Overview

Dataset statistics

Number of variables16
Number of observations642
Missing cells493
Missing cells (%)4.8%
Duplicate rows2
Duplicate rows (%)0.3%
Total size in memory84.1 KiB
Average record size in memory134.2 B

Variable types

Categorical3
Text5
DateTime2
Numeric6

Dataset

Description요양병원 현황(병원급)
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=ZB3Q91UH3YNE8655S8C5136365&infSeq=1

Alerts

Dataset has 2 (0.3%) duplicate rowsDuplicates
병상수(개) is highly overall correlated with 의료인수(명) and 2 other fieldsHigh correlation
의료인수(명) is highly overall correlated with 병상수(개) and 2 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 2 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 WGS84위도 and 1 other fieldsHigh correlation
의료기관종별명 is highly imbalanced (72.8%)Imbalance
폐업일자 has 464 (72.3%) missing valuesMissing
WGS84위도 has 8 (1.2%) missing valuesMissing
WGS84경도 has 8 (1.2%) missing valuesMissing
의료인수(명) has 7 (1.1%) zerosZeros
입원실수(개) has 9 (1.4%) zerosZeros

Reproduction

Analysis started2024-05-03 18:56:49.690648
Analysis finished2024-05-03 18:57:07.267608
Duration17.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
고양시
62 
안산시
59 
부천시
52 
용인시
49 
파주시
39 
Other values (26)
381 

Length

Max length4
Median length3
Mean length3.0576324
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
고양시 62
 
9.7%
안산시 59
 
9.2%
부천시 52
 
8.1%
용인시 49
 
7.6%
파주시 39
 
6.1%
수원시 35
 
5.5%
시흥시 34
 
5.3%
성남시 27
 
4.2%
동두천시 22
 
3.4%
남양주 22
 
3.4%
Other values (21) 241
37.5%

Length

2024-05-03T18:57:07.490922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고양시 62
 
9.7%
안산시 59
 
9.2%
부천시 52
 
8.1%
용인시 49
 
7.6%
파주시 39
 
6.1%
수원시 35
 
5.5%
시흥시 34
 
5.3%
성남시 27
 
4.2%
동두천시 22
 
3.4%
남양주 22
 
3.4%
Other values (21) 241
37.5%

사업장명
Text

Distinct448
Distinct (%)69.8%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2024-05-03T18:57:07.928890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length22
Mean length9.3146417
Min length4

Characters and Unicode

Total characters5980
Distinct characters291
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique272 ?
Unique (%)42.4%

Sample

1st row강변요양병원
2nd row청평활명요양병원
3rd row가평산속요양병원
4th row에이치제이매그놀리아국제요양병원
5th row가평푸른숲요양병원
ValueCountFrequency (%)
의료법인 43
 
5.4%
요양병원 15
 
1.9%
한사랑요양병원 5
 
0.6%
서울요양병원 5
 
0.6%
대효요양병원 4
 
0.5%
참사랑 4
 
0.5%
우리요양병원 4
 
0.5%
서울 4
 
0.5%
새생명요양병원 4
 
0.5%
연세요양병원 4
 
0.5%
Other values (491) 703
88.4%
2024-05-03T18:57:08.932126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 686
 
11.5%
병 643
 
10.8%
양 597
 
10.0%
요 577
 
9.6%
의 180
 
3.0%
료 169
 
2.8%
153
 
2.6%
인 152
 
2.5%
재 98
 
1.6%
단 89
 
1.5%
Other values (281) 2636
44.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5802
97.0%
Space Separator 153
 
2.6%
Close Punctuation 11
 
0.2%
Open Punctuation 8
 
0.1%
Uppercase Letter 6
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 686
 
11.8%
병 643
 
11.1%
양 597
 
10.3%
요 577
 
9.9%
의 180
 
3.1%
료 169
 
2.9%
인 152
 
2.6%
재 98
 
1.7%
단 89
 
1.5%
법 83
 
1.4%
Other values (275) 2528
43.6%
Uppercase Letter
ValueCountFrequency (%)
S 2
33.3%
R 2
33.3%
C 2
33.3%
Space Separator
ValueCountFrequency (%)
153
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5802
97.0%
Common 172
 
2.9%
Latin 6
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 686
 
11.8%
병 643
 
11.1%
양 597
 
10.3%
요 577
 
9.9%
의 180
 
3.1%
료 169
 
2.9%
인 152
 
2.6%
재 98
 
1.7%
단 89
 
1.5%
법 83
 
1.4%
Other values (275) 2528
43.6%
Common
ValueCountFrequency (%)
153
89.0%
) 11
 
6.4%
( 8
 
4.7%
Latin
ValueCountFrequency (%)
S 2
33.3%
R 2
33.3%
C 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5802
97.0%
ASCII 178
 
3.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 686
 
11.8%
병 643
 
11.1%
양 597
 
10.3%
요 577
 
9.9%
의 180
 
3.1%
료 169
 
2.9%
인 152
 
2.6%
재 98
 
1.7%
단 89
 
1.5%
법 83
 
1.4%
Other values (275) 2528
43.6%
ASCII
ValueCountFrequency (%)
153
86.0%
) 11
 
6.2%
( 8
 
4.5%
S 2
 
1.1%
R 2
 
1.1%
C 2
 
1.1%

인허가일자
Date

Distinct451
Distinct (%)70.2%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
Minimum1981-11-01 00:00:00
Maximum2024-03-11 00:00:00
2024-05-03T18:57:09.390245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:57:09.730040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

