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lambday/analysis.ipynb

Created June 17, 2020 00:09
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analysis.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 208,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from pandas.core.common import flatten\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"sns.set(style=\"darkgrid\")"
]
},
{
"cell_type": "code",
"execution_count": 301,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(r'/mnt/c/Users/sode/OneDrive - Microsoft/Work/Projects/plexstudy.csv')\n",
"\n",
"# Convert the header names to all uppercase\n",
"df.columns = df.columns.str.upper()\n",
"\n",
"# data cleanup\n",
"df['NAME'] = df['NAME'].str.upper().str.strip()\n",
"df['OUTCOME'] = df['OUTCOME'].str.upper().str.strip()\n",
"\n",
"df['ADDL RX'] = df['ADDL RX'].str.upper().str.strip()\n",
"df['ADDL RX'].fillna('NONE', inplace=True)\n",
"\n",
"df['NOVEL RX'].fillna('NO TPE', inplace=True)\n",
"df['NOVEL RX'] = df['NOVEL RX'].str.upper().str.strip()\n",
"\n",
"df['COMPLICATIONS'] = df['COMPLICATIONS'].str.upper().str.strip()\n",
"df['COMPLICATIONS'].fillna('NONE', inplace=True)\n",
"\n",
"df['% LUNG INVOLVEMENT'] = df['% LUNG INVOLVEMENT'].str.upper().str.strip()\n",
"df['% LUNG INVOLVEMENT'].mask(df['% LUNG INVOLVEMENT'] == '< 50', '<50%', inplace=True)\n",
"df['% LUNG INVOLVEMENT'].mask(df['% LUNG INVOLVEMENT'] == '<50', '<50%', inplace=True)\n",
"df['% LUNG INVOLVEMENT'].mask(df['% LUNG INVOLVEMENT'] == '>50', '>50%', inplace=True)\n",
"\n",
"df['COMORBIDS'] = df['COMORBIDS'].str.upper().str.strip()\n",
"df['COMORBIDS'].mask(df['COMORBIDS'] == 'NO', 'NONE', inplace=True)\n",
"df['COMORBIDS'].mask(df['COMORBIDS'] == 'NIL', 'NONE', inplace=True)\n",
"df['COMORBIDS'].mask(df['COMORBIDS'] == 'DM HTN', 'DM+HTN', inplace=True)\n",
"df['COMORBIDS'].mask(df['COMORBIDS'] == 'IHD, DM', 'IHD+DM', inplace=True)\n",
"df['COMORBIDS'].mask(df['COMORBIDS'] == 'IHD, HTN', 'IHD+HTN', inplace=True)\n",
"df['COMORBIDS'].mask(df['COMORBIDS'] == 'IHD, CKD', 'IHD+CKD', inplace=True)\n",
"\n",
"# de-normalizing multi-valued columns\n",
"a = map(lambda s: s.split('+') if '+' in s else [s], df['COMORBIDS'].unique())\n",
"a = set(flatten(a))\n",
"for c in a:\n",
" values = list(map(lambda x: c in x, df['COMORBIDS'].values))\n",
" normalized_colname = 'COMORBIDS:' + c\n",
" df.insert(4, normalized_colname, values, True)\n",
"df = df.drop(columns='COMORBIDS')\n",
"\n",
"a = map(lambda s: s.split(' + ') if '+' in s else [s], df['ADDL RX'].unique())\n",
"a = set(flatten(a))\n",
"for c in a:\n",
" values = list(map(lambda x: c in x, df['ADDL RX'].values))\n",
" normalized_colname = 'ADDL RX:' + c\n",
" df.insert(28, normalized_colname, values, True)\n",
"df = df.drop(columns='ADDL RX')\n",
"\n",
"# binning of continuous variables\n",
"\n",
"df.to_csv(r'/mnt/c/Users/sode/OneDrive - Microsoft/Work/Projects/plexstudy_v2.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 302,
