Overview

Dataset statistics

Number of variables7
Number of observations99
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.7 KiB
Average record size in memory59.3 B

Variable types

Categorical3
Text2
Numeric2

Dataset

DescriptionSample
Author㈜지오시스템리서치
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT09GSR005

Alerts

SIDO_NM is highly overall correlated with WASH_LC_LA and 2 other fieldsHigh correlation
SGG_NM is highly overall correlated with WASH_LC_LA and 3 other fieldsHigh correlation
WASH_LC_LA is highly overall correlated with SIDO_NM and 1 other fieldsHigh correlation
WASH_LC_LO is highly overall correlated with SIDO_NM and 1 other fieldsHigh correlation
WASH_TY_CD is highly overall correlated with SGG_NMHigh correlation
WASH_PHOTO_FILE_NM has unique valuesUnique

Reproduction

Analysis started2024-03-13 12:47:15.703258
Analysis finished2024-03-13 12:47:17.072917
Duration1.37 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SIDO_NM
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size924.0 B
경상북도
43 
전라남도
28 
충청남도
19 
경기도
8 
인천광역시
 
1

Length

Max length5
Median length4
Mean length3.9292929
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상북도 43
43.4%
전라남도 28
28.3%
충청남도 19
19.2%
경기도 8
 
8.1%
인천광역시 1
 
1.0%

Length

2024-03-13T21:47:17.213230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:47:17.418645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 43
43.4%
전라남도 28
28.3%
충청남도 19
19.2%
경기도 8
 
8.1%
인천광역시 1
 
1.0%

SGG_NM
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size924.0 B
울진군
24 
무안군
14 
영덕군
11 
태안군
11 
신안군
8 
Other values (12)
31 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique5 ?
Unique (%)5.1%

Sample

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

Common Values

ValueCountFrequency (%)
울진군 24
24.2%
무안군 14
14.1%
영덕군 11
11.1%
태안군 11
11.1%
신안군 8
 
8.1%
서천군 5
 
5.1%
화성시 4
 
4.0%
포항시 4
 
4.0%
안산시 4
 
4.0%
경주시 3
 
3.0%
Other values (7) 11
11.1%

Length

2024-03-13T21:47:17.595629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
울진군 24
24.2%
무안군 14
14.1%
영덕군 11
11.1%
태안군 11
11.1%
신안군 8
 
8.1%
서천군 5
 
5.1%
포항시 4
 
4.0%
안산시 4
 
4.0%
화성시 4
 
4.0%
경주시 3
 
3.0%
Other values (7) 11
11.1%

TRGET_AREA_NM
Text

Distinct55
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Memory size924.0 B
2024-03-13T21:47:17.884308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.1818182
Min length2

Characters and Unicode

Total characters315
Distinct characters94
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 (%)26.3%

Sample

1st row후정
2nd row후정
3rd row죽변항~봉평리
4th row온양리
5th row온양리
ValueCountFrequency (%)
서위 4
 
4.0%
궁평리 4
 
4.0%
장사 4
 
4.0%
마산~용정 4
 
4.0%
봉산리 3
 
3.0%
금음리 3
 
3.0%
명사십리 3
 
3.0%
도둔리 3
 
3.0%
진복~오산 3
 
3.0%
온양리 3
 
3.0%
Other values (45) 65
65.7%
2024-03-13T21:47:18.355257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 44
 
14.0%
~ 17
 
5.4%
산 15
 
4.8%
도 10
 
3.2%
사 8
 
2.5%
포 7
 
2.2%
송 7
 
2.2%
구 7
 
2.2%
정 7
 
2.2%
평 6
 
1.9%
Other values (84) 187
59.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 297
94.3%
Math Symbol 17
 
5.4%
Decimal Number 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 44
 
14.8%
산 15
 
5.1%
도 10
 
3.4%
사 8
 
2.7%
포 7
 
2.4%
송 7
 
2.4%
구 7
 
2.4%
정 7
 
2.4%
평 6
 
2.0%
금 6
 
2.0%
Other values (82) 180
60.6%
Math Symbol
ValueCountFrequency (%)
~ 17
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 297
94.3%
Common 18
 
