Overview

Dataset statistics

Number of variables20
Number of observations850
Missing cells26
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory145.4 KiB
Average record size in memory175.2 B

Variable types

Categorical4
Numeric15
Text1

Dataset

Description1. 직역구분: 지역, 직장 2. 연월: 2022년 1월 ~ 2023년 5월 3. 시도, 시군구: 서울특별시_구단위별 구분 4. 연령: 65세 ~ 99세(5세 단위), 100세 이상 5. 성별: 남자, 여자 1. 부과보험료 기준 자료 2. 지역보험료는 세대 단위로 산정됨에 따라, 세대주의 연령에 따라 자료 구분 1. 직장가입자의 보수월액 보험료이며, 산정보험료 기준임 2. 보수월액보험료는 사업장 단위로 부과되므로, 본 자료는 서울시에 소재한 사업장의 가입자를 기준으로 발췌됨 ※ 민원인의 제공 신청에 따른 제공 건으로서 2023-07-10 발췌
URLhttps://www.data.go.kr/data/15116515/fileData.do

Alerts

시도 has constant value ""Constant
65-69세(남자) is highly overall correlated with 65-69세(여자) and 5 other fieldsHigh correlation
65-69세(여자) is highly overall correlated with 65-69세(남자) and 2 other fieldsHigh correlation
70-74세(여자) is highly overall correlated with 65-69세(남자) and 13 other fieldsHigh correlation
75-79세(남자) is highly overall correlated with 65-69세(남자) and 13 other fieldsHigh correlation
75-79세(여자) is highly overall correlated with 65-69세(남자) and 13 other fieldsHigh correlation
80-84세(남자) is highly overall correlated with 65-69세(남자) and 13 other fieldsHigh correlation
80-84세(여자) is highly overall correlated with 70-74세(여자) and 12 other fieldsHigh correlation
85-89세(남자) is highly overall correlated with 65-69세(남자) and 13 other fieldsHigh correlation
85-89세(여자) is highly overall correlated with 70-74세(여자) and 12 other fieldsHigh correlation
90-94세(남자) is highly overall correlated with 70-74세(여자) and 12 other fieldsHigh correlation
90-94세(여자) is highly overall correlated with 70-74세(여자) and 12 other fieldsHigh correlation
95-99세(남자) is highly overall correlated with 70-74세(여자) and 12 other fieldsHigh correlation
95-99세(여자) is highly overall correlated with 70-74세(여자) and 11 other fieldsHigh correlation
100세이상(남자) is highly overall correlated with 75-79세(여자) and 10 other fieldsHigh correlation
100세이상(여자) is highly overall correlated with 70-74세(여자) and 12 other fieldsHigh correlation
직역구분 is highly overall correlated with 70-74세(여자) and 11 other fieldsHigh correlation
100세이상(남자) has 17 (2.0%) missing valuesMissing
100세이상(여자) has 9 (1.1%) missing valuesMissing
65-69세(남자) has unique valuesUnique
65-69세(여자) has unique valuesUnique
70-74세(남자) has unique valuesUnique
70-74세(여자) has unique valuesUnique
75-79세(남자) has unique valuesUnique
75-79세(여자) has unique valuesUnique
80-84세(남자) has unique valuesUnique
80-84세(여자) has unique valuesUnique
90-94세(여자) has 34 (4.0%) zerosZeros
95-99세(남자) has 95 (11.2%) zerosZeros
95-99세(여자) has 244 (28.7%) zerosZeros
100세이상(남자) has 318 (37.4%) zerosZeros
100세이상(여자) has 391 (46.0%) zerosZeros

Reproduction

Analysis started2023-12-12 06:41:35.481276
Analysis finished2023-12-12 06:42:02.579860
Duration27.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

직역구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
지역
425 
직장
425 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지역 425
50.0%
직장 425
50.0%

Length

2023-12-12T15:42:02.658206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:42:02.774739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지역 425
50.0%
직장 425
50.0%

연월
Categorical

Distinct17
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
2022년1월
 
50
2022년2월
 
50
2022년3월
 
50
2022년4월
 
50
2022년5월
 
50
Other values (12)
600 

Length

Max length8
Median length7
Mean length7.1764706
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022년1월
2nd row2022년1월
3rd row2022년1월
4th row2022년1월
5th row2022년1월

Common Values

ValueCountFrequency (%)
2022년1월 50
 
5.9%
2022년2월 50
 
5.9%
2022년3월 50
 
5.9%
2022년4월 50
 
5.9%
2022년5월 50
 
5.9%
2022년6월 50
 
5.9%
2022년7월 50
 
5.9%
2022년8월 50
 
5.9%
2022년9월 50
 
5.9%
2022년10월 50
 
5.9%
Other values (7) 350
41.2%

Length

2023-12-12T15:42:02.924076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022년1월 50
 
5.9%
2022년10월 50
 
5.9%
2023년4월 50
 
5.9%
2023년3월 50
 
5.9%
2023년2월 50
 
5.9%
2023년1월 50
 
5.9%
2022년12월 50
 
5.9%
2022년11월 50
 
5.9%
2022년9월 50
 
5.9%
2022년2월 50
 
5.9%
Other values (7) 350
41.2%

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
서울특별시
850 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 850
100.0%

Length

2023-12-12T15:42:03.085356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:42:03.178126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 850
100.0%

시군구
Categorical

Distinct25
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
종로구
 
34
중구
 
34
용산구
 
34
성동구
 
34
광진구
 
34
Other values (20)
680 

Length

Max length4
Median length3
Mean length3.08
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row종로구
2nd row중구
3rd row용산구
4th row성동구
5th row광진구

Common Values

ValueCountFrequency (%)
종로구 34
 
4.0%
중구 34
 
4.0%
용산구 34
 
4.0%
성동구 34
 
4.0%
광진구 34
 
4.0%
동대문구 34
 
4.0%
중랑구 34
 
4.0%
성북구 34
 
4.0%
강북구 34
 
4.0%
도봉구 34
 
4.0%
Other values (15) 510
60.0%

Length

2023-12-12T15:42:03.317507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
종로구 34
 
