Overview

Dataset statistics

Number of variables9
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory87.3 B

Variable types

Numeric8
Categorical1

Dataset

Description음주폐해예방 관련 지표- 알코올 생산과 소비 > 알코올 생산> 민속주 출고현황 지표데이터를 제공합니다.- 알코올 생산과 소비 > 알코올 생산> 민속주 출고현황 지표데이터를 제공합니다.
Author한국건강증진개발원
URLhttps://www.data.go.kr/data/15050203/fileData.do

Alerts

연도 is highly overall correlated with 합계 and 3 other fieldsHigh correlation
합계 is highly overall correlated with 연도 and 3 other fieldsHigh correlation
탁주 is highly overall correlated with 연도 and 3 other fieldsHigh correlation
증류식소주 is highly overall correlated with 합계High correlation
일반증류주 is highly overall correlated with 리큐르High correlation
리큐르 is highly overall correlated with 연도 and 2 other fieldsHigh correlation
기타주류 is highly overall correlated with 연도High correlation
과실주 is highly overall correlated with 합계 and 1 other fieldsHigh correlation
과실주 is highly imbalanced (65.4%)Imbalance
연도 has unique valuesUnique
합계 has unique valuesUnique
약주 has unique valuesUnique
기타주류 has 1 (4.8%) zerosZeros

Reproduction

Analysis started2023-12-12 06:00:07.944226
Analysis finished2023-12-12 06:00:14.626246
Duration6.68 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
2023-12-12T15:00:14.688622image/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
2023-12-12T15:00:14.843220image/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%
Mean1884.7143
Minimum882
Maximum3420
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T15:00:15.021729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum882
5-th percentile920
Q11089
median1742
Q32429
95-th percentile3042
Maximum3420
Range2538
Interquartile range (IQR)1340

Descriptive statistics

Standard deviation831.18892
Coefficient of variation (CV)0.44101587
Kurtosis-1.3030906
Mean1884.7143
Median Absolute Deviation (MAD)687
Skewness0.32737769
Sum39579
Variance690875.01
MonotonicityNot monotonic
2023-12-12T15:00:15.215369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1230 1
 
4.8%
1278 1
 
4.8%
2255 1
 
4.8%
2260 1
 
4.8%
2806 1
 
4.8%
2881 1
 
4.8%
3017 1
 
4.8%
3420 1
 
4.8%
3042 1
 
4.8%
2429 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
882 1
4.8%
920 1
4.8%
945 1
4.8%
967 1
4.8%
1019 1
4.8%
1089 1
4.8%
1230 1
4.8%
1278 1
4.8%
1354 1
4.8%
1635 1
4.8%
ValueCountFrequency (%)
3420 1
4.8%
3042 1
4.8%
3017 1
4.8%
2881 1
4.8%
2806 1
4.8%
2429 1
4.8%
2289 1
4.8%
2260 1
4.8%
2255 1
4.8%
2119 1
4.8%

약주
Real number (ℝ)

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean484
Minimum284
Maximum800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T15:00:15.334844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum284
5-th percentile366
Q1448
median472
Q3511
95-th percentile643
Maximum800
Range516
Interquartile range (IQR)63

Descriptive statistics

Standard deviation100.30603
Coefficient of variation (CV)0.20724387
Kurtosis4.8160461
Mean484
Median Absolute Deviation (MAD)39
Skewness1.3735373
Sum10164
Variance10061.3
MonotonicityNot monotonic
2023-12-12T15:00:15.494486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
413 1
 
4.8%
507 1
 
4.8%
518 1
 
4.8%
522 1
 
4.8%
643 1
 
4.8%
457 1
 
4.8%
464 1
 
4.8%
427 1
 
4.8%
472 1
 
4.8%
468 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
284 1
4.8%
366 1
4.8%
411 1
4.8%
413 1
4.8%
427 1
4.8%
448 1
4.8%
457 1
4.8%
464 1
4.8%
467 1
4.8%
468 1
4.8%
ValueCountFrequency (%)
800 1
4.8%
643 1
4.8%
522 1
4.8%
518 1
4.8%
515 1
4.8%
511 1
4.8%
508 1
4.8%
507 1
4.8%
485 1
4.8%
478 1
4.8%

탁주
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1049.4762
Minimum186
Maximum2677
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T15:00:15.642501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum186
5-th percentile220
Q1268
median940
Q31680
95-th percentile2260
Maximum2677
Range2491
Interquartile range (IQR)1412

Descriptive statistics

Standard deviation846.60738
Coefficient of variation (CV)0.80669518
Kurtosis-1.2969106
Mean1049.4762
Median Absolute Deviation (MAD)676
Skewness0.45676519
Sum22039
Variance716744.06
MonotonicityNot monotonic
2023-12-12T15:00:15.830159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1375 2
 
