Overview

Dataset statistics

Number of variables18
Number of observations66
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.7 KiB
Average record size in memory151.0 B

Variable types

Numeric5
Categorical10
Text3

Dataset

Description안양시 관내를 경유하는 버스의 노선정보(관할관청, 운행업체, 노선번호, 기점, 종점 , 인가거리 출퇴근 배차 주말 평일 배차 )데이터 현황 정보입니다.
URLhttps://www.data.go.kr/data/3045165/fileData.do

Alerts

주중하행막차 is highly overall correlated with 주말하행막차High correlation
운행업체 is highly overall correlated with 관할관청 and 2 other fieldsHigh correlation
주말하행막차 is highly overall correlated with 주중하행막차High correlation
주말상행막차 is highly overall correlated with 관할관청 and 2 other fieldsHigh correlation
관할관청 is highly overall correlated with 운행업체 and 2 other fieldsHigh correlation
주중하행첫차 is highly overall correlated with 주말하행첫차High correlation
주중상행첫차 is highly overall correlated with 주말상행첫차High correlation
주말하행첫차 is highly overall correlated with 주중하행첫차High correlation
주말상행첫차 is highly overall correlated with 주중상행첫차High correlation
주중상행막차 is highly overall correlated with 관할관청 and 2 other fieldsHigh correlation
출퇴근배차간격 is highly overall correlated with 평일배차간격 and 1 other fieldsHigh correlation
평일배차간격 is highly overall correlated with 출퇴근배차간격 and 1 other fieldsHigh correlation
주말배차간격(일요일 기준) is highly overall correlated with 출퇴근배차간격 and 1 other fieldsHigh correlation
순번 has unique valuesUnique
노선번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:14:23.809127
Analysis finished2023-12-12 23:14:28.568278
Duration4.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct66
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.5
Minimum1
Maximum66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size726.0 B
2023-12-13T08:14:28.630172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.25
Q117.25
median33.5
Q349.75
95-th percentile62.75
Maximum66
Range65
Interquartile range (IQR)32.5

Descriptive statistics

Standard deviation19.196354
Coefficient of variation (CV)0.57302549
Kurtosis-1.2
Mean33.5
Median Absolute Deviation (MAD)16.5
Skewness0
Sum2211
Variance368.5
MonotonicityStrictly increasing
2023-12-13T08:14:28.788710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.5%
51 1
 
1.5%
37 1
 
1.5%
38 1
 
1.5%
39 1
 
1.5%
40 1
 
1.5%
41 1
 
1.5%
42 1
 
1.5%
43 1
 
1.5%
44 1
 
1.5%
Other values (56) 56
84.8%
ValueCountFrequency (%)
1 1
1.5%
2 1
1.5%
3 1
1.5%
4 1
1.5%
5 1
1.5%
6 1
1.5%
7 1
1.5%
8 1
1.5%
9 1
1.5%
10 1
1.5%
ValueCountFrequency (%)
66 1
1.5%
65 1
1.5%
64 1
1.5%
63 1
1.5%
62 1
1.5%
61 1
1.5%
60 1
1.5%
59 1
1.5%
58 1
1.5%
57 1
1.5%

관할관청
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Memory size660.0 B
안양시
37 
광명시
6 
수원시
6 
안산시
4 
성남시
 
3
Other values (8)
10 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique7 ?
Unique (%)10.6%

Sample

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

Common Values

ValueCountFrequency (%)
안양시 37
56.1%
광명시 6
 
9.1%
수원시 6
 
9.1%
안산시 4
 
6.1%
성남시 3
 
4.5%
시흥시 3
 
4.5%
용인시 1
 
1.5%
구리시 1
 
1.5%
군포시 1
 
1.5%
김포시 1
 
1.5%
Other values (3) 3
 
4.5%

Length

2023-12-13T08:14:28.912130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
안양시 37
56.1%
광명시 6
 
9.1%
수원시 6
 
9.1%
안산시 4
 
6.1%
성남시 3
 
4.5%
시흥시 3
 
4.5%
용인시 1
 
1.5%
구리시 1
 
1.5%
군포시 1
 
1.5%
김포시 1
 
1.5%
Other values (3) 3
 
4.5%

운행업체
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Memory size660.0 B
삼영운수
25 
보영운수
12 
화영운수
7 
경원여객
4 
대원버스
3 
Other values (12)
15 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique10 ?
Unique (%)15.2%

Sample

1st row삼영운수
2nd row삼영운수
3rd row삼영운수
4th row삼영운수
5th row보영운수

Common Values

ValueCountFrequency (%)
삼영운수 25
37.9%
보영운수 12
18.2%
화영운수 7
 
10.6%
경원여객 4
 
6.1%
대원버스 3
 
4.5%
시흥교통 3
 
4.5%
성우운수 2
 
3.0%
대원고속 1
 
1.5%
용남고속 1
 
1.5%
제부여객 1
 
1.5%
Other values (7) 7
 
10.6%

Length

2023-12-13T08:14:29.020079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
삼영운수 25
37.9%
보영운수 12
18.2%
화영운수 7
 
