Overview

Dataset statistics

Number of variables13
Number of observations1487
Missing cells71
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory170.0 KiB
Average record size in memory117.1 B

Variable types

Numeric8
Categorical5

Dataset

Description경상남도 도로대장전산화 시스템 데이터의 중장기개방계획에 따른 데이터입니다. 시스템 상에서의 각 도로별 시설물 기본정보를 가지고 있으며, 도로대장의 낙석방지시설 데이터를 포함하고있습니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15091931

Alerts

식별번호 is highly overall correlated with 관리번호 and 2 other fieldsHigh correlation
관리번호 is highly overall correlated with 식별번호 and 2 other fieldsHigh correlation
노선번호 is highly overall correlated with 식별번호 and 2 other fieldsHigh correlation
위치_시점 is highly overall correlated with 위치_종점High correlation
위치_종점 is highly overall correlated with 위치_시점High correlation
도로종류 is highly overall correlated with 식별번호 and 2 other fieldsHigh correlation
관리기관 is highly imbalanced (96.2%)Imbalance
이력코드 is highly imbalanced (99.2%)Imbalance
종류 is highly imbalanced (75.7%)Imbalance
관리번호 has 71 (4.8%) missing valuesMissing
식별번호 has unique valuesUnique
높이 has 64 (4.3%) zerosZeros

Reproduction

Analysis started2023-12-10 23:56:08.914367
Analysis finished2023-12-10 23:56:18.005250
Duration9.09 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

식별번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1487
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean747.8581
Minimum1
Maximum1492
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-12-11T08:56:18.112585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile75.3
Q1376.5
median748
Q31119.5
95-th percentile1417.7
Maximum1492
Range1491
Interquartile range (IQR)743

Descriptive statistics

Standard deviation429.971
Coefficient of variation (CV)0.5749366
Kurtosis-1.1957938
Mean747.8581
Median Absolute Deviation (MAD)372
Skewness-0.001817931
Sum1112065
Variance184875.06
MonotonicityNot monotonic
2023-12-11T08:56:18.273846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 1
 
0.1%
1014 1
 
0.1%
1023 1
 
0.1%
1022 1
 
0.1%
1021 1
 
0.1%
1020 1
 
0.1%
1019 1
 
0.1%
1018 1
 
0.1%
1017 1
 
0.1%
1016 1
 
0.1%
Other values (1477) 1477
99.3%
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%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1492 1
0.1%
1491 1
0.1%
1490 1
0.1%
1489 1
0.1%
1488 1
0.1%
1487 1
0.1%
1486 1
0.1%
1485 1
0.1%
1484 1
0.1%
1483 1
0.1%

관리번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1404
Distinct (%)99.2%
Missing71
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean8571633.2
Minimum0
Maximum11024000
Zeros10
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-12-11T08:56:18.444171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile300003.75
Q110020054
median10210004
Q310370001
95-th percentile10890036
Maximum11024000
Range11024000
Interquartile range (IQR)349947.5

Descriptive statistics

Standard deviation3785467.5
Coefficient of variation (CV)0.44162733
Kurtosis0.91792132
Mean8571633.2
Median Absolute Deviation (MAD)169997
Skewness-1.6972766
Sum1.2137433 × 1010
Variance1.4329764 × 1013
MonotonicityNot monotonic
2023-12-11T08:56:18.610294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10
 
0.7%
600020 2
 
0.1%
1 2
 
0.1%
10400015 2
 
0.1%
10510057 1
 
0.1%
10510042 1
 
0.1%
10510036 1
 
0.1%
10510037 1
 
0.1%
10510038 1
 
0.1%
10510039 1
 
0.1%
Other values (1394) 1394
93.7%
(Missing) 71
 
4.8%
ValueCountFrequency (%)
0 10
0.7%
1 2
 
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%
9 1
 
0.1%
ValueCountFrequency (%)
11024000 1
0.1%
10990026 1
0.1%
10990025 1
0.1%
10990024 1
0.1%
10990023 1
0.1%
10990022 1
0.1%
10990021 1
0.1%
10990020 1
0.1%
10990019 1
0.1%
10990018 1
0.1%

