the rest of the scripts
This commit is contained in:
Mike Dijkhof
parent
bb4d0014b6
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# -*- coding: utf-8 -*-
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"""
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Created on Fri Apr 2 10:57:40 2021
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@author: Dijkhofmf
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"""
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import os
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import pandas as pd
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pd.options.mode.chained_assignment = None # default='warn'
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Path = r'I:\Mike Dijkhof\Connecare MGP\Data\FinalFiles'
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os.chdir(Path)
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FilenameOutc = 'SurgAdmComp.csv'
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FilenameSACM = 'DataSACM.csv'
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FilenameComplet = 'Complete.csv'
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DFComp = pd.DataFrame(pd.read_csv(FilenameOutc))
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DFComp = DFComp.set_index('Study ID')
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DFSACM = pd.DataFrame(pd.read_csv(FilenameSACM))
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DFSACM = DFSACM.set_index('Study ID')
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DFComplet = pd.DataFrame(pd.read_csv(FilenameComplet))
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DFComplet = DFComplet.set_index('Study ID')
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Startdate = pd.to_datetime(DFSACM['Start date Fitbit']).dt.date
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Enddate = pd.to_datetime(DFSACM['End date Fitbit']).dt.date
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DFDates = pd.DataFrame()
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DFDates['Study ID'] = DFComp.index
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DFDates = DFDates.set_index('Study ID')
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DFDates['Start'] = Startdate
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DFDates['Surgery'] = pd.to_datetime(DFComp['Date of surgery']).dt.date
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DFDates['Preop'] = DFDates['Surgery'] - DFDates['Start']
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DFDates['Discharge'] = pd.to_datetime(DFComp['Date of hospital discharge']).dt.date
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DFDates['LOS'] = DFDates['Discharge'] - DFDates['Surgery']
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DFDates['St2Dis'] = DFDates['Discharge'] - DFDates['Start']
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DFDates['First Comp'] = pd.to_datetime(DFComp['Date first complication at home']).dt.date
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DFDates['T2C'] = DFDates['First Comp'] - DFDates['Discharge']
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DFDates['First Read'] = pd.to_datetime(DFComp['Date (first) readmission']).dt.date
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DFDates['T2R'] = DFDates['First Read'] - DFDates['Discharge']
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DFDates['Sec Read'] = pd.to_datetime(DFComp['Date second readmission']).dt.date
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DFDates['T2SR'] = DFDates['Sec Read'] - DFDates['Discharge']
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DFDates['End'] = Enddate
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DFDates['Length'] = DFDates['End'] - DFDates['Start']
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DFDates = DFDates[DFComplet['Has patient completed study?']=='Yes']
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DFDates.to_csv('Dates.csv')
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# -*- coding: utf-8 -*-
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"""
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Created on Mon Mar 8 10:38:31 2021
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@author: Dijkhofmf
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"""
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# Import stuff
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import os
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import pandas as pd
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import seaborn as sns
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import matplotlib.pyplot as plt
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import seaborn as sns
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pd.options.mode.chained_assignment = None # default='warn'
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#%% Define filenames and path
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FilenameComplete = 'Complete.csv'
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FilenameDemo = 'DemoData.csv'
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Filename_T0 = 'FinalDF_T0.csv'
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Path = 'I:\Mike Dijkhof\Connecare MGP\Data\FinalFiles'
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# Set path
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os.chdir(Path)
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DFComplete = pd.DataFrame(pd.read_csv(FilenameComplete))
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DFDemo = pd.DataFrame(pd.read_csv(FilenameDemo))
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DFDemo['Complete data'] = DFComplete['Has patient completed study?']
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DFDemo = DFDemo.drop(DFDemo[DFDemo['Complete data'] !='Yes'].index)
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DFDemo['ASA-classification'] = DFDemo['ASA-classification'].str.replace('ASA ', '').astype('float64')
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DFDemo = DFDemo.replace('Unchecked', 0)
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DFDemo = DFDemo.replace('Checked', 1)
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Dropcols = ['Year of birth', 'Subject ID Connecare', 'Subject ID Connecare (version 2.0)','Date subject signed consent', 'Nationality', 'Language', 'Former occupation',
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'Does the patient have a smartphone that they use?', 'How many days a week is the smartphone used?',
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'Does the patient have a tablet that they use?','How many days a week is the tablet used?','Does the patient have a computer/pc that they use?',
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'How many days a week is the computer/pc used?','Smart device at home', 'Smart device at inclusion? (check all that apply) (choice=Fitbit)',
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'Smart device at inclusion? (check all that apply) (choice=Weight scale)','Indication Surgery', 'Comments', 'Complete?', 'Complete data']
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DFDemo = DFDemo.drop(Dropcols, axis=1)
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DFDemo = DFDemo.set_index('Study ID')
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# Calculate CCI score
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DFDemo.iloc[:,20:26] = DFDemo.iloc[:,20:26]*2
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DFDemo.iloc[:,26] = DFDemo.iloc[:,26]*3
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DFDemo.iloc[:,26:28] = DFDemo.iloc[:,26:28]*6
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ColMask = DFDemo.columns[10:29]
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DFDemo['Comorb'] = DFDemo[ColMask].sum(axis=1)
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DFDemo = DFDemo.drop(ColMask, axis=1)
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#%%
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DF_T0 = pd.DataFrame(pd.read_csv(Filename_T0))
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DF_T0 = DF_T0.set_index('Study ID')
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DFDemo['Type'] = DF_T0['Pt Type']
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#%% code variables
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DFDemo['Gender'] = DFDemo['Gender'].replace('Female', 0)
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DFDemo['Gender'] = DFDemo['Gender'].replace('Male', 1)
