Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows17
Duplicate rows (%)0.2%
Total size in memory576.2 KiB
Average record size in memory59.0 B

Variable types

DateTime2
Text1
Numeric3

Dataset

Description대구광역시 도로 내에 DSRC 장치를 통하여 여러 구간마다 차량이 지나는 속도를 측정하여 구간의 교통 흐름상태를 나타내는 자료입니다.
URLhttps://www.data.go.kr/data/15117323/fileData.do

Alerts

Dataset has 17 (0.2%) duplicate rowsDuplicates
원활 is highly overall correlated with 서행High correlation
서행 is highly overall correlated with 원활High correlation
원활 has 1841 (18.4%) zerosZeros
서행 has 6286 (62.9%) zerosZeros
정체 has 9303 (93.0%) zerosZeros

Reproduction

Analysis started2023-12-12 16:27:10.362163
Analysis finished2023-12-12 16:27:12.410532
Duration2.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년월일
Date

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2023-06-01 00:00:00
Maximum2023-06-09 00:00:00
2023-12-13T01:27:12.451186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:12.552207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)

시
Date

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2023-12-13 00:00:00
Maximum2023-12-13 23:00:00
2023-12-13T01:27:12.688213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:12.791463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)

링크명
Text

Distinct457
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T01:27:12.960314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length18
Mean length12.5117
Min length8

Characters and Unicode

Total characters125117
Distinct characters163
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 row달성네거리-고성네거리
2nd row율하역-반야월네거리
3rd row침산교남단(북)-팔달교(입구)
4th row동대구역네거리-MBC네거리
5th row무열대네거리-신천지네거리
ValueCountFrequency (%)
칠곡ic 51
 
0.5%
산격중학교삼거리-복현오거리 47
 
0.5%
공산수원지삼거리-연암네거리 46
 
0.5%
연암네거리-공산수원지삼거리 45
 
0.4%
산격중학교삼거리-공산수원지삼거리 44
 
0.4%
공산수원지삼거리-산격중학교삼거리 44
 
0.4%
침산교남단(남)-연암사거리 39
 
0.4%
연암사거리-침산교남단(남 39
 
0.4%
담티고개고가차도-연호네거리 35
 
0.3%
효목고가입구-큰고개오거리 34
 
0.3%
Other values (449) 9659
95.8%
2023-12-13T01:27:13.296285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 16023
 
12.8%
거 15656
 
12.5%
네 10810
 
8.6%
- 10000
 
8.0%
교 4829
 
3.9%
산 3097
 
2.5%
삼 2859
 
2.3%
남 2275
 
1.8%
대 2058
 
1.6%
단 1974
 
1.6%
Other values (153) 55536
44.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 110144
88.0%
Dash Punctuation 10000
 
8.0%
Uppercase Letter 2117
 
1.7%
Open Punctuation 1225
 
1.0%
Close Punctuation 1225
 
1.0%
Space Separator 225
 
0.2%
Decimal Number 181
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 16023
 
14.5%
거 15656
 
14.2%
네 10810
 
9.8%
교 4829
 
4.4%
산 3097
 
2.8%
삼 2859
 
2.6%
남 2275
 
2.1%
대 2058
 
1.9%
단 1974
 
1.8%
성 1894
 
1.7%
Other values (139) 48669
44.2%
Uppercase Letter
ValueCountFrequency (%)
C 677
32.0%
I 566
26.7%
M 230
 
10.9%
B 230
 
10.9%
T 138
 
6.5%
E 119
 
5.6%
R 119
 
5.6%
A 19
 
0.9%
P 19
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1225
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1225
100.0%
Space Separator
ValueCountFrequency (%)
225
100.0%
Decimal Number
ValueCountFrequency (%)
2 181
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 110144
88.0%
Common 12856
 
10.3%
Latin 2117
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 16023
 
14.5%
거 15656
 
14.2%
네 10810
 
9.8%
교 4829
 
4.4%
산 3097
 
2.8%
삼 2859
 
2.6%
남 2275
 
2.1%
대 2058
 
1.9%
단 1974
 
1.8%
성 1894
 
1.7%
Other values (139) 48669
44.2%
Latin
ValueCountFrequency (%)
C 677
32.0%
I 566
26.7%
M 230
 