영업상태명
Categorical

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
영업/정상
283 
폐업
162 
영업중
160 
휴업
 
21
취소/말소/만료/정지/중지
 
15

Length

Max length14
Median length5
Mean length3.8551402
Min length2

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
영업/정상 283
44.1%
폐업 162
25.2%
영업중 160
24.9%
휴업 21
 
3.3%
취소/말소/만료/정지/중지 15
 
2.3%
직권폐업 1
 
0.2%

Length

2024-05-03T18:57:10.179026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T18:57:10.575208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업/정상 283
44.1%
폐업 162
25.2%
영업중 160
24.9%
휴업 21
 
3.3%
취소/말소/만료/정지/중지 15
 
2.3%
직권폐업 1
 
0.2%

폐업일자
Date

MISSING 

Distinct173
Distinct (%)97.2%
Missing464
Missing (%)72.3%
Memory size5.1 KiB
Minimum2006-05-29 00:00:00
Maximum2024-04-15 00:00:00
2024-05-03T18:57:10.987153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:57:11.447197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

병상수(개)
Real number (ℝ)

HIGH CORRELATION 

Distinct211
Distinct (%)32.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.65576
Minimum0
Maximum554
Zeros4
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-05-03T18:57:11.871630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile44.1
Q195
median149.5
Q3198.75
95-th percentile295
Maximum554
Range554
Interquartile range (IQR)103.75

Descriptive statistics

Standard deviation82.516834
Coefficient of variation (CV)0.52673986
Kurtosis3.2841705
Mean156.65576
Median Absolute Deviation (MAD)50.5
Skewness1.2050167
Sum100573
Variance6809.028
MonotonicityNot monotonic
2024-05-03T18:57:12.318267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199 20
 
3.1%
99 13
 
2.0%
198 11
 
1.7%
182 10
 
1.6%
165 9
 
1.4%
30 9
 
1.4%
84 9
 
1.4%
144 9
 
1.4%
150 9
 
1.4%
66 8
 
1.2%
Other values (201) 535
83.3%
ValueCountFrequency (%)
0 4
0.6%
30 9
1.4%
31 1
 
0.2%
32 1
 
0.2%
34 2
 
0.3%
35 4
0.6%
36 1
 
0.2%
38 1
 
0.2%
39 1
 
0.2%
40 4
0.6%
ValueCountFrequency (%)
554 2
0.3%
545 1
0.2%
526 1
0.2%
523 1
0.2%
475 1
0.2%
473 1
0.2%
429 2
0.3%
382 1
0.2%
358 2
0.3%
357 2
0.3%

의료기관종별명
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
요양병원(일반요양병원)
612 
요양병원(노인병원)
 
30

Length

Max length12
Median length12
Mean length11.906542
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row요양병원(일반요양병원)
2nd row요양병원(일반요양병원)
3rd row요양병원(일반요양병원)
4th row요양병원(일반요양병원)
5th row요양병원(일반요양병원)

Common Values

ValueCountFrequency (%)
요양병원(일반요양병원) 612
95.3%
요양병원(노인병원) 30
 
4.7%

Length

2024-05-03T18:57:12.827543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T18:57:13.221779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
요양병원(일반요양병원 612
95.3%
요양병원(노인병원 30
 
4.7%

의료인수(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct69
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.971963
Minimum0
Maximum169
Zeros7
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-05-03T18:57:13.561863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q19
median19
Q328
95-th percentile47
Maximum169
Range169
Interquartile range (IQR)19

Descriptive statistics

Standard deviation17.023957
Coefficient of variation (CV)0.81174841
Kurtosis17.880593
Mean20.971963
Median Absolute Deviation (MAD)10
Skewness2.8384333
Sum13464
Variance289.81513
MonotonicityNot monotonic
2024-05-03T18:57:14.161593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 30
 
4.7%
19 26
 
4.0%
7 25
 
3.9%
6 23
 
3.6%
10 21
 
3.3%
3 21
 
3.3%
22 21
 
3.3%
20 20
 
3.1%
16 20
 
3.1%
12 19
 
3.0%
Other values (59) 416
64.8%
ValueCountFrequency (%)
0 7
 
1.1%
1 12
 
1.9%
2 12
 
1.9%
3 21
3.3%
4 16
2.5%
5 14
2.2%
6 23
3.6%
7 25
3.9%
8 30
4.7%
9 16
2.5%
ValueCountFrequency (%)
169 1
0.2%
166 1
0.2%
102 2
0.3%
83 1
0.2%
78 1
0.2%
75 1
0.2%
69 1
0.2%
68 1
0.2%
67 1
0.2%
64 2
0.3%

입원실수(개)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct77
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.202492
Minimum0
Maximum206
Zeros9
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-05-03T18:57:14.725478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q120
median30
Q342
95-th percentile64
Maximum206
Range206
Interquartile range (IQR)22

Descriptive statistics

Standard deviation21.092238
Coefficient of variation (CV)0.63526068
Kurtosis16.11093
Mean33.202492
Median Absolute Deviation (MAD)11
Skewness2.7467727
Sum21316
Variance444.88249
MonotonicityNot monotonic
2024-05-03T18:57:15.334263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 24
 
