Overview

Dataset statistics

Number of variables18
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows262
Duplicate rows (%)2.6%
Total size in memory1.5 MiB
Average record size in memory160.0 B

Variable types

Categorical5
Numeric7
Text6

Dataset

Description수산물 위판정보는 수협 산지조합에서 위판되는 정보를 관리하는 목록으로 기본적인 산지조합에 대한 기본정보, 위판장에 대한 기본정보, 일자별로 수산물에 대한 가격 및 물량정보에 대한 정보 입니다.
Author해양수산부
URLhttps://www.data.go.kr/data/15102791/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
Dataset has 262 (2.6%) duplicate rowsDuplicates
어종상태코드 is highly overall correlated with 어종상태명 and 1 other fieldsHigh correlation
어종상태명 is highly overall correlated with 어종상태코드 and 1 other fieldsHigh correlation
산지조합코드 is highly overall correlated with 위판장코드High correlation
위판장코드 is highly overall correlated with 산지조합코드High correlation
위판수량 is highly overall correlated with 위판단가(1킬로그램) and 1 other fieldsHigh correlation
위판중량 is highly overall correlated with 위판단가(1킬로그램) and 1 other fieldsHigh correlation
위판단가 is highly overall correlated with 위판단가(1킬로그램)High correlation
위판단가(1킬로그램) is highly overall correlated with 위판수량 and 2 other fieldsHigh correlation
위판금액 is highly overall correlated with 위판수량 and 1 other fieldsHigh correlation
상품단위명 is highly overall correlated with 어종상태코드 and 1 other fieldsHigh correlation
상품단위명 is highly imbalanced (53.7%)Imbalance
위판수량 is highly skewed (γ1 = 20.14268518)Skewed
위판수량 has 199 (2.0%) zerosZeros
위판단가 has 836 (8.4%) zerosZeros
위판단가(1킬로그램) has 836 (8.4%) zerosZeros

Reproduction

Analysis started2024-04-21 02:09:45.344098
Analysis finished2024-04-21 02:09:54.319582
Duration8.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

위판일자
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-04
4362 
2024-03-01
2811 
2024-03-02
1516 
2024-03-03
1227 
2024-03-05
 
84

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-03-01
2nd row2024-03-01
3rd row2024-03-01
4th row2024-03-03
5th row2024-03-04

Common Values

ValueCountFrequency (%)
2024-03-04 4362
43.6%
2024-03-01 2811
28.1%
2024-03-02 1516
 
15.2%
2024-03-03 1227
 
12.3%
2024-03-05 84
 
0.8%

Length

2024-04-21T11:09:54.394561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:09:54.508560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-03-04 4362
43.6%
2024-03-01 2811
28.1%
2024-03-02 1516
 
15.2%
2024-03-03 1227
 
12.3%
2024-03-05 84
 
0.8%

산지조합코드
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean354.269
Minimum301
Maximum517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T11:09:54.634223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum301
5-th percentile306
Q1327
median349
Q3362
95-th percentile511
Maximum517
Range216
Interquartile range (IQR)35

Descriptive statistics

Standard deviation47.740651
Coefficient of variation (CV)0.13475819
Kurtosis5.5314404
Mean354.269
Median Absolute Deviation (MAD)15
Skewness2.3488033
Sum3542690
Variance2279.1698
MonotonicityNot monotonic
2024-04-21T11:09:54.755172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
335 620
 
6.2%
351 587
 
5.9%
362 582
 
5.8%
374 577
 
5.8%
327 443
 
4.4%
357 389
 
3.9%
340 322
 
3.2%
339 307
 
3.1%
364 269
 
2.7%
311 261
 
2.6%
Other values (59) 5643
56.4%
ValueCountFrequency (%)
301 52
 
0.5%
302 45
 
0.4%
303 77
 
0.8%
304 53
 
0.5%
305 242
2.4%
306 197
2.0%
307 128
1.3%
308 141
1.4%
309 105
1.1%
310 41
 
0.4%
ValueCountFrequency (%)
517 140
 
1.4%
516 100
 
1.0%
515 9
 
0.1%
512 2
 
< 0.1%
511 252
2.5%
509 150
 
1.5%
507 10
 
0.1%
506 5
 
0.1%
503 29
 
0.3%
374 577
5.8%
Distinct69
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T11:09:54.942664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length9.8823
Min length9

Characters and Unicode

Total characters98823
Distinct characters98
Distinct categories4 ?
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 row후포수산업협동조합
2nd row장흥군수산업협동조합
3rd row옹진수산업협동조합
4th row인천수산업협동조합
5th row대천서부수산업협동조합
ValueCountFrequency (%)
고흥군수산업협동조합 620
 
6.0%
포항수산업협동조합 587
 
5.7%
통영수산업협동조합 582
 
5.6%
부산시수산업협동조합 577
 
5.6%
목포수산업협동조합 443
 
4.3%
삼천포수산업협동조합 389
 
3.8%
장흥군수산업협동조합 322
 
3.1%
수산업협동조합 307
 
3.0%
여수 307
 
3.0%
남해군수산업협동조합 269
 
2.6%
Other values (60) 5904
57.3%
2024-04-21T11:09:55.277803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11308
11.4%
10747
10.9%
10277
10.4%
10000
10.1%
10000
10.1%
10000
10.1%
10000
10.1%
2306
 
2.3%
2161
 
2.2%
1526
 
1.5%
Other values (88) 20498
20.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 98009
99.2%
Decimal Number 338
 
