Overview

Dataset statistics

Number of variables12
Number of observations51
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.5 KiB
Average record size in memory109.6 B

Variable types

Numeric11
Categorical1

Dataset

Description한국산업안전보건공단에서 실시하는 작업환경측정 사업에 대한 결과보고서 내용으로 규모별 측정 실시 및 초과 사업장 현황에 대한 내용을 제공합니다.
URLhttps://www.data.go.kr/data/15091876/fileData.do

Alerts

상반기_실시사업장수 is highly overall correlated with 상반기_실시사업장비율 and 7 other fieldsHigh correlation
상반기_실시사업장비율 is highly overall correlated with 상반기_실시사업장수 and 7 other fieldsHigh correlation
상반기_초과사업장수 is highly overall correlated with 상반기_실시사업장수 and 8 other fieldsHigh correlation
상반기_초과사업장비율 is highly overall correlated with 상반기_실시사업장수 and 7 other fieldsHigh correlation
상반기_초과율 is highly overall correlated with 하반기_초과율High correlation
하반기_실시사업장수 is highly overall correlated with 상반기_실시사업장수 and 7 other fieldsHigh correlation
하반기_실시사업장비율 is highly overall correlated with 상반기_실시사업장수 and 7 other fieldsHigh correlation
하반기_초과사업장수 is highly overall correlated with 상반기_실시사업장수 and 7 other fieldsHigh correlation
하반기_초과사업장비율 is highly overall correlated with 상반기_실시사업장수 and 7 other fieldsHigh correlation
하반기_초과율 is highly overall correlated with 상반기_초과사업장수 and 1 other fieldsHigh correlation
상시근로자수구분 is highly overall correlated with 상반기_실시사업장수 and 7 other fieldsHigh correlation
상반기_초과사업장수 has unique valuesUnique
하반기_실시사업장수 has unique valuesUnique
하반기_초과사업장수 has unique valuesUnique

Reproduction

Analysis started2023-12-12 01:35:48.446105
Analysis finished2023-12-12 01:36:04.642566
Duration16.2 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct6
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.6471
Minimum2016
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T10:36:04.698702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12017
median2019
Q32020
95-th percentile2021
Maximum2021
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.659199
Coefficient of variation (CV)0.00082193613
Kurtosis-1.1994593
Mean2018.6471
Median Absolute Deviation (MAD)1
Skewness-0.063865435
Sum102951
Variance2.7529412
MonotonicityIncreasing
2023-12-12T10:36:04.806224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2017 9
17.6%
2018 9
17.6%
2019 9
17.6%
2020 9
17.6%
2021 9
17.6%
2016 6
11.8%
ValueCountFrequency (%)
2016 6
11.8%
2017 9
17.6%
2018 9
17.6%
2019 9
17.6%
2020 9
17.6%
2021 9
17.6%
ValueCountFrequency (%)
2021 9
17.6%
2020 9
17.6%
2019 9
17.6%
2018 9
17.6%
2017 9
17.6%
2016 6
11.8%

상시근로자수구분
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Memory size540.0 B
5명 미만
50~99명
100~299명
300~999명
1000명 이상
Other values (5)
21 

Length

Max length8
Median length6
Mean length6.372549
Min length4

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row5명 미만
2nd row5~49명
3rd row50~99명
4th row100~299명
5th row300~999명

Common Values

ValueCountFrequency (%)
5명 미만 6
11.8%
50~99명 6
11.8%
100~299명 6
11.8%
300~999명 6
11.8%
1000명 이상 6
11.8%
5~9명 5
9.8%
10~19명 5
9.8%
20~29명 5
9.8%
30~49명 5
9.8%
5~49명 1
 
2.0%

Length

2023-12-12T10:36:04.948273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:36:05.092618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5명 6
9.5%
미만 6
9.5%
50~99명 6
9.5%
100~299명 6
9.5%
300~999명 6
9.5%
1000명 6
9.5%
이상 6
9.5%
5~9명 5
7.9%
10~19명 5
7.9%
20~29명 5
7.9%
Other values (2) 6
9.5%

상반기_실시사업장수
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6516.451
Minimum353
Maximum30826
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T10:36:05.271520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum353
5-th percentile924.5
Q14301.5
median6007
Q39097.5
95-th percentile11957
Maximum30826
Range30473
Interquartile range (IQR)4796

