Overview

Dataset statistics

Number of variables17
Number of observations100
Missing cells33
Missing cells (%)1.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.7 KiB
Average record size in memory140.3 B

Variable types

Numeric2
Categorical8
Text5
Boolean2

Dataset

Description한강홍수통제소에서 매년 대상년도를 전년도로하여 작성하는 수문연보에 등록되는 강수량관측소 정보입니다. 해당관측소의 코드, 관리기관, 주소와 명칭 등으로 구성되어 있습니다.
URLhttps://www.data.go.kr/data/15117641/fileData.do

Alerts

년도 has constant value ""Constant
관리기관명 has constant value ""Constant
관측방법 has constant value ""Constant
홍수통제소명 has constant value ""Constant
공인신청여부 has constant value ""Constant
전기간폐국여부 has constant value ""Constant
강수량관측소코드 is highly overall correlated with 수계명 and 1 other fieldsHigh correlation
해발고(m) is highly overall correlated with 기록방법 and 1 other fieldsHigh correlation
수계명 is highly overall correlated with 강수량관측소코드 and 1 other fieldsHigh correlation
기록방법 is highly overall correlated with 해발고(m)High correlation
관측개시일 is highly overall correlated with 강수량관측소코드 and 1 other fieldsHigh correlation
관측소등급 is highly overall correlated with 해발고(m)High correlation
수계명 is highly imbalanced (56.4%)Imbalance
기록방법 is highly imbalanced (63.4%)Imbalance
관측소등급 is highly imbalanced (85.9%)Imbalance
해발고(m) has 33 (33.0%) missing valuesMissing
강수량관측소코드 has unique valuesUnique
관측소명칭 has unique valuesUnique
관측소명칭영문 has unique valuesUnique
주소 has unique valuesUnique
위도 has unique valuesUnique

Reproduction

Analysis started2023-12-12 17:43:52.020946
Analysis finished2023-12-12 17:43:53.526696
Duration1.51 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

강수량관측소코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10231468
Minimum10064050
Maximum11014090
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-13T02:43:53.602341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10064050
5-th percentile10074029
Q110124088
median10164075
Q310204012
95-th percentile11014040
Maximum11014090
Range950040
Interquartile range (IQR)79925

Descriptive statistics

Standard deviation252103.6
Coefficient of variation (CV)0.024640023
Kurtosis5.9126644
Mean10231468
Median Absolute Deviation (MAD)39960
Skewness2.7105312
Sum1.0231468 × 109
Variance6.3556225 × 1010
MonotonicityStrictly increasing
2023-12-13T02:43:53.739465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10064050 1
 
1.0%
10184150 1
 
1.0%
10204010 1
 
1.0%
10194010 1
 
1.0%
10184230 1
 
1.0%
10184220 1
 
1.0%
10184210 1
 
1.0%
10184200 1
 
1.0%
10184190 1
 
1.0%
10184180 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
10064050 1
1.0%
10064070 1
1.0%
10064080 1
1.0%
10064090 1
1.0%
10074010 1
1.0%
10074030 1
1.0%
10074040 1
1.0%
10074050 1
1.0%
10074060 1
1.0%
10074070 1
1.0%
ValueCountFrequency (%)
11014090 1
1.0%
11014080 1
1.0%
11014070 1
1.0%
11014060 1
1.0%
11014050 1
1.0%
11014040 1
1.0%
11014030 1
1.0%
11014020 1
1.0%
11014010 1
1.0%
10234040 1
1.0%

년도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2016
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2016 100
100.0%

Length

2023-12-13T02:43:53.869404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:43:54.257182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 100
100.0%

관리기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
환경부
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
환경부 100
100.0%

Length

2023-12-13T02:43:54.352126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:43:54.438558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
환경부 100
100.0%

수계명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
한강
91 
안성천
 
9

Length

Max length3
Median length2
Mean length2.09
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
한강 91
91.0%
안성천 9
 
9.0%

Length

2023-12-13T02:43:54.524575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:43:54.623425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한강 91
91.0%
안성천 9
 
9.0%

관측소명칭
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-13T02:43:54.853319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length9.3
Min length8

Characters and Unicode

Total characters930
Distinct characters140
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

