Overview

Dataset statistics

Number of variables10
Number of observations50
Missing cells1
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.4 KiB
Average record size in memory90.6 B

Variable types

Numeric8
Text1
Categorical1

Dataset

Description2012년 주류산업 싵태조사 자료(시도별 생산주종, 주류시장현황, 탁주 원료사용현황 등)
Author농림축산식품부
URLhttps://www.data.go.kr/data/15054844/fileData.do

Alerts

순위 is highly overall correlated with 출고수량(㎘) and 2 other fieldsHigh correlation
출고수량(㎘) is highly overall correlated with 순위 and 2 other fieldsHigh correlation
출고금액(백만원) is highly overall correlated with 순위 and 2 other fieldsHigh correlation
점유율(%) is highly overall correlated with 순위 and 2 other fieldsHigh correlation
국내산 사용량(kg) is highly overall correlated with 국내산비율(%) and 1 other fieldsHigh correlation
수입산사용량(kg) is highly overall correlated with 국내산비율(%) and 1 other fieldsHigh correlation
국내산비율(%) is highly overall correlated with 국내산 사용량(kg) and 2 other fieldsHigh correlation
수입산비율(%) is highly overall correlated with 국내산 사용량(kg) and 2 other fieldsHigh correlation
점유율(%) has 1 (2.0%) missing valuesMissing
국내산 사용량(kg) has 20 (40.0%) zerosZeros
수입산사용량(kg) has 20 (40.0%) zerosZeros
국내산비율(%) has 20 (40.0%) zerosZeros
수입산비율(%) has 20 (40.0%) zerosZeros

Reproduction

Analysis started2023-12-11 23:04:39.234747
Analysis finished2023-12-11 23:04:45.725229
Duration6.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순위
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.66
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-12T08:04:45.787330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.45
Q18.25
median16
Q322.75
95-th percentile28.55
Maximum30
Range29
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation8.6274597
Coefficient of variation (CV)0.55092335
Kurtosis-1.1813477
Mean15.66
Median Absolute Deviation (MAD)7
Skewness-0.092853155
Sum783
Variance74.433061
MonotonicityIncreasing
2023-12-12T08:04:45.933865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
22 3
 
6.0%
1 2
 
4.0%
14 2
 
4.0%
29 2
 
4.0%
27 2
 
4.0%
26 2
 
4.0%
25 2
 
4.0%
23 2
 
4.0%
21 2
 
4.0%
18 2
 
4.0%
Other values (20) 29
58.0%
ValueCountFrequency (%)
1 2
4.0%
2 1
2.0%
3 2
4.0%
4 2
4.0%
5 1
2.0%
6 2
4.0%
7 2
4.0%
8 1
2.0%
9 1
2.0%
10 2
4.0%
ValueCountFrequency (%)
30 1
 
2.0%
29 2
4.0%
28 1
 
2.0%
27 2
4.0%
26 2
4.0%
25 2
4.0%
24 1
 
2.0%
23 2
4.0%
22 3
6.0%
21 2
4.0%

업체명
Text

Distinct30
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-12-12T08:04:46.146313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length8.5
Mean length6.9
Min length3

Characters and Unicode

Total characters345
Distinct characters70
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)22.0%

Sample

1st row서울탁주
2nd row서울탁주
3rd row(주)국순당
4th row부산합동양조장림제조장
5th row부산합동양조장림제조장
ValueCountFrequency (%)
원주탁주합동제조장 3
 
5.7%
대구탁주합동제1공장 2
 
3.8%
여수주조공사 2
 
3.8%
유)전주주류공사 2
 
3.8%
동해양조장 2
 
3.8%
포천일동주조(주 2
 
3.8%
오봉주조 2
 
3.8%
이동백운주조 2
 
3.8%
은척양조장 2
 
3.8%
조술당 2
 
3.8%
Other values (22) 32
60.4%
2023-12-12T08:04:46.521608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 58
 
16.8%
조 30
 
8.7%
) 19
 
5.5%
동 16
 
4.6%
탁 15
 
4.3%
장 14
 
4.1%
( 13
 
3.8%
제 11
 
3.2%
양 10
 
2.9%
산 9
 
2.6%
Other values (60) 150
43.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 306
88.7%
Close Punctuation 19
 
5.5%
Open Punctuation 13
 
3.8%
Space Separator 4
 
1.2%
Decimal Number 3
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 58
19.0%
조 30
 
