Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells10076
Missing cells (%)9.2%
Duplicate rows434
Duplicate rows (%)4.3%
Total size in memory1015.6 KiB
Average record size in memory104.0 B

Variable types

Numeric8
Categorical3

Alerts

관할기관명 has constant value ""Constant
Dataset has 434 (4.3%) duplicate rowsDuplicates
보관측소명칭 is highly overall correlated with 보관측소구분코드 and 4 other fieldsHigh correlation
보관측소주소 is highly overall correlated with 보관측소구분코드 and 4 other fieldsHigh correlation
보관측소구분코드 is highly overall correlated with 보상류수위값(단위:El.m)(El.m) and 3 other fieldsHigh correlation
보상류수위값(단위:El.m)(El.m) is highly overall correlated with 보관측소구분코드 and 3 other fieldsHigh correlation
보하류수위값(단위:El.m)(El.m) is highly overall correlated with 보관측소구분코드 and 3 other fieldsHigh correlation
유입량(단위:m^3/s)(㎥/s) is highly overall correlated with 총방류량(단위:m^3/s)(㎥/s)High correlation
저수량(단위:만m^3)(만m³/s) is highly overall correlated with 보관측소명칭 and 1 other fieldsHigh correlation
총방류량(단위:m^3/s)(㎥/s) is highly overall correlated with 유입량(단위:m^3/s)(㎥/s)High correlation
유입량(단위:m^3/s)(㎥/s) has 2519 (25.2%) missing valuesMissing
저수량(단위:만m^3)(만m³/s) has 2519 (25.2%) missing valuesMissing
공용량(단위:백만m^3)(백만m³/s) has 2519 (25.2%) missing valuesMissing
총방류량(단위:m^3/s)(㎥/s) has 2519 (25.2%) missing valuesMissing
공용량(단위:백만m^3)(백만m³/s) has 5324 (53.2%) zerosZeros

Reproduction

Analysis started2024-05-17 19:22:07.000163
Analysis finished2024-05-17 19:22:30.272919
Duration23.27 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

보관측일자
Real number (ℝ)

Distinct1918
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20191740
Minimum20160101
Maximum20240517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T04:22:30.591906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20160101
5-th percentile20160410
Q120170507
median20181229
Q320230112
95-th percentile20240210
Maximum20240517
Range80416
Interquartile range (IQR)59605

Descriptive statistics

Standard deviation26678.403
Coefficient of variation (CV)0.0013212533
Kurtosis-1.0030962
Mean20191740
Median Absolute Deviation (MAD)11122
Skewness0.60831941
Sum2.019174 × 1011
Variance7.1173721 × 108
MonotonicityNot monotonic
2024-05-18T04:22:31.304753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20181016 18
 
0.2%
20181027 15
 
0.1%
20190722 12
 
0.1%
20240217 12
 
0.1%
20170310 11
 
0.1%
20230411 11
 
0.1%
20180517 11
 
0.1%
20190515 11
 
0.1%
20240415 11
 
0.1%
20200118 11
 
0.1%
Other values (1908) 9877
98.8%
ValueCountFrequency (%)
20160101 2
 
< 0.1%
20160102 4
< 0.1%
20160103 1
 
< 0.1%
20160104 5
0.1%
20160105 4
< 0.1%
20160106 2
 
< 0.1%
20160107 6
0.1%
20160108 7
0.1%
20160109 6
0.1%
20160110 3
< 0.1%
ValueCountFrequency (%)
20240517 2
 
< 0.1%
20240516 6
0.1%
20240515 8
0.1%
20240514 6
0.1%
20240513 3
 
< 0.1%
20240512 6
0.1%
20240511 6
0.1%
20240510 8
0.1%
20240509 6
0.1%
20240508 2
 
< 0.1%

보관측소구분코드
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2156294.6
Minimum1007801
Maximum5004802
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T04:22:31.695500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1007801
5-th percentile1007801
Q11007803
median2011801
Q33010801
95-th percentile5004801
Maximum5004802
Range3997001
Interquartile range (IQR)2002998

