Overview

Dataset statistics

Number of variables15
Number of observations92
Missing cells57
Missing cells (%)4.1%
Duplicate rows1
Duplicate rows (%)1.1%
Total size in memory11.4 KiB
Average record size in memory127.4 B

Variable types

Categorical3
Text4
DateTime2
Numeric6

Alerts

의료기관종별명 has constant value ""Constant
Dataset has 1 (1.1%) duplicate rowsDuplicates
병상수(개) is highly overall correlated with 입원실수(개)High 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 의료인수(명) and 1 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
폐업일자 has 56 (60.9%) missing valuesMissing
소재지도로명주소 has 1 (1.1%) missing valuesMissing
병상수(개) has 1 (1.1%) zerosZeros
입원실수(개) has 2 (2.2%) zerosZeros
연면적(㎡) has 2 (2.2%) zerosZeros

Reproduction

Analysis started2024-03-16 05:13:58.225248
Analysis finished2024-03-16 05:14:13.305514
Duration15.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Memory size868.0 B
수원시
11 
고양시
10 
부천시
7 
안산시
7 
안양시
7 
Other values (19)
50 

Length

Max length4
Median length3
Mean length3.0434783
Min length3

Unique

Unique6 ?
Unique (%)6.5%

Sample

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

Common Values

ValueCountFrequency (%)
수원시 11
12.0%
고양시 10
 
10.9%
부천시 7
 
7.6%
안산시 7
 
7.6%
안양시 7
 
7.6%
용인시 5
 
5.4%
군포시 5
 
5.4%
성남시 5
 
5.4%
김포시 4
 
4.3%
파주시 4
 
4.3%
Other values (14) 27
29.3%

Length

2024-03-16T05:14:13.536531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수원시 11
12.0%
고양시 10
 
10.9%
부천시 7
 
7.6%
안산시 7
 
7.6%
안양시 7
 
7.6%
용인시 5
 
5.4%
군포시 5
 
5.4%
성남시 5
 
5.4%
김포시 4
 
4.3%
파주시 4
 
4.3%
Other values (14) 27
29.3%

사업장명
Text

Distinct80
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Memory size868.0 B
2024-03-16T05:14:14.015795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length17
Mean length6.8369565
Min length3

Characters and Unicode

Total characters629
Distinct characters138
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

Unique68 ?
Unique (%)73.9%

Sample

1st row에이치제이매그놀리아국제병원
2nd row국군고양병원
3rd row연세강인병원
4th row허유재병원
5th row그레이스병원
ValueCountFrequency (%)
의료법인 4
 
3.9%
초앤유여성병원 2
 
1.9%
의왕시티의료재단 2
 
1.9%
서울여성병원 2
 
1.9%
한빛여성병원 2
 
1.9%
봄빛병원 2
 
1.9%
고운여성병원 2
 
1.9%
시티병원 2
 
1.9%
평촌우리병원 2
 
1.9%
산본제일병원 2
 
1.9%
Other values (77) 81
78.6%
2024-03-16T05:14:15.004796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 98
 
15.6%
병 93
 
14.8%
성 34
 
5.4%
여 24
 
3.8%
의 17
 
2.7%
료 15
 
2.4%
인 14
 
2.2%
11
 
1.7%
산 10
 
1.6%
디 9
 
1.4%
Other values (128) 304
48.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 614
97.6%
Space Separator 11
 
1.7%
Open Punctuation 2
 
0.3%
Close Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 98
 
16.0%
병 93
 
15.1%
성 34
 
5.5%
여 24
 
3.9%
의 17
 
2.8%
료 15
 
2.4%
인 14
 
2.3%
산 10
 
1.6%
디 9
 
1.5%
재 9
 
1.5%
Other values (125) 291
47.4%
Space Separator
ValueCountFrequency (%)
11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 614
97.6%
Common 15
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 98
 
16.0%
병 93
 
15.1%
성 34
 
5.5%
여 24
 
3.9%
의 17
 
2.8%
료 15
 
2.4%
인 14
 
2.3%
산 10
 
1.6%
디 9
 
1.5%
재 9
 
1.5%
Other values (125) 291
47.4%
Common
ValueCountFrequency (%)
11
73.3%
( 2
 
13.3%
) 2
 
13.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 614
97.6%
ASCII 15
 
2.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 98
 
16.0%
병 93
 
15.1%
성 34
 
5.5%
여 24
 
3.9%
의 17
 
2.8%
료 15
 
2.4%
인 14
 
2.3%
산 10
 
1.6%
디 9
 
1.5%
재 9
 
1.5%
Other values (125) 291
47.4%
ASCII
ValueCountFrequency (%)
11
73.3%
( 2
 
13.3%
) 2
 
13.3%

인허가일자
Date

Distinct81
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size868.0 B
Minimum1977-01-15 00:00:00
Maximum2023-07-11 00:00:00
2024-03-16T05:14:15.421223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:15.963874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

