Overview

Dataset statistics

Number of variables7
Number of observations126
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 KiB
Average record size in memory62.0 B

Variable types

Categorical2
Numeric5

Dataset

Description- 5개년("18~"22년) 업종별ㆍ규모별 상용근로자 고용보험료 부과현황(건설업 등 자진신고사업 제외)* 보험료: 보험년도별 상용근로자 고용보험료 합계* 사업장규모: 상시근로자수 기준* 업종: 2016년까지 9차 업종 사용. 2017년 이후 10차 업종 사용. 아래 자료는 10차 업종 기준으로 표시함.※ 상시근로자수 미기재 또는 0명 사업장 제외
Author근로복지공단
URLhttps://www.data.go.kr/data/15124949/fileData.do

Alerts

2018년 is highly overall correlated with 2019년 and 3 other fieldsHigh correlation
2019년 is highly overall correlated with 2018년 and 3 other fieldsHigh correlation
2020년 is highly overall correlated with 2018년 and 3 other fieldsHigh correlation
2021년 is highly overall correlated with 2018년 and 3 other fieldsHigh correlation
2022년 is highly overall correlated with 2018년 and 3 other fieldsHigh correlation
2018년 has 3 (2.4%) zerosZeros
2019년 has 5 (4.0%) zerosZeros
2020년 has 4 (3.2%) zerosZeros
2021년 has 4 (3.2%) zerosZeros
2022년 has 4 (3.2%) zerosZeros

Reproduction

Analysis started2023-12-12 15:23:02.124638
Analysis finished2023-12-12 15:23:06.207743
Duration4.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종대분류명
Categorical

Distinct21
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
농업, 임업 및 어업(01~08)
 
6
광업(05~08)
 
6
제조업(10~34)
 
6
전기, 가스, 증기 및 공기조절 공급업(35)
 
6
수도, 하수 및 폐기물 처리, 원료 재생업(36 ~ 39)
 
6
Other values (16)
96 

Length

Max length35
Median length23
Mean length20
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row농업, 임업 및 어업(01~08)
2nd row농업, 임업 및 어업(01~08)
3rd row농업, 임업 및 어업(01~08)
4th row농업, 임업 및 어업(01~08)
5th row농업, 임업 및 어업(01~08)

Common Values

ValueCountFrequency (%)
농업, 임업 및 어업(01~08) 6
 
4.8%
광업(05~08) 6
 
4.8%
제조업(10~34) 6
 
4.8%
전기, 가스, 증기 및 공기조절 공급업(35) 6
 
4.8%
수도, 하수 및 폐기물 처리, 원료 재생업(36 ~ 39) 6
 
4.8%
건설업(41~42) * 건설장비운영업 등 부과고지만 대상 6
 
4.8%
도매 및 소매업(45~47) 6
 
4.8%
운수 및 창고업(49~52) 6
 
4.8%
숙박 및 음식점업(55~56) 6
 
4.8%
정보통신업(58~63) 6
 
4.8%
Other values (11) 66
52.4%

Length

2023-12-13T00:23:06.285923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
및 90
 
17.6%
12
 
2.4%
농업 6
 
1.2%
국방 6
 
1.2%
사회복지 6
 
1.2%
보건업 6
 
1.2%
서비스업(85 6
 
1.2%
교육 6
 
1.2%
행정(84 6
 
1.2%
사회보장 6
 
1.2%
Other values (60) 360
70.6%

사업장규모
Categorical

Distinct6
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1~5인미만
21 
5~10인미만
21 
10~30인미만
21 
30~100인미만
21 
100~300인미만
21 

Length

Max length10
Median length8.5
Mean length7.6666667
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1~5인미만
2nd row5~10인미만
3rd row10~30인미만
4th row30~100인미만
5th row100~300인미만

Common Values

ValueCountFrequency (%)
1~5인미만 21
16.7%
5~10인미만 21
16.7%
10~30인미만 21
16.7%
30~100인미만 21
16.7%
100~300인미만 21
16.7%
300인이상 21
16.7%

Length

2023-12-13T00:23:06.431761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:23:06.577066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1~5인미만 21
16.7%
5~10인미만 21
16.7%
10~30인미만 21
16.7%
30~100인미만 21
16.7%
100~300인미만 21
16.7%
300인이상 21
16.7%

