Overview

Dataset statistics

Number of variables11
Number of observations90
Missing cells12
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.5 KiB
Average record size in memory96.5 B

Variable types

Text2
Numeric4
Categorical5

Dataset

Description관내 원룸 및 오피스텔 현황자료로 대지위치, 도로명주소, 건축면적, 연면적, 용도, 세대수, 가구수 등이 확인가능함
Author강원특별자치도 태백시
URLhttps://www.data.go.kr/data/15127344/fileData.do

Alerts

(층)주용도코드명 has constant value ""Constant
대지면적 is highly overall correlated with 건축면적 and 1 other fieldsHigh correlation
건축면적 is highly overall correlated with 대지면적 and 2 other fieldsHigh correlation
연면적 is highly overall correlated with 대지면적 and 2 other fieldsHigh correlation
가구수 is highly overall correlated with 건축면적 and 1 other fieldsHigh correlation
주용도코드명 is highly overall correlated with 세대수High correlation
세대수 is highly overall correlated with 주용도코드명High correlation
주용도코드명 is highly imbalanced (66.4%)Imbalance
도로명주소 has 1 (1.1%) missing valuesMissing
대지면적 has 8 (8.9%) missing valuesMissing
가구수 has 3 (3.3%) missing valuesMissing
대지면적 has 4 (4.4%) zerosZeros
가구수 has 2 (2.2%) zerosZeros

Reproduction

Analysis started2024-04-06 08:08:01.606448
Analysis finished2024-04-06 08:08:07.667827
Duration6.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대지위치주소
Text

Distinct88
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size852.0 B
2024-04-06T17:08:08.049365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length25
Mean length24.588889
Min length20

Characters and Unicode

Total characters2213
Distinct characters38
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

Unique86 ?
Unique (%)95.6%

Sample

1st row강원특별자치도 태백시 황지동 0073-0025
2nd row강원특별자치도 태백시 황지동 0039-0047
3rd row강원특별자치도 태백시 황지동 0496-0001
4th row강원특별자치도 태백시 소도동 0023-0025
5th row강원특별자치도 태백시 황지동 0404
ValueCountFrequency (%)
강원특별자치도 90
24.9%
태백시 90
24.9%
황지동 64
17.7%
혈동 6
 
1.7%
소도동 6
 
1.7%
화전동 4
 
1.1%
통동 3
 
0.8%
0274-0082 2
 
0.6%
백산동 2
 
0.6%
동점동 2
 
0.6%
Other values (91) 92
25.5%
2024-04-06T17:08:09.130874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 332
 
15.0%
271
 
12.2%
도 96
 
4.3%
백 92
 
4.2%
동 92
 
4.2%
강 90
 
4.1%
특 90
 
4.1%
별 90
 
4.1%
자 90
 
4.1%
치 90
 
4.1%
Other values (28) 880
39.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1162
52.5%
Decimal Number 696
31.5%
Space Separator 271
 
12.2%
Dash Punctuation 84
 
3.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 96
 
8.3%
백 92
 
7.9%
동 92
 
7.9%
강 90
 
7.7%
특 90
 
7.7%
별 90
 
7.7%
자 90
 
7.7%
치 90
 
7.7%
태 90
 
7.7%
시 90
 
7.7%
Other values (16) 252
21.7%
Decimal Number
ValueCountFrequency (%)
0 332
47.7%
1 70
 
10.1%
2 55
 
7.9%
4 54
 
7.8%
3 44
 
6.3%
7 40
 
5.7%
6 36
 
5.2%
9 23
 
3.3%
5 22
 
3.2%
8 20
 
2.9%
Space Separator
ValueCountFrequency (%)
271
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 84
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1162
52.5%
Common 1051
47.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 96
 
8.3%
백 92
 
7.9%
동 92
 
7.9%
강 90
 
7.7%
특 90
 
7.7%
별 90
 
7.7%
자 90
 
7.7%
치 90
 
7.7%
태 90
 
7.7%
시 90
 
7.7%
Other values (16) 252
21.7%
Common
ValueCountFrequency (%)
0 332
31.6%
271
25.8%
- 84
 
8.0%
1 70
 
6.7%
2 55
 
5.2%
4 54
 
5.1%
3 44
 
4.2%
7 40
 
3.8%
6 36
 
3.4%
9 23
 
2.2%
Other values (2) 42
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1162
52.5%
ASCII 1051
47.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 332
31.6%
271
25.8%
- 84
 
