Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells8
Missing cells (%)< 0.1%
Duplicate rows370
Duplicate rows (%)3.7%
Total size in memory918.0 KiB
Average record size in memory94.0 B

Variable types

Unsupported1
Text3
Numeric6

Dataset

Description2020년 국토교통부_운행허가 가능한 도로의 규격 정보입니다.
Author국토교통부
URLhttps://www.data.go.kr/data/3047694/fileData.do

Alerts

Dataset has 370 (3.7%) duplicate rowsDuplicates
제한너비 is highly overall correlated with 제한높이High correlation
제한높이 is highly overall correlated with 제한너비 and 3 other fieldsHigh correlation
제한길이(단일차량) is highly overall correlated with 제한높이High correlation
제한길이(연결차량) is highly overall correlated with 제한높이 and 1 other fieldsHigh correlation
축중량 is highly overall correlated with 제한높이 and 1 other fieldsHigh correlation
도로명칭 is an unsupported type, check if it needs cleaning or further analysisUnsupported
축중량 has 234 (2.3%) zerosZeros

Reproduction

Analysis started2023-12-12 14:50:10.582615
Analysis finished2023-12-12 14:50:16.916621
Duration6.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

도로명칭
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size156.2 KiB

출발지 법정동명
Text

Distinct2236
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T23:50:17.074296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length9
Mean length8.7359
Min length5

Characters and Unicode

Total characters87359
Distinct characters327
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

Unique1081 ?
Unique (%)10.8%

Sample

1st row이천시부발읍아미리
2nd row부산시강서구송정동
3rd row서울시노원구상계동
4th row인천시동구송현동
5th row당진군석문면통정리
ValueCountFrequency (%)
부산시남구감만동 128
 
1.3%
창원시진해구 120
 
1.2%
부산시강서구대저2동 116
 
1.2%
당진군송악면고대리 115
 
1.1%
경남창원시진해구소사동 97
 
1.0%
평택시포승면도곡리 90
 
0.9%
부산시강서구송정동 84
 
0.8%
천안시동남구풍세면풍서리 78
 
0.8%
당진군송악면반촌리 74
 
0.7%
부산광역시기장군기장읍내리 73
 
0.7%
Other values (2227) 9051
90.3%
2023-12-12T23:50:17.446669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 7725
 
8.8%
리 5898
 
6.8%
동 5124
 
5.9%
면 4289
 
4.9%
구 3807
 
4.4%
산 3048
 
3.5%
군 2683
 
3.1%
천 1816
 
2.1%
읍 1758
 
2.0%
성 1650
 
1.9%
Other values (317) 49561
56.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 87056
99.7%
Decimal Number 275
 
0.3%
Space Separator 26
 
< 0.1%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 7725
 
8.9%
리 5898
 
6.8%
동 5124
 
5.9%
면 4289
 
4.9%
구 3807
 
4.4%
산 3048
 
3.5%
군 2683
 
3.1%
천 1816
 
2.1%
읍 1758
 
2.0%
성 1650
 
1.9%
Other values (309) 49258
56.6%
Decimal Number
ValueCountFrequency (%)
2 132
48.0%
3 60
21.8%
7 54
19.6%
1 28
 
10.2%
4 1
 
0.4%
Space Separator
ValueCountFrequency (%)
26
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 87056
99.7%
Common 303
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 7725
 
8.9%
리 5898
 
6.8%
동 5124
 
5.9%
면 4289
 
4.9%
구 3807
 
4.4%
산 3048
 
3.5%
군 2683
 
3.1%
천 1816
 
2.1%
읍 1758
 
2.0%
성 1650
 
1.9%
Other values (309) 49258
56.6%
Common
ValueCountFrequency (%)
2 132
43.6%
3 60
19.8%
7 54
17.8%
1 28
 
9.2%
26
 
8.6%
4 1
 
0.3%
) 1
 
0.3%
( 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 87056
99.7%
ASCII 303
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 7725
 
