Overview

Dataset statistics

Number of variables10
Number of observations179
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.8 KiB
Average record size in memory84.7 B

Variable types

Numeric4
Text2
Categorical4

Dataset

Description경상남도 진주시 버스노선정보 현황(노선번호, 노선ID, 구분, 방면, 거리, 운행횟수, 운행개시일, 운수회사)을 CSV 형태로 제공합니다 총 175건의 데이터를 제공합니다.
Author경상남도 진주시
URLhttps://www.data.go.kr/data/15088243/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
순번 is highly overall correlated with 노선아이디 and 1 other fieldsHigh correlation
노선아이디 is highly overall correlated with 순번 and 2 other fieldsHigh correlation
구분 is highly overall correlated with 노선아이디 and 2 other fieldsHigh correlation
운행개시일 is highly overall correlated with 순번 and 2 other fieldsHigh correlation
운수회사 is highly overall correlated with 구분High correlation
순번 has unique valuesUnique
노선아이디 has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:39:41.201117
Analysis finished2023-12-12 22:39:43.333496
Duration2.13 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct179
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90
Minimum1
Maximum179
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T07:39:43.390637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.9
Q145.5
median90
Q3134.5
95-th percentile170.1
Maximum179
Range178
Interquartile range (IQR)89

Descriptive statistics

Standard deviation51.816986
Coefficient of variation (CV)0.57574428
Kurtosis-1.2
Mean90
Median Absolute Deviation (MAD)45
Skewness0
Sum16110
Variance2685
MonotonicityStrictly increasing
2023-12-13T07:39:43.505008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
114 1
 
0.6%
116 1
 
0.6%
117 1
 
0.6%
118 1
 
0.6%
119 1
 
0.6%
120 1
 
0.6%
121 1
 
0.6%
122 1
 
0.6%
123 1
 
0.6%
Other values (169) 169
94.4%
ValueCountFrequency (%)
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
10 1
0.6%
ValueCountFrequency (%)
179 1
0.6%
178 1
0.6%
177 1
0.6%
176 1
0.6%
175 1
0.6%
174 1
0.6%
173 1
0.6%
172 1
0.6%
171 1
0.6%
170 1
0.6%

노선번호
Text

Distinct97
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-13T07:39:43.751570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.9553073
Min length1

Characters and Unicode

Total characters529
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

Unique15 ?
Unique (%)8.4%

Sample

1st row1
2nd row2
3rd row2-1
4th row3
5th row3-1
ValueCountFrequency (%)
253 2
 
1.1%
280 2
 
1.1%
350 2
 
1.1%
344 2
 
1.1%
343 2
 
1.1%
342 2
 
1.1%
341 2
 
1.1%
340 2
 
1.1%
295 2
 
1.1%
294 2
 
1.1%
Other values (87) 159
88.8%
2023-12-13T07:39:44.103815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 120
22.7%
3 88
16.6%
2 83
15.7%
0 67
12.7%
4 47
 
8.9%
5 37
 
7.0%
7 36
 
6.8%
6 23
 
4.3%
9 14
 
2.6%
8 8
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 523
98.9%
Dash Punctuation 6
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 120
22.9%
3 88
16.8%
2 83
15.9%
0 67
12.8%
4 47
 
9.0%
5 37
 
7.1%
7 36
 
6.9%
6 23
 
4.4%
9 14
 
2.7%
8 8
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 529
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 120
22.7%
3 88
16.6%
2 83
15.7%
0 67
12.7%
4 47
 
8.9%
5 37
 
7.0%
7 36
 
6.8%
6 23
 
4.3%
9 14
 
2.6%
8 8
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 529
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 120
22.7%
3 88
16.6%
2 83
15.7%
0 67
12.7%
4 47
 
8.9%
5 37
 
7.0%
7 36
 
6.8%
6 23
 
4.3%
9 14
 
2.6%
8 8
 
1.5%

노선아이디
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct179
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8102648 × 108
Minimum3.8100013 × 108
Maximum3.8108033 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T07:39:44.235156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.8100013 × 108
5-th percentile3.8100102 × 108
Q13.8101406 × 108
median3.8102611 × 108
Q33.8103612 × 108
95-th percentile3.8105504 × 108
Maximum3.8108033 × 108
Range80200
Interquartile range (IQR)22050

Descriptive statistics

Standard deviation16290.912
Coefficient of variation (CV)4.2755329 × 10-5
Kurtosis2.0601704
Mean3.8102648 × 108
Median Absolute Deviation (MAD)11210
Skewness1.0885903
Sum6.8203739 × 1010
Variance2.6539383 × 108
MonotonicityNot monotonic
2023-12-13T07:39:44.419858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
381000130 1
 
