Overview

Dataset statistics

Number of variables19
Number of observations3572
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory547.8 KiB
Average record size in memory157.0 B

Variable types

Numeric5
Categorical7
Text5
DateTime2

Dataset

Description광주광역시 관내 도로명 현황(도로명, 영문도로명, 고시일, 도로길이, 간격, 사유 등)을 제공하는 데이터입니다.
URLhttps://www.data.go.kr/data/15001637/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
도로폭 is highly overall correlated with 위계High correlation
도로고시길이 is highly overall correlated with 도로물리길이 and 1 other fieldsHigh correlation
도로물리길이 is highly overall correlated with 도로고시길이High correlation
기초간격 is highly overall correlated with 위계High correlation
위계 is highly overall correlated with 도로폭 and 2 other fieldsHigh correlation
위계 is highly imbalanced (69.5%)Imbalance
광역구분 is highly imbalanced (79.6%)Imbalance
종속구분 is highly imbalanced (99.6%)Imbalance
도로고시길이 is highly skewed (γ1 = 20.59726884)Skewed
기초간격 is highly skewed (γ1 = 26.52066775)Skewed

Reproduction

Analysis started2023-12-12 21:45:39.634762
Analysis finished2023-12-12 21:45:44.733437
Duration5.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

도로구간일련번호
Real number (ℝ)

Distinct1683
Distinct (%)47.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1168.3967
Minimum2
Maximum12868
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.5 KiB
2023-12-13T06:45:44.810186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile74.55
Q1290.75
median615
Q3952
95-th percentile3879.45
Maximum12868
Range12866
Interquartile range (IQR)661.25

Descriptive statistics

Standard deviation2283.7997
Coefficient of variation (CV)1.9546441
Kurtosis16.900466
Mean1168.3967
Median Absolute Deviation (MAD)330.5
Skewness4.1593036
Sum4173513
Variance5215741
MonotonicityNot monotonic
2023-12-13T06:45:44.973678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
617 5
 
0.1%
189 5
 
0.1%
226 5
 
0.1%
614 5
 
0.1%
198 5
 
0.1%
213 5
 
0.1%
225 5
 
0.1%
82 5
 
0.1%
606 5
 
0.1%
607 5
 
0.1%
Other values (1673) 3522
98.6%
ValueCountFrequency (%)
2 1
< 0.1%
8 1
< 0.1%
10 1
< 0.1%
13 1
< 0.1%
16 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
21 2
0.1%
ValueCountFrequency (%)
12868 1
< 0.1%
12788 1
< 0.1%
12774 1
< 0.1%
12773 1
< 0.1%
12772 1
< 0.1%
12768 1
< 0.1%
12588 1
< 0.1%
12570 1
< 0.1%
12568 1
< 0.1%
12553 1
< 0.1%

시군구
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
북구
892 
광산구
892 
남구
808 
서구
645 
동구
335 

Length

Max length3
Median length2
Mean length2.24972
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
북구 892
25.0%
광산구 892
25.0%
남구 808
22.6%
서구 645
18.1%
동구 335
 
9.4%

Length

2023-12-13T06:45:45.114838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:45:45.251147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
북구 892
25.0%
광산구 892
25.0%
남구 808
22.6%
서구 645
18.1%
동구 335
 
9.4%

위계
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
3106 
435 
대로
 
26
고속도로
 
5

Length

Max length4
Median length1
Mean length1.0114782
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대로
2nd row대로
3rd row대로
4th row
5th row

Common Values

ValueCountFrequency (%)
3106
87.0%
435
 
12.2%
대로 26
 
0.7%
고속도로 5
 
0.1%

Length

2023-12-13T06:45:45.434973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:45:45.562763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3106
87.0%
435
 
12.2%
대로 26
 
0.7%
고속도로 5
 
0.1%

광역구분
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
시군구
3393 
시도
 
144
행안부
 
35

Length

Max length3
Median length3
Mean length2.9596865
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
시군구 3393
95.0%
시도 144
 
4.0%
행안부 35
 
1.0%

Length

2023-12-13T06:45:45.689904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:45:45.812657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
시군구 3393
95.0%
시도 144
 
4.0%
행안부 35
 
1.0%

종속구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
주도로
3571 
1차 종속도로
 
1

Length

Max length7
Median length3
Mean length3.0011198
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
주도로 3571
> 99.9%
1차 종속도로 1
 
< 0.1%

Length

2023-12-13T06:45:45.954637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:45:46.094481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주도로 3571
99.9%
1차 1
 
< 0.1%
종속도로 1
 
< 0.1%
Distinct3478
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
2023-12-13T06:45:46.399854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length6.7609183
Min length3

Characters and Unicode

Total characters24150
Distinct characters284
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3397 ?
Unique (%)95.1%

Sample

1st row대남대로
2nd row서암대로
3rd row필문대로
4th row2순환로
5th row갈마로
ValueCountFrequency (%)
2순환로 5
 
0.1%
독립로 4
 
0.1%
경열로 3
 
0.1%
광주천자전거길 3
 
0.1%
영산강자전거길 3
 
0.1%
대남대로 3
 
0.1%
구성로 3
 
0.1%
상무대로 3
 
0.1%
천변좌로 3
 
0.1%
하남대로 3
 
0.1%
Other values (3468) 3539
99.1%
2023-12-13T06:45:46.919193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3265
 
