Overview

Dataset statistics

Number of variables9
Number of observations117
Missing cells4
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.9 KiB
Average record size in memory78.1 B

Variable types

Text3
Numeric5
Categorical1

Dataset

Description제주특별자치도 서귀포시 내 자전거도로의 관한 데이터로 노선명, 기점, 종점, 위도, 경도, 연장 정보를 제공합니다.
Author제주특별자치도 서귀포시
URLhttps://www.data.go.kr/data/3067626/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
기점 위도 is highly overall correlated with 기점 경도 and 2 other fieldsHigh correlation
기점 경도 is highly overall correlated with 기점 위도 and 2 other fieldsHigh correlation
종점 위도 is highly overall correlated with 기점 위도 and 2 other fieldsHigh correlation
종점 경도 is highly overall correlated with 기점 위도 and 2 other fieldsHigh correlation
노선명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 12:02:53.864693
Analysis finished2023-12-12 12:02:57.933907
Duration4.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

노선명
Text

UNIQUE 

Distinct117
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2023-12-12T21:02:58.239923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.1538462
Min length3

Characters and Unicode

Total characters486
Distinct characters129
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique117 ?
Unique (%)100.0%

Sample

1st row상모로
2nd row평화로
3rd row전세비로
4th row서삼중로
5th row동일하모로
ValueCountFrequency (%)
상모로 1
 
0.9%
서홍로 1
 
0.9%
남조로 1
 
0.9%
하례망장포로 1
 
0.9%
태신해안로 1
 
0.9%
신례동로 1
 
0.9%
신례상로 1
 
0.9%
수은로 1
 
0.9%
남태해안로 1
 
0.9%
일주동로 1
 
0.9%
Other values (107) 107
91.5%
2023-12-12T21:02:58.837550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 118
24.3%
중 20
 
4.1%
동 19
 
3.9%
신 16
 
3.3%
서 16
 
3.3%
하 11
 
2.3%
해 10
 
2.1%
앙 9
 
1.9%
성 9
 
1.9%
남 9
 
1.9%
Other values (119) 249
51.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 482
99.2%
Decimal Number 4
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 118
24.5%
중 20
 
4.1%
동 19
 
3.9%
신 16
 
3.3%
서 16
 
3.3%
하 11
 
2.3%
해 10
 
2.1%
앙 9
 
1.9%
성 9
 
1.9%
남 9
 
1.9%
Other values (117) 245
50.8%
Decimal Number
ValueCountFrequency (%)
1 2
50.0%
0 2
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 482
99.2%
Common 4
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 118
24.5%
중 20
 
4.1%
동 19
 
3.9%
신 16
 
3.3%
서 16
 
3.3%
하 11
 
2.3%
해 10
 
2.1%
앙 9
 
1.9%
성 9
 
1.9%
남 9
 
1.9%
Other values (117) 245
50.8%
Common
ValueCountFrequency (%)
1 2
50.0%
0 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 482
99.2%
ASCII 4
 
0.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 118
24.5%
중 20
 
4.1%
동 19
 
3.9%
신 16
 
3.3%
서 16
 
3.3%
하 11
 
2.3%
해 10
 
2.1%
앙 9
 
1.9%
성 9
 
1.9%
남 9
 
1.9%
Other values (117) 245
50.8%
ASCII
ValueCountFrequency (%)
1 2
50.0%
0 2
50.0%

기점
Text

Distinct114
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2023-12-12T21:02:59.333250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length27
Mean length24.282051
Min length19

Characters and Unicode

Total characters2841
Distinct characters85
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

Unique111 ?
Unique (%)94.9%

Sample

1st row제주특별자치도 서귀포시 대정읍 상모리 811-1
2nd row제주특별자치도 서귀포시 대정읍 안성리 1452-1
3rd row제주특별자치도 서귀포시 대정읍 영락리 1567
4th row제주특별자치도 서귀포시 대정읍 영락리 1960-1
5th row제주특별자치도 서귀포시 대정읍 일과리 1187-1
ValueCountFrequency (%)
제주특별자치도 117
22.1%
서귀포시 116
21.9%
남원읍 17
 