4.0%
마포구 34
 
4.0%
송파구 34
 
4.0%
강남구 34
 
4.0%
서초구 34
 
4.0%
관악구 34
 
4.0%
동작구 34
 
4.0%
영등포구 34
 
4.0%
금천구 34
 
4.0%
구로구 34
 
4.0%
Other values (15) 510
60.0%

65-69세(남자)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct850
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2794773 × 108
Minimum1.2457328 × 108
Maximum2.9942706 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2023-12-12T15:42:03.497155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.2457328 × 108
5-th percentile2.1883721 × 108
Q13.2348088 × 108
median4.9723978 × 108
Q37.1108261 × 108
95-th percentile1.6430398 × 109
Maximum2.9942706 × 109
Range2.8696973 × 109
Interquartile range (IQR)3.8760173 × 108

Descriptive statistics

Standard deviation4.6447162 × 108
Coefficient of variation (CV)0.73966606
Kurtosis6.1383522
Mean6.2794773 × 108
Median Absolute Deviation (MAD)1.8962837 × 108
Skewness2.2722598
Sum5.3375557 × 1011
Variance2.1573389 × 1017
MonotonicityNot monotonic
2023-12-12T15:42:03.670552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
234399670 1
 
0.1%
217781690 1
 
0.1%
227169220 1
 
0.1%
238608080 1
 
0.1%
578436400 1
 
0.1%
379667180 1
 
0.1%
593359800 1
 
0.1%
650111150 1
 
0.1%
623962470 1
 
0.1%
1147220510 1
 
0.1%
Other values (840) 840
98.8%
ValueCountFrequency (%)
124573280 1
0.1%
125243320 1
0.1%
134120720 1
0.1%
134343850 1
0.1%
137995360 1
0.1%
138267830 1
0.1%
139059500 1
0.1%
141155490 1
0.1%
141594460 1
0.1%
142271230 1
0.1%
ValueCountFrequency (%)
2994270620 1
0.1%
2925122990 1
0.1%
2786328260 1
0.1%
2764900410 1
0.1%
2764486400 1
0.1%
2712988640 1
0.1%
2711278300 1
0.1%
2697302120 1
0.1%
2678446900 1
0.1%
2605437920 1
0.1%

65-69세(여자)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct850
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0703298 × 108
Minimum50773240
Maximum9.2271929 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2023-12-12T15:42:03.823727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50773240
5-th percentile81981300
Q11.2185243 × 108
median1.5821426 × 108
Q32.3579562 × 108
95-th percentile4.8042816 × 108
Maximum9.2271929 × 108
Range8.7194605 × 108
Interquartile range (IQR)1.1394319 × 108

Descriptive statistics

Standard deviation1.3897462 × 108
Coefficient of variation (CV)0.67126802
Kurtosis6.4154709
Mean2.0703298 × 108
Median Absolute Deviation (MAD)47486430
Skewness2.3091803
Sum1.7597804 × 1011
Variance1.9313945 × 1016
MonotonicityNot monotonic
2023-12-12T15:42:03.961207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74420800 1
 
0.1%
91276200 1
 
0.1%
128427130 1
 
0.1%
100937450 1
 
0.1%
218313330 1
 
0.1%
131999610 1
 
0.1%
236377690 1
 
0.1%
187193270 1
 
0.1%
180212410 1
 
0.1%
382624590 1
 
0.1%
Other values (840) 840
98.8%
ValueCountFrequency (%)
50773240 1
0.1%
52464890 1
0.1%
54720540 1
0.1%
56248640 1
0.1%
57158110 1
0.1%
57863210 1
0.1%
58006000 1
0.1%
58772260 1
0.1%
59417710 1
0.1%
60328060 1
0.1%
ValueCountFrequency (%)
922719290 1
0.1%
901999330 1
0.1%
860815220 1
0.1%
859671530 1
0.1%
855284390 1
0.1%
853229240 1
0.1%
852366530 1
0.1%
844347130 1
0.1%
842166040 1
0.1%
815290690 1
0.1%

70-74세(남자)
Text

UNIQUE 

Distinct850
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
2023-12-12T15:42:04.300090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length9.0011765
Min length1

Characters and Unicode

Total characters7651
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique850 ?
Unique (%)100.0%

Sample

1st row159512720
2nd row102437870
3rd row270830220
4th row263779940
5th row367497610
ValueCountFrequency (%)
159512720 1
 
0.1%
126156630 1
 
0.1%
156272840 1
 
0.1%
134984340 1
 
0.1%
100698040 1
 
0.1%
85955870 1
 
0.1%
301259090 1
 
0.1%
158449530 1
 
0.1%
261935720 1
 
0.1%
277157990 1
 
0.1%
Other values (839) 839
98.8%
2023-12-12T15:42:04.736341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1454
19.0%
1 921
12.0%
2 712
9.3%
3 712
9.3%
4 691
9.0%
5 663
8.7%
6 650
8.5%
8 627
8.2%
9 617
8.1%
7 603
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7650
> 99.9%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1454
19.0%
1 921
12.0%
2 712
9.3%
3 712
9.3%
4 691
9.0%
5 663
8.7%
6 650
8.5%
8 627
8.2%
9 617
8.1%
7 603
7.9%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7651
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1454
19.0%
1 921
12.0%
2 712
9.3%
3 712
9.3%
4 691
9.0%
5 663
8.7%
6 650
8.5%
8 627
8.2%
9 617
8.1%
7 603
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7651
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1454
19.0%
1 921
12.0%
2 712
9.3%
3 712
9.3%
4 691
9.0%
5 663
8.7%
6 650
8.5%
8 627
8.2%
9 617
8.1%
7 603
7.9%

70-74세(여자)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct850
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1178316 × 108
Minimum26834210
Maximum5.2815431 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2023-12-12T15:42:04.909415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26834210
5-th percentile37428326
Q155642030
median87711990
Q31.3872719 × 108
95-th percentile2.6617652 × 108
Maximum5.2815431 × 108
Range5.013201 × 108
Interquartile range (IQR)83085160