9.5%
310 1
 
4.8%
1352 1
 
4.8%
1786 1
 
4.8%
2092 1
 
4.8%
2252 1
 
4.8%
2677 1
 
4.8%
2260 1
 
4.8%
1680 1
 
4.8%
1584 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
186 1
4.8%
220 1
4.8%
239 1
4.8%
263 1
4.8%
264 1
4.8%
268 1
4.8%
271 1
4.8%
310 1
4.8%
319 1
4.8%
326 1
4.8%
ValueCountFrequency (%)
2677 1
4.8%
2260 1
4.8%
2252 1
4.8%
2092 1
4.8%
1786 1
4.8%
1680 1
4.8%
1584 1
4.8%
1375 2
9.5%
1352 1
4.8%
940 1
4.8%

과실주
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size300.0 B
0
19 
7
 
1
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)9.5%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19
90.5%
7 1
 
4.8%
1 1
 
4.8%

Length

2023-12-12T15:00:15.992163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:00:16.114828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 19
90.5%
7 1
 
4.8%
1 1
 
4.8%

증류식소주
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean231.66667
Minimum143
Maximum323
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T15:00:16.543371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum143
5-th percentile164
Q1198
median225
Q3280
95-th percentile301
Maximum323
Range180
Interquartile range (IQR)82

Descriptive statistics

Standard deviation49.175536
Coefficient of variation (CV)0.2122685
Kurtosis-0.89936818
Mean231.66667
Median Absolute Deviation (MAD)30
Skewness0.20242156
Sum4865
Variance2418.2333
MonotonicityNot monotonic
2023-12-12T15:00:16.684008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
198 2
 
9.5%
205 2
 
9.5%
225 1
 
4.8%
286 1
 
4.8%
280 1
 
4.8%
294 1
 
4.8%
268 1
 
4.8%
235 1
 
4.8%
243 1
 
4.8%
239 1
 
4.8%
Other values (9) 9
42.9%
ValueCountFrequency (%)
143 1
4.8%
164 1
4.8%
184 1
4.8%
195 1
4.8%
197 1
4.8%
198 2
9.5%
200 1
4.8%
205 2
9.5%
225 1
4.8%
235 1
4.8%
ValueCountFrequency (%)
323 1
4.8%
301 1
4.8%
294 1
4.8%
286 1
4.8%
282 1
4.8%
280 1
4.8%
268 1
4.8%
243 1
4.8%
239 1
4.8%
235 1
4.8%

일반증류주
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.285714
Minimum2
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T15:00:16.846274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q17
median9
Q311
95-th percentile26
Maximum31
Range29
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.7686251
Coefficient of variation (CV)0.65806077
Kurtosis4.6510568
Mean10.285714
Median Absolute Deviation (MAD)2
Skewness2.0551502
Sum216
Variance45.814286
MonotonicityNot monotonic
2023-12-12T15:00:16.974356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 4
19.0%
11 3
14.3%
13 2
9.5%
9 2
9.5%
8 2
9.5%
5 2
9.5%
26 1
 
4.8%
31 1
 
4.8%
4 1
 
4.8%
2 1
 
4.8%
Other values (2) 2
9.5%
ValueCountFrequency (%)
2 1
 
4.8%
4 1
 
4.8%
5 2
9.5%
7 4
19.0%
8 2
9.5%
9 2
9.5%
10 1
 
4.8%
11 3
14.3%
12 1
 
4.8%
13 2
9.5%
ValueCountFrequency (%)
31 1
 
4.8%
26 1
 
4.8%
13 2
9.5%
12 1
 
4.8%
11 3
14.3%
10 1
 
4.8%
9 2
9.5%
8 2
9.5%
7 4
19.0%
5 2
9.5%

리큐르
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.52381
Minimum49
Maximum283
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T15:00:17.114099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum49
5-th percentile50
Q157
median67
Q384
95-th percentile199
Maximum283
Range234
Interquartile range (IQR)27

Descriptive statistics

Standard deviation63.776656
Coefficient of variation (CV)0.67471526
Kurtosis2.8405867
Mean94.52381
Median Absolute Deviation (MAD)12
Skewness1.8660689
Sum1985
Variance4067.4619
MonotonicityNot monotonic
2023-12-12T15:00:17.231404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
199 2
 
9.5%
60 2
 
9.5%
50 1
 
4.8%
84 1
 
4.8%
68 1
 
4.8%
69 1
 
4.8%
54 1
 
4.8%
56 1
 
4.8%
58 1
 
4.8%
49 1
 
4.8%
Other values (9) 9
42.9%
ValueCountFrequency (%)
49 1
4.8%
50 1
4.8%
54 1
4.8%
55 1
4.8%
56 1
4.8%
57 1
4.8%
58 1
4.8%
60 2
9.5%
63 1
4.8%
67 1
4.8%
ValueCountFrequency (%)
283 1
4.8%
199 2
9.5%
168 1
4.8%
127 1
4.8%
84 1
4.8%
81 1
4.8%
78 1
4.8%
69 1
4.8%
68 1
4.8%
67 1
4.8%

