Overview

Dataset statistics

Number of variables10
Number of observations26
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory89.1 B

Variable types

Numeric4
Text3
DateTime2
Categorical1

Dataset

Description홍성군내 아파트 현황으로 아파트명, 소재지 도로명주소, 승인일, 준공일 동수, 세대수, 데이터 기준일 등을 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=443&beforeMenuCd=DOM_000000201001001000&publicdatapk=3073568

Alerts

데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 동수High correlation
동수 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
세대수 is highly overall correlated with 동수 and 1 other fieldsHigh correlation
연면적(㎡) is highly overall correlated with 동수 and 1 other fieldsHigh correlation
연번 has unique valuesUnique
아파트명 has unique valuesUnique
소재지도로명주소 has unique valuesUnique
연면적(㎡) has unique valuesUnique
관리사무소연락처 has unique valuesUnique

Reproduction

Analysis started2024-01-09 21:37:09.297655
Analysis finished2024-01-09 21:37:10.752849
Duration1.46 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.5
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-01-10T06:37:10.799167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.25
Q17.25
median13.5
Q319.75
95-th percentile24.75
Maximum26
Range25
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation7.6485293
Coefficient of variation (CV)0.56655772
Kurtosis-1.2
Mean13.5
Median Absolute Deviation (MAD)6.5
Skewness0
Sum351
Variance58.5
MonotonicityStrictly increasing
2024-01-10T06:37:10.892706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 1
 
3.8%
15 1
 
3.8%
26 1
 
3.8%
25 1
 
3.8%
24 1
 
3.8%
23 1
 
3.8%
22 1
 
3.8%
21 1
 
3.8%
20 1
 
3.8%
19 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
1 1
3.8%
2 1
3.8%
3 1
3.8%
4 1
3.8%
5 1
3.8%
6 1
3.8%
7 1
3.8%
8 1
3.8%
9 1
3.8%
10 1
3.8%
ValueCountFrequency (%)
26 1
3.8%
25 1
3.8%
24 1
3.8%
23 1
3.8%
22 1
3.8%
21 1
3.8%
20 1
3.8%
19 1
3.8%
18 1
3.8%
17 1
3.8%

아파트명
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2024-01-10T06:37:11.055393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length9
Mean length6.2692308
Min length3

Characters and Unicode

Total characters163
Distinct characters83
Distinct categories6 ?
Distinct scripts3 ?
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 row대우
2nd row현광
3rd row청솔
4th row경성
5th row현대
ValueCountFrequency (%)
대우 1
 
3.4%
세청파크빌 1
 
3.4%
lh천년나무4단지 1
 
3.4%
주공4단지 1
 
3.4%
상록아파트 1
 
3.4%
모아엘가 1
 
3.4%
중흥s클래스 1
 
3.4%
경남아너스빌 1
 
3.4%
lh스타힐스 1
 
3.4%
효성해링턴플레이스 1
 
3.4%
Other values (19) 19
65.5%
2024-01-10T06:37:11.330401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16
 
9.8%
스 7
 
4.3%
아 6
 
3.7%
공 5
 
3.1%
파 5
 
3.1%
주 5
 
3.1%
단 4
 
2.5%
지 4
 
2.5%
H 3
 
1.8%
타 3
 
1.8%
Other values (73) 105
64.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 129
79.1%
Space Separator 16
 
9.8%
Uppercase Letter 7
 
4.3%
Decimal Number 7
 
4.3%
Open Punctuation 2
 
1.2%
Close Punctuation 2
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 7
 
5.4%
아 6
 
4.7%
공 5
 
3.9%
파 5
 
3.9%
주 5
 
3.9%
단 4
 
3.1%
지 4
 
3.1%
타 3
 
2.3%
빌 3
 
2.3%
차 3
 
2.3%
Other values (63) 84
65.1%
Decimal Number
ValueCountFrequency (%)
4 2
28.6%
1 2
28.6%
2 2
28.6%
3 1
14.3%
Uppercase Letter
ValueCountFrequency (%)
H 3
42.9%
L 3
42.9%
S 1
 
