Overview

Dataset statistics

Number of variables9
Number of observations135
Missing cells302
Missing cells (%)24.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.5 KiB
Average record size in memory80.0 B

Variable types

Numeric7
Text2

Dataset

Description인천광역시 연수구 자전거도로 및 이용시설 현황 자료로 자전거를 이용하는 주민들을 위하여 자전거도로 위치 및 연장 등을 제공하여 자전거 이용 활성화에 기여합니다.
Author인천광역시 연수구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=3068400&srcSe=7661IVAWM27C61E190

Alerts

총연장(킬로미터) is highly overall correlated with 분리형 겸용도로 연장(킬로미터)High correlation
분리형 겸용도로 연장(킬로미터) is highly overall correlated with 총연장(킬로미터)High correlation
전용도로 연장(킬로미터) has 85 (63.0%) missing valuesMissing
전용도로 폭원(미터) has 85 (63.0%) missing valuesMissing
분리형 겸용도로 연장(킬로미터) has 44 (32.6%) missing valuesMissing
분리형 겸용도로 자전거도로 폭원(미터) has 44 (32.6%) missing valuesMissing
분리형 겸용도로 보도폭원(미터) has 44 (32.6%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-04-29 13:45:00.478627
Analysis finished2024-04-29 13:45:06.817903
Duration6.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct135
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68
Minimum1
Maximum135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-04-29T22:45:06.905226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.7
Q134.5
median68
Q3101.5
95-th percentile128.3
Maximum135
Range134
Interquartile range (IQR)67

Descriptive statistics

Standard deviation39.115214
Coefficient of variation (CV)0.57522374
Kurtosis-1.2
Mean68
Median Absolute Deviation (MAD)34
Skewness0
Sum9180
Variance1530
MonotonicityStrictly increasing
2024-04-29T22:45:07.059364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.7%
94 1
 
0.7%
88 1
 
0.7%
89 1
 
0.7%
90 1
 
0.7%
91 1
 
0.7%
92 1
 
0.7%
93 1
 
0.7%
95 1
 
0.7%
2 1
 
0.7%
Other values (125) 125
92.6%
ValueCountFrequency (%)
1 1
0.7%
2 1
0.7%
3 1
0.7%
4 1
0.7%
5 1
0.7%
6 1
0.7%
7 1
0.7%
8 1
0.7%
9 1
0.7%
10 1
0.7%
ValueCountFrequency (%)
135 1
0.7%
134 1
0.7%
133 1
0.7%
132 1
0.7%
131 1
0.7%
130 1
0.7%
129 1
0.7%
128 1
0.7%
127 1
0.7%
126 1
0.7%

기점
Text

Distinct121
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-04-29T22:45:07.344542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length8.3185185
Min length3

Characters and Unicode

Total characters1123
Distinct characters184
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

Unique108 ?
Unique (%)80.0%

Sample

1st row옹암사거리
2nd row송학둥지아파트
3rd row청학사거리
4th row수리봉사거리
5th row인천여자고등학교
ValueCountFrequency (%)
송도동 43
 
20.8%
청학사거리 3
 
1.4%
송도3교 3
 
1.4%
가톨릭대학교 2
 
1.0%
커낼워크 2
 
1.0%
102동 2
 
1.0%
더샵그린스퀘어 2
 
1.0%
문학경기장사거리 2
 
1.0%
봄동 2
 
1.0%
더샵하버뷰 2
 
1.0%
Other values (131) 144
69.6%
2024-04-29T22:45:07.705554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
72
 
6.4%
동 68
 
6.1%
1 67
 
6.0%
송 53
 
4.7%
도 52
 
4.6%
지 49
 
4.4%
번 43
 
3.8%
2 35
 
3.1%
- 29
 
2.6%
0 25
 
2.2%
Other values (174) 630
56.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 798
71.1%
Decimal Number 212
 
18.9%
Space Separator 72
 
6.4%
Dash Punctuation 29
 
2.6%
Uppercase Letter 10
 
0.9%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 68
 
8.5%
송 53
 
6.6%
도 52
 
6.5%
지 49
 
6.1%
번 43
 
5.4%
리 24
 
3.0%
거 20
 
2.5%
학 20
 
2.5%
교 18
 
2.3%
사 17
 
2.1%
Other values (154) 434
54.4%
Decimal Number
ValueCountFrequency (%)
1 67
31.6%
2 35
16.5%
0 25
 
