Overview

Dataset statistics

Number of variables11
Number of observations250
Missing cells229
Missing cells (%)8.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.3 KiB
Average record size in memory95.5 B

Variable types

Categorical4
Numeric4
Text2
DateTime1

Dataset

Description인천광역시 동구 공동주택(다세대주택) 현황 데이터로, 법정동명, 본번, 부번, 건물명, 동명칭, 세대수, 층수, 승강기수, 사용승인일 등 항목을 게시하였습니다.
URLhttps://www.data.go.kr/data/15044934/fileData.do

Alerts

비상용승강기수 is highly overall correlated with 본번 and 6 other fieldsHigh correlation
지하층수 is highly overall correlated with 비상용승강기수High correlation
승용승강기수 is highly overall correlated with 세대수 and 2 other fieldsHigh correlation
법정동명 is highly overall correlated with 비상용승강기수High correlation
본번 is highly overall correlated with 부번 and 1 other fieldsHigh correlation
부번 is highly overall correlated with 본번 and 1 other fieldsHigh correlation
세대수 is highly overall correlated with 승용승강기수 and 1 other fieldsHigh correlation
지상층수 is highly overall correlated with 승용승강기수 and 1 other fieldsHigh correlation
승용승강기수 is highly imbalanced (54.0%)Imbalance
부번 has 3 (1.2%) missing valuesMissing
건물명 has 25 (10.0%) missing valuesMissing
동명칭 has 201 (80.4%) missing valuesMissing
부번 has 12 (4.8%) zerosZeros

Reproduction

Analysis started2023-12-12 00:16:52.283649
Analysis finished2023-12-12 00:16:54.218868
Duration1.94 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

법정동명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
화수동
74 
송림동
63 
만석동
63 
화평동
18 
금곡동
15 
Other values (2)
17 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row송림동
2nd row송현동
3rd row송현동
4th row송현동
5th row송림동

Common Values

ValueCountFrequency (%)
화수동 74
29.6%
송림동 63
25.2%
만석동 63
25.2%
화평동 18
 
7.2%
금곡동 15
 
6.0%
창영동 9
 
3.6%
송현동 8
 
3.2%

Length

2023-12-12T09:16:54.271964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:16:54.376158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
화수동 74
29.6%
송림동 63
25.2%
만석동 63
25.2%
화평동 18
 
7.2%
금곡동 15
 
6.0%
창영동 9
 
3.6%
송현동 8
 
3.2%

본번
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.86
Minimum2
Maximum471
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-12T09:16:54.492046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q18
median35.5
Q368
95-th percentile253.55
Maximum471
Range469
Interquartile range (IQR)60

Descriptive statistics

Standard deviation80.379224
Coefficient of variation (CV)1.3207234
Kurtosis6.2598507
Mean60.86
Median Absolute Deviation (MAD)27.5
Skewness2.3493598
Sum15215
Variance6460.8197
MonotonicityNot monotonic
2023-12-12T09:16:54.604150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 30
 
12.0%
5 15
 
6.0%
3 15
 
6.0%
9 12
 
4.8%
21 12
 
4.8%
12 10
 
4.0%
68 8
 
3.2%
34 7
 
2.8%
24 6
 
2.4%
14 6
 
2.4%
Other values (62) 129
51.6%
ValueCountFrequency (%)
2 4
 
1.6%
3 15
6.0%
4 3
 
1.2%
5 15
6.0%
7 3
 
1.2%
8 30
12.0%
9 12
 
4.8%
12 10
 
4.0%
13 1
 
0.4%
14 6
 
2.4%
ValueCountFrequency (%)
471 1
 
0.4%
464 1
 
0.4%
350 1
 
0.4%
290 5
2.0%
278 1
 
0.4%
275 1
 
0.4%
273 1
 
0.4%
254 2
 
0.8%
253 4
1.6%
244 1
 
0.4%

부번
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct124
Distinct (%)50.2%
Missing3
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean135.88664
Minimum0
Maximum840
Zeros12
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-12T09:16:54.719133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median30
Q3101.5
95-th percentile687.7
Maximum840
Range840
Interquartile range (IQR)95.5

Descriptive statistics

Standard deviation227.77677
Coefficient of variation (CV)1.6762264
Kurtosis2.0979085
Mean135.88664
Median Absolute Deviation (MAD)28
Skewness1.8801253
Sum33564
Variance51882.255
MonotonicityNot monotonic
2023-12-12T09:16:54.842594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 15
 
6.0%
0 12
 
4.8%
2 10
 
4.0%
4 8
 
3.2%
61 6
 
2.4%
6 6
 
2.4%
5 6
 
2.4%
3 6
 
2.4%
24 5
 
2.0%
15 5
 
2.0%
Other values (114) 168
67.2%
ValueCountFrequency (%)
0 12
4.8%
1 15
6.0%
2 10
4.0%
3 6
 
