Overview

Dataset statistics

Number of variables4
Number of observations1005
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.5 KiB
Average record size in memory35.1 B

Variable types

Numeric3
Text1

Dataset

Description전북특별자치도 진안군 도시계획정보시스템 건축물대장 총괄표제부에 대한 데이터로 건축물대장고유번호, 건축면적, 연면적, 기타 용도 정보를 제공합니다.
Author전북특별자치도 진안군
URLhttps://www.data.go.kr/data/15119149/fileData.do

Alerts

건축면적 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 건축면적High correlation
건축물대장고유번호 has unique valuesUnique

Reproduction

Analysis started2024-03-14 11:41:51.815511
Analysis finished2024-03-14 11:41:54.143574
Duration2.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

건축물대장고유번호
Real number (ℝ)

UNIQUE 

Distinct1005
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29605598
Minimum7
Maximum1.0018662 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-03-14T20:41:54.286164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile134.2
Q1489
median965
Q31.0017339 × 108
95-th percentile1.0018371 × 108
Maximum1.0018662 × 108
Range1.0018661 × 108
Interquartile range (IQR)1.001729 × 108

Descriptive statistics

Standard deviation45731783
Coefficient of variation (CV)1.5447006
Kurtosis-1.196651
Mean29605598
Median Absolute Deviation (MAD)552
Skewness0.89762704
Sum2.9753626 × 1010
Variance2.091396 × 1015
MonotonicityNot monotonic
2024-03-14T20:41:54.551584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
822 1
 
0.1%
100177250 1
 
0.1%
100171527 1
 
0.1%
100171685 1
 
0.1%
100171725 1
 
0.1%
100172126 1
 
0.1%
100172290 1
 
0.1%
100172309 1
 
0.1%
100180391 1
 
0.1%
100172766 1
 
0.1%
Other values (995) 995
99.0%
ValueCountFrequency (%)
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
11 1
0.1%
12 1
0.1%
13 1
0.1%
14 1
0.1%
15 1
0.1%
17 1
0.1%
ValueCountFrequency (%)
100186618 1
0.1%
100186541 1
0.1%
100186539 1
0.1%
100186420 1
0.1%
100186319 1
0.1%
100186299 1
0.1%
100186221 1
0.1%
100186199 1
0.1%
100186117 1
0.1%
100185978 1
0.1%

건축면적
Real number (ℝ)

HIGH CORRELATION 

Distinct989
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean701.53081
Minimum0
Maximum19154.8
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-03-14T20:41:54.809312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile71.98
Q1129.82
median229.54
Q3739.03
95-th percentile2727.02
Maximum19154.8
Range19154.8
Interquartile range (IQR)609.21

Descriptive statistics

Standard deviation1267.5395
Coefficient of variation (CV)1.8068194
Kurtosis55.976793
Mean701.53081
Median Absolute Deviation (MAD)135.13
Skewness5.71711
Sum705038.46
Variance1606656.3
MonotonicityNot monotonic
2024-03-14T20:41:55.218447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
142.34 2
 
0.2%
304.0 2
 
0.2%
187.2 2
 
0.2%
111.07 2
 
0.2%
128.1 2
 
0.2%
1786.4 2
 
0.2%
1677.7 2
 
0.2%
0.0 2
 
0.2%
1789.2 2
 
0.2%
1955.86 2
 
0.2%
Other values (979) 985
98.0%
ValueCountFrequency (%)
0.0 2
0.2%
33.32 1
0.1%
34.0 1
0.1%
35.04 1
0.1%
36.0 1
0.1%
36.69 1
0.1%
36.97 1
0.1%
38.86 1
0.1%
39.0 1
0.1%
40.8 1
0.1%
ValueCountFrequency (%)
19154.8 1
0.1%
10972.97 1
0.1%
8960.0 1
0.1%
8324.75 1
0.1%
8109.41 1
0.1%
7002.0 1
0.1%
6966.1 1
0.1%
6951.14 1
0.1%
6647.13 1
0.1%
6087.59 1
0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct990
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean842.44448
Minimum0
Maximum27857.32
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-03-14T20:41:55.500771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile72.428
Q1130.28
median240.29
Q3800.44
95-th percentile3388.554
Maximum27857.32
Range27857.32
Interquartile range (IQR)670.16

Descriptive statistics

Standard deviation1796.7174
Coefficient of variation (CV)2.1327428
Kurtosis73.716173
Mean842.44448
Median Absolute Deviation (MAD)145.48
Skewness6.9391613
Sum846656.71
Variance3228193.5
MonotonicityNot monotonic
2024-03-14T20:41:55.739787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111.07 2
 
0.2%
304.0 2
 
0.2%
1677.7 2
 
0.2%
1786.4 2
 
0.2%
128.1 2
 
0.2%
1955.86 2
 
0.2%
58.41 2
 
0.2%
138.47 2
 
0.2%
448.0 2
 
0.2%
1789.2 2
 
0.2%
Other values (980) 985
98.0%
ValueCountFrequency (%)
0.0 1
0.1%
33.32 1
0.1%
34.0 1
0.1%
35.04 1
0.1%
36.69 1
0.1%
36.97 1
0.1%
38.86 1
0.1%
39.0 1
0.1%
40.8 1
0.1%
41.81 1
0.1%
ValueCountFrequency (%)
27857.32 1
0.1%
19154.8 1
0.1%
15666.8 1
0.1%
14761.8 1
0.1%
13006.602 1
0.1%
11152.97 1
0.1%
8960.0 1
0.1%
8749.79 1
0.1%
8577.95 1
0.1%
8324.75 1
0.1%