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Housing = pd.get_dummies(DFDemo['Housing'], drop_first=True)
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Education = pd.get_dummies(DFDemo['Education'], drop_first=True)
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Smoking = pd.get_dummies(DFDemo['Smoking'], drop_first=True)
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Med_Dif = pd.get_dummies(DFDemo['Difficulty preparing medication?'], drop_first=True)
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Loc_Tu = pd.get_dummies(DFDemo['Location tumour'], drop_first=True)
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Prim_Mal = pd.get_dummies(DFDemo['Primary Malignancy'], drop_first=True)
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DFDemo['Recurrent disease?'] = DFDemo['Recurrent disease?'].replace('No', 0)
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DFDemo['Recurrent disease?'] = DFDemo['Recurrent disease?'].replace('Yes', 1)
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DFDemo = DFDemo.drop(['Marital State', 'Housing', 'Education', 'Tumour Stage', 'Smoking', 'Difficulty preparing medication?',
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'Location tumour', 'Primary Malignancy'], axis=1)
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#%%
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DFDemo = pd.concat([DFDemo, Housing, Education, Smoking, Med_Dif, Loc_Tu, Prim_Mal], axis=1)
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#%% Create Neoadjuvant therapy variable
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for i,r in DFDemo.iterrows():
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if (DFDemo.loc[i,'Neo-adjuvant therapy (choice=Chemotherapy)'] == 1) & (DFDemo.loc[i,'Neo-adjuvant therapy (choice=Radiotherapy)'] == 1):
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DFDemo.loc[i,'Neo'] = 1
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elif DFDemo.loc[i, 'Neo-adjuvant therapy (choice=Chemotherapy)'] == 1:
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DFDemo.loc[i,'Neo'] = 2
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elif DFDemo.loc[i,'Neo-adjuvant therapy (choice=Immunotherapy)'] == 1:
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DFDemo.loc[i,'Neo'] = 3
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elif DFDemo.loc[i,'Neo-adjuvant therapy (choice=Radiotherapy)'] == 1:
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DFDemo.loc[i,'Neo'] = 4
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elif DFDemo.loc[i,'Neo-adjuvant therapy (choice=Targeted Therapy)'] == 1:
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DFDemo.loc[i,'Neo'] = 5
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elif DFDemo.loc[i,'Neo-adjuvant therapy (choice=None)'] == 1:
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DFDemo.loc[i,'Neo'] = 0
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Neo = pd.get_dummies(DFDemo['Neo'], drop_first=True)
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NeoDrop = ['Neo-adjuvant therapy (choice=Chemotherapy)','Neo-adjuvant therapy (choice=Chemotherapy)','Neo-adjuvant therapy (choice=Immunotherapy)',
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'Neo-adjuvant therapy (choice=Radiotherapy)', 'Neo-adjuvant therapy (choice=None)', 'Neo-adjuvant therapy (choice=Targeted Therapy)', 'Neo']
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DFDemo = DFDemo.drop(NeoDrop, axis=1)
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DFDemo = pd.concat([DFDemo, Neo], axis=1)
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#%%
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plt.figure()
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sns.displot(DFDemo['Age (years)'])
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#%%
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DemoComp = DFDemo[DFDemo['Type'] != 'Healthy']
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DemoComp = DemoComp.drop('Type', axis=1)
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DemoNoComp = DFDemo[DFDemo['Type'] == 'Healthy']
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DemoNoComp = DemoNoComp.drop('Type', axis=1)
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from scipy import stats
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#outcome = pd.DataFrame(index=['stat', 'p-value'])
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outcomeT = stats.ttest_ind(DemoNoComp, DemoComp, nan_policy='omit')
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OutcomeT = outcomeT[1].tolist()
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OutcomeMW = []
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for column in DemoComp:
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print(column)
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outcomeMW = stats.mannwhitneyu(DemoNoComp[column], DemoComp[column])
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OutcomeMW.append(outcomeMW[1])
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#DFDemo.to_csv('FinalDemo.csv')
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# -*- coding: utf-8 -*-
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"""
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Created on Thu Feb 25 11:13:35 2021
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@author: Dijkhofmf
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"""
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# Import stuff
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import os
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import pandas as pd
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import numpy as np
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#import seaborn as sns
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#import matplotlib.pyplot as plt
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pd.options.mode.chained_assignment = None # default='warn'
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#%% Define filenames and path
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Filename_T0 = 'BaselineT0.csv'
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Filename_T1 = 'DischargeT1.csv'
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#Filename_T2 = 'FollowUpT2Data.csv'
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FilenameOutc = 'SurgAdmComp.csv'
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FilenameComplete = 'Complete.csv'
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Path = 'I:\Mike Dijkhof\Connecare MGP\Data\FinalFiles'
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# Set path
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os.chdir(Path)
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DFT0 = pd.DataFrame(pd.read_csv(Filename_T0))
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DFT1 = pd.DataFrame(pd.read_csv(Filename_T1))
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#DFT2 = pd.DataFrame(pd.read_csv(Filename_T2))
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DFComplete = pd.DataFrame(pd.read_csv(FilenameComplete))
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DFCompl = pd.DataFrame(pd.read_csv(FilenameOutc))
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#%%
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DFT0['Complete'] = DFComplete['Has patient completed study?']
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DFT0 = DFT0.drop(DFT0[DFT0['Complete'] !='Yes'].index)
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DFT0 = DFT0.astype('str')
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DFT0 = DFT0.set_index(['Study ID'])
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DFT1['Complete'] = DFComplete['Has patient completed study?']
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DFT1 = DFT1.drop(DFT1[DFT1['Complete'] !='Yes'].index)
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DFT1 = DFT1.astype('str')
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DFT1 = DFT1.set_index(['Study ID'])
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# DFT2['Complete data'] = DFComplete['Has patient completed study?']
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# DFT2 = DFT2.drop(DFT2[DFT2['Complete data'] !='Yes'].index)
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# DFT2 = DFT2.astype('str')
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# DFT2 = DFT2.set_index(['Study ID'])
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DFCompl['Complete'] = DFComplete['Has patient completed study?']
