Overview

Dataset statistics

Number of variables4
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 KiB
Average record size in memory38.4 B

Variable types

Text2
Numeric2

Dataset

Description대전광역시 버스전용차로 고정형 단속카메라 위치에 대한 데이터로 단속기점, 위치, 좌표(경도, 위도) 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15061885/fileData.do

Reproduction

Analysis started2023-12-12 14:35:00.056059
Analysis finished2023-12-12 14:35:00.773027
Duration0.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

버스전용차로단속기점
Text

Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-12T23:35:00.900059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.3
Min length2

Characters and Unicode

Total characters159
Distinct characters51
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)63.3%

Sample

1st row동서로네거리
2nd row큰마을네거리
3rd row유성네거리
4th row버드내네거리
5th row버드내네거리
ValueCountFrequency (%)
가수원네거리 5
 
16.7%
만년교 2
 
6.7%
버드내네거리 2
 
6.7%
향우네거리 2
 
6.7%
중리네거리 1
 
3.3%
동서로네거리 1
 
3.3%
만년네거리 1
 
3.3%
대전 1
 
3.3%
홍도육교네거리 1
 
3.3%
농수산오거리 1
 
3.3%
Other values (13) 13
43.3%
2023-12-12T23:35:01.206861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 24
15.1%
거 23
14.5%
네 20
 
12.6%
대 7
 
4.4%
수 6
 
3.8%
가 6
 
3.8%
교 5
 
3.1%
원 5
 
3.1%
전 4
 
2.5%
년 3
 
1.9%
Other values (41) 56
35.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 157
98.7%
Uppercase Letter 2
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 24
15.3%
거 23
14.6%
네 20
 
12.7%
대 7
 
4.5%
수 6
 
3.8%
가 6
 
3.8%
교 5
 
3.2%
원 5
 
3.2%
전 4
 
2.5%
년 3
 
1.9%
Other values (39) 54
34.4%
Uppercase Letter
ValueCountFrequency (%)
I 1
50.0%
C 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 157
98.7%
Latin 2
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 24
15.3%
거 23
14.6%
네 20
 
12.7%
대 7
 
4.5%
수 6
 
3.8%
가 6
 
3.8%
교 5
 
3.2%
원 5
 
3.2%
전 4
 
2.5%
년 3
 
1.9%
Other values (39) 54
34.4%
Latin
ValueCountFrequency (%)
I 1
50.0%
C 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 157
98.7%
ASCII 2
 
1.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 24
15.3%
거 23
14.6%
네 20
 
12.7%
대 7
 
4.5%
수 6
 
3.8%
가 6
 
3.8%
교 5
 
3.2%
원 5
 
3.2%
전 4
 
2.5%
년 3
 
1.9%
Other values (39) 54
34.4%
ASCII
ValueCountFrequency (%)
I 1
50.0%
C 1
50.0%

위치
Text

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-12T23:35:01.417629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length14.433333
Min length12

Characters and Unicode

Total characters433
Distinct characters46
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)86.7%

Sample

1st row대전 중구 계룡로 820
2nd row대전 서구 계룡로 387
3rd row대전 유성구 계룡로 142
4th row대전 중구 계백로1578
5th row대전 중구 태평로 15
ValueCountFrequency (%)
대전 30
25.4%
서구 11
 
9.3%
유성구 6
 
5.1%
대덕구 6
 
5.1%
중구 5
 
4.2%
도안동로 4
 
3.4%
계룡로 3
 
2.5%
도산로 3
 
2.5%
123 2
 
1.7%
331 2
 
1.7%
Other values (43) 46
39.0%
2023-12-12T23:35:01.822028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
88
20.3%
대 43
 
9.9%
전 31
 
7.2%
구 30
 
6.9%
1 28
 
6.5%
로 27
 
6.2%
3 18
 
4.2%
서 15
 
3.5%
동 11
 
2.5%
6 10
 
2.3%
Other values (36) 132
30.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 240
55.4%
Decimal Number 102
23.6%
Space Separator 88
 
