Overview

Dataset statistics

Number of variables11
Number of observations3193
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory283.9 KiB
Average record size in memory91.0 B

Variable types

Categorical6
Text2
Numeric3

Dataset

Description2023년도 계룡시 관내 가로등 데이터로서 가로등의 위도, 경도, 사용목적, 설치년도, 설치형태에 관한 공공데이터를 제공합니다.
URLhttps://www.data.go.kr/data/15093862/fileData.do

Alerts

관리기관 has constant value ""Constant
관리부서 has constant value ""Constant
사용목적 has constant value ""Constant
데이터기준일자 has constant value ""Constant
경도 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
소재지도로명주소 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
설치형태 is highly imbalanced (83.1%)Imbalance

Reproduction

Analysis started2023-12-12 08:22:36.345492
Analysis finished2023-12-12 08:22:38.245696
Duration1.9 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리기관
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 KiB
충청남도 계룡시
3193 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row충청남도 계룡시
2nd row충청남도 계룡시
3rd row충청남도 계룡시
4th row충청남도 계룡시
5th row충청남도 계룡시

Common Values

ValueCountFrequency (%)
충청남도 계룡시 3193
100.0%

Length

2023-12-12T17:22:38.306639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:22:38.394961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 3193
50.0%
계룡시 3193
50.0%

관리부서
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 KiB
건설교통실
3193 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
건설교통실 3193
100.0%

Length

2023-12-12T17:22:38.498966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:22:38.597429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건설교통실 3193
100.0%

관리번호
Text

Distinct3191
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size25.1 KiB
2023-12-12T17:22:38.839491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length7
Mean length7.1453179
Min length4

Characters and Unicode

Total characters22815
Distinct characters111
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3189 ?
Unique (%)99.9%

Sample

1st row계룡017
2nd row계룡018
3rd row계룡086
4th row계룡087
5th row계룡088
ValueCountFrequency (%)
터널등 15
 
0.5%
엄사 12
 
0.4%
평리4가 3
 
0.1%
연화ic 3
 
0.1%
터널 3
 
0.1%
터널등조명3 2
 
0.1%
2
 
0.1%
금암 2
 
0.1%
구 2
 
0.1%
금암1-592 2
 
0.1%
Other values (3200) 3200
98.6%
2023-12-12T17:22:39.278524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4110
18.0%
- 2926
 
12.8%
0 1215
 
5.3%
3 1080
 
4.7%
2 1054
 
4.6%
4 1019
 
4.5%
5 1007
 
4.4%
6 863
 
3.8%
사 751
 
3.3%
엄 749
 
3.3%
Other values (101) 8041
35.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12228
53.6%
Other Letter 7600
33.3%
Dash Punctuation 2926
 
12.8%
Space Separator 53
 
0.2%
Lowercase Letter 6
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 751
9.9%
엄 749
9.9%
암 722
9.5%
금 718
9.4%
두 617
8.1%
마 617
8.1%
도 602
7.9%
안 558
 
7.3%
신 558
 
7.3%
대 258
 
3.4%
Other values (85) 1450
19.1%
Decimal Number
ValueCountFrequency (%)
1 4110
33.6%
0 1215
 
9.9%
3 1080
 
8.8%
2 1054
 
8.6%
4 1019
 
8.3%
5 1007
 
8.2%
6 863
 
7.1%
9 646
 
5.3%
7 641
 
5.2%
8 593
 
4.8%
Lowercase Letter
ValueCountFrequency (%)
i 3
50.0%
c 3
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 2926
100.0%
Space Separator
ValueCountFrequency (%)
53
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15209
66.7%
Hangul 7600
33.3%
Latin 6
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 751
9.9%
엄 749
9.9%
암 722
9.5%
금 718
9.4%
두 617
8.1%
마 617
8.1%
도 602
7.9%
안 558
 
7.3%
신 558
 
7.3%
대 258
 
3.4%
Other values (85) 1450
19.1%
Common
ValueCountFrequency (%)
1 4110
27.0%
- 2926
19.2%
0 1215
 