3.7%
38 22
 
3.4%
31 21
 
3.3%
29 19
 
3.0%
17 17
 
2.6%
27 17
 
2.6%
40 16
 
2.5%
26 16
 
2.5%
16 16
 
2.5%
23 16
 
2.5%
Other values (67) 458
71.3%
ValueCountFrequency (%)
0 9
1.4%
3 1
 
0.2%
5 5
0.8%
6 9
1.4%
7 7
1.1%
8 8
1.2%
9 3
 
0.5%
10 5
0.8%
11 12
1.9%
12 5
0.8%
ValueCountFrequency (%)
206 1
0.2%
203 1
0.2%
140 2
0.3%
139 1
0.2%
117 2
0.3%
105 1
0.2%
104 1
0.2%
103 1
0.2%
83 2
0.3%
79 2
0.3%

진료과목내용
Text

Distinct428
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2024-05-03T18:57:15.968063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length214
Median length99
Mean length49.981308
Min length2

Characters and Unicode

Total characters32088
Distinct characters73
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

Unique256 ?
Unique (%)39.9%

Sample

1st row내과, 외과, 마취통증의학과, 산부인과, 한방내과
2nd row내과, 신경과, 외과, 정형외과, 신경외과, 심장혈관흉부외과, 마취통증의학과, 산부인과, 소아청소년과, 이비인후과, 피부과, 비뇨의학과, 영상의학과, 재활의학과, 가정의학과, 응급의학과, 한방내과, 한방부인과, 한방소아과, 한방안·이비인후·피부과, 한방신경정신과, 한방재활의학과, 사상체질과, 침구과
3rd row내과, 한방내과
4th row이비인후과, 한방내과, 한방부인과, 한방소아과, 한방안·이비인후·피부과, 한방신경정신과, 한방재활의학과, 사상체질과, 침구과
5th row내과, 가정의학과, 한방내과, 침구과
ValueCountFrequency (%)
내과 580
 
11.0%
한방내과 471
 
8.9%
가정의학과 466
 
8.8%
재활의학과 410
 
7.8%
외과 374
 
7.1%
신경과 340
 
6.5%
침구과 319
 
6.1%
정형외과 303
 
5.8%
신경외과 255
 
4.8%
한방재활의학과 220
 
4.2%
Other values (35) 1529
29.0%
2024-05-03T18:57:17.261876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 5272
16.4%
, 4625
14.4%
4625
14.4%
학 1408
 
4.4%
의 1403
 
4.4%
방 1274
 
4.0%
한 1265
 
3.9%
내 1052
 
3.3%
정 993
 
3.1%
외 988
 
3.1%
Other values (63) 9183
28.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22576
70.4%
Other Punctuation 4887
 
15.2%
Space Separator 4625
 
14.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 5272
23.4%
학 1408
 
6.2%
의 1403
 
6.2%
방 1274
 
5.6%
한 1265
 
5.6%
내 1052
 
4.7%
정 993
 
4.4%
외 988
 
4.4%
신 966
 
4.3%
경 745
 
3.3%
Other values (60) 7210
31.9%
Other Punctuation
ValueCountFrequency (%)
, 4625
94.6%
· 262
 
5.4%
Space Separator
ValueCountFrequency (%)
4625
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22576
70.4%
Common 9512
29.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 5272
23.4%
학 1408
 
6.2%
의 1403
 
6.2%
방 1274
 
5.6%
한 1265
 
5.6%
내 1052
 
4.7%
정 993
 
4.4%
외 988
 
4.4%
신 966
 
4.3%
경 745
 
3.3%
Other values (60) 7210
31.9%
Common
ValueCountFrequency (%)
, 4625
48.6%
4625
48.6%
· 262
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22576
70.4%
ASCII 9250
28.8%
None 262
 
0.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 5272
23.4%
학 1408
 
6.2%
의 1403
 
6.2%
방 1274
 
5.6%
한 1265
 
5.6%
내 1052
 
4.7%
정 993
 
4.4%
외 988
 
4.4%
신 966
 
4.3%
경 745
 
3.3%
Other values (60) 7210
31.9%
ASCII
ValueCountFrequency (%)
, 4625
50.0%
4625
50.0%
None
ValueCountFrequency (%)
· 262
100.0%

연면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct469
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3691.0572
Minimum0
Maximum82591
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-05-03T18:57:17.698276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile795.1775
Q11872.5275
median2972.145
Q34572.13
95-th percentile8175.7965
Maximum82591
Range82591
Interquartile range (IQR)2699.6025

Descriptive statistics

Standard deviation4265.5392
Coefficient of variation (CV)1.1556416
Kurtosis189.43011
Mean3691.0572
Median Absolute Deviation (MAD)1271.365
Skewness11.262798
Sum2369658.7
Variance18194824
MonotonicityNot monotonic
2024-05-03T18:57:18.610963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4941.36 4
 
0.6%
1327.3 3
 
0.5%
2245.5 3
 
0.5%
3207.0 3
 
0.5%
2992.56 2
 
0.3%
3030.19 2
 
0.3%
5258.02 2
 
0.3%
4446.24 2
 
0.3%
4149.02 2
 
0.3%
3720.46 2
 
0.3%
Other values (459) 617
96.1%
ValueCountFrequency (%)
0.0 1
0.2%
353.38 1
0.2%
374.52 1
0.2%
400.85 1
0.2%
427.88 1
0.2%
465.0 1
0.2%
490.5 1
0.2%
495.0 1
0.2%
513.51 1
0.2%
520.04 1
0.2%
ValueCountFrequency (%)
82591.0 1
0.2%
34244.47 1
0.2%
29931.48 1
0.2%
26094.0 1
0.2%
15193.99 2
0.3%
13683.74 2
0.3%
11764.12 2
0.3%
11437.0 1
0.2%
10954.15 2
0.3%
10583.98 2
0.3%

