Overview

Dataset statistics

Number of variables15
Number of observations515
Missing cells180
Missing cells (%)2.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory63.5 KiB
Average record size in memory126.3 B

Variable types

Numeric5
Categorical5
DateTime1
Text4

Dataset

Description하천기본계획코드,일련번호,시점측점번호,종점종점번호,하천기본계획 사업명,수립년도,하천명,지정일자,하천지정근거_고시번호,시점명,종점명,하천연장,도면번호,면적,법령
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15788/S/1/datasetView.do

Alerts

하천기본계획 사업명 is highly overall correlated with 하천기본계획코드 and 5 other fieldsHigh correlation
하천지정근거_고시번호 is highly overall correlated with 하천기본계획코드 and 5 other fieldsHigh correlation
수립년도 is highly overall correlated with 일련번호 and 4 other fieldsHigh correlation
하천명 is highly overall correlated with 하천기본계획코드 and 4 other fieldsHigh correlation
하천기본계획코드 is highly overall correlated with 하천기본계획 사업명 and 2 other fieldsHigh correlation
일련번호 is highly overall correlated with 시점측점번호 and 4 other fieldsHigh correlation
시점측점번호 is highly overall correlated with 일련번호 and 1 other fieldsHigh correlation
종점종점번호 is highly overall correlated with 시점측점번호High correlation
법령 is highly overall correlated with 하천기본계획 사업명 and 2 other fieldsHigh correlation
지정일자 has 177 (34.4%) missing valuesMissing
면적 is highly skewed (γ1 = 21.68064002)Skewed
일련번호 has unique valuesUnique
종점종점번호 has 76 (14.8%) zerosZeros

Reproduction

Analysis started2024-05-11 05:36:06.903937
Analysis finished2024-05-11 05:36:17.212802
Duration10.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

하천기본계획코드
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0286418 × 1012
Minimum1.0000102 × 1012
Maximum1.5003212 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2024-05-11T05:36:17.447823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.0000102 × 1012
5-th percentile1.0000102 × 1012
Q11.0050902 × 1012
median1.0251802 × 1012
Q31.0253002 × 1012
95-th percentile1.0255402 × 1012
Maximum1.5003212 × 1012
Range5.00311 × 1011
Interquartile range (IQR)2.021 × 1010

Descriptive statistics

Standard deviation7.3746517 × 1010
Coefficient of variation (CV)0.071693099
Kurtosis36.516871
Mean1.0286418 × 1012
Median Absolute Deviation (MAD)3.100003 × 108
Skewness6.1161269
Sum5.2975052 × 1014
Variance5.4385487 × 1021
MonotonicityNot monotonic
2024-05-11T05:36:17.928730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1000010202010 110
21.4%
1005380201510 36
 
7.0%
1025220201210 33
 
6.4%
1025540201510 30
 
5.8%
1025290201210 26
 
5.0%
1025350201410 23
 
4.5%
1005090201210 20
 
3.9%
1025500201510 17
 
3.3%
1025060201510 15
 
2.9%
1024910201510 15
 
2.9%
Other values (32) 190
36.9%
ValueCountFrequency (%)
1000010202010 110
21.4%
1005090201210 20
 
3.9%
1005380201510 36
 
7.0%
1015270201210 6
 
1.2%
1024880201510 4
 
0.8%
1024881201510 2
 
0.4%
1024900201210 12
 
2.3%
1024910201510 15
 
2.9%
1024920201510 4
 
0.8%
1024930201510 8
 
1.6%
ValueCountFrequency (%)
1500321201211 3
 
0.6%
1500321201210 9
 
1.7%
1025560201510 4
 
0.8%
1025550201510 2
 
0.4%
1025540201512 2
 
0.4%
1025540201510 30
5.8%
1025530201510 14
2.7%
1025500201510 17
3.3%
1025490201510 5
 
1.0%
1025370201411 2
 
0.4%

일련번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct515
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean258
Minimum1
Maximum515
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2024-05-11T05:36:18.388681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile26.7
Q1129.5
median258
Q3386.5
95-th percentile489.3
Maximum515
Range514
Interquartile range (IQR)257

Descriptive statistics

Standard deviation148.81196
Coefficient of variation (CV)0.57679055
Kurtosis-1.2
Mean258
Median Absolute Deviation (MAD)129
Skewness0
Sum132870
Variance22145
MonotonicityStrictly decreasing
2024-05-11T05:36:18.870575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
515 1
 
0.2%
192 1
 
0.2%
162 1
 
0.2%
163 1
 
0.2%
164 1
 
0.2%
165 1
 
0.2%
166 1
 
0.2%
167 1
 
0.2%
168 1
 
0.2%
169 1
 
0.2%
Other values (505) 505
98.1%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
515 1
0.2%
514 1
0.2%
513 1
0.2%
512 1
0.2%
511 1
0.2%
510 1
0.2%
509 1
0.2%
508 1
0.2%
507 1
0.2%
506 1
0.2%

시점측점번호
Real number (ℝ)

