Overview

Dataset statistics

Number of variables4
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory36.7 B

Variable types

Text2
Numeric2

Dataset

Description진주시 내 태풍 및 집중호우로 인한 침수 발생 우려 시 지역별 대피소(마을명, 대피시설명, 수용능력인원) 현황입니다.
URLhttps://www.data.go.kr/data/15114035/fileData.do

Reproduction

Analysis started2023-12-12 10:35:36.784512
Analysis finished2023-12-12 10:35:37.711348
Duration0.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

마을명
Text

Distinct48
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-12T19:35:37.991395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length28
Mean length9.755102
Min length3

Characters and Unicode

Total characters478
Distinct characters97
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)95.9%

Sample

1st row평거동1
2nd row판문동
3rd row평거동2
4th row신안동
5th row이현동
ValueCountFrequency (%)
사봉면 3
 
2.9%
대곡면 3
 
2.9%
진성면 3
 
2.9%
지수면 3
 
2.9%
상대동 2
 
2.0%
가호동(구 2
 
2.0%
단목리 2
 
2.0%
유곡리 2
 
2.0%
수곡면 2
 
2.0%
집현면 2
 
2.0%
Other values (76) 78
76.5%
2023-12-12T19:35:38.553365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 58
 
12.1%
53
 
11.1%
면 36
 
7.5%
, 26
 
5.4%
동 23
 
4.8%
) 18
 
3.8%
( 18
 
3.8%
곡 18
 
3.8%
대 12
 
2.5%
산 11
 
2.3%
Other values (87) 205
42.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 356
74.5%
Space Separator 53
 
11.1%
Other Punctuation 26
 
5.4%
Close Punctuation 18
 
3.8%
Open Punctuation 18
 
3.8%
Decimal Number 6
 
1.3%
Math Symbol 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 58
 
16.3%
면 36
 
10.1%
동 23
 
6.5%
곡 18
 
5.1%
대 12
 
3.4%
산 11
 
3.1%
사 8
 
2.2%
성 7
 
2.0%
가 7
 
2.0%
수 7
 
2.0%
Other values (80) 169
47.5%
Decimal Number
ValueCountFrequency (%)
1 3
50.0%
2 3
50.0%
Space Separator
ValueCountFrequency (%)
53
100.0%
Other Punctuation
ValueCountFrequency (%)
, 26
100.0%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 356
74.5%
Common 122
 
25.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 58
 
16.3%
면 36
 
10.1%
동 23
 
6.5%
곡 18
 
5.1%
대 12
 
3.4%
산 11
 
3.1%
사 8
 
2.2%
성 7
 
2.0%
가 7
 
2.0%
수 7
 
2.0%
Other values (80) 169
47.5%
Common
ValueCountFrequency (%)
53
43.4%
, 26
21.3%
) 18
 
14.8%
( 18
 
14.8%
1 3
 
2.5%
2 3
 
2.5%
+ 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 356
74.5%
ASCII 122
 
25.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 58
 
16.3%
면 36
 
10.1%
동 23
 
6.5%
곡 18
 
5.1%
대 12
 
3.4%
산 11
 
3.1%
사 8
 
2.2%
성 7
 
2.0%
가 7
 
2.0%
수 7
 
2.0%
Other values (80) 169
47.5%
ASCII
ValueCountFrequency (%)
53
43.4%
, 26
21.3%
) 18
 
14.8%
( 18
 
14.8%
1 3
 
2.5%
2 3
 
2.5%
+ 1
 
0.8%

대피소
Real number (ℝ)

Distinct6
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2857143
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T19:35:38.717612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6955825
Coefficient of variation (CV)0.74181734
Kurtosis7.7978208
Mean2.2857143
Median Absolute Deviation (MAD)1
Skewness2.310515
Sum112
Variance2.875
MonotonicityNot monotonic
2023-12-12T19:35:38.859993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 20
40.8%
2 14
28.6%
3 6
 
12.2%
4 4
 
8.2%
5 4
 
8.2%
10 1
 
2.0%
ValueCountFrequency (%)
1 20
40.8%
2 14
28.6%
3 6
 
12.2%
4 4
 
8.2%
5 4
 
8.2%
10 1
 
2.0%
ValueCountFrequency (%)
10 1
 
2.0%
5 4
 
8.2%
4 4
 
8.2%
3 6
 
12.2%
2 14
28.6%
1 20
40.8%

시설명
Text

Distinct42
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-12T19:35:39.112695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length71
Median length27
Mean length15.755102
Min length5

Characters and Unicode

Total characters772
Distinct characters145
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)77.6%

Sample

1st row진주시 능력개발관+박덕규미술관+양옥새마을회관
2nd row진양호 물홍보관+진주시전통예술회관+샛터마을회관+한국도로공사
3rd row진주임마뉴엘교회+대아중학교+대아고등학교
4th row대아중학교+대아고등학교
5th row각한마을회관+명석중학교+용우초등학교
ValueCountFrequency (%)
지수중학교 5
 
9.8%
한국국제대학교 2
 
3.9%
덕오초등학교(폐교 2
 
3.9%
연암공과대학교 2
 
3.9%
진주시 1
 
2.0%
대곡중고교+유동경로당+유동회관 1
 
2.0%
각한마을회관+명석중학교+용우초등학교 1
 
2.0%
경남과학고+월정마을회관 1
 
2.0%
진주새금산+진주우편집중국+진주동중학교 1
 
2.0%
진주평강순복음+사동마을회관 1
 
2.0%
Other values (34) 34
66.7%
2023-12-12T19:35:39.626452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
+ 62
 
