Overview

Dataset statistics

Number of variables20
Number of observations1207
Missing cells0
Missing cells (%)0.0%
Duplicate rows2
Duplicate rows (%)0.2%
Total size in memory198.1 KiB
Average record size in memory168.1 B

Variable types

Categorical12
Numeric5
Text3

Dataset

Description경기도 화성시_도로반사경에 대한 데이터로 지형지물부호, 관리번호, 행정읍면동, 관리기관, 도엽번호, 반사경재질, 반사경모양, 반사경관경, 반사경가로, 반사경세로, 대장초기화여부, 설치일자, 설치형식, 도로구간번호, 공사번호, 납품업체, 로딩일자, X좌표, Y좌표 등의 항목을 제공합니다.
Author경기도 화성시
URLhttps://www.data.go.kr/data/15093506/fileData.do

Alerts

지형지물부호 has constant value ""Constant
대장초기화여부 has constant value ""Constant
Dataset has 2 (0.2%) duplicate rowsDuplicates
반사경세로 is highly overall correlated with 관리기관 and 2 other fieldsHigh correlation
공사번호 is highly overall correlated with 관리기관 and 1 other fieldsHigh correlation
로딩일자 is highly overall correlated with 관리번호 and 7 other fieldsHigh correlation
관리기관 is highly overall correlated with 도로구간번호 and 7 other fieldsHigh correlation
반사경가로 is highly overall correlated with 관리기관 and 2 other fieldsHigh correlation
납품업체 is highly overall correlated with 관리번호 and 3 other fieldsHigh correlation
반사경모양 is highly overall correlated with 관리기관 and 4 other fieldsHigh correlation
관리번호 is highly overall correlated with 도로구간번호 and 2 other fieldsHigh correlation
도로구간번호 is highly overall correlated with 관리번호 and 3 other fieldsHigh correlation
X좌표 is highly overall correlated with 행정읍면동High correlation
Y좌표 is highly overall correlated with 행정읍면동High correlation
행정읍면동 is highly overall correlated with X좌표 and 2 other fieldsHigh correlation
반사경재질 is highly overall correlated with 로딩일자High correlation
설치형식 is highly overall correlated with 반사경모양 and 1 other fieldsHigh correlation
관리기관 is highly imbalanced (85.5%)Imbalance
반사경재질 is highly imbalanced (64.5%)Imbalance
반사경모양 is highly imbalanced (77.8%)Imbalance
반사경가로 is highly imbalanced (88.6%)Imbalance
반사경세로 is highly imbalanced (85.4%)Imbalance
설치형식 is highly imbalanced (90.4%)Imbalance
공사번호 is highly imbalanced (97.8%)Imbalance
납품업체 is highly imbalanced (70.0%)Imbalance
로딩일자 is highly imbalanced (68.9%)Imbalance
반사경관경 has 590 (48.9%) zerosZeros

Reproduction

Analysis started2024-04-21 01:21:30.161797
Analysis finished2024-04-21 01:21:37.857982
Duration7.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지형지물부호
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
도로반사경
1207 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
도로반사경 1207
100.0%

Length

2024-04-21T10:21:37.968940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:21:38.128477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도로반사경 1207
100.0%

관리번호
Real number (ℝ)

HIGH CORRELATION 

Distinct964
Distinct (%)79.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2660481 × 1012
Minimum100001
Maximum8.99919 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2024-04-21T10:21:38.522836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100001
5-th percentile100064.3
Q1500080.5
median9.992015 × 1010
Q39.9920151 × 1010
95-th percentile8.21619 × 1013
Maximum8.99919 × 1013
Range8.99919 × 1013
Interquartile range (IQR)9.9919651 × 1010

Descriptive statistics

Standard deviation2.1704038 × 1013
Coefficient of variation (CV)2.9870484
Kurtosis8.7083956
Mean7.2660481 × 1012
Median Absolute Deviation (MAD)9.991965 × 1010
Skewness3.1808614
Sum8.77012 × 1015
Variance4.7106525 × 1026
MonotonicityNot monotonic
2024-04-21T10:21:38.767308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89991600000000 23
 
1.9%
89991900000000 22
 
1.8%
511308000000 14
 
1.2%
888617000000 13
 
1.1%
215304000000 11
 
0.9%
12111700000000 10
 
0.8%
12111800000000 10
 
0.8%
82112000000000 10
 
0.8%
888615000000 10
 
0.8%
82161900000000 9
 
0.7%
Other values (954) 1075
89.1%
ValueCountFrequency (%)
100001 1
0.1%
100002 1
0.1%
100003 1
0.1%
100004 1
0.1%
100005 1
0.1%
100006 1
0.1%
100007 1
0.1%
100008 1
0.1%
100009 1
0.1%
100010 1
0.1%
ValueCountFrequency (%)
89991900000000 22
1.8%
89991600000000 23
1.9%
82162000000000 8
 