영업상태명
Categorical

Distinct4
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size868.0 B
영업/정상
47 
폐업
34 
영업중
9 
취소/말소/만료/정지/중지
 
2

Length

Max length14
Median length5
Mean length3.8913043
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영업/정상 47
51.1%
폐업 34
37.0%
영업중 9
 
9.8%
취소/말소/만료/정지/중지 2
 
2.2%

Length

2024-03-16T05:14:16.422152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T05:14:16.720154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업/정상 47
51.1%
폐업 34
37.0%
영업중 9
 
9.8%
취소/말소/만료/정지/중지 2
 
2.2%

폐업일자
Date

MISSING 

Distinct35
Distinct (%)97.2%
Missing56
Missing (%)60.9%
Memory size868.0 B
Minimum1997-01-14 00:00:00
Maximum2024-03-01 00:00:00
2024-03-16T05:14:17.038621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:17.401563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)

병상수(개)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)64.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.032609
Minimum0
Maximum342
Zeros1
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-03-16T05:14:17.832955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q141.5
median64.5
Q3118.5
95-th percentile237.2
Maximum342
Range342
Interquartile range (IQR)77

Descriptive statistics

Standard deviation68.105195
Coefficient of variation (CV)0.75645031
Kurtosis2.865678
Mean90.032609
Median Absolute Deviation (MAD)30.5
Skewness1.6906267
Sum8283
Variance4638.3176
MonotonicityNot monotonic
2024-03-16T05:14:18.319563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 7
 
7.6%
60 4
 
4.3%
85 3
 
3.3%
158 3
 
3.3%
36 3
 
3.3%
61 3
 
3.3%
50 3
 
3.3%
35 2
 
2.2%
131 2
 
2.2%
96 2
 
2.2%
Other values (49) 60
65.2%
ValueCountFrequency (%)
0 1
 
1.1%
20 1
 
1.1%
30 7
7.6%
31 1
 
1.1%
32 1
 
1.1%
33 2
 
2.2%
34 2
 
2.2%
35 2
 
2.2%
36 3
3.3%
37 1
 
1.1%
ValueCountFrequency (%)
342 1
 
1.1%
299 2
2.2%
269 1
 
1.1%
257 1
 
1.1%
221 1
 
1.1%
217 1
 
1.1%
201 1
 
1.1%
182 1
 
1.1%
164 1
 
1.1%
158 3
3.3%

의료기관종별명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size868.0 B
병원
92 

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 92
100.0%

Length

2024-03-16T05:14:18.708766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T05:14:19.028296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
병원 92
100.0%

의료인수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)60.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.597826
Minimum1
Maximum183
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-03-16T05:14:19.336570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113.5
median23.5
Q351
95-th percentile111.1
Maximum183
Range182
Interquartile range (IQR)37.5

Descriptive statistics

Standard deviation36.549848
Coefficient of variation (CV)0.99868906
Kurtosis4.7780242
Mean36.597826
Median Absolute Deviation (MAD)16.5
Skewness2.026571
Sum3367
Variance1335.8914
MonotonicityNot monotonic
2024-03-16T05:14:20.005438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 5
 
5.4%
21 4
 
4.3%
14 4
 
4.3%
51 3
 
3.3%
4 3
 
3.3%
7 3
 
3.3%
22 3
 
3.3%
6 3
 
3.3%
1 2
 
2.2%
69 2
 
2.2%
Other values (46) 60
65.2%
ValueCountFrequency (%)
1 2
2.2%
2 2
2.2%
3 2
2.2%
4 3
3.3%
5 1
 
1.1%
6 3
3.3%
7 3
3.3%
8 2
2.2%
10 2
2.2%
11 1
 
1.1%
ValueCountFrequency (%)
183 1
1.1%
169 1
1.1%
157 1
1.1%
132 2
2.2%
94 1
1.1%
88 1
1.1%
76 1
1.1%
75 1
1.1%
73 1
1.1%
70 1
1.1%

