Overview

Dataset statistics

Number of variables12
Number of observations106
Missing cells89
Missing cells (%)7.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.5 KiB
Average record size in memory101.2 B

Variable types

Numeric4
Categorical6
Text2

Dataset

Description승선근무예비역은 전시사변 또는 비상시 국민경제에 긴요한 물자와 군수물자를 수송하기 위한 업무 또는 이와 관련된 업무의 지원을 위하여 소집되어 승선근무하는 병역대체복무제도입니다.2024년 승선근무예비역에 대하여 해운업체, 수산업체별 인원배정한 명부입니다.
Author병무청
URLhttps://www.data.go.kr/data/3068269/fileData.do

Alerts

시도 is highly overall correlated with 시군구 and 1 other fieldsHigh correlation
지방청 is highly overall correlated with 시도 and 1 other fieldsHigh correlation
2024년 배정인원(계) is highly overall correlated with 2024년 배정인원(국가필수국제선박) and 2 other fieldsHigh correlation
2024년 배정인원(국가필수국제선박) is highly overall correlated with 2024년 배정인원(계) and 2 other fieldsHigh correlation
2024년 배정인원(기타선박) is highly overall correlated with 2024년 배정인원(계) and 1 other fieldsHigh correlation
기업규모 is highly overall correlated with 2024년 배정인원(계)High correlation
시군구 is highly overall correlated with 시도 and 1 other fieldsHigh correlation
관련협회 is highly overall correlated with 수산_해운(내_외항)High correlation
수산_해운(내_외항) is highly overall correlated with 2024년 배정인원(국가필수국제선박) and 1 other fieldsHigh correlation
시도 is highly imbalanced (54.4%)Imbalance
지방청 is highly imbalanced (58.0%)Imbalance
2024년 배정인원(국가필수국제선박) has 85 (80.2%) missing valuesMissing
2024년 배정인원(기타선박) has 4 (3.8%) missing valuesMissing
연번 has unique valuesUnique
업체명 has unique valuesUnique

Reproduction

Analysis started2024-03-14 18:34:45.313545
Analysis finished2024-03-14 18:34:51.196144
Duration5.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct106
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.5
Minimum1
Maximum106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-15T03:34:51.339053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.25
Q127.25
median53.5
Q379.75
95-th percentile100.75
Maximum106
Range105
Interquartile range (IQR)52.5

Descriptive statistics

Standard deviation30.743563
Coefficient of variation (CV)0.57464604
Kurtosis-1.2
Mean53.5
Median Absolute Deviation (MAD)26.5
Skewness0
Sum5671
Variance945.16667
MonotonicityStrictly increasing
2024-03-15T03:34:51.877133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.9%
81 1
 
0.9%
79 1
 
0.9%
78 1
 
0.9%
77 1
 
0.9%
76 1
 
0.9%
75 1
 
0.9%
74 1
 
0.9%
73 1
 
0.9%
72 1
 
0.9%
Other values (96) 96
90.6%
ValueCountFrequency (%)
1 1
0.9%
2 1
0.9%
3 1
0.9%
4 1
0.9%
5 1
0.9%
6 1
0.9%
7 1
0.9%
8 1
0.9%
9 1
0.9%
10 1
0.9%
ValueCountFrequency (%)
106 1
0.9%
105 1
0.9%
104 1
0.9%
103 1
0.9%
102 1
0.9%
101 1
0.9%
100 1
0.9%
99 1
0.9%
98 1
0.9%
97 1
0.9%

기업규모
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size976.0 B
중소기업
81 
대기업
17 
중견기업
 
8

Length

Max length4
Median length4
Mean length3.8396226
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row중소기업
2nd row대기업
3rd row중소기업
4th row중소기업
5th row중소기업

Common Values

ValueCountFrequency (%)
중소기업 81
76.4%
대기업 17
 
16.0%
중견기업 8
 
7.5%

Length

2024-03-15T03:34:52.167576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T03:34:52.344599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중소기업 81
76.4%
대기업 17
 