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DFCompl = DFCompl.drop(DFCompl[DFCompl['Complete'] !='Yes'].index)
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DFCompl = DFCompl.set_index(['Study ID'])
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#%%
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DFT0 = DFT0.apply(lambda x: x.str.replace(',','.'), axis=1)
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DFT1 = DFT1.apply(lambda x: x.str.replace(',','.'), axis=1)
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#DFT2 = DFT2.apply(lambda x: x.str.replace(',','.'), axis=1)
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#%%
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FinalDF_T0 = pd.DataFrame()
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FinalDF_T0[['BMI','GFI', 'HADS_A', 'HADS_D', 'ADL', 'iADL']] = DFT0[['BMI', 'Groningen Frailty Index', 'Anxiety - Hospital Anxiety Depression Scale', 'Depression - Hospital Anxiety Depression Scale', 'ADL', 'iADL']].astype('float64')
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FinalDF_T1 = pd.DataFrame()
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FinalDF_T1[['GFI', 'HADS_A', 'HADS_D', 'ADL', 'iADL']] = DFT1[['Groningen Frailty Index', 'Anxiety - Hospital Anxiety Depression Scale', 'Depression - Hospital Anxiety Depression Scale', 'ADL', 'iADL']].astype('float64')
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FinalDF_T2 = pd.DataFrame()
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#FinalDF_T2[['GFI', 'HADS_A', 'HADS_D', 'ADL', 'iADL']] = DFT2[['Groningen Frailty Index', 'Anxiety - Hospital Anxiety Depression Scale', 'Depression - Hospital Anxiety Depression Scale', 'ADL', 'iADL']].astype('float64')
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#%% TUG_T0
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FinalDF_T0['TUG1'] = DFT0['Timed to Up&Go - attempt 1 (sec)'].astype('float64').fillna(0)
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FinalDF_T0['TUG2'] = DFT0['Timed to Up&Go - attempt 2 (sec)'].astype('float64').fillna(0)
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for i, r in FinalDF_T0.iterrows():
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if FinalDF_T0.loc[i,'TUG1'] != 0 and FinalDF_T0.loc[i,'TUG2'] != 0:
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FinalDF_T0.loc[i,'TUGTot'] = (FinalDF_T0.loc[i,'TUG1']+FinalDF_T0.loc[i,'TUG2'])/2
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else:
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FinalDF_T0.loc[i,'TUGTot'] = (FinalDF_T0.loc[i,'TUG1']+FinalDF_T0.loc[i,'TUG2'])/1
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FinalDF_T0['TUG1'] = FinalDF_T0['TUG1'].replace(0, np.nan)
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FinalDF_T0['TUG2'] = FinalDF_T0['TUG2'].replace(0, np.nan)
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FinalDF_T0['TUGTot'] = FinalDF_T0['TUGTot'].replace(0, np.nan)
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FinalDF_T0 = FinalDF_T0.drop(['TUG1', 'TUG2'], axis=1)
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# TUG_T1 Asuming that all missing data were due to physical disabilties --> NaNs to 30 seconds
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FinalDF_T1['TUG1'] = DFT1['Timed to Up&Go - attempt 1 (sec)'].astype('float64').fillna(30)
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FinalDF_T1['TUG2'] = DFT1['Timed to Up&Go - attempt 2 (sec)'].astype('float64').fillna(30)
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FinalDF_T1['TUGTot'] = (FinalDF_T1['TUG1']+FinalDF_T1['TUG2'])/2
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FinalDF_T1 = FinalDF_T1.drop(['TUG1', 'TUG2'], axis=1)
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# TUG_T2
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#FinalDF_T2['TUG1'] = DFT2['Timed to Up&Go - attempt 1 (sec)'].astype('float64').fillna(0)
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#FinalDF_T2['TUG2'] = DFT2['Timed to Up&Go - attempt 2 (sec)'].astype('float64').fillna(0)
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# for i, r in FinalDF_T2.iterrows():
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# if FinalDF_T2.loc[i,'TUG1'] != 0 and FinalDF_T2.loc[i,'TUG2'] != 0:
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# FinalDF_T2.loc[i,'TUGTot'] = (FinalDF_T2.loc[i,'TUG1']+FinalDF_T2.loc[i,'TUG2'])/2
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# else:
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# FinalDF_T2.loc[i,'TUGTot'] = (FinalDF_T2.loc[i,'TUG1']+FinalDF_T2.loc[i,'TUG2'])/1
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# FinalDF_T2['TUG1'] = FinalDF_T2['TUG1'].replace(0, np.nan)
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# FinalDF_T2['TUG2'] = FinalDF_T2['TUG2'].replace(0, np.nan)
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# FinalDF_T2['TUGTot'] = FinalDF_T2['TUGTot'].replace(0, np.nan)
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#%%
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FinalDF_T0[['HGSR1','HGSR2','HGSR3']] = DFT0[['Handgrip Strength test Attempt 1 rigth','Handgrip Strength test Attempt 2 rigth','Handgrip Strength test Attempt 3 right']].astype('float64')
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FinalDF_T0['HGSRAvg'] = (FinalDF_T0['HGSR1'] + FinalDF_T0['HGSR2'] + FinalDF_T0['HGSR3'])/3
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FinalDF_T0[['HGSL1','HGSL2','HGSL3']] = DFT0[['Handgrip Strength test Attempt 1 left', 'Handgrip Strength test Attempt 2 left', 'Handgrip Strength test Attempt 3 left']].astype('float64')
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FinalDF_T0['HGSLAvg'] = (FinalDF_T0['HGSL1'] + FinalDF_T0['HGSL2'] + FinalDF_T0['HGSL3'])/3
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FinalDF_T0['Dominance'] = DFT0['Hand dominance']
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FinalDF_T1[['HGSR1','HGSR2','HGSR3']] = DFT1[['Handgrip Strength test Attempt 1 rigth','Handgrip Strength test Attempt 2 rigth','Handgrip Strength test Attempt 3 right']].astype('float64')
