Overview

Dataset statistics

Number of variables17
Number of observations10000
Missing cells20595
Missing cells (%)12.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory154.0 B

Variable types

Numeric10
Text5
Categorical1
Unsupported1

Dataset

Description경기부동산포털_건물_총괄표제부
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=SVTOOYGZR861O3HGNCET34183944&infSeq=1

Alerts

대지면적 is highly overall correlated with 건축면적 and 2 other fieldsHigh correlation
건축면적 is highly overall correlated with 대지면적 and 4 other fieldsHigh correlation
건폐율 is highly overall correlated with 용적율High correlation
연면적 is highly overall correlated with 대지면적 and 3 other fieldsHigh correlation
용적율 is highly overall correlated with 건축면적 and 3 other fieldsHigh correlation
주건축물수 is highly overall correlated with 건축면적High correlation
총주차수 is highly overall correlated with 대지면적 and 3 other fieldsHigh correlation
건물명 has 7992 (79.9%) missing valuesMissing
허가일 has 4242 (42.4%) missing valuesMissing
착공일 has 4514 (45.1%) missing valuesMissing
사용승인일 has 3846 (38.5%) missing valuesMissing
외필지수 is highly skewed (γ1 = 36.84657447)Skewed
대지면적 is highly skewed (γ1 = 27.61926935)Skewed
건축면적 is highly skewed (γ1 = 49.65824274)Skewed
건폐율 is highly skewed (γ1 = 84.37955351)Skewed
연면적 is highly skewed (γ1 = 87.73002134)Skewed
용적율 is highly skewed (γ1 = 97.84102323)Skewed
건물번호 has unique valuesUnique
사용승인일 is an unsupported type, check if it needs cleaning or further analysisUnsupported
외필지수 has 7398 (74.0%) zerosZeros
대지면적 has 1612 (16.1%) zerosZeros
건축면적 has 536 (5.4%) zerosZeros
건폐율 has 1631 (16.3%) zerosZeros
연면적 has 476 (4.8%) zerosZeros
용적율 has 1627 (16.3%) zerosZeros
주건축물수 has 298 (3.0%) zerosZeros
부속건축물수 has 8771 (87.7%) zerosZeros
총주차수 has 3851 (38.5%) zerosZeros

Reproduction

Analysis started2023-12-10 21:51:51.562874
Analysis finished2023-12-10 21:52:02.662084
Duration11.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

토지고유번호
Real number (ℝ)

Distinct9978
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1363581 × 1018
Minimum4.1171101 × 1017
Maximum4.183041 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:52:02.769896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.1171101 × 1017
5-th percentile4.1150102 × 1018
Q14.136025 × 1018
median4.150033 × 1018
Q34.159036 × 1018
95-th percentile4.182033 × 1018
Maximum4.183041 × 1018
Range3.77133 × 1018
Interquartile range (IQR)2.3011005 × 1016

Descriptive statistics

Standard deviation2.084319 × 1017
Coefficient of variation (CV)0.050390197
Kurtosis312.50147
Mean4.1363581 × 1018
Median Absolute Deviation (MAD)1.2997998 × 1016
Skewness-17.658654
Sum5.9805842 × 1018
Variance4.3443856 × 1034
MonotonicityNot monotonic
2023-12-11T06:52:02.918394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
415702562600000000 6
 
0.1%
412202592900000000 5
 
0.1%
4180025321103920001 2
 
< 0.1%
4136026223106830030 2
 
< 0.1%
4113111100101440000 2
 
< 0.1%
4115010500101680011 2
 
< 0.1%
412203102200000000 2
 
< 0.1%
4150035026103270020 2
 
< 0.1%
4113511400102200000 2
 
< 0.1%
415901330000000000 2
 
< 0.1%
Other values (9968) 9973
99.7%
ValueCountFrequency (%)
411711010000000000 1
 
< 0.1%
412201190000000000 1
 
< 0.1%
412201280000000000 1
 
< 0.1%
412202592900000000 5
0.1%
412203100000000000 1
 
< 0.1%
412203102200000000 2
 
< 0.1%
412203102700000000 1
 
< 0.1%
412731060000000000 1
 
< 0.1%
412811310000000000 1
 
< 0.1%
413701210000000000 1
 
< 0.1%
ValueCountFrequency (%)
4183041033103210000 1
< 0.1%
4183041032105960001 1
< 0.1%
4183041032105600004 1
< 0.1%
4183041032105350002 1
< 0.1%
4183041032102940001 1
< 0.1%
4183041032101030005 1
< 0.1%
4183041032100940000 1
< 0.1%
4183041029103910003 1
< 0.1%
4183041029103660000 1
< 0.1%
4183041029102450001 1
< 0.1%

건물번호
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T06:52:03.113181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length15
Mean length13.3409
Min length7

Characters and Unicode

Total characters133409
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st row41650-100212623
2nd row41210-100178750
3rd row41630-1000000000000001208254
4th row41550-100298656
5th row41610-721
ValueCountFrequency (%)
41650-100212623 1
 
< 0.1%
41590-100248554 1
 
< 0.1%
41360-100279116 1
 
< 0.1%
41220-2306 1
 
< 0.1%
41220-100410930 1
 
< 0.1%
41271-210 1
 
< 0.1%
41220-100190526 1
 
< 0.1%
41111-362 1
 
< 0.1%
41590-100266574 1
 
< 0.1%
41500-1716 1
 
< 0.1%
Other values (9990) 9990
99.9%
2023-12-11T06:52:03.385725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 30294
22.7%
1 25627
19.2%
4 15915
11.9%
2 10367
 