Unique100 ?
Unique (%)100.0%

Sample

1st row원주시(지정면사무소)
2nd row원주시(서곡초교)
3rd row횡성군(우항리)
4th row원주시(흥양초교)
5th row양평군(양평교)
ValueCountFrequency (%)
원주시(지정면사무소 1
 
1.0%
성남시(대장동 1
 
1.0%
김포시(김포시청 1
 
1.0%
성남시(구미초교 1
 
1.0%
남양주시(진접읍사무소 1
 
1.0%
안양시(인덕원초교 1
 
1.0%
서울시(월계2교 1
 
1.0%
서울시(동막골주차장 1
 
1.0%
성남시(성남북초교 1
 
1.0%
군포시(군포1동주민센터 1
 
1.0%
Other values (90) 90
90.0%
2023-12-13T02:43:55.289727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 100
 
10.8%
) 100
 
10.8%
시 68
 
7.3%
교 58
 
6.2%
천 43
 
4.6%
군 39
 
4.2%
초 35
 
3.8%
주 26
 
2.8%
양 16
 
1.7%
포 15
 
1.6%
Other values (130) 430
46.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 723
77.7%
Open Punctuation 100
 
10.8%
Close Punctuation 100
 
10.8%
Decimal Number 7
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 68
 
9.4%
교 58
 
8.0%
천 43
 
5.9%
군 39
 
5.4%
초 35
 
4.8%
주 26
 
3.6%
양 16
 
2.2%
포 15
 
2.1%
원 13
 
1.8%
리 13
 
1.8%
Other values (125) 397
54.9%
Decimal Number
ValueCountFrequency (%)
1 4
57.1%
2 2
28.6%
9 1
 
14.3%
Open Punctuation
ValueCountFrequency (%)
( 100
100.0%
Close Punctuation
ValueCountFrequency (%)
) 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 723
77.7%
Common 207
 
22.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 68
 
9.4%
교 58
 
8.0%
천 43
 
5.9%
군 39
 
5.4%
초 35
 
4.8%
주 26
 
3.6%
양 16
 
2.2%
포 15
 
2.1%
원 13
 
1.8%
리 13
 
1.8%
Other values (125) 397
54.9%
Common
ValueCountFrequency (%)
( 100
48.3%
) 100
48.3%
1 4
 
1.9%
2 2
 
1.0%
9 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 723
77.7%
ASCII 207
 
22.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 100
48.3%
) 100
48.3%
1 4
 
1.9%
2 2
 
1.0%
9 1
 
0.5%
Hangul
ValueCountFrequency (%)
시 68
 
9.4%
교 58
 
8.0%
천 43
 
5.9%
군 39
 
5.4%
초 35
 
4.8%
주 26
 
3.6%
양 16
 
2.2%
포 15
 
2.1%
원 13
 
1.8%
리 13
 
1.8%
Other values (125) 397
54.9%

관측소명칭영문
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-13T02:43:55.521354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length32
Mean length24.82
Min length16

Characters and Unicode

Total characters2482
Distinct characters45
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowWonjusi(Jijungmyeonsamuso)
2nd rowWonjusi(Seogokchogyo)
3rd rowHoengseonggun(Uhhangri)
4th rowWonjusi(Huengyangchogyo)
5th rowYangpyeonggun(Yangpyeonggyo)
ValueCountFrequency (%)
wonjusi(jijungmyeonsamuso 1
 
1.0%
seongnamsi(daejangdong 1
 
1.0%
gimposi(gimposichung 1
 
1.0%
seongnamsi(gumichogyo 1
 
1.0%
namyangjusi(jinjeopeupsamuso 1
 
1.0%
anyangsi(indukwonchogyo 1
 
1.0%
seoul(wallgae2gyo 1
 
1.0%
seoul(dongmakgoljuchajang 1
 
1.0%
seongnamsi(seongnambookchogyo 1
 
1.0%
gunposi(gunpo1dongjumincenter 1
 
1.0%
Other values (90) 90
90.0%
2023-12-13T02:43:55.890132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 335
13.5%
n 307
12.4%
g 242
 
9.8%
e 157
 
6.3%
a 141
 
5.7%
u 123
 
5.0%
h 111
 
4.5%
i 108
 
4.4%
s 104
 
4.2%
( 100
 
4.0%
Other values (35) 754
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2075
83.6%
Uppercase Letter 200
 
