Overview

Dataset statistics

Number of variables8
Number of observations35
Missing cells70
Missing cells (%)25.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory71.8 B

Variable types

Categorical1
Text2
Numeric2
Unsupported2
DateTime1

Dataset

Description부산광역시 해운대구의 자전거 도로에 관한 현황 데이터로 해운대구 자전거 도로의 시점, 종점, 길이, 보행자겸용 여부 등의 항목에 대한 정보를 제공합니다
Author부산광역시 해운대구
URLhttps://www.data.go.kr/data/3075706/fileData.do

Alerts

구군명 has constant value ""Constant
데이터 기준일자 has constant value ""Constant
구간길이 is highly overall correlated with 자전거보행자 겸용 길이High correlation
자전거보행자 겸용 길이 is highly overall correlated with 구간길이High correlation
자전거전용 길이 has 35 (100.0%) missing valuesMissing
자전거 우선도로 길이 has 35 (100.0%) missing valuesMissing
자전거전용 길이 is an unsupported type, check if it needs cleaning or further analysisUnsupported
자전거 우선도로 길이 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 05:45:13.518418
Analysis finished2023-12-12 05:45:14.346649
Duration0.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구군명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size412.0 B
부산광역시 해운대구
35 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 해운대구
2nd row부산광역시 해운대구
3rd row부산광역시 해운대구
4th row부산광역시 해운대구
5th row부산광역시 해운대구

Common Values

ValueCountFrequency (%)
부산광역시 해운대구 35
100.0%

Length

2023-12-12T14:45:14.406284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:45:14.517005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 35
50.0%
해운대구 35
50.0%

시작지점
Text

Distinct34
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size412.0 B
2023-12-12T14:45:14.753246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length8.3714286
Min length6

Characters and Unicode

Total characters293
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)94.3%

Sample

1st row반송동 산 15-2
2nd row반송동 756-255
3rd row반여동 1502-23
4th row석대동 591-6
5th row반여동 1621
ValueCountFrequency (%)
좌동 8
 
11.3%
우동 8
 
11.3%
중동 7
 
9.9%
재송동 4
 
5.6%
송정동 3
 
4.2%
반송동 2
 
2.8%
1756 2
 
2.8%
반여동 2
 
2.8%
135-5 1
 
1.4%
157-10 1
 
1.4%
Other values (33) 33
46.5%
2023-12-12T14:45:15.465114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 45
15.4%
36
12.3%
동 35
11.9%
6 19
 
6.5%
- 19
 
6.5%
5 15
 
5.1%
3 14
 
4.8%
2 13
 
4.4%
7 13
 
4.4%
9 11
 
3.8%
Other values (14) 73
24.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 155
52.9%
Other Letter 83
28.3%
Space Separator 36
 
12.3%
Dash Punctuation 19
 
6.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 35
42.2%
송 9
 
10.8%
우 8
 
9.6%
좌 8
 
9.6%
중 7
 
8.4%
재 4
 
4.8%
반 4
 
4.8%
정 3
 
3.6%
여 2
 
2.4%
석 1
 
1.2%
Other values (2) 2
 
2.4%
Decimal Number
ValueCountFrequency (%)
1 45
29.0%
6 19
12.3%
5 15
 
9.7%
3 14
 
9.0%
2 13
 
8.4%
7 13
 
8.4%
9 11
 
7.1%
0 10
 
6.5%
4 9
 
5.8%
8 6
 
3.9%
Space Separator
ValueCountFrequency (%)
36
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 210
71.7%
Hangul 83
 
28.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 45
21.4%
36
17.1%
6 19
9.0%
- 19
9.0%
5 15
 
7.1%
3 14
 
6.7%
2 13
 
6.2%
7 13
 
6.2%
9 11
 
5.2%
0 10
 
4.8%
Other values (2) 15
 
7.1%
Hangul
ValueCountFrequency (%)
동 35
42.2%
송 9
 
10.8%
우 8
 
9.6%
좌 8
 
9.6%
중 7
 
8.4%
재 4
 
4.8%
반 4
 
4.8%
정 3
 
3.6%
여 2
 
2.4%
석 1
 
1.2%
Other values (2) 2
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
71.7%
Hangul 83
 
28.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 45
21.4%
36
17.1%
6 19
9.0%
- 19
9.0%
5 15
 
7.1%
3 14
 
6.7%
2 13
 
6.2%
7 13
 
6.2%
9 11
 
5.2%
0 10
 
4.8%
Other values (2) 15
 
7.1%
Hangul
ValueCountFrequency (%)
동 35
42.2%
송 9
 
10.8%
우 8
 
9.6%
좌 8
 
9.6%
중 7
 
8.4%
재 4
 
4.8%
반 4
 
4.8%
정 3
 
3.6%
여 2
 
2.4%
석 1
 
1.2%
Other values (2) 2
 
2.4%

종료지점
Text

Distinct31
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Memory size412.0 B
2023-12-12T14:45:15.738275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length8.1428571
Min length7

