Overview

Dataset statistics

Number of variables34
Number of observations499
Missing cells2208
Missing cells (%)13.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory140.5 KiB
Average record size in memory288.3 B

Variable types

Numeric15
Categorical10
Text9

Dataset

Description하천기본계획코드,일련번호,하천기본계획 사업명,수립년도,시설물명,하천명,하천등급,좌우안,시점,종점,제방연장,시점_둑마루표고,종점_둑마루표고,둑마루폭1,둑마루폭2,비탈경사,하구종점거리,시점_계획홍수량,종점_계획홍수량,시점_계획홍수위,종점_계획홍수위,계획하폭1,계획하폭2,배수통관_수,배수암거_수,보_수,낙차공_수,기타주요시설_수,하천개황의 시점명,하천개황의 종점명,호안구분,호안연장,호안형식구분,호안형식명
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15791/S/1/datasetView.do

Alerts

하천등급 is highly imbalanced (51.2%)Imbalance
시설물명 has 26 (5.2%) missing valuesMissing
시점_둑마루표고 has 30 (6.0%) missing valuesMissing
종점_둑마루표고 has 42 (8.4%) missing valuesMissing
둑마루폭1 has 270 (54.1%) missing valuesMissing
둑마루폭2 has 430 (86.2%) missing valuesMissing
비탈경사 has 165 (33.1%) missing valuesMissing
시점_계획홍수량 has 227 (45.5%) missing valuesMissing
종점_계획홍수량 has 12 (2.4%) missing valuesMissing
계획하폭1 has 15 (3.0%) missing valuesMissing
계획하폭2 has 6 (1.2%) missing valuesMissing
배수통관_수 has 109 (21.8%) missing valuesMissing
배수암거_수 has 182 (36.5%) missing valuesMissing
보_수 has 393 (78.8%) missing valuesMissing
낙차공_수 has 296 (59.3%) missing valuesMissing
호안형식명 has 5 (1.0%) missing valuesMissing
일련번호 has unique valuesUnique
하구종점거리 has 106 (21.2%) zerosZeros
배수통관_수 has 6 (1.2%) zerosZeros
배수암거_수 has 11 (2.2%) zerosZeros
보_수 has 45 (9.0%) zerosZeros
낙차공_수 has 18 (3.6%) zerosZeros

Reproduction

Analysis started2024-05-11 01:48:25.721011
Analysis finished2024-05-11 01:48:27.106635
Duration1.39 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

하천기본계획코드
Real number (ℝ)

Distinct39
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0485777 × 1012
Minimum1.0050902 × 1012
Maximum1.5003212 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-05-11T01:48:27.386961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.0050902 × 1012
5-th percentile1.0053802 × 1012
Q11.0249302 × 1012
median1.0252102 × 1012
Q31.0253502 × 1012
95-th percentile1.5003212 × 1012
Maximum1.5003212 × 1012
Range4.95231 × 1011
Interquartile range (IQR)4.199999 × 108

Descriptive statistics

Standard deviation1.061254 × 1011
Coefficient of variation (CV)0.10120891
Kurtosis14.334425
Mean1.0485777 × 1012
Median Absolute Deviation (MAD)1.500002 × 108
Skewness4.0290908
Sum5.2324026 × 1014
Variance1.12626 × 1022
MonotonicityNot monotonic
2024-05-11T01:48:27.992016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
1025540201510 40
 
8.0%
1025350201410 37
 
7.4%
1025220201210 33
 
6.6%
1025060201510 31
 
6.2%
1024900201210 26
 
5.2%
1024910201510 26
 
5.2%
1500321201210 24
 
4.8%
1024930201510 22
 
4.4%
1025290201210 22
 
4.4%
1025200201210 17
 
3.4%
Other values (29) 221
44.3%
ValueCountFrequency (%)
1005090201210 12
2.4%
1005380201510 15
3.0%
1015270201210 15
3.0%
1024880201510 8
 
1.6%
1024881201510 4
 
0.8%
1024900201210 26
5.2%
1024910201510 26
5.2%
1024920201510 8
 
1.6%
1024930201510 22
4.4%
1025050201510 12
2.4%
ValueCountFrequency (%)
1500321201211 2
 
0.4%
1500321201210 24
4.8%
1025540201512 2
 
0.4%
1025540201510 40
8.0%
1025530201510 8
 
1.6%
1025500201510 14
 
2.8%
1025490201510 2
 
0.4%
1025370201411 2
 
0.4%
1025370201410 2
 
0.4%
1025360201410 16
 
3.2%

일련번호
Real number (ℝ)

UNIQUE 

Distinct499
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250
Minimum1
Maximum499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-05-11T01:48:28.497880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25.9
Q1125.5
median250
Q3374.5
95-th percentile474.1
Maximum499
Range498
Interquartile range (IQR)249

Descriptive statistics

Standard deviation144.19316
Coefficient of variation (CV)0.57677263
Kurtosis-1.2
Mean250
Median Absolute Deviation (MAD)125
Skewness0
Sum124750
Variance20791.667
MonotonicityNot monotonic
2024-05-11T01:48:29.191008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
412 1
 
0.2%
330 1
 
0.2%
343 1
 
0.2%
342 1
 
0.2%
341 1
 
0.2%
340 1
 
0.2%
339 1
 
0.2%
338 1
 
0.2%
337 1
 
0.2%
336 1
 
0.2%
Other values (489) 489
98.0%
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 (%)
499 1
0.2%
498 1
0.2%
497 1
0.2%
496 1
0.2%
495 1
0.2%
494 1
0.2%
493 1
0.2%
492 1
0.2%
491 1
0.2%
490 1
0.2%
Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
중랑천권역(서울특별시) 하천기본계획(변경)
180 
탄천 등 10개 하천기본계획
129 
안양천권역 하천기본계획
64 
홍제천 등 4개 하천기본계획
59 
아라천 하천기본계획
26 
Other values (2)
41 

Length

Max length23
Median length15
Mean length17.106212
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row탄천 등 10개 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) 180
36.1%
탄천 등 10개 하천기본계획 129
25.9%
안양천권역 하천기본계획 64
 
12.8%
홍제천 등 4개 하천기본계획 59
 
11.8%
아라천 하천기본계획 26
 
5.2%
망월천하천기본계획(변경) 26
 
5.2%
안양천 하천기본계획(변경) 15
 
3.0%

Length

2024-05-11T01:48:29.614715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:48:30.047963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
하천기본계획 278
20.6%
하천기본계획(변경 195
14.5%
188
13.9%
중랑천권역(서울특별시 180
13.4%
탄천 129
9.6%
10개 129
9.6%
안양천권역 64
 
4.7%
홍제천 59
 
4.4%
4개 59
 
4.4%
아라천 26
 
1.9%
Other values (2) 41
 
3.0%

수립년도
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2012
232 
2015
208 
2014
59 

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 (%)
2012 232
46.5%
2015 208
41.7%
2014 59
 
11.8%

Length

2024-05-11T01:48:30.582130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:48:31.146686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2012 232
46.5%
2015 208
41.7%
2014 59
 
11.8%

시설물명
Text

MISSING 

Distinct289
Distinct (%)61.1%
Missing26
Missing (%)5.2%
Memory size4.0 KiB
2024-05-11T01:48:31.851831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length5.5560254
Min length2

Characters and Unicode

Total characters2628
Distinct characters110
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique215 ?
Unique (%)45.5%

Sample

1st row좌2
2nd row좌2
3rd row좌1
4th row좌1
5th row우3
ValueCountFrequency (%)
좌1 13
 
2.5%
우1 13
 
2.5%
우2 12
 
2.3%
중랑천 12
 
2.3%
좌2 12
 
2.3%
좌안 11
 
2.1%
우안 11
 
2.1%
아라좌안1제 10
 
1.9%
도봉(2 10
 
1.9%
1지구 10
 
1.9%
Other values (272) 414
78.4%
2024-05-11T01:48:33.131178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
245
 
9.3%
211
 
8.0%
209
 
8.0%
198
 
7.5%
1 177
 
6.7%
151
 
5.7%
149
 
5.7%
2 115
 
4.4%
) 79
 
3.0%
( 79
 
3.0%
Other values (100) 1015
38.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1917
72.9%
Decimal Number 492
 
18.7%
Close Punctuation 79
 
3.0%
Open Punctuation 79
 
3.0%
Space Separator 55
 
2.1%
Uppercase Letter 6
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
245
 
12.8%
211
 
11.0%
209
 
10.9%
198
 
10.3%
151
 
7.9%
149
 
7.8%
36
 
1.9%
31
 
1.6%
29
 
1.5%
26
 
1.4%
Other values (84) 632
33.0%
Decimal Number
ValueCountFrequency (%)
1 177
36.0%
2 115
23.4%
3 62
 