Descriptive statistics

Standard deviation1161406.9
Coefficient of variation (CV)0.53861234
Kurtosis0.96934753
Mean2156294.6
Median Absolute Deviation (MAD)1001000
Skewness1.237618
Sum2.1562946 × 1010
Variance1.3488659 × 1012
MonotonicityNot monotonic
2024-05-18T04:22:32.220922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1007803 1089
 
10.9%
1007802 1027
 
10.3%
1007801 1007
 
10.1%
3010801 542
 
5.4%
2011801 532
 
5.3%
2009801 522
 
5.2%
2007801 521
 
5.2%
3012801 521
 
5.2%
2017801 517
 
5.2%
3012802 517
 
5.2%
Other values (7) 3205
32.0%
ValueCountFrequency (%)
1007801 1007
10.1%
1007802 1027
10.3%
1007803 1089
10.9%
2007801 521
5.2%
2009801 522
5.2%
2009802 517
5.2%
2011801 532
5.3%
2011802 516
5.2%
2014801 490
4.9%
2014802 489
4.9%
ValueCountFrequency (%)
5004802 499
5.0%
5004801 502
5.0%
3012802 517
5.2%
3012801 521
5.2%
3010801 542
5.4%
2022510 192
 
1.9%
2017801 517
5.2%
2014802 489
4.9%
2014801 490
4.9%
2011802 516
5.2%

보관측소명칭
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
이포보
1089 
여주보
1027 
강천보
1007 
세종보
 
542
칠곡보
 
532
Other values (12)
5803 

Length

Max length6
Median length3
Mean length3.4626
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row칠곡보
2nd row이포보
3rd row강천보
4th row승촌보
5th row구미보

Common Values

ValueCountFrequency (%)
이포보 1089
 
10.9%
여주보 1027
 
10.3%
강천보 1007
 
10.1%
세종보 542
 
5.4%
칠곡보 532
 
5.3%
낙단보 522
 
5.2%
상주보 521
 
5.2%
공주보 521
 
5.2%
창녕·함안보 517
 
5.2%
구미보 517
 
5.2%
Other values (7) 3205
32.0%

Length

2024-05-18T04:22:32.777477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
이포보 1089
 
10.9%
여주보 1027
 
10.3%
강천보 1007
 
10.1%
세종보 542
 
5.4%
칠곡보 532
 
5.3%
낙단보 522
 
5.2%
상주보 521
 
5.2%
공주보 521
 
5.2%
백제보 517
 
5.2%
창녕·함안보 517
 
5.2%
Other values (7) 3205
32.0%

보관측소주소
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
6221 
경상북도 칠곡군 약목면 관호리 408번지
 
532
경상북도 의성군 단밀면 생송리 1668-1(천)
 
522
경상북도 상주시 중동면 오상리 782-3번지
 
521
경상북도 구미시 해평면 원리 61-1
 
517
Other values (4)
1687 

Length

Max length26
Median length1
Mean length9.2227
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상북도 칠곡군 약목면 관호리 408번지
2nd row
3rd row
4th row
5th row경상북도 구미시 해평면 원리 61-1

Common Values

ValueCountFrequency (%)
6221
62.2%
경상북도 칠곡군 약목면 관호리 408번지 532
 
5.3%
경상북도 의성군 단밀면 생송리 1668-1(천) 522
 
5.2%
경상북도 상주시 중동면 오상리 782-3번지 521
 
5.2%
경상북도 구미시 해평면 원리 61-1 517
 
5.2%
대구광역시 달성군 다사읍 매곡리 1174번지 516
 
5.2%
경상북도 고령군 성산면 오곡리 56-3번지 490
 
4.9%
경상남도 창녕군 이방면 장천리 1169(천) 489
 
4.9%
부산광역시 사하구 하단동 192
 
1.9%

Length

2024-05-18T04:22:33.236137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T04:22:33.841733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 2582
 
13.9%
약목면 532
 
2.9%
관호리 532
 
2.9%
408번지 532
 
2.9%
칠곡군 532
 
2.9%
의성군 522
 
2.8%
단밀면 522
 
2.8%
생송리 522
 
2.8%
1668-1(천 522
 
2.8%
오상리 521
 
2.8%
Other values (24) 11192
60.5%

관할기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
한국수자원공사
10000 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
한국수자원공사 10000
100.0%

