Overview

Dataset statistics

Number of variables16
Number of observations119
Missing cells243
Missing cells (%)12.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.8 KiB
Average record size in memory136.1 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
자전거도로포장재질 is highly overall correlated with 전용도로 폭원 and 1 other fieldsHigh correlation
자전거도로종류 is highly overall correlated with 전용도로 폭원 and 4 other fieldsHigh correlation
전용차로 폭원 is highly overall correlated with 자전거도로종류 and 1 other fieldsHigh correlation
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 유효폭원 and 1 other fieldsHigh correlation
보도 겸용도로폭원 is highly overall correlated with 자전거도로종류High correlation
겸용도로 유효폭원 is highly overall correlated with 자전거도로 겸용도로 폭원 and 1 other fieldsHigh correlation
자전거도로포장재질 is highly imbalanced (67.7%)Imbalance
전용차로 폭원 is highly imbalanced (56.6%)Imbalance
전용도로 폭원 has 84 (70.6%) missing valuesMissing
자전거도로 겸용도로 폭원 has 52 (43.7%) missing valuesMissing
보도 겸용도로폭원 has 52 (43.7%) missing valuesMissing
겸용도로 유효폭원 has 55 (46.2%) missing valuesMissing
일련번호 has unique valuesUnique
노선명 has unique valuesUnique

Reproduction

Analysis started2024-01-28 11:30:09.891723
Analysis finished2024-01-28 11:30:13.107274
Duration3.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일련번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct119
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60
Minimum1
Maximum119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-01-28T20:30:13.159720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.9
Q130.5
median60
Q389.5
95-th percentile113.1
Maximum119
Range118
Interquartile range (IQR)59

Descriptive statistics

Standard deviation34.496377
Coefficient of variation (CV)0.57493961
Kurtosis-1.2
Mean60
Median Absolute Deviation (MAD)30
Skewness0
Sum7140
Variance1190
MonotonicityStrictly increasing
2024-01-28T20:30:13.284250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.8%
2 1
 
0.8%
89 1
 
0.8%
88 1
 
0.8%
87 1
 
0.8%
86 1
 
0.8%
85 1
 
0.8%
84 1
 
0.8%
83 1
 
0.8%
82 1
 
0.8%
Other values (109) 109
91.6%
ValueCountFrequency (%)
1 1
0.8%
2 1
0.8%
3 1
0.8%
4 1
0.8%
5 1
0.8%
6 1
0.8%
7 1
0.8%
8 1
0.8%
9 1
0.8%
10 1
0.8%
ValueCountFrequency (%)
119 1
0.8%
118 1
0.8%
117 1
0.8%
116 1
0.8%
115 1
0.8%
114 1
0.8%
113 1
0.8%
112 1
0.8%
111 1
0.8%
110 1
0.8%

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
인천광역시
119 

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 119
100.0%

Length

2024-01-28T20:30:13.381416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:30:13.447490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 119
100.0%

시군구
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
중구
119 

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 119
100.0%

Length

2024-01-28T20:30:13.517870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:30:13.582687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중구 119
100.0%

노선명
Text

UNIQUE 

Distinct119
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-01-28T20:30:13.763687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.0168067
Min length3

Characters and Unicode

Total characters716
Distinct characters78
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique119 ?
Unique (%)100.0%

Sample

1st row월미로1
2nd row월미로2
3rd row월미로3
4th row월미로7
5th row월미로8R
ValueCountFrequency (%)
영종대로 3
 
2.5%
은하수로450번길 1
 
0.8%
하늘별빛로1 1
 
0.8%
자연대로5 1
 
0.8%
자연대로4 1
 
0.8%
하늘달빛로l 1
 
0.8%
하늘달빛로r 1
 
0.8%
하늘중앙로2 1
 
0.8%
하늘중앙로1 1
 
0.8%
은하수로5 1
 
0.8%
Other values (110) 110
90.2%
2024-01-28T20:30:14.062563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 118
 
