Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory576.2 KiB
Average record size in memory59.0 B

Variable types

Numeric3
Categorical1
DateTime1
Text1

Dataset

Description한강홍수통제소에서 관측하는 수위 관측소 별 수위자료, 유량자료를 적용기간 별로 제공하는 수위유량곡선환산표 데이터입니다.
Author환경부 한강홍수통제소
URLhttps://www.data.go.kr/data/15085920/fileData.do

Alerts

수위관측소코드 is highly overall correlated with 수위관측소명High correlation
수위자료 is highly overall correlated with 유량자료High correlation
유량자료 is highly overall correlated with 수위자료High correlation
수위관측소명 is highly overall correlated with 수위관측소코드High correlation

Reproduction

Analysis started2023-12-12 17:31:43.185023
Analysis finished2023-12-12 17:31:45.687678
Duration2.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

수위관측소코드
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1001870.9
Minimum1001620
Maximum1002655
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:31:45.780635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001620
5-th percentile1001622
Q11001630
median1001670
Q31001690
95-th percentile1002650
Maximum1002655
Range1035
Interquartile range (IQR)60

Descriptive statistics

Standard deviation410.96064
Coefficient of variation (CV)0.00041019322
Kurtosis-0.21905995
Mean1001870.9
Median Absolute Deviation (MAD)40
Skewness1.3280224
Sum1.0018709 × 1010
Variance168888.65
MonotonicityNot monotonic
2023-12-13T02:31:45.931694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1001670 1483
14.8%
1001690 1366
13.7%
1001655 1261
12.6%
1001625 1239
12.4%
1001630 1207
12.1%
1002650 1007
10.1%
1002625 546
 
5.5%
1002640 419
 
4.2%
1001622 389
 
3.9%
1001620 224
 
2.2%
Other values (10) 859
8.6%
ValueCountFrequency (%)
1001620 224
 
2.2%
1001622 389
 
3.9%
1001625 1239
12.4%
1001626 141
 
1.4%
1001629 198
 
2.0%
1001630 1207
12.1%
1001645 74
 
0.7%
1001655 1261
12.6%
1001660 161
 
1.6%
1001670 1483
14.8%
ValueCountFrequency (%)
1002655 43
 
0.4%
1002650 1007
10.1%
1002640 419
 
4.2%
1002635 31
 
0.3%
1002625 546
 
5.5%
1002615 71
 
0.7%
1002610 61
 
0.6%
1002605 43
 
0.4%
1001690 1366
13.7%
1001680 36
 
0.4%

수위관측소명
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
영월군 거운교
1483 
영월군 영월대교
1366 
정선군 정선제1교
1261 
정선군 송천교
1239 
정선군 나전교
1207 
Other values (15)
3444 

Length

Max length10
Median length8
Mean length8.4721
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영월군 거운교
2nd row정선군 송계교
3rd row영월군 영월대교
4th row정선군 정선제1교
5th row영월군 영월대교

Common Values

ValueCountFrequency (%)
영월군 거운교 1483
14.8%
영월군 영월대교 1366
13.7%
정선군 정선제1교 1261
12.6%
정선군 송천교 1239
12.4%
정선군 나전교 1207
12.1%
평창군 평창교 1007
10.1%
평창군 선애교 546
 
5.5%
평창군 상방림교 419
 
4.2%
정선군 혈천교 389
 
3.9%
정선군 송계교 224
 
2.2%
Other values (10) 859
8.6%

Length

2023-12-13T02:31:46.122905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
정선군 4894
24.5%
영월군 2892
14.5%
평창군 2178
10.9%
거운교 1483
 
7.4%
영월대교 1366
 
6.8%
정선제1교 1261
 
6.3%
송천교 1239
 
6.2%
나전교 1207
 
6.0%
평창교 1007
 
5.0%
선애교 546
 
2.7%
Other values (13) 1891
 
9.5%

적용시작일자
Date

Distinct66
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum1900-01-01 00:00:00
Maximum2020-11-26 00:00:00
2023-12-13T02:31:46.266546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:46.459836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

적용종료일자
Text

Distinct64
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T02:31:46.766036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters100000
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

