Overview

Dataset statistics

Number of variables23
Number of observations44
Missing cells54
Missing cells (%)5.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.4 KiB
Average record size in memory195.0 B

Variable types

Numeric6
Categorical8
Text9

Dataset

Description하천기본계획코드,일련번호,하천기본계획 사업명,수립년도,수계명,하천명,본류명,지정일자,하천지정근거_고시번호,하천관리청,시점명,시점계획홍수량,시점계획홍수위,시점계획하폭,종점명,종점계획홍수량,종점계획홍수위,종점계획하폭,하천연장,시점주소,종점주소,지정년도,기준지점명
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15787/S/1/datasetView.do

Alerts

수계명 is highly imbalanced (73.3%)Imbalance
본류명 is highly imbalanced (73.3%)Imbalance
시점계획하폭 has 3 (6.8%) missing valuesMissing
종점계획홍수위 has 1 (2.3%) missing valuesMissing
시점주소 has 13 (29.5%) missing valuesMissing
종점주소 has 11 (25.0%) missing valuesMissing
기준지점명 has 26 (59.1%) missing valuesMissing
하천기본계획코드 has unique valuesUnique
일련번호 has unique valuesUnique
하천명 has unique valuesUnique
시점명 has unique valuesUnique
시점계획홍수위 has unique valuesUnique
하천연장 has unique valuesUnique

Reproduction

Analysis started2024-05-11 09:00:37.104906
Analysis finished2024-05-11 09:00:38.379952
Duration1.28 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

하천기본계획코드
Real number (ℝ)

UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0451412 × 1012
Minimum1.0000102 × 1012
Maximum1.5003212 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-05-11T09:00:38.780242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.0000102 × 1012
5-th percentile1.0068637 × 1012
Q11.0250577 × 1012
median1.0252452 × 1012
Q31.0254002 × 1012
95-th percentile1.0255787 × 1012
Maximum1.5003212 × 1012
Range5.00311 × 1011
Interquartile range (IQR)3.4249993 × 108

Descriptive statistics

Standard deviation1.0063933 × 1011
Coefficient of variation (CV)0.096292574
Kurtosis19.162662
Mean1.0451412 × 1012
Median Absolute Deviation (MAD)1.899997 × 108
Skewness4.49527
Sum4.5986212 × 1013
Variance1.0128276 × 1022
MonotonicityNot monotonic
2024-05-11T09:00:39.268208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1025580202210 1
 
2.3%
1025340201410 1
 
2.3%
1025360201410 1
 
2.3%
1005380201510 1
 
2.3%
1025050201510 1
 
2.3%
1025350201410 1
 
2.3%
1025550201510 1
 
2.3%
1025370201410 1
 
2.3%
1025570202210 1
 
2.3%
1025500201510 1
 
2.3%
Other values (34) 34
77.3%
ValueCountFrequency (%)
1000010202010 1
2.3%
1005090201210 1
2.3%
1005380201510 1
2.3%
1015270201210 1
2.3%
1024880201510 1
2.3%
1024881201510 1
2.3%
1024900201210 1
2.3%
1024910201510 1
2.3%
1024920201510 1
2.3%
1024930201510 1
2.3%
ValueCountFrequency (%)
1500321201211 1
2.3%
1500321201210 1
2.3%
1025580202210 1
2.3%
1025570202210 1
2.3%
1025560201510 1
2.3%
1025550201510 1
2.3%
1025540201512 1
2.3%
1025540201510 1
2.3%
1025530201510 1
2.3%
1025500201510 1
2.3%

일련번호
Real number (ℝ)

UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.5
Minimum1
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-05-11T09:00:39.885689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.15
Q111.75
median22.5
Q333.25
95-th percentile41.85
Maximum44
Range43
Interquartile range (IQR)21.5

Descriptive statistics

Standard deviation12.845233
Coefficient of variation (CV)0.57089923
Kurtosis-1.2
Mean22.5
Median Absolute Deviation (MAD)11
Skewness0
Sum990
Variance165
MonotonicityStrictly decreasing
2024-05-11T09:00:40.437106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
44 1
 
2.3%
21 1
 
2.3%
19 1
 
2.3%
18 1
 
2.3%
17 1
 
2.3%
16 1
 
2.3%
15 1
 
2.3%
14 1
 
2.3%
13 1
 
2.3%
12 1
 
2.3%
Other values (34) 34
77.3%
ValueCountFrequency (%)
1 1
2.3%
2 1
2.3%
3 1
2.3%
4 1
2.3%
5 1
2.3%
6 1
2.3%
7 1
2.3%
8 1
2.3%
9 1
2.3%
10 1
2.3%
ValueCountFrequency (%)
44 1
2.3%
43 1
2.3%
42 1
2.3%
41 1
2.3%
40 1
2.3%
39 1
2.3%
38 1
2.3%
37 1
2.3%
36 1
2.3%
35 1
2.3%

하천기본계획 사업명
Categorical

Distinct10
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Memory size484.0 B
중랑천권역(서울특별시) 하천기본계획(변경)
17 
탄천 등 10개 하천기본계획
9 
안양천권역 하천기본계획
6 
홍제천 등 4개 하천기본계획
5 
아라천 하천기본계획
2 
Other values (5)
5 

Length

Max length38
Median length36
Mean length18.522727
Min length10

Unique

Unique5 ?
Unique (%)11.4%

Sample

1st row한강하류권역 하천기본계획 용역(창릉천)
2nd row아라천 하천기본계획
3rd row망월천하천기본계획(변경)
4th row중랑천권역(서울특별시) 하천기본계획(변경)
5th row중랑천권역(서울특별시) 하천기본계획(변경)

