Overview

Dataset statistics

Number of variables5
Number of observations253
Missing cells3
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.3 KiB
Average record size in memory41.5 B

Variable types

Numeric1
Text3
Categorical1

Dataset

Description한강권역 강수량 관측소 현황
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=KKSPG3J7JLRGLAGN4HN524652265&infSeq=1

Alerts

강수량관측소구분 has unique valuesUnique
강수량관측소명 has unique valuesUnique

Reproduction

Analysis started2024-05-10 20:22:34.548467
Analysis finished2024-05-10 20:22:40.123910
Duration5.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

강수량관측소구분
Real number (ℝ)

UNIQUE 

Distinct253
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10231725
Minimum10014010
Maximum13034010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-05-10T20:22:40.325656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10014010
5-th percentile10014176
Q110044060
median10104173
Q310184060
95-th percentile11014094
Maximum13034010
Range3020000
Interquartile range (IQR)140000

Descriptive statistics

Standard deviation496481.48
Coefficient of variation (CV)0.048523731
Kurtosis19.654411
Mean10231725
Median Absolute Deviation (MAD)70003
Skewness4.3010524
Sum2.5886265 × 109
Variance2.4649386 × 1011
MonotonicityNot monotonic
2024-05-10T20:22:40.729433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10024230 1
 
0.4%
10014110 1
 
0.4%
10134140 1
 
0.4%
10144010 1
 
0.4%
10144020 1
 
0.4%
10144030 1
 
0.4%
10144040 1
 
0.4%
10144050 1
 
0.4%
10144060 1
 
0.4%
10014010 1
 
0.4%
Other values (243) 243
96.0%
ValueCountFrequency (%)
10014010 1
0.4%
10014020 1
0.4%
10014070 1
0.4%
10014080 1
0.4%
10014090 1
0.4%
10014100 1
0.4%
10014110 1
0.4%
10014120 1
0.4%
10014130 1
0.4%
10014140 1
0.4%
ValueCountFrequency (%)
13034010 1
0.4%
13024030 1
0.4%
13024010 1
0.4%
13014020 1
0.4%
13014010 1
0.4%
12024020 1
0.4%
12024010 1
0.4%
12014010 1
0.4%
11014140 1
0.4%
11014130 1
0.4%

강수량관측소명
Text

UNIQUE 

Distinct253
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-05-10T20:22:41.251998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length7.9407115
Min length2

Characters and Unicode

Total characters2009
Distinct characters195
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique253 ?
Unique (%)100.0%

Sample

1st row횡성군(부곡리)
2nd row원주시(황둔리)
3rd row횡성군(상안리)
4th row영월군(연덕리)
5th row단양군(영춘중교)
ValueCountFrequency (%)
횡성군(부곡리 1
 
0.4%
의암댐 1
 
0.4%
가평천 1
 
0.4%
홍천군(홍천농고 1
 
0.4%
홍천군(서석면사무소 1
 
0.4%
두촌2 1
 
0.4%
홍천군(내촌면사무소 1
 
0.4%
홍천군(반곡교 1
 
0.4%
홍천군(매산초교 1
 
0.4%
평창군(월정분교 1
 
0.4%
Other values (243) 243
96.0%
2024-05-10T20:22:42.208972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 212
 
10.6%
) 212
 
10.6%
군 116
 
5.8%
시 102
 
5.1%
리 82
 
4.1%
교 82
 
4.1%
천 79
 
3.9%
주 46
 
2.3%
평 41
 
2.0%
초 40
 
2.0%
Other values (185) 997
49.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1569
78.1%
Open Punctuation 212
 
10.6%
Close Punctuation 212
 
10.6%
Decimal Number 12
 
0.6%
Uppercase Letter 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 116
 
7.4%
시 102
 
6.5%
리 82
 
5.2%
교 82
 
5.2%
천 79
 
5.0%
주 46
 
2.9%
평 41
 
2.6%
초 40
 
2.5%
성 31
 
2.0%
양 27
 
1.7%
Other values (179) 923
58.8%
Decimal Number
ValueCountFrequency (%)
2 8
66.7%
1 4
33.3%
Uppercase Letter
ValueCountFrequency (%)
M 2
50.0%
T 2
50.0%
Open Punctuation
ValueCountFrequency (%)
( 212
100.0%
Close Punctuation
ValueCountFrequency (%)
) 212
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1569
78.1%
Common 436
 
21.7%
Latin 4
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 116
 
7.4%
시 102
 
6.5%
리 82
 
5.2%
교 82
 
5.2%
천 79
 
5.0%
주 46
 
2.9%
평 41
 
2.6%
초 40
 
2.5%
성 31
 
2.0%
양 27
 
1.7%
Other values (179) 923
58.8%
Common
ValueCountFrequency (%)
( 212
48.6%
) 212
48.6%
2 8
 
