Overview

Dataset statistics

Number of variables15
Number of observations26
Missing cells8
Missing cells (%)2.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory139.1 B

Variable types

Text1
Numeric14

Dataset

Description건축허가 통계자료 입니다.(신축, 증축, 착공연도 등)
Author경상남도
URLhttps://www.data.go.kr/data/15050513/fileData.do

Alerts

신축_계 is highly overall correlated with 신축_철근콘크리트 and 12 other fieldsHigh correlation
신축_철근콘크리트 is highly overall correlated with 신축_계 and 12 other fieldsHigh correlation
신축_철골 is highly overall correlated with 신축_계 and 12 other fieldsHigh correlation
신축_조적 is highly overall correlated with 신축_계 and 12 other fieldsHigh correlation
신축_철골철근 is highly overall correlated with 신축_계 and 12 other fieldsHigh correlation
신축_나무 is highly overall correlated with 신축_계 and 12 other fieldsHigh correlation
신축_기타 is highly overall correlated with 신축_계 and 12 other fieldsHigh correlation
증축_개축_이전_대수선_계 is highly overall correlated with 신축_계 and 12 other fieldsHigh correlation
증축_개축_이전_대수선철근콘크리트 is highly overall correlated with 신축_계 and 12 other fieldsHigh correlation
증축_개축_이전_대수선철골 is highly overall correlated with 신축_계 and 12 other fieldsHigh correlation
증축_개축_이전_대수선조적 is highly overall correlated with 신축_계 and 12 other fieldsHigh correlation
증축_개축_이전_대수선철골철근 is highly overall correlated with 신축_계 and 12 other fieldsHigh correlation
증축_개축_이전_대수선나무 is highly overall correlated with 신축_계 and 12 other fieldsHigh correlation
증축_개축_이전_대수선_기타 is highly overall correlated with 신축_계 and 12 other fieldsHigh correlation
신축_조적 has 2 (7.7%) missing valuesMissing
신축_철골철근 has 2 (7.7%) missing valuesMissing
신축_나무 has 4 (15.4%) missing valuesMissing
연별 및 용도별 has unique valuesUnique
신축_계 has unique valuesUnique
신축_철근콘크리트 has unique valuesUnique
신축_철골 has unique valuesUnique
신축_기타 has unique valuesUnique
증축_개축_이전_대수선_계 has unique valuesUnique
증축_개축_이전_대수선철근콘크리트 has unique valuesUnique
증축_개축_이전_대수선철골 has unique valuesUnique
증축_개축_이전_대수선조적 has 4 (15.4%) zerosZeros
증축_개축_이전_대수선철골철근 has 6 (23.1%) zerosZeros
증축_개축_이전_대수선나무 has 4 (15.4%) zerosZeros
증축_개축_이전_대수선_기타 has 2 (7.7%) zerosZeros

Reproduction

Analysis started2023-12-12 22:52:48.227323
Analysis finished2023-12-12 22:53:09.586693
Duration21.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연별 및 용도별
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-13T07:53:09.719982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length7.3846154
Min length5

Characters and Unicode

Total characters192
Distinct characters30
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

Unique26 ?
Unique (%)100.0%

Sample

1st row2013동 수
2nd row2013연면적
3rd row2014동 수
4th row2014연면적
5th row2015동 수
ValueCountFrequency (%)
수 7
14.0%
공 6
 
12.0%
업 4
 
8.0%
용연면적 4
 
8.0%
용동수 4
 
8.0%
주 2
 
4.0%
거 2
 
4.0%
상 2
 
4.0%
동수 1
 
2.0%
기타 1
 
2.0%
Other values (17) 17
34.0%
2023-12-13T07:53:10.074038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31
16.1%
수 15
 
7.8%
동 13
 
6.8%
연 13
 
6.8%
면 13
 
6.8%
적 13
 
6.8%
2 12
 
6.2%
0 12
 
6.2%
1 12
 
6.2%
용 12
 
6.2%
Other values (20) 46
24.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 111
57.8%
Decimal Number 48
25.0%
Space Separator 31
 
16.1%
Other Punctuation 2
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 15
13.5%
동 13
11.7%
연 13
11.7%
면 13
11.7%
적 13
11.7%
용 12
10.8%
공 6
 
5.4%
업 4
 
3.6%
육 2
 
1.8%
교 2
 
1.8%
Other values (9) 18
16.2%
Decimal Number
ValueCountFrequency (%)
2 12
25.0%
0 12
25.0%
1 12
25.0%
8 2
 
