Overview

Dataset statistics

Number of variables20
Number of observations1172
Missing cells2235
Missing cells (%)9.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory192.4 KiB
Average record size in memory168.1 B

Variable types

Categorical7
Text2
Numeric8
DateTime3

Dataset

Description2022년 이후 파주시 건축인허가에 대한 건축구분, 허가번호, 대지위치, 지목, 대지면적, 건축면적, 연면적, 증축연면적, 건폐율, 용적률, 구조, 취소구분, 허가취소일, 허가일, 주용도, 용도지역, 용도구역, 총주차대수, 총주차장면적 등의 정보를 제공합니다.
Author경기도 파주시
URLhttps://www.data.go.kr/data/15124463/fileData.do

Alerts

데이터기준일 has constant value ""Constant
대지면적 is highly overall correlated with 건축면적 and 2 other fieldsHigh correlation
건축면적 is highly overall correlated with 대지면적 and 5 other fieldsHigh correlation
연면적 is highly overall correlated with 대지면적 and 4 other fieldsHigh correlation
증축연면적 is highly overall correlated with 건축면적 and 5 other fieldsHigh correlation
건폐율 is highly overall correlated with 용적률High correlation
용적률 is highly overall correlated with 건폐율 and 1 other fieldsHigh correlation
총주차대수 is highly overall correlated with 대지면적 and 4 other fieldsHigh correlation
총주차장면적 is highly overall correlated with 건축면적 and 3 other fieldsHigh correlation
건축구분 is highly overall correlated with 증축연면적High correlation
용도지역 is highly overall correlated with 증축연면적 and 1 other fieldsHigh correlation
용도구역 is highly overall correlated with 건축면적 and 2 other fieldsHigh correlation
구조 is highly imbalanced (55.3%)Imbalance
취소구분 is highly imbalanced (88.2%)Imbalance
용도구역 is highly imbalanced (57.8%)Imbalance
증축연면적 has 1027 (87.6%) missing valuesMissing
허가취소일 has 1144 (97.6%) missing valuesMissing
총주차대수 has 31 (2.6%) missing valuesMissing
총주차장면적 has 31 (2.6%) missing valuesMissing
건축면적 is highly skewed (γ1 = 33.77725268)Skewed
총주차장면적 is highly skewed (γ1 = 32.79637714)Skewed
허가번호 has unique valuesUnique
총주차대수 has 35 (3.0%) zerosZeros
총주차장면적 has 132 (11.3%) zerosZeros

Reproduction

Analysis started2023-12-12 00:53:47.172948
Analysis finished2023-12-12 00:53:58.346588
Duration11.17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

건축구분
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
신축
787 
용도변경
199 
증축
146 
대수선
 
35
재축
 
4

Length

Max length4
Median length2
Mean length2.3694539
Min length2

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row대수선
2nd row용도변경
3rd row신축
4th row신축
5th row용도변경

Common Values

ValueCountFrequency (%)
신축 787
67.2%
용도변경 199
 
17.0%
증축 146
 
12.5%
대수선 35
 
3.0%
재축 4
 
0.3%
개축 1
 
0.1%

Length

2023-12-12T09:53:58.443007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:53:58.610484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신축 787
67.2%
용도변경 199
 
17.0%
증축 146
 
12.5%
대수선 35
 
3.0%
재축 4
 
0.3%
개축 1
 
0.1%

허가번호
Text

UNIQUE 

Distinct1172
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
2023-12-12T09:53:58.872831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length16.851536
Min length15

Characters and Unicode

Total characters19750
Distinct characters47
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1172 ?
Unique (%)100.0%

Sample

1st row2023-허가1과-대수선허가-7
2nd row2023-허가3과-용도변경허가-50
3rd row2023-허가1과-신축허가-88
4th row2023-허가1과-신축허가-89
5th row2023-허가3과-용도변경허가-48
ValueCountFrequency (%)
2023-허가1과-대수선허가-7 1
 
0.1%
2022-건축과-증축허가-39 1
 
0.1%
2022-건축과-신축허가-221 1
 
0.1%
2022-건축과-협의건축물-9 1
 
0.1%
2022-건축과-신축허가-223 1
 
0.1%
2022-건축과-신축허가-222 1
 
0.1%
2022-건축과-용도변경허가-58 1
 
0.1%
2022-주택과-신축허가-49 1
 
0.1%
2022-건축과-신축허가-219 1
 
0.1%
2022-건축과-증축허가-37 1
 
0.1%
Other values (1162) 1162
99.1%
2023-12-12T09:53:59.313379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 3563
18.0%
- 3516
17.8%
축 1606
8.1%
허 1510
7.6%
가 1510
7.6%
0 1319
 
6.7%
과 1145
 
5.8%
3 881
 
4.5%
신 767
 
3.9%
건 702
 
3.6%
Other values (37) 3231
16.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8667
43.9%
Decimal Number 7567
38.3%
Dash Punctuation 3516
17.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
축 1606
18.5%
허 1510
17.4%
가 1510
17.4%
과 1145
13.2%
신 767
8.8%
건 702
8.1%
용 224
 
2.6%
도 195
 
2.2%
변 195
 
2.2%
경 195
 
2.2%
Other values (26) 618
 
7.1%
Decimal Number
ValueCountFrequency (%)
2 3563
47.1%
0 1319
 
17.4%
3 881
 
11.6%
1 535
 
7.1%
4 298
 
3.9%
5 218
 
2.9%
6 211
 
2.8%
7 195
 
2.6%
8 187
 
2.5%
9 160
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 3516
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11083
56.1%
Hangul 8667
43.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
축 1606
18.5%
허 1510
17.4%
가 1510
17.4%
과 1145
13.2%
신 767
8.8%
건 702
8.1%
용 224
 
