Overview

Dataset statistics

Number of variables30
Number of observations10000
Missing cells29618
Missing cells (%)9.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 MiB
Average record size in memory254.0 B

Variable types

Text13
Categorical11
Numeric5
Unsupported1

Dataset

Description제어기명,온라인일,등록일시-설치일,제조회사,상태 (공통),교차로번호,제어기관리번호,시스템코드,형태코드,방식코드,전원코드,종류코드,고가 (공통),비고,교체일,구경찰서코드,구코드 (공통),동코드 (공통),지번,신경찰서코드 (공통),작업구분 (공통),표출구분 (공통),도로구분 (공통),관할사업소 (공통),신규정규화ID,공간데이터,이력ID,공사관리번호,제어기관리번호,공사형태 (공통)
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15538/S/1/datasetView.do

Alerts

상태 (공통) is highly imbalanced (90.6%)Imbalance
종류코드 is highly imbalanced (54.4%)Imbalance
고가 (공통) is highly imbalanced (98.1%)Imbalance
도로구분 (공통) is highly imbalanced (55.8%)Imbalance
온라인일 has 274 (2.7%) missing valuesMissing
등록일시-설치일 has 248 (2.5%) missing valuesMissing
제조회사 has 487 (4.9%) missing valuesMissing
교차로번호 has 340 (3.4%) missing valuesMissing
형태코드 has 313 (3.1%) missing valuesMissing
방식코드 has 276 (2.8%) missing valuesMissing
비고 has 9102 (91.0%) missing valuesMissing
교체일 has 250 (2.5%) missing valuesMissing
지번 has 177 (1.8%) missing valuesMissing
신규정규화ID has 7748 (77.5%) missing valuesMissing
공간데이터 has 10000 (100.0%) missing valuesMissing
공사관리번호 has 355 (3.5%) missing valuesMissing
공간데이터 is an unsupported type, check if it needs cleaning or further analysisUnsupported
교차로번호 has 230 (2.3%) zerosZeros
동코드 (공통) has 597 (6.0%) zerosZeros

Reproduction

Analysis started2024-05-04 02:54:25.564626
Analysis finished2024-05-04 02:54:30.357838
Duration4.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct6299
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-04T02:54:30.880007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length20
Mean length6.8498
Min length1

Characters and Unicode

Total characters68498
Distinct characters615
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

Unique4066 ?
Unique (%)40.7%

Sample

1st row
2nd row백련산힐스테이트204동
3rd row우이2교
4th row강북비타에듀학원
5th row공릉동부아파트
ValueCountFrequency (%)
표준제어기 9
 
0.1%
을지병원 7
 
0.1%
중흥초교 7
 
0.1%
서교동(보 7
 
0.1%
연무관앞 6
 
0.1%
사러가시장 6
 
0.1%
비우당교 6
 
0.1%
정훈단지(보 6
 
0.1%
상봉지하차도 6
 
0.1%
목동812동(보 6
 
0.1%
Other values (6301) 9730
99.3%
2024-05-04T02:54:32.067803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 3368
 
4.9%
) 3368
 
4.9%
2299
 
3.4%
1835
 
2.7%
1818
 
2.7%
1222
 
1.8%
1098
 
1.6%
1004
 
1.5%
879
 
1.3%
857
 
1.3%
Other values (605) 50750
74.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 56949
83.1%
Open Punctuation 3368
 
4.9%
Close Punctuation 3368
 
4.9%
Decimal Number 2682
 
3.9%
Uppercase Letter 1697
 
2.5%
Space Separator 274
 
0.4%
Other Punctuation 111
 
0.2%
Dash Punctuation 27
 
< 0.1%
Lowercase Letter 21
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2299
 
4.0%
1835
 
3.2%
1818
 
3.2%
1222
 
2.1%
1098
 
1.9%
1004
 
1.8%
879
 
1.5%
857
 
1.5%
854
 
1.5%
842
 
1.5%
Other values (555) 44241
77.7%
Uppercase Letter
ValueCountFrequency (%)
P 402
23.7%
T 363
21.4%
A 319
18.8%
B 106
 
6.2%
C 102
 
6.0%
K 63
 
3.7%
G 63
 
3.7%
S 59
 
3.5%
I 51
 
3.0%
L 40
 
2.4%
Other values (14) 129
 
7.6%
Decimal Number
ValueCountFrequency (%)
1 719
26.8%
2 432
16.1%
3 366
13.6%
4 269
 
10.0%
0 255
 
9.5%
5 179
 
6.7%
6 133
 
5.0%
9 122
 
4.5%
7 111
 
4.1%
8 96
 
3.6%
Lowercase Letter
ValueCountFrequency (%)
e 11
52.4%
t 4
 
19.0%
s 3
 
14.3%
c 1
 
4.8%
b 1
 
4.8%
d 1
 
4.8%
Other Punctuation
ValueCountFrequency (%)
, 47
42.3%
. 41
36.9%
# 14
 
12.6%
& 7
 
6.3%
? 2
 
1.8%
Open Punctuation
ValueCountFrequency (%)
( 3368
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3368
100.0%
Space Separator
ValueCountFrequency (%)
274
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 27
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 56949
83.1%
Common 9831
 
14.4%
Latin 1718
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2299
 
4.0%
1835
 
3.2%
1818
 
3.2%
1222
 
2.1%
1098
 
1.9%
1004
 
1.8%
879
 
1.5%
857
 
1.5%
854
 
1.5%
842
 
1.5%
Other values (555) 44241
77.7%
Latin
ValueCountFrequency (%)
P 402
23.4%
T 363
21.1%
A 319
18.6%
B 106
 
6.2%
C 102
 
5.9%
K 63
 
3.7%
G 63
 
3.7%
S 59
 
3.4%
I 51
 
3.0%
L 40
 
2.3%
Other values (20) 150
 
8.7%
Common
ValueCountFrequency (%)
( 3368
34.3%
) 3368
34.3%
1 719
 
7.3%
2 432
 
4.4%
3 366
 
3.7%
274
 
2.8%
4 269
 
2.7%
0 255
 
2.6%
5 179
 
1.8%
6 133
 
1.4%
Other values (10) 468
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 56942
83.1%
ASCII 11549
 
16.9%
Compat Jamo 7
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 3368
29.2%
) 3368
29.2%
1 719
 
6.2%
2 432
 
3.7%
P 402
 
3.5%
3 366
 
3.2%
T 363
 
3.1%
A 319
 
2.8%
274
 
2.4%
4 269
 
2.3%
Other values (40) 1669
14.5%
Hangul
ValueCountFrequency (%)
2299
 
4.0%
1835
 
3.2%
1818
 
3.2%
1222
 
2.1%
1098
 
1.9%
1004
 
1.8%
879
 
1.5%
857
 
1.5%
854
 
1.5%
842
 
1.5%
Other values (550) 44234
77.7%
Compat Jamo
ValueCountFrequency (%)
3
42.9%
1
 
14.3%
1
 
14.3%
1
 
14.3%
1
 
14.3%

온라인일
Text

MISSING 

Distinct1302
Distinct (%)13.4%
Missing274
Missing (%)2.7%
Memory size156.2 KiB
2024-05-04T02:54:32.842753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.8574954
Min length1

