Overview

Dataset statistics

Number of variables5
Number of observations555
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.4 KiB
Average record size in memory41.2 B

Variable types

Text2
DateTime2
Numeric1

Dataset

Description한국농어촌공사 금호호 배수갑문 일일방류량에 대한 데이터로 일자 및 방류량 등의 항목을 제공합니다. 방류일자 : 날짜형식 개문시작시간 : 00:00 시간:분 폐문시작시간 : 00:00 시간:분 조작문비 : 수문 개방 수량(갯수) 방류량 : 천톤
URLhttps://www.data.go.kr/data/15113821/fileData.do

Reproduction

Analysis started2023-12-12 00:45:43.856898
Analysis finished2023-12-12 00:45:44.369042
Duration0.51 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일자
Text

Distinct530
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2023-12-12T09:45:44.621879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5550
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

Unique505 ?
Unique (%)91.0%

Sample

1st row2012-01-10
2nd row2012-02-10
3rd row2012-03-08
4th row2012-03-09
5th row2012-04-04
ValueCountFrequency (%)
2012-08-29 2
 
0.4%
2016-07-04 2
 
0.4%
2012-09-17 2
 
0.4%
2021-07-08 2
 
0.4%
2019-10-02 2
 
0.4%
2012-09-02 2
 
0.4%
2012-09-01 2
 
0.4%
2012-08-30 2
 
0.4%
2019-09-03 2
 
0.4%
2012-09-15 2
 
0.4%
Other values (520) 535
96.4%
2023-12-12T09:45:45.183670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1319
23.8%
- 1110
20.0%
2 1013
18.3%
1 903
16.3%
3 191
 
3.4%
4 190
 
3.4%
7 188
 
3.4%
6 176
 
3.2%
8 170
 
3.1%
9 166
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4440
80.0%
Dash Punctuation 1110
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1319
29.7%
2 1013
22.8%
1 903
20.3%
3 191
 
4.3%
4 190
 
4.3%
7 188
 
4.2%
6 176
 
4.0%
8 170
 
3.8%
9 166
 
3.7%
5 124
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 1110
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5550
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1319
23.8%
- 1110
20.0%
2 1013
18.3%
1 903
16.3%
3 191
 
3.4%
4 190
 
3.4%
7 188
 
3.4%
6 176
 
3.2%
8 170
 
3.1%
9 166
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5550
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1319
23.8%
- 1110
20.0%
2 1013
18.3%
1 903
16.3%
3 191
 
3.4%
4 190
 
3.4%
7 188
 
3.4%
6 176
 
3.2%
8 170
 
3.1%
9 166
 
3.0%

개문시작시간
Date

Distinct353
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
Minimum2023-12-12 03:29:00
Maximum2023-12-12 22:41:00
2023-12-12T09:45:45.372520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:45:45.582983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

폐문종료시간
Date

Distinct350
Distinct (%)63.1%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
Minimum2023-12-12 00:10:00
Maximum2023-12-12 23:56:00
2023-12-12T09:45:45.771278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:45:45.929456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

조작문비
Real number (ℝ)

Distinct7
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2378378
Minimum1
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2023-12-12T09:45:46.056069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median4
Q35
95-th percentile5
Maximum40
Range39
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6944406
Coefficient of variation (CV)0.39983611
Kurtosis359.42659
Mean4.2378378
Median Absolute Deviation (MAD)0
Skewness16.867642
Sum2352
Variance2.8711289
MonotonicityNot monotonic
2023-12-12T09:45:46.159816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 358
64.5%
5 153
27.6%
3 23
 
4.1%
1 14
 
2.5%
6 5
 
0.9%
40 1
 
0.2%
2 1
 
0.2%
ValueCountFrequency (%)
1 14
 
2.5%
2 1
 
0.2%
3 23
 
4.1%
4 358
64.5%
5 153
27.6%
6 5
 
0.9%
40 1
 
0.2%
ValueCountFrequency (%)
40 1
 
0.2%
6 5
 
0.9%
5 153
27.6%
4 358
64.5%
3 23
 
4.1%
2 1
 
0.2%
1 14
 
2.5%

방류량(천톤)
Text

Distinct57
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2023-12-12T09:45:46.372349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.8414414
Min length1

Characters and Unicode

Total characters2132
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

Unique31 ?
Unique (%)5.6%

Sample

1st row708
2nd row1888
3rd row2360
4th row1180
5th row2360
ValueCountFrequency (%)
2360 56
 
10.1%
3068 40
 
7.2%
2124 37
 
6.7%
2832 37
 
6.7%
2596 35
 
6.3%
1416 35
 
6.3%
1888 32
 
5.8%
1652 31
 
5.6%
3540 29
 
5.2%
472 23
 
4.1%
Other values (45) 200
36.0%
2023-12-12T09:45:46.684458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 353
16.6%
4 258
12.1%
0 255
12.0%
6 253
11.9%
3 249
11.7%
1 248
11.6%
8 241
11.3%
5 113
 
5.3%
7 93
 
4.4%
9 65
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2128
99.8%
Dash Punctuation 4
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 353
16.6%
4 258
12.1%
0 255
12.0%
6 253
11.9%
3 249
11.7%
1 248
11.7%
8 241
11.3%
5 113
 
5.3%
7 93
 
4.4%
9 65
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2132
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 353
16.6%
4 258
12.1%
0 255
12.0%
6 253
11.9%
3 249
11.7%
1 248
11.6%
8 241
11.3%
5 113
 
5.3%
7 93
 
4.4%
9 65
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2132
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 353
16.6%
4 258
12.1%
0 255
12.0%
6 253
11.9%
3 249
11.7%
1 248
11.6%
8 241
11.3%
5 113
 
5.3%
7 93
 
4.4%
9 65
 
3.0%

Interactions

2023-12-12T09:45:44.058861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:45:46.768763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조작문비방류량(천톤)
조작문비1.0000.000
방류량(천톤)0.0001.000

Missing values

2023-12-12T09:45:44.224259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:45:44.325391image/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

일자개문시작시간폐문종료시간조작문비방류량(천톤)
02012-01-1006:3009:105708
12012-02-1007:4010:1531888
22012-03-0818:5020:4532360
32012-03-0906:4009:1031180
42012-04-0417:0018:3552360
52012-04-0504:5507:1553068
62012-04-0706:2009:0054012
72012-04-0919:4022:4053068
82012-04-1020:4023:0053304
92012-04-2218:4021:1553068
일자개문시작시간폐문종료시간조작문비방류량(천톤)
5452022-08-1016:2018:3843540
5462022-08-1710:1511:2051180
5472022-09-0109:2511:1452832
5482022-09-0715:4017:40402124
5492022-09-0816:3018:3042596
5502022-09-1509:3011:2042124
5512022-10-1409:0510:252708
5522022-11-3010:3511:101236
5532022-12-1308:5510:451708
5542022-12-2809:2011:1041652