Common Values

ValueCountFrequency (%)
중랑천권역(서울특별시) 하천기본계획(변경) 17
38.6%
탄천 등 10개 하천기본계획 9
20.5%
안양천권역 하천기본계획 6
 
13.6%
홍제천 등 4개 하천기본계획 5
 
11.4%
아라천 하천기본계획 2
 
4.5%
한강하류권역 하천기본계획 용역(창릉천) 1
 
2.3%
망월천하천기본계획(변경) 1
 
2.3%
안양천 하천기본계획(변경) 1
 
2.3%
차곡천 등 16개 하천기본계획(재수립) 및 하천시설관리대장 작성 용역 1
 
2.3%
한강(팔당댐~하구)하천기본계획 및 하천시설관리대장작성(보완) 용역 1
 
2.3%

Length

2024-05-11T09:00:40.992643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:00:41.553661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
하천기본계획 23
18.5%
하천기본계획(변경 18
14.5%
중랑천권역(서울특별시 17
13.7%
등 15
12.1%
탄천 9
 
7.3%
10개 9
 
7.3%
안양천권역 6
 
4.8%
홍제천 5
 
4.0%
4개 5
 
4.0%
아라천 2
 
1.6%
Other values (13) 15
12.1%

수립년도
Categorical

Distinct5
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size484.0 B
2012
20 
2015
16 
2014
5 
2022
 
2
2020
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)2.3%

Sample

1st row2022
2nd row2012
3rd row2012
4th row2012
5th row2012

Common Values

ValueCountFrequency (%)
2012 20
45.5%
2015 16
36.4%
2014 5
 
11.4%
2022 2
 
4.5%
2020 1
 
2.3%

Length

2024-05-11T09:00:42.114064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:00:42.519747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2012 20
45.5%
2015 16
36.4%
2014 5
 
11.4%
2022 2
 
4.5%
2020 1
 
2.3%

수계명
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size484.0 B
한강
42 
아라천
 
2

Length

Max length3
Median length2
Mean length2.0454545
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
한강 42
95.5%
아라천 2
 
4.5%

Length

2024-05-11T09:00:43.044440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:00:43.434522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한강 42
95.5%
아라천 2
 
4.5%

하천명
Text

UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size484.0 B
2024-05-11T09:00:43.920033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.1818182
Min length2

Characters and Unicode

Total characters140
Distinct characters67
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)100.0%

Sample

1st row창릉천
2nd row아라천
3rd row망월천
4th row월곡천
5th row정릉천
ValueCountFrequency (%)
창릉천 1
 
2.3%
아라천 1
 
2.3%
목감천 1
 
2.3%
녹번천지류 1
 
2.3%
불광천 1
 
2.3%
안양천 1
 
2.3%
세곡천 1
 
2.3%
홍제천 1
 
2.3%
봉천천 1
 
2.3%
녹번천 1
 
2.3%
Other values (34) 34
77.3%
2024-05-11T09:00:45.056555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 42
30.0%
도 4
 
2.9%
봉 4
 
2.9%
동 4
 
2.9%
대 3
 
2.1%
2 3
 
2.1%
류 3
 
2.1%
릉 2
 
1.4%
1 2
 
1.4%
묵 2
 
1.4%
Other values (57) 71
50.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 135
96.4%
Decimal Number 5
 
3.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 42
31.1%
도 4
 
3.0%
봉 4
 
3.0%
동 4
 
3.0%
대 3
 
2.2%
류 3
 
2.2%
릉 2
 
1.5%
묵 2
 
1.5%
양 2
 
1.5%
이 2
 
1.5%
Other values (55) 67
49.6%
Decimal Number
ValueCountFrequency (%)
2 3
60.0%
1 2
40.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 135
96.4%
Common 5
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 42
31.1%
도 4
 
3.0%
봉 4
 
3.0%
동 4
 
3.0%
대 3
 
2.2%
류 3
 
2.2%
릉 2
 
1.5%
묵 2
 
1.5%
양 2
 
1.5%
이 2
 
1.5%
Other values (55) 67
49.6%
Common
ValueCountFrequency (%)
2 3
60.0%
1 2
40.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 135
96.4%
ASCII 5
 
3.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 42
31.1%
도 4
 
3.0%
봉 4
 
3.0%
동 4
 
3.0%
대 3
 
2.2%
류 3
 
2.2%
릉 2
 
1.5%
묵 2
 
1.5%
양 2
 
1.5%
이 2
 
1.5%
Other values (55) 67
49.6%
ASCII
ValueCountFrequency (%)
2 3
60.0%
1 2
40.0%

본류명
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size484.0 B
한강
42 
아라천
 
2

Length

Max length3
Median length2
Mean length2.0454545
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
한강 42
95.5%
아라천 2
 
4.5%

Length

2024-05-11T09:00:45.567917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:00:45.990860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한강 42
95.5%
아라천 2
 
4.5%

지정일자
Categorical

Distinct13
Distinct (%)29.5%
Missing0
Missing (%)0.0%
Memory size484.0 B
1971-12-30 00:00:00.0
21 
1974-07-24 00:00:00.0
8 
2011-01-07 00:00:00.0
 
2
<NA>
 
2
1963-04-01 00:00:00.0
 
2
Other values (8)
9 

Length

Max length21
Median length21
Mean length20.227273
Min length4

Unique

Unique7 ?
Unique (%)15.9%

Sample

1st row2022-05-04 00:00:00.0
2nd row2011-01-07 00:00:00.0
3rd row<NA>
4th row1971-12-30 00:00:00.0
5th row1971-12-30 00:00:00.0

