Overview

Dataset statistics

Number of variables14
Number of observations44
Missing cells4
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.2 KiB
Average record size in memory121.9 B

Variable types

Numeric7
Text7

Dataset

Description인천광역시 시내버스 면허업체의 이름, 대표자명, 사업자등록번호, 주소, 전화, 팩스번호, 노선수, 버스종별 대수 등을 알 수 있습니다.
Author인천광역시
URLhttps://www.data.go.kr/data/15045238/fileData.do

Alerts

노선수 is highly overall correlated with 중형 and 2 other fieldsHigh correlation
대형 is highly overall correlated with 상용차High correlation
중형 is highly overall correlated with 노선수High correlation
상용차 is highly overall correlated with 노선수 and 2 other fieldsHigh correlation
예비차 is highly overall correlated with 노선수 and 1 other fieldsHigh correlation
법인등록번호 has 2 (4.5%) missing valuesMissing
사업자등록번호 has 1 (2.3%) missing valuesMissing
팩스번호 has 1 (2.3%) missing valuesMissing
번호 has unique valuesUnique
업체명 has unique valuesUnique
좌석형 has 35 (79.5%) zerosZeros
대형 has 14 (31.8%) zerosZeros
중형 has 23 (52.3%) zerosZeros
상용차 has 4 (9.1%) zerosZeros
예비차 has 8 (18.2%) zerosZeros

Reproduction

Analysis started2024-03-14 10:48:41.723580
Analysis finished2024-03-14 10:48:53.095612
Duration11.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.5
Minimum1
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size524.0 B
2024-03-14T19:48:53.221695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.15
Q111.75
median22.5
Q333.25
95-th percentile41.85
Maximum44
Range43
Interquartile range (IQR)21.5

Descriptive statistics

Standard deviation12.845233
Coefficient of variation (CV)0.57089923
Kurtosis-1.2
Mean22.5
Median Absolute Deviation (MAD)11
Skewness0
Sum990
Variance165
MonotonicityStrictly increasing
2024-03-14T19:48:53.483663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1 1
 
2.3%
24 1
 
2.3%
26 1
 
2.3%
27 1
 
2.3%
28 1
 
2.3%
29 1
 
2.3%
30 1
 
2.3%
31 1
 
2.3%
32 1
 
2.3%
33 1
 
2.3%
Other values (34) 34
77.3%
ValueCountFrequency (%)
1 1
2.3%
2 1
2.3%
3 1
2.3%
4 1
2.3%
5 1
2.3%
6 1
2.3%
7 1
2.3%
8 1
2.3%
9 1
2.3%
10 1
2.3%
ValueCountFrequency (%)
44 1
2.3%
43 1
2.3%
42 1
2.3%
41 1
2.3%
40 1
2.3%
39 1
2.3%
38 1
2.3%
37 1
2.3%
36 1
2.3%
35 1
2.3%

업체명
Text

UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size480.0 B
2024-03-14T19:48:54.357300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length4
Mean length4.5
Min length4

Characters and Unicode

Total characters198
Distinct characters75
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)100.0%

Sample

1st row강인교통
2nd row강인여객
3rd row강화교통
4th row공영급행
5th row더월드교통
ValueCountFrequency (%)
강인교통 1
 
2.3%
강인여객 1
 
2.3%
인천교통공사 1
 
2.3%
신강교통 1
 
2.3%
신동아교통 1
 
2.3%
신화여객 1
 
2.3%
신흥교통 1
 
2.3%
예성교통 1
 
2.3%
영종운수 1
 
2.3%
원진운수 1
 
2.3%
Other values (34) 34
77.3%
2024-03-14T19:48:55.467297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18
 
9.1%
18
 
9.1%
9
 
4.5%
9
 
4.5%
9
 
4.5%
9
 
4.5%
8
 
4.0%
7
 
3.5%
6
 
3.0%
4
 
2.0%
Other values (65) 101
51.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 195
98.5%
Other Symbol 1
 
0.5%
Open Punctuation 1
 
0.5%
Close Punctuation 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
18
 
9.2%
18
 
9.2%
9
 
4.6%
9
 
4.6%
9
 
4.6%
9
 
4.6%
8
 
4.1%
7
 
3.6%
6
 
3.1%
4
 
2.1%
Other values (62) 98
50.3%
Other Symbol
ValueCountFrequency (%)
1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 196
99.0%
Common 2
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
18
 
