Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows2
Duplicate rows (%)< 0.1%
Total size in memory556.6 KiB
Average record size in memory57.0 B

Variable types

Categorical1
DateTime1
Text3
Numeric1

Dataset

Description부산광역시 교통정보서비스센터에서 수집한 교통정보와 유관기관 교통정보를 통해 도로구간별(1-4레벨) 소통정보를 분석하여 요일별 시간별 형태(요일, 시분, 구간명, 시점, 종점, 속도)로 제공합니다.
Author부산광역시
URLhttps://www.data.go.kr/data/15041722/fileData.do

Alerts

Dataset has 2 (< 0.1%) duplicate rowsDuplicates
요일 is highly imbalanced (67.6%)Imbalance

Reproduction

Analysis started2024-04-21 01:09:03.467452
Analysis finished2024-04-21 01:09:05.523709
Duration2.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

요일
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일요일
9408 
월요일
 
592

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일요일
2nd row일요일
3rd row일요일
4th row일요일
5th row월요일

Common Values

ValueCountFrequency (%)
일요일 9408
94.1%
월요일 592
 
5.9%

Length

2024-04-21T10:09:05.585897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:09:05.673055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일요일 9408
94.1%
월요일 592
 
5.9%

시분
Date

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2024-04-21 18:00:00
Maximum2024-04-21 18:55:00
2024-04-21T10:09:05.740375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:09:05.825607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)

구간명
Text

Distinct839
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T10:09:06.061939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length4.9275
Min length3

Characters and Unicode

Total characters49275
Distinct characters250
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

Unique78 ?
Unique (%)0.8%

Sample

1st row동평로
2nd row센텀서로
3rd row낙동남로
4th row르노삼성대로
5th row법원북로
ValueCountFrequency (%)
중앙대로 295
 
2.9%
반송로 171
 
1.7%
해운대로 159
 
1.6%
낙동대로 154
 
1.5%
낙동남로 152
 
1.5%
기장대로 152
 
1.5%
가락대로 130
 
1.3%
번영로 112
 
1.1%
다대로 110
 
1.1%
강변대로 93
 
0.9%
Other values (829) 8472
84.7%
2024-04-21T10:09:06.426934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 9734
 
19.8%
대 3191
 
6.5%
산 2028
 
4.1%
길 1885
 
3.8%
번 1763
 
3.6%
1 1336
 
2.7%
동 1112
 
2.3%
2 986
 
2.0%
녹 832
 
1.7%
중 763
 
1.5%
Other values (240) 25645
52.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 43258
87.8%
Decimal Number 5981
 
12.1%
Uppercase Letter 36
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 9734
22.5%
대 3191
 
7.4%
산 2028
 
4.7%
길 1885
 
4.4%
번 1763
 
4.1%
동 1112
 
2.6%
녹 832
 
1.9%
중 763
 
1.8%
해 710
 
1.6%
단 607
 
1.4%
Other values (226) 20633
47.7%
Decimal Number
ValueCountFrequency (%)
1 1336
22.3%
2 986
16.5%
3 736
12.3%
4 492
 
8.2%
6 489
 
8.2%
7 472
 
7.9%
0 411
 
6.9%
8 384
 
6.4%
5 366
 
6.1%
9 309
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
A 9
25.0%
P 9
25.0%
E 9
25.0%
C 9
25.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 43258
87.8%
Common 5981
 
12.1%
Latin 36
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 9734
22.5%
대 3191
 
7.4%
산 2028
 
4.7%
길 1885
 
4.4%
번 1763
 
4.1%
동 1112
 
2.6%
녹 832
 
1.9%
중 763
 
1.8%
해 710
 
1.6%
단 607
 
1.4%
Other values (226) 20633
47.7%
Common
ValueCountFrequency (%)
1 1336
22.3%
2 986
16.5%
3 736
12.3%
4 492
 
8.2%
6 489
 
8.2%
7 472
 
7.9%
0 411
 
6.9%
8 384
 
6.4%
5 366
 
6.1%
9 309
 
5.2%
Latin
ValueCountFrequency (%)
A 9
25.0%
P 9
25.0%
E 9
25.0%
C 9
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 43258
87.8%
ASCII 6017
 
12.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 9734
22.5%
대 3191
 
7.4%
산 2028
 
4.7%
길 1885
 
4.4%
번 1763
 
4.1%
동 1112
 
2.6%
녹 832
 
1.9%
중 763
 
1.8%
해 710
 
1.6%
단 607
 
1.4%
Other values (226) 20633
47.7%
ASCII
ValueCountFrequency (%)
1 1336
22.2%
2 986
16.4%
3 736
12.2%
4 492
 
