Overview

Dataset statistics

Number of variables9
Number of observations40
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 KiB
Average record size in memory83.3 B

Variable types

Categorical2
Numeric7

Dataset

Description- 가구 소득에 따른 1년 동안 여가 생활을 위한 한 달 평균 지출 금액 통계를 제공합니다. - 표본수(명)를 제외한 금액대별 값들은 표본수에서 차지하는 비율(%)을 의미합니다. - 데이터 제공처: KOSIS 국가통계포털
Author제주데이터허브
URLhttps://www.jejudatahub.net/data/view/data/797

Alerts

3만원 미만 is highly overall correlated with 3~5만원 and 2 other fieldsHigh correlation
3~5만원 is highly overall correlated with 3만원 미만 and 2 other fieldsHigh correlation
5~7만원 is highly overall correlated with 3만원 미만 and 2 other fieldsHigh correlation
15만원이상 is highly overall correlated with 3만원 미만 and 2 other fieldsHigh correlation
표본수 is highly overall correlated with 가구소득별High correlation
가구소득별 is highly overall correlated with 표본수High correlation

Reproduction

Analysis started2023-12-11 20:06:39.328499
Analysis finished2023-12-11 20:06:45.765030
Duration6.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준일(년)
Categorical

Distinct5
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
2018
2019
2020
2021
2022

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2018 8
20.0%
2019 8
20.0%
2020 8
20.0%
2021 8
20.0%
2022 8
20.0%

Length

2023-12-12T05:06:45.827519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T05:06:45.950781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 8
20.0%
2019 8
20.0%
2020 8
20.0%
2021 8
20.0%
2022 8
20.0%

가구소득별
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
100만원 미만
200~300만원
300~400만원
400~500만원
500~600만원
Other values (4)
15 

Length

Max length11
Median length9
Mean length8.025
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100만원 미만
2nd row100만원~200만원
3rd row200~300만원
4th row300~400만원
5th row400~500만원

Common Values

ValueCountFrequency (%)
100만원 미만 5
12.5%
200~300만원 5
12.5%
300~400만원 5
12.5%
400~500만원 5
12.5%
500~600만원 5
12.5%
600만원 이상 5
12.5%
전체 5
12.5%
100만원~200만원 3
7.5%
100~200만원 2
 
5.0%

Length

2023-12-12T05:06:46.105167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T05:06:46.242428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
100만원 5
10.0%
미만 5
10.0%
200~300만원 5
10.0%
300~400만원 5
10.0%
400~500만원 5
10.0%
500~600만원 5
10.0%
600만원 5
10.0%
이상 5
10.0%
전체 5
10.0%
100만원~200만원 3
6.0%

3만원 미만
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.88
Minimum2.8
Maximum39.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T05:06:46.399363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.8
5-th percentile3.185
Q16.175
median9.3
Q314
95-th percentile29.79
Maximum39.1
Range36.3
Interquartile range (IQR)7.825

Descriptive statistics

Standard deviation8.889556
Coefficient of variation (CV)0.74827912
Kurtosis2.0950393
Mean11.88
Median Absolute Deviation (MAD)3.85
Skewness1.5878279
Sum475.2
Variance79.024205
MonotonicityNot monotonic
2023-12-12T05:06:46.572385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
7.5 2
 
5.0%
9.3 2
 
5.0%
9.8 2
 
5.0%
14.0 2
 
5.0%
6.6 2
 
5.0%
21.5 1
 
2.5%
9.7 1
 
2.5%
39.1 1
 
2.5%
23.3 1
 
2.5%
15.9 1
 
2.5%
Other values (25) 25
62.5%
ValueCountFrequency (%)
2.8 1
2.5%
2.9 1
2.5%
3.2 1
2.5%
3.3 1
2.5%
4.3 1
2.5%
4.5 1
2.5%
4.7 1
2.5%
5.1 1
2.5%
5.3 1
2.5%
5.8 1
2.5%
ValueCountFrequency (%)
39.1 1
2.5%
35.3 1
2.5%
29.5 1
2.5%
29.4 1
2.5%
23.3 1
2.5%
21.5 1
2.5%
19.5 1
2.5%
18.7 1
2.5%
15.9 1
2.5%
14.0 2
5.0%

3~5만원
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.21
Minimum3.3
Maximum18.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T05:06:46.769365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.3
5-th percentile4.09
Q16.525
median8.4
Q310.075
95-th percentile17.51
Maximum18.3
Range15
Interquartile range (IQR)3.55

