Overview

Dataset statistics

Number of variables8
Number of observations920
Missing cells1152
Missing cells (%)15.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory58.5 KiB
Average record size in memory65.1 B

Variable types

Text7
Numeric1

Dataset

Description공공데이터 중장기 개방계획에 따라 공개하는 경상남도 하천관리 시스템의 데이터 입니다. 하천관리시스템의 기본계획보고서 정보를 포함하고있습니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15093568

Alerts

보고서부록 has 68 (7.4%) missing valuesMissing
하천대장 has 78 (8.5%) missing valuesMissing
하천대장부록 has 116 (12.6%) missing valuesMissing
기타 has 890 (96.7%) missing valuesMissing
하천관리코드 has unique valuesUnique
보고서 has unique valuesUnique

Reproduction

Analysis started2023-12-11 00:14:23.397321
Analysis finished2023-12-11 00:14:24.301123
Duration0.9 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

하천관리코드
Text

UNIQUE 

Distinct920
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
2023-12-11T09:14:24.498635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters17480
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique920 ?
Unique (%)100.0%

Sample

1st row20233402010F01Q0101
2nd row20253802010F01Q0101
3rd row20254102010F02Q0101
4th row20261602010F02Q0101
5th row20261802010F01Q0101
ValueCountFrequency (%)
20233402010f01q0101 1
 
0.1%
27200202002f01q0101 1
 
0.1%
27200202017f02q0101 1
 
0.1%
27200302002f01q0101 1
 
0.1%
27200302016f02q0101 1
 
0.1%
27200402011f01q0101 1
 
0.1%
27200402014f02q0101 1
 
0.1%
27200502002f01q0101 1
 
0.1%
20273902006f02q0101 1
 
0.1%
27200502007f02q0101 1
 
0.1%
Other values (910) 910
98.9%
2023-12-11T09:14:24.879666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5999
34.3%
1 3336
19.1%
2 3311
18.9%
F 920
 
5.3%
Q 920
 
5.3%
7 544
 
3.1%
9 481
 
2.8%
4 461
 
2.6%
5 436
 
2.5%
6 408
 
2.3%
Other values (2) 664
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15640
89.5%
Uppercase Letter 1840
 
10.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5999
38.4%
1 3336
21.3%
2 3311
21.2%
7 544
 
3.5%
9 481
 
3.1%
4 461
 
2.9%
5 436
 
2.8%
6 408
 
2.6%
3 399
 
2.6%
8 265
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
F 920
50.0%
Q 920
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15640
89.5%
Latin 1840
 
10.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5999
38.4%
1 3336
21.3%
2 3311
21.2%
7 544
 
3.5%
9 481
 
3.1%
4 461
 
2.9%
5 436
 
2.8%
6 408
 
2.6%
3 399
 
2.6%
8 265
 
1.7%
Latin
ValueCountFrequency (%)
F 920
50.0%
Q 920
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5999
34.3%
1 3336
19.1%
2 3311
18.9%
F 920
 
5.3%
Q 920
 
5.3%
7 544
 
3.1%
9 481
 
2.8%
4 461
 
2.6%
5 436
 
2.5%
6 408
 
2.3%
Other values (2) 664
 
3.8%

하천명
Text

Distinct552
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
2023-12-11T09:14:25.304066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length2.9902174
Min length2

Characters and Unicode

Total characters2751
Distinct characters214
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

Unique328 ?
Unique (%)35.7%

Sample

1st row금성천
2nd row마곡천
3rd row독산천
4th row광정천
5th row옥열천
ValueCountFrequency (%)
대산천 9
 
1.0%
대곡천 8
 
0.9%
횡천강 6
 
0.7%
단장천 6
 
0.7%
동천 6
 
0.7%
계성천 5
 
0.5%
호계천 5
 
0.5%
의령천 5
 
0.5%
산양천 5
 
0.5%
광려천 5
 
0.5%
Other values (542) 860
93.5%
2023-12-11T09:14:25.853925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 952
34.6%
곡 103
 
3.7%
산 65
 
2.4%
대 54
 
2.0%
양 38
 
1.4%
동 37
 
1.3%
정 35
 
1.3%
전 33
 
1.2%
신 31
 
1.1%
강 30
 
1.1%
Other values (204) 1373
49.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2744
99.7%
Decimal Number 3
 