10.9%
B 230
 
10.9%
T 138
 
6.5%
E 119
 
5.6%
R 119
 
5.6%
A 19
 
0.9%
P 19
 
0.9%
Common
ValueCountFrequency (%)
- 10000
77.8%
( 1225
 
9.5%
) 1225
 
9.5%
225
 
1.8%
2 181
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 110144
88.0%
ASCII 14973
 
12.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 16023
 
14.5%
거 15656
 
14.2%
네 10810
 
9.8%
교 4829
 
4.4%
산 3097
 
2.8%
삼 2859
 
2.6%
남 2275
 
2.1%
대 2058
 
1.9%
단 1974
 
1.8%
성 1894
 
1.7%
Other values (139) 48669
44.2%
ASCII
ValueCountFrequency (%)
- 10000
66.8%
( 1225
 
8.2%
) 1225
 
8.2%
C 677
 
4.5%
I 566
 
3.8%
M 230
 
1.5%
B 230
 
1.5%
225
 
1.5%
2 181
 
1.2%
T 138
 
0.9%
Other values (4) 276
 
1.8%

원활
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6734
Minimum0
Maximum12
Zeros1841
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:27:13.412276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median12
Q312
95-th percentile12
Maximum12
Range12
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.8886933
Coefficient of variation (CV)0.56364209
Kurtosis-0.82322231
Mean8.6734
Median Absolute Deviation (MAD)0
Skewness-0.99383346
Sum86734
Variance23.899322
MonotonicityNot monotonic
2023-12-13T01:27:13.520586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
12 6115
61.2%
0 1841
 
18.4%
11 353
 
3.5%
9 202
 
2.0%
10 192
 
1.9%
1 189
 
1.9%
7 171
 
1.7%
2 164
 
1.6%
4 164
 
1.6%
8 157
 
1.6%
Other values (3) 452
 
4.5%
ValueCountFrequency (%)
0 1841
18.4%
1 189
 
1.9%
2 164
 
1.6%
3 150
 
1.5%
4 164
 
1.6%
5 155
 
1.6%
6 147
 
1.5%
7 171
 
1.7%
8 157
 
1.6%
9 202
 
2.0%
ValueCountFrequency (%)
12 6115
61.2%
11 353
 
3.5%
10 192
 
1.9%
9 202
 
2.0%
8 157
 
1.6%
7 171
 
1.7%
6 147
 
1.5%
5 155
 
1.6%
4 164
 
1.6%
3 150
 
1.5%

서행
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9065
Minimum0
Maximum12
Zeros6286
Zeros (%)62.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:27:13.621419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile12
Maximum12
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.5067714
Coefficient of variation (CV)1.5505837
Kurtosis-0.33546613
Mean2.9065
Median Absolute Deviation (MAD)0
Skewness1.173812
Sum29065
Variance20.310989
MonotonicityNot monotonic
2023-12-13T01:27:13.731212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 6286
62.9%
12 1226
 
12.3%
1 354
 
3.5%
11 255
 
2.5%
3 234
 
2.3%
2 230
 
2.3%
8 227
 
2.3%
10 223
 
2.2%
7 203
 
2.0%
5 199
 
2.0%
Other values (3) 563
 
5.6%
ValueCountFrequency (%)
0 6286
62.9%
1 354
 
3.5%
2 230
 
2.3%
3 234
 
2.3%
4 189
 
1.9%
5 199
 
2.0%
6 184
 
1.8%
7 203
 
2.0%
8 227
 
2.3%
9 190
 
1.9%
ValueCountFrequency (%)
12 1226
12.3%
11 255
 
2.5%
10 223
 
2.2%
9 190
 
1.9%
8 227
 
2.3%
7 203
 
2.0%
6 184
 
1.8%
5 199
 
2.0%
4 189
 
1.9%
3 234
 
2.3%

정체
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.417
Minimum0
Maximum12
Zeros9303
Zeros (%)93.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:27:13.858777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.8702569
Coefficient of variation (CV)4.4850285
Kurtosis25.774626
Mean0.417
Median Absolute Deviation (MAD)0
Skewness5.0704018
Sum4170
Variance3.4978608
MonotonicityNot monotonic
2023-12-13T01:27:13.960499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 9303
93.0%
12 146
 
