Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory830.1 KiB
Average record size in memory85.0 B

Variable types

Numeric4
Text3
DateTime1
Categorical1

Dataset

Description한강홍수통제소에서 관측하는 수위 관측소 별 수위유량곡선식입니다. 수위자료의 하한, 상한, 간격 및 관계식을 적용기간 별로 제공합니다.
Author환경부 한강홍수통제소
URLhttps://www.data.go.kr/data/15085917/fileData.do

Alerts

순번 is highly overall correlated with 수위자료하한 and 1 other fieldsHigh correlation
수위자료하한 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
수위자료상한 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
수위자료간격 is highly imbalanced (79.1%)Imbalance
수위자료하한 is highly skewed (γ1 = -21.83162608)Skewed

Reproduction

Analysis started2023-12-12 15:34:29.696901
Analysis finished2023-12-12 15:34:33.007717
Duration3.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

수위관측소코드
Real number (ℝ)

Distinct472
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2540672.2
Minimum1001620
Maximum5302620
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:34:33.124601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001620
5-th percentile1004690
Q11302648
median2101668
Q33203620
95-th percentile5003610
Maximum5302620
Range4301000
Interquartile range (IQR)1900972

Descriptive statistics

Standard deviation1235431.9
Coefficient of variation (CV)0.48626184
Kurtosis-0.64143563
Mean2540672.2
Median Absolute Deviation (MAD)1000007
Skewness0.47693665
Sum2.5406722 × 1010
Variance1.5262921 × 1012
MonotonicityNot monotonic
2023-12-13T00:34:33.317808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4005670 162
 
1.6%
2101668 126
 
1.3%
3012635 118
 
1.2%
3009673 115
 
1.1%
3011635 111
 
1.1%
5001650 101
 
1.0%
4005690 98
 
1.0%
1018693 98
 
1.0%
3011625 91
 
0.9%
1007650 85
 
0.9%
Other values (462) 8895
88.9%
ValueCountFrequency (%)
1001620 6
 
0.1%
1001622 11
 
0.1%
1001625 47
0.5%
1001626 8
 
0.1%
1001629 4
 
< 0.1%
1001630 31
0.3%
1001645 1
 
< 0.1%
1001655 40
0.4%
1001660 6
 
0.1%
1001670 30
0.3%
ValueCountFrequency (%)
5302620 9
 
0.1%
5301660 15
 
0.1%
5101690 7
 
0.1%
5101675 13
 
0.1%
5101670 78
0.8%
5101650 20
 
0.2%
5006630 45
0.4%
5006620 51
0.5%
5005680 39
0.4%
5005650 5
 
0.1%

지점명
Text

Distinct472
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T00:34:33.673382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length8
Mean length8.2799
Min length2

Characters and Unicode

Total characters82799
Distinct characters243
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

Unique26 ?
Unique (%)0.3%

Sample

1st row남양주시(진관교)
2nd row예천군(고평교)
3rd row구미시(구미대교)
4th row장흥군(별천교)
5th row광명시(시흥대교)
ValueCountFrequency (%)
남원시(동림교 162
 
1.6%
경주시(달성교 126
 
1.3%
공주시(국재교 118
 
1.2%
대전시(한밭대교 115
 
1.1%
청주시(팔결교 111
 
1.1%
광주광역시(유촌교 101
 
1.0%
남원시(요천대교 98
 
1.0%
광명시(시흥대교 98
 
1.0%
증평군(반탄교 91
 
0.9%
여주시(흥천대교 85
 
0.9%
Other values (462) 8895
88.9%
2023-12-13T00:34:34.163954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 9966
 
12.0%
) 9966
 
12.0%
교 9033
 
10.9%
시 5614
 
6.8%
군 4563
 
5.5%
주 2953
 
3.6%
천 2483
 
3.0%
대 1812
 
2.2%
산 1328
 
1.6%
성 1114
 
1.3%
Other values (233) 33967
41.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 62617
75.6%
Open Punctuation 9966
 
12.0%
Close Punctuation 9966
 
12.0%
Decimal Number 250
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 9033
 
