Overview

Dataset statistics

Number of variables9
Number of observations111
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.0 KiB
Average record size in memory74.2 B

Variable types

Numeric1
Categorical3
Text5

Dataset

Description서울특별시 양천구의 주정차단속 CCTV 설치 현황 자료를 제공합니다.설치번호, 행정구역, 도로명, 단속구간, 설치지점, 명칭, 설치년도, 단속 방식 등
Author서울특별시 양천구
URLhttps://www.data.go.kr/data/15039769/fileData.do

Alerts

단속방식 has constant value ""Constant
설치번호 is highly overall correlated with 설치년도High correlation
설치년도 is highly overall correlated with 설치번호High correlation
설치번호 has unique valuesUnique

Reproduction

Analysis started2024-03-14 16:56:18.404603
Analysis finished2024-03-14 16:56:20.127502
Duration1.72 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

설치번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct111
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean956.07207
Minimum900
Maximum1017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-15T01:56:20.332037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum900
5-th percentile905.5
Q1927.5
median955
Q3982.5
95-th percentile1011.5
Maximum1017
Range117
Interquartile range (IQR)55

Descriptive statistics

Standard deviation33.824604
Coefficient of variation (CV)0.035378718
Kurtosis-1.0972412
Mean956.07207
Median Absolute Deviation (MAD)28
Skewness0.12333525
Sum106124
Variance1144.1038
MonotonicityStrictly increasing
2024-03-15T01:56:20.734316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
900 1
 
0.9%
901 1
 
0.9%
982 1
 
0.9%
981 1
 
0.9%
980 1
 
0.9%
979 1
 
0.9%
978 1
 
0.9%
977 1
 
0.9%
976 1
 
0.9%
975 1
 
0.9%
Other values (101) 101
91.0%
ValueCountFrequency (%)
900 1
0.9%
901 1
0.9%
902 1
0.9%
903 1
0.9%
904 1
0.9%
905 1
0.9%
906 1
0.9%
907 1
0.9%
908 1
0.9%
909 1
0.9%
ValueCountFrequency (%)
1017 1
0.9%
1016 1
0.9%
1015 1
0.9%
1014 1
0.9%
1013 1
0.9%
1012 1
0.9%
1011 1
0.9%
1010 1
0.9%
1009 1
0.9%
1008 1
0.9%

행정구역
Categorical

Distinct18
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Memory size1016.0 B
목1동
12 
신정4동
11 
목5동
10 
목4동
10 
신정3동
9 
Other values (13)
59 

Length

Max length4
Median length4
Mean length3.6306306
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row목1동
2nd row목2동
3rd row목4동
4th row목4동
5th row목4동

Common Values

ValueCountFrequency (%)
목1동 12
10.8%
신정4동 11
9.9%
목5동 10
 
9.0%
목4동 10
 
9.0%
신정3동 9
 
8.1%
신정7동 9
 
8.1%
신월1동 8
 
7.2%
목2동 6
 
5.4%
신월4동 5
 
4.5%
신월2동 5
 
4.5%
Other values (8) 26
23.4%

Length

2024-03-15T01:56:21.290516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
목1동 12
10.8%
신정4동 11
9.9%
목5동 10
 
9.0%
목4동 10
 
9.0%
신정3동 9
 
8.1%
신정7동 9
 
8.1%
신월1동 8
 
7.2%
목2동 6
 
5.4%
신월2동 5
 
4.5%
신월4동 5
 
4.5%
Other values (8) 26
23.4%

도로명
Text

Distinct59
Distinct (%)53.2%
Missing0
Missing (%)0.0%
Memory size1016.0 B
2024-03-15T01:56:23.127168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.3063063
Min length3

Characters and Unicode

Total characters589
Distinct characters48
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

Unique40 ?
Unique (%)36.0%

Sample

1st row신목로
2nd row목동중앙본로
3rd row목동중앙로
4th row목동중앙로
5th row목동중앙서로
ValueCountFrequency (%)
오목로 13
 
