Overview

Dataset statistics

Number of variables13
Number of observations83
Missing cells1
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.2 KiB
Average record size in memory113.6 B

Variable types

Categorical5
Numeric7
Text1

Dataset

Description통영시 도시정보시스템의 중앙분리대에 대하여 지형지물부호,관리번호,행정읍면동,도엽번호,관리기관,도로구간번호,종류,연장,폭원,높이 등 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15062690/fileData.do

Alerts

지형지물부호 has constant value ""Constant
관리기관 has constant value ""Constant
대장초기화여부 has constant value ""Constant
관리번호 is highly overall correlated with 도로구간번호 and 1 other fieldsHigh correlation
도로구간번호 is highly overall correlated with 관리번호 and 2 other fieldsHigh correlation
폭원 is highly overall correlated with 도로구간번호 and 2 other fieldsHigh correlation
높이 is highly overall correlated with 종류High correlation
경도 is highly overall correlated with 폭원 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 경도 and 2 other fieldsHigh correlation
행정읍면동 is highly overall correlated with 관리번호 and 3 other fieldsHigh correlation
종류 is highly overall correlated with 폭원 and 3 other fieldsHigh correlation
도엽번호 has 1 (1.2%) missing valuesMissing
관리번호 has unique valuesUnique
경도 has unique valuesUnique
위도 has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:29:16.322971
Analysis finished2023-12-12 23:29:21.132658
Duration4.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지형지물부호
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size796.0 B
중앙분리대
83 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
중앙분리대 83
100.0%

Length

2023-12-13T08:29:21.195062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:29:21.279840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중앙분리대 83
100.0%

관리번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct83
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30872.506
Minimum1
Maximum450001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size879.0 B
2023-12-13T08:29:21.385309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.1
Q122.5
median47
Q367.5
95-th percentile181103.9
Maximum450001
Range450000
Interquartile range (IQR)45

Descriptive statistics

Standard deviation77719.664
Coefficient of variation (CV)2.5174394
Kurtosis9.955133
Mean30872.506
Median Absolute Deviation (MAD)23
Skewness2.8891146
Sum2562418
Variance6.0403462 × 109
MonotonicityStrictly increasing
2023-12-13T08:29:21.518693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.2%
59 1
 
1.2%
67 1
 
1.2%
66 1
 
1.2%
65 1
 
1.2%
64 1
 
1.2%
63 1
 
1.2%
62 1
 
1.2%
61 1
 
1.2%
60 1
 
1.2%
Other values (73) 73
88.0%
ValueCountFrequency (%)
1 1
1.2%
2 1
1.2%
3 1
1.2%
4 1
1.2%
5 1
1.2%
6 1
1.2%
7 1
1.2%
8 1
1.2%
9 1
1.2%
10 1
1.2%
ValueCountFrequency (%)
450001 1
1.2%
181107 1
1.2%
181106 1
1.2%
181105 1
1.2%
181104 1
1.2%
181103 1
1.2%
181102 1
1.2%
181101 1
1.2%
181002 1
1.2%
181001 1
1.2%

행정읍면동
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Memory size796.0 B
광도면
27 
도산면
26 
무전동
10 
<NA>
7 
용남면
4 
Other values (4)
9 

Length

Max length4
Median length3
Mean length3.0843373
Min length3

Unique

Unique2 ?
Unique (%)2.4%

Sample

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

Common Values

ValueCountFrequency (%)
광도면 27
32.5%
도산면 26
31.3%
무전동 10
 
12.0%
<NA> 7
 
8.4%
용남면 4
 
4.8%
봉평동 4
 
4.8%
도천동 3
 
3.6%
명정동 1
 
1.2%
북신동 1
 
1.2%

Length

2023-12-13T08:29:21.656700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:29:21.780840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광도면 27
32.5%
도산면 26
31.3%
무전동 10
 
12.0%
na 7
 
8.4%
용남면 4
 
4.8%
봉평동 4
 
4.8%
도천동 3
 
3.6%
명정동 1
 
1.2%
북신동 1
 
1.2%

도엽번호
Text

MISSING 

Distinct52
Distinct (%)63.4%
Missing1
Missing (%)1.2%
Memory size796.0 B
2023-12-13T08:29:22.032442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters820
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 (%)37.8%

Sample

1st row348021486B
2nd row348021476D
3rd row348021476D
4th row348021476D
5th row348021486B
ValueCountFrequency (%)
348021496b 4
 
