Overview

Dataset statistics

Number of variables6
Number of observations51
Missing cells1
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory52.6 B

Variable types

Numeric2
Categorical1
Text3

Dataset

Description경상남도 창녕군 농어촌버스 버스운행정보에 대한 데이터를 포함하고 있습니다.[노선번호, 기점, 경유지, 종점, 운행회수(편도), 운행시간별]
URLhttps://www.data.go.kr/data/15005337/fileData.do

Alerts

경유지 has 1 (2.0%) missing valuesMissing
노선번호 has unique valuesUnique
운행시간별 has unique valuesUnique

Reproduction

Analysis started2023-12-12 08:35:06.782700
Analysis finished2023-12-12 08:35:08.009275
Duration1.23 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

노선번호
Real number (ℝ)

UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26
Minimum1
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T17:35:08.089844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.5
Q113.5
median26
Q338.5
95-th percentile48.5
Maximum51
Range50
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.866069
Coefficient of variation (CV)0.57177187
Kurtosis-1.2
Mean26
Median Absolute Deviation (MAD)13
Skewness0
Sum1326
Variance221
MonotonicityStrictly increasing
2023-12-12T17:35:08.304015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
2.0%
2 1
 
2.0%
29 1
 
2.0%
30 1
 
2.0%
31 1
 
2.0%
32 1
 
2.0%
33 1
 
2.0%
34 1
 
2.0%
35 1
 
2.0%
36 1
 
2.0%
Other values (41) 41
80.4%
ValueCountFrequency (%)
1 1
2.0%
2 1
2.0%
3 1
2.0%
4 1
2.0%
5 1
2.0%
6 1
2.0%
7 1
2.0%
8 1
2.0%
9 1
2.0%
10 1
2.0%
ValueCountFrequency (%)
51 1
2.0%
50 1
2.0%
49 1
2.0%
48 1
2.0%
47 1
2.0%
46 1
2.0%
45 1
2.0%
44 1
2.0%
43 1
2.0%
42 1
2.0%

기점
Categorical

Distinct8
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Memory size540.0 B
창녕
30 
영산
10 
남지
4 
이방(옥야)
 
2
부곡
 
2
Other values (3)
 
3

Length

Max length6
Median length2
Mean length2.1568627
Min length2

Unique

Unique3 ?
Unique (%)5.9%

Sample

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

Common Values

ValueCountFrequency (%)
창녕 30
58.8%
영산 10
 
19.6%
남지 4
 
7.8%
이방(옥야) 2
 
3.9%
부곡 2
 
3.9%
감리 1
 
2.0%
박진 1
 
2.0%
이방 1
 
2.0%

Length

2023-12-12T17:35:08.502254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:35:08.675306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
창녕 30
58.8%
영산 10
 
19.6%
남지 4
 
7.8%
이방(옥야 2
 
3.9%
부곡 2
 
3.9%
감리 1
 
2.0%
박진 1
 
2.0%
이방 1
 
2.0%

경유지
Text

MISSING 

Distinct49
Distinct (%)98.0%
Missing1
Missing (%)2.0%
Memory size540.0 B
2023-12-12T17:35:09.069113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length34
Mean length25.26
Min length2

Characters and Unicode

Total characters1263
Distinct characters148
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)96.0%

Sample

1st row창서,왕산,모전,장기,무솔.십이리,성산,이천,운봉,연당.성곡.월곡
2nd row창서,왕산,모전,장기,무솔,십이리,성산,이천,운봉,원동방리,덕곡
3rd row창서.십이리.이천.운봉.안심.연화.운봉.방리.덕곡.가복
4th row창서,왕산,모전,장기,무솔,십이리,신안,평지,퇴산,옥산,용산,유산,용호
5th row창서,왕산,모전,장기,무솔,십이리,신안,평지,퇴산,옥산,용산,유산,용호
ValueCountFrequency (%)
창서,왕산,모전,장기,무솔,십이리,신안,평지,퇴산,옥산,용산,유산,용호 2
 
