Overview

Dataset statistics

Number of variables4
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory867.0 B
Average record size in memory41.3 B

Variable types

Text1
Numeric3

Dataset

Description경상북도의 저수지 갯수와 수혜면적, 유효 저수량 정보를 제공합니다.(저수량은 지역의 인구 및 기후변화와 농업용수 사용량에 영향을 받습니다.)
Author경상북도
URLhttps://www.data.go.kr/data/3050519/fileData.do

Alerts

저수지수 is highly overall correlated with 수혜면적(헥타르) and 1 other fieldsHigh correlation
수혜면적(헥타르) is highly overall correlated with 저수지수 and 1 other fieldsHigh correlation
유효저수량(천제곱미터) is highly overall correlated with 저수지수 and 1 other fieldsHigh correlation
시군명 has unique valuesUnique
저수지수 has unique valuesUnique
수혜면적(헥타르) has unique valuesUnique
유효저수량(천제곱미터) has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:48:51.119796
Analysis finished2023-12-12 23:48:52.029818
Duration0.91 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-13T08:48:52.155199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters63
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row포항시
2nd row경주시
3rd row김천시
4th row안동시
5th row구미시
ValueCountFrequency (%)
포항시 1
 
4.8%
청송군 1
 
4.8%
봉화군 1
 
4.8%
예천군 1
 
4.8%
칠곡군 1
 
4.8%
성주군 1
 
4.8%
고령군 1
 
4.8%
청도군 1
 
4.8%
영덕군 1
 
4.8%
영양군 1
 
4.8%
Other values (11) 11
52.4%
2023-12-13T08:48:52.452908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 11
17.5%
시 10
15.9%
영 4
 
6.3%
주 4
 
6.3%
경 3
 
4.8%
천 3
 
4.8%
성 2
 
3.2%
청 2
 
3.2%
봉 1
 
1.6%
양 1
 
1.6%
Other values (22) 22
34.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 63
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 11
17.5%
시 10
15.9%
영 4
 
6.3%
주 4
 
6.3%
경 3
 
4.8%
천 3
 
4.8%
성 2
 
3.2%
청 2
 
3.2%
봉 1
 
1.6%
양 1
 
1.6%
Other values (22) 22
34.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 63
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 11
17.5%
시 10
15.9%
영 4
 
6.3%
주 4
 
6.3%
경 3
 
4.8%
천 3
 
4.8%
성 2
 
3.2%
청 2
 
3.2%
봉 1
 
1.6%
양 1
 
1.6%
Other values (22) 22
34.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 63
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 11
17.5%
시 10
15.9%
영 4
 
6.3%
주 4
 
6.3%
경 3
 
4.8%
천 3
 
4.8%
성 2
 
3.2%
청 2
 
3.2%
봉 1
 
1.6%
양 1
 
1.6%
Other values (22) 22
34.9%

저수지수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean238.28571
Minimum21
Maximum985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T08:48:52.645193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile43
Q1116
median183
Q3278
95-th percentile696
Maximum985
Range964
Interquartile range (IQR)162

Descriptive statistics

Standard deviation225.72708
Coefficient of variation (CV)0.9472959
Kurtosis5.9138219
Mean238.28571
Median Absolute Deviation (MAD)83
Skewness2.3009612
Sum5004
Variance50952.714
MonotonicityNot monotonic
2023-12-13T08:48:52.794633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
278 1
 
4.8%
392 1
 
4.8%
21 1
 
4.8%
43 1
 
4.8%
116 1
 
4.8%
183 1
 
4.8%
176 1
 
4.8%
167 1
 
4.8%
287 1
 
4.8%
118 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
21 1
4.8%
43 1
4.8%
48 1
4.8%
63 1
4.8%
100 1
4.8%
116 1
4.8%
118 1
4.8%
167 1
4.8%
176 1
4.8%
178 1
4.8%
ValueCountFrequency (%)
985 1
4.8%
696 1
4.8%
392 1
4.8%
296 1
4.8%
287 1
4.8%
278 1
4.8%
252 1
4.8%
216 1
4.8%
200 1
4.8%
189 1
4.8%

