Overview

Dataset statistics

Number of variables19
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 KiB
Average record size in memory164.4 B

Variable types

DateTime3
Text3
Categorical7
Numeric6

Dataset

Description샘플 데이터
Author펌프킨
URLhttps://bigdata-region.kr/#/dataset/c4ad36bc-b37c-4ec7-a979-e8ff4303ee3d

Alerts

비고 has constant value ""Constant
생산_일시 has constant value ""Constant
시군구_명 is highly overall correlated with 시군구_코드 and 9 other fieldsHigh correlation
시도_명 is highly overall correlated with 시군구_코드 and 8 other fieldsHigh correlation
시도_코드 is highly overall correlated with 시군구_코드 and 8 other fieldsHigh correlation
시군구_코드 is highly overall correlated with 노선_거리 and 9 other fieldsHigh correlation
정류장_수 is highly overall correlated with 노선_거리 and 6 other fieldsHigh correlation
노선_거리 is highly overall correlated with 시군구_코드 and 10 other fieldsHigh correlation
최단_거리 is highly overall correlated with 시군구_코드 and 7 other fieldsHigh correlation
SOC사용량 is highly overall correlated with 시군구_코드 and 10 other fieldsHigh correlation
전력_사용량 is highly overall correlated with 시군구_코드 and 10 other fieldsHigh correlation
기점 is highly overall correlated with 시군구_코드 and 10 other fieldsHigh correlation
종점 is highly overall correlated with 시군구_코드 and 10 other fieldsHigh correlation
굴곡도 is highly overall correlated with 시군구_코드 and 9 other fieldsHigh correlation
버스번호 has unique valuesUnique
운행_종료_일시 has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:21:17.597517
Analysis finished2023-12-10 14:21:22.131482
Duration4.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

운행_시작_일시
Date

Distinct17
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2023-01-01 04:18:00
Maximum2023-01-01 05:10:00
2023-12-10T23:21:22.178253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:22.271405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)

버스번호
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:21:22.505073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters1920
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st rowf0122334b8983138276e43a1add81eaa17ce971f2ee362fa5cde056118249353
2nd rowbcbb9faa7b4a78cd7656e29945be725a2ef069f2d5080e50ed01f0f97c96e442
3rd row2056f593c8f2ebac33cd1040a3dd58bcbd2341ba22d6388df8599f70a2a604db
4th row3bf6556f8fe5486859c710c7c29a757b35adbdb9f6ddd2aef64286bd6994a860
5th row1aea4ff3da1501de8d7785af527f222c64bf1bb6c347c6c133713b0852e40ccf
ValueCountFrequency (%)
f0122334b8983138276e43a1add81eaa17ce971f2ee362fa5cde056118249353 1
 
3.3%
bcbb9faa7b4a78cd7656e29945be725a2ef069f2d5080e50ed01f0f97c96e442 1
 
3.3%
efa4f616aa2471744d75644614030991ae741208e6a85ec3d773e83d4293a424 1
 
3.3%
75fb23ff22f38ea50fa29fe9491623992f7ac513a52cbea8b441e729c1f41c90 1
 
3.3%
18956a50e18d26db24a554d9ac9b582c47814ef8b3a86d22c42aff06a1b9f75d 1
 
3.3%
61ddde3cfab1e6b1d2033025a4ffdf885f8a845f95245c1cf8afbd29b14d4476 1
 
3.3%
e32e8a27813cf5ed5d13dab7d00977ec15f8cb27248086fd93b304028edba4e2 1
 
3.3%
9cd22fc827c7c918edf07577fad59ba9b25df0cc2efe20f4b4697d3d4d93db36 1
 
3.3%
eafe60183883c1a456f32aff674895aa8efac196837d6f4c64658741d06ffca7 1
 
3.3%
1d2b16fab05eb9430f99032738619de9b4c590fe7769531f77f1e1eb1b4a1aa6 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T23:21:22.848629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 138
 
7.2%
9 137
 
7.1%
f 128
 
6.7%
4 126
 
6.6%
a 123
 
6.4%
8 121
 
6.3%
6 120
 
6.2%
1 119
 
6.2%
7 117
 
6.1%
c 117
 
6.1%
Other values (6) 674
35.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1204
62.7%
Lowercase Letter 716
37.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 138
11.5%
9 137
11.4%
4 126
10.5%
8 121
10.0%
6 120
10.0%
1 119
9.9%
7 117
9.7%
0 116
9.6%
5 107
8.9%
3 103
8.6%
Lowercase Letter
ValueCountFrequency (%)
f 128
17.9%
a 123
17.2%
c 117
16.3%
b 116
16.2%
e 116
16.2%
d 116
16.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1204
62.7%
Latin 716
37.3%

