Overview

Dataset statistics

Number of variables13
Number of observations241
Missing cells79
Missing cells (%)2.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.8 KiB
Average record size in memory109.5 B

Variable types

Numeric5
Text4
Categorical2
DateTime2

Dataset

Description강릉시에 소재한 공동주택현황 데이터로 단지명, 주소, 도로명주소, 대지면적, 건축연면적, 층수, 동수, 세대수, 형태, 사업승인일, 사용승인일 등을 제공합니다.
Author강원도 강릉시
URLhttps://www.data.go.kr/data/15084310/fileData.do

Alerts

종류 is highly overall correlated with 번호 and 5 other fieldsHigh correlation
형태 is highly overall correlated with 건축연면적 and 2 other fieldsHigh correlation
번호 is highly overall correlated with 건축연면적 and 3 other fieldsHigh correlation
건축연면적 is highly overall correlated with 번호 and 5 other fieldsHigh correlation
층수 is highly overall correlated with 번호 and 3 other fieldsHigh correlation
동수 is highly overall correlated with 건축연면적 and 3 other fieldsHigh correlation
세대수 is highly overall correlated with 번호 and 4 other fieldsHigh correlation
형태 is highly imbalanced (54.4%)Imbalance
종류 is highly imbalanced (87.8%)Imbalance
대지면적 has 76 (31.5%) missing valuesMissing
번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:25:10.441962
Analysis finished2023-12-12 16:25:13.795181
Duration3.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct241
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121
Minimum1
Maximum241
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T01:25:13.861182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q161
median121
Q3181
95-th percentile229
Maximum241
Range240
Interquartile range (IQR)120

Descriptive statistics

Standard deviation69.714896
Coefficient of variation (CV)0.57615616
Kurtosis-1.2
Mean121
Median Absolute Deviation (MAD)60
Skewness0
Sum29161
Variance4860.1667
MonotonicityStrictly increasing
2023-12-13T01:25:14.271763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
182 1
 
0.4%
154 1
 
0.4%
155 1
 
0.4%
156 1
 
0.4%
157 1
 
0.4%
158 1
 
0.4%
159 1
 
0.4%
160 1
 
0.4%
161 1
 
0.4%
Other values (231) 231
95.9%
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 (%)
241 1
0.4%
240 1
0.4%
239 1
0.4%
238 1
0.4%
237 1
0.4%
236 1
0.4%
235 1
0.4%
234 1
0.4%
233 1
0.4%
232 1
0.4%

단지명
Text

Distinct240
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-13T01:25:14.446593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length8.253112
Min length4

Characters and Unicode

Total characters1989
Distinct characters216
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

Unique239 ?
Unique (%)99.2%

Sample

1st row해당화주택
2nd row동부시장아파트
3rd row범아아파트
4th row금잔디아파트
5th row송정연립
ValueCountFrequency (%)
삼우그린아파트 2
 
0.8%
6단지 1
 
0.4%
동부시장아파트 1
 
0.4%
노암한라2차아파트 1
 
0.4%
고합노암아파트(우진주택 1
 
0.4%
초당유화3차아파트 1
 
0.4%
노암영진3차아파트 1
 
0.4%
경포현대아파트 1
 
0.4%
입암4주공아파트 1
 
0.4%
입암5주공아파트 1
 
0.4%
Other values (235) 235
95.5%
2023-12-13T01:25:14.800779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 183
 
9.2%
파 176
 
8.8%
트 173
 
8.7%
차 56
 
2.8%
동 44
 
2.2%
대 42
 
2.1%
주 37
 
1.9%
강 32
 
1.6%
2 31
 
1.6%
( 28
 
1.4%
Other values (206) 1187
59.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1803
90.6%
Decimal Number 100
 
