Overview

Dataset statistics

Number of variables6
Number of observations101
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.3 KiB
Average record size in memory53.3 B

Variable types

Numeric4
Text1
Categorical1

Dataset

Description가뭄 분석 정보 제공을 위해 수위관측소 중 K-water가 관리하는 대상 수위관측소에 대한 제원 정보 데이터 항목을 제공합니다.
Author한국수자원공사
URLhttps://www.data.go.kr/data/15049847/fileData.do

Alerts

수위관측소 코드 is highly overall correlated with 경도 and 2 other fieldsHigh correlation
경도 is highly overall correlated with 수위관측소 코드 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 수위관측소 코드High correlation
수계 is highly overall correlated with 수위관측소 코드 and 1 other fieldsHigh correlation
수위관측소 명 has unique valuesUnique
위도 has unique valuesUnique

Reproduction

Analysis started2023-12-12 02:05:36.754395
Analysis finished2023-12-12 02:05:39.228515
Duration2.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

수위관측소 코드
Real number (ℝ)

HIGH CORRELATION 

Distinct100
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2424460.5
Minimum1001683
Maximum5101680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T11:05:39.349182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001683
5-th percentile1004690
Q12002633
median2018665
Q33007670
95-th percentile4105665
Maximum5101680
Range4099997
Interquartile range (IQR)1005037

Descriptive statistics

Standard deviation1096575.7
Coefficient of variation (CV)0.4522968
Kurtosis-0.29188076
Mean2424460.5
Median Absolute Deviation (MAD)982975
Skewness0.67672237
Sum2.4487051 × 108
Variance1.2024783 × 1012
MonotonicityNot monotonic
2023-12-12T11:05:39.548546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2022696 2
 
2.0%
3001620 1
 
1.0%
2018630 1
 
1.0%
2021650 1
 
1.0%
2018695 1
 
1.0%
2018692 1
 
1.0%
2018685 1
 
1.0%
2018665 1
 
1.0%
2018655 1
 
1.0%
2018650 1
 
1.0%
Other values (90) 90
89.1%
ValueCountFrequency (%)
1001683 1
1.0%
1002655 1
1.0%
1002695 1
1.0%
1003630 1
1.0%
1003655 1
1.0%
1004690 1
1.0%
1006610 1
1.0%
1009650 1
1.0%
1009652 1
1.0%
1010640 1
1.0%
ValueCountFrequency (%)
5101680 1
1.0%
5101620 1
1.0%
5003604 1
1.0%
5001618 1
1.0%
4105670 1
1.0%
4105665 1
1.0%
4105660 1
1.0%
4104620 1
1.0%
4104610 1
1.0%
4104603 1
1.0%

수위관측소 명
Text

UNIQUE 

Distinct101
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
2023-12-12T11:05:39.834100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length8
Mean length8.3762376
Min length7

Characters and Unicode

Total characters846
Distinct characters129
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

Unique101 ?
Unique (%)100.0%

Sample

1st row장수군(연화교)
2nd row진안군(신용담교)
3rd row금산군(적벽교)
4th row영동군(호탄리)
5th row옥천군(이원대교)
ValueCountFrequency (%)
장수군(연화교 1
 
1.0%
광양시(수어댐 1
 
1.0%
낙동강하굿둑증설갑문(상 1
 
1.0%
양산시(대리 1
 
1.0%
진주시(판문동 1
 
1.0%
사천시(검정리 1
 
1.0%
하동군(대곡리 1
 
1.0%
산청군(하정리 1
 
1.0%
합천군(소오리 1
 
1.0%
산청군(수산교 1
 
1.0%
Other values (91) 91
90.1%
2023-12-12T11:05:40.256030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 103
 
12.2%
) 103
 
12.2%
군 67
 
7.9%
교 51
 
6.0%
시 37
 
4.4%
리 30
 
3.5%
천 27
 
3.2%
동 25
 
3.0%
산 19
 
2.2%
양 18
 
2.1%
Other values (119) 366
43.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 637
75.3%
Open Punctuation 103
 
12.2%
Close Punctuation 103
 
12.2%
Decimal Number 3
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 67
 
10.5%
교 51
 
8.0%
시 37
 
5.8%
리 30
 
4.7%
천 27
 
4.2%
동 25
 
3.9%
산 19
 
3.0%
양 18
 
2.8%
화 15
 
2.4%
댐 14
 
2.2%
Other values (115) 334
52.4%
Decimal Number
ValueCountFrequency (%)
2 2
66.7%
4 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 103
100.0%
Close Punctuation
ValueCountFrequency (%)
) 103
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 637
75.3%
Common 209
 
