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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Analysis notebook\n",
"\n",
"P. Benveniste $^1$, J. Alberge $^1$\n",
"\n",
"$^1$ Ecole Normale Supérieure Paris-Saclay\n",
"\n",
"In this Notebook, we perform the analysis of the final datasets after preprocessing and feature extraction."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"#Import of the librairies\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from tabulate import tabulate"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"We now import both datasets."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(55161, 10)\n",
"(48595, 10)\n"
]
}
],
"source": [
"#Loading of both datasets\n",
"plco_file = './preprocessed_plco.csv'\n",
"plco = pd.read_csv(plco_file)\n",
"nlst_file = './preprocessed_nlst.csv'\n",
"nlst = pd.read_csv(nlst_file)\n",
"\n",
"total_plco = len(plco)\n",
"print(plco.shape)\n",
"total_nlst = len(nlst)\n",
"print(nlst.shape)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we perform data analysis for each of the following features:\n",
"- `age`: This feature captures the person’s age.\n",
"- `ssmokea_f`: This feature describes the age at which the person stopped smoking.\n",
"- `cig_stat`: This feature describes if the person is a current or a former cigarette smoker at the beginning of the study.\n",
"- `pack_years`: This feature refers to the number of packs smoked per day multiplied by the number of years during which the person smoked.\n",
"- `smokea_f`: This feature indicates the age at which the person started smoking.\n",
"- `cig_years`: This feature describes the total number of years during which the person smoked. \n",
"- `lung_fh`: This feature describes if the person has close family (parents, siblings or child) who had lung cancer.\n",
"- `bmi`: This feature describes the person’s body mass index\n",
"- `lung_cancer`: This feature indicates if the person was diagnosed with lung cancer."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-------------- ----- ------ ----- ------\n",
"Age PLCO PLCO % NLST NLST %\n",
"<= 50 0 0.0 1 0.0\n",
"50 < ... <= 60 27337 49.6 24861 51.2\n",
"60 < ... <= 70 25120 45.5 20901 43.0\n",
"> 70 2704 4.9 2832 5.8\n",
"Missing 0 0.0 0 0.0\n",
"-------------- ----- ------ ----- ------\n"
]
}
],
"source": [
"table_age = [['Age', 'PLCO', 'PLCO %', 'NLST', 'NLST %'],\n",
" ['<= 50', plco[plco['age']<51]['age'].count(), round(plco[plco['age']<51]['age'].count() / total_plco * 100,1), nlst[nlst['age']<51]['age'].count(), round(nlst[nlst['age']<51]['age'].count() / total_nlst * 100,1)],\n",
" ['50 < ... <= 60',plco[(plco['age']>=51) & (plco['age']<61)]['age'].count(), round(plco[(plco['age']>=51) & (plco['age']<61)]['age'].count()/ total_plco * 100,1), nlst[(nlst['age']>=51) & (nlst['age']<61)]['age'].count(), round(nlst[(nlst['age']>=51) & (nlst['age']<61)]['age'].count() / total_nlst * 100,1)],\n",
" ['60 < ... <= 70',plco[(plco['age']>=61) & (plco['age']<71)]['age'].count(), round(plco[(plco['age']>=61) & (plco['age']<71)]['age'].count() / total_plco * 100,1), nlst[(nlst['age']>=61) & (nlst['age']<71)]['age'].count(), round(nlst[(nlst['age']>=61) & (nlst['age']<71)]['age'].count() / total_nlst * 100,1)],\n",