8.1%
Open Punctuation 100
 
4.0%
Close Punctuation 100
 
4.0%
Decimal Number 7
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 335
16.1%
n 307
14.8%
g 242
11.7%
e 157
7.6%
a 141
6.8%
u 123
 
5.9%
h 111
 
5.3%
i 108
 
5.2%
s 104
 
5.0%
y 98
 
4.7%
Other values (11) 349
16.8%
Uppercase Letter
ValueCountFrequency (%)
H 28
14.0%
S 27
13.5%
Y 26
13.0%
G 24
12.0%
P 18
9.0%
C 11
 
5.5%
J 11
 
5.5%
N 9
 
4.5%
D 8
 
4.0%
I 8
 
4.0%
Other values (9) 30
15.0%
Decimal Number
ValueCountFrequency (%)
1 4
57.1%
2 2
28.6%
9 1
 
14.3%
Open Punctuation
ValueCountFrequency (%)
( 100
100.0%
Close Punctuation
ValueCountFrequency (%)
) 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2275
91.7%
Common 207
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 335
14.7%
n 307
13.5%
g 242
10.6%
e 157
 
6.9%
a 141
 
6.2%
u 123
 
5.4%
h 111
 
4.9%
i 108
 
4.7%
s 104
 
4.6%
y 98
 
4.3%
Other values (30) 549
24.1%
Common
ValueCountFrequency (%)
( 100
48.3%
) 100
48.3%
1 4
 
1.9%
2 2
 
1.0%
9 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2482
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 335
13.5%
n 307
12.4%
g 242
 
9.8%
e 157
 
6.3%
a 141
 
5.7%
u 123
 
5.0%
h 111
 
4.5%
i 108
 
4.4%
s 104
 
4.2%
( 100
 
4.0%
Other values (35) 754
30.4%

기록방법
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
VHF/M2M
93 
VSAT/M2M
 
7

Length

Max length8
Median length7
Mean length7.07
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVHF/M2M
2nd rowVHF/M2M
3rd rowVHF/M2M
4th rowVHF/M2M
5th rowVHF/M2M

Common Values

ValueCountFrequency (%)
VHF/M2M 93
93.0%
VSAT/M2M 7
 
7.0%

Length

2023-12-13T02:43:56.032295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:43:56.123139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
vhf/m2m 93
93.0%
vsat/m2m 7
 
7.0%

관측방법
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
전도형우설량계
100 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전도형우설량계 100
100.0%

Length

2023-12-13T02:43:56.213556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:43:56.303226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전도형우설량계 100
100.0%

주소
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-13T02:43:56.543898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length30
Mean length25.11
Min length15

Characters and Unicode

Total characters2511
Distinct characters205
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row강원도 원주시 지정면 간현로 126 지정면사무소
2nd row강원도 원주시 판부면 용수골길 159 서곡초등학교
3rd row강원도 횡성군 우천면 우항리 658-3
4th row강원도 원주시 소초면 하초구길 6 흥양초등학교
5th row경기도 양평군 양평읍 양근리 양평교
ValueCountFrequency (%)
경기도 63
 
10.7%
강원도 30
 
5.1%
홍천군 10
 
1.7%
포천시 8
 
1.4%
용인시 7
 
1.2%
처인구 6
 
1.0%
서울특별시 5
 
0.9%
화천군 5
 
0.9%
가평군 5
 
0.9%
광주시 4
 
0.7%
Other values (383) 444
75.6%
2023-12-13T02:43:56.973127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
487
 
19.4%
도 98
 
3.9%
시 70
 
2.8%
경 67
 
2.7%
기 66
 
2.6%
면 65
 
2.6%
교 62
 
2.5%
로 52
 
2.1%
천 51
 
2.0%
원 50
 
2.0%
Other values (195) 1443
57.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1721
68.5%
Space Separator 487
 
19.4%
Decimal Number 277
 
11.0%
Dash Punctuation 22
 
0.9%
Uppercase Letter 2
 
0.1%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 98
 
5.7%
시 70
 
4.1%
경 67
 
3.9%
기 66
 
3.8%
면 65
 
3.8%
교 62
 
3.6%
로 52
 
3.0%
천 51
 
3.0%
원 50
 
2.9%
학 45
 
2.6%
Other values (179) 1095
63.6%
Decimal Number
ValueCountFrequency (%)
1 48
17.3%
2 36
13.0%
3 32
11.6%
4 30
10.8%
5 26
9.4%
7 25
9.0%
6 25
9.0%
0 20
7.2%
9 20
7.2%
8 15
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
T 1
50.0%
I 1
50.0%
Space Separator
ValueCountFrequency (%)
487
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1721
68.5%
Common 788
31.4%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 98
 