Most frequent character per script

Common
ValueCountFrequency (%)
2 138
11.5%
9 137
11.4%
4 126
10.5%
8 121
10.0%
6 120
10.0%
1 119
9.9%
7 117
9.7%
0 116
9.6%
5 107
8.9%
3 103
8.6%
Latin
ValueCountFrequency (%)
f 128
17.9%
a 123
17.2%
c 117
16.3%
b 116
16.2%
e 116
16.2%
d 116
16.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 138
 
7.2%
9 137
 
7.1%
f 128
 
6.7%
4 126
 
6.6%
a 123
 
6.4%
8 121
 
6.3%
6 120
 
6.2%
1 119
 
6.2%
7 117
 
6.1%
c 117
 
6.1%
Other values (6) 674
35.1%

노선_ID
Text

Distinct15
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:21:23.003094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length10.866667
Min length5

Characters and Unicode

Total characters326
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)26.7%

Sample

1st rowSOB100100273
2nd rowSOB100100286
3rd rowBSB5200188000
4th rowSOB100100346
5th rowSOB100100273
ValueCountFrequency (%)
sob100100286 5
16.7%
sob100100273 4
13.3%
sob100100346 4
13.3%
gjb1631 3
10.0%
bsb5200188000 2
 
6.7%
ghb21 2
 
6.7%
gjb1177 2
 
6.7%
bsb5201010000 1
 
3.3%
sob100100349 1
 
3.3%
bsb5200083100 1
 
3.3%
Other values (5) 5
16.7%
2023-12-10T23:21:23.259634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 97
29.8%
1 49
15.0%
B 35
 
10.7%
S 19
 
5.8%
2 19
 
5.8%
3 17
 
5.2%
O 14
 
4.3%
7 13
 
4.0%
8 12
 
3.7%
6 12
 
3.7%
Other values (8) 39
12.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 236
72.4%
Uppercase Letter 90
 
27.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97
41.1%
1 49
20.8%
2 19
 
8.1%
3 17
 
7.2%
7 13
 
5.5%
8 12
 
5.1%
6 12
 
5.1%
4 6
 
2.5%
5 6
 
2.5%
9 5
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
B 35
38.9%
S 19
21.1%
O 14
 
15.6%
G 7
 
7.8%
J 5
 
5.6%
C 4
 
4.4%
W 4
 
4.4%
H 2
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 236
72.4%
Latin 90
 
27.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 97
41.1%
1 49
20.8%
2 19
 
8.1%
3 17
 
7.2%
7 13
 
5.5%
8 12
 
5.1%
6 12
 
5.1%
4 6
 
2.5%
5 6
 
2.5%
9 5
 
2.1%
Latin
ValueCountFrequency (%)
B 35
38.9%
S 19
21.1%
O 14
 
15.6%
G 7
 
7.8%
J 5
 
5.6%
C 4
 
4.4%
W 4
 
4.4%
H 2
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 326
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 97
29.8%
1 49
15.0%
B 35
 
10.7%
S 19
 
5.8%
2 19
 
5.8%
3 17
 
5.2%
O 14
 
4.3%
7 13
 
4.0%
8 12
 
3.7%
6 12
 
3.7%
Other values (8) 39
12.0%

노선_번호
Text

Distinct15
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:21:23.419055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.4333333
Min length2

Characters and Unicode

Total characters103
Distinct characters10
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

Unique8 ?
Unique (%)26.7%

Sample

1st row5616
2nd row5712
3rd row188
4th row7612
5th row5616
ValueCountFrequency (%)
5712 5
16.7%
5616 4
13.3%
7612 4
13.3%
103 3
10.0%
188 2
 
6.7%
57 2
 
6.7%
101 2
 
6.7%
1010 1
 
3.3%
7713 1
 
3.3%
83-1 1
 
3.3%
Other values (5) 5
16.7%
2023-12-10T23:21:23.698869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 30
29.1%
7 14
13.6%
5 12
 
11.7%
2 12
 
11.7%
6 12
 
11.7%
0 9
 
8.7%
8 7
 
6.8%
3 5
 
4.9%
- 1
 
1.0%
4 1
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 102
99.0%
Dash Punctuation 1
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 30
29.4%
7 14
13.7%
5 12
 
11.8%
2 12
 
11.8%
6 12
 
11.8%
0 9
 
8.8%
8 7
 
6.9%
3 5
 
4.9%
4 1
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 103
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 30
29.1%
7 14
13.6%
5 12
 