입원실수(개)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)47.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.717391
Minimum0
Maximum89
Zeros2
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-03-16T05:14:20.462881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.65
Q116.75
median27.5
Q340
95-th percentile71
Maximum89
Range89
Interquartile range (IQR)23.25

Descriptive statistics

Standard deviation18.978488
Coefficient of variation (CV)0.61784178
Kurtosis0.8174473
Mean30.717391
Median Absolute Deviation (MAD)11.5
Skewness1.0612207
Sum2826
Variance360.18299
MonotonicityNot monotonic
2024-03-16T05:14:20.878145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
16 7
 
7.6%
43 6
 
6.5%
21 6
 
6.5%
14 5
 
5.4%
28 3
 
3.3%
30 3
 
3.3%
29 3
 
3.3%
19 3
 
3.3%
22 3
 
3.3%
34 3
 
3.3%
Other values (34) 50
54.3%
ValueCountFrequency (%)
0 2
 
2.2%
5 1
 
1.1%
6 1
 
1.1%
7 1
 
1.1%
10 2
 
2.2%
11 1
 
1.1%
12 2
 
2.2%
13 1
 
1.1%
14 5
5.4%
16 7
7.6%
ValueCountFrequency (%)
89 1
1.1%
80 2
2.2%
74 1
1.1%
71 2
2.2%
68 1
1.1%
64 2
2.2%
62 1
1.1%
56 1
1.1%
52 1
1.1%
51 1
1.1%

연면적(㎡)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct81
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4773.8767
Minimum0
Maximum22941.69
Zeros2
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-03-16T05:14:21.365049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1252.145
Q12323.1975
median3835.03
Q36626.9
95-th percentile10264.169
Maximum22941.69
Range22941.69
Interquartile range (IQR)4303.7025

Descriptive statistics

Standard deviation3573.3811
Coefficient of variation (CV)0.74852814
Kurtosis7.1953362
Mean4773.8767
Median Absolute Deviation (MAD)1735.86
Skewness2.1148263
Sum439196.66
Variance12769052
MonotonicityNot monotonic
2024-03-16T05:14:21.799760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2972.69 2
 
2.2%
8791.71 2
 
2.2%
3154.1 2
 
2.2%
2890.83 2
 
2.2%
7272.0 2
 
2.2%
6946.71 2
 
2.2%
0.0 2
 
2.2%
1639.03 2
 
2.2%
4502.78 2
 
2.2%
8055.68 2
 
2.2%
Other values (71) 72
78.3%
ValueCountFrequency (%)
0.0 2
2.2%
576.67 1
1.1%
812.59 1
1.1%
1195.0 1
1.1%
1298.9 1
1.1%
1418.92 1
1.1%
1639.03 2
2.2%
1698.03 1
1.1%
1734.13 1
1.1%
1795.32 1
1.1%
ValueCountFrequency (%)
22941.69 1
1.1%
16242.5 1
1.1%
13974.3 1
1.1%
11191.95 1
1.1%
10594.57 1
1.1%
9993.84 1
1.1%
9704.56 1
1.1%
9327.0 1
1.1%
8791.71 2
2.2%
8711.32 1
1.1%

소재지도로명주소
Text

MISSING 

Distinct81
Distinct (%)89.0%
Missing1
Missing (%)1.1%
Memory size868.0 B
2024-03-16T05:14:22.361991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length73
Median length47
Mean length30.714286
Min length17

Characters and Unicode

Total characters2795
Distinct characters187
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

Unique71 ?
Unique (%)78.0%

Sample

1st row경기도 가평군 설악면 미사리로 267-177
2nd row경기도 고양시 덕양구 혜음로 215 (벽제동)
3rd row경기도 고양시 덕양구 화중로 50, 4,5,6층 (화정동)
4th row경기도 고양시 일산동구 중앙로 1317, 지하1층~5층,6층 일부, 8층 (장항동)
5th row경기도 고양시 일산동구 중앙로 1073, (지하2층,지하1층,1층중일부,2~9층)/중앙로1071(1층중 일부, 2~4층) (백석동)
ValueCountFrequency (%)
경기도 91
 
