Overview

Dataset statistics

Number of variables10
Number of observations5010
Missing cells11221
Missing cells (%)22.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory406.2 KiB
Average record size in memory83.0 B

Variable types

Categorical2
Text3
DateTime2
Numeric3

Dataset

Description광주광역시 서구 관내의 원룸 및 오피스텔 대지위치, 허가일, 사용승인일, 주용도, 부속용도, 세대수, 호수, 가수구 등에 대한 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15117547/fileData.do

Alerts

세대수 is highly overall correlated with 가구수 and 1 other fieldsHigh correlation
호수 is highly overall correlated with 주용도High correlation
가구수 is highly overall correlated with 세대수 and 1 other fieldsHigh correlation
건축구분 is highly overall correlated with 주용도High correlation
주용도 is highly overall correlated with 세대수 and 3 other fieldsHigh correlation
건축구분 is highly imbalanced (64.6%)Imbalance
주용도 is highly imbalanced (64.4%)Imbalance
부속용도 has 1001 (20.0%) missing valuesMissing
세대수 has 4545 (90.7%) missing valuesMissing
호수 has 4922 (98.2%) missing valuesMissing
가구수 has 716 (14.3%) missing valuesMissing

Reproduction

Analysis started2023-12-12 22:23:56.791397
Analysis finished2023-12-12 22:23:58.619394
Duration1.83 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

건축구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.3 KiB
신축
4135 
증축
534 
용도변경
 
211
대수선
 
117
개축
 
11

Length

Max length9
Median length2
Mean length2.1103792
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
신축 4135
82.5%
증축 534
 
10.7%
용도변경 211
 
4.2%
대수선 117
 
2.3%
개축 11
 
0.2%
허가/신고사항변경 2
 
< 0.1%

Length

2023-12-13T07:23:58.703355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:23:58.856967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신축 4135
82.5%
증축 534
 
10.7%
용도변경 211
 
4.2%
대수선 117
 
2.3%
개축 11
 
0.2%
허가/신고사항변경 2
 
< 0.1%

허가번호
Text

Distinct4942
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size39.3 KiB
2023-12-13T07:23:59.155684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length17
Mean length16.68523
Min length15

Characters and Unicode

Total characters83593
Distinct characters44
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

Unique4874 ?
Unique (%)97.3%

Sample

1st row2022-건축과-신축허가-81
2nd row2022-건축과-신축허가-68
3rd row2022-건축과-신축허가-63
4th row2022-건축과-신축허가-61
5th row2022-건축과-신축허가-59
ValueCountFrequency (%)
2001-건축과-개발제한구역내 10
 
0.2%
2002-건축과-개발제한구역내 7
 
0.1%
2003-건축과-개발제한구역내 4
 
0.1%
건축허가-1 4
 
0.1%
건축허가-7 3
 
0.1%
건축허가-2 3
 
0.1%
2004-건축과-개발제한구역내 3
 
0.1%
2006-건축과-신축허가-6 2
 
< 0.1%
2014-건축과-신축신고-6 2
 
< 0.1%
2010-건축과-신축허가-144 2
 
< 0.1%
Other values (4927) 4997
99.2%
2023-12-13T07:23:59.551527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 15030
18.0%
축 9743
11.7%
0 9194
11.0%
2 7996
9.6%
건 5124
 
6.1%
과 5010
 
6.0%
신 4948
 
5.9%
1 4779
 
5.7%
가 4092
 
4.9%
허 4092
 
4.9%
Other values (34) 13585
16.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 36167
43.3%
Decimal Number 32275
38.6%
Dash Punctuation 15030
18.0%
Close Punctuation 47
 
0.1%
Open Punctuation 47
 
0.1%
Space Separator 27
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
축 9743
26.9%
건 5124
14.2%
과 5010
13.9%
신 4948
13.7%
가 4092
11.3%
허 4092
11.3%
고 831
 