Descriptive statistics

Standard deviation4901.1251
Coefficient of variation (CV)0.7521157
Kurtosis11.076548
Mean6516.451
Median Absolute Deviation (MAD)2996
Skewness2.4145706
Sum332339
Variance24021027
MonotonicityNot monotonic
2023-12-12T10:36:05.433553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
5981 2
 
3.9%
6455 2
 
3.9%
5861 1
 
2.0%
1776 1
 
2.0%
6137 1
 
2.0%
4818 1
 
2.0%
1759 1
 
2.0%
871 1
 
2.0%
10142 1
 
2.0%
11209 1
 
2.0%
Other values (39) 39
76.5%
ValueCountFrequency (%)
353 1
2.0%
386 1
2.0%
871 1
2.0%
978 1
2.0%
1202 1
2.0%
1218 1
2.0%
1290 1
2.0%
1332 1
2.0%
1511 1
2.0%
1759 1
2.0%
ValueCountFrequency (%)
30826 1
2.0%
12611 1
2.0%
12306 1
2.0%
11608 1
2.0%
11486 1
2.0%
11209 1
2.0%
11145 1
2.0%
10652 1
2.0%
10477 1
2.0%
10142 1
2.0%

상반기_실시사업장비율
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.768627
Minimum0.7
Maximum64.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T10:36:05.593801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile1.7
Q18.2
median11.1
Q315.95
95-th percentile20.7
Maximum64.3
Range63.6
Interquartile range (IQR)7.75

Descriptive statistics

Standard deviation9.6094223
Coefficient of variation (CV)0.81652872
Kurtosis17.342461
Mean11.768627
Median Absolute Deviation (MAD)4.6
Skewness3.2619674
Sum600.2
Variance92.340996
MonotonicityNot monotonic
2023-12-12T10:36:05.743736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
10.7 2
 
3.9%
11.5 2
 
3.9%
8.7 2
 
3.9%
8.4 2
 
3.9%
18.2 2
 
3.9%
11.7 2
 
3.9%
8.0 1
 
2.0%
3.1 1
 
2.0%
1.5 1
 
2.0%
16.8 1
 
2.0%
Other values (35) 35
68.6%
ValueCountFrequency (%)
0.7 1
2.0%
0.8 1
2.0%
1.5 1
2.0%
1.9 1
2.0%
2.0 1
2.0%
2.2 1
2.0%
2.4 1
2.0%
2.5 1
2.0%
2.9 1
2.0%
3.0 1
2.0%
ValueCountFrequency (%)
64.3 1
2.0%
21.5 1
2.0%
20.8 1
2.0%
20.6 1
2.0%
20.4 1
2.0%
20.0 1
2.0%
18.6 1
2.0%
18.2 2
3.9%
18.0 1
2.0%
17.7 1
2.0%

상반기_초과사업장수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean768.37255
Minimum51
Maximum4280
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T10:36:05.902689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum51
5-th percentile90.5
Q1402
median808
Q3967.5
95-th percentile1380.5
Maximum4280
Range4229
Interquartile range (IQR)565.5

Descriptive statistics

Standard deviation641.20399
Coefficient of variation (CV)0.83449622
Kurtosis17.492131
Mean768.37255
Median Absolute Deviation (MAD)232
Skewness3.2513903
Sum39187
Variance411142.56
MonotonicityNot monotonic
2023-12-12T10:36:06.067252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
430 1
 
2.0%
4280 1
 
2.0%
942 1
 
2.0%
808 1
 
2.0%
615 1
 
2.0%
139 1
 
2.0%
93 1
 
2.0%
576 1
 
2.0%
986 1
 
2.0%
1468 1
 
2.0%
Other values (41) 41
80.4%
ValueCountFrequency (%)
51 1
2.0%
60 1
2.0%
88 1
2.0%
93 1
2.0%
97 1
2.0%
120 1
2.0%
136 1
2.0%
137 1
2.0%
139 1
2.0%
181 1
2.0%
ValueCountFrequency (%)
4280 1
2.0%
1468 1
2.0%
1425 1
2.0%
1336 1
2.0%
1332 1
2.0%
1297 1
2.0%
1159 1
2.0%
1084 1
2.0%
997 1
2.0%
991 1
2.0%

상반기_초과사업장비율
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)78.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.764706
Minimum0.8
Maximum63.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T10:36:06.208905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile1.45
Q16.15
median12.5
Q314.85
95-th percentile21.55
Maximum63.7
Range62.9
Interquartile range (IQR)8.7

