Overview

Dataset statistics

Number of variables10
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory95.5 B

Variable types

Numeric10

Dataset

Description연차별 골재채취현황 등을 테이블로 서비스함. 허가사항인 바다골재, 하천골재, 산림골재, 육상골재 등 4개 골재원에 대한 연도별 허가실적 및 채취실적을 수치화함
Author국토교통부
URLhttps://www.data.go.kr/data/15122690/fileData.do

Alerts

등록년도 is highly overall correlated with 하천허가실적 and 4 other fieldsHigh correlation
하천허가실적 is highly overall correlated with 등록년도 and 4 other fieldsHigh correlation
바다허가실적 is highly overall correlated with 바다채취실적 and 1 other fieldsHigh correlation
산림허가실적 is highly overall correlated with 등록년도 and 4 other fieldsHigh correlation
육상허가실적 is highly overall correlated with 등록년도 and 3 other fieldsHigh correlation
하천채취실적 is highly overall correlated with 등록년도 and 2 other fieldsHigh correlation
바다채취실적 is highly overall correlated with 바다허가실적High correlation
육상채취실적 is highly overall correlated with 등록년도 and 4 other fieldsHigh correlation
등록년도 has unique valuesUnique
하천허가실적 has unique valuesUnique
바다허가실적 has unique valuesUnique
산림허가실적 has unique valuesUnique
육상허가실적 has unique valuesUnique
하천채취실적 has unique valuesUnique
바다채취실적 has unique valuesUnique
산림채취실적 has unique valuesUnique
육상채취실적 has unique valuesUnique

Reproduction

Analysis started2023-12-12 20:43:16.958327
Analysis finished2023-12-12 20:43:28.338146
Duration11.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

등록년도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2003.8333
Minimum1992
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T05:43:28.428355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1992
5-th percentile1993.15
Q11997.75
median2003.5
Q32009.25
95-th percentile2017.25
Maximum2019
Range27
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation7.6764273
Coefficient of variation (CV)0.0038308712
Kurtosis-0.724643
Mean2003.8333
Median Absolute Deviation (MAD)6
Skewness0.28706583
Sum48092
Variance58.927536
MonotonicityNot monotonic
2023-12-13T05:43:28.588505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2018 1
 
4.2%
2001 1
 
4.2%
2011 1
 
4.2%
2010 1
 
4.2%
2009 1
 
4.2%
2008 1
 
4.2%
2007 1
 
4.2%
2006 1
 
4.2%
2005 1
 
4.2%
2004 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
1992 1
4.2%
1993 1
4.2%
1994 1
4.2%
1995 1
4.2%
1996 1
4.2%
1997 1
4.2%
1998 1
4.2%
1999 1
4.2%
2000 1
4.2%
2001 1
4.2%
ValueCountFrequency (%)
2019 1
4.2%
2018 1
4.2%
2013 1
4.2%
2012 1
4.2%
2011 1
4.2%
2010 1
4.2%
2009 1
4.2%
2008 1
4.2%
2007 1
4.2%
2006 1
4.2%

등록업체수
Real number (ℝ)

Distinct22
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1553.875
Minimum848
Maximum2193
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T05:43:28.756009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum848
5-th percentile848
Q11419.75
median1549.5
Q31884
95-th percentile2081.25
Maximum2193
Range1345
Interquartile range (IQR)464.25

Descriptive statistics

Standard deviation388.07208
Coefficient of variation (CV)0.24974472
Kurtosis-0.39835605
Mean1553.875
Median Absolute Deviation (MAD)236
Skewness-0.28515035
Sum37293
Variance150599.94
MonotonicityNot monotonic
2023-12-13T05:43:28.919286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
848 3
 
12.5%
2082 1
 
4.2%
1562 1
 
4.2%
1494 1
 
4.2%
1496 1
 
4.2%
1914 1
 
4.2%
1957 1
 
4.2%
2077 1
 
4.2%
2193 1
 
4.2%
2044 1
 
4.2%
Other values (12) 12
50.0%
ValueCountFrequency (%)
848 3
12.5%
1121 1
 
4.2%
1274 1
 
4.2%
1353 1
 
4.2%
1442 1
 
4.2%
1446 1
 
4.2%
1463 1
 
4.2%
1494 1
 
4.2%
1496 1
 
4.2%
1544 1
 
4.2%
ValueCountFrequency (%)
2193 1
4.2%
2082 1
4.2%
2077 1
4.2%
2044 1
4.2%
1957 1
4.2%
1914 1
4.2%
1874 1
4.2%
1718 1
4.2%
1574 1
4.2%
1566 1
4.2%