Descriptive statistics

Standard deviation4.2071246
Coefficient of variation (CV)0.45679963
Kurtosis-0.07180127
Mean9.21
Median Absolute Deviation (MAD)1.75
Skewness0.93751683
Sum368.4
Variance17.699897
MonotonicityNot monotonic
2023-12-12T05:06:46.910316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5.7 2
 
5.0%
6.9 2
 
5.0%
5.3 2
 
5.0%
7.0 1
 
2.5%
9.4 1
 
2.5%
17.0 1
 
2.5%
15.7 1
 
2.5%
10.3 1
 
2.5%
8.6 1
 
2.5%
10.0 1
 
2.5%
Other values (27) 27
67.5%
ValueCountFrequency (%)
3.3 1
2.5%
3.9 1
2.5%
4.1 1
2.5%
4.5 1
2.5%
5.1 1
2.5%
5.3 2
5.0%
5.6 1
2.5%
5.7 2
5.0%
6.8 1
2.5%
6.9 2
5.0%
ValueCountFrequency (%)
18.3 1
2.5%
17.7 1
2.5%
17.5 1
2.5%
17.1 1
2.5%
17.0 1
2.5%
16.2 1
2.5%
15.7 1
2.5%
12.9 1
2.5%
11.4 1
2.5%
10.3 1
2.5%

5~7만원
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.8175
Minimum9.1
Maximum27.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T05:06:47.062851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.1
5-th percentile10.87
Q113.175
median17.15
Q318.925
95-th percentile24.195
Maximum27.7
Range18.6
Interquartile range (IQR)5.75

Descriptive statistics

Standard deviation4.456392
Coefficient of variation (CV)0.2649854
Kurtosis-0.2734805
Mean16.8175
Median Absolute Deviation (MAD)3.6
Skewness0.43588614
Sum672.7
Variance19.859429
MonotonicityNot monotonic
2023-12-12T05:06:47.209250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
17.4 3
 
7.5%
21.4 2
 
5.0%
16.5 1
 
2.5%
18.0 1
 
2.5%
22.8 1
 
2.5%
19.0 1
 
2.5%
18.7 1
 
2.5%
17.3 1
 
2.5%
12.5 1
 
2.5%
11.5 1
 
2.5%
Other values (27) 27
67.5%
ValueCountFrequency (%)
9.1 1
2.5%
10.3 1
2.5%
10.9 1
2.5%
11.0 1
2.5%
11.5 1
2.5%
11.9 1
2.5%
12.0 1
2.5%
12.3 1
2.5%
12.5 1
2.5%
13.1 1
2.5%
ValueCountFrequency (%)
27.7 1
2.5%
26.0 1
2.5%
24.1 1
2.5%
22.8 1
2.5%
22.1 1
2.5%
22.0 1
2.5%
21.7 1
2.5%
21.4 2
5.0%
19.0 1
2.5%
18.9 1
2.5%

7~9만원
Real number (ℝ)

Distinct27
Distinct (%)67.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.495
Minimum1.2
Maximum8.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T05:06:47.335440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile1.895
Q12.4
median3.05
Q33.5
95-th percentile6.82
Maximum8.4
Range7.2
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation1.6361227
Coefficient of variation (CV)0.46813238
Kurtosis1.5528812
Mean3.495
Median Absolute Deviation (MAD)0.6
Skewness1.4702121
Sum139.8
Variance2.6768974
MonotonicityNot monotonic
2023-12-12T05:06:47.489590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
3.5 4
 
10.0%
2.3 3
 
7.5%
2.4 3
 
7.5%
3.3 3
 
7.5%
3.0 2
 
5.0%
3.1 2
 
5.0%
2.9 2
 
5.0%
2.5 2
 
5.0%
1.8 1
 
2.5%
2.1 1
 
2.5%
Other values (17) 17
42.5%
ValueCountFrequency (%)
1.2 1
 
2.5%
1.8 1
 
2.5%
1.9 1
 
2.5%
2.1 1
 
2.5%
2.2 1
 
2.5%
2.3 3
7.5%
2.4 3
7.5%
2.5 2
5.0%
2.6 1
 
2.5%
2.7 1
 
2.5%
ValueCountFrequency (%)
8.4 1
 
2.5%
7.2 1
 
2.5%
6.8 1
 
2.5%
6.6 1
 
2.5%
6.4 1
 
2.5%
5.6 1
 
2.5%
5.4 1
 
2.5%
4.8 1
 
2.5%
3.7 1
 
2.5%
3.5 4
10.0%

9-15만원
Real number (ℝ)

