Overview

Dataset statistics

Number of variables16
Number of observations2432
Missing cells7
Missing cells (%)< 0.1%
Duplicate rows12
Duplicate rows (%)0.5%
Total size in memory323.1 KiB
Average record size in memory136.1 B

Variable types

Categorical6
Text2
Numeric8

Dataset

Description여수시 여객선 운임 할인정보, 요금종류(일반대인, 소인, 경로 등) 기본정보 입니다. 여수시 관내 9개 항로별 여객선사의 기본 운임 자료입니다.
Author전라남도 여수시
URLhttps://www.data.go.kr/data/15040006/fileData.do

Alerts

Dataset has 12 (0.5%) duplicate rowsDuplicates
시행일자 is highly overall correlated with 항로 and 4 other fieldsHigh correlation
항로 is highly overall correlated with 선박 and 3 other fieldsHigh correlation
입력일시 is highly overall correlated with 항로 and 4 other fieldsHigh correlation
일반대인 is highly overall correlated with 일반중고 and 7 other fieldsHigh correlation
일반중고 is highly overall correlated with 일반대인 and 7 other fieldsHigh correlation
일반경로 is highly overall correlated with 일반대인 and 7 other fieldsHigh correlation
일반소아 is highly overall correlated with 일반대인 and 7 other fieldsHigh correlation
도서대인 is highly overall correlated with 일반대인 and 6 other fieldsHigh correlation
도서중고 is highly overall correlated with 일반대인 and 6 other fieldsHigh correlation
도서경로 is highly overall correlated with 일반대인 and 6 other fieldsHigh correlation
도서소아 is highly overall correlated with 일반대인 and 6 other fieldsHigh correlation
선박 is highly overall correlated with 항로 and 4 other fieldsHigh correlation
요금종류 is highly overall correlated with 일반대인 and 8 other fieldsHigh correlation
객실등급 is highly overall correlated with 선박 and 3 other fieldsHigh correlation
요금종류 is highly imbalanced (52.0%)Imbalance
도서대인 has 72 (3.0%) zerosZeros
도서중고 has 69 (2.8%) zerosZeros
도서경로 has 69 (2.8%) zerosZeros
도서소아 has 69 (2.8%) zerosZeros

Reproduction

Analysis started2023-12-12 23:07:28.436152
Analysis finished2023-12-12 23:07:36.621525
Duration8.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

항로
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
녹동-동송
591 
여수-손죽-거문
536 
녹동-신지
454 
여수(사설)-둔병
208 
여수-안도-연도
168 
Other values (7)
475 

Length

Max length9
Median length8
Mean length6.6591283
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row녹동-거문도
2nd row녹동-거문도
3rd row녹동-거문도
4th row녹동-거문도
5th row녹동-거문도

Common Values

ValueCountFrequency (%)
녹동-동송 591
24.3%
여수-손죽-거문 536
22.0%
녹동-신지 454
18.7%
여수(사설)-둔병 208
 
8.6%
여수-안도-연도 168
 
6.9%
(뉴)여수-함구미 150
 
6.2%
여수-둔병 100
 
4.1%
녹동-거문도 83
 
3.4%
손죽-광도 40
 
1.6%
돌산(신기)-여천 36
 
1.5%
Other values (2) 66
 
2.7%

Length

2023-12-13T08:07:36.704775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
녹동-동송 591
24.3%
여수-손죽-거문 536
22.0%
녹동-신지 454
18.7%
여수(사설)-둔병 208
 
8.6%
여수-안도-연도 168
 
6.9%
뉴)여수-함구미 150
 
6.2%
여수-둔병 100
 
4.1%
녹동-거문도 83
 
3.4%
손죽-광도 40
 
1.6%
돌산(신기)-여천 36
 
1.5%
Other values (2) 66
 
2.7%

출발지
Text

Distinct54
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
2023-12-13T08:07:36.978730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.0530428
Min length2

Characters and Unicode

Total characters7425
Distinct characters77
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

Unique0 ?
Unique (%)0.0%

Sample

1st row거문도
2nd row거문도
3rd row거문도
4th row거문도
5th row금당도
ValueCountFrequency (%)
녹동 150
 
6.2%
여수 150
 
6.2%
손죽도 123
 
5.1%
금당도 121
 
5.0%
초도 109
 
4.5%
거문도 100
 
4.1%
연홍도 97
 
4.0%
나로도 95
 
3.9%
서도/동도 84
 
3.5%
금산_금진 81
 
3.3%
Other values (44) 1322
54.4%
2023-12-13T08:07:37.376625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 1645
22.2%
금 546
 
7.4%
동 373
 
5.0%
_ 291
 
3.9%
여 250
 
3.4%
산 231
 
3.1%
수 185
 
2.5%
녹 150
 
2.0%
오 143
 
1.9%
손 123
 
1.7%
Other values (67) 3488
47.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6908
93.0%
Connector Punctuation 291
 
