Overview

Dataset statistics

Number of variables16
Number of observations41
Missing cells41
Missing cells (%)6.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.5 KiB
Average record size in memory137.1 B

Variable types

Numeric5
Categorical7
Text3
DateTime1

Dataset

Description서울특별시 양천구 원룸 및 오피스텔 현황 데이터 입니다.유형, 건물명, 주소, 면적, 용도, 층수, 세대 등의 정보를 포함합니다.
Author서울특별시 양천구
URLhttps://www.data.go.kr/data/15126918/fileData.do

Alerts

유형 has constant value ""Constant
시군구명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
건축유형 is highly overall correlated with 연번 and 6 other fieldsHigh correlation
층용도 is highly overall correlated with 세대 and 1 other fieldsHigh correlation
지하층수 is highly overall correlated with 지상층수 and 2 other fieldsHigh correlation
연번 is highly overall correlated with 세대 and 1 other fieldsHigh correlation
대지면적(m2) is highly overall correlated with 건축면적(m2) and 1 other fieldsHigh correlation
건축면적(m2) is highly overall correlated with 대지면적(m2) and 1 other fieldsHigh correlation
지상층수 is highly overall correlated with 건축유형 and 3 other fieldsHigh correlation
세대 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
주용도 is highly overall correlated with 지상층수 and 2 other fieldsHigh correlation
세부용도 is highly overall correlated with 지상층수 and 3 other fieldsHigh correlation
건물명 has 8 (19.5%) missing valuesMissing
지상층수 has 6 (14.6%) missing valuesMissing
세대 has 27 (65.9%) missing valuesMissing
연번 has unique valuesUnique
세대 has 7 (17.1%) zerosZeros

Reproduction

Analysis started2024-03-14 16:43:06.059667
Analysis finished2024-03-14 16:43:13.961767
Duration7.9 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21
Minimum1
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size497.0 B
2024-03-15T01:43:14.180483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median21
Q331
95-th percentile39
Maximum41
Range40
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.979149
Coefficient of variation (CV)0.57043565
Kurtosis-1.2
Mean21
Median Absolute Deviation (MAD)10
Skewness0
Sum861
Variance143.5
MonotonicityStrictly increasing
2024-03-15T01:43:14.560916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1 1
 
2.4%
32 1
 
2.4%
24 1
 
2.4%
25 1
 
2.4%
26 1
 
2.4%
27 1
 
2.4%
28 1
 
2.4%
29 1
 
2.4%
30 1
 
2.4%
31 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
1 1
2.4%
2 1
2.4%
3 1
2.4%
4 1
2.4%
5 1
2.4%
6 1
2.4%
7 1
2.4%
8 1
2.4%
9 1
2.4%
10 1
2.4%
ValueCountFrequency (%)
41 1
2.4%
40 1
2.4%
39 1
2.4%
38 1
2.4%
37 1
2.4%
36 1
2.4%
35 1
2.4%
34 1
2.4%
33 1
2.4%
32 1
2.4%

유형
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size456.0 B
오피스텔
41 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
오피스텔 41
100.0%

Length

2024-03-15T01:43:14.862916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:43:15.059158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
오피스텔 41
100.0%

건축유형
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size456.0 B
일반건축물
35 
집합건축물
6 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반건축물 35
85.4%
집합건축물 6
 
14.6%

Length

2024-03-15T01:43:15.231682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:43:15.430686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반건축물 35
85.4%
집합건축물 6
 
14.6%

건물명
Text

MISSING 

Distinct19
Distinct (%)57.6%
Missing8
Missing (%)19.5%
Memory size456.0 B
2024-03-15T01:43:16.075865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.5757576
Min length4

Characters and Unicode

Total characters184
Distinct characters54
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)45.5%

Sample

1st row대풍오피스텔
2nd row목동오피스텔
3rd row하이드파크
4th row경원빌딩
5th row경원빌딩
ValueCountFrequency (%)
파크팰리스 7
20.6%
목동오피스텔 6
17.6%
경원빌딩 3
 
