Overview

Dataset statistics

Number of variables7
Number of observations76
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.4 KiB
Average record size in memory59.7 B

Variable types

Categorical1
Text3
Numeric2
DateTime1

Dataset

Description제주특별자치도 서귀포시 관내에 있는 리사무소에 관련한 데이터로 읍면동, 구분, 주소, 전화번호, 위도, 경도 등의 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15056415/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
위도 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
읍면동 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
구분 has unique valuesUnique
주소 has unique valuesUnique
전화번호 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2023-12-12 07:26:35.352374
Analysis finished2023-12-12 07:26:36.371646
Duration1.02 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

읍면동
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size740.0 B
대정읍
23 
남원읍
17 
성산읍
14 
안덕면
12 
표선면
10 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대정읍 23
30.3%
남원읍 17
22.4%
성산읍 14
18.4%
안덕면 12
15.8%
표선면 10
13.2%

Length

2023-12-12T16:26:36.439384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:26:36.552085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대정읍 23
30.3%
남원읍 17
22.4%
성산읍 14
18.4%
안덕면 12
15.8%
표선면 10
13.2%

구분
Text

UNIQUE 

Distinct76
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size740.0 B
2023-12-12T16:26:36.840266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.5657895
Min length3

Characters and Unicode

Total characters271
Distinct characters67
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

Unique76 ?
Unique (%)100.0%

Sample

1st row상모1리
2nd row상모2리
3rd row상모3리
4th row하모1리
5th row하모2리
ValueCountFrequency (%)
상모1리 1
 
1.3%
온평리 1
 
1.3%
사계리 1
 
1.3%
화순리 1
 
1.3%
신천리 1
 
1.3%
신풍리 1
 
1.3%
삼달2리 1
 
1.3%
삼달1리 1
 
1.3%
신산리 1
 
1.3%
상모2리 1
 
1.3%
Other values (66) 66
86.8%
2023-12-12T16:26:37.313162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 76
28.0%
1 17
 
6.3%
2 17
 
6.3%
신 12
 
4.4%
산 8
 
3.0%
성 7
 
2.6%
모 6
 
2.2%
흥 6
 
2.2%
3 6
 
2.2%
하 6
 
2.2%
Other values (57) 110
40.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 230
84.9%
Decimal Number 40
 
14.8%
Space Separator 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 76
33.0%
신 12
 
5.2%
산 8
 
3.5%
성 7
 
3.0%
모 6
 
2.6%
흥 6
 
2.6%
하 6
 
2.6%
상 5
 
2.2%
동 4
 
1.7%
수 4
 
1.7%
Other values (53) 96
41.7%
Decimal Number
ValueCountFrequency (%)
1 17
42.5%
2 17
42.5%
3 6
 
15.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 230
84.9%
Common 41
 
15.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 76
33.0%
신 12
 
5.2%
산 8
 
3.5%
성 7
 
3.0%
모 6
 
2.6%
흥 6
 
2.6%
하 6
 
2.6%
상 5
 
2.2%
동 4
 
1.7%
수 4
 
1.7%
Other values (53) 96
41.7%
Common
ValueCountFrequency (%)
1 17
41.5%
2 17
41.5%
3 6
 
14.6%
1
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 230
84.9%
ASCII 41
 
15.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 76
33.0%
신 12
 
5.2%
산 8
 
3.5%
성 7
 
3.0%
모 6
 
2.6%
흥 6
 
2.6%
하 6
 
2.6%
상 5
 
2.2%
동 4
 
1.7%
수 4
 
1.7%
Other values (53) 96
41.7%
ASCII
ValueCountFrequency (%)
1 17
41.5%
2 17
41.5%
3 6
 
14.6%
1
 
2.4%

주소
Text

UNIQUE 

Distinct76
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size740.0 B
2023-12-12T16:26:37.672469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length28
Mean length25.368421
Min length22

Characters and Unicode

Total characters1928
Distinct characters102
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