4.9%
348020831a 3
 
3.7%
348021476d 3
 
3.7%
348020899b 3
 
3.7%
348021433b 3
 
3.7%
348021444b 3
 
3.7%
348021486b 3
 
3.7%
348021496a 3
 
3.7%
348020800c 2
 
2.4%
348021435c 2
 
2.4%
Other values (42) 53
64.6%
2023-12-13T08:29:22.397291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 158
19.3%
0 124
15.1%
8 118
14.4%
3 111
13.5%
2 94
11.5%
1 61
 
7.4%
9 30
 
3.7%
B 30
 
3.7%
6 20
 
2.4%
D 19
 
2.3%
Other values (4) 55
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 738
90.0%
Uppercase Letter 82
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 158
21.4%
0 124
16.8%
8 118
16.0%
3 111
15.0%
2 94
12.7%
1 61
 
8.3%
9 30
 
4.1%
6 20
 
2.7%
7 13
 
1.8%
5 9
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
B 30
36.6%
D 19
23.2%
C 19
23.2%
A 14
17.1%

Most occurring scripts

ValueCountFrequency (%)
Common 738
90.0%
Latin 82
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 158
21.4%
0 124
16.8%
8 118
16.0%
3 111
15.0%
2 94
12.7%
1 61
 
8.3%
9 30
 
4.1%
6 20
 
2.7%
7 13
 
1.8%
5 9
 
1.2%
Latin
ValueCountFrequency (%)
B 30
36.6%
D 19
23.2%
C 19
23.2%
A 14
17.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 158
19.3%
0 124
15.1%
8 118
14.4%
3 111
13.5%
2 94
11.5%
1 61
 
7.4%
9 30
 
3.7%
B 30
 
3.7%
6 20
 
2.4%
D 19
 
2.3%
Other values (4) 55
 
6.7%

관리기관
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size796.0 B
통영시
83 

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 83
100.0%

Length

2023-12-13T08:29:22.518270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:29:22.604863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
통영시 83
100.0%

도로구간번호
Real number (ℝ)

HIGH CORRELATION 

Distinct65
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36433.518
Minimum82
Maximum450026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size879.0 B
2023-12-13T08:29:22.728686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum82
5-th percentile682
Q17015
median8259
Q38587
95-th percentile181107.8
Maximum450026
Range449944
Interquartile range (IQR)1572

Descriptive statistics

Standard deviation75532.674
Coefficient of variation (CV)2.0731644
Kurtosis10.576602
Mean36433.518
Median Absolute Deviation (MAD)1212
Skewness2.9508776
Sum3023982
Variance5.7051848 × 109
MonotonicityNot monotonic
2023-12-13T08:29:22.852710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7068 3
 
3.6%
8284 3
 
3.6%
682 3
 
3.6%
8360 3
 
3.6%
6089 2
 
2.4%
8587 2
 
2.4%
8336 2
 
2.4%
8606 2
 
2.4%
8505 2
 
2.4%
676 2
 
2.4%
Other values (55) 59
71.1%
ValueCountFrequency (%)
82 1
 
1.2%
676 2
2.4%
679 1
 
1.2%
682 3
3.6%
688 1
 
1.2%
3008 2
2.4%
3534 1
 
1.2%
5005 1
 
1.2%
5419 1
 
1.2%
5586 1
 
1.2%
ValueCountFrequency (%)
450026 1
1.2%
181114 1
1.2%
181110 2
2.4%
181108 1
1.2%
181106 1
1.2%
181103 2
2.4%
181007 1
1.2%
181006 1
1.2%
160042 1
1.2%
160035 1
1.2%

종류
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size796.0 B
가드레일
41 
녹지대
23 
기타
19 

Length

Max length4
Median length3
Mean length3.2650602
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가드레일 41
49.4%
녹지대 23
27.7%
기타 19
22.9%

Length

2023-12-13T08:29:22.978641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:29:23.097502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가드레일 41
49.4%
녹지대 23
27.7%
기타 19
22.9%

연장
Real number (ℝ)

Distinct82
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean284.42313
Minimum6.73
Maximum3610.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size879.0 B
2023-12-13T08:29:23.242505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.73
5-th percentile8.94
Q134.035
median75.66
Q3180.85
95-th percentile1224.008
Maximum3610.73
Range3604
Interquartile range (IQR)146.815