0.7%
82161900000000 9
 
0.7%
82161800000000 3
 
0.2%
82112000000000 10
0.8%
82111800000000 1
 
0.1%
42162000000000 1
 
0.1%
42161800000000 6
 
0.5%
42161700000000 7
 
0.6%

행정읍면동
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
<NA>
130 
봉담읍
112 
남양읍
94 
정남면
90 
팔탄면
90 
Other values (19)
691 

Length

Max length4
Median length3
Mean length3.1888981
Min length3

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 130
 
10.8%
봉담읍 112
 
9.3%
남양읍 94
 
7.8%
정남면 90
 
7.5%
팔탄면 90
 
7.5%
송산면 75
 
6.2%
마도면 69
 
5.7%
양감면 64
 
5.3%
향남읍 62
 
5.1%
서신면 53
 
4.4%
Other values (14) 368
30.5%

Length

2024-04-21T10:21:38.990972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 130
 
10.8%
봉담읍 112
 
9.3%
남양읍 94
 
7.8%
정남면 90
 
7.5%
팔탄면 90
 
7.5%
송산면 75
 
6.2%
마도면 69
 
5.7%
양감면 64
 
5.3%
향남읍 62
 
5.1%
서신면 53
 
4.4%
Other values (14) 368
30.5%

도엽번호
Text

Distinct873
Distinct (%)72.3%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
2024-04-21T10:21:39.844024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.998343
Min length8

Characters and Unicode

Total characters12068
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique635 ?
Unique (%)52.6%

Sample

1st row376160195A
2nd row376160193C
3rd row376160192D
4th row376160192C
5th row376160192C
ValueCountFrequency (%)
376160831b 8
 
0.7%
377130675c 5
 
0.4%
376161725c 5
 
0.4%
376160316d 5
 
0.4%
376151061a 5
 
0.4%
376160790b 5
 
0.4%
376161569a 4
 
0.3%
376160748b 4
 
0.3%
376161579a 4
 
0.3%
376160790d 4
 
0.3%
Other values (863) 1158
95.9%
2024-04-21T10:21:40.962264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 2010
16.7%
1 1916
15.9%
7 1879
15.6%
3 1820
15.1%
0 1097
9.1%
5 588
 
4.9%
2 464
 
3.8%
8 397
 
3.3%
4 369
 
3.1%
C 329
 
2.7%
Other values (4) 1199
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10862
90.0%
Uppercase Letter 1206
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 2010
18.5%
1 1916
17.6%
7 1879
17.3%
3 1820
16.8%
0 1097
10.1%
5 588
 
5.4%
2 464
 
4.3%
8 397
 
3.7%
4 369
 
3.4%
9 322
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
C 329
27.3%
B 306
25.4%
D 295
24.5%
A 276
22.9%

Most occurring scripts

ValueCountFrequency (%)
Common 10862
90.0%
Latin 1206
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 2010
18.5%
1 1916
17.6%
7 1879
17.3%
3 1820
16.8%
0 1097
10.1%
5 588
 
5.4%
2 464
 
4.3%
8 397
 
3.7%
4 369
 
3.4%
9 322
 
3.0%
Latin
ValueCountFrequency (%)
C 329
27.3%
B 306
25.4%
D 295
24.5%
A 276
22.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12068
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 2010
16.7%
1 1916
15.9%
7 1879
15.6%
3 1820
15.1%
0 1097
9.1%
5 588
 
4.9%
2 464
 
3.8%
8 397
 
3.3%
4 369
 
3.1%
C 329
 
2.7%
Other values (4) 1199
9.9%

관리기관
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
<NA>
1151 
도로과
 
52
화성시
 
3
교통건설과
 
1

Length

Max length5
Median length4
Mean length3.955261
Min length3

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 1151
95.4%
도로과 52
 
4.3%
화성시 3
 
0.2%
교통건설과 1
 
0.1%

Length

2024-04-21T10:21:41.211354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:21:41.411069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1151
95.4%
도로과 52
 
4.3%
화성시 3
 
0.2%
교통건설과 1
 
0.1%

반사경재질
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
스테인레스
1009 
기타
126 
알루미늄
 
67
아크릴
 
4
미분류
 
1

Length

Max length5
Median length5
Mean length4.6230323
Min length2

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
스테인레스 1009
83.6%
기타 126
 
10.4%
알루미늄 67
 
5.6%
아크릴 4
 
0.3%
미분류 1
 
0.1%

Length

2024-04-21T10:21:41.606429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:21:41.823945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
스테인레스 1009
83.6%
기타 126
 
10.4%
알루미늄 67
 
5.6%
아크릴 4
 
0.3%
미분류 1
 
0.1%

반사경모양
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
원형
1164 
원추형
 
43

Length

Max length3
Median length2
Mean length2.0356255
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
원형 1164
96.4%
원추형 43
 