0.6%
381030030 1
 
0.6%
381030130 1
 
0.6%
381080330 1
 
0.6%
381034010 1
 
0.6%
381034020 1
 
0.6%
381034120 1
 
0.6%
381034110 1
 
0.6%
381034220 1
 
0.6%
381034210 1
 
0.6%
Other values (169) 169
94.4%
ValueCountFrequency (%)
381000130 1
0.6%
381000230 1
0.6%
381000231 1
0.6%
381000330 1
0.6%
381000331 1
0.6%
381000430 1
0.6%
381000530 1
0.6%
381000630 1
0.6%
381001010 1
0.6%
381001020 1
0.6%
ValueCountFrequency (%)
381080330 1
0.6%
381080230 1
0.6%
381080120 1
0.6%
381080110 1
0.6%
381073120 1
0.6%
381073110 1
0.6%
381073020 1
0.6%
381073010 1
0.6%
381055130 1
0.6%
381055030 1
0.6%

구분
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
기점
82 
종점
82 
동부순환
 
8
순환
 
7

Length

Max length4
Median length2
Mean length2.0893855
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기점 82
45.8%
종점 82
45.8%
동부순환 8
 
4.5%
순환 7
 
3.9%

Length

2023-12-13T07:39:44.631250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:39:44.744596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기점 82
45.8%
종점 82
45.8%
동부순환 8
 
4.5%
순환 7
 
3.9%

방면
Text

Distinct80
Distinct (%)44.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-13T07:39:45.049286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length6.726257
Min length2

Characters and Unicode

Total characters1204
Distinct characters115
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)31.3%

Sample

1st row평촌(동촌, 발산)
2nd row남산(수목원)
3rd row남산(수목원)
4th row지수면사무소(무등)
5th row지수면사무소(무등)
ValueCountFrequency (%)
판문동차고지 22
 
12.2%
진양호차고지 19
 
10.5%
공영차고지(경상대 14
 
7.7%
공영차고지 13
 
7.2%
초전동 6
 
3.3%
진양호공원 5
 
2.8%
진양호차고지(이현동 4
 
2.2%
농산물도매시장 4
 
2.2%
이현동/혁신도시 4
 
2.2%
원동삼거리(집현 3
 
1.7%
Other values (72) 87
48.1%
2023-12-13T07:39:45.499418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 93
 
7.7%
( 91
 
7.6%
지 75
 
6.2%
차 74
 
6.1%
고 74
 
6.1%
동 48
 
4.0%
대 40
 
3.3%
공 37
 
3.1%
진 34
 
2.8%
양 30
 
2.5%
Other values (105) 608
50.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 997
82.8%
Close Punctuation 93
 
7.7%
Open Punctuation 91
 
7.6%
Other Punctuation 21
 
1.7%
Space Separator 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 75
 
7.5%
차 74
 
7.4%
고 74
 
7.4%
동 48
 
4.8%
대 40
 
4.0%
공 37
 
3.7%
진 34
 
3.4%
양 30
 
3.0%
호 29
 
2.9%
문 29
 
2.9%
Other values (100) 527
52.9%
Other Punctuation
ValueCountFrequency (%)
, 12
57.1%
/ 9
42.9%
Close Punctuation
ValueCountFrequency (%)
) 93
100.0%
Open Punctuation
ValueCountFrequency (%)
( 91
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 997
82.8%
Common 207
 
17.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 75
 
7.5%
차 74
 
7.4%
고 74
 
7.4%
동 48
 
4.8%
대 40
 
4.0%
공 37
 
3.7%
진 34
 
3.4%
양 30
 
3.0%
호 29
 
2.9%
문 29
 
2.9%
Other values (100) 527
52.9%
Common
ValueCountFrequency (%)
) 93
44.9%
( 91
44.0%
, 12
 
5.8%
/ 9
 
4.3%
2
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 997
82.8%
ASCII 207
 
17.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 93
44.9%
( 91
44.0%
, 12
 
5.8%
/ 9
 
4.3%
2
 
1.0%
Hangul
ValueCountFrequency (%)
지 75
 
7.5%
차 74
 
7.4%
고 74
 
7.4%
동 48
 
4.8%
대 40
 
4.0%
공 37
 
3.7%
진 34
 
3.4%
양 30
 
3.0%
호 29
 
2.9%
문 29
 
2.9%
Other values (100) 527
52.9%

거리(km)
Real number (ℝ)

Distinct176
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.343123
Minimum4.423
Maximum48.993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T07:39:45.637857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.423
5-th percentile11.2544
Q119.454
median24.971
Q330.642
95-th percentile41.0488
Maximum48.993
Range44.57
Interquartile range (IQR)11.188

Descriptive statistics

Standard deviation8.955686
Coefficient of variation (CV)0.35337736
Kurtosis0.12953789
Mean25.343123
Median Absolute Deviation (MAD)5.652
Skewness0.30896199
Sum4536.419
Variance80.204311
MonotonicityNot monotonic
2023-12-13T07:39:45.764350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.56 2
 