3.9%
Other Punctuation 84
 
1.1%
Close Punctuation 71
 
1.0%
Open Punctuation 71
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 1645
23.8%
금 546
 
7.9%
동 373
 
5.4%
여 250
 
3.6%
산 231
 
3.3%
수 185
 
2.7%
녹 150
 
2.2%
오 143
 
2.1%
손 123
 
1.8%
죽 123
 
1.8%
Other values (63) 3139
45.4%
Connector Punctuation
ValueCountFrequency (%)
_ 291
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 84
100.0%
Close Punctuation
ValueCountFrequency (%)
) 71
100.0%
Open Punctuation
ValueCountFrequency (%)
( 71
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6908
93.0%
Common 517
 
7.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 1645
23.8%
금 546
 
7.9%
동 373
 
5.4%
여 250
 
3.6%
산 231
 
3.3%
수 185
 
2.7%
녹 150
 
2.2%
오 143
 
2.1%
손 123
 
1.8%
죽 123
 
1.8%
Other values (63) 3139
45.4%
Common
ValueCountFrequency (%)
_ 291
56.3%
/ 84
 
16.2%
) 71
 
13.7%
( 71
 
13.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6908
93.0%
ASCII 517
 
7.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 1645
23.8%
금 546
 
7.9%
동 373
 
5.4%
여 250
 
3.6%
산 231
 
3.3%
수 185
 
2.7%
녹 150
 
2.2%
오 143
 
2.1%
손 123
 
1.8%
죽 123
 
1.8%
Other values (63) 3139
45.4%
ASCII
ValueCountFrequency (%)
_ 291
56.3%
/ 84
 
16.2%
) 71
 
13.7%
( 71
 
13.7%

도착지
Text

Distinct54
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
2023-12-13T08:07:37.638647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.0571546
Min length2

Characters and Unicode

Total characters7435
Distinct characters77
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

Unique0 ?
Unique (%)0.0%

Sample

1st row녹동
2nd row손죽도
3rd row초도
4th row대동
5th row녹동
ValueCountFrequency (%)
녹동 152
 
6.2%
여수 151
 
6.2%
손죽도 123
 
5.1%
금당도 122
 
5.0%
초도 108
 
4.4%
거문도 101
 
4.2%
나로도 95
 
3.9%
연홍도 90
 
3.7%
서도/동도 84
 
3.5%
금산_금진 74
 
3.0%
Other values (44) 1332
54.8%
2023-12-13T08:07:37.986460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 1648
22.2%
금 522
 
7.0%
동 376
 
5.1%
_ 278
 
3.7%
여 253
 
3.4%
산 219
 
2.9%
수 188
 
2.5%
녹 152
 
2.0%
오 143
 
1.9%
손 123
 
1.7%
Other values (67) 3533
47.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6905
92.9%
Connector Punctuation 278
 
3.7%
Open Punctuation 84
 
1.1%
Close Punctuation 84
 
1.1%
Other Punctuation 84
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 1648
23.9%
금 522
 
7.6%
동 376
 
5.4%
여 253
 
3.7%
산 219
 
3.2%
수 188
 
2.7%
녹 152
 
2.2%
오 143
 
2.1%
손 123
 
1.8%
죽 123
 
1.8%
Other values (63) 3158
45.7%
Connector Punctuation
ValueCountFrequency (%)
_ 278
100.0%
Open Punctuation
ValueCountFrequency (%)
( 84
100.0%
Close Punctuation
ValueCountFrequency (%)
) 84
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 84
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6905
92.9%
Common 530
 
7.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 1648
23.9%
금 522
 
7.6%
동 376
 
5.4%
여 253
 
3.7%
산 219
 
3.2%
수 188
 
2.7%
녹 152
 
2.2%
오 143
 
2.1%
손 123
 
1.8%
죽 123
 
1.8%
Other values (63) 3158
45.7%
Common
ValueCountFrequency (%)
_ 278
52.5%
( 84
 
15.8%
) 84
 
15.8%
/ 84
 
15.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6905
92.9%
ASCII 530
 
7.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 1648
23.9%
금 522
 
7.6%
동 376
 
5.4%
여 253
 
3.7%
산 219
 
3.2%
수 188
 
2.7%
녹 152
 
2.2%
오 143
 
2.1%
손 123
 
1.8%
죽 123
 
1.8%
Other values (63) 3158
45.7%
ASCII
ValueCountFrequency (%)
_ 278
52.5%
( 84
 
15.8%
) 84
 
15.8%
/ 84
 
15.8%

선박
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
남 해 퀸
224 
은해페리호
200 
평화훼리5호
199 
평화페리9호
197 
태평양1호
155 
Other values (26)
1457 