3.6%

Length

2024-04-21T10:21:42.044376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:21:42.213945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
원형 1164
96.4%
원추형 43
 
3.6%

반사경관경
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean488.36785
Minimum0
Maximum1200
Zeros590
Zeros (%)48.9%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2024-04-21T10:21:42.369280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median650
Q31000
95-th percentile1000
Maximum1200
Range1200
Interquartile range (IQR)1000

Descriptive statistics

Standard deviation482.74495
Coefficient of variation (CV)0.98848634
Kurtosis-1.9525297
Mean488.36785
Median Absolute Deviation (MAD)350
Skewness0.0082070589
Sum589460
Variance233042.69
MonotonicityNot monotonic
2024-04-21T10:21:42.570742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 590
48.9%
1000 478
39.6%
800 71
 
5.9%
850 16
 
1.3%
900 14
 
1.2%
650 13
 
1.1%
600 10
 
0.8%
1050 5
 
0.4%
750 4
 
0.3%
830 2
 
0.2%
Other values (3) 4
 
0.3%
ValueCountFrequency (%)
0 590
48.9%
600 10
 
0.8%
650 13
 
1.1%
700 1
 
0.1%
750 4
 
0.3%
800 71
 
5.9%
830 2
 
0.2%
850 16
 
1.3%
900 14
 
1.2%
1000 478
39.6%
ValueCountFrequency (%)
1200 1
 
0.1%
1100 2
 
0.2%
1050 5
 
0.4%
1000 478
39.6%
900 14
 
1.2%
850 16
 
1.3%
830 2
 
0.2%
800 71
 
5.9%
750 4
 
0.3%
700 1
 
0.1%

반사경가로
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
0.0
1164 
0.8
 
28
0.9
 
13
1.0
 
1
0.6
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1164
96.4%
0.8 28
 
2.3%
0.9 13
 
1.1%
1.0 1
 
0.1%
0.6 1
 
0.1%

Length

2024-04-21T10:21:42.783173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:21:42.960459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1164
96.4%
0.8 28
 
2.3%
0.9 13
 
1.1%
1.0 1
 
0.1%
0.6 1
 
0.1%

반사경세로
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
0.0
1164 
0.6
 
41
0.8
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1164
96.4%
0.6 41
 
3.4%
0.8 2
 
0.2%

Length

2024-04-21T10:21:43.150633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:21:43.319227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1164
96.4%
0.6 41
 
3.4%
0.8 2
 
0.2%

대장초기화여부
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
1
1207 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 1207
100.0%

Length

2024-04-21T10:21:43.499061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:21:43.659184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1207
100.0%

설치일자
Text

Distinct53
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
2024-04-21T10:21:44.152652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9030655
Min length1

Characters and Unicode

Total characters11953
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)1.2%

Sample

1st row2012-12-15
2nd row1990-01-01
3rd row1990-01-01
4th row1990-01-01
5th row1990-01-01
ValueCountFrequency (%)
1990-01-01 546
45.7%
1900-01-01 329
27.6%
2016-01-01 51
 
4.3%
2014-01-01 32
 
2.7%
2020-01-01 28
 
2.3%
2017-12-01 23
 
1.9%
2010-01-01 15
 
1.3%
2015-01-01 14
 
1.2%
2017-12-30 12
 
1.0%
2017-01-01 11
 
0.9%
Other values (42) 133
 
11.1%
2024-04-21T10:21:44.897681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3896
32.6%
1 3464
29.0%
- 2388
20.0%
9 1462
 
12.2%
2 425
 
3.6%
6 97
 
0.8%
7 79
 
0.7%
4 57
 
0.5%
3 38
 
0.3%
5 24
 
0.2%
Other values (2) 23
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9552
79.9%
Dash Punctuation 2388
 
20.0%
Space Separator 13
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3896
40.8%
1 3464
36.3%
9 1462
 
15.3%
2 425
 
4.4%
6 97
 
1.0%
7 79
 
0.8%
4 57
 
0.6%
3 38
 
0.4%
5 24
 
0.3%
8 10
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 2388
100.0%
Space Separator
ValueCountFrequency (%)
13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11953
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3896
32.6%
1 3464
29.0%
- 2388
20.0%
9 1462
 
12.2%
2 425
 
3.6%
6 97
 
0.8%
7 79
 
0.7%
4 57
 
0.5%
3 38
 
0.3%
5 24
 
0.2%
Other values (2) 23
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11953
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3896
32.6%
1 3464
29.0%
- 2388
20.0%
9 1462
 
