Overview

Dataset statistics

Number of variables8
Number of observations28
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory74.7 B

Variable types

Categorical2
Text1
Numeric5

Dataset

Description국민의 주택금융 이용실태 등을 파악하기 위해 전문조사기관과 함께 실시한 ‘2022년 주택금융 및 보금자리론 실태조사’ 결과 중 주택면적에 대한 데이터를 제공하며, 사례수, 면적범위별 비율, 합계 등의 데이터 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15120500/fileData.do

Alerts

계 has constant value ""Constant
60제곱미터 이하 (퍼센트) is highly overall correlated with 85제곱미터 초과 135제곱미터 이하 (퍼센트) and 1 other fieldsHigh correlation
85제곱미터 초과 135제곱미터 이하 (퍼센트) is highly overall correlated with 60제곱미터 이하 (퍼센트) and 1 other fieldsHigh correlation
135제곱미터 초과 (퍼센트) is highly overall correlated with 60제곱미터 이하 (퍼센트) and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 05:40:29.170419
Analysis finished2023-12-12 05:40:32.330904
Duration3.16 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

응답자 분류
Categorical

Distinct7
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
가구주 직업
6 
가구소득
5 
거주지역
4 
가구주 연령
4 
결혼여부
4 
Other values (2)
5 

Length

Max length8
Median length7
Mean length5.3571429
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row거주지역
2nd row거주지역
3rd row거주지역
4th row거주지역
5th row가구주 연령

Common Values

ValueCountFrequency (%)
가구주 직업 6
21.4%
가구소득 5
17.9%
거주지역 4
14.3%
가구주 연령 4
14.3%
결혼여부 4
14.3%
주택 보유/거주 3
10.7%
주택 보유여부 2
 
7.1%

Length

2023-12-12T14:40:32.432184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:40:32.584707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가구주 10
23.3%
직업 6
14.0%
가구소득 5
11.6%
주택 5
11.6%
거주지역 4
 
9.3%
연령 4
 
9.3%
결혼여부 4
 
9.3%
보유/거주 3
 
7.0%
보유여부 2
 
4.7%

응답자 세부분류
Text

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-12-12T14:40:32.779907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length4.4285714
Min length2

Characters and Unicode

Total characters124
Distinct characters54
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

Unique26 ?
Unique (%)92.9%

Sample

1st row서울
2nd row경기
3rd row광역시
4th row기타지역
5th row30대 이하
ValueCountFrequency (%)
유주택 3
 
7.9%
종사자 3
 
7.9%
기타 2
 
5.3%
임차거주 2
 
5.3%
무주택 2
 
5.3%
서울 1
 
2.6%
및 1
 
2.6%
전문가 1
 
2.6%
자가거주 1
 
2.6%
미혼 1
 
2.6%
Other values (21) 21
55.3%
2023-12-12T14:40:33.097239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10
 
8.1%
주 8
 
6.5%
기 6
 
4.8%
택 5
 
4.0%
자 5
 
4.0%
위 5
 
4.0%
분 5
 
4.0%
대 4
 
3.2%
사 4
 
3.2%
무 4
 
3.2%
Other values (44) 68
54.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 99
79.8%
Decimal Number 13
 
10.5%
Space Separator 10
 
8.1%
Other Punctuation 2
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 8
 
8.1%
기 6
 
6.1%
택 5
 
5.1%
자 5
 
5.1%
위 5
 
5.1%
분 5
 
5.1%
대 4
 
4.0%
사 4
 
4.0%
무 4
 
4.0%
혼 3
 
3.0%
Other values (35) 50
50.5%
Decimal Number
ValueCountFrequency (%)
0 4
30.8%
5 2
15.4%
3 2
15.4%
4 2
15.4%
2 1
 
7.7%
1 1
 
7.7%
6 1
 
7.7%
Space Separator
ValueCountFrequency (%)
10
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 99
79.8%
Common 25
 