5.7%
시 70
 
4.1%
경 67
 
3.9%
기 66
 
3.8%
면 65
 
3.8%
교 62
 
3.6%
로 52
 
3.0%
천 51
 
3.0%
원 50
 
2.9%
학 45
 
2.6%
Other values (179) 1095
63.6%
Common
ValueCountFrequency (%)
487
61.8%
1 48
 
6.1%
2 36
 
4.6%
3 32
 
4.1%
4 30
 
3.8%
5 26
 
3.3%
7 25
 
3.2%
6 25
 
3.2%
- 22
 
2.8%
0 20
 
2.5%
Other values (4) 37
 
4.7%
Latin
ValueCountFrequency (%)
T 1
50.0%
I 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1721
68.5%
ASCII 790
31.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
487
61.6%
1 48
 
6.1%
2 36
 
4.6%
3 32
 
4.1%
4 30
 
3.8%
5 26
 
3.3%
7 25
 
3.2%
6 25
 
3.2%
- 22
 
2.8%
0 20
 
2.5%
Other values (6) 39
 
4.9%
Hangul
ValueCountFrequency (%)
도 98
 
5.7%
시 70
 
4.1%
경 67
 
3.9%
기 66
 
3.8%
면 65
 
3.8%
교 62
 
3.6%
로 52
 
3.0%
천 51
 
3.0%
원 50
 
2.9%
학 45
 
2.6%
Other values (179) 1095
63.6%

경도
Text

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-13T02:43:57.308279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters900
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

Unique98 ?
Unique (%)98.0%

Sample

1st row127-50-23
2nd row127-56-22
3rd row128-03-56
4th row127-59-52
5th row127-29-24
ValueCountFrequency (%)
126-58-47 2
 
2.0%
127-15-47 1
 
1.0%
127-06-46 1
 
1.0%
127-07-04 1
 
1.0%
127-11-24 1
 
1.0%
127-03-00 1
 
1.0%
127-05-09 1
 
1.0%
127-09-05 1
 
1.0%
126-56-44 1
 
1.0%
126-48-45 1
 
1.0%
Other values (89) 89
89.0%
2023-12-13T02:43:57.724547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 200
22.2%
1 159
17.7%
2 149
16.6%
7 95
10.6%
0 61
 
6.8%
3 54
 
6.0%
5 52
 
5.8%
4 51
 
5.7%
6 37
 
4.1%
8 31
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 700
77.8%
Dash Punctuation 200
 
22.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 159
22.7%
2 149
21.3%
7 95
13.6%
0 61
 
8.7%
3 54
 
7.7%
5 52
 
7.4%
4 51
 
7.3%
6 37
 
5.3%
8 31
 
4.4%
9 11
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 900
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 200
22.2%
1 159
17.7%
2 149
16.6%
7 95
10.6%
0 61
 
6.8%
3 54
 
6.0%
5 52
 
5.8%
4 51
 
5.7%
6 37
 
4.1%
8 31
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 200
22.2%
1 159
17.7%
2 149
16.6%
7 95
10.6%
0 61
 
6.8%
3 54
 
6.0%
5 52
 
5.8%
4 51
 
5.7%
6 37
 
4.1%
8 31
 
3.4%

위도
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-13T02:43:58.010895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters900
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row37-21-44
2nd row37-17-52
3rd row37-27-41
4th row37-22-50
5th row37-29-11
ValueCountFrequency (%)
37-21-44 1
 
1.0%
37-21-36 1
 
1.0%
37-36-55 1
 
1.0%
37-20-15 1
 
1.0%
37-43-33 1
 
1.0%
37-23-47 1
 
1.0%
37-37-27 1
 
1.0%
37-40-29 1
 
1.0%
37-27-05 1
 
1.0%
37-21-11 1
 
1.0%
Other values (90) 90
90.0%
2023-12-13T02:43:58.471033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 200
22.2%
3 152
16.9%
7 104
11.6%
90
10.0%
0 63
 
