Overview

Dataset statistics

Number of variables13
Number of observations127
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.6 KiB
Average record size in memory110.0 B

Variable types

Numeric5
Categorical7
Text1

Dataset

Description제주특별자치도에서 제공하는 어린이보호구역 내 교차로 관련 도로명, 도로폭, 도로차로수, 교차로 등 정보 입니다.
Author제주특별자치도
URLhttps://www.data.go.kr/data/15075789/fileData.do

Alerts

시도명 has constant value ""Constant
교차로 분류 has constant value ""Constant
관리기관전화번호 has constant value ""Constant
데이터기준일자 has constant value ""Constant
관리기관명 is highly overall correlated with 관리번호 and 3 other fieldsHigh correlation
시군구명 is highly overall correlated with 관리번호 and 3 other fieldsHigh correlation
관리번호 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
도로폭 is highly overall correlated with 도로차로수 and 2 other fieldsHigh correlation
도로차로수 is highly overall correlated with 도로폭High correlation
위도 is highly overall correlated with 관리번호 and 3 other fieldsHigh correlation
경도 is highly overall correlated with 위도High correlation
관리번호 has unique valuesUnique

Reproduction

Analysis started2023-12-13 00:02:39.946853
Analysis finished2023-12-13 00:02:42.799528
Duration2.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct127
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64
Minimum1
Maximum127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-13T09:02:42.856313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.3
Q132.5
median64
Q395.5
95-th percentile120.7
Maximum127
Range126
Interquartile range (IQR)63

Descriptive statistics

Standard deviation36.805797
Coefficient of variation (CV)0.57509057
Kurtosis-1.2
Mean64
Median Absolute Deviation (MAD)32
Skewness0
Sum8128
Variance1354.6667
MonotonicityStrictly increasing
2023-12-13T09:02:42.984169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.8%
2 1
 
0.8%
95 1
 
0.8%
94 1
 
0.8%
93 1
 
0.8%
92 1
 
0.8%
91 1
 
0.8%
90 1
 
0.8%
89 1
 
0.8%
88 1
 
0.8%
Other values (117) 117
92.1%
ValueCountFrequency (%)
1 1
0.8%
2 1
0.8%
3 1
0.8%
4 1
0.8%
5 1
0.8%
6 1
0.8%
7 1
0.8%
8 1
0.8%
9 1
0.8%
10 1
0.8%
ValueCountFrequency (%)
127 1
0.8%
126 1
0.8%
125 1
0.8%
124 1
0.8%
123 1
0.8%
122 1
0.8%
121 1
0.8%
120 1
0.8%
119 1
0.8%
118 1
0.8%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
제주특별자치도
127 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주특별자치도 127
100.0%

Length

2023-12-13T09:02:43.083622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:02:43.153461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주특별자치도 127
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
제주시
100 
서귀포시
27 

Length

Max length4
Median length3
Mean length3.2125984
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주시 100
78.7%
서귀포시 27
 
21.3%

Length

2023-12-13T09:02:43.233139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:02:43.308578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주시 100
78.7%
서귀포시 27
 
21.3%

도로명
Text

Distinct90
Distinct (%)70.9%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-13T09:02:43.520196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length4.4173228
Min length3

Characters and Unicode

Total characters561
Distinct characters98
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63 ?
Unique (%)49.6%

Sample

1st row태위로
2nd row남조로
3rd row에듀시티로
4th row에듀시티로
5th row에듀시티로
ValueCountFrequency (%)
에듀시티로 4
 
3.1%
일주동로 4
 
3.1%
화삼로 3
 
2.4%
고마로13길 3
 
2.4%
구좌로 3
 
2.4%
남녕로 3
 
2.4%
이어도로 3
 
2.4%
구남동1길 3
 
2.4%
온평애향로 2
 
1.6%
용담로 2
 
1.6%
Other values (80) 97
76.4%
2023-12-13T09:02:43.853505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 101
 
18.0%
길 64
 
11.4%
1 30
 
5.3%
동 19
 
3.4%
남 15
 
2.7%
성 13
 
2.3%
2 10
 
1.8%
신 10
 
1.8%
서 9
 
1.6%
고 8
 
1.4%
Other values (88) 282
50.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 473
84.3%
Decimal Number 88
 
15.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 101
21.4%
길 64
 
13.5%
동 19
 
4.0%
남 15
 
3.2%
성 13
 
2.7%
신 10
 
2.1%
서 9
 
1.9%
고 8
 
1.7%
산 8
 
1.7%
구 8
 
1.7%
Other values (78) 218
46.1%
Decimal Number
ValueCountFrequency (%)
1 30
34.1%
2 10
 