Descriptive statistics

Standard deviation9.6298873
Coefficient of variation (CV)0.81854042
Kurtosis16.318728
Mean11.764706
Median Absolute Deviation (MAD)3.8
Skewness3.0910118
Sum600
Variance92.734729
MonotonicityNot monotonic
2023-12-12T10:36:06.363768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
6.4 2
 
3.9%
17.0 2
 
3.9%
14.5 2
 
3.9%
1.5 2
 
3.9%
14.7 2
 
3.9%
15.0 2
 
3.9%
12.9 2
 
3.9%
11.1 2
 
3.9%
14.9 2
 
3.9%
14.3 2
 
3.9%
Other values (30) 31
60.8%
ValueCountFrequency (%)
0.8 1
2.0%
0.9 1
2.0%
1.4 1
2.0%
1.5 2
3.9%
1.9 1
2.0%
2.0 1
2.0%
2.1 1
2.0%
2.2 1
2.0%
2.6 1
2.0%
2.9 1
2.0%
ValueCountFrequency (%)
63.7 1
2.0%
22.3 1
2.0%
22.1 1
2.0%
21.0 1
2.0%
20.7 1
2.0%
19.5 1
2.0%
17.0 2
3.9%
15.1 1
2.0%
15.0 2
3.9%
14.9 2
3.9%

상반기_초과율
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.076471
Minimum5.1
Maximum18.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T10:36:06.513788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.1
5-th percentile5.75
Q18.6
median12.8
Q314.7
95-th percentile17.45
Maximum18.3
Range13.2
Interquartile range (IQR)6.1

Descriptive statistics

Standard deviation3.7359116
Coefficient of variation (CV)0.30935459
Kurtosis-1.0049228
Mean12.076471
Median Absolute Deviation (MAD)2.6
Skewness-0.3416362
Sum615.9
Variance13.957035
MonotonicityNot monotonic
2023-12-12T10:36:06.672639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
11.3 2
 
3.9%
13.9 2
 
3.9%
14.4 2
 
3.9%
14.7 2
 
3.9%
14.5 2
 
3.9%
7.3 1
 
2.0%
12.8 1
 
2.0%
10.7 1
 
2.0%
5.7 1
 
2.0%
8.8 1
 
2.0%
Other values (36) 36
70.6%
ValueCountFrequency (%)
5.1 1
2.0%
5.2 1
2.0%
5.7 1
2.0%
5.8 1
2.0%
6.1 1
2.0%
6.7 1
2.0%
7.3 1
2.0%
7.4 1
2.0%
7.7 1
2.0%
7.9 1
2.0%
ValueCountFrequency (%)
18.3 1
2.0%
18.0 1
2.0%
17.9 1
2.0%
17.0 1
2.0%
16.5 1
2.0%
16.3 1
2.0%
16.2 1
2.0%
15.6 1
2.0%
15.5 1
2.0%
15.4 1
2.0%

하반기_실시사업장수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6221.0784
Minimum343
Maximum30865
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T10:36:06.839112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum343
5-th percentile972.5
Q14327.5
median5953
Q38085.5
95-th percentile11098
Maximum30865
Range30522
Interquartile range (IQR)3758

Descriptive statistics

Standard deviation4724.309
Coefficient of variation (CV)0.75940354
Kurtosis14.030346
Mean6221.0784
Median Absolute Deviation (MAD)1998
Skewness2.7968577
Sum317275
Variance22319096
MonotonicityNot monotonic
2023-12-12T10:36:07.382416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5443 1
 
2.0%
30865 1
 
2.0%
6408 1
 
2.0%
5953 1
 
2.0%
4751 1
 
2.0%
1745 1
 
2.0%
869 1
 
2.0%
8260 1
 
2.0%
9611 1
 
2.0%
11033 1
 
2.0%
Other values (41) 41
80.4%
ValueCountFrequency (%)
343 1
2.0%
406 1
2.0%
869 1
2.0%
1076 1
2.0%
1164 1
2.0%
1226 1
2.0%
1232 1
2.0%
1251 1
2.0%
1533 1
2.0%
1714 1
2.0%
ValueCountFrequency (%)
30865 1
2.0%
11643 1
2.0%
11163 1
2.0%
11033 1
2.0%
10971 1
2.0%
10198 1
2.0%
10168 1
2.0%
9611 1
2.0%
9564 1
2.0%
9278 1
2.0%

