Overview

Dataset statistics

Number of variables38
Number of observations96
Missing cells808
Missing cells (%)22.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.4 KiB
Average record size in memory324.4 B

Variable types

Numeric14
Categorical12
Text12

Dataset

Description하천기본계획코드,일련번호,하천기본계획 사업명,수립년도,시설물명,하천명,하천등급,상세주소,주소,유역면적,보호면적,시행기관,본류하천_유역면적,본류하천_홍수량,본류하천_계획빈도,제방고,계획홍수위,부지표고,기계실바닥표고,전기실바닥표고,배수장_최대배수량,배수장_계획빈도,초기흡입수위,전동기,펌프규격,전양정,실양정,토출수위,시설관리자,구분,배수문규격,배수문바닥표고,간선수로,지선수로,유수지표고_HWL,유수지표고_LWL,유수지면적,유수지용량
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15794/S/1/datasetView.do

Alerts

시행기관 is highly imbalanced (68.8%)Imbalance
전기실바닥표고 is highly imbalanced (71.0%)Imbalance
실양정 is highly imbalanced (81.7%)Imbalance
유수지표고_HWL is highly imbalanced (91.6%)Imbalance
주소 has 68 (70.8%) missing valuesMissing
유역면적 has 1 (1.0%) missing valuesMissing
보호면적 has 10 (10.4%) missing valuesMissing
본류하천_유역면적 has 5 (5.2%) missing valuesMissing
제방고 has 9 (9.4%) missing valuesMissing
계획홍수위 has 3 (3.1%) missing valuesMissing
부지표고 has 61 (63.5%) missing valuesMissing
기계실바닥표고 has 62 (64.6%) missing valuesMissing
배수장_최대배수량 has 1 (1.0%) missing valuesMissing
배수장_계획빈도 has 1 (1.0%) missing valuesMissing
초기흡입수위 has 24 (25.0%) missing valuesMissing
전양정 has 58 (60.4%) missing valuesMissing
토출수위 has 41 (42.7%) missing valuesMissing
구분 has 22 (22.9%) missing valuesMissing
배수문규격 has 33 (34.4%) missing valuesMissing
배수문바닥표고 has 42 (43.8%) missing valuesMissing
간선수로 has 89 (92.7%) missing valuesMissing
지선수로 has 89 (92.7%) missing valuesMissing
유수지표고_LWL has 86 (89.6%) missing valuesMissing
유수지면적 has 61 (63.5%) missing valuesMissing
유수지용량 has 42 (43.8%) missing valuesMissing
일련번호 has unique valuesUnique
유역면적 has 1 (1.0%) zerosZeros
배수문바닥표고 has 1 (1.0%) zerosZeros

Reproduction

Analysis started2024-05-11 08:27:31.365944
Analysis finished2024-05-11 08:27:31.912487
Duration0.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

하천기본계획코드
Real number (ℝ)

Distinct20
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0455907 × 1012
Minimum1.0050902 × 1012
Maximum1.5003212 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-05-11T17:27:31.961765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.0050902 × 1012
5-th percentile1.0050902 × 1012
Q11.0248802 × 1012
median1.0251352 × 1012
Q31.0255402 × 1012
95-th percentile1.1442505 × 1012
Maximum1.5003212 × 1012
Range4.95231 × 1011
Interquartile range (IQR)6.6 × 108

Descriptive statistics

Standard deviation1.0746243 × 1011
Coefficient of variation (CV)0.10277677
Kurtosis14.883518
Mean1.0455907 × 1012
Median Absolute Deviation (MAD)4.0500015 × 108
Skewness4.0556769
Sum1.003767 × 1014
Variance1.1548175 × 1022
MonotonicityNot monotonic
2024-05-11T17:27:32.072291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1025540201510 20
20.8%
1005090201210 20
20.8%
1024930201510 9
9.4%
1024910201510 9
9.4%
1025530201510 5
 
5.2%
1500321201210 5
 
5.2%
1024880201510 4
 
4.2%
1025340201410 4
 
4.2%
1025060201510 3
 
3.1%
1025290201210 3
 
3.1%
Other values (10) 14
14.6%
ValueCountFrequency (%)
1005090201210 20
20.8%
1015270201210 2
 
2.1%
1024880201510 4
 
4.2%
1024900201210 1
 
1.0%
1024910201510 9
9.4%
1024930201510 9
9.4%
1025060201510 3
 
3.1%
1025210201210 1
 
1.0%
1025220201210 2
 
2.1%
1025260201210 1
 
1.0%
ValueCountFrequency (%)
1500321201210 5
 
5.2%
1025560201510 2
 
2.1%
1025540201510 20
20.8%
1025530201510 5
 
5.2%
1025490201510 1
 
1.0%
1025360201410 2
 
2.1%
1025350201410 1
 
1.0%
1025340201410 4
 
4.2%
1025310201210 1
 
1.0%
1025290201210 3
 
3.1%

일련번호
Real number (ℝ)

UNIQUE 

Distinct96
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.5
Minimum1
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-05-11T17:27:32.180838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.75
Q124.75
median48.5
Q372.25
95-th percentile91.25
Maximum96
Range95
Interquartile range (IQR)47.5

Descriptive statistics

Standard deviation27.856777
Coefficient of variation (CV)0.57436653
Kurtosis-1.2
Mean48.5
Median Absolute Deviation (MAD)24
Skewness0
Sum4656
Variance776
MonotonicityNot monotonic
2024-05-11T17:27:32.301135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72 1
 
1.0%
29 1
 
1.0%
20 1
 
1.0%
19 1
 
1.0%
18 1
 
1.0%
17 1
 
1.0%
16 1
 
1.0%
15 1
 
1.0%
14 1
 
1.0%
13 1
 
1.0%
Other values (86) 86
89.6%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%
90 1
1.0%
89 1
1.0%
88 1
1.0%
87 1
1.0%
Distinct6
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size900.0 B
중랑천권역(서울특별시) 하천기본계획(변경)
30 
안양천권역 하천기본계획
28 
탄천 등 10개 하천기본계획
25 
홍제천 등 4개 하천기본계획
아라천 하천기본계획

Length

Max length23
Median length15
Mean length16.34375
Min length10

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row안양천권역 하천기본계획
2nd row탄천 등 10개 하천기본계획
3rd row탄천 등 10개 하천기본계획
4th row탄천 등 10개 하천기본계획
5th row탄천 등 10개 하천기본계획

Common Values

ValueCountFrequency (%)
중랑천권역(서울특별시) 하천기본계획(변경) 30
31.2%
안양천권역 하천기본계획 28
29.2%
탄천 등 10개 하천기본계획 25
26.0%
홍제천 등 4개 하천기본계획 7
 
7.3%
아라천 하천기본계획 5
 
5.2%
망월천하천기본계획(변경) 1
 
1.0%

Length

2024-05-11T17:27:32.435376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:27:32.552650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
하천기본계획 65
25.5%
32
12.5%
중랑천권역(서울특별시 30
11.8%
하천기본계획(변경 30
11.8%
안양천권역 28
11.0%
탄천 25
 
9.8%
10개 25
 
9.8%
홍제천 7
 
2.7%
4개 7
 
2.7%
아라천 5
 
2.0%

수립년도
Categorical

Distinct3
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size900.0 B
2015
53 
2012
36 
2014

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2015 53
55.2%
2012 36
37.5%
2014 7
 
7.3%

Length

2024-05-11T17:27:32.665143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:27:32.744766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2015 53
55.2%
2012 36
37.5%
2014 7
 
7.3%
Distinct60
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Memory size900.0 B
2024-05-11T17:27:32.936923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length4.40625
Min length2

Characters and Unicode

Total characters423
Distinct characters88
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

Unique44 ?
Unique (%)45.8%

Sample

1st row구로2 빗물펌프장
2nd row몽촌2
3rd row성내
4th row성내
5th row성내
ValueCountFrequency (%)
빗물펌프장 20
 
16.5%
대치 7
 
5.8%
문래 4
 
3.3%
개봉1 4
 
3.3%
고덕 4
 
3.3%
봉원 4
 
3.3%
구로4 3
 
2.5%
경서펌프장 3
 
2.5%
대림2동 3
 
2.5%
신림 3
 
2.5%
Other values (55) 66
54.5%
2024-05-11T17:27:33.247674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
35
 
8.3%
28
 
6.6%
28
 
6.6%
25
 
5.9%
21
 
5.0%
21
 
5.0%
2 16
 
3.8%
1 12
 
2.8%
12
 
2.8%
11
 
2.6%
Other values (78) 214
50.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 345
81.6%
Decimal Number 40
 
9.5%
Space Separator 25
 
5.9%
Close Punctuation 6
 
1.4%
Open Punctuation 6
 
1.4%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
35
 
10.1%
28
 
8.1%
28
 
8.1%
21
 
6.1%
21
 
6.1%
12
 
3.5%
11
 
3.2%
10
 
2.9%
9
 
2.6%
9
 
2.6%
Other values (70) 161
46.7%
Decimal Number
ValueCountFrequency (%)
2 16
40.0%
1 12
30.0%
4 7
17.5%
3 5
 
12.5%
Space Separator
ValueCountFrequency (%)
25
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 345
81.6%
Common 78
 
18.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
35
 
10.1%
28
 
8.1%
28
 
8.1%
21
 
6.1%
21
 
6.1%
12
 
3.5%
11
 
3.2%
10
 
2.9%
9
 
2.6%
9
 
2.6%
Other values (70) 161
46.7%
Common
ValueCountFrequency (%)
25
32.1%
2 16
20.5%
1 12
15.4%
4 7
 
9.0%
) 6
 
7.7%
( 6
 
7.7%
3 5
 
6.4%
, 1
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 345
81.6%
ASCII 78
 
18.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
35
 
10.1%
28
 
8.1%
28
 
8.1%
21
 
6.1%
21
 
6.1%
12
 
3.5%
11
 
3.2%
10
 
2.9%
9
 
2.6%
9
 
2.6%
Other values (70) 161
46.7%
ASCII
ValueCountFrequency (%)
25
32.1%
2 16
20.5%
1 12
15.4%
4 7
 
