Commit 6d15b53e authored by justus.taeger's avatar justus.taeger
Browse files

test

parent 8c0f95da
Patient_Id,City,Doctor
0,Boston,Dr. Bleimehl
1,Shanghai,Dr. Sommer
2,Munich,Dr. Jarasch
3,Tokyo,Dr. Bleimehl
4,New York,Dr. Sommer
5,Paris,Dr. Bleimehl
6,Tokyo,Dr. Bleimehl
7,Boston,Dr. Jarasch
8,Berlin,Dr. Täger
9,Munich,Dr. Sommer
10,Elmshorn,Dr. Bleimehl
11,Paris,Dr. Sommer
12,Munich,Dr. Jarasch
13,Munich,Dr. Sommer
14,London,Dr. Sommer
15,Munich,Dr. Sommer
16,Tokyo,Dr. Sommer
17,Boston,Dr. Sommer
18,New York,Dr. Jarasch
19,Shanghai,Dr. Täger
20,Boston,Dr. Täger
21,Berlin,Dr. Jarasch
22,Tokyo,Dr. Jarasch
23,Boston,Dr. Täger
24,Elmshorn,Dr. Sommer
25,Berlin,Dr. Täger
26,Boston,Dr. Sommer
27,Boston,Dr. Jarasch
28,Shanghai,Dr. Täger
29,Munich,Dr. Jarasch
30,Boston,Dr. Jarasch
31,Tokyo,Dr. Sommer
32,Paris,Dr. Sommer
33,London,Dr. Täger
34,Tokyo,Dr. Sommer
35,Shanghai,Dr. Sommer
36,Tokyo,Dr. Bleimehl
37,Elmshorn,Dr. Jarasch
38,Boston,Dr. Bleimehl
39,Boston,Dr. Täger
40,Boston,Dr. Täger
41,Berlin,Dr. Jarasch
42,Boston,Dr. Sommer
43,Boston,Dr. Bleimehl
44,Berlin,Dr. Bleimehl
45,Elmshorn,Dr. Jarasch
46,Elmshorn,Dr. Jarasch
47,Tokyo,Dr. Jarasch
48,Tokyo,Dr. Bleimehl
49,Boston,Dr. Täger
50,Tokyo,Dr. Bleimehl
51,Elmshorn,Dr. Jarasch
52,Munich,Dr. Bleimehl
53,Paris,Dr. Täger
54,Munich,Dr. Jarasch
55,Berlin,Dr. Bleimehl
56,Munich,Dr. Sommer
57,Munich,Dr. Jarasch
58,New York,Dr. Jarasch
59,London,Dr. Täger
0,Shanghai,Dr. Täger
1,Boston,Dr. Jarasch
2,New York,Dr. Täger
3,Paris,Dr. Jarasch
4,Boston,Dr. Jarasch
5,Paris,Dr. Jarasch
6,Elmshorn,Dr. Täger
7,Boston,Dr. Sommer
8,Boston,Dr. Bleimehl
9,Shanghai,Dr. Sommer
10,New York,Dr. Sommer
11,Munich,Dr. Täger
12,New York,Dr. Sommer
13,Shanghai,Dr. Bleimehl
14,Elmshorn,Dr. Jarasch
15,Munich,Dr. Bleimehl
16,Munich,Dr. Bleimehl
17,Tokyo,Dr. Täger
18,Elmshorn,Dr. Täger
19,Tokyo,Dr. Jarasch
20,New York,Dr. Bleimehl
21,New York,Dr. Jarasch
22,Paris,Dr. Sommer
23,New York,Dr. Jarasch
24,Berlin,Dr. Sommer
25,Elmshorn,Dr. Jarasch
26,Paris,Dr. Jarasch
27,New York,Dr. Täger
28,Berlin,Dr. Sommer
29,Elmshorn,Dr. Jarasch
30,Boston,Dr. Bleimehl
31,Elmshorn,Dr. Täger
32,London,Dr. Täger
33,London,Dr. Jarasch
34,Shanghai,Dr. Bleimehl
35,Berlin,Dr. Bleimehl
36,Paris,Dr. Sommer
37,Munich,Dr. Bleimehl
38,Elmshorn,Dr. Jarasch
39,Paris,Dr. Bleimehl