Descriptive statistics

Standard deviation82478890
Coefficient of variation (CV)0.73784718
Kurtosis5.9020464
Mean1.1178316 × 108
Median Absolute Deviation (MAD)35281805
Skewness2.1755308
Sum9.5015686 × 1010
Variance6.8027672 × 1015
MonotonicityNot monotonic
2023-12-12T15:42:05.069990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67375010 1
 
0.1%
30849490 1
 
0.1%
43569590 1
 
0.1%
32101630 1
 
0.1%
73396820 1
 
0.1%
44123370 1
 
0.1%
87478210 1
 
0.1%
56544930 1
 
0.1%
68099460 1
 
0.1%
139956550 1
 
0.1%
Other values (840) 840
98.8%
ValueCountFrequency (%)
26834210 1
0.1%
29063060 1
0.1%
29545220 1
0.1%
29855820 1
0.1%
29947900 1
0.1%
30469840 1
0.1%
30515260 1
0.1%
30550610 1
0.1%
30849490 1
0.1%
30863210 1
0.1%
ValueCountFrequency (%)
528154310 1
0.1%
525303350 1
0.1%
523057430 1
0.1%
522677880 1
0.1%
520977000 1
0.1%
492844480 1
0.1%
445142750 1
0.1%
444189150 1
0.1%
443980150 1
0.1%
443668340 1
0.1%

75-79세(남자)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct850
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3824676 × 108
Minimum12568590
Maximum1.5705095 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2023-12-12T15:42:05.216106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12568590
5-th percentile40467604
Q178680385
median1.9045838 × 108
Q33.1424729 × 108
95-th percentile5.6421252 × 108
Maximum1.5705095 × 109
Range1.5579409 × 109
Interquartile range (IQR)2.355669 × 108

Descriptive statistics

Standard deviation2.3638914 × 108
Coefficient of variation (CV)0.99220296
Kurtosis8.9660787
Mean2.3824676 × 108
Median Absolute Deviation (MAD)1.1481712 × 108
Skewness2.6523261
Sum2.0250974 × 1011
Variance5.5879825 × 1016
MonotonicityNot monotonic
2023-12-12T15:42:05.373143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
146616170 1
 
0.1%
46433650 1
 
0.1%
35346770 1
 
0.1%
40469160 1
 
0.1%
109520850 1
 
0.1%
45380870 1
 
0.1%
121369500 1
 
0.1%
94374030 1
 
0.1%
97602690 1
 
0.1%
194431130 1
 
0.1%
Other values (840) 840
98.8%
ValueCountFrequency (%)
12568590 1
0.1%
17308630 1
0.1%
23081230 1
0.1%
28456220 1
0.1%
30423660 1
0.1%
30441050 1
0.1%
30478700 1
0.1%
30980250 1
0.1%
31378880 1
0.1%
31977200 1
0.1%
ValueCountFrequency (%)
1570509490 1
0.1%
1563993580 1
0.1%
1532335270 1
0.1%
1530768760 1
0.1%
1530487600 1
0.1%
1476604500 1
0.1%
1201137370 1
0.1%
1190585160 1
0.1%
1173346630 1
0.1%
1165881740 1
0.1%

75-79세(여자)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct850
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75620780
Minimum3226090
Maximum4.6938875 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2023-12-12T15:42:05.562442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3226090
5-th percentile12747684
Q121206748
median61335550
Q395287545
95-th percentile1.9163051 × 108
Maximum4.6938875 × 108
Range4.6616266 × 108
Interquartile range (IQR)74080798

Descriptive statistics

Standard deviation76048326
Coefficient of variation (CV)1.0056538
Kurtosis7.6238608
Mean75620780
Median Absolute Deviation (MAD)39442475
Skewness2.453061
Sum6.4277663 × 1010
Variance5.7833479 × 1015
MonotonicityNot monotonic
2023-12-12T15:42:05.719371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67735070 1
 
0.1%
10942300 1
 
0.1%
14538990 1
 
0.1%
10675190 1
 
0.1%
29490670 1
 
0.1%
14351730 1
 
0.1%
27127010 1
 
0.1%
16590690 1
 
0.1%
20056380 1
 
0.1%
42998440 1
 
0.1%
Other values (840) 840
98.8%
ValueCountFrequency (%)
3226090 1
0.1%
6694440 1
0.1%
8021390 1
0.1%
8363350 1
0.1%
8954440 1
0.1%
9450550 1
0.1%
9581840 1
0.1%
9658240 1
0.1%
9824050 1
0.1%
10054290 1
0.1%
ValueCountFrequency (%)
469388750 1
0.1%
468919850 1
0.1%
468151540 1
0.1%
465210440 1
0.1%
463012240 1
0.1%
447450350 1
0.1%
390249120 1
0.1%
387898850 1
0.1%
381703980 1
0.1%
377595880 1
0.1%

80-84세(남자)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct850
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4779243 × 108
Minimum-11649160
Maximum1.1178973 × 109
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)0.2%
Memory size7.6 KiB
2023-12-12T15:42:05.876099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-11649160
5-th percentile15791998
Q134425150
median1.0504626 × 108
Q32.0707201 × 108
95-th percentile3.7405231 × 108
Maximum1.1178973 × 109
Range1.1295464 × 109
Interquartile range (IQR)1.7264686 × 108

Descriptive statistics

Standard deviation1.6297632 × 108
Coefficient of variation (CV)1.1027379
Kurtosis9.5212965
Mean1.4779243 × 108
Median Absolute Deviation (MAD)79860590
Skewness2.6718848
Sum1.2562356 × 1011
Variance2.656128 × 1016
MonotonicityNot monotonic
2023-12-12T15:42:06.032413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
131558690 1
 