0.3%
Space Separator 307
 
0.3%
Other Punctuation 169
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
11308
11.5%
10747
11.0%
10277
10.5%
10000
10.2%
10000
10.2%
10000
10.2%
10000
10.2%
2306
 
2.4%
2161
 
2.2%
1526
 
1.6%
Other values (81) 19684
20.1%
Decimal Number
ValueCountFrequency (%)
1 140
41.4%
2 140
41.4%
4 29
 
8.6%
3 29
 
8.6%
Other Punctuation
ValueCountFrequency (%)
/ 140
82.8%
. 29
 
17.2%
Space Separator
ValueCountFrequency (%)
307
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 98009
99.2%
Common 814
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
11308
11.5%
10747
11.0%
10277
10.5%
10000
10.2%
10000
10.2%
10000
10.2%
10000
10.2%
2306
 
2.4%
2161
 
2.2%
1526
 
1.6%
Other values (81) 19684
20.1%
Common
ValueCountFrequency (%)
307
37.7%
1 140
17.2%
2 140
17.2%
/ 140
17.2%
4 29
 
3.6%
3 29
 
3.6%
. 29
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 98009
99.2%
ASCII 814
 
0.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
11308
11.5%
10747
11.0%
10277
10.5%
10000
10.2%
10000
10.2%
10000
10.2%
10000
10.2%
2306
 
2.4%
2161
 
2.2%
1526
 
1.6%
Other values (81) 19684
20.1%
ASCII
ValueCountFrequency (%)
307
37.7%
1 140
17.2%
2 140
17.2%
/ 140
17.2%
4 29
 
3.6%
3 29
 
3.6%
. 29
 
3.6%

위판장코드
Real number (ℝ)

HIGH CORRELATION 

Distinct130
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean354275.32
Minimum301001
Maximum517005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T11:09:55.411374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum301001
5-th percentile306001
Q1327002
median349001
Q3362003
95-th percentile511001
Maximum517005
Range216004
Interquartile range (IQR)35001

Descriptive statistics

Standard deviation47741.763
Coefficient of variation (CV)0.13475893
Kurtosis5.530884
Mean354275.32
Median Absolute Deviation (MAD)15000
Skewness2.3486812
Sum3.5427532 × 109
Variance2.2792759 × 109
MonotonicityNot monotonic
2024-04-21T11:09:55.527310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
351001 587
 
5.9%
374002 440
 
4.4%
335002 430
 
4.3%
327002 361
 
3.6%
340001 322
 
3.2%
357003 268
 
2.7%
311001 261
 
2.6%
372001 258
 
2.6%
511001 252
 
2.5%
339001 229
 
2.3%
Other values (120) 6592
65.9%
ValueCountFrequency (%)
301001 39
 
0.4%
301004 13
 
0.1%
302001 45
 
0.4%
303001 64
0.6%
303002 13
 
0.1%
304001 34
 
0.3%
304003 19
 
0.2%
305001 118
1.2%
305002 80
0.8%
305003 44
 
0.4%
ValueCountFrequency (%)
517005 1
 
< 0.1%
517003 39
 
0.4%
517002 55
 
0.5%
517001 45
 
0.4%
516001 100
 
1.0%
515002 9
 
0.1%
512001 2
 
< 0.1%
511001 252
2.5%
509003 59
 
0.6%
509001 91
 
0.9%
Distinct97
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T11:09:55.765656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length10
Mean length5.7705
Min length2

Characters and Unicode

Total characters57705
Distinct characters127
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row총무지도판매사업과
2nd row정남진수산물위판장
3rd row연안위판장(옹진)
4th row판매팀
5th row판매과
ValueCountFrequency (%)
판매과 1591
 
15.3%
판매1과 858
 
8.2%
유통판매과 590
 
5.7%
유통사업과 569
 
5.5%
자갈치위판장(선어 440
 
4.2%
녹동지점(일반 430
 
4.1%
판매3과(활어 361
 
3.5%
정남진수산물위판장 322
 
3.1%
판매2과(활어 268
 
2.6%
1위판장 258
 
2.5%
Other values (90) 4720
45.4%
2024-04-21T11:09:56.173149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7455
 
12.9%
5156
 
8.9%
4808
 
8.3%
2448
 
4.2%
2311
 
4.0%
( 2192
 
3.8%
) 2192
 
3.8%
2026
 
3.5%
1923
 
3.3%
1584
 
2.7%
Other values (117) 25610
44.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 50796
88.0%
Open Punctuation 2192
 
3.8%
Close Punctuation 2192
 
3.8%
Decimal Number 2118
 
3.7%
Space Separator 407
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
7455
 
14.7%
5156
 
10.2%
4808
 
9.5%
2448
 
4.8%
2311
 
4.5%
2026
 
4.0%
1923
 
3.8%
1584
 
3.1%
1453
 
2.9%
1391
 
2.7%
Other values (110) 20241
39.8%
Decimal Number
ValueCountFrequency (%)
1 1249
59.0%
2 487
 
23.0%
3 361
 
17.0%
4 21
 
1.0%
Open Punctuation
ValueCountFrequency (%)
( 2192
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2192
100.0%
Space Separator
ValueCountFrequency (%)
407
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 50796
88.0%
Common 6909
 
