Overview

Dataset statistics

Number of variables9
Number of observations390
Missing cells549
Missing cells (%)15.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.1 KiB
Average record size in memory76.3 B

Variable types

Numeric4
Categorical1
Text4

Dataset

Description지능형 해상교통정보시스템(바다내비)에서 도선사예선지원서비스(SV51)를 위한 항만가이드라인 부두에 대한 테이블임
Author해양수산부
URLhttps://www.data.go.kr/data/15121238/fileData.do

Alerts

전면 수심 최소값 is highly overall correlated with 전면 수심 최대값High correlation
전면 수심 최대값 is highly overall correlated with 전면 수심 최소값High correlation
부두 구분 has 188 (48.2%) missing valuesMissing
부두 길이 has 6 (1.5%) missing valuesMissing
전면 수심 최소값 has 47 (12.1%) missing valuesMissing
전면 수심 최대값 has 47 (12.1%) missing valuesMissing
하역 능력 has 261 (66.9%) missing valuesMissing

Reproduction

Analysis started2023-12-12 09:52:41.714991
Analysis finished2023-12-12 09:52:44.746856
Duration3.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

Distinct79
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.297436
Minimum1
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-12T18:52:44.853313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median17.5
Q334
95-th percentile59.55
Maximum79
Range78
Interquartile range (IQR)25

Descriptive statistics

Standard deviation18.666709
Coefficient of variation (CV)0.80123446
Kurtosis0.15011274
Mean23.297436
Median Absolute Deviation (MAD)11.5
Skewness0.97011714
Sum9086
Variance348.44601
MonotonicityNot monotonic
2023-12-12T18:52:45.050152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 12
 
3.1%
6 12
 
3.1%
4 12
 
3.1%
3 12
 
3.1%
2 12
 
3.1%
1 12
 
3.1%
7 12
 
3.1%
8 12
 
3.1%
9 12
 
3.1%
10 12
 
3.1%
Other values (69) 270
69.2%
ValueCountFrequency (%)
1 12
3.1%
2 12
3.1%
3 12
3.1%
4 12
3.1%
5 12
3.1%
6 12
3.1%
7 12
3.1%
8 12
3.1%
9 12
3.1%
10 12
3.1%
ValueCountFrequency (%)
79 1
0.3%
78 1
0.3%
77 1
0.3%
76 1
0.3%
75 1
0.3%
74 1
0.3%
73 1
0.3%
72 1
0.3%
71 1
0.3%
70 1
0.3%

항구 명
Categorical

Distinct12
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
울산
79 
여수
59 
인천
56 
목포
39 
부산
33 
Other values (7)
124 

Length

Max length5
Median length2
Mean length2.1076923
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
울산 79
20.3%
여수 59
15.1%
인천 56
14.4%
목포 39
10.0%
부산 33
8.5%
제주 24
 
6.2%
마산 24
 
6.2%
대산 17
 
4.4%
동해 16
 
4.1%
포항 15
 
3.8%
Other values (2) 28
 
7.2%

Length

2023-12-12T18:52:45.210807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
울산 79
20.3%
여수 59
15.1%
인천 56
14.4%
목포 39
10.0%
부산 33
8.5%
제주 24
 
6.2%
마산 24
 
6.2%
대산 17
 
4.4%
동해 16
 
4.1%
포항 15
 
3.8%
Other values (2) 28
 
7.2%

부두 명
Text

Distinct63
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
2023-12-12T18:52:45.490453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length12
Mean length6.4333333
Min length2

Characters and Unicode

Total characters2509
Distinct characters103
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)3.8%

Sample

1st row남항
2nd row남항
3rd row남항
4th row남항
5th row남항
ValueCountFrequency (%)
광양항(여천지역 27
 
5.7%
마산항 24
 
5.0%
본항(국유 20
 
4.2%
남항 19
 
4.0%
광양항(광양지역 18
 
3.8%
북항 18
 
3.8%
대산항 15
 
3.1%
여수항 14
 
2.9%
기타계류시설(국유 14
 
2.9%
신항 13
 
2.7%
Other values (60) 295
61.8%
2023-12-12T18:52:45.882304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
항 370
 
