Overview

Dataset statistics

Number of variables8
Number of observations43
Missing cells1
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory68.1 B

Variable types

Categorical4
Text2
DateTime1
Numeric1

Alerts

전용차로연장(km) is highly overall correlated with 운영시간정보High correlation
전용차로종류명 is highly overall correlated with 운영시간구분명High correlation
운영시간구분명 is highly overall correlated with 전용차로종류명 and 1 other fieldsHigh correlation
운영시간정보 is highly overall correlated with 전용차로연장(km) and 1 other fieldsHigh correlation
시행시기일자 has 1 (2.3%) missing valuesMissing
구간명 has unique valuesUnique

Reproduction

Analysis started2024-03-12 23:53:10.499022
Analysis finished2024-03-12 23:53:11.067080
Duration0.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

Distinct16
Distinct (%)37.2%
Missing0
Missing (%)0.0%
Memory size476.0 B
수원시
7 
용인시
7 
성남시
5 
안양시
5 
부천시
3 
Other values (11)
16 

Length

Max length4
Median length3
Mean length3.0697674
Min length3

Unique

Unique7 ?
Unique (%)16.3%

Sample

1st row고양시
2nd row고양시
3rd row과천시
4th row과천시
5th row광명시

Common Values

ValueCountFrequency (%)
수원시 7
16.3%
용인시 7
16.3%
성남시 5
11.6%
안양시 5
11.6%
부천시 3
7.0%
평택시 3
7.0%
고양시 2
 
4.7%
과천시 2
 
4.7%
남양주시 2
 
4.7%
광명시 1
 
2.3%
Other values (6) 6
14.0%

Length

2024-03-13T08:53:11.116260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수원시 7
16.3%
용인시 7
16.3%
성남시 5
11.6%
안양시 5
11.6%
부천시 3
7.0%
평택시 3
7.0%
고양시 2
 
4.7%
과천시 2
 
4.7%
남양주시 2
 
4.7%
광명시 1
 
2.3%
Other values (6) 6
14.0%

구간명
Text

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
2024-03-13T08:53:11.292822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length15
Mean length11.697674
Min length8

Characters and Unicode

Total characters503
Distinct characters136
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)100.0%

Sample

1st row대화역-서울시계 수색
2nd row원흥역-삼송17단지
3rd row인덕원사거리-관문사거리
4th row관문사거리-남태령
5th row광명동굴 진입로
ValueCountFrequency (%)
대화역-서울시계 1
 
2.1%
동수원사거리-삼성전자삼거리 1
 
2.1%
용이1교차로-기남교차로 1
 
2.1%
석수역-안양육교 1
 
2.1%
삼거리 1
 
2.1%
안양육교삼거리-의왕시계 1
 
2.1%
비산사거리-인덕원사거리 1
 
2.1%
호계사거리-인덕원사거리 1
 
2.1%
범계역사거리-뉴코아아울렛앞사거리 1
 
2.1%
kt삼거리-금곡ic 1
 
2.1%
Other values (37) 37
78.7%
2024-03-13T08:53:11.594631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 40
 
8.0%
리 36
 
7.2%
거 34
 
6.8%
사 22
 
4.4%
교 20
 
4.0%
삼 15
 
3.0%
로 13
 
2.6%
시 12
 
2.4%
계 12
 
2.4%
원 11
 
2.2%
Other values (126) 288
57.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 427
84.9%
Dash Punctuation 40
 
8.0%
Uppercase Letter 18
 
3.6%
Decimal Number 10
 
2.0%
Space Separator 4
 
0.8%
Math Symbol 2
 
0.4%
Close Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 36
 
8.4%
거 34
 
8.0%
사 22
 
5.2%
교 20
 
4.7%
삼 15
 
3.5%
로 13
 
3.0%
시 12
 
2.8%
계 12
 
2.8%
원 11
 
2.6%
동 10
 
2.3%
Other values (110) 242
56.7%
Uppercase Letter
ValueCountFrequency (%)
C 6
33.3%
I 4
22.2%
T 4
22.2%
K 2
 
11.1%
J 2
 
11.1%
Decimal Number
ValueCountFrequency (%)
1 6
60.0%
3 1
 
10.0%
7 1
 
10.0%
0 1
 
10.0%
2 1
 
10.0%
Math Symbol
ValueCountFrequency (%)
→ 1
50.0%
~ 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 40
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 427
84.9%
Common 58
 