2.6%
도 195
 
2.2%
변 195
 
2.2%
경 195
 
2.2%
Other values (26) 618
 
7.1%
Common
ValueCountFrequency (%)
2 3563
32.1%
- 3516
31.7%
0 1319
 
11.9%
3 881
 
7.9%
1 535
 
4.8%
4 298
 
2.7%
5 218
 
2.0%
6 211
 
1.9%
7 195
 
1.8%
8 187
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11083
56.1%
Hangul 8667
43.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 3563
32.1%
- 3516
31.7%
0 1319
 
11.9%
3 881
 
7.9%
1 535
 
4.8%
4 298
 
2.7%
5 218
 
2.0%
6 211
 
1.9%
7 195
 
1.8%
8 187
 
1.7%
Hangul
ValueCountFrequency (%)
축 1606
18.5%
허 1510
17.4%
가 1510
17.4%
과 1145
13.2%
신 767
8.8%
건 702
8.1%
용 224
 
2.6%
도 195
 
2.2%
변 195
 
2.2%
경 195
 
2.2%
Other values (26) 618
 
7.1%

대지위치
Text

Distinct1078
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
2023-12-12T09:53:59.684358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length68
Median length36
Mean length21.921502
Min length14

Characters and Unicode

Total characters25692
Distinct characters155
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1022 ?
Unique (%)87.2%

Sample

1st row경기도 파주시 문산읍 선유리 1372-4
2nd row경기도 파주시 산남동 84-14
3rd row경기도 파주시 조리읍 봉일천리 215-15
4th row경기도 파주시 파주읍 봉암리 외2필지
5th row경기도 파주시 야당동 1074-2
ValueCountFrequency (%)
경기도 1172
19.6%
파주시 1172
19.6%
외1필지 254
 
4.3%
탄현면 163
 
2.7%
파주읍 123
 
2.1%
외2필지 102
 
1.7%
광탄면 94
 
1.6%
야당동 90
 
1.5%
조리읍 80
 
1.3%
문산읍 71
 
1.2%
Other values (1180) 2652
44.4%
2023-12-12T09:54:00.271729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4803
18.7%
파 1372
 
5.3%
주 1340
 
5.2%
1 1195
 
4.7%
도 1191
 
4.6%
기 1175
 
4.6%
시 1173
 
4.6%
경 1172
 
4.6%
- 975
 
3.8%
리 790
 
3.1%
Other values (145) 10506
40.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14569
56.7%
Decimal Number 5314
 
20.7%
Space Separator 4803
 
18.7%
Dash Punctuation 975
 
3.8%
Uppercase Letter 21
 
0.1%
Lowercase Letter 4
 
< 0.1%
Open Punctuation 3
 
< 0.1%
Close Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
파 1372
 
9.4%
주 1340
 
9.2%
도 1191
 
8.2%
기 1175
 
8.1%
시 1173
 
8.1%
경 1172
 
8.0%
리 790
 
5.4%
동 645
 
4.4%
지 607
 
4.2%
외 478
 
3.3%
Other values (123) 4626
31.8%
Decimal Number
ValueCountFrequency (%)
1 1195
22.5%
2 703
13.2%
3 648
12.2%
4 558
10.5%
5 518
9.7%
6 406
 
7.6%
7 353
 
6.6%
8 337
 
6.3%
0 303
 
5.7%
9 293
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
F 12
57.1%
N 5
23.8%
L 1
 
4.8%
B 1
 
4.8%
I 1
 
4.8%
C 1
 
4.8%
Lowercase Letter
ValueCountFrequency (%)
i 3
75.0%
d 1
 
25.0%
Space Separator
ValueCountFrequency (%)
4803
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 975
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14569
56.7%
Common 11098
43.2%
Latin 25
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
파 1372
 
9.4%
주 1340
 
9.2%
도 1191
 
8.2%
기 1175
 
8.1%
시 1173
 
8.1%
경 1172
 
8.0%
리 790
 
5.4%
동 645
 
4.4%
지 607
 
4.2%
외 478
 
3.3%
Other values (123) 4626
31.8%
Common
ValueCountFrequency (%)
4803
43.3%
1 1195
 
10.8%
- 975
 
8.8%
2 703
 
6.3%
3 648
 
5.8%
4 558
 
5.0%
5 518
 
4.7%
6 406
 
3.7%
7 353
 
3.2%
8 337
 
3.0%
Other values (4) 602
 
5.4%
Latin
ValueCountFrequency (%)
F 12
48.0%
N 5
20.0%
i 3
 
12.0%
d 1
 
4.0%
L 1
 
4.0%
B 1
 
4.0%
I 1
 
4.0%
C 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14569
56.7%
ASCII 11123
43.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4803
43.2%
1 1195
 
10.7%
- 975
 
8.8%
2 703
 
6.3%
3 648
 
5.8%
4 558
 
5.0%
5 518
 
4.7%
6 406
 
3.7%
7 353
 
3.2%
8 337
 
3.0%
Other values (12) 627
 
5.6%
Hangul
ValueCountFrequency (%)
파 1372
 
9.4%
주 1340
 
9.2%
도 1191
 
8.2%
기 1175
 
8.1%
시 1173
 
8.1%
경 1172
 
8.0%
리 790
 
5.4%
동 645
 
4.4%
지 607
 
4.2%
외 478
 
3.3%
Other values (123) 4626
31.8%

지목
Categorical

Distinct18
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
대
462 
임야
180 
전
163 
답
153 
공장용지
136 
Other values (13)
78 