0.1%
Open Punctuation 2
 
0.1%
Close Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 952
34.7%
곡 103
 
3.8%
산 65
 
2.4%
대 54
 
2.0%
양 38
 
1.4%
동 37
 
1.3%
정 35
 
1.3%
전 33
 
1.2%
신 31
 
1.1%
강 30
 
1.1%
Other values (200) 1366
49.8%
Decimal Number
ValueCountFrequency (%)
1 2
66.7%
2 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2744
99.7%
Common 7
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 952
34.7%
곡 103
 
3.8%
산 65
 
2.4%
대 54
 
2.0%
양 38
 
1.4%
동 37
 
1.3%
정 35
 
1.3%
전 33
 
1.2%
신 31
 
1.1%
강 30
 
1.1%
Other values (200) 1366
49.8%
Common
ValueCountFrequency (%)
1 2
28.6%
( 2
28.6%
) 2
28.6%
2 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2744
99.7%
ASCII 7
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 952
34.7%
곡 103
 
3.8%
산 65
 
2.4%
대 54
 
2.0%
양 38
 
1.4%
동 37
 
1.3%
정 35
 
1.3%
전 33
 
1.2%
신 31
 
1.1%
강 30
 
1.1%
Other values (200) 1366
49.8%
ASCII
ValueCountFrequency (%)
1 2
28.6%
( 2
28.6%
) 2
28.6%
2 1
14.3%

기본계획수립년도
Real number (ℝ)

Distinct30
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2008.525
Minimum1986
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-12-11T09:14:25.994637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1986
5-th percentile1994
Q12005
median2010
Q32013
95-th percentile2019
Maximum2020
Range34
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.4038946
Coefficient of variation (CV)0.0036862347
Kurtosis0.44428666
Mean2008.525
Median Absolute Deviation (MAD)5
Skewness-0.752313
Sum1847843
Variance54.817655
MonotonicityNot monotonic
2023-12-11T09:14:26.128164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2010 126
 
13.7%
2019 89
 
9.7%
2008 65
 
7.1%
2005 63
 
6.8%
2013 57
 
6.2%
2004 50
 
5.4%
2011 49
 
5.3%
2012 38
 
4.1%
2014 36
 
3.9%
2006 31
 
3.4%
Other values (20) 316
34.3%
ValueCountFrequency (%)
1986 16
1.7%
1988 2
 
0.2%
1992 1
 
0.1%
1993 11
1.2%
1994 22
2.4%
1995 21
2.3%
1996 16
1.7%
1997 15
1.6%
1998 3
 
0.3%
1999 5
 
0.5%
ValueCountFrequency (%)
2020 11
 
1.2%
2019 89
9.7%
2018 18
 
2.0%
2017 20
 
2.2%
2016 20
 
2.2%
2015 28
 
3.0%
2014 36
3.9%
2013 57
6.2%
2012 38
4.1%
2011 49
5.3%

보고서
Text

UNIQUE 

Distinct920
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
2023-12-11T09:14:26.324564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length23
Mean length23.013043
Min length23

Characters and Unicode

Total characters21172
Distinct characters20
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

Unique920 ?
Unique (%)100.0%

Sample

1st row20233402010F01Q0101_보고서
2nd row20253802010F01Q0101_보고서
3rd row20254102010F02Q0101_보고서
4th row20261602010F02Q0101_보고서
5th row20261802010F01Q0101_보고서
ValueCountFrequency (%)
20233402010f01q0101_보고서 1
 
0.1%
27200202002f01q0101_보고서 1
 
0.1%
27200202017f02q0101_보고서 1
 
0.1%
27200302002f01q0101_보고서 1
 
0.1%
27200302016f02q0101_보고서 1
 
0.1%
27200402011f01q0101_보고서 1
 
0.1%
27200402014f02q0101_보고서 1
 
0.1%
27200502002f01q0101_보고서 1
 
0.1%
20273902006f02q0101_보고서 1
 
0.1%
27200502007f02q0101_보고서 1
 
0.1%
Other values (910) 910
98.9%
2023-12-11T09:14:26.666208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5999
28.3%
1 3336
15.8%
2 3311
15.6%
_ 920
 