15.8%
수원시 11
 
1.9%
고양시 10
 
1.7%
안양시 7
 
1.2%
안산시 7
 
1.2%
원미구 7
 
1.2%
부천시 7
 
1.2%
일부 7
 
1.2%
성남시 5
 
0.9%
일산동구 5
 
0.9%
Other values (284) 419
72.7%
2024-03-16T05:14:23.360374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
488
 
17.5%
1 110
 
3.9%
동 96
 
3.4%
기 95
 
3.4%
경 94
 
3.4%
도 93
 
3.3%
로 92
 
3.3%
시 92
 
3.3%
) 89
 
3.2%
( 89
 
3.2%
Other values (177) 1457
52.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1555
55.6%
Space Separator 488
 
17.5%
Decimal Number 450
 
16.1%
Close Punctuation 89
 
3.2%
Open Punctuation 89
 
3.2%
Other Punctuation 83
 
3.0%
Math Symbol 21
 
0.8%
Dash Punctuation 19
 
0.7%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 96
 
6.2%
기 95
 
6.1%
경 94
 
6.0%
도 93
 
6.0%
로 92
 
5.9%
시 92
 
5.9%
구 54
 
3.5%
층 49
 
3.2%
원 35
 
2.3%
양 35
 
2.3%
Other values (159) 820
52.7%
Decimal Number
ValueCountFrequency (%)
1 110
24.4%
2 64
14.2%
3 46
10.2%
5 39
 
8.7%
4 35
 
7.8%
7 34
 
7.6%
0 33
 
7.3%
6 33
 
7.3%
9 31
 
6.9%
8 25
 
5.6%
Other Punctuation
ValueCountFrequency (%)
, 78
94.0%
/ 5
 
6.0%
Space Separator
ValueCountFrequency (%)
488
100.0%
Close Punctuation
ValueCountFrequency (%)
) 89
100.0%
Open Punctuation
ValueCountFrequency (%)
( 89
100.0%
Math Symbol
ValueCountFrequency (%)
~ 21
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1555
55.6%
Common 1239
44.3%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 96
 
6.2%
기 95
 
6.1%
경 94
 
6.0%
도 93
 
6.0%
로 92
 
5.9%
시 92
 
5.9%
구 54
 
3.5%
층 49
 
3.2%
원 35
 
2.3%
양 35
 
2.3%
Other values (159) 820
52.7%
Common
ValueCountFrequency (%)
488
39.4%
1 110
 
8.9%
) 89
 
7.2%
( 89
 
7.2%
, 78
 
6.3%
2 64
 
5.2%
3 46
 
3.7%
5 39
 
3.1%
4 35
 
2.8%
7 34
 
2.7%
Other values (7) 167
 
13.5%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1555
55.6%
ASCII 1240
44.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
488
39.4%
1 110
 
8.9%
) 89
 
7.2%
( 89
 
7.2%
, 78
 
6.3%
2 64
 
5.2%
3 46
 
3.7%
5 39
 
3.1%
4 35
 
2.8%
7 34
 
2.7%
Other values (8) 168
 
13.5%
Hangul
ValueCountFrequency (%)
동 96
 
6.2%
기 95
 
6.1%
경 94
 
6.0%
도 93
 
6.0%
로 92
 
5.9%
시 92
 
5.9%
구 54
 
3.5%
층 49
 
3.2%
원 35
 
2.3%
양 35
 
2.3%
Other values (159) 820
52.7%

소재지지번주소
Text

Distinct82
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Memory size868.0 B
2024-03-16T05:14:23.932175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length70
Median length46
Mean length27.326087
Min length17

Characters and Unicode

Total characters2514
Distinct characters167
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)78.3%

Sample

1st row경기도 가평군 설악면 송산리 460번지
2nd row경기도 고양시 덕양구 벽제동 335번지 1호
3rd row경기도 고양시 덕양구 화정동 984번지 1호 반석프라자 4층, 5층, 6층
4th row경기도 고양시 일산동구 장항동 780번지
5th row경기도 고양시 일산동구 백석동 1334번지
ValueCountFrequency (%)
경기도 92
 
17.1%
1호 13
 
2.4%
수원시 11
 
2.0%
고양시 9
 
1.7%
안양시 7
 
1.3%
부천시 7
 
1.3%
안산시 7
 
1.3%
원미구 7
 
1.3%
일부 5
 
0.9%
산본동 5
 
0.9%
Other values (260) 376
69.8%
2024-03-16T05:14:25.051528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
450
 
