Overview

Dataset statistics

Number of variables14
Number of observations54
Missing cells14
Missing cells (%)1.9%
Duplicate rows1
Duplicate rows (%)1.9%
Total size in memory6.7 KiB
Average record size in memory127.4 B

Variable types

Numeric9
Categorical4
Text1

Dataset

Description경상남도 도로대장전산화 시스템 데이터의 중장기개방계획에 따른 데이터입니다. 시스템 상에서의 각 도로의 유로도로, 터널 등의 정보를 가지고 있으며, 도로대장의 교차시설 데이터를 포함하고있습니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15091947

Alerts

관리기관 has constant value ""Constant
이력코드 has constant value ""Constant
Dataset has 1 (1.9%) duplicate rowsDuplicates
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
구간번호 is highly overall correlated with 교차방식High correlation
유효높이 is highly overall correlated with 식별번호High correlation
교차각도 is highly overall correlated with 도로종류High correlation
도로종류 is highly overall correlated with 관리번호 and 2 other fieldsHigh correlation
교차방식 is highly overall correlated with 구간번호High correlation
도로종류 is highly imbalanced (55.5%)Imbalance
관리번호 has 3 (5.6%) missing valuesMissing
교차시설명 has 11 (20.4%) missing valuesMissing
관리번호 has 1 (1.9%) zerosZeros
위치 has 1 (1.9%) zerosZeros
유효높이 has 24 (44.4%) zerosZeros
교차각도 has 5 (9.3%) zerosZeros

Reproduction

Analysis started2023-12-10 22:56:04.446608
Analysis finished2023-12-10 22:56:12.333495
Duration7.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

식별번호
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.462963
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-11T07:56:12.405650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.65
Q114.25
median27.5
Q340.75
95-th percentile51.35
Maximum53
Range52
Interquartile range (IQR)26.5

Descriptive statistics

Standard deviation15.670803
Coefficient of variation (CV)0.5706159
Kurtosis-1.2155746
Mean27.462963
Median Absolute Deviation (MAD)13.5
Skewness-0.012511397
Sum1483
Variance245.57407
MonotonicityIncreasing
2023-12-11T07:56:12.560608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 2
 
3.7%
1 1
 
1.9%
41 1
 
1.9%
30 1
 
1.9%
31 1
 
1.9%
32 1
 
1.9%
33 1
 
1.9%
34 1
 
1.9%
35 1
 
1.9%
36 1
 
1.9%
Other values (43) 43
79.6%
ValueCountFrequency (%)
1 1
1.9%
2 1
1.9%
3 1
1.9%
4 1
1.9%
5 1
1.9%
6 1
1.9%
7 1
1.9%
8 1
1.9%
9 1
1.9%
10 1
1.9%
ValueCountFrequency (%)
53 1
1.9%
52 2
3.7%
51 1
1.9%
50 1
1.9%
49 1
1.9%
48 1
1.9%
47 1
1.9%
46 1
1.9%
45 1
1.9%
44 1
1.9%

관리번호
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct50
Distinct (%)98.0%
Missing3
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean9146474.4
Minimum0
Maximum10800003
Zeros1
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-11T07:56:12.709973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile370002.5
Q110040002
median10200007
Q310420008
95-th percentile10800002
Maximum10800003
Range10800003
Interquartile range (IQR)380007

Descriptive statistics

Standard deviation3239760.8
Coefficient of variation (CV)0.3542087
Kurtosis4.0934331
Mean9146474.4
Median Absolute Deviation (MAD)219995
Skewness-2.4245228
Sum4.6647019 × 108
Variance1.049605 × 1013
MonotonicityNot monotonic
2023-12-11T07:56:12.872961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10800003 2
 
3.7%
10420008 1
 
1.9%
10770000 1
 
1.9%
10770001 1
 
1.9%
10060001 1
 
1.9%
10420001 1
 
1.9%
10420002 1
 
1.9%
10420003 1
 
1.9%
10420004 1
 
1.9%
10420005 1
 
1.9%
Other values (40) 40
74.1%
(Missing) 3
 
5.6%
ValueCountFrequency (%)
0 1
1.9%
370001 1
1.9%
370002 1
1.9%
370003 1
1.9%
600000 1
1.9%
600001 1
1.9%
10020000 1
1.9%
10020001 1
1.9%
10020002 1
1.9%
10030001 1
1.9%
ValueCountFrequency (%)
10800003 2
3.7%
10800002 1
1.9%
10800001 1
1.9%
10770001 1
1.9%
10770000 1
1.9%
10420015 1
1.9%
10420014 1
1.9%
10420013 1
1.9%
10420012 1
1.9%
10420011 1
1.9%