10.4%
목동동로 12
 
9.6%
목동서로 7
 
5.6%
신월로 5
 
4.0%
목동중앙본로 5
 
4.0%
목동중앙로 4
 
3.2%
목동중앙북로 4
 
3.2%
국회대로 3
 
2.4%
가로공원로 3
 
2.4%
남부순환로 3
 
2.4%
Other values (53) 66
52.8%
2024-03-15T01:56:24.749266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 115
19.5%
목 63
 
10.7%
동 59
 
10.0%
중 29
 
4.9%
앙 29
 
4.9%
길 29
 
4.9%
5 18
 
3.1%
18
 
3.1%
1 18
 
3.1%
2 16
 
2.7%
Other values (38) 195
33.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 473
80.3%
Decimal Number 96
 
16.3%
Space Separator 18
 
3.1%
Dash Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 115
24.3%
목 63
13.3%
동 59
12.5%
중 29
 
6.1%
앙 29
 
6.1%
길 29
 
6.1%
오 16
 
3.4%
신 12
 
2.5%
남 12
 
2.5%
서 9
 
1.9%
Other values (26) 100
21.1%
Decimal Number
ValueCountFrequency (%)
5 18
18.8%
1 18
18.8%
2 16
16.7%
4 10
10.4%
3 9
9.4%
9 6
 
6.2%
8 6
 
6.2%
7 6
 
6.2%
6 4
 
4.2%
0 3
 
3.1%
Space Separator
ValueCountFrequency (%)
18
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 473
80.3%
Common 116
 
19.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 115
24.3%
목 63
13.3%
동 59
12.5%
중 29
 
6.1%
앙 29
 
6.1%
길 29
 
6.1%
오 16
 
3.4%
신 12
 
2.5%
남 12
 
2.5%
서 9
 
1.9%
Other values (26) 100
21.1%
Common
ValueCountFrequency (%)
5 18
15.5%
18
15.5%
1 18
15.5%
2 16
13.8%
4 10
8.6%
3 9
7.8%
9 6
 
5.2%
8 6
 
5.2%
7 6
 
5.2%
6 4
 
3.4%
Other values (2) 5
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 473
80.3%
ASCII 116
 
19.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 115
24.3%
목 63
13.3%
동 59
12.5%
중 29
 
6.1%
앙 29
 
6.1%
길 29
 
6.1%
오 16
 
3.4%
신 12
 
2.5%
남 12
 
2.5%
서 9
 
1.9%
Other values (26) 100
21.1%
ASCII
ValueCountFrequency (%)
5 18
15.5%
18
15.5%
1 18
15.5%
2 16
13.8%
4 10
8.6%
3 9
7.8%
9 6
 
5.2%
8 6
 
5.2%
7 6
 
5.2%
6 4
 
3.4%
Other values (2) 5
 
4.3%

단속구간(방향)
Text

Distinct108
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size1016.0 B
2024-03-15T01:56:25.493020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length30
Mean length18.324324
Min length7

Characters and Unicode

Total characters2034
Distinct characters288
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique106 ?
Unique (%)95.5%

Sample

1st row청학스포츠타운 -> 목동중학교
2nd row달마을공원->목동문화체육센터 앞
3rd row영도초등학교 <-> 월촌초등학교
4th row목4동사무소<->신목중학교
5th row곰달래길 구간
ValueCountFrequency (%)
98
26.8%
앞 5
 
1.4%
신월1동(걷고싶은거리 3
 
0.8%
주변 3
 
0.8%
롯데캐슬 3
 
0.8%
104동 2
 
0.5%
남부순환로 2
 
0.5%
중앙로43길 2
 
0.5%
신원초정문 2
 
0.5%
목동14단지 2
 
0.5%
Other values (235) 244
66.7%
2024-03-15T01:56:26.983954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
277
 
