Overview

Dataset statistics

Number of variables19
Number of observations3592
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory592.9 KiB
Average record size in memory169.0 B

Variable types

Numeric17
Categorical1
Text1

Dataset

Description연도별, 농업용 지하수 정보제공(세부용도별 시설수, 이용량)를 아래와 같이 제공합니다. 제공정보 - 연도,시도,시군구,총계-개소수,총계-이용량,전작용-개소수,전작용-이용량,답작용-개소수,답작용-이용량,원예용-개소수,원예용-이용량,수산업용-개소수,수산업용-이용량,축산업용-개소수,축산업용-이용량,양어장용-개소수,양어장용-이용량,기타-개소수,기타-이용량
URLhttps://www.data.go.kr/data/15054543/fileData.do

Alerts

총계-개소수 is highly overall correlated with 총계-이용량 and 12 other fieldsHigh correlation
총계-이용량 is highly overall correlated with 총계-개소수 and 12 other fieldsHigh correlation
전작용-개소수 is highly overall correlated with 총계-개소수 and 12 other fieldsHigh correlation
전작용-이용량 is highly overall correlated with 총계-개소수 and 12 other fieldsHigh correlation
답작용-개소수 is highly overall correlated with 총계-개소수 and 12 other fieldsHigh correlation
답작용-이용량 is highly overall correlated with 총계-개소수 and 12 other fieldsHigh correlation
원예용-개소수 is highly overall correlated with 총계-개소수 and 12 other fieldsHigh correlation
원예용-이용량 is highly overall correlated with 총계-개소수 and 12 other fieldsHigh correlation
수산업용-개소수 is highly overall correlated with 수산업용-이용량 and 1 other fieldsHigh correlation
수산업용-이용량 is highly overall correlated with 수산업용-개소수 and 2 other fieldsHigh correlation
축산업용-개소수 is highly overall correlated with 총계-개소수 and 12 other fieldsHigh correlation
축산업용-이용량 is highly overall correlated with 총계-개소수 and 12 other fieldsHigh correlation
양어장용-개소수 is highly overall correlated with 총계-개소수 and 13 other fieldsHigh correlation
양어장용-이용량 is highly overall correlated with 총계-개소수 and 13 other fieldsHigh correlation
기타-개소수 is highly overall correlated with 총계-개소수 and 12 other fieldsHigh correlation
기타-이용량 is highly overall correlated with 총계-개소수 and 11 other fieldsHigh correlation
총계-이용량 is highly skewed (γ1 = 27.15860673)Skewed
수산업용-이용량 is highly skewed (γ1 = 28.88885265)Skewed
전작용-개소수 has 320 (8.9%) zerosZeros
전작용-이용량 has 362 (10.1%) zerosZeros
답작용-개소수 has 423 (11.8%) zerosZeros
답작용-이용량 has 466 (13.0%) zerosZeros
원예용-개소수 has 336 (9.4%) zerosZeros
원예용-이용량 has 379 (10.6%) zerosZeros
수산업용-개소수 has 2008 (55.9%) zerosZeros
수산업용-이용량 has 2087 (58.1%) zerosZeros
축산업용-개소수 has 881 (24.5%) zerosZeros
축산업용-이용량 has 888 (24.7%) zerosZeros
양어장용-개소수 has 1230 (34.2%) zerosZeros
양어장용-이용량 has 1254 (34.9%) zerosZeros
기타-개소수 has 215 (6.0%) zerosZeros
기타-이용량 has 235 (6.5%) zerosZeros

Reproduction

Analysis started2023-12-12 04:30:28.835845
Analysis finished2023-12-12 04:31:13.692110
Duration44.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct16
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.5192
Minimum2007
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.7 KiB
2023-12-12T13:31:13.787510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2007
5-th percentile2007
Q12011
median2015
Q32019
95-th percentile2022
Maximum2022
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.6116423
Coefficient of variation (CV)0.0022892024
Kurtosis-1.2083523
Mean2014.5192
Median Absolute Deviation (MAD)4
Skewness-0.0045536306
Sum7236153
Variance21.267244
MonotonicityIncreasing
2023-12-12T13:31:13.959031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2022 227
 
6.3%
2021 226
 
6.3%
2020 226
 
6.3%
2012 226
 
6.3%
2015 225
 
6.3%
2017 225
 
6.3%
2013 225
 
6.3%
2016 225
 
6.3%
2019 224
 
6.2%
2018 224
 
6.2%
Other values (6) 1339
37.3%
ValueCountFrequency (%)
2007 224
6.2%
2008 223
6.2%
2009 223
6.2%
2010 222
6.2%
2011 223
6.2%
2012 226
6.3%
2013 225
6.3%
2014 224
6.2%
2015 225
6.3%
2016 225
6.3%
ValueCountFrequency (%)
2022 227
6.3%
2021 226
6.3%
2020 226
6.3%
2019 224
6.2%
2018 224
6.2%
2017 225
6.3%
2016 225
6.3%
2015 225
6.3%
2014 224
6.2%
2013 225
6.3%

시도
Categorical

Distinct17
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size28.2 KiB
경기도
496 
경상북도
368 
전라남도
352 
서울특별시
350 
경상남도
294 
Other values (12)
1732 

Length

Max length7
Median length5
Mean length4.123608
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 496
13.8%
경상북도 368
10.2%
전라남도 352
9.8%
서울특별시 350
9.7%
경상남도 294
8.2%
강원도 288
8.0%
충청남도 245
6.8%
부산광역시 242
6.7%
전라북도 224
6.2%
충청북도 183
 