관리기관
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size564.0 B
1683
54 

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 54
100.0%

Length

2023-12-11T07:56:13.002575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:56:13.100207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1683 54
100.0%

도로종류
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size564.0 B
1504
49 
1507
5 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1507
2nd row1507
3rd row1507
4th row1504
5th row1504

Common Values

ValueCountFrequency (%)
1504 49
90.7%
1507 5
 
9.3%

Length

2023-12-11T07:56:13.194929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:56:13.299521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1504 49
90.7%
1507 5
 
9.3%

노선번호
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean941.40741
Minimum37
Maximum1080
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-11T07:56:13.422505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile51.95
Q11004.5
median1025.5
Q31042
95-th percentile1080
Maximum1080
Range1043
Interquartile range (IQR)37.5

Descriptive statistics

Standard deviation289.53909
Coefficient of variation (CV)0.30755982
Kurtosis6.5010579
Mean941.40741
Median Absolute Deviation (MAD)16.5
Skewness-2.8606457
Sum50836
Variance83832.887
MonotonicityNot monotonic
2023-12-11T07:56:13.553606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1042 16
29.6%
1020 8
14.8%
1080 4
 
7.4%
37 3
 
5.6%
1049 3
 
5.6%
1002 3
 
5.6%
1003 3
 
5.6%
1004 3
 
5.6%
60 2
 
3.7%
1077 2
 
3.7%
Other values (6) 7
13.0%
ValueCountFrequency (%)
37 3
 
5.6%
60 2
 
3.7%
1002 3
 
5.6%
1003 3
 
5.6%
1004 3
 
5.6%
1006 1
 
1.9%
1010 2
 
3.7%
1011 1
 
1.9%
1020 8
14.8%
1022 1
 
1.9%
ValueCountFrequency (%)
1080 4
 
7.4%
1077 2
 
3.7%
1049 3
 
5.6%
1042 16
29.6%
1037 1
 
1.9%
1029 1
 
1.9%
1022 1
 
1.9%
1020 8
14.8%
1011 1
 
1.9%
1010 2
 
3.7%

구간번호
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5740741
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-11T07:56:13.655443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile5.7
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7655172
Coefficient of variation (CV)0.68588438
Kurtosis3.6543381
Mean2.5740741
Median Absolute Deviation (MAD)1
Skewness1.6885767
Sum139
Variance3.117051
MonotonicityNot monotonic
2023-12-11T07:56:13.800543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 19
35.2%
3 13
24.1%
2 10
18.5%
4 8
14.8%
7 1
 
1.9%
9 1
 
1.9%
5 1
 
1.9%
8 1
 
1.9%
ValueCountFrequency (%)
1 19
35.2%
2 10
18.5%
3 13
24.1%
4 8
14.8%
5 1
 
1.9%
7 1
 
1.9%
8 1
 
1.9%
9 1
 
1.9%
ValueCountFrequency (%)
9 1
 
1.9%
8 1
 
1.9%
7 1
 
1.9%
5 1
 
1.9%
4 8
14.8%
3 13
24.1%
2 10
18.5%
1 19
35.2%

이력코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size564.0 B
0
54 

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 54
100.0%

Length

2023-12-11T07:56:13.944899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:56:14.034786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 54
100.0%

위치
Real number (ℝ)

ZEROS 

Distinct53
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6347593
Minimum0
Maximum13.086
Zeros1
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-11T07:56:14.143230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.16025
Q10.96
median3.865
Q37.55175
95-th percentile11.37295
Maximum13.086
Range13.086
Interquartile range (IQR)6.59175

Descriptive statistics

Standard deviation3.8940534
Coefficient of variation (CV)0.84018461
Kurtosis-1.0674009
Mean4.6347593
Median Absolute Deviation (MAD)3.4335
Skewness0.42142237
Sum250.277
Variance15.163652
MonotonicityNot monotonic
2023-12-11T07:56:14.293604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.21 2
 