8.8%
하이드파크 2
 
5.9%
파크아리온 1
 
2.9%
드어반 1
 
2.9%
대풍오피스텔 1
 
2.9%
지구촌선교센타 1
 
2.9%
목동솔리스타 1
 
2.9%
목동토레스타워 1
 
2.9%
Other values (10) 10
29.4%
2024-03-15T01:43:17.217682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 20
 
10.9%
파 11
 
6.0%
동 11
 
6.0%
텔 11
 
6.0%
피 11
 
6.0%
오 11
 
6.0%
크 11
 
6.0%
목 11
 
6.0%
리 10
 
5.4%
빌 7
 
3.8%
Other values (44) 70
38.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 182
98.9%
Space Separator 1
 
0.5%
Uppercase Letter 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 20
 
11.0%
파 11
 
6.0%
동 11
 
6.0%
텔 11
 
6.0%
피 11
 
6.0%
오 11
 
6.0%
크 11
 
6.0%
목 11
 
6.0%
리 10
 
5.5%
빌 7
 
3.8%
Other values (42) 68
37.4%
Space Separator
ValueCountFrequency (%)
1
100.0%
Uppercase Letter
ValueCountFrequency (%)
K 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 182
98.9%
Common 1
 
0.5%
Latin 1
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 20
 
11.0%
파 11
 
6.0%
동 11
 
6.0%
텔 11
 
6.0%
피 11
 
6.0%
오 11
 
6.0%
크 11
 
6.0%
목 11
 
6.0%
리 10
 
5.5%
빌 7
 
3.8%
Other values (42) 68
37.4%
Common
ValueCountFrequency (%)
1
100.0%
Latin
ValueCountFrequency (%)
K 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 182
98.9%
ASCII 2
 
1.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 20
 
11.0%
파 11
 
6.0%
동 11
 
6.0%
텔 11
 
6.0%
피 11
 
6.0%
오 11
 
6.0%
크 11
 
6.0%
목 11
 
6.0%
리 10
 
5.5%
빌 7
 
3.8%
Other values (42) 68
37.4%
ASCII
ValueCountFrequency (%)
1
50.0%
K 1
50.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size456.0 B
서울특별시 양천구
41 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시 양천구
2nd row서울특별시 양천구
3rd row서울특별시 양천구
4th row서울특별시 양천구
5th row서울특별시 양천구

Common Values

ValueCountFrequency (%)
서울특별시 양천구 41
100.0%

Length

2024-03-15T01:43:17.613874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:43:17.944782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 41
50.0%
양천구 41
50.0%

소재지주소(도로명)
Text

Distinct25
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Memory size456.0 B
2024-03-15T01:43:18.697685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length18.804878
Min length16

Characters and Unicode

Total characters771
Distinct characters37
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

Unique19 ?
Unique (%)46.3%

Sample

1st row서울특별시 양천구 등촌로 50-1
2nd row서울특별시 양천구 중앙로 328
3rd row서울특별시 양천구 신정중앙로21길 6-1
4th row서울특별시 양천구 목동로23길 7
5th row서울특별시 양천구 목동로23길 7
ValueCountFrequency (%)
서울특별시 41
25.0%
양천구 41
25.0%
중앙로 9
 
5.5%
남부순환로 8
 
4.9%
370 7
 
4.3%
328 6
 
3.7%
신정중앙로21길 5
 
3.0%
등촌로 5
 
3.0%
7 4
 
2.4%
목동로23길 3
 
1.8%
Other values (28) 35
21.3%
2024-03-15T01:43:19.835045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
123
16.0%
서 45
 
5.8%
특 41
 
5.3%
별 41
 
5.3%
시 41
 
5.3%
양 41
 
5.3%
천 41
 
5.3%
구 41
 
5.3%
울 41
 
5.3%
로 41
 
5.3%
Other values (27) 275
35.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 505
65.5%
Decimal Number 134
 
17.4%
Space Separator 123
 
16.0%
Dash Punctuation 9
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 45
 
