Overview

Dataset statistics

Number of variables5
Number of observations91
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory45.5 B

Variable types

Numeric3
Categorical2

Dataset

Description경상북도 구미시의 교통정보센터시스템 DB의 VMS소통정보로 VMS와 통신한 등록일시,속도,통행시간 정보등을 제공하고 있습니다.
Author경상북도 구미시
URLhttps://www.data.go.kr/data/15092238/fileData.do

Alerts

등록일시 has constant value ""Constant
속도 is highly overall correlated with 소통등급코드High correlation
소통등급코드 is highly overall correlated with 속도High correlation
정보제공구간식별자(VMS) has unique valuesUnique
통행시간 has 6 (6.6%) zerosZeros

Reproduction

Analysis started2023-12-12 01:19:31.832725
Analysis finished2023-12-12 01:19:33.015104
Duration1.18 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정보제공구간식별자(VMS)
Real number (ℝ)

UNIQUE 

Distinct91
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.802198
Minimum1
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size951.0 B
2023-12-12T10:19:33.099017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.5
Q124.5
median47
Q369.5
95-th percentile87.5
Maximum92
Range91
Interquartile range (IQR)45

Descriptive statistics

Standard deviation26.69133
Coefficient of variation (CV)0.57030079
Kurtosis-1.1886741
Mean46.802198
Median Absolute Deviation (MAD)23
Skewness-0.022305619
Sum4259
Variance712.42711
MonotonicityNot monotonic
2023-12-12T10:19:33.256220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71 1
 
1.1%
16 1
 
1.1%
5 1
 
1.1%
6 1
 
1.1%
7 1
 
1.1%
8 1
 
1.1%
9 1
 
1.1%
10 1
 
1.1%
11 1
 
1.1%
13 1
 
1.1%
Other values (81) 81
89.0%
ValueCountFrequency (%)
1 1
1.1%
2 1
1.1%
3 1
1.1%
4 1
1.1%
5 1
1.1%
6 1
1.1%
7 1
1.1%
8 1
1.1%
9 1
1.1%
10 1
1.1%
ValueCountFrequency (%)
92 1
1.1%
91 1
1.1%
90 1
1.1%
89 1
1.1%
88 1
1.1%
87 1
1.1%
86 1
1.1%
85 1
1.1%
84 1
1.1%
83 1
1.1%

등록일시
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size860.0 B
2022-10-12 13:55:00
91 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-10-12 13:55:00
2nd row2022-10-12 13:55:00
3rd row2022-10-12 13:55:00
4th row2022-10-12 13:55:00
5th row2022-10-12 13:55:00

Common Values

ValueCountFrequency (%)
2022-10-12 13:55:00 91
100.0%

Length

2023-12-12T10:19:33.404131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:19:33.509191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-10-12 91
50.0%
13:55:00 91
50.0%

속도
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)37.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33
Minimum10
Maximum57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size951.0 B
2023-12-12T10:19:33.634445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile17.5
Q127
median33
Q339
95-th percentile51
Maximum57
Range47
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.199129
Coefficient of variation (CV)0.3090645
Kurtosis-0.31838564
Mean33
Median Absolute Deviation (MAD)6
Skewness0.042918783
Sum3003
Variance104.02222
MonotonicityNot monotonic
2023-12-12T10:19:33.799824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
28 7
 
7.7%
34 5
 
5.5%
38 5
 
5.5%
37 5
 
5.5%
19 5
 
5.5%
40 5
 
5.5%
33 5
 
5.5%
39 4
 
4.4%
25 4
 
4.4%
30 4
 
4.4%
Other values (24) 42
46.2%
ValueCountFrequency (%)
10 1
 
1.1%
11 1
 
1.1%
15 2
 
2.2%
17 1
 
1.1%
18 2
 
2.2%
19 5
5.5%
20 1
 
1.1%
21 2
 
2.2%
23 1
 
1.1%
24 2
 
2.2%
ValueCountFrequency (%)
57 1
 
1.1%
53 2
 
2.2%
51 3
3.3%
50 1
 
1.1%
49 2
 
2.2%
47 1
 
1.1%
46 2
 
2.2%
43 3
3.3%
42 1
 
1.1%
40 5
5.5%

소통등급코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size860.0 B
1
79 
2
12 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 79
86.8%
2 12
 
13.2%

Length

2023-12-12T10:19:33.965374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:19:34.089813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 79
86.8%
2 12
 
13.2%

통행시간
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0659341
Minimum0
Maximum9
Zeros6
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size951.0 B
2023-12-12T10:19:34.217116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile6
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9020585
Coefficient of variation (CV)0.62038468
Kurtosis0.42818163
Mean3.0659341
Median Absolute Deviation (MAD)1
Skewness0.6167479
Sum279
Variance3.6178266
MonotonicityNot monotonic
2023-12-12T10:19:34.386308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 19
20.9%
4 16
17.6%
3 16
17.6%
1 14
15.4%
5 13
14.3%
0 6
 
6.6%
6 3
 
3.3%
8 2
 
2.2%
9 1
 
1.1%
7 1
 
1.1%
ValueCountFrequency (%)
0 6
 
6.6%
1 14
15.4%
2 19
20.9%
3 16
17.6%
4 16
17.6%
5 13
14.3%
6 3
 
3.3%
7 1
 
1.1%
8 2
 
2.2%
9 1
 
1.1%
ValueCountFrequency (%)
9 1
 
1.1%
8 2
 
2.2%
7 1
 
1.1%
6 3
 
3.3%
5 13
14.3%
4 16
17.6%
3 16
17.6%
2 19
20.9%
1 14
15.4%
0 6
 
6.6%

Interactions

2023-12-12T10:19:32.450762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:19:31.959508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:19:32.208239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:19:32.551997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:19:32.040915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:19:32.290661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:19:32.650017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:19:32.130004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:19:32.369308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:19:34.496184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정보제공구간식별자(VMS)속도소통등급코드통행시간
정보제공구간식별자(VMS)1.0000.3930.4490.336
속도0.3931.0001.0000.594
소통등급코드0.4491.0001.0000.182
통행시간0.3360.5940.1821.000
2023-12-12T10:19:34.626605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정보제공구간식별자(VMS)속도통행시간소통등급코드
정보제공구간식별자(VMS)1.000-0.292-0.1960.328
속도-0.2921.0000.2030.954
통행시간-0.1960.2031.0000.129
소통등급코드0.3280.9540.1291.000

Missing values

2023-12-12T10:19:32.798705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:19:32.965743image/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

정보제공구간식별자(VMS)등록일시속도소통등급코드통행시간
0712022-10-12 13:55:004715
1722022-10-12 13:55:002814
2732022-10-12 13:55:003914
3742022-10-12 13:55:001820
4752022-10-12 13:55:003714
5762022-10-12 13:55:001920
6772022-10-12 13:55:002514
7782022-10-12 13:55:003113
8792022-10-12 13:55:001924
9802022-10-12 13:55:003312
정보제공구간식별자(VMS)등록일시속도소통등급코드통행시간
81692022-10-12 13:55:004015
82552022-10-12 13:55:004015
83562022-10-12 13:55:003614
84572022-10-12 13:55:003015
85582022-10-12 13:55:005012
86592022-10-12 13:55:002410
87602022-10-12 13:55:005111
88612022-10-12 13:55:002812
89622022-10-12 13:55:002712
90632022-10-12 13:55:002714