16.5%
대 57
 
8.0%
2 39
 
5.4%
1 32
 
4.5%
R 31
 
4.3%
종 29
 
4.1%
영 29
 
4.1%
하 23
 
3.2%
L 21
 
2.9%
- 17
 
2.4%
Other values (68) 320
44.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 505
70.5%
Decimal Number 139
 
19.4%
Uppercase Letter 52
 
7.3%
Dash Punctuation 17
 
2.4%
Space Separator 3
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 118
23.4%
대 57
 
11.3%
종 29
 
5.7%
영 29
 
5.7%
하 23
 
4.6%
늘 16
 
3.2%
축 16
 
3.2%
항 16
 
3.2%
번 14
 
2.8%
길 14
 
2.8%
Other values (54) 173
34.3%
Decimal Number
ValueCountFrequency (%)
2 39
28.1%
1 32
23.0%
5 14
 
10.1%
3 13
 
9.4%
4 11
 
7.9%
7 10
 
7.2%
8 7
 
5.0%
9 5
 
3.6%
6 5
 
3.6%
0 3
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
R 31
59.6%
L 21
40.4%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 505
70.5%
Common 159
 
22.2%
Latin 52
 
7.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 118
23.4%
대 57
 
11.3%
종 29
 
5.7%
영 29
 
5.7%
하 23
 
4.6%
늘 16
 
3.2%
축 16
 
3.2%
항 16
 
3.2%
번 14
 
2.8%
길 14
 
2.8%
Other values (54) 173
34.3%
Common
ValueCountFrequency (%)
2 39
24.5%
1 32
20.1%
- 17
10.7%
5 14
 
8.8%
3 13
 
8.2%
4 11
 
6.9%
7 10
 
6.3%
8 7
 
4.4%
9 5
 
3.1%
6 5
 
3.1%
Other values (2) 6
 
3.8%
Latin
ValueCountFrequency (%)
R 31
59.6%
L 21
40.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 505
70.5%
ASCII 211
29.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 118
23.4%
대 57
 
11.3%
종 29
 
5.7%
영 29
 
5.7%
하 23
 
4.6%
늘 16
 
3.2%
축 16
 
3.2%
항 16
 
3.2%
번 14
 
2.8%
길 14
 
2.8%
Other values (54) 173
34.3%
ASCII
ValueCountFrequency (%)
2 39
18.5%
1 32
15.2%
R 31
14.7%
L 21
10.0%
- 17
8.1%
5 14
 
6.6%
3 13
 
6.2%
4 11
 
5.2%
7 10
 
4.7%
8 7
 
3.3%
Other values (4) 16
7.6%

기점
Text

Distinct102
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-01-28T20:30:14.259431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length19
Mean length17.689076
Min length12

Characters and Unicode

Total characters2105
Distinct characters103
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

Unique87 ?
Unique (%)73.1%

Sample

1st row인천광역시 중구 우회고가사거리
2nd row인천광역시 중구 8부두 앞
3rd row인천광역시 중구 인항철골
4th row인천광역시 중구 월미로260번길
5th row인천광역시 중구 월미공원후문
ValueCountFrequency (%)
인천광역시 119
27.9%
중구 119
27.9%
운남동 24
 
5.6%
중산동 22
 
5.2%
운서동 11
 
2.6%
제2경인고속도로 4
 
0.9%
시점 4
 
0.9%
운남동1113-23 2
 
0.5%
은골사거리 2
 
0.5%
1598-17 2
 
0.5%
Other values (103) 118
27.6%
2024-01-28T20:30:14.788565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
326
15.5%
중 145
 
6.9%
인 124
 
5.9%
시 123
 
5.8%
구 121
 
5.7%
천 119
 
5.7%
역 119
 
5.7%
광 119
 
5.7%
1 109
 
5.2%
동 80
 
3.8%
Other values (93) 720
34.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1312
62.3%
Decimal Number 407
 
