Overview

Dataset statistics

Number of variables6
Number of observations27
Missing cells59
Missing cells (%)36.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 KiB
Average record size in memory52.9 B

Variable types

Categorical1
DateTime5

Dataset

Description가뭄 분석 정보 제공을 위한 연간 다목적댐의 가뭄단계(관심, 주의, 경계, 심각) 발령일자 및 정상환원일자 확인정보 데이터 항목을 제공합니다.
Author한국수자원공사
URLhttps://www.data.go.kr/data/15044885/fileData.do

Alerts

심각단계 has constant value ""Constant
주의단계 has 11 (40.7%) missing valuesMissing
경계단계 has 21 (77.8%) missing valuesMissing
심각단계 has 26 (96.3%) missing valuesMissing
정상환원 has 1 (3.7%) missing valuesMissing

Reproduction

Analysis started2023-12-12 06:34:20.981473
Analysis finished2023-12-12 06:34:21.464054
Duration0.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct10
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
소양강댐&충주댐
5 
보령댐
5 
주암댐
5 
횡성댐
3 
안동댐&임하댐
3 
Other values (5)
6 

Length

Max length8
Median length3
Mean length4.3703704
Min length3

Unique

Unique4 ?
Unique (%)14.8%

Sample

1st row소양강댐&충주댐
2nd row소양강댐&충주댐
3rd row소양강댐&충주댐
4th row소양강댐&충주댐
5th row소양강댐&충주댐

Common Values

ValueCountFrequency (%)
소양강댐&충주댐 5
18.5%
보령댐 5
18.5%
주암댐 5
18.5%
횡성댐 3
11.1%
안동댐&임하댐 3
11.1%
합천댐 2
 
7.4%
밀양댐 1
 
3.7%
용담댐 1
 
3.7%
대청댐 1
 
3.7%
부안댐 1
 
3.7%

Length

2023-12-12T15:34:21.566816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:34:21.746603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
소양강댐&충주댐 5
18.5%
보령댐 5
18.5%
주암댐 5
18.5%
횡성댐 3
11.1%
안동댐&임하댐 3
11.1%
합천댐 2
 
7.4%
밀양댐 1
 
3.7%
용담댐 1
 
3.7%
대청댐 1
 
3.7%
부안댐 1
 
3.7%

관심단계
Date

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size348.0 B
Minimum2015-03-02 00:00:00
Maximum2021-06-21 00:00:00
2023-12-12T15:34:21.900420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:22.089514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)

주의단계
Date

MISSING 

Distinct15
Distinct (%)93.8%
Missing11
Missing (%)40.7%
Memory size348.0 B
Minimum2015-03-09 00:00:00
Maximum2021-07-25 00:00:00
2023-12-12T15:34:22.255542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:22.400464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)

경계단계
Date

MISSING 

Distinct6
Distinct (%)100.0%
Missing21
Missing (%)77.8%
Memory size348.0 B
Minimum2015-08-15 00:00:00
Maximum2021-08-16 00:00:00
2023-12-12T15:34:22.523504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:22.676787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)

심각단계
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing26
Missing (%)96.3%
Memory size348.0 B
Minimum2015-08-18 00:00:00
Maximum2015-08-18 00:00:00
2023-12-12T15:34:22.809930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:22.943431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

정상환원
Date

MISSING 

Distinct18
Distinct (%)69.2%
Missing1
Missing (%)3.7%
Memory size348.0 B
Minimum2016-04-01 00:00:00
Maximum2020-07-23 00:00:00
2023-12-12T15:34:23.079664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:34:23.268599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)

Correlations

2023-12-12T15:34:23.372222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분관심단계주의단계경계단계정상환원
구분1.0000.9570.8111.0000.000
관심단계0.9571.0000.9721.0000.989
주의단계0.8110.9721.0001.0000.736
경계단계1.0001.0001.0001.0001.000
정상환원0.0000.9890.7361.0001.000

Missing values

2023-12-12T15:34:21.183530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:34:21.285864image/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.
2023-12-12T15:34:21.396147image/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

구분관심단계주의단계경계단계심각단계정상환원
0소양강댐&충주댐2015-03-022015-05-19<NA><NA>2016-04-01
1소양강댐&충주댐2016-08-23<NA><NA><NA>2016-11-10
2소양강댐&충주댐2017-06-11<NA><NA><NA>2017-07-04
3소양강댐&충주댐2018-08-14<NA><NA><NA>2018-08-30
4소양강댐&충주댐2019-07-13<NA><NA><NA>2019-10-04
5횡성댐2015-03-022015-03-09<NA><NA>2016-05-01
6횡성댐2016-08-22<NA><NA><NA>2016-11-14
7횡성댐2018-08-12<NA><NA><NA>2018-08-30
8안동댐&임하댐2015-06-232015-07-08<NA><NA>2016-04-01
9안동댐&임하댐2017-06-28<NA><NA><NA>2017-08-20
구분관심단계주의단계경계단계심각단계정상환원
17보령댐2016-08-062016-08-212017-03-25<NA>2018-04-06
18보령댐2019-07-072019-07-242019-08-26<NA>2020-01-08
19보령댐2020-05-10<NA><NA><NA>2020-07-23
20보령댐2021-06-212021-07-252021-08-16<NA><NA>
21주암댐2015-08-302015-09-16<NA><NA>2016-04-01
22주암댐2016-08-242016-09-15<NA><NA>2016-09-18
23주암댐2017-06-292017-07-20<NA><NA>2018-05-09
24주암댐2018-06-072018-06-29<NA><NA>2018-07-02
25주암댐2018-08-09<NA><NA><NA>2018-08-27
26부안댐2017-11-28<NA><NA><NA>2018-03-28