HIGH CORRELATION 

Distinct390
Distinct (%)75.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37095.825
Minimum-13842
Maximum208050
Zeros2
Zeros (%)0.4%
Negative17
Negative (%)3.3%
Memory size4.7 KiB
2024-05-11T05:36:19.332118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-13842
5-th percentile426.9
Q14767
median15030
Q356080.5
95-th percentile161317.5
Maximum208050
Range221892
Interquartile range (IQR)51313.5

Descriptive statistics

Standard deviation47484.868
Coefficient of variation (CV)1.2800596
Kurtosis2.1685542
Mean37095.825
Median Absolute Deviation (MAD)13623
Skewness1.6875314
Sum19104350
Variance2.2548127 × 109
MonotonicityNot monotonic
2024-05-11T05:36:19.769687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42000 4
 
0.8%
162000 4
 
0.8%
12000 4
 
0.8%
170000 3
 
0.6%
76725 3
 
0.6%
70000 3
 
0.6%
1407 3
 
0.6%
53005 2
 
0.4%
11130 2
 
0.4%
869 2
 
0.4%
Other values (380) 485
94.2%
ValueCountFrequency (%)
-13842 1
0.2%
-13545 1
0.2%
-13084 1
0.2%
-11264 1
0.2%
-11014 1
0.2%
-10786 1
0.2%
-10018 1
0.2%
-9140 1
0.2%
-7719 1
0.2%
-7240 1
0.2%
ValueCountFrequency (%)
208050 2
0.4%
191074 1
0.2%
191000 1
0.2%
188052 2
0.4%
184060 1
0.2%
181000 2
0.4%
173000 1
0.2%
172034 1
0.2%
172002 1
0.2%
170016 1
0.2%

종점종점번호
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct356
Distinct (%)69.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29345.687
Minimum-15036
Maximum191074
Zeros76
Zeros (%)14.8%
Negative22
Negative (%)4.3%
Memory size4.7 KiB
2024-05-11T05:36:20.420005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-15036
5-th percentile0
Q11510
median10126
Q342000
95-th percentile142366.6
Maximum191074
Range206110
Interquartile range (IQR)40490

Descriptive statistics

Standard deviation43054.916
Coefficient of variation (CV)1.4671633
Kurtosis3.1382809
Mean29345.687
Median Absolute Deviation (MAD)10126
Skewness1.9167033
Sum15113029
Variance1.8537258 × 109
MonotonicityNot monotonic
2024-05-11T05:36:20.737448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 76
 
14.8%
6220 4
 
0.8%
42000 4
 
0.8%
1407 3
 
0.6%
26040 2
 
0.4%
-140 2
 
0.4%
11100 2
 
0.4%
10850 2
 
0.4%
10495 2
 
0.4%
2399 2
 
0.4%
Other values (346) 416
80.8%
ValueCountFrequency (%)
-15036 1
0.2%
-13842 1
0.2%
-13545 1
0.2%
-13084 1
0.2%
-11264 1
0.2%
-11014 1
0.2%
-10786 1
0.2%
-10018 1
0.2%
-9140 1
0.2%
-7719 1
0.2%
ValueCountFrequency (%)
191074 1
0.2%
191000 1
0.2%
184060 1
0.2%
173000 1
0.2%
172034 1
0.2%
172002 1
0.2%
170016 1
0.2%
170000 1
0.2%
169047 1
0.2%
169043 1
0.2%

하천기본계획 사업명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
중랑천권역(서울특별시) 하천기본계획(변경)
175 
한강(팔당댐~하구)하천기본계획 및 하천시설관리대장작성(보완) 용역
110 
안양천권역 하천기본계획
72 
탄천 등 10개 하천기본계획
66 
안양천 하천기본계획(변경)
36 
Other values (3)
56 

Length

Max length36
Median length23
Mean length21.551456
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row아라천 하천기본계획
2nd row아라천 하천기본계획
3rd row아라천 하천기본계획
4th row아라천 하천기본계획
5th row아라천 하천기본계획

Common Values

ValueCountFrequency (%)
중랑천권역(서울특별시) 하천기본계획(변경) 175
34.0%
한강(팔당댐~하구)하천기본계획 및 하천시설관리대장작성(보완) 용역 110
21.4%
안양천권역 하천기본계획 72
14.0%
탄천 등 10개 하천기본계획 66
 
12.8%
안양천 하천기본계획(변경) 36
 
7.0%
홍제천 등 4개 하천기본계획 32
 
6.2%
아라천 하천기본계획 12
 
2.3%
망월천하천기본계획(변경) 12
 
2.3%

Length

2024-05-11T05:36:20.999108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:36:21.357012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
하천기본계획(변경 211
14.7%
하천기본계획 182
12.7%
중랑천권역(서울특별시 175
12.2%
한강(팔당댐~하구)하천기본계획 110
7.7%
및 110
7.7%
하천시설관리대장작성(보완 110
7.7%
용역 110
7.7%
등 98
6.8%
안양천권역 72
 
