Overview

Dataset statistics

Number of variables13
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.2 KiB
Average record size in memory109.7 B

Variable types

Numeric3
Categorical8
Text2

Dataset

DescriptionSample
Author에이치더블유
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_000061

Alerts

PRDCT_SCTN has constant value ""Constant
PHOTO_INFO_ESSN_ID has constant value ""Constant
XPORT_NTN_NM is highly overall correlated with MNFCT_NTN_NM and 2 other fieldsHigh correlation
MNFCT_NTN_NM is highly overall correlated with XPORT_NTN_NM and 1 other fieldsHigh correlation
SEQ_NO is highly overall correlated with RN and 1 other fieldsHigh correlation
AVE_UNPRC_AMT is highly overall correlated with IMPORT_FOMHigh correlation
RN is highly overall correlated with SEQ_NO and 1 other fieldsHigh correlation
STRD_DTM is highly overall correlated with SEQ_NO and 1 other fieldsHigh correlation
IMPORT_PRPS is highly overall correlated with MNFCT_NTN_NM and 3 other fieldsHigh correlation
CTGRY_MLSFC_NM is highly overall correlated with XPORT_NTN_NM and 2 other fieldsHigh correlation
IMPORT_FOM is highly overall correlated with AVE_UNPRC_AMT and 2 other fieldsHigh correlation
STRD_DTM is highly imbalanced (66.8%)Imbalance
IMPORT_PRPS is highly imbalanced (52.5%)Imbalance
SEQ_NO has unique valuesUnique
RN has unique valuesUnique
AVE_UNPRC_AMT has 1 (2.0%) zerosZeros

Reproduction

Analysis started2023-12-10 14:47:59.641654
Analysis finished2023-12-10 14:48:01.366377
Duration1.72 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SEQ_NO
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean436
Minimum412
Maximum460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:48:01.440403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum412
5-th percentile414.4
Q1424
median436
Q3448
95-th percentile457.6
Maximum460
Range48
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.28869
Coefficient of variation (CV)0.032772225
Kurtosis-1.2
Mean436
Median Absolute Deviation (MAD)12
Skewness0
Sum21364
Variance204.16667
MonotonicityStrictly increasing
2023-12-10T23:48:01.596696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
412 1
 
2.0%
449 1
 
2.0%
439 1
 
2.0%
440 1
 
2.0%
441 1
 
2.0%
442 1
 
2.0%
443 1
 
2.0%
444 1
 
2.0%
445 1
 
2.0%
446 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
412 1
2.0%
413 1
2.0%
414 1
2.0%
415 1
2.0%
416 1
2.0%
417 1
2.0%
418 1
2.0%
419 1
2.0%
420 1
2.0%
421 1
2.0%
ValueCountFrequency (%)
460 1
2.0%
459 1
2.0%
458 1
2.0%
457 1
2.0%
456 1
2.0%
455 1
2.0%
454 1
2.0%
453 1
2.0%
452 1
2.0%
451 1
2.0%

STRD_DTM
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
11-Jan-2016 00:00:00
46 
04-Jan-2016 00:00:00
 
3

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row04-Jan-2016 00:00:00
2nd row04-Jan-2016 00:00:00
3rd row04-Jan-2016 00:00:00
4th row11-Jan-2016 00:00:00
5th row11-Jan-2016 00:00:00

Common Values

ValueCountFrequency (%)
11-Jan-2016 00:00:00 46
93.9%
04-Jan-2016 00:00:00 3
 
6.1%

Length

2023-12-10T23:48:01.721893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:48:01.812397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00:00 49
50.0%
11-jan-2016 46
46.9%
04-jan-2016 3
 
3.1%

PRDCT_SCTN
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
수산물
49 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
수산물 49
100.0%

Length

2023-12-10T23:48:01.908504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:48:01.993718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수산물 49
100.0%