9.8%
동 16
 
5.2%
탁 15
 
4.9%
장 14
 
4.6%
제 11
 
3.6%
양 10
 
3.3%
산 9
 
2.9%
공 8
 
2.6%
합 7
 
2.3%
Other values (55) 128
41.8%
Space Separator
ValueCountFrequency (%)
3
75.0%
　 1
 
25.0%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Decimal Number
ValueCountFrequency (%)
1 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 306
88.7%
Common 39
 
11.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 58
19.0%
조 30
 
9.8%
동 16
 
5.2%
탁 15
 
4.9%
장 14
 
4.6%
제 11
 
3.6%
양 10
 
3.3%
산 9
 
2.9%
공 8
 
2.6%
합 7
 
2.3%
Other values (55) 128
41.8%
Common
ValueCountFrequency (%)
) 19
48.7%
( 13
33.3%
1 3
 
7.7%
3
 
7.7%
　 1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 306
88.7%
ASCII 38
 
11.0%
None 1
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 58
19.0%
조 30
 
9.8%
동 16
 
5.2%
탁 15
 
4.9%
장 14
 
4.6%
제 11
 
3.6%
양 10
 
3.3%
산 9
 
2.9%
공 8
 
2.6%
합 7
 
2.3%
Other values (55) 128
41.8%
ASCII
ValueCountFrequency (%)
) 19
50.0%
( 13
34.2%
1 3
 
7.9%
3
 
7.9%
None
ValueCountFrequency (%)
　 1
100.0%

출고수량(㎘)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13615.76
Minimum1210
Maximum163489
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-12T08:04:46.645614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1210
5-th percentile1263.85
Q12054.75
median3000
Q37186
95-th percentile53512.1
Maximum163489
Range162279
Interquartile range (IQR)5131.25

Descriptive statistics

Standard deviation33023.555
Coefficient of variation (CV)2.4253919
Kurtosis16.525245
Mean13615.76
Median Absolute Deviation (MAD)1499.5
Skewness4.0435588
Sum680788
Variance1.0905552 × 109
MonotonicityDecreasing
2023-12-12T08:04:46.785681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
3000 6
 
12.0%
2000 5
 
10.0%
2219 3
 
6.0%
163489 2
 
4.0%
5274 2
 
4.0%
1240 2
 
4.0%
1306 2
 
4.0%
1700 2
 
4.0%
2310 2
 
4.0%
3380 2
 
4.0%
Other values (16) 22
44.0%
ValueCountFrequency (%)
1210 1
 
2.0%
1240 2
 
4.0%
1293 1
 
2.0%
1306 2
 
4.0%
1700 2
 
4.0%
2000 5
10.0%
2219 3
6.0%
2310 2
 
4.0%
2625 1
 
2.0%
2850 1
 
2.0%
ValueCountFrequency (%)
163489 2
4.0%
62000 1
2.0%
43138 2
4.0%
17720 2
4.0%
16500 1
2.0%
8545 2
4.0%
8473 2
4.0%
7223 1
2.0%
7075 1
2.0%
6950 2
4.0%

출고금액(백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13244.44
Minimum1166
Maximum128487
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-12T08:04:46.931127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1166
5-th percentile1170
Q11312.5
median2619.5
Q38425
95-th percentile63242.25
Maximum128487
Range127321
Interquartile range (IQR)7112.5

Descriptive statistics

Standard deviation27920.612
Coefficient of variation (CV)2.1081006
Kurtosis11.21493
Mean13244.44
Median Absolute Deviation (MAD)1451.5
Skewness3.3423105
Sum662222
Variance7.795606 × 108
MonotonicityNot monotonic
2023-12-12T08:04:47.068525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1200 6
 
12.0%
1506 3
 
6.0%
1300 3
 
6.0%
128487 2
 
4.0%
4500 2
 
4.0%
1795 2
 
4.0%
1166 2
 
4.0%
2000 2
 
4.0%
2800 2
 
4.0%
1170 2
 
4.0%
Other values (17) 24
48.0%
ValueCountFrequency (%)
1166 2
 
4.0%
1170 2
 
4.0%
1200 6
12.0%
1300 3
6.0%
1350 1
 
2.0%
1360 1
 
2.0%
1506 3
6.0%
1795 2
 
4.0%
1800 1
 
2.0%
2000 2
 
4.0%
ValueCountFrequency (%)
128487 2
4.0%
83895 1
2.0%
38000 2
4.0%
36652 2
4.0%
16539 2
4.0%
10000 1
2.0%
9865 2
4.0%
8900 1
2.0%
7000 2
4.0%
6700 1
2.0%