관리기관
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.7 KiB
1683
1481 
1684
 
6

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1683 1481
99.6%
1684 6
 
0.4%

Length

2023-12-11T08:56:18.726171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:56:18.811455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1683 1481
99.6%
1684 6
 
0.4%

도로종류
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.7 KiB
1504
1307 
1507
180 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1507
2nd row1507
3rd row1507
4th row1507
5th row1507

Common Values

ValueCountFrequency (%)
1504 1307
87.9%
1507 180
 
12.1%

Length

2023-12-11T08:56:18.903810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:56:18.995316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1504 1307
87.9%
1507 180
 
12.1%

노선번호
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean885.09751
Minimum30
Maximum1099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-12-11T08:56:19.104737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile37
Q11004
median1021
Q31037
95-th percentile1089
Maximum1099
Range1069
Interquartile range (IQR)33

Descriptive statistics

Standard deviation347.35924
Coefficient of variation (CV)0.39245307
Kurtosis1.9205296
Mean885.09751
Median Absolute Deviation (MAD)17
Skewness-1.9660344
Sum1316140
Variance120658.44
MonotonicityNot monotonic
2023-12-11T08:56:19.257084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1034 117
 
7.9%
1089 80
 
5.4%
1021 79
 
5.3%
37 75
 
5.0%
1051 68
 
4.6%
1022 64
 
4.3%
60 61
 
4.1%
1018 59
 
4.0%
1002 56
 
3.8%
1010 55
 
3.7%
Other values (33) 773
52.0%
ValueCountFrequency (%)
30 6
 
0.4%
37 75
5.0%
58 40
2.7%
60 61
4.1%
67 11
 
0.7%
69 26
 
1.7%
907 19
 
1.3%
1001 52
3.5%
1002 56
3.8%
1003 22
 
1.5%
ValueCountFrequency (%)
1099 26
 
1.7%
1089 80
5.4%
1084 20
 
1.3%
1080 22
 
1.5%
1077 20
 
1.3%
1051 68
4.6%
1049 32
 
2.2%
1047 28
 
1.9%
1042 3
 
0.2%
1041 27
 
1.8%

구간번호
Real number (ℝ)

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.332885
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-12-11T08:56:19.375310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q37
95-th percentile12
Maximum16
Range15
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5442048
Coefficient of variation (CV)0.66459426
Kurtosis-0.14828479
Mean5.332885
Median Absolute Deviation (MAD)2
Skewness0.84653825
Sum7930
Variance12.561388
MonotonicityNot monotonic
2023-12-11T08:56:19.755872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2 231
15.5%
4 188
12.6%
5 179
12.0%
3 159
10.7%
7 158
10.6%
1 156
10.5%
6 112
7.5%
11 103
6.9%
13 54
 
3.6%
9 48
 
3.2%
Other values (6) 99
6.7%
ValueCountFrequency (%)
1 156
10.5%
2 231
15.5%
3 159
10.7%
4 188
12.6%
5 179
12.0%
6 112
7.5%
7 158
10.6%
8 18
 
1.2%
9 48
 
3.2%
10 15
 
1.0%
ValueCountFrequency (%)
16 8
 
0.5%
15 3
 
0.2%
14 8
 
0.5%
13 54
 
3.6%
12 47
 
3.2%
11 103
6.9%
10 15
 
1.0%
9 48
 
3.2%
8 18
 
1.2%
7 158
10.6%

이력코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.7 KiB
0
1486 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 1486
99.9%
1 1
 
0.1%

Length

2023-12-11T08:56:19.872557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:56:19.960017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1486
99.9%
1 1
 
0.1%

위치_시점
Real number (ℝ)

HIGH CORRELATION 

Distinct1314
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6156826
Minimum0
Maximum28.76
Zeros5
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-12-11T08:56:20.061827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5945
Q12.4905
median5.13
Q38.0265
95-th percentile12.4531
Maximum28.76
Range28.76
Interquartile range (IQR)5.536

Descriptive statistics

Standard deviation3.7727509
Coefficient of variation (CV)0.6718241
Kurtosis0.9345161
Mean5.6156826
Median Absolute Deviation (MAD)2.727
Skewness0.79653989
Sum8350.52
Variance14.233649
MonotonicityNot monotonic
2023-12-11T08:56:20.204584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.0 17
 
