Overview

Dataset statistics

Number of variables12
Number of observations33
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory110.0 B

Variable types

Categorical2
Text1
Numeric9

Dataset

Description국민의 주택금융 이용실태 등을 파악하기 위해 전문조사기관과 함께 실시한 ‘2022년 주택금융 및 보금자리론 실태조사’ 결과 중 LTV에 대한 데이터를 제공하며, 응답자 분류, 응답자 세부분류, 사례수, 퍼센트, 합계, 평균값 등의 데이터 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15120553/fileData.do

Alerts

has constant value ""Constant
사례수 is highly overall correlated with 상대표준오차(퍼센트)High correlation
30퍼센트 미만(퍼센트) is highly overall correlated with 50-60퍼센트 미만(퍼센트) and 2 other fieldsHigh correlation
40-50퍼센트 미만(퍼센트) is highly overall correlated with (대푯값 - 퍼센트)중앙값(퍼센트)High correlation
50-60퍼센트 미만(퍼센트) is highly overall correlated with 30퍼센트 미만(퍼센트) and 2 other fieldsHigh correlation
(대푯값 - 퍼센트)평균(퍼센트) is highly overall correlated with 30퍼센트 미만(퍼센트) and 2 other fieldsHigh correlation
(대푯값 - 퍼센트)중앙값(퍼센트) is highly overall correlated with 30퍼센트 미만(퍼센트) and 3 other fieldsHigh correlation
상대표준오차(퍼센트) is highly overall correlated with 사례수High correlation
50-60퍼센트 미만(퍼센트) has unique valuesUnique
50-60퍼센트 미만(퍼센트) has 1 (3.0%) zerosZeros

Reproduction

Analysis started2023-12-12 09:40:26.196111
Analysis finished2023-12-12 09:40:37.213260
Duration11.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

응답자 분류
Categorical

Distinct8
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Memory size396.0 B
주택담보대출 이용상품
가구주 직업
가구소득
거주지역
가구주 연령
Other values (3)

Length

Max length11
Median length8
Mean length6.4242424
Min length4

Unique

Unique1 ?
Unique (%)3.0%

Sample

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

Common Values

ValueCountFrequency (%)
주택담보대출 이용상품 7
21.2%
가구주 직업 6
18.2%
가구소득 5
15.2%
거주지역 4
12.1%
가구주 연령 4
12.1%
결혼여부 4
12.1%
주택 보유/거주 2
 
6.1%
주택 보유여부 1
 
3.0%

Length

2023-12-12T18:40:37.336158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:40:37.509501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가구주 10
18.9%
주택담보대출 7
13.2%
이용상품 7
13.2%
직업 6
11.3%
가구소득 5
9.4%
거주지역 4
 
7.5%
연령 4
 
7.5%
결혼여부 4
 
7.5%
주택 3
 
5.7%
보유/거주 2
 
3.8%
Distinct32
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size396.0 B
2023-12-12T18:40:37.793885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length5.7272727
Min length2

Characters and Unicode

Total characters189
Distinct characters81
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

Unique31 ?
Unique (%)93.9%

Sample

1st row서울
2nd row경기
3rd row광역시
4th row기타지역
5th row30대 이하
ValueCountFrequency (%)
종사자 3
 
6.0%
유주택 3
 
6.0%
기타 2
 
4.0%
은행 2
 
4.0%
주택자금대출 2
 
4.0%
주택담보대출 2
 
4.0%
적격대출 1
 
2.0%
기혼 1
 
2.0%
미혼 1
 
2.0%
자가거주 1
 
2.0%
Other values (32) 32
64.0%
2023-12-12T18:40:38.231166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17
 
9.0%
10
 
5.3%
10
 
5.3%
9
 
4.8%
8
 
4.2%
6
 
3.2%
6
 
3.2%
5
 
2.6%
5
 
2.6%
5
 
2.6%
Other values (71) 108
57.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 154
81.5%
Space Separator 17
 
