Overview

Dataset statistics

Number of variables13
Number of observations276
Missing cells55
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.2 KiB
Average record size in memory108.5 B

Variable types

Numeric4
Text6
DateTime2
Categorical1

Dataset

Description경상남도 진주시 20세대 이상 공동주택 정보로 아파트명, 소재지지번주소, 소재지도로명주소 등의 정보를 제공합니다.
Author경상남도 진주시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15046124

Alerts

연번 is highly overall correlated with 세대수 and 1 other fieldsHigh correlation
세대수 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
지상층수 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
동수 is highly overall correlated with 세대수 High correlation
주택종류 is highly imbalanced (59.7%)Imbalance
입주일 has 13 (4.7%) missing valuesMissing
전화번호 has 42 (15.2%) missing valuesMissing
연번 has unique valuesUnique
아파트명 has unique valuesUnique
연면적 has unique valuesUnique

Reproduction

Analysis started2023-12-29 22:12:14.804315
Analysis finished2023-12-29 22:12:25.339309
Duration10.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct276
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.5
Minimum1
Maximum276
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-29T22:12:25.562131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14.75
Q169.75
median138.5
Q3207.25
95-th percentile262.25
Maximum276
Range275
Interquartile range (IQR)137.5

Descriptive statistics

Standard deviation79.818544
Coefficient of variation (CV)0.57630718
Kurtosis-1.2
Mean138.5
Median Absolute Deviation (MAD)69
Skewness0
Sum38226
Variance6371
MonotonicityStrictly increasing
2023-12-29T22:12:25.967696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
184 1
 
0.4%
190 1
 
0.4%
189 1
 
0.4%
188 1
 
0.4%
187 1
 
0.4%
186 1
 
0.4%
185 1
 
0.4%
183 1
 
0.4%
175 1
 
0.4%
Other values (266) 266
96.4%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
276 1
0.4%
275 1
0.4%
274 1
0.4%
273 1
0.4%
272 1
0.4%
271 1
0.4%
270 1
0.4%
269 1
0.4%
268 1
0.4%
267 1
0.4%

아파트명
Text

UNIQUE 

Distinct276
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2023-12-29T22:12:26.474152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length7.7173913
Min length4

Characters and Unicode

Total characters2130
Distinct characters245
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique276 ?
Unique (%)100.0%

Sample

1st row프리미어웰가
2nd row일진스위트포레강남
3rd row진주옥봉엘에이치아파트
4th row일동미라주
5th row중흥S클래스 더 퍼스트
ValueCountFrequency (%)
아파트 6
 
1.8%
2단지 4
 
1.2%
더 3
 
0.9%
미르젠 3
 
0.9%
중흥센트럴시티 3
 
0.9%
진주혁신도시 3
 
0.9%
초전 3
 
0.9%
3단지 2
 
0.6%
해모로 2
 
0.6%
4단지 2
 
0.6%
Other values (297) 302
90.7%
2023-12-29T22:12:27.476474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
트 151
 
7.1%
아 148
 
6.9%
파 146
 
6.9%
주 79
 
3.7%
대 70
 
3.3%
58
 
2.7%
한 46
 
2.2%
신 41
 
1.9%
진 37
 
1.7%
지 29
 
1.4%
Other values (235) 1325
62.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1978
92.9%
Space Separator 58
 
2.7%
Decimal Number 52
 
2.4%
Uppercase Letter 25
 
1.2%
Close Punctuation 5
 
0.2%
Open Punctuation 5
 
0.2%
Dash Punctuation 3
 
0.1%
Other Punctuation 3
 
0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
트 151
 
7.6%
아 148
 
7.5%
파 146
 
7.4%
주 79
 
4.0%
대 70
 
3.5%
한 46
 
2.3%
신 41
 
2.1%
진 37
 
1.9%
지 29
 
1.5%
빌 28
 
1.4%
Other values (209) 1203
60.8%
Uppercase Letter
ValueCountFrequency (%)
H 6
24.0%
L 5
20.0%
B 4
16.0%
A 3
12.0%
N 1
 