수혜면적(헥타르)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2642.1429
Minimum552
Maximum9727
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T08:48:53.229603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum552
5-th percentile604
Q11170
median2062
Q32681
95-th percentile6028
Maximum9727
Range9175
Interquartile range (IQR)1511

Descriptive statistics

Standard deviation2243.2122
Coefficient of variation (CV)0.84901246
Kurtosis4.0514995
Mean2642.1429
Median Absolute Deviation (MAD)880
Skewness1.9295682
Sum55485
Variance5032001
MonotonicityNot monotonic
2023-12-13T08:48:53.343544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
6028 1
 
4.8%
9727 1
 
4.8%
604 1
 
4.8%
841 1
 
4.8%
1772 1
 
4.8%
1024 1
 
4.8%
2681 1
 
4.8%
1544 1
 
4.8%
2428 1
 
4.8%
1755 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
552 1
4.8%
604 1
4.8%
841 1
4.8%
847 1
4.8%
1024 1
4.8%
1170 1
4.8%
1544 1
4.8%
1627 1
4.8%
1755 1
4.8%
1772 1
4.8%
ValueCountFrequency (%)
9727 1
4.8%
6028 1
4.8%
5528 1
4.8%
5176 1
4.8%
2942 1
4.8%
2681 1
4.8%
2471 1
4.8%
2428 1
4.8%
2409 1
4.8%
2297 1
4.8%

유효저수량(천제곱미터)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24286.571
Minimum6812
Maximum75045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T08:48:53.455160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6812
5-th percentile9743
Q111157
median19711
Q336149
95-th percentile45538
Maximum75045
Range68233
Interquartile range (IQR)24992

Descriptive statistics

Standard deviation16672.972
Coefficient of variation (CV)0.68650993
Kurtosis2.8802846
Mean24286.571
Median Absolute Deviation (MAD)9362
Skewness1.5159174
Sum510018
Variance2.7798801 × 108
MonotonicityNot monotonic
2023-12-13T08:48:53.577221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
45538 1
 
4.8%
75045 1
 
4.8%
6812 1
 
4.8%
12604 1
 
4.8%
10606 1
 
4.8%
11157 1
 
4.8%
39416 1
 
4.8%
9817 1
 
4.8%
22684 1
 
4.8%
21114 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
6812 1
4.8%
9743 1
4.8%
9817 1
4.8%
10349 1
4.8%
10606 1
4.8%
11157 1
4.8%
12604 1
4.8%
12767 1
4.8%
15535 1
4.8%
15748 1
4.8%
ValueCountFrequency (%)
75045 1
4.8%
45538 1
4.8%
39416 1
4.8%
37664 1
4.8%
36604 1
4.8%
36149 1
4.8%
33830 1
4.8%
27125 1
4.8%
22684 1
4.8%
21114 1
4.8%

Interactions

2023-12-13T08:48:51.643085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:51.211290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:51.425225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:51.716424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:51.282794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:51.490258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:51.790606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:51.345993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:51.559986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:48:53.678083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명저수지수수혜면적(헥타르)유효저수량(천제곱미터)
시군명1.0001.0001.0001.000
저수지수1.0001.0000.6680.574
수혜면적(헥타르)1.0000.6681.0000.623
유효저수량(천제곱미터)1.0000.5740.6231.000
2023-12-13T08:48:53.775466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
저수지수수혜면적(헥타르)유효저수량(천제곱미터)
저수지수1.0000.6320.578
수혜면적(헥타르)0.6321.0000.697
유효저수량(천제곱미터)0.5780.6971.000

Missing values

2023-12-13T08:48:51.898950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:48:51.997252image/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

시군명저수지수수혜면적(헥타르)유효저수량(천제곱미터)
0포항시278602845538
1경주시392972775045
2김천시25222979743
3안동시178247112767
4구미시200294219711
5영주시63206215535
6영천시985240936604
7상주시216517633830
8문경시48162736149
9경산시296117027125
시군명저수지수수혜면적(헥타르)유효저수량(천제곱미터)
11청송군18984715748
12영양군10055210349
13영덕군118175521114
14청도군287242822684
15고령군16715449817
16성주군176268139416
17칠곡군183102411157
18예천군116177210606
19봉화군4384112604
20울진군216046812