Overview

Dataset statistics

Number of variables6
Number of observations675
Missing cells950
Missing cells (%)23.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory33.1 KiB
Average record size in memory50.2 B

Variable types

Text3
Numeric2
Categorical1

Dataset

Description광주광역시 동구 관내 원룸 및 오피스텔 현황 입니다.원룸 및 오피스텔명, 소재지지번주소, 세대수, 용도 등을 포함하고 있습니다.
Author광주광역시 동구
URLhttps://www.data.go.kr/data/15077345/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
원룸및오피스텔명 has 610 (90.4%) missing valuesMissing
세대수 has 333 (49.3%) missing valuesMissing
건축연도 has 7 (1.0%) missing valuesMissing
세대수 has 303 (44.9%) zerosZeros

Reproduction

Analysis started2024-03-14 22:59:14.329465
Analysis finished2024-03-14 22:59:16.459500
Duration2.13 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

원룸및오피스텔명
Text

MISSING 

Distinct63
Distinct (%)96.9%
Missing610
Missing (%)90.4%
Memory size5.4 KiB
2024-03-15T07:59:17.116336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length18
Mean length12.153846
Min length2

Characters and Unicode

Total characters790
Distinct characters127
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)93.8%

Sample

1st row제1호
2nd row광주서석교회
3rd row가동
4th row계림 빌
5th row계림동 1116
ValueCountFrequency (%)
단독주택 18
 
12.2%
다가구주택 6
 
4.1%
지산동 6
 
4.1%
계림동 6
 
4.1%
서석동 5
 
3.4%
학동 4
 
2.7%
동명동 3
 
2.0%
산수동 3
 
2.0%
빌 2
 
1.4%
신축공사 2
 
1.4%
Other values (89) 92
62.6%
2024-03-15T07:59:18.355693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
89
 
11.3%
동 46
 
5.8%
주 46
 
5.8%
택 39
 
4.9%
1 30
 
3.8%
- 24
 
3.0%
( 23
 
2.9%
) 23
 
2.9%
독 19
 
2.4%
단 19
 
2.4%
Other values (117) 432
54.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 489
61.9%
Decimal Number 131
 
16.6%
Space Separator 89
 
11.3%
Dash Punctuation 24
 
3.0%
Open Punctuation 23
 
2.9%
Close Punctuation 23
 
2.9%
Lowercase Letter 5
 
0.6%
Uppercase Letter 4
 
0.5%
Other Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 46
 
9.4%
주 46
 
9.4%
택 39
 
8.0%
독 19
 
3.9%
단 19
 
3.9%
다 17
 
3.5%
가 17
 
3.5%
구 15
 
3.1%
림 12
 
2.5%
빌 10
 
2.0%
Other values (95) 249
50.9%
Decimal Number
ValueCountFrequency (%)
1 30
22.9%
5 17
13.0%
7 16
12.2%
4 16
12.2%
0 13
9.9%
3 12
 
9.2%
2 9
 
6.9%
6 8
 
6.1%
8 6
 
4.6%
9 4
 
3.1%
Lowercase Letter
ValueCountFrequency (%)
l 2
40.0%
e 1
20.0%
v 1
20.0%
i 1
20.0%
Uppercase Letter
ValueCountFrequency (%)
O 2
50.0%
W 1
25.0%
A 1
25.0%
Space Separator
ValueCountFrequency (%)
89
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 487
61.6%
Common 292
37.0%
Latin 9
 
1.1%
Han 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 46
 
9.4%
주 46
 
9.4%
택 39
 
8.0%
독 19
 
3.9%
단 19
 
3.9%
다 17
 
3.5%
가 17
 
3.5%
구 15
 
3.1%
림 12
 
2.5%
빌 10
 
2.1%
Other values (93) 247
50.7%
Common
ValueCountFrequency (%)
89
30.5%
1 30
 
10.3%
- 24
 
8.2%
( 23
 
7.9%
) 23
 
7.9%
5 17
 
5.8%
7 16
 
5.5%
4 16
 
5.5%
0 13
 
4.5%
3 12
 
4.1%
Other values (5) 29
 
9.9%
Latin
ValueCountFrequency (%)
l 2
22.2%
O 2
22.2%
W 1
11.1%
e 1
11.1%
v 1
11.1%
i 1
11.1%
A 1
11.1%
Han
ValueCountFrequency (%)
氏 1
50.0%
休 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 487
61.6%
ASCII 301
38.1%
CJK 2
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
89
29.6%
1 30
 