8.9%
특 41
 
8.1%
별 41
 
8.1%
시 41
 
8.1%
양 41
 
8.1%
천 41
 
8.1%
구 41
 
8.1%
울 41
 
8.1%
로 41
 
8.1%
중 17
 
3.4%
Other values (15) 115
22.8%
Decimal Number
ValueCountFrequency (%)
2 31
23.1%
1 25
18.7%
3 23
17.2%
7 17
12.7%
0 13
9.7%
8 10
 
7.5%
6 6
 
4.5%
5 4
 
3.0%
9 3
 
2.2%
4 2
 
1.5%
Space Separator
ValueCountFrequency (%)
123
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 505
65.5%
Common 266
34.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 45
 
8.9%
특 41
 
8.1%
별 41
 
8.1%
시 41
 
8.1%
양 41
 
8.1%
천 41
 
8.1%
구 41
 
8.1%
울 41
 
8.1%
로 41
 
8.1%
중 17
 
3.4%
Other values (15) 115
22.8%
Common
ValueCountFrequency (%)
123
46.2%
2 31
 
11.7%
1 25
 
9.4%
3 23
 
8.6%
7 17
 
6.4%
0 13
 
4.9%
8 10
 
3.8%
- 9
 
3.4%
6 6
 
2.3%
5 4
 
1.5%
Other values (2) 5
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 505
65.5%
ASCII 266
34.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
123
46.2%
2 31
 
11.7%
1 25
 
9.4%
3 23
 
8.6%
7 17
 
6.4%
0 13
 
4.9%
8 10
 
3.8%
- 9
 
3.4%
6 6
 
2.3%
5 4
 
1.5%
Other values (2) 5
 
1.9%
Hangul
ValueCountFrequency (%)
서 45
 
8.9%
특 41
 
8.1%
별 41
 
8.1%
시 41
 
8.1%
양 41
 
8.1%
천 41
 
8.1%
구 41
 
8.1%
울 41
 
8.1%
로 41
 
8.1%
중 17
 
3.4%
Other values (15) 115
22.8%

소재지주소(지번)
Text

Distinct26
Distinct (%)63.4%
Missing0
Missing (%)0.0%
Memory size456.0 B
2024-03-15T01:43:20.501491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length9.3414634
Min length8

Characters and Unicode

Total characters383
Distinct characters17
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

Unique20 ?
Unique (%)48.8%

Sample

1st row목동 783-9
2nd row신정동 939-7
3rd row신정동 888-22
4th row신정동 888-19
5th row신정동 888-19
ValueCountFrequency (%)
신정동 23
28.7%
신월동 8
 
10.0%
목동 8
 
10.0%
166-11 7
 
8.8%
939-7 5
 
6.2%
888-19 3
 
3.8%
888-22 2
 
2.5%
888-7 2
 
2.5%
894-27 2
 
2.5%
713-11 1
 
1.2%
Other values (19) 19
23.8%
2024-03-15T01:43:21.607463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 41
10.7%
동 41
10.7%
39
10.2%
1 37
9.7%
신 33
8.6%
8 31
8.1%
9 29
7.6%
정 25
 
6.5%
3 19
 
5.0%
7 19
 
5.0%
Other values (7) 69
18.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 188
49.1%
Other Letter 115
30.0%
Dash Punctuation 41
 
10.7%
Space Separator 39
 
10.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 37
19.7%
8 31
16.5%
9 29
15.4%
3 19
10.1%
7 19
10.1%
6 17
9.0%
2 16
8.5%
4 10
 
5.3%
5 9
 
4.8%
0 1
 
0.5%
Other Letter
ValueCountFrequency (%)
동 41
35.7%
신 33
28.7%
정 25
21.7%
월 8
 
7.0%
목 8
 
7.0%
Dash Punctuation
ValueCountFrequency (%)
- 41
100.0%
Space Separator
ValueCountFrequency (%)
39
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 268
70.0%
Hangul 115
30.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 41
15.3%
39
14.6%
1 37
13.8%
8 31
11.6%
9 29
10.8%
3 19
7.1%
7 19
7.1%
6 17
6.3%
2 16
 