Length

Max length5
Median length1
Mean length1.6578498
Min length1

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st row공장용지
2nd row대
3rd row답
4th row답
5th row대

Common Values

ValueCountFrequency (%)
대 462
39.4%
임야 180
 
15.4%
전 163
 
13.9%
답 153
 
13.1%
공장용지 136
 
11.6%
잡종지 31
 
2.6%
창고용지 8
 
0.7%
체육용지 6
 
0.5%
종교용지 5
 
0.4%
주차장 5
 
0.4%
Other values (8) 23
 
2.0%

Length

2023-12-12T09:54:00.444222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대 462
39.4%
임야 180
 
15.4%
전 163
 
13.9%
답 153
 
13.1%
공장용지 136
 
11.6%
잡종지 31
 
2.6%
창고용지 8
 
0.7%
체육용지 6
 
0.5%
na 5
 
0.4%
주차장 5
 
0.4%
Other values (8) 23
 
2.0%

대지면적
Real number (ℝ)

HIGH CORRELATION 

Distinct925
Distinct (%)78.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5123.5863
Minimum80
Maximum519939
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2023-12-12T09:54:00.622938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile279.755
Q1620
median1199
Q33011.75
95-th percentile10815.55
Maximum519939
Range519859
Interquartile range (IQR)2391.75

Descriptive statistics

Standard deviation29467.95
Coefficient of variation (CV)5.7514305
Kurtosis245.55758
Mean5123.5863
Median Absolute Deviation (MAD)734.5
Skewness14.86908
Sum6004843.2
Variance8.6836011 × 108
MonotonicityNot monotonic
2023-12-12T09:54:00.780884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
572.0 9
 
0.8%
630.0 9
 
0.8%
2834.0 9
 
0.8%
4758.6 7
 
0.6%
1994.8 6
 
0.5%
650.0 6
 
0.5%
482.0 6
 
0.5%
413.0 6
 
0.5%
557.0 5
 
0.4%
1000.0 5
 
0.4%
Other values (915) 1104
94.2%
ValueCountFrequency (%)
80.0 1
 
0.1%
99.0 1
 
0.1%
125.0 1
 
0.1%
133.0 4
0.3%
162.0 1
 
0.1%
175.0 1
 
0.1%
181.0 1
 
0.1%
184.0 1
 
0.1%
195.0 1
 
0.1%
199.0 1
 
0.1%
ValueCountFrequency (%)
519939.0 3
0.3%
278252.5 1
 
0.1%
180712.9 1
 
0.1%
154279.2 1
 
0.1%
119871.0 1
 
0.1%
117525.0 2
0.2%
86172.0 1
 
0.1%
72486.0 1
 
0.1%
71778.6 1
 
0.1%
68906.0 1
 
0.1%

건축면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct978
Distinct (%)83.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2051.0364
Minimum0
Maximum1132726
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2023-12-12T09:54:00.943529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile90.089
Q1197.9775
median382.585
Q3989.09
95-th percentile3528.0075
Maximum1132726
Range1132726
Interquartile range (IQR)791.1125

Descriptive statistics

Standard deviation33207.24
Coefficient of variation (CV)16.190468
Kurtosis1150.6964
Mean2051.0364
Median Absolute Deviation (MAD)243.845
Skewness33.777253
Sum2403814.7
Variance1.1027208 × 109
MonotonicityNot monotonic
2023-12-12T09:54:01.124229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
198.0 13
 
1.1%
396.0 10
 
0.9%
1693.32 9
 
0.8%
68.74 9
 
0.8%
360.0 9
 
0.8%
3310.66 7
 
0.6%
1369.81 6
 
0.5%
70.25 6
 
0.5%
976.06 6
 
0.5%
1281.2 5
 
0.4%
Other values (968) 1092
93.2%
ValueCountFrequency (%)
0.0 2
0.2%
39.59 1
 
0.1%
47.91 1
 
0.1%
49.46 3
0.3%
52.96 4
0.3%
53.44 1
 
0.1%
59.31 2
0.2%
60.1 1
 
0.1%
61.0 1
 
0.1%
64.64 1
 
0.1%
ValueCountFrequency (%)
1132726.0 1
0.1%
68398.75 1
0.1%
36272.9 1
0.1%
35595.78 1
0.1%
23631.0 1
0.1%
21534.53 1
0.1%
21420.92 1
0.1%
17359.06 1
0.1%
17220.9713 1
0.1%
17214.09 1
0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct1000
Distinct (%)85.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3426.8527
Minimum41.6
Maximum147577.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2023-12-12T09:54:01.662457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41.6
5-th percentile143.182
Q1360
median796.88
Q32050.715
95-th percentile18380.135
Maximum147577.75
Range147536.15
Interquartile range (IQR)1690.715

Descriptive statistics

Standard deviation9259.3906
Coefficient of variation (CV)2.7020101
Kurtosis72.192285
Mean3426.8527
Median Absolute Deviation (MAD)544.88
Skewness6.9929544
Sum4016271.3
Variance85736314
MonotonicityNot monotonic
2023-12-12T09:54:01.925016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
396.0 14
 
