severity = df.groupby(\"severity\").count()\n",
    "bed_grade = df.groupby(\"bed_grade\").count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 12;\n",
       "                var nbb_unformatted_code = \"admission_type = pd.DataFrame(admission_type)\\nseverity = pd.DataFrame(severity)\\nbed_grade = pd.DataFrame(bed_grade)\";\n",
       "                var nbb_formatted_code = \"admission_type = pd.DataFrame(admission_type)\\nseverity = pd.DataFrame(severity)\\nbed_grade = pd.DataFrame(bed_grade)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "admission_type = pd.DataFrame(admission_type)\n",
    "severity = pd.DataFrame(severity)\n",
    "bed_grade = pd.DataFrame(bed_grade)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f56af60760>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 13;\n",
       "                var nbb_unformatted_code = \"plt.title(\\\"Severity\\\")\\nsns.barplot(x=severity.index, y=severity.patientid)\";\n",
       "                var nbb_formatted_code = \"plt.title(\\\"Severity\\\")\\nsns.barplot(x=severity.index, y=severity.patientid)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Severity\")\n",
    "sns.barplot(x=severity.index, y=severity.patientid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f56c3958b0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 14;\n",
       "                var nbb_unformatted_code = \"plt.title(\\\"Admission Type\\\")\\nsns.barplot(x=admission_type.index, y=admission_type.patientid)\";\n",
       "                var nbb_formatted_code = \"plt.title(\\\"Admission Type\\\")\\nsns.barplot(x=admission_type.index, y=admission_type.patientid)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Admission Type\")\n",
    "sns.barplot(x=admission_type.index, y=admission_type.patientid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 15;\n",
       "                var nbb_unformatted_code = \"bed_grade = bed_grade.drop(100.0)\";\n",
       "                var nbb_formatted_code = \"bed_grade = bed_grade.drop(100.0)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bed_grade = bed_grade.drop(100.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>patientid</th>\n",
       "      <th>admission_type</th>\n",
       "      <th>severity</th>\n",
       "      <th>visitors</th>\n",
       "      <th>age</th>\n",
       "      <th>deposit</th>\n",
       "      <th>stay</th>\n",
       "      <th>percetage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bed_grade</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>1.0</th>\n",
       "      <td>26505</td>\n",
       "      <td>26505</td>\n",
       "      <td>26505</td>\n",
       "      <td>26505</td>\n",
       "      <td>26505</td>\n",
       "      <td>26505</td>\n",
       "      <td>26505</td>\n",
       "      <td>8.326396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>123671</td>\n",
       "      <td>123671</td>\n",
       "      <td>123671</td>\n",
       "      <td>123671</td>\n",
       "      <td>123671</td>\n",
       "      <td>123671</td>\n",
       "      <td>123671</td>\n",
       "      <td>38.850546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>110583</td>\n",
       "      <td>110583</td>\n",
       "      <td>110583</td>\n",
       "      <td>110583</td>\n",
       "      <td>110583</td>\n",
       "      <td>110583</td>\n",
       "      <td>110583</td>\n",
       "      <td>34.739025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>57566</td>\n",
       "      <td>57566</td>\n",
       "      <td>57566</td>\n",
       "      <td>57566</td>\n",
       "      <td>57566</td>\n",
       "      <td>57566</td>\n",
       "      <td>57566</td>\n",
       "      <td>18.084034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           patientid  admission_type  severity  visitors     age  deposit  \\\n",
       "bed_grade                                                                   \n",
       "1.0            26505           26505     26505     26505   26505    26505   \n",
       "2.0           123671          123671    123671    123671  123671   123671   \n",
       "3.0           110583          110583    110583    110583  110583   110583   \n",
       "4.0            57566           57566     57566     57566   57566    57566   \n",
       "\n",
       "             stay  percetage  \n",
       "bed_grade                     \n",
       "1.0         26505   8.326396  \n",
       "2.0        123671  38.850546  \n",
       "3.0        110583  34.739025  \n",
       "4.0         57566  18.084034  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 16;\n",
       "                var nbb_unformatted_code = \"bed_grade[\\\"percetage\\\"] = (bed_grade.patientid / bed_grade.patientid.sum()) * 100\\nbed_grade\";\n",
       "                var nbb_formatted_code = \"bed_grade[\\\"percetage\\\"] = (bed_grade.patientid / bed_grade.patientid.sum()) * 100\\nbed_grade\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bed_grade[\"percetage\"] = (bed_grade.patientid / bed_grade.patientid.sum()) * 100\n",
    "bed_grade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f56c3ee6d0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 17;\n",
       "                var nbb_unformatted_code = \"plt.title(\\\"Bed Grade\\\")\\nsns.barplot(x=bed_grade.index, y=bed_grade.patientid)\";\n",
       "                var nbb_formatted_code = \"plt.title(\\\"Bed Grade\\\")\\nsns.barplot(x=bed_grade.index, y=bed_grade.patientid)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Bed Grade\")\n",
    "sns.barplot(x=bed_grade.index, y=bed_grade.patientid)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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