13.6%
- 112
 
5.5%
> 111
 
5.5%
< 109
 
5.4%
동 65
 
3.2%
목 51
 
2.5%
신 36
 
1.8%
로 34
 
1.7%
트 33
 
1.6%
파 29
 
1.4%
Other values (278) 1177
57.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1264
62.1%
Space Separator 277
 
13.6%
Math Symbol 220
 
10.8%
Decimal Number 116
 
5.7%
Dash Punctuation 112
 
5.5%
Uppercase Letter 29
 
1.4%
Lowercase Letter 6
 
0.3%
Close Punctuation 4
 
0.2%
Open Punctuation 4
 
0.2%
Other Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 65
 
5.1%
목 51
 
4.0%
신 36
 
2.8%
로 34
 
2.7%
트 33
 
2.6%
파 29
 
2.3%
아 25
 
2.0%
길 23
 
1.8%
정 21
 
1.7%
리 20
 
1.6%
Other values (242) 927
73.3%
Uppercase Letter
ValueCountFrequency (%)
G 4
13.8%
E 4
13.8%
K 3
10.3%
S 3
10.3%
L 3
10.3%
I 2
6.9%
T 2
6.9%
B 2
6.9%
C 2
6.9%
N 1
 
3.4%
Other values (3) 3
10.3%
Decimal Number
ValueCountFrequency (%)
1 27
23.3%
4 19
16.4%
2 17
14.7%
3 15
12.9%
5 10
 
8.6%
6 8
 
6.9%
0 8
 
6.9%
9 6
 
5.2%
7 5
 
4.3%
8 1
 
0.9%
Lowercase Letter
ValueCountFrequency (%)
o 2
33.3%
k 1
16.7%
c 1
16.7%
a 1
16.7%
b 1
16.7%
Math Symbol
ValueCountFrequency (%)
> 111
50.5%
< 109
49.5%
Other Punctuation
ValueCountFrequency (%)
, 1
50.0%
. 1
50.0%
Space Separator
ValueCountFrequency (%)
277
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 112
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1264
62.1%
Common 735
36.1%
Latin 35
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 65
 
5.1%
목 51
 
4.0%
신 36
 
2.8%
로 34
 
2.7%
트 33
 
2.6%
파 29
 
2.3%
아 25
 
2.0%
길 23
 
1.8%
정 21
 
1.7%
리 20
 
1.6%
Other values (242) 927
73.3%
Common
ValueCountFrequency (%)
277
37.7%
- 112
15.2%
> 111
15.1%
< 109
 
14.8%
1 27
 
3.7%
4 19
 
2.6%
2 17
 
2.3%
3 15
 
2.0%
5 10
 
1.4%
6 8
 
1.1%
Other values (8) 30
 
4.1%
Latin
ValueCountFrequency (%)
G 4
11.4%
E 4
11.4%
K 3
 
8.6%
S 3
 
8.6%
L 3
 
8.6%
I 2
 
5.7%
o 2
 
5.7%
T 2
 
5.7%
B 2
 
5.7%
C 2
 
5.7%
Other values (8) 8
22.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1264
62.1%
ASCII 770
37.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
277
36.0%
- 112
14.5%
> 111
14.4%
< 109
 
14.2%
1 27
 
3.5%
4 19
 
2.5%
2 17
 
2.2%
3 15
 
1.9%
5 10
 
1.3%
6 8
 
1.0%
Other values (26) 65
 
8.4%
Hangul
ValueCountFrequency (%)
동 65
 
5.1%
목 51
 
4.0%
신 36
 
2.8%
로 34
 
2.7%
트 33
 
2.6%
파 29
 
2.3%
아 25
 
2.0%
길 23
 
1.8%
정 21
 
1.7%
리 20
 
1.6%
Other values (242) 927
73.3%

설치지점(지번주소)
Text

Distinct110
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size1016.0 B
2024-03-15T01:56:28.596707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length9.7747748
Min length7

Characters and Unicode

Total characters1085
Distinct characters19
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