6.0%
4 10
 
3.7%
Other values (2) 10
 
3.7%
Hangul
ValueCountFrequency (%)
동 41
35.7%
신 33
28.7%
정 25
21.7%
월 8
 
7.0%
목 8
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 268
70.0%
Hangul 115
30.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 41
15.3%
39
14.6%
1 37
13.8%
8 31
11.6%
9 29
10.8%
3 19
7.1%
7 19
7.1%
6 17
6.3%
2 16
 
6.0%
4 10
 
3.7%
Other values (2) 10
 
3.7%
Hangul
ValueCountFrequency (%)
동 41
35.7%
신 33
28.7%
정 25
21.7%
월 8
 
7.0%
목 8
 
7.0%

대지면적(m2)
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1038.6049
Minimum147
Maximum4252
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size497.0 B
2024-03-15T01:43:22.006695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum147
5-th percentile273.6
Q1441
median760.5
Q3923.4
95-th percentile3440
Maximum4252
Range4105
Interquartile range (IQR)482.4

Descriptive statistics

Standard deviation950.20446
Coefficient of variation (CV)0.91488542
Kurtosis4.2076998
Mean1038.6049
Median Absolute Deviation (MAD)319.5
Skewness2.1329496
Sum42582.8
Variance902888.51
MonotonicityNot monotonic
2024-03-15T01:43:22.400585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
810.8 4
 
9.8%
628.6 4
 
9.8%
405.4 2
 
4.9%
1372.8 1
 
2.4%
1799.6 1
 
2.4%
1104.3 1
 
2.4%
383.2 1
 
2.4%
2107.8 1
 
2.4%
357.3 1
 
2.4%
863.6 1
 
2.4%
Other values (24) 24
58.5%
ValueCountFrequency (%)
147.0 1
2.4%
271.8 1
2.4%
273.6 1
2.4%
357.3 1
2.4%
383.2 1
2.4%
405.4 2
4.9%
407.7 1
2.4%
421.2 1
2.4%
434.1 1
2.4%
441.0 1
2.4%
ValueCountFrequency (%)
4252.0 1
2.4%
3811.5 1
2.4%
3440.0 1
2.4%
2514.4 1
2.4%
2107.8 1
2.4%
1885.8 1
2.4%
1799.6 1
2.4%
1621.6 1
2.4%
1372.8 1
2.4%
1104.3 1
2.4%

건축면적(m2)
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean562.41024
Minimum82.89
Maximum2200.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size497.0 B
2024-03-15T01:43:22.849427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum82.89
5-th percentile162.96
Q1248.67
median381.64
Q3552.72
95-th percentile1707.12
Maximum2200.9
Range2118.01
Interquartile range (IQR)304.05

Descriptive statistics

Standard deviation502.73534
Coefficient of variation (CV)0.89389435
Kurtosis3.2908594
Mean562.41024
Median Absolute Deviation (MAD)137.2
Skewness1.920803
Sum23058.82
Variance252742.82
MonotonicityNot monotonic
2024-03-15T01:43:23.433355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
429.0 4
 
9.8%
314.18 4
 
9.8%
214.5 2
 
4.9%
818.88 1
 
2.4%
1076.54 1
 
2.4%
1094.6 1
 
2.4%
191.3 1
 
2.4%
1203.6 1
 
2.4%
214.02 1
 
2.4%
483.84 1
 
2.4%
Other values (24) 24
58.5%
ValueCountFrequency (%)
82.89 1
2.4%
159.28 1
2.4%
162.96 1
2.4%
191.3 1
2.4%
209.52 1
2.4%
212.97 1
2.4%
214.02 1
2.4%
214.5 2
4.9%
244.44 1
2.4%
248.67 1
2.4%
ValueCountFrequency (%)
2200.9 1
2.4%
2026.35 1
2.4%
1707.12 1
2.4%
1256.72 1
2.4%
1203.6 1
2.4%
1094.6 1
2.4%
1076.54 1
2.4%
942.54 1
2.4%
858.0 1
2.4%
818.88 1
2.4%

