-
Notifications
You must be signed in to change notification settings - Fork 0
/
01-1. SNUCH_InP.py
393 lines (288 loc) · 16.7 KB
/
01-1. SNUCH_InP.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
'''
This script is written 4 preprocessing SNUCH's Inpatients' Data.
Written Date: 2019.12.09
Written By: Peter JH Park
'''
### Import modules in needs
import os, sys, csv
import pandas as pd
import numpy as np
import datetime, time
print("\n Current Working Directory is: ", os.getcwd())
### READ Files & Check
InD = pd.read_csv("/Users/peterpark/Desktop/DATA_ANALYSIS/Research/Dev4PubChildCenter/RawData/2018SNUCHInDiag_Prep.csv", encoding="utf-8", low_memory=False)
InS = pd.read_csv("/Users/peterpark/Desktop/DATA_ANALYSIS/Research/Dev4PubChildCenter/RawData/2018SNUCHInSur_Prep.csv", encoding="utf-8", low_memory=False)
InsPayCri = pd.read_csv("/Users/peterpark/Desktop/DATA_ANALYSIS/Research/Dev4PubChildCenter/RawData/2018SNUCH_InsPayCri.csv", encoding="utf-8", low_memory=False)
InR = pd.read_csv("/Users/peterpark/Desktop/DATA_ANALYSIS/Research/Dev4PubChildCenter/RawData/2018SNUCHInSev_Prep.csv", encoding="utf-8", low_memory=False)
InV = pd.read_csv("/Users/peterpark/Desktop/DATA_ANALYSIS/Research/Dev4PubChildCenter/RawData/2018SNUCHInRare_Prep.csv", encoding="utf-8", low_memory=False)
'''
print(InD.columns)
print(InS.columns)
print(InR.columns)
'''
### Preprocessing
## 01. Renaming Columns
'''
Codes :
PACT_ID(Del); 환자번호 = PT_No; 생년월일 = Birth; 성별 = Gender; 주소 = Address; 급종 = Ins_Var; 급종보조 = Ins_Sub; 입원일 = In_Date; 입원진료과 = In_Dep; 퇴원일 = Dis_Date; 퇴원진료과 = Dis_Dep;
재원일수(입원일수) = In_Prd; DRGNO(Del); 급여본인부담 = Pay_InsSelf; 공단부담금 = Pay_InsCorp; 비급여 = Pay_NoIns; 선택진료료 = Pay_Sel; 주진단코드 = D_Code1; 주진단명 = D_Name1; 부진단코드# = D_Code#; 부진단명# = D_Name#;
수술일 = Sur_Date; 수술코드 = Sur_Code; 수술명 = Sur_Name; 소속진료과 = Sur_Dep; 수진자명(Del); KDRG = DRGNO; ADRG(Del); 질병군\n구분 = Severity; 요양개시일(Del); 진료과(Del); KDRG명칭(Del);
'''
InD.rename(columns={InD.columns[1] : 'PT_No', InD.columns[2] : 'Birth', InD.columns[3] : 'Gender', InD.columns[4] : 'Address', InD.columns[5] : 'Ins_Var', InD.columns[6] : 'Ins_Sub', InD.columns[7] : 'In_Date',
InD.columns[8] : 'In_Dep', InD.columns[9] : 'Dis_Date', InD.columns[10] : 'Dis_Dep', InD.columns[11] : 'In_Prd', InD.columns[13] : 'Pay_InsSelf', InD.columns[14] : 'Pay_InsCorp',
InD.columns[15] : 'Pay_NoIns', InD.columns[16] : 'Pay_Sel', InD.columns[17] : 'D_Code1', InD.columns[18] : 'D_Name1', InD.columns[19] : 'D_Code2', InD.columns[20] : 'D_Name2',
InD.columns[21] : 'D_Code3', InD.columns[22] : 'D_Name3', InD.columns[23] : 'D_Code4', InD.columns[24] : 'D_Name4', InD.columns[25] : 'D_Code5', InD.columns[26] : 'D_Name5',
InD.columns[27] : 'D_Code6', InD.columns[28] : 'D_Name6'}, inplace=True)