17.9%
1 119
 
4.7%
기 95
 
3.8%
지 94
 
3.7%
도 93
 
3.7%
동 93
 
3.7%
경 92
 
3.7%
시 90
 
3.6%
번 78
 
3.1%
4 59
 
2.3%
Other values (157) 1251
49.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1436
57.1%
Decimal Number 521
 
20.7%
Space Separator 450
 
17.9%
Dash Punctuation 43
 
1.7%
Other Punctuation 39
 
1.6%
Math Symbol 12
 
0.5%
Open Punctuation 6
 
0.2%
Close Punctuation 6
 
0.2%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 95
 
6.6%
지 94
 
6.5%
도 93
 
6.5%
동 93
 
6.5%
경 92
 
6.4%
시 90
 
6.3%
번 78
 
5.4%
구 53
 
3.7%
호 47
 
3.3%
층 41
 
2.9%
Other values (138) 660
46.0%
Decimal Number
ValueCountFrequency (%)
1 119
22.8%
4 59
11.3%
2 55
10.6%
3 54
10.4%
5 42
 
8.1%
8 42
 
8.1%
9 40
 
7.7%
7 38
 
7.3%
0 36
 
6.9%
6 36
 
6.9%
Other Punctuation
ValueCountFrequency (%)
, 35
89.7%
/ 3
 
7.7%
. 1
 
2.6%
Space Separator
ValueCountFrequency (%)
450
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43
100.0%
Math Symbol
ValueCountFrequency (%)
~ 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1437
57.2%
Common 1077
42.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 95
 
6.6%
지 94
 
6.5%
도 93
 
6.5%
동 93
 
6.5%
경 92
 
6.4%
시 90
 
6.3%
번 78
 
5.4%
구 53
 
3.7%
호 47
 
3.3%
층 41
 
2.9%
Other values (139) 661
46.0%
Common
ValueCountFrequency (%)
450
41.8%
1 119
 
11.0%
4 59
 
5.5%
2 55
 
5.1%
3 54
 
5.0%
- 43
 
4.0%
5 42
 
3.9%
8 42
 
3.9%
9 40
 
3.7%
7 38
 
3.5%
Other values (8) 135
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1436
57.1%
ASCII 1077
42.8%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
450
41.8%
1 119
 
11.0%
4 59
 
5.5%
2 55
 
5.1%
3 54
 
5.0%
- 43
 
4.0%
5 42
 
3.9%
8 42
 
3.9%
9 40
 
3.7%
7 38
 
3.5%
Other values (8) 135
 
12.5%
Hangul
ValueCountFrequency (%)
기 95
 
6.6%
지 94
 
6.5%
도 93
 
6.5%
동 93
 
6.5%
경 92
 
6.4%
시 90
 
6.3%
번 78
 
5.4%
구 53
 
3.7%
호 47
 
3.3%
층 41
 
2.9%
Other values (138) 660
46.0%
None
ValueCountFrequency (%)
㈜ 1
100.0%

소재지우편번호
Text

Distinct81
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size868.0 B
2024-03-16T05:14:25.619754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.2391304
Min length5

Characters and Unicode

Total characters482
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

Unique70 ?
Unique (%)76.1%

Sample

1st row12461
2nd row10271
3rd row10500
4th row10401
5th row10447
ValueCountFrequency (%)
15532 2
 
2.2%
14072 2
 
2.2%
15818 2
 
2.2%
14548 2
 
2.2%
16060 2
 
2.2%
10113 2
 
2.2%
15477 2
 
2.2%
10924 2
 
2.2%
14569 2
 
2.2%
10447 2
 
2.2%
Other values (71) 72
78.3%
2024-03-16T05:14:26.914069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 126
26.1%
4 61
12.7%
0 57
11.8%
5 44
 
9.1%
6 36
 
7.5%
2 34
 
7.1%
8 31
 
6.4%
7 29
 
6.0%
3 27
 
5.6%
9 26
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 471
97.7%
Dash Punctuation 11
 
2.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 126
26.8%
4 61
13.0%
0 57
12.1%
5 44
 
9.3%
6 36
 
7.6%
2 34
 
7.2%
8 31
 
6.6%
7 29
 
6.2%
3 27
 
5.7%
9 26
 
5.5%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 482
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 126
26.1%
4 61
12.7%
0 57
11.8%
5 44
 