8.0%
교 48
 
6.2%
학 46
 
6.0%
관 44
 
5.7%
회 41
 
5.3%
경 29
 
3.8%
마 27
 
3.5%
을 26
 
3.4%
로 23
 
3.0%
대 21
 
2.7%
Other values (135) 405
52.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 703
91.1%
Math Symbol 62
 
8.0%
Close Punctuation 2
 
0.3%
Space Separator 2
 
0.3%
Open Punctuation 2
 
0.3%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 48
 
6.8%
학 46
 
6.5%
관 44
 
6.3%
회 41
 
5.8%
경 29
 
4.1%
마 27
 
3.8%
을 26
 
3.7%
로 23
 
3.3%
대 21
 
3.0%
등 20
 
2.8%
Other values (130) 378
53.8%
Math Symbol
ValueCountFrequency (%)
+ 62
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 703
91.1%
Common 69
 
8.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 48
 
6.8%
학 46
 
6.5%
관 44
 
6.3%
회 41
 
5.8%
경 29
 
4.1%
마 27
 
3.8%
을 26
 
3.7%
로 23
 
3.3%
대 21
 
3.0%
등 20
 
2.8%
Other values (130) 378
53.8%
Common
ValueCountFrequency (%)
+ 62
89.9%
) 2
 
2.9%
2
 
2.9%
( 2
 
2.9%
, 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 703
91.1%
ASCII 69
 
8.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+ 62
89.9%
) 2
 
2.9%
2
 
2.9%
( 2
 
2.9%
, 1
 
1.4%
Hangul
ValueCountFrequency (%)
교 48
 
6.8%
학 46
 
6.5%
관 44
 
6.3%
회 41
 
5.8%
경 29
 
4.1%
마 27
 
3.8%
을 26
 
3.7%
로 23
 
3.3%
대 21
 
3.0%
등 20
 
2.8%
Other values (130) 378
53.8%

수용능력인원
Real number (ℝ)

Distinct36
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1358.5714
Minimum20
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T19:35:39.797545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile40
Q1140
median530
Q31200
95-th percentile6296
Maximum10000
Range9980
Interquartile range (IQR)1060

Descriptive statistics

Standard deviation2290.7832
Coefficient of variation (CV)1.6861706
Kurtosis7.692011
Mean1358.5714
Median Absolute Deviation (MAD)470
Skewness2.793539
Sum66570
Variance5247687.5
MonotonicityNot monotonic
2023-12-12T19:35:39.990961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
100 5
 
10.2%
60 2
 
4.1%
1200 2
 
4.1%
250 2
 
4.1%
1100 2
 
4.1%
5000 2
 
4.1%
1060 2
 
4.1%
10000 2
 
4.1%
40 2
 
4.1%
1250 2
 
4.1%
Other values (26) 26
53.1%
ValueCountFrequency (%)
20 1
 
2.0%
30 1
 
2.0%
40 2
 
4.1%
60 2
 
4.1%
80 1
 
2.0%
100 5
10.2%
140 1
 
2.0%
160 1
 
2.0%
170 1
 
2.0%
200 1
 
2.0%
ValueCountFrequency (%)
10000 2
4.1%
7160 1
2.0%
5000 2
4.1%
3200 1
2.0%
2120 1
2.0%
2100 1
2.0%
2000 1
2.0%
1390 1
2.0%
1250 2
4.1%
1200 2
4.1%

Interactions

2023-12-12T19:35:37.295573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:35:37.102155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:35:37.405483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:35:37.187904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:35:40.101574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
마을명대피소시설명수용능력인원
마을명1.0000.9820.9820.977
대피소0.9821.0001.0000.219
시설명0.9821.0001.0001.000
수용능력인원0.9770.2191.0001.000
2023-12-12T19:35:40.213404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대피소수용능력인원
대피소1.0000.210
수용능력인원0.2101.000

Missing values

2023-12-12T19:35:37.554636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:35:37.659972image/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

마을명대피소시설명수용능력인원
0평거동13진주시 능력개발관+박덕규미술관+양옥새마을회관440
1판문동4진양호 물홍보관+진주시전통예술회관+샛터마을회관+한국도로공사1100
2평거동22진주임마뉴엘교회+대아중학교+대아고등학교2120
3신안동1대아중학교+대아고등학교2000
4이현동3각한마을회관+명석중학교+용우초등학교220
5상봉동2진주보건대학교+봉원초등학교3200
6중앙동4봉래초등학교+수정초등학교+의곡사+봉원중학교1390
7명석면 독산리2산기마을회관+산강마을회관80
8천전동(구 강남동, 망경동)1망경초등학교720
9가호동(구 칠암동)1연암공과대학교10000
마을명대피소시설명수용능력인원
39수곡면 사곡리2우곡경로회관+반성초등학교540
40사봉면 무촌리1진주외국어고등학교400
41일반성면 운천리, 진성면(온수리, 대사리)2개암마을경로당+경남산림박물관1060
42수곡면 창촌리, 일반성면(답천리, 개암리)5원당마을회관+장곡경로당,포실경로당+남산마을경로회관+대사웃골노인회관160
43대곡면 대곡리1산방마을회관20
44금곡면 금곡리1지수중학교100
45지수면 압사리1지수중학교100
46지수면 용봉리1지수중학교100
47지수면 청담리11지수중학교100
48지수면(청담리2, 승산리)1지수중학교100