Overview

Dataset statistics

Number of variables14
Number of observations7975
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory903.5 KiB
Average record size in memory116.0 B

Variable types

Numeric4
Text4
Categorical5
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-21136/S/1/datasetView.do

Alerts

도형 속성코드 is highly overall correlated with 도형 대분류코드 and 2 other fieldsHigh correlation
도형 대분류코드 is highly overall correlated with 도형 중분류코드 and 1 other fieldsHigh correlation
도형 소분류코드 is highly overall correlated with 도형 중분류코드 and 1 other fieldsHigh correlation
도형 중분류코드 is highly overall correlated with 도형 대분류코드 and 2 other fieldsHigh correlation
면적(도형) is highly overall correlated with 길이(도형)High correlation
길이(도형) is highly overall correlated with 면적(도형)High correlation
도형 대분류코드 is highly imbalanced (67.4%)Imbalance
도형 중분류코드 is highly imbalanced (64.1%)Imbalance
도면번호 is highly imbalanced (99.4%)Imbalance
시군구코드 is highly skewed (γ1 = 62.7665626)Skewed
면적(도형) is highly skewed (γ1 = 21.24487173)Skewed
객체id has unique valuesUnique

Reproduction

Analysis started2024-05-11 01:46:11.735739
Analysis finished2024-05-11 01:46:22.337446
Duration10.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

객체id
Real number (ℝ)

UNIQUE 

Distinct7975
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean298377.94
Minimum294343
Maximum302365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2024-05-11T01:46:22.562913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum294343
5-th percentile294789.7
Q1296384.5
median298378
Q3300371.5
95-th percentile301966.3
Maximum302365
Range8022
Interquartile range (IQR)3987

Descriptive statistics

Standard deviation2302.4333
Coefficient of variation (CV)0.0077164997
Kurtosis-1.1997763
Mean298377.94
Median Absolute Deviation (MAD)1994
Skewness-0.00015911514
Sum2.3795641 × 109
Variance5301199
MonotonicityStrictly increasing
2024-05-11T01:46:23.165520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
294343 1
 
< 0.1%
299703 1
 
< 0.1%
299716 1
 
< 0.1%
299715 1
 
< 0.1%
299714 1
 
< 0.1%
299713 1
 
< 0.1%
299712 1
 
< 0.1%
299711 1
 
< 0.1%
299710 1
 
< 0.1%
299709 1
 
< 0.1%
Other values (7965) 7965
99.9%
ValueCountFrequency (%)
294343 1
< 0.1%
294344 1
< 0.1%
294345 1
< 0.1%
294346 1
< 0.1%
294347 1
< 0.1%
294348 1
< 0.1%
294349 1
< 0.1%
294350 1
< 0.1%
294351 1
< 0.1%
294352 1
< 0.1%
ValueCountFrequency (%)
302365 1
< 0.1%
302364 1
< 0.1%
302363 1
< 0.1%
302362 1
< 0.1%
302361 1
< 0.1%
302360 1
< 0.1%
302359 1
< 0.1%
302358 1
< 0.1%
302357 1
< 0.1%
302356 1
< 0.1%

현황도형 관리번호
Text

Distinct7885
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size62.4 KiB
2024-05-11T01:46:23.931954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters191400
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

Unique7845 ?
Unique (%)98.4%

Sample

1st row11000UQ111PS201912151888
2nd row11000UQ111PS201912152144
3rd row11000UQ111PS201912152145
4th row11000UQ111PS201912151968
5th row11000UQ111PS201912152024
ValueCountFrequency (%)
11000uq111ps201912153295 13
 
0.2%
11000uq111ps201912150878 10
 
0.1%
11000uq111ps201910160083 7
 
0.1%
11000uq111ps201910160084 7
 
0.1%
11000uq111ps201912153498 6
 
0.1%
11000uq111ps201912155654 5
 
0.1%
11000uq111ps201912154890 5
 
0.1%
11000uq111ps201912155790 4
 
0.1%
11000uq111ps201912154675 4
 
0.1%
11000uq111ps201912155655 4
 
0.1%
Other values (7875) 7910
99.2%
2024-05-11T01:46:25.110846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 63763
33.3%
0 40633
21.2%
2 20930
 
