Overview

Dataset statistics

Number of variables5
Number of observations557
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

DateTime3
Numeric1
Text1

Dataset

Description한국농어촌공사 영암호 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/15113822/fileData.do

Reproduction

Analysis started2023-12-11 23:17:52.893958
Analysis finished2023-12-11 23:17:53.299467
Duration0.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일자
Date

Distinct534
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
Minimum2012-03-09 00:00:00
Maximum2022-12-29 00:00:00
2023-12-12T08:17:53.366728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:17:53.515143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

개문시작시간
Date

Distinct384
Distinct (%)68.9%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
Minimum2023-12-12 02:38:00
Maximum2023-12-12 22:56:00
2023-12-12T08:17:53.670594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:17:54.116527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

폐문종료시간
Date

Distinct396
Distinct (%)71.1%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
Minimum2023-12-12 00:00:00
Maximum2023-12-12 23:48:00
2023-12-12T08:17:54.253345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:17:54.397050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

조작문비
Real number (ℝ)

Distinct12
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0143627
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2023-12-12T08:17:54.499044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median7
Q38
95-th percentile13
Maximum13
Range12
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.811169
Coefficient of variation (CV)0.40077326
Kurtosis0.11516967
Mean7.0143627
Median Absolute Deviation (MAD)2
Skewness0.35333242
Sum3907
Variance7.902671
MonotonicityNot monotonic
2023-12-12T08:17:54.608657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8 194
34.8%
5 153
27.5%
7 60
 
10.8%
13 50
 
9.0%
2 33
 
5.9%
10 24
 
4.3%
3 14
 
2.5%
4 9
 
1.6%
12 7
 
1.3%
1 7
 
1.3%
Other values (2) 6
 
1.1%
ValueCountFrequency (%)
1 7
 
1.3%
2 33
 
5.9%
3 14
 
2.5%
4 9
 
1.6%
5 153
27.5%
6 3
 
0.5%
7 60
 
10.8%
8 194
34.8%
9 3
 
0.5%
10 24
 
4.3%
ValueCountFrequency (%)
13 50
 
9.0%
12 7
 
1.3%
10 24
 
4.3%
9 3
 
0.5%
8 194
34.8%
7 60
 
10.8%
6 3
 
0.5%
5 153
27.5%
4 9
 
1.6%
3 14
 
2.5%

방류량(천톤)
Text

Distinct206
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2023-12-12T08:17:54.959698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.2423698
Min length1

Characters and Unicode

Total characters2363
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

Unique100 ?
Unique (%)18.0%

Sample

1st row6800
2nd row3400
3rd row7211
4th row7650
5th row7211
ValueCountFrequency (%)
6375 16
 
2.9%
6800 15
 
2.7%
10200 13
 
2.3%
8500 13
 
2.3%
5950 13
 
2.3%
5525 12
 
2.2%
3400 12
 
2.2%
850 11
 
2.0%
7650 10
 
1.8%
11475 10
 
1.8%
Other values (196) 432
77.6%
2023-12-12T08:17:55.520244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 455
19.3%
0 445
18.8%
1 318
13.5%
2 244
10.3%
4 169
 
7.2%
7 165
 
7.0%
8 146
 
6.2%
6 144
 
6.1%
9 142
 
6.0%
3 132
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2360
99.9%
Dash Punctuation 3
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 455
19.3%
0 445
18.9%
1 318
13.5%
2 244
10.3%
4 169
 
7.2%
7 165
 
7.0%
8 146
 
6.2%
6 144
 
6.1%
9 142
 
6.0%
3 132
 
5.6%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2363
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 455
19.3%
0 445
18.8%
1 318
13.5%
2 244
10.3%
4 169
 
7.2%
7 165
 
7.0%
8 146
 
6.2%
6 144
 
6.1%
9 142
 
6.0%
3 132
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2363
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 455
19.3%
0 445
18.8%
1 318
13.5%
2 244
10.3%
4 169
 
7.2%
7 165
 
7.0%
8 146
 
6.2%
6 144
 
6.1%
9 142
 
6.0%
3 132
 
5.6%

Interactions

2023-12-12T08:17:53.050368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2023-12-12T08:17:53.151892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:17:53.263429image/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)
02012-03-0906:4009:2566800
12012-04-0417:0518:3073400
22012-04-0505:1007:2077211
32012-04-1020:2023:1477650
42012-04-2319:1521:4577211
52012-04-2419:5022:0074670
62012-04-2520:2022:2573830
72012-05-0315:5718:0575525
82012-05-0718:3021:44710181
92012-05-0819:3021:1474675
일자개문시작시간폐문종료시간조작문비방류량(천톤)
5472022-08-1609:2511:25510214
5482022-09-0210:1511:2655969
5492022-09-0715:3017:1857669
5502022-09-0816:1618:23519144
5512022-09-1509:2610:4756795
5522022-09-2808:0510:0585525
5532022-10-0716:0017:55515300
5542022-11-3010:2511:3554680
5552022-12-1309:0011:001850
5562022-12-2910:1511:551425