1.2%
184.1 9
 
0.8%
18735.06 9
 
0.8%
360.0 8
 
0.7%
594.0 7
 
0.6%
42029.27 7
 
0.6%
976.06 6
 
0.5%
19499.79 6
 
0.5%
198.0 6
 
0.5%
123.19 6
 
0.5%
Other values (990) 1094
93.3%
ValueCountFrequency (%)
41.6 1
0.1%
60.1 1
0.1%
61.0 1
0.1%
66.1 1
0.1%
67.11 1
0.1%
72.88 1
0.1%
73.71 1
0.1%
74.12 1
0.1%
78.71 1
0.1%
79.18 1
0.1%
ValueCountFrequency (%)
147577.75 1
0.1%
94141.5 1
0.1%
78250.17 1
0.1%
71515.1 1
0.1%
68567.462 1
0.1%
64033.57 1
0.1%
60318.0542 1
0.1%
52222.3 1
0.1%
52134.29 1
0.1%
48536.6372 1
0.1%

증축연면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct141
Distinct (%)97.2%
Missing1027
Missing (%)87.6%
Infinite0
Infinite (%)0.0%
Mean1813.3554
Minimum0
Maximum77810.06
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2023-12-12T09:54:02.116121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile76.032
Q1195
median366.25
Q3754.94
95-th percentile5613.278
Maximum77810.06
Range77810.06
Interquartile range (IQR)559.94

Descriptive statistics

Standard deviation7473.3297
Coefficient of variation (CV)4.1212714
Kurtosis77.210725
Mean1813.3554
Median Absolute Deviation (MAD)203.77
Skewness8.2058143
Sum262936.53
Variance55850656
MonotonicityNot monotonic
2023-12-12T09:54:02.345530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
198.0 3
 
0.3%
195.0 3
 
0.3%
1119.8 1
 
0.1%
636.78 1
 
0.1%
1179.5 1
 
0.1%
28507.49 1
 
0.1%
162.8 1
 
0.1%
96.0 1
 
0.1%
645.84 1
 
0.1%
5652.5 1
 
0.1%
Other values (131) 131
 
11.2%
(Missing) 1027
87.6%
ValueCountFrequency (%)
0.0 1
0.1%
6.35 1
0.1%
15.21 1
0.1%
38.55 1
0.1%
56.98 1
0.1%
63.7 1
0.1%
64.34 1
0.1%
71.04 1
0.1%
96.0 1
0.1%
98.16 1
0.1%
ValueCountFrequency (%)
77810.06 1
0.1%
29702.0 1
0.1%
28507.49 1
0.1%
19905.47 1
0.1%
9967.47 1
0.1%
9434.89 1
0.1%
7172.0 1
0.1%
5652.5 1
0.1%
5456.39 1
0.1%
3630.0 1
0.1%

건폐율
Real number (ℝ)

HIGH CORRELATION 

Distinct932
Distinct (%)79.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean35.640735
Minimum0
Maximum86.771
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2023-12-12T09:54:02.578949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.00545
Q120.025
median36.64
Q341.3
95-th percentile67.235
Maximum86.771
Range86.771
Interquartile range (IQR)21.275

Descriptive statistics

Standard deviation16.084731
Coefficient of variation (CV)0.45130188
Kurtosis-0.34130759
Mean35.640735
Median Absolute Deviation (MAD)11.93
Skewness0.41534334
Sum41735.301
Variance258.71856
MonotonicityNot monotonic
2023-12-12T09:54:02.780694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68.67 9
 
0.8%
19.8 8
 
0.7%
59.75 7
 
0.6%
14.26 6
 
0.5%
19.9 6
 
0.5%
69.57 6
 
0.5%
39.96 6
 
0.5%
67.21 5
 
0.4%
66.41 5
 
0.4%
19.99 5
 
0.4%
Other values (922) 1108
94.5%
ValueCountFrequency (%)
0.0 1
0.1%
0.3799 1
0.1%
1.24 1
0.1%
1.64 1
0.1%
2.1257 2
0.2%
2.329 1
0.1%
3.22 1
0.1%
3.4699 1
0.1%
3.68 1
0.1%
5.6 1
0.1%
ValueCountFrequency (%)
86.771 1
0.1%
79.59 1
0.1%
79.29 2
0.2%
76.18 1
0.1%
75.76 1
0.1%
74.46 1
0.1%
73.85 2
0.2%
69.99 2
0.2%
69.97 1
0.1%
69.95 1
0.1%

용적률
Real number (ℝ)

HIGH CORRELATION 

Distinct1043
Distinct (%)89.1%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean100.66008
Minimum0
Maximum699.69
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2023-12-12T09:54:02.971119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.325
Q131.55
median60.89
Q399.49
95-th percentile429.5599
Maximum699.69
Range699.69
Interquartile range (IQR)67.94

Descriptive statistics

Standard deviation132.81747
Coefficient of variation (CV)1.3194652
Kurtosis7.9611919
Mean100.66008
Median Absolute Deviation (MAD)35.06
Skewness2.8562497
Sum117872.95
Variance17640.48
MonotonicityNot monotonic
2023-12-12T09:54:03.174036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
698.08 6
 
0.5%
599.19 6
 
0.5%
25.11 6
 
0.5%
398.51 6
 
0.5%
29.83 5
 
0.4%
499.71 5
 
0.4%
699.69 5
 
0.4%
99.92 4
 
0.3%
99.83 4
 
0.3%
98.44 4
 
0.3%
Other values (1033) 1120
95.6%
ValueCountFrequency (%)
0.0 1
0.1%
0.4166 1
0.1%
1.24 1
0.1%
1.64 1
0.1%
2.7963 1
0.1%
3.22 1
0.1%
3.4548 2
0.2%
3.68 1
0.1%
5.6 1
0.1%
6.24 1
0.1%
ValueCountFrequency (%)
699.69 5
0.4%
699.65 1
 