11.5%
Latin 18
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 36
 
8.4%
거 34
 
8.0%
사 22
 
5.2%
교 20
 
4.7%
삼 15
 
3.5%
로 13
 
3.0%
시 12
 
2.8%
계 12
 
2.8%
원 11
 
2.6%
동 10
 
2.3%
Other values (110) 242
56.7%
Common
ValueCountFrequency (%)
- 40
69.0%
1 6
 
10.3%
4
 
6.9%
→ 1
 
1.7%
3 1
 
1.7%
7 1
 
1.7%
0 1
 
1.7%
2 1
 
1.7%
~ 1
 
1.7%
) 1
 
1.7%
Latin
ValueCountFrequency (%)
C 6
33.3%
I 4
22.2%
T 4
22.2%
K 2
 
11.1%
J 2
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 427
84.9%
ASCII 75
 
14.9%
Arrows 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 40
53.3%
C 6
 
8.0%
1 6
 
8.0%
I 4
 
5.3%
4
 
5.3%
T 4
 
5.3%
K 2
 
2.7%
J 2
 
2.7%
3 1
 
1.3%
7 1
 
1.3%
Other values (5) 5
 
6.7%
Hangul
ValueCountFrequency (%)
리 36
 
8.4%
거 34
 
8.0%
사 22
 
5.2%
교 20
 
4.7%
삼 15
 
3.5%
로 13
 
3.0%
시 12
 
2.8%
계 12
 
2.8%
원 11
 
2.6%
동 10
 
2.3%
Other values (110) 242
56.7%
Arrows
ValueCountFrequency (%)
→ 1
100.0%

노선명
Text

Distinct32
Distinct (%)74.4%
Missing0
Missing (%)0.0%
Memory size476.0 B
2024-03-13T08:53:11.761816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.1395349
Min length3

Characters and Unicode

Total characters178
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)53.5%

Sample

1st row중앙로
2nd row삼송로
3rd row중앙로
4th row남태령로
5th row가학로85번길
ValueCountFrequency (%)
중앙로 4
 
9.1%
경수대로 2
 
4.5%
서동대로 2
 
4.5%
경부고속도로 2
 
4.5%
성남대로 2
 
4.5%
중부대로 2
 
4.5%
경춘로 2
 
4.5%
대왕판교로 2
 
4.5%
비전4로 2
 
4.5%
번영로 1
 
2.3%
Other values (23) 23
52.3%
2024-03-13T08:53:12.025443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 41
23.0%
대 16
 
9.0%
중 7
 
3.9%
경 7
 
3.9%
앙 5
 
2.8%
도 5
 
2.8%
서 4
 
2.2%
남 4
 
2.2%
동 4
 
2.2%
부 4
 
2.2%
Other values (52) 81
45.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 170
95.5%
Decimal Number 7
 
3.9%
Space Separator 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 41
24.1%
대 16
 
9.4%
중 7
 
4.1%
경 7
 
4.1%
앙 5
 
2.9%
도 5
 
2.9%
서 4
 
2.4%
남 4
 
2.4%
동 4
 
2.4%
부 4
 
2.4%
Other values (47) 73
42.9%
Decimal Number
ValueCountFrequency (%)
4 3
42.9%
8 2
28.6%
9 1
 
14.3%
5 1
 
14.3%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 170
95.5%
Common 8
 
4.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 41
24.1%
대 16
 
9.4%
중 7
 
4.1%
경 7
 
4.1%
앙 5
 
2.9%
도 5
 
2.9%
서 4
 
2.4%
남 4
 
2.4%
동 4
 
2.4%
부 4
 
2.4%
Other values (47) 73
42.9%
Common
ValueCountFrequency (%)
4 3
37.5%
8 2
25.0%
1
 
12.5%
9 1
 
12.5%
5 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 170
95.5%
ASCII 8
 
4.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 41
24.1%
대 16
 
9.4%
중 7
 
4.1%
경 7
 
4.1%
앙 5
 
2.9%
도 5
 
2.9%
서 4
 
2.4%
남 4
 
2.4%
동 4
 
2.4%
부 4
 
2.4%
Other values (47) 73
42.9%
ASCII
ValueCountFrequency (%)
4 3
37.5%
8 2
25.0%
1
 
12.5%
9 1
 
12.5%
5 1
 
12.5%

전용차로종류명
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
가로변
25 
중앙
14 
가로변(서울방향)
 
