Overview

Dataset statistics

Number of variables4
Number of observations7826
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory252.3 KiB
Average record size in memory33.0 B

Variable types

Numeric1
Text3

Dataset

Description부산광역시_지능형교통정보링크정보_20240131
Author부산광역시
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15041723

Alerts

링크번호 has unique valuesUnique

Reproduction

Analysis started2024-03-13 13:20:34.068532
Analysis finished2024-03-13 13:20:35.061900
Duration0.99 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

링크번호
Real number (ℝ)

UNIQUE 

Distinct7826
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.39551 × 109
Minimum1.3000001 × 109
Maximum4.1801579 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.9 KiB
2024-03-13T22:20:35.161383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3000001 × 109
5-th percentile1.3200112 × 109
Q11.3600199 × 109
median1.3900496 × 109
Q31.4110802 × 109
95-th percentile1.4500448 × 109
Maximum4.1801579 × 109
Range2.8801578 × 109
Interquartile range (IQR)51060251

Descriptive statistics

Standard deviation1.3063995 × 108
Coefficient of variation (CV)0.093614483
Kurtosis337.17271
Mean1.39551 × 109
Median Absolute Deviation (MAD)29967500
Skewness17.495061
Sum1.0921262 × 1013
Variance1.7066797 × 1016
MonotonicityNot monotonic
2024-03-13T22:20:35.386963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1390042802 1
 
< 0.1%
1330107401 1
 
< 0.1%
1320032701 1
 
< 0.1%
1330109301 1
 
< 0.1%
1330109201 1
 
< 0.1%
1440026300 1
 
< 0.1%
1440025800 1
 
< 0.1%
1440025400 1
 
< 0.1%
1440025300 1
 
< 0.1%
1440016000 1
 
< 0.1%
Other values (7816) 7816
99.9%
ValueCountFrequency (%)
1300000100 1
< 0.1%
1300000200 1
< 0.1%
1300000300 1
< 0.1%
1300000400 1
< 0.1%
1300000500 1
< 0.1%
1300000600 1
< 0.1%
1300000700 1
< 0.1%
1300000800 1
< 0.1%
1300000900 1
< 0.1%
1300001000 1
< 0.1%
ValueCountFrequency (%)
4180157900 1
< 0.1%
4180157800 1
< 0.1%
4180061000 1
< 0.1%
3880840700 1
< 0.1%
3880840600 1
< 0.1%
3870326400 1
< 0.1%
3870326300 1
< 0.1%
3870102800 1
< 0.1%
3870102700 1
< 0.1%
3870102600 1
< 0.1%

링크명
Text

Distinct867
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
2024-03-13T22:20:35.784219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length4.9197547
Min length3

Characters and Unicode

Total characters38502
Distinct characters252
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

Unique5 ?
Unique (%)0.1%

Sample

1st row장평로
2nd row장평로
3rd row장평로
4th row하신중앙로
5th row하신중앙로
ValueCountFrequency (%)
중앙대로 218
 
2.8%
낙동남로 132
 
1.7%
반송로 126
 
1.6%
해운대로 126
 
1.6%
낙동대로 117
 
1.5%
기장대로 112
 
1.4%
가락대로 108
 
1.4%
다대로 93
 
1.2%
번영로 90
 
1.2%
백양대로 80
 
1.0%
Other values (857) 6624
84.6%
2024-03-13T22:20:36.298324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 7601
 
19.7%
대 2541
 
6.6%
산 1556
 
4.0%
길 1497
 
3.9%
번 1400
 
3.6%
1 996
 
2.6%
동 887
 
2.3%
2 776
 
2.0%
녹 638
 
1.7%
3 604
 
1.6%
Other values (242) 20006
52.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 33778
87.7%
Decimal Number 4692
 
12.2%
Uppercase Letter 32
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 7601
22.5%
대 2541
 
7.5%
산 1556
 
4.6%
길 1497
 
4.4%
번 1400
 
4.1%
동 887
 
2.6%
녹 638
 
1.9%
해 574
 
1.7%
중 565
 
1.7%
단 468
 
1.4%
Other values (228) 16051
47.5%
Decimal Number
ValueCountFrequency (%)
1 996
21.2%
2 776
16.5%
3 604
12.9%
6 390
 