9.0%
Decimal Number 15
 
7.9%
Other Punctuation 3
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10
 
6.5%
10
 
6.5%
9
 
5.8%
8
 
5.2%
6
 
3.9%
6
 
3.9%
5
 
3.2%
5
 
3.2%
5
 
3.2%
5
 
3.2%
Other values (61) 85
55.2%
Decimal Number
ValueCountFrequency (%)
0 4
26.7%
3 3
20.0%
2 2
13.3%
5 2
13.3%
4 2
13.3%
6 1
 
6.7%
1 1
 
6.7%
Other Punctuation
ValueCountFrequency (%)
/ 2
66.7%
, 1
33.3%
Space Separator
ValueCountFrequency (%)
17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 154
81.5%
Common 35
 
18.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10
 
6.5%
10
 
6.5%
9
 
5.8%
8
 
5.2%
6
 
3.9%
6
 
3.9%
5
 
3.2%
5
 
3.2%
5
 
3.2%
5
 
3.2%
Other values (61) 85
55.2%
Common
ValueCountFrequency (%)
17
48.6%
0 4
 
11.4%
3 3
 
8.6%
/ 2
 
5.7%
2 2
 
5.7%
5 2
 
5.7%
4 2
 
5.7%
, 1
 
2.9%
6 1
 
2.9%
1 1
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 154
81.5%
ASCII 35
 
18.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17
48.6%
0 4
 
11.4%
3 3
 
8.6%
/ 2
 
5.7%
2 2
 
5.7%
5 2
 
5.7%
4 2
 
5.7%
, 1
 
2.9%
6 1
 
2.9%
1 1
 
2.9%
Hangul
ValueCountFrequency (%)
10
 
6.5%
10
 
6.5%
9
 
5.8%
8
 
5.2%
6
 
3.9%
6
 
3.9%
5
 
3.2%
5
 
3.2%
5
 
3.2%
5
 
3.2%
Other values (61) 85
55.2%

사례수
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean290.78788
Minimum9
Maximum1200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T18:40:38.431764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile17.6
Q148
median214
Q3349
95-th percentile1100.2
Maximum1200
Range1191
Interquartile range (IQR)301

Descriptive statistics

Standard deviation336.76984
Coefficient of variation (CV)1.1581289
Kurtosis2.2482497
Mean290.78788
Median Absolute Deviation (MAD)157
Skewness1.7195443
Sum9596
Variance113413.92
MonotonicityNot monotonic
2023-12-12T18:40:38.643071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
349 2
 
6.1%
22 2
 
6.1%
214 1
 
3.0%
23 1
 
3.0%
65 1
 
3.0%
105 1
 
3.0%
9 1
 
3.0%
11 1
 
3.0%
964 1
 
3.0%
1177 1
 
3.0%
Other values (21) 21
63.6%
ValueCountFrequency (%)
9 1
3.0%
11 1
3.0%
22 2
6.1%
23 1
3.0%
35 1
3.0%
39 1
3.0%
43 1
3.0%
48 1
3.0%
57 1
3.0%
65 1
3.0%
ValueCountFrequency (%)
1200 1
3.0%
1177 1
3.0%
1049 1
3.0%
964 1
3.0%
481 1
3.0%
416 1
3.0%
379 1
3.0%
349 2
6.1%
346 1
3.0%
338 1
3.0%

30퍼센트 미만(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.748485
Minimum5
Maximum53.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T18:40:38.834846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile16.68
Q127.8
median33.8
Q336.9
95-th percentile40.1
Maximum53.7
Range48.7
Interquartile range (IQR)9.1

Descriptive statistics

Standard deviation9.0169327
Coefficient of variation (CV)0.28401143
Kurtosis3.1382642
Mean31.748485
Median Absolute Deviation (MAD)4.6
Skewness-0.99367161
Sum1047.7
Variance81.305076
MonotonicityNot monotonic
2023-12-12T18:40:39.013855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
27.8 2
 