MNFCT_NTN_NM
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)46.9%
Missing0
Missing (%)0.0%
Memory size524.0 B
중국
15 
러시아
5 
일본
3 
노르웨이
 
2
파키스탄
 
2
Other values (18)
22 

Length

Max length6
Median length5
Mean length2.9795918
Min length2

Unique

Unique14 ?
Unique (%)28.6%

Sample

1st row베트남
2nd row중국
3rd row노르웨이
4th row아랍에미리트
5th row아르헨티나

Common Values

ValueCountFrequency (%)
중국 15
30.6%
러시아 5
 
10.2%
일본 3
 
6.1%
노르웨이 2
 
4.1%
파키스탄 2
 
4.1%
스페인 2
 
4.1%
인도네시아 2
 
4.1%
아일랜드 2
 
4.1%
페루 2
 
4.1%
대한민국 1
 
2.0%
Other values (13) 13
26.5%

Length

2023-12-10T23:48:02.112367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
중국 15
30.6%
러시아 5
 
10.2%
일본 3
 
6.1%
노르웨이 2
 
4.1%
파키스탄 2
 
4.1%
스페인 2
 
4.1%
인도네시아 2
 
4.1%
아일랜드 2
 
4.1%
페루 2
 
4.1%
멕시코 1
 
2.0%
Other values (13) 13
26.5%

XPORT_NTN_NM
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Memory size524.0 B
중국
15 
러시아
5 
일본
4 
태국
3 
스페인
3 
Other values (14)
19 

Length

Max length6
Median length2
Mean length2.8367347
Min length2

Unique

Unique9 ?
Unique (%)18.4%

Sample

1st row베트남
2nd row중국
3rd row노르웨이
4th row아랍에미리트
5th row태국

Common Values

ValueCountFrequency (%)
중국 15
30.6%
러시아 5
 
10.2%
일본 4
 
8.2%
태국 3
 
6.1%
스페인 3
 
6.1%
인도네시아 2
 
4.1%
파키스탄 2
 
4.1%
페루 2
 
4.1%
노르웨이 2
 
4.1%
아일랜드 2
 
4.1%
Other values (9) 9
18.4%

Length

2023-12-10T23:48:02.572676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
중국 15
30.6%
러시아 5
 
10.2%
일본 4
 
8.2%
태국 3
 
6.1%
스페인 3
 
6.1%
페루 2
 
4.1%
노르웨이 2
 
4.1%
아일랜드 2
 
4.1%
파키스탄 2
 
4.1%
인도네시아 2
 
4.1%
Other values (9) 9
18.4%

IMPORT_PRPS
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
판매용
44 
자사제품제조용
5 

Length

Max length7
Median length3
Mean length3.4081633
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
판매용 44
89.8%
자사제품제조용 5
 
10.2%

Length

2023-12-10T23:48:02.706053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:48:02.803883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
판매용 44
89.8%
자사제품제조용 5
 
10.2%

CTGRY_MLSFC_NM
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
어류
24 
패류 멍게류
10 
갑각류
8 
연체류 해물모듬
4 
젓갈류 해조류 해파리
3 

Length

Max length11
Median length8
Mean length4.0204082
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row연체류 해물모듬
2nd row패류 멍게류
3rd row어류
4th row어류
5th row갑각류

Common Values

ValueCountFrequency (%)
어류 24
49.0%
패류 멍게류 10
20.4%
갑각류 8
 
16.3%
연체류 해물모듬 4
 
8.2%
젓갈류 해조류 해파리 3
 
6.1%

Length

2023-12-10T23:48:02.913418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:48:03.012535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
어류 24
34.8%
패류 10
14.5%
멍게류 10
14.5%
갑각류 8
 
11.6%
연체류 4
 
5.8%
해물모듬 4
 
5.8%
젓갈류 3
 
4.3%
해조류 3
 
4.3%
해파리 3
 
4.3%

SOF
Text

Distinct29
Distinct (%)59.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:48:03.197092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length4
Min length1