하천허가실적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25481.083
Minimum51
Maximum64261
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T05:43:29.057452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum51
5-th percentile149.6
Q18814
median24791
Q340564.75
95-th percentile52289.6
Maximum64261
Range64210
Interquartile range (IQR)31750.75

Descriptive statistics

Standard deviation19755.009
Coefficient of variation (CV)0.77528135
Kurtosis-1.0165903
Mean25481.083
Median Absolute Deviation (MAD)18059
Skewness0.27468017
Sum611546
Variance3.9026037 × 108
MonotonicityNot monotonic
2023-12-13T05:43:29.206012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2112 1
 
4.2%
18693 1
 
4.2%
1104 1
 
4.2%
2568 1
 
4.2%
10896 1
 
4.2%
19200 1
 
4.2%
11853 1
 
4.2%
21589 1
 
4.2%
26402 1
 
4.2%
28079 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
51 1
4.2%
149 1
4.2%
153 1
4.2%
1104 1
4.2%
2112 1
4.2%
2568 1
4.2%
10896 1
4.2%
11853 1
4.2%
18693 1
4.2%
19200 1
4.2%
ValueCountFrequency (%)
64261 1
4.2%
52319 1
4.2%
52123 1
4.2%
52007 1
4.2%
47517 1
4.2%
47176 1
4.2%
38361 1
4.2%
34240 1
4.2%
31080 1
4.2%
28079 1
4.2%

바다허가실적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24468.667
Minimum330
Maximum36634
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T05:43:29.348975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum330
5-th percentile2484.6
Q119868.5
median26022
Q332496.25
95-th percentile35657.6
Maximum36634
Range36304
Interquartile range (IQR)12627.75

Descriptive statistics

Standard deviation9946.7038
Coefficient of variation (CV)0.4065078
Kurtosis0.951262
Mean24468.667
Median Absolute Deviation (MAD)6479.5
Skewness-1.1450418
Sum587248
Variance98936916
MonotonicityNot monotonic
2023-12-13T05:43:29.489938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
330 1
 
4.2%
30341 1
 
4.2%
21834 1
 
4.2%
25094 1
 
4.2%
27315 1
 
4.2%
29548 1
 
4.2%
15965 1
 
4.2%
11549 1
 
4.2%
32491 1
 
4.2%
19258 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
330 1
4.2%
885 1
4.2%
11549 1
4.2%
15546 1
4.2%
15965 1
4.2%
19258 1
4.2%
20072 1
4.2%
21834 1
4.2%
24011 1
4.2%
24827 1
4.2%
ValueCountFrequency (%)
36634 1
4.2%
36008 1
4.2%
33672 1
4.2%
33392 1
4.2%
33312 1
4.2%
32512 1
4.2%
32491 1
4.2%
30608 1
4.2%
30341 1
4.2%
29548 1
4.2%

산림허가실적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64095.375
Minimum17853
Maximum170396
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T05:43:29.651302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17853
5-th percentile24812.55
Q136856.75
median54730.5
Q384034.75
95-th percentile117638.1
Maximum170396
Range152543
Interquartile range (IQR)47178

Descriptive statistics

Standard deviation37034.599
Coefficient of variation (CV)0.57780454
Kurtosis1.4165238
Mean64095.375
Median Absolute Deviation (MAD)19194
Skewness1.1933993
Sum1538289
Variance1.3715615 × 109
MonotonicityNot monotonic
2023-12-13T05:43:29.790110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
67589 1
 
