Overview

Dataset statistics

Number of variables15
Number of observations1767
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory233.1 KiB
Average record size in memory135.1 B

Variable types

Numeric10
Categorical5

Dataset

Description경상남도 도로대장전산화 시스템 데이터의 중장기개방계획에 따른 데이터입니다. 시스템 상에서의 각 도로의 유로도로, 터널 등의 정보를 가지고 있으며, 도로대장의 절개면 데이터를 포함하고있습니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15091942

Alerts

관리기관 has constant value ""Constant
식별번호 is highly overall correlated with 도로종류 and 1 other fieldsHigh correlation
관리번호 is highly overall correlated with 노선번호 and 1 other fieldsHigh correlation
노선번호 is highly overall correlated with 관리번호 and 1 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
종류 is highly overall correlated with 식별번호High correlation
이력코드 is highly imbalanced (92.6%)Imbalance
높이_최소 is highly skewed (γ1 = 20.95153599)Skewed
식별번호 has unique valuesUnique
관리번호 has 222 (12.6%) zerosZeros
높이_최대 has 22 (1.2%) zerosZeros
높이_최소 has 269 (15.2%) zerosZeros
경사 has 214 (12.1%) zerosZeros

Reproduction

Analysis started2023-12-10 23:34:31.429653
Analysis finished2023-12-10 23:34:42.901107
Duration11.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

식별번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1767
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean884
Minimum1
Maximum1767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2023-12-11T08:34:42.988956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile89.3
Q1442.5
median884
Q31325.5
95-th percentile1678.7
Maximum1767
Range1766
Interquartile range (IQR)883

Descriptive statistics

Standard deviation510.23328
Coefficient of variation (CV)0.57718697
Kurtosis-1.2
Mean884
Median Absolute Deviation (MAD)442
Skewness0
Sum1562028
Variance260338
MonotonicityNot monotonic
2023-12-11T08:34:43.136895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
1151 1
 
0.1%
1162 1
 
0.1%
1161 1
 
0.1%
1160 1
 
0.1%
1159 1
 
0.1%
1158 1
 
0.1%
1157 1
 
0.1%
1156 1
 
0.1%
1155 1
 
0.1%
Other values (1757) 1757
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%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1767 1
0.1%
1766 1
0.1%
1765 1
0.1%
1764 1
0.1%
1763 1
0.1%
1762 1
0.1%
1761 1
0.1%
1760 1
0.1%
1759 1
0.1%
1758 1
0.1%

관리번호
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1481
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7649674.8
Minimum0
Maximum10990053
Zeros222
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2023-12-11T08:34:43.288965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1690050.5
median10100077
Q310290060
95-th percentile10890009
Maximum10990053
Range10990053
Interquartile range (IQR)9600010

Descriptive statistics

Standard deviation4388082.2
Coefficient of variation (CV)0.5736299
Kurtosis-0.83093069
Mean7649674.8
Median Absolute Deviation (MAD)239936
Skewness-1.0712527
Sum1.3516975 × 1010
Variance1.9255266 × 1013
MonotonicityNot monotonic
2023-12-11T08:34:43.419492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 222
 
12.6%
10240032 2
 
0.1%
10240049 2
 
0.1%
10240036 2
 
0.1%
10240060 2
 
0.1%
10240046 2
 
0.1%
10240044 2
 
0.1%
10240043 2
 
0.1%
10240042 2
 
0.1%
10240041 2
 
0.1%
Other values (1471) 1527
86.4%
ValueCountFrequency (%)
0 222
12.6%
300001 1
 
0.1%
300002 1
 
0.1%
370001 1
 
0.1%
370002 1
 
0.1%
370003 1
 
0.1%
370004 1
 
0.1%
370005 1
 
0.1%
580000 1
 
0.1%
580001 1
 
0.1%
ValueCountFrequency (%)
10990053 1
0.1%
10990052 1
0.1%
10990051 1
0.1%
10990050 1
0.1%
10990049 1
0.1%
10990048 1
0.1%
10990047 1
0.1%
10990046 1
0.1%
10990045 1
0.1%
10990044 1
0.1%

관리기관
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
1683
1767 

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 1767
100.0%

Length

2023-12-11T08:34:43.558841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:34:43.681023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1683 1767
100.0%