5.0%
Open Punctuation 28
 
1.4%
Close Punctuation 28
 
1.4%
Uppercase Letter 14
 
0.7%
Other Punctuation 7
 
0.4%
Space Separator 5
 
0.3%
Lowercase Letter 3
 
0.2%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 183
 
10.1%
파 176
 
9.8%
트 173
 
9.6%
차 56
 
3.1%
동 44
 
2.4%
대 42
 
2.3%
주 37
 
2.1%
강 32
 
1.8%
션 28
 
1.6%
지 27
 
1.5%
Other values (185) 1005
55.7%
Decimal Number
ValueCountFrequency (%)
2 31
31.0%
1 23
23.0%
3 17
17.0%
4 9
 
9.0%
5 7
 
7.0%
8 6
 
6.0%
6 5
 
5.0%
7 2
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
H 6
42.9%
L 6
42.9%
B 1
 
7.1%
A 1
 
7.1%
Lowercase Letter
ValueCountFrequency (%)
k 1
33.3%
t 1
33.3%
e 1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 4
57.1%
. 3
42.9%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1803
90.6%
Common 169
 
8.5%
Latin 17
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 183
 
10.1%
파 176
 
9.8%
트 173
 
9.6%
차 56
 
3.1%
동 44
 
2.4%
대 42
 
2.3%
주 37
 
2.1%
강 32
 
1.8%
션 28
 
1.6%
지 27
 
1.5%
Other values (185) 1005
55.7%
Common
ValueCountFrequency (%)
2 31
18.3%
( 28
16.6%
) 28
16.6%
1 23
13.6%
3 17
10.1%
4 9
 
5.3%
5 7
 
4.1%
8 6
 
3.6%
5
 
3.0%
6 5
 
3.0%
Other values (4) 10
 
5.9%
Latin
ValueCountFrequency (%)
H 6
35.3%
L 6
35.3%
k 1
 
5.9%
t 1
 
5.9%
B 1
 
5.9%
A 1
 
5.9%
e 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1803
90.6%
ASCII 186
 
9.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 183
 
10.1%
파 176
 
9.8%
트 173
 
9.6%
차 56
 
3.1%
동 44
 
2.4%
대 42
 
2.3%
주 37
 
2.1%
강 32
 
1.8%
션 28
 
1.6%
지 27
 
1.5%
Other values (185) 1005
55.7%
ASCII
ValueCountFrequency (%)
2 31
16.7%
( 28
15.1%
) 28
15.1%
1 23
12.4%
3 17
9.1%
4 9
 
4.8%
5 7
 
3.8%
H 6
 
3.2%
L 6
 
3.2%
8 6
 
3.2%
Other values (11) 25
13.4%

주소
Text

Distinct239
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-13T01:25:15.114933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length41
Mean length17.887967
Min length14

Characters and Unicode

Total characters4311
Distinct characters67
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

Unique237 ?
Unique (%)98.3%

Sample

1st row강원도 강릉시 송정동 39-4
2nd row강원도 강릉시 옥천동 223
3rd row강원도 강릉시 노암동 661, 661-14
4th row강원도 강릉시 옥천동 126-12
5th row강원도 강릉시 송정동 110-42
ValueCountFrequency (%)
강원도 241
23.6%
강릉시 241
23.6%
포남동 57
 
5.6%
교동 34
 
3.3%
입암동 28
 
2.7%
주문진읍 26
 
2.5%
교항리 19
 
1.9%
노암동 19
 
1.9%
송정동 14
 
1.4%
내곡동 13
 
1.3%
Other values (266) 329
32.2%
2023-12-13T01:25:15.571327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
780
18.1%
강 483
 
11.2%
1 253
 
5.9%
도 241
 
5.6%
릉 241
 
5.6%
시 241
 
5.6%
원 241
 
5.6%
동 212
 
4.9%
- 142
 
3.3%
0 119
 
2.8%
Other values (57) 1358
31.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2293
53.2%
Decimal Number 1055
24.5%
Space Separator 780
 
18.1%
Dash Punctuation 142
 
3.3%
Other Punctuation 20
 
0.5%
Close Punctuation 8
 
0.2%
Open Punctuation 8
 
0.2%
Math Symbol 3
 
0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
강 483
21.1%
도 241
10.5%
릉 241
10.5%
시 241
10.5%
원 241
10.5%
동 212
9.2%
남 59
 
