Overview

Dataset statistics

Number of variables16
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory146.0 B

Variable types

Categorical8
Numeric8

Dataset

Description대전광역시 도로관리시스템에 등재된 가로등 현황입니다. ※ 2022년 공공데이터 기업 매칭 지원사업으로 청년 인턴을 통해 정비·구축된 데이터입니다. 법적 효력이 없으므로 참고 목적으로만 활용하시기 바랍니다.
URLhttps://www.data.go.kr/data/15110054/fileData.do

Alerts

지형지물부호 has constant value ""Constant
대장초기화여부 has constant value ""Constant
등주형식 is highly overall correlated with 등주높이 and 4 other fieldsHigh correlation
등기구수량 is highly overall correlated with 등주높이 and 4 other fieldsHigh correlation
관리번호 is highly overall correlated with 행정읍면동High correlation
도엽번호 is highly overall correlated with 위도High correlation
등주높이 is highly overall correlated with 암길이 and 5 other fieldsHigh correlation
암길이 is highly overall correlated with 등주높이 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 도엽번호High correlation
경도 is highly overall correlated with 행정읍면동High correlation
행정읍면동 is highly overall correlated with 관리번호 and 1 other fieldsHigh correlation
등기구모형 is highly overall correlated with 등주높이 and 5 other fieldsHigh correlation
등주재질 is highly overall correlated with 등주높이 and 5 other fieldsHigh correlation
광원종류 is highly overall correlated with 등주높이 and 4 other fieldsHigh correlation
등기구모형 is highly imbalanced (94.1%)Imbalance
등주형식 is highly imbalanced (92.5%)Imbalance
등주재질 is highly imbalanced (93.3%)Imbalance
등기구수량 is highly imbalanced (90.7%)Imbalance
광원종류 is highly imbalanced (93.0%)Imbalance
암길이 is highly skewed (γ1 = 24.92002211)Skewed
관리번호 has unique valuesUnique
등주높이 has 9808 (98.1%) zerosZeros
암길이 has 9825 (98.2%) zerosZeros

Reproduction

Analysis started2023-12-12 05:17:15.405811
Analysis finished2023-12-12 05:17:26.801077
Duration11.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지형지물부호
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
가로등
10000 

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 10000
100.0%

Length

2023-12-12T14:17:26.881930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:17:27.007281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가로등 10000
100.0%

관리번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21647.57
Minimum5
Maximum43093
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:17:27.152063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile2170.9
Q110966.5
median21508.5
Q332351.5
95-th percentile41032.35
Maximum43093
Range43088
Interquartile range (IQR)21385

Descriptive statistics

Standard deviation12413.113
Coefficient of variation (CV)0.57341831
Kurtosis-1.1897055
Mean21647.57
Median Absolute Deviation (MAD)10720.5
Skewness-0.0067245118
Sum2.164757 × 108
Variance1.5408537 × 108
MonotonicityNot monotonic
2023-12-12T14:17:27.388070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10605 1
 
< 0.1%
28877 1
 
< 0.1%
36567 1
 
< 0.1%
36029 1
 
< 0.1%
40998 1
 
< 0.1%
32569 1
 
< 0.1%
22463 1
 
< 0.1%
31602 1
 
< 0.1%
4556 1
 
< 0.1%
33886 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
5 1
< 0.1%
8 1
< 0.1%
10 1
< 0.1%
15 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
22 1
< 0.1%
23 1
< 0.1%
25 1
< 0.1%
29 1
< 0.1%
ValueCountFrequency (%)
43093 1
< 0.1%
43089 1
< 0.1%
43087 1
< 0.1%
43085 1
< 0.1%
43084 1
< 0.1%
43082 1
< 0.1%
43076 1
< 0.1%
43067 1
< 0.1%
43064 1
< 0.1%
43058 1
< 0.1%

행정읍면동
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대전광역시 유성구
4035 
대전광역시 서구
2104 
대전광역시 동구
1377 
대전광역시 대덕구
1286 
대전광역시 중구
1198 

Length

Max length10
Median length10
Mean length9.5321
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대전광역시 대덕구
2nd row대전광역시 동구
3rd row대전광역시 동구
4th row대전광역시 중구
5th row대전광역시 중구

Common Values

ValueCountFrequency (%)
대전광역시 유성구 4035
40.4%
대전광역시 서구 2104
21.0%
대전광역시 동구 1377
 
13.8%
대전광역시 대덕구 1286
 
12.9%
대전광역시 중구 1198
 
12.0%

Length

2023-12-12T14:17:27.536487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:17:27.680047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 10000
50.0%
유성구 4035
20.2%
서구 2104
 