하반기_실시사업장비율
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)86.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.760784
Minimum0.6
Maximum64.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T10:36:07.589732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile1.8
Q18.3
median11.2
Q314.85
95-th percentile20.5
Maximum64.6
Range64
Interquartile range (IQR)6.55

Descriptive statistics

Standard deviation9.513613
Coefficient of variation (CV)0.80892674
Kurtosis18.649001
Mean11.760784
Median Absolute Deviation (MAD)3.4
Skewness3.4139149
Sum599.8
Variance90.508831
MonotonicityNot monotonic
2023-12-12T10:36:07.775095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
11.2 3
 
5.9%
2.2 2
 
3.9%
11.3 2
 
3.9%
3.1 2
 
3.9%
10.6 2
 
3.9%
11.7 2
 
3.9%
11.4 1
 
2.0%
10.5 1
 
2.0%
8.4 1
 
2.0%
1.5 1
 
2.0%
Other values (34) 34
66.7%
ValueCountFrequency (%)
0.6 1
2.0%
0.9 1
2.0%
1.5 1
2.0%
2.1 1
2.0%
2.2 2
3.9%
2.3 1
2.0%
2.6 1
2.0%
3.1 2
3.9%
3.2 1
2.0%
3.5 1
2.0%
ValueCountFrequency (%)
64.6 1
2.0%
21.4 1
2.0%
20.9 1
2.0%
20.1 1
2.0%
20.0 1
2.0%
19.7 1
2.0%
18.0 1
2.0%
17.6 1
2.0%
17.5 1
2.0%
16.9 1
2.0%

하반기_초과사업장수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean758.76471
Minimum47
Maximum4352
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T10:36:08.016985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile88.5
Q1376.5
median804
Q3963
95-th percentile1376
Maximum4352
Range4305
Interquartile range (IQR)586.5

Descriptive statistics

Standard deviation650.81772
Coefficient of variation (CV)0.85773325
Kurtosis18.130109
Mean758.76471
Median Absolute Deviation (MAD)257
Skewness3.3434428
Sum38697
Variance423563.7
MonotonicityNot monotonic
2023-12-12T10:36:08.203500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
417 1
 
2.0%
4352 1
 
2.0%
970 1
 
2.0%
804 1
 
2.0%
547 1
 
2.0%
129 1
 
2.0%
85 1
 
2.0%
513 1
 
2.0%
921 1
 
2.0%
1429 1
 
2.0%
Other values (41) 41
80.4%
ValueCountFrequency (%)
47 1
2.0%
57 1
2.0%
85 1
2.0%
92 1
2.0%
95 1
2.0%
119 1
2.0%
123 1
2.0%
128 1
2.0%
129 1
2.0%
159 1
2.0%
ValueCountFrequency (%)
4352 1
2.0%
1429 1
2.0%
1379 1
2.0%
1373 1
2.0%
1336 1
2.0%
1291 1
2.0%
1144 1
2.0%
1095 1
2.0%
995 1
2.0%
991 1
2.0%

하반기_초과사업장비율
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.760784
Minimum0.8
Maximum64.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T10:36:08.402838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile1.35
Q15.9
median12.6
Q315
95-th percentile22
Maximum64.6
Range63.8
Interquartile range (IQR)9.1

Descriptive statistics

Standard deviation9.8080799
Coefficient of variation (CV)0.83396478
Kurtosis16.228648
Mean11.760784
Median Absolute Deviation (MAD)4
Skewness3.0884615
Sum599.8
Variance96.198431
MonotonicityNot monotonic
2023-12-12T10:36:08.642452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
14.0 3
 
5.9%
22.1 2
 
3.9%
14.3 2
 
3.9%
15.1 2
 
3.9%
2.8 2
 
3.9%
0.8 2
 
3.9%
2.0 2
 
3.9%
14.9 2
 
3.9%
9.3 2
 
3.9%
6.2 1
 
2.0%
Other values (31) 31
60.8%
ValueCountFrequency (%)
0.8 2
3.9%
1.3 1
2.0%
1.4 1
2.0%
1.5 1
2.0%
1.8 1
2.0%
1.9 1
2.0%
2.0 2
3.9%
2.4 1
2.0%
2.8 2
3.9%
5.7 1
2.0%
ValueCountFrequency (%)
64.6 1
2.0%
22.1 2
3.9%
21.9 1
2.0%
20.9 1
2.0%
19.6 1
2.0%
17.6 1
2.0%
17.4 1
2.0%
15.6 1
2.0%
15.3 1
2.0%
15.2 1
2.0%