소재지도로명주소
Text

Distinct447
Distinct (%)70.3%
Missing6
Missing (%)0.9%
Memory size5.1 KiB
2024-05-03T18:57:19.396823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length82
Median length56
Mean length32.680818
Min length15

Characters and Unicode

Total characters20785
Distinct characters374
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique266 ?
Unique (%)41.8%

Sample

1st row경기도 가평군 가평읍 북한강변로 160
2nd row경기도 가평군 청평면 북한강로1604번길 64-28
3rd row경기도 가평군 상면 대보간선로 889
4th row경기도 가평군 설악면 미사리로 267-177
5th row경기도 가평군 상면 물골길 262-23
ValueCountFrequency (%)
경기도 636
 
15.4%
고양시 62
 
1.5%
안산시 58
 
1.4%
부천시 50
 
1.2%
용인시 48
 
1.2%
파주시 39
 
0.9%
단원구 35
 
0.8%
수원시 35
 
0.8%
일산동구 35
 
0.8%
시흥시 34
 
0.8%
Other values (1262) 3104
75.0%
2024-05-03T18:57:20.896977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3504
 
16.9%
, 779
 
3.7%
1 730
 
3.5%
기 689
 
3.3%
경 674
 
3.2%
도 667
 
3.2%
시 657
 
3.2%
동 615
 
3.0%
로 610
 
2.9%
( 508
 
2.4%
Other values (364) 11352
54.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11456
55.1%
Decimal Number 3657
 
17.6%
Space Separator 3504
 
16.9%
Other Punctuation 785
 
3.8%
Open Punctuation 508
 
2.4%
Close Punctuation 508
 
2.4%
Math Symbol 158
 
0.8%
Dash Punctuation 141
 
0.7%
Uppercase Letter 60
 
0.3%
Lowercase Letter 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 689
 
6.0%
경 674
 
5.9%
도 667
 
5.8%
시 657
 
5.7%
동 615
 
5.4%
로 610
 
5.3%
층 418
 
3.6%
구 315
 
2.7%
양 241
 
2.1%
원 238
 
2.1%
Other values (321) 6332
55.3%
Uppercase Letter
ValueCountFrequency (%)
B 14
23.3%
S 6
10.0%
D 4
 
6.7%
K 4
 
6.7%
A 4
 
6.7%
E 3
 
5.0%
U 3
 
5.0%
W 3
 
5.0%
I 3
 
5.0%
R 3
 
5.0%
Other values (8) 13
21.7%
Decimal Number
ValueCountFrequency (%)
1 730
20.0%
2 468
12.8%
0 404
11.0%
3 404
11.0%
4 387
10.6%
5 319
8.7%
6 282
 
7.7%
7 258
 
7.1%
8 204
 
5.6%
9 201
 
5.5%
Lowercase Letter
ValueCountFrequency (%)
e 2
40.0%
k 1
20.0%
b 1
20.0%
w 1
20.0%
Other Punctuation
ValueCountFrequency (%)
, 779
99.2%
. 4
 
0.5%
& 2
 
0.3%
Math Symbol
ValueCountFrequency (%)
~ 156
98.7%
∼ 2
 
1.3%
Letter Number
ValueCountFrequency (%)
Ⅱ 2
66.7%
Ⅲ 1
33.3%
Space Separator
ValueCountFrequency (%)
3504
100.0%
Open Punctuation
ValueCountFrequency (%)
( 508
100.0%
Close Punctuation
ValueCountFrequency (%)
) 508
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 141
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11456
55.1%
Common 9261
44.6%
Latin 68
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 689
 
6.0%
경 674
 
5.9%
도 667
 
5.8%
시 657
 
5.7%
동 615
 
5.4%
로 610
 
5.3%
층 418
 
3.6%
구 315
 
2.7%
양 241
 
2.1%
원 238
 
2.1%
Other values (321) 6332
55.3%
Latin
ValueCountFrequency (%)
B 14
20.6%
S 6
 
8.8%
D 4
 
5.9%
K 4
 
5.9%
A 4
 
5.9%
E 3
 
4.4%
U 3
 
4.4%
W 3
 
4.4%
I 3
 
4.4%
R 3
 
4.4%
Other values (14) 21
30.9%
Common
ValueCountFrequency (%)
3504
37.8%
, 779
 
8.4%
1 730
 
7.9%
( 508
 
5.5%
) 508
 
5.5%
2 468
 
5.1%
0 404
 
4.4%
3 404
 
4.4%
4 387
 
4.2%
5 319
 
3.4%
Other values (9) 1250
 
13.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11456
55.1%
ASCII 9324
44.9%
Number Forms 3
 
< 0.1%
Math Operators 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3504
37.6%
, 779
 
8.4%
1 730
 
7.8%
( 508
 
5.4%
) 508
 
5.4%
2 468
 
5.0%
0 404
 
4.3%
3 404
 
4.3%
4 387
 
4.2%
5 319
 
3.4%
Other values (30) 1313
 
14.1%
Hangul
ValueCountFrequency (%)
기 689
 
6.0%
경 674
 
5.9%
도 667
 
5.8%
시 657
 
5.7%
동 615
 
5.4%
로 610
 
5.3%
층 418
 
3.6%
구 315
 
2.7%
양 241
 
2.1%
원 238
 
2.1%
Other values (321) 6332
55.3%
Math Operators
ValueCountFrequency (%)
∼ 2
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 2
66.7%
Ⅲ 1
33.3%