10.6%
경원여객 4
 
6.1%
대원버스 3
 
4.5%
시흥교통 3
 
4.5%
성우운수 2
 
3.0%
수원여객 1
 
1.5%
경기고속 1
 
1.5%
삼경운수 1
 
1.5%
Other values (7) 7
 
10.6%

노선번호
Text

UNIQUE 

Distinct66
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size660.0 B
2023-12-13T08:14:29.254563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5.5
Mean length3.6212121
Min length1

Characters and Unicode

Total characters239
Distinct characters19
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)100.0%

Sample

1st row1
2nd row01월 01일
3rd row01월 02일
4th row01월 05일
5th row10
ValueCountFrequency (%)
01월 4
 
5.1%
02일 4
 
5.1%
11월 3
 
3.8%
05일 2
 
2.5%
01일 2
 
2.5%
03일 2
 
2.5%
08월 2
 
2.5%
15일 2
 
2.5%
jan-52 1
 
1.3%
5602 1
 
1.3%
Other values (56) 56
70.9%
2023-12-13T08:14:29.626820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 51
21.3%
1 31
13.0%
3 28
11.7%
5 23
9.6%
2 17
 
7.1%
8 14
 
5.9%
월 13
 
5.4%
13
 
5.4%
일 13
 
5.4%
6 10
 
4.2%
Other values (9) 26
10.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 189
79.1%
Other Letter 26
 
10.9%
Space Separator 13
 
5.4%
Uppercase Letter 5
 
2.1%
Lowercase Letter 4
 
1.7%
Dash Punctuation 2
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 51
27.0%
1 31
16.4%
3 28
14.8%
5 23
12.2%
2 17
 
9.0%
8 14
 
7.4%
6 10
 
5.3%
7 8
 
4.2%
9 5
 
2.6%
4 2
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
J 2
40.0%
M 2
40.0%
G 1
20.0%
Other Letter
ValueCountFrequency (%)
월 13
50.0%
일 13
50.0%
Lowercase Letter
ValueCountFrequency (%)
a 2
50.0%
n 2
50.0%
Space Separator
ValueCountFrequency (%)
13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 204
85.4%
Hangul 26
 
10.9%
Latin 9
 
3.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 51
25.0%
1 31
15.2%
3 28
13.7%
5 23
11.3%
2 17
 
8.3%
8 14
 
6.9%
13
 
6.4%
6 10
 
4.9%
7 8
 
3.9%
9 5
 
2.5%
Other values (2) 4
 
2.0%
Latin
ValueCountFrequency (%)
J 2
22.2%
a 2
22.2%
n 2
22.2%
M 2
22.2%
G 1
11.1%
Hangul
ValueCountFrequency (%)
월 13
50.0%
일 13
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 213
89.1%
Hangul 26
 
10.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 51
23.9%
1 31
14.6%
3 28
13.1%
5 23
10.8%
2 17
 
8.0%
8 14
 
6.6%
13
 
6.1%
6 10
 
4.7%
7 8
 
3.8%
9 5
 
2.3%
Other values (7) 13
 
6.1%
Hangul
ValueCountFrequency (%)
월 13
50.0%
일 13
50.0%

기점
Text

Distinct43
Distinct (%)65.2%
Missing0
Missing (%)0.0%
Memory size660.0 B
2023-12-13T08:14:29.850312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11.5
Mean length6.7575758
Min length3

Characters and Unicode

Total characters446
Distinct characters144
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)43.9%

Sample

1st row평촌차고지
2nd row월암종점
3rd row석수동버스공영차고지
4th row월암종점
5th row창박골
ValueCountFrequency (%)
월암종점 5
 
7.5%
군포공영차고지 3
 
4.5%
차고지입구 3
 
4.5%
평촌차고지 3
 
4.5%
창박골 3
 
4.5%
금강1단지 3
 
4.5%
숲속마을3.5단지 3
 
4.5%
경원여객안양차고지 2
 
3.0%
ktx광명역6번출구 2
 
3.0%
도촌동9단지앞 2
 
3.0%
Other values (34) 38
56.7%
2023-12-13T08:14:30.198805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 31
 
7.0%
차 22
 
4.9%
고 20
 
4.5%
단 9
 
2.0%
구 9
 
2.0%
수 9
 
2.0%
. 9
 
2.0%
종 8
 
1.8%
점 8
 
1.8%
트 8
 
1.8%
Other values (134) 313
70.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 407
91.3%
Decimal Number 17
 
3.8%
Other Punctuation 11
 
2.5%
Uppercase Letter 10
 
2.2%
Space Separator 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 31
 
7.6%
차 22
 
5.4%
고 20
 
4.9%
단 9
 
2.2%
구 9
 
2.2%
수 9
 
2.2%
종 8
 
2.0%
점 8
 
2.0%
트 8
 
2.0%
동 8
 
2.0%
Other values (119) 275
67.6%
Decimal Number
ValueCountFrequency (%)
3 4
23.5%
1 3
17.6%
5 3
17.6%
2 2
11.8%
6 2
11.8%
9 2
11.8%
4 1
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
K 3
30.0%
S 2
20.0%
T 2
20.0%
X 2
20.0%
A 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
. 9
81.8%
, 2
 
