Overview

Dataset statistics

Number of variables23
Number of observations10000
Missing cells23425
Missing cells (%)10.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 MiB
Average record size in memory201.0 B

Variable types

Text10
Categorical5
Numeric6
Unsupported1
DateTime1

Dataset

Description공공데이터 제공 표준데이터 속성정보(허용값, 표현형식/단위 등)는 [공공데이터 제공 표준] 전문을 참고하시기 바랍니다.(공공데이터포털>정보공유>자료실) 각 기관에서 등록한 표준데이터를 취합하여 제공하기 때문에 갱신주기는 개별 파일마다 다릅니다.(기관에서 등록한 데이터를 취합한 것으로 개별 파일별 갱신시점이 다름) ※ 데이터의 취합 기간중 무인교통단속장비 수치가 변경되어(속도, 위치 등) 차이가 있을 수 있습니다. 교통 환경의 변화에 유의하여 주시기 바랍니다.
Author경찰청(지방경찰청 기초자료 입력), 지방자치단체
URLhttps://www.data.go.kr/data/15028200/standard.do

Alerts

단속구간위치구분 is highly imbalanced (85.4%)Imbalance
도로노선번호 has 7226 (72.3%) missing valuesMissing
소재지도로명주소 has 4249 (42.5%) missing valuesMissing
소재지지번주소 has 1200 (12.0%) missing valuesMissing
과속단속구간길이 has 9681 (96.8%) missing valuesMissing
설치연도 has 1045 (10.4%) missing valuesMissing
단속구분 is an unsupported type, check if it needs cleaning or further analysisUnsupported
제한속도 has 2302 (23.0%) zerosZeros

Reproduction

Analysis started2024-05-04 08:16:49.900120
Analysis finished2024-05-04 08:16:55.785474
Duration5.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

무인교통단속카메라관리번호
Text

Distinct9040
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-04T08:16:56.398569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length5
Mean length5.0556
Min length1

Characters and Unicode

Total characters50556
Distinct characters205
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

Unique8747 ?
Unique (%)87.5%

Sample

1st rowH2510
2nd rowG8493
3rd rowH8104
4th rowJ1043
5th rowJ0132
ValueCountFrequency (%)
3 25
 
0.2%
단속 23
 
0.2%
주정차 23
 
0.2%
22
 
0.2%
1 22
 
0.2%
4 22
 
0.2%
19 21
 
0.2%
11 19
 
0.2%
6 18
 
0.2%
7 18
 
0.2%
Other values (9050) 9891
97.9%
2024-05-04T08:16:57.861363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4728
 
9.4%
1 4454
 
8.8%
2 3918
 
7.7%
3 3543
 
7.0%
6 3395
 
6.7%
7 3388
 
6.7%
8 3369
 
6.7%
9 3361
 
6.6%
4 3324
 
6.6%
5 3216
 
6.4%
Other values (195) 13860
27.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 36696
72.6%
Uppercase Letter 8376
 
16.6%
Other Letter 3785
 
7.5%
Dash Punctuation 1448
 
2.9%
Space Separator 105
 
0.2%
Lowercase Letter 104
 
0.2%
Open Punctuation 18
 
< 0.1%
Close Punctuation 10
 
< 0.1%
Other Punctuation 10
 
< 0.1%
Connector Punctuation 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 213
 
5.6%
주 203
 
5.4%
정 184
 
4.9%
차 178
 
4.7%
구 121
 
3.2%
완 117
 
3.1%
평 113
 
3.0%
안 105
 
2.8%
동 104
 
2.7%
진 102
 
2.7%
Other values (154) 2345
62.0%
Uppercase Letter
ValueCountFrequency (%)
G 2881
34.4%
H 2865
34.2%
F 982
 
11.7%
J 606
 
7.2%
P 266
 
3.2%
V 162
 
1.9%
E 134
 
1.6%
N 107
 
1.3%
C 85
 
1.0%
I 72
 
0.9%
Other values (9) 216
 
2.6%
Decimal Number
ValueCountFrequency (%)
0 4728
12.9%
1 4454
12.1%
2 3918
10.7%
3 3543
9.7%
6 3395
9.3%
7 3388
9.2%
8 3369
9.2%
9 3361
9.2%
4 3324
9.1%
5 3216
8.8%
Lowercase Letter
ValueCountFrequency (%)
c 26
25.0%
a 26
25.0%
m 13
12.5%
e 13
12.5%
r 13
12.5%
g 13
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 1448
100.0%
Space Separator
ValueCountFrequency (%)
105
100.0%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Other Punctuation
ValueCountFrequency (%)
. 10
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 38291
75.7%
Latin 8480
 
16.8%
Hangul 3785
 
7.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 213
 
5.6%
주 203
 
5.4%
정 184
 
4.9%
차 178
 
4.7%
구 121
 
3.2%
완 117
 
3.1%
평 113
 
3.0%
안 105
 
2.8%
동 104
 
2.7%
진 102
 
2.7%
Other values (154) 2345
62.0%
Latin
ValueCountFrequency (%)
G 2881
34.0%
H 2865
33.8%
F 982
 
11.6%
J 606
 
7.1%
P 266
 
3.1%
V 162
 
1.9%
E 134
 
1.6%
N 107
 
1.3%
C 85
 
1.0%
I 72
 
0.8%
Other values (15) 320
 
3.8%
Common
ValueCountFrequency (%)
0 4728
12.3%
1 4454
11.6%
2 3918
10.2%
3 3543
9.3%
6 3395
8.9%
7 3388
8.8%
8 3369
8.8%
9 3361
8.8%
4 3324
8.7%
5 3216
8.4%
Other values (6) 1595
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46771
92.5%
Hangul 3785
 
7.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4728
10.1%
1 4454
9.5%
2 3918
 
8.4%
3 3543
 
7.6%
6 3395
 
7.3%
7 3388
 
7.2%
8 3369
 
7.2%
9 3361
 
7.2%
4 3324
 
7.1%
5 3216
 
6.9%
Other values (31) 10075
21.5%
Hangul
ValueCountFrequency (%)
산 213
 
5.6%
주 203
 
5.4%
정 184
 
4.9%
차 178
 
4.7%
구 121
 
3.2%
완 117
 
3.1%
평 113
 
3.0%
안 105
 
2.8%
동 104
 
2.7%
진 102
 
2.7%
Other values (154) 2345
62.0%

시도명
Categorical

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경기도
2511 
서울특별시
1000 
충청남도
783 
경상남도
733 
경상북도
666 
Other values (14)
4307 

Length

Max length7
Median length5
Mean length4.3538
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상북도
2nd row서울특별시
3rd row경기도
4th row인천광역시
5th row경상북도

Common Values

ValueCountFrequency (%)
경기도 2511
25.1%
서울특별시 1000
 
10.0%
충청남도 783
 
7.8%
경상남도 733
 
7.3%
경상북도 666
 
6.7%
전라남도 603
 
6.0%
전북특별자치도 579
 
5.8%
인천광역시 432
 
4.3%
대전광역시 377
 
3.8%
부산광역시 370
 
3.7%
Other values (9) 1946
19.5%

Length

2024-05-04T08:16:58.433654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 2511
25.1%
서울특별시 1000
 
