Overview

Dataset statistics

Number of variables9
Number of observations90
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.1 KiB
Average record size in memory80.5 B

Variable types

Categorical2
Numeric6
Text1

Dataset

Description- 시도에 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/799

Alerts

3만원 미만 is highly overall correlated with 15만원이상High correlation
3~5만원 is highly overall correlated with 5~7만원 and 1 other fieldsHigh correlation
5~7만원 is highly overall correlated with 3~5만원 and 1 other fieldsHigh correlation
15만원이상 is highly overall correlated with 3만원 미만 and 2 other fieldsHigh correlation
표본수 is highly overall correlated with 시도별High correlation
시도별 is highly overall correlated with 표본수High correlation

Reproduction

Analysis started2023-12-11 19:52:34.251060
Analysis finished2023-12-11 19:52:39.738002
Duration5.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준일(년)
Categorical

Distinct5
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size852.0 B
2018
18 
2019
18 
2020
18 
2021
18 
2022
18 

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 18
20.0%
2019 18
20.0%
2020 18
20.0%
2021 18
20.0%
2022 18
20.0%

Length

2023-12-12T04:52:39.838195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T04:52:40.018903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 18
20.0%
2019 18
20.0%
2020 18
20.0%
2021 18
20.0%
2022 18
20.0%

시도별
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size852.0 B
강원
 
5
경기
 
5
경남
 
5
경북
 
5
광주
 
5
Other values (13)
65 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강원
2nd row경기
3rd row경남
4th row경북
5th row광주

Common Values

ValueCountFrequency (%)
강원 5
 
5.6%
경기 5
 
5.6%
경남 5
 
5.6%
경북 5
 
5.6%
광주 5
 
5.6%
대구 5
 
5.6%
대전 5
 
5.6%
부산 5
 
5.6%
서울 5
 
5.6%
세종 5
 
5.6%
Other values (8) 40
44.4%

Length

2023-12-12T04:52:40.212622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강원 5
 
5.6%
경기 5
 
5.6%
충남 5
 
5.6%
제주 5
 
5.6%
전체 5
 
5.6%
전북 5
 
5.6%
전남 5
 
5.6%
인천 5
 
5.6%
울산 5
 
5.6%
세종 5
 
5.6%
Other values (8) 40
44.4%

3만원 미만
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)74.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.68
Minimum0.6
Maximum19.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-12T04:52:40.419550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile3
Q17.1
median9.4
Q312.125
95-th percentile17.25
Maximum19.4
Range18.8
Interquartile range (IQR)5.025

Descriptive statistics

Standard deviation4.3299376
Coefficient of variation (CV)0.4473076
Kurtosis-0.31537931
Mean9.68
Median Absolute Deviation (MAD)2.45
Skewness0.39191046
Sum871.2
Variance18.74836
MonotonicityNot monotonic
2023-12-12T04:52:40.647624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.1 3
 
3.3%
7.2 3
 
3.3%
12.2 3
 
3.3%
9.9 3
 
3.3%
9.1 2
 
2.2%
9.8 2
 
2.2%
7.8 2
 
2.2%
4.6 2
 
2.2%
7.6 2
 
2.2%
8.6 2
 
2.2%
Other values (57) 66
73.3%
ValueCountFrequency (%)
0.6 1
1.1%
2.1 1
1.1%
2.6 1
1.1%
2.9 1
1.1%
3.0 2
2.2%
4.1 1
1.1%
4.2 1
1.1%
4.3 1
1.1%
4.6 2
2.2%
4.7 2
2.2%
ValueCountFrequency (%)
19.4 1
 
1.1%
19.3 1
 
1.1%
19.1 1
 
1.1%
19.0 1
 
1.1%
17.7 1
 
1.1%
16.7 1
 
1.1%
16.4 1
 
1.1%
16.3 1
 
1.1%
16.1 3
3.3%
15.8 1
 
1.1%

3~5만원
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1877778
Minimum1.8
Maximum18.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-12T04:52:40.884394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile3.2
Q15.8
median7.5
Q310.025
95-th percentile15.55
Maximum18.3
Range16.5
Interquartile range (IQR)4.225

Descriptive statistics

Standard deviation3.5811563
Coefficient of variation (CV)0.4373783
Kurtosis0.80666259
Mean8.1877778
Median Absolute Deviation (MAD)1.9
Skewness0.89193763
Sum736.9
Variance12.82468
MonotonicityNot monotonic
2023-12-12T04:52:41.114317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.2 5
 