5.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 44
 
14.8%
산 15
 
5.1%
도 10
 
3.4%
사 8
 
2.7%
포 7
 
2.4%
송 7
 
2.4%
구 7
 
2.4%
정 7
 
2.4%
평 6
 
2.0%
금 6
 
2.0%
Other values (82) 180
60.6%
Common
ValueCountFrequency (%)
~ 17
94.4%
1 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 297
94.3%
ASCII 18
 
5.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 44
 
14.8%
산 15
 
5.1%
도 10
 
3.4%
사 8
 
2.7%
포 7
 
2.4%
송 7
 
2.4%
구 7
 
2.4%
정 7
 
2.4%
평 6
 
2.0%
금 6
 
2.0%
Other values (82) 180
60.6%
ASCII
ValueCountFrequency (%)
~ 17
94.4%
1 1
 
5.6%

WASH_TY_CD
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
토사포락
50 
백사장침식
33 
사구포락
10 
호안붕괴
6 

Length

Max length5
Median length4
Mean length4.3333333
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
토사포락 50
50.5%
백사장침식 33
33.3%
사구포락 10
 
10.1%
호안붕괴 6
 
6.1%

Length

2024-03-13T21:47:18.581418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:47:18.696087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
토사포락 50
50.5%
백사장침식 33
33.3%
사구포락 10
 
10.1%
호안붕괴 6
 
6.1%

WASH_LC_LA
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.187556
Minimum34.247044
Maximum37.475781
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2024-03-13T21:47:18.888068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.247044
5-th percentile34.777009
Q135.079196
median36.436725
Q336.898538
95-th percentile37.177524
Maximum37.475781
Range3.2287361
Interquartile range (IQR)1.8193417

Descriptive statistics

Standard deviation0.8810362
Coefficient of variation (CV)0.024346386
Kurtosis-1.0627527
Mean36.187556
Median Absolute Deviation (MAD)0.49185278
Skewness-0.611169
Sum3582.5681
Variance0.77622479
MonotonicityNot monotonic
2024-03-13T21:47:19.114535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.89966667 2
 
2.0%
37.07641111 1
 
1.0%
35.04608611 1
 
1.0%
35.78028889 1
 
1.0%
34.32617778 1
 
1.0%
34.24704444 1
 
1.0%
34.79276667 1
 
1.0%
34.79211389 1
 
1.0%
34.77757222 1
 
1.0%
34.77691944 1
 
1.0%
Other values (88) 88
88.9%
ValueCountFrequency (%)
34.24704444 1
1.0%
34.32617778 1
1.0%
34.68105278 1
1.0%
34.68284167 1
1.0%
34.77691944 1
1.0%
34.77701944 1
1.0%
34.77757222 1
1.0%
34.79211389 1
1.0%
34.79276667 1
1.0%
34.84509167 1
1.0%
ValueCountFrequency (%)
37.47578056 1
1.0%
37.28316111 1
1.0%
37.28311389 1
1.0%
37.27787222 1
1.0%
37.27756944 1
1.0%
37.16640833 1
1.0%
37.13523056 1
1.0%
37.132 1
1.0%
37.12483333 1
1.0%
37.12091111 1
1.0%

WASH_LC_LO
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.67078
Minimum125.93974
Maximum130.89589
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2024-03-13T21:47:19.351457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum125.93974
5-th percentile126.05227
Q1126.34093
median126.56829
Q3129.41414
95-th percentile129.47115
Maximum130.89589
Range4.9561444
Interquartile range (IQR)3.0732167

Descriptive statistics

Standard deviation1.5582413
Coefficient of variation (CV)0.012205153
Kurtosis-1.8175626
Mean127.67078
Median Absolute Deviation (MAD)0.434375
Skewness0.31661629
Sum12639.407
Variance2.4281161
MonotonicityNot monotonic
2024-03-13T21:47:19.601121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.419525 2
 
2.0%
129.4057 1
 
1.0%
126.4219444 1
 
1.0%
126.4923861 1
 
1.0%
126.8277556 1
 
1.0%
126.0533583 1
 
1.0%
126.3392694 1
 
1.0%
126.3380417 1
 
1.0%
126.3069639 1
 
1.0%
125.9397417 1
 
1.0%
Other values (88) 88
88.9%
ValueCountFrequency (%)
125.9397417 1
1.0%
125.9400139 1
1.0%
126.0093306 1
1.0%
126.0156583 1
1.0%
126.0425194 1
1.0%
126.0533583 1
1.0%
126.1337806 1
1.0%
126.1339139 1
1.0%
126.1369083 1
1.0%
126.1597028 1
1.0%
ValueCountFrequency (%)
130.8958861 1
1.0%
129.4846611 1
1.0%
129.4755972 1
1.0%
129.4738889 1
1.0%
129.4716028 1
1.0%
129.4710944 1
1.0%
129.4694583 1
1.0%
129.4694472 1
1.0%
129.4677472 1
1.0%
129.4620167 1
1.0%