0.1%
9989430 1
 
0.1%
15616060 1
 
0.1%
14705030 1
 
0.1%
58541560 1
 
0.1%
18952430 1
 
0.1%
37795850 1
 
0.1%
41997300 1
 
0.1%
35568810 1
 
0.1%
73447770 1
 
0.1%
Other values (840) 840
98.8%
ValueCountFrequency (%)
-11649160 1
0.1%
-684480 1
0.1%
8621050 1
0.1%
9989430 1
0.1%
10199720 1
0.1%
10206200 1
0.1%
10268580 1
0.1%
10639910 1
0.1%
10744240 1
0.1%
10758470 1
0.1%
ValueCountFrequency (%)
1117897270 1
0.1%
1103973660 1
0.1%
1095807940 1
0.1%
1078907120 1
0.1%
1064776940 1
0.1%
1022621420 1
0.1%
734721170 1
0.1%
729346300 1
0.1%
724191260 1
0.1%
719093820 1
0.1%

80-84세(여자)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct850
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50244048
Minimum1147120
Maximum3.3999483 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2023-12-12T15:42:06.174037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1147120
5-th percentile3502624.5
Q17164870
median32450635
Q371473940
95-th percentile1.5628942 × 108
Maximum3.3999483 × 108
Range3.3884771 × 108
Interquartile range (IQR)64309070

Descriptive statistics

Standard deviation56264333
Coefficient of variation (CV)1.1198209
Kurtosis5.5154088
Mean50244048
Median Absolute Deviation (MAD)27043830
Skewness2.0457368
Sum4.2707441 × 1010
Variance3.1656752 × 1015
MonotonicityNot monotonic
2023-12-12T15:42:06.334928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59532260 1
 
0.1%
3282330 1
 
0.1%
2940420 1
 
0.1%
3746610 1
 
0.1%
12342620 1
 
0.1%
4759530 1
 
0.1%
8118220 1
 
0.1%
2792310 1
 
0.1%
6866810 1
 
0.1%
18213530 1
 
0.1%
Other values (840) 840
98.8%
ValueCountFrequency (%)
1147120 1
0.1%
1236010 1
0.1%
1703220 1
0.1%
2408500 1
0.1%
2445190 1
0.1%
2521250 1
0.1%
2591280 1
0.1%
2759320 1
0.1%
2792310 1
0.1%
2847980 1
0.1%
ValueCountFrequency (%)
339994830 1
0.1%
338370120 1
0.1%
337095890 1
0.1%
329256480 1
0.1%
325852380 1
0.1%
312197830 1
0.1%
263305510 1
0.1%
255517710 1
0.1%
252011120 1
0.1%
250413710 1
0.1%

85-89세(남자)
Real number (ℝ)

HIGH CORRELATION 

Distinct848
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57810947
Minimum-8460
Maximum4.1597766 × 108
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.1%
Memory size7.6 KiB
2023-12-12T15:42:06.481848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-8460
5-th percentile3929450
Q19031047.5
median38170260
Q380064952
95-th percentile1.7311349 × 108
Maximum4.1597766 × 108
Range4.1598612 × 108
Interquartile range (IQR)71033905

Descriptive statistics

Standard deviation67822589
Coefficient of variation (CV)1.173179
Kurtosis7.3646832
Mean57810947
Median Absolute Deviation (MAD)30646640
Skewness2.4527091
Sum4.9139305 × 1010
Variance4.5999036 × 1015
MonotonicityNot monotonic
2023-12-12T15:42:06.621486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2638050 2
 
0.2%
3949180 2
 
0.2%
67532390 1
 
0.1%
4091810 1
 
0.1%
621590 1
 
0.1%
5138750 1
 
0.1%
8020330 1
 
0.1%
5216790 1
 
0.1%
18334960 1
 
0.1%
3304160 1
 
0.1%
Other values (838) 838
98.6%
ValueCountFrequency (%)
-8460 1
0.1%
256950 1
0.1%
621590 1
0.1%
1369020 1
0.1%
1866360 1
0.1%
1937490 1
0.1%
2030730 1
0.1%
2172020 1
0.1%
2324560 1
0.1%
2357790 1
0.1%
ValueCountFrequency (%)
415977660 1
0.1%
409924310 1
0.1%
406627340 1
0.1%
403875350 1
0.1%
400881730 1
0.1%
376238610 1
0.1%
330308780 1
0.1%
329210690 1
0.1%
326716420 1
0.1%
326453480 1
0.1%

85-89세(여자)
Real number (ℝ)

HIGH CORRELATION 

Distinct818
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24345619
Minimum-4821510
Maximum1.9474603 × 108
Zeros0
Zeros (%)0.0%
Negative7
Negative (%)0.8%
Memory size7.6 KiB
2023-12-12T15:42:06.779060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4821510
5-th percentile946816
Q12206067.5
median16600155
Q338046628
95-th percentile87343824
Maximum1.9474603 × 108
Range1.9956754 × 108
Interquartile range (IQR)35840560

Descriptive statistics

Standard deviation29845871
Coefficient of variation (CV)1.2259237
Kurtosis6.1578033
Mean24345619
Median Absolute Deviation (MAD)14575220
Skewness2.0900124
Sum2.0693776 × 1010
Variance8.9077604 × 1014
MonotonicityNot monotonic
2023-12-12T15:42:06.955292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1055420 3
 
0.4%
1077580 3
 
0.4%
1813150 3
 
0.4%
1840610 3
 
0.4%
718320 3
 
0.4%
2193750 3
 
0.4%
1176000 2
 
0.2%
1015230 2
 
0.2%
2588710 2
 
0.2%
4316510 2
 
0.2%
Other values (808) 824
96.9%
ValueCountFrequency (%)
-4821510 1
0.1%
-3321440 1
0.1%
-1721720 1
0.1%
-1524760 1
0.1%
-966160 1
0.1%
-265490 1
0.1%
-23120 1
0.1%
194260 1
0.1%
293880 1
0.1%
299050 1
0.1%
ValueCountFrequency (%)
194746030 1
0.1%
190864140 1
0.1%
188913700 1
0.1%
188157530 1
0.1%
183206330 1
0.1%
170470710 1
0.1%
118086140 1
0.1%
117147090 1
0.1%
116525210 1
0.1%
116263620 1
0.1%

90-94세(남자)
Real number (ℝ)