2.4%
4 8
3.2%
5 6
 
2.4%
6 6
 
2.4%
7 1
 
0.4%
8 3
 
1.2%
9 4
 
1.6%
ValueCountFrequency (%)
840 1
0.4%
839 1
0.4%
838 1
0.4%
837 1
0.4%
836 1
0.4%
699 1
0.4%
698 1
0.4%
696 1
0.4%
695 1
0.4%
693 1
0.4%

건물명
Text

MISSING 

Distinct131
Distinct (%)58.2%
Missing25
Missing (%)10.0%
Memory size2.1 KiB
2023-12-12T09:16:55.123637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length4
Mean length4.7955556
Min length3

Characters and Unicode

Total characters1079
Distinct characters145
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)40.0%

Sample

1st row삼부다세대주택
2nd row삼부다세대주택
3rd row삼부다세대주택
4th row금하주택
5th row금하주택
ValueCountFrequency (%)
미래아트빌라 10
 
4.3%
대광빌라 9
 
3.8%
미래빌라 8
 
3.4%
만석빌리지 6
 
2.6%
힐타운 6
 
2.6%
수정빌라 6
 
2.6%
금곡빌라 6
 
2.6%
화원빌라 5
 
2.1%
안보빌라 4
 
1.7%
삼부다세대주택 3
 
1.3%
Other values (129) 171
73.1%
2023-12-12T09:16:55.518874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
빌 172
 
15.9%
라 156
 
14.5%
미 24
 
2.2%
대 23
 
2.1%
래 19
 
1.8%
아 19
 
1.8%
하 18
 
1.7%
동 18
 
1.7%
트 18
 
1.7%
이 17
 
1.6%
Other values (135) 595
55.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1047
97.0%
Decimal Number 10
 
0.9%
Space Separator 9
 
0.8%
Open Punctuation 4
 
0.4%
Close Punctuation 4
 
0.4%
Uppercase Letter 4
 
0.4%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
빌 172
 
16.4%
라 156
 
14.9%
미 24
 
2.3%
대 23
 
2.2%
래 19
 
1.8%
아 19
 
1.8%
하 18
 
1.7%
동 18
 
1.7%
트 18
 
1.7%
이 17
 
1.6%
Other values (124) 563
53.8%
Uppercase Letter
ValueCountFrequency (%)
K 1
25.0%
S 1
25.0%
A 1
25.0%
B 1
25.0%
Decimal Number
ValueCountFrequency (%)
2 6
60.0%
1 2
 
20.0%
3 2
 
20.0%
Space Separator
ValueCountFrequency (%)
9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1047
97.0%
Common 28
 
2.6%
Latin 4
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
빌 172
 
16.4%
라 156
 
14.9%
미 24
 
2.3%
대 23
 
2.2%
래 19
 
1.8%
아 19
 
1.8%
하 18
 
1.7%
동 18
 
1.7%
트 18
 
1.7%
이 17
 
1.6%
Other values (124) 563
53.8%
Common
ValueCountFrequency (%)
9
32.1%
2 6
21.4%
( 4
14.3%
) 4
14.3%
1 2
 
7.1%
3 2
 
7.1%
. 1
 
3.6%
Latin
ValueCountFrequency (%)
K 1
25.0%
S 1
25.0%
A 1
25.0%
B 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1047
97.0%
ASCII 32
 
3.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
빌 172
 
16.4%
라 156
 
14.9%
미 24
 
2.3%
대 23
 
2.2%
래 19
 
1.8%
아 19
 
1.8%
하 18
 
1.7%
동 18
 
1.7%
트 18
 
1.7%
이 17
 
1.6%
Other values (124) 563
53.8%
ASCII
ValueCountFrequency (%)
9
28.1%
2 6
18.8%
( 4
12.5%
) 4
12.5%
1 2
 
6.2%
3 2
 
6.2%
. 1
 
3.1%
K 1
 
3.1%
S 1
 
3.1%
A 1
 
3.1%

동명칭
Text

MISSING 

Distinct25
Distinct (%)51.0%
Missing201
Missing (%)80.4%
Memory size2.1 KiB
2023-12-12T09:16:55.684749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.244898
Min length2

Characters and Unicode

Total characters110
Distinct characters19
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)32.7%

Sample

1st row나동
2nd row다동
3rd row가동
4th rowA동
5th rowB동
ValueCountFrequency (%)
1동 7
14.3%
2동 6
 