14.3%
Space Separator
ValueCountFrequency (%)
16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 129
79.1%
Common 27
 
16.6%
Latin 7
 
4.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 7
 
5.4%
아 6
 
4.7%
공 5
 
3.9%
파 5
 
3.9%
주 5
 
3.9%
단 4
 
3.1%
지 4
 
3.1%
타 3
 
2.3%
빌 3
 
2.3%
차 3
 
2.3%
Other values (63) 84
65.1%
Common
ValueCountFrequency (%)
16
59.3%
4 2
 
7.4%
( 2
 
7.4%
) 2
 
7.4%
1 2
 
7.4%
2 2
 
7.4%
3 1
 
3.7%
Latin
ValueCountFrequency (%)
H 3
42.9%
L 3
42.9%
S 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 129
79.1%
ASCII 34
 
20.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16
47.1%
H 3
 
8.8%
L 3
 
8.8%
4 2
 
5.9%
( 2
 
5.9%
) 2
 
5.9%
1 2
 
5.9%
2 2
 
5.9%
S 1
 
2.9%
3 1
 
2.9%
Hangul
ValueCountFrequency (%)
스 7
 
5.4%
아 6
 
4.7%
공 5
 
3.9%
파 5
 
3.9%
주 5
 
3.9%
단 4
 
3.1%
지 4
 
3.1%
타 3
 
2.3%
빌 3
 
2.3%
차 3
 
2.3%
Other values (63) 84
65.1%

소재지도로명주소
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2024-01-10T06:37:11.487564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length18.038462
Min length15

Characters and Unicode

Total characters469
Distinct characters46
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 row충청남도 홍성읍 문화로72번길 41
2nd row충청남도 홍성읍 도청대로96번길 85-17
3rd row충청남도 홍성읍 내포로146번길 46
4th row충청남도 홍성읍 문화로 80번길 32-14
5th row충청남도 홍성읍 월계천길 41-11
ValueCountFrequency (%)
충청남도 26
24.8%
홍성읍 15
 
14.3%
홍북읍 9
 
8.6%
문화로72번길 4
 
3.8%
신대로 3
 
2.9%
홍학로 2
 
1.9%
남장중로 2
 
1.9%
월산로30번길 2
 
1.9%
37 1
 
1.0%
46-14 1
 
1.0%
Other values (40) 40
38.1%
2024-01-10T06:37:11.755522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
79
16.8%
충 29
 
6.2%
청 29
 
6.2%
남 28
 
6.0%
도 28
 
6.0%
홍 27
 
5.8%
로 25
 
5.3%
읍 25
 
5.3%
1 17
 
3.6%
성 15
 
3.2%
Other values (36) 167
35.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 291
62.0%
Decimal Number 95
 
20.3%
Space Separator 79
 
16.8%
Dash Punctuation 4
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
충 29
10.0%
청 29
10.0%
남 28
9.6%
도 28
9.6%
홍 27
9.3%
로 25
8.6%
읍 25
8.6%
성 15
 
5.2%
길 14
 
4.8%
번 13
 
4.5%
Other values (24) 58
19.9%
Decimal Number
ValueCountFrequency (%)
1 17
17.9%
2 14
14.7%
3 14
14.7%
4 10
10.5%
7 8
8.4%
0 8
8.4%
8 7
7.4%
5 7
7.4%
6 6
 
6.3%
9 4
 
4.2%
Space Separator
ValueCountFrequency (%)
79
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 291
62.0%
Common 178
38.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
충 29
10.0%
청 29
10.0%
남 28
9.6%
도 28
9.6%
홍 27
9.3%
로 25
8.6%
읍 25
8.6%
성 15
 
5.2%
길 14
 
4.8%
번 13
 
4.5%
Other values (24) 58
19.9%
Common
ValueCountFrequency (%)
79
44.4%
1 17
 
9.6%
2 14
 
7.9%
3 14
 
7.9%
4 10
 
5.6%
7 8
 
4.5%
0 8
 
4.5%
8 7
 
3.9%
5 7
 
3.9%
6 6
 
3.4%
Other values (2) 8
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 291
62.0%
ASCII 178
38.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
79
44.4%
1 17
 
