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136 lines
5.0 KiB
136 lines
5.0 KiB
# -*- 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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