7.0%
4 56
 
6.2%
2 55
 
6.1%
5 55
 
6.1%
1 47
 
5.2%
8 36
 
4.0%
Other values (2) 42
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 610
67.8%
Dash Punctuation 200
 
22.2%
Space Separator 90
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 152
24.9%
7 104
17.0%
0 63
10.3%
4 56
 
9.2%
2 55
 
9.0%
5 55
 
9.0%
1 47
 
7.7%
8 36
 
5.9%
9 23
 
3.8%
6 19
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 200
100.0%
Space Separator
ValueCountFrequency (%)
90
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 900
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 200
22.2%
3 152
16.9%
7 104
11.6%
90
10.0%
0 63
 
7.0%
4 56
 
6.2%
2 55
 
6.1%
5 55
 
6.1%
1 47
 
5.2%
8 36
 
4.0%
Other values (2) 42
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 200
22.2%
3 152
16.9%
7 104
11.6%
90
10.0%
0 63
 
7.0%
4 56
 
6.2%
2 55
 
6.1%
5 55
 
6.1%
1 47
 
5.2%
8 36
 
4.0%
Other values (2) 42
 
4.7%

관측개시일
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2001-10-06
12 
1962-07-01
9 
2002-10-01
8 
1998-03-01
6 
1996-12-01
6 
Other values (30)
59 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique18 ?
Unique (%)18.0%

Sample

1st row1966-09-01
2nd row1996-12-01
3rd row1998-03-01
4th row1998-03-01
5th row1914-01-01

Common Values

ValueCountFrequency (%)
2001-10-06 12
 
12.0%
1962-07-01 9
 
9.0%
2002-10-01 8
 
8.0%
1998-03-01 6
 
6.0%
1996-12-01 6
 
6.0%
2000-07-01 5
 
5.0%
1984-04-01 5
 
5.0%
1996-09-01 4
 
4.0%
1967-01-01 4
 
4.0%
1989-07-01 4
 
4.0%
Other values (25) 37
37.0%

Length

2023-12-13T02:43:58.668703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2001-10-06 12
 
12.0%
1962-07-01 9
 
9.0%
2002-10-01 8
 
8.0%
1998-03-01 6
 
6.0%
1996-12-01 6
 
6.0%
2000-07-01 5
 
5.0%
1984-04-01 5
 
5.0%
1996-09-01 4
 
4.0%
1967-01-01 4
 
4.0%
1989-07-01 4
 
4.0%
Other values (25) 37
37.0%

해발고(m)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct47
Distinct (%)70.1%
Missing33
Missing (%)33.0%
Infinite0
Infinite (%)0.0%
Mean132.22388
Minimum19
Maximum620
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-13T02:43:58.817823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile20
Q139.5
median91
Q3162
95-th percentile417
Maximum620
Range601
Interquartile range (IQR)122.5

Descriptive statistics

Standard deviation135.05451
Coefficient of variation (CV)1.0214079
Kurtosis4.2450151
Mean132.22388
Median Absolute Deviation (MAD)59
Skewness2.016967
Sum8859
Variance18239.722
MonotonicityNot monotonic
2023-12-13T02:43:58.954437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
20 8
 
8.0%
110 3
 
3.0%
31 3
 
3.0%
60 3
 
3.0%
180 2
 
2.0%
40 2
 
2.0%
130 2
 
2.0%
150 2
 
2.0%
620 2
 
2.0%
70 2
 
2.0%
Other values (37) 38
38.0%
(Missing) 33
33.0%
ValueCountFrequency (%)
19 1
 
1.0%
20 8
8.0%
24 1
 
1.0%
27 1
 
1.0%
31 3
 
3.0%
35 1
 
1.0%
36 1
 
1.0%
39 1
 
1.0%
40 2
 
2.0%
42 1
 
1.0%
ValueCountFrequency (%)
620 2
2.0%
480 1
1.0%
420 1
1.0%
410 1
1.0%
350 1
1.0%
320 1
1.0%
289 1
1.0%
260 1
1.0%
250 1
1.0%
219 1
1.0%

홍수통제소명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
한강홍수통제소
100 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
한강홍수통제소 100
100.0%

Length

2023-12-13T02:43:59.093949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:43:59.181904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한강홍수통제소 100
100.0%