2.3%
증 493
 
1.4%
용 220
 
0.6%
도 207
 
0.6%
Other values (20) 1407
 
3.9%
Decimal Number
ValueCountFrequency (%)
0 9194
28.5%
2 7996
24.8%
1 4779
14.8%
3 1943
 
6.0%
4 1632
 
5.1%
5 1530
 
4.7%
7 1418
 
4.4%
6 1384
 
4.3%
8 1238
 
3.8%
9 1161
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 15030
100.0%
Close Punctuation
ValueCountFrequency (%)
) 47
100.0%
Open Punctuation
ValueCountFrequency (%)
( 47
100.0%
Space Separator
ValueCountFrequency (%)
27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47426
56.7%
Hangul 36167
43.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
축 9743
26.9%
건 5124
14.2%
과 5010
13.9%
신 4948
13.7%
가 4092
11.3%
허 4092
11.3%
고 831
 
2.3%
증 493
 
1.4%
용 220
 
0.6%
도 207
 
0.6%
Other values (20) 1407
 
3.9%
Common
ValueCountFrequency (%)
- 15030
31.7%
0 9194
19.4%
2 7996
16.9%
1 4779
 
10.1%
3 1943
 
4.1%
4 1632
 
3.4%
5 1530
 
3.2%
7 1418
 
3.0%
6 1384
 
2.9%
8 1238
 
2.6%
Other values (4) 1282
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47426
56.7%
Hangul 36167
43.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 15030
31.7%
0 9194
19.4%
2 7996
16.9%
1 4779
 
10.1%
3 1943
 
4.1%
4 1632
 
3.4%
5 1530
 
3.2%
7 1418
 
3.0%
6 1384
 
2.9%
8 1238
 
2.6%
Other values (4) 1282
 
2.7%
Hangul
ValueCountFrequency (%)
축 9743
26.9%
건 5124
14.2%
과 5010
13.9%
신 4948
13.7%
가 4092
11.3%
허 4092
11.3%
고 831
 
2.3%
증 493
 
1.4%
용 220
 
0.6%
도 207
 
0.6%
Other values (20) 1407
 
3.9%

대지위치
Text

Distinct4369
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Memory size39.3 KiB
2023-12-13T07:23:59.956928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length24
Mean length18.946906
Min length11

Characters and Unicode

Total characters94924
Distinct characters53
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3855 ?
Unique (%)76.9%

Sample

1st row광주광역시 서구 화정동 846-19
2nd row광주광역시 서구 쌍촌동 978-14 외1필지
3rd row광주광역시 서구 서창동 37-1
4th row광주광역시 서구 쌍촌동 547-21
5th row광주광역시 서구 쌍촌동 331-38 외1필지
ValueCountFrequency (%)
광주광역시 5010
24.2%
서구 5010
24.2%
쌍촌동 1383
 
6.7%
풍암동 905
 
4.4%
금호동 527
 
2.5%
외1필지 507
 
2.5%
화정동 418
 
2.0%
농성동 355
 
1.7%
치평동 339
 
1.6%
유촌동 259
 
1.3%
Other values (3964) 5975
28.9%
2023-12-13T07:24:00.478513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15678
16.5%
광 10117
 
10.7%
동 5163
 
5.4%
서 5087
 
5.4%
구 5010
 
5.3%
주 5010
 
5.3%
시 5010
 
5.3%
역 5010
 
5.3%
1 4864
 
5.1%
- 4328
 
4.6%
Other values (43) 29647
31.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 51955
54.7%
Decimal Number 22963
24.2%
Space Separator 15678
 
16.5%
Dash Punctuation 4328
 
4.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 10117
19.5%
동 5163
9.9%
서 5087
9.8%
구 5010
9.6%
주 5010
9.6%
시 5010
9.6%
역 5010
9.6%
촌 1642
 
3.2%
쌍 1383
 
2.7%
풍 905
 
1.7%
Other values (31) 7618
14.7%
Decimal Number
ValueCountFrequency (%)
1 4864
21.2%
3 2427
10.6%
2 2370
10.3%
8 2358
10.3%
9 2068
9.0%
4 1994
8.7%
5 1833
 