9.1%
6 36
 
7.5%
2 34
 
7.1%
8 31
 
6.4%
7 29
 
6.0%
3 27
 
5.6%
9 26
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 482
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 126
26.1%
4 61
12.7%
0 57
11.8%
5 44
 
9.1%
6 36
 
7.5%
2 34
 
7.1%
8 31
 
6.4%
7 29
 
6.0%
3 27
 
5.6%
9 26
 
5.4%

WGS84위도
Real number (ℝ)

HIGH CORRELATION 

Distinct82
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.459656
Minimum37.047901
Maximum38.017425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-03-16T05:14:27.541846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.047901
5-th percentile37.148247
Q137.293007
median37.402762
Q337.632168
95-th percentile37.758994
Maximum38.017425
Range0.9695241
Interquartile range (IQR)0.33916156

Descriptive statistics

Standard deviation0.20815729
Coefficient of variation (CV)0.0055568392
Kurtosis-0.60574082
Mean37.459656
Median Absolute Deviation (MAD)0.13554414
Skewness0.33759549
Sum3446.2884
Variance0.043329457
MonotonicityNot monotonic
2024-03-16T05:14:28.047625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.3021011745 2
 
2.2%
37.4010609932 2
 
2.2%
37.5019133893 2
 
2.2%
37.3502838907 2
 
2.2%
37.6112219597 2
 
2.2%
37.3636568764 2
 
2.2%
37.4940588138 2
 
2.2%
37.390222391 2
 
2.2%
37.3091477016 2
 
2.2%
37.6447608487 2
 
2.2%
Other values (72) 72
78.3%
ValueCountFrequency (%)
37.0479010609 1
1.1%
37.055137185 1
1.1%
37.1385153838 1
1.1%
37.1412846971 1
1.1%
37.1469687275 1
1.1%
37.1492924517 1
1.1%
37.1992313944 1
1.1%
37.2165446873 1
1.1%
37.2177853774 1
1.1%
37.247607134 1
1.1%
ValueCountFrequency (%)
38.0174251575 1
1.1%
37.9159725251 1
1.1%
37.8508855498 1
1.1%
37.8227585625 1
1.1%
37.7610981986 1
1.1%
37.7572724013 1
1.1%
37.7557529111 1
1.1%
37.7488863458 1
1.1%
37.7384448943 1
1.1%
37.7379590057 1
1.1%

WGS84경도
Real number (ℝ)

HIGH CORRELATION 

Distinct82
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.97213
Minimum126.71281
Maximum127.58889
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2024-03-16T05:14:28.629306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.71281
5-th percentile126.75556
Q1126.82514
median126.95422
Q3127.07217
95-th percentile127.30707
Maximum127.58889
Range0.87608084
Interquartile range (IQR)0.24702426

Descriptive statistics

Standard deviation0.18050107
Coefficient of variation (CV)0.0014215802
Kurtosis1.2937834
Mean126.97213
Median Absolute Deviation (MAD)0.12296007
Skewness0.96820938
Sum11681.436
Variance0.032580636
MonotonicityNot monotonic
2024-03-16T05:14:29.107651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.8637678471 2
 
2.2%
126.9476364999 2
 
2.2%
126.7697801383 2
 
2.2%
126.9767891156 2
 
2.2%
126.7347224643 2
 
2.2%
126.9311296301 2
 
2.2%
126.7853658614 2
 
2.2%
126.9545167277 2
 
2.2%
126.8322694876 2
 
2.2%
126.785092015 2
 
2.2%
Other values (72) 72
78.3%
ValueCountFrequency (%)
126.712810666 1
1.1%
126.7212958431 1
1.1%
126.7347224643 2
2.2%
126.7516000019 1
1.1%
126.7587954312 1
1.1%
126.7667173351 1
1.1%
126.7697801383 2
2.2%
126.7699403885 1
1.1%
126.7739105615 1
1.1%
126.7747698389 1
1.1%
ValueCountFrequency (%)
127.5888915032 1
1.1%
127.5213805697 1
1.1%
127.4191564639 1
1.1%
127.4134712853 1
1.1%
127.310358584 1
1.1%
127.3043775936 1
1.1%
127.2073692113 1
1.1%
127.2052107223 1
1.1%
127.1799591601 1
1.1%
127.1378207406 1
1.1%