16.0%
중견기업 8
 
7.5%

업체명
Text

UNIQUE 

Distinct106
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size976.0 B
2024-03-15T03:34:53.316588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length7.8867925
Min length3

Characters and Unicode

Total characters836
Distinct characters143
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique106 ?
Unique (%)100.0%

Sample

1st row(주)KSS마린
2nd row(주)KSS해운
3rd row(주)가나마린
4th row(주)고려에스엠
5th row(주)그린마리타임
ValueCountFrequency (%)
주)kss마린 1
 
0.9%
알파해운(주 1
 
0.9%
우양상선(주 1
 
0.9%
우민해운(주 1
 
0.9%
우림해운(주 1
 
0.9%
엠에스에스엠(주 1
 
0.9%
엔디에스엠(주 1
 
0.9%
에이치엠엠오션서비스(주 1
 
0.9%
에이치엠엠(주 1
 
0.9%
에이치알쉬핑(주 1
 
0.9%
Other values (98) 98
90.7%
2024-03-15T03:34:54.775426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 99
 
11.8%
) 98
 
11.7%
( 98
 
11.7%
운 33
 
3.9%
해 31
 
3.7%
스 30
 
3.6%
에 21
 
2.5%
엠 19
 
2.3%
이 15
 
1.8%
선 14
 
1.7%
Other values (133) 378
45.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 623
74.5%
Close Punctuation 98
 
11.7%
Open Punctuation 98
 
11.7%
Uppercase Letter 8
 
1.0%
Other Symbol 6
 
0.7%
Space Separator 2
 
0.2%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 99
 
15.9%
운 33
 
5.3%
해 31
 
5.0%
스 30
 
4.8%
에 21
 
3.4%
엠 19
 
3.0%
이 15
 
2.4%
선 14
 
2.2%
마 13
 
2.1%
동 11
 
1.8%
Other values (126) 337
54.1%
Uppercase Letter
ValueCountFrequency (%)
S 5
62.5%
K 3
37.5%
Close Punctuation
ValueCountFrequency (%)
) 98
100.0%
Open Punctuation
ValueCountFrequency (%)
( 98
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 6
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 629
75.2%
Common 199
 
23.8%
Latin 8
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 99
 
15.7%
운 33
 
5.2%
해 31
 
4.9%
스 30
 
4.8%
에 21
 
3.3%
엠 19
 
3.0%
이 15
 
2.4%
선 14
 
2.2%
마 13
 
2.1%
동 11
 
1.7%
Other values (127) 343
54.5%
Common
ValueCountFrequency (%)
) 98
49.2%
( 98
49.2%
2
 
1.0%
- 1
 
0.5%
Latin
ValueCountFrequency (%)
S 5
62.5%
K 3
37.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 623
74.5%
ASCII 207
 
24.8%
None 6
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 99
 
15.9%
운 33
 
5.3%
해 31
 
5.0%
스 30
 
4.8%
에 21
 
3.4%
엠 19
 
3.0%
이 15
 
2.4%
선 14
 
2.2%
마 13
 
2.1%
동 11
 
1.8%
Other values (126) 337
54.1%
ASCII
ValueCountFrequency (%)
) 98
47.3%
( 98
47.3%
S 5
 
2.4%
K 3
 
1.4%
2
 
1.0%
- 1
 
0.5%
None
ValueCountFrequency (%)
㈜ 6
100.0%

시도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size976.0 B
부산
82 
서울
17 
울산
 
4
전남
 
2
제주
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)0.9%

Sample

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

Common Values

ValueCountFrequency (%)
부산 82
77.4%
서울 17
 
16.0%
울산 4
 
3.8%
전남 2
 
1.9%
제주 1
 
0.9%

Length

2024-03-15T03:34:55.116256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T03:34:55.434834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산 82
77.4%
서울 17
 
