Overview

Dataset statistics

Number of variables4
Number of observations664
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.5 KiB
Average record size in memory33.2 B

Variable types

Text1
DateTime2
Numeric1

Dataset

Description한국농어촌공사 아산방조제 배수갑문 일일방류량에 대한 데이터로 일자 및 방류량 등의 항목을 제공합니다.일자, 개문시작시간, 폐문종료시간, 방류량(천톤)
Author한국농어촌공사
URLhttps://www.data.go.kr/data/15114060/fileData.do

Reproduction

Analysis started2023-12-12 13:21:52.996503
Analysis finished2023-12-12 13:21:53.382729
Duration0.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일자
Text

Distinct616
Distinct (%)92.8%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
2023-12-12T22:21:53.664097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters6640
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

Unique568 ?
Unique (%)85.5%

Sample

1st row2012-01-09
2nd row2012-01-18
3rd row2012-03-06
4th row2012-03-23
5th row2012-04-03
ValueCountFrequency (%)
2021-05-03 2
 
0.3%
2022-08-11 2
 
0.3%
2018-10-06 2
 
0.3%
2022-08-13 2
 
0.3%
2023-07-04 2
 
0.3%
2019-09-04 2
 
0.3%
2018-08-28 2
 
0.3%
2018-07-02 2
 
0.3%
2018-07-01 2
 
0.3%
2018-06-26 2
 
0.3%
Other values (606) 644
97.0%
2023-12-12T22:21:54.101677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1602
24.1%
2 1398
21.1%
- 1328
20.0%
1 913
13.8%
3 243
 
3.7%
7 230
 
3.5%
8 229
 
3.4%
9 216
 
3.3%
4 176
 
2.7%
5 154
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5312
80.0%
Dash Punctuation 1328
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1602
30.2%
2 1398
26.3%
1 913
17.2%
3 243
 
4.6%
7 230
 
4.3%
8 229
 
4.3%
9 216
 
4.1%
4 176
 
3.3%
5 154
 
2.9%
6 151
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 1328
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6640
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1602
24.1%
2 1398
21.1%
- 1328
20.0%
1 913
13.8%
3 243
 
3.7%
7 230
 
3.5%
8 229
 
3.4%
9 216
 
3.3%
4 176
 
2.7%
5 154
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6640
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1602
24.1%
2 1398
21.1%
- 1328
20.0%
1 913
13.8%
3 243
 
3.7%
7 230
 
3.5%
8 229
 
3.4%
9 216
 
3.3%
4 176
 
2.7%
5 154
 
2.3%

개문시작시간
Date

Distinct161
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
Minimum2023-12-12 00:00:00
Maximum2023-12-12 23:50:00
2023-12-12T22:21:54.268293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:54.400524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

폐문종료시간
Date

Distinct172
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
Minimum2023-12-12 00:00:00
Maximum2023-12-12 23:50:00
2023-12-12T22:21:54.535005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:21:54.661526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

방류량(톤)
Real number (ℝ)

Distinct440
Distinct (%)66.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2021.1958
Minimum0
Maximum9880
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2023-12-12T22:21:54.788156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile146.75
Q11019.25
median1749
Q32743.5
95-th percentile4732.8
Maximum9880
Range9880
Interquartile range (IQR)1724.25

Descriptive statistics

Standard deviation1459.334
Coefficient of variation (CV)0.72201516
Kurtosis2.9333482
Mean2021.1958
Median Absolute Deviation (MAD)835.5
Skewness1.3506308
Sum1342074
Variance2129655.7
MonotonicityNot monotonic
2023-12-12T22:21:54.925450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73 11
 
1.7%
145 10
 
1.5%
2258 9
 
1.4%
437 8
 
1.2%
1821 7
 
1.1%
510 7
 
1.1%
364 6
 
0.9%
1602 6
 
0.9%
874 5
 
0.8%
157 5
 
0.8%
Other values (430) 590
88.9%
ValueCountFrequency (%)
0 1
 
0.2%
73 11
1.7%
78 4
 
0.6%
79 3
 
0.5%
145 10
1.5%
146 5
0.8%
151 1
 
0.2%
156 2
 
0.3%
157 5
0.8%
218 3
 
0.5%
ValueCountFrequency (%)
9880 1
0.2%
8641 1
0.2%
8419 1
0.2%
7375 1
0.2%
7034 1
0.2%
6934 1
0.2%
6749 1
0.2%
6492 1
0.2%
6470 1
0.2%
6455 1
0.2%

Interactions

2023-12-12T22:21:53.141706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2023-12-12T22:21:53.253406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:21:53.349755image/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

일자개문시작시간폐문종료시간방류량(톤)
02012-01-0909:4012:002867
12012-01-1815:4017:201675
22012-03-0609:0010:302108
32012-03-2310:0012:002373
42012-04-0307:0009:152389
52012-04-1909:0011:002459
62012-04-2121:0023:202943
72012-05-0207:0008:351795
82012-07-0219:0020:552111
92012-07-0510:0011:451912
일자개문시작시간폐문종료시간방류량(톤)
6542023-09-0813:0014:40729
6552023-09-1319:0021:202441
6562023-09-1520:1522:001966
6572023-09-2010:1012:302840
6582023-09-2022:4501:453857
6592023-09-2515:3017:001675
6602023-09-2617:0018:301749
6612023-09-2718:2019:301239
6622023-10-0410:1012:201748
6632023-10-1017:0018:301311