3.7%
0.272 1
 
1.9%
11.657 1
 
1.9%
7.542 1
 
1.9%
6.808 1
 
1.9%
6.865 1
 
1.9%
8.0 1
 
1.9%
0.708 1
 
1.9%
0.355 1
 
1.9%
1.205 1
 
1.9%
Other values (43) 43
79.6%
ValueCountFrequency (%)
0.0 1
1.9%
0.019 1
1.9%
0.105 1
1.9%
0.19 1
1.9%
0.21 2
3.7%
0.235 1
1.9%
0.243 1
1.9%
0.272 1
1.9%
0.301 1
1.9%
0.355 1
1.9%
ValueCountFrequency (%)
13.086 1
1.9%
11.705 1
1.9%
11.657 1
1.9%
11.22 1
1.9%
10.875 1
1.9%
10.7 1
1.9%
9.844 1
1.9%
9.258 1
1.9%
8.735 1
1.9%
8.412 1
1.9%

교차시설명
Text

MISSING 

Distinct28
Distinct (%)65.1%
Missing11
Missing (%)20.4%
Memory size564.0 B
2023-12-11T07:56:14.509912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.7674419
Min length2

Characters and Unicode

Total characters291
Distinct characters54
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)51.2%

Sample

1st row고속국도12호선
2nd row고속국도12호선
3rd row고속국도12호선
4th row고속국도10호선
5th row고속국도10호선
ValueCountFrequency (%)
고속국도10호선 8
18.6%
국도5 3
 
7.0%
일반국도2호선 3
 
7.0%
고속국도12호선 3
 
7.0%
일반국도14호선 2
 
4.7%
일반국도25호선 2
 
4.7%
등리삼거리 1
 
2.3%
철도 1
 
2.3%
유목2삼거리 1
 
2.3%
일반국도33호선 1
 
2.3%
Other values (18) 18
41.9%
2023-12-11T07:56:14.884929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 30
 
10.3%
국 25
 
8.6%
선 24
 
8.2%
호 24
 
8.2%
1 18
 
6.2%
고 15
 
5.2%
속 15
 
5.2%
리 14
 
4.8%
거 13
 
4.5%
0 11
 
3.8%
Other values (44) 102
35.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 233
80.1%
Decimal Number 53
 
18.2%
Uppercase Letter 5
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 30
12.9%
국 25
10.7%
선 24
10.3%
호 24
10.3%
고 15
 
6.4%
속 15
 
6.4%
리 14
 
6.0%
거 13
 
5.6%
삼 10
 
4.3%
일 9
 
3.9%
Other values (33) 54
23.2%
Decimal Number
ValueCountFrequency (%)
1 18
34.0%
0 11
20.8%
2 10
18.9%
5 7
 
13.2%
3 4
 
7.5%
4 3
 
5.7%
Uppercase Letter
ValueCountFrequency (%)
A 1
20.0%
R 1
20.0%
T 1
20.0%
C 1
20.0%
I 1
20.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 233
80.1%
Common 53
 
18.2%
Latin 5
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 30
12.9%
국 25
10.7%
선 24
10.3%
호 24
10.3%
고 15
 
6.4%
속 15
 
6.4%
리 14
 
6.0%
거 13
 
5.6%
삼 10
 
4.3%
일 9
 
3.9%
Other values (33) 54
23.2%
Common
ValueCountFrequency (%)
1 18
34.0%
0 11
20.8%
2 10
18.9%
5 7
 
13.2%
3 4
 
7.5%
4 3
 
5.7%
Latin
ValueCountFrequency (%)
A 1
20.0%
R 1
20.0%
T 1
20.0%
C 1
20.0%
I 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 233
80.1%
ASCII 58
 
19.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 30
12.9%
국 25
10.7%
선 24
10.3%
호 24
10.3%
고 15
 
6.4%
속 15
 
6.4%
리 14
 
6.0%
거 13
 
5.6%
삼 10
 
4.3%
일 9
 
3.9%
Other values (33) 54
23.2%
ASCII
ValueCountFrequency (%)
1 18
31.0%
0 11
19.0%
2 10
17.2%
5 7
 
12.1%
3 4
 
6.9%
4 3
 
5.2%
A 1
 
1.7%
R 1
 
1.7%
T 1
 
1.7%
C 1
 
1.7%

교차방식
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size564.0 B
1
35 
0
18 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.9%