점유율(%)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)30.6%
Missing1
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean3.0122449
Minimum0.3
Maximum28.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-12T08:04:47.178556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.3
Q10.3
median0.6
Q32
95-th percentile14.62
Maximum28.6
Range28.3
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation6.2699559
Coefficient of variation (CV)2.0814894
Kurtosis10.89638
Mean3.0122449
Median Absolute Deviation (MAD)0.3
Skewness3.298531
Sum147.6
Variance39.312347
MonotonicityNot monotonic
2023-12-12T08:04:47.279986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.3 17
34.0%
0.4 5
 
10.0%
1.0 4
 
8.0%
2.2 3
 
6.0%
1.5 3
 
6.0%
28.6 2
 
4.0%
8.2 2
 
4.0%
3.7 2
 
4.0%
1.6 2
 
4.0%
8.5 2
 
4.0%
Other values (5) 7
14.0%
ValueCountFrequency (%)
0.3 17
34.0%
0.4 5
 
10.0%
0.5 2
 
4.0%
0.6 2
 
4.0%
1.0 4
 
8.0%
1.3 1
 
2.0%
1.5 3
 
6.0%
1.6 2
 
4.0%
2.0 1
 
2.0%
2.2 3
 
6.0%
ValueCountFrequency (%)
28.6 2
4.0%
18.7 1
 
2.0%
8.5 2
4.0%
8.2 2
4.0%
3.7 2
4.0%
2.2 3
6.0%
2.0 1
 
2.0%
1.6 2
4.0%
1.5 3
6.0%
1.3 1
 
2.0%

재료
Categorical

Distinct6
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
쌀
30 
밀
16 
올리고당
 
1
복분자
 
1
울금
 
1

Length

Max length4
Median length1
Mean length1.14
Min length1

Unique

Unique4 ?
Unique (%)8.0%

Sample

1st row쌀
2nd row올리고당
3rd row쌀
4th row쌀
5th row밀

Common Values

ValueCountFrequency (%)
쌀 30
60.0%
밀 16
32.0%
올리고당 1
 
2.0%
복분자 1
 
2.0%
울금 1
 
2.0%
찹쌀 1
 
2.0%

Length

2023-12-12T08:04:47.427021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:04:47.524384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
쌀 30
60.0%
밀 16
32.0%
올리고당 1
 
2.0%
복분자 1
 
2.0%
울금 1
 
2.0%
찹쌀 1
 
2.0%

국내산 사용량(kg)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean330490.68
Minimum0
Maximum6241536
Zeros20
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-12T08:04:47.640703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median40135
Q3234065
95-th percentile1184944.2
Maximum6241536
Range6241536
Interquartile range (IQR)234065

Descriptive statistics

Standard deviation925316.9
Coefficient of variation (CV)2.7998275
Kurtosis35.32911
Mean330490.68
Median Absolute Deviation (MAD)40135
Skewness5.6050085
Sum16524534
Variance8.5621136 × 1011
MonotonicityNot monotonic
2023-12-12T08:04:47.760695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 20
40.0%
180000 2
 
4.0%
6241536 1
 
2.0%
139880 1
 
2.0%
190500 1
 
2.0%
1000 1
 
2.0%
198400 1
 
2.0%
300000 1
 
2.0%
61440 1
 
2.0%
1760 1
 
2.0%
Other values (20) 20
40.0%
ValueCountFrequency (%)
0 20
40.0%
1000 1
 
2.0%
1760 1
 
2.0%
15700 1
 
2.0%
36000 1
 
2.0%
39698 1
 
2.0%
40572 1
 
2.0%
42000 1
 
2.0%
61440 1
 
2.0%
66000 1
 
2.0%
ValueCountFrequency (%)
6241536 1
2.0%
1446000 1
2.0%
1199135 1
2.0%
1167600 1
2.0%
1090670 1
2.0%
909840 1
2.0%
729690 1
2.0%
584400 1
2.0%
313624 1
2.0%
300000 1
2.0%

수입산사용량(kg)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean772875
Minimum0
Maximum23480064
Zeros20
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-12T08:04:47.899205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median43414
Q3258807.5
95-th percentile1849134.5
Maximum23480064
Range23480064
Interquartile range (IQR)258807.5

Descriptive statistics

Standard deviation3360647.7
Coefficient of variation (CV)4.3482422
Kurtosis44.92692
Mean772875
Median Absolute Deviation (MAD)43414
Skewness6.5811788
Sum38643750
Variance1.1293953 × 1013
MonotonicityNot monotonic
2023-12-12T08:04:48.001726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 20
40.0%
23480064 1
 