4.2%
7 2
 
4.2%
6 2
 
4.2%
5 2
 
4.2%
4 2
 
4.2%
3 2
 
4.2%
Space Separator
ValueCountFrequency (%)
31
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 111
57.8%
Common 81
42.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 15
13.5%
동 13
11.7%
연 13
11.7%
면 13
11.7%
적 13
11.7%
용 12
10.8%
공 6
 
5.4%
업 4
 
3.6%
육 2
 
1.8%
교 2
 
1.8%
Other values (9) 18
16.2%
Common
ValueCountFrequency (%)
31
38.3%
2 12
 
14.8%
0 12
 
14.8%
1 12
 
14.8%
/ 2
 
2.5%
8 2
 
2.5%
7 2
 
2.5%
6 2
 
2.5%
5 2
 
2.5%
4 2
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 111
57.8%
ASCII 81
42.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31
38.3%
2 12
 
14.8%
0 12
 
14.8%
1 12
 
14.8%
/ 2
 
2.5%
8 2
 
2.5%
7 2
 
2.5%
6 2
 
2.5%
5 2
 
2.5%
4 2
 
2.5%
Hangul
ValueCountFrequency (%)
수 15
13.5%
동 13
11.7%
연 13
11.7%
면 13
11.7%
적 13
11.7%
용 12
10.8%
공 6
 
5.4%
업 4
 
3.6%
육 2
 
1.8%
교 2
 
1.8%
Other values (9) 18
16.2%

신축_계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2869900
Minimum108
Maximum14531805
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T07:53:10.193044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum108
5-th percentile920.75
Q111697
median45678.5
Q32299572.2
95-th percentile12442082
Maximum14531805
Range14531697
Interquartile range (IQR)2287875.2

Descriptive statistics

Standard deviation4810461.8
Coefficient of variation (CV)1.6761775
Kurtosis0.61420359
Mean2869900
Median Absolute Deviation (MAD)45249.5
Skewness1.4916068
Sum74617400
Variance2.3140542 × 1013
MonotonicityNot monotonic
2023-12-13T07:53:10.312273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
23363 1
 
3.8%
4850 1
 
3.8%
718451 1
 
3.8%
1753 1
 
3.8%
406005 1
 
3.8%
750 1
 
3.8%
66604 1
 
3.8%
108 1
 
3.8%
962478 1
 
3.8%
1433 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
108 1
3.8%
750 1
3.8%
1433 1
3.8%
1753 1
3.8%
3998 1
3.8%
4850 1
3.8%
8474 1
3.8%
21366 1
3.8%
22316 1
3.8%
23059 1
3.8%
ValueCountFrequency (%)
14531805 1
3.8%
12470188 1
3.8%
12357765 1
3.8%
10258739 1
3.8%
10094093 1
3.8%
7372351 1
3.8%
2567836 1
3.8%
1494781 1
3.8%
1156482 1
3.8%
962478 1
3.8%

신축_철근콘크리트
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1935172.3
Minimum47
Maximum10870790
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T07:53:10.436230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile78.25
Q14569.75
median28950
Q31882711.8
95-th percentile9017995.5
Maximum10870790
Range10870743
Interquartile range (IQR)1878142

Descriptive statistics

Standard deviation3440474.7
Coefficient of variation (CV)1.7778648
Kurtosis1.2148386
Mean1935172.3
Median Absolute Deviation (MAD)28898
Skewness1.6290218
Sum50314480
Variance1.1836866 × 1013
MonotonicityNot monotonic
2023-12-13T07:53:10.571442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
8360 1
 
3.8%
1672 1
 
3.8%
373896 1
 
3.8%
150 1
 
3.8%
280941 1
 
3.8%
399 1
 
3.8%
63235 1
 
3.8%
47 1
 
3.8%
83095 1
 
3.8%
142 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
47 1
3.8%
57 1
3.8%
142 1
3.8%
150 1
3.8%
399 1
3.8%
1672 1
3.8%
3953 1
3.8%
6420 1
3.8%
7703 1
3.8%
8360 1
3.8%
ValueCountFrequency (%)
10870790 1
3.8%
9060212 1
3.8%
8891346 1
3.8%
7188745 1
3.8%
6402309 1
3.8%
3923092 1
3.8%
2228863 1
3.8%
844258 1
3.8%
373896 1
3.8%
280941 1
3.8%

신축_철골
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean807815.19
Minimum59
Maximum3145710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T07:53:10.956908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum59
5-th percentile472
Q13525.25
median12728.5
Q31023985.5
95-th percentile3098843.2
Maximum3145710
Range3145651
Interquartile range (IQR)1020460.2

