Overview

Dataset statistics

Number of variables16
Number of observations136
Missing cells132
Missing cells (%)6.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.1 KiB
Average record size in memory136.0 B

Variable types

Categorical3
Text4
DateTime2
Numeric7

Dataset

Description종합병원 현황(병원급)
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=14X2BVE67VZ0043G1LO82121932&infSeq=1

Alerts

의료기관종별명 has constant value ""Constant
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
소재지우편번호 is highly overall correlated with WGS84위도 and 1 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 소재지우편번호 and 1 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 시군명High correlation
시군명 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
폐업일자 has 132 (97.1%) missing valuesMissing

Reproduction

Analysis started2024-05-10 21:32:16.477941
Analysis finished2024-05-10 21:32:33.214767
Duration16.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
성남시
14 
고양시
13 
부천시
12 
안산시
10 
평택시
9 
Other values (18)
78 

Length

Max length4
Median length3
Mean length3.0588235
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
성남시 14
 
10.3%
고양시 13
 
9.6%
부천시 12
 
8.8%
안산시 10
 
7.4%
평택시 9
 
6.6%
수원시 9
 
6.6%
용인시 8
 
5.9%
의정부시 6
 
4.4%
안양시 6
 
4.4%
시흥시 6
 
4.4%
Other values (13) 43
31.6%

Length

2024-05-10T21:32:33.384170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
성남시 14
 
10.3%
고양시 13
 
9.6%
부천시 12
 
8.8%
안산시 10
 
7.4%
평택시 9
 
6.6%
수원시 9
 
6.6%
용인시 8
 
5.9%
의정부시 6
 
4.4%
안양시 6
 
4.4%
시흥시 6
 
4.4%
Other values (13) 43
31.6%

사업장명
Text

Distinct77
Distinct (%)56.6%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-05-10T21:32:33.813543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length17
Mean length11.588235
Min length3

Characters and Unicode

Total characters1576
Distinct characters146
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)13.2%

Sample

1st row의료법인명지의료재단명지병원
2nd row의료법인성광의료재단일산차병원
3rd row국민건강보험공단일산병원
4th row인제대학교일산백병원
5th row동국대학교일산불교병원
ValueCountFrequency (%)
의료법인 24
 
11.0%
경기도의료원 5
 
2.3%
효산의료재단 4
 
1.8%
의료법인명지의료재단명지병원 2
 
0.9%
의료법인칠석의료재단사랑의병원 2
 
0.9%
시화병원 2
 
0.9%
남촌의료재단 2
 
0.9%
한림대학교성심병원 2
 
0.9%
안성성모병원 2
 
0.9%
단원병원 2
 
0.9%
Other values (98) 172
78.5%
2024-05-10T21:32:34.755013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 156
 
9.9%
병 132
 
8.4%
의 124
 
7.9%
료 109
 
6.9%
83
 
5.3%
단 62
 
3.9%
인 60
 
3.8%
재 56
 
3.6%
대 47
 
3.0%
법 44
 
2.8%
Other values (136) 703
44.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1487
94.4%
Space Separator 83
 
5.3%
Close Punctuation 3
 
0.2%
Open Punctuation 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 156
 
10.5%
병 132
 
8.9%
의 124
 
8.3%
료 109
 
7.3%
단 62
 
4.2%
인 60
 
4.0%
재 56
 
3.8%
대 47
 
3.2%
법 44
 
3.0%
학 42
 
2.8%
Other values (133) 655
44.0%
Space Separator
ValueCountFrequency (%)
83
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1487
94.4%
Common 89
 
5.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 156
 
10.5%
병 132
 
8.9%
의 124
 
8.3%
료 109
 
7.3%
단 62
 
4.2%
인 60
 
4.0%
재 56
 
3.8%
대 47
 
3.2%
법 44
 
3.0%
학 42
 
2.8%
Other values (133) 655
44.0%
Common
ValueCountFrequency (%)
83
93.3%
) 3
 
3.4%
( 3
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1487
94.4%
ASCII 89
 
5.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 156
 
10.5%
병 132
 
8.9%
의 124
 
8.3%
료 109
 
7.3%
단 62
 
4.2%
인 60
 
4.0%
재 56
 
3.8%
대 47
 
3.2%
법 44
 
3.0%
학 42
 
2.8%
Other values (133) 655
44.0%
ASCII
ValueCountFrequency (%)
83
93.3%
) 3
 
3.4%
( 3
 
3.4%

인허가일자
Date

Distinct74
Distinct (%)54.4%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
Minimum1965-12-21 00:00:00
Maximum2022-06-03 00:00:00
2024-05-10T21:32:35.150457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:35.578205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

영업상태명
Categorical

Distinct4
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
영업/정상
71 
영업중
61 
폐업
 
3
취소/말소/만료/정지/중지
 
1

Length

Max length14
Median length5
Mean length4.1029412
Min length2

Unique

Unique1 ?
Unique (%)0.7%

Sample

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

Common Values

ValueCountFrequency (%)
영업/정상 71
52.2%
영업중 61
44.9%
폐업 3
 
2.2%
취소/말소/만료/정지/중지 1
 
0.7%

Length

2024-05-10T21:32:36.039936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T21:32:36.309238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업/정상 71
52.2%
영업중 61
44.9%
폐업 3
 