12.6%
4 46
 
9.3%
5 31
 
6.3%
6 27
 
5.5%
7 19
 
3.9%
8 10
 
2.0%
9 3
 
0.6%
0 2
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
B 2
33.3%
O 2
33.3%
X 2
33.3%
Close Punctuation
ValueCountFrequency (%)
) 79
100.0%
Open Punctuation
ValueCountFrequency (%)
( 79
100.0%
Space Separator
ValueCountFrequency (%)
55
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1917
72.9%
Common 705
 
26.8%
Latin 6
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
245
 
12.8%
211
 
11.0%
209
 
10.9%
198
 
10.3%
151
 
7.9%
149
 
7.8%
36
 
1.9%
31
 
1.6%
29
 
1.5%
26
 
1.4%
Other values (84) 632
33.0%
Common
ValueCountFrequency (%)
1 177
25.1%
2 115
16.3%
) 79
11.2%
( 79
11.2%
3 62
 
8.8%
55
 
7.8%
4 46
 
6.5%
5 31
 
4.4%
6 27
 
3.8%
7 19
 
2.7%
Other values (3) 15
 
2.1%
Latin
ValueCountFrequency (%)
B 2
33.3%
O 2
33.3%
X 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1917
72.9%
ASCII 711
 
27.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
245
 
12.8%
211
 
11.0%
209
 
10.9%
198
 
10.3%
151
 
7.9%
149
 
7.8%
36
 
1.9%
31
 
1.6%
29
 
1.5%
26
 
1.4%
Other values (84) 632
33.0%
ASCII
ValueCountFrequency (%)
1 177
24.9%
2 115
16.2%
) 79
11.1%
( 79
11.1%
3 62
 
8.7%
55
 
7.7%
4 46
 
6.5%
5 31
 
4.4%
6 27
 
3.8%
7 19
 
2.7%
Other values (6) 21
 
3.0%

하천명
Categorical

Distinct39
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
도림천
40 
홍제천
37 
우이천
 
33
양재천
 
31
성내천
 
26
Other values (34)
332 

Length

Max length9
Median length3
Mean length3.0741483
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row양재천
2nd row양재천
3rd row양재천
4th row양재천
5th row세곡천

Common Values

ValueCountFrequency (%)
도림천 40
 
8.0%
홍제천 37
 
7.4%
우이천 33
 
6.6%
양재천 31
 
6.2%
성내천 26
 
5.2%
망월천 26
 
5.2%
아라천 24
 
4.8%
탄천 22
 
4.4%
정릉천 22
 
4.4%
당현천 17
 
3.4%
Other values (29) 221
44.3%

Length

2024-05-11T01:48:33.656914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
도림천 40
 
8.0%
홍제천 37
 
7.4%
우이천 33
 
6.6%
양재천 31
 
6.2%
성내천 26
 
5.2%
망월천 26
 
5.2%
아라천 24
 
4.8%
탄천 22
 
4.4%
정릉천 22
 
4.4%
당현천 17
 
3.4%
Other values (29) 221
44.3%

하천등급
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
지방하천
446 
국가하천
53 

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 446
89.4%
국가하천 53
 
10.6%

Length

2024-05-11T01:48:34.310526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:48:34.816513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방하천 446
89.4%
국가하천 53
 
10.6%

좌우안
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
좌안
255 
우안
242 
미지정
 
2

Length

Max length3
Median length2
Mean length2.004008
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row좌안
2nd row좌안
3rd row좌안
4th row좌안
5th row우안

Common Values

ValueCountFrequency (%)
좌안 255
51.1%
우안 242
48.5%
미지정 2
 
0.4%

Length

2024-05-11T01:48:35.263879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:48:35.671801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
좌안 255
51.1%
우안 242
48.5%
미지정 2
 
0.4%

시점
Text

Distinct308
Distinct (%)61.7%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-05-11T01:48:36.662867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length26
Mean length17.925852
Min length11

Characters and Unicode

Total characters8945
Distinct characters154
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

Unique216 ?
Unique (%)43.3%

Sample

1st row서울시 서초구 양재동 99-6
2nd row서울시 서초구 양재동 99-6
3rd row서울시 강남구 대치동 504-11
4th row서울시 강남구 대치동 504-11
5th row서울시 서초구 내곡동 1-2583
ValueCountFrequency (%)
서울시 412
 
20.5%
경기도 62
 
3.1%
도봉구 45
 
2.2%
송파구 38
 
1.9%
서초구 36
 
1.8%
성북구 35
 
1.7%
노원구 31
 
1.5%
신림동 28
 
1.4%
관악구 28
 
1.4%
강북구 24
 
1.2%
Other values (442) 1269
63.2%
2024-05-11T01:48:37.958854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1518
17.0%
526
 
5.9%
507
 
5.7%
465
 
5.2%
457
 
5.1%
414
 
4.6%
- 413
 
4.6%
1 362
 
4.0%
2 258
 
2.9%
4 237
 
2.6%
Other values (144) 3788
42.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5102
57.0%
Decimal Number 1912
 
21.4%
Space Separator 1518
 
17.0%
Dash Punctuation 413
 
4.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
526
 
10.3%
507
 
9.9%
465
 
9.1%
457
 
9.0%
414
 
8.1%
185
 
3.6%
183
 
3.6%
131
 
2.6%
80
 
1.6%
70
 
1.4%
Other values (132) 2084
40.8%
Decimal Number
ValueCountFrequency (%)
1 362
18.9%
2 258
13.5%
4 237
12.4%
3 196
10.3%
7 164
8.6%
8 152
7.9%
5 150
7.8%
6 145
7.6%
9 128
 
6.7%
0 120
 
6.3%
Space Separator
ValueCountFrequency (%)
1518
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 413
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5102
57.0%
Common 3843
43.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
526
 
10.3%
507
 
9.9%
465
 
9.1%
457
 
9.0%
414
 
8.1%
185
 
3.6%
183
 
3.6%
131
 
2.6%
80
 
1.6%
70
 
1.4%
Other values (132) 2084
40.8%
Common
ValueCountFrequency (%)
1518
39.5%
- 413
 
10.7%
1 362
 
9.4%
2 258
 
6.7%
4 237
 
6.2%
3 196
 
5.1%
7 164
 
4.3%
8 152
 
4.0%
5 150
 
3.9%
6 145
 
3.8%
Other values (2) 248
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5102
57.0%
ASCII 3843
43.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1518
39.5%
- 413
 
10.7%
1 362
 
9.4%
2 258
 
6.7%
4 237
 
6.2%
3 196
 
5.1%
7 164
 
4.3%
8 152
 
4.0%
5 150
 
3.9%
6 145
 
3.8%
Other values (2) 248
 
6.5%
Hangul
ValueCountFrequency (%)
526
 
10.3%
507
 
9.9%
465
 
9.1%
457
 
9.0%
414
 
8.1%
185
 
3.6%
183
 
3.6%
131
 
2.6%
80
 
1.6%
70
 
1.4%
Other values (132) 2084
40.8%

종점
Text

Distinct309
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-05-11T01:48:38.786845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length26
Mean length17.92986
Min length11

Characters and Unicode

Total characters8947
Distinct characters159
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

Unique220 ?
Unique (%)44.1%

Sample

1st row서울시 강남구 대치동 504-11
2nd row서울시 강남구 대치동 504-11
3rd row서울시 강남구 대치동 27-14
4th row서울시 강남구 대치동 27-14
5th row서울시 서초구 내곡동 12-411
ValueCountFrequency (%)
서울시 431
 
21.5%
도봉구 45
 
2.2%
송파구 42
 
2.1%
경기도 42
 
2.1%
마포구 38
 
1.9%
성북구 34
 
1.7%
강남구 33
 
1.6%
노원구 31
 
1.5%
서초구 28
 
1.4%
강동구 26
 
1.3%
Other values (445) 1255
62.6%
2024-05-11T01:48:40.026440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1516
16.9%
553
 
6.2%
521
 
5.8%
497
 
5.6%
485
 
5.4%
431
 
4.8%
- 390
 
4.4%
1 303
 
3.4%
2 242
 
2.7%
4 231
 
2.6%
Other values (149) 3778
42.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5207
58.2%
Decimal Number 1830
 
20.5%
Space Separator 1516
 
16.9%
Dash Punctuation 390
 
4.4%
Close Punctuation 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
553
 
10.6%
521
 
10.0%
497
 
9.5%
485
 
9.3%
431
 
8.3%
187
 
3.6%
185
 
3.6%
113
 
2.2%
100
 
1.9%
73
 
1.4%
Other values (135) 2062
39.6%
Decimal Number
ValueCountFrequency (%)
1 303
16.6%
2 242
13.2%
4 231
12.6%
3 200
10.9%
8 172
9.4%
7 160
8.7%
5 158
8.6%
6 146
8.0%
9 117
 
6.4%
0 101
 
5.5%
Space Separator
ValueCountFrequency (%)
1516
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 390
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5207
58.2%
Common 3740
41.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
553
 
10.6%
521
 
10.0%
497
 
9.5%
485
 
9.3%
431
 
8.3%
187
 
3.6%
185
 
3.6%
113
 
2.2%
100
 
1.9%
73
 
1.4%
Other values (135) 2062
39.6%
Common
ValueCountFrequency (%)
1516
40.5%
- 390
 