9.0%
) 6
 
7.7%
( 6
 
7.7%
3 5
 
6.4%
, 1
 
1.3%

하천명
Categorical

Distinct20
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Memory size900.0 B
도림천
20 
중랑천
20 
탄천
성내천
오류천
Other values (15)
33 

Length

Max length4
Median length3
Mean length2.9166667
Min length2

Unique

Unique6 ?
Unique (%)6.2%

Sample

1st row도림천
2nd row성내천
3rd row성내천
4th row성내천
5th row성내천

Common Values

ValueCountFrequency (%)
도림천 20
20.8%
중랑천 20
20.8%
탄천 9
9.4%
성내천 9
9.4%
오류천 5
 
5.2%
아라천 5
 
5.2%
고덕천 4
 
4.2%
봉원천 4
 
4.2%
양재천 3
 
3.1%
정릉천 3
 
3.1%
Other values (10) 14
14.6%

Length

2024-05-11T17:27:33.369615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
도림천 20
20.8%
중랑천 20
20.8%
탄천 9
9.4%
성내천 9
9.4%
오류천 5
 
5.2%
아라천 5
 
5.2%
고덕천 4
 
4.2%
봉원천 4
 
4.2%
양재천 3
 
3.1%
정릉천 3
 
3.1%
Other values (10) 14
14.6%

하천등급
Categorical

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size900.0 B
지방하천
71 
국가하천
25 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방하천 71
74.0%
국가하천 25
 
26.0%

Length

2024-05-11T17:27:33.472071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:27:33.549078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방하천 71
74.0%
국가하천 25
 
26.0%
Distinct59
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Memory size900.0 B
2024-05-11T17:27:33.774219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length14.083333
Min length6

Characters and Unicode

Total characters1352
Distinct characters95
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

Unique43 ?
Unique (%)44.8%

Sample

1st row서울시 구로구 구로4동 120-5
2nd row풍납동 471
3rd row방이동 88-13
4th row방이동 88-13
5th row방이동 88-13
ValueCountFrequency (%)
서울시 59
 
18.6%
구로구 14
 
4.4%
동대문구 13
 
4.1%
영등포구 10
 
3.2%
성동구 9
 
2.8%
대치동 7
 
2.2%
78-24 7
 
2.2%
송정동 5
 
1.6%
인천광역시 5
 
1.6%
서구 5
 
1.6%
Other values (107) 183
57.7%
2024-05-11T17:27:34.172648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
221
16.3%
119
 
8.8%
91
 
6.7%
1 72
 
5.3%
65
 
4.8%
64
 
4.7%
- 59
 
4.4%
59
 
4.4%
4 43
 
3.2%
8 41
 
3.0%
Other values (85) 518
38.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 720
53.3%
Decimal Number 352
26.0%
Space Separator 221
 
16.3%
Dash Punctuation 59
 
4.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
119
16.5%
91
12.6%
65
 
9.0%
64
 
8.9%
59
 
8.2%
24
 
3.3%
23
 
3.2%
20
 
2.8%
14
 
1.9%
14
 
1.9%
Other values (73) 227
31.5%
Decimal Number
ValueCountFrequency (%)
1 72
20.5%
4 43
12.2%
8 41
11.6%
5 38
10.8%
2 37
10.5%
9 32
9.1%
7 27
 
7.7%
3 27
 
7.7%
6 18
 
5.1%
0 17
 
4.8%
Space Separator
ValueCountFrequency (%)
221
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 59
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 720
53.3%
Common 632
46.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
119
16.5%
91
12.6%
65
 
9.0%
64
 
8.9%
59
 
8.2%
24
 
3.3%
23
 
3.2%
20
 
2.8%
14
 
1.9%
14
 
1.9%
Other values (73) 227
31.5%
Common
ValueCountFrequency (%)
221
35.0%
1 72
 
11.4%
- 59
 
9.3%
4 43
 
6.8%
8 41
 
6.5%
5 38
 
6.0%
2 37
 
5.9%
9 32
 
5.1%
7 27
 
4.3%
3 27
 
4.3%
Other values (2) 35
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 720
53.3%
ASCII 632
46.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
221
35.0%
1 72
 
11.4%
- 59
 
9.3%
4 43
 
6.8%
8 41
 
6.5%
5 38
 
6.0%
2 37
 
5.9%
9 32
 
5.1%
7 27
 
4.3%
3 27
 
4.3%
Other values (2) 35
 
5.5%
Hangul
ValueCountFrequency (%)
119
16.5%
91
12.6%
65
 
9.0%
64
 
8.9%
59
 
8.2%
24
 
3.3%
23
 
3.2%
20
 
2.8%
14
 
1.9%
14
 
1.9%
Other values (73) 227
31.5%

주소
Text

MISSING 

Distinct14
Distinct (%)50.0%
Missing68
Missing (%)70.8%
Memory size900.0 B
2024-05-11T17:27:34.320551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length14.321429
Min length13

Characters and Unicode

Total characters401
Distinct characters45
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

Unique7 ?
Unique (%)25.0%

Sample

1st row서울특별시 마포구 신정동
2nd row서울특별시 마포구 신정동
3rd row서울특별시 마포구 신정동
4th row서울특별시 마포구 성산동
5th row서울특별시 마포구 성산동
ValueCountFrequency (%)
서울특별시 27
32.1%
동대문구 8
 
9.5%
성동구 8
 
9.5%
마포구 6
 
7.1%
송정동 5
 
6.0%
신정동 4
 
4.8%
중랑구 3
 
3.6%
이문동 3
 
3.6%
장안동 3
 
3.6%
중화동 2
 
2.4%
Other values (13) 15
17.9%
2024-05-11T17:27:34.577924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
84
20.9%
44
11.0%
28
 
7.0%
28
 
7.0%
28
 
7.0%
27
 
6.7%
27
 
6.7%
27
 
6.7%
11
 
2.7%
11
 
2.7%
Other values (35) 86
21.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 316
78.8%
Space Separator 84
 
20.9%
Decimal Number 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
44
13.9%
28
 
8.9%
28
 
8.9%
28
 
8.9%
27
 
8.5%
27
 
8.5%
27
 
8.5%
11
 
3.5%
11
 
3.5%
9
 
2.8%
Other values (33) 76
24.1%
Space Separator
ValueCountFrequency (%)
84
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 316
78.8%
Common 85
 
21.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
44
13.9%
28
 
8.9%
28
 
8.9%
28
 
8.9%
27
 
8.5%
27
 
8.5%
27
 
8.5%
11
 
3.5%
11
 
3.5%
9
 
2.8%
Other values (33) 76
24.1%
Common
ValueCountFrequency (%)
84
98.8%
1 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 316
78.8%
ASCII 85
 
21.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
84
98.8%
1 1
 
1.2%
Hangul
ValueCountFrequency (%)
44
13.9%
28
 
8.9%
28
 
8.9%
28
 
8.9%
27
 
8.5%
27
 
8.5%
27
 
8.5%
11
 
3.5%
11
 
3.5%
9
 
2.8%
Other values (33) 76
24.1%

유역면적
Real number (ℝ)

MISSING  ZEROS 

Distinct36
Distinct (%)37.9%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean4.3778947
Minimum0
Maximum45.5
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-05-11T17:27:34.687995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.5
median1.2
Q35.8
95-th percentile8.41
Maximum45.5
Range45.5
Interquartile range (IQR)5.3

Descriptive statistics

Standard deviation9.0676322
Coefficient of variation (CV)2.0712312
Kurtosis16.109939
Mean4.3778947
Median Absolute Deviation (MAD)0.9
Skewness4.0199239
Sum415.9
Variance82.221953
MonotonicityNot monotonic
2024-05-11T17:27:34.816995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0.8 8
 
8.3%
5.8 7
 
7.3%
0.2 7
 
7.3%
0.3 7
 
7.3%
1.0 5
 
5.2%
0.4 5
 
5.2%
0.9 5
 
5.2%
8.2 4
 
4.2%
8.0 4
 
4.2%
0.5 4
 
4.2%
Other values (26) 39
40.6%
ValueCountFrequency (%)
0.0 1
 
1.0%
0.1 1
 
1.0%
0.2 7
7.3%
0.3 7
7.3%
0.4 5
5.2%
0.5 4
4.2%
0.6 3
 
3.1%
0.7 1
 
1.0%
0.8 8
8.3%
0.9 5
5.2%
ValueCountFrequency (%)
45.5 4
4.2%
8.9 1
 
1.0%
8.2 4
4.2%
8.0 4
4.2%
7.3 1
 
1.0%
6.3 3
3.1%
6.2 1
 
1.0%
5.8 7
7.3%
5.6 1
 
1.0%
5.3 1
 
1.0%

보호면적
Text

MISSING 

Distinct50
Distinct (%)58.1%
Missing10
Missing (%)10.4%
Memory size900.0 B
2024-05-11T17:27:35.010120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length6.6976744
Min length3

Characters and Unicode

Total characters576
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

Unique34 ?
Unique (%)39.5%

Sample

1st row382,000
2nd row580,000
3rd row1,130,000
4th row1,130,000
5th row1,130,000
ValueCountFrequency (%)
550,000 7
 
8.1%
498,000 4
 
4.7%
640,000 4
 
4.7%
307,000 4
 
4.7%
580,000 4
 
4.7%
120,000 3
 
3.5%
16,000 3
 
3.5%
769,600 3
 
3.5%
1,130,000 3
 
3.5%
0.9 3
 
3.5%
Other values (40) 48
55.8%
2024-05-11T17:27:35.555768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 287
49.8%
, 85
 
14.8%
5 35
 
6.1%
6 29
 
5.0%
3 26
 
4.5%
4 22
 
3.8%
1 22
 
3.8%
2 20
 
3.5%
8 18
 
3.1%
7 14
 
2.4%
Other values (2) 18
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 486
84.4%
Other Punctuation 90
 
15.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 287
59.1%
5 35
 
7.2%
6 29
 
6.0%
3 26
 
5.3%
4 22
 
4.5%
1 22
 
4.5%
2 20
 
4.1%
8 18
 
3.7%
7 14
 
2.9%
9 13
 
2.7%
Other Punctuation
ValueCountFrequency (%)
, 85
94.4%
. 5
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 576
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 287
49.8%
, 85
 
