Overview

Dataset statistics

Number of variables10
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory96.3 B

Variable types

Numeric10

Dataset

Description음주폐해예방 관련 지표- 알코올 생산과 소비 > 알코올 생산> 지역특산주 출고현황 지표데이터를 제공합니다.- 알코올 생산과 소비 > 알코올 생산> 지역특산주 출고현황 지표데이터를 제공합니다.
Author한국건강증진개발원
URLhttps://www.data.go.kr/data/15050199/fileData.do

Alerts

연도 is highly overall correlated with 합계 and 6 other fieldsHigh correlation
합계 is highly overall correlated with 연도 and 4 other fieldsHigh correlation
탁주 is highly overall correlated with 연도 and 4 other fieldsHigh correlation
약주 is highly overall correlated with 연도 and 5 other fieldsHigh correlation
청주 is highly overall correlated with 연도 and 2 other fieldsHigh correlation
증류식소주 is highly overall correlated with 연도 and 5 other fieldsHigh correlation
일반증류주 is highly overall correlated with 연도 and 5 other fieldsHigh correlation
기타주류 is highly overall correlated with 연도 and 3 other fieldsHigh correlation
연도 has unique valuesUnique
합계 has unique valuesUnique
약주 has unique valuesUnique
과실주 has unique valuesUnique
리큐르 has unique valuesUnique
기타주류 has unique valuesUnique
탁주 has 6 (28.6%) zerosZeros
청주 has 12 (57.1%) zerosZeros

Reproduction

Analysis started2023-12-12 21:13:33.517169
Analysis finished2023-12-12 21:13:43.364280
Duration9.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010
Minimum2000
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T06:13:43.427074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2001
Q12005
median2010
Q32015
95-th percentile2019
Maximum2020
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.2048368
Coefficient of variation (CV)0.0030869835
Kurtosis-1.2
Mean2010
Median Absolute Deviation (MAD)5
Skewness0
Sum42210
Variance38.5
MonotonicityStrictly increasing
2023-12-13T06:13:43.551792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2000 1
 
4.8%
2001 1
 
4.8%
2020 1
 
4.8%
2019 1
 
4.8%
2018 1
 
4.8%
2017 1
 
4.8%
2016 1
 
4.8%
2015 1
 
4.8%
2014 1
 
4.8%
2013 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
2000 1
4.8%
2001 1
4.8%
2002 1
4.8%
2003 1
4.8%
2004 1
4.8%
2005 1
4.8%
2006 1
4.8%
2007 1
4.8%
2008 1
4.8%
2009 1
4.8%
ValueCountFrequency (%)
2020 1
4.8%
2019 1
4.8%
2018 1
4.8%
2017 1
4.8%
2016 1
4.8%
2015 1
4.8%
2014 1
4.8%
2013 1
4.8%
2012 1
4.8%
2011 1
4.8%

합계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7093.2857
Minimum577
Maximum11182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T06:13:43.675968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum577
5-th percentile959
Q15831
median7200
Q39120
95-th percentile10930
Maximum11182
Range10605
Interquartile range (IQR)3289

Descriptive statistics

Standard deviation3021.6339
Coefficient of variation (CV)0.42598509
Kurtosis0.15930682
Mean7093.2857
Median Absolute Deviation (MAD)1741
Skewness-0.77340268
Sum148959
Variance9130271.6
MonotonicityNot monotonic
2023-12-13T06:13:43.775845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
577 1
 
4.8%
959 1
 
4.8%
10775 1
 
4.8%
9160 1
 
4.8%
6906 1
 
4.8%
5956 1
 
4.8%
7181 1
 
4.8%
8606 1
 
4.8%
11182 1
 
4.8%
10930 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
577 1
4.8%
959 1
4.8%
2570 1
4.8%
4655 1
4.8%
5459 1
4.8%
5831 1
4.8%
5956 1
4.8%
6906 1
4.8%
7168 1
4.8%
7181 1
4.8%
ValueCountFrequency (%)
11182 1
4.8%
10930 1
4.8%
10775 1
4.8%
10344 1
4.8%
9160 1
4.8%
9120 1
4.8%
8862 1
4.8%
8606 1
4.8%
8112 1
4.8%
7406 1
4.8%

탁주
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2318.619
Minimum0
Maximum7053
Zeros6
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T06:13:43.870697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2552
Q33872
95-th percentile6613
Maximum7053
Range7053
Interquartile range (IQR)3872

Descriptive statistics

Standard deviation2435.1461
Coefficient of variation (CV)1.0502571
Kurtosis-1.0532529
Mean2318.619
Median Absolute Deviation (MAD)2451
Skewness0.52882848
Sum48691
Variance5929936.3
MonotonicityNot monotonic
2023-12-13T06:13:43.970694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 6
28.6%
7053 1
 
