Overview

Dataset statistics

Number of variables16
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory138.5 B

Variable types

Categorical6
Text2
Numeric5
DateTime3

Dataset

Description인천광역시에서 운영되고 있는 광역버스 노선번호, 기점, 정류소, 종점, 운행간격 등의 항목에 대한 정보를 제공합니다.
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15045237&srcSe=7661IVAWM27C61E190

Alerts

노선구분 has constant value ""Constant
면허대수 is highly overall correlated with 인가대수 and 1 other fieldsHigh correlation
인가대수 is highly overall correlated with 면허대수 and 2 other fieldsHigh correlation
최소배차간격 is highly overall correlated with 인가대수 and 2 other fieldsHigh correlation
최대배차간격 is highly overall correlated with 최소배차간격 and 1 other fieldsHigh correlation
인가운행횟수 is highly overall correlated with 면허대수 and 3 other fieldsHigh correlation
회사명 is highly overall correlated with 노선유형 and 2 other fieldsHigh correlation
노선유형 is highly overall correlated with 회사명 and 2 other fieldsHigh correlation
종점정류소명 is highly overall correlated with 회사명 and 1 other fieldsHigh correlation
종점막차시간 is highly overall correlated with 회사명 and 1 other fieldsHigh correlation
노선유형 is highly imbalanced (75.0%)Imbalance
노선번호 has unique valuesUnique

Reproduction

Analysis started2024-04-17 06:03:24.566486
Analysis finished2024-04-17 06:03:27.575024
Duration3.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

회사명
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Memory size324.0 B
신강교통
5 
마니교통
4 
인강여객
4 
더월드교통
3 
선진여객
2 
Other values (5)
6 

Length

Max length6
Median length4
Mean length4.375
Min length4

Unique

Unique4 ?
Unique (%)16.7%

Sample

1st row더월드교통
2nd row더월드교통
3rd row더월드교통
4th row마니교통
5th row마니교통

Common Values

ValueCountFrequency (%)
신강교통 5
20.8%
마니교통 4
16.7%
인강여객 4
16.7%
더월드교통 3
12.5%
선진여객 2
 
8.3%
신동아교통 2
 
8.3%
수정관광화물 1
 
4.2%
신흥교통 1
 
4.2%
청룡교통 1
 
4.2%
한국철도공사 1
 
4.2%

Length

2024-04-17T15:03:27.643974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T15:03:27.760393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신강교통 5
20.8%
마니교통 4
16.7%
인강여객 4
16.7%
더월드교통 3
12.5%
선진여객 2
 
8.3%
신동아교통 2
 
8.3%
수정관광화물 1
 
4.2%
신흥교통 1
 
4.2%
청룡교통 1
 
4.2%
한국철도공사 1
 
4.2%

노선번호
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2024-04-17T15:03:27.926879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.25
Min length4

Characters and Unicode

Total characters102
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)100.0%

Sample

1st row1300
2nd row1301
3rd row1302
4th row1000
5th row1400
ValueCountFrequency (%)
1300 1
 
4.2%
1301 1
 
4.2%
m6724 1
 
4.2%
m6405 1
 
4.2%
9201 1
 
4.2%
9200 1
 
4.2%
9100 1
 
4.2%
m6751 1
 
4.2%
m6450 1
 
4.2%
m6439 1
 
4.2%
Other values (14) 14
58.3%
2024-04-17T15:03:28.206458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 31
30.4%
1 19
18.6%
6 9
 
8.8%
9 8
 
7.8%
2 7
 
6.9%
5 6
 
5.9%
3 5
 
4.9%
4 5
 
4.9%
M 5
 
4.9%
7 4
 
3.9%
Other values (2) 3
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 96
94.1%
Uppercase Letter 5
 
4.9%
Lowercase Letter 1
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 31
32.3%
1 19
19.8%
6 9
 
9.4%
9 8
 
8.3%
2 7
 
7.3%
5 6
 
6.2%
3 5
 
5.2%
4 5
 
5.2%
7 4
 
4.2%
8 2
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
M 5
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 96
94.1%
Latin 6
 
5.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 31
32.3%
1 19
19.8%
6 9
 
9.4%
9 8
 
8.3%
2 7
 
7.3%
5 6
 
6.2%
3 5
 
5.2%
4 5
 
5.2%
7 4
 
4.2%
8 2
 
2.1%
Latin
ValueCountFrequency (%)
M 5
83.3%
m 1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 102
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 31
30.4%
1 19
18.6%
6 9
 