11.8%
3 21
 
9.9%
7 14
 
6.6%
6 12
 
5.7%
8 12
 
5.7%
5 11
 
5.2%
4 8
 
3.8%
9 7
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
S 3
30.0%
B 2
20.0%
K 2
20.0%
C 1
 
10.0%
R 1
 
10.0%
I 1
 
10.0%
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 798
71.1%
Common 315
 
28.0%
Latin 10
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 68
 
8.5%
송 53
 
6.6%
도 52
 
6.5%
지 49
 
6.1%
번 43
 
5.4%
리 24
 
3.0%
거 20
 
2.5%
학 20
 
2.5%
교 18
 
2.3%
사 17
 
2.1%
Other values (154) 434
54.4%
Common
ValueCountFrequency (%)
72
22.9%
1 67
21.3%
2 35
11.1%
- 29
9.2%
0 25
 
7.9%
3 21
 
6.7%
7 14
 
4.4%
6 12
 
3.8%
8 12
 
3.8%
5 11
 
3.5%
Other values (4) 17
 
5.4%
Latin
ValueCountFrequency (%)
S 3
30.0%
B 2
20.0%
K 2
20.0%
C 1
 
10.0%
R 1
 
10.0%
I 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 798
71.1%
ASCII 325
28.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
72
22.2%
1 67
20.6%
2 35
10.8%
- 29
8.9%
0 25
 
7.7%
3 21
 
6.5%
7 14
 
4.3%
6 12
 
3.7%
8 12
 
3.7%
5 11
 
3.4%
Other values (10) 27
 
8.3%
Hangul
ValueCountFrequency (%)
동 68
 
8.5%
송 53
 
6.6%
도 52
 
6.5%
지 49
 
6.1%
번 43
 
5.4%
리 24
 
3.0%
거 20
 
2.5%
학 20
 
2.5%
교 18
 
2.3%
사 17
 
2.1%
Other values (154) 434
54.4%

종점
Text

Distinct117
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-04-29T22:45:07.950863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length8.6074074
Min length3

Characters and Unicode

Total characters1162
Distinct characters181
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

Unique99 ?
Unique (%)73.3%

Sample

1st row송학둥지아파트
2nd row청학사거리
3rd row수리봉사거리
4th row인천여자고등학교
5th row선학파출소
ValueCountFrequency (%)
송도동 43
 
20.3%
인천대학교 3
 
1.4%
송도ibs타워 2
 
0.9%
커낼워크 2
 
0.9%
더샵하버뷰 2
 
0.9%
158-3번지 2
 
0.9%
웰카운티 2
 
0.9%
103동 2
 
0.9%
겨울동 2
 
0.9%
커넬워크 2
 
0.9%
Other values (132) 150
70.8%
2024-04-29T22:45:08.325579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
77
 
6.6%
1 68
 
5.9%
동 65
 
5.6%
지 56
 
4.8%
도 55
 
4.7%
송 52
 
4.5%
번 43
 
3.7%
2 41
 
3.5%
- 34
 
2.9%
0 25
 
2.2%
Other values (171) 646
55.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 817
70.3%
Decimal Number 218
 
18.8%
Space Separator 77
 
6.6%
Dash Punctuation 34
 
2.9%
Uppercase Letter 12
 
1.0%
Open Punctuation 2
 
0.2%
Close Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 65
 
8.0%
지 56
 
6.9%
도 55
 
6.7%
송 52
 
6.4%
번 43
 
5.3%
리 22
 
2.7%
교 20
 
2.4%
거 18
 
2.2%
학 18
 
2.2%
사 15
 
1.8%
Other values (151) 453
55.4%
Decimal Number
ValueCountFrequency (%)
1 68
31.2%
2 41
18.8%
0 25
 
11.5%
8 16
 
7.3%
3 16
 
7.3%
4 14
 
6.4%
7 11
 
5.0%
6 9
 
4.1%
5 9
 
4.1%
9 9
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
S 4
33.3%
K 2
16.7%
I 2
16.7%
B 2
16.7%
F 1
 
8.3%
G 1
 
8.3%
Space Separator
ValueCountFrequency (%)
77
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 34
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 817
70.3%
Common 333
28.7%
Latin 12
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 65
 