"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>SN</th>\n",
" <th>NAME</th>\n",
" <th>AGE</th>\n",
" <th>COMORBIDS:NONE</th>\n",
" <th>COMORBIDS:COPD</th>\n",
" <th>COMORBIDS:CRF</th>\n",
" <th>COMORBIDS:RA-ILD</th>\n",
" <th>COMORBIDS:DOWN SYND</th>\n",
" <th>COMORBIDS:ASTHMA</th>\n",
" <th>COMORBIDS:OTHERS</th>\n",
" <th>...</th>\n",
" <th>ROUTINE RX</th>\n",
" <th>ADDL RX:VENT</th>\n",
" <th>ADDL RX:REMD</th>\n",
" <th>ADDL RX:NONE</th>\n",
" <th>ADDL RX:CP</th>\n",
" <th>ADDL RX:CPAP</th>\n",
" <th>ADDL RX:MSC</th>\n",
" <th>ADDL RX:TOCI</th>\n",
" <th>OUTCOME</th>\n",
" <th>COMPLICATIONS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>NCB ZUBAIR</td>\n",
" <td>35</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>C</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>RECOVERY</td>\n",
" <td>NONE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>P HAV SAFEER</td>\n",
" <td>50</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>B</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>RECOVERY</td>\n",
" <td>SEPTIC SHOCK</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>HAV IMRAN</td>\n",
" <td>35</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>D</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>RECOVERY</td>\n",
" <td>RESP FAILURE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>P LNK SHOUKAT</td>\n",
" <td>65</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>C</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>RECOVERY</td>\n",
" <td>CRS</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>P SUB RAZA</td>\n",
" <td>62</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>D</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>RECOVERY</td>\n",
" <td>ARDS</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>90</td>\n",
" <td>P/HAV YOUSAF</td>\n",
" <td>79</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>C</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>RECOVERY</td>\n",
" <td>NONE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>91</td>\n",
" <td>P/SUB YOUNUS</td>\n",
" <td>79</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>C</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>RECOVERY</td>\n",
" <td>NONE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>92</td>\n",
" <td>FL/LT KHER UL BASHAR</td>\n",
" <td>66</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>C</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>RECOVERY</td>\n",
" <td>NONE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91</th>\n",
" <td>93</td>\n",
" <td>F/O CAPT KASHIF</td>\n",
" <td>60</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>C</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>RECOVERY</td>\n",
" <td>NONE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>94</td>\n",
" <td>M/O CAPT AKASH</td>\n",
" <td>70</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>...</td>\n",
" <td>C</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>RECOVERY</td>\n",