14.4%
시 5614
 
9.0%
군 4563
 
7.3%
주 2953
 
4.7%
천 2483
 
4.0%
대 1812
 
2.9%
산 1328
 
2.1%
성 1114
 
1.8%
양 1107
 
1.8%
평 1000
 
1.6%
Other values (228) 31610
50.5%
Decimal Number
ValueCountFrequency (%)
2 134
53.6%
1 104
41.6%
3 12
 
4.8%
Open Punctuation
ValueCountFrequency (%)
( 9966
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9966
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 62617
75.6%
Common 20182
 
24.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 9033
 
14.4%
시 5614
 
9.0%
군 4563
 
7.3%
주 2953
 
4.7%
천 2483
 
4.0%
대 1812
 
2.9%
산 1328
 
2.1%
성 1114
 
1.8%
양 1107
 
1.8%
평 1000
 
1.6%
Other values (228) 31610
50.5%
Common
ValueCountFrequency (%)
( 9966
49.4%
) 9966
49.4%
2 134
 
0.7%
1 104
 
0.5%
3 12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 62617
75.6%
ASCII 20182
 
24.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 9966
49.4%
) 9966
49.4%
2 134
 
0.7%
1 104
 
0.5%
3 12
 
0.1%
Hangul
ValueCountFrequency (%)
교 9033
 
14.4%
시 5614
 
9.0%
군 4563
 
7.3%
주 2953
 
4.7%
천 2483
 
4.0%
대 1812
 
2.9%
산 1328
 
2.1%
성 1114
 
1.8%
양 1107
 
1.8%
평 1000
 
1.6%
Other values (228) 31610
50.5%

적용시작일자
Date

Distinct1533
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum1800-01-01 00:00:00
Maximum2022-05-03 00:00:00
2023-12-13T00:34:34.364761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:34:34.686938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

적용종료일자
Text

Distinct1553
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T00:34:35.075197image/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

Unique420 ?
Unique (%)4.2%

Sample

1st row2021-08-10
2nd row2020-12-31
3rd row9999-12-31
4th row2021-05-13
5th row2021-11-30
ValueCountFrequency (%)
9999-12-31 772
 
7.7%
2009-12-31 323
 
3.2%
2013-12-31 253
 
2.5%
2012-12-31 248
 
2.5%
2020-12-31 230
 
2.3%
2017-12-31 229
 
2.3%
2010-12-31 223
 
2.2%
2011-12-31 214
 
2.1%
2016-12-31 189
 
1.9%
2021-12-31 185
 
1.8%
Other values (1543) 7134
71.3%
2023-12-13T00:34:35.640749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 21349
21.3%
- 20000
20.0%
0 19569
19.6%
2 17764
17.8%
3 6168
 
6.2%
9 5748
 
5.7%
7 2397
 
2.4%
8 2134
 
2.1%
6 1914
 
1.9%
5 1661
 
1.7%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 21349
26.7%
0 19569
24.5%
2 17764
22.2%
3 6168
 
7.7%
9 5748
 
7.2%
7 2397
 
3.0%
8 2134
 
2.7%
6 1914
 
2.4%
5 1661
 
2.1%
4 1296
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 100000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 21349
21.3%
- 20000
20.0%
0 19569
19.6%
2 17764
17.8%
3 6168
 
6.2%
9 5748
 
5.7%
7 2397
 
2.4%
8 2134
 
2.1%
6 1914
 
1.9%
5 1661
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 21349
21.3%
- 20000
20.0%
0 19569
19.6%
2 17764
17.8%
3 6168
 
6.2%
9 5748
 
5.7%
7 2397
 
2.4%
8 2134
 
2.1%
6 1914
 
1.9%
5 1661
 
1.7%

순번
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3075
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:34:35.874930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2096653
Coefficient of variation (CV)0.52423197
Kurtosis-0.24995735
Mean2.3075
Median Absolute Deviation (MAD)1
Skewness0.66472666
Sum23075
Variance1.4632901
MonotonicityNot monotonic
2023-12-13T00:34:36.017011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 3273
32.7%
2 2692
26.9%
3 2295
22.9%
4 1249
 