5.1%
Other values (7) 550
15.3%

Length

2023-12-12T13:31:14.156174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 496
13.8%
경상북도 368
10.2%
전라남도 352
9.8%
서울특별시 350
9.7%
경상남도 294
8.2%
강원도 288
8.0%
충청남도 245
6.8%
부산광역시 242
6.7%
전라북도 224
6.2%
충청북도 183
 
5.1%
Other values (7) 550
15.3%
Distinct215
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size28.2 KiB
2023-12-12T13:31:14.606283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length2.9484967
Min length2

Characters and Unicode

Total characters10591
Distinct characters137
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

Unique3 ?
Unique (%)0.1%

Sample

1st row강릉시
2nd row고성군
3rd row동해시
4th row삼척시
5th row속초시
ValueCountFrequency (%)
서구 80
 
2.2%
동구 80
 
2.2%
중구 78
 
2.2%
남구 71
 
2.0%
북구 64
 
1.8%
강서구 32
 
0.9%
고성군 32
 
0.9%
중랑구 16
 
0.4%
연수구 16
 
0.4%
부평구 16
 
0.4%
Other values (205) 3107
86.5%
2023-12-12T13:31:15.103981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1382
 
13.0%
1255
 
11.8%
1098
 
10.4%
345
 
3.3%
320
 
3.0%
288
 
2.7%
273
 
2.6%
252
 
2.4%
241
 
2.3%
208
 
2.0%
Other values (127) 4929
46.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10591
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1382
 
13.0%
1255
 
11.8%
1098
 
10.4%
345
 
3.3%
320
 
3.0%
288
 
2.7%
273
 
2.6%
252
 
2.4%
241
 
2.3%
208
 
2.0%
Other values (127) 4929
46.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10591
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1382
 
13.0%
1255
 
11.8%
1098
 
10.4%
345
 
3.3%
320
 
3.0%
288
 
2.7%
273
 
2.6%
252
 
2.4%
241
 
2.3%
208
 
2.0%
Other values (127) 4929
46.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10591
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1382
 
13.0%
1255
 
11.8%
1098
 
10.4%
345
 
3.3%
320
 
3.0%
288
 
2.7%
273
 
2.6%
252
 
2.4%
241
 
2.3%
208
 
2.0%
Other values (127) 4929
46.5%

총계-개소수
Real number (ℝ)

HIGH CORRELATION 

Distinct2236
Distinct (%)62.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3147.5251
Minimum0
Maximum34769
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size31.7 KiB
2023-12-12T13:31:15.232583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q1249
median1415
Q34200
95-th percentile11770.9
Maximum34769
Range34769
Interquartile range (IQR)3951

Descriptive statistics

Standard deviation4473.2
Coefficient of variation (CV)1.4211801
Kurtosis9.8880131
Mean3147.5251
Median Absolute Deviation (MAD)1393
Skewness2.6579913
Sum11305910
Variance20009518
MonotonicityNot monotonic
2023-12-12T13:31:15.390742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 95
 
2.6%
2 78
 
2.2%
3 45
 
1.3%
5 39
 
1.1%
8 38
 
1.1%
6 38
 
1.1%
4 35
 
1.0%
9 25
 
0.7%
7 23
 
0.6%
15 18
 
0.5%
Other values (2226) 3158
87.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 95
2.6%
2 78
2.2%
3 45
1.3%
4 35
 
1.0%
5 39
1.1%
6 38
 
1.1%
7 23
 
0.6%
8 38
 
1.1%
9 25
 
0.7%
ValueCountFrequency (%)
34769 1
< 0.1%
34767 1
< 0.1%
34749 1
< 0.1%
34429 2
0.1%
34102 1
< 0.1%
34052 1
< 0.1%
33530 1
< 0.1%
28624 1
< 0.1%
28600 1
< 0.1%
28575 1
< 0.1%

총계-이용량
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3013
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9960425
Minimum0
Maximum1.6652633 × 109
Zeros25
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size31.7 KiB
2023-12-12T13:31:15.557007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3075
Q1489317.5
median3872372.5
Q312120389
95-th percentile29173045
Maximum1.6652633 × 109
Range1.6652633 × 109
Interquartile range (IQR)11631071

Descriptive statistics

Standard deviation53175770
Coefficient of variation (CV)5.3387049
Kurtosis806.25025
Mean9960425
Median Absolute Deviation (MAD)3804342.5
Skewness27.158607
Sum3.5777847 × 1010
Variance2.8276625 × 1015
MonotonicityNot monotonic
2023-12-12T13:31:15.713031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 25
 
0.7%
6096.0 14
 
0.4%
630.0 13
 
0.4%
5500.0 12
 
0.3%
800.0 11
 
0.3%
1049.0 9
 
0.3%
329549.0 7
 
0.2%
5773.0 7
 
0.2%
21482.0 6
 
0.2%
11768.0 6
 
0.2%
Other values (3003) 3482
96.9%
ValueCountFrequency (%)
0.0 25
0.7%
2.0 1
 
< 0.1%
39.0 1
 
< 0.1%
40.0 1
 
< 0.1%
46.0 1
 
< 0.1%
73.0 1
 
< 0.1%
74.0 1
 
< 0.1%
94.0 1
 
< 0.1%
96.0 1
 
< 0.1%
147.0 2
 
0.1%
ValueCountFrequency (%)
1665263280.0 1
< 0.1%
1665263277.0 1
< 0.1%
1659457464.0 1
< 0.1%
740266560.0 1
< 0.1%
714782877.0 1
< 0.1%
714782876.0 1
< 0.1%
66383834.0 1
< 0.1%
65617421.0 1
< 0.1%
65615818.0 1
< 0.1%
65466218.0 1
< 0.1%

