Overview

Dataset statistics

Number of variables12
Number of observations1948
Missing cells336
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory194.2 KiB
Average record size in memory102.1 B

Variable types

Numeric6
Text3
Categorical2
DateTime1

Dataset

Description경상남도 김해시 광역교통정보시스템 교통 현황에 대한 자료로 도로번호, 도로명, 시작지점, 끝지점, 구역길이, 통행량 등에 대한 데이터로 구성되어 있습니다.
Author경상남도 김해시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15092229

Alerts

일시 has constant value ""Constant
도로번호 is highly overall correlated with 소통 등급 코드High correlation
시작지점코드 is highly overall correlated with 끝지점코드High correlation
끝지점코드 is highly overall correlated with 시작지점코드High correlation
속도 is highly overall correlated with 소통 등급 코드High correlation
소통 등급 코드 is highly overall correlated with 도로번호 and 1 other fieldsHigh correlation
속도 has 168 (8.6%) missing valuesMissing
통행량 has 168 (8.6%) missing valuesMissing
도로번호 is highly skewed (γ1 = 40.54119264)Skewed
도로번호 has unique valuesUnique

Reproduction

Analysis started2024-01-05 22:07:38.567176
Analysis finished2024-01-05 22:07:51.458269
Duration12.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

도로번호
Real number (ℝ)

HIGH CORRELATION  SKEWED  UNIQUE 

Distinct1948
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8551306 × 109
Minimum3.8550278 × 109
Maximum3.8654388 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2024-01-05T22:07:51.697667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.8550278 × 109
5-th percentile3.8550375 × 109
Q13.8550766 × 109
median3.8551252 × 109
Q33.8551739 × 109
95-th percentile3.8552136 × 109
Maximum3.8654388 × 109
Range10411000
Interquartile range (IQR)97350

Descriptive statistics

Standard deviation240380.54
Coefficient of variation (CV)6.235341 × 10-5
Kurtosis1739.2574
Mean3.8551306 × 109
Median Absolute Deviation (MAD)48700
Skewness40.541193
Sum7.5097943 × 1012
Variance5.7782803 × 1010
MonotonicityStrictly increasing
2024-01-05T22:07:52.204597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3855027800 1
 
0.1%
3855155900 1
 
0.1%
3855158700 1
 
0.1%
3855158600 1
 
0.1%
3855158500 1
 
0.1%
3855158400 1
 
0.1%
3855158300 1
 
0.1%
3855158200 1
 
0.1%
3855158100 1
 
0.1%
3855158000 1
 
0.1%
Other values (1938) 1938
99.5%
ValueCountFrequency (%)
3855027800 1
0.1%
3855027900 1
0.1%
3855028000 1
0.1%
3855028100 1
0.1%
3855028200 1
0.1%
3855028300 1
0.1%
3855028400 1
0.1%
3855028500 1
0.1%
3855028600 1
0.1%
3855028700 1
0.1%
ValueCountFrequency (%)
3865438800 1
0.1%
3855224100 1
0.1%
3855224000 1
0.1%
3855223000 1
0.1%
3855222900 1
0.1%
3855222800 1
0.1%
3855222700 1
0.1%
3855222600 1
0.1%
3855222500 1
0.1%
3855222400 1
0.1%

도로명
Text

Distinct297
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
2024-01-05T22:07:52.739994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length97
Median length12
Mean length6.6575975
Min length3

Characters and Unicode

Total characters12969
Distinct characters113
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.3%

Sample

1st row대구부산간고속도로
2nd row일반국도14호선
3rd row대구부산간고속도로
4th row대구부산간고속도로
5th row대구부산간고속도로
ValueCountFrequency (%)
일반국도14호선 165
 
8.5%
지방도1042호선 66
 
3.4%
일반국도58호선 59
 
3.0%
지방도1020호선 58
 
3.0%
한림로 42
 
2.2%
남해고속도로 38
 
2.0%
가야로 36
 
1.8%
대청로 32
 
1.6%
분성로 32
 
1.6%
인제로 28
 
1.4%
Other values (282) 1392
71.5%
2024-01-05T22:07:53.644086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 1534
 