1.1%
3.0 10
 
0.7%
2.0 6
 
0.4%
5.0 5
 
0.3%
0.0 5
 
0.3%
4.0 4
 
0.3%
6.0 4
 
0.3%
7.152 3
 
0.2%
6.225 3
 
0.2%
1.203 3
 
0.2%
Other values (1304) 1427
96.0%
ValueCountFrequency (%)
0.0 5
0.3%
0.01 1
 
0.1%
0.011 1
 
0.1%
0.025 1
 
0.1%
0.031 1
 
0.1%
0.048 1
 
0.1%
0.049 1
 
0.1%
0.05 1
 
0.1%
0.068 1
 
0.1%
0.078 1
 
0.1%
ValueCountFrequency (%)
28.76 1
0.1%
21.842 1
0.1%
21.737 1
0.1%
19.02 1
0.1%
16.98 1
0.1%
16.827 1
0.1%
16.77 1
0.1%
16.508 1
0.1%
16.461 1
0.1%
16.353 1
0.1%

위치_종점
Real number (ℝ)

HIGH CORRELATION 

Distinct1316
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7158299
Minimum0.031
Maximum28.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-12-11T08:56:20.374000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.031
5-th percentile0.6862
Q12.63
median5.238
Q38.122
95-th percentile12.5581
Maximum28.82
Range28.789
Interquartile range (IQR)5.492

Descriptive statistics

Standard deviation3.7706648
Coefficient of variation (CV)0.65968808
Kurtosis0.92944073
Mean5.7158299
Median Absolute Deviation (MAD)2.729
Skewness0.7955651
Sum8499.439
Variance14.217913
MonotonicityNot monotonic
2023-12-11T08:56:20.512732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.0 18
 
1.2%
3.0 8
 
0.5%
6.0 6
 
0.4%
2.0 6
 
0.4%
1.7 4
 
0.3%
2.85 4
 
0.3%
4.0 4
 
0.3%
5.77 4
 
0.3%
4.672 3
 
0.2%
4.887 3
 
0.2%
Other values (1306) 1427
96.0%
ValueCountFrequency (%)
0.031 1
0.1%
0.065 1
0.1%
0.07 1
0.1%
0.1 2
0.1%
0.11 1
0.1%
0.121 1
0.1%
0.124 1
0.1%
0.131 1
0.1%
0.139 1
0.1%
0.14 2
0.1%
ValueCountFrequency (%)
28.82 1
0.1%
21.961 1
0.1%
21.817 1
0.1%
19.1 1
0.1%
17.068 1
0.1%
16.969 1
0.1%
16.89 1
0.1%
16.564 1
0.1%
16.505 1
0.1%
16.456 1
0.1%

위치_방향
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.7 KiB
1
921 
0
566 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 921
61.9%
0 566
38.1%

Length

2023-12-11T08:56:20.634912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:56:20.729774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 921
61.9%
0 566
38.1%

종류
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.7 KiB
3702
1340 
3701
143 
3703
 
3
3705
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row3701
2nd row3701
3rd row3702
4th row3701
5th row3701

Common Values

ValueCountFrequency (%)
3702 1340
90.1%
3701 143
 
9.6%
3703 3
 
0.2%
3705 1
 
0.1%

Length

2023-12-11T08:56:20.876778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:56:20.977154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3702 1340
90.1%
3701 143
 
9.6%
3703 3
 
0.2%
3705 1
 
0.1%

연장
Real number (ℝ)

Distinct282
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.93746
Minimum0
Maximum1421
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-12-11T08:56:21.095402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile26
Q152
median80
Q3123
95-th percentile249.7
Maximum1421
Range1421
Interquartile range (IQR)71

Descriptive statistics

Standard deviation87.84339
Coefficient of variation (CV)0.86173808
Kurtosis45.255602
Mean101.93746
Median Absolute Deviation (MAD)33
Skewness4.661255
Sum151581
Variance7716.4611
MonotonicityNot monotonic
2023-12-11T08:56:21.226485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 41
 