11.7%
2 12
 
11.7%
6 12
 
11.7%
0 9
 
8.7%
8 7
 
6.8%
3 5
 
4.9%
- 1
 
1.0%
4 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 30
29.1%
7 14
13.6%
5 12
 
11.7%
2 12
 
11.7%
6 12
 
11.7%
0 9
 
8.7%
8 7
 
6.8%
3 5
 
4.9%
- 1
 
1.0%
4 1
 
1.0%

기점
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
가산동기점(가상)
9 
정관
4 
홍연2교(기점가상)
4 
터미널(순환)
3 
진영시외주차장
2 
Other values (7)
8 

Length

Max length14
Median length10
Mean length7.4333333
Min length2

Unique

Unique6 ?
Unique (%)20.0%

Sample

1st row가산동기점(가상)
2nd row가산동기점(가상)
3rd row정관
4th row홍연2교(기점가상)
5th row가산동기점(가상)

Common Values

ValueCountFrequency (%)
가산동기점(가상) 9
30.0%
정관 4
13.3%
홍연2교(기점가상) 4
13.3%
터미널(순환) 3
 
10.0%
진영시외주차장 2
 
6.7%
거붕백병원 2
 
6.7%
홍연2교현대교통종점(가상) 1
 
3.3%
민락동 1
 
3.3%
월영마린애시앙APT 1
 
3.3%
월영아파트종점 1
 
3.3%
Other values (2) 2
 
6.7%

Length

2023-12-10T23:21:23.829758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
가산동기점(가상 9
29.0%
정관 4
12.9%
홍연2교(기점가상 4
12.9%
터미널(순환 3
 
9.7%
진영시외주차장 2
 
6.5%
거붕백병원 2
 
6.5%
홍연2교현대교통종점(가상 1
 
3.2%
민락동 1
 
3.2%
월영마린애시앙apt 1
 
3.2%
월영아파트종점 1
 
3.2%
Other values (3) 3
 
9.7%

종점
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
가산동종점(가상)
9 
홍연2교현대교통종점
5 
삼성기숙사
3 
안평역
2 
대방동종점
2 
Other values (7)
9 

Length

Max length10
Median length9
Mean length6.6333333
Min length2

Unique

Unique5 ?
Unique (%)16.7%

Sample

1st row가산동종점(가상)
2nd row가산동종점(가상)
3rd row안평역
4th row홍연2교현대교통종점
5th row가산동종점(가상)

Common Values

ValueCountFrequency (%)
가산동종점(가상) 9
30.0%
홍연2교현대교통종점 5
16.7%
삼성기숙사 3
 
10.0%
안평역 2
 
6.7%
대방동종점 2
 
6.7%
모정 2
 
6.7%
거붕백병원 2
 
6.7%
서면 1
 
3.3%
사직동 1
 
3.3%
반여농산물시장 1
 
3.3%
Other values (2) 2
 
6.7%

Length

2023-12-10T23:21:24.167422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
가산동종점(가상 9
30.0%
홍연2교현대교통종점 5
16.7%
삼성기숙사 3
 
10.0%
안평역 2
 
6.7%
대방동종점 2
 
6.7%
모정 2
 
6.7%
거붕백병원 2
 
6.7%
서면 1
 
3.3%
사직동 1
 
3.3%
반여농산물시장 1
 
3.3%
Other values (2) 2
 
6.7%

시도_코드
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
11
14 
48
9 
26
5 
41
2 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row11
3rd row26
4th row11
5th row11

Common Values

ValueCountFrequency (%)
11 14
46.7%
48 9
30.0%
26 5
 
16.7%
41 2
 
6.7%

Length

2023-12-10T23:21:24.279638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:21:24.375288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11 14
46.7%
48 9
30.0%
26 5
 
16.7%
41 2
 
6.7%

시군구_코드
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean391.83333
Minimum120
Maximum710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:21:24.468054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile120
Q1120
median410
Q3545
95-th percentile710
Maximum710
Range590
Interquartile range (IQR)425

Descriptive statistics

Standard deviation217.82025
Coefficient of variation (CV)0.55590025
Kurtosis-1.480391
Mean391.83333
Median Absolute Deviation (MAD)177.5
Skewness-0.1242543
Sum11755
Variance47445.661
MonotonicityNot monotonic
2023-12-10T23:21:24.567240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
545 9
30.0%
120 9
30.0%
410 5
16.7%
710 4
13.3%
190 2
 