Distinct32
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.3275
Minimum11.8
Maximum25.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T05:06:47.919566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11.8
5-th percentile14.195
Q118.7
median20.85
Q322.075
95-th percentile24.015
Maximum25.9
Range14.1
Interquartile range (IQR)3.375

Descriptive statistics

Standard deviation3.0473181
Coefficient of variation (CV)0.14991111
Kurtosis0.7961478
Mean20.3275
Median Absolute Deviation (MAD)1.6
Skewness-0.90500591
Sum813.1
Variance9.2861474
MonotonicityNot monotonic
2023-12-12T05:06:48.090925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
21.7 3
 
7.5%
20.8 2
 
5.0%
18.0 2
 
5.0%
21.4 2
 
5.0%
23.7 2
 
5.0%
21.9 2
 
5.0%
22.0 2
 
5.0%
20.9 1
 
2.5%
11.8 1
 
2.5%
19.3 1
 
2.5%
Other values (22) 22
55.0%
ValueCountFrequency (%)
11.8 1
2.5%
14.1 1
2.5%
14.2 1
2.5%
14.6 1
2.5%
16.0 1
2.5%
18.0 2
5.0%
18.2 1
2.5%
18.3 1
2.5%
18.4 1
2.5%
18.8 1
2.5%
ValueCountFrequency (%)
25.9 1
2.5%
24.3 1
2.5%
24.0 1
2.5%
23.7 2
5.0%
23.5 1
2.5%
22.9 1
2.5%
22.7 1
2.5%
22.4 1
2.5%
22.3 1
2.5%
22.0 2
5.0%

15만원이상
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.2725
Minimum4.5
Maximum63.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T05:06:48.258181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.5
5-th percentile11.955
Q128.6
median40.95
Q347.1
95-th percentile58.965
Maximum63.8
Range59.3
Interquartile range (IQR)18.5

Descriptive statistics

Standard deviation14.693989
Coefficient of variation (CV)0.38393075
Kurtosis-0.31966487
Mean38.2725
Median Absolute Deviation (MAD)8.3
Skewness-0.51261227
Sum1530.9
Variance215.91333
MonotonicityNot monotonic
2023-12-12T05:06:48.389088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
25.6 3
 
7.5%
40.3 2
 
5.0%
46.7 2
 
5.0%
40.4 1
 
2.5%
16.2 1
 
2.5%
29.6 1
 
2.5%
35.9 1
 
2.5%
36.6 1
 
2.5%
47.7 1
 
2.5%
56.5 1
 
2.5%
Other values (26) 26
65.0%
ValueCountFrequency (%)
4.5 1
 
2.5%
11.1 1
 
2.5%
12.0 1
 
2.5%
14.1 1
 
2.5%
16.2 1
 
2.5%
17.1 1
 
2.5%
21.2 1
 
2.5%
25.6 3
7.5%
29.6 1
 
2.5%
31.9 1
 
2.5%
ValueCountFrequency (%)
63.8 1
2.5%
62.1 1
2.5%
58.8 1
2.5%
57.2 1
2.5%
56.5 1
2.5%
53.1 1
2.5%
49.8 1
2.5%
49.3 1
2.5%
49.2 1
2.5%
47.7 1
2.5%

표본수
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2537.05
Minimum824
Maximum10498
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T05:06:48.553075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum824
5-th percentile922.9
Q11076.5
median1571.5
Q31803.75
95-th percentile10061.4
Maximum10498
Range9674
Interquartile range (IQR)727.25

Descriptive statistics

Standard deviation2938.5605
Coefficient of variation (CV)1.1582588
Kurtosis3.5423402
Mean2537.05
Median Absolute Deviation (MAD)340
Skewness2.2818802
Sum101482
Variance8635137.8
MonotonicityNot monotonic
2023-12-12T05:06:48.715156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1405 2
 
5.0%
999 2
 
5.0%
921 1
 
2.5%
1098 1
 
2.5%
1012 1
 
2.5%
1421 1
 
2.5%
1896 1
 
2.5%
1611 1
 
2.5%
1380 1
 
2.5%
1730 1
 
2.5%
Other values (28) 28
70.0%
ValueCountFrequency (%)
824 1
2.5%
921 1
2.5%
923 1
2.5%
929 1
2.5%
939 1
2.5%
980 1
2.5%
999 2
5.0%
1007 1
2.5%
1012 1
2.5%
1098 1
2.5%
ValueCountFrequency (%)
10498 1
2.5%
10088 1
2.5%
10060 1
2.5%
10049 1
2.5%
10046 1
2.5%
2246 1
2.5%
2214 1
2.5%
2184 1
2.5%
1896 1
2.5%
1824 1
2.5%

