Overview

Dataset statistics

Number of variables16
Number of observations339
Missing cells807
Missing cells (%)14.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory45.8 KiB
Average record size in memory138.4 B

Variable types

Numeric6
Categorical4
Text6

Dataset

Description경상남도 도로대장전산화 시스템 데이터의 중장기개방계획에 따른 데이터입니다. 시스템 상에서의 우회도로 및 구간정보를 가지고 있으며, 도로대장의 구간정보 데이터를 포함하고있습니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15091958

Alerts

관리기관 is highly overall correlated with 중용여부High correlation
중용여부 is highly overall correlated with 구간 연장 and 1 other fieldsHigh correlation
관리번호 is highly overall correlated with 노선번호High correlation
노선번호 is highly overall correlated with 관리번호High correlation
구간번호 is highly overall correlated with 구간 시점 노선 누적거리 High correlation
구간 시점 노선 누적거리 is highly overall correlated with 구간번호High correlation
구간 연장 is highly overall correlated with 중용여부High correlation
관리기관 is highly imbalanced (97.1%)Imbalance
도로종류 is highly imbalanced (92.3%)Imbalance
이력코드 is highly imbalanced (94.5%)Imbalance
중용내용 has 232 (68.4%) missing valuesMissing
중용이정 has 244 (72.0%) missing valuesMissing
비고 has 329 (97.1%) missing valuesMissing
관리번호 has 11 (3.2%) zerosZeros
구간 시점 노선 누적거리 has 21 (6.2%) zerosZeros

Reproduction

Analysis started2023-12-10 23:56:49.153107
Analysis finished2023-12-10 23:56:55.029634
Duration5.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

식별번호
Real number (ℝ)

Distinct337
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170.15339
Minimum0
Maximum338
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-12-11T08:56:55.134235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.9
Q185.5
median170
Q3255.5
95-th percentile323.1
Maximum338
Range338
Interquartile range (IQR)170

Descriptive statistics

Standard deviation98.492213
Coefficient of variation (CV)0.57884366
Kurtosis-1.1997393
Mean170.15339
Median Absolute Deviation (MAD)85
Skewness-0.002058581
Sum57682
Variance9700.716
MonotonicityNot monotonic
2023-12-11T08:56:55.308560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
335 2
 
0.6%
331 2
 
0.6%
221 1
 
0.3%
220 1
 
0.3%
219 1
 
0.3%
218 1
 
0.3%
217 1
 
0.3%
216 1
 
0.3%
215 1
 
0.3%
214 1
 
0.3%
Other values (327) 327
96.5%
ValueCountFrequency (%)
0 1
0.3%
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
ValueCountFrequency (%)
338 1
0.3%
337 1
0.3%
336 1
0.3%
335 2
0.6%
334 1
0.3%
333 1
0.3%
332 1
0.3%
331 2
0.6%
330 1
0.3%
329 1
0.3%

관리번호
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct324
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8341038.9
Minimum0
Maximum10990007
Zeros11
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-12-11T08:56:55.470067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile300006.9
Q110010016
median10160004
Q310340006
95-th percentile10890001
Maximum10990007
Range10990007
Interquartile range (IQR)329991

Descriptive statistics

Standard deviation3923911.4
Coefficient of variation (CV)0.47043437
Kurtosis0.31818864
Mean8341038.9
Median Absolute Deviation (MAD)149996
Skewness-1.5104233
Sum2.8276122 × 109
Variance1.5397081 × 1013
MonotonicityNot monotonic
2023-12-11T08:56:55.622373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
3.2%
10220001 2
 
0.6%
10420003 2
 
0.6%
10400001 2
 
0.6%
600001 2
 
0.6%
690001 2
 
0.6%
600002 1
 
0.3%
600000 1
 
0.3%
580002 1
 
0.3%
580001 1
 
0.3%
Other values (314) 314
92.6%
ValueCountFrequency (%)
0 11
3.2%
300001 1
 
0.3%
300002 1
 
0.3%
300003 1
 
0.3%
300004 1
 
0.3%
300005 1
 
0.3%
300006 1
 
0.3%
300007 1
 
0.3%
370001 1
 
0.3%
370002 1
 
0.3%
ValueCountFrequency (%)
10990007 1
0.3%
10990006 1
0.3%
10990005 1
0.3%
10990004 1
0.3%
10990003 1
0.3%
10990002 1
0.3%
10990001 1
0.3%
10890011 1
0.3%
10890010 1
0.3%
10890009 1
0.3%

관리기관
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
1683
338 
1681
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row1683
2nd row1683
3rd row1683
4th row1683
5th row1683

Common Values

ValueCountFrequency (%)
1683 338
99.7%
1681 1
 
0.3%

Length

2023-12-11T08:56:55.779009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:56:55.890990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1683 338
99.7%
1681 1
 