4.0%
F 1
 
4.0%
S 1
 
4.0%
I 1
 
4.0%
K 1
 
4.0%
R 1
 
4.0%
Decimal Number
ValueCountFrequency (%)
2 19
36.5%
1 15
28.8%
3 9
17.3%
4 4
 
7.7%
5 2
 
3.8%
0 1
 
1.9%
9 1
 
1.9%
8 1
 
1.9%
Other Punctuation
ValueCountFrequency (%)
, 2
66.7%
. 1
33.3%
Space Separator
ValueCountFrequency (%)
58
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1978
92.9%
Common 126
 
5.9%
Latin 26
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
트 151
 
7.6%
아 148
 
7.5%
파 146
 
7.4%
주 79
 
4.0%
대 70
 
3.5%
한 46
 
2.3%
신 41
 
2.1%
진 37
 
1.9%
지 29
 
1.5%
빌 28
 
1.4%
Other values (209) 1203
60.8%
Common
ValueCountFrequency (%)
58
46.0%
2 19
 
15.1%
1 15
 
11.9%
3 9
 
7.1%
) 5
 
4.0%
( 5
 
4.0%
4 4
 
3.2%
- 3
 
2.4%
, 2
 
1.6%
5 2
 
1.6%
Other values (4) 4
 
3.2%
Latin
ValueCountFrequency (%)
H 6
23.1%
L 5
19.2%
B 4
15.4%
A 3
11.5%
N 1
 
3.8%
F 1
 
3.8%
S 1
 
3.8%
e 1
 
3.8%
I 1
 
3.8%
K 1
 
3.8%
Other values (2) 2
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1978
92.9%
ASCII 152
 
7.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
트 151
 
7.6%
아 148
 
7.5%
파 146
 
7.4%
주 79
 
4.0%
대 70
 
3.5%
한 46
 
2.3%
신 41
 
2.1%
진 37
 
1.9%
지 29
 
1.5%
빌 28
 
1.4%
Other values (209) 1203
60.8%
ASCII
ValueCountFrequency (%)
58
38.2%
2 19
 
12.5%
1 15
 
9.9%
3 9
 
5.9%
H 6
 
3.9%
) 5
 
3.3%
( 5
 
3.3%
L 5
 
3.3%
B 4
 
2.6%
4 4
 
2.6%
Other values (16) 22
 
14.5%

새주소
Text

Distinct272
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2023-12-29T22:12:28.071875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length39
Mean length33.246377
Min length15

Characters and Unicode

Total characters9176
Distinct characters263
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique269 ?
Unique (%)97.5%

Sample

1st row경상남도 진주시 개양로 67
2nd row경상남도 진주시 망경로 308
3rd row경상남도 진주시 진산로 20
4th row경상남도 진주시 진주대로 891번길 56(강남동, 일동미라주)
5th row경상남도 진주시 충의로 146(충무공동, 중흥에스-클래스 더 퍼스트)
ValueCountFrequency (%)
경상남도 276
 
16.9%
진주시 276
 
16.9%
신안동 30
 
1.8%
하대동 28
 
1.7%
이현동 20
 
1.2%
평거동 20
 
1.2%
상대동 17
 
1.0%
상평동 14
 
0.9%
금산면 11
 
0.7%
7 11
 
0.7%
Other values (629) 930
57.0%
2023-12-29T22:12:29.095466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1371
 
14.9%
주 386
 
4.2%
진 358
 
3.9%
상 345
 
3.8%
남 319
 
3.5%
경 309
 
3.4%
시 293
 
3.2%
도 292
 
3.2%
동 279
 
3.0%
( 270
 
2.9%
Other values (253) 4954
54.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5903
64.3%
Space Separator 1371
 