공인신청여부
Boolean

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size232.0 B
True
100 
ValueCountFrequency (%)
True 100
100.0%
2023-12-13T02:43:59.263842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

관측소등급
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
우수(B)
98 
보통(C)
 
2

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row우수(B)
2nd row우수(B)
3rd row우수(B)
4th row우수(B)
5th row우수(B)

Common Values

ValueCountFrequency (%)
우수(B) 98
98.0%
보통(C) 2
 
2.0%

Length

2023-12-13T02:43:59.365502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:43:59.458989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
우수(b 98
98.0%
보통(c 2
 
2.0%

전기간폐국여부
Boolean

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size232.0 B
False
100 
ValueCountFrequency (%)
False 100
100.0%
2023-12-13T02:43:59.532442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2023-12-13T02:43:52.982577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:43:52.829050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:43:53.080928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:43:52.902008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:43:59.613418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강수량관측소코드수계명관측소명칭관측소명칭영문기록방법주소경도위도관측개시일해발고(m)관측소등급
강수량관측소코드1.0001.0001.0001.0000.0551.0000.0001.0000.9650.0710.000
수계명1.0001.0001.0001.0000.0001.0000.0001.0000.9610.0000.000
관측소명칭1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
관측소명칭영문1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
기록방법0.0550.0001.0001.0001.0001.0001.0001.0000.6280.5520.000
주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
경도0.0000.0001.0001.0001.0001.0001.0001.0000.9971.0001.000
위도1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
관측개시일0.9650.9611.0001.0000.6281.0000.9971.0001.0000.5280.000
해발고(m)0.0710.0001.0001.0000.5521.0001.0001.0000.5281.0000.644
관측소등급0.0000.0001.0001.0000.0001.0001.0001.0000.0000.6441.000
2023-12-13T02:43:59.753554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기록방법관측개시일관측소등급수계명
기록방법1.0000.4350.0000.000
관측개시일0.4351.0000.0000.744
관측소등급0.0000.0001.0000.000
수계명0.0000.7440.0001.000
2023-12-13T02:43:59.876055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강수량관측소코드해발고(m)수계명기록방법관측개시일관측소등급
강수량관측소코드1.000-0.2350.9950.0820.7180.000
해발고(m)-0.2351.0000.0000.5230.1820.614
수계명0.9950.0001.0000.0000.7440.000
기록방법0.0820.5230.0001.0000.4350.000
관측개시일0.7180.1820.7440.4351.0000.000
관측소등급0.0000.6140.0000.0000.0001.000

Missing values

2023-12-13T02:43:53.223527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:43:53.448683image/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.