" ['> 70',plco[(plco['age']>=71)]['age'].count(), round(plco[(plco['age']>=71)]['age'].count() / total_plco * 100,1), nlst[(nlst['age']>=71)]['age'].count(), round(nlst[(nlst['age']>=71)]['age'].count() / total_nlst * 100,1)],\n",
" ['Missing',plco['age'].isna().sum(), round(plco['age'].isna().sum() / total_plco * 100,1), nlst['age'].isna().sum(), round(nlst['age'].isna().sum() / total_nlst * 100,1)]] \n",
"print(tabulate(table_age))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--------------------- ----- ------ ----- ------\n",
"Smoking cessation age PLCO PLCO % NLST NLST %\n",
"<= 30 10470 19.0 2 0.0\n",
"30 < ... <= 40 11886 21.5 130 0.3\n",
"40 < ... <= 50 11447 20.8 7025 14.5\n",
"50 < ... <= 60 8649 15.7 14071 29.0\n",
"> 60 1942 3.5 4378 9.0\n",
"Missing 10767 19.5 22989 47.3\n",
"--------------------- ----- ------ ----- ------\n"
]
}
],
"source": [
"table_ssmokea_f = [['Smoking cessation age', 'PLCO', 'PLCO %', 'NLST', 'NLST %'],\n",
" ['<= 30', plco[plco['ssmokea_f']<31]['ssmokea_f'].count(), round(plco[plco['ssmokea_f']<31]['ssmokea_f'].count() / total_plco * 100,1), nlst[nlst['ssmokea_f']<31]['ssmokea_f'].count(), round(nlst[nlst['ssmokea_f']<31]['ssmokea_f'].count() / total_nlst * 100,1)],\n",
" ['30 < ... <= 40',plco[(plco['ssmokea_f']>=31) & (plco['ssmokea_f']<41)]['ssmokea_f'].count(), round(plco[(plco['ssmokea_f']>=31) & (plco['ssmokea_f']<41)]['ssmokea_f'].count()/ total_plco * 100,1), nlst[(nlst['ssmokea_f']>=31) & (nlst['ssmokea_f']<41)]['ssmokea_f'].count(), round(nlst[(nlst['ssmokea_f']>=31) & (nlst['ssmokea_f']<41)]['ssmokea_f'].count() / total_nlst * 100,1)],\n",
" ['40 < ... <= 50',plco[(plco['ssmokea_f']>=41) & (plco['ssmokea_f']<51)]['ssmokea_f'].count(), round(plco[(plco['ssmokea_f']>=41) & (plco['ssmokea_f']<51)]['ssmokea_f'].count() / total_plco * 100,1), nlst[(nlst['ssmokea_f']>=41) & (nlst['ssmokea_f']<51)]['ssmokea_f'].count(),round(nlst[(nlst['ssmokea_f']>=41) & (nlst['ssmokea_f']<51)]['ssmokea_f'].count() / total_nlst * 100,1)],\n",
" ['50 < ... <= 60',plco[(plco['ssmokea_f']>=51) & (plco['ssmokea_f']<61)]['ssmokea_f'].count(), round(plco[(plco['ssmokea_f']>=51) & (plco['ssmokea_f']<61)]['ssmokea_f'].count() / total_plco * 100,1), nlst[(nlst['ssmokea_f']>=51) & (nlst['ssmokea_f']<61)]['ssmokea_f'].count(),round(nlst[(nlst['ssmokea_f']>=51) & (nlst['ssmokea_f']<61)]['ssmokea_f'].count() / total_nlst * 100,1)],\n",
" ['> 60',plco[(plco['ssmokea_f']>=61)]['ssmokea_f'].count(), round(plco[(plco['ssmokea_f']>=61)]['ssmokea_f'].count() / total_plco * 100,1), nlst[(nlst['ssmokea_f']>=61)]['ssmokea_f'].count(), round(nlst[(nlst['ssmokea_f']>=61)]['ssmokea_f'].count() / total_nlst * 100,1)],\n",
" ['Missing',plco['ssmokea_f'].isna().sum(), round(plco['ssmokea_f'].isna().sum() / total_plco * 100,1), nlst['ssmokea_f'].isna().sum(), round(nlst['ssmokea_f'].isna().sum() / total_nlst * 100,1)]] \n",
"print(tabulate(table_ssmokea_f))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-------------- ----- ------ ----- ------\n",
"Smoking status PLCO PLCO % NLST NLST %\n",
"Active 9965 18.1 22842 47.0\n",
"Former 45196 81.9 25753 53.0\n",
"Missing 0 0.0 0 0.0\n",
"-------------- ----- ------ ----- ------\n"
]
}
],
"source": [
"table_cig_stat = [['Smoking status', 'PLCO', 'PLCO %', 'NLST', 'NLST %'],\n",
" ['Active', plco[plco['cig_stat']==1]['cig_stat'].count(),round(plco[plco['cig_stat']==1]['cig_stat'].count() / total_plco * 100,1), nlst[nlst['cig_stat']==1]['cig_stat'].count(), round(nlst[nlst['cig_stat']==1]['cig_stat'].count() / total_nlst * 100,1)],\n",
