Overview

Dataset statistics

Number of variables5
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows4
Duplicate rows (%)< 0.1%
Total size in memory488.3 KiB
Average record size in memory50.0 B

Variable types

DateTime2
Text1
Numeric2

Dataset

Description대구광역시 주요 도로구간 별(ex 매천교 -> 침산교) 속도를 측정한 통계치를 나타냅니다. 이에 대한 데이터로 구간이름,속도,통과시간 등이 있습니다.
URLhttps://www.data.go.kr/data/15117326/fileData.do

Alerts

Dataset has 4 (< 0.1%) duplicate rowsDuplicates

Reproduction

Analysis started2023-12-12 04:03:23.967176
Analysis finished2023-12-12 04:03:24.824744
Duration0.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년월일
Date

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2023-06-01 00:00:00
Maximum2023-06-10 00:00:00
2023-12-12T13:03:24.887738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:25.022707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)

시
Date

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2023-12-12 00:00:00
Maximum2023-12-12 23:00:00
2023-12-12T13:03:25.146578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:25.291188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)

구간이름
Text

Distinct359
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T13:03:25.754637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length10.0953
Min length7

Characters and Unicode

Total characters100953
Distinct characters161
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

Unique0 ?
Unique (%)0.0%

Sample

1st row수성교->두류4
2nd row효목4->MBC4
3rd row파계교->도곡4
4th row상동교->수성교
5th row칠곡우체국교차로->매천대교남단
ValueCountFrequency (%)
두류4-반월당4 180
 
1.8%
만촌4-연호4 160
 
1.6%
mbc4-무열대4 148
 
1.5%
신당4-성서ic 105
 
1.0%
범물4-관계3 97
 
1.0%
성서ic-남대구ic 96
 
1.0%
팔달교-성서ic 92
 
0.9%
산격대교-서변교 83
 
0.8%
성서ic-두류4 81
 
0.8%
대봉교-신천교 78
 
0.8%
Other values (352) 8963
88.9%
2023-12-12T13:03:26.140248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 10139
 
10.0%
> 7558
 
7.5%
교 6684
 
6.6%
4 5743
 
5.7%
리 3391
 
3.4%
거 3118
 
3.1%
대 2678
 
2.7%
성 2488
 
2.5%
네 2318
 
2.3%
산 2213
 
2.2%
Other values (151) 54623
54.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 69671
69.0%
Dash Punctuation 10139
 
10.0%
Math Symbol 7558
 
7.5%
Decimal Number 7247
 
7.2%
Uppercase Letter 4383
 
4.3%
Open Punctuation 799
 
0.8%
Close Punctuation 799
 
0.8%
Other Punctuation 184
 
0.2%
Space Separator 173
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 6684
 
9.6%
리 3391
 
4.9%
거 3118
 
4.5%
대 2678
 
3.8%
성 2488
 
3.6%
네 2318
 
3.3%
산 2213
 
3.2%
동 2072
 
3.0%
신 1952
 
2.8%
천 1927
 
2.8%
Other values (133) 40830
58.6%
Uppercase Letter
ValueCountFrequency (%)
C 1819
41.5%
I 1350
30.8%
M 469
 
10.7%
B 469
 
10.7%
P 92
 
2.1%
T 92
 
2.1%
A 92
 
2.1%
Decimal Number
ValueCountFrequency (%)
4 5743
79.2%
3 1016
 
14.0%
5 488
 
6.7%
Open Punctuation
ValueCountFrequency (%)
[ 662
82.9%
( 137
 
17.1%
Close Punctuation
ValueCountFrequency (%)
] 662
82.9%
) 137
 
17.1%
Dash Punctuation
ValueCountFrequency (%)
- 10139
100.0%
Math Symbol
ValueCountFrequency (%)
> 7558
100.0%
Other Punctuation
ValueCountFrequency (%)
. 184
100.0%
Space Separator
ValueCountFrequency (%)
173
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 69671
69.0%
Common 26899
 
26.6%
Latin 4383
 
4.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 6684
 
9.6%
리 3391
 
4.9%
거 3118
 
4.5%
대 2678
 
3.8%
성 2488
 
3.6%
네 2318
 
3.3%
산 2213
 
3.2%
동 2072
 
3.0%
신 1952
 
2.8%
천 1927
 
2.8%
Other values (133) 40830
58.6%
Common
ValueCountFrequency (%)
- 10139
37.7%
> 7558
28.1%
4 5743
21.4%
3 1016
 
3.8%
[ 662
 
2.5%
] 662
 
2.5%
5 488
 
1.8%
. 184
 
0.7%
173
 
0.6%
( 137
 
0.5%
Latin
ValueCountFrequency (%)
C 1819
41.5%
I 1350
30.8%
M 469
 
10.7%
B 469
 
10.7%
P 92
 
2.1%
T 92
 
2.1%
A 92
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 69671
69.0%
ASCII 31282
31.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 10139
32.4%
> 7558
24.2%
4 5743
18.4%
C 1819
 