4.8%
5576 1
 
4.8%
4256 1
 
4.8%
3461 1
 
4.8%
2861 1
 
4.8%
3576 1
 
4.8%
3872 1
 
4.8%
6613 1
 
4.8%
101 1
 
4.8%
Other values (6) 6
28.6%
ValueCountFrequency (%)
0 6
28.6%
81 1
 
4.8%
101 1
 
4.8%
246 1
 
4.8%
336 1
 
4.8%
2552 1
 
4.8%
2861 1
 
4.8%
3246 1
 
4.8%
3461 1
 
4.8%
3576 1
 
4.8%
ValueCountFrequency (%)
7053 1
4.8%
6613 1
4.8%
5576 1
4.8%
4861 1
4.8%
4256 1
4.8%
3872 1
4.8%
3576 1
4.8%
3461 1
4.8%
3246 1
4.8%
2861 1
4.8%

약주
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean467.19048
Minimum18
Maximum1424
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T06:13:44.087873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile34
Q1179
median509
Q3682
95-th percentile902
Maximum1424
Range1406
Interquartile range (IQR)503

Descriptive statistics

Standard deviation357.16307
Coefficient of variation (CV)0.76449134
Kurtosis0.87903934
Mean467.19048
Median Absolute Deviation (MAD)221
Skewness0.74482457
Sum9811
Variance127565.46
MonotonicityNot monotonic
2023-12-13T06:13:44.193238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
18 1
 
4.8%
48 1
 
4.8%
902 1
 
4.8%
1424 1
 
4.8%
682 1
 
4.8%
509 1
 
4.8%
601 1
 
4.8%
641 1
 
4.8%
697 1
 
4.8%
730 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
18 1
4.8%
34 1
4.8%
48 1
4.8%
72 1
4.8%
110 1
4.8%
179 1
4.8%
192 1
4.8%
199 1
4.8%
317 1
4.8%
459 1
4.8%
ValueCountFrequency (%)
1424 1
4.8%
902 1
4.8%
730 1
4.8%
723 1
4.8%
697 1
4.8%
682 1
4.8%
679 1
4.8%
641 1
4.8%
601 1
4.8%
595 1
4.8%

청주
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.952381
Minimum0
Maximum386
Zeros12
Zeros (%)57.1%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T06:13:44.311797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile100
Maximum386
Range386
Interquartile range (IQR)3

Descriptive statistics

Standard deviation85.709087
Coefficient of variation (CV)3.302552
Kurtosis17.57204
Mean25.952381
Median Absolute Deviation (MAD)0
Skewness4.1075298
Sum545
Variance7346.0476
MonotonicityNot monotonic
2023-12-13T06:13:44.411112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 12
57.1%
1 3
 
14.3%
4 2
 
9.5%
3 1
 
4.8%
45 1
 
4.8%
100 1
 
4.8%
386 1
 
4.8%
ValueCountFrequency (%)
0 12
57.1%
1 3
 
14.3%
3 1
 
4.8%
4 2
 
9.5%
45 1
 
4.8%
100 1
 
4.8%
386 1
 
4.8%
ValueCountFrequency (%)
386 1
 
4.8%
100 1
 
4.8%
45 1
 
4.8%
4 2
 
9.5%
3 1
 
4.8%
1 3
 
14.3%
0 12
57.1%

과실주
Real number (ℝ)

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3485.4286
Minimum201
Maximum9290
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T06:13:44.514362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201
5-th percentile584
Q11760
median3009
Q34285
95-th percentile7171
Maximum9290
Range9089
Interquartile range (IQR)2525

Descriptive statistics

Standard deviation2362.8314
Coefficient of variation (CV)0.67791705
Kurtosis0.4443143
Mean3485.4286
Median Absolute Deviation (MAD)1276
Skewness0.97919082
Sum73194
Variance5582972.5
MonotonicityNot monotonic
2023-12-13T06:13:44.622560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
201 1
 
4.8%
584 1
 
4.8%
1673 1
 
4.8%
1621 1
 
4.8%
1760 1
 
4.8%
1645 1
 
4.8%
1974 1
 
4.8%
3209 1
 
4.8%
2554 1
 
4.8%
3009 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
201 1
4.8%
584 1
4.8%
1621 1
4.8%
1645 1
4.8%
1673 1
4.8%
1760 1
4.8%
1963 1
4.8%
1974 1
4.8%
2554 1
4.8%
2887 1
4.8%
ValueCountFrequency (%)
9290 1
4.8%
7171 1
4.8%
7135 1
4.8%
6440 1
4.8%
5191 1
4.8%
4285 1
4.8%
3902 1
4.8%
3588 1
4.8%
3209 1
4.8%
3112 1
4.8%