" ['Former', plco[plco['cig_stat']==2]['cig_stat'].count(),round(plco[plco['cig_stat']==2]['cig_stat'].count() / total_plco * 100,1), nlst[nlst['cig_stat']==2]['cig_stat'].count(), round(nlst[nlst['cig_stat']==2]['cig_stat'].count() / total_nlst * 100,1)],\n",
" ['Missing', plco['cig_stat'].isna().sum(), round(plco['cig_stat'].isna().sum()/total_plco*100,1), nlst['cig_stat'].isna().sum(), round(nlst['cig_stat'].isna().sum() / total_nlst*100,1)]]\n",
" \n",
"print(tabulate(table_cig_stat))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--------------- ----- ------ ----- ------\n",
"Pack years PLCO PLCO % NLST NLST %\n",
"<= 25 26981 48.9 8 0.0\n",
"25 < ... <= 50 16147 29.3 26746 55.0\n",
"50 < ... <= 100 9448 17.1 19544 40.2\n",
"> 100 1434 2.6 2297 4.7\n",
"Missing 1151 2.1 0 0.0\n",
"--------------- ----- ------ ----- ------\n"
]
}
],
"source": [
"table_pack_years = [['Pack years', 'PLCO', 'PLCO %', 'NLST', 'NLST %'],\n",
" ['<= 25', plco[plco['pack_years']<26]['pack_years'].count(), round(plco[plco['pack_years']<26]['pack_years'].count() / total_plco * 100,1), nlst[nlst['pack_years']<26]['pack_years'].count(), round(nlst[nlst['pack_years']<26]['pack_years'].count() / total_nlst * 100,1)],\n",
" ['25 < ... <= 50',plco[(plco['pack_years']>=26) & (plco['pack_years']<51)]['pack_years'].count(), round(plco[(plco['pack_years']>=26) & (plco['pack_years']<51)]['pack_years'].count() / total_plco * 100,1), nlst[(nlst['pack_years']>=26) & (nlst['pack_years']<51)]['pack_years'].count(),round(nlst[(nlst['pack_years']>=26) & (nlst['pack_years']<51)]['pack_years'].count() / total_nlst * 100,1)],\n",
" ['50 < ... <= 100',plco[(plco['pack_years']>=51) & (plco['pack_years']<101)]['pack_years'].count(), round(plco[(plco['pack_years']>=51) & (plco['pack_years']<101)]['pack_years'].count()/ total_plco * 100,1), nlst[(nlst['pack_years']>=51) & (nlst['pack_years']<101)]['pack_years'].count(), round(nlst[(nlst['pack_years']>=51) & (nlst['pack_years']<101)]['pack_years'].count() / total_nlst * 100,1)],\n",
" ['> 100',plco[(plco['pack_years']>=101)]['pack_years'].count(), round(plco[(plco['pack_years']>=101)]['pack_years'].count() / total_plco * 100,1), nlst[(nlst['pack_years']>=101)]['pack_years'].count(), round(nlst[(nlst['pack_years']>=101)]['pack_years'].count() / total_nlst * 100,1)],\n",
" ['Missing',plco['pack_years'].isna().sum(), round(plco['pack_years'].isna().sum() / total_plco * 100,1), nlst['pack_years'].isna().sum(), round(nlst['pack_years'].isna().sum() / total_nlst * 100,1)]] \n",
"print(tabulate(table_pack_years))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------------- ----- ------ ----- ------\n",
"Smoking onset age PLCO PLCO % NLST NLST %\n",
"<= 15 10169 18.4 17927 36.9\n",
"15 < ... <= 20 33760 61.2 25411 52.3\n",
"> 20 10950 19.9 5256 10.8\n",
"Missing 282 0.5 1 0.0\n",
"----------------- ----- ------ ----- ------\n"
]
}
],
"source": [
"table_smokea_f = [['Smoking onset age', 'PLCO', 'PLCO %', 'NLST', 'NLST %'],\n",
" ['<= 15', plco[plco['smokea_f']<16]['smokea_f'].count(), round(plco[plco['smokea_f']<16]['smokea_f'].count() / total_plco * 100,1), nlst[nlst['smokea_f']<16]['smokea_f'].count(), round(nlst[nlst['smokea_f']<16]['smokea_f'].count() / total_nlst * 100,1)],\n",