10.5%
동구 1377
 
6.9%
대덕구 1286
 
6.4%
중구 1198
 
6.0%

도엽번호
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36710059
Minimum36710005
Maximum36710099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:17:27.841678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36710005
5-th percentile36710026
Q136710047
median36710063
Q336710074
95-th percentile36710086
Maximum36710099
Range94
Interquartile range (IQR)27

Descriptive statistics

Standard deviation17.418909
Coefficient of variation (CV)4.7449962 × 10-7
Kurtosis-0.23269496
Mean36710059
Median Absolute Deviation (MAD)11
Skewness-0.44510312
Sum3.6710059 × 1011
Variance303.4184
MonotonicityNot monotonic
2023-12-12T14:17:28.041985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36710064 556
 
5.6%
36710068 496
 
5.0%
36710056 460
 
4.6%
36710054 458
 
4.6%
36710067 422
 
4.2%
36710043 375
 
3.8%
36710066 364
 
3.6%
36710063 359
 
3.6%
36710026 337
 
3.4%
36710078 322
 
3.2%
Other values (48) 5851
58.5%
ValueCountFrequency (%)
36710005 25
 
0.2%
36710006 9
 
0.1%
36710010 1
 
< 0.1%
36710015 47
 
0.5%
36710016 20
 
0.2%
36710017 26
 
0.3%
36710018 40
 
0.4%
36710019 13
 
0.1%
36710026 337
3.4%
36710027 262
2.6%
ValueCountFrequency (%)
36710099 45
 
0.4%
36710096 5
 
0.1%
36710094 37
 
0.4%
36710089 174
1.7%
36710088 28
 
0.3%
36710087 16
 
0.2%
36710086 203
2.0%
36710085 37
 
0.4%
36710084 240
2.4%
36710083 135
1.4%

도로구간번호
Real number (ℝ)

Distinct6082
Distinct (%)60.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38644.881
Minimum1510
Maximum134875
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:17:28.210912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1510
5-th percentile13842
Q121501.5
median33985
Q343511.25
95-th percentile101386.65
Maximum134875
Range133365
Interquartile range (IQR)22009.75

Descriptive statistics

Standard deviation25909.606
Coefficient of variation (CV)0.67045377
Kurtosis3.4515145
Mean38644.881
Median Absolute Deviation (MAD)10919
Skewness1.9169949
Sum3.8644881 × 108
Variance6.7130769 × 108
MonotonicityNot monotonic
2023-12-12T14:17:28.425941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46986 28
 
0.3%
41367 18
 
0.2%
13792 17
 
0.2%
43406 16
 
0.2%
17169 13
 
0.1%
13860 13
 
0.1%
37201 12
 
0.1%
37161 12
 
0.1%
42446 12
 
0.1%
31607 12
 
0.1%
Other values (6072) 9847
98.5%
ValueCountFrequency (%)
1510 4
< 0.1%
2658 1
 
< 0.1%
2823 1
 
< 0.1%
2881 2
< 0.1%
2931 1
 
< 0.1%
3163 1
 
< 0.1%
3165 1
 
< 0.1%
3175 1
 
< 0.1%
3313 1
 
< 0.1%
3324 1
 
< 0.1%
ValueCountFrequency (%)
134875 1
< 0.1%
134851 1
< 0.1%
134845 1
< 0.1%
134839 1
< 0.1%
134826 1
< 0.1%
134823 1
< 0.1%
134822 1
< 0.1%
134761 1
< 0.1%
134756 2
< 0.1%
134753 1
< 0.1%

등기구모형
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
미분류
9831 
기타
 
95
가오스형(신),
 
33
LED
 
23
올챙이형
 
14

Length

Max length8
Median length3
Mean length3.0084
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미분류 9831
98.3%
기타 95
 
0.9%
가오스형(신), 33
 
0.3%
LED 23
 
0.2%
올챙이형 14
 
0.1%
사각형 4
 
< 0.1%

Length

2023-12-12T14:17:28.614329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:17:28.998260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미분류 9831
98.3%
기타 95
 
0.9%
가오스형(신 33
 
0.3%
led 23
 
0.2%
올챙이형 14
 
0.1%
사각형 4
 
< 0.1%

등주높이
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14285
Minimum0
Maximum11
Zeros9808
Zeros (%)98.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:17:29.134982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0594721
Coefficient of variation (CV)7.4166755
Kurtosis59.352057
Mean0.14285
Median Absolute Deviation (MAD)0
Skewness7.681306
Sum1428.5
Variance1.1224811
MonotonicityNot monotonic
2023-12-12T14:17:29.260931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.0 9808
98.1%
8.0 83
 