도로종류
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
1504
1535 
1507
232 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1504 1535
86.9%
1507 232
 
13.1%

Length

2023-12-11T08:34:43.781328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:34:43.901173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1504 1535
86.9%
1507 232
 
13.1%

노선번호
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean847.19525
Minimum30
Maximum1099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2023-12-11T08:34:44.007859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile58
Q11005
median1018
Q31034
95-th percentile1089
Maximum1099
Range1069
Interquartile range (IQR)29

Descriptive statistics

Standard deviation378.36641
Coefficient of variation (CV)0.44661064
Kurtosis0.55074209
Mean847.19525
Median Absolute Deviation (MAD)15
Skewness-1.5877818
Sum1496994
Variance143161.14
MonotonicityNot monotonic
2023-12-11T08:34:44.181921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1034 134
 
7.6%
60 124
 
7.0%
1024 122
 
6.9%
58 115
 
6.5%
1010 114
 
6.5%
1018 101
 
5.7%
1005 91
 
5.1%
1007 75
 
4.2%
69 74
 
4.2%
1029 65
 
3.7%
Other values (31) 752
42.6%
ValueCountFrequency (%)
30 2
 
0.1%
37 5
 
0.3%
58 115
6.5%
60 124
7.0%
67 11
 
0.6%
69 74
4.2%
907 9
 
0.5%
1001 12
 
0.7%
1002 19
 
1.1%
1003 35
 
2.0%
ValueCountFrequency (%)
1099 53
3.0%
1089 44
2.5%
1084 12
 
0.7%
1080 16
 
0.9%
1077 56
3.2%
1051 24
1.4%
1049 36
2.0%
1047 16
 
0.9%
1042 35
2.0%
1041 16
 
0.9%

구간번호
Real number (ℝ)

Distinct16
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4352009
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2023-12-11T08:34:44.553567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile13
Maximum19
Range18
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.5694319
Coefficient of variation (CV)0.65672492
Kurtosis2.1309903
Mean5.4352009
Median Absolute Deviation (MAD)2
Skewness1.2980008
Sum9604
Variance12.740844
MonotonicityNot monotonic
2023-12-11T08:34:44.655597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3 255
14.4%
6 229
13.0%
5 211
11.9%
7 204
11.5%
4 198
11.2%
1 181
10.2%
2 169
9.6%
11 63
 
3.6%
10 61
 
3.5%
9 51
 
2.9%
Other values (6) 145
8.2%
ValueCountFrequency (%)
1 181
10.2%
2 169
9.6%
3 255
14.4%
4 198
11.2%
5 211
11.9%
6 229
13.0%
7 204
11.5%
8 33
 
1.9%
9 51
 
2.9%
10 61
 
3.5%
ValueCountFrequency (%)
19 26
 
1.5%
15 23
 
1.3%
14 8
 
0.5%
13 34
 
1.9%
12 21
 
1.2%
11 63
 
3.6%
10 61
 
3.5%
9 51
 
2.9%
8 33
 
1.9%
7 204
11.5%

이력코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
0
1751 
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 1751
99.1%
1 16
 
0.9%

Length

2023-12-11T08:34:44.779525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:34:44.873744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1751
99.1%
1 16
 
0.9%

위치_시점
Real number (ℝ)

HIGH CORRELATION 

Distinct1607
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7969349
Minimum-0.21
Maximum28.725
Zeros6
Zeros (%)0.3%
Negative2
Negative (%)0.1%
Memory size15.7 KiB
2023-12-11T08:34:44.971465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.21
5-th percentile0.4291
Q12.3975
median4.81
Q38.192
95-th percentile14.2664
Maximum28.725
Range28.935
Interquartile range (IQR)5.7945

Descriptive statistics

Standard deviation4.6545645
Coefficient of variation (CV)0.80293544
Kurtosis3.4438683
Mean5.7969349
Median Absolute Deviation (MAD)2.842
Skewness1.5177418
Sum10243.184
Variance21.664971
MonotonicityNot monotonic
2023-12-11T08:34:45.128222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 6
 