14.9%
Decimal Number 1048
 
11.4%
Open Punctuation 270
 
2.9%
Close Punctuation 270
 
2.9%
Other Punctuation 268
 
2.9%
Dash Punctuation 35
 
0.4%
Uppercase Letter 10
 
0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 386
 
6.5%
진 358
 
6.1%
상 345
 
5.8%
남 319
 
5.4%
경 309
 
5.2%
시 293
 
5.0%
도 292
 
4.9%
동 279
 
4.7%
로 260
 
4.4%
대 166
 
2.8%
Other values (231) 2896
49.1%
Decimal Number
ValueCountFrequency (%)
1 262
25.0%
2 138
13.2%
5 102
 
9.7%
3 95
 
9.1%
4 85
 
8.1%
0 82
 
7.8%
8 73
 
7.0%
9 73
 
7.0%
6 69
 
6.6%
7 69
 
6.6%
Uppercase Letter
ValueCountFrequency (%)
L 3
30.0%
H 3
30.0%
S 1
 
10.0%
K 1
 
10.0%
F 1
 
10.0%
Y 1
 
10.0%
Space Separator
ValueCountFrequency (%)
1371
100.0%
Open Punctuation
ValueCountFrequency (%)
( 270
100.0%
Close Punctuation
ValueCountFrequency (%)
) 270
100.0%
Other Punctuation
ValueCountFrequency (%)
, 268
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 35
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5903
64.3%
Common 3262
35.5%
Latin 11
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 386
 
6.5%
진 358
 
6.1%
상 345
 
5.8%
남 319
 
5.4%
경 309
 
5.2%
시 293
 
5.0%
도 292
 
4.9%
동 279
 
4.7%
로 260
 
4.4%
대 166
 
2.8%
Other values (231) 2896
49.1%
Common
ValueCountFrequency (%)
1371
42.0%
( 270
 
8.3%
) 270
 
8.3%
, 268
 
8.2%
1 262
 
8.0%
2 138
 
4.2%
5 102
 
3.1%
3 95
 
2.9%
4 85
 
2.6%
0 82
 
2.5%
Other values (5) 319
 
9.8%
Latin
ValueCountFrequency (%)
L 3
27.3%
H 3
27.3%
S 1
 
9.1%
K 1
 
9.1%
e 1
 
9.1%
F 1
 
9.1%
Y 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5903
64.3%
ASCII 3273
35.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1371
41.9%
( 270
 
8.2%
) 270
 
8.2%
, 268
 
8.2%
1 262
 
8.0%
2 138
 
4.2%
5 102
 
3.1%
3 95
 
2.9%
4 85
 
2.6%
0 82
 
2.5%
Other values (12) 330
 
10.1%
Hangul
ValueCountFrequency (%)
주 386
 
6.5%
진 358
 
6.1%
상 345
 
5.8%
남 319
 
5.4%
경 309
 
5.2%
시 293
 
5.0%
도 292
 
4.9%
동 279
 
4.7%
로 260
 
4.4%
대 166
 
2.8%
Other values (231) 2896
49.1%

지번주소
Text

Distinct273
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2023-12-29T22:12:29.968909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length25
Mean length18.221014
Min length14

Characters and Unicode

Total characters5029
Distinct characters83
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique270 ?
Unique (%)97.8%

Sample

1st row경상남도 진주시 가좌동 1945
2nd row경상남도 진주시 강남동 141-1
3rd row경상남도 진주시 옥봉동 700-1
4th row경상남도 진주시 강남동 387
5th row경상남도 진주시 충무공동 275
ValueCountFrequency (%)
경상남도 276
24.1%
진주시 276
24.1%
하대동 34
 
3.0%
신안동 30
 
2.6%
평거동 26
 
2.3%
이현동 20
 
1.7%
충무공동 18
 
1.6%
상대동 17
 
1.5%
상평동 16
 
1.4%
초전동 14
 
1.2%
Other values (315) 420
36.6%
2023-12-29T22:12:31.055422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
885
17.6%
상 320
 