19.3%
Space Separator 326
 
15.5%
Dash Punctuation 54
 
2.6%
Uppercase Letter 4
 
0.2%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
중 145
11.1%
인 124
9.5%
시 123
9.4%
구 121
9.2%
천 119
9.1%
역 119
9.1%
광 119
9.1%
동 80
 
6.1%
운 52
 
4.0%
남 30
 
2.3%
Other values (77) 280
21.3%
Decimal Number
ValueCountFrequency (%)
1 109
26.8%
3 42
 
10.3%
8 39
 
9.6%
7 39
 
9.6%
2 39
 
9.6%
9 37
 
9.1%
0 32
 
7.9%
6 28
 
6.9%
5 24
 
5.9%
4 18
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
S 2
50.0%
K 2
50.0%
Space Separator
ValueCountFrequency (%)
326
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 54
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1312
62.3%
Common 789
37.5%
Latin 4
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
중 145
11.1%
인 124
9.5%
시 123
9.4%
구 121
9.2%
천 119
9.1%
역 119
9.1%
광 119
9.1%
동 80
 
6.1%
운 52
 
4.0%
남 30
 
2.3%
Other values (77) 280
21.3%
Common
ValueCountFrequency (%)
326
41.3%
1 109
 
13.8%
- 54
 
6.8%
3 42
 
5.3%
8 39
 
4.9%
7 39
 
4.9%
2 39
 
4.9%
9 37
 
4.7%
0 32
 
4.1%
6 28
 
3.5%
Other values (4) 44
 
5.6%
Latin
ValueCountFrequency (%)
S 2
50.0%
K 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1312
62.3%
ASCII 793
37.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
326
41.1%
1 109
 
13.7%
- 54
 
6.8%
3 42
 
5.3%
8 39
 
4.9%
7 39
 
4.9%
2 39
 
4.9%
9 37
 
4.7%
0 32
 
4.0%
6 28
 
3.5%
Other values (6) 48
 
6.1%
Hangul
ValueCountFrequency (%)
중 145
11.1%
인 124
9.5%
시 123
9.4%
구 121
9.2%
천 119
9.1%
역 119
9.1%
광 119
9.1%
동 80
 
6.1%
운 52
 
4.0%
남 30
 
2.3%
Other values (77) 280
21.3%

종점
Text

Distinct101
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-01-28T20:30:14.987785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length17.798319
Min length14

Characters and Unicode

Total characters2118
Distinct characters95
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

Unique86 ?
Unique (%)72.3%

Sample

1st row인천광역시 중구 8부두 앞
2nd row인천광역시 중구 월미도입구삼거리
3rd row인천광역시 중구 월미공원역
4th row인천광역시 중구 월미공원후문
5th row인천광역시 중구 월미로189번길
ValueCountFrequency (%)
인천광역시 119
28.0%
중구 119
28.0%
중산동 22
 
5.2%
운남동 18
 
4.2%
운서동 14
 
3.3%
서해대로 5
 
1.2%
293번길 5
 
1.2%
운북동 4
 
0.9%
연안사거리 2
 
0.5%
공원사거리 2
 
0.5%
Other values (102) 115
27.1%
2024-01-28T20:30:15.275948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
307
 
14.5%
중 146
 
6.9%
구 122
 
5.8%
인 121
 
5.7%
천 121
 
5.7%
역 120
 
5.7%
시 120
 
5.7%
광 119
 
5.6%
동 87
 
4.1%
1 82
 
3.9%
Other values (85) 773
36.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1313
62.0%
Decimal Number 431
 
20.3%
Space Separator 307
 
14.5%
Dash Punctuation 61
 
2.9%
Uppercase Letter 4
 
0.2%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
중 146
11.1%
구 122
9.3%
인 121
9.2%
천 121
9.2%
역 120
9.1%
시 120
9.1%
광 119
9.1%
동 87
 