2.6%
포 57
 
2.5%
교 53
 
2.3%
암 47
 
2.0%
Other values (39) 418
18.2%
Decimal Number
ValueCountFrequency (%)
1 253
24.0%
0 119
11.3%
3 104
9.9%
2 97
 
9.2%
6 96
 
9.1%
4 90
 
8.5%
5 83
 
7.9%
8 76
 
7.2%
9 71
 
6.7%
7 66
 
6.3%
Uppercase Letter
ValueCountFrequency (%)
B 1
50.0%
A 1
50.0%
Space Separator
ValueCountFrequency (%)
780
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 142
100.0%
Other Punctuation
ValueCountFrequency (%)
, 20
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2293
53.2%
Common 2016
46.8%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
강 483
21.1%
도 241
10.5%
릉 241
10.5%
시 241
10.5%
원 241
10.5%
동 212
9.2%
남 59
 
2.6%
포 57
 
2.5%
교 53
 
2.3%
암 47
 
2.0%
Other values (39) 418
18.2%
Common
ValueCountFrequency (%)
780
38.7%
1 253
 
12.5%
- 142
 
7.0%
0 119
 
5.9%
3 104
 
5.2%
2 97
 
4.8%
6 96
 
4.8%
4 90
 
4.5%
5 83
 
4.1%
8 76
 
3.8%
Other values (6) 176
 
8.7%
Latin
ValueCountFrequency (%)
B 1
50.0%
A 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2293
53.2%
ASCII 2018
46.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
780
38.7%
1 253
 
12.5%
- 142
 
7.0%
0 119
 
5.9%
3 104
 
5.2%
2 97
 
4.8%
6 96
 
4.8%
4 90
 
4.5%
5 83
 
4.1%
8 76
 
3.8%
Other values (8) 178
 
8.8%
Hangul
ValueCountFrequency (%)
강 483
21.1%
도 241
10.5%
릉 241
10.5%
시 241
10.5%
원 241
10.5%
동 212
9.2%
남 59
 
2.6%
포 57
 
2.5%
교 53
 
2.3%
암 47
 
2.0%
Other values (39) 418
18.2%

도로명주소
Text

Distinct240
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-13T01:25:15.823437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length85
Median length31
Mean length18.456432
Min length13

Characters and Unicode

Total characters4448
Distinct characters137
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique239 ?
Unique (%)99.2%

Sample

1st row강원도 강릉시 해안로 91
2nd row강원도 강릉시 옥천로 48
3rd row강원도 강릉시 임영로25번길 3-6, 3-7
4th row강원도 강릉시 수문길21번길 26
5th row강원도 강릉시 팔송길18번길 7
ValueCountFrequency (%)
강원도 247
23.9%
강릉시 247
23.9%
주문진읍 26
 
2.5%
강변로 10
 
1.0%
경강로 8
 
0.8%
연곡면 8
 
0.8%
용지로 8
 
0.8%
20 8
 
0.8%
15 8
 
0.8%
14 7
 
0.7%
Other values (282) 457
44.2%
2023-12-13T01:25:16.215989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
795
17.9%
강 553
 
12.4%
릉 257
 
5.8%
원 252
 
5.7%
시 248
 
5.6%
도 247
 
5.6%
길 181
 
4.1%
2 167
 
3.8%
1 162
 
3.6%
로 159
 
3.6%
Other values (127) 1427
32.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2720
61.2%
Decimal Number 859
 
19.3%
Space Separator 795
 
17.9%
Dash Punctuation 48
 
1.1%
Other Punctuation 10
 
0.2%
Open Punctuation 8
 
0.2%
Close Punctuation 8
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
강 553
20.3%
릉 257
 
9.4%
원 252
 
9.3%
시 248
 
9.1%
도 247
 
9.1%
길 181
 
6.7%
로 159
 
5.8%
번 93
 
3.4%
주 32
 
1.2%
진 32
 
1.2%
Other values (112) 666
24.5%
Decimal Number
ValueCountFrequency (%)
2 167
19.4%
1 162
18.9%
3 101
11.8%
4 89
10.4%
5 82
9.5%
9 57
 
