Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory732.4 KiB
Average record size in memory75.0 B

Variable types

Numeric3
Text3
DateTime1
Categorical1

Dataset

Description수산물 수협창고 재고 정보는 수협이 보유하고 있는 창고(이용가공)에 대한 기본정보 및 해당 창고에 재고 되는 품목에 대한 정보 및 품목별 재고 정보, 그리고 해당 창고를 이용하는 매출처에 대한 기본적인 정보를 제공하는 목록입니다.
Author해양수산부
URLhttps://www.data.go.kr/data/15102800/fileData.do

Alerts

조합코드 is highly overall correlated with 창고코드 and 1 other fieldsHigh correlation
창고코드 is highly overall correlated with 조합코드 and 1 other fieldsHigh correlation
창고명 is highly overall correlated with 조합코드 and 1 other fieldsHigh correlation
수량 has 289 (2.9%) zerosZeros

Reproduction

Analysis started2024-04-21 02:11:39.624086
Analysis finished2024-04-21 02:11:42.975372
Duration3.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

조합코드
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean353.8669
Minimum300
Maximum516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T11:11:43.054667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile306
Q1319
median349
Q3362
95-th percentile512
Maximum516
Range216
Interquartile range (IQR)43

Descriptive statistics

Standard deviation50.920767
Coefficient of variation (CV)0.14389808
Kurtosis4.4431249
Mean353.8669
Median Absolute Deviation (MAD)21
Skewness2.1660042
Sum3538669
Variance2592.9245
MonotonicityNot monotonic
2024-04-21T11:11:43.227827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
339 640
 
6.4%
311 560
 
5.6%
372 553
 
5.5%
325 479
 
4.8%
362 420
 
4.2%
353 416
 
4.2%
356 405
 
4.0%
351 367
 
3.7%
512 321
 
3.2%
361 316
 
3.2%
Other values (42) 5523
55.2%
ValueCountFrequency (%)
300 183
 
1.8%
303 76
 
0.8%
304 147
 
1.5%
305 55
 
0.5%
306 288
2.9%
307 156
 
1.6%
308 103
 
1.0%
311 560
5.6%
312 40
 
0.4%
313 124
 
1.2%
ValueCountFrequency (%)
516 79
 
0.8%
515 143
 
1.4%
512 321
3.2%
511 120
 
1.2%
509 17
 
0.2%
507 55
 
0.5%
505 47
 
0.5%
373 229
2.3%
372 553
5.5%
371 117
 
1.2%

창고코드
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3538674.3
Minimum3000002
Maximum5160005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T11:11:43.363807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3000002
5-th percentile3060002
Q13190002
median3490002
Q33620002
95-th percentile5120002
Maximum5160005
Range2160003
Interquartile range (IQR)430000

Descriptive statistics

Standard deviation509206.99
Coefficient of variation (CV)0.14389767
Kurtosis4.4431095
Mean3538674.3
Median Absolute Deviation (MAD)209997
Skewness2.1659993
Sum3.5386743 × 1010
Variance2.5929175 × 1011
MonotonicityNot monotonic
2024-04-21T11:11:43.498501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3390002 640
 
6.4%
3110002 560
 
5.6%
3720002 490
 
4.9%
3250002 479
 
4.8%
3620002 420
 
4.2%
3510002 367
 
3.7%
3560003 341
 
3.4%
5120002 321
 
3.2%
3610002 316
 
3.2%
3060002 288
 
2.9%
Other values (50) 5778
57.8%
ValueCountFrequency (%)
3000002 134
 
1.3%
3000114 49
 
0.5%
3030002 76
 
0.8%
3040002 147
 
1.5%
3050002 55
 
0.5%
3060002 288
2.9%
3070002 156
 
1.6%
3080007 103
 
1.0%
3110002 560
5.6%
3120002 40
 
0.4%
ValueCountFrequency (%)
5160005 79
 
0.8%
5150002 143
 
1.4%
5120002 321
3.2%
5110002 120
 
1.2%
5090002 17
 
0.2%
5070002 55
 
0.5%
5050002 47
 
0.5%
3730002 229
2.3%
3720004 63
 
0.6%
3720002 490
4.9%

표준어종코드
Text

Distinct247
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T11:11:43.808310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters60000
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row630403
2nd row610200
3rd row615601
4th row610100
5th row630201
ValueCountFrequency (%)
610802 326
 