6.6%
운 52
 
4.0%
서 30
 
2.3%
Other values (67) 275
20.9%
Decimal Number
ValueCountFrequency (%)
1 82
19.0%
3 64
14.8%
2 44
10.2%
7 41
9.5%
9 40
9.3%
0 40
9.3%
8 39
9.0%
6 32
 
7.4%
5 27
 
6.3%
4 22
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
G 1
25.0%
S 1
25.0%
C 1
25.0%
J 1
25.0%
Space Separator
ValueCountFrequency (%)
307
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 61
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1313
62.0%
Common 801
37.8%
Latin 4
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
중 146
11.1%
구 122
9.3%
인 121
9.2%
천 121
9.2%
역 120
9.1%
시 120
9.1%
광 119
9.1%
동 87
 
6.6%
운 52
 
4.0%
서 30
 
2.3%
Other values (67) 275
20.9%
Common
ValueCountFrequency (%)
307
38.3%
1 82
 
10.2%
3 64
 
8.0%
- 61
 
7.6%
2 44
 
5.5%
7 41
 
5.1%
9 40
 
5.0%
0 40
 
5.0%
8 39
 
4.9%
6 32
 
4.0%
Other values (4) 51
 
6.4%
Latin
ValueCountFrequency (%)
G 1
25.0%
S 1
25.0%
C 1
25.0%
J 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1313
62.0%
ASCII 805
38.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
307
38.1%
1 82
 
10.2%
3 64
 
8.0%
- 61
 
7.6%
2 44
 
5.5%
7 41
 
5.1%
9 40
 
5.0%
0 40
 
5.0%
8 39
 
4.8%
6 32
 
4.0%
Other values (8) 55
 
6.8%
Hangul
ValueCountFrequency (%)
중 146
11.1%
구 122
9.3%
인 121
9.2%
천 121
9.2%
역 120
9.1%
시 120
9.1%
광 119
9.1%
동 87
 
6.6%
운 52
 
4.0%
서 30
 
2.3%
Other values (67) 275
20.9%

도로연계성여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size251.0 B
True
119 
ValueCountFrequency (%)
True 119
100.0%
2024-01-28T20:30:15.386618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

자전거도로종류
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
겸용도로
67 
전용도로
35 
전용차로
17 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
겸용도로 67
56.3%
전용도로 35
29.4%
전용차로 17
 
14.3%

Length

2024-01-28T20:30:15.478438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:30:15.581069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
겸용도로 67
56.3%
전용도로 35
29.4%
전용차로 17
 
14.3%

자전거도로포장재질
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
아스콘
112 
보도블록
 
7

Length

Max length4
Median length3
Mean length3.0588235
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
아스콘 112
94.1%
보도블록 7
 
5.9%

Length

2024-01-28T20:30:15.665779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:30:15.734335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아스콘 112
94.1%
보도블록 7
 
5.9%

연장
Real number (ℝ)

Distinct100
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4717647
Minimum0.06
Maximum5.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-01-28T20:30:15.811592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.06
5-th percentile0.249
Q10.5
median1.04
Q31.965
95-th percentile4.294
Maximum5.7
Range5.64
Interquartile range (IQR)1.465

Descriptive statistics

Standard deviation1.3146022
Coefficient of variation (CV)0.89321495
Kurtosis2.4550142
Mean1.4717647
Median Absolute Deviation (MAD)0.65
Skewness1.6297012
Sum175.14
Variance1.7281791
MonotonicityNot monotonic
2024-01-28T20:30:15.918265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 3
 