6.4%
주 284
 
5.6%
경 282
 
5.6%
남 282
 
5.6%
도 276
 
5.5%
진 276
 
5.5%
시 276
 
5.5%
동 248
 
4.9%
1 241
 
4.8%
Other values (73) 1659
33.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2891
57.5%
Decimal Number 1064
 
21.2%
Space Separator 885
 
17.6%
Dash Punctuation 178
 
3.5%
Other Punctuation 11
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 320
11.1%
주 284
9.8%
경 282
9.8%
남 282
9.8%
도 276
9.5%
진 276
9.5%
시 276
9.5%
동 248
8.6%
대 52
 
1.8%
평 44
 
1.5%
Other values (60) 551
19.1%
Decimal Number
ValueCountFrequency (%)
1 241
22.7%
2 147
13.8%
3 125
11.7%
4 98
9.2%
7 93
 
8.7%
0 89
 
8.4%
6 85
 
8.0%
5 72
 
6.8%
9 69
 
6.5%
8 45
 
4.2%
Space Separator
ValueCountFrequency (%)
885
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 178
100.0%
Other Punctuation
ValueCountFrequency (%)
, 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2891
57.5%
Common 2138
42.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 320
11.1%
주 284
9.8%
경 282
9.8%
남 282
9.8%
도 276
9.5%
진 276
9.5%
시 276
9.5%
동 248
8.6%
대 52
 
1.8%
평 44
 
1.5%
Other values (60) 551
19.1%
Common
ValueCountFrequency (%)
885
41.4%
1 241
 
11.3%
- 178
 
8.3%
2 147
 
6.9%
3 125
 
5.8%
4 98
 
4.6%
7 93
 
4.3%
0 89
 
4.2%
6 85
 
4.0%
5 72
 
3.4%
Other values (3) 125
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2891
57.5%
ASCII 2138
42.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
885
41.4%
1 241
 
11.3%
- 178
 
8.3%
2 147
 
6.9%
3 125
 
5.8%
4 98
 
4.6%
7 93
 
4.3%
0 89
 
4.2%
6 85
 
4.0%
5 72
 
3.4%
Other values (3) 125
 
5.8%
Hangul
ValueCountFrequency (%)
상 320
11.1%
주 284
9.8%
경 282
9.8%
남 282
9.8%
도 276
9.5%
진 276
9.5%
시 276
9.5%
동 248
8.6%
대 52
 
1.8%
평 44
 
1.5%
Other values (60) 551
19.1%

우편번호
Text

Distinct129
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2023-12-29T22:12:31.650187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.0036232
Min length5

Characters and Unicode

Total characters1381
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

Unique63 ?
Unique (%)22.8%

Sample

1st row52826
2nd row52709
3rd row52760
4th row52709
5th row52853
ValueCountFrequency (%)
52649 8
 
2.9%
52675 8
 
2.9%
52694 7
 
2.5%
52693 6
 
2.2%
52814 6
 
2.2%
52677 6
 
2.2%
52826 5
 
1.8%
52731 5
 
1.8%
52678 5
 
1.8%
52738 5
 
1.8%
Other values (119) 215
77.9%
2023-12-29T22:12:32.598300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 335
24.3%
2 316
22.9%
7 190
13.8%
6 166
12.0%
8 106
 
7.7%
9 63
 
4.6%
1 58
 
4.2%
3 57
 
4.1%
4 51
 
3.7%
0 38
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1380
99.9%
Other Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 335
24.3%
2 316
22.9%
7 190
13.8%
6 166
12.0%
8 106
 
7.7%
9 63
 
4.6%
1 58
 
4.2%
3 57
 
4.1%
4 51
 
3.7%
0 38
 
2.8%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1381
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 335
24.3%
2 316
22.9%
7 190
13.8%
6 166
12.0%
8 106
 
7.7%
9 63
 
4.6%
1 58
 
4.2%
3 57
 
4.1%
4 51
 
3.7%
0 38
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1381
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 335
24.3%
2 316
22.9%
7 190
13.8%
6 166
12.0%
8 106
 