9.2%
18
 
9.2%
9
 
4.6%
9
 
4.6%
9
 
4.6%
9
 
4.6%
8
 
4.1%
7
 
3.6%
6
 
3.1%
4
 
2.0%
Other values (63) 99
50.5%
Common
ValueCountFrequency (%)
( 1
50.0%
) 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 195
98.5%
ASCII 2
 
1.0%
None 1
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
18
 
9.2%
18
 
9.2%
9
 
4.6%
9
 
4.6%
9
 
4.6%
9
 
4.6%
8
 
4.1%
7
 
3.6%
6
 
3.1%
4
 
2.1%
Other values (62) 98
50.3%
None
ValueCountFrequency (%)
1
100.0%
ASCII
ValueCountFrequency (%)
( 1
50.0%
) 1
50.0%
Distinct38
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Memory size480.0 B
2024-03-14T19:48:56.107887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length4.2272727
Min length2

Characters and Unicode

Total characters186
Distinct characters72
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)79.5%

Sample

1st row최영희
2nd row최영락, 최우석
3rd row노승후
4th row장진수, 신동완
5th row김해숙
ValueCountFrequency (%)
한강수 7
 
13.2%
장진수 3
 
5.7%
박진성 3
 
5.7%
최우석 2
 
3.8%
김해숙 2
 
3.8%
한문희 1
 
1.9%
류광신 1
 
1.9%
이병철 1
 
1.9%
안광헌,안생준 1
 
1.9%
송병진 1
 
1.9%
Other values (31) 31
58.5%
2024-03-14T19:48:56.970539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 12
 
6.5%
12
 
6.5%
11
 
5.9%
9
 
4.8%
9
 
4.8%
7
 
3.8%
7
 
3.8%
7
 
3.8%
6
 
3.2%
4
 
2.2%
Other values (62) 102
54.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 165
88.7%
Other Punctuation 12
 
6.5%
Space Separator 9
 
4.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12
 
7.3%
11
 
6.7%
9
 
5.5%
7
 
4.2%
7
 
4.2%
7
 
4.2%
6
 
3.6%
4
 
2.4%
4
 
2.4%
4
 
2.4%
Other values (60) 94
57.0%
Other Punctuation
ValueCountFrequency (%)
, 12
100.0%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 165
88.7%
Common 21
 
11.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12
 
7.3%
11
 
6.7%
9
 
5.5%
7
 
4.2%
7
 
4.2%
7
 
4.2%
6
 
3.6%
4
 
2.4%
4
 
2.4%
4
 
2.4%
Other values (60) 94
57.0%
Common
ValueCountFrequency (%)
, 12
57.1%
9
42.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 165
88.7%
ASCII 21
 
11.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 12
57.1%
9
42.9%
Hangul
ValueCountFrequency (%)
12
 
7.3%
11
 
6.7%
9
 
5.5%
7
 
4.2%
7
 
4.2%
7
 
4.2%
6
 
3.6%
4
 
2.4%
4
 
2.4%
4
 
2.4%
Other values (60) 94
57.0%

법인등록번호
Text

MISSING 

Distinct42
Distinct (%)100.0%
Missing2
Missing (%)4.5%
Memory size480.0 B
2024-03-14T19:48:57.715865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters588
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)100.0%

Sample

1st row120111-0489064
2nd row120111-0007733
3rd row120111-0415564
4th row120111-0482216
5th row120111-0508731
ValueCountFrequency (%)
120111-0309890 1
 
2.4%
120113-0004553 1
 
2.4%
124771-0000991 1
 
2.4%
120111-0617574 1
 
2.4%
120113-0005709 1
 
2.4%
124611-0264610 1
 
2.4%
120111-0077596 1
 
2.4%
120111-0155871 1
 
2.4%
120111-0008228 1
 
2.4%
120111-0218489 1
 
2.4%
Other values (32) 32
76.2%
2024-03-14T19:48:58.670645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 182
31.0%
0 121
20.6%
2 67
 