3.5%
용소,세거리,선소,거마,진창 2
 
3.5%
창서,왕산,모전,장기,무솔,십이리,성산,이천,운봉,원동방리,덕곡 1
 
1.8%
덕곡,부곡,인교,비봉,구산,학포 1
 
1.8%
월평 1
 
1.8%
퇴천,여초,계성,명리,영산,도천,신제1구,신제2구,십자둑,송진1구,송진2구 1
 
1.8%
덕곡,부곡,인교,비봉,학포,노리 1
 
1.8%
수다,인교,수성,비봉,구산,유산 1
 
1.8%
본포,월계,연동,북면사무소 1
 
1.8%
예리,덕곡,부곡,인교,비봉,구산,학포,노리,본포,인교,부곡 1
 
1.8%
Other values (45) 45
78.9%
2023-12-12T17:35:09.671706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 322
25.5%
산 48
 
3.8%
곡 38
 
3.0%
리 34
 
2.7%
구 27
 
2.1%
. 22
 
1.7%
계 22
 
1.7%
신 21
 
1.7%
포 20
 
1.6%
동 18
 
1.4%
Other values (138) 691
54.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 877
69.4%
Other Punctuation 344
 
27.2%
Decimal Number 17
 
1.3%
Open Punctuation 9
 
0.7%
Close Punctuation 9
 
0.7%
Space Separator 7
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 48
 
5.5%
곡 38
 
4.3%
리 34
 
3.9%
구 27
 
3.1%
계 22
 
2.5%
신 21
 
2.4%
포 20
 
2.3%
동 18
 
2.1%
전 18
 
2.1%
장 18
 
2.1%
Other values (130) 613
69.9%
Decimal Number
ValueCountFrequency (%)
2 9
52.9%
1 7
41.2%
3 1
 
5.9%
Other Punctuation
ValueCountFrequency (%)
, 322
93.6%
. 22
 
6.4%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 877
69.4%
Common 386
30.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 48
 
5.5%
곡 38
 
4.3%
리 34
 
3.9%
구 27
 
3.1%
계 22
 
2.5%
신 21
 
2.4%
포 20
 
2.3%
동 18
 
2.1%
전 18
 
2.1%
장 18
 
2.1%
Other values (130) 613
69.9%
Common
ValueCountFrequency (%)
, 322
83.4%
. 22
 
5.7%
( 9
 
2.3%
) 9
 
2.3%
2 9
 
2.3%
7
 
1.8%
1 7
 
1.8%
3 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 877
69.4%
ASCII 386
30.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 322
83.4%
. 22
 
5.7%
( 9
 
2.3%
) 9
 
2.3%
2 9
 
2.3%
7
 
1.8%
1 7
 
1.8%
3 1
 
0.3%
Hangul
ValueCountFrequency (%)
산 48
 
5.5%
곡 38
 
4.3%
리 34
 
3.9%
구 27
 
3.1%
계 22
 
2.5%
신 21
 
2.4%
포 20
 
2.3%
동 18
 
2.1%
전 18
 
2.1%
장 18
 
2.1%
Other values (130) 613
69.9%

종점
Text

Distinct40
Distinct (%)78.4%
Missing0
Missing (%)0.0%
Memory size540.0 B
2023-12-12T17:35:09.949678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.9215686
Min length2

Characters and Unicode

Total characters149
Distinct characters59
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)64.7%

Sample

1st row안심
2nd row가복
3rd row안심,가복
4th row목단
5th row목단,월포
ValueCountFrequency (%)
창녕 4
 
7.8%
남지 3
 
5.9%
부곡 3
 
5.9%
노리 2
 
3.9%
적교 2
 
3.9%
이방(옥야 2
 
3.9%
박진 2
 
3.9%
남지(신제 1
 
2.0%
옥천 1
 
2.0%
광계 1
 
2.0%
Other values (30) 30
58.8%
2023-12-12T17:35:10.446718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
곡 9
 
