Overview

Dataset statistics

Number of variables10
Number of observations144
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.9 KiB
Average record size in memory84.9 B

Variable types

Numeric3
Text3
DateTime1
Categorical3

Dataset

Description함안군의 공동주택(아파트, 연립주택, 다세대)현황 제공, 공동주택의 단지명, 공동주택의 도로명주소, 공동주택의 세대수, 공동주택의 연면적합계, 공동주택의 사용승인일, 공동주택의 동수,공동주택의 지하층수 ,지상층수 및 세부용도 등의 정보 포함
Author경상남도 함안군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=3080708

Alerts

구분 is highly overall correlated with 연면적합계(제곱미터) and 1 other fieldsHigh correlation
연면적합계(제곱미터) is highly overall correlated with 구분 and 1 other fieldsHigh correlation
동수 is highly overall correlated with 지상High correlation
지상 is highly overall correlated with 연면적합계(제곱미터) and 2 other fieldsHigh correlation
세부용도 is highly overall correlated with 구분 and 1 other fieldsHigh correlation
지하 is highly imbalanced (53.3%)Imbalance
구분 has unique valuesUnique
위 치(도로명 주소) has unique valuesUnique

Reproduction

Analysis started2023-12-10 23:25:58.421992
Analysis finished2023-12-10 23:26:00.057603
Duration1.64 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct144
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.5
Minimum1
Maximum144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T08:26:00.134445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.15
Q136.75
median72.5
Q3108.25
95-th percentile136.85
Maximum144
Range143
Interquartile range (IQR)71.5

Descriptive statistics

Standard deviation41.713307
Coefficient of variation (CV)0.57535596
Kurtosis-1.2
Mean72.5
Median Absolute Deviation (MAD)36
Skewness0
Sum10440
Variance1740
MonotonicityStrictly increasing
2023-12-11T08:26:00.294527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.7%
74 1
 
0.7%
94 1
 
0.7%
95 1
 
0.7%
96 1
 
0.7%
97 1
 
0.7%
98 1
 
0.7%
99 1
 
0.7%
100 1
 
0.7%
101 1
 
0.7%
Other values (134) 134
93.1%
ValueCountFrequency (%)
1 1
0.7%
2 1
0.7%
3 1
0.7%
4 1
0.7%
5 1
0.7%
6 1
0.7%
7 1
0.7%
8 1
0.7%
9 1
0.7%
10 1
0.7%
ValueCountFrequency (%)
144 1
0.7%
143 1
0.7%
142 1
0.7%
141 1
0.7%
140 1
0.7%
139 1
0.7%
138 1
0.7%
137 1
0.7%
136 1
0.7%
135 1
0.7%

아파트 단지명
Text

Distinct140
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-11T08:26:00.566457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length5.9722222
Min length3

Characters and Unicode

Total characters860
Distinct characters196
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique136 ?
Unique (%)94.4%

Sample

1st row대송아파트1차
2nd row대송아파트2차
3rd row대한아파트
4th row동명아파트
5th row무학아파트
ValueCountFrequency (%)
다솜빌라 3
 
1.9%
나동 3
 
1.9%
가동 3
 
1.9%
백산월드 3
 
1.9%
대광건설다세대 2
 
1.3%
구성빌라 2
 
1.3%
화성빌라 2
 
1.3%
정수장숙소 2
 
1.3%
무학아파트 2
 
1.3%
대진빌라 2
 
1.3%
Other values (134) 134
84.8%
2023-12-11T08:26:00.990319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
빌 58
 
6.7%
트 43
 
5.0%
파 43
 
5.0%
아 42
 
4.9%
라 35
 
4.1%
1 24
 
2.8%
동 23
 
2.7%
대 21
 
2.4%
스 18
 
2.1%
차 15
 
1.7%
Other values (186) 538
62.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 765
89.0%
Decimal Number 61
 
7.1%
Space Separator 14
 
1.6%
Other Symbol 8
 
0.9%
Uppercase Letter 7
 
0.8%
Open Punctuation 2
 
0.2%
Close Punctuation 2
 
0.2%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
빌 58
 
7.6%
트 43
 
5.6%
파 43
 
5.6%
아 42
 
5.5%
라 35
 
4.6%
동 23
 
3.0%
대 21
 
2.7%
스 18
 
2.4%
차 15
 
2.0%
다 11
 
1.4%
Other values (165) 456
59.6%
Decimal Number
ValueCountFrequency (%)
1 24
39.3%
2 14
23.0%
0 14
23.0%
3 3
 
