Overview

Dataset statistics

Number of variables9
Number of observations22
Missing cells80
Missing cells (%)40.4%
Duplicate rows1
Duplicate rows (%)4.5%
Total size in memory1.7 KiB
Average record size in memory79.0 B

Variable types

Unsupported3
Text5
Categorical1

Dataset

Description게이트웨이 개발사업 정보
Author새만금개발청
URLhttps://www.vworld.kr/dtmk/dtmk_ntads_s002.do?dsId=30152

Alerts

Dataset has 1 (4.5%) duplicate rowsDuplicates
테이블정의서 has 1 (4.5%) missing valuesMissing
Unnamed: 1 has 5 (22.7%) missing valuesMissing
Unnamed: 2 has 3 (13.6%) missing valuesMissing
Unnamed: 4 has 4 (18.2%) missing valuesMissing
Unnamed: 5 has 22 (100.0%) missing valuesMissing
Unnamed: 6 has 20 (90.9%) missing valuesMissing
Unnamed: 7 has 20 (90.9%) missing valuesMissing
Unnamed: 8 has 5 (22.7%) missing valuesMissing
테이블정의서 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 5 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-18 07:49:41.044156
Analysis finished2024-04-18 07:49:41.736599
Duration0.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

테이블정의서
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1
Missing (%)4.5%
Memory size308.0 B

Unnamed: 1
Text

MISSING 

Distinct17
Distinct (%)100.0%
Missing5
Missing (%)22.7%
Memory size308.0 B
2024-04-18T16:49:41.853661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length7.1176471
Min length4

Characters and Unicode

Total characters121
Distinct characters22
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)100.0%

Sample

1st row컬럼ID
2nd rowP_ID
3rd rowP_TY
4th rowP_TYNM
5th rowP_TY_SUB
ValueCountFrequency (%)
컬럼id 1
 
5.9%
p_disp_yn 1
 
5.9%
p_length 1
 
5.9%
p_perim 1
 
5.9%
p_area 1
 
5.9%
p_status 1
 
5.9%
p_lk_poly 1
 
5.9%
p_lk_layer 1
 
5.9%
p_desc 1
 
5.9%
p_id 1
 
5.9%
Other values (7) 7
41.2%
2024-04-18T16:49:42.126083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 23
19.0%
P 19
15.7%
Y 10
 
8.3%
T 8
 
6.6%
S 7
 
5.8%
L 6
 
5.0%
E 6
 
5.0%
N 6
 
5.0%
A 5
 
4.1%
R 4
 
3.3%
Other values (12) 27
22.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 96
79.3%
Connector Punctuation 23
 
19.0%
Other Letter 2
 
1.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 19
19.8%
Y 10
10.4%
T 8
 
8.3%
S 7
 
7.3%
L 6
 
6.2%
E 6
 
6.2%
N 6
 
6.2%
A 5
 
5.2%
R 4
 
4.2%
M 4
 
4.2%
Other values (9) 21
21.9%
Other Letter
ValueCountFrequency (%)
컬 1
50.0%
럼 1
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 96
79.3%
Common 23
 
19.0%
Hangul 2
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 19
19.8%
Y 10
10.4%
T 8
 
8.3%
S 7
 
7.3%
L 6
 
6.2%
E 6
 
6.2%
N 6
 
6.2%
A 5
 
5.2%
R 4
 
4.2%
M 4
 
4.2%
Other values (9) 21
21.9%
Hangul
ValueCountFrequency (%)
컬 1
50.0%
럼 1
50.0%
Common
ValueCountFrequency (%)
_ 23
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119
98.3%
Hangul 2
 
1.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 23
19.3%
P 19
16.0%
Y 10
 
8.4%
T 8
 
6.7%
S 7
 
5.9%
L 6
 
5.0%
E 6
 
5.0%
N 6
 
5.0%
A 5
 
4.2%
R 4
 
3.4%
Other values (10) 25
21.0%
Hangul
ValueCountFrequency (%)
컬 1
50.0%
럼 1
50.0%

Unnamed: 2
Text

MISSING 

Distinct19
Distinct (%)100.0%
Missing3
Missing (%)13.6%
Memory size308.0 B
2024-04-18T16:49:42.284733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length10
Mean length8
Min length3

Characters and Unicode

Total characters152
Distinct characters60
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)100.0%