12.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
7455
 
14.7%
5156
 
10.2%
4808
 
9.5%
2448
 
4.8%
2311
 
4.5%
2026
 
4.0%
1923
 
3.8%
1584
 
3.1%
1453
 
2.9%
1391
 
2.7%
Other values (110) 20241
39.8%
Common
ValueCountFrequency (%)
( 2192
31.7%
) 2192
31.7%
1 1249
18.1%
2 487
 
7.0%
407
 
5.9%
3 361
 
5.2%
4 21
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 50796
88.0%
ASCII 6909
 
12.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
7455
 
14.7%
5156
 
10.2%
4808
 
9.5%
2448
 
4.8%
2311
 
4.5%
2026
 
4.0%
1923
 
3.8%
1584
 
3.1%
1453
 
2.9%
1391
 
2.7%
Other values (110) 20241
39.8%
ASCII
ValueCountFrequency (%)
( 2192
31.7%
) 2192
31.7%
1 1249
18.1%
2 487
 
7.0%
407
 
5.9%
3 361
 
5.2%
4 21
 
0.3%
Distinct190
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T11:09:56.470686image/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

Unique25 ?
Unique (%)0.2%

Sample

1st row640201
2nd row640301
3rd row610201
4th row610201
5th row640601
ValueCountFrequency (%)
640301 1438
 
14.4%
610200 767
 
7.7%
640407 622
 
6.2%
618102 424
 
4.2%
620401 363
 
3.6%
640601 304
 
3.0%
699900 300
 
3.0%
612101 285
 
2.9%
614401 250
 
2.5%
610206 173
 
1.7%
Other values (180) 5074
50.7%
2024-04-21T11:09:56.869331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18153
30.3%
1 11457
19.1%
6 11088
18.5%
4 5004
 
8.3%
2 4664
 
7.8%
3 4008
 
6.7%
9 2041
 
3.4%
7 1495
 
2.5%
8 1141
 
1.9%
5 529
 
0.9%
Other values (3) 420
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 59580
99.3%
Uppercase Letter 420
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18153
30.5%
1 11457
19.2%
6 11088
18.6%
4 5004
 
8.4%
2 4664
 
7.8%
3 4008
 
6.7%
9 2041
 
3.4%
7 1495
 
2.5%
8 1141
 
1.9%
5 529
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
B 207
49.3%
A 129
30.7%
Z 84
20.0%

Most occurring scripts

ValueCountFrequency (%)
Common 59580
99.3%
Latin 420
 
0.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18153
30.5%
1 11457
19.2%
6 11088
18.6%
4 5004
 
8.4%
2 4664
 
7.8%
3 4008
 
6.7%
9 2041
 
3.4%
7 1495
 
2.5%
8 1141
 
1.9%
5 529
 
0.9%
Latin
ValueCountFrequency (%)
B 207
49.3%
A 129
30.7%
Z 84
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18153
30.3%
1 11457
19.1%
6 11088
18.5%
4 5004
 
8.3%
2 4664
 
7.8%
3 4008
 
6.7%
9 2041
 
3.4%
7 1495
 
2.5%
8 1141
 
1.9%
5 529
 
0.9%
Other values (3) 420
 
0.7%
Distinct190
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T11:09:57.151094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length3.0249
Min length1

Characters and Unicode

Total characters30249
Distinct characters176
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

Unique25 ?
Unique (%)0.2%

Sample

1st row화살꼴뚜기
2nd row낙지
3rd row도다리
4th row도다리
5th row주꾸미
ValueCountFrequency (%)
낙지 1438
 
14.4%
가자미류 767
 
7.7%
문어 622
 
6.2%
아귀 424
 
4.2%
굴(참굴 363
 
3.6%
주꾸미 304
 
3.0%
기타해면기타 300
 
3.0%
넙치 285
 
2.9%
멸치 250
 
2.5%
문치가자미 173
 
1.7%
Other values (180) 5074
50.7%
2024-04-21T11:09:57.551645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1966
 
6.5%
1794
 
5.9%
1629
 
5.4%
1604
 
5.3%
1539
 
5.1%
1503
 
5.0%
1395
 
4.6%
1216
 
4.0%
1153
 
3.8%
865
 
2.9%
Other values (166) 15585
51.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29417
97.2%
Open Punctuation 416
 
1.4%
Close Punctuation 416
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1966
 
6.7%
1794
 
6.1%
1629
 
5.5%
1604
 
5.5%
1539
 
5.2%
1503
 
5.1%
1395
 
4.7%
1216
 
4.1%
1153
 
3.9%
865
 
2.9%
Other values (164) 14753
50.2%
Open Punctuation
ValueCountFrequency (%)
( 416
100.0%
Close Punctuation
ValueCountFrequency (%)
) 416
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29417
97.2%
Common 832
 
2.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1966
 
6.7%
1794
 
6.1%
1629
 
5.5%
1604
 
5.5%
1539
 
5.2%
1503
 
5.1%
1395
 
4.7%
1216
 
4.1%
1153
 
3.9%
865
 
2.9%
Other values (164) 14753
50.2%
Common
ValueCountFrequency (%)
( 416
50.0%
) 416
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29417
97.2%
ASCII 832
 
2.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1966
 
6.7%
1794
 
6.1%
1629
 
5.5%
1604
 
5.5%
1539
 
5.2%
1503
 
5.1%
1395
 
4.7%
1216
 
4.1%
1153
 
3.9%
865
 
2.9%
Other values (164) 14753
50.2%
ASCII
ValueCountFrequency (%)
( 416
50.0%
) 416
50.0%

