Overview

Dataset statistics

Number of variables5
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.1 KiB
Average record size in memory42.3 B

Variable types

Categorical3
Text2

Dataset

Description판교제로시티 내 시설물 등에 대한 기준정보 조회
Author차세대융합기술연구원
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=BH3Y91Z3BGAWHEJJPNVK32001977&infSeq=1

Alerts

데이터획득일시 is highly overall correlated with 위치정보High correlation
위치정보 is highly overall correlated with 데이터획득일시High correlation
구분자 has unique valuesUnique
GPS정보 has unique valuesUnique

Reproduction

Analysis started2024-03-23 02:22:41.607636
Analysis finished2024-03-23 02:22:42.882896
Duration1.28 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

데이터획득일시
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2019-04-15 00:00:00
69 
2018-03-12 00:00:00
27 
2019-03-12 00:00:00
 
4

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-04-15 00:00:00
2nd row2019-04-15 00:00:00
3rd row2019-04-15 00:00:00
4th row2019-04-15 00:00:00
5th row2019-04-15 00:00:00

Common Values

ValueCountFrequency (%)
2019-04-15 00:00:00 69
69.0%
2018-03-12 00:00:00 27
 
27.0%
2019-03-12 00:00:00 4
 
4.0%

Length

2024-03-23T02:22:43.124370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T02:22:43.452184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00:00 100
50.0%
2019-04-15 69
34.5%
2018-03-12 27
 
13.5%
2019-03-12 4
 
2.0%

구분자
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-03-23T02:22:44.025994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1200
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowARO-SDB-3091
2nd rowARO-SDB-3092
3rd rowARO-SDB-3122
4th rowARO-SDB-3121
5th rowARO-SDB-3101
ValueCountFrequency (%)
aro-sdb-3091 1
 
1.0%
aro-sdc-3071 1
 
1.0%
cit-sdb-1035 1
 
1.0%
cit-sdb-1031 1
 
1.0%
aro-sdc-1061 1
 
1.0%
aro-sdc-1011 1
 
1.0%
aro-sdc-1021 1
 
1.0%
aro-sdc-1031 1
 
1.0%
aro-sdc-1041 1
 
1.0%
aro-sdc-1051 1
 
1.0%
Other values (90) 90
90.0%
2024-03-23T02:22:45.134130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 200
16.7%
1 120
10.0%
S 100
8.3%
D 100
8.3%
3 94
7.8%
0 85
7.1%
B 76
 
6.3%
R 72
 
6.0%
A 72
 
6.0%
O 72
 
6.0%
Other values (10) 209
17.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 600
50.0%
Decimal Number 400
33.3%
Dash Punctuation 200
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 120
30.0%
3 94
23.5%
0 85
21.2%
2 43
 
10.8%
4 14
 
3.5%
5 11
 
2.8%
9 10
 
2.5%
8 10
 
2.5%
7 7
 
1.8%
6 6
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
S 100
16.7%
D 100
16.7%
B 76
12.7%
R 72
12.0%
A 72
12.0%
O 72
12.0%
C 52
8.7%
I 28
 
4.7%
T 28
 
4.7%
Dash Punctuation
ValueCountFrequency (%)
- 200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 600
50.0%
Latin 600
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 200
33.3%
1 120
20.0%
3 94
15.7%
0 85
14.2%
2 43
 
7.2%
4 14
 
2.3%
5 11
 
1.8%
9 10
 
1.7%
8 10
 
1.7%
7 7
 
1.2%
Latin
ValueCountFrequency (%)
S 100
16.7%
D 100
16.7%
B 76
12.7%
R 72
12.0%
A 72
12.0%
O 72
12.0%
C 52
8.7%
I 28
 
4.7%
T 28
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 200
16.7%
1 120
10.0%
S 100
8.3%
D 100
8.3%
3 94
7.8%
0 85
7.1%
B 76
 
6.3%
R 72
 
6.0%
A 72
 
6.0%
O 72
 
6.0%
Other values (10) 209
17.4%

서비스구분코드
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
48 
5
28 
2
24 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 48
48.0%
5 28
28.0%
2 24
24.0%

Length

2024-03-23T02:22:45.566515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T02:22:45.854071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 48
48.0%
5 28
28.0%
2 24
24.0%

위치정보
Categorical

HIGH CORRELATION 

Distinct33
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
판교제2테크노밸리내 도로
9 
엔씨소프트 R&D 센터 전면 사거리
 
