Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells11128
Missing cells (%)11.1%
Duplicate rows780
Duplicate rows (%)7.8%
Total size in memory927.7 KiB
Average record size in memory95.0 B

Variable types

Numeric6
Text2
Categorical2

Dataset

Description경기도 광주시 도시계획정보시스템의 건축물주제도 현황에 관한 데이터로 지번코드, 층수, 건폐율, 용적율 등에 대한 항목을 제공합니다.
Author경기도 광주시
URLhttps://www.data.go.kr/data/15122764/fileData.do

Alerts

시군구코드 has constant value ""Constant
시군구 has constant value ""Constant
Dataset has 780 (7.8%) duplicate rowsDuplicates
층수 is highly overall correlated with 용적율High correlation
건폐율 is highly overall correlated with 용적율High correlation
용적율 is highly overall correlated with 층수 and 1 other fieldsHigh correlation
건물군관리번호 has 2326 (23.3%) missing valuesMissing
건축물용도 has 2328 (23.3%) missing valuesMissing
구조 has 3371 (33.7%) missing valuesMissing
건축년도 has 3101 (31.0%) missing valuesMissing
층수 has 3370 (33.7%) zerosZeros
건폐율 has 3854 (38.5%) zerosZeros
용적율 has 3852 (38.5%) zerosZeros

Reproduction

Analysis started2024-04-17 09:40:53.425633
Analysis finished2024-04-17 09:40:57.440476
Duration4.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지번코드
Real number (ℝ)

Distinct6429
Distinct (%)64.3%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4.1610236 × 1018
Minimum4.1610101 × 1018
Maximum4.161037 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T18:40:57.504516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.1610101 × 1018
5-th percentile4.1610103 × 1018
Q14.1610113 × 1018
median4.1610253 × 1018
Q34.1610259 × 1018
95-th percentile4.161035 × 1018
Maximum4.161037 × 1018
Range2.6928102 × 1013
Interquartile range (IQR)1.4633999 × 1013

Descriptive statistics

Standard deviation8.2902155 × 1012
Coefficient of variation (CV)1.99235 × 10-6
Kurtosis-0.85607801
Mean4.1610236 × 1018
Median Absolute Deviation (MAD)6.1000393 × 1011
Skewness-0.48610368
Sum4.5061888 × 1018
Variance6.8727673 × 1025
MonotonicityNot monotonic
2024-04-17T18:40:57.853586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4161010300101130240 21
 
0.2%
4161025921104529920 16
 
0.2%
4161010300101000192 16
 
0.2%
4161011000104670208 15
 
0.1%
4161025022108550144 15
 
0.1%
4161010100101349888 14
 
0.1%
4161010100101519872 13
 
0.1%
4161010300103500288 13
 
0.1%
4161010100100109824 13
 
0.1%
4161010300101179904 13
 
0.1%
Other values (6419) 9849
98.5%
ValueCountFrequency (%)
4161010100100089856 1
 
< 0.1%
4161010100100109824 13
0.1%
4161010100100120064 1
 
< 0.1%
4161010100100140032 1
 
< 0.1%
4161010100100170240 2
 
< 0.1%
4161010100100190208 1
 
< 0.1%
4161010100100199936 3
 
< 0.1%
4161010100100219904 1
 
< 0.1%
4161010100100239872 2
 
< 0.1%
4161010100100250112 1
 
< 0.1%
ValueCountFrequency (%)
4161037028201739776 1
< 0.1%
4161037028201589760 1
< 0.1%
4161037028200229888 1
< 0.1%
4161037028103059968 1
< 0.1%
4161037028102969856 2
< 0.1%
4161037028102819840 2
< 0.1%
4161037028102580224 1
< 0.1%
4161037028102309888 1
< 0.1%
4161037028102280192 1
< 0.1%
4161037028102099968 1
< 0.1%

건물군관리번호
Real number (ℝ)

MISSING 

Distinct7128
Distinct (%)92.9%
Missing2326
Missing (%)23.3%
Infinite0
Infinite (%)0.0%
Mean33342618
Minimum1
Maximum1.0030385 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T18:40:57.972658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1027.3
Q114093.5
median26091
Q31.001927 × 108
95-th percentile1.0027631 × 108
Maximum1.0030385 × 108
Range1.0030385 × 108
Interquartile range (IQR)1.0017861 × 108