4.3%
고 920
 
4.3%
보 920
 
4.3%
서 920
 
4.3%
Q 920
 
4.3%
F 920
 
4.3%
7 544
 
2.6%
Other values (10) 2462
11.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15640
73.9%
Other Letter 2760
 
13.0%
Uppercase Letter 1840
 
8.7%
Connector Punctuation 920
 
4.3%
Lowercase Letter 9
 
< 0.1%
Other Punctuation 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5999
38.4%
1 3336
21.3%
2 3311
21.2%
7 544
 
3.5%
9 481
 
3.1%
4 461
 
2.9%
5 436
 
2.8%
6 408
 
2.6%
3 399
 
2.6%
8 265
 
1.7%
Other Letter
ValueCountFrequency (%)
고 920
33.3%
보 920
33.3%
서 920
33.3%
Lowercase Letter
ValueCountFrequency (%)
p 3
33.3%
d 3
33.3%
f 3
33.3%
Uppercase Letter
ValueCountFrequency (%)
Q 920
50.0%
F 920
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 920
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16563
78.2%
Hangul 2760
 
13.0%
Latin 1849
 
8.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5999
36.2%
1 3336
20.1%
2 3311
20.0%
_ 920
 
5.6%
7 544
 
3.3%
9 481
 
2.9%
4 461
 
2.8%
5 436
 
2.6%
6 408
 
2.5%
3 399
 
2.4%
Other values (2) 268
 
1.6%
Latin
ValueCountFrequency (%)
Q 920
49.8%
F 920
49.8%
p 3
 
0.2%
d 3
 
0.2%
f 3
 
0.2%
Hangul
ValueCountFrequency (%)
고 920
33.3%
보 920
33.3%
서 920
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18412
87.0%
Hangul 2760
 
13.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5999
32.6%
1 3336
18.1%
2 3311
18.0%
_ 920
 
5.0%
Q 920
 
5.0%
F 920
 
5.0%
7 544
 
3.0%
9 481
 
2.6%
4 461
 
2.5%
5 436
 
2.4%
Other values (7) 1084
 
5.9%
Hangul
ValueCountFrequency (%)
고 920
33.3%
보 920
33.3%
서 920
33.3%

보고서부록
Text

MISSING 

Distinct852
Distinct (%)100.0%
Missing68
Missing (%)7.4%
Memory size7.3 KiB
2023-12-11T09:14:26.919645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length25
Mean length25.003521
Min length25

Characters and Unicode

Total characters21303
Distinct characters22
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

Unique852 ?
Unique (%)100.0%

Sample

1st row20233402010F01Q0101_보고서부록
2nd row20253802010F01Q0101_보고서부록
3rd row20254102010F02Q0101_보고서부록
4th row20261602010F02Q0101_보고서부록
5th row20261802010F01Q0101_보고서부록
ValueCountFrequency (%)
27214502010f01q0101_보고서부록 1
 
0.1%
27202301995f01q0101_보고서부록 1
 
0.1%
24201502013f02q0101_보고서부록 1
 
0.1%
24200602013f02q0101_보고서부록 1
 
0.1%
24200702005f01q0101_보고서부록 1
 
0.1%
24200702013f02q0101_보고서부록 1
 
0.1%
24200802013f02q0101_보고서부록 1
 
0.1%
24200902005f02q0101_보고서부록 1
 
0.1%
24200902013f02q0101_보고서부록 1
 
0.1%
24201101997f01q0101_보고서부록 1
 
0.1%
Other values (842) 842
98.8%
2023-12-11T09:14:27.369099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5540
26.0%
1 3114
14.6%
2 3086
14.5%
록 852
 
4.0%
부 852
 
4.0%
F 852
 
4.0%
Q 852
 
4.0%
_ 852
 
4.0%
고 849
 
4.0%
서 849
 
4.0%
Other values (12) 3605
16.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14484
68.0%
Other Letter 4251
 
20.0%
Uppercase Letter 1704
 
8.0%
Connector Punctuation 852
 
4.0%
Lowercase Letter 9
 
< 0.1%
Other Punctuation 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5540
38.2%
1 3114
21.5%
2 3086
21.3%
7 503
 