Descriptive statistics

Standard deviation1238417.4
Coefficient of variation (CV)1.5330455
Kurtosis-0.34528715
Mean807815.19
Median Absolute Deviation (MAD)12592.5
Skewness1.221482
Sum21003195
Variance1.5336777 × 1012
MonotonicityNot monotonic
2023-12-13T07:53:11.069918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
11677 1
 
3.8%
2681 1
 
3.8%
340836 1
 
3.8%
1520 1
 
3.8%
69012 1
 
3.8%
213 1
 
3.8%
2999 1
 
3.8%
59 1
 
3.8%
855807 1
 
3.8%
1249 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
59 1
3.8%
213 1
3.8%
1249 1
3.8%
1520 1
3.8%
2681 1
3.8%
2999 1
3.8%
3476 1
3.8%
3673 1
3.8%
11665 1
3.8%
11677 1
3.8%
ValueCountFrequency (%)
3145710 1
3.8%
3122220 1
3.8%
3028713 1
3.8%
2987963 1
3.8%
2870829 1
3.8%
2640010 1
3.8%
1080045 1
3.8%
855807 1
3.8%
514487 1
3.8%
340836 1
3.8%

신축_조적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)100.0%
Missing2
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean26660.708
Minimum19
Maximum154714
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T07:53:11.185635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile35.3
Q1233
median1463.5
Q323480
95-th percentile127897.85
Maximum154714
Range154695
Interquartile range (IQR)23247

Descriptive statistics

Standard deviation46918.974
Coefficient of variation (CV)1.7598547
Kurtosis2.004381
Mean26660.708
Median Absolute Deviation (MAD)1373.5
Skewness1.7902922
Sum639857
Variance2.2013901 × 109
MonotonicityNot monotonic
2023-12-13T07:53:11.290817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
10507 1
 
3.8%
1580 1
 
3.8%
35 1
 
3.8%
514 1
 
3.8%
37 1
 
3.8%
143 1
 
3.8%
19 1
 
3.8%
13046 1
 
3.8%
153 1
 
3.8%
17104 1
 
3.8%
Other values (14) 14
53.8%
(Missing) 2
 
7.7%
ValueCountFrequency (%)
19 1
3.8%
35 1
3.8%
37 1
3.8%
143 1
3.8%
153 1
3.8%
221 1
3.8%
237 1
3.8%
514 1
3.8%
702 1
3.8%
896 1
3.8%
ValueCountFrequency (%)
154714 1
3.8%
130346 1
3.8%
114025 1
3.8%
90188 1
3.8%
56807 1
3.8%
42608 1
3.8%
17104 1
3.8%
13046 1
3.8%
10507 1
3.8%
1824 1
3.8%

신축_철골철근
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)91.7%
Missing2
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean76119.167
Minimum2
Maximum450472
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T07:53:11.396291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3.5
Q168.75
median501
Q3120093.5
95-th percentile311957.75
Maximum450472
Range450470
Interquartile range (IQR)120024.75

Descriptive statistics

Standard deviation128093.47
Coefficient of variation (CV)1.6828018
Kurtosis2.0792944
Mean76119.167
Median Absolute Deviation (MAD)499
Skewness1.6956681
Sum1826860
Variance1.6407938 × 1010
MonotonicityNot monotonic
2023-12-13T07:53:11.519583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
18 2
 
7.7%
2 2
 
7.7%
858 1
 
3.8%
50974 1
 
3.8%
22919 1
 
3.8%
12 1
 
3.8%
3420 1
 
3.8%
108007 1
 
3.8%
47 1
 
3.8%
3563 1
 
3.8%
Other values (12) 12
46.2%
(Missing) 2
 
7.7%
ValueCountFrequency (%)
2 2
7.7%
12 1
3.8%
18 2
7.7%
47 1
3.8%
76 1
3.8%
79 1
3.8%
90 1
3.8%
99 1
3.8%
113 1
3.8%
144 1
3.8%
ValueCountFrequency (%)
450472 1
3.8%
316685 1
3.8%
285170 1
3.8%
237998 1
3.8%
189741 1
3.8%
156353 1
3.8%
108007 1
3.8%
50974 1
3.8%
22919 1
3.8%
3563 1
3.8%

신축_나무
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing4
Missing (%)15.4%
Infinite0
Infinite (%)0.0%
Mean34602.955
Minimum6
Maximum138532
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T07:53:11.613940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile32.3
Q1793.75
median1575
Q374517.5
95-th percentile130350.4
Maximum138532
Range138526
Interquartile range (IQR)73723.75