12.2%
2 425
 
3.6%
6 97
 
0.8%
7 79
 
0.7%
4 57
 
0.5%
3 38
 
0.3%
5 24
 
0.2%
Other values (2) 23
 
0.2%

설치형식
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
1160 
단주식
 
15
독립식
 
15
일면식
 
8
규격규정없슴
 
2
Other values (6)
 
7

Length

Max length12
Median length1
Mean length1.1118476
Min length1

Unique

Unique5 ?
Unique (%)0.4%

Sample

1st row일면식
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
1160
96.1%
단주식 15
 
1.2%
독립식 15
 
1.2%
일면식 8
 
0.7%
규격규정없슴 2
 
0.2%
자체지주에 설치 2
 
0.2%
견착식 1
 
0.1%
단주식1000/900 1
 
0.1%
단주식1000/1000 1
 
0.1%
1000/800 1
 
0.1%

Length

2024-04-21T10:21:45.122459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
단주식 15
30.6%
독립식 15
30.6%
일면식 8
16.3%
규격규정없슴 2
 
4.1%
자체지주에 2
 
4.1%
설치 2
 
4.1%
견착식 1
 
2.0%
단주식1000/900 1
 
2.0%
단주식1000/1000 1
 
2.0%
1000/800 1
 
2.0%

비고
Text

Distinct188
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
2024-04-21T10:21:45.791893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length1
Mean length7.8823529
Min length1

Characters and Unicode

Total characters9514
Distinct characters175
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

Unique149 ?
Unique (%)12.3%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
gis 176
 
11.2%
db 142
 
9.0%
구축용역 92
 
5.8%
및 71
 
4.5%
도로 63
 
4.0%
지하시설물 52
 
3.3%
보도설치공사 38
 
2.4%
구축 33
 
2.1%
화성 27
 
1.7%
db구축 26
 
1.6%
Other values (281) 856
54.3%
2024-04-21T10:21:46.751622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2067
21.7%
0 468
 
4.9%
1 327
 
3.4%
도 282
 
3.0%
구 279
 
2.9%
2 274
 
2.9%
사 231
 
2.4%
축 224
 
2.4%
로 212
 
2.2%
S 208
 
2.2%
Other values (165) 4942
51.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4458
46.9%
Space Separator 2067
21.7%
Decimal Number 1606
 
16.9%
Uppercase Letter 1010
 
10.6%
Close Punctuation 131
 
1.4%
Open Punctuation 131
 
1.4%
Dash Punctuation 58
 
0.6%
Other Punctuation 36
 
0.4%
Math Symbol 9
 
0.1%
Connector Punctuation 8
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 282
 
6.3%
구 279
 
6.3%
사 231
 
5.2%
축 224
 
5.0%
로 212
 
4.8%
설 163
 
3.7%
공 157
 
3.5%
지 156
 
3.5%
역 153
 
3.4%
용 152
 
3.4%
Other values (141) 2449
54.9%
Decimal Number
ValueCountFrequency (%)
0 468
29.1%
1 327
20.4%
2 274
17.1%
3 205
12.8%
7 83
 
5.2%
8 69
 
4.3%
6 66
 
4.1%
5 41
 
2.6%
4 40
 
2.5%
9 33
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
S 208
20.6%
I 208
20.6%
G 208
20.6%
D 194
19.2%
B 191
18.9%
H 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
/ 19
52.8%
, 17
47.2%
Space Separator
ValueCountFrequency (%)
2067
100.0%
Close Punctuation
ValueCountFrequency (%)
) 131
100.0%
Open Punctuation
ValueCountFrequency (%)
( 131
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 58
100.0%
Math Symbol
ValueCountFrequency (%)
~ 9
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4458
46.9%
Common 4046
42.5%
Latin 1010
 
10.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 282
 
6.3%
구 279
 
6.3%
사 231
 
5.2%
축 224
 
5.0%
로 212
 
4.8%
설 163
 
3.7%
공 157
 
3.5%
지 156
 
3.5%
역 153
 
3.4%
용 152
 
3.4%
Other values (141) 2449
54.9%
Common
ValueCountFrequency (%)
2067
51.1%
0 468
 
11.6%
1 327
 
8.1%
2 274
 
6.8%
3 205
 
5.1%
) 131
 
3.2%
( 131
 
3.2%
7 83
 
2.1%
8 69
 
1.7%
6 66
 
1.6%
Other values (8) 225
 
5.6%
Latin
ValueCountFrequency (%)
S 208
20.6%
I 208
20.6%
G 208
20.6%
D 194
19.2%
B 191
18.9%
H 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5056
53.1%
Hangul 4458
46.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2067
40.9%
0 468
 
9.3%
1 327
 
6.5%
2 274
 
5.4%
S 208
 
4.1%
I 208
 
4.1%
G 208
 
4.1%
3 205
 
4.1%
D 194
 
3.8%
B 191
 
3.8%
Other values (14) 706
 
14.0%
Hangul
ValueCountFrequency (%)
도 282
 
6.3%
구 279
 
6.3%
사 231
 
5.2%
축 224
 
5.0%
로 212
 
4.8%
설 163
 
3.7%
공 157
 
3.5%
지 156
 
3.5%
역 153
 
3.4%
용 152
 
3.4%
Other values (141) 2449
54.9%

도로구간번호
Real number (ℝ)