Sample

1st row국토관리지역개발
2nd row컬럼명
3rd row폴리곤 아이디
4th row폴리곤 분류
5th row폴리곤 분류명
ValueCountFrequency (%)
폴리곤 9
23.1%
다른 2
 
5.1%
이동 2
 
5.1%
국토관리지역개발 1
 
2.6%
구분 1
 
2.6%
레이어종류 1
 
2.6%
길이(m 1
 
2.6%
선의 1
 
2.6%
둘레길이(m 1
 
2.6%
면적(㎡ 1
 
2.6%
Other values (19) 19
48.7%
2024-04-18T16:49:42.570333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20
 
13.2%
리 13
 
8.6%
곤 12
 
7.9%
폴 12
 
7.9%
이 9
 
5.9%
류 5
 
3.3%
명 4
 
2.6%
분 4
 
2.6%
( 3
 
2.0%
레 3
 
2.0%
Other values (50) 67
44.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 123
80.9%
Space Separator 20
 
13.2%
Open Punctuation 3
 
2.0%
Close Punctuation 3
 
2.0%
Lowercase Letter 2
 
1.3%
Other Symbol 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 13
 
10.6%
곤 12
 
9.8%
폴 12
 
9.8%
이 9
 
7.3%
류 5
 
4.1%
명 4
 
3.3%
분 4
 
3.3%
레 3
 
2.4%
세 3
 
2.4%
부 3
 
2.4%
Other values (45) 55
44.7%
Space Separator
ValueCountFrequency (%)
20
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 2
100.0%
Other Symbol
ValueCountFrequency (%)
㎡ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 123
80.9%
Common 27
 
17.8%
Latin 2
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 13
 
10.6%
곤 12
 
9.8%
폴 12
 
9.8%
이 9
 
7.3%
류 5
 
4.1%
명 4
 
3.3%
분 4
 
3.3%
레 3
 
2.4%
세 3
 
2.4%
부 3
 
2.4%
Other values (45) 55
44.7%
Common
ValueCountFrequency (%)
20
74.1%
( 3
 
11.1%
) 3
 
11.1%
㎡ 1
 
3.7%
Latin
ValueCountFrequency (%)
m 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 123
80.9%
ASCII 28
 
18.4%
CJK Compat 1
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20
71.4%
( 3
 
10.7%
) 3
 
10.7%
m 2
 
7.1%
Hangul
ValueCountFrequency (%)
리 13
 
10.6%
곤 12
 
9.8%
폴 12
 
9.8%
이 9
 
7.3%
류 5
 
4.1%
명 4
 
3.3%
분 4
 
3.3%
레 3
 
2.4%
세 3
 
2.4%
부 3
 
2.4%
Other values (45) 55
44.7%
CJK Compat
ValueCountFrequency (%)
㎡ 1
100.0%

Unnamed: 3
Categorical

Distinct4
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size308.0 B
VARCHAR2
16 
<NA>
4 
테이블ID
 
1
타입
 
1

Length

Max length8
Median length8
Mean length6.8636364
Min length2

Unique

Unique2 ?
Unique (%)9.1%

Sample

1st row테이블ID
2nd row<NA>
3rd row타입
4th rowVARCHAR2
5th rowVARCHAR2

Common Values

ValueCountFrequency (%)
VARCHAR2 16
72.7%
<NA> 4
 
18.2%
테이블ID 1
 
4.5%
타입 1
 
4.5%

Length

2024-04-18T16:49:42.688536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T16:49:42.787816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
varchar2 16
72.7%
na 4
 
18.2%
테이블id 1
 
4.5%
타입 1
 
4.5%

Unnamed: 4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4
Missing (%)18.2%
Memory size308.0 B

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing22
Missing (%)100.0%
Memory size330.0 B

Unnamed: 6
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing20
Missing (%)90.9%
Memory size308.0 B
2024-04-18T16:49:42.888000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4.5
Mean length4.5
Min length4

Characters and Unicode

Total characters9
Distinct characters8
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row테이블명
2nd rowPK/FK
ValueCountFrequency (%)
테이블명 1
50.0%
pk/fk 1
50.0%
2024-04-18T16:49:43.129833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
K 2
22.2%
테 1
11.1%
이 1
11.1%
블 1
11.1%
명 1
11.1%
P 1
11.1%
/ 1
11.1%
F 1
11.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4
44.4%
Other Letter 4
44.4%
Other Punctuation 1
 