소재지지번주소
Text

Distinct468
Distinct (%)73.2%
Missing3
Missing (%)0.5%
Memory size5.1 KiB
2024-05-03T18:57:21.747349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length76
Median length52
Mean length28.604069
Min length8

Characters and Unicode

Total characters18278
Distinct characters325
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique300 ?
Unique (%)46.9%

Sample

1st row경기도 가평군 가평읍 금대리 585번지
2nd row경기도 가평군 청평면 삼회리 502번지
3rd row경기도 가평군 상면 항사리 2번지
4th row경기도 가평군 설악면 송산리 460번지
5th row경기도 가평군 상면 봉수리 49-4번지
ValueCountFrequency (%)
경기도 634
 
16.6%
고양시 60
 
1.6%
안산시 57
 
1.5%
부천시 52
 
1.4%
1호 49
 
1.3%
용인시 49
 
1.3%
파주시 39
 
1.0%
수원시 35
 
0.9%
일산동구 35
 
0.9%
단원구 34
 
0.9%
Other values (1136) 2779
72.7%
2024-05-03T18:57:23.352202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3200
 
17.5%
1 798
 
4.4%
기 676
 
3.7%
도 653
 
3.6%
지 651
 
3.6%
시 650
 
3.6%
경 640
 
3.5%
동 593
 
3.2%
번 518
 
2.8%
2 435
 
2.4%
Other values (315) 9464
51.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10461
57.2%
Decimal Number 3760
 
20.6%
Space Separator 3200
 
17.5%
Other Punctuation 365
 
2.0%
Dash Punctuation 277
 
1.5%
Math Symbol 108
 
0.6%
Uppercase Letter 52
 
0.3%
Close Punctuation 23
 
0.1%
Open Punctuation 23
 
0.1%
Lowercase Letter 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 676
 
6.5%
도 653
 
6.2%
지 651
 
6.2%
시 650
 
6.2%
경 640
 
6.1%
동 593
 
5.7%
번 518
 
5.0%
호 348
 
3.3%
층 315
 
3.0%
구 308
 
2.9%
Other values (273) 5109
48.8%
Uppercase Letter
ValueCountFrequency (%)
B 10
19.2%
S 6
11.5%
D 4
 
7.7%
K 4
 
7.7%
U 4
 
7.7%
A 4
 
7.7%
R 3
 
5.8%
E 3
 
5.8%
I 3
 
5.8%
C 2
 
3.8%
Other values (6) 9
17.3%
Decimal Number
ValueCountFrequency (%)
1 798
21.2%
2 435
11.6%
3 431
11.5%
0 389
10.3%
4 370
9.8%
5 352
9.4%
6 283
 
7.5%
7 271
 
7.2%
8 237
 
6.3%
9 194
 
5.2%
Other Punctuation
ValueCountFrequency (%)
, 355
97.3%
. 8
 
2.2%
/ 1
 
0.3%
& 1
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
e 2
40.0%
w 1
20.0%
b 1
20.0%
k 1
20.0%
Math Symbol
ValueCountFrequency (%)
~ 106
98.1%
∼ 2
 
1.9%
Letter Number
ValueCountFrequency (%)
Ⅱ 3
75.0%
Ⅲ 1
 
25.0%
Space Separator
ValueCountFrequency (%)
3200
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 277
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10461
57.2%
Common 7756
42.4%
Latin 61
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 676
 
6.5%
도 653
 
6.2%
지 651
 
6.2%
시 650
 
6.2%
경 640
 
6.1%
동 593
 
5.7%
번 518
 
5.0%
호 348
 
3.3%
층 315
 
3.0%
구 308
 
2.9%
Other values (273) 5109
48.8%
Latin
ValueCountFrequency (%)
B 10
16.4%
S 6
 
9.8%
D 4
 
6.6%
K 4
 
6.6%
U 4
 
6.6%
A 4
 
6.6%
R 3
 
4.9%
E 3
 
4.9%
I 3
 
4.9%
Ⅱ 3
 
4.9%
Other values (12) 17
27.9%
Common
ValueCountFrequency (%)
3200
41.3%
1 798
 
10.3%
2 435
 
5.6%
3 431
 
5.6%
0 389
 
5.0%
4 370
 
4.8%
, 355
 
4.6%
5 352
 
4.5%
6 283
 
3.6%
- 277
 
3.6%
Other values (10) 866
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10461
57.2%
ASCII 7811
42.7%
Number Forms 4
 
< 0.1%
Math Operators 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3200
41.0%
1 798
 
10.2%
2 435
 
5.6%
3 431
 
5.5%
0 389
 
5.0%
4 370
 
4.7%
, 355
 
4.5%
5 352
 
4.5%
6 283
 
3.6%
- 277
 
3.5%
Other values (29) 921
 
11.8%
Hangul
ValueCountFrequency (%)
기 676
 
6.5%
도 653
 
6.2%
지 651
 
6.2%
시 650
 
6.2%
경 640
 
6.1%
동 593
 
5.7%
번 518
 
5.0%
호 348
 
3.3%
층 315
 
3.0%
구 308
 
2.9%
Other values (273) 5109
48.8%
Number Forms
ValueCountFrequency (%)
Ⅱ 3
75.0%
Ⅲ 1
 