Characters and Unicode

Total characters76422
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

Unique574 ?
Unique (%)5.9%

Sample

1st row20000101
2nd row20111205
3rd row20070119
4th row20110101
5th row20210428
ValueCountFrequency (%)
20000101 1411
 
14.8%
20020103 477
 
5.0%
20020322 376
 
3.9%
20020326 309
 
3.2%
20020325 237
 
2.5%
20020430 189
 
2.0%
20020106 179
 
1.9%
20000902 162
 
1.7%
20010102 159
 
1.7%
20020105 157
 
1.6%
Other values (1291) 5872
61.6%
2024-05-04T02:54:34.014806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 33224
43.5%
2 19188
25.1%
1 11769
 
15.4%
3 3918
 
5.1%
9 1728
 
2.3%
5 1652
 
2.2%
6 1583
 
2.1%
8 1229
 
1.6%
7 1175
 
1.5%
4 758
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 76224
99.7%
Space Separator 198
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 33224
43.6%
2 19188
25.2%
1 11769
 
15.4%
3 3918
 
5.1%
9 1728
 
2.3%
5 1652
 
2.2%
6 1583
 
2.1%
8 1229
 
1.6%
7 1175
 
1.5%
4 758
 
1.0%
Space Separator
ValueCountFrequency (%)
198
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 76422
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 33224
43.5%
2 19188
25.1%
1 11769
 
15.4%
3 3918
 
5.1%
9 1728
 
2.3%
5 1652
 
2.2%
6 1583
 
2.1%
8 1229
 
1.6%
7 1175
 
1.5%
4 758
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 76422
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33224
43.5%
2 19188
25.1%
1 11769
 
15.4%
3 3918
 
5.1%
9 1728
 
2.3%
5 1652
 
2.2%
6 1583
 
2.1%
8 1229
 
1.6%
7 1175
 
1.5%
4 758
 
1.0%
Distinct1869
Distinct (%)19.2%
Missing248
Missing (%)2.5%
Memory size156.2 KiB
2024-05-04T02:54:34.900329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.8578753
Min length1

Characters and Unicode

Total characters76630
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

Unique712 ?
Unique (%)7.3%

Sample

1st row00010101
2nd row20111205
3rd row20030401
4th row20110101
5th row20210428
ValueCountFrequency (%)
20041120 321
 
3.4%
20020201 222
 
2.3%
20010201 205
 
2.1%
20010101 157
 
1.6%
00010101 136
 
1.4%
20011001 115
 
1.2%
20020801 101
 
1.1%
20001201 99
 
1.0%
20080101 96
 
1.0%
20020701 75
 
0.8%
Other values (1858) 8027
84.0%
2024-05-04T02:54:36.316456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 29456
38.4%
1 15053
19.6%
2 14784
19.3%
9 4342
 
5.7%
3 2581
 
3.4%
4 2394
 
3.1%
5 2390
 
3.1%
8 2139
 
2.8%
6 1736
 
2.3%
7 1557
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 76432
99.7%
Space Separator 198
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29456
38.5%
1 15053
19.7%
2 14784
19.3%
9 4342
 
5.7%
3 2581
 
3.4%
4 2394
 
3.1%
5 2390
 
3.1%
8 2139
 
2.8%
6 1736
 
2.3%
7 1557
 
2.0%
Space Separator
ValueCountFrequency (%)
198
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 76630
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29456
38.4%
1 15053
19.6%
2 14784
19.3%
9 4342
 
5.7%
3 2581
 
3.4%
4 2394
 
3.1%
5 2390
 
3.1%
8 2139
 
2.8%
6 1736
 
2.3%
7 1557
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 76630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29456
38.4%
1 15053
19.6%
2 14784
19.3%
9 4342
 
5.7%
3 2581
 
3.4%
4 2394
 
3.1%
5 2390
 
3.1%
8 2139
 
2.8%
6 1736
 
2.3%
7 1557
 
2.0%

제조회사
Text

MISSING 

Distinct123
Distinct (%)1.3%
Missing487
Missing (%)4.9%
Memory size156.2 KiB
2024-05-04T02:54:37.159205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length5.2150741
Min length1

Characters and Unicode

Total characters49611
Distinct characters146
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)0.3%

Sample

1st row
2nd row코스텍
3rd row에틴(주)
4th row(주)서돌전자통신
5th row위운용사복지회
ValueCountFrequency (%)
진우산전 1449
15.6%
코스텍 798
 
8.6%
주)서돌전자통신 794
 
8.5%
주)세인시스템 569
 
6.1%
대한신호 507
 
5.5%
미상 371
 
4.0%
대흥기업 357
 
3.8%
세인(주 307
 
3.3%
주)신호 298
 
3.2%
주)t&c 258
 
2.8%
Other values (112) 3592
38.6%
2024-05-04T02:54:38.335962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3730
 
7.5%
( 3561
 
7.2%
) 3561
 
7.2%
3556
 
7.2%
2314
 
4.7%
1820
 
3.7%
1682
 
3.4%
1609
 
3.2%
1605
 
3.2%
1590
 
3.2%
Other values (136) 24583
49.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 39835
80.3%
Open Punctuation 3561
 
7.2%
Close Punctuation 3561
 
7.2%
Uppercase Letter 2085
 
4.2%
Other Punctuation 276
 
0.6%
Space Separator 243
 
0.5%
Decimal Number 38
 
0.1%
Dash Punctuation 10
 
< 0.1%
Modifier Symbol 1
 
< 0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3730
 
9.4%
3556
 
8.9%
2314
 
5.8%
1820
 
4.6%
1682
 
4.2%
1609
 
4.0%
1605
 
4.0%
1590
 
4.0%
1473
 
3.7%
1467
 
3.7%
Other values (109) 18989
47.7%
Uppercase Letter
ValueCountFrequency (%)
T 667
32.0%
C 497
23.8%
G 357
17.1%
L 356
17.1%
S 99
 
4.7%
I 17
 
0.8%
O 17
 
0.8%
V 17
 
0.8%
Z 16
 
0.8%
R 16
 
0.8%
Other values (6) 26
 
1.2%
Decimal Number
ValueCountFrequency (%)
0 29
76.3%
9 7
 
18.4%
3 2
 
5.3%
Other Punctuation
ValueCountFrequency (%)
& 258
93.5%
. 18
 
6.5%
Open Punctuation
ValueCountFrequency (%)
( 3561
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3561
100.0%
Space Separator
ValueCountFrequency (%)
243
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 39836
80.3%
Common 7690
 
15.5%
Latin 2085
 
4.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3730
 
9.4%
3556
 
8.9%
2314
 
5.8%
1820
 
4.6%
1682
 
4.2%
1609
 
4.0%
1605
 
4.0%
1590
 
4.0%
1473
 
3.7%
1467
 
3.7%
Other values (110) 18990
47.7%
Latin
ValueCountFrequency (%)
T 667
32.0%
C 497
23.8%
G 357
17.1%
L 356
17.1%
S 99
 
