Overview

Dataset statistics

Number of variables10
Number of observations46
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory88.9 B

Variable types

Text2
Numeric6
Categorical1
DateTime1

Dataset

Description경상남도 양산시 소류지 현황에 대한 데이터로 소류지명, 위치, 위도, 경도, 수혜면적(ha), 제당높이(m), 제당길이(m), 유효저수량(만㎥) 등의 항목을 제공합니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15074082

Alerts

출처 has constant value ""Constant
기준일자 has constant value ""Constant
수혜면적(ha) is highly overall correlated with 유효저수량(만㎥)High correlation
제당높이(m) is highly overall correlated with 제당길이(m)High correlation
제당길이(m) is highly overall correlated with 제당높이(m)High correlation
유효저수량(만㎥) is highly overall correlated with 수혜면적(ha)High correlation
소류지명 has unique valuesUnique
위치 has unique valuesUnique
위도 has unique valuesUnique

Reproduction

Analysis started2023-12-11 00:52:09.892222
Analysis finished2023-12-11 00:52:14.055722
Duration4.16 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

소류지명
Text

UNIQUE 

Distinct46
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size500.0 B
2023-12-11T09:52:14.225259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.326087
Min length2

Characters and Unicode

Total characters107
Distinct characters65
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)100.0%

Sample

1st row매곡
2nd row남낙골
3rd row명곡
4th row소매골
5th row삼용
ValueCountFrequency (%)
명곡 2
 
4.3%
남락밖 2
 
4.3%
매곡 1
 
2.2%
성천 1
 
2.2%
넙적 1
 
2.2%
내석 1
 
2.2%
상삼 1
 
2.2%
위천 1
 
2.2%
효감 1
 
2.2%
소노 1
 
2.2%
Other values (34) 34
73.9%
2023-12-11T09:52:14.639325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
남 6
 
5.6%
4
 
3.7%
명 4
 
3.7%
골 4
 
3.7%
곡 4
 
3.7%
락 4
 
3.7%
주 3
 
2.8%
내 3
 
2.8%
소 3
 
2.8%
삼 3
 
2.8%
Other values (55) 69
64.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 99
92.5%
Space Separator 4
 
3.7%
Decimal Number 4
 
3.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 6
 
6.1%
명 4
 
4.0%
골 4
 
4.0%
곡 4
 
4.0%
락 4
 
4.0%
주 3
 
3.0%
내 3
 
3.0%
소 3
 
3.0%
삼 3
 
3.0%
지 3
 
3.0%
Other values (52) 62
62.6%
Decimal Number
ValueCountFrequency (%)
1 2
50.0%
2 2
50.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 99
92.5%
Common 8
 
7.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 6
 
6.1%
명 4
 
4.0%
골 4
 
4.0%
곡 4
 
4.0%
락 4
 
4.0%
주 3
 
3.0%
내 3
 
3.0%
소 3
 
3.0%
삼 3
 
3.0%
지 3
 
3.0%
Other values (52) 62
62.6%
Common
ValueCountFrequency (%)
4
50.0%
1 2
25.0%
2 2
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 99
92.5%
ASCII 8
 
7.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
남 6
 
6.1%
명 4
 
4.0%
골 4
 
4.0%
곡 4
 
4.0%
락 4
 
4.0%
주 3
 
3.0%
내 3
 
3.0%
소 3
 
3.0%
삼 3
 
3.0%
지 3
 
3.0%
Other values (52) 62
62.6%
ASCII
ValueCountFrequency (%)
4
50.0%
1 2
25.0%
2 2
25.0%

위치
Text

UNIQUE 

Distinct46
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size500.0 B
2023-12-11T09:52:14.902008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length22
Mean length19.804348
Min length14

Characters and Unicode

Total characters911
Distinct characters61
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)100.0%

Sample

1st row경상남도 양산시 매곡동 산28-1
2nd row경상남도 양산시 명동 100-1
3rd row경상남도 양산시 명동 89
4th row경상남도 양산시 명동 155-1
5th row경상남도 양산시 삼호동 48(산58-4)
ValueCountFrequency (%)
경상남도 46
21.4%
양산시 46
21.4%
하북면 11
 
