Overview

Dataset statistics

Number of variables5
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows52
Duplicate rows (%)0.5%
Total size in memory478.5 KiB
Average record size in memory49.0 B

Variable types

Categorical3
Text1
Numeric1

Dataset

Description대구광역시 내의 주요 링크별 교통 흐름의 시간 및 속도의 데이터입니다. 이에 대한 데이터로는 시간,링크명,측정 일시 등입니다.
URLhttps://www.data.go.kr/data/15117329/fileData.do

Alerts

Dataset has 52 (0.5%) duplicate rowsDuplicates

Reproduction

Analysis started2023-12-12 10:17:51.924677
Analysis finished2023-12-12 10:17:52.658388
Duration0.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년월일
Categorical

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-06-02
 
386
2023-06-14
 
364
2023-06-15
 
351
2023-06-23
 
351
2023-06-27
 
348
Other values (25)
8200 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-06-25
2nd row2023-06-29
3rd row2023-06-18
4th row2023-06-02
5th row2023-06-01

Common Values

ValueCountFrequency (%)
2023-06-02 386
 
3.9%
2023-06-14 364
 
3.6%
2023-06-15 351
 
3.5%
2023-06-23 351
 
3.5%
2023-06-27 348
 
3.5%
2023-06-22 348
 
3.5%
2023-06-30 347
 
3.5%
2023-06-04 344
 
3.4%
2023-06-29 340
 
3.4%
2023-06-01 339
 
3.4%
Other values (20) 6482
64.8%

Length

2023-12-12T19:17:52.736029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2023-06-02 386
 
3.9%
2023-06-14 364
 
3.6%
2023-06-15 351
 
3.5%
2023-06-23 351
 
3.5%
2023-06-27 348
 
3.5%
2023-06-22 348
 
3.5%
2023-06-30 347
 
3.5%
2023-06-04 344
 
3.4%
2023-06-29 340
 
3.4%
2023-06-01 339
 
3.4%
Other values (20) 6482
64.8%

시
Categorical

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
13:00
 
458
23:00
 
456
09:00
 
448
04:00
 
441
21:00
 
438
Other values (19)
7759 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row14:00
2nd row11:00
3rd row02:00
4th row16:00
5th row11:00

Common Values

ValueCountFrequency (%)
13:00 458
 
4.6%
23:00 456
 
4.6%
09:00 448
 
4.5%
04:00 441
 
4.4%
21:00 438
 
4.4%
17:00 432
 
4.3%
19:00 430
 
4.3%
14:00 429
 
4.3%
02:00 421
 
4.2%
01:00 418
 
4.2%
Other values (14) 5629
56.3%

Length

2023-12-12T19:17:52.891557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
13:00 458
 
4.6%
23:00 456
 
4.6%
09:00 448
 
4.5%
04:00 441
 
4.4%
21:00 438
 
4.4%
17:00 432
 
4.3%
19:00 430
 
4.3%
14:00 429
 
4.3%
02:00 421
 
4.2%
01:00 418
 
4.2%
Other values (14) 5629
56.3%

가로명
Categorical

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
신천대로
1169 
구마로대명로
1043 
월배로성당로
1025 
공항로팔공로
956 
신천동로
810 
Other values (16)
4997 

Length

Max length9
Median length8
Mean length5.2451
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row팔달로칠곡로
2nd row신천대로
3rd row구마로대명로
4th row팔달로칠곡로
5th row신천동로

Common Values

ValueCountFrequency (%)
신천대로 1169
11.7%
구마로대명로 1043
10.4%
월배로성당로 1025
10.2%
공항로팔공로 956
9.6%
신천동로 810
8.1%
팔달로칠곡로 795
8.0%
두류공원로서대구로 621
 
6.2%
호국로 596
 
6.0%
노원로동북로 555
 
5.5%
상화로앞산순환로 467
 
4.7%
Other values (11) 1963
19.6%

Length

2023-12-12T19:17:53.019904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
신천대로 1169
11.7%
구마로대명로 1043
10.4%
월배로성당로 1025
10.2%
공항로팔공로 956
9.6%
신천동로 810
8.1%
팔달로칠곡로 795
8.0%
두류공원로서대구로 621
 