0.8%
4.0 34
 
0.3%
9.0 27
 
0.3%
5.5 23
 
0.2%
10.0 16
 
0.2%
11.0 9
 
0.1%
ValueCountFrequency (%)
0.0 9808
98.1%
4.0 34
 
0.3%
5.5 23
 
0.2%
8.0 83
 
0.8%
9.0 27
 
0.3%
10.0 16
 
0.2%
11.0 9
 
0.1%
ValueCountFrequency (%)
11.0 9
 
0.1%
10.0 16
 
0.2%
9.0 27
 
0.3%
8.0 83
 
0.8%
5.5 23
 
0.2%
4.0 34
 
0.3%
0.0 9808
98.1%

등주형식
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
미분류
9808 
기본형
 
58
원형
 
53
사각
 
51
2등형
 
30

Length

Max length3
Median length3
Mean length2.9896
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미분류 9808
98.1%
기본형 58
 
0.6%
원형 53
 
0.5%
사각 51
 
0.5%
2등형 30
 
0.3%

Length

2023-12-12T14:17:29.407228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:17:29.536781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미분류 9808
98.1%
기본형 58
 
0.6%
원형 53
 
0.5%
사각 51
 
0.5%
2등형 30
 
0.3%

등주재질
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
미분류
9808 
강관
 
90
강판
 
51
스텐레스
 
31
STEEL
 
14

Length

Max length5
Median length3
Mean length2.9912
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미분류 9808
98.1%
강관 90
 
0.9%
강판 51
 
0.5%
스텐레스 31
 
0.3%
STEEL 14
 
0.1%
주철 6
 
0.1%

Length

2023-12-12T14:17:29.700716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:17:29.854649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미분류 9808
98.1%
강관 90
 
0.9%
강판 51
 
0.5%
스텐레스 31
 
0.3%
steel 14
 
0.1%
주철 6
 
0.1%

암길이
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05975
Minimum0
Maximum25
Zeros9825
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:17:29.968036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum25
Range25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.95639762
Coefficient of variation (CV)16.006655
Kurtosis645.04034
Mean0.05975
Median Absolute Deviation (MAD)0
Skewness24.920022
Sum597.5
Variance0.91469641
MonotonicityNot monotonic
2023-12-12T14:17:30.106866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.0 9825
98.2%
1.0 80
 
0.8%
2.0 58
 
0.6%
2.5 17
 
0.2%
25.0 14
 
0.1%
1.5 6
 
0.1%
ValueCountFrequency (%)
0.0 9825
98.2%
1.0 80
 
0.8%
1.5 6
 
0.1%
2.0 58
 
0.6%
2.5 17
 
0.2%
25.0 14
 
0.1%
ValueCountFrequency (%)
25.0 14
 
0.1%
2.5 17
 
0.2%
2.0 58
 
0.6%
1.5 6
 
0.1%
1.0 80
 
0.8%
0.0 9825
98.2%

등기구수량
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9808 
1
 
167
2
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 9808
98.1%
1 167
 
1.7%
2 25
 
0.2%

Length

2023-12-12T14:17:30.265559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:17:30.378191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9808
98.1%
1 167
 
1.7%
2 25
 
0.2%

광원종류
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
미분류
9808 
기타
 
132
메탈할라이드
 
33
고압나트륨램프
 
23
CDM
 
4

Length

Max length7
Median length3
Mean length3.0059
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미분류 9808
98.1%
기타 132
 
1.3%
메탈할라이드 33
 
0.3%
고압나트륨램프 23
 
0.2%
CDM 4
 
< 0.1%

Length

2023-12-12T14:17:30.532377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:17:30.664832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미분류 9808
98.1%
기타 132
 
1.3%
메탈할라이드 33
 
0.3%
고압나트륨램프 23
 
0.2%
cdm 4
 
< 0.1%
Distinct1362
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8890004 × 109
Minimum1
Maximum1 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:17:30.803949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile227
Q11058
median1 × 1010
Q31 × 1010
95-th percentile1 × 1010
Maximum1 × 1010
Range1 × 1010
Interquartile range (IQR)9.9999989 × 109