기타주류
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)47.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.380952
Minimum0
Maximum139
Zeros1
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T15:00:17.339146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median4
Q312
95-th percentile35
Maximum139
Range139
Interquartile range (IQR)9

Descriptive statistics

Standard deviation29.905645
Coefficient of variation (CV)2.0795316
Kurtosis16.893831
Mean14.380952
Median Absolute Deviation (MAD)1
Skewness3.9773306
Sum302
Variance894.34762
MonotonicityNot monotonic
2023-12-12T15:00:17.496058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3 6
28.6%
4 6
28.6%
12 2
 
9.5%
0 1
 
4.8%
25 1
 
4.8%
16 1
 
4.8%
139 1
 
4.8%
35 1
 
4.8%
19 1
 
4.8%
2 1
 
4.8%
ValueCountFrequency (%)
0 1
 
4.8%
2 1
 
4.8%
3 6
28.6%
4 6
28.6%
12 2
 
9.5%
16 1
 
4.8%
19 1
 
4.8%
25 1
 
4.8%
35 1
 
4.8%
139 1
 
4.8%
ValueCountFrequency (%)
139 1
 
4.8%
35 1
 
4.8%
25 1
 
4.8%
19 1
 
4.8%
16 1
 
4.8%
12 2
 
9.5%
4 6
28.6%
3 6
28.6%
2 1
 
4.8%
0 1
 
4.8%

Interactions

2023-12-12T15:00:13.459898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:08.238552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:09.032309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:09.686095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:10.587969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:11.167883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:11.875829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:12.638457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:13.564547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:08.374075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:09.130642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:09.785539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:10.665916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:11.269033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:11.977612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:12.738175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:13.675510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:08.473209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:09.210917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:10.114378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:10.737863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:11.350981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:12.078369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:12.839297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:13.768805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:08.577820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:09.298856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:10.195114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:10.808075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:11.434197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:12.166323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:12.925210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:13.875138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:08.669854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:09.363779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:10.270285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:10.869834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:11.517657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:12.244005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:13.009099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:13.983995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:08.778852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:09.447706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:10.357682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:10.945375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:11.613780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:12.336902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:13.125555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:14.093015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:08.870319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:09.523125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:10.444634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:11.016064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:11.699398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:12.422679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:13.250825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:14.189370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:08.947037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:09.591611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:10.511259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:11.084300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:11.782042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:12.533274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:13.362155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:00:17.625057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도합계약주탁주과실주증류식소주일반증류주리큐르기타주류
연도1.0000.8550.4000.6150.3180.7570.5640.3970.285
합계0.8551.0000.8330.9261.0000.8020.6180.3010.000
약주0.4000.8331.0000.6020.0000.7000.8020.7940.000
탁주0.6150.9260.6021.0000.8300.8610.0000.0000.000
과실주0.3181.0000.0000.8301.0000.8330.5670.0000.130
증류식소주0.7570.8020.7000.8610.8331.0000.8680.3180.000
일반증류주0.5640.6180.8020.0000.5670.8681.0000.8360.000
리큐르0.3970.3010.7940.0000.0000.3180.8361.0000.738
기타주류0.2850.0000.0000.0000.1300.0000.0000.7381.000
2023-12-12T15:00:17.787470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도합계약주탁주증류식소주일반증류주리큐르기타주류과실주
연도1.0000.7220.1780.7910.227-0.104-0.535-0.5160.162
합계0.7221.0000.2220.8540.505-0.151-0.428-0.4210.782
약주0.1780.2221.000-0.1470.3960.4550.4310.0410.000
탁주0.7910.854-0.1471.0000.212-0.361-0.610-0.4090.656
증류식소주0.2270.5050.3960.2121.0000.3350.272-0.4730.379
일반증류주-0.104-0.1510.455-0.3610.3351.0000.6830.1190.232
리큐르-0.535-0.4280.431-0.6100.2720.6831.0000.2770.000
기타주류-0.516-0.4210.041-0.409-0.4730.1190.2771.0000.080
과실주0.1620.7820.0000.6560.3790.2320.0000.0801.000

Missing values

2023-12-12T15:00:14.380619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:00:14.551963image/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

연도합계약주탁주과실주증류식소주일반증류주리큐르기타주류
020001230413310030171990
12001127850723902822619925
22002163580018603233128312
32003108948526401841312716
42004882366271014398112
52005135451531902058168139
6200694541126302058553
7200796746726801644604
820081019511220019511784
920099202843260197116735
연도합계약주탁주과실주증류식소주일반증류주리큐르기타주류
1120112119478137501987574
1220122289448158401985504
1320132429468168012252494
14201430424722260023910583
1520153420427267702439604
1620163017464225202357563
1720172881457209202687543
18201828066431786029411693
19201922605221375028012683
20202022555181352028613842