8.0%
7 1778
 
7.7%
6 1730
 
7.5%
0 1541
 
6.7%
Space Separator
ValueCountFrequency (%)
15678
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4328
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 51955
54.7%
Common 42969
45.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 10117
19.5%
동 5163
9.9%
서 5087
9.8%
구 5010
9.6%
주 5010
9.6%
시 5010
9.6%
역 5010
9.6%
촌 1642
 
3.2%
쌍 1383
 
2.7%
풍 905
 
1.7%
Other values (31) 7618
14.7%
Common
ValueCountFrequency (%)
15678
36.5%
1 4864
 
11.3%
- 4328
 
10.1%
3 2427
 
5.6%
2 2370
 
5.5%
8 2358
 
5.5%
9 2068
 
4.8%
4 1994
 
4.6%
5 1833
 
4.3%
7 1778
 
4.1%
Other values (2) 3271
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 51955
54.7%
ASCII 42969
45.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15678
36.5%
1 4864
 
11.3%
- 4328
 
10.1%
3 2427
 
5.6%
2 2370
 
5.5%
8 2358
 
5.5%
9 2068
 
4.8%
4 1994
 
4.6%
5 1833
 
4.3%
7 1778
 
4.1%
Other values (2) 3271
 
7.6%
Hangul
ValueCountFrequency (%)
광 10117
19.5%
동 5163
9.9%
서 5087
9.8%
구 5010
9.6%
주 5010
9.6%
시 5010
9.6%
역 5010
9.6%
촌 1642
 
3.2%
쌍 1383
 
2.7%
풍 905
 
1.7%
Other values (31) 7618
14.7%

허가일
Date

Distinct2771
Distinct (%)55.7%
Missing36
Missing (%)0.7%
Memory size39.3 KiB
Minimum1978-04-20 00:00:00
Maximum2023-03-31 00:00:00
2023-12-13T07:24:00.599410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:24:00.718397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

사용승인일
Date

Distinct2978
Distinct (%)59.5%
Missing1
Missing (%)< 0.1%
Memory size39.3 KiB
Minimum1978-09-01 00:00:00
Maximum2023-07-24 00:00:00
2023-12-13T07:24:00.847726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:24:00.986141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

주용도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.3 KiB
단독주택
4672 
업무시설
 
338

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
단독주택 4672
93.3%
업무시설 338
 
6.7%

Length

2023-12-13T07:24:01.118801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:24:01.205662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
단독주택 4672
93.3%
업무시설 338
 
6.7%

부속용도
Text

MISSING 

Distinct526
Distinct (%)13.1%
Missing1001
Missing (%)20.0%
Memory size39.3 KiB
2023-12-13T07:24:01.350124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length37
Mean length7.9416313
Min length1

Characters and Unicode

Total characters31838
Distinct characters166
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique379 ?
Unique (%)9.5%

Sample

1st row다가구주택+소매점
2nd row다가구주택+오피스텔
3rd row다가구주택+소매점
4th row다가구주택+소매점
5th row다가구주택+오피스텔+소매점
ValueCountFrequency (%)
다가구주택 1427
34.6%
근린생활시설 417
 
10.1%
제2종근린생활시설 183
 
4.4%
다가구주택+근린생활시설 166
 
4.0%
제1종근린생활시설 144
 
3.5%
단독주택 122
 
3.0%
다가구주택+소매점 116
 
2.8%
다가구주택+사무소 99
 
2.4%
일반음식점 67
 
1.6%
다가구주택+일반음식점 66
 
1.6%
Other values (476) 1318
32.0%
2023-12-13T07:24:01.693502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 2744
 
8.6%
택 2729
 
8.6%
구 2422
 
7.6%
가 2415
 
7.6%
다 2408
 
7.6%
시 1572
 
4.9%
설 1569
 
4.9%
+ 1527
 
4.8%
근 1379
 
4.3%
생 1379
 
4.3%
Other values (156) 11694
36.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29116
91.5%
Math Symbol 1527
 