12.2%
b동 5
 
10.2%
a동 4
 
8.2%
3동 3
 
6.1%
6동 2
 
4.1%
5동 2
 
4.1%
7동 2
 
4.1%
c동 2
 
4.1%
8동 1
 
2.0%
Other values (15) 15
30.6%
2023-12-12T09:16:56.098709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 49
44.5%
1 18
 
16.4%
2 8
 
7.3%
B 5
 
4.5%
A 4
 
3.6%
3 4
 
3.6%
6 3
 
2.7%
5 3
 
2.7%
7 3
 
2.7%
4 2
 
1.8%
Other values (9) 11
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 54
49.1%
Decimal Number 45
40.9%
Uppercase Letter 11
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 18
40.0%
2 8
17.8%
3 4
 
8.9%
6 3
 
6.7%
5 3
 
6.7%
7 3
 
6.7%
4 2
 
4.4%
0 2
 
4.4%
9 1
 
2.2%
8 1
 
2.2%
Other Letter
ValueCountFrequency (%)
동 49
90.7%
가 1
 
1.9%
다 1
 
1.9%
나 1
 
1.9%
롯 1
 
1.9%
데 1
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
B 5
45.5%
A 4
36.4%
C 2
 
18.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 54
49.1%
Common 45
40.9%
Latin 11
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 18
40.0%
2 8
17.8%
3 4
 
8.9%
6 3
 
6.7%
5 3
 
6.7%
7 3
 
6.7%
4 2
 
4.4%
0 2
 
4.4%
9 1
 
2.2%
8 1
 
2.2%
Hangul
ValueCountFrequency (%)
동 49
90.7%
가 1
 
1.9%
다 1
 
1.9%
나 1
 
1.9%
롯 1
 
1.9%
데 1
 
1.9%
Latin
ValueCountFrequency (%)
B 5
45.5%
A 4
36.4%
C 2
 
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56
50.9%
Hangul 54
49.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 49
90.7%
가 1
 
1.9%
다 1
 
1.9%
나 1
 
1.9%
롯 1
 
1.9%
데 1
 
1.9%
ASCII
ValueCountFrequency (%)
1 18
32.1%
2 8
14.3%
B 5
 
8.9%
A 4
 
7.1%
3 4
 
7.1%
6 3
 
5.4%
5 3
 
5.4%
7 3
 
5.4%
4 2
 
3.6%
0 2
 
3.6%
Other values (3) 4
 
7.1%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.752
Minimum2
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-12T09:16:56.288676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q18
median9
Q312
95-th percentile16
Maximum36
Range34
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.1472031
Coefficient of variation (CV)0.42526693
Kurtosis6.9973731
Mean9.752
Median Absolute Deviation (MAD)1
Skewness1.519289
Sum2438
Variance17.199293
MonotonicityNot monotonic
2023-12-12T09:16:56.759469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
8 73
29.2%
10 48
19.2%
12 24
 
9.6%
9 14
 
5.6%
13 11
 
4.4%
14 10
 
4.0%
16 9
 
3.6%
2 8
 
3.2%
4 8
 
3.2%
3 7
 
2.8%
Other values (10) 38
15.2%
ValueCountFrequency (%)
2 8
 
3.2%
3 7
 
2.8%
4 8
 
3.2%
5 7
 
2.8%
6 5
 
2.0%
7 5
 
2.0%
8 73
29.2%
9 14
 
5.6%
10 48
19.2%
11 5
 
2.0%
ValueCountFrequency (%)
36 1
 
0.4%
24 3
 
1.2%
20 2
 
0.8%
19 2
 
0.8%
18 1
 
0.4%
16 9
 
3.6%
15 7
 
2.8%
14 10
4.0%
13 11
4.4%
12 24
9.6%

지하층수
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
1
134 
0
112 
<NA>
 
4

Length

Max length4
Median length1
Mean length1.048
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 134
53.6%
0 112
44.8%
<NA> 4
 
1.6%

Length

2023-12-12T09:16:56.904509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:16:57.006240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 134
53.6%
0 112
44.8%
na 4
 
1.6%

지상층수
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.044
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-12T09:16:57.107130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median4
Q34
95-th percentile5
Maximum10
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.83236731
Coefficient of variation (CV)0.20582772
Kurtosis11.772351
Mean4.044
Median Absolute Deviation (MAD)0
Skewness1.5599148
Sum1011
Variance0.69283534
MonotonicityNot monotonic
2023-12-12T09:16:57.217362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 166
66.4%
5 41
 
