Overview

Dataset statistics

Number of variables14
Number of observations38
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.4 KiB
Average record size in memory119.5 B

Variable types

Numeric3
Text2
Categorical8
DateTime1

Dataset

Description객체id,현황도형 관리번호,도형 대분류코드,도형 중분류코드,도형 소분류코드,도형 속성코드,도형 조서관리 코드,결정고시관리코드,라벨명,시군구코드,도면번호,현황도형 생성일시,면적(도형),길이(도형)
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-21162/S/1/datasetView.do

Alerts

도형 대분류코드 has constant value ""Constant
도형 중분류코드 has constant value ""Constant
도형 소분류코드 has constant value ""Constant
도형 속성코드 has constant value ""Constant
시군구코드 has constant value ""Constant
현황도형 생성일시 has constant value ""Constant
면적(도형) is highly overall correlated with 길이(도형) and 1 other fieldsHigh correlation
길이(도형) is highly overall correlated with 면적(도형) and 1 other fieldsHigh correlation
결정고시관리코드 is highly overall correlated with 면적(도형) and 1 other fieldsHigh correlation
라벨명 is highly overall correlated with 도면번호High correlation
도면번호 is highly overall correlated with 라벨명High correlation
객체id has unique valuesUnique
현황도형 관리번호 has unique valuesUnique

Reproduction

Analysis started2024-05-11 09:32:31.585971
Analysis finished2024-05-11 09:32:36.922221
Duration5.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

객체id
Real number (ℝ)

UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10865.184
Minimum10589
Maximum10914
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2024-05-11T09:32:37.228892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10589
5-th percentile10590.85
Q110886.25
median10895.5
Q310904.75
95-th percentile10912.15
Maximum10914
Range325
Interquartile range (IQR)18.5

Descriptive statistics

Standard deviation95.943355
Coefficient of variation (CV)0.0088303477
Kurtosis5.3061552
Mean10865.184
Median Absolute Deviation (MAD)9.5
Skewness-2.630729
Sum412877
Variance9205.1273
MonotonicityStrictly increasing
2024-05-11T09:32:37.764171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
10589 1
 
2.6%
10906 1
 
2.6%
10899 1
 
2.6%
10900 1
 
2.6%
10901 1
 
2.6%
10902 1
 
2.6%
10903 1
 
2.6%
10904 1
 
2.6%
10905 1
 
2.6%
10907 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
10589 1
2.6%
10590 1
2.6%
10591 1
2.6%
10592 1
2.6%
10881 1
2.6%
10882 1
2.6%
10883 1
2.6%
10884 1
2.6%
10885 1
2.6%
10886 1
2.6%
ValueCountFrequency (%)
10914 1
2.6%
10913 1
2.6%
10912 1
2.6%
10911 1
2.6%
10910 1
2.6%
10909 1
2.6%
10908 1
2.6%
10907 1
2.6%
10906 1
2.6%
10905 1
2.6%

현황도형 관리번호
Text

UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size436.0 B
2024-05-11T09:32:38.501461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters912
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

Unique38 ?
Unique (%)100.0%

Sample

1st row11000UQ128PS201912150037
2nd row11000UQ128PS201912150038
3rd row11000UQ128PS201912150039
4th row11000UQ128PS201912150001
5th row11000UQ128PS201912150002
ValueCountFrequency (%)
11000uq128ps201912150037 1
 
2.6%
11000uq128ps201912150023 1
 
2.6%
11000uq128ps201912150033 1
 
2.6%
11000uq128ps201912150017 1
 
2.6%
11000uq128ps201912150018 1
 
2.6%
11000uq128ps201912150019 1
 
2.6%
11000uq128ps201912150020 1
 
2.6%
11000uq128ps201912150021 1
 
2.6%
11000uq128ps201912150022 1
 
2.6%
11000uq128ps201912150025 1
 
2.6%
Other values (28) 28
73.7%
2024-05-11T09:32:39.662223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 241
26.4%
0 240
26.3%
2 128
14.0%
8 42
 
4.6%
9 42
 
4.6%
5 42
 
4.6%
U 38
 
4.2%
Q 38
 
4.2%
P 38
 
4.2%
S 38
 
4.2%
Other values (4) 25
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 760
83.3%
Uppercase Letter 152
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 241
31.7%
0 240
31.6%
2 128
16.8%
8 42
 
