Overview

Dataset statistics

Number of variables17
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory71.4 KiB
Average record size in memory146.3 B

Variable types

Numeric7
Categorical10

Dataset

DescriptionSample
Author엔에스원소프트㈜
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT10NS1006

Alerts

gtr_ym has constant value ""Constant
shp_knd_nm has constant value ""Constant
shp_no is highly overall correlated with shp_la and 11 other fieldsHigh correlation
shp_prpos_nm is highly overall correlated with shp_la and 9 other fieldsHigh correlation
shp_hedp_stt_cd is highly overall correlated with shp_la and 6 other fieldsHigh correlation
shp_nm is highly overall correlated with shp_la and 11 other fieldsHigh correlation
nssid is highly overall correlated with shp_cog and 7 other fieldsHigh correlation
rgshb_nm is highly overall correlated with shp_la and 10 other fieldsHigh correlation
shp_qtmt_nm is highly overall correlated with shp_cog and 7 other fieldsHigh correlation
data_sn is highly overall correlated with gtr_ymdhmsHigh correlation
shp_la is highly overall correlated with shp_lo and 5 other fieldsHigh correlation
shp_lo is highly overall correlated with shp_la and 5 other fieldsHigh correlation
shp_cog is highly overall correlated with nssid and 4 other fieldsHigh correlation
shp_gtnn is highly overall correlated with shp_lt and 5 other fieldsHigh correlation
shp_lt is highly overall correlated with shp_gtnn and 5 other fieldsHigh correlation
gtr_ymdhms is highly overall correlated with data_snHigh correlation
shp_sog is highly overall correlated with shp_lo and 4 other fieldsHigh correlation
shp_sog is highly imbalanced (89.4%)Imbalance
data_sn has unique valuesUnique
shp_cog has 7 (1.4%) zerosZeros

Reproduction

Analysis started2024-03-13 12:42:45.830913
Analysis finished2024-03-13 12:42:55.283168
Duration9.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

data_sn
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.5
Minimum1
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T21:42:55.383049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25.95
Q1125.75
median250.5
Q3375.25
95-th percentile475.05
Maximum500
Range499
Interquartile range (IQR)249.5

Descriptive statistics

Standard deviation144.48183
Coefficient of variation (CV)0.57677378
Kurtosis-1.2
Mean250.5
Median Absolute Deviation (MAD)125
Skewness0
Sum125250
Variance20875
MonotonicityStrictly increasing
2024-03-13T21:42:55.564198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
331 1
 
0.2%
344 1
 
0.2%
343 1
 
0.2%
342 1
 
0.2%
341 1
 
0.2%
340 1
 
0.2%
339 1
 
0.2%
338 1
 
0.2%
337 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
500 1
0.2%
499 1
0.2%
498 1
0.2%
497 1
0.2%
496 1
0.2%
495 1
0.2%
494 1
0.2%
493 1
0.2%
492 1
0.2%
491 1
0.2%

gtr_ym
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
202210
500 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row202210
2nd row202210
3rd row202210
4th row202210
5th row202210

Common Values

ValueCountFrequency (%)
202210 500
100.0%

Length

2024-03-13T21:42:55.731182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:42:55.875547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202210 500
100.0%

nssid
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
34******
179 
33******
116 
35******
111 
36******
68 
31******
21 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row35******
2nd row33******
3rd row34******
4th row36******
5th row35******

Common Values

ValueCountFrequency (%)
34****** 179
35.8%
33****** 116
23.2%
35****** 111
22.2%
36****** 68
 
13.6%
31****** 21
 
4.2%
32****** 5
 
1.0%

Length

2024-03-13T21:42:56.051635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:42:56.260310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
34 179
35.8%
33 116
23.2%
35 111
22.2%
36 68
 
13.6%
31 21
 
4.2%
32 5
 
1.0%

shp_la
Real number (ℝ)

HIGH CORRELATION 

Distinct137
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.932524
Minimum35.2296
Maximum36.4232
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T21:42:56.483494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.2296
5-th percentile35.932775
Q135.9356
median35.9373
Q335.94055
95-th percentile35.9863
Maximum36.4232
Range1.1936
Interquartile range (IQR)0.00495

