Overview

Dataset statistics

Number of variables16
Number of observations546
Missing cells369
Missing cells (%)4.2%
Duplicate rows2
Duplicate rows (%)0.4%
Total size in memory71.6 KiB
Average record size in memory134.2 B

Variable types

Categorical3
Text5
DateTime2
Numeric6

Alerts

의료기관종별명 has constant value ""Constant
Dataset has 2 (0.4%) duplicate rowsDuplicates
병상수(개) is highly overall correlated with 입원실수(개) and 1 other fieldsHigh correlation
의료인수(명) is highly overall correlated with 입원실수(개) and 1 other fieldsHigh correlation
입원실수(개) is highly overall correlated with 병상수(개) and 2 other fieldsHigh correlation
연면적(㎡) is highly overall correlated with 병상수(개) and 3 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 시군명High correlation
WGS84경도 is highly overall correlated with 시군명High correlation
시군명 is highly overall correlated with 연면적(㎡) and 2 other fieldsHigh correlation
소재지도로명주소 has 6 (1.1%) missing valuesMissing
폐업일자 has 351 (64.3%) missing valuesMissing
연면적(㎡) is highly skewed (γ1 = 23.14581014)Skewed
병상수(개) has 7 (1.3%) zerosZeros
입원실수(개) has 16 (2.9%) zerosZeros

Reproduction

Analysis started2024-03-16 05:32:06.175856
Analysis finished2024-03-16 05:32:21.857764
Duration15.68 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
고양시
51 
안산시
45 
수원시
40 
부천시
39 
용인시
37 
Other values (25)
334 

Length

Max length4
Median length3
Mean length3.0494505
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row가평군
2nd row가평군
3rd row가평군
4th row고양시
5th row고양시

Common Values

ValueCountFrequency (%)
고양시 51
 
9.3%
안산시 45
 
8.2%
수원시 40
 
7.3%
부천시 39
 
7.1%
용인시 37
 
6.8%
성남시 31
 
5.7%
화성시 27
 
4.9%
파주시 26
 
4.8%
안양시 26
 
4.8%
김포시 26
 
4.8%
Other values (20) 198
36.3%

Length

2024-03-16T05:32:22.099278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고양시 51
 
9.3%
안산시 45
 
8.2%
수원시 40
 
7.3%
부천시 39
 
7.1%
용인시 37
 
6.8%
성남시 31
 
5.7%
화성시 27
 
4.9%
파주시 26
 
4.8%
안양시 26
 
4.8%
김포시 26
 
4.8%
Other values (20) 198
36.3%

사업장명
Text

Distinct455
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2024-03-16T05:32:22.735076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length7.0952381
Min length3

Characters and Unicode

Total characters3874
Distinct characters302
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique370 ?
Unique (%)67.8%

Sample

1st row노체리안드리자애병원
2nd row에이치제이매그놀리아국제병원
3rd row국군청평병원
4th row연세마두병원
5th row해븐리병원
ValueCountFrequency (%)
의료법인 25
 
4.0%
우리병원 5
 
0.8%
병원 4
 
0.6%
늘편한병원 3
 
0.5%
고든병원 3
 
0.5%
시티병원 3
 
0.5%
서울병원 3
 
0.5%
바른병원 3
 
0.5%
온누리병원 3
 
0.5%
토마스의료재단 2
 
0.3%
Other values (484) 570
91.3%
2024-03-16T05:32:23.830253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 572
 
14.8%
병 548
 
14.1%
의 120
 
3.1%
료 105
 
2.7%
78
 
2.0%
인 78
 
2.0%
성 76
 
2.0%
재 74
 
1.9%
스 67
 
1.7%
단 61
 
1.6%
Other values (292) 2095
54.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3737
96.5%
Space Separator 78
 
2.0%
Decimal Number 17
 
0.4%
Open Punctuation 16
 
0.4%
Close Punctuation 16
 
0.4%
Uppercase Letter 10
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 572
 
15.3%
병 548
 
14.7%
의 120
 
3.2%
료 105
 
2.8%
인 78
 
2.1%
성 76
 
2.0%
재 74
 
2.0%
스 67
 
1.8%
단 61
 
1.6%
세 55
 
1.5%
Other values (280) 1981
53.0%
Decimal Number
ValueCountFrequency (%)
2 7
41.2%
1 7
41.2%
3 1
 
5.9%
6 1
 
5.9%
5 1
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
S 4
40.0%
R 2
20.0%
C 2
20.0%
H 2
20.0%
Space Separator
ValueCountFrequency (%)
78
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3737
96.5%
Common 127
 
3.3%
Latin 10
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 572
 
15.3%
병 548
 
14.7%
의 120
 
3.2%
료 105
 
2.8%
인 78
 
2.1%
성 76
 
2.0%
재 74
 
2.0%
스 67
 
1.8%
단 61
 
1.6%
세 55
 
1.5%
Other values (280) 1981
53.0%
Common
ValueCountFrequency (%)
78
61.4%
( 16
 
12.6%
) 16
 
12.6%
2 7
 
5.5%
1 7
 
5.5%
3 1
 
0.8%
6 1
 
0.8%
5 1
 
0.8%
Latin
ValueCountFrequency (%)
S 4
40.0%
R 2
20.0%
C 2
20.0%
H 2
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3737
96.5%
ASCII 137
 