16.0%
울산 4
 
3.8%
전남 2
 
1.9%
제주 1
 
0.9%

시군구
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Memory size976.0 B
중구
51 
동구
18 
서구
8 
해운대구
7 
남구
 
4
Other values (10)
18 

Length

Max length4
Median length2
Mean length2.3301887
Min length2

Unique

Unique5 ?
Unique (%)4.7%

Sample

1st row중구
2nd row중구
3rd row동구
4th row중구
5th row해운대구

Common Values

ValueCountFrequency (%)
중구 51
48.1%
동구 18
 
17.0%
서구 8
 
7.5%
해운대구 7
 
6.6%
남구 4
 
3.8%
영도구 3
 
2.8%
영등포구 3
 
2.8%
종로구 3
 
2.8%
강서구 2
 
1.9%
강남구 2
 
1.9%
Other values (5) 5
 
4.7%

Length

2024-03-15T03:34:55.884830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
중구 51
48.1%
동구 18
 
17.0%
서구 8
 
7.5%
해운대구 7
 
6.6%
남구 4
 
3.8%
영도구 3
 
2.8%
영등포구 3
 
2.8%
종로구 3
 
2.8%
강서구 2
 
1.9%
강남구 2
 
1.9%
Other values (5) 5
 
4.7%

이하주소
Text

Distinct104
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size976.0 B
2024-03-15T03:34:56.896811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length27
Mean length22.424528
Min length7

Characters and Unicode

Total characters2377
Distinct characters179
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique103 ?
Unique (%)97.2%

Sample

1st row중앙대로146, 8층
2nd row중앙대로146 (중앙동4가, 대한항공빌딩8층)
3rd row중앙대로180번길 6-12 12층 (초량동, DK빌딩)
4th row충장대로 7, 10층(중앙동4가)
5th row센텀중앙로 97, 센텀스카이비즈 2502호
ValueCountFrequency (%)
중앙대로 25
 
6.2%
중앙동4가 12
 
3.0%
중앙대로180번길 8
 
2.0%
초량동 7
 
1.7%
8층 6
 
1.5%
3층 6
 
1.5%
6층 5
 
1.2%
97 5
 
1.2%
충장대로9번길 5
 
1.2%
센텀중앙로 5
 
1.2%
Other values (253) 322
79.3%
2024-03-15T03:34:58.501737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
312
 
13.1%
1 125
 
5.3%
로 108
 
4.5%
, 102
 
4.3%
( 81
 
3.4%
동 81
 
3.4%
) 81
 
3.4%
대 79
 
3.3%
중 79
 
3.3%
앙 76
 
3.2%
Other values (169) 1253
52.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1215
51.1%
Decimal Number 556
23.4%
Space Separator 312
 
13.1%
Other Punctuation 102
 
4.3%
Open Punctuation 81
 
3.4%
Close Punctuation 81
 
3.4%
Dash Punctuation 15
 
0.6%
Uppercase Letter 14
 
0.6%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 108
 
8.9%
동 81
 
6.7%
대 79
 
6.5%
중 79
 
6.5%
앙 76
 
6.3%
층 56
 
4.6%
빌 48
 
4.0%
딩 48
 
4.0%
가 37
 
3.0%
호 36
 
3.0%
Other values (143) 567
46.7%
Decimal Number
ValueCountFrequency (%)
1 125
22.5%
0 66
11.9%
3 58
10.4%
2 57
10.3%
6 51
9.2%
4 47
 