Descriptive statistics

Standard deviation543.58649
Coefficient of variation (CV)1.9111894
Kurtosis17.255953
Mean284.42313
Median Absolute Deviation (MAD)60.65
Skewness3.6375554
Sum23607.12
Variance295486.27
MonotonicityNot monotonic
2023-12-13T08:29:23.385914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.55 2
 
2.4%
81.42 1
 
1.2%
952.58 1
 
1.2%
12.44 1
 
1.2%
43.33 1
 
1.2%
26.89 1
 
1.2%
15.7 1
 
1.2%
15.01 1
 
1.2%
1471.65 1
 
1.2%
7.55 1
 
1.2%
Other values (72) 72
86.7%
ValueCountFrequency (%)
6.73 1
1.2%
7.25 1
1.2%
7.5 1
1.2%
7.55 1
1.2%
8.93 1
1.2%
9.03 1
1.2%
12.14 1
1.2%
12.26 1
1.2%
12.39 1
1.2%
12.44 1
1.2%
ValueCountFrequency (%)
3610.73 1
1.2%
1862.82 1
1.2%
1478.88 1
1.2%
1471.65 1
1.2%
1230.88 1
1.2%
1162.16 1
1.2%
1152.45 1
1.2%
961.01 1
1.2%
952.58 1
1.2%
900.87 1
1.2%

폭원
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.61831325
Minimum0.05
Maximum2.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size879.0 B
2023-12-13T08:29:23.543746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.08
Q10.23
median0.4
Q31
95-th percentile2
Maximum2.5
Range2.45
Interquartile range (IQR)0.77

Descriptive statistics

Standard deviation0.59443111
Coefficient of variation (CV)0.96137533
Kurtosis2.1132304
Mean0.61831325
Median Absolute Deviation (MAD)0.32
Skewness1.6064013
Sum51.32
Variance0.35334834
MonotonicityNot monotonic
2023-12-13T08:29:23.646679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.4 25
30.1%
0.08 19
22.9%
1.0 13
15.7%
0.5 11
13.3%
2.0 5
 
6.0%
0.05 2
 
2.4%
2.5 2
 
2.4%
1.3 2
 
2.4%
2.02 1
 
1.2%
0.55 1
 
1.2%
Other values (2) 2
 
2.4%
ValueCountFrequency (%)
0.05 2
 
2.4%
0.08 19
22.9%
0.38 1
 
1.2%
0.4 25
30.1%
0.5 11
13.3%
0.55 1
 
1.2%
0.65 1
 
1.2%
1.0 13
15.7%
1.3 2
 
2.4%
2.0 5
 
6.0%
ValueCountFrequency (%)
2.5 2
 
2.4%
2.02 1
 
1.2%
2.0 5
 
6.0%
1.3 2
 
2.4%
1.0 13
15.7%
0.65 1
 
1.2%
0.55 1
 
1.2%
0.5 11
13.3%
0.4 25
30.1%
0.38 1
 
1.2%

높이
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.74939759
Minimum0.2
Maximum1.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size879.0 B
2023-12-13T08:29:23.752029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.5
Q10.7
median0.8
Q30.8
95-th percentile0.9
Maximum1.5
Range1.3
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.21618843
Coefficient of variation (CV)0.28848295
Kurtosis4.4149503
Mean0.74939759
Median Absolute Deviation (MAD)0.05
Skewness1.0120339
Sum62.2
Variance0.046737438
MonotonicityNot monotonic
2023-12-13T08:29:23.854451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.8 32
38.6%
0.75 20
24.1%
0.5 11
 
13.3%
0.9 6
 
7.2%
0.6 4
 
4.8%
0.65 2
 
2.4%
0.4 2
 
2.4%
1.4 2
 
2.4%
1.5 2
 
2.4%
0.2 2
 
2.4%
ValueCountFrequency (%)
0.2 2
 
2.4%
0.4 2
 
2.4%
0.5 11
 
13.3%
0.6 4
 
4.8%
0.65 2
 
2.4%
0.75 20
24.1%
0.8 32
38.6%
0.9 6
 
7.2%
1.4 2
 
2.4%
1.5 2
 
2.4%
ValueCountFrequency (%)
1.5 2
 
2.4%
1.4 2
 
2.4%
0.9 6
 
7.2%
0.8 32
38.6%
0.75 20
24.1%
0.65 2
 
2.4%
0.6 4
 
4.8%
0.5 11
 
13.3%
0.4 2
 
2.4%
0.2 2
 
2.4%

대장초기화여부
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size796.0 B
1
83 

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 83
100.0%

Length

2023-12-13T08:29:23.989630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:29:24.085906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 83
100.0%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct83
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.40305
Minimum128.34747
Maximum128.46784
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size879.0 B
2023-12-13T08:29:24.190793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.34747
5-th percentile128.34938
Q1128.37688
median128.41489
Q3128.4266
95-th percentile128.43821
Maximum128.46784
Range0.1203638
Interquartile range (IQR)0.0497215