3.5%
9 435
 
3.0%
5 417
 
2.9%
4 395
 
2.7%
6 377
 
2.6%
3 375
 
2.6%
8 242
 
1.7%
Other Letter
ValueCountFrequency (%)
록 852
20.0%
부 852
20.0%
고 849
20.0%
서 849
20.0%
보 849
20.0%
Lowercase Letter
ValueCountFrequency (%)
p 3
33.3%
d 3
33.3%
f 3
33.3%
Uppercase Letter
ValueCountFrequency (%)
F 852
50.0%
Q 852
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 852
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15339
72.0%
Hangul 4251
 
20.0%
Latin 1713
 
8.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5540
36.1%
1 3114
20.3%
2 3086
20.1%
_ 852
 
5.6%
7 503
 
3.3%
9 435
 
2.8%
5 417
 
2.7%
4 395
 
2.6%
6 377
 
2.5%
3 375
 
2.4%
Other values (2) 245
 
1.6%
Hangul
ValueCountFrequency (%)
록 852
20.0%
부 852
20.0%
고 849
20.0%
서 849
20.0%
보 849
20.0%
Latin
ValueCountFrequency (%)
F 852
49.7%
Q 852
49.7%
p 3
 
0.2%
d 3
 
0.2%
f 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17052
80.0%
Hangul 4251
 
20.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5540
32.5%
1 3114
18.3%
2 3086
18.1%
F 852
 
5.0%
Q 852
 
5.0%
_ 852
 
5.0%
7 503
 
2.9%
9 435
 
2.6%
5 417
 
2.4%
4 395
 
2.3%
Other values (7) 1006
 
5.9%
Hangul
ValueCountFrequency (%)
록 852
20.0%
부 852
20.0%
고 849
20.0%
서 849
20.0%
보 849
20.0%

하천대장
Text

MISSING 

Distinct842
Distinct (%)100.0%
Missing78
Missing (%)8.5%
Memory size7.3 KiB
2023-12-11T09:14:27.633002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length24
Mean length24.014252
Min length24

Characters and Unicode

Total characters20220
Distinct characters21
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

Unique842 ?
Unique (%)100.0%

Sample

1st row20233402010F01Q0101_하천대장
2nd row20253802010F01Q0101_하천대장
3rd row20254102010F02Q0101_하천대장
4th row20261602010F02Q0101_하천대장
5th row20261802010F01Q0101_하천대장
ValueCountFrequency (%)
27212702010f01q0101_하천대장 1
 
0.1%
20275902018f02q0101_하천대장 1
 
0.1%
24201302013f01q0101_하천대장 1
 
0.1%
24200502013f02q0101_하천대장 1
 
0.1%
24200502017f02q0101_하천대장 1
 
0.1%
20276201993f01q0101_하천대장 1
 
0.1%
20276202009f02q0101_하천대장 1
 
0.1%
20276202011f02q0101_하천대장 1
 
0.1%
20276302009f01q0101_하천대장 1
 
0.1%
24200202010f02q0101_하천대장 1
 
0.1%
Other values (832) 832
98.8%
2023-12-11T09:14:28.038174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5501
27.2%
1 3069
15.2%
2 3063
15.1%
하 842
 
4.2%
대 842
 
4.2%
천 842
 
4.2%
장 842
 
4.2%
_ 842
 
4.2%
Q 842
 
4.2%
F 842
 
4.2%
Other values (11) 2693
13.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14314
70.8%
Other Letter 3368
 
16.7%
Uppercase Letter 1684
 
8.3%
Connector Punctuation 842
 
4.2%
Lowercase Letter 9
 
< 0.1%
Other Punctuation 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5501
38.4%
1 3069
21.4%
2 3063
21.4%
7 496
 
3.5%
9 418
 
2.9%
5 412
 
2.9%
4 398
 
2.8%
6 364
 
2.5%
3 356
 
2.5%
8 237
 
1.7%
Other Letter
ValueCountFrequency (%)
하 842
25.0%
대 842
25.0%
천 842
25.0%
장 842
25.0%
Lowercase Letter
ValueCountFrequency (%)
p 3
33.3%
d 3
33.3%
f 3
33.3%
Uppercase Letter
ValueCountFrequency (%)
Q 842
50.0%
F 842
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 842
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15159
75.0%
Hangul 3368
 