Common Values

ValueCountFrequency (%)
1971-12-30 00:00:00.0 21
47.7%
1974-07-24 00:00:00.0 8
 
18.2%
2011-01-07 00:00:00.0 2
 
4.5%
<NA> 2
 
4.5%
1963-04-01 00:00:00.0 2
 
4.5%
1965-03-01 00:00:00.0 2
 
4.5%
2022-05-04 00:00:00.0 1
 
2.3%
1930-10-03 00:00:00.0 1
 
2.3%
1991-12-26 00:00:00.0 1
 
2.3%
2021-09-01 00:00:00.0 1
 
2.3%
Other values (3) 3
 
6.8%

Length

2024-05-11T09:00:46.504717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00.0 42
48.8%
1971-12-30 21
24.4%
1974-07-24 8
 
9.3%
2011-01-07 2
 
2.3%
na 2
 
2.3%
1963-04-01 2
 
2.3%
1965-03-01 2
 
2.3%
2022-05-04 1
 
1.2%
1930-10-03 1
 
1.2%
1991-12-26 1
 
1.2%
Other values (4) 4
 
4.7%

하천지정근거_고시번호
Categorical

Distinct13
Distinct (%)29.5%
Missing0
Missing (%)0.0%
Memory size484.0 B
서울268호
21 
서울146호
8 
국토해양부 제2011-3호
 
2
<NA>
 
2
대통령령 제16535호
 
2
Other values (8)
9 

Length

Max length17
Median length6
Mean length7.4090909
Min length4

Unique

Unique7 ?
Unique (%)15.9%

Sample

1st row경기도고시 제2022-5078호
2nd row국토해양부 제2011-3호
3rd row<NA>
4th row서울268호
5th row서울268호

Common Values

ValueCountFrequency (%)
서울268호 21
47.7%
서울146호 8
 
18.2%
국토해양부 제2011-3호 2
 
4.5%
<NA> 2
 
4.5%
대통령령 제16535호 2
 
4.5%
경기3148호 2
 
4.5%
경기도고시 제2022-5078호 1
 
2.3%
대통령령 제17315호 1
 
2.3%
서울353호 1
 
2.3%
대통령령제17315호 1
 
2.3%
Other values (3) 3
 
6.8%

Length

2024-05-11T09:00:47.018293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울268호 21
41.2%
서울146호 8
 
15.7%
대통령령 3
 
5.9%
국토해양부 2
 
3.9%
제2011-3호 2
 
3.9%
na 2
 
3.9%
제16535호 2
 
3.9%
경기3148호 2
 
3.9%
경기도고시 2
 
3.9%
제2022-5078호 1
 
2.0%
Other values (6) 6
 
11.8%

하천관리청
Categorical

Distinct9
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Memory size484.0 B
서울특별시
30 
서울시
4 
국토해양부
 
2
국토교통부
 
2
경기도
 
2
Other values (4)
4 

Length

Max length10
Median length5
Mean length4.9090909
Min length3

Unique

Unique4 ?
Unique (%)9.1%

Sample

1st row서울지방국토관리청
2nd row국토해양부
3rd row경기도, 서울특별시
4th row서울특별시
5th row서울특별시

Common Values

ValueCountFrequency (%)
서울특별시 30
68.2%
서울시 4
 
9.1%
국토해양부 2
 
4.5%
국토교통부 2
 
4.5%
경기도 2
 
4.5%
서울지방국토관리청 1
 
2.3%
경기도, 서울특별시 1
 
2.3%
<NA> 1
 
2.3%
건설교통부 1
 
2.3%

Length

2024-05-11T09:00:47.488891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:00:47.999133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 31
68.9%
서울시 4
 
8.9%
경기도 3
 
6.7%
국토해양부 2
 
4.4%
국토교통부 2
 
4.4%
서울지방국토관리청 1
 
2.2%
na 1
 
2.2%
건설교통부 1
 
2.2%

시점명
Text

UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size484.0 B
2024-05-11T09:00:48.938770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length31
Mean length22.363636
Min length11

Characters and Unicode

Total characters984
Distinct characters131
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)100.0%

Sample

1st row경기도 고양시 덕양구 효자동 신도리
2nd row서울특별시 강서구 개화동 한강분기점
3rd row경기도 하남시 풍산동 284-5번지선
4th row서울특별시 강북구 미아동 837지선
5th row서울특별시 성북구 정릉동 산1-1번지선
ValueCountFrequency (%)
서울시 24
 
11.5%
서울특별시 13
 
6.2%
경기도 7
 
3.3%
강북구 5
 
2.4%
중랑구 3
 
1.4%
3
 
1.4%
은평구 3
 
1.4%
도봉구 3
 
1.4%
수유동 3
 
1.4%
불광동 3
 
1.4%
Other values (121) 142
67.9%
2024-05-11T09:00:50.227952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
167
 
17.0%
동 50
 
5.1%
시 49
 
5.0%
서 47
 
4.8%
구 43
 
4.4%
울 40
 
4.1%
지 29
 
2.9%
번 28
 
2.8%
- 28
 
2.8%
1 25
 
2.5%
Other values (121) 478
48.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 618
62.8%
Space Separator 167
 
17.0%
Decimal Number 147
 
14.9%
Dash Punctuation 28
 
2.8%
Other Punctuation 12
 
1.2%
Close Punctuation 5
 
0.5%
Open Punctuation 5
 
0.5%
Lowercase Letter 1
 
0.1%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 50
 
8.1%
시 49
 
7.9%
서 47
 
7.6%
구 43
 
7.0%
울 40
 
6.5%
지 29
 
4.7%
번 28
 
4.5%
도 21
 
3.4%
선 21
 
3.4%
특 13
 
2.1%
Other values (103) 277
44.8%
Decimal Number
ValueCountFrequency (%)
1 25
17.0%
2 21
14.3%
3 20
13.6%
4 18
12.2%
7 14
9.5%
5 14
9.5%
0 12
8.2%
8 11
7.5%
6 8
 