4
판교테크노파크공원 진입 사거리
 
4
판교테크노중앙사거리
 
4
SK케미칼 연구소 전면사거리
 
4
Other values (28)
75 

Length

Max length25
Median length19
Mean length14.36
Min length5

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row봇들5단지 휴먼시아 아파트 503동 전면도로
2nd row봇들5단지 휴먼시아 아파트 503동 전면도로
3rd row삼환하이펙스 B동 전면도로
4th row삼환하이펙스 B동 전면도로
5th rowSK가스 본사 전면도로

Common Values

ValueCountFrequency (%)
판교제2테크노밸리내 도로 9
 
9.0%
엔씨소프트 R&D 센터 전면 사거리 4
 
4.0%
판교테크노파크공원 진입 사거리 4
 
4.0%
판교테크노중앙사거리 4
 
4.0%
SK케미칼 연구소 전면사거리 4
 
4.0%
봇들사거리 4
 
4.0%
LH판교기업성장센터 전면 사거리 4
 
4.0%
금토동삼거리인근 3
 
3.0%
동안교 진입전 삼거리 3
 
3.0%
판교유스페이스몰 전면도로 3
 
3.0%
Other values (23) 58
58.0%

Length

2024-03-23T02:22:46.245922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전면도로 36
 
14.1%
판교제2테크노밸리 15
 
5.9%
사거리 12
 
4.7%
도로 11
 
4.3%
전면 11
 
4.3%
삼거리 9
 
3.5%
판교제2테크노밸리내 9
 
3.5%
엔씨소프트 7
 
2.7%
lh판교기업성장센터 7
 
2.7%
후면도로 6
 
2.4%
Other values (40) 132
51.8%

GPS정보
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-03-23T02:22:46.883674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length46
Mean length45.78
Min length44

Characters and Unicode

Total characters4578
Distinct characters21
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowPOINT Z(127.113112105682, 37.4013020743719, 0)
2nd rowPOINT Z(127.113112326873, 37.4013049646348, 0)
3rd rowPOINT Z(127.109776763566, 37.4012021394122, 0)
4th rowPOINT Z(127.109777280671, 37.4011973815424, 0)
5th rowPOINT Z(127.110907512104, 37.4031438666896, 0)
ValueCountFrequency (%)
point 100
25.0%
0 100
25.0%
37.4124958411739 1
 
0.2%
37.3963069685493 1
 
0.2%
z(127.095282366495 1
 
0.2%
37.4086436348225 1
 
0.2%
z(127.095846515419 1
 
0.2%
37.4098870959457 1
 
0.2%
z(127.095106159311 1
 
0.2%
37.411754163664 1
 
0.2%
Other values (192) 192
48.0%
2024-03-23T02:22:47.951461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 418
 
9.1%
7 411
 
9.0%
0 397
 
8.7%
3 330
 
7.2%
2 306
 
6.7%
9 302
 
6.6%
300
 
6.6%
4 283
 
6.2%
5 225
 
4.9%
6 205
 
4.5%
Other values (11) 1401
30.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3078
67.2%
Uppercase Letter 600
 
13.1%
Other Punctuation 400
 
8.7%
Space Separator 300
 
6.6%
Open Punctuation 100
 
2.2%
Close Punctuation 100
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 418
13.6%
7 411
13.4%
0 397
12.9%
3 330
10.7%
2 306
9.9%
9 302
9.8%
4 283
9.2%
5 225
7.3%
6 205
6.7%
8 201
6.5%
Uppercase Letter
ValueCountFrequency (%)
P 100
16.7%
O 100
16.7%
Z 100
16.7%
T 100
16.7%
N 100
16.7%
I 100
16.7%
Other Punctuation
ValueCountFrequency (%)
, 200
50.0%
. 200
50.0%
Space Separator
ValueCountFrequency (%)
300
100.0%
Open Punctuation
ValueCountFrequency (%)
( 100
100.0%
Close Punctuation
ValueCountFrequency (%)
) 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3978
86.9%
Latin 600
 
13.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 418
10.5%
7 411
10.3%
0 397
10.0%
3 330
8.3%
2 306
 
7.7%
9 302
 
7.6%
300
 
7.5%
4 283
 
7.1%
5 225
 
5.7%
6 205
 
5.2%
Other values (5) 801
20.1%
Latin
ValueCountFrequency (%)
P 100
16.7%
O 100
16.7%
Z 100
16.7%
T 100
16.7%
N 100
16.7%
I 100
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4578
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 418
 