6.7%
500 1
 
3.3%
ValueCountFrequency (%)
120 9
30.0%
190 2
 
6.7%
410 5
16.7%
500 1
 
3.3%
545 9
30.0%
710 4
13.3%
ValueCountFrequency (%)
710 4
13.3%
545 9
30.0%
500 1
 
3.3%
410 5
16.7%
190 2
 
6.7%
120 9
30.0%

시도_명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
서울특별시
14 
경상남도
9 
부산광역시
5 
경기도
2 

Length

Max length5
Median length5
Mean length4.5666667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시
2nd row서울특별시
3rd row부산광역시
4th row서울특별시
5th row서울특별시

Common Values

ValueCountFrequency (%)
서울특별시 14
46.7%
경상남도 9
30.0%
부산광역시 5
 
16.7%
경기도 2
 
6.7%

Length

2023-12-10T23:21:24.704494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:21:24.814057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 14
46.7%
경상남도 9
30.0%
부산광역시 5
 
16.7%
경기도 2
 
6.7%

시군구_명
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
금천구
9 
창원시
9 
서대문구
5 
기장군
4 
부천시
2 

Length

Max length4
Median length3
Mean length3.1666667
Min length3

Unique

Unique1 ?
Unique (%)3.3%

Sample

1st row금천구
2nd row금천구
3rd row기장군
4th row서대문구
5th row금천구

Common Values

ValueCountFrequency (%)
금천구 9
30.0%
창원시 9
30.0%
서대문구 5
16.7%
기장군 4
13.3%
부천시 2
 
6.7%
수영구 1
 
3.3%

Length

2023-12-10T23:21:24.943837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:21:25.060852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
금천구 9
30.0%
창원시 9
30.0%
서대문구 5
16.7%
기장군 4
13.3%
부천시 2
 
6.7%
수영구 1
 
3.3%

정류장_수
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.9
Minimum22
Maximum159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:21:25.158714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile22
Q147
median90
Q3113
95-th percentile158.55
Maximum159
Range137
Interquartile range (IQR)66

Descriptive statistics

Standard deviation43.026335
Coefficient of variation (CV)0.5067884
Kurtosis-0.98377793
Mean84.9
Median Absolute Deviation (MAD)43
Skewness0.23191262
Sum2547
Variance1851.2655
MonotonicityNot monotonic
2023-12-10T23:21:25.262671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
47 6
20.0%
113 5
16.7%
90 5
16.7%
22 3
10.0%
159 2
 
6.7%
38 2
 
6.7%
94 1
 
3.3%
67 1
 
3.3%
106 1
 
3.3%
74 1
 
3.3%
Other values (3) 3
10.0%
ValueCountFrequency (%)
22 3
10.0%
38 2
 
6.7%
47 6
20.0%
67 1
 
3.3%
74 1
 
3.3%
90 5
16.7%
94 1
 
3.3%
106 1
 
3.3%
113 5
16.7%
139 1
 
3.3%
ValueCountFrequency (%)
159 2
 
6.7%
158 1
 
3.3%
152 1
 
3.3%
139 1
 
3.3%
113 5
16.7%
106 1
 
3.3%
94 1
 
3.3%
90 5
16.7%
74 1
 
3.3%
67 1
 
3.3%

노선_거리
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.566667
Minimum8
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:21:25.362490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile8
Q111
median36
Q355.75
95-th percentile75
Maximum75
Range67
Interquartile range (IQR)44.75

Descriptive statistics

Standard deviation24.122794
Coefficient of variation (CV)0.64213294
Kurtosis-1.2608305
Mean37.566667
Median Absolute Deviation (MAD)25
Skewness0.26036961
Sum1127
Variance581.9092
MonotonicityNot monotonic
2023-12-10T23:21:25.457573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
75 5
16.7%
36 4
13.3%
11 4
13.3%
8 3
10.0%
62 2
 
6.7%
28 2
 
6.7%
9 2
 
6.7%
55 1
 
3.3%
18 1
 
3.3%
35 1
 
3.3%
Other values (5) 5
16.7%
ValueCountFrequency (%)
8 3
10.0%
9 2
6.7%
11 4
13.3%
18 1
 
3.3%
28 2
6.7%
34 1
 
3.3%
35 1
 
3.3%
36 4
13.3%
44 1
 
3.3%
48 1
 
3.3%
ValueCountFrequency (%)
75 5
16.7%
62 2
 
6.7%
56 1
 
3.3%
55 1
 
3.3%
52 1
 
3.3%
48 1
 
3.3%
44 1
 
3.3%
36 4
13.3%
35 1
 
3.3%
34 1
 
3.3%

최단_거리
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.233333
Minimum4
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:21:25.548821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q16
median19
Q331.75
95-th percentile42
Maximum42
Range38
Interquartile range (IQR)25.75