8.9%
리 5898
 
6.8%
동 5124
 
5.9%
면 4289
 
4.9%
구 3807
 
4.4%
산 3048
 
3.5%
군 2683
 
3.1%
천 1816
 
2.1%
읍 1758
 
2.0%
성 1650
 
1.9%
Other values (309) 49258
56.6%
ASCII
ValueCountFrequency (%)
2 132
43.6%
3 60
19.8%
7 54
17.8%
1 28
 
9.2%
26
 
8.6%
4 1
 
0.3%
) 1
 
0.3%
( 1
 
0.3%

출발지 이름
Text

Distinct2773
Distinct (%)27.7%
Missing1
Missing (%)< 0.1%
Memory size156.2 KiB
2023-12-12T23:50:17.853815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length6.0048005
Min length2

Characters and Unicode

Total characters60042
Distinct characters439
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

Unique1577 ?
Unique (%)15.8%

Sample

1st row이천IC입구
2nd row농심사거리
3rd row녹천교교차로
4th row송현4(동국제강앞)
5th row석문방조제
ValueCountFrequency (%)
부산신항ic입구 120
 
1.2%
현대제철r 115
 
1.1%
감만시민부두입구 111
 
1.1%
서부산ic입구 110
 
1.1%
진해ic입구 96
 
0.9%
서평택ic입구 90
 
0.9%
남풍세ic입구 78
 
0.8%
당진ic입구 74
 
0.7%
포항철강산업단지 72
 
0.7%
포항철강3단지 72
 
0.7%
Other values (2915) 9339
90.9%
2023-12-12T23:50:18.733116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 5163
 
8.6%
입 4846
 
8.1%
C 4707
 
7.8%
I 4670
 
7.8%
리 1799
 
3.0%
교 1562
 
2.6%
산 1524
 
2.5%
거 1358
 
2.3%
로 1081
 
1.8%
차 1029
 
1.7%
Other values (429) 32303
53.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 48462
80.7%
Uppercase Letter 9951
 
16.6%
Decimal Number 1235
 
2.1%
Space Separator 278
 
0.5%
Open Punctuation 41
 
0.1%
Close Punctuation 41
 
0.1%
Other Punctuation 34
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 5163
 
10.7%
입 4846
 
10.0%
리 1799
 
3.7%
교 1562
 
3.2%
산 1524
 
3.1%
거 1358
 
2.8%
로 1081
 
2.2%
차 1029
 
2.1%
사 880
 
1.8%
대 871
 
1.8%
Other values (399) 28349
58.5%
Uppercase Letter
ValueCountFrequency (%)
C 4707
47.3%
I 4670
46.9%
R 464
 
4.7%
J 25
 
0.3%
D 21
 
0.2%
S 18
 
0.2%
T 15
 
0.2%
G 11
 
0.1%
X 9
 
0.1%
P 4
 
< 0.1%
Other values (6) 7
 
0.1%
Decimal Number
ValueCountFrequency (%)
3 482
39.0%
4 444
36.0%
1 108
 
8.7%
2 89
 
7.2%
5 73
 
5.9%
6 18
 
1.5%
7 12
 
1.0%
9 6
 
0.5%
0 3
 
0.2%
Other Punctuation
ValueCountFrequency (%)
/ 28
82.4%
, 6
 
17.6%
Space Separator
ValueCountFrequency (%)
278
100.0%
Open Punctuation
ValueCountFrequency (%)
( 41
100.0%
Close Punctuation
ValueCountFrequency (%)
) 41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 48462
80.7%
Latin 9951
 
16.6%
Common 1629
 
2.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 5163
 
10.7%
입 4846
 
10.0%
리 1799
 
3.7%
교 1562
 
3.2%
산 1524
 
3.1%
거 1358
 
2.8%
로 1081
 
2.2%
차 1029
 
2.1%
사 880
 
1.8%
대 871
 
1.8%
Other values (399) 28349
58.5%
Latin
ValueCountFrequency (%)
C 4707
47.3%
I 4670
46.9%
R 464
 