1
전용도로
 
1
중앙전용
 
1

Length

Max length9
Median length3
Mean length2.8372093
Min length2

Unique

Unique4 ?
Unique (%)9.3%

Sample

1st row중앙
2nd row중앙
3rd row가로변
4th row가로변
5th row가로변

Common Values

ValueCountFrequency (%)
가로변 25
58.1%
중앙 14
32.6%
가로변(서울방향) 1
 
2.3%
전용도로 1
 
2.3%
중앙전용 1
 
2.3%
중안 1
 
2.3%

Length

2024-03-13T08:53:12.141452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:53:12.231514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가로변 25
58.1%
중앙 14
32.6%
가로변(서울방향 1
 
2.3%
전용도로 1
 
2.3%
중앙전용 1
 
2.3%
중안 1
 
2.3%

운영시간구분명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size476.0 B
시간제
28 
전일제
15 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전일제
2nd row전일제
3rd row시간제
4th row시간제
5th row시간제

Common Values

ValueCountFrequency (%)
시간제 28
65.1%
전일제 15
34.9%

Length

2024-03-13T08:53:12.322121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:53:12.401081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
시간제 28
65.1%
전일제 15
34.9%

시행시기일자
Date

MISSING 

Distinct33
Distinct (%)78.6%
Missing1
Missing (%)2.3%
Memory size476.0 B
Minimum1994-10-01 00:00:00
Maximum2020-08-06 00:00:00
2024-03-13T08:53:12.488460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:53:12.620085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)

전용차로연장(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9627907
Minimum0.2
Maximum43.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-03-13T08:53:12.743523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.51
Q11.15
median2.6
Q35.45
95-th percentile33.15
Maximum43.6
Range43.4
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation9.9448724
Coefficient of variation (CV)1.6678218
Kurtosis8.7242801
Mean5.9627907
Median Absolute Deviation (MAD)1.8
Skewness3.0383182
Sum256.4
Variance98.900487
MonotonicityNot monotonic
2024-03-13T08:53:12.838763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1.1 3
 
7.0%
1.6 3
 
7.0%
0.8 2
 
4.7%
4.4 2
 
4.7%
1.5 2
 
4.7%
0.2 1
 
2.3%
4.7 1
 
2.3%
1.0 1
 
2.3%
9.8 1
 
2.3%
4.0 1
 
2.3%
Other values (26) 26
60.5%
ValueCountFrequency (%)
0.2 1
 
2.3%
0.4 1
 
2.3%
0.5 1
 
2.3%
0.6 1
 
2.3%
0.8 2
4.7%
0.9 1
 
2.3%
1.0 1
 
2.3%
1.1 3
7.0%
1.2 1
 
2.3%
1.3 1
 
2.3%
ValueCountFrequency (%)
43.6 1
2.3%
41.0 1
2.3%
35.1 1
2.3%
15.6 1
2.3%
10.9 1
2.3%
9.8 1
2.3%
6.8 1
2.3%
6.2 1
2.3%
5.9 1
2.3%
5.8 1
2.3%

운영시간정보
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Memory size476.0 B
24시간 전일제
15 
평일 06:00-22:00
6 
평일 07:00-10:00 17:00-20:00
6 
평일 07:00-10:00 17:00-21:00
5 
평일 07:30-09:30 17:00-20:00
5 
Other values (5)
6 

Length

Max length37
Median length36
Mean length17.767442
Min length8

Unique

Unique4 ?
Unique (%)9.3%

Sample

1st row24시간 전일제
2nd row24시간 전일제
3rd row평일 07:00-10:00 17:00-21:00
4th row평일 07:00-21:00
5th row(매년)7. 1 - 8. 31 09:00-18:00(토·공휴일 포함

Common Values

ValueCountFrequency (%)
24시간 전일제 15
34.9%
평일 06:00-22:00 6
 
14.0%
평일 07:00-10:00 17:00-20:00 6
 
14.0%
평일 07:00-10:00 17:00-21:00 5
 
11.6%
평일 07:30-09:30 17:00-20:00 5
 
11.6%
휴일 07:00-21:00 2
 
4.7%
평일 07:00-21:00 1
 
2.3%
(매년)7. 1 - 8. 31 09:00-18:00(토·공휴일 포함 1
 
2.3%
평일 07:00-10:00(서울행) 17:00-21:00(인천행) 1
 
2.3%
평일 07:00-21:00 휴일 07:00-21:00 1
 
2.3%

Length

2024-03-13T08:53:12.942782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:53:13.055741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
평일 25
22.7%
24시간 15
13.6%
전일제 15
13.6%
07:00-10:00 11
10.0%
17:00-20:00 11
10.0%
06:00-22:00 6
 