5.4%
9 4
 
2.7%
Other Punctuation
ValueCountFrequency (%)
, 11
91.7%
. 1
 
8.3%
Space Separator
ValueCountFrequency (%)
167
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Lowercase Letter
ValueCountFrequency (%)
o 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 618
62.8%
Common 364
37.0%
Latin 2
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 50
 
8.1%
시 49
 
7.9%
서 47
 
7.6%
구 43
 
7.0%
울 40
 
6.5%
지 29
 
4.7%
번 28
 
4.5%
도 21
 
3.4%
선 21
 
3.4%
특 13
 
2.1%
Other values (103) 277
44.8%
Common
ValueCountFrequency (%)
167
45.9%
- 28
 
7.7%
1 25
 
6.9%
2 21
 
5.8%
3 20
 
5.5%
4 18
 
4.9%
7 14
 
3.8%
5 14
 
3.8%
0 12
 
3.3%
8 11
 
3.0%
Other values (6) 34
 
9.3%
Latin
ValueCountFrequency (%)
o 1
50.0%
N 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 618
62.8%
ASCII 366
37.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
167
45.6%
- 28
 
7.7%
1 25
 
6.8%
2 21
 
5.7%
3 20
 
5.5%
4 18
 
4.9%
7 14
 
3.8%
5 14
 
3.8%
0 12
 
3.3%
8 11
 
3.0%
Other values (8) 36
 
9.8%
Hangul
ValueCountFrequency (%)
동 50
 
8.1%
시 49
 
7.9%
서 47
 
7.6%
구 43
 
7.0%
울 40
 
6.5%
지 29
 
4.7%
번 28
 
4.5%
도 21
 
3.4%
선 21
 
3.4%
특 13
 
2.1%
Other values (103) 277
44.8%

시점계획홍수량
Text

Distinct38
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Memory size484.0 B
2024-05-11T09:00:50.780616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.5909091
Min length2

Characters and Unicode

Total characters114
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

Unique33 ?
Unique (%)75.0%

Sample

1st row979
2nd row40
3rd row35
4th row23
5th row81
ValueCountFrequency (%)
35 3
 
6.8%
77 2
 
4.5%
50 2
 
4.5%
112 2
 
4.5%
15 2
 
4.5%
73 1
 
2.3%
67 1
 
2.3%
127 1
 
2.3%
36 1
 
2.3%
24 1
 
2.3%
Other values (28) 28
63.6%
2024-05-11T09:00:51.926353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 19
16.7%
2 15
13.2%
0 13
11.4%
3 12
10.5%
6 12
10.5%
5 11
9.6%
7 11
9.6%
4 7
 
6.1%
, 5
 
4.4%
9 5
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 109
95.6%
Other Punctuation 5
 
4.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 19
17.4%
2 15
13.8%
0 13
11.9%
3 12
11.0%
6 12
11.0%
5 11
10.1%
7 11
10.1%
4 7
 
6.4%
9 5
 
4.6%
8 4
 
3.7%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 114
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 19
16.7%
2 15
13.2%
0 13
11.4%
3 12
10.5%
6 12
10.5%
5 11
9.6%
7 11
9.6%
4 7
 
6.1%
, 5
 
4.4%
9 5
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 19
16.7%
2 15
13.2%
0 13
11.4%
3 12
10.5%
6 12
10.5%
5 11
9.6%
7 11
9.6%
4 7
 
6.1%
, 5
 
4.4%
9 5
 
4.4%

시점계획홍수위
Real number (ℝ)

UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.573182
Minimum6.52
Maximum153.44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-05-11T09:00:52.502768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.52
5-th percentile8.05
Q124.2825
median36.335
Q367.0075
95-th percentile109.8565
Maximum153.44
Range146.92
Interquartile range (IQR)42.725

Descriptive statistics

Standard deviation35.406868
Coefficient of variation (CV)0.72893862
Kurtosis1.5260632
Mean48.573182
Median Absolute Deviation (MAD)24.03
Skewness1.2414402
Sum2137.22
Variance1253.6463
MonotonicityNot monotonic
2024-05-11T09:00:53.081959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
12.13 1
 
2.3%
14.53 1
 
2.3%
29.18 1
 
2.3%
25.19 1
 
2.3%
67.56 1
 
2.3%
105.87 1
 
2.3%
60.76 1
 
2.3%
70.41 1
 
2.3%
7.33 1
 
2.3%
25.62 1
 
2.3%
Other values (34) 34
77.3%
ValueCountFrequency (%)
6.52 1
2.3%
6.55 1
2.3%
7.33 1
2.3%
12.13 1
2.3%
12.48 1
2.3%
14.53 1
2.3%
17.7 1
2.3%
17.94 1
2.3%
18.2 1
2.3%
22.91 1
2.3%
ValueCountFrequency (%)
153.44 1
2.3%
149.88 1
2.3%
110.56 1
2.3%
105.87 1
2.3%
92.92 1
2.3%
86.31 1
2.3%
77.03 1
2.3%
70.41 1
2.3%
69.42 1
2.3%
67.56 1
2.3%

시점계획하폭
Real number (ℝ)

MISSING 

Distinct28
Distinct (%)68.3%
Missing3
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean40.465854
Minimum2
Maximum407
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-05-11T09:00:53.704553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q17
median10
Q322
95-th percentile153
Maximum407
Range405
Interquartile range (IQR)15