0.3%
2.48 5
 
0.3%
4.83 4
 
0.2%
1.03 4
 
0.2%
2.82 3
 
0.2%
11.16 3
 
0.2%
5.85 3
 
0.2%
0.81 3
 
0.2%
0.05 3
 
0.2%
3.15 3
 
0.2%
Other values (1597) 1730
97.9%
ValueCountFrequency (%)
-0.21 1
 
0.1%
-0.11 1
 
0.1%
0.0 6
0.3%
0.007 1
 
0.1%
0.01 1
 
0.1%
0.025 1
 
0.1%
0.026 2
 
0.1%
0.027 1
 
0.1%
0.038 2
 
0.1%
0.05 3
0.2%
ValueCountFrequency (%)
28.725 1
0.1%
28.386 1
0.1%
28.208 1
0.1%
28.129 1
0.1%
27.375 1
0.1%
27.223 1
0.1%
27.103 1
0.1%
25.746 1
0.1%
25.046 1
0.1%
24.969 1
0.1%

위치_종점
Real number (ℝ)

HIGH CORRELATION 

Distinct1606
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9600407
Minimum-0.11
Maximum28.825
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)0.1%
Memory size15.7 KiB
2023-12-11T08:34:45.276558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.11
5-th percentile0.5879
Q12.544
median4.971
Q38.357
95-th percentile14.3764
Maximum28.825
Range28.935
Interquartile range (IQR)5.813

Descriptive statistics

Standard deviation4.6485961
Coefficient of variation (CV)0.77996045
Kurtosis3.4020824
Mean5.9600407
Median Absolute Deviation (MAD)2.839
Skewness1.5070405
Sum10531.392
Variance21.609445
MonotonicityNot monotonic
2023-12-11T08:34:45.417202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.03 4
 
0.2%
1.7 3
 
0.2%
1.88 3
 
0.2%
4.95 3
 
0.2%
0.275 3
 
0.2%
1.803 3
 
0.2%
0.4 3
 
0.2%
5.23 3
 
0.2%
0.7 3
 
0.2%
0.872 3
 
0.2%
Other values (1596) 1736
98.2%
ValueCountFrequency (%)
-0.11 1
0.1%
-0.05 1
0.1%
0.05 1
0.1%
0.07 1
0.1%
0.097 1
0.1%
0.099 1
0.1%
0.109 1
0.1%
0.12 1
0.1%
0.125 1
0.1%
0.133 1
0.1%
ValueCountFrequency (%)
28.825 1
0.1%
28.758 1
0.1%
28.243 1
0.1%
28.171 1
0.1%
27.64 1
0.1%
27.367 1
0.1%
27.178 1
0.1%
25.772 1
0.1%
25.138 1
0.1%
25.03 1
0.1%

위치_방향
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
1
963 
0
804 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 963
54.5%
0 804
45.5%

Length

2023-12-11T08:34:45.534788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:34:45.640626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 963
54.5%
0 804
45.5%

연장
Real number (ℝ)

Distinct441
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean163.74754
Minimum0
Maximum3328
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2023-12-11T08:34:45.756767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32
Q172
median118
Q3198
95-th percentile439.4
Maximum3328
Range3328
Interquartile range (IQR)126

Descriptive statistics

Standard deviation176.30671
Coefficient of variation (CV)1.0766984
Kurtosis84.769023
Mean163.74754
Median Absolute Deviation (MAD)57
Skewness6.5384339
Sum289341.9
Variance31084.056
MonotonicityNot monotonic
2023-12-11T08:34:45.909922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80.0 35
 
2.0%
40.0 29
 
1.6%
90.0 22
 
1.2%
60.0 22
 
1.2%
120.0 20
 
1.1%
50.0 19
 
1.1%
75.0 18
 
1.0%
100.0 17
 
1.0%
140.0 16
 
0.9%
95.0 14
 
0.8%
Other values (431) 1555
88.0%
ValueCountFrequency (%)
0.0 2
 
0.1%
6.0 3
0.2%
10.0 4
0.2%
11.0 1
 
0.1%
12.0 4
0.2%
12.2 1
 
0.1%
12.4 1
 
0.1%
13.0 1
 
0.1%
14.0 2
 
0.1%
15.0 5
0.3%
ValueCountFrequency (%)
3328.0 1
0.1%
2557.0 1
0.1%
1697.0 1
0.1%
1239.0 1
0.1%
1190.0 1
0.1%
1170.0 1
0.1%
1076.0 1
0.1%
1055.0 1
0.1%
1048.0 1
0.1%
995.0 1
0.1%