10.4%
1 303
 
8.1%
2 242
 
6.5%
4 231
 
6.2%
3 200
 
5.3%
8 172
 
4.6%
7 160
 
4.3%
5 158
 
4.2%
6 146
 
3.9%
Other values (4) 222
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5207
58.2%
ASCII 3740
41.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1516
40.5%
- 390
 
10.4%
1 303
 
8.1%
2 242
 
6.5%
4 231
 
6.2%
3 200
 
5.3%
8 172
 
4.6%
7 160
 
4.3%
5 158
 
4.2%
6 146
 
3.9%
Other values (4) 222
 
5.9%
Hangul
ValueCountFrequency (%)
553
 
10.6%
521
 
10.0%
497
 
9.5%
485
 
9.3%
431
 
8.3%
187
 
3.6%
185
 
3.6%
113
 
2.2%
100
 
1.9%
73
 
1.4%
Other values (135) 2062
39.6%
Distinct255
Distinct (%)51.1%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-05-11T01:48:41.231312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.9118236
Min length2

Characters and Unicode

Total characters1952
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

Unique126 ?
Unique (%)25.3%

Sample

1st row1,399
2nd row1,399
3rd row2,300
4th row2,300
5th row1,591
ValueCountFrequency (%)
12,504 10
 
2.0%
15,692 9
 
1.8%
6,085 9
 
1.8%
900 8
 
1.6%
4,485 8
 
1.6%
3,550 6
 
1.2%
222 6
 
1.2%
2,280 6
 
1.2%
3,145 6
 
1.2%
815 5
 
1.0%
Other values (245) 426
85.4%
2024-05-11T01:48:43.051369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 287
14.7%
1 264
13.5%
5 249
12.8%
, 221
11.3%
2 193
9.9%
9 145
7.4%
8 131
6.7%
4 122
6.2%
3 121
6.2%
6 112
 
5.7%
Other values (2) 107
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1723
88.3%
Other Punctuation 229
 
11.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 287
16.7%
1 264
15.3%
5 249
14.5%
2 193
11.2%
9 145
8.4%
8 131
7.6%
4 122
7.1%
3 121
7.0%
6 112
 
6.5%
7 99
 
5.7%
Other Punctuation
ValueCountFrequency (%)
, 221
96.5%
. 8
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1952
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 287
14.7%
1 264
13.5%
5 249
12.8%
, 221
11.3%
2 193
9.9%
9 145
7.4%
8 131
6.7%
4 122
6.2%
3 121
6.2%
6 112
 
5.7%
Other values (2) 107
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 287
14.7%
1 264
13.5%
5 249
12.8%
, 221
11.3%
2 193
9.9%
9 145
7.4%
8 131
6.7%
4 122
6.2%
3 121
6.2%
6 112
 
5.7%
Other values (2) 107
 
5.5%

시점_둑마루표고
Real number (ℝ)

MISSING 

Distinct271
Distinct (%)57.8%
Missing30
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean30.710128
Minimum6.2
Maximum120.54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-05-11T01:48:43.779464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile7.66
Q118.66
median23.26
Q336.03
95-th percentile75.92
Maximum120.54
Range114.34
Interquartile range (IQR)17.37

Descriptive statistics

Standard deviation19.993658
Coefficient of variation (CV)0.65104445
Kurtosis2.8336308
Mean30.710128
Median Absolute Deviation (MAD)6.41
Skewness1.7177114
Sum14403.05
Variance399.74638
MonotonicityNot monotonic
2024-05-11T01:48:44.424156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.6 20
 
4.0%
18.0 14
 
2.8%
36.3 12
 
2.4%
83.97 10
 
2.0%
21.68 8
 
1.6%
25.98 5
 
1.0%
23.5 4
 
0.8%
53.55 4
 
0.8%
18.2 4
 
0.8%
26.67 4
 
0.8%
Other values (261) 384
77.0%
(Missing) 30
 
6.0%
ValueCountFrequency (%)
6.2 2
 
0.4%
7.3 2
 
0.4%
7.6 20
4.0%
7.75 2
 
0.4%
11.77 1
 
0.2%
12.05 1
 
0.2%
12.38 2
 
0.4%
12.53 2
 
0.4%
12.63 1
 
0.2%
12.73 2
 
0.4%
ValueCountFrequency (%)
120.54 1
 
0.2%
111.21 1
 
0.2%
110.89 1
 
0.2%
99.19 2
 
0.4%
93.56 2
 
0.4%
91.91 1
 
0.2%
89.29 1
 
0.2%
89.09 1
 
0.2%
83.97 10
2.0%
80.02 1
 
0.2%

종점_둑마루표고
Real number (ℝ)

MISSING 

Distinct276
Distinct (%)60.4%
Missing42
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean26.233676
Minimum6.1
Maximum111.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-05-11T01:48:45.394371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.1
5-th percentile7.54
Q118.01
median20.93
Q330.37
95-th percentile58.86
Maximum111.89
Range105.79
Interquartile range (IQR)12.36

Descriptive statistics

Standard deviation16.311059
Coefficient of variation (CV)0.62176031
Kurtosis6.6043316
Mean26.233676
Median Absolute Deviation (MAD)5.47
Skewness2.2329303
Sum11988.79
Variance266.05063
MonotonicityNot monotonic
2024-05-11T01:48:46.205158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.16 10
 
2.0%
18.2 10
 
2.0%
6.2 10
 
2.0%
14.76 10
 
2.0%
6.1 9
 
1.8%
13.17 6
 
1.2%
18.64 4
 
0.8%
20.15 4
 
0.8%
51.18 4
 
0.8%
20.32 4
 
0.8%
Other values (266) 386
77.4%
(Missing) 42
 
8.4%
ValueCountFrequency (%)
6.1 9
1.8%
6.2 10
2.0%
6.5 2
 
0.4%
7.3 2
 
0.4%
7.6 3
 
0.6%
7.83 3
 
0.6%
8.26 1
 
0.2%
11.89 2
 
0.4%
12.11 1
 
0.2%
12.33 1
 
0.2%
ValueCountFrequency (%)
111.89 1
0.2%
109.75 2
0.4%
96.78 1
0.2%
91.6 1
0.2%
91.4 1
0.2%
85.58 2
0.4%
79.96 1
0.2%
76.54 1
0.2%
76.26 1
0.2%
75.63 1
0.2%

둑마루폭1
Real number (ℝ)

MISSING 

Distinct29
Distinct (%)12.7%
Missing270
Missing (%)54.1%
Infinite0
Infinite (%)0.0%
Mean4.3387773
Minimum0.4
Maximum12.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-05-11T01:48:46.871717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile2.16
Q13.95
median4
Q35
95-th percentile8
Maximum12.7
Range12.3
Interquartile range (IQR)1.05

Descriptive statistics

Standard deviation1.7374225
Coefficient of variation (CV)0.40044059
Kurtosis5.3904343
Mean4.3387773
Median Absolute Deviation (MAD)1
Skewness1.7889711
Sum993.58
Variance3.0186371
MonotonicityNot monotonic
2024-05-11T01:48:47.410582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
4.0 102
 
20.4%
3.0 34
 
6.8%
5.0 31
 
6.2%
8.0 10
 
2.0%
7.0 9
 
1.8%
2.0 6
 
1.2%
2.4 4
 
0.8%
6.0 4
 
0.8%
4.6 2
 
0.4%
2.8 2
 
0.4%
Other values (19) 25
 
5.0%
(Missing) 270
54.1%
ValueCountFrequency (%)
0.4 2
 
0.4%
1.2 1
 
0.2%
1.32 1
 
0.2%
1.8 1
 
0.2%
1.9 1
 
0.2%
2.0 6
1.2%
2.4 4
0.8%
2.5 2
 
0.4%
2.7 1
 
0.2%
2.8 2
 
0.4%
ValueCountFrequency (%)
12.7 2
 
0.4%
10.59 1
 
0.2%
9.8 2
 
0.4%
9.2 1
 
0.2%
8.0 10
 
2.0%
7.0 9
 
1.8%
6.0 4
 
0.8%
5.7 2
 
0.4%
5.5 1
 
0.2%
5.0 31
6.2%

둑마루폭2
Real number (ℝ)

MISSING 

Distinct31
Distinct (%)44.9%
Missing430
Missing (%)86.2%
Infinite0
Infinite (%)0.0%
Mean8.6033333
Minimum2.2
Maximum25.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-05-11T01:48:47.890471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile4.08
Q16.5
median8
Q310
95-th percentile14.7
Maximum25.6
Range23.4
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation3.7336127
Coefficient of variation (CV)0.4339728
Kurtosis5.218846
Mean8.6033333
Median Absolute Deviation (MAD)2
Skewness1.5786624
Sum593.63
Variance13.939864
MonotonicityNot monotonic
2024-05-11T01:48:48.266853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
8.0 10
 
