Overview

Dataset statistics

Number of variables9
Number of observations26
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory84.1 B

Variable types

Categorical3
Text1
Numeric5

Dataset

Description국민의 주택금융 이용실태 등을 파악하기 위해 전문조사기관과 함께 실시한 ‘2022년 주택금융 및 보금자리론 실태조사’ 결과 중 보유주택취득방법에 대한 데이터를 제공하며, 응답자 분류, 응답자 세부분류, 사례수, 신규주택 분양 또는 구입(재건축 포함)(%), 기존주택 구입(%), 개인주택 신축(%), 증여나 상속(%), 기타(%), 계(%) 등의 데이터 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15120512/fileData.do

Alerts

계(퍼센트) has constant value ""Constant
신규주택 분양 또는 구입(재건축 포함)(퍼센트) is highly overall correlated with 기존주택 구입(퍼센트)High correlation
기존주택 구입(퍼센트) is highly overall correlated with 신규주택 분양 또는 구입(재건축 포함)(퍼센트) and 1 other fieldsHigh correlation
개인주택 신축(퍼센트) is highly overall correlated with 기존주택 구입(퍼센트) and 1 other fieldsHigh correlation
기타(퍼센트) is highly overall correlated with 개인주택 신축(퍼센트)High correlation
사례수 has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:21:32.240508
Analysis finished2023-12-12 23:21:34.775159
Duration2.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

응답자 분류
Categorical

Distinct7
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Memory size340.0 B
가구주 직업
6 
가구소득
5 
거주지역
4 
가구주 연령
4 
결혼여부
4 
Other values (2)
3 

Length

Max length8
Median length7.5
Mean length5.1923077
Min length4

Unique

Unique1 ?
Unique (%)3.8%

Sample

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

Common Values

ValueCountFrequency (%)
가구주 직업 6
23.1%
가구소득 5
19.2%
거주지역 4
15.4%
가구주 연령 4
15.4%
결혼여부 4
15.4%
주택 보유/거주 2
 
7.7%
주택 보유여부 1
 
3.8%

Length

2023-12-13T08:21:34.835685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:21:34.935946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가구주 10
25.6%
직업 6
15.4%
가구소득 5
12.8%
거주지역 4
 
10.3%
연령 4
 
10.3%
결혼여부 4
 
10.3%
주택 3
 
7.7%
보유/거주 2
 
5.1%
보유여부 1
 
2.6%

응답자 세부분류
Text

Distinct25
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-13T08:21:35.096583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length4.3461538
Min length2

Characters and Unicode

Total characters113
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

Unique24 ?
Unique (%)92.3%

Sample

1st row서울
2nd row경기
3rd row광역시
4th row기타지역
5th row30대 이하
ValueCountFrequency (%)
유주택 3
 
8.6%
종사자 3
 
8.6%
기타 2
 
5.7%
경기 1
 
2.9%
관리자 1
 
2.9%
자가거주 1
 
2.9%
미혼 1
 
2.9%
기혼 1
 
2.9%
신혼 1
 
2.9%
기능원/단순노무직 1
 
2.9%
Other values (20) 20
57.1%
2023-12-13T08:21:35.464739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9
 
8.0%
기 6
 
5.3%
위 5
 
4.4%
자 5
 
4.4%
분 5
 
4.4%
주 5
 
4.4%
0 4
 
3.5%
사 4
 
3.5%
대 4
 
3.5%
유 3
 
2.7%
Other values (44) 63
55.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89
78.8%
Decimal Number 13
 
11.5%
Space Separator 9
 
8.0%
Other Punctuation 2
 
1.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 6
 
6.7%
위 5
 
5.6%
자 5
 
5.6%
분 5
 
5.6%
주 5
 
5.6%
사 4
 
4.5%
대 4
 
4.5%
유 3
 
3.4%
혼 3
 
3.4%
택 3
 
3.4%
Other values (35) 46
51.7%
Decimal Number
ValueCountFrequency (%)
0 4
30.8%
5 2
15.4%
4 2
15.4%
3 2
15.4%
2 1
 
7.7%
1 1
 
7.7%
6 1
 
7.7%
Space Separator
ValueCountFrequency (%)
9
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89
78.8%
Common 24
 
21.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 6
 
6.7%
위 5
 
5.6%
자 5
 
5.6%
분 5
 
5.6%
주 5
 
5.6%
사 4
 
4.5%
대 4
 
4.5%
유 3
 
3.4%
혼 3
 
3.4%
택 3
 
3.4%
Other values (35) 46
51.7%
Common
ValueCountFrequency (%)
9
37.5%
0 4
16.7%
/ 2
 