하반기_초과율
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.090196
Minimum4.5
Maximum17.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T10:36:08.852876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.5
5-th percentile6.4
Q19.7
median12.7
Q314.95
95-th percentile16.9
Maximum17.9
Range13.4
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation3.5156937
Coefficient of variation (CV)0.2907888
Kurtosis-0.90345319
Mean12.090196
Median Absolute Deviation (MAD)2.6
Skewness-0.37642817
Sum616.6
Variance12.360102
MonotonicityNot monotonic
2023-12-12T10:36:09.070966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
9.8 2
 
3.9%
14.0 2
 
3.9%
15.2 2
 
3.9%
6.6 2
 
3.9%
17.9 2
 
3.9%
11.4 1
 
2.0%
12.4 1
 
2.0%
11.5 1
 
2.0%
7.4 1
 
2.0%
6.2 1
 
2.0%
Other values (36) 36
70.6%
ValueCountFrequency (%)
4.5 1
2.0%
6.0 1
2.0%
6.2 1
2.0%
6.6 2
3.9%
7.2 1
2.0%
7.3 1
2.0%
7.4 1
2.0%
7.7 1
2.0%
7.8 1
2.0%
7.9 1
2.0%
ValueCountFrequency (%)
17.9 2
3.9%
17.2 1
2.0%
16.6 1
2.0%
16.2 1
2.0%
15.8 1
2.0%
15.7 1
2.0%
15.6 1
2.0%
15.5 1
2.0%
15.3 1
2.0%
15.2 2
3.9%

Interactions

2023-12-12T10:36:02.984206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:48.866055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:50.023117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:51.222056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:53.033043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:54.543658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:56.039463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:57.421601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:58.624032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:59.995053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:01.651605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:03.111212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:48.980277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:50.124902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:51.344980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:53.152316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:54.671524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:56.171404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:57.531115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:58.743719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:00.130317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:01.754100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:03.260288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:49.079562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:50.242069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:51.492645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:53.275059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:54.826330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:56.300534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:57.666204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:58.870611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:00.247936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:01.880725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:03.388796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:49.179198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:50.369498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:51.647690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:53.437053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:54.958015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:56.417376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:57.784661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:59.001720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:00.365855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:02.015324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:03.501861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:49.285211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:50.465244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:51.747603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:53.554458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:55.078453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:56.513834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:57.881325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:59.107679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:00.472258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:02.134961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:03.615762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:49.403387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:50.571322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:51.846532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:53.740192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:55.223568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:56.638274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:57.988133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:59.224420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:00.595300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:02.251480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:03.729885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:49.508007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:50.687513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:52.068681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:53.855989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:55.387446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:56.777799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:58.092917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:59.336645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:00.721049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:02.377609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:03.833038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:49.612980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:50.781306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:52.192819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:53.985192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:55.528122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:56.912866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:58.189722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:59.475213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:01.190484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:02.493881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:03.976057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:49.718609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:50.899138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:52.658784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:54.131820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:55.666541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:57.043840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:58.300062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:59.613109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:01.315148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:02.638562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:04.081976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:49.811413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:51.002190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:52.763581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:54.265376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:55.775389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:57.164161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:58.396901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:59.729941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:01.418233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:02.752675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:04.224074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:49.918066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:51.128012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:52.872214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:54.405492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:55.912220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:57.285401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:58.512390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:35:59.862589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:01.521359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:36:02.850828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:36:09.228250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도상시근로자수구분상반기_실시사업장수상반기_실시사업장비율상반기_초과사업장수상반기_초과사업장비율상반기_초과율하반기_실시사업장수하반기_실시사업장비율하반기_초과사업장수하반기_초과사업장비율하반기_초과율
연도1.0000.0000.0000.0000.0000.0000.5520.0000.0000.0000.0000.000
상시근로자수구분0.0001.0000.9131.0000.9890.9920.8340.9930.9970.9850.9900.756
상반기_실시사업장수0.0000.9131.0000.8980.8220.8170.4040.9030.9010.8400.8320.370
상반기_실시사업장비율0.0001.0000.8981.0000.9760.9760.8140.9950.9990.9660.9750.724
상반기_초과사업장수0.0000.9890.8220.9761.0000.9960.6210.9590.9830.9970.9960.679
상반기_초과사업장비율0.0000.9920.8170.9760.9961.0000.7140.9600.9650.9960.9990.697
상반기_초과율0.5520.8340.4040.8140.6210.7141.0000.7600.7550.5030.6780.927
하반기_실시사업장수0.0000.9930.9030.9950.9590.9600.7601.0000.9930.9520.9580.729
하반기_실시사업장비율0.0000.9970.9010.9990.9830.9650.7550.9931.0000.9730.9640.678
하반기_초과사업장수0.0000.9850.8400.9660.9970.9960.5030.9520.9731.0000.9980.588
하반기_초과사업장비율0.0000.9900.8320.9750.9960.9990.6780.9580.9640.9981.0000.660
하반기_초과율0.0000.7560.3700.7240.6790.6970.9270.7290.6780.5880.6601.000
2023-12-12T10:36:09.434248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도상반기_실시사업장수상반기_실시사업장비율상반기_초과사업장수상반기_초과사업장비율상반기_초과율하반기_실시사업장수하반기_실시사업장비율하반기_초과사업장수하반기_초과사업장비율하반기_초과율상시근로자수구분
연도1.0000.205-0.021-0.0060.042-0.4310.1660.0190.0150.043-0.3150.000
상반기_실시사업장수0.2051.0000.9520.7740.791-0.2340.9910.9600.7880.803-0.0620.749
상반기_실시사업장비율-0.0210.9521.0000.7620.756-0.2060.9520.9850.7640.769-0.0610.914
상반기_초과사업장수-0.0060.7740.7621.0000.9840.3460.7860.7900.9810.9760.5060.804
상반기_초과사업장비율0.0420.7910.7560.9841.0000.3190.8070.7770.9820.9860.4820.820
상반기_초과율-0.431-0.234-0.2060.3460.3191.000-0.198-0.1760.3160.2950.9620.398
하반기_실시사업장수0.1660.9910.9520.7860.807-0.1981.0000.9640.8070.821-0.0330.834
하반기_실시사업장비율0.0190.9600.9850.7900.777-0.1760.9641.0000.7950.797-0.0230.874
하반기_초과사업장수0.0150.7880.7640.9810.9820.3160.8070.7951.0000.9970.4890.780
하반기_초과사업장비율0.0430.8030.7690.9760.9860.2950.8210.7970.9971.0000.4690.811
하반기_초과율-0.315-0.062-0.0610.5060.4820.962-0.033-0.0230.4890.4691.0000.350
상시근로자수구분0.0000.7490.9140.8040.8200.3980.8340.8740.7800.8110.3501.000