2.0%
7.0 8
 
1.6%
6.0 4
 
0.8%
10.0 4
 
0.8%
5.0 4
 
0.8%
9.0 3
 
0.6%
11.0 3
 
0.6%
12.7 2
 
0.4%
6.5 2
 
0.4%
11.8 2
 
0.4%
Other values (21) 27
 
5.4%
(Missing) 430
86.2%
ValueCountFrequency (%)
2.2 1
 
0.2%
3.0 2
0.4%
4.0 1
 
0.2%
4.2 1
 
0.2%
4.36 1
 
0.2%
4.68 1
 
0.2%
4.95 1
 
0.2%
5.0 4
0.8%
5.8 1
 
0.2%
6.0 4
0.8%
ValueCountFrequency (%)
25.6 1
 
0.2%
16.2 2
0.4%
14.7 2
0.4%
13.1 2
0.4%
12.7 2
0.4%
12.0 1
 
0.2%
11.8 2
0.4%
11.3 2
0.4%
11.0 3
0.6%
10.0 4
0.8%

비탈경사
Text

MISSING 

Distinct74
Distinct (%)22.2%
Missing165
Missing (%)33.1%
Memory size4.0 KiB
2024-05-11T01:48:48.927565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length10.592814
Min length2

Characters and Unicode

Total characters3538
Distinct characters20
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)12.3%

Sample

1st row직립,1:1.5~2.0
2nd row직립,1:1.5~2.0
3rd row직립,1:1.5~2.0
4th row직립,1:1.5~2.0
5th row직립,1:1.5~2.0
ValueCountFrequency (%)
직립,1:1.5~2.0 97
23.3%
직립 75
18.0%
12:01:02 26
 
6.2%
오전 26
 
6.2%
1:2.0~1:3.5 19
 
4.6%
1:2.0 16
 
3.8%
직립,1:0.3 10
 
2.4%
1:1.5~2.0 10
 
2.4%
직립,1:1.5 9
 
2.2%
직립,1:2.0 8
 
1.9%
Other values (64) 120
28.8%
2024-05-11T01:48:50.478577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 635
17.9%
. 495
14.0%
: 427
12.1%
0 333
9.4%
2 303
8.6%
231
 
6.5%
231
 
6.5%
~ 181
 
5.1%
, 179
 
5.1%
5 179
 
5.1%
Other values (10) 344
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1617
45.7%
Other Punctuation 1144
32.3%
Other Letter 514
 
14.5%
Math Symbol 181
 
5.1%
Space Separator 82
 
2.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 635
39.3%
0 333
20.6%
2 303
18.7%
5 179
 
11.1%
3 91
 
5.6%
6 20
 
1.2%
9 19
 
1.2%
4 18
 
1.1%
7 12
 
0.7%
8 7
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 495
43.3%
: 427
37.3%
, 179
 
15.6%
? 43
 
3.8%
Other Letter
ValueCountFrequency (%)
231
44.9%
231
44.9%
26
 
5.1%
26
 
5.1%
Math Symbol
ValueCountFrequency (%)
~ 181
100.0%
Space Separator
ValueCountFrequency (%)
82
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3024
85.5%
Hangul 514
 
14.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 635
21.0%
. 495
16.4%
: 427
14.1%
0 333
11.0%
2 303
10.0%
~ 181
 
6.0%
, 179
 
5.9%
5 179
 
5.9%
3 91
 
3.0%
82
 
2.7%
Other values (6) 119
 
3.9%
Hangul
ValueCountFrequency (%)
231
44.9%
231
44.9%
26
 
5.1%
26
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3024
85.5%
Hangul 514
 
14.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 635
21.0%
. 495
16.4%
: 427
14.1%
0 333
11.0%
2 303
10.0%
~ 181
 
6.0%
, 179
 
5.9%
5 179
 
5.9%
3 91
 
3.0%
82
 
2.7%
Other values (6) 119
 
3.9%
Hangul
ValueCountFrequency (%)
231
44.9%
231
44.9%
26
 
5.1%
26
 
5.1%

하구종점거리
Real number (ℝ)

ZEROS 

Distinct203
Distinct (%)40.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.443044
Minimum0
Maximum15549
Zeros106
Zeros (%)21.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-05-11T01:48:51.119510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.4395
median1.81
Q35.306
95-th percentile10.72
Maximum15549
Range15549
Interquartile range (IQR)4.8665

Descriptive statistics

Standard deviation994.78489
Coefficient of variation (CV)10.105182
Kurtosis147.57516
Mean98.443044
Median Absolute Deviation (MAD)1.81
Skewness11.591093
Sum49123.079
Variance989596.98
MonotonicityNot monotonic
2024-05-11T01:48:51.596588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 106
 
21.2%
1.581 19
 
3.8%
6.455 10
 
2.0%
1.2 9
 
1.8%
1.009 6
 
1.2%
1.34 6
 
1.2%
1.407 5
 
1.0%
2.65 4
 
0.8%
3.9 4
 
0.8%
0.4 4
 
0.8%
Other values (193) 326
65.3%
ValueCountFrequency (%)
0.0 106
21.2%
0.012 2
 
0.4%
0.05 2
 
0.4%
0.12 2
 
0.4%
0.132 2
 
0.4%
0.306 1
 
0.2%
0.311 1
 
0.2%
0.317 2
 
0.4%
0.341 1
 
0.2%
0.4 4
 
0.8%
ValueCountFrequency (%)
15549.0 1
 
0.2%
7998.0 4
0.8%
19.164 1
 
0.2%
18.952 1
 
0.2%
18.497 1
 
0.2%
17.635 1
 
0.2%
17.53 1
 
0.2%
17.273 2
0.4%
17.011 1
 
0.2%
14.917 1
 
0.2%
Distinct75
Distinct (%)27.6%
Missing227
Missing (%)45.5%
Memory size4.0 KiB
2024-05-11T01:48:52.307737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.0625
Min length2

Characters and Unicode

Total characters833
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

Unique9 ?
Unique (%)3.3%

Sample

1st row65
2nd row147
3rd row147
4th row147
5th row65
ValueCountFrequency (%)
352 14
 
5.1%
164 14
 
5.1%
40 12
 
4.4%
1,687 12
 
4.4%
112 12
 
4.4%
313 11
 
4.0%
282 10
 
3.7%
1,375 10
 
3.7%
111 8
 
2.9%
147 6
 
2.2%
Other values (65) 163
59.9%
2024-05-11T01:48:53.229160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 185
22.2%
2 95
11.4%
6 86
10.3%
3 81
9.7%
4 76
9.1%
8 63
 
7.6%
7 62
 
7.4%
0 59
 
7.1%
5 55
 
6.6%
, 45
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 788
94.6%
Other Punctuation 45
 
5.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 185
23.5%
2 95
12.1%
6 86
10.9%
3 81
10.3%
4 76
9.6%
8 63
 
8.0%
7 62
 
7.9%
0 59
 
7.5%
5 55
 
7.0%
9 26
 
3.3%
Other Punctuation
ValueCountFrequency (%)
, 45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 833
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 185
22.2%
2 95
11.4%
6 86
10.3%
3 81
9.7%
4 76
9.1%
8 63
 
7.6%
7 62
 
7.4%
0 59
 
7.1%
5 55
 
6.6%
, 45
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 833
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 185
22.2%
2 95
11.4%
6 86
10.3%
3 81
9.7%
4 76
9.1%
8 63
 
7.6%
7 62
 
7.4%
0 59
 
7.1%
5 55
 
6.6%
, 45
 
5.4%
Distinct92
Distinct (%)18.9%
Missing12
Missing (%)2.4%
Memory size4.0 KiB
2024-05-11T01:48:53.740969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.0759754
Min length2

Characters and Unicode

Total characters1498
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

Unique9 ?
Unique (%)1.8%

Sample

1st row646
2nd row646
3rd row646
4th row646
5th row147
ValueCountFrequency (%)
1,480 19
 
3.9%
164 17
 
3.5%
135 16
 
3.3%
257 15
 
3.1%
333 14
 
2.9%
231 14
 
2.9%
447 14
 
2.9%
646 14
 
2.9%
497 13
 
2.7%
282 13
 
2.7%
Other values (82) 338
69.4%
2024-05-11T01:48:54.720749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 271
18.1%
3 181
12.1%
4 163
10.9%
6 161
10.7%
2 155
10.3%
0 129
8.6%
7 111
7.4%
5 100
 
6.7%
8 96
 
6.4%
9 69
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1436
95.9%
Other Punctuation 62
 
4.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 271
18.9%
3 181
12.6%
4 163
11.4%
6 161
11.2%
2 155
10.8%
0 129
9.0%
7 111
7.7%
5 100
 
7.0%
8 96
 
6.7%
9 69
 
4.8%
Other Punctuation
ValueCountFrequency (%)
, 62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 271
18.1%
3 181
12.1%
4 163
10.9%
6 161
10.7%
2 155
10.3%
0 129
8.6%
7 111
7.4%
5 100
 
