Overview

Dataset statistics

Number of variables10
Number of observations109
Missing cells2
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.2 KiB
Average record size in memory86.2 B

Variable types

Numeric5
Text3
DateTime1
Categorical1

Dataset

Description부산광역시북구공동주택현황_20221017
Author부산광역시 북구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=3069542

Alerts

단지규모(동수) is highly overall correlated with 단지규모(세대수) and 1 other fieldsHigh correlation
단지규모(세대수) is highly overall correlated with 단지규모(동수) and 1 other fieldsHigh correlation
승강기(대) is highly overall correlated with 단지규모(동수) and 1 other fieldsHigh correlation
구분 is highly imbalanced (58.9%)Imbalance
승강기(대) has 2 (1.8%) missing valuesMissing
연번 has unique valuesUnique
단지명 has unique valuesUnique
승강기(대) has 7 (6.4%) zerosZeros

Reproduction

Analysis started2023-12-10 16:01:25.076417
Analysis finished2023-12-10 16:01:29.224056
Duration4.15 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct109
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55
Minimum1
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T01:01:29.365985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.4
Q128
median55
Q382
95-th percentile103.6
Maximum109
Range108
Interquartile range (IQR)54

Descriptive statistics

Standard deviation31.609598
Coefficient of variation (CV)0.57471996
Kurtosis-1.2
Mean55
Median Absolute Deviation (MAD)27
Skewness0
Sum5995
Variance999.16667
MonotonicityStrictly increasing
2023-12-11T01:01:29.754066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.9%
70 1
 
0.9%
81 1
 
0.9%
80 1
 
0.9%
79 1
 
0.9%
78 1
 
0.9%
77 1
 
0.9%
76 1
 
0.9%
75 1
 
0.9%
74 1
 
0.9%
Other values (99) 99
90.8%
ValueCountFrequency (%)
1 1
0.9%
2 1
0.9%
3 1
0.9%
4 1
0.9%
5 1
0.9%
6 1
0.9%
7 1
0.9%
8 1
0.9%
9 1
0.9%
10 1
0.9%
ValueCountFrequency (%)
109 1
0.9%
108 1
0.9%
107 1
0.9%
106 1
0.9%
105 1
0.9%
104 1
0.9%
103 1
0.9%
102 1
0.9%
101 1
0.9%
100 1
0.9%

단지명
Text

UNIQUE 

Distinct109
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1004.0 B
2023-12-11T01:01:30.114113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length7.3302752
Min length2

Characters and Unicode

Total characters799
Distinct characters180
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique109 ?
Unique (%)100.0%

Sample

1st row구포삼정그린코아
2nd row구포현대아파트
3rd row유림노르웨이숲
4th row구포대성맨션
5th row구포에이스타운
ValueCountFrequency (%)
금곡주공 8
 
5.6%
1차 4
 
2.8%
2차 3
 
2.1%
화명코오롱하늘채 2
 
1.4%
1단지 2
 
1.4%
금곡유림 2
 
1.4%
덕천주공 2
 
1.4%
2단지 2
 
1.4%
럭키만덕 2
 
1.4%
율리역 2
 
1.4%
Other values (113) 113
79.6%
2023-12-11T01:01:30.867966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
33
 
4.1%
화 27
 
3.4%
명 26
 
3.3%
덕 26
 
3.3%
금 19
 
2.4%
만 17
 
2.1%
지 16
 
2.0%
2 16
 
2.0%
대 16
 
2.0%
곡 16
 
2.0%
Other values (170) 587
73.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 712
89.1%
Decimal Number 36
 
4.5%
Space Separator 33
 
4.1%
Close Punctuation 6
 
0.8%
Open Punctuation 6
 
0.8%
Uppercase Letter 4
 
0.5%
Lowercase Letter 1
 
0.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
화 27
 
3.8%
명 26
 
3.7%
덕 26
 
3.7%
금 19
 
2.7%
만 17
 
2.4%
지 16
 
2.2%
대 16
 
2.2%
곡 16
 
2.2%
포 15
 
2.1%
그 14
 
2.0%
Other values (152) 520
73.0%
Decimal Number
ValueCountFrequency (%)
2 16
44.4%
1 9
25.0%
3 3
 