Length

2024-05-18T04:22:34.301534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T04:22:34.622301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한국수자원공사 10000
100.0%

보상류수위값(단위:El.m)(El.m)
Real number (ℝ)

HIGH CORRELATION 

Distinct1952
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.582418
Minimum-1.6
Maximum47.4
Zeros30
Zeros (%)0.3%
Negative39
Negative (%)0.4%
Memory size166.0 KiB
2024-05-18T04:22:34.972045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.6
5-th percentile1.53
Q17.5
median25.54
Q333.07
95-th percentile46.41
Maximum47.4
Range49
Interquartile range (IQR)25.57

Descriptive statistics

Standard deviation14.385801
Coefficient of variation (CV)0.66655187
Kurtosis-1.4157983
Mean21.582418
Median Absolute Deviation (MAD)12.69
Skewness0.048361317
Sum215824.18
Variance206.95128
MonotonicityNot monotonic
2024-05-18T04:22:35.388099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.02 117
 
1.2%
33.03 117
 
1.2%
33.02 109
 
1.1%
38.01 104
 
1.0%
28.02 101
 
1.0%
28.03 101
 
1.0%
28.01 92
 
0.9%
33.04 81
 
0.8%
38.03 76
 
0.8%
47.02 70
 
0.7%
Other values (1942) 9032
90.3%
ValueCountFrequency (%)
-1.6 1
< 0.1%
-1.54 1
< 0.1%
-1.52 1
< 0.1%
-1.49 1
< 0.1%
-1.48 1
< 0.1%
-1.47 1
< 0.1%
-1.46 1
< 0.1%
-1.4 2
< 0.1%
-1.34 2
< 0.1%
-1.33 1
< 0.1%
ValueCountFrequency (%)
47.4 1
 
< 0.1%
47.33 1
 
< 0.1%
47.32 2
< 0.1%
47.3 1
 
< 0.1%
47.25 1
 
< 0.1%
47.24 1
 
< 0.1%
47.23 4
< 0.1%
47.22 1
 
< 0.1%
47.2 1
 
< 0.1%
47.19 1
 
< 0.1%

보하류수위값(단위:El.m)(El.m)
Real number (ℝ)

HIGH CORRELATION 

Distinct3192
Distinct (%)31.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.324114
Minimum-1.96
Maximum53.75
Zeros40
Zeros (%)0.4%
Negative575
Negative (%)5.8%
Memory size166.0 KiB
2024-05-18T04:22:35.714485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.96
5-th percentile-0.68025
Q14.28
median18.41
Q328.31
95-th percentile39.72
Maximum53.75
Range55.71
Interquartile range (IQR)24.03

Descriptive statistics

Standard deviation13.207181
Coefficient of variation (CV)0.76235822
Kurtosis-1.478523
Mean17.324114
Median Absolute Deviation (MAD)13.48
Skewness0.042737011
Sum173241.14
Variance174.42962
MonotonicityNot monotonic
2024-05-18T04:22:36.059895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.13 71
 
0.7%
33.14 65
 
0.7%
33.16 60
 
0.6%
33.12 56
 
0.6%
33.15 54
 
0.5%
33.11 45
 
0.4%
0.0 40
 
0.4%
33.1 38
 
0.4%
33.17 38
 
0.4%
1.53 35
 
0.4%
Other values (3182) 9498
95.0%
ValueCountFrequency (%)
-1.96 1
 
< 0.1%
-1.93 1
 
< 0.1%
-1.865 1
 
< 0.1%
-1.858 1
 
< 0.1%
-1.84 3
< 0.1%
-1.79 1
 
< 0.1%
-1.76 1
 
< 0.1%
-1.73 2
< 0.1%
-1.72 1
 
< 0.1%
-1.71 1
 
< 0.1%
ValueCountFrequency (%)
53.75 1
< 0.1%
41.639 1
< 0.1%
41.356 1
< 0.1%
41.192 1
< 0.1%
40.864 1
< 0.1%
40.749 1
< 0.1%
40.622 1
< 0.1%
40.57 1
< 0.1%
40.47 2
< 0.1%
40.463 1
< 0.1%