0.1%
698.08 6
0.5%
677.29 1
 
0.1%
669.11 2
 
0.2%
666.56 1
 
0.1%
620.44 2
 
0.2%
599.64 2
 
0.2%
599.51 1
 
0.1%
599.42 2
 
0.2%

구조
Categorical

IMBALANCE 

Distinct17
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
철근콘크리트구조
519 
일반철골구조
490 
일반목구조
55 
<NA>
 
35
경량철골구조
 
30
Other values (12)
 
43

Length

Max length13
Median length12
Mean length6.8114334
Min length3

Unique

Unique4 ?
Unique (%)0.3%

Sample

1st row<NA>
2nd row<NA>
3rd row일반철골구조
4th row일반철골구조
5th row철근콘크리트구조

Common Values

ValueCountFrequency (%)
철근콘크리트구조 519
44.3%
일반철골구조 490
41.8%
일반목구조 55
 
4.7%
<NA> 35
 
3.0%
경량철골구조 30
 
2.6%
벽돌구조 14
 
1.2%
철골철근콘크리트구조 5
 
0.4%
공업화박판강구조(PEB) 4
 
0.3%
철골콘크리트구조 4
 
0.3%
블록구조 3
 
0.3%
Other values (7) 13
 
1.1%

Length

2023-12-12T09:54:03.416827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
철근콘크리트구조 519
44.3%
일반철골구조 490
41.8%
일반목구조 55
 
4.7%
na 35
 
3.0%
경량철골구조 30
 
2.6%
벽돌구조 14
 
1.2%
철골철근콘크리트구조 5
 
0.4%
철골콘크리트구조 4
 
0.3%
공업화박판강구조(peb 4
 
0.3%
블록구조 3
 
0.3%
Other values (7) 13
 
1.1%

취소구분
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
미취소
1144 
취소
 
14
전환
 
14

Length

Max length3
Median length3
Mean length2.9761092
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미취소 1144
97.6%
취소 14
 
1.2%
전환 14
 
1.2%

Length

2023-12-12T09:54:03.597625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:54:03.773619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미취소 1144
97.6%
취소 14
 
1.2%
전환 14
 
1.2%

허가취소일
Date

MISSING 

Distinct22
Distinct (%)78.6%
Missing1144
Missing (%)97.6%
Memory size9.3 KiB
Minimum2022-04-21 00:00:00
Maximum2023-09-01 00:00:00
2023-12-12T09:54:03.892046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:54:04.004426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)

허가일
Date

Distinct382
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
Minimum2022-01-03 00:00:00
Maximum2023-10-11 00:00:00
2023-12-12T09:54:04.145017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:54:04.304993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

주용도
Categorical

Distinct24
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
제2종근린생활시설
268 
공장
210 
공동주택
187 
제1종근린생활시설
179 
단독주택
148 
Other values (19)
180 

Length

Max length10
Median length9
Mean length5.7244027
Min length2

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row공장
2nd row제2종근린생활시설
3rd row제2종근린생활시설
4th row방송통신시설
5th row제1종근린생활시설

Common Values

ValueCountFrequency (%)
제2종근린생활시설 268
22.9%
공장 210
17.9%
공동주택 187
16.0%
제1종근린생활시설 179
15.3%
단독주택 148
12.6%
창고시설 42
 
3.6%
업무시설 29
 
2.5%
방송통신시설 13
 
1.1%
의료시설 12
 
1.0%
자동차관련시설 12
 
1.0%
Other values (14) 72
 
6.1%

Length

2023-12-12T09:54:04.516687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
제2종근린생활시설 268
22.9%
공장 210
17.9%
공동주택 187
16.0%
제1종근린생활시설 179
15.3%
단독주택 148
12.6%
창고시설 42
 
3.6%
업무시설 29
 
2.5%
방송통신시설 13
 
1.1%
의료시설 12
 
1.0%
자동차관련시설 12
 
1.0%
Other values (14) 72
 
6.1%

용도지역
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
계획관리지역
603 
도시지역
134 
자연녹지지역
107 
제1종일반주거지역
73 
생산관리지역
 
43
Other values (20)
212 

Length

Max length11
Median length6
Mean length5.9812287
Min length4

Unique

Unique4 ?
Unique (%)0.3%

Sample

1st row일반공업지역
2nd row계획관리지역
3rd row도시지역
4th row계획관리지역
5th row일반상업지역

Common Values

ValueCountFrequency (%)
계획관리지역 603
51.5%
도시지역 134
 
11.4%
자연녹지지역 107
 
9.1%
제1종일반주거지역 73
 
6.2%
생산관리지역 43
 
3.7%
일반공업지역 41
 
3.5%
일반상업지역 30
 
2.6%
중심상업지역 25
 
2.1%
근린상업지역 24
 
2.0%
제2종일반주거지역 21
 
1.8%
Other values (15) 71
 
6.1%

Length

2023-12-12T09:54:04.702722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
계획관리지역 603
51.5%
도시지역 134
 
11.4%
자연녹지지역 107
 
9.1%
제1종일반주거지역 73
 
6.2%
생산관리지역 43
 
3.7%
일반공업지역 41
 
3.5%
일반상업지역 30
 
2.6%
중심상업지역 25
 
2.1%
근린상업지역 24
 
2.0%
제2종일반주거지역 21
 
1.8%
Other values (15) 71
 
6.1%

용도구역
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct25
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
<NA>
796 
제1종지구단위계획구역
108 
상대보호구역
 