10.9%
5 9247
 
4.8%
9 8659
 
4.5%
U 7975
 
4.2%
Q 7975
 
4.2%
P 7975
 
4.2%
S 7975
 
4.2%
3 3752
 
2.0%
Other values (4) 12516
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 159500
83.3%
Uppercase Letter 31900
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 63763
40.0%
0 40633
25.5%
2 20930
 
13.1%
5 9247
 
5.8%
9 8659
 
5.4%
3 3752
 
2.4%
7 3551
 
2.2%
4 3419
 
2.1%
6 3196
 
2.0%
8 2350
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
U 7975
25.0%
Q 7975
25.0%
P 7975
25.0%
S 7975
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 159500
83.3%
Latin 31900
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 63763
40.0%
0 40633
25.5%
2 20930
 
13.1%
5 9247
 
5.8%
9 8659
 
5.4%
3 3752
 
2.4%
7 3551
 
2.2%
4 3419
 
2.1%
6 3196
 
2.0%
8 2350
 
1.5%
Latin
ValueCountFrequency (%)
U 7975
25.0%
Q 7975
25.0%
P 7975
25.0%
S 7975
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 191400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 63763
33.3%
0 40633
21.2%
2 20930
 
10.9%
5 9247
 
4.8%
9 8659
 
4.5%
U 7975
 
4.2%
Q 7975
 
4.2%
P 7975
 
4.2%
S 7975
 
4.2%
3 3752
 
2.0%
Other values (4) 12516
 
6.5%

도형 대분류코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size62.4 KiB
UQA100
6547 
UQA400
 
607
UQA200
 
540
UQA999
 
227
UQA300
 
51
Other values (3)
 
3

Length

Max length6
Median length6
Mean length5.999373
Min length1

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowUQA100
2nd rowUQA100
3rd rowUQA100
4th rowUQA100
5th rowUQA100

Common Values

ValueCountFrequency (%)
UQA100 6547
82.1%
UQA400 607
 
7.6%
UQA200 540
 
6.8%
UQA999 227
 
2.8%
UQA300 51
 
0.6%
1
 
< 0.1%
UQ1200 1
 
< 0.1%
UQA120 1
 
< 0.1%

Length

2024-05-11T01:46:25.690333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:46:26.195509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
uqa100 6547
82.1%
uqa400 607
 
7.6%
uqa200 540
 
6.8%
uqa999 227
 
2.8%
uqa300 51
 
0.6%
uq1200 1
 
< 0.1%
uqa120 1
 
< 0.1%

도형 중분류코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size62.4 KiB
UQA120
5950 
UQA430
 
589
UQA130
 
539
UQA220
 
476
 
228
Other values (11)
 
193

Length

Max length6
Median length6
Mean length5.8570533
Min length1

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st rowUQA120
2nd rowUQA120
3rd rowUQA120
4th rowUQA120
5th rowUQA120

Common Values

ValueCountFrequency (%)
UQA120 5950
74.6%
UQA430 589
 
7.4%
UQA130 539
 
6.8%
UQA220 476
 
6.0%
228
 
2.9%
UQA330 51
 
0.6%
UQA230 49
 
0.6%
UQA110 48
 
0.6%
UQA420 17
 
0.2%
UQA240 15
 
0.2%
Other values (6) 13
 
0.2%

Length

2024-05-11T01:46:26.779274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
uqa120 5950
76.8%
uqa430 589
 
7.6%
uqa130 539
 
7.0%
uqa220 476
 
6.1%
uqa330 51
 
0.7%
uqa230 49
 
0.6%
uqa110 48
 
0.6%
uqa420 17
 
0.2%
uqa240 15
 
0.2%
uqa190 6
 
0.1%
Other values (5) 7
 
0.1%

도형 소분류코드
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size62.4 KiB
1971 
UQA122
1898 
UQA121
1484 
UQA124
1361 
UQA123
1203 
Other values (8)
 
58

Length

Max length6
Median length6
Mean length4.7640125
Min length1

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowUQA124
2nd rowUQA124
3rd rowUQA124
4th rowUQA124
5th rowUQA124

Common Values

ValueCountFrequency (%)
1971
24.7%
UQA122 1898
23.8%
UQA121 1484
18.6%
UQA124 1361
17.1%
UQA123 1203
15.1%
UQA111 41
 
0.5%
UQA129 5
 
0.1%
UQA112 4
 
0.1%
UQA119 3
 
< 0.1%
UQA220 2
 
< 0.1%
Other values (3) 3
 
< 0.1%

Length

2024-05-11T01:46:27.511764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
uqa122 1898
31.6%
uqa121 1484
24.7%
uqa124 1361
22.7%
uqa123 1203
20.0%
uqa111 41
 