1.1%
8.891 2
 
1.1%
7.743 2
 
1.1%
31.984 1
 
0.6%
38.903 1
 
0.6%
41.857 1
 
0.6%
41.884 1
 
0.6%
40.959 1
 
0.6%
40.911 1
 
0.6%
45.964 1
 
0.6%
Other values (166) 166
92.7%
ValueCountFrequency (%)
4.423 1
0.6%
4.456 1
0.6%
7.743 2
1.1%
8.427 1
0.6%
8.833 1
0.6%
8.891 2
1.1%
9.08 1
0.6%
11.496 1
0.6%
11.549 1
0.6%
11.815 1
0.6%
ValueCountFrequency (%)
48.993 1
0.6%
48.87 1
0.6%
48.678 1
0.6%
47.291 1
0.6%
45.964 1
0.6%
45.962 1
0.6%
41.989 1
0.6%
41.884 1
0.6%
41.857 1
0.6%
40.959 1
0.6%

운행횟수
Real number (ℝ)

Distinct35
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.558659
Minimum1
Maximum112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T07:39:45.922307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median8
Q324
95-th percentile56
Maximum112
Range111
Interquartile range (IQR)19

Descriptive statistics

Standard deviation21.523979
Coefficient of variation (CV)1.2258327
Kurtosis8.2944888
Mean17.558659
Median Absolute Deviation (MAD)5
Skewness2.7052606
Sum3143
Variance463.28165
MonotonicityNot monotonic
2023-12-13T07:39:46.092411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
4 18
 
10.1%
5 16
 
8.9%
6 15
 
8.4%
2 12
 
6.7%
7 11
 
6.1%
8 11
 
6.1%
35 10
 
5.6%
30 10
 
5.6%
9 8
 
4.5%
24 6
 
3.4%
Other values (25) 62
34.6%
ValueCountFrequency (%)
1 4
 
2.2%
2 12
6.7%
3 4
 
2.2%
4 18
10.1%
5 16
8.9%
6 15
8.4%
7 11
6.1%
8 11
6.1%
9 8
4.5%
10 2
 
1.1%
ValueCountFrequency (%)
112 2
 
1.1%
110 2
 
1.1%
96 2
 
1.1%
79 2
 
1.1%
56 2
 
1.1%
42 1
 
0.6%
38 1
 
0.6%
37 4
 
2.2%
36 2
 
1.1%
35 10
5.6%

운행개시일
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2017-04-21
18 
2014-11-15
17 
2014-11-14
16 
2014-11-16
15 
2017-04-19
12 
Other values (20)
101 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row2020-07-29
2nd row2020-07-29
3rd row2020-07-29
4th row2020-07-29
5th row2020-07-29

Common Values

ValueCountFrequency (%)
2017-04-21 18
 
10.1%
2014-11-15 17
 
9.5%
2014-11-14 16
 
8.9%
2014-11-16 15
 
8.4%
2017-04-19 12
 
6.7%
2017-03-13 12
 
6.7%
2020-07-29 10
 
5.6%
2017-04-22 10
 
5.6%
2017-04-20 8
 
4.5%
2017-03-14 8
 
4.5%
Other values (15) 53
29.6%

Length

2023-12-13T07:39:46.227641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-04-21 18
 
10.1%
2014-11-15 17
 
9.5%
2014-11-14 16
 
8.9%
2014-11-16 15
 
8.4%
2017-04-19 12
 
6.7%
2017-03-13 12
 
6.7%
2020-07-29 10
 
5.6%
2017-04-22 10
 
5.6%
2017-04-20 8
 
4.5%
2017-03-14 8
 
4.5%
Other values (15) 53
29.6%

운수회사
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
삼성교통
56 
시민버스
56 
부일교통
30 
부산교통
22 
부산교통 부일교통 삼성교통 시민버스
8 
Other values (4)
7 

Length

Max length19
Median length4
Mean length4.9217877
Min length4

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row부산교통 부일교통 삼성교통 시민버스
2nd row부산교통 부일교통 삼성교통 시민버스
3rd row부산교통 부일교통 삼성교통 시민버스
4th row부산교통 부일교통 삼성교통 시민버스
5th row부산교통 부일교통 삼성교통 시민버스

Common Values

ValueCountFrequency (%)
삼성교통 56
31.3%
시민버스 56
31.3%
부일교통 30
16.8%
부산교통 22
 
12.3%
부산교통 부일교통 삼성교통 시민버스 8
 
4.5%
삼성교통 시민버스 부산교통 2
 
1.1%
삼성교통 부일교통 2
 
1.1%
부산교통 부일교통 2
 
1.1%
삼성교통 시민버스 1
 
0.6%

Length

2023-12-13T07:39:46.376688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:39:46.542646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
삼성교통 69
32.5%
시민버스 67
31.6%
부일교통 42
19.8%
부산교통 34
16.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2022-07-25
179 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-07-25
2nd row2022-07-25
3rd row2022-07-25
4th row2022-07-25
5th row2022-07-25