8.3%
5 2
 
5.6%
4 2
 
5.6%
7 1
 
2.8%
6 1
 
2.8%
8 1
 
2.8%
9 1
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
L 1
25.0%
A 1
25.0%
B 1
25.0%
H 1
25.0%
Space Separator
ValueCountFrequency (%)
33
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 712
89.1%
Common 82
 
10.3%
Latin 5
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
화 27
 
3.8%
명 26
 
3.7%
덕 26
 
3.7%
금 19
 
2.7%
만 17
 
2.4%
지 16
 
2.2%
대 16
 
2.2%
곡 16
 
2.2%
포 15
 
2.1%
그 14
 
2.0%
Other values (152) 520
73.0%
Common
ValueCountFrequency (%)
33
40.2%
2 16
19.5%
1 9
 
11.0%
) 6
 
7.3%
( 6
 
7.3%
3 3
 
3.7%
5 2
 
2.4%
4 2
 
2.4%
7 1
 
1.2%
6 1
 
1.2%
Other values (3) 3
 
3.7%
Latin
ValueCountFrequency (%)
L 1
20.0%
A 1
20.0%
B 1
20.0%
e 1
20.0%
H 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 712
89.1%
ASCII 87
 
10.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
33
37.9%
2 16
18.4%
1 9
 
10.3%
) 6
 
6.9%
( 6
 
6.9%
3 3
 
3.4%
5 2
 
2.3%
4 2
 
2.3%
L 1
 
1.1%
A 1
 
1.1%
Other values (8) 8
 
9.2%
Hangul
ValueCountFrequency (%)
화 27
 
3.8%
명 26
 
3.7%
덕 26
 
3.7%
금 19
 
2.7%
만 17
 
2.4%
지 16
 
2.2%
대 16
 
2.2%
곡 16
 
2.2%
포 15
 
2.1%
그 14
 
2.0%
Other values (152) 520
73.0%

소재지
Text

Distinct108
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size1004.0 B
2023-12-11T01:01:31.319248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length9.2385321
Min length5

Characters and Unicode

Total characters1007
Distinct characters51
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

Unique107 ?
Unique (%)98.2%

Sample

1st row백양대로1016번길 10
2nd row백양대로 1003
3rd row낙동북로 737
4th row구남언덕로20번길 7
5th row백양대로 1098
ValueCountFrequency (%)
화명신도시로 10
 
4.6%
효열로 9
 
4.1%
덕천로 6
 
2.8%
금곡대로 6
 
2.8%
화명대로 5
 
2.3%
만덕1로 5
 
2.3%
백양대로 4
 
1.8%
양달로 4
 
1.8%
10 3
 
1.4%
29 3
 
1.4%
Other values (130) 162
74.7%
2023-12-11T01:01:32.048828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
108
 
10.7%
로 107
 
10.6%
1 88
 
8.7%
2 53
 
5.3%
길 47
 
4.7%
번 45
 
4.5%
0 40
 
4.0%
대 37
 
3.7%
3 36
 
3.6%
5 36
 
3.6%
Other values (41) 410
40.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 496
49.3%
Decimal Number 395
39.2%
Space Separator 108
 
10.7%
Dash Punctuation 8
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 107
21.6%
길 47
 
9.5%
번 45
 
9.1%
대 37
 
7.5%
덕 31
 
6.2%
만 19
 
3.8%
화 16
 
3.2%
명 16
 
3.2%
시 15
 
3.0%
천 15
 
3.0%
Other values (29) 148
29.8%
Decimal Number
ValueCountFrequency (%)
1 88
22.3%
2 53
13.4%
0 40
10.1%
3 36
9.1%
5 36
9.1%
4 33
 
8.4%
8 29
 
7.3%
7 28
 
7.1%
6 28
 
7.1%
9 24
 
6.1%
Space Separator
ValueCountFrequency (%)
108
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 511
50.7%
Hangul 496
49.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 107
21.6%
길 47
 
9.5%
번 45
 
9.1%
대 37
 
7.5%
덕 31
 
6.2%
만 19
 
3.8%
화 16
 
3.2%
명 16
 
3.2%
시 15
 
3.0%
천 15
 
3.0%
Other values (29) 148
29.8%
Common
ValueCountFrequency (%)
108
21.1%
1 88
17.2%
2 53
10.4%
0 40
 