2.5%
0.31 3
 
2.5%
1.03 3
 
2.5%
0.25 3
 
2.5%
0.86 3
 
2.5%
0.24 2
 
1.7%
1.0 2
 
1.7%
3.8 2
 
1.7%
2.76 2
 
1.7%
2.8 2
 
1.7%
Other values (90) 94
79.0%
ValueCountFrequency (%)
0.06 1
 
0.8%
0.2 1
 
0.8%
0.21 1
 
0.8%
0.23 1
 
0.8%
0.24 2
1.7%
0.25 3
2.5%
0.27 1
 
0.8%
0.29 2
1.7%
0.3 1
 
0.8%
0.31 3
2.5%
ValueCountFrequency (%)
5.7 1
0.8%
5.68 1
0.8%
5.67 1
0.8%
5.6 1
0.8%
5.37 1
0.8%
4.6 1
0.8%
4.26 1
0.8%
4.23 1
0.8%
3.8 2
1.7%
3.4 1
0.8%

전용도로 폭원
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)28.6%
Missing84
Missing (%)70.6%
Infinite0
Infinite (%)0.0%
Mean1.8
Minimum1
Maximum3.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-01-28T20:30:16.004461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.55
median1.8
Q32
95-th percentile2.65
Maximum3.4
Range2.4
Interquartile range (IQR)0.45

Descriptive statistics

Standard deviation0.50642925
Coefficient of variation (CV)0.28134958
Kurtosis2.5366373
Mean1.8
Median Absolute Deviation (MAD)0.2
Skewness1.0043911
Sum63
Variance0.25647059
MonotonicityNot monotonic
2024-01-28T20:30:16.085488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.8 8
 
6.7%
2.0 8
 
6.7%
1.7 5
 
4.2%
1.0 4
 
3.4%
1.5 3
 
2.5%
1.3 2
 
1.7%
2.5 2
 
1.7%
1.6 1
 
0.8%
3.4 1
 
0.8%
3.0 1
 
0.8%
(Missing) 84
70.6%
ValueCountFrequency (%)
1.0 4
3.4%
1.3 2
 
1.7%
1.5 3
 
2.5%
1.6 1
 
0.8%
1.7 5
4.2%
1.8 8
6.7%
2.0 8
6.7%
2.5 2
 
1.7%
3.0 1
 
0.8%
3.4 1
 
0.8%
ValueCountFrequency (%)
3.4 1
 
0.8%
3.0 1
 
0.8%
2.5 2
 
1.7%
2.0 8
6.7%
1.8 8
6.7%
1.7 5
4.2%
1.6 1
 
0.8%
1.5 3
 
2.5%
1.3 2
 
1.7%
1.0 4
3.4%

전용차로 폭원
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
<NA>
102 
2.0
14 
1.2
 
3

Length

Max length4
Median length4
Mean length3.8571429
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 102
85.7%
2.0 14
 
11.8%
1.2 3
 
2.5%

Length

2024-01-28T20:30:16.171028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:30:16.243338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 102
85.7%
2.0 14
 
11.8%
1.2 3
 
2.5%

자전거도로 겸용도로 폭원
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)19.4%
Missing52
Missing (%)43.7%
Infinite0
Infinite (%)0.0%
Mean1.5059701
Minimum1
Maximum2.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-01-28T20:30:16.321970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.1
Q11.35
median1.5
Q31.7
95-th percentile2
Maximum2.5
Range1.5
Interquartile range (IQR)0.35

Descriptive statistics

Standard deviation0.28597552
Coefficient of variation (CV)0.18989455
Kurtosis1.3831869
Mean1.5059701
Median Absolute Deviation (MAD)0.15
Skewness0.84561306
Sum100.9
Variance0.081781999
MonotonicityNot monotonic
2024-01-28T20:30:16.432266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1.5 17
 
