Overview

Dataset statistics

Number of variables16
Number of observations174
Missing cells364
Missing cells (%)13.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.1 KiB
Average record size in memory135.8 B

Variable types

Numeric6
Categorical6
Text3
Boolean1

Dataset

Description인천광역시 중구 자전거도로에 관한 정보입니다.
Author인천광역시
URLhttps://www.incheon.go.kr/data/DATA010201/view?docId=3079633

Alerts

시도 has constant value ""Constant
시군구 has constant value ""Constant
도로연계성여부 has constant value ""Constant
데이터기준일자 has constant value ""Constant
일련번호 is highly overall correlated with 전용도로 폭원High correlation
전용도로 폭원 is highly overall correlated with 일련번호 and 2 other fieldsHigh correlation
자전거도로 겸용도로 폭원 is highly overall correlated with 겸용도로 유효폭원High correlation
겸용도로 유효폭원 is highly overall correlated with 자전거도로 겸용도로 폭원High correlation
자전거도로종류 is highly overall correlated with 전용도로 폭원High correlation
자전거도로포장재질 is highly overall correlated with 전용도로 폭원High correlation
자전거도로포장재질 is highly imbalanced (75.7%)Imbalance
전용차로 폭원 is highly imbalanced (94.9%)Imbalance
기점 has 17 (9.8%) missing valuesMissing
종점 has 17 (9.8%) missing valuesMissing
전용도로 폭원 has 99 (56.9%) missing valuesMissing
자전거도로 겸용도로 폭원 has 77 (44.3%) missing valuesMissing
보도 겸용도로폭원 has 77 (44.3%) missing valuesMissing
겸용도로 유효폭원 has 77 (44.3%) missing valuesMissing
일련번호 has unique valuesUnique

Reproduction

Analysis started2024-01-28 11:30:18.590791
Analysis finished2024-01-28 11:30:22.160126
Duration3.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일련번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct174
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.5
Minimum1
Maximum174
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-01-28T20:30:22.215771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.65
Q144.25
median87.5
Q3130.75
95-th percentile165.35
Maximum174
Range173
Interquartile range (IQR)86.5

Descriptive statistics

Standard deviation50.373604
Coefficient of variation (CV)0.57569833
Kurtosis-1.2
Mean87.5
Median Absolute Deviation (MAD)43.5
Skewness0
Sum15225
Variance2537.5
MonotonicityStrictly increasing
2024-01-28T20:30:22.319194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
121 1
 
0.6%
113 1
 
0.6%
114 1
 
0.6%
115 1
 
0.6%
116 1
 
0.6%
117 1
 
0.6%
118 1
 
0.6%
119 1
 
0.6%
120 1
 
0.6%
Other values (164) 164
94.3%
ValueCountFrequency (%)
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
10 1
0.6%
ValueCountFrequency (%)
174 1
0.6%
173 1
0.6%
172 1
0.6%
171 1
0.6%
170 1
0.6%
169 1
0.6%
168 1
0.6%
167 1
0.6%
166 1
0.6%
165 1
0.6%

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
인천광역시
174 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인천광역시 174
100.0%

Length

2024-01-28T20:30:22.421654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:30:22.494429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 174
100.0%

시군구
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
중구
174 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
중구 174
100.0%

Length

2024-01-28T20:30:22.570597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:30:22.642844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중구 174
100.0%

노선명
Text

Distinct173
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-01-28T20:30:22.858150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length7.2528736
Min length3

Characters and Unicode

Total characters1262
Distinct characters108
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique172 ?
Unique (%)98.9%

Sample

1st row월미로1
2nd row월미로2
3rd row월미로3
4th row월미로4
5th row월미로5
ValueCountFrequency (%)
영종대로 21
 
8.5%
주변 17
 
6.9%
녹지 16
 
6.5%
운남동 6
 
2.4%
중산동 6
 
2.4%
은하수로 2
 
0.8%
운남1교 2
 
0.8%
하늘별빛로2r 1
 
0.4%
하늘별빛로1 1
 
0.4%
구읍로 1
 
0.4%
Other values (173) 173
70.3%
2024-01-28T20:30:23.222883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 155
 