9.6%
2 14
 
7.9%
3 14
 
7.9%
4 10
 
5.6%
7 8
 
4.5%
0 8
 
4.5%
8 7
 
3.9%
5 7
 
3.9%
6 6
 
3.4%
Other values (2) 8
 
4.5%
Hangul
ValueCountFrequency (%)
충 29
10.0%
청 29
10.0%
남 28
9.6%
도 28
9.6%
홍 27
9.3%
로 25
8.6%
읍 25
8.6%
성 15
 
5.2%
길 14
 
4.8%
번 13
 
4.5%
Other values (24) 58
19.9%

승인일
Date

Distinct24
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Memory size340.0 B
Minimum1993-01-14 00:00:00
Maximum2013-12-24 00:00:00
2024-01-10T06:37:11.849146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:37:11.939598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)

준공일
Date

Distinct25
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size340.0 B
Minimum1995-03-30 00:00:00
Maximum2017-05-30 00:00:00
2024-01-10T06:37:12.027037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:37:12.116110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2307692
Minimum2
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-01-10T06:37:12.201743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.25
Q14
median8
Q312
95-th percentile22.75
Maximum28
Range26
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.4949685
Coefficient of variation (CV)0.70362158
Kurtosis2.3479787
Mean9.2307692
Median Absolute Deviation (MAD)4
Skewness1.4739626
Sum240
Variance42.184615
MonotonicityNot monotonic
2024-01-10T06:37:12.284058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
4 5
19.2%
2 2
 
7.7%
8 2
 
7.7%
12 2
 
7.7%
9 2
 
7.7%
6 2
 
7.7%
13 2
 
7.7%
5 1
 
3.8%
3 1
 
3.8%
7 1
 
3.8%
Other values (6) 6
23.1%
ValueCountFrequency (%)
2 2
 
7.7%
3 1
 
3.8%
4 5
19.2%
5 1
 
3.8%
6 2
 
7.7%
7 1
 
3.8%
8 2
 
7.7%
9 2
 
7.7%
10 1
 
3.8%
11 1
 
3.8%
ValueCountFrequency (%)
28 1
3.8%
25 1
3.8%
16 1
3.8%
15 1
3.8%
13 2
7.7%
12 2
7.7%
11 1
3.8%
10 1
3.8%
9 2
7.7%
8 2
7.7%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean681.23077
Minimum248
Maximum2127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-01-10T06:37:12.368352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum248
5-th percentile252.5
Q1352.75
median513.5
Q3907.5
95-th percentile1560
Maximum2127
Range1879
Interquartile range (IQR)554.75

Descriptive statistics

Standard deviation457.2373
Coefficient of variation (CV)0.67119296
Kurtosis3.1634411
Mean681.23077
Median Absolute Deviation (MAD)223
Skewness1.6804329
Sum17712
Variance209065.94
MonotonicityNot monotonic
2024-01-10T06:37:12.465591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
684 2
 
7.7%
260 1
 
3.8%
885 1
 
3.8%
394 1
 
3.8%
518 1
 
3.8%
497 1
 
3.8%
1260 1
 
3.8%
1660 1
 
3.8%
990 1
 
3.8%
2127 1
 
3.8%
Other values (15) 15
57.7%
ValueCountFrequency (%)
248 1
3.8%
250 1
3.8%
260 1
3.8%
270 1
3.8%
284 1
3.8%
297 1
3.8%
339 1
3.8%
394 1
3.8%
430 1
3.8%
434 1
3.8%
ValueCountFrequency (%)
2127 1
3.8%
1660 1
3.8%
1260 1
3.8%
996 1
3.8%
990 1
3.8%
938 1
3.8%
915 1
3.8%
885 1
3.8%
716 1
3.8%
684 2
7.7%