14.7%
( 203
 
8.1%
) 203
 
8.1%
두 89
 
3.5%
부 89
 
3.5%
87
 
3.5%
산 85
 
3.4%
유 80
 
3.2%
양 76
 
3.0%
광 63
 
2.5%
Other values (93) 1164
46.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1984
79.1%
Open Punctuation 203
 
8.1%
Close Punctuation 203
 
8.1%
Space Separator 87
 
3.5%
Uppercase Letter 18
 
0.7%
Other Punctuation 14
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
항 370
 
18.6%
두 89
 
4.5%
부 89
 
4.5%
산 85
 
4.3%
유 80
 
4.0%
양 76
 
3.8%
광 63
 
3.2%
국 51
 
2.6%
역 48
 
2.4%
천 47
 
2.4%
Other values (86) 986
49.7%
Uppercase Letter
ValueCountFrequency (%)
C 6
33.3%
O 6
33.3%
T 6
33.3%
Open Punctuation
ValueCountFrequency (%)
( 203
100.0%
Close Punctuation
ValueCountFrequency (%)
) 203
100.0%
Space Separator
ValueCountFrequency (%)
87
100.0%
Other Punctuation
ValueCountFrequency (%)
· 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1984
79.1%
Common 507
 
20.2%
Latin 18
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
항 370
 
18.6%
두 89
 
4.5%
부 89
 
4.5%
산 85
 
4.3%
유 80
 
4.0%
양 76
 
3.8%
광 63
 
3.2%
국 51
 
2.6%
역 48
 
2.4%
천 47
 
2.4%
Other values (86) 986
49.7%
Common
ValueCountFrequency (%)
( 203
40.0%
) 203
40.0%
87
17.2%
· 14
 
2.8%
Latin
ValueCountFrequency (%)
C 6
33.3%
O 6
33.3%
T 6
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1984
79.1%
ASCII 511
 
20.4%
None 14
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
항 370
 
18.6%
두 89
 
4.5%
부 89
 
4.5%
산 85
 
4.3%
유 80
 
4.0%
양 76
 
3.8%
광 63
 
3.2%
국 51
 
2.6%
역 48
 
2.4%
천 47
 
2.4%
Other values (86) 986
49.7%
ASCII
ValueCountFrequency (%)
( 203
39.7%
) 203
39.7%
87
17.0%
C 6
 
1.2%
O 6
 
1.2%
T 6
 
1.2%
None
ValueCountFrequency (%)
· 14
100.0%

부두 세부 명
Text

Distinct302
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
2023-12-12T18:52:46.158767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length13
Mean length5.6871795
Min length1

Characters and Unicode

Total characters2218
Distinct characters202
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique265 ?
Unique (%)67.9%

Sample

1st row남항모래부두(1)
2nd row남항모래부두(2)
3rd row영진부두
4th row대한통운부두
5th rowSICT부두
ValueCountFrequency (%)
물양장 19
 
4.1%
돌핀 14
 
3.0%
1 11
 
2.4%
감천 8
 
1.7%
제3부두 7
 
1.5%
제2부두 7
 
1.5%
부두 7
 
1.5%
컨테이너 6
 
1.3%
제4부두 6
 
1.3%
제1부두 6
 
1.3%
Other values (281) 370
80.3%
2023-12-12T18:52:46.580618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
부 305
 
13.8%
두 300
 
13.5%
71
 
3.2%
제 71
 
3.2%
1 53
 
2.4%
양 41
 
1.8%
2 39
 
1.8%
장 39
 
1.8%
물 37
 
1.7%
항 36
 
1.6%
Other values (192) 1226
55.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1719
77.5%
Decimal Number 179
 
