Overview

Dataset statistics

Number of variables8
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory74.7 B

Variable types

Numeric7
Categorical1

Dataset

Description국민연금공단의 가입된 국민연금 가입자의 데이터를 기반으로 만들어진 국민연금 가입자의 성별, 가입종별 가입자 수 입니다.
Author국민연금공단
URLhttps://www.data.go.kr/data/15094026/fileData.do

Alerts

계 is highly overall correlated with 사업장가입자 and 4 other fieldsHigh correlation
사업장가입자 is highly overall correlated with 계 and 4 other fieldsHigh correlation
지역 소득신고 is highly overall correlated with 임의 and 2 other fieldsHigh correlation
지역 납부예외 is highly overall correlated with 계 and 2 other fieldsHigh correlation
임의 is highly overall correlated with 계 and 4 other fieldsHigh correlation
임계 is highly overall correlated with 계 and 4 other fieldsHigh correlation
구분 is highly overall correlated with 계 and 5 other fieldsHigh correlation
계 has unique valuesUnique
사업장가입자 has unique valuesUnique
지역 소득신고 has unique valuesUnique
지역 납부예외 has unique valuesUnique
임의 has unique valuesUnique
임계 has unique valuesUnique

Reproduction

Analysis started2024-04-17 17:42:54.061920
Analysis finished2024-04-17 17:42:58.035030
Duration3.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월
Real number (ℝ)

Distinct12
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202106.5
Minimum202101
Maximum202112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-04-18T02:42:58.077782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum202101
5-th percentile202101
Q1202103.75
median202106.5
Q3202109.25
95-th percentile202112
Maximum202112
Range11
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.5010203
Coefficient of variation (CV)1.732265 × 10-5
Kurtosis-1.217232
Mean202106.5
Median Absolute Deviation (MAD)3
Skewness0
Sum7275834
Variance12.257143
MonotonicityDecreasing
2024-04-18T02:42:58.163346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
202112 3
8.3%
202111 3
8.3%
202110 3
8.3%
202109 3
8.3%
202108 3
8.3%
202107 3
8.3%
202106 3
8.3%
202105 3
8.3%
202104 3
8.3%
202103 3
8.3%
Other values (2) 6
16.7%
ValueCountFrequency (%)
202101 3
8.3%
202102 3
8.3%
202103 3
8.3%
202104 3
8.3%
202105 3
8.3%
202106 3
8.3%
202107 3
8.3%
202108 3
8.3%
202109 3
8.3%
202110 3
8.3%
ValueCountFrequency (%)
202112 3
8.3%
202111 3
8.3%
202110 3
8.3%
202109 3
8.3%
202108 3
8.3%
202107 3
8.3%
202106 3
8.3%
202105 3
8.3%
202104 3
8.3%
202103 3
8.3%

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size420.0 B
계
12 
남자
12 
여자
12 

Length

Max length2
Median length2
Mean length1.6666667
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row계
2nd row남자
3rd row여자
4th row계
5th row남자

Common Values

ValueCountFrequency (%)
계 12
33.3%
남자 12
33.3%
여자 12
33.3%

Length

2024-04-18T02:42:58.254581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T02:42:58.329226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
계 12
33.3%
남자 12
33.3%
여자 12
33.3%

계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14717361
Minimum9916601
Maximum22347586
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-04-18T02:42:58.414415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9916601
5-th percentile9937531.5
Q110106074
median12026484
Q321961822
95-th percentile22161520
Maximum22347586
Range12430985
Interquartile range (IQR)11855748

Descriptive statistics

Standard deviation5342627.3
Coefficient of variation (CV)0.3630153
Kurtosis-1.5421731
Mean14717361
Median Absolute Deviation (MAD)1968212.5
Skewness0.65932756
Sum5.2982499 × 108
Variance2.8543666 × 1013
MonotonicityNot monotonic
2024-04-18T02:42:58.518185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
22347586 1
 