연면적(㎡)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87953.542
Minimum17224.26
Maximum311816.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-01-10T06:37:12.561861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17224.26
5-th percentile18020.45
Q133621.74
median66553.315
Q3131901.61
95-th percentile246079.34
Maximum311816.04
Range294591.78
Interquartile range (IQR)98279.865

Descriptive statistics

Standard deviation75974.172
Coefficient of variation (CV)0.86379889
Kurtosis2.3620785
Mean87953.542
Median Absolute Deviation (MAD)35603.465
Skewness1.6009668
Sum2286792.1
Variance5.7720748 × 109
MonotonicityNot monotonic
2024-01-10T06:37:12.646606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
25390.42 1
 
3.8%
19836.56 1
 
3.8%
51696.26 1
 
3.8%
28843.78 1
 
3.8%
64920.6 1
 
3.8%
190740.69 1
 
3.8%
264525.56 1
 
3.8%
147537.28 1
 
3.8%
311816.04 1
 
3.8%
136842.06 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
17224.26 1
3.8%
17415.08 1
3.8%
19836.56 1
3.8%
25390.42 1
3.8%
28843.78 1
3.8%
33055.92 1
3.8%
33387.11 1
3.8%
34325.63 1
3.8%
37822.0 1
3.8%
46076.36 1
3.8%
ValueCountFrequency (%)
311816.04 1
3.8%
264525.56 1
3.8%
190740.69 1
3.8%
147537.28 1
3.8%
143879.09 1
3.8%
141973.26 1
3.8%
136842.06 1
3.8%
117080.24 1
3.8%
83641.37 1
3.8%
79298.5 1
3.8%

관리사무소연락처
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2024-01-10T06:37:12.798981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters312
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

Unique26 ?
Unique (%)100.0%

Sample

1st row041-634-8615
2nd row041-634-6034
3rd row041-634-4844
4th row041-634-8048
5th row041-633-5324
ValueCountFrequency (%)
041-634-8615 1
 
3.8%
041-634-6034 1
 
3.8%
041-631-6404 1
 
3.8%
041-634-2985 1
 
3.8%
041-631-8936 1
 
3.8%
041-631-3585 1
 
3.8%
041-634-3036 1
 
3.8%
041-633-0433 1
 
3.8%
041-635-1126 1
 
3.8%
041-634-7263 1
 
3.8%
Other values (16) 16
61.5%
2024-01-10T06:37:13.048157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 52
16.7%
- 52
16.7%
1 46
14.7%
6 39
12.5%
3 39
12.5%
0 36
11.5%
9 14
 
4.5%
8 11
 
3.5%
5 11
 
3.5%
2 10
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260
83.3%
Dash Punctuation 52
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 52
20.0%
1 46
17.7%
6 39
15.0%
3 39
15.0%
0 36
13.8%
9 14
 
5.4%
8 11
 
4.2%
5 11
 
4.2%
2 10
 
3.8%
7 2
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
- 52
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 312
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 52
16.7%
- 52
16.7%
1 46
14.7%
6 39
12.5%
3 39
12.5%
0 36
11.5%
9 14
 
4.5%
8 11
 
3.5%
5 11
 
3.5%
2 10
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 52
16.7%
- 52
16.7%
1 46
14.7%
6 39
12.5%
3 39
12.5%
0 36
11.5%
9 14
 
4.5%
8 11
 
3.5%
5 11
 
3.5%
2 10
 
3.2%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size340.0 B
2021-08-31
26 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-08-31
2nd row2021-08-31
3rd row2021-08-31
4th row2021-08-31
5th row2021-08-31

Common Values

ValueCountFrequency (%)
2021-08-31 26
100.0%

Length

2024-01-10T06:37:13.151238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:37:13.217832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-08-31 26
100.0%