4.9%
8 1
 
1.6%
4 1
 
1.6%
6 1
 
1.6%
7 1
 
1.6%
5 1
 
1.6%
9 1
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
B 2
28.6%
A 2
28.6%
C 1
14.3%
L 1
14.3%
H 1
14.3%
Other Symbol
ValueCountFrequency (%)
○ 6
75.0%
㈜ 2
 
25.0%
Space Separator
ValueCountFrequency (%)
14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 767
89.2%
Common 86
 
10.0%
Latin 7
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
빌 58
 
7.6%
트 43
 
5.6%
파 43
 
5.6%
아 42
 
5.5%
라 35
 
4.6%
동 23
 
3.0%
대 21
 
2.7%
스 18
 
2.3%
차 15
 
2.0%
다 11
 
1.4%
Other values (166) 458
59.7%
Common
ValueCountFrequency (%)
1 24
27.9%
2 14
16.3%
0 14
16.3%
14
16.3%
○ 6
 
7.0%
3 3
 
3.5%
( 2
 
2.3%
) 2
 
2.3%
8 1
 
1.2%
, 1
 
1.2%
Other values (5) 5
 
5.8%
Latin
ValueCountFrequency (%)
B 2
28.6%
A 2
28.6%
C 1
14.3%
L 1
14.3%
H 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 765
89.0%
ASCII 87
 
10.1%
Geometric Shapes 6
 
0.7%
None 2
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
빌 58
 
7.6%
트 43
 
5.6%
파 43
 
5.6%
아 42
 
5.5%
라 35
 
4.6%
동 23
 
3.0%
대 21
 
2.7%
스 18
 
2.4%
차 15
 
2.0%
다 11
 
1.4%
Other values (165) 456
59.6%
ASCII
ValueCountFrequency (%)
1 24
27.6%
2 14
16.1%
0 14
16.1%
14
16.1%
3 3
 
3.4%
B 2
 
2.3%
( 2
 
2.3%
A 2
 
2.3%
) 2
 
2.3%
8 1
 
1.1%
Other values (9) 9
 
10.3%
Geometric Shapes
ValueCountFrequency (%)
○ 6
100.0%
None
ValueCountFrequency (%)
㈜ 2
100.0%

위 치(도로명 주소)
Text

UNIQUE 

Distinct144
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-11T08:26:01.326125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length34
Mean length31.284722
Min length27

Characters and Unicode

Total characters4505
Distinct characters97
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique144 ?
Unique (%)100.0%

Sample

1st row경상남도 함안군 가야읍 말산리 103(중앙남 3길 6)
2nd row경상남도 함안군 가야읍 말산리 102(중앙남 3길 6)
3rd row경상남도 함안군 가야읍 도항리 455-2(고분2길 6)
4th row경상남도 함안군 가야읍 말산리 180(중앙남길 50)
5th row경상남도 함안군 가야읍 말산리 184(말산1길 20)
ValueCountFrequency (%)
경상남도 144
 
16.6%
함안군 144
 
16.6%
가야읍 67
 
7.7%
칠원읍 35
 
4.0%
말산리 30
 
3.5%
도항리 22
 
2.5%
군북면 19
 
2.2%
중암리 16
 
1.8%
검암리 15
 
1.7%
구성리 11
 
1.3%
Other values (289) 366
42.1%
2023-12-11T08:26:01.813466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
726
 
16.1%
1 216
 
4.8%
도 176
 
3.9%
함 165
 
3.7%
2 164
 
3.6%
군 163
 
3.6%
남 160
 
3.6%
안 159
 
3.5%
경 150
 
3.3%
리 145
 
3.2%
Other values (87) 2281
50.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2359
52.4%
Decimal Number 996
22.1%
Space Separator 726
 
16.1%
Open Punctuation 144
 
3.2%
Close Punctuation 143
 
3.2%
Dash Punctuation 137
 
3.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 176
 
7.5%
함 165
 
7.0%
군 163
 
6.9%
남 160
 
6.8%
안 159
 
6.7%
경 150
 
6.4%
리 145
 
6.1%
상 145
 
6.1%
길 111
 
4.7%
읍 102
 
4.3%
Other values (73) 883
37.4%
Decimal Number
ValueCountFrequency (%)
1 216
21.7%
2 164
16.5%
3 112
11.2%
5 99
9.9%
6 76
 
