Overview

Dataset statistics

Number of variables18
Number of observations614
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory90.7 KiB
Average record size in memory151.2 B

Variable types

Categorical11
Numeric6
Text1

Dataset

Description통영시 도시정보시스템의 차도와 보도의 포장에 대한 지형지물부호,관리번호,행정읍면동,도엽번호,관리기관,도로구간번호,공사번호,보수종류,보수공종,연장,면적,폭원,차도포장재질 등 제공합니다.
Author경상남도 통영시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15062705

Alerts

관리기관 has constant value ""Constant
차도포장재질 is highly overall correlated with 폭원 and 5 other fieldsHigh correlation
공사번호 is highly overall correlated with 관리번호 and 10 other fieldsHigh correlation
대장초기화여부 is highly overall correlated with 관리번호 and 14 other fieldsHigh correlation
보도포장재질 is highly overall correlated with 지형지물부호 and 3 other fieldsHigh correlation
이전보도포장재질 is highly overall correlated with 공사번호 and 1 other fieldsHigh correlation
행정읍면동 is highly overall correlated with 공사번호 and 4 other fieldsHigh correlation
보수공종 is highly overall correlated with 구분ID and 3 other fieldsHigh correlation
보수종류 is highly overall correlated with 행정읍면동 and 2 other fieldsHigh correlation
이전차도포장재질 is highly overall correlated with 관리번호 and 3 other fieldsHigh correlation
지형지물부호 is highly overall correlated with 폭원 and 4 other fieldsHigh correlation
관리번호 is highly overall correlated with 도로구간번호 and 3 other fieldsHigh correlation
도로구간번호 is highly overall correlated with 관리번호 and 3 other fieldsHigh correlation
연장 is highly overall correlated with 면적 and 1 other fieldsHigh correlation
면적 is highly overall correlated with 연장 and 2 other fieldsHigh correlation
폭원 is highly overall correlated with 면적 and 3 other fieldsHigh correlation
구분ID is highly overall correlated with 보수공종 and 1 other fieldsHigh correlation
보수종류 is highly imbalanced (70.4%)Imbalance
이전차도포장재질 is highly imbalanced (58.3%)Imbalance
이전보도포장재질 is highly imbalanced (89.4%)Imbalance
대장초기화여부 is highly imbalanced (83.4%)Imbalance
구분ID has unique valuesUnique

Reproduction

Analysis started2023-12-11 00:23:00.802894
Analysis finished2023-12-11 00:23:07.028856
Duration6.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지형지물부호
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
보도포장
334 
차도포장
238 
차도면
 
24
보도면
 
18

Length

Max length4
Median length4
Mean length3.9315961
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row보도포장
2nd row차도포장
3rd row보도포장
4th row보도포장
5th row보도포장

Common Values

ValueCountFrequency (%)
보도포장 334
54.4%
차도포장 238
38.8%
차도면 24
 
3.9%
보도면 18
 
2.9%

Length

2023-12-11T09:23:07.093826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:23:07.204251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
보도포장 334
54.4%
차도포장 238
38.8%
차도면 24
 
3.9%
보도면 18
 
2.9%

관리번호
Real number (ℝ)

HIGH CORRELATION 

Distinct612
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3177274 × 108
Minimum1
Maximum2.02301 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2023-12-11T09:23:07.344186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile29.65
Q1161.25
median160003.5
Q3181131.75
95-th percentile2.02108 × 109
Maximum2.02301 × 109
Range2.02301 × 109
Interquartile range (IQR)180970.5

Descriptive statistics

Standard deviation4.9919003 × 108
Coefficient of variation (CV)3.7882646
Kurtosis10.514875
Mean1.3177274 × 108
Median Absolute Deviation (MAD)159595
Skewness3.532794
Sum8.0908465 × 1010
Variance2.4919068 × 1017
MonotonicityNot monotonic
2023-12-11T09:23:07.502749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2
 
0.3%
2 2
 
0.3%
2022080001 1
 
0.2%
167 1
 
0.2%
202 1
 
0.2%
174 1
 
0.2%
176 1
 
0.2%
166 1
 
0.2%
164 1
 
0.2%
177 1
 
0.2%
Other values (602) 602
98.0%
ValueCountFrequency (%)
1 2
0.3%
2 2
0.3%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
2023010002 1
0.2%
2023010001 1
0.2%
2022080002 1
0.2%
2022080001 1
0.2%
2021120007 1
0.2%
2021120006 1
0.2%
2021120005 1
0.2%
2021120004 1
0.2%
2021120003 1
0.2%
2021120002 1
0.2%