14.8%
5 35
 
6.1%
6 29
 
5.0%
3 26
 
4.5%
4 22
 
3.8%
1 22
 
3.8%
2 20
 
3.5%
8 18
 
3.1%
7 14
 
2.4%
Other values (2) 18
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 287
49.8%
, 85
 
14.8%
5 35
 
6.1%
6 29
 
5.0%
3 26
 
4.5%
4 22
 
3.8%
1 22
 
3.8%
2 20
 
3.5%
8 18
 
3.1%
7 14
 
2.4%
Other values (2) 18
 
3.1%

시행기관
Categorical

IMBALANCE 

Distinct4
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size900.0 B
<NA>
86 
한국수자원공사
 
5
마포구청
 
4
강동구청
 
1

Length

Max length7
Median length4
Mean length4.15625
Min length4

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 86
89.6%
한국수자원공사 5
 
5.2%
마포구청 4
 
4.2%
강동구청 1
 
1.0%

Length

2024-05-11T17:27:35.697018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:27:35.787729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 86
89.6%
한국수자원공사 5
 
5.2%
마포구청 4
 
4.2%
강동구청 1
 
1.0%

본류하천_유역면적
Real number (ℝ)

MISSING 

Distinct22
Distinct (%)24.2%
Missing5
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean110.9989
Minimum3.8
Maximum302.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-05-11T17:27:35.871191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile18.9
Q134.7
median42.5
Q3221.2
95-th percentile302.4
Maximum302.4
Range298.6
Interquartile range (IQR)186.5

Descriptive statistics

Standard deviation103.96409
Coefficient of variation (CV)0.93662268
Kurtosis-1.1538345
Mean110.9989
Median Absolute Deviation (MAD)21.4
Skewness0.75233098
Sum10100.9
Variance10808.532
MonotonicityNot monotonic
2024-05-11T17:27:35.975118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
42.5 20
20.8%
239.6 11
11.5%
302.4 7
 
7.3%
29.6 7
 
7.3%
206.6 6
 
6.2%
34.7 4
 
4.2%
18.9 4
 
4.2%
45.5 4
 
4.2%
153.2 4
 
4.2%
51.4 3
 
3.1%
Other values (12) 21
21.9%
(Missing) 5
 
5.2%
ValueCountFrequency (%)
3.8 1
 
1.0%
10.3 1
 
1.0%
10.5 1
 
1.0%
18.9 4
4.2%
19.2 3
3.1%
21.1 2
 
2.1%
27.3 2
 
2.1%
29.6 7
7.3%
34.7 4
4.2%
35.3 2
 
2.1%
ValueCountFrequency (%)
302.4 7
7.3%
296.9 3
 
3.1%
239.6 11
11.5%
235.8 2
 
2.1%
206.6 6
6.2%
153.2 4
 
4.2%
139.8 1
 
1.0%
51.4 3
 
3.1%
47.7 2
 
2.1%
45.5 4
 
4.2%
Distinct24
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size900.0 B
2,040
11 
<NA>
2,198
484
282
Other values (19)
55 

Length

Max length6
Median length5.5
Mean length3.9166667
Min length2

Unique

Unique4 ?
Unique (%)4.2%

Sample

1st row549
2nd row282
3rd row282
4th row282
5th row282

Common Values

ValueCountFrequency (%)
2,040 11
 
11.5%
<NA> 9
 
9.4%
2,198 7
 
7.3%
484 7
 
7.3%
282 7
 
7.3%
747 6
 
6.2%
1,950 6
 
6.2%
1,480 5
 
5.2%
549 4
 
4.2%
384 4
 
4.2%
Other values (14) 30
31.2%

Length

2024-05-11T17:27:36.096882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,040 11
 
11.5%
na 9
 
9.4%
2,198 7
 
7.3%
484 7
 
7.3%
282 7
 
7.3%
747 6
 
6.2%
1,950 6
 
6.2%
1,480 5
 
5.2%
549 4
 
4.2%
384 4
 
4.2%
Other values (14) 30
31.2%
Distinct4
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size900.0 B
100
64 
80
19 
<NA>
200
 
4

Length

Max length4
Median length3
Mean length2.8958333
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100
2nd row80
3rd row80
4th row80
5th row80

Common Values

ValueCountFrequency (%)
100 64
66.7%
80 19
 
19.8%
<NA> 9
 
9.4%
200 4
 
4.2%

Length

2024-05-11T17:27:36.232495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:27:36.328409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
100 64
66.7%
80 19
 
19.8%
na 9
 
9.4%
200 4
 
4.2%

제방고
Real number (ℝ)

MISSING 

Distinct55
Distinct (%)63.2%
Missing9
Missing (%)9.4%
Infinite0
Infinite (%)0.0%
Mean17.744943
Minimum6.1
Maximum22.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-05-11T17:27:36.423149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.1
5-th percentile8.181
Q116.62
median18.13
Q320.345
95-th percentile21.979
Maximum22.56
Range16.46
Interquartile range (IQR)3.725

Descriptive statistics

Standard deviation3.6585678
Coefficient of variation (CV)0.20617524
Kurtosis3.4131356
Mean17.744943
Median Absolute Deviation (MAD)1.67
Skewness-1.658922
Sum1543.81
Variance13.385118
MonotonicityNot monotonic
2024-05-11T17:27:36.543062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.75 7
 
7.3%
16.62 4
 
4.2%
21.93 4
 
4.2%
14.24 4
 
4.2%
17.22 3
 
3.1%
6.2 3
 
3.1%
17.64 3
 
3.1%
17.05 3
 
3.1%
16.46 3
 
3.1%
17.72 3
 
3.1%
Other values (45) 50
52.1%
(Missing) 9
 
9.4%
ValueCountFrequency (%)
6.1 1
 
1.0%
6.2 3
3.1%
6.75 1
 
1.0%
11.52 1
 
1.0%
14.24 4
4.2%
14.51 1
 
1.0%
14.57 1
 
1.0%
14.74 1
 
1.0%
15.22 2
2.1%
15.4 1
 
1.0%
ValueCountFrequency (%)
22.56 1
 
1.0%
22.13 2
 
2.1%
22.0 2
 
2.1%
21.93 4
4.2%
21.7 1
 
1.0%
21.62 1
 
1.0%
21.38 1
 
1.0%
21.23 1
 
1.0%
21.16 1
 
1.0%
20.75 7
7.3%

계획홍수위
Real number (ℝ)

MISSING 

Distinct30
Distinct (%)32.3%
Missing3
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean15.643871
Minimum5
Maximum21.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-05-11T17:27:36.646836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8.906
Q114.07
median17.05
Q317.32
95-th percentile19.072
Maximum21.48
Range16.48
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation3.154996
Coefficient of variation (CV)0.20167617
Kurtosis4.6451014
Mean15.643871
Median Absolute Deviation (MAD)1.3
Skewness-1.8741417
Sum1454.88
Variance9.9540001
MonotonicityNot monotonic
2024-05-11T17:27:36.775006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
17.05 20
20.8%
18.01 12
12.5%
14.07 7
 
7.3%
19.84 4
 
4.2%
13.48 4
 
4.2%
14.0 4
 
4.2%
14.53 4
 
4.2%
14.85 3
 
3.1%
5.01 3
 
3.1%
15.75 3
 
3.1%
Other values (20) 29
30.2%
ValueCountFrequency (%)
5.0 1
 
1.0%
5.01 3
3.1%
5.36 1
 
1.0%
11.27 1
 
1.0%
13.48 4
4.2%
13.5 2
 
2.1%
13.54 2
 
2.1%
14.0 4
4.2%
14.07 7
7.3%
14.31 3
3.1%
ValueCountFrequency (%)
21.48 1
 
1.0%
19.84 4
 
4.2%
18.56 2
 
2.1%
18.23 1
 
1.0%
18.18 1
 
1.0%
18.01 12
12.5%
17.67 1
 
1.0%
17.53 1
 
1.0%
17.32 1
 
1.0%
17.3 1
 
1.0%

부지표고
Real number (ℝ)

MISSING 

Distinct14
Distinct (%)40.0%
Missing61
Missing (%)63.5%
Infinite0
Infinite (%)0.0%
Mean12.991714
Minimum1.6
Maximum18.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-05-11T17:27:36.898656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile6.42
Q112
median13.1
Q315.5
95-th percentile18.2
Maximum18.2
Range16.6
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation3.6881515
Coefficient of variation (CV)0.2838849
Kurtosis1.8478289
Mean12.991714
Median Absolute Deviation (MAD)1.1
Skewness-1.2328605
Sum454.71
Variance13.602462
MonotonicityNot monotonic
2024-05-11T17:27:37.006008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
12.0 7
 
7.3%
14.14 4
 
4.2%
15.5 4
 
4.2%
13.1 4
 
4.2%
18.2 3
 
3.1%
16.4 3
 
3.1%
6.6 3
 
3.1%
14.18 1
 
1.0%
12.69 1
 
1.0%
13.54 1
 
1.0%
Other values (4) 4
 
4.2%
(Missing) 61
63.5%
ValueCountFrequency (%)
1.6 1
 
1.0%
6.0 1
 
1.0%
6.6 3
3.1%
12.0 7
7.3%
12.69 1
 
1.0%
12.74 1
 
1.0%
13.1 4
4.2%
13.54 1
 
1.0%
14.14 4
4.2%
14.18 1
 
1.0%
ValueCountFrequency (%)
18.2 3
3.1%
16.4 3
3.1%
15.5 4
4.2%
15.4 1
 
1.0%
14.18 1
 
1.0%
14.14 4
4.2%
13.54 1
 
1.0%
13.1 4
4.2%
12.74 1
 
1.0%
12.69 1
 
1.0%

기계실바닥표고
Real number (ℝ)