8.0%
지 56
 
6.9%
도 55
 
6.7%
송 52
 
6.4%
번 43
 
5.3%
리 22
 
2.7%
교 20
 
2.4%
거 18
 
2.2%
학 18
 
2.2%
사 15
 
1.8%
Other values (151) 453
55.4%
Common
ValueCountFrequency (%)
77
23.1%
1 68
20.4%
2 41
12.3%
- 34
10.2%
0 25
 
7.5%
8 16
 
4.8%
3 16
 
4.8%
4 14
 
4.2%
7 11
 
3.3%
6 9
 
2.7%
Other values (4) 22
 
6.6%
Latin
ValueCountFrequency (%)
S 4
33.3%
K 2
16.7%
I 2
16.7%
B 2
16.7%
F 1
 
8.3%
G 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 817
70.3%
ASCII 345
29.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
77
22.3%
1 68
19.7%
2 41
11.9%
- 34
9.9%
0 25
 
7.2%
8 16
 
4.6%
3 16
 
4.6%
4 14
 
4.1%
7 11
 
3.2%
6 9
 
2.6%
Other values (10) 34
9.9%
Hangul
ValueCountFrequency (%)
동 65
 
8.0%
지 56
 
6.9%
도 55
 
6.7%
송 52
 
6.4%
번 43
 
5.3%
리 22
 
2.7%
교 20
 
2.4%
거 18
 
2.2%
학 18
 
2.2%
사 15
 
1.8%
Other values (151) 453
55.4%

총연장(킬로미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3407407
Minimum0.2
Maximum4.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-04-29T22:45:08.461633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.47
Q10.65
median1
Q31.6
95-th percentile3.5
Maximum4.5
Range4.3
Interquartile range (IQR)0.95

Descriptive statistics

Standard deviation0.97194192
Coefficient of variation (CV)0.72492905
Kurtosis2.2108117
Mean1.3407407
Median Absolute Deviation (MAD)0.4
Skewness1.5946041
Sum181
Variance0.94467109
MonotonicityNot monotonic
2024-04-29T22:45:08.581239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0.8 16
 
11.9%
0.6 14
 
10.4%
0.5 13
 
9.6%
1.0 11
 
8.1%
1.6 11
 
8.1%
0.7 7
 
5.2%
1.1 7
 
5.2%
1.2 5
 
3.7%
0.3 4
 
3.0%
1.3 4
 
3.0%
Other values (21) 43
31.9%
ValueCountFrequency (%)
0.2 1
 
0.7%
0.3 4
 
3.0%
0.4 2
 
1.5%
0.5 13
9.6%
0.6 14
10.4%
0.7 7
5.2%
0.8 16
11.9%
0.9 3
 
2.2%
1.0 11
8.1%
1.1 7
5.2%
ValueCountFrequency (%)
4.5 2
1.5%
4.4 2
1.5%
4.2 1
0.7%
3.8 1
0.7%
3.5 2
1.5%
3.2 2
1.5%
3.1 1
0.7%
3.0 1
0.7%
2.7 1
0.7%
2.6 1
0.7%

전용도로 연장(킬로미터)
Real number (ℝ)

MISSING 

Distinct18
Distinct (%)36.0%
Missing85
Missing (%)63.0%
Infinite0
Infinite (%)0.0%
Mean1.504
Minimum0.3
Maximum4.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-04-29T22:45:08.688627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.5
Q10.65
median1.3
Q31.9
95-th percentile3.885
Maximum4.5
Range4.2
Interquartile range (IQR)1.25

Descriptive statistics

Standard deviation1.061566
Coefficient of variation (CV)0.70582848
Kurtosis1.6070647
Mean1.504
Median Absolute Deviation (MAD)0.7
Skewness1.4034414
Sum75.2
Variance1.1269224
MonotonicityNot monotonic
2024-04-29T22:45:08.795169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1.6 8
 