행정읍면동
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
도산면
219 
사량면
160 
광도면
107 
북신동
44 
용남면
43 
Other values (10)
41 

Length

Max length3
Median length3
Mean length2.985342
Min length2

Unique

Unique5 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
도산면 219
35.7%
사량면 160
26.1%
광도면 107
17.4%
북신동 44
 
7.2%
용남면 43
 
7.0%
동호동 9
 
1.5%
당동 9
 
1.5%
산양읍 8
 
1.3%
한산면 7
 
1.1%
욕지면 3
 
0.5%
Other values (5) 5
 
0.8%

Length

2023-12-11T09:23:07.649439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
도산면 219
35.7%
사량면 160
26.1%
광도면 107
17.4%
북신동 44
 
7.2%
용남면 43
 
7.0%
동호동 9
 
1.5%
당동 9
 
1.5%
산양읍 8
 
1.3%
한산면 7
 
1.1%
욕지면 3
 
0.5%
Other values (5) 5
 
0.8%

도엽번호
Text

Distinct104
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
2023-12-11T09:23:07.892297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters6140
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

Unique31 ?
Unique (%)5.0%

Sample

1st row348021315D
2nd row348021315D
3rd row348021315D
4th row348021315D
5th row348021315D
ValueCountFrequency (%)
348021315b 47
 
7.7%
348021315d 41
 
6.7%
348021305c 41
 
6.7%
348021315a 35
 
5.7%
348021496b 25
 
4.1%
348021316c 24
 
3.9%
348021305d 16
 
2.6%
348021850c 15
 
2.4%
348021496a 15
 
2.4%
348021497a 14
 
2.3%
Other values (94) 341
55.5%
2023-12-11T09:23:08.305386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 958
15.6%
4 909
14.8%
0 819
13.3%
1 778
12.7%
2 700
11.4%
8 671
10.9%
5 292
 
4.8%
B 203
 
3.3%
9 203
 
3.3%
D 150
 
2.4%
Other values (4) 457
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5526
90.0%
Uppercase Letter 614
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 958
17.3%
4 909
16.4%
0 819
14.8%
1 778
14.1%
2 700
12.7%
8 671
12.1%
5 292
 
5.3%
9 203
 
3.7%
6 135
 
2.4%
7 61
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
B 203
33.1%
D 150
24.4%
C 132
21.5%
A 129
21.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5526
90.0%
Latin 614
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 958
17.3%
4 909
16.4%
0 819
14.8%
1 778
14.1%
2 700
12.7%
8 671
12.1%
5 292
 
5.3%
9 203
 
3.7%
6 135
 
2.4%
7 61
 
1.1%
Latin
ValueCountFrequency (%)
B 203
33.1%
D 150
24.4%
C 132
21.5%
A 129
21.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 958
15.6%
4 909
14.8%
0 819
13.3%
1 778
12.7%
2 700
11.4%
8 671
10.9%
5 292
 
4.8%
B 203
 
3.3%
9 203
 
3.3%
D 150
 
2.4%
Other values (4) 457
7.4%

관리기관
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
통영시
614 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
통영시 614
100.0%

Length

2023-12-11T09:23:08.474210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:23:08.609626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
통영시 614
100.0%

도로구간번호
Real number (ℝ)

HIGH CORRELATION 

Distinct262
Distinct (%)42.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3177659 × 108
Minimum2
Maximum2.02301 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2023-12-11T09:23:08.756438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10007
Q110069
median160015
Q3181101.75
95-th percentile2.02108 × 109
Maximum2.02301 × 109
Range2.02301 × 109
Interquartile range (IQR)171032.75

Descriptive statistics

Standard deviation4.9918901 × 108
Coefficient of variation (CV)3.7881465
Kurtosis10.514876
Mean1.3177659 × 108
Median Absolute Deviation (MAD)148832.5
Skewness3.5327941
Sum8.0910823 × 1010
Variance2.4918967 × 1017
MonotonicityNot monotonic
2023-12-11T09:23:08.931339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
181007 15
 