8.0%
3 1080
 
7.1%
2 1054
 
6.9%
4 1019
 
6.7%
5 1007
 
6.6%
6 863
 
5.7%
9 646
 
4.2%
7 641
 
4.2%
Other values (4) 648
 
4.3%
Latin
ValueCountFrequency (%)
i 3
50.0%
c 3
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15215
66.7%
Hangul 7600
33.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4110
27.0%
- 2926
19.2%
0 1215
 
8.0%
3 1080
 
7.1%
2 1054
 
6.9%
4 1019
 
6.7%
5 1007
 
6.6%
6 863
 
5.7%
9 646
 
4.2%
7 641
 
4.2%
Other values (6) 654
 
4.3%
Hangul
ValueCountFrequency (%)
사 751
9.9%
엄 749
9.9%
암 722
9.5%
금 718
9.4%
두 617
8.1%
마 617
8.1%
도 602
7.9%
안 558
 
7.3%
신 558
 
7.3%
대 258
 
3.4%
Other values (85) 1450
19.1%

소재지도로명주소
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size25.1 KiB
충청남도 계룡시 두마면
883 
충청남도 계룡시 엄사면
821 
충청남도 계룡시 금암동
762 
충청남도 계룡시 신도안면
563 
충청남도 계룡시
164 

Length

Max length14
Median length13
Mean length13.022236
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row충청남도 계룡시
2nd row충청남도 계룡시
3rd row충청남도 계룡시
4th row충청남도 계룡시
5th row충청남도 계룡시

Common Values

ValueCountFrequency (%)
충청남도 계룡시 두마면 883
27.7%
충청남도 계룡시 엄사면 821
25.7%
충청남도 계룡시 금암동 762
23.9%
충청남도 계룡시 신도안면 563
17.6%
충청남도 계룡시 164
 
5.1%

Length

2023-12-12T17:22:39.474101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:22:39.623327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 3193
33.9%
계룡시 3193
33.9%
두마면 883
 
9.4%
엄사면 821
 
8.7%
금암동 762
 
8.1%
신도안면 563
 
6.0%

소재지지번주소
Text

Distinct467
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Memory size25.1 KiB
2023-12-12T17:22:40.053800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length23
Mean length17.56436
Min length10

Characters and Unicode

Total characters56083
Distinct characters46
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique253 ?
Unique (%)7.9%

Sample

1st row충청남도 계룡시
2nd row충청남도 계룡시
3rd row충청남도 계룡시
4th row충청남도 계룡시
5th row충청남도 계룡시
ValueCountFrequency (%)
충청남도 3193
24.6%
계룡시 3193
24.6%
두마면 883
 
6.8%
엄사면 821
 
6.3%
금암동 762
 
5.9%
엄사리 633
 
4.9%
신도안면 563
 
4.3%
남선리 443
 
3.4%
두계리 412
 
3.2%
농소리 243
 
1.9%
Other values (456) 1809
14.0%
2023-12-12T17:22:40.550104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11882
21.2%
도 3790
 
6.8%
남 3636
 
6.5%
계 3626
 
6.5%
충 3193
 
5.7%
룡 3193
 
5.7%
시 3193
 
5.7%
청 3193
 
5.7%
면 2267
 
4.0%
리 2267
 
4.0%
Other values (36) 15843
28.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 38841
69.3%
Space Separator 11882
 
21.2%
Decimal Number 4665
 
8.3%
Dash Punctuation 695
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 3790
9.8%
남 3636
9.4%
계 3626
9.3%
충 3193
 
8.2%
룡 3193
 
8.2%
시 3193
 
8.2%
청 3193
 
8.2%
면 2267
 
5.8%
리 2267
 
5.8%
엄 1454
 
3.7%
Other values (24) 9029
23.2%
Decimal Number
ValueCountFrequency (%)
1 961
20.6%
2 641
13.7%
4 564
12.1%
6 452
9.7%
3 415
8.9%
0 404
8.7%
5 387
8.3%
8 336
 
7.2%
7 304
 
6.5%
9 201
 
4.3%
Space Separator
ValueCountFrequency (%)
11882
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 695
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 38841
69.3%
Common 17242
30.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 3790
9.8%
남 3636
9.4%
계 3626
9.3%
충 3193
 