24.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 67
 
10.5%
교 51
 
8.0%
시 37
 
5.8%
리 30
 
4.7%
천 27
 
4.2%
동 25
 
3.9%
산 19
 
3.0%
양 18
 
2.8%
화 15
 
2.4%
댐 14
 
2.2%
Other values (115) 334
52.4%
Common
ValueCountFrequency (%)
( 103
49.3%
) 103
49.3%
2 2
 
1.0%
4 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 637
75.3%
ASCII 209
 
24.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 103
49.3%
) 103
49.3%
2 2
 
1.0%
4 1
 
0.5%
Hangul
ValueCountFrequency (%)
군 67
 
10.5%
교 51
 
8.0%
시 37
 
5.8%
리 30
 
4.7%
천 27
 
4.2%
동 25
 
3.9%
산 19
 
3.0%
양 18
 
2.8%
화 15
 
2.4%
댐 14
 
2.2%
Other values (115) 334
52.4%

수계
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Memory size940.0 B
낙동강
40 
한강
19 
금강
10 
섬진강
10 
섬진강남해
6 
Other values (9)
16 

Length

Max length7
Median length3
Mean length3.039604
Min length2

Unique

Unique3 ?
Unique (%)3.0%

Sample

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

Common Values

ValueCountFrequency (%)
낙동강 40
39.6%
한강 19
18.8%
금강 10
 
9.9%
섬진강 10
 
9.9%
섬진강남해 6
 
5.9%
낙동강남해 3
 
3.0%
금강서해 2
 
2.0%
영산강 2
 
2.0%
탐진강 2
 
2.0%
태화강 2
 
2.0%
Other values (4) 5
 
5.0%

Length

2023-12-12T11:05:40.417014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
낙동강 40
38.1%
한강 19
18.1%
금강 10
 
9.5%
섬진강 10
 
9.5%
섬진강남해 6
 
5.7%
낙동강남해 3
 
2.9%
태화강 2
 
1.9%
· 2
 
1.9%
낙동강동해 2
 
1.9%
탐진강 2
 
1.9%
Other values (7) 9
 
8.6%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.04238
Minimum126.64556
Maximum129.37361
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T11:05:40.549845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.64556
5-th percentile126.98667
Q1127.52778
median127.99583
Q3128.65667
95-th percentile129.08861
Maximum129.37361
Range2.7280556
Interquartile range (IQR)1.1288889

Descriptive statistics

Standard deviation0.7102489
Coefficient of variation (CV)0.0055469827
Kurtosis-0.99957362
Mean128.04238
Median Absolute Deviation (MAD)0.55416667
Skewness0.038296835
Sum12932.281
Variance0.50445349
MonotonicityNot monotonic
2023-12-12T11:05:40.725020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.95055556 3
 
3.0%
127.71666667 2
 
2.0%
127.68277778 1
 
1.0%
128.98305556 1
 
1.0%
128.03027778 1
 
1.0%
128.03277778 1
 
1.0%
127.92027778 1
 
1.0%
127.99583333 1
 
1.0%
128.1175 1
 
1.0%
127.94 1
 
1.0%
Other values (88) 88
87.1%
ValueCountFrequency (%)
126.64555556 1
1.0%
126.64861111 1
1.0%
126.87 1
1.0%
126.89222222 1
1.0%
126.98638889 1
1.0%
126.98666667 1
1.0%
126.98972222 1
1.0%
127.01388889 1
1.0%
127.01583333 1
1.0%
127.09527778 1
1.0%
ValueCountFrequency (%)
129.37361111 1
1.0%
129.3075 1
1.0%
129.19472222 1
1.0%
129.18333333 1
1.0%
129.10805556 1
1.0%
129.08861111 1
1.0%
129.08 1
1.0%
129.06722222 1
1.0%
129.06527778 1
1.0%
129.04444444 1
1.0%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct101
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.109092
Minimum34.649167
Maximum38.656667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T11:05:40.893817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.649167
5-th percentile34.828056
Q135.267222
median35.819167
Q336.614722
95-th percentile38.216111
Maximum38.656667
Range4.0075
Interquartile range (IQR)1.3475

Descriptive statistics

Standard deviation1.0473958
Coefficient of variation (CV)0.029006429
Kurtosis-0.22570195
Mean36.109092
Median Absolute Deviation (MAD)0.71111111
Skewness0.79709621
Sum3647.0183
Variance1.097038
MonotonicityNot monotonic
2023-12-12T11:05:41.072562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.78583333 1
 