증류식소주
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean186.57143
Minimum29
Maximum368
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T06:13:44.727737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile55
Q1132
median191
Q3214
95-th percentile346
Maximum368
Range339
Interquartile range (IQR)82

Descriptive statistics

Standard deviation85.470797
Coefficient of variation (CV)0.458113
Kurtosis0.26354046
Mean186.57143
Median Absolute Deviation (MAD)53
Skewness0.46472958
Sum3918
Variance7305.2571
MonotonicityNot monotonic
2023-12-13T06:13:44.820491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
131 2
 
9.5%
192 1
 
4.8%
368 1
 
4.8%
310 1
 
4.8%
204 1
 
4.8%
243 1
 
4.8%
214 1
 
4.8%
138 1
 
4.8%
193 1
 
4.8%
207 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
29 1
4.8%
55 1
4.8%
119 1
4.8%
131 2
9.5%
132 1
4.8%
138 1
4.8%
141 1
4.8%
148 1
4.8%
165 1
4.8%
191 1
4.8%
ValueCountFrequency (%)
368 1
4.8%
346 1
4.8%
310 1
4.8%
261 1
4.8%
243 1
4.8%
214 1
4.8%
207 1
4.8%
204 1
4.8%
193 1
4.8%
192 1
4.8%

일반증류주
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean157.42857
Minimum12
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T06:13:44.923429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile13
Q117
median54
Q3137
95-th percentile714
Maximum1000
Range988
Interquartile range (IQR)120

Descriptive statistics

Standard deviation253.74152
Coefficient of variation (CV)1.6117882
Kurtosis6.3465684
Mean157.42857
Median Absolute Deviation (MAD)41
Skewness2.5364669
Sum3306
Variance64384.757
MonotonicityNot monotonic
2023-12-13T06:13:45.032309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
13 3
 
14.3%
36 1
 
4.8%
1000 1
 
4.8%
714 1
 
4.8%
326 1
 
4.8%
293 1
 
4.8%
215 1
 
4.8%
137 1
 
4.8%
130 1
 
4.8%
72 1
 
4.8%
Other values (9) 9
42.9%
ValueCountFrequency (%)
12 1
 
4.8%
13 3
14.3%
14 1
 
4.8%
17 1
 
4.8%
23 1
 
4.8%
36 1
 
4.8%
38 1
 
4.8%
53 1
 
4.8%
54 1
 
4.8%
57 1
 
4.8%
ValueCountFrequency (%)
1000 1
4.8%
714 1
4.8%
326 1
4.8%
293 1
4.8%
215 1
4.8%
137 1
4.8%
130 1
4.8%
76 1
4.8%
72 1
4.8%
57 1
4.8%

리큐르
Real number (ℝ)

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean255.71429
Minimum141
Maximum414
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T06:13:45.133898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum141
5-th percentile170
Q1192
median257
Q3306
95-th percentile387
Maximum414
Range273
Interquartile range (IQR)114

Descriptive statistics

Standard deviation75.581177
Coefficient of variation (CV)0.29556885
Kurtosis-0.47765161
Mean255.71429
Median Absolute Deviation (MAD)54
Skewness0.53558946
Sum5370
Variance5712.5143
MonotonicityNot monotonic
2023-12-13T06:13:45.231731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
213 1
 
4.8%
257 1
 
4.8%
387 1
 
4.8%
276 1
 
4.8%
263 1
 
4.8%
267 1
 
4.8%
310 1
 
4.8%
229 1
 
4.8%
190 1
 
4.8%
172 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
141 1
4.8%
170 1
4.8%
172 1
4.8%
177 1
4.8%
190 1
4.8%
192 1
4.8%
203 1
4.8%
213 1
4.8%
229 1
4.8%
230 1
4.8%
ValueCountFrequency (%)
414 1
4.8%
387 1
4.8%
361 1
4.8%
335 1
4.8%
310 1
4.8%
306 1
4.8%
277 1
4.8%
276 1
4.8%
267 1
4.8%
263 1
4.8%

기타주류
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean196.38095
Minimum1
Maximum505
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T06:13:45.345128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q151
median136
Q3287
95-th percentile483
Maximum505
Range504
Interquartile range (IQR)236

Descriptive statistics

Standard deviation163.65863
Coefficient of variation (CV)0.83337324
Kurtosis-0.81340541
Mean196.38095
Median Absolute Deviation (MAD)109
Skewness0.60800265
Sum4124
Variance26784.148
MonotonicityNot monotonic
2023-12-13T06:13:45.459612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 1
 