2.4%
11098 12
 
2.0%
2036 9
 
1.5%
181109 8
 
1.3%
181005 8
 
1.3%
171103 8
 
1.3%
191201 7
 
1.1%
181008 7
 
1.1%
181006 6
 
1.0%
13003 6
 
1.0%
Other values (252) 528
86.0%
ValueCountFrequency (%)
2 1
 
0.2%
777 1
 
0.2%
2036 9
1.5%
6618 3
 
0.5%
6619 1
 
0.2%
6620 2
 
0.3%
8166 2
 
0.3%
10001 2
 
0.3%
10002 2
 
0.3%
10003 3
 
0.5%
ValueCountFrequency (%)
2023010002 1
0.2%
2023010001 1
0.2%
2022080001 2
0.3%
2021120008 2
0.3%
2021120006 1
0.2%
2021120005 1
0.2%
2021120003 1
0.2%
2021120002 1
0.2%
2021120001 1
0.2%
2021080013 1
0.2%

공사번호
Categorical

HIGH CORRELATION 

Distinct38
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
RD20170001
262 
<NA>
118 
RD20190007
28 
RD20190003
 
21
RD20130006
 
21
Other values (33)
164 

Length

Max length10
Median length10
Mean length8.8469055
Min length4

Unique

Unique7 ?
Unique (%)1.1%

Sample

1st rowRD20170001
2nd rowRD20170001
3rd rowRD20170001
4th rowRD20170001
5th rowRD20170001

Common Values

ValueCountFrequency (%)
RD20170001 262
42.7%
<NA> 118
19.2%
RD20190007 28
 
4.6%
RD20190003 21
 
3.4%
RD20130006 21
 
3.4%
RD20170003 15
 
2.4%
RD20210005 14
 
2.3%
RD20130001 14
 
2.3%
RD20190010 13
 
2.1%
RD20160001 10
 
1.6%
Other values (28) 98
 
16.0%

Length

2023-12-11T09:23:09.147947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rd20170001 262
42.7%
na 118
19.2%
rd20190007 28
 
4.6%
rd20190003 21
 
3.4%
rd20130006 21
 
3.4%
rd20170003 15
 
2.4%
rd20210005 14
 
2.3%
rd20130001 14
 
2.3%
rd20190010 13
 
2.1%
rd20160001 10
 
1.6%
Other values (28) 98
 
16.0%

보수종류
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
신설
557 
전면개수
 
22
기타
 
18
미분류
 
17

Length

Max length4
Median length2
Mean length2.0993485
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
신설 557
90.7%
전면개수 22
 
3.6%
기타 18
 
2.9%
미분류 17
 
2.8%

Length

2023-12-11T09:23:09.339912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:23:09.480503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신설 557
90.7%
전면개수 22
 
3.6%
기타 18
 
2.9%
미분류 17
 
2.8%

보수공종
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
미분류
408 
기타
206 

Length

Max length3
Median length3
Mean length2.6644951
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미분류 408
66.4%
기타 206
33.6%

Length

2023-12-11T09:23:09.620074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:23:09.779567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미분류 408
66.4%
기타 206
33.6%

연장
Real number (ℝ)

HIGH CORRELATION 

Distinct551
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.254577
Minimum0
Maximum1030.83
Zeros2
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2023-12-11T09:23:09.936323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.552
Q112.0975
median42.6
Q389.57
95-th percentile290.7545
Maximum1030.83
Range1030.83
Interquartile range (IQR)77.4725

Descriptive statistics

Standard deviation130.38348
Coefficient of variation (CV)1.6246236
Kurtosis19.808914
Mean80.254577
Median Absolute Deviation (MAD)35.23
Skewness3.9679402
Sum49276.31
Variance16999.852
MonotonicityNot monotonic
2023-12-11T09:23:10.073633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.08 5
 
0.8%
2.79 4
 
0.7%
196.47 3
 
0.5%
1.9 3
 
0.5%
2.85 3
 
0.5%
60.62 3
 
0.5%
7.45 3
 
0.5%
3.19 3
 
0.5%
94.9 3
 
0.5%
1.77 3
 
0.5%
Other values (541) 581
94.6%
ValueCountFrequency (%)
0.0 2
0.3%
0.55 1
 
0.2%
1.12 1
 
0.2%
1.3 1
 
0.2%
1.33 1
 
0.2%
1.49 1
 
0.2%
1.59 1
 
0.2%
1.6 1
 
0.2%
1.69 1
 
0.2%
1.77 3
0.5%
ValueCountFrequency (%)
1030.83 1
0.2%
1017.62 1
0.2%
966.0 2
0.3%
775.16 1
0.2%
664.9 1
0.2%
648.5 1
0.2%
633.27 1
0.2%
606.04 1
0.2%
593.78 1
0.2%
583.9 2
0.3%