InS.rename(columns={InS.columns[1] : 'PT_No', InS.columns[2] : 'Birth', InS.columns[3] : 'Gender', InS.columns[4] : 'Address', InS.columns[5] : 'Ins_Var', InS.columns[6] : 'Ins_Sub', InS.columns[7] : 'In_Date',
InS.columns[8] : 'In_Dep', InS.columns[9] : 'Dis_Date', InS.columns[10] : 'Dis_Dep', InS.columns[11] : 'In_Prd', InS.columns[13] : 'Pay_InsSelf', InS.columns[14] : 'Pay_InsCorp',
InS.columns[15] : 'Pay_NoIns', InS.columns[16] : 'Pay_Sel', InS.columns[17] : 'Sur_Date', InS.columns[18] : 'Prescript_Code', InS.columns[19] : 'Sur_Code', InS.columns[20] : 'Sur_Name'}, inplace=True)
InR.rename(columns={InR.columns[1] : "DRGNO", InR.columns[3] : "Severity", InR.columns[4] : "PT_No", InR.columns[5] : "In_Date"}, inplace=True)
InV.rename(columns={InV.columns[0] : "PACT_ID", InV.columns[1] : "PT_No", InV.columns[2] : "Diag_ID", InV.columns[3] : "SevCode", InV.columns[4] : "VCode", InV.columns[5] : "VCodeSub"}, inplace=True)
'''
print(InD.columns)
print(InS.columns)
print(InR.columns)
'''
## 02. Deleting Unnecessary Columns
'''
print(InD.loc[0:5])
'''
InD['D_Code1'] = InD['D_Code1'].fillna('NoDiag')
InD['D_Code1'] = InD['D_Code1'].astype(str); InD['D_Code1'] = InD['D_Code1'].str.replace('/',',')
InD['D_Name1'] = InD['D_Name1'].fillna('NoDiag')
InD['D_Name1'] = InD['D_Name1'].astype(str); InD['D_Name1'] = InD['D_Name1'].str.replace('/',',')
InD['D_Code2'] = InD['D_Code2'].fillna('NoDiag')
InD['D_Code2'] = InD['D_Code2'].astype(str); InD['D_Code2'] = InD['D_Code2'].str.replace('/',',')
InD['D_Name2'] = InD['D_Name2'].fillna('NoDiag')
InD['D_Name2'] = InD['D_Name2'].astype(str); InD['D_Name2'] = InD['D_Name2'].str.replace('/',',')
InD['D_Code3'] = InD['D_Code3'].fillna('NoDiag')
InD['D_Code3'] = InD['D_Code3'].astype(str); InD['D_Code3'] = InD['D_Code3'].str.replace('/',',')
InD['D_Name3'] = InD['D_Name3'].fillna('NoDiag')
InD['D_Name3'] = InD['D_Name3'].astype(str); InD['D_Name3'] = InD['D_Name3'].str.replace('/',',')
InD['D_Code4'] = InD['D_Code4'].fillna('NoDiag')
InD['D_Code4'] = InD['D_Code4'].astype(str); InD['D_Code4'] = InD['D_Code4'].str.replace('/',',')
InD['D_Name4'] = InD['D_Name4'].fillna('NoDiag')
InD['D_Name4'] = InD['D_Name4'].astype(str); InD['D_Name4'] = InD['D_Name4'].str.replace('/',',')
InD['D_Code5'] = InD['D_Code5'].fillna('NoDiag')
InD['D_Code5'] = InD['D_Code5'].astype(str); InD['D_Code5'] = InD['D_Code5'].str.replace('/',',')
InD['D_Name5'] = InD['D_Name5'].fillna('NoDiag')
InD['D_Name5'] = InD['D_Name5'].astype(str); InD['D_Name5'] = InD['D_Name5'].str.replace('/',',')
InD['D_Code6'] = InD['D_Code6'].fillna('NoDiag')
InD['D_Code6'] = InD['D_Code6'].astype(str); InD['D_Code6'] = InD['D_Code6'].str.replace('/',',')
InD['D_Name6'] = InD['D_Name6'].fillna('NoDiag')