2.2%
취소/말소/만료/정지/중지 1
 
0.7%

폐업일자
Date

MISSING 

Distinct3
Distinct (%)75.0%
Missing132
Missing (%)97.1%
Memory size1.2 KiB
Minimum2011-01-17 00:00:00
Maximum2023-03-14 00:00:00
2024-05-10T21:32:36.571962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:37.066379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)

병상수(개)
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)55.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean402.26471
Minimum100
Maximum1335
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-10T21:32:37.483989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile141.5
Q1212.5
median299
Q3550
95-th percentile863
Maximum1335
Range1235
Interquartile range (IQR)337.5

Descriptive statistics

Standard deviation254.53115
Coefficient of variation (CV)0.63274542
Kurtosis1.713462
Mean402.26471
Median Absolute Deviation (MAD)98.5
Skewness1.3892558
Sum54708
Variance64786.107
MonotonicityNot monotonic
2024-05-10T21:32:37.962791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
299 12
 
8.8%
200 6
 
4.4%
203 3
 
2.2%
206 3
 
2.2%
167 2
 
1.5%
255 2
 
1.5%
300 2
 
1.5%
647 2
 
1.5%
272 2
 
1.5%
681 2
 
1.5%
Other values (66) 100
73.5%
ValueCountFrequency (%)
100 1
 
0.7%
120 1
 
0.7%
135 2
 
1.5%
139 1
 
0.7%
140 2
 
1.5%
142 1
 
0.7%
153 2
 
1.5%
167 2
 
1.5%
192 2
 
1.5%
200 6
4.4%
ValueCountFrequency (%)
1335 2
1.5%
1161 1
0.7%
897 2
1.5%
881 2
1.5%
857 2
1.5%
850 1
0.7%
844 2
1.5%
833 2
1.5%
813 2
1.5%
808 1
0.7%

의료기관종별명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
종합병원
136 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
종합병원 136
100.0%

Length

2024-05-10T21:32:38.398588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T21:32:38.738642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
종합병원 136
100.0%

의료인수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct113
Distinct (%)83.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean552.63971
Minimum7
Maximum3196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-10T21:32:39.114859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile61
Q1143.25
median302
Q3830.75
95-th percentile1582.5
Maximum3196
Range3189
Interquartile range (IQR)687.5

Descriptive statistics

Standard deviation590.99635
Coefficient of variation (CV)1.0694062
Kurtosis4.7903756
Mean552.63971
Median Absolute Deviation (MAD)205
Skewness1.9567195
Sum75159
Variance349276.69
MonotonicityNot monotonic
2024-05-10T21:32:39.578048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81 3
 
2.2%
126 3
 
2.2%
34 3
 
2.2%
138 3
 
2.2%
179 2
 
1.5%
105 2
 
1.5%
61 2
 
1.5%
256 2
 
1.5%
245 2
 
1.5%
863 2
 
1.5%
Other values (103) 112
82.4%
ValueCountFrequency (%)
7 1
 
0.7%
21 1
 
0.7%
34 3
2.2%
48 1
 
0.7%
61 2
1.5%
74 1
 
0.7%
77 1
 
0.7%
81 3
2.2%
84 2
1.5%
86 1
 
0.7%
ValueCountFrequency (%)
3196 1
0.7%
3133 1
0.7%
2435 1
0.7%
1847 1
0.7%
1794 1
0.7%
1598 1
0.7%
1587 1
0.7%
1581 2
1.5%
1560 1
0.7%
1536 1
0.7%

입원실수(개)
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)50.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.77941
Minimum20
Maximum363
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-10T21:32:40.040460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile36
Q160.25
median84
Q3152.5
95-th percentile245.5
Maximum363
Range343
Interquartile range (IQR)92.25

Descriptive statistics

Standard deviation70.626161
Coefficient of variation (CV)0.63183515
Kurtosis1.2526515
Mean111.77941
Median Absolute Deviation (MAD)33
Skewness1.2473464
Sum15202
Variance4988.0547
MonotonicityNot monotonic
2024-05-10T21:32:40.635638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51 7
 
5.1%
58 6
 
4.4%
93 5
 
3.7%
75 4
 
2.9%
67 4
 
2.9%
117 4
 
2.9%
69 3
 
2.2%
84 3
 
2.2%
65 3
 
2.2%
73 3
 
2.2%
Other values (59) 94
69.1%
ValueCountFrequency (%)
20 1
 
0.7%
25 1
 
0.7%
32 2
1.5%
33 1
 
0.7%
36 3
2.2%
38 2
1.5%
44 1
 
0.7%
47 1
 
0.7%
48 1
 
0.7%
49 1
 
0.7%
ValueCountFrequency (%)
363 2
1.5%
287 1
0.7%
269 2
1.5%
250 2
1.5%
244 1
0.7%
239 2
1.5%
232 1
0.7%
225 2
1.5%
213 2
1.5%
207 2
1.5%

진료과목내용
Text

Distinct78
Distinct (%)57.4%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-05-10T21:32:41.174153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length208
Median length159
Mean length135.96324
Min length51

Characters and Unicode

Total characters18491
Distinct characters70
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)19.1%