8.0%
1 70
 
6.7%
2 55
 
5.2%
4 54
 
5.1%
3 44
 
4.2%
7 40
 
3.8%
6 36
 
3.4%
9 23
 
2.2%
Other values (2) 42
 
4.0%
Hangul
ValueCountFrequency (%)
도 96
 
8.3%
백 92
 
7.9%
동 92
 
7.9%
강 90
 
7.7%
특 90
 
7.7%
별 90
 
7.7%
자 90
 
7.7%
치 90
 
7.7%
태 90
 
7.7%
시 90
 
7.7%
Other values (16) 252
21.7%

도로명주소
Text

MISSING 

Distinct88
Distinct (%)98.9%
Missing1
Missing (%)1.1%
Memory size852.0 B
2024-04-06T17:08:09.736410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length23
Mean length19.550562
Min length17

Characters and Unicode

Total characters1740
Distinct characters86
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

Unique87 ?
Unique (%)97.8%

Sample

1st row강원특별자치도 태백시 황지남2길 14
2nd row강원특별자치도 태백시 황지북2길 8
3rd row강원특별자치도 태백시 서학로 940-8
4th row강원특별자치도 태백시 태백산로 4879
5th row강원특별자치도 태백시 대학길 47
ValueCountFrequency (%)
강원특별자치도 89
25.0%
태백시 89
25.0%
태백산로 8
 
2.2%
문화로3길 6
 
1.7%
태백로 5
 
1.4%
대학길 5
 
1.4%
번영로 5
 
1.4%
서학로 4
 
1.1%
8 3
 
0.8%
황지로 3
 
0.8%
Other values (118) 139
39.0%
2024-04-06T17:08:10.791464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
267
15.3%
태 105
 
6.0%
백 104
 
6.0%
원 91
 
5.2%
치 90
 
5.2%
도 90
 
5.2%
시 90
 
5.2%
강 89
 
5.1%
특 89
 
5.1%
별 89
 
5.1%
Other values (76) 636
36.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1204
69.2%
Space Separator 267
 
15.3%
Decimal Number 256
 
14.7%
Dash Punctuation 13
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
태 105
 
8.7%
백 104
 
8.6%
원 91
 
7.6%
치 90
 
7.5%
도 90
 
7.5%
시 90
 
7.5%
강 89
 
7.4%
특 89
 
7.4%
별 89
 
7.4%
자 89
 
7.4%
Other values (64) 278
23.1%
Decimal Number
ValueCountFrequency (%)
1 53
20.7%
2 36
14.1%
3 36
14.1%
4 30
11.7%
5 20
 
7.8%
9 18
 
7.0%
6 17
 
6.6%
0 16
 
6.2%
7 15
 
5.9%
8 15
 
5.9%
Space Separator
ValueCountFrequency (%)
267
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1204
69.2%
Common 536
30.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
태 105
 
8.7%
백 104
 
8.6%
원 91
 
7.6%
치 90
 
7.5%
도 90
 
7.5%
시 90
 
7.5%
강 89
 
7.4%
특 89
 
7.4%
별 89
 
7.4%
자 89
 
7.4%
Other values (64) 278
23.1%
Common
ValueCountFrequency (%)
267
49.8%
1 53
 
9.9%
2 36
 
6.7%
3 36
 
6.7%
4 30
 
5.6%
5 20
 
3.7%
9 18
 
3.4%
6 17
 
3.2%
0 16
 
3.0%
7 15
 
2.8%
Other values (2) 28
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1204
69.2%
ASCII 536
30.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
267
49.8%
1 53
 
9.9%
2 36
 
6.7%
3 36
 
6.7%
4 30
 
5.6%
5 20
 
3.7%
9 18
 
3.4%
6 17
 
3.2%
0 16
 
3.0%
7 15
 
2.8%
Other values (2) 28
 
5.2%
Hangul
ValueCountFrequency (%)
태 105
 
8.7%
백 104
 
8.6%
원 91
 
7.6%
치 90
 
7.5%
도 90
 
7.5%
시 90
 
7.5%
강 89
 
7.4%
특 89
 
7.4%
별 89
 
7.4%
자 89
 
7.4%
Other values (64) 278
23.1%

대지면적
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct76
Distinct (%)92.7%
Missing8
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean880.47561
Minimum0
Maximum3312
Zeros4
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size942.0 B
2024-04-06T17:08:11.142096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile154
Q1334.75
median697.5
Q31083.75
95-th percentile2470.7
Maximum3312
Range3312
Interquartile range (IQR)749