Sample

강수량관측소코드년도관리기관명수계명관측소명칭관측소명칭영문기록방법관측방법주소경도위도관측개시일해발고(m)홍수통제소명공인신청여부관측소등급전기간폐국여부
0100640502016환경부한강원주시(지정면사무소)Wonjusi(Jijungmyeonsamuso)VHF/M2M전도형우설량계강원도 원주시 지정면 간현로 126 지정면사무소127-50-2337-21-441966-09-0170한강홍수통제소T우수(B)F
1100640702016환경부한강원주시(서곡초교)Wonjusi(Seogokchogyo)VHF/M2M전도형우설량계강원도 원주시 판부면 용수골길 159 서곡초등학교127-56-2237-17-521996-12-01<NA>한강홍수통제소T우수(B)F
2100640802016환경부한강횡성군(우항리)Hoengseonggun(Uhhangri)VHF/M2M전도형우설량계강원도 횡성군 우천면 우항리 658-3128-03-5637-27-411998-03-01<NA>한강홍수통제소T우수(B)F
3100640902016환경부한강원주시(흥양초교)Wonjusi(Huengyangchogyo)VHF/M2M전도형우설량계강원도 원주시 소초면 하초구길 6 흥양초등학교127-59-5237-22-501998-03-01<NA>한강홍수통제소T우수(B)F
4100740102016환경부한강양평군(양평교)Yangpyeonggun(Yangpyeonggyo)VHF/M2M전도형우설량계경기도 양평군 양평읍 양근리 양평교127-29-2437-29-111914-01-0131한강홍수통제소T우수(B)F
5100740302016환경부한강여주시(여주대교)Yeojusi(Yeojudaegyo)VHF/M2M전도형우설량계경기도 여주시 상동 여주대교 상류 좌안127-38-5337-17-431962-07-0145한강홍수통제소T우수(B)F
6100740402016환경부한강양평군(청운면사무소)Yangpyeonggun(Cheongunmyeonsamuso)VHF/M2M전도형우설량계경기도 양평군 청운면 용두로 170 청운면사무소127-42-3837-33-231964-07-01110한강홍수통제소T우수(B)F
7100740502016환경부한강음성군(생극면사무소)Eumseonggun(Seanggeukmyeonsamuso)VHF/M2M전도형우설량계충청북도 음성군 생극면 음성로 1646 생극면사무소127-36-25037-02-031965-11-01100한강홍수통제소T우수(B)F
8100740602016환경부한강이천시(이천남초교)Icheonsi(Icheonnamchogyo)VHF/M2M전도형우설량계경기도 이천시 경충대로 2614번길 32 이천남초등학교127-26-3537-16-371974-07-0163한강홍수통제소T우수(B)F
9100740702016환경부한강여주시(금당초교)Yeojusi(Kumdangchogyo)VHF/M2M전도형우설량계경기도 여주시 가남읍 가남로 649 금당초등학교127-36-2037-11-511988-12-01<NA>한강홍수통제소T우수(B)F
강수량관측소코드년도관리기관명수계명관측소명칭관측소명칭영문기록방법관측방법주소경도위도관측개시일해발고(m)홍수통제소명공인신청여부관측소등급전기간폐국여부
90102340402016환경부한강파주시(용연초교)Pajusi(Yongyeonchogyo)VHF/M2M전도형우설량계경기도 파주시 파평면 청송로 402번길 31 용연초등학교126-51-2837-55-432001-10-0639한강홍수통제소T우수(B)F
91110140102016환경부안성천평택시(진위면사무소)Pyeongtaeksi(Jinwimyeonsamuso)VHF/M2M전도형우설량계경기도 평택시 진위면 봉남길 61 진위면사무소127-05-2537-05-571919-07-0170한강홍수통제소T우수(B)F
92110140202016환경부안성천안성시(안성시청)Anseongsi(Anseongsichung)VHF/M2M전도형우설량계경기도 안성시 시청길 25 안성시청127-16-4437-00-291927-01-0140한강홍수통제소T우수(B)F
93110140302016환경부안성천용인시(원삼중교)Yonginsi(Wonsamjoonggyo)VHF/M2M전도형우설량계경기도 용인시 처인구 원삼면 고당로 16번길 29 원삼중학교127-18-36037-09-581962-07-01140한강홍수통제소T우수(B)F
94110140402016환경부안성천화성시(양감119지역대)Hwaseongsi(Yanggam119jiyeokdae)VHF/M2M전도형우설량계경기도 화성시 양감면 생이길 8 양감119지역대126-56-3937-04-541968-07-0120한강홍수통제소T우수(B)F
95110140502016환경부안성천천안시(대홍초교)Cheonansi(Daeheungchogyo)VHF/M2M전도형우설량계충청남도 천안시 서북구 성환읍 잠실길 4 대홍초등학교127-08-5336-56-591968-07-0120한강홍수통제소T우수(B)F
96110140602016환경부안성천화성시(수원대학교)Hwaseongsi(Suwondaehackgyo)VHF/M2M전도형우설량계경기도 화성시 봉담읍 와우안길 17 수원대학교(IT대학)126-58-4737-12-371996-09-01<NA>한강홍수통제소T우수(B)F
97110140702016환경부안성천평택시(회화리)Pyeongtaeksi(Hoehwari)VHF/M2M전도형우설량계경기도 평택시 서탄면 회화리 388127-01-4437-05-461996-09-01<NA>한강홍수통제소T우수(B)F
98110140802016환경부안성천평택시(군문교)Pyeongtaeksi(Gunmoongyo)VHF/M2M전도형우설량계경기도 평택시 군문동 군문교127-04-4836-59-011996-09-01<NA>한강홍수통제소T우수(B)F
99110140902016환경부안성천용인시(신갈저수지)Yonginsi(Singalgersugi)VHF/M2M전도형우설량계경기도 용인시 기흥구 고매동 87-1 신갈저수지 제방127-05-3837-13-341996-09-01<NA>한강홍수통제소T우수(B)F