Overview

Dataset statistics

Number of variables12
Number of observations299
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.6 KiB
Average record size in memory101.4 B

Variable types

Numeric5
Text3
DateTime1
Categorical3

Dataset

Description부산광역시_사상구_공동주택현황_20231005
Author부산광역시 사상구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=3078899

Alerts

비고(임대 등 기재) is highly overall correlated with 연번 and 3 other fieldsHigh correlation
관리방식 is highly overall correlated with 연번 and 5 other fieldsHigh correlation
의무여부 is highly overall correlated with 연번 and 5 other fieldsHigh correlation
연번 is highly overall correlated with 의무여부 and 2 other fieldsHigh correlation
층수 is highly overall correlated with 연면적 and 2 other fieldsHigh correlation
연면적 is highly overall correlated with 층수 and 3 other fieldsHigh correlation
세대수 is highly overall correlated with 연면적 and 3 other fieldsHigh correlation
비고(임대 등 기재) is highly imbalanced (53.6%)Imbalance
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-10 16:37:05.804783
Analysis finished2023-12-10 16:37:10.409789
Duration4.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct299
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150
Minimum1
Maximum299
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-11T01:37:10.491415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15.9
Q175.5
median150
Q3224.5
95-th percentile284.1
Maximum299
Range298
Interquartile range (IQR)149

Descriptive statistics

Standard deviation86.458082
Coefficient of variation (CV)0.57638722
Kurtosis-1.2
Mean150
Median Absolute Deviation (MAD)75
Skewness0
Sum44850
Variance7475
MonotonicityStrictly increasing
2023-12-11T01:37:10.685478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
207 1
 
0.3%
205 1
 
0.3%
204 1
 
0.3%
203 1
 
0.3%
202 1
 
0.3%
201 1
 
0.3%
200 1
 
0.3%
199 1
 
0.3%
198 1
 
0.3%
Other values (289) 289
96.7%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
299 1
0.3%
298 1
0.3%
297 1
0.3%
296 1
0.3%
295 1
0.3%
294 1
0.3%
293 1
0.3%
292 1
0.3%
291 1
0.3%
290 1
0.3%

단지명
Text

Distinct146
Distinct (%)48.8%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2023-12-11T01:37:10.955953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length6.0367893
Min length2

Characters and Unicode

Total characters1805
Distinct characters192
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

Unique119 ?
Unique (%)39.8%

Sample

1st row엄궁아파트
2nd row학장동양
3rd row주례럭키
4th row대성아파트
5th row주례현대아파트
ValueCountFrequency (%)
벽산블루밍 17
 
5.5%
엄궁롯데캐슬리버 16
 
5.1%
엄궁쌍용스윗닷홈 13
 
4.2%
모라삼정그린코아 11
 
3.5%
학장무학 11
 
3.5%
엄궁한신1차 8
 
2.6%
엄궁코오롱 8
 
2.6%
반도보라매머드타운 8
 
2.6%
무학다솜 8
 
2.6%
학장삼성2차 7
 
2.3%
Other values (147) 204
65.6%
2023-12-11T01:37:11.405177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
학 59
 
3.3%
엄 58
 
3.2%
궁 58
 
3.2%
아 54
 
3.0%
차 45
 
2.5%
동 44
 
2.4%
라 42
 
2.3%
신 41
 
2.3%
장 38
 
2.1%
주 37
 
2.0%
Other values (182) 1329
73.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1702
94.3%
Decimal Number 51
 
2.8%
Uppercase Letter 28
 
1.6%
Space Separator 12
 
0.7%
Open Punctuation 6
 
0.3%
Close Punctuation 6
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 59
 
3.5%
엄 58
 
3.4%
궁 58
 
3.4%
아 54
 
3.2%
차 45
 
2.6%
동 44
 
2.6%
라 42
 
2.5%
신 41
 
2.4%
장 38
 
2.2%
주 37
 
2.2%
Other values (172) 1226
72.0%
Decimal Number
ValueCountFrequency (%)
1 29
56.9%
2 20
39.2%
3 1
 