51
토지거래계약에관한허가구역
 
45
성장관리계획구역
 
40
Other values (20)
132 

Length

Max length13
Median length4
Mean length5.6186007
Min length2

Unique

Unique7 ?
Unique (%)0.6%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row지구단위계획구역

Common Values

ValueCountFrequency (%)
<NA> 796
67.9%
제1종지구단위계획구역 108
 
9.2%
상대보호구역 51
 
4.4%
토지거래계약에관한허가구역 45
 
3.8%
성장관리계획구역 40
 
3.4%
제2종지구단위계획구역 32
 
2.7%
가축사육제한구역 28
 
2.4%
지구단위계획구역 25
 
2.1%
기타용지 11
 
0.9%
접도구역 7
 
0.6%
Other values (15) 29
 
2.5%

Length

2023-12-12T09:54:04.891324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 796
67.9%
제1종지구단위계획구역 108
 
9.2%
상대보호구역 51
 
4.4%
토지거래계약에관한허가구역 45
 
3.8%
성장관리계획구역 40
 
3.4%
제2종지구단위계획구역 32
 
2.7%
가축사육제한구역 28
 
2.4%
지구단위계획구역 25
 
2.1%
기타용지 11
 
0.9%
접도구역 7
 
0.6%
Other values (15) 29
 
2.5%

총주차대수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct126
Distinct (%)11.0%
Missing31
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean31.672217
Minimum0
Maximum2970
Zeros35
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2023-12-12T09:54:05.043561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median8
Q317
95-th percentile166
Maximum2970
Range2970
Interquartile range (IQR)14

Descriptive statistics

Standard deviation117.0528
Coefficient of variation (CV)3.6957563
Kurtosis363.03258
Mean31.672217
Median Absolute Deviation (MAD)5
Skewness16.034196
Sum36138
Variance13701.357
MonotonicityNot monotonic
2023-12-12T09:54:05.221215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 135
 
11.5%
8 117
 
10.0%
4 88
 
7.5%
3 79
 
6.7%
9 70
 
6.0%
6 60
 
5.1%
5 49
 
4.2%
10 41
 
3.5%
1 37
 
3.2%
0 35
 
3.0%
Other values (116) 430
36.7%
ValueCountFrequency (%)
0 35
 
3.0%
1 37
 
3.2%
2 135
11.5%
3 79
6.7%
4 88
7.5%
5 49
 
4.2%
6 60
5.1%
7 33
 
2.8%
8 117
10.0%
9 70
6.0%
ValueCountFrequency (%)
2970 1
 
0.1%
1239 1
 
0.1%
942 1
 
0.1%
625 1
 
0.1%
595 1
 
0.1%
517 1
 
0.1%
476 1
 
0.1%
445 2
 
0.2%
387 7
0.6%
379 1
 
0.1%

총주차장면적
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct325
Distinct (%)28.5%
Missing31
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean774.75295
Minimum0
Maximum358057
Zeros132
Zeros (%)11.3%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2023-12-12T09:54:05.440466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q125
median87.5
Q3198.5
95-th percentile2250.17
Maximum358057
Range358057
Interquartile range (IQR)173.5

Descriptive statistics

Standard deviation10692.745
Coefficient of variation (CV)13.801489
Kurtosis1096.191
Mean774.75295
Median Absolute Deviation (MAD)62.5
Skewness32.796377
Sum883993.12
Variance1.1433479 × 108
MonotonicityNot monotonic
2023-12-12T09:54:05.649612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 132
 