Length

Max length7
Median length6
Mean length5.5925164
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row평화훼리5호
2nd row평화훼리5호
3rd row평화훼리5호
4th row평화훼리5호
5th row평화훼리5호

Common Values

ValueCountFrequency (%)
남 해 퀸 224
 
9.2%
은해페리호 200
 
8.2%
평화훼리5호 199
 
8.2%
평화페리9호 197
 
8.1%
태평양1호 155
 
6.4%
금오페리5호 149
 
6.1%
제5은성페리호 149
 
6.1%
평화훼리호 132
 
5.4%
니나 116
 
4.8%
파라다이스 112
 
4.6%
Other values (21) 799
32.9%

Length

2023-12-13T08:07:38.114273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
남 224
 
7.6%
퀸 224
 
7.6%
해 224
 
7.6%
은해페리호 200
 
6.8%
평화훼리5호 199
 
6.7%
평화페리9호 197
 
6.7%
태평양1호 155
 
5.3%
금오페리5호 149
 
5.1%
제5은성페리호 149
 
5.1%
평화훼리호 132
 
4.5%
Other values (24) 1097
37.2%

요금종류
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
일반요금
2180 
할증요금
252 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반요금 2180
89.6%
할증요금 252
 
10.4%

Length

2023-12-13T08:07:38.251884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:07:38.351744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반요금 2180
89.6%
할증요금 252
 
10.4%

객실등급
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
등급없음
1136 
일반실
605 
1층
170 
2층
170 
3등실
149 
Other values (4)
202 

Length

Max length6
Median length4
Mean length3.4679276
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
등급없음 1136
46.7%
일반실 605
24.9%
1층 170
 
7.0%
2층 170
 
7.0%
3등실 149
 
6.1%
일반석 84
 
3.5%
2층VIP1 56
 
2.3%
2층VIP2 56
 
2.3%
일반객실 6
 
0.2%

Length

2023-12-13T08:07:38.481398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:07:38.612420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
등급없음 1136
46.7%
일반실 605
24.9%
1층 170
 
7.0%
2층 170
 
7.0%
3등실 149
 
6.1%
일반석 84
 
3.5%
2층vip1 56
 
2.3%
2층vip2 56
 
2.3%
일반객실 6
 
0.2%

일반대인
Real number (ℝ)

HIGH CORRELATION 

Distinct157
Distinct (%)6.5%
Missing7
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean9542.3299
Minimum600
Maximum40100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.5 KiB
2023-12-13T08:07:38.736888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum600
5-th percentile1600
Q13900
median6000
Q311500
95-th percentile31800
Maximum40100
Range39500
Interquartile range (IQR)7600

Descriptive statistics

Standard deviation8929.5842
Coefficient of variation (CV)0.93578657
Kurtosis2.3240624
Mean9542.3299
Median Absolute Deviation (MAD)3500
Skewness1.7216167
Sum23140150
Variance79737474
MonotonicityNot monotonic
2023-12-13T08:07:38.882215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3900 95
 
3.9%
4000 83
 
3.4%
4500 77
 
3.2%
2500 60
 
2.5%
3500 55
 
2.3%
2200 54
 
2.2%
10900 52
 
2.1%
5000 50
 
2.1%
8500 45
 
1.9%
5800 44
 
1.8%
Other values (147) 1810
74.4%
ValueCountFrequency (%)
600 6
 
0.2%
800 18
0.7%
900 6
 
0.2%
1000 8
 
0.3%
1300 6
 
0.2%
1400 24
1.0%
1500 40
1.6%
1600 42
1.7%
1800 20
0.8%
2000 31
1.3%
ValueCountFrequency (%)
40100 18
0.7%
39600 9
0.4%
38600 9
0.4%
36600 10
0.4%
36100 20
0.8%
35100 10
0.4%
33400 9
0.4%
33000 2
 
0.1%
32800 9
0.4%
31900 9
0.4%

일반중고
Real number (ℝ)

HIGH CORRELATION 

Distinct163
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8617.0169
Minimum500
Maximum36300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.5 KiB
2023-12-13T08:07:39.035531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile1400
Q13500
median5400
Q310500
95-th percentile28700
Maximum36300
Range35800
Interquartile range (IQR)7000

Descriptive statistics

Standard deviation8074.4673
Coefficient of variation (CV)0.93703743
Kurtosis2.3231896
Mean8617.0169
Median Absolute Deviation (MAD)3100
Skewness1.7206146
Sum20956585
Variance65197023
MonotonicityNot monotonic
2023-12-13T08:07:39.186237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3500 109
 