13.5%
2705
 
11.2%
2419
 
10.0%
1 1207
 
5.0%
2 909
 
3.8%
3 739
 
3.1%
4 603
 
2.5%
5 525
 
2.2%
6 480
 
2.0%
456
 
1.9%
Other values (274) 10842
44.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 17952
74.3%
Decimal Number 6197
 
25.7%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3265
18.2%
2705
 
15.1%
2419
 
13.5%
456
 
2.5%
333
 
1.9%
285
 
1.6%
236
 
1.3%
196
 
1.1%
177
 
1.0%
169
 
0.9%
Other values (263) 7711
43.0%
Decimal Number
ValueCountFrequency (%)
1 1207
19.5%
2 909
14.7%
3 739
11.9%
4 603
9.7%
5 525
8.5%
6 480
 
7.7%
7 451
 
7.3%
0 446
 
7.2%
8 422
 
6.8%
9 415
 
6.7%
Other Punctuation
ValueCountFrequency (%)
· 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 17952
74.3%
Common 6198
 
25.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3265
18.2%
2705
 
15.1%
2419
 
13.5%
456
 
2.5%
333
 
1.9%
285
 
1.6%
236
 
1.3%
196
 
1.1%
177
 
1.0%
169
 
0.9%
Other values (263) 7711
43.0%
Common
ValueCountFrequency (%)
1 1207
19.5%
2 909
14.7%
3 739
11.9%
4 603
9.7%
5 525
8.5%
6 480
 
7.7%
7 451
 
7.3%
0 446
 
7.2%
8 422
 
6.8%
9 415
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 17952
74.3%
ASCII 6197
 
25.7%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3265
18.2%
2705
 
15.1%
2419
 
13.5%
456
 
2.5%
333
 
1.9%
285
 
1.6%
236
 
1.3%
196
 
1.1%
177
 
1.0%
169
 
0.9%
Other values (263) 7711
43.0%
ASCII
ValueCountFrequency (%)
1 1207
19.5%
2 909
14.7%
3 739
11.9%
4 603
9.7%
5 525
8.5%
6 480
 
7.7%
7 451
 
7.3%
0 446
 
7.2%
8 422
 
6.8%
9 415
 
6.7%
None
ValueCountFrequency (%)
· 1
100.0%
Distinct3479
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
2023-12-13T06:45:47.235643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length28
Mean length19.879899
Min length6

Characters and Unicode

Total characters71011
Distinct characters55
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3399 ?
Unique (%)95.2%

Sample

1st rowDaenam-daero
2nd rowSeoam-daero
3rd rowPilmun-daero
4th row2sunhwan-ro
5th rowGalma-ro
ValueCountFrequency (%)
gunbun-ro 73
 
1.1%
cheonbyeonjwa-ro 61
 
1.0%
1-gil 58
 
0.9%
2-gil 58
 
0.9%
daenam-daero 56
 
0.9%
dongnip-ro 43
 
0.7%
sangmu-daero 41
 
0.6%
1-ro 38
 
0.6%
seomun-daero 38
 
0.6%
hwajeong-ro 37
 
0.6%
Other values (1650) 5860
92.1%
2023-12-13T06:45:47.700571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 8321
11.7%
n 7346
 
10.3%
g 6265
 
8.8%
- 5940
 
8.4%
e 5228
 
7.4%
i 4051
 
5.7%
l 3909
 
5.5%
a 3463
 
4.9%
b 2873
 
4.0%
r 2830
 
4.0%
Other values (45) 20785
29.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 52508
73.9%
Decimal Number 6197
 
8.7%
Dash Punctuation 5940
 
8.4%
Uppercase Letter 3574
 
5.0%
Space Separator 2791
 
3.9%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 8321
15.8%
n 7346
14.0%
g 6265
11.9%
e 5228
10.0%
i 4051
7.7%
l 3909
7.4%
a 3463
6.6%
b 2873
 
5.5%
r 2830
 
5.4%
u 1754
 
3.3%
Other values (13) 6468
12.3%
Uppercase Letter
ValueCountFrequency (%)
S 717
20.1%
G 402
11.2%
B 326
9.1%
H 308
8.6%
D 307
8.6%
J 280
 
7.8%
Y 217
 
6.1%
C 190
 
5.3%
P 180
 
5.0%
W 153
 
4.3%
Other values (9) 494
13.8%
Decimal Number
ValueCountFrequency (%)
1 1207
19.5%
2 909
14.7%
3 739
11.9%
4 603
9.7%
5 525
8.5%
6 480
 
7.7%
7 451
 
7.3%
0 446
 
7.2%
8 422
 
6.8%
9 415
 
6.7%
Dash Punctuation
ValueCountFrequency (%)
- 5940
100.0%
Space Separator
ValueCountFrequency (%)
2791
100.0%
Other Punctuation
ValueCountFrequency (%)
· 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 56082
79.0%
Common 14929
 
21.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 8321
14.8%
n 7346
13.1%
g 6265
11.2%
e 5228
9.3%
i 4051
7.2%
l 3909
 