0.7%
uqa129 5
 
0.1%
uqa112 4
 
0.1%
uqa119 3
 
< 0.1%
uqa220 2
 
< 0.1%
na 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

도형 속성코드
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size62.4 KiB
UQA122
1897 
UQA121
1485 
UQA124
1361 
UQA123
1204 
UQA430
589 
Other values (16)
1439 

Length

Max length6
Median length6
Mean length5.999373
Min length1

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowUQA124
2nd rowUQA124
3rd rowUQA124
4th rowUQA124
5th rowUQA124

Common Values

ValueCountFrequency (%)
UQA122 1897
23.8%
UQA121 1485
18.6%
UQA124 1361
17.1%
UQA123 1204
15.1%
UQA430 589
 
7.4%
UQA130 539
 
6.8%
UQA220 476
 
6.0%
UQA999 227
 
2.8%
UQA330 51
 
0.6%
UQA230 49
 
0.6%
Other values (11) 97
 
1.2%

Length

2024-05-11T01:46:28.023468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
uqa122 1897
23.8%
uqa121 1485
18.6%
uqa124 1361
17.1%
uqa123 1204
15.1%
uqa430 589
 
7.4%
uqa130 539
 
6.8%
uqa220 476
 
6.0%
uqa999 227
 
2.8%
uqa330 51
 
0.6%
uqa230 49
 
0.6%
Other values (10) 96
 
1.2%

도형 조서관리 코드
Text

Distinct2257
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Memory size62.4 KiB
2024-05-11T01:46:29.216119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters159500
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

Unique2173 ?
Unique (%)27.2%

Sample

1st row11000ARZ000000001111
2nd row11000ARZ000000001111
3rd row11000ARZ000000001111
4th row11000ARZ000000001111
5th row11000ARZ000000001111
ValueCountFrequency (%)
11000arz000000001111 5557
69.7%
11650arz202105210001 10
 
0.1%
11000arz201306242004 10
 
0.1%
11410arz202203020003 8
 
0.1%
11560arz201908050002 7
 
0.1%
11560arz201908050006 7
 
0.1%
11000arz201807051156 5
 
0.1%
11000arz202203290001 5
 
0.1%
11000arz200209301219 4
 
0.1%
11000arz202001280002 4
 
0.1%
Other values (2247) 2358
29.6%
2024-05-11T01:46:30.868152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 76747
48.1%
1 43322
27.2%
A 7975
 
5.0%
Z 7975
 
5.0%
R 7974
 
5.0%
2 6201
 
3.9%
3 1701
 
1.1%
5 1489
 
0.9%
9 1402
 
0.9%
4 1391
 
0.9%
Other values (4) 3323
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 135575
85.0%
Uppercase Letter 23925
 
15.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 76747
56.6%
1 43322
32.0%
2 6201
 
4.6%
3 1701
 
1.3%
5 1489
 
1.1%
9 1402
 
1.0%
4 1391
 
1.0%
8 1152
 
0.8%
6 1146
 
0.8%
7 1024
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
A 7975
33.3%
Z 7975
33.3%
R 7974
33.3%
G 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 135575
85.0%
Latin 23925
 
15.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 76747
56.6%
1 43322
32.0%
2 6201
 
4.6%
3 1701
 
1.3%
5 1489
 
1.1%
9 1402
 
1.0%
4 1391
 
1.0%
8 1152
 
0.8%
6 1146
 
0.8%
7 1024
 
0.8%
Latin
ValueCountFrequency (%)
A 7975
33.3%
Z 7975
33.3%
R 7974
33.3%
G 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 159500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 76747
48.1%
1 43322
27.2%
A 7975
 
5.0%
Z 7975
 
5.0%
R 7974
 
5.0%
2 6201
 
3.9%
3 1701
 
1.1%
5 1489
 
0.9%
9 1402
 
0.9%
4 1391
 
0.9%
Other values (4) 3323
 
2.1%

결정고시관리코드
Text

Distinct1342
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Memory size62.4 KiB
2024-05-11T01:46:31.674413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length1
Mean length6.710721
Min length1

Characters and Unicode

Total characters53518
Distinct characters14
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

Unique795 ?
Unique (%)10.0%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
11000ntc200101270199 23
 