Descriptive statistics

Standard deviation0.10823915
Coefficient of variation (CV)0.0030122891
Kurtosis32.53118
Mean35.932524
Median Absolute Deviation (MAD)0.0017
Skewness-5.0364257
Sum17966.262
Variance0.011715714
MonotonicityNot monotonic
2024-03-13T21:42:56.724962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.9373 130
26.0%
35.9375 70
14.0%
35.9355 65
13.0%
35.9356 37
 
7.4%
35.937 16
 
3.2%
36.0693 10
 
2.0%
35.9337 6
 
1.2%
35.9382 6
 
1.2%
35.9863 4
 
0.8%
35.9615 3
 
0.6%
Other values (127) 153
30.6%
ValueCountFrequency (%)
35.2296 1
 
0.2%
35.2315 3
0.6%
35.2316 1
 
0.2%
35.2317 2
0.4%
35.232 1
 
0.2%
35.283 1
 
0.2%
35.3336 1
 
0.2%
35.3956 1
 
0.2%
35.9245 1
 
0.2%
35.9297 1
 
0.2%
ValueCountFrequency (%)
36.4232 1
 
0.2%
36.2194 2
 
0.4%
36.2128 1
 
0.2%
36.0695 1
 
0.2%
36.0693 10
2.0%
36.0587 1
 
0.2%
36.0585 1
 
0.2%
36.0413 1
 
0.2%
36.0397 1
 
0.2%
36.0367 1
 
0.2%

shp_lo
Real number (ℝ)

HIGH CORRELATION 

Distinct112
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.4486
Minimum125.1201
Maximum126.5326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T21:42:56.919977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum125.1201
5-th percentile125.86099
Q1126.43465
median126.5315
Q3126.5317
95-th percentile126.5319
Maximum126.5326
Range1.4125
Interquartile range (IQR)0.09705

Descriptive statistics

Standard deviation0.21676043
Coefficient of variation (CV)0.0017142177
Kurtosis17.192365
Mean126.4486
Median Absolute Deviation (MAD)0.0004
Skewness-4.0346747
Sum63224.299
Variance0.046985083
MonotonicityNot monotonic
2024-03-13T21:42:57.125500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.5319 102
20.4%
126.5315 69
13.8%
126.5316 64
 
12.8%
126.5317 60
 
12.0%
126.5322 10
 
2.0%
125.8608 9
 
1.8%
126.427 7
 
1.4%
126.4345 7
 
1.4%
126.532 6
 
1.2%
126.5307 6
 
1.2%
Other values (102) 160
32.0%
ValueCountFrequency (%)
125.1201 1
 
0.2%
125.2877 1
 
0.2%
125.2901 4
0.8%
125.2902 2
0.4%
125.2906 1
 
0.2%
125.3266 1
 
0.2%
125.5071 1
 
0.2%
125.821 1
 
0.2%
125.8227 1
 
0.2%
125.8243 1
 
0.2%
ValueCountFrequency (%)
126.5326 1
 
0.2%
126.5322 10
 
2.0%
126.5321 1
 
0.2%
126.532 6
 
1.2%
126.5319 102
20.4%
126.5317 60
12.0%
126.5316 64
12.8%
126.5315 69
13.8%
126.5313 1
 
0.2%
126.5312 1
 
0.2%

shp_cog
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct96
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.94
Minimum0
Maximum358
Zeros7
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T21:42:57.352077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q172
median166
Q3297
95-th percentile348.1
Maximum358
Range358
Interquartile range (IQR)225

Descriptive statistics

Standard deviation116.26852
Coefficient of variation (CV)0.69646892
Kurtosis-1.3627147
Mean166.94
Median Absolute Deviation (MAD)106
Skewness0.20644219
Sum83470
Variance13518.369
MonotonicityNot monotonic
2024-03-13T21:42:57.566016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72 66
 
13.2%
316 64
 
12.8%
30 39
 
7.8%
166 38
 
7.6%
178 32
 
6.4%
272 13
 
2.6%
352 10
 
2.0%
186 10
 
2.0%
130 10
 
2.0%
210 9
 
1.8%
Other values (86) 209
41.8%
ValueCountFrequency (%)
0 7
1.4%
2 4
0.8%
4 7
1.4%
6 2
 