8.1%
Uppercase Letter 167
 
7.5%
Space Separator 71
 
3.2%
Close Punctuation 26
 
1.2%
Open Punctuation 26
 
1.2%
Other Symbol 11
 
0.5%
Lowercase Letter 8
 
0.4%
Dash Punctuation 7
 
0.3%
Other Punctuation 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 305
 
17.7%
두 300
 
17.5%
제 71
 
4.1%
양 41
 
2.4%
장 39
 
2.3%
물 37
 
2.2%
항 36
 
2.1%
선 29
 
1.7%
돌 24
 
1.4%
핀 24
 
1.4%
Other values (155) 813
47.3%
Uppercase Letter
ValueCountFrequency (%)
S 35
21.0%
T 22
13.2%
K 21
12.6%
O 14
 
8.4%
G 12
 
7.2%
N 11
 
6.6%
C 11
 
6.6%
I 10
 
6.0%
L 9
 
5.4%
P 7
 
4.2%
Other values (8) 15
9.0%
Decimal Number
ValueCountFrequency (%)
1 53
29.6%
2 39
21.8%
3 24
13.4%
4 18
 
10.1%
6 13
 
7.3%
5 12
 
6.7%
7 10
 
5.6%
8 7
 
3.9%
9 2
 
1.1%
0 1
 
0.6%
Lowercase Letter
ValueCountFrequency (%)
l 4
50.0%
i 4
50.0%
Other Punctuation
ValueCountFrequency (%)
/ 2
50.0%
, 2
50.0%
Space Separator
ValueCountFrequency (%)
71
100.0%
Close Punctuation
ValueCountFrequency (%)
) 26
100.0%
Open Punctuation
ValueCountFrequency (%)
( 26
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1730
78.0%
Common 313
 
14.1%
Latin 175
 
7.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 305
 
17.6%
두 300
 
17.3%
제 71
 
4.1%
양 41
 
2.4%
장 39
 
2.3%
물 37
 
2.1%
항 36
 
2.1%
선 29
 
1.7%
돌 24
 
1.4%
핀 24
 
1.4%
Other values (156) 824
47.6%
Latin
ValueCountFrequency (%)
S 35
20.0%
T 22
12.6%
K 21
12.0%
O 14
 
8.0%
G 12
 
6.9%
N 11
 
6.3%
C 11
 
6.3%
I 10
 
5.7%
L 9
 
5.1%
P 7
 
4.0%
Other values (10) 23
13.1%
Common
ValueCountFrequency (%)
71
22.7%
1 53
16.9%
2 39
12.5%
) 26
 
8.3%
( 26
 
8.3%
3 24
 
7.7%
4 18
 
5.8%
6 13
 
4.2%
5 12
 
3.8%
7 10
 
3.2%
Other values (6) 21
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1719
77.5%
ASCII 488
 
22.0%
None 11
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
부 305
 
17.7%
두 300
 
17.5%
제 71
 
4.1%
양 41
 
2.4%
장 39
 
2.3%
물 37
 
2.2%
항 36
 
2.1%
선 29
 
1.7%
돌 24
 
1.4%
핀 24
 
1.4%
Other values (155) 813
47.3%
ASCII
ValueCountFrequency (%)
71
14.5%
1 53
 
10.9%
2 39
 
8.0%
S 35
 
7.2%
) 26
 
5.3%
( 26
 
5.3%
3 24
 
4.9%
T 22
 
4.5%
K 21
 
4.3%
4 18
 
3.7%
Other values (26) 153
31.4%
None
ValueCountFrequency (%)
㈜ 11
100.0%

부두 구분
Text

MISSING 

Distinct110
Distinct (%)54.5%
Missing188
Missing (%)48.2%
Memory size3.2 KiB
2023-12-12T18:52:46.827106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length20
Mean length5.5346535
Min length2

Characters and Unicode

Total characters1118
Distinct characters129
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

Unique85 ?
Unique (%)42.1%

Sample

1st row유류, LPG
2nd row유류, LPG
3rd rowLPG
4th rowLNG
5th rowLNG
ValueCountFrequency (%)
잡화 42
 