3.3%
610300 255
 
2.5%
640501 240
 
2.4%
614401 215
 
2.1%
61a402 204
 
2.0%
61a404 204
 
2.0%
616808 202
 
2.0%
610200 187
 
1.9%
699900 183
 
1.8%
616200 170
 
1.7%
Other values (237) 7814
78.1%
2024-04-21T11:11:44.309752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15533
25.9%
1 11855
19.8%
6 11631
19.4%
9 4587
 
7.6%
4 4028
 
6.7%
2 3818
 
6.4%
3 3493
 
5.8%
8 1725
 
2.9%
5 1423
 
2.4%
7 1064
 
1.8%
Other values (3) 843
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 59157
98.6%
Uppercase Letter 843
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15533
26.3%
1 11855
20.0%
6 11631
19.7%
9 4587
 
7.8%
4 4028
 
6.8%
2 3818
 
6.5%
3 3493
 
5.9%
8 1725
 
2.9%
5 1423
 
2.4%
7 1064
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
A 539
63.9%
Z 186
 
22.1%
B 118
 
14.0%

Most occurring scripts

ValueCountFrequency (%)
Common 59157
98.6%
Latin 843
 
1.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15533
26.3%
1 11855
20.0%
6 11631
19.7%
9 4587
 
7.8%
4 4028
 
6.8%
2 3818
 
6.5%
3 3493
 
5.9%
8 1725
 
2.9%
5 1423
 
2.4%
7 1064
 
1.8%
Latin
ValueCountFrequency (%)
A 539
63.9%
Z 186
 
22.1%
B 118
 
14.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15533
25.9%
1 11855
19.8%
6 11631
19.4%
9 4587
 
7.6%
4 4028
 
6.7%
2 3818
 
6.4%
3 3493
 
5.8%
8 1725
 
2.9%
5 1423
 
2.4%
7 1064
 
1.8%
Other values (3) 843
 
1.4%

기준일자
Date

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2024-03-01 00:00:00
Maximum2024-03-31 00:00:00
2024-04-21T11:11:44.449037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:11:44.568827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)

조합명
Text

Distinct52
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T11:11:44.769098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length9.7706
Min length9

Characters and Unicode

Total characters97706
Distinct characters83
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경인북부수산업협동조합
2nd row강구수산업협동조합
3rd row강릉시수산업협동조합
4th row동해시수산업협동조합
5th row서산수산업협동조합
ValueCountFrequency (%)
여수 640
 
6.0%
수산업협동조합 640
 
6.0%
강릉시수산업협동조합 560
 
5.3%
한림수산업협동조합 553
 
5.2%
부안수산업협동조합 479
 
4.5%
통영수산업협동조합 420
 
3.9%
거제수산업협동조합 416
 
3.9%
마산수산업협동조합 405
 
3.8%
포항수산업협동조합 367
 
3.4%
근해통발수산업협동조합 321
 
3.0%
Other values (43) 5839
54.9%
2024-04-21T11:11:45.100754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 11079
11.3%
수 10958
11.2%
동 10288
10.5%
업 10000
10.2%
협 10000
10.2%
조 10000
10.2%
합 10000
10.2%
포 1266
 
1.3%
시 1259
 
1.3%
부 1197
 
1.2%
Other values (73) 21659
22.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 97066
99.3%
Space Separator 640
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 11079
11.4%
수 10958
11.3%
동 10288
10.6%
업 10000
10.3%
협 10000
10.3%
조 10000
10.3%
합 10000
10.3%
포 1266
 
1.3%
시 1259
 
1.3%
부 1197
 
1.2%
Other values (72) 21019
21.7%
Space Separator
ValueCountFrequency (%)
640
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 97066
99.3%
Common 640
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 11079
11.4%
수 10958
11.3%
동 10288
10.6%
업 10000
10.3%
협 10000
10.3%
조 10000
10.3%
합 10000
10.3%
포 1266
 
1.3%
시 1259
 
1.3%
부 1197
 
1.2%
Other values (72) 21019
21.7%
Common
ValueCountFrequency (%)
640
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 97066
99.3%
ASCII 640
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 11079
11.4%
수 10958
11.3%
동 10288
10.6%
업 10000
10.3%
협 10000
10.3%
조 10000
10.3%
합 10000
10.3%
포 1266
 
1.3%
시 1259
 
1.3%
부 1197
 
1.2%
Other values (72) 21019
21.7%
ASCII
ValueCountFrequency (%)
640
100.0%

창고명
Categorical

HIGH CORRELATION 

Distinct46
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
이용가공과(이용)
1551 
유통사업과(이용)
641 
이용가공과 (여수수협제빙냉동공장)(이용)
640 
제빙냉동공장(이용)
 