5.5%
9 42
 
5.5%
5 42
 
5.5%
3 13
 
1.7%
7 4
 
0.5%
6 4
 
0.5%
4 4
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
U 38
25.0%
Q 38
25.0%
P 38
25.0%
S 38
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 760
83.3%
Latin 152
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 241
31.7%
0 240
31.6%
2 128
16.8%
8 42
 
5.5%
9 42
 
5.5%
5 42
 
5.5%
3 13
 
1.7%
7 4
 
0.5%
6 4
 
0.5%
4 4
 
0.5%
Latin
ValueCountFrequency (%)
U 38
25.0%
Q 38
25.0%
P 38
25.0%
S 38
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 912
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 241
26.4%
0 240
26.3%
2 128
14.0%
8 42
 
4.6%
9 42
 
4.6%
5 42
 
4.6%
U 38
 
4.2%
Q 38
 
4.2%
P 38
 
4.2%
S 38
 
4.2%
Other values (4) 25
 
2.7%

도형 대분류코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size436.0 B
UQM120
38 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUQM120
2nd rowUQM120
3rd rowUQM120
4th rowUQM120
5th rowUQM120

Common Values

ValueCountFrequency (%)
UQM120 38
100.0%

Length

2024-05-11T09:32:40.087567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:32:40.450405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
uqm120 38
100.0%

도형 중분류코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size436.0 B
38 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
38
100.0%

Length

2024-05-11T09:32:40.990994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:32:41.419906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

도형 소분류코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size436.0 B
38 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
38
100.0%

Length

2024-05-11T09:32:41.780847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:32:42.136179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

도형 속성코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size436.0 B
UQM120
38 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUQM120
2nd rowUQM120
3rd rowUQM120
4th rowUQM120
5th rowUQM120

Common Values

ValueCountFrequency (%)
UQM120 38
100.0%

Length

2024-05-11T09:32:42.532130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:32:42.944728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
uqm120 38
100.0%

도형 조서관리 코드
Text

Distinct29
Distinct (%)76.3%
Missing0
Missing (%)0.0%
Memory size436.0 B
2024-05-11T09:32:43.524135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters760
Distinct characters13
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

Unique28 ?
Unique (%)73.7%

Sample

1st row11000DSZ000000001281
2nd row11000DSZ200909155354
3rd row11000DSZ200910133002
4th row11000DSZ000000001281
5th row11000DSZ000000001281
ValueCountFrequency (%)
11000dsz000000001281 10
26.3%
11000dsz201605200845 1
 
2.6%
11000dsz200903188283 1
 
2.6%
11000dsz200903188287 1
 
2.6%
11000dsz200903177272 1
 
2.6%
11000dsz200903200293 1
 
2.6%
11000dsz200903188289 1
 
2.6%
11000dsz201602044818 1
 
2.6%
11000dsz201602044814 1
 
2.6%
11000dsz201602044816 1
 
2.6%
Other values (19) 19
50.0%
2024-05-11T09:32:44.732628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 283
37.2%
1 136
17.9%
2 64
 
8.4%
D 38
 
5.0%
S 38
 
5.0%
Z 38
 
5.0%
8 30
 
3.9%
5 29
 
3.8%
9 24
 
3.2%
3 24
 
3.2%
Other values (3) 56
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 646
85.0%
Uppercase Letter 114
 
15.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 283
43.8%
1 136
21.1%
2 64
 
9.9%
8 30
 
4.6%
5 29
 
4.5%
9 24
 
3.7%
3 24
 
3.7%
6 24
 
3.7%
4 21
 
3.3%
7 11
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
D 38
33.3%
S 38
33.3%
Z 38
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 646
85.0%
Latin 114
 
15.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 283
43.8%
1 136
21.1%
2 64
 
9.9%
8 30
 
4.6%
5 29
 
4.5%
9 24
 
3.7%
3 24
 
3.7%
6 24
 
3.7%
4 21
 
3.3%
7 11
 
1.7%
Latin
ValueCountFrequency (%)
D 38
33.3%
S 38
33.3%
Z 38
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 283
37.2%
1 136
17.9%
2 64
 