" <td>NONE</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>93 rows × 37 columns</p>\n",
"</div>"
],
"text/plain": [
" SN NAME AGE COMORBIDS:NONE COMORBIDS:COPD \\\n",
"0 1 NCB ZUBAIR 35 True False \n",
"1 2 P HAV SAFEER 50 False False \n",
"2 3 HAV IMRAN 35 True False \n",
"3 4 P LNK SHOUKAT 65 False False \n",
"4 5 P SUB RAZA 62 False False \n",
".. .. ... ... ... ... \n",
"88 90 P/HAV YOUSAF 79 False False \n",
"89 91 P/SUB YOUNUS 79 False False \n",
"90 92 FL/LT KHER UL BASHAR 66 False False \n",
"91 93 F/O CAPT KASHIF 60 True False \n",
"92 94 M/O CAPT AKASH 70 False False \n",
"\n",
" COMORBIDS:CRF COMORBIDS:RA-ILD COMORBIDS:DOWN SYND COMORBIDS:ASTHMA \\\n",
"0 False False False False \n",
"1 False False False False \n",
"2 False False False False \n",
"3 False False False False \n",
"4 False False False False \n",
".. ... ... ... ... \n",
"88 False False False False \n",
"89 False False False False \n",
"90 False False False False \n",
"91 False False False False \n",
"92 False False False False \n",
"\n",
" COMORBIDS:OTHERS ... ROUTINE RX ADDL RX:VENT ADDL RX:REMD \\\n",
"0 False ... C False False \n",
"1 False ... B False False \n",
"2 False ... D True False \n",
"3 False ... C False False \n",
"4 False ... D False False \n",
".. ... ... ... ... ... \n",
"88 False ... C False False \n",
"89 False ... C False False \n",
"90 False ... C False False \n",
"91 False ... C False False \n",
"92 False ... C False False \n",
"\n",
" ADDL RX:NONE ADDL RX:CP ADDL RX:CPAP ADDL RX:MSC ADDL RX:TOCI \\\n",
"0 True False False False False \n",
"1 True False False False False \n",
"2 False False False False False \n",
"3 True False False False False \n",
"4 False True True False False \n",
".. ... ... ... ... ... \n",
"88 False True True False False \n",
"89 False True True False False \n",
"90 True False False False False \n",
"91 True False False False False \n",
"92 False True True False False \n",
"\n",
" OUTCOME COMPLICATIONS \n",
"0 RECOVERY NONE \n",
"1 RECOVERY SEPTIC SHOCK \n",
"2 RECOVERY RESP FAILURE \n",
"3 RECOVERY CRS \n",
"4 RECOVERY ARDS \n",
".. ... ... \n",
"88 RECOVERY NONE \n",
"89 RECOVERY NONE \n",
"90 RECOVERY NONE \n",
"91 RECOVERY NONE \n",
"92 RECOVERY NONE \n",
"\n",
"[93 rows x 37 columns]"
]
},
"execution_count": 302,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 299,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 93 entries, 0 to 92\n",
"Data columns (total 37 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 SN 93 non-null int64 \n",
" 1 NAME 93 non-null object \n",
" 2 AGE 93 non-null int64 \n",
" 3 COMORBIDS:NONE 93 non-null bool \n",
" 4 COMORBIDS:COPD 93 non-null bool \n",
" 5 COMORBIDS:CRF 93 non-null bool \n",
" 6 COMORBIDS:RA-ILD 93 non-null bool \n",
" 7 COMORBIDS:DOWN SYND 93 non-null bool \n",
" 8 COMORBIDS:ASTHMA 93 non-null bool \n",
" 9 COMORBIDS:OTHERS 93 non-null bool \n",
" 10 COMORBIDS:HDN 93 non-null bool \n",
" 11 COMORBIDS:CLD 93 non-null bool \n",
" 12 COMORBIDS:MULTIPLES 93 non-null bool \n",