Sample

1st row내과, 신경과, 정신건강의학과, 외과, 정형외과, 신경외과, 심장혈관흉부외과, 성형외과, 마취통증의학과, 산부인과, 소아청소년과, 안과, 이비인후과, 피부과, 비뇨의학과, 영상의학과, 방사선종양학과, 병리과, 진단검사의학과, 재활의학과, 가정의학과, 핵의학과, 직업환경의학과, 응급의학과, 치과
2nd row내과, 신경과, 정신건강의학과, 외과, 성형외과, 마취통증의학과, 산부인과, 소아청소년과, 피부과, 비뇨의학과, 영상의학과, 방사선종양학과, 병리과, 진단검사의학과, 재활의학과, 가정의학과, 핵의학과, 한방내과
3rd row내과, 신경과, 정신건강의학과, 외과, 정형외과, 신경외과, 심장혈관흉부외과, 성형외과, 마취통증의학과, 산부인과, 소아청소년과, 안과, 이비인후과, 피부과, 비뇨의학과, 영상의학과, 방사선종양학과, 병리과, 진단검사의학과, 재활의학과, 가정의학과, 핵의학과, 응급의학과, 구강악안면외과, 치과보철과, 치과교정과, 치주과, 치과보존과
4th row내과, 신경과, 정신건강의학과, 외과, 정형외과, 신경외과, 심장혈관흉부외과, 성형외과, 마취통증의학과, 산부인과, 소아청소년과, 안과, 이비인후과, 피부과, 비뇨의학과, 영상의학과, 방사선종양학과, 병리과, 진단검사의학과, 재활의학과, 가정의학과, 핵의학과, 응급의학과, 구강악안면외과, 치과보철과, 치과교정과, 통합치의학과
5th row내과, 신경과, 정신건강의학과, 외과, 정형외과, 신경외과, 심장혈관흉부외과, 성형외과, 마취통증의학과, 산부인과, 소아청소년과, 안과, 이비인후과, 피부과, 비뇨의학과, 영상의학과, 방사선종양학과, 병리과, 진단검사의학과, 재활의학과, 가정의학과, 핵의학과, 응급의학과, 구강악안면외과
ValueCountFrequency (%)
내과 136
 
4.9%
외과 136
 
4.9%
마취통증의학과 136
 
4.9%
영상의학과 136
 
4.9%
정형외과 132
 
4.7%
신경과 130
 
4.7%
진단검사의학과 130
 
4.7%
산부인과 126
 
4.5%
신경외과 122
 
4.4%
소아청소년과 120
 
4.3%
Other values (32) 1486
53.3%
2024-05-10T21:32:42.123811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 2920
15.8%
, 2654
14.4%
2654
14.4%
학 1135
 
6.1%
의 1091
 
5.9%
외 590
 
3.2%
정 366
 
2.0%
신 342
 
1.8%
경 312
 
1.7%
부 270
 
1.5%
Other values (60) 6157
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13183
71.3%
Other Punctuation 2654
 
14.4%
Space Separator 2654
 
14.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 2920
22.1%
학 1135
 
8.6%
의 1091
 
8.3%
외 590
 
4.5%
정 366
 
2.8%
신 342
 
2.6%
경 312
 
2.4%
부 270
 
2.0%
소 263
 
2.0%
치 259
 
2.0%
Other values (58) 5635
42.7%
Other Punctuation
ValueCountFrequency (%)
, 2654
100.0%
Space Separator
ValueCountFrequency (%)
2654
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13183
71.3%
Common 5308
28.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 2920
22.1%
학 1135
 
8.6%
의 1091
 
8.3%
외 590
 
4.5%
정 366
 
2.8%
신 342
 
2.6%
경 312
 
2.4%
부 270
 
2.0%
소 263
 
2.0%
치 259
 
2.0%
Other values (58) 5635
42.7%
Common
ValueCountFrequency (%)
, 2654
50.0%
2654
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13183
71.3%
ASCII 5308
28.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 2920
22.1%
학 1135
 
8.6%
의 1091
 
8.3%
외 590
 
4.5%
정 366
 
2.8%
신 342
 
2.6%
경 312
 
2.4%
부 270
 
2.0%
소 263
 
2.0%
치 259
 
2.0%
Other values (58) 5635
42.7%
ASCII
ValueCountFrequency (%)
, 2654
50.0%
2654
50.0%

연면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)55.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52690.306
Minimum2999.81
Maximum868162.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-10T21:32:42.498420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2999.81
5-th percentile6475.9475
Q112785.47
median23896
Q358139.815
95-th percentile122189.48
Maximum868162.6
Range865162.79
Interquartile range (IQR)45354.345

Descriptive statistics

Standard deviation107617.35
Coefficient of variation (CV)2.0424507
Kurtosis48.070281
Mean52690.306
Median Absolute Deviation (MAD)14836.14
Skewness6.5764676
Sum7165881.7
Variance1.1581495 × 1010
MonotonicityNot monotonic
2024-05-10T21:32:42.926123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75723.87 2
 