InD['D_Name6'] = InD['D_Name6'].astype(str); InD['D_Name6'] = InD['D_Name6'].str.replace('/',',')
InD.loc[InD.D_Code1 != 'NoDiag', 'D_Code1'] = InD.D_Code1.str[0:4]
InD.loc[InD.D_Code2 != 'NoDiag', 'D_Code2'] = InD.D_Code2.str[0:4]
InD.loc[InD.D_Code3 != 'NoDiag', 'D_Code3'] = InD.D_Code3.str[0:4]
InD.loc[InD.D_Code4 != 'NoDiag', 'D_Code4'] = InD.D_Code4.str[0:4]
InD.loc[InD.D_Code5 != 'NoDiag', 'D_Code5'] = InD.D_Code5.str[0:4]
InD.loc[InD.D_Code6 != 'NoDiag', 'D_Code6'] = InD.D_Code6.str[0:4]
InD['D_Code'] = InD['D_Code1']+'/'+InD['D_Code2']+'/'+InD['D_Code3']+'/'+InD['D_Code4']+'/'+InD['D_Code5']+'/'+InD['D_Code6']
InD['D_Name'] = InD['D_Name1']+'/'+InD['D_Name2']+'/'+InD['D_Name3']+'/'+InD['D_Name4']+'/'+InD['D_Name5']+'/'+InD['D_Name6']
del InD['D_Code1']; del InD['D_Code2']; del InD['D_Code3']; del InD['D_Code4']; del InD['D_Code5']; del InD['D_Code6']
del InD['D_Name1']; del InD['D_Name2']; del InD['D_Name3']; del InD['D_Name4']; del InD['D_Name5']; del InD['D_Name6']
del InD['Ins_Sub']
'''
print(InD.loc[0:5,'D_Code'])
'''
InD.drop('DRGNO', axis=1, inplace=True)
InS.pop('DRGNO')
del InR['수진자명']; del InR['ADRG']; del InR['요양개시일']; del InR['진료과']; del InR['KDRG명칭']; del InR['입원일수']
'''
print(InD.columns)
print(InS.columns)
print(InR.columns)
'''
## 03. Refine InD(Criteria: columns[0:15], Base)
'''
print(len(InD))
'''
InD.drop_duplicates(['PT_No', 'In_Date'], inplace=True)
'''
print(len(InD))
'''
print(InD.columns)
## 04. Combine duplicated from InS(Criteria: ['PT_No', 'In_Date'])
del InS['Birth']; del InS['Gender']; del InS['Address']; del InS['Ins_Var']; del InS['Ins_Sub']; del InS['In_Dep']; del InS['Dis_Date']; del InS['Dis_Dep']
del InS['In_Prd']; del InS['Pay_InsSelf']; del InS['Pay_InsCorp']; del InS['Pay_NoIns']; del InS['Pay_Sel']; del InS['Prescript_Code']
'''
print(InS.loc[0:5, ['PT_No', 'In_Date', 'Sur_Code']])
'''
print(InsPayCri.columns)
InsPayCri.drop(columns=['보험구분', '수가구분', ' 보험단가 ', ' 건강보험수가 ', ' 일반단가 '], inplace=True)
InsPayCri.rename(columns={'수가코드':'Sur_Code', '수가명':'Sur_NameM', 'EDI 대응수가':'Sur_CodeM'}, inplace=True)
InsPayCri['Sur_CodeM'] = InsPayCri['Sur_CodeM'].fillna('GroupPay_SNUCH')
InsPayCri['Sur_CodeM'].replace({'0': 'GroupPay_SNUCH'}, inplace=True)
InsPayCri.reset_index(drop=True, inplace=True)
print(InsPayCri.columns)
InS.reset_index(drop=True, inplace=True)
InS = pd.merge(InS, InsPayCri, on='Sur_Code', how='left')
InS.drop(columns=['Sur_Code', 'Sur_Name'], inplace=True)
InS.rename(columns={'Sur_CodeM':'Sur_Code', 'Sur_NameM':'Sur_Name'}, inplace=True)
InS = InS[['PT_No', 'In_Date', 'Sur_Date', 'Sur_Code', 'Sur_Name']]
InS['Sur_Date'] = InS['Sur_Date'].fillna('NoSur')
InS['Sur_Code'] = InS['Sur_Code'].fillna('NoSur')