7.8%
- 10000
 
7.5%
5 8997
 
6.7%
3 8516
 
6.4%
6 7086
 
5.3%
7 6052
 
4.5%
8 5648
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 123409
92.5%
Dash Punctuation 10000
 
7.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30294
24.5%
1 25627
20.8%
4 15915
12.9%
2 10367
 
8.4%
5 8997
 
7.3%
3 8516
 
6.9%
6 7086
 
5.7%
7 6052
 
4.9%
8 5648
 
4.6%
9 4907
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 133409
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30294
22.7%
1 25627
19.2%
4 15915
11.9%
2 10367
 
7.8%
- 10000
 
7.5%
5 8997
 
6.7%
3 8516
 
6.4%
6 7086
 
5.3%
7 6052
 
4.5%
8 5648
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 133409
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30294
22.7%
1 25627
19.2%
4 15915
11.9%
2 10367
 
7.8%
- 10000
 
7.5%
5 8997
 
6.7%
3 8516
 
6.4%
6 7086
 
5.3%
7 6052
 
4.5%
8 5648
 
4.2%
Distinct9977
Distinct (%)99.8%
Missing1
Missing (%)< 0.1%
Memory size156.2 KiB
2023-12-11T06:52:03.673453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length16.265927
Min length8

Characters and Unicode

Total characters162643
Distinct characters309
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9962 ?
Unique (%)99.6%

Sample

1st row포천시 신북면 덕둔리 277
2nd row광명시 소하동 1289
3rd row양주시 옥정동 1097
4th row안성시 발화동 193-1
5th row광주시 초월읍 지월리 127-1
ValueCountFrequency (%)
김포시 1166
 
3.2%
남양주시 996
 
2.7%
평택시 818
 
2.2%
이천시 762
 
2.1%
안성시 750
 
2.0%
용인시처인구 717
 
1.9%
광주시 528
 
1.4%
양주시 493
 
1.3%
화성시 405
 
1.1%
가평군 384
 
1.0%
Other values (7921) 29809
80.9%
2023-12-11T06:52:04.070212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
30175
 
18.6%
8986
 
5.5%
- 7198
 
4.4%
1 6973
 
4.3%
6848
 
4.2%
2 5056
 
3.1%
3 4468
 
2.7%
4 4047
 
2.5%
4040
 
2.5%
3832
 
2.4%
Other values (299) 81020
49.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 87337
53.7%
Decimal Number 37931
23.3%
Space Separator 30175
 
18.6%
Dash Punctuation 7198
 
4.4%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
8986
 
10.3%
6848
 
7.8%
4040
 
4.6%
3832
 
4.4%
3290
 
3.8%
2805
 
3.2%
2074
 
2.4%
2007
 
2.3%
2004
 
2.3%
1899
 
2.2%
Other values (285) 49552
56.7%
Decimal Number
ValueCountFrequency (%)
1 6973
18.4%
2 5056
13.3%
3 4468
11.8%
4 4047
10.7%
5 3604
9.5%
6 3293
8.7%
7 2975
7.8%
8 2635
 
6.9%
9 2482
 
6.5%
0 2398
 
6.3%
Space Separator
ValueCountFrequency (%)
30175
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7198
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 87337
53.7%
Common 75306
46.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
8986
 
10.3%
6848
 
7.8%
4040
 
4.6%
3832
 
4.4%
3290
 
3.8%
2805
 
3.2%
2074
 
2.4%
2007
 
2.3%
2004
 
2.3%
1899
 
2.2%
Other values (285) 49552
56.7%
Common
ValueCountFrequency (%)
30175
40.1%
- 7198
 
9.6%
1 6973
 
9.3%
2 5056
 
6.7%
3 4468
 
5.9%
4 4047
 
5.4%
5 3604
 
4.8%
6 3293
 
4.4%
7 2975
 
4.0%
8 2635
 
3.5%
Other values (4) 4882
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 87337
53.7%
ASCII 75306
46.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
30175
40.1%
- 7198
 
9.6%
1 6973
 
9.3%
2 5056
 
6.7%
3 4468
 
5.9%
4 4047
 
5.4%
5 3604
 
4.8%
6 3293
 
4.4%
7 2975
 
4.0%
8 2635
 
3.5%
Other values (4) 4882
 
6.5%
Hangul
ValueCountFrequency (%)
8986
 
10.3%
6848
 
7.8%
4040
 
4.6%
3832
 
4.4%
3290
 
3.8%
2805
 
3.2%
2074
 
2.4%
2007
 
2.3%
2004
 
2.3%
1899
 
2.2%
Other values (285) 49552
56.7%

건물명
Text

MISSING 

Distinct1860
Distinct (%)92.6%
Missing7992
Missing (%)79.9%
Memory size156.2 KiB
2023-12-11T06:52:04.311719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length32
Mean length9.8027888
Min length1

Characters and Unicode

Total characters19684
Distinct characters611
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1791 ?
Unique (%)89.2%

