Overview

Dataset statistics

Number of variables5
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory45.9 B

Variable types

Text4
Numeric1

Dataset

Description대구광역시 동구 오피스텔 현황 데이터 입니다. 이 데이터는 건물명, 주소, 세대수, 준공년월 항목을 포함합니다. 감사합니다.
URLhttps://www.data.go.kr/data/15076827/fileData.do

Alerts

건물명 has unique valuesUnique
도로명주소 has unique valuesUnique
지번주소 has unique valuesUnique

Reproduction

Analysis started2023-12-12 17:58:33.321920
Analysis finished2023-12-12 17:58:33.772818
Duration0.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

건물명
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-13T02:58:33.894048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length16
Mean length9.962963
Min length4

Characters and Unicode

Total characters269
Distinct characters104
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row서한코보스카운티
2nd row유성푸르나임
3rd row신천 까사밀라
4th row정성라임오피스텔
5th row더블유스퀘어
ValueCountFrequency (%)
오피스텔 7
 
14.3%
동대구역 3
 
6.1%
태왕아너스 2
 
4.1%
각산역더클래스ⅱ 1
 
2.0%
오피세틀 1
 
2.0%
밀레니엄오피스텔 1
 
2.0%
대구신서 1
 
2.0%
혁신도시 1
 
2.0%
하우스디어반 1
 
2.0%
서원프레쉬빌 1
 
2.0%
Other values (30) 30
61.2%
2023-12-13T02:58:34.227618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 23
 
8.6%
22
 
8.2%
텔 13
 
4.8%
피 13
 
4.8%
오 13
 
4.8%
동 7
 
2.6%
역 7
 
2.6%
아 6
 
2.2%
대 5
 
1.9%
구 5
 
1.9%
Other values (94) 155
57.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 243
90.3%
Space Separator 22
 
8.2%
Lowercase Letter 2
 
0.7%
Uppercase Letter 1
 
0.4%
Letter Number 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 23
 
9.5%
텔 13
 
5.3%
피 13
 
5.3%
오 13
 
5.3%
동 7
 
2.9%
역 7
 
2.9%
아 6
 
2.5%
대 5
 
2.1%
구 5
 
2.1%
티 5
 
2.1%
Other values (89) 146
60.1%
Lowercase Letter
ValueCountFrequency (%)
d 1
50.0%
s 1
50.0%
Space Separator
ValueCountFrequency (%)
22
100.0%
Uppercase Letter
ValueCountFrequency (%)
D 1
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 243
90.3%
Common 22
 
8.2%
Latin 4
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 23
 
9.5%
텔 13
 
5.3%
피 13
 
5.3%
오 13
 
5.3%
동 7
 
2.9%
역 7
 
2.9%
아 6
 
2.5%
대 5
 
2.1%
구 5
 
2.1%
티 5
 
2.1%
Other values (89) 146
60.1%
Latin
ValueCountFrequency (%)
D 1
25.0%
Ⅱ 1
25.0%
d 1
25.0%
s 1
25.0%
Common
ValueCountFrequency (%)
22
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 243
90.3%
ASCII 25
 
9.3%
Number Forms 1
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 23
 
9.5%
텔 13
 
5.3%
피 13
 
5.3%
오 13
 
5.3%
동 7
 
2.9%
역 7
 
2.9%
아 6
 
2.5%
대 5
 
2.1%
구 5
 
2.1%
티 5
 
2.1%
Other values (89) 146
60.1%
ASCII
ValueCountFrequency (%)
22
88.0%
D 1
 
4.0%
d 1
 
4.0%
s 1
 
4.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

도로명주소
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-13T02:58:34.423715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length17.037037
Min length14

Characters and Unicode

Total characters460
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

Unique27 ?
Unique (%)100.0%

Sample

1st row대구광역시 동구 동부로22길 2
2nd row대구광역시 동구 동부로22길 48
3rd row대구광역시 동구 동부로 33
4th row대구광역시 동구 신암남로 105
5th row대구광역시 동구 신암남로 103
ValueCountFrequency (%)
대구광역시 27
25.0%
동구 27
25.0%
동부로 4
 
3.7%
동부로22길 4
 
3.7%
동대구로 3
 
2.8%
동촌로 3
 
2.8%
33 2
 
1.9%
동부로26길 2
 
1.9%
첨복로 2
 
1.9%
동부로30길 2
 
1.9%
Other values (31) 32
29.6%
2023-12-13T02:58:34.790848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
81
17.6%
구 57
12.4%
동 45
9.8%
대 30
 
6.5%
광 27
 
5.9%
역 27
 
5.9%
시 27
 
5.9%
로 27
 
5.9%
2 17
 
3.7%
3 14
 
3.0%
Other values (27) 108
23.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 291
63.3%
Decimal Number 86
 