8.5%
5 40
 
7.2%
8 39
 
7.0%
9 38
 
6.8%
7 35
 
6.3%
Uppercase Letter
ValueCountFrequency (%)
K 4
28.6%
D 2
14.3%
C 1
 
7.1%
J 1
 
7.1%
S 1
 
7.1%
I 1
 
7.1%
E 1
 
7.1%
B 1
 
7.1%
A 1
 
7.1%
T 1
 
7.1%
Space Separator
ValueCountFrequency (%)
312
100.0%
Other Punctuation
ValueCountFrequency (%)
, 102
100.0%
Open Punctuation
ValueCountFrequency (%)
( 81
100.0%
Close Punctuation
ValueCountFrequency (%)
) 81
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1216
51.2%
Common 1147
48.3%
Latin 14
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 108
 
8.9%
동 81
 
6.7%
대 79
 
6.5%
중 79
 
6.5%
앙 76
 
6.2%
층 56
 
4.6%
빌 48
 
3.9%
딩 48
 
3.9%
가 37
 
3.0%
호 36
 
3.0%
Other values (144) 568
46.7%
Common
ValueCountFrequency (%)
312
27.2%
1 125
10.9%
, 102
 
8.9%
( 81
 
7.1%
) 81
 
7.1%
0 66
 
5.8%
3 58
 
5.1%
2 57
 
5.0%
6 51
 
4.4%
4 47
 
4.1%
Other values (5) 167
14.6%
Latin
ValueCountFrequency (%)
K 4
28.6%
D 2
14.3%
C 1
 
7.1%
J 1
 
7.1%
S 1
 
7.1%
I 1
 
7.1%
E 1
 
7.1%
B 1
 
7.1%
A 1
 
7.1%
T 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1215
51.1%
ASCII 1161
48.8%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
312
26.9%
1 125
10.8%
, 102
 
8.8%
( 81
 
7.0%
) 81
 
7.0%
0 66
 
5.7%
3 58
 
5.0%
2 57
 
4.9%
6 51
 
4.4%
4 47
 
4.0%
Other values (15) 181
15.6%
Hangul
ValueCountFrequency (%)
로 108
 
8.9%
동 81
 
6.7%
대 79
 
6.5%
중 79
 
6.5%
앙 76
 
6.3%
층 56
 
4.6%
빌 48
 
4.0%
딩 48
 
4.0%
가 37
 
3.0%
호 36
 
3.0%
Other values (143) 567
46.7%
None
ValueCountFrequency (%)
㈜ 1
100.0%

관련협회
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size976.0 B
선박관리
46 
해운조합
23 
해운협회
22 
원양협회
12 
수협중앙회
 
3

Length

Max length5
Median length4
Mean length4.0283019
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row선박관리
2nd row해운협회
3rd row선박관리
4th row선박관리
5th row선박관리

Common Values

ValueCountFrequency (%)
선박관리 46
43.4%
해운조합 23
21.7%
해운협회 22
20.8%
원양협회 12
 
11.3%
수협중앙회 3
 
2.8%

Length

2024-03-15T03:34:59.006382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T03:34:59.304835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
선박관리 46
43.4%
해운조합 23
21.7%
해운협회 22
20.8%
원양협회 12
 
11.3%
수협중앙회 3
 
2.8%

수산_해운(내_외항)
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size976.0 B
해운(외항)
67 
해운(내항)
15 
수산(외항)
12 
해운(내,외항)
9 
수산(내항)
 
2

Length

Max length8
Median length6
Mean length6.1320755
Min length2

Unique

Unique1 ?
Unique (%)0.9%

Sample

1st row해운(외항)
2nd row해운(외항)
3rd row해운(외항)
4th row해운(외항)
5th row해운(외항)

Common Values

ValueCountFrequency (%)
해운(외항) 67
63.2%
해운(내항) 15
 
14.2%
수산(외항) 12
 
11.3%
해운(내,외항) 9
 
8.5%
수산(내항) 2
 
1.9%
수산 1
 
0.9%

Length

2024-03-15T03:34:59.684565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T03:35:00.104116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
해운(외항 67
63.2%
해운(내항 15
 