0.4%
8 3
0.6%
10 1
 
0.2%
12 6
1.2%
14 3
0.6%
16 1
 
0.2%
18 5
1.0%
ValueCountFrequency (%)
358 2
 
0.4%
356 4
 
0.8%
354 8
1.6%
352 10
2.0%
350 1
 
0.2%
348 8
1.6%
346 4
 
0.8%
342 3
 
0.6%
340 1
 
0.2%
338 1
 
0.2%

shp_sog
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
493 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 493
98.6%
1 7
 
1.4%

Length

2024-03-13T21:42:57.755979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:42:57.884545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 493
98.6%
1 7
 
1.4%

shp_no
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
10070*********
116 
04040*********
69 
02020*********
67 
15090*********
64 
03040*********
47 
Other values (17)
137 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique4 ?
Unique (%)0.8%

Sample

1st row04040*********
2nd row10070*********
3rd row03050*********
4th row15090*********
5th row99090*********

Common Values

ValueCountFrequency (%)
10070********* 116
23.2%
04040********* 69
13.8%
02020********* 67
13.4%
15090********* 64
12.8%
03040********* 47
9.4%
02120********* 26
 
5.2%
99080********* 23
 
4.6%
99090********* 15
 
3.0%
07050********* 14
 
2.8%
95124********* 11
 
2.2%
Other values (12) 48
9.6%

Length

2024-03-13T21:42:58.040769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10070 116
23.2%
04040 69
13.8%
02020 67
13.4%
15090 64
12.8%
03040 47
9.4%
02120 26
 
5.2%
99080 23
 
4.6%
99090 15
 
3.0%
07050 14
 
2.8%
95124 11
 
2.2%
Other values (12) 48
9.6%

shp_nm
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
제****호
127 
금*호
69 
흥*호
67 
혜*호
64 
태*호
47 
Other values (15)
126 

Length

Max length7
Median length3
Mean length3.874
Min length3

Unique

Unique3 ?
Unique (%)0.6%

Sample

1st row금*호
2nd row제****호
3rd row국*호
4th row혜*호
5th row영*호

Common Values

ValueCountFrequency (%)
제****호 127
25.4%
금*호 69
13.8%
흥*호 67
13.4%
혜*호 64
12.8%
태*호 47
 
9.4%
신*호 26
 
5.2%
성*호 23
 
4.6%
영*호 15
 
3.0%
명*호 14
 
2.8%
제***호 11
 
2.2%
Other values (10) 37
 
7.4%

Length

2024-03-13T21:42:58.233035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
제****호 127
25.4%
금*호 69
13.8%
흥*호 67
13.4%
혜*호 64
12.8%
태*호 47
 
9.4%
신*호 26
 
5.2%
성*호 23
 
4.6%
영*호 15
 
3.0%
명*호 14
 
2.8%
제***호 11
 
2.2%
Other values (10) 37
 
7.4%

shp_knd_nm
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
기선
500 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기선 500
100.0%

Length

2024-03-13T21:42:58.453032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:42:58.616315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기선 500
100.0%

shp_qtmt_nm
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
FRP
367 
133 

Length

Max length3
Median length3
Mean length2.468
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFRP
2nd row
3rd rowFRP
4th rowFRP
5th rowFRP

Common Values

ValueCountFrequency (%)
FRP 367
73.4%
133
 
26.6%

Length

2024-03-13T21:42:58.842894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:42:59.021694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
frp 367
73.4%
133
 
26.6%

rgshb_nm
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
전북 군산시 옥도면
299 
경남 남해군 미조면
116 
전북 부안군 변산면
41 
충남 태안군 근흥면
 
16
부산시 중구
 
14
Other values (6)
 
14

Length

Max length10
Median length10
Mean length9.822
Min length6

Unique

Unique3 ?
Unique (%)0.6%

Sample

1st row전북 군산시 옥도면
2nd row경남 남해군 미조면
3rd row전북 군산시 옥도면
4th row전북 군산시 옥도면
5th row전북 부안군 변산면