14.3%
1.4 9
 
7.6%
1.8 8
 
6.7%
1.35 8
 
6.7%
1.1 5
 
4.2%
1.2 4
 
3.4%
1.7 4
 
3.4%
2.0 4
 
3.4%
1.6 2
 
1.7%
1.0 2
 
1.7%
Other values (3) 4
 
3.4%
(Missing) 52
43.7%
ValueCountFrequency (%)
1.0 2
 
1.7%
1.1 5
 
4.2%
1.2 4
 
3.4%
1.3 2
 
1.7%
1.35 8
6.7%
1.4 9
7.6%
1.5 17
14.3%
1.6 2
 
1.7%
1.7 4
 
3.4%
1.8 8
6.7%
ValueCountFrequency (%)
2.5 1
 
0.8%
2.2 1
 
0.8%
2.0 4
 
3.4%
1.8 8
6.7%
1.7 4
 
3.4%
1.6 2
 
1.7%
1.5 17
14.3%
1.4 9
7.6%
1.35 8
6.7%
1.3 2
 
1.7%

보도 겸용도로폭원
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)28.4%
Missing52
Missing (%)43.7%
Infinite0
Infinite (%)0.0%
Mean2.2492537
Minimum0.5
Maximum4.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-01-28T20:30:16.529933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1.56
Q12
median2.2
Q32.45
95-th percentile3.1
Maximum4.6
Range4.1
Interquartile range (IQR)0.45

Descriptive statistics

Standard deviation0.56390767
Coefficient of variation (CV)0.25070878
Kurtosis5.1270808
Mean2.2492537
Median Absolute Deviation (MAD)0.2
Skewness1.1257787
Sum150.7
Variance0.31799186
MonotonicityNot monotonic
2024-01-28T20:30:16.604753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2.0 20
 
16.8%
2.3 10
 
8.4%
2.2 7
 
5.9%
2.5 5
 
4.2%
2.7 3
 
2.5%
1.7 3
 
2.5%
1.8 2
 
1.7%
3.1 2
 
1.7%
3.0 2
 
1.7%
2.1 2
 
1.7%
Other values (9) 11
 
9.2%
(Missing) 52
43.7%
ValueCountFrequency (%)
0.5 1
 
0.8%
1.4 1
 
0.8%
1.5 2
 
1.7%
1.7 3
 
2.5%
1.8 2
 
1.7%
1.9 1
 
0.8%
2.0 20
16.8%
2.1 2
 
1.7%
2.2 7
 
5.9%
2.3 10
8.4%
ValueCountFrequency (%)
4.6 1
 
0.8%
3.7 1
 
0.8%
3.5 1
 
0.8%
3.1 2
 
1.7%
3.0 2
 
1.7%
2.8 2
 
1.7%
2.7 3
 
2.5%
2.5 5
4.2%
2.4 1
 
0.8%
2.3 10
8.4%

겸용도로 유효폭원
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)20.3%
Missing55
Missing (%)46.2%
Infinite0
Infinite (%)0.0%
Mean1.50625
Minimum1
Maximum2.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-01-28T20:30:16.682762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.1
Q11.35
median1.5
Q31.7
95-th percentile2
Maximum2.5
Range1.5
Interquartile range (IQR)0.35

Descriptive statistics

Standard deviation0.28778023
Coefficient of variation (CV)0.19105741
Kurtosis1.4489858
Mean1.50625
Median Absolute Deviation (MAD)0.15
Skewness0.86977924
Sum96.4
Variance0.08281746
MonotonicityNot monotonic
2024-01-28T20:30:16.770720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1.5 16
 
13.4%
1.4 9
 
7.6%
1.35 8
 
6.7%
1.8 7
 
5.9%
1.1 5
 
4.2%
1.7 4
 
3.4%
2.0 4
 
3.4%
1.2 3
 
2.5%
1.6 2
 
1.7%
1.0 2
 
1.7%
Other values (3) 4
 
3.4%
(Missing) 55
46.2%
ValueCountFrequency (%)
1.0 2
 
1.7%
1.1 5
 
4.2%
1.2 3
 
2.5%
1.3 2
 
1.7%
1.35 8
6.7%
1.4 9
7.6%
1.5 16
13.4%
1.6 2
 
1.7%
1.7 4
 
3.4%
1.8 7
5.9%
ValueCountFrequency (%)
2.5 1
 
0.8%
2.2 1
 
0.8%
2.0 4
 
3.4%
1.8 7
5.9%
1.7 4
 
3.4%
1.6 2
 
1.7%
1.5 16
13.4%
1.4 9
7.6%
1.35 8
6.7%
1.3 2
 
1.7%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2020-09-15
119 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-09-15
2nd row2020-09-15
3rd row2020-09-15
4th row2020-09-15
5th row2020-09-15