4.7%
J 25
 
0.3%
D 21
 
0.2%
S 18
 
0.2%
T 15
 
0.2%
G 11
 
0.1%
X 9
 
0.1%
P 4
 
< 0.1%
Other values (6) 7
 
0.1%
Common
ValueCountFrequency (%)
3 482
29.6%
4 444
27.3%
278
17.1%
1 108
 
6.6%
2 89
 
5.5%
5 73
 
4.5%
( 41
 
2.5%
) 41
 
2.5%
/ 28
 
1.7%
6 18
 
1.1%
Other values (4) 27
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 48462
80.7%
ASCII 11580
 
19.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 5163
 
10.7%
입 4846
 
10.0%
리 1799
 
3.7%
교 1562
 
3.2%
산 1524
 
3.1%
거 1358
 
2.8%
로 1081
 
2.2%
차 1029
 
2.1%
사 880
 
1.8%
대 871
 
1.8%
Other values (399) 28349
58.5%
ASCII
ValueCountFrequency (%)
C 4707
40.6%
I 4670
40.3%
3 482
 
4.2%
R 464
 
4.0%
4 444
 
3.8%
278
 
2.4%
1 108
 
0.9%
2 89
 
0.8%
5 73
 
0.6%
( 41
 
0.4%
Other values (20) 224
 
1.9%

도착지 이름
Text

Distinct3368
Distinct (%)33.7%
Missing7
Missing (%)0.1%
Memory size156.2 KiB
2023-12-12T23:50:19.166895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length5.9755829
Min length2

Characters and Unicode

Total characters59714
Distinct characters471
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

Unique1916 ?
Unique (%)19.2%

Sample

1st row탄천교
2nd row39번신호등R
3rd row북동삼거리
4th row감천항한보부두
5th row봉화읍 거촌리
ValueCountFrequency (%)
가락ic입구 73
 
0.7%
청량ic입구 69
 
0.7%
서평택ic입구 61
 
0.6%
서부산ic입구 52
 
0.5%
양산ic입구 52
 
0.5%
대동ic입구 51
 
0.5%
송악ic입구 50
 
0.5%
남풍세ic입구 50
 
0.5%
광양ic입구 50
 
0.5%
현대제철r 49
 
0.5%
Other values (3529) 9764
94.6%
2023-12-12T23:50:19.788644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 5061
 
8.5%
입 4752
 
8.0%
C 4623
 
7.7%
I 4558
 
7.6%
리 1907
 
3.2%
교 1634
 
2.7%
산 1567
 
2.6%
거 1374
 
2.3%
로 1053
 
1.8%
차 1019
 
1.7%
Other values (461) 32166
53.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 48305
80.9%
Uppercase Letter 9883
 
16.6%
Decimal Number 1129
 
1.9%
Space Separator 328
 
0.5%
Open Punctuation 28
 
< 0.1%
Close Punctuation 28
 
< 0.1%
Other Punctuation 13
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 5061
 
10.5%
입 4752
 
9.8%
리 1907
 
3.9%
교 1634
 
3.4%
산 1567
 
3.2%
거 1374
 
2.8%
로 1053
 
2.2%
차 1019
 
2.1%
대 856
 
1.8%
사 837
 
1.7%
Other values (427) 28245
58.5%
Uppercase Letter
ValueCountFrequency (%)
C 4623
46.8%
I 4558
46.1%
R 396
 
4.0%
T 81
 
0.8%
G 62
 
0.6%
D 33
 
0.3%
J 32
 
0.3%
S 24
 
0.2%
L 22
 
0.2%
X 22
 
0.2%
Other values (9) 30
 
0.3%
Decimal Number
ValueCountFrequency (%)
3 436
38.6%
4 390
34.5%
2 139
 
12.3%
1 77
 
6.8%
5 75
 
6.6%
7 5
 
0.4%
6 3
 
0.3%
9 2
 
0.2%
8 1
 
0.1%
0 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
/ 11
84.6%
, 2
 
15.4%
Space Separator
ValueCountFrequency (%)
328
100.0%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 48305
80.9%
Latin 9883
 
16.6%
Common 1526
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 5061
 
10.5%
입 4752
 
9.8%
리 1907
 
3.9%
교 1634
 
3.4%
산 1567
 
3.2%
거 1374
 
2.8%
로 1053
 
2.2%
차 1019
 
2.1%
대 856
 
1.8%
사 837
 
1.7%
Other values (427) 28245
58.5%
Latin
ValueCountFrequency (%)
C 4623
46.8%
I 4558
46.1%
R 396
 