Unique76 ?
Unique (%)100.0%

Sample

1st row제주특별자치도 서귀포시 대정읍 상모인성로 18
2nd row제주특별자치도 서귀포시 대정읍 상모로274번길 10
3rd row제주특별자치도 서귀포시 대정읍 영서중로 45-4
4th row제주특별자치도 서귀포시 대정읍 최남단해안로125번길 47
5th row제주특별자치도 서귀포시 대정읍 신영로94번길 7
ValueCountFrequency (%)
제주특별자치도 76
20.0%
서귀포시 76
20.0%
대정읍 23
 
6.1%
남원읍 17
 
4.5%
성산읍 14
 
3.7%
안덕면 12
 
3.2%
표선면 10
 
2.6%
일주동로 4
 
1.1%
태위로 3
 
0.8%
중산간서로 3
 
0.8%
Other values (130) 142
37.4%
2023-12-12T16:26:38.195716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
304
 
15.8%
서 89
 
4.6%
주 80
 
4.1%
도 78
 
4.0%
자 77
 
4.0%
포 77
 
4.0%
시 77
 
4.0%
제 76
 
3.9%
로 76
 
3.9%
특 76
 
3.9%
Other values (92) 918
47.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1394
72.3%
Space Separator 304
 
15.8%
Decimal Number 220
 
11.4%
Dash Punctuation 10
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 89
 
6.4%
주 80
 
5.7%
도 78
 
5.6%
자 77
 
5.5%
포 77
 
5.5%
시 77
 
5.5%
제 76
 
5.5%
로 76
 
5.5%
특 76
 
5.5%
별 76
 
5.5%
Other values (80) 612
43.9%
Decimal Number
ValueCountFrequency (%)
1 34
15.5%
2 27
12.3%
3 26
11.8%
5 24
10.9%
8 21
9.5%
4 21
9.5%
9 18
8.2%
7 18
8.2%
6 16
7.3%
0 15
6.8%
Space Separator
ValueCountFrequency (%)
304
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1394
72.3%
Common 534
 
27.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 89
 
6.4%
주 80
 
5.7%
도 78
 
5.6%
자 77
 
5.5%
포 77
 
5.5%
시 77
 
5.5%
제 76
 
5.5%
로 76
 
5.5%
특 76
 
5.5%
별 76
 
5.5%
Other values (80) 612
43.9%
Common
ValueCountFrequency (%)
304
56.9%
1 34
 
6.4%
2 27
 
5.1%
3 26
 
4.9%
5 24
 
4.5%
8 21
 
3.9%
4 21
 
3.9%
9 18
 
3.4%
7 18
 
3.4%
6 16
 
3.0%
Other values (2) 25
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1394
72.3%
ASCII 534
 
27.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
304
56.9%
1 34
 
6.4%
2 27
 
5.1%
3 26
 
4.9%
5 24
 
4.5%
8 21
 
3.9%
4 21
 
3.9%
9 18
 
3.4%
7 18
 
3.4%
6 16
 
3.0%
Other values (2) 25
 
4.7%
Hangul
ValueCountFrequency (%)
서 89
 
6.4%
주 80
 
5.7%
도 78
 
5.6%
자 77
 
5.5%
포 77
 
5.5%
시 77
 
5.5%
제 76
 
5.5%
로 76
 
5.5%
특 76
 
5.5%
별 76
 
5.5%
Other values (80) 612
43.9%

전화번호
Text

UNIQUE 

Distinct76
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size740.0 B
2023-12-12T16:26:38.522512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters912
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

Unique76 ?
Unique (%)100.0%

Sample

1st row064-794-3589
2nd row064-794-3490
3rd row064-794-4390
4th row064-794-2755
5th row064-794-2107
ValueCountFrequency (%)
064-794-3589 1
 
1.3%
064-782-2766 1
 
1.3%
064-794-2611 1
 
1.3%
064-794-9008 1
 
1.3%
064-787-0157 1
 
1.3%
064-782-0311 1
 
1.3%
064-782-3886 1
 
1.3%
064-782-3990 1
 
1.3%
064-782-3784 1
 
1.3%
064-794-3490 1
 
1.3%
Other values (66) 66
86.8%
2023-12-12T16:26:38.966664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 152
16.7%
4 138
15.1%
0 122
13.4%
7 117
12.8%
6 113
12.4%
8 57
 