주용도
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size456.0 B
업무시설
29 
제1종근린생활시설
7 
공동주택
3 
단독주택
 
1
제2종근린생활시설
 
1

Length

Max length9
Median length4
Mean length4.9756098
Min length4

Unique

Unique2 ?
Unique (%)4.9%

Sample

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

Common Values

ValueCountFrequency (%)
업무시설 29
70.7%
제1종근린생활시설 7
 
17.1%
공동주택 3
 
7.3%
단독주택 1
 
2.4%
제2종근린생활시설 1
 
2.4%

Length

2024-03-15T01:43:23.851684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:43:24.208865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
업무시설 29
70.7%
제1종근린생활시설 7
 
17.1%
공동주택 3
 
7.3%
단독주택 1
 
2.4%
제2종근린생활시설 1
 
2.4%

세부용도
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)48.8%
Missing0
Missing (%)0.0%
Memory size456.0 B
근린시설/오피스텔/다가구주택
7 
오피스텔,근린생활시설,주택
6 
업무시설(오피스텔)
5 
오피스텔,근린생활시설,단독주택
3 
업무시설
3 
Other values (15)
17 

Length

Max length29
Median length22
Mean length14.121951
Min length4

Unique

Unique13 ?
Unique (%)31.7%

Sample

1st row업무시설(오피스텔)
2nd row오피스텔,근린생활시설,주택
3rd row업무시설(오피스텔)
4th row오피스텔,근린생활시설,단독주택
5th row오피스텔,근린생활시설,단독주택

Common Values

ValueCountFrequency (%)
근린시설/오피스텔/다가구주택 7
17.1%
오피스텔,근린생활시설,주택 6
14.6%
업무시설(오피스텔) 5
12.2%
오피스텔,근린생활시설,단독주택 3
 
7.3%
업무시설 3
 
7.3%
업무시설(오피스텔) 및 단독주택 2
 
4.9%
오피스텔 및 다중주택 2
 
4.9%
업무시설,제1종근린생활시설 1
 
2.4%
업무시설,근린생활시설 1
 
2.4%
업무시설,근린생활시설및단독주택 1
 
2.4%
Other values (10) 10
24.4%

Length

2024-03-15T01:43:24.599800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
업무시설(오피스텔 9
14.1%
및 9
14.1%
근린시설/오피스텔/다가구주택 7
10.9%
업무시설 6
9.4%
오피스텔,근린생활시설,주택 6
9.4%
단독주택 5
7.8%
오피스텔,근린생활시설,단독주택 3
 
4.7%
근린생활시설 3
 
4.7%
오피스텔 2
 
3.1%
다중주택 2
 
3.1%
Other values (12) 12
18.8%

지하층수
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size456.0 B
1
18 
0
16 
<NA>
6 
4
 
1

Length

Max length4
Median length1
Mean length1.4390244
Min length1

Unique

Unique1 ?
Unique (%)2.4%

Sample

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

Common Values

ValueCountFrequency (%)
1 18
43.9%
0 16
39.0%
<NA> 6
 
14.6%
4 1
 
2.4%

Length

2024-03-15T01:43:24.850922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:43:25.170512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 18
43.9%
0 16
39.0%
na 6
 
14.6%
4 1
 
2.4%

지상층수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)20.0%
Missing6
Missing (%)14.6%
Infinite0
Infinite (%)0.0%
Mean6.5714286
Minimum4
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size497.0 B
2024-03-15T01:43:25.485424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4.7
Q16
median6
Q38
95-th percentile9
Maximum10
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6321353
Coefficient of variation (CV)0.24836841
Kurtosis-0.73332156
Mean6.5714286
Median Absolute Deviation (MAD)1
Skewness0.61849268
Sum230
Variance2.6638655
MonotonicityNot monotonic
2024-03-15T01:43:25.834326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 16
39.0%
9 7
17.1%
5 6
 
