Overview

Dataset statistics

Number of variables9
Number of observations328
Missing cells28
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.5 KiB
Average record size in memory73.4 B

Variable types

Text3
DateTime4
Numeric1
Categorical1

Dataset

Description경기도 광주시 관내 노선버스, 시외버스, 공항버스 운행현황에 대한 데이터로 기점, 종점, 상행첫차,상행막차 등을 제공합니다.
Author경기도 광주시
URLhttps://www.data.go.kr/data/3079770/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
하행첫차 has 13 (4.0%) missing valuesMissing
하행막차 has 13 (4.0%) missing valuesMissing
노선번호 has unique valuesUnique
배차간격 has 272 (82.9%) zerosZeros

Reproduction

Analysis started2023-12-12 18:43:07.080563
Analysis finished2023-12-12 18:43:09.004933
Duration1.92 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

노선번호
Text

UNIQUE 

Distinct328
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2023-12-13T03:43:09.379419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.2439024
Min length2

Characters and Unicode

Total characters1720
Distinct characters15
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

Unique328 ?
Unique (%)100.0%

Sample

1st rowB1
2nd rowB1-12
3rd rowB1-13
4th rowB1-20
5th rowB1-21
ValueCountFrequency (%)
b1 1
 
0.3%
b918-1 1
 
0.3%
b916b 1
 
0.3%
b916-2b 1
 
0.3%
b916-2 1
 
0.3%
b916-1 1
 
0.3%
b916 1
 
0.3%
b915-3 1
 
0.3%
b915-2 1
 
0.3%
b915-1 1
 
0.3%
Other values (318) 318
97.0%
2023-12-13T03:43:10.077939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 341
19.8%
- 226
13.1%
1 206
12.0%
3 195
11.3%
2 161
9.4%
9 154
9.0%
5 98
 
5.7%
0 75
 
4.4%
8 73
 
4.2%
7 60
 
3.5%
Other values (5) 131
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1129
65.6%
Uppercase Letter 365
 
21.2%
Dash Punctuation 226
 
13.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 206
18.2%
3 195
17.3%
2 161
14.3%
9 154
13.6%
5 98
8.7%
0 75
 
6.6%
8 73
 
6.5%
7 60
 
5.3%
4 55
 
4.9%
6 52
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
B 341
93.4%
M 12
 
3.3%
A 11
 
3.0%
G 1
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
- 226
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1355
78.8%
Latin 365
 
21.2%

Most frequent character per script

Common
ValueCountFrequency (%)
- 226
16.7%
1 206
15.2%
3 195
14.4%
2 161
11.9%
9 154
11.4%
5 98
7.2%
0 75
 
5.5%
8 73
 
5.4%
7 60
 
4.4%
4 55
 
4.1%
Latin
ValueCountFrequency (%)
B 341
93.4%
M 12
 
3.3%
A 11
 
3.0%
G 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 341
19.8%
- 226
13.1%
1 206
12.0%
3 195
11.3%
2 161
9.4%
9 154
9.0%
5 98
 
5.7%
0 75
 
4.4%
8 73
 
4.2%
7 60
 
3.5%
Other values (5) 131
 
7.6%

기점
Text

Distinct84
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2023-12-13T03:43:10.460928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length5.1006098
Min length2

Characters and Unicode

Total characters1673
Distinct characters157
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

Unique54 ?
Unique (%)16.5%

Sample

1st row광주하남교육청
2nd row삼동역
3rd row경기광주역
4th row보건소.공설운동장
5th row광주하남교육청
ValueCountFrequency (%)
여주역 89
27.1%
곤지암터미널 30
 
9.1%
광주시축협 20
 
6.1%
보건소.공설운동장 17
 
5.2%
경기광주역 13
 
4.0%
장호원초등학교 13
 
4.0%
광주차고지 8
 
2.4%
광주터미널 7
 
2.1%
하동제일시장 7
 
2.1%
태평터미널 6
 
1.8%
Other values (74) 118
36.0%
2023-12-13T03:43:11.095719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 147
 
8.8%
역 116
 
6.9%
여 93
 
5.6%
미 59
 
3.5%
광 58
 
3.5%
터 58
 
3.5%
널 58
 
3.5%
지 49
 
2.9%
동 45
 
2.7%
장 45
 
2.7%
Other values (147) 945
56.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1618
96.7%
Other Punctuation 34
 
2.0%
Decimal Number 16
 
1.0%
Uppercase Letter 3
 
0.2%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 147
 
9.1%
역 116
 
7.2%
여 93
 
5.7%
미 59
 
3.6%
광 58
 
3.6%
터 58
 
3.6%
널 58
 
3.6%
지 49
 
3.0%
동 45
 
2.8%
장 45
 
2.8%
Other values (137) 890
55.0%
Decimal Number
ValueCountFrequency (%)
1 10
62.5%
2 4
 