4.7%
I 17
 
0.8%
O 17
 
0.8%
V 17
 
0.8%
Z 16
 
0.8%
R 16
 
0.8%
Other values (6) 26
 
1.2%
Common
ValueCountFrequency (%)
( 3561
46.3%
) 3561
46.3%
& 258
 
3.4%
243
 
3.2%
0 29
 
0.4%
. 18
 
0.2%
- 10
 
0.1%
9 7
 
0.1%
3 2
 
< 0.1%
` 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 39833
80.3%
ASCII 9775
 
19.7%
Compat Jamo 2
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3730
 
9.4%
3556
 
8.9%
2314
 
5.8%
1820
 
4.6%
1682
 
4.2%
1609
 
4.0%
1605
 
4.0%
1590
 
4.0%
1473
 
3.7%
1467
 
3.7%
Other values (107) 18987
47.7%
ASCII
ValueCountFrequency (%)
( 3561
36.4%
) 3561
36.4%
T 667
 
6.8%
C 497
 
5.1%
G 357
 
3.7%
L 356
 
3.6%
& 258
 
2.6%
243
 
2.5%
S 99
 
1.0%
0 29
 
0.3%
Other values (16) 147
 
1.5%
Compat Jamo
ValueCountFrequency (%)
1
50.0%
1
50.0%
None
ValueCountFrequency (%)
1
100.0%

상태 (공통)
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
001
9755 
 
198
<NA>
 
35
004
 
12

Length

Max length4
Median length3
Mean length2.9639
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row001
2nd row001
3rd row001
4th row001
5th row001

Common Values

ValueCountFrequency (%)
001 9755
97.5%
198
 
2.0%
<NA> 35
 
0.4%
004 12
 
0.1%

Length

2024-05-04T02:54:38.753988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T02:54:39.132524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
001 9755
99.5%
na 35
 
0.4%
004 12
 
0.1%

교차로번호
Real number (ℝ)

MISSING  ZEROS 

Distinct4017
Distinct (%)41.6%
Missing340
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean2913.4565
Minimum0
Maximum8905
Zeros230
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T02:54:39.583379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile126
Q11039.75
median2502.5
Q34311.25
95-th percentile7115.4
Maximum8905
Range8905
Interquartile range (IQR)3271.5

Descriptive statistics

Standard deviation2195.4975
Coefficient of variation (CV)0.75357139
Kurtosis-0.28162561
Mean2913.4565
Median Absolute Deviation (MAD)1581
Skewness0.69606799
Sum28143990
Variance4820209.2
MonotonicityNot monotonic
2024-05-04T02:54:40.004748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 230
 
2.3%
8393 110
 
1.1%
1031 9
 
0.1%
2956 8
 
0.1%
2880 8
 
0.1%
3804 8
 
0.1%
2098 8
 
0.1%
3163 8
 
0.1%
1658 8
 
0.1%
2608 7
 
0.1%
Other values (4007) 9256
92.6%
(Missing) 340
 
3.4%
ValueCountFrequency (%)
0 230
2.3%
4 4
 
< 0.1%
5 3
 
< 0.1%
6 2
 
< 0.1%
32 2
 
< 0.1%
33 5
 
0.1%
35 3
 
< 0.1%
36 2
 
< 0.1%
37 5
 
0.1%
38 5
 
0.1%
ValueCountFrequency (%)
8905 1
< 0.1%
8890 1
< 0.1%
8883 1
< 0.1%
8882 1
< 0.1%
8877 1
< 0.1%
8875 1
< 0.1%
8873 1
< 0.1%
8867 1
< 0.1%
8858 1
< 0.1%
8857 1
< 0.1%
Distinct4709
Distinct (%)47.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-04T02:54:40.652498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters130000
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

Unique2012 ?
Unique (%)20.1%

Sample

1st row01-0000003753
2nd row01-0000004371
3rd row01-0000002980
4th row01-0000004343
5th row01-0000007225
ValueCountFrequency (%)
01-0000001321 9
 
0.1%
01-0000002536 9
 
0.1%
01-0000001505 8
 
0.1%
01-0000002515 8
 
0.1%
01-0000001658 7
 
0.1%
01-0000001005 7
 
0.1%
01-0000000828 7
 
0.1%
01-0000000656 7
 
0.1%
01-0000003586 7
 
0.1%
01-0000000792 7
 
0.1%
Other values (4699) 9924
99.2%
2024-05-04T02:54:41.626707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 75417
58.0%
1 15321
 
11.8%
- 10000
 
7.7%
2 5076
 
3.9%
3 4759
 
3.7%
4 3986
 
3.1%
6 3617
 
2.8%
7 3332
 
2.6%
5 3046
 
2.3%
9 2804
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 120000
92.3%
Dash Punctuation 10000
 
7.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 75417
62.8%
1 15321
 
12.8%
2 5076
 
4.2%
3 4759
 
4.0%
4 3986
 
3.3%
6 3617
 
3.0%
7 3332
 
2.8%
5 3046
 
2.5%
9 2804
 
2.3%
8 2642
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 130000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 75417
58.0%
1 15321
 
11.8%
- 10000
 
7.7%
2 5076
 
3.9%
3 4759
 
3.7%
4 3986
 
3.1%
6 3617
 
2.8%
7 3332
 
2.6%
5 3046
 
2.3%
9 2804
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 75417
58.0%
1 15321
 
11.8%
- 10000
 
7.7%
2 5076
 
3.9%
3 4759
 
3.7%
4 3986
 
3.1%
6 3617
 
2.8%
7 3332
 
2.6%
5 3046
 
2.3%
9 2804
 
2.2%

시스템코드
Real number (ℝ)

Distinct23
Distinct (%)0.2%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean7.4245698
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T02:54:42.037890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q18
median8
Q38
95-th percentile12
Maximum27
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.7519083
Coefficient of variation (CV)0.37064885
Kurtosis2.8537583
Mean7.4245698
Median Absolute Deviation (MAD)0
Skewness-0.34784478
Sum74216
Variance7.5729991
MonotonicityNot monotonic
2024-05-04T02:54:42.434893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
8 6568
65.7%
12 820
 
8.2%
1 705
 
7.0%
6 495
 
5.0%
3 394
 
3.9%
9 394
 
3.9%
4 303
 
3.0%
2 168
 
1.7%
5 35
 
0.4%
11 29
 
0.3%
Other values (13) 85
 
0.9%
ValueCountFrequency (%)
1 705
 
7.0%
2 168
 
1.7%
3 394
 
3.9%
4 303
 
3.0%
5 35
 
0.4%
6 495
 
5.0%
8 6568
65.7%
9 394
 
3.9%
10 19
 
0.2%
11 29
 
0.3%
ValueCountFrequency (%)
27 1
 
< 0.1%
25 1
 
< 0.1%
24 4
 
< 0.1%
23 2
 
< 0.1%
20 17
0.2%
19 13
0.1%
18 4
 
< 0.1%
17 4
 
< 0.1%
16 2
 
< 0.1%
15 4
 
< 0.1%

형태코드
Real number (ℝ)