560
한림수협(이용과)
 
490
Other values (41)
6118 

Length

Max length22
Median length16
Mean length10.8295
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row판매사업과(이용)
2nd row이용가공과(이용)
3rd row제빙냉동공장(이용)
4th row유통가공과(이용)
5th row서산수협제빙냉동공장(이용)

Common Values

ValueCountFrequency (%)
이용가공과(이용) 1551
 
15.5%
유통사업과(이용) 641
 
6.4%
이용가공과 (여수수협제빙냉동공장)(이용) 640
 
6.4%
제빙냉동공장(이용) 560
 
5.6%
한림수협(이용과) 490
 
4.9%
격포냉동공장(이용) 479
 
4.8%
마산수협 제빙공장(이용) 341
 
3.4%
냉동냉장창고(이용) 321
 
3.2%
이용팀(이용) 316
 
3.2%
이용과(이용) 303
 
3.0%
Other values (36) 4358
43.6%

Length

2024-04-21T11:11:45.292008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
이용가공과(이용 1551
 
13.3%
유통사업과(이용 641
 
5.5%
이용가공과 640
 
5.5%
여수수협제빙냉동공장)(이용 640
 
5.5%
제빙냉동공장(이용 560
 
4.8%
제빙공장(이용 534
 
4.6%
한림수협(이용과 490
 
4.2%
격포냉동공장(이용 479
 
4.1%
마산수협 405
 
3.5%
거제수협 337
 
2.9%
Other values (42) 5406
46.3%

표준어종명
Text

Distinct246
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T11:11:45.603116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length3.2961
Min length1

Characters and Unicode

Total characters32961
Distinct characters183
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row젓새우
2nd row가자미류
3rd row방어
4th row가오리류
5th row꽃게
ValueCountFrequency (%)
고등어 326
 
3.3%
갈치류 255
 
2.5%
살오징어 240
 
2.4%
멸치 215
 
2.1%
청어 204
 
2.0%
정어리 204
 
2.0%
삼치 202
 
2.0%
가자미류 187
 
1.9%
기타해면기타 183
 
1.8%
복류 170
 
1.7%
Other values (236) 7814
78.1%
2024-04-21T11:11:46.049984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
류 3617
 
11.0%
어 3000
 
9.1%
기 2340
 
7.1%
타 1603
 
4.9%
치 1550
 
4.7%
오 831
 
2.5%
징 678
 
2.1%
새 599
 
1.8%
우 593
 
1.8%
고 576
 
1.7%
Other values (173) 17574
53.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 32757
99.4%
Open Punctuation 102
 
0.3%
Close Punctuation 102
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
류 3617
 
11.0%
어 3000
 
9.2%
기 2340
 
7.1%
타 1603
 
4.9%
치 1550
 
4.7%
오 831
 
2.5%
징 678
 
2.1%
새 599
 
1.8%
우 593
 
1.8%
고 576
 
1.8%
Other values (171) 17370
53.0%
Open Punctuation
ValueCountFrequency (%)
( 102
100.0%
Close Punctuation
ValueCountFrequency (%)
) 102
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 32757
99.4%
Common 204
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
류 3617
 
11.0%
어 3000
 
9.2%
기 2340
 
7.1%
타 1603
 
4.9%
치 1550
 
4.7%
오 831
 
2.5%
징 678
 
2.1%
새 599
 
1.8%
우 593
 
1.8%
고 576
 
1.8%
Other values (171) 17370
53.0%
Common
ValueCountFrequency (%)
( 102
50.0%
) 102
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 32757
99.4%
ASCII 204
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
류 3617
 
11.0%
어 3000
 
9.2%
기 2340
 
7.1%
타 1603
 
4.9%
치 1550
 
4.7%
오 831
 
2.5%
징 678
 
2.1%
새 599
 
1.8%
우 593
 
1.8%
고 576
 
1.8%
Other values (171) 17370
53.0%
ASCII
ValueCountFrequency (%)
( 102
50.0%
) 102
50.0%

수량
Real number (ℝ)

ZEROS 

Distinct2203
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2376.5108
Minimum0
Maximum252799
Zeros289
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T11:11:46.192292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q130
median174
Q3824
95-th percentile8495.55
Maximum252799
Range252799
Interquartile range (IQR)794