Sample

1st row휴먼시아
2nd row양주옥정 유림노르웨이숲
3rd row이목동 242-7 단독주택 (지숙영)
4th row양서면 복포리 의료시설 (의료법인지연의료재단한길요양병원)
5th row김포시장애인복지관
ValueCountFrequency (%)
공장 85
 
2.4%
동.식물관련시설 50
 
1.4%
제2종근린생활시설 49
 
1.4%
단독주택 42
 
1.2%
제1종근린생활시설 27
 
0.7%
24
 
0.7%
가동,나동 17
 
0.5%
용문면 17
 
0.5%
고덕면 14
 
0.4%
1단지 14
 
0.4%
Other values (2656) 3269
90.6%
2023-12-11T06:52:04.688247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1606
 
8.2%
) 470
 
2.4%
( 468
 
2.4%
467
 
2.4%
416
 
2.1%
401
 
2.0%
1 345
 
1.8%
2 306
 
1.6%
291
 
1.5%
278
 
1.4%
Other values (601) 14636
74.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14723
74.8%
Space Separator 1606
 
8.2%
Decimal Number 1575
 
8.0%
Close Punctuation 471
 
2.4%
Open Punctuation 469
 
2.4%
Uppercase Letter 317
 
1.6%
Other Punctuation 245
 
1.2%
Dash Punctuation 240
 
1.2%
Lowercase Letter 36
 
0.2%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
467
 
3.2%
416
 
2.8%
401
 
2.7%
291
 
2.0%
278
 
1.9%
257
 
1.7%
256
 
1.7%
247
 
1.7%
226
 
1.5%
225
 
1.5%
Other values (541) 11659
79.2%
Uppercase Letter
ValueCountFrequency (%)
S 30
 
9.5%
A 28
 
8.8%
C 26
 
8.2%
B 23
 
7.3%
L 20
 
6.3%
E 20
 
6.3%
I 19
 
6.0%
H 17
 
5.4%
T 16
 
5.0%
G 16
 
5.0%
Other values (13) 102
32.2%
Lowercase Letter
ValueCountFrequency (%)
e 11
30.6%
c 3
 
8.3%
a 3
 
8.3%
t 3
 
8.3%
i 3
 
8.3%
h 2
 
5.6%
w 2
 
5.6%
o 2
 
5.6%
u 1
 
2.8%
s 1
 
2.8%
Other values (5) 5
13.9%
Decimal Number
ValueCountFrequency (%)
1 345
21.9%
2 306
19.4%
3 195
12.4%
0 133
 
8.4%
4 123
 
7.8%
5 122
 
7.7%
6 121
 
7.7%
9 83
 
5.3%
7 77
 
4.9%
8 70
 
4.4%
Other Punctuation
ValueCountFrequency (%)
, 147
60.0%
. 88
35.9%
& 5
 
2.0%
/ 4
 
1.6%
' 1
 
0.4%
Close Punctuation
ValueCountFrequency (%)
) 470
99.8%
] 1
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 468
99.8%
[ 1
 
0.2%
Space Separator
ValueCountFrequency (%)
1606
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 240
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14721
74.8%
Common 4608
 
23.4%
Latin 353
 
1.8%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
467
 
3.2%
416
 
2.8%
401
 
2.7%
291
 
2.0%
278
 
1.9%
257
 
1.7%
256
 
1.7%
247
 
1.7%
226
 
1.5%
225
 
1.5%
Other values (539) 11657
79.2%
Latin
ValueCountFrequency (%)
S 30
 
8.5%
A 28
 
7.9%
C 26
 
7.4%
B 23
 
6.5%
L 20
 
5.7%
E 20
 
5.7%
I 19
 
5.4%
H 17
 
4.8%
T 16
 
4.5%
G 16
 
4.5%
Other values (28) 138
39.1%
Common
ValueCountFrequency (%)
1606
34.9%
) 470
 
10.2%
( 468
 
10.2%
1 345
 
7.5%
2 306
 
6.6%
- 240
 
5.2%
3 195
 
4.2%
, 147
 
3.2%
0 133
 
2.9%
4 123
 
2.7%
Other values (12) 575
 
12.5%
Han
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14721
74.8%
ASCII 4961
 
25.2%
CJK 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1606
32.4%
) 470
 
9.5%
( 468
 
9.4%
1 345
 
7.0%
2 306
 
6.2%
- 240
 
4.8%
3 195
 
3.9%
, 147
 
3.0%
0 133
 
2.7%
4 123
 
2.5%
Other values (50) 928
18.7%
Hangul
ValueCountFrequency (%)
467
 
3.2%
416
 
2.8%
401
 
2.7%
291
 
2.0%
278
 
1.9%
257
 
1.7%
256
 
1.7%
247
 
1.7%
226
 
1.5%
225
 
1.5%
Other values (539) 11657
79.2%
CJK
ValueCountFrequency (%)
1
50.0%
1
50.0%

외필지수
Real number (ℝ)

SKEWED  ZEROS 

Distinct37
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6676
Minimum0
Maximum249
Zeros7398
Zeros (%)74.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:52:04.797383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum249
Range249
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.7938041
Coefficient of variation (CV)5.6827503
Kurtosis2024.9627
Mean0.6676
Median Absolute Deviation (MAD)0
Skewness36.846574
Sum6676
Variance14.39295
MonotonicityNot monotonic
2023-12-11T06:52:04.909497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 7398
74.0%
1 1437
 
