Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory107.0 B

Variable types

Numeric11

Dataset

Description전북특별자치도 진안군 도시계획정보시스템 건축물주제도에 대한 데이터로 공간정보관리번호, X좌표 및 Y좌표 정보, 건물군관리번호, 필지고유번호(PNU) 등 정보를 제공합니다.
Author전북특별자치도 진안군
URLhttps://www.data.go.kr/data/15119140/fileData.do

Alerts

공간정보관리번호 is highly overall correlated with 필지고유번호(PNU)High correlation
X좌표최소값 is highly overall correlated with X좌표최대값High correlation
Y좌표최소값 is highly overall correlated with Y좌표최대값High correlation
X좌표최대값 is highly overall correlated with X좌표최소값High correlation
Y좌표최대값 is highly overall correlated with Y좌표최소값High correlation
건폐율 is highly overall correlated with 용적율High correlation
필지고유번호(PNU) is highly overall correlated with 공간정보관리번호High correlation
용적율 is highly overall correlated with 건폐율High correlation
공간정보관리번호 has unique valuesUnique
건물군관리번호 has unique valuesUnique
건폐율 has 2784 (27.8%) zerosZeros
층수 has 134 (1.3%) zerosZeros
용적율 has 2780 (27.8%) zerosZeros

Reproduction

Analysis started2024-03-14 13:47:18.289802
Analysis finished2024-03-14 13:47:50.344696
Duration32.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

공간정보관리번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6132.666
Minimum1
Maximum12147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T22:47:50.479690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile629.95
Q13123.75
median6186.5
Q39203.25
95-th percentile11548.05
Maximum12147
Range12146
Interquartile range (IQR)6079.5

Descriptive statistics

Standard deviation3512.4417
Coefficient of variation (CV)0.57274302
Kurtosis-1.2041878
Mean6132.666
Median Absolute Deviation (MAD)3038
Skewness-0.026087463
Sum61326660
Variance12337247
MonotonicityNot monotonic
2024-03-14T22:47:50.737663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3700 1
 
< 0.1%
9003 1
 
< 0.1%
6979 1
 
< 0.1%
4998 1
 
< 0.1%
11335 1
 
< 0.1%
7835 1
 
< 0.1%
7624 1
 
< 0.1%
10635 1
 
< 0.1%
8226 1
 
< 0.1%
4122 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
ValueCountFrequency (%)
12147 1
< 0.1%
12145 1
< 0.1%
12144 1
< 0.1%
12143 1
< 0.1%
12142 1
< 0.1%
12141 1
< 0.1%
12140 1
< 0.1%
12139 1
< 0.1%
12138 1
< 0.1%
12136 1
< 0.1%

X좌표최소값
Real number (ℝ)

HIGH CORRELATION 

Distinct9937
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23840812
Minimum22440102
Maximum25574377
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T22:47:50.987234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22440102
5-th percentile22868736
Q123340906
median23810472
Q324156003
95-th percentile25132256
Maximum25574377
Range3134275
Interquartile range (IQR)815097.5

Descriptive statistics

Standard deviation675340.18
Coefficient of variation (CV)0.028327062
Kurtosis-0.38389991
Mean23840812
Median Absolute Deviation (MAD)454076
Skewness0.55266779
Sum2.3840812 × 1011
Variance4.5608436 × 1011
MonotonicityNot monotonic
2024-03-14T22:47:51.317711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23850660 3
 
< 0.1%
23820457 2
 
< 0.1%
23419764 2
 
< 0.1%
23833504 2
 
< 0.1%
23439436 2
 
< 0.1%
23556450 2
 
< 0.1%
22936318 2
 
< 0.1%
23126247 2
 
< 0.1%
23303950 2
 
< 0.1%
23807666 2
 
< 0.1%
Other values (9927) 9979
99.8%
ValueCountFrequency (%)
22440102 1
< 0.1%
22443254 1
< 0.1%
22444773 1
< 0.1%
22457198 1
< 0.1%
22458321 1
< 0.1%
22483910 1
< 0.1%
22493316 1
< 0.1%
22540610 1
< 0.1%
22541794 1
< 0.1%
22542108 1
< 0.1%
ValueCountFrequency (%)
25574377 1
< 0.1%
25573069 1
< 0.1%
25571786 1
< 0.1%
25567808 1
< 0.1%
25565980 1
< 0.1%
25563581 1
< 0.1%
25561643 1
< 0.1%
25559470 1
< 0.1%
25555237 1
< 0.1%
25555073 1
< 0.1%