8.8%
9 8
 
7.8%
2 7
 
6.9%
5 6
 
5.9%
3 5
 
4.9%
4 5
 
4.9%
M 5
 
4.9%
7 4
 
3.9%
Other values (2) 3
 
2.9%

노선구분
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
광역형
24 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
광역형 24
100.0%

Length

2024-04-17T15:03:28.352826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T15:03:28.434297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광역형 24
100.0%

노선유형
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
직행좌석형 시내버스
23 
좌석형 시내버스
 
1

Length

Max length10
Median length10
Mean length9.9166667
Min length8

Unique

Unique1 ?
Unique (%)4.2%

Sample

1st row직행좌석형 시내버스
2nd row직행좌석형 시내버스
3rd row직행좌석형 시내버스
4th row직행좌석형 시내버스
5th row직행좌석형 시내버스

Common Values

ValueCountFrequency (%)
직행좌석형 시내버스 23
95.8%
좌석형 시내버스 1
 
4.2%

Length

2024-04-17T15:03:28.525297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T15:03:28.620726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
시내버스 24
50.0%
직행좌석형 23
47.9%
좌석형 1
 
2.1%

기점정류소명
Text

Distinct19
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
2024-04-17T15:03:28.758312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length11
Mean length7.0833333
Min length3

Characters and Unicode

Total characters170
Distinct characters85
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)58.3%

Sample

1st row힐스테이트레이크송도2차(201동)
2nd row송도공영차고지
3rd row극지연구소
4th row경남아너스빌
5th row인천터미널
ValueCountFrequency (%)
공단사거리 2
 
7.7%
e편한세상 2
 
7.7%
정문 2
 
7.7%
마전지구버스차고지 2
 
7.7%
경남아너스빌 2
 
7.7%
인천터미널 2
 
7.7%
힐스테이트레이크송도2차(201동 1
 
3.8%
인하대후문 1
 
3.8%
연세대 1
 
3.8%
웰카운티 1
 
3.8%
Other values (10) 10
38.5%
2024-04-17T15:03:29.034942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 7
 
4.1%
스 6
 
3.5%
인 5
 
2.9%
아 5
 
2.9%
트 5
 
2.9%
공 4
 
2.4%
차 4
 
2.4%
파 4
 
2.4%
도 3
 
1.8%
고 3
 
1.8%
Other values (75) 124
72.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 151
88.8%
Decimal Number 6
 
3.5%
Open Punctuation 3
 
1.8%
Close Punctuation 3
 
1.8%
Lowercase Letter 2
 
1.2%
Space Separator 2
 
1.2%
Uppercase Letter 2
 
1.2%
Dash Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 7
 
4.6%
스 6
 
4.0%
인 5
 
3.3%
아 5
 
3.3%
트 5
 
3.3%
공 4
 
2.6%
차 4
 
2.6%
파 4
 
2.6%
도 3
 
2.0%
고 3
 
2.0%
Other values (65) 105
69.5%
Decimal Number
ValueCountFrequency (%)
2 3
50.0%
1 2
33.3%
0 1
 
16.7%
Uppercase Letter
ValueCountFrequency (%)
T 1
50.0%
B 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 151
88.8%
Common 15
 
8.8%
Latin 4
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 7
 
4.6%
스 6
 
4.0%
인 5
 
3.3%
아 5
 
3.3%
트 5
 
3.3%
공 4
 
2.6%
차 4
 
2.6%
파 4
 
2.6%
도 3
 
2.0%
고 3
 
2.0%
Other values (65) 105
69.5%
Common
ValueCountFrequency (%)
( 3
20.0%
) 3
20.0%
2 3
20.0%
1 2
13.3%
2
13.3%
0 1
 
6.7%
- 1
 
6.7%
Latin
ValueCountFrequency (%)
e 2
50.0%
T 1
25.0%
B 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 151
88.8%
ASCII 19
 
11.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 7
 
4.6%
스 6
 
4.0%
인 5
 
3.3%
아 5
 
3.3%
트 5
 
3.3%
공 4
 
2.6%
차 4
 
2.6%
파 4
 
2.6%
도 3
 
2.0%
고 3
 
2.0%
Other values (65) 105
69.5%
ASCII
ValueCountFrequency (%)
( 3
15.8%
) 3
15.8%
2 3
15.8%
1 2
10.5%
e 2
10.5%
2
10.5%
0 1
 