Descriptive statistics

Standard deviation11627.337
Coefficient of variation (CV)4.8926085
Kurtosis181.87919
Mean2376.5108
Median Absolute Deviation (MAD)165
Skewness12.003592
Sum23765108
Variance1.3519497 × 108
MonotonicityNot monotonic
2024-04-21T11:11:46.343046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 289
 
2.9%
1.0 189
 
1.9%
2.0 130
 
1.3%
7.0 123
 
1.2%
10.0 122
 
1.2%
3.0 108
 
1.1%
5.0 108
 
1.1%
4.0 103
 
1.0%
15.0 94
 
0.9%
8.0 85
 
0.9%
Other values (2193) 8649
86.5%
ValueCountFrequency (%)
0.0 289
2.9%
1.0 189
1.9%
2.0 130
1.3%
3.0 108
 
1.1%
4.0 103
 
1.0%
5.0 108
 
1.1%
6.0 75
 
0.8%
7.0 123
1.2%
8.0 85
 
0.9%
8.2 2
 
< 0.1%
ValueCountFrequency (%)
252799.0 1
< 0.1%
238790.0 1
< 0.1%
238534.0 1
< 0.1%
235994.0 1
< 0.1%
231518.0 1
< 0.1%
228715.0 1
< 0.1%
227295.0 1
< 0.1%
169933.0 1
< 0.1%
166361.0 1
< 0.1%
166151.0 1
< 0.1%

Interactions

2024-04-21T11:11:42.446018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:11:41.875426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:11:42.197916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:11:42.529464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:11:42.017028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:11:42.284878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:11:42.622768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:11:42.104285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:11:42.364546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T11:11:46.442424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조합코드창고코드기준일자조합명창고명수량
조합코드1.0001.0000.0001.0000.9950.136
창고코드1.0001.0000.0001.0000.9860.136
기준일자0.0000.0001.0000.0000.0000.000
조합명1.0001.0000.0001.0000.9980.610
창고명0.9950.9860.0000.9981.0000.437
수량0.1360.1360.0000.6100.4371.000
2024-04-21T11:11:46.551824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조합코드창고코드수량창고명
조합코드1.0001.0000.1060.908
창고코드1.0001.0000.1060.908
수량0.1060.1061.0000.171
창고명0.9080.9080.1711.000

Missing values

2024-04-21T11:11:42.752339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T11:11:42.906770image/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

조합코드창고코드표준어종코드기준일자조합명창고명표준어종명수량
734530030000026304032024-03-07경인북부수산업협동조합판매사업과(이용)젓새우88.0
2298834634600026102002024-03-19강구수산업협동조합이용가공과(이용)가자미류184.0
1748131131100026156012024-03-15강릉시수산업협동조합제빙냉동공장(이용)방어7.0
1490630630600026101002024-03-13동해시수산업협동조합유통가공과(이용)가오리류9.0
511631631600026302012024-03-05서산수산업협동조합서산수협제빙냉동공장(이용)꽃게786.0
1667934634600026999002024-03-14강구수산업협동조합이용가공과(이용)기타해면기타486.0
1914933933900026131012024-03-16여수 수산업협동조합이용가공과 (여수수협제빙냉동공장)(이용)대구7.0
188735135100026304002024-03-02포항수산업협동조합유통사업과(이용)새우류560.75
604351251200026191012024-03-05근해통발수산업협동조합냉동냉장창고(이용)붕장어42861.0
2126331131100026210992024-03-18강릉시수산업협동조합제빙냉동공장(이용)기타바지락류368.0
조합코드창고코드표준어종코드기준일자조합명창고명표준어종명수량
557135135100026108022024-03-05포항수산업협동조합유통사업과(이용)고등어4114.0
3781430630600026145012024-03-31동해시수산업협동조합유통가공과(이용)명태115.0
361151251200026191992024-03-03근해통발수산업협동조합냉동냉장창고(이용)기타장어류1196.0
2022332532500026123002024-03-17부안수산업협동조합격포냉동공장(이용)노래미류1.0
3561233133100028108012024-03-29진도군수산업협동조합제빙냉동사업소(이용)톳6.0
3266230630600026140012024-03-27동해시수산업협동조합유통가공과(이용)벌레문치65.0
200335635600036302062024-03-02마산수산업협동조합마산수협 제빙공장(이용)민꽃게303.0
869331131100026405022024-03-08강릉시수산업협동조합제빙냉동공장(이용)빨강오징어127.0
3066134934900026304002024-03-25죽변수산업협동조합사업이용가공과(이용)새우류314.0
2247230630600026404072024-03-19동해시수산업협동조합유통가공과(이용)문어18.0