2.0%
4 1
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
L 14
50.0%
G 12
42.9%
H 2
 
7.1%
Space Separator
ValueCountFrequency (%)
12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1702
94.3%
Common 75
 
4.2%
Latin 28
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 59
 
3.5%
엄 58
 
3.4%
궁 58
 
3.4%
아 54
 
3.2%
차 45
 
2.6%
동 44
 
2.6%
라 42
 
2.5%
신 41
 
2.4%
장 38
 
2.2%
주 37
 
2.2%
Other values (172) 1226
72.0%
Common
ValueCountFrequency (%)
1 29
38.7%
2 20
26.7%
12
16.0%
( 6
 
8.0%
) 6
 
8.0%
3 1
 
1.3%
4 1
 
1.3%
Latin
ValueCountFrequency (%)
L 14
50.0%
G 12
42.9%
H 2
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1702
94.3%
ASCII 103
 
5.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
학 59
 
3.5%
엄 58
 
3.4%
궁 58
 
3.4%
아 54
 
3.2%
차 45
 
2.6%
동 44
 
2.6%
라 42
 
2.5%
신 41
 
2.4%
장 38
 
2.2%
주 37
 
2.2%
Other values (172) 1226
72.0%
ASCII
ValueCountFrequency (%)
1 29
28.2%
2 20
19.4%
L 14
13.6%
12
11.7%
G 12
11.7%
( 6
 
5.8%
) 6
 
5.8%
H 2
 
1.9%
3 1
 
1.0%
4 1
 
1.0%

위치(새주소)
Text

Distinct146
Distinct (%)48.8%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2023-12-11T01:37:11.665915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length25
Mean length13.571906
Min length5

Characters and Unicode

Total characters4058
Distinct characters58
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique119 ?
Unique (%)39.8%

Sample

1st row엄궁로 202
2nd row주례로 231
3rd row가야대로284번길 12
4th row양지로 3
5th row양지로30번길 5
ValueCountFrequency (%)
백양대로 50
 
7.5%
학감대로 21
 
3.1%
39 18
 
2.7%
사상로342번길 17
 
2.5%
엄궁로 17
 
2.5%
엄궁북로 16
 
2.4%
61(401∼404 16
 
2.4%
62(101∼302 16
 
2.4%
84 16
 
2.4%
대동로 15
 
2.2%
Other values (211) 468
69.9%
2023-12-11T01:37:12.182145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
372
 
9.2%
로 300
 
7.4%
1 294
 
7.2%
2 277
 
6.8%
4 243
 
6.0%
3 223
 
5.5%
0 186
 
4.6%
대 164
 
4.0%
번 155
 
3.8%
길 155
 
3.8%
Other values (48) 1689
41.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1746
43.0%
Other Letter 1565
38.6%
Space Separator 372
 
9.2%
Dash Punctuation 109
 
2.7%
Close Punctuation 107
 
2.6%
Open Punctuation 100
 
2.5%
Math Symbol 33
 
0.8%
Other Punctuation 21
 
0.5%
Uppercase Letter 5
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 300
19.2%
대 164
10.5%
번 155
9.9%
길 155
9.9%
동 115
 
7.3%
양 85
 
5.4%
백 78
 
5.0%
궁 51
 
3.3%
엄 51
 
3.3%
상 43
 
2.7%
Other values (27) 368
23.5%
Decimal Number
ValueCountFrequency (%)
1 294
16.8%
2 277
15.9%
4 243
13.9%
3 223
12.8%
0 186
10.7%
6 122
7.0%
9 116
 