14.4%
2 558
 
5.6%
3 258
 
2.6%
4 113
 
1.1%
5 76
 
0.8%
6 37
 
0.4%
7 25
 
0.2%
8 23
 
0.2%
9 13
 
0.1%
Other values (27) 62
 
0.6%
ValueCountFrequency (%)
0 7398
74.0%
1 1437
 
14.4%
2 558
 
5.6%
3 258
 
2.6%
4 113
 
1.1%
5 76
 
0.8%
6 37
 
0.4%
7 25
 
0.2%
8 23
 
0.2%
9 13
 
0.1%
ValueCountFrequency (%)
249 1
< 0.1%
112 1
< 0.1%
98 1
< 0.1%
81 1
< 0.1%
75 1
< 0.1%
69 2
< 0.1%
52 1
< 0.1%
51 1
< 0.1%
48 2
< 0.1%
45 1
< 0.1%

대지면적
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct4873
Distinct (%)48.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5578.2693
Minimum0
Maximum1580952
Zeros1612
Zeros (%)16.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:52:05.013595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1635.3
median1381.5
Q33269.25
95-th percentile19542.25
Maximum1580952
Range1580952
Interquartile range (IQR)2633.95

Descriptive statistics

Standard deviation29646.721
Coefficient of variation (CV)5.3146808
Kurtosis1121.3646
Mean5578.2693
Median Absolute Deviation (MAD)1078.5
Skewness27.619269
Sum55782693
Variance8.7892806 × 108
MonotonicityNot monotonic
2023-12-11T06:52:05.128669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1612
 
16.1%
660.0 85
 
0.9%
990.0 70
 
0.7%
998.0 59
 
0.6%
992.0 30
 
0.3%
980.0 28
 
0.3%
995.0 27
 
0.3%
1653.0 24
 
0.2%
988.0 24
 
0.2%
1322.0 20
 
0.2%
Other values (4863) 8021
80.2%
ValueCountFrequency (%)
0.0 1612
16.1%
1.523 1
 
< 0.1%
10.323 1
 
< 0.1%
102.0 1
 
< 0.1%
102.7 1
 
< 0.1%
109.1 1
 
< 0.1%
120.75 1
 
< 0.1%
125.0 1
 
< 0.1%
136.0 1
 
< 0.1%
137.0 1
 
< 0.1%
ValueCountFrequency (%)
1580952.0 1
< 0.1%
1121127.6 1
< 0.1%
696993.0 1
< 0.1%
676347.0 1
< 0.1%
647766.1 1
< 0.1%
516875.0 1
< 0.1%
479758.0 1
< 0.1%
444734.0 1
< 0.1%
430135.0 1
< 0.1%
420647.0 1
< 0.1%

건축면적
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct8175
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1240.5073
Minimum0
Maximum414707.2
Zeros536
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:52:05.241076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1211.8785
median449.13
Q3990
95-th percentile4404.7453
Maximum414707.2
Range414707.2
Interquartile range (IQR)778.1215

Descriptive statistics

Standard deviation5369.6867
Coefficient of variation (CV)4.3286216
Kurtosis3577.6236
Mean1240.5073
Median Absolute Deviation (MAD)286.215
Skewness49.658243
Sum12405073
Variance28833535
MonotonicityNot monotonic
2023-12-11T06:52:05.344497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 536
 
5.4%
396.0 164
 
1.6%
495.0 49
 
0.5%
499.2 47
 
0.5%
594.0 30
 
0.3%
990.0 22
 
0.2%
330.0 22
 
0.2%
492.0 19
 
0.2%
792.0 18
 
0.2%
494.0 17
 
0.2%
Other values (8165) 9076
90.8%
ValueCountFrequency (%)
0.0 536
5.4%
9.18 1
 
< 0.1%
14.0 1
 
< 0.1%
20.6 1
 
< 0.1%
21.6 1
 
< 0.1%
23.91 1
 
< 0.1%
24.0 1
 
< 0.1%
25.9 1
 
< 0.1%
26.5 1
 
< 0.1%
30.0 1
 
< 0.1%
ValueCountFrequency (%)
414707.202 1
< 0.1%
121749.5 1
< 0.1%
103782.93 1
< 0.1%
88137.92 1
< 0.1%
80096.98 1
< 0.1%
76391.19 1
< 0.1%
65412.77 1
< 0.1%
63801.108 1
< 0.1%
56103.03 1
< 0.1%
52197.923 1
< 0.1%

건폐율
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct4268
Distinct (%)42.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.938825
Minimum0
Maximum5356
Zeros1631
Zeros (%)16.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:52:05.451317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115.2875
median28.34
Q339.55
95-th percentile56.9505
Maximum5356
Range5356
Interquartile range (IQR)24.2625

Descriptive statistics

Standard deviation56.392161
Coefficient of variation (CV)2.0184156
Kurtosis7971.5997
Mean27.938825
Median Absolute Deviation (MAD)11.48
Skewness84.379554
Sum279388.25
Variance3180.0758
MonotonicityNot monotonic
2023-12-11T06:52:05.557288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1631
 
16.3%
50.0 42
 
0.4%
19.98 38
 
0.4%
19.95 31
 
0.3%
39.98 25
 
0.2%
50.02 23
 
0.2%
19.96 23
 
0.2%
39.94 22
 
0.2%
39.92 21
 
0.2%
19.94 21
 
0.2%
Other values (4258) 8123
81.2%
ValueCountFrequency (%)
0.0 1631
16.3%
0.0256596 1
 