6.2%
호국로 596
 
6.0%
노원로동북로 555
 
5.5%
상화로앞산순환로 467
 
4.7%
Other values (11) 1963
19.6%

링크명
Text

Distinct124
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T19:17:53.269397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length12.6432
Min length8

Characters and Unicode

Total characters126432
Distinct characters103
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

Unique0 ?
Unique (%)0.0%

Sample

1st row태전삼거리-칠곡네거리
2nd row도청교남단-침산교남단(북)
3rd row남대구IC삼거리-성서공단네거리
4th row태전삼거리-칠곡네거리
5th row수성교북단-신천교북단
ValueCountFrequency (%)
산격중학교삼거리-공산수원지삼거리 324
 
3.1%
복현오거리-산격중학교삼거리 305
 
3.0%
공산수원지삼거리-산격중학교삼거리 295
 
2.9%
칠곡ic 148
 
1.4%
네거리 141
 
1.4%
현충삼거리-앞산네거리 96
 
0.9%
상동교북단-중동교북단 93
 
0.9%
불로삼거리-공항교 93
 
0.9%
진천역네거리-상인네거리 92
 
0.9%
앞산네거리-두류공원네거리 92
 
0.9%
Other values (116) 8610
83.7%
2023-12-12T19:17:53.724463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 15192
 
12.0%
거 14562
 
11.5%
네 10154
 
8.0%
- 10000
 
7.9%
교 7041
 
5.6%
단 3262
 
2.6%
산 3109
 
2.5%
삼 3062
 
2.4%
천 2755
 
2.2%
남 2651
 
2.1%
Other values (93) 54644
43.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 110844
87.7%
Dash Punctuation 10000
 
7.9%
Uppercase Letter 2153
 
1.7%
Close Punctuation 1388
 
1.1%
Open Punctuation 1388
 
1.1%
Decimal Number 370
 
0.3%
Space Separator 289
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 15192
 
13.7%
거 14562
 
13.1%
네 10154
 
9.2%
교 7041
 
6.4%
단 3262
 
2.9%
산 3109
 
2.8%
삼 3062
 
2.8%
천 2755
 
2.5%
남 2651
 
2.4%
공 2314
 
2.1%
Other values (83) 46742
42.2%
Uppercase Letter
ValueCountFrequency (%)
I 964
44.8%
C 964
44.8%
T 75
 
3.5%
P 75
 
3.5%
A 75
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1388
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1388
100.0%
Decimal Number
ValueCountFrequency (%)
2 370
100.0%
Space Separator
ValueCountFrequency (%)
289
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 110844
87.7%
Common 13435
 
10.6%
Latin 2153
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 15192
 
13.7%
거 14562
 
13.1%
네 10154
 
9.2%
교 7041
 
6.4%
단 3262
 
2.9%
산 3109
 
2.8%
삼 3062
 
2.8%
천 2755
 
2.5%
남 2651
 
2.4%
공 2314
 
2.1%
Other values (83) 46742
42.2%
Common
ValueCountFrequency (%)
- 10000
74.4%
) 1388
 
10.3%
( 1388
 
10.3%
2 370
 
2.8%
289
 
2.2%
Latin
ValueCountFrequency (%)
I 964
44.8%
C 964
44.8%
T 75
 
3.5%
P 75
 
3.5%
A 75
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 110844
87.7%
ASCII 15588
 
12.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 15192
 
13.7%
거 14562
 
13.1%
네 10154
 
9.2%
교 7041
 
6.4%
단 3262
 
2.9%
산 3109
 
2.8%
삼 3062
 
2.8%
천 2755
 
2.5%
남 2651
 
2.4%
공 2314
 
2.1%
Other values (83) 46742
42.2%
ASCII
ValueCountFrequency (%)
- 10000
64.2%
) 1388
 
8.9%
( 1388
 
8.9%
I 964
 
6.2%
C 964
 
6.2%
2 370
 
2.4%
289
 
1.9%
T 75
 
0.5%
P 75
 
0.5%
A 75
 
0.5%

속도
Real number (ℝ)

Distinct75
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.0962
Minimum6
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T19:17:53.914687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile17
Q125
median33
Q349
95-th percentile65
Maximum80
Range74
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.775932
Coefficient of variation (CV)0.42527084
Kurtosis-0.19469095
Mean37.0962
Median Absolute Deviation (MAD)10
Skewness0.69878558
Sum370962
Variance248.88003
MonotonicityNot monotonic
2023-12-12T19:17:54.069028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 454
 