1.0%
35.40194444 1
 
1.0%
35.48138889 1
 
1.0%
35.47666667 1
 
1.0%
35.16888889 1
 
1.0%
35.06916667 1
 
1.0%
35.1675 1
 
1.0%
35.31722222 1
 
1.0%
35.4 1
 
1.0%
35.3375 1
 
1.0%
Other values (91) 91
90.1%
ValueCountFrequency (%)
34.64916667 1
1.0%
34.65222222 1
1.0%
34.80527778 1
1.0%
34.80805556 1
1.0%
34.81333333 1
1.0%
34.82805556 1
1.0%
34.89027778 1
1.0%
34.94416667 1
1.0%
34.95083333 1
1.0%
34.98527778 1
1.0%
ValueCountFrequency (%)
38.65666667 1
1.0%
38.59305556 1
1.0%
38.31888889 1
1.0%
38.285 1
1.0%
38.23166667 1
1.0%
38.21611111 1
1.0%
38.21111111 1
1.0%
38.20777778 1
1.0%
38.105 1
1.0%
38.10055556 1
1.0%

관측개시일
Real number (ℝ)

Distinct28
Distinct (%)27.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1995.3465
Minimum1965
Maximum2013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T11:05:41.260230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1965
5-th percentile1978
Q11988
median1995
Q32004
95-th percentile2010
Maximum2013
Range48
Interquartile range (IQR)16

Descriptive statistics

Standard deviation10.536067
Coefficient of variation (CV)0.0052803195
Kurtosis-0.2974828
Mean1995.3465
Median Absolute Deviation (MAD)8
Skewness-0.36542599
Sum201530
Variance111.00871
MonotonicityNot monotonic
2023-12-12T11:05:41.440482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1987 11
 
10.9%
1994 8
 
7.9%
2008 8
 
7.9%
1996 6
 
5.9%
1992 6
 
5.9%
2004 6
 
5.9%
2010 6
 
5.9%
1991 5
 
5.0%
1988 4
 
4.0%
2000 4
 
4.0%
Other values (18) 37
36.6%
ValueCountFrequency (%)
1965 1
 
1.0%
1972 2
 
2.0%
1976 2
 
2.0%
1978 3
 
3.0%
1979 3
 
3.0%
1985 3
 
3.0%
1987 11
10.9%
1988 4
 
4.0%
1990 2
 
2.0%
1991 5
5.0%
ValueCountFrequency (%)
2013 1
 
1.0%
2012 2
 
2.0%
2011 1
 
1.0%
2010 6
5.9%
2009 2
 
2.0%
2008 8
7.9%
2005 3
 
3.0%
2004 6
5.9%
2002 3
 
3.0%
2001 1
 
1.0%

Interactions

2023-12-12T11:05:38.488251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:37.067989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:37.525734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:37.993987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:38.593169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:37.207953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:37.641364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:38.112624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:38.703893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:37.328453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:37.757563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:38.242282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:38.843683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:37.433424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:37.895222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:05:38.372288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:05:41.882825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수위관측소 코드수계경도위도관측개시일
수위관측소 코드1.0000.9940.7920.7230.518
수계0.9941.0000.8620.7200.577
경도0.7920.8621.0000.7150.505
위도0.7230.7200.7151.0000.317
관측개시일0.5180.5770.5050.3171.000
2023-12-12T11:05:41.978231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수위관측소 코드경도위도관측개시일수계
수위관측소 코드1.000-0.578-0.8020.0440.953
경도-0.5781.0000.347-0.0510.582
위도-0.8020.3471.000-0.0150.393
관측개시일0.044-0.051-0.0151.0000.355
수계0.9530.5820.3930.3551.000

Missing values

2023-12-12T11:05:39.003090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:05:39.172292image/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

수위관측소 코드수위관측소 명수계경도위도관측개시일
03001620장수군(연화교)금강127.52722235.7858331999
13002630진안군(신용담교)금강127.52777835.9513892001
23004620금산군(적벽교)금강127.59333336.0488891992
33004650영동군(호탄리)금강127.65222236.1288891987
43006680옥천군(이원대교)금강127.67138936.23751979
53007670옥천군(산계리)금강127.73722236.3108331979
61001683영월군(삼옥교)한강128.51027837.2072222008
71002655영월군(판운교)한강128.34388937.2966671992
81003630단양군(오사리)한강128.51083337.0986111985
91004690충주시(향산리)한강127.92444436.9251985
수위관측소 코드수위관측소 명수계경도위도관측개시일
913001640진안군(성산리)금강127.54416735.8191671999
924002610임실군(회문리)섬진강127.13972235.5183331990
934008650순천시(광천교)섬진강127.40638935.1330561991
941002695영월군(북쌍리)한강128.40916737.1941671985
951003655제천시(물태리)한강128.1837.00251992
961009652화천군(평화나래교)한강127.85138938.2077782010
971010640양구군(각시교)한강128.5538.3188891998
983008690청주시(대청댐)금강127.48388936.4758331979
993005680영동군(율리)금강127.80722236.2269441992
1004008655곡성군(목사동2교)섬진강127.25888935.1266672008