3.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 572
 
15.3%
병 548
 
14.7%
의 120
 
3.2%
료 105
 
2.8%
인 78
 
2.1%
성 76
 
2.0%
재 74
 
2.0%
스 67
 
1.8%
단 61
 
1.6%
세 55
 
1.5%
Other values (280) 1981
53.0%
ASCII
ValueCountFrequency (%)
78
56.9%
( 16
 
11.7%
) 16
 
11.7%
2 7
 
5.1%
1 7
 
5.1%
S 4
 
2.9%
R 2
 
1.5%
C 2
 
1.5%
H 2
 
1.5%
3 1
 
0.7%
Other values (2) 2
 
1.5%

소재지우편번호
Text

Distinct374
Distinct (%)68.9%
Missing3
Missing (%)0.5%
Memory size4.4 KiB
2024-03-16T05:32:24.581751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.1878453
Min length5

Characters and Unicode

Total characters2817
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique250 ?
Unique (%)46.0%

Sample

1st row12432
2nd row12461
3rd row12452
4th row10414
5th row10338
ValueCountFrequency (%)
10071 6
 
1.1%
11932 5
 
0.9%
10447 4
 
0.7%
10892 4
 
0.7%
15359 4
 
0.7%
14427 4
 
0.7%
10338 4
 
0.7%
10896 4
 
0.7%
15494 4
 
0.7%
10826 4
 
0.7%
Other values (364) 500
92.1%
2024-03-16T05:32:25.703247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 748
26.6%
4 319
11.3%
0 277
 
9.8%
5 245
 
8.7%
6 218
 
7.7%
8 217
 
7.7%
2 210
 
7.5%
3 203
 
7.2%
7 168
 
6.0%
9 161
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2766
98.2%
Dash Punctuation 51
 
1.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 748
27.0%
4 319
11.5%
0 277
 
10.0%
5 245
 
8.9%
6 218
 
7.9%
8 217
 
7.8%
2 210
 
7.6%
3 203
 
7.3%
7 168
 
6.1%
9 161
 
5.8%
Dash Punctuation
ValueCountFrequency (%)
- 51
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2817
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 748
26.6%
4 319
11.3%
0 277
 
9.8%
5 245
 
8.7%
6 218
 
7.7%
8 217
 
7.7%
2 210
 
7.5%
3 203
 
7.2%
7 168
 
6.0%
9 161
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2817
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 748
26.6%
4 319
11.3%
0 277
 
9.8%
5 245
 
8.7%
6 218
 
7.7%
8 217
 
7.7%
2 210
 
7.5%
3 203
 
7.2%
7 168
 
6.0%
9 161
 
5.7%

소재지도로명주소
Text

MISSING 

Distinct454
Distinct (%)84.1%
Missing6
Missing (%)1.1%
Memory size4.4 KiB
2024-03-16T05:32:26.406768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length76
Median length53.5
Mean length32.424074
Min length14

Characters and Unicode

Total characters17509
Distinct characters368
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique372 ?
Unique (%)68.9%

Sample

1st row경기도 가평군 조종면 꽃동네길 46 (노체리안드리자애병원)
2nd row경기도 가평군 설악면 미사리로 267-177
3rd row경기도 가평군 청평면 경춘로 926
4th row경기도 고양시 일산동구 강석로 123 (마두동, 뉴삼창마트)
5th row경기도 고양시 일산동구 중산로 117, 지하1층 일부, 1층 일부, 2~5층 (중산동)
ValueCountFrequency (%)
경기도 540
 
15.2%
고양시 51
 
1.4%
안산시 45
 
1.3%
수원시 40
 
1.1%
부천시 39
 
1.1%
용인시 37
 
1.0%
성남시 30
 
0.8%
단원구 29
 
0.8%
파주시 26
 
0.7%
화성시 26
 
0.7%
Other values (1225) 2681
75.6%
2024-03-16T05:32:27.513408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3008
 
17.2%
, 659
 
3.8%
경 585
 
3.3%
1 578
 
3.3%
시 573
 
3.3%
기 567
 
3.2%
도 553
 
3.2%
동 549
 
3.1%
로 535
 
3.1%
) 490
 
2.8%
Other values (358) 9412
53.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9620
54.9%
Space Separator 3008
 
17.2%
Decimal Number 2925
 
16.7%
Other Punctuation 668
 
3.8%
Close Punctuation 490
 
2.8%
Open Punctuation 490
 
2.8%
Math Symbol 152
 
0.9%
Dash Punctuation 95
 
0.5%
Uppercase Letter 46
 
0.3%
Lowercase Letter 15
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 585
 
6.1%
시 573
 
6.0%
기 567
 
5.9%
도 553
 
5.7%
동 549
 
5.7%
로 535
 
5.6%
층 348
 
3.6%
구 301
 
3.1%
원 202
 
2.1%
양 178
 
1.9%
Other values (314) 5229
54.4%
Uppercase Letter
ValueCountFrequency (%)
B 7
15.2%
S 6
13.0%
I 5
10.9%
C 4
8.7%
N 3
 
6.5%
Y 3
 
6.5%
D 3
 
6.5%
R 2
 
4.3%
A 2
 
4.3%
T 2
 
4.3%
Other values (7) 9
19.6%
Decimal Number
ValueCountFrequency (%)
1 578
19.8%
2 430
14.7%
3 328
11.2%
4 267
9.1%
5 267
9.1%
0 259
8.9%
7 227
 
7.8%
6 220
 
7.5%
8 181
 
6.2%
9 168
 
5.7%
Lowercase Letter
ValueCountFrequency (%)
a 3
20.0%
l 3
20.0%
e 2
13.3%
r 2
13.3%
c 1
 