5.1%
동면 8
 
3.7%
상북면 6
 
2.8%
원동면 6
 
2.8%
화제리 6
 
2.8%
순지리 3
 
1.4%
명동 3
 
1.4%
여락리 3
 
1.4%
Other values (70) 77
35.8%
2023-12-11T09:52:15.359535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
172
18.9%
산 53
 
5.8%
상 53
 
5.8%
남 47
 
5.2%
경 46
 
5.0%
도 46
 
5.0%
양 46
 
5.0%
시 46
 
5.0%
면 31
 
3.4%
리 31
 
3.4%
Other values (51) 340
37.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 543
59.6%
Space Separator 172
 
18.9%
Decimal Number 166
 
18.2%
Dash Punctuation 26
 
2.9%
Open Punctuation 2
 
0.2%
Close Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 53
9.8%
상 53
9.8%
남 47
8.7%
경 46
 
8.5%
도 46
 
8.5%
양 46
 
8.5%
시 46
 
8.5%
면 31
 
5.7%
리 31
 
5.7%
동 29
 
5.3%
Other values (37) 115
21.2%
Decimal Number
ValueCountFrequency (%)
1 25
15.1%
2 22
13.3%
5 18
10.8%
6 17
10.2%
8 17
10.2%
3 15
9.0%
4 14
8.4%
0 14
8.4%
7 12
7.2%
9 12
7.2%
Space Separator
ValueCountFrequency (%)
172
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 543
59.6%
Common 368
40.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 53
9.8%
상 53
9.8%
남 47
8.7%
경 46
 
8.5%
도 46
 
8.5%
양 46
 
8.5%
시 46
 
8.5%
면 31
 
5.7%
리 31
 
5.7%
동 29
 
5.3%
Other values (37) 115
21.2%
Common
ValueCountFrequency (%)
172
46.7%
- 26
 
7.1%
1 25
 
6.8%
2 22
 
6.0%
5 18
 
4.9%
6 17
 
4.6%
8 17
 
4.6%
3 15
 
4.1%
4 14
 
3.8%
0 14
 
3.8%
Other values (4) 28
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 543
59.6%
ASCII 368
40.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
172
46.7%
- 26
 
7.1%
1 25
 
6.8%
2 22
 
6.0%
5 18
 
4.9%
6 17
 
4.6%
8 17
 
4.6%
3 15
 
4.1%
4 14
 
3.8%
0 14
 
3.8%
Other values (4) 28
 
7.6%
Hangul
ValueCountFrequency (%)
산 53
9.8%
상 53
9.8%
남 47
8.7%
경 46
 
8.5%
도 46
 
8.5%
양 46
 
8.5%
시 46
 
8.5%
면 31
 
5.7%
리 31
 
5.7%
동 29
 
5.3%
Other values (37) 115
21.2%

위도
Real number (ℝ)

UNIQUE 

Distinct46
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.398588
Minimum35.286584
Maximum35.501765
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-11T09:52:15.544202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.286584
5-th percentile35.304985
Q135.35383
median35.393833
Q335.439097
95-th percentile35.495019
Maximum35.501765
Range0.215181
Interquartile range (IQR)0.0852675

Descriptive statistics

Standard deviation0.059914533
Coefficient of variation (CV)0.0016925685
Kurtosis-0.83230525
Mean35.398588
Median Absolute Deviation (MAD)0.042254
Skewness0.098521439
Sum1628.335
Variance0.0035897512
MonotonicityNot monotonic
2023-12-11T09:52:15.720430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
35.374864 1
 
2.2%
35.458577 1
 
2.2%
35.441866 1
 
2.2%
35.427475 1
 
2.2%
35.407281 1
 
2.2%
35.389162 1
 
2.2%
35.378905 1
 
2.2%
35.430791 1
 
2.2%
35.483089 1
 
2.2%
35.478082 1
 
2.2%
Other values (36) 36
78.3%
ValueCountFrequency (%)
35.286584 1
2.2%
35.301236 1
2.2%
35.304942 1
2.2%
35.305113 1
2.2%
35.307453 1
2.2%
35.315728 1
2.2%
35.342197 1
2.2%
35.345322 1
2.2%
35.349981 1
2.2%
35.350094 1
2.2%
ValueCountFrequency (%)
35.501765 1
2.2%
35.498857 1
2.2%
35.495201 1
2.2%
35.494472 1
2.2%
35.490394 1
2.2%
35.483089 1
2.2%
35.483016 1
2.2%
35.478082 1
2.2%
35.466478 1
2.2%
35.458577 1
2.2%