12.3%
대 76
 
6.0%
72
 
5.7%
1 60
 
4.8%
2 55
 
4.4%
R 55
 
4.4%
L 45
 
3.6%
종 42
 
3.3%
영 42
 
3.3%
- 29
 
2.3%
Other values (98) 631
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 804
63.7%
Decimal Number 255
 
20.2%
Uppercase Letter 100
 
7.9%
Space Separator 72
 
5.7%
Dash Punctuation 29
 
2.3%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 155
19.3%
대 76
 
9.5%
종 42
 
5.2%
영 42
 
5.2%
하 25
 
3.1%
길 23
 
2.9%
번 23
 
2.9%
항 20
 
2.5%
축 20
 
2.5%
늘 18
 
2.2%
Other values (82) 360
44.8%
Decimal Number
ValueCountFrequency (%)
1 60
23.5%
2 55
21.6%
7 24
 
9.4%
3 22
 
8.6%
5 22
 
8.6%
6 20
 
7.8%
8 17
 
6.7%
4 15
 
5.9%
9 13
 
5.1%
0 7
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
R 55
55.0%
L 45
45.0%
Space Separator
ValueCountFrequency (%)
72
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 804
63.7%
Common 358
28.4%
Latin 100
 
7.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 155
19.3%
대 76
 
9.5%
종 42
 
5.2%
영 42
 
5.2%
하 25
 
3.1%
길 23
 
2.9%
번 23
 
2.9%
항 20
 
2.5%
축 20
 
2.5%
늘 18
 
2.2%
Other values (82) 360
44.8%
Common
ValueCountFrequency (%)
72
20.1%
1 60
16.8%
2 55
15.4%
- 29
8.1%
7 24
 
6.7%
3 22
 
6.1%
5 22
 
6.1%
6 20
 
5.6%
8 17
 
4.7%
4 15
 
4.2%
Other values (4) 22
 
6.1%
Latin
ValueCountFrequency (%)
R 55
55.0%
L 45
45.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 804
63.7%
ASCII 458
36.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 155
19.3%
대 76
 
9.5%
종 42
 
5.2%
영 42
 
5.2%
하 25
 
3.1%
길 23
 
2.9%
번 23
 
2.9%
항 20
 
2.5%
축 20
 
2.5%
늘 18
 
2.2%
Other values (82) 360
44.8%
ASCII
ValueCountFrequency (%)
72
15.7%
1 60
13.1%
2 55
12.0%
R 55
12.0%
L 45
9.8%
- 29
6.3%
7 24
 
5.2%
3 22
 
4.8%
5 22
 
4.8%
6 20
 
4.4%
Other values (6) 54
11.8%

기점
Text

MISSING 

Distinct113
Distinct (%)72.0%
Missing17
Missing (%)9.8%
Memory size1.5 KiB
2024-01-28T20:30:23.486212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length8.4840764
Min length3

Characters and Unicode

Total characters1332
Distinct characters112
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)45.2%

Sample

1st row우회고가 사거리
2nd row8부두 앞
3rd row인항철골
4th row월미공원 정문
5th row갑문입구
ValueCountFrequency (%)
운남동 29
 
11.1%
중산동 26
 
10.0%
운서동 17
 
6.5%
운북동 6
 
2.3%
시점 4
 
1.5%
제2경인고속도로 4
 
1.5%
연안부두로 4
 
1.5%
을왕동 3
 
1.1%
영종순환로900번길 2
 
0.8%
운북동1272-1 2
 
0.8%
Other values (118) 164
62.8%
2024-01-28T20:30:23.836958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 137
 
10.3%
105
 
7.9%
동 101
 
7.6%
운 73
 
5.5%
- 68
 
5.1%
3 56
 
4.2%
2 52
 
3.9%
8 47
 
3.5%
7 47
 
3.5%
9 46
 
3.5%
Other values (102) 600
45.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 637
47.8%
Decimal Number 514
38.6%
Space Separator 105
 