14.2%
수산(외항 12
 
11.3%
해운(내,외항 9
 
8.5%
수산(내항 2
 
1.9%
수산 1
 
0.9%

2024년 배정인원(계)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.4339623
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-15T03:35:00.460057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q39
95-th percentile42
Maximum93
Range92
Interquartile range (IQR)7

Descriptive statistics

Standard deviation15.908081
Coefficient of variation (CV)1.6862565
Kurtosis12.156315
Mean9.4339623
Median Absolute Deviation (MAD)2
Skewness3.3342674
Sum1000
Variance253.06703
MonotonicityNot monotonic
2024-03-15T03:35:00.686360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2 25
23.6%
1 17
16.0%
3 10
 
9.4%
4 8
 
7.5%
5 7
 
6.6%
6 5
 
4.7%
10 5
 
4.7%
7 5
 
4.7%
9 4
 
3.8%
24 4
 
3.8%
Other values (14) 16
15.1%
ValueCountFrequency (%)
1 17
16.0%
2 25
23.6%
3 10
 
9.4%
4 8
 
7.5%
5 7
 
6.6%
6 5
 
4.7%
7 5
 
4.7%
8 1
 
0.9%
9 4
 
3.8%
10 5
 
4.7%
ValueCountFrequency (%)
93 1
 
0.9%
81 1
 
0.9%
66 1
 
0.9%
60 1
 
0.9%
49 1
 
0.9%
46 1
 
0.9%
30 1
 
0.9%
29 1
 
0.9%
24 4
3.8%
23 2
1.9%

2024년 배정인원(국가필수국제선박)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)76.2%
Missing85
Missing (%)80.2%
Infinite0
Infinite (%)0.0%
Mean11.095238
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-15T03:35:00.881691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median10
Q316
95-th percentile30
Maximum32
Range31
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.0714098
Coefficient of variation (CV)0.81759488
Kurtosis0.3528816
Mean11.095238
Median Absolute Deviation (MAD)6
Skewness0.99495295
Sum233
Variance82.290476
MonotonicityNot monotonic
2024-03-15T03:35:01.382004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
10 3
 
2.8%
1 2
 
1.9%
16 2
 
1.9%
2 2
 
1.9%
23 1
 
0.9%
3 1
 
0.9%
6 1
 
0.9%
8 1
 
0.9%
32 1
 
0.9%
12 1
 
0.9%
Other values (6) 6
 
5.7%
(Missing) 85
80.2%
ValueCountFrequency (%)
1 2
1.9%
2 2
1.9%
3 1
 
0.9%
4 1
 
0.9%
5 1
 
0.9%
6 1
 
0.9%
7 1
 
0.9%
8 1
 
0.9%
10 3
2.8%
12 1
 
0.9%
ValueCountFrequency (%)
32 1
 
0.9%
30 1
 
0.9%
23 1
 
0.9%
18 1
 
0.9%
17 1
 
0.9%
16 2
1.9%
12 1
 
0.9%
10 3
2.8%
8 1
 
0.9%
7 1
 
0.9%

2024년 배정인원(기타선박)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)23.5%
Missing4
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean7.5196078
Minimum1
Maximum71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-15T03:35:01.790536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q37
95-th percentile29.7
Maximum71
Range70
Interquartile range (IQR)5

Descriptive statistics

Standard deviation11.882869
Coefficient of variation (CV)1.5802512
Kurtosis12.732916
Mean7.5196078
Median Absolute Deviation (MAD)2
Skewness3.3895593
Sum767
Variance141.20258
MonotonicityNot monotonic
2024-03-15T03:35:02.193161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2 24
22.6%
1 17
16.0%
3 11
10.4%
5 10
9.4%
4 8
 