8.3%
5 2
 
8.3%
4 2
 
8.3%
3 2
 
8.3%
2 1
 
4.2%
1 1
 
4.2%
6 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89
78.8%
ASCII 24
 
21.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9
37.5%
0 4
16.7%
/ 2
 
8.3%
5 2
 
8.3%
4 2
 
8.3%
3 2
 
8.3%
2 1
 
4.2%
1 1
 
4.2%
6 1
 
4.2%
Hangul
ValueCountFrequency (%)
기 6
 
6.7%
위 5
 
5.6%
자 5
 
5.6%
분 5
 
5.6%
주 5
 
5.6%
사 4
 
4.5%
대 4
 
4.5%
유 3
 
3.4%
혼 3
 
3.4%
택 3
 
3.4%
Other values (35) 46
51.7%

사례수
Real number (ℝ)

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean832
Minimum89
Maximum3090
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:21:35.614770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile106.75
Q1294.5
median597.5
Q3839.25
95-th percentile2891.5
Maximum3090
Range3001
Interquartile range (IQR)544.75

Descriptive statistics

Standard deviation833.26049
Coefficient of variation (CV)1.001515
Kurtosis2.8602489
Mean832
Median Absolute Deviation (MAD)290.5
Skewness1.8622828
Sum21632
Variance694323.04
MonotonicityNot monotonic
2023-12-13T08:21:35.789015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
543 1
 
3.8%
155 1
 
3.8%
106 1
 
3.8%
2984 1
 
3.8%
3090 1
 
3.8%
279 1
 
3.8%
89 1
 
3.8%
2614 1
 
3.8%
109 1
 
3.8%
341 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
89 1
3.8%
106 1
3.8%
109 1
3.8%
112 1
3.8%
155 1
3.8%
226 1
3.8%
279 1
3.8%
341 1
3.8%
520 1
3.8%
543 1
3.8%
ValueCountFrequency (%)
3090 1
3.8%
2984 1
3.8%
2614 1
3.8%
1392 1
3.8%
1173 1
3.8%
1017 1
3.8%
860 1
3.8%
777 1
3.8%
765 1
3.8%
762 1
3.8%

신규주택 분양 또는 구입(재건축 포함)(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.157692
Minimum6.2
Maximum21.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:21:35.935503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile7.925
Q112.45
median14.2
Q315.25
95-th percentile19.35
Maximum21.5
Range15.3
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation3.3947221
Coefficient of variation (CV)0.23977934
Kurtosis0.89815241
Mean14.157692
Median Absolute Deviation (MAD)1.7
Skewness-0.22560534
Sum368.1
Variance11.524138
MonotonicityNot monotonic
2023-12-13T08:21:36.082817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
15.1 2
 
7.7%
14.2 2
 
7.7%
14.6 2
 
7.7%
17.6 1
 
3.8%
21.5 1
 
3.8%
13.3 1
 
3.8%
11.7 1
 
3.8%
6.2 1
 
3.8%
15.3 1
 
3.8%
10.1 1
 
3.8%
Other values (13) 13
50.0%
ValueCountFrequency (%)
6.2 1
3.8%
7.2 1
3.8%
10.1 1
3.8%
11.7 1
3.8%
12.0 1
3.8%
12.2 1
3.8%
12.4 1
3.8%
12.6 1
3.8%
13.2 1
3.8%
13.3 1
3.8%
ValueCountFrequency (%)
21.5 1
3.8%
19.6 1
3.8%
18.6 1
3.8%
17.6 1
3.8%
17.5 1
3.8%
16.9 1
3.8%
15.3 1
3.8%
15.1 2
7.7%
14.9 1
3.8%
14.6 2
7.7%

기존주택 구입(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.261538
Minimum53.5
Maximum84.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:21:36.253187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum53.5
5-th percentile66.125
Q170.9
median77.1
Q379.925
95-th percentile83.725
Maximum84.3
Range30.8
Interquartile range (IQR)9.025

Descriptive statistics

Standard deviation7.0384133
Coefficient of variation (CV)0.093519392
Kurtosis2.176511
Mean75.261538
Median Absolute Deviation (MAD)3.5
Skewness-1.2620299
Sum1956.8
Variance49.539262
MonotonicityNot monotonic
2023-12-13T08:21:36.400647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
77.8 3
 