6.1%
34.4 2
 
6.1%
33.8 2
 
6.1%
53.7 1
 
3.0%
33.2 1
 
3.0%
40.7 1
 
3.0%
23.3 1
 
3.0%
25.3 1
 
3.0%
5.0 1
 
3.0%
7.5 1
 
3.0%
Other values (20) 20
60.6%
ValueCountFrequency (%)
5.0 1
3.0%
7.5 1
3.0%
22.8 1
3.0%
23.3 1
3.0%
25.3 1
3.0%
25.5 1
3.0%
26.2 1
3.0%
27.0 1
3.0%
27.8 2
6.1%
28.3 1
3.0%
ValueCountFrequency (%)
53.7 1
3.0%
40.7 1
3.0%
39.7 1
3.0%
39.2 1
3.0%
38.5 1
3.0%
38.4 1
3.0%
38.2 1
3.0%
38.1 1
3.0%
36.9 1
3.0%
36.6 1
3.0%
Distinct28
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.645455
Minimum10.1
Maximum32.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T18:40:39.165824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10.1
5-th percentile16.68
Q120.5
median22.1
Q324.1
95-th percentile32.12
Maximum32.8
Range22.7
Interquartile range (IQR)3.6

Descriptive statistics

Standard deviation4.5336996
Coefficient of variation (CV)0.20020351
Kurtosis1.992754
Mean22.645455
Median Absolute Deviation (MAD)1.8
Skewness0.27734638
Sum747.3
Variance20.554432
MonotonicityNot monotonic
2023-12-12T18:40:39.718244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
20.5 2
 
6.1%
24.3 2
 
6.1%
24.1 2
 
6.1%
22.1 2
 
6.1%
21.0 2
 
6.1%
17.2 1
 
3.0%
30.7 1
 
3.0%
22.6 1
 
3.0%
15.9 1
 
3.0%
32.3 1
 
3.0%
Other values (18) 18
54.5%
ValueCountFrequency (%)
10.1 1
3.0%
15.9 1
3.0%
17.2 1
3.0%
19.6 1
3.0%
19.8 1
3.0%
20.2 1
3.0%
20.3 1
3.0%
20.5 2
6.1%
20.8 1
3.0%
20.9 1
3.0%
ValueCountFrequency (%)
32.8 1
3.0%
32.3 1
3.0%
32.0 1
3.0%
30.7 1
3.0%
25.9 1
3.0%
24.3 2
6.1%
24.1 2
6.1%
23.9 1
3.0%
23.7 1
3.0%
23.5 1
3.0%

40-50퍼센트 미만(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.690909
Minimum7.2
Maximum36.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T18:40:39.884399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.2
5-th percentile10.16
Q116.4
median18.2
Q320.4
95-th percentile29.94
Maximum36.1
Range28.9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.8770403
Coefficient of variation (CV)0.31443309
Kurtosis2.5470155
Mean18.690909
Median Absolute Deviation (MAD)2
Skewness1.0384216
Sum616.8
Variance34.539602
MonotonicityNot monotonic
2023-12-12T18:40:40.068337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
17.0 2
 
6.1%
18.9 2
 
6.1%
33.9 1
 
3.0%
18.5 1
 
3.0%
18.3 1
 
3.0%
21.4 1
 
3.0%
12.2 1
 
3.0%
18.2 1
 
3.0%
17.9 1
 
3.0%
12.0 1
 
3.0%
Other values (21) 21
63.6%
ValueCountFrequency (%)
7.2 1
3.0%
9.8 1
3.0%
10.4 1
3.0%
12.0 1
3.0%
12.2 1
3.0%
15.5 1
3.0%
16.0 1
3.0%
16.2 1
3.0%
16.4 1
3.0%
16.5 1
3.0%
ValueCountFrequency (%)
36.1 1
3.0%
33.9 1
3.0%
27.3 1
3.0%
24.3 1
3.0%
23.6 1
3.0%
22.0 1
3.0%
21.4 1
3.0%
21.3 1
3.0%
20.4 1
3.0%
19.7 1
3.0%

