Overview

Dataset statistics

Number of variables14
Number of observations131
Missing cells103
Missing cells (%)5.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.2 KiB
Average record size in memory119.0 B

Variable types

Categorical8
Text1
Numeric5

Dataset

Description울산항의 전체현황과 시설의 제원을 확인할 수 있습니다. 총괄, 항만별 시설(울산본항, 온산항, 미포항, 신항, 기타계류시설)별 길이, 수심, 접안능력, 하역능력, 주요 취급화물, 부두 운영사 정보입니다. * 기존 엑셀파일에서 csv파일로 변경
Author울산항만공사
URLhttps://www.data.go.kr/data/15021870/fileData.do

Alerts

부두형태 is highly overall correlated with 최저수심(M) and 5 other fieldsHigh correlation
구분 is highly overall correlated with 부두형태 and 4 other fieldsHigh correlation
분류 is highly overall correlated with 접안능력_척수 and 3 other fieldsHigh correlation
운영사 is highly overall correlated with 하역능력(천톤) and 5 other fieldsHigh correlation
접안능력_척수 is highly overall correlated with 길이(M) and 11 other fieldsHigh correlation
비고 is highly overall correlated with 최저수심(M) and 9 other fieldsHigh correlation
주요취급화물 is highly overall correlated with 하역능력(천톤) and 6 other fieldsHigh correlation
선석구분 is highly overall correlated with 접안능력_척수 and 1 other fieldsHigh correlation
길이(M) is highly overall correlated with 하역능력(천톤) and 1 other fieldsHigh correlation
최저수심(M) is highly overall correlated with 최고수심(M) and 4 other fieldsHigh correlation
최고수심(M) is highly overall correlated with 최저수심(M) and 4 other fieldsHigh correlation
접안능력_톤수(DWT) is highly overall correlated with 최저수심(M) and 3 other fieldsHigh correlation
하역능력(천톤) is highly overall correlated with 길이(M) and 3 other fieldsHigh correlation
접안능력_척수 is highly imbalanced (51.0%)Imbalance
비고 is highly imbalanced (76.6%)Imbalance
길이(M) has 5 (3.8%) missing valuesMissing
최저수심(M) has 14 (10.7%) missing valuesMissing
최고수심(M) has 14 (10.7%) missing valuesMissing
접안능력_톤수(DWT) has 14 (10.7%) missing valuesMissing
하역능력(천톤) has 56 (42.7%) missing valuesMissing

Reproduction

Analysis started2023-12-12 02:55:22.284470
Analysis finished2023-12-12 02:55:28.051290
Duration5.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

분류
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
본항
61 
온산항
33 
울산신항
22 
기타계류시설
14 
미포항
 
1

Length

Max length6
Median length4
Mean length3.0229008
Min length2

Unique

Unique1 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
본항 61
46.6%
온산항 33
25.2%
울산신항 22
 
16.8%
기타계류시설 14
 
10.7%
미포항 1
 
0.8%

Length

2023-12-12T11:55:28.148918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:55:28.314817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
본항 61
46.6%
온산항 33
25.2%
울산신항 22
 
16.8%
기타계류시설 14
 
10.7%
미포항 1
 
0.8%

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
국유
77 
민유
54 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
국유 77
58.8%
민유 54
41.2%

Length

2023-12-12T11:55:28.482964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:55:28.620046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국유 77
58.8%
민유 54
41.2%

부두형태
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
중력식
61 
돌핀
31 
잔교식
14 
<NA>
14 
원유부이
 
5
Other values (2)
 
6

Length

Max length7
Median length3
Mean length2.9694656
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row잔교식(돌핀)
2nd row잔교식
3rd row중력식
4th row잔교식
5th row잔교식

Common Values

ValueCountFrequency (%)
중력식 61
46.6%
돌핀 31
23.7%
잔교식 14
 
10.7%
<NA> 14
 
10.7%
원유부이 5
 
3.8%
방파제 4
 
3.1%
잔교식(돌핀) 2
 
1.5%

Length

2023-12-12T11:55:28.762011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:55:28.883682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중력식 61
46.6%
돌핀 31
23.7%
잔교식 14
 