7.9%
Dash Punctuation 68
 
5.1%
Uppercase Letter 4
 
0.3%
Close Punctuation 2
 
0.2%
Open Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 101
 
15.9%
운 73
 
11.5%
남 34
 
5.3%
서 32
 
5.0%
산 29
 
4.6%
중 28
 
4.4%
로 20
 
3.1%
리 16
 
2.5%
북 16
 
2.5%
거 16
 
2.5%
Other values (86) 272
42.7%
Decimal Number
ValueCountFrequency (%)
1 137
26.7%
3 56
10.9%
2 52
 
10.1%
8 47
 
9.1%
7 47
 
9.1%
9 46
 
8.9%
0 37
 
7.2%
6 36
 
7.0%
5 32
 
6.2%
4 24
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
K 2
50.0%
S 2
50.0%
Space Separator
ValueCountFrequency (%)
105
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 68
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 691
51.9%
Hangul 637
47.8%
Latin 4
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 101
 
15.9%
운 73
 
11.5%
남 34
 
5.3%
서 32
 
5.0%
산 29
 
4.6%
중 28
 
4.4%
로 20
 
3.1%
리 16
 
2.5%
북 16
 
2.5%
거 16
 
2.5%
Other values (86) 272
42.7%
Common
ValueCountFrequency (%)
1 137
19.8%
105
15.2%
- 68
9.8%
3 56
8.1%
2 52
 
7.5%
8 47
 
6.8%
7 47
 
6.8%
9 46
 
6.7%
0 37
 
5.4%
6 36
 
5.2%
Other values (4) 60
8.7%
Latin
ValueCountFrequency (%)
K 2
50.0%
S 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 695
52.2%
Hangul 637
47.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 137
19.7%
105
15.1%
- 68
9.8%
3 56
8.1%
2 52
 
7.5%
8 47
 
6.8%
7 47
 
6.8%
9 46
 
6.6%
0 37
 
5.3%
6 36
 
5.2%
Other values (6) 64
9.2%
Hangul
ValueCountFrequency (%)
동 101
 
15.9%
운 73
 
11.5%
남 34
 
5.3%
서 32
 
5.0%
산 29
 
4.6%
중 28
 
4.4%
로 20
 
3.1%
리 16
 
2.5%
북 16
 
2.5%
거 16
 
2.5%
Other values (86) 272
42.7%

종점
Text

MISSING 

Distinct112
Distinct (%)71.3%
Missing17
Missing (%)9.8%
Memory size1.5 KiB
2024-01-28T20:30:24.082282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length8.955414
Min length5

Characters and Unicode

Total characters1406
Distinct characters104
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)45.2%

Sample

1st row8부두 앞
2nd row월미도입구 삼거리
3rd row월미공원역
4th row갑문 입구
5th row문타운입구
ValueCountFrequency (%)
운서동 30
 
10.8%
중산동 26
 
9.4%
운남동 21
 
7.6%
운북동 7
 
2.5%
신흥동3가 6
 
2.2%
서해대로 5
 
1.8%
293번길 5
 
1.8%
1654-4 3
 
1.1%
연안부두로 3
 
1.1%
운서동833 2
 
0.7%
Other values (125) 170
61.2%
2024-01-28T20:30:24.423929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
123
 
8.7%
동 112
 
8.0%
1 102
 
7.3%
3 89
 
6.3%
- 83
 
5.9%
운 74
 
5.3%
2 55
 
3.9%
0 54
 
3.8%
7 50
 
3.6%
9 47
 
3.3%
Other values (94) 617
43.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 637
45.3%
Decimal Number 555
39.5%
Space Separator 123
 