면적
Real number (ℝ)

HIGH CORRELATION 

Distinct600
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean584.89614
Minimum0
Maximum8663.39
Zeros2
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2023-12-11T09:23:10.219286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.1055
Q147.285
median170.015
Q3531.01
95-th percentile2726.186
Maximum8663.39
Range8663.39
Interquartile range (IQR)483.725

Descriptive statistics

Standard deviation1167.9417
Coefficient of variation (CV)1.9968361
Kurtosis14.7958
Mean584.89614
Median Absolute Deviation (MAD)155.325
Skewness3.6402293
Sum359126.23
Variance1364087.9
MonotonicityNot monotonic
2023-12-11T09:23:10.385070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.28 2
 
0.3%
0.0 2
 
0.3%
43.33 2
 
0.3%
2064.05 2
 
0.3%
6.84 2
 
0.3%
6.27 2
 
0.3%
6.46 2
 
0.3%
121.73 2
 
0.3%
38.07 2
 
0.3%
6.75 2
 
0.3%
Other values (590) 594
96.7%
ValueCountFrequency (%)
0.0 2
0.3%
0.74 1
0.2%
1.92 1
0.2%
2.07 1
0.2%
2.26 1
0.2%
2.81 1
0.2%
3.5 1
0.2%
3.54 1
0.2%
3.59 1
0.2%
3.6 1
0.2%
ValueCountFrequency (%)
8663.39 1
0.2%
7126.66 1
0.2%
7025.61 1
0.2%
6838.23 1
0.2%
6774.85 2
0.3%
6661.04 1
0.2%
5885.38 1
0.2%
5705.56 1
0.2%
5635.13 1
0.2%
5591.45 1
0.2%

폭원
Real number (ℝ)

HIGH CORRELATION 

Distinct251
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9512215
Minimum0
Maximum37
Zeros2
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2023-12-11T09:23:10.534998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.9
Q12.1625
median3.8
Q38.03
95-th percentile16
Maximum37
Range37
Interquartile range (IQR)5.8675

Descriptive statistics

Standard deviation5.0093178
Coefficient of variation (CV)0.84172935
Kurtosis5.1440608
Mean5.9512215
Median Absolute Deviation (MAD)1.81
Skewness1.8894774
Sum3654.05
Variance25.093265
MonotonicityNot monotonic
2023-12-11T09:23:10.684852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.0 48
 
7.8%
8.0 41
 
6.7%
3.0 21
 
3.4%
10.0 20
 
3.3%
4.0 20
 
3.3%
2.5 16
 
2.6%
6.0 9
 
1.5%
8.02 7
 
1.1%
2.02 7
 
1.1%
1.99 7
 
1.1%
Other values (241) 418
68.1%
ValueCountFrequency (%)
0.0 2
 
0.3%
1.0 4
0.7%
1.2 1
 
0.2%
1.3 1
 
0.2%
1.45 1
 
0.2%
1.5 5
0.8%
1.53 1
 
0.2%
1.54 1
 
0.2%
1.6 4
0.7%
1.66 1
 
0.2%
ValueCountFrequency (%)
37.0 1
 
0.2%
32.38 1
 
0.2%
27.0 1
 
0.2%
26.0 2
0.3%
25.0 1
 
0.2%
24.0 1
 
0.2%
23.0 1
 
0.2%
22.0 3
0.5%
20.0 3
0.5%
19.5 1
 
0.2%

차도포장재질
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
미분류
348 
아스팔트콘크리트
235 
콘크리트
 
17
<NA>
 
14

Length

Max length8
Median length3
Mean length4.9641694
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미분류 348
56.7%
아스팔트콘크리트 235
38.3%
콘크리트 17
 
2.8%
<NA> 14
 
2.3%

Length

2023-12-11T09:23:10.843450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:23:10.949791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미분류 348
56.7%
아스팔트콘크리트 235
38.3%
콘크리트 17
 