MISSING 

Distinct10
Distinct (%)0.1%
Missing313
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean2.1600083
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T02:54:43.050259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile8
Maximum11
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6745902
Coefficient of variation (CV)0.77527026
Kurtosis6.5336577
Mean2.1600083
Median Absolute Deviation (MAD)1
Skewness2.4854071
Sum20924
Variance2.8042522
MonotonicityNot monotonic
2024-05-04T02:54:43.373508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 4226
42.3%
2 2552
25.5%
3 2320
23.2%
8 521
 
5.2%
4 22
 
0.2%
9 15
 
0.1%
5 14
 
0.1%
10 9
 
0.1%
11 7
 
0.1%
6 1
 
< 0.1%
(Missing) 313
 
3.1%
ValueCountFrequency (%)
1 4226
42.3%
2 2552
25.5%
3 2320
23.2%
4 22
 
0.2%
5 14
 
0.1%
6 1
 
< 0.1%
8 521
 
5.2%
9 15
 
0.1%
10 9
 
0.1%
11 7
 
0.1%
ValueCountFrequency (%)
11 7
 
0.1%
10 9
 
0.1%
9 15
 
0.1%
8 521
 
5.2%
6 1
 
< 0.1%
5 14
 
0.1%
4 22
 
0.2%
3 2320
23.2%
2 2552
25.5%
1 4226
42.3%

방식코드
Text

MISSING 

Distinct147
Distinct (%)1.5%
Missing276
Missing (%)2.8%
Memory size156.2 KiB
2024-05-04T02:54:44.007211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9518717
Min length1

Characters and Unicode

Total characters28704
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

Unique27 ?
Unique (%)0.3%

Sample

1st row40
2nd row133
3rd row56
4th row40
5th row173
ValueCountFrequency (%)
88 830
 
8.7%
91 695
 
7.3%
98 527
 
5.5%
133 503
 
5.3%
135 389
 
4.1%
70 355
 
3.7%
63 294
 
3.1%
123 282
 
3.0%
22 276
 
2.9%
27 261
 
2.7%
Other values (121) 5114
53.7%
2024-05-04T02:54:45.304468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 6464
22.5%
5504
19.2%
8 2899
10.1%
3 2576
 
9.0%
9 2189
 
7.6%
2 1861
 
6.5%
7 1633
 
5.7%
6 1579
 
5.5%
5 1547
 
5.4%
0 1504
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23200
80.8%
Space Separator 5504
 
19.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6464
27.9%
8 2899
12.5%
3 2576
 
11.1%
9 2189
 
9.4%
2 1861
 
8.0%
7 1633
 
7.0%
6 1579
 
6.8%
5 1547
 
6.7%
0 1504
 
6.5%
4 948
 
4.1%
Space Separator
ValueCountFrequency (%)
5504
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28704
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6464
22.5%
5504
19.2%
8 2899
10.1%
3 2576
 
9.0%
9 2189
 
7.6%
2 1861
 
6.5%
7 1633
 
5.7%
6 1579
 
5.5%
5 1547
 
5.4%
0 1504
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28704
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6464
22.5%
5504
19.2%
8 2899
10.1%
3 2576
 
9.0%
9 2189
 
7.6%
2 1861
 
6.5%
7 1633
 
5.7%
6 1579
 
5.5%
5 1547
 
5.4%
0 1504
 
5.2%

전원코드
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
001
7499 
002
1829 
<NA>
 
599
003
 
73

Length

Max length4
Median length3
Mean length3.0599
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row001
2nd row001
3rd row001
4th row001
5th row001

Common Values

ValueCountFrequency (%)
001 7499
75.0%
002 1829
 
18.3%
<NA> 599
 
6.0%
003 73
 
0.7%

Length

2024-05-04T02:54:45.790970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T02:54:46.215702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
001 7499
75.0%
002 1829
 
18.3%
na 599
 
6.0%
003 73
 
0.7%

종류코드
Categorical

IMBALANCE 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
004
6735 
001
1440 
009
799 
002
 
542
005
 
372
Other values (6)
 
112

Length

Max length4
Median length3
Mean length2.9999
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row001
2nd row004
3rd row004
4th row004
5th row004

Common Values

ValueCountFrequency (%)
004 6735
67.3%
001 1440
 
14.4%
009 799
 
8.0%
002 542
 
5.4%
005 372
 
3.7%
008 54
 
0.5%
007 28
 
0.3%
006 19
 
0.2%
011 9
 
0.1%
<NA> 1
 
< 0.1%

Length

2024-05-04T02:54:46.641012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
004 6735
67.4%
001 1440
 
14.4%
009 799
 
8.0%
002 542
 
5.4%
005 372
 
3.7%
008 54
 
0.5%
007 28
 
0.3%
006 19
 
0.2%
011 9
 
0.1%
na 1
 
< 0.1%

고가 (공통)
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
001
9972 
<NA>
 
22
002
 
6

Length

Max length4
Median length3
Mean length3.0022
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row001
2nd row001
3rd row001
4th row001
5th row001

Common Values

ValueCountFrequency (%)
001 9972
99.7%
<NA> 22
 
0.2%
002 6
 
0.1%

Length

2024-05-04T02:54:47.040599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T02:54:47.478990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
001 9972
99.7%
na 22
 
0.2%
002 6
 
0.1%

비고
Text

MISSING 

Distinct389
Distinct (%)43.3%
Missing9102
Missing (%)91.0%
Memory size156.2 KiB
2024-05-04T02:54:48.208447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length6.7171492
Min length1

Characters and Unicode

Total characters6032
Distinct characters322
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

Unique330 ?
Unique (%)36.7%

Sample

1st row
2nd row
3rd row누전차단기, 한국전기안전
4th row누전차단기, 연남파출소
5th row누전차단기, 화곡중고
ValueCountFrequency (%)
누전차단기 521
51.0%
시간제 8
 
0.8%
220v 8
 
0.8%
110v 7
 
0.7%
2현시 7
 
0.7%
인입 4
 
0.4%
사용 4
 
0.4%
3
 
0.3%
구경약국 3
 
0.3%
lg산전 3
 
0.3%
Other values (392) 453
44.4%
2024-05-04T02:54:49.912960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
736
 
12.2%
547
 
9.1%
534
 
8.9%
531
 
8.8%
527
 
8.7%
525
 
8.7%
, 394
 
6.5%
R 79
 
1.3%
55
 
0.9%
46
 
0.8%
Other values (312) 2058
34.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4579
75.9%
Space Separator 736
 
12.2%
Other Punctuation 399
 
6.6%
Decimal Number 160
 
2.7%
Uppercase Letter 136
 
2.3%
Open Punctuation 10
 
0.2%
Close Punctuation 5
 
0.1%
Math Symbol 3
 
< 0.1%
Lowercase Letter 3
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
547
 
11.9%
534
 
11.7%
531
 
11.6%
527
 
11.5%
525
 
11.5%
55
 
1.2%
46
 
1.0%
41
 
0.9%
40
 
0.9%
40
 
0.9%
Other values (279) 1693
37.0%
Uppercase Letter
ValueCountFrequency (%)
R 79
58.1%
V 15
 