Descriptive statistics

Standard deviation47214587
Coefficient of variation (CV)1.4160432
Kurtosis-1.4948939
Mean33342618
Median Absolute Deviation (MAD)15678.5
Skewness0.71098148
Sum2.5587125 × 1011
Variance2.2292172 × 1015
MonotonicityNot monotonic
2024-04-17T18:40:58.105147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
778 5
 
0.1%
1397 5
 
0.1%
28619 5
 
0.1%
770 4
 
< 0.1%
16930 4
 
< 0.1%
1237 4
 
< 0.1%
22362 4
 
< 0.1%
19717 3
 
< 0.1%
23888 3
 
< 0.1%
11127 3
 
< 0.1%
Other values (7118) 7634
76.3%
(Missing) 2326
 
23.3%
ValueCountFrequency (%)
1 2
< 0.1%
3 1
< 0.1%
5 1
< 0.1%
10 1
< 0.1%
12 1
< 0.1%
13 2
< 0.1%
17 1
< 0.1%
19 1
< 0.1%
23 1
< 0.1%
24 1
< 0.1%
ValueCountFrequency (%)
100303851 1
< 0.1%
100303842 1
< 0.1%
100303804 1
< 0.1%
100303642 1
< 0.1%
100303602 1
< 0.1%
100303462 1
< 0.1%
100303438 1
< 0.1%
100303321 1
< 0.1%
100303298 1
< 0.1%
100303218 1
< 0.1%

층수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3407
Minimum0
Maximum24
Zeros3370
Zeros (%)33.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T18:40:58.218961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum24
Range24
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4525269
Coefficient of variation (CV)1.0834094
Kurtosis22.622804
Mean1.3407
Median Absolute Deviation (MAD)1
Skewness2.4463148
Sum13407
Variance2.1098345
MonotonicityNot monotonic
2024-04-17T18:40:58.315616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 3370
33.7%
1 3171
31.7%
2 1693
16.9%
4 1250
 
12.5%
3 436
 
4.4%
5 55
 
0.5%
6 10
 
0.1%
8 4
 
< 0.1%
20 4
 
< 0.1%
11 3
 
< 0.1%
Other values (2) 4
 
< 0.1%
ValueCountFrequency (%)
0 3370
33.7%
1 3171
31.7%
2 1693
16.9%
3 436
 
4.4%
4 1250
 
12.5%
5 55
 
0.5%
6 10
 
0.1%
7 2
 
< 0.1%
8 4
 
< 0.1%
11 3
 
< 0.1%
ValueCountFrequency (%)
24 2
 
< 0.1%
20 4
 
< 0.1%
11 3
 
< 0.1%
8 4
 
< 0.1%
7 2
 
< 0.1%
6 10
 
0.1%
5 55
 
0.5%
4 1250
12.5%
3 436
 
4.4%
2 1693
16.9%

건축물용도
Text

MISSING 

Distinct160
Distinct (%)2.1%
Missing2328
Missing (%)23.3%
Memory size156.2 KiB
2024-04-17T18:40:58.495165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.3179093
Min length4

Characters and Unicode

Total characters33127
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)0.6%

Sample

1st row1001
2nd row1001
3rd row2003
4th row19000
5th row2000
ValueCountFrequency (%)
1001 1928
25.1%
2003 880
11.5%
17100 726
 
9.5%
18001 554
 
7.2%
4005 367
 
4.8%
17000 327
 
4.3%
4402 307
 
4.0%
3001 279
 
3.6%
1003 278
 
3.6%
4001 209
 
2.7%
Other values (150) 1817
23.7%
2024-04-17T18:40:58.796994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15081
45.5%
1 9022
27.2%
2 1934
 
5.8%
3 1829
 
5.5%
4 1668
 
5.0%
7 1133
 
3.4%
9 1008
 
3.0%
8 822
 
2.5%
5 464
 
1.4%
6 114
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33075
99.8%
Uppercase Letter 52
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15081
45.6%
1 9022
27.3%
2 1934
 