18.7%
Space Separator 81
 
17.6%
Dash Punctuation 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 57
19.6%
동 45
15.5%
대 30
10.3%
광 27
9.3%
역 27
9.3%
시 27
9.3%
로 27
9.3%
부 12
 
4.1%
길 9
 
3.1%
월 3
 
1.0%
Other values (15) 27
9.3%
Decimal Number
ValueCountFrequency (%)
2 17
19.8%
3 14
16.3%
1 14
16.3%
5 11
12.8%
0 11
12.8%
9 5
 
5.8%
6 5
 
5.8%
7 4
 
4.7%
4 3
 
3.5%
8 2
 
2.3%
Space Separator
ValueCountFrequency (%)
81
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 291
63.3%
Common 169
36.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 57
19.6%
동 45
15.5%
대 30
10.3%
광 27
9.3%
역 27
9.3%
시 27
9.3%
로 27
9.3%
부 12
 
4.1%
길 9
 
3.1%
월 3
 
1.0%
Other values (15) 27
9.3%
Common
ValueCountFrequency (%)
81
47.9%
2 17
 
10.1%
3 14
 
8.3%
1 14
 
8.3%
5 11
 
6.5%
0 11
 
6.5%
9 5
 
3.0%
6 5
 
3.0%
7 4
 
2.4%
4 3
 
1.8%
Other values (2) 4
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 291
63.3%
ASCII 169
36.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
81
47.9%
2 17
 
10.1%
3 14
 
8.3%
1 14
 
8.3%
5 11
 
6.5%
0 11
 
6.5%
9 5
 
3.0%
6 5
 
3.0%
7 4
 
2.4%
4 3
 
1.8%
Other values (2) 4
 
2.4%
Hangul
ValueCountFrequency (%)
구 57
19.6%
동 45
15.5%
대 30
10.3%
광 27
9.3%
역 27
9.3%
시 27
9.3%
로 27
9.3%
부 12
 
4.1%
길 9
 
3.1%
월 3
 
1.0%
Other values (15) 27
9.3%

지번주소
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-13T02:58:34.995913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length17.925926
Min length16

Characters and Unicode

Total characters484
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

Unique27 ?
Unique (%)100.0%

Sample

1st row대구광역시 동구 신천동 285-1
2nd row대구광역시 동구 신천동 292-6
3rd row대구광역시 동구 신천동 538-15
4th row대구광역시 동구 신암동 259-7
5th row대구광역시 동구 신암동 259-6
ValueCountFrequency (%)
대구광역시 27
25.0%
동구 27
25.0%
신천동 13
12.0%
신암동 4
 
3.7%
신서동 4
 
3.7%
방촌동 2
 
1.9%
327-3 1
 
0.9%
255-14 1
 
0.9%
235-1 1
 
0.9%
518-1 1
 
0.9%
Other values (27) 27
25.0%
2023-12-13T02:58:35.332952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
81
16.7%
동 55
11.4%
구 54
11.2%
대 27
 
5.6%
광 27
 
5.6%
역 27
 
5.6%
시 27
 
5.6%
1 27
 
5.6%
- 22
 
4.5%
신 21
 
4.3%
Other values (21) 116
24.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 270
55.8%
Decimal Number 111
22.9%
Space Separator 81
 
16.7%
Dash Punctuation 22
 
4.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 55
20.4%
구 54
20.0%
대 27
10.0%
광 27
10.0%
역 27
10.0%
시 27
10.0%
신 21
 
7.8%
천 13
 
4.8%
암 4
 
1.5%
서 4
 
1.5%
Other values (9) 11
 
4.1%
Decimal Number
ValueCountFrequency (%)
1 27
24.3%
2 17
15.3%
5 17
15.3%
3 13
11.7%
8 9
 
8.1%
9 8
 
7.2%
6 6
 
5.4%
7 5
 
4.5%
4 5
 
4.5%
0 4
 
3.6%
Space Separator
ValueCountFrequency (%)
81
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 270
55.8%
Common 214
44.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 55
20.4%
구 54
20.0%
대 27
10.0%
광 27
10.0%
역 27
10.0%
시 27
10.0%
신 21
 
7.8%
천 13
 
4.8%
암 4
 
1.5%
서 4
 
1.5%
Other values (9) 11
 
4.1%
Common
ValueCountFrequency (%)
81
37.9%
1 27
 
12.6%
- 22
 
10.3%
2 17
 
7.9%
5 17
 
7.9%
3 13
 
6.1%
8 9
 
4.2%
9 8
 
3.7%
6 6
 
2.8%
7 5
 
2.3%
Other values (2) 9
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 270
55.8%
ASCII 214
44.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
81
37.9%
1 27
 
12.6%
- 22
 
10.3%
2 17
 
7.9%
5 17
 
7.9%
3 13
 
6.1%
8 9
 
4.2%
9 8
 
3.7%
6 6
 
2.8%
7 5
 
2.3%
Other values (2) 9
 
4.2%
Hangul
ValueCountFrequency (%)
동 55
20.4%
구 54
20.0%
대 27
10.0%
광 27
10.0%
역 27
10.0%
시 27
10.0%
신 21
 