4.5%
3600 83
 
3.4%
1400 62
 
2.5%
4100 58
 
2.4%
2000 58
 
2.4%
9800 56
 
2.3%
4500 50
 
2.1%
3200 45
 
1.9%
2300 44
 
1.8%
1900 42
 
1.7%
Other values (153) 1825
75.0%
ValueCountFrequency (%)
500 6
 
0.2%
700 18
 
0.7%
800 6
 
0.2%
860 1
 
< 0.1%
900 2
 
0.1%
1000 18
 
0.7%
1200 6
 
0.2%
1300 24
 
1.0%
1350 8
 
0.3%
1400 62
2.5%
ValueCountFrequency (%)
36300 18
0.7%
35800 9
0.4%
34800 9
0.4%
33150 10
0.4%
32650 20
0.8%
31650 10
0.4%
30300 9
0.4%
29900 2
 
0.1%
29700 9
0.4%
28800 9
0.4%

일반경로
Real number (ℝ)

HIGH CORRELATION 

Distinct139
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7721.5049
Minimum500
Maximum32500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.5 KiB
2023-12-13T08:07:39.322589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile1300
Q13100
median4800
Q39400
95-th percentile25600
Maximum32500
Range32000
Interquartile range (IQR)6300

Descriptive statistics

Standard deviation7218.6067
Coefficient of variation (CV)0.93487044
Kurtosis2.3029414
Mean7721.5049
Median Absolute Deviation (MAD)2800
Skewness1.7148385
Sum18778700
Variance52108283
MonotonicityNot monotonic
2023-12-13T08:07:39.440461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3100 109
 
4.5%
3600 104
 
4.3%
2000 76
 
3.1%
1800 70
 
2.9%
4600 66
 
2.7%
3200 63
 
2.6%
6200 57
 
2.3%
2800 55
 
2.3%
8700 52
 
2.1%
4400 52
 
2.1%
Other values (129) 1728
71.1%
ValueCountFrequency (%)
500 6
 
0.2%
600 18
0.7%
700 6
 
0.2%
800 2
 
0.1%
1000 24
1.0%
1100 21
0.9%
1200 16
 
0.7%
1300 42
1.7%
1400 32
1.3%
1600 23
0.9%
ValueCountFrequency (%)
32500 18
0.7%
32000 9
 
0.4%
31000 9
 
0.4%
29700 10
 
0.4%
29200 20
0.8%
28200 10
 
0.4%
27100 9
 
0.4%
26700 2
 
0.1%
26600 9
 
0.4%
25600 36
1.5%

일반소아
Real number (ℝ)

HIGH CORRELATION 

Distinct126
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4766.0773
Minimum0
Maximum24400
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size21.5 KiB
2023-12-13T08:07:39.564801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile800
Q12000
median3000
Q35800
95-th percentile15900
Maximum24400
Range24400
Interquartile range (IQR)3800

Descriptive statistics

Standard deviation4472.8769
Coefficient of variation (CV)0.93848181
Kurtosis2.5312701
Mean4766.0773
Median Absolute Deviation (MAD)1700
Skewness1.7602171
Sum11591100
Variance20006627
MonotonicityNot monotonic
2023-12-13T08:07:39.677779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000 182
 
7.5%
1100 98
 
4.0%
800 74
 
3.0%
2900 69
 
2.8%
2400 67
 
2.8%
5500 61
 
2.5%
2300 55
 
2.3%
3900 55
 
2.3%
2500 50
 
2.1%
1800 49
 
2.0%
Other values (116) 1672
68.8%
ValueCountFrequency (%)
0 3
 
0.1%
300 6
 
0.2%
400 18
 
0.7%
500 14
 
0.6%
700 30
 
1.2%
750 7
 
0.3%
800 74
3.0%
900 20
 
0.8%
1000 45
1.9%
1100 98
4.0%
ValueCountFrequency (%)
24400 2
 
0.1%
20050 18
0.7%
19800 9
0.4%
19300 9
0.4%
18300 10
0.4%
18050 20
0.8%
17550 10
0.4%
16550 9
0.4%
16400 9
0.4%
15950 9
0.4%

도서대인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct112
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7897.8207
Minimum0
Maximum31600
Zeros72
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size21.5 KiB
2023-12-13T08:07:39.839313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1300
Q13200
median5000
Q39800
95-th percentile26100
Maximum31600
Range31600
Interquartile range (IQR)6600

Descriptive statistics

Standard deviation7552.6228
Coefficient of variation (CV)0.95629201
Kurtosis2.1133905
Mean7897.8207
Median Absolute Deviation (MAD)3000
Skewness1.6752459
Sum19207500
Variance57042112
MonotonicityNot monotonic
2023-12-13T08:07:39.993408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3500 111
 