Characters and Unicode

Total characters196
Distinct characters62
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

Unique19 ?
Unique (%)38.8%

Sample

1st row쭈꾸미
2nd row바지락
3rd row연어
4th row갈치
5th row새우
ValueCountFrequency (%)
게 6
 
8.0%
백합 4
 
5.3%
대합 4
 
5.3%
도미 4
 
5.3%
감성돔 4
 
5.3%
돔류 4
 
5.3%
조개 4
 
5.3%
오징어 3
 
4.0%
참치 3
 
4.0%
새치류 3
 
4.0%
Other values (30) 36
48.0%
2023-12-10T23:48:03.563141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
30
 
15.3%
치 11
 
5.6%
합 9
 
4.6%
어 8
 
4.1%
돔 8
 
4.1%
미 7
 
3.6%
류 7
 
3.6%
게 6
 
3.1%
대 6
 
3.1%
조 5
 
2.6%
Other values (52) 99
50.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 166
84.7%
Space Separator 30
 
15.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
치 11
 
6.6%
합 9
 
5.4%
어 8
 
4.8%
돔 8
 
4.8%
미 7
 
4.2%
류 7
 
4.2%
게 6
 
3.6%
대 6
 
3.6%
조 5
 
3.0%
우 5
 
3.0%
Other values (51) 94
56.6%
Space Separator
ValueCountFrequency (%)
30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 166
84.7%
Common 30
 
15.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
치 11
 
6.6%
합 9
 
5.4%
어 8
 
4.8%
돔 8
 
4.8%
미 7
 
4.2%
류 7
 
4.2%
게 6
 
3.6%
대 6
 
3.6%
조 5
 
3.0%
우 5
 
3.0%
Other values (51) 94
56.6%
Common
ValueCountFrequency (%)
30
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 166
84.7%
ASCII 30
 
15.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
30
100.0%
Hangul
ValueCountFrequency (%)
치 11
 
6.6%
합 9
 
5.4%
어 8
 
4.8%
돔 8
 
4.8%
미 7
 
4.2%
류 7
 
4.2%
게 6
 
3.6%
대 6
 
3.6%
조 5
 
3.0%
우 5
 
3.0%
Other values (51) 94
56.6%

DETL_SOF
Text

Distinct40
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-10T23:48:03.796451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length2.9591837
Min length1

Characters and Unicode

Total characters145
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)69.4%

Sample

1st row주꾸미
2nd row바지락
3rd row연어
4th row갈치
5th row아르헨티나붉은새우
ValueCountFrequency (%)
게 4
 
8.2%
오징어 3
 
6.1%
해삼 2
 
4.1%
갈치 2
 
4.1%
장문볼락 2
 
4.1%
참돔 2
 
4.1%
소주목탁가자미 1
 
2.0%
톳 1
 
2.0%
스피노잠 1
 
2.0%
맛조개 1
 
2.0%
Other values (30) 30
61.2%
2023-12-10T23:48:04.192232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 10
 
6.9%
게 6
 
4.1%
치 5
 
3.4%
참 5
 
3.4%
다 5
 
3.4%
돔 4
 
2.8%
대 4
 
2.8%
오 4
 
2.8%
조 3
 
2.1%
해 3
 
2.1%
Other values (66) 96
66.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 145
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 10
 
6.9%
게 6
 
4.1%
치 5
 
3.4%
참 5
 
3.4%
다 5
 
3.4%
돔 4
 
2.8%
대 4
 
2.8%
오 4
 
2.8%
조 3
 
2.1%
해 3
 
2.1%
Other values (66) 96
66.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 145
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 10
 
6.9%
게 6
 
4.1%
치 5
 
3.4%
참 5
 
3.4%
다 5
 
3.4%
돔 4
 
2.8%
대 4
 
2.8%
오 4
 
2.8%
조 3
 
2.1%
해 3
 
2.1%
Other values (66) 96
66.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 145
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 10
 