40,New York,Dr. Sommer
41,New York,Dr. Täger
42,New York,Dr. Täger
43,London,Dr. Bleimehl
44,Paris,Dr. Sommer
45,Boston,Dr. Sommer
46,Elmshorn,Dr. Bleimehl
47,Munich,Dr. Bleimehl
48,London,Dr. Täger
49,London,Dr. Jarasch
50,Tokyo,Dr. Täger
51,Berlin,Dr. Sommer
52,Berlin,Dr. Täger
53,Munich,Dr. Jarasch
54,Elmshorn,Dr. Sommer
55,Tokyo,Dr. Bleimehl
56,Tokyo,Dr. Sommer
57,Munich,Dr. Sommer
58,Paris,Dr. Sommer
59,Boston,Dr. Täger
60,London,Dr. Sommer
61,London,Dr. Bleimehl
62,Tokyo,Dr. Sommer
63,Berlin,Dr. Sommer
64,Berlin,Dr. Sommer
65,Elmshorn,Dr. Bleimehl
66,Munich,Dr. Jarasch
67,Shanghai,Dr. Täger
68,Munich,Dr. Sommer
69,Munich,Dr. Jarasch
70,Shanghai,Dr. Sommer
71,Berlin,Dr. Sommer
72,Elmshorn,Dr. Jarasch
73,Paris,Dr. Sommer
74,New York,Dr. Jarasch
75,New York,Dr. Täger
76,Munich,Dr. Bleimehl
77,Paris,Dr. Sommer
78,Boston,Dr. Bleimehl
79,New York,Dr. Täger
80,Boston,Dr. Täger
81,Munich,Dr. Bleimehl
82,Paris,Dr. Sommer
83,London,Dr. Bleimehl
84,Munich,Dr. Jarasch
85,Boston,Dr. Sommer
86,Shanghai,Dr. Jarasch
87,Munich,Dr. Sommer
88,Munich,Dr. Jarasch
89,Elmshorn,Dr. Bleimehl
90,Boston,Dr. Bleimehl
91,Paris,Dr. Täger
92,Boston,Dr. Sommer
93,Munich,Dr. Täger
94,London,Dr. Täger
95,Tokyo,Dr. Bleimehl
61,Tokyo,Dr. Bleimehl
62,Munich,Dr. Sommer
63,London,Dr. Jarasch
64,London,Dr. Jarasch
65,Paris,Dr. Täger
66,Shanghai,Dr. Bleimehl
67,Elmshorn,Dr. Sommer
68,Munich,Dr. Täger
69,Paris,Dr. Täger
70,Munich,Dr. Sommer
71,Boston,Dr. Jarasch
72,Berlin,Dr. Bleimehl
73,Elmshorn,Dr. Bleimehl
74,Elmshorn,Dr. Bleimehl
75,Paris,Dr. Jarasch
76,Tokyo,Dr. Sommer
77,Boston,Dr. Bleimehl
78,Berlin,Dr. Bleimehl
79,Paris,Dr. Täger
80,Munich,Dr. Sommer
81,Elmshorn,Dr. Täger
82,Shanghai,Dr. Jarasch
83,New York,Dr. Jarasch
84,Boston,Dr. Bleimehl
85,Tokyo,Dr. Jarasch
86,Tokyo,Dr. Jarasch
87,Paris,Dr. Sommer
88,New York,Dr. Sommer
89,London,Dr. Bleimehl
90,Berlin,Dr. Täger
91,Shanghai,Dr. Täger
92,Tokyo,Dr. Bleimehl
93,Tokyo,Dr. Jarasch
94,Shanghai,Dr. Jarasch
95,London,Dr. Bleimehl
96,Tokyo,Dr. Jarasch
97,Berlin,Dr. Sommer
98,New York,Dr. Sommer
99,Elmshorn,Dr. Jarasch
100,New York,Dr. Sommer
101,Elmshorn,Dr. Jarasch
102,Tokyo,Dr. Bleimehl
103,Tokyo,Dr. Sommer
104,Munich,Dr. Jarasch
105,Elmshorn,Dr. Jarasch
97,New York,Dr. Bleimehl
98,London,Dr. Jarasch
99,New York,Dr. Jarasch