13.5%
유류 25
 
8.0%
액체화학 21
 
6.8%
시멘트 16
 
5.1%
자동차 11
 
3.5%
철재 11
 
3.5%
컨테이너 10
 
3.2%
감천 8
 
2.6%
수산물 7
 
2.3%
석탄 6
 
1.9%
Other values (89) 154
49.5%
2023-12-12T18:52:47.191412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
109
 
9.7%
, 87
 
7.8%
화 74
 
6.6%
잡 44
 
3.9%
유 39
 
3.5%
류 33
 
3.0%
부 28
 
2.5%
재 28
 
2.5%
두 27
 
2.4%
철 27
 
2.4%
Other values (119) 622
55.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 834
74.6%
Space Separator 109
 
9.7%
Other Punctuation 87
 
7.8%
Uppercase Letter 54
 
4.8%
Decimal Number 18
 
1.6%
Open Punctuation 8
 
0.7%
Close Punctuation 8
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
화 74
 
8.9%
잡 44
 
5.3%
유 39
 
4.7%
류 33
 
4.0%
부 28
 
3.4%
재 28
 
3.4%
두 27
 
3.2%
철 27
 
3.2%
액 24
 
2.9%
체 24
 
2.9%
Other values (100) 486
58.3%
Decimal Number
ValueCountFrequency (%)
2 4
22.2%
1 4
22.2%
3 2
11.1%
7 2
11.1%
4 2
11.1%
5 2
11.1%
6 1
 
5.6%
8 1
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
G 11
20.4%
N 11
20.4%
L 11
20.4%
P 10
18.5%
I 5
9.3%
T 5
9.3%
C 1
 
1.9%
Space Separator
ValueCountFrequency (%)
109
100.0%
Other Punctuation
ValueCountFrequency (%)
, 87
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 834
74.6%
Common 230
 
20.6%
Latin 54
 
4.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
화 74
 
8.9%
잡 44
 
5.3%
유 39
 
4.7%
류 33
 
4.0%
부 28
 
3.4%
재 28
 
3.4%
두 27
 
3.2%
철 27
 
3.2%
액 24
 
2.9%
체 24
 
2.9%
Other values (100) 486
58.3%
Common
ValueCountFrequency (%)
109
47.4%
, 87
37.8%
( 8
 
3.5%
) 8
 
3.5%
2 4
 
1.7%
1 4
 
1.7%
3 2
 
0.9%
7 2
 
0.9%
4 2
 
0.9%
5 2
 
0.9%
Other values (2) 2
 
0.9%
Latin
ValueCountFrequency (%)
G 11
20.4%
N 11
20.4%
L 11
20.4%
P 10
18.5%
I 5
9.3%
T 5
9.3%
C 1
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 834
74.6%
ASCII 284
 
25.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
109
38.4%
, 87
30.6%
G 11
 
3.9%
N 11
 
3.9%
L 11
 
3.9%
P 10
 
3.5%
( 8
 
2.8%
) 8
 
2.8%
I 5
 
1.8%
T 5
 
1.8%
Other values (9) 19
 
6.7%
Hangul
ValueCountFrequency (%)
화 74
 
8.9%
잡 44
 
5.3%
유 39
 
4.7%
류 33
 
4.0%
부 28
 
3.4%
재 28
 
3.4%
두 27
 
3.2%
철 27
 
3.2%
액 24
 
2.9%
체 24
 
2.9%
Other values (100) 486
58.3%

부두 길이
Real number (ℝ)

MISSING 

Distinct211
Distinct (%)54.9%
Missing6
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean470.76185
Minimum0
Maximum4400
Zeros2
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-12T18:52:47.341033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile80
Q1189.5
median282.5
Q3544.25
95-th percentile1396.25
Maximum4400
Range4400
Interquartile range (IQR)354.75