WASH_PHOTO_FILE_NM
Text

UNIQUE 

Distinct99
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
2024-03-13T21:47:19.972748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length23
Mean length20.535354
Min length18

Characters and Unicode

Total characters2033
Distinct characters110
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique99 ?
Unique (%)100.0%

Sample

1st row20210622_후정_0076.JPG
2nd row20210622_후정_0160.JPG
3rd row20211109_죽변봉평_0662.jpg
4th row20210622_온양리_285.JPG
5th row20210622_온양리_063.JPG
ValueCountFrequency (%)
20210622_후정_0076.jpg 1
 
1.0%
20210518_송림리_0043.jpg 1
 
1.0%
20211102_전촌나정_395.jpg 1
 
1.0%
20210407_신지명사십리_0171.jpg 1
 
1.0%
20210601_관매도_0821.jpg 1
 
1.0%
2021057_장좌도_0176.jpg 1
 
1.0%
2021057_장좌도_0066.jpg 1
 
1.0%
20210527_혼불_284.jpg 1
 
1.0%
20210603_신안명사십리_323.jpg 1
 
1.0%
20210421_백길009.jpg 1
 
1.0%
Other values (89) 89
89.9%
2024-03-13T21:47:20.480406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 321
15.8%
2 264
13.0%
1 241
11.9%
_ 197
 
9.7%
. 99
 
4.9%
6 76
 
3.7%
J 71
 
3.5%
P 71
 
3.5%
G 71
 
3.5%
4 56
 
2.8%
Other values (100) 566
27.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1153
56.7%
Other Letter 280
 
13.8%
Uppercase Letter 213
 
10.5%
Connector Punctuation 197
 
9.7%
Other Punctuation 99
 
4.9%
Lowercase Letter 84
 
4.1%
Math Symbol 7
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 37
 
13.2%
산 12
 
4.3%
정 10
 
3.6%
도 10
 
3.6%
사 8
 
2.9%
송 7
 
2.5%
포 7
 
2.5%
신 7
 
2.5%
금 6
 
2.1%
평 6
 
2.1%
Other values (81) 170
60.7%
Decimal Number
ValueCountFrequency (%)
0 321
27.8%
2 264
22.9%
1 241
20.9%
6 76
 
6.6%
4 56
 
4.9%
9 42
 
3.6%
5 40
 
3.5%
7 40
 
3.5%
3 40
 
3.5%
8 33
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
J 71
33.3%
P 71
33.3%
G 71
33.3%
Lowercase Letter
ValueCountFrequency (%)
j 28
33.3%
p 28
33.3%
g 28
33.3%
Connector Punctuation
ValueCountFrequency (%)
_ 197
100.0%
Other Punctuation
ValueCountFrequency (%)
. 99
100.0%
Math Symbol
ValueCountFrequency (%)
~ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1456
71.6%
Latin 297
 
14.6%
Hangul 280
 
13.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 37
 
13.2%
산 12
 
4.3%
정 10
 
3.6%
도 10
 
3.6%
사 8
 
2.9%
송 7
 
2.5%
포 7
 
2.5%
신 7
 
2.5%
금 6
 
2.1%
평 6
 
2.1%
Other values (81) 170
60.7%
Common
ValueCountFrequency (%)
0 321
22.0%
2 264
18.1%
1 241
16.6%
_ 197
13.5%
. 99
 
6.8%
6 76
 
5.2%
4 56
 
3.8%
9 42
 
2.9%
5 40
 
2.7%
7 40
 
2.7%
Other values (3) 80
 
5.5%
Latin
ValueCountFrequency (%)
J 71
23.9%
P 71
23.9%
G 71
23.9%
j 28
 
9.4%
p 28
 
9.4%
g 28
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1753
86.2%
Hangul 280
 
13.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 321
18.3%
2 264
15.1%
1 241
13.7%
_ 197
11.2%
. 99
 