0.3%

도로종류
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
1504
334 
1054
 
4
1507
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row1504
2nd row1504
3rd row1504
4th row1504
5th row1504

Common Values

ValueCountFrequency (%)
1504 334
98.5%
1054 4
 
1.2%
1507 1
 
0.3%

Length

2023-12-11T08:56:56.026954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:56:56.151606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1504 334
98.5%
1054 4
 
1.2%
1507 1
 
0.3%

노선번호
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean865.19174
Minimum30
Maximum1099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-12-11T08:56:56.288915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile58
Q11002
median1018
Q31034
95-th percentile1089
Maximum1099
Range1069
Interquartile range (IQR)32

Descriptive statistics

Standard deviation365.39724
Coefficient of variation (CV)0.42233094
Kurtosis1.1493954
Mean865.19174
Median Absolute Deviation (MAD)16
Skewness-1.7607692
Sum293300
Variance133515.14
MonotonicityNot monotonic
2023-12-11T08:56:56.463084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
60 20
 
5.9%
1001 16
 
4.7%
1021 16
 
4.7%
1034 15
 
4.4%
1002 14
 
4.1%
1022 13
 
3.8%
1040 12
 
3.5%
1024 11
 
3.2%
1018 11
 
3.2%
1089 11
 
3.2%
Other values (33) 200
59.0%
ValueCountFrequency (%)
30 7
 
2.1%
37 6
 
1.8%
58 7
 
2.1%
60 20
5.9%
67 9
2.7%
69 8
 
2.4%
907 3
 
0.9%
1001 16
4.7%
1002 14
4.1%
1003 9
2.7%
ValueCountFrequency (%)
1099 7
2.1%
1089 11
3.2%
1084 10
2.9%
1080 8
2.4%
1077 8
2.4%
1051 2
 
0.6%
1049 3
 
0.9%
1047 4
 
1.2%
1042 4
 
1.2%
1041 8
2.4%

구간번호
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5250737
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-12-11T08:56:56.622006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12.5
median5
Q38
95-th percentile13
Maximum19
Range18
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.8594144
Coefficient of variation (CV)0.69852722
Kurtosis0.75682498
Mean5.5250737
Median Absolute Deviation (MAD)3
Skewness1.0464225
Sum1873
Variance14.89508
MonotonicityNot monotonic
2023-12-11T08:56:56.753071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2 43
12.7%
1 42
12.4%
3 41
12.1%
4 37
10.9%
5 35
10.3%
6 29
8.6%
7 26
7.7%
8 19
5.6%
9 15
 
4.4%
10 12
 
3.5%
Other values (9) 40
11.8%
ValueCountFrequency (%)
1 42
12.4%
2 43
12.7%
3 41
12.1%
4 37
10.9%
5 35
10.3%
6 29
8.6%
7 26
7.7%
8 19
5.6%
9 15
 
4.4%
10 12
 
3.5%
ValueCountFrequency (%)
19 2
 
0.6%
18 1
 
0.3%
17 1
 
0.3%
16 3
 
0.9%
15 4
 
1.2%
14 5
1.5%
13 7
2.1%
12 7
2.1%
11 10
2.9%
10 12
3.5%

이력코드
Categorical

IMBALANCE 

Distinct4
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
335 
1
 
2
3
 
1
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
0 335
98.8%
1 2
 
0.6%
3 1
 
0.3%
2 1
 
0.3%

Length

2023-12-11T08:56:56.917023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:56:57.072978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 335
98.8%
1 2
 
0.6%
3 1
 
0.3%
2 1
 
0.3%

구간 시점 노선 누적거리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct312
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44291.354
Minimum0
Maximum1180130
Zeros21
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-12-11T08:56:57.204390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114041
median29269
Q358604.5
95-th percentile118049.5
Maximum1180130
Range1180130
Interquartile range (IQR)44563.5

Descriptive statistics

Standard deviation71788.41
Coefficient of variation (CV)1.6208222
Kurtosis185.96193
Mean44291.354
Median Absolute Deviation (MAD)18831
Skewness11.916748
Sum15014769
Variance5.1535758 × 109
MonotonicityNot monotonic
2023-12-11T08:56:57.383760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 21
 
6.2%
26961.0 2
 
0.6%
11000.0 2
 
0.6%
122504.0 2
 
0.6%
26000.0 2
 
0.6%
37000.0 2
 
0.6%
61000.0 2
 
0.6%
44024.0 2
 
0.6%
115966.0 1
 
0.3%
62897.0 1
 
0.3%
Other values (302) 302
89.1%
ValueCountFrequency (%)
0.0 21
6.2%
2.55 1
 
0.3%
253.698 1
 
0.3%
500.0 1
 
0.3%
794.182 1
 
0.3%
860.0 1
 
0.3%
940.0 1
 
0.3%
1200.0 1
 
0.3%
2200.0 1
 
0.3%
2530.0 1
 
0.3%
ValueCountFrequency (%)
1180130.0 1
0.3%
185584.0 1
0.3%
170806.0 1
0.3%
160000.0 1
0.3%
148969.0 1
0.3%
148834.0 1
0.3%
145469.0 1
0.3%
139838.0 1
0.3%
136220.0 1
0.3%
129736.0 1
0.3%