< 0.1%
0.085 1
 
< 0.1%
0.13 1
 
< 0.1%
0.16 1
 
< 0.1%
0.1607717 1
 
< 0.1%
0.21 1
 
< 0.1%
0.22 1
 
< 0.1%
0.2503 1
 
< 0.1%
0.2772 1
 
< 0.1%
ValueCountFrequency (%)
5356.0 1
< 0.1%
237.78 1
< 0.1%
168.95 1
< 0.1%
151.36 1
< 0.1%
146.5 1
< 0.1%
132.73 1
< 0.1%
120.02 1
< 0.1%
110.47 1
< 0.1%
100.0 1
< 0.1%
98.8 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct8347
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6407.3338
Minimum0
Maximum10535170
Zeros476
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:52:05.665437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38.4385
Q1265.985
median543.68
Q31356.5525
95-th percentile17382.389
Maximum10535170
Range10535170
Interquartile range (IQR)1090.5675

Descriptive statistics

Standard deviation110397.17
Coefficient of variation (CV)17.229814
Kurtosis8292.6753
Mean6407.3338
Median Absolute Deviation (MAD)378.185
Skewness87.730021
Sum64073338
Variance1.2187534 × 1010
MonotonicityNot monotonic
2023-12-11T06:52:06.017144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 476
 
4.8%
396.0 158
 
1.6%
495.0 52
 
0.5%
499.2 48
 
0.5%
594.0 30
 
0.3%
792.0 23
 
0.2%
990.0 23
 
0.2%
492.0 19
 
0.2%
330.0 18
 
0.2%
494.0 17
 
0.2%
Other values (8337) 9136
91.4%
ValueCountFrequency (%)
0.0 476
4.8%
9.18 1
 
< 0.1%
14.0 1
 
< 0.1%
19.53 1
 
< 0.1%
20.6 1
 
< 0.1%
21.6 1
 
< 0.1%
24.0 1
 
< 0.1%
25.9 1
 
< 0.1%
26.5 1
 
< 0.1%
29.75 1
 
< 0.1%
ValueCountFrequency (%)
10535170.0 1
< 0.1%
2179941.0 1
< 0.1%
1214012.133 1
< 0.1%
443448.4199 1
< 0.1%
429279.37 1
< 0.1%
330955.867 1
< 0.1%
302545.4766 1
< 0.1%
299842.81 1
< 0.1%
288393.2 1
< 0.1%
283612.7255 1
< 0.1%

용적율
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct5474
Distinct (%)54.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.029844
Minimum0
Maximum45090
Zeros1627
Zeros (%)16.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:52:06.132133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117.68
median35.52
Q350.928525
95-th percentile174.9118
Maximum45090
Range45090
Interquartile range (IQR)33.248525

Descriptive statistics

Standard deviation453.73144
Coefficient of variation (CV)8.8914918
Kurtosis9712.4404
Mean51.029844
Median Absolute Deviation (MAD)16.91
Skewness97.841023
Sum510298.44
Variance205872.22
MonotonicityNot monotonic
2023-12-11T06:52:06.238022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1627
 
16.3%
50.0 42
 
0.4%
50.02 24
 
0.2%
39.92 17
 
0.2%
39.94 16
 
0.2%
50.04 14
 
0.1%
39.87 13
 
0.1%
50.1 11
 
0.1%
39.98 11
 
0.1%
39.89 11
 
0.1%
Other values (5464) 8214
82.1%
ValueCountFrequency (%)
0.0 1627
16.3%
0.0256596 1
 
< 0.1%
0.085 1
 
< 0.1%
0.13 1
 
< 0.1%
0.16 1
 
< 0.1%
0.1607717 1
 
< 0.1%
0.21 1
 
< 0.1%
0.2503 1
 
< 0.1%
0.2772 1
 
< 0.1%
0.2906929 1
 
< 0.1%
ValueCountFrequency (%)
45090.0 1
< 0.1%
738.59 1
< 0.1%
699.06 1
< 0.1%
679.59 1
< 0.1%
570.45 1
< 0.1%
538.52 1
< 0.1%
503.98 1
< 0.1%
496.93 1
< 0.1%
449.74 1
< 0.1%
448.09 1
< 0.1%

주용도코드
Categorical

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
17000
1943 
01000
1883 
04000
1562 
21000
1208 
02000
1040 
Other values (33)
2364 

Length

Max length5
Median length5
Mean length4.9823
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row01000
2nd row02000
3rd row02000
4th row11000
5th row21000

Common Values

ValueCountFrequency (%)
17000 1943
19.4%
01000 1883
18.8%
04000 1562
15.6%
21000 1208
12.1%
02000 1040
10.4%
03000 771
 
7.7%
18000 460
 
4.6%
<NA> 177
 
1.8%
10000 157
 
1.6%
20000 115
 
1.1%
Other values (28) 684
 
6.8%

Length

2023-12-11T06:52:06.337403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
17000 1943
19.4%
01000 1883
18.8%
04000 1562
15.6%
21000 1208
12.1%
02000 1040
10.4%
03000 771
 