6.2%
2 55
 
6.0%
9 53
 
5.8%
3 48
 
5.3%
1 42
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 760
83.3%
Dash Punctuation 152
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 138
18.2%
0 122
16.1%
7 117
15.4%
6 113
14.9%
8 57
7.5%
2 55
 
7.2%
9 53
 
7.0%
3 48
 
6.3%
1 42
 
5.5%
5 15
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 152
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 912
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 152
16.7%
4 138
15.1%
0 122
13.4%
7 117
12.8%
6 113
12.4%
8 57
 
6.2%
2 55
 
6.0%
9 53
 
5.8%
3 48
 
5.3%
1 42
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 912
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 152
16.7%
4 138
15.1%
0 122
13.4%
7 117
12.8%
6 113
12.4%
8 57
 
6.2%
2 55
 
6.0%
9 53
 
5.8%
3 48
 
5.3%
1 42
 
4.6%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct76
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.30248
Minimum33.118036
Maximum33.476152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size816.0 B
2023-12-12T16:26:39.136882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.118036
5-th percentile33.221391
Q133.258299
median33.287206
Q333.330221
95-th percentile33.445099
Maximum33.476152
Range0.35811616
Interquartile range (IQR)0.071921633

Descriptive statistics

Standard deviation0.071089767
Coefficient of variation (CV)0.0021346688
Kurtosis0.40510364
Mean33.30248
Median Absolute Deviation (MAD)0.037263865
Skewness0.59038267
Sum2530.9885
Variance0.005053755
MonotonicityNot monotonic
2023-12-12T16:26:39.613975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.22637138 1
 
1.3%
33.38017987 1
 
1.3%
33.25464328 1
 
1.3%
33.23109441 1
 
1.3%
33.2463413 1
 
1.3%
33.34418406 1
 
1.3%
33.36120098 1
 
1.3%
33.36940843 1
 
1.3%
33.37409847 1
 
1.3%
33.40117874 1
 
1.3%
Other values (66) 66
86.8%
ValueCountFrequency (%)
33.11803584 1
1.3%
33.1664382 1
1.3%
33.21562549 1
1.3%
33.22025119 1
1.3%
33.22177088 1
1.3%
33.22188439 1
1.3%
33.22421306 1
1.3%
33.22637138 1
1.3%
33.22853984 1
1.3%
33.23109441 1
1.3%
ValueCountFrequency (%)
33.476152 1
1.3%
33.46363045 1
1.3%
33.46124364 1
1.3%
33.44588031 1
1.3%
33.44483911 1
1.3%
33.44213101 1
1.3%
33.43851848 1
1.3%
33.41316973 1
1.3%
33.40812252 1
1.3%
33.40117874 1
1.3%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct76
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.55091
Minimum126.17475
Maximum126.93405
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size816.0 B
2023-12-12T16:26:39.761746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.17475
5-th percentile126.20882
Q1126.27336
median126.63385
Q3126.79206
95-th percentile126.90314
Maximum126.93405
Range0.7592944
Interquartile range (IQR)0.51869985

Descriptive statistics

Standard deviation0.26509323
Coefficient of variation (CV)0.0020947557
Kurtosis-1.7437381
Mean126.55091
Median Absolute Deviation (MAD)0.2652547
Skewness-0.04113812
Sum9617.8689
Variance0.070274419
MonotonicityNot monotonic
2023-12-12T16:26:39.947575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.2694824 1
 
