Overview

Dataset statistics

Number of variables5
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 KiB
Average record size in memory48.7 B

Variable types

Categorical1
Text1
Numeric3

Dataset

Description매년 광주광역시 주요 간선도로에 대한 차량속도를 조사하여 속도 현황 정보를 제공(제1순환도로내, 제1순환도로외, 도로명(23개 구간), 연장, 주행속도, 여행속도)
Author광주광역시
URLhttps://www.data.go.kr/data/15056406/fileData.do

Alerts

주행속도 is highly overall correlated with 여행속도 and 1 other fieldsHigh correlation
여행속도 is highly overall correlated with 주행속도 and 1 other fieldsHigh correlation
구 분 is highly overall correlated with 주행속도 and 1 other fieldsHigh correlation
도로명 has unique valuesUnique
연 장(킬로미터) has unique valuesUnique

Reproduction

Analysis started2024-03-23 05:52:07.311090
Analysis finished2024-03-23 05:52:09.055261
Duration1.74 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구 분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
제1순환도로외
14 
제1순환도로내
9 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row제1순환도로내
2nd row제1순환도로내
3rd row제1순환도로내
4th row제1순환도로내
5th row제1순환도로내

Common Values

ValueCountFrequency (%)
제1순환도로외 14
60.9%
제1순환도로내 9
39.1%

Length

2024-03-23T14:52:09.173428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T14:52:09.314423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제1순환도로외 14
60.9%
제1순환도로내 9
39.1%

도로명
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2024-03-23T14:52:09.530494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length4.1304348
Min length3

Characters and Unicode

Total characters95
Distinct characters40
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row금남로
2nd row제봉로
3rd row중앙로
4th row구성로
5th row독립로
ValueCountFrequency (%)
상무대로 2
 
8.3%
금남로 1
 
4.2%
무진대로-2 1
 
4.2%
북문대로 1
 
4.2%
하남대로 1
 
4.2%
사암로 1
 
4.2%
우치로 1
 
4.2%
빛고을대로 1
 
4.2%
운천로 1
 
4.2%
남문대로 1
 
4.2%
Other values (13) 13
54.2%
2024-03-23T14:52:09.948682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 24
25.3%
대 10
 
10.5%
무 5
 
5.3%
문 4
 
4.2%
제 3
 
3.2%
남 3
 
3.2%
서 2
 
2.1%
진 2
 
2.1%
2 2
 
2.1%
도 2
 
2.1%
Other values (30) 38
40.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 87
91.6%
Decimal Number 4
 
4.2%
Dash Punctuation 2
 
2.1%
Space Separator 1
 
1.1%
Other Punctuation 1
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 24
27.6%
대 10
 
11.5%
무 5
 
5.7%
문 4
 
4.6%
제 3
 
3.4%
남 3
 
3.4%
서 2
 
2.3%
진 2
 
2.3%
도 2
 
2.3%
환 2
 
2.3%
Other values (25) 30
34.5%
Decimal Number
ValueCountFrequency (%)
2 2
50.0%
1 2
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 87
91.6%
Common 8
 
8.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 24
27.6%
대 10
 
11.5%
무 5
 
5.7%
문 4
 
4.6%
제 3
 
3.4%
남 3
 
3.4%
서 2
 
2.3%
진 2
 
2.3%
도 2
 
2.3%
환 2
 
2.3%
Other values (25) 30
34.5%
Common
ValueCountFrequency (%)
2 2
25.0%
1 2
25.0%
- 2
25.0%
1
12.5%
, 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 87
91.6%
ASCII 8
 
8.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 24
27.6%
대 10
 
11.5%
무 5
 
5.7%
문 4
 
4.6%
제 3
 
3.4%
남 3
 
3.4%
서 2
 
2.3%
진 2
 
2.3%
도 2
 
2.3%
환 2
 
2.3%
Other values (25) 30
34.5%
ASCII
ValueCountFrequency (%)
2 2
25.0%
1 2
25.0%
- 2
25.0%
1
12.5%
, 1
12.5%

연 장(킬로미터)
Real number (ℝ)

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8026087
Minimum1.11
Maximum37.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T14:52:10.138483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.11
5-th percentile2.014
Q12.72
median3.51
Q35.06
95-th percentile13.672
Maximum37.86
Range36.75
Interquartile range (IQR)2.34

Descriptive statistics

Standard deviation7.5039857
Coefficient of variation (CV)1.293209
Kurtosis16.563182
Mean5.8026087
Median Absolute Deviation (MAD)1.26
Skewness3.9058915
Sum133.46
Variance56.309802
MonotonicityNot monotonic
2024-03-23T14:52:10.340509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
3.8 1
 
4.3%
3.32 1
 
4.3%
1.11 1
 
4.3%
5.45 1
 
4.3%
5.05 1
 
4.3%
9.28 1
 
4.3%
2.23 1
 
4.3%
5.07 1
 
4.3%
3.5 1
 
4.3%
3.4 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
1.11 1
4.3%
1.99 1
4.3%
2.23 1
4.3%
2.52 1
4.3%
2.59 1
4.3%
2.64 1
4.3%
2.8 1
4.3%
3.26 1
4.3%
3.32 1
4.3%
3.4 1
4.3%
ValueCountFrequency (%)
37.86 1
4.3%
14.16 1
4.3%
9.28 1
4.3%
6.31 1
4.3%
5.45 1
4.3%
5.07 1
4.3%
5.05 1
4.3%
4.88 1
4.3%
4.77 1
4.3%
3.96 1
4.3%