12.5%
5 418
 
4.2%
6 65
 
0.7%
7 7
 
0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
1 3273
32.7%
2 2692
26.9%
3 2295
22.9%
4 1249
 
12.5%
5 418
 
4.2%
6 65
 
0.7%
7 7
 
0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 7
 
0.1%
6 65
 
0.7%
5 418
 
4.2%
4 1249
 
12.5%
3 2295
22.9%
2 2692
26.9%
1 3273
32.7%

관계식
Text

Distinct6272
Distinct (%)62.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T00:34:36.308040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length36
Mean length22.7977
Min length1

Characters and Unicode

Total characters227977
Distinct characters21
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4587 ?
Unique (%)45.9%

Sample

1st row35.900*((h+1)-0.690)^2.155
2nd row39.443*(h-0.900)^1.516
3rd row35.571*((h+6)+0.100)^2.010
4th row48.337*(h-0.960)^2.278
5th row30.908*(h-0.370)^2.480
ValueCountFrequency (%)
149.966*(h-1.060)^1.921 36
 
0.4%
91.204*(h-1.880)^1.775 26
 
0.3%
24.663*(h-0.490)^2.998 26
 
0.3%
68.765*(h-0.500)^1.892 26
 
0.3%
125.296*(h-1.090)^2.970 25
 
0.2%
92.150*(h-1.940)^1.879 24
 
0.2%
72.386*(h-0.600)^1.378 24
 
0.2%
125.020*(h-0.750)^1.603 22
 
0.2%
171.965*(h-1.110)^1.621 22
 
0.2%
40.760*(h-0.350)^1.797 22
 
0.2%
Other values (6254) 9747
97.5%
2023-12-13T00:34:36.867799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 29774
13.1%
0 27754
12.2%
1 20050
 
8.8%
2 16613
 
7.3%
) 11790
 
5.2%
( 11789
 
5.2%
5 11707
 
5.1%
3 10216
 
4.5%
9 10111
 
4.4%
* 10057
 
4.4%
Other values (11) 68116
29.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 132504
58.1%
Other Punctuation 39833
 
17.5%
Close Punctuation 11790
 
5.2%
Open Punctuation 11789
 
5.2%
Modifier Symbol 9989
 
4.4%
Lowercase Letter 9083
 
4.0%
Dash Punctuation 8748
 
3.8%
Math Symbol 3194
 
1.4%
Uppercase Letter 967
 
0.4%
Space Separator 80
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 27754
20.9%
1 20050
15.1%
2 16613
12.5%
5 11707
8.8%
3 10216
 
7.7%
9 10111
 
7.6%
4 9274
 
7.0%
8 8993
 
6.8%
6 8916
 
6.7%
7 8870
 
6.7%
Other Punctuation
ValueCountFrequency (%)
. 29774
74.7%
* 10057
 
25.2%
/ 2
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 11790
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11789
100.0%
Modifier Symbol
ValueCountFrequency (%)
^ 9989
100.0%
Lowercase Letter
ValueCountFrequency (%)
h 9083
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8748
100.0%
Math Symbol
ValueCountFrequency (%)
+ 3194
100.0%
Uppercase Letter
ValueCountFrequency (%)
H 967
100.0%
Space Separator
ValueCountFrequency (%)
80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 217927
95.6%
Latin 10050
 
4.4%

Most frequent character per script

Common
ValueCountFrequency (%)
. 29774
13.7%
0 27754
12.7%
1 20050
 
9.2%
2 16613
 
7.6%
) 11790
 
5.4%
( 11789
 
5.4%
5 11707
 
5.4%
3 10216
 
4.7%
9 10111
 
4.6%
* 10057
 
4.6%
Other values (9) 58066
26.6%
Latin
ValueCountFrequency (%)
h 9083
90.4%
H 967
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 227977
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 29774
13.1%
0 27754
12.2%
1 20050
 
8.8%
2 16613
 
7.3%
) 11790
 
5.2%
( 11789
 
5.2%
5 11707
 
5.1%
3 10216
 
4.5%
9 10111
 
4.4%
* 10057
 
4.4%
Other values (11) 68116
29.9%

수위자료하한
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct771
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3474088
Minimum-100
Maximum14.02
Zeros99
Zeros (%)1.0%
Negative1082
Negative (%)10.8%
Memory size166.0 KiB
2023-12-13T00:34:37.080111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-0.49
Q10.4
median1.06
Q32.08
95-th percentile4.1
Maximum14.02
Range114.02
Interquartile range (IQR)1.68