11.4%
- 42
 
7.1%
7 38
 
6.5%
4 35
 
6.0%
6 28
 
4.8%
8 22
 
3.7%
5 21
 
3.6%
9 17
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 546
92.9%
Dash Punctuation 42
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 182
33.3%
0 121
22.2%
2 67
 
12.3%
7 38
 
7.0%
4 35
 
6.4%
6 28
 
5.1%
8 22
 
4.0%
5 21
 
3.8%
9 17
 
3.1%
3 15
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 588
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 182
31.0%
0 121
20.6%
2 67
 
11.4%
- 42
 
7.1%
7 38
 
6.5%
4 35
 
6.0%
6 28
 
4.8%
8 22
 
3.7%
5 21
 
3.6%
9 17
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 588
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 182
31.0%
0 121
20.6%
2 67
 
11.4%
- 42
 
7.1%
7 38
 
6.5%
4 35
 
6.0%
6 28
 
4.8%
8 22
 
3.7%
5 21
 
3.6%
9 17
 
2.9%

사업자등록번호
Text

MISSING 

Distinct43
Distinct (%)100.0%
Missing1
Missing (%)2.3%
Memory size480.0 B
2024-03-14T19:48:59.474076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters516
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)100.0%

Sample

1st row122-86-04260
2nd row137-81-04568
3rd row137-81-77789
4th row121-81-94589
5th row131-86-18486
ValueCountFrequency (%)
122-86-04260 1
 
2.3%
121-81-77159 1
 
2.3%
139-82-02409 1
 
2.3%
122-86-25284 1
 
2.3%
121-81-36070 1
 
2.3%
137-81-39728 1
 
2.3%
131-81-23908 1
 
2.3%
121-81-25587 1
 
2.3%
137-81-13413 1
 
2.3%
121-81-36575 1
 
2.3%
Other values (33) 33
76.7%
2024-03-14T19:49:00.486623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 111
21.5%
- 86
16.7%
8 63
12.2%
2 50
9.7%
3 45
8.7%
7 32
 
6.2%
5 29
 
5.6%
6 27
 
5.2%
0 27
 
5.2%
4 25
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 430
83.3%
Dash Punctuation 86
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 111
25.8%
8 63
14.7%
2 50
11.6%
3 45
10.5%
7 32
 
7.4%
5 29
 
6.7%
6 27
 
6.3%
0 27
 
6.3%
4 25
 
5.8%
9 21
 
4.9%
Dash Punctuation
ValueCountFrequency (%)
- 86
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 516
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 111
21.5%
- 86
16.7%
8 63
12.2%
2 50
9.7%
3 45
8.7%
7 32
 
6.2%
5 29
 
5.6%
6 27
 
5.2%
0 27
 
5.2%
4 25
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 516
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 111
21.5%
- 86
16.7%
8 63
12.2%
2 50
9.7%
3 45
8.7%
7 32
 
6.2%
5 29
 
5.6%
6 27
 
5.2%
0 27
 
5.2%
4 25
 
4.8%
Distinct43
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size480.0 B
2024-03-14T19:49:01.244188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length12
Mean length12.818182
Min length9

Characters and Unicode

Total characters564
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)95.5%

Sample

1st row032-581-1738
2nd row032-578-1738
3rd row032-933-8677032-886-8638
4th row032-432-2295
5th row032-424-7878
ValueCountFrequency (%)
032-887-2842 2
 
4.5%
032-581-1738 1
 
2.3%
032-773-8885 1
 
2.3%
032-867-7065 1
 
2.3%
032-516-7422 1
 
2.3%
032-888-3516 1
 
2.3%
032-746-2728 1
 
2.3%
032-568-5552 1
 
2.3%
032-575-4816 1
 
2.3%
032-885-6900 1
 
2.3%
Other values (33) 33
75.0%
2024-03-14T19:49:02.468138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 93
16.5%
3 75
13.3%
2 73
12.9%
0 70
12.4%
8 61
10.8%
5 50
8.9%
7 35
 
6.2%
6 32
 
5.7%
1 31
 
5.5%
4 24
 
4.3%
Other values (3) 20
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 469
83.2%
Dash Punctuation 93
 