10.0%
충청남도 783
 
7.8%
경상남도 733
 
7.3%
경상북도 666
 
6.7%
전라남도 603
 
6.0%
전북특별자치도 579
 
5.8%
인천광역시 432
 
4.3%
대전광역시 377
 
3.8%
부산광역시 370
 
3.7%
Other values (9) 1946
19.5%

시군구명
Text

Distinct221
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-04T08:16:59.294549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.0996
Min length2

Characters and Unicode

Total characters30996
Distinct characters134
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

Unique2 ?
Unique (%)< 0.1%

Sample

1st row의성군
2nd row노원구
3rd row오산시
4th row부평구
5th row경주시
ValueCountFrequency (%)
고양시 252
 
2.4%
평택시 250
 
2.4%
전주시 210
 
2.0%
서구 192
 
1.9%
중구 189
 
1.8%
천안시 186
 
1.8%
성남시 175
 
1.7%
완산구 168
 
1.6%
화성시 166
 
1.6%
북구 164
 
1.6%
Other values (210) 8397
81.1%
2024-05-04T08:17:00.565534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 5316
 
17.2%
구 3365
 
10.9%
군 1910
 
6.2%
주 1324
 
4.3%
산 1182
 
3.8%
성 953
 
3.1%
천 921
 
3.0%
양 870
 
2.8%
남 838
 
2.7%
서 641
 
2.1%
Other values (124) 13676
44.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30647
98.9%
Space Separator 349
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 5316
 
17.3%
구 3365
 
11.0%
군 1910
 
6.2%
주 1324
 
4.3%
산 1182
 
3.9%
성 953
 
3.1%
천 921
 
3.0%
양 870
 
2.8%
남 838
 
2.7%
서 641
 
2.1%
Other values (123) 13327
43.5%
Space Separator
ValueCountFrequency (%)
349
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30647
98.9%
Common 349
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 5316
 
17.3%
구 3365
 
11.0%
군 1910
 
6.2%
주 1324
 
4.3%
산 1182
 
3.9%
성 953
 
3.1%
천 921
 
3.0%
양 870
 
2.8%
남 838
 
2.7%
서 641
 
2.1%
Other values (123) 13327
43.5%
Common
ValueCountFrequency (%)
349
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30647
98.9%
ASCII 349
 
1.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 5316
 
17.3%
구 3365
 
11.0%
군 1910
 
6.2%
주 1324
 
4.3%
산 1182
 
3.9%
성 953
 
3.1%
천 921
 
3.0%
양 870
 
2.8%
남 838
 
2.7%
서 641
 
2.1%
Other values (123) 13327
43.5%
ASCII
ValueCountFrequency (%)
349
100.0%

도로종류
Categorical

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
기타
5045 
시도
1683 
일반국도
1387 
지방도
1034 
구도
 
449
Other values (4)
 
402

Length

Max length7
Median length2
Mean length2.445
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기타 5045
50.4%
시도 1683
 
16.8%
일반국도 1387
 
13.9%
지방도 1034
 
10.3%
구도 449
 
4.5%
고속국도 162
 
1.6%
특별시도 114
 
1.1%
군도 108
 
1.1%
국가지원지방도 18
 
0.2%

Length

2024-05-04T08:17:01.168412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T08:17:01.542727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 5045
50.4%
시도 1683
 
16.8%
일반국도 1387
 
13.9%
지방도 1034
 
10.3%
구도 449
 
4.5%
고속국도 162
 
1.6%
특별시도 114
 
1.1%
군도 108
 
1.1%
국가지원지방도 18
 
0.2%

도로노선번호
Text

MISSING 

Distinct550
Distinct (%)19.8%
Missing7226
Missing (%)72.3%
Memory size156.2 KiB
2024-05-04T08:17:02.446397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length8
Mean length2.3976208
Min length1

Characters and Unicode

Total characters6651
Distinct characters50
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique258 ?
Unique (%)9.3%

Sample

1st row840
2nd row42
3rd row318
4th row69
5th row113
ValueCountFrequency (%)
1 207
 
7.3%
3 154
 
5.5%
2 58
 
2.1%
23 56
 
2.0%
14 55
 
2.0%
7 50
 
1.8%
30 48
 
1.7%
17 47
 
1.7%
13 41
 
1.5%
38 39
 
1.4%
Other values (536) 2065
73.2%
2024-05-04T08:17:03.620448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1046
15.7%
3 1010
15.2%
2 723
10.9%
7 533
8.0%
4 520
7.8%
0 449
6.8%
5 442
6.6%
번 382
 
5.7%
9 366
 
5.5%
8 320
 
4.8%
Other values (40) 860
12.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5727
86.1%
Other Letter 823
 
12.4%
Dash Punctuation 51
 
0.8%
Space Separator 46
 
0.7%
Connector Punctuation 2
 
< 0.1%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
번 382
46.4%
호 183
22.2%
도 41
 
5.0%
선 36
 
4.4%
국 31
 
3.8%
로 23
 
2.8%
중 20
 
2.4%
소 13
 
1.6%
지 10
 
1.2%
방 10
 
1.2%
Other values (25) 74
 
9.0%
Decimal Number
ValueCountFrequency (%)
1 1046
18.3%
3 1010
17.6%
2 723
12.6%
7 533
9.3%
4 520
9.1%
0 449
7.8%
5 442
7.7%
9 366
 
6.4%
8 320
 
5.6%
6 318
 
5.6%
Dash Punctuation
ValueCountFrequency (%)
- 51
100.0%
Space Separator
ValueCountFrequency (%)
46
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5828
87.6%
Hangul 823
 
12.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
번 382
46.4%
호 183
22.2%
도 41
 
5.0%
선 36
 
4.4%
국 31
 
3.8%
로 23
 
2.8%
중 20
 
2.4%
소 13
 
1.6%
지 10
 
1.2%
방 10
 
1.2%
Other values (25) 74
 
9.0%
Common
ValueCountFrequency (%)
1 1046
17.9%
3 1010
17.3%
2 723
12.4%
7 533
9.1%
4 520
8.9%
0 449
7.7%
5 442
7.6%
9 366
 
6.3%
8 320
 
5.5%
6 318
 
5.5%
Other values (5) 101
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5828
87.6%
Hangul 823
 
12.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1046
17.9%
3 1010
17.3%
2 723
12.4%
7 533
9.1%
4 520
8.9%
0 449
7.7%
5 442
7.6%
9 366
 
6.3%
8 320
 
5.5%
6 318
 
5.5%
Other values (5) 101
 
1.7%
Hangul
ValueCountFrequency (%)
번 382
46.4%
호 183
22.2%
도 41
 
5.0%
선 36
 
4.4%
국 31
 
3.8%
로 23
 
2.8%
중 20
 
2.4%
소 13
 
1.6%
지 10
 
1.2%
방 10
 
1.2%
Other values (25) 74
 
9.0%

도로노선명
Text

Distinct4596
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-04T08:17:04.406620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length3
Mean length3.8959
Min length1

Characters and Unicode

Total characters38959
Distinct characters506
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2671 ?
Unique (%)26.7%

Sample

1st row문소1길
2nd row노원로
3rd row오산로132번길
4th row수변로57번길
5th row강변로
ValueCountFrequency (%)
중앙로 139
 
1.4%
국도 51
 
0.5%
경충대로 39
 
0.4%
번영로 39
 
0.4%
동해대로 35
 
0.3%
호국로 32
 
0.3%
대학로 26
 
0.3%
평화로 26
 
0.3%
지방도 25
 
0.2%
경부고속도로 24
 
0.2%
Other values (4614) 9703
95.7%
2024-05-04T08:17:06.160135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 9117
 
