Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory781.2 KiB
Average record size in memory80.0 B

Variable types

Numeric5
Categorical3

Dataset

Description2024년 1월 현황입니다.광주광역시 교통 소통 정보(구축시기 : 2009년도)주요 교차로 노변기지국 및 신호연동으로 원활한 교통소통 도모 및 교통서비스 제고
Author광주광역시
URLhttps://www.data.go.kr/data/15042700/fileData.do

Alerts

교통운영상태 has constant value ""Constant
점유율 has constant value ""Constant
속도 is highly overall correlated with 혼잡도레벨High correlation
통행시간 is highly overall correlated with 교통량High correlation
교통량 is highly overall correlated with 통행시간High correlation
혼잡도레벨 is highly overall correlated with 속도High correlation
연계번호 is highly skewed (γ1 = 22.35851721)Skewed

Reproduction

Analysis started2024-03-15 00:50:54.709941
Analysis finished2024-03-15 00:51:02.966145
Duration8.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

수집일시
Real number (ℝ)

Distinct331
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0240115 × 1013
Minimum2.0240115 × 1013
Maximum2.0240115 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T09:51:03.105139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0240115 × 1013
5-th percentile2.0240115 × 1013
Q12.0240115 × 1013
median2.0240115 × 1013
Q32.0240115 × 1013
95-th percentile2.0240115 × 1013
Maximum2.0240115 × 1013
Range39202
Interquartile range (IQR)18603

Descriptive statistics

Standard deviation11598.242
Coefficient of variation (CV)5.730324 × 10-10
Kurtosis-1.0001509
Mean2.0240115 × 1013
Median Absolute Deviation (MAD)9702
Skewness0.25069234
Sum2.0240115 × 1017
Variance1.3451921 × 108
MonotonicityNot monotonic
2024-03-15T09:51:03.580628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20240115092900 76
 
0.8%
20240115093600 70
 
0.7%
20240115092800 68
 
0.7%
20240115093000 65
 
0.7%
20240115092700 65
 
0.7%
20240115093100 65
 
0.7%
20240115093500 63
 
0.6%
20240115092600 62
 
0.6%
20240115085301 62
 
0.6%
20240115093701 58
 
0.6%
Other values (321) 9346
93.5%
ValueCountFrequency (%)
20240115085100 49
0.5%
20240115085101 4
 
< 0.1%
20240115085200 52
0.5%
20240115085201 41
0.4%
20240115085300 35
0.4%
20240115085301 62
0.6%
20240115085500 51
0.5%
20240115085501 54
0.5%
20240115085600 54
0.5%
20240115085601 46
0.5%
ValueCountFrequency (%)
20240115124302 33
0.3%
20240115124301 45
0.4%
20240115124300 8
 
0.1%
20240115124203 14
 
0.1%
20240115124202 30
0.3%
20240115124201 48
0.5%
20240115124200 8
 
0.1%
20240115124003 19
 
0.2%
20240115124002 39
0.4%
20240115124001 43
0.4%

연계번호
Real number (ℝ)

SKEWED 

Distinct998
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7764359 × 109
Minimum1.7500029 × 109
Maximum3.4201513 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T09:51:03.884223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.7500029 × 109
5-th percentile1.7500258 × 109
Q11.760132 × 109
median1.780006 × 109
Q31.7801317 × 109
95-th percentile1.7901991 × 109
Maximum3.4201513 × 109
Range1.6701484 × 109
Interquartile range (IQR)19999725

Descriptive statistics

Standard deviation70975761
Coefficient of variation (CV)0.039954024
Kurtosis515.13644
Mean1.7764359 × 109
Median Absolute Deviation (MAD)10012200
Skewness22.358517
Sum1.7764359 × 1013
Variance5.0375587 × 1015
MonotonicityNot monotonic
2024-03-15T09:51:04.150462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1760135400 35
 