14.6%
8 2
 
4.9%
4 2
 
4.9%
7 1
 
2.4%
10 1
 
2.4%
(Missing) 6
 
14.6%
ValueCountFrequency (%)
4 2
 
4.9%
5 6
 
14.6%
6 16
39.0%
7 1
 
2.4%
8 2
 
4.9%
9 7
17.1%
10 1
 
2.4%
ValueCountFrequency (%)
10 1
 
2.4%
9 7
17.1%
8 2
 
4.9%
7 1
 
2.4%
6 16
39.0%
5 6
 
14.6%
4 2
 
4.9%

세대
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct7
Distinct (%)50.0%
Missing27
Missing (%)65.9%
Infinite0
Infinite (%)0.0%
Mean10.357143
Minimum0
Maximum28
Zeros7
Zeros (%)17.1%
Negative0
Negative (%)0.0%
Memory size497.0 B
2024-03-15T01:43:26.197141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q322.5
95-th percentile27.35
Maximum28
Range28
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation11.790525
Coefficient of variation (CV)1.1383955
Kurtosis-1.7644962
Mean10.357143
Median Absolute Deviation (MAD)4
Skewness0.43889943
Sum145
Variance139.01648
MonotonicityNot monotonic
2024-03-15T01:43:26.415863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 7
 
17.1%
24 2
 
4.9%
8 1
 
2.4%
27 1
 
2.4%
28 1
 
2.4%
18 1
 
2.4%
16 1
 
2.4%
(Missing) 27
65.9%
ValueCountFrequency (%)
0 7
17.1%
8 1
 
2.4%
16 1
 
2.4%
18 1
 
2.4%
24 2
 
4.9%
27 1
 
2.4%
28 1
 
2.4%
ValueCountFrequency (%)
28 1
 
2.4%
27 1
 
2.4%
24 2
 
4.9%
18 1
 
2.4%
16 1
 
2.4%
8 1
 
2.4%
0 7
17.1%

층용도
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)31.7%
Missing0
Missing (%)0.0%
Memory size456.0 B
오피스텔
20 
<NA>
6 
단독주택
3 
소매점
3 
조산소
 
1
Other values (8)
8 

Length

Max length8
Median length4
Mean length3.902439
Min length2

Unique

Unique9 ?
Unique (%)22.0%

Sample

1st row오피스텔
2nd row오피스텔
3rd row조산소
4th row단독주택
5th row소매점

Common Values

ValueCountFrequency (%)
오피스텔 20
48.8%
<NA> 6
 
14.6%
단독주택 3
 
7.3%
소매점 3
 
7.3%
조산소 1
 
2.4%
사무소 1
 
2.4%
세차장 1
 
2.4%
다가구주택 1
 
2.4%
의원 1
 
2.4%
휴게음식점 1
 
2.4%
Other values (3) 3
 
7.3%

Length

2024-03-15T01:43:26.862008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
오피스텔 20
48.8%
na 6
 
