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
Categorical2

Dataset

Description경상남도 양산시 소류지 현황에 대한 데이터로 소류지명, 위치, 위도, 경도, 수혜면적(ha), 제당높이(m), 제당길이(m), 유효저수량(만㎥) 등의 항목을 제공합니다.
Author경상남도 양산시
URLhttps://www.data.go.kr/data/15074082/fileData.do

Alerts

출처 has constant value ""Constant
위도 is highly overall correlated with 기준일자High correlation
수혜면적(ha) is highly overall correlated with 유효저수량High correlation
제당높이(m) is highly overall correlated with 제당길이(m)High correlation
제당길이(m) is highly overall correlated with 제당높이(m)High correlation
유효저수량 is highly overall correlated with 수혜면적(ha)High correlation
기준일자 is highly overall correlated with 위도High correlation
기준일자 is highly imbalanced (84.9%)Imbalance
소류지명 has unique valuesUnique
위치 has unique valuesUnique
위도 has unique valuesUnique

Reproduction

Analysis started2023-12-12 00:31:26.791467
Analysis finished2023-12-12 00:31:30.484946
Duration3.69 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
2023-12-12T09:31:30.637991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.326087
Min length2

Characters and Unicode

Total characters107
Distinct characters66
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%
매곡 1
 
2.2%
성천 1
 
2.2%
회현 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 (35) 35
76.1%
2023-12-12T09:31:31.047747image/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 (56) 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 (53) 62
62.6%
Decimal Number
ValueCountFrequency (%)
2 2
50.0%
1 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 (53) 62
62.6%
Common
ValueCountFrequency (%)
4
50.0%
2 2
25.0%
1 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 (53) 62
62.6%
ASCII
ValueCountFrequency (%)
4
50.0%
2 2
25.0%
1 2
25.0%

위치
Text

UNIQUE 

Distinct46
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size500.0 B
2023-12-12T09:31:31.325660image/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%
2023-12-12T09:31:31.744101image/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)

HIGH CORRELATION  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
2023-12-12T09:31:31.899522image/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
2023-12-12T09:31:32.054208image/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
2023-12-12T09:31:32.190445image/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
2023-12-12T09:31:32.320918image/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
2023-12-12T09:31:32.471579image/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
2023-12-12T09:31:32.639513image/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
2023-12-12T09:31:32.785351image/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
2023-12-12T09:31:32.983109image/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
2023-12-12T09:31:33.149337image/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
2023-12-12T09:31:33.302923image/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
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
2023-12-12T09:31:33.441479image/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
2023-12-12T09:31:33.564837image/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

2023-12-12T09:31:33.732724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:31:33.849996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기본현황 46
100.0%

기준일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size500.0 B
2020-06-30
45 
2021-08-09
 
1

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique1 ?
Unique (%)2.2%

Sample

1st row2020-06-30
2nd row2020-06-30
3rd row2020-06-30
4th row2020-06-30
5th row2020-06-30

Common Values

ValueCountFrequency (%)
2020-06-30 45
97.8%
2021-08-09 1
 
2.2%

Length

2023-12-12T09:31:33.955217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:31:34.060047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-06-30 45
97.8%
2021-08-09 1
 