23.4%
대 1466
 
3.8%
길 1225
 
3.1%
산 740
 
1.9%
동 692
 
1.8%
1 581
 
1.5%
도 516
 
1.3%
번 485
 
1.2%
중 478
 
1.2%
서 475
 
1.2%
Other values (496) 23184
59.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 36347
93.3%
Decimal Number 2422
 
6.2%
Space Separator 139
 
0.4%
Dash Punctuation 18
 
< 0.1%
Other Punctuation 12
 
< 0.1%
Open Punctuation 7
 
< 0.1%
Close Punctuation 6
 
< 0.1%
Math Symbol 6
 
< 0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 9117
25.1%
대 1466
 
4.0%
길 1225
 
3.4%
산 740
 
2.0%
동 692
 
1.9%
도 516
 
1.4%
번 485
 
1.3%
중 478
 
1.3%
서 475
 
1.3%
천 466
 
1.3%
Other values (477) 20687
56.9%
Decimal Number
ValueCountFrequency (%)
1 581
24.0%
2 412
17.0%
3 330
13.6%
5 229
 
9.5%
4 225
 
9.3%
6 162
 
6.7%
7 138
 
5.7%
8 122
 
5.0%
9 115
 
4.7%
0 108
 
4.5%
Math Symbol
ValueCountFrequency (%)
~ 4
66.7%
+ 2
33.3%
Uppercase Letter
ValueCountFrequency (%)
C 1
50.0%
I 1
50.0%
Space Separator
ValueCountFrequency (%)
139
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Other Punctuation
ValueCountFrequency (%)
. 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 36347
93.3%
Common 2610
 
6.7%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 9117
25.1%
대 1466
 
4.0%
길 1225
 
3.4%
산 740
 
2.0%
동 692
 
1.9%
도 516
 
1.4%
번 485
 
1.3%
중 478
 
1.3%
서 475
 
1.3%
천 466
 
1.3%
Other values (477) 20687
56.9%
Common
ValueCountFrequency (%)
1 581
22.3%
2 412
15.8%
3 330
12.6%
5 229
 
8.8%
4 225
 
8.6%
6 162
 
6.2%
139
 
5.3%
7 138
 
5.3%
8 122
 
4.7%
9 115
 
4.4%
Other values (7) 157
 
6.0%
Latin
ValueCountFrequency (%)
C 1
50.0%
I 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 36347
93.3%
ASCII 2612
 
6.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 9117
25.1%
대 1466
 
4.0%
길 1225
 
3.4%
산 740
 
2.0%
동 692
 
1.9%
도 516
 
1.4%
번 485
 
1.3%
중 478
 
1.3%
서 475
 
1.3%
천 466
 
1.3%
Other values (477) 20687
56.9%
ASCII
ValueCountFrequency (%)
1 581
22.2%
2 412
15.8%
3 330
12.6%
5 229
 
8.8%
4 225
 
8.6%
6 162
 
6.2%
139
 
5.3%
7 138
 
5.3%
8 122
 
4.7%
9 115
 
4.4%
Other values (9) 159
 
6.1%

도로노선방향
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
3
4619 
1
2966 
2
2415 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row3
4th row3
5th row1

Common Values

ValueCountFrequency (%)
3 4619
46.2%
1 2966
29.7%
2 2415
24.1%

Length

2024-05-04T08:17:06.714226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T08:17:07.139462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 4619
46.2%
1 2966
29.7%
2 2415
24.1%

소재지도로명주소
Text

MISSING 

Distinct5235
Distinct (%)91.0%
Missing4249
Missing (%)42.5%
Memory size156.2 KiB
2024-05-04T08:17:07.895530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length34
Mean length18.498348
Min length9

Characters and Unicode

Total characters106384
Distinct characters483
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4821 ?
Unique (%)83.8%

Sample

1st row경상북도 의성군 의성읍 구봉길 172
2nd row경기도 오산시 오산로132번길 28-5
3rd row인천광역시 부평구 수변로57번길
4th row경상북도 경주시 용황로 6
5th row경상남도 창원시 마산회원구 내서읍 삼계10길
ValueCountFrequency (%)
경기도 838
 
3.4%
서울특별시 832
 
3.4%
충청남도 733
 
3.0%
경상남도 610
 
2.5%
경상북도 568
 
2.3%
인천광역시 378
 
1.5%
대구광역시 282
 
1.2%
강원특별자치도 268
 
1.1%
대전광역시 239
 
1.0%
전라북도 217
 
0.9%
Other values (6051) 19444
79.7%
2024-05-04T08:17:09.451834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18660
 
17.5%
로 5219
 
4.9%
시 5115
 
4.8%
도 3711
 
3.5%
구 3464
 
3.3%
1 2996
 
2.8%
남 2356
 
2.2%
경 2265
 
2.1%
2 2027
 
1.9%
서 1773
 
1.7%
Other values (473) 58798
55.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 72548
68.2%
Space Separator 18660
 
17.5%
Decimal Number 14240
 
13.4%
Dash Punctuation 512
 
0.5%
Close Punctuation 168
 
0.2%
Open Punctuation 168
 
0.2%
Other Punctuation 86
 
0.1%
Connector Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 5219
 
7.2%
시 5115
 
7.1%
도 3711
 
5.1%
구 3464
 
4.8%
남 2356
 
3.2%
경 2265
 
3.1%
서 1773
 
2.4%
광 1756
 
2.4%
산 1746
 
2.4%
대 1585
 
2.2%
Other values (455) 43558
60.0%
Decimal Number
ValueCountFrequency (%)
1 2996
21.0%
2 2027
14.2%
3 1634
11.5%
4 1330
9.3%
5 1234
8.7%
7 1124
 
7.9%
6 1110
 
7.8%
8 982
 
6.9%
0 966
 
6.8%
9 837
 
5.9%
Other Punctuation
ValueCountFrequency (%)
, 76
88.4%
. 7
 
8.1%
· 3
 
3.5%
Space Separator
ValueCountFrequency (%)
18660
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 512
100.0%
Close Punctuation
ValueCountFrequency (%)
) 168
100.0%
Open Punctuation
ValueCountFrequency (%)
( 168
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 72548
68.2%
Common 33836
31.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 5219
 
7.2%
시 5115
 
7.1%
도 3711
 
5.1%
구 3464
 
4.8%
남 2356
 
3.2%
경 2265
 
3.1%
서 1773
 
2.4%
광 1756
 
2.4%
산 1746
 
2.4%
대 1585
 
2.2%
Other values (455) 43558
60.0%
Common
ValueCountFrequency (%)
18660
55.1%
1 2996
 
8.9%
2 2027
 
6.0%
3 1634
 
4.8%
4 1330
 
3.9%
5 1234
 
3.6%
7 1124
 
3.3%
6 1110
 
3.3%
8 982
 
2.9%
0 966
 
2.9%
Other values (8) 1773
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 72548
68.2%
ASCII 33833
31.8%
None 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18660
55.2%
1 2996
 