5.8%
I 1350
 
4.3%
3 1016
 
3.2%
[ 662
 
2.1%
] 662
 
2.1%
5 488
 
1.6%
M 469
 
1.5%
Other values (8) 1376
 
4.4%
Hangul
ValueCountFrequency (%)
교 6684
 
9.6%
리 3391
 
4.9%
거 3118
 
4.5%
대 2678
 
3.8%
성 2488
 
3.6%
네 2318
 
3.3%
산 2213
 
3.2%
동 2072
 
3.0%
신 1952
 
2.8%
천 1927
 
2.8%
Other values (133) 40830
58.6%

속도
Real number (ℝ)

Distinct84
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.9562
Minimum7
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:03:26.294144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile18
Q127
median33
Q344
95-th percentile67
Maximum91
Range84
Interquartile range (IQR)17

Descriptive statistics

Standard deviation14.784104
Coefficient of variation (CV)0.40004395
Kurtosis0.41891418
Mean36.9562
Median Absolute Deviation (MAD)8
Skewness0.93735776
Sum369562
Variance218.56974
MonotonicityNot monotonic
2023-12-12T13:03:26.445656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 391
 
3.9%
32 380
 
3.8%
30 379
 
3.8%
31 378
 
3.8%
28 375
 
3.8%
27 358
 
3.6%
35 335
 
3.4%
29 330
 
3.3%
34 324
 
3.2%
26 318
 
3.2%
Other values (74) 6432
64.3%
ValueCountFrequency (%)
7 2
 
< 0.1%
8 4
 
< 0.1%
9 5
 
0.1%
10 13
 
0.1%
11 15
 
0.1%
12 20
 
0.2%
13 30
 
0.3%
14 51
0.5%
15 72
0.7%
16 80
0.8%
ValueCountFrequency (%)
91 1
 
< 0.1%
89 3
 
< 0.1%
88 3
 
< 0.1%
87 5
0.1%
86 8
0.1%
85 10
0.1%
84 12
0.1%
83 6
0.1%
82 9
0.1%
81 3
 
< 0.1%

통과시간
Real number (ℝ)

Distinct413
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean483.0481
Minimum60
Maximum2280
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:03:26.645629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile180
Q1300
median435
Q3600
95-th percentile965
Maximum2280
Range2220
Interquartile range (IQR)300

Descriptive statistics

Standard deviation249.9328
Coefficient of variation (CV)0.51740768
Kurtosis3.410301
Mean483.0481
Median Absolute Deviation (MAD)140
Skewness1.3711529
Sum4830481
Variance62466.403
MonotonicityNot monotonic
2023-12-12T13:03:26.806399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300 472
 
4.7%
240 411
 
4.1%
420 310
 
3.1%
180 289
 
2.9%
360 242
 
2.4%
480 213
 
2.1%
120 166
 
1.7%
430 116
 
1.2%
425 102
 
1.0%
540 101
 
1.0%
Other values (403) 7578
75.8%
ValueCountFrequency (%)
60 44
0.4%
65 5
 
0.1%
70 4
 
< 0.1%
75 3
 
< 0.1%
82 2
 
< 0.1%
85 6
 
0.1%
87 1
 
< 0.1%
90 2
 
< 0.1%
93 1
 
< 0.1%
95 2
 
< 0.1%
ValueCountFrequency (%)
2280 1
< 0.1%
2170 1
< 0.1%
2150 1
< 0.1%
2145 1
< 0.1%
2115 1
< 0.1%
2000 1
< 0.1%
1970 1
< 0.1%
1955 1
< 0.1%
1940 1
< 0.1%
1935 1
< 0.1%

Interactions

2023-12-12T13:03:24.459931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:24.293246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:24.546638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:24.374779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:03:26.923118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월일시속도통과시간
년월일1.0000.2300.1310.070
시0.2301.0000.3530.208
속도0.1310.3531.0000.493
통과시간0.0700.2080.4931.000
2023-12-12T13:03:27.054554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
속도통과시간
속도1.000-0.399
통과시간-0.3991.000

Missing values

2023-12-12T13:03:24.682667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:03:24.779770image/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

년월일시구간이름속도통과시간
154902023-06-0212:00수성교->두류4261040
440272023-06-0506:00효목4->MBC433310
231962023-06-0305:00파계교->도곡439510
535762023-06-0604:00상동교->수성교68330
514132023-06-0523:00칠곡우체국교차로->매천대교남단21460
951132023-06-1005:00호림4->신당421385
152142023-06-0211:00성서IC-두류427585
690392023-06-0716:00만촌4-연호432240
423062023-06-0502:00두류4-반월당431320
456832023-06-0510:00성당4->남대구IC30545
년월일시구간이름속도통과시간
536822023-06-0604:00복현5->효목고가22580
103822023-06-0200:00산격대교-서변교24775
53002023-06-0112:00범어4-궁전맨션323435
358002023-06-0411:00동산4-서성435245
500422023-06-0520:00성서IC-두류435420
252452023-06-0310:00이곡역네거리->대한제분앞7365
96242023-06-0122:00성서IC-두류428545
763812023-06-0809:00두류네거리->신평리네거리31565
613922023-06-0622:00반월당->영대병원426445
475932023-06-0514:00상인공원교차로->청소년수련원교차로52371

Duplicate rows

Most frequently occurring

년월일시구간이름속도통과시간# duplicates
02023-06-0307:00수성교->상동교[신천동로]572502
12023-06-0509:00수성교->상동교[신천동로]612402
22023-06-0523:00수성교->상동교[신천대로]653002
32023-06-0817:00수성교->상동교[신천대로]217852