Descriptive statistics

Standard deviation722.17426
Coefficient of variation (CV)0.82020927
Kurtosis1.1927461
Mean880.47561
Median Absolute Deviation (MAD)385
Skewness1.2488585
Sum72199
Variance521535.66
MonotonicityNot monotonic
2024-04-06T17:08:11.480612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
4.4%
154 2
 
2.2%
1635 2
 
2.2%
964 2
 
2.2%
204 1
 
1.1%
1963 1
 
1.1%
2480 1
 
1.1%
755 1
 
1.1%
661 1
 
1.1%
286 1
 
1.1%
Other values (66) 66
73.3%
(Missing) 8
 
8.9%
ValueCountFrequency (%)
0 4
4.4%
154 2
2.2%
159 1
 
1.1%
161 1
 
1.1%
200 1
 
1.1%
204 1
 
1.1%
208 1
 
1.1%
210 1
 
1.1%
240 1
 
1.1%
241 1
 
1.1%
ValueCountFrequency (%)
3312 1
1.1%
2724 1
1.1%
2532 1
1.1%
2526 1
1.1%
2480 1
1.1%
2294 1
1.1%
2256 1
1.1%
2120 1
1.1%
1963 1
1.1%
1884 1
1.1%

건축면적
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean366.31111
Minimum2
Maximum1479
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2024-04-06T17:08:11.749231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile77.35
Q1172
median267
Q3526.25
95-th percentile858.05
Maximum1479
Range1477
Interquartile range (IQR)354.25

Descriptive statistics

Standard deviation275.76222
Coefficient of variation (CV)0.75280877
Kurtosis2.7416279
Mean366.31111
Median Absolute Deviation (MAD)122.5
Skewness1.5064778
Sum32968
Variance76044.801
MonotonicityNot monotonic
2024-04-06T17:08:12.158728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
147 2
 
2.2%
543 2
 
2.2%
185 2
 
2.2%
735 1
 
1.1%
195 1
 
1.1%
273 1
 
1.1%
568 1
 
1.1%
667 1
 
1.1%
354 1
 
1.1%
673 1
 
1.1%
Other values (77) 77
85.6%
ValueCountFrequency (%)
2 1
1.1%
34 1
1.1%
39 1
1.1%
53 1
1.1%
58 1
1.1%
101 1
1.1%
107 1
1.1%
111 1
1.1%
118 1
1.1%
119 1
1.1%
ValueCountFrequency (%)
1479 1
1.1%
1160 1
1.1%
1103 1
1.1%
1056 1
1.1%
881 1
1.1%
830 1
1.1%
753 1
1.1%
735 1
1.1%
712 1
1.1%
687 1
1.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct89
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean823.04444
Minimum2
Maximum3076
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2024-04-06T17:08:12.519656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile120
Q1300.5
median492.5
Q31087.5
95-th percentile2408.7
Maximum3076
Range3074
Interquartile range (IQR)787

Descriptive statistics

Standard deviation751.04034
Coefficient of variation (CV)0.91251493
Kurtosis0.707352
Mean823.04444
Median Absolute Deviation (MAD)291
Skewness1.2844326
Sum74074
Variance564061.59
MonotonicityNot monotonic
2024-04-06T17:08:13.008836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
351 2
 
2.2%
2194 1
 
1.1%
330 1
 
1.1%
370 1
 
1.1%
530 1
 
1.1%
568 1
 
1.1%
1990 1
 
1.1%
268 1
 
1.1%
1034 1
 
1.1%
1988 1
 
1.1%
Other values (79) 79
87.8%
ValueCountFrequency (%)
2 1
1.1%
34 1
1.1%
39 1
1.1%
58 1
1.1%
111 1
1.1%
131 1
1.1%
148 1
1.1%
152 1
1.1%
163 1
1.1%
172 1
1.1%
ValueCountFrequency (%)
3076 1
1.1%
2735 1
1.1%
2726 1
1.1%
2629 1
1.1%
2415 1
1.1%
2401 1
1.1%
2265 1
1.1%
2214 1
1.1%
2194 1
1.1%
1990 1
1.1%

주용도코드명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size852.0 B
단독주택
77 
제2종근린생활시설
 
6
제1종근린생활시설
 
4
자동차관련시설
 
1
노유자시설
 
1

Length

Max length10
Median length4
Mean length4.6666667
Min length4

Unique

Unique3 ?
Unique (%)3.3%

Sample

1st row단독주택
2nd row단독주택
3rd row단독주택
4th row제2종근린생활시설
5th row단독주택

Common Values

ValueCountFrequency (%)
단독주택 77
85.6%
제2종근린생활시설 6
 
6.7%
제1종근린생활시설 4
 
4.4%
자동차관련시설 1
 
1.1%
노유자시설 1
 
1.1%
위험물저장및처리시설 1
 
1.1%

Length

2024-04-06T17:08:13.394669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:08:13.698215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
단독주택 77
85.6%
제2종근린생활시설 6
 