Descriptive statistics

Standard deviation75.014751
Coefficient of variation (CV)1.853779
Kurtosis14.220893
Mean40.465854
Median Absolute Deviation (MAD)5
Skewness3.4595485
Sum1659.1
Variance5627.2128
MonotonicityNot monotonic
2024-05-11T09:00:54.240751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
6.0 4
 
9.1%
7.0 4
 
9.1%
5.0 3
 
6.8%
9.0 3
 
6.8%
22.0 2
 
4.5%
10.0 2
 
4.5%
16.0 2
 
4.5%
48.0 1
 
2.3%
153.0 1
 
2.3%
141.0 1
 
2.3%
Other values (18) 18
40.9%
(Missing) 3
 
6.8%
ValueCountFrequency (%)
2.0 1
 
2.3%
3.0 1
 
2.3%
4.0 1
 
2.3%
5.0 3
6.8%
6.0 4
9.1%
7.0 4
9.1%
8.0 1
 
2.3%
9.0 3
6.8%
9.6 1
 
2.3%
10.0 2
4.5%
ValueCountFrequency (%)
407.0 1
2.3%
199.0 1
2.3%
153.0 1
2.3%
141.0 1
2.3%
111.0 1
2.3%
110.0 1
2.3%
96.0 1
2.3%
48.0 1
2.3%
47.0 1
2.3%
40.0 1
2.3%

종점명
Text

Distinct43
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size484.0 B
2024-05-11T09:00:55.430515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length30
Mean length23.863636
Min length19

Characters and Unicode

Total characters1050
Distinct characters129
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)95.5%

Sample

1st row경기도 고양시 덕양구 강매동 한강합류점
2nd row인천광역시 서구 오류동 해안 서해배수문
3rd row서울특별시 강동구 고덕동 고덕천 합류점
4th row서울특별시 성북구 하월곡동 정릉천합류점
5th row서울특별시 동대문구 용두동 청계천합류점
ValueCountFrequency (%)
합류점 32
 
14.2%
서울시 25
 
11.1%
서울특별시 14
 
6.2%
한강(국가 5
 
2.2%
중랑천(국가 4
 
1.8%
노원구 3
 
1.3%
송파구 3
 
1.3%
영등포구 3
 
1.3%
도봉구 3
 
1.3%
안양천(국가 3
 
1.3%
Other values (101) 131
58.0%
2024-05-11T09:00:56.639970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
183
 
17.4%
동 52
 
5.0%
시 45
 
4.3%
구 45
 
4.3%
서 44
 
4.2%
점 42
 
4.0%
류 42
 
4.0%
합 41
 
3.9%
울 39
 
3.7%
천 36
 
3.4%
Other values (119) 481
45.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 757
72.1%
Space Separator 183
 
17.4%
Decimal Number 37
 
3.5%
Open Punctuation 29
 
2.8%
Close Punctuation 29
 
2.8%
Dash Punctuation 6
 
0.6%
Other Punctuation 6
 
0.6%
Lowercase Letter 2
 
0.2%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 52
 
6.9%
시 45
 
5.9%
구 45
 
5.9%
서 44
 
5.8%
점 42
 
5.5%
류 42
 
5.5%
합 41
 
5.4%
울 39
 
5.2%
천 36
 
4.8%
강 18
 
2.4%
Other values (101) 353
46.6%
Decimal Number
ValueCountFrequency (%)
1 9
24.3%
3 7
18.9%
4 5
13.5%
0 4
10.8%
6 3
 
8.1%
2 3
 
8.1%
8 3
 
8.1%
7 2
 
5.4%
9 1
 
2.7%
Other Punctuation
ValueCountFrequency (%)
, 5
83.3%
. 1
 
16.7%
Lowercase Letter
ValueCountFrequency (%)
o 1
50.0%
m 1
50.0%
Space Separator
ValueCountFrequency (%)
183
100.0%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 757
72.1%
Common 290
 
27.6%
Latin 3
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 52
 
6.9%
시 45
 
5.9%
구 45
 
5.9%
서 44
 
5.8%
점 42
 
5.5%
류 42
 
5.5%
합 41
 
5.4%
울 39
 
5.2%
천 36
 
4.8%
강 18
 
2.4%
Other values (101) 353
46.6%
Common
ValueCountFrequency (%)
183
63.1%
( 29
 
10.0%
) 29
 
10.0%
1 9
 
3.1%
3 7
 
2.4%
- 6
 
2.1%
4 5
 
1.7%
, 5
 
1.7%
0 4
 
1.4%
6 3
 
1.0%
Other values (5) 10
 
3.4%
Latin
ValueCountFrequency (%)
N 1
33.3%
o 1
33.3%
m 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 757
72.1%
ASCII 293
 
27.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
183
62.5%
( 29
 
9.9%
) 29
 
9.9%
1 9
 
3.1%
3 7
 
2.4%
- 6
 
2.0%
4 5
 
1.7%
, 5
 
1.7%
0 4
 
1.4%
6 3
 
1.0%
Other values (8) 13
 
4.4%
Hangul
ValueCountFrequency (%)
동 52
 
6.9%
시 45
 
5.9%
구 45
 
5.9%
서 44
 
5.8%
점 42
 
5.5%
류 42
 
5.5%
합 41
 
5.4%
울 39
 
5.2%
천 36
 
4.8%
강 18
 
2.4%
Other values (101) 353
46.6%

종점계획홍수량
Text

Distinct43
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size484.0 B
2024-05-11T09:00:57.111446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.1136364
Min length2

Characters and Unicode

Total characters137
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

Unique42 ?
Unique (%)95.5%

Sample

1st row153
2nd row1,480
3rd row135
4th row105
5th row316
ValueCountFrequency (%)
105 2
 