전작용-개소수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1401
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean820.6172
Minimum0
Maximum11893
Zeros320
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size31.7 KiB
2023-12-12T13:31:15.856415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q168.75
median328
Q3963.5
95-th percentile3503.25
Maximum11893
Range11893
Interquartile range (IQR)894.75

Descriptive statistics

Standard deviation1298.5514
Coefficient of variation (CV)1.5824083
Kurtosis15.994847
Mean820.6172
Median Absolute Deviation (MAD)318
Skewness3.3127981
Sum2947657
Variance1686235.9
MonotonicityNot monotonic
2023-12-12T13:31:15.994595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 320
 
8.9%
1 92
 
2.6%
2 40
 
1.1%
3 30
 
0.8%
6 25
 
0.7%
10 19
 
0.5%
13 18
 
0.5%
8 17
 
0.5%
5 15
 
0.4%
9 14
 
0.4%
Other values (1391) 3002
83.6%
ValueCountFrequency (%)
0 320
8.9%
1 92
 
2.6%
2 40
 
1.1%
3 30
 
0.8%
4 13
 
0.4%
5 15
 
0.4%
6 25
 
0.7%
7 11
 
0.3%
8 17
 
0.5%
9 14
 
0.4%
ValueCountFrequency (%)
11893 2
0.1%
11677 2
0.1%
11676 1
< 0.1%
11472 1
< 0.1%
10484 2
0.1%
10330 1
< 0.1%
10287 1
< 0.1%
10057 2
0.1%
7192 1
< 0.1%
7158 1
< 0.1%

전작용-이용량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2621
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2225209.4
Minimum0
Maximum49298299
Zeros362
Zeros (%)10.1%
Negative0
Negative (%)0.0%
Memory size31.7 KiB
2023-12-12T13:31:16.156022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1139537
median884716.5
Q32542522.8
95-th percentile8363759.5
Maximum49298299
Range49298299
Interquartile range (IQR)2402985.8

Descriptive statistics

Standard deviation4402385.3
Coefficient of variation (CV)1.9784139
Kurtosis42.324884
Mean2225209.4
Median Absolute Deviation (MAD)869539.5
Skewness5.5616106
Sum7.9929523 × 109
Variance1.9380997 × 1013
MonotonicityNot monotonic
2023-12-12T13:31:16.346570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 362
 
10.1%
2115.0 9
 
0.3%
6000.0 8
 
0.2%
5475.0 8
 
0.2%
220.0 7
 
0.2%
7436.0 6
 
0.2%
17320.0 5
 
0.1%
139537.0 5
 
0.1%
300.0 5
 
0.1%
18600.0 5
 
0.1%
Other values (2611) 3172
88.3%
ValueCountFrequency (%)
0.0 362
10.1%
2.0 1
 
< 0.1%
11.0 2
 
0.1%
29.0 1
 
< 0.1%
30.0 1
 
< 0.1%
73.0 1
 
< 0.1%
74.0 1
 
< 0.1%
94.0 1
 
< 0.1%
120.0 1
 
< 0.1%
122.0 1
 
< 0.1%
ValueCountFrequency (%)
49298299.0 1
< 0.1%
47919017.0 2
0.1%
46940321.0 1
< 0.1%
46709926.0 1
< 0.1%
46476214.0 2
0.1%
44920466.0 2
0.1%
44735058.0 1
< 0.1%
42270141.0 2
0.1%
38256785.0 1
< 0.1%
37800021.0 1
< 0.1%

답작용-개소수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1687
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1851.5354
Minimum0
Maximum30841
Zeros423
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size31.7 KiB
2023-12-12T13:31:16.526908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q128
median484
Q32106
95-th percentile7744.5
Maximum30841
Range30841
Interquartile range (IQR)2078

Descriptive statistics

Standard deviation3409.172
Coefficient of variation (CV)1.8412676
Kurtosis20.611676
Mean1851.5354
Median Absolute Deviation (MAD)483
Skewness3.8143616
Sum6650715
Variance11622454
MonotonicityNot monotonic
2023-12-12T13:31:16.663595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 423
 
11.8%
1 91
 
2.5%
2 61
 
1.7%
8 29
 
0.8%
4 27
 
0.8%
10 23
 
0.6%
5 21
 
0.6%
21 20
 
0.6%
7 20
 
0.6%
3 19
 
0.5%
Other values (1677) 2858
79.6%
ValueCountFrequency (%)
0 423
11.8%
1 91
 
2.5%
2 61
 
1.7%
3 19
 
0.5%
4 27
 
0.8%
5 21
 
0.6%
6 13
 
0.4%
7 20
 
0.6%
8 29
 
0.8%
9 11
 
0.3%
ValueCountFrequency (%)
30841 2
0.1%
30838 1
< 0.1%
30647 2
0.1%
30512 1
< 0.1%
30504 1
< 0.1%
30117 1
< 0.1%
26596 1
< 0.1%
26589 1
< 0.1%
26585 1
< 0.1%
26556 1
< 0.1%

답작용-이용량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2443
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4212509.2
Minimum0
Maximum60546119
Zeros466
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size31.7 KiB
2023-12-12T13:31:16.804614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q148850.75
median1062488
Q35238486.5
95-th percentile16428410
Maximum60546119
Range60546119
Interquartile range (IQR)5189635.8