5.9%
0.5 7
 
5.2%
0.6 5
 
3.7%
1.0 5
 
3.7%
0.8 4
 
3.0%
1.5 3
 
2.2%
2.0 2
 
1.5%
2.2 2
 
1.5%
0.9 2
 
1.5%
4.5 2
 
1.5%
Other values (8) 10
 
7.4%
(Missing) 85
63.0%
ValueCountFrequency (%)
0.3 1
 
0.7%
0.5 7
5.2%
0.6 5
3.7%
0.8 4
3.0%
0.9 2
 
1.5%
1.0 5
3.7%
1.2 1
 
0.7%
1.4 1
 
0.7%
1.5 3
 
2.2%
1.6 8
5.9%
ValueCountFrequency (%)
4.5 2
 
1.5%
4.2 1
 
0.7%
3.5 2
 
1.5%
2.6 1
 
0.7%
2.5 2
 
1.5%
2.3 1
 
0.7%
2.2 2
 
1.5%
2.0 2
 
1.5%
1.6 8
5.9%
1.5 3
 
2.2%

전용도로 폭원(미터)
Real number (ℝ)

MISSING 

Distinct13
Distinct (%)26.0%
Missing85
Missing (%)63.0%
Infinite0
Infinite (%)0.0%
Mean1.794
Minimum1.2
Maximum2.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-04-29T22:45:08.892873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile1.445
Q11.5
median1.8
Q31.9
95-th percentile2.4
Maximum2.9
Range1.7
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.34782707
Coefficient of variation (CV)0.19388354
Kurtosis1.8460843
Mean1.794
Median Absolute Deviation (MAD)0.3
Skewness1.1132652
Sum89.7
Variance0.12098367
MonotonicityNot monotonic
2024-04-29T22:45:09.020988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1.5 17
 
12.6%
1.9 12
 
8.9%
1.8 6
 
4.4%
2.0 4
 
3.0%
2.4 2
 
1.5%
2.2 2
 
1.5%
2.1 1
 
0.7%
1.3 1
 
0.7%
1.2 1
 
0.7%
1.7 1
 
0.7%
Other values (3) 3
 
2.2%
(Missing) 85
63.0%
ValueCountFrequency (%)
1.2 1
 
0.7%
1.3 1
 
0.7%
1.4 1
 
0.7%
1.5 17
12.6%
1.7 1
 
0.7%
1.8 6
 
4.4%
1.9 12
8.9%
2.0 4
 
3.0%
2.1 1
 
0.7%
2.2 2
 
1.5%
ValueCountFrequency (%)
2.9 1
 
0.7%
2.8 1
 
0.7%
2.4 2
 
1.5%
2.2 2
 
1.5%
2.1 1
 
0.7%
2.0 4
 
3.0%
1.9 12
8.9%
1.8 6
 
4.4%
1.7 1
 
0.7%
1.5 17
12.6%

분리형 겸용도로 연장(킬로미터)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)29.7%
Missing44
Missing (%)32.6%
Infinite0
Infinite (%)0.0%
Mean1.1626374
Minimum0.1
Maximum4.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-04-29T22:45:09.152754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.25
Q10.6
median0.8
Q31.3
95-th percentile3.15
Maximum4.4
Range4.3
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.91197568
Coefficient of variation (CV)0.78440252
Kurtosis3.051914
Mean1.1626374
Median Absolute Deviation (MAD)0.3
Skewness1.7641731
Sum105.8
Variance0.83169963
MonotonicityNot monotonic
2024-04-29T22:45:09.286170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.8 13
 
9.6%
0.6 12
 
8.9%
1.0 8
 
5.9%
0.5 8
 
5.9%
1.1 6
 
4.4%
1.2 4
 
3.0%
0.3 4
 
3.0%
1.6 3
 
2.2%
1.3 3
 
2.2%
1.7 3
 
2.2%
Other values (17) 27
20.0%
(Missing) 44
32.6%
ValueCountFrequency (%)
0.1 3
 
2.2%
0.2 2
 
1.5%
0.3 4
 
3.0%
0.4 1
 
0.7%
0.5 8
5.9%
0.6 12
8.9%
0.7 3
 
2.2%
0.8 13
9.6%
0.9 2
 
1.5%
1.0 8
5.9%
ValueCountFrequency (%)
4.4 2
1.5%
3.8 1
0.7%
3.2 2
1.5%
3.1 1
0.7%
3.0 1
0.7%
2.7 1
0.7%
2.5 2
1.5%
2.4 2
1.5%
2.3 1
0.7%
2.0 1
0.7%

분리형 겸용도로 자전거도로 폭원(미터)
Real number (ℝ)