Y좌표최소값
Real number (ℝ)

HIGH CORRELATION 

Distinct9965
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25676076
Minimum23670040
Maximum27979338
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T22:47:51.873392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23670040
5-th percentile24234628
Q124935608
median25490138
Q326258202
95-th percentile27487556
Maximum27979338
Range4309298
Interquartile range (IQR)1322594.8

Descriptive statistics

Standard deviation968263.23
Coefficient of variation (CV)0.037710716
Kurtosis-0.5673667
Mean25676076
Median Absolute Deviation (MAD)654201
Skewness0.41073134
Sum2.5676076 × 1011
Variance9.3753369 × 1011
MonotonicityNot monotonic
2024-03-14T22:47:52.318467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25250862 3
 
< 0.1%
25104422 2
 
< 0.1%
25302763 2
 
< 0.1%
25505473 2
 
< 0.1%
24564812 2
 
< 0.1%
25093961 2
 
< 0.1%
25530893 2
 
< 0.1%
24877978 2
 
< 0.1%
24020089 2
 
< 0.1%
26009881 2
 
< 0.1%
Other values (9955) 9979
99.8%
ValueCountFrequency (%)
23670040 1
< 0.1%
23694144 1
< 0.1%
23698721 1
< 0.1%
23717424 1
< 0.1%
23744380 1
< 0.1%
23766729 1
< 0.1%
23768390 1
< 0.1%
23774842 1
< 0.1%
23775602 1
< 0.1%
23785596 1
< 0.1%
ValueCountFrequency (%)
27979338 1
< 0.1%
27949950 1
< 0.1%
27915388 1
< 0.1%
27913281 1
< 0.1%
27912130 1
< 0.1%
27911321 1
< 0.1%
27909557 1
< 0.1%
27909309 1
< 0.1%
27908657 1
< 0.1%
27907507 1
< 0.1%

X좌표최대값
Real number (ℝ)

HIGH CORRELATION 

Distinct9941
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23844488
Minimum22443656
Maximum25577671
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T22:47:52.724818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22443656
5-th percentile22871240
Q123343670
median23813618
Q324159418
95-th percentile25134876
Maximum25577671
Range3134015
Interquartile range (IQR)815748.25

Descriptive statistics

Standard deviation675191.64
Coefficient of variation (CV)0.028316466
Kurtosis-0.38400061
Mean23844488
Median Absolute Deviation (MAD)453618
Skewness0.55262606
Sum2.3844488 × 1011
Variance4.5588375 × 1011
MonotonicityNot monotonic
2024-03-14T22:47:53.160690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24674072 2
 
< 0.1%
23961634 2
 
< 0.1%
23300932 2
 
< 0.1%
24495742 2
 
< 0.1%
24083348 2
 
< 0.1%
23196148 2
 
< 0.1%
25139075 2
 
< 0.1%
23777006 2
 
< 0.1%
23891111 2
 
< 0.1%
23448141 2
 
< 0.1%
Other values (9931) 9980
99.8%
ValueCountFrequency (%)
22443656 1
< 0.1%
22446563 1
< 0.1%
22446913 1
< 0.1%
22461115 1
< 0.1%
22466092 1
< 0.1%
22486390 1
< 0.1%
22494888 1
< 0.1%
22541514 1
< 0.1%
22543818 1
< 0.1%
22544751 1
< 0.1%
ValueCountFrequency (%)
25577671 1
< 0.1%
25575603 1
< 0.1%
25574568 1
< 0.1%
25571693 1
< 0.1%
25568170 1
< 0.1%
25566036 1
< 0.1%
25565253 1
< 0.1%
25562750 1
< 0.1%
25558714 1
< 0.1%
25557789 1
< 0.1%

Y좌표최대값
Real number (ℝ)