Descriptive statistics

Standard deviation7534237.9
Coefficient of variation (CV)1.7885392
Kurtosis13.289573
Mean4212509.2
Median Absolute Deviation (MAD)1062488
Skewness3.2825183
Sum1.5131333 × 1010
Variance5.6764741 × 1013
MonotonicityNot monotonic
2023-12-12T13:31:16.955806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 466
 
13.0%
630.0 13
 
0.4%
2100.0 10
 
0.3%
11242.0 9
 
0.3%
374313.0 7
 
0.2%
328500.0 7
 
0.2%
554.0 7
 
0.2%
9867.0 7
 
0.2%
970.0 6
 
0.2%
11828.0 6
 
0.2%
Other values (2433) 3054
85.0%
ValueCountFrequency (%)
0.0 466
13.0%
2.0 1
 
< 0.1%
32.0 1
 
< 0.1%
40.0 1
 
< 0.1%
46.0 1
 
< 0.1%
50.0 4
 
0.1%
73.0 1
 
< 0.1%
96.0 1
 
< 0.1%
112.0 1
 
< 0.1%
197.0 1
 
< 0.1%
ValueCountFrequency (%)
60546119.0 1
< 0.1%
59922702.0 1
< 0.1%
56156802.0 1
< 0.1%
56152328.0 1
< 0.1%
56077843.0 1
< 0.1%
55527935.0 1
< 0.1%
55524622.0 1
< 0.1%
55513778.0 1
< 0.1%
55508947.0 1
< 0.1%
44443353.0 2
0.1%

원예용-개소수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct679
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean216.09744
Minimum0
Maximum7653
Zeros336
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size31.7 KiB
2023-12-12T13:31:17.140515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median38
Q3201.25
95-th percentile905.15
Maximum7653
Range7653
Interquartile range (IQR)194.25

Descriptive statistics

Standard deviation563.60078
Coefficient of variation (CV)2.6080863
Kurtosis75.839934
Mean216.09744
Median Absolute Deviation (MAD)37
Skewness7.4204362
Sum776222
Variance317645.84
MonotonicityNot monotonic
2023-12-12T13:31:17.378459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 336
 
9.4%
2 136
 
3.8%
1 120
 
3.3%
5 89
 
2.5%
6 88
 
2.4%
3 73
 
2.0%
9 65
 
1.8%
7 63
 
1.8%
8 57
 
1.6%
14 53
 
1.5%
Other values (669) 2512
69.9%
ValueCountFrequency (%)
0 336
9.4%
1 120
 
3.3%
2 136
3.8%
3 73
 
2.0%
4 44
 
1.2%
5 89
 
2.5%
6 88
 
2.4%
7 63
 
1.8%
8 57
 
1.6%
9 65
 
1.8%
ValueCountFrequency (%)
7653 2
0.1%
7492 1
< 0.1%
7363 2
0.1%
7294 1
< 0.1%
7243 1
< 0.1%
7149 1
< 0.1%
7054 1
< 0.1%
4940 1
< 0.1%
4274 1
< 0.1%
4262 2
0.1%

원예용-이용량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2142
Distinct (%)59.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean642113.29
Minimum0
Maximum21141768
Zeros379
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size31.7 KiB
2023-12-12T13:31:17.552470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114795
median96793
Q3472222.5
95-th percentile2719499.6
Maximum21141768
Range21141768
Interquartile range (IQR)457427.5

Descriptive statistics

Standard deviation1762488.5
Coefficient of variation (CV)2.7448247
Kurtosis57.244054
Mean642113.29
Median Absolute Deviation (MAD)96709
Skewness6.68607
Sum2.3064709 × 109
Variance3.1063655 × 1012
MonotonicityNot monotonic
2023-12-12T13:31:17.718913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 379
 
10.6%
19800 16
 
0.4%
5500 12
 
0.3%
39948 11
 
0.3%
300 10
 
0.3%
14795 10
 
0.3%
2605 9
 
0.3%
12510 9
 
0.3%
1186 9
 
0.3%
117892 8
 
0.2%
Other values (2132) 3119
86.8%
ValueCountFrequency (%)
0 379
10.6%
13 1
 
< 0.1%
22 1
 
< 0.1%
39 1
 
< 0.1%
43 3
 
0.1%
55 2
 
0.1%
64 1
 
< 0.1%
84 3
 
0.1%
91 1
 
< 0.1%
100 2
 
0.1%
ValueCountFrequency (%)
21141768 2
0.1%
21103031 1
< 0.1%
20363244 1
< 0.1%
20351422 2
0.1%
19560114 1
< 0.1%
19523847 1
< 0.1%
16430392 1
< 0.1%
16262598 1
< 0.1%
16114743 1
< 0.1%
16012979 2
0.1%

수산업용-개소수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct82
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4629733
Minimum0
Maximum798
Zeros2008
Zeros (%)55.9%
Negative0
Negative (%)0.0%
Memory size31.7 KiB
2023-12-12T13:31:17.860278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile15
Maximum798
Range798
Interquartile range (IQR)2

Descriptive statistics

Standard deviation57.22638
Coefficient of variation (CV)6.761971
Kurtosis123.44079
Mean8.4629733
Median Absolute Deviation (MAD)0
Skewness10.846985
Sum30399
Variance3274.8586
MonotonicityNot monotonic
2023-12-12T13:31:18.015577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2008
55.9%
1 473
 