25.0%
Math Operators
ValueCountFrequency (%)
∼ 2
100.0%

소재지우편번호
Text

Distinct366
Distinct (%)57.4%
Missing4
Missing (%)0.6%
Memory size5.1 KiB
2024-05-03T18:57:24.468549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.1253918
Min length5

Characters and Unicode

Total characters3270
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

Unique180 ?
Unique (%)28.2%

Sample

1st row12428
2nd row12458
3rd row12445
4th row12461
5th row12440
ValueCountFrequency (%)
11518 7
 
1.1%
15455 7
 
1.1%
16953 6
 
0.9%
12919 6
 
0.9%
15457 6
 
0.9%
14643 5
 
0.8%
10826 5
 
0.8%
13644 5
 
0.8%
15062 5
 
0.8%
14912 5
 
0.8%
Other values (356) 581
91.1%
2024-05-03T18:57:26.073488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 871
26.6%
4 353
10.8%
0 319
 
9.8%
5 312
 
9.5%
2 275
 
8.4%
8 241
 
7.4%
3 239
 
7.3%
6 234
 
7.2%
7 210
 
6.4%
9 176
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3230
98.8%
Dash Punctuation 40
 
1.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 871
27.0%
4 353
10.9%
0 319
 
9.9%
5 312
 
9.7%
2 275
 
8.5%
8 241
 
7.5%
3 239
 
7.4%
6 234
 
7.2%
7 210
 
6.5%
9 176
 
5.4%
Dash Punctuation
ValueCountFrequency (%)
- 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3270
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 871
26.6%
4 353
10.8%
0 319
 
9.8%
5 312
 
9.5%
2 275
 
8.4%
8 241
 
7.4%
3 239
 
7.3%
6 234
 
7.2%
7 210
 
6.4%
9 176
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3270
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 871
26.6%
4 353
10.8%
0 319
 
9.8%
5 312
 
9.5%
2 275
 
8.4%
8 241
 
7.4%
3 239
 
7.3%
6 234
 
7.2%
7 210
 
6.4%
9 176
 
5.4%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct407
Distinct (%)64.2%
Missing8
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean37.476458
Minimum36.989891
Maximum38.049565
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-05-03T18:57:26.750733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.989891
5-th percentile37.114797
Q137.301077
median37.44643
Q337.672356
95-th percentile37.877746
Maximum38.049565
Range1.0596741
Interquartile range (IQR)0.37127875

Descriptive statistics

Standard deviation0.2306688
Coefficient of variation (CV)0.0061550322
Kurtosis-0.77418128
Mean37.476458
Median Absolute Deviation (MAD)0.17175375
Skewness0.1391326
Sum23760.074
Variance0.053208097
MonotonicityNot monotonic
2024-05-03T18:57:27.486130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.3011341461 7
 
1.1%
37.3358363471 4
 
0.6%
37.8599307827 4
 
0.6%
37.9081637185 4
 
0.6%
37.6896660344 4
 
0.6%
37.462268405 4
 
0.6%
37.8408474989 4
 
0.6%
37.2713814124 3
 
0.5%
37.4848896105 3
 
0.5%
37.3185798127 3
 
0.5%
Other values (397) 594
92.5%
(Missing) 8
 
1.2%
ValueCountFrequency (%)
36.9898906563 1
0.2%
36.9919683829 2
0.3%
36.9939711588 1
0.2%
36.9942873572 2
0.3%
36.9971749018 2
0.3%
37.0007602696 2
0.3%
37.0049595787 1
0.2%
37.0098700968 2
0.3%
37.0106468665 1
0.2%
37.0150965847 1
0.2%
ValueCountFrequency (%)
38.0495647776 1
 
0.2%
38.0174229847 1
 
0.2%
37.9421798717 2
0.3%
37.9348602658 1
 
0.2%
37.9198109083 1
 
0.2%
37.9172633702 2
0.3%
37.9125441079 1
 
0.2%
37.9081637185 4
0.6%
37.9068599484 2
0.3%
37.901009932 1
 
0.2%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct407
Distinct (%)64.2%
Missing8
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean126.99441
Minimum126.60208
Maximum127.78078
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-05-03T18:57:27.929438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.60208
5-th percentile126.73936
Q1126.79804
median126.95367
Q3127.13609
95-th percentile127.40756
Maximum127.78078
Range1.178699
Interquartile range (IQR)0.33804662

Descriptive statistics

Standard deviation0.22360269
Coefficient of variation (CV)0.0017607286
Kurtosis0.35438255
Mean126.99441
Median Absolute Deviation (MAD)0.16432101
Skewness0.82369397
Sum80514.454
Variance0.049998161
MonotonicityNot monotonic
2024-05-03T18:57:28.628598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.8133164971 7
 
1.1%
126.7479559003 4
 
0.6%
126.7865996065 4
 
0.6%
127.0363051342 4
 
0.6%
126.6020784224 4
 
0.6%
127.1433639275 4
 
0.6%
127.304209243 4
 
0.6%
127.009492248 3
 
0.5%
126.7879416273 3
 
0.5%
126.8409665896 3
 
0.5%
Other values (397) 594
92.5%
(Missing) 8
 
1.2%
ValueCountFrequency (%)
126.6020784224 4
0.6%
126.6191440503 2
0.3%
126.6215805592 1
 