Common Values

ValueCountFrequency (%)
2020-09-15 119
100.0%

Length

2024-01-28T20:30:16.868905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:30:16.935862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-09-15 119
100.0%

Interactions

2024-01-28T20:30:12.340521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:10.364349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:10.861393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:11.234624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:11.594152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:11.990070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:12.408690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:10.432123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:10.937440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:11.298782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:11.660169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:12.049235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:12.473706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:10.546123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:10.993566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:11.359522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:11.727304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:12.104913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:12.529018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:10.626364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:11.052876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:11.419820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:11.785731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:12.164599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:12.603052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:10.705787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:11.116297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:11.479705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:11.855494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:12.226301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:12.674935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:10.775344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:11.170742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:11.537820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:11.916105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:12.276938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T20:30:16.982961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호자전거도로종류자전거도로포장재질연장전용도로 폭원전용차로 폭원자전거도로 겸용도로 폭원보도 겸용도로폭원겸용도로 유효폭원
일련번호1.0000.4560.4830.5920.6910.0000.5330.3060.546
자전거도로종류0.4561.0000.1080.295NaNNaNNaNNaNNaN
자전거도로포장재질0.4830.1081.0000.186NaNNaN0.0000.0000.000
연장0.5920.2950.1861.0000.8900.3720.0000.4250.000
전용도로 폭원0.691NaNNaN0.8901.000NaNNaNNaNNaN
전용차로 폭원0.000NaNNaN0.372NaN1.000NaNNaNNaN
자전거도로 겸용도로 폭원0.533NaN0.0000.000NaNNaN1.0000.0001.000
보도 겸용도로폭원0.306NaN0.0000.425NaNNaN0.0001.0000.000
겸용도로 유효폭원0.546NaN0.0000.000NaNNaN1.0000.0001.000
2024-01-28T20:30:17.075045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자전거도로포장재질자전거도로종류전용차로 폭원
자전거도로포장재질1.0000.1781.000
자전거도로종류0.1781.0001.000
전용차로 폭원1.0001.0001.000
2024-01-28T20:30:17.146290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호연장전용도로 폭원자전거도로 겸용도로 폭원보도 겸용도로폭원겸용도로 유효폭원자전거도로종류자전거도로포장재질전용차로 폭원
일련번호1.0000.4100.672-0.129-0.079-0.1660.2950.3590.000
연장0.4101.0000.432-0.122-0.012-0.1070.1770.1350.390
전용도로 폭원0.6720.4321.000NaNNaNNaN1.0001.0000.000
자전거도로 겸용도로 폭원-0.129-0.122NaN1.0000.0970.9881.0000.0000.000
보도 겸용도로폭원-0.079-0.012NaN0.0971.0000.0391.0000.0000.000
겸용도로 유효폭원-0.166-0.107NaN0.9880.0391.0001.0000.0000.000
자전거도로종류0.2950.1771.0001.0001.0001.0001.0000.1781.000
자전거도로포장재질0.3590.1351.0000.0000.0000.0000.1781.0001.000
전용차로 폭원0.0000.3900.0000.0000.0000.0001.0001.0001.000