Descriptive statistics

Standard deviation526.13711
Coefficient of variation (CV)1.117629
Kurtosis16.57943
Mean470.76185
Median Absolute Deviation (MAD)137.5
Skewness3.390075
Sum180772.55
Variance276820.25
MonotonicityNot monotonic
2023-12-12T18:52:47.474901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
210.0 23
 
5.9%
240.0 13
 
3.3%
420.0 11
 
2.8%
250.0 10
 
2.6%
270.0 10
 
2.6%
130.0 7
 
1.8%
80.0 6
 
1.5%
140.0 6
 
1.5%
150.0 6
 
1.5%
160.0 6
 
1.5%
Other values (201) 286
73.3%
ValueCountFrequency (%)
0.0 2
0.5%
21.0 1
 
0.3%
22.0 1
 
0.3%
25.0 1
 
0.3%
26.0 1
 
0.3%
29.0 1
 
0.3%
40.0 1
 
0.3%
46.0 1
 
0.3%
50.0 3
0.8%
52.0 1
 
0.3%
ValueCountFrequency (%)
4400.0 1
0.3%
4145.0 1
0.3%
3040.0 1
0.3%
2520.0 1
0.3%
2470.0 1
0.3%
2240.0 1
0.3%
2200.0 1
0.3%
2040.0 1
0.3%
2021.0 1
0.3%
2000.0 1
0.3%

전면 수심 최소값
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct58
Distinct (%)16.9%
Missing47
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean9.7823615
Minimum0.15
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-12T18:52:47.623811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.15
5-th percentile3.05
Q16.7
median10
Q312
95-th percentile16
Maximum27
Range26.85
Interquartile range (IQR)5.3

Descriptive statistics

Standard deviation4.4368986
Coefficient of variation (CV)0.4535611
Kurtosis2.3810228
Mean9.7823615
Median Absolute Deviation (MAD)2.5
Skewness0.91942016
Sum3355.35
Variance19.68607
MonotonicityNot monotonic
2023-12-12T18:52:47.756844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.0 53
13.6%
11.0 38
 
9.7%
14.0 22
 
5.6%
7.5 21
 
5.4%
9.0 19
 
4.9%
7.0 16
 
4.1%
5.0 13
 
3.3%
4.0 13
 
3.3%
10.0 11
 
2.8%
6.0 10
 
2.6%
Other values (48) 127
32.6%
(Missing) 47
 
12.1%
ValueCountFrequency (%)
0.15 1
 
0.3%
0.2 1
 
0.3%
1.5 3
 
0.8%
1.7 1
 
0.3%
2.0 2
 
0.5%
2.8 1
 
0.3%
3.0 9
2.3%
3.5 1
 
0.3%
4.0 13
3.3%
4.5 5
 
1.3%
ValueCountFrequency (%)
27.0 4
1.0%
26.0 1
 
0.3%
25.5 1
 
0.3%
23.5 1
 
0.3%
21.5 1
 
0.3%
21.0 1
 
0.3%
18.0 3
 
0.8%
17.0 2
 
0.5%
16.5 1
 
0.3%
16.0 9
2.3%

전면 수심 최대값
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct56
Distinct (%)16.3%
Missing47
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean10.579621
Minimum0.2
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-12T18:52:47.881377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile4
Q17.3
median11
Q312.75
95-th percentile17.09
Maximum27
Range26.8
Interquartile range (IQR)5.45

Descriptive statistics

Standard deviation4.4948273
Coefficient of variation (CV)0.42485712
Kurtosis1.8479588
Mean10.579621
Median Absolute Deviation (MAD)3
Skewness0.85601044
Sum3628.81
Variance20.203473
MonotonicityNot monotonic
2023-12-12T18:52:48.013040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.0 49
 