Unique109 ?
Unique (%)98.2%

Sample

1st row목1동 405-87
2nd row목2동 30-18
3rd row목4동 753-7
4th row목4동 762-10
5th row목4동 797-8
ValueCountFrequency (%)
목4동 10
 
4.5%
신정4동 10
 
4.5%
목1동 9
 
4.1%
신정3동 9
 
4.1%
목5동 8
 
3.6%
신월1동 8
 
3.6%
신정7동 7
 
3.2%
목2동 6
 
2.7%
목동 6
 
2.7%
신월2동 5
 
2.3%
Other values (120) 144
64.9%
2024-03-15T01:56:30.120959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 126
11.6%
112
10.3%
동 111
10.2%
- 97
 
8.9%
2 75
 
6.9%
4 75
 
6.9%
신 69
 
6.4%
9 56
 
5.2%
3 50
 
4.6%
7 47
 
4.3%
Other values (9) 267
24.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 583
53.7%
Other Letter 293
27.0%
Space Separator 112
 
10.3%
Dash Punctuation 97
 
8.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 126
21.6%
2 75
12.9%
4 75
12.9%
9 56
9.6%
3 50
 
8.6%
7 47
 
8.1%
0 44
 
7.5%
5 39
 
6.7%
8 38
 
6.5%
6 33
 
5.7%
Other Letter
ValueCountFrequency (%)
동 111
37.9%
신 69
23.5%
목 42
 
14.3%
정 37
 
12.6%
월 32
 
10.9%
번 1
 
0.3%
지 1
 
0.3%
Space Separator
ValueCountFrequency (%)
112
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 97
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 792
73.0%
Hangul 293
 
27.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 126
15.9%
112
14.1%
- 97
12.2%
2 75
9.5%
4 75
9.5%
9 56
7.1%
3 50
 
6.3%
7 47
 
5.9%
0 44
 
5.6%
5 39
 
4.9%
Other values (2) 71
9.0%
Hangul
ValueCountFrequency (%)
동 111
37.9%
신 69
23.5%
목 42
 
14.3%
정 37
 
12.6%
월 32
 
10.9%
번 1
 
0.3%
지 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 792
73.0%
Hangul 293
 
27.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 126
15.9%
112
14.1%
- 97
12.2%
2 75
9.5%
4 75
9.5%
9 56
7.1%
3 50
 
6.3%
7 47
 
5.9%
0 44
 
5.6%
5 39
 
4.9%
Other values (2) 71
9.0%
Hangul
ValueCountFrequency (%)
동 111
37.9%
신 69
23.5%
목 42
 
14.3%
정 37
 
12.6%
월 32
 
10.9%
번 1
 
0.3%
지 1
 
0.3%

설치지점(도로명주소)
Text

Distinct110
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size1016.0 B
2024-03-15T01:56:31.186994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length18.387387
Min length14

Characters and Unicode

Total characters2041
Distinct characters54
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

Unique109 ?
Unique (%)98.2%

Sample

1st row서울특별시 양천구 신목로 95-1
2nd row서울특별시 양천구 목동중앙본로 74
3rd row서울특별시 양천구 목동중앙로 95
4th row서울특별시 양천구 목동중앙로 73
5th row서울특별시 양천구 목동중앙서로 30
ValueCountFrequency (%)
서울특별시 111
25.3%
양천구 111
25.3%
목동동로 13
 
3.0%
오목로 12
 
2.7%
목동서로 7
 
1.6%
남부순환로 5
 
1.1%
22 5
 
1.1%
목동중앙본로 4
 
0.9%
목동중앙로 4
 
0.9%
73 3
 
0.7%
Other values (133) 164
37.4%
2024-03-15T01:56:32.668352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
335
16.4%
서 120
 
5.9%
양 114
 
5.6%
로 113
 
5.5%
천 112
 
5.5%
구 111
 
5.4%
울 111
 
5.4%
시 111
 
5.4%
특 111
 
5.4%
별 111
 
5.4%
Other values (44) 692
33.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1367
67.0%
Space Separator 335
 