6.9%
게 6
 
4.1%
치 5
 
3.4%
참 5
 
3.4%
다 5
 
3.4%
돔 4
 
2.8%
대 4
 
2.8%
오 4
 
2.8%
조 3
 
2.1%
해 3
 
2.1%
Other values (66) 96
66.2%

IMPORT_FOM
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Memory size524.0 B
냉동
16 
활
9 
냉장
6 
냉동,필렛(F)
2 
냉동,절단
2 
Other values (13)
14 

Length

Max length11
Median length8
Mean length3.2653061
Min length1

Unique

Unique12 ?
Unique (%)24.5%

Sample

1st row냉동
2nd row활
3rd row냉장
4th row냉동
5th row냉동

Common Values

ValueCountFrequency (%)
냉동 16
32.7%
활 9
18.4%
냉장 6
 
12.2%
냉동,필렛(F) 2
 
4.1%
냉동,절단 2
 
4.1%
냉동,살,자숙 2
 
4.1%
건조 1
 
2.0%
냉동,동체,자숙 1
 
2.0%
냉동,다리 1
 
2.0%
냉장,필렛(F) 1
 
2.0%
Other values (8) 8
16.3%

Length

2023-12-10T23:48:04.338718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
냉동 16
32.7%
활 9
18.4%
냉장 6
 
12.2%
냉동,필렛(f 2
 
4.1%
냉동,절단 2
 
4.1%
냉동,살,자숙 2
 
4.1%
건조,자숙 1
 
2.0%
냉동,필렛(f),횟감 1
 
2.0%
냉동,지느러미 1
 
2.0%
냉동,살 1
 
2.0%
Other values (8) 8
16.3%

AVE_UNPRC_AMT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)34.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3877551
Minimum0
Maximum78
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:48:04.451750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q38
95-th percentile24
Maximum78
Range78
Interquartile range (IQR)6

Descriptive statistics

Standard deviation12.010094
Coefficient of variation (CV)1.6256756
Kurtosis25.512146
Mean7.3877551
Median Absolute Deviation (MAD)2
Skewness4.6223751
Sum362
Variance144.24235
MonotonicityNot monotonic
2023-12-10T23:48:04.576183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2 9
18.4%
3 7
14.3%
1 6
12.2%
4 6
12.2%
7 3
 
6.1%
5 3
 
6.1%
10 3
 
6.1%
13 2
 
4.1%
8 2
 
4.1%
14 1
 
2.0%
Other values (7) 7
14.3%
ValueCountFrequency (%)
0 1
 
2.0%
1 6
12.2%
2 9
18.4%
3 7
14.3%
4 6
12.2%
5 3
 
6.1%
6 1
 
2.0%
7 3
 
6.1%
8 2
 
4.1%
10 3
 
6.1%
ValueCountFrequency (%)
78 1
 
2.0%
29 1
 
2.0%
28 1
 
2.0%
18 1
 
2.0%
14 1
 
2.0%
13 2
4.1%
12 1
 
2.0%
10 3
6.1%
8 2
4.1%
7 3
6.1%

PHOTO_INFO_ESSN_ID
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
data/1607816233_aTFS
49 

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdata/1607816233_aTFS
2nd rowdata/1607816233_aTFS
3rd rowdata/1607816233_aTFS
4th rowdata/1607816233_aTFS
5th rowdata/1607816233_aTFS

Common Values

ValueCountFrequency (%)
data/1607816233_aTFS 49
100.0%

Length

2023-12-10T23:48:04.732810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:48:04.849872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
data/1607816233_atfs 49
100.0%

RN
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26
Minimum2
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:48:04.974144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.4
Q114
median26
Q338
95-th percentile47.6
Maximum50
Range48
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.28869
Coefficient of variation (CV)0.54956501
Kurtosis-1.2
Mean26
Median Absolute Deviation (MAD)12
Skewness0
Sum1274
Variance204.16667
MonotonicityStrictly increasing
2023-12-10T23:48:05.134780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2 1
 