기타 용도
Text

Distinct164
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Memory size8.0 KiB
2024-03-14T20:41:56.478308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length26
Mean length7.2
Min length2

Characters and Unicode

Total characters7236
Distinct characters130
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique106 ?
Unique (%)10.5%

Sample

1st row문화및집회시설, 단독주택
2nd row아파트
3rd row제2종근린생활시설
4th row단독주택
5th row단독주택
ValueCountFrequency (%)
단독주택 376
31.6%
동물및식물관련시설 182
15.3%
주택 99
 
8.3%
창고시설 83
 
7.0%
제2종근린생활시설 83
 
7.0%
제1종근린생활시설 49
 
4.1%
동물관련시설 36
 
3.0%
근린생활시설 33
 
2.8%
공장 19
 
1.6%
교육연구시설 17
 
1.4%
Other values (101) 211
17.8%
2024-03-14T20:41:57.415232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 633
 
8.7%
설 633
 
8.7%
주 514
 
7.1%
택 514
 
7.1%
물 436
 
6.0%
독 403
 
5.6%
단 403
 
5.6%
관 252
 
3.5%
동 249
 
3.4%
련 245
 
3.4%
Other values (120) 2954
40.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6630
91.6%
Other Punctuation 235
 
3.2%
Space Separator 183
 
2.5%
Decimal Number 161
 
2.2%
Open Punctuation 13
 
0.2%
Close Punctuation 13
 
0.2%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 633
 
9.5%
설 633
 
9.5%
주 514
 
7.8%
택 514
 
7.8%
물 436
 
6.6%
독 403
 
6.1%
단 403
 
6.1%
관 252
 
3.8%
동 249
 
3.8%
련 245
 
3.7%
Other values (108) 2348
35.4%
Other Punctuation
ValueCountFrequency (%)
, 214
91.1%
. 16
 
6.8%
/ 4
 
1.7%
: 1
 
0.4%
Decimal Number
ValueCountFrequency (%)
2 102
63.4%
1 57
35.4%
5 1
 
0.6%
6 1
 
0.6%
Space Separator
ValueCountFrequency (%)
183
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6630
91.6%
Common 606
 
8.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 633
 
9.5%
설 633
 
9.5%
주 514
 
7.8%
택 514
 
7.8%
물 436
 
6.6%
독 403
 
6.1%
단 403
 
6.1%
관 252
 
3.8%
동 249
 
3.8%
련 245
 
3.7%
Other values (108) 2348
35.4%
Common
ValueCountFrequency (%)
, 214
35.3%
183
30.2%
2 102
16.8%
1 57
 
9.4%
. 16
 
2.6%
( 13
 
2.1%
) 13
 
2.1%
/ 4
 
0.7%
5 1
 
0.2%
: 1
 
0.2%
Other values (2) 2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6629
91.6%
ASCII 606
 
8.4%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 633
 
9.5%
설 633
 
9.5%
주 514
 
7.8%
택 514
 
7.8%
물 436
 
6.6%
독 403
 
6.1%
단 403
 
6.1%
관 252
 
3.8%
동 249
 
3.8%
련 245
 
3.7%
Other values (107) 2347
35.4%
ASCII
ValueCountFrequency (%)
, 214
35.3%
183
30.2%
2 102
16.8%
1 57
 
9.4%
. 16
 
2.6%
( 13
 
2.1%
) 13
 
2.1%
/ 4
 
0.7%
5 1
 
0.2%
: 1
 
0.2%
Other values (2) 2
 
0.3%
Compat Jamo
ValueCountFrequency (%)
ㆍ 1
100.0%

Interactions

2024-03-14T20:41:53.297308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:41:52.044139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:41:52.656188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:41:53.462579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:41:52.307587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:41:52.939384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:41:53.640459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:41:52.492078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:41:53.131390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T20:41:57.822776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건축물대장고유번호건축면적연면적
건축물대장고유번호1.0000.0910.092
건축면적0.0911.0000.873
연면적0.0920.8731.000
2024-03-14T20:41:57.973262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건축물대장고유번호건축면적연면적
건축물대장고유번호1.000-0.001-0.005
건축면적-0.0011.0000.985
연면적-0.0050.9851.000

Missing values

2024-03-14T20:41:53.844427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T20:41:54.081893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

건축물대장고유번호건축면적연면적기타 용도
0822155.11155.11문화및집회시설, 단독주택
1832803.03773.92아파트
2833220.997531.647제2종근린생활시설
3834144.82144.82단독주택
4835168.2168.2단독주택
5836145.59145.59단독주택
683774.5574.55단독주택
7838157.83157.83단독주택
883933.3233.32단독주택
9840220.965441.93제2종근린생활시설
건축물대장고유번호건축면적연면적기타 용도
995100185357972.0972.0작물재배사
996100185875304.0304.0창고시설
997100183393138.5138.5단독주택
998100183541109.76101.12단독주택
999100184314136.18136.18단독주택
100010018462192.092.0단독주택, 제1종근린생활시설
1001100184977470.46470.46동물및식물관련시설
1002100184987498.97494.53동물및식물관련시설
10031001856001955.861955.86동물및식물관련시설
10041001851931017.241251.942종근린생활시설