유입량(단위:m^3/s)(㎥/s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3545
Distinct (%)47.4%
Missing2519
Missing (%)25.2%
Infinite0
Infinite (%)0.0%
Mean129.8738
Minimum0
Maximum10444.874
Zeros37
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T04:22:36.465697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.8
Q142.998
median72.6
Q3120.8
95-th percentile390.8
Maximum10444.874
Range10444.874
Interquartile range (IQR)77.802

Descriptive statistics

Standard deviation270.27204
Coefficient of variation (CV)2.0810358
Kurtosis332.3519
Mean129.8738
Median Absolute Deviation (MAD)35.3
Skewness12.868116
Sum971585.93
Variance73046.977
MonotonicityNot monotonic
2024-05-18T04:22:36.826586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 37
 
0.4%
59.6 13
 
0.1%
58.2 12
 
0.1%
35.7 12
 
0.1%
45.5 12
 
0.1%
59.8 12
 
0.1%
33.6 11
 
0.1%
69.1 11
 
0.1%
57.4 11
 
0.1%
38.9 11
 
0.1%
Other values (3535) 7339
73.4%
(Missing) 2519
 
25.2%
ValueCountFrequency (%)
0.0 37
0.4%
0.5 1
 
< 0.1%
2.3 1
 
< 0.1%
2.6 1
 
< 0.1%
3.0 1
 
< 0.1%
3.06 1
 
< 0.1%
3.4 1
 
< 0.1%
3.6 1
 
< 0.1%
3.7 1
 
< 0.1%
4.2 1
 
< 0.1%
ValueCountFrequency (%)
10444.874 1
< 0.1%
4062.2 1
< 0.1%
3836.8 2
< 0.1%
3496.1 1
< 0.1%
3330.977 1
< 0.1%
3318.2 1
< 0.1%
3088.9 1
< 0.1%
3047.4 1
< 0.1%
2954.8 1
< 0.1%
2919.6 2
< 0.1%

저수량(단위:만m^3)(만m³/s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1594
Distinct (%)21.3%
Missing2519
Missing (%)25.2%
Infinite0
Infinite (%)0.0%
Mean34.300235
Minimum0
Maximum307.493
Zeros30
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T04:22:37.171922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.45
Q110.63
median16.3
Q353.58
95-th percentile97.37
Maximum307.493
Range307.493
Interquartile range (IQR)42.95

Descriptive statistics

Standard deviation37.87832
Coefficient of variation (CV)1.1043166
Kurtosis19.38866
Mean34.300235
Median Absolute Deviation (MAD)10.68
Skewness3.3690606
Sum256600.06
Variance1434.7671
MonotonicityNot monotonic
2024-05-18T04:22:37.582333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.82 99
 
1.0%
8.77 97
 
1.0%
14.39 93
 
0.9%
11.4 92
 
0.9%
11.35 91
 
0.9%
14.43 83
 
0.8%
14.48 73
 
0.7%
11.31 66
 
0.7%
11.44 63
 
0.6%
24.26 60
 
0.6%
Other values (1584) 6664
66.6%
(Missing) 2519
 
25.2%
ValueCountFrequency (%)
0.0 30
0.3%
0.95 22
0.2%
0.96 22
0.2%
0.962 1
 
< 0.1%
0.963 6
 
0.1%
0.964 5
 
0.1%
0.965 5
 
0.1%
0.966 4
 
< 0.1%
0.967 1
 
< 0.1%
0.968 6
 
0.1%
ValueCountFrequency (%)
307.493 1
 
< 0.1%
306.092 3
< 0.1%
305.625 5
0.1%
305.159 1
 
< 0.1%
304.694 7
0.1%
304.229 2
 
< 0.1%
303.764 6
0.1%
303.299 6
0.1%
302.835 7
0.1%
302.372 4
< 0.1%

공용량(단위:백만m^3)(백만m³/s)
Real number (ℝ)