11.8%
1 793
 
6.1%
752
 
5.8%
번 681
 
5.3%
길 661
 
5.1%
도 477
 
3.7%
2 470
 
3.6%
4 432
 
3.3%
호 412
 
3.2%
선 412
 
3.2%
Other values (103) 6345
48.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8986
69.3%
Decimal Number 3136
 
24.2%
Space Separator 752
 
5.8%
Dash Punctuation 95
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 1534
17.1%
번 681
 
7.6%
길 661
 
7.4%
도 477
 
5.3%
호 412
 
4.6%
선 412
 
4.6%
반 282
 
3.1%
국 280
 
3.1%
지 244
 
2.7%
일 232
 
2.6%
Other values (91) 3771
42.0%
Decimal Number
ValueCountFrequency (%)
1 793
25.3%
2 470
15.0%
4 432
13.8%
0 324
10.3%
6 260
 
8.3%
5 231
 
7.4%
9 201
 
6.4%
8 151
 
4.8%
3 148
 
4.7%
7 126
 
4.0%
Space Separator
ValueCountFrequency (%)
752
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 95
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8986
69.3%
Common 3983
30.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 1534
17.1%
번 681
 
7.6%
길 661
 
7.4%
도 477
 
5.3%
호 412
 
4.6%
선 412
 
4.6%
반 282
 
3.1%
국 280
 
3.1%
지 244
 
2.7%
일 232
 
2.6%
Other values (91) 3771
42.0%
Common
ValueCountFrequency (%)
1 793
19.9%
752
18.9%
2 470
11.8%
4 432
10.8%
0 324
8.1%
6 260
 
6.5%
5 231
 
5.8%
9 201
 
5.0%
8 151
 
3.8%
3 148
 
3.7%
Other values (2) 221
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8986
69.3%
ASCII 3983
30.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 1534
17.1%
번 681
 
7.6%
길 661
 
7.4%
도 477
 
5.3%
호 412
 
4.6%
선 412
 
4.6%
반 282
 
3.1%
국 280
 
3.1%
지 244
 
2.7%
일 232
 
2.6%
Other values (91) 3771
42.0%
ASCII
ValueCountFrequency (%)
1 793
19.9%
752
18.9%
2 470
11.8%
4 432
10.8%
0 324
8.1%
6 260
 
6.5%
5 231
 
5.8%
9 201
 
5.0%
8 151
 
3.8%
3 148
 
3.7%
Other values (2) 221
 
5.5%

방향
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
상행
975 
하행
973 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row상행
2nd row상행
3rd row하행
4th row상행
5th row하행

Common Values

ValueCountFrequency (%)
상행 975
50.1%
하행 973
49.9%

Length

2024-01-05T22:07:54.033085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-05T22:07:54.330747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상행 975
50.1%
하행 973
49.9%

시작지점
Text

Distinct591
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
2024-01-05T22:07:54.774467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length7.7833676
Min length2

Characters and Unicode

Total characters15162
Distinct characters375
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)2.8%

Sample

1st row상동IC
2nd row좌곤교
3rd row상동IC삼거리
4th row상동IC
5th row상동IC
ValueCountFrequency (%)
서김해ic 13
 
0.7%
공설운동장교차로 13
 
0.7%
풍유동내교차로 13
 
0.7%
장유팔판마을서측교차로 13
 
0.7%
냉정jc 12
 
0.6%
활천삼거리 10
 
0.5%
대동jc 9
 
0.5%
진례ic 9
 
0.5%
대동분기점앞교차로 8
 
0.4%
봉황교사거리 8
 
0.4%
Other values (584) 1851
94.5%
2024-01-05T22:07:55.803127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 1238
 
8.2%
차 998
 
6.6%
로 933
 
6.2%
앞 838
 
5.5%
리 717
 
4.7%
거 664
 
4.4%
사 489
 
3.2%
삼 309
 
2.0%
동 304
 
2.0%
구 248
 
1.6%
Other values (365) 8424
55.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14523
95.8%
Uppercase Letter 308
 
2.0%
Decimal Number 274
 
1.8%
Lowercase Letter 16
 
0.1%
Space Separator 11
 
0.1%
Dash Punctuation 11
 
0.1%
Open Punctuation 11
 
0.1%
Close Punctuation 7
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 1238
 