5.0%
탄천 66
 
4.6%
Other values (6) 190
13.2%

수립년도
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2012
199 
2015
174 
2020
110 
2014
32 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2012
2nd row2012
3rd row2012
4th row2012
5th row2012

Common Values

ValueCountFrequency (%)
2012 199
38.6%
2015 174
33.8%
2020 110
21.4%
2014 32
 
6.2%

Length

2024-05-11T05:36:21.787655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:36:22.128357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2012 199
38.6%
2015 174
33.8%
2020 110
21.4%
2014 32
 
6.2%

하천명
Categorical

HIGH CORRELATION 

Distinct37
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
한강
110 
안양천
36 
우이천
33 
도림천
32 
정릉천
 
26
Other values (32)
278 

Length

Max length10
Median length3
Mean length2.8563107
Min length2

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
한강 110
21.4%
안양천 36
 
7.0%
우이천 33
 
6.4%
도림천 32
 
6.2%
정릉천 26
 
5.0%
홍제천 23
 
4.5%
중랑천 20
 
3.9%
목감천 17
 
3.3%
도봉천 17
 
3.3%
성내천 15
 
2.9%
Other values (27) 186
36.1%

Length

2024-05-11T05:36:22.538103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한강 110
21.4%
안양천 36
 
7.0%
우이천 33
 
6.4%
도림천 32
 
6.2%
정릉천 26
 
5.0%
홍제천 23
 
4.5%
중랑천 20
 
3.9%
목감천 17
 
3.3%
도봉천 17
 
3.3%
성내천 15
 
2.9%
Other values (27) 186
36.1%

지정일자
Date

MISSING 

Distinct7
Distinct (%)2.1%
Missing177
Missing (%)34.4%
Memory size4.2 KiB
Minimum1930-10-03 00:00:00
Maximum1999-08-09 00:00:00
2024-05-11T05:36:22.901345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:36:23.356328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)

하천지정근거_고시번호
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
<NA>
165 
서울지방국토관리청고시 제2020-100~103호
110 
서울 268호
80 
서울 146호
43 
대통령령제17315호
36 
Other values (6)
81 

Length

Max length26
Median length16
Mean length10.815534
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row국토해양부고시 제2011-3호
2nd row국토해양부고시 제2011-3호
3rd row국토해양부고시 제2011-3호
4th row국토해양부고시 제2011-3호
5th row국토해양부고시 제2011-3호

Common Values

ValueCountFrequency (%)
<NA> 165
32.0%
서울지방국토관리청고시 제2020-100~103호 110
21.4%
서울 268호 80
15.5%
서울 146호 43
 
8.3%
대통령령제17315호 36
 
7.0%
대통령령 제16535호 20
 
3.9%
경기3148호 17
 
3.3%
서울 353호 14
 
2.7%
국토해양부고시 제2011-3호 12
 
2.3%
서울268호 12
 
2.3%

Length

2024-05-11T05:36:23.742531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 165
20.6%
서울 137
17.1%
서울지방국토관리청고시 110
13.8%
제2020-100~103호 110
13.8%
268호 80
10.0%
146호 43
 
5.4%
대통령령제17315호 36
 
4.5%
대통령령 26
 
3.2%
제16535호 20
 
2.5%
경기3148호 17
 
2.1%
Other values (5) 56
 
7.0%

시점명
Text

Distinct442
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2024-05-11T05:36:24.292025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length38
Mean length21.858252
Min length11

Characters and Unicode

Total characters11257
Distinct characters196
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique391 ?
Unique (%)75.9%

Sample

1st row서울특별시 강서구 개화동 11-1 한기분기점
2nd row김포시 고촌읍 신곡리 104-9 아라김포여객터미널
3rd row인천광역시 계양구 장기동 106-5
4th row인천광역시 서구 시천동 산61-7 시천교
5th row인천광역시 서구 오류동 아라인천여객터미널 시점
ValueCountFrequency (%)
서울시 323
 
14.4%
지번선 166
 
7.4%
경기도 59
 
2.6%
도봉구 48
 
2.1%
성북구 41
 
1.8%
김포시 41
 
1.8%
서울특별시 41
 
1.8%
강북구 29
 
1.3%
관악구 26
 
1.2%
송파구 24
 
1.1%
Other values (683) 1441
64.4%
2024-05-11T05:36:25.165225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1724
 
15.3%
동 516
 
4.6%
시 515
 
4.6%
- 476
 
4.2%
1 466
 
4.1%
구 432
 
3.8%
서 410
 
3.6%
울 364
 
3.2%
2 335
 
3.0%
선 293
 
2.6%
Other values (186) 5726
50.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5804
51.6%
Decimal Number 2608
23.2%
Space Separator 1724
 