HIGH CORRELATION 

Distinct786
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14161962
Minimum-10720150
Maximum92180480
Zeros0
Zeros (%)0.0%
Negative6
Negative (%)0.7%
Memory size7.6 KiB
2023-12-12T15:42:07.146713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-10720150
5-th percentile414604.5
Q11971435
median9429080
Q320362215
95-th percentile50774262
Maximum92180480
Range1.0290063 × 108
Interquartile range (IQR)18390780

Descriptive statistics

Standard deviation16401244
Coefficient of variation (CV)1.1581195
Kurtosis4.7629014
Mean14161962
Median Absolute Deviation (MAD)7904440
Skewness2.0063476
Sum1.2037668 × 1010
Variance2.690008 × 1014
MonotonicityNot monotonic
2023-12-12T15:42:07.307752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3499530 5
 
0.6%
2705190 5
 
0.6%
407850 4
 
0.5%
230880 4
 
0.5%
1011130 3
 
0.4%
596890 3
 
0.4%
1323370 3
 
0.4%
1585880 3
 
0.4%
99070 3
 
0.4%
1597410 3
 
0.4%
Other values (776) 814
95.8%
ValueCountFrequency (%)
-10720150 1
 
0.1%
-6605290 1
 
0.1%
-5069070 1
 
0.1%
-3610260 1
 
0.1%
-506030 1
 
0.1%
-1900 1
 
0.1%
99070 3
0.4%
100120 1
 
0.1%
117790 1
 
0.1%
150490 1
 
0.1%
ValueCountFrequency (%)
92180480 1
0.1%
91429970 1
0.1%
88059140 1
0.1%
86954760 1
0.1%
84014300 1
0.1%
83186960 1
0.1%
79349220 1
0.1%
79154110 1
0.1%
78783220 1
0.1%
77674570 1
0.1%

90-94세(여자)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct664
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8467637.6
Minimum-8447930
Maximum75990410
Zeros34
Zeros (%)4.0%
Negative11
Negative (%)1.3%
Memory size7.6 KiB
2023-12-12T15:42:07.462949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-8447930
5-th percentile0
Q1569770
median4769415
Q311877868
95-th percentile33741711
Maximum75990410
Range84438340
Interquartile range (IQR)11308098

Descriptive statistics

Standard deviation11114581
Coefficient of variation (CV)1.3125953
Kurtosis8.3099816
Mean8467637.6
Median Absolute Deviation (MAD)4529425
Skewness2.3980002
Sum7.1974919 × 109
Variance1.2353391 × 1014
MonotonicityNot monotonic
2023-12-12T15:42:07.647695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 34
 
4.0%
752550 11
 
1.3%
741950 8
 
0.9%
132190 7
 
0.8%
1996630 7
 
0.8%
45430 6
 
0.7%
84630 5
 
0.6%
68130 5
 
0.6%
85840 5
 
0.6%
3217790 5
 
0.6%
Other values (654) 757
89.1%
ValueCountFrequency (%)
-8447930 1
0.1%
-1580010 1
0.1%
-1318860 1
0.1%
-606380 1
0.1%
-596040 1
0.1%
-360990 1
0.1%
-268320 1
0.1%
-122350 1
0.1%
-112560 1
0.1%
-105890 1
0.1%
ValueCountFrequency (%)
75990410 1
0.1%
75658630 1
0.1%
75037080 1
0.1%
73393680 1
0.1%
72179700 1
0.1%
66789420 1
0.1%
46052040 1
0.1%
44442630 1
0.1%
44098160 1
0.1%
42323420 1
0.1%

95-99세(남자)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct549
Distinct (%)64.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2308684.7
Minimum-2912240
Maximum17803230
Zeros95
Zeros (%)11.2%
Negative6
Negative (%)0.7%
Memory size7.6 KiB
2023-12-12T15:42:08.203999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2912240
5-th percentile0
Q1208240
median1381860
Q33192962.5
95-th percentile9404306
Maximum17803230
Range20715470
Interquartile range (IQR)2984722.5

Descriptive statistics

Standard deviation2899822
Coefficient of variation (CV)1.2560494
Kurtosis5.2301765
Mean2308684.7
Median Absolute Deviation (MAD)1294490
Skewness2.1116765
Sum1.962382 × 109
Variance8.4089676 × 1012
MonotonicityNot monotonic
2023-12-12T15:42:08.373599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 95
 
11.2%
76040 11
 
1.3%
208240 9
 
1.1%
352400 9
 
1.1%
103970 6
 
0.7%
901590 6
 
0.7%
69900 6
 
0.7%
399760 6
 
0.7%
101170 5
 
0.6%
106350 5
 
0.6%
Other values (539) 692
81.4%
ValueCountFrequency (%)
-2912240 1
 
0.1%
-2718790 1
 
0.1%
-1343370 1
 
0.1%
-1023940 1
 
0.1%
-637920 1
 
0.1%
-11300 1
 
0.1%
0 95
11.2%
27960 2
 
0.2%
34950 2
 
0.2%
35450 3
 
0.4%
ValueCountFrequency (%)
17803230 1
0.1%
16502440 1
0.1%
16290970 1
0.1%
16270000 1
0.1%
15960760 1
0.1%
13914500 1
0.1%
12939090 1
0.1%
12814040 1
0.1%
12397760 1
0.1%
12269700 1
0.1%

95-99세(여자)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct482
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1484293.2
Minimum-9869040
Maximum14361180
Zeros244
Zeros (%)28.7%
Negative8
Negative (%)0.9%
Memory size7.6 KiB
2023-12-12T15:42:08.521756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-9869040
5-th percentile0
Q10
median789690
Q32071145
95-th percentile5328054.5
Maximum14361180
Range24230220
Interquartile range (IQR)2071145

Descriptive statistics

Standard deviation2298304.2
Coefficient of variation (CV)1.5484165
Kurtosis9.8573583
Mean1484293.2
Median Absolute Deviation (MAD)789690
Skewness2.47811
Sum1.2616492 × 109
Variance5.2822021 × 1012
MonotonicityNot monotonic
2023-12-12T15:42:08.678145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 244
28.7%
161550 11
 