7.7%
18000 460
 
4.6%
na 177
 
1.8%
10000 157
 
1.6%
20000 115
 
1.1%
Other values (28) 684
 
6.8%

주건축물수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1274
Minimum0
Maximum173
Zeros298
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:52:06.433461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median2
Q33
95-th percentile8
Maximum173
Range173
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4.2511147
Coefficient of variation (CV)1.3593128
Kurtosis475.0162
Mean3.1274
Median Absolute Deviation (MAD)0
Skewness16.442609
Sum31274
Variance18.071976
MonotonicityNot monotonic
2023-12-11T06:52:06.545421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 5968
59.7%
3 1779
 
17.8%
4 711
 
7.1%
5 318
 
3.2%
0 298
 
3.0%
6 201
 
2.0%
1 115
 
1.1%
7 107
 
1.1%
8 84
 
0.8%
10 67
 
0.7%
Other values (41) 352
 
3.5%
ValueCountFrequency (%)
0 298
 
3.0%
1 115
 
1.1%
2 5968
59.7%
3 1779
 
17.8%
4 711
 
7.1%
5 318
 
3.2%
6 201
 
2.0%
7 107
 
1.1%
8 84
 
0.8%
9 65
 
0.7%
ValueCountFrequency (%)
173 1
< 0.1%
145 1
< 0.1%
111 1
< 0.1%
89 1
< 0.1%
71 2
< 0.1%
62 1
< 0.1%
61 1
< 0.1%
59 1
< 0.1%
57 1
< 0.1%
56 1
< 0.1%

부속건축물수
Real number (ℝ)

ZEROS 

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4368
Minimum0
Maximum47
Zeros8771
Zeros (%)87.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:52:06.644323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum47
Range47
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.9307974
Coefficient of variation (CV)4.4203237
Kurtosis80.598812
Mean0.4368
Median Absolute Deviation (MAD)0
Skewness7.4576721
Sum4368
Variance3.7279786
MonotonicityNot monotonic
2023-12-11T06:52:06.729828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 8771
87.7%
1 641
 
6.4%
2 153
 
1.5%
3 87
 
0.9%
4 50
 
0.5%
5 44
 
0.4%
10 36
 
0.4%
6 34
 
0.3%
8 32
 
0.3%
7 27
 
0.3%
Other values (17) 125
 
1.2%
ValueCountFrequency (%)
0 8771
87.7%
1 641
 
6.4%
2 153
 
1.5%
3 87
 
0.9%
4 50
 
0.5%
5 44
 
0.4%
6 34
 
0.3%
7 27
 
0.3%
8 32
 
0.3%
9 22
 
0.2%
ValueCountFrequency (%)
47 1
 
< 0.1%
28 1
 
< 0.1%
25 2
 
< 0.1%
24 2
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 6
0.1%
19 2
 
< 0.1%
18 2
 
< 0.1%
17 6
0.1%

총주차수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct472
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.3903
Minimum0
Maximum11090
Zeros3851
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:52:06.828297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile78.05
Maximum11090
Range11090
Interquartile range (IQR)6

Descriptive statistics

Standard deviation215.90596
Coefficient of variation (CV)5.9330634
Kurtosis748.54776
Mean36.3903
Median Absolute Deviation (MAD)2
Skewness19.262822
Sum363903
Variance46615.382
MonotonicityNot monotonic
2023-12-11T06:52:06.935874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3851
38.5%
2 1002
 
10.0%
3 880
 
8.8%
4 649
 
6.5%
1 490
 
4.9%
5 448
 
4.5%
6 305
 
3.0%
7 228
 
2.3%
8 220
 
2.2%
9 151
 
1.5%
Other values (462) 1776
17.8%
ValueCountFrequency (%)
0 3851
38.5%
1 490
 
4.9%
2 1002
 
10.0%
3 880
 
8.8%
4 649
 
6.5%
5 448
 
4.5%
6 305
 
3.0%
7 228
 
2.3%
8 220
 
2.2%
9 151
 
1.5%
ValueCountFrequency (%)
11090 1
< 0.1%
4242 1
< 0.1%
3564 1
< 0.1%
3558 1
< 0.1%
3329 1
< 0.1%
2790 1
< 0.1%
2615 1
< 0.1%
2594 1
< 0.1%
2510 1
< 0.1%
2364 1
< 0.1%

허가일
Text

MISSING 

Distinct3560
Distinct (%)61.8%
Missing4242
Missing (%)42.4%
Memory size156.2 KiB
2023-12-11T06:52:07.152426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.9980896
Min length4

Characters and Unicode

Total characters46053
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2120 ?
Unique (%)36.8%

Sample

1st row20130218
2nd row20041004
3rd row20191023
4th row20190118
5th row20080626
ValueCountFrequency (%)
20060711 22
 
0.4%
20091222 11
 
0.2%
20060710 9
 
0.2%
20160912 7
 
0.1%
20080513 6
 
0.1%
20180330 6
 
0.1%
20130528 6
 
0.1%
20140428 6
 
0.1%
20071024 6
 
0.1%
20051230 6
 
0.1%
Other values (3554) 5677
98.5%
2023-12-11T06:52:07.485920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 14696
31.9%
2 10212
22.2%
1 8870
19.3%
9 1965
 
