Overview

Dataset statistics

Number of variables4
Number of observations120
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 KiB
Average record size in memory36.1 B

Variable types

Numeric3
Categorical1

Dataset

Description다목적댐의 연도별 부유물 수거현황의 정보를 아래와 같이 제공합니다.o 제공기간 : 2017년~2022년o 제공댐(20개) : 영주댐, 성덕댐, 보현산댐, 김천부항댐, 장흥댐, 보령댐, 부안댐, 주암댐, 섬진강댐, 군위댐, 밀양댐, 남강댐, 합천댐, 소양강댐, 충주댐, 횡성댐, 용담댐, 대청댐, 안동댐, 임하댐o 제공항목 : 발생량(톤),수거처리비(백만원)
Author한국수자원공사
URLhttps://www.data.go.kr/data/15038199/fileData.do

Alerts

발생량(톤) is highly overall correlated with 수거처리비(백만원)High correlation
수거처리비(백만원) is highly overall correlated with 발생량(톤)High correlation
발생량(톤) has 14 (11.7%) zerosZeros
수거처리비(백만원) has 12 (10.0%) zerosZeros

Reproduction

Analysis started2024-01-14 13:25:47.006840
Analysis finished2024-01-14 13:25:48.669785
Duration1.66 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Real number (ℝ)

Distinct6
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.5
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-01-14T22:25:48.740885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2019.5
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7149859
Coefficient of variation (CV)0.0008492131
Kurtosis-1.2713429
Mean2019.5
Median Absolute Deviation (MAD)1.5
Skewness0
Sum242340
Variance2.9411765
MonotonicityNot monotonic
2024-01-14T22:25:48.910022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2017 20
16.7%
2018 20
16.7%
2019 20
16.7%
2020 20
16.7%
2021 20
16.7%
2022 20
16.7%
ValueCountFrequency (%)
2017 20
16.7%
2018 20
16.7%
2019 20
16.7%
2020 20
16.7%
2021 20
16.7%
2022 20
16.7%
ValueCountFrequency (%)
2022 20
16.7%
2021 20
16.7%
2020 20
16.7%
2019 20
16.7%
2018 20
16.7%
2017 20
16.7%

다목적댐
Categorical

Distinct20
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
영주댐
 
6
성덕댐
 
6
보현산댐
 
6
김천부항댐
 
6
장흥댐
 
6
Other values (15)
90 

Length

Max length5
Median length3
Mean length3.25
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영주댐
2nd row영주댐
3rd row영주댐
4th row영주댐
5th row영주댐

Common Values

ValueCountFrequency (%)
영주댐 6
 
5.0%
성덕댐 6
 
5.0%
보현산댐 6
 
5.0%
김천부항댐 6
 
5.0%
장흥댐 6
 
5.0%
보령댐 6
 
5.0%
부안댐 6
 
5.0%
주암댐 6
 
5.0%
섬진강댐 6
 
5.0%
군위댐 6
 
5.0%
Other values (10) 60
50.0%

Length

2024-01-14T22:25:49.047666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
영주댐 6
 
5.0%
성덕댐 6
 
5.0%
안동댐 6
 
5.0%
대청댐 6
 
5.0%
용담댐 6
 
5.0%
횡성댐 6
 
5.0%
충주댐 6
 
5.0%
소양강댐 6
 
5.0%
합천댐 6
 
5.0%
남강댐 6
 
5.0%
Other values (10) 60
50.0%

발생량(톤)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct105
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3641.875
Minimum0
Maximum37344
Zeros14
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-01-14T22:25:49.189837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1211.25
median882.5
Q32983.25
95-th percentile17411.4
Maximum37344
Range37344
Interquartile range (IQR)2772

Descriptive statistics

Standard deviation7194.5275
Coefficient of variation (CV)1.9755009
Kurtosis9.9383169
Mean3641.875
Median Absolute Deviation (MAD)847.5
Skewness3.100603
Sum437025
Variance51761226
MonotonicityNot monotonic
2024-01-14T22:25:49.352288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
11.7%
290 2
 