1.3%
90870 8
 
0.9%
1286190 6
 
0.7%
165940 6
 
0.7%
307550 6
 
0.7%
610260 5
 
0.6%
2429220 5
 
0.6%
618990 5
 
0.6%
629870 5
 
0.6%
Other values (472) 549
64.6%
ValueCountFrequency (%)
-9869040 1
 
0.1%
-5661950 1
 
0.1%
-1524500 1
 
0.1%
-746120 1
 
0.1%
-655620 1
 
0.1%
-576890 1
 
0.1%
-515650 1
 
0.1%
-149570 1
 
0.1%
0 244
28.7%
14040 1
 
0.1%
ValueCountFrequency (%)
14361180 1
0.1%
14263930 1
0.1%
14095640 1
0.1%
13587830 1
0.1%
13445880 1
0.1%
13081200 1
0.1%
12836370 1
0.1%
12785110 1
0.1%
12336190 1
0.1%
12098800 1
0.1%

100세이상(남자)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct245
Distinct (%)29.4%
Missing17
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean248528.21
Minimum-165250
Maximum2334230
Zeros318
Zeros (%)37.4%
Negative2
Negative (%)0.2%
Memory size7.6 KiB
2023-12-12T15:42:08.827612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-165250
5-th percentile0
Q10
median80670
Q3338120
95-th percentile1118902
Maximum2334230
Range2499480
Interquartile range (IQR)338120

Descriptive statistics

Standard deviation398161.7
Coefficient of variation (CV)1.6020785
Kurtosis6.6262008
Mean248528.21
Median Absolute Deviation (MAD)80670
Skewness2.4654412
Sum2.07024 × 108
Variance1.5853274 × 1011
MonotonicityNot monotonic
2023-12-12T15:42:08.975277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 318
37.4%
140640 11
 
1.3%
20520 11
 
1.3%
524250 10
 
1.2%
69660 8
 
0.9%
11790 8
 
0.9%
1858710 8
 
0.9%
554510 7
 
0.8%
13420 6
 
0.7%
10260 6
 
0.7%
Other values (235) 440
51.8%
(Missing) 17
 
2.0%
ValueCountFrequency (%)
-165250 1
 
0.1%
-84390 1
 
0.1%
0 318
37.4%
2870 5
 
0.6%
10260 6
 
0.7%
11790 8
 
0.9%
13180 2
 
0.2%
13420 6
 
0.7%
13470 5
 
0.6%
16290 4
 
0.5%
ValueCountFrequency (%)
2334230 1
 
0.1%
2003730 1
 
0.1%
1869330 1
 
0.1%
1868830 2
 
0.2%
1858710 8
0.9%
1833550 2
 
0.2%
1810680 1
 
0.1%
1808690 2
 
0.2%
1789430 2
 
0.2%
1784770 1
 
0.1%

100세이상(여자)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct262
Distinct (%)31.2%
Missing9
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean217739.68
Minimum-277700
Maximum2837010
Zeros391
Zeros (%)46.0%
Negative1
Negative (%)0.1%
Memory size7.6 KiB
2023-12-12T15:42:09.132021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-277700
5-th percentile0
Q10
median18020
Q3223280
95-th percentile1168590
Maximum2837010
Range3114710
Interquartile range (IQR)223280

Descriptive statistics

Standard deviation440629.72
Coefficient of variation (CV)2.0236538
Kurtosis8.9957823
Mean217739.68
Median Absolute Deviation (MAD)18020
Skewness2.906252
Sum1.8311907 × 108
Variance1.9415455 × 1011
MonotonicityNot monotonic
2023-12-12T15:42:09.319623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 391
46.0%
4600 8
 
