Overview

Dataset statistics

Number of variables6
Number of observations46
Missing cells12
Missing cells (%)4.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory56.9 B

Variable types

Numeric6

Dataset

Description근로복지공단 소속병원 연도별 일반검진,특수검진,채용신검,보건관리대행,작업환경측정 현황 통계입니다. *1977년부터 2022년까지
URLhttps://www.data.go.kr/data/15051522/fileData.do

Alerts

년도 is highly overall correlated with 일반검진(명) and 2 other fieldsHigh correlation
일반검진(명) is highly overall correlated with 년도 and 2 other fieldsHigh correlation
채용신검(명) is highly overall correlated with 년도 and 2 other fieldsHigh correlation
보건관리대행(명) is highly overall correlated with 작업환경측정(개소)High correlation
작업환경측정(개소) is highly overall correlated with 년도 and 3 other fieldsHigh correlation
보건관리대행(명) has 11 (23.9%) missing valuesMissing
작업환경측정(개소) has 1 (2.2%) missing valuesMissing
년도 has unique valuesUnique
일반검진(명) has unique valuesUnique
특수검진(명) has unique valuesUnique
채용신검(명) has unique valuesUnique

Reproduction

Analysis started2023-12-12 06:37:15.206101
Analysis finished2023-12-12 06:37:19.355576
Duration4.15 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct46
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1999.5
Minimum1977
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-12T15:37:19.434252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1977
5-th percentile1979.25
Q11988.25
median1999.5
Q32010.75
95-th percentile2019.75
Maximum2022
Range45
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation13.422618
Coefficient of variation (CV)0.0067129871
Kurtosis-1.2
Mean1999.5
Median Absolute Deviation (MAD)11.5
Skewness0
Sum91977
Variance180.16667
MonotonicityStrictly increasing
2023-12-12T15:37:19.596477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
1977 1
 
2.2%
2012 1
 
2.2%
2003 1
 
2.2%
2004 1
 
2.2%
2005 1
 
2.2%
2006 1
 
2.2%
2007 1
 
2.2%
2008 1
 
2.2%
2009 1
 
2.2%
2010 1
 
2.2%
Other values (36) 36
78.3%
ValueCountFrequency (%)
1977 1
2.2%
1978 1
2.2%
1979 1
2.2%
1980 1
2.2%
1981 1
2.2%
1982 1
2.2%
1983 1
2.2%
1984 1
2.2%
1985 1
2.2%
1986 1
2.2%
ValueCountFrequency (%)
2022 1
2.2%
2021 1
2.2%
2020 1
2.2%
2019 1
2.2%
2018 1
2.2%
2017 1
2.2%
2016 1
2.2%
2015 1
2.2%
2014 1
2.2%
2013 1
2.2%

일반검진(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct46
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127056.43
Minimum3399
Maximum247829
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-12T15:37:19.773793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3399
5-th percentile13784.5
Q159502.75
median146528
Q3184193.75
95-th percentile214361.75
Maximum247829
Range244430
Interquartile range (IQR)124691

Descriptive statistics

Standard deviation70129.064
Coefficient of variation (CV)0.55195208
Kurtosis-1.2321859
Mean127056.43
Median Absolute Deviation (MAD)62209
Skewness-0.22843879
Sum5844596
Variance4.9180856 × 109
MonotonicityNot monotonic
2023-12-12T15:37:19.958761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
3399 1
 
2.2%
186004 1
 
2.2%
167575 1
 
2.2%
168124 1
 
2.2%
178763 1
 
2.2%
225156 1
 
2.2%
215220 1
 
2.2%
247829 1
 
2.2%
211787 1
 
2.2%
195129 1
 
2.2%
Other values (36) 36
78.3%
ValueCountFrequency (%)
3399 1
2.2%
3495 1
2.2%
10708 1
2.2%
23014 1
2.2%
23836 1
2.2%
35311 1
2.2%
48257 1
2.2%
51377 1
2.2%
51979 1
2.2%
54861 1
2.2%
ValueCountFrequency (%)
247829 1
2.2%
225156 1
2.2%
215220 1
2.2%
211787 1
2.2%
209373 1
2.2%
209205 1
2.2%
208841 1
2.2%
208780 1
2.2%
201189 1
2.2%
200457 1
2.2%

특수검진(명)
Real number (ℝ)

UNIQUE 

Distinct46
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81482.978
Minimum8640
Maximum128637
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-12T15:37:20.115760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8640
5-th percentile12932.25
Q170313.5
median85913.5
Q3103729.5
95-th percentile122007.5
Maximum128637
Range119997
Interquartile range (IQR)33416

Descriptive statistics

Standard deviation31385.769
Coefficient of variation (CV)0.3851819
Kurtosis0.33152937
Mean81482.978
Median Absolute Deviation (MAD)17802.5
Skewness-0.91671266
Sum3748217
Variance9.8506648 × 108
MonotonicityNot monotonic
2023-12-12T15:37:20.286262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
8640 1
 