8.5%
차 998
 
6.9%
로 933
 
6.4%
앞 838
 
5.8%
리 717
 
4.9%
거 664
 
4.6%
사 489
 
3.4%
삼 309
 
2.1%
동 304
 
2.1%
구 248
 
1.7%
Other values (333) 7785
53.6%
Uppercase Letter
ValueCountFrequency (%)
C 100
32.5%
I 78
25.3%
J 24
 
7.8%
A 22
 
7.1%
T 15
 
4.9%
G 14
 
4.5%
M 11
 
3.6%
P 11
 
3.6%
L 9
 
2.9%
S 7
 
2.3%
Other values (4) 17
 
5.5%
Decimal Number
ValueCountFrequency (%)
1 94
34.3%
2 92
33.6%
3 29
 
10.6%
5 21
 
7.7%
4 9
 
3.3%
6 9
 
3.3%
9 7
 
2.6%
8 7
 
2.6%
7 6
 
2.2%
Lowercase Letter
ValueCountFrequency (%)
s 6
37.5%
g 4
25.0%
e 4
25.0%
k 2
 
12.5%
Space Separator
ValueCountFrequency (%)
11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14523
95.8%
Latin 324
 
2.1%
Common 315
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 1238
 
8.5%
차 998
 
6.9%
로 933
 
6.4%
앞 838
 
5.8%
리 717
 
4.9%
거 664
 
4.6%
사 489
 
3.4%
삼 309
 
2.1%
동 304
 
2.1%
구 248
 
1.7%
Other values (333) 7785
53.6%
Latin
ValueCountFrequency (%)
C 100
30.9%
I 78
24.1%
J 24
 
7.4%
A 22
 
6.8%
T 15
 
4.6%
G 14
 
4.3%
M 11
 
3.4%
P 11
 
3.4%
L 9
 
2.8%
S 7
 
2.2%
Other values (8) 33
 
10.2%
Common
ValueCountFrequency (%)
1 94
29.8%
2 92
29.2%
3 29
 
9.2%
5 21
 
6.7%
11
 
3.5%
- 11
 
3.5%
( 11
 
3.5%
4 9
 
2.9%
6 9
 
2.9%
9 7
 
2.2%
Other values (4) 21
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14523
95.8%
ASCII 639
 
4.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 1238
 
8.5%
차 998
 
6.9%
로 933
 
6.4%
앞 838
 
5.8%
리 717
 
4.9%
거 664
 
4.6%
사 489
 
3.4%
삼 309
 
2.1%
동 304
 
2.1%
구 248
 
1.7%
Other values (333) 7785
53.6%
ASCII
ValueCountFrequency (%)
C 100
15.6%
1 94
14.7%
2 92
14.4%
I 78
12.2%
3 29
 
4.5%
J 24
 
3.8%
A 22
 
3.4%
5 21
 
3.3%
T 15
 
2.3%
G 14
 
2.2%
Other values (22) 150
23.5%

끝지점
Text

Distinct591
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
2024-01-05T22:07:56.199968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length7.7951745
Min length2

Characters and Unicode

Total characters15185
Distinct characters375
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)2.8%

Sample

1st row상동IC삼거리
2nd row화곡삼거리
3rd row상동IC
4th row상동IC삼거리
5th row상동IC
ValueCountFrequency (%)
공설운동장교차로 13
 
0.7%
풍유동내교차로 13
 
0.7%
장유팔판마을서측교차로 13
 
0.7%
서김해ic 13
 
0.7%
냉정jc 12
 
0.6%
활천삼거리 10
 
0.5%
진례ic 9
 
0.5%
대동jc 9
 
0.5%
대동분기점앞교차로 9
 
0.5%
25시약국사거리 8
 
0.4%
Other values (584) 1850
94.4%
2024-01-05T22:07:57.161925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 1235
 
8.1%
차 999
 
6.6%
로 937
 
6.2%
앞 838
 
5.5%
리 719
 
4.7%
거 666
 
4.4%
사 491
 
3.2%
삼 311
 
2.0%
동 306
 
2.0%
구 248
 
1.6%
Other values (365) 8435
55.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14547
95.8%
Uppercase Letter 308
 