10.0%
- 24
 
8.0%
( 23
 
7.6%
) 23
 
7.6%
5 17
 
5.6%
7 16
 
5.3%
4 16
 
5.3%
0 13
 
4.3%
3 12
 
4.0%
Other values (12) 38
12.6%
Hangul
ValueCountFrequency (%)
동 46
 
9.4%
주 46
 
9.4%
택 39
 
8.0%
독 19
 
3.9%
단 19
 
3.9%
다 17
 
3.5%
가 17
 
3.5%
구 15
 
3.1%
림 12
 
2.5%
빌 10
 
2.1%
Other values (93) 247
50.7%
CJK
ValueCountFrequency (%)
氏 1
50.0%
休 1
50.0%

소재지지번주소
Text

Distinct673
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
2024-03-15T07:59:19.820211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length21.24
Min length16

Characters and Unicode

Total characters14337
Distinct characters44
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

Unique671 ?
Unique (%)99.4%

Sample

1st row광주광역시 동구 동명동 0174
2nd row광주광역시 동구 금남로2가 0010
3rd row광주광역시 동구 수기동 0068-0001
4th row광주광역시 동구 대인동 0096
5th row광주광역시 동구 동명동 0245-0016
ValueCountFrequency (%)
광주광역시 675
25.0%
동구 675
25.0%
지산동 156
 
5.8%
계림동 143
 
5.3%
산수동 95
 
3.5%
동명동 70
 
2.6%
서석동 68
 
2.5%
학동 39
 
1.4%
소태동 23
 
0.9%
운림동 16
 
0.6%
Other values (679) 740
27.4%
2024-03-15T07:59:21.577913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2378
16.6%
2025
14.1%
동 1407
9.8%
광 1350
 
9.4%
구 675
 
4.7%
주 675
 
4.7%
시 675
 
4.7%
역 675
 
4.7%
- 580
 
4.0%
1 518
 
3.6%
Other values (34) 3379
23.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6699
46.7%
Decimal Number 5033
35.1%
Space Separator 2025
 
14.1%
Dash Punctuation 580
 
4.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1407
21.0%
광 1350
20.2%
구 675
10.1%
주 675
10.1%
시 675
10.1%
역 675
10.1%
산 255
 
3.8%
림 159
 
2.4%
지 156
 
2.3%
계 143
 
2.1%
Other values (22) 529
 
7.9%
Decimal Number
ValueCountFrequency (%)
0 2378
47.2%
1 518
 
10.3%
5 403
 
8.0%
4 343
 
6.8%
2 332
 
6.6%
3 244
 
4.8%
7 243
 
4.8%
6 241
 
4.8%
8 173
 
3.4%
9 158
 
3.1%
Space Separator
ValueCountFrequency (%)
2025
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 580
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7638
53.3%
Hangul 6699
46.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1407
21.0%
광 1350
20.2%
구 675
10.1%
주 675
10.1%
시 675
10.1%
역 675
10.1%
산 255
 
3.8%
림 159
 
2.4%
지 156
 
2.3%
계 143
 
2.1%
Other values (22) 529
 
7.9%
Common
ValueCountFrequency (%)
0 2378
31.1%
2025
26.5%
- 580
 
7.6%
1 518
 
6.8%
5 403
 
5.3%
4 343
 
4.5%
2 332
 
4.3%
3 244
 
3.2%
7 243
 
3.2%
6 241
 
3.2%
Other values (2) 331
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7638
53.3%
Hangul 6699
46.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2378
31.1%
2025
26.5%
- 580
 
7.6%
1 518
 
6.8%
5 403
 
5.3%
4 343
 
4.5%
2 332
 
4.3%
3 244
 
3.2%
7 243
 
3.2%
6 241
 
3.2%
Other values (2) 331
 
4.3%
Hangul
ValueCountFrequency (%)
동 1407
21.0%
광 1350
20.2%
구 675
10.1%
주 675
10.1%
시 675
10.1%
역 675
10.1%
산 255
 
3.8%
림 159
 
2.4%
지 156
 
2.3%
계 143
 
2.1%
Other values (22) 529
 
7.9%

세대수
Real number (ℝ)

MISSING  ZEROS 

Distinct17
Distinct (%)5.0%
Missing333
Missing (%)49.3%
Infinite0
Infinite (%)0.0%
Mean2.997076
Minimum0
Maximum224
Zeros303
Zeros (%)44.9%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-03-15T07:59:21.797649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile9
Maximum224
Range224
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17.661446
Coefficient of variation (CV)5.8928923
Kurtosis93.920507
Mean2.997076
Median Absolute Deviation (MAD)0
Skewness9.0697494
Sum1025
Variance311.92668
MonotonicityNot monotonic
2024-03-15T07:59:22.187434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 303
44.9%
2 9
 