7.8%
3 36
 
7.0%
5 36
 
7.0%
4 33
 
6.5%
8 29
 
5.7%
7 28
 
5.5%
6 28
 
5.5%
Other values (2) 32
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 511
50.7%
Hangul 496
49.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
108
21.1%
1 88
17.2%
2 53
10.4%
0 40
 
7.8%
3 36
 
7.0%
5 36
 
7.0%
4 33
 
6.5%
8 29
 
5.7%
7 28
 
5.5%
6 28
 
5.5%
Other values (2) 32
 
6.3%
Hangul
ValueCountFrequency (%)
로 107
21.6%
길 47
 
9.5%
번 45
 
9.1%
대 37
 
7.5%
덕 31
 
6.2%
만 19
 
3.8%
화 16
 
3.2%
명 16
 
3.2%
시 15
 
3.0%
천 15
 
3.0%
Other values (29) 148
29.8%

단지규모(층수)
Real number (ℝ)

Distinct23
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.633028
Minimum5
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T01:01:32.290559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile12.4
Q115
median20
Q325
95-th percentile29
Maximum48
Range43
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.7297853
Coefficient of variation (CV)0.32616567
Kurtosis2.1438015
Mean20.633028
Median Absolute Deviation (MAD)5
Skewness0.42552823
Sum2249
Variance45.29001
MonotonicityNot monotonic
2023-12-11T01:01:32.472655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
15 28
25.7%
20 15
13.8%
25 14
12.8%
24 9
 
8.3%
29 6
 
5.5%
22 4
 
3.7%
21 4
 
3.7%
5 4
 
3.7%
18 4
 
3.7%
23 3
 
2.8%
Other values (13) 18
16.5%
ValueCountFrequency (%)
5 4
 
3.7%
6 1
 
0.9%
12 1
 
0.9%
13 1
 
0.9%
15 28
25.7%
16 1
 
0.9%
17 1
 
0.9%
18 4
 
3.7%
19 1
 
0.9%
20 15
13.8%
ValueCountFrequency (%)
48 1
 
0.9%
37 1
 
0.9%
35 2
 
1.8%
30 1
 
0.9%
29 6
5.5%
28 2
 
1.8%
27 2
 
1.8%
26 3
 
2.8%
25 14
12.8%
24 9
8.3%

단지규모(동수)
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4587156
Minimum1
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T01:01:32.662742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile16
Maximum48
Range47
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.8184555
Coefficient of variation (CV)0.91415947
Kurtosis13.2925
Mean7.4587156
Median Absolute Deviation (MAD)3
Skewness2.9311049
Sum813
Variance46.491335
MonotonicityNot monotonic
2023-12-11T01:01:33.201694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
6 13
11.9%
1 13
11.9%
3 11
10.1%
2 10
9.2%
8 8
 
7.3%
4 7
 
6.4%
5 6
 
5.5%
11 6
 
5.5%
7 6
 
5.5%
13 5
 
4.6%
Other values (9) 24
22.0%
ValueCountFrequency (%)
1 13
11.9%
2 10
9.2%
3 11
10.1%
4 7
6.4%
5 6
5.5%
6 13
11.9%
7 6
5.5%
8 8
7.3%
9 4
 
3.7%
10 4
 
3.7%
ValueCountFrequency (%)
48 1
 
0.9%
35 1
 
0.9%
30 1
 
0.9%
17 1
 
0.9%
16 4
3.7%
14 4
3.7%
13 5
4.6%
12 4
3.7%
11 6
5.5%
10 4
3.7%

단지규모(세대수)
Real number (ℝ)

HIGH CORRELATION 

Distinct100
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean781.88073
Minimum103
Maximum5239
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T01:01:33.397879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum103
5-th percentile176.4
Q1280
median576
Q3989
95-th percentile1920.2
Maximum5239
Range5136
Interquartile range (IQR)709