Sample

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

Common Values

ValueCountFrequency (%)
1 35
64.8%
0 18
33.3%
2 1
 
1.9%

Length

2023-12-11T07:56:15.045585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:56:15.148676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 35
64.8%
0 18
33.3%
2 1
 
1.9%

교차연장
Real number (ℝ)

Distinct46
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.415
Minimum7.2
Maximum160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-11T07:56:15.255487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.2
5-th percentile7.8895
Q118.325
median27.485
Q336.3125
95-th percentile75.7
Maximum160
Range152.8
Interquartile range (IQR)17.9875

Descriptive statistics

Standard deviation26.084858
Coefficient of variation (CV)0.80471567
Kurtosis10.481793
Mean32.415
Median Absolute Deviation (MAD)9.45
Skewness2.7344241
Sum1750.41
Variance680.41983
MonotonicityNot monotonic
2023-12-11T07:56:15.408049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
20.0 3
 
5.6%
12.0 2
 
3.7%
7.87 2
 
3.7%
45.0 2
 
3.7%
30.0 2
 
3.7%
33.0 2
 
3.7%
22.0 2
 
3.7%
15.41 1
 
1.9%
75.0 1
 
1.9%
24.0 1
 
1.9%
Other values (36) 36
66.7%
ValueCountFrequency (%)
7.2 1
1.9%
7.87 2
3.7%
7.9 1
1.9%
8.56 1
1.9%
8.71 1
1.9%
8.82 1
1.9%
10.55 1
1.9%
12.0 2
3.7%
12.62 1
1.9%
14.46 1
1.9%
ValueCountFrequency (%)
160.0 1
1.9%
100.0 1
1.9%
77.0 1
1.9%
75.0 1
1.9%
67.66 1
1.9%
62.0 1
1.9%
52.0 1
1.9%
50.0 1
1.9%
46.0 1
1.9%
45.0 2
3.7%

교차시설폭원
Real number (ℝ)

Distinct45
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.881481
Minimum3.6
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-11T07:56:15.563232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.6
5-th percentile7.455
Q110
median17.255
Q325.625
95-th percentile35
Maximum49
Range45.4
Interquartile range (IQR)15.625

Descriptive statistics

Standard deviation10.087737
Coefficient of variation (CV)0.5342662
Kurtosis0.11459585
Mean18.881481
Median Absolute Deviation (MAD)7.265
Skewness0.78095688
Sum1019.6
Variance101.76245
MonotonicityNot monotonic
2023-12-11T07:56:15.744748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
20.0 4
 
7.4%
35.0 3
 
5.6%
14.18 2
 
3.7%
9.0 2
 
3.7%
10.0 2
 
3.7%
9.98 2
 
3.7%
22.96 1
 
1.9%
14.0 1
 
1.9%
49.0 1
 
1.9%
21.0 1
 
1.9%
Other values (35) 35
64.8%
ValueCountFrequency (%)
3.6 1
1.9%
6.5 1
1.9%
7.0 1
1.9%
7.7 1
1.9%
8.0 1
1.9%
8.1 1
1.9%
8.6 1
1.9%
8.71 1
1.9%
8.78 1
1.9%
9.0 2
3.7%
ValueCountFrequency (%)
49.0 1
 
1.9%
40.0 1
 
1.9%
35.0 3
5.6%
33.0 1
 
1.9%
32.9 1
 
1.9%
31.94 1
 
1.9%
30.0 1
 
1.9%
29.32 1
 
1.9%
28.0 1
 
1.9%
27.6 1
 
1.9%

유효높이
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5166667
Minimum0
Maximum25
Zeros24
Zeros (%)44.4%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-11T07:56:15.897157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.3
Q35.375
95-th percentile22.35
Maximum25
Range25
Interquartile range (IQR)5.375

Descriptive statistics

Standard deviation6.2666458
Coefficient of variation (CV)1.3874492
Kurtosis4.0332911
Mean4.5166667
Median Absolute Deviation (MAD)4.3
Skewness2.0542579
Sum243.9
Variance39.270849
MonotonicityNot monotonic
2023-12-11T07:56:16.011159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.0 24
44.4%
4.5 7
 