Missing values

2023-12-12T10:36:04.379475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:36:04.573747image/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

연도상시근로자수구분상반기_실시사업장수상반기_실시사업장비율상반기_초과사업장수상반기_초과사업장비율상반기_초과율하반기_실시사업장수하반기_실시사업장비율하반기_초과사업장수하반기_초과사업장비율하반기_초과율
020165명 미만586112.24306.47.3544311.44176.27.7
120165~49명3082664.3428063.713.93086564.6435264.614.1
2201650~99명552411.599114.817.9561811.896514.317.2
32016100~299명41578.775911.318.341868.875111.217.9
42016300~999명12022.51952.916.212322.61912.815.5
520161000명 이상3860.8600.915.54060.9570.814.0
620175명 미만600711.74426.47.4548711.23996.17.3
720175~9명919218.087112.89.5822016.892014.011.2
8201710~19명1065220.8133219.512.51019820.9129119.612.7
9201720~29명598111.797314.316.3583211.992414.015.8
연도상시근로자수구분상반기_실시사업장수상반기_실시사업장비율상반기_초과사업장수상반기_초과사업장비율상반기_초과율하반기_실시사업장수하반기_실시사업장비율하반기_초과사업장수하반기_초과사업장비율하반기_초과율
4120201000명 이상12182.0971.58.011642.1921.47.9
4220215명 미만994516.25218.15.2787814.45168.26.6
4320215~9명1114518.293914.78.4927816.993514.910.1
44202110~19명1261120.6142522.311.31097120.0137321.912.5
45202120~29명672011.094714.814.1639711.792814.814.5
46202130~49명648210.696015.014.8615411.293914.915.3
47202150~99명59379.780112.513.5583310.678312.413.4
482021100~299명51328.45949.311.651629.45879.311.4
492021300~999명19733.21201.96.119263.51282.06.6
5020211000명 이상13322.2881.46.712262.2951.57.8