13.2%
2 303
 
8.4%
3 135
 
3.8%
4 134
 
3.7%
5 53
 
1.5%
6 53
 
1.5%
8 45
 
1.3%
12 36
 
1.0%
7 35
 
1.0%
Other values (72) 317
 
8.8%
ValueCountFrequency (%)
0 2008
55.9%
1 473
 
13.2%
2 303
 
8.4%
3 135
 
3.8%
4 134
 
3.7%
5 53
 
1.5%
6 53
 
1.5%
7 35
 
1.0%
8 45
 
1.3%
9 29
 
0.8%
ValueCountFrequency (%)
798 1
< 0.1%
787 1
< 0.1%
781 1
< 0.1%
764 1
< 0.1%
741 1
< 0.1%
718 1
< 0.1%
717 1
< 0.1%
660 2
0.1%
658 1
< 0.1%
656 1
< 0.1%

수산업용-이용량
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct458
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1966879.5
Minimum0
Maximum1.6330197 × 109
Zeros2087
Zeros (%)58.1%
Negative0
Negative (%)0.0%
Memory size31.7 KiB
2023-12-12T13:31:18.177631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37300
95-th percentile86384.05
Maximum1.6330197 × 109
Range1.6330197 × 109
Interquartile range (IQR)7300

Descriptive statistics

Standard deviation51238825
Coefficient of variation (CV)26.05082
Kurtosis876.35026
Mean1966879.5
Median Absolute Deviation (MAD)0
Skewness28.888853
Sum7.0650313 × 109
Variance2.6254172 × 1015
MonotonicityNot monotonic
2023-12-12T13:31:18.351890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2087
58.1%
7300 44
 
1.2%
3600 22
 
0.6%
6000 19
 
0.5%
5600 18
 
0.5%
9000 17
 
0.5%
1460 17
 
0.5%
4968 16
 
0.4%
23725 16
 
0.4%
270 16
 
0.4%
Other values (448) 1320
36.7%
ValueCountFrequency (%)
0 2087
58.1%
2 2
 
0.1%
26 2
 
0.1%
53 6
 
0.2%
54 1
 
< 0.1%
65 1
 
< 0.1%
73 1
 
< 0.1%
83 1
 
< 0.1%
92 1
 
< 0.1%
104 1
 
< 0.1%
ValueCountFrequency (%)
1633019746 1
< 0.1%
1633019743 1
< 0.1%
1619576793 1
< 0.1%
719178410 1
< 0.1%
695696577 1
< 0.1%
695696576 1
< 0.1%
2347277 1
< 0.1%
2013050 1
< 0.1%
1451045 1
< 0.1%
750785 1
< 0.1%

축산업용-개소수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct285
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.169822
Minimum0
Maximum568
Zeros881
Zeros (%)24.5%
Negative0
Negative (%)0.0%
Memory size31.7 KiB
2023-12-12T13:31:18.545968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median10
Q356
95-th percentile202
Maximum568
Range568
Interquartile range (IQR)55

Descriptive statistics

Standard deviation73.45633
Coefficient of variation (CV)1.6630434
Kurtosis9.2269734
Mean44.169822
Median Absolute Deviation (MAD)10
Skewness2.7055378
Sum158658
Variance5395.8325
MonotonicityNot monotonic
2023-12-12T13:31:19.023321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 881
24.5%
1 160
 
4.5%
2 140
 
3.9%
3 112
 
3.1%
5 103
 
2.9%
4 101
 
2.8%
7 98
 
2.7%
6 84
 
2.3%
9 56
 
1.6%
15 43
 
1.2%
Other values (275) 1814
50.5%
ValueCountFrequency (%)
0 881
24.5%
1 160
 
4.5%
2 140
 
3.9%
3 112
 
3.1%
4 101
 
2.8%
5 103
 
2.9%
6 84
 
2.3%
7 98
 
2.7%
8 37
 
1.0%
9 56
 
1.6%
ValueCountFrequency (%)
568 2
0.1%
561 1
< 0.1%
552 2
0.1%
543 1
< 0.1%
508 1
< 0.1%
496 1
< 0.1%
480 1
< 0.1%
463 1
< 0.1%
457 1
< 0.1%
442 1
< 0.1%

축산업용-이용량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1524
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122581.13
Minimum0
Maximum1811355
Zeros888
Zeros (%)24.7%
Negative0
Negative (%)0.0%
Memory size31.7 KiB
2023-12-12T13:31:19.203999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1229.25
median26042.5
Q3159971.25
95-th percentile513373.45
Maximum1811355
Range1811355
Interquartile range (IQR)159742

Descriptive statistics

Standard deviation221126.03
Coefficient of variation (CV)1.8039157
Kurtosis15.721649
Mean122581.13
Median Absolute Deviation (MAD)26042.5
Skewness3.4583138
Sum4.4031142 × 108
Variance4.889672 × 1010
MonotonicityNot monotonic
2023-12-12T13:31:19.410585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 888
 
24.7%
480 16
 
0.4%
1908 16
 
0.4%
6480 16
 
0.4%
9950 16
 
0.4%
14600 15
 
0.4%
25550 15
 
0.4%
300 14
 
0.4%
353 11
 
0.3%
4628 11
 
0.3%
Other values (1514) 2574
71.7%
ValueCountFrequency (%)
0 888
24.7%
31 1
 
< 0.1%
69 1
 
< 0.1%
83 1
 
< 0.1%
84 4
 
0.1%
109 1
 
< 0.1%
115 1
 
< 0.1%
170 1
 
< 0.1%
249 1
 
< 0.1%
252 2
 
0.1%
ValueCountFrequency (%)
1811355 1
< 0.1%
1669821 2
0.1%
1635129 2
0.1%
1627206 2
0.1%
1625980 1
< 0.1%
1613732 1
< 0.1%
1609821 1
< 0.1%
1609382 1
< 0.1%
1601303 1
< 0.1%
1589990 1
< 0.1%