2018년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct124
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9658103 × 1010
Minimum0
Maximum1.6993851 × 1012
Zeros3
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-13T00:23:06.719229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27412605
Q13.4501893 × 109
median1.7950317 × 1010
Q36.8207117 × 1010
95-th percentile2.9551226 × 1011
Maximum1.6993851 × 1012
Range1.6993851 × 1012
Interquartile range (IQR)6.4756928 × 1010

Descriptive statistics

Standard deviation1.7369036 × 1011
Coefficient of variation (CV)2.4934695
Kurtosis62.919775
Mean6.9658103 × 1010
Median Absolute Deviation (MAD)1.7366808 × 1010
Skewness7.1033693
Sum8.776921 × 1012
Variance3.0168341 × 1022
MonotonicityNot monotonic
2023-12-13T00:23:06.891212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3
 
2.4%
3456336950 1
 
0.8%
28337470130 1
 
0.8%
13417725150 1
 
0.8%
16135478270 1
 
0.8%
163686388990 1
 
0.8%
18422547220 1
 
0.8%
15903824790 1
 
0.8%
8834872870 1
 
0.8%
2664362610 1
 
0.8%
Other values (114) 114
90.5%
ValueCountFrequency (%)
0 3
2.4%
37200 1
 
0.8%
6041810 1
 
0.8%
6567220 1
 
0.8%
19282330 1
 
0.8%
51803430 1
 
0.8%
126228220 1
 
0.8%
208848330 1
 
0.8%
294075200 1
 
0.8%
441452940 1
 
0.8%
ValueCountFrequency (%)
1699385143800 1
0.8%
554312926230 1
0.8%
425959334660 1
0.8%
413919345960 1
0.8%
331905997520 1
0.8%
316837781120 1
0.8%
299865004120 1
0.8%
282454010220 1
0.8%
227014507200 1
0.8%
216380926860 1
0.8%

2019년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct122
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7006749 × 1010
Minimum0
Maximum1.7785712 × 1012
Zeros5
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-13T00:23:07.068187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15100655
Q13.8999671 × 109
median2.2059742 × 1010
Q37.9143615 × 1010
95-th percentile3.317554 × 1011
Maximum1.7785712 × 1012
Range1.7785712 × 1012
Interquartile range (IQR)7.5243648 × 1010

Descriptive statistics

Standard deviation1.8426351 × 1011
Coefficient of variation (CV)2.3928229
Kurtosis58.980621
Mean7.7006749 × 1010
Median Absolute Deviation (MAD)2.1287398 × 1010
Skewness6.8193842
Sum9.7028504 × 1012
Variance3.3953041 × 1022
MonotonicityNot monotonic
2023-12-13T00:23:07.214269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
4.0%
3968215490 1
 
0.8%
28400473510 1
 
0.8%
19533467800 1
 
0.8%
193562586930 1
 
0.8%
22584534270 1
 
0.8%
19194713140 1
 
0.8%
9713254610 1
 
0.8%
2921168220 1
 
0.8%
1532930800 1
 
0.8%
Other values (112) 112
88.9%
ValueCountFrequency (%)
0 5
4.0%
4945200 1
 
0.8%
12847840 1
 
0.8%
21859100 1
 
0.8%
37327490 1
 
0.8%
134162760 1
 
0.8%
214439410 1
 
0.8%
448588540 1
 
0.8%
563070390 1
 
0.8%
566295160 1
 
0.8%
ValueCountFrequency (%)
1778571175690 1
0.8%
599609976680 1
0.8%
460211456120 1
0.8%
448684682440 1
0.8%
360850560610 1
0.8%
352289422990 1
0.8%
337144764520 1
0.8%
315587321120 1
0.8%
260278539980 1
0.8%
249346561290 1
0.8%

2020년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct123
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.9429049 × 1010
Minimum0
Maximum2.0133837 × 1012
Zeros4
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-13T00:23:07.344637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23391082
Q14.6446789 × 109
median2.4337319 × 1010
Q39.3248214 × 1010
95-th percentile3.8164826 × 1011
Maximum2.0133837 × 1012
Range2.0133837 × 1012
Interquartile range (IQR)8.8603535 × 1010

Descriptive statistics

Standard deviation2.099543 × 1011
Coefficient of variation (CV)2.3477193
Kurtosis57.174754
Mean8.9429049 × 1010
Median Absolute Deviation (MAD)2.3434688 × 1010
Skewness6.6856502
Sum1.126806 × 1013
Variance4.408081 × 1022
MonotonicityNot monotonic
2023-12-13T00:23:07.506027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
3.2%
4630469270 1
 