7.5%
7 5
 
4.7%
9 4
 
3.8%
6 4
 
3.8%
24 2
 
1.9%
10 2
 
1.9%
Other values (14) 15
14.2%
(Missing) 4
 
3.8%
ValueCountFrequency (%)
1 17
16.0%
2 24
22.6%
3 11
10.4%
4 8
 
7.5%
5 10
9.4%
6 4
 
3.8%
7 5
 
4.7%
8 2
 
1.9%
9 4
 
3.8%
10 2
 
1.9%
ValueCountFrequency (%)
71 1
0.9%
61 1
0.9%
49 1
0.9%
42 1
0.9%
36 1
0.9%
30 1
0.9%
24 2
1.9%
23 1
0.9%
20 1
0.9%
19 1
0.9%

지방청
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size976.0 B
부산울산청
86 
서울청
17 
광주전남청
 
2
제주청
 
1

Length

Max length5
Median length5
Mean length4.6603774
Min length3

Unique

Unique1 ?
Unique (%)0.9%

Sample

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

Common Values

ValueCountFrequency (%)
부산울산청 86
81.1%
서울청 17
 
16.0%
광주전남청 2
 
1.9%
제주청 1
 
0.9%

Length

2024-03-15T03:35:02.586550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T03:35:02.814920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산울산청 86
81.1%
서울청 17
 
16.0%
광주전남청 2
 
1.9%
제주청 1
 
0.9%

Interactions

2024-03-15T03:34:49.249199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:34:46.458815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:34:47.439234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:34:48.408141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:34:49.501386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:34:46.697846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:34:47.673215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:34:48.601383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:34:49.663011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:34:46.935387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:34:47.906817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:34:48.739527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:34:49.857476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:34:47.183772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:34:48.153082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:34:48.989489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T03:35:02.950791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번기업규모시도시군구관련협회수산_해운(내_외항)2024년 배정인원(계)2024년 배정인원(국가필수국제선박)2024년 배정인원(기타선박)지방청
연번1.0000.2110.3060.4660.4000.2060.0000.0000.0000.281
기업규모0.2111.0000.0000.5450.4080.5460.7160.7060.7980.000
시도0.3060.0001.0000.9990.5870.2240.0000.0000.0001.000
시군구0.4660.5450.9991.0000.7220.6340.0000.6280.0000.970
관련협회0.4000.4080.5870.7221.0000.9050.0000.5520.0000.323
수산_해운(내_외항)0.2060.5460.2240.6340.9051.0000.0000.7970.0000.106
2024년 배정인원(계)0.0000.7160.0000.0000.0000.0001.0000.6740.9650.000
2024년 배정인원(국가필수국제선박)0.0000.7060.0000.6280.5520.7970.6741.0000.6570.000
2024년 배정인원(기타선박)0.0000.7980.0000.0000.0000.0000.9650.6571.0000.000
지방청0.2810.0001.0000.9700.3230.1060.0000.0000.0001.000
2024-03-15T03:35:03.230901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구시도기업규모관련협회지방청수산_해운(내_외항)
시군구1.0000.8960.2770.3740.8740.339
시도0.8961.0000.0000.2530.9950.151
기업규모0.2770.0001.0000.3350.0000.263
관련협회0.3740.2530.3351.0000.2670.847
지방청0.8740.9950.0000.2671.0000.065
수산_해운(내_외항)0.3390.1510.2630.8470.0651.000
2024-03-15T03:35:03.536725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번2024년 배정인원(계)2024년 배정인원(국가필수국제선박)2024년 배정인원(기타선박)기업규모시도시군구관련협회수산_해운(내_외항)지방청
연번1.0000.0370.039-0.0140.0510.1230.1820.1960.1320.164
2024년 배정인원(계)0.0371.0000.8440.9670.5560.0000.0000.0000.0000.000
2024년 배정인원(국가필수국제선박)0.0390.8441.0000.6740.4920.0000.2010.3330.5010.000
2024년 배정인원(기타선박)-0.0140.9670.6741.0000.4980.0000.0000.0000.0000.000
기업규모0.0510.5560.4920.4981.0000.0000.2770.3350.2630.000
시도0.1230.0000.0000.0000.0001.0000.8960.2530.1510.995
시군구0.1820.0000.2010.0000.2770.8961.0000.3740.3390.874
관련협회0.1960.0000.3330.0000.3350.2530.3741.0000.8470.267
수산_해운(내_외항)0.1320.0000.5010.0000.2630.1510.3390.8471.0000.065
지방청0.1640.0000.0000.0000.0000.9950.8740.2670.0651.000