HIGH CORRELATION 

Distinct9947
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25679805
Minimum23696544
Maximum28033127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T22:47:53.597716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23696544
5-th percentile24242106
Q124939361
median25492374
Q326261466
95-th percentile27492492
Maximum28033127
Range4336583
Interquartile range (IQR)1322104.5

Descriptive statistics

Standard deviation968272.43
Coefficient of variation (CV)0.037705598
Kurtosis-0.5683287
Mean25679805
Median Absolute Deviation (MAD)654587.5
Skewness0.41164688
Sum2.5679805 × 1011
Variance9.375515 × 1011
MonotonicityNot monotonic
2024-03-14T22:47:54.027055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25261839 3
 
< 0.1%
25255822 2
 
< 0.1%
25944503 2
 
< 0.1%
26640230 2
 
< 0.1%
27456353 2
 
< 0.1%
26054453 2
 
< 0.1%
24480394 2
 
< 0.1%
24874834 2
 
< 0.1%
26919718 2
 
< 0.1%
24349459 2
 
< 0.1%
Other values (9937) 9979
99.8%
ValueCountFrequency (%)
23696544 1
< 0.1%
23701398 1
< 0.1%
23749731 1
< 0.1%
23764408 1
< 0.1%
23770182 1
< 0.1%
23772950 1
< 0.1%
23777407 1
< 0.1%
23788274 1
< 0.1%
23794570 1
< 0.1%
23822656 1
< 0.1%
ValueCountFrequency (%)
28033127 1
< 0.1%
27980978 1
< 0.1%
27959037 1
< 0.1%
27924141 1
< 0.1%
27916390 1
< 0.1%
27915522 1
< 0.1%
27913801 1
< 0.1%
27911932 1
< 0.1%
27911590 1
< 0.1%
27911444 1
< 0.1%

건폐율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3769
Distinct (%)37.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.487069
Minimum0
Maximum260.8
Zeros2784
Zeros (%)27.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T22:47:54.440967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median16.5
Q327.72
95-th percentile51.1035
Maximum260.8
Range260.8
Interquartile range (IQR)27.72

Descriptive statistics

Standard deviation18.512275
Coefficient of variation (CV)1.0013635
Kurtosis12.251403
Mean18.487069
Median Absolute Deviation (MAD)13.695
Skewness2.078082
Sum184870.69
Variance342.70434
MonotonicityNot monotonic
2024-03-14T22:47:54.903101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2784
 
27.8%
15.0 15
 
0.1%
14.5 12
 
0.1%
20.0 11
 
0.1%
24.5 10
 
0.1%
19.8 9
 
0.1%
10.0 9
 
0.1%
13.9 9
 
0.1%
15.1 9
 
0.1%
25.0 9
 
0.1%
Other values (3759) 7123
71.2%
ValueCountFrequency (%)
0.0 2784
27.8%
0.03 1
 
< 0.1%
0.04 2
 
< 0.1%
0.066 1
 
< 0.1%
0.07 3
 
< 0.1%
0.08 1
 
< 0.1%
0.1 1
 
< 0.1%
0.14 1
 
< 0.1%
0.15 1
 
< 0.1%
0.18 2
 
< 0.1%
ValueCountFrequency (%)
260.8 1
< 0.1%
254.5 1
< 0.1%
222.49 1
< 0.1%
187.87 1
< 0.1%
184.0 1
< 0.1%
176.88 1
< 0.1%
163.95 1
< 0.1%
156.24 1
< 0.1%
149.11 1
< 0.1%
148.96 1
< 0.1%

층수
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0556
Minimum0
Maximum5
Zeros134
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T22:47:55.275796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.32911924
Coefficient of variation (CV)0.31178404
Kurtosis30.772196
Mean1.0556
Median Absolute Deviation (MAD)0
Skewness4.1123739
Sum10556
Variance0.10831947
MonotonicityNot monotonic
2024-03-14T22:47:55.627054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 9289
92.9%
2 491
 