MISSING 

Distinct13
Distinct (%)38.2%
Missing62
Missing (%)64.6%
Infinite0
Infinite (%)0.0%
Mean13.400882
Minimum3
Maximum19.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-05-11T17:27:37.113889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q19.975
median14.12
Q318
95-th percentile19.5
Maximum19.5
Range16.5
Interquartile range (IQR)8.025

Descriptive statistics

Standard deviation5.4791027
Coefficient of variation (CV)0.40886134
Kurtosis-0.59707033
Mean13.400882
Median Absolute Deviation (MAD)4.205
Skewness-0.72066392
Sum455.63
Variance30.020566
MonotonicityNot monotonic
2024-05-11T17:27:37.232609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
12.0 7
 
7.3%
18.65 4
 
4.2%
19.5 4
 
4.2%
9.3 4
 
4.2%
18.0 3
 
3.1%
16.7 3
 
3.1%
3.0 3
 
3.1%
16.39 1
 
1.0%
17.25 1
 
1.0%
15.84 1
 
1.0%
Other values (3) 3
 
3.1%
(Missing) 62
64.6%
ValueCountFrequency (%)
3.0 3
3.1%
3.05 1
 
1.0%
3.8 1
 
1.0%
9.3 4
4.2%
12.0 7
7.3%
12.4 1
 
1.0%
15.84 1
 
1.0%
16.39 1
 
1.0%
16.7 3
3.1%
17.25 1
 
1.0%
ValueCountFrequency (%)
19.5 4
4.2%
18.65 4
4.2%
18.0 3
3.1%
17.25 1
 
1.0%
16.7 3
3.1%
16.39 1
 
1.0%
15.84 1
 
1.0%
12.4 1
 
1.0%
12.0 7
7.3%
9.3 4
4.2%

전기실바닥표고
Categorical

IMBALANCE 

Distinct4
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size900.0 B
<NA>
87 
13.6
 
4
6.6
 
4
5.8
 
1

Length

Max length4
Median length4
Mean length3.9479167
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 87
90.6%
13.6 4
 
4.2%
6.6 4
 
4.2%
5.8 1
 
1.0%

Length

2024-05-11T17:27:37.343446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:27:37.440993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 87
90.6%
13.6 4
 
4.2%
6.6 4
 
4.2%
5.8 1
 
1.0%
Distinct56
Distinct (%)58.9%
Missing1
Missing (%)1.0%
Memory size900.0 B
2024-05-11T17:27:37.612072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.5263158
Min length1

Characters and Unicode

Total characters335
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

Unique40 ?
Unique (%)42.1%

Sample

1st row21.9
2nd row21.1
3rd row75
4th row75
5th row75
ValueCountFrequency (%)
52 7
 
7.4%
16.7 7
 
7.4%
23.9 4
 
4.2%
172.2 4
 
4.2%
66.2 4
 
4.2%
5.6 4
 
4.2%
21.9 3
 
3.2%
75 3
 
3.2%
238 3
 
3.2%
11.7 3
 
3.2%
Other values (46) 53
55.8%
2024-05-11T17:27:37.917506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 56
16.7%
2 54
16.1%
1 40
11.9%
5 33
9.9%
6 32
9.6%
7 25
7.5%
3 22
 
6.6%
0 20
 
6.0%
9 17
 
5.1%
8 17
 
5.1%
Other values (2) 19
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 268
80.0%
Other Punctuation 67
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 54
20.1%
1 40
14.9%
5 33
12.3%
6 32
11.9%
7 25
9.3%
3 22
8.2%
0 20
 
7.5%
9 17
 
6.3%
8 17
 
6.3%
4 8
 
3.0%
Other Punctuation
ValueCountFrequency (%)
. 56
83.6%
, 11
 
16.4%

Most occurring scripts

ValueCountFrequency (%)
Common 335
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 56
16.7%
2 54
16.1%
1 40
11.9%
5 33
9.9%
6 32
9.6%
7 25
7.5%
3 22
 
6.6%
0 20
 
6.0%
9 17
 
5.1%
8 17
 
5.1%
Other values (2) 19
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 335
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 56
16.7%
2 54
16.1%
1 40
11.9%
5 33
9.9%
6 32
9.6%
7 25
7.5%
3 22
 
6.6%
0 20
 
6.0%
9 17
 
5.1%
8 17
 
5.1%
Other values (2) 19
 
5.7%

배수장_계획빈도
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)6.3%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean19.778947
Minimum2
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-05-11T17:27:38.026475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8.5
Q110
median20
Q330
95-th percentile36
Maximum50
Range48
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.540105
Coefficient of variation (CV)0.58345394
Kurtosis0.21229008
Mean19.778947
Median Absolute Deviation (MAD)10
Skewness0.79558914
Sum1879
Variance133.17402
MonotonicityNot monotonic
2024-05-11T17:27:38.117127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
10 38
39.6%
30 29
30.2%
20 18
18.8%
50 5
 
5.2%
5 3
 
3.1%
2 2
 
2.1%
(Missing) 1
 
1.0%
ValueCountFrequency (%)
2 2
 
2.1%
5 3
 
3.1%
10 38
39.6%
20 18
18.8%
30 29
30.2%
50 5
 
5.2%
ValueCountFrequency (%)
50 5
 
5.2%
30 29
30.2%
20 18
18.8%
10 38
39.6%
5 3
 
3.1%
2 2
 
2.1%

초기흡입수위
Real number (ℝ)

MISSING 

Distinct26
Distinct (%)36.1%
Missing24
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean7.5944444
Minimum-2.5
Maximum12.7
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)3.1%
Memory size996.0 B
2024-05-11T17:27:38.218052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2.5
5-th percentile1.55
Q16.4
median7.45
Q39.6
95-th percentile12.3
Maximum12.7
Range15.2
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation3.1444961
Coefficient of variation (CV)0.41405216
Kurtosis2.8692521
Mean7.5944444
Median Absolute Deviation (MAD)1.35
Skewness-1.259154
Sum546.8
Variance9.887856
MonotonicityNot monotonic
2024-05-11T17:27:38.352625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
6.4 7
 
7.3%
9.6 5
 
5.2%
6.5 5
 
5.2%
8.2 4
 
4.2%
6.1 4
 
4.2%
12.3 4
 
4.2%
10.6 4
 
4.2%
4.8 4
 
4.2%
6.8 4
 
4.2%
7.0 3
 
3.1%
Other values (16) 28
29.2%
(Missing) 24
25.0%
ValueCountFrequency (%)
-2.5 3
3.1%
1.0 1
 
1.0%
2.0 1
 
1.0%
4.8 4
4.2%
6.1 4
4.2%
6.4 7
7.3%
6.5 5
5.2%
6.6 2
 
2.1%
6.8 4
4.2%
7.0 3
3.1%
ValueCountFrequency (%)
12.7 1
 
1.0%
12.3 4
4.2%
11.8 2
 
2.1%
10.6 4
4.2%
10.3 3
3.1%
10.0 3
3.1%
9.6 5
5.2%
9.5 1
 
1.0%
9.2 1
 
1.0%
9.1 2
 
2.1%
Distinct60
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Memory size900.0 B
2024-05-11T17:27:38.608102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length25
Mean length13.4375
Min length4

Characters and Unicode

Total characters1290
Distinct characters27
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)45.8%

Sample

1st row250×3, 120×1, 870×2
2nd row800×4, 200×2
3rd row950×10, 1,100×3
4th row950×10, 1,100×3
5th row950×10, 1,100×3
ValueCountFrequency (%)
1,000x6 7
 
3.5%
800x6 7
 
3.5%
250×1 7
 
3.5%
1,000×8 5
 
2.5%
1,080×2 4
 
2.0%
× 4
 
2.0%
850x9 4
 
2.0%
1,000x2 4
 
2.0%
1,340×8 4
 
2.0%
900×4 4
 
2.0%
Other values (99) 150
75.0%
2024-05-11T17:27:38.972987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 290
22.5%
, 128
9.9%
× 113
 
8.8%
112
 
8.7%
1 101
 
7.8%
5 94
 
7.3%
2 84
 
6.5%
3 60
 
4.7%
4 45
 
3.5%
8 42
 
3.3%
Other values (17) 221
17.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 806
62.5%
Other Punctuation 132
 
10.2%
Math Symbol 113
 
8.8%
Space Separator 112
 
8.7%
Uppercase Letter 56
 
4.3%
Lowercase Letter 54
 
4.2%
Other Letter 7
 
0.5%
Close Punctuation 4
 
0.3%
Open Punctuation 4
 
0.3%
Other Symbol 2
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 290
36.0%
1 101
 
12.5%
5 94
 
11.7%
2 84
 
10.4%
3 60
 
7.4%
4 45
 
5.6%
8 42
 
5.2%
6 36
 
4.5%
7 30
 
3.7%
9 24
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
X 35
62.5%
W 7
 
12.5%
H 5
 
8.9%
P 5
 
8.9%
D 4
 
7.1%
Other Punctuation
ValueCountFrequency (%)
, 128
97.0%
/ 3
 
2.3%
? 1
 
0.8%
Lowercase Letter
ValueCountFrequency (%)
x 37
68.5%
m 10
 
18.5%
k 7
 
13.0%
Math Symbol
ValueCountFrequency (%)
× 113
100.0%
Space Separator
ValueCountFrequency (%)
112
100.0%
Other Letter
ValueCountFrequency (%)
7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Other Symbol
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1173
90.9%
Latin 110
 
8.5%
Hangul 7
 
0.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 290
24.7%
, 128
10.9%
× 113
 
9.6%
112
 
9.5%
1 101
 
8.6%
5 94
 
8.0%
2 84
 
7.2%
3 60
 
5.1%
4 45
 
3.8%
8 42
 
3.6%
Other values (8) 104
 
8.9%
Latin
ValueCountFrequency (%)
x 37
33.6%
X 35
31.8%
m 10
 
9.1%
W 7
 
6.4%
k 7
 
6.4%
H 5
 
4.5%
P 5
 
4.5%
D 4
 
3.6%
Hangul
ValueCountFrequency (%)
7
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1168
90.5%
None 113
 
8.8%
Hangul 7
 
0.5%
CJK Compat 2
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 290
24.8%
, 128
11.0%
112
 