2.0%
Decimal Number 273
 
1.8%
Lowercase Letter 16
 
0.1%
Space Separator 11
 
0.1%
Open Punctuation 11
 
0.1%
Dash Punctuation 11
 
0.1%
Close Punctuation 7
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 1235
 
8.5%
차 999
 
6.9%
로 937
 
6.4%
앞 838
 
5.8%
리 719
 
4.9%
거 666
 
4.6%
사 491
 
3.4%
삼 311
 
2.1%
동 306
 
2.1%
구 248
 
1.7%
Other values (333) 7797
53.6%
Uppercase Letter
ValueCountFrequency (%)
C 100
32.5%
I 78
25.3%
J 24
 
7.8%
A 22
 
7.1%
T 15
 
4.9%
G 14
 
4.5%
P 11
 
3.6%
M 11
 
3.6%
L 9
 
2.9%
K 7
 
2.3%
Other values (4) 17
 
5.5%
Decimal Number
ValueCountFrequency (%)
1 94
34.4%
2 91
33.3%
3 29
 
10.6%
5 21
 
7.7%
6 9
 
3.3%
4 9
 
3.3%
8 7
 
2.6%
9 7
 
2.6%
7 6
 
2.2%
Lowercase Letter
ValueCountFrequency (%)
s 6
37.5%
e 4
25.0%
g 4
25.0%
k 2
 
12.5%
Space Separator
ValueCountFrequency (%)
11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14547
95.8%
Latin 324
 
2.1%
Common 314
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 1235
 
8.5%
차 999
 
6.9%
로 937
 
6.4%
앞 838
 
5.8%
리 719
 
4.9%
거 666
 
4.6%
사 491
 
3.4%
삼 311
 
2.1%
동 306
 
2.1%
구 248
 
1.7%
Other values (333) 7797
53.6%
Latin
ValueCountFrequency (%)
C 100
30.9%
I 78
24.1%
J 24
 
7.4%
A 22
 
6.8%
T 15
 
4.6%
G 14
 
4.3%
P 11
 
3.4%
M 11
 
3.4%
L 9
 
2.8%
K 7
 
2.2%
Other values (8) 33
 
10.2%
Common
ValueCountFrequency (%)
1 94
29.9%
2 91
29.0%
3 29
 
9.2%
5 21
 
6.7%
11
 
3.5%
( 11
 
3.5%
- 11
 
3.5%
6 9
 
2.9%
4 9
 
2.9%
8 7
 
2.2%
Other values (4) 21
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14547
95.8%
ASCII 638
 
4.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 1235
 
8.5%
차 999
 
6.9%
로 937
 
6.4%
앞 838
 
5.8%
리 719
 
4.9%
거 666
 
4.6%
사 491
 
3.4%
삼 311
 
2.1%
동 306
 
2.1%
구 248
 
1.7%
Other values (333) 7797
53.6%
ASCII
ValueCountFrequency (%)
C 100
15.7%
1 94
14.7%
2 91
14.3%
I 78
12.2%
3 29
 
4.5%
J 24
 
3.8%
A 22
 
3.4%
5 21
 
3.3%
T 15
 
2.4%
G 14
 
2.2%
Other values (22) 150
23.5%

구역길이
Real number (ℝ)

Distinct855
Distinct (%)43.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean594.79312
Minimum11
Maximum14727
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2024-01-05T22:07:57.656028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile69.35
Q1168
median294
Q3575.25
95-th percentile2359.45
Maximum14727
Range14716
Interquartile range (IQR)407.25

Descriptive statistics

Standard deviation917.65397
Coefficient of variation (CV)1.542812
Kurtosis43.031423
Mean594.79312
Median Absolute Deviation (MAD)159
Skewness4.9809439
Sum1158657
Variance842088.8
MonotonicityNot monotonic
2024-01-05T22:07:58.185829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
186 12
 
0.6%
157 12
 
0.6%
121 12
 
0.6%
125 12
 
0.6%
253 11
 
0.6%
216 11
 
0.6%
166 11
 
0.6%
123 11
 
0.6%
113 10
 
0.5%
190 10
 
0.5%
Other values (845) 1836
94.3%
ValueCountFrequency (%)
11 1
 
0.1%
13 1
 
0.1%
17 1
 
0.1%
18 2
 
0.1%
22 2
 
0.1%
29 5
0.3%
32 3
0.2%
35 2
 
0.1%
36 1
 
0.1%
37 1
 
0.1%
ValueCountFrequency (%)
14727 1
0.1%
8388 1
0.1%
8372 1
0.1%
7110 1
0.1%
7090 1
0.1%
6567 1
0.1%
6543 1
0.1%
5991 1
0.1%
5986 1
0.1%
5515 1
0.1%