18.2%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 407
91.3%
Common 29
 
6.5%
Latin 10
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 31
 
7.6%
차 22
 
5.4%
고 20
 
4.9%
단 9
 
2.2%
구 9
 
2.2%
수 9
 
2.2%
종 8
 
2.0%
점 8
 
2.0%
트 8
 
2.0%
동 8
 
2.0%
Other values (119) 275
67.6%
Common
ValueCountFrequency (%)
. 9
31.0%
3 4
13.8%
1 3
 
10.3%
5 3
 
10.3%
2 2
 
6.9%
, 2
 
6.9%
6 2
 
6.9%
9 2
 
6.9%
4 1
 
3.4%
1
 
3.4%
Latin
ValueCountFrequency (%)
K 3
30.0%
S 2
20.0%
T 2
20.0%
X 2
20.0%
A 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 407
91.3%
ASCII 39
 
8.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 31
 
7.6%
차 22
 
5.4%
고 20
 
4.9%
단 9
 
2.2%
구 9
 
2.2%
수 9
 
2.2%
종 8
 
2.0%
점 8
 
2.0%
트 8
 
2.0%
동 8
 
2.0%
Other values (119) 275
67.6%
ASCII
ValueCountFrequency (%)
. 9
23.1%
3 4
10.3%
1 3
 
7.7%
K 3
 
7.7%
5 3
 
7.7%
2 2
 
5.1%
, 2
 
5.1%
S 2
 
5.1%
T 2
 
5.1%
6 2
 
5.1%
Other values (5) 7
17.9%

종점
Text

Distinct45
Distinct (%)68.2%
Missing0
Missing (%)0.0%
Memory size660.0 B
2023-12-13T08:14:30.406235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length14
Mean length6.7121212
Min length3

Characters and Unicode

Total characters443
Distinct characters135
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)54.5%

Sample

1st row구로디지털단지(중)
2nd row사당역(중)
3rd row월암동.부곡중
4th row옥박골
5th row의왕보건소
ValueCountFrequency (%)
안양역 10
 
15.2%
사당역(중 4
 
6.1%
석수역 3
 
4.5%
석수역(중 3
 
4.5%
의왕보건소 2
 
3.0%
갯마을앞(중 2
 
3.0%
잠실종합운동장 2
 
3.0%
창박골 2
 
3.0%
인덕원역4호선 2
 
3.0%
고천.의왕시청 1
 
1.5%
Other values (35) 35
53.0%
2023-12-13T08:14:30.805158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
역 35
 
7.9%
중 15
 
3.4%
안 14
 
3.2%
( 14
 
3.2%
) 14
 
3.2%
구 14
 
3.2%
지 12
 
2.7%
양 11
 
2.5%
사 9
 
2.0%
번 9
 
2.0%
Other values (125) 296
66.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 387
87.4%
Decimal Number 16
 
3.6%
Open Punctuation 14
 
3.2%
Close Punctuation 14
 
3.2%
Other Punctuation 9
 
2.0%
Uppercase Letter 3
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 35
 
9.0%
중 15
 
3.9%
안 14
 
3.6%
구 14
 
3.6%
지 12
 
3.1%
양 11
 
2.8%
사 9
 
2.3%
번 9
 
2.3%
단 7
 
1.8%
원 7
 
1.8%
Other values (113) 254
65.6%
Decimal Number
ValueCountFrequency (%)
1 5
31.2%
4 4
25.0%
2 4
25.0%
3 2
 
12.5%
8 1
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
K 1
33.3%
X 1
33.3%
T 1
33.3%
Other Punctuation
ValueCountFrequency (%)
. 8
88.9%
, 1
 
11.1%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 387
87.4%
Common 53
 
12.0%
Latin 3
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 35
 
9.0%
중 15
 
3.9%
안 14
 
3.6%
구 14
 
3.6%
지 12
 
3.1%
양 11
 
2.8%
사 9
 
2.3%
번 9
 
2.3%
단 7
 
1.8%
원 7
 
1.8%
Other values (113) 254
65.6%
Common
ValueCountFrequency (%)
( 14
26.4%
) 14
26.4%
. 8
15.1%
1 5
 
9.4%
4 4
 
7.5%
2 4
 
7.5%
3 2
 
3.8%
, 1
 
1.9%
8 1
 
1.9%
Latin
ValueCountFrequency (%)
K 1
33.3%
X 1
33.3%
T 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 387
87.4%
ASCII 56
 
12.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
역 35
 
9.0%
중 15
 
3.9%
안 14
 
3.6%
구 14
 
3.6%
지 12
 
3.1%
양 11
 
2.8%
사 9
 
2.3%
번 9
 
2.3%
단 7
 
1.8%
원 7
 
1.8%
Other values (113) 254
65.6%
ASCII
ValueCountFrequency (%)
( 14
25.0%
) 14
25.0%
. 8
14.3%
1 5
 