Descriptive statistics

Standard deviation13.382138
Coefficient of variation (CV)0.63024196
Kurtosis-1.2102691
Mean21.233333
Median Absolute Deviation (MAD)13
Skewness0.18418622
Sum637
Variance179.08161
MonotonicityNot monotonic
2023-12-10T23:21:25.640033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
42 5
16.7%
19 4
13.3%
6 4
13.3%
32 3
10.0%
16 3
10.0%
4 3
10.0%
29 2
 
6.7%
5 2
 
6.7%
24 1
 
3.3%
25 1
 
3.3%
Other values (2) 2
 
6.7%
ValueCountFrequency (%)
4 3
10.0%
5 2
6.7%
6 4
13.3%
16 3
10.0%
19 4
13.3%
23 1
 
3.3%
24 1
 
3.3%
25 1
 
3.3%
29 2
6.7%
31 1
 
3.3%
ValueCountFrequency (%)
42 5
16.7%
32 3
10.0%
31 1
 
3.3%
29 2
 
6.7%
25 1
 
3.3%
24 1
 
3.3%
23 1
 
3.3%
19 4
13.3%
16 3
10.0%
6 4
13.3%

굴곡도
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2
11 
3
9 
0
7 
5
2 
6
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.3%

Sample

1st row3
2nd row3
3rd row5
4th row2
5th row3

Common Values

ValueCountFrequency (%)
2 11
36.7%
3 9
30.0%
0 7
23.3%
5 2
 
6.7%
6 1
 
3.3%

Length

2023-12-10T23:21:25.741059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:21:25.827039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 11
36.7%
3 9
30.0%
0 7
23.3%
5 2
 
6.7%
6 1
 
3.3%

운행_종료_일시
Date

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2023-01-01 05:43:00
Maximum2023-01-01 14:58:00
2023-12-10T23:21:25.911032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:26.026366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)

SOC사용량
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.7
Minimum5
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:21:26.126258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q16
median22
Q334
95-th percentile45
Maximum45
Range40
Interquartile range (IQR)28

Descriptive statistics

Standard deviation14.614943
Coefficient of variation (CV)0.64383008
Kurtosis-1.3032256
Mean22.7
Median Absolute Deviation (MAD)16
Skewness0.22423685
Sum681
Variance213.59655
MonotonicityNot monotonic
2023-12-10T23:21:26.230478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
45 5
16.7%
6 5
16.7%
22 4
13.3%
5 4
13.3%
38 2
 
6.7%
34 2
 
6.7%
21 2
 
6.7%
17 2
 
6.7%
11 1
 
3.3%
27 1
 
3.3%
Other values (2) 2
 
6.7%
ValueCountFrequency (%)
5 4
13.3%
6 5
16.7%
11 1
 
3.3%
17 2
 
6.7%
21 2
 
6.7%
22 4
13.3%
27 1
 
3.3%
29 1
 
3.3%
31 1
 
3.3%
34 2
 
6.7%
ValueCountFrequency (%)
45 5
16.7%
38 2
 
6.7%
34 2
 
6.7%
31 1
 
3.3%
29 1
 
3.3%
27 1
 
3.3%
22 4
13.3%
21 2
 
6.7%
17 2
 
6.7%
11 1
 
3.3%

전력_사용량
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.033333
Minimum17
Maximum160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:21:26.325786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile17
Q123
median76.5
Q3119.5
95-th percentile159.55
Maximum160
Range143
Interquartile range (IQR)96.5

Descriptive statistics

Standard deviation51.541402
Coefficient of variation (CV)0.64399919
Kurtosis-1.2791656
Mean80.033333
Median Absolute Deviation (MAD)53.5
Skewness0.24322482
Sum2401
Variance2656.5161
MonotonicityNot monotonic
2023-12-10T23:21:26.423859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
23 4
13.3%
77 3
10.0%
159 3
10.0%
17 3
10.0%
133 2
 
6.7%
160 2
 
6.7%
18 2
 
6.7%
60 2
 
6.7%
73 1
 
3.3%
120 1
 
3.3%
Other values (7) 7
23.3%
ValueCountFrequency (%)
17 3
10.0%
18 2
6.7%
23 4
13.3%
39 1
 