종류
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size13.9 KiB
4202
705 
4201
507 
4203
405 
4200
76 
4299
74 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4202
2nd row4202
3rd row4201
4th row4203
5th row4203

Common Values

ValueCountFrequency (%)
4202 705
39.9%
4201 507
28.7%
4203 405
22.9%
4200 76
 
4.3%
4299 74
 
4.2%

Length

2023-12-11T08:34:46.052118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:34:46.162884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4202 705
39.9%
4201 507
28.7%
4203 405
22.9%
4200 76
 
4.3%
4299 74
 
4.2%

높이_최대
Real number (ℝ)

ZEROS 

Distinct308
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7224278
Minimum0
Maximum189.95
Zeros22
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2023-12-11T08:34:46.361151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5.5
Q39.25
95-th percentile20
Maximum189.95
Range189.95
Interquartile range (IQR)6.25

Descriptive statistics

Standard deviation9.9428159
Coefficient of variation (CV)1.2875246
Kurtosis108.1978
Mean7.7224278
Median Absolute Deviation (MAD)2.5
Skewness8.3391505
Sum13645.53
Variance98.859589
MonotonicityNot monotonic
2023-12-11T08:34:46.519525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.0 194
 
11.0%
3.0 128
 
7.2%
10.0 104
 
5.9%
6.0 98
 
5.5%
4.0 96
 
5.4%
8.0 95
 
5.4%
7.0 93
 
5.3%
2.0 80
 
4.5%
15.0 51
 
2.9%
9.0 45
 
2.5%
Other values (298) 783
44.3%
ValueCountFrequency (%)
0.0 22
1.2%
0.09 3
 
0.2%
0.1 5
 
0.3%
0.11 1
 
0.1%
0.12 2
 
0.1%
0.13 2
 
0.1%
0.15 1
 
0.1%
0.17 1
 
0.1%
0.2 3
 
0.2%
0.24 1
 
0.1%
ValueCountFrequency (%)
189.95 1
0.1%
125.2 2
0.1%
121.62 1
0.1%
102.71 1
0.1%
101.5 1
0.1%
75.0 2
0.1%
70.84 1
0.1%
65.84 1
0.1%
56.0 1
0.1%
54.66 1
0.1%

높이_최소
Real number (ℝ)

SKEWED  ZEROS 

Distinct192
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8759593
Minimum0
Maximum201
Zeros269
Zeros (%)15.2%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2023-12-11T08:34:46.715029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.545
median1
Q32
95-th percentile5
Maximum201
Range201
Interquartile range (IQR)1.455

Descriptive statistics

Standard deviation6.8577185
Coefficient of variation (CV)3.6555797
Kurtosis522.41263
Mean1.8759593
Median Absolute Deviation (MAD)0.5
Skewness20.951536
Sum3314.82
Variance47.028303
MonotonicityNot monotonic
2023-12-11T08:34:46.897533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 656
37.1%
0.0 269
15.2%
2.0 194
 
11.0%
0.5 77
 
4.4%
3.0 67
 
3.8%
1.5 57
 
3.2%
4.0 16
 
0.9%
1.2 15
 
0.8%
5.0 14
 
0.8%
0.3 14
 
0.8%
Other values (182) 388
22.0%
ValueCountFrequency (%)
0.0 269
15.2%
0.01 2
 
0.1%
0.02 1
 
0.1%
0.03 1
 
0.1%
0.04 1
 
0.1%
0.05 2
 
0.1%
0.06 2
 
0.1%
0.07 3
 
0.2%
0.08 1
 
0.1%
0.1 14
 
0.8%
ValueCountFrequency (%)
201.0 1
0.1%
125.2 2
0.1%
55.09 1
0.1%
28.4 1
0.1%
26.6 1
0.1%
25.6 1
0.1%
25.0 1
0.1%
23.1 1
0.1%
22.9 1
0.1%
20.53 1
0.1%

경사
Real number (ℝ)

ZEROS 

Distinct258
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.223543
Minimum0
Maximum212.5
Zeros214
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size15.7 KiB
2023-12-11T08:34:47.053356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q149.95
median67
Q3100
95-th percentile140
Maximum212.5
Range212.5
Interquartile range (IQR)50.05