" 13 COMORBIDS:IHD 93 non-null bool \n",
" 14 COMORBIDS:HTN 93 non-null bool \n",
" 15 COMORBIDS:CKD 93 non-null bool \n",
" 16 COMORBIDS:DM 93 non-null bool \n",
" 17 DAYS OF ILLNESS 93 non-null int64 \n",
" 18 DURATION OF FEVER > 101 93 non-null int64 \n",
" 19 % LUNG INVOLVEMENT 93 non-null object \n",
" 20 O2 SUPPORT IN L 93 non-null int64 \n",
" 21 MAX CRP 93 non-null float64\n",
" 22 MAX FERRITIN 93 non-null int64 \n",
" 23 MAX LDH 93 non-null int64 \n",
" 24 D-DIMERS 93 non-null int64 \n",
" 25 ALC 93 non-null object \n",
" 26 NOVEL RX 93 non-null object \n",
" 27 ROUTINE RX 93 non-null object \n",
" 28 ADDL RX:VENT 93 non-null bool \n",
" 29 ADDL RX:REMD 93 non-null bool \n",
" 30 ADDL RX:NONE 93 non-null bool \n",
" 31 ADDL RX:CP 93 non-null bool \n",
" 32 ADDL RX:CPAP 93 non-null bool \n",
" 33 ADDL RX:MSC 93 non-null bool \n",
" 34 ADDL RX:TOCI 93 non-null bool \n",
" 35 OUTCOME 93 non-null object \n",
" 36 COMPLICATIONS 93 non-null object \n",
"dtypes: bool(21), float64(1), int64(8), object(7)\n",
"memory usage: 13.7+ KB\n"
]
}
],
"source": [
"df.info(verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 180,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 458.35x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.catplot(x='AGE', y='NOVEL RX', hue='OUTCOME', data=df);"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {},
"outputs": [],
"source": [
"# sns.pairplot(df)"
]
},
{
"cell_type": "code",
"execution_count": 161,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fa0c312deb8>"
]
},
"execution_count": 161,
"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": [
"# sns.distplot(df['Max CRP'], kde=False)\n",
"# sns.boxplot(df['Max CRP'])\n",
"# sns.distplot(df['Max Ferritin'], kde=False)\n",
"# sns.boxplot(df['Max Ferritin'])\n",
"# sns.distplot(df['Max LDH'], kde=False)\n",
"# sns.boxplot(df['Max LDH'])\n",
"# sns.distplot(df['D-Dimers'], kde=False)\n",
"# sns.boxplot(df['D-Dimers'])\n",
"sns.distplot(df[df['ALC'] != \">1000\"]['ALC'], kde=False)\n",
"# sns.distplot(df['O2 Support in L'], kde=False)\n",
"# sns.distplot(df['Duration of fever > 101'], kde=False)\n",
"# sns.distplot(df['Days of illness'], kde=False)\n",
"# sns.distplot(df['AGE'], kde=False)\n",
"# sns.boxplot(df['AGE'])"
]
},
{
"cell_type": "code",
"execution_count": 181,
"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>SN</th>\n",
" <th>NAME</th>\n",
" <th>AGE</th>\n",
" <th>COMORBIDS</th>\n",
" <th>DAYS OF ILLNESS</th>\n",
" <th>DURATION OF FEVER &gt; 101</th>\n",
" <th>% LUNG INVOLVEMENT</th>\n",
" <th>O2 SUPPORT IN L</th>\n",
" <th>MAX CRP</th>\n",
" <th>MAX FERRITIN</th>\n",
" <th>MAX LDH</th>\n",
" <th>D-DIMERS</th>\n",
" <th>ALC</th>\n",
" <th>NOVEL RX</th>\n",
" <th>ROUTINE RX</th>\n",
" <th>ADDL RX</th>\n",
" <th>OUTCOME</th>\n",
" <th>COMPLICATIONS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>NCB ZUBAIR</td>\n",
" <td>35</td>\n",
" <td>NONE</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>FEW GGOS</td>\n",