MISSING  ZEROS 

Distinct616
Distinct (%)8.2%
Missing2519
Missing (%)25.2%
Infinite0
Infinite (%)0.0%
Mean1.9731747
Minimum-0.31
Maximum51.88
Zeros5324
Zeros (%)53.2%
Negative4
Negative (%)< 0.1%
Memory size166.0 KiB
2024-05-18T04:22:38.027957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.31
5-th percentile0
Q10
median0
Q30.29
95-th percentile13.39
Maximum51.88
Range52.19
Interquartile range (IQR)0.29

Descriptive statistics

Standard deviation5.3952234
Coefficient of variation (CV)2.7342857
Kurtosis25.399507
Mean1.9731747
Median Absolute Deviation (MAD)0
Skewness4.2885039
Sum14761.32
Variance29.108435
MonotonicityNot monotonic
2024-05-18T04:22:38.570815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5324
53.2%
4.7 29
 
0.3%
4.68 28
 
0.3%
0.03 26
 
0.3%
13.11 25
 
0.2%
0.06 24
 
0.2%
0.88 24
 
0.2%
0.08 23
 
0.2%
0.82 22
 
0.2%
4.69 21
 
0.2%
Other values (606) 1935
 
19.4%
(Missing) 2519
25.2%
ValueCountFrequency (%)
-0.31 2
 
< 0.1%
-0.13 1
 
< 0.1%
-0.09 1
 
< 0.1%
0.0 5324
53.2%
0.01 20
 
0.2%
0.03 26
 
0.3%
0.04 12
 
0.1%
0.05 20
 
0.2%
0.06 24
 
0.2%
0.07 14
 
0.1%
ValueCountFrequency (%)
51.88 2
< 0.1%
51.81 2
< 0.1%
51.74 3
< 0.1%
51.67 2
< 0.1%
51.6 1
 
< 0.1%
51.52 1
 
< 0.1%
51.45 3
< 0.1%
51.38 1
 
< 0.1%
51.31 2
< 0.1%
50.01 1
 
< 0.1%

총방류량(단위:m^3/s)(㎥/s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3551
Distinct (%)47.5%
Missing2519
Missing (%)25.2%
Infinite0
Infinite (%)0.0%
Mean129.58987
Minimum0
Maximum9814.892
Zeros42
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T04:22:38.973515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.326
Q142.9
median72.8
Q3120.9
95-th percentile396.7
Maximum9814.892
Range9814.892
Interquartile range (IQR)78

Descriptive statistics

Standard deviation265.46953
Coefficient of variation (CV)2.0485361
Kurtosis286.73029
Mean129.58987
Median Absolute Deviation (MAD)35.6
Skewness12.039786
Sum969461.82
Variance70474.072
MonotonicityNot monotonic
2024-05-18T04:22:39.380815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 42
 
0.4%
35.1 15
 
0.1%
38.2 14
 
0.1%
68.6 14
 
0.1%
65.5 13
 
0.1%
32.6 13
 
0.1%
58.2 13
 
0.1%
30.0 12
 
0.1%
51.5 12
 
0.1%
45.1 12
 
0.1%
Other values (3541) 7321
73.2%
(Missing) 2519
 
25.2%
ValueCountFrequency (%)
0.0 42
0.4%
0.2 1
 
< 0.1%
0.5 2
 
< 0.1%
0.7 1
 
< 0.1%
0.9 1
 
< 0.1%
1.3 1
 
< 0.1%
1.5 1
 
< 0.1%
1.9 1
 
< 0.1%
2.3 1
 
< 0.1%
2.5 1
 
< 0.1%
ValueCountFrequency (%)
9814.892 1
< 0.1%
3991.7 1
< 0.1%
3836.3 2
< 0.1%
3389.355 1
< 0.1%
3307.8 1
< 0.1%
3243.3 1
< 0.1%
3152.6 1
< 0.1%
3030.6 1
< 0.1%
2823.3 1
< 0.1%
2751.7 2
< 0.1%

