Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells208
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory722.7 KiB
Average record size in memory74.0 B

Variable types

Numeric2
Categorical2
Text3
DateTime1

Dataset

Description인천광역시 UTIS 시스템에 등록된 법정동코드, 행정동코드, 시도명, 시군구명, 읍면동명, 동리명, 생성일자, 등록일시 등에 관한 데이터입니다.
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15089879&srcSe=7661IVAWM27C61E190

Alerts

등록일시 has constant value ""Constant
법정동코드 is highly overall correlated with 행정동코드 and 1 other fieldsHigh correlation
행정동코드 is highly overall correlated with 법정동코드 and 1 other fieldsHigh correlation
시도명 is highly overall correlated with 법정동코드 and 1 other fieldsHigh correlation
읍면동명 has 134 (1.3%) missing valuesMissing

Reproduction

Analysis started2024-01-28 15:17:32.570327
Analysis finished2024-01-28 15:17:34.043376
Duration1.47 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct9665
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.266174 × 109
Minimum1.111 × 109
Maximum5.013032 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-29T00:17:34.100391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.111 × 109
5-th percentile2.6500104 × 109
Q14.213031 × 109
median4.5113116 × 109
Q34.715043 × 109
95-th percentile4.882035 × 109
Maximum5.013032 × 109
Range3.902032 × 109
Interquartile range (IQR)5.02012 × 108

Descriptive statistics

Standard deviation8.0587859 × 108
Coefficient of variation (CV)0.1888996
Kurtosis6.2301796
Mean4.266174 × 109
Median Absolute Deviation (MAD)2.2827956 × 108
Skewness-2.5121108
Sum4.266174 × 1013
Variance6.4944029 × 1017
MonotonicityNot monotonic
2024-01-29T00:17:34.212648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4611010200 7
 
0.1%
1135010500 6
 
0.1%
2720010300 6
 
0.1%
1150010300 6
 
0.1%
4119510800 5
 
0.1%
4691000000 5
 
0.1%
4117110100 5
 
0.1%
1147010300 5
 
0.1%
1162010200 5
 
0.1%
1162010100 5
 
0.1%
Other values (9655) 9945
99.5%
ValueCountFrequency (%)
1111000000 1
< 0.1%
1111010200 1
< 0.1%
1111010500 1
< 0.1%
1111010600 1
< 0.1%
1111010700 1
< 0.1%
1111010800 1
< 0.1%
1111010900 1
< 0.1%
1111011200 1
< 0.1%
1111011300 1
< 0.1%
1111011600 1
< 0.1%
ValueCountFrequency (%)
5013032024 1
< 0.1%
5013032023 1
< 0.1%
5013031030 1
< 0.1%
5013031029 1
< 0.1%
5013031028 1
< 0.1%
5013031026 1
< 0.1%
5013031023 1
< 0.1%
5013031022 1
< 0.1%
5013031021 1
< 0.1%
5013025928 1
< 0.1%

행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct2881
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2661846 × 109
Minimum1.111 × 109
Maximum5.013062 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-29T00:17:34.356604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.111 × 109
5-th percentile2.6500749 × 109
Q14.213035 × 109
median4.5113616 × 109
Q34.7150442 × 109
95-th percentile4.882035 × 109
Maximum5.013062 × 109
Range3.902062 × 109
Interquartile range (IQR)5.0200925 × 108

Descriptive statistics

Standard deviation8.0586757 × 108
Coefficient of variation (CV)0.18889655
Kurtosis6.2302257
Mean4.2661846 × 109
Median Absolute Deviation (MAD)2.283296 × 108
Skewness-2.5121151
Sum4.2661846 × 1013
Variance6.4942255 × 1017
MonotonicityNot monotonic
2024-01-29T00:17:34.488971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4311425300 19
 
0.2%
4690036000 17
 
0.2%
4167025000 17
 
0.2%
4315032000 16
 
0.2%
4311132000 16
 
0.2%
3611034000 16
 
0.2%
4372039000 15
 
0.1%
4280025000 15
 
0.1%
4611065500 15
 
0.1%
4273025000 14
 
0.1%
Other values (2871) 9840
98.4%
ValueCountFrequency (%)
1111000000 1
 
< 0.1%
1111051500 5
 
0.1%
1111053000 7
0.1%
1111054000 3
 
< 0.1%
1111055000 1
 
< 0.1%
1111056000 1
 
< 0.1%
1111058000 2
 
< 0.1%
1111060000 1
 
< 0.1%
1111061500 14
0.1%
1111063000 2
 
< 0.1%
ValueCountFrequency (%)
5013062000 2
< 0.1%
5013061000 2
< 0.1%
5013060000 1
< 0.1%
5013059000 1
< 0.1%
5013058000 1
< 0.1%
5013056000 1
< 0.1%
5013055000 1
< 0.1%
5013054000 1
< 0.1%
5013051000 2
< 0.1%
5013032000 2
< 0.1%

