Overview

Dataset statistics

Number of variables17
Number of observations3516
Missing cells6923
Missing cells (%)11.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory494.6 KiB
Average record size in memory144.0 B

Variable types

Numeric7
Categorical7
Text3

Dataset

Description경기도 의왕시 보행안전지수 지도 시각화에 사용된 의왕시 교통표지판에 대한 정보를 담고 있는 csv형태의 데이터를 제공합니다.
Author경기도 의왕시
URLhttps://www.data.go.kr/data/15108924/fileData.do

Alerts

공사번호 is highly overall correlated with 관리번호 and 8 other fieldsHigh correlation
대장초기화여부 is highly overall correlated with 행정동읍면코드 and 3 other fieldsHigh correlation
관리기관코드 is highly overall correlated with 행정동읍면코드 and 1 other fieldsHigh correlation
지형지물부호 is highly overall correlated with 공사번호High correlation
관리번호 is highly overall correlated with 도로구간 번호 and 2 other fieldsHigh correlation
행정동읍면코드 is highly overall correlated with 경도 and 3 other fieldsHigh correlation
도로구간 번호 is highly overall correlated with 관리번호 and 2 other fieldsHigh correlation
높이 is highly overall correlated with 관리번호 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 행정동읍면코드 and 2 other fieldsHigh correlation
위치구분 is highly overall correlated with 공사번호 and 1 other fieldsHigh correlation
지형지물부호 is highly imbalanced (98.7%)Imbalance
관리기관코드 is highly imbalanced (97.2%)Imbalance
지주형식 is highly imbalanced (60.2%)Imbalance
공사번호 is highly imbalanced (78.9%)Imbalance
대장초기화여부 is highly imbalanced (91.8%)Imbalance
행정동읍면코드 has 1790 (50.9%) missing valuesMissing
도엽번호 has 1790 (50.9%) missing valuesMissing
규격 has 1854 (52.7%) missing valuesMissing
기재사항 has 1489 (42.3%) missing valuesMissing
공간지리식별번호 has unique valuesUnique
도로구간 번호 has 98 (2.8%) zerosZeros
높이 has 1662 (47.3%) zerosZeros

Reproduction

Analysis started2023-12-12 18:30:50.658174
Analysis finished2023-12-12 18:30:58.646696
Duration7.99 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

공간지리식별번호
Real number (ℝ)

UNIQUE 

Distinct3516
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2555.1578
Minimum1
Maximum5120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-12-13T03:30:58.792482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile207.75
Q11179.75
median2520
Q33992.5
95-th percentile4908.25
Maximum5120
Range5119
Interquartile range (IQR)2812.75

Descriptive statistics

Standard deviation1557.0745
Coefficient of variation (CV)0.60938486
Kurtosis-1.3058107
Mean2555.1578
Median Absolute Deviation (MAD)1394.5
Skewness0.035870962
Sum8983935
Variance2424481
MonotonicityNot monotonic
2023-12-13T03:30:59.002382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
142 1
 
< 0.1%
3579 1
 
< 0.1%
5051 1
 
< 0.1%
5052 1
 
< 0.1%
3554 1
 
< 0.1%
3556 1
 
< 0.1%
3560 1
 
< 0.1%
3561 1
 
< 0.1%
3574 1
 
< 0.1%
3575 1
 
< 0.1%
Other values (3506) 3506
99.7%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
5120 1
< 0.1%
5119 1
< 0.1%
5118 1
< 0.1%
5083 1
< 0.1%
5082 1
< 0.1%
5081 1
< 0.1%
5080 1
< 0.1%
5079 1
< 0.1%
5078 1
< 0.1%
5077 1
< 0.1%

지형지물부호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.6 KiB
AE211
3512 
AE221
 
4

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAE211
2nd rowAE211
3rd rowAE211
4th rowAE211
5th rowAE211

Common Values

ValueCountFrequency (%)
AE211 3512
99.9%
AE221 4
 
0.1%

Length

2023-12-13T03:30:59.184337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:30:59.312387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ae211 3512
99.9%
ae221 4
 
0.1%

관리번호
Real number (ℝ)

HIGH CORRELATION 

Distinct3490
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6023222 × 108
Minimum1
Maximum2.10901 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-12-13T03:30:59.509507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile357.75
Q11204.75
median30025.5
Q3402102.25
95-th percentile2.0073003 × 109
Maximum2.10901 × 109
Range2.10901 × 109
Interquartile range (IQR)400897.5

Descriptive statistics

Standard deviation6.7566163 × 108
Coefficient of variation (CV)2.5963796
Kurtosis2.9039562
Mean2.6023222 × 108
Median Absolute Deviation (MAD)29640
Skewness2.2137249
Sum9.1497649 × 1011
Variance4.5651864 × 1017
MonotonicityNot monotonic
2023-12-13T03:30:59.713503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
676 2
 