20.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 8
 
8.1%
기 6
 
6.1%
택 5
 
5.1%
자 5
 
5.1%
위 5
 
5.1%
분 5
 
5.1%
대 4
 
4.0%
사 4
 
4.0%
무 4
 
4.0%
혼 3
 
3.0%
Other values (35) 50
50.5%
Common
ValueCountFrequency (%)
10
40.0%
0 4
 
16.0%
5 2
 
8.0%
3 2
 
8.0%
/ 2
 
8.0%
4 2
 
8.0%
2 1
 
4.0%
1 1
 
4.0%
6 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 99
79.8%
ASCII 25
 
20.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10
40.0%
0 4
 
16.0%
5 2
 
8.0%
3 2
 
8.0%
/ 2
 
8.0%
4 2
 
8.0%
2 1
 
4.0%
1 1
 
4.0%
6 1
 
4.0%
Hangul
ValueCountFrequency (%)
주 8
 
8.1%
기 6
 
6.1%
택 5
 
5.1%
자 5
 
5.1%
위 5
 
5.1%
분 5
 
5.1%
대 4
 
4.0%
사 4
 
4.0%
무 4
 
4.0%
혼 3
 
3.0%
Other values (35) 50
50.5%

사례수
Real number (ℝ)

Distinct26
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1250
Minimum106
Maximum3439
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T14:40:33.221821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum106
5-th percentile171.35
Q1870.5
median1082
Q31605.25
95-th percentile3052.9
Maximum3439
Range3333
Interquartile range (IQR)734.75

Descriptive statistics

Standard deviation840.72649
Coefficient of variation (CV)0.67258119
Kurtosis1.2402441
Mean1250
Median Absolute Deviation (MAD)477.5
Skewness1.147903
Sum35000
Variance706821.04
MonotonicityNot monotonic
2023-12-12T14:40:33.383364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1910 2
 
7.1%
994 2
 
7.1%
951 1
 
3.6%
1142 1
 
3.6%
106 1
 
3.6%
2984 1
 
3.6%
3090 1
 
3.6%
503 1
 
3.6%
629 1
 
3.6%
3439 1
 
3.6%
Other values (16) 16
57.1%
ValueCountFrequency (%)
106 1
3.6%
136 1
3.6%
237 1
3.6%
429 1
3.6%
444 1
3.6%
503 1
3.6%
629 1
3.6%
951 1
3.6%
994 2
7.1%
997 1
3.6%
ValueCountFrequency (%)
3439 1
3.6%
3090 1
3.6%
2984 1
3.6%
1910 2
7.1%
1886 1
3.6%
1669 1
3.6%
1584 1
3.6%
1245 1
3.6%
1220 1
3.6%
1156 1
3.6%

60제곱미터 이하 (퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.507143
Minimum3.6
Maximum62.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T14:40:33.522830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.6
5-th percentile5.65
Q116.05
median22.35
Q333.2
95-th percentile43.19
Maximum62.8
Range59.2
Interquartile range (IQR)17.15

Descriptive statistics

Standard deviation13.889536
Coefficient of variation (CV)0.5667546
Kurtosis0.68019094
Mean24.507143
Median Absolute Deviation (MAD)8.8
Skewness0.8183752
Sum686.2
Variance192.91921
MonotonicityNot monotonic
2023-12-12T14:40:33.651391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
42.8 2
 
7.1%
23.4 1
 
3.6%
22.1 1
 
3.6%
34.1 1
 
3.6%
8.9 1
 
3.6%
9.8 1
 
3.6%
42.4 1
 
3.6%
62.8 1
 
3.6%
12.1 1
 
3.6%
22.6 1
 
3.6%
Other values (17) 17
60.7%
ValueCountFrequency (%)
3.6 1
3.6%
3.9 1
3.6%
8.9 1
3.6%
9.8 1
3.6%
9.9 1
3.6%
12.1 1
3.6%
15.0 1
3.6%
16.4 1
3.6%
18.3 1
3.6%
18.7 1
3.6%
ValueCountFrequency (%)
62.8 1
3.6%
43.4 1
3.6%
42.8 2
7.1%
42.4 1
3.6%
41.0 1
3.6%
34.1 1
3.6%
32.9 1
3.6%
24.9 1
3.6%
24.1 1
3.6%
23.4 1
3.6%

60제곱미터 초과 85제곱미터 이하 (퍼센트)
Real number (ℝ)

Distinct22
Distinct (%)78.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.907143
Minimum26.6
Maximum43.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T14:40:33.765787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26.6
5-th percentile27.265
Q134.1
median37.65
Q338.8
95-th percentile40.2
Maximum43.1
Range16.5
Interquartile range (IQR)4.7