Common Values

ValueCountFrequency (%)
전북 군산시 옥도면 299
59.8%
경남 남해군 미조면 116
 
23.2%
전북 부안군 변산면 41
 
8.2%
충남 태안군 근흥면 16
 
3.2%
부산시 중구 14
 
2.8%
충청남도 서천군 5
 
1.0%
충청남도 보령시 4
 
0.8%
전북 군산시 2
 
0.4%
충남 서천군 비인면 1
 
0.2%
제주시 추자면 1
 
0.2%

Length

2024-03-13T21:42:59.199102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전북 342
23.2%
군산시 301
20.4%
옥도면 299
20.3%
경남 116
 
7.9%
남해군 116
 
7.9%
미조면 116
 
7.9%
부안군 41
 
2.8%
변산면 41
 
2.8%
충남 17
 
1.2%
근흥면 16
 
1.1%
Other values (10) 68
 
4.6%

shp_gtnn
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.86546
Minimum2.99
Maximum283
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T21:42:59.379499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.99
5-th percentile7.93
Q17.93
median9.77
Q339
95-th percentile39
Maximum283
Range280.01
Interquartile range (IQR)31.07

Descriptive statistics

Standard deviation35.964993
Coefficient of variation (CV)1.5728961
Kurtosis25.975019
Mean22.86546
Median Absolute Deviation (MAD)1.84
Skewness4.8019431
Sum11432.73
Variance1293.4808
MonotonicityNot monotonic
2024-03-13T21:42:59.517008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
9.77 178
35.6%
7.93 161
32.2%
39.0 116
23.2%
29.0 12
 
2.4%
197.0 11
 
2.2%
24.0 5
 
1.0%
4.99 5
 
1.0%
28.0 4
 
0.8%
283.0 3
 
0.6%
63.0 2
 
0.4%
Other values (3) 3
 
0.6%
ValueCountFrequency (%)
2.99 1
 
0.2%
4.99 5
 
1.0%
7.93 161
32.2%
9.77 178
35.6%
24.0 5
 
1.0%
28.0 4
 
0.8%
29.0 12
 
2.4%
39.0 116
23.2%
42.0 1
 
0.2%
63.0 2
 
0.4%
ValueCountFrequency (%)
283.0 3
 
0.6%
197.0 11
 
2.2%
101.0 1
 
0.2%
63.0 2
 
0.4%
42.0 1
 
0.2%
39.0 116
23.2%
29.0 12
 
2.4%
28.0 4
 
0.8%
24.0 5
 
1.0%
9.77 178
35.6%

shp_lt
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.75212
Minimum8.92
Maximum51.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T21:42:59.686615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.92
5-th percentile13.05
Q114.2
median16.1
Q322.1
95-th percentile23
Maximum51.1
Range42.18
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation5.7083322
Coefficient of variation (CV)0.32155777
Kurtosis11.212387
Mean17.75212
Median Absolute Deviation (MAD)2.3
Skewness2.847104
Sum8876.06
Variance32.585056
MonotonicityNot monotonic
2024-03-13T21:42:59.860645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
22.1 116
23.2%
13.8 69
13.8%
16.1 67
13.4%
16.7 64
12.8%
14.2 47
9.4%
14.22 26
 
5.2%
14.6 23
 
4.6%
13.05 15
 
3.0%
13.86 14
 
2.8%
41.44 11
 
2.2%
Other values (13) 48
9.6%
ValueCountFrequency (%)
8.92 1
 
0.2%
10.4 5
 
1.0%
12.8 1
 
0.2%
12.86 6
 
1.2%
13.05 15
 
3.0%
13.8 69
13.8%
13.86 14
 
2.8%
14.2 47
9.4%
14.22 26
 
5.2%
14.6 23
 
4.6%
ValueCountFrequency (%)
51.1 3
 
0.6%
41.44 11
 
2.2%
37.5 1
 
0.2%
23.43 1
 
0.2%
23.1 1
 
0.2%
23.05 4
 
0.8%
23.0 11
 
2.2%
22.5 5
 
1.0%
22.1 116
23.2%
19.76 2
 
0.4%

shp_prpos_nm
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
연안복합어업
167 
근해형망어업
138 
서남해구쌍끌이중형저인망어업
116 
근해안강망어업
21 
연안자망어업
20 
Other values (6)
38 

Length

Max length14
Median length6
Mean length7.976
Min length5

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row연안복합어업
2nd row서남해구쌍끌이중형저인망어업
3rd row연안조망어업
4th row근해형망어업
5th row연안자망어업