6.0%
) 8
 
5.4%
( 8
 
5.4%
리 6
 
4.0%
창 5
 
3.4%
녕 5
 
3.4%
산 5
 
3.4%
부 5
 
3.4%
영 4
 
2.7%
지 4
 
2.7%
Other values (49) 90
60.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 129
86.6%
Close Punctuation 8
 
5.4%
Open Punctuation 8
 
5.4%
Other Punctuation 4
 
2.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
곡 9
 
7.0%
리 6
 
4.7%
창 5
 
3.9%
녕 5
 
3.9%
산 5
 
3.9%
부 5
 
3.9%
영 4
 
3.1%
지 4
 
3.1%
남 4
 
3.1%
옥 4
 
3.1%
Other values (46) 78
60.5%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 129
86.6%
Common 20
 
13.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
곡 9
 
7.0%
리 6
 
4.7%
창 5
 
3.9%
녕 5
 
3.9%
산 5
 
3.9%
부 5
 
3.9%
영 4
 
3.1%
지 4
 
3.1%
남 4
 
3.1%
옥 4
 
3.1%
Other values (46) 78
60.5%
Common
ValueCountFrequency (%)
) 8
40.0%
( 8
40.0%
, 4
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 129
86.6%
ASCII 20
 
13.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
곡 9
 
7.0%
리 6
 
4.7%
창 5
 
3.9%
녕 5
 
3.9%
산 5
 
3.9%
부 5
 
3.9%
영 4
 
3.1%
지 4
 
3.1%
남 4
 
3.1%
옥 4
 
3.1%
Other values (46) 78
60.5%
ASCII
ValueCountFrequency (%)
) 8
40.0%
( 8
40.0%
, 4
20.0%

운행회수(편도)
Real number (ℝ)

Distinct13
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T17:35:10.634428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12.5
median5
Q36
95-th percentile17.5
Maximum25
Range24
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation5.2115257
Coefficient of variation (CV)0.86858761
Kurtosis3.7623497
Mean6
Median Absolute Deviation (MAD)2
Skewness1.8715497
Sum306
Variance27.16
MonotonicityNot monotonic
2023-12-12T17:35:10.787636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
6 11
21.6%
1 7
13.7%
2 6
11.8%
4 6
11.8%
3 5
9.8%
5 4
 
7.8%
8 3
 
5.9%
10 2
 
3.9%
20 2
 
3.9%
12 2
 
3.9%
Other values (3) 3
 
5.9%
ValueCountFrequency (%)
1 7
13.7%
2 6
11.8%
3 5
9.8%
4 6
11.8%
5 4
 
7.8%
6 11
21.6%
8 3
 
5.9%
10 2
 
3.9%
12 2
 
3.9%
14 1
 
2.0%
ValueCountFrequency (%)
25 1
 
2.0%
20 2
 
3.9%
15 1
 
2.0%
14 1
 
2.0%
12 2
 
3.9%
10 2
 
3.9%
8 3
 
5.9%
6 11
21.6%
5 4
 
7.8%
4 6
11.8%

운행시간별
Text

UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size540.0 B
2023-12-12T17:35:11.067684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length166
Median length81
Mean length53.235294
Min length11

Characters and Unicode

Total characters2715
Distinct characters93
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

Unique51 ?
Unique (%)100.0%

Sample

1st row창녕-안심 10:20,16:30, 안심-창녕 07:10(편도),11:00,17:00
2nd row창녕-가복 08:20,14:10, 가복-창녕 06:50(편도),08:50,14:50
3rd row창녕-안심,가복 18:20(편도)
4th row창녕-목단 13:30, 목단-창녕 14:00
5th row창녕-목단,월포 07:10,17:40, 목단-월포,창녕 07:40,18:05
ValueCountFrequency (%)
영산-창녕 2
 