InS['Sur_Name'] = InS['Sur_Name'].fillna('NoSur')
print(InS.columns)
print(InS)
InS['Sur_Date'] = InS['Sur_Date'].astype(str); InS['Sur_Date'] = InS['Sur_Date'].str.replace('/',',')
InS['Sur_Code'] = InS['Sur_Code'].astype(str); InS['Sur_Code'] = InS['Sur_Code'].str.replace('/',',')
InS['Sur_Name'] = InS['Sur_Name'].astype(str); InS['Sur_Name'] = InS['Sur_Name'].str.replace('/',',')
InS = InS.groupby(['PT_No', 'In_Date']).agg({'Sur_Date': lambda a: '/'.join(a), 'Sur_Code': lambda b: '/'.join(b), 'Sur_Name': lambda c: '/'.join(c)}).reset_index()
'''
print(InS.loc[0:5, ['PT_No', 'In_Date', 'Sur_Code']])
'''
'''
InS['PT_No'] = InS['PT_No'].astype(str)
print(InS.loc[(InS['PT_No'].str.contains('78671328')), ['In_Date', 'Sur_Code']])
'''
print(InS.columns)
## 05. Combine duplicated from InR(Criteria: ['PT_No', 'In_Date'])
print(InR)
InR['Severity'] = InR['Severity'].map({'전문' : 'Severe', '일반' : 'Normal', '단순' : 'Simple', '분류오류' : 'SortError'})
InR['Severity'] = InR['Severity'].map({'Severe' : 4, 'Normal' : 3, 'Simple' : 2, 'SortError' : 1})
InRsub = InR.copy()
InR = InR.groupby(['PT_No', 'In_Date'], as_index= False)['Severity'].agg(lambda x: x.max())
InR = InR.merge(InRsub, on=['PT_No', 'In_Date', 'Severity'], how='left')
InR.drop_duplicates(subset =['PT_No', 'In_Date', 'Severity'], inplace = True)
InR.reset_index(drop=True, inplace=True)
InR['Severity'] = InR['Severity'].map({4:'Severe', 3:'Normal', 2:'Simple', 1:'SortError'})
print(InR)
#InR['DRGNO'] = InR['DRGNO'].astype(str); InR['DRGNO'] = InR['DRGNO'].str.replace('/',',')
#InR['Severity'] = InR['Severity'].astype(str); InR['Severity'] = InR['Severity'].str.replace('/',',')
#InR = InR.groupby(['PT_No', 'In_Date']).agg({'DRGNO': lambda a: '/'.join(a), 'Severity': lambda b: '/'.join(b)}).reset_index()
print(InR.columns)
## 06. Combine duplicated from InR(Criteria: ['PT_No', 'In_Date'])
print(InV)
print(InV.columns)
InV.fillna('', inplace=True)
InV.loc[(InV.SevCode == 'V193'), 'VCode'] = 'V193'
InV.loc[(InV.VCode == InV.VCodeSub), 'VCodeSub'] = ''
InV.loc[(InV.SevCode == 'V193'), 'SevCode'] = ''
InV.loc[(InV.VCode == ''), 'VCode'] = InV.VCodeSub
del InV['PACT_ID']; del InV['Diag_ID']; del InV['SevCode']; del InV['VCodeSub']
InV.drop_duplicates(['PT_No','VCode'], inplace=True)
InV = InV.groupby('PT_No').agg({'VCode': lambda a: '/'.join(a)}).reset_index()
InV.rename(columns={'VCode':'Ins_Sub'}, inplace=True)
print(InV)
## 07. Combine InD, InD, InR(Criteria: ['PT_No', 'In_Date'])
'''
InD['PT_No'] = InD['PT_No'].astype(str)
print(InD.loc[(InD['PT_No'].str.contains('71255934')), 'D_Code'])
InR['PT_No'] = InR['PT_No'].astype(str)
print(InR.loc[(InR['PT_No'].str.contains('71255934')), 'DRGNO'])
'''
'''
print(len(InD))
'''
InDR = pd.merge(InD, InR, how='left', on=['PT_No', 'In_Date'])