2.8%
40 41
 
2.8%
100 31
 
2.1%
80 31
 
2.1%
90 29
 
2.0%
70 25
 
1.7%
50 23
 
1.5%
55 21
 
1.4%
61 21
 
1.4%
30 20
 
1.3%
Other values (272) 1204
81.0%
ValueCountFrequency (%)
0 1
 
0.1%
6 1
 
0.1%
7 1
 
0.1%
8 1
 
0.1%
10 1
 
0.1%
11 1
 
0.1%
12 7
0.5%
13 2
 
0.1%
14 3
0.2%
15 2
 
0.1%
ValueCountFrequency (%)
1421 1
0.1%
875 1
0.1%
790 1
0.1%
670 1
0.1%
630 1
0.1%
620 1
0.1%
580 1
0.1%
570 1
0.1%
551 1
0.1%
536 1
0.1%

높이
Real number (ℝ)

ZEROS 

Distinct49
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2822798
Minimum0
Maximum35
Zeros64
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-12-11T08:56:21.386289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.8
Q12.7
median2.8
Q32.8
95-th percentile8
Maximum35
Range35
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation3.2574793
Coefficient of variation (CV)0.99244415
Kurtosis32.496541
Mean3.2822798
Median Absolute Deviation (MAD)0.1
Skewness5.2414786
Sum4880.75
Variance10.611172
MonotonicityNot monotonic
2023-12-11T08:56:21.546960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2.8 547
36.8%
2.7 279
18.8%
3.0 162
 