6.7%
p 1
 
6.7%
n 1
 
6.7%
b 1
 
6.7%
k 1
 
6.7%
Other Punctuation
ValueCountFrequency (%)
, 659
98.7%
/ 5
 
0.7%
. 4
 
0.6%
Space Separator
ValueCountFrequency (%)
3008
100.0%
Close Punctuation
ValueCountFrequency (%)
) 490
100.0%
Open Punctuation
ValueCountFrequency (%)
( 490
100.0%
Math Symbol
ValueCountFrequency (%)
~ 152
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 95
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9620
54.9%
Common 7828
44.7%
Latin 61
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 585
 
6.1%
시 573
 
6.0%
기 567
 
5.9%
도 553
 
5.7%
동 549
 
5.7%
로 535
 
5.6%
층 348
 
3.6%
구 301
 
3.1%
원 202
 
2.1%
양 178
 
1.9%
Other values (314) 5229
54.4%
Latin
ValueCountFrequency (%)
B 7
 
11.5%
S 6
 
9.8%
I 5
 
8.2%
C 4
 
6.6%
N 3
 
4.9%
Y 3
 
4.9%
a 3
 
4.9%
l 3
 
4.9%
D 3
 
4.9%
R 2
 
3.3%
Other values (16) 22
36.1%
Common
ValueCountFrequency (%)
3008
38.4%
, 659
 
8.4%
1 578
 
7.4%
) 490
 
6.3%
( 490
 
6.3%
2 430
 
5.5%
3 328
 
4.2%
4 267
 
3.4%
5 267
 
3.4%
0 259
 
3.3%
Other values (8) 1052
 
13.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9620
54.9%
ASCII 7889
45.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3008
38.1%
, 659
 
8.4%
1 578
 
7.3%
) 490
 
6.2%
( 490
 
6.2%
2 430
 
5.5%
3 328
 
4.2%
4 267
 
3.4%
5 267
 
3.4%
0 259
 
3.3%
Other values (34) 1113
 
14.1%
Hangul
ValueCountFrequency (%)
경 585
 
6.1%
시 573
 
6.0%
기 567
 
5.9%
도 553
 
5.7%
동 549
 
5.7%
로 535
 
5.6%
층 348
 
3.6%
구 301
 
3.1%
원 202
 
2.1%
양 178
 
1.9%
Other values (314) 5229
54.4%

소재지지번주소
Text

Distinct467
Distinct (%)85.5%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2024-03-16T05:32:28.250562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length70
Median length52
Mean length27.364469
Min length7

Characters and Unicode

Total characters14941
Distinct characters322
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique389 ?
Unique (%)71.2%

Sample

1st row경기도 가평군 조종면 하판리 540번지 2호
2nd row경기도 가평군 설악면 송산리 460번지
3rd row경기도 가평군 청평면 청평리 567번지 21호 사서함
4th row경기도 고양시 일산동구 마두동 802번지 뉴삼창마트 3층,6층,7층
5th row경기도 고양시 일산동구 중산동 1696 지하1층 일부, 1층 일부, 2~5층
ValueCountFrequency (%)
경기도 542
 
16.9%
1호 46
 
1.4%
고양시 46
 
1.4%
안산시 41
 
1.3%
수원시 40
 
1.2%
부천시 39
 
1.2%
용인시 37
 
1.2%
2호 31
 
1.0%
성남시 30
 
0.9%
화성시 27
 
0.8%
Other values (1102) 2324
72.6%
2024-03-16T05:32:29.507126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2667
 
17.9%
1 620
 
4.1%
시 563
 
3.8%
기 560
 
3.7%
도 552
 
3.7%
경 545
 
3.6%
동 544
 
3.6%
지 509
 
3.4%
번 405
 
2.7%
2 371
 
2.5%
Other values (312) 7605
50.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8513
57.0%
Decimal Number 3081
 
20.6%
Space Separator 2667
 
17.9%
Other Punctuation 255
 
1.7%
Dash Punctuation 248
 
1.7%
Math Symbol 91
 
0.6%
Uppercase Letter 38
 
0.3%
Close Punctuation 16
 
0.1%
Open Punctuation 16
 
0.1%
Lowercase Letter 15
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 563
 
6.6%
기 560
 
6.6%
도 552
 
6.5%
경 545
 
6.4%
동 544
 
6.4%
지 509
 
6.0%
번 405
 
4.8%
호 292
 
3.4%
구 291
 
3.4%
층 254
 
3.0%
Other values (268) 3998
47.0%
Uppercase Letter
ValueCountFrequency (%)
S 6
15.8%
B 5
13.2%
I 5
13.2%
C 3
7.9%
N 3
7.9%
A 2
 
5.3%
R 2
 
5.3%
Y 2
 
5.3%
D 2
 
5.3%
T 2
 
5.3%
Other values (6) 6
15.8%
Decimal Number
ValueCountFrequency (%)
1 620
20.1%
2 371
12.0%
3 327
10.6%
4 324
10.5%
5 286
9.3%
6 261
8.5%
0 247
 
8.0%
7 235
 
7.6%
8 208
 
6.8%
9 202
 
6.6%
Lowercase Letter
ValueCountFrequency (%)
l 3
20.0%
a 3
20.0%
r 2
13.3%
e 2
13.3%
c 1
 
6.7%
p 1
 
6.7%
n 1
 
6.7%
b 1
 
6.7%
k 1
 
6.7%
Other Punctuation
ValueCountFrequency (%)
, 247
96.9%
. 5
 
2.0%
/ 3
 
1.2%
Space Separator
ValueCountFrequency (%)
2667
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 248
100.0%
Math Symbol
ValueCountFrequency (%)
~ 91
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8514
57.0%
Common 6374
42.7%
Latin 53
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 563
 