13.0%
5.0 5
 
9.3%
4.3 4
 
7.4%
5.8 3
 
5.6%
8.0 2
 
3.7%
23.0 2
 
3.7%
5.5 2
 
3.7%
10.0 1
 
1.9%
25.0 1
 
1.9%
Other values (3) 3
 
5.6%
ValueCountFrequency (%)
0.0 24
44.4%
4.3 4
 
7.4%
4.5 7
 
13.0%
5.0 5
 
9.3%
5.5 2
 
3.7%
5.8 3
 
5.6%
7.8 1
 
1.9%
8.0 2
 
3.7%
10.0 1
 
1.9%
15.0 1
 
1.9%
ValueCountFrequency (%)
25.0 1
 
1.9%
23.0 2
 
3.7%
22.0 1
 
1.9%
15.0 1
 
1.9%
10.0 1
 
1.9%
8.0 2
 
3.7%
7.8 1
 
1.9%
5.8 3
5.6%
5.5 2
 
3.7%
5.0 5
9.3%

교차각도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)68.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.896296
Minimum0
Maximum192
Zeros5
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-11T07:56:16.150832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q168.5
median90.15
Q3110
95-th percentile133.85
Maximum192
Range192
Interquartile range (IQR)41.5

Descriptive statistics

Standard deviation41.55096
Coefficient of variation (CV)0.50124024
Kurtosis0.38589414
Mean82.896296
Median Absolute Deviation (MAD)20.5
Skewness-0.46446278
Sum4476.4
Variance1726.4823
MonotonicityNot monotonic
2023-12-11T07:56:16.291904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.0 5
 
9.3%
90.0 4
 
7.4%
95.0 3
 
5.6%
70.0 2
 
3.7%
88.0 2
 
3.7%
130.0 2
 
3.7%
45.0 2
 
3.7%
96.0 2
 
3.7%
111.0 2
 
3.7%
110.0 2
 
3.7%
Other values (27) 28
51.9%
ValueCountFrequency (%)
0.0 5
9.3%
3.0 1
 
1.9%
23.0 1
 
1.9%
30.0 1
 
1.9%
35.0 1
 
1.9%
40.0 1
 
1.9%
45.0 2
 
3.7%
61.9 1
 
1.9%
68.0 1
 
1.9%
70.0 2
 
3.7%
ValueCountFrequency (%)
192.0 1
1.9%
148.0 1
1.9%
141.0 1
1.9%
130.0 2
3.7%
122.0 1
1.9%
120.0 1
1.9%
117.2 1
1.9%
116.0 1
1.9%
113.0 1
1.9%
112.0 1
1.9%