4.2%
48832 1
 
4.2%
36211 1
 
4.2%
26153 1
 
4.2%
29738 1
 
4.2%
37534 1
 
4.2%
24576 1
 
4.2%
47251 1
 
4.2%
62644 1
 
4.2%
96064 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
17853 1
4.2%
24576 1
4.2%
26153 1
4.2%
29738 1
4.2%
34862 1
4.2%
36211 1
4.2%
37072 1
4.2%
37534 1
4.2%
42564 1
4.2%
47251 1
4.2%
ValueCountFrequency (%)
170396 1
4.2%
118248 1
4.2%
114182 1
4.2%
107858 1
4.2%
96199 1
4.2%
96064 1
4.2%
80025 1
4.2%
67589 1
4.2%
66740 1
4.2%
66237 1
4.2%

육상허가실적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8241.875
Minimum320
Maximum15544
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T05:43:29.950017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum320
5-th percentile1140.2
Q14466
median8757.5
Q311319.25
95-th percentile15244.55
Maximum15544
Range15224
Interquartile range (IQR)6853.25

Descriptive statistics

Standard deviation4583.6734
Coefficient of variation (CV)0.5561445
Kurtosis-1.0486135
Mean8241.875
Median Absolute Deviation (MAD)4048.5
Skewness-0.09156681
Sum197805
Variance21010062
MonotonicityNot monotonic
2023-12-13T05:43:30.106496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
320 1
 
4.2%
9254 1
 
4.2%
4139 1
 
4.2%
4843 1
 
4.2%
4575 1
 
4.2%
7724 1
 
4.2%
7632 1
 
4.2%
13298 1
 
4.2%
14562 1
 
4.2%
10860 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
320 1
4.2%
1043 1
4.2%
1691 1
4.2%
3348 1
4.2%
3650 1
4.2%
4139 1
4.2%
4575 1
4.2%
4843 1
4.2%
5700 1
4.2%
7632 1
4.2%
ValueCountFrequency (%)
15544 1
4.2%
15365 1
4.2%
14562 1
4.2%
13298 1
4.2%
13118 1
4.2%
11341 1
4.2%
11312 1
4.2%
11066 1
4.2%
10860 1
4.2%
9905 1
4.2%

하천채취실적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24239.25
Minimum375
Maximum59815
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T05:43:30.253748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum375
5-th percentile1751.2
Q115026
median20305.5
Q332474
95-th percentile49149
Maximum59815
Range59440
Interquartile range (IQR)17448

Descriptive statistics

Standard deviation15753.979
Coefficient of variation (CV)0.64993672
Kurtosis-0.18862687
Mean24239.25
Median Absolute Deviation (MAD)7675.5
Skewness0.5585839
Sum581742
Variance2.4818784 × 108
MonotonicityNot monotonic
2023-12-13T05:43:30.406538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
59815 1
 
4.2%
19781 1
 
4.2%
12718 1
 
4.2%
8337 1
 
4.2%
15519 1
 
4.2%
13547 1
 
4.2%
18780 1
 
4.2%
19189 1
 
4.2%
17573 1
 
4.2%
23797 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
375 1
4.2%
1714 1
4.2%
1962 1
4.2%
8337 1
4.2%
12718 1
4.2%
13547 1
4.2%
15519 1
4.2%
17573 1
4.2%
18780 1
4.2%
19189 1
4.2%
ValueCountFrequency (%)
59815 1
4.2%
49437 1
4.2%
47517 1
4.2%
44405 1
4.2%
43084 1
4.2%
35393 1
4.2%
31501 1
4.2%
28069 1
4.2%
26770 1
4.2%
23797 1
4.2%

바다채취실적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21839
Minimum333
Maximum33698
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T05:43:30.551983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum333
5-th percentile1996.75
Q118987.5
median23252.5
Q327762.25
95-th percentile32750.85
Maximum33698
Range33365
Interquartile range (IQR)8774.75

Descriptive statistics

Standard deviation8744.6815
Coefficient of variation (CV)0.40041584
Kurtosis1.3724749
Mean21839
Median Absolute Deviation (MAD)4598.5
Skewness-1.136765
Sum524136
Variance76469455
MonotonicityNot monotonic
2023-12-13T05:43:30.728873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
333 1
 