" <td>0</td>\n",
" <td>16.0</td>\n",
" <td>3000</td>\n",
" <td>466</td>\n",
" <td>500</td>\n",
" <td>&gt;1000</td>\n",
" <td>TPE</td>\n",
" <td>C</td>\n",
" <td>NONE</td>\n",
" <td>RECOVERY</td>\n",
" <td>NONE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>P HAV SAFEER</td>\n",
" <td>50</td>\n",
" <td>DM</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>&lt;50%</td>\n",
" <td>0</td>\n",
" <td>73.0</td>\n",
" <td>1923</td>\n",
" <td>470</td>\n",
" <td>250</td>\n",
" <td>&gt;1000</td>\n",
" <td>NO TPE</td>\n",
" <td>B</td>\n",
" <td>NONE</td>\n",
" <td>RECOVERY</td>\n",
" <td>SEPTIC SHOCK</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>HAV IMRAN</td>\n",
" <td>35</td>\n",
" <td>NONE</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>FEW GGOS</td>\n",
" <td>0</td>\n",
" <td>69.0</td>\n",
" <td>667</td>\n",
" <td>220</td>\n",
" <td>400</td>\n",
" <td>&gt;1000</td>\n",
" <td>TPE</td>\n",
" <td>D</td>\n",
" <td>VENT</td>\n",
" <td>RECOVERY</td>\n",
" <td>RESP FAILURE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>P LNK SHOUKAT</td>\n",
" <td>65</td>\n",
" <td>DM+IHD+HDN</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>FEW GGOS</td>\n",
" <td>0</td>\n",
" <td>32.0</td>\n",
" <td>1021</td>\n",
" <td>444</td>\n",
" <td>250</td>\n",
" <td>&gt;1000</td>\n",
" <td>NO TPE</td>\n",
" <td>C</td>\n",
" <td>NONE</td>\n",
" <td>RECOVERY</td>\n",
" <td>CRS</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>P SUB RAZA</td>\n",
" <td>62</td>\n",
" <td>DM</td>\n",
" <td>7</td>\n",
" <td>4</td>\n",
" <td>&gt;50%</td>\n",
" <td>5</td>\n",
" <td>179.0</td>\n",
" <td>1300</td>\n",
" <td>432</td>\n",
" <td>600</td>\n",
" <td>&gt;1000</td>\n",
" <td>TPE</td>\n",
" <td>D</td>\n",
" <td>CPAP</td>\n",
" <td>RECOVERY</td>\n",
" <td>ARDS</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>90</td>\n",
" <td>P/HAV YOUSAF</td>\n",
" <td>79</td>\n",
" <td>IHD, DM</td>\n",
" <td>8</td>\n",
" <td>4</td>\n",
" <td>&gt;50%</td>\n",
" <td>15</td>\n",
" <td>98.0</td>\n",
" <td>1050</td>\n",
" <td>789</td>\n",
" <td>452</td>\n",
" <td>445</td>\n",
" <td>NO TPE</td>\n",
" <td>C</td>\n",
" <td>CPAP</td>\n",
" <td>RECOVERY</td>\n",
" <td>NONE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>91</td>\n",
" <td>P/SUB YOUNUS</td>\n",
" <td>79</td>\n",
" <td>IHD, CKD</td>\n",
" <td>9</td>\n",
" <td>3</td>\n",
" <td>&gt;50%</td>\n",
" <td>8</td>\n",
" <td>209.0</td>\n",
" <td>1213</td>\n",
" <td>1152</td>\n",
" <td>780</td>\n",
" <td>&gt;1000</td>\n",
" <td>NO TPE</td>\n",
" <td>C</td>\n",
" <td>CPAP</td>\n",
" <td>RECOVERY</td>\n",
" <td>NONE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>92</td>\n",
" <td>FL/LT KHER UL BASHAR</td>\n",
" <td>66</td>\n",
" <td>DM+IHD+HDN</td>\n",
" <td>8</td>\n",
" <td>6</td>\n",
" <td>&lt;50%</td>\n",
" <td>2</td>\n",
" <td>114.0</td>\n",
" <td>1038</td>\n",
" <td>345</td>\n",
" <td>250</td>\n",
" <td>&gt;1000</td>\n",
" <td>NO TPE</td>\n",
" <td>C</td>\n",
" <td>NONE</td>\n",
" <td>RECOVERY</td>\n",