7.0%
a 3463
 
6.2%
b 2873
 
5.1%
r 2830
 
5.0%
u 1754
 
3.1%
Other values (32) 10042
17.9%
Common
ValueCountFrequency (%)
- 5940
39.8%
2791
18.7%
1 1207
 
8.1%
2 909
 
6.1%
3 739
 
5.0%
4 603
 
4.0%
5 525
 
3.5%
6 480
 
3.2%
7 451
 
3.0%
0 446
 
3.0%
Other values (3) 838
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71010
> 99.9%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 8321
11.7%
n 7346
 
10.3%
g 6265
 
8.8%
- 5940
 
8.4%
e 5228
 
7.4%
i 4051
 
5.7%
l 3909
 
5.5%
a 3463
 
4.9%
b 2873
 
4.0%
r 2830
 
4.0%
Other values (44) 20784
29.3%
None
ValueCountFrequency (%)
· 1
100.0%
Distinct106
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
Minimum2008-08-25 00:00:00
Maximum2022-11-25 00:00:00
2023-12-13T06:45:47.849846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:48.015082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

시점
Text

Distinct3509
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
2023-12-13T06:45:48.417037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length24
Mean length8.7567189
Min length3

Characters and Unicode

Total characters31279
Distinct characters156
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3452 ?
Unique (%)96.6%

Sample

1st row학동51-5
2nd row계림동 595
3rd row계림동 1032
4th row문흥동 산48
5th row산수동 536-34
ValueCountFrequency (%)
월산동 111
 
1.9%
쌍촌동 103
 
1.7%
화정동 88
 
1.5%
농성동 78
 
1.3%
주월동 58
 
1.0%
백운동 56
 
0.9%
풍암동 55
 
0.9%
송정동 54
 
0.9%
양동 51
 
0.9%
신가동 50
 
0.8%
Other values (3565) 5231
88.1%
2023-12-13T06:45:48.944232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3631
 
11.6%
1 3093
 
9.9%
- 2968
 
9.5%
2366
 
7.6%
2 1802
 
5.8%
3 1473
 
4.7%
5 1402
 
4.5%
4 1344
 
4.3%
6 1277
 
4.1%
9 1218
 
3.9%
Other values (146) 10705
34.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14892
47.6%
Other Letter 10988
35.1%
Dash Punctuation 2968
 
9.5%
Space Separator 2366
 
7.6%
Open Punctuation 30
 
0.1%
Close Punctuation 30
 
0.1%
Uppercase Letter 4
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3631
33.0%
627
 
5.7%
472
 
4.3%
255
 
2.3%
250
 
2.3%
198
 
1.8%
194
 
1.8%
194
 
1.8%
180
 
1.6%
160
 
1.5%
Other values (128) 4827
43.9%
Decimal Number
ValueCountFrequency (%)
1 3093
20.8%
2 1802
12.1%
3 1473
9.9%
5 1402
9.4%
4 1344
9.0%
6 1277
8.6%
9 1218
 
8.2%
8 1136
 
7.6%
7 1074
 
7.2%
0 1073
 
7.2%
Uppercase Letter
ValueCountFrequency (%)
C 2
50.0%
J 1
25.0%
I 1
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 2968
100.0%
Space Separator
ValueCountFrequency (%)
2366
100.0%
Open Punctuation
ValueCountFrequency (%)
( 30
100.0%
Close Punctuation
ValueCountFrequency (%)
) 30
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20287
64.9%
Hangul 10988
35.1%
Latin 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3631
33.0%
627
 
5.7%
472
 
4.3%
255
 
2.3%
250
 
2.3%
198
 
1.8%
194
 
1.8%
194
 
1.8%
180
 
1.6%
160
 
1.5%
Other values (128) 4827
43.9%
Common
ValueCountFrequency (%)
1 3093
15.2%
- 2968
14.6%
2366
11.7%
2 1802
8.9%
3 1473
7.3%
5 1402
6.9%
4 1344
6.6%
6 1277
6.3%
9 1218
 
6.0%
8 1136
 
5.6%
Other values (5) 2208
10.9%
Latin
ValueCountFrequency (%)
C 2
50.0%
J 1
25.0%
I 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20291
64.9%
Hangul 10988
35.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3631
33.0%
627
 
5.7%
472
 
4.3%
255
 
2.3%
250
 
2.3%
198
 
1.8%
194
 
1.8%
194
 
1.8%
180
 
1.6%
160
 
1.5%
Other values (128) 4827
43.9%
ASCII
ValueCountFrequency (%)
1 3093
15.2%
- 2968
14.6%
2366
11.7%
2 1802
8.9%
3 1473
7.3%
5 1402
6.9%
4 1344
6.6%
6 1277
6.3%
9 1218
 
6.0%
8 1136
 
5.6%
Other values (8) 2212
10.9%

종점
Text

Distinct3465
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
2023-12-13T06:45:49.307577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length28
Mean length8.6598544
Min length4

Characters and Unicode

Total characters30933
Distinct characters169
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3363 ?
Unique (%)94.1%

Sample

1st row서구 농성동148-2
2nd row계림동 1011
3rd row계림동 1641
4th row신창동14
5th row두암동969-9
ValueCountFrequency (%)
월산동 129
 