20.3%
Dash Punctuation 3
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 43
17.9%
전 31
12.9%
구 30
12.5%
로 27
11.2%
서 15
 
6.2%
동 11
 
4.6%
계 9
 
3.8%
성 7
 
2.9%
도 7
 
2.9%
덕 7
 
2.9%
Other values (24) 53
22.1%
Decimal Number
ValueCountFrequency (%)
1 28
27.5%
3 18
17.6%
6 10
 
9.8%
5 10
 
9.8%
2 8
 
7.8%
8 8
 
7.8%
4 6
 
5.9%
7 6
 
5.9%
0 6
 
5.9%
9 2
 
2.0%
Space Separator
ValueCountFrequency (%)
88
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 240
55.4%
Common 193
44.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 43
17.9%
전 31
12.9%
구 30
12.5%
로 27
11.2%
서 15
 
6.2%
동 11
 
4.6%
계 9
 
3.8%
성 7
 
2.9%
도 7
 
2.9%
덕 7
 
2.9%
Other values (24) 53
22.1%
Common
ValueCountFrequency (%)
88
45.6%
1 28
 
14.5%
3 18
 
9.3%
6 10
 
5.2%
5 10
 
5.2%
2 8
 
4.1%
8 8
 
4.1%
4 6
 
3.1%
7 6
 
3.1%
0 6
 
3.1%
Other values (2) 5
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 240
55.4%
ASCII 193
44.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
88
45.6%
1 28
 
14.5%
3 18
 
9.3%
6 10
 
5.2%
5 10
 
5.2%
2 8
 
4.1%
8 8
 
4.1%
4 6
 
3.1%
7 6
 
3.1%
0 6
 
3.1%
Other values (2) 5
 
2.6%
Hangul
ValueCountFrequency (%)
대 43
17.9%
전 31
12.9%
구 30
12.5%
로 27
11.2%
서 15
 
6.2%
동 11
 
4.6%
계 9
 
3.8%
성 7
 
2.9%
도 7
 
2.9%
덕 7
 
2.9%
Other values (24) 53
22.1%

X좌표
Real number (ℝ)

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.3833
Minimum127.34581
Maximum127.44248
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T23:35:01.964897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.34581
5-th percentile127.34586
Q1127.35187
median127.37916
Q3127.41322
95-th percentile127.43424
Maximum127.44248
Range0.0966702
Interquartile range (IQR)0.06135005

Descriptive statistics

Standard deviation0.03242697
Coefficient of variation (CV)0.00025456217
Kurtosis-1.3613885
Mean127.3833
Median Absolute Deviation (MAD)0.03078885
Skewness0.30183851
Sum3821.499
Variance0.0010515084
MonotonicityNot monotonic
2023-12-12T23:35:02.120400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
127.35369 2
 
6.7%
127.3458646 2
 
6.7%
127.3483719 2
 
6.7%
127.4069043 1
 
3.3%
127.3799847 1
 
3.3%
127.4158487 1
 
3.3%
127.4167144 1
 
3.3%
127.419201 1
 
3.3%
127.4110904 1
 
3.3%
127.3482624 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
127.3458082 1
3.3%
127.3458646 2
6.7%
127.346123 1
3.3%
127.3482624 1
3.3%
127.3483719 2
6.7%
127.3512679 1
3.3%
127.35369 2
6.7%
127.3541717 1
3.3%
127.3579776 1
3.3%
127.3713392 1
3.3%
ValueCountFrequency (%)
127.4424784 1
3.3%
127.436454 1
3.3%
127.4315445 1
3.3%
127.4278273 1
3.3%
127.419201 1
3.3%
127.4167144 1
3.3%
127.4158487 1
3.3%
127.4139345 1
3.3%
127.4110904 1
3.3%
127.4069043 1
3.3%