4.6%
3600 87
 
3.6%
4000 74
 
3.0%
1300 73
 
3.0%
0 72
 
3.0%
3200 72
 
3.0%
5200 70
 
2.9%
8600 59
 
2.4%
12100 56
 
2.3%
31600 56
 
2.3%
Other values (102) 1702
70.0%
ValueCountFrequency (%)
0 72
3.0%
500 6
 
0.2%
700 20
 
0.8%
800 7
 
0.3%
1000 6
 
0.2%
1200 9
 
0.4%
1300 73
3.0%
1400 14
 
0.6%
1500 10
 
0.4%
1600 20
 
0.8%
ValueCountFrequency (%)
31600 56
2.3%
31100 20
 
0.8%
28400 2
 
0.1%
26250 36
1.5%
26100 38
1.6%
25750 20
 
0.8%
25250 20
 
0.8%
22600 38
1.6%
22500 16
 
0.7%
20300 10
 
0.4%

도서중고
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct109
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7140.44
Minimum0
Maximum28500
Zeros69
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size21.5 KiB
2023-12-13T08:07:40.119683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1200
Q12900
median4500
Q38800
95-th percentile23550
Maximum28500
Range28500
Interquartile range (IQR)5900

Descriptive statistics

Standard deviation6809.2097
Coefficient of variation (CV)0.95361207
Kurtosis2.1050713
Mean7140.44
Median Absolute Deviation (MAD)2700
Skewness1.6731878
Sum17365550
Variance46365337
MonotonicityNot monotonic
2023-12-13T08:07:40.252794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3200 176
 
7.2%
4100 95
 
3.9%
1200 74
 
3.0%
3600 72
 
3.0%
4700 70
 
2.9%
2900 69
 
2.8%
0 69
 
2.8%
28500 56
 
2.3%
11000 56
 
2.3%
7850 56
 
2.3%
Other values (99) 1639
67.4%
ValueCountFrequency (%)
0 69
2.8%
500 6
 
0.2%
600 20
 
0.8%
700 6
 
0.2%
1000 6
 
0.2%
1100 8
 
0.3%
1200 74
3.0%
1300 14
 
0.6%
1400 30
1.2%
1500 14
 
0.6%
ValueCountFrequency (%)
28500 56
2.3%
28000 20
 
0.8%
25500 2
 
0.1%
23750 36
1.5%
23550 38
1.6%
23250 20
 
0.8%
22750 20
 
0.8%
20400 38
1.6%
20300 16
 
0.7%
18300 2
 
0.1%

도서경로
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct94
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6400.6168
Minimum0
Maximum33000
Zeros69
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size21.5 KiB
2023-12-13T08:07:40.390714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1000
Q12600
median4000
Q37800
95-th percentile21000
Maximum33000
Range33000
Interquartile range (IQR)5200

Descriptive statistics

Standard deviation6115.5593
Coefficient of variation (CV)0.95546406
Kurtosis2.195032
Mean6400.6168
Median Absolute Deviation (MAD)2400
Skewness1.6832773
Sum15566300
Variance37400065
MonotonicityNot monotonic
2023-12-13T08:07:40.549172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2800 119
 
4.9%
1000 88
 
3.6%
2600 83
 
3.4%
3200 78
 
3.2%
4200 77
 
3.2%
1400 71
 
2.9%
0 69
 
2.8%
9900 68
 
2.8%
4000 59
 
2.4%
2900 58
 
2.4%
Other values (84) 1662
68.3%
ValueCountFrequency (%)
0 69
2.8%
400 6
 
0.2%
600 26
 
1.1%
1000 88
3.6%
1100 2
 
0.1%
1300 42
1.7%
1400 71
2.9%
1500 44
1.8%
1600 38
1.6%
1700 10
 
0.4%
ValueCountFrequency (%)
33000 2
 
0.1%
25400 56
2.3%
24900 20
 
0.8%
22700 2
 
0.1%
21200 36
1.5%
21000 38
1.6%
20700 20
 
0.8%
20200 20
 
0.8%
18200 38
1.6%
18000 16
 
0.7%

도서소아
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct91
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3981.1472
Minimum0
Maximum21300
Zeros69
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size21.5 KiB
2023-12-13T08:07:41.002535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile700
Q11600
median2500
Q34900
95-th percentile13050
Maximum21300
Range21300
Interquartile range (IQR)3300

Descriptive statistics

Standard deviation3799.2621
Coefficient of variation (CV)0.95431339
Kurtosis2.2853025
Mean3981.1472
Median Absolute Deviation (MAD)1500
Skewness1.7032798
Sum9682150
Variance14434392
MonotonicityNot monotonic
2023-12-13T08:07:41.133195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800 200
 