5.6%
5.8 4
 
4.4%
7.9 4
 
4.4%
7.4 2
 
2.2%
17.3 2
 
2.2%
6.0 2
 
2.2%
8.6 2
 
2.2%
5.2 2
 
2.2%
9.4 2
 
2.2%
15.0 2
 
2.2%
Other values (53) 63
70.0%
ValueCountFrequency (%)
1.8 1
1.1%
2.4 1
1.1%
2.7 1
1.1%
3.0 1
1.1%
3.2 2
2.2%
3.5 1
1.1%
3.6 1
1.1%
3.7 1
1.1%
3.8 1
1.1%
4.6 1
1.1%
ValueCountFrequency (%)
18.3 1
1.1%
18.1 1
1.1%
17.3 2
2.2%
16.0 1
1.1%
15.0 2
2.2%
13.8 1
1.1%
12.9 1
1.1%
12.3 1
1.1%
12.2 1
1.1%
12.1 2
2.2%

5~7만원
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.352222
Minimum5.3
Maximum27.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-12T04:52:41.339832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.3
5-th percentile9.435
Q112.925
median15.1
Q318.15
95-th percentile21.065
Maximum27.9
Range22.6
Interquartile range (IQR)5.225

Descriptive statistics

Standard deviation4.0915157
Coefficient of variation (CV)0.26650967
Kurtosis0.70608263
Mean15.352222
Median Absolute Deviation (MAD)2.7
Skewness0.30831125
Sum1381.7
Variance16.740501
MonotonicityNot monotonic
2023-12-12T04:52:41.593347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.1 3
 
3.3%
14.8 3
 
3.3%
18.7 2
 
2.2%
17.1 2
 
2.2%
17.4 2
 
2.2%
13.2 2
 
2.2%
19.3 2
 
2.2%
18.4 2
 
2.2%
11.5 2
 
2.2%
16.8 2
 
2.2%
Other values (62) 68
75.6%
ValueCountFrequency (%)
5.3 1
1.1%
5.8 1
1.1%
8.3 1
1.1%
8.4 1
1.1%
9.3 1
1.1%
9.6 1
1.1%
9.9 1
1.1%
10.0 1
1.1%
10.3 1
1.1%
10.6 1
1.1%
ValueCountFrequency (%)
27.9 1
1.1%
26.5 1
1.1%
24.3 1
1.1%
23.1 1
1.1%
21.2 1
1.1%
20.9 1
1.1%
20.8 1
1.1%
20.3 1
1.1%
20.2 2
2.2%
19.9 1
1.1%

7~9만원
Text

Distinct52
Distinct (%)57.8%
Missing0
Missing (%)0.0%
Memory size852.0 B
2023-12-12T04:52:41.955833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.7777778
Min length1

Characters and Unicode

Total characters250
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)28.9%

Sample

1st row4.1
2nd row7.2
3rd row7.6
4th row2.9
5th row4.2
ValueCountFrequency (%)
2.9 4
 
4.4%
1.5 4
 
4.4%
1.7 3
 
3.3%
1 3
 
3.3%
3 3
 
3.3%
3.4 3
 
3.3%
2.1 3
 
3.3%
3.3 3
 
3.3%
4.2 3
 
3.3%
1.6 3
 
3.3%
Other values (42) 58
64.4%
2023-12-12T04:52:42.502854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 79
31.6%
1 34
13.6%
2 26
 
10.4%
3 24
 
9.6%
7 17
 
6.8%
5 15
 
6.0%
6 14
 
5.6%
4 13
 
5.2%
9 11
 
4.4%
0 8
 
3.2%
Other values (2) 9
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 170
68.0%
Other Punctuation 79
31.6%
Dash Punctuation 1
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 34
20.0%
2 26
15.3%
3 24
14.1%
7 17
10.0%
5 15
8.8%
6 14
8.2%
4 13
 
7.6%
9 11
 
6.5%
0 8
 
4.7%
8 8
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 79
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 250
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 79
31.6%
1 34
13.6%
2 26
 