8.9%
4 4
 
7.1%
2 4
 
7.1%
3 2
 
3.6%
, 1
 
1.8%
8 1
 
1.8%
K 1
 
1.8%
Other values (2) 2
 
3.6%

인가거리(왕복)
Real number (ℝ)

Distinct57
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.263636
Minimum5
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size726.0 B
2023-12-13T08:14:30.967067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile23.95
Q133.8
median47.5
Q366.95
95-th percentile78.75
Maximum92
Range87
Interquartile range (IQR)33.15

Descriptive statistics

Standard deviation20.155981
Coefficient of variation (CV)0.40914522
Kurtosis-0.88264041
Mean49.263636
Median Absolute Deviation (MAD)15.9
Skewness0.19905165
Sum3251.4
Variance406.26358
MonotonicityNot monotonic
2023-12-13T08:14:31.100602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.0 5
 
7.6%
54.0 2
 
3.0%
36.4 2
 
3.0%
72.0 2
 
3.0%
62.0 2
 
3.0%
25.6 2
 
3.0%
54.6 1
 
1.5%
67.8 1
 
1.5%
32.2 1
 
1.5%
25.8 1
 
1.5%
Other values (47) 47
71.2%
ValueCountFrequency (%)
5.0 1
 
1.5%
20.0 1
 
1.5%
21.2 1
 
1.5%
23.6 1
 
1.5%
25.0 1
 
1.5%
25.6 2
 
3.0%
25.8 1
 
1.5%
26.0 5
7.6%
30.6 1
 
1.5%
31.0 1
 
1.5%
ValueCountFrequency (%)
92.0 1
1.5%
88.0 1
1.5%
86.2 1
1.5%
78.8 1
1.5%
78.6 1
1.5%
78.0 1
1.5%
77.0 1
1.5%
76.6 1
1.5%
75.0 1
1.5%
74.0 1
1.5%

출퇴근배차간격
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.515152
Minimum5
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size726.0 B
2023-12-13T08:14:31.259597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q16.25
median10
Q320
95-th percentile38.75
Maximum50
Range45
Interquartile range (IQR)13.75

Descriptive statistics

Standard deviation11.054052
Coefficient of variation (CV)0.76155267
Kurtosis2.0850765
Mean14.515152
Median Absolute Deviation (MAD)4
Skewness1.5885408
Sum958
Variance122.19207
MonotonicityNot monotonic
2023-12-13T08:14:31.403781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
8 10
15.2%
6 9
13.6%
5 8
12.1%
10 6
9.1%
20 5
7.6%
15 5
7.6%
30 4
 
6.1%
25 3
 
4.5%
40 2
 
3.0%
11 2
 
3.0%
Other values (9) 12
18.2%
ValueCountFrequency (%)
5 8
12.1%
6 9
13.6%
7 2
 
3.0%
8 10
15.2%
9 1
 
1.5%
10 6
9.1%
11 2
 
3.0%
12 2
 
3.0%
13 1
 
1.5%
14 1
 
1.5%
ValueCountFrequency (%)
50 2
 
3.0%
40 2
 
3.0%
35 1
 
1.5%
30 4
6.1%
27 1
 
1.5%
25 3
4.5%
20 5
7.6%
16 1
 
1.5%
15 5
7.6%
14 1
 
1.5%

평일배차간격
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.727273
Minimum6
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size726.0 B
2023-12-13T08:14:31.540146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile7.25
Q110
median15
Q330
95-th percentile47.5
Maximum80
Range74
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.292578
Coefficient of variation (CV)0.70384249
Kurtosis2.4653703
Mean21.727273
Median Absolute Deviation (MAD)6
Skewness1.491382
Sum1434
Variance233.86294
MonotonicityNot monotonic
2023-12-13T08:14:31.682455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
15 11
16.7%
40 8
12.1%
20 6
9.1%
9 5
7.6%
8 5
7.6%
30 5
7.6%
10 5
7.6%
25 3
 
4.5%
6 3
 
4.5%
35 3
 
4.5%
Other values (9) 12
18.2%
ValueCountFrequency (%)
6 3
 
4.5%
7 1
 
1.5%
8 5
7.6%
9 5
7.6%
10 5
7.6%
11 2
 
3.0%
12 2
 
3.0%
13 1
 
1.5%
14 1
 
1.5%
15 11
16.7%
ValueCountFrequency (%)
80 1
 
1.5%
60 2
 
3.0%
50 1
 
1.5%
40 8
12.1%
35 3
 
4.5%
30 5
7.6%
25 3
 
4.5%
20 6
9.1%
16 1
 
1.5%
15 11
16.7%

주말배차간격(일요일 기준)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)39.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.560606
Minimum8
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size726.0 B
2023-12-13T08:14:31.820600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile10
Q116.25
median24.5
Q340
95-th percentile60.75
Maximum100
Range92
Interquartile range (IQR)23.75