Characters and Unicode

Total characters285
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)80.0%

Sample

1st row석대동 산 58-7
2nd row반여동 1502-49
3rd row반여동 1473-15
4th row우동 1494
5th row반여동 1486
ValueCountFrequency (%)
좌동 12
16.9%
우동 10
 
14.1%
반여동 3
 
4.2%
중동 3
 
4.2%
1427-2 3
 
4.2%
재송동 3
 
4.2%
송정동 3
 
4.2%
1420 2
 
2.8%
1483 2
 
2.8%
1317 1
 
1.4%
Other values (29) 29
40.8%
2023-12-12T14:45:16.154854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
38
13.3%
1 37
13.0%
동 35
12.3%
4 22
 
7.7%
3 16
 
5.6%
2 16
 
5.6%
- 16
 
5.6%
7 15
 
5.3%
좌 12
 
4.2%
5 10
 
3.5%
Other values (14) 68
23.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 150
52.6%
Other Letter 81
28.4%
Space Separator 38
 
13.3%
Dash Punctuation 16
 
5.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 35
43.2%
좌 12
 
14.8%
우 10
 
12.3%
송 6
 
7.4%
정 3
 
3.7%
중 3
 
3.7%
반 3
 
3.7%
재 3
 
3.7%
여 3
 
3.7%
석 1
 
1.2%
Other values (2) 2
 
2.5%
Decimal Number
ValueCountFrequency (%)
1 37
24.7%
4 22
14.7%
3 16
10.7%
2 16
10.7%
7 15
10.0%
5 10
 
6.7%
8 9
 
6.0%
0 9
 
6.0%
6 9
 
6.0%
9 7
 
4.7%
Space Separator
ValueCountFrequency (%)
38
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 204
71.6%
Hangul 81
 
28.4%

Most frequent character per script

Common
ValueCountFrequency (%)
38
18.6%
1 37
18.1%
4 22
10.8%
3 16
7.8%
2 16
7.8%
- 16
7.8%
7 15
 
7.4%
5 10
 
4.9%
8 9
 
4.4%
0 9
 
4.4%
Other values (2) 16
7.8%
Hangul
ValueCountFrequency (%)
동 35
43.2%
좌 12
 
14.8%
우 10
 
12.3%
송 6
 
7.4%
정 3
 
3.7%
중 3
 
3.7%
반 3
 
3.7%
재 3
 
3.7%
여 3
 
3.7%
석 1
 
1.2%
Other values (2) 2
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 204
71.6%
Hangul 81
 
28.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
38
18.6%
1 37
18.1%
4 22
10.8%
3 16
7.8%
2 16
7.8%
- 16
7.8%
7 15
 
7.4%
5 10
 
4.9%
8 9
 
4.4%
0 9
 
4.4%
Other values (2) 16
7.8%
Hangul
ValueCountFrequency (%)
동 35
43.2%
좌 12
 
14.8%
우 10
 
12.3%
송 6
 
7.4%
정 3
 
3.7%
중 3
 
3.7%
반 3
 
3.7%
재 3
 
3.7%
여 3
 
3.7%
석 1
 
1.2%
Other values (2) 2
 
2.5%

구간길이
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8445714
Minimum0.15
Maximum6.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T14:45:16.335164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.15
5-th percentile0.306
Q10.635
median1.66
Q32.2
95-th percentile5.563
Maximum6.12
Range5.97
Interquartile range (IQR)1.565

Descriptive statistics

Standard deviation1.5467791
Coefficient of variation (CV)0.83855744
Kurtosis1.9812878
Mean1.8445714
Median Absolute Deviation (MAD)0.84
Skewness1.5460607
Sum64.56
Variance2.3925255
MonotonicityNot monotonic
2023-12-12T14:45:16.466128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.6 3
 
8.6%
1.66 2
 
5.7%
1.4 1
 
2.9%
2.1 1
 
2.9%
0.15 1
 
2.9%
0.36 1
 
2.9%
0.62 1
 
2.9%
0.82 1
 
2.9%
4.99 1
 
2.9%
1.32 1
 
2.9%
Other values (22) 22
62.9%
ValueCountFrequency (%)
0.15 1
 
2.9%
0.18 1
 
2.9%
0.36 1
 
2.9%
0.45 1
 
2.9%
0.54 1
 
2.9%
0.6 3
8.6%
0.62 1
 
2.9%
0.65 1
 
2.9%
0.8 1
 
2.9%
0.82 1
 
2.9%
ValueCountFrequency (%)
6.12 1
2.9%
5.57 1
2.9%
5.56 1
2.9%
4.99 1
2.9%
2.77 1
2.9%
2.56 1
2.9%
2.5 1
2.9%
2.4 1
2.9%
2.3 1
2.9%
2.1 1
2.9%

자전거전용 길이
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing35
Missing (%)100.0%
Memory size447.0 B