16.5%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 75
16.0%
2 73
15.6%
0 70
14.9%
8 61
13.0%
5 50
10.7%
7 35
7.5%
6 32
6.8%
1 31
6.6%
4 24
 
5.1%
9 18
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
- 93
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 564
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 93
16.5%
3 75
13.3%
2 73
12.9%
0 70
12.4%
8 61
10.8%
5 50
8.9%
7 35
 
6.2%
6 32
 
5.7%
1 31
 
5.5%
4 24
 
4.3%
Other values (3) 20
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 93
16.5%
3 75
13.3%
2 73
12.9%
0 70
12.4%
8 61
10.8%
5 50
8.9%
7 35
 
6.2%
6 32
 
5.7%
1 31
 
5.5%
4 24
 
4.3%
Other values (3) 20
 
3.5%

팩스번호
Text

MISSING 

Distinct40
Distinct (%)93.0%
Missing1
Missing (%)2.3%
Memory size480.0 B
2024-03-14T19:49:03.348888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.976744
Min length11

Characters and Unicode

Total characters515
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)88.4%

Sample

1st row032-574-8445
2nd row032-574-8445
3rd row032-886-8633
4th row032-432-2297
5th row032-424-9009
ValueCountFrequency (%)
032-574-8445 3
 
7.0%
032-887-2847 2
 
4.7%
032-584-6305 1
 
2.3%
032-429-9977 1
 
2.3%
032-885-7606 1
 
2.3%
032-773-8886 1
 
2.3%
032-867-7180 1
 
2.3%
032-527-5526 1
 
2.3%
032-888-3519 1
 
2.3%
032-551-2728 1
 
2.3%
Other values (30) 30
69.8%
2024-03-14T19:49:04.604693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 86
16.7%
2 69
13.4%
3 68
13.2%
0 61
11.8%
8 51
9.9%
5 42
8.2%
7 35
6.8%
4 32
 
6.2%
1 28
 
5.4%
6 23
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 429
83.3%
Dash Punctuation 86
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 69
16.1%
3 68
15.9%
0 61
14.2%
8 51
11.9%
5 42
9.8%
7 35
8.2%
4 32
7.5%
1 28
6.5%
6 23
 
5.4%
9 20
 
4.7%
Dash Punctuation
ValueCountFrequency (%)
- 86
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 515
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 86
16.7%
2 69
13.4%
3 68
13.2%
0 61
11.8%
8 51
9.9%
5 42
8.2%
7 35
6.8%
4 32
 
6.2%
1 28
 
5.4%
6 23
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 515
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 86
16.7%
2 69
13.4%
3 68
13.2%
0 61
11.8%
8 51
9.9%
5 42
8.2%
7 35
6.8%
4 32
 
6.2%
1 28
 
5.4%
6 23
 
4.5%
Distinct38
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Memory size480.0 B
2024-03-14T19:49:05.711457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length28
Mean length19.272727
Min length8

Characters and Unicode

Total characters848
Distinct characters122
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)79.5%

Sample

1st row부평구 백범로 570(십정동)
2nd row부평구 백범로 570(십정동)
3rd row강화군 선원면 중앙로219(김포시 양촌읍 향동로 20)
4th row원당대로 227-10(오류동 434-154)
5th row연수구 아카데미로 51번길 42
ValueCountFrequency (%)
서구 12
 
7.5%
중구 10
 
6.3%
부평구 7
 
4.4%
남동구 5
 
3.1%
연수구 4
 
2.5%
백범로 4
 
2.5%
원창로 4
 
2.5%
570(십정동 4
 
2.5%
축항대로86번길 4
 
2.5%
129(항동7가 3
 
1.9%
Other values (90) 102
64.2%
2024-03-14T19:49:07.289359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
115
 
13.6%
45
 
5.3%
41
 
4.8%
38
 
4.5%
2 35
 
4.1%
( 33
 
3.9%
) 33
 
3.9%
1 32
 
3.8%
7 22
 
2.6%
0 20
 
2.4%
Other values (112) 434
51.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 443
52.2%
Decimal Number 207
24.4%
Space Separator 115
 