시작지점코드
Real number (ℝ)

HIGH CORRELATION 

Distinct650
Distinct (%)33.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8387863 × 109
Minimum1.4100079 × 109
Maximum3.8800013 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2024-01-05T22:07:58.832177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4100079 × 109
5-th percentile3.8500042 × 109
Q13.8500251 × 109
median3.8501055 × 109
Q33.8501488 × 109
95-th percentile3.8501906 × 109
Maximum3.8800013 × 109
Range2.4699934 × 109
Interquartile range (IQR)123700

Descriptive statistics

Standard deviation1.6552413 × 108
Coefficient of variation (CV)0.043118872
Kurtosis211.92895
Mean3.8387863 × 109
Median Absolute Deviation (MAD)60700
Skewness-14.617756
Sum7.4779557 × 1012
Variance2.7398239 × 1016
MonotonicityNot monotonic
2024-01-05T22:07:59.291269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3850016700 6
 
0.3%
3850002400 5
 
0.3%
3850128900 5
 
0.3%
3850021200 5
 
0.3%
3850095400 5
 
0.3%
3850094800 4
 
0.2%
3850043700 4
 
0.2%
3850107800 4
 
0.2%
3850037900 4
 
0.2%
3850003601 4
 
0.2%
Other values (640) 1902
97.6%
ValueCountFrequency (%)
1410007900 1
0.1%
1410008900 1
0.1%
1410033100 1
0.1%
1410036400 1
0.1%
1410039300 1
0.1%
1410039600 1
0.1%
1410039700 1
0.1%
1410039800 1
0.1%
1410039900 1
0.1%
3790011300 1
0.1%
ValueCountFrequency (%)
3880001300 1
 
0.1%
3860020000 1
 
0.1%
3860001800 1
 
0.1%
3850202900 3
0.2%
3850202800 1
 
0.1%
3850202500 3
0.2%
3850202400 3
0.2%
3850202300 3
0.2%
3850202200 1
 
0.1%
3850202100 2
0.1%

끝지점코드
Real number (ℝ)

HIGH CORRELATION 

Distinct650
Distinct (%)33.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8388118 × 109
Minimum1.4100079 × 109
Maximum3.8800013 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2024-01-05T22:07:59.808747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4100079 × 109
5-th percentile3.8500041 × 109
Q13.8500251 × 109
median3.8501049 × 109
Q33.8501487 × 109
95-th percentile3.8501905 × 109
Maximum3.8800013 × 109
Range2.4699934 × 109
Interquartile range (IQR)123649.25

Descriptive statistics

Standard deviation1.6552014 × 108
Coefficient of variation (CV)0.043117544
Kurtosis211.95875
Mean3.8388118 × 109
Median Absolute Deviation (MAD)61000
Skewness-14.619266
Sum7.4780055 × 1012
Variance2.7396916 × 1016
MonotonicityNot monotonic
2024-01-05T22:08:00.254827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3850016700 6
 
0.3%
3850095400 5
 
0.3%
3850021200 5
 
0.3%
3850128900 5
 
0.3%
3850002400 5
 
0.3%
3850186100 4
 
0.2%
3850003601 4
 
0.2%
3850003600 4
 
0.2%
3850002300 4
 
0.2%
3850150400 4
 
0.2%
Other values (640) 1902
97.6%
ValueCountFrequency (%)
1410007900 1
0.1%
1410008900 1
0.1%
1410033100 1
0.1%
1410036400 1
0.1%
1410039300 1
0.1%
1410039600 1
0.1%
1410039700 1
0.1%
1410039800 1
0.1%
1410039900 1
0.1%
3790011300 1
0.1%
ValueCountFrequency (%)
3880001300 1
 