11.4%
6 8
 
9.1%
3 8
 
9.1%
9 7
 
8.0%
5 6
 
6.8%
0 6
 
6.8%
4 5
 
5.7%
8 4
 
4.5%
7 4
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 473
84.3%
Common 88
 
15.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 101
21.4%
길 64
 
13.5%
동 19
 
4.0%
남 15
 
3.2%
성 13
 
2.7%
신 10
 
2.1%
서 9
 
1.9%
고 8
 
1.7%
산 8
 
1.7%
구 8
 
1.7%
Other values (78) 218
46.1%
Common
ValueCountFrequency (%)
1 30
34.1%
2 10
 
11.4%
6 8
 
9.1%
3 8
 
9.1%
9 7
 
8.0%
5 6
 
6.8%
0 6
 
6.8%
4 5
 
5.7%
8 4
 
4.5%
7 4
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 473
84.3%
ASCII 88
 
15.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 101
21.4%
길 64
 
13.5%
동 19
 
4.0%
남 15
 
3.2%
성 13
 
2.7%
신 10
 
2.1%
서 9
 
1.9%
고 8
 
1.7%
산 8
 
1.7%
구 8
 
1.7%
Other values (78) 218
46.1%
ASCII
ValueCountFrequency (%)
1 30
34.1%
2 10
 
11.4%
6 8
 
9.1%
3 8
 
9.1%
9 7
 
8.0%
5 6
 
6.8%
0 6
 
6.8%
4 5
 
5.7%
8 4
 
4.5%
7 4
 
4.5%

도로폭
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.299213
Minimum4
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-13T09:02:43.953960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6
Q18
median10
Q315
95-th percentile20
Maximum30
Range26
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.9446379
Coefficient of variation (CV)0.43760907
Kurtosis1.0552303
Mean11.299213
Median Absolute Deviation (MAD)3
Skewness0.98683308
Sum1435
Variance24.449444
MonotonicityNot monotonic
2023-12-13T09:02:44.044684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
8 20
15.7%
15 14
11.0%
6 13
10.2%
7 11
8.7%
12 9
 
7.1%
9 8
 
6.3%
16 8
 
6.3%
10 8
 
6.3%
11 7
 
5.5%
14 6
 
4.7%
Other values (10) 23
18.1%
ValueCountFrequency (%)
4 4
 
3.1%
5 2
 
1.6%
6 13
10.2%
7 11
8.7%
8 20
15.7%
9 8
 
6.3%
10 8
 
6.3%
11 7
 
5.5%
12 9
7.1%
13 3
 
2.4%
ValueCountFrequency (%)
30 1
 
0.8%
25 1
 
0.8%
24 2
 
1.6%
22 1
 
0.8%
20 5
 
3.9%
18 3
 
2.4%
17 1
 
0.8%
16 8
6.3%
15 14
11.0%
14 6
4.7%

도로차로수
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.976378
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-13T09:02:44.134885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.87708542
Coefficient of variation (CV)0.44378426
Kurtosis5.7650283
Mean1.976378
Median Absolute Deviation (MAD)0
Skewness1.9822895
Sum251
Variance0.76927884
MonotonicityNot monotonic
2023-12-13T09:02:44.220195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 84
66.1%
1 30
 
23.6%
4 5
 
3.9%
3 4
 
3.1%
5 3
 
2.4%
6 1
 
0.8%
ValueCountFrequency (%)
1 30
 
23.6%
2 84
66.1%
3 4
 
3.1%
4 5
 
3.9%
5 3
 
2.4%
6 1
 
0.8%
ValueCountFrequency (%)
6 1
 
0.8%
5 3
 
2.4%
4 5
 
3.9%
3 4
 
3.1%
2 84
66.1%
1 30
 
23.6%

보차분리여부
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
분리
101 
미분리
26 

Length

Max length3
Median length2
Mean length2.2047244
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
분리 101
79.5%
미분리 26
 
20.5%

Length

2023-12-13T09:02:44.314027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:02:44.385686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
분리 101
79.5%
미분리 26
 
20.5%

교차로 분류
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
고원식
127 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
고원식 127
100.0%

Length

2023-12-13T09:02:44.461614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:02:44.530655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고원식 127
100.0%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct126
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.442294
Minimum33.169136
Maximum33.554993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-13T09:02:44.610734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.169136
5-th percentile33.248102
Q133.409443
median33.488093
Q333.51033
95-th percentile33.537437
Maximum33.554993
Range0.385857
Interquartile range (IQR)0.100887