5.8%
3 1829
 
5.5%
4 1668
 
5.0%
7 1133
 
3.4%
9 1008
 
3.0%
8 822
 
2.5%
5 464
 
1.4%
6 114
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
Z 52
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33075
99.8%
Latin 52
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15081
45.6%
1 9022
27.3%
2 1934
 
5.8%
3 1829
 
5.5%
4 1668
 
5.0%
7 1133
 
3.4%
9 1008
 
3.0%
8 822
 
2.5%
5 464
 
1.4%
6 114
 
0.3%
Latin
ValueCountFrequency (%)
Z 52
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33127
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15081
45.5%
1 9022
27.2%
2 1934
 
5.8%
3 1829
 
5.5%
4 1668
 
5.0%
7 1133
 
3.4%
9 1008
 
3.0%
8 822
 
2.5%
5 464
 
1.4%
6 114
 
0.3%

구조
Real number (ℝ)

MISSING 

Distinct15
Distinct (%)0.2%
Missing3371
Missing (%)33.7%
Infinite0
Infinite (%)0.0%
Mean26.260069
Minimum11
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T18:40:58.908310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q121
median21
Q331
95-th percentile51
Maximum99
Range88
Interquartile range (IQR)10

Descriptive statistics

Standard deviation11.315493
Coefficient of variation (CV)0.43090111
Kurtosis2.7441626
Mean26.260069
Median Absolute Deviation (MAD)10
Skewness1.2248049
Sum174078
Variance128.04038
MonotonicityNot monotonic
2024-04-17T18:40:58.993495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
21 2593
25.9%
31 1362
13.6%
51 698
 
7.0%
32 669
 
6.7%
11 524
 
5.2%
12 428
 
4.3%
19 240
 
2.4%
41 47
 
0.5%
33 16
 
0.2%
22 14
 
0.1%
Other values (5) 38
 
0.4%
(Missing) 3371
33.7%
ValueCountFrequency (%)
11 524
 
5.2%
12 428
 
4.3%
19 240
 
2.4%
21 2593
25.9%
22 14
 
0.1%
29 3
 
< 0.1%
31 1362
13.6%
32 669
 
6.7%
33 16
 
0.2%
39 10
 
0.1%
ValueCountFrequency (%)
99 11
 
0.1%
52 2
 
< 0.1%
51 698
7.0%
42 12
 
0.1%
41 47
 
0.5%
39 10
 
0.1%
33 16
 
0.2%
32 669
6.7%
31 1362
13.6%
29 3
 
< 0.1%

건축년도
Text

MISSING 

Distinct4208
Distinct (%)61.0%
Missing3101
Missing (%)31.0%
Memory size156.2 KiB
2024-04-17T18:40:59.205087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters68990
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2524 ?
Unique (%)36.6%

Sample

1st row1985-07-29
2nd row1980-07-16
3rd row2011-05-27
4th row2001-01-06
5th row2013-12-09
ValueCountFrequency (%)
2016-10-31 13
 
0.2%
2008-08-07 8
 
0.1%
1985-05-28 7
 
0.1%
2001-06-18 7
 
0.1%
2008-03-03 7
 
0.1%
1995-07-06 7
 
0.1%
2011-01-31 7
 
0.1%
1997-01-13 7
 
0.1%
1997-11-19 6
 
0.1%
1993-08-04 6
 
0.1%
Other values (4198) 6824
98.9%
2024-04-17T18:40:59.537853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15407
22.3%
- 13798
20.0%
1 11558
16.8%
2 9333
13.5%
9 6102
 
8.8%
3 2392
 
3.5%
8 2237
 
3.2%
7 2198
 
3.2%
6 2172
 
3.1%
5 1966
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 55192
80.0%
Dash Punctuation 13798
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15407
27.9%
1 11558
20.9%
2 9333
16.9%
9 6102
 
11.1%
3 2392
 
4.3%
8 2237
 
4.1%
7 2198
 
4.0%
6 2172
 
3.9%
5 1966
 
3.6%
4 1827
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 13798
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 68990
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15407
22.3%
- 13798
20.0%
1 11558
16.8%
2 9333
13.5%
9 6102
 
8.8%
3 2392
 
3.5%
8 2237
 
3.2%
7 2198
 
3.2%
6 2172
 
3.1%
5 1966
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68990
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15407
22.3%
- 13798
20.0%
1 11558
16.8%
2 9333
13.5%
9 6102
 