10.7%
na 14
 
10.7%
원유부이 5
 
3.8%
방파제 4
 
3.1%
잔교식(돌핀 2
 
1.5%

부두구분
Text

Distinct78
Distinct (%)59.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-12T11:55:29.212127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length5.7709924
Min length3

Characters and Unicode

Total characters756
Distinct characters96
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)35.9%

Sample

1st row석탄부두
2nd row1부두
3rd row2부두
4th row2부두
5th row2부두
ValueCountFrequency (%)
일반부두 7
 
4.7%
소형선부두 7
 
4.7%
sk5부두 5
 
3.3%
sk2부두 5
 
3.3%
신항컨부두 4
 
2.7%
6부두 4
 
2.7%
sk4부두 3
 
2.0%
정일스톨트헤븐 3
 
2.0%
3부두 3
 
2.0%
s-oil2부두 3
 
2.0%
Other values (72) 106
70.7%
2023-12-12T11:55:30.009318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
부 131
17.3%
두 125
 
16.5%
S 38
 
5.0%
K 28
 
3.7%
항 22
 
2.9%
신 21
 
2.8%
일 20
 
2.6%
19
 
2.5%
2 18
 
2.4%
O 16
 
2.1%
Other values (86) 318
42.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 531
70.2%
Uppercase Letter 100
 
13.2%
Decimal Number 65
 
8.6%
Lowercase Letter 24
 
3.2%
Space Separator 19
 
2.5%
Dash Punctuation 12
 
1.6%
Other Punctuation 5
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 131
24.7%
두 125
23.5%
항 22
 
4.1%
신 21
 
4.0%
일 20
 
3.8%
선 11
 
2.1%
포 10
 
1.9%
반 10
 
1.9%
산 9
 
1.7%
소 8
 
1.5%
Other values (65) 164
30.9%
Decimal Number
ValueCountFrequency (%)
2 18
27.7%
1 11
16.9%
4 10
15.4%
3 8
12.3%
5 7
 
10.8%
6 5
 
7.7%
8 3
 
4.6%
7 2
 
3.1%
9 1
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
S 38
38.0%
K 28
28.0%
O 16
16.0%
T 13
 
13.0%
U 4
 
4.0%
L 1
 
1.0%
Lowercase Letter
ValueCountFrequency (%)
i 12
50.0%
l 12
50.0%
Other Punctuation
ValueCountFrequency (%)
/ 4
80.0%
& 1
 
20.0%
Space Separator
ValueCountFrequency (%)
19
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 531
70.2%
Latin 124
 
16.4%
Common 101
 
13.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 131
24.7%
두 125
23.5%
항 22
 
4.1%
신 21
 
4.0%
일 20
 
3.8%
선 11
 
2.1%
포 10
 
1.9%
반 10
 
1.9%
산 9
 
1.7%
소 8
 
1.5%
Other values (65) 164
30.9%
Common
ValueCountFrequency (%)
19
18.8%
2 18
17.8%
- 12
11.9%
1 11
10.9%
4 10
9.9%
3 8
7.9%
5 7
 
6.9%
6 5
 
5.0%
/ 4
 
4.0%
8 3
 
3.0%
Other values (3) 4
 
4.0%
Latin
ValueCountFrequency (%)
S 38
30.6%
K 28
22.6%
O 16
12.9%
T 13
 
10.5%
i 12
 
9.7%
l 12
 
9.7%
U 4
 
3.2%
L 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 531
70.2%
ASCII 225
29.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
부 131
24.7%
두 125
23.5%
항 22
 
4.1%
신 21
 
4.0%
일 20
 
3.8%
선 11
 
2.1%
포 10
 
1.9%
반 10
 
1.9%
산 9
 
1.7%
소 8
 
1.5%
Other values (65) 164
30.9%
ASCII
ValueCountFrequency (%)
S 38
16.9%
K 28
12.4%
19
8.4%
2 18
 