8.7%
Dash Punctuation 83
 
5.9%
Uppercase Letter 4
 
0.3%
Close Punctuation 2
 
0.1%
Open Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 112
17.6%
운 74
 
11.6%
서 43
 
6.8%
중 29
 
4.6%
남 28
 
4.4%
산 28
 
4.4%
로 18
 
2.8%
번 16
 
2.5%
리 16
 
2.5%
거 16
 
2.5%
Other values (76) 257
40.3%
Decimal Number
ValueCountFrequency (%)
1 102
18.4%
3 89
16.0%
2 55
9.9%
0 54
9.7%
7 50
9.0%
9 47
8.5%
6 46
8.3%
8 41
7.4%
5 39
 
7.0%
4 32
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
C 1
25.0%
J 1
25.0%
S 1
25.0%
G 1
25.0%
Space Separator
ValueCountFrequency (%)
123
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 83
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 765
54.4%
Hangul 637
45.3%
Latin 4
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 112
17.6%
운 74
 
11.6%
서 43
 
6.8%
중 29
 
4.6%
남 28
 
4.4%
산 28
 
4.4%
로 18
 
2.8%
번 16
 
2.5%
리 16
 
2.5%
거 16
 
2.5%
Other values (76) 257
40.3%
Common
ValueCountFrequency (%)
123
16.1%
1 102
13.3%
3 89
11.6%
- 83
10.8%
2 55
7.2%
0 54
7.1%
7 50
6.5%
9 47
 
6.1%
6 46
 
6.0%
8 41
 
5.4%
Other values (4) 75
9.8%
Latin
ValueCountFrequency (%)
C 1
25.0%
J 1
25.0%
S 1
25.0%
G 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 769
54.7%
Hangul 637
45.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
123
16.0%
1 102
13.3%
3 89
11.6%
- 83
10.8%
2 55
7.2%
0 54
7.0%
7 50
6.5%
9 47
 
6.1%
6 46
 
6.0%
8 41
 
5.3%
Other values (8) 79
10.3%
Hangul
ValueCountFrequency (%)
동 112
17.6%
운 74
 
11.6%
서 43
 
6.8%
중 29
 
4.6%
남 28
 
4.4%
산 28
 
4.4%
로 18
 
2.8%
번 16
 
2.5%
리 16
 
2.5%
거 16
 
2.5%
Other values (76) 257
40.3%

도로연계성여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size306.0 B
True
174 
ValueCountFrequency (%)
True 174
100.0%
2024-01-28T20:30:24.518998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

자전거도로종류
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
겸용도로
95 
전용도로
78 
전용차로
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row겸용도로
2nd row겸용도로
3rd row전용도로
4th row겸용도로
5th row겸용도로

Common Values

ValueCountFrequency (%)
겸용도로 95
54.6%
전용도로 78
44.8%
전용차로 1
 
0.6%

Length

2024-01-28T20:30:24.611093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:30:24.710648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
겸용도로 95
54.6%
전용도로 78
44.8%
전용차로 1
 
0.6%

자전거도로포장재질
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
아스콘
167 
보도
 
7

Length

Max length3
Median length3
Mean length2.9597701
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
아스콘 167
96.0%
보도 7
 
4.0%

Length

2024-01-28T20:30:24.802487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:30:24.880984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아스콘 167
96.0%
보도 7
 
4.0%

연장
Real number (ℝ)

Distinct114
Distinct (%)65.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1695287
Minimum0.06
Maximum5.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-01-28T20:30:24.963589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.06
5-th percentile0.2
Q10.41
median0.775
Q31.69
95-th percentile3.0935
Maximum5.87
Range5.81
Interquartile range (IQR)1.28

Descriptive statistics

Standard deviation1.1039135
Coefficient of variation (CV)0.94389601
Kurtosis5.1324452
Mean1.1695287
Median Absolute Deviation (MAD)0.44
Skewness2.0433932
Sum203.498
Variance1.218625
MonotonicityNot monotonic
2024-01-28T20:30:25.072320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.45 4
 