9.6%
1 101
 
8.6%
5 94
 
8.0%
2 84
 
7.2%
3 60
 
5.1%
4 45
 
3.9%
8 42
 
3.6%
x 37
 
3.2%
Other values (14) 175
15.0%
None
ValueCountFrequency (%)
× 113
100.0%
Hangul
ValueCountFrequency (%)
7
100.0%
CJK Compat
ValueCountFrequency (%)
2
100.0%

펌프규격
Categorical

Distinct32
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size900.0 B
<NA>
29 
Φ1,500x6, Φ1,350x6
1400×4 (250㎥/min×4대)
 
4
1,500×11
 
4
1,500×4, 1,000×1
 
4
Other values (27)
48 

Length

Max length34
Median length25
Mean length12.895833
Min length4

Unique

Unique15 ?
Unique (%)15.6%

Sample

1st row1,100×3, 700×1, 1650×2
2nd row1,400×4, 700×2
3rd row1,500×10, 1,800×3
4th row1,500×10, 1,800×3
5th row1,500×10, 1,800×3

Common Values

ValueCountFrequency (%)
<NA> 29
30.2%
Φ1,500x6, Φ1,350x6 7
 
7.3%
1400×4 (250㎥/min×4대) 4
 
4.2%
1,500×11 4
 
4.2%
1,500×4, 1,000×1 4
 
4.2%
1,800×8, 2,200×8, 1,800×2 4
 
4.2%
1,100×3, 700×1, 1650×2 3
 
3.1%
1,000×3, 700×1 3
 
3.1%
44m³/분×2대, 50m³/분×3대 3
 
3.1%
1,200×5 3
 
3.1%
Other values (22) 32
33.3%

Length

2024-05-11T17:27:39.095342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 29
 
16.0%
700×1 8
 
4.4%
1,000×1 7
 
3.9%
× 7
 
3.9%
φ1,500x6 7
 
3.9%
φ1,350x6 7
 
3.9%
1,800×8 5
 
2.8%
1,500×2 5
 
2.8%
1,500×4 5
 
2.8%
1,500×11 4
 
2.2%
Other values (52) 97
53.6%

전양정
Real number (ℝ)

MISSING 

Distinct8
Distinct (%)21.1%
Missing58
Missing (%)60.4%
Infinite0
Infinite (%)0.0%
Mean9.9821053
Minimum3.82
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-05-11T17:27:39.183089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.82
5-th percentile7.475
Q18
median10
Q311.75
95-th percentile18
Maximum18
Range14.18
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation3.0377367
Coefficient of variation (CV)0.30431824
Kurtosis2.3586585
Mean9.9821053
Median Absolute Deviation (MAD)2
Skewness1.1285776
Sum379.32
Variance9.2278441
MonotonicityNot monotonic
2024-05-11T17:27:39.284284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
8.0 12
 
12.5%
10.0 9
 
9.4%
12.0 7
 
7.3%
9.0 4
 
4.2%
18.0 3
 
3.1%
11.0 1
 
1.0%
4.5 1
 
1.0%
3.82 1
 
1.0%
(Missing) 58
60.4%
ValueCountFrequency (%)
3.82 1
 
1.0%
4.5 1
 
1.0%
8.0 12
12.5%
9.0 4
 
4.2%
10.0 9
9.4%
11.0 1
 
1.0%
12.0 7
7.3%
18.0 3
 
3.1%
ValueCountFrequency (%)
18.0 3
 
3.1%
12.0 7
7.3%
11.0 1
 
1.0%
10.0 9
9.4%
9.0 4
 
4.2%
8.0 12
12.5%
4.5 1
 
1.0%
3.82 1
 
1.0%

실양정
Categorical

IMBALANCE 

Distinct4
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size900.0 B
<NA>
91 
13.5
 
3
4.0
 
1
3.02
 
1

Length

Max length4
Median length4
Mean length3.9895833
Min length3

Unique

Unique2 ?
Unique (%)2.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 91
94.8%
13.5 3
 
3.1%
4.0 1
 
1.0%
3.02 1
 
1.0%

Length

2024-05-11T17:27:39.389848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:27:39.471501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 91
94.8%
13.5 3
 
3.1%
4.0 1
 
1.0%
3.02 1
 
1.0%

토출수위
Real number (ℝ)

MISSING 

Distinct19
Distinct (%)34.5%
Missing41
Missing (%)42.7%
Infinite0
Infinite (%)0.0%
Mean10.957455
Minimum5
Maximum14.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-05-11T17:27:39.555132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile7
Q19.06
median11
Q313.7
95-th percentile14.5
Maximum14.5
Range9.5
Interquartile range (IQR)4.64

Descriptive statistics

Standard deviation2.5964728
Coefficient of variation (CV)0.23695949
Kurtosis-0.71065219
Mean10.957455
Median Absolute Deviation (MAD)2
Skewness-0.30461864
Sum602.66
Variance6.7416712
MonotonicityNot monotonic
2024-05-11T17:27:39.663509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
10.0 7
 
7.3%
14.0 7
 
7.3%
14.5 5
 
5.2%
7.0 4
 
4.2%
13.0 4
 
4.2%
9.06 4
 
4.2%
11.0 3
 
3.1%
11.4 3
 
3.1%
8.5 3
 
3.1%
13.7 3
 
3.1%
Other values (9) 12
 
12.5%
(Missing) 41
42.7%
ValueCountFrequency (%)
5.0 1
 
1.0%
5.02 1
 
1.0%
7.0 4
4.2%
8.25 2
 
2.1%
8.5 3
3.1%
9.0 1
 
1.0%
9.06 4
4.2%
9.5 1
 
1.0%
10.0 7
7.3%
10.5 3
3.1%
ValueCountFrequency (%)
14.5 5
5.2%
14.0 7
7.3%
13.7 3
3.1%
13.0 4
4.2%
12.5 1
 
1.0%
11.6 1
 
1.0%
11.5 1
 
1.0%
11.4 3
3.1%
11.0 3
3.1%
10.5 3
3.1%

시설관리자
Categorical

Distinct14
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Memory size900.0 B
<NA>
36 
영등포구
10 
송파구
구로구
강동구
Other values (9)
28 

Length

Max length9
Median length4
Mean length3.59375
Min length3

Unique

Unique3 ?
Unique (%)3.1%

Sample

1st row구로구
2nd row송파구
3rd row강동구
4th row강동구
5th row강동구

Common Values

ValueCountFrequency (%)
<NA> 36
37.5%
영등포구 10
 
10.4%
송파구 8
 
8.3%
구로구 7
 
7.3%
강동구 7
 
7.3%
강남구 7
 
7.3%
마포구 6
 
6.2%
구로구청 4
 
4.2%
서초구 3
 
3.1%
관악구 3
 
3.1%
Other values (4) 5
 
5.2%

Length

2024-05-11T17:27:39.773814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 36
37.1%
영등포구 10
 
10.3%
송파구 8
 
8.2%
강동구 8
 
8.2%
구로구 7
 
7.2%
강남구 7
 
7.2%
마포구 6
 
6.2%
구로구청 4
 
4.1%
서초구 3
 
3.1%
관악구 3
 
3.1%
Other values (4) 5
 
5.2%

구분
Text

MISSING 

Distinct61
Distinct (%)82.4%
Missing22
Missing (%)22.9%
Memory size900.0 B
2024-05-11T17:27:39.981349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length11
Mean length6.472973
Min length2

Characters and Unicode

Total characters479
Distinct characters70
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

Unique57 ?
Unique (%)77.0%

Sample

1st row구로2 유수지
2nd row성내천 고지수문
3rd row성내 배수문(내)-성내천
4th row성내 배수문(내)-탄천
5th row성내 배수문(외)-성내천
ValueCountFrequency (%)
유수지 14
 
12.3%
성내천 4
 
3.5%
문래 4
 
3.5%
배수문 4
 
3.5%
고덕천 3
 
2.6%
대림2동 3
 
2.6%
성내 3
 
2.6%
양재 3
 
2.6%
방류수문 2
 
1.8%
방류수로 2
 
1.8%
Other values (65) 72
63.2%
2024-05-11T17:27:40.341939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
64
 
13.4%
49
 
10.2%
40
 
8.4%
21
 
4.4%
20
 
4.2%
19
 
4.0%
18
 
3.8%
15
 
3.1%
2 13
 
2.7%
) 12
 
2.5%
Other values (60) 208
43.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 364
76.0%
Space Separator 40
 
8.4%
Decimal Number 39
 
8.1%
Close Punctuation 12
 
2.5%
Open Punctuation 12
 
2.5%
Dash Punctuation 8
 
1.7%
Other Punctuation 4
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
64
17.6%
49
 
13.5%
21
 
5.8%
20
 
5.5%
19
 
5.2%
18
 
4.9%
15
 
4.1%
11
 
3.0%
11
 
3.0%
10
 
2.7%
Other values (46) 126
34.6%
Decimal Number
ValueCountFrequency (%)
2 13
33.3%
1 12
30.8%
3 4
 
10.3%
4 3
 
7.7%
7 2
 
5.1%
9 2
 
5.1%
5 1
 
2.6%
0 1
 
2.6%
8 1
 
2.6%
Space Separator
ValueCountFrequency (%)
40
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 364
76.0%
Common 115
 
24.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
64
17.6%
49
 
13.5%
21
 
5.8%
20
 
5.5%
19
 
5.2%
18
 
4.9%
15
 
4.1%
11
 
3.0%
11
 
3.0%
10
 
2.7%
Other values (46) 126
34.6%
Common
ValueCountFrequency (%)
40
34.8%
2 13
 
11.3%
) 12
 
10.4%
1 12
 
10.4%
( 12
 
10.4%
- 8
 
7.0%
, 4
 
3.5%
3 4
 
3.5%
4 3
 
2.6%
7 2
 
1.7%
Other values (4) 5
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 364
76.0%
ASCII 115
 