어종상태코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
10
5854 
20
3757 
40
 
254
30
 
91
99
 
44

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row10

Common Values

ValueCountFrequency (%)
10 5854
58.5%
20 3757
37.6%
40 254
 
2.5%
30 91
 
0.9%
99 44
 
0.4%

Length

2024-04-21T11:09:57.674356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:09:57.765884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10 5854
58.5%
20 3757
37.6%
40 254
 
2.5%
30 91
 
0.9%
99 44
 
0.4%

어종상태명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
활어
5854 
선어
3757 
건어
 
254
냉동
 
91
없음
 
44

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
활어 5854
58.5%
선어 3757
37.6%
건어 254
 
2.5%
냉동 91
 
0.9%
없음 44
 
0.4%

Length

2024-04-21T11:09:57.859949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:09:57.949735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
활어 5854
58.5%
선어 3757
37.6%
건어 254
 
2.5%
냉동 91
 
0.9%
없음 44
 
0.4%
Distinct69
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T11:09:58.125956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length6
Mean length6.4003
Min length3

Characters and Unicode

Total characters64003
Distinct characters85
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row대형정치망
2nd row연안통발어업
3rd row연안자망어업
4th row연안자망어업
5th row연안개량안강망어업
ValueCountFrequency (%)
연안자망어업 2339
23.4%
연안복합어업 1571
15.7%
연안통발어업 1480
14.8%
근해자망어업 556
 
5.6%
천해양식어업 485
 
4.9%
정치망어업 397
 
4.0%
기타어업 303
 
3.0%
근해연승어업 273
 
2.7%
외끌이서남해구기선저인망어업 273
 
2.7%
외끌이대형기선저인망어업 259
 
2.6%
Other values (59) 2064
20.6%
2024-04-21T11:09:58.438231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9930
15.5%
9920
15.5%
6129
 
9.6%
5880
 
9.2%
4646
 
7.3%
2926
 
4.6%
2090
 
3.3%
1626
 
2.5%
1585
 
2.5%
1585
 
2.5%
Other values (75) 17686
27.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 63899
99.8%
Other Punctuation 33
 
0.1%
Open Punctuation 32
 
< 0.1%
Close Punctuation 32
 
< 0.1%
Decimal Number 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
9930
15.5%
9920
15.5%
6129
 
9.6%
5880
 
9.2%
4646
 
7.3%
2926
 
4.6%
2090
 
3.3%
1626
 
2.5%
1585
 
2.5%
1585
 
2.5%
Other values (71) 17582
27.5%
Other Punctuation
ValueCountFrequency (%)
/ 33
100.0%
Open Punctuation
ValueCountFrequency (%)
( 32
100.0%
Close Punctuation
ValueCountFrequency (%)
) 32
100.0%
Decimal Number
ValueCountFrequency (%)
2 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 63899
99.8%
Common 104
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
9930
15.5%
9920
15.5%
6129
 
9.6%
5880
 
9.2%
4646
 
7.3%
2926
 
4.6%
2090
 
3.3%
1626
 
2.5%
1585
 
2.5%
1585
 
2.5%
Other values (71) 17582
27.5%
Common
ValueCountFrequency (%)
/ 33
31.7%
( 32
30.8%
) 32
30.8%
2 7
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 63899
99.8%
ASCII 104
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
9930
15.5%
9920
15.5%
6129
 
9.6%
5880
 
9.2%
4646
 
7.3%
2926
 
4.6%
2090
 
3.3%
1626
 
2.5%
1585
 
2.5%
1585
 
2.5%
Other values (71) 17582
27.5%
ASCII
ValueCountFrequency (%)
/ 33
31.7%
( 32
30.8%
) 32
30.8%
2 7
 
6.7%

위판수량
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct469
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.412826
Minimum0
Maximum5036
Zeros199
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T11:09:58.582335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q39
95-th percentile98
Maximum5036
Range5036
Interquartile range (IQR)8

Descriptive statistics

Standard deviation138.1581
Coefficient of variation (CV)5.6592424
Kurtosis539.7057
Mean24.412826
Median Absolute Deviation (MAD)1
Skewness20.142685
Sum244128.26
Variance19087.661
MonotonicityNot monotonic
2024-04-21T11:09:58.747909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 4624
46.2%
2.0 854
 
8.5%
3.0 501
 
5.0%
4.0 286
 
2.9%
6.0 216
 
2.2%
0.0 199
 
2.0%
5.0 195
 
1.9%
10.0 154
 
1.5%
7.0 132
 
1.3%
8.0 110
 
1.1%
Other values (459) 2729
27.3%
ValueCountFrequency (%)
0.0 199
2.0%
0.1 1
 
< 0.1%
0.2 9
 
0.1%
0.3 4
 
< 0.1%
0.4 3
 
< 0.1%
0.5 19
 
0.2%
0.6 7
 
0.1%
0.7 11
 
0.1%
0.8 12
 
0.1%
0.9 7
 
0.1%
ValueCountFrequency (%)
5036.0 1
< 0.1%
4500.0 1
< 0.1%
4286.0 1
< 0.1%
3660.0 1
< 0.1%
3420.0 1
< 0.1%
2850.0 1
< 0.1%
2550.0 1
< 0.1%
2431.0 1
< 0.1%
2380.0 1
< 0.1%
2150.0 1
< 0.1%

위판중량
Real number (ℝ)