시도명
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경상북도
1518 
전라남도
1409 
경상남도
1160 
경기도
1143 
충청남도
1059 
Other values (12)
3711 

Length

Max length7
Median length4
Mean length3.983
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row충청북도
2nd row충청남도
3rd row전라남도
4th row세종특별자치시
5th row전라북도

Common Values

ValueCountFrequency (%)
경상북도 1518
15.2%
전라남도 1409
14.1%
경상남도 1160
11.6%
경기도 1143
11.4%
충청남도 1059
10.6%
전라북도 866
8.7%
충청북도 756
7.6%
강원도 715
7.1%
서울특별시 363
 
3.6%
대구광역시 182
 
1.8%
Other values (7) 829
8.3%

Length

2024-01-29T00:17:34.618612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경상북도 1518
15.2%
전라남도 1409
14.1%
경상남도 1160
11.6%
경기도 1143
11.4%
충청남도 1059
10.6%
전라북도 866
8.7%
충청북도 756
7.6%
강원도 715
7.1%
서울특별시 363
 
3.6%
대구광역시 182
 
1.8%
Other values (7) 829
8.3%

시군구명
Text

Distinct232
Distinct (%)2.3%
Missing74
Missing (%)0.7%
Memory size156.2 KiB
2024-01-29T00:17:34.918613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.3165424
Min length2

Characters and Unicode

Total characters32920
Distinct characters141
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row충주시
2nd row예산군
3rd row강진군
4th row고창군
5th row창원시 마산합포구
ValueCountFrequency (%)
창원시 175
 
1.6%
청주시 167
 
1.6%
중구 145
 
1.3%
북구 132
 
1.2%
포항시 114
 
1.1%
상주시 114
 
1.1%
고성군 112
 
1.0%
남구 108
 
1.0%
영천시 103
 
1.0%
천안시 102
 
0.9%
Other values (228) 9498
88.2%
2024-01-29T00:17:35.310989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 4621
 
14.0%
시 4425
 
13.4%
구 2021
 
6.1%
주 1360
 
4.1%
천 1177
 
3.6%
산 1017
 
3.1%
성 1007
 
3.1%
844
 
2.6%
양 766
 
2.3%
안 697
 
2.1%
Other values (131) 14985
45.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 32076
97.4%
Space Separator 844
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 4621
 
14.4%
시 4425
 
13.8%
구 2021
 
6.3%
주 1360
 
4.2%
천 1177
 
3.7%
산 1017
 
3.2%
성 1007
 
3.1%
양 766
 
2.4%
안 697
 
2.2%
영 576
 
1.8%
Other values (130) 14409
44.9%
Space Separator
ValueCountFrequency (%)
844
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 32076
97.4%
Common 844
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 4621
 
14.4%
시 4425
 
13.8%
구 2021
 
6.3%
주 1360
 
4.2%
천 1177
 
3.7%
산 1017
 
3.2%
성 1007
 
3.1%
양 766
 
2.4%
안 697
 
2.2%
영 576
 
1.8%
Other values (130) 14409
44.9%
Common
ValueCountFrequency (%)
844
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 32076
97.4%
ASCII 844
 
2.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 4621
 
14.4%
시 4425
 
13.8%
구 2021
 
6.3%
주 1360
 
4.2%
천 1177
 
3.7%
산 1017
 
3.2%
성 1007
 
3.1%
양 766
 
2.4%
안 697
 
2.2%
영 576
 
1.8%
Other values (130) 14409
44.9%
ASCII
ValueCountFrequency (%)
844
100.0%

읍면동명
Text

MISSING 

Distinct2473
Distinct (%)25.1%
Missing134
Missing (%)1.3%
Memory size156.2 KiB
2024-01-29T00:17:35.583591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length3
Mean length3.1280154
Min length2

Characters and Unicode

Total characters30861
Distinct characters336
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

Unique871 ?
Unique (%)8.8%

Sample

1st row신니면
2nd row대술면
3rd row작천면
4th row한솔동
5th row성송면
ValueCountFrequency (%)
중앙동 77
 