6.6%
8 56
 
6.5%
6 54
 
6.3%
7 51
 
5.9%
0 40
 
4.7%
Space Separator
ValueCountFrequency (%)
795
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48
100.0%
Other Punctuation
ValueCountFrequency (%)
, 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2720
61.2%
Common 1728
38.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
강 553
20.3%
릉 257
 
9.4%
원 252
 
9.3%
시 248
 
9.1%
도 247
 
9.1%
길 181
 
6.7%
로 159
 
5.8%
번 93
 
3.4%
주 32
 
1.2%
진 32
 
1.2%
Other values (112) 666
24.5%
Common
ValueCountFrequency (%)
795
46.0%
2 167
 
9.7%
1 162
 
9.4%
3 101
 
5.8%
4 89
 
5.2%
5 82
 
4.7%
9 57
 
3.3%
8 56
 
3.2%
6 54
 
3.1%
7 51
 
3.0%
Other values (5) 114
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2720
61.2%
ASCII 1728
38.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
795
46.0%
2 167
 
9.7%
1 162
 
9.4%
3 101
 
5.8%
4 89
 
5.2%
5 82
 
4.7%
9 57
 
3.3%
8 56
 
3.2%
6 54
 
3.1%
7 51
 
3.0%
Other values (5) 114
 
6.6%
Hangul
ValueCountFrequency (%)
강 553
20.3%
릉 257
 
9.4%
원 252
 
9.3%
시 248
 
9.1%
도 247
 
9.1%
길 181
 
6.7%
로 159
 
5.8%
번 93
 
3.4%
주 32
 
1.2%
진 32
 
1.2%
Other values (112) 666
24.5%

대지면적
Text

MISSING 

Distinct164
Distinct (%)99.4%
Missing76
Missing (%)31.5%
Memory size2.0 KiB
2023-12-13T01:25:16.543443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.3151515
Min length4

Characters and Unicode

Total characters877
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

Unique163 ?
Unique (%)98.8%

Sample

1st row1653
2nd row3305
3rd row1716
4th row18814
5th row1466
ValueCountFrequency (%)
31897 2
 
1.2%
18657.3 1
 
0.6%
18977.4 1
 
0.6%
23,789,40 1
 
0.6%
23317 1
 
0.6%
28221 1
 
0.6%
19100 1
 
0.6%
47685 1
 
0.6%
6567 1
 
0.6%
17911 1
 
0.6%
Other values (154) 154
93.3%
2023-12-13T01:25:16.997755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 119
13.6%
1 106
12.1%
3 91
10.4%
7 88
10.0%
6 82
9.4%
0 73
8.3%
4 71
8.1%
8 69
7.9%
5 63
7.2%
. 59
6.7%
Other values (2) 56
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 816
93.0%
Other Punctuation 61
 
7.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 119
14.6%
1 106
13.0%
3 91
11.2%
7 88
10.8%
6 82
10.0%
0 73
8.9%
4 71
8.7%
8 69
8.5%
5 63
7.7%
9 54
6.6%
Other Punctuation
ValueCountFrequency (%)
. 59
96.7%
, 2
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 877
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 119
13.6%
1 106
12.1%
3 91
10.4%
7 88
10.0%
6 82
9.4%
0 73
8.3%
4 71
8.1%
8 69
7.9%
5 63
7.2%
. 59
6.7%
Other values (2) 56
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 877
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 119
13.6%
1 106
12.1%
3 91
10.4%
7 88
10.0%
6 82
9.4%
0 73
8.3%
4 71
8.1%
8 69
7.9%
5 63
7.2%
. 59
6.7%
Other values (2) 56
6.4%

건축연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct236
Distinct (%)98.7%
Missing2
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean22170.996
Minimum29.933
Maximum191083
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T01:25:17.135089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29.933
5-th percentile1517.85
Q13369.5075
median8151
Q328151.33
95-th percentile78450.719
Maximum191083
Range191053.07
Interquartile range (IQR)24781.823