4.8%
Decimal Number 650
 
2.0%
Open Punctuation 203
 
0.6%
Close Punctuation 202
 
0.6%
Space Separator 117
 
0.4%
Other Punctuation 19
 
0.1%
Uppercase Letter 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 2744
 
9.4%
택 2729
 
9.4%
구 2422
 
8.3%
가 2415
 
8.3%
다 2408
 
8.3%
시 1572
 
5.4%
설 1569
 
5.4%
근 1379
 
4.7%
생 1379
 
4.7%
린 1376
 
4.7%
Other values (143) 9123
31.3%
Decimal Number
ValueCountFrequency (%)
2 365
56.2%
1 284
43.7%
0 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 15
78.9%
: 3
 
15.8%
& 1
 
5.3%
Open Punctuation
ValueCountFrequency (%)
( 200
98.5%
[ 3
 
1.5%
Close Punctuation
ValueCountFrequency (%)
) 199
98.5%
] 3
 
1.5%
Math Symbol
ValueCountFrequency (%)
+ 1527
100.0%
Space Separator
ValueCountFrequency (%)
117
100.0%
Uppercase Letter
ValueCountFrequency (%)
O 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29116
91.5%
Common 2718
 
8.5%
Latin 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 2744
 
9.4%
택 2729
 
9.4%
구 2422
 
8.3%
가 2415
 
8.3%
다 2408
 
8.3%
시 1572
 
5.4%
설 1569
 
5.4%
근 1379
 
4.7%
생 1379
 
4.7%
린 1376
 
4.7%
Other values (143) 9123
31.3%
Common
ValueCountFrequency (%)
+ 1527
56.2%
2 365
 
13.4%
1 284
 
10.4%
( 200
 
7.4%
) 199
 
7.3%
117
 
4.3%
. 15
 
0.6%
[ 3
 
0.1%
: 3
 
0.1%
] 3
 
0.1%
Other values (2) 2
 
0.1%
Latin
ValueCountFrequency (%)
O 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29115
91.4%
ASCII 2722
 
8.5%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 2744
 
9.4%
택 2729
 
9.4%
구 2422
 
8.3%
가 2415
 
8.3%
다 2408
 
8.3%
시 1572
 
5.4%
설 1569
 
5.4%
근 1379
 
4.7%
생 1379
 
4.7%
린 1376
 
4.7%
Other values (142) 9122
31.3%
ASCII
ValueCountFrequency (%)
+ 1527
56.1%
2 365
 
13.4%
1 284
 
10.4%
( 200
 
7.3%
) 199
 
7.3%
117
 
4.3%
. 15
 
0.6%
O 4
 
0.1%
[ 3
 
0.1%
: 3
 
0.1%
Other values (3) 5
 
0.2%
Compat Jamo
ValueCountFrequency (%)
ㅅ 1
100.0%

세대수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct31
Distinct (%)6.7%
Missing4545
Missing (%)90.7%
Infinite0
Infinite (%)0.0%
Mean8.1053763
Minimum1
Maximum762
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.2 KiB
2023-12-13T07:24:01.825564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile16
Maximum762
Range761
Interquartile range (IQR)3

Descriptive statistics

Standard deviation42.327282
Coefficient of variation (CV)5.2221242
Kurtosis225.53632
Mean8.1053763
Median Absolute Deviation (MAD)0
Skewness13.70453
Sum3769
Variance1791.5988
MonotonicityNot monotonic
2023-12-13T07:24:01.970778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 282
 