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FinalDF_T1['HGSRAvg'] = (FinalDF_T1['HGSR1'] + FinalDF_T1['HGSR2'] + FinalDF_T1['HGSR3'])/3
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FinalDF_T1[['HGSL1','HGSL2','HGSL3']] = DFT1[['Handgrip Strength test Attempt 1 left', 'Handgrip Strength test Attempt 2 left', 'Handgrip Strength test Attempt 3 left']].astype('float64')
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FinalDF_T1['HGSLAvg'] = (FinalDF_T1['HGSL1'] + FinalDF_T1['HGSL2'] + FinalDF_T1['HGSL3'])/3
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for i, r in DFT1.iterrows():
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if DFT1.loc[i,'Handgrip Strength Test'] == 'No':
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FinalDF_T1.loc[i,['HGSR1','HGSR2','HGSR3','HGSRAvg','HGSL1','HGSL2','HGSL3','HGSLAvg']] = 0
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for index, rows in FinalDF_T1.iterrows():
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if FinalDF_T0.loc[index, 'Dominance'] == 'Rigth':
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FinalDF_T0.loc[index, 'HGSDom'] = FinalDF_T0.loc[index,'HGSRAvg']
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FinalDF_T1.loc[index, 'HGSDom'] = FinalDF_T1.loc[index,'HGSRAvg']
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elif FinalDF_T0.loc[index, 'Dominance'] == 'Left':
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FinalDF_T0.loc[index, 'HGSDom'] = FinalDF_T0.loc[index,'HGSLAvg']
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FinalDF_T1.loc[index, 'HGSDom'] = FinalDF_T1.loc[index,'HGSLAvg']
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else:
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FinalDF_T0.loc[index, 'HGSDom'] = (FinalDF_T0.loc[index,'HGSRAvg']+FinalDF_T0.loc[index,'HGSLAvg'])/2
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FinalDF_T1.loc[index, 'HGSDom'] = (FinalDF_T1.loc[index,'HGSRAvg']+FinalDF_T1.loc[index,'HGSLAvg'])/2
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FinalDF_T0 = FinalDF_T0.drop(['HGSR1', 'HGSR2', 'HGSR3', 'HGSRAvg','HGSL1', 'HGSL2', 'HGSL3', 'HGSLAvg', 'Dominance'], axis=1)
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FinalDF_T1 = FinalDF_T1.drop(['HGSR1', 'HGSR2', 'HGSR3', 'HGSRAvg','HGSL1', 'HGSL2', 'HGSL3', 'HGSLAvg'], axis=1)
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# FinalDF_T2[['HGSR1','HGSR2','HGSR3']] = DFT2[['Handgrip Strength test Attempt 1 right','Handgrip Strength test Attempt 2 right','Handgrip Strength test Attempt 3 right']].astype('float64')
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# FinalDF_T2['HGSRAvg'] = (FinalDF_T2['HGSR1'] + FinalDF_T2['HGSR2'] + FinalDF_T2['HGSR3'])/3
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# FinalDF_T2[['HGSL1','HGSL2','HGSL3']] = DFT2[['Handgrip Strength test Attempt 1 left', 'Handgrip Strength test Attempt 2 left', 'Handgrip Strength test Attempt 3 left']].astype('float64')
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# FinalDF_T2['HGSLAvg'] = (FinalDF_T2['HGSL1'] + FinalDF_T2['HGSL2'] + FinalDF_T2['HGSL3'])/3
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# if FinalDF_T0['Dominance'] == 'Right':
|
||||
# FinalDF_T2['HSGDom'] = FinalDF_T0['HGSRAvg']
|
||||
# elif FinalDF_T0['Dominance'] == 'Left':
|
||||
# FinalDF_T2['HSGDom'] = FinalDF_T0['HGSLAvg']
|
||||
# else:
|
||||
# FinalDF_T2['HGSDom'] = (FinalDF_T0['HGSRAvg']+FinalDF_T0['HGSLAvg'])/2
|
||||
|
||||
#%%
|
||||
EORTCCols = DFT0.columns[15:59].tolist()
|
||||
EORTCScoresT0 = DFT0[EORTCCols]
|
||||
#EORTCScoresT2 = DFT2[EORTCCols]
|
||||
|
||||
|
||||
#%%
|
||||
os.chdir('I:\Mike Dijkhof\Python')
|
||||
|
||||
import EORTC as eor
|
||||
import SQUASH as sq
|
||||
|
||||
NewEORTCScoresT0 = eor.EORTCCalculator(EORTCScoresT0, EORTCCols)
|
||||
#NewEORTCScoresT2 = eor.EORTCCalculator(EORTCScoresT2, EORTCCols)
|
||||
|
||||
EORTCT0 = eor.EORTCScore(EORTCScoresT0)
|
||||
#EORTCT2 = eor.EORTCScore(EORTCScoresT2)
|
||||
|
||||
os.chdir(Path)
|
||||
|
||||
#%% plaatjes
|
||||
|
||||
# for index, row in EORTC.iterrows():
|
||||
# plt.figure(figsize=(20,8))
|
||||
# plt.title('EORTC preoperative outcomes pt ' + str(index))
|
||||
# sns.barplot(x=EORTC.columns, y=EORTC.loc[index,:])
|
||||
|
||||
#%%
|
||||
|
||||
SQUASHScoresT0 = sq.SQUASHParse(DFT0)
|
||||
#SQUASHScoresT2 = sq.SQUASHParse(DFT2)
|
||||
|
||||
ColsToDrop = ['SQUASH baseline afgenomen?', 'Woon werkverkeer?', 'Werk?', 'Huishoudelijk werk?']
|
||||
|
||||
SQUASHScoresT0 = SQUASHScoresT0.drop(ColsToDrop, axis=1)
|
||||
SQUASHScoresT0 = SQUASHScoresT0.astype('float64')
|
||||
|
||||
#SQUASHScoresT2 = SQUASHScoresT2.drop(ColsToDrop, axis=1)
|
||||
#SQUASHScoresT2 = SQUASHScoresT2.astype('float64')
|
||||
|
||||
SQUASHT0 = sq.SQUASHScore(SQUASHScoresT0)
|
||||
#SQUASHT2 = sq.SQUASHScore(SQUASHScoresT2)
|
||||
|
||||
#%%
|
||||
FinalDF_T0['Pt Type'] = DFCompl.loc[:,'Complications at home during monitoring ? '].values
|
||||
FinalDF_T1['Pt Type'] = DFCompl.loc[:,'Complications at home during monitoring ? '].values
|
||||
#FinalDF_T2['Pt Type'] = DFCompl.loc[:,'Complications at home during monitoring ? '].values
|
||||
|
||||
|
||||
FinalDF_T0['Pt Type'] = FinalDF_T0['Pt Type'].str.replace('Yes', 'Complication')
|
||||
FinalDF_T0['Pt Type'] = FinalDF_T0['Pt Type'].str.replace('No', 'Healthy')
|
||||
|
||||
FinalDF_T1['Pt Type'] = FinalDF_T1['Pt Type'].str.replace('Yes', 'Complication')
|
||||
FinalDF_T1['Pt Type'] = FinalDF_T1['Pt Type'].str.replace('No', 'Healthy')
|
||||
|
||||
#FinalDF_T2['Pt Type'] = FinalDF_T2['Pt Type'].str.replace('Yes', 'Complication')
|
||||
#FinalDF_T2['Pt Type'] = FinalDF_T2['Pt Type'].str.replace('No', 'Healthy')
|
||||
|
||||
|
||||
#%% Save FinalDF to .csv file
|
||||
|
||||
FinalDF_T0.to_csv('FinalDF_T0.csv')
|
||||
FinalDF_T1.to_csv('FinalDF_T1.csv')
|
||||
#FinalDF_T2.to_csv('FinalDF_T2.csv')
|
||||
|
|
@ -0,0 +1,415 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Script for parsing the Fitbit data into graphs.