14.6%
단독주택 3
 
7.3%
소매점 3
 
7.3%
조산소 1
 
2.4%
사무소 1
 
2.4%
세차장 1
 
2.4%
다가구주택 1
 
2.4%
의원 1
 
2.4%
휴게음식점 1
 
2.4%
Other values (3) 3
 
7.3%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size456.0 B
Minimum2024-02-20 00:00:00
Maximum2024-02-20 00:00:00
2024-03-15T01:43:27.429882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:43:27.893994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-15T01:43:11.922681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:43:07.288186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:43:08.832357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:43:10.101375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:43:11.144269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:43:12.182044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:43:07.577208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:43:09.092578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:43:10.355301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:43:11.320697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:43:12.362945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:43:07.845379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:43:09.318351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:43:10.582782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:43:11.459147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:43:12.591761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:43:08.069737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:43:09.559693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:43:10.813866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:43:11.594584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:43:12.797362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:43:08.585793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:43:09.850151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:43:10.990043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:43:11.750998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T01:43:28.197103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번건축유형건물명소재지주소(도로명)소재지주소(지번)대지면적(m2)건축면적(m2)주용도세부용도지하층수지상층수세대층용도
연번1.0000.9760.0000.3230.3960.3020.0000.1470.7660.0000.4870.7310.605
건축유형0.9761.0001.0001.0001.0000.2080.3210.5340.957NaNNaN1.000NaN
건물명0.0001.0001.0001.0001.0000.6580.8431.0001.0001.0001.0001.0000.000
소재지주소(도로명)0.3231.0001.0001.0001.0000.6710.9071.0001.0001.0001.0001.0000.000
소재지주소(지번)0.3961.0001.0001.0001.0000.5850.8981.0001.0001.0001.0001.0000.000
대지면적(m2)0.3020.2080.6580.6710.5851.0000.9710.0000.0000.4610.5610.0000.000
건축면적(m2)0.0000.3210.8430.9070.8980.9711.0000.0000.2100.5350.5360.0000.000
주용도0.1470.5341.0001.0001.0000.0000.0001.0000.9970.2770.7520.6130.000
세부용도0.7660.9571.0001.0001.0000.0000.2100.9971.0000.9990.9480.7270.000
지하층수0.000NaN1.0001.0001.0000.4610.5350.2770.9991.0000.8260.0000.000
지상층수0.487NaN1.0001.0001.0000.5610.5360.7520.9480.8261.0000.0000.000
세대0.7311.0001.0001.0001.0000.0000.0000.6130.7270.0000.0001.000NaN
층용도0.605NaN0.0000.0000.0000.0000.0000.0000.0000.0000.000NaN1.000
2024-03-15T01:43:28.915427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건축유형층용도주용도세부용도지하층수
건축유형1.0001.0000.6200.6151.000
층용도1.0001.0000.0000.0000.000
주용도0.6200.0001.0000.7080.256
세부용도0.6150.0000.7081.0000.764
지하층수1.0000.0000.2560.7641.000
2024-03-15T01:43:29.322237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번대지면적(m2)건축면적(m2)지상층수세대건축유형주용도세부용도지하층수층용도
연번1.0000.0680.011-0.1830.6490.7710.1490.2970.0000.118
대지면적(m2)0.0681.0000.9770.304-0.5550.1550.0000.0000.3120.000
건축면적(m2)0.0110.9771.0000.290-0.5500.2130.0000.0000.3610.000
지상층수-0.1830.3040.2901.0000.0001.0000.5980.6870.7380.000
세대0.649-0.555-0.5500.0001.0000.8160.2050.1590.0001.000
건축유형0.7710.1550.2131.0000.8161.0000.6200.6151.0001.000
주용도0.1490.0000.0000.5980.2050.6201.0000.7080.2560.000
세부용도0.2970.0000.0000.6870.1590.6150.7081.0000.7640.000
지하층수0.0000.3120.3610.7380.0001.0000.2560.7641.0000.000
층용도0.1180.0000.0000.0001.0001.0000.0000.0000.0001.000

Missing values

2024-03-15T01:43:13.013786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T01:43:13.486294image/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.
2024-03-15T01:43:13.753903image/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