경도
Real number (ℝ)

Distinct45
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.08489
Minimum128.97089
Maximum129.18522
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-11T09:52:15.888694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.97089
5-th percentile128.98402
Q1129.05442
median129.08493
Q3129.13518
95-th percentile129.17578
Maximum129.18522
Range0.21433
Interquartile range (IQR)0.08075375

Descriptive statistics

Standard deviation0.059022671
Coefficient of variation (CV)0.00045723919
Kurtosis-0.55049594
Mean129.08489
Median Absolute Deviation (MAD)0.036565
Skewness-0.15215354
Sum5937.905
Variance0.0034836757
MonotonicityNot monotonic
2023-12-11T09:52:16.053917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
129.149451 2
 
4.3%
129.185221 1
 
2.2%
129.084791 1
 
2.2%
129.053136 1
 
2.2%
129.062876 1
 
2.2%
129.045988 1
 
2.2%
129.058289 1
 
2.2%
129.047414 1
 
2.2%
129.096261 1
 
2.2%
129.090677 1
 
2.2%
Other values (35) 35
76.1%
ValueCountFrequency (%)
128.970891 1
2.2%
128.973718 1
2.2%
128.983446 1
2.2%
128.985744 1
2.2%
128.988122 1
2.2%
128.989471 1
2.2%
129.014338 1
2.2%
129.026578 1
2.2%
129.034991 1
2.2%
129.045988 1
2.2%
ValueCountFrequency (%)
129.185221 1
2.2%
129.184511 1
2.2%
129.176484 1
2.2%
129.173654 1
2.2%
129.171308 1
2.2%
129.169985 1
2.2%
129.150398 1
2.2%
129.149451 2
4.3%
129.143811 1
2.2%
129.142211 1
2.2%

수혜면적(ha)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)52.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9391304
Minimum0.4
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-11T09:52:16.184614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile2
Q13
median5
Q310
95-th percentile22
Maximum32
Range31.6
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.2941827
Coefficient of variation (CV)0.91876343
Kurtosis1.8084721
Mean7.9391304
Median Absolute Deviation (MAD)3
Skewness1.5330413
Sum365.2
Variance53.205101
MonotonicityNot monotonic
2023-12-11T09:52:16.328941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2.0 8
17.4%
7.0 5
 
10.9%
3.0 4
 
8.7%
10.0 3
 
6.5%
4.0 3
 
6.5%
5.0 3
 
6.5%
22.0 2
 
4.3%
8.0 2
 
4.3%
4.6 1
 
2.2%
2.6 1
 
2.2%
Other values (14) 14
30.4%
ValueCountFrequency (%)
0.4 1
 
2.2%
1.6 1
 
2.2%
2.0 8
17.4%
2.6 1
 
2.2%
3.0 4
8.7%
3.2 1
 
2.2%
4.0 3
 
6.5%
4.5 1
 
2.2%
4.6 1
 
2.2%
5.0 3
 
6.5%
ValueCountFrequency (%)
32.0 1
 
2.2%
24.0 1
 
2.2%
22.0 2
4.3%
21.0 1
 
2.2%
19.0 1
 
2.2%
18.0 1
 
2.2%
15.2 1
 
2.2%
15.0 1
 
2.2%
12.1 1
 
2.2%
10.0 3
6.5%

제당높이(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)60.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6434783
Minimum2.8
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-11T09:52:16.456175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.8
5-th percentile3.5
Q14.05
median5.85
Q38.2
95-th percentile13.15
Maximum16
Range13.2
Interquartile range (IQR)4.15

Descriptive statistics

Standard deviation3.1220116
Coefficient of variation (CV)0.4699363
Kurtosis1.3962651
Mean6.6434783
Median Absolute Deviation (MAD)2.05
Skewness1.2382178
Sum305.6
Variance9.7469565
MonotonicityNot monotonic
2023-12-11T09:52:16.574152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
3.5 4
 