0.1%
719 2
 
0.1%
686 2
 
0.1%
2034 2
 
0.1%
100007 2
 
0.1%
100010 2
 
0.1%
402236 2
 
0.1%
674 2
 
0.1%
402225 2
 
0.1%
402291 2
 
0.1%
Other values (3480) 3496
99.4%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
10 1
< 0.1%
14 1
< 0.1%
ValueCountFrequency (%)
2109010014 1
< 0.1%
2109010013 1
< 0.1%
2109010012 1
< 0.1%
2109010011 1
< 0.1%
2109010010 1
< 0.1%
2109010009 1
< 0.1%
2109010008 1
< 0.1%
2109010007 1
< 0.1%
2109010006 1
< 0.1%
2109010005 1
< 0.1%

행정동읍면코드
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)0.6%
Missing1790
Missing (%)50.9%
Infinite0
Infinite (%)0.0%
Mean4.1430115 × 109
Minimum4.1430101 × 109
Maximum4.143053 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-12-13T03:30:59.868820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.1430101 × 109
5-th percentile4.1430103 × 109
Q14.1430106 × 109
median4.1430106 × 109
Q34.1430109 × 109
95-th percentile4.143011 × 109
Maximum4.143053 × 109
Range42900
Interquartile range (IQR)300

Descriptive statistics

Standard deviation5891.6043
Coefficient of variation (CV)1.4220584 × 10-6
Kurtosis45.780249
Mean4.1430115 × 109
Median Absolute Deviation (MAD)200
Skewness6.9030678
Sum7.1508378 × 1012
Variance34711001
MonotonicityNot monotonic
2023-12-13T03:30:59.994127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
4143010600 522
 
14.8%
4143010900 339
 
9.6%
4143010300 337
 
9.6%
4143010700 208
 
5.9%
4143010800 167
 
4.7%
4143011100 42
 
1.2%
4143011000 34
 
1.0%
4143053000 34
 
1.0%
4143010500 21
 
0.6%
4143010200 14
 
0.4%
(Missing) 1790
50.9%
ValueCountFrequency (%)
4143010100 8
 
0.2%
4143010200 14
 
0.4%
4143010300 337
9.6%
4143010500 21
 
0.6%
4143010600 522
14.8%
4143010700 208
 
5.9%
4143010800 167
 
4.7%
4143010900 339
9.6%
4143011000 34
 
1.0%
4143011100 42
 
1.2%
ValueCountFrequency (%)
4143053000 34
 
1.0%
4143011100 42
 
1.2%
4143011000 34
 
1.0%
4143010900 339
9.6%
4143010800 167
 
4.7%
4143010700 208
 
5.9%
4143010600 522
14.8%
4143010500 21
 
0.6%
4143010300 337
9.6%
4143010200 14
 
0.4%

도엽번호
Text

MISSING 

Distinct154
Distinct (%)8.9%
Missing1790
Missing (%)50.9%
Memory size27.6 KiB
2023-12-13T03:31:00.355919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.8673233
Min length9

Characters and Unicode

Total characters17031
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

Unique20 ?
Unique (%)1.2%

Sample

1st row376121967B
2nd row376121968A
3rd row376121968A
4th row376121967B
5th row376121967B
ValueCountFrequency (%)
376121098 73
 
4.2%
376121508 71
 
4.1%
377091151d 54
 
3.1%
376122052d 50
 
2.9%
377091152b 48
 
2.8%
376122062b 46
 
2.7%
376121507 45
 
2.6%
377091153a 41
 
2.4%
376121950d 41
 
2.4%
377091151b 37
 
2.1%
Other values (144) 1220
70.7%
2023-12-13T03:31:00.871141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3732
21.9%
7 2540
14.9%
3 2027
11.9%
2 1792
10.5%
6 1428
 
8.4%
0 1423
 
8.4%
9 1070
 
6.3%
5 1055
 
6.2%
A 440
 
2.6%
B 415
 
2.4%
Other values (4) 1109
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15534
91.2%
Uppercase Letter 1497
 
8.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3732
24.0%
7 2540
16.4%
3 2027
13.0%
2 1792
11.5%
6 1428
 
9.2%
0 1423
 
9.2%
9 1070
 
6.9%
5 1055
 
6.8%
8 308
 
2.0%
4 159
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
A 440
29.4%
B 415
27.7%
D 372
24.8%
C 270
18.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15534
91.2%
Latin 1497
 
8.8%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3732
24.0%
7 2540
16.4%
3 2027
13.0%
2 1792
11.5%
6 1428
 
9.2%
0 1423
 
9.2%
9 1070
 
6.9%
5 1055
 
6.8%
8 308
 
2.0%
4 159
 
1.0%
Latin
ValueCountFrequency (%)
A 440
29.4%
B 415
27.7%
D 372
24.8%
C 270
18.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17031
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3732
21.9%
7 2540
14.9%
3 2027
11.9%
2 1792
10.5%
6 1428
 
8.4%
0 1423
 
8.4%
9 1070
 
6.3%
5 1055
 
6.2%
A 440
 
2.6%
B 415
 
2.4%
Other values (4) 1109
 
6.5%

관리기관코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.6 KiB
MNG001
3506 
MNG000
 