Interactions

2023-12-12T05:06:44.870553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:39.719047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:40.536841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:41.594180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:42.457282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:43.213336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:44.007656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:44.975675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:39.846681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:40.673347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:41.730600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:42.586629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:43.322698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:44.127891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:45.095450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:39.963391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:40.809223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:41.849130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:42.696304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:43.432061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:44.278952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:45.184683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:40.089213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:40.926341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:41.981086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:42.794957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:43.552812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:44.424015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:45.266671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:40.205864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:41.035144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:42.116391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:42.897389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:43.656672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:44.533463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:45.353827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:40.325059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:41.151291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:42.247990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:42.995427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:43.786520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:44.661829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:45.433536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:40.437188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:41.477701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:42.348300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:43.109886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:43.896831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:44.776650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:06:48.807446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일(년)가구소득별3만원 미만3~5만원5~7만원7~9만원9-15만원15만원이상표본수
기준일(년)1.0000.0000.3960.3150.6210.3680.0000.0000.000
가구소득별0.0001.0000.7910.8640.6760.3500.6030.7540.987
3만원 미만0.3960.7911.0000.8260.5990.0000.8070.7030.000
3~5만원0.3150.8640.8261.0000.6980.6360.4420.7700.000
5~7만원0.6210.6760.5990.6981.0000.0000.2780.8490.000
7~9만원0.3680.3500.0000.6360.0001.0000.7980.4130.000
9-15만원0.0000.6030.8070.4420.2780.7981.0000.6300.000
15만원이상0.0000.7540.7030.7700.8490.4130.6301.0000.000
표본수0.0000.9870.0000.0000.0000.0000.0000.0001.000
2023-12-12T05:06:48.943605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가구소득별기준일(년)
가구소득별1.0000.000
기준일(년)0.0001.000
2023-12-12T05:06:49.037221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
3만원 미만3~5만원5~7만원7~9만원9-15만원15만원이상표본수기준일(년)가구소득별
3만원 미만1.0000.8000.742-0.198-0.401-0.841-0.4460.2160.359
3~5만원0.8001.0000.8380.113-0.324-0.924-0.3590.2210.439
5~7만원0.7420.8381.0000.183-0.052-0.935-0.4070.2720.375
7~9만원-0.1980.1130.1831.0000.350-0.1590.1070.2110.083
9-15만원-0.401-0.324-0.0520.3501.0000.1740.3720.0000.218
15만원이상-0.841-0.924-0.935-0.1590.1741.0000.4380.0000.457
표본수-0.446-0.359-0.4070.1070.3720.4381.0000.0000.791
기준일(년)0.2160.2210.2720.2110.0000.0000.0001.0000.000
가구소득별0.3590.4390.3750.0830.2180.4570.7910.0001.000

Missing values

2023-12-12T05:06:45.562244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:06:45.712570image/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

기준일(년)가구소득별3만원 미만3~5만원5~7만원7~9만원9-15만원15만원이상표본수
02018100만원 미만21.517.522.13.518.417.1921
12018100만원~200만원11.612.921.47.221.425.61007
22018200~300만원3.38.517.45.625.939.31706
32018300~400만원5.37.213.16.822.944.72246
42018400~500만원3.26.913.95.423.746.92214
52018500~600만원2.96.914.38.421.745.71405
62018600만원 이상2.84.110.96.618.357.2999
72018전체5.17.814.86.422.343.610498
82019100만원 미만29.518.324.12.414.611.1939
92019100만원~200만원18.711.422.03.518.825.6929
기준일(년)가구소득별3만원 미만3~5만원5~7만원7~9만원9-15만원15만원이상표본수
302021600만원 이상6.35.711.51.918.056.51730
312021전체14.09.516.52.819.937.310049
322022100만원 미만29.417.127.73.318.04.51098
332022100~200만원14.08.826.02.123.525.6824
342022200~300만원10.95.114.62.924.342.11357
352022300~400만원9.35.312.32.321.749.31764
362022400~500만원7.65.611.93.122.049.81797
372022500~600만원4.73.310.32.320.758.81532
382022600만원 이상6.64.59.11.814.263.81674
392022전체9.35.713.22.520.149.210046