8.2%
6 489
 
8.1%
7 472
 
7.8%
0 411
 
6.8%
8 384
 
6.4%
5 366
 
6.1%
9 309
 
5.1%
Other values (4) 36
 
0.6%

시점
Text

Distinct2489
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T10:09:06.623261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length6.7123
Min length2

Characters and Unicode

Total characters67123
Distinct characters605
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique358 ?
Unique (%)3.6%

Sample

1st row양정동512-2
2nd row뮤지엄다
3rd row한주아파트
4th row을숙도대교
5th row거제동1499-1
ValueCountFrequency (%)
속성변화점 94
 
0.9%
거제역10번출구 27
 
0.3%
명지ic 27
 
0.3%
대동사거리 24
 
0.2%
청강교 23
 
0.2%
제2지하차도 22
 
0.2%
제1지하차도 21
 
0.2%
민락교차로 19
 
0.2%
덕천ic 19
 
0.2%
버스정류장 18
 
0.2%
Other values (2479) 9706
97.1%
2024-04-21T10:09:06.946363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 2890
 
4.3%
동 2273
 
3.4%
리 1632
 
2.4%
산 1350
 
2.0%
차 1333
 
2.0%
대 1250
 
1.9%
1 1205
 
1.8%
로 1189
 
1.8%
사 1129
 
1.7%
부 1123
 
1.7%
Other values (595) 51749
77.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 59842
89.2%
Decimal Number 4951
 
7.4%
Uppercase Letter 1469
 
2.2%
Dash Punctuation 694
 
1.0%
Open Punctuation 66
 
0.1%
Close Punctuation 66
 
0.1%
Lowercase Letter 26
 
< 0.1%
Other Punctuation 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 2890
 
4.8%
동 2273
 
3.8%
리 1632
 
2.7%
산 1350
 
2.3%
차 1333
 
2.2%
대 1250
 
2.1%
로 1189
 
2.0%
사 1129
 
1.9%
부 1123
 
1.9%
거 1071
 
1.8%
Other values (554) 44602
74.5%
Uppercase Letter
ValueCountFrequency (%)
C 361
24.6%
I 290
19.7%
S 133
 
9.1%
G 120
 
8.2%
K 100
 
6.8%
T 92
 
6.3%
B 57
 
3.9%
E 41
 
2.8%
H 40
 
2.7%
J 36
 
2.5%
Other values (12) 199
13.5%
Decimal Number
ValueCountFrequency (%)
1 1205
24.3%
2 812
16.4%
3 495
10.0%
5 469
 
9.5%
4 468
 
9.5%
6 374
 
7.6%
7 370
 
7.5%
9 285
 
5.8%
0 253
 
5.1%
8 220
 
4.4%
Lowercase Letter
ValueCountFrequency (%)
i 9
34.6%
l 9
34.6%
k 4
15.4%
s 4
15.4%
Other Punctuation
ValueCountFrequency (%)
& 5
55.6%
, 4
44.4%
Dash Punctuation
ValueCountFrequency (%)
- 694
100.0%
Open Punctuation
ValueCountFrequency (%)
( 66
100.0%
Close Punctuation
ValueCountFrequency (%)
) 66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 59842
89.2%
Common 5786
 
8.6%
Latin 1495
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 2890
 
4.8%
동 2273
 
3.8%
리 1632
 
2.7%
산 1350
 
2.3%
차 1333
 
2.2%
대 1250
 
2.1%
로 1189
 
2.0%
사 1129
 
1.9%
부 1123
 
1.9%
거 1071
 
1.8%
Other values (554) 44602
74.5%
Latin
ValueCountFrequency (%)
C 361
24.1%
I 290
19.4%
S 133
 
8.9%
G 120
 
8.0%
K 100
 
6.7%
T 92
 
6.2%
B 57
 
3.8%
E 41
 
2.7%
H 40
 
2.7%
J 36
 
2.4%
Other values (16) 225
15.1%
Common
ValueCountFrequency (%)
1 1205
20.8%
2 812
14.0%
- 694
12.0%
3 495
8.6%
5 469
 
8.1%
4 468
 
8.1%
6 374
 
6.5%
7 370
 
6.4%
9 285
 
4.9%
0 253
 
4.4%
Other values (5) 361
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 59842
89.2%
ASCII 7281
 