Descriptive statistics

Standard deviation51616.875
Coefficient of variation (CV)1.4916898
Kurtosis-0.52154162
Mean34602.955
Median Absolute Deviation (MAD)1557
Skewness1.1047968
Sum761265
Variance2.6643018 × 109
MonotonicityNot monotonic
2023-12-13T07:53:11.716023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
726 1
 
3.8%
114 1
 
3.8%
6 1
 
3.8%
3453 1
 
3.8%
76 1
 
3.8%
3745 1
 
3.8%
30 1
 
3.8%
8810 1
 
3.8%
159 1
 
3.8%
62426 1
 
3.8%
Other values (12) 12
46.2%
(Missing) 4
 
15.4%
ValueCountFrequency (%)
6 1
3.8%
30 1
3.8%
76 1
3.8%
114 1
3.8%
159 1
3.8%
726 1
3.8%
997 1
3.8%
1308 1
3.8%
1322 1
3.8%
1527 1
3.8%
ValueCountFrequency (%)
138532 1
3.8%
130544 1
3.8%
126672 1
3.8%
101383 1
3.8%
97737 1
3.8%
78548 1
3.8%
62426 1
3.8%
8810 1
3.8%
3745 1
3.8%
3453 1
3.8%

신축_기타
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2759.3462
Minimum2
Maximum16142
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T07:53:11.818861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q187.25
median323.5
Q34337
95-th percentile9895.75
Maximum16142
Range16140
Interquartile range (IQR)4249.75

Descriptive statistics

Standard deviation4259.8377
Coefficient of variation (CV)1.5437852
Kurtosis2.6719269
Mean2759.3462
Median Absolute Deviation (MAD)314.5
Skewness1.750969
Sum71743
Variance18146217
MonotonicityNot monotonic
2023-12-13T07:53:11.922154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
101 1
 
3.8%
54 1
 
3.8%
1167 1
 
3.8%
40 1
 
3.8%
1111 1
 
3.8%
7 1
 
3.8%
370 1
 
3.8%
2 1
 
3.8%
514 1
 
3.8%
11 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
2 1
3.8%
7 1
3.8%
11 1
3.8%
40 1
3.8%
54 1
3.8%
80 1
3.8%
83 1
3.8%
100 1
3.8%
101 1
3.8%
107 1
3.8%
ValueCountFrequency (%)
16142 1
3.8%
10027 1
3.8%
9502 1
3.8%
8453 1
3.8%
7422 1
3.8%
5720 1
3.8%
4635 1
3.8%
3443 1
3.8%
2115 1
3.8%
1167 1
3.8%

증축_개축_이전_대수선_계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2442675.5
Minimum51
Maximum13087997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T07:53:12.039524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum51
5-th percentile298
Q18703
median28029.5
Q32155478.8
95-th percentile11121593
Maximum13087997
Range13087946
Interquartile range (IQR)2146775.8

Descriptive statistics

Standard deviation4234987.5
Coefficient of variation (CV)1.7337495
Kurtosis0.96750013
Mean2442675.5
Median Absolute Deviation (MAD)27899.5
Skewness1.5754141
Sum63509564
Variance1.7935119 × 1013
MonotonicityNot monotonic
2023-12-13T07:53:12.184222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
15753 1
 
3.8%
2731 1
 
3.8%
572499 1
 
3.8%
1163 1
 
3.8%
172016 1
 
3.8%
209 1
 
3.8%
38004 1
 
3.8%
51 1
 
3.8%
488190 1
 
3.8%
565 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
51 1
3.8%
209 1
3.8%
565 1
3.8%
1163 1
3.8%
1257 1
3.8%
2731 1
3.8%
7209 1
3.8%
13185 1
3.8%
15635 1
3.8%
15753 1
3.8%
ValueCountFrequency (%)
13087997 1
3.8%
11142067 1
3.8%
11060171 1
3.8%
8867460 1
3.8%
8285291 1
3.8%
5478870 1
3.8%
2490724 1
3.8%
1149743 1
3.8%
572499 1
3.8%
567694 1
3.8%

증축_개축_이전_대수선철근콘크리트
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1793656.3
Minimum16
Maximum10279368
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T07:53:12.303903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile34
Q13942.25
median19657
Q31807738
95-th percentile8559390.8
Maximum10279368
Range10279352
Interquartile range (IQR)1803795.8

Descriptive statistics

Standard deviation3239321.8
Coefficient of variation (CV)1.805988
Kurtosis1.3618828
Mean1793656.3
Median Absolute Deviation (MAD)19611
Skewness1.6641
Sum46635064
Variance1.0493205 × 1013
MonotonicityNot monotonic
2023-12-13T07:53:12.436977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
6338 1
 