2.0%
39 1
 
2.0%
29 1
 
2.0%
30 1
 
2.0%
31 1
 
2.0%
32 1
 
2.0%
33 1
 
2.0%
34 1
 
2.0%
35 1
 
2.0%
36 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
2 1
2.0%
3 1
2.0%
4 1
2.0%
5 1
2.0%
6 1
2.0%
7 1
2.0%
8 1
2.0%
9 1
2.0%
10 1
2.0%
11 1
2.0%
ValueCountFrequency (%)
50 1
2.0%
49 1
2.0%
48 1
2.0%
47 1
2.0%
46 1
2.0%
45 1
2.0%
44 1
2.0%
43 1
2.0%
42 1
2.0%
41 1
2.0%

Interactions

2023-12-10T23:48:00.793130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:00.214336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:00.503309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:00.886836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:00.297214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:00.591631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:00.988607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:00.398114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:48:00.705123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:48:05.268901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQ_NOSTRD_DTMMNFCT_NTN_NMXPORT_NTN_NMIMPORT_PRPSCTGRY_MLSFC_NMSOFDETL_SOFIMPORT_FOMAVE_UNPRC_AMTRN
SEQ_NO1.0000.7600.8780.8220.5430.4160.6370.8100.0000.1371.000
STRD_DTM0.7601.0000.3170.4970.0000.0001.0001.0000.0000.0000.859
MNFCT_NTN_NM0.8780.3171.0001.0000.8430.8410.5800.0000.6610.8310.890
XPORT_NTN_NM0.8220.4971.0001.0000.8590.8470.8430.0000.5680.8330.842
IMPORT_PRPS0.5430.0000.8430.8591.0000.5471.0001.0000.9890.0000.463
CTGRY_MLSFC_NM0.4160.0000.8410.8470.5471.0001.0001.0000.8750.5840.405
SOF0.6371.0000.5800.8431.0001.0001.0001.0000.8550.5600.602
DETL_SOF0.8101.0000.0000.0001.0001.0001.0001.0000.7280.8570.843
IMPORT_FOM0.0000.0000.6610.5680.9890.8750.8550.7281.0000.8650.000
AVE_UNPRC_AMT0.1370.0000.8310.8330.0000.5840.5600.8570.8651.0000.156
RN1.0000.8590.8900.8420.4630.4050.6020.8430.0000.1561.000
2023-12-10T23:48:05.445231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CTGRY_MLSFC_NMIMPORT_FOMXPORT_NTN_NMIMPORT_PRPSMNFCT_NTN_NMSTRD_DTM
CTGRY_MLSFC_NM1.0000.5730.5110.6400.4630.000
IMPORT_FOM0.5731.0000.1720.7390.1980.000
XPORT_NTN_NM0.5110.1721.0000.6410.9310.344
IMPORT_PRPS0.6400.7390.6411.0000.5710.000
MNFCT_NTN_NM0.4630.1980.9310.5711.0000.183
STRD_DTM0.0000.0000.3440.0000.1831.000
2023-12-10T23:48:05.581640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQ_NOAVE_UNPRC_AMTRNSTRD_DTMMNFCT_NTN_NMXPORT_NTN_NMIMPORT_PRPSCTGRY_MLSFC_NMIMPORT_FOM
SEQ_NO1.000-0.1281.0000.6280.4610.4280.3840.1270.000
AVE_UNPRC_AMT-0.1281.000-0.1280.0000.4510.4910.0000.2480.558
RN1.000-0.1281.0000.6280.4610.4280.3840.1270.000
STRD_DTM0.6280.0000.6281.0000.1830.3440.0000.0000.000
MNFCT_NTN_NM0.4610.4510.4610.1831.0000.9310.5710.4630.198
XPORT_NTN_NM0.4280.4910.4280.3440.9311.0000.6410.5110.172
IMPORT_PRPS0.3840.0000.3840.0000.5710.6411.0000.6400.739
CTGRY_MLSFC_NM0.1270.2480.1270.0000.4630.5110.6401.0000.573
IMPORT_FOM0.0000.5580.0000.0000.1980.1720.7390.5731.000