7.6%
4 74
 
7.4%
7 73
 
7.3%
0 71
 
7.1%
8 67
 
6.7%
9 44
 
4.4%
Space Separator
ValueCountFrequency (%)
726
100.0%
Open Punctuation
ValueCountFrequency (%)
( 144
100.0%
Close Punctuation
ValueCountFrequency (%)
) 143
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 137
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2359
52.4%
Common 2146
47.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 176
 
7.5%
함 165
 
7.0%
군 163
 
6.9%
남 160
 
6.8%
안 159
 
6.7%
경 150
 
6.4%
리 145
 
6.1%
상 145
 
6.1%
길 111
 
4.7%
읍 102
 
4.3%
Other values (73) 883
37.4%
Common
ValueCountFrequency (%)
726
33.8%
1 216
 
10.1%
2 164
 
7.6%
( 144
 
6.7%
) 143
 
6.7%
- 137
 
6.4%
3 112
 
5.2%
5 99
 
4.6%
6 76
 
3.5%
4 74
 
3.4%
Other values (4) 255
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2359
52.4%
ASCII 2146
47.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
726
33.8%
1 216
 
10.1%
2 164
 
7.6%
( 144
 
6.7%
) 143
 
6.7%
- 137
 
6.4%
3 112
 
5.2%
5 99
 
4.6%
6 76
 
3.5%
4 74
 
3.4%
Other values (4) 255
 
11.9%
Hangul
ValueCountFrequency (%)
도 176
 
7.5%
함 165
 
7.0%
군 163
 
6.9%
남 160
 
6.8%
안 159
 
6.7%
경 150
 
6.4%
리 145
 
6.1%
상 145
 
6.1%
길 111
 
4.7%
읍 102
 
4.3%
Other values (73) 883
37.4%

세대수
Text

Distinct62
Distinct (%)43.1%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-11T08:26:02.053226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length1.8958333
Min length1

Characters and Unicode

Total characters273
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

Unique44 ?
Unique (%)30.6%

Sample

1st row50
2nd row50
3rd row34
4th row40
5th row50
ValueCountFrequency (%)
8 26
18.1%
16 18
 
12.5%
12 8
 
5.6%
18 6
 
4.2%
6 5
 
3.5%
19 5
 
3.5%
50 5
 
3.5%
4 4
 
2.8%
15 3
 
2.1%
40 3
 
2.1%
Other values (52) 61
42.4%
2023-12-11T08:26:02.423647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 65
23.8%
8 43
15.8%
6 32
11.7%
5 24
 
8.8%
0 24
 
8.8%
2 22
 
8.1%
4 22
 
8.1%
9 15
 
5.5%
3 13
 
4.8%
7 12
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 272
99.6%
Other Punctuation 1
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 65
23.9%
8 43
15.8%
6 32
11.8%
5 24
 
8.8%
0 24
 
8.8%
2 22
 
8.1%
4 22
 
8.1%
9 15
 
5.5%
3 13
 
4.8%
7 12
 
4.4%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 273
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 65
23.8%
8 43
15.8%
6 32
11.7%
5 24
 
8.8%
0 24
 
8.8%
2 22
 
8.1%
4 22
 
8.1%
9 15
 
5.5%
3 13
 
4.8%
7 12
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 273
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 65
23.8%
8 43
15.8%
6 32
11.7%
5 24
 
8.8%
0 24
 
8.8%
2 22
 
8.1%
4 22
 
8.1%
9 15
 
5.5%
3 13
 
4.8%
7 12
 
4.4%

연면적합계(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct135
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8475.6707
Minimum170.67
Maximum223909.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T08:26:02.557634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum170.67
5-th percentile593.4725
Q1659.7
median1412.275
Q35020.7
95-th percentile35968.62
Maximum223909.99
Range223739.32
Interquartile range (IQR)4361

Descriptive statistics

Standard deviation23219.23
Coefficient of variation (CV)2.7395153
Kurtosis54.130188
Mean8475.6707
Median Absolute Deviation (MAD)759.835
Skewness6.5629133
Sum1220496.6
Variance5.3913263 × 108
MonotonicityNot monotonic
2023-12-11T08:26:02.686987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
657.32 4
 