InDR['DRGNO'] = InDR['DRGNO'].fillna('NoDRG')
InDR['Severity'] = InDR['Severity'].fillna('NoDRG')
'''
print(len(InDR))
'''
'''
print(InDR.loc[0:5, ['PT_No', 'In_Date', 'D_Code', 'DRGNO']])
print(InDR.columns)
'''
'''
print(len(InDR))
'''
InDRS = pd.merge(InDR, InS, how='left', on=['PT_No', 'In_Date'])
InDRS['Sur_Date'] = InDRS['Sur_Date'].fillna('NoSur')
InDRS['Sur_Code'] = InDRS['Sur_Code'].fillna('NoSur')
InDRS['Sur_Name'] = InDRS['Sur_Name'].fillna('NoSur')
'''
print(len(SNUCHIn))
'''
SNUCHIn = pd.merge(InDRS, InV, how='left', on='PT_No')
SNUCHIn.drop_duplicates(['PT_No', 'In_Date'], inplace=True)
SNUCHIn.reset_index(drop=True, inplace=True)
SNUCHIn['Address'] = SNUCHIn['Address'].fillna('NoAdd')
SNUCHIn['Ins_Var'] = SNUCHIn['Ins_Var'].fillna('NoInsVar')
SNUCHIn['Ins_Sub'] = SNUCHIn['Ins_Sub'].fillna('NoVCode')
SNUCHIn['In_Prd'] = SNUCHIn['In_Prd'].fillna(0)
SNUCHIn['Pay_InsSelf'] = SNUCHIn['Pay_InsSelf'].fillna(0)
SNUCHIn['Pay_InsCorp'] = SNUCHIn['Pay_InsCorp'].fillna(0)
SNUCHIn['Pay_NoIns'] = SNUCHIn['Pay_NoIns'].fillna(0)
SNUCHIn['Pay_Sel'] = SNUCHIn['Pay_Sel'].fillna(0)
'''
print(SNUCHIn.loc[0:5, ['PT_No', 'Ins_Var', 'D_Code', 'DRGNO', 'Sur_Code']])
print(SNUCHIn.columns)
'''
## 08. Refine SNUCHIn
'''
print(len(SNUCHIn))
'''
SNUCHIn.dropna(how='any')
'''
print(len(SNUCHIn))
'''
SNUCHIn['PT_No'] = SNUCHIn['PT_No'].astype(str)
SNUCHIn['Birth'] = SNUCHIn['Birth'].astype(str); SNUCHIn['Birth'] = SNUCHIn['Birth'].str.replace('-',''); SNUCHIn['Birth'] = SNUCHIn['Birth'].astype(int)
SNUCHIn['Gender'] = SNUCHIn['Gender'].astype(str)
SNUCHIn['Address'] = SNUCHIn['Address'].astype(str)
SNUCHIn['Ins_Var'] = SNUCHIn['Ins_Var'].astype(str)
SNUCHIn['Ins_Sub'] = SNUCHIn['Ins_Sub'].astype(str)
SNUCHIn['In_Date'] = SNUCHIn['In_Date'].astype(str); SNUCHIn['In_Date'] = SNUCHIn['In_Date'].str.replace('-',''); SNUCHIn['In_Date'] = SNUCHIn['In_Date'].astype(int)
SNUCHIn['In_Dep'] = SNUCHIn['In_Dep'].astype(str)
SNUCHIn['Dis_Date'] = SNUCHIn['Dis_Date'].astype(str); SNUCHIn['Dis_Date'] = SNUCHIn['Dis_Date'].str.replace('-',''); SNUCHIn['Dis_Date'] = SNUCHIn['Dis_Date'].astype(int)
SNUCHIn['Dis_Dep'] = SNUCHIn['Dis_Dep'].astype(str)
SNUCHIn['In_Prd'] = SNUCHIn['In_Prd'].astype(int)
SNUCHIn['Pay_InsSelf'] = SNUCHIn['Pay_InsSelf'].astype(int)
SNUCHIn['Pay_InsCorp'] = SNUCHIn['Pay_InsCorp'].astype(int)
SNUCHIn['Pay_NoIns'] = SNUCHIn['Pay_NoIns'].astype(int)
SNUCHIn['Pay_Sel'] = SNUCHIn['Pay_Sel'].astype(int)
SNUCHIn['D_Code'] = SNUCHIn['D_Code'].astype(str)
SNUCHIn['D_Name'] = SNUCHIn['D_Name'].astype(str)
SNUCHIn['DRGNO'] = SNUCHIn['DRGNO'].astype(str)
SNUCHIn['Severity'] = SNUCHIn['Severity'].astype(str)
SNUCHIn['Sur_Date'] = SNUCHIn['Sur_Date'].astype(str)