Descriptive statistics

Standard deviation4.9205788 × 109
Coefficient of variation (CV)0.83555417
Kurtosis-1.8697555
Mean5.8890004 × 109
Median Absolute Deviation (MAD)0
Skewness-0.36141185
Sum5.8890004 × 1013
Variance2.4212096 × 1019
MonotonicityNot monotonic
2023-12-12T14:17:30.987463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9999999999 5889
58.9%
1210 12
 
0.1%
1293 12
 
0.1%
681 11
 
0.1%
1021 11
 
0.1%
1292 11
 
0.1%
1206 11
 
0.1%
735 10
 
0.1%
1097 10
 
0.1%
1252 10
 
0.1%
Other values (1352) 4013
40.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 3
< 0.1%
3 2
< 0.1%
4 2
< 0.1%
5 1
 
< 0.1%
6 3
< 0.1%
7 1
 
< 0.1%
8 2
< 0.1%
9 2
< 0.1%
10 3
< 0.1%
ValueCountFrequency (%)
9999999999 5889
58.9%
1627 1
 
< 0.1%
1626 6
 
0.1%
1624 2
 
< 0.1%
1622 1
 
< 0.1%
1621 1
 
< 0.1%
1620 3
 
< 0.1%
1619 4
 
< 0.1%
1618 7
 
0.1%
1616 2
 
< 0.1%

대장초기화여부
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
10000 

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 10000
100.0%

Length

2023-12-12T14:17:31.143855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:17:31.261656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 10000
100.0%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct9992
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.353495
Minimum36.25002
Maximum36.482975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:17:31.390691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.25002
5-th percentile36.293
Q136.322819
median36.34772
Q336.377663
95-th percentile36.437744
Maximum36.482975
Range0.23295495
Interquartile range (IQR)0.054844585

Descriptive statistics

Standard deviation0.043037403
Coefficient of variation (CV)0.0011838587
Kurtosis-0.091654237
Mean36.353495
Median Absolute Deviation (MAD)0.026865055
Skewness0.55241063
Sum363534.95
Variance0.001852218
MonotonicityNot monotonic
2023-12-12T14:17:31.549959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.34253321 2
 
< 0.1%
36.43054287 2
 
< 0.1%
36.31045711 2
 
< 0.1%
36.29155162 2
 
< 0.1%
36.34021976 2
 
< 0.1%
36.29542473 2
 
< 0.1%
36.3657342 2
 
< 0.1%
36.34091589 2
 
< 0.1%
36.39458941 1
 
< 0.1%
36.38308767 1
 
< 0.1%
Other values (9982) 9982
99.8%
ValueCountFrequency (%)
36.25002036 1
< 0.1%
36.25092386 1
< 0.1%
36.25176667 1
< 0.1%
36.2518575 1
< 0.1%
36.25194508 1
< 0.1%
36.25236618 1
< 0.1%
36.25272545 1
< 0.1%
36.25319526 1
< 0.1%
36.25331576 1
< 0.1%
36.25331774 1
< 0.1%
ValueCountFrequency (%)
36.48297531 1
< 0.1%
36.48252541 1
< 0.1%
36.48244965 1
< 0.1%
36.48229535 1
< 0.1%
36.48213068 1
< 0.1%
36.48135542 1
< 0.1%
36.48127333 1
< 0.1%
36.48112899 1
< 0.1%
36.48090232 1
< 0.1%
36.48032039 1
< 0.1%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct9971
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.38198
Minimum127.2584
Maximum127.47628
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:17:31.731847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.2584
5-th percentile127.3086
Q1127.34178
median127.38526
Q3127.4188
95-th percentile127.4564
Maximum127.47628
Range0.2178806
Interquartile range (IQR)0.077018825

Descriptive statistics

Standard deviation0.045852408
Coefficient of variation (CV)0.00035995992
Kurtosis-0.96377769
Mean127.38198
Median Absolute Deviation (MAD)0.03826075
Skewness-0.024338573
Sum1273819.8
Variance0.0021024434
MonotonicityNot monotonic
2023-12-12T14:17:31.927215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.319659 2
 