6.6%
기 560
 
6.6%
도 552
 
6.5%
경 545
 
6.4%
동 544
 
6.4%
지 509
 
6.0%
번 405
 
4.8%
호 292
 
3.4%
구 291
 
3.4%
층 254
 
3.0%
Other values (269) 3999
47.0%
Latin
ValueCountFrequency (%)
S 6
 
11.3%
B 5
 
9.4%
I 5
 
9.4%
C 3
 
5.7%
l 3
 
5.7%
N 3
 
5.7%
a 3
 
5.7%
A 2
 
3.8%
r 2
 
3.8%
e 2
 
3.8%
Other values (15) 19
35.8%
Common
ValueCountFrequency (%)
2667
41.8%
1 620
 
9.7%
2 371
 
5.8%
3 327
 
5.1%
4 324
 
5.1%
5 286
 
4.5%
6 261
 
4.1%
- 248
 
3.9%
0 247
 
3.9%
, 247
 
3.9%
Other values (8) 776
 
12.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8513
57.0%
ASCII 6427
43.0%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2667
41.5%
1 620
 
9.6%
2 371
 
5.8%
3 327
 
5.1%
4 324
 
5.0%
5 286
 
4.4%
6 261
 
4.1%
- 248
 
3.9%
0 247
 
3.8%
, 247
 
3.8%
Other values (33) 829
 
12.9%
Hangul
ValueCountFrequency (%)
시 563
 
6.6%
기 560
 
6.6%
도 552
 
6.5%
경 545
 
6.4%
동 544
 
6.4%
지 509
 
6.0%
번 405
 
4.8%
호 292
 
3.4%
구 291
 
3.4%
층 254
 
3.0%
Other values (268) 3998
47.0%
None
ValueCountFrequency (%)
㈜ 1
100.0%

인허가일자
Date

Distinct456
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
Minimum1977-01-15 00:00:00
Maximum2024-01-25 00:00:00
2024-03-16T05:32:29.931391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:30.392084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

영업상태명
Categorical

Distinct5
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
영업/정상
279 
폐업
188 
영업중
64 
취소/말소/만료/정지/중지
 
8
휴업
 
7

Length

Max length14
Median length5
Mean length3.8260073
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영업/정상
2nd row영업/정상
3rd row폐업
4th row영업/정상
5th row영업/정상

Common Values

ValueCountFrequency (%)
영업/정상 279
51.1%
폐업 188
34.4%
영업중 64
 
11.7%
취소/말소/만료/정지/중지 8
 
1.5%
휴업 7
 
1.3%

Length

2024-03-16T05:32:30.813699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T05:32:31.295206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업/정상 279
51.1%
폐업 188
34.4%
영업중 64
 
11.7%
취소/말소/만료/정지/중지 8
 
1.5%
휴업 7
 
1.3%

폐업일자
Date

MISSING 

Distinct187
Distinct (%)95.9%
Missing351
Missing (%)64.3%
Memory size4.4 KiB
Minimum1997-01-14 00:00:00
Maximum2024-03-01 00:00:00
2024-03-16T05:32:31.708158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:32.112525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

의료기관종별명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
병원
546 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row병원
2nd row병원
3rd row병원
4th row병원
5th row병원

Common Values

ValueCountFrequency (%)
병원 546
100.0%

Length

2024-03-16T05:32:32.519647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T05:32:32.801087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
병원 546
100.0%

병상수(개)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct160
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.946886
Minimum0
Maximum350
Zeros7
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2024-03-16T05:32:33.121042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q150
median72
Q3120
95-th percentile200.75
Maximum350
Range350
Interquartile range (IQR)70

Descriptive statistics

Standard deviation59.345659
Coefficient of variation (CV)0.65253096
Kurtosis3.0081096
Mean90.946886
Median Absolute Deviation (MAD)28
Skewness1.5775282
Sum49657
Variance3521.9073
MonotonicityNot monotonic
2024-03-16T05:32:33.586433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 30
 
5.5%
99 17
 
3.1%
50 13
 
2.4%
60 12
 
2.2%
61 10
 
1.8%
120 10
 
1.8%
62 9
 
1.6%
65 9
 
1.6%
51 8
 
1.5%
54 8
 
1.5%
Other values (150) 420
76.9%
ValueCountFrequency (%)
0 7
 
1.3%
20 1
 
0.2%
30 30
5.5%
31 6
 
1.1%
32 4
 
0.7%
33 3
 
0.5%
34 5
 
0.9%
35 4
 
0.7%
36 8
 
1.5%
37 6
 
1.1%
ValueCountFrequency (%)
350 1
0.2%
342 1
0.2%
340 1
0.2%
318 2
0.4%
299 2
0.4%
294 1
0.2%
293 1
0.2%
288 2
0.4%
269 1
0.2%
268 1
0.2%

의료인수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.155678
Minimum0
Maximum183
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2024-03-16T05:32:34.001385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median20.5
Q339
95-th percentile91.5
Maximum183
Range183
Interquartile range (IQR)31

Descriptive statistics

Standard deviation29.139014
Coefficient of variation (CV)0.99942846
Kurtosis4.5452658
Mean29.155678
Median Absolute Deviation (MAD)14.5
Skewness1.9042216
Sum15919
Variance849.08214
MonotonicityNot monotonic
2024-03-16T05:32:34.454215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 24
 