5.6%
2 40
 
0.8%
3 25
 
0.5%
5 19
 
0.4%
9 11
 
0.2%
4 10
 
0.2%
12 10
 
0.2%
7 9
 
0.2%
6 7
 
0.1%
11 6
 
0.1%
Other values (21) 46
 
0.9%
(Missing) 4545
90.7%
ValueCountFrequency (%)
1 282
5.6%
2 40
 
0.8%
3 25
 
0.5%
4 10
 
0.2%
5 19
 
0.4%
6 7
 
0.1%
7 9
 
0.2%
8 4
 
0.1%
9 11
 
0.2%
10 5
 
0.1%
ValueCountFrequency (%)
762 1
 
< 0.1%
325 1
 
< 0.1%
165 4
0.1%
134 1
 
< 0.1%
124 1
 
< 0.1%
82 1
 
< 0.1%
67 1
 
< 0.1%
62 1
 
< 0.1%
56 1
 
< 0.1%
43 1
 
< 0.1%

호수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)26.1%
Missing4922
Missing (%)98.2%
Infinite0
Infinite (%)0.0%
Mean42.931818
Minimum1
Maximum838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.2 KiB
2023-12-13T07:24:02.118432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median5
Q37.25
95-th percentile251
Maximum838
Range837
Interquartile range (IQR)6.25

Descriptive statistics

Standard deviation129.00199
Coefficient of variation (CV)3.0048107
Kurtosis19.854553
Mean42.931818
Median Absolute Deviation (MAD)3
Skewness4.2513728
Sum3778
Variance16641.513
MonotonicityNot monotonic
2023-12-13T07:24:02.233385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 24
 
0.5%
5 16
 
0.3%
4 10
 
0.2%
6 9
 
0.2%
7 6
 
0.1%
8 4
 
0.1%
251 2
 
< 0.1%
10 2
 
< 0.1%
33 1
 
< 0.1%
838 1
 
< 0.1%
Other values (13) 13
 
0.3%
(Missing) 4922
98.2%
ValueCountFrequency (%)
1 24
0.5%
2 1
 
< 0.1%
4 10
0.2%
5 16
0.3%
6 9
 
0.2%
7 6
 
0.1%
8 4
 
0.1%
10 2
 
< 0.1%
11 1
 
< 0.1%
25 1
 
< 0.1%
ValueCountFrequency (%)
838 1
< 0.1%
505 1
< 0.1%
486 1
< 0.1%
451 1
< 0.1%
251 2
< 0.1%
217 1
< 0.1%
118 1
< 0.1%
117 1
< 0.1%
63 1
< 0.1%
54 1
< 0.1%

가구수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)0.5%
Missing716
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Mean6.9613414
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.2 KiB
2023-12-13T07:24:02.346238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile16
Maximum31
Range30
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.0207485
Coefficient of variation (CV)0.72123291
Kurtosis-0.21768177
Mean6.9613414
Median Absolute Deviation (MAD)4
Skewness0.68327084
Sum29892
Variance25.207916
MonotonicityNot monotonic
2023-12-13T07:24:02.467152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 662
13.2%
2 400
8.0%
3 370
 
7.4%
9 365
 
7.3%
5 349
 
7.0%
7 312
 
6.2%
12 247
 
4.9%
4 243
 
4.9%
15 222
 
4.4%
6 199
 
4.0%
Other values (10) 925
18.5%
(Missing) 716
14.3%
ValueCountFrequency (%)
1 662
13.2%
2 400
8.0%
3 370
7.4%
4 243
 
4.9%
5 349
7.0%
6 199
 
4.0%
7 312
6.2%
8 197
 
3.9%
9 365
7.3%
10 135
 
2.7%
ValueCountFrequency (%)
31 4
 
0.1%
19 74
 
1.5%
18 91
 
1.8%
17 35
 
0.7%
16 47
 
0.9%
15 222
4.4%
14 85
 
1.7%
13 94
 
1.9%
12 247
4.9%
11 163
3.3%

Interactions

2023-12-13T07:23:57.912073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:23:57.307651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:23:57.622476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:23:58.011318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:23:57.418046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:23:57.711910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:23:58.088338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:23:57.527063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:23:57.817382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:24:02.564636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건축구분주용도세대수호수가구수
건축구분1.0000.7990.4400.0000.259
주용도0.7991.0000.5600.8820.986
세대수0.4400.5601.000NaNNaN
호수0.0000.882NaN1.000NaN
가구수0.2590.986NaNNaN1.000
2023-12-13T07:24:02.660187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주용도건축구분
주용도1.0000.603
건축구분0.6031.000
2023-12-13T07:24:02.747445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대수호수가구수건축구분주용도
세대수1.000NaN0.9810.3720.675
호수NaN1.0000.4890.0000.679
가구수0.9810.4891.0000.1440.894
건축구분0.3720.0000.1441.0000.603
주용도0.6750.6790.8940.6031.000