Interactions

2023-12-11T07:56:10.978965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:04.935766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:05.715744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:06.575744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:07.621732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:08.498375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:09.143808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:09.787920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:10.379441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:11.048115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:05.024107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:05.812180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:06.653781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:07.711636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:08.577430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:09.211904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:09.848866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:10.442151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:11.119332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:05.107130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:05.913051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:06.737683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:07.815676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:08.652283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:09.278824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:09.915117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:10.507026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:11.184750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:05.191840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:06.003581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:07.085689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:07.924682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:08.728428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:09.345105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:09.976483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:10.568416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:11.271251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:05.274068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:06.090787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:07.191176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:08.041477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:08.809233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:09.420836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:10.048258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:10.639287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:11.342439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:05.362236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:06.190264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:07.280705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:08.134280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:08.876337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:09.495071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:10.115205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:10.707544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:11.423306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:05.455632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:06.289205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:07.373303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:08.240625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:08.947241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:09.569453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:10.187333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:10.787933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:11.499795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:05.527874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:06.388696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:07.460289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:08.318703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:09.009261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:09.635927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:10.248411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:10.848458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:11.571747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:05.606269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:06.480830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:07.535807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:08.407909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:09.072584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:09.707153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:10.308321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:56:10.907741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:56:16.403944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식별번호관리번호도로종류노선번호구간번호위치교차시설명교차방식교차연장교차시설폭원유효높이교차각도
식별번호1.0000.5560.6860.6860.7110.7500.9310.5890.3820.3850.4820.432
관리번호0.5561.0000.9490.9490.1480.0000.6350.0000.1870.3250.4450.514
도로종류0.6860.9491.0000.9840.0000.0001.0000.0000.2200.0000.5890.585
노선번호0.6860.9490.9841.0000.0000.0001.0000.0000.2200.0000.5890.585
구간번호0.7110.1480.0000.0001.0000.4620.8140.8190.0000.0000.8980.000
위치0.7500.0000.0000.0000.4621.0000.8030.0680.0000.3750.0000.000
교차시설명0.9310.6351.0001.0000.8140.8031.0001.0000.9880.0000.0000.851
교차방식0.5890.0000.0000.0000.8190.0681.0001.0000.2120.2110.6240.332
교차연장0.3820.1870.2200.2200.0000.0000.9880.2121.0000.5510.3230.324
교차시설폭원0.3850.3250.0000.0000.0000.3750.0000.2110.5511.0000.0000.000
유효높이0.4820.4450.5890.5890.8980.0000.0000.6240.3230.0001.0000.458
교차각도0.4320.5140.5850.5850.0000.0000.8510.3320.3240.0000.4581.000
2023-12-11T07:56:16.526061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로종류교차방식
도로종류1.0000.000
교차방식0.0001.000
2023-12-11T07:56:16.919242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식별번호관리번호노선번호구간번호위치교차연장교차시설폭원유효높이교차각도도로종류교차방식
식별번호1.0000.7080.784-0.3600.0230.1950.108-0.513-0.3430.4580.399
관리번호0.7081.0000.922-0.1580.0660.0680.117-0.398-0.3020.7960.000
노선번호0.7840.9221.000-0.2570.0590.1210.105-0.291-0.4560.8880.000
구간번호-0.360-0.158-0.2571.0000.004-0.1170.0240.3900.2200.0000.715
위치0.0230.0660.0590.0041.0000.2320.263-0.2440.0490.0000.000
교차연장0.1950.0680.121-0.1170.2321.0000.491-0.0840.2570.2190.132
교차시설폭원0.1080.1170.1050.0240.2630.4911.0000.1600.1720.0000.070
유효높이-0.513-0.398-0.2910.390-0.244-0.0840.1601.000-0.0380.3430.473
교차각도-0.343-0.302-0.4560.2200.0490.2570.172-0.0381.0000.5480.118
도로종류0.4580.7960.8880.0000.0000.2190.0000.3430.5481.0000.000
교차방식0.3990.0000.0000.7150.0000.1320.0700.4730.1180.0001.000

Missing values

2023-12-11T07:56:11.943653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:56:12.102522image/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.
2023-12-11T07:56:12.282347image/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

식별번호관리번호관리기관도로종류노선번호구간번호이력코드위치교차시설명교차방식교차연장교차시설폭원유효높이교차각도
013700011683150737100.272고속국도12호선115.4114.1810.023.0
123700021683150737101.26고속국도12호선120.714.188.030.0
233700031683150737200.019고속국도12호선18.568.7825.090.3
3410290001168315041029402.954고속국도10호선118.120.423.093.0
4510110001168315041011101.177고속국도10호선125.1323.075.0117.2
5610100002168315041010709.258일반국도33호선128.226.14.5112.0
6710100001168315041010903.476고속국도35호선126.724.222.077.0
7810040001168315041004300.508지방도1011호선167.6616.58.0111.0
8910040002168315041004304.212철도010.553.60.0141.0
910100400031683150410043013.086고속국도10호선134.2529.3223.0110.0
식별번호관리번호관리기관도로종류노선번호구간번호이력코드위치교차시설명교차방식교차연장교차시설폭원유효높이교차각도
444510420012168315041042207.555성원ART삼거리030.020.00.088.0
454610420013168315041042208.09임호사거리045.035.00.097.0
464710420014168315041042208.412무접삼거리029.020.00.085.0
474810420009168315041042200.105<NA>122.013.00.070.0
484910420010168315041042200.301<NA>131.014.00.0130.0
4950108000011683150410801011.22국도5127.122.965.00.0
505110800002168315041080300.19중부내륙고속국도4518.8212.394.30.0
515210800003168315041080300.21국도517.879.984.30.0
525210800003168315041080300.21국도517.879.984.30.0
53530168315041042103.85고속국도10호선152.049.05.561.9

Duplicate rows

Most frequently occurring

식별번호관리번호관리기관도로종류노선번호구간번호이력코드위치교차시설명교차방식교차연장교차시설폭원유효높이교차각도# duplicates
05210800003168315041080300.21국도517.879.984.30.02