3.8%
888 1
 
3.8%
343655 1
 
3.8%
108 1
 
3.8%
106812 1
 
3.8%
125 1
 
3.8%
36022 1
 
3.8%
16 1
 
3.8%
42015 1
 
3.8%
70 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
16 1
3.8%
22 1
3.8%
70 1
3.8%
108 1
3.8%
125 1
3.8%
888 1
3.8%
3635 1
3.8%
4864 1
3.8%
6029 1
3.8%
6338 1
3.8%
ValueCountFrequency (%)
10279368 1
3.8%
8597246 1
3.8%
8445825 1
3.8%
6717223 1
3.8%
5743367 1
3.8%
3404662 1
3.8%
2193405 1
3.8%
650737 1
3.8%
343655 1
3.8%
106812 1
3.8%

증축_개축_이전_대수선철골
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean553872.5
Minimum35
Maximum2245069
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T07:53:12.550578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile166
Q12207.75
median8496
Q3507928
95-th percentile2214338.8
Maximum2245069
Range2245034
Interquartile range (IQR)505720.25

Descriptive statistics

Standard deviation870452.23
Coefficient of variation (CV)1.5715751
Kurtosis-0.16101446
Mean553872.5
Median Absolute Deviation (MAD)8449.5
Skewness1.2920128
Sum14400685
Variance7.5768709 × 1011
MonotonicityNot monotonic
2023-12-13T07:53:12.666433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
7394 1
 
3.8%
1728 1
 
3.8%
227861 1
 
3.8%
1024 1
 
3.8%
27109 1
 
3.8%
58 1
 
3.8%
1982 1
 
3.8%
35 1
 
3.8%
443752 1
 
3.8%
490 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
35 1
3.8%
58 1
3.8%
490 1
3.8%
1024 1
3.8%
1193 1
3.8%
1728 1
3.8%
1982 1
3.8%
2885 1
3.8%
7394 1
3.8%
7413 1
3.8%
ValueCountFrequency (%)
2245069 1
3.8%
2219364 1
3.8%
2199263 1
3.8%
2151217 1
3.8%
1857789 1
3.8%
1815321 1
3.8%
529320 1
3.8%
443752 1
3.8%
397418 1
3.8%
230347 1
3.8%

증축_개축_이전_대수선조적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14548.885
Minimum0
Maximum99079
Zeros4
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T07:53:12.805428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.75
median443
Q33854.75
95-th percentile90379
Maximum99079
Range99079
Interquartile range (IQR)3848

Descriptive statistics

Standard deviation30901.432
Coefficient of variation (CV)2.1239726
Kurtosis2.9386756
Mean14548.885
Median Absolute Deviation (MAD)442.5
Skewness2.0796653
Sum378271
Variance9.5489849 × 108
MonotonicityNot monotonic
2023-12-13T07:53:12.940421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 4
 
15.4%
919 1
 
3.8%
99079 1
 
3.8%
243 1
 
3.8%
4 1
 
3.8%
2 1
 
3.8%
1 1
 
3.8%
2150 1
 
3.8%
16 1
 
3.8%
546 1
 
3.8%
Other values (13) 13
50.0%
ValueCountFrequency (%)
0 4
15.4%
1 1
 
3.8%
2 1
 
3.8%
4 1
 
3.8%
15 1
 
3.8%
16 1
 
3.8%
60 1
 
3.8%
96 1
 
3.8%
243 1
 
3.8%
340 1
 
3.8%
ValueCountFrequency (%)
99079 1
3.8%
94352 1
3.8%
78460 1
3.8%
60453 1
3.8%
27662 1
3.8%
7364 1
3.8%
4423 1
3.8%
2150 1
3.8%
919 1
3.8%
778 1
3.8%

증축_개축_이전_대수선철골철근
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54155.885
Minimum0
Maximum382875
Zeros6
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T07:53:13.048615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.25
median43.5
Q380409
95-th percentile256061.25
Maximum382875
Range382875
Interquartile range (IQR)80406.75

Descriptive statistics

Standard deviation100589.03
Coefficient of variation (CV)1.8573979
Kurtosis3.9899356
Mean54155.885
Median Absolute Deviation (MAD)43.5
Skewness2.0630133
Sum1408053
Variance1.0118152 × 1010
MonotonicityNot monotonic
2023-12-13T07:53:13.183963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 6
23.1%
44 1
 