Interactions

2024-03-16T05:14:10.191123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:02.239370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:03.818022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:05.736602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:07.143240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:08.582263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:10.523408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:02.499902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:04.102930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:05.975210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:07.401862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:08.856494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:10.811669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:02.800919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:04.552084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:06.217789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:07.650917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:09.235184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:11.078085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:03.086711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:04.793921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:06.421362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:07.912842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:09.490487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:11.313566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:03.337282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:05.101082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:06.650806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:08.147311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:09.727518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:11.582730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:03.577906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:05.406543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:06.909727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:08.368214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T05:14:09.970072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-16T05:14:29.393129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명사업장명인허가일자영업상태명폐업일자병상수(개)의료인수(명)입원실수(개)연면적(㎡)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
시군명1.0000.9950.9990.3671.0000.5210.5830.5020.8081.0001.0001.0000.9830.946
사업장명0.9951.0001.0000.0001.0001.0000.9890.9940.9431.0001.0001.0000.9961.000
인허가일자0.9991.0001.0000.0001.0000.9981.0000.9990.9951.0001.0001.0000.9970.987
영업상태명0.3670.0000.0001.0001.0000.3650.3700.1800.4410.0000.0000.0000.1990.126
폐업일자1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
병상수(개)0.5211.0000.9980.3651.0001.0000.6930.7770.5471.0001.0001.0000.6580.000
의료인수(명)0.5830.9891.0000.3701.0000.6931.0000.6490.5441.0001.0001.0000.4310.424
입원실수(개)0.5020.9940.9990.1801.0000.7770.6491.0000.6501.0001.0000.9990.3110.000
연면적(㎡)0.8080.9430.9950.4411.0000.5470.5440.6501.0001.0001.0001.0000.5180.576
소재지도로명주소1.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지지번주소1.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지우편번호1.0001.0001.0000.0001.0001.0001.0000.9991.0001.0001.0001.0001.0001.000
WGS84위도0.9830.9960.9970.1991.0000.6580.4310.3110.5181.0001.0001.0001.0000.807
WGS84경도0.9461.0000.9870.1261.0000.0000.4240.0000.5761.0001.0001.0000.8071.000
2024-03-16T05:14:29.809057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영업상태명시군명
영업상태명1.0000.150
시군명0.1501.000
2024-03-16T05:14:30.054759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
병상수(개)의료인수(명)입원실수(개)연면적(㎡)WGS84위도WGS84경도시군명영업상태명
병상수(개)1.0000.4380.7300.4750.0430.1370.1920.216
의료인수(명)0.4381.0000.6150.6310.0420.0380.2330.236
입원실수(개)0.7300.6151.0000.6150.0000.0090.1820.099
연면적(㎡)0.4750.6310.6151.000-0.0260.0680.3870.202
WGS84위도0.0430.0420.000-0.0261.000-0.2360.8140.111
WGS84경도0.1370.0380.0090.068-0.2361.0000.6750.065
시군명0.1920.2330.1820.3870.8140.6751.0000.150
영업상태명0.2160.2360.0990.2020.1110.0650.1501.000