9.1%
7 411
 
9.0%
0 397
 
8.7%
3 330
 
7.2%
2 306
 
6.7%
9 302
 
6.6%
300
 
6.6%
4 283
 
6.2%
5 225
 
4.9%
6 205
 
4.5%
Other values (11) 1401
30.6%

Correlations

2024-03-23T02:22:48.223952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
데이터획득일시구분자서비스구분코드위치정보GPS정보
데이터획득일시1.0001.0000.6431.0001.000
구분자1.0001.0001.0001.0001.000
서비스구분코드0.6431.0001.0000.8411.000
위치정보1.0001.0000.8411.0001.000
GPS정보1.0001.0001.0001.0001.000
2024-03-23T02:22:48.513406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
데이터획득일시위치정보서비스구분코드
데이터획득일시1.0000.8310.306
위치정보0.8311.0000.494
서비스구분코드0.3060.4941.000
2024-03-23T02:22:48.771797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
데이터획득일시서비스구분코드위치정보
데이터획득일시1.0000.3060.831
서비스구분코드0.3061.0000.494
위치정보0.8310.4941.000

Missing values

2024-03-23T02:22:42.251812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T02:22:42.630502image/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

데이터획득일시구분자서비스구분코드위치정보GPS정보
02019-04-15 00:00:00ARO-SDB-30911봇들5단지 휴먼시아 아파트 503동 전면도로POINT Z(127.113112105682, 37.4013020743719, 0)
12019-04-15 00:00:00ARO-SDB-30921봇들5단지 휴먼시아 아파트 503동 전면도로POINT Z(127.113112326873, 37.4013049646348, 0)
22019-04-15 00:00:00ARO-SDB-31221삼환하이펙스 B동 전면도로POINT Z(127.109776763566, 37.4012021394122, 0)
32019-04-15 00:00:00ARO-SDB-31211삼환하이펙스 B동 전면도로POINT Z(127.109777280671, 37.4011973815424, 0)
42019-04-15 00:00:00ARO-SDB-31011SK가스 본사 전면도로POINT Z(127.110907512104, 37.4031438666896, 0)
52019-04-15 00:00:00ARO-SDB-31021SK가스 본사 전면도로POINT Z(127.110911402028, 37.4031439603511, 0)
62019-04-15 00:00:00ARO-SDB-31111미래에셋벤처타워 전면도로POINT Z(127.107376577289, 37.4031318005028, 0)
72019-04-15 00:00:00ARO-SDB-31121미래에셋벤처타워 전면도로POINT Z(127.107379600102, 37.4031323033051, 0)
82019-04-15 00:00:00ARO-SDB-30311NHN플레이허브 진입도로POINT Z(127.10357551802, 37.4052442069628, 0)
92019-04-15 00:00:00ARO-SDB-30321NHN플레이허브 진입도로POINT Z(127.103572552903, 37.4052464810322, 0)
데이터획득일시구분자서비스구분코드위치정보GPS정보
902019-04-15 00:00:00CIT-SDB-30225판교테크노중앙사거리POINT Z(127.104730716759, 37.4031160406472, 0)
912019-04-15 00:00:00CIT-SDB-30355판교테크노파크공원 진입 사거리POINT Z(127.106366121619, 37.3996329166102, 0)
922019-04-15 00:00:00CIT-SDB-30315판교테크노파크공원 진입 사거리POINT Z(127.106365071508, 37.3996346691398, 0)
932019-04-15 00:00:00CIT-SDB-30335판교테크노파크공원 진입 사거리POINT Z(127.106367783064, 37.3996353983297, 0)
942019-04-15 00:00:00CIT-SDB-30325판교테크노파크공원 진입 사거리POINT Z(127.106367864107, 37.3996328078213, 0)
952019-04-15 00:00:00CIT-SDB-31045엔씨소프트 R&D 센터 전면 사거리POINT Z(127.109473005204, 37.3999495668107, 0)
962019-04-15 00:00:00CIT-SDB-31025엔씨소프트 R&D 센터 전면 사거리POINT Z(127.109476440946, 37.3999498208706, 0)
972019-04-15 00:00:00CIT-SDB-31015엔씨소프트 R&D 센터 전면 사거리POINT Z(127.109474987544, 37.3999512985398, 0)
982019-04-15 00:00:00CIT-SDB-31035엔씨소프트 R&D 센터 전면 사거리POINT Z(127.109473938394, 37.3999482297326, 0)
992019-04-15 00:00:00CIT-SDB-30735삼평사거리POINT Z(127.113120065162, 37.4000311065793, 0)