13.6%
Open Punctuation 33
 
3.9%
Close Punctuation 33
 
3.9%
Dash Punctuation 12
 
1.4%
Other Punctuation 5
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
45
 
10.2%
41
 
9.3%
38
 
8.6%
18
 
4.1%
18
 
4.1%
18
 
4.1%
13
 
2.9%
12
 
2.7%
11
 
2.5%
10
 
2.3%
Other values (96) 219
49.4%
Decimal Number
ValueCountFrequency (%)
2 35
16.9%
1 32
15.5%
7 22
10.6%
0 20
9.7%
3 20
9.7%
4 18
8.7%
9 16
7.7%
5 16
7.7%
6 15
7.2%
8 13
 
6.3%
Other Punctuation
ValueCountFrequency (%)
, 4
80.0%
. 1
 
20.0%
Space Separator
ValueCountFrequency (%)
115
100.0%
Open Punctuation
ValueCountFrequency (%)
( 33
100.0%
Close Punctuation
ValueCountFrequency (%)
) 33
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 443
52.2%
Common 405
47.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
45
 
10.2%
41
 
9.3%
38
 
8.6%
18
 
4.1%
18
 
4.1%
18
 
4.1%
13
 
2.9%
12
 
2.7%
11
 
2.5%
10
 
2.3%
Other values (96) 219
49.4%
Common
ValueCountFrequency (%)
115
28.4%
2 35
 
8.6%
( 33
 
8.1%
) 33
 
8.1%
1 32
 
7.9%
7 22
 
5.4%
0 20
 
4.9%
3 20
 
4.9%
4 18
 
4.4%
9 16
 
4.0%
Other values (6) 61
15.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 443
52.2%
ASCII 405
47.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
115
28.4%
2 35
 
8.6%
( 33
 
8.1%
) 33
 
8.1%
1 32
 
7.9%
7 22
 
5.4%
0 20
 
4.9%
3 20
 
4.9%
4 18
 
4.4%
9 16
 
4.0%
Other values (6) 61
15.1%
Hangul
ValueCountFrequency (%)
45
 
10.2%
41
 
9.3%
38
 
8.6%
18
 
4.1%
18
 
4.1%
18
 
4.1%
13
 
2.9%
12
 
2.7%
11
 
2.5%
10
 
2.3%
Other values (96) 219
49.4%

노선수
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0681818
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size524.0 B
2024-03-14T19:49:07.535276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4.5
Q37
95-th percentile10.7
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.781983
Coefficient of variation (CV)0.54891144
Kurtosis-0.065450006
Mean5.0681818
Median Absolute Deviation (MAD)1.5
Skewness0.59170061
Sum223
Variance7.7394292
MonotonicityNot monotonic
2024-03-14T19:49:07.736192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
4 8
18.2%
6 6
13.6%
3 6
13.6%
7 6
13.6%
2 4
9.1%
1 4
9.1%
5 3
 
6.8%
8 2
 
4.5%
9 2
 
4.5%
11 2
 
4.5%
ValueCountFrequency (%)
1 4
9.1%
2 4
9.1%
3 6
13.6%
4 8
18.2%
5 3
 
6.8%
6 6
13.6%
7 6
13.6%
8 2
 
4.5%
9 2
 
4.5%
11 2
 
4.5%
ValueCountFrequency (%)
12 1
 
2.3%
11 2
 
4.5%
9 2
 
4.5%
8 2
 
4.5%
7 6
13.6%
6 6
13.6%
5 3
 
6.8%
4 8
18.2%
3 6
13.6%
2 4
9.1%

좌석형
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5
Minimum0
Maximum62
Zeros35
Zeros (%)79.5%
Negative0
Negative (%)0.0%
Memory size524.0 B
2024-03-14T19:49:08.100103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile55.1
Maximum62
Range62
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17.180979
Coefficient of variation (CV)2.2907972
Kurtosis4.1948833
Mean7.5
Median Absolute Deviation (MAD)0
Skewness2.310224
Sum330
Variance295.18605
MonotonicityNot monotonic
2024-03-14T19:49:08.288467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 35
79.5%
50 1
 