8.0%
O 16
 
7.1%
T 13
 
5.8%
- 12
 
5.3%
i 12
 
5.3%
l 12
 
5.3%
1 11
 
4.9%
Other values (11) 46
20.4%

길이(M)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct53
Distinct (%)42.1%
Missing5
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean410.18611
Minimum21
Maximum990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T11:55:30.209258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile96
Q1228.5
median340
Q3597.75
95-th percentile920
Maximum990
Range969
Interquartile range (IQR)369.25

Descriptive statistics

Standard deviation253.02295
Coefficient of variation (CV)0.61684915
Kurtosis-0.42286103
Mean410.18611
Median Absolute Deviation (MAD)125.5
Skewness0.80557731
Sum51683.45
Variance64020.616
MonotonicityNot monotonic
2023-12-12T11:55:30.412107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
679.0 7
 
5.3%
270.0 6
 
4.6%
210.0 6
 
4.6%
340.0 5
 
3.8%
280.0 5
 
3.8%
798.0 5
 
3.8%
990.0 4
 
3.1%
150.0 4
 
3.1%
920.0 4
 
3.1%
430.0 4
 
3.1%
Other values (43) 76
58.0%
(Missing) 5
 
3.8%
ValueCountFrequency (%)
21.0 1
 
0.8%
25.0 1
 
0.8%
50.0 1
 
0.8%
80.0 3
2.3%
88.0 1
 
0.8%
120.0 1
 
0.8%
130.0 1
 
0.8%
135.0 1
 
0.8%
140.0 2
1.5%
149.0 1
 
0.8%
ValueCountFrequency (%)
990.0 4
3.1%
920.0 4
3.1%
858.0 1
 
0.8%
830.0 3
2.3%
810.0 3
2.3%
798.0 5
3.8%
679.0 7
5.3%
650.0 2
 
1.5%
602.0 3
2.3%
585.0 3
2.3%

최저수심(M)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)13.7%
Missing14
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean11.525641
Minimum7
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T11:55:30.582847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7
Q18
median11.5
Q312
95-th percentile16.4
Maximum27
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.246117
Coefficient of variation (CV)0.36840614
Kurtosis5.3643368
Mean11.525641
Median Absolute Deviation (MAD)2.5
Skewness1.9529273
Sum1348.5
Variance18.029509
MonotonicityNot monotonic
2023-12-12T11:55:30.732360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
12.0 27
20.6%
7.0 19
14.5%
11.0 15
11.5%
14.0 11
8.4%
9.0 8
 
6.1%
7.5 6
 
4.6%
8.0 6
 
4.6%
27.0 5
 
3.8%
11.5 4
 
3.1%
16.0 4
 
3.1%
Other values (6) 12
9.2%
(Missing) 14
10.7%
ValueCountFrequency (%)
7.0 19
14.5%
7.5 6
 
4.6%
8.0 6
 
4.6%
9.0 8
 
6.1%
10.0 3
 
2.3%
11.0 15
11.5%
11.5 4
 
3.1%
12.0 27
20.6%
12.5 2
 
1.5%
13.0 1
 
0.8%
ValueCountFrequency (%)
27.0 5
 
3.8%
18.0 1
 
0.8%
16.0 4
 
3.1%
15.5 3
 
2.3%
15.0 2
 
1.5%
14.0 11
8.4%
13.0 1
 
0.8%
12.5 2
 
1.5%
12.0 27
20.6%
11.5 4
 
3.1%

최고수심(M)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)13.7%
Missing14
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean11.876068
Minimum7
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T11:55:30.887160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7
Q19
median12
Q314
95-th percentile16.4
Maximum27
Range20
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.1449523
Coefficient of variation (CV)0.34901721
Kurtosis5.4424874
Mean11.876068
Median Absolute Deviation (MAD)2
Skewness1.9148769
Sum1389.5
Variance17.180629
MonotonicityNot monotonic
2023-12-12T11:55:31.071948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
12.0 24
18.3%
11.0 20
15.3%
14.0 17
13.0%
7.0 14
10.7%
7.5 6
 