12.6%
11.0 41
 
10.5%
14.0 31
 
7.9%
7.5 19
 
4.9%
7.0 16
 
4.1%
5.0 14
 
3.6%
10.0 13
 
3.3%
9.0 12
 
3.1%
15.0 11
 
2.8%
8.0 10
 
2.6%
Other values (46) 127
32.6%
(Missing) 47
 
12.1%
ValueCountFrequency (%)
0.2 1
 
0.3%
2.0 1
 
0.3%
3.0 9
2.3%
3.4 1
 
0.3%
4.0 9
2.3%
4.5 5
 
1.3%
5.0 14
3.6%
5.1 1
 
0.3%
5.11 1
 
0.3%
5.5 5
 
1.3%
ValueCountFrequency (%)
27.0 4
1.0%
26.0 1
 
0.3%
25.5 1
 
0.3%
23.5 2
0.5%
22.5 2
0.5%
21.0 1
 
0.3%
20.5 1
 
0.3%
19.5 2
0.5%
18.0 3
0.8%
17.1 1
 
0.3%

하역 능력
Text

MISSING 

Distinct113
Distinct (%)87.6%
Missing261
Missing (%)66.9%
Memory size3.2 KiB
2023-12-12T18:52:48.280420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length16
Mean length6.8062016
Min length3

Characters and Unicode

Total characters878
Distinct characters33
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique101 ?
Unique (%)78.3%

Sample

1st row1,050천TEU
2nd row1,050천TEU
3rd row517 천톤
4th row894 천톤
5th row628 천톤
ValueCountFrequency (%)
천톤 14
 
9.0%
기당 5
 
3.2%
1,836천톤 4
 
2.6%
661천톤 3
 
1.9%
102 3
 
1.9%
930천톤 2
 
1.3%
1,360천톤 2
 
1.3%
1,050천teu 2
 
1.3%
1480 2
 
1.3%
925천톤 2
 
1.3%
Other values (110) 116
74.8%
2023-12-12T18:52:48.662882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 103
11.7%
0 93
10.6%
톤 87
 
9.9%
1 78
 
8.9%
2 62
 
7.1%
, 58
 
6.6%
3 52
 
5.9%
6 49
 
5.6%
5 40
 
4.6%
8 36
 
4.1%
Other values (23) 220
25.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 505
57.5%
Other Letter 208
23.7%
Other Punctuation 65
 
7.4%
Uppercase Letter 52
 
5.9%
Space Separator 26
 
3.0%
Lowercase Letter 10
 
1.1%
Open Punctuation 6
 
0.7%
Close Punctuation 5
 
0.6%
Math Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 103
49.5%
톤 87
41.8%
당 5
 
2.4%
기 5
 
2.4%
컨 2
 
1.0%
만 2
 
1.0%
잡 1
 
0.5%
화 1
 
0.5%
일 1
 
0.5%
반 1
 
0.5%
Decimal Number
ValueCountFrequency (%)
0 93
18.4%
1 78
15.4%
2 62
12.3%
3 52
10.3%
6 49
9.7%
5 40
7.9%
8 36
 
7.1%
7 33
 
6.5%
4 32
 
6.3%
9 30
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
T 18
34.6%
E 16
30.8%
U 16
30.8%
R 2
 
3.8%
Other Punctuation
ValueCountFrequency (%)
, 58
89.2%
/ 5
 
7.7%
' 2
 
3.1%
Lowercase Letter
ValueCountFrequency (%)
m 5
50.0%
h 5
50.0%
Space Separator
ValueCountFrequency (%)
26
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 608
69.2%
Hangul 208
 
23.7%
Latin 62
 
7.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 93
15.3%
1 78
12.8%
2 62
10.2%
, 58
9.5%
3 52
8.6%
6 49
8.1%
5 40
6.6%
8 36
 
5.9%
7 33
 
5.4%
4 32
 
5.3%
Other values (7) 75
12.3%
Hangul
ValueCountFrequency (%)
천 103
49.5%
톤 87
41.8%
당 5
 