6.7%
8 96
 
6.4%
9 69
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 271
18.1%
3 181
12.1%
4 163
10.9%
6 161
10.7%
2 155
10.3%
0 129
8.6%
7 111
7.4%
5 100
 
6.7%
8 96
 
6.4%
9 69
 
4.6%

시점_계획홍수위
Real number (ℝ)

Distinct211
Distinct (%)42.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.002445
Minimum4.99
Maximum118.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-05-11T01:48:55.144607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.99
5-th percentile6.68
Q117.05
median21.85
Q334.555
95-th percentile77.065
Maximum118.61
Range113.62
Interquartile range (IQR)17.505

Descriptive statistics

Standard deviation21.062145
Coefficient of variation (CV)0.7020143
Kurtosis2.7748632
Mean30.002445
Median Absolute Deviation (MAD)6.23
Skewness1.7261879
Sum14971.22
Variance443.61397
MonotonicityNot monotonic
2024-05-11T01:48:55.553148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.01 24
 
4.8%
17.05 14
 
2.8%
16.19 14
 
2.8%
6.55 12
 
2.4%
33.85 11
 
2.2%
6.48 10
 
2.0%
13.95 10
 
2.0%
82.74 10
 
2.0%
18.34 8
 
1.6%
35.48 6
 
1.2%
Other values (201) 380
76.2%
ValueCountFrequency (%)
4.99 2
 
0.4%
6.48 10
2.0%
6.55 12
2.4%
6.68 2
 
0.4%
8.0 2
 
0.4%
10.63 2
 
0.4%
10.84 2
 
0.4%
11.0 1
 
0.2%
11.11 1
 
0.2%
11.98 2
 
0.4%
ValueCountFrequency (%)
118.61 1
0.2%
118.27 1
0.2%
110.63 1
0.2%
106.0 1
0.2%
105.87 2
0.4%
97.7 2
0.4%
92.69 2
0.4%
90.3 1
0.2%
88.4 2
0.4%
85.57 1
0.2%

종점_계획홍수위
Real number (ℝ)

Distinct188
Distinct (%)37.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.4002
Minimum4.96
Maximum110.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-05-11T01:48:56.157412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.96
5-th percentile6.55
Q115.87
median18.56
Q327.61
95-th percentile55.58
Maximum110.63
Range105.67
Interquartile range (IQR)11.74

Descriptive statistics

Standard deviation16.057259
Coefficient of variation (CV)0.65807899
Kurtosis7.6532189
Mean24.4002
Median Absolute Deviation (MAD)4.61
Skewness2.427735
Sum12175.7
Variance257.83558
MonotonicityNot monotonic
2024-05-11T01:48:56.597791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.01 34
 
6.8%
13.54 27
 
5.4%
17.05 24
 
4.8%
4.99 19
 
3.8%
13.95 14
 
2.8%
35.87 10
 
2.0%
16.78 8
 
1.6%
18.56 8
 
1.6%
15.87 6
 
1.2%
19.03 6
 
1.2%
Other values (178) 343
68.7%
ValueCountFrequency (%)
4.96 2
 
0.4%
4.99 19
3.8%
6.52 2
 
0.4%
6.55 3
 
0.6%
8.0 2
 
0.4%
10.5 2
 
0.4%
10.63 2
 
0.4%
10.75 2
 
0.4%
10.91 2
 
0.4%
11.0 1
 
0.2%
ValueCountFrequency (%)
110.63 1
0.2%
110.25 1
0.2%
104.65 2
0.4%
94.56 1
0.2%
89.92 2
0.4%
84.65 2
0.4%
78.01 1
0.2%
76.61 1
0.2%
76.24 1
0.2%
67.54 1
0.2%

계획하폭1
Real number (ℝ)

MISSING 

Distinct86
Distinct (%)17.8%
Missing15
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean48.537603
Minimum3
Maximum255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-05-11T01:48:56.915533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q116
median30
Q360
95-th percentile153.4
Maximum255
Range252
Interquartile range (IQR)44

Descriptive statistics

Standard deviation51.372634
Coefficient of variation (CV)1.0584089
Kurtosis4.6011762
Mean48.537603
Median Absolute Deviation (MAD)17
Skewness2.1261622
Sum23492.2
Variance2639.1475
MonotonicityNot monotonic
2024-05-11T01:48:57.399292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.0 35
 
7.0%
5.0 24
 
4.8%
11.0 24
 
4.8%
96.0 21
 
4.2%
46.0 14
 
2.8%
21.0 14
 
2.8%
34.0 13
 
2.6%
27.0 12
 
2.4%
8.0 12
 
2.4%
28.0 12
 
2.4%
Other values (76) 303
60.7%
(Missing) 15
 
3.0%
ValueCountFrequency (%)
3.0 2
 
0.4%
4.0 8
 
1.6%
5.0 24
4.8%
6.0 7
 
1.4%
7.0 6
 
1.2%
8.0 12
2.4%
9.0 9
 
1.8%
9.6 2
 
0.4%
10.0 2
 
0.4%
11.0 24
4.8%
ValueCountFrequency (%)
255.0 4
0.8%
238.0 2
 
0.4%
234.0 2
 
0.4%
230.0 1
 
0.2%
228.0 4
0.8%
212.0 6
1.2%
203.0 1
 
0.2%
198.0 1
 
0.2%
182.0 1
 
0.2%
161.0 1
 
0.2%

계획하폭2
Real number (ℝ)

MISSING 

Distinct114
Distinct (%)23.1%
Missing6
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean83.726978
Minimum6
Maximum621
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-05-11T01:48:57.794190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile12
Q130
median57
Q3103
95-th percentile249.4
Maximum621
Range615
Interquartile range (IQR)73

Descriptive statistics

Standard deviation88.569815
Coefficient of variation (CV)1.0578408
Kurtosis9.6789394
Mean83.726978
Median Absolute Deviation (MAD)31
Skewness2.7184131
Sum41277.4
Variance7844.6121
MonotonicityNot monotonic
2024-05-11T01:48:58.197646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58.0 15
 
3.0%
49.0 14
 
2.8%
97.0 12
 
2.4%
23.0 12
 
2.4%
75.0 12
 
2.4%
30.0 11
 
2.2%
33.0 11
 
2.2%
120.0 11
 
2.2%
151.0 11
 
2.2%
167.0 10
 
2.0%
Other values (104) 374
74.9%
ValueCountFrequency (%)
6.0 4
0.8%
7.0 2
 
0.4%
8.0 8
1.6%
9.0 6
1.2%
10.0 1
 
0.2%
11.0 3
 
0.6%
12.0 4
0.8%
13.0 8
1.6%
14.0 4
0.8%
16.0 6
1.2%
ValueCountFrequency (%)
621.0 2
0.4%
485.0 4
0.8%
449.0 2
0.4%
415.0 2
0.4%
393.0 2
0.4%
316.0 1
 
0.2%
312.0 1
 
0.2%
280.0 2
0.4%
274.0 2
0.4%
273.0 2
0.4%

배수통관_수
Real number (ℝ)

MISSING  ZEROS 

Distinct35
Distinct (%)9.0%
Missing109
Missing (%)21.8%
Infinite0
Infinite (%)0.0%
Mean7.9512821
Minimum0
Maximum60
Zeros6
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-05-11T01:48:58.690521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4.5
Q39.75
95-th percentile26.55
Maximum60
Range60
Interquartile range (IQR)7.75

Descriptive statistics

Standard deviation8.937382
Coefficient of variation (CV)1.1240177
Kurtosis6.0895972
Mean7.9512821
Median Absolute Deviation (MAD)3.5
Skewness2.2236385
Sum3101
Variance79.876798
MonotonicityNot monotonic
2024-05-11T01:48:59.175156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
1 62
12.4%
2 49
9.8%
4 42
 
8.4%
3 36
 
7.2%
8 30
 
6.0%
5 24
 
4.8%
7 19
 
3.8%
6 13
 
2.6%
9 11
 
2.2%
34 9
 
1.8%
Other values (25) 95
19.0%
(Missing) 109
21.8%
ValueCountFrequency (%)
0 6
 
1.2%
1 62
12.4%
2 49
9.8%
3 36
7.2%
4 42
8.4%
5 24
 
4.8%
6 13
 
2.6%
7 19
 
3.8%
8 30
6.0%
9 11
 
2.2%
ValueCountFrequency (%)
60 1
 
0.2%
52 1
 
0.2%
45 1
 
0.2%
41 1
 
0.2%
39 1
 
0.2%
37 1
 
0.2%
34 9
1.8%
31 1
 
0.2%
28 3
 
0.6%
27 1
 
0.2%

배수암거_수
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)4.1%
Missing182
Missing (%)36.5%
Infinite0
Infinite (%)0.0%
Mean4.126183
Minimum0
Maximum23
Zeros11
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-05-11T01:48:59.637272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q36
95-th percentile13
Maximum23
Range23
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.5217312
Coefficient of variation (CV)1.095863
Kurtosis7.4840698
Mean4.126183
Median Absolute Deviation (MAD)2
Skewness2.5316099
Sum1308
Variance20.446053
MonotonicityNot monotonic
2024-05-11T01:48:59.969563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 86
17.2%
2 60
 