1.3%
126.8755463 1
 
1.3%
126.3064933 1
 
1.3%
126.2980953 1
 
1.3%
126.3363012 1
 
1.3%
126.8497937 1
 
1.3%
126.8354438 1
 
1.3%
126.8687192 1
 
1.3%
126.8472648 1
 
1.3%
126.8707916 1
 
1.3%
Other values (66) 66
86.8%
ValueCountFrequency (%)
126.174754 1
1.3%
126.1845098 1
1.3%
126.1917484 1
1.3%
126.2029867 1
1.3%
126.210761 1
1.3%
126.223601 1
1.3%
126.2352653 1
1.3%
126.2366024 1
1.3%
126.2397066 1
1.3%
126.2430731 1
1.3%
ValueCountFrequency (%)
126.9340484 1
1.3%
126.9174462 1
1.3%
126.9148198 1
1.3%
126.9120221 1
1.3%
126.9001736 1
1.3%
126.8989935 1
1.3%
126.8829278 1
1.3%
126.8755463 1
1.3%
126.8707916 1
1.3%
126.8687192 1
1.3%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size740.0 B
Minimum2023-05-01 00:00:00
Maximum2023-05-01 00:00:00
2023-12-12T16:26:40.081101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:26:40.188753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T16:26:35.925905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:26:35.734342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:26:36.023438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:26:35.822639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:26:40.272776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동구분주소전화번호위도경도
읍면동1.0001.0001.0001.0000.8840.905
구분1.0001.0001.0001.0001.0001.000
주소1.0001.0001.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.0001.000
위도0.8841.0001.0001.0001.0000.679
경도0.9051.0001.0001.0000.6791.000
2023-12-12T16:26:40.378953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도읍면동
위도1.0000.8540.542
경도0.8541.0000.819
읍면동0.5420.8191.000

Missing values

2023-12-12T16:26:36.166681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:26:36.310607image/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

읍면동구분주소전화번호위도경도데이터기준일자
0대정읍상모1리제주특별자치도 서귀포시 대정읍 상모인성로 18064-794-358933.226371126.2694822023-05-01
1대정읍상모2리제주특별자치도 서귀포시 대정읍 상모로274번길 10064-794-349033.224213126.2599422023-05-01
2대정읍상모3리제주특별자치도 서귀포시 대정읍 영서중로 45-4064-794-439033.221884126.2584322023-05-01
3대정읍하모1리제주특별자치도 서귀포시 대정읍 최남단해안로125번길 47064-794-275533.215625126.2574972023-05-01
4대정읍하모2리제주특별자치도 서귀포시 대정읍 신영로94번길 7064-794-210733.220251126.2534542023-05-01
5대정읍하모3리제주특별자치도 서귀포시 대정읍 하모상가로 9064-794-799533.221771126.2497532023-05-01
6대정읍동일1리제주특별자치도 서귀포시 대정읍 동일하모로 99064-794-227633.22854126.2430732023-05-01
7대정읍동일2리제주특별자치도 서귀포시 대정읍 암반수마농로329번길 18064-794-457733.257842126.2366022023-05-01
8대정읍일과1리제주특별자치도 서귀포시 대정읍 암반수마농로55번길 34064-794-237233.234671126.2397072023-05-01
9대정읍일과2리제주특별자치도 서귀포시 대정읍 서림로 8064-794-309433.246316126.2236012023-05-01
읍면동구분주소전화번호위도경도데이터기준일자
66표선면표선리제주특별자치도 서귀포시 표선면 표선동서로 234064-787-002433.32472126.8323522023-05-01
67표선면하천리제주특별자치도 서귀포시 표선면 하천로 59064-787-040433.346522126.840962023-05-01
68표선면성읍1리제주특별자치도 서귀포시 표선면 중산간동로 4658064-787-130633.389957126.7977812023-05-01
69표선면성읍2리제주특별자치도 서귀포시 표선면 성읍이리로58번길 8064-787-136733.41317126.7719382023-05-01
70표선면가시리제주특별자치도 서귀포시 표선면 가시로565번길 20064-787-130533.353609126.7708822023-05-01
71표선면세화1리제주특별자치도 서귀포시 표선면 세성로 302064-787-330933.332122126.7979942023-05-01
72표선면세화2리제주특별자치도 서귀포시 표선면 일주동로 6276064-787-331033.307043126.8012292023-05-01
73표선면세화3리제주특별자치도 서귀포시 표선면 세화로 168064-787-150433.31885126.7901582023-05-01
74표선면토산1리제주특별자치도 서귀포시 표선면 중산간동로5570번길 2064-787-330733.326466126.7657392023-05-01
75표선면토산2리제주특별자치도 서귀포시 표선면 토산중앙로16번길 1064-787-330833.308875126.7797212023-05-01