Overview

Dataset statistics

Number of variables9
Number of observations95
Missing cells9
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.1 KiB
Average record size in memory76.4 B

Variable types

Categorical1
Text4
Numeric3
DateTime1

Dataset

Description서울특별시 광진구 관내 아파트 단지명, 주소, 동수, 세대수, 층수, 관리사무소연락처 및 팩스번호 정보 제공합니다.
Author서울특별시 광진구
URLhttps://www.data.go.kr/data/15046141/fileData.do

Alerts

구분 has constant value ""Constant
데이터기준일자 has constant value ""Constant
동수 is highly overall correlated with 세대수High correlation
세대수 is highly overall correlated with 동수High correlation
관리사무소연락처 has 1 (1.1%) missing valuesMissing
관리사무소팩스 has 8 (8.4%) missing valuesMissing
단지명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 03:13:30.462467
Analysis finished2023-12-12 03:13:33.119840
Duration2.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size892.0 B
아파트
95 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
아파트 95
100.0%

Length

2023-12-12T12:13:33.204653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:13:33.334672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아파트 95
100.0%

단지명
Text

UNIQUE 

Distinct95
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size892.0 B
2023-12-12T12:13:33.679556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length5.6526316
Min length2

Characters and Unicode

Total characters537
Distinct characters119
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

Unique95 ?
Unique (%)100.0%

Sample

1st row삼민
2nd row중곡2차
3rd row중곡1차
4th row중곡성원
5th row광덕
ValueCountFrequency (%)
삼민 1
 
1.0%
삼성1차 1
 
1.0%
태원강변 1
 
1.0%
금강 1
 
1.0%
자양8차현대홈타운 1
 
1.0%
자양9차현대홈타운 1
 
1.0%
대원리버빌 1
 
1.0%
자양7차현대홈타운 1
 
1.0%
자양동삼성 1
 
1.0%
자양현대6차 1
 
1.0%
Other values (86) 86
89.6%
2023-12-12T12:13:34.268303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
차 29
 
5.4%
대 27
 
5.0%
양 26
 
4.8%
현 24
 
4.5%
자 24
 
4.5%
성 17
 
3.2%
광 15
 
2.8%
리 14
 
2.6%
동 13
 
2.4%
한 13
 
2.4%
Other values (109) 335
62.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 495
92.2%
Decimal Number 35
 
6.5%
Open Punctuation 2
 
0.4%
Close Punctuation 2
 
0.4%
Lowercase Letter 2
 
0.4%
Space Separator 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
차 29
 
5.9%
대 27
 
5.5%
양 26
 
5.3%
현 24
 
4.8%
자 24
 
4.8%
성 17
 
3.4%
광 15
 
3.0%
리 14
 
2.8%
동 13
 
2.6%
한 13
 
2.6%
Other values (94) 293
59.2%
Decimal Number
ValueCountFrequency (%)
2 8
22.9%
1 7
20.0%
5 4
11.4%
3 4
11.4%
7 3
 
8.6%
6 3
 
8.6%
9 2
 
5.7%
8 2
 
5.7%
0 1
 
2.9%
4 1
 
2.9%
Lowercase Letter
ValueCountFrequency (%)
s 1
50.0%
k 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 495
92.2%
Common 40
 
7.4%
Latin 2
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
차 29
 
5.9%
대 27
 
5.5%
양 26
 
5.3%
현 24
 
4.8%
자 24
 
4.8%
성 17
 
3.4%
광 15
 
3.0%
리 14
 
2.8%
동 13
 
2.6%
한 13
 
2.6%
Other values (94) 293
59.2%
Common
ValueCountFrequency (%)
2 8
20.0%
1 7
17.5%
5 4
10.0%
3 4
10.0%
7 3
 
7.5%
6 3
 
7.5%
9 2
 
5.0%
8 2
 
5.0%
( 2
 
5.0%
) 2
 
5.0%
Other values (3) 3
 
7.5%
Latin
ValueCountFrequency (%)
s 1
50.0%
k 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 495
92.2%
ASCII 42
 