Descriptive statistics

Standard deviation0.030678666
Coefficient of variation (CV)0.00023892474
Kurtosis-0.84912913
Mean128.40305
Median Absolute Deviation (MAD)0.013949
Skewness-0.58439502
Sum10657.453
Variance0.00094118054
MonotonicityNot monotonic
2023-12-13T08:29:24.320060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.4285112 1
 
1.2%
128.3595209 1
 
1.2%
128.3708782 1
 
1.2%
128.3937121 1
 
1.2%
128.3927973 1
 
1.2%
128.3658401 1
 
1.2%
128.3652688 1
 
1.2%
128.3643944 1
 
1.2%
128.3591131 1
 
1.2%
128.3587405 1
 
1.2%
Other values (73) 73
88.0%
ValueCountFrequency (%)
128.3474736 1
1.2%
128.3475869 1
1.2%
128.3476223 1
1.2%
128.347928 1
1.2%
128.3492853 1
1.2%
128.3502218 1
1.2%
128.3509959 1
1.2%
128.3517709 1
1.2%
128.3529814 1
1.2%
128.3545286 1
1.2%
ValueCountFrequency (%)
128.4678374 1
1.2%
128.4500816 1
1.2%
128.4414343 1
1.2%
128.4411249 1
1.2%
128.438243 1
1.2%
128.4378817 1
1.2%
128.4365725 1
1.2%
128.4318369 1
1.2%
128.4292663 1
1.2%
128.4289397 1
1.2%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct83
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.885206
Minimum34.826514
Maximum34.960295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size879.0 B
2023-12-13T08:29:24.471679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.826514
5-th percentile34.839601
Q134.859795
median34.881259
Q334.903376
95-th percentile34.936712
Maximum34.960295
Range0.13378065
Interquartile range (IQR)0.04358108

Descriptive statistics

Standard deviation0.031470902
Coefficient of variation (CV)0.00090212747
Kurtosis-0.62919101
Mean34.885206
Median Absolute Deviation (MAD)0.0219889
Skewness0.28222938
Sum2895.4721
Variance0.00099041769
MonotonicityNot monotonic
2023-12-13T08:29:24.866084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.85788833 1
 
1.2%
34.92318815 1
 
1.2%
34.90198588 1
 
1.2%
34.9032475 1
 
1.2%
34.90352055 1
 
1.2%
34.9035038 1
 
1.2%
34.90370428 1
 
1.2%
34.90403523 1
 
1.2%
34.9080254 1
 
1.2%
34.91366967 1
 
1.2%
Other values (73) 73
88.0%
ValueCountFrequency (%)
34.82651444 1
1.2%
34.82681627 1
1.2%
34.82941875 1
1.2%
34.82969664 1
1.2%
34.83958013 1
1.2%
34.83979129 1
1.2%
34.8433535 1
1.2%
34.85186696 1
1.2%
34.85255953 1
1.2%
34.85310769 1
1.2%
ValueCountFrequency (%)
34.96029509 1
1.2%
34.9401081 1
1.2%
34.9396693 1
1.2%
34.9393221 1
1.2%
34.93691819 1
1.2%
34.93486021 1
1.2%
34.9344482 1
1.2%
34.93410847 1
1.2%
34.93376986 1
1.2%
34.93324185 1
1.2%