2.3%
17 1
 
2.3%
57 1
 
2.3%
27 1
 
2.3%
20 1
 
2.3%
62 1
 
2.3%
56 1
 
2.3%
30 1
 
2.3%
11 1
 
2.3%
ValueCountFrequency (%)
0 35
79.5%
11 1
 
2.3%
17 1
 
2.3%
20 1
 
2.3%
27 1
 
2.3%
30 1
 
2.3%
50 1
 
2.3%
56 1
 
2.3%
57 1
 
2.3%
62 1
 
2.3%
ValueCountFrequency (%)
62 1
 
2.3%
57 1
 
2.3%
56 1
 
2.3%
50 1
 
2.3%
30 1
 
2.3%
27 1
 
2.3%
20 1
 
2.3%
17 1
 
2.3%
11 1
 
2.3%
0 35
79.5%

대형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)56.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.5
Minimum0
Maximum103
Zeros14
Zeros (%)31.8%
Negative0
Negative (%)0.0%
Memory size524.0 B
2024-03-14T19:49:08.594288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median24.5
Q349.5
95-th percentile73.85
Maximum103
Range103
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation28.564493
Coefficient of variation (CV)0.93654074
Kurtosis-0.67145953
Mean30.5
Median Absolute Deviation (MAD)24.5
Skewness0.54015479
Sum1342
Variance815.93023
MonotonicityNot monotonic
2024-03-14T19:49:08.814591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 14
31.8%
20 2
 
4.5%
19 2
 
4.5%
42 2
 
4.5%
43 2
 
4.5%
16 2
 
4.5%
60 2
 
4.5%
58 1
 
2.3%
51 1
 
2.3%
69 1
 
2.3%
Other values (15) 15
34.1%
ValueCountFrequency (%)
0 14
31.8%
9 1
 
2.3%
10 1
 
2.3%
16 2
 
4.5%
19 2
 
4.5%
20 2
 
4.5%
29 1
 
2.3%
31 1
 
2.3%
38 1
 
2.3%
41 1
 
2.3%
ValueCountFrequency (%)
103 1
2.3%
84 1
2.3%
74 1
2.3%
73 1
2.3%
71 1
2.3%
69 1
2.3%
60 2
4.5%
59 1
2.3%
58 1
2.3%
51 1
2.3%

중형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.340909
Minimum0
Maximum58
Zeros23
Zeros (%)52.3%
Negative0
Negative (%)0.0%
Memory size524.0 B
2024-03-14T19:49:09.016012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q319.75
95-th percentile47.7
Maximum58
Range58
Interquartile range (IQR)19.75

Descriptive statistics

Standard deviation18.191415
Coefficient of variation (CV)1.4740742
Kurtosis0.18765648
Mean12.340909
Median Absolute Deviation (MAD)0
Skewness1.2712582
Sum543
Variance330.92759
MonotonicityNot monotonic
2024-03-14T19:49:09.214906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 23
52.3%
5 3
 
6.8%
40 3
 
6.8%
46 1
 
2.3%
58 1
 
2.3%
4 1
 
2.3%
2 1
 
2.3%
18 1
 
2.3%
13 1
 
2.3%
48 1
 
2.3%
Other values (8) 8
 
18.2%
ValueCountFrequency (%)
0 23
52.3%
2 1
 
2.3%
4 1
 
2.3%
5 3
 
6.8%
7 1
 
2.3%
12 1
 
2.3%
13 1
 
2.3%
16 1
 
2.3%
18 1
 
2.3%
25 1
 
2.3%
ValueCountFrequency (%)
58 1
 
2.3%
56 1
 
2.3%
48 1
 
2.3%
46 1
 
2.3%
40 3
6.8%
37 1
 
2.3%
34 1
 
2.3%
32 1
 
2.3%
25 1
 
2.3%
18 1
 
2.3%

상용차
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.340909
Minimum0
Maximum121
Zeros4
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size524.0 B
2024-03-14T19:49:09.427103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q142.75
median50.5
Q360
95-th percentile101.95
Maximum121
Range121
Interquartile range (IQR)17.25

Descriptive statistics

Standard deviation26.980564
Coefficient of variation (CV)0.53595703
Kurtosis0.91917609
Mean50.340909
Median Absolute Deviation (MAD)8.5
Skewness0.1002959
Sum2215
Variance727.95085
MonotonicityNot monotonic
2024-03-14T19:49:09.721302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 4
 