구간 연장
Real number (ℝ)

HIGH CORRELATION 

Distinct300
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8643.2717
Minimum33.934
Maximum57882
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-12-11T08:56:57.570393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.934
5-th percentile769
Q13556.5
median8000
Q311510
95-th percentile19562.2
Maximum57882
Range57848.066
Interquartile range (IQR)7953.5

Descriptive statistics

Standard deviation7205.6517
Coefficient of variation (CV)0.83367178
Kurtosis10.851244
Mean8643.2717
Median Absolute Deviation (MAD)3839
Skewness2.4744706
Sum2930069.1
Variance51921416
MonotonicityNot monotonic
2023-12-11T08:56:57.750280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
800.0 3
 
0.9%
3200.0 3
 
0.9%
4500.0 3
 
0.9%
500.0 3
 
0.9%
1000.0 3
 
0.9%
8300.0 3
 
0.9%
8100.0 3
 
0.9%
6000.0 2
 
0.6%
920.0 2
 
0.6%
2460.0 2
 
0.6%
Other values (290) 312
92.0%
ValueCountFrequency (%)
33.934 1
 
0.3%
80.0 1
 
0.3%
98.0 1
 
0.3%
110.0 1
 
0.3%
300.0 1
 
0.3%
325.0 1
 
0.3%
376.0 1
 
0.3%
400.0 1
 
0.3%
500.0 3
0.9%
582.0 1
 
0.3%
ValueCountFrequency (%)
57882.0 1
0.3%
46100.0 1
0.3%
43100.0 1
0.3%
38645.0 1
0.3%
36860.0 1
0.3%
36200.0 1
0.3%
29800.0 1
0.3%
28903.0 1
0.3%
26400.0 1
0.3%
26330.0 1
0.3%

시점명
Text

Distinct297
Distinct (%)87.9%
Missing1
Missing (%)0.3%
Memory size2.8 KiB
2023-12-11T08:56:58.160306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length11
Mean length11.517751
Min length5

Characters and Unicode

Total characters3893
Distinct characters200
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique261 ?
Unique (%)77.2%

Sample

1st row함안군 군북면 오곡리
2nd row함안군 군북면 중암리
3rd row함안군 군북면 중암리
4th row산청군 차황면 장박리
5th row산청군 오부면 양촌리
ValueCountFrequency (%)
거창군 20
 
2.4%
함안군 20
 
2.4%
하동군 19
 
2.2%
진주시 19
 
2.2%
고성군 19
 
2.2%
합천군 16
 
1.9%
산청군 16
 
1.9%
사천시 15
 
1.8%
함양군 15
 
1.8%
통영시 13
 
1.5%
Other values (434) 674
79.7%
2023-12-11T08:56:58.739843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
508
 
13.0%
리 283
 
7.3%
면 272
 
7.0%
군 215
 
5.5%
시 132
 
3.4%
산 90
 
2.3%
동 84
 
2.2%
양 78
 
2.0%
천 74
 
1.9%
- 67
 
1.7%
Other values (190) 2090
53.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3163
81.2%
Space Separator 508
 
13.0%
Decimal Number 150
 
3.9%
Dash Punctuation 67
 
1.7%
Close Punctuation 2
 
0.1%
Open Punctuation 2
 
0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 283
 
8.9%
면 272
 
8.6%
군 215
 
6.8%
시 132
 
4.2%
산 90
 
2.8%
동 84
 
2.7%
양 78
 
2.5%
천 74
 
2.3%
도 63
 
2.0%
분 58
 
1.8%
Other values (175) 1814
57.4%
Decimal Number
ValueCountFrequency (%)
0 39
26.0%
1 33
22.0%
3 15
 
10.0%
2 14
 
9.3%
4 10
 
6.7%
5 10
 
6.7%
7 10
 
6.7%
9 8
 
5.3%
8 6
 
4.0%
6 5
 
3.3%
Space Separator
ValueCountFrequency (%)
508
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 67
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3163
81.2%
Common 729
 