2.8%
na 14
 
2.3%

보도포장재질
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
소형고압블록
290 
미분류
254 
콘크리트
 
20
SB
 
16
<NA>
 
14
Other values (4)
 
20

Length

Max length10
Median length7
Mean length4.5439739
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
소형고압블록 290
47.2%
미분류 254
41.4%
콘크리트 20
 
3.3%
SB 16
 
2.6%
<NA> 14
 
2.3%
아스팔트 6
 
1.0%
칼라아스팔트콘크리트 6
 
1.0%
사각 4
 
0.7%
투수성콘크리트 4
 
0.7%

Length

2023-12-11T09:23:11.078557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:23:11.208659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
소형고압블록 290
47.2%
미분류 254
41.4%
콘크리트 20
 
3.3%
sb 16
 
2.6%
na 14
 
2.3%
아스팔트 6
 
1.0%
칼라아스팔트콘크리트 6
 
1.0%
사각 4
 
0.7%
투수성콘크리트 4
 
0.7%

이전차도포장재질
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
미분류
486 
GSP
117 
아스팔트콘크리트
 
10
콘크리트
 
1

Length

Max length8
Median length3
Mean length3.0830619
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
미분류 486
79.2%
GSP 117
 
19.1%
아스팔트콘크리트 10
 
1.6%
콘크리트 1
 
0.2%

Length

2023-12-11T09:23:11.345101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:23:11.456931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미분류 486
79.2%
gsp 117
 
19.1%
아스팔트콘크리트 10
 
1.6%
콘크리트 1
 
0.2%

이전보도포장재질
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
미분류
598 
소형고압블록
 
7
SB
 
7
투수성콘크리트
 
2

Length

Max length7
Median length3
Mean length3.0358306
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미분류 598
97.4%
소형고압블록 7
 
1.1%
SB 7
 
1.1%
투수성콘크리트 2
 
0.3%

Length

2023-12-11T09:23:11.575418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:23:11.689113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미분류 598
97.4%
소형고압블록 7
 
1.1%
sb 7
 
1.1%
투수성콘크리트 2
 
0.3%

대장초기화여부
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
1
599 
<NA>
 
15

Length

Max length4
Median length1
Mean length1.0732899
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 599
97.6%
<NA> 15
 
2.4%

Length

2023-12-11T09:23:11.811467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:23:11.910090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 599
97.6%
na 15
 
2.4%

구분ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct614
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean306.5
Minimum0
Maximum613
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2023-12-11T09:23:12.018397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30.65
Q1153.25
median306.5
Q3459.75
95-th percentile582.35
Maximum613
Range613
Interquartile range (IQR)306.5

Descriptive statistics

Standard deviation177.39081
Coefficient of variation (CV)0.57876284
Kurtosis-1.2
Mean306.5
Median Absolute Deviation (MAD)153.5
Skewness0
Sum188191
Variance31467.5
MonotonicityStrictly increasing
2023-12-11T09:23:12.157831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.2%
413 1
 
0.2%
406 1
 
0.2%
407 1
 
0.2%
408 1
 
0.2%
409 1
 
0.2%
410 1
 
0.2%
411 1
 
0.2%
412 1
 
0.2%
414 1
 
0.2%
Other values (604) 604
98.4%
ValueCountFrequency (%)
0 1
0.2%
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
ValueCountFrequency (%)
613 1
0.2%
612 1
0.2%
611 1
0.2%
610 1
0.2%
609 1
0.2%
608 1
0.2%
607 1
0.2%
606 1
0.2%
605 1
0.2%
604 1
0.2%