" ['15 < ... <= 20',plco[(plco['smokea_f']>=16) & (plco['smokea_f']<21)]['smokea_f'].count(), round(plco[(plco['smokea_f']>=16) & (plco['smokea_f']<21)]['smokea_f'].count()/ total_plco * 100,1), nlst[(nlst['smokea_f']>=16) & (nlst['smokea_f']<21)]['smokea_f'].count(), round(nlst[(nlst['smokea_f']>=16) & (nlst['smokea_f']<21)]['smokea_f'].count() / total_nlst * 100,1)],\n",
" ['> 20',plco[(plco['smokea_f']>=21)]['smokea_f'].count(), round(plco[(plco['smokea_f']>=21)]['smokea_f'].count() / total_plco * 100,1), nlst[(nlst['smokea_f']>=21)]['smokea_f'].count(), round(nlst[(nlst['smokea_f']>=21)]['smokea_f'].count() / total_nlst * 100,1)],\n",
" ['Missing',plco['smokea_f'].isna().sum(), round(plco['smokea_f'].isna().sum() / total_plco * 100,1), nlst['smokea_f'].isna().sum(), round(nlst['smokea_f'].isna().sum() / total_nlst * 100,1)]] \n",
"print(tabulate(table_smokea_f))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-------------- ----- ------ ----- ------\n",
"Smoking years PLCO PLCO % NLST NLST %\n",
"<= 10 8800 16.0 2 0.0\n",
"10 < ... <= 20 11761 21.3 292 0.6\n",
"20 < ... <= 30 11532 20.9 5134 10.6\n",
"30 < ... <= 40 13037 23.6 21620 44.5\n",
"> 40 8963 16.2 21547 44.3\n",
"Missing 1068 1.9 0 0.0\n",
"-------------- ----- ------ ----- ------\n"
]
}
],
"source": [
"table_cig_years = [['Smoking years', 'PLCO', 'PLCO %', 'NLST', 'NLST %'],\n",
" ['<= 10', plco[plco['cig_years']<11]['cig_years'].count(), round(plco[plco['cig_years']<11]['cig_years'].count() / total_plco * 100,1), nlst[nlst['cig_years']<11]['cig_years'].count(), round(nlst[nlst['cig_years']<11]['cig_years'].count() / total_nlst * 100,1)],\n",
" ['10 < ... <= 20',plco[(plco['cig_years']>=11) & (plco['cig_years']<21)]['cig_years'].count(), round(plco[(plco['cig_years']>=11) & (plco['cig_years']<21)]['cig_years'].count()/ total_plco * 100,1), nlst[(nlst['cig_years']>=11) & (nlst['cig_years']<21)]['cig_years'].count(), round(nlst[(nlst['cig_years']>=11) & (nlst['cig_years']<21)]['cig_years'].count() / total_nlst * 100,1)],\n",
" ['20 < ... <= 30',plco[(plco['cig_years']>=21) & (plco['cig_years']<31)]['cig_years'].count(), round(plco[(plco['cig_years']>=21) & (plco['cig_years']<31)]['cig_years'].count() / total_plco * 100,1), nlst[(nlst['cig_years']>=21) & (nlst['cig_years']<31)]['cig_years'].count(),round(nlst[(nlst['cig_years']>=21) & (nlst['cig_years']<31)]['cig_years'].count() / total_nlst * 100,1)],\n",
" ['30 < ... <= 40',plco[(plco['cig_years']>=31) & (plco['cig_years']<41)]['cig_years'].count(), round(plco[(plco['cig_years']>=31) & (plco['cig_years']<41)]['cig_years'].count() / total_plco * 100,1), nlst[(nlst['cig_years']>=31) & (nlst['cig_years']<41)]['cig_years'].count(),round(nlst[(nlst['cig_years']>=31) & (nlst['cig_years']<41)]['cig_years'].count() / total_nlst * 100,1)],\n",
" ['> 40',plco[(plco['cig_years']>=41)]['cig_years'].count(), round(plco[(plco['cig_years']>=41)]['cig_years'].count() / total_plco * 100,1), nlst[(nlst['cig_years']>=41)]['cig_years'].count(), round(nlst[(nlst['cig_years']>=41)]['cig_years'].count() / total_nlst * 100,1)],\n",
" ['Missing',plco['cig_years'].isna().sum(), round(plco['cig_years'].isna().sum() / total_plco * 100,1), nlst['cig_years'].isna().sum(), round(nlst['cig_years'].isna().sum() / total_nlst * 100,1)]] \n",