3.3%
60 2
6.7%
73 1
 
3.3%
74 1
 
3.3%
76 1
 
3.3%
77 3
10.0%
95 1
 
3.3%
ValueCountFrequency (%)
160 2
6.7%
159 3
10.0%
133 2
6.7%
120 1
 
3.3%
118 1
 
3.3%
110 1
 
3.3%
103 1
 
3.3%
95 1
 
3.3%
77 3
10.0%
76 1
 
3.3%

비고
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-04-11 전기버스 노선 굴곡도 대비 에너지 사용량
30 

Length

Max length33
Median length33
Mean length33
Min length33

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-04-11 전기버스 노선 굴곡도 대비 에너지 사용량
2nd row2023-04-11 전기버스 노선 굴곡도 대비 에너지 사용량
3rd row2023-04-11 전기버스 노선 굴곡도 대비 에너지 사용량
4th row2023-04-11 전기버스 노선 굴곡도 대비 에너지 사용량
5th row2023-04-11 전기버스 노선 굴곡도 대비 에너지 사용량

Common Values

ValueCountFrequency (%)
2023-04-11 전기버스 노선 굴곡도 대비 에너지 사용량 30
100.0%

Length

2023-12-10T23:21:26.567814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:21:26.665389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-04-11 30
14.3%
전기버스 30
14.3%
노선 30
14.3%
굴곡도 30
14.3%
대비 30
14.3%
에너지 30
14.3%
사용량 30
14.3%

생산_일시
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2023-04-11 11:29:01
Maximum2023-04-11 11:29:01
2023-12-10T23:21:26.776715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:26.888513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-10T23:21:21.236600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:18.538624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:19.053233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:19.620914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:20.164781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:20.735268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:21.323646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:18.625503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:19.146666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:19.694772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:20.254348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:20.812140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:21.407733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:18.711461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:19.250525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:19.787943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:20.354651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:20.891882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:21.480813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:18.791169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:19.347880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:19.902406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:20.444510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:20.981810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:21.580524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:18.871766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:19.452819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:19.983056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:20.553397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:21.062483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:21.698164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:18.960208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:19.541200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:20.080848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:20.650716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:21:21.144359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:21:26.981779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
운행_시작_일시버스번호노선_ID노선_번호기점종점시도_코드시군구_코드시도_명시군구_명정류장_수노선_거리최단_거리굴곡도운행_종료_일시SOC사용량전력_사용량
운행_시작_일시1.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.6150.0000.6050.0001.0000.0000.000
버스번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
노선_ID0.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
노선_번호0.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
기점0.0001.0001.0001.0001.0000.9941.0001.0001.0001.0000.9240.9290.9530.9441.0000.9500.939
종점0.0001.0001.0001.0000.9941.0001.0001.0001.0001.0000.9610.9290.9770.9151.0000.9200.909
시도_코드0.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.7710.9060.6620.6341.0000.8650.794
시군구_코드0.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.6880.8730.8550.7601.0000.8730.840
시도_명0.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.7710.9060.6620.6341.0000.8650.794
시군구_명0.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.6880.8730.8550.7601.0000.8730.840
정류장_수0.6151.0001.0001.0000.9240.9610.7710.6880.7710.6881.0000.9120.9230.7761.0000.8790.873
노선_거리0.0001.0001.0001.0000.9290.9290.9060.8730.9060.8730.9121.0000.9270.9921.0000.9810.970
최단_거리0.6051.0001.0001.0000.9530.9770.6620.8550.6620.8550.9230.9271.0000.6781.0000.9400.953
굴곡도0.0001.0001.0001.0000.9440.9150.6340.7600.6340.7600.7760.9920.6781.0001.0000.9300.933
운행_종료_일시1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
SOC사용량0.0001.0001.0001.0000.9500.9200.8650.8730.8650.8730.8790.9810.9400.9301.0001.0000.999
전력_사용량0.0001.0001.0001.0000.9390.9090.7940.8400.7940.8400.8730.9700.9530.9331.0000.9991.000
2023-12-10T23:21:27.167587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구_명기점굴곡도시도_명종점시도_코드
시군구_명1.0000.8660.6200.9610.8660.961
기점0.8661.0000.7410.8320.8410.832
굴곡도0.6200.7411.0000.5500.6830.550
시도_명0.9610.8320.5501.0000.8321.000
종점0.8660.8410.6830.8321.0000.832
시도_코드0.9610.8320.5501.0000.8321.000
2023-12-10T23:21:27.308060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구_코드정류장_수노선_거리최단_거리SOC사용량전력_사용량기점종점시도_코드시도_명시군구_명굴곡도
시군구_코드1.0000.4300.5760.5540.5770.5740.8660.8660.9610.9611.0000.620
정류장_수0.4301.0000.7470.7290.7390.7450.6370.7290.3930.3930.4440.580
노선_거리0.5760.7471.0000.9860.9970.9980.7040.7020.6930.6930.6390.776
최단_거리0.5540.7290.9861.0000.9830.9840.5780.7080.4460.4460.5160.485
SOC사용량0.5770.7390.9970.9831.0000.9950.7330.6890.7490.7490.6870.806
전력_사용량0.5740.7450.9980.9840.9951.0000.7080.6340.5820.5820.5630.788
기점0.8660.6370.7040.5780.7330.7081.0000.8410.8320.8320.8660.741
종점0.8660.7290.7020.7080.6890.6340.8411.0000.8320.8320.8660.683
시도_코드0.9610.3930.6930.4460.7490.5820.8320.8321.0001.0000.9610.550
시도_명0.9610.3930.6930.4460.7490.5820.8320.8321.0001.0000.9610.550
시군구_명1.0000.4440.6390.5160.6870.5630.8660.8660.9610.9611.0000.620
굴곡도0.6200.5800.7760.4850.8060.7880.7410.6830.5500.5500.6201.000