7.7%
9 63
 
4.6%
1 58
 
4.2%
3 57
 
4.1%
4 51
 
3.7%
0 38
 
2.8%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct182
Distinct (%)65.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean304.71377
Minimum20
Maximum1813
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-29T22:12:32.939250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile24
Q150
median151
Q3461
95-th percentile1039.25
Maximum1813
Range1793
Interquartile range (IQR)411

Descriptive statistics

Standard deviation351.55579
Coefficient of variation (CV)1.1537247
Kurtosis2.7433359
Mean304.71377
Median Absolute Deviation (MAD)114.5
Skewness1.6894103
Sum84101
Variance123591.47
MonotonicityNot monotonic
2023-12-29T22:12:33.427306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 15
 
5.4%
60 9
 
3.3%
30 9
 
3.3%
20 8
 
2.9%
24 7
 
2.5%
48 6
 
2.2%
40 6
 
2.2%
90 5
 
1.8%
190 4
 
1.4%
100 4
 
1.4%
Other values (172) 203
73.6%
ValueCountFrequency (%)
20 8
2.9%
21 3
 
1.1%
22 1
 
0.4%
24 7
2.5%
25 3
 
1.1%
26 2
 
0.7%
27 1
 
0.4%
30 9
3.3%
31 1
 
0.4%
33 1
 
0.4%
ValueCountFrequency (%)
1813 1
0.4%
1709 1
0.4%
1530 1
0.4%
1465 1
0.4%
1421 1
0.4%
1372 1
0.4%
1308 1
0.4%
1268 1
0.4%
1256 1
0.4%
1152 1
0.4%

지상층수
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.373188
Minimum3
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-29T22:12:34.214548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q15
median13
Q319
95-th percentile29
Maximum39
Range36
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.3641142
Coefficient of variation (CV)0.62543904
Kurtosis0.10759752
Mean13.373188
Median Absolute Deviation (MAD)7
Skewness0.78239623
Sum3691
Variance69.958406
MonotonicityNot monotonic
2023-12-29T22:12:34.795648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
5 62
22.5%
15 49
17.8%
20 21
 
7.6%
6 18
 
6.5%
10 14
 
5.1%
3 13
 
4.7%
25 11
 
4.0%
12 9
 
3.3%
18 8
 
2.9%
21 8
 
2.9%
Other values (25) 63
22.8%
ValueCountFrequency (%)
3 13
 
4.7%
4 6
 
2.2%
5 62
22.5%
6 18
 
6.5%
7 1
 
0.4%
8 1
 
0.4%
9 4
 
1.4%
10 14
 
5.1%
11 5
 
1.8%
12 9
 
3.3%
ValueCountFrequency (%)
39 2
0.7%
38 1
 
0.4%
36 1
 
0.4%
35 2
0.7%
34 1
 
0.4%
33 3
1.1%
32 2
0.7%
30 1
 
0.4%
29 2
0.7%
28 2
0.7%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0833333
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-29T22:12:35.218894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q36
95-th percentile13
Maximum22
Range21
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.2678857
Coefficient of variation (CV)1.0451965
Kurtosis2.2844701
Mean4.0833333
Median Absolute Deviation (MAD)1
Skewness1.637474
Sum1127
Variance18.214848
MonotonicityNot monotonic
2023-12-29T22:12:35.767019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 110
39.9%
2 43
 
15.6%
3 30
 
10.9%
6 11
 
4.0%
5 10
 
3.6%
9 9
 
3.3%
4 9
 
3.3%
7 9
 
3.3%
10 8
 
2.9%
8 8
 
2.9%
Other values (10) 29
 
10.5%
ValueCountFrequency (%)
1 110
39.9%
2 43
 
15.6%
3 30
 
10.9%
4 9
 
3.3%
5 10
 
3.6%
6 11
 
4.0%
7 9
 
3.3%
8 8
 
2.9%
9 9
 
3.3%
10 8
 
2.9%
ValueCountFrequency (%)
22 1
 
0.4%
21 1
 
0.4%
19 1
 
0.4%
18 1
 
0.4%
16 2
 
0.7%
15 1
 
0.4%
14 5
1.8%
13 6
2.2%
12 5
1.8%
11 6
2.2%

연면적
Text

UNIQUE 

Distinct276
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2023-12-29T22:12:36.587524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length7.7862319
Min length4