0.2%
126.6760927289 2
0.3%
126.6864519306 2
0.3%
126.6870055362 1
 
0.2%
126.7072280688 1
 
0.2%
126.7095488701 1
 
0.2%
126.7109962005 2
0.3%
126.7177453991 2
0.3%
ValueCountFrequency (%)
127.7807774627 2
0.3%
127.7542881725 1
0.2%
127.7021476459 1
0.2%
127.6913745235 1
0.2%
127.6686207363 1
0.2%
127.6288883839 2
0.3%
127.6201407328 2
0.3%
127.5892647695 1
0.2%
127.5858092755 2
0.3%
127.5611511576 1
0.2%

Interactions

2024-05-03T18:57:02.978967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:56:52.223371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:56:54.361407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:56:56.469156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:56:58.618203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:57:00.761764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:57:03.318075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:56:52.503230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:56:54.665597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:56:56.722389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:56:58.922997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:57:01.033076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:57:03.713347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:56:52.854432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:56:54.960587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:56:57.075343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:56:59.215125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:57:01.319151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:57:04.086279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:56:53.223066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:56:55.290011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:56:57.463650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:56:59.567341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:57:01.711075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:57:04.505663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:56:53.558659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:56:55.695929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:56:57.885777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:57:00.198256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:57:01.993938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:57:04.999673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:56:53.841954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:56:56.032351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:56:58.224950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:57:00.473227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T18:57:02.383951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-03T18:57:29.008987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명영업상태명병상수(개)의료기관종별명의료인수(명)입원실수(개)연면적(㎡)WGS84위도WGS84경도
시군명1.0000.1900.4350.2340.3340.4560.2110.9640.920
영업상태명0.1901.0000.3770.1570.3490.4110.0000.0750.000
병상수(개)0.4350.3771.0000.4030.8600.8290.7960.2420.283
의료기관종별명0.2340.1570.4031.0000.3420.3930.1370.1750.251
의료인수(명)0.3340.3490.8600.3421.0000.8370.3800.1700.135
입원실수(개)0.4560.4110.8290.3930.8371.0000.5280.1930.197
연면적(㎡)0.2110.0000.7960.1370.3800.5281.0000.1430.201
WGS84위도0.9640.0750.2420.1750.1700.1930.1431.0000.625
WGS84경도0.9200.0000.2830.2510.1350.1970.2010.6251.000
2024-05-03T18:57:29.433613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명의료기관종별명영업상태명
시군명1.0000.1950.082
의료기관종별명0.1951.0000.113
영업상태명0.0820.1131.000
2024-05-03T18:57:29.829278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
병상수(개)의료인수(명)입원실수(개)연면적(㎡)WGS84위도WGS84경도시군명영업상태명의료기관종별명
병상수(개)1.0000.8450.7990.7320.010-0.0910.1650.2080.308
의료인수(명)0.8451.0000.7400.6850.005-0.0340.1430.2160.365
입원실수(개)0.7990.7401.0000.8230.0990.0350.1790.2430.306
연면적(㎡)0.7320.6850.8231.0000.0160.0860.0980.0000.167
WGS84위도0.0100.0050.0990.0161.000-0.1900.7720.0390.134
WGS84경도-0.091-0.0340.0350.086-0.1901.0000.6330.0000.191
시군명0.1650.1430.1790.0980.7720.6331.0000.0820.195
영업상태명0.2080.2160.2430.0000.0390.0000.0821.0000.113
의료기관종별명0.3080.3650.3060.1670.1340.1910.1950.1131.000

Missing values

2024-05-03T18:57:05.609954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T18:57:06.453930image/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.
2024-05-03T18:57:06.995777image/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