3.2%
대정읍 14
 
2.6%
성산읍 13
 
2.5%
안덕면 9
 
1.7%
서귀동 9
 
1.7%
표선면 8
 
1.5%
동홍동 7
 
1.3%
강정동 5
 
0.9%
Other values (155) 214
40.5%
2023-12-12T21:03:00.012788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
414
 
14.6%
서 137
 
4.8%
1 131
 
4.6%
귀 125
 
4.4%
시 120
 
4.2%
제 118
 
4.2%
주 118
 
4.2%
특 117
 
4.1%
별 117
 
4.1%
자 117
 
4.1%
Other values (75) 1327
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1827
64.3%
Decimal Number 507
 
17.8%
Space Separator 414
 
14.6%
Dash Punctuation 93
 
3.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 137
 
7.5%
귀 125
 
6.8%
시 120
 
6.6%
제 118
 
6.5%
주 118
 
6.5%
특 117
 
6.4%
별 117
 
6.4%
자 117
 
6.4%
치 117
 
6.4%
도 117
 
6.4%
Other values (63) 624
34.2%
Decimal Number
ValueCountFrequency (%)
1 131
25.8%
2 66
13.0%
5 50
 
9.9%
3 49
 
9.7%
4 42
 
8.3%
6 40
 
7.9%
7 34
 
6.7%
8 34
 
6.7%
0 33
 
6.5%
9 28
 
5.5%
Space Separator
ValueCountFrequency (%)
414
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 93
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1827
64.3%
Common 1014
35.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 137
 
7.5%
귀 125
 
6.8%
시 120
 
6.6%
제 118
 
6.5%
주 118
 
6.5%
특 117
 
6.4%
별 117
 
6.4%
자 117
 
6.4%
치 117
 
6.4%
도 117
 
6.4%
Other values (63) 624
34.2%
Common
ValueCountFrequency (%)
414
40.8%
1 131
 
12.9%
- 93
 
9.2%
2 66
 
6.5%
5 50
 
4.9%
3 49
 
4.8%
4 42
 
4.1%
6 40
 
3.9%
7 34
 
3.4%
8 34
 
3.4%
Other values (2) 61
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1827
64.3%
ASCII 1014
35.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
414
40.8%
1 131
 
12.9%
- 93
 
9.2%
2 66
 
6.5%
5 50
 
4.9%
3 49
 
4.8%
4 42
 
4.1%
6 40
 
3.9%
7 34
 
3.4%
8 34
 
3.4%
Other values (2) 61
 
6.0%
Hangul
ValueCountFrequency (%)
서 137
 
7.5%
귀 125
 
6.8%
시 120
 
6.6%
제 118
 
6.5%
주 118
 
6.5%
특 117
 
6.4%
별 117
 
6.4%
자 117
 
6.4%
치 117
 
6.4%
도 117
 
6.4%
Other values (63) 624
34.2%

기점 위도
Real number (ℝ)

HIGH CORRELATION 

Distinct113
Distinct (%)97.4%
Missing1
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean33.283904
Minimum33.220314
Maximum33.48233
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T21:03:00.215068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.220314
5-th percentile33.227571
Q133.247883
median33.258574
Q333.290277
95-th percentile33.456402
Maximum33.48233
Range0.2620155
Interquartile range (IQR)0.04239385

Descriptive statistics

Standard deviation0.06454764
Coefficient of variation (CV)0.001939305
Kurtosis2.7003942
Mean33.283904
Median Absolute Deviation (MAD)0.0150304
Skewness1.9056607
Sum3860.9328
Variance0.0041663978
MonotonicityNot monotonic
2023-12-12T21:03:00.427788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.3033114 2
 
1.7%
33.243544 2
 
1.7%
33.24938228 2
 
1.7%
33.2264212 1
 
0.9%
33.267145 1
 
0.9%
33.2790662 1
 
0.9%
33.2798408 1
 
0.9%
33.264002 1
 
0.9%
33.289758 1
 
0.9%
33.285611 1
 
0.9%
Other values (103) 103
88.0%
ValueCountFrequency (%)
33.2203142 1
0.9%
33.2215362 1
0.9%
33.22352715 1
0.9%
33.22377136 1
0.9%
33.22523857 1
0.9%
33.2264212 1
0.9%
33.227954 1
0.9%
33.228511 1
0.9%
33.2325771 1
0.9%
33.2348571 1
0.9%
ValueCountFrequency (%)
33.4823297 1
0.9%
33.481812 1
0.9%
33.4703831 1
0.9%
33.4656657 1
0.9%
33.46406154 1
0.9%
33.461045 1
0.9%
33.4548548 1
0.9%
33.4496 1
0.9%
33.444414 1
0.9%
33.420277 1
0.9%