|
||||
@author M.F. Dijkhof
|
||||
"""
|
||||
# Import stuff
|
||||
import os
|
||||
import pandas as pd
|
||||
import seaborn as sns
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
# Disable copy overwrite warning
|
||||
pd.options.mode.chained_assignment = None # default='warn'
|
||||
|
||||
#%% Define filenames and path
|
||||
|
||||
FilenameComp = 'SurgeryAndAdmission2.csv' #Surg and Adm + Complications
|
||||
FilenamePA = 'PA_Data.csv'
|
||||
FilenameSteps = 'StepData.csv'
|
||||
FilenameComplete = 'Complete.csv'
|
||||
FilenameOutcome = 'Complications.csv'
|
||||
|
||||
Path = 'I:\Mike Dijkhof\Connecare MGP\Data'
|
||||
|
||||
# Set path
|
||||
os.chdir(Path)
|
||||
|
||||
#%% Create DF from files
|
||||
|
||||
DFComp = pd.DataFrame(pd.read_csv(FilenameComp))
|
||||
DFPA = pd.DataFrame(pd.read_csv(FilenamePA))
|
||||
DFSteps = pd.DataFrame(pd.read_csv(FilenameSteps))
|
||||
DFComplete = pd.DataFrame(pd.read_csv(FilenameComplete))
|
||||
DFOutcome = pd.DataFrame(pd.read_csv(FilenameOutcome))
|
||||
|
||||
DFComp = DFComp.set_index('Study ID')
|
||||
DFPA = DFPA.set_index('Study ID')
|
||||
DFSteps = DFSteps.set_index('Study ID')
|
||||
DFComplete = DFComplete.set_index('Study ID')
|
||||
DFOutcome = DFOutcome.set_index('Study ID')
|
||||
|
||||
#%%
|
||||
# Clear all uncomplete cases
|
||||
CompleteCheck= DFComplete['Has patient completed study?'] == 'Yes'
|
||||
|
||||
DFComp = DFComp[CompleteCheck]
|
||||
DFPA = DFPA[CompleteCheck]
|
||||
DFOutcome = DFOutcome[CompleteCheck]
|
||||
DFSteps = DFSteps[CompleteCheck]
|
||||
|
||||
|
||||
# Transpose PA data into the right format
|
||||
NewDF= pd.DataFrame(DFPA.iloc[0]).transpose()
|
||||
|
||||
counter = range(1, len(DFPA))
|
||||
|
||||
for i in counter:
|
||||
NewRow = DFPA.iloc[i].transpose()
|
||||
NewDF = NewDF.append(NewRow)
|
||||
|
||||
NewDF = NewDF.drop(['Complete?'], axis=1)
|
||||
|
||||
# Do the same for Step data
|
||||
NewStepDF = pd.DataFrame(DFSteps.iloc[0]).transpose()
|
||||
|
||||
counter = range(1, len(DFSteps))
|
||||
|
||||
for i in counter:
|
||||
NewRow = DFSteps.iloc[i].transpose()
|
||||
NewStepDF = NewStepDF.append(NewRow)
|
||||
|
||||
NewStepDF = NewStepDF.drop(['Complete?'], axis=1)
|
||||
|
||||
#%% Create DF with important dates
|
||||
|
||||
DFDates = DFComp [['Date of surgery','Date of hospital discharge',
|
||||
'Date first complication at home', 'Date (first) readmission',
|
||||
'Date discharge after first readmission', 'Date second readmission',
|
||||
'Date discharge second readmission']]
|
||||
|
||||
for i in DFDates:
|
||||
DFDates[i] = pd.to_datetime(DFDates[i]).dt.date
|
||||
|
||||
DFDates['LOS'] = DFDates['Date of hospital discharge'] - DFDates['Date of surgery'] #LOS = Length of stay
|
||||
DFDates['TTC'] = DFDates['Date first complication at home'] - DFDates['Date of surgery'] #TTC = Time to complication
|
||||
DFDates['TTR'] = DFDates['Date (first) readmission'] - DFDates['Date of surgery'] #TTR = Time to readmission
|
||||
DFDates['TT2R'] = DFDates['Date second readmission'] - DFDates['Date of surgery'] #TT2R = Time to second readmission
|
||||
|
||||
#%% Create coordinates from the dates for the plots
|
||||
|
||||
AXVcoord = pd.DataFrame(columns= ['LOS', 'TTC', 'TTR', 'TT2R'])
|
||||
|
||||
for rows, index in DFDates.iterrows():
|
||||
AXVcoord.loc[rows, 'LOS'] = DFDates['LOS'].loc[rows].days
|
||||
AXVcoord.loc[rows, 'TTC'] = DFDates['TTC'].loc[rows].days
|
||||
AXVcoord.loc[rows, 'TTR'] = DFDates['TTR'].loc[rows].days
|
||||
AXVcoord.loc[rows, 'TT2R'] = DFDates['TT2R'].loc[rows].days
|
||||
|
||||
AXVcomb = AXVcoord.values.tolist()
|
||||
AXVArray = np.array(AXVcomb)
|
||||
|
||||
|
||||
#%% Create DFs for each PA level
|
||||
|
||||
NoActDF = NewDF.loc[:, :'No activity After Surgery: 90']
|
||||
LowActDF = NewDF.loc[:, 'Low activity Before Surgery: -1 ':'Low activity After Surgery: 90']
|
||||
MedActDF = NewDF.loc[:, 'Medium activity Before Surgery: -1':'Medium activity After Surgery: 90']
|
||||
HighActDF = NewDF.loc[:, 'High activity Before Surgery: -1 ':'High activity After Surgery: 90']
|
||||
|
||||
def MakeStepDF(NewDF):
|
||||
StepDF = NewDF.iloc[:,321:427]
|
||||
StepDF = StepDF.drop('Days Fitbit prescribed after surgery', axis=1)
|
||||
StepDF = StepDF.replace(' ', '')
|
||||
StepDF = StepDF.replace('N.A.', np.nan)
|
||||
StepDF = StepDF.replace('N.A. ', np.nan)
|
||||
StepDF = StepDF.replace('NA.', np.nan)
|
||||
StepDF = StepDF.replace('n.a.', np.nan)
|
||||
StepDF = StepDF.replace('N.A', np.nan)
|
||||
StepDF = StepDF.replace('NaN', np.nan)
|
||||
StepDF = StepDF.astype('float64')
|
||||
return StepDF
|
||||
|
||||
StepDF = MakeStepDF(NewStepDF)
|
||||
|
||||
#%% Day -14 to surgery were in the wrong order so we have to flip the first 14 days
|
||||
|
||||
def DayFlipper(DF):
|