16.7%
Latin 1693
 
8.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5501
36.3%
1 3069
20.2%
2 3063
20.2%
_ 842
 
5.6%
7 496
 
3.3%
9 418
 
2.8%
5 412
 
2.7%
4 398
 
2.6%
6 364
 
2.4%
3 356
 
2.3%
Other values (2) 240
 
1.6%
Latin
ValueCountFrequency (%)
Q 842
49.7%
F 842
49.7%
p 3
 
0.2%
d 3
 
0.2%
f 3
 
0.2%
Hangul
ValueCountFrequency (%)
하 842
25.0%
대 842
25.0%
천 842
25.0%
장 842
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16852
83.3%
Hangul 3368
 
16.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5501
32.6%
1 3069
18.2%
2 3063
18.2%
_ 842
 
5.0%
Q 842
 
5.0%
F 842
 
5.0%
7 496
 
2.9%
9 418
 
2.5%
5 412
 
2.4%
4 398
 
2.4%
Other values (7) 969
 
5.8%
Hangul
ValueCountFrequency (%)
하 842
25.0%
대 842
25.0%
천 842
25.0%
장 842
25.0%

하천대장부록
Text

MISSING 

Distinct804
Distinct (%)100.0%
Missing116
Missing (%)12.6%
Memory size7.3 KiB
2023-12-11T09:14:28.296999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length26
Mean length26.014925
Min length26

Characters and Unicode

Total characters20916
Distinct characters23
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

Unique804 ?
Unique (%)100.0%

Sample

1st row20233402010F01Q0101_하천대장부록
2nd row20253802010F01Q0101_하천대장부록
3rd row20254102010F02Q0101_하천대장부록
4th row20261602010F02Q0101_하천대장부록
5th row20261802010F01Q0101_하천대장부록
ValueCountFrequency (%)
27203002010f01q0101_하천대장부록 1
 
0.1%
20274602002f01q0101_하천대장부록 1
 
0.1%
20275402011f02q0101_하천대장부록 1
 
0.1%
20274702007f02q0101_하천대장부록 1
 
0.1%
20274202001f02q0101_하천대장부록 1
 
0.1%
20274202011f02q0101_하천대장부록 1
 
0.1%
20274302010f01q0101_하천대장부록 1
 
0.1%
20274402008f01q0101_하천대장부록 1
 
0.1%
20274402012f02q0101_하천대장부록 1
 
0.1%
20274402013f02q0101_하천대장부록 1
 
0.1%
Other values (794) 794
98.8%
2023-12-11T09:14:28.665297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5286
25.3%
2 2960
14.2%
1 2920
14.0%
하 804
 
3.8%
록 804
 
3.8%
부 804
 
3.8%
대 804
 
3.8%
천 804
 
3.8%
장 804
 
3.8%
_ 804
 
3.8%
Other values (13) 4122
19.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13668
65.3%
Other Letter 4824
 
23.1%
Uppercase Letter 1608
 
7.7%
Connector Punctuation 804
 
3.8%
Lowercase Letter 9
 
< 0.1%
Other Punctuation 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5286
38.7%
2 2960
21.7%
1 2920
21.4%
7 466
 
3.4%
5 391
 
2.9%
4 380
 
2.8%
9 350
 
2.6%
3 347
 
2.5%
6 338
 
2.5%
8 230
 
1.7%
Other Letter
ValueCountFrequency (%)
하 804
16.7%
록 804
16.7%
부 804
16.7%
대 804
16.7%
천 804
16.7%
장 804
16.7%
Lowercase Letter
ValueCountFrequency (%)
p 3
33.3%
d 3
33.3%
f 3
33.3%
Uppercase Letter
ValueCountFrequency (%)
Q 804
50.0%
F 804
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 804
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14475
69.2%
Hangul 4824
 
23.1%
Latin 1617
 
7.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5286
36.5%
2 2960
20.4%
1 2920
20.2%
_ 804
 