5.5%
07:30-09:30 5
 
4.5%
07:00-21:00 5
 
4.5%
17:00-21:00 5
 
4.5%
휴일 3
 
2.7%
Other values (9) 9
 
8.2%

Interactions

2024-03-13T08:53:10.843797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T08:53:13.151769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명구간명노선명전용차로종류명운영시간구분명시행시기일자전용차로연장(km)운영시간정보
시군명1.0001.0000.9130.8100.6830.9680.0000.693
구간명1.0001.0001.0001.0001.0001.0001.0001.000
노선명0.9131.0001.0000.9890.8090.9690.0000.923
전용차로종류명0.8101.0000.9891.0000.9420.9970.0000.256
운영시간구분명0.6831.0000.8090.9421.0000.9150.0001.000
시행시기일자0.9681.0000.9690.9970.9151.0000.7880.805
전용차로연장(km)0.0001.0000.0000.0000.0000.7881.0000.796
운영시간정보0.6931.0000.9230.2561.0000.8050.7961.000
2024-03-13T08:53:13.245924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
운영시간구분명시군명운영시간정보전용차로종류명
운영시간구분명1.0000.4370.8970.745
시군명0.4371.0000.3090.472
운영시간정보0.8970.3091.0000.106
전용차로종류명0.7450.4720.1061.000
2024-03-13T08:53:13.335253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전용차로연장(km)시군명전용차로종류명운영시간구분명운영시간정보
전용차로연장(km)1.0000.0000.0000.0000.545
시군명0.0001.0000.4720.4370.309
전용차로종류명0.0000.4721.0000.7450.106
운영시간구분명0.0000.4370.7451.0000.897
운영시간정보0.5450.3090.1060.8971.000

Missing values

2024-03-13T08:53:10.930164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T08:53:11.028821image/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

시군명구간명노선명전용차로종류명운영시간구분명시행시기일자전용차로연장(km)운영시간정보
0고양시대화역-서울시계 수색중앙로중앙전일제2006-10-2715.624시간 전일제
1고양시원흥역-삼송17단지삼송로중앙전일제2016-06-011.224시간 전일제
2과천시인덕원사거리-관문사거리중앙로가로변시간제1998-10-015.8평일 07:00-10:00 17:00-21:00
3과천시관문사거리-남태령남태령로가로변시간제1998-10-010.8평일 07:00-21:00
4광명시광명동굴 진입로가학로85번길가로변시간제2017-07-010.9(매년)7. 1 - 8. 31 09:00-18:00(토·공휴일 포함
5구리시서울시계-왕숙교경춘로중앙전일제2009-12-273.124시간 전일제
6김포시사우삼거리-신곡사거리48번 국도가로변(서울방향)시간제2015-04-015.2평일 07:00-10:00 17:00-21:00
7남양주시구리시계-도농삼거리경춘로중앙전일제2009-12-272.324시간 전일제
8남양주시구리시계-별내역순화궁로가로변시간제2014-11-172.6평일 07:00-10:00 17:00-21:00
9부천시구산사거리-동신아파트앞교차로경인로가로변시간제1998-07-016.8평일 07:00-10:00(서울행) 17:00-21:00(인천행)
시군명구간명노선명전용차로종류명운영시간구분명시행시기일자전용차로연장(km)운영시간정보
33용인시기흥역→수원신갈IC중부대로가로변시간제2012-01-301.6평일 07:30-09:30 17:00-20:00
34용인시보정삼거리-벽산삼거리용구대로가로변시간제2006-12-202.4평일 07:30-09:30 17:00-20:00
35용인시만당주유소삼거리-현대홈타운번영로가로변시간제2010-01-010.6평일 06:00-22:00
36용인시KT삼거리-수지중삼거리화합로가로변시간제2010-01-010.4평일 06:00-22:00
37의정부시장암고가-호장교북단서계로중안전일제2018-10-014.424시간 전일제
38평택시용이1교차로-굿모닝병원사거리서동대로가로변시간제2017-11-011.3평일 07:00-10:00 17:00-20:00
39평택시굿모닝병원사거리-서재교비전4로중앙전일제2017-11-012.924시간 전일제
40평택시서재교-동삭1교차로비전4로중앙전일제2020-08-061.124시간 전일제
41하남시하남시 창우동-서울시계하남대로중앙전일제2011-03-195.424시간 전일제
42화성시병점-동탄신도시중앙로중앙전일제2007-01-015.524시간 전일제