4.5%
27 349
 
3.5%
30 326
 
3.3%
28 324
 
3.2%
26 315
 
3.1%
23 314
 
3.1%
25 308
 
3.1%
31 307
 
3.1%
22 292
 
2.9%
32 287
 
2.9%
Other values (65) 6724
67.2%
ValueCountFrequency (%)
6 5
 
0.1%
7 10
 
0.1%
8 12
 
0.1%
9 6
 
0.1%
10 27
 
0.3%
11 35
 
0.4%
12 54
0.5%
13 60
0.6%
14 54
0.5%
15 88
0.9%
ValueCountFrequency (%)
80 171
1.7%
79 20
 
0.2%
78 16
 
0.2%
77 30
 
0.3%
76 17
 
0.2%
75 14
 
0.1%
74 16
 
0.2%
73 20
 
0.2%
72 25
 
0.2%
71 18
 
0.2%

Interactions

2023-12-12T19:17:52.344449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:17:54.206650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월일시가로명속도
년월일1.0000.0420.0000.079
시0.0421.0000.0000.336
가로명0.0000.0001.0000.684
속도0.0790.3360.6841.000
2023-12-12T19:17:54.308253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월일시가로명
년월일1.0000.0100.000
시0.0101.0000.000
가로명0.0000.0001.000
2023-12-12T19:17:54.402650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
속도년월일시가로명
속도1.0000.0250.1300.331
년월일0.0251.0000.0100.000
시0.1300.0101.0000.000
가로명0.3310.0000.0001.000

Missing values

2023-12-12T19:17:52.489434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:17:52.611674image/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

년월일시가로명링크명속도
41902023-06-2514:00팔달로칠곡로태전삼거리-칠곡네거리29
287632023-06-2911:00신천대로도청교남단-침산교남단(북)30
464902023-06-1802:00구마로대명로남대구IC삼거리-성서공단네거리54
36402023-06-0216:00팔달로칠곡로태전삼거리-칠곡네거리26
345712023-06-0111:00신천동로수성교북단-신천교북단55
681262023-06-1914:00신천대로성서IC-매천대교남단(지상)67
242962023-06-2308:00신천대로상동교네거리-중동교남단49
663792023-06-0619:00공항로팔공로불로삼거리-공항교37
919452023-06-0606:00공항로팔공로복현오거리-산격중학교삼거리30
722542023-06-1114:00신천대로침산교남단(북)-팔달교(입구)77
년월일시가로명링크명속도
836372023-06-0521:00호국로서변교네거리-산격대교네거리52
233402023-06-1312:00신천대로중동교-상동교네거리62
866542023-06-1103:00노원로동북로산격중학교삼거리-공산수원지삼거리59
489932023-06-0209:00구마로대명로본리네거리-감천네거리31
425692023-06-0417:00달서대로신당네거리-호림네거리32
317562023-06-0404:00신천동로상동교북단-중동교북단33
27912023-06-2707:00팔달로칠곡로칠곡네거리-칠곡우체국사거리53
849312023-06-2919:00호국로도곡네거리-서변교네거리30
258212023-06-2621:00신천대로동신교남-신천교남단(남)62
852712023-06-1323:00호국로산격대교네거리-서변교네거리51

Duplicate rows

Most frequently occurring

년월일시가로명링크명속도# duplicates
02023-06-0101:00공항로팔공로복현오거리-산격중학교삼거리272
12023-06-0205:00공항로팔공로산격중학교삼거리-공산수원지삼거리602
22023-06-0207:00공항로팔공로산격중학교삼거리-공산수원지삼거리412
32023-06-0209:00공항로팔공로공산수원지삼거리-산격중학교삼거리362
42023-06-0302:00노원로동북로공산수원지삼거리-산격중학교삼거리512
52023-06-0310:00노원로동북로산격중학교삼거리-공산수원지삼거리432
62023-06-0315:00노원로동북로공산수원지삼거리-산격중학교삼거리282
72023-06-0414:00공항로팔공로공산수원지삼거리-산격중학교삼거리302
82023-06-0509:00노원로동북로복현오거리-산격중학교삼거리152
92023-06-0513:00노원로동북로산격중학교삼거리-공산수원지삼거리342