8.3%
4 389
 
8.3%
7 377
 
8.0%
5 309
 
6.6%
8 305
 
6.5%
0 302
 
6.4%
9 244
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
P 8
25.0%
E 8
25.0%
A 8
25.0%
C 8
25.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 33778
87.7%
Common 4692
 
12.2%
Latin 32
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 7601
22.5%
대 2541
 
7.5%
산 1556
 
4.6%
길 1497
 
4.4%
번 1400
 
4.1%
동 887
 
2.6%
녹 638
 
1.9%
해 574
 
1.7%
중 565
 
1.7%
단 468
 
1.4%
Other values (228) 16051
47.5%
Common
ValueCountFrequency (%)
1 996
21.2%
2 776
16.5%
3 604
12.9%
6 390
 
8.3%
4 389
 
8.3%
7 377
 
8.0%
5 309
 
6.6%
8 305
 
6.5%
0 302
 
6.4%
9 244
 
5.2%
Latin
ValueCountFrequency (%)
P 8
25.0%
E 8
25.0%
A 8
25.0%
C 8
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 33778
87.7%
ASCII 4724
 
12.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 7601
22.5%
대 2541
 
7.5%
산 1556
 
4.6%
길 1497
 
4.4%
번 1400
 
4.1%
동 887
 
2.6%
녹 638
 
1.9%
해 574
 
1.7%
중 565
 
1.7%
단 468
 
1.4%
Other values (228) 16051
47.5%
ASCII
ValueCountFrequency (%)
1 996
21.1%
2 776
16.4%
3 604
12.8%
6 390
 
8.3%
4 389
 
8.2%
7 377
 
8.0%
5 309
 
6.5%
8 305
 
6.5%
0 302
 
6.4%
9 244
 
5.2%
Other values (4) 32
 
0.7%

시점
Text

Distinct2627
Distinct (%)33.6%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
2024-03-13T22:20:36.547047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length6.7173524
Min length2

Characters and Unicode

Total characters52570
Distinct characters614
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

Unique240 ?
Unique (%)3.1%

Sample

1st row신평시장입구사거리
2nd row배고개사거리
3rd row신평동11-6
4th row서빙고
5th row신평역4번출구
ValueCountFrequency (%)
속성변화점 78
 
1.0%
명지ic 29
 
0.4%
버스정류장 17
 
0.2%
덕천ic 16
 
0.2%
청강교 15
 
0.2%
거제역10번출구 15
 
0.2%
대동화명대교ic 14
 
0.2%
좌천삼거리 14
 
0.2%
제1지하차도 13
 
0.2%
제2지하차도 13
 
0.2%
Other values (2617) 7602
97.1%
2024-03-13T22:20:36.992939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 2269
 
4.3%
동 1720
 
3.3%
리 1299
 
2.5%
산 1089
 
2.1%
차 1022
 
1.9%
1 968
 
1.8%
대 967
 
1.8%
로 925
 
1.8%
부 907
 
1.7%
사 891
 
1.7%
Other values (604) 40513
77.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 46806
89.0%
Decimal Number 3958
 
7.5%
Uppercase Letter 1134
 
2.2%
Dash Punctuation 560
 
1.1%
Close Punctuation 45
 
0.1%
Open Punctuation 45
 
0.1%
Lowercase Letter 14
 
< 0.1%
Other Punctuation 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 2269
 
4.8%
동 1720
 
3.7%
리 1299
 
2.8%
산 1089
 
2.3%
차 1022
 
2.2%
대 967
 
2.1%
로 925
 
2.0%
부 907
 
1.9%
사 891
 
1.9%
거 860
 
1.8%
Other values (562) 34857
74.5%
Uppercase Letter
ValueCountFrequency (%)
C 288
25.4%
I 226
19.9%
G 102
 
9.0%
S 97
 
8.6%
T 72
 
6.3%
K 71
 
6.3%
B 40
 
3.5%
E 31
 
2.7%
N 28
 
2.5%
U 26
 
2.3%
Other values (13) 153
13.5%
Decimal Number
ValueCountFrequency (%)
1 968
24.5%
2 664
16.8%
3 400
10.1%
4 385
 