2.8%
659.76 3
 
2.1%
659.7 3
 
2.1%
998.64 2
 
1.4%
658.66 2
 
1.4%
827.21 1
 
0.7%
1309.67 1
 
0.7%
2297.28 1
 
0.7%
2915.81 1
 
0.7%
654.3 1
 
0.7%
Other values (125) 125
86.8%
ValueCountFrequency (%)
170.67 1
0.7%
319.41 1
0.7%
383.56 1
0.7%
395.8 1
0.7%
475.9 1
0.7%
513.9 1
0.7%
586.65 1
0.7%
593.27 1
0.7%
594.62 1
0.7%
600.8 1
0.7%
ValueCountFrequency (%)
223909.99 1
0.7%
105132.15 1
0.7%
71756.85 1
0.7%
69932.71 1
0.7%
49039.03 1
0.7%
44482.03 1
0.7%
42009.46 1
0.7%
36016.51 1
0.7%
35697.24 1
0.7%
31638.45 1
0.7%

사용승인일
Date

Distinct132
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum1905-06-06 00:00:00
Maximum2020-10-13 00:00:00
2023-12-11T08:26:02.810063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:02.940811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9444444
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T08:26:03.059124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31.25
95-th percentile7
Maximum22
Range21
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation2.8573981
Coefficient of variation (CV)1.469519
Kurtosis23.873394
Mean1.9444444
Median Absolute Deviation (MAD)0
Skewness4.5651577
Sum280
Variance8.1647242
MonotonicityNot monotonic
2023-12-11T08:26:03.146863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 108
75.0%
2 19
 
13.2%
3 4
 
2.8%
4 2
 
1.4%
5 2
 
1.4%
7 2
 
1.4%
9 2
 
1.4%
13 1
 
0.7%
17 1
 
0.7%
22 1
 
0.7%
Other values (2) 2
 
1.4%
ValueCountFrequency (%)
1 108
75.0%
2 19
 
13.2%
3 4
 
2.8%
4 2
 
1.4%
5 2
 
1.4%
7 2
 
1.4%
8 1
 
0.7%
9 2
 
1.4%
12 1
 
0.7%
13 1
 
0.7%
ValueCountFrequency (%)
22 1
 
0.7%
17 1
 
0.7%
13 1
 
0.7%
12 1
 
0.7%
9 2
1.4%
8 1
 
0.7%
7 2
1.4%
5 2
1.4%
4 2
1.4%
3 4
2.8%

지하
Categorical

IMBALANCE 

Distinct4
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
0
108 
1
33 
2
 
2
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.7%

Sample

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

Common Values

ValueCountFrequency (%)
0 108
75.0%
1 33
 
22.9%
2 2
 
1.4%
3 1
 
0.7%

Length

2023-12-11T08:26:03.261782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:26:03.358988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 108
75.0%
1 33
 
22.9%
2 2
 
1.4%
3 1
 
0.7%

지상
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
5
50 
4
37 
15
14 
3
10 
9
 
4
Other values (17)
29 

Length

Max length5
Median length1
Mean length1.3472222
Min length1

Unique

Unique9 ?
Unique (%)6.2%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 50
34.7%
4 37
25.7%
15 14
 
9.7%
3 10
 
6.9%
9 4
 
2.8%
2 4
 
2.8%
6 4
 
2.8%
14 2
 
1.4%
4~5 2
 
1.4%
10~15 2
 
1.4%
Other values (12) 15
 
10.4%

Length

2023-12-11T08:26:03.512195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5 50
34.7%
4 37
25.7%
15 14
 
9.7%
3 10
 
6.9%
9 4
 
2.8%
2 4
 
2.8%
6 4
 
2.8%
7 2
 
1.4%
13 2
 
1.4%
10 2
 
1.4%
Other values (12) 15
 
10.4%

세부용도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
아파트
59 
다세대
51 
연립주택
34 

Length

Max length4
Median length3
Mean length3.2361111
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
아파트 59
41.0%
다세대 51
35.4%
연립주택 34
23.6%

Length

2023-12-11T08:26:03.625330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:26:03.720085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아파트 59
41.0%
다세대 51
35.4%
연립주택 34
23.6%