11.3%
25.0 108
 
9.2%
50.0 76
 
6.5%
100.0 69
 
5.9%
37.5 55
 
4.7%
75.0 50
 
4.3%
112.5 45
 
3.8%
62.5 30
 
2.6%
87.5 28
 
2.4%
125.0 26
 
2.2%
Other values (315) 522
44.5%
(Missing) 31
 
2.6%
ValueCountFrequency (%)
0.0 132
11.3%
11.5 7
 
0.6%
12.0 2
 
0.2%
12.5 25
 
2.1%
15.0 1
 
0.1%
18.804 1
 
0.1%
23.0 11
 
0.9%
24.0 2
 
0.2%
24.5 3
 
0.3%
25.0 108
9.2%
ValueCountFrequency (%)
358057.0 1
 
0.1%
15370.5 1
 
0.1%
12615.44 7
0.6%
12429.09 2
 
0.2%
8370.23 2
 
0.2%
7983.86 1
 
0.1%
7904.5 1
 
0.1%
6180.4 1
 
0.1%
5984.84 1
 
0.1%
5668.83 9
0.8%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
Minimum2023-10-12 00:00:00
Maximum2023-10-12 00:00:00
2023-12-12T09:54:05.784012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:54:05.892497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T09:53:56.630290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:48.873550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:49.873563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:51.006917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:52.085915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:53.260689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:54.601691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:55.612489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:56.757576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:48.977587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:49.978126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:51.121404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:52.196478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:53.371700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:54.717277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:55.737465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:56.867760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:49.081297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:50.130484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:51.230590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:52.305669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:53.487020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:54.840450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:55.872259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:56.993884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:49.228009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:50.262456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:51.360280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:52.485160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:53.620199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:54.958116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:56.005820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:57.142207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:49.341388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:50.394248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:51.476865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:52.687295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:53.774480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:55.075509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:56.129570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:57.266280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:49.463064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:50.506044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:51.642366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:52.832057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:53.862733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:55.201559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:56.227552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:57.368833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:49.606724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:50.671748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:51.794370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:52.977267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:54.309053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:55.341321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:56.355525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:57.467681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:49.739000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:50.836555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:51.950000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:53.110201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:54.460467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:55.469126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:56.492325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:54:06.009383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건축구분지목대지면적건축면적연면적증축연면적건폐율용적률구조취소구분허가취소일주용도용도지역용도구역총주차대수총주차장면적
건축구분1.0000.6390.2560.0000.273NaN0.3310.3930.3880.0851.0000.6640.5950.5150.1170.026
지목0.6391.0000.3420.0000.4970.6890.6410.3570.2630.0891.0000.8260.4730.5570.6120.000
대지면적0.2560.3421.0000.0000.7440.8720.4110.0000.3040.000NaN0.7160.5420.0000.8670.000
건축면적0.0000.0000.0001.0000.000NaN0.0000.0000.0000.000NaN0.0000.067NaN0.0000.000
연면적0.2730.4970.7440.0001.0000.8490.3990.5180.4550.000NaN0.7440.6380.0000.8850.000
증축연면적NaN0.6890.872NaN0.8491.0000.1650.0000.6680.000NaN0.7370.7880.7380.991NaN
건폐율0.3310.6410.4110.0000.3990.1651.0000.8530.4330.0000.8860.6900.7900.6740.4440.226
용적률0.3930.3570.0000.0000.5180.0000.8531.0000.4040.0001.0000.6590.8620.5130.6150.274
구조0.3880.2630.3040.0000.4550.6680.4330.4041.0000.0001.0000.7940.5960.4670.0000.000
취소구분0.0850.0890.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.0000.0000.000
허가취소일1.0001.000NaNNaNNaNNaN0.8861.0001.0001.0001.0001.0000.9241.000NaNNaN
주용도0.6640.8260.7160.0000.7440.7370.6900.6590.7940.0001.0001.0000.7220.5850.7120.000
용도지역0.5950.4730.5420.0670.6380.7880.7900.8620.5960.0000.9240.7221.0000.8210.4030.148
용도구역0.5150.5570.000NaN0.0000.7380.6740.5130.4670.0001.0000.5850.8211.0000.000NaN
총주차대수0.1170.6120.8670.0000.8850.9910.4440.6150.0000.000NaN0.7120.4030.0001.0000.000
총주차장면적0.0260.0000.0000.0000.000NaN0.2260.2740.0000.000NaN0.0000.148NaN0.0001.000
2023-12-12T09:54:06.196746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구조용도지역건축구분주용도용도구역취소구분지목
구조1.0000.2090.1970.3540.1920.0000.087
용도지역0.2091.0000.2810.2100.3940.0000.156
건축구분0.1970.2811.0000.3300.2590.0350.364
주용도0.3540.2100.3301.0000.1950.0000.403
용도구역0.1920.3940.2590.1951.0000.0000.213
취소구분0.0000.0000.0350.0000.0001.0000.047
지목0.0870.1560.3640.4030.2130.0471.000
2023-12-12T09:54:06.361770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대지면적건축면적연면적증축연면적건폐율용적률총주차대수총주차장면적건축구분지목구조취소구분주용도용도지역용도구역
대지면적1.0000.8820.6880.465-0.209-0.2760.5880.3790.0950.1680.1500.0000.3730.2470.000
건축면적0.8821.0000.8890.5390.2050.1070.7660.5310.0000.0000.0000.0000.0000.0521.000
연면적0.6880.8891.0000.5250.3940.4440.8970.6330.1550.2330.1750.0000.3630.2790.000
증축연면적0.4650.5390.5251.0000.1820.2370.5590.3601.0000.4580.4370.0000.4660.5640.511
건폐율-0.2090.2050.3940.1821.0000.8450.3700.2980.1810.3120.1850.0000.3320.4330.315
용적률-0.2760.1070.4440.2370.8451.0000.4630.3700.2190.1460.1700.0000.3070.5370.210
총주차대수0.5880.7660.8970.5590.3700.4631.0000.7210.0790.3420.0000.0000.3690.1720.000
총주차장면적0.3790.5310.6330.3600.2980.3700.7211.0000.0320.0000.0000.0000.0000.1161.000
건축구분0.0950.0000.1551.0000.1810.2190.0790.0321.0000.3640.1970.0350.3300.2810.259
지목0.1680.0000.2330.4580.3120.1460.3420.0000.3641.0000.0870.0470.4030.1560.213
구조0.1500.0000.1750.4370.1850.1700.0000.0000.1970.0871.0000.0000.3540.2090.192
취소구분0.0000.0000.0000.0000.0000.0000.0000.0000.0350.0470.0001.0000.0000.0000.000
주용도0.3730.0000.3630.4660.3320.3070.3690.0000.3300.4030.3540.0001.0000.2100.195
용도지역0.2470.0520.2790.5640.4330.5370.1720.1160.2810.1560.2090.0000.2101.0000.394
용도구역0.0001.0000.0000.5110.3150.2100.0001.0000.2590.2130.1920.0000.1950.3941.000

Missing values

2023-12-12T09:53:57.652446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:53:57.989586image/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.
2023-12-12T09:53:58.212578image/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