9.7%
5 364
 
9.2%
7 277
 
7.0%
6 266
 
6.7%
9 246
 
6.2%
0 200
 
5.1%
8 188
 
4.7%
Lowercase Letter
ValueCountFrequency (%)
l 5
35.7%
i 5
35.7%
k 2
 
14.3%
s 2
 
14.3%
Other Punctuation
ValueCountFrequency (%)
& 5
62.5%
, 3
37.5%
Dash Punctuation
ValueCountFrequency (%)
- 560
100.0%
Close Punctuation
ValueCountFrequency (%)
) 45
100.0%
Open Punctuation
ValueCountFrequency (%)
( 45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 46806
89.0%
Common 4616
 
8.8%
Latin 1148
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 2269
 
4.8%
동 1720
 
3.7%
리 1299
 
2.8%
산 1089
 
2.3%
차 1022
 
2.2%
대 967
 
2.1%
로 925
 
2.0%
부 907
 
1.9%
사 891
 
1.9%
거 860
 
1.8%
Other values (562) 34857
74.5%
Latin
ValueCountFrequency (%)
C 288
25.1%
I 226
19.7%
G 102
 
8.9%
S 97
 
8.4%
T 72
 
6.3%
K 71
 
6.2%
B 40
 
3.5%
E 31
 
2.7%
N 28
 
2.4%
U 26
 
2.3%
Other values (17) 167
14.5%
Common
ValueCountFrequency (%)
1 968
21.0%
2 664
14.4%
- 560
12.1%
3 400
8.7%
4 385
 
8.3%
5 364
 
7.9%
7 277
 
6.0%
6 266
 
5.8%
9 246
 
5.3%
0 200
 
4.3%
Other values (5) 286
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 46806
89.0%
ASCII 5764
 
11.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 2269
 
4.8%
동 1720
 
3.7%
리 1299
 
2.8%
산 1089
 
2.3%
차 1022
 
2.2%
대 967
 
2.1%
로 925
 
2.0%
부 907
 
1.9%
사 891
 
1.9%
거 860
 
1.8%
Other values (562) 34857
74.5%
ASCII
ValueCountFrequency (%)
1 968
16.8%
2 664
11.5%
- 560
9.7%
3 400
 
6.9%
4 385
 
6.7%
5 364
 
6.3%
C 288
 
5.0%
7 277
 
4.8%
6 266
 
4.6%
9 246
 
4.3%
Other values (32) 1346
23.4%

종점
Text

Distinct2625
Distinct (%)33.5%
Missing0
Missing (%)0.0%
Memory size61.3 KiB
2024-03-13T22:20:37.254117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length6.7182469
Min length2

Characters and Unicode

Total characters52577
Distinct characters613
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

Unique233 ?
Unique (%)3.0%

Sample

1st row신평사거리
2nd row신평동11-6
3rd row신평시장입구사거리
4th row신평역4번출구
5th row서빙고
ValueCountFrequency (%)
속성변화점 78
 
1.0%
명지ic 29
 
0.4%
덕천ic 17
 
0.2%
버스정류장 16
 
0.2%
거제역10번출구 15
 
0.2%
좌천삼거리 15
 
0.2%
청강교 15
 
0.2%
대동화명대교ic 14
 
0.2%
제1지하차도 13
 
0.2%
제2지하차도 13
 
0.2%
Other values (2615) 7601
97.1%
2024-03-13T22:20:37.748480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 2268
 
4.3%
동 1713
 
3.3%
리 1299
 
2.5%
산 1094
 
2.1%
차 1025
 
1.9%
대 968
 
1.8%
1 960
 
1.8%
로 930
 
1.8%
부 911
 
1.7%
사 891
 
1.7%
Other values (603) 40518
77.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 46841
89.1%
Decimal Number 3930
 
7.5%
Uppercase Letter 1138
 
2.2%
Dash Punctuation 556
 
1.1%
Open Punctuation 45
 
0.1%
Close Punctuation 45
 
0.1%
Lowercase Letter 14
 
< 0.1%
Other Punctuation 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 2268
 