3.8%
149793 1
 
3.8%
36345 1
 
3.8%
3 1
 
3.8%
2266 1
 
3.8%
2 1
 
3.8%
95097 1
 
3.8%
6 1
 
3.8%
3547 1
 
3.8%
Other values (11) 11
42.3%
ValueCountFrequency (%)
0 6
23.1%
2 1
 
3.8%
3 1
 
3.8%
6 1
 
3.8%
17 1
 
3.8%
28 1
 
3.8%
40 1
 
3.8%
43 1
 
3.8%
44 1
 
3.8%
56 1
 
3.8%
ValueCountFrequency (%)
382875 1
3.8%
278687 1
3.8%
188184 1
3.8%
149793 1
3.8%
137255 1
3.8%
133702 1
3.8%
95097 1
3.8%
36345 1
3.8%
3547 1
3.8%
2266 1
3.8%

증축_개축_이전_대수선나무
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24728.385
Minimum0
Maximum115731
Zeros4
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T07:53:13.351449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q135.25
median1051.5
Q343364
95-th percentile111194.75
Maximum115731
Range115731
Interquartile range (IQR)43328.75

Descriptive statistics

Standard deviation41727.092
Coefficient of variation (CV)1.6874168
Kurtosis0.10431257
Mean24728.385
Median Absolute Deviation (MAD)1044.5
Skewness1.3427966
Sum642938
Variance1.7411502 × 109
MonotonicityNot monotonic
2023-12-13T07:53:13.476375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 4
 
15.4%
979 1
 
3.8%
86128 1
 
3.8%
106 1
 
3.8%
3 1
 
3.8%
1731 1
 
3.8%
22 1
 
3.8%
1968 1
 
3.8%
11 1
 
3.8%
4964 1
 
3.8%
Other values (13) 13
50.0%
ValueCountFrequency (%)
0 4
15.4%
3 1
 
3.8%
11 1
 
3.8%
22 1
 
3.8%
75 1
 
3.8%
106 1
 
3.8%
559 1
 
3.8%
670 1
 
3.8%
979 1
 
3.8%
999 1
 
3.8%
ValueCountFrequency (%)
115731 1
3.8%
115494 1
3.8%
98297 1
3.8%
90585 1
3.8%
86128 1
3.8%
64933 1
3.8%
56164 1
3.8%
4964 1
3.8%
1968 1
3.8%
1731 1
3.8%

증축_개축_이전_대수선_기타
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1713.5769
Minimum0
Maximum8961
Zeros2
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T07:53:13.600024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q120.25
median86
Q32688.5
95-th percentile7462.5
Maximum8961
Range8961
Interquartile range (IQR)2668.25

Descriptive statistics

Standard deviation2746.7495
Coefficient of variation (CV)1.6029333
Kurtosis1.2040303
Mean1713.5769
Median Absolute Deviation (MAD)84.5
Skewness1.55088
Sum44553
Variance7544632.9
MonotonicityNot monotonic
2023-12-13T07:53:13.713483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 2
 
7.7%
19 2
 
7.7%
79 1
 
3.8%
2838 1
 
3.8%
634 1
 
3.8%
24 1
 
3.8%
1 1
 
3.8%
155 1
 
3.8%
2 1
 
3.8%
2240 1
 
3.8%
Other values (14) 14
53.8%
ValueCountFrequency (%)
0 2
7.7%
1 1
3.8%
2 1
3.8%
15 1
3.8%
19 2
7.7%
24 1
3.8%
44 1
3.8%
53 1
3.8%
63 1
3.8%
71 1
3.8%
ValueCountFrequency (%)
8961 1
3.8%
7661 1
3.8%
6867 1
3.8%
5542 1
3.8%
4738 1
3.8%
3339 1
3.8%
2838 1
3.8%
2240 1
3.8%
981 1
3.8%
634 1
3.8%