HIGH CORRELATION 

Distinct843
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean221.66852
Minimum0.08
Maximum35040
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T11:09:58.876931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile1.2
Q15
median10
Q330
95-th percentile360
Maximum35040
Range35039.92
Interquartile range (IQR)25

Descriptive statistics

Standard deviation1484.4523
Coefficient of variation (CV)6.6967213
Kurtosis144.79351
Mean221.66852
Median Absolute Deviation (MAD)7
Skewness10.943653
Sum2216685.2
Variance2203598.7
MonotonicityNot monotonic
2024-04-21T11:09:58.998414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.0 1081
 
10.8%
5.0 928
 
9.3%
20.0 526
 
5.3%
3.0 442
 
4.4%
15.0 372
 
3.7%
8.0 317
 
3.2%
19.0 317
 
3.2%
2.0 303
 
3.0%
4.0 295
 
2.9%
1.0 237
 
2.4%
Other values (833) 5182
51.8%
ValueCountFrequency (%)
0.08 14
0.1%
0.15 14
0.1%
0.16 20
0.2%
0.2 2
 
< 0.1%
0.24 21
0.2%
0.25 5
 
0.1%
0.3 13
0.1%
0.32 15
0.1%
0.4 17
0.2%
0.45 5
 
0.1%
ValueCountFrequency (%)
35040.0 1
< 0.1%
29160.0 1
< 0.1%
26760.0 1
< 0.1%
24360.0 1
< 0.1%
24240.0 1
< 0.1%
22920.0 1
< 0.1%
21840.0 1
< 0.1%
21720.0 1
< 0.1%
21600.0 1
< 0.1%
21480.0 1
< 0.1%

위판단가
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1457
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72155.455
Minimum0
Maximum3050000
Zeros836
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T11:09:59.120624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110500
median36000
Q390000
95-th percentile285900
Maximum3050000
Range3050000
Interquartile range (IQR)79500

Descriptive statistics

Standard deviation104876.82
Coefficient of variation (CV)1.4534843
Kurtosis99.318085
Mean72155.455
Median Absolute Deviation (MAD)31500
Skewness5.9986418
Sum7.2155455 × 108
Variance1.0999148 × 1010
MonotonicityNot monotonic
2024-04-21T11:09:59.252357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 836
 
8.4%
70000 159
 
1.6%
30000 150
 
1.5%
20000 136
 
1.4%
40000 129
 
1.3%
50000 122
 
1.2%
60000 118
 
1.2%
100000 112
 
1.1%
10000 103
 
1.0%
90000 95
 
0.9%
Other values (1447) 8040
80.4%
ValueCountFrequency (%)
0 836
8.4%
300 3
 
< 0.1%
500 4
 
< 0.1%
690 1
 
< 0.1%
700 1
 
< 0.1%
750 1
 
< 0.1%
770 1
 
< 0.1%
830 1
 
< 0.1%
890 1
 
< 0.1%
900 1
 
< 0.1%
ValueCountFrequency (%)
3050000 1
< 0.1%
2030000 1
< 0.1%
1810000 1
< 0.1%
1420000 1
< 0.1%
1390000 1
< 0.1%
1280000 1
< 0.1%
1200000 1
< 0.1%
1161000 1
< 0.1%
960000 1
< 0.1%
930000 1
< 0.1%

위판단가(1킬로그램)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3150
Distinct (%)31.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7922.9535
Minimum0
Maximum377500
Zeros836
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T11:09:59.378639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1381.75
median2631
Q310000
95-th percentile32000
Maximum377500
Range377500
Interquartile range (IQR)9618.25

Descriptive statistics

Standard deviation13302.517
Coefficient of variation (CV)1.6789846
Kurtosis73.216242
Mean7922.9535
Median Absolute Deviation (MAD)2569.5
Skewness5.0477772
Sum79229535
Variance1.7695696 × 108
MonotonicityNot monotonic
2024-04-21T11:09:59.519398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 836
 
8.4%
2000 85
 
0.9%
10000 70
 
0.7%
20000 70
 
0.7%
5000 68
 
0.7%
4000 60
 
0.6%
14000 57
 
0.6%
3000 56
 
0.6%
8000 54
 
0.5%
1000 52
 
0.5%
Other values (3140) 8592
85.9%
ValueCountFrequency (%)
0 836
8.4%
1 5
 
0.1%
2 7
 
0.1%
3 2
 
< 0.1%
4 5
 
0.1%
5 3
 
< 0.1%
6 3
 
< 0.1%
7 8
 
0.1%
8 6
 
0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
377500 1
< 0.1%
165000 1
< 0.1%
152000 1
< 0.1%
127083 1
< 0.1%
116665 1
< 0.1%
115000 2
< 0.1%
113980 1
< 0.1%
110000 1
< 0.1%
106666 1
< 0.1%
104545 1
< 0.1%

위판금액
Real number (ℝ)

HIGH CORRELATION 

Distinct3160
Distinct (%)31.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean936392.19
Minimum300
Maximum1.66188 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T11:09:59.643247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile16800
Q152000
median123600
Q3300000
95-th percentile3080800
Maximum1.66188 × 108
Range1.661877 × 108
Interquartile range (IQR)248000

Descriptive statistics

Standard deviation4720256
Coefficient of variation (CV)5.0408964
Kurtosis275.12622
Mean936392.19
Median Absolute Deviation (MAD)86400
Skewness13.328235
Sum9.3639219 × 109
Variance2.2280817 × 1013
MonotonicityNot monotonic
2024-04-21T11:09:59.986498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60000 148
 