4.4%
3 21
 
3.8%
5 20
 
3.7%
4 19
 
3.5%
7 17
 
3.1%
2 17
 
3.1%
10 17
 
3.1%
8 15
 
2.7%
16 15
 
2.7%
22 14
 
2.6%
Other values (89) 367
67.2%
ValueCountFrequency (%)
0 1
 
0.2%
1 11
2.0%
2 17
3.1%
3 21
3.8%
4 19
3.5%
5 20
3.7%
6 24
4.4%
7 17
3.1%
8 15
2.7%
9 12
2.2%
ValueCountFrequency (%)
183 1
0.2%
169 1
0.2%
157 1
0.2%
155 1
0.2%
151 1
0.2%
137 1
0.2%
132 2
0.4%
129 1
0.2%
126 2
0.4%
106 1
0.2%

입원실수(개)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct67
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.622711
Minimum0
Maximum93
Zeros16
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2024-03-16T05:32:34.953734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q114
median21
Q332
95-th percentile54
Maximum93
Range93
Interquartile range (IQR)18

Descriptive statistics

Standard deviation15.891737
Coefficient of variation (CV)0.64540972
Kurtosis2.9194241
Mean24.622711
Median Absolute Deviation (MAD)8
Skewness1.472665
Sum13444
Variance252.5473
MonotonicityNot monotonic
2024-03-16T05:32:35.570400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 29
 
5.3%
23 25
 
4.6%
18 24
 
4.4%
16 24
 
4.4%
12 23
 
4.2%
13 21
 
3.8%
11 21
 
3.8%
20 17
 
3.1%
15 17
 
3.1%
0 16
 
2.9%
Other values (57) 329
60.3%
ValueCountFrequency (%)
0 16
2.9%
2 1
 
0.2%
4 3
 
0.5%
5 2
 
0.4%
6 4
 
0.7%
7 5
 
0.9%
8 6
 
1.1%
9 12
2.2%
10 14
2.6%
11 21
3.8%
ValueCountFrequency (%)
93 1
 
0.2%
91 2
0.4%
89 1
 
0.2%
85 2
0.4%
80 2
0.4%
74 1
 
0.2%
71 4
0.7%
68 1
 
0.2%
66 2
0.4%
65 1
 
0.2%

진료과목내용
Text

Distinct379
Distinct (%)69.5%
Missing1
Missing (%)0.2%
Memory size4.4 KiB
2024-03-16T05:32:36.065143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length160
Median length82
Mean length41.618349
Min length2

Characters and Unicode

Total characters22682
Distinct characters74
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique286 ?
Unique (%)52.5%

Sample

1st row내과, 정신건강의학과, 외과, 영상의학과
2nd row내과, 신경과, 정신건강의학과, 외과, 정형외과, 성형외과, 마취통증의학과, 산부인과, 영상의학과, 재활의학과, 가정의학과
3rd row내과, 신경과, 정신건강의학과, 외과, 정형외과, 신경외과, 심장혈관흉부외과, 안과, 이비인후과, 피부과, 비뇨의학과, 영상의학과, 진단검사의학과, 재활의학과, 가정의학과, 응급의학과, 치과
4th row내과, 신경과, 소아청소년과, 이비인후과, 재활의학과
5th row내과, 신경과, 정형외과, 영상의학과, 재활의학과, 가정의학과
ValueCountFrequency (%)
내과 478
12.7%
정형외과 375
 
9.9%
영상의학과 294
 
7.8%
신경외과 285
 
7.6%
마취통증의학과 265
 
7.0%
외과 250
 
6.6%
소아청소년과 239
 
6.3%
재활의학과 234
 
6.2%
가정의학과 234
 
6.2%
신경과 155
 
4.1%
Other values (35) 963
25.5%
2024-03-16T05:32:37.111792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 3790
16.7%
, 3227
14.2%
3227
14.2%
학 1318
 
5.8%
의 1317
 
5.8%
외 980
 
4.3%
정 673
 
3.0%
내 532
 
2.3%
신 507
 
2.2%
소 498
 
2.2%
Other values (64) 6613
29.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16200
71.4%
Other Punctuation 3255
 
14.4%
Space Separator 3227
 
14.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 3790
23.4%
학 1318
 
8.1%
의 1317
 
8.1%
외 980
 
6.0%
정 673
 
4.2%
내 532
 
3.3%
신 507
 
3.1%
소 498
 
3.1%
경 470
 
2.9%
형 418
 
2.6%
Other values (61) 5697
35.2%
Other Punctuation
ValueCountFrequency (%)
, 3227
99.1%
· 28
 
0.9%
Space Separator
ValueCountFrequency (%)
3227
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 16200
71.4%
Common 6482
28.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 3790
23.4%
학 1318
 
8.1%
의 1317
 
8.1%
외 980
 
6.0%
정 673
 
4.2%
내 532
 
3.3%
신 507
 
3.1%
소 498
 
3.1%
경 470
 
2.9%
형 418
 
2.6%
Other values (61) 5697
35.2%
Common
ValueCountFrequency (%)
, 3227
49.8%
3227
49.8%
· 28
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 16200
71.4%
ASCII 6454
 
28.5%
None 28
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 3790
23.4%
학 1318
 
8.1%
의 1317
 
8.1%
외 980
 
6.0%
정 673
 
4.2%
내 532
 
3.3%
신 507
 
3.1%
소 498
 
3.1%
경 470
 
2.9%
형 418
 
2.6%
Other values (61) 5697
35.2%
ASCII
ValueCountFrequency (%)
, 3227
50.0%
3227
50.0%
None
ValueCountFrequency (%)
· 28
100.0%

연면적(㎡)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct466
Distinct (%)85.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5535.4569
Minimum0
Maximum999999
Zeros5
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2024-03-16T05:32:37.553927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile995.6875
Q11824.9875
median2812.38
Q34537.965
95-th percentile8942.94
Maximum999999
Range999999
Interquartile range (IQR)2712.9775

