Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory390.6 KiB
Average record size in memory40.0 B

Variable types

Text3
Categorical1

Dataset

Description경상남도 도로대장전산화 시스템 데이터의 중장기개방계획에 따른 데이터입니다. 시스템 상에서의 각 도로별 구조물 정보를 가지고 있으며, 도로대장의 구조물도면 정보 데이터를 포함하고있습니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15091937

Reproduction

Analysis started2023-12-10 23:25:32.011855
Analysis finished2023-12-10 23:25:32.890991
Duration0.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구조물코드
Text

Distinct530
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T08:25:33.105215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters80000
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)0.3%

Sample

1st row1003B040
2nd row0058B520
3rd row1037B100
4th row1089B040
5th row0060B190
ValueCountFrequency (%)
0058b570 473
 
4.7%
0058b520 387
 
3.9%
0058b300 356
 
3.6%
0058b490 246
 
2.5%
1020t010 218
 
2.2%
1047t010 189
 
1.9%
1020b090 183
 
1.8%
0058b320 174
 
1.7%
0030b040 155
 
1.6%
0058b380 154
 
1.5%
Other values (520) 7465
74.7%
2023-12-11T08:25:33.466982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 32879
41.1%
B 9547
 
11.9%
1 8956
 
11.2%
5 6351
 
7.9%
8 5290
 
6.6%
2 3644
 
4.6%
4 3198
 
4.0%
3 3159
 
3.9%
7 2364
 
3.0%
9 2190
 
2.7%
Other values (4) 2422
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70000
87.5%
Uppercase Letter 10000
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 32879
47.0%
1 8956
 
12.8%
5 6351
 
9.1%
8 5290
 
7.6%
2 3644
 
5.2%
4 3198
 
4.6%
3 3159
 
4.5%
7 2364
 
3.4%
9 2190
 
3.1%
6 1969
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
B 9547
95.5%
T 427
 
4.3%
S 19
 
0.2%
P 7
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 70000
87.5%
Latin 10000
 
12.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 32879
47.0%
1 8956
 
12.8%
5 6351
 
9.1%
8 5290
 
7.6%
2 3644
 
5.2%
4 3198
 
4.6%
3 3159
 
4.5%
7 2364
 
3.4%
9 2190
 
3.1%
6 1969
 
2.8%
Latin
ValueCountFrequency (%)
B 9547
95.5%
T 427
 
4.3%
S 19
 
0.2%
P 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 32879
41.1%
B 9547
 
11.9%
1 8956
 
11.2%
5 6351
 
7.9%
8 5290
 
6.6%
2 3644
 
4.6%
4 3198
 
4.0%
3 3159
 
3.9%
7 2364
 
3.0%
9 2190
 
2.7%
Other values (4) 2422
 
3.0%

파일명
Text

Distinct9865
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T08:25:33.666275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length12.9992
Min length11

Characters and Unicode

Total characters129992
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9741 ?
Unique (%)97.4%

Sample

1st row100305B040013
2nd row005807B520021
3rd row103703B100080
4th row108903B040010
5th row006007B190004
ValueCountFrequency (%)
102113b110001 3
 
< 0.1%
100101b010001 3
 
< 0.1%
100116b320001 3
 
< 0.1%
100111b200001 3
 
< 0.1%
100114b280001 3
 
< 0.1%
108911b150001 3
 
< 0.1%
104202b040001 3
 
< 0.1%
100113b220001 3
 
< 0.1%
100102b110001 3
 
< 0.1%
108410b200001 3
 
< 0.1%
Other values (9855) 9970
99.7%
2023-12-11T08:25:33.961549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 52827
40.6%
1 15210
 
11.7%
B 9547
 
7.3%
5 8708
 
6.7%
7 8011
 
6.2%
2 7476
 
5.8%
8 7243
 
5.6%
3 6940
 
5.3%
4 5695
 
4.4%
9 4114
 
3.2%
Other values (4) 4221
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 119992
92.3%
Uppercase Letter 10000
 
7.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 52827
44.0%
1 15210
 
12.7%
5 8708
 
7.3%
7 8011
 
6.7%
2 7476
 
6.2%
8 7243
 
6.0%
3 6940
 
5.8%
4 5695
 
4.7%
9 4114
 
3.4%
6 3768
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
B 9547
95.5%
T 427
 