Interactions

2024-05-18T04:22:26.846360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:10.435838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:12.908195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:15.240357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:17.505076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:19.683695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:22.061099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:24.593670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:27.110098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:10.779067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:13.256708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:15.501891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:17.806551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:19.910802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:22.420938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:24.917274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:27.422920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:11.132085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:13.561045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:15.786774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:18.098599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:20.215912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:22.756665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:25.250842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:27.667924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:11.407254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:13.795036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:16.061792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:18.362643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:20.543194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:23.048203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:25.554182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:27.896573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:11.740330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:14.090644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:16.314180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:18.623751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:20.843857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:23.365980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:25.823655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:28.173027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:12.056380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:14.377906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:16.551038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:18.892958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:21.090884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:23.663410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:26.107262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:28.470470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:12.336051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:14.675522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:16.793726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:19.173854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:21.334555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:23.953067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:26.370948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:28.749722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:12.623371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:14.943184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:17.058402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:19.441828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:21.646565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:24.273288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T04:22:26.612053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T04:22:39.667360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
보관측일자보관측소구분코드보관측소명칭보관측소주소보상류수위값(단위:El.m)(El.m)보하류수위값(단위:El.m)(El.m)유입량(단위:m^3/s)(㎥/s)저수량(단위:만m^3)(만m³/s)공용량(단위:백만m^3)(백만m³/s)총방류량(단위:m^3/s)(㎥/s)
보관측일자1.0000.0000.1330.1150.1520.0680.0480.1190.2640.079
보관측소구분코드0.0001.0001.0000.6810.8400.8100.0270.6430.4040.035
보관측소명칭0.1331.0001.0001.0000.9880.9790.0800.9250.6140.073
보관측소주소0.1150.6811.0001.0000.9320.9610.0400.8500.4810.019
보상류수위값(단위:El.m)(El.m)0.1520.8400.9880.9321.0000.9670.0330.7170.6820.000
보하류수위값(단위:El.m)(El.m)0.0680.8100.9790.9610.9671.0000.0710.6250.4290.067
유입량(단위:m^3/s)(㎥/s)0.0480.0270.0800.0400.0330.0711.0000.6620.0000.946
저수량(단위:만m^3)(만m³/s)0.1190.6430.9250.8500.7170.6250.6621.0000.3090.642
공용량(단위:백만m^3)(백만m³/s)0.2640.4040.6140.4810.6820.4290.0000.3091.0000.000
총방류량(단위:m^3/s)(㎥/s)0.0790.0350.0730.0190.0000.0670.9460.6420.0001.000
2024-05-18T04:22:40.012416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
보관측소명칭보관측소주소
보관측소명칭1.0001.000
보관측소주소1.0001.000
2024-05-18T04:22:40.260258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
보관측일자보관측소구분코드보상류수위값(단위:El.m)(El.m)보하류수위값(단위:El.m)(El.m)유입량(단위:m^3/s)(㎥/s)저수량(단위:만m^3)(만m³/s)공용량(단위:백만m^3)(백만m³/s)총방류량(단위:m^3/s)(㎥/s)보관측소명칭보관측소주소
보관측일자1.0000.012-0.040-0.0310.085-0.064-0.0140.0840.0560.056
보관측소구분코드0.0121.000-0.871-0.887-0.3840.1590.395-0.3830.9990.518
보상류수위값(단위:El.m)(El.m)-0.040-0.8711.0000.9800.227-0.081-0.3290.2260.9410.776
보하류수위값(단위:El.m)(El.m)-0.031-0.8870.9801.0000.233-0.162-0.3310.2340.8930.673
유입량(단위:m^3/s)(㎥/s)0.085-0.3840.2270.2331.0000.120-0.2070.9910.0410.023
저수량(단위:만m^3)(만m³/s)-0.0640.159-0.081-0.1620.1201.0000.0310.1150.7560.673
공용량(단위:백만m^3)(백만m³/s)-0.0140.395-0.329-0.331-0.2070.0311.000-0.1980.2920.242
총방류량(단위:m^3/s)(㎥/s)0.084-0.3830.2260.2340.9910.115-0.1981.0000.0340.009
보관측소명칭0.0560.9990.9410.8930.0410.7560.2920.0341.0001.000
보관측소주소0.0560.5180.7760.6730.0230.6730.2420.0091.0001.000

Missing values

2024-05-18T04:22:29.061656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T04:22:29.642372image/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.
2024-05-18T04:22:30.069538image/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