||||
ListCol = DF.columns.tolist()
|
||||
ListCol[0:14] = ListCol[0:14][::-1]
|
||||
DF = DF[ListCol]
|
||||
return(DF)
|
||||
|
||||
NoActDF = DayFlipper(NoActDF)
|
||||
print(NoActDF.columns)
|
||||
LowActDF = DayFlipper(LowActDF)
|
||||
print(LowActDF.columns)
|
||||
MedActDF = DayFlipper(MedActDF)
|
||||
print(MedActDF.columns)
|
||||
HighActDF = DayFlipper(HighActDF)
|
||||
print(HighActDF.columns)
|
||||
StepDF = DayFlipper(StepDF)
|
||||
print(StepDF.columns)
|
||||
|
||||
#%%
|
||||
OldColumns = LowActDF.columns
|
||||
NewColumns = range(-14, 91)
|
||||
|
||||
LowActDF.columns = NewColumns
|
||||
MedActDF.columns = NewColumns
|
||||
HighActDF.columns = NewColumns
|
||||
StepDF.columns = NewColumns
|
||||
|
||||
# Set NaN to zeroes in order to calculate the total amount of activity
|
||||
LowActDFZeroes = LowActDF.fillna(0)
|
||||
MedActDFZeroes = MedActDF.fillna(0)
|
||||
HighActDFZeroes = HighActDF.fillna(0)
|
||||
StepDFZeroes = StepDF.fillna(0)
|
||||
|
||||
TotActDF = LowActDF + MedActDF + HighActDF
|
||||
TotActDFZeroes = LowActDFZeroes + MedActDFZeroes + HighActDFZeroes
|
||||
|
||||
# Remove pts that reported less than threshold PA days
|
||||
Threshold = 200
|
||||
|
||||
NaNCount = LowActDF.isnull().sum(axis=1) # Count days without data per patient
|
||||
NaNRowDrop = (LowActDF.isnull().sum(axis=1)) < Threshold
|
||||
|
||||
NoActDFClean = NoActDF[NaNRowDrop]
|
||||
LowActDFClean = LowActDFZeroes[NaNRowDrop]
|
||||
MedActDFClean = MedActDFZeroes[NaNRowDrop]
|
||||
HighActDFClean = HighActDFZeroes[NaNRowDrop]
|
||||
TotActDFClean = TotActDFZeroes[NaNRowDrop]
|
||||
|
||||
#%%
|
||||
|
||||
# NoActDFClean['Group'] = 'Complication'
|
||||
# LowActDFClean['Group'] = 'Complication'
|
||||
# MedActDFClean['Group'] = 'Complication'
|
||||
# HighActDFClean['Group'] = 'Complication'
|
||||
# TotActDFClean['Group'] = 'Complication'
|
||||
# StepDF['Group'] = 'Complication'
|
||||
|
||||
def Grouper(DF):
|
||||
DF['Group'] = 'Complication'
|
||||
DF['Group'] = DF['Group'].where(DFOutcome['Complications at home during monitoring ? '] == 'Yes', other='No Comp')
|
||||
return DF
|
||||
|
||||
NoActDFClean = Grouper(NoActDFClean)
|
||||
LowActDFClean = Grouper(LowActDFClean)
|
||||
MedActDFClean = Grouper(MedActDFClean)
|
||||
HighActDFClean = Grouper(HighActDFClean)
|
||||
TotActDFClean = Grouper(TotActDFClean)
|
||||
StepDF = Grouper(StepDF)
|
||||
|
||||
# #%% Divide Comps, Non-comps and Unknown-Comps
|
||||
|
||||
# LowActComp = LowActDFClean.loc[NewDF['Complications at Home'] == 'Yes']
|
||||
# MedActComp = MedActDFClean.loc[NewDF['Complications at Home'] == 'Yes']
|
||||
# HighActComp = HighActDFClean.loc[NewDF['Complications at Home'] == 'Yes']
|
||||
# TotActComp = TotActDFClean.loc[NewDF['Complications at Home'] == 'Yes']
|
||||
|
||||
# LowActNoComp = LowActDFClean.loc[NewDF['Complications at Home'] == 'No']
|
||||
# MedActNoComp = MedActDFClean.loc[NewDF['Complications at Home'] == 'No']
|
||||
# HighActNoComp = HighActDFClean.loc[NewDF['Complications at Home'] == 'No']
|
||||
# TotActNoComp = TotActDFClean.loc[NewDF['Complications at Home'] == 'No']
|
||||
|
||||
# LowActUnk = LowActDFClean.loc[(NewDF['Complications at Home'] != 'Yes') & (NewDF['Complications at Home'] != 'No')]
|
||||
# MedActUnk = MedActDFClean.loc[(NewDF['Complications at Home'] != 'Yes') & (NewDF['Complications at Home'] != 'No')]
|
||||
# HighActUnk = HighActDFClean.loc[(NewDF['Complications at Home'] != 'Yes') & (NewDF['Complications at Home'] != 'No')]
|
||||
# TotActUnk = TotActDFClean.loc[(NewDF['Complications at Home'] != 'Yes') & (NewDF['Complications at Home'] != 'No')]
|
||||
|
||||
#%% Plot comps, non-comps amd unknown patient data with event-dates
|
||||
|
||||
colors = ['k','c','r', 'r'] # k=discharge, c=complication, r=readmissions
|
||||
|
||||
def PAPlotter(Low, Med, High, Tot, Step, AXV):
|
||||
|
||||
for index, row in Tot.iterrows():
|
||||
|
||||
counter = index-1
|
||||
fig, ax1 = plt.subplots(figsize=(20,8))
|
||||
|
||||
ax1.plot(Low.loc[index], 'b:')
|
||||
ax1.plot(Med.loc[index], 'r:')
|
||||
ax1.plot(High.loc[index], 'y:')
|
||||
ax1.plot(Tot.loc[index])
|
||||
ax1.set_ylabel('Minutes of PA')
|
||||
ax1.set_xlabel('Days')
|
||||
plt.ylim(0,1440)
|
||||
|
||||
plt.vlines(x=0, ymin=0, ymax=1440, linestyle='dashed')
|
||||
plt.vlines(AXV[counter], ymin= 0, ymax= 1440, colors=colors, linestyle='dotted')
|
||||
|
||||
ax2 = ax1.twinx()
|
||||
ax2.plot(Step.loc[index], 'k')
|
||||
ax2.set_ylabel('Steps per day')
|
||||
|
||||
plt.title('PA levels comp pt' + str(index))
|
||||
plt.ylim(0,25000)
|
||||
|
||||
PAPlotter(LowActDFClean, MedActDFClean, HighActDFClean, TotActDFClean, StepDF, AXVcomb)
|
||||
#PAPlotter(LowActNoComp, MedActNoComp, HighActNoComp,TotActNoComp, StepDF, AXVcomb, 'No Complication')