Interactions

2023-12-11T09:23:05.645843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:02.080877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:02.786595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:03.621756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:04.302522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:05.015587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:05.772108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:02.188571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:02.925324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:03.742589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:04.406937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:05.125335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:05.890561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:02.294619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:03.081568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:03.869642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:04.537834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:05.260753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:06.000301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:02.418975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:03.217534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:03.993881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:04.651602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:05.352265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:06.153522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:02.539344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:03.348806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:04.087616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:04.761111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:05.451008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:06.275478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:02.663007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:03.500719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:04.191215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:04.893235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:05.544409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:23:12.260895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지형지물부호관리번호행정읍면동도로구간번호공사번호보수종류보수공종연장면적폭원차도포장재질보도포장재질이전차도포장재질이전보도포장재질구분ID
지형지물부호1.0000.0740.6500.0740.8960.3570.3140.2640.4040.7260.7520.8820.6620.0000.249
관리번호0.0741.0000.5451.0000.9800.0720.2730.0000.0000.0000.0240.6470.7480.0000.552
행정읍면동0.6500.5451.0000.5450.9900.7380.9520.4710.4220.4130.8360.6560.5520.6130.745
도로구간번호0.0741.0000.5451.0000.9800.0720.2730.0000.0000.0000.0240.6470.7480.0000.552
공사번호0.8960.9800.9900.9801.0000.9800.8690.7730.5880.5300.9050.8680.9020.9270.836
보수종류0.3570.0720.7380.0720.9801.0000.3250.0000.1380.4110.1950.2920.1090.0000.521
보수공종0.3140.2730.9520.2730.8690.3251.0000.2940.2970.3600.0850.3710.5270.1400.771
연장0.2640.0000.4710.0000.7730.0000.2941.0000.7800.2440.4570.2290.2680.5100.160
면적0.4040.0000.4220.0000.5880.1380.2970.7801.0000.7050.4950.2670.3600.2760.268
폭원0.7260.0000.4130.0000.5300.4110.3600.2440.7051.0000.7850.5880.3390.0000.310
차도포장재질0.7520.0240.8360.0240.9050.1950.0850.4570.4950.7851.0000.7820.2510.1290.283
보도포장재질0.8820.6470.6560.6470.8680.2920.3710.2290.2670.5880.7821.0000.3770.7350.335
이전차도포장재질0.6620.7480.5520.7480.9020.1090.5270.2680.3600.3390.2510.3771.0000.0000.367
이전보도포장재질0.0000.0000.6130.0000.9270.0000.1400.5100.2760.0000.1290.7350.0001.0000.239
구분ID0.2490.5520.7450.5520.8360.5210.7710.1600.2680.3100.2830.3350.3670.2391.000
2023-12-11T09:23:12.772290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차도포장재질공사번호대장초기화여부보도포장재질이전보도포장재질행정읍면동보수공종보수종류이전차도포장재질지형지물부호
차도포장재질1.0000.6691.0000.6950.1210.5700.1400.1850.2390.802
공사번호0.6691.0001.0000.5410.8240.8810.7500.8730.7140.671
대장초기화여부1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
보도포장재질0.6950.5411.0001.0000.4030.3500.2770.1340.1750.568
이전보도포장재질0.1210.8241.0000.4031.0000.3940.0930.0000.0000.000
행정읍면동0.5700.8811.0000.3500.3941.0000.9430.5190.3430.427
보수공종0.1400.7501.0000.2770.0930.9431.0000.2160.3580.209
보수종류0.1850.8731.0000.1340.0000.5190.2161.0000.0430.146
이전차도포장재질0.2390.7141.0000.1750.0000.3430.3580.0431.0000.313
지형지물부호0.8020.6711.0000.5680.0000.4270.2090.1460.3131.000
2023-12-11T09:23:12.974875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호도로구간번호연장면적폭원구분ID지형지물부호행정읍면동공사번호보수종류보수공종차도포장재질보도포장재질이전차도포장재질이전보도포장재질대장초기화여부
관리번호1.0000.9100.1600.1720.0740.1050.0490.4950.9170.0470.1760.0400.4890.5400.0001.000
도로구간번호0.9101.0000.1280.1630.0720.0680.0490.4950.9170.0470.1760.0400.4890.5400.0001.000
연장0.1600.1281.0000.8500.217-0.0210.1710.2110.3990.0000.2920.2260.1140.1730.3511.000
면적0.1720.1630.8501.0000.6290.0470.2510.1700.2410.0820.2260.3400.1300.2220.1671.000
폭원0.0740.0720.2170.6291.0000.0770.5290.1660.2120.2540.2750.6640.3290.2100.0001.000
구분ID0.1050.068-0.0210.0470.0771.0000.1500.3860.4660.3370.6040.1750.1670.2260.1441.000
지형지물부호0.0490.0490.1710.2510.5290.1501.0000.4270.6710.1460.2090.8020.5680.3130.0001.000
행정읍면동0.4950.4950.2110.1700.1660.3860.4271.0000.8810.5190.9430.5700.3500.3430.3941.000
공사번호0.9170.9170.3990.2410.2120.4660.6710.8811.0000.8730.7500.6690.5410.7140.8241.000
보수종류0.0470.0470.0000.0820.2540.3370.1460.5190.8731.0000.2160.1850.1340.0430.0001.000
보수공종0.1760.1760.2920.2260.2750.6040.2090.9430.7500.2161.0000.1400.2770.3580.0931.000
차도포장재질0.0400.0400.2260.3400.6640.1750.8020.5700.6690.1850.1401.0000.6950.2390.1211.000
보도포장재질0.4890.4890.1140.1300.3290.1670.5680.3500.5410.1340.2770.6951.0000.1750.4031.000
이전차도포장재질0.5400.5400.1730.2220.2100.2260.3130.3430.7140.0430.3580.2390.1751.0000.0001.000
이전보도포장재질0.0000.0000.3510.1670.0000.1440.0000.3940.8240.0000.0930.1210.4030.0001.0001.000
대장초기화여부1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-11T09:23:06.736418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:23:06.946515image/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