15.3%
Dash Punctuation 476
 
4.2%
Other Punctuation 112
 
1.0%
Uppercase Letter 110
 
1.0%
Open Punctuation 110
 
1.0%
Lowercase Letter 110
 
1.0%
Close Punctuation 110
 
1.0%
Math Symbol 93
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 516
 
8.9%
시 515
 
8.9%
구 432
 
7.4%
서 410
 
7.1%
울 364
 
6.3%
선 293
 
5.0%
번 292
 
5.0%
지 290
 
5.0%
도 138
 
2.4%
봉 81
 
1.4%
Other values (166) 2473
42.6%
Decimal Number
ValueCountFrequency (%)
1 466
17.9%
2 335
12.8%
6 276
10.6%
4 260
10.0%
3 245
9.4%
7 241
9.2%
5 236
9.0%
8 210
8.1%
0 191
7.3%
9 148
 
5.7%
Other Punctuation
ValueCountFrequency (%)
. 110
98.2%
? 1
 
0.9%
？ 1
 
0.9%
Space Separator
ValueCountFrequency (%)
1724
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 476
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 110
100.0%
Open Punctuation
ValueCountFrequency (%)
( 110
100.0%
Lowercase Letter
ValueCountFrequency (%)
o 110
100.0%
Close Punctuation
ValueCountFrequency (%)
) 110
100.0%
Math Symbol
ValueCountFrequency (%)
+ 93
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5804
51.6%
Common 5233
46.5%
Latin 220
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 516
 
8.9%
시 515
 
8.9%
구 432
 
7.4%
서 410
 
7.1%
울 364
 
6.3%
선 293
 
5.0%
번 292
 
5.0%
지 290
 
5.0%
도 138
 
2.4%
봉 81
 
1.4%
Other values (166) 2473
42.6%
Common
ValueCountFrequency (%)
1724
32.9%
- 476
 
9.1%
1 466
 
8.9%
2 335
 
6.4%
6 276
 
5.3%
4 260
 
5.0%
3 245
 
4.7%
7 241
 
4.6%
5 236
 
4.5%
8 210
 
4.0%
Other values (8) 764
14.6%
Latin
ValueCountFrequency (%)
N 110
50.0%
o 110
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5804
51.6%
ASCII 5452
48.4%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1724
31.6%
- 476
 
8.7%
1 466
 
8.5%
2 335
 
6.1%
6 276
 
5.1%
4 260
 
4.8%
3 245
 
4.5%
7 241
 
4.4%
5 236
 
4.3%
8 210
 
3.9%
Other values (9) 983
18.0%
Hangul
ValueCountFrequency (%)
동 516
 
8.9%
시 515
 
8.9%
구 432
 
7.4%
서 410
 
7.1%
울 364
 
6.3%
선 293
 
5.0%
번 292
 
5.0%
지 290
 
5.0%
도 138
 
2.4%
봉 81
 
1.4%
Other values (166) 2473
42.6%
None
ValueCountFrequency (%)
？ 1
100.0%

종점명
Text

Distinct432
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2024-05-11T05:36:25.766378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length38
Mean length21.862136
Min length11

Characters and Unicode

Total characters11259
Distinct characters191
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique375 ?
Unique (%)72.8%

Sample

1st row김포시 고촌읍 전호리 68-5
2nd row인천광역시 계양구 장기동 109-2 계양대교
3rd row인천광역시 서구 시천동 163-18
4th row인천광역시 서구 오류동 아라인천여객터미널 시점
5th row인천광역시 서구 오류동 서해배수문
ValueCountFrequency (%)
서울시 328
 
14.6%
지번선 161
 
7.2%
경기도 58
 
2.6%
도봉구 47
 
2.1%
김포시 43
 
1.9%
성북구 41
 
1.8%
서울특별시 39
 
1.7%
구로구 26
 
1.2%
강북구 25
 
1.1%
송파구 25
 
1.1%
Other values (659) 1447
64.6%
2024-05-11T05:36:26.728043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1726
 
15.3%
동 526
 
4.7%
시 507
 
4.5%
- 480
 
4.3%
1 461
 
4.1%
구 447
 
4.0%
서 412
 
3.7%
울 367
 
3.3%
2 339
 
3.0%
번 293
 
2.6%
Other values (181) 5701
50.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5807
51.6%
Decimal Number 2603
23.1%
Space Separator 1726
 
15.3%
Dash Punctuation 480
 
4.3%
Close Punctuation 111
 
1.0%
Open Punctuation 111
 
1.0%
Other Punctuation 111
 
1.0%
Lowercase Letter 110
 
1.0%
Uppercase Letter 110
 
1.0%
Math Symbol 90
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 526
 
9.1%
시 507
 
8.7%
구 447
 
7.7%
서 412
 
7.1%
울 367
 
6.3%
번 293
 
5.0%
선 292
 
5.0%
지 291
 
5.0%
도 136
 
2.3%
성 87
 
1.5%
Other values (162) 2449
42.2%
Decimal Number
ValueCountFrequency (%)
1 461
17.7%
2 339
13.0%
6 275
10.6%
4 269
10.3%
7 242
9.3%
3 239
9.2%
5 232
8.9%
8 208
8.0%
0 184
 