Descriptive statistics

Standard deviation29785.849
Coefficient of variation (CV)1.3434601
Kurtosis6.4286361
Mean22170.996
Median Absolute Deviation (MAD)6192.36
Skewness2.3012029
Sum5298868.1
Variance8.8719682 × 108
MonotonicityNot monotonic
2023-12-13T01:25:17.283247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1958.64 2
 
0.8%
64167.25 2
 
0.8%
2282.81 2
 
0.8%
22637.44 1
 
0.4%
13740.57 1
 
0.4%
9386.26 1
 
0.4%
7785.57 1
 
0.4%
40632.79 1
 
0.4%
28549.88 1
 
0.4%
17359.16 1
 
0.4%
Other values (226) 226
93.8%
(Missing) 2
 
0.8%
ValueCountFrequency (%)
29.933 1
0.4%
821.34 1
0.4%
987.12 1
0.4%
1239.75 1
0.4%
1315.0 1
0.4%
1338.34 1
0.4%
1362.96 1
0.4%
1365.89 1
0.4%
1415.88 1
0.4%
1428.94 1
0.4%
ValueCountFrequency (%)
191083.0 1
0.4%
131015.82 1
0.4%
129804.74 1
0.4%
129522.59 1
0.4%
125665.16 1
0.4%
118343.51 1
0.4%
117750.0 1
0.4%
105135.51 1
0.4%
93001.5502 1
0.4%
92277.25 1
0.4%

층수
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7344398
Minimum2
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T01:25:17.398042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q15
median9
Q315
95-th percentile20
Maximum39
Range37
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.9536418
Coefficient of variation (CV)0.611606
Kurtosis1.0939299
Mean9.7344398
Median Absolute Deviation (MAD)5
Skewness0.83078011
Sum2346
Variance35.445851
MonotonicityNot monotonic
2023-12-13T01:25:17.534894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
5 66
27.4%
15 45
18.7%
3 31
12.9%
14 12
 
5.0%
13 12
 
5.0%
12 11
 
4.6%
20 10
 
4.1%
4 10
 
4.1%
10 10
 
4.1%
18 8
 
3.3%
Other values (11) 26
 
10.8%
ValueCountFrequency (%)
2 2
 
0.8%
3 31
12.9%
4 10
 
4.1%
5 66
27.4%
6 5
 
2.1%
7 2
 
0.8%
8 4
 
1.7%
9 5
 
2.1%
10 10
 
4.1%
11 1
 
0.4%
ValueCountFrequency (%)
39 1
 
0.4%
25 2
 
0.8%
23 1
 
0.4%
22 1
 
0.4%
20 10
 
4.1%
19 2
 
0.8%
18 8
 
3.3%
15 45
18.7%
14 12
 
5.0%
13 12
 
5.0%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4149378
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T01:25:17.658423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile10
Maximum20
Range19
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.2548081
Coefficient of variation (CV)0.95310906
Kurtosis5.806702
Mean3.4149378
Median Absolute Deviation (MAD)1
Skewness2.2023285
Sum823
Variance10.593776
MonotonicityNot monotonic
2023-12-13T01:25:17.783884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 81
33.6%
2 55
22.8%
3 24
 
10.0%
4 20
 
8.3%
5 17
 
7.1%
7 13
 
5.4%
6 11
 
4.6%
8 4
 
1.7%
11 3
 
1.2%
14 2
 
0.8%
Other values (7) 11
 
4.6%
ValueCountFrequency (%)
1 81
33.6%
2 55
22.8%
3 24
 
10.0%
4 20
 
8.3%
5 17
 
7.1%
6 11
 
4.6%
7 13
 
5.4%
8 4
 
1.7%
9 2
 
0.8%
10 2
 
0.8%
ValueCountFrequency (%)
20 1
 
0.4%
17 2
0.8%
15 1
 
0.4%
14 2
0.8%
13 2
0.8%
12 1
 
0.4%
11 3
1.2%
10 2
0.8%
9 2
0.8%
8 4
1.7%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct158
Distinct (%)65.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean223.05394
Minimum12
Maximum1620
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T01:25:17.912782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile24
Q150
median102
Q3306
95-th percentile750
Maximum1620
Range1608
Interquartile range (IQR)256