0.8%
312435398040 1
 
0.8%
21809546950 1
 
0.8%
247722584340 1
 
0.8%
28269646880 1
 
0.8%
23600097740 1
 
0.8%
12664539490 1
 
0.8%
3776195460 1
 
0.8%
2067363890 1
 
0.8%
Other values (113) 113
89.7%
ValueCountFrequency (%)
0 4
3.2%
1752470 1
 
0.8%
5046590 1
 
0.8%
16385230 1
 
0.8%
44408640 1
 
0.8%
178428580 1
 
0.8%
216556010 1
 
0.8%
309231170 1
 
0.8%
524128250 1
 
0.8%
561741610 1
 
0.8%
ValueCountFrequency (%)
2013383705380 1
0.8%
695429299340 1
0.8%
521449601830 1
0.8%
510920975840 1
0.8%
416561820440 1
0.8%
411103744230 1
0.8%
389965737100 1
0.8%
356695814340 1
0.8%
312435398040 1
0.8%
279992774720 1
0.8%

2021년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct123
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6756426 × 1010
Minimum0
Maximum2.2656352 × 1012
Zeros4
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-13T00:23:07.679155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24089452
Q14.9793372 × 109
median2.5236435 × 1010
Q39.9174512 × 1010
95-th percentile4.0622992 × 1011
Maximum2.2656352 × 1012
Range2.2656352 × 1012
Interquartile range (IQR)9.4195175 × 1010

Descriptive statistics

Standard deviation2.3337753 × 1011
Coefficient of variation (CV)2.4120107
Kurtosis60.480758
Mean9.6756426 × 1010
Median Absolute Deviation (MAD)2.4141278 × 1010
Skewness6.9163979
Sum1.219131 × 1013
Variance5.4465072 × 1022
MonotonicityNot monotonic
2023-12-13T00:23:07.824900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
3.2%
4929236180 1
 
0.8%
19871632610 1
 
0.8%
19813733680 1
 
0.8%
24714680030 1
 
0.8%
255439248320 1
 
0.8%
30168263980 1
 
0.8%
25774863300 1
 
0.8%
12663554390 1
 
0.8%
3777412540 1
 
0.8%
Other values (113) 113
89.7%
ValueCountFrequency (%)
0 4
3.2%
5355470 1
 
0.8%
8070140 1
 
0.8%
9797050 1
 
0.8%
66966660 1
 
0.8%
193753200 1
 
0.8%
310367570 1
 
0.8%
337682070 1
 
0.8%
345199760 1
 
0.8%
474788420 1
 
0.8%
ValueCountFrequency (%)
2265635189140 1
0.8%
755694516310 1
0.8%
548277959110 1
0.8%
539711027680 1
0.8%
481434356460 1
0.8%
441338936150 1
0.8%
413487494100 1
0.8%
384457179770 1
0.8%
340603289770 1
0.8%
295589609610 1
0.8%

2022년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct123
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0929093 × 1011
Minimum0
Maximum2.5863716 × 1012
Zeros4
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-13T00:23:07.979187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23075742
Q15.4061736 × 109
median2.8277114 × 1010
Q31.137692 × 1011
95-th percentile4.597535 × 1011
Maximum2.5863716 × 1012
Range2.5863716 × 1012
Interquartile range (IQR)1.0836303 × 1011

Descriptive statistics

Standard deviation2.6446854 × 1011
Coefficient of variation (CV)2.4198581
Kurtosis62.332501
Mean1.0929093 × 1011
Median Absolute Deviation (MAD)2.7386792 × 1010
Skewness7.0332057
Sum1.3770657 × 1013
Variance6.9943611 × 1022
MonotonicityNot monotonic
2023-12-13T00:23:08.134051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
3.2%
5276290370 1
 