Descriptive statistics

Standard deviation4.3606769
Coefficient of variation (CV)0.12144316
Kurtosis-0.056265685
Mean35.907143
Median Absolute Deviation (MAD)1.95
Skewness-0.88011521
Sum1005.4
Variance19.015503
MonotonicityNot monotonic
2023-12-12T14:40:33.910347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
37.8 3
 
10.7%
35.7 2
 
7.1%
39.4 2
 
7.1%
40.2 2
 
7.1%
26.6 2
 
7.1%
32.2 1
 
3.6%
34.6 1
 
3.6%
28.5 1
 
3.6%
38.2 1
 
3.6%
37.3 1
 
3.6%
Other values (12) 12
42.9%
ValueCountFrequency (%)
26.6 2
7.1%
28.5 1
3.6%
29.0 1
3.6%
30.2 1
3.6%
32.2 1
3.6%
32.6 1
3.6%
34.6 1
3.6%
35.0 1
3.6%
35.7 2
7.1%
35.9 1
3.6%
ValueCountFrequency (%)
43.1 1
 
3.6%
40.2 2
7.1%
39.8 1
 
3.6%
39.4 2
7.1%
39.1 1
 
3.6%
38.7 1
 
3.6%
38.5 1
 
3.6%
38.2 1
 
3.6%
38.0 1
 
3.6%
37.8 3
10.7%

85제곱미터 초과 135제곱미터 이하 (퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.725
Minimum8.6
Maximum52.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T14:40:34.055739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.6
5-th percentile17.32
Q127.075
median33.1
Q338.375
95-th percentile48.445
Maximum52.3
Range43.7
Interquartile range (IQR)11.3

Descriptive statistics

Standard deviation10.345786
Coefficient of variation (CV)0.31614318
Kurtosis0.012162657
Mean32.725
Median Absolute Deviation (MAD)5.55
Skewness-0.32985854
Sum916.3
Variance107.03528
MonotonicityNot monotonic
2023-12-12T14:40:34.203869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
31.6 2
 
7.1%
19.2 2
 
7.1%
38.1 1
 
3.6%
28.2 1
 
3.6%
43.7 1
 
3.6%
43.1 1
 
3.6%
16.9 1
 
3.6%
8.6 1
 
3.6%
41.0 1
 
3.6%
34.9 1
 
3.6%
Other values (16) 16
57.1%
ValueCountFrequency (%)
8.6 1
3.6%
16.9 1
3.6%
18.1 1
3.6%
19.2 2
7.1%
23.2 1
3.6%
23.7 1
3.6%
28.2 1
3.6%
31.3 1
3.6%
31.5 1
3.6%
31.6 2
7.1%
ValueCountFrequency (%)
52.3 1
3.6%
51.0 1
3.6%
43.7 1
3.6%
43.5 1
3.6%
43.1 1
3.6%
41.0 1
3.6%
39.2 1
3.6%
38.1 1
3.6%
37.6 1
3.6%
37.5 1
3.6%

135제곱미터 초과 (퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8607143
Minimum2
Maximum18.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T14:40:34.321611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.235
Q13.475
median6.3
Q38.675
95-th percentile14.815
Maximum18.5
Range16.5
Interquartile range (IQR)5.2

Descriptive statistics

Standard deviation4.2376915
Coefficient of variation (CV)0.61767497
Kurtosis0.96937596
Mean6.8607143
Median Absolute Deviation (MAD)2.85
Skewness1.1173234
Sum192.1
Variance17.958029
MonotonicityNot monotonic
2023-12-12T14:40:34.747736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2.3 2
 
7.1%
9.2 2
 
7.1%
6.3 2
 
7.1%
12.9 1
 
3.6%
14.1 1
 
3.6%
9.3 1
 
3.6%
3.4 1
 
3.6%
2.0 1
 
3.6%
8.5 1
 
3.6%
2.2 1
 
3.6%
Other values (15) 15
53.6%
ValueCountFrequency (%)
2.0 1
3.6%
2.2 1
3.6%
2.3 2
7.1%
2.6 1
3.6%
3.2 1
3.6%
3.4 1
3.6%
3.5 1
3.6%
4.0 1
3.6%
4.2 1
3.6%
4.6 1
3.6%
ValueCountFrequency (%)
18.5 1
3.6%
15.2 1
3.6%
14.1 1
3.6%
12.9 1
3.6%
9.3 1
3.6%
9.2 2
7.1%
8.5 1
3.6%
8.2 1
3.6%
8.0 1
3.6%
7.8 1
3.6%