Common Values

ValueCountFrequency (%)
2022-07-25 179
100.0%

Length

2023-12-13T07:39:46.706268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:39:46.819264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-07-25 179
100.0%

Interactions

2023-12-13T07:39:42.764024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:41.785632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:42.106644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:42.432256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:42.834550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:41.868475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:42.198133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:42.526526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:42.916254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:41.954931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:42.279751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:42.604796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:42.987936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:42.028796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:42.357787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:42.680470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:39:47.143876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번노선번호노선아이디구분방면거리(km)운행횟수운행개시일운수회사
순번1.0000.9990.8610.5650.8720.7680.4600.9280.720
노선번호0.9991.0001.0000.8020.8050.9841.0000.9990.994
노선아이디0.8611.0001.0000.8640.8850.4800.3780.9240.704
구분0.5650.8020.8641.0000.9670.3990.0000.8350.724
방면0.8720.8050.8850.9671.0000.8580.0000.7910.843
거리(km)0.7680.9840.4800.3990.8581.0000.5070.7850.459
운행횟수0.4601.0000.3780.0000.0000.5071.0000.6470.740
운행개시일0.9280.9990.9240.8350.7910.7850.6471.0000.837
운수회사0.7200.9940.7040.7240.8430.4590.7400.8371.000
2023-12-13T07:39:47.248398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분운행개시일운수회사
구분1.0000.5760.554
운행개시일0.5761.0000.491
운수회사0.5540.4911.000
2023-12-13T07:39:47.348624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번노선아이디거리(km)운행횟수구분운행개시일운수회사
순번1.0000.9620.336-0.0300.3670.6270.426
노선아이디0.9621.0000.330-0.0410.5470.6640.446
거리(km)0.3360.3301.000-0.4490.2440.3940.227
운행횟수-0.030-0.041-0.4491.0000.0000.3040.480
구분0.3670.5470.2440.0001.0000.5760.554
운행개시일0.6270.6640.3940.3040.5761.0000.491
운수회사0.4260.4460.2270.4800.5540.4911.000

Missing values

2023-12-13T07:39:43.100889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:39:43.265659image/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

순번노선번호노선아이디구분방면거리(km)운행횟수운행개시일운수회사데이터기준일자
011381000130동부순환평촌(동촌, 발산)31.984142020-07-29부산교통 부일교통 삼성교통 시민버스2022-07-25
122381000230동부순환남산(수목원)26.61982020-07-29부산교통 부일교통 삼성교통 시민버스2022-07-25
232-1381000231동부순환남산(수목원)23.96172020-07-29부산교통 부일교통 삼성교통 시민버스2022-07-25
343381000330동부순환지수면사무소(무등)36.26782020-07-29부산교통 부일교통 삼성교통 시민버스2022-07-25
453-1381000331동부순환지수면사무소(무등)36.32882020-07-29부산교통 부일교통 삼성교통 시민버스2022-07-25
564381000430동부순환안계48.99352020-07-29부산교통 부일교통 삼성교통 시민버스2022-07-25
675381000530동부순환부계(부동,저동)29.07852020-07-29부산교통 부일교통 삼성교통 시민버스2022-07-25
786381000630동부순환하촌(구천)28.51962020-07-29부산교통 부일교통 삼성교통 시민버스2022-07-25
8910381001010기점석교(금산)26.60882020-02-21삼성교통2022-07-25
91010381001020종점공영차고지26.19582020-02-21삼성교통2022-07-25
순번노선번호노선아이디구분방면거리(km)운행횟수운행개시일운수회사데이터기준일자
169170470381047020기점공영차고지21.676162017-04-22삼성교통2022-07-25
170171470381047010종점원동삼거리(집현)22.201162017-04-22삼성교통2022-07-25
171172471381047120기점공영차고지19.861232017-06-12삼성교통2022-07-25
172173471381047110종점원동삼거리(집현)19.686232017-06-12삼성교통2022-07-25
173174550381055030순환청락원26.4992018-03-10삼성교통2022-07-25
174175551381055130순환청락원26.4892018-03-10삼성교통2022-07-25
175176730381073010기점원동삼거리(집현)19.84262017-04-22시민버스2022-07-25
176177730381073020종점판문동차고지19.84762017-04-22시민버스2022-07-25
177178731381073120기점판문동차고지32.19922017-04-22시민버스2022-07-25
178179731381073110종점어옥,덕진(미천)40.12822017-04-22시민버스2022-07-25