Interactions

2023-12-13T07:53:07.775057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:48.679322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:50.054781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:51.315150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:52.929171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:54.338077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:55.708080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:57.256479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:58.986642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:00.536391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:02.131353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:03.623345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:04.891474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:06.377093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:07.910144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:48.772918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:50.147629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:51.444725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:53.027775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:54.436096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:55.821806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:57.393582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:59.082675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:00.698497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:02.243448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:03.715371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:05.225789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:06.455591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:08.017840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:48.886594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:50.232661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:51.546188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:53.115884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:54.526228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:55.940370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:57.489560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:59.168472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:00.830392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:02.346105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:03.796132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:05.317499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:06.542864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:08.127571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:48.983622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:50.330237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:51.644416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:53.229149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:54.604270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:56.027557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:57.584497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:59.258542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:00.946449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:02.454158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:03.880608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:05.429442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:06.647168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:08.234041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:49.073520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:50.420683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:51.731156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:53.335674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:54.711567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:56.145752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:57.673908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:59.364919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:01.052248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:02.577416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:03.980550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:05.530446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:06.790748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:08.329463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:49.171801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:50.493193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:51.803892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:53.446402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:54.782777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:56.239329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:57.762174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:59.462431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:01.130124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:02.674239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:04.055125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:05.612544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:06.879231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:08.418692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:49.274366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:50.584025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:52.199023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:53.559308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:54.875996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:56.347788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:57.886428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:59.561368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:01.252285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:02.786620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:04.161753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:05.707783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:06.981692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:08.514217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:49.385487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:50.672749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:52.306141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:53.673145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:54.986675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:56.472608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:57.998468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:59.678457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:01.360884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:02.899320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:04.261177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:05.805843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:07.096950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:08.606669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:49.478309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:50.769626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:52.404601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:53.790606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:55.079551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:56.583708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:58.090307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:59.809350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:01.471621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:03.017395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:04.369429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:05.887831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:07.198806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:08.712613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:49.573208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:50.851462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:52.481864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:53.876708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:55.188149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:56.694970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:58.504963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:59.917265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:01.575778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:03.112507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:04.455334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:05.972485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:07.291228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:08.784035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:49.670811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:50.940599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:52.560157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:53.959803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:55.279764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:56.798567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:58.610808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:00.026756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:01.667156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:03.198566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:04.530257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:06.046349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:07.380649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:08.865948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:49.771506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:51.022190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:52.651142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:54.049337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:55.403381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:56.907396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:58.700198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:00.151688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:01.781657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:03.289297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:04.612725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:06.135384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:07.477875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:08.969027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:49.878433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:51.126553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:52.752306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:54.149569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:55.518848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:57.050616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:58.798629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:00.288409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:01.934301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:03.416928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:04.710939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:06.220016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:07.584283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:09.048350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:49.977415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:51.235866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:52.829817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:54.238892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:55.607701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:57.155215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:58.889048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:00.418856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:02.038460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:03.530190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:04.804621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:06.305947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:53:07.668857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:53:13.836219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연별 및 용도별신축_계신축_철근콘크리트신축_철골신축_조적신축_철골철근신축_나무신축_기타증축_개축_이전_대수선_계증축_개축_이전_대수선철근콘크리트증축_개축_이전_대수선철골증축_개축_이전_대수선조적증축_개축_이전_대수선철골철근증축_개축_이전_대수선나무증축_개축_이전_대수선_기타
연별 및 용도별1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
신축_계1.0001.0000.9990.7090.9730.9670.8860.9240.9620.9990.8150.9130.9170.9110.957
신축_철근콘크리트1.0000.9991.0000.6630.9510.9420.9430.9081.0001.0000.7760.9130.8760.9480.944
신축_철골1.0000.7090.6631.0000.7430.7530.7650.8350.7200.6630.9040.6910.9280.7200.859
신축_조적1.0000.9730.9510.7431.0001.0000.8830.9900.9360.9510.7621.0001.0000.9360.980
신축_철골철근1.0000.9670.9420.7531.0001.0000.8940.9400.9620.9420.7641.0001.0000.9620.992
신축_나무1.0000.8860.9430.7650.8830.8941.0000.9210.9070.9430.9010.8670.7271.0000.932
신축_기타1.0000.9240.9080.8350.9900.9400.9211.0000.9180.9080.8980.8580.9730.9571.000
증축_개축_이전_대수선_계1.0000.9621.0000.7200.9360.9620.9070.9181.0001.0000.7090.8310.9430.9880.981
증축_개축_이전_대수선철근콘크리트1.0000.9991.0000.6630.9510.9420.9430.9081.0001.0000.7760.9130.8760.9480.944
증축_개축_이전_대수선철골1.0000.8150.7760.9040.7620.7640.9010.8980.7090.7761.0000.8620.6780.7090.911
증축_개축_이전_대수선조적1.0000.9130.9130.6911.0001.0000.8670.8580.8310.9130.8621.0000.8900.7920.950
증축_개축_이전_대수선철골철근1.0000.9170.8760.9281.0001.0000.7270.9730.9430.8760.6780.8901.0000.9620.991
증축_개축_이전_대수선나무1.0000.9110.9480.7200.9360.9621.0000.9570.9880.9480.7090.7920.9621.0000.981
증축_개축_이전_대수선_기타1.0000.9570.9440.8590.9800.9920.9321.0000.9810.9440.9110.9500.9910.9811.000
2023-12-13T07:53:14.069251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
신축_계신축_철근콘크리트신축_철골신축_조적신축_철골철근신축_나무신축_기타증축_개축_이전_대수선_계증축_개축_이전_대수선철근콘크리트증축_개축_이전_대수선철골증축_개축_이전_대수선조적증축_개축_이전_대수선철골철근증축_개축_이전_대수선나무증축_개축_이전_대수선_기타
신축_계1.0000.9840.9320.8950.9530.9300.9570.9970.9840.9650.7730.7760.8220.873
신축_철근콘크리트0.9841.0000.8990.8780.9610.9190.9320.9881.0000.9400.7240.7970.8110.840
신축_철골0.9320.8991.0000.8480.9430.8660.9510.9270.8990.9750.7880.7550.8130.928
신축_조적0.8950.8780.8481.0000.8350.8890.8800.9000.8780.8650.9400.7230.9100.901
신축_철골철근0.9530.9610.9430.8351.0000.9340.9140.9520.9610.9630.7020.8600.8500.846
신축_나무0.9300.9190.8660.8890.9341.0000.8660.9210.9190.9220.8460.8970.9940.816
신축_기타0.9570.9320.9510.8800.9140.8661.0000.9560.9320.9440.7530.6870.7920.879
증축_개축_이전_대수선_계0.9970.9880.9270.9000.9520.9210.9561.0000.9880.9630.7720.7690.8220.871
증축_개축_이전_대수선철근콘크리트0.9841.0000.8990.8780.9610.9190.9320.9881.0000.9400.7240.7970.8110.840
증축_개축_이전_대수선철골0.9650.9400.9750.8650.9630.9220.9440.9630.9401.0000.8050.7970.8430.916
증축_개축_이전_대수선조적0.7730.7240.7880.9400.7020.8460.7530.7720.7240.8051.0000.6940.8650.895
증축_개축_이전_대수선철골철근0.7760.7970.7550.7230.8600.8970.6870.7690.7970.7970.6941.0000.8210.732
증축_개축_이전_대수선나무0.8220.8110.8130.9100.8500.9940.7920.8220.8110.8430.8650.8211.0000.818
증축_개축_이전_대수선_기타0.8730.8400.9280.9010.8460.8160.8790.8710.8400.9160.8950.7320.8181.000