0.4%
1760113200 31
 
0.3%
1790068800 28
 
0.3%
1760004101 28
 
0.3%
1760132000 26
 
0.3%
1780002502 24
 
0.2%
1770051700 24
 
0.2%
1760104500 23
 
0.2%
1760003800 23
 
0.2%
1760003302 23
 
0.2%
Other values (988) 9735
97.4%
ValueCountFrequency (%)
1750002900 8
0.1%
1750003000 7
0.1%
1750003101 11
0.1%
1750003102 15
0.1%
1750003201 8
0.1%
1750003202 9
0.1%
1750003300 6
 
0.1%
1750003400 7
0.1%
1750003501 7
0.1%
1750003601 9
0.1%
ValueCountFrequency (%)
3420151300 11
0.1%
3420151200 7
0.1%
1790435300 5
 
0.1%
1790435200 8
0.1%
1790434700 8
0.1%
1790434600 9
0.1%
1790433700 4
 
< 0.1%
1790433600 14
0.1%
1790433500 6
0.1%
1790433400 10
0.1%

속도
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.9218
Minimum1
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T09:51:04.512362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q118
median25
Q335
95-th percentile79
Maximum97
Range96
Interquartile range (IQR)17

Descriptive statistics

Standard deviation19.243724
Coefficient of variation (CV)0.62233518
Kurtosis1.4988501
Mean30.9218
Median Absolute Deviation (MAD)8
Skewness1.5072554
Sum309218
Variance370.32092
MonotonicityNot monotonic
2024-03-15T09:51:04.960662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 429
 
4.3%
17 428
 
4.3%
18 424
 
4.2%
19 418
 
4.2%
23 413
 
4.1%
22 393
 
3.9%
16 372
 
3.7%
20 353
 
3.5%
24 337
 
3.4%
25 306
 
3.1%
Other values (86) 6127
61.3%
ValueCountFrequency (%)
1 2
 
< 0.1%
2 6
 
0.1%
3 22
 
0.2%
4 21
 
0.2%
5 22
 
0.2%
6 16
 
0.2%
7 54
0.5%
8 58
0.6%
9 86
0.9%
10 108
1.1%
ValueCountFrequency (%)
97 2
 
< 0.1%
96 2
 
< 0.1%
95 1
 
< 0.1%
94 1
 
< 0.1%
93 5
 
0.1%
91 1
 
< 0.1%
90 2
 
< 0.1%
89 13
0.1%
88 10
 
0.1%
87 32
0.3%

통행시간
Real number (ℝ)

HIGH CORRELATION 

Distinct680
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean276.8352
Minimum30
Maximum4108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T09:51:05.381068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile99.95
Q1157
median245
Q3352
95-th percentile559
Maximum4108
Range4078
Interquartile range (IQR)195

Descriptive statistics

Standard deviation173.44946
Coefficient of variation (CV)0.62654408
Kurtosis60.803606
Mean276.8352
Median Absolute Deviation (MAD)95
Skewness4.2849564
Sum2768352
Variance30084.714
MonotonicityNot monotonic
2024-03-15T09:51:05.762952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128 72
 
0.7%
245 59
 
0.6%
148 58
 
0.6%
252 54
 
0.5%
130 51
 
0.5%
145 50
 
0.5%
143 50
 
0.5%
132 50
 
0.5%
144 49
 
0.5%
158 49
 
0.5%
Other values (670) 9458
94.6%
ValueCountFrequency (%)
30 1
 
< 0.1%
33 1
 
< 0.1%
35 1
 
< 0.1%
38 9
0.1%
39 5
0.1%
41 5
0.1%
42 6
0.1%
43 5
0.1%
44 6
0.1%
45 6
0.1%
ValueCountFrequency (%)
4108 2
 
< 0.1%
2367 5
0.1%
1606 1
 
< 0.1%
1311 3
 
< 0.1%
1276 4
< 0.1%
1235 8
0.1%
1174 1
 
< 0.1%
1123 1
 
< 0.1%
1114 1
 
< 0.1%
1102 1
 
< 0.1%

교통운영상태
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10000
100.0%

Length

2024-03-15T09:51:06.096176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T09:51:06.298215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

교통량
Real number (ℝ)

HIGH CORRELATION 

Distinct402
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.1868
Minimum1
Maximum570
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T09:51:06.587497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q111
median29
Q376
95-th percentile192
Maximum570
Range569
Interquartile range (IQR)65