1.0%
11560ntc201908050001 18
 
0.8%
11260ntc202112160004 16
 
0.7%
11000ntc201304256817 11
 
0.5%
11000ntc202204270002 11
 
0.5%
11000ntc202106250006 10
 
0.4%
11110ntc201908130003 10
 
0.4%
11000ntc201004224815 10
 
0.4%
11000ntc202203290001 9
 
0.4%
11530ntc202205180003 9
 
0.4%
Other values (1331) 2270
94.7%
2024-05-11T01:46:32.961778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15801
29.5%
1 9236
17.3%
2 6177
 
11.5%
5578
 
10.4%
T 2401
 
4.5%
C 2397
 
4.5%
N 2393
 
4.5%
3 1654
 
3.1%
9 1392
 
2.6%
6 1386
 
2.6%
Other values (4) 5103
 
9.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40749
76.1%
Uppercase Letter 7191
 
13.4%
Space Separator 5578
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15801
38.8%
1 9236
22.7%
2 6177
 
15.2%
3 1654
 
4.1%
9 1392
 
3.4%
6 1386
 
3.4%
7 1319
 
3.2%
8 1307
 
3.2%
5 1277
 
3.1%
4 1200
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
T 2401
33.4%
C 2397
33.3%
N 2393
33.3%
Space Separator
ValueCountFrequency (%)
5578
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 46327
86.6%
Latin 7191
 
13.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15801
34.1%
1 9236
19.9%
2 6177
 
13.3%
5578
 
12.0%
3 1654
 
3.6%
9 1392
 
3.0%
6 1386
 
3.0%
7 1319
 
2.8%
8 1307
 
2.8%
5 1277
 
2.8%
Latin
ValueCountFrequency (%)
T 2401
33.4%
C 2397
33.3%
N 2393
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53518
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15801
29.5%
1 9236
17.3%
2 6177
 
11.5%
5578
 
10.4%
T 2401
 
4.5%
C 2397
 
4.5%
N 2393
 
4.5%
3 1654
 
3.1%
9 1392
 
2.6%
6 1386
 
2.6%
Other values (4) 5103
 
9.5%

라벨명
Text

Distinct74
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size62.4 KiB
2024-05-11T01:46:33.653272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length9
Mean length9.2599373
Min length1

Characters and Unicode

Total characters73848
Distinct characters80
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)0.6%

Sample

1st row제2종일반주거지역(7층이하)
2nd row제2종일반주거지역(7층이하)
3rd row제2종일반주거지역(7층이하)
4th row제2종일반주거지역(7층이하)
5th row제2종일반주거지역(7층이하)
ValueCountFrequency (%)
제2종일반주거지역 1841
22.4%
제1종일반주거지역 1477
18.0%
제2종일반주거지역(7층이하 1261
15.3%
제3종일반주거지역 1202
14.6%
자연녹지지역 586
 
7.1%
준주거지역 544
 
6.6%
일반상업지역 470
 
5.7%
기타 227
 
2.8%
도시지역 227
 
2.8%
제2종일반주거지역(7층 76
 
0.9%
Other values (63) 310
 
3.8%
2024-05-11T01:46:34.709840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 8575
11.6%
역 7972
10.8%
주 6542
8.9%
거 6541
8.9%
일 6420
8.7%
반 6420
8.7%
종 5987
8.1%
제 5977
8.1%
2 3320
 
4.5%
1 1590
 
2.2%
Other values (70) 14504
19.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 63270
85.7%
Decimal Number 7475
 
10.1%
Open Punctuation 1408
 
1.9%
Close Punctuation 1408
 
1.9%
Space Separator 248
 
0.3%
Other Punctuation 28
 
< 0.1%
Dash Punctuation 10
 
< 0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 8575
13.6%
역 7972
12.6%
주 6542
10.3%
거 6541
10.3%
일 6420
10.1%
반 6420
10.1%
종 5987
9.5%
제 5977
9.4%
층 1408
 
2.2%
이 1294
 
2.0%
Other values (59) 6134
9.7%
Decimal Number
ValueCountFrequency (%)
2 3320
44.4%
1 1590
21.3%
7 1356
18.1%
3 1208
 
16.2%
5 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 1408
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1408
100.0%
Space Separator
ValueCountFrequency (%)
248
100.0%
Other Punctuation
ValueCountFrequency (%)
？ 28
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 63258
85.7%
Common 10578
 