8.2%
700 96
 
3.9%
2600 95
 
3.9%
1000 86
 
3.5%
1600 84
 
3.5%
2000 81
 
3.3%
2500 74
 
3.0%
3500 72
 
3.0%
900 71
 
2.9%
0 69
 
2.8%
Other values (81) 1504
61.8%
ValueCountFrequency (%)
0 69
2.8%
300 6
 
0.2%
350 2
 
0.1%
400 25
 
1.0%
500 6
 
0.2%
600 8
 
0.3%
650 1
 
< 0.1%
700 96
3.9%
800 20
 
0.8%
900 71
2.9%
ValueCountFrequency (%)
21300 2
 
0.1%
15850 10
 
0.4%
15800 46
1.9%
15550 20
0.8%
13150 36
1.5%
13050 38
1.6%
12900 20
0.8%
12650 20
0.8%
12500 2
 
0.1%
11350 10
 
0.4%

시행일자
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
2019-09-16 00:00
542 
2020-09-29 00:00
405 
2019-09-17 00:00
130 
2019-07-08 00:00
114 
2021-04-14 00:00
 
112
Other values (25)
1129 

Length

Max length16
Median length16
Mean length16
Min length16

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-09-16 00:00
2nd row2019-09-16 00:00
3rd row2019-09-16 00:00
4th row2019-09-16 00:00
5th row2019-09-16 00:00

Common Values

ValueCountFrequency (%)
2019-09-16 00:00 542
22.3%
2020-09-29 00:00 405
16.7%
2019-09-17 00:00 130
 
5.3%
2019-07-08 00:00 114
 
4.7%
2021-04-14 00:00 112
 
4.6%
2020-11-09 00:00 100
 
4.1%
2020-01-01 00:00 88
 
3.6%
2020-05-22 00:00 87
 
3.6%
2020-04-20 00:00 82
 
3.4%
2020-03-12 00:00 81
 
3.3%
Other values (20) 691
28.4%

Length

2023-12-13T08:07:41.248873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00 2432
50.0%
2019-09-16 542
 
11.1%
2020-09-29 405
 
8.3%
2019-09-17 130
 
2.7%
2019-07-08 114
 
2.3%
2021-04-14 112
 
2.3%
2020-11-09 100
 
2.1%
2020-01-01 88
 
1.8%
2020-05-22 87
 
1.8%
2020-04-20 82
 
1.7%
Other values (21) 772
 
15.9%

입력일시
Categorical

HIGH CORRELATION 

Distinct40
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
2019-09-16 09:47
542 
2020-09-28 16:37
181 
2020-09-29 19:37
168 
2019-09-17 09:20
 
130
2020-11-09 11:59
 
100
Other values (35)
1311 

Length

Max length16
Median length16
Mean length16
Min length16

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st row2019-09-16 09:47
2nd row2019-09-16 09:47
3rd row2019-09-16 09:47
4th row2019-09-16 09:47
5th row2019-09-16 09:47

Common Values

ValueCountFrequency (%)
2019-09-16 09:47 542
22.3%
2020-09-28 16:37 181
 
7.4%
2020-09-29 19:37 168
 
6.9%
2019-09-17 09:20 130
 
5.3%
2020-11-09 11:59 100
 
4.1%
2019-07-08 13:55 90
 
3.7%
2020-01-01 12:58 88
 
3.6%
2020-05-22 10:10 87
 
3.6%
2020-04-20 09:25 82
 
3.4%
2020-03-12 13:43 81
 
3.3%
Other values (30) 883
36.3%

Length

2023-12-13T08:07:41.372067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2019-09-16 542
 