HIGH CORRELATION 

Distinct624
Distinct (%)51.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0033369 × 1012
Minimum0
Maximum8.99919 × 1013
Zeros8
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2024-04-21T10:21:47.166609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.208413 × 109
Q19.003626 × 109
median9.992015 × 1010
Q39.9920151 × 1010
95-th percentile8.21619 × 1013
Maximum8.99919 × 1013
Range8.99919 × 1013
Interquartile range (IQR)9.0916525 × 1010

Descriptive statistics

Standard deviation2.2768773 × 1013
Coefficient of variation (CV)2.84491
Kurtosis7.2523797
Mean8.0033369 × 1012
Median Absolute Deviation (MAD)7.8758745 × 1010
Skewness2.960039
Sum9.6600276 × 1015
Variance5.1841702 × 1026
MonotonicityNot monotonic
2024-04-21T10:21:47.589997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89991600000000 23
 
1.9%
89991900000000 20
 
1.7%
82161900000000 20
 
1.7%
99920150348 18
 
1.5%
99920150667 13
 
1.1%
12161900000000 13
 
1.1%
99920150013 13
 
1.1%
99920150597 12
 
1.0%
99920150568 11
 
0.9%
99920150686 10
 
0.8%
Other values (614) 1054
87.3%
ValueCountFrequency (%)
0 8
0.7%
613000583 1
 
0.1%
613000591 1
 
0.1%
614530548 1
 
0.1%
614630002 1
 
0.1%
614640524 1
 
0.1%
614710391 1
 
0.1%
614710393 1
 
0.1%
614710399 1
 
0.1%
614810242 1
 
0.1%
ValueCountFrequency (%)
89991900000000 20
1.7%
89991600000000 23
1.9%
82992000000000 1
 
0.1%
82162000000000 10
0.8%
82161900000000 20
1.7%
82161800000000 2
 
0.2%
82112000000000 10
0.8%
82111700000000 1
 
0.1%
42162000000000 1
 
0.1%
42161800000000 7
 
0.6%

공사번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
1203 
5
 
3
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
1203
99.7%
5 3
 
0.2%
1 1
 
0.1%

Length

2024-04-21T10:21:47.988642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:21:48.302054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 3
75.0%
1 1
 
25.0%

납품업체
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
1026 
㈜진성이엔씨
 
61
새한항업
 
24
㈜선진이엔씨
 
19
(주)한라지리정보
 
19
Other values (6)
 
58

Length

Max length10
Median length1
Mean length1.8757249
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
1026
85.0%
㈜진성이엔씨 61
 
5.1%
새한항업 24
 
2.0%
㈜선진이엔씨 19
 
1.6%
(주)한라지리정보 19
 
1.6%
(주)성원공간정보 18
 
1.5%
(주)진성이엔씨 12
 
1.0%
㈜한양지에스티 11
 
0.9%
(주)지아이에스21 11
 
0.9%
(주)선진이엔씨 5
 
0.4%

Length

2024-04-21T10:21:48.636956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
㈜진성이엔씨 61
33.7%
새한항업 24
 
13.3%
㈜선진이엔씨 19
 
10.5%
주)한라지리정보 19
 
10.5%
주)성원공간정보 18
 
9.9%
주)진성이엔씨 12
 
6.6%
㈜한양지에스티 11
 
6.1%
주)지아이에스21 11
 
6.1%
주)선진이엔씨 5
 
2.8%
㈜대광지오텍 1
 
0.6%

로딩일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct49
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
955 
2021-04-16
 
33
2019-01-05
 
23
2017-08-31
 
17
2020-03-13
 
12
Other values (44)
167 

Length

Max length10
Median length1
Mean length2.8790389
Min length1

Unique

Unique19 ?
Unique (%)1.6%

Sample

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

Common Values

ValueCountFrequency (%)
955
79.1%
2021-04-16 33
 
2.7%
2019-01-05 23
 
1.9%
2017-08-31 17
 
1.4%
2020-03-13 12
 
1.0%
2020-05-20 11
 
0.9%
2021-03-23 11
 
0.9%
2019-12-04 11
 
0.9%
2017-12-20 10
 
0.8%
2020-04-08 10
 
0.8%
Other values (39) 114
 
9.4%

Length

2024-04-21T10:21:48.996591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-04-16 33
 