기점 경도
Real number (ℝ)

HIGH CORRELATION 

Distinct113
Distinct (%)97.4%
Missing1
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean126.56348
Minimum126.18834
Maximum126.93376
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T21:03:00.585794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.18834
5-th percentile126.2487
Q1126.43303
median126.56674
Q3126.65806
95-th percentile126.89946
Maximum126.93376
Range0.7454181
Interquartile range (IQR)0.22503588

Descriptive statistics

Standard deviation0.19321928
Coefficient of variation (CV)0.0015266591
Kurtosis-0.589519
Mean126.56348
Median Absolute Deviation (MAD)0.1033295
Skewness0.026572576
Sum14681.363
Variance0.037333691
MonotonicityNot monotonic
2023-12-12T21:03:00.802468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.2883683 2
 
1.7%
126.560345 2
 
1.7%
126.5073585 2
 
1.7%
126.2865372 1
 
0.9%
126.642544 1
 
0.9%
126.7158289 1
 
0.9%
126.7193492 1
 
0.9%
126.635117 1
 
0.9%
126.751968 1
 
0.9%
126.634673 1
 
0.9%
Other values (103) 103
88.0%
ValueCountFrequency (%)
126.188339 1
0.9%
126.2119958 1
0.9%
126.2256252 1
0.9%
126.2341423 1
0.9%
126.2475593 1
0.9%
126.2478314 1
0.9%
126.2489941 1
0.9%
126.2497212 1
0.9%
126.2503673 1
0.9%
126.2554543 1
0.9%
ValueCountFrequency (%)
126.9337571 1
0.9%
126.931445 1
0.9%
126.913099 1
0.9%
126.907342 1
0.9%
126.9065279 1
0.9%
126.8999074 1
0.9%
126.8993091 1
0.9%
126.898039 1
0.9%
126.8977633 1
0.9%
126.896331 1
0.9%

종점
Text

Distinct115
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2023-12-12T21:03:01.251741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length25
Mean length23.991453
Min length19

Characters and Unicode

Total characters2807
Distinct characters80
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

Unique113 ?
Unique (%)96.6%

Sample

1st row제주특별자치도 서귀포시 대정읍 하모리 824-1
2nd row제주특별자치도 서귀포시 안덕면 광평리 산124
3rd row제주특별자치도 서귀포시 대정읍 영락리 815-3
4th row제주특별자치도 서귀포시 대정읍 신도리 1830-7
5th row제주특별자치도 서귀포시 대정읍 상모리 3732-9
ValueCountFrequency (%)
제주특별자치도 117
22.1%
서귀포시 116
21.9%
남원읍 16
 
3.0%
대정읍 16
 
3.0%
성산읍 12
 
2.3%
서호동 9
 
1.7%
표선면 9
 
1.7%
동홍동 8
 
1.5%
안덕면 8
 
1.5%
서귀동 7
 
1.3%
Other values (159) 212
40.0%
2023-12-12T21:03:01.826025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
414
 
14.7%
서 139
 
5.0%
귀 123
 
4.4%
시 120
 
4.3%
도 119
 
4.2%
주 118
 
4.2%
제 118
 
4.2%
치 117
 
4.2%
자 117
 
4.2%
포 117
 
4.2%
Other values (70) 1305
46.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1832
65.3%
Decimal Number 472
 
16.8%
Space Separator 414
 
14.7%
Dash Punctuation 89
 
3.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 139
 
7.6%
귀 123
 
6.7%
시 120
 
6.6%
도 119
 
6.5%
주 118
 
6.4%
제 118
 
6.4%
치 117
 
6.4%
자 117
 
6.4%
포 117
 
6.4%
별 117
 
6.4%
Other values (58) 627
34.2%
Decimal Number
ValueCountFrequency (%)
1 104
22.0%
3 63
13.3%
2 60
12.7%
8 43
9.1%
4 37
 