7.8%
천 13
 
4.8%
암 4
 
1.5%
서 4
 
1.5%
Other values (9) 11
 
4.1%

세대수
Real number (ℝ)

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean205.14815
Minimum14
Maximum1046
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T02:58:35.458755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile18.4
Q135.5
median151
Q3256.5
95-th percentile623.4
Maximum1046
Range1032
Interquartile range (IQR)221

Descriptive statistics

Standard deviation239.11579
Coefficient of variation (CV)1.1655762
Kurtosis5.1082216
Mean205.14815
Median Absolute Deviation (MAD)111
Skewness2.0919148
Sum5539
Variance57176.362
MonotonicityNot monotonic
2023-12-13T02:58:35.604068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
31 2
 
7.4%
193 1
 
3.7%
63 1
 
3.7%
253 1
 
3.7%
50 1
 
3.7%
16 1
 
3.7%
308 1
 
3.7%
225 1
 
3.7%
180 1
 
3.7%
24 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
14 1
3.7%
16 1
3.7%
24 1
3.7%
26 1
3.7%
30 1
3.7%
31 2
7.4%
40 1
3.7%
42 1
3.7%
50 1
3.7%
63 1
3.7%
ValueCountFrequency (%)
1046 1
3.7%
672 1
3.7%
510 1
3.7%
482 1
3.7%
326 1
3.7%
308 1
3.7%
260 1
3.7%
253 1
3.7%
231 1
3.7%
225 1
3.7%

준공년월
Text

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-13T02:58:35.796308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.7407407
Min length3

Characters and Unicode

Total characters263
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)92.6%

Sample

1st row2014-07-30
2nd row2014-11-20
3rd row2015-07-10
4th row2020-03-04
5th row2017-12-13
ValueCountFrequency (%)
2015-07-10 2
 
7.4%
2014-07-30 1
 
3.7%
2017-12-26 1
 
3.7%
2022-05-30 1
 
3.7%
미착공 1
 
3.7%
2022-03-22 1
 
3.7%
2023-06-29 1
 
3.7%
2021-09-27 1
 
3.7%
2016-06-22 1
 
3.7%
2021-12-30 1
 
3.7%
Other values (16) 16
59.3%
2023-12-13T02:58:36.184160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 60
22.8%
0 57
21.7%
- 52
19.8%
1 38
14.4%
3 12
 
4.6%
5 9
 
3.4%
6 9
 
3.4%
7 8
 
3.0%
4 7
 
2.7%
9 7
 
2.7%
Other values (4) 4
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 208
79.1%
Dash Punctuation 52
 
19.8%
Other Letter 3
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 60
28.8%
0 57
27.4%
1 38
18.3%
3 12
 
5.8%
5 9
 
4.3%
6 9
 
4.3%
7 8
 
3.8%
4 7
 
3.4%
9 7
 
3.4%
8 1
 
0.5%
Other Letter
ValueCountFrequency (%)
미 1
33.3%
착 1
33.3%
공 1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 52
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 260
98.9%
Hangul 3
 
1.1%

Most frequent character per script

Common
ValueCountFrequency (%)
2 60
23.1%
0 57
21.9%
- 52
20.0%
1 38
14.6%
3 12
 
4.6%
5 9
 
3.5%
6 9
 
3.5%
7 8
 
3.1%
4 7
 
2.7%
9 7
 
2.7%
Hangul
ValueCountFrequency (%)
미 1
33.3%
착 1
33.3%
공 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260
98.9%
Hangul 3
 
1.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 60
23.1%
0 57
21.9%
- 52
20.0%
1 38
14.6%
3 12
 
4.6%
5 9
 
3.5%
6 9
 
3.5%
7 8
 
3.1%
4 7
 
2.7%
9 7
 
2.7%
Hangul
ValueCountFrequency (%)
미 1
33.3%
착 1
33.3%
공 1
33.3%

Interactions

2023-12-13T02:58:33.547254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:58:36.289105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건물명도로명주소지번주소세대수준공년월
건물명1.0001.0001.0001.0001.000
도로명주소1.0001.0001.0001.0001.000
지번주소1.0001.0001.0001.0001.000
세대수1.0001.0001.0001.0001.000
준공년월1.0001.0001.0001.0001.000

Missing values

2023-12-13T02:58:33.649513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:58:33.738463image/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

건물명도로명주소지번주소세대수준공년월
0서한코보스카운티대구광역시 동구 동부로22길 2대구광역시 동구 신천동 285-11932014-07-30
1유성푸르나임대구광역시 동구 동부로22길 48대구광역시 동구 신천동 292-66722014-11-20
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3정성라임오피스텔대구광역시 동구 신암남로 105대구광역시 동구 신암동 259-71622020-03-04
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