5.3%
T 1
 
5.3%
B 1
 
5.3%
- 1
 
5.3%

종점정류소명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
서울역
11 
강남역
5 
시민의숲양재꽃시장
3 
연세대학교앞
 
1
역삼역.포스코타워역삼
 
1
Other values (3)
3 

Length

Max length11
Median length3
Mean length4.875
Min length3

Unique

Unique5 ?
Unique (%)20.8%

Sample

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

Common Values

ValueCountFrequency (%)
서울역 11
45.8%
강남역 5
20.8%
시민의숲양재꽃시장 3
 
12.5%
연세대학교앞 1
 
4.2%
역삼역.포스코타워역삼 1
 
4.2%
한국무역센터.삼성역 1
 
4.2%
공덕오거리 1
 
4.2%
KTX광명역4번출구 1
 
4.2%

Length

2024-04-17T15:03:29.157532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T15:03:29.263370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울역 11
45.8%
강남역 5
20.8%
시민의숲양재꽃시장 3
 
12.5%
연세대학교앞 1
 
4.2%
역삼역.포스코타워역삼 1
 
4.2%
한국무역센터.삼성역 1
 
4.2%
공덕오거리 1
 
4.2%
ktx광명역4번출구 1
 
4.2%

면허대수
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.083333
Minimum5
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-04-17T15:03:29.364002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5.45
Q110
median13.5
Q315.25
95-th percentile21.4
Maximum23
Range18
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation4.5196832
Coefficient of variation (CV)0.34545349
Kurtosis0.24619771
Mean13.083333
Median Absolute Deviation (MAD)2.5
Skewness0.23042314
Sum314
Variance20.427536
MonotonicityNot monotonic
2024-04-17T15:03:29.461444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
15 4
16.7%
16 3
12.5%
12 3
12.5%
14 2
8.3%
8 2
8.3%
5 2
8.3%
10 2
8.3%
13 1
 
4.2%
9 1
 
4.2%
18 1
 
4.2%
Other values (3) 3
12.5%
ValueCountFrequency (%)
5 2
8.3%
8 2
8.3%
9 1
 
4.2%
10 2
8.3%
11 1
 
4.2%
12 3
12.5%
13 1
 
4.2%
14 2
8.3%
15 4
16.7%
16 3
12.5%
ValueCountFrequency (%)
23 1
 
4.2%
22 1
 
4.2%
18 1
 
4.2%
16 3
12.5%
15 4
16.7%
14 2
8.3%
13 1
 
4.2%
12 3
12.5%
11 1
 
4.2%
10 2
8.3%

관할관청
Categorical

Distinct6
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
연수구
9 
서구
9 
미추홀구
3 
계양구
1 
부평구
1 

Length

Max length4
Median length3
Mean length2.7083333
Min length2

Unique

Unique3 ?
Unique (%)12.5%

Sample

1st row연수구
2nd row연수구
3rd row연수구
4th row서구
5th row미추홀구

Common Values

ValueCountFrequency (%)
연수구 9
37.5%
서구 9
37.5%
미추홀구 3
 
12.5%
계양구 1
 
4.2%
부평구 1
 
4.2%
중구 1
 
4.2%

Length

2024-04-17T15:03:29.580196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T15:03:29.679727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
연수구 9
37.5%
서구 9
37.5%
미추홀구 3
 
12.5%
계양구 1
 
4.2%
부평구 1
 
4.2%
중구 1
 
4.2%

인가대수
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.25
Minimum5
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-04-17T15:03:29.766482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5.15
Q18
median11.5
Q314
95-th percentile16.7
Maximum23
Range18
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.2758167
Coefficient of variation (CV)0.3800726
Kurtosis0.90979266
Mean11.25
Median Absolute Deviation (MAD)3.5
Skewness0.68212878
Sum270
Variance18.282609
MonotonicityNot monotonic
2024-04-17T15:03:29.862683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
15 3
12.5%
13 3
12.5%
8 3
12.5%
7 2
8.3%
12 2
8.3%
14 2
8.3%
5 2
8.3%
10 2
8.3%
9 1
 