6.6%
7 102
 
5.8%
5 99
 
5.7%
8 84
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
C 3
60.0%
A 1
 
20.0%
B 1
 
20.0%
Math Symbol
ValueCountFrequency (%)
∼ 32
97.0%
+ 1
 
3.0%
Other Punctuation
ValueCountFrequency (%)
, 20
95.2%
: 1
 
4.8%
Space Separator
ValueCountFrequency (%)
372
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 109
100.0%
Close Punctuation
ValueCountFrequency (%)
) 107
100.0%
Open Punctuation
ValueCountFrequency (%)
( 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2488
61.3%
Hangul 1565
38.6%
Latin 5
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 300
19.2%
대 164
10.5%
번 155
9.9%
길 155
9.9%
동 115
 
7.3%
양 85
 
5.4%
백 78
 
5.0%
궁 51
 
3.3%
엄 51
 
3.3%
상 43
 
2.7%
Other values (27) 368
23.5%
Common
ValueCountFrequency (%)
372
15.0%
1 294
11.8%
2 277
11.1%
4 243
9.8%
3 223
9.0%
0 186
7.5%
6 122
 
4.9%
9 116
 
4.7%
- 109
 
4.4%
) 107
 
4.3%
Other values (8) 439
17.6%
Latin
ValueCountFrequency (%)
C 3
60.0%
A 1
 
20.0%
B 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2461
60.6%
Hangul 1565
38.6%
Math Operators 32
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
372
15.1%
1 294
11.9%
2 277
11.3%
4 243
9.9%
3 223
9.1%
0 186
7.6%
6 122
 
5.0%
9 116
 
4.7%
- 109
 
4.4%
) 107
 
4.3%
Other values (10) 412
16.7%
Hangul
ValueCountFrequency (%)
로 300
19.2%
대 164
10.5%
번 155
9.9%
길 155
9.9%
동 115
 
7.3%
양 85
 
5.4%
백 78
 
5.0%
궁 51
 
3.3%
엄 51
 
3.3%
상 43
 
2.7%
Other values (27) 368
23.5%
Math Operators
ValueCountFrequency (%)
∼ 32
100.0%

동수
Real number (ℝ)

Distinct17
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4882943
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-11T01:37:12.366942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q38
95-th percentile18
Maximum18
Range17
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.9408274
Coefficient of variation (CV)0.90024826
Kurtosis0.418152
Mean5.4882943
Median Absolute Deviation (MAD)3
Skewness1.1880513
Sum1641
Variance24.411775
MonotonicityNot monotonic
2023-12-11T01:37:12.538217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2 63
21.1%
1 62
20.7%
4 31
10.4%
3 21
 
7.0%
18 17
 
5.7%
8 16
 
5.4%
6 15
 
5.0%
11 14
 
4.7%
5 11
 
3.7%
7 11
 
3.7%
Other values (7) 38
12.7%
ValueCountFrequency (%)
1 62
20.7%
2 63
21.1%
3 21
 
7.0%
4 31
10.4%
5 11
 
3.7%
6 15
 
5.0%
7 11
 
3.7%
8 16
 
5.4%
9 7
 
2.3%
10 9
 
3.0%
ValueCountFrequency (%)
18 17
5.7%
16 5
 
1.7%
15 5
 
1.7%
14 1
 
0.3%
13 2
 
0.7%
12 9
3.0%
11 14
4.7%
10 9
3.0%
9 7
2.3%
8 16
5.4%

층수
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.347826
Minimum2
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-11T01:37:12.716164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q115
median19
Q322.5
95-th percentile26
Maximum29
Range27
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation6.6971461
Coefficient of variation (CV)0.38605103
Kurtosis-0.42979995
Mean17.347826
Median Absolute Deviation (MAD)4
Skewness-0.69297786
Sum5187
Variance44.851765
MonotonicityNot monotonic
2023-12-11T01:37:12.911677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
15 38
12.7%
20 26
 