10.4%
3 24
 
9.6%
7 17
 
6.8%
5 15
 
6.0%
6 14
 
5.6%
4 13
 
5.2%
9 11
 
4.4%
0 8
 
3.2%
Other values (2) 9
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 250
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 79
31.6%
1 34
13.6%
2 26
 
10.4%
3 24
 
9.6%
7 17
 
6.8%
5 15
 
6.0%
6 14
 
5.6%
4 13
 
5.2%
9 11
 
4.4%
0 8
 
3.2%
Other values (2) 9
 
3.6%

9-15만원
Real number (ℝ)

Distinct69
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.355556
Minimum11.3
Maximum29.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-12T04:52:42.788590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11.3
5-th percentile15.145
Q118.025
median20.1
Q322.45
95-th percentile26.3
Maximum29.7
Range18.4
Interquartile range (IQR)4.425

Descriptive statistics

Standard deviation3.6714847
Coefficient of variation (CV)0.1803677
Kurtosis0.262843
Mean20.355556
Median Absolute Deviation (MAD)2.2
Skewness0.094045136
Sum1832
Variance13.4798
MonotonicityNot monotonic
2023-12-12T04:52:43.056069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.3 3
 
3.3%
19.1 3
 
3.3%
20.5 3
 
3.3%
19.9 3
 
3.3%
24.4 2
 
2.2%
21.5 2
 
2.2%
16.7 2
 
2.2%
22.0 2
 
2.2%
16.2 2
 
2.2%
22.5 2
 
2.2%
Other values (59) 66
73.3%
ValueCountFrequency (%)
11.3 1
1.1%
11.9 2
2.2%
13.7 1
1.1%
15.1 1
1.1%
15.2 1
1.1%
15.4 1
1.1%
15.6 1
1.1%
15.9 1
1.1%
16.2 2
2.2%
16.6 1
1.1%
ValueCountFrequency (%)
29.7 1
1.1%
29.6 1
1.1%
27.7 1
1.1%
26.9 1
1.1%
26.3 2
2.2%
25.8 1
1.1%
25.6 1
1.1%
25.4 1
1.1%
25.2 1
1.1%
25.0 1
1.1%

15만원이상
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.948889
Minimum18.6
Maximum69.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-12T04:52:43.329515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18.6
5-th percentile27.645
Q136.65
median44.3
Q348.7
95-th percentile60.885
Maximum69.1
Range50.5
Interquartile range (IQR)12.05

Descriptive statistics

Standard deviation9.9227833
Coefficient of variation (CV)0.23103702
Kurtosis0.02662203
Mean42.948889
Median Absolute Deviation (MAD)6.5
Skewness-0.013347776
Sum3865.4
Variance98.461628
MonotonicityNot monotonic
2023-12-12T04:52:43.605653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42.2 3
 
3.3%
45.4 3
 
3.3%
49.0 2
 
2.2%
44.4 2
 
2.2%
33.0 2
 
2.2%
48.7 2
 
2.2%
41.5 2
 
2.2%
47.8 2
 
2.2%
40.4 2
 
2.2%
44.6 2
 
2.2%
Other values (66) 68
75.6%
ValueCountFrequency (%)
18.6 1
1.1%
22.0 1
1.1%
23.9 1
1.1%
26.4 1
1.1%
27.6 1
1.1%
27.7 1
1.1%
28.1 1
1.1%
28.2 1
1.1%
28.3 1
1.1%
28.4 1
1.1%
ValueCountFrequency (%)
69.1 1
1.1%
63.0 1
1.1%
62.9 1
1.1%
62.4 1
1.1%
61.2 1
1.1%
60.5 1
1.1%
58.9 1
1.1%
56.3 1
1.1%
54.1 1
1.1%
52.8 1
1.1%

표본수
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1127.5778
Minimum61
Maximum10498
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-12T04:52:43.867231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile210
Q1448
median535
Q3680.25
95-th percentile6608.9
Maximum10498
Range10437
Interquartile range (IQR)232.25

Descriptive statistics

Standard deviation2229.1076
Coefficient of variation (CV)1.9768992
Kurtosis13.060565
Mean1127.5778
Median Absolute Deviation (MAD)127
Skewness3.7904383
Sum101482
Variance4968920.8
MonotonicityNot monotonic
2023-12-12T04:52:44.100669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
475 3
 