10.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 2890
 
4.8%
동 2273
 
3.8%
리 1632
 
2.7%
산 1350
 
2.3%
차 1333
 
2.2%
대 1250
 
2.1%
로 1189
 
2.0%
사 1129
 
1.9%
부 1123
 
1.9%
거 1071
 
1.8%
Other values (554) 44602
74.5%
ASCII
ValueCountFrequency (%)
1 1205
16.5%
2 812
11.2%
- 694
 
9.5%
3 495
 
6.8%
5 469
 
6.4%
4 468
 
6.4%
6 374
 
5.1%
7 370
 
5.1%
C 361
 
5.0%
I 290
 
4.0%
Other values (31) 1743
23.9%

종점
Text

Distinct2479
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T10:09:07.173887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length6.7024
Min length2

Characters and Unicode

Total characters67024
Distinct characters608
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique356 ?
Unique (%)3.6%

Sample

1st row양정동512-2
2nd row센텀가온어린이집
3rd row하구둑교차로
4th row울숙도대교
5th row아이마루도서관
ValueCountFrequency (%)
속성변화점 110
 
1.1%
명지ic 27
 
0.3%
제2지하차도 24
 
0.2%
청강교 23
 
0.2%
대동화명대교ic 21
 
0.2%
거제역10번출구 21
 
0.2%
부전사거리 19
 
0.2%
제1지하차도 19
 
0.2%
버스정류장 18
 
0.2%
송도교차로 18
 
0.2%
Other values (2469) 9700
97.0%
2024-04-21T10:09:07.519704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 2918
 
4.4%
동 2163
 
3.2%
리 1716
 
2.6%
산 1428
 
2.1%
차 1336
 
2.0%
대 1248
 
1.9%
1 1227
 
1.8%
로 1186
 
1.8%
부 1159
 
1.7%
사 1136
 
1.7%
Other values (598) 51507
76.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 59798
89.2%
Decimal Number 4958
 
7.4%
Uppercase Letter 1419
 
2.1%
Dash Punctuation 668
 
1.0%
Close Punctuation 73
 
0.1%
Open Punctuation 73
 
0.1%
Lowercase Letter 20
 
< 0.1%
Other Punctuation 15
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 2918
 
4.9%
동 2163
 
3.6%
리 1716
 
2.9%
산 1428
 
2.4%
차 1336
 
2.2%
대 1248
 
2.1%
로 1186
 
2.0%
부 1159
 
1.9%
사 1136
 
1.9%
거 1127
 
1.9%
Other values (556) 44381
74.2%
Uppercase Letter
ValueCountFrequency (%)
C 382
26.9%
I 294
20.7%
G 136
 
9.6%
S 117
 
8.2%
T 73
 
5.1%
K 64
 
4.5%
N 41
 
2.9%
U 39
 
2.7%
B 38
 
2.7%
H 38
 
2.7%
Other values (13) 197
13.9%
Decimal Number
ValueCountFrequency (%)
1 1227
24.7%
2 840
16.9%
3 478
 
9.6%
4 472
 
9.5%
5 440
 
8.9%
6 341
 
6.9%
7 340
 
6.9%
9 308
 
6.2%
0 274
 
5.5%
8 238
 
4.8%
Lowercase Letter
ValueCountFrequency (%)
l 6
30.0%
i 6
30.0%
k 4
20.0%
s 4
20.0%
Other Punctuation
ValueCountFrequency (%)
& 10
66.7%
, 5
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 668
100.0%
Close Punctuation
ValueCountFrequency (%)
) 73
100.0%
Open Punctuation
ValueCountFrequency (%)
( 73
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 59798
89.2%
Common 5787
 
8.6%
Latin 1439
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 2918
 
4.9%
동 2163
 
3.6%
리 1716
 
2.9%
산 1428
 
2.4%
차 1336
 
2.2%
대 1248
 
2.1%
로 1186
 
2.0%
부 1159
 
1.9%
사 1136
 
1.9%
거 1127
 
1.9%
Other values (556) 44381
74.2%
Latin
ValueCountFrequency (%)
C 382
26.5%
I 294
20.4%
G 136
 
9.5%
S 117
 
8.1%
T 73
 
5.1%
K 64
 
4.4%
N 41
 
2.8%
U 39
 
2.7%
B 38
 
2.6%
H 38
 
2.6%
Other values (17) 217
15.1%
Common
ValueCountFrequency (%)
1 1227
21.2%
2 840
14.5%
- 668
11.5%
3 478
 