9.1%
50 3
 
6.8%
56 3
 
6.8%
42 3
 
6.8%
43 3
 
6.8%
62 2
 
4.5%
60 2
 
4.5%
58 2
 
4.5%
51 2
 
4.5%
46 2
 
4.5%
Other values (18) 18
40.9%
ValueCountFrequency (%)
0 4
9.1%
2 1
 
2.3%
11 1
 
2.3%
16 1
 
2.3%
40 1
 
2.3%
42 3
6.8%
43 3
6.8%
44 1
 
2.3%
46 2
4.5%
48 1
 
2.3%
ValueCountFrequency (%)
121 1
2.3%
105 1
2.3%
103 1
2.3%
96 1
2.3%
79 1
2.3%
73 1
2.3%
69 1
2.3%
66 1
2.3%
62 2
4.5%
60 2
4.5%

예비차
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1818182
Minimum0
Maximum8
Zeros8
Zeros (%)18.2%
Negative0
Negative (%)0.0%
Memory size524.0 B
2024-03-14T19:49:09.966493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34.25
95-th percentile6.85
Maximum8
Range8
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation2.1050629
Coefficient of variation (CV)0.66159118
Kurtosis-0.45298651
Mean3.1818182
Median Absolute Deviation (MAD)1
Skewness0.062165129
Sum140
Variance4.4312896
MonotonicityNot monotonic
2024-03-14T19:49:10.168501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3 10
22.7%
4 9
20.5%
0 8
18.2%
5 6
13.6%
2 4
 
9.1%
7 2
 
4.5%
1 2
 
4.5%
6 2
 
4.5%
8 1
 
2.3%
ValueCountFrequency (%)
0 8
18.2%
1 2
 
4.5%
2 4
 
9.1%
3 10
22.7%
4 9
20.5%
5 6
13.6%
6 2
 
4.5%
7 2
 
4.5%
8 1
 
2.3%
ValueCountFrequency (%)
8 1
 
2.3%
7 2
 
4.5%
6 2
 
4.5%
5 6
13.6%
4 9
20.5%
3 10
22.7%
2 4
 
9.1%
1 2
 
4.5%
0 8
18.2%

Interactions

2024-03-14T19:48:51.225827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:42.658690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:44.345588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:46.052554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:47.756045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:48.827848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:50.128973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:51.368511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:42.897315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:44.575840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:46.286955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:47.888819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:49.171374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:50.264593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:51.518000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:43.134738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:44.812131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:46.534320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:48.025235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:49.313064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:50.405135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:51.671536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:43.377685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:45.058885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:46.779150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:48.170389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:49.456647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:50.552482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:51.819783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:43.610081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:45.301259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:47.019163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:48.305007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:49.597239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:50.690701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:51.973395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:43.851469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:45.548226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:47.264199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:48.449907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:49.740263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:50.839478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:52.134858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:44.091722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:45.792597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:47.513213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:48.591419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:49.918909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:48:50.984780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T19:49:10.345010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호업체명대표자법인등록번호사업자등록번호전화번호팩스번호주사무소노선수좌석형대형중형상용차예비차
번호1.0001.0000.6461.0001.0000.9150.9180.6830.4810.0000.4930.0000.5790.000
업체명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
대표자0.6461.0001.0001.0001.0001.0000.9770.9850.8520.0000.0000.0000.0000.869
법인등록번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
사업자등록번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
전화번호0.9151.0001.0001.0001.0001.0001.0001.0000.9601.0001.0000.0001.0001.000
팩스번호0.9181.0000.9771.0001.0001.0001.0001.0000.9860.0000.9790.9311.0000.912
주사무소0.6831.0000.9851.0001.0001.0001.0001.0000.9630.0000.0000.9170.7070.750
노선수0.4811.0000.8521.0001.0000.9600.9860.9631.0000.0000.4650.5350.7420.736
좌석형0.0001.0000.0001.0001.0001.0000.0000.0000.0001.0000.6270.0000.4910.319
대형0.4931.0000.0001.0001.0001.0000.9790.0000.4650.6271.0000.0000.8250.530
중형0.0001.0000.0001.0001.0000.0000.9310.9170.5350.0000.0001.0000.0000.671
상용차0.5791.0000.0001.0001.0001.0001.0000.7070.7420.4910.8250.0001.0000.784
예비차0.0001.0000.8691.0001.0001.0000.9120.7500.7360.3190.5300.6710.7841.000
2024-03-14T19:49:10.591577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호노선수좌석형대형중형상용차예비차
번호1.000-0.306-0.129-0.277-0.101-0.281-0.276
노선수-0.3061.000-0.0190.2890.5670.6090.637
좌석형-0.129-0.0191.000-0.182-0.3840.135-0.043
대형-0.2770.289-0.1821.000-0.1980.6680.341
중형-0.1010.567-0.384-0.1981.0000.0980.406
상용차-0.2810.6090.1350.6680.0981.0000.514
예비차-0.2760.637-0.0430.3410.4060.5141.000