4.2%
6 1
 
4.2%
Other values (3) 3
12.5%
ValueCountFrequency (%)
5 2
8.3%
6 1
 
4.2%
7 2
8.3%
8 3
12.5%
9 1
 
4.2%
10 2
8.3%
11 1
 
4.2%
12 2
8.3%
13 3
12.5%
14 2
8.3%
ValueCountFrequency (%)
23 1
 
4.2%
17 1
 
4.2%
15 3
12.5%
14 2
8.3%
13 3
12.5%
12 2
8.3%
11 1
 
4.2%
10 2
8.3%
9 1
 
4.2%
8 3
12.5%

최소배차간격
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.083333
Minimum6
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-04-17T15:03:29.958408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile10
Q115
median25
Q340
95-th percentile60
Maximum60
Range54
Interquartile range (IQR)25

Descriptive statistics

Standard deviation16.589459
Coefficient of variation (CV)0.57041119
Kurtosis-0.62414247
Mean29.083333
Median Absolute Deviation (MAD)11.5
Skewness0.62872336
Sum698
Variance275.21014
MonotonicityNot monotonic
2024-04-17T15:03:30.052190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
20 4
16.7%
40 4
16.7%
60 3
12.5%
15 3
12.5%
25 2
8.3%
30 2
8.3%
10 2
8.3%
50 1
 
4.2%
35 1
 
4.2%
12 1
 
4.2%
ValueCountFrequency (%)
6 1
 
4.2%
10 2
8.3%
12 1
 
4.2%
15 3
12.5%
20 4
16.7%
25 2
8.3%
30 2
8.3%
35 1
 
4.2%
40 4
16.7%
50 1
 
4.2%
ValueCountFrequency (%)
60 3
12.5%
50 1
 
4.2%
40 4
16.7%
35 1
 
4.2%
30 2
8.3%
25 2
8.3%
20 4
16.7%
15 3
12.5%
12 1
 
4.2%
10 2
8.3%

최대배차간격
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.125
Minimum20
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-04-17T15:03:30.148848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile25
Q134.25
median37.5
Q360
95-th percentile120
Maximum120
Range100
Interquartile range (IQR)25.75

Descriptive statistics

Standard deviation29.795407
Coefficient of variation (CV)0.58279526
Kurtosis1.6164478
Mean51.125
Median Absolute Deviation (MAD)10
Skewness1.5610952
Sum1227
Variance887.7663
MonotonicityNot monotonic
2024-04-17T15:03:30.261274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
35 6
25.0%
60 5
20.8%
120 3
12.5%
40 2
 
8.3%
30 2
 
8.3%
25 2
 
8.3%
45 1
 
4.2%
70 1
 
4.2%
32 1
 
4.2%
20 1
 
4.2%
ValueCountFrequency (%)
20 1
 
4.2%
25 2
 
8.3%
30 2
 
8.3%
32 1
 
4.2%
35 6
25.0%
40 2
 
8.3%
45 1
 
4.2%
60 5
20.8%
70 1
 
4.2%
120 3
12.5%
ValueCountFrequency (%)
120 3
12.5%
70 1
 
4.2%
60 5
20.8%
45 1
 
4.2%
40 2
 
8.3%
35 6
25.0%
32 1
 
4.2%
30 2
 
8.3%
25 2
 
8.3%
20 1
 
4.2%

인가운행횟수
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.666667
Minimum20
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-04-17T15:03:30.360051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20.15
Q131.5
median44
Q361.75
95-th percentile82.95
Maximum98
Range78
Interquartile range (IQR)30.25

Descriptive statistics

Standard deviation22.18434
Coefficient of variation (CV)0.46540573
Kurtosis-0.53583422
Mean47.666667
Median Absolute Deviation (MAD)16
Skewness0.57700406
Sum1144
Variance492.14493
MonotonicityNot monotonic
2024-04-17T15:03:30.463209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
60 2
 
8.3%
36 2
 
8.3%
72 2
 
8.3%
20 2
 
8.3%
21 1
 
4.2%
84 1
 
4.2%
48 1
 
4.2%
98 1
 
4.2%
54 1
 
4.2%
32 1
 
4.2%
Other values (10) 10
41.7%
ValueCountFrequency (%)
20 2
8.3%
21 1
4.2%
22 1
4.2%
25 1
4.2%
30 1
4.2%
32 1
4.2%
33 1
4.2%
35 1
4.2%
36 2
8.3%
40 1
4.2%
ValueCountFrequency (%)
98 1
4.2%
84 1
4.2%
77 1
4.2%
72 2
8.3%
67 1
4.2%
60 2
8.3%
54 1
4.2%
52 1
4.2%
50 1
4.2%
48 1
4.2%