8.7%
5 23
 
7.7%
25 20
 
6.7%
24 20
 
6.7%
23 19
 
6.4%
21 19
 
6.4%
22 19
 
6.4%
19 17
 
5.7%
6 14
 
4.7%
Other values (16) 84
28.1%
ValueCountFrequency (%)
2 3
 
1.0%
3 5
 
1.7%
4 2
 
0.7%
5 23
7.7%
6 14
4.7%
9 2
 
0.7%
10 2
 
0.7%
11 2
 
0.7%
12 3
 
1.0%
13 6
 
2.0%
ValueCountFrequency (%)
29 2
 
0.7%
28 3
 
1.0%
27 5
 
1.7%
26 6
 
2.0%
25 20
6.7%
24 20
6.7%
23 19
6.4%
22 19
6.4%
21 19
6.4%
20 26
8.7%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct144
Distinct (%)48.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77249.992
Minimum697.95
Maximum325227
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-11T01:37:13.088254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum697.95
5-th percentile1979.3
Q122507
median58153
Q3103998
95-th percentile325227
Maximum325227
Range324529.05
Interquartile range (IQR)81491

Descriptive statistics

Standard deviation77287.217
Coefficient of variation (CV)1.0004819
Kurtosis3.3090222
Mean77249.992
Median Absolute Deviation (MAD)41024
Skewness1.7774825
Sum23097748
Variance5.9733139 × 109
MonotonicityNot monotonic
2023-12-11T01:37:13.280836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48835.0 17
 
5.7%
325227.0 16
 
5.4%
77813.0 13
 
4.3%
113055.0 11
 
3.7%
92762.0 11
 
3.7%
213588.0 8
 
2.7%
34723.0 8
 
2.7%
133619.0 8
 
2.7%
25916.0 8
 
2.7%
89059.0 7
 
2.3%
Other values (134) 192
64.2%
ValueCountFrequency (%)
697.95 1
0.3%
698.08 1
0.3%
720.94 1
0.3%
738.038 1
0.3%
773.639 1
0.3%
774.165 1
0.3%
886.0 1
0.3%
950.6275 1
0.3%
1130.8 1
0.3%
1710.0 2
0.7%
ValueCountFrequency (%)
325227.0 16
5.4%
213588.0 8
2.7%
183390.0 1
 
0.3%
179623.0 2
 
0.7%
153713.0 1
 
0.3%
143352.0 5
 
1.7%
133619.0 8
2.7%
133472.0 6
 
2.0%
131561.0 1
 
0.3%
129594.0 1
 
0.3%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct115
Distinct (%)38.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean571.8194
Minimum20
Maximum2529
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-11T01:37:13.486151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile31.8
Q1209
median438
Q3831
95-th percentile1852
Maximum2529
Range2509
Interquartile range (IQR)622

Descriptive statistics

Standard deviation487.64347
Coefficient of variation (CV)0.85279281
Kurtosis1.8936524
Mean571.8194
Median Absolute Deviation (MAD)303
Skewness1.3690579
Sum170974
Variance237796.16
MonotonicityNot monotonic
2023-12-11T01:37:13.700489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
350 17
 
5.7%
1852 16
 
5.4%
607 13
 
4.3%
893 11
 
3.7%
438 11
 
3.7%
498 10
 
3.3%
1206 8
 
2.7%
387 8
 
2.7%
1158 8
 
2.7%
200 8
 
2.7%
Other values (105) 189
63.2%
ValueCountFrequency (%)
20 5
1.7%
22 1
 
0.3%
24 3
1.0%
26 1
 
0.3%
28 1
 
0.3%
29 2
 
0.7%
30 2
 
0.7%
32 3
1.0%
34 2
 
0.7%
36 1
 
0.3%
ValueCountFrequency (%)
2529 1
 
0.3%
2385 1
 
0.3%
1963 1
 
0.3%
1852 16
5.4%
1620 1
 
0.3%
1206 8
2.7%
1158 8
2.7%
1110 2
 
0.7%
1094 1
 
0.3%
1080 1
 
0.3%

사용승인일
Date

Distinct139
Distinct (%)46.5%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
Minimum1975-06-12 00:00:00
Maximum2021-10-08 00:00:00
2023-12-11T01:37:13.873463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:14.087956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