8.8%
3 2392
 
3.5%
8 2237
 
3.2%
7 2198
 
3.2%
6 2172
 
3.1%
5 1966
 
2.8%

건폐율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3097
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.028204
Minimum0
Maximum244.18
Zeros3854
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T18:40:59.662614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median19.96
Q337.8
95-th percentile52.9805
Maximum244.18
Range244.18
Interquartile range (IQR)37.8

Descriptive statistics

Standard deviation19.511023
Coefficient of variation (CV)0.92785019
Kurtosis0.43745067
Mean21.028204
Median Absolute Deviation (MAD)19.96
Skewness0.40238077
Sum210282.04
Variance380.68004
MonotonicityNot monotonic
2024-04-17T18:40:59.773890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3854
38.5%
19.96 22
 
0.2%
19.98 21
 
0.2%
19.94 19
 
0.2%
19.97 18
 
0.2%
19.95 16
 
0.2%
39.74 16
 
0.2%
19.99 16
 
0.2%
19.88 16
 
0.2%
19.93 16
 
0.2%
Other values (3087) 5986
59.9%
ValueCountFrequency (%)
0.0 3854
38.5%
0.48 3
 
< 0.1%
0.51 5
 
0.1%
0.86 2
 
< 0.1%
0.99 1
 
< 0.1%
1.34 1
 
< 0.1%
2.12 1
 
< 0.1%
2.2293074 1
 
< 0.1%
2.52 1
 
< 0.1%
2.59 1
 
< 0.1%
ValueCountFrequency (%)
244.18 1
< 0.1%
100.0 2
< 0.1%
82.8 1
< 0.1%
81.33 1
< 0.1%
75.06 1
< 0.1%
74.38 1
< 0.1%
73.08 1
< 0.1%
69.91 1
< 0.1%
69.8 1
< 0.1%
69.76 1
< 0.1%

용적율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4059
Distinct (%)40.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.151246
Minimum0
Maximum399.94
Zeros3852
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T18:40:59.885306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median28.645
Q352.0375
95-th percentile142.481
Maximum399.94
Range399.94
Interquartile range (IQR)52.0375

Descriptive statistics

Standard deviation46.49732
Coefficient of variation (CV)1.2187628
Kurtosis3.5566202
Mean38.151246
Median Absolute Deviation (MAD)28.645
Skewness1.6797144
Sum381512.46
Variance2162.0007
MonotonicityNot monotonic
2024-04-17T18:41:00.004261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3852
38.5%
99.91 11
 
0.1%
39.97 10
 
0.1%
99.79 10
 
0.1%
99.9 10
 
0.1%
50.0 10
 
0.1%
99.78 9
 
0.1%
39.92 9
 
0.1%
39.9 8
 
0.1%
99.87 8
 
0.1%
Other values (4049) 6063
60.6%
ValueCountFrequency (%)
0.0 3852
38.5%
0.049 1
 
< 0.1%
0.61 3
 
< 0.1%
0.64 5
 
0.1%
1.1 1
 
< 0.1%
1.34 1
 
< 0.1%
1.73 2
 
< 0.1%
2.12 1
 
< 0.1%
2.2293074 1
 
< 0.1%
2.59 1
 
< 0.1%
ValueCountFrequency (%)
399.94 1
< 0.1%
395.5 1
< 0.1%
347.5 1
< 0.1%
320.51 1
< 0.1%
316.8 1
< 0.1%
314.29 1
< 0.1%
287.03 1
< 0.1%
283.25 2
< 0.1%
276.79 1
< 0.1%
273.28 1
< 0.1%

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
41610
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row41610
2nd row41610
3rd row41610
4th row41610
5th row41610

Common Values

ValueCountFrequency (%)
41610 10000
100.0%

Length

2024-04-17T18:41:00.116895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T18:41:00.192974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
41610 10000
100.0%

시군구
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
광주시
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
광주시 10000
100.0%

Length

2024-04-17T18:41:00.273609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T18:41:00.345211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광주시 10000
100.0%