Descriptive statistics

Standard deviation44.256005
Coefficient of variation (CV)0.60439584
Kurtosis-0.62096555
Mean73.223543
Median Absolute Deviation (MAD)24.7
Skewness0.20802219
Sum129386
Variance1958.594
MonotonicityNot monotonic
2023-12-11T08:34:47.240356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67.0 494
28.0%
140.0 350
19.8%
0.0 214
12.1%
40.0 56
 
3.2%
100.0 49
 
2.8%
63.4 49
 
2.8%
39.8 30
 
1.7%
54.0 29
 
1.6%
70.5 24
 
1.4%
83.3 23
 
1.3%
Other values (248) 449
25.4%
ValueCountFrequency (%)
0.0 214
12.1%
0.1 1
 
0.1%
4.4 1
 
0.1%
5.6 1
 
0.1%
6.0 1
 
0.1%
7.3 1
 
0.1%
9.0 1
 
0.1%
9.7 1
 
0.1%
9.9 1
 
0.1%
12.0 3
 
0.2%
ValueCountFrequency (%)
212.5 1
 
0.1%
196.4 1
 
0.1%
194.0 1
 
0.1%
179.6 1
 
0.1%
175.7 1
 
0.1%
172.7 1
 
0.1%
163.9 1
 
0.1%
161.1 1
 
0.1%
151.3 1
 
0.1%
150.0 21
1.2%

Interactions

2023-12-11T08:34:41.594808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:32.298431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:33.562450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:34.523494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:35.489770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:36.523782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:37.421101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:38.317175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:39.548826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:40.656941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:41.728599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:32.386655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:33.661711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:34.627326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:35.603842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:36.615919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:37.530529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:38.409281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:39.660230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:40.757689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:41.848629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:32.470384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:33.746068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:34.716434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:35.717987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:36.702221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:37.614506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:38.512190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:39.773653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:40.849467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:41.926730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:32.570364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:33.834946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:34.806057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:35.820530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:36.785547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:37.694165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:38.601502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:39.885153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:40.932602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:42.036740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:32.677397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:33.960273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:34.897382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:35.920446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:36.871221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:37.783195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:38.699104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:39.986019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:41.024827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:42.128496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:32.766285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:34.053807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:34.991901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:36.021347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:36.955694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:37.870580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:38.790290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:40.084876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:41.121258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:42.209933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:32.922258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:34.138484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:35.083827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:36.110504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:37.046264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:37.961056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:39.143474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:40.197140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:41.213032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:42.297179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:33.008478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:34.228492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:35.171354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:36.208757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:37.131253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:38.049736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:39.240189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:40.296993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:41.307821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:42.387511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:33.382927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:34.322004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:35.265574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:36.306157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:37.241894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:38.139309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:39.356493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:40.424072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:41.397659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:42.517421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:33.482906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:34.432781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:35.383896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:36.440718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:37.338359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:38.240986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:39.462616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:40.551653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:34:41.495448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:34:47.349686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식별번호관리번호도로종류노선번호구간번호이력코드위치_시점위치_종점위치_방향연장종류높이_최대높이_최소경사
식별번호1.0000.6730.8400.6020.4990.3620.4950.4940.0940.1220.8600.1840.1670.789
관리번호0.6731.0000.4140.9940.5630.0280.1650.1610.0380.0000.2880.1160.0000.529
도로종류0.8400.4141.0000.5420.2600.0240.1060.0930.0520.0260.1460.0830.0000.255
노선번호0.6020.9940.5421.0000.6160.0190.2020.2010.0360.0000.1900.1090.0000.447
구간번호0.4990.5630.2600.6161.0000.1810.2730.2740.1540.0000.4190.4060.1940.352
이력코드0.3620.0280.0240.0190.1811.0000.0540.0570.0000.0000.1380.0000.0000.291
위치_시점0.4950.1650.1060.2020.2730.0541.0001.0000.0650.0000.6390.0000.0000.215
위치_종점0.4940.1610.0930.2010.2740.0571.0001.0000.0710.0000.6400.0000.0000.226
위치_방향0.0940.0380.0520.0360.1540.0000.0650.0711.0000.0000.0950.0180.0000.158
연장0.1220.0000.0260.0000.0000.0000.0000.0000.0001.0000.0960.1660.0000.097
종류0.8600.2880.1460.1900.4190.1380.6390.6400.0950.0961.0000.1280.3240.805
높이_최대0.1840.1160.0830.1090.4060.0000.0000.0000.0180.1660.1281.0000.7090.145
높이_최소0.1670.0000.0000.0000.1940.0000.0000.0000.0000.0000.3240.7091.0000.087
경사0.7890.5290.2550.4470.3520.2910.2150.2260.1580.0970.8050.1450.0871.000
2023-12-11T08:34:47.498781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
종류이력코드위치_방향도로종류
종류1.0000.1690.1160.179
이력코드0.1691.0000.0000.015
위치_방향0.1160.0001.0000.033
도로종류0.1790.0150.0331.000
2023-12-11T08:34:47.601078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식별번호관리번호노선번호구간번호위치_시점위치_종점연장높이_최대높이_최소경사도로종류이력코드위치_방향종류
식별번호1.0000.1770.3530.094-0.033-0.036-0.1360.067-0.284-0.2640.6710.2780.0720.527
관리번호0.1771.0000.718-0.1140.0160.0170.0190.120-0.0360.0680.6500.0470.0630.227
노선번호0.3530.7181.000-0.179-0.116-0.117-0.0580.038-0.2240.0180.8090.0320.0600.145
구간번호0.094-0.114-0.1791.0000.1230.1230.0070.0100.1310.0510.2590.1800.1440.255
위치_시점-0.0330.016-0.1160.1231.0000.999-0.0130.0680.014-0.0630.0810.0410.0510.317
위치_종점-0.0360.017-0.1170.1230.9991.0000.0210.0770.018-0.0540.0710.0440.0540.318
연장-0.1360.019-0.0580.007-0.0130.0211.0000.2620.1570.2030.0280.0000.0000.061
높이_최대0.0670.1200.0380.0100.0680.0770.2621.0000.1830.0680.0890.0000.0200.081
높이_최소-0.284-0.036-0.2240.1310.0140.0180.1570.1831.0000.2910.0000.0000.0000.126
경사-0.2640.0680.0180.051-0.063-0.0540.2030.0680.2911.0000.1980.2230.1210.461
도로종류0.6710.6500.8090.2590.0810.0710.0280.0890.0000.1981.0000.0150.0330.179
이력코드0.2780.0470.0320.1800.0410.0440.0000.0000.0000.2230.0151.0000.0000.169
위치_방향0.0720.0630.0600.1440.0510.0540.0000.0200.0000.1210.0330.0001.0000.116
종류0.5270.2270.1450.2550.3170.3180.0610.0810.1260.4610.1790.1690.1161.000