Descriptive statistics

Standard deviation255.37347
Coefficient of variation (CV)1.1448956
Kurtosis5.099528
Mean223.05394
Median Absolute Deviation (MAD)72
Skewness2.0447869
Sum53756
Variance65215.61
MonotonicityNot monotonic
2023-12-13T01:25:18.045464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 13
 
5.4%
40 6
 
2.5%
50 6
 
2.5%
60 6
 
2.5%
45 5
 
2.1%
24 5
 
2.1%
42 4
 
1.7%
99 4
 
1.7%
21 4
 
1.7%
100 4
 
1.7%
Other values (148) 184
76.3%
ValueCountFrequency (%)
12 1
 
0.4%
20 2
 
0.8%
21 4
 
1.7%
22 1
 
0.4%
24 5
 
2.1%
27 2
 
0.8%
28 1
 
0.4%
30 13
5.4%
31 2
 
0.8%
32 1
 
0.4%
ValueCountFrequency (%)
1620 1
0.4%
1144 1
0.4%
1107 1
0.4%
1054 1
0.4%
1019 1
0.4%
1017 1
0.4%
922 1
0.4%
917 1
0.4%
914 1
0.4%
820 1
0.4%

형태
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
아파트
196 
연립
44 
아파트
 
1

Length

Max length4
Median length3
Mean length2.8215768
Min length2

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
아파트 196
81.3%
연립 44
 
18.3%
아파트 1
 
0.4%

Length

2023-12-13T01:25:18.180815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:25:18.296268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아파트 197
81.7%
연립 44
 
18.3%

종류
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
<NA>
237 
주상복합
 
4

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row주상복합
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 237
98.3%
주상복합 4
 
1.7%

Length

2023-12-13T01:25:18.401722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:25:18.494117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 237
98.3%
주상복합 4
 
1.7%

사업승인일
Date

Distinct204
Distinct (%)85.0%
Missing1
Missing (%)0.4%
Memory size2.0 KiB
Minimum1976-12-30 00:00:00
Maximum2018-10-31 00:00:00
2023-12-13T01:25:18.598626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:25:18.737964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

사용승인일
Date

Distinct222
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
Minimum1979-07-05 00:00:00
Maximum2021-06-28 00:00:00
2023-12-13T01:25:18.893536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:25:19.030652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-13T01:25:12.907879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:25:10.989857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:25:11.457023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:25:11.909133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:25:12.416420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:25:13.001032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:25:11.084821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:25:11.548995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:25:12.001959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:25:12.514611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:25:13.104306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:25:11.186456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:25:11.642679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:25:12.123996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:25:12.628139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:25:13.215558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:25:11.276212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:25:11.720993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:25:12.224824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:25:12.719219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:25:13.306734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:25:11.366576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:25:11.815793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:25:12.319770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:25:12.810390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:25:19.115786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호건축연면적층수동수세대수형태
번호1.0000.4820.6970.4640.4470.595
건축연면적0.4821.0000.5900.8350.8210.809
층수0.6970.5901.0000.4400.6880.471
동수0.4640.8350.4401.0000.8240.808
세대수0.4470.8210.6880.8241.0000.694
형태0.5950.8090.4710.8080.6941.000
2023-12-13T01:25:19.205611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
종류형태
종류1.0001.000
형태1.0001.000
2023-12-13T01:25:19.292132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호건축연면적층수동수세대수형태종류
번호1.0000.7270.8360.3180.6640.4341.000
건축연면적0.7271.0000.8210.6560.9500.7301.000
층수0.8360.8211.0000.2970.7600.3361.000
동수0.3180.6560.2971.0000.7170.6921.000
세대수0.6640.9500.7600.7171.0000.3971.000
형태0.4340.7300.3360.6920.3971.0001.000
종류1.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-13T01:25:13.446549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:25:13.618442image/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.
2023-12-13T01:25:13.736177image/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