지형지물부호관리번호행정읍면동도엽번호관리기관도로구간번호공사번호보수종류보수공종연장면적폭원차도포장재질보도포장재질이전차도포장재질이전보도포장재질대장초기화여부구분ID
0보도포장48도산면348021315D통영시10027RD20170001신설기타59.81139.292.33미분류소형고압블록미분류미분류10
1차도포장49도산면348021315D통영시10027RD20170001신설기타59.8626.0510.47아스팔트콘크리트미분류미분류미분류11
2보도포장33도산면348021315D통영시10107RD20170001신설기타3.216.311.97미분류소형고압블록미분류미분류12
3보도포장32도산면348021315D통영시10074RD20170001신설기타1.94.092.15미분류소형고압블록미분류미분류13
4보도포장41도산면348021315D통영시10073RD20170001신설기타31.2867.682.16미분류소형고압블록미분류미분류14
5차도포장40도산면348021315D통영시10073RD20170001신설기타30.28246.18.13아스팔트콘크리트미분류미분류미분류15
6차도포장150014사량면348012027A통영시150014<NA>신설미분류223.392412.329.82아스팔트콘크리트미분류아스팔트콘크리트미분류16
7차도포장181203광도면348021434D통영시181005RD20190003전면개수미분류225.34296.0317.0아스팔트콘크리트미분류미분류미분류17
8보도포장181226광도면348021434D통영시181006RD20190003전면개수미분류16.9358.643.6미분류콘크리트미분류미분류18
9보도포장181208광도면348021434D통영시181006RD20190003전면개수미분류82.49296.773.6미분류콘크리트미분류미분류19
지형지물부호관리번호행정읍면동도엽번호관리기관도로구간번호공사번호보수종류보수공종연장면적폭원차도포장재질보도포장재질이전차도포장재질이전보도포장재질대장초기화여부구분ID
604차도포장191201용남면348021469B통영시191201RD20200001신설미분류288.562329.847.8아스팔트콘크리트미분류미분류미분류1604
605보도포장191207용남면348021469A통영시191201RD20200001신설미분류37.0174.432.0미분류소형고압블록미분류미분류1605
606보도포장191206용남면348021469B통영시191201RD20200001신설미분류36.7972.362.0미분류소형고압블록미분류미분류1606
607보도포장191205용남면348021469B통영시191201RD20200001신설미분류28.7957.512.0미분류소형고압블록미분류미분류1607
608보도포장191204용남면348021469B통영시191201RD20200001신설미분류37.7775.032.0미분류소형고압블록미분류미분류1608
609보도포장191203용남면348021469B통영시191201RD20200001신설미분류117.04232.282.0미분류소형고압블록미분류미분류1609
610보도포장191202용남면348021497D통영시191201RD20200001신설미분류1.31.922.0미분류소형고압블록미분류미분류1610
611차도포장181031용남면348021561A통영시181036RD20190009신설미분류91.43749.6612.0아스팔트콘크리트미분류미분류미분류1611
612보도포장181035용남면348021561A통영시181036RD20190009신설미분류36.82135.043.8미분류칼라아스팔트콘크리트미분류미분류1612
613보도포장181036용남면348021561A통영시181036RD20190009신설미분류43.71235.65.3미분류칼라아스팔트콘크리트미분류미분류1613