Interactions

2024-01-10T06:37:10.320579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:37:09.556871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:37:09.813817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:37:10.053130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:37:10.381768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:37:09.624996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:37:09.874457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:37:10.117333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:37:10.444878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:37:09.684647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:37:09.928436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:37:10.184090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:37:10.514918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:37:09.753028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:37:09.992085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:37:10.252820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T06:37:13.266064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번아파트명소재지도로명주소승인일준공일동수세대수연면적(㎡)관리사무소연락처
연번1.0001.0001.0000.7981.0000.5480.4710.4701.000
아파트명1.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지도로명주소1.0001.0001.0001.0001.0001.0001.0001.0001.000
승인일0.7981.0001.0001.0000.9620.9211.0000.8501.000
준공일1.0001.0001.0000.9621.0001.0001.0001.0001.000
동수0.5481.0001.0000.9211.0001.0000.8420.9791.000
세대수0.4711.0001.0001.0001.0000.8421.0000.9221.000
연면적(㎡)0.4701.0001.0000.8501.0000.9790.9221.0001.000
관리사무소연락처1.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-01-10T06:37:13.356467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번동수세대수연면적(㎡)
연번1.0000.6040.4940.446
동수0.6041.0000.7920.832
세대수0.4940.7921.0000.921
연면적(㎡)0.4460.8320.9211.000

Missing values

2024-01-10T06:37:10.601268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:37:10.707597image/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

연번아파트명소재지도로명주소승인일준공일동수세대수연면적(㎡)관리사무소연락처데이터기준일자
01대우충청남도 홍성읍 문화로72번길 411993-01-141995-03-30226025390.42041-634-86152021-08-31
12현광충청남도 홍성읍 도청대로96번길 85-171994-10-291997-05-01333933387.11041-634-60342021-08-31
23청솔충청남도 홍성읍 내포로146번길 461994-12-291997-05-01229734325.63041-634-48442021-08-31
34경성충청남도 홍성읍 문화로 80번길 32-141995-02-171997-01-10427033055.92041-634-80482021-08-31
45현대충청남도 홍성읍 월계천길 41-111995-03-311997-04-11443446076.36041-633-53242021-08-31
56동진충청남도 홍북읍 도청대로 2111996-09-241998-01-10424817224.26041-634-94972021-08-31
67주공 1차(남장주공그린빌)충청남도 홍성읍 문화로72번길 921998-06-012001-09-081399683641.37041-631-61622021-08-31
78부영1차충청남도 홍성읍 월산로30번길 392001-09-182003-05-26668468186.03041-631-56522021-08-31
89부영2차충청남도 홍성읍 월산로30번길 382001-09-182004-08-23868472747.29041-631-56112021-08-31
910코오롱하늘채충청남도 홍성읍 문화로33번길 112004-08-042006-10-181350979298.5041-631-44502021-08-31
연번아파트명소재지도로명주소승인일준공일동수세대수연면적(㎡)관리사무소연락처데이터기준일자
1617홍성광천 LH충청남도 광천읍 광천로428번길 46-142008-06-272013-11-19428417415.08041-641-60092021-08-31
1718극동스타클래스충청남도 홍북읍 신대로 332011-09-012014-05-3010938141973.26041-634-72632021-08-31
1819효성해링턴플레이스충청남도 홍북읍 신대로 542011-10-282014-09-229915136842.06041-635-11262021-08-31
1920LH스타힐스충청남도 홍북읍 홍예로 2132010-11-012015-08-21252127311816.04041-633-04332021-08-31
2021경남아너스빌충청남도 홍북읍 청사로 152013-03-222016-01-2911990147537.28041-634-30362021-08-31
2122중흥S클래스충청남도 홍북읍 자경로 172013-09-022016-04-05281660264525.56041-631-35852021-08-31
2223모아엘가충청남도 홍북읍 홍학로 882013-02-182016-04-08151260190740.69041-631-89362021-08-31
2324상록아파트충청남도 홍북읍 홍학로 252013-11-132016-07-251649764920.6041-634-29852021-08-31
2425주공4단지 (LH천년나무4단지)충청남도 홍성읍 남장중로 372013-12-242016-11-30451828843.78041-631-64042021-08-31
2526이안아파트충청남도 홍성읍 문화로72번길 502008-03-282017-05-30839451696.26041-632-29842021-08-31