10

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMNG001
2nd rowMNG001
3rd rowMNG001
4th rowMNG001
5th rowMNG001

Common Values

ValueCountFrequency (%)
MNG001 3506
99.7%
MNG000 10
 
0.3%

Length

2023-12-13T03:31:01.059993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:31:01.235706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
mng001 3506
99.7%
mng000 10
 
0.3%

도로구간 번호
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct525
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4993312 × 108
Minimum0
Maximum2.10901 × 109
Zeros98
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-12-13T03:31:01.397763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27
Q1609
median2103
Q3200119.5
95-th percentile2.0073002 × 109
Maximum2.10901 × 109
Range2.10901 × 109
Interquartile range (IQR)199510.5

Descriptive statistics

Standard deviation6.6415943 × 108
Coefficient of variation (CV)2.6573486
Kurtosis3.2178413
Mean2.4993312 × 108
Median Absolute Deviation (MAD)2103
Skewness2.2834548
Sum8.7876484 × 1011
Variance4.4110774 × 1017
MonotonicityNot monotonic
2023-12-13T03:31:01.604129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 98
 
2.8%
30012 83
 
2.4%
911041 60
 
1.7%
2002 53
 
1.5%
200152 53
 
1.5%
911039 46
 
1.3%
1014 40
 
1.1%
30018 39
 
1.1%
30006 35
 
1.0%
450 34
 
1.0%
Other values (515) 2975
84.6%
ValueCountFrequency (%)
0 98
2.8%
1 5
 
0.1%
2 13
 
0.4%
4 21
 
0.6%
5 2
 
0.1%
9 7
 
0.2%
11 8
 
0.2%
12 6
 
0.2%
13 7
 
0.2%
15 7
 
0.2%
ValueCountFrequency (%)
2109010001 14
0.4%
2103240003 3
 
0.1%
2103240002 3
 
0.1%
2103240001 2
 
0.1%
2103020001 10
0.3%
2012310002 9
0.3%
2012310001 8
0.2%
2009010014 1
 
< 0.1%
2009010012 2
 
0.1%
2009010009 9
0.3%

위치구분
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size27.6 KiB
DRT001
1716 
DRT002
1598 
DRT003
 
121
DRT000
 
49
PLC001
 
18

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDRT001
2nd rowDRT002
3rd rowDRT002
4th rowDRT001
5th rowDRT002

Common Values

ValueCountFrequency (%)
DRT001 1716
48.8%
DRT002 1598
45.4%
DRT003 121
 
3.4%
DRT000 49
 
1.4%
PLC001 18
 
0.5%
PLC002 14
 
0.4%

Length

2023-12-13T03:31:01.750386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:31:01.915628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
drt001 1716
48.8%
drt002 1598
45.4%
drt003 121
 
3.4%
drt000 49
 
1.4%
plc001 18
 
0.5%
plc002 14
 
0.4%

표지판구분
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.6 KiB
AET009
1317 
AET010
941 
AET008
657 
AET011
358 
AET012
243 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAET009
2nd rowAET008
3rd rowAET008
4th rowAET008
5th rowAET008

Common Values

ValueCountFrequency (%)
AET009 1317
37.5%
AET010 941
26.8%
AET008 657
18.7%
AET011 358
 
10.2%
AET012 243
 
6.9%

Length

2023-12-13T03:31:02.068873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:31:02.193818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
aet009 1317
37.5%
aet010 941
26.8%
aet008 657
18.7%
aet011 358
 
10.2%
aet012 243
 
6.9%

지주형식
Categorical

IMBALANCE 

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size27.6 KiB
IPG001
1879 
IPG999
1512 
IPG002
 
118
IPG003
 
2
IPG006
 
2
Other values (3)
 
3

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowIPG001
2nd rowIPG001
3rd rowIPG001
4th rowIPG001
5th rowIPG001

Common Values

ValueCountFrequency (%)
IPG001 1879
53.4%
IPG999 1512
43.0%
IPG002 118
 
3.4%
IPG003 2
 
0.1%
IPG006 2
 
0.1%
IPG000 1
 
< 0.1%
IPG923 1
 
< 0.1%
IPG915 1
 
< 0.1%

Length

2023-12-13T03:31:02.680442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:31:02.824206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ipg001 1879
53.4%
ipg999 1512
43.0%
ipg002 118
 
3.4%
ipg003 2
 
0.1%
ipg006 2
 
0.1%
ipg000 1
 
< 0.1%
ipg923 1
 
< 0.1%
ipg915 1
 
< 0.1%

높이
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct112
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5947753
Minimum0
Maximum10
Zeros1662
Zeros (%)47.3%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-12-13T03:31:03.009178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q32.7
95-th percentile5
Maximum10
Range10
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation1.7829895
Coefficient of variation (CV)1.1180192
Kurtosis0.37002855
Mean1.5947753
Median Absolute Deviation (MAD)2
Skewness0.91820839
Sum5607.23
Variance3.1790515
MonotonicityNot monotonic
2023-12-13T03:31:03.203627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1662
47.3%
2.0 424
 