기점첫차시간
Date

Distinct6
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
Minimum2024-04-17 04:40:00
Maximum2024-04-17 06:35:00
2024-04-17T15:03:30.561853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:30.649214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)

기점막차시간
Date

Distinct9
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
Minimum2024-04-17 00:00:00
Maximum2024-04-17 23:40:00
2024-04-17T15:03:30.740791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:30.823021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)

종점첫차시간
Date

Distinct10
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Memory size324.0 B
Minimum2024-04-17 05:00:00
Maximum2024-04-17 07:50:00
2024-04-17T15:03:30.907716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:31.005975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)

종점막차시간
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
00:30
14 
00:50
3 
00:20
 
1
00:00
 
1
24:10:00
 
1
Other values (4)
4 

Length

Max length8
Median length5
Mean length5.125
Min length5

Unique

Unique7 ?
Unique (%)29.2%

Sample

1st row00:20
2nd row00:00
3rd row24:10:00
4th row00:30
5th row00:30

Common Values

ValueCountFrequency (%)
00:30 14
58.3%
00:50 3
 
12.5%
00:20 1
 
4.2%
00:00 1
 
4.2%
24:10:00 1
 
4.2%
01:00 1
 
4.2%
22:45 1
 
4.2%
21:30 1
 
4.2%
20:30 1
 
4.2%

Length

2024-04-17T15:03:31.127940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T15:03:31.233083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:30 14
58.3%
00:50 3
 
12.5%
00:20 1
 
4.2%
00:00 1
 
4.2%
24:10:00 1
 
4.2%
01:00 1
 
4.2%
22:45 1
 
4.2%
21:30 1
 
4.2%
20:30 1
 
4.2%

Interactions

2024-04-17T15:03:26.629913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:25.094110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:25.480771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:25.871081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:26.262676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:26.710090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:25.176212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:25.559397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:25.948730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:26.336400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:26.795569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:25.251778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:25.642684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:26.028263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:26.417693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:26.867342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:25.326247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:25.714667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:26.100872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:26.489241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:26.942428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:25.398501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:25.790582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:26.168238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:26.554611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T15:03:31.326703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회사명노선번호노선유형기점정류소명종점정류소명면허대수관할관청인가대수최소배차간격최대배차간격인가운행횟수기점첫차시간기점막차시간종점첫차시간종점막차시간
회사명1.0001.0001.0000.8120.9070.5060.6310.6170.6840.7590.5940.9310.8470.8450.852
노선번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
노선유형1.0001.0001.0000.0001.0000.4650.0000.0000.0000.0000.0001.0001.0000.0001.000
기점정류소명0.8121.0000.0001.0000.0000.7460.9770.7930.0000.4040.8670.0000.0000.3440.882
종점정류소명0.9071.0001.0000.0001.0000.6650.4740.5950.3970.6460.0000.9650.7350.8490.715
면허대수0.5061.0000.4650.7460.6651.0000.5690.8050.6930.4940.6380.4750.2870.5880.273
관할관청0.6311.0000.0000.9770.4740.5691.0000.0000.4580.0000.7860.6560.0000.7090.000
인가대수0.6171.0000.0000.7930.5950.8050.0001.0000.1000.5530.7870.0000.3970.0000.560
최소배차간격0.6841.0000.0000.0000.3970.6930.4580.1001.0000.9090.0000.0000.6570.6260.000
최대배차간격0.7591.0000.0000.4040.6460.4940.0000.5530.9091.0000.0000.0000.5760.2090.000
인가운행횟수0.5941.0000.0000.8670.0000.6380.7860.7870.0000.0001.0000.4480.0000.4850.000
기점첫차시간0.9311.0001.0000.0000.9650.4750.6560.0000.0000.0000.4481.0000.7680.8970.870
기점막차시간0.8471.0001.0000.0000.7350.2870.0000.3970.6570.5760.0000.7681.0000.7430.924
종점첫차시간0.8451.0000.0000.3440.8490.5880.7090.0000.6260.2090.4850.8970.7431.0000.732
종점막차시간0.8521.0001.0000.8820.7150.2730.0000.5600.0000.0000.0000.8700.9240.7321.000
2024-04-17T15:03:31.458306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회사명종점막차시간종점정류소명노선유형관할관청
회사명1.0000.5740.6850.7980.324
종점막차시간0.5741.0000.4210.8260.000
종점정류소명0.6850.4211.0000.8530.242
노선유형0.7980.8260.8531.0000.000
관할관청0.3240.0000.2420.0001.000
2024-04-17T15:03:31.556846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면허대수인가대수최소배차간격최대배차간격인가운행횟수회사명노선유형종점정류소명관할관청종점막차시간
면허대수1.0000.837-0.411-0.3530.6560.2460.3690.2790.0000.149
인가대수0.8371.000-0.531-0.4840.8410.3530.0000.1110.0000.274
최소배차간격-0.411-0.5311.0000.902-0.5560.3550.0000.1420.1830.000
최대배차간격-0.353-0.4840.9021.000-0.6410.4640.0000.3930.0000.000
인가운행횟수0.6560.841-0.556-0.6411.0000.1500.0000.0000.4860.000
회사명0.2460.3530.3550.4640.1501.0000.7980.6850.3240.574
노선유형0.3690.0000.0000.0000.0000.7981.0000.8530.0000.826
종점정류소명0.2790.1110.1420.3930.0000.6850.8531.0000.2420.421
관할관청0.0000.0000.1830.0000.4860.3240.0000.2421.0000.000
종점막차시간0.1490.2740.0000.0000.0000.5740.8260.4210.0001.000