5.6%
7 466
 
3.2%
5 391
 
2.7%
4 380
 
2.6%
9 350
 
2.4%
3 347
 
2.4%
6 338
 
2.3%
Other values (2) 233
 
1.6%
Hangul
ValueCountFrequency (%)
하 804
16.7%
록 804
16.7%
부 804
16.7%
대 804
16.7%
천 804
16.7%
장 804
16.7%
Latin
ValueCountFrequency (%)
Q 804
49.7%
F 804
49.7%
p 3
 
0.2%
d 3
 
0.2%
f 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16092
76.9%
Hangul 4824
 
23.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5286
32.8%
2 2960
18.4%
1 2920
18.1%
_ 804
 
5.0%
Q 804
 
5.0%
F 804
 
5.0%
7 466
 
2.9%
5 391
 
2.4%
4 380
 
2.4%
9 350
 
2.2%
Other values (7) 927
 
5.8%
Hangul
ValueCountFrequency (%)
하 804
16.7%
록 804
16.7%
부 804
16.7%
대 804
16.7%
천 804
16.7%
장 804
16.7%

기타
Text

MISSING 

Distinct30
Distinct (%)100.0%
Missing890
Missing (%)96.7%
Memory size7.3 KiB
2023-12-11T09:14:28.874640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters660
Distinct characters15
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row20233402010F01Q0101_기타
2nd row20253802010F01Q0101_기타
3rd row20254102010F02Q0101_기타
4th row20261602010F02Q0101_기타
5th row20261802010F01Q0101_기타
ValueCountFrequency (%)
20253802010f01q0101_기타 1
 
3.3%
20254102010f02q0101_기타 1
 
3.3%
27204002010f01q0101_기타 1
 
3.3%
40227702010f01q0101_기타 1
 
3.3%
40227102010f01q0101_기타 1
 
3.3%
27214502010f01q0101_기타 1
 
3.3%
27213602010f01q0101_기타 1
 
3.3%
27212702010f01q0101_기타 1
 
3.3%
27212302009f01q0101_기타 1
 
3.3%
27212202009f01q0101_기타 1
 
3.3%
Other values (20) 20
66.7%
2023-12-11T09:14:29.191273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 202
30.6%
1 129
19.5%
2 107
16.2%
F 30
 
4.5%
Q 30
 
4.5%
_ 30
 
4.5%
기 30
 
4.5%
타 30
 
4.5%
7 22
 
3.3%
3 16
 
2.4%
Other values (5) 34
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 510
77.3%
Uppercase Letter 60
 
9.1%
Other Letter 60
 
9.1%
Connector Punctuation 30
 
4.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 202
39.6%
1 129
25.3%
2 107
21.0%
7 22
 
4.3%
3 16
 
3.1%
6 11
 
2.2%
4 9
 
1.8%
5 6
 
1.2%
8 4
 
0.8%
9 4
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
F 30
50.0%
Q 30
50.0%
Other Letter
ValueCountFrequency (%)
기 30
50.0%
타 30
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 540
81.8%
Latin 60
 
9.1%
Hangul 60
 
9.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 202
37.4%
1 129
23.9%
2 107
19.8%
_ 30
 
5.6%
7 22
 
4.1%
3 16
 
3.0%
6 11
 
2.0%
4 9
 
1.7%
5 6
 
1.1%
8 4
 
0.7%
Latin
ValueCountFrequency (%)
F 30
50.0%
Q 30
50.0%
Hangul
ValueCountFrequency (%)
기 30
50.0%
타 30
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 600
90.9%
Hangul 60
 
9.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 202
33.7%
1 129
21.5%
2 107
17.8%
F 30
 
5.0%
Q 30
 
5.0%
_ 30
 
5.0%
7 22
 
3.7%
3 16
 
2.7%
6 11
 
1.8%
4 9
 
1.5%
Other values (3) 14
 
2.3%
Hangul
ValueCountFrequency (%)
기 30
50.0%
타 30
50.0%

Interactions

2023-12-11T09:14:23.735404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:14:29.274641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본계획수립년도기타
기본계획수립년도1.0001.000
기타1.0001.000

Missing values

2023-12-11T09:14:23.892190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:14:24.011003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-11T09:14:24.197948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