Missing values

2023-12-13T07:23:58.223544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:23:58.391300image/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.
2023-12-13T07:23:58.531872image/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

건축구분허가번호대지위치허가일사용승인일주용도부속용도세대수호수가구수
0신축2022-건축과-신축허가-81광주광역시 서구 화정동 846-192022-10-112023-07-24단독주택<NA><NA><NA>1
1신축2022-건축과-신축허가-68광주광역시 서구 쌍촌동 978-14 외1필지2022-08-092023-05-18단독주택다가구주택+소매점<NA><NA>14
2신축2022-건축과-신축허가-63광주광역시 서구 서창동 37-12022-07-262023-07-03단독주택<NA><NA><NA>1
3신축2022-건축과-신축허가-61광주광역시 서구 쌍촌동 547-212022-07-182023-05-08단독주택다가구주택+오피스텔<NA>69
4신축2022-건축과-신축허가-59광주광역시 서구 쌍촌동 331-38 외1필지2022-07-142023-02-07단독주택다가구주택+소매점<NA><NA>7
5신축2022-건축과-신축허가-54광주광역시 서구 쌍촌동 328-82022-06-282023-02-01단독주택다가구주택+소매점<NA><NA>7
6신축2022-건축과-신축허가-50광주광역시 서구 농성동 646-452022-06-082023-04-17단독주택다가구주택+오피스텔+소매점<NA>510
7신축2022-건축과-신축허가-47광주광역시 서구 유촌동 872022-05-312022-11-10단독주택<NA><NA><NA>1
8신축2022-건축과-신축허가-46광주광역시 서구 치평동 1193-32022-05-302023-02-10단독주택다가구주택+제1종근린생활시설<NA><NA>9
9신축2022-건축과-신축허가-41광주광역시 서구 화정동 770-282022-05-032022-11-21단독주택다가구주택+소매점<NA><NA>19
건축구분허가번호대지위치허가일사용승인일주용도부속용도세대수호수가구수
5000용도변경2005-건축과-용도변경신고-14광주광역시 서구 치평동 1240-102005-04-042005-04-08업무시설오피스텔<NA><NA><NA>
5001용도변경2005-건축과-용도변경신고-13광주광역시 서구 치평동 1187-22005-04-022005-04-21업무시설<NA>67<NA><NA>
5002증축2004-건축과-증축신고-42광주광역시 서구 농성동 161-12004-10-262005-01-07업무시설사무실<NA><NA><NA>
5003용도변경2004-건축과-용도변경신고-25광주광역시 서구 화정동 177-12004-10-132004-11-04업무시설<NA><NA><NA><NA>
5004용도변경2004-건축과-용도변경신고-8광주광역시 서구 광천동 650-354 외2필지2004-03-292004-04-27업무시설<NA><NA><NA><NA>
5005증축2004-건축과-증축신고-14광주광역시 서구 농성동 393-352004-03-152006-07-20업무시설오피스텔<NA><NA><NA>
5006대수선2004-건축과-대수선신고-1광주광역시 서구 치평동 1210-72004-02-172004-04-01업무시설근린생활시설<NA><NA><NA>
5007용도변경2003-건축과-용도변경신고-14광주광역시 서구 쌍촌동 994-12003-08-282003-09-09업무시설<NA><NA><NA><NA>
5008용도변경2002-건축과-용도변경신고-38광주광역시 서구 농성동 417-402002-12-302003-01-07업무시설근린생활시설+운동시설+위락시설<NA><NA><NA>
5009용도변경2001-건축과-용도변경신고-312광주광역시 서구 치평동 1218-22001-11-242001-11-27업무시설숙박+ 근린생활시설<NA><NA><NA>