Common Values

ValueCountFrequency (%)
연안복합어업 167
33.4%
근해형망어업 138
27.6%
서남해구쌍끌이중형저인망어업 116
23.2%
근해안강망어업 21
 
4.2%
연안자망어업 20
 
4.0%
연안개량안강망어업 14
 
2.8%
대형선망어업 12
 
2.4%
연안조망어업 6
 
1.2%
선망운반선 3
 
0.6%
어획물운반선 2
 
0.4%

Length

2024-03-13T21:43:00.046930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
연안복합어업 167
33.4%
근해형망어업 138
27.6%
서남해구쌍끌이중형저인망어업 116
23.2%
근해안강망어업 21
 
4.2%
연안자망어업 20
 
4.0%
연안개량안강망어업 14
 
2.8%
대형선망어업 12
 
2.4%
연안조망어업 6
 
1.2%
선망운반선 3
 
0.6%
어획물운반선 2
 
0.4%

shp_hedp_stt_cd
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
341 
0
159 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 341
68.2%
0 159
31.8%

Length

2024-03-13T21:43:00.238930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:43:00.403094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 341
68.2%
0 159
31.8%

gtr_ymdhms
Real number (ℝ)

HIGH CORRELATION 

Distinct391
Distinct (%)78.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0221001 × 1013
Minimum2.0221001 × 1013
Maximum2.0221001 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T21:43:01.019147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0221001 × 1013
5-th percentile2.0221001 × 1013
Q12.0221001 × 1013
median2.0221001 × 1013
Q32.0221001 × 1013
95-th percentile2.0221001 × 1013
Maximum2.0221001 × 1013
Range4100
Interquartile range (IQR)2049.75

Descriptive statistics

Standard deviation1184.704
Coefficient of variation (CV)5.8587801 × 10-11
Kurtosis-1.2235625
Mean2.0221001 × 1013
Median Absolute Deviation (MAD)1067.5
Skewness0.0090774179
Sum1.0110501 × 1016
Variance1403523.5
MonotonicityIncreasing
2024-03-13T21:43:01.194919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20221001000734 4
 
0.8%
20221001003434 3
 
0.6%
20221001004000 3
 
0.6%
20221001000634 3
 
0.6%
20221001000334 3
 
0.6%
20221001000934 3
 
0.6%
20221001003404 3
 
0.6%
20221001000234 3
 
0.6%
20221001003305 2
 
0.4%
20221001001605 2
 
0.4%
Other values (381) 471
94.2%
ValueCountFrequency (%)
20221001000003 1
0.2%
20221001000004 2
0.4%
20221001000005 1
0.2%
20221001000016 1
0.2%
20221001000030 1
0.2%
20221001000033 1
0.2%
20221001000035 1
0.2%
20221001000100 1
0.2%
20221001000103 2
0.4%
20221001000104 1
0.2%
ValueCountFrequency (%)
20221001004103 1
0.2%
20221001004100 1
0.2%
20221001004038 1
0.2%
20221001004035 1
0.2%
20221001004034 1
0.2%
20221001004033 2
0.4%
20221001004031 1
0.2%
20221001004030 2
0.4%
20221001004027 1
0.2%
20221001004014 1
0.2%