0.9%
387800 7
 
0.8%
62400 7
 
0.8%
10260 6
 
0.7%
18020 6
 
0.7%
68150 6
 
0.7%
183020 6
 
0.7%
13420 6
 
0.7%
1592370 6
 
0.7%
Other values (252) 392
46.1%
(Missing) 9
 
1.1%
ValueCountFrequency (%)
-277700 1
 
0.1%
0 391
46.0%
4600 8
 
0.9%
4670 1
 
0.1%
10260 6
 
0.7%
11490 2
 
0.2%
13420 6
 
0.7%
14650 1
 
0.1%
14930 4
 
0.5%
18020 6
 
0.7%
ValueCountFrequency (%)
2837010 1
 
0.1%
2621520 2
0.2%
2407020 1
 
0.1%
2337900 1
 
0.1%
2152000 2
0.2%
2085080 3
0.4%
1935390 1
 
0.1%
1935110 2
0.2%
1926380 1
 
0.1%
1924070 1
 
0.1%

Interactions

2023-12-12T15:41:59.937254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:36.828981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:38.060276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:39.460262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:41.127842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:42.589833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:44.227300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:46.166509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:48.283307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:49.699294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:51.325679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:53.112829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:55.229169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:56.583519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:58.237091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:42:00.030418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:36.911574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:38.179065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:39.848542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:41.236628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:42.726449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:44.345226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:46.323306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:48.388988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:49.793193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:51.423550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:53.222673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:55.355905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:56.676732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:58.345501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:42:00.122602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:36.981057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:38.257655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:39.942369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:41.335703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:42.823004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:44.449055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:46.413653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:48.492793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:49.877940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:51.541817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:53.325682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:55.433435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:56.751985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:58.448913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:42:00.224947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:37.069060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:38.354306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:40.036049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:41.430318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:42.919783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:44.583558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:46.522049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:48.591320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:49.971113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:51.650597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:53.433915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:55.510730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:56.837856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:58.544803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:42:00.343039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:37.154026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:38.443217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:40.134958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:41.563865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:43.022271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:44.735928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:46.703649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:48.687178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:50.082165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:51.777205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:53.575671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:55.616450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:56.954133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:58.669198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:42:00.483887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:37.228701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:38.540498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:40.220830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:41.673873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:43.120025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:44.856787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:46.853570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:48.772922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:50.177847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:51.891562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:53.691645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:55.696235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:57.137487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:58.789454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:42:00.598171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:37.311388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:38.632191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:40.317163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:41.767923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:43.203984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:44.993681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:46.969273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:48.860673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:50.327588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:52.076936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:53.833976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:55.781729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:57.227019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:58.918375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:42:00.715078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:37.397201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:38.734213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:40.418952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:41.876214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:43.301594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:45.121727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:47.372722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:48.972419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:50.454874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:52.201382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:53.987231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:55.884130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:57.329547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:59.046464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:42:00.819828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:37.477487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:38.812696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:40.507263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:41.969806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:43.391902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:45.243052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:47.490478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:49.051010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:50.557927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:52.299811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:54.097822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:55.973896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:57.435186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:59.156881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:42:00.958665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:37.565902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:38.918909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:40.599937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:42.073875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:43.491252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:45.384634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:47.619505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:49.144979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:50.665827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:52.413096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:54.239627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:56.065977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:57.541463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:59.297171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:42:01.052257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:37.641726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:38.999398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:40.677268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:42.169559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:43.587251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:45.509715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:47.725004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:49.231991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:50.771837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:52.514556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:54.379400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:56.154779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:57.639089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:59.408457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:42:01.180009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:37.721692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:39.075795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:40.760556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:42.260341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:43.681093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:45.643202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:47.842111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:49.313485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:50.877894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:52.647712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:54.494993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:56.243114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:57.745658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:59.520192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:42:01.288547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:37.798870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:39.164009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:40.870203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:42.339574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:43.766614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:45.759830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:47.946986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:49.400040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:50.992563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:52.746954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:54.586523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:56.318855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:57.861824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:59.617197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:42:01.396637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:37.886918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:39.265254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:40.961297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:42.421359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:43.909180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:45.885921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:48.050085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:49.493563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:51.111866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:52.869597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:54.700655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:56.406803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:57.986326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:59.720305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:42:01.498024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:37.966871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:39.369737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:41.037660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:42.503440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:44.096747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:46.013505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:48.155272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:49.598120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:51.226491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:52.994047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:55.130398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:56.491039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:58.114891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:41:59.846413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:42:09.460409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
직역구분연월시군구65-69세(남자)65-69세(여자)70-74세(여자)75-79세(남자)75-79세(여자)80-84세(남자)80-84세(여자)85-89세(남자)85-89세(여자)90-94세(남자)90-94세(여자)95-99세(남자)95-99세(여자)100세이상(남자)100세이상(여자)
직역구분1.0000.0000.0000.3840.2630.7880.6490.9530.7730.8430.9610.8930.9040.7730.7940.6380.5390.922
연월0.0001.0000.0000.0000.2100.2350.0000.3110.0070.2880.0450.1860.0000.0000.1020.1250.0000.000
시군구0.0000.0001.0000.8170.8210.8120.7440.7130.6880.6740.7040.6660.7300.6330.6970.7270.7110.713
65-69세(남자)0.3840.0000.8171.0000.9680.9200.7780.8230.6620.6370.6380.6220.7550.5400.7410.5920.3750.640
65-69세(여자)0.2630.2100.8210.9681.0000.9250.7760.8290.6530.6700.6440.6840.7870.5780.7780.5780.4290.576
70-74세(여자)0.7880.2350.8120.9200.9251.0000.9380.9620.8820.8660.8460.8590.9140.7790.8940.7710.6280.792
75-79세(남자)0.6490.0000.7440.7780.7760.9381.0000.8880.9700.8460.8580.8590.8250.7950.8390.7330.6190.662
75-79세(여자)0.9530.3110.7130.8230.8290.9620.8881.0000.9200.9180.9190.8810.9330.8260.9060.8230.6240.842
80-84세(남자)0.7730.0070.6880.6620.6530.8820.9700.9201.0000.8830.9180.8640.8540.8130.8270.7660.6300.703
80-84세(여자)0.8430.2880.6740.6370.6700.8660.8460.9180.8831.0000.9140.9760.8590.9210.7830.7970.7840.740
85-89세(남자)0.9610.0450.7040.6380.6440.8460.8580.9190.9180.9141.0000.8880.8760.8290.8270.9070.6160.711
85-89세(여자)0.8930.1860.6660.6220.6840.8590.8590.8810.8640.9760.8881.0000.8630.9620.8610.7750.7700.705
90-94세(남자)0.9040.0000.7300.7550.7870.9140.8250.9330.8540.8590.8760.8631.0000.8230.8980.7920.5900.864
90-94세(여자)0.7730.0000.6330.5400.5780.7790.7950.8260.8130.9210.8290.9620.8231.0000.8600.7310.7720.657
95-99세(남자)0.7940.1020.6970.7410.7780.8940.8390.9060.8270.7830.8270.8610.8980.8601.0000.7360.6080.725
95-99세(여자)0.6380.1250.7270.5920.5780.7710.7330.8230.7660.7970.9070.7750.7920.7310.7361.0000.4750.697
100세이상(남자)0.5390.0000.7110.3750.4290.6280.6190.6240.6300.7840.6160.7700.5900.7720.6080.4751.0000.488
100세이상(여자)0.9220.0000.7130.6400.5760.7920.6620.8420.7030.7400.7110.7050.8640.6570.7250.6970.4881.000
2023-12-12T15:42:09.683402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
직역구분시군구연월
직역구분1.0000.0000.000
시군구0.0001.0000.000
연월0.0000.0001.000
2023-12-12T15:42:09.821558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
65-69세(남자)65-69세(여자)70-74세(여자)75-79세(남자)75-79세(여자)80-84세(남자)80-84세(여자)85-89세(남자)85-89세(여자)90-94세(남자)90-94세(여자)95-99세(남자)95-99세(여자)100세이상(남자)100세이상(여자)직역구분연월시군구
65-69세(남자)1.0000.9220.8310.7420.6100.6050.4640.5380.4490.4700.3700.3890.3300.0720.2120.2920.0000.450
65-69세(여자)0.9221.0000.7520.5840.4900.4530.3440.3980.3090.3300.2440.2760.176-0.0190.0760.2010.0830.455
70-74세(여자)0.8310.7521.0000.9400.9060.8750.8110.8360.7690.7950.7200.6510.6420.3810.5650.6210.0930.443
75-79세(남자)0.7420.5840.9401.0000.9530.9570.8810.9130.8630.8600.8050.7310.7490.4560.6960.6980.0000.429
75-79세(여자)0.6100.4900.9060.9531.0000.9690.9540.9510.9220.9100.8870.7840.8150.5600.7690.8110.1250.338
80-84세(남자)0.6050.4530.8750.9570.9691.0000.9460.9680.9280.9160.8820.8070.8390.5680.7910.8360.0000.374
80-84세(여자)0.4640.3440.8110.8810.9540.9461.0000.9490.9400.9200.9190.8020.8420.6290.8280.8700.1180.330
85-89세(남자)0.5380.3980.8360.9130.9510.9680.9491.0000.9510.9280.9090.8250.8420.5890.8090.8240.0170.366
85-89세(여자)0.4490.3090.7690.8630.9220.9280.9400.9511.0000.9090.9130.8040.8790.6080.8320.9270.0750.323
90-94세(남자)0.4700.3300.7950.8600.9100.9160.9200.9280.9091.0000.9110.8090.8310.5730.7830.7400.0000.355
90-94세(여자)0.3700.2440.7200.8050.8870.8820.9190.9090.9130.9111.0000.7930.8280.6340.8190.7900.0000.297
95-99세(남자)0.3890.2760.6510.7310.7840.8070.8020.8250.8040.8090.7931.0000.7430.5790.6970.6260.0320.323
95-99세(여자)0.3300.1760.6420.7490.8150.8390.8420.8420.8790.8310.8280.7431.0000.6210.8130.4820.0490.363
100세이상(남자)0.072-0.0190.3810.4560.5600.5680.6290.5890.6080.5730.6340.5790.6211.0000.6710.5400.0000.362
100세이상(여자)0.2120.0760.5650.6960.7690.7910.8280.8090.8320.7830.8190.6970.8130.6711.0000.7630.0000.337
직역구분0.2920.2010.6210.6980.8110.8360.8700.8240.9270.7400.7900.6260.4820.5400.7631.0000.0000.000
연월0.0000.0830.0930.0000.1250.0000.1180.0170.0750.0000.0000.0320.0490.0000.0000.0001.0000.000
시군구0.4500.4550.4430.4290.3380.3740.3300.3660.3230.3550.2970.3230.3630.3620.3370.0000.0001.000