0.8%
남면 76
 
0.8%
북면 54
 
0.5%
서면 41
 
0.4%
동면 26
 
0.3%
금성면 25
 
0.3%
청풍면 22
 
0.2%
대덕면 22
 
0.2%
봉산면 21
 
0.2%
청산면 21
 
0.2%
Other values (2463) 9481
96.1%
2024-01-29T00:17:35.959218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
면 6253
 
20.3%
동 2689
 
8.7%
읍 1419
 
4.6%
산 904
 
2.9%
성 574
 
1.9%
남 521
 
1.7%
천 453
 
1.5%
양 417
 
1.4%
북 380
 
1.2%
서 361
 
1.2%
Other values (326) 16890
54.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29965
97.1%
Decimal Number 818
 
2.7%
Other Punctuation 78
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 6253
 
20.9%
동 2689
 
9.0%
읍 1419
 
4.7%
산 904
 
3.0%
성 574
 
1.9%
남 521
 
1.7%
천 453
 
1.5%
양 417
 
1.4%
북 380
 
1.3%
서 361
 
1.2%
Other values (314) 15994
53.4%
Decimal Number
ValueCountFrequency (%)
1 295
36.1%
2 271
33.1%
3 128
15.6%
4 68
 
8.3%
5 22
 
2.7%
6 16
 
2.0%
7 9
 
1.1%
8 4
 
0.5%
9 3
 
0.4%
0 2
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 75
96.2%
, 3
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29965
97.1%
Common 896
 
2.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 6253
 
20.9%
동 2689
 
9.0%
읍 1419
 
4.7%
산 904
 
3.0%
성 574
 
1.9%
남 521
 
1.7%
천 453
 
1.5%
양 417
 
1.4%
북 380
 
1.3%
서 361
 
1.2%
Other values (314) 15994
53.4%
Common
ValueCountFrequency (%)
1 295
32.9%
2 271
30.2%
3 128
14.3%
. 75
 
8.4%
4 68
 
7.6%
5 22
 
2.5%
6 16
 
1.8%
7 9
 
1.0%
8 4
 
0.4%
, 3
 
0.3%
Other values (2) 5
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29965
97.1%
ASCII 896
 
2.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
면 6253
 
20.9%
동 2689
 
9.0%
읍 1419
 
4.7%
산 904
 
3.0%
성 574
 
1.9%
남 521
 
1.7%
천 453
 
1.5%
양 417
 
1.4%
북 380
 
1.3%
서 361
 
1.2%
Other values (314) 15994
53.4%
ASCII
ValueCountFrequency (%)
1 295
32.9%
2 271
30.2%
3 128
14.3%
. 75
 
8.4%
4 68
 
7.6%
5 22
 
2.5%
6 16
 
1.8%
7 9
 
1.0%
8 4
 
0.4%
, 3
 
0.3%
Other values (2) 5
 
0.6%

동리명
Text

Distinct6500
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-29T00:17:36.232987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.0359
Min length2

Characters and Unicode

Total characters30359
Distinct characters385
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4902 ?
Unique (%)49.0%

Sample

1st row모남리
2nd row송석리
3rd row내기리
4th row한솔동
5th row판정리
ValueCountFrequency (%)
대곡리 19
 
0.2%
금곡리 18
 
0.2%
신촌리 16
 
0.2%
신흥리 16
 
0.2%
용산리 16
 
0.2%
마산리 16
 
0.2%
신기리 15
 
0.1%
덕산리 15
 
0.1%
남산리 14
 
0.1%
화산리 13
 
0.1%
Other values (6490) 9842
98.4%
2024-01-29T00:17:36.604812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 7044
23.2%
동 2626
 
8.6%
산 803
 
2.6%
면 564
 
1.9%
곡 514
 
1.7%
신 493
 
1.6%
대 435
 
1.4%
천 430
 
1.4%
가 386
 
1.3%
정 384
 
1.3%
Other values (375) 16680
54.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30127
99.2%
Decimal Number 226
 
0.7%
Open Punctuation 3
 
< 0.1%
Close Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 7044
23.4%
동 2626
 
8.7%
산 803
 
2.7%
면 564
 
1.9%
곡 514
 
1.7%
신 493
 
1.6%
대 435
 
1.4%
천 430
 
1.4%
가 386
 
1.3%
정 384
 
1.3%
Other values (366) 16448
54.6%
Decimal Number
ValueCountFrequency (%)
1 74
32.7%
2 60
26.5%
3 50
22.1%
4 25
 
11.1%
6 6
 
2.7%
5 6
 
2.7%
7 5
 
2.2%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30121
99.2%
Common 232
 
0.8%
Han 6
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 7044
23.4%
동 2626
 