18.7%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 283
 
8.9%
면 272
 
8.6%
군 215
 
6.8%
시 132
 
4.2%
산 90
 
2.8%
동 84
 
2.7%
양 78
 
2.5%
천 74
 
2.3%
도 63
 
2.0%
분 58
 
1.8%
Other values (175) 1814
57.4%
Common
ValueCountFrequency (%)
508
69.7%
- 67
 
9.2%
0 39
 
5.3%
1 33
 
4.5%
3 15
 
2.1%
2 14
 
1.9%
4 10
 
1.4%
5 10
 
1.4%
7 10
 
1.4%
9 8
 
1.1%
Other values (4) 15
 
2.1%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3163
81.2%
ASCII 730
 
18.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
508
69.6%
- 67
 
9.2%
0 39
 
5.3%
1 33
 
4.5%
3 15
 
2.1%
2 14
 
1.9%
4 10
 
1.4%
5 10
 
1.4%
7 10
 
1.4%
9 8
 
1.1%
Other values (5) 16
 
2.2%
Hangul
ValueCountFrequency (%)
리 283
 
8.9%
면 272
 
8.6%
군 215
 
6.8%
시 132
 
4.2%
산 90
 
2.8%
동 84
 
2.7%
양 78
 
2.5%
천 74
 
2.3%
도 63
 
2.0%
분 58
 
1.8%
Other values (175) 1814
57.4%

종점명
Text

Distinct305
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2023-12-11T08:56:59.125146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length11
Mean length11.548673
Min length5

Characters and Unicode

Total characters3915
Distinct characters199
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique275 ?
Unique (%)81.1%

Sample

1st row함안군 군북면 중암리
2nd row의령군 정곡면 죽전리
3rd row함안군 군북면 중암리
4th row합천군 대병면 회양리
5th row산청군 차황면 실매리
ValueCountFrequency (%)
거창군 23
 
2.7%
함안군 21
 
2.5%
합천군 20
 
2.3%
진주시 19
 
2.2%
고성군 17
 
2.0%
하동군 17
 
2.0%
산청군 16
 
1.9%
창녕군 15
 
1.8%
함양군 13
 
1.5%
통영시 11
 
1.3%
Other values (453) 683
79.9%
2023-12-11T08:56:59.678415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
517
 
13.2%
리 280
 
7.2%
면 277
 
7.1%
군 217
 
5.5%
시 130
 
3.3%
산 93
 
2.4%
양 80
 
2.0%
천 72
 
1.8%
동 71
 
1.8%
- 67
 
1.7%
Other values (189) 2111
53.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3172
81.0%
Space Separator 517
 
13.2%
Decimal Number 158
 
4.0%
Dash Punctuation 67
 
1.7%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 280
 
8.8%
면 277
 
8.7%
군 217
 
6.8%
시 130
 
4.1%
산 93
 
2.9%
양 80
 
2.5%
천 72
 
2.3%
동 71
 
2.2%
도 64
 
2.0%
창 64
 
2.0%
Other values (176) 1824
57.5%
Decimal Number
ValueCountFrequency (%)
1 36
22.8%
0 36
22.8%
3 21
13.3%
2 15
9.5%
7 11
 
7.0%
4 11
 
7.0%
5 10
 
6.3%
9 9
 
5.7%
8 6
 
3.8%
6 3
 
1.9%
Space Separator
ValueCountFrequency (%)
517
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 67
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3172
81.0%
Common 742
 
19.0%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 280
 
8.8%
면 277
 
8.7%
군 217
 
6.8%
시 130
 
4.1%
산 93
 
2.9%
양 80
 
2.5%
천 72
 
2.3%
동 71
 
2.2%
도 64
 
2.0%
창 64
 
2.0%
Other values (176) 1824
57.5%
Common
ValueCountFrequency (%)
517
69.7%
- 67
 
9.0%
1 36
 
4.9%
0 36
 
4.9%
3 21
 
2.8%
2 15
 
2.0%
7 11
 
1.5%
4 11
 
1.5%
5 10
 
1.3%
9 9
 
1.2%
Other values (2) 9
 
1.2%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3172
81.0%
ASCII 743
 
19.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
517
69.6%
- 67
 
9.0%
1 36
 
4.8%
0 36
 
4.8%
3 21
 
2.8%
2 15
 
2.0%
7 11
 
1.5%
4 11
 
1.5%
5 10
 
1.3%
9 9
 
1.2%
Other values (3) 10
 
1.3%
Hangul
ValueCountFrequency (%)
리 280
 
8.8%
면 277
 
8.7%
군 217
 
6.8%
시 130
 
4.1%
산 93
 
2.9%
양 80
 
2.5%
천 72
 
2.3%
동 71
 
2.2%
도 64
 
2.0%
창 64
 
2.0%
Other values (176) 1824
57.5%

구간명
Text

Distinct327
Distinct (%)96.7%
Missing1
Missing (%)0.3%
Memory size2.8 KiB
2023-12-11T08:57:00.056766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length5
Mean length5.0295858
Min length3

Characters and Unicode

Total characters1700
Distinct characters184
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique318 ?
Unique (%)94.1%

Sample

1st row오곡-중암
2nd row중암-죽전
3rd row중암-중암
4th row장박~회양
5th row양촌~실매
ValueCountFrequency (%)
국도3중용 3
 