Descriptive statistics

Standard deviation19.342364
Coefficient of variation (CV)0.6329182
Kurtosis1.9220946
Mean30.560606
Median Absolute Deviation (MAD)10.5
Skewness1.3618127
Sum2017
Variance374.12704
MonotonicityNot monotonic
2023-12-13T08:14:31.956486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
30 8
 
12.1%
20 6
 
9.1%
12 5
 
7.6%
40 4
 
6.1%
50 4
 
6.1%
15 3
 
4.5%
60 3
 
4.5%
25 3
 
4.5%
23 3
 
4.5%
13 3
 
4.5%
Other values (16) 24
36.4%
ValueCountFrequency (%)
8 1
 
1.5%
9 1
 
1.5%
10 3
4.5%
12 5
7.6%
13 3
4.5%
15 3
4.5%
16 1
 
1.5%
17 1
 
1.5%
18 2
 
3.0%
19 1
 
1.5%
ValueCountFrequency (%)
100 1
 
1.5%
80 2
3.0%
61 1
 
1.5%
60 3
4.5%
55 2
3.0%
50 4
6.1%
45 2
3.0%
40 4
6.1%
35 2
3.0%
33 1
 
1.5%

주중상행첫차
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Memory size660.0 B
05:00
20 
04:50
8 
04:30
6 
05:20
5 
05:30
4 
Other values (14)
23 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique6 ?
Unique (%)9.1%

Sample

1st row04:50
2nd row04:40
3rd row05:00
4th row04:50
5th row05:00

Common Values

ValueCountFrequency (%)
05:00 20
30.3%
04:50 8
 
12.1%
04:30 6
 
9.1%
05:20 5
 
7.6%
05:30 4
 
6.1%
04:40 3
 
4.5%
04:42 2
 
3.0%
04:45 2
 
3.0%
05:10 2
 
3.0%
04:35 2
 
3.0%
Other values (9) 12
18.2%

Length

2023-12-13T08:14:32.111305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
05:00 20
30.3%
04:50 8
 
12.1%
04:30 6
 
9.1%
05:20 5
 
7.6%
05:30 4
 
6.1%
04:40 3
 
4.5%
04:55 2
 
3.0%
06:00 2
 
3.0%
04:10 2
 
3.0%
04:35 2
 
3.0%
Other values (9) 12
18.2%

주중상행막차
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Memory size660.0 B
23:00
16 
22:40
14 
23:20
7 
22:30
6 
22:50
5 
Other values (12)
18 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique9 ?
Unique (%)13.6%

Sample

1st row22:40
2nd row22:30
3rd row22:40
4th row22:40
5th row23:00

Common Values

ValueCountFrequency (%)
23:00 16
24.2%
22:40 14
21.2%
23:20 7
10.6%
22:30 6
 
9.1%
22:50 5
 
7.6%
22:20 4
 
6.1%
23:30 3
 
4.5%
22:00 2
 
3.0%
22:35 1
 
1.5%
23:10 1
 
1.5%
Other values (7) 7
10.6%

Length

2023-12-13T08:14:32.226941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
23:00 16
24.2%
22:40 14
21.2%
23:20 7
10.6%
22:30 6
 
9.1%
22:50 5
 
7.6%
22:20 4
 
6.1%
23:30 3
 
4.5%
22:00 2
 
3.0%
22:15 1
 
1.5%
21:20 1
 
1.5%
Other values (7) 7
10.6%

주말상행첫차
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Memory size660.0 B
05:00
19 
04:50
8 
04:30
6 
05:30
6 
05:20
4 
Other values (14)
23 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique6 ?
Unique (%)9.1%

Sample

1st row04:50
2nd row04:40
3rd row05:00
4th row04:50
5th row05:00

Common Values

ValueCountFrequency (%)
05:00 19
28.8%
04:50 8
12.1%
04:30 6
 
9.1%
05:30 6
 
9.1%
05:20 4
 
6.1%
04:40 3
 
4.5%
04:42 2
 
3.0%
04:45 2
 
3.0%
04:35 2
 
3.0%
04:10 2
 
3.0%
Other values (9) 12
18.2%

Length

2023-12-13T08:14:32.347249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
05:00 19
28.8%
04:50 8
12.1%
04:30 6
 
9.1%
05:30 6
 
9.1%
05:20 4
 
6.1%
04:40 3
 
4.5%
05:10 2
 
3.0%
04:55 2
 
3.0%
06:00 2
 
3.0%
04:10 2
 
3.0%
Other values (9) 12
18.2%

주말상행막차
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Memory size660.0 B
23:00
15 
22:40
13 
23:20
7 
22:30
6 
22:50
5 
Other values (13)
20 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique10 ?
Unique (%)15.2%

Sample

1st row22:40
2nd row22:30
3rd row22:40
4th row22:40
5th row23:00

Common Values

ValueCountFrequency (%)
23:00 15
22.7%
22:40 13
19.7%
23:20 7
10.6%
22:30 6
 
9.1%
22:50 5
 
7.6%
22:20 4
 
6.1%
23:30 4
 
6.1%
22:00 2
 
3.0%
22:15 1
 
1.5%
23:10 1
 
1.5%
Other values (8) 8
12.1%

Length

2023-12-13T08:14:32.468482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
23:00 15
22.7%
22:40 13
19.7%
23:20 7
10.6%
22:30 6
 