7.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
차 29
 
5.9%
대 27
 
5.5%
양 26
 
5.3%
현 24
 
4.8%
자 24
 
4.8%
성 17
 
3.4%
광 15
 
3.0%
리 14
 
2.8%
동 13
 
2.6%
한 13
 
2.6%
Other values (94) 293
59.2%
ASCII
ValueCountFrequency (%)
2 8
19.0%
1 7
16.7%
5 4
9.5%
3 4
9.5%
7 3
 
7.1%
6 3
 
7.1%
9 2
 
4.8%
8 2
 
4.8%
( 2
 
4.8%
) 2
 
4.8%
Other values (5) 5
11.9%

아파트소재지
Text

Distinct93
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size892.0 B
2023-12-12T12:13:34.668183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length32
Mean length28.536842
Min length25

Characters and Unicode

Total characters2711
Distinct characters58
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

Unique91 ?
Unique (%)95.8%

Sample

1st row서울특별시 광진구 긴고랑로15길 32(중곡2동609)
2nd row서울특별시 광진구 긴고랑로1길 55(중곡3동190-26)
3rd row서울특별시 광진구 동일로72길 17(중곡3동191-77)
4th row서울특별시 광진구 동일로 459(중곡3동681)
5th row서울특별시 광진구 동일로76가길 17(중곡3동683)
ValueCountFrequency (%)
서울특별시 95
23.8%
광진구 95
23.8%
아차산로 13
 
3.2%
구의강변로 6
 
1.5%
뚝섬로34길 4
 
1.0%
천호대로 3
 
0.8%
아차산로70길 3
 
0.8%
광나루로 3
 
0.8%
능동로4길 3
 
0.8%
뚝섬로 3
 
0.8%
Other values (158) 172
43.0%
2023-12-12T12:13:35.330840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
305
 
11.3%
구 122
 
4.5%
광 121
 
4.5%
동 109
 
4.0%
5 103
 
3.8%
3 101
 
3.7%
울 95
 
3.5%
로 95
 
3.5%
) 95
 
3.5%
서 95
 
3.5%
Other values (48) 1470
54.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1464
54.0%
Decimal Number 718
26.5%
Space Separator 305
 
11.3%
Close Punctuation 95
 
3.5%
Open Punctuation 95
 
3.5%
Dash Punctuation 34
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 122
 
8.3%
광 121
 
8.3%
동 109
 
7.4%
울 95
 
6.5%
로 95
 
6.5%
서 95
 
6.5%
진 95
 
6.5%
시 95
 
6.5%
별 95
 
6.5%
특 95
 
6.5%
Other values (34) 447
30.5%
Decimal Number
ValueCountFrequency (%)
5 103
14.3%
3 101
14.1%
1 92
12.8%
6 90
12.5%
4 70
9.7%
2 69
9.6%
7 68
9.5%
8 52
7.2%
9 38
 
5.3%
0 35
 
4.9%
Space Separator
ValueCountFrequency (%)
305
100.0%
Close Punctuation
ValueCountFrequency (%)
) 95
100.0%
Open Punctuation
ValueCountFrequency (%)
( 95
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1464
54.0%
Common 1247
46.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 122
 
8.3%
광 121
 
8.3%
동 109
 
7.4%
울 95
 
6.5%
로 95
 
6.5%
서 95
 
6.5%
진 95
 
6.5%
시 95
 
6.5%
별 95
 
6.5%
특 95
 
6.5%
Other values (34) 447
30.5%
Common
ValueCountFrequency (%)
305
24.5%
5 103
 
8.3%
3 101
 
8.1%
) 95
 
7.6%
( 95
 
7.6%
1 92
 
7.4%
6 90
 
7.2%
4 70
 
5.6%
2 69
 
5.5%
7 68
 
5.5%
Other values (4) 159
12.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1464
54.0%
ASCII 1247
46.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
305
24.5%
5 103
 