1.5%
40000 144
 
1.4%
100000 131
 
1.3%
30000 122
 
1.2%
70000 114
 
1.1%
90000 110
 
1.1%
50000 104
 
1.0%
80000 99
 
1.0%
20000 98
 
1.0%
140000 88
 
0.9%
Other values (3150) 8842
88.4%
ValueCountFrequency (%)
300 1
< 0.1%
500 2
< 0.1%
600 1
< 0.1%
690 1
< 0.1%
770 1
< 0.1%
1000 2
< 0.1%
1220 1
< 0.1%
1320 1
< 0.1%
1500 2
< 0.1%
1700 1
< 0.1%
ValueCountFrequency (%)
166188000 1
< 0.1%
120780000 1
< 0.1%
88797200 1
< 0.1%
81600000 1
< 0.1%
81405000 1
< 0.1%
71137000 1
< 0.1%
70499000 1
< 0.1%
65751700 1
< 0.1%
64397600 1
< 0.1%
64320000 1
< 0.1%
Distinct55
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T11:10:00.153805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length1.7862
Min length1

Characters and Unicode

Total characters17862
Distinct characters31
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.1%

Sample

1st row
2nd row
3rd row없음
4th row없음
5th row없음
ValueCountFrequency (%)
없음 6431
64.3%
2469
 
24.7%
221
 
2.2%
167
 
1.7%
파품 166
 
1.7%
5kg이하 147
 
1.5%
69
 
0.7%
20미 56
 
0.6%
kg 30
 
0.3%
3방 29
 
0.3%
Other values (45) 215
 
2.1%
2024-04-21T11:10:00.457729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6431
36.0%
6431
36.0%
2471
 
13.8%
341
 
1.9%
g 234
 
1.3%
k 232
 
1.3%
5 209
 
1.2%
168
 
0.9%
166
 
0.9%
166
 
0.9%
Other values (21) 1013
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16682
93.4%
Decimal Number 651
 
3.6%
Lowercase Letter 466
 
2.6%
Other Punctuation 28
 
0.2%
Math Symbol 28
 
0.2%
Uppercase Letter 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6431
38.6%
6431
38.6%
2471
 
14.8%
341
 
2.0%
168
 
1.0%
166
 
1.0%
166
 
1.0%
157
 
0.9%
157
 
0.9%
89
 
0.5%
Other values (4) 105
 
0.6%
Decimal Number
ValueCountFrequency (%)
5 209
32.1%
2 120
18.4%
0 120
18.4%
3 76
 
11.7%
1 62
 
9.5%
4 34
 
5.2%
9 12
 
1.8%
6 11
 
1.7%
7 4
 
0.6%
8 3
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
G 3
42.9%
M 2
28.6%
L 2
28.6%
Lowercase Letter
ValueCountFrequency (%)
g 234
50.2%
k 232
49.8%
Other Punctuation
ValueCountFrequency (%)
. 28
100.0%
Math Symbol
ValueCountFrequency (%)
~ 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 16682
93.4%
Common 707
 
4.0%
Latin 473
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6431
38.6%
6431
38.6%
2471
 
14.8%
341
 
2.0%
168
 
1.0%
166
 
1.0%
166
 
1.0%
157
 
0.9%
157
 
0.9%
89
 
0.5%
Other values (4) 105
 
0.6%
Common
ValueCountFrequency (%)
5 209
29.6%
2 120
17.0%
0 120
17.0%
3 76
 
10.7%
1 62
 
8.8%
4 34
 
4.8%
. 28
 
4.0%
~ 28
 
4.0%
9 12
 
1.7%
6 11
 
1.6%
Other values (2) 7
 
1.0%
Latin
ValueCountFrequency (%)
g 234
49.5%
k 232
49.0%
G 3
 
0.6%
M 2
 
0.4%
L 2
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 16682
93.4%
ASCII 1180
 
6.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
6431
38.6%
6431
38.6%
2471
 
14.8%
341
 
2.0%
168
 
1.0%
166
 
1.0%
166
 
1.0%
157
 
0.9%
157
 
0.9%
89
 
0.5%
Other values (4) 105
 
0.6%
ASCII
ValueCountFrequency (%)
g 234
19.8%
k 232
19.7%
5 209
17.7%
2 120
10.2%
0 120
10.2%
3 76
 
6.4%
1 62
 
5.3%
4 34
 
2.9%
. 28
 
2.4%
~ 28
 
2.4%
Other values (7) 37
 
3.1%

상품단위명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
상자(C/S)
5649 
없음
2468 
미(마리)
668 
 
271
 
237
Other values (14)
707 

Length

Max length7
Median length7
Mean length4.992
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row상자(C/S)
2nd row상자(C/S)
3rd row없음
4th row상자(C/S)
5th row없음

Common Values

ValueCountFrequency (%)
상자(C/S) 5649
56.5%
없음 2468
24.7%
미(마리) 668
 
6.7%
271
 
2.7%
237
 
2.4%
Kg 208
 
2.1%
그물망 170
 
1.7%
두름 97
 
1.0%
봉지 65
 
0.7%
40
 
0.4%
Other values (9) 127
 
1.3%

Length

2024-04-21T11:10:00.622407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
상자(c/s 5649
56.5%
없음 2468
24.7%
미(마리 668
 