Missing values

2023-12-13T07:53:09.186494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:53:09.390680image/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.
2023-12-13T07:53:09.528440image/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

연별 및 용도별신축_계신축_철근콘크리트신축_철골신축_조적신축_철골철근신축_나무신축_기타증축_개축_이전_대수선_계증축_개축_이전_대수선철근콘크리트증축_개축_이전_대수선철골증축_개축_이전_대수선조적증축_개축_이전_대수선철골철근증축_개축_이전_대수선나무증축_개축_이전_대수선_기타
02013동 수23363836011677182479132210115753633873949194497979
12013연면적100940936402309314571015471428517097737845382852915743367219926399079149793861287661
22014동 수23059842711665147676130810716094657576717134399993
32014연면적12470188906021230287131140251563531013839502110601718597246215121778460133702905858961
42015동 수235998468119181451901527145169316743818777856110463
52015연면적123577658891346287082913034631668513853210027111420678445825221936494352278687982975542
62016동 수2475390961258612241441588115180557298880559563122371
72016연면적1453180510870790298796390188450472126672572013087997102793682245069604533828751154944738
82017동 수223167703119428961131562100156356029799034040119244
92017연면적1025873971887452640010568072379981305444635886746067172231815321276621881841157313339
연별 및 용도별신축_계신축_철근콘크리트신축_철골신축_조적신축_철골철근신축_나무신축_기타증축_개축_이전_대수선_계증축_개축_이전_대수선철근콘크리트증축_개축_이전_대수선철골증축_개축_이전_대수선조적증축_개축_이전_대수선철골철근증축_개축_이전_대수선나무증축_개축_이전_대수선_기타
16농수산용동수39985736731532308312572211931601115
17농수산용연면적115648248804108004513046342037457422567694320165293202150019682240
18공 업 용동수143314212491912<NA>11565704901202
19공 업 용연면적9624788309585580714322919<NA>51448819042015443752222660155
20공 공 용동수1084759<NA><NA><NA>25116350000
21공 공 용연면적66604632352999<NA><NA><NA>370380043602219820000
22교육/사회용동 수75039921337187672091255803221
23교육/사회용연면적40600528094169012514509743453111117201610681227109036345173119
24기타 동수175315015203526401163108102440324
25기타연면적718451373896340836158085811411675724993436552278612430106634