16.4%
3 29
 
11.6%
2 9
 
3.6%
7 3
 
1.2%
6 1
 
0.4%
10 1
 
0.4%
ValueCountFrequency (%)
2 9
 
3.6%
3 29
 
11.6%
4 166
66.4%
5 41
 
16.4%
6 1
 
0.4%
7 3
 
1.2%
10 1
 
0.4%
ValueCountFrequency (%)
10 1
 
0.4%
7 3
 
1.2%
6 1
 
0.4%
5 41
 
16.4%
4 166
66.4%
3 29
 
11.6%
2 9
 
3.6%

승용승강기수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
0
200 
<NA>
37 
1
 
11
2
 
2

Length

Max length4
Median length1
Mean length1.444
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 200
80.0%
<NA> 37
 
14.8%
1 11
 
4.4%
2 2
 
0.8%

Length

2023-12-12T09:16:57.345257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:16:57.448596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 200
80.0%
na 37
 
14.8%
1 11
 
4.4%
2 2
 
0.8%

비상용승강기수
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
0
202 
<NA>
48 

Length

Max length4
Median length1
Mean length1.576
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 202
80.8%
<NA> 48
 
19.2%

Length

2023-12-12T09:16:57.563037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:16:57.660347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 202
80.8%
na 48
 
19.2%

사용승인일
Date

Distinct187
Distinct (%)74.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
Minimum1986-09-25 00:00:00
Maximum2021-12-07 00:00:00
2023-12-12T09:16:57.784280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:57.958340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-12T09:16:53.564084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:52.643709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:52.919254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:53.247181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:53.639179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:52.700979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:52.989196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:53.326957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:53.713167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:52.763753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:53.078531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:53.397124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:53.794723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:52.834960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:53.161858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:53.479580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:16:58.060886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동명본번부번동명칭세대수지하층수지상층수승용승강기수
법정동명1.0000.4570.4420.0000.3380.2200.6780.135
본번0.4571.0000.0000.0000.2540.1060.1740.274
부번0.4420.0001.0000.0000.6150.3680.3280.000
동명칭0.0000.0000.0001.0000.4400.4580.884NaN
세대수0.3380.2540.6150.4401.0000.5850.5320.752
지하층수0.2200.1060.3680.4580.5851.0000.4440.105
지상층수0.6780.1740.3280.8840.5320.4441.0000.740
승용승강기수0.1350.2740.000NaN0.7520.1050.7401.000
2023-12-12T09:16:58.183709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비상용승강기수지하층수승용승강기수법정동명
비상용승강기수1.0001.0001.0001.000
지하층수1.0001.0000.1740.232
승용승강기수1.0000.1741.0000.089
법정동명1.0000.2320.0891.000
2023-12-12T09:16:58.290816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
본번부번세대수지상층수법정동명지하층수승용승강기수비상용승강기수
본번1.000-0.528-0.153-0.0400.2610.1040.1221.000
부번-0.5281.0000.096-0.1210.2420.2770.0001.000
세대수-0.1530.0961.0000.4070.1860.4210.6821.000
지상층수-0.040-0.1210.4071.0000.2940.4710.6611.000
법정동명0.2610.2420.1860.2941.0000.2320.0891.000
지하층수0.1040.2770.4210.4710.2321.0000.1741.000
승용승강기수0.1220.0000.6820.6610.0890.1741.0001.000
비상용승강기수1.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-12T09:16:53.909498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:16:54.044463image/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.
2023-12-12T09:16:54.149521image/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

법정동명본번부번건물명동명칭세대수지하층수지상층수승용승강기수비상용승강기수사용승인일
0송림동5461<NA><NA>713001986-09-25
1송현동1261삼부다세대주택나동612001986-10-29
2송현동1261삼부다세대주택다동612001986-10-29
3송현동1261삼부다세대주택가동302001986-10-29
4송림동12568<NA><NA>312001987-12-05
5송현동1342<NA><NA>612001987-12-22
6금곡동5710<NA><NA>313001988-06-15
7송림동9139금하주택A동813001990-12-15
8송림동9139금하주택B동813001990-12-15
9송림동2118한서빌라<NA>813001991-01-29
법정동명본번부번건물명동명칭세대수지하층수지상층수승용승강기수비상용승강기수사용승인일
240금곡동5417<NA><NA>7151<NA>2018-12-21
241송림동8839호진다온채2동<NA>1605<NA><NA>2018-12-31
242송림동8840호진다온채3동<NA>1605<NA><NA>2018-12-31
243송림동1155해드림<NA>8051<NA>2018-12-31
244송림동8439호진다온채1동<NA>2405<NA><NA>2019-04-11
245금곡동483해드림B<NA>805<NA><NA>2019-10-02
246금곡동48166해드림A<NA>805<NA><NA>2019-10-02
247만석동693한마을<NA>36052<NA>2020-01-28
248화평동2730파인빌리지<NA>1005102021-05-13
249화수동7320화수정원마을 행복주택 2<NA>2005102021-12-07