Overview

Dataset statistics

Number of variables5
Number of observations64
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory44.1 B

Variable types

Categorical2
Text1
Numeric2

Dataset

Description전라북도 군산시에 소재한 원룸 및 오피스텔현황으로 주택유형구분, 주소, 가구수, 건축년도 등의 데이터를 제공합니다.
Author전라북도
URLhttps://www.bigdatahub.go.kr/index.jeonbuk?startPage=2&menuCd=DOM_000000103007001000&pListTypeStr=&pId=15077157

Alerts

가구수 is highly overall correlated with 건축연도 and 1 other fieldsHigh correlation
건축연도 is highly overall correlated with 가구수 and 1 other fieldsHigh correlation
주택유형구분 is highly overall correlated with 가구수 and 2 other fieldsHigh correlation
용도 is highly overall correlated with 주택유형구분High correlation
주소 has unique valuesUnique

Reproduction

Analysis started2024-03-14 03:02:39.857927
Analysis finished2024-03-14 03:02:40.477649
Duration0.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

주택유형구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size644.0 B
원룸
39 
오피스텔
25 

Length

Max length4
Median length2
Mean length2.78125
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
원룸 39
60.9%
오피스텔 25
39.1%

Length

2024-03-14T12:02:40.537498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T12:02:40.619806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
원룸 39
60.9%
오피스텔 25
39.1%

주소
Text

UNIQUE 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size644.0 B
2024-03-14T12:02:40.807671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length25
Mean length22.0625
Min length19

Characters and Unicode

Total characters1412
Distinct characters57
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

Unique64 ?
Unique (%)100.0%

Sample

1st row전북특별자치도 군산시 문화동 893-4
2nd row전북특별자치도 군산시 대명동 387-12
3rd row전북특별자치도 군산시 소룡동 1608-1
4th row전북특별자치도 군산시 미룡동 871-1
5th row전북특별자치도 군산시 미룡동 875-1
ValueCountFrequency (%)
전북특별자치도 64
23.9%
군산시 64
23.9%
미룡동 16
 
6.0%
오식도동 11
 
4.1%
옥서면 9
 
3.4%
옥봉리 9
 
3.4%
지곡동 8
 
3.0%
소룡동 8
 
3.0%
임피면 2
 
0.7%
월하리 2
 
0.7%
Other values (74) 75
28.0%
2024-03-14T12:02:41.161303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
204
 
14.4%
도 75
 
5.3%
전 64
 
4.5%
북 64
 
4.5%
특 64
 
4.5%
별 64
 
4.5%
자 64
 
4.5%
치 64
 
4.5%
군 64
 
4.5%
산 64
 
4.5%
Other values (47) 621
44.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 879
62.3%
Decimal Number 272
 
19.3%
Space Separator 204
 
14.4%
Dash Punctuation 57
 
4.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 75
 
8.5%
전 64
 
7.3%
북 64
 
7.3%
특 64
 
7.3%
별 64
 
7.3%
자 64
 
7.3%
치 64
 
7.3%
군 64
 
7.3%
산 64
 
7.3%
시 64
 
7.3%
Other values (35) 228
25.9%
Decimal Number
ValueCountFrequency (%)
4 42
15.4%
1 39
14.3%
5 35
12.9%
8 32
11.8%
3 29
10.7%
6 25
9.2%
7 22
8.1%
2 22
8.1%
0 15
 
5.5%
9 11
 
4.0%
Space Separator
ValueCountFrequency (%)
204
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 57
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 879
62.3%
Common 533
37.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 75
 
8.5%
전 64
 
7.3%
북 64
 
7.3%
특 64
 
7.3%
별 64
 
7.3%
자 64
 
7.3%
치 64
 
7.3%
군 64
 
7.3%
산 64
 
7.3%
시 64
 
7.3%
Other values (35) 228
25.9%
Common
ValueCountFrequency (%)
204
38.3%
- 57
 
10.7%
4 42
 
7.9%
1 39
 
7.3%
5 35
 
6.6%
8 32
 
6.0%
3 29
 
5.4%
6 25
 
4.7%
7 22
 
4.1%
2 22
 
4.1%
Other values (2) 26
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 879
62.3%
ASCII 533
37.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
204
38.3%
- 57
 