100,Paris,Dr. Sommer
101,Shanghai,Dr. Jarasch
102,Tokyo,Dr. Täger
103,Tokyo,Dr. Bleimehl
104,Munich,Dr. Sommer
105,London,Dr. Sommer
106,New York,Dr. Täger
107,Tokyo,Dr. Täger
108,London,Dr. Täger
109,Tokyo,Dr. Sommer
110,Tokyo,Dr. Sommer
111,Paris,Dr. Bleimehl
112,Munich,Dr. Bleimehl
113,Munich,Dr. Jarasch
114,Elmshorn,Dr. Bleimehl
115,Shanghai,Dr. Sommer
116,London,Dr. Täger
117,Tokyo,Dr. Täger
118,Paris,Dr. Jarasch
119,Boston,Dr. Jarasch
120,Berlin,Dr. Jarasch
121,Berlin,Dr. Täger
122,Elmshorn,Dr. Jarasch
123,London,Dr. Täger
124,New York,Dr. Sommer
125,Paris,Dr. Täger
126,Munich,Dr. Sommer
127,London,Dr. Täger
128,Paris,Dr. Jarasch
129,Paris,Dr. Täger
130,Shanghai,Dr. Täger
131,New York,Dr. Jarasch
132,London,Dr. Bleimehl
133,Elmshorn,Dr. Sommer
134,New York,Dr. Bleimehl
135,Shanghai,Dr. Bleimehl
136,Shanghai,Dr. Täger
137,London,Dr. Sommer
138,London,Dr. Bleimehl
139,Shanghai,Dr. Bleimehl
140,Paris,Dr. Sommer
141,Elmshorn,Dr. Jarasch
142,Paris,Dr. Bleimehl
143,Tokyo,Dr. Jarasch
144,Berlin,Dr. Täger
145,New York,Dr. Bleimehl
146,London,Dr. Bleimehl
147,London,Dr. Sommer
148,Boston,Dr. Täger
149,London,Dr. Täger
107,Paris,Dr. Sommer
108,Paris,Dr. Jarasch
109,Paris,Dr. Täger
110,Elmshorn,Dr. Täger
111,New York,Dr. Bleimehl
112,Paris,Dr. Bleimehl
113,Paris,Dr. Bleimehl
114,Berlin,Dr. Täger
115,Elmshorn,Dr. Jarasch
116,Tokyo,Dr. Sommer
117,Paris,Dr. Täger
118,Tokyo,Dr. Bleimehl
119,Elmshorn,Dr. Täger
120,Boston,Dr. Bleimehl
121,Elmshorn,Dr. Jarasch
122,London,Dr. Täger
123,London,Dr. Sommer
124,Boston,Dr. Sommer
Influenca,Cancer,Tubercolosis,Diabetes,Diagnose_ID
0,0,0,0,0
0,0,0,1,1
0,0,1,0,2
0,0,1,1,3
0,1,0,0,4
0,1,0,1,5
0,1,1,0,6
0,1,1,1,7
1,0,0,0,8
1,0,0,1,9
1,0,1,0,10
1,0,1,1,11
1,1,0,0,12
1,1,0,1,13
1,1,1,0,14
1,1,1,1,15
Pneumonia,Stroke,Diabetes,Influenca,Alzheimer,Diagnose_ID
0,0,0,0,0,0
0,0,0,0,1,1
0,0,0,1,0,2
0,0,0,1,1,3
0,0,1,0,0,4
0,0,1,0,1,5
0,0,1,1,0,6
0,0,1,1,1,7
0,1,0,0,0,8
0,1,0,0,1,9
0,1,0,1,0,10
0,1,0,1,1,11
0,1,1,0,0,12
0,1,1,0,1,13
0,1,1,1,0,14
0,1,1,1,1,15
1,0,0,0,0,16
1,0,0,0,1,17
1,0,0,1,0,18
1,0,0,1,1,19
1,0,1,0,0,20
1,0,1,0,1,21
1,0,1,1,0,22
1,0,1,1,1,23
1,1,0,0,0,24
1,1,0,0,1,25
1,1,0,1,0,26
1,1,0,1,1,27
1,1,1,0,0,28
1,1,1,0,1,29
1,1,1,1,0,30
1,1,1,1,1,31
%% Cell type:code id: tags:
```
import random
import csv