12.1%
5.0 213
 
6.1%
3.0 149
 
4.2%
2.70000005 105
 
3.0%
2.0999999 84
 
2.4%
2.5 82
 
2.3%
6.0 72
 
2.0%
2.9000001 69
 
2.0%
3.5 63
 
1.8%
Other values (102) 593
 
16.9%
ValueCountFrequency (%)
0.0 1662
47.3%
0.72 1
 
< 0.1%
0.89999998 1
 
< 0.1%
1.0 1
 
< 0.1%
1.14999998 1
 
< 0.1%
1.15999997 15
 
0.4%
1.20000005 1
 
< 0.1%
1.29999995 1
 
< 0.1%
1.39999998 2
 
0.1%
1.5 24
 
0.7%
ValueCountFrequency (%)
10.0 3
 
0.1%
8.0 4
 
0.1%
7.84000015 12
 
0.3%
7.0 2
 
0.1%
6.5 4
 
0.1%
6.0 72
2.0%
5.5 4
 
0.1%
5.4000001 2
 
0.1%
5.3 1
 
< 0.1%
5.2 1
 
< 0.1%

규격
Text

MISSING 

Distinct173
Distinct (%)10.4%
Missing1854
Missing (%)52.7%
Memory size27.6 KiB
2023-12-13T03:31:03.523433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length23
Mean length7.0481348
Min length3

Characters and Unicode

Total characters11714
Distinct characters33
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)4.2%

Sample

1st row역삼각700
2nd row삼각700
3rd row삼각700
4th row삼각700
5th row삼각700
ValueCountFrequency (%)
ø600 282
 
16.6%
오각0.6x0.2x0.6 153
 
9.0%
원형600 107
 
6.3%
0.6변 79
 
4.6%
450x600 75
 
4.4%
0.3*1.0 66
 
3.9%
원형900 53
 
3.1%
0.9x0.9x0.9 44
 
2.6%
600 44
 
2.6%
1.0x1.7 41
 
2.4%
Other values (160) 755
44.4%
2023-12-13T03:31:03.974970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3737
31.9%
. 1710
14.6%
6 1225
 
10.5%
X 988
 
8.4%
각 515
 
4.4%
1 482
 
4.1%
9 429
 
3.7%
Ø 319
 
2.7%
2 317
 
2.7%
형 254
 
2.2%
Other values (23) 1738
14.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6838
58.4%
Other Punctuation 1872
 
16.0%
Other Letter 1625
 
13.9%
Uppercase Letter 1313
 
11.2%
Space Separator 37
 
0.3%
Lowercase Letter 18
 
0.2%
Math Symbol 7
 
0.1%
Other Symbol 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3737
54.7%
6 1225
 
17.9%
1 482
 
7.0%
9 429
 
6.3%
2 317
 
4.6%
5 163
 
2.4%
4 135
 
2.0%
3 126
 
1.8%
7 122
 
1.8%
8 102
 
1.5%
Other Letter
ValueCountFrequency (%)
각 515
31.7%
형 254
15.6%
오 196
 
12.1%
삼 187
 
11.5%
원 169
 
10.4%
사 130
 
8.0%
변 89
 
5.5%
역 64
 
3.9%
정 19
 
1.2%
팔 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 1710
91.3%
* 127
 
6.8%
, 31
 
1.7%
@ 4
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
X 988
75.2%
Ø 319
 
24.3%
Φ 5
 
0.4%
D 1
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
x 16
88.9%
ø 2
 
11.1%
Space Separator
ValueCountFrequency (%)
37
100.0%
Math Symbol
ValueCountFrequency (%)
+ 7
100.0%
Other Symbol
ValueCountFrequency (%)
△ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8758
74.8%
Hangul 1625
 
13.9%
Latin 1326
 
11.3%
Greek 5
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3737
42.7%
. 1710
19.5%
6 1225
 
14.0%
1 482
 
5.5%
9 429
 
4.9%
2 317
 
3.6%
5 163
 
1.9%
4 135
 
1.5%
* 127
 
1.5%
3 126
 
1.4%
Other values (7) 307
 
3.5%
Hangul
ValueCountFrequency (%)
각 515
31.7%
형 254
15.6%
오 196
 
12.1%
삼 187
 
11.5%
원 169
 
10.4%
사 130
 
8.0%
변 89
 
5.5%
역 64
 
3.9%
정 19
 
1.2%
팔 2
 
0.1%
Latin
ValueCountFrequency (%)
X 988
74.5%
Ø 319
 
24.1%
x 16
 
1.2%
ø 2
 
0.2%
D 1
 
0.1%
Greek
ValueCountFrequency (%)
Φ 5
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9759
83.3%
Hangul 1625
 
13.9%
None 326
 
2.8%
Geometric Shapes 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3737
38.3%
. 1710
17.5%
6 1225
 