7.8%
7 36
 
7.6%
9 35
 
7.4%
6 34
 
7.2%
5 34
 
7.2%
0 26
 
5.5%
Space Separator
ValueCountFrequency (%)
414
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 89
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1832
65.3%
Common 975
34.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 139
 
7.6%
귀 123
 
6.7%
시 120
 
6.6%
도 119
 
6.5%
주 118
 
6.4%
제 118
 
6.4%
치 117
 
6.4%
자 117
 
6.4%
포 117
 
6.4%
별 117
 
6.4%
Other values (58) 627
34.2%
Common
ValueCountFrequency (%)
414
42.5%
1 104
 
10.7%
- 89
 
9.1%
3 63
 
6.5%
2 60
 
6.2%
8 43
 
4.4%
4 37
 
3.8%
7 36
 
3.7%
9 35
 
3.6%
6 34
 
3.5%
Other values (2) 60
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1832
65.3%
ASCII 975
34.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
414
42.5%
1 104
 
10.7%
- 89
 
9.1%
3 63
 
6.5%
2 60
 
6.2%
8 43
 
4.4%
4 37
 
3.8%
7 36
 
3.7%
9 35
 
3.6%
6 34
 
3.5%
Other values (2) 60
 
6.2%
Hangul
ValueCountFrequency (%)
서 139
 
7.6%
귀 123
 
6.7%
시 120
 
6.6%
도 119
 
6.5%
주 118
 
6.4%
제 118
 
6.4%
치 117
 
6.4%
자 117
 
6.4%
포 117
 
6.4%
별 117
 
6.4%
Other values (58) 627
34.2%

종점 위도
Real number (ℝ)

HIGH CORRELATION 

Distinct114
Distinct (%)98.3%
Missing1
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean33.290821
Minimum33.206434
Maximum33.481098
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T21:03:02.020321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.206434
5-th percentile33.223008
Q133.250819
median33.267004
Q333.310502
95-th percentile33.452842
Maximum33.481098
Range0.274664
Interquartile range (IQR)0.0596826

Descriptive statistics

Standard deviation0.064227547
Coefficient of variation (CV)0.001929287
Kurtosis1.8388041
Mean33.290821
Median Absolute Deviation (MAD)0.02289195
Skewness1.54928
Sum3861.7352
Variance0.0041251778
MonotonicityNot monotonic
2023-12-12T21:03:02.178571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.254125 2
 
1.7%
33.2781342 2
 
1.7%
33.2220084 1
 
0.9%
33.25727006 1
 
0.9%
33.312104 1
 
0.9%
33.403434 1
 
0.9%
33.258246 1
 
0.9%
33.306749 1
 
0.9%
33.316156 1
 
0.9%
33.294796 1
 
0.9%
Other values (104) 104
88.9%
ValueCountFrequency (%)
33.2064342 1
0.9%
33.20739732 1
0.9%
33.2082658 1
0.9%
33.2205974 1
0.9%
33.2220084 1
0.9%
33.22210714 1
0.9%
33.22330839 1
0.9%
33.22360079 1
0.9%
33.22816659 1
0.9%
33.2281831 1
0.9%
ValueCountFrequency (%)
33.4810982 1
0.9%
33.4719124 1
0.9%
33.4688738 1
0.9%
33.46548335 1
0.9%
33.46536093 1
0.9%
33.4603547 1
0.9%
33.450338 1
0.9%
33.4473503 1
0.9%
33.436519 1
0.9%
33.435747 1
0.9%

종점 경도
Real number (ℝ)

HIGH CORRELATION 

Distinct114
Distinct (%)98.3%
Missing1
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean126.56457
Minimum126.18145
Maximum126.9358
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T21:03:02.330355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.18145
5-th percentile126.25426
Q1126.45584
median126.56956
Q3126.65027
95-th percentile126.91731
Maximum126.9358
Range0.7543483
Interquartile range (IQR)0.19443378

Descriptive statistics

Standard deviation0.19677741
Coefficient of variation (CV)0.0015547591
Kurtosis-0.51239356
Mean126.56457
Median Absolute Deviation (MAD)0.1056825
Skewness0.06300216
Sum14681.49
Variance0.038721349
MonotonicityNot monotonic
2023-12-12T21:03:02.473966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.576844 2
 