8.7%
7.0 3
 
6.5%
9.0 3
 
6.5%
3.7 3
 
6.5%
4.0 3
 
6.5%
8.2 2
 
4.3%
4.2 2
 
4.3%
8.0 2
 
4.3%
5.0 2
 
4.3%
4.5 2
 
4.3%
Other values (18) 20
43.5%
ValueCountFrequency (%)
2.8 1
 
2.2%
3.5 4
8.7%
3.7 3
6.5%
3.9 1
 
2.2%
4.0 3
6.5%
4.2 2
4.3%
4.4 1
 
2.2%
4.5 2
4.3%
5.0 2
4.3%
5.3 1
 
2.2%
ValueCountFrequency (%)
16.0 1
 
2.2%
15.0 1
 
2.2%
13.7 1
 
2.2%
11.5 1
 
2.2%
10.0 2
4.3%
9.7 1
 
2.2%
9.0 3
6.5%
8.4 1
 
2.2%
8.2 2
4.3%
8.0 2
4.3%

제당길이(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.88478
Minimum32
Maximum250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-11T09:52:16.711720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile45.75
Q163.25
median94.1
Q3158.25
95-th percentile221
Maximum250
Range218
Interquartile range (IQR)95

Descriptive statistics

Standard deviation59.707096
Coefficient of variation (CV)0.53846069
Kurtosis-0.37732907
Mean110.88478
Median Absolute Deviation (MAD)37.1
Skewness0.78997594
Sum5100.7
Variance3564.9373
MonotonicityNot monotonic
2023-12-11T09:52:16.858469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
250.0 2
 
4.3%
70.0 2
 
4.3%
73.0 1
 
2.2%
48.0 1
 
2.2%
166.0 1
 
2.2%
193.0 1
 
2.2%
222.0 1
 
2.2%
121.0 1
 
2.2%
136.0 1
 
2.2%
54.0 1
 
2.2%
Other values (34) 34
73.9%
ValueCountFrequency (%)
32.0 1
2.2%
44.0 1
2.2%
45.0 1
2.2%
48.0 1
2.2%
48.5 1
2.2%
49.0 1
2.2%
52.0 1
2.2%
54.0 1
2.2%
56.0 1
2.2%
58.0 1
2.2%
ValueCountFrequency (%)
250.0 2
4.3%
222.0 1
2.2%
218.0 1
2.2%
201.0 1
2.2%
193.0 1
2.2%
181.0 1
2.2%
173.0 1
2.2%
169.0 1
2.2%
166.0 1
2.2%
161.0 1
2.2%

유효저수량(만㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3195652
Minimum0.2
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-11T09:52:17.027603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.3
Q10.625
median0.85
Q31.425
95-th percentile9.25
Maximum20
Range19.8
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation4.2627909
Coefficient of variation (CV)1.8377543
Kurtosis10.679019
Mean2.3195652
Median Absolute Deviation (MAD)0.35
Skewness3.2513014
Sum106.7
Variance18.171386
MonotonicityNot monotonic
2023-12-11T09:52:17.238695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.7 6
13.0%
0.8 5
10.9%
0.3 5
10.9%
1.2 3
 
6.5%
0.9 3
 
6.5%
0.6 3
 
6.5%
1.1 3
 
6.5%
0.4 3
 
6.5%
1.0 2
 
4.3%
1.5 2
 
4.3%
Other values (10) 11
23.9%
ValueCountFrequency (%)
0.2 1
 
2.2%
0.3 5
10.9%
0.4 3
6.5%
0.6 3
6.5%
0.7 6
13.0%
0.8 5
10.9%
0.9 3
6.5%
1.0 2
 
4.3%
1.1 3
6.5%
1.2 3
6.5%
ValueCountFrequency (%)
20.0 1
2.2%
19.0 1
2.2%
9.6 1
2.2%
8.2 1
2.2%
7.8 1
2.2%
6.5 1
2.2%
2.4 1
2.2%
2.0 2
4.3%
1.7 1
2.2%
1.5 2
4.3%

출처
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
기본현황
46 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기본현황 46
100.0%

Length

2023-12-11T09:52:17.425238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:52:17.555640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기본현황 46
100.0%