10.7%
4 42
 
7.9%
1 39
 
7.3%
5 35
 
6.6%
8 32
 
6.0%
3 29
 
5.4%
6 25
 
4.7%
7 22
 
4.1%
2 22
 
4.1%
Other values (2) 26
 
4.9%
Hangul
ValueCountFrequency (%)
도 75
 
8.5%
전 64
 
7.3%
북 64
 
7.3%
특 64
 
7.3%
별 64
 
7.3%
자 64
 
7.3%
치 64
 
7.3%
군 64
 
7.3%
산 64
 
7.3%
시 64
 
7.3%
Other values (35) 228
25.9%

가구수
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)42.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.40625
Minimum2
Maximum216
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-03-14T12:02:41.263156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q14
median9
Q324
95-th percentile86.5
Maximum216
Range214
Interquartile range (IQR)20

Descriptive statistics

Standard deviation32.33835
Coefficient of variation (CV)1.6663884
Kurtosis22.391064
Mean19.40625
Median Absolute Deviation (MAD)7
Skewness4.2905762
Sum1242
Variance1045.7688
MonotonicityNot monotonic
2024-03-14T12:02:41.368496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
2 12
18.8%
7 4
 
6.2%
4 4
 
6.2%
6 4
 
6.2%
12 4
 
6.2%
24 3
 
4.7%
5 3
 
4.7%
28 3
 
4.7%
18 2
 
3.1%
14 2
 
3.1%
Other values (17) 23
35.9%
ValueCountFrequency (%)
2 12
18.8%
3 2
 
3.1%
4 4
 
6.2%
5 3
 
4.7%
6 4
 
6.2%
7 4
 
6.2%
8 2
 
3.1%
9 2
 
3.1%
12 4
 
6.2%
13 2
 
3.1%
ValueCountFrequency (%)
216 1
 
1.6%
98 1
 
1.6%
96 1
 
1.6%
94 1
 
1.6%
44 1
 
1.6%
38 1
 
1.6%
32 2
3.1%
28 3
4.7%
27 1
 
1.6%
26 1
 
1.6%

건축연도
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2009.1094
Minimum1974
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-03-14T12:02:41.476506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1974
5-th percentile1997
Q12005.75
median2009
Q32017
95-th percentile2022
Maximum2023
Range49
Interquartile range (IQR)11.25

Descriptive statistics

Standard deviation9.3352331
Coefficient of variation (CV)0.0046464534
Kurtosis2.9221806
Mean2009.1094
Median Absolute Deviation (MAD)7
Skewness-1.1133431
Sum128583
Variance87.146577
MonotonicityNot monotonic
2024-03-14T12:02:41.576739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2007 14
21.9%
2002 7
10.9%
2011 5
 
7.8%
2020 4
 
6.2%
2009 4
 
6.2%
2010 4
 
6.2%
2022 4
 
6.2%
2017 3
 
4.7%
2001 2
 
3.1%
2013 2
 
3.1%
Other values (12) 15
23.4%
ValueCountFrequency (%)
1974 1
 
1.6%
1980 1
 
1.6%
1996 1
 
1.6%
1997 2
 
3.1%
1998 1
 
1.6%
2000 1
 
1.6%
2001 2
 
3.1%
2002 7
10.9%
2007 14
21.9%
2009 4
 
6.2%
ValueCountFrequency (%)
2023 1
 
1.6%
2022 4
6.2%
2021 2
3.1%
2020 4
6.2%
2019 1
 
1.6%
2018 2
3.1%
2017 3
4.7%
2016 1
 
1.6%
2015 1
 
1.6%
2013 2
3.1%

용도
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Memory size644.0 B
업무시설
25 
단독주택(다가구주택)
15 
다가구주택
7 
제2종근린생활시설, 단독주택(다가구주택)
4 
다가구주택 및 제2종근린생활시설
3 
Other values (9)
10 