5.6%
6 76
 
4.3%
J 71
 
4.1%
P 71
 
4.1%
G 71
 
4.1%
4 56
 
3.2%
Other values (9) 286
16.3%
Hangul
ValueCountFrequency (%)
리 37
 
13.2%
산 12
 
4.3%
정 10
 
3.6%
도 10
 
3.6%
사 8
 
2.9%
송 7
 
2.5%
포 7
 
2.5%
신 7
 
2.5%
금 6
 
2.1%
평 6
 
2.1%
Other values (81) 170
60.7%

Interactions

2024-03-13T21:47:16.473382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:16.194717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:16.594242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:47:16.338215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:47:20.657110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SIDO_NMSGG_NMTRGET_AREA_NMWASH_TY_CDWASH_LC_LAWASH_LC_LOWASH_PHOTO_FILE_NM
SIDO_NM1.0001.0001.0000.5540.8930.9041.000
SGG_NM1.0001.0000.9970.7830.9710.9641.000
TRGET_AREA_NM1.0000.9971.0000.7120.9950.9991.000
WASH_TY_CD0.5540.7830.7121.0000.5580.5181.000
WASH_LC_LA0.8930.9710.9950.5581.0000.8891.000
WASH_LC_LO0.9040.9640.9990.5180.8891.0001.000
WASH_PHOTO_FILE_NM1.0001.0001.0001.0001.0001.0001.000
2024-03-13T21:47:20.835582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WASH_TY_CDSIDO_NMSGG_NM
WASH_TY_CD1.0000.4780.527
SIDO_NM0.4781.0000.934
SGG_NM0.5270.9341.000
2024-03-13T21:47:20.988565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WASH_LC_LAWASH_LC_LOSIDO_NMSGG_NMWASH_TY_CD
WASH_LC_LA1.0000.4070.5620.8310.355
WASH_LC_LO0.4071.0000.5760.8370.443
SIDO_NM0.5620.5761.0000.9340.478
SGG_NM0.8310.8370.9341.0000.527
WASH_TY_CD0.3550.4430.4780.5271.000

Missing values

2024-03-13T21:47:16.817887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:47:16.992567image/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

SIDO_NMSGG_NMTRGET_AREA_NMWASH_TY_CDWASH_LC_LAWASH_LC_LOWASH_PHOTO_FILE_NM
0경상북도울진군후정백사장침식37.076411129.405720210622_후정_0076.JPG
1경상북도울진군후정백사장침식37.074528129.40934220210622_후정_0160.JPG
2경상북도울진군죽변항~봉평리백사장침식37.043389129.41314720211109_죽변봉평_0662.jpg
3경상북도울진군온양리백사장침식37.022778129.41019420210622_온양리_285.JPG
4경상북도울진군온양리백사장침식37.030078129.4120210622_온양리_063.JPG
5경상북도울진군온양리백사장침식37.014403129.41398320210622_온양리_478.JPG
6경상북도울진군산포리백사장침식36.966111129.41430320210702_산포리_0443.JPG
7경상북도울진군진복~오산백사장침식36.899667129.41952520211110_오산리_0836.jpg
8경상북도울진군진복~오산토사포락36.904678129.41703120210618_오산리_0616.jpg
9경상북도울진군덕신리백사장침식36.878353129.41923120210618_덕신리_0991.jpg
SIDO_NMSGG_NMTRGET_AREA_NMWASH_TY_CDWASH_LC_LAWASH_LC_LOWASH_PHOTO_FILE_NM
89전라남도무안군현화토사포락35.048789126.42501920211013_현화_0135.JPG
90전라남도신안군자라토사포락34.681053126.16799420211018_자라_0301.jpg
91전라남도신안군자라토사포락34.682842126.16507220211018_자라_0530.jpg
92경기도안산시서위토사포락37.283161126.56803120210908_서위_426.JPG
93경기도안산시서위토사포락37.277872126.56381120210908_서위_215.JPG
94경기도화성시궁평리토사포락37.132126.67966120210906_궁평리_094.jpg
95전라남도신안군우전토사포락34.971233126.13690820211020_우전_0494.jpg
96경상북도울진군덕신리토사포락36.878939129.41922520211110_덕신리_0745.jpg
97경상북도울진군진복~오산백사장침식36.899667129.41952520211110_오산리_0841.jpg
98경상북도영덕군금진~하저리백사장침식36.386497129.40661120211104_금진~하저_0360.jpg