Missing values

2023-12-12T15:42:01.698925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:42:02.007012image/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.
2023-12-12T15:42:02.504965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

직역구분연월시도시군구65-69세(남자)65-69세(여자)70-74세(남자)70-74세(여자)75-79세(남자)75-79세(여자)80-84세(남자)80-84세(여자)85-89세(남자)85-89세(여자)90-94세(남자)90-94세(여자)95-99세(남자)95-99세(여자)100세이상(남자)100세이상(여자)
0지역2022년1월서울특별시종로구234399670744208001595127206737501014661617067735070131558690595322606753239025771050250027502384813035313801991850668370165940
1지역2022년1월서울특별시중구147901730594177101024378704021774080227950320324806425935031460450232634701752734066451101064307040301705302602336704600
2지역2022년1월서울특별시용산구32837638011201712027083022010202328021136565085541150183039240869504909972281055526370429971201615761062287802759970342510695830
3지역2022년1월서울특별시성동구36431764011121850026377994083211510196180020758551701356839805568670056310840259588001123106092685801885510217560040197010260
4지역2022년1월서울특별시광진구4869550901287448003674976109586133032160219083219440205841540609576607349424035596830303314401014359034421301115100664550307600
5지역2022년1월서울특별시동대문구44020247012257886010250520023712890084238320166575610669412805483188024383790130201701089801027601301040270185871018020
6지역2022년1월서울특별시중랑구4968554301242213803279543409456624023273868072277420131477980494627704469303018158170816530054128701022840285370<NA><NA>
7지역2022년1월서울특별시성북구49347706014835452032481094097086300277853230921360401943126407739270084514410458170502260273011637920370497016458806966071200
8지역2022년1월서울특별시강북구25478267086574010165865170648383201342457205561509097348790335819903259268017922810503104030866102183750147844026840193280
9지역2022년1월서울특별시도봉구4735250401133611803027937908549043023664344066596590142743390514221505013882022578760832445011207160133835017401002052098520
직역구분연월시도시군구65-69세(남자)65-69세(여자)70-74세(남자)70-74세(여자)75-79세(남자)75-79세(여자)80-84세(남자)80-84세(여자)85-89세(남자)85-89세(여자)90-94세(남자)90-94세(여자)95-99세(남자)95-99세(여자)100세이상(남자)100세이상(여자)
840직장2023년5월서울특별시강서구7125790002958840303094265701054895801518350703569677047370870912447010124580248937017724903013200354501090000
841직장2023년5월서울특별시구로구7647431802333388503359573307404135011506375022852980541753305269960957644031362801938020331340265870000
842직장2023년5월서울특별시금천구7395474702160367303400152108437955011327607028958530494378808982680167508902144150233864032177900000
843직장2023년5월서울특별시영등포구1327566590467675070549403640176251420230096470577512809855721019717590313192607362090153600901159660152786017940000
844직장2023년5월서울특별시동작구311158280134061250119098130553787005950030018569760308347208115090833030098499025625401206230165810000
845직장2023년5월서울특별시관악구33148161015151509017494255074670310808319202540348029088210891477053346801977560359400010050503763018283000
846직장2023년5월서울특별시서초구21488318206044704408769843402534581704336684709620293019924713032807970766412009716930181906708401830206875012153000
847직장2023년5월서울특별시강남구29942706209227192901328178850338751790615341140141938050257631220532403309534400030905470173831908621780852753029633000
848직장2023년5월서울특별시송파구1648563610436808170678450580176237280239436890624811907068438026127280210702102516560100774501037990827520848101063500
849직장2023년5월서울특별시강동구53389796024020059023477505082354350964632002536249034658910752198066663601813150157584062594010635001036800