8.3%
3 101
 
8.1%
) 95
 
7.6%
( 95
 
7.6%
1 92
 
7.4%
6 90
 
7.2%
4 70
 
5.6%
2 69
 
5.5%
7 68
 
5.5%
Other values (4) 159
12.8%
Hangul
ValueCountFrequency (%)
구 122
 
8.3%
광 121
 
8.3%
동 109
 
7.4%
울 95
 
6.5%
로 95
 
6.5%
서 95
 
6.5%
진 95
 
6.5%
시 95
 
6.5%
별 95
 
6.5%
특 95
 
6.5%
Other values (34) 447
30.5%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3368421
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-12T12:13:35.515819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile11.3
Maximum15
Range14
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.1609672
Coefficient of variation (CV)0.94729299
Kurtosis5.0236624
Mean3.3368421
Median Absolute Deviation (MAD)1
Skewness2.2493069
Sum317
Variance9.9917133
MonotonicityNot monotonic
2023-12-12T12:13:35.698969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 27
28.4%
2 26
27.4%
3 14
14.7%
4 8
 
8.4%
5 7
 
7.4%
6 4
 
4.2%
15 2
 
2.1%
14 1
 
1.1%
11 1
 
1.1%
10 1
 
1.1%
Other values (4) 4
 
4.2%
ValueCountFrequency (%)
1 27
28.4%
2 26
27.4%
3 14
14.7%
4 8
 
8.4%
5 7
 
7.4%
6 4
 
4.2%
7 1
 
1.1%
8 1
 
1.1%
10 1
 
1.1%
11 1
 
1.1%
ValueCountFrequency (%)
15 2
 
2.1%
14 1
 
1.1%
13 1
 
1.1%
12 1
 
1.1%
11 1
 
1.1%
10 1
 
1.1%
8 1
 
1.1%
7 1
 
1.1%
6 4
4.2%
5 7
7.4%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean292.95789
Minimum28
Maximum1606
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-12T12:13:35.913796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile42.1
Q197
median204
Q3356
95-th percentile944
Maximum1606
Range1578
Interquartile range (IQR)259

Descriptive statistics

Standard deviation307.4689
Coefficient of variation (CV)1.0495327
Kurtosis6.7322049
Mean292.95789
Median Absolute Deviation (MAD)122
Skewness2.4257586
Sum27831
Variance94537.126
MonotonicityNot monotonic
2023-12-12T12:13:36.164967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 2
 
2.1%
182 2
 
2.1%
200 2
 
2.1%
204 2
 
2.1%
28 2
 
2.1%
43 2
 
2.1%
252 2
 
2.1%
119 2
 
2.1%
36 1
 
1.1%
625 1
 
1.1%
Other values (77) 77
81.1%
ValueCountFrequency (%)
28 2
2.1%
33 1
1.1%
36 1
1.1%
40 1
1.1%
43 2
2.1%
47 1
1.1%
48 1
1.1%
54 1
1.1%
55 1
1.1%
56 1
1.1%
ValueCountFrequency (%)
1606 1
1.1%
1592 1
1.1%
1177 1
1.1%
1170 1
1.1%
1056 1
1.1%
896 1
1.1%
854 1
1.1%
656 1
1.1%
654 1
1.1%
625 1
1.1%

층수
Real number (ℝ)

Distinct26
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.915789
Minimum4
Maximum58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-12T12:13:36.389690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q19.5
median15
Q322
95-th percentile29
Maximum58
Range54
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation8.3483508
Coefficient of variation (CV)0.52453262
Kurtosis5.6159981
Mean15.915789
Median Absolute Deviation (MAD)6
Skewness1.5400273
Sum1512
Variance69.694961
MonotonicityNot monotonic
2023-12-12T12:13:36.586639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
9 8
 
8.4%
23 7
 
7.4%
15 7
 
7.4%
12 6
 
6.3%
25 6
 
6.3%
14 6
 
6.3%
11 5
 
5.3%
18 4
 
4.2%
22 4
 
4.2%
7 4
 
4.2%
Other values (16) 38
40.0%
ValueCountFrequency (%)
4 3
 
3.2%
5 3
 
3.2%
6 3
 
3.2%
7 4
4.2%
8 3
 
3.2%
9 8
8.4%
10 3
 
3.2%
11 5
5.3%
12 6
6.3%
13 3
 
3.2%
ValueCountFrequency (%)
58 1
 
1.1%
37 1
 
1.1%
30 1
 
1.1%
29 3
3.2%
25 6
6.3%
24 2
 
2.1%
23 7
7.4%
22 4
4.2%
21 3
3.2%
20 2
 
2.1%

관리사무소연락처
Text

MISSING 

Distinct94
Distinct (%)100.0%
Missing1
Missing (%)1.1%
Memory size892.0 B
2023-12-12T12:13:36.973634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length11.255319
Min length11