11.0%
P 13
 
9.6%
B 9
 
6.6%
T 6
 
4.4%
A 5
 
3.7%
G 3
 
2.2%
L 3
 
2.2%
S 1
 
0.7%
H 1
 
0.7%
Decimal Number
ValueCountFrequency (%)
1 39
24.4%
2 37
23.1%
3 28
17.5%
0 26
16.2%
4 11
 
6.9%
5 7
 
4.4%
6 4
 
2.5%
7 3
 
1.9%
9 3
 
1.9%
8 2
 
1.2%
Other Punctuation
ValueCountFrequency (%)
, 394
98.7%
@ 3
 
0.8%
. 1
 
0.3%
: 1
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
t 1
33.3%
p 1
33.3%
a 1
33.3%
Space Separator
ValueCountFrequency (%)
736
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Math Symbol
ValueCountFrequency (%)
3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4579
75.9%
Common 1314
 
21.8%
Latin 139
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
547
 
11.9%
534
 
11.7%
531
 
11.6%
527
 
11.5%
525
 
11.5%
55
 
1.2%
46
 
1.0%
41
 
0.9%
40
 
0.9%
40
 
0.9%
Other values (279) 1693
37.0%
Common
ValueCountFrequency (%)
736
56.0%
, 394
30.0%
1 39
 
3.0%
2 37
 
2.8%
3 28
 
2.1%
0 26
 
2.0%
4 11
 
0.8%
( 10
 
0.8%
5 7
 
0.5%
) 5
 
0.4%
Other values (9) 21
 
1.6%
Latin
ValueCountFrequency (%)
R 79
56.8%
V 15
 
10.8%
P 13
 
9.4%
B 9
 
6.5%
T 6
 
4.3%
A 5
 
3.6%
G 3
 
2.2%
L 3
 
2.2%
S 1
 
0.7%
H 1
 
0.7%
Other values (4) 4
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4579
75.9%
ASCII 1450
 
24.0%
Arrows 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
736
50.8%
, 394
27.2%
R 79
 
5.4%
1 39
 
2.7%
2 37
 
2.6%
3 28
 
1.9%
0 26
 
1.8%
V 15
 
1.0%
P 13
 
0.9%
4 11
 
0.8%
Other values (22) 72
 
5.0%
Hangul
ValueCountFrequency (%)
547
 
11.9%
534
 
11.7%
531
 
11.6%
527
 
11.5%
525
 
11.5%
55
 
1.2%
46
 
1.0%
41
 
0.9%
40
 
0.9%
40
 
0.9%
Other values (279) 1693
37.0%
Arrows
ValueCountFrequency (%)
3
100.0%

교체일
Text

MISSING 

Distinct1999
Distinct (%)20.5%
Missing250
Missing (%)2.5%
Memory size156.2 KiB
2024-05-04T02:54:51.152668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.8578462
Min length1

Characters and Unicode

Total characters76614
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

Unique832 ?
Unique (%)8.5%

Sample

1st row00010101
2nd row20170809
3rd row20030401
4th row20110101
5th row20210428
ValueCountFrequency (%)
20041120 354
 
3.7%
20020201 177
 
1.9%
20221101 159
 
1.7%
00010101 143
 
1.5%
20010201 111
 
1.2%
20051020 107
 
1.1%
20010101 99
 
1.0%
20051230 97
 
1.0%
20040705 88
 
0.9%
20020801 86
 
0.9%
Other values (1988) 8131
85.1%
2024-05-04T02:54:54.282505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 28755
37.5%
2 16904
22.1%
1 14137
18.5%
5 2822
 
3.7%
3 2755
 
3.6%
9 2732
 
3.6%
8 2434
 
3.2%
4 2134
 
2.8%
6 2042
 
2.7%
7 1701
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 76416
99.7%
Space Separator 198
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28755
37.6%
2 16904
22.1%
1 14137
18.5%
5 2822
 
3.7%
3 2755
 
3.6%
9 2732
 
3.6%
8 2434
 
3.2%
4 2134
 
2.8%
6 2042
 
2.7%
7 1701
 
2.2%
Space Separator
ValueCountFrequency (%)
198
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 76614
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28755
37.5%
2 16904
22.1%
1 14137
18.5%
5 2822
 
3.7%
3 2755
 
3.6%
9 2732
 
3.6%
8 2434
 
3.2%
4 2134
 
2.8%
6 2042
 
2.7%
7 1701
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 76614
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28755
37.5%
2 16904
22.1%
1 14137
18.5%
5 2822
 
3.7%
3 2755
 
3.6%
9 2732
 
3.6%
8 2434
 
3.2%
4 2134
 
2.8%
6 2042
 
2.7%
7 1701
 
2.2%
Distinct58
Distinct (%)0.6%
Missing13
Missing (%)0.1%
Memory size156.2 KiB
2024-05-04T02:54:55.132167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.8076499
Min length1

Characters and Unicode

Total characters77975
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

Unique4 ?
Unique (%)< 0.1%

Sample

1st row140
2nd row240
3rd row250
4th row150
5th row370
ValueCountFrequency (%)
210 566
 
5.8%
300 491
 
5.0%
360 465
 
4.8%
170 441
 
4.5%
310 430
 
4.4%
350 422
 
4.3%
340 405
 
4.1%
270 398
 
4.1%
200 392
 
4.0%
260 390
 
4.0%
Other values (21) 5389
55.1%
2024-05-04T02:54:56.549623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
48608
62.3%
0 11025
 
14.1%
3 4340
 
5.6%
2 4229
 
5.4%
1 3469
 
4.4%
4 1541
 
2.0%
6 1170
 
1.5%
7 1167
 
1.5%
5 828
 
1.1%
8 808
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Space Separator 48608
62.3%
Decimal Number 29367
37.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11025
37.5%
3 4340
 
14.8%
2 4229
 
14.4%
1 3469
 
11.8%
4 1541
 
5.2%
6 1170
 
4.0%
7 1167
 
4.0%
5 828
 
2.8%
8 808
 
2.8%
9 790
 
2.7%
Space Separator
ValueCountFrequency (%)
48608
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 77975
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
48608
62.3%
0 11025
 
14.1%
3 4340
 
5.6%
2 4229
 
5.4%
1 3469
 
4.4%
4 1541
 
2.0%
6 1170
 
1.5%
7 1167
 
1.5%
5 828
 
1.1%
8 808
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77975
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
48608
62.3%
0 11025
 
14.1%
3 4340
 
5.6%
2 4229
 
5.4%
1 3469
 
4.4%
4 1541
 
2.0%
6 1170
 
1.5%
7 1167
 
1.5%
5 828
 
1.1%
8 808
 
1.0%
Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
710
 
553
650
 
550
560
 
547
440
 
542
680
 
529
Other values (22)
7279 

Length

Max length4
Median length3
Mean length2.9631
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row110
2nd row380
3rd row320
4th row230
5th row350

Common Values

ValueCountFrequency (%)
710 553
 
5.5%
650 550
 
5.5%
560 547
 
5.5%
440 542
 
5.4%
680 529
 
5.3%
500 501
 
5.0%
380 470
 
4.7%
350 448
 
4.5%
740 424
 
4.2%
230 414
 
4.1%
Other values (17) 5022
50.2%

Length

2024-05-04T02:54:57.455868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
710 553
 