< 0.1%
127.3109828 2
 
< 0.1%
127.4554824 2
 
< 0.1%
127.3865666 2
 
< 0.1%
127.3341837 2
 
< 0.1%
127.3353142 2
 
< 0.1%
127.3202595 2
 
< 0.1%
127.4152972 2
 
< 0.1%
127.4185 2
 
< 0.1%
127.3861679 2
 
< 0.1%
Other values (9961) 9980
99.8%
ValueCountFrequency (%)
127.2583953 1
< 0.1%
127.259325 1
< 0.1%
127.2593952 1
< 0.1%
127.2596675 1
< 0.1%
127.2604437 1
< 0.1%
127.2609424 1
< 0.1%
127.2611002 1
< 0.1%
127.2613334 1
< 0.1%
127.2614786 1
< 0.1%
127.2619731 1
< 0.1%
ValueCountFrequency (%)
127.4762759 1
< 0.1%
127.4752563 1
< 0.1%
127.4751071 1
< 0.1%
127.4748851 1
< 0.1%
127.4748436 1
< 0.1%
127.4748162 1
< 0.1%
127.4748145 1
< 0.1%
127.4747811 1
< 0.1%
127.4747522 1
< 0.1%
127.474709 1
< 0.1%

Interactions

2023-12-12T14:17:25.454545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:18.732863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:19.523497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:20.291939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:21.297924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:22.610258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:23.564482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:24.560536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:25.549077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:18.837246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:19.621937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:20.397417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:21.397273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:22.736377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:23.703695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:24.688144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:25.685502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:18.926399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:19.724621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:20.514654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:21.511274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:22.847844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:23.813017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:24.787333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:25.789811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:19.029629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:19.823061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:20.623491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:21.903546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:22.993206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:23.928405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:24.924341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:25.885984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:19.144125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:19.920457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:20.721558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:22.033341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:23.125933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:24.054721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:25.060471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:25.983219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:19.240018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:20.016487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:20.824906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:22.161463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:23.244581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:24.176782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:25.162324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:26.108205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:19.336607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:20.102415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:20.948871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:22.291001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:23.347228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:24.305023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:25.260092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:26.222927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:19.437514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:20.198736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:21.063155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:22.443110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:23.446584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:24.432778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:17:25.353073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:17:32.094315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호행정읍면동도엽번호도로구간번호등기구모형등주높이등주형식등주재질암길이등기구수량광원종류가로등제어기관리번호위도경도
관리번호1.0000.9490.7870.6650.3200.3400.4610.3400.1900.4420.4610.3670.7630.795
행정읍면동0.9491.0000.7690.7230.1060.0980.1840.1220.0430.1110.2380.1240.7730.898
도엽번호0.7870.7691.0000.7000.4890.5580.6890.4750.0980.4550.5450.1840.9780.578
도로구간번호0.6650.7230.7001.0000.4260.3900.5270.3450.0760.3990.5190.2130.7280.650
등기구모형0.3200.1060.4890.4261.0000.9580.7750.9600.9690.9360.9130.1230.6400.639
등주높이0.3400.0980.5580.3900.9581.0000.8710.9580.9120.9730.8050.0990.6040.606
등주형식0.4610.1840.6890.5270.7750.8711.0000.8680.5580.9010.9320.0730.7830.625
등주재질0.3400.1220.4750.3450.9600.9580.8681.0000.9850.9780.8010.1020.5560.479
암길이0.1900.0430.0980.0760.9690.9120.5580.9851.0000.8210.4400.0080.1190.113
등기구수량0.4420.1110.4550.3990.9360.9730.9010.9780.8211.0000.7300.0300.6310.382
광원종류0.4610.2380.5450.5190.9130.8050.9320.8010.4400.7301.0000.0690.7230.503
가로등제어기관리번호0.3670.1240.1840.2130.1230.0990.0730.1020.0080.0300.0691.0000.2100.262
위도0.7630.7730.9780.7280.6400.6040.7830.5560.1190.6310.7230.2101.0000.605
경도0.7950.8980.5780.6500.6390.6060.6250.4790.1130.3820.5030.2620.6051.000
2023-12-12T14:17:32.287401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등주형식등기구수량광원종류행정읍면동등주재질등기구모형
등주형식1.0000.9510.6370.0700.7880.655
등기구수량0.9511.0000.7230.0840.8180.693
광원종류0.6370.7231.0000.0910.6900.866
행정읍면동0.0700.0840.0911.0000.0820.071
등주재질0.7880.8180.6900.0821.0000.705
등기구모형0.6550.6930.8660.0710.7051.000
2023-12-12T14:17:32.441190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호도엽번호도로구간번호등주높이암길이가로등제어기관리번호위도경도행정읍면동등기구모형등주형식등주재질등기구수량광원종류
관리번호1.000-0.0890.121-0.161-0.154-0.1590.021-0.3670.6910.1740.2090.1860.2970.209
도엽번호-0.0891.000-0.391-0.088-0.103-0.064-0.9690.1950.4250.3350.3700.2900.3930.317
도로구간번호0.121-0.3911.0000.1220.119-0.0690.352-0.3780.3830.2400.2460.1890.2620.241
등주높이-0.161-0.0880.1221.0000.9530.0540.083-0.0600.0660.6950.7940.6980.7980.696
암길이-0.154-0.1030.1190.9531.0000.0470.103-0.0410.0271.0000.3971.0000.2870.323
가로등제어기관리번호-0.159-0.064-0.0690.0540.0471.0000.0900.1000.1520.0880.0890.0740.0500.084
위도0.021-0.9690.3520.0830.1030.0901.000-0.0430.4290.4050.4390.3330.4770.383
경도-0.3670.195-0.378-0.060-0.0410.100-0.0431.0000.5850.4030.3080.2760.2480.232
행정읍면동0.6910.4250.3830.0660.0270.1520.4290.5851.0000.0710.0700.0820.0840.091
등기구모형0.1740.3350.2400.6951.0000.0880.4050.4030.0711.0000.6550.7050.6930.866
등주형식0.2090.3700.2460.7940.3970.0890.4390.3080.0700.6551.0000.7880.9510.637
등주재질0.1860.2900.1890.6981.0000.0740.3330.2760.0820.7050.7881.0000.8180.690
등기구수량0.2970.3930.2620.7980.2870.0500.4770.2480.0840.6930.9510.8181.0000.723
광원종류0.2090.3170.2410.6960.3230.0840.3830.2320.0910.8660.6370.6900.7231.000