Characters and Unicode

Total characters1058
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

Unique94 ?
Unique (%)100.0%

Sample

1st row02-454-6402
2nd row02-469-2265
3rd row02-467-1019
4th row02-492-9505
5th row02-461-3582
ValueCountFrequency (%)
02-454-6402 1
 
1.1%
02-457-7500 1
 
1.1%
02-444-6370 1
 
1.1%
02-446-7676 1
 
1.1%
02-3437-9297 1
 
1.1%
02-3436-4385 1
 
1.1%
02-456-8111 1
 
1.1%
02-3436-1331 1
 
1.1%
02-452-4500 1
 
1.1%
02-458-0973 1
 
1.1%
Other values (84) 84
89.4%
2023-12-12T12:13:37.623602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 188
17.8%
4 154
14.6%
0 150
14.2%
2 146
13.8%
5 79
7.5%
3 65
 
6.1%
7 62
 
5.9%
6 61
 
5.8%
1 59
 
5.6%
9 51
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 870
82.2%
Dash Punctuation 188
 
17.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 154
17.7%
0 150
17.2%
2 146
16.8%
5 79
9.1%
3 65
7.5%
7 62
7.1%
6 61
 
7.0%
1 59
 
6.8%
9 51
 
5.9%
8 43
 
4.9%
Dash Punctuation
ValueCountFrequency (%)
- 188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1058
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 188
17.8%
4 154
14.6%
0 150
14.2%
2 146
13.8%
5 79
7.5%
3 65
 
6.1%
7 62
 
5.9%
6 61
 
5.8%
1 59
 
5.6%
9 51
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1058
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 188
17.8%
4 154
14.6%
0 150
14.2%
2 146
13.8%
5 79
7.5%
3 65
 
6.1%
7 62
 
5.9%
6 61
 
5.8%
1 59
 
5.6%
9 51
 
4.8%

관리사무소팩스
Text

MISSING 

Distinct87
Distinct (%)100.0%
Missing8
Missing (%)8.4%
Memory size892.0 B
2023-12-12T12:13:38.015794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length11.333333
Min length11

Characters and Unicode

Total characters986
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

Unique87 ?
Unique (%)100.0%

Sample

1st row02-454-6402
2nd row02-467-1019
3rd row02-492-9505
4th row02-461-3582
5th row02-3437-4184
ValueCountFrequency (%)
02-454-6402 1
 
1.1%
02-3436-9336 1
 
1.1%
02-446-7676 1
 
1.1%
02-3437-9298 1
 
1.1%
02-6214-4385 1
 
1.1%
02-3436-1351 1
 
1.1%
02-452-4533 1
 
1.1%
02-458-0973 1
 
1.1%
02-457-8388 1
 
1.1%
02-6235-1689 1
 
1.1%
Other values (77) 77
88.5%
2023-12-12T12:13:38.674274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 174
17.6%
2 143
14.5%
0 133
13.5%
4 131
13.3%
6 72
7.3%
5 68
 
6.9%
3 63
 
6.4%
7 61
 
6.2%
1 52
 
5.3%
9 49
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 812
82.4%
Dash Punctuation 174
 
17.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 143
17.6%
0 133
16.4%
4 131
16.1%
6 72
8.9%
5 68
8.4%
3 63
7.8%
7 61
7.5%
1 52
 
6.4%
9 49
 
6.0%
8 40
 
4.9%
Dash Punctuation
ValueCountFrequency (%)
- 174
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 986
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 174
17.6%
2 143
14.5%
0 133
13.5%
4 131
13.3%
6 72
7.3%
5 68
 
6.9%
3 63
 
6.4%
7 61
 
6.2%
1 52
 
5.3%
9 49
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 986
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 174
17.6%
2 143
14.5%
0 133
13.5%
4 131
13.3%
6 72
7.3%
5 68
 