4.2%
31203 1
 
4.2%
21694 1
 
4.2%
26348 1
 
4.2%
23419 1
 
4.2%
23503 1
 
4.2%
15483 1
 
4.2%
19521 1
 
4.2%
27319 1
 
4.2%
10773 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
333 1
4.2%
448 1
4.2%
10773 1
4.2%
15483 1
4.2%
15546 1
4.2%
18122 1
4.2%
19276 1
4.2%
19521 1
4.2%
21339 1
4.2%
21694 1
4.2%
ValueCountFrequency (%)
33698 1
4.2%
33024 1
4.2%
31203 1
4.2%
30591 1
4.2%
29179 1
4.2%
29092 1
4.2%
27319 1
4.2%
26348 1
4.2%
24836 1
4.2%
24586 1
4.2%

산림채취실적
Real number (ℝ)

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49080.875
Minimum11282
Maximum64843
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T05:43:30.886344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11282
5-th percentile35920.25
Q145092
median49849.5
Q357651.25
95-th percentile64611.65
Maximum64843
Range53561
Interquartile range (IQR)12559.25

Descriptive statistics

Standard deviation11709.424
Coefficient of variation (CV)0.23857407
Kurtosis3.5618683
Mean49080.875
Median Absolute Deviation (MAD)6337
Skewness-1.3528652
Sum1177941
Variance1.3711061 × 108
MonotonicityNot monotonic
2023-12-13T05:43:31.033550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
51727 1
 
4.2%
57418 1
 
4.2%
46691 1
 
4.2%
45208 1
 
4.2%
48040 1
 
4.2%
48172 1
 
4.2%
48004 1
 
4.2%
51440 1
 
4.2%
51774 1
 
4.2%
63652 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
11282 1
4.2%
35717 1
4.2%
37072 1
4.2%
37483 1
4.2%
39474 1
4.2%
44744 1
4.2%
45208 1
4.2%
46691 1
4.2%
48004 1
4.2%
48040 1
4.2%
ValueCountFrequency (%)
64843 1
4.2%
64781 1
4.2%
63652 1
4.2%
60252 1
4.2%
59688 1
4.2%
58351 1
4.2%
57418 1
4.2%
52429 1
4.2%
51774 1
4.2%
51727 1
4.2%

육상채취실적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6040.5833
Minimum333
Maximum10488
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T05:43:31.192227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum333
5-th percentile938.75
Q14025.5
median6461.5
Q37799.25
95-th percentile9569.7
Maximum10488
Range10155
Interquartile range (IQR)3773.75

Descriptive statistics

Standard deviation2751.8496
Coefficient of variation (CV)0.45556024
Kurtosis-0.31065897
Mean6040.5833
Median Absolute Deviation (MAD)2078.5
Skewness-0.46268722
Sum144974
Variance7572676.2
MonotonicityNot monotonic
2023-12-13T05:43:31.361529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
333 1
 
4.2%
6443 1
 
4.2%
3963 1
 
4.2%
4730 1
 
4.2%
5408 1
 
4.2%
6480 1
 
4.2%
5833 1
 
4.2%
6822 1
 
4.2%
9445 1
 
4.2%
8593 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
333 1
4.2%
806 1
4.2%
1691 1
4.2%
3576 1
4.2%
3963 1
4.2%
4012 1
4.2%
4030 1
4.2%
4730 1
4.2%
5408 1
4.2%
5833 1
4.2%
ValueCountFrequency (%)
10488 1
4.2%
9585 1
4.2%
9483 1
4.2%
9445 1
4.2%
8593 1
4.2%
8487 1
4.2%
7570 1
4.2%
7195 1
4.2%
7078 1
4.2%
6822 1
4.2%