1.5%
20405.37 2
 
1.5%
71255.17 2
 
1.5%
29658.33 2
 
1.5%
6852.56 2
 
1.5%
35517.82 2
 
1.5%
31760.0 2
 
1.5%
53829.71 2
 
1.5%
25269.78 2
 
1.5%
8070.37 2
 
1.5%
Other values (66) 116
85.3%
ValueCountFrequency (%)
2999.81 2
1.5%
4929.67 1
0.7%
5083.76 1
0.7%
5559.18 2
1.5%
5783.39 1
0.7%
6706.8 2
1.5%
6852.56 2
1.5%
6855.76 1
0.7%
8070.37 2
1.5%
8097.48 2
1.5%
ValueCountFrequency (%)
868162.6 2
1.5%
176171.05 2
1.5%
174347.61 2
1.5%
150397.2 1
0.7%
112786.91 2
1.5%
109537.0 2
1.5%
108719.52 2
1.5%
105649.0 2
1.5%
105511.2 2
1.5%
96987.39 2
1.5%

소재지도로명주소
Text

Distinct75
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-05-10T21:32:43.417006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length91
Median length37
Mean length29.963235
Min length17

Characters and Unicode

Total characters4075
Distinct characters197
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)10.3%

Sample

1st row경기도 고양시 덕양구 화수로14번길 55 (화정동)
2nd row경기도 고양시 일산동구 중앙로 1205, 지하1층(일부),지하2층,지하4층(일부),1층(일부),2층(일부),3층(일부),4~5층,6층(일부), 7~10층 (장항동)
3rd row경기도 고양시 일산동구 일산로 100 (백석동,백석1동 1241외1필지 4층)
4th row경기도 고양시 일산서구 주화로 170 (대화동)
5th row경기도 고양시 일산동구 동국로 27 (식사동, 동국대학교일산병원)
ValueCountFrequency (%)
경기도 136
 
16.5%
성남시 14
 
1.7%
고양시 13
 
1.6%
부천시 12
 
1.5%
안산시 10
 
1.2%
수원시 9
 
1.1%
평택시 9
 
1.1%
원미구 8
 
1.0%
용인시 8
 
1.0%
분당구 8
 
1.0%
Other values (277) 599
72.5%
2024-05-10T21:32:44.606095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
690
 
16.9%
동 152
 
3.7%
시 148
 
3.6%
기 147
 
3.6%
경 146
 
3.6%
( 145
 
3.6%
) 145
 
3.6%
로 143
 
3.5%
도 139
 
3.4%
1 107
 
2.6%
Other values (187) 2113
51.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2459
60.3%
Space Separator 690
 
16.9%
Decimal Number 528
 
13.0%
Open Punctuation 145
 
3.6%
Close Punctuation 145
 
3.6%
Other Punctuation 84
 
2.1%
Dash Punctuation 12
 
0.3%
Math Symbol 8
 
0.2%
Uppercase Letter 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 152
 
6.2%
시 148
 
6.0%
기 147
 
6.0%
경 146
 
5.9%
로 143
 
5.8%
도 139
 
5.7%
원 87
 
3.5%
구 80
 
3.3%
부 55
 
2.2%
병 46
 
1.9%
Other values (168) 1316
53.5%
Decimal Number
ValueCountFrequency (%)
1 107
20.3%
2 72
13.6%
3 67
12.7%
7 55
10.4%
5 43
8.1%
0 42
 
8.0%
8 40
 
7.6%
4 38
 
7.2%
6 33
 
6.2%
9 31
 
5.9%
Other Punctuation
ValueCountFrequency (%)
, 81
96.4%
/ 3
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
G 2
50.0%
A 2
50.0%
Space Separator
ValueCountFrequency (%)
690
100.0%
Open Punctuation
ValueCountFrequency (%)
( 145
100.0%
Close Punctuation
ValueCountFrequency (%)
) 145
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%
Math Symbol
ValueCountFrequency (%)
~ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2459
60.3%
Common 1612
39.6%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 152
 
6.2%
시 148
 
6.0%
기 147
 
6.0%
경 146
 
5.9%
로 143
 
5.8%
도 139
 
5.7%
원 87
 
3.5%
구 80
 
3.3%
부 55
 
2.2%
병 46
 
1.9%
Other values (168) 1316
53.5%
Common
ValueCountFrequency (%)
690
42.8%
( 145
 
9.0%
) 145
 
9.0%
1 107
 
6.6%
, 81
 
5.0%
2 72
 
4.5%
3 67
 
4.2%
7 55
 
3.4%
5 43
 
2.7%
0 42
 
2.6%
Other values (7) 165
 
10.2%
Latin
ValueCountFrequency (%)
G 2
50.0%
A 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2459
60.3%
ASCII 1616
39.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
690
42.7%
( 145
 
9.0%
) 145
 
9.0%
1 107
 
6.6%
, 81
 
5.0%
2 72
 
4.5%
3 67
 
4.1%
7 55
 
3.4%
5 43
 
2.7%
0 42
 
2.6%
Other values (9) 169
 
10.5%
Hangul
ValueCountFrequency (%)
동 152
 
6.2%
시 148
 
6.0%
기 147
 
6.0%
경 146
 
5.9%
로 143
 
5.8%
도 139
 
5.7%
원 87
 
3.5%
구 80
 
3.3%
부 55
 
2.2%
병 46
 
1.9%
Other values (168) 1316
53.5%

소재지지번주소
Text

Distinct75
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-05-10T21:32:45.251304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length37
Mean length25.595588
Min length15

Characters and Unicode

Total characters3481
Distinct characters173
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)10.3%