11.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
테 1
25.0%
이 1
25.0%
블 1
25.0%
명 1
25.0%
Uppercase Letter
ValueCountFrequency (%)
K 2
50.0%
P 1
25.0%
F 1
25.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4
44.4%
Hangul 4
44.4%
Common 1
 
11.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
테 1
25.0%
이 1
25.0%
블 1
25.0%
명 1
25.0%
Latin
ValueCountFrequency (%)
K 2
50.0%
P 1
25.0%
F 1
25.0%
Common
ValueCountFrequency (%)
/ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5
55.6%
Hangul 4
44.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
K 2
40.0%
P 1
20.0%
/ 1
20.0%
F 1
20.0%
Hangul
ValueCountFrequency (%)
테 1
25.0%
이 1
25.0%
블 1
25.0%
명 1
25.0%

Unnamed: 7
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing20
Missing (%)90.9%
Memory size308.0 B
2024-04-18T16:49:43.256335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length11
Min length7

Characters and Unicode

Total characters22
Distinct characters19
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row(사업별)게이트웨이 개발사업
2nd rowDefault
ValueCountFrequency (%)
사업별)게이트웨이 1
33.3%
개발사업 1
33.3%
default 1
33.3%
2024-04-18T16:49:43.534227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
업 2
 
9.1%
이 2
 
9.1%
사 2
 
9.1%
( 1
 
4.5%
발 1
 
4.5%
l 1
 
4.5%
u 1
 
4.5%
a 1
 
4.5%
f 1
 
4.5%
e 1
 
4.5%
Other values (9) 9
40.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12
54.5%
Lowercase Letter 6
27.3%
Open Punctuation 1
 
4.5%
Uppercase Letter 1
 
4.5%
Space Separator 1
 
4.5%
Close Punctuation 1
 
4.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 2
16.7%
이 2
16.7%
사 2
16.7%
발 1
8.3%
개 1
8.3%
웨 1
8.3%
트 1
8.3%
게 1
8.3%
별 1
8.3%
Lowercase Letter
ValueCountFrequency (%)
l 1
16.7%
u 1
16.7%
a 1
16.7%
f 1
16.7%
e 1
16.7%
t 1
16.7%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
D 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12
54.5%
Latin 7
31.8%
Common 3
 
13.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 2
16.7%
이 2
16.7%
사 2
16.7%
발 1
8.3%
개 1
8.3%
웨 1
8.3%
트 1
8.3%
게 1
8.3%
별 1
8.3%
Latin
ValueCountFrequency (%)
l 1
14.3%
u 1
14.3%
a 1
14.3%
f 1
14.3%
e 1
14.3%
D 1
14.3%
t 1
14.3%
Common
ValueCountFrequency (%)
( 1
33.3%
1
33.3%
) 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12
54.5%
ASCII 10
45.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
업 2
16.7%
이 2
16.7%
사 2
16.7%
발 1
8.3%
개 1
8.3%
웨 1
8.3%
트 1
8.3%
게 1
8.3%
별 1
8.3%
ASCII
ValueCountFrequency (%)
( 1
10.0%
l 1
10.0%
u 1
10.0%
a 1
10.0%
f 1
10.0%
e 1
10.0%
D 1
10.0%
1
10.0%
) 1
10.0%
t 1
10.0%

Unnamed: 8
Text

MISSING 

Distinct13
Distinct (%)76.5%
Missing5
Missing (%)22.7%
Memory size308.0 B
2024-04-18T16:49:43.755533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length128
Median length65
Mean length34
Min length9

Characters and Unicode

Total characters578
Distinct characters143
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)52.9%

Sample

1st row참조테이블명/비고
2nd row지도코드(2)+대분류(2)+소분류(2)+BLOCK(4)+SEQ(4) 예.게이트웨이 개발사업+공공공지+1블럭+1번째 폴리곤(GDOS0000010001)
3rd row호텔(HT) 펜션(PE) 콘도(CD) 상업시설(BA) 수로및하천(WR) 연수단지(SS) 공공업무시설(PO) 테마파크(TP) 공원(PK) 보행자도로(PR) 주차장(PL) 녹지(GR) 공공공지(OS) 공연문화시설(CF) 주유소(PS)
4th row호텔(HT) 펜션(PE) 콘도(CD) 상업시설(BA) 수로및하천(WR) 연수단지(SS) 공공업무시설(PO) 테마파크(TP) 공원(PK) 보행자도로(PR) 주차장(PL) 녹지(GR) 공공공지(OS) 공연문화시설(CF) 주유소(PS)
5th row세부분류가 있는경우만 사용
ValueCountFrequency (%)
값산출됨 3
 