Missing values

2024-03-14T19:48:52.454294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T19:48:52.796664image/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.
2024-03-14T19:48:53.003106image/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

번호업체명대표자법인등록번호사업자등록번호전화번호팩스번호주사무소노선수좌석형대형중형상용차예비차
01강인교통최영희120111-0489064122-86-04260032-581-1738032-574-8445부평구 백범로 570(십정동)45000505
12강인여객최영락, 최우석120111-0007733137-81-04568032-578-1738032-574-8445부평구 백범로 570(십정동)40600607
23강화교통노승후120111-0415564137-81-77789032-933-8677032-886-8638032-886-8633강화군 선원면 중앙로219(김포시 양촌읍 향동로 20)617290463
34공영급행장진수, 신동완120111-0482216121-81-94589032-432-2295032-432-2297원당대로 227-10(오류동 434-154)1201925444
45더월드교통김해숙120111-0508731131-86-18486032-424-7878032-424-9009연수구 아카데미로 51번길 4230420420
56대인교통김용옥,서여경120111-0077562131-81-23645032-507-5938032-525-3656부평구 대정로 38-1, 3층(부평동)701932515
67도영운수김명화124611-0266632131-81-67565032-816-0966032-816-0964연수구 먼우금로19(동남상가 212)604312552
78동화운수홍일원120111-0008161122-81-12237032-547-1371032-547-1373계양구 효서로 565010301033
89마니교통박수응, 김영모120111-0868648427-87-00583032-584-8523032-569-5819서구 원창로 20(원창동)8574801053
910명진교통한강수, 박호정124611-0261872137-81-41035032-330-3172032-330-3173서구 보도진로30번길 26(가좌동)80056564
번호업체명대표자법인등록번호사업자등록번호전화번호팩스번호주사무소노선수좌석형대형중형상용차예비차
3435인천제물포교통한강수<NA>137-81-93815032-887-2842032-887-2847중구 축항대로86번길 129(항동7가)30510513
3536청라교통김영한, 홍윤성120111-0489056122-86-04275032-584-1738032-574-8445부평구 백범로 570(십정동)430200505
3637청룡교통김해숙, 백억120111-0011445137-81-01009032-584-0890032-584-0891서구 한서로53번길 16(백석동 212-115)40420423
3738태양여객주상준120111-0181842122-81-56424032-503-9112032-503-9114부평구 송내대로373번길 54110058584
3839한국철도공사한문희160171-0004321314-82-10024042-615-5831(02-2639-3880)02-361-8413중구 제물량로 269(인천역)11100111
3940해성운수정곤120111-0502311137-81-96262032-571-7290032-571-7690서구 거북로 27, 301호(석남동)1102046668
4041수정관광화물이병철174711-0004775505-81-22696054-532-6883<NA>경북 상주시 사벌면 상풍로 390100000
4142인천관광공사민민흥120171-0007141<NA>032-899-7300032-899-7309미추홀타워 1706호(관광인프라팀)200000
4243강서관광㈜황호선<NA>136-81-13443032-434-0003032-429-9977남동구 구월로 42. 301,302호200000
4344셔틀콕모빌리티(주)박무열134811-0429200818-81-009621668-081502-3275-2019경기도 화성시 봉담읍 와우로73번길 6100000