6.7%
271
 
2.7%
237
 
2.4%
kg 208
 
2.1%
그물망 170
 
1.7%
두름 97
 
1.0%
봉지 65
 
0.7%
40
 
0.4%
Other values (9) 127
 
1.3%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-31
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-03-31
2nd row2024-03-31
3rd row2024-03-31
4th row2024-03-31
5th row2024-03-31

Common Values

ValueCountFrequency (%)
2024-03-31 10000
100.0%

Length

2024-04-21T11:10:00.746195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:10:00.829076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-03-31 10000
100.0%

Interactions

2024-04-21T11:09:53.118714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:49.173962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:49.899732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:50.551878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:51.167249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:51.806499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:52.485697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:53.210991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:49.329917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:49.993188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:50.633645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:51.253536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:51.901357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:52.569500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:53.288388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:49.416762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:50.079106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:50.713679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:51.336642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:51.982851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:52.656577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:53.367153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:49.515108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:50.162750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:50.794590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:51.420705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:52.061579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:52.741591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:53.647463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:49.616869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:50.253304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:50.889949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:51.514007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:52.165395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:52.844157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:53.736752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:49.704971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:50.353305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:50.980150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:51.612603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:52.263792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:52.944123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:53.821153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:49.798933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:50.453673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:51.069117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:51.712415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:52.380553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:09:53.030873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T11:10:00.915156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위판일자산지조합코드산지조합명위판장코드위판장명어종상태코드어종상태명업종명위판수량위판중량위판단가위판단가(1킬로그램)위판금액상품규격명상품단위명
위판일자1.0000.4430.6970.4430.6960.2140.2140.4600.0440.0720.0470.0340.0440.2900.241
산지조합코드0.4431.0001.0001.0000.9810.6470.6470.8650.1140.1750.1030.0650.1160.5760.578
산지조합명0.6971.0001.0001.0000.9980.8340.8340.9720.7890.4170.2450.2070.7530.9160.924
위판장코드0.4431.0001.0001.0000.9750.6470.6470.8650.1140.1720.1030.0650.1150.5740.578
위판장명0.6960.9810.9980.9751.0000.9640.9640.9550.6600.7800.3670.2110.7780.9250.924
어종상태코드0.2140.6470.8340.6470.9641.0001.0000.8570.0880.6050.1920.0640.3980.4860.794
어종상태명0.2140.6470.8340.6470.9641.0001.0000.8570.0880.6050.1920.0640.3980.4860.794
업종명0.4600.8650.9720.8650.9550.8570.8571.0000.6490.5030.2330.1580.6400.8040.777
위판수량0.0440.1140.7890.1140.6600.0880.0880.6491.0000.3070.0000.0000.8150.7130.157
위판중량0.0720.1750.4170.1720.7800.6050.6050.5030.3071.0000.2570.0000.8910.0000.385
위판단가0.0470.1030.2450.1030.3670.1920.1920.2330.0000.2571.0000.3880.3120.3750.123
위판단가(1킬로그램)0.0340.0650.2070.0650.2110.0640.0640.1580.0000.0000.3881.0000.0000.0000.106
위판금액0.0440.1160.7530.1150.7780.3980.3980.6400.8150.8910.3120.0001.0000.6930.344
상품규격명0.2900.5760.9160.5740.9250.4860.4860.8040.7130.0000.3750.0000.6931.0000.870
상품단위명0.2410.5780.9240.5780.9240.7940.7940.7770.1570.3850.1230.1060.3440.8701.000
2024-04-21T11:10:01.064301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
어종상태코드상품단위명어종상태명위판일자
어종상태코드1.0000.5471.0000.081
상품단위명0.5471.0000.5470.122
어종상태명1.0000.5471.0000.081
위판일자0.0810.1220.0811.000
2024-04-21T11:10:01.162329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
산지조합코드위판장코드위판수량위판중량위판단가위판단가(1킬로그램)위판금액위판일자어종상태코드어종상태명상품단위명
산지조합코드1.0001.000-0.0090.2400.075-0.1050.1040.1800.2910.2910.331
위판장코드1.0001.000-0.0090.2370.073-0.1040.1030.1800.2910.2910.331
위판수량-0.009-0.0091.0000.439-0.241-0.5130.5100.0180.0370.0370.059
위판중량0.2400.2370.4391.0000.221-0.5120.6930.0300.2940.2940.154
위판단가0.0750.073-0.2410.2211.0000.6310.3580.0290.1190.1190.051
위판단가(1킬로그램)-0.105-0.104-0.513-0.5120.6311.000-0.2380.0230.0430.0430.049
위판금액0.1040.1030.5100.6930.358-0.2381.0000.0270.2580.2580.151
위판일자0.1800.1800.0180.0300.0290.0230.0271.0000.0810.0810.122
어종상태코드0.2910.2910.0370.2940.1190.0430.2580.0811.0001.0000.547
어종상태명0.2910.2910.0370.2940.1190.0430.2580.0811.0001.0000.547
상품단위명0.3310.3310.0590.1540.0510.0490.1510.1220.5470.5471.000

Missing values

2024-04-21T11:09:53.969869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T11:09:54.195695image/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