2.8%
12021137 1
 
2.8%
21999490 1
 
2.8%
12004518 1
 
2.8%
9994972 1
 
2.8%
21992493 1
 
2.8%
12013168 1
 
2.8%
9979325 1
 
2.8%
21886867 1
 
2.8%
11970266 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
9916601 1
2.8%
9927036 1
2.8%
9941030 1
2.8%
9979325 1
2.8%
9994972 1
2.8%
10039393 1
2.8%
10077149 1
2.8%
10079662 1
2.8%
10088258 1
2.8%
10112012 1
2.8%
ValueCountFrequency (%)
22347586 1
2.8%
22169342 1
2.8%
22158912 1
2.8%
22157234 1
2.8%
22132550 1
2.8%
22108067 1
2.8%
22060530 1
2.8%
21999490 1
2.8%
21992493 1
2.8%
21951598 1
2.8%

사업장가입자
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9616979.9
Minimum5946567
Maximum14621411
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-04-18T02:42:58.621095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5946567
5-th percentile5970035
Q16176003.5
median8341876
Q314257523
95-th percentile14578253
Maximum14621411
Range8674844
Interquartile range (IQR)8081519.5

Descriptive statistics

Standard deviation3573012.5
Coefficient of variation (CV)0.37153166
Kurtosis-1.5385284
Mean9616979.9
Median Absolute Deviation (MAD)2234992.5
Skewness0.52033548
Sum3.4621128 × 108
Variance1.2766418 × 1013
MonotonicityNot monotonic
2024-04-18T02:42:58.714966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
14580825 1
 
2.8%
8327608 1
 
2.8%
14382818 1
 
2.8%
8320987 1
 
2.8%
6061831 1
 
2.8%
14332316 1
 
2.8%
8305103 1
 
2.8%
6027213 1
 
2.8%
14230373 1
 
2.8%
8259166 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
5946567 1
2.8%
5966519 1
2.8%
5971207 1
2.8%
6027213 1
2.8%
6061831 1
2.8%
6092291 1
2.8%
6121476 1
2.8%
6136240 1
2.8%
6165862 1
2.8%
6179384 1
2.8%
ValueCountFrequency (%)
14621411 1
2.8%
14580825 1
2.8%
14577396 1
2.8%
14533341 1
2.8%
14506112 1
2.8%
14477620 1
2.8%
14419899 1
2.8%
14382818 1
2.8%
14332316 1
2.8%
14232592 1
2.8%

지역 소득신고
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2431560.1
Minimum1706612
Maximum3742040
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-04-18T02:42:58.812029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1706612
5-th percentile1724585.2
Q11803745
median1880072.5
Q33586470
95-th percentile3720258.8
Maximum3742040
Range2035428
Interquartile range (IQR)1782725

Descriptive statistics

Standard deviation874332.45
Coefficient of variation (CV)0.35957674
Kurtosis-1.525639
Mean2431560.1
Median Absolute Deviation (MAD)114549.5
Skewness0.73125998
Sum87536164
Variance7.6445724 × 1011
MonotonicityNot monotonic
2024-04-18T02:42:58.937744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
3742040 1
 
2.8%
1758605 1
 
2.8%
3655570 1
 
2.8%
1772441 1
 
2.8%
1883129 1
 
2.8%
3674932 1
 
2.8%
1783804 1
 
2.8%
1891128 1
 
2.8%
3679475 1
 
2.8%
1789633 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
1706612 1
2.8%
1719492 1
2.8%
1726283 1
2.8%
1737047 1
2.8%
1752322 1
2.8%
1758605 1
2.8%
1772441 1
2.8%
1783804 1
2.8%
1789633 1
2.8%
1808449 1
2.8%
ValueCountFrequency (%)
3742040 1
2.8%
3741948 1
2.8%
3713029 1
2.8%
3679475 1
2.8%
3674932 1
2.8%
3655570 1
2.8%
3631199 1
2.8%
3629338 1
2.8%
3602265 1
2.8%
3581205 1
2.8%