Descriptive statistics

Standard deviation0.099194915
Coefficient of variation (CV)0.0029661516
Kurtosis-0.053832826
Mean33.442294
Median Absolute Deviation (MAD)0.026167
Skewness-1.1598716
Sum4247.1714
Variance0.0098396312
MonotonicityNot monotonic
2023-12-13T09:02:44.718429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.407553 2
 
1.6%
33.28442 1
 
0.8%
33.492139 1
 
0.8%
33.492797 1
 
0.8%
33.491435 1
 
0.8%
33.491886 1
 
0.8%
33.491056 1
 
0.8%
33.511346 1
 
0.8%
33.512616 1
 
0.8%
33.512067 1
 
0.8%
Other values (116) 116
91.3%
ValueCountFrequency (%)
33.169136 1
0.8%
33.169389 1
0.8%
33.243323 1
0.8%
33.246392 1
0.8%
33.246915 1
0.8%
33.247184 1
0.8%
33.248073 1
0.8%
33.24817 1
0.8%
33.24932 1
0.8%
33.250093 1
0.8%
ValueCountFrequency (%)
33.554993 1
0.8%
33.554761 1
0.8%
33.551981 1
0.8%
33.550985 1
0.8%
33.539816 1
0.8%
33.537616 1
0.8%
33.537491 1
0.8%
33.537312 1
0.8%
33.53438 1
0.8%
33.523231 1
0.8%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct126
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.51196
Minimum126.17682
Maximum126.89772
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-13T09:02:44.827855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.17682
5-th percentile126.27003
Q1126.45102
median126.51403
Q3126.57107
95-th percentile126.82056
Maximum126.89772
Range0.720906
Interquartile range (IQR)0.1200505

Descriptive statistics

Standard deviation0.15910113
Coefficient of variation (CV)0.0012575975
Kurtosis0.24266043
Mean126.51196
Median Absolute Deviation (MAD)0.062266
Skewness0.3041426
Sum16067.019
Variance0.025313168
MonotonicityNot monotonic
2023-12-13T09:02:44.941010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.897723 2
 
1.6%
126.633114 1
 
0.8%
126.539532 1
 
0.8%
126.540575 1
 
0.8%
126.483672 1
 
0.8%
126.484984 1
 
0.8%
126.485625 1
 
0.8%
126.510671 1
 
0.8%
126.510423 1
 
0.8%
126.512279 1
 
0.8%
Other values (116) 116
91.3%
ValueCountFrequency (%)
126.176817 1
0.8%
126.177996 1
0.8%
126.182716 1
0.8%
126.251913 1
0.8%
126.260284 1
0.8%
126.261989 1
0.8%
126.269717 1
0.8%
126.270744 1
0.8%
126.270854 1
0.8%
126.275633 1
0.8%
ValueCountFrequency (%)
126.897723 2
1.6%
126.896377 1
0.8%
126.858917 1
0.8%
126.858039 1
0.8%
126.856418 1
0.8%
126.829235 1
0.8%
126.800325 1
0.8%
126.798316 1
0.8%
126.798315 1
0.8%
126.777879 1
0.8%

관리기관명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
제주시
100 
서귀포시
27 

Length

Max length4
Median length3
Mean length3.2125984
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주시 100
78.7%
서귀포시 27
 
21.3%

Length

2023-12-13T09:02:45.048315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:02:45.124774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주시 100
78.7%
서귀포시 27
 
21.3%

관리기관전화번호
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
064-120
127 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row064-120
2nd row064-120
3rd row064-120
4th row064-120
5th row064-120

Common Values

ValueCountFrequency (%)
064-120 127
100.0%

Length

2023-12-13T09:02:45.211205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:02:45.281270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
064-120 127
100.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2020-12-31
127 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-31
2nd row2020-12-31
3rd row2020-12-31
4th row2020-12-31
5th row2020-12-31

Common Values

ValueCountFrequency (%)
2020-12-31 127
100.0%

Length

2023-12-13T09:02:45.354588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:02:45.426244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-31 127
100.0%