보관측일자보관측소구분코드보관측소명칭보관측소주소관할기관명보상류수위값(단위:El.m)(El.m)보하류수위값(단위:El.m)(El.m)유입량(단위:m^3/s)(㎥/s)저수량(단위:만m^3)(만m³/s)공용량(단위:백만m^3)(백만m³/s)총방류량(단위:m^3/s)(㎥/s)
429202404262011801칠곡보경상북도 칠곡군 약목면 관호리 408번지한국수자원공사25.618.336<NA><NA><NA><NA>
22911201804181007803이포보한국수자원공사28.225.3177.415.240.0176.4
12580201911251007801강천보한국수자원공사38.1633.31101.1949.4520.0101.718
34097201606285004801승촌보한국수자원공사7.523.5514.49.010.014.4
25100201712152009802구미보경상북도 구미시 해평면 원리 61-1한국수자원공사32.5225.7639.652.860.050.2
20545201808242009802구미보경상북도 구미시 해평면 원리 61-1한국수자원공사32.5825.6754.453.250.052.1
21666201806272007801상주보경상북도 상주시 중동면 오상리 782-3번지한국수자원공사47.0239.9660.527.490.062.4
12219201912131007802여주보한국수자원공사33.0328.2489.34311.3980.090.344
12145201912172009801낙단보경상북도 의성군 단밀면 생송리 1668-1(천)한국수자원공사39.8832.3829.98133.9730.027.944
29226201703111007803이포보한국수자원공사28.0225.3497.714.430.097.7
보관측일자보관측소구분코드보관측소명칭보관측소주소관할기관명보상류수위값(단위:El.m)(El.m)보하류수위값(단위:El.m)(El.m)유입량(단위:m^3/s)(㎥/s)저수량(단위:만m^3)(만m³/s)공용량(단위:백만m^3)(백만m³/s)총방류량(단위:m^3/s)(㎥/s)
36551201602201007801강천보한국수자원공사38.033.1237.28.730.037.2
36636201602151007803이포보한국수자원공사28.0125.4398.614.390.0100.7
4260202310162009801낙단보경상북도 의성군 단밀면 생송리 1668-1(천)한국수자원공사39.8532.568<NA><NA><NA><NA>
31359201611192014801달성보경상북도 고령군 성산면 오곡리 56-3번지한국수자원공사14.0310.5769.958.850.072.1
31924201610201007801강천보한국수자원공사38.1233.1487.69.270.087.0
6854202306081007802여주보한국수자원공사33.1128.356<NA><NA><NA><NA>
3236202312072009802구미보경상북도 구미시 해평면 원리 61-1한국수자원공사32.5825.708<NA><NA><NA><NA>
4174202310211007801강천보한국수자원공사38.1933.211<NA><NA><NA><NA>
14280201908313012801공주보한국수자원공사3.723.1770.4542.090.070.677
17727201902251007802여주보한국수자원공사33.0428.29124.211.440.0124.2

Duplicate rows

Most frequently occurring

보관측일자보관측소구분코드보관측소명칭보관측소주소관할기관명보상류수위값(단위:El.m)(El.m)보하류수위값(단위:El.m)(El.m)유입량(단위:m^3/s)(㎥/s)저수량(단위:만m^3)(만m³/s)공용량(단위:백만m^3)(백만m³/s)총방류량(단위:m^3/s)(㎥/s)# duplicates
200201810161007801강천보한국수자원공사38.2633.33141.39.910.0141.94
206201810271007801강천보한국수자원공사38.1333.3136.59.320.0137.03
207201810271007803이포보한국수자원공사26.4125.18172.15.698.65172.13
331202301101007803이포보한국수자원공사28.0425.26<NA><NA><NA><NA>3
0201601081007801강천보한국수자원공사38.0233.1358.48.820.057.32
1201601081007803이포보한국수자원공사28.0325.362.914.480.062.42
2201601171007802여주보한국수자원공사33.0428.0456.911.440.056.42
3201601181007803이포보한국수자원공사28.0225.3161.314.430.060.82
4201601191007801강천보한국수자원공사37.9633.0950.28.60.1251.62
5201601201007802여주보한국수자원공사33.028.1771.611.270.071.62