50-60퍼센트 미만(퍼센트)
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.881818
Minimum0
Maximum44.8
Zeros1
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T18:40:40.252521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.68
Q113.5
median15.9
Q319.2
95-th percentile26.82
Maximum44.8
Range44.8
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation7.4358277
Coefficient of variation (CV)0.44046368
Kurtosis5.6914517
Mean16.881818
Median Absolute Deviation (MAD)3.1
Skewness1.4768284
Sum557.1
Variance55.291534
MonotonicityNot monotonic
2023-12-12T18:40:40.436156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
9.8 1
 
3.0%
0.0 1
 
3.0%
19.2 1
 
3.0%
15.3 1
 
3.0%
24.6 1
 
3.0%
9.5 1
 
3.0%
15.6 1
 
3.0%
15.9 1
 
3.0%
13.5 1
 
3.0%
14.6 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
0.0 1
3.0%
9.5 1
3.0%
9.8 1
3.0%
9.9 1
3.0%
10.3 1
3.0%
11.2 1
3.0%
12.2 1
3.0%
12.8 1
3.0%
13.5 1
3.0%
13.6 1
3.0%
ValueCountFrequency (%)
44.8 1
3.0%
27.9 1
3.0%
26.1 1
3.0%
24.6 1
3.0%
22.8 1
3.0%
22.3 1
3.0%
21.9 1
3.0%
21.4 1
3.0%
19.2 1
3.0%
18.6 1
3.0%
Distinct27
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.036364
Minimum4
Maximum27.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T18:40:40.612657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5.08
Q18.2
median9.1
Q310.2
95-th percentile17.18
Maximum27.3
Range23.3
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.3644027
Coefficient of variation (CV)0.43485897
Kurtosis6.9758232
Mean10.036364
Median Absolute Deviation (MAD)1.1
Skewness2.142197
Sum331.2
Variance19.048011
MonotonicityNot monotonic
2023-12-12T18:40:40.770391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
9.1 3
 
9.1%
4.0 2
 
6.1%
10.0 2
 
6.1%
9.7 2
 
6.1%
9.0 2
 
6.1%
16.9 1
 
3.0%
10.2 1
 
3.0%
17.6 1
 
3.0%
5.8 1
 
3.0%
7.1 1
 
3.0%
Other values (17) 17
51.5%
ValueCountFrequency (%)
4.0 2
6.1%
5.8 1
3.0%
6.0 1
3.0%
6.2 1
3.0%
6.6 1
3.0%
7.1 1
3.0%
7.6 1
3.0%
8.2 1
3.0%
8.7 1
3.0%
8.8 1
3.0%
ValueCountFrequency (%)
27.3 1
3.0%
17.6 1
3.0%
16.9 1
3.0%
15.3 1
3.0%
13.0 1
3.0%
12.1 1
3.0%
11.7 1
3.0%
11.4 1
3.0%
10.2 1
3.0%
10.1 1
3.0%


Categorical

CONSTANT 

Distinct1
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size396.0 B
100
33 

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 33
100.0%

Length

2023-12-12T18:40:40.938361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:40:41.089733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
100 33
100.0%

(대푯값 - 퍼센트)평균(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.445455
Minimum30.5
Maximum48.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T18:40:41.232157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30.5
5-th percentile34.1
Q136.7
median38.2
Q340.3
95-th percentile42.18
Maximum48.3
Range17.8
Interquartile range (IQR)3.6

Descriptive statistics

Standard deviation3.1339961
Coefficient of variation (CV)0.081517989
Kurtosis2.6108826
Mean38.445455
Median Absolute Deviation (MAD)1.9
Skewness0.44533509
Sum1268.7
Variance9.8219318
MonotonicityNot monotonic
2023-12-12T18:40:41.423878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
40.3 3
 