4.9%
0 134
 
1.3%
3 64
 
0.6%
4 17
 
0.2%
5 5
 
0.1%
ValueCountFrequency (%)
0 134
 
1.3%
1 9289
92.9%
2 491
 
4.9%
3 64
 
0.6%
4 17
 
0.2%
5 5
 
0.1%
ValueCountFrequency (%)
5 5
 
0.1%
4 17
 
0.2%
3 64
 
0.6%
2 491
 
4.9%
1 9289
92.9%
0 134
 
1.3%

건물군관리번호
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15937484
Minimum173
Maximum1.001869 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T22:47:56.022444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum173
5-th percentile3922.75
Q18144.75
median12089
Q315952.25
95-th percentile1.0018192 × 108
Maximum1.001869 × 108
Range1.0018673 × 108
Interquartile range (IQR)7807.5

Descriptive statistics

Standard deviation36631042
Coefficient of variation (CV)2.2984206
Kurtosis1.4797085
Mean15937484
Median Absolute Deviation (MAD)3904
Skewness1.8653183
Sum1.5937484 × 1011
Variance1.3418332 × 1015
MonotonicityNot monotonic
2024-03-14T22:47:56.468156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17313 1
 
< 0.1%
9472 1
 
< 0.1%
12578 1
 
< 0.1%
9369 1
 
< 0.1%
10988 1
 
< 0.1%
13577 1
 
< 0.1%
8985 1
 
< 0.1%
12638 1
 
< 0.1%
14228 1
 
< 0.1%
7636 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
173 1
< 0.1%
176 1
< 0.1%
182 1
< 0.1%
289 1
< 0.1%
291 1
< 0.1%
295 1
< 0.1%
381 1
< 0.1%
387 1
< 0.1%
396 1
< 0.1%
406 1
< 0.1%
ValueCountFrequency (%)
100186900 1
< 0.1%
100186880 1
< 0.1%
100186861 1
< 0.1%
100186860 1
< 0.1%
100186840 1
< 0.1%
100186819 1
< 0.1%
100186809 1
< 0.1%
100186805 1
< 0.1%
100186804 1
< 0.1%
100186803 1
< 0.1%

필지고유번호(PNU)
Real number (ℝ)

HIGH CORRELATION 

Distinct7359
Distinct (%)73.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5720332 × 1018
Minimum4.572025 × 1018
Maximum4.57204 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T22:47:56.882097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.572025 × 1018
5-th percentile4.572025 × 1018
Q14.572025 × 1018
median4.572035 × 1018
Q34.572038 × 1018
95-th percentile4.57204 × 1018
Maximum4.57204 × 1018
Range1.50051 × 1013
Interquartile range (IQR)1.2987994 × 1013

Descriptive statistics

Standard deviation5.3119813 × 1012
Coefficient of variation (CV)1.1618422 × 10-6
Kurtosis-1.1374361
Mean4.5720332 × 1018
Median Absolute Deviation (MAD)3.0019994 × 1012
Skewness-0.55198515
Sum-9.1462821 × 1018
Variance2.8217146 × 1025
MonotonicityNot monotonic
2024-03-14T22:47:57.258174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4572025021104030000 26
 
0.3%
4572025023100320000 26
 
0.3%
4572025021104060000 26
 
0.3%
4572025023102840000 20
 
0.2%
4572025021104790000 17
 
0.2%
4572031025110530000 17
 
0.2%
4572025022101250000 14
 
0.1%
4572025022101260000 14
 
0.1%
4572038029105030000 14
 
0.1%
4572040024109060000 13
 
0.1%
Other values (7349) 9813
98.1%
ValueCountFrequency (%)
4572025021100330000 1
 
< 0.1%
4572025021100450000 1
 
< 0.1%
4572025021100540000 1
 
< 0.1%
4572025021100560000 4
< 0.1%
4572025021100570000 2
< 0.1%
4572025021100600000 1
 
< 0.1%
4572025021100610000 1
 
< 0.1%
4572025021100620000 1
 
< 0.1%
4572025021100630000 1
 
< 0.1%
4572025021100700000 1
 
< 0.1%
ValueCountFrequency (%)
4572040026200720000 1
< 0.1%
4572040026200520000 2
< 0.1%
4572040026200280000 1
< 0.1%
4572040026110460000 1
< 0.1%
4572040026109930000 1
< 0.1%
4572040026109900000 1
< 0.1%
4572040026109880000 1
< 0.1%
4572040026109800000 1
< 0.1%
4572040026109770000 1
< 0.1%
4572040026109760000 1
< 0.1%