위판일자산지조합코드산지조합명위판장코드위판장명수산물표준코드수산물표준코드명어종상태코드어종상태명업종명위판수량위판중량위판단가위판단가(1킬로그램)위판금액상품규격명상품단위명데이터기준일자
97742024-03-01352후포수산업협동조합352001총무지도판매사업과640201화살꼴뚜기10활어대형정치망1.05.015990031980159900상자(C/S)2024-03-31
58772024-03-01340장흥군수산업협동조합340001정남진수산물위판장640301낙지10활어연안통발어업43.06.453660567157380상자(C/S)2024-03-31
62024-03-01301옹진수산업협동조합301001연안위판장(옹진)610201도다리10활어연안자망어업0.076.0700092532000없음없음2024-03-31
253212024-03-03303인천수산업협동조합303001판매팀610201도다리10활어연안자망어업97.097.0650067630500없음상자(C/S)2024-03-31
362852024-03-04319대천서부수산업협동조합319001판매과640601주꾸미10활어연안개량안강망어업26.026.0312001200811200없음없음2024-03-31
291432024-03-03335고흥군수산업협동조합335009회천지점(일반)640301낙지10활어연안복합어업1.06.619380029363193800상자(C/S)2024-03-31
359352024-03-04317서천군수산업협동조합317001판매사업팀640601주꾸미10활어연안복합어업79.079.0284003592243600kgKg2024-03-31
155312024-03-01511굴수하식수산업협동조합511001유통판매과620401굴(참굴)10활어천해양식어업10.0100.066000660660000없음상자(C/S)2024-03-31
567052024-03-04516제주어류양식수산업협동조합516001유통사업팀612101넙치10활어육상양식어업2.8100.0180001801800000없음없음2024-03-31
235752024-03-02363하동군수산업협동조합363001판매과613701감성돔10활어연안복합어업1.015.000155000없음없음2024-03-31
위판일자산지조합코드산지조합명위판장코드위판장명수산물표준코드수산물표준코드명어종상태코드어종상태명업종명위판수량위판중량위판단가위판단가(1킬로그램)위판금액상품규격명상품단위명데이터기준일자
117602024-03-01358울산수산업협동조합358003공판장613100대구류20선어연안자망어업4.060.01900031676000없음상자(C/S)2024-03-31
187002024-03-02332해남군수산업협동조합332009해남군수협남창사업소810303돌김30냉동연안자망어업6.0720.02700003751620000없음2024-03-31
426472024-03-04345경주시수산업협동조합345001유통판매과61A402청어20선어중형기선저인망17.0170.0800047136000없음상자(C/S)2024-03-31
450562024-03-04356마산수산업협동조합356002경제부 남성공판610200가자미류20선어연안통발어업1.05.0300006000300005kg이하상자(C/S)2024-03-31
123252024-03-01361진해수산업협동조합361001판매사업과640400문어류20선어연안통발어업2.02.017000850034000kg상자(C/S)2024-03-31
442072024-03-04351포항수산업협동조합351001판매1과640407문어20선어연안자망어업1.05.000102000없음없음2024-03-31
279462024-03-03331진도군수산업협동조합331006초사낙지 위판장640301낙지10활어연안통발어업197.0197.0280014551600미(마리)2024-03-31
133592024-03-01362통영수산업협동조합362003견유위판장630206민꽃게10활어연안통발어업1.02.0200001000020000없음PP대2024-03-31
431552024-03-04349죽변수산업협동조합349001일반사업610205기름가자미20선어동해구기선저인망어업6.0120.087900732527400없음없음2024-03-31
268542024-03-03316서산수산업협동조합316001안흥판매사업소611302물메기20선어연안복합어업1.010.026000260026000없음상자(C/S)2024-03-31

Duplicate rows

Most frequently occurring

위판일자산지조합코드산지조합명위판장코드위판장명수산물표준코드수산물표준코드명어종상태코드어종상태명업종명위판수량위판중량위판단가위판단가(1킬로그램)위판금액상품규격명상품단위명데이터기준일자# duplicates
612024-03-01511굴수하식수산업협동조합511001유통판매과620401굴(참굴)10활어천해양식어업6.060.0670001116402000없음상자(C/S)2024-03-319
682024-03-01511굴수하식수산업협동조합511001유통판매과620401굴(참굴)10활어천해양식어업10.0100.067000670670000없음상자(C/S)2024-03-317
162024-03-01337나로도수산업협동조합337001유통사업과640301낙지10활어기타어업1.026.000655000없음상자(C/S)2024-03-316
152024-03-01337나로도수산업협동조합337001유통사업과640301낙지10활어기타어업1.025.000630000없음상자(C/S)2024-03-315
582024-03-01511굴수하식수산업협동조합511001유통판매과620401굴(참굴)10활어천해양식어업4.040.0660001650264000없음상자(C/S)2024-03-315
652024-03-01511굴수하식수산업협동조합511001유통판매과620401굴(참굴)10활어천해양식어업9.090.067000744603000없음상자(C/S)2024-03-315
172024-03-01337나로도수산업협동조합337001유통사업과640301낙지10활어기타어업1.027.000680000없음상자(C/S)2024-03-314
182024-03-01337나로도수산업협동조합337001유통사업과640301낙지10활어기타어업1.028.000706000없음상자(C/S)2024-03-314
572024-03-01511굴수하식수산업협동조합511001유통판매과620401굴(참굴)10활어천해양식어업3.030.0660002200198000없음상자(C/S)2024-03-314
672024-03-01511굴수하식수산업협동조합511001유통판매과620401굴(참굴)10활어천해양식어업10.0100.066000660660000없음상자(C/S)2024-03-314