Overview

Dataset statistics

Number of variables13
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.3 KiB
Average record size in memory111.7 B

Variable types

Numeric4
DateTime1
Categorical8

Dataset

DescriptionSample
Author에이치더블유
URLhttps://www.bigdata-sea.kr/datasearch/base/view.do?prodId=PROD_000063

Alerts

WRKNG_AREA has constant value ""Constant
SHIP_NM has constant value ""Constant
WT_KG has constant value ""Constant
PHOTO_INFO_ESSN_ID has constant value ""Constant
CTGRY_MLSFC_NM is highly overall correlated with SEQ_NO and 2 other fieldsHigh correlation
QLT_MSRM_RSLT is highly overall correlated with SEQ_NO and 4 other fieldsHigh correlation
SOF is highly overall correlated with SEQ_NO and 4 other fieldsHigh correlation
STNDR is highly overall correlated with SEQ_NO and 4 other fieldsHigh correlation
SEQ_NO is highly overall correlated with TME and 6 other fieldsHigh correlation
TME is highly overall correlated with SEQ_NO and 4 other fieldsHigh correlation
QTY is highly overall correlated with SEQ_NO and 2 other fieldsHigh correlation
RN is highly overall correlated with SEQ_NO and 6 other fieldsHigh correlation
CTGRY_MLSFC_NM is highly imbalanced (59.2%)Imbalance
SEQ_NO has unique valuesUnique
RN has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:57:23.971708
Analysis finished2023-12-10 14:57:28.139236
Duration4.17 seconds
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%
Mean368
Minimum344
Maximum392
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:57:28.271250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum344
5-th percentile346.4
Q1356
median368
Q3380
95-th percentile389.6
Maximum392
Range48
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.28869
Coefficient of variation (CV)0.038827962
Kurtosis-1.2
Mean368
Median Absolute Deviation (MAD)12
Skewness0
Sum18032
Variance204.16667
MonotonicityStrictly increasing
2023-12-10T23:57:28.608613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
344 1
 
2.0%
381 1
 
2.0%
371 1
 
2.0%
372 1
 
2.0%
373 1
 
2.0%
374 1
 
2.0%
375 1
 
2.0%
376 1
 
2.0%
377 1
 
2.0%
378 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
344 1
2.0%
345 1
2.0%
346 1
2.0%
347 1
2.0%
348 1
2.0%
349 1
2.0%
350 1
2.0%
351 1
2.0%
352 1
2.0%
353 1
2.0%
ValueCountFrequency (%)
392 1
2.0%
391 1
2.0%
390 1
2.0%
389 1
2.0%
388 1
2.0%
387 1
2.0%
386 1
2.0%
385 1
2.0%
384 1
2.0%
383 1
2.0%

WRKNG_YMD
Date

Distinct3
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
Minimum2013-04-30 00:00:00
Maximum2013-05-08 00:00:00
2023-12-10T23:57:28.842039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:29.520549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)

WRKNG_AREA
Categorical

CONSTANT 

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

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동중국해 49
100.0%

Length

2023-12-10T23:57:29.777760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:57:29.974436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동중국해 49
100.0%

SHIP_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
75동명, 76동명
49 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row75동명, 76동명
2nd row75동명, 76동명
3rd row75동명, 76동명
4th row75동명, 76동명
5th row75동명, 76동명

Common Values

ValueCountFrequency (%)
75동명, 76동명 49
100.0%

Length

2023-12-10T23:57:30.151749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:57:30.351731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
75동명 49
50.0%
76동명 49
50.0%

TME
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9387755
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:57:30.530643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5.6
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6634322
Coefficient of variation (CV)0.85798084
Kurtosis4.3546898
Mean1.9387755
Median Absolute Deviation (MAD)0
Skewness2.1395822
Sum95
Variance2.7670068
MonotonicityNot monotonic
2023-12-10T23:57:30.750457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 31
63.3%
2 7
 