Missing values

2024-03-16T05:14:12.121248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-16T05:14:12.755815image/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.
2024-03-16T05:14:13.160533image/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)연면적(㎡)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
0가평군에이치제이매그놀리아국제병원2003-05-17영업/정상<NA>99병원514022941.69경기도 가평군 설악면 미사리로 267-177경기도 가평군 설악면 송산리 460번지1246137.691127127.521381
1고양시국군고양병원1994-06-07영업/정상<NA>150병원692911191.95경기도 고양시 덕양구 혜음로 215 (벽제동)경기도 고양시 덕양구 벽제동 335번지 1호1027137.719372126.898091
2고양시연세강인병원2012-01-25영업/정상<NA>85병원18162101.6경기도 고양시 덕양구 화중로 50, 4,5,6층 (화정동)경기도 고양시 덕양구 화정동 984번지 1호 반석프라자 4층, 5층, 6층1050037.631707126.830686
3고양시허유재병원2004-03-02영업/정상<NA>77병원69438427.19경기도 고양시 일산동구 중앙로 1317, 지하1층~5층,6층 일부, 8층 (장항동)경기도 고양시 일산동구 장항동 780번지1040137.662929126.76994
4고양시그레이스병원1999-01-07영업/정상<NA>60병원59324721.17경기도 고양시 일산동구 중앙로 1073, (지하2층,지하1층,1층중일부,2~9층)/중앙로1071(1층중 일부, 2~4층) (백석동)경기도 고양시 일산동구 백석동 1334번지1044737.644761126.785092
5고양시그레이스병원1999-01-07영업중<NA>60병원60324721.17경기도 고양시 일산동구 중앙로 1073, (지하2층,지하1층,1층중일부,2~9층)/중앙로1071(1층중 일부, 2~4층) (백석동)경기도 고양시 일산동구 백석동 1334번지1044737.644761126.785092
6고양시명성병원2005-05-05취소/말소/만료/정지/중지2016-01-08299병원22394616.99경기도 고양시 덕양구 내유길 146-4 (내유동)경기도 고양시 덕양구 내유동 114번지 1호1026437.722693126.86282
7고양시봄여성병원2003-04-03폐업2019-06-2136병원42223653.77경기도 고양시 덕양구 중앙로 620 (화정동)경기도 고양시 덕양구 화정2동 1148번지 4호1050337.627187126.829933
8고양시일산제일병원2000-06-10폐업2020-03-0936병원17214278.9경기도 고양시 일산동구 장백로 174 (장항동, /장백로 172, 6~7층/ 중앙로 1161, 3층)경기도 고양시 일산동구 장항동 898번지 장항동 898/장항동 896-2.삼성화재㈜일산사옥 3층/장항동 898-1 6층7층1041437.650466126.777612
9고양시능곡병원1991-10-12폐업2006-05-0863병원701884.0경기도 고양시 덕양구 지도로 105 (토당동)경기도 고양시덕양구 토당동 840번지 3호1050737.626483126.823435
시군명사업장명인허가일자영업상태명폐업일자병상수(개)의료기관종별명의료인수(명)입원실수(개)연면적(㎡)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
82파주시운정와이즈병원2019-12-06영업/정상<NA>95병원67475260.2경기도 파주시 금바위로 50, 2~7,10,11층 (와동동)경기도 파주시 와동동 1378번지 2~7,10,11층1089437.728989126.758795
83파주시미래여성병원2002-12-04폐업2018-05-0130병원5142359.0경기도 파주시 후곡로 19 (금촌동)경기도 파주시 금촌동 953번지 5호1092437.755753126.773911
84파주시메디인병원2001-07-23폐업2022-04-01221병원88745108.51경기도 파주시 금릉역로 190, 메디인병원 1동 (금촌동)경기도 파주시 금촌동 495-11091337.761098126.766717
85평택시의료법인 성림의료재단 메디웰병원2006-12-28취소/말소/만료/정지/중지2014-01-02146병원29384756.03경기도 평택시 정암로 78-14 (이충동)경기도 평택시 이충동 410번지459-82537.055137127.063535
86평택시고덕탑병원2020-06-10폐업2021-08-2030병원14105229.0경기도 평택시 고덕면 고덕중앙로 218, 4~8층경기도 평택시 고덕면 여염리 4318번지 1호1777037.047901127.044964
87포천시국군포천병원1994-05-17영업/정상<NA>100병원73209327.0경기도 포천시 화현면 화동로 564경기도 포천시 화현면 화현리 14-2번지1112337.915973127.310359
88하남시한림병원2005-05-26폐업2010-05-1050병원6101195.0경기도 하남시 신장로 93-1 (신장동)경기도 하남시 신장동 432번지 17호1296437.536771127.207369
89화성시화성디에스병원2019-08-16영업/정상<NA>98병원12293474.75경기도 화성시 남양읍 시청로160번길 46-13, 1,3~8층경기도 화성시 남양읍 남양리 2319번지 2호1827037.199231126.825712
90화성시의료법인상운의료재단 동탄제일병원2011-05-12영업/정상<NA>201병원668013974.3경기도 화성시 삼성1로 144-6 (석우동)경기도 화성시 석우동 42-1번지1845037.216545127.078499
91화성시화성유일병원2019-06-17영업/정상<NA>118병원765110594.57경기도 화성시 남양읍 남양로920번길 6경기도 화성시 남양읍 북양리 691-1번지1825637.217785126.832659

Duplicate rows

Most frequently occurring

시군명사업장명인허가일자영업상태명폐업일자병상수(개)의료기관종별명의료인수(명)입원실수(개)연면적(㎡)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도# duplicates
0부천시고운여성병원2007-02-22폐업2024-03-0140병원7211639.03경기도 부천시 원미구 부천로 110-1, 2~6층 (원미동)경기도 부천시 원미구 원미동 111-3번지1456937.494059126.7853662