Missing values

2023-12-10T23:21:21.845887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:21:22.051595image/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

운행_시작_일시버스번호노선_ID노선_번호기점종점시도_코드시군구_코드시도_명시군구_명정류장_수노선_거리최단_거리굴곡도운행_종료_일시SOC사용량전력_사용량비고생산_일시
02023-01-01 04:18:00f0122334b8983138276e43a1add81eaa17ce971f2ee362fa5cde056118249353SOB1001002735616가산동기점(가상)가산동종점(가상)11545서울특별시금천구113361932023-01-01 07:21:0022772023-04-11 전기버스 노선 굴곡도 대비 에너지 사용량2023-04-11 11:29:01
12023-01-01 04:29:00bcbb9faa7b4a78cd7656e29945be725a2ef069f2d5080e50ed01f0f97c96e442SOB1001002865712가산동기점(가상)가산동종점(가상)11545서울특별시금천구90754232023-01-01 07:15:00451592023-04-11 전기버스 노선 굴곡도 대비 에너지 사용량2023-04-11 11:29:01
22023-01-01 04:30:002056f593c8f2ebac33cd1040a3dd58bcbd2341ba22d6388df8599f70a2a604dbBSB5200188000188정관안평역26710부산광역시기장군159623252023-01-01 11:36:00381332023-04-11 전기버스 노선 굴곡도 대비 에너지 사용량2023-04-11 11:29:01
32023-01-01 04:32:003bf6556f8fe5486859c710c7c29a757b35adbdb9f6ddd2aef64286bd6994a860SOB1001003467612홍연2교(기점가상)홍연2교현대교통종점11410서울특별시서대문구4711622023-01-01 05:43:006232023-04-11 전기버스 노선 굴곡도 대비 에너지 사용량2023-04-11 11:29:01
42023-01-01 04:35:001aea4ff3da1501de8d7785af527f222c64bf1bb6c347c6c133713b0852e40ccfSOB1001002735616가산동기점(가상)가산동종점(가상)11545서울특별시금천구113361932023-01-01 07:37:0022772023-04-11 전기버스 노선 굴곡도 대비 에너지 사용량2023-04-11 11:29:01
52023-01-01 04:41:008d07be1f7b61e1d2481ff8cb204ee35adb99023989c12e8027a16b0bc0336575SOB1001002865712가산동기점(가상)가산동종점(가상)11545서울특별시금천구90754232023-01-01 07:25:00451602023-04-11 전기버스 노선 굴곡도 대비 에너지 사용량2023-04-11 11:29:01
62023-01-01 04:47:004815faecbc6615d700fb5ae2a50698ae7eccba2dc844608cf496cc0b71246472SOB1001003467612홍연2교(기점가상)홍연2교현대교통종점11410서울특별시서대문구4711622023-01-01 05:58:006232023-04-11 전기버스 노선 굴곡도 대비 에너지 사용량2023-04-11 11:29:01
72023-01-01 04:49:0069cc40b7a7b2e48066f38a105c8240cd4eab862898e6202a730433d59fe9512bSOB1001002865712가산동기점(가상)가산동종점(가상)11545서울특별시금천구90754232023-01-01 07:36:00451602023-04-11 전기버스 노선 굴곡도 대비 에너지 사용량2023-04-11 11:29:01
82023-01-01 04:50:006795d5f170c4e6f8d43c073fc34a77d76368cdcc7ca0a68e9e979a5de1505cb8BSB52010100001010정관서면26710부산광역시기장군94552922023-01-01 10:59:00341182023-04-11 전기버스 노선 굴곡도 대비 에너지 사용량2023-04-11 11:29:01