4.5%
114 1
 
2.3%
210 1
 
2.3%
35 1
 
2.3%
352 1
 
2.3%
2,440 1
 
2.3%
264 1
 
2.3%
639 1
 
2.3%
175 1
 
2.3%
144 1
 
2.3%
Other values (33) 33
75.0%
2024-05-11T09:00:58.134653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 30
21.9%
0 17
12.4%
3 14
10.2%
4 14
10.2%
2 13
9.5%
5 12
 
8.8%
6 11
 
8.0%
7 10
 
7.3%
, 6
 
4.4%
9 5
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 131
95.6%
Other Punctuation 6
 
4.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 30
22.9%
0 17
13.0%
3 14
10.7%
4 14
10.7%
2 13
9.9%
5 12
 
9.2%
6 11
 
8.4%
7 10
 
7.6%
9 5
 
3.8%
8 5
 
3.8%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 137
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 30
21.9%
0 17
12.4%
3 14
10.2%
4 14
10.2%
2 13
9.5%
5 12
 
8.8%
6 11
 
8.0%
7 10
 
7.3%
, 6
 
4.4%
9 5
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 137
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 30
21.9%
0 17
12.4%
3 14
10.2%
4 14
10.2%
2 13
9.5%
5 12
 
8.8%
6 11
 
8.0%
7 10
 
7.3%
, 6
 
4.4%
9 5
 
3.6%

종점계획홍수위
Real number (ℝ)

MISSING 

Distinct36
Distinct (%)83.7%
Missing1
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean20.556977
Minimum4.96
Maximum96.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-05-11T09:00:58.758978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.96
5-th percentile7.1
Q114.515
median18.01
Q322.795
95-th percentile35.913
Maximum96.36
Range91.4
Interquartile range (IQR)8.28

Descriptive statistics

Standard deviation13.750772
Coefficient of variation (CV)0.66891023
Kurtosis22.401215
Mean20.556977
Median Absolute Deviation (MAD)3.84
Skewness4.1605008
Sum883.95
Variance189.08373
MonotonicityNot monotonic
2024-05-11T09:00:59.237828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
17.05 5
 
11.4%
18.01 2
 
4.5%
13.95 2
 
4.5%
13.54 2
 
4.5%
18.07 1
 
2.3%
19.84 1
 
2.3%
18.56 1
 
2.3%
22.76 1
 
2.3%
15.87 1
 
2.3%
36.4 1
 
2.3%
Other values (26) 26
59.1%
ValueCountFrequency (%)
4.96 1
2.3%
6.68 1
2.3%
7.0 1
2.3%
8.0 1
2.3%
13.32 1
2.3%
13.54 2
4.5%
13.95 2
4.5%
14.17 1
2.3%
14.5 1
2.3%
14.53 1
2.3%
ValueCountFrequency (%)
96.36 1
2.3%
37.24 1
2.3%
36.4 1
2.3%
31.53 1
2.3%
29.23 1
2.3%
28.43 1
2.3%
25.91 1
2.3%
25.64 1
2.3%
25.57 1
2.3%
24.28 1
2.3%

종점계획하폭
Text

Distinct40
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Memory size484.0 B
2024-05-11T09:00:59.613727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.4545455
Min length1

Characters and Unicode

Total characters108
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

Unique36 ?
Unique (%)81.8%

Sample

1st row14
2nd row244
3rd row19
4th row44
5th row66
ValueCountFrequency (%)
31 2
 
4.5%
21 2
 
4.5%
16 2
 
4.5%
110 2
 
4.5%
60 1
 
2.3%
87 1
 
2.3%
14 1
 
2.3%
75 1
 
2.3%
185 1
 
2.3%
47 1
 
2.3%
Other values (30) 30
68.2%
2024-05-11T09:01:00.497614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 21
19.4%
2 15
13.9%
4 15
13.9%
0 11
10.2%
3 10
9.3%
6 10
9.3%
5 10
9.3%
8 5
 
4.6%
9 4
 
3.7%
7 4
 
3.7%
Other values (2) 3
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 105
97.2%
Other Punctuation 3
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 21
20.0%
2 15
14.3%
4 15
14.3%
0 11
10.5%
3 10
9.5%
6 10
9.5%
5 10
9.5%
8 5
 
4.8%
9 4
 
3.8%
7 4
 
3.8%
Other Punctuation
ValueCountFrequency (%)
. 2
66.7%
, 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 108
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 21
19.4%
2 15
13.9%
4 15
13.9%
0 11
10.2%
3 10
9.3%
6 10
9.3%
5 10
9.3%
8 5
 
4.6%
9 4
 
3.7%
7 4
 
3.7%
Other values (2) 3
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 21
19.4%
2 15
13.9%
4 15
13.9%
0 11
10.2%
3 10
9.3%
6 10
9.3%
5 10
9.3%
8 5
 
4.6%
9 4
 
3.7%
7 4
 
3.7%
Other values (2) 3
 
2.8%

하천연장
Real number (ℝ)

UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9448864
Minimum0.45
Maximum91.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-05-11T09:01:00.976008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.45
5-th percentile1.2015
Q12.295
median4.785
Q38.3025
95-th percentile20.2195
Maximum91.64
Range91.19
Interquartile range (IQR)6.0075

Descriptive statistics

Standard deviation13.955884
Coefficient of variation (CV)1.7565869
Kurtosis31.442289
Mean7.9448864
Median Absolute Deviation (MAD)3.02
Skewness5.2740425
Sum349.575
Variance194.76669
MonotonicityNot monotonic
2024-05-11T09:01:01.719921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
18.42 1
 