1.7%
670 2
 
1.7%
902 1
 
0.8%
568 1
 
0.8%
765 1
 
0.8%
17240 1
 
0.8%
2499 1
 
0.8%
37344 1
 
0.8%
1307 1
 
0.8%
Other values (95) 95
79.2%
ValueCountFrequency (%)
0 14
11.7%
8 1
 
0.8%
15 1
 
0.8%
20 1
 
0.8%
30 1
 
0.8%
40 1
 
0.8%
58 1
 
0.8%
65 1
 
0.8%
70 1
 
0.8%
78 1
 
0.8%
ValueCountFrequency (%)
37344 1
0.8%
35007 1
0.8%
33581 1
0.8%
30977 1
0.8%
22238 1
0.8%
20668 1
0.8%
17240 1
0.8%
17212 1
0.8%
14306 1
0.8%
13831 1
0.8%

수거처리비(백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct96
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean285.24167
Minimum0
Maximum3388
Zeros12
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-01-14T22:25:49.557729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q121
median90.5
Q3296.25
95-th percentile1417.7
Maximum3388
Range3388
Interquartile range (IQR)275.25

Descriptive statistics

Standard deviation483.06434
Coefficient of variation (CV)1.6935266
Kurtosis15.058416
Mean285.24167
Median Absolute Deviation (MAD)87
Skewness3.3565645
Sum34229
Variance233351.16
MonotonicityNot monotonic
2024-01-14T22:25:49.759349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12
 
10.0%
4 3
 
2.5%
21 2
 
1.7%
353 2
 
1.7%
244 2
 
1.7%
37 2
 
1.7%
89 2
 
1.7%
73 2
 
1.7%
3 2
 
1.7%
1 2
 
1.7%
Other values (86) 89
74.2%
ValueCountFrequency (%)
0 12
10.0%
1 2
 
1.7%
3 2
 
1.7%
4 3
 
2.5%
6 2
 
1.7%
7 1
 
0.8%
8 1
 
0.8%
11 1
 
0.8%
15 1
 
0.8%
17 1
 
0.8%
ValueCountFrequency (%)
3388 1
0.8%
1703 1
0.8%
1686 1
0.8%
1654 1
0.8%
1460 1
0.8%
1450 1
0.8%
1416 1
0.8%
1226 1
0.8%
1051 1
0.8%
1046 1
0.8%

Interactions

2024-01-14T22:25:47.973739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:25:47.200486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:25:47.555923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:25:48.111274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:25:47.337572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:25:47.694526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:25:48.248384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:25:47.451765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:25:47.857352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-14T22:25:49.862161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도다목적댐발생량(톤)수거처리비(백만원)
년도1.0000.0000.2740.213
다목적댐0.0001.0000.1460.563
발생량(톤)0.2740.1461.0000.883
수거처리비(백만원)0.2130.5630.8831.000
2024-01-14T22:25:49.973289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도발생량(톤)수거처리비(백만원)다목적댐
년도1.0000.0480.0840.000
발생량(톤)0.0481.0000.9760.041
수거처리비(백만원)0.0840.9761.0000.264
다목적댐0.0000.0410.2641.000

Missing values

2024-01-14T22:25:48.459625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-14T22:25:48.605913image/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

년도다목적댐발생량(톤)수거처리비(백만원)
02017영주댐00
12018영주댐00
22019영주댐65361
32020영주댐3614327
42021영주댐69768
52022영주댐56159
62017성덕댐00
72018성덕댐85080
82019성덕댐20318
92020성덕댐1219171
년도다목적댐발생량(톤)수거처리비(백만원)
1102019안동댐27837
1112020안동댐3727199
1122021안동댐969113
1132022안동댐93666
1142017임하댐00
1152018임하댐17212939
1162019임하댐143061046
1172020임하댐133511051
1182021임하댐4294353
1192022임하댐2100286