건축구분허가번호대지위치지목대지면적건축면적연면적증축연면적건폐율용적률구조취소구분허가취소일허가일주용도용도지역용도구역총주차대수총주차장면적데이터기준일
0대수선2023-허가1과-대수선허가-7경기도 파주시 문산읍 선유리 1372-4공장용지11779.47585.7837671.15<NA>64.4236.39<NA>미취소<NA>2023-10-11공장일반공업지역<NA>1792237.52023-10-12
1용도변경2023-허가3과-용도변경허가-50경기도 파주시 산남동 84-14대414.0165.18370.16<NA>39.963.31<NA>미취소<NA>2023-10-11제2종근린생활시설계획관리지역<NA>374.12023-10-12
2신축2023-허가1과-신축허가-88경기도 파주시 조리읍 봉일천리 215-15답661.0360.0720.0<NA>54.46108.93일반철골구조미취소<NA>2023-10-11제2종근린생활시설도시지역<NA>8100.02023-10-12
3신축2023-허가1과-신축허가-89경기도 파주시 파주읍 봉암리 외2필지답5467.02185.92860.76<NA>39.9852.33일반철골구조미취소<NA>2023-10-11방송통신시설계획관리지역<NA>20250.02023-10-12
4용도변경2023-허가3과-용도변경허가-48경기도 파주시 야당동 1074-2대2005.71403.4413407.03<NA>69.97486.82철근콘크리트구조미취소<NA>2023-10-10제1종근린생활시설일반상업지역지구단위계획구역1043473.452023-10-12
5용도변경2023-허가3과-용도변경허가-49경기도 파주시 와동동 1434대1651.21109.8212434.69<NA>67.21499.71철근콘크리트구조미취소<NA>2023-10-10제1종근린생활시설중심상업지역지구단위계획구역983849.712023-10-12
6신축2023-허가2과-신축허가-80경기도 파주시 광탄면 신산리 491 외1필지답3966.0396.0396.0<NA>9.989.98일반철골구조미취소<NA>2023-10-10제2종근린생활시설생산녹지지역<NA>450.02023-10-12
7용도변경2023-허가2과-용도변경허가-12경기도 파주시 탄현면 법흥리 1652-577대933.8230.26412.59<NA>24.6644.19<NA>미취소<NA>2023-10-05문화및집회시설계획관리지역상업용지334.52023-10-12
8용도변경2023-허가2과-용도변경허가-13경기도 파주시 적성면 마지리 291-2대476.060.160.1<NA>12.626112.6261일반목구조미취소<NA>2023-10-05제2종근린생활시설자연녹지지역<NA>00.02023-10-12
9용도변경2023-허가3과-용도변경허가-47경기도 파주시 동패동 2077-6대1227.0736.077224.65<NA>59.99378.4<NA>미취소<NA>2023-10-05제2종근린생활시설근린상업지역제1종지구단위계획구역842438.182023-10-12
건축구분허가번호대지위치지목대지면적건축면적연면적증축연면적건폐율용적률구조취소구분허가취소일허가일주용도용도지역용도구역총주차대수총주차장면적데이터기준일
1162신축2022-건축과-신축허가-12경기도 파주시 문산읍 내포리 661-1전2780.0703.442097.91<NA>25.348.78철근콘크리트구조미취소<NA>2022-01-07제2종근린생활시설계획관리지역<NA>340.02023-10-12
1163신축2022-건축과-신축허가-6경기도 파주시 상지석동 554-298공장용지587.0233.6586.88<NA>39.7999.97철근콘크리트구조미취소<NA>2022-01-06공동주택계획관리지역<NA>8100.02023-10-12
1164신축2022-건축과-신축허가-7경기도 파주시 송촌동 556-3전788.0191.43283.13<NA>24.293135.9302일반철골구조미취소<NA>2022-01-06제1종근린생활시설계획관리지역상대보호구역382.52023-10-12
1165용도변경2022-건축과-용도변경허가-3경기도 파주시 광탄면 분수리 145-1대958.0382.67934.82<NA>39.9497.58일반철골구조미취소<NA>2022-01-05제2종근린생활시설계획관리지역가축사육제한구역8100.02023-10-12
1166신축2022-주택과-신축허가-1경기도 파주시 야당동 45-141전335.0121.128334.6048<NA>36.1699.88철근콘크리트구조미취소<NA>2022-01-04공동주택계획관리지역성장관리계획구역450.02023-10-12
1167용도변경2022-건축과-용도변경허가-2경기도 파주시 검산동 466-1대998.0190.28190.28<NA>19.0719.07일반목구조미취소<NA>2022-01-04제1종근린생활시설계획관리지역<NA>223.02023-10-12
1168신축2022-건축과-신축허가-2경기도 파주시 탄현면 법흥리 1752-4대475.6189.12245.09<NA>39.7651.53철근콘크리트구조미취소<NA>2022-01-04단독주택계획관리지역제2종지구단위계획구역20.02023-10-12
1169신축2022-건축과-신축허가-344경기도 파주시 산남동 299-1 외1필지임야3742.01372.051679.55<NA>36.666244.8838일반철골구조미취소<NA>2022-01-04제1종근린생활시설계획관리지역<NA>17212.52023-10-12
1170신축2022-건축과-신축허가-372경기도 파주시 산남동 299-8 외1필지임야1265.0415.311245.93<NA>32.8398.49일반철골구조미취소<NA>2022-01-04제2종근린생활시설계획관리지역<NA>12142.22023-10-12
1171용도변경2022-건축과-용도변경허가-1경기도 파주시 조리읍 뇌조리 150-5대1457.0264.68284.68<NA>18.1718.17<NA>미취소<NA>2022-01-03제1종근린생활시설도시지역<NA><NA><NA>2023-10-12