지역 납부예외
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2047879.4
Minimum1354100
Maximum3104084
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-04-18T02:42:59.051808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1354100
5-th percentile1356409.5
Q11380503.8
median1703388
Q33058747.5
95-th percentile3089658
Maximum3104084
Range1749984
Interquartile range (IQR)1678243.8

Descriptive statistics

Standard deviation747290
Coefficient of variation (CV)0.36490917
Kurtosis-1.5418193
Mean2047879.4
Median Absolute Deviation (MAD)333905
Skewness0.62691039
Sum73723660
Variance5.5844234 × 1011
MonotonicityNot monotonic
2024-04-18T02:42:59.153666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
3084969 1
 
2.8%
1699712 1
 
2.8%
3033001 1
 
2.8%
1678901 1
 
2.8%
1354100 1
 
2.8%
3061238 1
 
2.8%
1693258 1
 
2.8%
1367980 1
 
2.8%
3059769 1
 
2.8%
1692471 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
1354100 1
2.8%
1354266 1
2.8%
1357124 1
2.8%
1357475 1
2.8%
1367298 1
2.8%
1367980 1
2.8%
1370986 1
2.8%
1372298 1
2.8%
1373567 1
2.8%
1382816 1
2.8%
ValueCountFrequency (%)
3104084 1
2.8%
3095370 1
2.8%
3087754 1
2.8%
3084969 1
2.8%
3083816 1
2.8%
3073279 1
2.8%
3064210 1
2.8%
3061238 1
2.8%
3059769 1
2.8%
3058407 1
2.8%

임의
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean255070.5
Minimum54429
Maximum396632
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-04-18T02:42:59.249754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum54429
5-th percentile56139
Q160064.5
median324945
Q3377869.5
95-th percentile388756
Maximum396632
Range342203
Interquartile range (IQR)317805

Descriptive statistics

Standard deviation143179.32
Coefficient of variation (CV)0.56133233
Kurtosis-1.5419101
Mean255070.5
Median Absolute Deviation (MAD)59043.5
Skewness-0.63959624
Sum9182538
Variance2.0500317 × 1010
MonotonicityNot monotonic
2024-04-18T02:42:59.346071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
396632 1
 
2.8%
58395 1
 
2.8%
381157 1
 
2.8%
57374 1
 
2.8%
323783 1
 
2.8%
379206 1
 
2.8%
56585 1
 
2.8%
322621 1
 
2.8%
377424 1
 
2.8%
56281 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
54429 1
2.8%
55713 1
2.8%
56281 1
2.8%
56585 1
2.8%
57374 1
2.8%
58395 1
2.8%
58784 1
2.8%
59393 1
2.8%
59472 1
2.8%
60262 1
2.8%
ValueCountFrequency (%)
396632 1
2.8%
391555 1
2.8%
387823 1
2.8%
385795 1
2.8%
384313 1
2.8%
384144 1
2.8%
383833 1
2.8%
381157 1
2.8%
379206 1
2.8%
377424 1
2.8%

임계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean365870.94
Minimum171202
Maximum562359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-04-18T02:42:59.441446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum171202
5-th percentile173390
Q1177382.5
median373660.5
Q3541389
95-th percentile556021.5
Maximum562359
Range391157
Interquartile range (IQR)364006.5

Descriptive statistics

Standard deviation154778.76
Coefficient of variation (CV)0.42304195
Kurtosis-1.539363
Mean365870.94
Median Absolute Deviation (MAD)174797
Skewness-0.073632857
Sum13171354
Variance2.3956464 × 1010
MonotonicityNot monotonic
2024-04-18T02:42:59.537053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
543120 1
 