13.1%
2019-01-05 23
 
9.1%
2017-08-31 17
 
6.7%
2020-03-13 12
 
4.8%
2020-05-20 11
 
4.4%
2021-03-23 11
 
4.4%
2019-12-04 11
 
4.4%
2017-12-20 10
 
4.0%
2020-04-08 10
 
4.0%
2018-08-01 10
 
4.0%
Other values (38) 104
41.3%

X좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct1205
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean191671.14
Minimum166441.06
Maximum212583.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2024-04-21T10:21:49.353461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum166441.06
5-th percentile173670.59
Q1183816.26
median192145.02
Q3199173.39
95-th percentile209876.65
Maximum212583.07
Range46142.011
Interquartile range (IQR)15357.131

Descriptive statistics

Standard deviation10399.939
Coefficient of variation (CV)0.054259283
Kurtosis-0.76224304
Mean191671.14
Median Absolute Deviation (MAD)7903.426
Skewness-0.046572886
Sum2.3134707 × 108
Variance1.0815873 × 108
MonotonicityNot monotonic
2024-04-21T10:21:49.780056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180995.76 2
 
0.2%
180878.707 2
 
0.2%
179594.44 1
 
0.1%
201043.972 1
 
0.1%
171764.289 1
 
0.1%
171623.137 1
 
0.1%
171517.324 1
 
0.1%
171491.021 1
 
0.1%
203125.131 1
 
0.1%
200296.06 1
 
0.1%
Other values (1195) 1195
99.0%
ValueCountFrequency (%)
166441.059 1
0.1%
166731.742 1
0.1%
166961.146 1
0.1%
167123.023 1
0.1%
169589.495 1
0.1%
169764.036 1
0.1%
169840.278 1
0.1%
170019.19 1
0.1%
170280.443 1
0.1%
170350.445 1
0.1%
ValueCountFrequency (%)
212583.07 1
0.1%
212370.06 1
0.1%
212046.0 1
0.1%
212031.596 1
0.1%
212027.225 1
0.1%
211902.47 1
0.1%
211792.83 1
0.1%
211700.3 1
0.1%
211681.18 1
0.1%
211496.08 1
0.1%

Y좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct1205
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean508794.07
Minimum492341.42
Maximum521103.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.7 KiB
2024-04-21T10:21:50.170046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum492341.42
5-th percentile497700.28
Q1506340.47
median509470.59
Q3513167.72
95-th percentile516193.28
Maximum521103.35
Range28761.928
Interquartile range (IQR)6827.247

Descriptive statistics

Standard deviation5686.0594
Coefficient of variation (CV)0.011175561
Kurtosis0.00025920947
Mean508794.07
Median Absolute Deviation (MAD)3476.95
Skewness-0.65435792
Sum6.1411444 × 108
Variance32331271
MonotonicityNot monotonic
2024-04-21T10:21:50.586024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
493462.695 2
 
0.2%
493497.966 2
 
0.2%
511623.24 1
 
0.1%
511726.776 1
 
0.1%
515150.55 1
 
0.1%
515273.773 1
 
0.1%
515355.127 1
 
0.1%
515359.504 1
 
0.1%
512455.987 1
 
0.1%
512383.43 1
 
0.1%
Other values (1195) 1195
99.0%
ValueCountFrequency (%)
492341.42 1
0.1%
492679.36 1
0.1%
492816.76 1
0.1%
492946.45 1
0.1%
492995.3 1
0.1%
493032.16 1
0.1%
493108.51 1
0.1%
493139.36 1
0.1%
493292.46 1
0.1%
493300.7 1
0.1%
ValueCountFrequency (%)
521103.348 1
0.1%
520846.35 1
0.1%
520772.531 1
0.1%
520718.156 1
0.1%
520693.92 1
0.1%
520693.09 1
0.1%
520687.25 1
0.1%
520488.98 1
0.1%
520183.659 1
0.1%
520124.22 1
0.1%