8.4%
D 38
 
5.0%
S 38
 
5.0%
Z 38
 
5.0%
8 30
 
3.9%
5 29
 
3.8%
9 24
 
3.2%
3 24
 
3.2%
Other values (3) 56
 
7.4%

결정고시관리코드
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Memory size436.0 B
10 
11000NTC201512317693
4 
11000NTC201512317690
4 
11000NTC200909103808
3 
11000NTC200908203735
3 
Other values (11)
14 

Length

Max length20
Median length20
Mean length15
Min length1

Unique

Unique9 ?
Unique (%)23.7%

Sample

1st row
2nd row11000NTC200909103808
3rd row11000NTC200909243878
4th row
5th row

Common Values

ValueCountFrequency (%)
10
26.3%
11000NTC201512317693 4
 
10.5%
11000NTC201512317690 4
 
10.5%
11000NTC200909103808 3
 
7.9%
11000NTC200908203735 3
 
7.9%
11000NTC201305166830 3
 
7.9%
11000NTC200909243878 2
 
5.3%
11000NTC201712280075 1
 
2.6%
11000NTC201010215491 1
 
2.6%
11000NTC201412047344 1
 
2.6%
Other values (6) 6
15.8%

Length

2024-05-11T09:32:45.350904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11000ntc201512317693 4
14.3%
11000ntc201512317690 4
14.3%
11000ntc200909103808 3
10.7%
11000ntc200908203735 3
10.7%
11000ntc201305166830 3
10.7%
11000ntc200909243878 2
 
7.1%
11000ntc201712280075 1
 
3.6%
11000ntc201010215491 1
 
3.6%
11000ntc201412047344 1
 
3.6%
11000ntc200902122778 1
 
3.6%
Other values (5) 5
17.9%

라벨명
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Memory size436.0 B
집단취락지구
27 
취락지구
3 
부암2
 
2
홍지
 
2
평창
 
2

Length

Max length6
Median length6
Mean length5.1052632
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
집단취락지구 27
71.1%
취락지구 3
 
7.9%
부암2 2
 
5.3%
홍지 2
 
5.3%
평창 2
 
5.3%
부암1 2
 
5.3%

Length

2024-05-11T09:32:45.954901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:32:46.359340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
집단취락지구 27
71.1%
취락지구 3
 
7.9%
부암2 2
 
5.3%
홍지 2
 
5.3%
평창 2
 
5.3%
부암1 2
 
5.3%

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size436.0 B
11000
38 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11000
2nd row11000
3rd row11000
4th row11000
5th row11000

Common Values

ValueCountFrequency (%)
11000 38
100.0%

Length

2024-05-11T09:32:46.731281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:32:47.143713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11000 38
100.0%

도면번호
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Memory size436.0 B
26 
1
4 
2
3 
3
3 
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
26
68.4%
1 4
 
10.5%
2 3
 
7.9%
3 3
 
7.9%
4 2
 
5.3%

Length

2024-05-11T09:32:47.526499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:32:47.971527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 4
33.3%
2 3
25.0%
3 3
25.0%
4 2
16.7%

현황도형 생성일시
Date

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size436.0 B
Minimum2019-12-15 00:00:00
Maximum2019-12-15 00:00:00
2024-05-11T09:32:48.307385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:32:48.903326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

면적(도형)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)71.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17902.898
Minimum3034.9995
Maximum68767.979
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2024-05-11T09:32:49.352564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3034.9995
5-th percentile4458.4366
Q111643.194
median16279.022
Q320857.041
95-th percentile29024.52
Maximum68767.979
Range65732.979
Interquartile range (IQR)9213.8469

Descriptive statistics

Standard deviation11060.071
Coefficient of variation (CV)0.61778101
Kurtosis11.504129
Mean17902.898
Median Absolute Deviation (MAD)4578.0185
Skewness2.641606
Sum680310.13
Variance1.2232516 × 108
MonotonicityNot monotonic
2024-05-11T09:32:49.894645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
20857.040602 4
 