MISSING 

Distinct20
Distinct (%)22.0%
Missing44
Missing (%)32.6%
Infinite0
Infinite (%)0.0%
Mean2.0725275
Minimum1
Maximum5.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-04-29T22:45:09.430280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.4
Q11.65
median2
Q32.1
95-th percentile3
Maximum5.1
Range4.1
Interquartile range (IQR)0.45

Descriptive statistics

Standard deviation0.67331121
Coefficient of variation (CV)0.32487444
Kurtosis7.9523099
Mean2.0725275
Median Absolute Deviation (MAD)0.1
Skewness2.3382147
Sum188.6
Variance0.45334799
MonotonicityNot monotonic
2024-04-29T22:45:09.568282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2.0 23
17.0%
2.1 20
14.8%
1.5 12
 
8.9%
3.0 5
 
3.7%
1.4 4
 
3.0%
1.8 4
 
3.0%
1.6 3
 
2.2%
1.9 3
 
2.2%
2.8 2
 
1.5%
2.9 2
 
1.5%
Other values (10) 13
 
9.6%
(Missing) 44
32.6%
ValueCountFrequency (%)
1.0 1
 
0.7%
1.2 1
 
0.7%
1.3 2
 
1.5%
1.4 4
 
3.0%
1.5 12
8.9%
1.6 3
 
2.2%
1.7 2
 
1.5%
1.8 4
 
3.0%
1.9 3
 
2.2%
2.0 23
17.0%
ValueCountFrequency (%)
5.1 2
 
1.5%
3.8 1
 
0.7%
3.5 1
 
0.7%
3.0 5
 
3.7%
2.9 2
 
1.5%
2.8 2
 
1.5%
2.6 1
 
0.7%
2.4 1
 
0.7%
2.2 1
 
0.7%
2.1 20
14.8%

분리형 겸용도로 보도폭원(미터)
Real number (ℝ)

MISSING 

Distinct11
Distinct (%)12.1%
Missing44
Missing (%)32.6%
Infinite0
Infinite (%)0.0%
Mean1.5472527
Minimum1.1
Maximum3.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-04-29T22:45:09.681516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile1.2
Q11.4
median1.5
Q31.5
95-th percentile1.9
Maximum3.2
Range2.1
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.34587752
Coefficient of variation (CV)0.223543
Kurtosis10.869158
Mean1.5472527
Median Absolute Deviation (MAD)0.1
Skewness2.8782744
Sum140.8
Variance0.11963126
MonotonicityNot monotonic
2024-04-29T22:45:09.801308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1.5 41
30.4%
1.4 19
14.1%
1.9 9
 