2.0%
264000 1
 
2.0%
12000 1
 
2.0%
192000 1
 
2.0%
1000 1
 
2.0%
180000 1
 
2.0%
8000 1
 
2.0%
121600 1
 
2.0%
15360 1
 
2.0%
Other values (21) 21
42.0%
ValueCountFrequency (%)
0 20
40.0%
1000 1
 
2.0%
8000 1
 
2.0%
12000 1
 
2.0%
15360 1
 
2.0%
23828 1
 
2.0%
63000 1
 
2.0%
90000 1
 
2.0%
100000 1
 
2.0%
105120 1
 
2.0%
ValueCountFrequency (%)
23480064 1
2.0%
4554000 1
2.0%
1970000 1
2.0%
1701410 1
2.0%
1440000 1
2.0%
905502 1
2.0%
580000 1
2.0%
560000 1
2.0%
416200 1
2.0%
407000 1
2.0%

국내산비율(%)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.986
Minimum0
Maximum100
Zeros20
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-12T08:04:48.111000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median27.05
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)100

Descriptive statistics

Standard deviation46.750148
Coefficient of variation (CV)0.99498038
Kurtosis-1.9239282
Mean46.986
Median Absolute Deviation (MAD)27.05
Skewness0.15633893
Sum2349.3
Variance2185.5763
MonotonicityNot monotonic
2023-12-12T08:04:48.235729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.0 20
40.0%
100.0 20
40.0%
21.0 1
 
2.0%
24.1 1
 
2.0%
75.0 1
 
2.0%
4.2 1
 
2.0%
30.0 1
 
2.0%
63.0 1
 
2.0%
20.0 1
 
2.0%
10.0 1
 
2.0%
Other values (2) 2
 
4.0%
ValueCountFrequency (%)
0.0 20
40.0%
4.2 1
 
2.0%
10.0 1
 
2.0%
20.0 1
 
2.0%
21.0 1
 
2.0%
24.1 1
 
2.0%
30.0 1
 
2.0%
40.0 1
 
2.0%
62.0 1
 
2.0%
63.0 1
 
2.0%
ValueCountFrequency (%)
100.0 20
40.0%
75.0 1
 
2.0%
63.0 1
 
2.0%
62.0 1
 
2.0%
40.0 1
 
2.0%
30.0 1
 
2.0%
24.1 1
 
2.0%
21.0 1
 
2.0%
20.0 1
 
2.0%
10.0 1
 
2.0%

수입산비율(%)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.014
Minimum0
Maximum100
Zeros20
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-12T08:04:48.398516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median72.95
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)100

Descriptive statistics

Standard deviation46.750148
Coefficient of variation (CV)0.88184532
Kurtosis-1.9239282
Mean53.014
Median Absolute Deviation (MAD)27.05
Skewness-0.15633893
Sum2650.7
Variance2185.5763
MonotonicityNot monotonic
2023-12-12T08:04:48.504683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
100.0 20
40.0%
0.0 20
40.0%
79.0 1
 
