Overview

Dataset statistics

Number of variables5
Number of observations107
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 KiB
Average record size in memory43.2 B

Variable types

Categorical2
Text1
Numeric2

Dataset

Description서울특별시 중랑구의 오피스텔 현황입니다. 구분,소재지지번주소,주용도,호수,사용승인년도를 제공합니다. 참고해주시기 바랍니다. 감사합니다.
Author서울특별시 중랑구
URLhttps://www.data.go.kr/data/15107654/fileData.do

Alerts

구분 is highly imbalanced (92.4%)Imbalance
소재지 지번주소 has unique valuesUnique

Reproduction

Analysis started2023-12-12 17:37:28.505301
Analysis finished2023-12-12 17:37:29.480412
Duration0.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size988.0 B
오피스텔
106 
오피스텔
 
1

Length

Max length5
Median length4
Mean length4.0093458
Min length4

Unique

Unique1 ?
Unique (%)0.9%

Sample

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

Common Values

ValueCountFrequency (%)
오피스텔 106
99.1%
오피스텔 1
 
0.9%

Length

2023-12-13T02:37:29.592022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:37:29.714865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
오피스텔 107
100.0%

소재지 지번주소
Text

UNIQUE 

Distinct107
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size988.0 B
2023-12-13T02:37:30.024346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length19.448598
Min length17

Characters and Unicode

Total characters2081
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique107 ?
Unique (%)100.0%

Sample

1st row서울특별시 중랑구 신내동 646
2nd row서울특별시 중랑구 묵동 176-11
3rd row서울특별시 중랑구 묵동 176-12
4th row서울특별시 중랑구 상봉동 88-61
5th row서울특별시 중랑구 상봉동 113-33
ValueCountFrequency (%)
서울특별시 107
25.0%
중랑구 107
25.0%
상봉동 49
11.4%
면목동 32
 
7.5%
망우동 11
 
2.6%
묵동 9
 
2.1%
중화동 4
 
0.9%
신내동 2
 
0.5%
518-35 1
 
0.2%
490-19 1
 
0.2%
Other values (105) 105
24.5%
2023-12-13T02:37:30.612590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
321
15.4%
1 124
 
6.0%
중 111
 
5.3%
서 107
 
5.1%
울 107
 
5.1%
특 107
 
5.1%
별 107
 
5.1%
시 107
 
5.1%
랑 107
 
5.1%
구 107
 
5.1%
Other values (21) 776
37.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1168
56.1%
Decimal Number 493
23.7%
Space Separator 321
 
15.4%
Dash Punctuation 99
 
4.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
중 111
9.5%
서 107
9.2%
울 107
9.2%
특 107
9.2%
별 107
9.2%
시 107
9.2%
랑 107
9.2%
구 107
9.2%
동 107
9.2%
봉 49
 
4.2%
Other values (9) 152
13.0%
Decimal Number
ValueCountFrequency (%)
1 124
25.2%
2 55
11.2%
3 52
10.5%
0 50
10.1%
4 41
 
8.3%
6 41
 
8.3%
5 37
 
7.5%
7 35
 
7.1%
9 29
 
5.9%
8 29
 
5.9%
Space Separator
ValueCountFrequency (%)
321
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 99
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1168
56.1%
Common 913
43.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
중 111
9.5%
서 107
9.2%
울 107
9.2%
특 107
9.2%
별 107
9.2%
시 107
9.2%
랑 107
9.2%
구 107
9.2%
동 107
9.2%
봉 49
 
4.2%
Other values (9) 152
13.0%
Common
ValueCountFrequency (%)
321
35.2%
1 124
 
13.6%
- 99
 
10.8%
2 55
 
6.0%
3 52
 
5.7%
0 50
 
5.5%
4 41
 
4.5%
6 41
 
4.5%
5 37
 
4.1%
7 35
 
3.8%
Other values (2) 58
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1168
56.1%
ASCII 913
43.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
321
35.2%
1 124
 
13.6%
- 99
 
10.8%
2 55
 
6.0%
3 52
 
5.7%
0 50
 
5.5%
4 41
 
4.5%
6 41
 
4.5%
5 37
 
4.1%
7 35
 
3.8%
Other values (2) 58
 
6.4%
Hangul
ValueCountFrequency (%)
중 111
9.5%
서 107
9.2%
울 107
9.2%
특 107
9.2%
별 107
9.2%
시 107
9.2%
랑 107
9.2%
구 107
9.2%
동 107
9.2%
봉 49
 
4.2%
Other values (9) 152
13.0%

주용도
Categorical

Distinct4
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size988.0 B
공동주택
61 
업무시설
44 
단독주택
 
1
제1종근린생활시설
 
1

Length

Max length9
Median length4
Mean length4.046729
Min length4

Unique

Unique2 ?
Unique (%)1.9%

Sample

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

Common Values

ValueCountFrequency (%)
공동주택 61
57.0%
업무시설 44
41.1%
단독주택 1
 
0.9%
제1종근린생활시설 1
 
0.9%

Length

2023-12-13T02:37:30.755804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:37:30.869137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공동주택 61
57.0%
업무시설 44
41.1%
단독주택 1
 
0.9%
제1종근린생활시설 1
 
0.9%

호수
Real number (ℝ)