Descriptive statistics

Standard deviation42773.389
Coefficient of variation (CV)7.727165
Kurtosis539.04255
Mean5535.4569
Median Absolute Deviation (MAD)1186.59
Skewness23.14581
Sum3022359.5
Variance1.8295628 × 109
MonotonicityNot monotonic
2024-03-16T05:32:38.012957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5
 
0.9%
7272.0 3
 
0.5%
1929.13 2
 
0.4%
3357.63 2
 
0.4%
3435.41 2
 
0.4%
4319.45 2
 
0.4%
2657.9 2
 
0.4%
3090.22 2
 
0.4%
12620.83 2
 
0.4%
1643.15 2
 
0.4%
Other values (456) 522
95.6%
ValueCountFrequency (%)
0.0 5
0.9%
1.0 2
 
0.4%
576.67 1
 
0.2%
643.95 2
 
0.4%
694.8 1
 
0.2%
705.91 1
 
0.2%
710.72 1
 
0.2%
737.13 1
 
0.2%
744.16 1
 
0.2%
792.07 1
 
0.2%
ValueCountFrequency (%)
999999.0 1
0.2%
40149.8 1
0.2%
35348.33 1
0.2%
22941.69 1
0.2%
19750.09 1
0.2%
18891.09 1
0.2%
16242.5 1
0.2%
13974.3 1
0.2%
12620.83 2
0.4%
12333.16 1
0.2%

WGS84위도
Real number (ℝ)

HIGH CORRELATION 

Distinct417
Distinct (%)76.9%
Missing4
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean37.452477
Minimum36.98405
Maximum38.017425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2024-03-16T05:32:38.579194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.98405
5-th percentile37.115658
Q137.300868
median37.432752
Q337.647035
95-th percentile37.754161
Maximum38.017425
Range1.0333754
Interquartile range (IQR)0.34616645

Descriptive statistics

Standard deviation0.21057113
Coefficient of variation (CV)0.0056223554
Kurtosis-0.69744449
Mean37.452477
Median Absolute Deviation (MAD)0.16326511
Skewness0.0012656485
Sum20299.242
Variance0.044340202
MonotonicityNot monotonic
2024-03-16T05:32:39.053159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.8492976171 5
 
0.9%
37.3502838907 3
 
0.5%
37.4744258198 3
 
0.5%
37.3188431542 3
 
0.5%
37.6000192981 3
 
0.5%
37.6885486984 3
 
0.5%
37.4384494234 3
 
0.5%
37.3168654316 3
 
0.5%
37.4095182128 3
 
0.5%
37.2933628028 3
 
0.5%
Other values (407) 510
93.4%
(Missing) 4
 
0.7%
ValueCountFrequency (%)
36.9840497212 1
0.2%
36.9881523917 1
0.2%
36.9902752468 1
0.2%
36.9903562328 1
0.2%
36.991858189 1
0.2%
36.9935903917 1
0.2%
36.9939711588 1
0.2%
36.994463971 1
0.2%
36.995635887 1
0.2%
36.9988810256 1
0.2%
ValueCountFrequency (%)
38.0174251575 1
 
0.2%
37.9159725251 1
 
0.2%
37.9088541736 1
 
0.2%
37.9068599484 1
 
0.2%
37.8981434009 1
 
0.2%
37.8848827889 1
 
0.2%
37.8639614261 1
 
0.2%
37.854250207 1
 
0.2%
37.8508855498 1
 
0.2%
37.8492976171 5
0.9%

WGS84경도
Real number (ℝ)

HIGH CORRELATION 

Distinct417
Distinct (%)76.9%
Missing4
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean126.97224
Minimum126.60017
Maximum127.63292
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2024-03-16T05:32:39.538384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.60017
5-th percentile126.73053
Q1126.80584
median126.96449
Q3127.10852
95-th percentile127.24279
Maximum127.63292
Range1.0327501
Interquartile range (IQR)0.30267589

Descriptive statistics

Standard deviation0.18891714
Coefficient of variation (CV)0.0014878617
Kurtosis0.51797374
Mean126.97224
Median Absolute Deviation (MAD)0.14783214
Skewness0.58034805
Sum68818.957
Variance0.035689685
MonotonicityNot monotonic
2024-03-16T05:32:40.053414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.8724505895 5
 
0.9%
126.9767891156 3
 
0.5%
126.8523236727 3
 
0.5%
126.834869809 3
 
0.5%
127.1392295602 3
 
0.5%
126.8657208575 3
 
0.5%
126.789037523 3
 
0.5%
127.0848461455 3
 
0.5%
127.2562056006 3
 
0.5%
127.1214738877 3
 
0.5%
Other values (407) 510
93.4%
(Missing) 4
 
0.7%
ValueCountFrequency (%)
126.6001738151 1
0.2%
126.6020784224 2
0.4%
126.6084077369 2
0.4%
126.6232141562 2
0.4%
126.6235711257 1
0.2%
126.6248942624 1
0.2%
126.6266409118 2
0.4%
126.627571937 1
0.2%
126.661361931 1
0.2%
126.6788350165 1
0.2%
ValueCountFrequency (%)
127.6329239251 1
0.2%
127.6288883839 2
0.4%
127.6253165893 1
0.2%
127.6121106081 1
0.2%
127.5888915032 1
0.2%
127.5213805697 1
0.2%
127.517861738 1
0.2%
127.5034843038 1
0.2%
127.4503587588 1
0.2%
127.4490609315 1
0.2%