0.9%
부곡-하남 3
 
0.9%
덕촌-장대 2
 
0.6%
연초-장목 2
 
0.6%
화정-의령 2
 
0.6%
묵계도로 2
 
0.6%
산포-산포 2
 
0.6%
진례~주촌 2
 
0.6%
중용 2
 
0.6%
33호선 2
 
0.6%
Other values (321) 322
93.6%
2023-12-11T08:57:00.643156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 283
 
16.6%
산 53
 
3.1%
대 52
 
3.1%
동 47
 
2.8%
곡 43
 
2.5%
도 35
 
2.1%
정 32
 
1.9%
~ 27
 
1.6%
남 27
 
1.6%
중 26
 
1.5%
Other values (174) 1075
63.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1363
80.2%
Dash Punctuation 283
 
16.6%
Math Symbol 27
 
1.6%
Decimal Number 20
 
1.2%
Space Separator 6
 
0.4%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 53
 
3.9%
대 52
 
3.8%
동 47
 
3.4%
곡 43
 
3.2%
도 35
 
2.6%
정 32
 
2.3%
남 27
 
2.0%
중 26
 
1.9%
포 25
 
1.8%
용 24
 
1.8%
Other values (163) 999
73.3%
Decimal Number
ValueCountFrequency (%)
3 7
35.0%
7 4
20.0%
9 3
15.0%
2 2
 
10.0%
1 2
 
10.0%
5 1
 
5.0%
6 1
 
5.0%
Dash Punctuation
ValueCountFrequency (%)
- 283
100.0%
Math Symbol
ValueCountFrequency (%)
~ 27
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1363
80.2%
Common 337
 
19.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 53
 
3.9%
대 52
 
3.8%
동 47
 
3.4%
곡 43
 
3.2%
도 35
 
2.6%
정 32
 
2.3%
남 27
 
2.0%
중 26
 
1.9%
포 25
 
1.8%
용 24
 
1.8%
Other values (163) 999
73.3%
Common
ValueCountFrequency (%)
- 283
84.0%
~ 27
 
8.0%
3 7
 
2.1%
6
 
1.8%
7 4
 
1.2%
9 3
 
0.9%
2 2
 
0.6%
1 2
 
0.6%
5 1
 
0.3%
6 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1363
80.2%
ASCII 337
 
19.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 283
84.0%
~ 27
 
8.0%
3 7
 
2.1%
6
 
1.8%
7 4
 
1.2%
9 3
 
0.9%
2 2
 
0.6%
1 2
 
0.6%
5 1
 
0.3%
6 1
 
0.3%
Hangul
ValueCountFrequency (%)
산 53
 
3.9%
대 52
 
3.8%
동 47
 
3.4%
곡 43
 
3.2%
도 35
 
2.6%
정 32
 
2.3%
남 27
 
2.0%
중 26
 
1.9%
포 25
 
1.8%
용 24
 
1.8%
Other values (163) 999
73.3%

중용여부
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
245 
1
93 
<NA>
 
1

Length

Max length4
Median length1
Mean length1.0088496
Min length1

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 245
72.3%
1 93
 
27.4%
<NA> 1
 
0.3%

Length

2023-12-11T08:57:00.821007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:57:00.935280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 245
72.3%
1 93
 
27.4%
na 1
 
0.3%

중용내용
Text

MISSING 

Distinct71
Distinct (%)66.4%
Missing232
Missing (%)68.4%
Memory size2.8 KiB
2023-12-11T08:57:01.166996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length8.4018692
Min length1

Characters and Unicode

Total characters899
Distinct characters25
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)47.7%

Sample

1st row일반국도79호선
2nd row지방도1089호선
3rd row일반국도58호선
4th row국가지원지방도60호선
5th row일반국도25호선
ValueCountFrequency (%)
0 7
 
6.4%
국도03호선0구간 4
 
3.6%
국도14호선0구간 4
 
3.6%
국도24호선0구간 4
 
3.6%
일반국도2호선 4
 
3.6%
국도33호선0구간 3
 
2.7%
국도79호선0구간 3
 
2.7%
일반국도79호선 3
 
2.7%
일반국도14호선 2
 
1.8%
지방도 2
 
1.8%
Other values (63) 74
67.3%
2023-12-11T08:57:01.862430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 115
12.8%
호 99
11.0%
선 99
11.0%
도 98
10.9%
국 80
8.9%
구 54
 
6.0%
간 54
 
6.0%
1 37
 
4.1%
지 34
 
3.8%
2 30
 
3.3%
Other values (15) 199
22.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 582
64.7%
Decimal Number 310
34.5%
Other Punctuation 3
 
0.3%
Space Separator 3
 
0.3%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 99
17.0%
선 99
17.0%
도 98
16.8%
국 80
13.7%
구 54
9.3%
간 54
9.3%
지 34
 
