Overview

Dataset statistics

Number of variables5
Number of observations509
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.0 KiB
Average record size in memory42.3 B

Variable types

DateTime3
Numeric2

Dataset

Description한국농어촌공사 석문호 배수갑문 일일방류량에 대한 데이터로 일자 및 방류량 등의 항목을 제공합니다. 일자 : 날짜형식 방류시작시간 : 00:00 시간:분 방류종료시간 : 00:00 시간:분 개문수 : 수문 개방 수량(갯수) 총 배제량(천톤) : 천톤
URLhttps://www.data.go.kr/data/15113934/fileData.do

Reproduction

Analysis started2023-12-12 16:52:52.827103
Analysis finished2023-12-12 16:52:53.782271
Duration0.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일자
Date

Distinct480
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
Minimum2012-03-08 00:00:00
Maximum2022-12-26 00:00:00
2023-12-13T01:52:53.860498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:52:53.995721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

방류시작시간
Date

Distinct95
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
Minimum2023-12-13 00:00:00
Maximum2023-12-13 23:30:00
2023-12-13T01:52:54.167677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:52:54.312525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

종류종료시간
Date

Distinct102
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
Minimum2023-12-13 00:00:00
Maximum2023-12-13 23:50:00
2023-12-13T01:52:54.454425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:52:54.591865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

개문수
Real number (ℝ)

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0785855
Minimum2
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-12-13T01:52:54.695035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median6
Q38
95-th percentile8
Maximum8
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9036106
Coefficient of variation (CV)0.31316671
Kurtosis-1.2500897
Mean6.0785855
Median Absolute Deviation (MAD)2
Skewness-0.41720059
Sum3094
Variance3.6237334
MonotonicityNot monotonic
2023-12-13T01:52:54.800434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
8 201
39.5%
4 96
18.9%
6 55
 
10.8%
5 55
 
10.8%
7 51
 
10.0%
3 38
 
7.5%
2 13
 
2.6%
ValueCountFrequency (%)
2 13
 
2.6%
3 38
 
7.5%
4 96
18.9%
5 55
 
10.8%
6 55
 
10.8%
7 51
 
10.0%
8 201
39.5%
ValueCountFrequency (%)
8 201
39.5%
7 51
 
10.0%
6 55
 
10.8%
5 55
 
10.8%
4 96
18.9%
3 38
 
7.5%
2 13
 
2.6%

방류량(천톤)
Real number (ℝ)

Distinct391
Distinct (%)76.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3068.2233
Minimum378.896
Maximum13595.663
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-12-13T01:52:54.967955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum378.896
5-th percentile1287.5624
Q12203.478
median3011.014
Q33791.289
95-th percentile5527.6964
Maximum13595.663
Range13216.767
Interquartile range (IQR)1587.811

Descriptive statistics

Standard deviation1462.1866
Coefficient of variation (CV)0.47655808
Kurtosis8.1124101
Mean3068.2233
Median Absolute Deviation (MAD)780.275
Skewness1.8851233
Sum1561725.7
Variance2137989.8
MonotonicityNot monotonic
2023-12-13T01:52:55.141151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3044.074 10
 
2.0%
3036.705 8
 
1.6%
3051.449 7
 
1.4%
3791.289 7
 
1.4%
3809.702 6
 
1.2%
3059.994 6
 
1.2%
2280.29 5
 
1.0%
2285.821 5
 
1.0%
2298.053 4
 
0.8%
2655.504 4
 
0.8%
Other values (381) 447
87.8%
ValueCountFrequency (%)
378.896 1
0.2%
385.889 1
0.2%
451.322 1
0.2%
455.173 1
0.2%
531.744 1
0.2%
611.618 1
0.2%
680.527 1
0.2%
681.186 1
0.2%
760.465 1
0.2%
760.834 1
0.2%
ValueCountFrequency (%)
13595.663 1
0.2%
11480.473 1
0.2%
9187.651 1
0.2%
8729.946 1
0.2%
7855.838 1
0.2%
7742.131 1
0.2%
7543.217 1
0.2%
7472.04 1
0.2%
7056.309 2
0.4%
6936.467 1
0.2%

Interactions

2023-12-13T01:52:53.307444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:52:53.065600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:52:53.435144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:52:53.187207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:52:55.243872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
방류시작시간개문수방류량(천톤)
방류시작시간1.0000.0000.784
개문수0.0001.0000.269
방류량(천톤)0.7840.2691.000
2023-12-13T01:52:55.337164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
개문수방류량(천톤)
개문수1.0000.388
방류량(천톤)0.3881.000

Missing values

2023-12-13T01:52:53.604221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:52:53.730200image/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

일자방류시작시간종류종료시간개문수방류량(천톤)
02012-03-0809:0011:3064108.508
12012-04-0509:0011:0063746.485
22012-04-0609:0011:2064010.49
32012-04-2310:4011:4061594.46
42012-04-2411:3013:3063016.14
52012-05-1719:0021:5053809.702
62012-05-2311:3013:0061814.079
72012-06-0409:3011:4063864.771
82012-06-2715:0016:0071594.46
92012-06-3017:3020:0084341.531
일자방류시작시간종류종료시간개문수방류량(천톤)
4992022-09-1010:0012:1082874.39
5002022-09-1612:4015:2083397.696
5012022-09-2911:5013:5041900.011
5022022-10-0416:0017:4082837.522
5032022-10-0618:5021:2084033.888
5042022-10-1713:4015:4083047.762
5052022-11-0113:5016:2083878.847
5062022-11-1513:2015:2083119.798
5072022-11-2811:3014:3064178.01
5082022-12-2610:3013:5064418.291