Characters and Unicode

Total characters2149
Distinct characters12
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

Unique276 ?
Unique (%)100.0%

Sample

1st row80968.3766
2nd row18368.9464
3rd row32355.89
4th row86398.9322
5th row149217.85
ValueCountFrequency (%)
80968.3766 1
 
0.4%
6300.99 1
 
0.4%
1633.8 1
 
0.4%
2029.5 1
 
0.4%
20860.79 1
 
0.4%
67123.04 1
 
0.4%
5015.51 1
 
0.4%
11337.58 1
 
0.4%
12654.91 1
 
0.4%
6513.75 1
 
0.4%
Other values (266) 266
96.4%
2023-12-29T22:12:37.952326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 268
12.5%
1 253
11.8%
2 206
9.6%
4 192
8.9%
6 191
8.9%
7 185
8.6%
3 183
8.5%
9 180
8.4%
5 179
8.3%
8 175
8.1%
Other values (2) 137
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1879
87.4%
Other Punctuation 270
 
12.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 253
13.5%
2 206
11.0%
4 192
10.2%
6 191
10.2%
7 185
9.8%
3 183
9.7%
9 180
9.6%
5 179
9.5%
8 175
9.3%
0 135
7.2%
Other Punctuation
ValueCountFrequency (%)
. 268
99.3%
, 2
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 2149
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 268
12.5%
1 253
11.8%
2 206
9.6%
4 192
8.9%
6 191
8.9%
7 185
8.6%
3 183
8.5%
9 180
8.4%
5 179
8.3%
8 175
8.1%
Other values (2) 137
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2149
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 268
12.5%
1 253
11.8%
2 206
9.6%
4 192
8.9%
6 191
8.9%
7 185
8.6%
3 183
8.5%
9 180
8.4%
5 179
8.3%
8 175
8.1%
Other values (2) 137
6.4%

건축년도
Date

Distinct261
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
Minimum1979-06-15 00:00:00
Maximum2021-12-07 00:00:00
2023-12-29T22:12:38.859079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-29T22:12:40.233656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

입주일
Date

MISSING 

Distinct251
Distinct (%)95.4%
Missing13
Missing (%)4.7%
Memory size2.3 KiB
Minimum1979-06-15 00:00:00
Maximum2020-07-16 00:00:00
2023-12-29T22:12:40.899743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-29T22:12:41.673950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

주택종류
Categorical

IMBALANCE 

Distinct8
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
아파트
218 
연립주택
 
21
주상복합
 
20
공공임대
 
6
민간임대
 
3
Other values (3)
 
8

Length

Max length4
Median length3
Mean length3.2101449
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row민간임대
2nd row주상복합
3rd row행복주택
4th row주상복합
5th row아파트

Common Values

ValueCountFrequency (%)
아파트 218
79.0%
연립주택 21
 
7.6%
주상복합 20
 
7.2%
공공임대 6
 
2.2%
민간임대 3
 
1.1%
행복주택 3
 
1.1%
국민임대 3
 
1.1%
영구임대 2
 
0.7%

Length

2023-12-29T22:12:42.311096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-29T22:12:42.935689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아파트 218
79.0%
연립주택 21
 
7.6%
주상복합 20
 
7.2%
공공임대 6
 
2.2%
민간임대 3
 
1.1%
행복주택 3
 
1.1%
국민임대 3
 
1.1%
영구임대 2
 
0.7%

전화번호
Text

MISSING 

Distinct227
Distinct (%)97.0%
Missing42
Missing (%)15.2%
Memory size2.3 KiB
2023-12-29T22:12:43.655350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.021368
Min length12