8.7%
산 803
 
2.7%
면 564
 
1.9%
곡 514
 
1.7%
신 493
 
1.6%
대 435
 
1.4%
천 430
 
1.4%
가 386
 
1.3%
정 384
 
1.3%
Other values (361) 16442
54.6%
Common
ValueCountFrequency (%)
1 74
31.9%
2 60
25.9%
3 50
21.6%
4 25
 
10.8%
6 6
 
2.6%
5 6
 
2.6%
7 5
 
2.2%
( 3
 
1.3%
) 3
 
1.3%
Han
ValueCountFrequency (%)
岩 2
33.3%
基 1
16.7%
坪 1
16.7%
沙 1
16.7%
岐 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30121
99.2%
ASCII 232
 
0.8%
CJK 6
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 7044
23.4%
동 2626
 
8.7%
산 803
 
2.7%
면 564
 
1.9%
곡 514
 
1.7%
신 493
 
1.6%
대 435
 
1.4%
천 430
 
1.4%
가 386
 
1.3%
정 384
 
1.3%
Other values (361) 16442
54.6%
ASCII
ValueCountFrequency (%)
1 74
31.9%
2 60
25.9%
3 50
21.6%
4 25
 
10.8%
6 6
 
2.6%
5 6
 
2.6%
7 5
 
2.2%
( 3
 
1.3%
) 3
 
1.3%
CJK
ValueCountFrequency (%)
岩 2
33.3%
基 1
16.7%
坪 1
16.7%
沙 1
16.7%
岐 1
16.7%

생성일자
Date

Distinct313
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum1980-04-01 00:00:00
Maximum2015-09-23 00:00:00
2024-01-29T00:17:36.717939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:17:36.814081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

등록일시
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2015-10-05
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015-10-05
2nd row2015-10-05
3rd row2015-10-05
4th row2015-10-05
5th row2015-10-05

Common Values

ValueCountFrequency (%)
2015-10-05 10000
100.0%

Length

2024-01-29T00:17:36.904583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T00:17:36.969665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2015-10-05 10000
100.0%

Interactions

2024-01-29T00:17:33.384903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:17:33.213593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:17:33.486593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:17:33.301164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-29T00:17:37.013997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동코드행정동코드시도명
법정동코드1.0001.0000.995
행정동코드1.0001.0000.995
시도명0.9950.9951.000
2024-01-29T00:17:37.079169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동코드행정동코드시도명
법정동코드1.0001.0000.978
행정동코드1.0001.0000.978
시도명0.9780.9781.000

Missing values

2024-01-29T00:17:33.608089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-29T00:17:33.704955image/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.
2024-01-29T00:17:34.004643image/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

법정동코드행정동코드시도명시군구명읍면동명동리명생성일자등록일시
1151343130350264313035000충청북도충주시신니면모남리1995-01-012015-10-05
858044810310264481031000충청남도예산군대술면송석리1988-04-232015-10-05
633646810370244681037000전라남도강진군작천면내기리1988-04-232015-10-05
1592636110106003611051000세종특별자치시<NA>한솔동한솔동2012-07-012015-10-05
1045945790370264579037000전라북도고창군성송면판정리1988-04-232015-10-05
314348125119004812562000경상남도창원시 마산합포구노산동상남동2010-07-012015-10-05
858644810320004481032000충청남도예산군신양면신양면1988-04-232015-10-05
1673729140118002914074500광주광역시서구치평동쌍촌동2003-02-172015-10-05
1250331710310243171031000울산광역시울주군서생면화산리1997-07-152015-10-05
688046730350004673035000전라남도구례군광의면광의면1988-04-232015-10-05
법정동코드행정동코드시도명시군구명읍면동명동리명생성일자등록일시
491747730440434773044000경상북도의성군다인면덕미리1988-10-012015-10-05
2009848250250214825025000경상남도김해시진영읍우동리1995-05-102015-10-05
1125443770320244377032000충청북도음성군원남면상당리1988-04-232015-10-05
1773142230310224223031000강원도삼척시근덕면하맹방리1995-05-192015-10-05
17944230107004423051000충청남도논산시취암동지산동1996-03-012015-10-05
1835230200139003020054800대전광역시유성구노은3동반석동2015-07-202015-10-05
1435341670250274167025000경기도여주시가남읍심석리2013-09-232015-10-05
1472441570350004157035000경기도김포시월곶면월곶면1998-04-012015-10-05
374447850320334785032000경상북도칠곡군동명면학명리1988-10-012015-10-05
592847170350274717035000경상북도안동시일직면평팔리1995-01-012015-10-05