2.2%

Interactions

2023-12-12T09:31:29.760455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:27.101484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:27.582015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:28.043015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:28.511729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:29.022087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:29.837209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:27.184055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:27.665723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:28.115315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:28.587967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:29.092661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:29.909530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:27.264891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:27.734248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:28.181200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:28.667844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:29.162874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:29.989243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:27.348242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:27.818011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:28.257334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:28.742843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:29.234838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:30.068291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:27.425014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:27.895880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:28.344959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:28.836722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:29.321354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:30.143807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:27.501253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:27.958556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:28.415166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:28.921673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:31:29.670535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:31:34.123636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소류지명위치위도경도수혜면적(ha)제당높이(m)제당길이(m)유효저수량기준일자
소류지명1.0001.0001.0001.0001.0001.0001.0001.0001.000
위치1.0001.0001.0001.0001.0001.0001.0001.0001.000
위도1.0001.0001.0000.6610.0000.3560.0000.0001.000
경도1.0001.0000.6611.0000.0000.5850.0000.2470.000
수혜면적(ha)1.0001.0000.0000.0001.0000.7700.5680.8120.000
제당높이(m)1.0001.0000.3560.5850.7701.0000.4640.5860.000
제당길이(m)1.0001.0000.0000.0000.5680.4641.0000.6790.000
유효저수량1.0001.0000.0000.2470.8120.5860.6791.0000.000
기준일자1.0001.0001.0000.0000.0000.0000.0000.0001.000
2023-12-12T09:31:34.274881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도수혜면적(ha)제당높이(m)제당길이(m)유효저수량기준일자
위도1.0000.1900.359-0.0860.1260.3040.905
경도0.1901.0000.1660.256-0.2270.4140.000
수혜면적(ha)0.3590.1661.0000.0370.2820.5030.000
제당높이(m)-0.0860.2560.0371.000-0.6110.1820.000
제당길이(m)0.126-0.2270.282-0.6111.0000.1920.000
유효저수량0.3040.4140.5030.1820.1921.0000.000
기준일자0.9050.0000.0000.0000.0000.0001.000

Missing values

2023-12-12T09:31:30.259138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:31:30.405656image/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)유효저수량출처기준일자
0매곡경상남도 양산시 매곡동 산28-135.374864129.18522110.08.273.01.5기본현황2020-06-30
1남낙골경상남도 양산시 명동 100-135.396518129.1713087.03.5153.00.7기본현황2020-06-30
2명곡경상남도 양산시 명동 8935.400017129.17648415.015.082.019.0기본현황2020-06-30
3소매골경상남도 양산시 명동 155-135.391147129.1699854.59.045.00.8기본현황2020-06-30
4삼용경상남도 양산시 삼호동 48(산58-4)35.416843129.1845112.010.058.00.8기본현황2020-06-30
5백동2경상남도 양산시 소주동 113735.405891129.14381122.016.095.09.6기본현황2020-06-30
6백동1경상남도 양산시 소주동 62535.401894129.1494518.03.7169.02.4기본현황2020-06-30
7소주경상남도 양산시 소주동 90035.409069129.1494517.06.071.01.1기본현황2020-06-30
8주진경상남도 양산시 주진동 58435.391089129.14005624.013.794.57.8기본현황2020-06-30
9당촌경상남도 양산시 용당동 109635.430731129.17365422.09.093.78.2기본현황2020-06-30
소류지명위치위도경도수혜면적(ha)제당높이(m)제당길이(m)유효저수량출처기준일자
36지내경상남도 양산시 하북면 순지리 28935.498857129.08479132.07.0112.06.5기본현황2020-06-30
37정자경상남도 양산시 하북면 순지리 180-135.495201129.0926787.03.5120.01.1기본현황2020-06-30
38달방경상남도 양산시 하북면 순지리 145-235.490394129.0941132.03.564.00.4기본현황2020-06-30
39한들경상남도 양산시 하북면 지산리 659-535.494472129.06113115.28.283.02.0기본현황2020-06-30
40갈밭경상남도 양산시 하북면 지산리 73-235.501765129.0754815.04.4201.00.7기본현황2020-06-30
41초산경상남도 양산시 하북면 초산리 43335.483016129.08147421.05.3161.01.0기본현황2020-06-30
42명곡경상남도 양산시 명곡동 65635.342197129.0767192.65.663.00.6기본현황2020-06-30
43호계경상남도 양산시 호계동 51035.359415129.0719143.08.452.00.6기본현황2020-06-30
44회현경상남도 양산시 교동 406-235.349981129.0143382.04.5250.00.9기본현황2020-06-30
45용연경상남도 양산시 하북면 용연리 76335.443297129.0700027.03.5250.020.0기본현황2020-06-30