5.6%
650 550
 
5.6%
560 547
 
5.6%
440 542
 
5.5%
680 529
 
5.4%
500 501
 
5.1%
380 470
 
4.8%
350 448
 
4.6%
740 424
 
4.3%
230 414
 
4.2%
Other values (16) 4818
49.2%

동코드 (공통)
Real number (ℝ)

ZEROS 

Distinct82
Distinct (%)0.8%
Missing20
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean10409.238
Minimum0
Maximum18600
Zeros597
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T02:54:58.367634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110200
median10600
Q311100
95-th percentile13500
Maximum18600
Range18600
Interquartile range (IQR)900

Descriptive statistics

Standard deviation2934.2279
Coefficient of variation (CV)0.28188689
Kurtosis7.2174208
Mean10409.238
Median Absolute Deviation (MAD)400
Skewness-2.2871997
Sum1.038842 × 108
Variance8609693.2
MonotonicityNot monotonic
2024-05-04T02:54:59.100478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10100 1255
12.6%
10200 1100
 
11.0%
10300 879
 
8.8%
10500 651
 
6.5%
0 597
 
6.0%
10700 591
 
5.9%
10800 587
 
5.9%
10600 547
 
5.5%
10400 462
 
4.6%
10900 409
 
4.1%
Other values (72) 2902
29.0%
ValueCountFrequency (%)
0 597
6.0%
10100 1255
12.6%
10200 1100
11.0%
10300 879
8.8%
10400 462
 
4.6%
10500 651
6.5%
10600 547
5.5%
10700 591
5.9%
10800 587
5.9%
10900 409
 
4.1%
ValueCountFrequency (%)
18600 8
0.1%
18500 3
 
< 0.1%
18400 8
0.1%
18300 10
0.1%
18200 4
 
< 0.1%
18100 1
 
< 0.1%
17500 15
0.1%
17400 12
0.1%
17300 4
 
< 0.1%
17200 8
0.1%

지번
Text

MISSING 

Distinct5074
Distinct (%)51.7%
Missing177
Missing (%)1.8%
Memory size156.2 KiB
2024-05-04T02:55:00.217459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.8820116
Min length1

Characters and Unicode

Total characters57779
Distinct characters37
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

Unique2911 ?
Unique (%)29.6%

Sample

1st row64-2도
2nd row241-15대
3rd row92-34도
4th row189 학
5th row790도
ValueCountFrequency (%)
1103
 
9.7%
385
 
3.4%
51
 
0.4%
46
 
0.4%
36
 
0.3%
30
 
0.3%
19
 
0.2%
140도 17
 
0.1%
16
 
0.1%
16
 
0.1%
Other values (5056) 9707
85.0%
2024-05-04T02:55:01.710480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 7743
13.4%
- 7541
13.1%
5985
10.4%
2 4930
8.5%
3 4259
 
7.4%
4 3797
 
6.6%
6 3226
 
5.6%
5 3119
 
5.4%
7 3025
 
5.2%
0 2862
 
5.0%
Other values (27) 11292
19.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38224
66.2%
Other Letter 9999
 
17.3%
Dash Punctuation 7541
 
13.1%
Space Separator 2015
 
3.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
5985
59.9%
1858
 
18.6%
386
 
3.9%
348
 
3.5%
329
 
3.3%
160
 
1.6%
153
 
1.5%
150
 
1.5%
104
 
1.0%
103
 
1.0%
Other values (15) 423
 
4.2%
Decimal Number
ValueCountFrequency (%)
1 7743
20.3%
2 4930
12.9%
3 4259
11.1%
4 3797
9.9%
6 3226
8.4%
5 3119
8.2%
7 3025
 
7.9%
0 2862
 
7.5%
9 2657
 
7.0%
8 2606
 
6.8%
Dash Punctuation
ValueCountFrequency (%)
- 7541
100.0%
Space Separator
ValueCountFrequency (%)
2015
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47780
82.7%
Hangul 9999
 
17.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
5985
59.9%
1858
 
18.6%
386
 
3.9%
348
 
3.5%
329
 
3.3%
160
 
1.6%
153
 
1.5%
150
 
1.5%
104
 
1.0%
103
 
1.0%
Other values (15) 423
 
4.2%
Common
ValueCountFrequency (%)
1 7743
16.2%
- 7541
15.8%
2 4930
10.3%
3 4259
8.9%
4 3797
7.9%
6 3226
6.8%
5 3119
6.5%
7 3025
 
6.3%
0 2862
 
6.0%
9 2657
 
5.6%
Other values (2) 4621
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47780
82.7%
Hangul 9999
 
17.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7743
16.2%
- 7541
15.8%
2 4930
10.3%
3 4259
8.9%
4 3797
7.9%
6 3226
6.8%
5 3119
6.5%
7 3025
 
6.3%
0 2862
 
6.0%
9 2657
 
5.6%
Other values (2) 4621
9.7%
Hangul
ValueCountFrequency (%)
5985
59.9%
1858
 
18.6%
386
 
3.9%
348
 
3.5%
329
 
3.3%
160
 
1.6%
153
 
1.5%
150
 
1.5%
104
 
1.0%
103
 
1.0%
Other values (15) 423
 
4.2%
Distinct33
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
360
 
565
210
 
549
170
 
549
300
 
501
370
 
449
Other values (28)
7387 

Length

Max length4
Median length3
Mean length2.9612
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row120
2nd row240
3rd row400
4th row200
5th row370

Common Values

ValueCountFrequency (%)
360 565
 
5.7%
210 549
 
5.5%
170 549
 
5.5%
300 501
 
5.0%
370 449
 
4.5%
310 429
 
4.3%
200 414
 
4.1%
350 412
 
4.1%
340 400
 
4.0%
270 393
 
3.9%
Other values (23) 5339
53.4%

Length

2024-05-04T02:55:02.126967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
360 565
 
5.8%
210 549
 
5.6%
170 549
 
5.6%
300 501
 
5.1%
370 449
 
4.6%
310 429
 
4.4%
200 414
 
4.2%
350 412
 
4.2%
340 400
 
4.1%
270 393
 
4.0%
Other values (22) 5141
52.4%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
4
5853 
1
3314 
2
 
451
6
 
247
3
 
135

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row4
4th row4
5th row1

Common Values

ValueCountFrequency (%)
4 5853
58.5%
1 3314
33.1%
2 451
 
4.5%
6 247
 
2.5%
3 135
 
1.4%

Length

2024-05-04T02:55:02.558885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T02:55:02.896364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 5853
58.5%
1 3314
33.1%
2 451
 
4.5%
6 247
 
2.5%
3 135
 
1.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
6932 
2
3068 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 6932
69.3%
2 3068
30.7%

Length

2024-05-04T02:55:03.251760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T02:55:03.568768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 6932
69.3%
2 3068
30.7%

도로구분 (공통)
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
001
7157 
002
2815 
<NA>
 
27
 
1

Length

Max length4
Median length3
Mean length3.0025
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row001
2nd row001
3rd row002
4th row001
5th row002

Common Values

ValueCountFrequency (%)
001 7157
71.6%
002 2815
 
28.1%
<NA> 27
 
0.3%
1
 
< 0.1%

Length

2024-05-04T02:55:04.052849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T02:55:04.468388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
001 7157
71.6%
002 2815
 