Sample

1st row경기도 고양시 덕양구 화정1동 697번지 24호
2nd row경기도 고양시 일산동구 장항동 889 차움 라이프센터
3rd row경기도 고양시 일산동구 백석1동 1232번지 백석1동 1241외1필지 4층
4th row경기도 고양시 일산서구 대화동 2240번지
5th row경기도 고양시 일산동구 식사동 814번지 동국대학교일산병원
ValueCountFrequency (%)
경기도 136
 
18.5%
고양시 13
 
1.8%
부천시 12
 
1.6%
성남시 12
 
1.6%
안산시 10
 
1.4%
수원시 9
 
1.2%
평택시 9
 
1.2%
일산동구 8
 
1.1%
원미구 8
 
1.1%
용인시 8
 
1.1%
Other values (233) 510
69.4%
2024-05-10T21:32:46.661760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
601
 
17.3%
기 147
 
4.2%
시 146
 
4.2%
동 142
 
4.1%
경 141
 
4.1%
도 139
 
4.0%
지 134
 
3.8%
1 124
 
3.6%
번 117
 
3.4%
3 90
 
2.6%
Other values (163) 1700
48.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2169
62.3%
Decimal Number 616
 
17.7%
Space Separator 601
 
17.3%
Dash Punctuation 49
 
1.4%
Other Punctuation 20
 
0.6%
Close Punctuation 10
 
0.3%
Open Punctuation 10
 
0.3%
Uppercase Letter 4
 
0.1%
Math Symbol 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 147
 
6.8%
시 146
 
6.7%
동 142
 
6.5%
경 141
 
6.5%
도 139
 
6.4%
지 134
 
6.2%
번 117
 
5.4%
원 84
 
3.9%
구 76
 
3.5%
병 47
 
2.2%
Other values (144) 996
45.9%
Decimal Number
ValueCountFrequency (%)
1 124
20.1%
3 90
14.6%
4 67
10.9%
5 57
9.3%
2 56
9.1%
9 55
8.9%
6 47
 
7.6%
0 44
 
7.1%
8 41
 
6.7%
7 35
 
5.7%
Other Punctuation
ValueCountFrequency (%)
, 16
80.0%
/ 4
 
20.0%
Uppercase Letter
ValueCountFrequency (%)
A 2
50.0%
G 2
50.0%
Space Separator
ValueCountFrequency (%)
601
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2169
62.3%
Common 1308
37.6%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 147
 
6.8%
시 146
 
6.7%
동 142
 
6.5%
경 141
 
6.5%
도 139
 
6.4%
지 134
 
6.2%
번 117
 
5.4%
원 84
 
3.9%
구 76
 
3.5%
병 47
 
2.2%
Other values (144) 996
45.9%
Common
ValueCountFrequency (%)
601
45.9%
1 124
 
9.5%
3 90
 
6.9%
4 67
 
5.1%
5 57
 
4.4%
2 56
 
4.3%
9 55
 
4.2%
- 49
 
3.7%
6 47
 
3.6%
0 44
 
3.4%
Other values (7) 118
 
9.0%
Latin
ValueCountFrequency (%)
A 2
50.0%
G 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2169
62.3%
ASCII 1312
37.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
601
45.8%
1 124
 
9.5%
3 90
 
6.9%
4 67
 
5.1%
5 57
 
4.3%
2 56
 
4.3%
9 55
 
4.2%
- 49
 
3.7%
6 47
 
3.6%
0 44
 
3.4%
Other values (9) 122
 
9.3%
Hangul
ValueCountFrequency (%)
기 147
 
6.8%
시 146
 
6.7%
동 142
 
6.5%
경 141
 
6.5%
도 139
 
6.4%
지 134
 
6.2%
번 117
 
5.4%
원 84
 
3.9%
구 76
 
3.5%
병 47
 
2.2%
Other values (144) 996
45.9%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14320.434
Minimum10086
Maximum18592
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-10T21:32:47.226404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10086
5-th percentile10380
Q112030.25
median14513
Q316483.5
95-th percentile18138
Maximum18592
Range8506
Interquartile range (IQR)4453.25

Descriptive statistics

Standard deviation2530.8961
Coefficient of variation (CV)0.1767332
Kurtosis-1.1208241
Mean14320.434
Median Absolute Deviation (MAD)2465
Skewness-0.10066069
Sum1947579
Variance6405434.9
MonotonicityNot monotonic
2024-05-10T21:32:47.617970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10475 2
 
1.5%
16316 2
 
1.5%
16247 2
 
1.5%
10414 2
 
1.5%
14905 2
 
1.5%
15034 2
 
1.5%
15324 2
 
1.5%
15355 2
 
1.5%
15367 2
 
1.5%
15291 2
 
1.5%
Other values (65) 116
85.3%
ValueCountFrequency (%)
10086 2
1.5%
10099 2
1.5%
10326 2
1.5%
10380 2
1.5%
10408 2
1.5%
10414 2
1.5%
10444 2
1.5%
10475 2
1.5%
10518 1
0.7%
10913 2
1.5%
ValueCountFrequency (%)
18592 1
0.7%
18450 2
1.5%
18356 2
1.5%
18144 2
1.5%
18136 1
0.7%
17909 2
1.5%
17874 2
1.5%
17825 2
1.5%
17784 2
1.5%
17739 1
0.7%