"print(tabulate(table_cig_years))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-------------------------- ----- ------ ----- ------\n",
"Lung cancer family history PLCO PLCO % NLST NLST %\n",
"No 48415 87.8 37302 76.8\n",
"Yes 6323 11.5 10598 21.8\n",
"Missing 423 0.8 695 1.4\n",
"-------------------------- ----- ------ ----- ------\n"
]
}
],
"source": [
"table_lung_fh = [['Lung cancer family history', 'PLCO', 'PLCO %', 'NLST', 'NLST %'],\n",
" ['No', plco[plco['lung_fh']==0]['lung_fh'].count(),round(plco[plco['lung_fh']==0]['lung_fh'].count() / total_plco * 100,1), nlst[nlst['lung_fh']==0]['lung_fh'].count(), round(nlst[nlst['lung_fh']==0]['lung_fh'].count() / total_nlst * 100,1)],\n",
" ['Yes', plco[plco['lung_fh']==1]['lung_fh'].count(),round(plco[plco['lung_fh']==1]['lung_fh'].count() / total_plco * 100,1), nlst[nlst['lung_fh']==1]['lung_fh'].count(), round(nlst[nlst['lung_fh']==1]['lung_fh'].count() / total_nlst * 100,1)],\n",
" ['Missing', plco['lung_fh'].isna().sum(), round(plco['lung_fh'].isna().sum()/total_plco*100,1), nlst['lung_fh'].isna().sum(), round(nlst['lung_fh'].isna().sum() / total_nlst*100,1)]]\n",
"print(tabulate(table_lung_fh))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"------------------------------------ ----- ------ ----- ------\n",
"Body Mass Index PLCO PLCO % NLST NLST %\n",
"Underweight (... <= 18.4) 295 0.5 347 0.7\n",
"Healthy weight (18.5 <= ... <= 24.9) 17556 31.8 13404 27.6\n",
"Overweight (25 <= ... <= 29.9) 23920 43.4 20894 43.0\n",
"Obesity (... >= 30) 12631 22.9 13696 28.2\n",
"Missing 759 1.4 234 0.5\n",
"------------------------------------ ----- ------ ----- ------\n"
]
}
],
"source": [
"table_bmi = [['Body Mass Index', 'PLCO', 'PLCO %', 'NLST', 'NLST %'],\n",
" ['Underweight (... <= 18.4)', plco[plco['bmi']<18.5]['bmi'].count(), round(plco[plco['bmi']<18.4]['bmi'].count() / total_plco * 100,1), nlst[nlst['bmi']<18.4]['bmi'].count(), round(nlst[nlst['bmi']<18.4]['bmi'].count() / total_nlst * 100,1)],\n",
" ['Healthy weight (18.5 <= ... <= 24.9)',plco[(plco['bmi']>=18.5) & (plco['bmi']<25)]['bmi'].count(), round(plco[(plco['bmi']>=18.5) & (plco['bmi']<25)]['bmi'].count()/ total_plco * 100,1), nlst[(nlst['bmi']>=18.5) & (nlst['bmi']<25)]['bmi'].count(), round(nlst[(nlst['bmi']>=18.5) & (nlst['bmi']<25)]['bmi'].count() / total_nlst * 100,1)],\n",
" ['Overweight (25 <= ... <= 29.9)',plco[(plco['bmi']>=25) & (plco['bmi']<30)]['bmi'].count(), round(plco[(plco['bmi']>=25) & (plco['bmi']<30)]['bmi'].count() / total_plco * 100,1), nlst[(nlst['bmi']>=25) & (nlst['bmi']<30)]['bmi'].count(),round(nlst[(nlst['bmi']>=25) & (nlst['bmi']<30)]['bmi'].count() / total_nlst * 100,1)],\n",
" ['Obesity (... >= 30)',plco[(plco['bmi']>=30)]['bmi'].count(), round(plco[(plco['bmi']>=30)]['bmi'].count() / total_plco * 100,1), nlst[(nlst['bmi']>=30)]['bmi'].count(), round(nlst[(nlst['bmi']>=30)]['bmi'].count() / total_nlst * 100,1)],\n",
" ['Missing',plco['bmi'].isna().sum(), round(plco['bmi'].isna().sum() / total_plco * 100,1), nlst['bmi'].isna().sum(), round(nlst['bmi'].isna().sum() / total_nlst * 100,1)]] \n",
"print(tabulate(table_bmi))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------- ----- ------ ----- ------\n",
"Lung cancer PLCO PLCO % NLST NLST %\n",
"Negative 52409 95.0 47084 96.9\n",
"Positive 2752 5.0 1511 3.1\n",
"Missing 0 0.0 0 0.0\n",