11.1%
09:47 542
 
11.1%
2020-09-29 224
 
4.6%
2020-09-28 211
 
4.3%
16:37 181
 
3.7%
19:37 168
 
3.5%
13:43 136
 
2.8%
2019-09-17 130
 
2.7%
09:20 130
 
2.7%
2019-07-08 114
 
2.3%
Other values (59) 2486
51.1%

Interactions

2023-12-13T08:07:35.413868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:29.689426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:30.377110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:31.097188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:31.813285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:32.560605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:33.401994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:34.206600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:35.505397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:29.770525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:30.506949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:31.196054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:31.895690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:32.664154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:33.530842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:34.326785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:35.606617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:29.850796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:30.588381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:31.279313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:31.977339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:32.781020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:33.652264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:34.431794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:35.722443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:29.927996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:30.667082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:31.360583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:32.068225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:32.877728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:33.741580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:34.826602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:35.832880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:30.013631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:30.750266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:31.438783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:32.151860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:32.973309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:33.830594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:34.931300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:35.930815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:30.100133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:30.848991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:31.557486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:32.247416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:33.097149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:33.938508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:35.051869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:36.040358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:30.188226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:30.934932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:31.666492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:32.343931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:33.185058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:34.037476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:35.179555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:36.138623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:30.279151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:31.022082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:31.742022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:32.443160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:33.300030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:34.124421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:07:35.296723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:07:41.467728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
항로출발지도착지선박요금종류객실등급일반대인일반중고일반경로일반소아도서대인도서중고도서경로도서소아시행일자입력일시
항로1.0000.9690.9690.9780.7940.7770.5990.6010.5950.6030.6030.6030.6140.6260.9880.993
출발지0.9691.0000.9310.9090.6790.7780.7420.7470.7460.7280.7470.7460.7690.7420.9110.915
도착지0.9690.9311.0000.9080.6790.7760.7310.7260.7210.7210.7510.7510.7760.7440.9060.904
선박0.9780.9090.9081.0000.7490.9660.5970.5990.5940.6680.5630.5620.5750.6790.9910.993
요금종류0.7940.6790.6790.7491.0000.6380.6760.6720.6810.7180.5690.5680.4440.4670.8090.880
객실등급0.7770.7780.7760.9660.6381.0000.5120.5130.5100.5910.4620.4610.6120.7420.9300.974
일반대인0.5990.7420.7310.5970.6760.5121.0001.0001.0000.9810.9730.9730.9340.9200.6240.662
일반중고0.6010.7470.7260.5990.6720.5131.0001.0001.0000.9800.9740.9740.9290.9160.6250.662
일반경로0.5950.7460.7210.5940.6810.5101.0001.0001.0000.9830.9690.9690.9270.9150.6190.658
일반소아0.6030.7280.7210.6680.7180.5910.9810.9800.9831.0000.9690.9690.9390.9570.7060.727
도서대인0.6030.7470.7510.5630.5690.4620.9730.9740.9690.9691.0001.0000.9480.9610.6060.640
도서중고0.6030.7460.7510.5620.5680.4610.9730.9740.9690.9691.0001.0000.9490.9600.6050.640
도서경로0.6140.7690.7760.5750.4440.6120.9340.9290.9270.9390.9480.9491.0000.9900.5750.618
도서소아0.6260.7420.7440.6790.4670.7420.9200.9160.9150.9570.9610.9600.9901.0000.6780.706
시행일자0.9880.9110.9060.9910.8090.9300.6240.6250.6190.7060.6060.6050.5750.6781.0001.000
입력일시0.9930.9150.9040.9930.8800.9740.6620.6620.6580.7270.6400.6400.6180.7061.0001.000
2023-12-13T08:07:41.645083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
요금종류시행일자선박항로객실등급입력일시
요금종류1.0000.6660.6530.6360.6430.742
시행일자0.6661.0000.8240.8720.6860.998
선박0.6530.8241.0000.8380.8090.838
항로0.6360.8720.8381.0000.4710.919
객실등급0.6430.6860.8090.4711.0000.830
입력일시0.7420.9980.8380.9190.8301.000
2023-12-13T08:07:41.765353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일반대인일반중고일반경로일반소아도서대인도서중고도서경로도서소아항로선박요금종류객실등급시행일자입력일시
일반대인1.0000.9990.9950.9970.9170.9190.9160.9180.3020.2540.5260.2620.2470.272
일반중고0.9991.0000.9940.9960.9160.9180.9150.9170.3030.2560.5220.2630.2480.272
일반경로0.9950.9941.0000.9920.9130.9150.9130.9140.2990.2520.5300.2610.2440.269
일반소아0.9970.9960.9921.0000.9180.9200.9170.9200.3050.3030.5610.3180.3030.321
도서대인0.9170.9160.9130.9181.0000.9970.9950.9970.3050.2330.4380.2310.2360.257
도서중고0.9190.9180.9150.9200.9971.0000.9970.9990.3040.2330.4380.2300.2360.257
도서경로0.9160.9150.9130.9170.9950.9971.0000.9960.3150.2530.4440.2360.2530.275
도서소아0.9180.9170.9140.9200.9970.9990.9961.0000.3240.3280.4680.3230.3260.343
항로0.3020.3030.2990.3050.3050.3040.3150.3241.0000.8380.6360.4710.8720.919
선박0.2540.2560.2520.3030.2330.2330.2530.3280.8381.0000.6530.8090.8240.838
요금종류0.5260.5220.5300.5610.4380.4380.4440.4680.6360.6531.0000.6430.6660.742
객실등급0.2620.2630.2610.3180.2310.2300.2360.3230.4710.8090.6431.0000.6860.830
시행일자0.2470.2480.2440.3030.2360.2360.2530.3260.8720.8240.6660.6861.0000.998
입력일시0.2720.2720.2690.3210.2570.2570.2750.3430.9190.8380.7420.8300.9981.000

Missing values

2023-12-13T08:07:36.293270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:07:36.531088image/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