Interactions

2024-03-16T05:32:17.928373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:08.364849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:10.066372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:12.012671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:13.612340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:16.023802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:18.353465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:08.636696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:10.456510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:12.273788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:14.051593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:16.315549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:18.679318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:08.918247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:10.856528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:12.552323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:14.463069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:16.681571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:18.934884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:09.237222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:11.110766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:12.816277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:14.760311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:16.940390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:19.256664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:09.531134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:11.412752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:13.114252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:15.053584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:17.294347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:19.587002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:09.799536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:11.692067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:13.356829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:15.691447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:32:17.570095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-16T05:32:40.347510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명영업상태명병상수(개)의료인수(명)입원실수(개)연면적(㎡)WGS84위도WGS84경도
시군명1.0000.3580.5060.1110.5350.6710.9860.972
영업상태명0.3581.0000.2520.5010.4370.0000.3250.000
병상수(개)0.5060.2521.0000.6430.8440.5570.2090.288
의료인수(명)0.1110.5010.6431.0000.6490.1390.1720.220
입원실수(개)0.5350.4370.8440.6491.0000.0610.3090.326
연면적(㎡)0.6710.0000.5570.1390.0611.0000.0000.429
WGS84위도0.9860.3250.2090.1720.3090.0001.0000.720
WGS84경도0.9720.0000.2880.2200.3260.4290.7201.000
2024-03-16T05:32:40.895587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영업상태명시군명
영업상태명1.0000.159
시군명0.1591.000
2024-03-16T05:32:41.367188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
병상수(개)의료인수(명)입원실수(개)연면적(㎡)WGS84위도WGS84경도시군명영업상태명
병상수(개)1.0000.4530.7500.6120.0460.0510.1780.113
의료인수(명)0.4531.0000.5710.676-0.0030.0440.0330.230
입원실수(개)0.7500.5711.0000.687-0.0090.0280.1940.195
연면적(㎡)0.6120.6760.6871.000-0.0200.0480.5290.000
WGS84위도0.046-0.003-0.009-0.0201.000-0.2290.7950.140
WGS84경도0.0510.0440.0280.048-0.2291.0000.7290.000
시군명0.1780.0330.1940.5290.7950.7291.0000.159
영업상태명0.1130.2300.1950.0000.1400.0000.1591.000