14.3%
3 4
 
8.2%
4 3
 
6.1%
5 1
 
2.0%
6 1
 
2.0%
7 1
 
2.0%
8 1
 
2.0%
ValueCountFrequency (%)
1 31
63.3%
2 7
 
14.3%
3 4
 
8.2%
4 3
 
6.1%
5 1
 
2.0%
6 1
 
2.0%
7 1
 
2.0%
8 1
 
2.0%
ValueCountFrequency (%)
8 1
 
2.0%
7 1
 
2.0%
6 1
 
2.0%
5 1
 
2.0%
4 3
 
6.1%
3 4
 
8.2%
2 7
 
14.3%
1 31
63.3%

CTGRY_MLSFC_NM
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
어류
45 
연체류 해물모둠
 
4

Length

Max length8
Median length2
Mean length2.4897959
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row어류
2nd row어류
3rd row연체류 해물모둠
4th row연체류 해물모둠
5th row연체류 해물모둠

Common Values

ValueCountFrequency (%)
어류 45
91.8%
연체류 해물모둠 4
 
8.2%

Length

2023-12-10T23:57:31.138883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:57:31.339378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
어류 45
84.9%
연체류 4
 
7.5%
해물모둠 4
 
7.5%

SOF
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
삼치
27 
병어
14 
갑오징어
4 
아귀
 
2
방어
 
2

Length

Max length4
Median length2
Mean length2.1632653
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row아귀
2nd row아귀
3rd row갑오징어
4th row갑오징어
5th row갑오징어

Common Values

ValueCountFrequency (%)
삼치 27
55.1%
병어 14
28.6%
갑오징어 4
 
8.2%
아귀 2
 
4.1%
방어 2
 
4.1%

Length

2023-12-10T23:57:31.599070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:57:31.867302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
삼치 27
55.1%
병어 14
28.6%
갑오징어 4
 
8.2%
아귀 2
 
4.1%
방어 2
 
4.1%

STNDR
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
무표
36 
상
13 

Length

Max length2
Median length2
Mean length1.7346939
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
무표 36
73.5%
상 13
 
26.5%

Length

2023-12-10T23:57:32.127234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:57:32.337548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
무표 36
73.5%
상 13
 
26.5%

QTY
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.265306
Minimum1
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-10T23:57:32.528634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q315
95-th percentile63.8
Maximum73
Range72
Interquartile range (IQR)14

Descriptive statistics

Standard deviation19.966405
Coefficient of variation (CV)1.6278766
Kurtosis2.9324358
Mean12.265306
Median Absolute Deviation (MAD)1
Skewness1.9796784
Sum601
Variance398.65731
MonotonicityNot monotonic
2023-12-10T23:57:32.786563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 19
38.8%
2 7
 
14.3%
3 4
 
8.2%
4 3
 
6.1%
23 2
 
4.1%
24 1
 
2.0%
73 1
 
2.0%
70 1
 
2.0%
62 1
 
2.0%
40 1
 
2.0%
Other values (9) 9
18.4%
ValueCountFrequency (%)
1 19
38.8%
2 7
 
14.3%
3 4
 
8.2%
4 3
 
6.1%
5 1
 
2.0%
6 1
 
2.0%
12 1
 
2.0%
15 1
 
2.0%
17 1
 
2.0%
23 2
 
4.1%
ValueCountFrequency (%)
73 1
2.0%
70 1
2.0%
65 1
2.0%
62 1
2.0%
46 1
2.0%
40 1
2.0%
35 1
2.0%
28 1
2.0%
24 1
2.0%
23 2
4.1%

WT_KG
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
18
49 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row18
2nd row18
3rd row18
4th row18
5th row18

Common Values

ValueCountFrequency (%)
18 49
100.0%

Length

2023-12-10T23:57:33.044863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:57:33.242762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
18 49
100.0%

QLT_MSRM_RSLT
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
무표
36 
상
13 

Length

Max length2
Median length2
Mean length1.7346939
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
무표 36
73.5%
상 13
 
26.5%

Length

2023-12-10T23:57:33.449524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:57:33.672253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
무표 36
73.5%
상 13
 
26.5%

PHOTO_INFO_ESSN_ID
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
data/1607816270_BU7n
49 