10.5%
11443.7586425 2
 
5.3%
15205.6121987 2
 
5.3%
16065.5400892 2
 
5.3%
19859.9394289 2
 
5.3%
28420.4066003 2
 
5.3%
12241.4990187 2
 
5.3%
17488.4030508 2
 
5.3%
10058.9787552 2
 
5.3%
14024.844655 1
 
2.6%
Other values (17) 17
44.7%
ValueCountFrequency (%)
3034.99948172 1
2.6%
3198.56893195 1
2.6%
4680.7661422 1
2.6%
5885.54609232 1
2.6%
9555.36304764 1
2.6%
9779.87816645 1
2.6%
10058.9787552 2
5.3%
11443.7586425 2
5.3%
12241.4990187 2
5.3%
13703.9898791 1
2.6%
ValueCountFrequency (%)
68767.9786069 1
 
2.6%
32447.8272725 1
 
2.6%
28420.4066003 2
5.3%
26961.1752173 1
 
2.6%
26290.8467907 1
 
2.6%
25856.890046 1
 
2.6%
20857.040602 4
10.5%
20744.885037 1
 
2.6%
20573.6647733 1
 
2.6%
19859.9394289 2
5.3%

길이(도형)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)71.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1008.1871
Minimum331.815
Maximum6138.0601
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2024-05-11T09:32:50.419059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum331.815
5-th percentile423.8084
Q1582.28101
median820.34299
Q31188.78
95-th percentile1465.74
Maximum6138.0601
Range5806.2451
Interquartile range (IQR)606.49902

Descriptive statistics

Standard deviation909.75584
Coefficient of variation (CV)0.90236809
Kurtosis28.923375
Mean1008.1871
Median Absolute Deviation (MAD)241.40298
Skewness5.0618187
Sum38311.108
Variance827655.68
MonotonicityNot monotonic
2024-05-11T09:32:50.937767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1232.79003906 4
 
10.5%
582.28100586 2
 
5.3%
700.99499512 2
 
5.3%
1465.73999023 2
 
5.3%
743.50097656 2
 
5.3%
1123.17004395 2
 
5.3%
580.95697021 2
 
5.3%
955.59802246 2
 
5.3%
578.94000244 2
 
5.3%
770.82299805 1
 
2.6%
Other values (17) 17
44.7%
ValueCountFrequency (%)
331.81500244 1
2.6%
341.79699707 1
2.6%
438.28100586 1
2.6%
468.15100098 1
2.6%
578.56799316 1
2.6%
578.94000244 2
5.3%
580.95697021 2
5.3%
582.28100586 2
5.3%
685.2789917 1
2.6%
700.99499512 2
5.3%
ValueCountFrequency (%)
6138.06005859 1
 
2.6%
1465.73999023 2
5.3%
1269.40002441 1
 
2.6%
1260.44995117 1
 
2.6%
1232.79003906 4
10.5%
1202.0300293 1
 
2.6%
1149.0300293 1
 
2.6%
1123.17004395 2
5.3%
1050.40002441 1
 
2.6%
983.93499756 1
 
2.6%

Interactions

2024-05-11T09:32:34.592078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:32:32.355422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:32:33.535730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:32:34.889933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:32:32.771068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:32:33.867096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:32:35.171346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:32:33.196755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:32:34.218366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T09:32:51.267711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
객체id현황도형 관리번호도형 조서관리 코드결정고시관리코드라벨명도면번호면적(도형)길이(도형)
객체id1.0001.0000.0000.0000.0000.0000.1860.000
현황도형 관리번호1.0001.0001.0001.0001.0001.0001.0001.000
도형 조서관리 코드0.0001.0001.0001.0001.0001.0000.8770.900
결정고시관리코드0.0001.0001.0001.0000.1920.0000.9300.945
라벨명0.0001.0001.0000.1921.0000.8830.7110.704
도면번호0.0001.0001.0000.0000.8831.0000.0000.244
면적(도형)0.1861.0000.8770.9300.7110.0001.0000.984
길이(도형)0.0001.0000.9000.9450.7040.2440.9841.000
2024-05-11T09:32:51.599779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
결정고시관리코드도면번호라벨명
결정고시관리코드1.0000.0000.000
도면번호0.0001.0000.800
라벨명0.0000.8001.000
2024-05-11T09:32:52.222526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
객체id면적(도형)길이(도형)결정고시관리코드라벨명도면번호
객체id1.000-0.421-0.2210.0000.0000.000
면적(도형)-0.4211.0000.8160.6340.3290.000
길이(도형)-0.2210.8161.0000.5840.3620.150
결정고시관리코드0.0000.6340.5841.0000.0000.000
라벨명0.0000.3290.3620.0001.0000.800
도면번호0.0000.0000.1500.0000.8001.000