12.0%
3 44
 
8.8%
6 22
 
4.4%
7 18
 
3.6%
4 17
 
3.4%
5 14
 
2.8%
8 13
 
2.6%
9 12
 
2.4%
0 11
 
2.2%
Other values (3) 20
 
4.0%
(Missing) 182
36.5%
ValueCountFrequency (%)
0 11
 
2.2%
1 86
17.2%
2 60
12.0%
3 44
8.8%
4 17
 
3.4%
5 14
 
2.8%
6 22
 
4.4%
7 18
 
3.6%
8 13
 
2.6%
9 12
 
2.4%
ValueCountFrequency (%)
23 10
 
2.0%
15 1
 
0.2%
13 9
 
1.8%
9 12
 
2.4%
8 13
 
2.6%
7 18
3.6%
6 22
4.4%
5 14
 
2.8%
4 17
 
3.4%
3 44
8.8%

보_수
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)6.6%
Missing393
Missing (%)78.8%
Infinite0
Infinite (%)0.0%
Mean0.86792453
Minimum0
Maximum7
Zeros45
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-05-11T01:49:00.279176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1133431
Coefficient of variation (CV)1.2827648
Kurtosis9.5551264
Mean0.86792453
Median Absolute Deviation (MAD)1
Skewness2.5027563
Sum92
Variance1.2395328
MonotonicityNot monotonic
2024-05-11T01:49:00.630941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 45
 
9.0%
1 44
 
8.8%
2 10
 
2.0%
3 4
 
0.8%
5 1
 
0.2%
4 1
 
0.2%
7 1
 
0.2%
(Missing) 393
78.8%
ValueCountFrequency (%)
0 45
9.0%
1 44
8.8%
2 10
 
2.0%
3 4
 
0.8%
4 1
 
0.2%
5 1
 
0.2%
7 1
 
0.2%
ValueCountFrequency (%)
7 1
 
0.2%
5 1
 
0.2%
4 1
 
0.2%
3 4
 
0.8%
2 10
 
2.0%
1 44
8.8%
0 45
9.0%

낙차공_수
Real number (ℝ)

MISSING  ZEROS 

Distinct12
Distinct (%)5.9%
Missing296
Missing (%)59.3%
Infinite0
Infinite (%)0.0%
Mean2.591133
Minimum0
Maximum27
Zeros18
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-05-11T01:49:01.043223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile6
Maximum27
Range27
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.6322701
Coefficient of variation (CV)1.4018077
Kurtosis22.74452
Mean2.591133
Median Absolute Deviation (MAD)1
Skewness4.3495515
Sum526
Variance13.193386
MonotonicityNot monotonic
2024-05-11T01:49:01.599334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 75
 
15.0%
2 53
 
10.6%
4 19
 
3.8%
0 18
 
3.6%
3 17
 
3.4%
5 8
 
1.6%
6 3
 
0.6%
27 2
 
0.4%
17 2
 
0.4%
15 2
 
0.4%
Other values (2) 4
 
0.8%
(Missing) 296
59.3%
ValueCountFrequency (%)
0 18
 
3.6%
1 75
15.0%
2 53
10.6%
3 17
 
3.4%
4 19
 
3.8%
5 8
 
1.6%
6 3
 
0.6%
7 2
 
0.4%
14 2
 
0.4%
15 2
 
0.4%
ValueCountFrequency (%)
27 2
 
0.4%
17 2
 
0.4%
15 2
 
0.4%
14 2
 
0.4%
7 2
 
0.4%
6 3
 
0.6%
5 8
 
1.6%
4 19
 
3.8%
3 17
 
3.4%
2 53
10.6%
Distinct31
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
<NA>
234 
0
59 
2
43 
1
37 
3
35 
Other values (26)
91 

Length

Max length18
Median length8
Mean length2.5891784
Min length1

Unique

Unique14 ?
Unique (%)2.8%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 234
46.9%
0 59
 
11.8%
2 43
 
8.6%
1 37
 
7.4%
3 35
 
7.0%
4 18
 
3.6%
6 13
 
2.6%
8 11
 
2.2%
5 11
 
2.2%
7 7
 
1.4%
Other values (21) 31
 
6.2%

Length

2024-05-11T01:49:02.128923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 234
46.1%
0 59
 
11.6%
2 43
 
8.5%
1 37
 
7.3%
3 35
 
6.9%
4 18
 
3.5%
6 13
 
2.6%
8 11
 
2.2%
5 11
 
2.2%
7 7
 
1.4%
Other values (23) 40
 
7.9%
Distinct39
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
서울시 관악구 신림동 808-126번지선
40 
서울시 종로구 평창동 복개시점부
37 
서울특별시 강북구 우이동 200번지선 (북한교)
 
33
서울시 서초구 우면동 749
 
31
서울시 송파구 마천동 277-48번지 (서울,경기도계)
 
26
Other values (34)
332 

Length

Max length45
Median length31
Mean length22.216433
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울시 서초구 우면동 749
2nd row서울시 서초구 우면동 749
3rd row서울시 서초구 우면동 749
4th row서울시 서초구 우면동 749
5th row서울시 서초구 내곡동 1-2588번지선

Common Values

ValueCountFrequency (%)
서울시 관악구 신림동 808-126번지선 40
 
8.0%
서울시 종로구 평창동 복개시점부 37
 
7.4%
서울특별시 강북구 우이동 200번지선 (북한교) 33
 
6.6%
서울시 서초구 우면동 749 31
 
6.2%
서울시 송파구 마천동 277-48번지 (서울,경기도계) 26
 
5.2%
경기도 하남시 풍산동 284-5번지선 26
 
5.2%
서울특별시 강서구 개화동 한강분기점 24
 
4.8%
서울시 강남구 세곡동 13-4 서울, 경기도계 22
 
4.4%
서울특별시 성북구 정릉동 산1-1번지선 22
 
4.4%
서울시 노원구 상계동 40-1,308-2(중계) 17
 
3.4%
Other values (29) 221
44.3%

Length

2024-05-11T01:49:02.613886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울시 296
 
12.8%
서울특별시 142
 
6.1%
경기도 67
 
2.9%
서초구 59
 
2.5%
종로구 52
 
2.2%
강북구 49
 
2.1%
신림동 42
 
1.8%
관악구 42
 
1.8%
808-126번지선 40
 
1.7%
평창동 37
 
1.6%
Other values (107) 1494
64.4%
Distinct38
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
서울시 영등포구 문래동 안양천(국가) 합류점
40 
서울시 서대문구 망원동 한강 합류점
37 
서울특별시 노원구 월계동 중랑천 합류점
 
33
서울시 강남구 대치동 74-4, 탄천(지방) 합류점
 
31
서울시 송파구 신천동 6번지 한강(국가) 합류점
 
26
Other values (33)
332 

Length

Max length31
Median length29
Mean length23.45491
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울시 강남구 대치동 74-4, 탄천(지방) 합류점
2nd row서울시 강남구 대치동 74-4, 탄천(지방) 합류점
3rd row서울시 강남구 대치동 74-4, 탄천(지방) 합류점
4th row서울시 강남구 대치동 74-4, 탄천(지방) 합류점
5th row서울시 서초구 내곡동 13-2, 탄천(지방) 합류점

Common Values

ValueCountFrequency (%)
서울시 영등포구 문래동 안양천(국가) 합류점 40
 
8.0%
서울시 서대문구 망원동 한강 합류점 37
 
7.4%
서울특별시 노원구 월계동 중랑천 합류점 33
 
6.6%
서울시 강남구 대치동 74-4, 탄천(지방) 합류점 31
 
6.2%
서울시 송파구 신천동 6번지 한강(국가) 합류점 26
 
5.2%
서울특별시 강동구 고덕동 고덕천 합류점 26
 
5.2%
인천광역시 서구 오류동 해안 서해배수문 24
 
4.8%
서울시 송파구 삼성동 111-24, 한강(국가) 합류점 22
 
4.4%
서울특별시 동대문구 용두동 청계천합류점 22
 
4.4%
서울시 노원구 상계동 중랑천(국가) 합류점 17
 
3.4%
Other values (28) 221
44.3%

Length

2024-05-11T01:49:03.177933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
합류점 379
 
15.0%
서울시 317
 
12.5%
서울특별시 156
 
6.2%
노원구 63
 
2.5%
한강(국가 58
 
2.3%
송파구 56
 
2.2%
안양천(국가 56
 
2.2%
영등포구 55
 
2.2%
탄천(지방 43
 
1.7%
문래동 40
 
1.6%
Other values (85) 1307
51.7%

호안구분
Categorical

Distinct24
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
고수호안
121 
1
103 
2
87 
저수호안
71 
제방호안
22 
Other values (19)
95 