8.9%
2 2027
 
6.0%
3 1634
 
4.8%
4 1330
 
3.9%
5 1234
 
3.6%
7 1124
 
3.3%
6 1110
 
3.3%
8 982
 
2.9%
0 966
 
2.9%
Other values (7) 1770
 
5.2%
Hangul
ValueCountFrequency (%)
로 5219
 
7.2%
시 5115
 
7.1%
도 3711
 
5.1%
구 3464
 
4.8%
남 2356
 
3.2%
경 2265
 
3.1%
서 1773
 
2.4%
광 1756
 
2.4%
산 1746
 
2.4%
대 1585
 
2.2%
Other values (455) 43558
60.0%
None
ValueCountFrequency (%)
· 3
100.0%

소재지지번주소
Text

MISSING 

Distinct8339
Distinct (%)94.8%
Missing1200
Missing (%)12.0%
Memory size156.2 KiB
2024-05-04T08:17:10.295749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length38
Mean length20.347386
Min length12

Characters and Unicode

Total characters179057
Distinct characters448
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7903 ?
Unique (%)89.8%

Sample

1st row경상북도 의성군 의성읍 도동리 670-1
2nd row서울특별시 노원구 중계동 511-2
3rd row경상북도 경주시 황성동 800-13
4th row경상남도 창원시 마산회원구 내서읍 삼계리 51
5th row인천광역시 동구 화평동 1-48
ValueCountFrequency (%)
경기도 2164
 
5.4%
경상남도 717
 
1.8%
충청남도 697
 
1.7%
경상북도 642
 
1.6%
전라남도 581
 
1.5%
전북특별자치도 565
 
1.4%
서울특별시 496
 
1.2%
대전광역시 387
 
1.0%
인천광역시 364
 
0.9%
강원특별자치도 341
 
0.9%
Other values (10926) 32922
82.6%
2024-05-04T08:17:11.889631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31097
 
17.4%
시 7347
 
4.1%
1 7075
 
4.0%
도 6784
 
3.8%
동 6472
 
3.6%
- 6193
 
3.5%
2 4501
 
2.5%
구 4253
 
2.4%
3 3784
 
2.1%
경 3720
 
2.1%
Other values (438) 97831
54.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 106625
59.5%
Decimal Number 34984
 
19.5%
Space Separator 31097
 
17.4%
Dash Punctuation 6193
 
3.5%
Close Punctuation 64
 
< 0.1%
Open Punctuation 63
 
< 0.1%
Other Punctuation 15
 
< 0.1%
Uppercase Letter 11
 
< 0.1%
Math Symbol 4
 
< 0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 7347
 
6.9%
도 6784
 
6.4%
동 6472
 
6.1%
구 4253
 
4.0%
경 3720
 
3.5%
리 3387
 
3.2%
남 3294
 
3.1%
산 3087
 
2.9%
기 2344
 
2.2%
전 2339
 
2.2%
Other values (413) 63598
59.6%
Decimal Number
ValueCountFrequency (%)
1 7075
20.2%
2 4501
12.9%
3 3784
10.8%
4 3327
9.5%
6 3118
8.9%
5 3033
8.7%
7 2718
 
7.8%
8 2614
 
7.5%
0 2409
 
6.9%
9 2405
 
6.9%
Uppercase Letter
ValueCountFrequency (%)
G 3
27.3%
S 2
18.2%
N 2
18.2%
A 1
 
9.1%
H 1
 
9.1%
T 1
 
9.1%
E 1
 
9.1%
Math Symbol
ValueCountFrequency (%)
→ 3
75.0%
~ 1
 
25.0%
Space Separator
ValueCountFrequency (%)
31097
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6193
100.0%
Close Punctuation
ValueCountFrequency (%)
) 64
100.0%
Open Punctuation
ValueCountFrequency (%)
( 63
100.0%
Other Punctuation
ValueCountFrequency (%)
, 15
100.0%
Lowercase Letter
ValueCountFrequency (%)
k 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 106625
59.5%
Common 72420
40.4%
Latin 12
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 7347
 
6.9%
도 6784
 
6.4%
동 6472
 
6.1%
구 4253
 
4.0%
경 3720
 
3.5%
리 3387
 
3.2%
남 3294
 
3.1%
산 3087
 
2.9%
기 2344
 
2.2%
전 2339
 
2.2%
Other values (413) 63598
59.6%
Common
ValueCountFrequency (%)
31097
42.9%
1 7075
 
9.8%
- 6193
 
8.6%
2 4501
 
6.2%
3 3784
 
5.2%
4 3327
 
4.6%
6 3118
 
4.3%
5 3033
 
4.2%
7 2718
 
3.8%
8 2614
 
3.6%
Other values (7) 4960
 
6.8%
Latin
ValueCountFrequency (%)
G 3
25.0%
S 2
16.7%
N 2
16.7%
A 1
 
8.3%
H 1
 
8.3%
T 1
 
8.3%
E 1
 
8.3%
k 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 106625
59.5%
ASCII 72429
40.5%
Arrows 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31097
42.9%
1 7075
 
9.8%
- 6193
 
8.6%
2 4501
 
6.2%
3 3784
 
5.2%
4 3327
 
4.6%
6 3118
 
4.3%
5 3033
 
4.2%
7 2718
 
3.8%
8 2614
 
3.6%
Other values (14) 4969
 
6.9%
Hangul
ValueCountFrequency (%)
시 7347
 
6.9%
도 6784
 
6.4%
동 6472
 
6.1%
구 4253
 
4.0%
경 3720
 
3.5%
리 3387
 
3.2%
남 3294
 
3.1%
산 3087
 
2.9%
기 2344
 
2.2%
전 2339
 
2.2%
Other values (413) 63598
59.6%
Arrows
ValueCountFrequency (%)
→ 3
100.0%

위도
Real number (ℝ)

Distinct9588
Distinct (%)96.0%
Missing12
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean36.544152
Minimum33.244096
Maximum38.468251
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T08:17:12.493063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.244096
5-th percentile34.956532
Q135.776149
median36.772592
Q337.474384
95-th percentile37.752283
Maximum38.468251
Range5.2241553
Interquartile range (IQR)1.6982344

Descriptive statistics

Standard deviation0.99600404
Coefficient of variation (CV)0.027254813
Kurtosis-0.86622055
Mean36.544152
Median Absolute Deviation (MAD)0.80322561
Skewness-0.4317523
Sum365002.99
Variance0.99202404
MonotonicityNot monotonic
2024-05-04T08:17:13.046091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.9601511 4
 
< 0.1%
35.8234244255 4
 
< 0.1%
35.16826961 4
 
< 0.1%
35.94228639 4
 
< 0.1%
35.30295991 4
 
< 0.1%
37.4072358 3
 
< 0.1%
35.6856503 3
 
< 0.1%
35.818353535 3
 
< 0.1%
37.87306566 3
 
< 0.1%
37.62579933 3
 
< 0.1%
Other values (9578) 9953
99.5%
(Missing) 12
 
0.1%
ValueCountFrequency (%)
33.2440957 1
< 0.1%
33.24984233 1
< 0.1%
33.25081525 1
< 0.1%
33.25240173 2
< 0.1%
33.25387751 1
< 0.1%
33.25442912 1
< 0.1%
33.2582778 1
< 0.1%
33.25928007 1
< 0.1%
33.25958563 1
< 0.1%
33.26290153 1
< 0.1%
ValueCountFrequency (%)
38.468251 1
< 0.1%
38.37488965 1
< 0.1%
38.3721916 1
< 0.1%
38.3657285 1
< 0.1%
38.2958388 1
< 0.1%
38.27263324 1
< 0.1%
38.2672583 1
< 0.1%
38.25748 1
< 0.1%
38.2566343 1
< 0.1%
38.25637469 2
< 0.1%

경도
Real number (ℝ)