4.3%
레이어가 3
 
4.3%
공연문화시설(cf 2
 
2.9%
이동할 2
 
2.9%
펜션(pe 2
 
2.9%
폴리곤일때 2
 
2.9%
n(no 2
 
2.9%
y(yes 2
 
2.9%
사용 2
 
2.9%
있는경우만 2
 
2.9%
Other values (34) 48
68.6%
2024-04-18T16:49:44.209719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 45
 
7.8%
) 45
 
7.8%
31
 
5.4%
24
 
4.2%
공 17
 
2.9%
P 17
 
2.9%
이 10
 
1.7%
S 10
 
1.7%
지 9
 
1.6%
n 8
 
1.4%
Other values (133) 362
62.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 267
46.2%
Uppercase Letter 88
 
15.2%
Open Punctuation 45
 
7.8%
Close Punctuation 45
 
7.8%
Lowercase Letter 39
 
6.7%
Control 31
 
5.4%
Space Separator 24
 
4.2%
Decimal Number 17
 
2.9%
Other Punctuation 9
 
1.6%
Math Symbol 7
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
공 17
 
6.4%
이 10
 
3.7%
지 9
 
3.4%
설 8
 
3.0%
도 7
 
2.6%
시 6
 
2.2%
리 6
 
2.2%
폴 6
 
2.2%
곤 6
 
2.2%
주 5
 
1.9%
Other values (85) 187
70.0%
Uppercase Letter
ValueCountFrequency (%)
P 17
19.3%
S 10
11.4%
C 7
 
8.0%
O 6
 
6.8%
R 6
 
6.8%
N 4
 
4.5%
L 4
 
4.5%
Y 4
 
4.5%
D 4
 
4.5%
T 4
 
4.5%
Other values (9) 22
25.0%
Lowercase Letter
ValueCountFrequency (%)
n 8
20.5%
s 5
12.8%
i 4
10.3%
o 3
 
7.7%
e 3
 
7.7%
l 2
 
5.1%
a 2
 
5.1%
g 2
 
5.1%
h 2
 
5.1%
t 2
 
5.1%
Other values (6) 6
15.4%
Decimal Number
ValueCountFrequency (%)
0 8
47.1%
1 4
23.5%
2 3
 
17.6%
4 2
 
11.8%
Other Punctuation
ValueCountFrequency (%)
, 4
44.4%
/ 3
33.3%
. 2
22.2%
Open Punctuation
ValueCountFrequency (%)
( 45
100.0%
Close Punctuation
ValueCountFrequency (%)
) 45
100.0%
Control
ValueCountFrequency (%)
31
100.0%
Space Separator
ValueCountFrequency (%)
24
100.0%
Math Symbol
ValueCountFrequency (%)
+ 7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 267
46.2%
Common 184
31.8%
Latin 127
22.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
공 17
 
6.4%
이 10
 
3.7%
지 9
 
3.4%
설 8
 
3.0%
도 7
 
2.6%
시 6
 
2.2%
리 6
 
2.2%
폴 6
 
2.2%
곤 6
 
2.2%
주 5
 
1.9%
Other values (85) 187
70.0%
Latin
ValueCountFrequency (%)
P 17
 
13.4%
S 10
 
7.9%
n 8
 
6.3%
C 7
 
5.5%
O 6
 
4.7%
R 6
 
4.7%
s 5
 
3.9%
N 4
 
3.1%
L 4
 
3.1%
Y 4
 
3.1%
Other values (25) 56
44.1%
Common
ValueCountFrequency (%)
( 45
24.5%
) 45
24.5%
31
16.8%
24
13.0%
0 8
 
4.3%
+ 7
 
3.8%
- 6
 
3.3%
, 4
 
2.2%
1 4
 
2.2%
/ 3
 
1.6%
Other values (3) 7
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 311
53.8%
Hangul 267
46.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 45
14.5%
) 45
14.5%
31
 