6.7%
제1종근린생활시설 4
 
4.4%
자동차관련시설 1
 
1.1%
노유자시설 1
 
1.1%
위험물저장및처리시설 1
 
1.1%

지상층수
Categorical

Distinct5
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size852.0 B
2
39 
3
22 
1
14 
4
13 
5
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row2
4th row2
5th row3

Common Values

ValueCountFrequency (%)
2 39
43.3%
3 22
24.4%
1 14
 
15.6%
4 13
 
14.4%
5 2
 
2.2%

Length

2024-04-06T17:08:13.984858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:08:14.263551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 39
43.3%
3 22
24.4%
1 14
 
15.6%
4 13
 
14.4%
5 2
 
2.2%

지하층수
Categorical

Distinct3
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size852.0 B
0
69 
<NA>
12 
1
9 

Length

Max length4
Median length1
Mean length1.4
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 69
76.7%
<NA> 12
 
13.3%
1 9
 
10.0%

Length

2024-04-06T17:08:14.487826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:08:14.686943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 69
76.7%
na 12
 
13.3%
1 9
 
10.0%

세대수
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size852.0 B
<NA>
58 
0
30 
2
 
1
76
 
1

Length

Max length4
Median length4
Mean length2.9444444
Min length1

Unique

Unique2 ?
Unique (%)2.2%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
<NA> 58
64.4%
0 30
33.3%
2 1
 
1.1%
76 1
 
1.1%

Length

2024-04-06T17:08:14.919664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:08:15.133196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 58
64.4%
0 30
33.3%
2 1
 
1.1%
76 1
 
1.1%

(층)주용도코드명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size852.0 B
다가구주택
90 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
다가구주택 90
100.0%

Length

2024-04-06T17:08:15.353263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:08:15.557853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
다가구주택 90
100.0%

가구수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct29
Distinct (%)33.3%
Missing3
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean11.965517
Minimum0
Maximum80
Zeros2
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size942.0 B
2024-04-06T17:08:15.770333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q312
95-th percentile52.8
Maximum80
Range80
Interquartile range (IQR)10

Descriptive statistics

Standard deviation17.710494
Coefficient of variation (CV)1.4801277
Kurtosis5.0758648
Mean11.965517
Median Absolute Deviation (MAD)2
Skewness2.3544313
Sum1041
Variance313.66159
MonotonicityNot monotonic
2024-04-06T17:08:16.015933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
4 16
17.8%
2 13
14.4%
1 8
 
8.9%
3 8
 
8.9%
6 6
 
6.7%
8 4
 
4.4%
12 4
 
4.4%
5 3
 
3.3%
0 2
 
2.2%
14 2
 
2.2%
Other values (19) 21
23.3%
(Missing) 3
 
3.3%
ValueCountFrequency (%)
0 2
 
2.2%
1 8
8.9%
2 13
14.4%
3 8
8.9%
4 16
17.8%
5 3
 
3.3%
6 6
 
6.7%
7 1
 
1.1%
8 4
 
4.4%
9 1
 
1.1%
ValueCountFrequency (%)
80 1
1.1%
76 1
1.1%
72 1
1.1%
56 1
1.1%
54 1
1.1%
50 1
1.1%
45 1
1.1%
44 2
2.2%
36 1
1.1%
33 1
1.1%