|
||||
#PAPlotter(LowActUnk, MedActUnk, HighActUnk, TotActUnk, StepDF, AXVcomb, 'Unknown Complication')
|
||||
|
||||
|
||||
#%% Calculate differences between comp PA and no comp PA
|
||||
|
||||
def PAStats(DF, group):
|
||||
MeanTotPA = DF.mean().mean()
|
||||
StdTotPA = DF.std().std()
|
||||
PreMean= DF.loc[:,-14:-1].mean().mean()
|
||||
PreStd = DF.loc[:,-14:-1].std().std()
|
||||
Post30Mean = DF.loc[:,0:30].mean().mean()
|
||||
Post30Std = DF.loc[:,0:30].std().std()
|
||||
Post60Mean = DF.loc[:,0:60].mean().mean()
|
||||
Post60Std = DF.loc[:,0:60].std().std()
|
||||
Post90Mean = DF.loc[:,0:90].mean().mean()
|
||||
Post90Std = DF.loc[:,0:90].std().std()
|
||||
|
||||
print('Stats '+ group + ':', '\n')
|
||||
print('Total Mean min PA ='+ str(MeanTotPA),'Std=' + str(StdTotPA))
|
||||
print('Preoperative Mean min PA =' + str(PreMean), 'Std=' + str(PreStd))
|
||||
print('30 days Postop. Mean min PA =' + str(Post30Mean), 'Std=' + str(Post30Std))
|
||||
print('60 days Postop. Mean min PA =' + str(Post60Mean), 'Std=' + str(Post60Std))
|
||||
print('90 days Postop. Mean min PA =' + str(Post90Mean), 'Std=' + str(Post90Std),'\n')
|
||||
|
||||
PAStats(TotActComp, 'complication')
|
||||
PAStats(TotActNoComp, 'no complication')
|
||||
PAStats(TotActUnk, 'unkown')
|
||||
|
||||
|
||||
#%% Plot histogram number of missing values
|
||||
CountDF = pd.DataFrame(NaNCount)
|
||||
CountDF['Complication'] = DFCompl['Complications at home during monitoring ? ']
|
||||
CountDF.columns = ['Count', 'Complication']
|
||||
|
||||
sns.displot(CountDF, x='Count', bins=[10, 20, 30, 40, 50, 60, 70, 80, 90], hue='Complication')
|
||||
sns.color_palette ('colorblind')
|
||||
|
||||
#%%
|
||||
|
||||
def RollingAvAct(DF, windowsize):
|
||||
AvDF = pd.DataFrame()
|
||||
|
||||
for index, row in DF.iterrows():
|
||||
AvDF = AvDF.append(row.rolling(windowsize, min_periods=1).mean())
|
||||
return(AvDF)
|
||||
|
||||
AvTotActComp =pd.DataFrame(RollingAvAct(TotActComp, 3))
|
||||
AvTotActNoComp = pd.DataFrame(RollingAvAct(TotActNoComp, 3))
|
||||
|
||||
|
||||
#%%
|
||||
def Trendliner(DF, Dates, group):
|
||||
newPASlopePre = pd.DataFrame(columns=['Slope', 'Int', 'Group'])
|
||||
newPASlopeLOS = pd.DataFrame(columns=['Slope', 'Int', 'Group'])
|
||||
newPASlopePost = pd.DataFrame(columns=['Slope', 'Int', 'Group'])
|
||||
|
||||
for index, row in DF.iterrows():
|
||||
|
||||
counter = index-1
|
||||
DisDay = int(AXVArray[counter,0])
|
||||
DisDay2 = int(DisDay+15)
|
||||
DisDay3 = int(DisDay2-1)
|
||||
|
||||
# Calculate trendline pre-op
|
||||
Xpre = DF.columns[0:15]
|
||||
Ypre = DF.loc[index,-14:0]
|
||||
z_pre = np.polyfit(Xpre, Ypre, 1)
|
||||
p_pre = np.poly1d(z_pre)
|
||||
newPASlopePre.loc[index,'Slope'] = z_pre[0]
|
||||
newPASlopePre.loc[index,'Int'] = z_pre[1]
|
||||
newPASlopePre.loc[index, 'Group'] = group
|
||||
|
||||
# Calculate trendline LOS
|
||||
Xlos = DF.columns[14:DisDay2]
|
||||
Ylos = DF.loc[index,0:DisDay]
|
||||
z_los = np.polyfit(Xlos, Ylos, 1)
|
||||
p_los = np.poly1d(z_los)
|
||||
newPASlopeLOS.loc[index,'Slope'] = z_los[0]
|
||||
newPASlopeLOS.loc[index,'Int'] = z_los[1]
|
||||
newPASlopeLOS.loc[index, 'Group'] = group
|
||||
|
||||
# Calculate trendline post-op
|
||||
Xpost = DF.columns[DisDay3:]
|
||||
Ypost = DF.loc[index,DisDay:]
|
||||
z_post = np.polyfit(Xpost, Ypost, 1)
|
||||
p_post = np.poly1d(z_post)
|
||||
newPASlopePost.loc[index,'Slope'] = z_post[0]
|
||||
newPASlopePost.loc[index,'Int'] = z_post[1]
|
||||
newPASlopePost.loc[index, 'Group'] = group
|
||||
|
||||
# Plot figures
|
||||
plt.figure(figsize=(24,8))
|
||||
plt.plot(DF.loc[index])
|
||||
plt.plot(Xpost,p_post(Xpost),'r--')
|
||||
plt.plot(Xpre, p_pre(Xpre), 'b--')
|
||||
plt.plot(Xlos, p_los(Xlos), 'k--')
|
||||
plt.vlines(x=0, ymin=0, ymax=1440, linestyle='dashed')
|
||||
plt.vlines(Dates[counter], ymin= 0, ymax= 1440, colors=colors, linestyle='dotted')
|
||||
plt.xlim(-14,105)
|
||||
plt.ylim(0,1440)
|
||||
plt.ylabel('Minutes of PA')
|
||||
plt.xlabel('Days')
|
||||
plt.title('Mov Avg PA levels pt' + str(index) + '_' + group)
|
||||
|
||||
d = {'Pre': newPASlopePre, 'LOS':newPASlopeLOS, 'Post': newPASlopePost}
|
||||
|
||||
return(d)
|
||||
|
||||
TrendDictComp = Trendliner(AvTotActComp, AXVcomb, 'complication')
|
||||
TrendDictNoComp= Trendliner(AvTotActNoComp, AXVcomb, 'no complication')
|
||||
|
||||
|
||||
#%%
|
||||
|
||||
# def SlopeStats(SlopeDict, group):
|
||||
# MeanSlopePre, MeanIntPre = SlopeDict['Pre'].mean()
|
||||
# StdSlopePre, StdIntPre = SlopeDict['Pre'].std()
|
||||
# MeanSlopeLOS, MeanIntLOS = SlopeDict['LOS'].mean()
|
||||
# StdSlopeLOS, StdIntLOS = SlopeDict['LOS'].std()
|
||||
# MeanSlopePost, MeanIntPost = SlopeDict['Post'].mean()
|
||||
# StdSlopePost, StdIntPost = SlopeDict['Post'].std()
|
||||
|
||||