24.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
64
17.6%
49
 
13.5%
21
 
5.8%
20
 
5.5%
19
 
5.2%
18
 
4.9%
15
 
4.1%
11
 
3.0%
11
 
3.0%
10
 
2.7%
Other values (46) 126
34.6%
ASCII
ValueCountFrequency (%)
40
34.8%
2 13
 
11.3%
) 12
 
10.4%
1 12
 
10.4%
( 12
 
10.4%
- 8
 
7.0%
, 4
 
3.5%
3 4
 
3.5%
4 3
 
2.6%
7 2
 
1.7%
Other values (4) 5
 
4.3%

배수문규격
Text

MISSING 

Distinct50
Distinct (%)79.4%
Missing33
Missing (%)34.4%
Memory size900.0 B
2024-05-11T17:27:40.573425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length9
Mean length12.650794
Min length6

Characters and Unicode

Total characters797
Distinct characters50
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)65.1%

Sample

1st row3.00×2.00@2
2nd row4.0×4.0×10
3rd row4.0×3.0×2
4th row2.5×1.5×1
5th row6.0×3.0×2
ValueCountFrequency (%)
2.1×2.18@1 4
 
4.5%
→방수로 4
 
4.5%
gate 4
 
4.5%
수문(flap 3
 
3.4%
1.1×1.1×1 3
 
3.4%
2.5×2.5×1 3
 
3.4%
토출수조 2
 
2.3%
2.1×1.5@1 2
 
2.3%
5.0×5.0@2 2
 
2.3%
2.7×2.5×1 2
 
2.3%
Other values (56) 59
67.0%
2024-05-11T17:27:40.905754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 125
15.7%
1 102
12.8%
× 89
11.2%
2 67
 
8.4%
0 65
 
8.2%
5 50
 
6.3%
@ 29
 
3.6%
3 25
 
3.1%
25
 
3.1%
4 15
 
1.9%
Other values (40) 205
25.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 352
44.2%
Other Punctuation 169
21.2%
Math Symbol 99
 
12.4%
Other Letter 56
 
7.0%
Uppercase Letter 51
 
6.4%
Space Separator 25
 
3.1%
Lowercase Letter 19
 
2.4%
Close Punctuation 13
 
1.6%
Open Punctuation 13
 
1.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 8
15.7%
L 8
15.7%
P 4
7.8%
T 4
7.8%
E 4
7.8%
D 4
7.8%
G 4
7.8%
F 3
 
5.9%
H 3
 
5.9%
B 3
 
5.9%
Other values (6) 6
11.8%
Other Letter
ValueCountFrequency (%)
14
25.0%
7
12.5%
6
10.7%
5
 
8.9%
4
 
7.1%
4
 
7.1%
4
 
7.1%
4
 
7.1%
3
 
5.4%
2
 
3.6%
Other values (2) 3
 
5.4%
Decimal Number
ValueCountFrequency (%)
1 102
29.0%
2 67
19.0%
0 65
18.5%
5 50
14.2%
3 25
 
7.1%
4 15
 
4.3%
6 11
 
3.1%
8 10
 
2.8%
7 6
 
1.7%
9 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 125
74.0%
@ 29
 
17.2%
, 14
 
8.3%
? 1
 
0.6%
Math Symbol
ValueCountFrequency (%)
× 89
89.9%
= 5
 
5.1%
5
 
5.1%
Lowercase Letter
ValueCountFrequency (%)
m 11
57.9%
x 8
42.1%
Space Separator
ValueCountFrequency (%)
25
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 671
84.2%
Latin 70
 
8.8%
Hangul 56
 
7.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 125
18.6%
1 102
15.2%
× 89
13.3%
2 67
10.0%
0 65
9.7%
5 50
 
7.5%
@ 29
 
4.3%
3 25
 
3.7%
25
 
3.7%
4 15
 
2.2%
Other values (10) 79
11.8%
Latin
ValueCountFrequency (%)
m 11
15.7%
x 8
11.4%
A 8
11.4%
L 8
11.4%
P 4
 
5.7%
T 4
 
5.7%
E 4
 
5.7%
D 4
 
5.7%
G 4
 
5.7%
F 3
 
4.3%
Other values (8) 12
17.1%
Hangul
ValueCountFrequency (%)
14
25.0%
7
12.5%
6
10.7%
5
 
8.9%
4
 
7.1%
4
 
7.1%
4
 
7.1%
4
 
7.1%
3
 
5.4%
2
 
3.6%
Other values (2) 3
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 647
81.2%
None 89
 
11.2%
Hangul 56
 
7.0%
Arrows 5
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 125
19.3%
1 102
15.8%
2 67
10.4%
0 65
10.0%
5 50
 
7.7%
@ 29
 
4.5%
3 25
 
3.9%
25
 
3.9%
4 15
 
2.3%
, 14
 
2.2%
Other values (26) 130
20.1%
None
ValueCountFrequency (%)
× 89
100.0%
Hangul
ValueCountFrequency (%)
14
25.0%
7
12.5%
6
10.7%
5
 
8.9%
4
 
7.1%
4
 
7.1%
4
 
7.1%
4
 
7.1%
3
 
5.4%
2
 
3.6%
Other values (2) 3
 
5.4%
Arrows
ValueCountFrequency (%)
5
100.0%

배수문바닥표고
Real number (ℝ)

MISSING  ZEROS 

Distinct39
Distinct (%)72.2%
Missing42
Missing (%)43.8%
Infinite0
Infinite (%)0.0%
Mean7.9213889
Minimum0
Maximum14.5
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2024-05-11T17:27:41.041758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.5075
Q14.6
median8.05
Q311.1375
95-th percentile13.1715
Maximum14.5
Range14.5
Interquartile range (IQR)6.5375

Descriptive statistics

Standard deviation3.5976382
Coefficient of variation (CV)0.4541676
Kurtosis-0.79721325
Mean7.9213889
Median Absolute Deviation (MAD)3.45
Skewness-0.17243435
Sum427.755
Variance12.943
MonotonicityNot monotonic
2024-05-11T17:27:41.178452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
4.6 4
 
4.2%
8.9 3
 
3.1%
7.85 2
 
2.1%
8.4 2
 
2.1%
11.9 2
 
2.1%
11.8 2
 
2.1%
3.0 2
 
2.1%
11.84 2
 
2.1%
3.75 2
 
2.1%
8.3 2
 
2.1%
Other values (29) 31
32.3%
(Missing) 42
43.8%
ValueCountFrequency (%)
0.0 1
 
1.0%
1.0 1
 
1.0%
2.15 1
 
1.0%
2.7 1
 
1.0%
3.0 2
2.1%
3.525 1
 
1.0%
3.75 2
2.1%
3.9 1
 
1.0%
4.3 1
 
1.0%
4.6 4
4.2%
ValueCountFrequency (%)
14.5 1
1.0%
14.0 1
1.0%
13.49 1
1.0%
13.0 1
1.0%
12.4 2
2.1%
12.04 1
1.0%
11.9 2
2.1%
11.84 2
2.1%
11.8 2
2.1%
11.4 1
1.0%

간선수로
Text

MISSING 

Distinct7
Distinct (%)100.0%
Missing89
Missing (%)92.7%
Memory size900.0 B
2024-05-11T17:27:41.345852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length23
Mean length24.571429
Min length15

Characters and Unicode

Total characters172
Distinct characters52
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)100.0%

Sample

1st row1.2x4.2~6.8@1 / 713m
2nd row1.2x0.8@1 / 27m
3rd row?600~1,100@1 / 452m
4th row2.7x6.8~7.0@1 / 79m
5th rowL=780km, B15m(합류부 유인수로)
ValueCountFrequency (%)
6
28.6%
1.2x4.2~6.8@1 1
 
4.8%
713m 1
 
4.8%
1.2x0.8@1 1
 
4.8%
27m 1
 
4.8%
600~1,100@1 1
 
4.8%
452m 1
 
4.8%
2.7x6.8~7.0@1 1
 
4.8%
79m 1
 
4.8%
l=780km 1
 
4.8%
Other values (6) 6
28.6%
2024-05-11T17:27:41.619369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15
 
8.7%
1 13
 
7.6%
0 10
 
5.8%
. 10
 
5.8%
m 9
 
5.2%
2 8
 
4.7%
8 6
 
3.5%
7 6
 
3.5%
( 5
 
2.9%
/ 5
 
2.9%
Other values (42) 85
49.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 58
33.7%
Other Punctuation 28
16.3%
Other Letter 28
16.3%
Lowercase Letter 19
 
11.0%
Space Separator 15
 
8.7%
Math Symbol 7
 
4.1%
Uppercase Letter 7
 
4.1%
Open Punctuation 5
 
2.9%
Close Punctuation 5
 
2.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4
14.3%
3
10.7%
3
10.7%
3
10.7%
3
10.7%
1
 
3.6%
1
 
3.6%
1
 
3.6%
1
 
3.6%
1
 
3.6%
Other values (7) 7
25.0%
Decimal Number
ValueCountFrequency (%)
1 13
22.4%
0 10
17.2%
2 8
13.8%
8 6
10.3%
7 6
10.3%
3 4
 
6.9%
5 4
 
6.9%
6 3
 
5.2%
4 3
 
5.2%
9 1
 
1.7%
Lowercase Letter
ValueCountFrequency (%)
m 9
47.4%
x 3
 
15.8%
s 2
 
10.5%
a 1
 
5.3%
p 1
 
5.3%
y 1
 
5.3%
b 1
 
5.3%
k 1
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 10
35.7%
/ 5
17.9%
, 5
17.9%
@ 4
 
14.3%
: 2
 
7.1%
? 2
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
B 2
28.6%
L 2
28.6%
O 1
14.3%
D 1
14.3%
X 1
14.3%
Math Symbol
ValueCountFrequency (%)
~ 3
42.9%
= 2
28.6%
× 2
28.6%
Space Separator
ValueCountFrequency (%)
15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 118
68.6%
Hangul 28
 
16.3%
Latin 26
 
15.1%

Most frequent character per script

Common
ValueCountFrequency (%)
15
12.7%
1 13
 
11.0%
0 10
 
8.5%
. 10
 
8.5%
2 8
 
6.8%
8 6
 
5.1%
7 6
 
5.1%
( 5
 
4.2%
/ 5
 
4.2%
, 5
 
4.2%
Other values (12) 35
29.7%
Hangul
ValueCountFrequency (%)
4
14.3%
3
10.7%
3
10.7%
3
10.7%
3
10.7%
1
 