Distinct9573
Distinct (%)95.8%
Missing12
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean127.48909
Minimum125.93645
Maximum129.55291
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T08:17:13.516030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum125.93645
5-th percentile126.60223
Q1126.91442
median127.13118
Q3128.0368
95-th percentile129.1275
Maximum129.55291
Range3.6164585
Interquartile range (IQR)1.1223819

Descriptive statistics

Standard deviation0.82088749
Coefficient of variation (CV)0.0064388845
Kurtosis-0.39103107
Mean127.48909
Median Absolute Deviation (MAD)0.3312006
Skewness0.93740752
Sum1273361
Variance0.67385627
MonotonicityNot monotonic
2024-05-04T08:17:14.030313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.1426289162 4
 
< 0.1%
126.785493 4
 
< 0.1%
126.6886522 4
 
< 0.1%
128.9464763 4
 
< 0.1%
126.9748498 4
 
< 0.1%
127.1475790047 3
 
< 0.1%
127.1113604927 3
 
< 0.1%
128.2606988 3
 
< 0.1%
127.8548814 3
 
< 0.1%
126.6162534 3
 
< 0.1%
Other values (9563) 9953
99.5%
(Missing) 12
 
0.1%
ValueCountFrequency (%)
125.936448 1
< 0.1%
126.1014286 1
< 0.1%
126.1112163 1
< 0.1%
126.1252917 1
< 0.1%
126.125394 1
< 0.1%
126.138045 1
< 0.1%
126.1536472 1
< 0.1%
126.1611619 1
< 0.1%
126.1667783 1
< 0.1%
126.1741976 1
< 0.1%
ValueCountFrequency (%)
129.5529065 1
< 0.1%
129.5242956 1
< 0.1%
129.511165 1
< 0.1%
129.483622 1
< 0.1%
129.479925 2
< 0.1%
129.4712744 1
< 0.1%
129.4707029 2
< 0.1%
129.4618796 1
< 0.1%
129.4602535 1
< 0.1%
129.4592732 1
< 0.1%

설치장소
Text

Distinct9782
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-04T08:17:14.895269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length65
Median length48
Mean length16.3295
Min length2

Characters and Unicode

Total characters163295
Distinct characters806
Distinct categories11 ?
Distinct scripts5 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9639 ?
Unique (%)96.4%

Sample

1st row의성남부초등학교(의성군청방면)
2nd row노원경찰서건너편(용동초)(노원경찰서교차로→용동초교교차로)
3rd row원일초등학교(오산복합물류센터→원일중학교)
4th row부개고등학교 앞(부개역코오롱하늘채→부평SK뷰해모로)
5th row강변철재(포항방면)
ValueCountFrequency (%)
앞 769
 
4.8%
부근 284
 
1.8%
주변 252
 
1.6%
정문 149
 
0.9%
사거리 146
 
0.9%
삼거리 124
 
0.8%
입구 91
 
0.6%
맞은편 64
 
0.4%
후문 59
 
0.4%
건너편 52
 
0.3%
Other values (11744) 13935
87.5%
2024-05-04T08:17:16.657762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 6994
 
4.3%
) 6991
 
4.3%
교 6940
 
4.2%
5943
 
3.6%
→ 5633
 
3.4%
초 4325
 
2.6%
리 3909
 
2.4%
앞 3175
 
1.9%
동 2995
 
1.8%
거 2910
 
1.8%
Other values (796) 113480
69.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 128684
78.8%
Open Punctuation 7026
 
4.3%
Close Punctuation 7023
 
4.3%
Space Separator 5943
 
3.6%
Decimal Number 5868
 
3.6%
Math Symbol 5789
 
3.5%
Uppercase Letter 1793
 
1.1%
Dash Punctuation 420
 
0.3%
Other Punctuation 403
 
0.2%
Lowercase Letter 341
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 6940
 
5.4%
초 4325
 
3.4%
리 3909
 
3.0%
앞 3175
 
2.5%
동 2995
 
2.3%
거 2910
 
2.3%
로 2777
 
2.2%
학 2745
 
2.1%
사 2233
 
1.7%
등 2146
 
1.7%
Other values (721) 94529
73.5%
Uppercase Letter
ValueCountFrequency (%)
C 529
29.5%
I 451
25.2%
K 144
 
8.0%
G 102
 
5.7%
R 89
 
5.0%
T 85
 
4.7%
S 78
 
4.4%
L 55
 
3.1%
A 42
 
2.3%
M 31
 
1.7%
Other values (17) 187
 
10.4%
Lowercase Letter
ValueCountFrequency (%)
m 181
53.1%
k 101
29.6%
e 28
 
8.2%
c 10
 
2.9%
s 4
 
1.2%
n 3
 
0.9%
g 3
 
0.9%
i 2
 
0.6%
o 2
 
0.6%
t 2
 
0.6%
Other values (4) 5
 
1.5%
Decimal Number
ValueCountFrequency (%)
1 1441
24.6%
2 1028
17.5%
4 679
11.6%
3 675
11.5%
0 583
9.9%
5 366
 
6.2%
6 298
 
5.1%
9 271
 
4.6%
8 265
 
4.5%
7 262
 
4.5%
Other Punctuation
ValueCountFrequency (%)
. 159
39.5%
/ 104
25.8%
, 95
23.6%
@ 29
 
7.2%
? 6
 
1.5%
& 4
 
1.0%
; 2
 
0.5%
· 2
 
0.5%
: 2
 
0.5%
Math Symbol
ValueCountFrequency (%)
→ 5633
97.3%
> 110
 
1.9%
↔ 22
 
0.4%
~ 16
 
0.3%
< 4
 
0.1%
+ 4
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 6994
99.5%
【 17
 
0.2%
[ 15
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 6991
99.5%
】 17
 
0.2%
] 15
 
0.2%
Space Separator
ValueCountFrequency (%)
5943
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 420
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 128683
78.8%
Common 32477
 
19.9%
Latin 2133
 
1.3%
Cyrillic 1
 
< 0.1%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 6940
 
5.4%
초 4325
 
3.4%
리 3909
 
3.0%
앞 3175
 
2.5%
동 2995
 
2.3%
거 2910
 
2.3%
로 2777
 
2.2%
학 2745
 
2.1%
사 2233
 
1.7%
등 2146
 
1.7%
Other values (720) 94528
73.5%
Latin
ValueCountFrequency (%)
C 529
24.8%
I 451
21.1%
m 181
 
8.5%
K 144
 
6.8%
G 102
 
4.8%
k 101
 
4.7%
R 89
 
4.2%
T 85
 
4.0%
S 78
 
3.7%
L 55
 
2.6%
Other values (30) 318
14.9%
Common
ValueCountFrequency (%)
( 6994
21.5%
) 6991
21.5%
5943
18.3%
→ 5633
17.3%
1 1441
 
4.4%
2 1028
 
3.2%
4 679
 
2.1%
3 675
 
2.1%
0 583
 
1.8%
- 420
 
1.3%
Other values (24) 2090
 
6.4%
Cyrillic
ValueCountFrequency (%)
Ю 1
100.0%
Han
ValueCountFrequency (%)
騁 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 128680
78.8%
ASCII 28919
 
17.7%
Arrows 5655
 
3.5%
None 36
 
< 0.1%
Compat Jamo 3
 
< 0.1%
Cyrillic 1
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 6994
24.2%
) 6991
24.2%
5943
20.6%
1 1441
 