12.6%
X 988
 
10.1%
1 482
 
4.9%
9 429
 
4.4%
2 317
 
3.2%
5 163
 
1.7%
4 135
 
1.4%
* 127
 
1.3%
Other values (9) 446
 
4.6%
Hangul
ValueCountFrequency (%)
각 515
31.7%
형 254
15.6%
오 196
 
12.1%
삼 187
 
11.5%
원 169
 
10.4%
사 130
 
8.0%
변 89
 
5.5%
역 64
 
3.9%
정 19
 
1.2%
팔 2
 
0.1%
None
ValueCountFrequency (%)
Ø 319
97.9%
Φ 5
 
1.5%
ø 2
 
0.6%
Geometric Shapes
ValueCountFrequency (%)
△ 4
100.0%

기재사항
Text

MISSING 

Distinct289
Distinct (%)14.3%
Missing1489
Missing (%)42.3%
Memory size27.6 KiB
2023-12-13T03:31:04.216909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length33
Mean length6.8051307
Min length2

Characters and Unicode

Total characters13794
Distinct characters235
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique153 ?
Unique (%)7.5%

Sample

1st row서행
2nd row과속방지턱
3rd row우좌로이중굽은도로
4th row과속방지턱
5th row과속방지턱
ValueCountFrequency (%)
횡단보도 186
 
8.6%
자전거횡단 145
 
6.7%
주정차금지 144
 
6.7%
견인지역 104
 
4.8%
갈매기표지판 86
 
4.0%
정차·주차금지 78
 
3.6%
최고속도제한 73
 
3.4%
과속방지턱 70
 
3.2%
자전거및보행자통행구분 62
 
2.9%
최고속도제한40 53
 
2.5%
Other values (289) 1158
53.6%
2023-12-13T03:31:04.637237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 715
 
5.2%
도 642
 
4.7%
보 583
 
4.2%
차 557
 
4.0%
자 483
 
3.5%
전 430
 
3.1%
속 426
 
3.1%
단 418
 
3.0%
주 398
 
2.9%
금 375
 
2.7%
Other values (225) 8767
63.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12566
91.1%
Decimal Number 457
 
3.3%
Other Punctuation 263
 
1.9%
Space Separator 132
 
1.0%
Uppercase Letter 115
 
0.8%
Math Symbol 90
 
0.7%
Open Punctuation 72
 
0.5%
Close Punctuation 70
 
0.5%
Lowercase Letter 23
 
0.2%
Dash Punctuation 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 715
 
5.7%
도 642
 
5.1%
보 583
 
4.6%
차 557
 
4.4%
자 483
 
3.8%
전 430
 
3.4%
속 426
 
3.4%
단 418
 
3.3%
주 398
 
3.2%
금 375
 
3.0%
Other values (185) 7539
60.0%
Decimal Number
ValueCountFrequency (%)
0 222
48.6%
3 78
 
17.1%
4 58
 
12.7%
5 41
 
9.0%
6 29
 
6.3%
2 10
 
2.2%
1 7
 
1.5%
7 7
 
1.5%
8 3
 
0.7%
9 2
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
M 39
33.9%
C 24
20.9%
K 21
18.3%
T 13
 
11.3%
V 12
 
10.4%
Y 2
 
1.7%
B 1
 
0.9%
U 1
 
0.9%
S 1
 
0.9%
E 1
 
0.9%
Other Punctuation
ValueCountFrequency (%)
, 148
56.3%
· 78
29.7%
' 24
 
9.1%
% 8
 
3.0%
/ 3
 
1.1%
@ 1
 
0.4%
. 1
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
m 12
52.2%
k 4
 
17.4%
c 2
 
8.7%
t 2
 
8.7%
v 1
 
4.3%
a 1
 
4.3%
r 1
 
4.3%
Math Symbol
ValueCountFrequency (%)
+ 89
98.9%
> 1
 
1.1%
Space Separator
ValueCountFrequency (%)
132
100.0%
Open Punctuation
ValueCountFrequency (%)
( 72
100.0%
Close Punctuation
ValueCountFrequency (%)
) 70
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12566
91.1%
Common 1090
 
7.9%
Latin 138
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 715
 
5.7%
도 642
 
5.1%
보 583
 
4.6%
차 557
 
4.4%
자 483
 
3.8%
전 430
 
3.4%
속 426
 
3.4%
단 418
 
3.3%
주 398
 
3.2%
금 375
 
3.0%
Other values (185) 7539
60.0%
Common
ValueCountFrequency (%)
0 222
20.4%
, 148
13.6%
132
12.1%
+ 89
8.2%
3 78
 
7.2%
· 78
 
7.2%
( 72
 
6.6%
) 70
 
6.4%
4 58
 
5.3%
5 41
 
3.8%
Other values (13) 102
9.4%
Latin
ValueCountFrequency (%)
M 39
28.3%
C 24
17.4%
K 21
15.2%
T 13
 