3.6%
1
 
3.6%
1
 
3.6%
1
 
3.6%
1
 
3.6%
Other values (7) 7
25.0%
Latin
ValueCountFrequency (%)
m 9
34.6%
x 3
 
11.5%
s 2
 
7.7%
B 2
 
7.7%
L 2
 
7.7%
O 1
 
3.8%
a 1
 
3.8%
p 1
 
3.8%
y 1
 
3.8%
b 1
 
3.8%
Other values (3) 3
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 142
82.6%
Hangul 28
 
16.3%
None 2
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15
 
10.6%
1 13
 
9.2%
0 10
 
7.0%
. 10
 
7.0%
m 9
 
6.3%
2 8
 
5.6%
8 6
 
4.2%
7 6
 
4.2%
( 5
 
3.5%
/ 5
 
3.5%
Other values (24) 55
38.7%
Hangul
ValueCountFrequency (%)
4
14.3%
3
10.7%
3
10.7%
3
10.7%
3
10.7%
1
 
3.6%
1
 
3.6%
1
 
3.6%
1
 
3.6%
1
 
3.6%
Other values (7) 7
25.0%
None
ValueCountFrequency (%)
× 2
100.0%

지선수로
Text

MISSING 

Distinct7
Distinct (%)100.0%
Missing89
Missing (%)92.7%
Memory size900.0 B
2024-05-11T17:27:41.767747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length20
Mean length24.571429
Min length16

Characters and Unicode

Total characters172
Distinct characters43
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)100.0%

Sample

1st row1.2x4.2~7.0@1 / 713m
2nd row1.5x1.5~2.0x1@1 / 252m
3rd row?800~1,350@1 / 171m
4th row1.8x1.15@2 / 24m
5th rowL=1,150m, H2m×B3m×2련(유입암거1)
ValueCountFrequency (%)
4
17.4%
자연방류관(강관 2
 
8.7%
1.2x4.2~7.0@1 1
 
4.3%
h2m×b3m×2련(유입암거1 1
 
4.3%
21.0m 1
 
4.3%
l 1
 
4.3%
d1,000mm 1
 
4.3%
고지 1
 
4.3%
b3m(유입수로2 1
 
4.3%
l=498m 1
 
4.3%
Other values (9) 9
39.1%
2024-05-11T17:27:42.027129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 20
 
11.6%
18
 
10.5%
m 12
 
7.0%
2 11
 
6.4%
0 10
 
5.8%
. 9
 
5.2%
5 6
 
3.5%
, 6
 
3.5%
/ 5
 
2.9%
) 4
 
2.3%
Other values (33) 71
41.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 61
35.5%
Other Letter 29
16.9%
Other Punctuation 25
14.5%
Space Separator 18
 
10.5%
Lowercase Letter 16
 
9.3%
Math Symbol 8
 
4.7%
Uppercase Letter 7
 
4.1%
Close Punctuation 4
 
2.3%
Open Punctuation 4
 
2.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4
13.8%
2
 
6.9%
2
 
6.9%
2
 
6.9%
2
 
6.9%
2
 
6.9%
2
 
6.9%
2
 
6.9%
2
 
6.9%
2
 
6.9%
Other values (7) 7
24.1%
Decimal Number
ValueCountFrequency (%)
1 20
32.8%
2 11
18.0%
0 10
16.4%
5 6
 
9.8%
3 4
 
6.6%
4 3
 
4.9%
8 3
 
4.9%
7 3
 
4.9%
9 1
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 9
36.0%
, 6
24.0%
/ 5
20.0%
@ 4
16.0%
? 1
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
L 3
42.9%
B 2
28.6%
H 1
 
14.3%
D 1
 
14.3%
Math Symbol
ValueCountFrequency (%)
= 3
37.5%
~ 3
37.5%
× 2
25.0%
Lowercase Letter
ValueCountFrequency (%)
m 12
75.0%
x 4
 
25.0%
Space Separator
ValueCountFrequency (%)
18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 120
69.8%
Hangul 29
 
16.9%
Latin 23
 
13.4%

Most frequent character per script

Common
ValueCountFrequency (%)
1 20
16.7%
18
15.0%
2 11
9.2%
0 10
 
8.3%
. 9
 
7.5%
5 6
 
5.0%
, 6
 
5.0%
/ 5
 
4.2%
) 4
 
3.3%
3 4
 
3.3%
Other values (10) 27
22.5%
Hangul
ValueCountFrequency (%)
4
13.8%
2
 
6.9%
2
 
6.9%
2
 
6.9%
2
 
6.9%
2
 
6.9%
2
 
6.9%
2
 
6.9%
2
 
6.9%
2
 
6.9%
Other values (7) 7
24.1%
Latin
ValueCountFrequency (%)
m 12
52.2%
x 4
 
17.4%
L 3
 
13.0%
B 2
 
8.7%
H 1
 
4.3%
D 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 141
82.0%
Hangul 29
 
16.9%
None 2
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 20
14.2%
18
12.8%
m 12
 
8.5%
2 11
 
7.8%
0 10
 
7.1%
. 9
 
6.4%
5 6
 
4.3%
, 6
 
4.3%
/ 5
 
3.5%
) 4
 
2.8%
Other values (15) 40
28.4%
Hangul
ValueCountFrequency (%)
4
13.8%
2
 
6.9%
2
 
6.9%
2
 
6.9%
2
 
6.9%
2
 
6.9%
2
 
6.9%
2
 
6.9%
2
 
6.9%
2
 
6.9%
Other values (7) 7
24.1%
None
ValueCountFrequency (%)
× 2
100.0%

유수지표고_HWL
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size900.0 B
<NA>
95 
2.5
 
1

Length

Max length4
Median length4
Mean length3.9895833
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 95
99.0%
2.5 1
 
1.0%

Length

2024-05-11T17:27:42.140537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:27:42.226658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 95
99.0%
2.5 1
 
1.0%

유수지표고_LWL
Real number (ℝ)

MISSING 

Distinct10
Distinct (%)100.0%
Missing86
Missing (%)89.6%
Infinite0
Infinite (%)0.0%
Mean6.177
Minimum-3
Maximum11.2
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)2.1%
Memory size996.0 B
2024-05-11T17:27:42.303087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3
5-th percentile-1.6905
Q15.175
median7.98
Q38.825
95-th percentile10.435
Maximum11.2
Range14.2
Interquartile range (IQR)3.65

Descriptive statistics

Standard deviation4.4791171
Coefficient of variation (CV)0.72512823
Kurtosis0.81434255
Mean6.177
Median Absolute Deviation (MAD)1.75
Skewness-1.2565923
Sum61.77
Variance20.06249
MonotonicityNot monotonic
2024-05-11T17:27:42.396791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
9.5 1
 
1.0%
6.0 1
 
1.0%
9.0 1
 
1.0%
8.26 1
 
1.0%
11.2 1
 
1.0%
8.3 1
 
1.0%
7.7 1
 
1.0%
4.9 1
 
1.0%
-0.09 1
 
1.0%
-3.0 1
 
1.0%
(Missing) 86
89.6%
ValueCountFrequency (%)
-3.0 1
1.0%
-0.09 1
1.0%
4.9 1
1.0%
6.0 1
1.0%
7.7 1
1.0%
8.26 1
1.0%
8.3 1
1.0%
9.0 1
1.0%
9.5 1
1.0%
11.2 1
1.0%
ValueCountFrequency (%)
11.2 1
1.0%
9.5 1
1.0%
9.0 1
1.0%
8.3 1
1.0%
8.26 1
1.0%
7.7 1
1.0%
6.0 1
1.0%
4.9 1
1.0%
-0.09 1
1.0%
-3.0 1
1.0%

유수지면적
Text

MISSING 

Distinct33
Distinct (%)94.3%
Missing61
Missing (%)63.5%
Memory size900.0 B
2024-05-11T17:27:42.562590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.2857143
Min length3

Characters and Unicode

Total characters185
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

Unique31 ?
Unique (%)88.6%

Sample

1st row962
2nd row59,508
3rd row61,714
4th row55,720
5th row82,942
ValueCountFrequency (%)
17,400 2
 
5.7%
160,625 2
 
5.7%
478 1
 
2.9%
1,776 1
 
2.9%
2,307 1
 
2.9%
14,418 1
 
2.9%
1,333 1
 
2.9%
2,003 1
 
2.9%
13,300 1
 
2.9%
2,874 1
 
2.9%
Other values (23) 23
65.7%
2024-05-11T17:27:43.107005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 32
17.3%
, 29
15.7%
1 26
14.1%
4 18
9.7%
2 18
9.7%
3 14
7.6%
6 12
 
6.5%
5 11
 
5.9%
7 10
 
5.4%
8 8
 
4.3%
Other values (2) 7
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 155
83.8%
Other Punctuation 30
 
16.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 32
20.6%
1 26
16.8%
4 18
11.6%
2 18
11.6%
3 14
9.0%
6 12
 
7.7%
5 11
 
7.1%
7 10
 
6.5%
8 8
 
5.2%
9 6
 
3.9%
Other Punctuation
ValueCountFrequency (%)
, 29
96.7%
. 1
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 185
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 32
17.3%
, 29
15.7%
1 26
14.1%
4 18
9.7%
2 18
9.7%
3 14
7.6%
6 12
 
6.5%
5 11
 
5.9%
7 10
 
5.4%
8 8
 
4.3%
Other values (2) 7
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 185
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 32
17.3%
, 29
15.7%
1 26
14.1%
4 18
9.7%
2 18
9.7%
3 14
7.6%
6 12
 
6.5%
5 11
 
5.9%
7 10
 
5.4%
8 8
 
4.3%
Other values (2) 7
 
3.8%

유수지용량
Text

MISSING 

Distinct50
Distinct (%)92.6%
Missing42
Missing (%)43.8%
Memory size900.0 B
2024-05-11T17:27:43.305490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.2592593
Min length2