Interactions

2024-03-13T21:42:53.720772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:47.372712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:48.315633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:49.370265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:50.365461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:51.279027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:52.360987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:53.891149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:47.490171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:48.478283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:49.518096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:50.498001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:51.410182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:52.494501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:54.067613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:47.618918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:48.655370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:49.652908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:50.632450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:51.619663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:52.622966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:54.222374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:47.741183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:48.799777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:49.788446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:50.773550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:51.802173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:53.191723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:54.369126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:47.885994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:48.921320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:49.929739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:50.910571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:51.944897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:53.326706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:54.537976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:48.033973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:49.050642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:50.079698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:51.047259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:52.086957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:53.460637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:54.658424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:48.184484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:49.183981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:50.232783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:51.159292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:52.214917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:53.571863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:43:01.334970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
data_snnssidshp_lashp_loshp_cogshp_sogshp_noshp_nmshp_qtmt_nmrgshb_nmshp_gtnnshp_ltshp_prpos_nmshp_hedp_stt_cdgtr_ymdhms
data_sn1.0000.1440.2260.3250.3150.1990.4470.4690.0000.2910.2010.2780.3160.1230.997
nssid0.1441.0000.5770.7200.7780.6881.0000.9950.9910.9060.8420.6550.8700.7940.149
shp_la0.2260.5771.0000.9080.6170.3740.9140.8430.3150.7940.7070.8810.8290.6670.220
shp_lo0.3250.7200.9081.0000.5640.7430.9590.9130.2730.8830.7660.7270.8620.4880.301
shp_cog0.3150.7780.6170.5641.0000.3110.8920.9220.8160.6480.6240.6060.7620.8310.277
shp_sog0.1990.6880.3740.7430.3111.0000.7950.8560.0430.5320.0000.1220.4940.0000.196
shp_no0.4471.0000.9140.9590.8920.7951.0000.9991.0001.0001.0000.9991.0001.0000.441
shp_nm0.4690.9950.8430.9130.9220.8560.9991.0001.0000.9850.9630.9740.9810.9940.459
shp_qtmt_nm0.0000.9910.3150.2730.8160.0431.0001.0001.0001.0001.0000.8291.0000.4210.070
rgshb_nm0.2910.9060.7940.8830.6480.5321.0000.9851.0001.0000.9610.9440.9800.6340.284
shp_gtnn0.2010.8420.7070.7660.6240.0001.0000.9631.0000.9611.0000.9830.9610.5790.207
shp_lt0.2780.6550.8810.7270.6060.1220.9990.9740.8290.9440.9831.0000.9640.4300.296
shp_prpos_nm0.3160.8700.8290.8620.7620.4941.0000.9811.0000.9800.9610.9641.0000.7400.331
shp_hedp_stt_cd0.1230.7940.6670.4880.8310.0001.0000.9940.4210.6340.5790.4300.7401.0000.086
gtr_ymdhms0.9970.1490.2200.3010.2770.1960.4410.4590.0700.2840.2070.2960.3310.0861.000
2024-03-13T21:43:01.534876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
shp_sogshp_noshp_prpos_nmshp_hedp_stt_cdshp_nmnssidrgshb_nmshp_qtmt_nm
shp_sog1.0000.6400.4710.0000.7010.5040.5080.027
shp_no0.6401.0000.9860.9620.9900.9780.9860.980
shp_prpos_nm0.4710.9861.0000.7190.8850.6850.7400.991
shp_hedp_stt_cd0.0000.9620.7191.0000.9160.5960.6090.277
shp_nm0.7010.9900.8850.9161.0000.9640.9040.978
nssid0.5040.9780.6850.5960.9641.0000.7550.913
rgshb_nm0.5080.9860.7400.6090.9040.7551.0000.991
shp_qtmt_nm0.0270.9800.9910.2770.9780.9130.9911.000
2024-03-13T21:43:01.728389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
data_snshp_lashp_loshp_cogshp_gtnnshp_ltgtr_ymdhmsnssidshp_sogshp_noshp_nmshp_qtmt_nmrgshb_nmshp_prpos_nmshp_hedp_stt_cd
data_sn1.0000.032-0.0610.047-0.060-0.0281.0000.0750.1510.1800.1640.0000.1270.1400.093
shp_la0.0321.000-0.5090.249-0.398-0.3120.0310.3910.3980.6880.5610.3350.5530.6050.716
shp_lo-0.061-0.5091.000-0.2160.3310.201-0.0610.4570.7540.7990.6710.2710.6690.6300.486
shp_cog0.0470.249-0.2161.000-0.190-0.1100.0460.5550.2370.5970.5720.6440.3430.4550.658
shp_gtnn-0.060-0.3980.331-0.1901.0000.917-0.0590.4600.0000.9840.8470.9910.8880.8880.418
shp_lt-0.028-0.3120.201-0.1100.9171.000-0.0270.4640.1300.9820.8710.8950.8420.9000.458
gtr_ymdhms1.0000.031-0.0610.046-0.059-0.0271.0000.0810.1490.1770.1600.0540.1240.1460.069
nssid0.0750.3910.4570.5550.4600.4640.0811.0000.5040.9780.9640.9130.7550.6850.596
shp_sog0.1510.3980.7540.2370.0000.1300.1490.5041.0000.6400.7010.0270.5080.4710.000
shp_no0.1800.6880.7990.5970.9840.9820.1770.9780.6401.0000.9900.9800.9860.9860.962
shp_nm0.1640.5610.6710.5720.8470.8710.1600.9640.7010.9901.0000.9780.9040.8850.916
shp_qtmt_nm0.0000.3350.2710.6440.9910.8950.0540.9130.0270.9800.9781.0000.9910.9910.277
rgshb_nm0.1270.5530.6690.3430.8880.8420.1240.7550.5080.9860.9040.9911.0000.7400.609
shp_prpos_nm0.1400.6050.6300.4550.8880.9000.1460.6850.4710.9860.8850.9910.7401.0000.719
shp_hedp_stt_cd0.0930.7160.4860.6580.4180.4580.0690.5960.0000.9620.9160.2770.6090.7191.000