4.0%
T 81
 
0.8%
G 62
 
0.6%
D 33
 
0.3%
J 32
 
0.3%
S 24
 
0.2%
L 22
 
0.2%
X 22
 
0.2%
Other values (9) 30
 
0.3%
Common
ValueCountFrequency (%)
3 436
28.6%
4 390
25.6%
328
21.5%
2 139
 
9.1%
1 77
 
5.0%
5 75
 
4.9%
( 28
 
1.8%
) 28
 
1.8%
/ 11
 
0.7%
7 5
 
0.3%
Other values (5) 9
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 48305
80.9%
ASCII 11409
 
19.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 5061
 
10.5%
입 4752
 
9.8%
리 1907
 
3.9%
교 1634
 
3.4%
산 1567
 
3.2%
거 1374
 
2.8%
로 1053
 
2.2%
차 1019
 
2.1%
대 856
 
1.8%
사 837
 
1.7%
Other values (427) 28245
58.5%
ASCII
ValueCountFrequency (%)
C 4623
40.5%
I 4558
40.0%
3 436
 
3.8%
R 396
 
3.5%
4 390
 
3.4%
328
 
2.9%
2 139
 
1.2%
T 81
 
0.7%
1 77
 
0.7%
5 75
 
0.7%
Other values (24) 306
 
2.7%

제한너비
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.23971
Minimum1
Maximum4.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:50:19.968477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.3
Q14.2
median4.4
Q34.5
95-th percentile4.5
Maximum4.5
Range3.5
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.40963508
Coefficient of variation (CV)0.096618655
Kurtosis1.9311837
Mean4.23971
Median Absolute Deviation (MAD)0.1
Skewness-1.7168456
Sum42397.1
Variance0.1678009
MonotonicityNot monotonic
2023-12-12T23:50:20.123205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
4.5 4604
46.0%
4.4 1763
 
17.6%
4.3 1020
 
10.2%
3.4 630
 
6.3%
4.2 462
 
4.6%
4.0 415
 
4.2%
3.5 281
 
2.8%
3.2 243
 
2.4%
3.3 194
 
1.9%
4.1 102
 
1.0%
Other values (15) 286
 
2.9%
ValueCountFrequency (%)
1.0 1
 
< 0.1%
2.0 1
 
< 0.1%
2.3 1
 
< 0.1%
2.4 1
 
< 0.1%
2.5 7
 
0.1%
2.6 7
 
0.1%
2.7 4
 
< 0.1%
2.8 2
 
< 0.1%
2.9 10
 
0.1%
3.0 63
0.6%
ValueCountFrequency (%)
4.5 4604
46.0%
4.4 1763
 
17.6%
4.3 1020
 
10.2%
4.2 462
 
4.6%
4.1 102
 
1.0%
4.0 415
 
4.2%
3.9 38
 
0.4%
3.8 46
 
0.5%
3.7 42
 
0.4%
3.6 45
 
0.4%

제한높이
Real number (ℝ)

HIGH CORRELATION 

Distinct120
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.69115
Minimum1.9
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:50:20.278879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile4.5
Q113
median19
Q321
95-th percentile24
Maximum25
Range23.1
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.8902499
Coefficient of variation (CV)0.35289659
Kurtosis-0.15559159
Mean16.69115
Median Absolute Deviation (MAD)3
Skewness-0.89735677
Sum166911.5
Variance34.695044
MonotonicityNot monotonic
2023-12-12T23:50:20.456815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.0 2782
27.8%
24.0 1186
11.9%
4.5 980
 