주행속도
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.482609
Minimum26.2
Maximum69.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T14:52:10.500909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26.2
5-th percentile27.06
Q130.65
median34.5
Q344
95-th percentile54.45
Maximum69.2
Range43
Interquartile range (IQR)13.35

Descriptive statistics

Standard deviation10.510413
Coefficient of variation (CV)0.27312111
Kurtosis1.9367383
Mean38.482609
Median Absolute Deviation (MAD)4.2
Skewness1.3929632
Sum885.1
Variance110.46877
MonotonicityNot monotonic
2024-03-23T14:52:10.647599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
30.3 2
 
8.7%
34.5 1
 
4.3%
38.7 1
 
4.3%
34.4 1
 
4.3%
36.7 1
 
4.3%
46.3 1
 
4.3%
54.0 1
 
4.3%
35.6 1
 
4.3%
38.6 1
 
4.3%
54.5 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
26.2 1
4.3%
26.7 1
4.3%
30.3 2
8.7%
30.4 1
4.3%
30.5 1
4.3%
30.8 1
4.3%
33.2 1
4.3%
33.3 1
4.3%
33.4 1
4.3%
34.4 1
4.3%
ValueCountFrequency (%)
69.2 1
4.3%
54.5 1
4.3%
54.0 1
4.3%
49.5 1
4.3%
46.3 1
4.3%
46.2 1
4.3%
41.8 1
4.3%
38.7 1
4.3%
38.6 1
4.3%
36.7 1
4.3%

여행속도
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.052174
Minimum15.4
Maximum69.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T14:52:10.790740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15.4
5-th percentile15.7
Q117.85
median23.9
Q330.85
95-th percentile50.5
Maximum69.2
Range53.8
Interquartile range (IQR)13

Descriptive statistics

Standard deviation13.186352
Coefficient of variation (CV)0.48744149
Kurtosis3.8189071
Mean27.052174
Median Absolute Deviation (MAD)6.4
Skewness1.8646707
Sum622.2
Variance173.87988
MonotonicityNot monotonic
2024-03-23T14:52:11.338262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
15.7 2
 
8.7%
25.9 2
 
8.7%
23.9 2
 
8.7%
19.4 1
 
4.3%
24.6 1
 
4.3%
30.4 1
 
4.3%
39.3 1
 
4.3%
51.5 1
 
4.3%
17.5 1
 
4.3%
31.3 1
 
4.3%
Other values (10) 10
43.5%
ValueCountFrequency (%)
15.4 1
4.3%
15.7 2
8.7%
17.3 1
4.3%
17.5 1
4.3%
17.7 1
4.3%
18.0 1
4.3%
18.6 1
4.3%
19.4 1
4.3%
20.0 1
4.3%
23.0 1
4.3%
ValueCountFrequency (%)
69.2 1
4.3%
51.5 1
4.3%
41.5 1
4.3%
39.3 1
4.3%
36.5 1
4.3%
31.3 1
4.3%
30.4 1
4.3%
25.9 2
8.7%
24.6 1
4.3%
23.9 2
8.7%

Interactions

2024-03-23T14:52:08.334221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:52:07.535427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:52:07.981019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:52:08.464571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:52:07.657932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:52:08.124005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:52:08.593676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:52:07.833225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:52:08.224156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T14:52:11.447212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구 분도로명연 장(킬로미터)주행속도여행속도
구 분1.0001.0000.0000.9100.813
도로명1.0001.0001.0001.0001.000
연 장(킬로미터)0.0001.0001.0000.7100.638
주행속도0.9101.0000.7101.0000.942
여행속도0.8131.0000.6380.9421.000
2024-03-23T14:52:11.567470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연 장(킬로미터)주행속도여행속도구 분
연 장(킬로미터)1.0000.2460.3120.000
주행속도0.2461.0000.8950.654
여행속도0.3120.8951.0000.768
구 분0.0000.6540.7681.000

Missing values

2024-03-23T14:52:08.804353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T14:52:08.989569image/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

구 분도로명연 장(킬로미터)주행속도여행속도
0제1순환도로내금남로3.834.519.4
1제1순환도로내제봉로3.3230.418.0
2제1순환도로내중앙로3.2630.315.7
3제1순환도로내구성로1.9926.715.4
4제1순환도로내독립로3.9630.517.3
5제1순환도로내상무대로, 태봉로2.6430.815.7
6제1순환도로내무등로4.7733.220.0
7제1순환도로내천변로4.8830.317.7
8제1순환도로내제1순환도로14.1626.218.6
9제1순환도로외제2순환도로37.8669.269.2
구 분도로명연 장(킬로미터)주행속도여행속도
13제1순환도로외동문대로3.5138.723.0
14제1순환도로외남문대로2.846.231.3
15제1순환도로외운천로3.433.417.5
16제1순환도로외빛고을대로3.554.551.5
17제1순환도로외상무대로5.0738.623.9
18제1순환도로외우치로2.2335.625.9
19제1순환도로외사암로9.2854.039.3
20제1순환도로외하남대로5.0546.330.4
21제1순환도로외북문대로5.4536.725.9
22제1순환도로외하서로1.1134.423.9