9.4%
V 12
 
8.7%
m 12
 
8.7%
k 4
 
2.9%
c 2
 
1.4%
t 2
 
1.4%
Y 2
 
1.4%
Other values (7) 7
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12548
91.0%
ASCII 1150
 
8.3%
None 78
 
0.6%
Compat Jamo 18
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 715
 
5.7%
도 642
 
5.1%
보 583
 
4.6%
차 557
 
4.4%
자 483
 
3.8%
전 430
 
3.4%
속 426
 
3.4%
단 418
 
3.3%
주 398
 
3.2%
금 375
 
3.0%
Other values (182) 7521
59.9%
ASCII
ValueCountFrequency (%)
0 222
19.3%
, 148
12.9%
132
11.5%
+ 89
7.7%
3 78
 
6.8%
( 72
 
6.3%
) 70
 
6.1%
4 58
 
5.0%
5 41
 
3.6%
M 39
 
3.4%
Other values (29) 201
17.5%
None
ValueCountFrequency (%)
· 78
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅏ 9
50.0%
ㅓ 8
44.4%
ㅜ 1
 
5.6%

공사번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct13
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size27.6 KiB
<NA>
3112 
0
 
163
RD20190002
 
138
20060004
 
26
RD20160010
 
20
Other values (8)
 
57

Length

Max length10
Median length4
Mean length4.2573948
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRD20170002
2nd rowRD20170002
3rd rowRD20170002
4th rowRD20170002
5th rowRD20170002

Common Values

ValueCountFrequency (%)
<NA> 3112
88.5%
0 163
 
4.6%
RD20190002 138
 
3.9%
20060004 26
 
0.7%
RD20160010 20
 
0.6%
RD20170002 15
 
0.4%
RD20190003 14
 
0.4%
RD20170006 8
 
0.2%
RD20170008 6
 
0.2%
RD20170004 5
 
0.1%
Other values (3) 9
 
0.3%

Length

2023-12-13T03:31:04.781958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 3112
88.5%
0 163
 
4.6%
rd20190002 138
 
3.9%
20060004 26
 
0.7%
rd20160010 20
 
0.6%
rd20170002 15
 
0.4%
rd20190003 14
 
0.4%
rd20170006 8
 
0.2%
rd20170008 6
 
0.2%
rd20170004 5
 
0.1%
Other values (3) 9
 
0.3%

대장초기화여부
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.6 KiB
1
3480 
0
 
36

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 3480
99.0%
0 36
 
1.0%

Length

2023-12-13T03:31:04.909207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:31:05.013203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3480
99.0%
0 36
 
1.0%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct2894
Distinct (%)82.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198450.61
Minimum194163.55
Maximum203384.54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-12-13T03:31:05.134145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum194163.55
5-th percentile195459.31
Q1197088.16
median198224.75
Q3200035.44
95-th percentile201255.6
Maximum203384.54
Range9220.984
Interquartile range (IQR)2947.2772

Descriptive statistics

Standard deviation1893.2715
Coefficient of variation (CV)0.0095402657
Kurtosis-0.57542423
Mean198450.61
Median Absolute Deviation (MAD)1624.2904
Skewness0.17246672
Sum6.9775234 × 108
Variance3584477.1
MonotonicityNot monotonic
2023-12-13T03:31:05.311135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201097.1685 4
 
0.1%
197631.368 4
 
0.1%
195760.278 4
 
0.1%
199919.9529 4
 
0.1%
200330.776 4
 
0.1%
195223.776 4
 
0.1%
195327.78 4
 
0.1%
199949.2667 4
 
0.1%
200289.8575 4
 
0.1%
203000.851 4
 
0.1%
Other values (2884) 3476
98.9%
ValueCountFrequency (%)
194163.551 1
< 0.1%
194174.29 1
< 0.1%
194174.371 1
< 0.1%
194187.549 1
< 0.1%
194214.157 1
< 0.1%
194232.346 1
< 0.1%
194248.601 1
< 0.1%
194267.087 1
< 0.1%
194294.451 1
< 0.1%
194565.733 1
< 0.1%
ValueCountFrequency (%)
203384.535 1
< 0.1%
203372.782 1
< 0.1%
203372.4196 1
< 0.1%
203371.352 1
< 0.1%
203352.019 1
< 0.1%
203341.789 1
< 0.1%
203328.307 1
< 0.1%
203326.313 1
< 0.1%
203324.7393 1
< 0.1%
203312.146 1
< 0.1%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct2894
Distinct (%)82.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean529429.1
Minimum522364.93
Maximum534508.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.0 KiB
2023-12-13T03:31:05.488487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum522364.93
5-th percentile524263.6
Q1527238.92
median530340.51
Q3532131.24
95-th percentile533318.33
Maximum534508.73
Range12143.797
Interquartile range (IQR)4892.3233