# print('Stats '+ group + ':', '\n')
|
||||
# print('Mean slope PA Pre-op = '+ str(MeanSlopePre),'Std= ' + str(StdSlopePre))
|
||||
# print('Mean slope PA hospitalization = '+ str(MeanSlopeLOS),'Std= ' + str(StdSlopeLOS))
|
||||
# print('Mean slope PA Post-op = '+ str(MeanSlopePost),'Std= ' + str(StdSlopePost))
|
||||
# print('Mean intersept PA Pre-op = '+ str(MeanIntPre),'Std= ' + str(StdIntPre))
|
||||
# print('Mean intercept PA hospitalization = '+ str(MeanIntLOS),'Std= ' + str(StdIntLOS))
|
||||
# print('Mean intercept PA Post-op = '+ str(MeanIntPre),'Std= ' + str(StdIntPre), '\n')
|
||||
|
||||
# return(MeanSlopePre, StdSlopePre, MeanSlopeLOS, StdSlopeLOS, MeanSlopePost, StdSlopePost)
|
||||
|
||||
# MeanSlopePreComp, StdSlopePreComp, MeanSlopeLOSComp, StdSlopLOSComp, MeanSlopePostComp, StdSlopeComp, = SlopeStats(TrendDictComp, 'complications')
|
||||
# MeanSlopePreNoComp, StdSlopePreNoComp, MeanSlopeLOSNoComp, StdSlopLOSNoComp, MeanSlopePostNoComp, StdSlopeNoComp = SlopeStats(TrendDictNoComp, 'no complications')
|
||||
|
||||
|
||||
#%%
|
||||
|
||||
# SlopeIntPreComp = pd.DataFrame(TrendDictComp['Pre'])
|
||||
# SlopeIntPreComp['Period'] = 'Pre'
|
||||
# SlopeIntPreNoComp= pd.DataFrame(TrendDictNoComp['Pre'])
|
||||
# SlopeIntPreNoComp['Period'] = 'Pre'
|
||||
# SlopeIntLOSComp = pd.DataFrame(TrendDictComp['LOS'])
|
||||
# SlopeIntLOSComp['Period'] = 'LOS'
|
||||
# SlopeIntLOSNoComp= pd.DataFrame(TrendDictNoComp['LOS'])
|
||||
# SlopeIntLOSNoComp['Period'] = 'LOS'
|
||||
|
||||
# SlopeIntPostComp = pd.DataFrame(TrendDictComp['Post'])
|
||||
# SlopeIntPostComp['Period'] = 'Post'
|
||||
# SlopeIntPostNoComp= pd.DataFrame(TrendDictNoComp['Post'])
|
||||
# SlopeIntPostNoComp['Period'] = 'Post'
|
||||
|
||||
# Slope = pd.DataFrame()
|
||||
# Slope = Slope.append([SlopeIntPreComp, SlopeIntPreNoComp, SlopeIntLOSComp, SlopeIntLOSNoComp, SlopeIntPostComp, SlopeIntPostNoComp])
|
||||
# Slope['Slope'] = Slope['Slope'].astype('float64')
|
||||
# Slope['Int'] = Slope['Int'].astype('float64')
|
||||
|
||||
#%%
|
||||
# plt.figure(figsize=(12,8))
|
||||
# sns.set_theme(style="darkgrid")
|
||||
# sns.violinplot(x=Slope['Period'], y=Slope['Slope'],hue=Slope['Group'], palette="muted", split=True)
|
||||
# plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
|
||||
|
||||
# plt.figure(figsize=(12,8))
|
||||
# sns.set_theme(style="darkgrid")
|
||||
# sns.violinplot(x=Slope['Period'], y=Slope['Int'],hue=Slope['Group'], palette="muted", split=True)
|
||||
# plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
|
||||
|
||||
#%%
|
||||
#fig, axes = plt.subplots(1,2, sharey=True)
|
||||
#sns.violinplot(data=newPASlopeComp['Intercept'], ax=axes[0], color='b')
|
||||
#sns.violinplot(data=newPASlopeNoComp['Intercept'], ax=axes[1], color='r')
|
||||
|
|
@ -0,0 +1,73 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Fri May 14 09:18:32 2021
|
||||
|
||||
@author: Dijkhofmf
|
||||
"""
|
||||
|
||||
# Import stuff
|
||||
import os
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sns
|
||||
from sklearn import preprocessing
|
||||
|
||||
#%% Import data and path
|
||||
|
||||
Path = 'I:\Mike Dijkhof\Connecare MGP\Data\FinalFiles'
|
||||
|
||||
# Set path
|
||||
os.chdir(Path)
|
||||
|
||||
#%% Create DF
|
||||
FinalDF = pd.DataFrame(pd.read_csv('FinalDataset.csv'))
|
||||
|
||||
X = pd.DataFrame(FinalDF)
|
||||
|
||||
cols = X.drop('Pt Type', axis=1)
|
||||
|
||||
|
||||
ID = X['Study ID']
|
||||
y = X['Pt Type']
|
||||
y= y.replace('Healthy', 'No-complication')
|
||||
X = X.drop(['Pt Type', 'Study ID'], axis=1)
|
||||
|
||||
#%%
|
||||
X1 = pd.DataFrame(preprocessing.scale(X), columns=X.columns)
|
||||
|
||||
X1['Pt Type'] = y
|
||||
X1.set_index(ID)
|
||||
|
||||
#%%
|
||||
|
||||
X1.columns = ['Age (years)', 'Gender', 'Daily alcohol use', 'Medication',
|
||||
'ASA-classification', 'Recurrent disease?', 'Comorb',
|
||||
'Independent, with others', 'Smokes cigarettes/sigar', 'BMI', 'GFI',
|
||||
'HADS_A', 'HADS Depression', 'ADL', 'iADL', 'TUG', 'Handgrip strength',
|
||||
'Avg. Steps/day', 'Avg. MVPA/day', 'Pt Type']
|
||||
|
||||
plots = X1.columns
|
||||
|
||||
#%%
|
||||
import matplotlib.pylab as pylab
|
||||
|
||||
params = {'legend.fontsize': 'x-large',
|
||||
'axes.labelsize': 'x-large',
|
||||
'axes.titlesize':'x-large',
|
||||
'xtick.labelsize':'x-large',
|
||||
'ytick.labelsize':'x-large'}
|
||||
|
||||
pylab.rcParams.update(params)
|
||||
|
||||
plots = plots[1:]
|
||||
namecount=0
|
||||
|
||||
for x in plots:
|
||||
name = str(plots[namecount])
|
||||
plt.figure(dpi=720)
|
||||
sns.boxplot(x='Pt Type', y=x, data=X1, boxprops=dict(alpha=0.5))
|
||||
sns.swarmplot(x='Pt Type', y=x, data=X1)
|
||||
plt.title('Swarm-boxplot ' + name)
|
||||
namecount = namecount +1
|
||||
|
||||
|
||||
Loading…
Reference in New Issue