1.7%
126.2728064 2
 
1.7%
126.2543493 1
 
0.9%
126.520364 1
 
0.9%
126.695655 1
 
0.9%
126.682278 1
 
0.9%
126.639436 1
 
0.9%
126.775559 1
 
0.9%
126.633403 1
 
0.9%
126.632588 1
 
0.9%
Other values (104) 104
88.9%
ValueCountFrequency (%)
126.1814517 1
0.9%
126.1860435 1
0.9%
126.2002625 1
0.9%
126.212968 1
0.9%
126.2499248 1
0.9%
126.2539752 1
0.9%
126.2543493 1
0.9%
126.2552755 1
0.9%
126.2579641 1
0.9%
126.2582473 1
0.9%
ValueCountFrequency (%)
126.9358 1
0.9%
126.9354213 1
0.9%
126.9331057 1
0.9%
126.922377 1
0.9%
126.9209 1
0.9%
126.9189078 1
0.9%
126.916782 1
0.9%
126.9121735 1
0.9%
126.9090514 1
0.9%
126.904323 1
0.9%

연장(km)
Real number (ℝ)

Distinct65
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.017094
Minimum0.4
Maximum57.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T21:03:02.648817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.5
Q11.2
median2.2
Q34.5
95-th percentile16.12
Maximum57.2
Range56.8
Interquartile range (IQR)3.3

Descriptive statistics

Standard deviation8.8024809
Coefficient of variation (CV)1.7544979
Kurtosis19.347519
Mean5.017094
Median Absolute Deviation (MAD)1.4
Skewness4.1143733
Sum587
Variance77.483671
MonotonicityNot monotonic
2023-12-12T21:03:02.803084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8 7
 
6.0%
0.7 6
 
5.1%
1.3 5
 
4.3%
1.2 5
 
4.3%
0.4 5
 
4.3%
1.4 5
 
4.3%
0.5 4
 
3.4%
0.6 4
 
3.4%
3.5 3
 
2.6%
1.6 3
 
2.6%
Other values (55) 70
59.8%
ValueCountFrequency (%)
0.4 5
4.3%
0.5 4
3.4%
0.6 4
3.4%
0.7 6
5.1%
0.8 7
6.0%
0.9 2
 
1.7%
1.1 1
 
0.9%
1.2 5
4.3%
1.3 5
4.3%
1.4 5
4.3%
ValueCountFrequency (%)
57.2 1
0.9%
53.9 1
0.9%
36.2 1
0.9%
30.2 1
0.9%
28.0 1
0.9%
17.4 1
0.9%
15.8 1
0.9%
15.6 1
0.9%
12.8 1
0.9%
12.6 1
0.9%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2022-09-30
117 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-09-30
2nd row2022-09-30
3rd row2022-09-30
4th row2022-09-30
5th row2022-09-30

Common Values

ValueCountFrequency (%)
2022-09-30 117
100.0%

Length

2023-12-12T21:03:02.942063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:03:03.055005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-09-30 117
100.0%

Interactions

2023-12-12T21:02:56.580313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:02:54.238587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:02:54.717440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:02:55.301038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:02:55.915514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:02:56.679514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:02:54.337008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:02:54.826999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:02:55.412764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:02:56.023018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:02:56.797603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:02:54.445239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:02:54.972088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:02:55.533885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:02:56.198722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:02:56.902620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:02:54.526270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:02:55.077324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:02:55.692082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:02:56.328062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:02:57.027727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:02:54.632441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:02:55.194984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:02:55.801427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:02:56.466877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:03:03.113935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기점 위도기점 경도종점 위도종점 경도연장(km)
기점 위도1.0000.8660.9140.8370.362
기점 경도0.8661.0000.8640.9690.000
종점 위도0.9140.8641.0000.8890.398
종점 경도0.8370.9690.8891.0000.043
연장(km)0.3620.0000.3980.0431.000
2023-12-12T21:03:03.239801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기점 위도기점 경도종점 위도종점 경도연장(km)
기점 위도1.0000.7710.8080.6940.144
기점 경도0.7711.0000.6730.9350.007
종점 위도0.8080.6731.0000.6810.321
종점 경도0.6940.9350.6811.000-0.020
연장(km)0.1440.0070.321-0.0201.000

Missing values

2023-12-12T21:02:57.178989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:02:57.359965image/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.
2023-12-12T21:02:57.842201image/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