2.4%
기 5
 
2.4%
컨 2
 
1.0%
만 2
 
1.0%
잡 1
 
0.5%
화 1
 
0.5%
일 1
 
0.5%
반 1
 
0.5%
Latin
ValueCountFrequency (%)
T 18
29.0%
E 16
25.8%
U 16
25.8%
m 5
 
8.1%
h 5
 
8.1%
R 2
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 670
76.3%
Hangul 208
 
23.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 103
49.5%
톤 87
41.8%
당 5
 
2.4%
기 5
 
2.4%
컨 2
 
1.0%
만 2
 
1.0%
잡 1
 
0.5%
화 1
 
0.5%
일 1
 
0.5%
반 1
 
0.5%
ASCII
ValueCountFrequency (%)
0 93
13.9%
1 78
11.6%
2 62
9.3%
, 58
8.7%
3 52
 
7.8%
6 49
 
7.3%
5 40
 
6.0%
8 36
 
5.4%
7 33
 
4.9%
4 32
 
4.8%
Other values (13) 137
20.4%

Interactions

2023-12-12T18:52:43.577790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:52:42.236176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:52:42.700828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:52:43.152275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:52:43.987999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:52:42.363253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:52:42.833938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:52:43.259490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:52:44.081689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:52:42.475218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:52:42.933465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:52:43.355428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:52:44.198710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:52:42.612884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:52:43.037882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:52:43.463090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:52:48.755422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번항구 명부두 명부두 길이전면 수심 최소값전면 수심 최대값
순번1.0000.4790.9340.0000.3180.329
항구 명0.4791.0001.0000.3880.2890.365
부두 명0.9341.0001.0000.7100.8090.839
부두 길이0.0000.3880.7101.0000.0000.515
전면 수심 최소값0.3180.2890.8090.0001.0000.976
전면 수심 최대값0.3290.3650.8390.5150.9761.000
2023-12-12T18:52:48.847492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번부두 길이전면 수심 최소값전면 수심 최대값항구 명
순번1.000-0.2070.1190.0520.223
부두 길이-0.2071.0000.2290.4320.174
전면 수심 최소값0.1190.2291.0000.9030.127
전면 수심 최대값0.0520.4320.9031.0000.164
항구 명0.2230.1740.1270.1641.000

Missing values

2023-12-12T18:52:44.348456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:52:44.507953image/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.
2023-12-12T18:52:44.659807image/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

순번항구 명부두 명부두 세부 명부두 구분부두 길이전면 수심 최소값전면 수심 최대값하역 능력
031인천남항남항모래부두(1)<NA>547.04.04.0<NA>
132인천남항남항모래부두(2)<NA>375.04.04.0<NA>
233인천남항영진부두<NA>170.09.09.0<NA>
334인천남항대한통운부두<NA>225.04.04.0<NA>
435인천남항SICT부두<NA>407.011.011.0<NA>
536인천남항ICT부두<NA>600.014.014.0<NA>
637인천남항제1국제여객부두(1부두)<NA>224.07.57.5<NA>
738인천남항제1국제여객부두(2부두)<NA>243.09.59.5<NA>
839인천남항제1국제여객부두(3부두)<NA>220.07.57.5<NA>
940인천남항국내여객부두<NA>184.07.57.5<NA>
순번항구 명부두 명부두 세부 명부두 구분부두 길이전면 수심 최소값전면 수심 최대값하역 능력
38021인천내항5부두<NA>1150.012.512.58,530천톤
38122인천내항6부두<NA>1218.07.512.53,040천톤
38223인천내항7부두<NA>1216.012.014.06,520천톤
38324인천내항8부두<NA>602.011.511.51,760천톤
38425인천남항한일탱크터미널 돌핀케미칼29.07.07.0<NA>
38526인천남항E1부두<NA>250.012.012.0<NA>
38627인천남항쌍용양회 돌핀<NA>186.010.010.0<NA>
38728인천남항RH시멘트 돌핀<NA>188.09.09.0<NA>
38829인천남항동양시멘트 돌핀<NA>188.09.09.0<NA>
38930인천남항한일,대우시멘트 돌핀<NA>200.0<NA><NA><NA>