1.0%
왕복운행 2
 
1.0%
창녕-영산 2
 
1.0%
옥천-창녕 2
 
1.0%
13:10 2
 
1.0%
13:30 2
 
1.0%
영산-남지 2
 
1.0%
창녕-옥천 2
 
1.0%
남지-영산 2
 
1.0%
창녕-안심 1
 
0.5%
Other values (174) 174
90.2%
2023-12-12T17:35:11.862568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 462
17.0%
: 316
11.6%
, 298
 
11.0%
1 283
 
10.4%
142
 
5.2%
5 99
 
3.6%
- 97
 
3.6%
2 82
 
3.0%
3 81
 
3.0%
4 74
 
2.7%
Other values (83) 781
28.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1264
46.6%
Other Punctuation 619
22.8%
Other Letter 539
19.9%
Space Separator 142
 
5.2%
Dash Punctuation 97
 
3.6%
Close Punctuation 27
 
1.0%
Open Punctuation 27
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
녕 64
 
11.9%
창 64
 
11.9%
산 31
 
5.8%
곡 28
 
5.2%
영 27
 
5.0%
지 19
 
3.5%
남 18
 
3.3%
리 17
 
3.2%
부 16
 
3.0%
이 12
 
2.2%
Other values (66) 243
45.1%
Decimal Number
ValueCountFrequency (%)
0 462
36.6%
1 283
22.4%
5 99
 
7.8%
2 82
 
6.5%
3 81
 
6.4%
4 74
 
5.9%
7 59
 
4.7%
8 53
 
4.2%
9 39
 
3.1%
6 32
 
2.5%
Other Punctuation
ValueCountFrequency (%)
: 316
51.1%
, 298
48.1%
. 5
 
0.8%
Space Separator
ValueCountFrequency (%)
142
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 97
100.0%
Close Punctuation
ValueCountFrequency (%)
) 27
100.0%
Open Punctuation
ValueCountFrequency (%)
( 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2176
80.1%
Hangul 539
 
19.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
녕 64
 
11.9%
창 64
 
11.9%
산 31
 
5.8%
곡 28
 
5.2%
영 27
 
5.0%
지 19
 
3.5%
남 18
 
3.3%
리 17
 
3.2%
부 16
 
3.0%
이 12
 
2.2%
Other values (66) 243
45.1%
Common
ValueCountFrequency (%)
0 462
21.2%
: 316
14.5%
, 298
13.7%
1 283
13.0%
142
 
6.5%
5 99
 
4.5%
- 97
 
4.5%
2 82
 
3.8%
3 81
 
3.7%
4 74
 
3.4%
Other values (7) 242
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2176
80.1%
Hangul 539
 
19.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 462
21.2%
: 316
14.5%
, 298
13.7%
1 283
13.0%
142
 
6.5%
5 99
 
4.5%
- 97
 
4.5%
2 82
 
3.8%
3 81
 
3.7%
4 74
 
3.4%
Other values (7) 242
11.1%
Hangul
ValueCountFrequency (%)
녕 64
 
11.9%
창 64
 
11.9%
산 31
 
5.8%
곡 28
 
5.2%
영 27
 
5.0%
지 19
 
3.5%
남 18
 
3.3%
리 17
 
3.2%
부 16
 
3.0%
이 12
 
2.2%
Other values (66) 243
45.1%

Interactions

2023-12-12T17:35:07.483267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:07.277617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:07.623016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:07.378341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:35:11.997553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선번호기점경유지종점운행회수(편도)운행시간별
노선번호1.0000.6001.0000.8360.4591.000
기점0.6001.0001.0000.1120.0001.000
경유지1.0001.0001.0000.9740.9781.000
종점0.8360.1120.9741.0000.0001.000
운행회수(편도)0.4590.0000.9780.0001.0001.000
운행시간별1.0001.0001.0001.0001.0001.000
2023-12-12T17:35:12.128636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선번호운행회수(편도)기점
노선번호1.000-0.0840.382
운행회수(편도)-0.0841.0000.000
기점0.3820.0001.000