4.6%
8.0 6
 
4.6%
27.0 5
 
3.8%
9.0 5
 
3.8%
11.5 4
 
3.1%
16.0 4
 
3.1%
Other values (6) 12
9.2%
(Missing) 14
10.7%
ValueCountFrequency (%)
7.0 14
10.7%
7.5 6
 
4.6%
8.0 6
 
4.6%
9.0 5
 
3.8%
10.0 3
 
2.3%
11.0 20
15.3%
11.5 4
 
3.1%
12.0 24
18.3%
12.5 2
 
1.5%
13.0 1
 
0.8%
ValueCountFrequency (%)
27.0 5
 
3.8%
18.0 1
 
0.8%
16.0 4
 
3.1%
15.5 3
 
2.3%
15.0 2
 
1.5%
14.0 17
13.0%
13.0 1
 
0.8%
12.5 2
 
1.5%
12.0 24
18.3%
11.5 4
 
3.1%

선석구분
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
1선석
65 
2선석
31 
<NA>
14 
3선석
12 
4선석
 
5
Other values (3)
 
4

Length

Max length4
Median length3
Mean length3.1068702
Min length3

Unique

Unique2 ?
Unique (%)1.5%

Sample

1st row1선석
2nd row1선석
3rd row1선석
4th row2선석
5th row3선석

Common Values

ValueCountFrequency (%)
1선석 65
49.6%
2선석 31
23.7%
<NA> 14
 
10.7%
3선석 12
 
9.2%
4선석 5
 
3.8%
5선석 2
 
1.5%
6선석 1
 
0.8%
7선석 1
 
0.8%

Length

2023-12-12T11:55:31.268119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:55:31.453190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1선석 65
49.6%
2선석 31
23.7%
na 14
 
10.7%
3선석 12
 
9.2%
4선석 5
 
3.8%
5선석 2
 
1.5%
6선석 1
 
0.8%
7선석 1
 
0.8%

접안능력_톤수(DWT)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)17.9%
Missing14
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean37252.137
Minimum1000
Maximum350000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T11:55:31.638200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1800
Q15000
median20000
Q340000
95-th percentile134000
Maximum350000
Range349000
Interquartile range (IQR)35000

Descriptive statistics

Standard deviation66845.603
Coefficient of variation (CV)1.7944099
Kurtosis13.895165
Mean37252.137
Median Absolute Deviation (MAD)15000
Skewness3.7323942
Sum4358500
Variance4.4683346 × 109
MonotonicityNot monotonic
2023-12-12T11:55:31.797688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
20000 20
15.3%
30000 18
13.7%
5000 12
9.2%
50000 12
9.2%
3000 10
7.6%
40000 9
6.9%
10000 9
6.9%
1000 6
 
4.6%
4000 4
 
3.1%
325000 4
 
3.1%
Other values (11) 13
9.9%
(Missing) 14
10.7%
ValueCountFrequency (%)
1000 6
4.6%
2000 2
 
1.5%
3000 10
7.6%
4000 4
 
3.1%
5000 12
9.2%
6000 1
 
0.8%
8000 1
 
0.8%
8500 1
 
0.8%
10000 9
6.9%
15000 2
 
1.5%
ValueCountFrequency (%)
350000 1
 
0.8%
325000 4
 
3.1%
150000 1
 
0.8%
130000 1
 
0.8%
120000 1
 
0.8%
80000 1
 
0.8%
70000 1
 
0.8%
50000 12
9.2%
40000 9
6.9%
30000 18
13.7%

접안능력_척수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
1
117 
<NA>
14 

Length

Max length4
Median length1
Mean length1.3206107
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 117
89.3%
<NA> 14
 
10.7%

Length

2023-12-12T11:55:31.985553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:55:32.118868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 117
89.3%
na 14
 