2.3%
0.62 4
 
2.3%
0.36 4
 
2.3%
0.72 4
 
2.3%
0.35 3
 
1.7%
0.24 3
 
1.7%
0.61 3
 
1.7%
0.78 3
 
1.7%
2.13 3
 
1.7%
1.08 3
 
1.7%
Other values (104) 140
80.5%
ValueCountFrequency (%)
0.06 1
 
0.6%
0.1 1
 
0.6%
0.111 1
 
0.6%
0.15 1
 
0.6%
0.18 3
1.7%
0.2 3
1.7%
0.21 2
1.1%
0.22 1
 
0.6%
0.23 1
 
0.6%
0.24 3
1.7%
ValueCountFrequency (%)
5.87 1
0.6%
5.85 1
0.6%
5.7 1
0.6%
5.39 1
0.6%
4.09 1
0.6%
3.66 2
1.1%
3.64 1
0.6%
3.23 1
0.6%
3.02 1
0.6%
3.0 1
0.6%

전용도로 폭원
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)13.3%
Missing99
Missing (%)56.9%
Infinite0
Infinite (%)0.0%
Mean1.8346667
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-01-28T20:30:25.159078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.14
Q11.7
median2
Q32
95-th percentile2
Maximum3
Range2
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.35125002
Coefficient of variation (CV)0.19145168
Kurtosis1.7751112
Mean1.8346667
Median Absolute Deviation (MAD)0
Skewness-0.47948196
Sum137.6
Variance0.12337658
MonotonicityNot monotonic
2024-01-28T20:30:25.242116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2.0 44
25.3%
1.8 7
 
4.0%
1.7 5
 
2.9%
1.5 5
 
2.9%
1.0 4
 
2.3%
1.2 4
 
2.3%
1.3 2
 
1.1%
2.5 2
 
1.1%
1.6 1
 
0.6%
3.0 1
 
0.6%
(Missing) 99
56.9%
ValueCountFrequency (%)
1.0 4
 
2.3%
1.2 4
 
2.3%
1.3 2
 
1.1%
1.5 5
 
2.9%
1.6 1
 
0.6%
1.7 5
 
2.9%
1.8 7
 
4.0%
2.0 44
25.3%
2.5 2
 
1.1%
3.0 1
 
0.6%
ValueCountFrequency (%)
3.0 1
 
0.6%
2.5 2
 
1.1%
2.0 44
25.3%
1.8 7
 
4.0%
1.7 5
 
2.9%
1.6 1
 
0.6%
1.5 5
 
2.9%
1.3 2
 
1.1%
1.2 4
 
2.3%
1.0 4
 
2.3%

전용차로 폭원
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
173 
3.4
 
1

Length

Max length4
Median length4
Mean length3.9942529
Min length3

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 173
99.4%
3.4 1
 
0.6%

Length

2024-01-28T20:30:25.337582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:30:25.421525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 173
99.4%
3.4 1
 
0.6%

자전거도로 겸용도로 폭원
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)18.6%
Missing77
Missing (%)44.3%
Infinite0
Infinite (%)0.0%
Mean1.6463918
Minimum1
Maximum4.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-01-28T20:30:25.493003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.1
Q11.35
median1.5
Q31.7
95-th percentile3.22
Maximum4.5
Range3.5
Interquartile range (IQR)0.35

Descriptive statistics

Standard deviation0.64194721
Coefficient of variation (CV)0.38991158
Kurtosis9.1577931
Mean1.6463918
Median Absolute Deviation (MAD)0.15
Skewness2.8662219
Sum159.7
Variance0.41209622
MonotonicityNot monotonic
2024-01-28T20:30:25.600294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1.5 32
18.4%
1.8 11
 