Descriptive statistics

Standard deviation3050.3076
Coefficient of variation (CV)0.0057615034
Kurtosis-1.0564444
Mean529429.1
Median Absolute Deviation (MAD)2271.768
Skewness-0.45200344
Sum1.8614727 × 109
Variance9304376.3
MonotonicityNot monotonic
2023-12-13T03:31:05.722168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
532555.4865 4
 
0.1%
527967.626 4
 
0.1%
525465.686 4
 
0.1%
531030.9397 4
 
0.1%
530512.344 4
 
0.1%
524955.649 4
 
0.1%
525062.74 4
 
0.1%
531400.4284 4
 
0.1%
530679.128 4
 
0.1%
532663.466 4
 
0.1%
Other values (2884) 3476
98.9%
ValueCountFrequency (%)
522364.931 1
< 0.1%
522386.282 1
< 0.1%
522421.085 1
< 0.1%
522654.025 1
< 0.1%
522684.995 1
< 0.1%
522716.1157 1
< 0.1%
522749.3142 1
< 0.1%
522762.4111 1
< 0.1%
522854.7727 1
< 0.1%
522868.913 1
< 0.1%
ValueCountFrequency (%)
534508.728 2
0.1%
534493.248 2
0.1%
534484.8694 2
0.1%
534226.1574 2
0.1%
534217.3139 2
0.1%
534156.4651 1
< 0.1%
534155.6767 1
< 0.1%
534152.9073 1
< 0.1%
534144.2373 1
< 0.1%
534128.1797 1
< 0.1%

Interactions

2023-12-13T03:30:56.866422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:52.126903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:52.883346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:53.584811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:54.635881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:55.378547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:56.090740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:57.028769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:52.221008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:53.002392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:53.689607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:54.752389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:55.475333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:56.191183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:57.137974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:52.320034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:53.091573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:53.812252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:54.867303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:55.577438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:56.294959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:57.256751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:52.426880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:53.194003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:53.911791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:54.962412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:55.688426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:56.417884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:57.374610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:52.529905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:53.287082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:54.002939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:55.053924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:55.780265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:56.514567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:57.499402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:52.636656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:53.385650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:54.098439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:55.158098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:55.880987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:56.619974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:57.605739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:52.746728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:53.472072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:54.528617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:55.272543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:55.987484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:56.732439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:31:05.888612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공간지리식별번호지형지물부호관리번호행정동읍면코드관리기관코드도로구간 번호위치구분표지판구분지주형식높이공사번호대장초기화여부위도경도
공간지리식별번호1.0000.1281.0000.5140.0360.9980.1680.3330.1940.5730.7880.1260.6970.811
지형지물부호0.1281.0000.0000.0000.0000.0000.0000.0310.0000.000NaN0.0000.0600.054
관리번호1.0000.0001.0000.1200.0000.9990.0910.2060.2590.549NaN0.0480.6410.854
행정동읍면코드0.5140.0000.1201.000NaN0.1160.0200.0810.1220.133NaNNaN0.7721.000
관리기관코드0.0360.0000.000NaN1.0000.0000.0320.0340.0300.080NaN0.7050.1230.293
도로구간 번호0.9980.0000.9990.1160.0001.0000.0870.2120.2590.553NaN0.0460.6610.869
위치구분0.1680.0000.0910.0200.0320.0871.0000.1850.1370.3940.9230.8100.5460.280
표지판구분0.3330.0310.2060.0810.0340.2120.1851.0000.2410.4990.5060.1140.4130.356
지주형식0.1940.0000.2590.1220.0300.2590.1370.2411.0000.4710.7240.2330.3050.230
높이0.5730.0000.5490.1330.0800.5530.3940.4990.4711.0000.7610.6990.6090.650
공사번호0.788NaNNaNNaNNaNNaN0.9230.5060.7240.7611.0001.0000.9320.946
대장초기화여부0.1260.0000.048NaN0.7050.0460.8100.1140.2330.6991.0001.0000.2310.166
위도0.6970.0600.6410.7720.1230.6610.5460.4130.3050.6090.9320.2311.0000.874
경도0.8110.0540.8541.0000.2930.8690.2800.3560.2300.6500.9460.1660.8741.000
2023-12-13T03:31:06.078639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공사번호대장초기화여부관리기관코드지형지물부호표지판구분위치구분지주형식
공사번호1.0000.9871.0001.0000.3060.6550.438
대장초기화여부0.9871.0000.4980.0000.1390.6140.175
관리기관코드1.0000.4981.0000.0000.0410.0230.022
지형지물부호1.0000.0000.0001.0000.0380.0000.000
표지판구분0.3060.1390.0410.0381.0000.1260.150
위치구분0.6550.6140.0230.0000.1261.0000.076
지주형식0.4380.1750.0220.0000.1500.0761.000
2023-12-13T03:31:06.270105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공간지리식별번호관리번호행정동읍면코드도로구간 번호높이위도경도지형지물부호관리기관코드위치구분표지판구분지주형식공사번호대장초기화여부
공간지리식별번호1.0000.438-0.1740.4090.2980.2510.0060.0980.0280.0890.1450.0930.4830.096
관리번호0.4381.000-0.0890.9110.7100.2740.0550.0000.0000.0650.2510.1941.0000.031
행정동읍면코드-0.174-0.0891.000-0.065-0.1110.1710.6380.0001.0000.0340.0990.0911.0001.000
도로구간 번호0.4090.911-0.0651.0000.6240.2750.0700.0000.0000.0630.2590.1941.0000.030
높이0.2980.710-0.1110.6241.0000.2490.2370.0390.0850.2170.2060.2490.4470.527
위도0.2510.2740.1710.2750.2491.0000.7270.0460.0950.3230.1830.1500.8070.177
경도0.0060.0550.6380.0700.2370.7271.0000.0410.2240.1500.1550.1160.7940.127
지형지물부호0.0980.0000.0000.0000.0390.0460.0411.0000.0000.0000.0380.0001.0000.000
관리기관코드0.0280.0001.0000.0000.0850.0950.2240.0001.0000.0230.0410.0221.0000.498
위치구분0.0890.0650.0340.0630.2170.3230.1500.0000.0231.0000.1260.0760.6550.614
표지판구분0.1450.2510.0990.2590.2060.1830.1550.0380.0410.1261.0000.1500.3060.139
지주형식0.0930.1940.0910.1940.2490.1500.1160.0000.0220.0760.1501.0000.4380.175
공사번호0.4831.0001.0001.0000.4470.8070.7941.0001.0000.6550.3060.4381.0000.987
대장초기화여부0.0960.0311.0000.0300.5270.1770.1270.0000.4980.6140.1390.1750.9871.000