8.2%
룡 3193
 
8.2%
시 3193
 
8.2%
청 3193
 
8.2%
면 2267
 
5.8%
리 2267
 
5.8%
엄 1454
 
3.7%
Other values (24) 9029
23.2%
Common
ValueCountFrequency (%)
11882
68.9%
1 961
 
5.6%
- 695
 
4.0%
2 641
 
3.7%
4 564
 
3.3%
6 452
 
2.6%
3 415
 
2.4%
0 404
 
2.3%
5 387
 
2.2%
8 336
 
1.9%
Other values (2) 505
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 38841
69.3%
ASCII 17242
30.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11882
68.9%
1 961
 
5.6%
- 695
 
4.0%
2 641
 
3.7%
4 564
 
3.3%
6 452
 
2.6%
3 415
 
2.4%
0 404
 
2.3%
5 387
 
2.2%
8 336
 
1.9%
Other values (2) 505
 
2.9%
Hangul
ValueCountFrequency (%)
도 3790
9.8%
남 3636
9.4%
계 3626
9.3%
충 3193
 
8.2%
룡 3193
 
8.2%
시 3193
 
8.2%
청 3193
 
8.2%
면 2267
 
5.8%
리 2267
 
5.8%
엄 1454
 
3.7%
Other values (24) 9029
23.2%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct3084
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.25027
Minimum127.20528
Maximum127.28194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.2 KiB
2023-12-12T17:22:40.727814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.20528
5-th percentile127.22631
Q1127.23972
median127.25038
Q3127.25933
95-th percentile127.27583
Maximum127.28194
Range0.076663463
Interquartile range (IQR)0.019613357

Descriptive statistics

Standard deviation0.014939209
Coefficient of variation (CV)0.00011740021
Kurtosis-0.34723469
Mean127.25027
Median Absolute Deviation (MAD)0.0099370675
Skewness-0.053456317
Sum406310.12
Variance0.00022317995
MonotonicityNot monotonic
2023-12-12T17:22:40.936421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.23593611111112 7
 
0.2%
127.23594166666666 5
 
0.2%
127.23593055555556 4
 
0.1%
127.25188055555556 3
 
0.1%
127.23594722222222 3
 
0.1%
127.23279166666666 3
 
0.1%
127.23604722222223 3
 
0.1%
127.23915277777776 3
 
0.1%
127.23963055555556 3
 
0.1%
127.26771666666666 2
 
0.1%
Other values (3074) 3157
98.9%
ValueCountFrequency (%)
127.20527777777777 1
< 0.1%
127.21163333333332 1
< 0.1%
127.21173611111112 1
< 0.1%
127.21184444444444 1
< 0.1%
127.21212777777777 1
< 0.1%
127.21225 1
< 0.1%
127.21228333333332 1
< 0.1%
127.21228476370592 1
< 0.1%
127.2123142394402 1
< 0.1%
127.21234166666666 1
< 0.1%
ValueCountFrequency (%)
127.28194124048808 1
< 0.1%
127.28141230309372 1
< 0.1%
127.28136111111112 1
< 0.1%
127.28118032555795 1
< 0.1%
127.28101666666666 1
< 0.1%
127.28093737206382 1
< 0.1%
127.28092736535974 1
< 0.1%
127.28077639053184 1
< 0.1%
127.2807432414108 1
< 0.1%
127.28071388888888 1
< 0.1%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct3041
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.278867
Minimum36.247433
Maximum36.325244
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.2 KiB
2023-12-12T17:22:41.120767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.247433
5-th percentile36.253331
Q136.267672
median36.27525
Q336.288419
95-th percentile36.309355
Maximum36.325244
Range0.07781107
Interquartile range (IQR)0.020747281

Descriptive statistics

Standard deviation0.016158953
Coefficient of variation (CV)0.00044540953
Kurtosis-0.25689679
Mean36.278867
Median Absolute Deviation (MAD)0.010472222
Skewness0.47148348
Sum115838.42
Variance0.00026111176
MonotonicityNot monotonic
2023-12-12T17:22:41.282352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.29993055555556 7
 