8.3%
4 472
 
8.2%
5 440
 
7.6%
6 341
 
5.9%
7 340
 
5.9%
9 308
 
5.3%
0 274
 
4.7%
Other values (5) 399
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 59798
89.2%
ASCII 7226
 
10.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 2918
 
4.9%
동 2163
 
3.6%
리 1716
 
2.9%
산 1428
 
2.4%
차 1336
 
2.2%
대 1248
 
2.1%
로 1186
 
2.0%
부 1159
 
1.9%
사 1136
 
1.9%
거 1127
 
1.9%
Other values (556) 44381
74.2%
ASCII
ValueCountFrequency (%)
1 1227
17.0%
2 840
11.6%
- 668
 
9.2%
3 478
 
6.6%
4 472
 
6.5%
5 440
 
6.1%
C 382
 
5.3%
6 341
 
4.7%
7 340
 
4.7%
9 308
 
4.3%
Other values (32) 1730
23.9%

속도
Real number (ℝ)

Distinct100
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.939
Minimum4
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T10:09:07.647726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile11
Q119
median26
Q336
95-th percentile66
Maximum105
Range101
Interquartile range (IQR)17

Descriptive statistics

Standard deviation16.296418
Coefficient of variation (CV)0.54432072
Kurtosis2.5569918
Mean29.939
Median Absolute Deviation (MAD)8
Skewness1.4939513
Sum299390
Variance265.57324
MonotonicityNot monotonic
2024-04-21T10:09:07.762913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 438
 
4.4%
23 387
 
3.9%
25 383
 
3.8%
24 374
 
3.7%
19 373
 
3.7%
22 370
 
3.7%
20 362
 
3.6%
17 356
 
3.6%
26 354
 
3.5%
18 335
 
3.4%
Other values (90) 6268
62.7%
ValueCountFrequency (%)
4 42
 
0.4%
5 27
 
0.3%
6 39
 
0.4%
7 48
 
0.5%
8 81
0.8%
9 70
0.7%
10 109
1.1%
11 125
1.2%
12 133
1.3%
13 143
1.4%
ValueCountFrequency (%)
105 2
 
< 0.1%
103 3
 
< 0.1%
102 3
 
< 0.1%
100 2
 
< 0.1%
99 1
 
< 0.1%
98 2
 
< 0.1%
97 5
 
0.1%
96 14
0.1%
95 10
0.1%
94 5
 
0.1%

Interactions

2024-04-21T10:09:05.215178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T10:09:07.847935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
요일시분속도
요일1.0000.7920.051
시분0.7921.0000.035
속도0.0510.0351.000
2024-04-21T10:09:07.923880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
속도요일
속도1.0000.039
요일0.0391.000

Missing values

2024-04-21T10:09:05.377000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T10:09:05.473774image/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

요일시분구간명시점종점속도
40018일요일18:25동평로양정동512-2양정동512-236
25127일요일18:15센텀서로뮤지엄다센텀가온어린이집31
41107일요일18:25낙동남로한주아파트하구둑교차로20
30528일요일18:15르노삼성대로을숙도대교울숙도대교58
98177월요일18:00법원북로거제동1499-1아이마루도서관20
50315일요일18:30해운대해변로우3동주민센터한국해양소년단부산연맹32
51317일요일18:30녹산산업중로61번길신원정공동황물산19
63995일요일18:40녹산산단335로동남식당KEB하나은행녹산공단지점25
1763일요일18:00해돋이로속성변화점초장교회29
51689일요일18:30쌍미천로연미시장사거리성민교회20
요일시분구간명시점종점속도
74654일요일18:45해운대로송정삼거리송정동447-1628
8258일요일18:05괴정로남경어린이집동매교사거리21
97089월요일18:00생곡로생곡동1613세산교차로37
56733일요일18:35신반송로반송동757-309반송동89221
17044일요일18:10서전로이아커피&세라믹전포사거리9
42027일요일18:25쇠미로사직동595-48사직초등학교앞교차로23
15572일요일18:05새싹로인본사회연구소범전우편취급국13
32139일요일18:20절영로영선2치안센터2송도삼거리22
40405일요일18:25구덕로남포사거리남포동교차로25
61158일요일18:35녹산산단322로리키아지에스우성에퍼트코리아37

Duplicate rows

Most frequently occurring

요일시분구간명시점종점속도# duplicates
0일요일18:15광안해변로378번길환경생태공학연구원환경생태공학연구원182
1일요일18:45중앙대로308번길제2지하차도제2지하차도182