28.2%
na 27
 
0.3%
Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
108
1981 
107
1703 
104
1665 
105
1610 
106
1588 
Other values (3)
1453 

Length

Max length4
Median length3
Mean length3.0016
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row107
2nd row108
3rd row107
4th row109
5th row107

Common Values

ValueCountFrequency (%)
108 1981
19.8%
107 1703
17.0%
104 1665
16.7%
105 1610
16.1%
106 1588
15.9%
109 1413
14.1%
<NA> 32
 
0.3%
8
 
0.1%

Length

2024-05-04T02:55:04.902425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T02:55:05.373974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
108 1981
19.8%
107 1703
17.0%
104 1665
16.7%
105 1610
16.1%
106 1588
15.9%
109 1413
14.1%
na 32
 
0.3%

신규정규화ID
Text

MISSING 

Distinct1839
Distinct (%)81.7%
Missing7748
Missing (%)77.5%
Memory size156.2 KiB
2024-05-04T02:55:06.417643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length9.7415631
Min length1

Characters and Unicode

Total characters21938
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

Unique1693 ?
Unique (%)75.2%

Sample

1st row
2nd row2375051
3rd row6225713
4th row3292752
5th row2028088
ValueCountFrequency (%)
3326061 2
 
0.1%
5351335 2
 
0.1%
4320305 2
 
0.1%
5356472 2
 
0.1%
2456934 2
 
0.1%
1148114 2
 
0.1%
2136705 2
 
0.1%
4362281 2
 
0.1%
1134833 2
 
0.1%
3193308 2
 
0.1%
Other values (1825) 1963
99.0%
2024-05-04T02:55:08.044033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8055
36.7%
2 2611
 
11.9%
1 2130
 
9.7%
3 1857
 
8.5%
4 1598
 
7.3%
5 1313
 
6.0%
6 1018
 
4.6%
0 980
 
4.5%
7 840
 
3.8%
8 783
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13883
63.3%
Space Separator 8055
36.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2611
18.8%
1 2130
15.3%
3 1857
13.4%
4 1598
11.5%
5 1313
9.5%
6 1018
 
7.3%
0 980
 
7.1%
7 840
 
6.1%
8 783
 
5.6%
9 753
 
5.4%
Space Separator
ValueCountFrequency (%)
8055
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21938
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8055
36.7%
2 2611
 
11.9%
1 2130
 
9.7%
3 1857
 
8.5%
4 1598
 
7.3%
5 1313
 
6.0%
6 1018
 
4.6%
0 980
 
4.5%
7 840
 
3.8%
8 783
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21938
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8055
36.7%
2 2611
 
11.9%
1 2130
 
9.7%
3 1857
 
8.5%
4 1598
 
7.3%
5 1313
 
6.0%
6 1018
 
4.6%
0 980
 
4.5%
7 840
 
3.8%
8 783
 
3.6%

공간데이터
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

이력ID
Real number (ℝ)

Distinct9603
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10195.482
Minimum2
Maximum20598
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T02:55:08.629950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile1039.95
Q15298.75
median10369.5
Q314962.25
95-th percentile19617.15
Maximum20598
Range20596
Interquartile range (IQR)9663.5

Descriptive statistics

Standard deviation5763.8307
Coefficient of variation (CV)0.56533184
Kurtosis-1.1161215
Mean10195.482
Median Absolute Deviation (MAD)4825.5
Skewness-0.020644648
Sum1.0195482 × 108
Variance33221744
MonotonicityNot monotonic
2024-05-04T02:55:09.207566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16561 3
 
< 0.1%
11978 2
 
< 0.1%
12239 2
 
< 0.1%
16417 2
 
< 0.1%
12706 2
 
< 0.1%
13823 2
 
< 0.1%
14477 2
 
< 0.1%
17844 2
 
< 0.1%
5731 2
 
< 0.1%
11730 2
 
< 0.1%
Other values (9593) 9979
99.8%
ValueCountFrequency (%)
2 1
< 0.1%
3 1
< 0.1%
9 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
22 1
< 0.1%
24 1
< 0.1%
25 1
< 0.1%
27 1
< 0.1%
28 1
< 0.1%
ValueCountFrequency (%)
20598 1
< 0.1%
20596 1
< 0.1%
20595 1
< 0.1%
20594 1
< 0.1%
20593 1
< 0.1%
20592 1
< 0.1%
20591 1
< 0.1%
20589 1
< 0.1%
20588 1
< 0.1%
20587 1
< 0.1%

공사관리번호
Text

MISSING 

Distinct2024
Distinct (%)21.0%
Missing355
Missing (%)3.5%
Memory size156.2 KiB
2024-05-04T02:55:10.075538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters125385
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

Unique841 ?
Unique (%)8.7%

Sample

1st row2000-0000-000
2nd row2017-0201-042
3rd row2000-0000-000
4th row2000-0000-000
5th row2021-0101-010
ValueCountFrequency (%)
2000-0000-000 2582
 
26.8%
2009-0201-001 136
 
1.4%
2009-0201-005 69
 
0.7%
2009-0101-100 46
 
0.5%
2004-0101-044 45
 
0.5%
2010-0101-042 42
 
0.4%
2007-0201-457 41
 
0.4%
2009-1101-063 36
 
0.4%
2003-0201-004 34
 
0.4%
2003-0201-009 33
 
0.3%
Other values (2014) 6581
68.2%
2024-05-04T02:55:11.485002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 57461
45.8%
1 19387
 
15.5%
- 19290
 
15.4%
2 15183
 
12.1%
4 2749
 
2.2%
9 2220
 
1.8%
5 2114
 
1.7%
3 2023
 
1.6%
6 1815
 
1.4%
7 1583
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 106095
84.6%
Dash Punctuation 19290
 
15.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 57461
54.2%
1 19387
 
18.3%
2 15183
 
14.3%
4 2749
 
2.6%
9 2220
 
2.1%
5 2114
 
2.0%
3 2023
 
1.9%
6 1815
 
1.7%
7 1583
 
1.5%
8 1560
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 19290
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 125385
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 57461
45.8%
1 19387
 
15.5%
- 19290
 
15.4%
2 15183
 
12.1%
4 2749
 
2.2%
9 2220
 
1.8%
5 2114
 
1.7%
3 2023
 
1.6%
6 1815
 
1.4%
7 1583
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125385
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 57461
45.8%
1 19387
 
15.5%
- 19290
 
15.4%
2 15183
 
12.1%
4 2749
 
2.2%
9 2220
 
1.8%
5 2114
 
1.7%
3 2023
 
1.6%
6 1815
 
1.4%
7 1583
 
1.3%
Distinct4706
Distinct (%)47.1%
Missing11
Missing (%)0.1%
Memory size156.2 KiB
2024-05-04T02:55:12.499265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters89901
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

Unique2010 ?
Unique (%)20.1%

Sample

1st row01-003753
2nd row01-004371
3rd row01-002980
4th row01-004343
5th row01-007225
ValueCountFrequency (%)
01-002536 9
 