Descriptive statistics

Standard deviation2.1037598
Coefficient of variation (CV)1.5613375
Kurtosis1077.4504
Mean1.3474088
Median Absolute Deviation (MAD)0.8
Skewness-21.831626
Sum13474.088
Variance4.4258055
MonotonicityNot monotonic
2023-12-13T00:34:37.614652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.0 142
 
1.4%
3.0 133
 
1.3%
1.0 121
 
1.2%
1.2 110
 
1.1%
0.5 102
 
1.0%
0.0 99
 
1.0%
0.4 91
 
0.9%
0.1 89
 
0.9%
1.5 81
 
0.8%
0.7 79
 
0.8%
Other values (761) 8953
89.5%
ValueCountFrequency (%)
-100.0 2
< 0.1%
-6.695 1
< 0.1%
-5.5 1
< 0.1%
-5.47 1
< 0.1%
-5.3 1
< 0.1%
-5.149 2
< 0.1%
-4.85 1
< 0.1%
-4.8 1
< 0.1%
-4.65 1
< 0.1%
-4.58 1
< 0.1%
ValueCountFrequency (%)
14.02 2
< 0.1%
12.8 1
< 0.1%
12.5 1
< 0.1%
11.5 1
< 0.1%
11.2 1
< 0.1%
11.12 2
< 0.1%
11.09 1
< 0.1%
10.86 1
< 0.1%
10.77 1
< 0.1%
10.5 1
< 0.1%

수위자료상한
Real number (ℝ)

HIGH CORRELATION 

Distinct1095
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5041587
Minimum-3.6
Maximum100
Zeros16
Zeros (%)0.2%
Negative355
Negative (%)3.5%
Memory size166.0 KiB
2023-12-13T00:34:37.804678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.6
5-th percentile0.2
Q11.1
median2.33
Q35.2725
95-th percentile10
Maximum100
Range103.6
Interquartile range (IQR)4.1725

Descriptive statistics

Standard deviation3.5316955
Coefficient of variation (CV)1.0078583
Kurtosis112.14882
Mean3.5041587
Median Absolute Deviation (MAD)1.52
Skewness5.1430041
Sum35041.587
Variance12.472873
MonotonicityNot monotonic
2023-12-13T00:34:38.020462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.0 132
 
1.3%
3.0 130
 
1.3%
1.2 103
 
1.0%
1.0 95
 
0.9%
6.0 90
 
0.9%
10.0 83
 
0.8%
1.9 76
 
0.8%
1.5 76
 
0.8%
5.0 76
 
0.8%
2.7 73
 
0.7%
Other values (1085) 9066
90.7%
ValueCountFrequency (%)
-3.6 2
< 0.1%
-3.16 1
 
< 0.1%
-2.94 1
 
< 0.1%
-2.9 1
 
< 0.1%
-2.61 1
 
< 0.1%
-2.14 1
 
< 0.1%
-2.12 1
 
< 0.1%
-2.08 1
 
< 0.1%
-2.02 1
 
< 0.1%
-2.0 3
< 0.1%
ValueCountFrequency (%)
100.0 2
< 0.1%
25.0 1
 
< 0.1%
21.15 3
< 0.1%
20.0 2
< 0.1%
19.58 1
 
< 0.1%
19.49 1
 
< 0.1%
18.8 2
< 0.1%
18.45 1
 
< 0.1%
18.16 1
 
< 0.1%
18.0 1
 
< 0.1%

수위자료간격
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0.01
9181 
0.1
 
653
0.2
 
100
0.5
 
65
0.05
 
1

Length

Max length4
Median length4
Mean length3.9182
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.01
2nd row0.01
3rd row0.01
4th row0.01
5th row0.01