WGS84위도
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.422912
Minimum36.990565
Maximum37.903093
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-10T21:32:48.007148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.990565
5-th percentile37.008374
Q137.279164
median37.392894
Q337.634992
95-th percentile37.755095
Maximum37.903093
Range0.91252859
Interquartile range (IQR)0.35582726

Descriptive statistics

Standard deviation0.21743189
Coefficient of variation (CV)0.0058101277
Kurtosis-0.61298321
Mean37.422912
Median Absolute Deviation (MAD)0.12484756
Skewness-0.048948838
Sum5089.516
Variance0.047276629
MonotonicityNot monotonic
2024-05-10T21:32:48.389195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.6424745722 2
 
1.5%
37.2918869067 2
 
1.5%
37.2778994265 2
 
1.5%
37.6541226556 2
 
1.5%
37.4449686532 2
 
1.5%
37.3499087489 2
 
1.5%
37.3167670968 2
 
1.5%
37.3177037052 2
 
1.5%
37.3340553037 2
 
1.5%
37.3269215026 2
 
1.5%
Other values (65) 116
85.3%
ValueCountFrequency (%)
36.9905649024 2
1.5%
36.9930565731 2
1.5%
37.0058059754 2
1.5%
37.0083742374 2
1.5%
37.0166996541 1
0.7%
37.0482621367 2
1.5%
37.055137185 1
0.7%
37.1313495282 1
0.7%
37.1412846971 2
1.5%
37.154202255 1
0.7%
ValueCountFrequency (%)
37.9030934888 1
0.7%
37.8274877024 2
1.5%
37.7610981986 2
1.5%
37.7585227082 1
0.7%
37.7550951627 2
1.5%
37.7534456401 2
1.5%
37.7497753415 1
0.7%
37.7453057969 1
0.7%
37.7410761753 1
0.7%
37.7154360459 2
1.5%

WGS84경도
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.98048
Minimum126.66026
Maximum127.43322
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-10T21:32:48.748086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.66026
5-th percentile126.73701
Q1126.80547
median127.00218
Q3127.12583
95-th percentile127.22337
Maximum127.43322
Range0.77296159
Interquartile range (IQR)0.3203633

Descriptive statistics

Standard deviation0.17585782
Coefficient of variation (CV)0.0013849201
Kurtosis-0.97224213
Mean126.98048
Median Absolute Deviation (MAD)0.15207578
Skewness0.12625939
Sum17269.345
Variance0.030925974
MonotonicityNot monotonic
2024-05-10T21:32:49.160010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.8317454892 2
 
1.5%
126.9963395675 2
 
1.5%
127.0274529236 2
 
1.5%
126.7757402631 2
 
1.5%
126.7894393407 2
 
1.5%
126.7370103825 2
 
1.5%
126.873993316 2
 
1.5%
126.8246661096 2
 
1.5%
126.8076211068 2
 
1.5%
126.844923301 2
 
1.5%
Other values (65) 116
85.3%
ValueCountFrequency (%)
126.660255538 2
1.5%
126.7105517913 2
1.5%
126.7284520798 2
1.5%
126.7370103825 2
1.5%
126.7503817665 2
1.5%
126.7621105079 2
1.5%
126.7667173351 2
1.5%
126.7694862939 2
1.5%
126.7757402631 2
1.5%
126.7798200252 2
1.5%
ValueCountFrequency (%)
127.4332171305 2
1.5%
127.2707076272 2
1.5%
127.2599526938 2
1.5%
127.2592199036 1
0.7%
127.2114163575 2
1.5%
127.2043260272 2
1.5%
127.2015749087 1
0.7%
127.2010748933 2
1.5%
127.1993221569 2
1.5%
127.1983489735 1
0.7%