1.8%
1 4
 
0.9%
Latin
ValueCountFrequency (%)
M 2
50.0%
T 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1569
78.1%
ASCII 440
 
21.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 212
48.2%
) 212
48.2%
2 8
 
1.8%
1 4
 
0.9%
M 2
 
0.5%
T 2
 
0.5%
Hangul
ValueCountFrequency (%)
군 116
 
7.4%
시 102
 
6.5%
리 82
 
5.2%
교 82
 
5.2%
천 79
 
5.0%
주 46
 
2.9%
평 41
 
2.6%
초 40
 
2.5%
성 31
 
2.0%
양 27
 
1.7%
Other values (179) 923
58.8%

관할기관명
Categorical

Distinct4
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
환경부
158 
한국수자원공사
65 
한국수력원자력
29 
기타
 
1

Length

Max length7
Median length3
Mean length4.4822134
Min length2

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
환경부 158
62.5%
한국수자원공사 65
25.7%
한국수력원자력 29
 
11.5%
기타 1
 
0.4%

Length

2024-05-10T20:22:42.609429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T20:22:42.952553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
환경부 158
62.5%
한국수자원공사 65
25.7%
한국수력원자력 29
 
11.5%
기타 1
 
0.4%

주소
Text

Distinct81
Distinct (%)32.1%
Missing1
Missing (%)0.4%
Memory size2.1 KiB
2024-05-10T20:22:43.531124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length9.3452381
Min length5

Characters and Unicode

Total characters2355
Distinct characters92
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

Unique40 ?
Unique (%)15.9%

Sample

1st row강원특별자치도 횡성군
2nd row강원특별자치도 원주시
3rd row강원특별자치도 횡성군
4th row강원특별자치도 영월군
5th row충청북도 단양군
ValueCountFrequency (%)
강원특별자치도 110
20.9%
경기도 83
15.8%
충청북도 36
 
6.8%
평창군 24
 
4.6%
홍천군 16
 
3.0%
인제군 15
 
2.9%
괴산군 11
 
2.1%
강원도 10
 
1.9%
화천군 9
 
1.7%
춘천시 9
 
1.7%
Other values (79) 203
38.6%
2024-05-10T20:22:44.357770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
274
 
11.6%
도 243
 
10.3%
군 143
 
6.1%
원 134
 
5.7%
강 124
 
5.3%
특 117
 
5.0%
별 117
 
5.0%
시 113
 
4.8%
자 110
 
4.7%
치 110
 
4.7%
Other values (82) 870
36.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2080
88.3%
Space Separator 274
 
11.6%
Decimal Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 243
 
11.7%
군 143
 
6.9%
원 134
 
6.4%
강 124
 
6.0%
특 117
 
5.6%
별 117
 
5.6%
시 113
 
5.4%
자 110
 
5.3%
치 110
 
5.3%
경 85
 
4.1%
Other values (80) 784
37.7%
Space Separator
ValueCountFrequency (%)
274
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2080
88.3%
Common 275
 
11.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 243
 
11.7%
군 143
 
6.9%
원 134
 
6.4%
강 124
 
6.0%
특 117
 
5.6%
별 117
 
5.6%
시 113
 
5.4%
자 110
 
5.3%
치 110
 
5.3%
경 85
 
4.1%
Other values (80) 784
37.7%
Common
ValueCountFrequency (%)
274
99.6%
2 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2080
88.3%
ASCII 275
 
11.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
274
99.6%
2 1
 
0.4%
Hangul
ValueCountFrequency (%)
도 243
 
11.7%
군 143
 
6.9%
원 134
 
6.4%
강 124
 
6.0%
특 117
 
5.6%
별 117
 
5.6%
시 113
 
5.4%
자 110
 
5.3%
치 110
 
5.3%
경 85
 
4.1%
Other values (80) 784
37.7%

상세주소
Text

Distinct251
Distinct (%)100.0%
Missing2
Missing (%)0.8%
Memory size2.1 KiB
2024-05-10T20:22:44.957695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length26
Mean length14.896414
Min length4

Characters and Unicode

Total characters3739
Distinct characters272
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

Unique251 ?
Unique (%)100.0%

Sample

1st row안흥면 부곡2 793
2nd row신림면 황둔리 1558-8
3rd row안흥면 상안리 733-2
4th row북면 연덕리 산222-1
5th row영춘면 온달평강로 105 영춘중학교
ValueCountFrequency (%)
처인구 7
 
0.8%
산 6
 
0.7%
북면 6
 
0.7%
남면 5
 
0.6%
서면 4
 
0.5%
17 4
 
0.5%
화천읍 4
 
0.5%
봉평면 4
 
0.5%
320 3
 
0.4%
하장면 3
 
0.4%
Other values (718) 794
94.5%
2024-05-10T20:22:45.984341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
595
 