Missing values

2023-12-13T03:30:57.817284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:30:58.250849image/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.
2023-12-13T03:30:58.503096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

공간지리식별번호지형지물부호관리번호행정동읍면코드도엽번호관리기관코드도로구간 번호위치구분표지판구분지주형식높이규격기재사항공사번호대장초기화여부위도경도
0142AE2116000044143011100376121967BMNG001600008DRT001AET009IPG0012.2역삼각700서행RD201700021194163.551524418.039
1143AE2116000054143011100376121968AMNG001600008DRT002AET008IPG0012.2삼각700과속방지턱RD201700021194267.087524479.709
2144AE2116000064143011100376121968AMNG001600008DRT002AET008IPG0012.2삼각700우좌로이중굽은도로RD201700021194248.601524492.278
3145AE2116000074143011100376121967BMNG001600008DRT001AET008IPG0012.2삼각700과속방지턱RD201700021194232.346524481.549
4146AE2116000084143011100376121967BMNG001600008DRT002AET008IPG0012.2삼각700과속방지턱RD201700021194174.29524463.238
5147AE2116000094143011100376121967BMNG001600008DRT001AET008IPG0012.2삼각700과속방지턱RD201700021194174.371524429.097
6148AE2116000114143011100376121979AMNG001600009DRT001AET009IPG9993.0역삼각700서행RD201700021194734.3211523929.478
7149AE2116000124143011100376121968DMNG001600002DRT002AET009IPG0012.2역삼각700서행RD201700021194627.183524012.813
8150AE211300224143010900376121509MNG00130006DRT002AET009IPG9992.7<NA>정차·주차금지<NA>1199226.08533396.492
9151AE211300304143010900376121507MNG00130031DRT002AET009IPG9992.7<NA>정차·주차금지<NA>1198620.731533016.984
공간지리식별번호지형지물부호관리번호행정동읍면코드도엽번호관리기관코드도로구간 번호위치구분표지판구분지주형식높이규격기재사항공사번호대장초기화여부위도경도
35064447AE2111220<NA><NA>MNG001994DRT001AET008IPG0010.0<NA><NA><NA>1197761.1222527453.9293
35074448AE2111103<NA><NA>MNG001520DRT002AET009IPG9990.0<NA><NA><NA>1197467.1888528165.9026
35084449AE211997<NA><NA>MNG001520DRT002AET011IPG0010.0<NA><NA><NA>1197432.8288528212.2927
35094450AE2111252<NA><NA>MNG001518DRT002AET009IPG0010.0<NA><NA><NA>1197748.6214527557.1874
35104451AE2111260<NA><NA>MNG001558DRT001AET008IPG0010.0<NA><NA><NA>1197679.6826527418.5699
35114452AE2111143<NA><NA>MNG001518DRT001AET009IPG0010.0<NA><NA><NA>1197666.6205527668.8853
35124453AE2111033<NA><NA>MNG001572DRT001AET011IPG0010.0<NA><NA><NA>1197485.769528399.0628
35134454AE2111101<NA><NA>MNG001520DRT002AET008IPG0010.0<NA><NA><NA>1197533.6489528045.4924
35144456AE2111262<NA><NA>MNG001556DRT001AET009IPG0010.0<NA><NA><NA>1197721.6824527438.8695
35154457AE2111126<NA><NA>MNG001607DRT001AET009IPG9990.0<NA><NA><NA>1197529.4089528098.2124