5.8%
방 24
 
4.1%
일 17
 
2.9%
반 17
 
2.9%
Other values (2) 6
 
1.0%
Decimal Number
ValueCountFrequency (%)
0 115
37.1%
1 37
 
11.9%
2 30
 
9.7%
3 26
 
8.4%
7 23
 
7.4%
4 21
 
6.8%
5 20
 
6.5%
9 18
 
5.8%
8 11
 
3.5%
6 9
 
2.9%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 582
64.7%
Common 317
35.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 115
36.3%
1 37
 
11.7%
2 30
 
9.5%
3 26
 
8.2%
7 23
 
7.3%
4 21
 
6.6%
5 20
 
6.3%
9 18
 
5.7%
8 11
 
3.5%
6 9
 
2.8%
Other values (3) 7
 
2.2%
Hangul
ValueCountFrequency (%)
호 99
17.0%
선 99
17.0%
도 98
16.8%
국 80
13.7%
구 54
9.3%
간 54
9.3%
지 34
 
5.8%
방 24
 
4.1%
일 17
 
2.9%
반 17
 
2.9%
Other values (2) 6
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 582
64.7%
ASCII 317
35.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 115
36.3%
1 37
 
11.7%
2 30
 
9.5%
3 26
 
8.2%
7 23
 
7.3%
4 21
 
6.6%
5 20
 
6.3%
9 18
 
5.7%
8 11
 
3.5%
6 9
 
2.8%
Other values (3) 7
 
2.2%
Hangul
ValueCountFrequency (%)
호 99
17.0%
선 99
17.0%
도 98
16.8%
국 80
13.7%
구 54
9.3%
간 54
9.3%
지 34
 
5.8%
방 24
 
4.1%
일 17
 
2.9%
반 17
 
2.9%
Other values (2) 6
 
1.0%

중용이정
Text

MISSING 

Distinct80
Distinct (%)84.2%
Missing244
Missing (%)72.0%
Memory size2.8 KiB
2023-12-11T08:57:02.119208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.989474
Min length10

Characters and Unicode

Total characters1044
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73 ?
Unique (%)76.8%

Sample

1st row00000-00800
2nd row00000-00000
3rd row00000-00500
4th row00000-00000
5th row00000-01200
ValueCountFrequency (%)
00000-00000 9
 
9.5%
00000-00920 3
 
3.2%
00000-00800 2
 
2.1%
00000-03000 2
 
2.1%
00000-04500 2
 
2.1%
00000-00500 2
 
2.1%
00000-01200 2
 
2.1%
00000-00760 1
 
1.1%
00000-02574 1
 
1.1%
07050-06970 1
 
1.1%
Other values (70) 70
73.7%
2023-12-11T08:57:02.534001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 730
69.9%
- 95
 
9.1%
2 36
 
3.4%
1 34
 
3.3%
6 27
 
2.6%
4 26
 
2.5%
3 23
 
2.2%
5 23
 
2.2%
7 22
 
2.1%
8 15
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 949
90.9%
Dash Punctuation 95
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 730
76.9%
2 36
 
3.8%
1 34
 
3.6%
6 27
 
2.8%
4 26
 
2.7%
3 23
 
2.4%
5 23
 
2.4%
7 22
 
2.3%
8 15
 
1.6%
9 13
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 95
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1044
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 730
69.9%
- 95
 
9.1%
2 36
 
3.4%
1 34
 
3.3%
6 27
 
2.6%
4 26
 
2.5%
3 23
 
2.2%
5 23
 
2.2%
7 22
 
2.1%
8 15
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1044
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 730
69.9%
- 95
 
9.1%
2 36
 
3.4%
1 34
 
3.3%
6 27
 
2.6%
4 26
 
2.5%
3 23
 
2.2%
5 23
 
2.2%
7 22
 
2.1%
8 15
 
1.4%

비고
Text

MISSING 

Distinct7
Distinct (%)70.0%
Missing329
Missing (%)97.1%
Memory size2.8 KiB
2023-12-11T08:57:02.742818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length7.2
Min length1

Characters and Unicode

Total characters72
Distinct characters40
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)50.0%

Sample

1st row미개통(L=6.463km)
2nd row미개통
3rd row연장에서제외
4th row연장에서제외
5th row0
ValueCountFrequency (%)
미개통 3
30.0%
연장에서제외 2
20.0%
미개통(l=6.463km 1
 
10.0%
0 1
 
10.0%
2+400~4+800(미포장 1
 
10.0%
국지60호선비전산화구간 1
 
10.0%
미개통및별도발주 1
 
10.0%
2023-12-11T08:57:03.034991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
미 6
 