9.1%
39.0 2
 
6.1%
34.1 2
 
6.1%
38.2 2
 
6.1%
30.5 1
 
3.0%
42.6 1
 
3.0%
41.0 1
 
3.0%
48.3 1
 
3.0%
41.9 1
 
3.0%
41.8 1
 
3.0%
Other values (18) 18
54.5%
ValueCountFrequency (%)
30.5 1
3.0%
34.1 2
6.1%
34.9 1
3.0%
36.0 1
3.0%
36.2 1
3.0%
36.4 1
3.0%
36.5 1
3.0%
36.7 1
3.0%
36.8 1
3.0%
37.1 1
3.0%
ValueCountFrequency (%)
48.3 1
 
3.0%
42.6 1
 
3.0%
41.9 1
 
3.0%
41.8 1
 
3.0%
41.2 1
 
3.0%
41.0 1
 
3.0%
40.5 1
 
3.0%
40.3 3
9.1%
40.1 1
 
3.0%
39.6 1
 
3.0%

(대푯값 - 퍼센트)중앙값(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)51.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.906061
Minimum25.6
Maximum49.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T18:40:41.581434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25.6
5-th percentile32.94
Q134.3
median35.7
Q340
95-th percentile42.72
Maximum49.5
Range23.9
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation4.1847894
Coefficient of variation (CV)0.1133903
Kurtosis2.4197455
Mean36.906061
Median Absolute Deviation (MAD)2.4
Skewness0.39730675
Sum1217.9
Variance17.512462
MonotonicityNot monotonic
2023-12-12T18:40:41.761746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
40.0 5
15.2%
33.3 5
15.2%
35.7 4
12.1%
37.5 3
9.1%
35.1 3
9.1%
34.3 2
 
6.1%
25.6 1
 
3.0%
42.2 1
 
3.0%
32.4 1
 
3.0%
40.5 1
 
3.0%
Other values (7) 7
21.2%
ValueCountFrequency (%)
25.6 1
 
3.0%
32.4 1
 
3.0%
33.3 5
15.2%
34.3 2
 
6.1%
35.0 1
 
3.0%
35.1 3
9.1%
35.7 4
12.1%
36.3 1
 
3.0%
37.2 1
 
3.0%
37.5 3
9.1%
ValueCountFrequency (%)
49.5 1
 
3.0%
43.5 1
 
3.0%
42.2 1
 
3.0%
41.2 1
 
3.0%
40.5 1
 
3.0%
40.0 5
15.2%
38.8 1
 
3.0%
37.5 3
9.1%
37.2 1
 
3.0%
36.3 1
 
3.0%

상대표준오차(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9545455
Minimum1.5
Maximum17.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T18:40:41.933712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile1.56
Q12.5
median3.4
Q36.6
95-th percentile12.44
Maximum17.2
Range15.7
Interquartile range (IQR)4.1

Descriptive statistics

Standard deviation3.7766628
Coefficient of variation (CV)0.76226221
Kurtosis2.748764
Mean4.9545455
Median Absolute Deviation (MAD)1.6
Skewness1.7195672
Sum163.5
Variance14.263182
MonotonicityNot monotonic
2023-12-12T18:40:42.100153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
2.9 3
 
9.1%
3.4 2
 
6.1%
3.9 2
 
6.1%
1.5 2
 
6.1%
2.8 2
 
6.1%
4.1 1
 
3.0%
1.6 1
 
3.0%
8.1 1
 
3.0%
5.1 1
 
3.0%
5.2 1
 
3.0%
Other values (17) 17
51.5%
ValueCountFrequency (%)
1.5 2
6.1%
1.6 1
3.0%
1.7 1
3.0%
2.0 1
3.0%
2.1 1
3.0%
2.2 1
3.0%
2.4 1
3.0%
2.5 1
3.0%
2.6 1
3.0%
2.8 2
6.1%
ValueCountFrequency (%)
17.2 1
3.0%
13.1 1
3.0%
12.0 1
3.0%
11.8 1
3.0%
8.1 1
3.0%
7.4 1
3.0%
6.9 1
3.0%
6.7 1
3.0%
6.6 1
3.0%
5.3 1
3.0%