Interactions

2023-12-13T09:02:41.925469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:40.327219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:40.711106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:41.093859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:41.477367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:42.014044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:40.391102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:40.784935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:41.164394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:41.558944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:42.083047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:40.456352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:40.848650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:41.236563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:41.663604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:42.157149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:40.521439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:40.915384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:41.309221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:41.750229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:42.241555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:40.622533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:41.013431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:41.396346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:02:41.837254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T09:02:45.481369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호시군구명도로명도로폭도로차로수보차분리여부위도경도관리기관명
관리번호1.0000.9990.9920.6510.4740.5270.7160.7380.999
시군구명0.9991.0000.9850.6800.4450.3440.8820.4320.999
도로명0.9920.9851.0000.9340.9090.9961.0000.9970.985
도로폭0.6510.6800.9341.0000.8950.4890.4210.3280.680
도로차로수0.4740.4450.9090.8951.0000.6490.0000.2480.445
보차분리여부0.5270.3440.9960.4890.6491.0000.2470.1790.344
위도0.7160.8821.0000.4210.0000.2471.0000.7030.882
경도0.7380.4320.9970.3280.2480.1790.7031.0000.432
관리기관명0.9990.9990.9850.6800.4450.3440.8820.4321.000
2023-12-13T09:02:45.569648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리기관명시군구명보차분리여부
관리기관명1.0000.9760.224
시군구명0.9761.0000.224
보차분리여부0.2240.2241.000
2023-12-13T09:02:45.644264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호도로폭도로차로수위도경도시군구명보차분리여부관리기관명
관리번호1.0000.250-0.3290.7390.3010.9450.3920.945
도로폭0.2501.0000.5600.2990.1720.5120.3630.512
도로차로수-0.3290.5601.000-0.211-0.1100.3150.4660.315
위도0.7390.299-0.2111.0000.5610.8920.2390.892
경도0.3010.172-0.1100.5611.0000.3210.1310.321
시군구명0.9450.5120.3150.8920.3211.0000.2240.976
보차분리여부0.3920.3630.4660.2390.1310.2241.0000.224
관리기관명0.9450.5120.3150.8920.3210.9760.2241.000

Missing values

2023-12-13T09:02:42.600675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T09:02:42.746520image/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

관리번호시도명시군구명도로명도로폭도로차로수보차분리여부교차로 분류위도경도관리기관명관리기관전화번호데이터기준일자
01제주특별자치도서귀포시태위로112분리고원식33.28442126.633114서귀포시064-1202020-12-31
12제주특별자치도서귀포시남조로132분리고원식33.31005126.714918서귀포시064-1202020-12-31
23제주특별자치도서귀포시에듀시티로62분리고원식33.288562126.282912서귀포시064-1202020-12-31
34제주특별자치도서귀포시에듀시티로62분리고원식33.290489126.283727서귀포시064-1202020-12-31
45제주특별자치도서귀포시에듀시티로62분리고원식33.296121126.287531서귀포시064-1202020-12-31
56제주특별자치도서귀포시에듀시티로62분리고원식33.296219126.286309서귀포시064-1202020-12-31
67제주특별자치도서귀포시글로벌에듀로260번길92분리고원식33.29589126.289939서귀포시064-1202020-12-31
78제주특별자치도서귀포시솔동산로11번길162분리고원식33.243323126.565836서귀포시064-1202020-12-31
89제주특별자치도서귀포시신동로67번길92분리고원식33.255704126.513761서귀포시064-1202020-12-31
910제주특별자치도서귀포시신동로67번길52분리고원식33.255683126.513626서귀포시064-1202020-12-31
관리번호시도명시군구명도로명도로폭도로차로수보차분리여부교차로 분류위도경도관리기관명관리기관전화번호데이터기준일자
117118제주특별자치도제주시고마로13길81미분리고원식33.509935126.542242제주시064-1202020-12-31
118119제주특별자치도제주시동광로21길81미분리고원식33.510855126.541841제주시064-1202020-12-31
119120제주특별자치도제주시고마로13길81미분리고원식33.510726126.542671제주시064-1202020-12-31
120121제주특별자치도제주시고마로13길71미분리고원식33.51189126.542969제주시064-1202020-12-31
121122제주특별자치도제주시동광로19길71미분리고원식33.511518126.540071제주시064-1202020-12-31
122123제주특별자치도제주시고마로7길71미분리고원식33.51134126.539444제주시064-1202020-12-31
123124제주특별자치도제주시국기로2길101미분리고원식33.479995126.491951제주시064-1202020-12-31
124125제주특별자치도제주시연화로2길81미분리고원식33.478195126.489121제주시064-1202020-12-31
125126제주특별자치도제주시수덕로184분리고원식33.48558126.471748제주시064-1202020-12-31
126127제주특별자치도제주시수덕로184분리고원식33.484838126.470848제주시064-1202020-12-31