5.0%
2 1028
 
3.6%
4 679
 
2.3%
3 675
 
2.3%
0 583
 
2.0%
C 529
 
1.8%
I 451
 
1.6%
Other values (59) 3605
12.5%
Hangul
ValueCountFrequency (%)
교 6940
 
5.4%
초 4325
 
3.4%
리 3909
 
3.0%
앞 3175
 
2.5%
동 2995
 
2.3%
거 2910
 
2.3%
로 2777
 
2.2%
학 2745
 
2.1%
사 2233
 
1.7%
등 2146
 
1.7%
Other values (719) 94525
73.5%
Arrows
ValueCountFrequency (%)
→ 5633
99.6%
↔ 22
 
0.4%
None
ValueCountFrequency (%)
【 17
47.2%
】 17
47.2%
· 2
 
5.6%
Compat Jamo
ValueCountFrequency (%)
ㅡ 3
100.0%
Cyrillic
ValueCountFrequency (%)
Ю 1
100.0%
CJK
ValueCountFrequency (%)
騁 1
100.0%

단속구분
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size156.2 KiB

제한속도
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.465
Minimum0
Maximum110
Zeros2302
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T08:17:17.190586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q130
median30
Q350
95-th percentile80
Maximum110
Range110
Interquartile range (IQR)20

Descriptive statistics

Standard deviation25.041695
Coefficient of variation (CV)0.70609601
Kurtosis-0.44733253
Mean35.465
Median Absolute Deviation (MAD)20
Skewness0.18561061
Sum354650
Variance627.08648
MonotonicityNot monotonic
2024-05-04T08:17:17.624711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
30 3550
35.5%
0 2302
23.0%
50 1535
15.3%
60 1274
 
12.7%
70 463
 
4.6%
80 400
 
4.0%
40 274
 
2.7%
100 130
 
1.3%
110 37
 
0.4%
90 26
 
0.3%
ValueCountFrequency (%)
0 2302
23.0%
20 9
 
0.1%
30 3550
35.5%
40 274
 
2.7%
50 1535
15.3%
60 1274
 
12.7%
70 463
 
4.6%
80 400
 
4.0%
90 26
 
0.3%
100 130
 
1.3%
ValueCountFrequency (%)
110 37
 
0.4%
100 130
 
1.3%
90 26
 
0.3%
80 400
 
4.0%
70 463
 
4.6%
60 1274
 
12.7%
50 1535
15.3%
40 274
 
2.7%
30 3550
35.5%
20 9
 
0.1%

단속구간위치구분
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9687 
2
 
165
1
 
148

Length

Max length4
Median length4
Mean length3.9061
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9687
96.9%
2 165
 
1.7%
1 148
 
1.5%

Length

2024-05-04T08:17:18.049660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T08:17:18.498322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9687
96.9%
2 165
 
1.7%
1 148
 
1.5%

과속단속구간길이
Real number (ℝ)

MISSING 

Distinct184
Distinct (%)57.7%
Missing9681
Missing (%)96.8%
Infinite0
Infinite (%)0.0%
Mean158.60501
Minimum0
Maximum18637
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T08:17:18.902482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.7744
Q14.675
median9.29
Q316.2
95-th percentile200
Maximum18637
Range18637
Interquartile range (IQR)11.525

Descriptive statistics

Standard deviation1139.6471
Coefficient of variation (CV)7.1854417
Kurtosis220.04667
Mean158.60501
Median Absolute Deviation (MAD)5.302
Skewness13.972257
Sum50594.999
Variance1298795.5
MonotonicityNot monotonic
2024-05-04T08:17:19.348802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45.0 28
 
0.3%
200.0 27
 
0.3%
3.3 10
 
0.1%
0.0 8
 
0.1%
10.04 4
 
< 0.1%
10.34 4
 
< 0.1%
1.801 3
 
< 0.1%
15.78 3
 
< 0.1%
7.854 3
 
< 0.1%
9.014 3
 
< 0.1%
Other values (174) 226
 
2.3%
(Missing) 9681
96.8%
ValueCountFrequency (%)
0.0 8
0.1%
0.894 2
 
< 0.1%
1.294 1
 
< 0.1%
1.296 1
 
< 0.1%
1.466 1
 
< 0.1%
1.531 1
 
< 0.1%
1.535 2
 
< 0.1%
1.801 3
 
< 0.1%
1.807 1
 
< 0.1%
1.824 1
 
< 0.1%
ValueCountFrequency (%)
18637.0 1
 
< 0.1%
4348.0 1
 
< 0.1%
3887.0 1
 
< 0.1%
3883.0 1
 
< 0.1%
3518.0 1
 
< 0.1%
1655.0 2
 
< 0.1%
1392.0 2
 
< 0.1%
1296.0 1
 
< 0.1%
200.0 27
0.3%
150.0 2
 
< 0.1%

보호구역구분
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
99
3773 
2
3635 
<NA>
2521 
1
 
71

Length

Max length4
Median length2
Mean length2.1336
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row99

Common Values

ValueCountFrequency (%)
99 3773
37.7%
2 3635
36.4%
<NA> 2521
25.2%
1 71
 
0.7%

Length

2024-05-04T08:17:19.874146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T08:17:20.221337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
99 3773
37.7%
2 3635
36.4%
na 2521
25.2%
1 71
 
0.7%

설치연도
Real number (ℝ)

MISSING 

Distinct23
Distinct (%)0.3%
Missing1045
Missing (%)10.4%
Infinite0
Infinite (%)0.0%
Mean2019.1859
Minimum2002
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T08:17:20.571228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2002
5-th percentile2011
Q12018
median2020
Q32021
95-th percentile2023
Maximum2024
Range22
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.4877082
Coefficient of variation (CV)0.0017272843
Kurtosis3.2495003
Mean2019.1859
Median Absolute Deviation (MAD)2
Skewness-1.7402251
Sum18081810
Variance12.164108
MonotonicityNot monotonic
2024-05-04T08:17:20.915468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2021 2031
20.3%
2020 1458
14.6%
2022 1425
14.2%
2018 692
 
6.9%
2019 687
 
6.9%
2023 680
 
6.8%
2017 506
 
5.1%
2016 386
 
3.9%
2015 201
 
2.0%
2014 189
 
1.9%
Other values (13) 700
 
7.0%
(Missing) 1045
10.4%
ValueCountFrequency (%)
2002 2
 
< 0.1%
2003 1
 
< 0.1%
2004 6
 
0.1%
2005 36
 
0.4%
2006 44
0.4%
2007 52
0.5%
2008 72
0.7%
2009 59
0.6%
2010 92
0.9%
2011 90
0.9%
ValueCountFrequency (%)
2024 8
 
0.1%
2023 680
 
6.8%
2022 1425
14.2%
2021 2031
20.3%
2020 1458
14.6%
2019 687
 
6.9%
2018 692
 
6.9%
2017 506
 
5.1%
2016 386
 
3.9%
2015 201
 
2.0%

관리기관명
Text

Distinct154
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-04T08:17:21.473164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length18
Mean length7.4116
Min length3

Characters and Unicode

Total characters74116
Distinct characters118
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

Unique6 ?
Unique (%)0.1%

Sample

1st row경상북도경찰청
2nd row서울경찰청
3rd row경기남부경찰청
4th row인천경찰청
5th row경상북도경찰청
ValueCountFrequency (%)
경기남부경찰청 1056
 