Unique2 ?
Unique (%)< 0.1%

Sample

1st row2010-12-31
2nd row2016-01-01
3rd row2020-01-01
4th row2010-12-31
5th row9999-12-31
ValueCountFrequency (%)
9999-12-31 1295
13.0%
2010-12-31 940
 
9.4%
2020-01-01 739
 
7.4%
2018-01-01 665
 
6.7%
2012-12-31 628
 
6.3%
2019-01-01 613
 
6.1%
2016-01-01 592
 
5.9%
2015-01-01 565
 
5.7%
2014-01-01 562
 
5.6%
2013-12-31 465
 
4.7%
Other values (54) 2936
29.4%
2023-12-13T02:31:47.204826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 24439
24.4%
0 22443
22.4%
- 20000
20.0%
2 15360
15.4%
9 6347
 
6.3%
3 5035
 
5.0%
8 1501
 
1.5%
5 1479
 
1.5%
6 1302
 
1.3%
7 1265
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 80000
80.0%
Dash Punctuation 20000
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 24439
30.5%
0 22443
28.1%
2 15360
19.2%
9 6347
 
7.9%
3 5035
 
6.3%
8 1501
 
1.9%
5 1479
 
1.8%
6 1302
 
1.6%
7 1265
 
1.6%
4 829
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
- 20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 100000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 24439
24.4%
0 22443
22.4%
- 20000
20.0%
2 15360
15.4%
9 6347
 
6.3%
3 5035
 
5.0%
8 1501
 
1.5%
5 1479
 
1.5%
6 1302
 
1.3%
7 1265
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 24439
24.4%
0 22443
22.4%
- 20000
20.0%
2 15360
15.4%
9 6347
 
6.3%
3 5035
 
5.0%
8 1501
 
1.5%
5 1479
 
1.5%
6 1302
 
1.3%
7 1265
 
1.3%

수위자료
Real number (ℝ)

HIGH CORRELATION 

Distinct1419
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.400016
Minimum-1.28
Maximum14.6
Zeros5
Zeros (%)< 0.1%
Negative303
Negative (%)3.0%
Memory size166.0 KiB
2023-12-13T02:31:47.415193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.28
5-th percentile0.3295
Q11.95
median3.86
Q36.22
95-th percentile10.9605
Maximum14.6
Range15.88
Interquartile range (IQR)4.27

Descriptive statistics

Standard deviation3.1753214
Coefficient of variation (CV)0.72166133
Kurtosis0.11155245
Mean4.400016
Median Absolute Deviation (MAD)2.08
Skewness0.79127812
Sum44000.16
Variance10.082666
MonotonicityNot monotonic
2023-12-13T02:31:47.583638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.9 25
 
0.2%
4.3 24
 
0.2%
2.13 23
 
0.2%
4.65 23
 
0.2%
2.0 22
 
0.2%
3.59 22
 
0.2%
3.8 22
 
0.2%
3.9 22
 
0.2%
1.62 21
 
0.2%
6.3 20
 
0.2%
Other values (1409) 9776
97.8%
ValueCountFrequency (%)
-1.28 1
< 0.1%
-1.24 1
< 0.1%
-1.23 1
< 0.1%
-1.2 1
< 0.1%
-1.13 1
< 0.1%
-1.11 1
< 0.1%
-1.1 1
< 0.1%
-1.09 1
< 0.1%
-1.08 2
< 0.1%
-1.07 1
< 0.1%
ValueCountFrequency (%)
14.6 1
 
< 0.1%
13.96 1
 
< 0.1%
13.93 1
 
< 0.1%
13.9 1
 
< 0.1%
13.88 3
< 0.1%
13.86 2
< 0.1%
13.85 1
 
< 0.1%
13.84 1
 
< 0.1%
13.82 1
 
< 0.1%
13.78 1
 
< 0.1%

유량자료
Real number (ℝ)

HIGH CORRELATION 

Distinct8162
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5265.4885
Minimum0
Maximum1098671.8
Zeros90
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:31:47.803587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.739
Q1189.95
median1201.875
Q33391.14
95-th percentile8829.8775
Maximum1098671.8
Range1098671.8
Interquartile range (IQR)3201.19