연번유형건축유형건물명시군구명소재지주소(도로명)소재지주소(지번)대지면적(m2)건축면적(m2)주용도세부용도지하층수지상층수세대층용도데이터기준일자
01오피스텔일반건축물대풍오피스텔서울특별시 양천구서울특별시 양천구 등촌로 50-1목동 783-91372.8818.88업무시설업무시설(오피스텔)068오피스텔2024-02-20
12오피스텔일반건축물목동오피스텔서울특별시 양천구서울특별시 양천구 중앙로 328신정동 939-72514.41256.72업무시설오피스텔,근린생활시설,주택16<NA>오피스텔2024-02-20
23오피스텔일반건축물하이드파크서울특별시 양천구서울특별시 양천구 신정중앙로21길 6-1신정동 888-22456.3273.72업무시설업무시설(오피스텔)06<NA>조산소2024-02-20
34오피스텔일반건축물경원빌딩서울특별시 양천구서울특별시 양천구 목동로23길 7신정동 888-19441.0248.67업무시설오피스텔,근린생활시설,단독주택06<NA>단독주택2024-02-20
45오피스텔일반건축물경원빌딩서울특별시 양천구서울특별시 양천구 목동로23길 7신정동 888-19147.082.89업무시설오피스텔,근린생활시설,단독주택06<NA>소매점2024-02-20
56오피스텔일반건축물파크팰리스서울특별시 양천구서울특별시 양천구 남부순환로 370신월동 166-11810.8429.0제1종근린생활시설근린시설/오피스텔/다가구주택19<NA>소매점2024-02-20
67오피스텔일반건축물목동오피스텔서울특별시 양천구서울특별시 양천구 중앙로 328신정동 939-7628.6314.18업무시설오피스텔,근린생활시설,주택16<NA>소매점2024-02-20
78오피스텔일반건축물파크팰리스서울특별시 양천구서울특별시 양천구 남부순환로 370신월동 166-11810.8429.0제1종근린생활시설근린시설/오피스텔/다가구주택19<NA>사무소2024-02-20
89오피스텔일반건축물목동오피스텔서울특별시 양천구서울특별시 양천구 중앙로 328신정동 939-7628.6314.18업무시설오피스텔,근린생활시설,주택16<NA>세차장2024-02-20
910오피스텔일반건축물<NA>서울특별시 양천구서울특별시 양천구 신정중앙로21길 12신정동 888-7273.6159.28업무시설업무시설(오피스텔) 및 단독주택16<NA>단독주택2024-02-20
연번유형건축유형건물명시군구명소재지주소(도로명)소재지주소(지번)대지면적(m2)건축면적(m2)주용도세부용도지하층수지상층수세대층용도데이터기준일자
3132오피스텔일반건축물명성빌딩서울특별시 양천구서울특별시 양천구 목동중앙서로1길 11목동 792-8863.6483.84업무시설업무시설,제2종근린생활시설040오피스텔2024-02-20
3233오피스텔일반건축물지구촌선교센타서울특별시 양천구서울특별시 양천구 목동서로 379신정동 323-51799.61076.54업무시설문화 및 집회시설, 업무시설4100오피스텔2024-02-20
3334오피스텔일반건축물파크팰리스서울특별시 양천구서울특별시 양천구 남부순환로 370신월동 166-11405.4214.5제1종근린생활시설근린시설/오피스텔/다가구주택19<NA>오피스텔2024-02-20
3435오피스텔일반건축물<NA>서울특별시 양천구서울특별시 양천구 목동로27길 7-1신정동 895-14434.1212.97업무시설업무시설, 근린생활시설 및 단독주택05<NA>오피스텔2024-02-20
3536오피스텔집합건축물파크아리온서울특별시 양천구서울특별시 양천구 목동중앙북로7가길 38목동 607-22846.9433.39업무시설근린생활시설,오피스텔 및 도시형주택<NA><NA>27<NA>2024-02-20
3637오피스텔집합건축물목동월드파크빌서울특별시 양천구서울특별시 양천구 등촌로 120목동 713-11774.7381.64공동주택도시형생활주택(단지형다세대주택)및 업무시설(오피스텔)<NA><NA>28<NA>2024-02-20
3738오피스텔집합건축물엠펠리체서울특별시 양천구서울특별시 양천구 등촌로 118-1목동 713-14643.5321.38공동주택오피스텔및다세대주택<NA><NA>24<NA>2024-02-20
3839오피스텔집합건축물목동토레스타워서울특별시 양천구서울특별시 양천구 등촌로 106목동 721-5859.0394.62공동주택업무시설(오피스텔)<NA><NA>24<NA>2024-02-20
3940오피스텔집합건축물목동솔리스타서울특별시 양천구서울특별시 양천구 등촌로 20목동 793-6707.0343.72업무시설업무시설(오피스텔 및 공동주택,근린생활시설)<NA><NA>18<NA>2024-02-20
4041오피스텔집합건축물드어반 트라움서울특별시 양천구서울특별시 양천구 남부순환로 645신정동 733-53724.0295.17업무시설오피스텔,도시형생활주택(단지형다세대주택)<NA><NA>16<NA>2024-02-20