2.0%
75.9 1
 
2.0%
25.0 1
 
2.0%
95.8 1
 
2.0%
70.0 1
 
2.0%
37.0 1
 
2.0%
80.0 1
 
2.0%
90.0 1
 
2.0%
Other values (2) 2
 
4.0%
ValueCountFrequency (%)
0.0 20
40.0%
25.0 1
 
2.0%
37.0 1
 
2.0%
38.0 1
 
2.0%
60.0 1
 
2.0%
70.0 1
 
2.0%
75.9 1
 
2.0%
79.0 1
 
2.0%
80.0 1
 
2.0%
90.0 1
 
2.0%
ValueCountFrequency (%)
100.0 20
40.0%
95.8 1
 
2.0%
90.0 1
 
2.0%
80.0 1
 
2.0%
79.0 1
 
2.0%
75.9 1
 
2.0%
70.0 1
 
2.0%
60.0 1
 
2.0%
38.0 1
 
2.0%
37.0 1
 
2.0%

Interactions

2023-12-12T08:04:44.656093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:39.532378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:40.116498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:40.761286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:41.408975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:42.086584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:42.859504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:43.811150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:44.737070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:39.598425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:40.189997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:40.832292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:41.488283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:42.176489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:42.935550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:43.914810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:44.846501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:39.671760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:40.269741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:40.915967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:41.572120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:42.279596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:43.310415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:44.019992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:44.928853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:39.742047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:40.342032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:41.004351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:41.647096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:42.394860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:43.390192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:44.102751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:45.034236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:39.821282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:40.417775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:41.090316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:41.729797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:42.491615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:43.486220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:44.194522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:45.128829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:39.898838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:40.502234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:41.167184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:41.856530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:42.590410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:43.567899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:44.297735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:45.236190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:39.978715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:40.592161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:41.243128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:41.929639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:42.689809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:43.644522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:44.457253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:45.348435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:40.050880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:40.687062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:41.317168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:42.011376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:42.773768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:43.724369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:04:44.557734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:04:48.596049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순위업체명출고수량(㎘)출고금액(백만원)점유율(%)재료국내산 사용량(kg)수입산사용량(kg)국내산비율(%)수입산비율(%)
순위1.0001.0000.7860.7700.7750.0000.4100.1590.1910.259
업체명1.0001.0001.0001.0001.0000.0000.7450.8620.0000.711
출고수량(㎘)0.7861.0001.0000.9970.9970.2620.7470.8210.2090.000
출고금액(백만원)0.7701.0000.9971.0001.0000.4270.7480.8210.3910.339
점유율(%)0.7751.0000.9971.0001.0000.4310.7480.8210.2770.161
재료0.0000.0000.2620.4270.4311.0000.0000.0000.3170.221
국내산 사용량(kg)0.4100.7450.7470.7480.7480.0001.0001.0000.6080.781
수입산사용량(kg)0.1590.8620.8210.8210.8210.0001.0001.0000.5920.597
국내산비율(%)0.1910.0000.2090.3910.2770.3170.6080.5921.0001.000
수입산비율(%)0.2590.7110.0000.3390.1610.2210.7810.5971.0001.000
2023-12-12T08:04:48.713699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순위출고수량(㎘)출고금액(백만원)점유율(%)국내산 사용량(kg)수입산사용량(kg)국내산비율(%)수입산비율(%)재료
순위1.000-0.999-0.825-0.834-0.182-0.4510.110-0.1100.000
출고수량(㎘)-0.9991.0000.8170.8320.1810.460-0.1160.1160.178
출고금액(백만원)-0.8250.8171.0000.9790.2170.2480.050-0.0500.306
점유율(%)-0.8340.8320.9791.0000.2260.2670.043-0.0430.303
국내산 사용량(kg)-0.1820.1810.2170.2261.000-0.4110.816-0.8160.000
수입산사용량(kg)-0.4510.4600.2480.267-0.4111.000-0.7660.7660.000
국내산비율(%)0.110-0.1160.0500.0430.816-0.7661.000-1.0000.147
수입산비율(%)-0.1100.116-0.050-0.043-0.8160.766-1.0001.0000.108
재료0.0000.1780.3060.3030.0000.0000.1470.1081.000

Missing values

2023-12-12T08:04:45.521459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:04:45.665586image/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

순위업체명출고수량(㎘)출고금액(백만원)점유율(%)재료국내산 사용량(kg)수입산사용량(kg)국내산비율(%)수입산비율(%)
01서울탁주16348912848728.6쌀62415362348006421.079.0
11서울탁주16348912848728.6올리고당04070000.0100.0
22(주)국순당620008389518.7쌀1446000455400024.175.9
33부산합동양조장림제조장43138366528.2쌀014400000.0100.0
43부산합동양조장림제조장43138366528.2밀05800000.0100.0
54대구탁주합동제1공장17720165393.7쌀10906700100.00.0
64대구탁주합동제1공장17720165393.7밀017014100.0100.0
75인천탁주제조제1공장16500100002.2쌀019700000.0100.0
86울산탁주 태화루854598652.2쌀9098400100.00.0
96울산탁주 태화루854598652.2밀01380000.0100.0
순위업체명출고수량(㎘)출고금액(백만원)점유율(%)재료국내산 사용량(kg)수입산사용량(kg)국내산비율(%)수입산비율(%)
4025이동백운주조200011660.3쌀19840012160062.038.0
4125이동백운주조200011660.3밀080000.0100.0
4226오봉주조170012000.3쌀1800000100.00.0
4326오봉주조170012000.3밀01800000.0100.0
4427부산산성양조130617950.4쌀10000100.00.0
4527부산산성양조130617950.4밀010000.0100.0
4628진주탁주공동운영체129313500.3쌀01920000.0100.0
4729동해양조장124012000.3쌀1905000100.00.0
4829동해양조장124012000.3밀0120000.0100.0
4930주)당진면천주조12101300<NA>쌀1398800100.00.0