" <td>NONE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91</th>\n",
" <td>93</td>\n",
" <td>F/O CAPT KASHIF</td>\n",
" <td>60</td>\n",
" <td>NONE</td>\n",
" <td>9</td>\n",
" <td>6</td>\n",
" <td>&lt;50%</td>\n",
" <td>5</td>\n",
" <td>95.0</td>\n",
" <td>2270</td>\n",
" <td>255</td>\n",
" <td>545</td>\n",
" <td>500</td>\n",
" <td>TPE</td>\n",
" <td>C</td>\n",
" <td>NONE</td>\n",
" <td>RECOVERY</td>\n",
" <td>NONE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>94</td>\n",
" <td>M/O CAPT AKASH</td>\n",
" <td>70</td>\n",
" <td>DM</td>\n",
" <td>9</td>\n",
" <td>5</td>\n",
" <td>&gt;50%</td>\n",
" <td>12</td>\n",
" <td>145.0</td>\n",
" <td>1100</td>\n",
" <td>750</td>\n",
" <td>252</td>\n",
" <td>700</td>\n",
" <td>TPE</td>\n",
" <td>C</td>\n",
" <td>CPAP</td>\n",
" <td>RECOVERY</td>\n",
" <td>NONE</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>93 rows × 18 columns</p>\n",
"</div>"
],
"text/plain": [
" SN NAME AGE COMORBIDS DAYS OF ILLNESS \\\n",
"0 1 NCB ZUBAIR 35 NONE 7 \n",
"1 2 P HAV SAFEER 50 DM 4 \n",
"2 3 HAV IMRAN 35 NONE 4 \n",
"3 4 P LNK SHOUKAT 65 DM+IHD+HDN 5 \n",
"4 5 P SUB RAZA 62 DM 7 \n",
".. .. ... ... ... ... \n",
"88 90 P/HAV YOUSAF 79 IHD, DM 8 \n",
"89 91 P/SUB YOUNUS 79 IHD, CKD 9 \n",
"90 92 FL/LT KHER UL BASHAR 66 DM+IHD+HDN 8 \n",
"91 93 F/O CAPT KASHIF 60 NONE 9 \n",
"92 94 M/O CAPT AKASH 70 DM 9 \n",
"\n",
" DURATION OF FEVER > 101 % LUNG INVOLVEMENT O2 SUPPORT IN L MAX CRP \\\n",
"0 5 FEW GGOS 0 16.0 \n",
"1 4 <50% 0 73.0 \n",
"2 4 FEW GGOS 0 69.0 \n",
"3 4 FEW GGOS 0 32.0 \n",
"4 4 >50% 5 179.0 \n",
".. ... ... ... ... \n",
"88 4 >50% 15 98.0 \n",
"89 3 >50% 8 209.0 \n",
"90 6 <50% 2 114.0 \n",
"91 6 <50% 5 95.0 \n",
"92 5 >50% 12 145.0 \n",
"\n",
" MAX FERRITIN MAX LDH D-DIMERS ALC NOVEL RX ROUTINE RX ADDL RX \\\n",
"0 3000 466 500 >1000 TPE C NONE \n",
"1 1923 470 250 >1000 NO TPE B NONE \n",
"2 667 220 400 >1000 TPE D VENT \n",
"3 1021 444 250 >1000 NO TPE C NONE \n",
"4 1300 432 600 >1000 TPE D CPAP \n",
".. ... ... ... ... ... ... ... \n",
"88 1050 789 452 445 NO TPE C CPAP \n",
"89 1213 1152 780 >1000 NO TPE C CPAP \n",
"90 1038 345 250 >1000 NO TPE C NONE \n",
"91 2270 255 545 500 TPE C NONE \n",
"92 1100 750 252 700 TPE C CPAP \n",
"\n",
" OUTCOME COMPLICATIONS \n",
"0 RECOVERY NONE \n",
"1 RECOVERY SEPTIC SHOCK \n",
"2 RECOVERY RESP FAILURE \n",
"3 RECOVERY CRS \n",
"4 RECOVERY ARDS \n",
".. ... ... \n",
"88 RECOVERY NONE \n",
"89 RECOVERY NONE \n",
"90 RECOVERY NONE \n",
"91 RECOVERY NONE \n",
"92 RECOVERY NONE \n",
"\n",
"[93 rows x 18 columns]"
]
},
"execution_count": 181,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {},
"outputs": [],
"source": [
"# sns.jointplot(x='AGE', y='Max LDH', data=df, kind='kde')"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"# sns.catplot(x='AGE', y='Novel Rx', hue='Outcome', data=df);"
]
},
{
"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
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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