diseases = ['Cancer', 'Diabetes', 'Heart Disease', 'Stroke', 'Alzheimer', 'Influenca', 'Syphilis', 'Pneumonia', 'Asthma', 'Tubercolosis']
diseases_selection = []
number_of_diseases = input("Anzahl an Krankheiten (bitte 1 bis 10 eingeben): ")
number_of_patients = input("Anzahl an Patienten: ")
number_of_patients = int(number_of_patients)
number_of_diseases = int(number_of_diseases)
for i in range(number_of_diseases):
tmp = random.choice(diseases)
diseases_selection.append(tmp)
diseases.remove(tmp)
disease_csv = []
disease_csv.append(diseases_selection)
import itertools
tmp_list = []
counter = -1
for i in range(len(diseases_selection)):
tmp_list.append([0,1])
for element in itertools.product(*tmp_list):
counter += 1
element = list(element)
element.append(counter)
disease_csv.append(element)
diseases_selection.append('Diagnose_ID')
with open('disease.csv', 'w', newline='') as file:
writer = csv.writer(file)
writer.writerows(disease_csv)
patients = []
patient_header = ['Patient_Id', 'Age', 'Height', 'Weight', 'BMI', 'Sex', 'Diagnose_ID']
gender = ['f', 'm']
diganose_IDs = []
for i in range(len(disease_csv)-1):
diganose_IDs.append(i)
patients.append(patient_header)
for i in range(number_of_patients):
patient = []
patient.append(i)
patient.append(random.randint(10,80))
height = random.randint(150,200)
patient.append(height)
weight = random.randint(45,120)
patient.append(weight)
patient.append(weight / (height/100)**2)
patient.append(random.choice(gender))
patient.append(random.choice(diganose_IDs))
patients.append(patient)
with open('patient.csv', 'w', newline='') as file:
writer = csv.writer(file)
writer.writerows(patients)
cities = ['Munich', 'London', 'Tokyo', 'Shanghai', 'Berlin', 'New York', 'Boston', 'Paris', 'Elmshorn']
doctors = ['Dr. Sommer', 'Dr. Bleimehl', 'Dr. Jarasch', 'Dr. Täger']
city_doctor_csv = []
city_doctor_csv_header = ['Patient_Id', 'City', 'Doctor']
city_doctor_csv.append(city_doctor_csv_header)
for i in range(len(patients)-1):
city_doctor = []
city_doctor.append(i)
city_doctor.append(random.choice(cities))
city_doctor.append(random.choice(doctors))
city_doctor_csv.append(city_doctor)
with open('city_doctor.csv', 'w', newline='') as file:
writer = csv.writer(file)
writer.writerows(city_doctor_csv)
```
%% Cell type:code id: tags:
```
```
......