92023-01-01 04:53:007529de2b3aaf2a5b64dec7e6c29c94a4c981bdbd19eb2fce2981cecba0141a6aSOB1001002735616가산동기점(가상)가산동종점(가상)11545서울특별시금천구113361932023-01-01 07:54:0022762023-04-11 전기버스 노선 굴곡도 대비 에너지 사용량2023-04-11 11:29:01
운행_시작_일시버스번호노선_ID노선_번호기점종점시도_코드시군구_코드시도_명시군구_명정류장_수노선_거리최단_거리굴곡도운행_종료_일시SOC사용량전력_사용량비고생산_일시
202023-01-01 05:03:00d7c81c964e0bc685495f724028f1a475cb922cf2f76f9716921145ca69afc32cGJB1177101거붕백병원거붕백병원48120경상남도창원시389502023-01-01 12:15:006182023-04-11 전기버스 노선 굴곡도 대비 에너지 사용량2023-04-11 11:29:01
212023-01-01 05:08:001d2b16fab05eb9430f99032738619de9b4c590fe7769531f77f1e1eb1b4a1aa6GJB1631103터미널(순환)삼성기숙사48120경상남도창원시228402023-01-01 14:38:005172023-04-11 전기버스 노선 굴곡도 대비 에너지 사용량2023-04-11 11:29:01
222023-01-01 05:08:00eafe60183883c1a456f32aff674895aa8efac196837d6f4c64658741d06ffca7CWB379001050105월영아파트종점대방동종점48120경상남도창원시152523222023-01-01 13:24:00311102023-04-11 전기버스 노선 굴곡도 대비 에너지 사용량2023-04-11 11:29:01
232023-01-01 05:09:009cd22fc827c7c918edf07577fad59ba9b25df0cc2efe20f4b4697d3d4d93db36GHB2157진영시외주차장모정41190경기도부천시47281602023-01-01 14:09:0017602023-04-11 전기버스 노선 굴곡도 대비 에너지 사용량2023-04-11 11:29:01
242023-01-01 05:09:00e32e8a27813cf5ed5d13dab7d00977ec15f8cb27248086fd93b304028edba4e2SOB1001002865712가산동기점(가상)가산동종점(가상)11545서울특별시금천구90754232023-01-01 07:55:00451592023-04-11 전기버스 노선 굴곡도 대비 에너지 사용량2023-04-11 11:29:01
252023-01-01 05:09:0061ddde3cfab1e6b1d2033025a4ffdf885f8a845f95245c1cf8afbd29b14d4476SOB1001002735616가산동기점(가상)가산동종점(가상)11545서울특별시금천구113361932023-01-01 08:11:0022772023-04-11 전기버스 노선 굴곡도 대비 에너지 사용량2023-04-11 11:29:01
262023-01-01 05:09:0018956a50e18d26db24a554d9ac9b582c47814ef8b3a86d22c42aff06a1b9f75dSOB1001003467612홍연2교(기점가상)홍연2교현대교통종점11410서울특별시서대문구4711622023-01-01 06:18:006232023-04-11 전기버스 노선 굴곡도 대비 에너지 사용량2023-04-11 11:29:01
272023-01-01 05:10:0075fb23ff22f38ea50fa29fe9491623992f7ac513a52cbea8b441e729c1f41c90BSB5200188000188정관안평역26710부산광역시기장군159623252023-01-01 12:16:00381332023-04-11 전기버스 노선 굴곡도 대비 에너지 사용량2023-04-11 11:29:01
282023-01-01 05:10:00efa4f616aa2471744d75644614030991ae741208e6a85ec3d773e83d4293a424CWB379001220122성주사역 환승센터월영아파트종점48120경상남도창원시139482922023-01-01 12:24:00291032023-04-11 전기버스 노선 굴곡도 대비 에너지 사용량2023-04-11 11:29:01
292023-01-01 05:10:0091fabeb6e8b0c70546978e7a969267814460d194d64348d70b5e8f49f92a4efdCWB37900027027가포고등학교북면종점48120경상남도창원시158563122023-01-01 12:22:00341202023-04-11 전기버스 노선 굴곡도 대비 에너지 사용량2023-04-11 11:29:01