2.2%
쌍촌동 105
 
1.8%
화정동 89
 
1.5%
농성동 76
 
1.3%
송정동 59
 
1.0%
방림동 55
 
0.9%
주월동 55
 
0.9%
양동 53
 
0.9%
풍암동 52
 
0.9%
백운동 50
 
0.8%
Other values (3505) 5255
87.9%
2023-12-13T06:45:50.151091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3646
 
11.8%
1 2971
 
9.6%
- 2787
 
9.0%
2409
 
7.8%
2 1725
 
5.6%
3 1452
 
4.7%
5 1449
 
4.7%
4 1378
 
4.5%
9 1173
 
3.8%
6 1168
 
3.8%
Other values (159) 10775
34.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14590
47.2%
Other Letter 11060
35.8%
Dash Punctuation 2787
 
9.0%
Space Separator 2409
 
7.8%
Close Punctuation 40
 
0.1%
Open Punctuation 40
 
0.1%
Uppercase Letter 6
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3646
33.0%
661
 
6.0%
474
 
4.3%
257
 
2.3%
241
 
2.2%
207
 
1.9%
205
 
1.9%
194
 
1.8%
188
 
1.7%
171
 
1.5%
Other values (141) 4816
43.5%
Decimal Number
ValueCountFrequency (%)
1 2971
20.4%
2 1725
11.8%
3 1452
10.0%
5 1449
9.9%
4 1378
9.4%
9 1173
 
8.0%
6 1168
 
8.0%
7 1115
 
7.6%
0 1089
 
7.5%
8 1070
 
7.3%
Uppercase Letter
ValueCountFrequency (%)
C 3
50.0%
I 2
33.3%
J 1
 
16.7%
Dash Punctuation
ValueCountFrequency (%)
- 2787
100.0%
Space Separator
ValueCountFrequency (%)
2409
100.0%
Close Punctuation
ValueCountFrequency (%)
) 40
100.0%
Open Punctuation
ValueCountFrequency (%)
( 40
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19867
64.2%
Hangul 11060
35.8%
Latin 6
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3646
33.0%
661
 
6.0%
474
 
4.3%
257
 
2.3%
241
 
2.2%
207
 
1.9%
205
 
1.9%
194
 
1.8%
188
 
1.7%
171
 
1.5%
Other values (141) 4816
43.5%
Common
ValueCountFrequency (%)
1 2971
15.0%
- 2787
14.0%
2409
12.1%
2 1725
8.7%
3 1452
7.3%
5 1449
7.3%
4 1378
6.9%
9 1173
 
5.9%
6 1168
 
5.9%
7 1115
 
5.6%
Other values (5) 2240
11.3%
Latin
ValueCountFrequency (%)
C 3
50.0%
I 2
33.3%
J 1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19873
64.2%
Hangul 11060
35.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3646
33.0%
661
 
6.0%
474
 
4.3%
257
 
2.3%
241
 
2.2%
207
 
1.9%
205
 
1.9%
194
 
1.8%
188
 
1.7%
171
 
1.5%
Other values (141) 4816
43.5%
ASCII
ValueCountFrequency (%)
1 2971
14.9%
- 2787
14.0%
2409
12.1%
2 1725
8.7%
3 1452
7.3%
5 1449
7.3%
4 1378
6.9%
9 1173
 
5.9%
6 1168
 
5.9%
7 1115
 
5.6%
Other values (8) 2246
11.3%

도로폭
Real number (ℝ)

HIGH CORRELATION 

Distinct293
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1899555
Minimum1
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.5 KiB
2023-12-13T06:45:50.290257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q16
median8
Q310
95-th percentile21
Maximum80
Range79
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.666472
Coefficient of variation (CV)0.72540852
Kurtosis14.858916
Mean9.1899555
Median Absolute Deviation (MAD)2
Skewness2.992793
Sum32826.521
Variance44.441849
MonotonicityNot monotonic
2023-12-13T06:45:50.432988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.0 764
21.4%
6.0 455
12.7%
10.0 335
9.4%
5.0 250
 
7.0%
4.0 244
 
6.8%
3.0 173
 
4.8%
15.0 171
 
4.8%
9.0 148
 
4.1%
7.0 140
 
3.9%
20.0 109
 
3.1%
Other values (283) 783
21.9%
ValueCountFrequency (%)
1.0 20
0.6%
1.453 1
 
< 0.1%
1.487 1
 
< 0.1%
1.522 1
 
< 0.1%
1.532 1
 
< 0.1%
1.927 1
 
< 0.1%
1.959 1
 
< 0.1%
1.965 1
 
< 0.1%
1.976 1
 
< 0.1%
1.987 1
 
< 0.1%
ValueCountFrequency (%)
80.0 2
 
0.1%
61.0 1
 
< 0.1%
60.0 1
 
< 0.1%
55.0 1
 
< 0.1%
50.0 2
 
0.1%
46.0 1
 
< 0.1%
42.0 1
 
< 0.1%
41.0 2
 
0.1%
40.0 14
0.4%
39.0 3
 
0.1%

도로고시길이
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1244
Distinct (%)34.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1083.2756
Minimum0
Maximum194220
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size31.5 KiB
2023-12-13T06:45:50.587375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile97
Q1178
median290
Q3519
95-th percentile2593.8782
Maximum194220
Range194220
Interquartile range (IQR)341