Missing values

2024-01-28T20:30:12.781857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T20:30:12.943291image/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.
2024-01-28T20:30:13.050921image/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
01인천광역시중구월미로1인천광역시 중구 우회고가사거리인천광역시 중구 8부두 앞Y겸용도로아스콘0.2<NA><NA>1.81.41.82020-09-15
12인천광역시중구월미로2인천광역시 중구 8부두 앞인천광역시 중구 월미도입구삼거리Y전용도로아스콘0.061.8<NA><NA><NA><NA>2020-09-15
23인천광역시중구월미로3인천광역시 중구 인항철골인천광역시 중구 월미공원역Y전용도로아스콘0.621.3<NA><NA><NA><NA>2020-09-15
34인천광역시중구월미로7인천광역시 중구 월미로260번길인천광역시 중구 월미공원후문Y겸용도로아스콘0.44<NA><NA>1.62.21.62020-09-15
45인천광역시중구월미로8R인천광역시 중구 월미공원후문인천광역시 중구 월미로189번길Y전용도로아스콘0.291.0<NA><NA><NA><NA>2020-09-15
56인천광역시중구월미로8L인천광역시 중구 월미로189번길인천광역시 중구 월미공원후문Y전용도로아스콘0.311.0<NA><NA><NA><NA>2020-09-15
67인천광역시중구축항대로1인천광역시 중구 라이프아파트정문인천광역시 중구 연안부두로75번길Y겸용도로아스콘0.23<NA><NA>2.22.32.22020-09-15
78인천광역시중구축항대로2R인천광역시 중구 연안부두로75번길인천광역시 중구 연안부두로33번길Y겸용도로보도블록0.33<NA><NA>1.02.01.02020-09-15
89인천광역시중구연안부두로1인천광역시 중구 연안부두로33번길인천광역시 중구 어시장사거리Y겸용도로아스콘0.25<NA><NA>2.52.02.52020-09-15
910인천광역시중구축항대로4인천광역시 중구 연안부두로33번길인천광역시 중구 축항대로118번길Y겸용도로보도블록0.27<NA><NA>1.22.01.22020-09-15
일련번호시도시군구노선명기점종점도로연계성여부자전거도로종류자전거도로포장재질연장전용도로 폭원전용차로 폭원자전거도로 겸용도로 폭원보도 겸용도로폭원겸용도로 유효폭원데이터기준일자
109110인천광역시중구영종대로2-4R인천광역시 중구 운서동 3084-2인천광역시 중구 운서동 3073-3Y겸용도로아스콘0.45<NA><NA>1.72.01.72020-09-15
110111인천광역시중구영종대로2-5R인천광역시 중구 운서동 3050-6인천광역시 중구 운서동 3049-2Y전용도로아스콘0.251.8<NA><NA><NA><NA>2020-09-15
111112인천광역시중구운남로1인천광역시 중구 운남동 1710-6인천광역시 중구 운남동 1654-4Y전용차로아스콘1.5<NA>1.2<NA><NA><NA>2020-09-15
112113인천광역시중구운남로2인천광역시 중구 운남동 211-17인천광역시 중구 운남동 1515Y겸용도로아스콘2.05<NA><NA>2.01.72.02020-09-15
113114인천광역시중구운남로3인천광역시 중구 운남동1565-2인천광역시 중구 운남동1537-9Y겸용도로아스콘0.5<NA><NA>2.01.82.02020-09-15
114115인천광역시중구하늘달빛로2번길인천광역시 중구 중산동2002인천광역시 중구 중산동2001Y전용차로아스콘2.02<NA>2.0<NA><NA><NA>2020-09-15
115116인천광역시중구용유서로R인천광역시 중구 을왕동931인천광역시 중구 을왕동936-2Y전용도로아스콘3.82.5<NA><NA><NA><NA>2020-09-15
116117인천광역시중구남북로R인천광역시 중구 을왕동936-2인천광역시 중구 남북동493-2Y전용도로아스콘2.92.5<NA><NA><NA><NA>2020-09-15
117118인천광역시중구용유로R인천광역시 중구 남북동493-2인천광역시 중구 덕교동128-4Y전용도로아스콘3.253.0<NA><NA><NA><NA>2020-09-15
118119인천광역시중구용유로L인천광역시 중구 을왕동 산104번지인천광역시 중구 남북동990Y겸용도로아스콘5.37<NA><NA>1.10.51.12020-09-15