Length

Max length6
Median length5
Mean length2.7354709
Min length1

Unique

Unique4 ?
Unique (%)0.8%

Sample

1st row저수호안
2nd row고수호안
3rd row저수호안
4th row고수호안
5th row고수호안

Common Values

ValueCountFrequency (%)
고수호안 121
24.2%
1 103
20.6%
2 87
17.4%
저수호안 71
14.2%
제방호안 22
 
4.4%
호안1 18
 
3.6%
<NA> 13
 
2.6%
호안 9
 
1.8%
호안2 9
 
1.8%
호안3 7
 
1.4%
Other values (14) 39
 
7.8%

Length

2024-05-11T01:49:03.792028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고수호안 121
24.2%
1 103
20.6%
2 87
17.4%
저수호안 71
14.2%
제방호안 22
 
4.4%
호안1 18
 
3.6%
na 13
 
2.6%
호안 9
 
1.8%
호안2 9
 
1.8%
호안3 7
 
1.4%
Other values (14) 39
 
7.8%
Distinct309
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-05-11T01:49:04.666045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.6192385
Min length1

Characters and Unicode

Total characters1806
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

Unique174 ?
Unique (%)34.9%

Sample

1st row1,399
2nd row1,399
3rd row2,300
4th row2,300
5th row1,591
ValueCountFrequency (%)
200 6
 
1.2%
299 5
 
1.0%
362 5
 
1.0%
905 4
 
0.8%
639 4
 
0.8%
215 4
 
0.8%
398 4
 
0.8%
405 4
 
0.8%
801 4
 
0.8%
400 4
 
0.8%
Other values (299) 455
91.2%
2024-05-11T01:49:06.701389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 286
15.8%
1 241
13.3%
5 214
11.8%
2 197
10.9%
, 171
9.5%
3 134
7.4%
9 126
7.0%
7 118
6.5%
8 113
 
6.3%
6 103
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1635
90.5%
Other Punctuation 171
 
9.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 286
17.5%
1 241
14.7%
5 214
13.1%
2 197
12.0%
3 134
8.2%
9 126
7.7%
7 118
7.2%
8 113
 
6.9%
6 103
 
6.3%
4 103
 
6.3%
Other Punctuation
ValueCountFrequency (%)
, 171
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1806
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 286
15.8%
1 241
13.3%
5 214
11.8%
2 197
10.9%
, 171
9.5%
3 134
7.4%
9 126
7.0%
7 118
6.5%
8 113
 
6.3%
6 103
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 286
15.8%
1 241
13.3%
5 214
11.8%
2 197
10.9%
, 171
9.5%
3 134
7.4%
9 126
7.0%
7 118
6.5%
8 113
 
6.3%
6 103
 
5.7%
Distinct14
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
<NA>
320 
F112
52 
F110
50 
F114
 
25
F113
 
16
Other values (9)
36 

Length

Max length4
Median length4
Mean length3.9378758
Min length2

Unique

Unique3 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 320
64.1%
F112 52
 
10.4%
F110 50
 
10.0%
F114 25
 
5.0%
F113 16
 
3.2%
옹벽 11
 
2.2%
F116 9
 
1.8%
돌쌓기 5
 
1.0%
석축,옹 3
 
0.6%
석축/옹 3
 
0.6%
Other values (4) 5
 
1.0%

Length

2024-05-11T01:49:07.473089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 320
64.1%
f112 52
 
10.4%
f110 50
 
10.0%
f114 25
 
5.0%
f113 16
 
3.2%
옹벽 11
 
2.2%
f116 9
 
1.8%
돌쌓기 5
 
1.0%
석축,옹 3
 
0.6%
석축/옹 3
 
0.6%
Other values (4) 5
 
1.0%

호안형식명
Text

MISSING 

Distinct112
Distinct (%)22.7%
Missing5
Missing (%)1.0%
Memory size4.0 KiB
2024-05-11T01:49:08.254405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length15
Mean length5.6336032
Min length2

Characters and Unicode

Total characters2783
Distinct characters77
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)9.7%

Sample

1st row돌쌓기,방틀
2nd row호안블록.토사
3rd row돌쌓기,블록
4th row토사
5th row석축
ValueCountFrequency (%)
석축 85
 
14.1%
옹벽 83
 
13.8%
호안블럭 44
 
7.3%
돌쌓기 44
 
7.3%
호안블록 25
 
4.2%
돌붙임 20
 
3.3%
자연석쌓기 19
 
3.2%
콘크리트 18
 
3.0%
옹벽(복개 16
 
2.7%
box(복개 14
 
2.3%
Other values (76) 233
38.8%
2024-05-11T01:49:09.646169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 227
 
8.2%
169
 
6.1%
167
 
6.0%
158
 
5.7%
152
 
5.5%
147
 
5.3%
147
 
5.3%
127
 
4.6%
121
 
4.3%
119
 
4.3%
Other values (67) 1249
44.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2230
80.1%
Other Punctuation 237
 
8.5%
Space Separator 119
 
4.3%
Uppercase Letter 66
 
2.4%
Close Punctuation 52
 
1.9%
Open Punctuation 52
 
1.9%
Math Symbol 16
 
0.6%
Lowercase Letter 11
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
169
 
7.6%
167
 
7.5%
158
 
7.1%
152
 
6.8%
147
 
6.6%
147
 
6.6%
127
 
5.7%
121
 
5.4%
115
 
5.2%
115
 
5.2%
Other values (47) 812
36.4%
Lowercase Letter
ValueCountFrequency (%)
t 2
18.2%
i 2
18.2%
l 2
18.2%
a 1
9.1%
h 1
9.1%
m 1
9.1%
e 1
9.1%
n 1
9.1%
Uppercase Letter
ValueCountFrequency (%)
X 21
31.8%
O 21
31.8%
B 21
31.8%
S 2
 
3.0%
R 1
 
1.5%
Other Punctuation
ValueCountFrequency (%)
, 227
95.8%
. 9
 
3.8%
/ 1
 
0.4%
Space Separator
ValueCountFrequency (%)
119
100.0%
Close Punctuation
ValueCountFrequency (%)
) 52
100.0%
Open Punctuation
ValueCountFrequency (%)
( 52
100.0%
Math Symbol
ValueCountFrequency (%)
+ 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2230
80.1%
Common 476
 
17.1%
Latin 77
 
2.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
169
 
7.6%
167
 
7.5%
158
 
7.1%
152
 
6.8%
147
 
6.6%
147
 
6.6%
127
 
5.7%
121
 
5.4%
115
 
5.2%
115
 
5.2%
Other values (47) 812
36.4%
Latin
ValueCountFrequency (%)
X 21
27.3%
O 21
27.3%
B 21
27.3%
t 2
 
2.6%
i 2
 
2.6%
l 2
 
2.6%
S 2
 
2.6%
R 1
 
1.3%
a 1
 
1.3%
h 1
 
1.3%
Other values (3) 3
 
3.9%
Common
ValueCountFrequency (%)
, 227
47.7%
119
25.0%
) 52
 
10.9%
( 52
 
10.9%
+ 16
 
3.4%
. 9
 
1.9%
/ 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2230
80.1%
ASCII 553
 
19.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 227
41.0%
119
21.5%
) 52
 