Descriptive statistics

Standard deviation7147.8889
Coefficient of variation (CV)6.5984027
Kurtosis484.46736
Mean1083.2756
Median Absolute Deviation (MAD)139.5
Skewness20.597269
Sum3869460.6
Variance51092316
MonotonicityNot monotonic
2023-12-13T06:45:50.744483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
147.0 19
 
0.5%
155.0 17
 
0.5%
200.0 16
 
0.4%
223.0 16
 
0.4%
137.0 15
 
0.4%
158.0 15
 
0.4%
125.0 14
 
0.4%
220.0 14
 
0.4%
151.0 14
 
0.4%
198.0 13
 
0.4%
Other values (1234) 3419
95.7%
ValueCountFrequency (%)
0.0 3
0.1%
39.0 1
 
< 0.1%
42.0 1
 
< 0.1%
43.0 2
0.1%
45.0 1
 
< 0.1%
46.0 2
0.1%
47.0 1
 
< 0.1%
48.0 1
 
< 0.1%
49.0 1
 
< 0.1%
56.0 2
0.1%
ValueCountFrequency (%)
194220.0 2
 
0.1%
175640.0 1
 
< 0.1%
128031.0 3
0.1%
69300.0 1
 
< 0.1%
47600.0 2
 
0.1%
42500.0 1
 
< 0.1%
41300.0 1
 
< 0.1%
40099.0 1
 
< 0.1%
37672.0 2
 
0.1%
27810.0 5
0.1%

도로물리길이
Real number (ℝ)

HIGH CORRELATION 

Distinct3550
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean596.66476
Minimum0.459
Maximum21270.406
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.5 KiB
2023-12-13T06:45:50.913079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.459
5-th percentile102.47755
Q1178.22975
median289.98
Q3515.69225
95-th percentile2000.7381
Maximum21270.406
Range21269.947
Interquartile range (IQR)337.4625

Descriptive statistics

Standard deviation1147.9432
Coefficient of variation (CV)1.9239333
Kurtosis67.196817
Mean596.66476
Median Absolute Deviation (MAD)138.383
Skewness6.8196204
Sum2131286.5
Variance1317773.5
MonotonicityNot monotonic
2023-12-13T06:45:51.054443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
260.002 3
 
0.1%
174.46 2
 
0.1%
223.993 2
 
0.1%
127.269 2
 
0.1%
282.171 2
 
0.1%
107.005 2
 
0.1%
309.964 2
 
0.1%
179.934 2
 
0.1%
69.16 2
 
0.1%
129.888 2
 
0.1%
Other values (3540) 3551
99.4%
ValueCountFrequency (%)
0.459 1
< 0.1%
20.114 1
< 0.1%
44.908 1
< 0.1%
52.376 1
< 0.1%
52.601 1
< 0.1%
54.11 1
< 0.1%
55.575 1
< 0.1%
55.596 1
< 0.1%
55.734 1
< 0.1%
60.587 1
< 0.1%
ValueCountFrequency (%)
21270.406 1
< 0.1%
14336.39 1
< 0.1%
13064.116 1
< 0.1%
12406.882 1
< 0.1%
11920.929 1
< 0.1%
11564.247 1
< 0.1%
11477.339 1
< 0.1%
11310.186 1
< 0.1%
11231.765 1
< 0.1%
9861.768 1
< 0.1%

기초간격
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct29
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.901456
Minimum5
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.5 KiB
2023-12-13T06:45:51.185508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10
Q111
median20
Q320
95-th percentile22
Maximum2000
Range1995
Interquartile range (IQR)9

Descriptive statistics

Standard deviation74.295586
Coefficient of variation (CV)3.7331734
Kurtosis704.62561
Mean19.901456
Median Absolute Deviation (MAD)1
Skewness26.520668
Sum71088
Variance5519.834
MonotonicityNot monotonic
2023-12-13T06:45:51.378772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
20 1770
49.6%
10 594
 
16.6%
11 426
 
11.9%
21 313
 
8.8%
22 132
 
3.7%
12 125
 
3.5%
19 51
 
1.4%
23 47
 
1.3%
13 31
 
0.9%
24 22
 
0.6%
Other values (19) 61
 
1.7%
ValueCountFrequency (%)
5 1
 
< 0.1%
6 2
 
0.1%
7 1
 
< 0.1%
8 2
 
0.1%
9 4
 
0.1%
10 594
16.6%
11 426
11.9%
12 125
 
3.5%
13 31
 
0.9%
14 3
 
0.1%
ValueCountFrequency (%)
2000 5
 
0.1%
42 2
 
0.1%
41 1
 
< 0.1%
39 1
 
< 0.1%
29 1
 
< 0.1%
28 3
 
0.1%
27 2
 
0.1%
26 4
 
0.1%
25 6
 
0.2%
24 22
0.6%

부여사유
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
지명·자연마을이름
1952 
행정구역 명칭
910 
역사적인물·기념
502 
기타
 
124
문화재·유적
 
84

Length

Max length9
Median length9
Mean length8.0363942
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row역사적인물·기념
2nd row역사적인물·기념
3rd row역사적인물·기념
4th row기타
5th row지명·자연마을이름