10.7%

하역능력(천톤)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)45.3%
Missing56
Missing (%)42.7%
Infinite0
Infinite (%)0.0%
Mean2551.2
Minimum380
Maximum13083
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T11:55:32.254253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum380
5-th percentile670
Q11087.5
median1496
Q32242.5
95-th percentile10623.9
Maximum13083
Range12703
Interquartile range (IQR)1155

Descriptive statistics

Standard deviation3056.0091
Coefficient of variation (CV)1.1978712
Kurtosis6.408324
Mean2551.2
Median Absolute Deviation (MAD)571
Skewness2.7078229
Sum191340
Variance9339191.7
MonotonicityNot monotonic
2023-12-12T11:55:32.423468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
2160 7
 
5.3%
670 5
 
3.8%
13083 4
 
3.1%
3561 4
 
3.1%
1496 4
 
3.1%
1485 3
 
2.3%
1360 3
 
2.3%
2690 3
 
2.3%
9570 3
 
2.3%
2060 3
 
2.3%
Other values (24) 36
27.5%
(Missing) 56
42.7%
ValueCountFrequency (%)
380 1
 
0.8%
460 1
 
0.8%
530 1
 
0.8%
670 5
3.8%
761 1
 
0.8%
830 2
 
1.5%
925 2
 
1.5%
930 2
 
1.5%
940 1
 
0.8%
979 1
 
0.8%
ValueCountFrequency (%)
13083 4
3.1%
9570 3
2.3%
3561 4
3.1%
3060 1
 
0.8%
2690 3
2.3%
2363 2
 
1.5%
2325 2
 
1.5%
2160 7
5.3%
2060 3
2.3%
1859 2
 
1.5%

주요취급화물
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
유류
30 
액체화학
28 
잡화
20 
<NA>
14 
잡화, 액체화학
8 
Other values (15)
31 

Length

Max length10
Median length8
Mean length3.4885496
Min length2

Unique

Unique7 ?
Unique (%)5.3%

Sample

1st row석탄
2nd row잡화
3rd row잡화, 시멘트
4th row잡화, 시멘트
5th row잡화, 시멘트

Common Values

ValueCountFrequency (%)
유류 30
22.9%
액체화학 28
21.4%
잡화 20
15.3%
<NA> 14
10.7%
잡화, 액체화학 8
 
6.1%
컨테이너 5
 
3.8%
원유 4
 
3.1%
잡화, 시멘트 3
 
2.3%
자동차 3
 
2.3%
철재, 잡화 3
 
2.3%
Other values (10) 13
9.9%

Length

2023-12-12T11:55:32.600374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
잡화 36
24.0%
액체화학 36
24.0%
유류 31
20.7%
na 14
 
9.3%
컨테이너 5
 
3.3%
원유 4
 
2.7%
시멘트 4
 
2.7%
철재 4
 
2.7%
자동차 3
 
2.0%
목재 2
 
1.3%
Other values (9) 11
 
7.3%

운영사
Categorical

HIGH CORRELATION 

Distinct41
Distinct (%)31.3%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
SK에너지㈜
24 
공용
20 
S-OIL㈜
12 
㈜정일스톨트헤븐울산
7 
선박블록 하역
 
5
Other values (36)
63 

Length

Max length12
Median length10
Mean length6.1755725
Min length2

Unique

Unique23 ?
Unique (%)17.6%

Sample

1st rowCJ대한통운㈜
2nd row울산항만운영㈜
3rd row울산항만운영㈜
4th row울산항만운영㈜
5th row공용

Common Values

ValueCountFrequency (%)
SK에너지㈜ 24
18.3%
공용 20
15.3%
S-OIL㈜ 12
 
9.2%
㈜정일스톨트헤븐울산 7
 
5.3%
선박블록 하역 5
 
3.8%
㈜태영인더스트리 4
 
3.1%
UNCT㈜ 4
 
3.1%
오드펠터미널코리아㈜ 4
 
3.1%
울산항만운영㈜ 4
 
3.1%
현대자동차㈜ 3
 
2.3%
Other values (31) 44
33.6%

Length

2023-12-12T11:55:32.754555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sk에너지㈜ 24
16.8%
공용 20
 