10.9%
2.5 158
 
10.6%
0.0 64
 
4.3%
2.0 59
 
4.0%
2.9 34
 
2.3%
2.1 27
 
1.8%
15.0 20
 
1.3%
2.4 12
 
0.8%
Other values (39) 125
 
8.4%
ValueCountFrequency (%)
0.0 64
4.3%
0.7 4
 
0.3%
0.8 2
 
0.1%
1.1 1
 
0.1%
1.2 1
 
0.1%
1.8 7
 
0.5%
1.9 1
 
0.1%
2.0 59
4.0%
2.1 27
1.8%
2.11 1
 
0.1%
ValueCountFrequency (%)
35.0 1
 
0.1%
30.0 5
 
0.3%
25.0 4
 
0.3%
24.0 1
 
0.1%
20.0 7
 
0.5%
19.0 2
 
0.1%
18.0 2
 
0.1%
17.0 1
 
0.1%
15.0 20
1.3%
13.0 2
 
0.1%

Interactions

2023-12-11T08:56:16.789093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:10.185884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:10.988281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:11.877324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:12.771100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:13.603950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:14.773462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:15.780358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:16.892395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:10.275642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:11.080363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:11.991770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:12.873234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:13.727591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:14.894404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:15.912295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:17.005567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:10.366935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:11.181201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:12.113978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:12.973958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:13.837301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:15.017576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:16.038394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:17.127004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:10.452512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:11.275179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:12.223278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:13.077863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:13.964942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:15.120905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:16.133446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:17.261853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:10.548755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:11.379599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:12.342202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:13.191907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:14.064408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:15.296388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:16.266471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:17.357873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:10.645166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:11.487985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:12.468565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:13.289550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:14.419279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:15.405996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:16.395668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:17.453593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:10.739355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:11.611671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:12.567656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:13.380627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:14.524451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:15.499754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:16.503063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:17.573504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:10.871582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:11.759404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:12.668987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:13.475668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:14.639109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:15.619390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:56:16.644840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:56:21.672311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식별번호관리번호관리기관도로종류노선번호구간번호이력코드위치_시점위치_종점위치_방향종류연장높이
식별번호1.0000.8850.2270.9060.6890.6920.0000.2570.2590.1690.2030.0750.240
관리번호0.8851.0000.0000.9630.7540.1950.0000.2620.2700.0000.1920.0600.183
관리기관0.2270.0001.0000.0000.0000.1040.0000.0690.0710.0640.0000.0000.000
도로종류0.9060.9630.0001.0000.6260.1890.0000.1990.2120.0000.1720.0840.160
노선번호0.6890.7540.0000.6261.0000.3930.0000.4660.4740.0000.0830.0000.082
구간번호0.6920.1950.1040.1890.3931.0000.0620.1980.2000.1640.0000.0000.221
이력코드0.0000.0000.0000.0000.0000.0621.0000.0000.0000.0000.0000.0000.000
위치_시점0.2570.2620.0690.1990.4660.1980.0001.0001.0000.0310.1580.0000.000
위치_종점0.2590.2700.0710.2120.4740.2000.0001.0001.0000.0460.1630.0000.000
위치_방향0.1690.0000.0640.0000.0000.1640.0000.0310.0461.0000.0000.0600.086
종류0.2030.1920.0000.1720.0830.0000.0000.1580.1630.0001.0000.0000.602
연장0.0750.0600.0000.0840.0000.0000.0000.0000.0000.0600.0001.0000.098
높이0.2400.1830.0000.1600.0820.2210.0000.0000.0000.0860.6020.0981.000
2023-12-11T08:56:21.864452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
종류도로종류관리기관이력코드위치_방향
종류1.0000.1140.0000.0000.000
도로종류0.1141.0000.0000.0000.000
관리기관0.0000.0001.0000.0000.041
이력코드0.0000.0000.0001.0000.000
위치_방향0.0000.0000.0410.0001.000
2023-12-11T08:56:21.971617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식별번호관리번호노선번호구간번호위치_시점위치_종점연장높이관리기관도로종류이력코드위치_방향종류
식별번호1.0000.5390.6030.153-0.080-0.0800.0400.1000.1730.7430.0000.1290.122
관리번호0.5391.0000.975-0.006-0.173-0.174-0.0010.1090.0000.8250.0000.0000.127
노선번호0.6030.9751.000-0.039-0.166-0.1670.0100.1220.0000.8930.0000.0000.078
구간번호0.153-0.006-0.0391.0000.0270.024-0.0450.0000.0850.1720.0000.1210.000
위치_시점-0.080-0.173-0.1660.0271.0000.999-0.0110.0310.0690.1990.0000.0310.101
위치_종점-0.080-0.174-0.1670.0240.9991.0000.0080.0300.0710.2110.0000.0460.104
연장0.040-0.0010.010-0.045-0.0110.0081.0000.0220.0000.0630.0000.0450.000
높이0.1000.1090.1220.0000.0310.0300.0221.0000.0000.1150.0000.0520.403
관리기관0.1730.0000.0000.0850.0690.0710.0000.0001.0000.0000.0000.0410.000
도로종류0.7430.8250.8930.1720.1990.2110.0630.1150.0001.0000.0000.0000.114
이력코드0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
위치_방향0.1290.0000.0000.1210.0310.0460.0450.0520.0410.0000.0001.0000.000
종류0.1220.1270.0780.0000.1010.1040.0000.4030.0000.1140.0000.0001.000

Missing values

2023-12-11T08:56:17.743231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:56:17.940220image/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

식별번호관리번호관리기관도로종류노선번호구간번호이력코드위치_시점위치_종점위치_방향종류연장높이
0253700061683150737209.97410.041037016710.0
12637000716831507372010.09810.8880370179015.0
22737000816831507372010.72810.919037021912.7
32837000916831507372010.91910.949037013020.0
42937001016831507372010.98611.057137017112.0
53037001116831507372011.06211.12313702612.7
63137001216831507372012.22312.25403702312.7
73237001316831507372012.38612.507037021212.7
83337001416831507372012.5512.5803702302.7
93437001516831507372012.68412.75403702702.7
식별번호관리번호관리기관도로종류노선번호구간번호이력코드위치_시점위치_종점위치_방향종류연장높이
1477213700021683150737205.3535.498037021452.7
1478223700031683150737206.026.04703702272.7
1479233700041683150737209.8319.8803702492.7
1480243700051683150737209.93510.486037025512.7
148114870168315041029501.4151.45503701405.0
148214880168315041029501.4651.5303701655.0
148314890168315041029502.7812.8503701695.0
148414900168315041077803.63.6403702402.5
1485149101683150410401300.00.14137011402.0
1486149201683150410401300.00.14137021402.5