10.0%
24
 
7.7%
P 17
 
5.5%
S 10
 
3.2%
n 8
 
2.6%
0 8
 
2.6%
C 7
 
2.3%
+ 7
 
2.3%
Other values (38) 109
35.0%
Hangul
ValueCountFrequency (%)
공 17
 
6.4%
이 10
 
3.7%
지 9
 
3.4%
설 8
 
3.0%
도 7
 
2.6%
시 6
 
2.2%
리 6
 
2.2%
폴 6
 
2.2%
곤 6
 
2.2%
주 5
 
1.9%
Other values (85) 187
70.0%

Correlations

2024-04-18T16:49:44.295746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 6Unnamed: 7Unnamed: 8
Unnamed: 11.0001.0001.000NaNNaN1.000
Unnamed: 21.0001.0001.0000.0000.0001.000
Unnamed: 31.0001.0001.0000.0000.0001.000
Unnamed: 6NaN0.0000.0001.0000.000NaN
Unnamed: 7NaN0.0000.0000.0001.000NaN
Unnamed: 81.0001.0001.000NaNNaN1.000

Missing values

2024-04-18T16:49:41.509085image/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.
2024-04-18T16:49:41.629313image/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

테이블정의서Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8
0주제영역명<NA>국토관리지역개발테이블IDP_GD_20181231<NA>테이블명(사업별)게이트웨이 개발사업<NA>
1테이블설명<NA><NA><NA>NaN<NA><NA><NA><NA>
2No컬럼ID컬럼명타입길이(Byte)<NA>PK/FKDefault참조테이블명/비고
31P_ID폴리곤 아이디VARCHAR214<NA><NA><NA>지도코드(2)+대분류(2)+소분류(2)+BLOCK(4)+SEQ(4) 예.게이트웨이 개발사업+공공공지+1블럭+1번째 폴리곤(GDOS0000010001)
42P_TY폴리곤 분류VARCHAR22<NA><NA><NA>호텔(HT) 펜션(PE) 콘도(CD) 상업시설(BA) 수로및하천(WR) 연수단지(SS) 공공업무시설(PO) 테마파크(TP) 공원(PK) 보행자도로(PR) 주차장(PL) 녹지(GR) 공공공지(OS) 공연문화시설(CF) 주유소(PS)
53P_TYNM폴리곤 분류명VARCHAR2100<NA><NA><NA>호텔(HT) 펜션(PE) 콘도(CD) 상업시설(BA) 수로및하천(WR) 연수단지(SS) 공공업무시설(PO) 테마파크(TP) 공원(PK) 보행자도로(PR) 주차장(PL) 녹지(GR) 공공공지(OS) 공연문화시설(CF) 주유소(PS)
64P_TY_SUB폴리곤 세분류VARCHAR22<NA><NA><NA>세부분류가 있는경우만 사용
75P_TY_SUBNM폴리곤 세류명VARCHAR2100<NA><NA><NA>세부분류가 있는경우만 사용
86P_SRCH_YN검색대상 폴리곤여부VARCHAR21<NA><NA><NA>Y(Yes), N(No)
97P_NAME폴리곤 이름VARCHAR2100<NA><NA><NA>세부용지이름(예.수상호텔)
테이블정의서Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8
1210P_LK_LAYER다른 레이어로 이동VARCHAR24<NA><NA><NA>이동할 주제도 코드
1311P_LK_POLY다른 폴리곤으로 이동VARCHAR214<NA><NA><NA>이동할 폴리곤 이름
1412P_STATUS계획 진행 상태VARCHAR21<NA><NA><NA>P-계획중(Planning), F-계획완료(planning finshish), C-건설완료(Constructed)
1513P_AREA폴리곤 면적(㎡)VARCHAR210<NA><NA><NA>레이어가 폴리곤일때 값산출됨
1614P_PERIM폴리곤 둘레길이(m)VARCHAR210<NA><NA><NA>레이어가 폴리곤일때 값산출됨
1715P_LENGTH선의 길이(m)VARCHAR210<NA><NA><NA>레이어가 라인일때 값산출됨
1816P_LY_TY레이어종류 구분VARCHAR21<NA><NA><NA>P-폴리곤/L-라인/D-도트(점)
19인덱스명<NA>인덱스키<NA>NaN<NA><NA><NA><NA>
20NaN<NA><NA><NA>NaN<NA><NA><NA><NA>
21업무규칙<NA><NA><NA>NaN<NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 6Unnamed: 7Unnamed: 8# duplicates
0<NA><NA><NA><NA><NA><NA>3