8.3%
0 6
 
8.3%
통 5
 
6.9%
개 5
 
6.9%
장 3
 
4.2%
4 3
 
4.2%
6 3
 
4.2%
+ 2
 
2.8%
( 2
 
2.8%
외 2
 
2.8%
Other values (30) 35
48.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 45
62.5%
Decimal Number 15
 
20.8%
Math Symbol 4
 
5.6%
Open Punctuation 2
 
2.8%
Close Punctuation 2
 
2.8%
Lowercase Letter 2
 
2.8%
Other Punctuation 1
 
1.4%
Uppercase Letter 1
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
미 6
 
13.3%
통 5
 
11.1%
개 5
 
11.1%
장 3
 
6.7%
외 2
 
4.4%
제 2
 
4.4%
서 2
 
4.4%
에 2
 
4.4%
연 2
 
4.4%
비 1
 
2.2%
Other values (15) 15
33.3%
Decimal Number
ValueCountFrequency (%)
0 6
40.0%
4 3
20.0%
6 3
20.0%
2 1
 
6.7%
8 1
 
6.7%
3 1
 
6.7%
Math Symbol
ValueCountFrequency (%)
+ 2
50.0%
~ 1
25.0%
= 1
25.0%
Lowercase Letter
ValueCountFrequency (%)
m 1
50.0%
k 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
L 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 45
62.5%
Common 24
33.3%
Latin 3
 
4.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
미 6
 
13.3%
통 5
 
11.1%
개 5
 
11.1%
장 3
 
6.7%
외 2
 
4.4%
제 2
 
4.4%
서 2
 
4.4%
에 2
 
4.4%
연 2
 
4.4%
비 1
 
2.2%
Other values (15) 15
33.3%
Common
ValueCountFrequency (%)
0 6
25.0%
4 3
12.5%
6 3
12.5%
+ 2
 
8.3%
( 2
 
8.3%
) 2
 
8.3%
2 1
 
4.2%
8 1
 
4.2%
~ 1
 
4.2%
3 1
 
4.2%
Other values (2) 2
 
8.3%
Latin
ValueCountFrequency (%)
m 1
33.3%
k 1
33.3%
L 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 45
62.5%
ASCII 27
37.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
미 6
 
13.3%
통 5
 
11.1%
개 5
 
11.1%
장 3
 
6.7%
외 2
 
4.4%
제 2
 
4.4%
서 2
 
4.4%
에 2
 
4.4%
연 2
 
4.4%
비 1
 
2.2%
Other values (15) 15
33.3%
ASCII
ValueCountFrequency (%)
0 6
22.2%
4 3
11.1%
6 3
11.1%
+ 2
 
7.4%
( 2
 
7.4%
) 2
 
7.4%
2 1
 
3.7%
8 1
 
3.7%
~ 1
 
3.7%
m 1
 
3.7%
Other values (5) 5
18.5%

Interactions

2023-12-11T08:56:53.647744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:50.076530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:50.702103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:51.293872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:51.998104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:52.676773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:53.794601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:50.177390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:50.795720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:51.428017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:52.114681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:52.792622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:53.903469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:50.272347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:50.881163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:51.538656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:52.213660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:52.887909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:54.045324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:50.376773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:50.974068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:51.651133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:52.321614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:53.001660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:54.152564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:50.480024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:51.078240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:51.760366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:52.439132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:53.126251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:54.268647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:50.598365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:51.191148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:51.891640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:52.552733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:53.531218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:57:03.152313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식별번호관리번호관리기관도로종류노선번호구간번호이력코드구간 시점 노선 누적거리구간 연장중용여부중용내용중용이정비고
식별번호1.0000.6580.0000.3080.6880.1490.0000.1700.1540.0280.8500.0000.879
관리번호0.6581.0000.0470.4190.9990.1300.0670.0000.2810.0000.0000.8280.537
관리기관0.0000.0471.0000.0000.0000.3780.0000.0000.000NaNNaNNaNNaN
도로종류0.3080.4190.0001.0000.1690.5520.4930.0000.0000.000NaNNaNNaN
노선번호0.6880.9990.0000.1691.0000.1560.0000.1510.3210.0080.0000.8280.537
구간번호0.1490.1300.3780.5520.1561.0000.1360.6290.1860.0000.0000.8260.378
이력코드0.0000.0670.0000.4930.0000.1361.0000.0000.0000.000NaNNaNNaN
구간 시점 노선 누적거리0.1700.0000.0000.0000.1510.6290.0001.0000.4240.0000.1780.8221.000
구간 연장0.1540.2810.0000.0000.3210.1860.0000.4241.0000.5430.0000.0000.520
중용여부0.0280.000NaN0.0000.0080.0000.0000.0000.5431.0000.9831.0001.000
중용내용0.8500.000NaNNaN0.0000.000NaN0.1780.0000.9831.0000.0001.000
중용이정0.0000.828NaNNaN0.8280.826NaN0.8220.0001.0000.0001.0001.000
비고0.8790.537NaNNaN0.5370.378NaN1.0000.5201.0001.0001.0001.000
2023-12-11T08:57:03.309054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리기관중용여부도로종류이력코드
관리기관1.0001.0000.0000.000
중용여부1.0001.0000.0000.000
도로종류0.0000.0001.0000.491
이력코드0.0000.0000.4911.000
2023-12-11T08:57:03.437262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식별번호관리번호노선번호구간번호구간 시점 노선 누적거리구간 연장관리기관도로종류이력코드중용여부
식별번호1.0000.1020.2180.1810.084-0.0380.0000.1910.0000.019
관리번호0.1021.0000.890-0.0700.043-0.0510.0780.1570.0630.000
노선번호0.2180.8901.000-0.075-0.071-0.0870.0000.0520.0000.012
구간번호0.181-0.070-0.0751.0000.762-0.0100.2870.3900.0800.000
구간 시점 노선 누적거리0.0840.043-0.0710.7621.0000.1740.0000.0000.0000.000
구간 연장-0.038-0.051-0.087-0.0100.1741.0000.0000.0000.0000.540
관리기관0.0000.0780.0000.2870.0000.0001.0000.0000.0001.000
도로종류0.1910.1570.0520.3900.0000.0000.0001.0000.4910.000
이력코드0.0000.0630.0000.0800.0000.0000.0000.4911.0000.000
중용여부0.0190.0000.0120.0000.0000.5401.0000.0000.0001.000