Common Values

ValueCountFrequency (%)
0.01 9181
91.8%
0.1 653
 
6.5%
0.2 100
 
1.0%
0.5 65
 
0.7%
0.05 1
 
< 0.1%

Length

2023-12-13T00:34:38.187409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:34:38.325939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01 9181
91.8%
0.1 653
 
6.5%
0.2 100
 
1.0%
0.5 65
 
0.7%
0.05 1
 
< 0.1%

Interactions

2023-12-13T00:34:32.255262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:34:30.701178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:34:31.222744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:34:31.785759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:34:32.368909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:34:30.805789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:34:31.416790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:34:31.886467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:34:32.492513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:34:30.958340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:34:31.566618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:34:32.026324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:34:32.601191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:34:31.092913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:34:31.674648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:34:32.146122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:34:38.430844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수위관측소코드순번수위자료하한수위자료상한수위자료간격
수위관측소코드1.0000.1270.0970.1560.366
순번0.1271.0000.4970.4840.077
수위자료하한0.0970.4971.0000.7360.032
수위자료상한0.1560.4840.7361.0000.056
수위자료간격0.3660.0770.0320.0561.000
2023-12-13T00:34:38.577575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수위관측소코드순번수위자료하한수위자료상한수위자료간격
수위관측소코드1.000-0.0190.0630.0510.235
순번-0.0191.0000.6740.6230.047
수위자료하한0.0630.6741.0000.7950.024
수위자료상한0.0510.6230.7951.0000.046
수위자료간격0.2350.0470.0240.0461.000

Missing values

2023-12-13T00:34:32.770267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:34:32.933690image/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

수위관측소코드지점명적용시작일자적용종료일자순번관계식수위자료하한수위자료상한수위자료간격
34381018630남양주시(진관교)2021-07-042021-08-10135.900*((h+1)-0.690)^2.155-0.310.020.01
64032004655예천군(고평교)2020-08-112020-12-31239.443*(h-0.900)^1.5162.142.840.01
81932011640구미시(구미대교)2011-10-019999-12-31135.571*((h+6)+0.100)^2.010-5.5-2.90.01
205335101650장흥군(별천교)2021-02-162021-05-13348.337*(h-0.960)^2.2781.547.170.01
40941018693광명시(시흥대교)2021-11-082021-11-30330.908*(h-0.370)^2.4802.333.20.01
158233301630완주군(용봉교)2009-07-012009-09-25280.847*(h-0.182)^1.9330.561.650.01
198865003620화순군(신성교)2009-01-012009-07-152101.536*(h-0.74)^1.2831.012.340.01
200055003680나주시(남평교)1997-01-011998-12-312911.304*(H-0.589)^2.50.741.00.01
165483301670완주군(삼례교)2011-05-112016-12-31334.307*(h-0.11)^2.1913.05.750.01
138323011665청주시(미호천교)2018-03-052018-10-05244.004*(h-0.250)^1.6801.521.970.01
수위관측소코드지점명적용시작일자적용종료일자순번관계식수위자료하한수위자료상한수위자료간격
170524001610진안군(좌포교)2009-04-232009-08-05261.308*(h-0.18)^3.1490.921.470.01
41811018697서울시(오금교)2017-09-202017-11-11218.339*(h-0.400)^2.1100.941.10.01
561001625정선군(송천교)2013-01-012013-07-15233.790*(h-0.300)^2.5000.381.040.01
126583009680대전시(원촌교)1998-01-011998-12-311103.673*(H-0.42)^2.5960.592.740.01
63502004655예천군(고평교)2017-07-312017-12-31237.327*((h+2)-0.020)^1.738-1.3-0.020.01
55932002685안동시(묵계교)2011-01-012011-07-11417.924*((h+2)-1.000)^2.6000.381.850.01
24821014630홍천군(주음치교)2018-01-012019-01-01486.052*(h-0.650)^1.8351.367.10.01
170854001660순창군(운암교)2001-01-012009-12-31161.517*(H-0.0009)^3.5440.180.440.01
195585002660광주광역시(용진교)2017-07-012018-12-31254.01*(h-0.33)^1.980.641.00.01
12181006650횡성군(횡성교)2019-01-012019-06-0718.212*((h+1)-0.410)^2.001-0.59-0.260.01