6.9%
3 63
 
6.4%
7 61
 
6.2%
1 52
 
5.3%
9 49
 
5.0%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size892.0 B
Minimum2021-02-08 00:00:00
Maximum2021-02-08 00:00:00
2023-12-12T12:13:38.864737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:39.007195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T12:13:32.211772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:31.069184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:31.420757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:32.329627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:31.179786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:31.572889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:32.488501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:31.291789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:31.721431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:13:39.147512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단지명아파트소재지동수세대수층수관리사무소연락처관리사무소팩스
단지명1.0001.0001.0001.0001.0001.0001.000
아파트소재지1.0001.0000.1660.0000.9861.0001.000
동수1.0000.1661.0000.8860.0001.0001.000
세대수1.0000.0000.8861.0000.5121.0001.000
층수1.0000.9860.0000.5121.0001.0001.000
관리사무소연락처1.0001.0001.0001.0001.0001.0001.000
관리사무소팩스1.0001.0001.0001.0001.0001.0001.000
2023-12-12T12:13:39.306564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동수세대수층수
동수1.0000.7820.110
세대수0.7821.0000.297
층수0.1100.2971.000

Missing values

2023-12-12T12:13:32.677036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:13:32.887286image/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.
2023-12-12T12:13:33.047914image/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

구분단지명아파트소재지동수세대수층수관리사무소연락처관리사무소팩스데이터기준일자
0아파트삼민서울특별시 광진구 긴고랑로15길 32(중곡2동609)136602-454-640202-454-64022021-02-08
1아파트중곡2차서울특별시 광진구 긴고랑로1길 55(중곡3동190-26)3120502-469-2265<NA>2021-02-08
2아파트중곡1차서울특별시 광진구 동일로72길 17(중곡3동191-77)3150502-467-101902-467-10192021-02-08
3아파트중곡성원서울특별시 광진구 동일로 459(중곡3동681)1912402-492-950502-492-95052021-02-08
4아파트광덕서울특별시 광진구 동일로76가길 17(중곡3동683)1551202-461-358202-461-35822021-02-08
5아파트중곡에스케이서울특별시 광진구 용마산로 174(중곡4동292)31822102-3437-418502-3437-41842021-02-08
6아파트구의동새한서울특별시 광진구 자양로26길 71(구의1동656)5126902-456-9109070-8976-49182021-02-08
7아파트아차산한라(한라녹턴)서울특별시 광진구 영화사로16길 43(구의2동 662)272602-455-099802-444-74972021-02-08
8아파트아차산 휴먼시아서울특별시 광진구 천호대로 716(구의2동 663)31251202-455-012602-455-01362021-02-08
9아파트파크타운서울특별시 광진구 천호대로 671(구의2동 665)128902-456-090102-456-09022021-02-08
구분단지명아파트소재지동수세대수층수관리사무소연락처관리사무소팩스데이터기준일자
85아파트삼성쉐르빌서울특별시 광진구 구의강변로 106(구의3동 199-18)12522402-2201-424602-2201-42472021-02-08
86아파트구의아크로리버서울특별시 광진구 구의강변로 64(구의3동 589-10)22203702-455-169502-455-16982021-02-08
87아파트강변sk뷰서울특별시 광진구 아차산로 431(구의동667)21972902-457-072502-457-07262021-02-08
88아파트래미안프리미어팰리스서울특별시 광진구 아차산로345(자양1동 778-6)22652902-456-509002-456-50922021-02-08
89아파트광진트라팰리스서울특별시 광진구 뚝섬로34길 67(자양3동 854번지)22042902-464-321002-461-32102021-02-08
90아파트더샵스타시티서울특별시 광진구 아차산로 262(자양3동 227-7)411775802-2024-711902-2024-71112021-02-08
91아파트이튼타워리버3차서울특별시 광진구 능동로 18(자양3동 855)32602502-461-327002-461-32052021-02-08
92아파트래미안구의파크스위트서울특별시 광진구 광나루로 545(구의2동 668)128542302-3494-948402-3394-94862021-02-08
93아파트테라팰리스건대1차서울특별시 광진구 아차산로25길 60(화양동 531)154702-462-991702-462-99182021-02-08
94아파트테라팰리스건대2차서울특별시 광진구 아차산로25길 60(화양동 531)2781402-446-906302-447-90632021-02-08