9.4%
( 52
 
9.4%
X 21
 
3.8%
O 21
 
3.8%
B 21
 
3.8%
+ 16
 
2.9%
. 9
 
1.6%
t 2
 
0.4%
Other values (10) 13
 
2.4%
Hangul
ValueCountFrequency (%)
169
 
7.6%
167
 
7.5%
158
 
7.1%
152
 
6.8%
147
 
6.6%
147
 
6.6%
127
 
5.7%
121
 
5.4%
115
 
5.2%
115
 
5.2%
Other values (47) 812
36.4%

Sample

하천기본계획코드일련번호하천기본계획 사업명수립년도시설물명하천명하천등급좌우안시점종점제방연장시점_둑마루표고종점_둑마루표고둑마루폭1둑마루폭2비탈경사하구종점거리시점_계획홍수량종점_계획홍수량시점_계획홍수위종점_계획홍수위계획하폭1계획하폭2배수통관_수배수암거_수보_수낙차공_수기타주요시설_수하천개황의 시점명하천개황의 종점명호안구분호안연장호안형식구분호안형식명
01025060201510412탄천 등 10개 하천기본계획2015좌2양재천지방하천좌안서울시 서초구 양재동 99-6서울시 강남구 대치동 504-111,39921.1420.354.0<NA>직립,1:1.5~2.02.3<NA>64618.0118.0192.0115.0<NA>1<NA><NA><NA>서울시 서초구 우면동 749서울시 강남구 대치동 74-4, 탄천(지방) 합류점저수호안1,399<NA>돌쌓기,방틀
11025060201510411탄천 등 10개 하천기본계획2015좌2양재천지방하천좌안서울시 서초구 양재동 99-6서울시 강남구 대치동 504-111,39921.1420.354.0<NA>직립,1:1.5~2.02.3<NA>64618.0118.0192.0115.0<NA>1<NA><NA><NA>서울시 서초구 우면동 749서울시 강남구 대치동 74-4, 탄천(지방) 합류점고수호안1,399<NA>호안블록.토사
21025060201510410탄천 등 10개 하천기본계획2015좌1양재천지방하천좌안서울시 강남구 대치동 504-11서울시 강남구 대치동 27-142,30020.3520.644.0<NA>직립,1:1.5~2.00.0<NA>64618.0118.0199.0193.0<NA><NA><NA><NA><NA>서울시 서초구 우면동 749서울시 강남구 대치동 74-4, 탄천(지방) 합류점저수호안2,300<NA>돌쌓기,블록
31025060201510409탄천 등 10개 하천기본계획2015좌1양재천지방하천좌안서울시 강남구 대치동 504-11서울시 강남구 대치동 27-142,30020.3520.644.0<NA>직립,1:1.5~2.00.0<NA>64618.0118.0199.0193.0<NA><NA><NA><NA><NA>서울시 서초구 우면동 749서울시 강남구 대치동 74-4, 탄천(지방) 합류점고수호안2,300<NA>토사
41025050201510408탄천 등 10개 하천기본계획2015우3세곡천지방하천우안서울시 서초구 내곡동 1-2583서울시 서초구 내곡동 12-4111,59167.535.714.0<NA><NA>3.2896514767.5635.485.022.05<NA>1<NA><NA>서울시 서초구 내곡동 1-2588번지선서울시 서초구 내곡동 13-2, 탄천(지방) 합류점고수호안1,591<NA>석축
51025050201510407탄천 등 10개 하천기본계획2015우2세곡천지방하천우안서울시 서초구 내곡동 12-411경기도 성남시 수정구 신촌동 165-12,28035.71<NA>4.0<NA><NA>1.00914726435.4819.0317.046.084<NA><NA><NA>서울시 서초구 내곡동 1-2588번지선서울시 서초구 내곡동 13-2, 탄천(지방) 합류점고수호안727<NA>석축,토사
61025050201510406탄천 등 10개 하천기본계획2015우2세곡천지방하천우안서울시 서초구 내곡동 12-411경기도 성남시 수정구 신촌동 165-12,28035.71<NA>4.0<NA><NA>1.00914726435.4819.0317.046.084<NA><NA><NA>서울시 서초구 내곡동 1-2588번지선서울시 서초구 내곡동 13-2, 탄천(지방) 합류점고수호안671<NA>돌쌓기
71025050201510405탄천 등 10개 하천기본계획2015우2세곡천지방하천우안서울시 서초구 내곡동 12-411경기도 성남시 수정구 신촌동 165-12,28035.71<NA>4.0<NA><NA>1.00914726435.4819.0317.046.084<NA><NA><NA>서울시 서초구 내곡동 1-2588번지선서울시 서초구 내곡동 13-2, 탄천(지방) 합류점고호.저호882<NA>블록,돌쌓기,토사
81025050201510404탄천 등 10개 하천기본계획2015우1세곡천지방하천우안경기도 성남시 수정구 신촌동 165-1서울시 강남구 세곡동 13-21,009<NA>18.174.0<NA>직립,1:1.5~2.00.0<NA>26419.0318.3238.047.012<NA><NA><NA>서울시 서초구 내곡동 1-2588번지선서울시 서초구 내곡동 13-2, 탄천(지방) 합류점저수호안287<NA>돌쌓기,방틀
91025050201510403탄천 등 10개 하천기본계획2015우1세곡천지방하천우안경기도 성남시 수정구 신촌동 165-1서울시 강남구 세곡동 13-21,009<NA>18.174.0<NA>직립,1:1.5~2.00.0<NA>26419.0318.3238.047.012<NA><NA><NA>서울시 서초구 내곡동 1-2588번지선서울시 서초구 내곡동 13-2, 탄천(지방) 합류점고수호안1,009<NA>돌쌓기,블록,옹벽
하천기본계획코드일련번호하천기본계획 사업명수립년도시설물명하천명하천등급좌우안시점종점제방연장시점_둑마루표고종점_둑마루표고둑마루폭1둑마루폭2비탈경사하구종점거리시점_계획홍수량종점_계획홍수량시점_계획홍수위종점_계획홍수위계획하폭1계획하폭2배수통관_수배수암거_수보_수낙차공_수기타주요시설_수하천개황의 시점명하천개황의 종점명호안구분호안연장호안형식구분호안형식명
489150032120121011아라천 하천기본계획2012아라좌안2제아라천국가하천좌안경기도 김포시 고촌읍 전호리 439-8 굴포교인천광역시 계양구 노오지동 120-2 연결수로 종점2,9827.67.65.0<NA>1:2.5~1:3.114.185401856.556.55137.0167.0<NA>1<NA><NA><NA>서울특별시 강서구 개화동 한강분기점인천광역시 서구 오류동 해안 서해배수문제방호안2,980F112돌붙임
490150032120121010아라천 하천기본계획2012아라좌안1제아라천국가하천좌안인천광역시 계양구 귤현동 4-12인천광역시 서구 오류동 아라인천여객터미널 시점12,5047.66.25.0<NA>1:2.0~1:3.51.5811,3751,4806.484.9996.0151.0423<NA><NA><NA>서울특별시 강서구 개화동 한강분기점인천광역시 서구 오류동 해안 서해배수문제방호안1,504F114돌쌓기+식생매트
49115003212012109아라천 하천기본계획2012아라좌안1제아라천국가하천좌안인천광역시 계양구 귤현동 4-12인천광역시 서구 오류동 아라인천여객터미널 시점12,5047.66.25.0<NA>1:2.0~1:3.51.5811,3751,4806.484.9996.0151.0423<NA><NA><NA>서울특별시 강서구 개화동 한강분기점인천광역시 서구 오류동 해안 서해배수문제방호안1,400F114돌쌓기+식생매트
49215003212012108아라천 하천기본계획2012아라좌안1제아라천국가하천좌안인천광역시 계양구 귤현동 4-12인천광역시 서구 오류동 아라인천여객터미널 시점12,5047.66.25.0<NA>1:2.0~1:3.51.5811,3751,4806.484.9996.0151.0423<NA><NA><NA>서울특별시 강서구 개화동 한강분기점인천광역시 서구 오류동 해안 서해배수문제방호안700F116직립호안(강널말뚝)
49315003212012107아라천 하천기본계획2012아라좌안1제아라천국가하천좌안인천광역시 계양구 귤현동 4-12인천광역시 서구 오류동 아라인천여객터미널 시점12,5047.66.25.0<NA>1:2.0~1:3.51.5811,3751,4806.484.9996.0151.0423<NA><NA><NA>서울특별시 강서구 개화동 한강분기점인천광역시 서구 오류동 해안 서해배수문제방호안400F114돌쌓기+식생매트
49415003212012106아라천 하천기본계획2012아라좌안1제아라천국가하천좌안인천광역시 계양구 귤현동 4-12인천광역시 서구 오류동 아라인천여객터미널 시점12,5047.66.25.0<NA>1:2.0~1:3.51.5811,3751,4806.484.9996.0151.0423<NA><NA><NA>서울특별시 강서구 개화동 한강분기점인천광역시 서구 오류동 해안 서해배수문제방호안2,060F116직립호안(강널말뚝)
4951025210201210264중랑천권역(서울특별시) 하천기본계획(변경)2012묵동(1)우안3제묵동천1지방하천우안서울시 노원구 공릉동 39-34번지서울시 노원구 공릉동 58-2번지14323.1422.48<NA><NA><NA>1.591<NA>7921.8120.9721.026.01<NA><NA>1<NA>서울시 중랑구 신내동 670번지선서울시 노원구 공릉동 중랑천(국가) 합류점2143<NA>석축, 호안블럭
4961025210201210265중랑천권역(서울특별시) 하천기본계획(변경)2012묵동(1)우안4제묵동천1지방하천우안경기도 의정부시 의정부동 476-3번지경기도 의정부시 의정부동 548-4번지18324.0222.29<NA><NA><NA>1.734<NA>7923.221.6317.022.0<NA><NA><NA>22서울시 중랑구 신내동 670번지선서울시 노원구 공릉동 중랑천(국가) 합류점2183<NA>옹벽
4971025210201210266중랑천권역(서울특별시) 하천기본계획(변경)2012묵동(1)우안5제묵동천1지방하천우안경기도 의정부시 의정부동 476-3번지경기도 의정부시 의정부동 548-4번지34424.9323.71<NA><NA><NA>1.91<NA>7924.4123.0219.027.0<NA>2<NA>32서울시 중랑구 신내동 670번지선서울시 노원구 공릉동 중랑천(국가) 합류점2344<NA>옹벽
4981025210201210267중랑천권역(서울특별시) 하천기본계획(변경)2012묵동(1)우안6제묵동천1지방하천우안경기도 의정부시 의정부동 476-3번지경기도 의정부시 의정부동 548-4번지25226.6824.93<NA><NA><NA>2.271377925.9124.4120.027.02<NA><NA>2<NA>서울시 중랑구 신내동 670번지선서울시 노원구 공릉동 중랑천(국가) 합류점2252<NA>옹벽, 호안블럭