Interactions

2024-05-10T21:32:30.441308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:19.926006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:21.639443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:23.406434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:25.219745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:26.976745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:28.490151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:30.683568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:20.223016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:21.891316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:23.656268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:25.489116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:27.196102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:28.748837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:30.954785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:20.495716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:22.157081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:23.917980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:25.760534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:27.464497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:29.005491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:31.183479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:20.756068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:22.426761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:24.173692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:25.964760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:27.706087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:29.260619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:31.439296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:21.009618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:22.646745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:24.448132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:26.222869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:27.891480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:29.503567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:31.692221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:21.210169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:22.885753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:24.698588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:26.487640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:28.077466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:29.752246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:31.948630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:21.398289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:23.130559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:24.947447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:26.757057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:28.260964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:32:29.939363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-10T21:32:49.500397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명사업장명인허가일자영업상태명폐업일자병상수(개)의료인수(명)입원실수(개)진료과목내용연면적(㎡)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
시군명1.0000.9990.9990.5481.0000.5900.5420.6040.9980.0000.9990.9990.9900.9800.966
사업장명0.9991.0001.0000.8271.0000.9970.9990.9991.0001.0001.0001.0001.0001.0001.000
인허가일자0.9991.0001.0000.8401.0000.9930.9970.9981.0001.0001.0001.0001.0001.0001.000
영업상태명0.5480.8270.8401.0001.0000.0000.0000.0000.8490.0000.8330.8330.4320.1030.000
폐업일자1.0001.0001.0001.0001.0001.000NaN1.0001.000NaN1.0001.0001.0001.0001.000
병상수(개)0.5900.9970.9930.0001.0001.0000.9140.9620.9770.8090.9980.9980.4740.3730.412
의료인수(명)0.5420.9990.9970.000NaN0.9141.0000.8920.9790.9330.9990.9990.4190.3050.314
입원실수(개)0.6040.9990.9980.0001.0000.9620.8921.0000.9740.8360.9990.9990.5530.4490.500
진료과목내용0.9981.0001.0000.8491.0000.9770.9790.9741.0000.9161.0001.0000.9960.9980.997
연면적(㎡)0.0001.0001.0000.000NaN0.8090.9330.8360.9161.0001.0001.0000.3250.0000.172
소재지도로명주소0.9991.0001.0000.8331.0000.9980.9990.9991.0001.0001.0001.0001.0001.0001.000
소재지지번주소0.9991.0001.0000.8331.0000.9980.9990.9991.0001.0001.0001.0001.0001.0001.000
소재지우편번호0.9901.0001.0000.4321.0000.4740.4190.5530.9960.3251.0001.0001.0000.9320.793
WGS84위도0.9801.0001.0000.1031.0000.3730.3050.4490.9980.0001.0001.0000.9321.0000.640
WGS84경도0.9661.0001.0000.0001.0000.4120.3140.5000.9970.1721.0001.0000.7930.6401.000
2024-05-10T21:32:49.854146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영업상태명시군명
영업상태명1.0000.298
시군명0.2981.000
2024-05-10T21:32:50.141829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
병상수(개)의료인수(명)입원실수(개)연면적(㎡)소재지우편번호WGS84위도WGS84경도시군명영업상태명
병상수(개)1.0000.8870.9610.819-0.1620.051-0.1090.2560.000
의료인수(명)0.8871.0000.8900.853-0.2050.077-0.1980.2330.000
입원실수(개)0.9610.8901.0000.860-0.1690.059-0.1340.2650.000
연면적(㎡)0.8190.8530.8601.000-0.1930.075-0.1110.0000.000
소재지우편번호-0.162-0.205-0.169-0.1931.000-0.9160.2670.8900.267
WGS84위도0.0510.0770.0590.075-0.9161.000-0.2820.8420.056
WGS84경도-0.109-0.198-0.134-0.1110.267-0.2821.0000.7840.000
시군명0.2560.2330.2650.0000.8900.8420.7841.0000.298
영업상태명0.0000.0000.0000.0000.2670.0560.0000.2981.000

Missing values

2024-05-10T21:32:32.360876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-10T21:32:33.021202image/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.