11.5%
70.9 2
 
7.7%
77.4 1
 
3.8%
80.0 1
 
3.8%
69.4 1
 
3.8%
75.9 1
 
3.8%
75.7 1
 
3.8%
67.3 1
 
3.8%
83.9 1
 
3.8%
76.0 1
 
3.8%
Other values (13) 13
50.0%
ValueCountFrequency (%)
53.5 1
3.8%
66.0 1
3.8%
66.5 1
3.8%
67.3 1
3.8%
67.8 1
3.8%
69.4 1
3.8%
70.9 2
7.7%
75.7 1
3.8%
75.9 1
3.8%
76.0 1
3.8%
ValueCountFrequency (%)
84.3 1
 
3.8%
83.9 1
 
3.8%
83.2 1
 
3.8%
82.9 1
 
3.8%
80.9 1
 
3.8%
80.3 1
 
3.8%
80.0 1
 
3.8%
79.7 1
 
3.8%
77.8 3
11.5%
77.4 1
 
3.8%

개인주택 신축(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6153846
Minimum1.1
Maximum17.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:21:36.535791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile2.125
Q12.775
median5.25
Q35.925
95-th percentile10.9
Maximum17.5
Range16.4
Interquartile range (IQR)3.15

Descriptive statistics

Standard deviation3.6580533
Coefficient of variation (CV)0.65143415
Kurtosis3.2344064
Mean5.6153846
Median Absolute Deviation (MAD)2.1
Skewness1.6032579
Sum146
Variance13.381354
MonotonicityNot monotonic
2023-12-13T08:21:36.672828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
5.4 3
 
11.5%
5.7 2
 
7.7%
2.4 2
 
7.7%
10.6 2
 
7.7%
2.6 2
 
7.7%
2.2 1
 
3.8%
9.2 1
 
3.8%
1.1 1
 
3.8%
17.5 1
 
3.8%
3.6 1
 
3.8%
Other values (10) 10
38.5%
ValueCountFrequency (%)
1.1 1
3.8%
2.1 1
3.8%
2.2 1
3.8%
2.4 2
7.7%
2.6 2
7.7%
3.3 1
3.8%
3.6 1
3.8%
4.0 1
3.8%
4.4 1
3.8%
4.7 1
3.8%
ValueCountFrequency (%)
17.5 1
 
3.8%
11.0 1
 
3.8%
10.6 2
7.7%
9.2 1
 
3.8%
7.5 1
 
3.8%
6.0 1
 
3.8%
5.7 2
7.7%
5.5 1
 
3.8%
5.4 3
11.5%
5.1 1
 
3.8%

증여나 상속(퍼센트)
Real number (ℝ)

Distinct23
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8807692
Minimum1.9
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:21:36.819926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile2.3
Q12.925
median3.85
Q36.725
95-th percentile9.775
Maximum12
Range10.1
Interquartile range (IQR)3.8

Descriptive statistics

Standard deviation2.6977056
Coefficient of variation (CV)0.55272139
Kurtosis0.57006884
Mean4.8807692
Median Absolute Deviation (MAD)1.05
Skewness1.2020713
Sum126.9
Variance7.2776154
MonotonicityNot monotonic
2023-12-13T08:21:36.959568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2.7 2
 
7.7%
2.9 2
 
7.7%
4.0 2
 
7.7%
2.6 1
 
3.8%
12.0 1
 
3.8%
3.7 1
 
3.8%
10.1 1
 
3.8%
8.8 1
 
3.8%
3.2 1
 
3.8%
3.3 1
 
3.8%
Other values (13) 13
50.0%
ValueCountFrequency (%)
1.9 1
3.8%
2.2 1
3.8%
2.6 1
3.8%
2.7 2
7.7%
2.9 2
7.7%
3.0 1
3.8%
3.2 1
3.8%
3.3 1
3.8%
3.4 1
3.8%
3.7 1
3.8%
ValueCountFrequency (%)
12.0 1
3.8%
10.1 1
3.8%
8.8 1
3.8%
8.6 1
3.8%
7.8 1
3.8%
7.2 1
3.8%
6.9 1
3.8%
6.2 1
3.8%
4.8 1
3.8%
4.3 1
3.8%

기타(퍼센트)
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Memory size340.0 B
0.0
14 
0.1
7 
0.2
3 
0.8
 
1
0.4
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique2 ?
Unique (%)7.7%

Sample

1st row0.2
2nd row0.0
3rd row0.1
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14
53.8%
0.1 7
26.9%
0.2 3
 
11.5%
0.8 1
 
3.8%
0.4 1
 
3.8%

Length

2023-12-13T08:21:37.100903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:21:37.193184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14
53.8%
0.1 7
26.9%
0.2 3
 
11.5%
0.8 1
 
3.8%
0.4 1
 
3.8%

계(퍼센트)
Categorical

CONSTANT 

Distinct1
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size340.0 B
100
26 