Missing values

2024-03-13T21:42:54.840913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:42:55.170107image/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

data_sngtr_ymnssidshp_lashp_loshp_cogshp_sogshp_noshp_nmshp_knd_nmshp_qtmt_nmrgshb_nmshp_gtnnshp_ltshp_prpos_nmshp_hedp_stt_cdgtr_ymdhms
0120221035******35.9373126.5317110004040*********금*호기선FRP전북 군산시 옥도면7.9313.8연안복합어업120221001000003
1220221033******35.9355126.531972010070*********제****호기선경남 남해군 미조면39.022.1서남해구쌍끌이중형저인망어업120221001000004
2320221034******35.9473126.400820003050*********국*호기선FRP전북 군산시 옥도면7.9312.86연안조망어업020221001000004
3420221036******35.9375126.5316316015090*********혜*호기선FRP전북 군산시 옥도면9.7716.7근해형망어업120221001000005
4520221035******35.9595126.42726099090*********영*호기선FRP전북 부안군 변산면7.9313.05연안자망어업020221001000016
5620221034******35.9373126.5315166002020*********흥*호기선FRP전북 군산시 옥도면9.7716.1근해형망어업120221001000030
6720221035******35.9373126.5317136004040*********금*호기선FRP전북 군산시 옥도면7.9313.8연안복합어업120221001000033
7820221036******35.9375126.5316316015090*********혜*호기선FRP전북 군산시 옥도면9.7716.7근해형망어업120221001000035
8920221034******35.9373126.5315166002020*********흥*호기선FRP전북 군산시 옥도면9.7716.1근해형망어업120221001000100
91020221033******35.9356126.531930010070*********제****호기선경남 남해군 미조면39.022.1서남해구쌍끌이중형저인망어업120221001000103
data_sngtr_ymnssidshp_lashp_loshp_cogshp_sogshp_noshp_nmshp_knd_nmshp_qtmt_nmrgshb_nmshp_gtnnshp_ltshp_prpos_nmshp_hedp_stt_cdgtr_ymdhms
49049120221034******35.9373126.5315178002020*********흥*호기선FRP전북 군산시 옥도면9.7716.1근해형망어업120221001004030
49149220221034******35.9633126.436514003040*********태*호기선FRP전북 군산시 옥도면7.9314.2연안복합어업020221001004030
49249320221034******35.9633126.436514099080*********성*호기선FRP전북 군산시 옥도면7.9314.6연안복합어업020221001004031
49349420221033******35.9356126.531930010070*********제****호기선경남 남해군 미조면39.022.1서남해구쌍끌이중형저인망어업120221001004033
49449520221035******35.9373126.531778004040*********금*호기선FRP전북 군산시 옥도면7.9313.8연안복합어업120221001004033
49549620221033******35.9355126.531972010070*********제****호기선경남 남해군 미조면39.022.1서남해구쌍끌이중형저인망어업120221001004034
49649720221036******35.9375126.5316316015090*********혜*호기선FRP전북 군산시 옥도면9.7716.7근해형망어업120221001004035
49749820221031******35.9863126.1666352009080*********2**호기선FRP충청남도 서천군4.9910.4연안자망어업020221001004038
49849920221034******35.9637126.436714003040*********태*호기선FRP전북 군산시 옥도면7.9314.2연안복합어업020221001004100
49950020221033******35.9356126.531930010070*********제****호기선경남 남해군 미조면39.022.1서남해구쌍끌이중형저인망어업120221001004103