항로출발지도착지선박요금종류객실등급일반대인일반중고일반경로일반소아도서대인도서중고도서경로도서소아시행일자입력일시
0녹동-거문도거문도녹동평화훼리5호일반요금등급없음25000225002000012500225002030018000113002019-09-16 00:002019-09-16 09:47
1녹동-거문도거문도손죽도평화훼리5호일반요금등급없음1090098008700550098008800780049002019-09-16 00:002019-09-16 09:47
2녹동-거문도거문도초도평화훼리5호일반요금등급없음1090098008700550098008800780049002019-09-16 00:002019-09-16 09:47
3녹동-거문도거문도대동평화훼리5호일반요금등급없음1090098008700550098008800780049002019-09-16 00:002019-09-16 09:47
4녹동-거문도금당도녹동평화훼리5호일반요금등급없음580053004700290005800520029002019-09-16 00:002019-09-16 09:47
5녹동-거문도녹동거문도평화훼리5호일반요금등급없음26500240002150013100225002030018000113002019-09-16 00:002019-09-16 09:47
6녹동-거문도녹동금당도평화훼리5호일반요금등급없음635058505250310058005200460029002019-09-16 00:002019-09-16 09:47
7녹동-거문도녹동손죽도평화훼리5호일반요금등급없음24000217501950011850203001800015800102002019-09-16 00:002019-09-16 09:47
8녹동-거문도녹동초도평화훼리5호일반요금등급없음202001840016500985016900152001350083002019-09-16 00:002019-09-16 09:47
9녹동-거문도녹동대동평화훼리5호일반요금등급없음202001840016500980016900152001350083002019-09-16 00:002019-09-16 09:47
항로출발지도착지선박요금종류객실등급일반대인일반중고일반경로일반소아도서대인도서중고도서경로도서소아시행일자입력일시
2422여수(사설)-둔병하화도상화도태평양3호일반요금일반실20001800160010001800160014009002020-03-12 00:002020-03-12 13:43
2423여수(사설)-둔병하화도제도태평양3호일반요금일반실350031502800175000002020-03-12 00:002020-03-12 13:43
2424여수(사설)-둔병하화도여수(사설)태평양3호일반요금일반실720065005800360065005900520033002020-03-12 00:002020-03-12 13:43
2425여수(사설)-둔병여수(사설)여석태평양3호일반요금일반실<NA>61005400340061005500490031002020-03-12 00:002020-03-12 13:43
2426여수(사설)-둔병여수(사설)모전태평양3호일반요금일반실<NA>63005600350063005700510032002020-03-12 00:002020-03-12 13:43
2427여수(사설)-둔병여수(사설)낭도태평양3호일반요금일반실<NA>11700106006400101009100810051002020-03-12 00:002020-03-12 13:43
2428여수(사설)-둔병여수(사설)백야도태평양3호일반요금일반실<NA>41003600230040004500360023002020-03-12 00:002020-03-12 13:43
2429여수(사설)-둔병여수(사설)사도태평양3호일반요금일반실<NA>91508300500079007200640040002020-03-12 00:002020-03-12 13:43
2430여수(사설)-둔병여수(사설)상화도태평양3호일반요금일반실<NA>65005800330065005900520033002020-03-12 00:002020-03-12 13:43
2431여수(사설)-둔병여수(사설)하화도태평양3호일반요금일반실<NA>65005800360065005900520033002020-03-12 00:002020-03-12 13:43

Duplicate rows

Most frequently occurring

항로출발지도착지선박요금종류객실등급일반대인일반중고일반경로일반소아도서대인도서중고도서경로도서소아시행일자입력일시# duplicates
0돌산(신기)-여천금오_여천돌산도한림페리11호일반요금등급없음560050004500280050004500400025002020-02-17 00:002020-02-17 09:532
1돌산(신기)-여천금오_여천돌산도한림페리9호일반요금등급없음560050004500280050004500400025002019-09-17 00:002019-09-17 09:202
2돌산(신기)-여천금오_여천화태한림페리11호일반요금등급없음330030002600170030002700240015002020-02-17 00:002020-02-17 09:532
3돌산(신기)-여천금오_여천화태한림페리9호일반요금등급없음330030002600170030002700240015002019-09-17 00:002019-09-17 09:202
4돌산(신기)-여천돌산도금오_여천한림페리11호일반요금등급없음560050004500280050004500400025002020-02-17 00:002020-02-17 09:532
5돌산(신기)-여천돌산도금오_여천한림페리9호일반요금등급없음560050004500280050004500400025002019-09-17 00:002019-09-17 09:202
6돌산(신기)-여천돌산도화태한림페리11호일반요금등급없음240022002000120022002000180011002020-02-17 00:002020-02-17 09:532
7돌산(신기)-여천돌산도화태한림페리9호일반요금등급없음240022002000120022002000180011002019-09-17 00:002019-09-17 09:202
8돌산(신기)-여천화태금오_여천한림페리11호일반요금등급없음330030002600170030002700240015002020-02-17 00:002020-02-17 09:532
9돌산(신기)-여천화태금오_여천한림페리9호일반요금등급없음330030002600170030002700240015002019-09-17 00:002019-09-17 09:202