Characters and Unicode

Total characters2813
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique221 ?
Unique (%)94.4%

Sample

1st row055-763-1988
2nd row055- 761-1910
3rd row055-746-3510
4th row055-761-0571
5th row055-759-0702
ValueCountFrequency (%)
055-754-1220 3
 
1.3%
055-755-2656 2
 
0.9%
055-753-9877 2
 
0.9%
055-748-3490 2
 
0.9%
055-759-5115 2
 
0.9%
055-745-7118 2
 
0.9%
055-745-9070 1
 
0.4%
055-746-7164 1
 
0.4%
055-759-0573 1
 
0.4%
055-744-4753 1
 
0.4%
Other values (218) 218
92.8%
2023-12-29T22:12:44.937737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 693
24.6%
- 468
16.6%
0 366
13.0%
7 346
12.3%
4 188
 
6.7%
2 150
 
5.3%
6 139
 
4.9%
1 131
 
4.7%
3 127
 
4.5%
8 107
 
3.8%
Other values (3) 98
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2343
83.3%
Dash Punctuation 468
 
16.6%
Math Symbol 1
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 693
29.6%
0 366
15.6%
7 346
14.8%
4 188
 
8.0%
2 150
 
6.4%
6 139
 
5.9%
1 131
 
5.6%
3 127
 
5.4%
8 107
 
4.6%
9 96
 
4.1%
Dash Punctuation
ValueCountFrequency (%)
- 468
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2813
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 693
24.6%
- 468
16.6%
0 366
13.0%
7 346
12.3%
4 188
 
6.7%
2 150
 
5.3%
6 139
 
4.9%
1 131
 
4.7%
3 127
 
4.5%
8 107
 
3.8%
Other values (3) 98
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2813
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 693
24.6%
- 468
16.6%
0 366
13.0%
7 346
12.3%
4 188
 
6.7%
2 150
 
5.3%
6 139
 
4.9%
1 131
 
4.7%
3 127
 
4.5%
8 107
 
3.8%
Other values (3) 98
 
3.5%

Interactions

2023-12-29T22:12:23.120470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-29T22:12:20.377556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-29T22:12:21.388817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-29T22:12:22.309663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-29T22:12:23.468100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-29T22:12:20.667265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-29T22:12:21.545507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-29T22:12:22.503830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-29T22:12:23.716325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-29T22:12:20.950399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-29T22:12:21.798264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-29T22:12:22.674131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-29T22:12:23.972163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-29T22:12:21.195391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-29T22:12:22.060230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-29T22:12:22.851098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-29T22:12:45.361315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번세대수지상층수동수주택종류
연번1.0000.5320.7990.4300.514
세대수0.5321.0000.7180.8940.204
지상층수0.7990.7181.0000.5570.445
동수0.4300.8940.5571.0000.000
주택종류0.5140.2040.4450.0001.000
2023-12-29T22:12:45.669039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번세대수지상층수동수주택종류
연번1.000-0.549-0.792-0.3640.274
세대수-0.5491.0000.7810.8040.097
지상층수-0.7920.7811.0000.4570.227
동수-0.3640.8040.4571.0000.000
주택종류0.2740.0970.2270.0001.000

Missing values

2023-12-29T22:12:24.362862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-29T22:12:24.836002image/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.
2023-12-29T22:12:25.199104image/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