14.0%
s-oil㈜ 12
 
8.4%
㈜정일스톨트헤븐울산 7
 
4.9%
하역 6
 
4.2%
선박블록 5
 
3.5%
㈜태영인더스트리 4
 
2.8%
unct㈜ 4
 
2.8%
오드펠터미널코리아㈜ 4
 
2.8%
울산항만운영㈜ 4
 
2.8%
Other values (37) 53
37.1%

비고
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
<NA>
126 
1기
 
5

Length

Max length4
Median length4
Mean length3.9236641
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 126
96.2%
1기 5
 
3.8%

Length

2023-12-12T11:55:32.939968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:55:33.075954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 126
96.2%
1기 5
 
3.8%

Interactions

2023-12-12T11:55:26.240751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:55:23.512211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:55:24.106204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:55:24.726740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:55:25.495394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:55:26.361290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:55:23.606492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:55:24.197011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:55:24.868280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:55:25.621826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:55:26.508097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:55:23.714127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:55:24.311288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:55:25.011880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:55:25.777949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:55:26.658844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:55:23.831780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:55:24.456223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:55:25.163028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:55:25.941956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:55:26.811715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:55:23.966244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:55:24.601439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:55:25.351366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:55:26.093744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:55:33.187756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류구분부두형태부두구분길이(M)최저수심(M)최고수심(M)선석구분접안능력_톤수(DWT)하역능력(천톤)주요취급화물운영사
분류1.0000.3100.4971.0000.5020.5330.6240.0000.0000.3550.8860.991
구분0.3101.0000.9330.9980.4510.2650.3060.0000.4470.1540.8250.948
부두형태0.4970.9331.0000.9940.4990.7210.7450.0000.8560.3650.8870.674
부두구분1.0000.9980.9941.0000.9991.0001.0000.0000.9261.0001.0000.998
길이(M)0.5020.4510.4990.9991.0000.5360.5660.3440.0000.7930.8660.848
최저수심(M)0.5330.2650.7211.0000.5361.0000.9970.0000.8240.2820.7440.554
최고수심(M)0.6240.3060.7451.0000.5660.9971.0000.0000.8200.3530.7140.404
선석구분0.0000.0000.0000.0000.3440.0000.0001.0000.0000.2130.0000.000
접안능력_톤수(DWT)0.0000.4470.8560.9260.0000.8240.8200.0001.0000.2750.6070.000
하역능력(천톤)0.3550.1540.3651.0000.7930.2820.3530.2130.2751.0000.9040.964
주요취급화물0.8860.8250.8871.0000.8660.7440.7140.0000.6070.9041.0000.957
운영사0.9910.9480.6740.9980.8480.5540.4040.0000.0000.9640.9571.000
2023-12-12T11:55:33.404379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부두형태구분분류운영사접안능력_척수비고주요취급화물선석구분
부두형태1.0000.7550.3390.3251.0001.0000.6300.000
구분0.7551.0000.3740.7031.0001.0000.7060.000
분류0.3390.3741.0000.7491.0001.0000.6660.000
운영사0.3250.7030.7491.0001.0001.0000.6170.000
접안능력_척수1.0001.0001.0001.0001.0001.0001.0001.000
비고1.0001.0001.0001.0001.0001.0001.0001.000
주요취급화물0.6300.7060.6660.6171.0001.0001.0000.000
선석구분0.0000.0000.0000.0001.0001.0000.0001.000
2023-12-12T11:55:33.599738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
길이(M)최저수심(M)최고수심(M)접안능력_톤수(DWT)하역능력(천톤)분류구분부두형태선석구분접안능력_척수주요취급화물운영사비고
길이(M)1.000-0.0150.1360.0470.7690.3130.4390.3100.1861.0000.4640.4360.000
최저수심(M)-0.0151.0000.9480.8070.0240.3630.3620.5620.0001.0000.3490.2361.000
최고수심(M)0.1360.9481.0000.7870.1070.4320.3690.5950.0001.0000.3750.2031.000
접안능력_톤수(DWT)0.0470.8070.7871.0000.2290.0000.3360.4830.0001.0000.3260.0001.000
하역능력(천톤)0.7690.0240.1070.2291.0000.2860.1730.2740.1391.0000.6780.6830.000
분류0.3130.3630.4320.0000.2861.0000.3740.3390.0001.0000.6660.7491.000
구분0.4390.3620.3690.3360.1730.3741.0000.7550.0001.0000.7060.7031.000
부두형태0.3100.5620.5950.4830.2740.3390.7551.0000.0001.0000.6300.3251.000
선석구분0.1860.0000.0000.0000.1390.0000.0000.0001.0001.0000.0000.0001.000
접안능력_척수1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
주요취급화물0.4640.3490.3750.3260.6780.6660.7060.6300.0001.0001.0000.6171.000
운영사0.4360.2360.2030.0000.6830.7490.7030.3250.0001.0000.6171.0001.000
비고0.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-12T11:55:27.033440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:55:27.322526image/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.
2023-12-12T11:55:27.564179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