Length

Max length32
Median length22
Mean length9.9375
Min length4

Unique

Unique8 ?
Unique (%)12.5%

Sample

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

Common Values

ValueCountFrequency (%)
업무시설 25
39.1%
단독주택(다가구주택) 15
23.4%
다가구주택 7
 
10.9%
제2종근린생활시설, 단독주택(다가구주택) 4
 
6.2%
다가구주택 및 제2종근린생활시설 3
 
4.7%
단독주택(다가구주택),제2종근린생활시설 2
 
3.1%
근린생활시설, 다가구주택 1
 
1.6%
1종근린생활시설, 단독주택(다가구주택), 제2종근린생활시설 1
 
1.6%
단독주택(다가구주택), 제1종근린생활시설 1
 
1.6%
제1종근린생활시설, 단독주택(다가구주택) 1
 
1.6%
Other values (4) 4
 
6.2%

Length

2024-03-14T12:02:41.690128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
업무시설 25
31.2%
단독주택(다가구주택 22
27.5%
다가구주택 12
15.0%
제2종근린생활시설 9
 
11.2%
및 3
 
3.8%
단독주택(다가구주택),제2종근린생활시설 2
 
2.5%
제1종근린생활시설 2
 
2.5%
근린생활시설 1
 
1.2%
1종근린생활시설 1
 
1.2%
다가구주택,2종근린생활시설 1
 
1.2%
Other values (2) 2
 
2.5%

Interactions

2024-03-14T12:02:40.178647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:02:40.008790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:02:40.265388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:02:40.091380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T12:02:41.755418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주택유형구분주소가구수건축연도용도
주택유형구분1.0001.0000.9150.7351.000
주소1.0001.0001.0001.0001.000
가구수0.9151.0001.0000.4860.000
건축연도0.7351.0000.4861.0000.755
용도1.0001.0000.0000.7551.000
2024-03-14T12:02:41.834242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
용도주택유형구분
용도1.0000.898
주택유형구분0.8981.000
2024-03-14T12:02:41.905703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가구수건축연도주택유형구분용도
가구수1.0000.6260.7250.000
건축연도0.6261.0000.7550.316
주택유형구분0.7250.7551.0000.898
용도0.0000.3160.8981.000

Missing values

2024-03-14T12:02:40.363454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T12:02:40.448510image/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

주택유형구분주소가구수건축연도용도
0오피스텔전북특별자치도 군산시 문화동 893-4271998업무시설
1오피스텔전북특별자치도 군산시 대명동 387-1281980업무시설
2오피스텔전북특별자치도 군산시 소룡동 1608-1202009업무시설
3오피스텔전북특별자치도 군산시 미룡동 871-1322010업무시설
4오피스텔전북특별자치도 군산시 미룡동 875-1382011업무시설
5오피스텔전북특별자치도 군산시 나운동 754-892013업무시설
6오피스텔전북특별자치도 군산시 소룡동 772-8242013업무시설
7오피스텔전북특별자치도 군산시 소룡동 7682162015업무시설
8오피스텔전북특별자치도 군산시 미룡동 853-2282016업무시설
9오피스텔전북특별자치도 군산시 소룡동 831982017업무시설
주택유형구분주소가구수건축연도용도
54원룸전북특별자치도 군산시 오식도동 617-1492009다가구주택 및 제2종근린생활시설
55원룸전북특별자치도 군산시 오식도동 634-372009단독주택(다가구주택),제1종근린생활시설
56원룸전북특별자치도 군산시 미룡동 385-1152010다가구주택
57원룸전북특별자치도 군산시 수송동 848-1222010다가구주택, 제2종근린생활시설
58원룸전북특별자치도 군산시 오식도동 594-662010다가구주택 및 제2종근린생활시설
59원룸전북특별자치도 군산시 오식도동 634-4122011다가구주택
60원룸전북특별자치도 군산시 오식도동 636-1452011다가구주택,제2종근린생활시설
61원룸전북특별자치도 군산시 오식도동 646-472011다가구주택 및 제2종근린생활시설
62원룸전북특별자치도 군산시 지곡동 559132011다가구주택
63원룸전북특별자치도 군산시 조촌동 862-4132020다가구주택