Characters and Unicode

Total characters284
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

Unique46 ?
Unique (%)85.2%

Sample

1st row1,904
2nd row237,000
3rd row216,000
4th row200,300
5th row261,000
ValueCountFrequency (%)
6,000 2
 
3.7%
4,500 2
 
3.7%
700,000 2
 
3.7%
65,000 2
 
3.7%
2,000 1
 
1.9%
3,700 1
 
1.9%
1,904 1
 
1.9%
69 1
 
1.9%
4,800 1
 
1.9%
43 1
 
1.9%
Other values (40) 40
74.1%
2024-05-11T17:27:43.641414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 112
39.4%
, 47
16.5%
6 19
 
6.7%
5 18
 
6.3%
3 18
 
6.3%
1 18
 
6.3%
7 16
 
5.6%
2 13
 
4.6%
4 11
 
3.9%
9 7
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 237
83.5%
Other Punctuation 47
 
16.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 112
47.3%
6 19
 
8.0%
5 18
 
7.6%
3 18
 
7.6%
1 18
 
7.6%
7 16
 
6.8%
2 13
 
5.5%
4 11
 
4.6%
9 7
 
3.0%
8 5
 
2.1%
Other Punctuation
ValueCountFrequency (%)
, 47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 284
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 112
39.4%
, 47
16.5%
6 19
 
6.7%
5 18
 
6.3%
3 18
 
6.3%
1 18
 
6.3%
7 16
 
5.6%
2 13
 
4.6%
4 11
 
3.9%
9 7
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 284
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 112
39.4%
, 47
16.5%
6 19
 
6.7%
5 18
 
6.3%
3 18
 
6.3%
1 18
 
6.3%
7 16
 
5.6%
2 13
 
4.6%
4 11
 
3.9%
9 7
 
2.5%

Sample

하천기본계획코드일련번호하천기본계획 사업명수립년도시설물명하천명하천등급상세주소주소유역면적보호면적시행기관본류하천_유역면적본류하천_홍수량본류하천_계획빈도제방고계획홍수위부지표고기계실바닥표고전기실바닥표고배수장_최대배수량배수장_계획빈도초기흡입수위전동기펌프규격전양정실양정토출수위시설관리자구분배수문규격배수문바닥표고간선수로지선수로유수지표고_HWL유수지표고_LWL유수지면적유수지용량
0102554020151072안양천권역 하천기본계획2015구로2 빗물펌프장도림천지방하천서울시 구로구 구로4동 120-5<NA>0.5382,000<NA>42.554910016.4614.85<NA><NA><NA>21.9306.8250×3, 120×1, 870×21,100×3, 700×1, 1650×2<NA><NA>8.5구로구구로2 유수지3.00×2.00@27.9<NA><NA><NA><NA>9621,904
1102491020151033탄천 등 10개 하천기본계획2015몽촌2성내천지방하천풍납동 471<NA>8.2580,000<NA>29.62828022.1314.014.1418.65<NA>21.15010.6800×4, 200×21,400×4, 700×28.0<NA>14.0송파구성내천 고지수문4.0×4.0×103.0<NA><NA><NA><NA><NA><NA>
2102491020151034탄천 등 10개 하천기본계획2015성내성내천지방하천방이동 88-13<NA>6.31,130,000<NA>29.62828017.7214.3118.218.0<NA>753010.0950×10, 1,100×31,500×10, 1,800×39.0<NA>13.0강동구성내 배수문(내)-성내천4.0×3.0×28.4<NA><NA><NA>9.559,508237,000
3102491020151035탄천 등 10개 하천기본계획2015성내성내천지방하천방이동 88-13<NA>6.31,130,000<NA>29.62828017.7214.3118.218.0<NA>753010.0950×10, 1,100×31,500×10, 1,800×39.0<NA>13.0강동구성내 배수문(내)-탄천2.5×1.5×17.83<NA><NA><NA><NA><NA><NA>
4102491020151036탄천 등 10개 하천기본계획2015성내성내천지방하천방이동 88-13<NA>6.31,130,000<NA>29.62828017.7214.3118.218.0<NA>753010.0950×10, 1,100×31,500×10, 1,800×39.0<NA>13.0강동구성내 배수문(외)-성내천6.0×3.0×28.3<NA><NA><NA><NA><NA><NA>
5102493020151037탄천 등 10개 하천기본계획2015대치탄천지방하천대치동 78-24<NA>5.8550,000<NA>302.42,19810020.7518.0112.012.0<NA>52306.41,000x6, 800x6Φ1,500x6, Φ1,350x612.0<NA>10.0강남구탄천4수문2.5×2.0×112.04<NA><NA><NA>6.061,714216,000
6102493020151038탄천 등 10개 하천기본계획2015대치탄천지방하천대치동 78-24<NA>5.8550,000<NA>302.42,19810020.7518.0112.012.0<NA>52306.41,000x6, 800x6Φ1,500x6, Φ1,350x612.0<NA>10.0강남구대치배수문3.5×2.5×15.12<NA><NA><NA><NA><NA><NA>
7102493020151039탄천 등 10개 하천기본계획2015대치탄천지방하천대치동 78-24<NA>5.8550,000<NA>302.42,19810020.7518.0112.012.0<NA>52306.41,000x6, 800x6Φ1,500x6, Φ1,350x612.0<NA>10.0강남구압구1수문1.1×1.1×111.9<NA><NA><NA><NA><NA><NA>
8102493020151040탄천 등 10개 하천기본계획2015대치탄천지방하천대치동 78-24<NA>5.8550,000<NA>302.42,19810020.7518.0112.012.0<NA>52306.41,000x6, 800x6Φ1,500x6, Φ1,350x612.0<NA>10.0강남구압구2수문1.1×1.1×19.15<NA><NA><NA><NA><NA><NA>
9102493020151041탄천 등 10개 하천기본계획2015대치탄천지방하천대치동 78-24<NA>5.8550,000<NA>302.42,19810020.7518.0112.012.0<NA>52306.41,000x6, 800x6Φ1,500x6, Φ1,350x612.0<NA>10.0강남구탄천1수문1.1×1.1×19.4<NA><NA><NA><NA><NA><NA>
하천기본계획코드일련번호하천기본계획 사업명수립년도시설물명하천명하천등급상세주소주소유역면적보호면적시행기관본류하천_유역면적본류하천_홍수량본류하천_계획빈도제방고계획홍수위부지표고기계실바닥표고전기실바닥표고배수장_최대배수량배수장_계획빈도초기흡입수위전동기펌프규격전양정실양정토출수위시설관리자구분배수문규격배수문바닥표고간선수로지선수로유수지표고_HWL유수지표고_LWL유수지면적유수지용량
86102529020121055중랑천권역(서울특별시) 하천기본계획(변경)2012제기1정릉천지방하천서울시 동대문구 제기동 268-47<NA>0.346,000<NA>19.231610017.9717.05<NA><NA><NA>6.710<NA>125×4<NA><NA><NA><NA><NA>유수지<NA><NA><NA><NA><NA><NA><NA>667
87102531020121056중랑천권역(서울특별시) 하천기본계획(변경)2012답십리4(간이)전농천지방하천서울시 동대문구 답십리동 4-304<NA>0.194,000<NA>3.87210011.5211.27<NA><NA><NA>2.810<NA>100×2, 50×1<NA><NA><NA><NA><NA>유수지<NA><NA><NA><NA><NA><NA><NA>603
8810050902012102중랑천권역(서울특별시) 하천기본계획(변경)2012행당중랑천국가하천서울시 성동구 행당1동 90-4서울특별시 성동구 행당동1.7240,000<NA>296.92,40010018.7617.05<NA><NA><NA>2,06510<NA>800X71500<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1,4446,500
89150032120121096아라천 하천기본계획2012백석펌프장아라천국가하천인천광역시 서구 백석동인천광역시 서구 백석동1.80.6한국수자원공사139.81,4801006.755.361.63.85.8240202.0?800mm×75HP×4대60m3/min × ?800mm × 4대3.823.025.02<NA>방류수로L=23.5m, D800mm×4련 (토출수조 →방수로), 수문(FLAP GATE)4.3개수로(유입수로)4.8×3.1×2련(유입BOX)<NA><NA><NA><NA><NA>
9010050902012103중랑천권역(서울특별시) 하천기본계획(변경)2012뚝섬중랑천국가하천서울시 성동구 성수1가2동 685서울특별시 성동구 성수동1가5.1510,000<NA>296.92,40010018.9617.05<NA><NA><NA>2,870108.5450X9, 900X51000, 1500<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>60,000180,000
9110050902012104중랑천권역(서울특별시) 하천기본계획(변경)2012전농1,2중랑천국가하천서울시 성동구 송정동 78-1서울특별시 성동구 송정동6.2620,000<NA>239.62,04010018.8917.05<NA><NA><NA>3,395207.6850X7, 500X2, 1300X3<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>20,500102,000
9210050902012105중랑천권역(서울특별시) 하천기본계획(변경)2012장안중랑천국가하천서울시 동대문구 송정동 78-1서울특별시 성동구 송정동2.8480,000<NA>239.62,04010017.8717.05<NA><NA><NA>1,350207.6650X3, 550X2, 450X2, 300X2<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>17,40070,000
9310050902012106중랑천권역(서울특별시) 하천기본계획(변경)2012장안2중랑천국가하천서울시 동대문구 장안동 356서울특별시 동대문구 장안동0.2200,000<NA>239.62,04010018.9217.05<NA><NA><NA>12010<NA>150X2, 60X1600, 400<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>600
9410050902012107중랑천권역(서울특별시) 하천기본계획(변경)2012장안4 (구장안3)중랑천국가하천서울시 동대문구 장안4동 301-4서울특별시 동대문구 장안동1.21,150,000<NA>239.62,04010018.817.05<NA><NA><NA>65010<NA>850X2, 510X1<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>3,250
9510050902012108중랑천권역(서울특별시) 하천기본계획(변경)2012장안1 (구장안4)중랑천국가하천서울시 동대문구 장안1동 379서울특별시 동대문구 장안동0.3341,000<NA>239.62,04010018.7317.05<NA><NA><NA>69020<NA>750X5<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>3,000