Interactions

2023-12-11T08:25:59.526903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:25:58.844125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:25:59.076807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:25:59.623366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:25:58.920008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:25:59.389338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:25:59.692066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:25:58.990225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:25:59.456245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:26:03.801211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분세대수연면적합계(제곱미터)동수지하지상세부용도
구분1.0000.7130.2880.1740.3730.6510.947
세대수0.7131.0001.0001.0000.9590.9880.760
연면적합계(제곱미터)0.2881.0001.0000.8560.5460.9440.403
동수0.1741.0000.8561.0000.4980.8420.196
지하0.3730.9590.5460.4981.0000.7020.321
지상0.6510.9880.9440.8420.7021.0000.782
세부용도0.9470.7600.4030.1960.3210.7821.000
2023-12-11T08:26:03.922994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지하세부용도지상
지하1.0000.3080.426
세부용도0.3081.0000.547
지상0.4260.5471.000
2023-12-11T08:26:04.013880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분연면적합계(제곱미터)동수지하지상세부용도
구분1.000-0.748-0.1370.2250.2900.914
연면적합계(제곱미터)-0.7481.0000.4970.3800.7480.181
동수-0.1370.4971.0000.3620.5300.131
지하0.2250.3800.3621.0000.4260.308
지상0.2900.7480.5300.4261.0000.547
세부용도0.9140.1810.1310.3080.5471.000

Missing values

2023-12-11T08:25:59.847348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:25:59.990227image/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

구분아파트 단지명위 치(도로명 주소)세대수연면적합계(제곱미터)사용승인일동수지하지상세부용도
01대송아파트1차경상남도 함안군 가야읍 말산리 103(중앙남 3길 6)504150.01990-01-12105아파트
12대송아파트2차경상남도 함안군 가야읍 말산리 102(중앙남 3길 6)504521.241990-11-30105아파트
23대한아파트경상남도 함안군 가야읍 도항리 455-2(고분2길 6)342069.081989-06-08105아파트
34동명아파트경상남도 함안군 가야읍 말산리 180(중앙남길 50)403112.61990-01-20105아파트
45무학아파트경상남도 함안군 가야읍 말산리 184(말산1길 20)504406.21990-07-25105아파트
56금강그린아파트경상남도 함안군 가야읍 말산리 284(중앙본길 18)605476.851990-08-13205아파트
67천일아라아파트1차경상남도 함안군 가야읍 검암리 932-1(가야20길 24)605904.011990-12-31205아파트
78천일아라아파트2차경상남도 함안군 가야읍 검암리 932-8(가야20길 24)403072.211991-10-21105아파트
89동신아파트1차경상남도 함안군 가야읍 도항리 224-4(도항2길 65)17316467.741991-12-301115아파트
910동신아파트2차경상남도 함안군 가야읍 도항리 225-2(도항 2길 55)28520900.561992-04-182110~15아파트
구분아파트 단지명위 치(도로명 주소)세대수연면적합계(제곱미터)사용승인일동수지하지상세부용도
134135정수장숙소경상남도 함안군 칠원읍 예곡리 735(오곡로 70-24)82140.381905-06-06112다세대
135136대진빌라경상남도 함안군 칠원읍 용정리 249-1(석전1길 111-4)8658.82005-09-08104다세대
136137새롬파크빌101경상남도 함안군 칠원읍 예곡리 145-3(예곡1길 46-6)6657.322014-03-10104다세대
137138새롬파크빌102경상남도 함안군 칠원읍 예곡리 146-2(예곡1길 46-8)6657.322014-03-10104다세대
138139새롬파크빌103경상남도 함안군 칠원읍 예곡리 413(예곡1길 46-10)9842.512014-03-12104다세대
139140새롬파크빌104경상남도 함안군 칠원읍 예곡리 145(예곡1길 46-14)8656.142014-07-21104다세대
140141새롬파크빌105경상남도 함안군 칠원읍 예곡리 145-5(예곡1길 46-12)8657.322014-07-21104다세대
141142새롬파크빌106경상남도 함안군 칠원읍 예곡리 142-7(예곡1길 46-16)8657.322014-07-04104다세대
142143새롬파크빌107경상남도 함안군 칠원읍 예곡리 139-5(예곡1길 46-17)8658.562016-11-10104다세대
143144새롬파크빌108,109경상남도 함안군 칠원읍 예곡리 138(예곡1길 46-18)161318.322016-11-10204다세대