Missing values

2024-03-16T05:32:20.078433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-16T05:32:20.963585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-16T05:32:21.585513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시군명사업장명소재지우편번호소재지도로명주소소재지지번주소인허가일자영업상태명폐업일자의료기관종별명병상수(개)의료인수(명)입원실수(개)진료과목내용연면적(㎡)WGS84위도WGS84경도
0가평군노체리안드리자애병원12432경기도 가평군 조종면 꽃동네길 46 (노체리안드리자애병원)경기도 가평군 조종면 하판리 540번지 2호1995-12-18영업/정상<NA>병원621612내과, 정신건강의학과, 외과, 영상의학과6511.3437.884883127.351696
1가평군에이치제이매그놀리아국제병원12461경기도 가평군 설악면 미사리로 267-177경기도 가평군 설악면 송산리 460번지2003-05-17영업/정상<NA>병원995140내과, 신경과, 정신건강의학과, 외과, 정형외과, 성형외과, 마취통증의학과, 산부인과, 영상의학과, 재활의학과, 가정의학과22941.6937.691127127.521381
2가평군국군청평병원12452경기도 가평군 청평면 경춘로 926경기도 가평군 청평면 청평리 567번지 21호 사서함1994-05-23폐업2017-09-19병원350656내과, 신경과, 정신건강의학과, 외과, 정형외과, 신경외과, 심장혈관흉부외과, 안과, 이비인후과, 피부과, 비뇨의학과, 영상의학과, 진단검사의학과, 재활의학과, 가정의학과, 응급의학과, 치과999999.037.743691127.429584
3고양시연세마두병원10414경기도 고양시 일산동구 강석로 123 (마두동, 뉴삼창마트)경기도 고양시 일산동구 마두동 802번지 뉴삼창마트 3층,6층,7층2013-05-27영업/정상<NA>병원1052518내과, 신경과, 소아청소년과, 이비인후과, 재활의학과2257.4737.651906126.778789
4고양시해븐리병원10338경기도 고양시 일산동구 중산로 117, 지하1층 일부, 1층 일부, 2~5층 (중산동)경기도 고양시 일산동구 중산동 1696 지하1층 일부, 1층 일부, 2~5층2008-07-11영업/정상<NA>병원1783140내과, 신경과, 정형외과, 영상의학과, 재활의학과, 가정의학과4356.6337.682547126.781032
5고양시원당연세병원10461경기도 고양시 덕양구 호국로 781, 2,3,4,6,7층 (주교동)경기도 고양시 덕양구 주교동 614-51999-12-01영업/정상<NA>병원685320내과, 외과, 정형외과, 신경외과, 마취통증의학과, 피부과, 비뇨의학과, 영상의학과, 가정의학과2273.4537.656195126.835375
6고양시의료법인자인의료재단(더자인병원)10518경기도 고양시 덕양구 중앙로 555, 지하1,1-10층 (행신동)경기도 고양시 덕양구 행신동 995번지 지하1,1-10층2002-01-16영업/정상<NA>병원835415내과, 신경과, 외과, 정형외과, 신경외과, 마취통증의학과, 영상의학과, 재활의학과, 가정의학과, 한방내과, 한방재활의학과5522.5537.622983126.835682
7고양시의료법인 세경의료재단 새빛안과병원10447경기도 고양시 일산동구 중앙로 1065 (백석동)경기도 고양시 일산동구 백석2동 1334-32005-10-19영업/정상<NA>병원505814내과, 마취통증의학과, 안과4090.9937.64426126.785743
8고양시일산하이병원10338경기도 고양시 일산동구 고양대로 761 (중산동)경기도 고양시 일산동구 중산동 1695번지2012-08-17영업/정상<NA>병원762618내과, 정형외과, 신경외과, 마취통증의학과, 영상의학과5331.737.682553126.780601
9고양시일산이십일세기병원10447경기도 고양시 일산동구 중앙로 1059 (백석동, ISD빌딩 3~8층)경기도 고양시 일산동구 백석동 1332번지 ISD빌딩 3~8층2014-05-09영업/정상<NA>병원913118내과, 정형외과, 신경외과, 마취통증의학과, 영상의학과3602.9937.643929126.786165
시군명사업장명소재지우편번호소재지도로명주소소재지지번주소인허가일자영업상태명폐업일자의료기관종별명병상수(개)의료인수(명)입원실수(개)진료과목내용연면적(㎡)WGS84위도WGS84경도
536화성시씨이오병원18611경기도 화성시 향남읍 상신하길로298번길 7-7, 3~6층경기도 화성시 향남읍 하길리 1472번지 2호2018-08-07폐업2019-06-14병원69625내과, 정형외과, 성형외과, 소아청소년과, 이비인후과, 피부과, 영상의학과, 가정의학과, 직업환경의학과3316.037.115646126.91058
537화성시동탄드림병원18466경기도 화성시 동탄순환대로 704, A동 5,6,7층 (영천동)경기도 화성시 영천동 681번지 3호2016-03-16폐업2020-11-06병원45428내과, 소아청소년과, 안과, 이비인후과, 피부과, 영상의학과1070.9437.207587127.11078
538화성시의료법인 은혜와감사의료재단 향남백병원18591경기도 화성시 향남읍 발안로 107, 3,5층 (태성프라자)경기도 화성시 향남읍 행정리 474번지 6호 태성프라자3층,5층(일부)2009-03-24폐업2016-11-01병원30102내과, 소아청소년과, 피부과, 비뇨의학과744.1637.131722126.922378
539화성시센트럴병원18434경기도 화성시 동탄지성로 116, 2~4층 (반송동, 강남프라자)경기도 화성시 반송동 41번지 5호 강남프라자 2~4층2008-03-10폐업2018-09-28병원48111내과, 정형외과, 마취통증의학과, 소아청소년과, 재활의학과1621.4437.208979127.062853
540화성시새봄병원18244경기도 화성시 꽃내음1길 5-4, 3층 일부, 4층 (새솔동)경기도 화성시 새솔동 78-1번지 3층 일부, 4층2020-12-24폐업2023-06-01병원763721내과, 외과, 정형외과, 신경외과, 마취통증의학과, 소아청소년과, 영상의학과, 재활의학과, 가정의학과, 응급의학과2774.8837.284993126.816836
541화성시(사)한국장애인케어협회 굿닥터병원445-310경기도 화성시 효행로265번길 16 (기안동)경기도 화성시 기안동 353번지2009-09-17폐업2014-09-02병원50812내과, 정형외과, 마취통증의학과, 소아청소년과, 이비인후과, 피부과, 비뇨의학과1370.9337.225085126.973699
542화성시의료법인 녹산의료재단 동수원남양병원445-010경기도 화성시 남양성지로 254-3 (남양동)경기도 화성시 남양동 1417-21999-03-26폐업2014-10-01병원89424내과, 외과, 정형외과, 신경외과, 마취통증의학과, 소아청소년과, 가정의학과, 응급의학과4099.037.213406126.829444
543화성시이화병원18573<NA>경기도 화성시 우정읍 이화리 439번지 1호2006-06-23폐업2007-06-13병원59211내과, 외과, 정형외과, 신경외과, 소아청소년과8227.68<NA><NA>
544화성시동탄플러스병원18455경기도 화성시 동탄반석로 134, 5,6,7층 (반송동)경기도 화성시 반송동 104번지 1호2018-03-12폐업2018-05-29병원104730성형외과, 재활의학과2270.8837.20106127.071362
545화성시의료법인 신명진의료재단 퍼스트힐화성병원18262경기도 화성시 남양읍 시청로 11, 1,3,4층 102~104, 107~112, 3,4층전체호경기도 화성시 남양읍 남양리 2252-32021-07-16휴업<NA>병원30717내과, 신경과, 마취통증의학과, 재활의학과, 가정의학과2895.6237.208255126.819433

Duplicate rows

Most frequently occurring

시군명사업장명소재지우편번호소재지도로명주소소재지지번주소인허가일자영업상태명폐업일자의료기관종별명병상수(개)의료인수(명)입원실수(개)진료과목내용연면적(㎡)WGS84위도WGS84경도# duplicates
0부천시고운여성병원14569경기도 부천시 원미구 부천로 110-1, 2~6층 (원미동)경기도 부천시 원미구 원미동 111-3번지2007-02-22폐업2024-03-01병원40721내과, 외과, 성형외과, 산부인과, 소아청소년과, 피부과1639.0337.494059126.7853662
1부천시새울병원14774경기도 부천시 소사구 소사로 80, 한일프라자 2,3,4,5,6층 (소사본동)경기도 부천시 소사구 소사본동 404-4 한일프라자 2,3,4,5,6층2012-11-29폐업2024-02-29병원391012내과, 정형외과, 신경외과, 마취통증의학과, 영상의학과, 가정의학과1643.1537.466001126.7990782