0.1%
3860001800 1
 
0.1%
3850202900 3
0.2%
3850202800 1
 
0.1%
3850202500 3
0.2%
3850202400 1
 
0.1%
3850202300 3
0.2%
3850202200 1
 
0.1%
3850202100 3
0.2%
3850201900 3
0.2%

속도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct81
Distinct (%)4.6%
Missing168
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean27.666292
Minimum3
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2024-01-05T22:08:00.686689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile7
Q117
median26
Q336
95-th percentile53
Maximum100
Range97
Interquartile range (IQR)19

Descriptive statistics

Standard deviation15.325103
Coefficient of variation (CV)0.55392689
Kurtosis3.3272575
Mean27.666292
Median Absolute Deviation (MAD)9
Skewness1.3211933
Sum49246
Variance234.85878
MonotonicityNot monotonic
2024-01-05T22:08:01.189598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 61
 
3.1%
19 58
 
3.0%
22 58
 
3.0%
30 58
 
3.0%
24 57
 
2.9%
21 54
 
2.8%
23 53
 
2.7%
26 52
 
2.7%
25 50
 
2.6%
31 49
 
2.5%
Other values (71) 1230
63.1%
(Missing) 168
 
8.6%
ValueCountFrequency (%)
3 3
 
0.2%
4 40
2.1%
5 15
 
0.8%
6 21
1.1%
7 24
1.2%
8 24
1.2%
9 30
1.5%
10 24
1.2%
11 40
2.1%
12 37
1.9%
ValueCountFrequency (%)
100 1
 
0.1%
99 4
0.2%
96 3
0.2%
95 3
0.2%
94 1
 
0.1%
92 3
0.2%
91 1
 
0.1%
90 1
 
0.1%
84 1
 
0.1%
80 4
0.2%

통행량
Real number (ℝ)

MISSING 

Distinct289
Distinct (%)16.2%
Missing168
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean78.400562
Minimum2
Maximum1867
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.2 KiB
2024-01-05T22:08:01.514157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile11
Q124
median45
Q383
95-th percentile247
Maximum1867
Range1865
Interquartile range (IQR)59

Descriptive statistics

Standard deviation121.8679
Coefficient of variation (CV)1.5544264
Kurtosis67.962384
Mean78.400562
Median Absolute Deviation (MAD)25
Skewness6.7013992
Sum139553
Variance14851.786
MonotonicityNot monotonic
2024-01-05T22:08:01.773174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 38
 
2.0%
25 35
 
1.8%
22 34
 
1.7%
29 32
 
1.6%
17 31
 
1.6%
18 31
 
1.6%
19 29
 
1.5%
15 29
 
1.5%
21 28
 
1.4%
20 28
 
1.4%
Other values (279) 1465
75.2%
(Missing) 168
 
8.6%
ValueCountFrequency (%)
2 9
0.5%
3 3
 
0.2%
4 6
 
0.3%
5 8
0.4%
6 14
0.7%
7 7
0.4%
8 15
0.8%
9 11
0.6%
10 11
0.6%
11 15
0.8%
ValueCountFrequency (%)
1867 1
0.1%
1688 1
0.1%
1380 1
0.1%
1318 1
0.1%
1128 1
0.1%
1068 1
0.1%
1061 1
0.1%
892 1
0.1%
850 1
0.1%
837 1
0.1%

소통 등급 코드
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
소통원활[LTC1]
956 
부분지체[LTC2]
489 
정체[LTC3]
335 
<NA>
168 

Length

Max length10
Median length10
Mean length9.1386037
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row소통원활[LTC1]
3rd row소통원활[LTC1]
4th row소통원활[LTC1]
5th row소통원활[LTC1]

Common Values

ValueCountFrequency (%)
소통원활[LTC1] 956
49.1%
부분지체[LTC2] 489
25.1%
정체[LTC3] 335
 
17.2%
<NA> 168
 
8.6%

Length

2024-01-05T22:08:02.315666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-05T22:08:02.571187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
소통원활[ltc1 956
49.1%
부분지체[ltc2 489
25.1%
정체[ltc3 335
 