4.8%
동 1713
 
3.7%
리 1299
 
2.8%
산 1094
 
2.3%
차 1025
 
2.2%
대 968
 
2.1%
로 930
 
2.0%
부 911
 
1.9%
사 891
 
1.9%
거 862
 
1.8%
Other values (561) 34880
74.5%
Uppercase Letter
ValueCountFrequency (%)
C 290
25.5%
I 228
20.0%
G 102
 
9.0%
S 96
 
8.4%
T 72
 
6.3%
K 71
 
6.2%
B 40
 
3.5%
E 31
 
2.7%
N 28
 
2.5%
U 26
 
2.3%
Other values (13) 154
13.5%
Decimal Number
ValueCountFrequency (%)
1 960
24.4%
2 665
16.9%
3 398
10.1%
4 382
 
9.7%
5 364
 
9.3%
7 272
 
6.9%
6 262
 
6.7%
9 244
 
6.2%
0 200
 
5.1%
8 183
 
4.7%
Lowercase Letter
ValueCountFrequency (%)
i 5
35.7%
l 5
35.7%
s 2
 
14.3%
k 2
 
14.3%
Other Punctuation
ValueCountFrequency (%)
& 5
62.5%
, 3
37.5%
Dash Punctuation
ValueCountFrequency (%)
- 556
100.0%
Open Punctuation
ValueCountFrequency (%)
( 45
100.0%
Close Punctuation
ValueCountFrequency (%)
) 45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 46841
89.1%
Common 4584
 
8.7%
Latin 1152
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 2268
 
4.8%
동 1713
 
3.7%
리 1299
 
2.8%
산 1094
 
2.3%
차 1025
 
2.2%
대 968
 
2.1%
로 930
 
2.0%
부 911
 
1.9%
사 891
 
1.9%
거 862
 
1.8%
Other values (561) 34880
74.5%
Latin
ValueCountFrequency (%)
C 290
25.2%
I 228
19.8%
G 102
 
8.9%
S 96
 
8.3%
T 72
 
6.2%
K 71
 
6.2%
B 40
 
3.5%
E 31
 
2.7%
N 28
 
2.4%
U 26
 
2.3%
Other values (17) 168
14.6%
Common
ValueCountFrequency (%)
1 960
20.9%
2 665
14.5%
- 556
12.1%
3 398
8.7%
4 382
 
8.3%
5 364
 
7.9%
7 272
 
5.9%
6 262
 
5.7%
9 244
 
5.3%
0 200
 
4.4%
Other values (5) 281
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 46841
89.1%
ASCII 5736
 
10.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 2268
 
4.8%
동 1713
 
3.7%
리 1299
 
2.8%
산 1094
 
2.3%
차 1025
 
2.2%
대 968
 
2.1%
로 930
 
2.0%
부 911
 
1.9%
사 891
 
1.9%
거 862
 
1.8%
Other values (561) 34880
74.5%
ASCII
ValueCountFrequency (%)
1 960
16.7%
2 665
11.6%
- 556
9.7%
3 398
 
6.9%
4 382
 
6.7%
5 364
 
6.3%
C 290
 
5.1%
7 272
 
4.7%
6 262
 
4.6%
9 244
 
4.3%
Other values (32) 1343
23.4%

Interactions

2024-03-13T22:20:34.771860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2024-03-13T22:20:34.898259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T22:20:35.007088image/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

링크번호링크명시점종점
01390042802장평로신평시장입구사거리신평사거리
11390042803장평로배고개사거리신평동11-6
21390042804장평로신평동11-6신평시장입구사거리
31390042900하신중앙로서빙고신평역4번출구
41390043000하신중앙로신평역4번출구서빙고
51390043100감천로비락우유감천대리점비락우유감천대리점
61390043200감천로비락우유감천대리점비락우유감천대리점
71390043700감천로비락우유감천대리점감천삼거리
81390043800감천로감천삼거리비락우유감천대리점
91390044500하신번영로신평역1번출구신평역9번출구
링크번호링크명시점종점
78161410947200공항로금호지하차도북측금호지하차도남측
78171410947500공항로금호지하차도남측금호지하차도북측
78181451152600동뫼길지하차도지하차도
78191451152700동뫼길지하차도지하차도
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