연번아파트명새주소지번주소우편번호세대수지상층수동수연면적건축년도입주일주택종류전화번호
01프리미어웰가경상남도 진주시 개양로 67경상남도 진주시 가좌동 19455282670228980968.37662021-12-07<NA>민간임대055-763-1988
12일진스위트포레강남경상남도 진주시 망경로 308경상남도 진주시 강남동 141-15270911527118368.94642021-10-12<NA>주상복합055- 761-1910
23진주옥봉엘에이치아파트경상남도 진주시 진산로 20경상남도 진주시 옥봉동 700-15276050015332355.892021-09-08<NA>행복주택055-746-3510
34일동미라주경상남도 진주시 진주대로 891번길 56(강남동, 일동미라주)경상남도 진주시 강남동 3875270939435386398.93222021-01-29<NA>주상복합055-761-0571
45중흥S클래스 더 퍼스트경상남도 진주시 충의로 146(충무공동, 중흥에스-클래스 더 퍼스트)경상남도 진주시 충무공동 275528537262010149217.852020-08-19<NA>아파트055-759-0702
56중흥센트럴시티 4단지경상남도 진주시 에나로 175번길 11(충무공동, 중흥센트럴시티 4단지)경상남도 진주시 충무공동 2955285544434597212.642020-07-162020-07-16주상복합055-763-4020
67중흥센트럴시티 3단지경상남도 진주시 에나로 175번길 12(충무공동, 중흥센트럴시티 3단지)경상남도 진주시 충무공동 2985285533332477796.422020-07-162020-07-16주상복합055-759-2982
78중흥센트럴시티 2단지경상남도 진주시 에나로 190(충무공동, 중흥센트럴시티 2단지)경상남도 진주시 충무공동 29352856560328147347.67482020-07-162020-07-16주상복합055-754-4985
89시티프라디움 2차경상남도 진주시 진주역로 114(가좌동, 시티프라디움)경상남도 진주시 가좌동 20585282640739373464.49822020-07-10<NA>주상복합055-762-5630
910명석 블리시움아파트경상남도 진주시 광제산로26번길 5(명석면, 명석 블리시움아파트)경상남도 진주시 명석면 우수리 102-152642361013867.452020-04-01<NA>아파트<NA>
연번아파트명새주소지번주소우편번호세대수지상층수동수연면적건축년도입주일주택종류전화번호
266267하대연립조합주택경상남도 진주시 말띠고개로108번길 13(하대동, 하대연립조합주택)경상남도 진주시 하대동 641-45274250333299.041983-04-301983-04-30연립주택<NA>
267268상봉한주조합주택경상남도 진주시 상봉대룡길 29 (상봉동, 상봉연립조합주택)경상남도 진주시 상봉동 741-15265521311456.321983-03-111983-03-11연립주택<NA>
268269모란아파트경상남도 진주시 솔밭로 137 (상대동, 모란아파트)경상남도 진주시 상대동 287-245278830521592.561982-12-021982-12-02아파트<NA>
269270하대주공아파트경상남도 진주시 상대로 125 (하대동, 주공아파트)경상남도 진주시 하대동 73-15277950051023725.71982-07-061982-07-06아파트055-752-5844
270271상평촉석아파트경상남도 진주시 솔밭로92번길 5 (상평동, 촉석맨션)경상남도 진주시 상평동 219-91527961755614450.081980-12-271980-12-27아파트055-752-3380
271272상대주공아파트경상남도 진주시 상대로 101 (하대동, 상대아파트)경상남도 진주시 하대동 725278051051121687.321980-09-061980-09-06아파트055-752-2304
272273상평태화연립경상남도 진주시 공단로 27 (상평동, 태화연립)경상남도 진주시 상평동 275-75280720311406.451980-05-121980-05-12연립주택<NA>
273274대동아파트경상남도 진주시 도동천로184번길 7 (상대동, 대동아파트)경상남도 진주시 상대동 331-1527761605510789.791979-11-121979-11-12아파트055-758-1076
274275상봉(1)주공아파트경상남도 진주시 창렬로 129 (상봉동, 상봉주공아파트)경상남도 진주시 상봉동 800-1 외 1필지5265865051312793.71979-06-151979-06-15아파트055-741-5345
275276영남연립경상남도 진주시 비봉로 80(계동, 영남연립)경상남도 진주시 계동 275275022311777.111979-06-151979-06-15연립주택<NA>