Overview

Dataset statistics

Number of variables8
Number of observations194
Missing cells17
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.2 KiB
Average record size in memory69.7 B

Variable types

DateTime3
Numeric4
Categorical1

Dataset

Description한국농어촌공사 금강사업단 금강하구둑 일일방류량 자료입니다.일자, 개문시작시간, 개문종료시간, 방류량(천통) 등
Author한국농어촌공사
URLhttps://www.data.go.kr/data/15104593/fileData.do

Alerts

조작문비 has constant value ""Constant
폐문종료수위 is highly overall correlated with 방류량(천톤)High correlation
개도(m) is highly overall correlated with 방류량(천톤)High correlation
방류량(천톤) is highly overall correlated with 폐문종료수위 and 1 other fieldsHigh correlation
개문시작시각 has 4 (2.1%) missing valuesMissing
개문시작수위 has 5 (2.6%) missing valuesMissing
폐문종료시각 has 4 (2.1%) missing valuesMissing
폐문종료수위 has 4 (2.1%) missing valuesMissing
방류량(천톤) has unique valuesUnique

Reproduction

Analysis started2024-03-14 13:43:19.649357
Analysis finished2024-03-14 13:43:24.963565
Duration5.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일자
Date

Distinct154
Distinct (%)79.4%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
Minimum2023-01-03 00:00:00
Maximum2023-12-29 00:00:00
2024-03-14T22:43:25.163120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:43:25.594879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

개문시작시각
Date

MISSING 

Distinct172
Distinct (%)90.5%
Missing4
Missing (%)2.1%
Memory size1.6 KiB
Minimum2024-03-14 00:01:00
Maximum2024-03-14 23:58:00
2024-03-14T22:43:26.232315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:43:26.652211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

개문시작수위
Real number (ℝ)

MISSING 

Distinct86
Distinct (%)45.5%
Missing5
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean1.837619
Minimum0.97
Maximum3.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-03-14T22:43:26.994725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.97
5-th percentile1.348
Q11.68
median1.87
Q31.97
95-th percentile2.366
Maximum3.19
Range2.22
Interquartile range (IQR)0.29

Descriptive statistics

Standard deviation0.29842876
Coefficient of variation (CV)0.16239969
Kurtosis3.3086893
Mean1.837619
Median Absolute Deviation (MAD)0.12
Skewness0.66212248
Sum347.31
Variance0.089059726
MonotonicityNot monotonic
2024-03-14T22:43:27.243733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.85 7
 
3.6%
1.92 6
 
3.1%
1.98 6
 
3.1%
1.89 6
 
3.1%
1.9 6
 
3.1%
1.87 6
 
3.1%
1.97 6
 
3.1%
1.99 5
 
2.6%
1.96 5
 
2.6%
1.94 4
 
2.1%
Other values (76) 132
68.0%
(Missing) 5
 
2.6%
ValueCountFrequency (%)
0.97 1
0.5%
1.11 1
0.5%
1.17 1
0.5%
1.26 1
0.5%
1.28 1
0.5%
1.29 1
0.5%
1.31 1
0.5%
1.32 1
0.5%
1.33 1
0.5%
1.34 1
0.5%
ValueCountFrequency (%)
3.19 1
0.5%
2.97 1
0.5%
2.69 1
0.5%
2.62 1
0.5%
2.54 1
0.5%
2.5 1
0.5%
2.48 1
0.5%
2.45 1
0.5%
2.42 1
0.5%
2.37 1
0.5%

폐문종료시각
Date

MISSING 

Distinct169
Distinct (%)88.9%
Missing4
Missing (%)2.1%
Memory size1.6 KiB
Minimum2024-03-14 00:01:00
Maximum2024-03-14 23:59:00
2024-03-14T22:43:27.488256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:43:27.921487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

폐문종료수위
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct110
Distinct (%)57.9%
Missing4
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean0.96168421
Minimum-0.17
Maximum1.69
Zeros0
Zeros (%)0.0%
Negative5
Negative (%)2.6%
Memory size1.8 KiB
2024-03-14T22:43:28.325992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.17
5-th percentile0.1645
Q10.705
median1.005
Q31.2975
95-th percentile1.57
Maximum1.69
Range1.86
Interquartile range (IQR)0.5925

Descriptive statistics

Standard deviation0.4400677
Coefficient of variation (CV)0.45760105
Kurtosis-0.39914298
Mean0.96168421
Median Absolute Deviation (MAD)0.295
Skewness-0.56144014
Sum182.72
Variance0.19365958
MonotonicityNot monotonic
2024-03-14T22:43:28.784215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.99 5
 
2.6%
1.0 5
 
2.6%
1.3 4
 
2.1%
1.45 4
 
2.1%
0.4 4
 
2.1%
1.04 4
 
2.1%
1.19 4
 
2.1%
1.5 4
 
2.1%
1.35 4
 
2.1%
1.17 4
 
2.1%
Other values (100) 148
76.3%
(Missing) 4
 
2.1%
ValueCountFrequency (%)
-0.17 1
0.5%
-0.16 1
0.5%
-0.09 1
0.5%
-0.05 1
0.5%
-0.04 1
0.5%
0.04 2
1.0%
0.05 1
0.5%
0.11 1
0.5%
0.16 1
0.5%
0.17 1
0.5%
ValueCountFrequency (%)
1.69 1
0.5%
1.64 1
0.5%
1.63 2
1.0%
1.61 1
0.5%
1.6 2
1.0%
1.59 1
0.5%
1.58 1
0.5%
1.57 2
1.0%
1.56 1
0.5%
1.55 1
0.5%

조작문비
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
20
194 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20
2nd row20
3rd row20
4th row20
5th row20