관리사무소연락처
Text

Distinct116
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2023-12-11T01:37:14.382633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters3588
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

Unique88 ?
Unique (%)29.4%

Sample

1st row051-322-3373
2nd row051-322-2288
3rd row051-325-8801
4th row051-322-5557
5th row051-327-0071
ValueCountFrequency (%)
051-000-0000 31
 
10.4%
051-305-7222 17
 
5.7%
051-314-3358 16
 
5.4%
051-312-9407 13
 
4.3%
051-327-7905 11
 
3.7%
051-324-9175 11
 
3.7%
051-312-2230 8
 
2.7%
051-324-7211 8
 
2.7%
051-323-8410 8
 
2.7%
051-311-1333 8
 
2.7%
Other values (106) 168
56.2%
2023-12-11T01:37:14.759748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 684
19.1%
- 598
16.7%
1 551
15.4%
3 448
12.5%
5 438
12.2%
2 300
8.4%
7 159
 
4.4%
4 124
 
3.5%
8 117
 
3.3%
9 89
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2990
83.3%
Dash Punctuation 598
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 684
22.9%
1 551
18.4%
3 448
15.0%
5 438
14.6%
2 300
10.0%
7 159
 
5.3%
4 124
 
4.1%
8 117
 
3.9%
9 89
 
3.0%
6 80
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 598
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3588
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 684
19.1%
- 598
16.7%
1 551
15.4%
3 448
12.5%
5 438
12.2%
2 300
8.4%
7 159
 
4.4%
4 124
 
3.5%
8 117
 
3.3%
9 89
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3588
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 684
19.1%
- 598
16.7%
1 551
15.4%
3 448
12.5%
5 438
12.2%
2 300
8.4%
7 159
 
4.4%
4 124
 
3.5%
8 117
 
3.3%
9 89
 
2.5%

의무여부
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
의무
233 
비의무
66 

Length

Max length3
Median length2
Mean length2.2207358
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
의무 233
77.9%
비의무 66
 
22.1%

Length

2023-12-11T01:37:14.895646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:37:15.008187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
의무 233
77.9%
비의무 66
 
22.1%

관리방식
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
위탁관리
193 
미신고
65 
자치관리
41 

Length

Max length4
Median length4
Mean length3.7826087
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row자치관리
2nd row자치관리
3rd row위탁관리
4th row자치관리
5th row자치관리

Common Values

ValueCountFrequency (%)
위탁관리 193
64.5%
미신고 65
 
21.7%
자치관리 41
 
13.7%

Length

2023-12-11T01:37:15.147116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:37:15.266295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
위탁관리 193
64.5%
미신고 65
 
21.7%
자치관리 41
 
13.7%

비고(임대 등 기재)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
의무
226 
임의
66 
영구임대
 
6
공공임대
 
1

Length

Max length4
Median length2
Mean length2.0468227
Min length2

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
의무 226
75.6%
임의 66
 
22.1%
영구임대 6
 
2.0%
공공임대 1
 
0.3%

Length

2023-12-11T01:37:15.405499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:37:15.506595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
의무 226
75.6%
임의 66
 