기준일자
Date

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
Minimum2020-12-01 00:00:00
Maximum2020-12-01 00:00:00
2023-12-11T09:52:17.649385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:18.133734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-11T09:52:13.072382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:10.185858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:10.671516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:11.393564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:11.901465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:12.421247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:13.185256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:10.256774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:10.985044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:11.463086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:11.984715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:12.556512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:13.292158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:10.335631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:11.048259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:11.539127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:12.061136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:12.659987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:13.408767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:10.442096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:11.133126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:11.622095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:12.145180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:12.752375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:13.497323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:10.524485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:11.207755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:11.709912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:12.222744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:12.845433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:13.597798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:10.593497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:11.312720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:11.800449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:12.316799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:52:12.948714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:52:18.256891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소류지명위치위도경도수혜면적(ha)제당높이(m)제당길이(m)유효저수량(만㎥)
소류지명1.0001.0001.0001.0001.0001.0001.0001.000
위치1.0001.0001.0001.0001.0001.0001.0001.000
위도1.0001.0001.0000.6610.0000.3560.0000.000
경도1.0001.0000.6611.0000.0000.5850.0000.247
수혜면적(ha)1.0001.0000.0000.0001.0000.7700.5680.812
제당높이(m)1.0001.0000.3560.5850.7701.0000.4640.586
제당길이(m)1.0001.0000.0000.0000.5680.4641.0000.679
유효저수량(만㎥)1.0001.0000.0000.2470.8120.5860.6791.000
2023-12-11T09:52:18.405802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도수혜면적(ha)제당높이(m)제당길이(m)유효저수량(만㎥)
위도1.0000.1900.359-0.0860.1260.304
경도0.1901.0000.1660.256-0.2270.414
수혜면적(ha)0.3590.1661.0000.0370.2820.503
제당높이(m)-0.0860.2560.0371.000-0.6110.182
제당길이(m)0.126-0.2270.282-0.6111.0000.192
유효저수량(만㎥)0.3040.4140.5030.1820.1921.000

Missing values

2023-12-11T09:52:13.780711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:52:13.974728image/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

소류지명위치위도경도수혜면적(ha)제당높이(m)제당길이(m)유효저수량(만㎥)출처기준일자
0매곡경상남도 양산시 매곡동 산28-135.374864129.18522110.08.273.01.5기본현황2020-12-01
1남낙골경상남도 양산시 명동 100-135.396518129.1713087.03.5153.00.7기본현황2020-12-01
2명곡경상남도 양산시 명동 8935.400017129.17648415.015.082.019.0기본현황2020-12-01
3소매골경상남도 양산시 명동 155-135.391147129.1699854.59.045.00.8기본현황2020-12-01
4삼용경상남도 양산시 삼호동 48(산58-4)35.416843129.1845112.010.058.00.8기본현황2020-12-01
5백동2경상남도 양산시 소주동 113735.405891129.14381122.016.095.09.6기본현황2020-12-01
6백동1경상남도 양산시 소주동 62535.401894129.1494518.03.7169.02.4기본현황2020-12-01
7소주경상남도 양산시 소주동 90035.409069129.1494517.06.071.01.1기본현황2020-12-01
8주진경상남도 양산시 주진동 58435.391089129.14005624.013.794.57.8기본현황2020-12-01
9당촌경상남도 양산시 용당동 109635.430731129.17365422.09.093.78.2기본현황2020-12-01
소류지명위치위도경도수혜면적(ha)제당높이(m)제당길이(m)유효저수량(만㎥)출처기준일자
36지내경상남도 양산시 하북면 순지리 28935.498857129.08479132.07.0112.06.5기본현황2020-12-01
37정자경상남도 양산시 하북면 순지리 180-135.495201129.0926787.03.5120.01.1기본현황2020-12-01
38달방경상남도 양산시 하북면 순지리 145-235.490394129.0941132.03.564.00.4기본현황2020-12-01
39한들경상남도 양산시 하북면 지산리 659-535.494472129.06113115.28.283.02.0기본현황2020-12-01
40갈밭경상남도 양산시 하북면 지산리 73-235.501765129.0754815.04.4201.00.7기본현황2020-12-01
41초산경상남도 양산시 하북면 초산리 43335.483016129.08147421.05.3161.01.0기본현황2020-12-01
42명곡경상남도 양산시 명곡동 65635.342197129.0767192.65.663.00.6기본현황2020-12-01
43호계경상남도 양산시 호계동 51035.359415129.0719143.08.452.00.6기본현황2020-12-01
44회현경상남도 양산시 교동 406-235.349981129.0143382.04.5250.00.9기본현황2020-12-01
45용연경상남도 양산시 하북면 용연리 76335.443297129.0700027.03.5250.020.0기본현황2020-12-01