Interactions

2024-04-17T18:40:56.619614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:54.229561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:54.700578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:55.213533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:55.702265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:56.158377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:56.702784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:54.307090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:54.785633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:55.296969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:55.779839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:56.237899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:56.792251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:54.385003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:54.879864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:55.384421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:55.853637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:56.317900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:56.870149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:54.455894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:54.959798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:55.456170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:55.925960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:56.389046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:56.948830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:54.536193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:55.033299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:55.529223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:55.995170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:56.463479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:57.027401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:54.615871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:55.111991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:55.610312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:56.072614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:40:56.536460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T18:41:00.396005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지번코드건물군관리번호층수구조건폐율용적율
지번코드1.0000.0960.1700.2870.1590.234
건물군관리번호0.0961.0000.3320.5430.1920.456
층수0.1700.3321.0000.4400.2670.619
구조0.2870.5430.4401.0000.4630.466
건폐율0.1590.1920.2670.4631.0000.586
용적율0.2340.4560.6190.4660.5861.000
2024-04-17T18:41:00.482676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지번코드건물군관리번호층수구조건폐율용적율
지번코드1.000-0.069-0.1890.165-0.129-0.207
건물군관리번호-0.0691.0000.356-0.0000.1160.231
층수-0.1890.3561.000-0.2330.4820.606
구조0.165-0.000-0.2331.000-0.048-0.138
건폐율-0.1290.1160.482-0.0481.0000.904
용적율-0.2070.2310.606-0.1380.9041.000

Missing values

2024-04-17T18:40:57.141085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T18:40:57.271543image/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.
2024-04-17T18:40:57.375090image/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

지번코드건물군관리번호층수건축물용도구조건축년도건폐율용적율시군구코드시군구
2058741610101001013498882803121001211985-07-290.00.041610광주시
1967141610102001022699522406221001211980-07-160.00.041610광주시
349434161010500105769984<NA>0<NA><NA><NA>0.00.041610광주시
36158416101070010309990410019962842003212011-05-2739.2999.8241610광주시
435324161033028101020160<NA>0<NA><NA><NA>0.00.041610광주시
349941610103001032401921377019000<NA>2001-01-0612.217.2141610광주시
43759416102502310127001677702000<NA><NA>21.7479.8741610광주시
5039416102533010733977610023007704000<NA>2013-12-0938.445.1341610광주시
34723416102532610471014410027033804000<NA>2016-04-0136.836.841610광주시
42269416103702210275020810029884903000<NA>2017-05-2349.9366.3841610광주시
지번코드건물군관리번호층수건축물용도구조건축년도건폐율용적율시군구코드시군구
3890416102502610720000025842120999311993-12-2841.7541.7541610광주시
45577416103402910317004810029403911001322017-02-287.37.341610광주시
7144161011100105849856711811003211998-07-2419.2918.9241610광주시
210264161011000103989760<NA>0<NA><NA><NA>0.00.041610광주시
17084416101030010119987232150318001211982-01-0728.6485.941610광주시
4552241610253271012198401890414005121991-08-190.00.041610광주시
48649416102532910354995210021925704000<NA>2013-02-2623.8823.8841610광주시
3547241610253301055400961006018000<NA>2007-06-2839.6339.6341610광주시
11507416102502710537984010025453904000<NA>2015-06-1945.8945.8941610광주시
19431416103402210285004810025265842003212015-05-0733.9398.9641610광주시

Duplicate rows

Most frequently occurring

지번코드건물군관리번호층수건축물용도구조건축년도건폐율용적율시군구코드시군구# duplicates
5834161025934105420288<NA>0<NA><NA><NA>0.00.041610광주시9
1184161010800102110208<NA>0<NA><NA><NA>0.00.041610광주시6
1684161011200100270080<NA>0<NA><NA><NA>0.00.041610광주시6
14161010100100109824<NA>0<NA><NA><NA>0.00.041610광주시5
394161010300101009920<NA>0<NA><NA><NA>0.00.041610광주시5
434161010300101179904<NA>0<NA><NA><NA>0.00.041610광주시5
228416102502310140979277802000<NA><NA>15.91199.2741610광주시5
5754161025934101490176286191100151<NA>0.00.041610광주시5
6044161025936200220160<NA>0<NA><NA><NA>0.00.041610광주시5
66941610330261020999681397013000<NA>1993-09-250.510.6441610광주시5