4.3%
S 19
 
0.2%
P 7
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 119992
92.3%
Latin 10000
 
7.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 52827
44.0%
1 15210
 
12.7%
5 8708
 
7.3%
7 8011
 
6.7%
2 7476
 
6.2%
8 7243
 
6.0%
3 6940
 
5.8%
4 5695
 
4.7%
9 4114
 
3.4%
6 3768
 
3.1%
Latin
ValueCountFrequency (%)
B 9547
95.5%
T 427
 
4.3%
S 19
 
0.2%
P 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129992
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 52827
40.6%
1 15210
 
11.7%
B 9547
 
7.3%
5 8708
 
6.7%
7 8011
 
6.2%
2 7476
 
5.8%
8 7243
 
5.6%
3 6940
 
5.3%
4 5695
 
4.4%
9 4114
 
3.2%
Other values (4) 4221
 
3.2%

도면명
Text

Distinct7674
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T08:25:34.188443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length36
Mean length11.0638
Min length2

Characters and Unicode

Total characters110638
Distinct characters503
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6853 ?
Unique (%)68.5%

Sample

1st row편구배도
2nd row케이슨횡방향벽체철근배치도,TYPE1
3rd row장박교LAUNCHING NOSE상세도(5)
4th row도탄교 난간 상세도
5th row덕진교교명판
ValueCountFrequency (%)
교명주 232
 
1.6%
교대 230
 
1.6%
위치도 224
 
1.5%
정면 203
 
1.4%
교명판 196
 
1.3%
교각 193
 
1.3%
일반도 177
 
1.2%
측면 164
 
1.1%
상세도 164
 
1.1%
135
 
0.9%
Other values (6455) 12816
87.0%
2023-12-11T08:25:34.558283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 7305
 
6.6%
( 6215
 
5.6%
) 6145
 
5.6%
교 5670
 
5.1%
4747
 
4.3%
1 2916
 
2.6%
상 2855
 
2.6%
2 2388
 
2.2%
배 2197
 
2.0%
근 2057
 
1.9%
Other values (493) 68143
61.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 69232
62.6%
Uppercase Letter 11338
 
10.2%
Decimal Number 10063
 
9.1%
Open Punctuation 6215
 
5.6%
Close Punctuation 6145
 
5.6%
Space Separator 4747
 
4.3%
Other Punctuation 1259
 
1.1%
Dash Punctuation 997
 
0.9%
Lowercase Letter 356
 
0.3%
Math Symbol 265
 
0.2%
Other values (2) 21
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 7305
 
10.6%
교 5670
 
8.2%
상 2855
 
4.1%
배 2197
 
3.2%
근 2057
 
3.0%
세 2030
 
2.9%
면 1387
 
2.0%
강 1292
 
1.9%
치 1188
 
1.7%
일 1181
 
1.7%
Other values (415) 42070
60.8%
Uppercase Letter
ValueCountFrequency (%)
E 1450
12.8%
P 1169
 
10.3%
T 844
 
7.4%
S 832
 
7.3%
A 825
 
7.3%
N 781
 
6.9%
R 689
 
6.1%
G 560
 
4.9%
M 512
 
4.5%
O 512
 
4.5%
Other values (16) 3164
27.9%
Lowercase Letter
ValueCountFrequency (%)
w 94
26.4%
g 88
24.7%
d 88
24.7%
m 25
 
7.0%
e 15
 
4.2%
o 6
 
1.7%
r 5
 
1.4%
t 5
 
1.4%
l 5
 
1.4%
p 5
 
1.4%
Other values (9) 20
 
5.6%
Decimal Number
ValueCountFrequency (%)
1 2916
29.0%
2 2388
23.7%
3 1356
13.5%
4 743
 
7.4%
5 662
 
6.6%
6 470
 
4.7%
7 437
 
4.3%
0 420
 
4.2%
8 380
 
3.8%
9 291
 
2.9%
Other Punctuation
ValueCountFrequency (%)
, 759
60.3%
. 433
34.4%
' 23
 
1.8%
: 21
 
1.7%
/ 12
 
1.0%
* 5
 
0.4%
@ 2
 
0.2%
" 2
 
0.2%
& 2
 
0.2%
Letter Number
ValueCountFrequency (%)
Ⅲ 5
31.2%
Ⅳ 3
18.8%
Ⅴ 3
18.8%
Ⅵ 3
18.8%
Ⅱ 1
 