Patient_Id,Age,Height,Weight,BMI,Gender,Diagnose_ID
0,58,189,83,23.23563170124017,m,5
1,64,188,74,20.937075599818925,m,10
2,22,183,111,33.14521186061094,m,13
3,25,178,89,28.089887640449437,div,13
4,59,159,94,37.18207349392824,f,12
5,69,172,103,34.81611681990265,div,4
6,71,194,66,17.53640131788713,div,13
7,61,186,89,25.725517400855587,m,3
8,36,155,64,26.638917793964616,m,6
9,11,183,84,25.082863029651524,m,13
10,42,195,117,30.76923076923077,f,0
11,17,150,95,42.22222222222222,m,10
12,36,177,70,22.34351559258195,m,0
13,50,152,82,35.49168975069252,f,11
14,20,184,57,16.836011342155007,div,15
15,80,172,71,23.99945916711736,f,15
16,73,169,103,36.0631630545149,div,11
17,51,158,116,46.466912353789446,f,12
18,44,152,100,43.282548476454295,div,14
19,50,184,116,34.262759924385634,div,0
20,34,162,113,43.057460752934,f,3
21,31,194,82,21.787650122223404,div,8
22,30,177,47,15.00207475501931,div,15
23,57,152,48,20.775623268698062,f,5
24,69,166,66,23.95122659311947,f,14
25,56,154,98,41.32231404958678,f,14
26,53,163,53,19.94805976890361,m,15
27,72,179,119,37.13991448456665,div,1
28,38,163,67,25.21735857578381,m,15
29,53,167,47,16.85252249991036,div,9
30,40,183,69,20.603780345785182,m,14
31,37,168,87,30.824829931972793,m,8
32,10,174,113,37.323292376800104,f,0
33,43,169,109,38.163929834389556,div,11
34,64,165,57,20.936639118457304,div,6
35,48,162,89,33.91251333638164,m,14
36,48,199,117,29.544708466957903,m,12
37,40,194,114,30.290147730895953,f,8
38,33,191,102,27.959759875003428,m,8
39,45,152,59,25.536703601108034,f,7
40,16,198,88,22.44668911335578,m,8
41,64,165,91,33.425160697887975,f,0
42,62,189,55,15.397105344195293,div,1
43,18,158,66,26.43807082198365,f,1
44,59,180,49,15.123456790123456,div,5
45,21,164,56,20.820939916716245,m,8
46,49,197,66,17.006364503079183,div,11
47,38,152,46,19.909972299168974,f,10
48,17,150,65,28.88888888888889,f,3
49,66,163,100,37.63784862057285,div,3
50,71,175,85,27.755102040816325,div,4
51,34,151,83,36.40191219683347,div,0
52,43,184,106,31.30907372400756,f,0
53,19,169,59,20.65754000210077,f,0
54,67,185,84,24.543462381300216,div,11
55,20,189,97,27.154894879762605,m,14
56,61,153,59,25.20398137468495,m,13
57,14,174,48,15.854141894569956,f,5
58,64,164,113,42.013682331945276,div,5
59,47,166,99,35.9268398896792,div,8
60,27,152,113,48.909279778393355,f,15
61,48,198,68,17.345168860320378,m,1
62,26,192,118,32.009548611111114,f,12
63,80,179,56,17.477606816266658,m,9
64,20,183,64,19.1107527844964,f,15
65,53,184,50,14.768431001890358,m,10
66,52,195,74,19.46088099934254,div,8
67,76,154,57,24.034407151290267,f,15
68,53,154,118,49.75543936582898,m,14
69,53,170,72,24.913494809688583,m,15
70,63,161,59,22.76146753597469,div,6
71,26,170,58,20.06920415224914,div,2
72,31,167,110,39.442073935960416,m,12
73,25,197,87,22.41748048133165,f,14
74,39,189,77,21.55594748187341,m,8
75,21,159,75,29.666548000474663,f,5
76,52,191,78,21.380992845590857,div,0