16.4%
Decimal Number 331
 
16.2%
Dash Punctuation 8
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 120
8.8%
양 114
 
8.3%
로 113
 
8.3%
천 112
 
8.2%
구 111
 
8.1%
울 111
 
8.1%
시 111
 
8.1%
특 111
 
8.1%
별 111
 
8.1%
목 64
 
4.7%
Other values (32) 289
21.1%
Decimal Number
ValueCountFrequency (%)
1 74
22.4%
2 47
14.2%
3 43
13.0%
5 38
11.5%
4 30
9.1%
7 25
 
7.6%
6 21
 
6.3%
0 20
 
6.0%
9 17
 
5.1%
8 16
 
4.8%
Space Separator
ValueCountFrequency (%)
335
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1367
67.0%
Common 674
33.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 120
8.8%
양 114
 
8.3%
로 113
 
8.3%
천 112
 
8.2%
구 111
 
8.1%
울 111
 
8.1%
시 111
 
8.1%
특 111
 
8.1%
별 111
 
8.1%
목 64
 
4.7%
Other values (32) 289
21.1%
Common
ValueCountFrequency (%)
335
49.7%
1 74
 
11.0%
2 47
 
7.0%
3 43
 
6.4%
5 38
 
5.6%
4 30
 
4.5%
7 25
 
3.7%
6 21
 
3.1%
0 20
 
3.0%
9 17
 
2.5%
Other values (2) 24
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1367
67.0%
ASCII 674
33.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
335
49.7%
1 74
 
11.0%
2 47
 
7.0%
3 43
 
6.4%
5 38
 
5.6%
4 30
 
4.5%
7 25
 
3.7%
6 21
 
3.1%
0 20
 
3.0%
9 17
 
2.5%
Other values (2) 24
 
3.6%
Hangul
ValueCountFrequency (%)
서 120
8.8%
양 114
 
8.3%
로 113
 
8.3%
천 112
 
8.2%
구 111
 
8.1%
울 111
 
8.1%
시 111
 
8.1%
특 111
 
8.1%
별 111
 
8.1%
목 64
 
4.7%
Other values (32) 289
21.1%

명칭
Text

Distinct109
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size1016.0 B
2024-03-15T01:56:33.482189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length17
Mean length11.468468
Min length6

Characters and Unicode

Total characters1273
Distinct characters218
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

Unique107 ?
Unique (%)96.4%

Sample

1st row목1동 오목교역7번출구
2nd row목2동 문화체육센터 앞
3rd row목4동 청우빌딩 앞
4th row목4동 영도초등학교 앞
5th row목4동 태학관 앞
ValueCountFrequency (%)
앞 63
 
21.3%
목4동 10
 
3.4%
목1동 9
 
3.0%
사거리 9
 
3.0%
통학로 8
 
2.7%
신월1동 7
 
2.4%
목5동 6
 
2.0%
삼거리 6
 
2.0%
주변 6
 
2.0%
목2동 5
 
1.7%
Other values (138) 167
56.4%
2024-03-15T01:56:34.781131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
186
 
14.6%
동 92
 
7.2%
앞 68
 
5.3%
목 52
 
4.1%
신 49
 
3.8%
정 24
 
1.9%
리 24
 
1.9%
트 22
 
1.7%
월 21
 
1.6%
1 20
 
1.6%
Other values (208) 715
56.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 981
77.1%
Space Separator 186
 
14.6%
Decimal Number 86
 
6.8%
Uppercase Letter 12
 
0.9%
Open Punctuation 3
 
0.2%
Close Punctuation 3
 
0.2%
Math Symbol 1
 
0.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 92
 
9.4%
앞 68
 
6.9%
목 52
 
5.3%
신 49
 
5.0%
정 24
 
2.4%
리 24
 
2.4%
트 22
 
2.2%
월 21
 
2.1%
교 19
 
1.9%
학 18
 
1.8%
Other values (186) 592
60.3%
Uppercase Letter
ValueCountFrequency (%)
S 3
25.0%
T 2
16.7%
C 1
 