Descriptive statistics

Standard deviation734.49258
Coefficient of variation (CV)0.93939209
Kurtosis13.317365
Mean781.88073
Median Absolute Deviation (MAD)324
Skewness2.97098
Sum85225
Variance539479.35
MonotonicityNot monotonic
2023-12-11T01:01:33.610720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240 3
 
2.8%
600 2
 
1.8%
299 2
 
1.8%
499 2
 
1.8%
576 2
 
1.8%
900 2
 
1.8%
211 2
 
1.8%
360 2
 
1.8%
720 1
 
0.9%
390 1
 
0.9%
Other values (90) 90
82.6%
ValueCountFrequency (%)
103 1
0.9%
119 1
0.9%
156 1
0.9%
164 1
0.9%
173 1
0.9%
174 1
0.9%
180 1
0.9%
181 1
0.9%
190 1
0.9%
203 1
0.9%
ValueCountFrequency (%)
5239 1
0.9%
3382 1
0.9%
3160 1
0.9%
1969 1
0.9%
1958 1
0.9%
1937 1
0.9%
1895 1
0.9%
1780 1
0.9%
1741 1
0.9%
1677 1
0.9%

승강기(대)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct40
Distinct (%)37.4%
Missing2
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean16.420561
Minimum0
Maximum74
Zeros7
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T01:01:33.785108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median13
Q322
95-th percentile42.1
Maximum74
Range74
Interquartile range (IQR)16

Descriptive statistics

Standard deviation14.110104
Coefficient of variation (CV)0.85929492
Kurtosis3.6943991
Mean16.420561
Median Absolute Deviation (MAD)7
Skewness1.6988345
Sum1757
Variance199.09504
MonotonicityNot monotonic
2023-12-11T01:01:33.984020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
8 10
 
9.2%
6 9
 
8.3%
0 7
 
6.4%
5 6
 
5.5%
17 5
 
4.6%
12 5
 
4.6%
16 4
 
3.7%
30 4
 
3.7%
7 4
 
3.7%
24 4
 
3.7%
Other values (30) 49
45.0%
ValueCountFrequency (%)
0 7
6.4%
2 1
 
0.9%
3 2
 
1.8%
4 3
 
2.8%
5 6
5.5%
6 9
8.3%
7 4
 
3.7%
8 10
9.2%
9 1
 
0.9%
10 4
 
3.7%
ValueCountFrequency (%)
74 1
0.9%
69 1
0.9%
58 1
0.9%
51 1
0.9%
49 1
0.9%
43 1
0.9%
40 1
0.9%
39 1
0.9%
38 1
0.9%
33 2
1.8%

사용검사
Date

Distinct103
Distinct (%)94.5%
Missing0
Missing (%)0.0%
Memory size1004.0 B
Minimum1979-08-31 00:00:00
Maximum2022-08-23 00:00:00
2023-12-11T01:01:34.192100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:34.415032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

전화번호
Text

Distinct108
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size1004.0 B
2023-12-11T01:01:34.755219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.009174
Min length12

Characters and Unicode

Total characters1309
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

Unique107 ?
Unique (%)98.2%

Sample

1st row051-338-3006
2nd row051-335-8191
3rd row051-335-4112
4th row051-334-3232
5th row051-341-4206
ValueCountFrequency (%)
051-361-9914 2
 
1.8%
051-331-0384 1
 
0.9%
051-333-1110 1
 
0.9%
051-362-1710 1
 
0.9%
051-362-0635 1
 
0.9%
051-362-6814 1
 
0.9%
051-362-6813 1
 
0.9%
051-362-6812 1
 
0.9%
051-362-5561 1
 
0.9%
051-362-0077 1
 
0.9%
Other values (98) 98
89.9%
2023-12-11T01:01:35.264082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 218
16.7%
3 208
15.9%
1 185
14.1%
0 169
12.9%
5 165
12.6%
6 91
7.0%
4 73
 
5.6%
2 73
 
5.6%
7 45
 
3.4%
9 44
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1091
83.3%
Dash Punctuation 218
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 208
19.1%
1 185
17.0%
0 169
15.5%
5 165
15.1%
6 91
8.3%
4 73
 
6.7%
2 73
 
6.7%
7 45
 
4.1%
9 44
 
4.0%
8 38
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
- 218
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1309
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 218
16.7%
3 208
15.9%
1 185
14.1%
0 169
12.9%
5 165
12.6%
6 91
7.0%
4 73
 