9.1%
22:50 5
 
7.6%
22:20 4
 
6.1%
23:30 4
 
6.1%
22:00 2
 
3.0%
22:55 1
 
1.5%
22:45 1
 
1.5%
Other values (8) 8
12.1%

주중하행첫차
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size660.0 B
05:30
10 
05:50
9 
05:40
6 
05:45
5 
06:30
 
3
Other values (17)
33 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique5 ?
Unique (%)7.6%

Sample

1st row05:50
2nd row05:40
3rd row05:50
4th row05:50
5th row05:40

Common Values

ValueCountFrequency (%)
05:30 10
15.2%
05:50 9
13.6%
05:40 6
 
9.1%
05:45 5
 
7.6%
06:30 3
 
4.5%
06:10 3
 
4.5%
05:00 3
 
4.5%
05:25 3
 
4.5%
05:10 3
 
4.5%
06:50 2
 
3.0%
Other values (12) 19
28.8%

Length

2023-12-13T08:14:32.583151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
05:30 10
15.2%
05:50 9
13.6%
05:40 6
 
9.1%
05:45 5
 
7.6%
06:30 3
 
4.5%
06:10 3
 
4.5%
05:00 3
 
4.5%
05:25 3
 
4.5%
05:10 3
 
4.5%
06:00 2
 
3.0%
Other values (12) 19
28.8%

주중하행막차
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)31.8%
Missing0
Missing (%)0.0%
Memory size660.0 B
23:30
11 
23:40
8 
23:50
7 
23:20
7 
23:00
6 
Other values (16)
27 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique11 ?
Unique (%)16.7%

Sample

1st row23:40
2nd row23:20
3rd row23:30
4th row23:40
5th row23:30

Common Values

ValueCountFrequency (%)
23:30 11
16.7%
23:40 8
12.1%
23:50 7
10.6%
23:20 7
10.6%
23:00 6
9.1%
00:00 6
9.1%
23:10 3
 
4.5%
23:15 3
 
4.5%
23:55 2
 
3.0%
23:35 2
 
3.0%
Other values (11) 11
16.7%

Length

2023-12-13T08:14:32.700372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
23:30 11
16.7%
23:40 8
12.1%
23:50 7
10.6%
23:20 7
10.6%
23:00 6
9.1%
00:00 6
9.1%
23:10 3
 
4.5%
23:15 3
 
4.5%
23:55 2
 
3.0%
23:35 2
 
3.0%
Other values (11) 11
16.7%

주말하행첫차
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)34.8%
Missing0
Missing (%)0.0%
Memory size660.0 B
05:30
10 
05:50
9 
05:40
6 
05:45
5 
06:30
 
3
Other values (18)
33 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique7 ?
Unique (%)10.6%

Sample

1st row05:50
2nd row05:40
3rd row05:50
4th row05:50
5th row05:40

Common Values

ValueCountFrequency (%)
05:30 10
15.2%
05:50 9
13.6%
05:40 6
 
9.1%
05:45 5
 
7.6%
06:30 3
 
4.5%
06:10 3
 
4.5%
05:00 3
 
4.5%
05:25 3
 
4.5%
05:10 3
 
4.5%
06:50 2
 
3.0%
Other values (13) 19
28.8%

Length

2023-12-13T08:14:32.803775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
05:30 10
15.2%
05:50 9
13.6%
05:40 6
 
9.1%
05:45 5
 
7.6%
06:30 3
 
4.5%
06:10 3
 
4.5%
05:00 3
 
4.5%
05:25 3
 
4.5%
05:10 3
 
4.5%
05:55 2
 
3.0%
Other values (13) 19
28.8%

주말하행막차
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)31.8%
Missing0
Missing (%)0.0%
Memory size660.0 B
23:30
11 
23:20
8 
23:40
8 
23:50
7 
00:00
6 
Other values (16)
26 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique11 ?
Unique (%)16.7%

Sample

1st row23:40
2nd row23:20
3rd row23:30
4th row23:40
5th row23:30

Common Values

ValueCountFrequency (%)
23:30 11
16.7%
23:20 8
12.1%
23:40 8
12.1%
23:50 7
10.6%
00:00 6
9.1%
23:00 5
7.6%
23:10 3
 
4.5%
23:15 3
 
4.5%
23:55 2
 
3.0%
23:35 2
 
3.0%
Other values (11) 11
16.7%

Length

2023-12-13T08:14:32.903617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
23:30 11
16.7%
23:40 8
12.1%
23:20 8
12.1%
23:50 7
10.6%
00:00 6
9.1%
23:00 5
7.6%
23:10 3
 