양어장용-개소수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct86
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6082962
Minimum0
Maximum185
Zeros1230
Zeros (%)34.2%
Negative0
Negative (%)0.0%
Memory size31.7 KiB
2023-12-12T13:31:19.660432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q37
95-th percentile26
Maximum185
Range185
Interquartile range (IQR)7

Descriptive statistics

Standard deviation19.441591
Coefficient of variation (CV)2.5553147
Kurtosis43.839219
Mean7.6082962
Median Absolute Deviation (MAD)2
Skewness6.1854474
Sum27329
Variance377.97546
MonotonicityNot monotonic
2023-12-12T13:31:19.879876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1230
34.2%
1 371
 
10.3%
2 288
 
8.0%
4 200
 
5.6%
3 196
 
5.5%
5 169
 
4.7%
7 157
 
4.4%
6 125
 
3.5%
9 91
 
2.5%
8 77
 
2.1%
Other values (76) 688
19.2%
ValueCountFrequency (%)
0 1230
34.2%
1 371
 
10.3%
2 288
 
8.0%
3 196
 
5.5%
4 200
 
5.6%
5 169
 
4.7%
6 125
 
3.5%
7 157
 
4.4%
8 77
 
2.1%
9 91
 
2.5%
ValueCountFrequency (%)
185 2
0.1%
181 1
< 0.1%
179 1
< 0.1%
178 2
0.1%
174 2
0.1%
172 2
0.1%
170 1
< 0.1%
168 2
0.1%
161 1
< 0.1%
160 1
< 0.1%

양어장용-이용량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct802
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100011.43
Minimum0
Maximum6220480
Zeros1254
Zeros (%)34.9%
Negative0
Negative (%)0.0%
Memory size31.7 KiB
2023-12-12T13:31:20.051266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9700
Q351398
95-th percentile339707.35
Maximum6220480
Range6220480
Interquartile range (IQR)51398

Descriptive statistics

Standard deviation445202.34
Coefficient of variation (CV)4.4515145
Kurtosis102.40233
Mean100011.43
Median Absolute Deviation (MAD)9700
Skewness9.582757
Sum3.5924107 × 108
Variance1.9820513 × 1011
MonotonicityNot monotonic
2023-12-12T13:31:20.236365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1254
34.9%
3600 25
 
0.7%
24000 21
 
0.6%
32850 18
 
0.5%
7300 18
 
0.5%
37800 16
 
0.4%
14400 16
 
0.4%
30110 16
 
0.4%
117260 16
 
0.4%
21485 15
 
0.4%
Other values (792) 2177
60.6%
ValueCountFrequency (%)
0 1254
34.9%
56 1
 
< 0.1%
83 1
 
< 0.1%
109 1
 
< 0.1%
116 1
 
< 0.1%
150 14
 
0.4%
234 10
 
0.3%
296 1
 
< 0.1%
332 2
 
0.1%
336 1
 
< 0.1%
ValueCountFrequency (%)
6220480 1
 
< 0.1%
5996883 1
 
< 0.1%
5508175 2
0.1%
5504525 3
0.1%
5255774 2
0.1%
5255773 1
 
< 0.1%
5181967 1
 
< 0.1%
5094567 1
 
< 0.1%
5080167 2
0.1%
4668088 1
 
< 0.1%

기타-개소수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct564
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199.03396
Minimum0
Maximum9323
Zeros215
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size31.7 KiB
2023-12-12T13:31:20.411169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median43
Q3142
95-th percentile599.6
Maximum9323
Range9323
Interquartile range (IQR)134

Descriptive statistics

Standard deviation697.88542
Coefficient of variation (CV)3.5063635
Kurtosis94.897291
Mean199.03396
Median Absolute Deviation (MAD)41
Skewness8.9358139
Sum714930
Variance487044.06
MonotonicityNot monotonic
2023-12-12T13:31:20.561516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 215
 
6.0%
1 197
 
5.5%
2 97
 
2.7%
3 83
 
2.3%
4 75
 
2.1%
5 73
 
2.0%
8 63
 
1.8%
9 57
 
1.6%
6 54
 
1.5%
7 52
 
1.4%
Other values (554) 2626
73.1%
ValueCountFrequency (%)
0 215
6.0%
1 197
5.5%
2 97
2.7%
3 83
 
2.3%
4 75
 
2.1%
5 73
 
2.0%
6 54
 
1.5%
7 52
 
1.4%
8 63
 
1.8%
9 57
 
1.6%
ValueCountFrequency (%)
9323 1
< 0.1%
9147 1
< 0.1%
9025 1
< 0.1%
8953 1
< 0.1%
8920 1
< 0.1%
8886 1
< 0.1%
8883 2
0.1%
8866 1
< 0.1%
8861 1
< 0.1%
7120 1
< 0.1%

기타-이용량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2266
Distinct (%)63.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean691121.06
Minimum0
Maximum40236451
Zeros235
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size31.7 KiB
2023-12-12T13:31:20.758610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q122738
median149882.5
Q3540131
95-th percentile2947503.3
Maximum40236451
Range40236451
Interquartile range (IQR)517393

Descriptive statistics

Standard deviation2301423.3
Coefficient of variation (CV)3.3299857
Kurtosis200.74866
Mean691121.06
Median Absolute Deviation (MAD)148012.5
Skewness12.651671
Sum2.4825069 × 109
Variance5.296549 × 1012
MonotonicityNot monotonic
2023-12-12T13:31:20.935777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 235
 