하천관리코드하천명기본계획수립년도보고서보고서부록하천대장하천대장부록기타
020233402010F01Q0101금성천201020233402010F01Q0101_보고서20233402010F01Q0101_보고서부록20233402010F01Q0101_하천대장20233402010F01Q0101_하천대장부록20233402010F01Q0101_기타
120253802010F01Q0101마곡천201020253802010F01Q0101_보고서20253802010F01Q0101_보고서부록20253802010F01Q0101_하천대장20253802010F01Q0101_하천대장부록20253802010F01Q0101_기타
220254102010F02Q0101독산천201020254102010F02Q0101_보고서20254102010F02Q0101_보고서부록20254102010F02Q0101_하천대장20254102010F02Q0101_하천대장부록20254102010F02Q0101_기타
320261602010F02Q0101광정천201020261602010F02Q0101_보고서20261602010F02Q0101_보고서부록20261602010F02Q0101_하천대장20261602010F02Q0101_하천대장부록20261602010F02Q0101_기타
420261802010F01Q0101옥열천201020261802010F01Q0101_보고서20261802010F01Q0101_보고서부록20261802010F01Q0101_하천대장20261802010F01Q0101_하천대장부록20261802010F01Q0101_기타
520262202010F02Q0101대사천201020262202010F02Q0101_보고서20262202010F02Q0101_보고서부록20262202010F02Q0101_하천대장20262202010F02Q0101_하천대장부록20262202010F02Q0101_기타
620263302012F03Q0301덕곡천201220263302012F03Q0301_보고서20263302012F03Q0301_보고서부록<NA><NA>20263302012F03Q0301_기타
720263402010F02Q0101광려천201020263402010F02Q0101_보고서20263402010F02Q0101_보고서부록20263402010F02Q0101_하천대장20263402010F02Q0101_하천대장부록20263402010F02Q0101_기타
820263502010F01Q0101삼계천201020263502010F01Q0101_보고서20263502010F01Q0101_보고서부록20263502010F01Q0101_하천대장20263502010F01Q0101_하천대장부록20263502010F01Q0101_기타
920264502010F01Q0101이령천201020264502010F01Q0101_보고서20264502010F01Q0101_보고서부록20264502010F01Q0101_하천대장20264502010F01Q0101_하천대장부록20264502010F01Q0101_기타
하천관리코드하천명기본계획수립년도보고서보고서부록하천대장하천대장부록기타
91027209802007F01Q0101용정천200727209802007F01Q0101_보고서<NA><NA><NA><NA>
91127209901997F02Q0101죽천천199727209901997F02Q0101_보고서<NA><NA><NA><NA>
91220238002020F01Q0101토곡천202020238002020F01Q0101_보고서20238002020F01Q0101_보고서부록20238002020F01Q0101_하천대장20238002020F01Q0101_하천대장부록<NA>
91320257502020F02Q0101대곡천202020257502020F02Q0101_보고서20257502020F02Q0101_보고서부록20257502020F02Q0101_하천대장20257502020F02Q0101_하천대장부록<NA>
91426210002020F01Q0101여락천202026210002020F01Q0101_보고서26210002020F01Q0101_보고서부록26210002020F01Q0101_하천대장26210002020F01Q0101_하천대장부록<NA>
91527209402020F02Q0101삼천포천202027209402020F02Q0101_보고서27209402020F02Q0101_보고서부록27209402020F02Q0101_하천대장27209402020F02Q0101_하천대장부록<NA>
91627211002020F02Q0101대축천202027211002020F02Q0101_보고서27211002020F02Q0101_보고서부록27211002020F02Q0101_하천대장27211002020F02Q0101_하천대장부록<NA>
91720259202020F02Q0101의령천202020259202020F02Q0101_보고서.pdf20259202020F02Q0101_부록.pdf20259202020F02Q0101_하천대장.pdf20259202020F02Q0101_하천대장부록.pdf<NA>
91827205702020F02Q0101안정천202027205702020F02Q0101_보고서.pdf27205702020F02Q0101_부록.pdf27205702020F02Q0101_하천대장.pdf27205702020F02Q0101_하천대장부록.pdf<NA>
91927206302020F02Q0101고현천202027206302020F02Q0101_보고서.pdf27206302020F02Q0101_부록.pdf27206302020F02Q0101_하천대장.pdf27206302020F02Q0101_하천대장부록.pdf<NA>