14.3%
Han 12
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 8575
13.6%
역 7972
12.6%
주 6542
10.3%
거 6541
10.3%
일 6420
10.1%
반 6420
10.1%
종 5987
9.5%
제 5977
9.4%
층 1408
 
2.2%
이 1294
 
2.0%
Other values (55) 6122
9.7%
Common
ValueCountFrequency (%)
2 3320
31.4%
1 1590
15.0%
( 1408
13.3%
) 1408
13.3%
7 1356
12.8%
3 1208
 
11.4%
248
 
2.3%
？ 28
 
0.3%
- 10
 
0.1%
5 1
 
< 0.1%
Han
ValueCountFrequency (%)
二 4
33.3%
吏 4
33.3%
醫 3
25.0%
以 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 63258
85.7%
ASCII 10550
 
14.3%
None 28
 
< 0.1%
CJK 8
 
< 0.1%
CJK Compat Ideographs 4
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 8575
13.6%
역 7972
12.6%
주 6542
10.3%
거 6541
10.3%
일 6420
10.1%
반 6420
10.1%
종 5987
9.5%
제 5977
9.4%
층 1408
 
2.2%
이 1294
 
2.0%
Other values (55) 6122
9.7%
ASCII
ValueCountFrequency (%)
2 3320
31.5%
1 1590
15.1%
( 1408
13.3%
) 1408
13.3%
7 1356
12.9%
3 1208
 
11.5%
248
 
2.4%
- 10
 
0.1%
5 1
 
< 0.1%
_ 1
 
< 0.1%
None
ValueCountFrequency (%)
？ 28
100.0%
CJK
ValueCountFrequency (%)
二 4
50.0%
醫 3
37.5%
以 1
 
12.5%
CJK Compat Ideographs
ValueCountFrequency (%)
吏 4
100.0%

시군구코드
Real number (ℝ)

SKEWED 

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11039.242
Minimum11000
Maximum99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2024-05-11T01:46:35.165036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11000
5-th percentile11000
Q111000
median11000
Q311000
95-th percentile11000
Maximum99999
Range88999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1411.8042
Coefficient of variation (CV)0.12788959
Kurtosis3953.9175
Mean11039.242
Median Absolute Deviation (MAD)0
Skewness62.766563
Sum88037958
Variance1993191
MonotonicityNot monotonic
2024-05-11T01:46:35.760021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
11000 7627
95.6%
11590 37
 
0.5%
11560 36
 
0.5%
11170 28
 
0.4%
11380 25
 
0.3%
11230 22
 
0.3%
11110 22
 
0.3%
11440 20
 
0.3%
11140 16
 
0.2%
11620 16
 
0.2%
Other values (17) 126
 
1.6%
ValueCountFrequency (%)
11000 7627
95.6%
11110 22
 
0.3%
11140 16
 
0.2%
11170 28
 
0.4%
11200 12
 
0.2%
11215 15
 
0.2%
11230 22
 
0.3%
11260 5
 
0.1%
11290 15
 
0.2%
11305 10
 
0.1%
ValueCountFrequency (%)
99999 2
 
< 0.1%
11740 1
 
< 0.1%
11710 6
 
0.1%
11680 7
 
0.1%
11650 9
 
0.1%
11620 16
0.2%
11590 37
0.5%
11560 36
0.5%
11545 7
 
0.1%
11530 12
 
0.2%

도면번호
Categorical

IMBALANCE 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size62.4 KiB
7964 
2
 
4
6
 
2
①
 
1
<NA>
 
1
Other values (3)
 
3

Length

Max length4
Median length1
Mean length1.0003762
Min length1

Unique

Unique5 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
7964
99.9%
2 4
 
0.1%
6 2
 
< 0.1%
① 1
 
< 0.1%
<NA> 1
 
< 0.1%
1 1
 
< 0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%

Length

2024-05-11T01:46:36.265309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:46:36.632665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 4
36.4%
6 2
18.2%
① 1
 
9.1%
na 1
 
9.1%
1 1
 
9.1%
3 1
 
9.1%
5 1
 
9.1%

현황도형 생성일시
Date

Distinct381
Distinct (%)4.8%
Missing1
Missing (%)< 0.1%
Memory size62.4 KiB
Minimum1899-12-29 23:27:52
Maximum2024-04-24 00:00:00
2024-05-11T01:46:37.066214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:46:37.739310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