0.2%
36.29993333333333 7
 
0.2%
36.29975 5
 
0.2%
36.29978055555556 4
 
0.1%
36.29974722222222 4
 
0.1%
36.299927777777775 4
 
0.1%
36.25089166666666 4
 
0.1%
36.29974166666666 4
 
0.1%
36.28830277777777 3
 
0.1%
36.29993888888889 3
 
0.1%
Other values (3031) 3148
98.6%
ValueCountFrequency (%)
36.247433333333326 1
< 0.1%
36.24744444444445 1
< 0.1%
36.24773888888889 1
< 0.1%
36.24776944444445 1
< 0.1%
36.24807222222223 1
< 0.1%
36.24808333333333 1
< 0.1%
36.24839166666666 1
< 0.1%
36.24840555555556 1
< 0.1%
36.24861111111111 1
< 0.1%
36.24863884926241 1
< 0.1%
ValueCountFrequency (%)
36.32524440370062 1
< 0.1%
36.32494084060252 1
< 0.1%
36.3248908351393 1
< 0.1%
36.32455356923965 1
< 0.1%
36.32453051341378 1
< 0.1%
36.32422030544973 1
< 0.1%
36.32416792150769 1
< 0.1%
36.32387364392683 1
< 0.1%
36.323818961612815 1
< 0.1%
36.32365082547765 1
< 0.1%

설치연도
Real number (ℝ)

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.9139
Minimum2011
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.2 KiB
2023-12-12T17:22:41.462103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2015
Q12015
median2015
Q32016
95-th percentile2021
Maximum2023
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.708496
Coefficient of variation (CV)0.00084750444
Kurtosis3.5467753
Mean2015.9139
Median Absolute Deviation (MAD)0
Skewness2.0501097
Sum6436813
Variance2.9189585
MonotonicityNot monotonic
2023-12-12T17:22:41.636765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2015 2184
68.4%
2018 323
 
10.1%
2016 301
 
9.4%
2017 164
 
5.1%
2021 153
 
4.8%
2022 37
 
1.2%
2019 18
 
0.6%
2020 7
 
0.2%
2023 5
 
0.2%
2011 1
 
< 0.1%
ValueCountFrequency (%)
2011 1
 
< 0.1%
2015 2184
68.4%
2016 301
 
9.4%
2017 164
 
5.1%
2018 323
 
10.1%
2019 18
 
0.6%
2020 7
 
0.2%
2021 153
 
4.8%
2022 37
 
1.2%
2023 5
 
0.2%
ValueCountFrequency (%)
2023 5
 
0.2%
2022 37
 
1.2%
2021 153
 
4.8%
2020 7
 
0.2%
2019 18
 
0.6%
2018 323
 
10.1%
2017 164
 
5.1%
2016 301
 
9.4%
2015 2184
68.4%
2011 1
 
< 0.1%

설치형태
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.1 KiB
전용주
3113 
한전주
 
80

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전용주 3113
97.5%
한전주 80
 
2.5%

Length

2023-12-12T17:22:41.767045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:22:41.873238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전용주 3113
97.5%
한전주 80
 
2.5%

사용목적
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 KiB
가로등
3193 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가로등 3193
100.0%

Length

2023-12-12T17:22:41.968259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:22:42.092112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가로등 3193
100.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 KiB
2023-06-23
3193 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-06-23
2nd row2023-06-23
3rd row2023-06-23
4th row2023-06-23
5th row2023-06-23

Common Values

ValueCountFrequency (%)
2023-06-23 3193
100.0%

Length

2023-12-12T17:22:42.206298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:22:42.324809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-06-23 3193
100.0%

Interactions

2023-12-12T17:22:37.645796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:22:36.868137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:22:37.223386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:22:37.775647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:22:36.986170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:22:37.390318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:22:37.901352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:22:37.107033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:22:37.538539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:22:42.749135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지도로명주소경도위도설치연도설치형태
소재지도로명주소1.0000.9130.9520.4950.023
경도0.9131.0000.8340.4730.043
위도0.9520.8341.0000.5950.142
설치연도0.4950.4730.5951.0000.284
설치형태0.0230.0430.1420.2841.000
2023-12-12T17:22:42.880232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지도로명주소설치형태
소재지도로명주소1.0000.028
설치형태0.0281.000
2023-12-12T17:22:43.002313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경도위도설치연도소재지도로명주소설치형태
경도1.000-0.694-0.1110.6110.033
위도-0.6941.0000.1320.6990.109
설치연도-0.1110.1321.0000.3460.303
소재지도로명주소0.6110.6990.3461.0000.028
설치형태0.0330.1090.3030.0281.000