17.2%
na 168
 
8.6%

일시
Date

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
Minimum2023-12-22 09:20:00
Maximum2023-12-22 09:20:00
2024-01-05T22:08:02.746166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:08:02.993609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-01-05T22:07:48.689307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:40.227328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:41.921213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:43.339826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:45.077701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:47.089856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:48.977079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:40.391469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:42.219944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:43.599549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:45.505003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:47.388992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:49.232296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:40.678182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:42.497308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:43.968414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:45.782984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:47.650696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:49.506543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:40.992789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:42.769097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:44.252666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:46.168137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:47.908142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:49.768152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:41.415857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:42.962371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:44.523457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:46.507360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:48.171032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:50.030389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:41.593726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:43.122437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:44.796118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:46.813487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-05T22:07:48.423899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-05T22:08:03.225898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로번호방향구역길이시작지점코드끝지점코드속도통행량소통 등급 코드
도로번호1.0000.0001.0000.0000.000NaNNaNNaN
방향0.0001.0000.0000.0130.0130.0000.0000.000
구역길이1.0000.0001.0000.1560.1560.3230.5440.212
시작지점코드0.0000.0130.1561.0000.2830.0000.0000.026
끝지점코드0.0000.0130.1560.2831.0000.0000.0000.026
속도NaN0.0000.3230.0000.0001.0000.0910.893
통행량NaN0.0000.5440.0000.0000.0911.0000.258
소통 등급 코드NaN0.0000.2120.0260.0260.8930.2581.000
2024-01-05T22:08:03.613898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소통 등급 코드방향
소통 등급 코드1.0000.000
방향0.0001.000
2024-01-05T22:08:03.871642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로번호구역길이시작지점코드끝지점코드속도통행량방향소통 등급 코드
도로번호1.000-0.1700.1030.101-0.2790.0110.0001.000
구역길이-0.1701.000-0.106-0.1100.2980.4690.0000.089
시작지점코드0.103-0.1061.0000.712-0.079-0.0120.0200.030
끝지점코드0.101-0.1100.7121.000-0.111-0.0130.0200.041
속도-0.2790.298-0.079-0.1111.000-0.2130.0000.838
통행량0.0110.469-0.012-0.013-0.2131.0000.0000.117
방향0.0000.0000.0200.0200.0000.0001.0000.000
소통 등급 코드1.0000.0890.0300.0410.8380.1170.0001.000

Missing values

2024-01-05T22:07:50.421491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-05T22:07:50.952502image/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.
2024-01-05T22:07:51.304950image/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

도로번호도로명방향시작지점끝지점구역길이시작지점코드끝지점코드속도통행량소통 등급 코드일시
03855027800대구부산간고속도로상행상동IC상동IC삼거리130138500261003850186100<NA><NA><NA>2023-12-22 9:20
13855027900일반국도14호선상행좌곤교화곡삼거리2353850019902385002010039120소통원활[LTC1]2023-12-22 9:20
23855028000대구부산간고속도로하행상동IC삼거리상동IC843385018610038500260005515소통원활[LTC1]2023-12-22 9:20
33855028100대구부산간고속도로상행상동IC상동IC삼거리1350385002600038501861005951소통원활[LTC1]2023-12-22 9:20
43855028200대구부산간고속도로하행상동IC상동IC11173850026100385002600047103소통원활[LTC1]2023-12-22 9:20
53855028300대구부산간고속도로상행상동IC상동IC1104385002600038500261009542소통원활[LTC1]2023-12-22 9:20
63855028400대구부산간고속도로하행상동IC대동JC48893850026000385002630010039소통원활[LTC1]2023-12-22 9:20
73855028500대구부산간고속도로상행대동JC상동IC49003850026300385002600092191소통원활[LTC1]2023-12-22 9:20
83855028600대구부산간고속도로하행대동JC대동JC13583850026300385001940096183소통원활[LTC1]2023-12-22 9:20
93855028700대구부산간고속도로상행대동JC대동JC99338500194003850026300<NA><NA><NA>2023-12-22 9:20
도로번호도로명방향시작지점끝지점구역길이시작지점코드끝지점코드속도통행량소통 등급 코드일시
19383855222400활천로214번길하행대우유토피아후문사거리어방주공2차입구교차로36638501209003850115600<NA><NA><NA>2023-12-22 9:20
19393855222500국가지원지방도69호선하행용산마을입구교차로상동농협주유소837238500250033850023800<NA><NA><NA>2023-12-22 9:20
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