4.8%
2 1
 
4.8%
483 1
 
4.8%
459 1
 
4.8%
165 1
 
4.8%
134 1
 
4.8%
287 1
 
4.8%
377 1
 
4.8%
365 1
 
4.8%
127 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
1 1
4.8%
2 1
4.8%
4 1
4.8%
38 1
4.8%
45 1
4.8%
51 1
4.8%
87 1
4.8%
127 1
4.8%
132 1
4.8%
134 1
4.8%
ValueCountFrequency (%)
505 1
4.8%
483 1
4.8%
459 1
4.8%
377 1
4.8%
365 1
4.8%
287 1
4.8%
274 1
4.8%
245 1
4.8%
207 1
4.8%
165 1
4.8%

Interactions

2023-12-13T06:13:42.195961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:33.757836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:34.852817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:35.719869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:36.789341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:37.829242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:38.726087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:39.461247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:40.415933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:41.308162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:42.291255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:33.840886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:34.935664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:35.834424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:36.919160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:37.950235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:38.820935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:39.558686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:40.492664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:41.402136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:42.375638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:33.908058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:35.014269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:35.980544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:37.038785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:38.030312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:38.889942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:39.635554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:40.559599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:41.508874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:42.479220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:33.993955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:35.112155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:36.083749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:37.143888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:38.118536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:38.965857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:39.709953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:40.627807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:41.595965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:42.567034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:34.080579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:35.187151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:36.193557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:37.251245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:38.215507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:39.043214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:39.778077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:40.706395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:41.680850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:42.668062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:34.175864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:35.282241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:36.288477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:37.376505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:38.306218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:39.117261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:39.850368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:40.853125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:41.781940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:42.756416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:34.260372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:35.377384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:36.376271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:37.470842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:38.387955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:39.183647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:39.921912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:40.924563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:41.857078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:42.847341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:34.338581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:35.486683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:36.479847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:37.562907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:38.482394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:39.254511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:39.998964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:41.054758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:41.949210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:42.923282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:34.425456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:35.571291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:36.578758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:37.643047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:38.568387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:39.321050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:40.058553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:41.136276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:42.034488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:42.995233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:34.772012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:35.644151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:36.669382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:37.728004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:38.647105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:39.396837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:40.125839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:41.217506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:13:42.119334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:13:45.541055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도합계탁주약주청주과실주증류식소주일반증류주리큐르기타주류
연도1.0000.6710.8710.7730.4300.4620.5190.4810.4330.658
합계0.6711.0000.0000.0000.0000.6500.5670.0000.6920.266
탁주0.8710.0001.0000.7450.7670.0000.4810.7580.0000.000
약주0.7730.0000.7451.0000.9640.0000.9340.7610.4000.686
청주0.4300.0000.7670.9641.0000.0000.9111.0000.0000.876
과실주0.4620.6500.0000.0000.0001.0000.0000.0000.0000.000
증류식소주0.5190.5670.4810.9340.9110.0001.0000.6250.2500.000
일반증류주0.4810.0000.7580.7611.0000.0000.6251.0000.0000.800
리큐르0.4330.6920.0000.4000.0000.0000.2500.0001.0000.000
기타주류0.6580.2660.0000.6860.8760.0000.0000.8000.0001.000
2023-12-13T06:13:45.891536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도합계탁주약주청주과실주증류식소주일반증류주리큐르기타주류
연도1.0000.5810.8690.8260.771-0.2730.7330.879-0.0560.516
합계0.5811.0000.7200.7970.1270.2340.5440.441-0.2810.630
탁주0.8690.7201.0000.8130.437-0.2490.6040.720-0.3860.332
약주0.8260.7970.8131.0000.470-0.1170.8310.834-0.1010.616
청주0.7710.1270.4370.4701.000-0.3190.5710.6850.3640.446
과실주-0.2730.234-0.249-0.117-0.3191.000-0.168-0.412-0.0970.247
증류식소주0.7330.5440.6040.8310.571-0.1681.0000.7960.0520.459
일반증류주0.8790.4410.7200.8340.685-0.4120.7961.0000.1380.515
리큐르-0.056-0.281-0.386-0.1010.364-0.0970.0520.1381.0000.378
기타주류0.5160.6300.3320.6160.4460.2470.4590.5150.3781.000

Missing values

2023-12-13T06:13:43.117095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:13:43.297105image/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

연도합계탁주약주청주과실주증류식소주일반증류주리큐르기타주류
020005770180201131132131
12001959048058455132572
22002257003401963119364144
32003465501100390213214361136
420045831072051912917277245
52005912007230713534676335505
6200672001011790644014823177132
72007103442461990929013112192274
820088112811921717114113306207
920095459336317142851653823087
연도합계탁주약주청주과실주증류식소주일반증류주리큐르기타주류
11201174063246595031121915417038
12201288624861679028871925714145
1320131093066137300300920772172127
14201411182705369702554193130190365
1520158606387264133209138137229377
1620167181357660141974214215310287
1720175956286150941645243293267134
18201869063461682451760204326263165
1920199160425614241001621310714276459
20202010775557690238616733681000387483