Missing values

2023-12-12T17:22:38.033836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:22:38.184257image/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

관리기관관리부서관리번호소재지도로명주소소재지지번주소경도위도설치연도설치형태사용목적데이터기준일자
0충청남도 계룡시건설교통실계룡017충청남도 계룡시충청남도 계룡시127.24378236.290052015전용주가로등2023-06-23
1충청남도 계룡시건설교통실계룡018충청남도 계룡시충청남도 계룡시127.24366736.2909752015전용주가로등2023-06-23
2충청남도 계룡시건설교통실계룡086충청남도 계룡시충청남도 계룡시127.23868936.3094862015전용주가로등2023-06-23
3충청남도 계룡시건설교통실계룡087충청남도 계룡시충청남도 계룡시127.23844736.3098942015전용주가로등2023-06-23
4충청남도 계룡시건설교통실계룡088충청남도 계룡시충청남도 계룡시127.23875636.3090832015전용주가로등2023-06-23
5충청남도 계룡시건설교통실계룡089충청남도 계룡시충청남도 계룡시127.24166736.2907922015전용주가로등2023-06-23
6충청남도 계룡시건설교통실계룡099충청남도 계룡시충청남도 계룡시127.23575636.2995612015전용주가로등2023-06-23
7충청남도 계룡시건설교통실계룡100충청남도 계룡시충청남도 계룡시127.23547536.2997332015전용주가로등2023-06-23
8충청남도 계룡시건설교통실계룡1003충청남도 계룡시충청남도 계룡시127.24806136.2802392015전용주가로등2023-06-23
9충청남도 계룡시건설교통실계룡1004충청남도 계룡시충청남도 계룡시127.24848136.2800582015전용주가로등2023-06-23
관리기관관리부서관리번호소재지도로명주소소재지지번주소경도위도설치연도설치형태사용목적데이터기준일자
3183충청남도 계룡시건설교통실팥거리길지하차도2충청남도 계룡시 두마면충청남도 계룡시 두마면 두계리 49-10127.27394836.2676112017전용주가로등2023-06-23
3184충청남도 계룡시건설교통실팥거리길지하차도20충청남도 계룡시 두마면충청남도 계룡시 두마면 두계리 154-1127.27436236.2680142017전용주가로등2023-06-23
3185충청남도 계룡시건설교통실팥거리길지하차도3충청남도 계룡시 두마면충청남도 계룡시 두마면 두계리 49-9127.2738936.2676722017전용주가로등2023-06-23
3186충청남도 계룡시건설교통실팥거리길지하차도4충청남도 계룡시 두마면충청남도 계룡시 두마면 두계리 160-12127.27383736.267722017전용주가로등2023-06-23
3187충청남도 계룡시건설교통실팥거리길지하차도5충청남도 계룡시 두마면충청남도 계룡시 두마면 두계리 53-6127.27379336.2677692017전용주가로등2023-06-23
3188충청남도 계룡시건설교통실팥거리길지하차도6충청남도 계룡시 두마면충청남도 계룡시 두마면 두계리 53-6127.27372436.2678262017전용주가로등2023-06-23
3189충청남도 계룡시건설교통실팥거리길지하차도7충청남도 계룡시 두마면충청남도 계룡시 두마면 두계리 53-6127.27368536.2678872017전용주가로등2023-06-23
3190충청남도 계룡시건설교통실팥거리길지하차도8충청남도 계룡시 두마면충청남도 계룡시 두마면 두계리 53-6127.27368236.2679522017전용주가로등2023-06-23
3191충청남도 계룡시건설교통실팥거리길지하차도9충청남도 계룡시 두마면충청남도 계룡시 두마면 두계리 53-6127.27373336.2680152017전용주가로등2023-06-23
3192충청남도 계룡시건설교통실호남철도 지하차도 터널충청남도 계룡시 금암동충청남도 계룡시 금암동 452127.25172336.2783912020한전주가로등2023-06-23