7.1%
9 154
 
5.9%
Other Punctuation
ValueCountFrequency (%)
. 110
99.1%
？ 1
 
0.9%
Space Separator
ValueCountFrequency (%)
1726
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 480
100.0%
Close Punctuation
ValueCountFrequency (%)
) 111
100.0%
Open Punctuation
ValueCountFrequency (%)
( 111
100.0%
Lowercase Letter
ValueCountFrequency (%)
o 110
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 110
100.0%
Math Symbol
ValueCountFrequency (%)
+ 90
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5807
51.6%
Common 5232
46.5%
Latin 220
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 526
 
9.1%
시 507
 
8.7%
구 447
 
7.7%
서 412
 
7.1%
울 367
 
6.3%
번 293
 
5.0%
선 292
 
5.0%
지 291
 
5.0%
도 136
 
2.3%
성 87
 
1.5%
Other values (162) 2449
42.2%
Common
ValueCountFrequency (%)
1726
33.0%
- 480
 
9.2%
1 461
 
8.8%
2 339
 
6.5%
6 275
 
5.3%
4 269
 
5.1%
7 242
 
4.6%
3 239
 
4.6%
5 232
 
4.4%
8 208
 
4.0%
Other values (7) 761
14.5%
Latin
ValueCountFrequency (%)
o 110
50.0%
N 110
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5807
51.6%
ASCII 5451
48.4%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1726
31.7%
- 480
 
8.8%
1 461
 
8.5%
2 339
 
6.2%
6 275
 
5.0%
4 269
 
4.9%
7 242
 
4.4%
3 239
 
4.4%
5 232
 
4.3%
8 208
 
3.8%
Other values (8) 980
18.0%
Hangul
ValueCountFrequency (%)
동 526
 
9.1%
시 507
 
8.7%
구 447
 
7.7%
서 412
 
7.1%
울 367
 
6.3%
번 293
 
5.0%
선 292
 
5.0%
지 291
 
5.0%
도 136
 
2.3%
성 87
 
1.5%
Other values (162) 2449
42.2%
None
ValueCountFrequency (%)
？ 1
100.0%

하천연장
Text

Distinct407
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2024-05-11T05:36:27.421776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.8543689
Min length1

Characters and Unicode

Total characters2500
Distinct characters12
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

Unique316 ?
Unique (%)61.4%

Sample

1st row1.570
2nd row4.860
3rd row5.462
4th row5.370
5th row1.581
ValueCountFrequency (%)
186 4
 
0.8%
0.215 4
 
0.8%
1.200 3
 
0.6%
0.602 3
 
0.6%
0.550 3
 
0.6%
0.150 3
 
0.6%
0.175 3
 
0.6%
1.280 3
 
0.6%
350 3
 
0.6%
0.185 3
 
0.6%
Other values (397) 483
93.8%
2024-05-11T05:36:28.550310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 536
21.4%
. 459
18.4%
1 284
11.4%
5 204
 
8.2%
2 203
 
8.1%
3 155
 
6.2%
6 135
 
5.4%
8 133
 
5.3%
4 133
 
5.3%
7 119
 
4.8%
Other values (2) 139
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2020
80.8%
Other Punctuation 480
 
19.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 536
26.5%
1 284
14.1%
5 204
 
10.1%
2 203
 
10.0%
3 155
 
7.7%
6 135
 
6.7%
8 133
 
6.6%
4 133
 
6.6%
7 119
 
5.9%
9 118
 
5.8%
Other Punctuation
ValueCountFrequency (%)
. 459
95.6%
, 21
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 536
21.4%
. 459
18.4%
1 284
11.4%
5 204
 
8.2%
2 203
 
8.1%
3 155
 
6.2%
6 135
 
5.4%
8 133
 
5.3%
4 133
 
5.3%
7 119
 
4.8%
Other values (2) 139
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 536
21.4%
. 459
18.4%
1 284
11.4%
5 204
 
8.2%
2 203
 
8.1%
3 155
 
6.2%
6 135
 
5.4%
8 133
 
5.3%
4 133
 
5.3%
7 119
 
4.8%
Other values (2) 139
 
5.6%

도면번호
Text

Distinct348
Distinct (%)67.6%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2024-05-11T05:36:29.103466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length30
Mean length6.6407767
Min length3

Characters and Unicode

Total characters3420
Distinct characters72
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

Unique217 ?
Unique (%)42.1%

Sample

1st row아라천26,27,29
2nd row아라천16,17,18,19, 22,23,24,25,26
3rd row아라천10,11,12,13,14,15,16
4th row아라천4,5,6,7,8,9,10
5th row아라천1,2,3,4
ValueCountFrequency (%)
양재 15
 
2.5%
hj10 9
 
1.5%
정릉12 5
 
0.8%
대동02 5
 
0.8%
한강19 5
 
0.8%
한강58 4
 
0.7%
한강53 4
 
0.7%
감이 4
 
0.7%
한강45 4
 
0.7%
한강60 4
 
0.7%
Other values (360) 534
90.1%
2024-05-11T05:36:29.948272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 398
 