Common Values

ValueCountFrequency (%)
20 194
100.0%

Length

2024-03-14T22:43:29.206775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T22:43:29.508980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20 194
100.0%

개도(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9051546
Minimum0.5
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-03-14T22:43:29.808417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1
Q11.5
median1.5
Q32
95-th percentile3
Maximum10
Range9.5
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation1.2919336
Coefficient of variation (CV)0.67812533
Kurtosis17.167785
Mean1.9051546
Median Absolute Deviation (MAD)0.5
Skewness3.8733755
Sum369.6
Variance1.6690925
MonotonicityNot monotonic
2024-03-14T22:43:30.176846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1.5 83
42.8%
2.0 54
27.8%
1.0 25
 
12.9%
3.0 11
 
5.7%
1.2 4
 
2.1%
6.0 3
 
1.5%
2.5 2
 
1.0%
1.3 2
 
1.0%
0.8 2
 
1.0%
7.0 2
 
1.0%
Other values (6) 6
 
3.1%
ValueCountFrequency (%)
0.5 1
 
0.5%
0.6 1
 
0.5%
0.8 2
 
1.0%
1.0 25
 
12.9%
1.2 4
 
2.1%
1.3 2
 
1.0%
1.5 83
42.8%
2.0 54
27.8%
2.5 2
 
1.0%
3.0 11
 
5.7%
ValueCountFrequency (%)
10.0 1
 
0.5%
9.0 1
 
0.5%
8.0 1
 
0.5%
7.0 2
 
1.0%
6.0 3
 
1.5%
5.0 1
 
0.5%
3.0 11
 
5.7%
2.5 2
 
1.0%
2.0 54
27.8%
1.5 83
42.8%

방류량(천톤)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct194
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39785.758
Minimum615
Maximum988416
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-03-14T22:43:30.578221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum615
5-th percentile8990.8
Q116615.5
median25343.5
Q335166.75
95-th percentile65987.45
Maximum988416
Range987801
Interquartile range (IQR)18551.25

Descriptive statistics

Standard deviation88840.653
Coefficient of variation (CV)2.2329763
Kurtosis75.067147
Mean39785.758
Median Absolute Deviation (MAD)8965
Skewness8.0758513
Sum7718437
Variance7.8926616 × 109
MonotonicityNot monotonic
2024-03-14T22:43:31.027247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17042 1
 
0.5%
46369 1
 
0.5%
41686 1
 
0.5%
11569 1
 
0.5%
7046 1
 
0.5%
16626 1
 
0.5%
15918 1
 
0.5%
12212 1
 
0.5%
34284 1
 
0.5%
42023 1
 
0.5%
Other values (184) 184
94.8%
ValueCountFrequency (%)
615 1
0.5%
1899 1
0.5%
4920 1
0.5%
5391 1
0.5%
7046 1
0.5%
7562 1
0.5%
7898 1
0.5%
8342 1
0.5%
8607 1
0.5%
8892 1
0.5%
ValueCountFrequency (%)
988416 1
0.5%
528980 1
0.5%
475200 1
0.5%
302400 1
0.5%
264600 1
0.5%
91681 1
0.5%
85457 1
0.5%
82407 1
0.5%
70216 1
0.5%
67214 1
0.5%

Interactions

2024-03-14T22:43:22.951396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:43:19.909684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:43:20.899604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:43:21.962340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:43:23.187894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:43:20.145034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:43:21.150741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:43:22.203297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:43:23.461611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:43:20.409839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:43:21.426662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:43:22.477163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:43:23.700781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:43:20.661906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:43:21.697330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:43:22.707492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T22:43:31.294924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
개문시작수위폐문종료수위개도(m)방류량(천톤)
개문시작수위1.0000.4290.715NaN
폐문종료수위0.4291.0000.3240.171
개도(m)0.7150.3241.0000.975
방류량(천톤)NaN0.1710.9751.000
2024-03-14T22:43:31.563797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
개문시작수위폐문종료수위개도(m)방류량(천톤)
개문시작수위1.0000.2440.2810.336
폐문종료수위0.2441.000-0.440-0.756
개도(m)0.281-0.4401.0000.654
방류량(천톤)0.336-0.7560.6541.000

Missing values

2024-03-14T22:43:24.050744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T22:43:24.470157image/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.
2024-03-14T22:43:24.792749image/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

일자개문시작시각개문시작수위폐문종료시각폐문종료수위조작문비개도(m)방류량(천톤)
02023-01-0314:071.8417:511.3201.017042
12023-01-0517:001.5819:051.29201.09044
22023-01-1119:561.9923:561.17201.025899
32023-01-1508:342.0212:551.05201.530512
42023-01-1915:041.9619:020.92202.032437
52023-01-2608:091.8711:471.0201.527126
62023-02-0214:401.9718:101.28202.021880
72023-02-0718:481.9823:470.79201.536927
82023-02-1612:021.8717:490.82202.032468
92023-02-2709:031.9611:591.29202.021246
일자개문시작시각개문시작수위폐문종료시각폐문종료수위조작문비개도(m)방류량(천톤)
1842023-12-1706:471.6809:560.95202.022516
1852023-12-1807:292.0811:261.47201.519594
1862023-12-1821:301.9500:171.5201.014394
1872023-12-1908:271.8911:051.63201.58342
1882023-12-1921:551.9701:380.98201.530976
1892023-12-2021:431.9301:580.93201.531162
1902023-12-2122:411.8403:061.07201.524052
1912023-12-2501:431.9405:151.3201.320286
1922023-12-2704:071.907:011.25201.520522
1932023-12-2919:071.8722:521.17201.521993