1.3%
4 4
 
0.6%
12 3
 
0.4%
3 3
 
0.4%
56 2
 
0.3%
9 2
 
0.3%
5 2
 
0.3%
140 2
 
0.3%
29 2
 
0.3%
Other values (7) 10
 
1.5%
(Missing) 333
49.3%
ValueCountFrequency (%)
0 303
44.9%
2 9
 
1.3%
3 3
 
0.4%
4 4
 
0.6%
5 2
 
0.3%
6 2
 
0.3%
9 2
 
0.3%
12 3
 
0.4%
19 1
 
0.1%
22 2
 
0.3%
ValueCountFrequency (%)
224 1
 
0.1%
140 2
0.3%
88 1
 
0.1%
56 2
0.3%
29 2
0.3%
28 2
0.3%
25 1
 
0.1%
22 2
0.3%
19 1
 
0.1%
12 3
0.4%

건축연도
Real number (ℝ)

MISSING 

Distinct44
Distinct (%)6.6%
Missing7
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean2006.3428
Minimum1973
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-03-15T07:59:22.452186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1973
5-th percentile1994
Q12002
median2006
Q32013
95-th percentile2020
Maximum2023
Range50
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.5471174
Coefficient of variation (CV)0.0042600484
Kurtosis0.57851039
Mean2006.3428
Median Absolute Deviation (MAD)5
Skewness-0.39664441
Sum1340237
Variance73.053217
MonotonicityNot monotonic
2024-03-15T07:59:22.792217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
2002 89
 
13.2%
2003 56
 
8.3%
2001 41
 
6.1%
2013 37
 
5.5%
2015 36
 
5.3%
2006 31
 
4.6%
1995 25
 
3.7%
2004 24
 
3.6%
2008 23
 
3.4%
2012 23
 
3.4%
Other values (34) 283
41.9%
ValueCountFrequency (%)
1973 1
 
0.1%
1976 2
0.3%
1977 3
0.4%
1978 1
 
0.1%
1980 1
 
0.1%
1982 1
 
0.1%
1986 2
0.3%
1987 2
0.3%
1988 2
0.3%
1989 1
 
0.1%
ValueCountFrequency (%)
2023 4
 
0.6%
2022 11
 
1.6%
2021 9
 
1.3%
2020 20
3.0%
2019 16
2.4%
2018 9
 
1.3%
2017 8
 
1.2%
2016 19
2.8%
2015 36
5.3%
2014 21
3.1%

용도
Text

Distinct217
Distinct (%)32.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
2024-03-15T07:59:23.632821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length28
Mean length10.395556
Min length4

Characters and Unicode

Total characters7017
Distinct characters104
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

Unique157 ?
Unique (%)23.3%

Sample

1st row업무시설(오피스텔),근린생활시설
2nd row업무시설(오피스텔), 근린생활시설
3rd row업무시설(오피스텔),근린생활시설
4th row업무시설(오피스텔)
5th row업무시설(오피스텔)
ValueCountFrequency (%)
다가구주택 299
35.2%
단독주택(다가구주택 67
 
7.9%
근린생활시설 36
 
4.2%
사무소 25
 
2.9%
일용품소매점 21
 
2.5%
다가구주택(2가구 19
 
2.2%
단독주택(다가구 18
 
2.1%
다가구주택(12가구 15
 
1.8%
다가구주택(15가구 11
 
1.3%
제2종근린생활시설 10
 
1.2%
Other values (166) 328
38.6%
2024-03-15T07:59:24.904100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 845
12.0%
가 845
12.0%
주 778
 
11.1%
택 776
 
11.1%
다 652
 
9.3%
( 307
 
4.4%
) 306
 
4.4%
, 238
 
3.4%
175
 
2.5%
단 147
 
2.1%
Other values (94) 1948
27.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5560
79.2%
Decimal Number 361
 
5.1%
Open Punctuation 308
 
4.4%
Close Punctuation 307
 
4.4%
Other Punctuation 271
 
3.9%
Space Separator 175
 
2.5%
Dash Punctuation 35
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 845
15.2%
가 845
15.2%
주 778
14.0%
택 776
14.0%
다 652
11.7%
단 147
 
2.6%
독 147
 
2.6%
시 110
 
2.0%
설 107
 
1.9%
생 93
 
1.7%
Other values (73) 1060
19.1%
Decimal Number
ValueCountFrequency (%)
1 133
36.8%
2 73
20.2%
8 36
 
10.0%
5 33
 
9.1%
9 23
 
6.4%
4 19
 
5.3%
3 17
 
4.7%
6 15
 
4.2%
0 6
 
1.7%
7 6
 
1.7%
Other Punctuation
ValueCountFrequency (%)
, 238
87.8%
: 18
 
6.6%
/ 13
 
4.8%
; 1
 
0.4%
. 1
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 307
99.7%
[ 1
 
0.3%
Close Punctuation
ValueCountFrequency (%)
) 306
99.7%
] 1
 
0.3%
Space Separator
ValueCountFrequency (%)
175
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5560
79.2%
Common 1457
 