11.6%
0 304
 
8.9%
2 229
 
6.7%
~ 205
 
6.0%
, 196
 
5.7%
3 162
 
4.7%
4 121
 
3.5%
5 119
 
3.5%
한 110
 
3.2%
강 110
 
3.2%
Other values (62) 1466
42.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1627
47.6%
Other Letter 1202
35.1%
Other Punctuation 234
 
6.8%
Math Symbol 205
 
6.0%
Space Separator 78
 
2.3%
Uppercase Letter 64
 
1.9%
Dash Punctuation 6
 
0.2%
Open Punctuation 2
 
0.1%
Close Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
한 110
 
9.2%
강 110
 
9.2%
이 56
 
4.7%
도 53
 
4.4%
우 52
 
4.3%
양 51
 
4.2%
동 44
 
3.7%
릉 39
 
3.2%
정 39
 
3.2%
안 36
 
3.0%
Other values (39) 612
50.9%
Decimal Number
ValueCountFrequency (%)
1 398
24.5%
0 304
18.7%
2 229
14.1%
3 162
10.0%
4 121
 
7.4%
5 119
 
7.3%
6 90
 
5.5%
7 77
 
4.7%
9 66
 
4.1%
8 61
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
J 23
35.9%
H 23
35.9%
B 8
 
12.5%
N 4
 
6.2%
G 4
 
6.2%
S 2
 
3.1%
Other Punctuation
ValueCountFrequency (%)
, 196
83.8%
? 38
 
16.2%
Math Symbol
ValueCountFrequency (%)
~ 205
100.0%
Space Separator
ValueCountFrequency (%)
78
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2154
63.0%
Hangul 1202
35.1%
Latin 64
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
한 110
 
9.2%
강 110
 
9.2%
이 56
 
4.7%
도 53
 
4.4%
우 52
 
4.3%
양 51
 
4.2%
동 44
 
3.7%
릉 39
 
3.2%
정 39
 
3.2%
안 36
 
3.0%
Other values (39) 612
50.9%
Common
ValueCountFrequency (%)
1 398
18.5%
0 304
14.1%
2 229
10.6%
~ 205
9.5%
, 196
9.1%
3 162
7.5%
4 121
 
5.6%
5 119
 
5.5%
6 90
 
4.2%
78
 
3.6%
Other values (7) 252
11.7%
Latin
ValueCountFrequency (%)
J 23
35.9%
H 23
35.9%
B 8
 
12.5%
N 4
 
6.2%
G 4
 
6.2%
S 2
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2218
64.9%
Hangul 1202
35.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 398
17.9%
0 304
13.7%
2 229
10.3%
~ 205
9.2%
, 196
8.8%
3 162
7.3%
4 121
 
5.5%
5 119
 
5.4%
6 90
 
4.1%
78
 
3.5%
Other values (13) 316
14.2%
Hangul
ValueCountFrequency (%)
한 110
 
9.2%
강 110
 
9.2%
이 56
 
4.7%
도 53
 
4.4%
우 52
 
4.3%
양 51
 
4.2%
동 44
 
3.7%
릉 39
 
3.2%
정 39
 
3.2%
안 36
 
3.0%
Other values (39) 612
50.9%

면적
Real number (ℝ)

SKEWED 

Distinct509
Distinct (%)99.4%
Missing3
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean512302.94
Minimum0
Maximum1.2000853 × 108
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2024-05-11T05:36:30.387358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile515.2
Q14603.5
median22465
Q399573.25
95-th percentile1873536.9
Maximum1.2000853 × 108
Range1.2000853 × 108
Interquartile range (IQR)94969.75

Descriptive statistics

Standard deviation5369878.2
Coefficient of variation (CV)10.481842
Kurtosis482.59442
Mean512302.94
Median Absolute Deviation (MAD)20303
Skewness21.68064
Sum2.622991 × 108
Variance2.8835592 × 1013
MonotonicityNot monotonic
2024-05-11T05:36:30.777646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2395.0 2
 
0.4%
18119.0 2
 
0.4%
485.0 2
 
0.4%
10841.0 1
 
0.2%
1665876.0 1
 
0.2%
7399.0 1
 
0.2%
301416.0 1
 
0.2%
32019.0 1
 
0.2%
22237.0 1
 
0.2%
99367.0 1
 
0.2%
Other values (499) 499
96.9%
(Missing) 3
 
0.6%
ValueCountFrequency (%)
0.0 1
0.2%
30.0 1
0.2%
35.0 1
0.2%
87.0 1
0.2%
96.0 1
0.2%
147.0 1
0.2%
205.0 1
0.2%
221.0 1
0.2%
242.0 1
0.2%
253.0 1
0.2%
ValueCountFrequency (%)
120008525.96 1
0.2%
12621214.0 1
0.2%
6235126.0 1
0.2%
4769742.0 1
0.2%
4738945.0 1
0.2%
4575510.0 1
0.2%
4485429.0 1
0.2%
4242350.0 1
0.2%
3968134.0 1
0.2%
3309098.0 1
0.2%