번호단지명주소도로명주소대지면적건축연면적층수동수세대수형태종류사업승인일사용승인일
01해당화주택강원도 강릉시 송정동 39-4강원도 강릉시 해안로 91<NA>821.342230연립<NA>1978-09-251979-07-05
12동부시장아파트강원도 강릉시 옥천동 223강원도 강릉시 옥천로 48<NA>3968.195188아파트주상복합1976-12-301979-10-12
23범아아파트강원도 강릉시 노암동 661, 661-14강원도 강릉시 임영로25번길 3-6, 3-7<NA>3980.724250연립<NA>1979-09-101980-07-09
34금잔디아파트강원도 강릉시 옥천동 126-12강원도 강릉시 수문길21번길 2616533341.575145아파트<NA>1980-03-061980-11-20
45송정연립강원도 강릉시 송정동 110-42강원도 강릉시 팔송길18번길 7<NA>2861.082660연립<NA>1980-08-161980-12-05
56포남1주공아파트강원도 강릉시 포남동 1065강원도 강릉시 보래미상길 65<NA>27050.056640아파트<NA>1981-03-201981-12-28
67주문진교항주공아파트강원도 강릉시 주문진읍 교항리 387-6강원도 강릉시 주문진읍 신리천로 7233058151.055190아파트<NA>1982-02-251982-12-18
78개나리아파트강원도 강릉시 노암동 337-2강원도 강릉시 노암길41번길 14<NA>6640.2834102연립<NA>1982-06-291983-02-02
89옥포연립강원도 강릉시 포남동 1005-134강원도 강릉시 용지각길8번길 29<NA>6786.4833102연립<NA>1982-09-141983-02-28
910성덕연립주택(금모래)강원도 강릉시 노암동 267-8강원도 강릉시 강변로312번길 3<NA>2381.093142연립<NA>1982-09-011983-04-08
번호단지명주소도로명주소대지면적건축연면적층수동수세대수형태종류사업승인일사용승인일
231232송정한신더휴아파트강원도 강릉시 송정동 1063강원도 강릉시 경강로 244817703.549790.3118204353아파트<NA>2016-07-262019-03-18
232233강변코아루오투리움강원도 강릉시 입암동 712강원도 강릉시 월대산로 2320251.451004.313157427아파트<NA>2016-07-252019-07-17
233234썬라이즈강원도 강릉시 포남동 1324강원도 강릉시 난설헌로78번길 29-172100.61415.884144연립<NA>2018-08-032019-10-28
234235영진코아루비치테라스강원도 강릉시 연곡면 영진리 381강원도 강릉시 연곡면 홍질목길 126580.923343.2118191298아파트<NA>2017-05-112019-12-18
235236강릉아이파크강원도 강릉시 송정동 1064강원도 강릉시 경강로 251124907.372563.32207492아파트<NA>2017-09-282019-12-31
236237송정신원아침도시강원도 강릉시 송정동 1065강원도 강릉시 경강로 249825471.568724.4145188477아파트<NA>2017-07-202020-04-29
237238유천더테라스아리스타강원도 강릉시 유천동 793강원도 강릉시 위촌길 302331718655.8446131연립<NA>2017-06-082020-06-24
238239주문진 서희스타힐스아파트강원도 강릉시 주문진읍 교항리 21강원도 강릉시 주문진읍 연주로 281-2123,789,4045847.52205416아파트<NA>2017-09-132020-12-11
239240주문진벽산블루밍오션힐스강원도 강릉시 주문진읍 교항리 1269강원도 강릉시 주문진읍 장성로 256371.514137.4584153113아파트<NA>2018-10-312021-03-22
240241강릉유천유승한내들강원도 강릉시 유천동 781강원도 강릉시 선수촌로 79-14<NA><NA>395788아파트<NA>2018-01-032021-06-28