Missing values

2024-04-17T15:03:27.340142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T15:03:27.507421image/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

회사명노선번호노선구분노선유형기점정류소명종점정류소명면허대수관할관청인가대수최소배차간격최대배차간격인가운행횟수기점첫차시간기점막차시간종점첫차시간종점막차시간
0더월드교통1300광역형직행좌석형 시내버스힐스테이트레이크송도2차(201동)서울역14연수구7601202105:0022:3006:3000:20
1더월드교통1301광역형직행좌석형 시내버스송도공영차고지서울역16연수구12601203305:0022:3006:0000:00
2더월드교통1302광역형직행좌석형 시내버스극지연구소서울역12연수구12601203605:0022:3006:0024:10:00
3마니교통1000광역형직행좌석형 시내버스경남아너스빌서울역14서구1420357205:0023:3006:1000:30
4마니교통1400광역형직행좌석형 시내버스인천터미널서울역15미추홀구1520356705:0023:1006:2000:30
5마니교통1500광역형직행좌석형 시내버스롯데마트서울역15계양구1525357705:0023:3006:1000:30
6마니교통9500광역형직행좌석형 시내버스부평역시민의숲양재꽃시장13부평구1330405205:0023:1006:3500:30
7선진여객1200광역형직행좌석형 시내버스범양아파트(종점)서울역8서구840603005:0000:0006:0001:00
8선진여객9300광역형직행좌석형 시내버스청라국제업무단지(골드클래스)강남역12서구940602005:0023:0006:1000:50
9수정관광화물M6628광역형직행좌석형 시내버스경남아너스빌연세대학교앞5서구515302006:3521:3507:5022:45
회사명노선번호노선구분노선유형기점정류소명종점정류소명면허대수관할관청인가대수최소배차간격최대배차간격인가운행횟수기점첫차시간기점막차시간종점첫차시간종점막차시간
14신강교통9802광역형직행좌석형 시내버스공단사거리시민의숲양재꽃시장10서구835453604:4023:1006:1500:30
15신동아교통M6439광역형직행좌석형 시내버스인천터미널역삼역.포스코타워역삼5서구540602505:3023:0006:5000:30
16신동아교통M6450광역형직행좌석형 시내버스e편한세상 정문한국무역센터.삼성역10연수구630702205:0023:0006:1000:30
17신흥교통m6751광역형좌석형 시내버스e편한세상 정문공덕오거리8연수구812323205:1020:0006:2021:30
18인강여객9100광역형직행좌석형 시내버스대우아파트강남역18미추홀구1410356005:0023:0006:1000:30
19인강여객9200광역형직행좌석형 시내버스송도파크레인동일하이빌강남역22연수구1710257205:0023:0006:1000:30
20인강여객9201광역형직행좌석형 시내버스성호아파트강남역16연수구1320355405:0023:0006:0500:30
21인강여객M6405광역형직행좌석형 시내버스웰카운티강남역23연수구236209805:0023:3006:1000:30
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