25.0%
4 1
 
6.2%
3 1
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
D 1
33.3%
A 1
33.3%
K 1
33.3%
Other Punctuation
ValueCountFrequency (%)
. 34
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1618
96.7%
Common 52
 
3.1%
Latin 3
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 147
 
9.1%
역 116
 
7.2%
여 93
 
5.7%
미 59
 
3.6%
광 58
 
3.6%
터 58
 
3.6%
널 58
 
3.6%
지 49
 
3.0%
동 45
 
2.8%
장 45
 
2.8%
Other values (137) 890
55.0%
Common
ValueCountFrequency (%)
. 34
65.4%
1 10
 
19.2%
2 4
 
7.7%
) 1
 
1.9%
( 1
 
1.9%
4 1
 
1.9%
3 1
 
1.9%
Latin
ValueCountFrequency (%)
D 1
33.3%
A 1
33.3%
K 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1618
96.7%
ASCII 55
 
3.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 147
 
9.1%
역 116
 
7.2%
여 93
 
5.7%
미 59
 
3.6%
광 58
 
3.6%
터 58
 
3.6%
널 58
 
3.6%
지 49
 
3.0%
동 45
 
2.8%
장 45
 
2.8%
Other values (137) 890
55.0%
ASCII
ValueCountFrequency (%)
. 34
61.8%
1 10
 
18.2%
2 4
 
7.3%
) 1
 
1.8%
D 1
 
1.8%
( 1
 
1.8%
4 1
 
1.8%
3 1
 
1.8%
A 1
 
1.8%
K 1
 
1.8%

종점
Text

Distinct171
Distinct (%)52.1%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2023-12-13T03:43:11.471235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length17
Mean length6.0030488
Min length2

Characters and Unicode

Total characters1969
Distinct characters249
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique105 ?
Unique (%)32.0%

Sample

1st row전원마을입구
2nd row태전효성해링턴플레이스
3rd row태전효성해링턴플레이스
4th row직동
5th row직동
ValueCountFrequency (%)
태평터미널 16
 
4.9%
여주역 13
 
4.0%
곤지암터미널 7
 
2.1%
프레미엄아울렛 5
 
1.5%
잠실광역환승센터 5
 
1.5%
도척성당 5
 
1.5%
산성리 5
 
1.5%
멱곡1통 5
 
1.5%
수청1리 5
 
1.5%
양동역 5
 
1.5%
Other values (161) 257
78.4%
2023-12-13T03:43:12.118117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 102
 
5.2%
터 59
 
3.0%
미 58
 
2.9%
역 54
 
2.7%
. 48
 
2.4%
널 43
 
2.2%
동 31
 
1.6%
1 30
 
1.5%
주 30
 
1.5%
트 29
 
1.5%
Other values (239) 1485
75.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1779
90.4%
Decimal Number 72
 
3.7%
Other Punctuation 48
 
2.4%
Close Punctuation 24
 
1.2%
Open Punctuation 24
 
1.2%
Uppercase Letter 17
 
0.9%
Lowercase Letter 5
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 102
 
5.7%
터 59
 
3.3%
미 58
 
3.3%
역 54
 
3.0%
널 43
 
2.4%
동 31
 
1.7%
주 30
 
1.7%
트 29
 
1.6%
아 29
 
1.6%
점 28
 
1.6%
Other values (219) 1316
74.0%
Decimal Number
ValueCountFrequency (%)
1 30
41.7%
2 17
23.6%
3 12
 
16.7%
6 6
 
8.3%
5 2
 
2.8%
4 2
 
2.8%
7 2
 
2.8%
8 1
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
K 4
23.5%
D 4
23.5%
A 3
17.6%
C 2
11.8%
T 2
11.8%
R 1
 
5.9%
B 1
 
5.9%
Lowercase Letter
ValueCountFrequency (%)
c 4
80.0%
e 1
 
20.0%
Other Punctuation
ValueCountFrequency (%)
. 48
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1779
90.4%
Common 168
 
8.5%
Latin 22
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 102
 
5.7%
터 59
 
3.3%
미 58
 
3.3%
역 54
 
3.0%
널 43
 
2.4%
동 31
 
1.7%
주 30
 
1.7%
트 29
 
1.6%
아 29
 
1.6%
점 28
 
1.6%
Other values (219) 1316
74.0%
Common
ValueCountFrequency (%)
. 48
28.6%
1 30
17.9%
) 24
14.3%
( 24
14.3%
2 17
 
10.1%
3 12
 
7.1%
6 6
 
3.6%
5 2
 
1.2%
4 2
 
1.2%
7 2
 
1.2%
Latin
ValueCountFrequency (%)
K 4
18.2%
D 4
18.2%
c 4
18.2%
A 3
13.6%
C 2
9.1%
T 2
9.1%
R 1
 