Y좌표
Real number (ℝ)

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.340458
Minimum36.296538
Maximum36.422122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T23:35:02.251953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.296538
5-th percentile36.298518
Q136.317747
median36.336871
Q336.357123
95-th percentile36.398165
Maximum36.422122
Range0.12558377
Interquartile range (IQR)0.039375548

Descriptive statistics

Standard deviation0.0313996
Coefficient of variation (CV)0.00086403976
Kurtosis1.1955068
Mean36.340458
Median Absolute Deviation (MAD)0.019845915
Skewness0.922714
Sum1090.2137
Variance0.0009859349
MonotonicityNot monotonic
2023-12-12T23:35:02.723170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
36.29653779 2
 
6.7%
36.31639502 2
 
6.7%
36.33497446 2
 
6.7%
36.32658145 1
 
3.3%
36.36593794 1
 
3.3%
36.42212156 1
 
3.3%
36.42118849 1
 
3.3%
36.34608172 1
 
3.3%
36.35203544 1
 
3.3%
36.33876812 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
36.29653779 2
6.7%
36.30093925 1
3.3%
36.3044531 1
3.3%
36.30591587 1
3.3%
36.31639502 2
6.7%
36.31765573 1
3.3%
36.31802262 1
3.3%
36.31826028 1
3.3%
36.32076371 1
3.3%
36.32658145 1
3.3%
ValueCountFrequency (%)
36.42212156 1
3.3%
36.42118849 1
3.3%
36.37002439 1
3.3%
36.36717312 1
3.3%
36.36593794 1
3.3%
36.36303037 1
3.3%
36.36055858 1
3.3%
36.35840228 1
3.3%
36.35328516 1
3.3%
36.35203544 1
3.3%

Interactions

2023-12-12T23:35:00.391161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:35:00.235960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:35:00.477773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:35:00.311934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:35:02.828697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
버스전용차로단속기점위치X좌표Y좌표
버스전용차로단속기점1.0000.9870.8820.848
위치0.9871.0001.0001.000
X좌표0.8821.0001.0000.669
Y좌표0.8481.0000.6691.000
2023-12-12T23:35:02.939007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
X좌표Y좌표
X좌표1.0000.435
Y좌표0.4351.000

Missing values

2023-12-12T23:35:00.631343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:35:00.731848image/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

버스전용차로단속기점위치X좌표Y좌표
0동서로네거리대전 중구 계룡로 820127.40690436.326581
1큰마을네거리대전 서구 계룡로 387127.37133936.351815
2유성네거리대전 유성구 계룡로 142127.34612336.351996
3버드내네거리대전 중구 계백로1578127.39542336.317656
4버드내네거리대전 중구 태평로 15127.39134436.318023
5유천네거리대전 중구 계백로1581127.39556336.31826
6도마네거리대전 서구 계백로1460127.3536936.296538
7가수원네거리대전 서구 벌곡로1384번길 131127.35797836.305916
8가수원네거리대전 서구 계백로 1095127.34580836.304453
9향우네거리대전 서구 도산로 137127.37755836.320764
버스전용차로단속기점위치X좌표Y좌표
20대전IC앞네거리대전 대덕구 동서대로 1761번길 1127.44247836.353285
21만년교대전 유성구 도안동로 331127.34837236.334974
22가수원네거리대전 유성구 도안동로 331127.34837236.334974
23만년교대전 서구 도안동로 123127.34586536.316395
24가수원네거리대전 서구 도안동로 123127.34586536.316395
25가수원네거리대전 유성구 봉명로 16127.34826236.338768
26농수산오거리대전 대덕구 오정로 65127.4110936.352035
27홍도육교네거리대전 동구 대전로 1014127.41920136.346082
28대전대전 대덕구 상서동 385-5127.41671436.421188
29세종대전 대덕구 상서동 361-3127.41584936.422122