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdata/1607816270_BU7n
2nd rowdata/1607816270_BU7n
3rd rowdata/1607816270_BU7n
4th rowdata/1607816270_BU7n
5th rowdata/1607816270_BU7n

Common Values

ValueCountFrequency (%)
data/1607816270_BU7n 49
100.0%

Length

2023-12-10T23:57:33.879386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:57:34.093969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
data/1607816270_bu7n 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
2023-12-10T23:57:34.320137image/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
2023-12-10T23:57:34.673010image/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

2023-12-10T23:57:26.857124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:24.726644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:25.394634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:26.119573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:27.000906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:24.863425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:25.556837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:26.291117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:27.172171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:25.063756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:25.762989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:26.486474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:27.341125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:25.253911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:25.957437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:57:26.694614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:57:34.868841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQ_NOWRKNG_YMDTMECTGRY_MLSFC_NMSOFSTNDRQTYQLT_MSRM_RSLTRN
SEQ_NO1.0000.9490.0000.8600.9190.9840.5130.9841.000
WRKNG_YMD0.9491.0000.0000.5240.7560.3040.3730.3040.954
TME0.0000.0001.0000.0000.0000.7060.0000.7060.000
CTGRY_MLSFC_NM0.8600.5240.0001.0001.0000.0000.0000.0000.736
SOF0.9190.7560.0001.0001.0000.8200.0000.8200.925
STNDR0.9840.3040.7060.0000.8201.0000.0000.9960.985
QTY0.5130.3730.0000.0000.0000.0001.0000.0000.533
QLT_MSRM_RSLT0.9840.3040.7060.0000.8200.9960.0001.0000.985
RN1.0000.9540.0000.7360.9250.9850.5330.9851.000
2023-12-10T23:57:35.130861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CTGRY_MLSFC_NMQLT_MSRM_RSLTSOFSTNDR
CTGRY_MLSFC_NM1.0000.0000.9680.000
QLT_MSRM_RSLT0.0001.0000.9150.947
SOF0.9680.9151.0000.915
STNDR0.0000.9470.9151.000
2023-12-10T23:57:35.340148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQ_NOTMEQTYRNCTGRY_MLSFC_NMSOFSTNDRQLT_MSRM_RSLT
SEQ_NO1.000-0.5390.7721.0000.5230.5960.8120.812
TME-0.5391.000-0.622-0.5390.0000.0000.5020.502
QTY0.772-0.6221.0000.7720.0000.0000.0000.000
RN1.000-0.5390.7721.0000.5230.5960.8120.812
CTGRY_MLSFC_NM0.5230.0000.0000.5231.0000.9680.0000.000
SOF0.5960.0000.0000.5960.9681.0000.9150.915
STNDR0.8120.5020.0000.8120.0000.9151.0000.947
QLT_MSRM_RSLT0.8120.5020.0000.8120.0000.9150.9471.000

Missing values

2023-12-10T23:57:27.604395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:57:28.005333image/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_NOWRKNG_YMDWRKNG_AREASHIP_NMTMECTGRY_MLSFC_NMSOFSTNDRQTYWT_KGQLT_MSRM_RSLTPHOTO_INFO_ESSN_IDRN
034430-Apr-2013 00:00:00동중국해75동명, 76동명1어류아귀무표118무표data/1607816270_BU7n2
134530-Apr-2013 00:00:00동중국해75동명, 76동명2어류아귀무표118무표data/1607816270_BU7n3
234630-Apr-2013 00:00:00동중국해75동명, 76동명1연체류 해물모둠갑오징어무표118무표data/1607816270_BU7n4
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534930-Apr-2013 00:00:00동중국해75동명, 76동명1연체류 해물모둠갑오징어무표218무표data/1607816270_BU7n7
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4138508-May-2013 00:00:00동중국해75동명, 76동명1어류삼치무표2818무표data/1607816270_BU7n43
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4438808-May-2013 00:00:00동중국해75동명, 76동명1어류삼치무표4018무표data/1607816270_BU7n46
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4639008-May-2013 00:00:00동중국해75동명, 76동명1어류삼치무표6218무표data/1607816270_BU7n48
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