SNUCHIn['Sur_Code'] = SNUCHIn['Sur_Code'].astype(str)
SNUCHIn['Sur_Name'] = SNUCHIn['Sur_Name'].astype(str)
# 08-1) Change values in 'Gender' Column ( '1' : Male, '2' : Female)
SNUCHIn.Gender.replace(['1', '2'], ['Male', 'Female'], inplace=True)
# 08-2) Create 'Age' Column
# df.loc[df['column name'] condition, 'new column name'] = 'value if condition is met'
SNUCHIn.loc[(SNUCHIn['In_Date'] % 10000 <= SNUCHIn['Birth'] % 10000) & (SNUCHIn['In_Date'] // 10000 >= SNUCHIn['Birth'] // 10000), 'Age'] = (SNUCHIn['In_Date'] - SNUCHIn['Birth'])// 10000
SNUCHIn.loc[(SNUCHIn['In_Date'] % 10000 > SNUCHIn['Birth'] % 10000) & (SNUCHIn['In_Date'] // 10000 == SNUCHIn['Birth'] // 10000), 'Age'] = (SNUCHIn['In_Date'] - SNUCHIn['Birth'])// 10000
SNUCHIn.loc[(SNUCHIn['In_Date'] % 10000 > SNUCHIn['Birth'] % 10000) & (SNUCHIn['In_Date'] // 10000 > SNUCHIn['Birth'] // 10000), 'Age'] = (SNUCHIn['In_Date'] - SNUCHIn['Birth'])// 10000 - 1
SNUCHIn['Age'] = SNUCHIn['Age'].astype(int)
print(SNUCHIn.columns)
print(SNUCHIn['Age'])
# 08-3) Rephrase 'Address' Values
class dict_partial(dict):
def __getitem__(self, value):
for k in self.keys():
if k in value:
return self.get(k)
else:
return self.get(None)
address_map = dict_partial({'서울': 'seoul', '부산': 'busan', '울산': 'ulsan', '대구': 'daegu', '광주': 'gwangju', '대전': 'daejeon', '제주': 'jeju', '세종': 'sejong', '인천': 'incheon',
'전남': 'jeonnam', '전라남도': 'jeonnam', '전북': 'jeonbuk', '전라북도' : 'jeonbuk', '경남': 'gyeongnam', '경상남도': 'gyeongnam', '경북': 'gyeongbuk', '경상북도': 'gyeongbuk',
'충남': 'chungnam', '충청남도': 'chungnam', '충북': 'chungbuk', '충청북도' : 'chungbuk', '강원': 'gangwon', '경기': 'gyeonggi'})
SNUCHIn['Address'] = SNUCHIn['Address'].apply(lambda x: address_map[x])
SNUCHIn['Address'] = SNUCHIn['Address'].fillna('NoAdd')
'''
print(SNUCHIn['Address'].value_counts())
print(SNUCHIn['Address'])
'''
# 08-4) Rephrase 'Ins_Var' Values
'''
print(SNUCHIn['Ins_Var'].value_counts())
'''
SNUCHIn['Ins_Var'] = SNUCHIn['Ins_Var'].map({'국민건강보험공단' : 'NHIS', '의료급여1종' : 'MedCareT1', '의료급여2종' : 'MedCareT2', '의료급여장애인' : 'MedCareDis'})
SNUCHIn['Ins_Var'] = SNUCHIn['Ins_Var'].fillna('Others')
'''
print(SNUCHIn['Ins_Var'].value_counts())
print(SNUCHIn['Ins_Var'])
'''
# 08-5) Change Order of Columns
SNUCHIn['InstName'] = np.nan
SNUCHIn['InstName'] = SNUCHIn['InstName'].fillna('SNUCH')
SNUCHIn = SNUCHIn[['InstName', 'PT_No', 'Birth', 'Age', 'Gender', 'Address', 'Ins_Var', 'Ins_Sub', 'In_Date', 'In_Dep', 'Dis_Date', 'Dis_Dep', 'In_Prd', 'Pay_InsSelf',
'Pay_InsCorp', 'Pay_NoIns', 'Pay_Sel', 'D_Code', 'D_Name', 'DRGNO', 'Severity', 'Sur_Date', 'Sur_Code', 'Sur_Name']]
print(SNUCHIn.columns)
print(SNUCHIn.info)
### Save as CSV in '/Users/peterpark/Desktop/PY_START/SNUH_Project01'
SNUCHIn.to_csv('./SNUCH/SNUCHInP_R4A.csv', index=False)