Missing values

2024-05-11T09:32:35.646991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T09:32:36.555028image/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

객체id현황도형 관리번호도형 대분류코드도형 중분류코드도형 소분류코드도형 속성코드도형 조서관리 코드결정고시관리코드라벨명시군구코드도면번호현황도형 생성일시면적(도형)길이(도형)
01058911000UQ128PS201912150037UQM120UQM12011000DSZ000000001281집단취락지구110002019-12-15 00:00:00.09555.363048438.281006
11059011000UQ128PS201912150038UQM120UQM12011000DSZ20090915535411000NTC200909103808집단취락지구110002019-12-15 00:00:00.019859.939429743.500977
21059111000UQ128PS201912150039UQM120UQM12011000DSZ20091013300211000NTC200909243878집단취락지구110002019-12-15 00:00:00.025856.890046983.934998
31059211000UQ128PS201912150001UQM120UQM12011000DSZ000000001281집단취락지구110002019-12-15 00:00:00.019859.939429743.500977
41088111000UQ128PS201912150002UQM120UQM12011000DSZ000000001281집단취락지구110002019-12-15 00:00:00.026290.8467911050.400024
51088211000UQ128PS201912150003UQM120UQM12011000DSZ20090915535111000NTC200908203735집단취락지구110002019-12-15 00:00:00.010058.978755578.940002
61088311000UQ128PS201912150004UQM120UQM12011000DSZ20090915534711000NTC200908203735집단취락지구110002019-12-15 00:00:00.020573.6647731269.400024
71088411000UQ128PS201912150005UQM120UQM12011000DSZ20090915534911000NTC200909103808집단취락지구110002019-12-15 00:00:00.017488.403051955.598022
81088511000UQ128PS201912150006UQM120UQM12011000DSZ20090915535511000NTC200909103808집단취락지구110002019-12-15 00:00:00.020857.0406021232.790039
91088611000UQ128PS201912150007UQM120UQM12011000DSZ000000001281집단취락지구110002019-12-15 00:00:00.020857.0406021232.790039
객체id현황도형 관리번호도형 대분류코드도형 중분류코드도형 소분류코드도형 속성코드도형 조서관리 코드결정고시관리코드라벨명시군구코드도면번호현황도형 생성일시면적(도형)길이(도형)
281090511000UQ128PS201912150024UQM120UQM12011000DSZ20160204481811000NTC201512317690평창1100042019-12-15 00:00:00.015205.612199700.994995
291090611000UQ128PS201912150025UQM120UQM12011000DSZ20090318828911000NTC200902122778집단취락지구110002019-12-15 00:00:00.013703.989879685.278992
301090711000UQ128PS201912150026UQM120UQM12011000DSZ20090320029311000NTC200902122779집단취락지구110002019-12-15 00:00:00.03034.999482331.815002
311090811000UQ128PS201912150027UQM120UQM12011000DSZ20090317727211000NTC200902122774집단취락지구110002019-12-15 00:00:00.05885.546092869.862976
321090911000UQ128PS201912150028UQM120UQM12011000DSZ20090318828711000NTC200902122777집단취락지구110002019-12-15 00:00:00.018931.164956747.606995
331091011000UQ128PS201912150029UQM120UQM12011000DSZ20090318828311000NTC200902122775집단취락지구110002019-12-15 00:00:00.014024.844655770.822998
341091111000UQ128PS201912150030UQM120UQM12011000DSZ20090603331011000NTC200904163187집단취락지구110002019-12-15 00:00:00.04680.766142468.151001
351091211000UQ128PS201912150032UQM120UQM12011000DSZ000000001281집단취락지구110002019-12-15 00:00:00.010058.978755578.940002
361091311000UQ128PS201912150033UQM120UQM12011000DSZ000000001281집단취락지구110002019-12-15 00:00:00.020744.8850371260.449951
371091411000UQ128PS201912150034UQM120UQM12011000DSZ000000001281집단취락지구110002019-12-15 00:00:00.017488.403051955.598022