6.7%
1.2 6
 
4.4%
1.8 4
 
3.0%
1.1 4
 
3.0%
1.3 3
 
2.2%
3.0 2
 
1.5%
1.7 1
 
0.7%
3.2 1
 
0.7%
(Missing) 44
32.6%
ValueCountFrequency (%)
1.1 4
 
3.0%
1.2 6
 
4.4%
1.3 3
 
2.2%
1.4 19
14.1%
1.5 41
30.4%
1.7 1
 
0.7%
1.8 4
 
3.0%
1.9 9
 
6.7%
2.0 1
 
0.7%
3.0 2
 
1.5%
ValueCountFrequency (%)
3.2 1
 
0.7%
3.0 2
 
1.5%
2.0 1
 
0.7%
1.9 9
 
6.7%
1.8 4
 
3.0%
1.7 1
 
0.7%
1.5 41
30.4%
1.4 19
14.1%
1.3 3
 
2.2%
1.2 6
 
4.4%

Interactions

2024-04-29T22:45:05.641715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:02.315016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:02.994353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:03.477886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:04.027519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:04.589933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:05.087389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:05.732858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:02.455272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:03.075477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:03.548203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:04.106925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:04.662880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:05.168303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:05.808811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:02.529466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:03.137543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:03.626055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:04.193143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:04.725819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:05.244285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:05.905346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:02.608796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:03.201276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:03.692027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:04.263615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:04.812330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:05.334325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:05.988479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:02.705967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:03.281626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:03.765296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:04.343349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:04.886984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:05.425092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:06.058746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:02.796448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:03.345310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:03.845476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:04.414031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:04.948572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:05.498570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:06.132235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:02.893456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:03.413029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:03.928622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:04.509141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:05.018788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T22:45:05.573292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-29T22:45:09.913787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번총연장(킬로미터)전용도로 연장(킬로미터)전용도로 폭원(미터)분리형 겸용도로 연장(킬로미터)분리형 겸용도로 자전거도로 폭원(미터)분리형 겸용도로 보도폭원(미터)
연번1.0000.6280.6340.5890.7260.5970.483
총연장(킬로미터)0.6281.0000.2730.0000.9440.4820.379
전용도로 연장(킬로미터)0.6340.2731.0000.6760.5860.2250.483
전용도로 폭원(미터)0.5890.0000.6761.0000.3200.3160.319
분리형 겸용도로 연장(킬로미터)0.7260.9440.5860.3201.0000.6520.552
분리형 겸용도로 자전거도로 폭원(미터)0.5970.4820.2250.3160.6521.0000.607
분리형 겸용도로 보도폭원(미터)0.4830.3790.4830.3190.5520.6071.000
2024-04-29T22:45:10.041650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번총연장(킬로미터)전용도로 연장(킬로미터)전용도로 폭원(미터)분리형 겸용도로 연장(킬로미터)분리형 겸용도로 자전거도로 폭원(미터)분리형 겸용도로 보도폭원(미터)
연번1.000-0.0840.1180.2510.0400.349-0.009
총연장(킬로미터)-0.0841.000-0.167-0.1770.830-0.1140.070
전용도로 연장(킬로미터)0.118-0.1671.0000.141-0.0890.066-0.111
전용도로 폭원(미터)0.251-0.1770.1411.000-0.0830.114-0.207
분리형 겸용도로 연장(킬로미터)0.0400.830-0.089-0.0831.0000.0480.018
분리형 겸용도로 자전거도로 폭원(미터)0.349-0.1140.0660.1140.0481.0000.136
분리형 겸용도로 보도폭원(미터)-0.0090.070-0.111-0.2070.0180.1361.000

Missing values

2024-04-29T22:45:06.243222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-29T22:45:06.396095image/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.
2024-04-29T22:45:06.744049image/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

연번기점종점총연장(킬로미터)전용도로 연장(킬로미터)전용도로 폭원(미터)분리형 겸용도로 연장(킬로미터)분리형 겸용도로 자전거도로 폭원(미터)분리형 겸용도로 보도폭원(미터)
01옹암사거리송학둥지아파트1.3<NA><NA>0.51.71.5
12송학둥지아파트청학사거리1.5<NA><NA>1.52.01.5
23청학사거리수리봉사거리1.2<NA><NA><NA><NA><NA>
34수리봉사거리인천여자고등학교0.7<NA><NA>0.72.01.5
45인천여자고등학교선학파출소0.90.82.40.92.01.5
56선학파출소인천여자고등학교0.9<NA><NA>0.92.01.5
67인천여자고등학교청학보도육교2.31.21.82.32.01.5
78송도3교외암도사거리2.5<NA><NA><NA><NA><NA>
89연수새마을금고벽산2차아파트1.1<NA><NA>1.02.01.2
910벽산2차아파트연수새마을금고1.1<NA><NA>1.02.01.2
연번기점종점총연장(킬로미터)전용도로 연장(킬로미터)전용도로 폭원(미터)분리형 겸용도로 연장(킬로미터)분리형 겸용도로 자전거도로 폭원(미터)분리형 겸용도로 보도폭원(미터)
125126송도동 144번지송도동 144번지1.2<NA><NA>1.21.51.9
126127더샵파크에비뉴 1601동더샵퍼스트파크 13F 1301동3.1<NA><NA>3.13.01.5
127128해모로월드뷰 303동아메리칸타운 103동1.0<NA><NA>1.01.51.5
128129송도동 174-7번지송도동 173-1번지0.6<NA><NA>0.61.51.5
129130송도동 177-4번지송도동 173-5번지0.6<NA><NA>0.62.11.5
130131송도동 245번지송도동 187-1번지2.5<NA><NA>2.52.11.5
131132송도동 195-1번지송도동 195-1번지0.8<NA><NA>0.82.11.4
132133한국외국어대학교한국외국어대학교0.7<NA><NA>0.72.11.5
133134더샵그린에비뉴 805동자이하버뷰 105동0.8<NA><NA>0.82.11.5
134135더샵그린워크 1101동더샵그린워크 3차 1803동0.8<NA><NA>0.82.11.5