분류구분부두형태부두구분길이(M)최저수심(M)최고수심(M)선석구분접안능력_톤수(DWT)접안능력_척수하역능력(천톤)주요취급화물운영사비고
0본항국유잔교식(돌핀)석탄부두270.012.012.01선석4000011560석탄CJ대한통운㈜<NA>
1본항국유잔교식1부두149.08.08.01선석50001380잡화울산항만운영㈜<NA>
2본항국유중력식2부두602.09.012.01선석2000012060잡화, 시멘트울산항만운영㈜<NA>
3본항국유잔교식2부두602.09.012.02선석4000012060잡화, 시멘트울산항만운영㈜<NA>
4본항국유잔교식2부두602.09.012.03선석500012060잡화, 시멘트공용<NA>
5본항국유중력식3부두347.09.09.01선석1000011266잡화, 액체화학울산항만운영㈜<NA>
6본항국유중력식3부두347.09.09.02선석1000011266잡화, 액체화학신흥사㈜<NA>
7본항국유중력식4부두322.011.011.01선석500011495잡화, 액체화학신흥사㈜<NA>
8본항국유중력식4부두322.011.011.02선석2000011495잡화, 액체화학신흥사㈜<NA>
9본항국유중력식5부두220.011.511.51선석200001530잡화고려항만㈜<NA>
분류구분부두형태부두구분길이(M)최저수심(M)최고수심(M)선석구분접안능력_톤수(DWT)접안능력_척수하역능력(천톤)주요취급화물운영사비고
121기타계류시설국유<NA>미포조선 안벽135.0<NA><NA><NA><NA><NA><NA><NA>신조선 의장안벽<NA>
122기타계류시설국유<NA>신항예부선부두140.0<NA><NA><NA><NA><NA><NA><NA>예부선정계지<NA>
123기타계류시설국유<NA>장생포 소형선부두300.0<NA><NA><NA><NA><NA><NA><NA>소형선(잡종선) 계류지<NA>
124기타계류시설국유<NA>온산항 소형선부두219.0<NA><NA><NA><NA><NA><NA><NA>잡종선(어선) 계류지<NA>
125기타계류시설국유<NA>한전 소형선부두21.0<NA><NA><NA><NA><NA><NA><NA>공사자재 등 하역<NA>
126기타계류시설국유<NA>남화 예선부두359.45<NA><NA><NA><NA><NA><NA><NA>예선 정계지<NA>
127기타계류시설민유<NA>처용 소형선부두25.0<NA><NA><NA><NA><NA><NA><NA>선박블록 하역<NA>
128기타계류시설민유<NA>이진 소형선부두50.0<NA><NA><NA><NA><NA><NA><NA>선박블록 하역<NA>
129기타계류시설민유<NA>원산 소형선부두80.0<NA><NA><NA><NA><NA><NA><NA>선박블록 하역<NA>
130기타계류시설민유<NA>이진 소형선부두80.0<NA><NA><NA><NA><NA><NA><NA>선박블록 하역<NA>