면적(도형)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct7852
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85201.138
Minimum0
Maximum20968960
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2024-05-11T01:46:38.293584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.4207582
Q11395.2277
median10182.636
Q342292.295
95-th percentile262986.43
Maximum20968960
Range20968960
Interquartile range (IQR)40897.068

Descriptive statistics

Standard deviation571030.2
Coefficient of variation (CV)6.7021429
Kurtosis565.53651
Mean85201.138
Median Absolute Deviation (MAD)10063.157
Skewness21.244872
Sum6.7947907 × 108
Variance3.2607549 × 1011
MonotonicityNot monotonic
2024-05-11T01:46:38.869222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
108192.33280801 10
 
0.1%
40462.70964938 8
 
0.1%
80.41972918 5
 
0.1%
1343.79765862 4
 
0.1%
286.864463 4
 
0.1%
127452.38791959 4
 
0.1%
0.0006585 4
 
0.1%
7585.56642794 4
 
0.1%
249.18700122 3
 
< 0.1%
4066.47418999 3
 
< 0.1%
Other values (7842) 7926
99.4%
ValueCountFrequency (%)
0.0 1
 
< 0.1%
8.794e-05 1
 
< 0.1%
0.0006585 4
0.1%
0.00341802 1
 
< 0.1%
0.00359357 1
 
< 0.1%
0.00364012 1
 
< 0.1%
0.0044337 1
 
< 0.1%
0.004558 1
 
< 0.1%
0.0046161 1
 
< 0.1%
0.0047279 1
 
< 0.1%
ValueCountFrequency (%)
20968960.184733 1
< 0.1%
17523067.9555952 1
< 0.1%
16535936.1506327 1
< 0.1%
13697999.1170577 1
< 0.1%
12284179.1735757 1
< 0.1%
10890628.6113886 1
< 0.1%
10510314.5286172 1
< 0.1%
9697471.7484725 1
< 0.1%
9169537.04875847 1
< 0.1%
8790953.12064867 1
< 0.1%

길이(도형)
Real number (ℝ)

HIGH CORRELATION 

Distinct7846
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1464.8789
Minimum0
Maximum136718.04
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2024-05-11T01:46:39.473588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile37.851105
Q1216.19085
median573.73641
Q31367.192
95-th percentile5472.948
Maximum136718.04
Range136718.04
Interquartile range (IQR)1151.0012

Descriptive statistics

Standard deviation3715.7273
Coefficient of variation (CV)2.5365423
Kurtosis327.79919
Mean1464.8789
Median Absolute Deviation (MAD)431.50292
Skewness13.584859
Sum11682409
Variance13806629
MonotonicityNot monotonic
2024-05-11T01:46:39.961706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1609.99212575 10
 
0.1%
1740.40594112 8
 
0.1%
81.65300163 5
 
0.1%
145.24008958 4
 
0.1%
5594.93074492 4
 
0.1%
0.14327141 4
 
0.1%
80.7661672 4
 
0.1%
380.09925477 4
 
0.1%
173.42904194 3
 
< 0.1%
136.02299212 3
 
< 0.1%
Other values (7836) 7926
99.4%
ValueCountFrequency (%)
0.0 1
 
< 0.1%
0.116897 1
 
< 0.1%
0.14327141 4
0.1%
1.26213596 1
 
< 0.1%
1.32463171 1
 
< 0.1%
1.44799569 1
 
< 0.1%
1.49449806 1
 
< 0.1%
1.53601185 1
 
< 0.1%
1.59970724 1
 
< 0.1%
1.81426135 1
 
< 0.1%
ValueCountFrequency (%)
136718.03627726 1
< 0.1%
83567.21487144 1
< 0.1%
81593.90324953 1
< 0.1%
60910.45569796 1
< 0.1%
60702.46359381 1
< 0.1%
55507.04157824 1
< 0.1%
52250.07519019 1
< 0.1%
50456.9303414 1
< 0.1%
45702.14415603 1
< 0.1%
45046.82776143 1
< 0.1%