4.5%
B 1
 
4.5%
e 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1779
90.4%
ASCII 190
 
9.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 102
 
5.7%
터 59
 
3.3%
미 58
 
3.3%
역 54
 
3.0%
널 43
 
2.4%
동 31
 
1.7%
주 30
 
1.7%
트 29
 
1.6%
아 29
 
1.6%
점 28
 
1.6%
Other values (219) 1316
74.0%
ASCII
ValueCountFrequency (%)
. 48
25.3%
1 30
15.8%
) 24
12.6%
( 24
12.6%
2 17
 
8.9%
3 12
 
6.3%
6 6
 
3.2%
K 4
 
2.1%
D 4
 
2.1%
c 4
 
2.1%
Other values (10) 17
 
8.9%

상행첫차
Date

Distinct126
Distinct (%)38.5%
Missing1
Missing (%)0.3%
Memory size2.7 KiB
Minimum2023-12-13 04:00:00
Maximum2023-12-13 23:45:00
2023-12-13T03:43:12.396116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:43:12.670239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

상행막차
Date

Distinct131
Distinct (%)40.1%
Missing1
Missing (%)0.3%
Memory size2.7 KiB
Minimum2023-12-13 00:00:00
Maximum2023-12-13 23:30:00
2023-12-13T03:43:12.907930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:43:13.107411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

하행첫차
Date

MISSING 

Distinct117
Distinct (%)37.1%
Missing13
Missing (%)4.0%
Memory size2.7 KiB
Minimum2023-12-13 00:00:00
Maximum2023-12-13 22:00:00
2023-12-13T03:43:13.332834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:43:13.595923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

하행막차
Date

MISSING 

Distinct130
Distinct (%)41.3%
Missing13
Missing (%)4.0%
Memory size2.7 KiB
Minimum2023-12-13 00:00:00
Maximum2023-12-13 23:55:00
2023-12-13T03:43:13.836053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:43:14.054999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

배차간격
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1280488
Minimum0
Maximum125
Zeros272
Zeros (%)82.9%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-13T03:43:14.281121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile30
Maximum125
Range125
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14.695338
Coefficient of variation (CV)2.8656784
Kurtosis27.323706
Mean5.1280488
Median Absolute Deviation (MAD)0
Skewness4.5294708
Sum1682
Variance215.95297
MonotonicityNot monotonic
2023-12-13T03:43:14.484403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 272
82.9%
15 10
 
3.0%
20 9
 
2.7%
25 8
 
2.4%
40 6
 
1.8%
30 6
 
1.8%
10 4
 
1.2%
35 3
 
0.9%
17 2
 
0.6%
60 2
 
0.6%
Other values (6) 6
 
1.8%
ValueCountFrequency (%)
0 272
82.9%
8 1
 
0.3%
10 4
 
1.2%
15 10
 
3.0%
17 2
 
0.6%
20 9
 
2.7%
25 8
 
2.4%
30 6
 
1.8%
35 3
 
0.9%
40 6
 
1.8%
ValueCountFrequency (%)
125 1
 
0.3%
120 1
 
0.3%
80 1
 
0.3%
60 2
 
0.6%
55 1
 
0.3%
45 1
 
0.3%
40 6
1.8%
35 3
 
0.9%
30 6
1.8%
25 8
2.4%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2022-10-06
328 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-10-06
2nd row2022-10-06
3rd row2022-10-06
4th row2022-10-06
5th row2022-10-06

Common Values

ValueCountFrequency (%)
2022-10-06 328
100.0%

Length

2023-12-13T03:43:14.723903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:43:14.894112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-10-06 328
100.0%

Interactions

2023-12-13T03:43:07.768743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:43:14.990742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기점배차간격
기점1.0000.883
배차간격0.8831.000

Missing values

2023-12-13T03:43:07.983638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:43:08.242171image/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.
2023-12-13T03:43:08.923550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

노선번호기점종점상행첫차상행막차하행첫차하행막차배차간격데이터기준일자
0B1광주하남교육청전원마을입구05:4021:4005:5022:2002022-10-06
1B1-12삼동역태전효성해링턴플레이스08:2020:1508:5020:4502022-10-06
2B1-13경기광주역태전효성해링턴플레이스06:3006:3007:2507:2502022-10-06
3B1-20보건소.공설운동장직동15:2015:2016:1016:1002022-10-06
4B1-21광주하남교육청직동06:2021:3007:1020:4502022-10-06
5B1-22보건소.공설운동장직동07:1507:1508:0508:0502022-10-06
6B1-23광주하남교육청광주시축협06:5006:5009:1509:1502022-10-06
7B10이천터미널태평터미널06:5021:5006:1021:5002022-10-06
8B100광주터미널시청07:0021:3007:2522:00802022-10-06
9B111여주대하차장불교회관05:5521:2006:4022:10602022-10-06
노선번호기점종점상행첫차상행막차하행첫차하행막차배차간격데이터기준일자
318BM4101상현역숭례문05:1022:5006:0023:4082022-10-06
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