시군명사업장명인허가일자영업상태명폐업일자병상수(개)의료기관종별명의료인수(명)입원실수(개)진료과목내용연면적(㎡)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
0가평군강변요양병원2020-03-06영업/정상<NA>66요양병원(일반요양병원)1040내과, 외과, 마취통증의학과, 산부인과, 한방내과2992.56경기도 가평군 가평읍 북한강변로 160경기도 가평군 가평읍 금대리 585번지1242837.747333127.525983
1가평군청평활명요양병원2017-08-14영업/정상<NA>98요양병원(일반요양병원)1143내과, 신경과, 외과, 정형외과, 신경외과, 심장혈관흉부외과, 마취통증의학과, 산부인과, 소아청소년과, 이비인후과, 피부과, 비뇨의학과, 영상의학과, 재활의학과, 가정의학과, 응급의학과, 한방내과, 한방부인과, 한방소아과, 한방안·이비인후·피부과, 한방신경정신과, 한방재활의학과, 사상체질과, 침구과2997.94경기도 가평군 청평면 북한강로1604번길 64-28경기도 가평군 청평면 삼회리 502번지1245837.663666127.38766
2가평군가평산속요양병원2012-10-08영업/정상<NA>83요양병원(일반요양병원)644내과, 한방내과2250.0경기도 가평군 상면 대보간선로 889경기도 가평군 상면 항사리 2번지1244537.788938127.404382
3가평군에이치제이매그놀리아국제요양병원2003-05-18영업/정상<NA>62요양병원(일반요양병원)729이비인후과, 한방내과, 한방부인과, 한방소아과, 한방안·이비인후·피부과, 한방신경정신과, 한방재활의학과, 사상체질과, 침구과2021.64경기도 가평군 설악면 미사리로 267-177경기도 가평군 설악면 송산리 460번지1246137.691127127.521381
4가평군가평푸른숲요양병원2017-09-01영업/정상<NA>146요양병원(일반요양병원)1877내과, 가정의학과, 한방내과, 침구과6883.0경기도 가평군 상면 물골길 262-23경기도 가평군 상면 봉수리 49-4번지1244037.840847127.304209
5가평군에이치제이매그놀리아국제요양병원2003-05-18영업중<NA>62요양병원(일반요양병원)829이비인후과, 한방내과, 한방부인과, 한방소아과, 한방안·이비인후·피부과, 한방신경정신과, 한방재활의학과, 사상체질과, 침구과2021.64경기도 가평군 설악면 미사리로 267-177경기도 가평군 설악면 송산리 460번지1246137.691127127.521381
6가평군강변요양병원2020-03-06영업중<NA>66요양병원(일반요양병원)840내과, 외과, 마취통증의학과, 산부인과, 한방내과2992.56경기도 가평군 가평읍 북한강변로 160경기도 가평군 가평읍 금대리 585번지1242837.747333127.525983
7가평군청평활명요양병원2017-08-14영업중<NA>98요양병원(일반요양병원)1043내과, 신경과, 외과, 정형외과, 신경외과, 심장혈관흉부외과, 마취통증의학과, 산부인과, 소아청소년과, 이비인후과, 피부과, 비뇨의학과, 영상의학과, 재활의학과, 가정의학과, 응급의학과, 한방내과, 한방부인과, 한방소아과, 한방안·이비인후·피부과, 한방신경정신과, 한방재활의학과, 사상체질과, 침구과2997.94경기도 가평군 청평면 북한강로1604번길 64-28경기도 가평군 청평면 삼회리 502번지1245837.663666127.38766
8가평군가평푸른숲요양병원2017-09-01영업중<NA>146요양병원(일반요양병원)2277내과, 가정의학과, 한방내과, 침구과6883.0경기도 가평군 상면 물골길 262-23경기도 가평군 상면 봉수리 49-4번지1244037.840847127.304209
9가평군가평산속요양병원2012-10-08영업중<NA>83요양병원(일반요양병원)644내과, 한방내과2250.0경기도 가평군 상면 대보간선로 889경기도 가평군 상면 항사리 2번지1244537.788938127.404382
시군명사업장명인허가일자영업상태명폐업일자병상수(개)의료기관종별명의료인수(명)입원실수(개)진료과목내용연면적(㎡)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
632화성시사)한국장애인케어협회 복음요양병원2014-09-02폐업2017-07-1276요양병원(일반요양병원)514내과, 외과, 정형외과, 마취통증의학과, 소아청소년과, 피부과, 비뇨의학과1432.33경기도 화성시 효행로265번길 16 (기안동)경기도 화성시 기안동 353번지1833737.225085126.973699
633화성시서울요양병원2016-09-06폐업2019-10-2299요양병원(일반요양병원)515외과, 정형외과, 한방내과1736.39경기도 화성시 효행로 223 (기안동, 대원빌딩)경기도 화성시 기안동 333-6번지 대원빌딩1833737.227836126.971887
634화성시화성성심요양병원2009-09-04폐업2011-01-2489요양병원(일반요양병원)316내과, 소아청소년과, 이비인후과, 가정의학과2928.36경기도 화성시 마도면 화성로 848-16경기도 화성시 마도면 두곡리 418-2번지1853737.204484126.782246
635화성시동탄성모요양병원2015-09-25폐업2017-03-02108요양병원(일반요양병원)338내과, 신경과, 외과, 산부인과3504.77경기도 화성시 동탄솔빛로 57 (반송동, 삼성미즈빌딩)경기도 화성시 반송동 220-3번지 삼성미즈빌딩1844337.194109127.073564
636화성시의료법인 일선의료재단 화성현대요양병원2010-12-30폐업2011-04-15220요양병원(일반요양병원)840내과, 외과, 정형외과, 신경외과1854.74경기도 화성시 우정읍 남양만로 662경기도 화성시 우정읍 이화리 439번지 1호445-95437.042361126.798045
637화성시화성제일요양병원2014-05-21폐업2016-05-26118요양병원(일반요양병원)1216내과, 신경과, 신경외과, 산부인과, 소아청소년과, 피부과, 재활의학과, 가정의학과, 치과, 침구과2105.59경기도 화성시 효행로 990, 2층,3층,5층 (진안동, 비젼월드)경기도 화성시 진안동 512번지 1호 2층, 3층 일부, 5층1840137.211213127.036367
638화성시호산나요양병원2013-05-22폐업2013-08-16100요양병원(일반요양병원)611내과, 가정의학과, 한방내과, 한방부인과, 한방소아과, 한방안·이비인후·피부과, 한방신경정신과, 한방재활의학과, 사상체질과, 침구과1148.64경기도 화성시 송산면 사강로 161, 2~3층경기도 화성시 송산면 사강리 694번지 8호445-87437.213195126.733949
639화성시의료법인 로뎀호산나의료재단 호산나요양병원2013-08-22폐업2016-07-0798요양병원(일반요양병원)311내과, 가정의학과, 한방내과, 한방부인과, 한방소아과, 한방안·이비인후·피부과, 한방신경정신과, 한방재활의학과, 사상체질과, 침구과, 한방응급과1148.64경기도 화성시 송산면 사강로 161, 2~3층경기도 화성시 송산면 사강리 694번지 8호445-87437.213195126.733949
640화성시동탄플러스요양병원2018-05-29폐업2019-12-20108요양병원(일반요양병원)730내과, 정형외과, 성형외과, 한방내과, 침구과821.9경기도 화성시 동탄반석로 134 (반송동)경기도 화성시 반송동 104번지 1호1845537.20106127.071362
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