법령
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
제1호
159 
1
57 
1호
51 
3호
46 
제2호
33 
Other values (25)
169 

Length

Max length14
Median length9
Mean length3.0776699
Min length1

Unique

Unique9 ?
Unique (%)1.7%

Sample

1st row3호
2nd row1호
3rd row5호
4th row1호
5th row3호

Common Values

ValueCountFrequency (%)
제1호 159
30.9%
1 57
 
11.1%
1호 51
 
9.9%
3호 46
 
8.9%
제2호 33
 
6.4%
제1,2호 27
 
5.2%
제1호 및 제2호 26
 
5.0%
제3호 23
 
4.5%
2 17
 
3.3%
5호 14
 
2.7%
Other values (20) 62
 
12.0%

Length

2024-05-11T05:36:31.100001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
제1호 188
32.1%
1 61
 
10.4%
제2호 61
 
10.4%
1호 52
 
8.9%
3호 50
 
8.5%
및 28
 
4.8%
제1,2호 27
 
4.6%
제3호 25
 
4.3%
2 20
 
3.4%
5호 16
 
2.7%
Other values (13) 57
 
9.7%

Interactions

2024-05-11T05:36:14.492050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:36:08.662513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:36:10.135198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:36:11.747930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:36:13.354677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:36:14.758140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:36:08.931960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:36:10.412612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:36:12.039326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:36:13.639627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:36:15.015227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:36:09.227710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:36:10.686994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:36:12.333114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:36:13.902953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:36:15.341801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:36:09.487678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:36:11.134478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:36:12.641953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:36:14.125274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:36:15.675216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:36:09.809620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:36:11.471932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:36:12.937840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:36:14.298410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T05:36:31.449813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
하천기본계획코드일련번호시점측점번호종점종점번호하천기본계획 사업명수립년도하천명지정일자하천지정근거_고시번호면적법령
하천기본계획코드1.0000.3170.4010.2691.0000.0991.000NaN1.000NaN1.000
일련번호0.3171.0000.7450.6940.8690.8820.9730.8310.9520.0000.853
시점측점번호0.4010.7451.0000.9640.5990.6510.7770.4900.7370.0000.624
종점종점번호0.2690.6940.9641.0000.5080.5830.7140.4490.6820.0000.649
하천기본계획 사업명1.0000.8690.5990.5081.0001.0000.9990.9371.0000.0000.921
수립년도0.0990.8820.6510.5831.0001.0001.0000.9091.0000.0390.928
하천명1.0000.9730.7770.7140.9991.0001.0001.0001.0000.0000.874
지정일자NaN0.8310.4900.4490.9370.9091.0001.0001.0000.0000.836
하천지정근거_고시번호1.0000.9520.7370.6821.0001.0001.0001.0001.0000.0000.845
면적NaN0.0000.0000.0000.0000.0390.0000.0000.0001.0000.000
법령1.0000.8530.6240.6490.9210.9280.8740.8360.8450.0001.000
2024-05-11T05:36:31.786120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
하천기본계획 사업명법령하천지정근거_고시번호수립년도하천명
하천기본계획 사업명1.0000.6720.9930.9960.967
법령0.6721.0000.5060.7470.366
하천지정근거_고시번호0.9930.5061.0000.9900.987
수립년도0.9960.7470.9901.0000.967
하천명0.9670.3660.9870.9671.000
2024-05-11T05:36:31.984811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
하천기본계획코드일련번호시점측점번호종점종점번호면적하천기본계획 사업명수립년도하천명하천지정근거_고시번호법령
하천기본계획코드1.0000.448-0.119-0.168-0.4660.9940.1790.9650.9880.341
일련번호0.4481.0000.5530.356-0.3290.6600.7460.8040.6280.452
시점측점번호-0.1190.5531.0000.8660.0490.3370.4480.3910.3090.242
종점종점번호-0.1680.3560.8661.000-0.0250.2700.3850.3300.2700.257
면적-0.466-0.3290.049-0.0251.0000.0000.0360.0000.0000.000
하천기본계획 사업명0.9940.6600.3370.2700.0001.0000.9960.9670.9930.672
수립년도0.1790.7460.4480.3850.0360.9961.0000.9670.9900.747
하천명0.9650.8040.3910.3300.0000.9670.9671.0000.9870.366
하천지정근거_고시번호0.9880.6280.3090.2700.0000.9930.9900.9871.0000.506
법령0.3410.4520.2420.2570.0000.6720.7470.3660.5061.000

Missing values

2024-05-11T05:36:16.098075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T05:36:16.688128image/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.
2024-05-11T05:36:17.048095image/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