4.5%
23:15 3
 
4.5%
23:55 2
 
3.0%
23:35 2
 
3.0%
Other values (11) 11
16.7%

Interactions

2023-12-13T08:14:27.827696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:25.621062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:26.067326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:26.858232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:27.343960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:27.900129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:25.707793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:26.464785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:26.956284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:27.461059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:27.966941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:25.794795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:26.542845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:27.047845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:27.559248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:28.053553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:25.885663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:26.651139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:27.153192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:27.658258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:28.152219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:25.985735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:26.775546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:27.253785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:14:27.750559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:14:32.989921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번관할관청운행업체노선번호기점종점인가거리(왕복)출퇴근배차간격평일배차간격주말배차간격(일요일 기준)주중상행첫차주중상행막차주말상행첫차주말상행막차주중하행첫차주중하행막차주말하행첫차주말하행막차
순번1.0000.4580.5601.0000.7910.8220.4890.0000.0000.1880.6440.0000.6470.0000.4820.3210.5010.284
관할관청0.4581.0000.9901.0000.9880.0000.6390.5490.6930.3920.8020.9220.8200.9300.6980.1960.7290.273
운행업체0.5600.9901.0001.0000.9890.0000.6930.5910.7020.3350.4770.9470.5840.8980.6890.0000.7080.000
노선번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
기점0.7910.9880.9891.0001.0000.0000.8990.0000.8370.7150.8330.9660.8220.9680.9330.8670.9440.844
종점0.8220.0000.0001.0000.0001.0000.0000.8900.5740.8700.0000.0000.0000.0000.0000.0000.0000.000
인가거리(왕복)0.4890.6390.6931.0000.8990.0001.0000.2740.3450.3160.4450.5090.4960.5660.6230.0000.6300.000
출퇴근배차간격0.0000.5490.5911.0000.0000.8900.2741.0000.8120.8330.4660.6170.5250.7000.5180.0000.5010.000
평일배차간격0.0000.6930.7021.0000.8370.5740.3450.8121.0000.9660.7000.6820.6640.6630.2760.0000.2440.000
주말배차간격(일요일 기준)0.1880.3920.3351.0000.7150.8700.3160.8330.9661.0000.6230.6740.6220.7070.2850.0000.3020.000
주중상행첫차0.6440.8020.4771.0000.8330.0000.4450.4660.7000.6231.0000.6811.0000.6690.6890.8150.6860.814
주중상행막차0.0000.9220.9471.0000.9660.0000.5090.6170.6820.6740.6811.0000.6910.9990.7830.5640.7670.554
주말상행첫차0.6470.8200.5841.0000.8220.0000.4960.5250.6640.6221.0000.6911.0000.6640.7390.8120.6960.820
주말상행막차0.0000.9300.8981.0000.9680.0000.5660.7000.6630.7070.6690.9990.6641.0000.7950.3790.8270.390
주중하행첫차0.4820.6980.6891.0000.9330.0000.6230.5180.2760.2850.6890.7830.7390.7951.0000.2231.0000.339
주중하행막차0.3210.1960.0001.0000.8670.0000.0000.0000.0000.0000.8150.5640.8120.3790.2231.0000.5011.000
주말하행첫차0.5010.7290.7081.0000.9440.0000.6300.5010.2440.3020.6860.7670.6960.8271.0000.5011.0000.542
주말하행막차0.2840.2730.0001.0000.8440.0000.0000.0000.0000.0000.8140.5540.8200.3900.3391.0000.5421.000
2023-12-13T08:14:33.501214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주중하행막차운행업체주말하행막차주말상행막차관할관청주중하행첫차주중상행첫차주말하행첫차주말상행첫차주중상행막차
주중하행막차1.0000.0000.9900.0860.0000.0000.3710.1290.3680.180
운행업체0.0001.0000.0000.5360.9010.2520.1460.2590.2020.530
주말하행막차0.9900.0001.0000.0910.0440.0510.3700.1500.3760.175
주말상행막차0.0860.5360.0911.0000.6420.3420.2520.3720.2480.977
관할관청0.0000.9010.0440.6421.0000.2750.3950.2950.4180.630
주중하행첫차0.0000.2520.0510.3420.2751.0000.2470.9890.2860.332
주중상행첫차0.3710.1460.3700.2520.3950.2471.0000.2380.9790.266
주말하행첫차0.1290.2590.1500.3720.2950.9890.2381.0000.2460.309
주말상행첫차0.3680.2020.3760.2480.4180.2860.9790.2461.0000.273
주중상행막차0.1800.5300.1750.9770.6300.3320.2660.3090.2731.000
2023-12-13T08:14:33.634542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번인가거리(왕복)출퇴근배차간격평일배차간격주말배차간격(일요일 기준)관할관청운행업체주중상행첫차주중상행막차주말상행첫차주말상행막차주중하행첫차주중하행막차주말하행첫차주말하행막차
순번1.000-0.1060.1590.1640.1380.1700.2130.2520.0000.2570.0000.1820.1170.1870.103
인가거리(왕복)-0.1061.0000.2730.3500.3160.3150.3270.1530.2000.1800.2270.2430.0000.2430.000
출퇴근배차간격0.1590.2731.0000.9410.9150.2660.2600.1840.2700.2160.2740.2050.0000.1970.000
평일배차간격0.1640.3500.9411.0000.9590.3830.3500.3410.3320.3110.3140.0690.0000.0410.000
주말배차간격(일요일 기준)0.1380.3160.9150.9591.0000.1720.1190.2800.3260.2790.3510.0740.0000.0780.000
관할관청0.1700.3150.2660.3830.1721.0000.9010.3950.6300.4180.6420.2750.0000.2950.044
운행업체0.2130.3270.2600.3500.1190.9011.0000.1460.5300.2020.5360.2520.0000.2590.000
주중상행첫차0.2520.1530.1840.3410.2800.3950.1461.0000.2660.9790.2520.2470.3710.2380.370
주중상행막차0.0000.2000.2700.3320.3260.6300.5300.2661.0000.2730.9770.3320.1800.3090.175
주말상행첫차0.2570.1800.2160.3110.2790.4180.2020.9790.2731.0000.2480.2860.3680.2460.376
주말상행막차0.0000.2270.2740.3140.3510.6420.5360.2520.9770.2481.0000.3420.0860.3720.091
주중하행첫차0.1820.2430.2050.0690.0740.2750.2520.2470.3320.2860.3421.0000.0000.9890.051
주중하행막차0.1170.0000.0000.0000.0000.0000.0000.3710.1800.3680.0860.0001.0000.1290.990
주말하행첫차0.1870.2430.1970.0410.0780.2950.2590.2380.3090.2460.3720.9890.1291.0000.150
주말하행막차0.1030.0000.0000.0000.0000.0440.0000.3700.1750.3760.0910.0510.9900.1501.000