계
Categorical

CONSTANT 

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size356.0 B
100
28 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100
2nd row100
3rd row100
4th row100
5th row100

Common Values

ValueCountFrequency (%)
100 28
100.0%

Length

2023-12-12T14:40:34.942203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:40:35.053032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
100 28
100.0%

Interactions

2023-12-12T14:40:31.564961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:40:29.480423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:40:29.995500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:40:30.472804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:40:31.018729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:40:31.677995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:40:29.614996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:40:30.087586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:40:30.581722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:40:31.152559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:40:31.773746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:40:29.707093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:40:30.178528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:40:30.694967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:40:31.276576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:40:31.890424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:40:29.810808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:40:30.272594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:40:30.800382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:40:31.379498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:40:31.983389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:40:29.894320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:40:30.364130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:40:30.901897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:40:31.467413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:40:35.113533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
응답자 분류응답자 세부분류사례수60제곱미터 이하 (퍼센트)60제곱미터 초과 85제곱미터 이하 (퍼센트)85제곱미터 초과 135제곱미터 이하 (퍼센트)135제곱미터 초과 (퍼센트)
응답자 분류1.0000.9260.6150.3750.0000.4240.462
응답자 세부분류0.9261.0001.0000.9651.0000.6940.990
사례수0.6151.0001.0000.0000.0000.5370.233
60제곱미터 이하 (퍼센트)0.3750.9650.0001.0000.4760.9610.682
60제곱미터 초과 85제곱미터 이하 (퍼센트)0.0001.0000.0000.4761.0000.5780.690
85제곱미터 초과 135제곱미터 이하 (퍼센트)0.4240.6940.5370.9610.5781.0000.579
135제곱미터 초과 (퍼센트)0.4620.9900.2330.6820.6900.5791.000
2023-12-12T14:40:35.251996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사례수60제곱미터 이하 (퍼센트)60제곱미터 초과 85제곱미터 이하 (퍼센트)85제곱미터 초과 135제곱미터 이하 (퍼센트)135제곱미터 초과 (퍼센트)응답자 분류
사례수1.000-0.1390.3190.137-0.0930.366
60제곱미터 이하 (퍼센트)-0.1391.000-0.178-0.973-0.7050.177
60제곱미터 초과 85제곱미터 이하 (퍼센트)0.319-0.1781.0000.118-0.2800.000
85제곱미터 초과 135제곱미터 이하 (퍼센트)0.137-0.9730.1181.0000.6850.176
135제곱미터 초과 (퍼센트)-0.093-0.705-0.2800.6851.0000.243
응답자 분류0.3660.1770.0000.1760.2431.000

Missing values

2023-12-12T14:40:32.110225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:40:32.263878image/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

응답자 분류응답자 세부분류사례수60제곱미터 이하 (퍼센트)60제곱미터 초과 85제곱미터 이하 (퍼센트)85제곱미터 초과 135제곱미터 이하 (퍼센트)135제곱미터 초과 (퍼센트)계
0거주지역서울95123.432.231.612.9100
1거주지역경기122022.140.233.64.2100
2거주지역광역시124522.939.131.66.4100
3거주지역기타지역158421.635.937.64.9100
4가구주 연령30대 이하115641.032.623.23.2100
5가구주 연령40대102216.439.837.56.3100
6가구주 연령50대115315.037.839.28.0100
7가구주 연령60대 이상166918.337.835.78.2100
8가구소득1분위101543.435.018.13.5100
9가구소득2분위99732.939.423.74.0100
응답자 분류응답자 세부분류사례수60제곱미터 이하 (퍼센트)60제곱미터 초과 85제곱미터 이하 (퍼센트)85제곱미터 초과 135제곱미터 이하 (퍼센트)135제곱미터 초과 (퍼센트)계
18가구주 직업기타44424.137.531.56.9100
19결혼여부신혼42922.640.234.92.2100
20결혼여부기혼343912.138.541.08.5100
21결혼여부미혼62962.826.68.62.0100
22결혼여부기타50342.437.316.93.4100
23주택 보유여부유주택30909.837.843.19.2100
24주택 보유여부무주택191042.835.719.22.3100
25주택 보유/거주유주택 자가거주29848.938.243.79.2100
26주택 보유/거주유주택 임차거주10634.128.528.29.3100
27주택 보유/거주무주택 임차거주191042.835.719.22.3100