9.8%
22.0 798
 
8.0%
16.7 539
 
5.4%
16.0 522
 
5.2%
21.0 426
 
4.3%
17.0 376
 
3.8%
18.0 220
 
2.2%
13.0 177
 
1.8%
Other values (110) 1994
19.9%
ValueCountFrequency (%)
1.9 1
 
< 0.1%
2.0 1
 
< 0.1%
2.1 1
 
< 0.1%
2.4 4
< 0.1%
3.3 1
 
< 0.1%
3.4 1
 
< 0.1%
3.6 1
 
< 0.1%
3.9 1
 
< 0.1%
4.0 7
0.1%
4.1 3
< 0.1%
ValueCountFrequency (%)
25.0 40
 
0.4%
24.7 2
 
< 0.1%
24.5 2
 
< 0.1%
24.0 1186
11.9%
23.5 4
 
< 0.1%
23.0 108
 
1.1%
22.0 798
8.0%
21.7 3
 
< 0.1%
21.0 426
 
4.3%
20.9 1
 
< 0.1%

제한길이(단일차량)
Real number (ℝ)

HIGH CORRELATION 

Distinct129
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.06368
Minimum1.9
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:50:20.634484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile9
Q116
median19
Q320
95-th percentile24
Maximum100
Range98.1
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.6657387
Coefficient of variation (CV)0.25829392
Kurtosis11.934047
Mean18.06368
Median Absolute Deviation (MAD)2.3
Skewness0.18603093
Sum180636.8
Variance21.769118
MonotonicityNot monotonic
2023-12-12T23:50:20.844337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.0 3680
36.8%
24.0 1363
 
13.6%
16.7 540
 
5.4%
16.0 533
 
5.3%
25.0 359
 
3.6%
22.0 334
 
3.3%
21.0 278
 
2.8%
17.0 237
 
2.4%
18.0 218
 
2.2%
12.0 190
 
1.9%
Other values (119) 2268
22.7%
ValueCountFrequency (%)
1.9 1
 
< 0.1%
2.0 1
 
< 0.1%
2.4 10
0.1%
3.4 1
 
< 0.1%
3.6 1
 
< 0.1%
4.0 6
0.1%
4.5 4
 
< 0.1%
5.0 1
 
< 0.1%
5.2 1
 
< 0.1%
6.0 14
0.1%
ValueCountFrequency (%)
100.0 1
 
< 0.1%
69.0 1
 
< 0.1%
65.0 1
 
< 0.1%
49.0 1
 
< 0.1%
29.0 3
 
< 0.1%
27.0 1
 
< 0.1%
25.0 359
3.6%
24.8 1
 
< 0.1%
24.7 16
 
0.2%
24.5 5
 
0.1%

제한길이(연결차량)
Real number (ℝ)

HIGH CORRELATION 

Distinct186
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.80795
Minimum1.2
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:50:21.034632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile11
Q124
median40
Q340
95-th percentile40
Maximum40
Range38.8
Interquartile range (IQR)16

Descriptive statistics

Standard deviation10.104979
Coefficient of variation (CV)0.30800396
Kurtosis-0.21275847
Mean32.80795
Median Absolute Deviation (MAD)0
Skewness-1.0624218
Sum328079.5
Variance102.11059
MonotonicityNot monotonic
2023-12-12T23:50:21.230095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.0 5787
57.9%
24.0 622
 
6.2%
30.0 374
 
3.7%
20.0 308
 
3.1%
25.0 292
 
2.9%
22.0 291
 
2.9%
10.0 226
 
2.3%
15.0 191
 
1.9%
39.0 171
 
1.7%
32.0 138
 
1.4%
Other values (176) 1600
 
16.0%
ValueCountFrequency (%)
1.2 1
 
< 0.1%
2.9 1
 
< 0.1%
3.0 5
 
0.1%
3.5 2
 
< 0.1%
3.8 1
 
< 0.1%
4.0 3
 
< 0.1%
4.5 29
0.3%
5.0 37
0.4%
5.1 1
 
< 0.1%
5.5 6
 
0.1%
ValueCountFrequency (%)
40.0 5787
57.9%
39.9 47
 
0.5%
39.8 10
 
0.1%
39.7 1
 
< 0.1%
39.6 17
 
0.2%
39.5 12
 
0.1%
39.4 5
 
0.1%
39.3 2
 
< 0.1%
39.2 6
 
0.1%
39.1 20
 
0.2%

총중량
Real number (ℝ)