Missing values

2023-12-13T08:14:28.261085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:14:28.495534image/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

순번관할관청운행업체노선번호기점종점인가거리(왕복)출퇴근배차간격평일배차간격주말배차간격(일요일 기준)주중상행첫차주중상행막차주말상행첫차주말상행막차주중하행첫차주중하행막차주말하행첫차주말하행막차
01안양시삼영운수1평촌차고지구로디지털단지(중)41.416253004:5022:4004:5022:4005:5023:4005:5023:40
12안양시삼영운수01월 01일월암종점사당역(중)52.611152004:4022:3004:4022:3005:4023:2005:4023:20
23안양시삼영운수01월 02일석수동버스공영차고지월암동.부곡중35.0691205:0022:4005:0022:4005:5023:3005:5023:30
34안양시삼영운수01월 05일월암종점옥박골41.040608004:5022:4004:5022:4005:5023:4005:5023:40
45안양시보영운수10창박골의왕보건소26.0681205:0023:0005:0023:0005:4023:3005:4023:30
56광명시화영운수101화영운수차고지석수역(중)26.0691505:1023:2005:1023:2005:4500:0005:4500:00
67성남시대원버스103도촌동9단지앞사당역(중)61.820304005:0022:2005:0022:2006:1023:4006:1023:40
78광명시화영운수11광명돔경륜장.광남문안양역42.630355505:0023:1005:0023:1005:2023:3005:2023:30
89안양시보영운수11월 02일군포공영차고지갯마을앞(중)50.28101604:3023:0004:3023:0005:1023:5005:1023:50
910안양시보영운수11월 03일창박골잠실종합운동장54.06101804:5522:3004:5522:3005:5023:3005:5023:30
순번관할관청운행업체노선번호기점종점인가거리(왕복)출퇴근배차간격평일배차간격주말배차간격(일요일 기준)주중상행첫차주중상행막차주말상행첫차주말상행막차주중하행첫차주중하행막차주말하행첫차주말하행막차
5657안양시삼영운수9충훈부종점사당역(중)37.48101304:4222:5004:4222:5004:4222:5004:4222:50
5758안양시삼영운수09월 03일충훈부종점사당역4번출구35.66101204:4223:0004:4223:0004:4223:0004:4223:00
5859수원시성우운수900경희대학교보라매공원(중)78.68121504:1022:4004:1022:4005:3000:2005:3000:20
5960안양시보영운수917군포공영차고지잠실종합운동장67.415203504:4022:2004:4022:2006:0023:4006:0023:40
6061부천시소신여객G8808부천소풍터미널범계역72.030405005:2021:4005:2021:4006:4523:0006:4523:00
6162안양시삼영운수M5333동안경찰서,범계역잠실역1번.11번출구67.610305005:0022:3005:0022:3005:5523:3005:5523:30
6263안양시삼영운수33숲속마을3.5단지롯데프리미엄아울렛타임빌라스후문30.615203005:0022:4005:0022:4005:4523:2505:4523:25
6364안양시보영운수87백운사입구금정역26.08112305:0023:0005:0023:0005:4523:4505:4523:45
6465안양시보영운수55금강1단지금정역3번출구21.230405505:2022:5005:2022:5005:5023:1005:5023:10
6566안양시화영운수M5556석수3동행정복지센터사당역3번출구33.420404004:5023:3004:5023:3005:4000:1005:4000:10