자전거보행자 겸용 길이
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8445714
Minimum0.15
Maximum6.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T14:45:16.602669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.15
5-th percentile0.306
Q10.635
median1.66
Q32.2
95-th percentile5.563
Maximum6.12
Range5.97
Interquartile range (IQR)1.565

Descriptive statistics

Standard deviation1.5467791
Coefficient of variation (CV)0.83855744
Kurtosis1.9812878
Mean1.8445714
Median Absolute Deviation (MAD)0.84
Skewness1.5460607
Sum64.56
Variance2.3925255
MonotonicityNot monotonic
2023-12-12T14:45:16.730638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.6 3
 
8.6%
1.66 2
 
5.7%
1.4 1
 
2.9%
2.1 1
 
2.9%
0.15 1
 
2.9%
0.36 1
 
2.9%
0.62 1
 
2.9%
0.82 1
 
2.9%
4.99 1
 
2.9%
1.32 1
 
2.9%
Other values (22) 22
62.9%
ValueCountFrequency (%)
0.15 1
 
2.9%
0.18 1
 
2.9%
0.36 1
 
2.9%
0.45 1
 
2.9%
0.54 1
 
2.9%
0.6 3
8.6%
0.62 1
 
2.9%
0.65 1
 
2.9%
0.8 1
 
2.9%
0.82 1
 
2.9%
ValueCountFrequency (%)
6.12 1
2.9%
5.57 1
2.9%
5.56 1
2.9%
4.99 1
2.9%
2.77 1
2.9%
2.56 1
2.9%
2.5 1
2.9%
2.4 1
2.9%
2.3 1
2.9%
2.1 1
2.9%

자전거 우선도로 길이
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing35
Missing (%)100.0%
Memory size447.0 B

데이터 기준일자
Date

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size412.0 B
Minimum2022-04-15 00:00:00
Maximum2022-04-15 00:00:00
2023-12-12T14:45:16.829767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:45:16.914362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T14:45:13.915090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:45:13.731416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:45:14.026718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:45:13.818731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:45:16.982287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시작지점종료지점구간길이자전거보행자 겸용 길이
시작지점1.0000.9850.0000.000
종료지점0.9851.0000.9180.918
구간길이0.0000.9181.0001.000
자전거보행자 겸용 길이0.0000.9181.0001.000
2023-12-12T14:45:17.090630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구간길이자전거보행자 겸용 길이
구간길이1.0001.000
자전거보행자 겸용 길이1.0001.000

Missing values

2023-12-12T14:45:14.170879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:45:14.295269image/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

구군명시작지점종료지점구간길이자전거전용 길이자전거보행자 겸용 길이자전거 우선도로 길이데이터 기준일자
0부산광역시 해운대구반송동 산 15-2석대동 산 58-72.5<NA>2.5<NA>2022-04-15
1부산광역시 해운대구반송동 756-255반여동 1502-492.4<NA>2.4<NA>2022-04-15
2부산광역시 해운대구반여동 1502-23반여동 1473-151.87<NA>1.87<NA>2022-04-15
3부산광역시 해운대구석대동 591-6우동 14946.12<NA>6.12<NA>2022-04-15
4부산광역시 해운대구반여동 1621반여동 14861.2<NA>1.2<NA>2022-04-15
5부산광역시 해운대구재송동 920-17우동 14832.3<NA>2.3<NA>2022-04-15
6부산광역시 해운대구재송동 1198재송동 12130.9<NA>0.9<NA>2022-04-15
7부산광역시 해운대구재송동 1200재송동 12000.65<NA>0.65<NA>2022-04-15
8부산광역시 해운대구재송동 1206재송동 12070.6<NA>0.6<NA>2022-04-15
9부산광역시 해운대구우동 1466우동 14930.6<NA>0.6<NA>2022-04-15
구군명시작지점종료지점구간길이자전거전용 길이자전거보행자 겸용 길이자전거 우선도로 길이데이터 기준일자
25부산광역시 해운대구좌동 1371-1좌동 13021.74<NA>1.74<NA>2022-04-15
26부산광역시 해운대구좌동 1393-1좌동 13312.01<NA>2.01<NA>2022-04-15
27부산광역시 해운대구좌동 1282좌동 1459-61.32<NA>1.32<NA>2022-04-15
28부산광역시 해운대구좌동 1461-6좌동 1486-10.82<NA>0.82<NA>2022-04-15
29부산광역시 해운대구좌동 395-3좌동 13170.62<NA>0.62<NA>2022-04-15
30부산광역시 해운대구좌동 1336좌동 13380.36<NA>0.36<NA>2022-04-15
31부산광역시 해운대구좌동 1419좌동 14200.15<NA>0.15<NA>2022-04-15
32부산광역시 해운대구송정동 157-10송정동 77-72.1<NA>2.1<NA>2022-04-15
33부산광역시 해운대구송정동 135-5송정동 81-11.4<NA>1.4<NA>2022-04-15
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