Missing values

2023-12-10T23:48:01.114699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:48:01.294227image/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

SEQ_NOSTRD_DTMPRDCT_SCTNMNFCT_NTN_NMXPORT_NTN_NMIMPORT_PRPSCTGRY_MLSFC_NMSOFDETL_SOFIMPORT_FOMAVE_UNPRC_AMTPHOTO_INFO_ESSN_IDRN
041204-Jan-2016 00:00:00수산물베트남베트남판매용연체류 해물모듬쭈꾸미주꾸미냉동4data/1607816233_aTFS2
141304-Jan-2016 00:00:00수산물중국중국판매용패류 멍게류바지락바지락활1data/1607816233_aTFS3
241404-Jan-2016 00:00:00수산물노르웨이노르웨이판매용어류연어연어냉장10data/1607816233_aTFS4
341511-Jan-2016 00:00:00수산물아랍에미리트아랍에미리트판매용어류갈치갈치냉동3data/1607816233_aTFS5
441611-Jan-2016 00:00:00수산물아르헨티나태국판매용갑각류새우아르헨티나붉은새우냉동14data/1607816233_aTFS6
541711-Jan-2016 00:00:00수산물호주일본판매용어류참치 새치류남방참다랑어냉동,목살7data/1607816233_aTFS7
641811-Jan-2016 00:00:00수산물캐나다태국판매용패류 멍게류조개 백합 대합북방대합냉동,살7data/1607816233_aTFS8
741911-Jan-2016 00:00:00수산물칠레칠레자사제품제조용연체류 해물모듬오징어오징어냉동,지느러미1data/1607816233_aTFS9
842011-Jan-2016 00:00:00수산물중국중국판매용어류민물붕어붕어활3data/1607816233_aTFS10
942111-Jan-2016 00:00:00수산물중국중국판매용어류잉어잉어활3data/1607816233_aTFS11
SEQ_NOSTRD_DTMPRDCT_SCTNMNFCT_NTN_NMXPORT_NTN_NMIMPORT_PRPSCTGRY_MLSFC_NMSOFDETL_SOFIMPORT_FOMAVE_UNPRC_AMTPHOTO_INFO_ESSN_IDRN
3945111-Jan-2016 00:00:00수산물파키스탄파키스탄판매용갑각류게게냉동,절단2data/1607816233_aTFS41
4045211-Jan-2016 00:00:00수산물포르투갈스페인판매용어류우럭 볼락장문볼락냉동2data/1607816233_aTFS42
4145311-Jan-2016 00:00:00수산물러시아러시아판매용어류가오리가오리냉동2data/1607816233_aTFS43
4245411-Jan-2016 00:00:00수산물러시아러시아판매용어류명태명태냉동,곤이2data/1607816233_aTFS44
4345511-Jan-2016 00:00:00수산물러시아러시아판매용어류가자미까지가자미냉동1data/1607816233_aTFS45
4445611-Jan-2016 00:00:00수산물러시아러시아판매용갑각류게왕게냉장28data/1607816233_aTFS46
4545711-Jan-2016 00:00:00수산물러시아러시아판매용갑각류게대게냉장2data/1607816233_aTFS47
4645811-Jan-2016 00:00:00수산물시에라리온시에라리온판매용어류서대 박대 페루다홍서대냉동2data/1607816233_aTFS48
4745911-Jan-2016 00:00:00수산물세네갈세네갈판매용어류갈치갈치냉동4data/1607816233_aTFS49
4846011-Jan-2016 00:00:00수산물태국태국판매용갑각류새우흰다리새우냉동8data/1607816233_aTFS50