Distinct47
Distinct (%)43.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.542056
Minimum2
Maximum368
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T02:37:31.018456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q18
median14
Q325.5
95-th percentile112
Maximum368
Range366
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation55.243591
Coefficient of variation (CV)1.8087712
Kurtosis20.475334
Mean30.542056
Median Absolute Deviation (MAD)7
Skewness4.3011017
Sum3268
Variance3051.8543
MonotonicityNot monotonic
2023-12-13T02:37:31.170839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
6 9
 
8.4%
10 8
 
7.5%
4 7
 
6.5%
12 6
 
5.6%
21 6
 
5.6%
11 5
 
4.7%
7 4
 
3.7%
14 4
 
3.7%
18 4
 
3.7%
8 4
 
3.7%
Other values (37) 50
46.7%
ValueCountFrequency (%)
2 2
 
1.9%
3 1
 
0.9%
4 7
6.5%
5 2
 
1.9%
6 9
8.4%
7 4
3.7%
8 4
3.7%
9 2
 
1.9%
10 8
7.5%
11 5
4.7%
ValueCountFrequency (%)
368 1
0.9%
314 1
0.9%
229 1
0.9%
196 1
0.9%
142 1
0.9%
118 1
0.9%
98 1
0.9%
74 1
0.9%
71 1
0.9%
57 1
0.9%

사용승인년도
Real number (ℝ)

Distinct17
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.6449
Minimum2003
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T02:37:31.320385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2003
5-th percentile2006
Q12016
median2019
Q32021
95-th percentile2022
Maximum2022
Range19
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.4472852
Coefficient of variation (CV)0.0022041963
Kurtosis2.7020692
Mean2017.6449
Median Absolute Deviation (MAD)2
Skewness-1.7294481
Sum215888
Variance19.778346
MonotonicityIncreasing
2023-12-13T02:37:31.472237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2021 19
17.8%
2018 16
15.0%
2022 13
12.1%
2019 12
11.2%
2020 11
10.3%
2016 11
10.3%
2017 8
7.5%
2012 4
 
3.7%
2006 3
 
2.8%
2015 2
 
1.9%
Other values (7) 8
7.5%
ValueCountFrequency (%)
2003 1
 
0.9%
2004 2
1.9%
2005 1
 
0.9%
2006 3
2.8%
2007 1
 
0.9%
2009 1
 
0.9%
2012 4
3.7%
2013 1
 
0.9%
2014 1
 
0.9%
2015 2
1.9%
ValueCountFrequency (%)
2022 13
12.1%
2021 19
17.8%
2020 11
10.3%
2019 12
11.2%
2018 16
15.0%
2017 8
7.5%
2016 11
10.3%
2015 2
 
1.9%
2014 1
 
0.9%
2013 1
 
0.9%

Interactions

2023-12-13T02:37:28.989116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:28.720141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:29.111494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:28.831518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:37:31.575938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분주용도호수사용승인년도
구분1.0000.0000.0000.000
주용도0.0001.0000.0000.000
호수0.0000.0001.0000.000
사용승인년도0.0000.0000.0001.000
2023-12-13T02:37:31.672852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분주용도
구분1.0000.000
주용도0.0001.000
2023-12-13T02:37:31.779208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
호수사용승인년도구분주용도
호수1.0000.0190.0000.000
사용승인년도0.0191.0000.0000.000
구분0.0000.0001.0000.000
주용도0.0000.0000.0001.000

Missing values

2023-12-13T02:37:29.293674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:37:29.428075image/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

구분소재지 지번주소주용도호수사용승인년도
0오피스텔서울특별시 중랑구 신내동 646업무시설2292003
1오피스텔서울특별시 중랑구 묵동 176-11업무시설122004
2오피스텔서울특별시 중랑구 묵동 176-12업무시설122004
3오피스텔서울특별시 중랑구 상봉동 88-61업무시설142005
4오피스텔서울특별시 중랑구 상봉동 113-33업무시설62006
5오피스텔서울특별시 중랑구 상봉동 115-70업무시설182006
6오피스텔서울특별시 중랑구 상봉동 128-9업무시설322006
7오피스텔서울특별시 중랑구 면목동 633-9업무시설712007
8오피스텔서울특별시 중랑구 면목동 105-43업무시설552009
9오피스텔서울특별시 중랑구 망우동 508-34공동주택112012
구분소재지 지번주소주용도호수사용승인년도
97오피스텔서울특별시 중랑구 면목동 617-9공동주택102022
98오피스텔서울특별시 중랑구 면목동 634-42업무시설112022
99오피스텔서울특별시 중랑구 상봉동 105-1공동주택52022
100오피스텔서울특별시 중랑구 상봉동 105-97공동주택142022
101오피스텔서울특별시 중랑구 상봉동 128-32공동주택92022
102오피스텔서울특별시 중랑구 상봉동 128-34공동주택472022
103오피스텔서울특별시 중랑구 상봉동 130-90공동주택22022
104오피스텔서울특별시 중랑구 상봉동 85-17업무시설122022
105오피스텔서울특별시 중랑구 상봉동 89-27업무시설1422022
106오피스텔서울특별시 중랑구 중화동 305-34공동주택72022