Distinct82
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.55997
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:50:21.377436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.5
Q110
median10
Q310
95-th percentile40
Maximum50
Range49
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.7551443
Coefficient of variation (CV)0.71940752
Kurtosis3.3687556
Mean13.55997
Median Absolute Deviation (MAD)0
Skewness2.2774275
Sum135599.7
Variance95.162841
MonotonicityNot monotonic
2023-12-12T23:50:21.521142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.0 7859
78.6%
40.0 1166
 
11.7%
9.0 122
 
1.2%
8.0 90
 
0.9%
9.9 81
 
0.8%
9.5 64
 
0.6%
5.0 60
 
0.6%
19.0 54
 
0.5%
13.2 40
 
0.4%
18.9 40
 
0.4%
Other values (72) 424
 
4.2%
ValueCountFrequency (%)
1.0 15
0.1%
1.1 4
 
< 0.1%
1.5 2
 
< 0.1%
2.0 5
 
0.1%
2.3 3
 
< 0.1%
2.4 1
 
< 0.1%
2.5 2
 
< 0.1%
3.0 11
0.1%
3.1 3
 
< 0.1%
3.5 1
 
< 0.1%
ValueCountFrequency (%)
50.0 1
 
< 0.1%
40.0 1166
11.7%
32.4 5
 
0.1%
32.0 7
 
0.1%
29.0 1
 
< 0.1%
25.0 1
 
< 0.1%
24.3 3
 
< 0.1%
23.0 1
 
< 0.1%
19.0 54
 
0.5%
18.9 40
 
0.4%

축중량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct210
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.72526
Minimum0
Maximum89.8
Zeros234
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T23:50:21.696469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.4
Q115
median40
Q340
95-th percentile40
Maximum89.8
Range89.8
Interquartile range (IQR)25

Descriptive statistics

Standard deviation13.332766
Coefficient of variation (CV)0.44853321
Kurtosis-1.0378993
Mean29.72526
Median Absolute Deviation (MAD)0
Skewness-0.75756002
Sum297252.6
Variance177.76265
MonotonicityNot monotonic
2023-12-12T23:50:21.847219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.0 5371
53.7%
10.0 1430
 
14.3%
30.0 321
 
3.2%
20.0 270
 
2.7%
0.0 234
 
2.3%
25.0 216
 
2.2%
15.0 176
 
1.8%
39.0 173
 
1.7%
32.0 70
 
0.7%
14.0 61
 
0.6%
Other values (200) 1678
 
16.8%
ValueCountFrequency (%)
0.0 234
2.3%
1.2 2
 
< 0.1%
2.5 1
 
< 0.1%
2.6 4
 
< 0.1%
2.9 1
 
< 0.1%
3.0 6
 
0.1%
3.5 2
 
< 0.1%
3.8 2
 
< 0.1%
4.0 3
 
< 0.1%
4.5 37
 
0.4%
ValueCountFrequency (%)
89.8 1
 
< 0.1%
48.0 1
 
< 0.1%
45.0 2
 
< 0.1%
44.0 15
 
0.1%
43.0 3
 
< 0.1%
42.0 1
 
< 0.1%
40.0 5371
53.7%
39.9 45
 
0.4%
39.8 10
 
0.1%
39.7 3
 
< 0.1%

Interactions

2023-12-12T23:50:15.796232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:12.602022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:13.400930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:14.045027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:14.583519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:15.172227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:15.916077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:12.751717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:13.545663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:14.154990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:14.706555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:15.271066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:16.021683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:12.882596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:13.630552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:14.235236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:14.785863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:15.363244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:16.133207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:13.017642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:13.749797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:14.328881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:14.880667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:15.463031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:16.291728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:13.157907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:13.862082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:14.418554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:14.979017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:15.576835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:16.385355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:13.272261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:13.965597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:14.496564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:15.066525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:50:15.688083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:50:21.941084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
제한너비제한높이제한길이(단일차량)제한길이(연결차량)총중량축중량
제한너비1.0000.6340.2670.6270.8170.469
제한높이0.6341.0000.7010.7680.6790.547
제한길이(단일차량)0.2670.7011.0000.3920.3500.329
제한길이(연결차량)0.6270.7680.3921.0000.6500.809
총중량0.8170.6790.3500.6501.0000.528
축중량0.4690.5470.3290.8090.5281.000
2023-12-12T23:50:22.064115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
제한너비제한높이제한길이(단일차량)제한길이(연결차량)총중량축중량
제한너비1.0000.5780.1830.485-0.5000.490
제한높이0.5781.0000.6110.589-0.4230.565
제한길이(단일차량)0.1830.6111.0000.3520.1040.355
제한길이(연결차량)0.4850.5890.3521.000-0.1900.846
총중량-0.500-0.4230.104-0.1901.000-0.301
축중량0.4900.5650.3550.846-0.3011.000