6.2%
Ⅰ 1
 
6.2%
Math Symbol
ValueCountFrequency (%)
~ 187
70.6%
= 60
 
22.6%
+ 18
 
6.8%
Open Punctuation
ValueCountFrequency (%)
( 6215
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6145
100.0%
Space Separator
ValueCountFrequency (%)
4747
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 997
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 69232
62.6%
Common 29696
26.8%
Latin 11710
 
10.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 7305
 
10.6%
교 5670
 
8.2%
상 2855
 
4.1%
배 2197
 
3.2%
근 2057
 
3.0%
세 2030
 
2.9%
면 1387
 
2.0%
강 1292
 
1.9%
치 1188
 
1.7%
일 1181
 
1.7%
Other values (415) 42070
60.8%
Latin
ValueCountFrequency (%)
E 1450
12.4%
P 1169
 
10.0%
T 844
 
7.2%
S 832
 
7.1%
A 825
 
7.0%
N 781
 
6.7%
R 689
 
5.9%
G 560
 
4.8%
M 512
 
4.4%
O 512
 
4.4%
Other values (41) 3536
30.2%
Common
ValueCountFrequency (%)
( 6215
20.9%
) 6145
20.7%
4747
16.0%
1 2916
9.8%
2 2388
 
8.0%
3 1356
 
4.6%
- 997
 
3.4%
, 759
 
2.6%
4 743
 
2.5%
5 662
 
2.2%
Other values (17) 2768
9.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 69232
62.6%
ASCII 41390
37.4%
Number Forms 16
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 7305
 
10.6%
교 5670
 
8.2%
상 2855
 
4.1%
배 2197
 
3.2%
근 2057
 
3.0%
세 2030
 
2.9%
면 1387
 
2.0%
강 1292
 
1.9%
치 1188
 
1.7%
일 1181
 
1.7%
Other values (415) 42070
60.8%
ASCII
ValueCountFrequency (%)
( 6215
15.0%
) 6145
14.8%
4747
 
11.5%
1 2916
 
7.0%
2 2388
 
5.8%
E 1450
 
3.5%
3 1356
 
3.3%
P 1169
 
2.8%
- 997
 
2.4%
T 844
 
2.0%
Other values (62) 13163
31.8%
Number Forms
ValueCountFrequency (%)
Ⅲ 5
31.2%
Ⅳ 3
18.8%
Ⅴ 3
18.8%
Ⅵ 3
18.8%
Ⅱ 1
 
6.2%
Ⅰ 1
 
6.2%

입력방식
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
R
7082 
P
2031 
V
886 
p
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowR
2nd rowR
3rd rowR
4th rowR
5th rowP

Common Values

ValueCountFrequency (%)
R 7082
70.8%
P 2031
 
20.3%
V 886
 
8.9%
p 1
 
< 0.1%

Length

2023-12-11T08:25:34.713911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:25:34.831390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
r 7082
70.8%
p 2032
 
20.3%
v 886
 
8.9%

Missing values

2023-12-11T08:25:32.763386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:25:32.845625image/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

구조물코드파일명도면명입력방식
134961003B040100305B040013편구배도R
37740058B520005807B520021케이슨횡방향벽체철근배치도,TYPE1R
93481037B100103703B100080장박교LAUNCHING NOSE상세도(5)R
159321089B040108903B040010도탄교 난간 상세도R
112690060B190006007B190004덕진교교명판P
154711080B070108001B070003대지교 교명주P
127551010B100101003B100004주철근조립도 및 배근도V
52260058B570005807B570189P7-1파일기초,파일철근배치도(2)R
131711004B100100403B100022주형상세도(15)R
19970058B370005807B370069시.종점부정착구보강상세R
구조물코드파일명도면명입력방식
157361084B110108403B110003춘전교 교명주P
70250069B610006908B610219교대2구조도(1)R
138010067B010006701B010117상부슬라브수평브레이싱(1)R
158711084B190108406B190010ARCHRIB배근도(1)V
71150069B350006906B350019배근도(시점부)R
31670058B490005807B490222맨홀상세도R
81031007B210100705B210002교명주P
103381077B090107703B090010어영교일반도V
2640058B270005807B270102강재재료표(11)R
71230069B350006906B350027차도용난간받침상세도R