Interactions

2023-12-12T18:40:35.587091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:26.636834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:27.587973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:28.553275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:30.047330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:31.513397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:32.390544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:33.536981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:34.564062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:35.728651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:26.752197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:27.719254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:28.666511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:30.201534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:31.615326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:32.517282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:33.665692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:34.666840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:35.851775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:26.866746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:27.817281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:28.894868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:30.339271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:31.702308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:32.637471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:33.773088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:34.768404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:35.986336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:26.987563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:27.907607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:29.067976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:30.465379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:31.786787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:32.766385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:33.892429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:34.874541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:36.101532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:27.094599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:28.022450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:29.232846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:30.598469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:31.878859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:32.888843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:34.000183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:34.997651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:36.205595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:27.200938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:28.123876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:29.396313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:31.043612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:31.974682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:32.999927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:34.100089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:35.105004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:36.327476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:27.295711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:28.230725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:29.547085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:31.151046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:32.053764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:33.160823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:34.206482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:35.228860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:36.459491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:27.401736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:28.356947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:29.702106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:31.272300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:32.163579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:33.292810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:34.333549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:35.345361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:36.594967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:27.486806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:28.462808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:29.869192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:31.389089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:32.278279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:33.404656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:34.459804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:40:35.458479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:40:42.244197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
응답자 분류응답자 세부분류사례수30퍼센트 미만(퍼센트)30-40퍼센트 미만(퍼센트)40-50퍼센트 미만(퍼센트)50-60퍼센트 미만(퍼센트)60퍼센트 이상(퍼센트)(대푯값 - 퍼센트)평균(퍼센트)(대푯값 - 퍼센트)중앙값(퍼센트)상대표준오차(퍼센트)
응답자 분류1.0000.9550.6090.0000.0000.0000.0000.0000.0000.0000.000
응답자 세부분류0.9551.0001.0000.8950.0000.9581.0000.7900.8651.0000.000
사례수0.6091.0001.0000.1660.0000.0000.0000.0000.3890.0000.424
30퍼센트 미만(퍼센트)0.0000.8950.1661.0000.7550.6080.6440.5670.9490.8390.581
30-40퍼센트 미만(퍼센트)0.0000.0000.0000.7551.0000.5590.7880.8570.8480.5690.809
40-50퍼센트 미만(퍼센트)0.0000.9580.0000.6080.5591.0000.6330.6350.7220.8650.739
50-60퍼센트 미만(퍼센트)0.0001.0000.0000.6440.7880.6331.0000.5880.8590.6260.578
60퍼센트 이상(퍼센트)0.0000.7900.0000.5670.8570.6350.5881.0000.4600.0000.782
(대푯값 - 퍼센트)평균(퍼센트)0.0000.8650.3890.9490.8480.7220.8590.4601.0000.8680.534
(대푯값 - 퍼센트)중앙값(퍼센트)0.0001.0000.0000.8390.5690.8650.6260.0000.8681.0000.800
상대표준오차(퍼센트)0.0000.0000.4240.5810.8090.7390.5780.7820.5340.8001.000
2023-12-12T18:40:42.427920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사례수30퍼센트 미만(퍼센트)30-40퍼센트 미만(퍼센트)40-50퍼센트 미만(퍼센트)50-60퍼센트 미만(퍼센트)60퍼센트 이상(퍼센트)(대푯값 - 퍼센트)평균(퍼센트)(대푯값 - 퍼센트)중앙값(퍼센트)상대표준오차(퍼센트)응답자 분류
사례수1.0000.287-0.029-0.125-0.0690.086-0.407-0.332-0.9280.372
30퍼센트 미만(퍼센트)0.2871.000-0.111-0.484-0.7860.026-0.878-0.913-0.0920.000
30-40퍼센트 미만(퍼센트)-0.029-0.1111.000-0.277-0.005-0.036-0.140-0.1640.0440.000
40-50퍼센트 미만(퍼센트)-0.125-0.484-0.2771.0000.165-0.1860.3810.616-0.0560.000
50-60퍼센트 미만(퍼센트)-0.069-0.786-0.0050.1651.000-0.2410.6680.669-0.0890.000
60퍼센트 이상(퍼센트)0.0860.026-0.036-0.186-0.2411.0000.2270.0480.0250.000
(대푯값 - 퍼센트)평균(퍼센트)-0.407-0.878-0.1400.3810.6680.2271.0000.9040.2920.000
(대푯값 - 퍼센트)중앙값(퍼센트)-0.332-0.913-0.1640.6160.6690.0480.9041.0000.1400.000
상대표준오차(퍼센트)-0.928-0.0920.044-0.056-0.0890.0250.2920.1401.0000.000
응답자 분류0.3720.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-12T18:40:36.825866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:40:37.106443image/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 세부분류사례수30퍼센트 미만(퍼센트)30-40퍼센트 미만(퍼센트)40-50퍼센트 미만(퍼센트)50-60퍼센트 미만(퍼센트)60퍼센트 이상(퍼센트)(대푯값 - 퍼센트)평균(퍼센트)(대푯값 - 퍼센트)중앙값(퍼센트)상대표준오차(퍼센트)
0거주지역서울21453.720.512.09.84.010030.525.64.1
1거주지역경기34938.422.417.014.67.610036.033.32.8
2거주지역광역시29926.220.523.617.512.110040.340.02.0
3거주지역기타지역33825.524.318.618.613.010041.240.02.9
4가구주 연령30대 이하14627.019.624.322.86.210040.140.03.4
5가구주 연령40대34930.124.118.916.910.010038.437.52.6
6가구주 연령50대37938.222.516.214.09.110036.433.32.2
7가구주 연령60대 이상32538.120.817.012.811.410036.835.13.9
8가구소득1분위6628.324.315.516.715.310040.535.711.8
9가구소득2분위13427.820.920.422.38.710039.640.03.9
응답자 분류응답자 세부분류사례수30퍼센트 미만(퍼센트)30-40퍼센트 미만(퍼센트)40-50퍼센트 미만(퍼센트)50-60퍼센트 미만(퍼센트)60퍼센트 이상(퍼센트)(대푯값 - 퍼센트)평균(퍼센트)(대푯값 - 퍼센트)중앙값(퍼센트)상대표준오차(퍼센트)
23주택 보유여부유주택120034.422.118.215.69.610037.535.71.5
24주택 보유/거주유주택 자가거주117734.422.117.915.99.710037.635.71.5
25주택 보유/거주유주택 임차거주2238.523.533.90.04.010034.134.312.0
26주택담보대출 이용상품은행 주택담보대출96436.621.918.813.59.110036.735.01.7
27주택담보대출 이용상품은행 적격대출2233.810.118.99.927.310041.842.213.1
28주택담보대출 이용상품생애최초 주택자금대출117.532.336.114.49.710041.943.57.4
29주택담보대출 이용상품근로자서민 주택자금대출95.015.927.344.87.110048.349.55.2
30주택담보대출 이용상품주택금융공사 보금자리론10525.324.116.927.95.810041.040.03.4
31주택담보대출 이용상품내집마련 디딤돌 대출6523.322.610.426.117.610042.640.55.1
32주택담보대출 이용상품제2,3금융권 주택담보대출2340.730.77.211.210.210034.132.48.1