Interactions

2024-05-11T01:46:19.977410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:46:15.804136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:46:17.740406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:46:18.863665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:46:20.337516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:46:16.503300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:46:18.036577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:46:19.155741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:46:20.607298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:46:16.954244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:46:18.298081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:46:19.428653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:46:20.885302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:46:17.314100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:46:18.520065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:46:19.691115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T01:46:40.347186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
객체id도형 대분류코드도형 중분류코드도형 소분류코드도형 속성코드라벨명시군구코드도면번호면적(도형)길이(도형)
객체id1.0000.0850.1020.1160.1570.1690.2740.0310.0000.000
도형 대분류코드0.0851.0000.9980.7940.9840.9930.0000.0000.1190.144
도형 중분류코드0.1020.9981.0000.8580.9920.9930.0000.0000.1030.127
도형 소분류코드0.1160.7940.8581.0000.9870.9920.0000.0000.0000.125
도형 속성코드0.1570.9840.9920.9871.0000.9990.0000.0000.0900.160
라벨명0.1690.9930.9930.9920.9991.0000.0000.2230.7140.288
시군구코드0.2740.0000.0000.0000.0000.0001.0000.000NaNNaN
도면번호0.0310.0000.0000.0000.0000.2230.0001.0000.0000.000
면적(도형)0.0000.1190.1030.0000.0900.714NaN0.0001.0000.756
길이(도형)0.0000.1440.1270.1250.1600.288NaN0.0000.7561.000
2024-05-11T01:46:40.873792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도형 속성코드도면번호도형 대분류코드도형 소분류코드도형 중분류코드
도형 속성코드1.0000.0000.9240.9040.931
도면번호0.0001.0000.0000.0000.000
도형 대분류코드0.9240.0001.0000.4860.925
도형 소분류코드0.9040.0000.4861.0000.521
도형 중분류코드0.9310.0000.9250.5211.000
2024-05-11T01:46:41.208441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
객체id시군구코드면적(도형)길이(도형)도형 대분류코드도형 중분류코드도형 소분류코드도형 속성코드도면번호
객체id1.000-0.019-0.003-0.0050.0400.0400.0490.0580.016
시군구코드-0.0191.0000.0490.0620.0000.0000.0000.0000.000
면적(도형)-0.0030.0491.0000.9390.0570.0400.0000.0330.000
길이(도형)-0.0050.0620.9391.0000.0480.0450.0530.0660.000
도형 대분류코드0.0400.0000.0570.0481.0000.9250.4860.9240.000
도형 중분류코드0.0400.0000.0400.0450.9251.0000.5210.9310.000
도형 소분류코드0.0490.0000.0000.0530.4860.5211.0000.9040.000
도형 속성코드0.0580.0000.0330.0660.9240.9310.9041.0000.000
도면번호0.0160.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2024-05-11T01:46:21.546593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T01:46:22.081867image/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)
029434311000UQ111PS201912151888UQA100UQA120UQA124UQA12411000ARZ000000001111제2종일반주거지역(7층이하)110002019-12-15 00:00:00.0152954.9006444227.108135
129434411000UQ111PS201912152144UQA100UQA120UQA124UQA12411000ARZ000000001111제2종일반주거지역(7층이하)110002019-12-15 00:00:00.0108067.8620981749.209302
229434511000UQ111PS201912152145UQA100UQA120UQA124UQA12411000ARZ000000001111제2종일반주거지역(7층이하)110002019-12-15 00:00:00.014.684567110.004502
329434611000UQ111PS201912151968UQA100UQA120UQA124UQA12411000ARZ000000001111제2종일반주거지역(7층이하)110002019-12-15 00:00:00.06165.861378322.816516
429434711000UQ111PS201912152024UQA100UQA120UQA124UQA12411000ARZ000000001111제2종일반주거지역(7층이하)110002019-12-15 00:00:00.0457830.7868087774.463035
529434811000UQ111PS201912153565UQA100UQA120UQA122UQA12211000ARZ000000001111제2종일반주거지역110002019-12-15 00:00:00.027587.712678954.538723
629434911000UQ111PS201912152311UQA100UQA120UQA121UQA12111000ARZ000000001111제1종일반주거지역110002019-12-15 00:00:00.0100240.2867274227.558148
729435011000UQ111PS201912154250UQA100UQA120UQA123UQA12311000ARZ000000001111제3종일반주거지역110002019-12-15 00:00:00.0141849.2761075919.309664
829435111000UQ111PS201912155548UQA100UQA120UQA122UQA12211000ARZ20180813003111000NTC201806143176제2종일반주거지역110002019-12-15 00:00:00.0200542.0566344472.758837
929435211000UQ111PS201912155555UQA100UQA120UQA122UQA12211000ARZ000000001111제2종일반주거지역110002019-12-15 00:00:00.0271545.690642899.692749
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