2.3%
1.3 1
 
2.3%
7.32 1
 
2.3%
20.47 1
 
2.3%
4.88 1
 
2.3%
11.13 1
 
2.3%
5.0 1
 
2.3%
3.2 1
 
2.3%
4.74 1
 
2.3%
12.33 1
 
2.3%
Other values (34) 34
77.3%
ValueCountFrequency (%)
0.45 1
2.3%
0.88 1
2.3%
1.2 1
2.3%
1.21 1
2.3%
1.225 1
2.3%
1.3 1
2.3%
1.34 1
2.3%
1.44 1
2.3%
1.67 1
2.3%
1.91 1
2.3%
ValueCountFrequency (%)
91.64 1
2.3%
20.81 1
2.3%
20.47 1
2.3%
18.8 1
2.3%
18.42 1
2.3%
12.33 1
2.3%
11.13 1
2.3%
11.0 1
2.3%
9.05 1
2.3%
8.81 1
2.3%

시점주소
Text

MISSING 

Distinct28
Distinct (%)90.3%
Missing13
Missing (%)29.5%
Memory size484.0 B
2024-05-11T09:01:02.339519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length14.096774
Min length12

Characters and Unicode

Total characters437
Distinct characters81
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)83.9%

Sample

1st row경기도 고양시 덕양구 효자동
2nd row서울특별시 강서구 개화동
3rd row경기도 하남시 풍산동
4th row서울특별시 성북구 정릉동
5th row서울특별시 성북구 성북동
ValueCountFrequency (%)
서울특별시 23
23.7%
경기도 8
 
8.2%
수유동 3
 
3.1%
강북구 3
 
3.1%
은평구 2
 
2.1%
불광동 2
 
2.1%
서초구 2
 
2.1%
덕양구 2
 
2.1%
성북구 2
 
2.1%
고양시 2
 
2.1%
Other values (47) 48
49.5%
2024-05-11T09:01:03.566663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
97
22.2%
동 35
 
8.0%
시 33
 
7.6%
구 28
 
6.4%
서 26
 
5.9%
별 24
 
5.5%
특 23
 
5.3%
울 23
 
5.3%
도 10
 
2.3%
기 9
 
2.1%
Other values (71) 129
29.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 340
77.8%
Space Separator 97
 
22.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 35
 
10.3%
시 33
 
9.7%
구 28
 
8.2%
서 26
 
7.6%
별 24
 
7.1%
특 23
 
6.8%
울 23
 
6.8%
도 10
 
2.9%
기 9
 
2.6%
경 8
 
2.4%
Other values (70) 121
35.6%
Space Separator
ValueCountFrequency (%)
97
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 340
77.8%
Common 97
 
22.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 35
 
10.3%
시 33
 
9.7%
구 28
 
8.2%
서 26
 
7.6%
별 24
 
7.1%
특 23
 
6.8%
울 23
 
6.8%
도 10
 
2.9%
기 9
 
2.6%
경 8
 
2.4%
Other values (70) 121
35.6%
Common
ValueCountFrequency (%)
97
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 340
77.8%
ASCII 97
 
22.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
97
100.0%
Hangul
ValueCountFrequency (%)
동 35
 
10.3%
시 33
 
9.7%
구 28
 
8.2%
서 26
 
7.6%
별 24
 
7.1%
특 23
 
6.8%
울 23
 
6.8%
도 10
 
2.9%
기 9
 
2.6%
경 8
 
2.4%
Other values (70) 121
35.6%

종점주소
Text

MISSING 

Distinct28
Distinct (%)84.8%
Missing11
Missing (%)25.0%
Memory size484.0 B
2024-05-11T09:01:04.340345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length14.424242
Min length13

Characters and Unicode

Total characters476
Distinct characters75
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

Unique23 ?
Unique (%)69.7%

Sample

1st row경기도 고양시 덕양구 강매동
2nd row인천광역시 서구 오류동
3rd row서울특별시 강동구 고덕동
4th row서울특별시 성북구 하월곡동
5th row서울특별시 동대문구 용두동
ValueCountFrequency (%)
서울특별시 30
29.7%
강남구 4
 
4.0%
영등포구 3
 
3.0%
마포구 3
 
3.0%
강북구 3
 
3.0%
수유동 2
 
2.0%
강매동 2
 
2.0%
성동구 2
 
2.0%
동대문구 2
 
2.0%
개봉동 2
 
2.0%
Other values (40) 48
47.5%
2024-05-11T09:01:05.369941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
101
21.2%
동 39
 
8.2%
구 35
 
7.4%
시 34
 
7.1%
서 31
 
6.5%
특 30
 
6.3%
별 30
 
6.3%
울 30
 
6.3%
강 11
 
2.3%
포 6
 
1.3%
Other values (65) 129
27.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 372
78.2%
Space Separator 101
 
21.2%
Decimal Number 3
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 39
 
10.5%
구 35
 
9.4%
시 34
 
9.1%
서 31
 
8.3%
특 30
 
8.1%
별 30
 
8.1%
울 30
 
8.1%
강 11
 
3.0%
포 6
 
1.6%
양 5
 
1.3%
Other values (63) 121
32.5%
Space Separator
ValueCountFrequency (%)
101
100.0%
Decimal Number
ValueCountFrequency (%)
1 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 372
78.2%
Common 104
 
21.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 39
 
10.5%
구 35
 
9.4%
시 34
 
9.1%
서 31
 
8.3%
특 30
 
8.1%
별 30
 
8.1%
울 30
 
8.1%
강 11
 
3.0%
포 6
 
1.6%
양 5
 
1.3%
Other values (63) 121
32.5%
Common
ValueCountFrequency (%)
101
97.1%
1 3
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 372
78.2%
ASCII 104
 