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 26
100.0%

Length

2023-12-13T08:21:37.314685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:21:37.416417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
100 26
100.0%

Interactions

2023-12-13T08:21:34.260727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:21:32.533598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:21:32.942891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:21:33.603949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:21:33.936811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:21:34.323585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:21:32.606586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:21:33.022979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:21:33.666602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:21:34.001814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:21:34.394574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:21:32.705374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:21:33.129086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:21:33.736539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:21:34.071522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:21:34.461351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:21:32.785043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:21:33.214174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:21:33.799239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:21:34.137354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:21:34.528003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:21:32.872498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:21:33.293461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:21:33.872005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:21:34.199769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:21:37.480418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
응답자 분류응답자 세부분류사례수신규주택 분양 또는 구입(재건축 포함)(퍼센트)기존주택 구입(퍼센트)개인주택 신축(퍼센트)증여나 상속(퍼센트)기타(퍼센트)
응답자 분류1.0000.9300.7310.0000.0000.0000.3010.000
응답자 세부분류0.9301.0001.0000.8680.0000.0000.0000.000
사례수0.7311.0001.0000.0000.4280.5510.0000.330
신규주택 분양 또는 구입(재건축 포함)(퍼센트)0.0000.8680.0001.0000.6060.4340.0000.538
기존주택 구입(퍼센트)0.0000.0000.4280.6061.0000.8320.6210.625
개인주택 신축(퍼센트)0.0000.0000.5510.4340.8321.0000.7760.751
증여나 상속(퍼센트)0.3010.0000.0000.0000.6210.7761.0000.671
기타(퍼센트)0.0000.0000.3300.5380.6250.7510.6711.000
2023-12-13T08:21:37.598255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
응답자 분류기타(퍼센트)
응답자 분류1.0000.000
기타(퍼센트)0.0001.000
2023-12-13T08:21:37.691406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사례수신규주택 분양 또는 구입(재건축 포함)(퍼센트)기존주택 구입(퍼센트)개인주택 신축(퍼센트)증여나 상속(퍼센트)응답자 분류기타(퍼센트)
사례수1.0000.197-0.0350.240-0.3830.3160.180
신규주택 분양 또는 구입(재건축 포함)(퍼센트)0.1971.000-0.6110.333-0.2950.0000.291
기존주택 구입(퍼센트)-0.035-0.6111.000-0.870-0.3870.0000.466
개인주택 신축(퍼센트)0.2400.333-0.8701.0000.4560.0000.539
증여나 상속(퍼센트)-0.383-0.295-0.3870.4561.0000.1110.448
응답자 분류0.3160.0000.0000.0000.1111.0000.000
기타(퍼센트)0.1800.2910.4660.5390.4480.0001.000

Missing values

2023-12-13T08:21:34.618359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:21:34.730482image/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 세부분류사례수신규주택 분양 또는 구입(재건축 포함)(퍼센트)기존주택 구입(퍼센트)개인주택 신축(퍼센트)증여나 상속(퍼센트)기타(퍼센트)계(퍼센트)
0거주지역서울54317.677.42.22.70.2100
1거주지역경기75312.482.92.62.20.0100
2거주지역광역시77715.177.84.02.90.1100
3거주지역기타지역101714.267.811.06.90.0100
4가구주 연령30대 이하2267.283.22.47.20.0100
5가구주 연령40대61213.980.92.13.00.2100
6가구주 연령50대86012.277.86.04.00.0100
7가구주 연령60대 이상139217.570.97.53.90.1100
8가구소득1분위52014.966.510.67.80.2100
9가구소득2분위55612.677.05.54.80.0100
응답자 분류응답자 세부분류사례수신규주택 분양 또는 구입(재건축 포함)(퍼센트)기존주택 구입(퍼센트)개인주택 신축(퍼센트)증여나 상속(퍼센트)기타(퍼센트)계(퍼센트)
16가구주 직업서비스/판매 종사자117312.080.34.72.90.0100
17가구주 직업기능원/단순노무직76515.177.83.63.40.0100
18가구주 직업기타34119.653.517.58.60.8100
19결혼여부신혼10910.184.32.43.30.0100
20결혼여부기혼261415.376.05.43.20.1100
21결혼여부미혼896.283.91.18.80.0100
22결혼여부기타27911.767.310.610.10.4100
23주택 보유여부유주택309014.675.75.74.00.1100
24주택 보유/거주유주택 자가거주298414.675.95.73.70.1100
25주택 보유/거주유주택 임차거주10613.369.45.412.00.0100