77,58,162,67,25.529644871208653,div,1
78,47,197,86,22.159808291891057,m,7
79,69,182,112,33.8123415046492,m,1
80,23,198,106,27.038057341087644,m,4
81,47,195,47,12.360289283366207,f,3
82,16,167,68,24.38237297859371,m,13
83,13,193,99,26.577894708582782,div,14
84,66,166,52,18.870663376397157,f,2
85,62,157,102,41.380989086778364,m,1
86,15,157,74,30.021501886486266,f,12
87,46,152,64,27.700831024930746,div,10
88,28,184,64,18.90359168241966,f,2
89,26,192,45,12.20703125,m,9
90,77,193,75,20.13476871862332,m,6
91,62,181,120,36.62891853118037,f,6
92,48,198,114,29.07866544230181,div,6
93,49,159,50,19.77769866698311,f,15
94,40,191,61,16.721032866423617,f,1
95,69,156,62,25.476660092044707,div,1
96,46,193,108,28.99406695481758,m,15
97,77,164,84,31.231409875074366,m,6
98,20,166,73,26.491508201480624,f,4
99,16,150,120,53.333333333333336,f,6
100,65,170,99,34.256055363321806,m,1
101,47,177,50,15.959653994701394,div,3
102,60,171,88,30.094730002393902,m,8
103,60,190,98,27.146814404432135,div,5
104,54,175,112,36.57142857142857,m,14
105,30,170,66,22.837370242214536,f,15
106,11,182,101,30.491486535442576,div,13
107,52,162,67,25.529644871208653,f,12
108,53,182,114,34.416133317232216,m,9
109,44,196,54,14.056643065389423,f,2
110,48,175,118,38.53061224489796,f,9
111,26,196,54,14.056643065389423,div,8
112,77,183,99,29.56194571351787,m,10
113,67,173,55,18.376825152861773,m,0
114,65,200,82,20.5,m,3
115,78,151,58,25.437480812245077,div,2
116,62,151,78,34.209025919915796,div,6
117,73,182,119,35.92561284868977,m,1
118,10,172,81,27.379664683612766,div,3
119,39,181,46,14.041085436952473,f,8
120,55,186,82,23.702162099664697,m,5
121,64,200,90,22.5,m,11
122,33,181,100,30.52409877598364,f,12
123,67,163,88,33.12130678610411,div,4
124,15,187,59,16.872086705367607,f,5
125,59,182,88,26.56683975365294,m,5
126,80,164,53,19.705532421177875,m,8
127,18,192,79,21.43012152777778,f,10
128,63,156,109,44.7896120973044,f,15
129,75,181,114,34.79747260462135,f,12
130,73,191,109,29.878566925248762,div,3
131,46,156,60,24.654832347140037,f,3
132,24,179,77,24.031709372366656,m,4
133,68,177,56,17.87481247406556,m,4
134,79,180,120,37.03703703703704,m,6
135,67,173,72,24.0569347455645,div,1
136,29,191,109,29.878566925248762,f,9
137,31,199,54,13.636019292442109,div,9
138,23,194,54,14.347964714634925,f,10
139,79,177,47,15.00207475501931,div,14
140,54,178,106,33.45537179649034,div,6
141,61,166,73,26.491508201480624,m,4
142,15,189,75,20.99605274208449,m,3
143,80,173,45,15.035584215977813,f,13
144,11,160,55,21.484374999999996,div,14
145,73,178,95,29.983587930816814,div,15
146,43,199,69,17.423802429231586,div,7
147,42,190,61,16.897506925207757,div,9
148,13,192,111,30.110677083333336,m,13
149,44,174,50,16.514731140177037,div,13
Patient_Id,Age,Height,Weight,BMI,Sex,Diagnose_ID
0,27,184,109,32.19517958412098,m,13
1,50,182,92,27.77442337881898,f,23
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