20.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 845
15.2%
가 845
15.2%
주 778
14.0%
택 776
14.0%
다 652
11.7%
단 147
 
2.6%
독 147
 
2.6%
시 110
 
2.0%
설 107
 
1.9%
생 93
 
1.7%
Other values (73) 1060
19.1%
Common
ValueCountFrequency (%)
( 307
21.1%
) 306
21.0%
, 238
16.3%
175
12.0%
1 133
9.1%
2 73
 
5.0%
8 36
 
2.5%
- 35
 
2.4%
5 33
 
2.3%
9 23
 
1.6%
Other values (11) 98
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5560
79.2%
ASCII 1457
 
20.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 845
15.2%
가 845
15.2%
주 778
14.0%
택 776
14.0%
다 652
11.7%
단 147
 
2.6%
독 147
 
2.6%
시 110
 
2.0%
설 107
 
1.9%
생 93
 
1.7%
Other values (73) 1060
19.1%
ASCII
ValueCountFrequency (%)
( 307
21.1%
) 306
21.0%
, 238
16.3%
175
12.0%
1 133
9.1%
2 73
 
5.0%
8 36
 
2.5%
- 35
 
2.4%
5 33
 
2.3%
9 23
 
1.6%
Other values (11) 98
 
6.7%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
2024-02-23
675 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-02-23
2nd row2024-02-23
3rd row2024-02-23
4th row2024-02-23
5th row2024-02-23

Common Values

ValueCountFrequency (%)
2024-02-23 675
100.0%

Length

2024-03-15T07:59:25.395233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T07:59:25.627216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-02-23 675
100.0%

Interactions

2024-03-15T07:59:15.276445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T07:59:14.734836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T07:59:15.508669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T07:59:14.972362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T07:59:25.947587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
원룸및오피스텔명세대수건축연도
원룸및오피스텔명1.0001.0000.000
세대수1.0001.0000.000
건축연도0.0000.0001.000
2024-03-15T07:59:26.095082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대수건축연도
세대수1.0000.041
건축연도0.0411.000

Missing values

2024-03-15T07:59:15.845477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T07:59:16.061463image/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.
2024-03-15T07:59:16.317627image/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

원룸및오피스텔명소재지지번주소세대수건축연도용도데이터기준일자
0제1호광주광역시 동구 동명동 0174<NA>2023업무시설(오피스텔),근린생활시설2024-02-23
1<NA>광주광역시 동구 금남로2가 0010<NA>2022업무시설(오피스텔), 근린생활시설2024-02-23
2광주서석교회광주광역시 동구 수기동 0068-0001<NA>2022업무시설(오피스텔),근린생활시설2024-02-23
3<NA>광주광역시 동구 대인동 0096<NA>2022업무시설(오피스텔)2024-02-23
4<NA>광주광역시 동구 동명동 0245-0016<NA>2020업무시설(오피스텔)2024-02-23
5<NA>광주광역시 동구 충장로5가 0096-0004<NA>2023업무시설(오피스텔)2024-02-23
6<NA>광주광역시 동구 금동 0019-0003<NA>2022업무시설(오피스텔),제1종근린생활시설(부동산중개사무소)2024-02-23
7<NA>광주광역시 동구 소태동 0532-0083<NA>2020업무시설(오피스텔)2024-02-23
8<NA>광주광역시 동구 대인동 0150<NA>2019업무시설(오피스텔),근린생활시설2024-02-23
9<NA>광주광역시 동구 대인동 0148-0001<NA>2019아파트(도시형생활주택-원룸형),업무시설(오피스텔),근린생활시설2024-02-23
원룸및오피스텔명소재지지번주소세대수건축연도용도데이터기준일자
665<NA>광주광역시 동구 학동 0674<NA>2015다가구주택-15가구2024-02-23
666<NA>광주광역시 동구 학동 0674-0002<NA>2015다가구주택-18가구2024-02-23
667<NA>광주광역시 동구 학동 0674-0003<NA>1997다가구주택2024-02-23
668<NA>광주광역시 동구 학동 0713-0009<NA>2009다가구주택2024-02-23
669<NA>광주광역시 동구 학동 0716-0005<NA>2016단독주택(다가구주택)2024-02-23
670<NA>광주광역시 동구 학동 0719-0004<NA>1991다가구주택2024-02-23
671<NA>광주광역시 동구 학동 0731-0004<NA>2002다가구주택2024-02-23
672<NA>광주광역시 동구 학동 0789-0002<NA>2017다가구주택2024-02-23
673<NA>광주광역시 동구 학동 0789-0003<NA>2017다가구주택2024-02-23
674<NA>광주광역시 동구 학동 0804<NA>2018단독주택(다가구주택),소매점,사무소2024-02-23