15.9%
면 175
 
4.7%
리 136
 
3.6%
1 133
 
3.6%
2 113
 
3.0%
- 91
 
2.4%
교 87
 
2.3%
로 78
 
2.1%
3 74
 
2.0%
5 71
 
1.9%
Other values (262) 2186
58.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2309
61.8%
Decimal Number 713
 
19.1%
Space Separator 595
 
15.9%
Dash Punctuation 91
 
2.4%
Close Punctuation 14
 
0.4%
Open Punctuation 14
 
0.4%
Uppercase Letter 2
 
0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 175
 
7.6%
리 136
 
5.9%
교 87
 
3.8%
로 78
 
3.4%
산 56
 
2.4%
학 55
 
2.4%
동 52
 
2.3%
초 49
 
2.1%
읍 47
 
2.0%
등 43
 
1.9%
Other values (245) 1531
66.3%
Decimal Number
ValueCountFrequency (%)
1 133
18.7%
2 113
15.8%
3 74
10.4%
5 71
10.0%
4 68
9.5%
6 58
8.1%
0 56
7.9%
9 54
7.6%
7 52
 
7.3%
8 34
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
I 1
50.0%
T 1
50.0%
Space Separator
ValueCountFrequency (%)
595
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 91
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2309
61.8%
Common 1427
38.2%
Latin 3
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 175
 
7.6%
리 136
 
5.9%
교 87
 
3.8%
로 78
 
3.4%
산 56
 
2.4%
학 55
 
2.4%
동 52
 
2.3%
초 49
 
2.1%
읍 47
 
2.0%
등 43
 
1.9%
Other values (245) 1531
66.3%
Common
ValueCountFrequency (%)
595
41.7%
1 133
 
9.3%
2 113
 
7.9%
- 91
 
6.4%
3 74
 
5.2%
5 71
 
5.0%
4 68
 
4.8%
6 58
 
4.1%
0 56
 
3.9%
9 54
 
3.8%
Other values (4) 114
 
8.0%
Latin
ValueCountFrequency (%)
I 1
33.3%
T 1
33.3%
m 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2309
61.8%
ASCII 1430
38.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
595
41.6%
1 133
 
9.3%
2 113
 
7.9%
- 91
 
6.4%
3 74
 
5.2%
5 71
 
5.0%
4 68
 
4.8%
6 58
 
4.1%
0 56
 
3.9%
9 54
 
3.8%
Other values (7) 117
 
8.2%
Hangul
ValueCountFrequency (%)
면 175
 
7.6%
리 136
 
5.9%
교 87
 
3.8%
로 78
 
3.4%
산 56
 
2.4%
학 55
 
2.4%
동 52
 
2.3%
초 49
 
2.1%
읍 47
 
2.0%
등 43
 
1.9%
Other values (245) 1531
66.3%

Interactions

2024-05-10T20:22:39.137080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-10T20:22:46.247063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강수량관측소구분관할기관명주소
강수량관측소구분1.0000.1150.955
관할기관명0.1151.0000.920
주소0.9550.9201.000
2024-05-10T20:22:46.480477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강수량관측소구분관할기관명
강수량관측소구분1.0000.044
관할기관명0.0441.000

Missing values

2024-05-10T20:22:39.508773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-10T20:22:39.758422image/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.
2024-05-10T20:22:40.020218image/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

강수량관측소구분강수량관측소명관할기관명주소상세주소
010024230횡성군(부곡리)한국수자원공사강원특별자치도 횡성군안흥면 부곡2 793
110024240원주시(황둔리)한국수자원공사강원특별자치도 원주시신림면 황둔리 1558-8
210024250횡성군(상안리)한국수자원공사강원특별자치도 횡성군안흥면 상안리 733-2
310024260영월군(연덕리)한국수자원공사강원특별자치도 영월군북면 연덕리 산222-1
410034050단양군(영춘중교)환경부충청북도 단양군영춘면 온달평강로 105 영춘중학교
510034060제천시(덕주휴게소)환경부충청북도 제천시한수면 송계리 1149 덕주휴게소
610034070단양군(올산리)환경부충청북도 단양군대강면 올산리 159-2
710034080제천시(평동리)한국수자원공사충청북도 제천시백운면 평동리 229-1
810184130성남시(대장동)환경부경기도 성남시분당구 대장동 310
910184140서울시(송정동)환경부서울특별시 성동구송정동 75-30
강수량관측소구분강수량관측소명관할기관명주소상세주소
24310184110남양주시(진관교)환경부경기도 남양주시퇴계원면 퇴계원리 진관교
24410184120포천시(진목리)환경부경기도 포천시내촌면 진목리 638-3
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