노선명기점기점 위도기점 경도종점종점 위도종점 경도연장(km)데이터기준일자
0상모로제주특별자치도 서귀포시 대정읍 상모리 811-133.226421126.286537제주특별자치도 서귀포시 대정읍 하모리 824-133.222008126.2543493.32022-09-30
1평화로제주특별자치도 서귀포시 대정읍 안성리 1452-133.250223126.281599제주특별자치도 서귀포시 안덕면 광평리 산12433.340209126.35568412.82022-09-30
2전세비로제주특별자치도 서귀포시 대정읍 영락리 156733.243873126.211996제주특별자치도 서귀포시 대정읍 영락리 815-333.254029126.2129681.22022-09-30
3서삼중로제주특별자치도 서귀포시 대정읍 영락리 1960-133.281355126.188339제주특별자치도 서귀포시 대정읍 신도리 1830-733.282057126.1860432.82022-09-30
4동일하모로제주특별자치도 서귀포시 대정읍 일과리 1187-133.23491126.234142제주특별자치도 서귀포시 대정읍 상모리 3732-933.228183126.2579642.42022-09-30
5노을해안로제주특별자치도 서귀포시 대정읍 일과리 1452-433.24422126.225625제주특별자치도 제주시 한경면 고산리<NA><NA>8.02022-09-30
6최남단해안로제주특별자치도 서귀포시 대정읍 하모리 938-133.220314126.249721제주특별자치도 서귀포시 대정읍 상모리 13833.208266126.2876085.92022-09-30
7신영로제주특별자치도 서귀포시 대정읍 동일리 2998-133.225239126.247559제주특별자치도 서귀포시 대정읍 하모리 818-333.223601126.2552751.32022-09-30
8에듀시티로제주특별자치도 서귀포시 대정읍 구억리 산1-633.296463126.285242제주특별자치도 서귀포시 대정읍 보성리 194-333.278134126.2728062.52022-09-30
9상모대서로제주특별자치도 서귀포시 대정읍 상모리 393833.223771126.255454제주특별자치도 서귀포시 대정읍 상모리 3732-233.228167126.2601160.72022-09-30
노선명기점기점 위도기점 경도종점종점 위도종점 경도연장(km)데이터기준일자
107고성오조로제주특별자치도 서귀포시 성산읍 고성리 101133.444414126.913099제주특별자치도 서귀포시 성산읍 오조리 808-133.460355126.9090512.02022-09-30
108고성동서로제주특별자치도 서귀포시 성산읍 고성리 141733.4496126.907342제주특별자치도 서귀포시 성산읍 고성리 296-133.450338126.92091.42022-09-30
109성산중앙로제주특별자치도 서귀포시 성산읍 고성리 388-233.461045126.931445제주특별자치도 서귀포시 성산읍 성산리 298-433.465483126.9354210.72022-09-30
110신양로제주특별자치도 서귀포시 성산읍 고성리 839-733.407646126.897763제주특별자치도 서귀포시 성산읍 고성리 206-1333.436519126.9223771.22022-09-30
111성산등용로제주특별자치도 서귀포시 성산읍 성산리 229-133.464062126.933757제주특별자치도 서귀포시 성산읍 성산리 347-933.471912126.9331061.32022-09-30
112시흥상동로제주특별자치도 서귀포시 성산읍 시흥리 27633.470383126.899907제주특별자치도 서귀포시 성산읍 시흥리 1207-133.481098126.8957611.62022-09-30
113삼달신풍로제주특별자치도 서귀포시 성산읍 신풍리 690-133.364938126.83287제주특별자치도 서귀포시 성산읍 삼달리 1152-133.376381126.843471.72022-09-30
114한도로제주특별자치도 서귀포시 성산읍 오조리 1314-133.465666126.906528제주특별자치도 서귀포시 성산읍 성산리 298-1833.465361126.93582.72022-09-30
115온평동로제주특별자치도 서귀포시 성산읍 온평리 1475-733.403784126.896331제주특별자치도 서귀포시 성산읍 온평리 554-833.410598126.9043231.42022-09-30
116서호중앙로제주특별자치도 서귀포시 서호동 160633.259075126.518703제주특별자치도 서귀포시 서호동 161033.249129126.51704211.42022-09-30