Missing values

2023-12-11T08:34:42.632520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:34:42.828082image/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_방향연장종류높이_최대높이_최소경사
010168315041049306.566.750190.0420214.00.545.0
120168315041049306.566.751190.0420213.20.550.0
230168315041049305.445.841400.0420124.00.540.0
340168315041049305.095.14150.042032.01.5172.7
450168315041049305.0125.072160.042033.53.0149.6
560168315041049304.9264.955129.042033.02.5103.7
670168315041049304.0584.4491391.042032.01.5116.5
780168315041049302.8372.885148.042035.55.0101.9
890168315041049301.0851.1940109.042035.55.0115.6
9100168315041049300.9211.0790158.042035.04.5116.8
식별번호관리번호관리기관도로종류노선번호구간번호이력코드위치_시점위치_종점위치_방향연장종류높이_최대높이_최소경사
1757175860011316831507601908.378.4130.0420317.00.040.0
1758175960011016831507601907.758.0761326.04203189.950.055.0
1759176060010916831507601907.2737.4351162.0420354.660.055.0
1760176160010816831507601909.289.322042.0420314.390.034.0
1761176260010616831507601908.4858.51025.042033.610.034.0
1762176360010516831507601908.1058.159054.0420315.490.063.0
1763176460010416831507601908.0148.06046.0420331.080.063.0
1764176560010316831507601907.777.9850215.0420365.840.051.0
1765176660010216831507601907.2057.4510246.04203121.620.051.0
1766176760011216831507601908.238.255125.0420327.110.045.0