Missing values

2024-03-15T03:34:50.235883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T03:34:50.796283image/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.
2024-03-15T03:34:51.102101image/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

연번기업규모업체명시도시군구이하주소관련협회수산_해운(내_외항)2024년 배정인원(계)2024년 배정인원(국가필수국제선박)2024년 배정인원(기타선박)지방청
01중소기업(주)KSS마린부산중구중앙대로146, 8층선박관리해운(외항)1<NA>1부산울산청
12대기업(주)KSS해운부산중구중앙대로146 (중앙동4가, 대한항공빌딩8층)해운협회해운(외항)24<NA>24부산울산청
23중소기업(주)가나마린부산동구중앙대로180번길 6-12 12층 (초량동, DK빌딩)선박관리해운(외항)2<NA>2부산울산청
34중소기업(주)고려에스엠부산중구충장대로 7, 10층(중앙동4가)선박관리해운(외항)301218부산울산청
45중소기업(주)그린마리타임부산해운대구센텀중앙로 97, 센텀스카이비즈 2502호선박관리해운(외항)1<NA>1부산울산청
56중소기업(주)그린에스엠부산해운대구센텀중앙로 97, 2505호(센템스카이비즈)선박관리해운(외항)1<NA>1부산울산청
67중소기업(주)남북수산부산중구백산길 17 602호 (동광동3가, 삼성빌딩)원양협회수산(외항)2<NA>2부산울산청
78중소기업(주)동남(승선)부산서구원양로 171 (암남동)원양협회수산(외항)1<NA>1부산울산청
89중소기업(주)리앤쉽핑부산해운대구센텀중앙로 97, A동 3101호(센텀스카이비즈)선박관리해운(외항)1<NA>1부산울산청
910중소기업(주)사조오양부산서구충무대로 170 (남부민동)원양협회수산(외항)2<NA>2부산울산청
연번기업규모업체명시도시군구이하주소관련협회수산_해운(내_외항)2024년 배정인원(계)2024년 배정인원(국가필수국제선박)2024년 배정인원(기타선박)지방청
9697중소기업한선해운(주)서울강남구삼성로 508, 909호해운조합해운(내항)2<NA>2서울청
9798대기업한성기업(주)부산영도구태종로 63(대교동1가)원양협회수산(외항)1<NA>1부산울산청
9899중소기업현대엘엔지해운(주)서울중구세종대로 39(남대문로4가,7층)해운협회해운(외항)24168서울청
99100중소기업화이브오션(주)서울중구무교로 6, 9층(을지로1가, 금세기빌딩)해운협회해운(외항)2<NA>2서울청
100101중소기업효동선박(주)부산중구충장대로9번길16 (중앙동4가, 효동빌딩 6층)해운조합해운(내항)3<NA>3부산울산청
101102중소기업훼어선박(주)부산중구중앙대로 126 502호 (중앙동4가, 부일빌딩)선박관리해운(외항)7<NA>7부산울산청
102103중소기업대양해운㈜제주제주시동문로 125, 대양해운㈜해운조합해운(내항)1<NA>1제주청
103104중소기업영우해운㈜울산남구장생포고래로263, 기영빌딩 8층해운조합해운(내항)1<NA>1부산울산청
104105중소기업동국상선㈜부산동구중앙대로180번길 6-12, 3층선박관리해운(외항)2<NA>2부산울산청
105106중소기업엔제이에스엠㈜부산동구중앙대로196번길 6-7, 혁정빌딩 302호선박관리해운(외항)2<NA>2부산울산청