Common Values

ValueCountFrequency (%)
지명·자연마을이름 1952
54.6%
행정구역 명칭 910
25.5%
역사적인물·기념 502
 
14.1%
기타 124
 
3.5%
문화재·유적 84
 
2.4%

Length

2023-12-13T06:45:51.545091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:45:51.676527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지명·자연마을이름 1952
43.6%
행정구역 910
20.3%
명칭 910
20.3%
역사적인물·기념 502
 
11.2%
기타 124
 
2.8%
문화재·유적 84
 
1.9%

부여방식
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
기초번호
2401 
고유명사
791 
일련번호
 
203
방위
 
75
기타
 
69

Length

Max length4
Median length4
Mean length3.9193729
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기초번호 2401
67.2%
고유명사 791
 
22.1%
일련번호 203
 
5.7%
방위 75
 
2.1%
기타 69
 
1.9%
복합명사 33
 
0.9%

Length

2023-12-13T06:45:51.862215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:45:51.999136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기초번호 2401
67.2%
고유명사 791
 
22.1%
일련번호 203
 
5.7%
방위 75
 
2.1%
기타 69
 
1.9%
복합명사 33
 
0.9%
Distinct180
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
Minimum2000-03-02 00:00:00
Maximum2022-11-25 00:00:00
2023-12-13T06:45:52.129820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:52.308125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3357
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
2023-12-13T06:45:52.678014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length65
Median length58
Mean length33.580907
Min length4

Characters and Unicode

Total characters119951
Distinct characters585
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3236 ?
Unique (%)90.6%

Sample

1st row1968년 자매결연한 대남시명을 반영
2nd row한말의병장 양진여 장군의 아호
3rd row조선문인 이선제선생의 호를 따서 명명
4th row광주의 제2순환도로에서 유래
5th row갈마촌이라 불리던 옛 지명에서 유래
ValueCountFrequency (%)
도로 2535
 
10.8%
분기되는 2475
 
10.6%
1831
 
7.8%
왼쪽으로 1137
 
4.9%
오른쪽으로 1110
 
4.7%
시작지점에서부터 1092
 
4.7%
지점에서 721
 
3.1%
시작지점으로부터 693
 
3.0%
시작점에서부터 493
 
2.1%
반영 394
 
1.7%
Other values (3934) 10920
46.7%
2023-12-13T06:45:53.212299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19890
 
16.6%
8442
 
7.0%
4545
 
3.8%
4474
 
3.7%
4432
 
3.7%
4390
 
3.7%
3096
 
2.6%
2791
 
2.3%
0 2764
 
2.3%
2728
 
2.3%
Other values (575) 62399
52.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 88258
73.6%
Space Separator 19890
 
16.6%
Decimal Number 8227
 
6.9%
Lowercase Letter 2255
 
1.9%
Other Punctuation 938
 
0.8%
Close Punctuation 187
 
0.2%
Open Punctuation 187
 
0.2%
Uppercase Letter 4
 
< 0.1%
Final Punctuation 2
 
< 0.1%
Initial Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
8442
 
9.6%
4545
 
5.1%
4474
 
5.1%
4432
 
5.0%
4390
 
5.0%
3096
 
3.5%
2791
 
3.2%
2728
 
3.1%
2677
 
3.0%
2603
 
2.9%
Other values (550) 48080
54.5%
Decimal Number
ValueCountFrequency (%)
0 2764
33.6%
1 1060
 
12.9%
2 906
 
11.0%
4 667
 
8.1%
3 638
 
7.8%
6 516
 
6.3%
8 486
 
5.9%
5 455
 
5.5%
7 385
 
4.7%
9 350
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 926
98.7%
. 7
 
0.7%
' 4
 
0.4%
· 1
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
m 2254
> 99.9%
k 1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 184
98.4%
3
 
1.6%
Open Punctuation
ValueCountFrequency (%)
( 184
98.4%
3
 
1.6%
Space Separator
ValueCountFrequency (%)
19890
100.0%
Uppercase Letter
ValueCountFrequency (%)
M 4
100.0%
Final Punctuation
ValueCountFrequency (%)
2
100.0%
Initial Punctuation
ValueCountFrequency (%)
2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 88231
73.6%
Common 29434
 
24.5%
Latin 2259
 
1.9%
Han 27
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
8442
 
9.6%
4545
 
5.2%
4474
 
5.1%
4432
 
5.0%
4390
 
5.0%
3096
 
3.5%
2791
 
3.2%
2728
 
3.1%
2677
 
3.0%
2603
 
3.0%
Other values (526) 48053
54.5%
Han
ValueCountFrequency (%)
3
 
11.1%
2
 
7.4%
1
 
3.7%
1
 
3.7%
1
 
3.7%
1
 
3.7%
1
 
3.7%
1
 
3.7%
1
 
3.7%
1
 
3.7%
Other values (14) 14
51.9%
Common
ValueCountFrequency (%)
19890
67.6%
0 2764
 
9.4%
1 1060
 
3.6%
, 926
 
3.1%
2 906
 
3.1%
4 667
 
2.3%
3 638
 
2.2%
6 516
 
1.8%
8 486
 
1.7%
5 455
 
1.5%
Other values (12) 1126
 
3.8%
Latin
ValueCountFrequency (%)
m 2254
99.8%
M 4
 
0.2%
k 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 88231
73.6%
ASCII 31682
 
