Overview

Dataset statistics

Number of variables5
Number of observations28
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 KiB
Average record size in memory47.7 B

Variable types

Categorical1
Text1
Numeric3

Dataset

Description국민의 주택금융 이용실태 등을 파악하기 위해 전문조사기관과 함께 실시한 ‘2022년 주택금융 및 보금자리론 실태조사’ 결과 중 주택여부에 대한 데이터를 제공하며, 응답자 분류, 응답자 세부분류, 사례수, 주택 보유여부 등의 데이터 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15120545/fileData.do

Alerts

주택 보유(퍼센트) is highly overall correlated with 주택 미보유(퍼센트)High correlation
주택 미보유(퍼센트) is highly overall correlated with 주택 보유(퍼센트)High correlation
주택 보유(퍼센트) has 2 (7.1%) zerosZeros
주택 미보유(퍼센트) has 3 (10.7%) zerosZeros

Reproduction

Analysis started2023-12-12 18:55:03.220787
Analysis finished2023-12-12 18:55:05.921567
Duration2.7 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

2023-12-13T03:55:06.076982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:55:06.315325image/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
2023-12-13T03:55:06.620064image/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%
2023-12-13T03:55:07.154741image/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)

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1218.0357
Minimum99
Maximum3439
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-13T03:55:07.366458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum99
5-th percentile116.5
Q1597.5
median1082
Q31605.25
95-th percentile3052.9
Maximum3439
Range3340
Interquartile range (IQR)1007.75

Descriptive statistics

Standard deviation867.41047
Coefficient of variation (CV)0.71213878
Kurtosis1.0154566
Mean1218.0357
Median Absolute Deviation (MAD)540.5
Skewness1.0721355
Sum34105
Variance752400.92
MonotonicityNot monotonic
2023-12-13T03:55:07.571422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1910 2
 
7.1%
951 1
 
3.6%
1220 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%
429 1
 
3.6%
Other values (17) 17
60.7%
ValueCountFrequency (%)
99 1
3.6%
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 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%

주택 보유(퍼센트)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.417857
Minimum0
Maximum100
Zeros2
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-13T03:55:07.775849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.935
Q154.35
median62.3
Q376.05
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)21.7

Descriptive statistics

Standard deviation26.829117
Coefficient of variation (CV)0.45153289
Kurtosis0.3955318
Mean59.417857
Median Absolute Deviation (MAD)12.95
Skewness-0.76863743
Sum1663.7
Variance719.80152
MonotonicityNot monotonic
2023-12-13T03:55:07.998266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
100.0 3
 
10.7%
0.0 2
 
7.1%
57.1 1
 
3.6%
61.7 1
 
3.6%
55.4 1
 
3.6%
14.1 1
 
3.6%
76.0 1
 
3.6%
25.4 1
 
3.6%
76.8 1
 
3.6%
66.2 1
 
3.6%
Other values (15) 15
53.6%
ValueCountFrequency (%)
0.0 2
7.1%
14.1 1
3.6%
19.5 1
3.6%
25.4 1
3.6%
47.6 1
3.6%
51.2 1
3.6%
55.4 1
3.6%
55.7 1
3.6%
57.1 1
3.6%
58.7 1
3.6%
ValueCountFrequency (%)
100.0 3
10.7%
83.4 1
 
3.6%
82.5 1
 
3.6%
76.8 1
 
3.6%
76.2 1
 
3.6%
76.0 1
 
3.6%
74.5 1
 
3.6%
67.3 1
 
3.6%
66.2 1
 
3.6%
65.6 1
 
3.6%

주택 미보유(퍼센트)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.582143
Minimum0
Maximum100
Zeros3
Zeros (%)10.7%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-13T03:55:08.209656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q123.95
median37.7
Q345.65
95-th percentile95.065
Maximum100
Range100
Interquartile range (IQR)21.7

Descriptive statistics

Standard deviation26.829117
Coefficient of variation (CV)0.66110647
Kurtosis0.3955318
Mean40.582143
Median Absolute Deviation (MAD)12.95
Skewness0.76863743
Sum1136.3
Variance719.80152
MonotonicityNot monotonic
2023-12-13T03:55:08.414370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.0 3
 
10.7%
100.0 2
 
7.1%
42.9 1
 
3.6%
38.3 1
 
3.6%
44.6 1
 
3.6%
85.9 1
 
3.6%
24.0 1
 
3.6%
74.6 1
 
3.6%
23.2 1
 
3.6%
33.8 1
 
3.6%
Other values (15) 15
53.6%
ValueCountFrequency (%)
0.0 3
10.7%
16.6 1
 
3.6%
17.5 1
 
3.6%
23.2 1
 
3.6%
23.8 1
 
3.6%
24.0 1
 
3.6%
25.5 1
 
3.6%
32.7 1
 
3.6%
33.8 1
 
3.6%
34.4 1
 
3.6%
ValueCountFrequency (%)
100.0 2
7.1%
85.9 1
3.6%
80.5 1
3.6%
74.6 1
3.6%
52.4 1
3.6%
48.8 1
3.6%
44.6 1
3.6%
44.3 1
3.6%
42.9 1
3.6%
41.3 1
3.6%

Interactions

2023-12-13T03:55:05.160923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:55:03.599302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:55:04.603080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:55:05.325709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:55:03.776262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:55:04.805380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:55:05.476482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:55:03.953083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:55:05.010778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:55:08.551721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
응답자 분류응답자 세부분류사례수주택 보유(퍼센트)주택 미보유(퍼센트)
응답자 분류1.0000.9260.5450.5150.513
응답자 세부분류0.9261.0001.0000.9820.978
사례수0.5451.0001.0000.6320.595
주택 보유(퍼센트)0.5150.9820.6321.0001.000
주택 미보유(퍼센트)0.5130.9780.5951.0001.000
2023-12-13T03:55:08.721846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사례수주택 보유(퍼센트)주택 미보유(퍼센트)응답자 분류
사례수1.0000.054-0.0540.305
주택 보유(퍼센트)0.0541.000-1.0000.266
주택 미보유(퍼센트)-0.054-1.0001.0000.265
응답자 분류0.3050.2660.2651.000

Missing values

2023-12-13T03:55:05.686237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:55:05.855546image/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 세부분류사례수주택 보유(퍼센트)주택 미보유(퍼센트)
0거주지역서울95157.142.9
1거주지역경기122061.738.3
2거주지역광역시124562.437.6
3거주지역기타지역158464.235.8
4가구주 연령30대 이하115619.580.5
5가구주 연령40대102260.040.0
6가구주 연령50대115374.525.5
7가구주 연령60대 이상166983.416.6
8가구소득1분위101551.248.8
9가구소득2분위99755.744.3
응답자 분류응답자 세부분류사례수주택 보유(퍼센트)주택 미보유(퍼센트)
18가구주 직업기타44476.823.2
19결혼여부신혼42925.474.6
20결혼여부기혼343976.024.0
21결혼여부미혼62914.185.9
22결혼여부기타50355.444.6
23주택 보유여부유주택3090100.00.0
24주택 보유여부무주택19100.0100.0
25주택 보유/거주유주택 자가거주2984100.00.0
26주택 보유/거주유주택 임차거주106100.00.0
27주택 보유/거주무주택 임차거주19100.0100.0