6.5%
9971.0 16
 
0.4%
5773.0 15
 
0.4%
1049.0 15
 
0.4%
3030.0 14
 
0.4%
730.0 11
 
0.3%
10250.0 11
 
0.3%
800.0 11
 
0.3%
778.0 10
 
0.3%
42.0 10
 
0.3%
Other values (2256) 3244
90.3%
ValueCountFrequency (%)
0.0 235
6.5%
1.0 2
 
0.1%
42.0 10
 
0.3%
200.0 1
 
< 0.1%
256.0 2
 
0.1%
292.0 7
 
0.2%
365.0 2
 
0.1%
366.0 8
 
0.2%
412.0 8
 
0.2%
438.0 4
 
0.1%
ValueCountFrequency (%)
40236451.0 1
< 0.1%
40000940.0 1
< 0.1%
39234730.0 1
< 0.1%
39230230.0 1
< 0.1%
39227890.0 1
< 0.1%
39125440.0 1
< 0.1%
39099940.0 2
0.1%
39084940.0 1
< 0.1%
15390872.0 1
< 0.1%
13991561.0 1
< 0.1%

Interactions

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2023-12-12T13:30:46.630178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:49.336488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:53.157882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:54.690114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:56.513251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:59.034809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:01.358580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:03.607475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:06.047723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:08.360635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:10.012953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:12.306493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:34.888812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:37.290421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:39.686242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:42.571256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:44.750675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:46.759862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:49.540235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:53.273978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:54.763356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:56.639938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:59.144398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:01.465891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:03.708652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:06.182569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:08.449806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:10.135808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:12.416970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:35.021955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:37.436857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:39.834609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:42.728525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:44.864422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:46.902407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:49.747906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:53.377448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:54.851620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:56.795485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:59.265230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:01.613054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:03.807509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:06.345748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:08.548949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:10.276897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:12.514397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:35.158021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:37.598967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:39.975582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:42.851591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:44.972259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:47.032422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:49.954943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:53.473040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:54.937470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:56.962106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:59.414739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:01.737121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:03.920583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:06.508768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:08.645839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:10.401733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:12.620174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:35.302029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:37.742108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:40.107177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:42.981672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:45.136637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:47.198497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:50.155259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:53.570007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:55.024920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:57.092442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:59.559566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:01.870002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:04.021509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:06.627467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:08.740548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:10.518043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:12.725099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:35.450883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:37.899178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:40.232156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:43.108954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:45.219487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:47.326443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:50.335365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:53.658104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:55.118835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:57.198159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:59.705703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:01.977852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:04.117547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:06.740392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:08.832010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:31:10.617631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:31:21.047096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도시도총계-개소수총계-이용량전작용-개소수전작용-이용량답작용-개소수답작용-이용량원예용-개소수원예용-이용량수산업용-개소수수산업용-이용량축산업용-개소수축산업용-이용량양어장용-개소수양어장용-이용량기타-개소수기타-이용량
연도1.0000.0000.0000.0170.1260.0980.0000.1070.0000.0240.0000.0170.1690.1690.0180.0190.0000.000
시도0.0001.0000.5600.4820.4950.6320.5990.4680.4000.3980.5580.4820.4980.4570.4000.3550.3260.272
총계-개소수0.0000.5601.0000.0000.5850.5560.9130.6790.5200.3680.4390.0000.6960.6150.6490.6260.3840.447
총계-이용량0.0170.4820.0001.0000.1700.6860.0000.0000.0000.0000.9451.0000.0000.2580.0000.0000.0000.232
전작용-개소수0.1260.4950.5850.1701.0000.7030.5300.3130.1620.2270.1480.1700.4230.3740.5370.5720.2390.272
전작용-이용량0.0980.6320.5560.6860.7031.0000.3120.2540.0000.1750.4800.6860.4080.4520.3080.3410.2320.325
답작용-개소수0.0000.5990.9130.0000.5300.3121.0000.8420.3920.2400.6600.0000.4570.4630.7850.7630.1750.093
답작용-이용량0.1070.4680.6790.0000.3130.2540.8421.0000.6060.6900.1450.0000.4090.3570.6350.6140.1650.131
원예용-개소수0.0000.4000.5200.0000.1620.0000.3920.6061.0000.8120.1580.0000.5130.3210.3010.2970.1390.113
원예용-이용량0.0240.3980.3680.0000.2270.1750.2400.6900.8121.0000.1450.0000.4110.3230.4580.4900.2680.130
수산업용-개소수0.0000.5580.4390.9450.1480.4800.6600.1450.1580.1451.0000.9450.2950.4830.0000.0000.1530.137
수산업용-이용량0.0170.4820.0001.0000.1700.6860.0000.0000.0000.0000.9451.0000.0000.2580.0000.0000.0000.232
축산업용-개소수0.1690.4980.6960.0000.4230.4080.4570.4090.5130.4110.2950.0001.0000.8580.4210.2320.2360.355
축산업용-이용량0.1690.4570.6150.2580.3740.4520.4630.3570.3210.3230.4830.2580.8581.0000.4530.2790.1690.313
양어장용-개소수0.0180.4000.6490.0000.5370.3080.7850.6350.3010.4580.0000.0000.4210.4531.0000.9040.3160.273
양어장용-이용량0.0190.3550.6260.0000.5720.3410.7630.6140.2970.4900.0000.0000.2320.2790.9041.0000.3980.336
기타-개소수0.0000.3260.3840.0000.2390.2320.1750.1650.1390.2680.1530.0000.2360.1690.3160.3981.0000.814
기타-이용량0.0000.2720.4470.2320.2720.3250.0930.1310.1130.1300.1370.2320.3550.3130.2730.3360.8141.000
2023-12-12T13:31:21.228008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도총계-개소수총계-이용량전작용-개소수전작용-이용량답작용-개소수답작용-이용량원예용-개소수원예용-이용량수산업용-개소수수산업용-이용량축산업용-개소수축산업용-이용량양어장용-개소수양어장용-이용량기타-개소수기타-이용량시도
연도1.0000.086-0.0150.1940.1150.045-0.0820.1400.0220.1220.1280.1460.1240.1440.1230.0930.0770.000
총계-개소수0.0861.0000.9100.8580.8080.9430.8620.7990.7780.4980.4830.8180.8020.6980.6610.7060.6600.256
총계-이용량-0.0150.9101.0000.7790.8530.8490.9090.7150.7780.4360.4340.7780.8140.6410.6350.6780.7000.298
전작용-개소수0.1940.8580.7791.0000.9240.7270.6390.6560.6210.4250.4200.7680.7540.6530.6030.6480.6080.222
전작용-이용량0.1150.8080.8530.9241.0000.6930.7010.6090.6430.3940.4030.7330.7600.6090.5930.6400.6590.306
답작용-개소수0.0450.9430.8490.7270.6931.0000.9270.7840.7700.4830.4670.7780.7520.6640.6370.6270.6010.288
답작용-이용량-0.0820.8620.9090.6390.7010.9271.0000.7240.7920.4210.4160.7290.7430.6100.6050.6190.6370.207
원예용-개소수0.1400.7990.7150.6560.6090.7840.7241.0000.9380.4400.4320.7000.6750.6180.5930.6210.5650.179
원예용-이용량0.0220.7780.7780.6210.6430.7700.7920.9381.0000.3940.3930.6880.6900.5860.5780.6320.6200.170
수산업용-개소수0.1220.4980.4360.4250.3940.4830.4210.4400.3941.0000.9530.4920.4650.5380.4900.2850.2650.302
수산업용-이용량0.1280.4830.4340.4200.4030.4670.4160.4320.3930.9531.0000.4820.4710.5360.5060.2740.2610.298
축산업용-개소수0.1460.8180.7780.7680.7330.7780.7290.7000.6880.4920.4821.0000.9510.7380.6870.5960.5840.218
축산업용-이용량0.1240.8020.8140.7540.7600.7520.7430.6750.6900.4650.4710.9511.0000.7040.6750.6010.6170.196
양어장용-개소수0.1440.6980.6410.6530.6090.6640.6100.6180.5860.5380.5360.7380.7041.0000.9340.5250.4950.168
양어장용-이용량0.1230.6610.6350.6030.5930.6370.6050.5930.5780.4900.5060.6870.6750.9341.0000.5150.5130.150
기타-개소수0.0930.7060.6780.6480.6400.6270.6190.6210.6320.2850.2740.5960.6010.5250.5151.0000.9050.153
기타-이용량0.0770.6600.7000.6080.6590.6010.6370.5650.6200.2650.2610.5840.6170.4950.5130.9051.0000.143
시도0.0000.2560.2980.2220.3060.2880.2070.1790.1700.3020.2980.2180.1960.1680.1500.1530.1431.000