4.3%
3 1837
 
4.0%
7 1816
 
3.9%
8 1746
 
3.8%
6 1706
 
3.7%
5 1614
 
3.5%
4 1586
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46048
> 99.9%
Space Separator 5
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14696
31.9%
2 10212
22.2%
1 8870
19.3%
9 1965
 
4.3%
3 1837
 
4.0%
7 1816
 
3.9%
8 1746
 
3.8%
6 1706
 
3.7%
5 1614
 
3.5%
4 1586
 
3.4%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 46053
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14696
31.9%
2 10212
22.2%
1 8870
19.3%
9 1965
 
4.3%
3 1837
 
4.0%
7 1816
 
3.9%
8 1746
 
3.8%
6 1706
 
3.7%
5 1614
 
3.5%
4 1586
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46053
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14696
31.9%
2 10212
22.2%
1 8870
19.3%
9 1965
 
4.3%
3 1837
 
4.0%
7 1816
 
3.9%
8 1746
 
3.8%
6 1706
 
3.7%
5 1614
 
3.5%
4 1586
 
3.4%

착공일
Text

MISSING 

Distinct3537
Distinct (%)64.5%
Missing4514
Missing (%)45.1%
Memory size156.2 KiB
2023-12-11T06:52:07.771636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.9963544
Min length4

Characters and Unicode

Total characters43868
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2226 ?
Unique (%)40.6%

Sample

1st row20130227
2nd row20070531
3rd row20200103
4th row20190402
5th row20021109
ValueCountFrequency (%)
20160715 8
 
0.1%
20080901 8
 
0.1%
20150417 7
 
0.1%
20140520 6
 
0.1%
20161101 6
 
0.1%
20070531 6
 
0.1%
20131216 6
 
0.1%
20181119 6
 
0.1%
20160615 5
 
0.1%
20190520 5
 
0.1%
Other values (3532) 5428
98.9%
2023-12-11T06:52:08.170970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 14260
32.5%
2 9596
21.9%
1 8386
19.1%
3 1844
 
4.2%
9 1747
 
4.0%
5 1681
 
3.8%
6 1615
 
3.7%
8 1611
 
3.7%
7 1609
 
3.7%
4 1512
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43861
> 99.9%
Space Separator 7
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14260
32.5%
2 9596
21.9%
1 8386
19.1%
3 1844
 
4.2%
9 1747
 
4.0%
5 1681
 
3.8%
6 1615
 
3.7%
8 1611
 
3.7%
7 1609
 
3.7%
4 1512
 
3.4%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 43868
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14260
32.5%
2 9596
21.9%
1 8386
19.1%
3 1844
 
4.2%
9 1747
 
4.0%
5 1681
 
3.8%
6 1615
 
3.7%
8 1611
 
3.7%
7 1609
 
3.7%
4 1512
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43868
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14260
32.5%
2 9596
21.9%
1 8386
19.1%
3 1844
 
4.2%
9 1747
 
4.0%
5 1681
 
3.8%
6 1615
 
3.7%
8 1611
 
3.7%
7 1609
 
3.7%
4 1512
 
3.4%

사용승인일
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3846
Missing (%)38.5%
Memory size156.2 KiB

Interactions

2023-12-11T06:52:01.263912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:54.275581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:55.035383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:55.744065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:56.717932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:57.475872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:58.231144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:59.032512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:59.751642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:52:00.541700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:52:01.538696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:54.355504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:55.115125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:55.829551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:56.798397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:57.556445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:58.310635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:59.111092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:59.830292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:52:00.637862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:52:01.608394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:54.427997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:55.183341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:55.903085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:56.872572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:57.625643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:58.394099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:59.184559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:59.905005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:52:00.710813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:52:01.690117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:54.513649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:55.261014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:55.986153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:56.953524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:57.707862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:58.471046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:59.267672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:52:00.022538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:52:00.792688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:52:01.757418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:54.585511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:55.325230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:56.061728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:57.024625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:57.773639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:58.539486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:59.341647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:52:00.096400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:52:00.858770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:52:01.830196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:54.659637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:55.393490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:56.135333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:57.120928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:57.847003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:58.630694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:59.410067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:52:00.175937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:52:00.924849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:52:01.900100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:54.737014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:55.462525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:56.232027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:57.197644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:57.941501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:58.732780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:59.480812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:52:00.249461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:52:00.993485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:52:01.971006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:54.806762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:55.530499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:56.310821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:57.264256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:58.016816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:58.801283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:59.545953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:52:00.319876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:52:01.060353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:52:02.056816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:54.887114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:55.605494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:56.569330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:57.341415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:58.091435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:58.878660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:59.619602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:52:00.397946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:52:01.132906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:52:02.142862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:54.957954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:55.676375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:56.641873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:57.407941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:58.157907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:58.959200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:51:59.684829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:52:00.468400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:52:01.197095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:52:08.258190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
토지고유번호외필지수대지면적건축면적건폐율연면적용적율주용도코드주건축물수부속건축물수총주차수
토지고유번호1.0000.0000.0000.0000.0000.0000.0000.0520.0000.1300.044
외필지수0.0001.0000.6220.3290.0000.0000.0000.5430.7400.1130.356
대지면적0.0000.6221.0000.5200.0000.2200.0000.4020.6690.2960.467
건축면적0.0000.3290.5201.0000.0000.5080.0000.1260.3400.3240.406
건폐율0.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.000
연면적0.0000.0000.2200.5080.0001.0000.0000.0000.3550.7080.647
용적율0.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.000
주용도코드0.0520.5430.4020.1260.0000.0000.0001.0000.6660.3990.303
주건축물수0.0000.7400.6690.3400.0000.3550.0000.6661.0000.3630.515
부속건축물수0.1300.1130.2960.3240.0000.7080.0000.3990.3631.0000.730
총주차수0.0440.3560.4670.4060.0000.6470.0000.3030.5150.7301.000
2023-12-11T06:52:08.367165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
토지고유번호외필지수대지면적건축면적건폐율연면적용적율주건축물수부속건축물수총주차수주용도코드
토지고유번호1.0000.0920.043-0.054-0.073-0.121-0.1540.018-0.040-0.0490.046
외필지수0.0921.0000.2910.2300.0820.2140.0650.1560.0200.0960.269
대지면적0.0430.2911.0000.8350.2340.7620.3850.4800.3170.5660.177
건축면적-0.0540.2300.8351.0000.4000.9240.5060.5200.2980.5320.065
건폐율-0.0730.0820.2340.4001.0000.3960.8130.023-0.0410.3030.000
연면적-0.1210.2140.7620.9240.3961.0000.6090.4700.3030.6130.000
용적율-0.1540.0650.3850.5060.8130.6091.0000.1400.2000.5440.000
주건축물수0.0180.1560.4800.5200.0230.4700.1401.0000.2430.2420.311
부속건축물수-0.0400.0200.3170.298-0.0410.3030.2000.2431.0000.2360.175
총주차수-0.0490.0960.5660.5320.3030.6130.5440.2420.2361.0000.147
주용도코드0.0460.2690.1770.0650.0000.0000.0000.3110.1750.1471.000