하천기본계획코드일련번호시점측점번호종점종점번호하천기본계획 사업명수립년도하천명지정일자하천지정근거_고시번호시점명종점명하천연장도면번호면적법령
01500321201210515188052173000아라천 하천기본계획2012아라천<NA>국토해양부고시 제2011-3호서울특별시 강서구 개화동 11-1 한기분기점김포시 고촌읍 전호리 68-51.570아라천26,27,29180574.03호
11500321201210514173000124032아라천 하천기본계획2012아라천<NA>국토해양부고시 제2011-3호김포시 고촌읍 신곡리 104-9 아라김포여객터미널인천광역시 계양구 장기동 109-2 계양대교4.860아라천16,17,18,19, 22,23,24,25,26621954.01호
2150032120121051312403269070아라천 하천기본계획2012아라천<NA>국토해양부고시 제2011-3호인천광역시 계양구 장기동 106-5인천광역시 서구 시천동 163-185.462아라천10,11,12,13,14,15,16477927.05호
315003212012105126907016000아라천 하천기본계획2012아라천<NA>국토해양부고시 제2011-3호인천광역시 서구 시천동 산61-7 시천교인천광역시 서구 오류동 아라인천여객터미널 시점5.370아라천4,5,6,7,8,9,10414126.01호
41500321201210511160000아라천 하천기본계획2012아라천<NA>국토해양부고시 제2011-3호인천광역시 서구 오류동 아라인천여객터미널 시점인천광역시 서구 오류동 서해배수문1.581아라천1,2,3,4284689.03호
51500321201210510188052172002아라천 하천기본계획2012아라천<NA>국토해양부고시 제2011-3호서울특별시 강서구 개화동 30-2김포시 고촌읍 전호리 284-61.676아라천26,27,29234549.03호
61500321201210509172002142000아라천 하천기본계획2012아라천<NA>국토해양부고시 제2011-3호김포시 고촌읍 전호리 439-8 굴포교인천광역시 계양구 노오지동 120-22.982아라천19, 22,23,24,25,26247783.05호
7150032120121050814100016000아라천 하천기본계획2012아라천<NA>국토해양부고시 제2011-3호인천광역시 계양구 귤현동 4-12인천광역시 서구 오류동 아라인천여객터미널 시점12.504아라천3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,191151176.05호
81500321201210507160000아라천 하천기본계획2012아라천<NA>국토해양부고시 제2011-3호인천광역시 서구 오류동 아라인천여객터미널 시점인천광역시 서구 오류동 서해배수문1.581아라천1,2,3,4326052.03호
910249002012105068701477000망월천하천기본계획(변경)2012망월천<NA><NA>경기도 하남시 풍산동 302번지선경기도 하남시 풍산동 85번지선0.510망월6?망월78430.0제2호
하천기본계획코드일련번호시점측점번호종점종점번호하천기본계획 사업명수립년도하천명지정일자하천지정근거_고시번호시점명종점명하천연장도면번호면적법령
50510250602015101021000탄천 등 10개 하천기본계획2015양재천<NA><NA>서울시 강남구 개포동 648-1번지선서울시 강남구 대치동 27-14번지선2.100양재 1~7183309.0제1,2호
5061025060201510976846087탄천 등 10개 하천기본계획2015양재천<NA><NA>경기도 과천시 주암동 691-4번지선경기도 과천시 주암동 736번지선1.597양재 18~2128135.4제3호
5071025060201510860875691탄천 등 10개 하천기본계획2015양재천<NA><NA>경기도 과천시 주암동 736번지선서울시 서초구 우면동 143번지선0.396양재 17,180.0제1호
5081025060201510756914894탄천 등 10개 하천기본계획2015양재천<NA><NA>서울시 서초구 우면동 143번지선서울시 서초구 양재동 126-1번지선0.797양재 15,16,1736152.0제1호
5091025060201510648943699탄천 등 10개 하천기본계획2015양재천<NA><NA>서울시 서초구 양재동 126-1번지선서울시 서초구 양재동 99-6번지선1.195양재 12~1538378.0제1,2호
5101025060201510536992300탄천 등 10개 하천기본계획2015양재천<NA><NA>서울시 서초구 양재동 99-6번지선서울시 강남구 대치동 504-11번지선1.399양재 8~1288432.0제1,2호
5111025060201510423000탄천 등 10개 하천기본계획2015양재천<NA><NA>서울시 강남구 대치동 504-11번지선서울시 강남구 대치동 27-14번지선2.300양재 1~8130528.0제1,2호
51215003212012113120001000아라천 하천기본계획2012아라천<NA>국토해양부고시 제2011-3호인천광역시 계양구 귤현동 26-10 연결수로시점인천광역시 계양구 노오지동 120-11.053아라천18,19,20,2176062.01호
51315003212012112120007075아라천 하천기본계획2012아라천<NA>국토해양부고시 제2011-3호인천광역시 계양구 귤현동 68-8 연결수로 시점인천광역시 계양구 귤현동 394-270.408아라천18,19,20,2122440.01호
5141500321201211170750아라천 하천기본계획2012아라천<NA>국토해양부고시 제2011-3호인천광역시 계양구 귤현동 27-1 귤현1교인천광역시 계양구 귤현동 4-10 연결수로 종점0.745아라천17,18,19268659.01호, 4호