0.1%
01-001321 9
 
0.1%
01-001505 8
 
0.1%
01-002515 8
 
0.1%
01-000828 7
 
0.1%
01-001658 7
 
0.1%
01-002541 7
 
0.1%
01-000251 7
 
0.1%
01-001005 7
 
0.1%
01-003586 7
 
0.1%
Other values (4696) 9913
99.2%
2024-05-04T02:55:14.110837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 35382
39.4%
1 15311
17.0%
- 9989
 
11.1%
2 5068
 
5.6%
3 4752
 
5.3%
4 3986
 
4.4%
6 3608
 
4.0%
7 3326
 
3.7%
5 3042
 
3.4%
9 2804
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79912
88.9%
Dash Punctuation 9989
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 35382
44.3%
1 15311
19.2%
2 5068
 
6.3%
3 4752
 
5.9%
4 3986
 
5.0%
6 3608
 
4.5%
7 3326
 
4.2%
5 3042
 
3.8%
9 2804
 
3.5%
8 2633
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 9989
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 89901
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 35382
39.4%
1 15311
17.0%
- 9989
 
11.1%
2 5068
 
5.6%
3 4752
 
5.3%
4 3986
 
4.4%
6 3608
 
4.0%
7 3326
 
3.7%
5 3042
 
3.4%
9 2804
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89901
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 35382
39.4%
1 15311
17.0%
- 9989
 
11.1%
2 5068
 
5.6%
3 4752
 
5.3%
4 3986
 
4.4%
6 3608
 
4.0%
7 3326
 
3.7%
5 3042
 
3.4%
9 2804
 
3.1%
Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
002
3072 
001
2308 
<NA>
2193 
006
915 
009
750 
Other values (7)
762 

Length

Max length4
Median length3
Mean length3.1857
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row006
3rd row<NA>
4th row<NA>
5th row001

Common Values

ValueCountFrequency (%)
002 3072
30.7%
001 2308
23.1%
<NA> 2193
21.9%
006 915
 
9.2%
009 750
 
7.5%
003 362
 
3.6%
168
 
1.7%
008 88
 
0.9%
007 87
 
0.9%
004 51
 
0.5%
Other values (2) 6
 
0.1%

Length

2024-05-04T02:55:14.684370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
002 3072
31.2%
001 2308
23.5%
na 2193
22.3%
006 915
 
9.3%
009 750
 
7.6%
003 362
 
3.7%
008 88
 
0.9%
007 87
 
0.9%
004 51
 
0.5%
010 3
 
< 0.1%

Sample

제어기명온라인일등록일시-설치일제조회사상태 (공통)교차로번호제어기관리번호시스템코드형태코드방식코드전원코드종류코드고가 (공통)비고교체일구경찰서코드구코드 (공통)동코드 (공통)지번신경찰서코드 (공통)작업구분 (공통)표출구분 (공통)도로구분 (공통)관할사업소 (공통)신규정규화ID공간데이터이력ID공사관리번호제어기관리번호.1공사형태 (공통)
202292000010100010101001001-000000375300100140001001001000101011401101210064-2도12021001107<NA>58492000-0000-00001-003753
3353백련산힐스테이트204동2011120520111205코스텍00183001-0000004371008002133001004001<NA>2017080924038010700241-15대240120011082375051<NA>139982017-0201-04201-004371006
5196우이2교2007011920030401에틴(주)001422901-000000298000800156001004001<NA>2003040125032010700<NA>40041002107<NA><NA>77032000-0000-00001-002980<NA>
3518강북비타에듀학원2011010120110101<NA>001628601-000000434300800140001004001<NA>201101011502301010092-34도20041001109<NA><NA>139922000-0000-00001-004343<NA>
1141공릉동부아파트2021042820210428(주)서돌전자통신001388201-0000007225008003173001004001<NA>2021042837035010300189 학37012002107<NA><NA>201922021-0101-01001-007225001
16523가로공원2000090520010214위운용사복지회00113001-0000001061008003123002004001<NA>2009010435047010300790도35041001104<NA><NA>112852000-0000-00001-001061<NA>
11504화곡제일성심병원2002013120020801진우산전001292401-000000001500300188001001001<NA>200208013005001030094-4대30041001104<NA><NA>124132000-0000-00001-000015<NA>
7561답십리두산APT2002101220070901코스텍001345301-0000002604008001114002004001<NA>2007110920023010500261-1도20041001109<NA><NA>46672000-0000-00001-002604<NA>
6909인건빌딩(이산부인과)2003012319980901위운용사복지회001385001-0000002346003008123002001001<NA>2008122635050010300939-2도30041001104<NA><NA>83122000-0000-00001-002346<NA>
3640천호동공원앞(보)2010021020070912한국전기교통(주)001522601-0000003602008001123001004001<NA>2009122331074010900323-9대310120011066225713<NA>119372009-0101-10001-003602009
제어기명온라인일등록일시-설치일제조회사상태 (공통)교차로번호제어기관리번호시스템코드형태코드방식코드전원코드종류코드고가 (공통)비고교체일구경찰서코드구코드 (공통)동코드 (공통)지번신경찰서코드 (공통)작업구분 (공통)표출구분 (공통)도로구분 (공통)관할사업소 (공통)신규정규화ID공간데이터이력ID공사관리번호제어기관리번호.1공사형태 (공통)
9992청량리굴다리2002032520041120세인(주)001340301-0000002131008002101001004001<NA>2004112020023010400673대20041002109<NA><NA>170632004-0101-02901-002131009
3388송파파크데일1단지2011093020110930양광001626001-0000004305008001115001004001<NA>2011093036071011400산1-5 공360120021066185702<NA>163882011-1101-05501-004305001
3326이태원입구2012010720120107(주)서돌전자통신00131201-0000000817008003174001004001<NA>2021090716017013000196-3도160120011083283511<NA>160782021-0101-08301-000817002
4565유광사산부인과(보)2007122420071224코스텍001564801-0000003638008001114001004001<NA>2007122430050001089-29도30041001104<NA><NA>13412000-0000-00001-003638<NA>
3632정신여고2010021020091223(주)서돌전자통신001204201-0000002608006001174001002001<NA>202312073607101010064도36032002106<NA><NA>119482023-0101-10401-002608002
5223영화초교(남부교회)2007011920061222(주)세인시스템001164401-000000015400800291001004001<NA>200701222205901080017-59도22041002105<NA><NA>40762006-0201-03001-000154002
16433성원아파트104동2001010120010201한국전기001323701-00000034160010019001001001<NA>2001020127026010600561도27041001109<NA><NA>55862000-0000-00001-003416<NA>
6113동선약국(보)2006020320060131(주)서돌전자통신001356001-000000216600800198001004001<NA>20060131190290118001-1도19041002107<NA><NA>154552010-0101-06101-002166006
5978월드빌아파트(보)2006060120060531(주)세인시스템001545301-000000348200800191001004001<NA>2006053124038011000199-4도24041001108<NA><NA>47392005-0101-08801-003482001
11451단암빌딩2002013120050430서돌전자(주)00185701-000000157900800186002004001<NA>200505301301401180017-7대13012001108<NA><NA>113912004-1101-22301-001579002