Interactions

2023-12-13T08:29:20.108194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:16.652102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:17.116656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:17.578025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:18.096009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:18.682885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:19.499209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:20.185750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:16.710296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:17.174337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:17.644758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:18.173367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:18.765291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:19.586991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:20.328921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:16.775049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:17.231432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:17.710795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:18.240635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:18.843015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:19.668575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:20.414031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:16.852345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:17.301545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:17.781191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:18.322207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:19.206536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:19.758694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:20.499299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:16.914891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:17.361877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:17.847481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:18.402131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:19.277885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:19.842288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:20.599585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:16.985460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:17.427978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:17.934027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:18.485726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:19.348970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:19.923430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:20.689160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:17.048916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:17.508246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:18.013986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:18.578702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:19.419177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:29:20.023547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:29:24.989509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호행정읍면동도엽번호도로구간번호종류연장폭원높이경도위도
관리번호1.0000.9650.7501.0000.1450.0000.4310.5830.0000.496
행정읍면동0.9651.0000.9990.9650.6550.2580.8420.6010.7540.848
도엽번호0.7500.9991.0000.7500.9461.0000.9400.9740.9960.998
도로구간번호1.0000.9650.7501.0000.1450.0000.4310.5830.0000.496
종류0.1450.6550.9460.1451.0000.2980.9490.7100.6550.721
연장0.0000.2581.0000.0000.2981.0000.0000.0000.7490.466
폭원0.4310.8420.9400.4310.9490.0001.0000.7960.5480.746
높이0.5830.6010.9740.5830.7100.0000.7961.0000.1310.551
경도0.0000.7540.9960.0000.6550.7490.5480.1311.0000.893
위도0.4960.8480.9980.4960.7210.4660.7460.5510.8931.000
2023-12-13T08:29:25.103746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정읍면동종류
행정읍면동1.0000.512
종류0.5121.000
2023-12-13T08:29:25.184599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호도로구간번호연장폭원높이경도위도행정읍면동종류
관리번호1.0000.908-0.152-0.4540.069-0.3590.1280.7300.135
도로구간번호0.9081.000-0.183-0.5220.171-0.3880.1560.7300.135
연장-0.152-0.1831.0000.2840.2760.132-0.0210.1350.202
폭원-0.454-0.5220.2841.000-0.2930.576-0.4010.4330.931
높이0.0690.1710.276-0.2931.0000.023-0.1330.3720.611
경도-0.359-0.3880.1320.5760.0231.000-0.8270.4830.471
위도0.1280.156-0.021-0.401-0.133-0.8271.0000.6150.556
행정읍면동0.7300.7300.1350.4330.3720.4830.6151.0000.512
종류0.1350.1350.2020.9310.6110.4710.5560.5121.000

Missing values

2023-12-13T08:29:20.816235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:29:21.047370image/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

지형지물부호관리번호행정읍면동도엽번호관리기관도로구간번호종류연장폭원높이대장초기화여부경도위도
0중앙분리대1무전동348021486B통영시676녹지대40.732.00.651128.42851134.857888
1중앙분리대2무전동348021476D통영시682녹지대28.192.020.751128.4288434.860289
2중앙분리대3무전동348021476D통영시682녹지대35.162.00.91128.4289434.861016
3중앙분리대4무전동348021476D통영시682녹지대40.292.00.91128.42888834.860634
4중앙분리대5무전동348021486B통영시679녹지대24.042.00.61128.42870534.8593
5중앙분리대6무전동348021486B통영시676녹지대56.062.00.651128.42857734.858368
6중앙분리대7용남면348021489A통영시5968가드레일91.760.40.81128.44143434.858149
7중앙분리대8무전동348021476A통영시688가드레일284.950.40.81128.4271834.86294
8중앙분리대9무전동348021477C통영시3008가드레일277.760.40.81128.43183734.860615
9중앙분리대10무전동348021488A통영시3008가드레일721.680.40.81128.43657334.858316
지형지물부호관리번호행정읍면동도엽번호관리기관도로구간번호종류연장폭원높이대장초기화여부경도위도
73중앙분리대181001광도면348021424C통영시181007녹지대93.51.30.21128.4176334.884661
74중앙분리대181002광도면348021434D통영시181006녹지대142.51.30.21128.4184434.881259
75중앙분리대181101<NA>348021496A통영시181110가드레일68.490.40.81128.42609334.853871
76중앙분리대181102<NA>348021496A통영시181110가드레일63.630.40.81128.42711634.85353
77중앙분리대181103<NA>348021496B통영시181108가드레일6.730.40.81128.42786734.85328
78중앙분리대181104<NA>348021496B통영시181106가드레일75.660.40.81128.42840734.853108
79중앙분리대181105<NA>348021496D통영시181103가드레일63.680.40.81128.42817234.851867
80중앙분리대181106<NA>348021496A통영시181114가드레일150.570.40.81128.42511634.853371
81중앙분리대181107<NA>348021496B통영시181103가드레일45.630.40.81128.42878734.85256
82중앙분리대450001북신동348021496B통영시450026가드레일17.00.50.91128.42926634.85311