"----------- ----- ------ ----- ------\n"
]
}
],
"source": [
"table_lung_cancer = [['Lung cancer', 'PLCO', 'PLCO %', 'NLST', 'NLST %'],\n",
" ['Negative', plco[plco['lung_cancer']==0]['lung_cancer'].count(),round(plco[plco['lung_cancer']==0]['lung_cancer'].count() / total_plco * 100,1), nlst[nlst['lung_cancer']==0]['lung_cancer'].count(), round(nlst[nlst['lung_cancer']==0]['lung_cancer'].count() / total_nlst * 100,1)],\n",
" ['Positive', plco[plco['lung_cancer']==1]['lung_cancer'].count(),round(plco[plco['lung_cancer']==1]['lung_cancer'].count() / total_plco * 100,1), nlst[nlst['lung_cancer']==1]['lung_cancer'].count(), round(nlst[nlst['lung_cancer']==1]['lung_cancer'].count() / total_nlst * 100,1)],\n",
" ['Missing', plco['lung_cancer'].isna().sum(), round(plco['lung_cancer'].isna().sum()/total_plco*100,1), nlst['lung_cancer'].isna().sum(), round(nlst['lung_cancer'].isna().sum() / total_nlst*100,1)]]\n",
"print(tabulate(table_lung_cancer))"
]
},
{
"attachments": {},
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"source": [
"### Saving a txt file\n",
"\n",
"Now we write a text file to concatenate these analyses. "
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"File edited\n"
]
}
],
"source": [
"with open('./data_analysis.txt', 'w') as f:\n",
" f.write('------------ PRE-PROCESSED DATA ANALYSIS ------------ \\n \\n')\n",
" f.write('We perform data analysis on each features of the PLCO and NLST dataset.\\n')\n",
" f.write('Number of participants: \\n')\n",
" f.write(' - PLCO: ' + str(total_plco) + '\\n')\n",
" f.write(' - NLST: ' + str(total_nlst) + '\\n \\n')\n",
" f.write('--- Feature analysis --- \\n\\n')\n",
" f.write('Age: This feature captures the person’s age. \\n')\n",
" f.write(tabulate(table_age))\n",
" f.write('\\n\\n')\n",
" f.write('Smoking cessation age: This feature describes the age at which the person stopped smoking. \\n')\n",
" f.write(tabulate(table_ssmokea_f))\n",
" f.write('\\n\\n')\n",
" f.write('Smoking status: This feature describes if the person is a current or a former cigarette smoker at the beginning of the study. \\n')\n",
" f.write(tabulate(table_cig_stat))\n",
" f.write('\\n\\n')\n",
" f.write('Pack-years: This feature refers to the number of packs smoked per day multiplied by the number of years during which the person smoked. \\n')\n",
" f.write(tabulate(table_pack_years))\n",
" f.write('\\n\\n')\n",
" f.write('Smoking onset age: This feature indicates the age at which the person started smoking. \\n')\n",
" f.write(tabulate(table_smokea_f))\n",
" f.write('\\n\\n')\n",
" f.write('Years smoked: This feature describes the total number of years during which the person smoked. \\n')\n",
" f.write(tabulate(table_cig_years))\n",
" f.write('\\n\\n')\n",
" f.write('Lung family history: This feature describes if the person has close family (parents, siblings or child) who had lung cancer. \\n')\n",
" f.write(tabulate(table_lung_fh))\n",
" f.write('\\n\\n')\n",
" f.write('BMI: This feature describes the person’s body mass index. \\n')\n",
" f.write(tabulate(table_bmi))\n",
" f.write('\\n\\n')\n",
" f.write('Lung cancer: This feature indicates if the person was diagnosed with lung cancer. \\n')\n",
" f.write(tabulate(table_lung_cancer))\n",
" f.write('\\n\\n\\n')\n",
"print(\"File edited\")"
]
}
],
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