3.3%
681 2
 
2.2%
463 2
 
2.2%
302 2
 
2.2%
580 2
 
2.2%
460 2
 
2.2%
201 2
 
2.2%
221 2
 
2.2%
515 1
 
1.1%
1316 1
 
1.1%
Other values (71) 71
78.9%
ValueCountFrequency (%)
61 1
1.1%
120 1
1.1%
189 1
1.1%
201 2
2.2%
221 2
2.2%
294 1
1.1%
300 1
1.1%
302 2
2.2%
303 1
1.1%
308 1
1.1%
ValueCountFrequency (%)
10498 1
1.1%
10088 1
1.1%
10060 1
1.1%
10049 1
1.1%
10046 1
1.1%
2408 1
1.1%
1931 1
1.1%
1344 1
1.1%
1326 1
1.1%
1325 1
1.1%

Interactions

2023-12-12T04:52:38.633031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:34.707948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:35.463520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:36.162076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:36.731605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:37.742473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:38.763885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:34.843301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:35.593781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:36.277000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:36.832213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:37.869076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:38.871583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:34.963142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:35.724251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:36.379795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:36.927163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:38.061640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:38.975132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:35.079082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:35.841392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:36.462277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:37.027434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:38.202674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:39.115003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:35.212207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:35.952703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:36.557662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:37.143723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:38.390281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:39.236825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:35.348561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:36.061599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:36.644095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:37.625985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:52:38.528220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T04:52:44.289054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일(년)시도별3만원 미만3~5만원5~7만원7~9만원9-15만원15만원이상표본수
기준일(년)1.0000.0000.4720.5700.2930.6260.0000.5870.000
시도별0.0001.0000.0000.3660.3490.4110.0900.2530.928
3만원 미만0.4720.0001.0000.2220.4790.6950.4990.7800.000
3~5만원0.5700.3660.2221.0000.6350.6750.0600.8300.000
5~7만원0.2930.3490.4790.6351.0000.8140.6690.8550.000
7~9만원0.6260.4110.6950.6750.8141.0000.5420.7720.000
9-15만원0.0000.0900.4990.0600.6690.5421.0000.5440.000
15만원이상0.5870.2530.7800.8300.8550.7720.5441.0000.050
표본수0.0000.9280.0000.0000.0000.0000.0000.0501.000
2023-12-12T04:52:44.497504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도별기준일(년)
시도별1.0000.000
기준일(년)0.0001.000
2023-12-12T04:52:44.674200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
3만원 미만3~5만원5~7만원9-15만원15만원이상표본수기준일(년)시도별
3만원 미만1.0000.4790.413-0.317-0.571-0.0840.2060.000
3~5만원0.4791.0000.519-0.097-0.772-0.0400.2670.180
5~7만원0.4130.5191.000-0.074-0.7790.0360.1170.125
9-15만원-0.317-0.097-0.0741.000-0.1470.1320.0000.000
15만원이상-0.571-0.772-0.779-0.1471.0000.0370.2720.081
표본수-0.084-0.0400.0360.1320.0371.0000.0000.728
기준일(년)0.2060.2670.1170.0000.2720.0001.0000.000
시도별0.0000.1800.1250.0000.0810.7280.0001.000

Missing values

2023-12-12T04:52:39.419905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T04:52:39.652611image/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)시도별3만원 미만3~5만원5~7만원7~9만원9-15만원15만원이상표본수
02018강원9.17.418.74.125.235.6478
12018경기2.96.815.37.225.642.21325
22018경남3.03.210.37.627.748.2713
32018경북9.56.714.42.919.147.4666
42018광주2.65.514.94.226.346.5475
52018대구6.27.119.12.120.844.6638
62018대전7.216.020.28.520.527.7493
72018부산2.17.210.67.818.354.1734
82018서울4.35.712.66.721.449.31220
92018세종9.610.415.43.416.844.4189
기준일(년)시도별3만원 미만3~5만원5~7만원7~9만원9-15만원15만원이상표본수
802022서울9.67.214.02.119.747.41164
812022세종8.43.55.30.222.160.5221
822022울산7.82.49.61.116.862.4393
832022인천10.85.812.30.919.251.0617
842022전남7.04.616.11.329.641.5515
852022전북15.84.816.9119.042.5523
862022전체9.35.713.22.520.149.210046
872022제주4.67.911.96.421.547.8300
882022충남6.33.212.97.717.852.2558
892022충북6.43.611.80.525.851.8482