Sample

시군명사업장명인허가일자영업상태명폐업일자병상수(개)의료기관종별명의료인수(명)입원실수(개)진료과목내용연면적(㎡)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
0고양시의료법인명지의료재단명지병원1983-03-28영업/정상<NA>558종합병원1019152내과, 신경과, 정신건강의학과, 외과, 정형외과, 신경외과, 심장혈관흉부외과, 성형외과, 마취통증의학과, 산부인과, 소아청소년과, 안과, 이비인후과, 피부과, 비뇨의학과, 영상의학과, 방사선종양학과, 병리과, 진단검사의학과, 재활의학과, 가정의학과, 핵의학과, 직업환경의학과, 응급의학과, 치과75723.87경기도 고양시 덕양구 화수로14번길 55 (화정동)경기도 고양시 덕양구 화정1동 697번지 24호1047537.642475126.831745
1고양시의료법인성광의료재단일산차병원2019-12-26영업/정상<NA>260종합병원562112내과, 신경과, 정신건강의학과, 외과, 성형외과, 마취통증의학과, 산부인과, 소아청소년과, 피부과, 비뇨의학과, 영상의학과, 방사선종양학과, 병리과, 진단검사의학과, 재활의학과, 가정의학과, 핵의학과, 한방내과29658.33경기도 고양시 일산동구 중앙로 1205, 지하1층(일부),지하2층,지하4층(일부),1층(일부),2층(일부),3층(일부),4~5층,6층(일부), 7~10층 (장항동)경기도 고양시 일산동구 장항동 889 차움 라이프센터1041437.654123126.77574
2고양시국민건강보험공단일산병원2000-01-18영업/정상<NA>850종합병원1581244내과, 신경과, 정신건강의학과, 외과, 정형외과, 신경외과, 심장혈관흉부외과, 성형외과, 마취통증의학과, 산부인과, 소아청소년과, 안과, 이비인후과, 피부과, 비뇨의학과, 영상의학과, 방사선종양학과, 병리과, 진단검사의학과, 재활의학과, 가정의학과, 핵의학과, 응급의학과, 구강악안면외과, 치과보철과, 치과교정과, 치주과, 치과보존과112786.91경기도 고양시 일산동구 일산로 100 (백석동,백석1동 1241외1필지 4층)경기도 고양시 일산동구 백석1동 1232번지 백석1동 1241외1필지 4층1044437.645475126.792963
3고양시인제대학교일산백병원1999-12-06영업/정상<NA>645종합병원1146141내과, 신경과, 정신건강의학과, 외과, 정형외과, 신경외과, 심장혈관흉부외과, 성형외과, 마취통증의학과, 산부인과, 소아청소년과, 안과, 이비인후과, 피부과, 비뇨의학과, 영상의학과, 방사선종양학과, 병리과, 진단검사의학과, 재활의학과, 가정의학과, 핵의학과, 응급의학과, 구강악안면외과, 치과보철과, 치과교정과, 통합치의학과73977.34경기도 고양시 일산서구 주화로 170 (대화동)경기도 고양시 일산서구 대화동 2240번지1038037.674271126.750382
4고양시동국대학교일산불교병원2005-05-31영업/정상<NA>566종합병원951176내과, 신경과, 정신건강의학과, 외과, 정형외과, 신경외과, 심장혈관흉부외과, 성형외과, 마취통증의학과, 산부인과, 소아청소년과, 안과, 이비인후과, 피부과, 비뇨의학과, 영상의학과, 방사선종양학과, 병리과, 진단검사의학과, 재활의학과, 가정의학과, 핵의학과, 응급의학과, 구강악안면외과81344.0경기도 고양시 일산동구 동국로 27 (식사동, 동국대학교일산병원)경기도 고양시 일산동구 식사동 814번지 동국대학교일산병원1032637.676439126.805563
5고양시국립암센터2000-09-08영업/정상<NA>550종합병원1069194내과, 신경과, 정신건강의학과, 외과, 정형외과, 신경외과, 심장혈관흉부외과, 마취통증의학과, 산부인과, 소아청소년과, 안과, 이비인후과, 피부과, 비뇨의학과, 영상의학과, 방사선종양학과, 병리과, 진단검사의학과, 재활의학과, 가정의학과, 핵의학과, 구강악안면외과109537.0경기도 고양시 일산동구 일산로 323 (마두동)경기도 고양시 일산동구 마두1동 809번지1040837.663237126.783396
6고양시동국대학교일산불교병원2005-05-31영업중<NA>566종합병원905176내과, 신경과, 정신건강의학과, 외과, 정형외과, 신경외과, 심장혈관흉부외과, 성형외과, 마취통증의학과, 산부인과, 소아청소년과, 안과, 이비인후과, 피부과, 비뇨의학과, 영상의학과, 방사선종양학과, 병리과, 진단검사의학과, 재활의학과, 가정의학과, 핵의학과, 응급의학과, 구강악안면외과81344.0경기도 고양시 일산동구 동국로 27 (식사동, 동국대학교일산병원)경기도 고양시 일산동구 식사동 814번지 동국대학교일산병원1032637.676439126.805563
7고양시의료법인성광의료재단일산차병원2019-12-26영업중<NA>263종합병원563112내과, 신경과, 정신건강의학과, 외과, 성형외과, 마취통증의학과, 산부인과, 소아청소년과, 피부과, 비뇨의학과, 영상의학과, 방사선종양학과, 병리과, 진단검사의학과, 재활의학과, 가정의학과, 핵의학과, 한방내과29658.33경기도 고양시 일산동구 중앙로 1205, 지하1층(일부),지하2층,지하4층(일부),1층(일부),2층(일부),3층(일부),4~5층,6층(일부), 7~10층 (장항동)경기도 고양시 일산동구 장항동 889 차움 라이프센터1041437.654123126.77574
8고양시의료법인자인의료재단(더자인병원)2002-01-16영업중<NA>277종합병원8464내과, 신경과, 외과, 정형외과, 신경외과, 마취통증의학과, 산부인과, 영상의학과, 진단검사의학과, 재활의학과, 가정의학과, 한방내과, 한방재활의학과16214.38경기도 고양시 덕양구 중앙로 555, /중앙로557번길 14, 지하1(일부),1-6층 (행신동)경기도 고양시 덕양구 행신동 995 자인메디병원1051837.622983126.835682
9고양시인제대학교일산백병원1999-12-06영업중<NA>640종합병원1114139내과, 신경과, 정신건강의학과, 외과, 정형외과, 신경외과, 심장혈관흉부외과, 성형외과, 마취통증의학과, 산부인과, 소아청소년과, 안과, 이비인후과, 피부과, 비뇨의학과, 영상의학과, 방사선종양학과, 병리과, 진단검사의학과, 재활의학과, 가정의학과, 핵의학과, 응급의학과, 구강악안면외과, 치과보철과, 치과교정과, 통합치의학과73977.34경기도 고양시 일산서구 주화로 170 (대화동)경기도 고양시 일산서구 대화동 2240번지1038037.674271126.750382
시군명사업장명인허가일자영업상태명폐업일자병상수(개)의료기관종별명의료인수(명)입원실수(개)진료과목내용연면적(㎡)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
126평택시의료법인 갈렌의료재단 박병원2003-01-20영업중<NA>153종합병원13832내과, 신경과, 외과, 정형외과, 신경외과, 마취통증의학과, 산부인과, 영상의학과, 진단검사의학과, 재활의학과, 가정의학과, 직업환경의학과, 응급의학과5559.18경기도 평택시 송탄로 33 (장당동)경기도 평택시 장당동 470-4번지1778437.048262127.057007
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133화성시의료법인 원광의료재단 원광종합병원2017-06-12영업/정상<NA>283종합병원3465내과, 신경과, 정신건강의학과, 외과, 정형외과, 신경외과, 마취통증의학과, 산부인과, 비뇨의학과, 영상의학과, 병리과, 가정의학과, 직업환경의학과, 응급의학과10550.03경기도 화성시 화산북로 21 (송산동)경기도 화성시 송산동 200-4번지1835637.210062127.00803
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