6.3%
1.4 9
 
5.2%
1.35 8
 
4.6%
1.1 7
 
4.0%
1.2 5
 
2.9%
1.0 4
 
2.3%
1.7 4
 
2.3%
2.0 4
 
2.3%
1.6 2
 
1.1%
Other values (8) 11
 
6.3%
(Missing) 77
44.3%
ValueCountFrequency (%)
1.0 4
 
2.3%
1.1 7
 
4.0%
1.2 5
 
2.9%
1.3 2
 
1.1%
1.35 8
 
4.6%
1.4 9
 
5.2%
1.5 32
18.4%
1.6 2
 
1.1%
1.7 4
 
2.3%
1.8 11
 
6.3%
ValueCountFrequency (%)
4.5 2
 
1.1%
4.0 1
 
0.6%
3.3 2
 
1.1%
3.2 1
 
0.6%
2.7 1
 
0.6%
2.5 1
 
0.6%
2.2 1
 
0.6%
2.0 4
 
2.3%
1.8 11
6.3%
1.7 4
 
2.3%

보도 겸용도로폭원
Real number (ℝ)

MISSING 

Distinct24
Distinct (%)24.7%
Missing77
Missing (%)44.3%
Infinite0
Infinite (%)0.0%
Mean2.2706186
Minimum0.5
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-01-28T20:30:25.699560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1.48
Q12
median2.1
Q32.4
95-th percentile3.3
Maximum8
Range7.5
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.82075145
Coefficient of variation (CV)0.36146602
Kurtosis24.635105
Mean2.2706186
Median Absolute Deviation (MAD)0.2
Skewness3.8168585
Sum220.25
Variance0.67363295
MonotonicityNot monotonic
2024-01-28T20:30:25.791437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2.0 27
 
15.5%
2.3 15
 
8.6%
1.5 7
 
4.0%
2.2 7
 
4.0%
2.5 5
 
2.9%
2.1 4
 
2.3%
3.0 4
 
2.3%
2.8 3
 
1.7%
1.7 3
 
1.7%
1.8 3
 
1.7%
Other values (14) 19
 
10.9%
(Missing) 77
44.3%
ValueCountFrequency (%)
0.5 1
 
0.6%
1.3 2
 
1.1%
1.35 1
 
0.6%
1.4 1
 
0.6%
1.5 7
 
4.0%
1.7 3
 
1.7%
1.8 3
 
1.7%
1.9 1
 
0.6%
2.0 27
15.5%
2.1 4
 
2.3%
ValueCountFrequency (%)
8.0 1
 
0.6%
4.6 1
 
0.6%
3.7 1
 
0.6%
3.5 1
 
0.6%
3.3 2
1.1%
3.2 1
 
0.6%
3.1 2
1.1%
3.0 4
2.3%
2.8 3
1.7%
2.7 3
1.7%

겸용도로 유효폭원
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)18.6%
Missing77
Missing (%)44.3%
Infinite0
Infinite (%)0.0%
Mean1.6463918
Minimum1
Maximum4.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-01-28T20:30:25.879448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.1
Q11.35
median1.5
Q31.7
95-th percentile3.22
Maximum4.5
Range3.5
Interquartile range (IQR)0.35

Descriptive statistics

Standard deviation0.64194721
Coefficient of variation (CV)0.38991158
Kurtosis9.1577931
Mean1.6463918
Median Absolute Deviation (MAD)0.15
Skewness2.8662219
Sum159.7
Variance0.41209622
MonotonicityNot monotonic
2024-01-28T20:30:25.969264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1.5 32
18.4%
1.8 11
 
6.3%
1.4 9
 
5.2%
1.35 8
 
4.6%
1.1 7
 
4.0%
1.2 5
 
2.9%
1.0 4
 
2.3%
1.7 4
 
2.3%
2.0 4
 
2.3%
1.6 2
 
1.1%
Other values (8) 11
 
6.3%
(Missing) 77
44.3%
ValueCountFrequency (%)
1.0 4
 
2.3%
1.1 7
 
4.0%
1.2 5
 
2.9%
1.3 2
 
1.1%
1.35 8
 
4.6%
1.4 9
 
5.2%
1.5 32
18.4%
1.6 2
 
1.1%
1.7 4
 
2.3%
1.8 11
 
6.3%
ValueCountFrequency (%)
4.5 2
 
1.1%
4.0 1
 
0.6%
3.3 2
 
1.1%
3.2 1
 
0.6%
2.7 1
 
0.6%
2.5 1
 
0.6%
2.2 1
 
0.6%
2.0 4
 
2.3%
1.8 11
6.3%
1.7 4
 
2.3%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2021-07-20
174 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-07-20
2nd row2021-07-20
3rd row2021-07-20
4th row2021-07-20
5th row2021-07-20