Missing values

2023-12-12T17:35:07.794277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:35:07.953652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

노선번호기점경유지종점운행회수(편도)운행시간별
01창녕창서,왕산,모전,장기,무솔.십이리,성산,이천,운봉,연당.성곡.월곡안심5창녕-안심 10:20,16:30, 안심-창녕 07:10(편도),11:00,17:00
12창녕창서,왕산,모전,장기,무솔,십이리,성산,이천,운봉,원동방리,덕곡가복5창녕-가복 08:20,14:10, 가복-창녕 06:50(편도),08:50,14:50
23창녕창서.십이리.이천.운봉.안심.연화.운봉.방리.덕곡.가복안심,가복1창녕-안심,가복 18:20(편도)
34창녕창서,왕산,모전,장기,무솔,십이리,신안,평지,퇴산,옥산,용산,유산,용호목단2창녕-목단 13:30, 목단-창녕 14:00
45창녕창서,왕산,모전,장기,무솔,십이리,신안,평지,퇴산,옥산,용산,유산,용호목단,월포4창녕-목단,월포 07:10,17:40, 목단-월포,창녕 07:40,18:05
56창녕창서,왕산,모전,장기,무솔,십이리,대견,월포,합리,신안월포4창녕-월포 09:30,12:50, 월포-창녕 09:50,13:10
67창녕도야,고암(중대),계팔,신기감리14창녕-감리 06:50,08:00,09:20,10:30,11:30,12:50,14:00,15:30,17:40,19:00, 감리-창녕 07:15,08:25,09:50,11:00,12:00,13:20,14:30,16:00,18:10,19:20
78창녕창서,왕산,모전,장기,무솔,십이리,신안, 평지,퇴산,옥산,울기,계동,대곡,구미이방(옥야)6창녕-이방(옥야) 07:40,11:30,15:30, 이방(옥야)-창녕 08:20,12:20,16:40
89감리감리,청간청간10감리-청간 07:10,09:40,13:10,15:50,18:00, 청간-감리 07:15,09:50,13:20,16:00,18:10
910창녕도야,고암(중대),대암,간상간적6창녕-간적 08:40,12:00,16:20, 간적-창녕 09:00,12:20,16:40
노선번호기점경유지종점운행회수(편도)운행시간별
4142영산봉산,연포,작포,월령1구,월령2구,십자둑,송진2구,송진1구남지6영산-월령,남지 07:35,12:00,15:50, 남지-월령,영산 09:50,15:20,18:00
4243영산신제3구,신제1구,신제2구,상대포남지(신제)2영산-남지 15:20, 남지-영산 13:10
4344남지상대포,성사,아지,고곡,반포박진8남지-박진 08:45,12:00,16:20,19:00, 박진-남지 07:20,09:10,12:40,17:00
4445남지송진1구,송진2구,우강,오호,증산,임해진,온정부곡5남지-부곡 08:20,16:00, 부곡-남지 09:50,14:30,18:10
4546남지송진2구,우강,길곡,온정,부곡,덕곡,영산영산(길곡)1남지-길곡,부곡 12:30
4647창녕신창여중,창녕농협,탐하환곡4창녕-환곡 09:30,14:30,18:50, 환곡-창녕 07:40,09:45,14:45
4748영산<NA>구계리6영산-구계리 08:40,12:20,17:30, 구계리-영산 08:55,12:35,17:45
4849남지상대포.신전.매전.도동.유리장마6남지-장마 09:30,11:00,17:30, 장마-남지 09:45,11:15,17:45
4950영산월평광계6영산-광계 09:10,11:40,16:30, 광계-영산 09:20,11:10,16:40
5051이방죽전등림6이방-등림 07:40,11:40,14:50, 등림-이방 08:10,12:10,15:10