건축년도
Real number (ℝ)

Distinct104
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1979.1947
Minimum1871
Maximum2013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T22:47:57.546209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1871
5-th percentile1937
Q11955
median1986.5
Q32002
95-th percentile2011
Maximum2013
Range142
Interquartile range (IQR)47

Descriptive statistics

Standard deviation25.96285
Coefficient of variation (CV)0.013117886
Kurtosis-1.2435951
Mean1979.1947
Median Absolute Deviation (MAD)21.5
Skewness-0.37470239
Sum19791947
Variance674.0696
MonotonicityNot monotonic
2024-03-14T22:47:57.842886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1997 332
 
3.3%
1949 330
 
3.3%
2011 296
 
3.0%
2012 292
 
2.9%
2009 286
 
2.9%
2008 282
 
2.8%
2010 278
 
2.8%
1996 263
 
2.6%
1999 263
 
2.6%
2000 260
 
2.6%
Other values (94) 7118
71.2%
ValueCountFrequency (%)
1871 1
 
< 0.1%
1890 1
 
< 0.1%
1899 2
 
< 0.1%
1900 1
 
< 0.1%
1902 1
 
< 0.1%
1906 1
 
< 0.1%
1909 1
 
< 0.1%
1910 2
 
< 0.1%
1913 1
 
< 0.1%
1919 5
0.1%
ValueCountFrequency (%)
2013 43
 
0.4%
2012 292
2.9%
2011 296
3.0%
2010 278
2.8%
2009 286
2.9%
2008 282
2.8%
2007 213
2.1%
2006 213
2.1%
2005 202
2.0%
2004 184
1.8%

용적율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3857
Distinct (%)38.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.955812
Minimum0
Maximum1011
Zeros2780
Zeros (%)27.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T22:47:58.149060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median16.47
Q328.21
95-th percentile54.9825
Maximum1011
Range1011
Interquartile range (IQR)28.21

Descriptive statistics

Standard deviation25.43171
Coefficient of variation (CV)1.2744012
Kurtosis247.50802
Mean19.955812
Median Absolute Deviation (MAD)14.1
Skewness8.72377
Sum199558.12
Variance646.77189
MonotonicityNot monotonic
2024-03-14T22:47:58.487179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2780
 
27.8%
15.0 16
 
0.2%
24.5 12
 
0.1%
20.0 12
 
0.1%
25.0 11
 
0.1%
14.5 11
 
0.1%
15.1 10
 
0.1%
13.5 10
 
0.1%
10.0 9
 
0.1%
16.5 8
 
0.1%
Other values (3847) 7121
71.2%
ValueCountFrequency (%)
0.0 2780
27.8%
0.03 1
 
< 0.1%
0.04 2
 
< 0.1%
0.066 1
 
< 0.1%
0.07 3
 
< 0.1%
0.08 1
 
< 0.1%
0.1 1
 
< 0.1%
0.14 1
 
< 0.1%
0.15 1
 
< 0.1%
0.18 2
 
< 0.1%
ValueCountFrequency (%)
1011.0 1
< 0.1%
308.65 1
< 0.1%
268.48 1
< 0.1%
263.83 1
< 0.1%
260.8 1
< 0.1%
257.98 1
< 0.1%
256.39 1
< 0.1%
254.5 1
< 0.1%
251.16 1
< 0.1%
246.86 1
< 0.1%