26.4%
CJK 26
 
< 0.1%
None 7
 
< 0.1%
Punctuation 4
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19890
62.8%
0 2764
 
8.7%
m 2254
 
7.1%
1 1060
 
3.3%
, 926
 
2.9%
2 906
 
2.9%
4 667
 
2.1%
3 638
 
2.0%
6 516
 
1.6%
8 486
 
1.5%
Other values (10) 1575
 
5.0%
Hangul
ValueCountFrequency (%)
8442
 
9.6%
4545
 
5.2%
4474
 
5.1%
4432
 
5.0%
4390
 
5.0%
3096
 
3.5%
2791
 
3.2%
2728
 
3.1%
2677
 
3.0%
2603
 
3.0%
Other values (526) 48053
54.5%
CJK
ValueCountFrequency (%)
3
 
11.5%
2
 
7.7%
1
 
3.8%
1
 
3.8%
1
 
3.8%
1
 
3.8%
1
 
3.8%
1
 
3.8%
1
 
3.8%
1
 
3.8%
Other values (13) 13
50.0%
None
ValueCountFrequency (%)
3
42.9%
3
42.9%
· 1
 
14.3%
Punctuation
ValueCountFrequency (%)
2
50.0%
2
50.0%
CJK Compat Ideographs
ValueCountFrequency (%)
1
100.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.0 KiB
2023-07-26
3572 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-07-26
2nd row2023-07-26
3rd row2023-07-26
4th row2023-07-26
5th row2023-07-26

Common Values

ValueCountFrequency (%)
2023-07-26 3572
100.0%

Length

2023-12-13T06:45:53.346708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:45:53.454513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-07-26 3572
100.0%

Interactions

2023-12-13T06:45:43.682330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:41.333732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:41.932967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:42.494798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:43.113009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:43.803803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:41.438069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:42.050507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:42.607918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:43.214763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:43.928786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:41.566490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:42.162528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:42.726626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:43.328216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:44.043652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:41.690949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:42.264886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:42.870056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:43.441371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:44.131653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:41.797103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:42.397784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:42.979943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:43.543832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:45:53.524170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로구간일련번호시군구위계광역구분종속구분도로폭도로고시길이도로물리길이기초간격부여사유부여방식
도로구간일련번호1.0000.2600.3760.4970.0820.2160.5220.3650.5260.3440.488
시군구0.2601.0000.0460.1170.0100.1540.0000.0430.0160.3300.262
위계0.3760.0461.0000.4350.0000.7470.6770.6291.0000.2210.457
광역구분0.4970.1170.4351.0000.0000.5800.7640.4290.2300.2990.553
종속구분0.0820.0100.0000.0001.0000.0000.0000.0000.0000.0000.150
도로폭0.2160.1540.7470.5800.0001.0000.3900.5070.2960.1950.331
도로고시길이0.5220.0000.6770.7640.0000.3901.0000.5470.9790.2310.418
도로물리길이0.3650.0430.6290.4290.0000.5070.5471.0000.5080.1680.322
기초간격0.5260.0161.0000.2300.0000.2960.9790.5081.0000.1590.083
부여사유0.3440.3300.2210.2990.0000.1950.2310.1680.1591.0000.251
부여방식0.4880.2620.4570.5530.1500.3310.4180.3220.0830.2511.000
2023-12-13T06:45:53.669783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구광역구분위계종속구분부여방식부여사유
시군구1.0000.0880.0370.0120.1800.128
광역구분0.0881.0000.4280.0000.2710.237
위계0.0370.4281.0000.0000.3110.182
종속구분0.0120.0000.0001.0000.1080.000
부여방식0.1800.2710.3110.1081.0000.173
부여사유0.1280.2370.1820.0000.1731.000
2023-12-13T06:45:53.770842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로구간일련번호도로폭도로고시길이도로물리길이기초간격시군구위계광역구분종속구분부여사유부여방식
도로구간일련번호1.000-0.026-0.026-0.026-0.0250.1790.2510.2360.0590.2410.194
도로폭-0.0261.0000.3390.3240.0520.0890.5900.3100.0000.1130.171
도로고시길이-0.0260.3391.0000.9680.0380.0000.5070.4420.0000.1580.162
도로물리길이-0.0260.3240.9681.0000.0330.0260.3220.3030.0000.1030.185
기초간격-0.0250.0520.0380.0331.0000.0201.0000.3760.0000.1950.059
시군구0.1790.0890.0000.0260.0201.0000.0370.0880.0120.1280.180
위계0.2510.5900.5070.3221.0000.0371.0000.4280.0000.1820.311
광역구분0.2360.3100.4420.3030.3760.0880.4281.0000.0000.2370.271
종속구분0.0590.0000.0000.0000.0000.0120.0000.0001.0000.0000.108
부여사유0.2410.1130.1580.1030.1950.1280.1820.2370.0001.0000.173
부여방식0.1940.1710.1620.1850.0590.1800.3110.2710.1080.1731.000

Missing values

2023-12-13T06:45:44.333405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:45:44.634388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