1.5%
1 115
 
1.1%
2 85
 
0.9%
4 72
 
0.7%
3 61
 
0.6%
5 41
 
0.4%
6 39
 
0.4%
8 34
 
0.3%
7 29
 
0.3%
Other values (3) 75
 
0.8%
ValueCountFrequency (%)
0 9303
93.0%
1 115
 
1.1%
2 85
 
0.9%
3 61
 
0.6%
4 72
 
0.7%
5 41
 
0.4%
6 39
 
0.4%
7 29
 
0.3%
8 34
 
0.3%
9 25
 
0.2%
ValueCountFrequency (%)
12 146
1.5%
11 23
 
0.2%
10 27
 
0.3%
9 25
 
0.2%
8 34
 
0.3%
7 29
 
0.3%
6 39
 
0.4%
5 41
 
0.4%
4 72
0.7%
3 61
0.6%

Interactions

2023-12-13T01:27:11.874049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:11.064213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:11.533525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:11.976865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:11.296770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:11.637064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:12.076134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:11.423828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:11.760686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:27:14.036158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월일시원활서행정체
년월일1.0000.1980.0670.0660.064
시0.1981.0000.3350.3020.233
원활0.0670.3351.0000.9900.447
서행0.0660.3020.9901.0000.804
정체0.0640.2330.4470.8041.000
2023-12-13T01:27:14.117671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
원활서행정체
원활1.000-0.947-0.423
서행-0.9471.0000.190
정체-0.4230.1901.000

Missing values

2023-12-13T01:27:12.257247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:27:12.364931image/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

년월일시링크명원활서행정체
166612023-06-0211:00달성네거리-고성네거리0102
480382023-06-0507:00율하역-반야월네거리1200
135852023-06-0205:00침산교남단(북)-팔달교(입구)1200
690922023-06-0704:00동대구역네거리-MBC네거리1020
106812023-06-0122:00무열대네거리-신천지네거리1200
856422023-06-0816:00월곡네거리-상인네거리(고가)1200
4792023-06-0101:00두류공원네거리-성당네거리1200
922282023-06-0906:00청구네거리-수성네거리1200
885072023-06-0822:00궁전맨션삼거리-범어네거리1200
501142023-06-0511:00침산네거리-원대오거리0120
년월일시링크명원활서행정체
901322023-06-0901:00고성네거리-달성네거리0120
149702023-06-0208:00공항교-복현오거리048
628992023-06-0615:00복현오거리-산격중학교삼거리0120
247922023-06-0305:00율하역-반야월삼거리750
673552023-06-0700:00서변교사거리-침산교남단(북)1200
753392023-06-0718:00신평리네거리-평리네거리0120
740872023-06-0715:00담티고개고가차도-연호네거리1200
173062023-06-0213:00팔달교(입구)-침산교남단(북)1200
323932023-06-0321:00범어네거리-MBC네거리840
309262023-06-0318:00대구역네거리-태평네거리1110

Duplicate rows

Most frequently occurring

년월일시링크명원활서행정체# duplicates
02023-06-0104:00산격중학교삼거리-복현오거리12002
12023-06-0104:00연암사거리-침산교남단(남)12002
22023-06-0115:00복현오거리-산격중학교삼거리01202
32023-06-0118:00산격중학교삼거리-복현오거리01112
42023-06-0219:00산격중학교삼거리-공산수원지삼거리12002
52023-06-0310:00공산수원지삼거리-연암네거리7502
62023-06-0314:00복현오거리-산격중학교삼거리01022
72023-06-0321:00복현오거리-산격중학교삼거리01202
82023-06-0404:00연암사거리-침산교남단(남)12002
92023-06-0506:00연암네거리-공산수원지삼거리12002