Common Values

ValueCountFrequency (%)
2021-07-20 174
100.0%

Length

2024-01-28T20:30:26.070134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:30:26.147883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-07-20 174
100.0%

Interactions

2024-01-28T20:30:21.339628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:19.035538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:19.452641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:19.855082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:20.261966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:20.672205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:21.419251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:19.102875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:19.515476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:19.936578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:20.332744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:20.743125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:21.484142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:19.168858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:19.578405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:20.020823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:20.402113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:20.812857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:21.538802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:19.239807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:19.649779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:20.095685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:20.460332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:21.101673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:21.613852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:19.310580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:19.717456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:20.152121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:20.531418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:21.170979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:21.699283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:19.381881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:19.784699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:20.206258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:20.604190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:21.253654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T20:30:26.209565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호자전거도로종류자전거도로포장재질연장전용도로 폭원자전거도로 겸용도로 폭원보도 겸용도로폭원겸용도로 유효폭원
일련번호1.0000.4730.3970.3720.6380.5640.3350.564
자전거도로종류0.4731.0000.0930.501NaN0.0000.0000.000
자전거도로포장재질0.3970.0931.0000.272NaN0.0000.0000.000
연장0.3720.5010.2721.0000.6800.0000.3720.000
전용도로 폭원0.638NaNNaN0.6801.000NaNNaNNaN
자전거도로 겸용도로 폭원0.5640.0000.0000.000NaN1.0000.3811.000
보도 겸용도로폭원0.3350.0000.0000.372NaN0.3811.0000.381
겸용도로 유효폭원0.5640.0000.0000.000NaN1.0000.3811.000
2024-01-28T20:30:26.526316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자전거도로포장재질자전거도로종류전용차로 폭원
자전거도로포장재질1.0000.153NaN
자전거도로종류0.1531.000NaN
전용차로 폭원NaNNaN1.000
2024-01-28T20:30:26.604916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호연장전용도로 폭원자전거도로 겸용도로 폭원보도 겸용도로폭원겸용도로 유효폭원자전거도로종류자전거도로포장재질전용차로 폭원
일련번호1.0000.3210.580-0.217-0.120-0.2170.3160.297NaN
연장0.3211.0000.263-0.1820.123-0.1820.3620.200NaN
전용도로 폭원0.5800.2631.000NaNNaNNaN1.0001.0000.000
자전거도로 겸용도로 폭원-0.217-0.182NaN1.0000.0061.0000.0000.0000.000
보도 겸용도로폭원-0.1200.123NaN0.0061.0000.0060.0000.0000.000
겸용도로 유효폭원-0.217-0.182NaN1.0000.0061.0000.0000.0000.000
자전거도로종류0.3160.3621.0000.0000.0000.0001.0000.153NaN
자전거도로포장재질0.2970.2001.0000.0000.0000.0000.1531.000NaN
전용차로 폭원NaNNaN0.0000.0000.0000.000NaNNaN1.000

Missing values

2024-01-28T20:30:21.802369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T20:30:21.971015image/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.
2024-01-28T20:30:22.093434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

일련번호시도시군구노선명기점종점도로연계성여부자전거도로종류자전거도로포장재질연장전용도로 폭원전용차로 폭원자전거도로 겸용도로 폭원보도 겸용도로폭원겸용도로 유효폭원데이터기준일자
01인천광역시중구월미로1우회고가 사거리8부두 앞Y겸용도로아스콘0.2<NA><NA>1.81.41.82021-07-20
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