도로구간일련번호시군구위계광역구분종속구분도로명영문도로명고시일자시점종점도로폭도로고시길이도로물리길이기초간격부여사유부여방식부여일자부여사유설명데이터기준일자
068동구대로시도주도로대남대로Daenam-daero2009-07-27학동51-5서구 농성동148-240.04916.0277.49820역사적인물·기념고유명사2009-06-301968년 자매결연한 대남시명을 반영2023-07-26
13144동구대로시도주도로서암대로Seoam-daero2009-07-27계림동 595계림동 101139.03200.0308.7620역사적인물·기념고유명사2009-06-30한말의병장 양진여 장군의 아호2023-07-26
22동구대로시도주도로필문대로Pilmun-daero2009-07-27계림동 1032계림동 164136.6744200.01061.79120역사적인물·기념고유명사2009-07-27조선문인 이선제선생의 호를 따서 명명2023-07-26
3358동구시도주도로2순환로2sunhwan-ro2009-07-27문흥동 산48신창동1435.027810.06928.32520기타고유명사2009-06-30광주의 제2순환도로에서 유래2023-07-26
477동구시도주도로갈마로Galma-ro2009-11-19산수동 536-34두암동969-924.0778.0791.78420지명·자연마을이름고유명사2009-11-19갈마촌이라 불리던 옛 지명에서 유래2023-07-26
5353동구시도주도로경양로Gyeongyang-ro2009-07-27북구 임동101-156산수동553-33(산수5거리)15.03500.01879.63120지명·자연마을이름고유명사2009-06-30옛날 경양방죽이 위치하였던 곳의 명칭2023-07-26
6114동구시군구주도로계림로Gyerim-ro2009-11-26동명동 258-0계림동 525-110.0670.0307.75720행정구역 명칭고유명사2009-11-26법정동(계림동)을 이용하여 계림로로 명명2023-07-26
770동구시도주도로구성로Guseong-ro2009-07-27남구 월산동361-25계림동287-1215.02756.01423.87420역사적인물·기념고유명사2009-06-30정유호란시 충신인 전상의 장군을 기리기 위하여 명명2023-07-26
8356동구시도주도로금남로Geumnam-ro2009-07-27임동94-224금남로1가1-127.02300.01032.71520역사적인물·기념고유명사2009-06-30이괄의 난, 정유호란시 무장 정충신을 기리기 위하여 명명2023-07-26
967동구시군구주도로남문로Nammun-ro2009-07-10선교동 산185학동 58-435.07830.07827.82320지명·자연마을이름고유명사2009-06-25남광주 방향 진입로2023-07-26
도로구간일련번호시군구위계광역구분종속구분도로명영문도로명고시일자시점종점도로폭도로고시길이도로물리길이기초간격부여사유부여방식부여일자부여사유설명데이터기준일자
3562735광산구시군구주도로하남지실안길Hanamjisiran-gil2009-11-11산정동 272-3산정동 산765.0298.0303.47310지명·자연마을이름고유명사2009-11-11하남지실길 안길에 위치한 길2023-07-26
3563756광산구시군구주도로하림길Harim-gil2009-11-11임곡동 451-2임곡동 4295.0785.0775.46110지명·자연마을이름고유명사2009-09-29임곡을 상하로 구분해 아래쪽에 있다하여 붙여진 자연마을이름 반영2023-07-26
3564577광산구시군구주도로하완길Hawan-gil2009-11-11수완동 727수완동 273-112.0872.0894.29710지명·자연마을이름고유명사2009-11-11마을앞 저수지의 형상이 말구시통 같은데서 유래하여 붙여진 자연마을이름 반영2023-07-26
3565724광산구시군구주도로하흑석길Haheukseok-gil2009-11-11선동 111-6선동 4456.0289.0286.29110지명·자연마을이름고유명사2009-09-28땅이 기름지고 검게 보인다 하여 붙여진 자연마을이름 반영2023-07-26
356611828광산구시군구주도로호남대길Honamdae-gil2018-12-11서봉동 94서봉동 산2066.0713.2822098.9920기타고유명사2018-12-11도로명 주소 국민불편 개선 시범사업과 관련하여 신청2023-07-26
3567504광산구시군구주도로호송길Hosong-gil2009-09-14박호동 262박호동 3296.0432.0440.10910지명·자연마을이름고유명사2009-09-14개울이 흐르고 솔나무가 무성하다하여 개솔의 한자표기인 호송을 이용하여 반영2023-07-26
356812548광산구시군구주도로황룡강1자전거길Hwangnyonggang 1jajeongeo-gil2020-10-22송대동 687-2황룡동 405-23.03011.3173011.31720기타기초번호2020-10-15자전거길 도로명 부여2023-07-26
356912549광산구시군구주도로황룡강2자전거길Hwangnyonggang 2jajeongeo-gil2020-10-22송대동 85지죽동 196-23.06969.2976969.29720기타기초번호2020-10-15자전거길 도로명 부여2023-07-26
357012550광산구시군구주도로황룡강3자전거길Hwangnyonggang 3jajeongeo-gil2020-10-22서봉동 82-16서봉동 484-13.02619.3962565.0920기타기초번호2020-10-15자전거길 도로명 부여2023-07-26
357112552광산구시군구주도로황룡강4자전거길Hwangnyonggang 4jajeongeo-gil2020-10-22송산동 362-1임곡동 610-43.07654.4237654.42320기타기초번호2020-10-15자전거길 도로명 부여2023-07-26