Missing values

2023-12-12T14:17:26.383666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:17:26.652832image/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

지형지물부호관리번호행정읍면동도엽번호도로구간번호등기구모형등주높이등주형식등주재질암길이등기구수량광원종류가로등제어기관리번호대장초기화여부위도경도
10596가로등10605대전광역시 대덕구3671004836309미분류0.0미분류미분류0.00미분류9999999999136.376837127.427412
4922가로등4928대전광역시 동구3671006820784미분류0.0미분류미분류0.00미분류9999999999136.345164127.446184
14087가로등14099대전광역시 동구3671008913533미분류0.0미분류미분류0.00미분류9999999999136.288152127.457784
16637가로등16649대전광역시 중구3671006825539미분류0.0미분류미분류0.00미분류9999999999136.32585127.43196
41944가로등41957대전광역시 중구3671006727543미분류0.0미분류미분류0.00미분류879136.33865127.413581
18516가로등18528대전광역시 서구3671008633476미분류0.0미분류미분류0.00미분류9999999999136.299562127.378508
18831가로등18843대전광역시 서구3671008413770미분류0.0미분류미분류0.00미분류9999999999136.291859127.327822
14972가로등14984대전광역시 동구3671007923476미분류0.0미분류미분류0.00미분류1246136.319538127.461857
19123가로등19135대전광역시 서구3671008413689미분류0.0미분류미분류0.00미분류9999999999136.291811127.333901
38204가로등38217대전광역시 유성구3671003613784미분류0.0미분류미분류0.00미분류9999999999136.411489127.381454
지형지물부호관리번호행정읍면동도엽번호도로구간번호등기구모형등주높이등주형식등주재질암길이등기구수량광원종류가로등제어기관리번호대장초기화여부위도경도
19788가로등19800대전광역시 서구3671008434403미분류0.0미분류미분류0.00미분류1097136.296047127.33466
38144가로등38157대전광역시 유성구3671003642870미분류0.0미분류미분류0.00미분류9999999999136.408581127.377943
33061가로등33074대전광역시 유성구3671005444686미분류0.0미분류미분류0.00미분류701136.357399127.349569
13755가로등13767대전광역시 동구3671008914167미분류0.0미분류미분류0.00미분류9999999999136.277624127.464207
12622가로등12631대전광역시 대덕구3671002835955미분류0.0미분류미분류0.00미분류9999999999136.440543127.428652
41360가로등41373대전광역시 중구3671007722479미분류0.0미분류미분류0.00미분류888136.321087127.415213
34464가로등34477대전광역시 유성구3671005343697미분류0.0미분류미분류0.00미분류9999999999136.372588127.319401
37927가로등37940대전광역시 유성구3671003739524미분류0.0미분류미분류0.00미분류9999999999136.421538127.404504
41391가로등41404대전광역시 중구3671007766666미분류0.0미분류미분류0.00미분류891136.323139127.413858
6145가로등6152대전광역시 대덕구3671002738577미분류0.0미분류미분류0.00미분류9999999999136.445417127.407573