21.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
101
97.1%
1 3
 
2.9%
Hangul
ValueCountFrequency (%)
동 39
 
10.5%
구 35
 
9.4%
시 34
 
9.1%
서 31
 
8.3%
특 30
 
8.1%
별 30
 
8.1%
울 30
 
8.1%
강 11
 
3.0%
포 6
 
1.6%
양 5
 
1.3%
Other values (63) 121
32.5%

지정년도
Categorical

Distinct6
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Memory size484.0 B
2009
15 
2014
10 
2011
8 
<NA>
6 
2013
4 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)2.3%

Sample

1st row<NA>
2nd row2011
3rd row2008
4th row2009
5th row2009

Common Values

ValueCountFrequency (%)
2009 15
34.1%
2014 10
22.7%
2011 8
18.2%
<NA> 6
 
13.6%
2013 4
 
9.1%
2008 1
 
2.3%

Length

2024-05-11T09:01:06.014841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:01:06.425511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2009 15
34.1%
2014 10
22.7%
2011 8
18.2%
na 6
 
13.6%
2013 4
 
9.1%
2008 1
 
2.3%

기준지점명
Text

MISSING 

Distinct18
Distinct (%)100.0%
Missing26
Missing (%)59.1%
Memory size484.0 B
2024-05-11T09:01:06.840301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.4444444
Min length2

Characters and Unicode

Total characters80
Distinct characters43
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

Unique18 ?
Unique (%)100.0%

Sample

1st row아라천
2nd row망월천하구
3rd row중랑천(하구)
4th row대방천 하구
5th row도림천 하구
ValueCountFrequency (%)
하구 5
21.7%
아라천 1
 
4.3%
임진강합류전 1
 
4.3%
여의천 1
 
4.3%
고덕천 1
 
4.3%
성내천 1
 
4.3%
대사골천 1
 
4.3%
감이천 1
 
4.3%
도림2지류 1
 
4.3%
탄천 1
 
4.3%
Other values (9) 9
39.1%
2024-05-11T09:01:07.875561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 17
21.2%
하 7
 
8.8%
구 7
 
8.8%
5
 
6.2%
류 3
 
3.8%
대 2
 
2.5%
도 2
 
2.5%
림 2
 
2.5%
아 1
 
1.2%
골 1
 
1.2%
Other values (33) 33
41.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 72
90.0%
Space Separator 5
 
6.2%
Decimal Number 1
 
1.2%
Open Punctuation 1
 
1.2%
Close Punctuation 1
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 17
23.6%
하 7
 
9.7%
구 7
 
9.7%
류 3
 
4.2%
대 2
 
2.8%
도 2
 
2.8%
림 2
 
2.8%
아 1
 
1.4%
골 1
 
1.4%
탄 1
 
1.4%
Other values (29) 29
40.3%
Space Separator
ValueCountFrequency (%)
5
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 72
90.0%
Common 8
 
10.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 17
23.6%
하 7
 
9.7%
구 7
 
9.7%
류 3
 
4.2%
대 2
 
2.8%
도 2
 
2.8%
림 2
 
2.8%
아 1
 
1.4%
골 1
 
1.4%
탄 1
 
1.4%
Other values (29) 29
40.3%
Common
ValueCountFrequency (%)
5
62.5%
2 1
 
12.5%
( 1
 
12.5%
) 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 72
90.0%
ASCII 8
 
10.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 17
23.6%
하 7
 
9.7%
구 7
 
9.7%
류 3
 
4.2%
대 2
 
2.8%
도 2
 
2.8%
림 2
 
2.8%
아 1
 
1.4%
골 1
 
1.4%
탄 1
 
1.4%
Other values (29) 29
40.3%
ASCII
ValueCountFrequency (%)
5
62.5%
2 1
 
12.5%
( 1
 
12.5%
) 1
 
12.5%

Sample

하천기본계획코드일련번호하천기본계획 사업명수립년도수계명하천명본류명지정일자하천지정근거_고시번호하천관리청시점명시점계획홍수량시점계획홍수위시점계획하폭종점명종점계획홍수량종점계획홍수위종점계획하폭하천연장시점주소종점주소지정년도기준지점명
0102558020221044한강하류권역 하천기본계획 용역(창릉천)2022한강창릉천한강2022-05-04 00:00:00.0경기도고시 제2022-5078호서울지방국토관리청경기도 고양시 덕양구 효자동 신도리97912.13199.0경기도 고양시 덕양구 강매동 한강합류점15396.361418.42경기도 고양시 덕양구 효자동경기도 고양시 덕양구 강매동<NA><NA>
1150032120121043아라천 하천기본계획2012아라천아라천아라천2011-01-07 00:00:00.0국토해양부 제2011-3호국토해양부서울특별시 강서구 개화동 한강분기점406.5522.0인천광역시 서구 오류동 해안 서해배수문1,4804.9624418.8서울특별시 강서구 개화동인천광역시 서구 오류동2011아라천
2102490020121042망월천하천기본계획(변경)2012한강망월천한강<NA><NA>경기도, 서울특별시경기도 하남시 풍산동 284-5번지선3524.8116.0서울특별시 강동구 고덕동 고덕천 합류점13514.5194.36경기도 하남시 풍산동서울특별시 강동구 고덕동2008망월천하구
3102530020121041중랑천권역(서울특별시) 하천기본계획(변경)2012한강월곡천한강1971-12-30 00:00:00.0서울268호서울특별시서울특별시 강북구 미아동 837지선2362.063.0서울특별시 성북구 하월곡동 정릉천합류점10525.57443.5<NA>서울특별시 성북구 하월곡동2009<NA>
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