22.1%
영구임대 6
 
2.0%
공공임대 1
 
0.3%

Interactions

2023-12-11T01:37:09.393187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:06.446950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:07.182340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:07.837899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:08.423260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:09.514023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:06.594857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:07.319464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:07.978244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:08.568168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:09.653403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:06.750714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:07.447041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:08.105884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:09.014201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:09.753615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:06.916989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:07.569611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:08.199313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:09.133937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:09.893597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:07.062909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:07.699342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:08.301603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:37:09.257668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:37:15.583837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번동수층수연면적세대수의무여부관리방식비고(임대 등 기재)
연번1.0000.8080.7390.7220.7180.9970.8250.780
동수0.8081.0000.3090.6910.6390.4900.4940.389
층수0.7390.3091.0000.4720.4210.9050.6810.620
연면적0.7220.6910.4721.0000.9490.9100.6720.775
세대수0.7180.6390.4210.9491.0000.9340.6750.870
의무여부0.9970.4900.9050.9100.9341.0000.8001.000
관리방식0.8250.4940.6810.6720.6750.8001.0000.668
비고(임대 등 기재)0.7800.3890.6200.7750.8701.0000.6681.000
2023-12-11T01:37:15.707446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비고(임대 등 기재)관리방식의무여부
비고(임대 등 기재)1.0000.6960.997
관리방식0.6961.0000.989
의무여부0.9970.9891.000
2023-12-11T01:37:15.794998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번동수층수연면적세대수의무여부관리방식비고(임대 등 기재)
연번1.000-0.279-0.184-0.491-0.4580.9380.7200.591
동수-0.2791.0000.2950.3420.3790.3720.3370.239
층수-0.1840.2951.0000.5140.4840.7340.5280.418
연면적-0.4910.3420.5141.0000.9400.7360.5490.439
세대수-0.4580.3790.4840.9401.0000.7690.5510.548
의무여부0.9380.3720.7340.7360.7691.0000.9890.997
관리방식0.7200.3370.5280.5490.5510.9891.0000.696
비고(임대 등 기재)0.5910.2390.4180.4390.5480.9970.6961.000

Missing values

2023-12-11T01:37:10.054151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:37:10.308426image/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

연번단지명위치(새주소)동수층수연면적세대수사용승인일관리사무소연락처의무여부관리방식비고(임대 등 기재)
01엄궁아파트엄궁로 2022522094.04601983-11-25051-322-3373의무자치관리의무
12학장동양주례로 2318629565.04421984-11-01051-322-2288의무자치관리의무
23주례럭키가야대로284번길 1281575412.019631987-04-11051-325-8801의무위탁관리의무
34대성아파트양지로 33529291.04201988-02-02051-322-5557의무자치관리의무
45주례현대아파트양지로30번길 541028108.02591989-05-24051-327-0071의무자치관리의무
56부원파크타운백양대로804번길 311635813.04201989-06-23051-303-6005의무자치관리의무
67모라우성1차백양대로 906214109291.07961989-07-20051-327-7880의무자치관리의무
78모라우성1차백양대로 906215109291.07961989-07-20051-327-7880의무자치관리의무
89덕포자유백양대로 82021570650.07801989-07-20051-303-4000의무위탁관리의무
910모라동원백양대로 879115103586.011101989-09-26051-327-7888의무자치관리의무
연번단지명위치(새주소)동수층수연면적세대수사용승인일관리사무소연락처의무여부관리방식비고(임대 등 기재)
289290올유 레스틴 뷰괘법동 541-3(사상로202번길 60)1153710.113612018-05-18051-000-0000비의무미신고임의
290291굿모닝뷰괘법동 523-4(낙동대로1210번길 84)1195608.44662018-11-19051-000-0000비의무미신고임의
291292괘법포르투나오피스텔괘법동 273-1(사상로224번길 16)1208051.81852019-01-04051-000-0000비의무미신고임의
292293천아하늘정원괘법동 524-17(사상로243번길 7)1196696.88642019-05-20051-000-0000비의무미신고임의
293294아이더블유타워괘법동 542-15(광장로97번길 7)1144090.264322019-08-27051-000-0000비의무미신고임의
294295네오리더스 사상괘법동 545-4(광장로97번길 7)1279436.981252019-09-03051-000-0000비의무미신고임의
295296비룡벨로스텔라덕포동 416-7(사상로 320)22012746.331432020-02-17051-000-0000비의무미신고임의
296297사상역 경보센트리안괘법동 525-10(사상로223번길 24)12011625.521192020-06-30051-000-0000비의무미신고임의
297298파크블루11차괘법동 270-9(광장로87번길77)1155220.01122021-03-11051-000-0000비의무미신고임의
298299감전엘크루센트로학감대로267번길2012010429.0902021-10-08051-000-0000비의무미신고임의