Interactions

2024-04-21T10:21:36.407047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:21:32.881401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:21:33.871165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:21:34.869040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:21:35.658339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:21:36.560324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:21:33.054578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:21:34.138504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:21:35.013657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:21:35.803605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:21:36.719895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:21:33.218631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:21:34.398768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:21:35.171830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:21:35.963573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:21:36.871020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:21:33.362963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:21:34.555815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:21:35.316576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:21:36.109213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:21:37.020183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:21:33.580795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:21:34.708318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:21:35.462544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:21:36.254762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T10:21:51.083054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호행정읍면동관리기관반사경재질반사경모양반사경관경반사경가로반사경세로설치일자설치형식도로구간번호공사번호납품업체로딩일자X좌표Y좌표
관리번호1.0000.4420.1670.6800.1370.2750.2720.1530.9340.6540.9930.0000.9690.9900.5150.399
행정읍면동0.4421.0000.8440.4960.4590.5780.4380.4820.9110.6930.4280.5050.5180.8430.9360.869
관리기관0.1670.8441.0000.000NaN0.420NaNNaN0.8090.1160.584NaN0.8681.0000.5530.000
반사경재질0.6800.4960.0001.0000.2420.3090.4160.2720.8520.2120.6200.0000.6010.8300.5740.203
반사경모양0.1370.459NaN0.2421.0000.1721.0001.0000.8390.5580.1230.0000.3660.7190.2800.440
반사경관경0.2750.5780.4200.3090.1721.0000.1080.1770.8150.1680.2060.0880.3100.6390.4150.254
반사경가로0.2720.438NaN0.4161.0000.1081.0001.0000.8220.5340.2450.0000.3630.6350.3860.466
반사경세로0.1530.482NaN0.2721.0000.1771.0001.0000.7400.5440.1370.0000.3880.6530.2440.376
설치일자0.9340.9110.8090.8520.8390.8150.8220.7401.0000.9150.8960.9330.9520.9880.8480.689
설치형식0.6540.6930.1160.2120.5580.1680.5340.5440.9151.0000.6200.6170.6900.8810.4440.226
도로구간번호0.9930.4280.5840.6200.1230.2060.2450.1370.8960.6201.0000.0000.9550.9930.4930.419
공사번호0.0000.505NaN0.0000.0000.0880.0000.0000.9330.6170.0001.0000.0000.8980.1150.000
납품업체0.9690.5180.8680.6010.3660.3100.3630.3880.9520.6900.9550.0001.0000.9950.4770.544
로딩일자0.9900.8431.0000.8300.7190.6390.6350.6530.9880.8810.9930.8980.9951.0000.8360.732
X좌표0.5150.9360.5530.5740.2800.4150.3860.2440.8480.4440.4930.1150.4770.8361.0000.579
Y좌표0.3990.8690.0000.2030.4400.2540.4660.3760.6890.2260.4190.0000.5440.7320.5791.000
2024-04-21T10:21:51.462842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
반사경세로행정읍면동공사번호반사경재질설치형식로딩일자관리기관반사경가로납품업체반사경모양
반사경세로1.0000.2840.0000.2120.3770.4071.0000.9990.2461.000
행정읍면동0.2841.0000.3020.2680.3210.3480.6300.2450.2270.397
공사번호0.0000.3021.0000.0000.4500.7071.0000.0000.0000.000
반사경재질0.2120.2680.0001.0000.1180.5460.0000.1660.3840.295
설치형식0.3770.3210.4500.1181.0000.5200.1040.3280.2670.536
로딩일자0.4070.3480.7070.5460.5201.0000.8580.3440.9260.600
관리기관1.0000.6301.0000.0000.1040.8581.0001.0000.5521.000
반사경가로0.9990.2450.0000.1660.3280.3441.0001.0000.2090.999
납품업체0.2460.2270.0000.3840.2670.9260.5520.2091.0000.349
반사경모양1.0000.3970.0000.2950.5360.6001.0000.9990.3491.000
2024-04-21T10:21:51.795280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호반사경관경도로구간번호X좌표Y좌표행정읍면동관리기관반사경재질반사경모양반사경가로반사경세로설치형식공사번호납품업체로딩일자
관리번호1.000-0.1140.617-0.258-0.0140.2330.1190.3150.1670.1040.1160.4330.0000.9280.912
반사경관경-0.1141.000-0.3760.3540.1870.2910.4080.2030.1840.0690.1200.0830.0590.1580.293
도로구간번호0.617-0.3761.000-0.321-0.2030.2240.5220.2740.1500.0940.1030.4010.0000.8920.929
X좌표-0.2580.354-0.3211.0000.0070.7110.3960.2760.2140.1700.1490.2000.0680.2270.463
Y좌표-0.0140.187-0.2030.0071.0000.5550.0000.0860.3370.2110.2430.0980.0000.2670.341
행정읍면동0.2330.2910.2240.7110.5551.0000.6300.2680.3970.2450.2840.3210.3020.2270.348
관리기관0.1190.4080.5220.3960.0000.6301.0000.0001.0001.0001.0000.1041.0000.5520.858
반사경재질0.3150.2030.2740.2760.0860.2680.0001.0000.2950.1660.2120.1180.0000.3840.546
반사경모양0.1670.1840.1500.2140.3370.3971.0000.2951.0000.9991.0000.5360.0000.3490.600
반사경가로0.1040.0690.0940.1700.2110.2451.0000.1660.9991.0000.9990.3280.0000.2090.344
반사경세로0.1160.1200.1030.1490.2430.2841.0000.2121.0000.9991.0000.3770.0000.2460.407
설치형식0.4330.0830.4010.2000.0980.3210.1040.1180.5360.3280.3771.0000.4500.2670.520
공사번호0.0000.0590.0000.0680.0000.3021.0000.0000.0000.0000.0000.4501.0000.0000.707
납품업체0.9280.1580.8920.2270.2670.2270.5520.3840.3490.2090.2460.2670.0001.0000.926
로딩일자0.9120.2930.9290.4630.3410.3480.8580.5460.6000.3440.4070.5200.7070.9261.000