8.3%
P 1
 
8.3%
A 1
 
8.3%
M 1
 
8.3%
W 1
 
8.3%
B 1
 
8.3%
K 1
 
8.3%
Decimal Number
ValueCountFrequency (%)
1 20
23.3%
4 18
20.9%
5 12
14.0%
7 10
11.6%
2 10
11.6%
3 10
11.6%
6 3
 
3.5%
0 3
 
3.5%
Space Separator
ValueCountFrequency (%)
186
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 981
77.1%
Common 280
 
22.0%
Latin 12
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 92
 
9.4%
앞 68
 
6.9%
목 52
 
5.3%
신 49
 
5.0%
정 24
 
2.4%
리 24
 
2.4%
트 22
 
2.2%
월 21
 
2.1%
교 19
 
1.9%
학 18
 
1.8%
Other values (186) 592
60.3%
Common
ValueCountFrequency (%)
186
66.4%
1 20
 
7.1%
4 18
 
6.4%
5 12
 
4.3%
7 10
 
3.6%
2 10
 
3.6%
3 10
 
3.6%
6 3
 
1.1%
( 3
 
1.1%
0 3
 
1.1%
Other values (3) 5
 
1.8%
Latin
ValueCountFrequency (%)
S 3
25.0%
T 2
16.7%
C 1
 
8.3%
P 1
 
8.3%
A 1
 
8.3%
M 1
 
8.3%
W 1
 
8.3%
B 1
 
8.3%
K 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 981
77.1%
ASCII 292
 
22.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
186
63.7%
1 20
 
6.8%
4 18
 
6.2%
5 12
 
4.1%
7 10
 
3.4%
2 10
 
3.4%
3 10
 
3.4%
6 3
 
1.0%
( 3
 
1.0%
0 3
 
1.0%
Other values (12) 17
 
5.8%
Hangul
ValueCountFrequency (%)
동 92
 
9.4%
앞 68
 
6.9%
목 52
 
5.3%
신 49
 
5.0%
정 24
 
2.4%
리 24
 
2.4%
트 22
 
2.2%
월 21
 
2.1%
교 19
 
1.9%
학 18
 
1.8%
Other values (186) 592
60.3%

설치년도
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Memory size1016.0 B
2008
11 
2021
11 
2010
10 
2022
10 
2009
8 
Other values (11)
61 

Length

Max length9
Median length4
Mean length4.0900901
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2008
2nd row2008
3rd row2008
4th row2008
5th row2008

Common Values

ValueCountFrequency (%)
2008 11
9.9%
2021 11
9.9%
2010 10
 
9.0%
2022 10
 
9.0%
2009 8
 
7.2%
2015 8
 
7.2%
2011 7
 
6.3%
2020 7
 
6.3%
2023 7
 
6.3%
2016 6
 
5.4%
Other values (6) 26
23.4%

Length

2024-03-15T01:56:35.194962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2008 11
9.9%
2021 11
9.9%
2010 10
 
9.0%
2022 10
 
9.0%
2009 8
 
7.2%
2015 8
 
7.2%
2011 7
 
6.3%
2020 7
 
6.3%
2023 7
 
6.3%
2016 6
 
5.4%
Other values (6) 26
23.4%

단속방식
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1016.0 B
자동
111 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
자동 111
100.0%

Length

2024-03-15T01:56:35.569550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:56:35.863759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자동 111
100.0%

Interactions

2024-03-15T01:56:19.145905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T01:56:36.049292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치번호행정구역도로명설치년도
설치번호1.0000.4610.7540.962
행정구역0.4611.0000.9280.476
도로명0.7540.9281.0000.658
설치년도0.9620.4760.6581.000
2024-03-15T01:56:36.206789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치년도행정구역
설치년도1.0000.165
행정구역0.1651.000
2024-03-15T01:56:36.460692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치번호행정구역설치년도
설치번호1.0000.1770.805
행정구역0.1771.0000.165
설치년도0.8050.1651.000