Missing values

2023-12-11T08:56:54.435290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:56:54.733887image/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.
2023-12-11T08:56:54.921096image/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

식별번호관리번호관리기관도로종류노선번호구간번호이력코드구간 시점 노선 누적거리구간 연장시점명종점명구간명중용여부중용내용중용이정비고
024102900021683150410292020942.06468.0함안군 군북면 오곡리함안군 군북면 중암리오곡-중암0<NA><NA><NA>
125102900041683150410294032802.011060.0함안군 군북면 중암리의령군 정곡면 죽전리중암-죽전0<NA><NA><NA>
226102900031683150410293021742.0800.0함안군 군북면 중암리함안군 군북면 중암리중암-중암1일반국도79호선00000-00800<NA>
327102600031683150410263019400.07260.0산청군 차황면 장박리합천군 대병면 회양리장박~회양0<NA><NA>미개통(L=6.463km)
428102600011683150410261011640.011640.0산청군 오부면 양촌리산청군 차황면 실매리양촌~실매0<NA><NA><NA>
529102600051683150410265045040.021000.0합천군 대병면 양리합천군 대양면 정양리양리~정양0<NA><NA><NA>
630102600041683150410264024040.04640.0합천군 대병면 회양리합천군 대병면 양리회양~양리1지방도1089호선00000-00000<NA>
731102600021683150410262012140.0500.0산청군 차황면 실매리산청군 차황면 장박리실매~장박1일반국도58호선00000-00500<NA>
832102300051683150410235043447.02500.0함양군 마천면 가흥리함양군 마천면 의탄리가흥~의탄1국가지원지방도60호선00000-00000<NA>
933102300061683150410236055627.012180.0함양군 마천면 의탄리함양군 함양읍 구룡리의탄~구룡0<NA><NA><NA>
식별번호관리번호관리기관도로종류노선번호구간번호이력코드구간 시점 노선 누적거리구간 연장시점명종점명구간명중용여부중용내용중용이정비고
3291430000416831504304022055.06123.0밀양시 무안면 신법리창녕군 부곡면 수다리신법-수다0<NA><NA><NA>
3301530000616831504306027953.05800.0창녕군 부곡면 수다리창녕군 부곡면 학포리수다-학포0<NA><NA><NA>
3311630000716831504307043231.015278.0창녕군 부곡면 학포리창원시 동읍 용잠리학포-용잠0<NA><NA><NA>
3321737000416831504374040270.012640.0함양군 서상면 중남리거창군 북상면 월성리중남-월성0<NA><NA><NA>
3331837000616831504376057882.07008.0거창군 북상면 갈계리거창군 마리면 율리갈계-율리0<NA><NA><NA>
3341937000516831504375050874.010604.0거창군 북상면 월성리거창군 북상면 갈계리월성-갈계0<NA><NA><NA>
3352037000216831504372021230.018700.0함양군 백전면 경백리함양군 서하면 송계리경백-송계0<NA><NA><NA>
336213700011683150437102530.02530.0함양군 백전면 오천리함양군 백전면 경백리오천-경백0<NA><NA><NA>
3372237000316831504373027630.06400.0함양군 서하면 송계리함양군 서상면 중남리송계-중남1일반국도26호선00000-06400<NA>
33823102900011683150410291014474.014474.0마산시 진전면 양촌리함안군 군북면 오곡리양촌-오곡0<NA><NA><NA>