8.0%
경기도 883
 
6.7%
충청남도경찰청 620
 
4.7%
서울경찰청 569
 
4.3%
전라북도경찰청 561
 
4.2%
경상북도경찰청 527
 
4.0%
전라남도경찰청 510
 
3.9%
경상남도경찰청 500
 
3.8%
서울특별시 431
 
3.3%
경기북부경찰청 398
 
3.0%
Other values (166) 7160
54.2%
2024-05-04T08:17:22.622979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
경 10980
14.8%
청 10277
13.9%
찰 7102
 
9.6%
도 5411
 
7.3%
남 3697
 
5.0%
3215
 
4.3%
시 2660
 
3.6%
기 2480
 
3.3%
북 2312
 
3.1%
전 2113
 
2.9%
Other values (108) 23869
32.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 70901
95.7%
Space Separator 3215
 
4.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 10980
15.5%
청 10277
14.5%
찰 7102
 
10.0%
도 5411
 
7.6%
남 3697
 
5.2%
시 2660
 
3.8%
기 2480
 
3.5%
북 2312
 
3.3%
전 2113
 
3.0%
부 2105
 
3.0%
Other values (107) 21764
30.7%
Space Separator
ValueCountFrequency (%)
3215
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 70901
95.7%
Common 3215
 
4.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 10980
15.5%
청 10277
14.5%
찰 7102
 
10.0%
도 5411
 
7.6%
남 3697
 
5.2%
시 2660
 
3.8%
기 2480
 
3.5%
북 2312
 
3.3%
전 2113
 
3.0%
부 2105
 
3.0%
Other values (107) 21764
30.7%
Common
ValueCountFrequency (%)
3215
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 70901
95.7%
ASCII 3215
 
4.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
경 10980
15.5%
청 10277
14.5%
찰 7102
 
10.0%
도 5411
 
7.6%
남 3697
 
5.2%
시 2660
 
3.8%
기 2480
 
3.5%
북 2312
 
3.3%
전 2113
 
3.0%
부 2105
 
3.0%
Other values (107) 21764
30.7%
ASCII
ValueCountFrequency (%)
3215
100.0%

관리기관전화번호
Text

Distinct197
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-04T08:17:23.200146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.9052
Min length7

Characters and Unicode

Total characters119052
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)0.5%

Sample

1st row054-824-2152
2nd row02-700-5047
3rd row031-888-3952
4th row032-455-2152
5th row054-824-2152
ValueCountFrequency (%)
031-888-3952 1127
 
11.3%
041-336-2152 673
 
6.7%
02-700-5047 569
 
5.7%
063-280-8153 564
 
5.6%
054-824-2152 526
 
5.3%
061-289-3296 507
 
5.1%
055-233-2257 500
 
5.0%
031-961-2651 379
 
3.8%
032-455-2152 324
 
3.2%
051-899-2153 314
 
3.1%
Other values (187) 4517
45.2%
2024-05-04T08:17:24.489527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 19875
16.7%
0 16222
13.6%
2 15948
13.4%
3 12690
10.7%
5 12440
10.4%
8 9450
7.9%
1 9317
7.8%
4 7000
 
5.9%
6 6795
 
5.7%
9 5102
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 99177
83.3%
Dash Punctuation 19875
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16222
16.4%
2 15948
16.1%
3 12690
12.8%
5 12440
12.5%
8 9450
9.5%
1 9317
9.4%
4 7000
7.1%
6 6795
6.9%
9 5102
 
5.1%
7 4213
 
4.2%
Dash Punctuation
ValueCountFrequency (%)
- 19875
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 119052
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 19875
16.7%
0 16222
13.6%
2 15948
13.4%
3 12690
10.7%
5 12440
10.4%
8 9450
7.9%
1 9317
7.8%
4 7000
 
5.9%
6 6795
 
5.7%
9 5102
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119052
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 19875
16.7%
0 16222
13.6%
2 15948
13.4%
3 12690
10.7%
5 12440
10.4%
8 9450
7.9%
1 9317
7.8%
4 7000
 
5.9%
6 6795
 
5.7%
9 5102
 
4.3%

데이터기준일자
Date

Distinct112
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-09-01 00:00:00
Maximum2024-04-04 00:00:00
2024-05-04T08:17:25.415625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:17:26.229089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

제공기관코드
Real number (ℝ)

Distinct142
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2295153.3
Minimum1320000
Maximum6300000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T08:17:26.808995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1320000
5-th percentile1320000
Q11320000
median1320000
Q33680000
95-th percentile5350000
Maximum6300000
Range4980000
Interquartile range (IQR)2360000

Descriptive statistics

Standard deviation1471713.2
Coefficient of variation (CV)0.64122654
Kurtosis-0.32532559
Mean2295153.3
Median Absolute Deviation (MAD)0
Skewness1.0814782
Sum2.2951533 × 1010
Variance2.1659397 × 1012
MonotonicityNot monotonic
2024-05-04T08:17:27.443670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1320000 6704
67.0%
3910000 162
 
1.6%
3940000 160
 
1.6%
6300000 125
 
1.2%
3220000 109
 
1.1%
4640000 107
 
1.1%
4641000 103
 
1.0%
3780000 100
 
1.0%
4490000 82
 
0.8%
4070000 70
 
0.7%
Other values (132) 2278
 
22.8%
ValueCountFrequency (%)
1320000 6704
67.0%
3000000 31
 
0.3%
3040000 19
 
0.2%
3050000 20
 
0.2%
3060000 31
 
0.3%
3070000 27
 
0.3%
3080000 8
 
0.1%
3110000 29
 
0.3%
3120000 20
 
0.2%
3130000 34
 
0.3%
ValueCountFrequency (%)
6300000 125
1.2%
6280000 3
 
< 0.1%
6260000 11
 
0.1%
5700000 53
0.5%
5680000 5
 
0.1%
5670000 46
 
0.5%
5600000 19
 
0.2%
5590000 27
 
0.3%
5540000 51
0.5%
5530000 32
 
0.3%

제공기관명
Text

Distinct142
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-04T08:17:28.450348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length3
Mean length4.6475
Min length3

Characters and Unicode

Total characters46475
Distinct characters102
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

Unique7 ?
Unique (%)0.1%

Sample

1st row경찰청
2nd row경찰청
3rd row경찰청
4th row경찰청
5th row경찰청
ValueCountFrequency (%)
경찰청 6704
51.0%
경기도 1151
 
8.7%
서울특별시 431
 
3.3%
경상남도 224
 
1.7%
전주시 210
 
1.6%
충청남도 198
 
1.5%
대전광역시 182
 
1.4%
평택시 162
 
1.2%
고양시 160
 
1.2%
전라북도 156
 
1.2%
Other values (128) 3579
27.2%
2024-05-04T08:17:30.271273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
경 8245
17.7%
청 6942
14.9%
찰 6704
14.4%
3157
 
6.8%
시 3040
 
6.5%
도 2345
 
5.0%
기 1151
 
2.5%
구 929
 
2.0%
남 911
 
2.0%
전 813
 
1.7%
Other values (92) 12238
26.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 43318
93.2%
Space Separator 3157
 
6.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 8245
19.0%
청 6942
16.0%
찰 6704
15.5%
시 3040
 
7.0%
도 2345
 
5.4%
기 1151
 
2.7%
구 929
 
2.1%
남 911
 
2.1%
전 813
 
1.9%
광 715
 
1.7%
Other values (91) 11523
26.6%
Space Separator
ValueCountFrequency (%)
3157
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 43318
93.2%
Common 3157
 