Missing values

2023-12-12T13:31:13.282757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:31:13.570521image/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

연도시도시군구총계-개소수총계-이용량전작용-개소수전작용-이용량답작용-개소수답작용-이용량원예용-개소수원예용-이용량수산업용-개소수수산업용-이용량축산업용-개소수축산업용-이용량양어장용-개소수양어장용-이용량기타-개소수기타-이용량
02007강원도강릉시8714897022.04402070119.02931275368.0365330892447687422972362585916116.0
12007강원도고성군5904118608.067115042.04923582176.06166200032400190021401470.0
22007강원도동해시1481310050.089504291.037436404.03288000023540017340201.0
32007강원도삼척시5296390686.02704565049.02461709593.0772440021560013200390000.0
42007강원도속초시2630165.02018577.025494.00000000046094.0
52007강원도양구군20133758767.0138453610.018493257057.05120000019340000022100.0
62007강원도양양군6454188697.07273045.0539939273.07414700010834063123900112669.0
72007강원도영월군4853913455.03242426015.0101781550.0141816500055824052350036442500.0
82007강원도원주시516616832750.012254419103.0354510812162.0237713385130001113297031313115034424247.0
92007강원도인제군102519207.036161813.022147315.0114400002196333100038162716.0
연도시도시군구총계-개소수총계-이용량전작용-개소수전작용-이용량답작용-개소수답작용-이용량원예용-개소수원예용-이용량수산업용-개소수수산업용-이용량축산업용-개소수축산업용-이용량양어장용-개소수양어장용-이용량기타-개소수기타-이용량
35822022충청북도단양군4821230373.0352795894.03934602.0101284600321552641222745359278.0
35832022충청북도보은군559510588828.022815488896.025523330919.02754460690012419222271124703561018252.0
35842022충청북도영동군575116683664.040499616799.08592484587.04043050297001174174664400013181074514.0
35852022충청북도옥천군826213107134.029166348875.033193534966.01690206126342530016831265719287155146536918.0
35862022충청북도음성군883119076772.0452310016538.031674551624.0292113696426000330121981010609525072084884.0
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35882022충청북도증평군21053384745.0491978922.013921996435.0549513127300136239416273002860241.0
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35902022충청북도청주시2569626275460.041488223857.01983614554070.0996168118541340037974634920118244313938355.0
35912022충청북도충주시1119025671348.0524710048063.020842128888.01692247505424968257760755156550817517524745445.0