Missing values

2024-04-21T10:21:37.267291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T10:21:37.693624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

지형지물부호관리번호행정읍면동도엽번호관리기관반사경재질반사경모양반사경관경반사경가로반사경세로대장초기화여부설치일자설치형식비고도로구간번호공사번호납품업체로딩일자X좌표Y좌표
0도로반사경211602000000마도면376160195A<NA>스테인레스원형10000.00.012012-12-15일면식9000333504179594.44511623.24
1도로반사경99920150495마도면376160193C<NA>스테인레스원형00.00.011990-01-0199920150651178760.27511370.08
2도로반사경99920150496마도면376160192D<NA>스테인레스원형00.00.011990-01-0199920150651178582.77511322.73
3도로반사경99920150497마도면376160192C<NA>스테인레스원형00.00.011990-01-0199920150651178452.72511298.08
4도로반사경99920150498마도면376160192C<NA>스테인레스원형00.00.011990-01-0199920150651178348.44511286.5
5도로반사경99920150552마도면376150599A<NA>스테인레스원형00.00.011990-01-0199920150651177111.53511716.05
6도로반사경99920150480남양읍376122253C<NA>스테인레스원형00.00.011990-01-0199920150568183359.33519221.28
7도로반사경99920150474남양읍376122294A<NA>스테인레스원형00.00.011990-01-0199920150568183706.57517156.85
8도로반사경99920150476남양읍376122284B<NA>스테인레스원형00.00.011990-01-0199920150568183824.56517787.18
9도로반사경22111900000000동탄1동377130275C화성시알루미늄원형10000.00.012019-11-01단주식H3 도로개선공사 GIS DB 구축용역2100319010(주)성원공간정보2020-02-14206240.8865512346.7485
지형지물부호관리번호행정읍면동도엽번호관리기관반사경재질반사경모양반사경관경반사경가로반사경세로대장초기화여부설치일자설치형식비고도로구간번호공사번호납품업체로딩일자X좌표Y좌표
1197도로반사경100045정남면376161062D<NA>스테인레스원형10000.00.011900-01-011331011013196251.074507509.671
1198도로반사경99920150353매송면376122389C<NA>스테인레스원형00.00.011990-01-0199920150175190372.52517510.2
1199도로반사경99920150354매송면376122300B<NA>스테인레스원형00.00.011990-01-0199920150175190963.89517261.46
1200도로반사경99920150336매송면376160418A<NA>스테인레스원형00.00.011990-01-0199920150192194408.29516179.5
1201도로반사경42161600000000비봉면376160324C<NA>기타원형10000.00.012016-01-01비봉 도시계획도로(중로2-3호선)개설공사 GIS DB구축 사업2006913009(주)선진이엔씨2018-04-18188139.426515145.244
1202도로반사경42161600000000비봉면376160324D<NA>기타원형8000.00.012016-01-01비봉 도시계획도로(중로2-3호선)개설공사 GIS DB구축 사업42161600000000(주)선진이엔씨2018-04-18188356.816515114.518
1203도로반사경99920150402<NA>376160734D<NA>스테인레스원형00.00.011990-01-0199920150597183855.04509067.58
1204도로반사경22162000000000<NA>376161733B도로과알루미늄원형10000.00.012020-03-01단주식우정도시계획도로 중로3-3호선 개설공사 GIS DB 구축용역22162000000000(주)성원공간정보2020-06-10183461.898498270.682
1205도로반사경12161700000000송산면376150438D<NA>기타원형10000.00.012017-10-12송산면 고포리 보도설치공사 GIS DB 구축용역12161700000000㈜진성이엔씨2018-08-01172473.711514788.833
1206도로반사경211606000000봉담읍376160575B<NA>스테인레스원형9000.00.012012-06-01형식규정업슴태안도시계획도로(대3-3호선 외2개소)개설공사 GIS DB구축21160575003197592.198512736.669

Duplicate rows

Most frequently occurring

지형지물부호관리번호행정읍면동도엽번호관리기관반사경재질반사경모양반사경관경반사경가로반사경세로대장초기화여부설치일자설치형식비고도로구간번호공사번호납품업체로딩일자X좌표Y좌표# duplicates
0도로반사경211621000000<NA>376162118C<NA>스테인레스원형10000.00.012013-01-01이화-석천간(2공구)도로확포장공사 GIS DB 구축 사업21162129001180878.707493497.9662
1도로반사경211621000000<NA>376162128A<NA>스테인레스원형10000.00.012013-01-01이화-석천간(2공구)도로확포장공사 GIS DB 구축 사업21162129001180995.76493462.6952