5.6%
2 73
 
5.6%
7 45
 
3.4%
9 44
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1309
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 218
16.7%
3 208
15.9%
1 185
14.1%
0 169
12.9%
5 165
12.6%
6 91
7.0%
4 73
 
5.6%
2 73
 
5.6%
7 45
 
3.4%
9 44
 
3.4%

구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1004.0 B
의무
100 
임대
 
9

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
의무 100
91.7%
임대 9
 
8.3%

Length

2023-12-11T01:01:35.446752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:01:35.573699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
의무 100
91.7%
임대 9
 
8.3%

Interactions

2023-12-11T01:01:28.120549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:25.608426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:26.219091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:26.841749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:27.472020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:28.247782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:25.722475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:26.324911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:26.967453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:27.600269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:28.384845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:25.838848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:26.449808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:27.082598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:27.719069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:28.519121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:25.975231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:26.572296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:27.206549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:27.844492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:28.674815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:26.102416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:26.698806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:27.339344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:27.972107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:01:35.666781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번단지규모(층수)단지규모(동수)단지규모(세대수)승강기(대)구분
연번1.0000.2700.1780.0000.2840.171
단지규모(층수)0.2701.0000.4700.5130.6300.235
단지규모(동수)0.1780.4701.0000.9830.8550.000
단지규모(세대수)0.0000.5130.9831.0000.8390.178
승강기(대)0.2840.6300.8550.8391.0000.064
구분0.1710.2350.0000.1780.0641.000
2023-12-11T01:01:35.834323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번단지규모(층수)단지규모(동수)단지규모(세대수)승강기(대)구분
연번1.0000.3090.2820.2100.2870.118
단지규모(층수)0.3091.0000.1270.2270.2620.225
단지규모(동수)0.2820.1271.0000.8900.7730.000
단지규모(세대수)0.2100.2270.8901.0000.8640.185
승강기(대)0.2870.2620.7730.8641.0000.056
구분0.1180.2250.0000.1850.0561.000

Missing values

2023-12-11T01:01:28.866254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:01:29.111833image/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

연번단지명소재지단지규모(층수)단지규모(동수)단지규모(세대수)승강기(대)사용검사전화번호구분
01구포삼정그린코아백양대로1016번길 10217668171999-01-15051-338-3006의무
12구포현대아파트백양대로 100315161741581994-02-28051-335-8191의무
23유림노르웨이숲낙동북로 73721131176302005-06-15051-335-4112의무
34구포대성맨션구남언덕로20번길 75849901986-08-20051-334-3232의무
45구포에이스타운백양대로 109820116421997-11-22051-341-4206의무
56벽산그린타운시랑로182번길 1917117461993-09-27051-338-2879의무
67구포태평양시랑로 17615123271992-09-09051-937-6546의무
78태경해피타운시랑로138번길 818121161992-09-08051-342-4074의무
89삼경장미시랑로21번길 5023118141995-02-24051-342-9051의무
910구포대우모분재로105번길 43-715122581992-10-01051-336-9127의무
연번단지명소재지단지규모(층수)단지규모(동수)단지규모(세대수)승강기(대)사용검사전화번호구분
99100화명플리체 비스타동원대천천길 109296447112022-07-12051-364-0573의무
100101신만덕베스티움에코포레상리로 861512604262022-04-25051-336-8932의무
101102포레나 부산덕천만덕대로 81256636192022-08-23051-338-2785의무
102103이편한세상 금정산상학로 3628131969432021-04-12051-336-1663의무
103104화명푸르지오 헤리센트신성로28359886<NA>2021-03-24051-925-3330의무
104105리버파크 반도유보라백양대로 11362811790242021-06-21051-337-6994의무
105106덕천역 이즈 카운티금곡대로8번길 8726225052019-10-29051-341-7333의무
106107금정산LH뉴웰시티상학로 3529141677322019-10-15051-336-9570의무
107108구포윤창비에이치타운낙동대로1762번가길 2015115632018-12-28051-343-4145의무
108109구포도시시랑로114번길 12121103<NA>1999-08-27051-338-7260임대