Interactions

2024-03-14T22:47:46.890037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:19.589433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:22.592361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:25.551183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:29.438123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:32.126331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:34.509118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:37.389088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:39.484497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:42.612225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:44.806377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:47.057492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:19.859170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:22.842704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:25.922732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:29.710658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:32.395886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:34.741677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:37.656855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:39.754297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:42.930052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:44.981518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:47.229472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:20.140174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:23.091844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:26.363941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:29.993799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:32.665657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:35.014454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:37.850175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:40.026023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:43.117530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:45.210089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:47.388090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:20.404699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:23.356359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:26.772296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:30.258146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:32.930543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:35.274444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:38.008068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:40.286582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:43.287886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:45.412090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:47.645008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:20.685213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:23.632887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:27.144577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:30.535104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:33.202728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:35.544463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:38.248457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:40.561460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:43.475115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:45.595770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:47.892830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:20.954063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:23.895134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:27.544590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:30.699790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:33.458330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:35.802669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:38.408480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:40.822747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:43.648419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:45.763239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:48.150414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:21.218745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:24.161752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:27.898828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:30.867942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:33.673879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:36.060508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:38.566666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:41.080878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:43.820498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:45.933353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:48.408611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:21.487024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:24.427884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:28.154218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:31.032848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:33.832433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:36.316266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:38.720576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:41.341973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:44.050472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:46.102861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:48.676909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:21.755801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:24.699379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:28.627419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:31.290549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:33.998211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:36.579052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:38.883425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:41.789872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:44.253046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:46.276118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:48.961235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:22.046735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:24.984549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:28.907312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:31.579218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:34.176713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:36.855788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:39.060080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:42.072295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:44.443944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:46.539832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:49.239615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:22.325970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:25.267212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:29.180474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:31.861918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:34.350688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:37.132452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:39.233793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:42.351962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:44.631456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:47:46.720844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T22:47:58.664935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공간정보관리번호X좌표최소값Y좌표최소값X좌표최대값Y좌표최대값건폐율층수건물군관리번호필지고유번호(PNU)건축년도용적율
공간정보관리번호1.0000.8490.8580.8490.8570.2140.1760.4050.8570.3580.201
X좌표최소값0.8491.0000.7321.0000.7330.1230.1220.0720.7910.2830.147
Y좌표최소값0.8580.7321.0000.7330.9980.1760.1340.0680.8300.2300.192
X좌표최대값0.8491.0000.7331.0000.7340.1220.1220.0720.7910.2810.147
Y좌표최대값0.8570.7330.9980.7341.0000.1830.1300.0570.8300.2270.196
건폐율0.2140.1230.1760.1220.1831.0000.1700.0810.0880.1580.827
층수0.1760.1220.1340.1220.1300.1701.0000.2240.1150.1810.353
건물군관리번호0.4050.0720.0680.0720.0570.0810.2241.0000.0660.5710.000
필지고유번호(PNU)0.8570.7910.8300.7910.8300.0880.1150.0661.0000.2740.102
건축년도0.3580.2830.2300.2810.2270.1580.1810.5710.2741.0000.099
용적율0.2010.1470.1920.1470.1960.8270.3530.0000.1020.0991.000
2024-03-14T22:47:58.901531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공간정보관리번호X좌표최소값Y좌표최소값X좌표최대값Y좌표최대값건폐율층수건물군관리번호필지고유번호(PNU)건축년도용적율
공간정보관리번호1.000-0.2950.161-0.2950.161-0.035-0.0690.0790.6790.201-0.037
X좌표최소값-0.2951.0000.4431.0000.4430.0190.0310.024-0.466-0.0020.021
Y좌표최소값0.1610.4431.0000.4431.000-0.0630.0340.0070.2260.102-0.060
X좌표최대값-0.2951.0000.4431.0000.4430.0170.0320.023-0.465-0.0010.019
Y좌표최대값0.1610.4431.0000.4431.000-0.0640.0340.0060.2260.103-0.060
건폐율-0.0350.019-0.0630.017-0.0641.0000.0940.101-0.0400.1600.987
층수-0.0690.0310.0340.0320.0340.0941.000-0.011-0.1020.1060.159
건물군관리번호0.0790.0240.0070.0230.0060.101-0.0111.0000.0090.3840.101
필지고유번호(PNU)0.679-0.4660.226-0.4650.226-0.040-0.1020.0091.0000.081-0.047
건축년도0.201-0.0020.102-0.0010.1030.1600.1060.3840.0811.0000.165
용적율-0.0370.021-0.0600.019-0.0600.9870.1590.101-0.0470.1651.000

Missing values

2024-03-14T22:47:49.606140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T22:47:50.121816image/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

공간정보관리번호X좌표최소값Y좌표최소값X좌표최대값Y좌표최대값건폐율층수건물군관리번호필지고유번호(PNU)건축년도용적율
333437002506302226728444250649562672994843.571173134572032022109910000199643.57
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