2.8%
176817 1
 
2.8%
546944 1
 
2.8%
174815 1
 
2.8%
372129 1
 
2.8%
544801 1
 
2.8%
174418 1
 
2.8%
370383 1
 
2.8%
539826 1
 
2.8%
172715 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
171202 1
2.8%
172715 1
2.8%
173615 1
2.8%
173928 1
2.8%
174407 1
2.8%
174418 1
2.8%
174815 1
2.8%
176713 1
2.8%
176817 1
2.8%
177571 1
2.8%
ValueCountFrequency (%)
562359 1
2.8%
560400 1
2.8%
554562 1
2.8%
553275 1
2.8%
552009 1
2.8%
549971 1
2.8%
546944 1
2.8%
544801 1
2.8%
543120 1
2.8%
540812 1
2.8%

Interactions

2024-04-18T02:42:57.361364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:54.273956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:54.735712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:55.200953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:55.671675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:56.190923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:56.862494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:57.425036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:54.332090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:54.800455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:55.260776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:55.744012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:56.257956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:56.930951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:57.490403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:54.398606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:54.873282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:55.339858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:55.813833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:56.322203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:56.998317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:57.551748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:54.470785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:54.930815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:55.405855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:55.875948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:56.382476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:57.066463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:57.642743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:54.538153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:55.003085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:55.479650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:55.947782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:56.664163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:57.156776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:57.727314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:54.602932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:55.069372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:55.542228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:56.021815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:56.728187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:57.226635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:57.800323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:54.668190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:55.133621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:55.605137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:56.114926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:56.792672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:42:57.293611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T02:42:59.608060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월구분계사업장가입자지역 소득신고지역 납부예외임의임계
기준년월1.0000.0000.0000.0000.0000.0000.0000.000
구분0.0001.0001.0001.0000.9471.0001.0001.000
계0.0001.0001.0001.0000.9491.0001.0001.000
사업장가입자0.0001.0001.0001.0000.9411.0001.0001.000
지역 소득신고0.0000.9470.9490.9411.0000.6800.7190.792
지역 납부예외0.0001.0001.0001.0000.6801.0000.9780.978
임의0.0001.0001.0001.0000.7190.9781.0000.978
임계0.0001.0001.0001.0000.7920.9780.9781.000
2024-04-18T02:42:59.696421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월계사업장가입자지역 소득신고지역 납부예외임의임계구분
기준년월1.0000.2950.325-0.186-0.0260.3250.1690.000
계0.2951.0000.9890.3980.9100.5410.5031.000
사업장가입자0.3250.9891.0000.3740.8820.5500.5071.000
지역 소득신고-0.1860.3980.3741.0000.4760.8300.8060.720
지역 납부예외-0.0260.9100.8820.4761.0000.4400.4410.985
임의0.3250.5410.5500.8300.4401.0000.9440.985
임계0.1690.5030.5070.8060.4410.9441.0000.985
구분0.0001.0001.0000.7200.9850.9850.9851.000

Missing values

2024-04-18T02:42:57.903920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T02:42:57.999616image/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

기준년월구분계사업장가입자지역 소득신고지역 납부예외임의임계
0202112계223475861458082537420403084969396632543120
1202112남자1216955784014411819617171398363314171202
2202112여자10178029617938419224231370986333318371918
3202111계221693421462141135479983058407391555549971
4202111남자1205733084107481706612170414161422174407
5202111여자10112012621066318413861354266330133375564
6202110계221589121457739635690833064210387823560400
7202110남자1204589983802201719492170708660262178839
8202110여자10113013619717618495911357124327561381561
9202109계221080671453334135812053055933384313553275
기준년월구분계사업장가입자지역 소득신고지역 납부예외임의임계
26202104여자9979325602721318911281367980322621370383
27202103계218868671423037336794753059769377424539826
28202103남자1197026682591661789633169247156281172715
29202103여자9916601597120718898421367298321143367111
30202102계219478281423259237130293087754373641540812
31202102남자1200679882660731808449170263555713173928
32202102여자9941030596651919045801385119317928366884
33202101계219515981421093637419483095370365746537598
34202101남자1202456282643691825754170639554429173615
35202101여자9927036594656719161941388975311317363983