Descriptive statistics

Standard deviation79.716186
Coefficient of variation (CV)1.3700046
Kurtosis11.140379
Mean58.1868
Median Absolute Deviation (MAD)23
Skewness3.0076337
Sum581868
Variance6354.6704
MonotonicityNot monotonic
2024-03-15T09:51:06.846833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 411
 
4.1%
2 250
 
2.5%
3 242
 
2.4%
4 240
 
2.4%
11 231
 
2.3%
7 227
 
2.3%
5 218
 
2.2%
12 214
 
2.1%
6 212
 
2.1%
10 208
 
2.1%
Other values (392) 7547
75.5%
ValueCountFrequency (%)
1 411
4.1%
2 250
2.5%
3 242
2.4%
4 240
2.4%
5 218
2.2%
6 212
2.1%
7 227
2.3%
8 196
2.0%
9 204
2.0%
10 208
2.1%
ValueCountFrequency (%)
570 2
 
< 0.1%
553 1
 
< 0.1%
545 1
 
< 0.1%
543 1
 
< 0.1%
537 2
 
< 0.1%
527 3
< 0.1%
526 1
 
< 0.1%
519 2
 
< 0.1%
515 7
0.1%
511 1
 
< 0.1%

혼잡도레벨
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
7021 
2
2692 
3
 
287

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 7021
70.2%
2 2692
 
26.9%
3 287
 
2.9%

Length

2024-03-15T09:51:07.260628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T09:51:07.581922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 7021
70.2%
2 2692
 
26.9%
3 287
 
2.9%

점유율
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10000
100.0%

Length

2024-03-15T09:51:07.924803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T09:51:08.223487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

Interactions

2024-03-15T09:51:00.942582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:50:55.852531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:50:57.324837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:50:58.421424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:50:59.458983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:51:01.234023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:50:56.152505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:50:57.616342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:50:58.609070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:50:59.840952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:51:01.511390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:50:56.452541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:50:57.848853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:50:58.877976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:51:00.336983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:51:01.775146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:50:56.734447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:50:58.018166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:50:59.036824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:51:00.525663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:51:02.068301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:50:57.031069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:50:58.246364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:50:59.223561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T09:51:00.715338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T09:51:08.406680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수집일시연계번호속도통행시간교통량혼잡도레벨
수집일시1.0000.0000.0930.1310.1530.084
연계번호0.0001.0000.2250.1500.0000.014
속도0.0930.2251.0000.4460.6310.914
통행시간0.1310.1500.4461.0000.2270.716
교통량0.1530.0000.6310.2271.0000.363
혼잡도레벨0.0840.0140.9140.7160.3631.000
2024-03-15T09:51:08.639157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수집일시연계번호속도통행시간교통량혼잡도레벨
수집일시1.000-0.030-0.0310.0540.1400.036
연계번호-0.0301.0000.370-0.1140.2440.024
속도-0.0310.3701.000-0.4950.4460.876
통행시간0.054-0.114-0.4951.000-0.5770.396
교통량0.1400.2440.446-0.5771.0000.234
혼잡도레벨0.0360.0240.8760.3960.2341.000

Missing values

2024-03-15T09:51:02.490170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T09:51:02.874732image/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

수집일시연계번호속도통행시간교통운영상태교통량혼잡도레벨점유율
701022024011511100217601542001645602120
52500202401151039011770004600145840220
201902024011509300017801950008325101510
199242024011509300017800879003117904810
4846320240115103402179007690078136048610
767172024011511250217700526001929902720
148972024011509090117900959002335401110
960872024011512390017800771002821209410
809222024011511410217601252001441601020
200772024011509300017900688003714401110
수집일시연계번호속도통행시간교통운영상태교통량혼잡도레벨점유율
248002024011509350017800693003511403010
842532024011512110217600874001830901120
392322024011510170234201513007254604410
899682024011512310117800985003314508010
522962024011510380317800283001639101520
26061202401150936001760072000301180610
774272024011511320117800555002517008010
721542024011511140217901397002529701410
149872024011509090117601131001823405720
98762024011509040117601075001836303320