6.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 8245
19.0%
청 6942
16.0%
찰 6704
15.5%
시 3040
 
7.0%
도 2345
 
5.4%
기 1151
 
2.7%
구 929
 
2.1%
남 911
 
2.1%
전 813
 
1.9%
광 715
 
1.7%
Other values (91) 11523
26.6%
Common
ValueCountFrequency (%)
3157
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 43318
93.2%
ASCII 3157
 
6.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
경 8245
19.0%
청 6942
16.0%
찰 6704
15.5%
시 3040
 
7.0%
도 2345
 
5.4%
기 1151
 
2.7%
구 929
 
2.1%
남 911
 
2.1%
전 813
 
1.9%
광 715
 
1.7%
Other values (91) 11523
26.6%
ASCII
ValueCountFrequency (%)
3157
100.0%

Sample

무인교통단속카메라관리번호시도명시군구명도로종류도로노선번호도로노선명도로노선방향소재지도로명주소소재지지번주소위도경도설치장소단속구분제한속도단속구간위치구분과속단속구간길이보호구역구분설치연도관리기관명관리기관전화번호데이터기준일자제공기관코드제공기관명
34802H2510경상북도의성군기타<NA>문소1길1경상북도 의성군 의성읍 구봉길 172경상북도 의성군 의성읍 도동리 670-136.341051128.700029의성남부초등학교(의성군청방면)230<NA><NA>22021경상북도경찰청054-824-21522024-04-041320000경찰청
26459G8493서울특별시노원구기타<NA>노원로2<NA>서울특별시 노원구 중계동 511-237.644664127.069472노원경찰서건너편(용동초)(노원경찰서교차로→용동초교교차로)230<NA><NA>22021서울경찰청02-700-50472024-04-041320000경찰청
36383H8104경기도오산시기타<NA>오산로132번길3경기도 오산시 오산로132번길 28-5<NA>37.138564127.072379원일초등학교(오산복합물류센터→원일중학교)230<NA><NA>22023경기남부경찰청031-888-39522024-04-041320000경찰청
7806J1043인천광역시부평구기타<NA>수변로57번길3인천광역시 부평구 수변로57번길<NA>37.491868126.738408부개고등학교 앞(부개역코오롱하늘채→부평SK뷰해모로)230<NA><NA>22023인천경찰청032-455-21522024-04-041320000경찰청
12195J0132경상북도경주시기타<NA>강변로1경상북도 경주시 용황로 6경상북도 경주시 황성동 800-1335.881959129.218803강변철재(포항방면)260<NA><NA>992022경상북도경찰청054-824-21522024-04-041320000경찰청
3039165경상남도마산회원구시도<NA>삼계로2경상남도 창원시 마산회원구 내서읍 삼계10길경상남도 창원시 마산회원구 내서읍 삼계리 5135.234342128.50273창원시 마산회원구 내서읍 삼계리 51 마산삼계주공상가 앞460<NA><NA><NA>2018마산회원구 경제교통과055-230-44642021-10-255670000경상남도 창원시
97인천광역시동구구도<NA>송화로2<NA>인천광역시 동구 화평동 1-4837.48033126.632892송화로 송현초교 앞130<NA><NA>22018인천지방경찰청032-455-23522023-10-203500000인천광역시 동구
1848654부산광역시연제구기타<NA>고분로3부산광역시 연제구 고분로 129<NA>35.185803129.096524연산동 이마트트레이더스 연산정 후문(경남아파트 맞은편)40<NA><NA><NA>2021부산광역시 연제구청 교통행정과051-665-45642023-11-193370000부산광역시 연제구
150P-7701경기도고양시시도<NA>호수로3<NA>경기도 고양시 일산서구 대화동 252737.672323126.749019대화동 2527 (장촌초등학교 앞)40<NA><NA><NA>2011경기도 고양시031-8075-25782023-11-243940000경기도 고양시
20247G3817경기도고양시기타<NA>행신로1<NA>경기도 고양시 덕양구 행신동 97637.618318126.837885무원고교앞교차로(가라뫼사거리→행주대교)250<NA><NA>992018경기북부경찰청031-961-26512024-04-041320000경찰청
무인교통단속카메라관리번호시도명시군구명도로종류도로노선번호도로노선명도로노선방향소재지도로명주소소재지지번주소위도경도설치장소단속구분제한속도단속구간위치구분과속단속구간길이보호구역구분설치연도관리기관명관리기관전화번호데이터기준일자제공기관코드제공기관명
15592935서울특별시중랑구구도<NA>망우로3서울특별시 중랑구 망우로220서울특별시 중랑구 상봉동 12937.593945127.077148동부시장교차로540<NA><NA>992020서울특별시 중랑구청02-2094-26352023-07-133060000서울특별시 중랑구
9526천안서북-F0163충청남도천안시 서북구시도<NA>두정로3충청남도 천안시 서북구 두정로 289충청남도 천안시 서북구 두정동 85-536.832766127.148856두정역 1번 출구40<NA><NA><NA>2023충청남도 천안시 서북구청041-521-64202023-07-014490000충청남도 천안시
22743F9441전북특별자치도군산시기타<NA>구암로1<NA>전북특별자치도 군산시 경암동 599-235.981421126.723902경암사거리(연안사거리→미원사거리)250<NA><NA>992017전라북도경찰청063-280-81532024-04-041320000경찰청
24032G8850경상남도고성군기타<NA>엑스포로2경상남도 고성군 회화면 엑스포로 58경상남도 고성군 회화면 배둔리 205-435.064611128.37919천년빌회전교차로후방(당항포관광단지→배둔삼거리)140<NA><NA>992021경상남도경찰청055-233-22572024-04-041320000경찰청
19015G2338경기도수원시기타<NA>정자천로3<NA>경기도 수원시 장안구 정자동 89737.294858126.994873대유평사거리(일월저수지->수원중부경찰서)250<NA><NA>992018경기남부경찰청031-888-39522024-04-041320000경찰청
30262H4264전북특별자치도군산시기타<NA>동군산로2<NA>전북특별자치도 군산시 성산면 창오리447-335.983551126.815766창오초등학교앞(성산산업단지→창오교차로)130<NA><NA>22021전라북도경찰청063-280-81532024-04-041320000경찰청
27302G8105충청남도공주시일반국도23차령로3충청남도 공주시 우성면 차령로충청남도 공주시 우성면 목천리 8-236.511788127.122991목천교차로(정안→의당)270<NA><NA>992021충청남도경찰청041-336-21522024-04-041320000경찰청
28785G9842서울특별시동작구기타<NA>상도로2<NA>서울특별시 동작구 상도동 533-137.498552126.949178샛별유치원건너편(상도열린복지센터→상도센트럴파크아파트)130<NA><NA>22021서울경찰청02-700-50472024-04-041320000경찰청
24112G8765전라남도목포시기타<NA>삼학로373번길2전라남도 목포시 이로로9번길 3전라남도 목포시 용해동 240-234.805119126.406822이로초등학교후문(한국전력→의료원)130<NA><NA>22020전라남도경찰청061-289-32962024-04-041320000경찰청
15245G6301대전광역시동구시도<NA>현암로2<NA>대전광역시 동구 삼성동 366-936.342631127.420723현암초교 앞0230<NA><NA>22020대전광역시042-1202022-12-156300000대전광역시