Missing values

2024-03-15T01:56:19.498269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T01:56:19.951632image/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

설치번호행정구역도로명단속구간(방향)설치지점(지번주소)설치지점(도로명주소)명칭설치년도단속방식
0900목1동신목로청학스포츠타운 -> 목동중학교목1동 405-87서울특별시 양천구 신목로 95-1목1동 오목교역7번출구2008자동
1901목2동목동중앙본로달마을공원->목동문화체육센터 앞목2동 30-18서울특별시 양천구 목동중앙본로 74목2동 문화체육센터 앞2008자동
2902목4동목동중앙로영도초등학교 <-> 월촌초등학교목4동 753-7서울특별시 양천구 목동중앙로 95목4동 청우빌딩 앞2008자동
3903목4동목동중앙로목4동사무소<->신목중학교목4동 762-10서울특별시 양천구 목동중앙로 73목4동 영도초등학교 앞2008자동
4904목4동목동중앙서로곰달래길 구간목4동 797-8서울특별시 양천구 목동중앙서로 30목4동 태학관 앞2008자동
5905신정6동목동서로구의회 앞길 <-> 센트럴프라자신정6동 322-1서울특별시 양천구 목동서로 355신정6동 서울강림교회 앞2008자동
6906신정4동국회대로신정4동 <->제물포로신정4동 881-1서울특별시 양천구 국회대로 214신정4동 한서빌딩 앞2008자동
7907신월2동오목로양강초후문 <-> 양강중 <-> 대광주택신월2동 495-1서울특별시 양천구 오목로 101양강초등학교 후문 통학로2021년이전설치자동
8908신정4동목동로목동아파트703동앞<->KT<->예술인회관목동 925번지서울특별시 양천구 목동로 212목동아파트 703동앞-KT2021년이전설치자동
9909신월7동지양로과학수사연구소 <->신월체육센터신월7동 928-1서울특별시 양천구 지양로 78신월7동 우성상가 앞2008자동
설치번호행정구역도로명단속구간(방향)설치지점(지번주소)설치지점(도로명주소)명칭설치년도단속방식
1011008신정3동중앙로23길 22남명초등학교 <-> 원달러김밥신정3동 1148-7서울특별시 양천구 중앙로23길 22남명초등학교 진입로2022자동
1021009신정3동중앙로29길 11중앙로29길(호반써밋목동아파트 정문)신정3동 1150-41서울특별시 양천구 중앙로29길 11호반서밋목동아파트 정문2022자동
1031010신정7동중앙로14나길 39중앙로14나길 <-> 안양천로39길신정7동 177-8서울특별시 양천구 중앙로14나길 39향림사 인근2022자동
1041011신월2동오목로9길 12신목동파라곤아파트 104동 앞신월2동 488-13서울특별시 양천구 오목로9길 12신목동파라곤아파트 104동 앞 (기부체납)2023자동
1051012신월2동오목로 71이마트편의점 신월양강점 주변신월2동 491-8서울특별시 양천구 오목로 71이마트편의점 신월양강점 주변 (기부체납)2023자동
1061013신정3동신정로7길 81-4서울신정유치원 주변신정3동 1319-3서울특별시 양천구 신정로7길 81-4서울신정유치원 주변2023자동
1071014목1동오목로 276-5레디앙아파트 삼거리목1동 408-219서울특별시 양천구 오목로 276-5레디앙아파트 삼거리2023자동
1081015신월2동중앙로55길 65신강초교 체육관 앞 주변신월2동 447-12서울특별시 양천구 중앙로55길 65신강초교 체육관 앞 주변2023자동
1091016신정4동중앙로52길 73앞산어린이공원신정4동 910-9서울특별시 양천구 중앙로52길 73앞산어린이공원2023자동
1101017목5동목동동로 350목동5단지510동 사거리목5동 912서울특별시 양천구 목동동로 350목동5단지510동 사거리2023자동