Missing values

2023-12-12T23:50:16.544333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:50:16.735331image/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.
2023-12-12T23:50:16.864227image/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

도로명칭출발지 법정동명출발지 이름도착지 이름제한너비제한높이제한길이(단일차량)제한길이(연결차량)총중량축중량
416502018이천시부발읍아미리이천IC입구탄천교4.319.019.040.06.040.0
5733국지58부산시강서구송정동농심사거리39번신호등R3.34.419.021.040.010.0
257012018서울시노원구상계동녹천교교차로북동삼거리4.019.019.030.010.030.0
697592018인천시동구송현동송현4(동국제강앞)감천항한보부두4.421.019.040.010.040.0
552802018당진군석문면통정리석문방조제봉화읍 거촌리4.521.021.040.010.040.0
7383시도전주시덕진구송천동1가송천역4전미교차로3.04.416.724.040.010.0
366782018아산시염치읍곡교리곡교R신평산업단지교차로4.015.012.040.010.040.0
508372018공주시정안면광정리정안IC입구천안IC입구4.319.019.030.010.030.0
390322018시흥시정왕동환경사업소교차로문래동44.119.09.932.010.09.9
818632018함안군칠서면무릉리칠서IC입구남양대교4.519.012.022.010.00.0
도로명칭출발지 법정동명출발지 이름도착지 이름제한너비제한높이제한길이(단일차량)제한길이(연결차량)총중량축중량
938922018인천시계양구병방동계양IC입구평택항4.521.025.040.010.040.0
472152018아산시염치읍석정리석정3운산교차로4.216.016.011.010.011.0
742482018경주시내남면용장리내남면 용장리북평산업단지4.512.912.940.010.040.0
540322018창원시성산구적현동마산5부두소성삼거리4.322.022.040.010.040.0
180892018평택시팽성읍도두리팽성R신왕34.519.019.032.010.032.0
257702018완주군용진면상운리완주IC입구장지IC입구4.416.716.740.010.040.0
291502018음성군금왕읍육령리음성IC입구서대구IC입구4.519.019.040.010.040.0
922432018경남창원시진해구소사동진해IC입구칠서IC입구4.416.716.725.010.025.0
783172018김해시어방동동김해IC입구단양IC입구4.512.812.839.210.039.2
781772018부산시남구감만동감만시민부두입구동창원IC입구4.416.716.740.010.040.0

Duplicate rows

Most frequently occurring

출발지 법정동명출발지 이름도착지 이름제한너비제한높이제한길이(단일차량)제한길이(연결차량)총중량축중량# duplicates
172양주시광적면석우리섬말교차로현대제철R4.519.019.040.010.040.010
259창원시진해구부산신항IC입구남대구IC입구4.416.716.740.010.040.09
116부산시남구감만동감만시민부두입구동창원IC입구4.416.716.740.010.040.08
257창원시성산구적현동마산5부두소성삼거리4.322.022.040.010.040.08
21경주시건천읍천포리건천IC입구함안IC입구4.518.018.040.010.040.07
31고양시일산동구식사동식사3미사IC입구4.019.019.040.010.040.07
147서산시운산면갈산리서산IC입구유성IC입구4.519.019.040.010.040.07
245진주시판문동서진주IC입구김제IC입구4.319.019.040.010.040.07
276천안시동남구풍세면풍서리남풍세IC입구전주IC입구4.519.019.040.010.040.07
283청주시흥덕구석소동청주IC입구신복R4.519.019.040.010.040.07