Interactions

2023-12-13T05:43:27.108023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:17.211397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:18.192003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:19.222710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:20.070069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:21.231302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:22.306925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:23.479418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:24.521085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:25.566794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:27.207760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:17.297707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:18.314643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:19.305573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:20.423309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:21.332633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:22.417356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:23.593061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:24.609796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:25.652860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:27.321142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:17.395148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:18.479099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:19.399019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:20.510262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:21.432704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:22.520318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:23.703919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:24.711616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:25.758112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:27.408947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:17.484752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:18.568094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:19.475445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:20.595039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:21.550515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:22.642011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:23.818592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:24.821146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:25.874016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:27.493269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:17.582295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:18.659153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:19.556105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:20.684738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:21.669812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:22.748520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:23.930750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:24.937075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:26.023315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:27.576533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:17.694084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:18.746784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:19.641772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:20.775552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:21.776721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:22.845163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:24.027938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:25.046933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:26.177438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:27.661802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:17.789302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:18.839692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:19.738050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:20.869631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:21.880677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:22.953453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:24.145499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:25.173143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:26.356151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:27.753293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:17.896933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:18.929242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:19.822051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:20.962970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:21.998811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:23.081236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:24.232051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:25.270988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:26.829720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:27.859479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:17.985382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:19.036092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:19.910292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:21.059010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:22.105963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:23.236893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:24.331286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:25.369511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:26.929067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:27.957847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:18.081154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:19.136400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:19.983698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:21.147943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:22.197700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:23.360847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:24.423180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:25.468937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:43:27.017133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:43:31.476378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록년도등록업체수하천허가실적바다허가실적산림허가실적육상허가실적하천채취실적바다채취실적산림채취실적육상채취실적
등록년도1.0000.7960.8460.5540.6830.8180.9480.5370.7890.875
등록업체수0.7961.0000.8010.5180.5260.2660.7010.5730.6820.384
하천허가실적0.8460.8011.0000.5300.6900.3920.6920.0000.6500.685
바다허가실적0.5540.5180.5301.0000.0000.4360.0000.9010.0000.684
산림허가실적0.6830.5260.6900.0001.0000.5240.5710.0000.6030.804
육상허가실적0.8180.2660.3920.4360.5241.0000.1000.4910.8500.547
하천채취실적0.9480.7010.6920.0000.5710.1001.0000.1470.4200.675
바다채취실적0.5370.5730.0000.9010.0000.4910.1471.0000.4750.698
산림채취실적0.7890.6820.6500.0000.6030.8500.4200.4751.0000.376
육상채취실적0.8750.3840.6850.6840.8040.5470.6750.6980.3761.000
2023-12-13T05:43:31.622687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록년도등록업체수하천허가실적바다허가실적산림허가실적육상허가실적하천채취실적바다채취실적산림채취실적육상채취실적
등록년도1.0000.466-0.949-0.221-0.615-0.575-0.751-0.178-0.027-0.503
등록업체수0.4661.000-0.344-0.049-0.0760.062-0.210-0.0180.4890.098
하천허가실적-0.949-0.3441.0000.2050.7240.6650.8030.1300.0420.609
바다허가실적-0.221-0.0490.2051.0000.3300.442-0.0320.9030.4600.579
산림허가실적-0.615-0.0760.7240.3301.0000.6880.7210.2140.4630.671
육상허가실적-0.5750.0620.6650.4420.6881.0000.3480.4050.3570.939
하천채취실적-0.751-0.2100.803-0.0320.7210.3481.000-0.0780.0920.297
바다채취실적-0.178-0.0180.1300.9030.2140.405-0.0781.0000.4300.490
산림채취실적-0.0270.4890.0420.4600.4630.3570.0920.4301.0000.443
육상채취실적-0.5030.0980.6090.5790.6710.9390.2970.4900.4431.000

Missing values

2023-12-13T05:43:28.101558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:43:28.267110image/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

등록년도등록업체수하천허가실적바다허가실적산림허가실적육상허가실적하천채취실적바다채취실적산림채취실적육상채취실적
0201820822112330675893205981533351727333
12019144215388517853104337544811282806
22012146314924827425643348196221717447443576
3201314465133672348623650171424836524294030
4199284847517155463707216914751715546370721691
5199384852123200725211657004440518122394744012
61994848520072401180025113414308421339374837570
71995112152319306081078581554435393230863571710488
8199612746426133392170396153654943730591494169585
9199715664717632512114182131183150129092648438487
등록년도등록업체수하천허가실적바다허가실적산림허가실적육상허가실적하천채취실적바다채취실적산림채취실적육상채취실적
142002156226433360086674084102067633024583517195
1520031718231803663457345110661993533698647819483
1620041874280791925896064108602379710773636528593
1720052044264023249162644145621757327319517749445
1820062193215891154947251132981918919521514406822
192007207711853159652457676321878015483480045833
202008195719200295483753477241354723503481726480
212009191410896273152973845751551923419480405408
2220101496256825094261534843833726348452084730
23201114941104218343621141391271821694466913963