Missing values

2023-12-11T06:52:02.283231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:52:02.458133image/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.
2023-12-11T06:52:02.589715image/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

토지고유번호건물번호토지소재지건물명외필지수대지면적건축면적건폐율연면적용적율주용도코드주건축물수부속건축물수총주차수허가일착공일사용승인일
78444416503403210277000041650-100212623포천시 신북면 덕둔리 277<NA>01049.0193.6518.46248.8523.7201000203201302182013022720141218
79412101040011289000041210-100178750광명시 소하동 1289휴먼시아028785.04743.10116.4854577.692158.810200096463200410042007053120091218.0
51460416301140011097000041630-1000000000000001208254양주시 옥정동 1097양주옥정 유림노르웨이숲063788.19051.203414.19190023.6344199.950200020171565201910232020010320230126
25892415501320010193000141550-100298656안성시 발화동 193-1<NA>03560.0704.0219.78805.8122.6411000205201901182019040220190906.0
41332416102532910127000141610-721광주시 초월읍 지월리 127-1<NA>12941.0774.9426.35774.9426.3521000500<NA>2002110920030705
62519418303802710155000741830-100183536양평군 양동면 삼산리 155-7<NA>0600.0128.6621.44128.6621.4421000200200806262008070320081030
1674415003603010499000741500-100175621이천시 모가면 어농리 499-7<NA>21352.0293.7321.73288.0321.301000202200805272008053020080908.0
43879415702562610728000041570-100259286김포시 양촌읍 학운리 728<NA>01000.0396.039.6396.039.60400020320120430<NA>20130214
73256415902622210174000141590-100258217화성시 남양읍 신남리 174-1<NA>01562.0289.118.51349.922.401000303200807112008090220091210
75273415903603110007003541590-100187263화성시 팔탄면 서근리 7-35<NA>11751.0686.2839.19874.8849.9617000204200607092007073120080121
토지고유번호건물번호토지소재지건물명외필지수대지면적건축면적건폐율연면적용적율주용도코드주건축물수부속건축물수총주차수허가일착공일사용승인일
46104414612562611286000641461-100471014용인시처인구 이동읍 덕성리 1286-6서해전설02855.81342.6547.011693.9559.3117000208202011262020122220210907
7508418203302310237000541820-100192982가평군 상면 행현리 237-5<NA>01350.0206.3515.29206.3515.2904000202201011012011012420110715.0
80800414651050010650000141465-100408381용인시수지구 신봉동 650-1디에스알앤디34318.01072.1524.831921.7544.51040002147201710112017111120180919
62135413601040010561001341360-866남양주시 일패동 561-13<NA>02314.0998.443.15998.443.1521000400200108202003053120031125
75278412811190010278005241281-100459447고양시덕양구 내유동 278-52라피네 103동, 104동01025.0357.4434.871234.1699.76020002016201707262019072020220622
49051414611030010296000241461-1361용인시처인구 삼가동 296-2<NA>0692.0257.437.21233.18148.7902000200<NA><NA>NaN
16629418002532110347000041800-736연천군 전곡읍 전곡리 347<NA>00.088.040.088.040.001000300<NA><NA>NaN
30000415503202210316000141550-100241881안성시 금광면 개산리 316-1<NA>01450.0242.6616.74279.3719.2701000201201506162015061920150904.0
68751418303102111205000041830-100195167양평군 강상면 병산리 1205양평현대성우아파트1단지012937.32087.142216.1326370.0488149.220200024197200705162007090320100331
79824415902592510928000241590-100346072화성시 향남읍 구문천리 928-2<NA>01983.91492.375.221631.6982.2517000205201311272013121620140325