Descriptive statistics

Standard deviation47091.085
Coefficient of variation (CV)8.9433459
Kurtosis320.58649
Mean5265.4885
Median Absolute Deviation (MAD)1148.525
Skewness17.332907
Sum52654885
Variance2.2175703 × 109
MonotonicityNot monotonic
2023-12-13T02:31:47.989869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 90
 
0.9%
0.01 28
 
0.3%
0.03 21
 
0.2%
0.02 15
 
0.1%
0.11 10
 
0.1%
0.13 10
 
0.1%
0.05 10
 
0.1%
0.09 8
 
0.1%
0.06 8
 
0.1%
0.14 8
 
0.1%
Other values (8152) 9792
97.9%
ValueCountFrequency (%)
0.0 90
0.9%
0.01 28
 
0.3%
0.02 15
 
0.1%
0.03 21
 
0.2%
0.04 7
 
0.1%
0.05 10
 
0.1%
0.06 8
 
0.1%
0.07 6
 
0.1%
0.08 1
 
< 0.1%
0.09 8
 
0.1%
ValueCountFrequency (%)
1098671.82 1
< 0.1%
1093631.72 1
< 0.1%
1076066.58 1
< 0.1%
1056135.76 1
< 0.1%
1026527.86 1
< 0.1%
1014293.7 1
< 0.1%
992424.66 1
< 0.1%
977954.51 1
< 0.1%
963571.99 1
< 0.1%
906922.82 1
< 0.1%

Interactions

2023-12-13T02:31:45.075260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:43.874614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:44.303919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:45.191551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:43.997557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:44.793434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:45.324994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:44.170781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:44.937671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:31:48.123738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수위관측소코드수위관측소명적용시작일자적용종료일자수위자료유량자료
수위관측소코드1.0001.0000.6650.5990.4070.121
수위관측소명1.0001.0000.9180.9080.6070.218
적용시작일자0.6650.9181.0000.9950.3240.097
적용종료일자0.5990.9080.9951.0000.3670.610
수위자료0.4070.6070.3240.3671.0000.080
유량자료0.1210.2180.0970.6100.0801.000
2023-12-13T02:31:48.256358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수위관측소코드수위자료유량자료수위관측소명
수위관측소코드1.0000.0500.1480.999
수위자료0.0501.0000.9420.236
유량자료0.1480.9421.0000.088
수위관측소명0.9990.2360.0881.000

Missing values

2023-12-13T02:31:45.471598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:31:45.624315image/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

수위관측소코드수위관측소명적용시작일자적용종료일자수위자료유량자료
484411001670영월군 거운교2011-01-012010-12-3112.448339.49
8721001620정선군 송계교2015-08-252016-01-011.16121.61
720361001690영월군 영월대교2018-01-012020-01-0111.0510002.79
340171001655정선군 정선제1교2011-01-012010-12-314.043343.32
723791001690영월군 영월대교2020-01-019999-12-310.9733.13
822881002640평창군 상방림교2013-07-222017-01-012.72626.9
82041001625정선군 송천교2014-01-012014-01-312.71549.29
490611001670영월군 거운교2012-01-012012-12-315.962291.41
206981001629정선군 남평대교2019-01-019999-12-3110.255176.45
880491002650평창군 평창교2013-07-222013-12-312.1471.48
수위관측소코드수위관측소명적용시작일자적용종료일자수위자료유량자료
547581001670영월군 거운교2016-01-012017-01-015.772166.34
922281002650평창군 평창교2018-01-012019-01-012.55636.08
463771001670영월군 거운교2010-01-012010-12-314.481267.62
339371001655정선군 정선제1교2011-01-012010-12-313.242202.22
121041001625정선군 송천교2016-07-062016-12-314.461342.28
706901001690영월군 영월대교2014-01-012018-01-0112.3911239.02
570591001670영월군 거운교2018-01-012019-01-014.771545.03
585291001670영월군 거운교2019-01-012020-01-017.413312.84
271141001630정선군 나전교2016-07-052017-01-013.99791.72
594641001670영월군 거운교2020-01-019999-12-314.71314.23