0.8%
22261776970 1
 
0.8%
22789974160 1
 
0.8%
28474023190 1
 
0.8%
266200745820 1
 
0.8%
32720519180 1
 
0.8%
27903119120 1
 
0.8%
15477162420 1
 
0.8%
4236744480 1
 
0.8%
Other values (113) 113
89.7%
ValueCountFrequency (%)
0 4
3.2%
3543000 1
 
0.8%
5369840 1
 
0.8%
5768070 1
 
0.8%
74998760 1
 
0.8%
216974880 1
 
0.8%
309772250 1
 
0.8%
364145220 1
 
0.8%
401898820 1
 
0.8%
438723230 1
 
0.8%
ValueCountFrequency (%)
2586371644370 1
0.8%
810614324810 1
0.8%
610152226980 1
0.8%
609325562290 1
0.8%
543585209970 1
0.8%
492116532290 1
0.8%
469896959650 1
0.8%
429323130740 1
0.8%
393606950360 1
0.8%
341733811050 1
0.8%

Interactions

2023-12-13T00:23:05.121565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:02.492799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:03.175855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:03.838906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:04.486745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:05.226459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:02.658608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:03.313668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:03.980702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:04.631963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:05.336666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:02.815928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:03.425398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:04.129505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:04.764152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:05.461205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:02.957240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:03.532230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:04.255120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:04.888961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:05.558249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:03.063001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:03.727784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:04.361474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:05.013680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:23:08.243747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종대분류명사업장규모2018년2019년2020년2021년2022년
업종대분류명1.0000.0000.4650.5580.5230.3880.388
사업장규모0.0001.0000.2530.3090.2410.2390.239
2018년0.4650.2531.0000.9980.9940.9960.996
2019년0.5580.3090.9981.0000.9980.9930.993
2020년0.5230.2410.9940.9981.0000.9980.998
2021년0.3880.2390.9960.9930.9981.0001.000
2022년0.3880.2390.9960.9930.9981.0001.000
2023-12-13T00:23:08.371708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종대분류명사업장규모
업종대분류명1.0000.000
사업장규모0.0001.000
2023-12-13T00:23:08.468589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2018년2019년2020년2021년2022년업종대분류명사업장규모
2018년1.0000.9970.9950.9930.9920.2300.172
2019년0.9971.0000.9990.9980.9970.2900.213
2020년0.9950.9991.0000.9990.9980.2660.164
2021년0.9930.9980.9991.0000.9990.1840.162
2022년0.9920.9970.9980.9991.0000.1840.162
업종대분류명0.2300.2900.2660.1840.1841.0000.000
사업장규모0.1720.2130.1640.1620.1620.0001.000

Missing values

2023-12-13T00:23:06.045237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:23:06.162580image/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

업종대분류명사업장규모2018년2019년2020년2021년2022년
0농업, 임업 및 어업(01~08)1~5인미만34563369503968215490463046927049292361805276290370
1농업, 임업 및 어업(01~08)5~10인미만29775386503190384960384358485040496852504517874280
2농업, 임업 및 어업(01~08)10~30인미만41073413104515214610548074774057852568706230231890
3농업, 임업 및 어업(01~08)30~100인미만60868048806339889980729198089076489750008400112960
4농업, 임업 및 어업(01~08)100~300인미만16052107301676670300176096936022234004402253531660
5농업, 임업 및 어업(01~08)300인이상499655860563070390561741610538676240598942550
6광업(05~08)1~5인미만582141340566295160584031890474788420438723230
7광업(05~08)5~10인미만519197010589017860628649940579254930620329790
8광업(05~08)10~30인미만20192043802206116830255916193026883369102829052720
9광업(05~08)30~100인미만14924460101610029000181456026020731593302428440130
업종대분류명사업장규모2018년2019년2020년2021년2022년
116가구내 고용활동및 달리 분류되지않은 자가소비생산활동(97~98)10~30인미만372000005369840
117가구내 고용활동및 달리 분류되지않은 자가소비생산활동(97~98)30~100인미만00080701400
118가구내 고용활동및 달리 분류되지않은 자가소비생산활동(97~98)100~300인미만00000
119가구내 고용활동및 달리 분류되지않은 자가소비생산활동(97~98)300인이상00000
120국제 및 외국기관(99)1~5인미만126228220134162760178428580193753200216974880
121국제 및 외국기관(99)5~10인미만1928233021859100444086406696666074998760
122국제 및 외국기관(99)10~30인미만208848330214439410309231170345199760401898820
123국제 및 외국기관(99)30~100인미만5180343037327490216556010337682070364145220
124국제 및 외국기관(99)100~300인미만527438300576710530590482950310367570309772250
125국제 및 외국기관(99)300인이상1256281066012800463870139109089501498412141015639339190