Interactions

2024-04-06T17:08:05.528642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:02.681127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:03.741621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:04.553477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:05.781632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:02.949447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:03.953173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:04.877082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:05.956636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:03.271773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:04.146600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:05.046530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:06.623096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:03.574057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:04.364967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:05.261493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:08:16.264904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대지위치주소도로명주소대지면적건축면적연면적주용도코드명지상층수지하층수세대수가구수
대지위치주소1.0001.0001.0001.0001.0001.0000.9591.0001.0001.000
도로명주소1.0001.0001.0001.0001.0001.0000.8981.0001.0001.000
대지면적1.0001.0001.0000.6740.7670.3770.4860.3370.0000.624
건축면적1.0001.0000.6741.0000.8140.0000.4770.3560.2430.704
연면적1.0001.0000.7670.8141.0000.0000.8200.5060.4750.773
주용도코드명1.0001.0000.3770.0000.0001.0000.1360.4760.9330.000
지상층수0.9590.8980.4860.4770.8200.1361.0000.1770.0000.556
지하층수1.0001.0000.3370.3560.5060.4760.1771.0000.2590.316
세대수1.0001.0000.0000.2430.4750.9330.0000.2591.0000.000
가구수1.0001.0000.6240.7040.7730.0000.5560.3160.0001.000
2024-04-06T17:08:16.508980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주용도코드명지상층수지하층수세대수
주용도코드명1.0000.0880.3330.683
지상층수0.0881.0000.2110.000
지하층수0.3330.2111.0000.412
세대수0.6830.0000.4121.000
2024-04-06T17:08:16.679684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대지면적건축면적연면적가구수주용도코드명지상층수지하층수세대수
대지면적1.0000.7240.6210.4410.2000.2120.3180.000
건축면적0.7241.0000.9030.7150.0000.3110.3670.170
연면적0.6210.9031.0000.6800.0000.4640.3670.302
가구수0.4410.7150.6801.0000.0000.3650.3630.000
주용도코드명0.2000.0000.0000.0001.0000.0880.3330.683
지상층수0.2120.3110.4640.3650.0881.0000.2110.000
지하층수0.3180.3670.3670.3630.3330.2111.0000.412
세대수0.0000.1700.3020.0000.6830.0000.4121.000

Missing values

2024-04-06T17:08:06.900654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:08:07.212791image/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.
2024-04-06T17:08:07.479753image/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

대지위치주소도로명주소대지면적건축면적연면적주용도코드명지상층수지하층수세대수(층)주용도코드명가구수
0강원특별자치도 태백시 황지동 0073-0025강원특별자치도 태백시 황지남2길 1410607352194단독주택40<NA>다가구주택56
1강원특별자치도 태백시 황지동 0039-0047강원특별자치도 태백시 황지북2길 81016388650단독주택20<NA>다가구주택6
2강원특별자치도 태백시 황지동 0496-0001강원특별자치도 태백시 서학로 940-81400275427단독주택20<NA>다가구주택12
3강원특별자치도 태백시 소도동 0023-0025강원특별자치도 태백시 태백산로 4879515147247제2종근린생활시설20<NA>다가구주택3
4강원특별자치도 태백시 황지동 0404강원특별자치도 태백시 대학길 4710715431604단독주택300다가구주택54
5강원특별자치도 태백시 혈동 0023-0002강원특별자치도 태백시 사내골길 9-250153173단독주택2<NA><NA>다가구주택3
6강원특별자치도 태백시 황지동 0175-0017강원특별자치도 태백시 번영로 3221208302265단독주택30<NA>다가구주택36
7강원특별자치도 태백시 황지동 0666-0002강원특별자치도 태백시 해지개길 13-109245511486단독주택30<NA>다가구주택28
8강원특별자치도 태백시 황지동 0426-0002강원특별자치도 태백시 서학로 9962526208334자동차관련시설20<NA>다가구주택5
9강원특별자치도 태백시 황지동 0059-0157강원특별자치도 태백시 문화로 15-60175351단독주택200다가구주택8
대지위치주소도로명주소대지면적건축면적연면적주용도코드명지상층수지하층수세대수(층)주용도코드명가구수
80강원특별자치도 태백시 황지동 0114-0035강원특별자치도 태백시 상장로 31383220220단독주택10<NA>다가구주택5
81강원특별자치도 태백시 황지동 0049-0125강원특별자치도 태백시 문예1길 38200107148단독주택200다가구주택1
82강원특별자치도 태백시 황지동 0008-0114강원특별자치도 태백시 황지북3길 23-29326522214단독주택310다가구주택72
83강원특별자치도 태백시 황지동 0164-0039강원특별자치도 태백시 함태길 667973741041단독주택300다가구주택0
84강원특별자치도 태백시 황지동 0073-0044강원특별자치도 태백시 문화로3길 5410857532415단독주택40<NA>다가구주택50
85강원특별자치도 태백시 황지동 0274-0011강원특별자치도 태백시 장수4길 24<NA>4591696단독주택41<NA>다가구주택7
86강원특별자치도 태백시 황지동 0169-0007강원특별자치도 태백시 상장남2길 39520147205단독주택20<NA>다가구주택2
87강원특별자치도 태백시 황지동 0274-0082강원특별자치도 태백시 장수2길 8154111111단독주택10<NA>다가구주택3
88강원특별자치도 태백시 황지동 0028-0011강원특별자치도 태백시 문화로3길 10110806871589단독주택400다가구주택33
89강원특별자치도 태백시 황지동 0253-0079강원특별자치도 태백시 먹거리길 105240186632단독주택40<NA>다가구주택6