2.2%
80676 1
 
2.2%
65490 1
 
2.2%
72152 1
 
2.2%
70184 1
 
2.2%
80024 1
 
2.2%
81745 1
 
2.2%
91101 1
 
2.2%
79644 1
 
2.2%
81392 1
 
2.2%
Other values (36) 36
78.3%
ValueCountFrequency (%)
8640 1
2.2%
10275 1
2.2%
10935 1
2.2%
18924 1
2.2%
24995 1
2.2%
25568 1
2.2%
51762 1
2.2%
56429 1
2.2%
63092 1
2.2%
64672 1
2.2%
ValueCountFrequency (%)
128637 1
2.2%
126517 1
2.2%
124068 1
2.2%
115826 1
2.2%
115185 1
2.2%
114670 1
2.2%
112505 1
2.2%
110643 1
2.2%
107938 1
2.2%
104832 1
2.2%

채용신검(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct46
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38659.565
Minimum1817
Maximum78630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-12T15:37:20.774542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1817
5-th percentile6053.5
Q120361
median35477
Q357895.25
95-th percentile74686.5
Maximum78630
Range76813
Interquartile range (IQR)37534.25

Descriptive statistics

Standard deviation22271.403
Coefficient of variation (CV)0.57609036
Kurtosis-1.145885
Mean38659.565
Median Absolute Deviation (MAD)17798
Skewness0.17632913
Sum1778340
Variance4.9601538 × 108
MonotonicityNot monotonic
2023-12-12T15:37:20.966806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
1817 1
 
2.2%
58523 1
 
2.2%
48701 1
 
2.2%
55130 1
 
2.2%
56012 1
 
2.2%
42199 1
 
2.2%
44345 1
 
2.2%
50492 1
 
2.2%
47349 1
 
2.2%
60375 1
 
2.2%
Other values (36) 36
78.3%
ValueCountFrequency (%)
1817 1
2.2%
4381 1
2.2%
5049 1
2.2%
9067 1
2.2%
9911 1
2.2%
11057 1
2.2%
13907 1
2.2%
15134 1
2.2%
18454 1
2.2%
18456 1
2.2%
ValueCountFrequency (%)
78630 1
2.2%
75412 1
2.2%
74852 1
2.2%
74190 1
2.2%
71210 1
2.2%
68037 1
2.2%
67912 1
2.2%
65505 1
2.2%
60375 1
2.2%
60316 1
2.2%

보건관리대행(명)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct35
Distinct (%)100.0%
Missing11
Missing (%)23.9%
Infinite0
Infinite (%)0.0%
Mean59093.2
Minimum321
Maximum70305
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-12T15:37:21.155907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum321
5-th percentile28172.1
Q157450.5
median64716
Q367911
95-th percentile69258.9
Maximum70305
Range69984
Interquartile range (IQR)10460.5

Descriptive statistics

Standard deviation14547.118
Coefficient of variation (CV)0.24617245
Kurtosis8.1255056
Mean59093.2
Median Absolute Deviation (MAD)4076
Skewness-2.6931735
Sum2068262
Variance2.1161864 × 108
MonotonicityNot monotonic
2023-12-12T15:37:21.295652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
22185 1
 
2.2%
68792 1
 
2.2%
65686 1
 
2.2%
67772 1
 
2.2%
67244 1
 
2.2%
68681 1
 
2.2%
66197 1
 
2.2%
64716 1
 
2.2%
67508 1
 
2.2%
58365 1
 
2.2%
Other values (25) 25
54.3%
(Missing) 11
23.9%
ValueCountFrequency (%)
321 1
2.2%
22185 1
2.2%
30738 1
2.2%
49726 1
2.2%
50316 1
2.2%
53911 1
2.2%
55337 1
2.2%
55824 1
2.2%
56936 1
2.2%
57965 1
2.2%
ValueCountFrequency (%)
70305 1
2.2%
70290 1
2.2%
68817 1
2.2%
68792 1
2.2%
68681 1
2.2%
68459 1
2.2%
68408 1
2.2%
68350 1
2.2%
68050 1
2.2%
67772 1
2.2%

작업환경측정(개소)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct45
Distinct (%)100.0%
Missing1
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean2691.4
Minimum12
Maximum4228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-12T15:37:21.446270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile62.2
Q11532
median3547
Q33774
95-th percentile4048
Maximum4228
Range4216
Interquartile range (IQR)2242

Descriptive statistics

Standard deviation1483.2859
Coefficient of variation (CV)0.55112056
Kurtosis-0.88890399
Mean2691.4
Median Absolute Deviation (MAD)378
Skewness-0.89740685
Sum121113
Variance2200137
MonotonicityNot monotonic
2023-12-12T15:37:21.609117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
12 1
 
2.2%
3809 1
 
2.2%
4062 1
 
2.2%
4228 1
 
2.2%
3798 1
 
2.2%
3642 1
 
2.2%
3756 1
 
2.2%
3774 1
 
2.2%
3677 1
 
2.2%
3754 1
 
2.2%
Other values (35) 35
76.1%
ValueCountFrequency (%)
12 1
2.2%
34 1
2.2%
58 1
2.2%
79 1
2.2%
86 1
2.2%
304 1
2.2%
348 1
2.2%
395 1
2.2%
448 1
2.2%
511 1
2.2%
ValueCountFrequency (%)
4228 1
2.2%
4072 1
2.2%
4062 1
2.2%
3992 1
2.2%
3925 1
2.2%
3900 1
2.2%
3849 1
2.2%
3847 1
2.2%
3838 1
2.2%
3809 1
2.2%

Interactions

2023-12-12T15:37:18.474663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:15.425706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:15.978257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:16.522371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:17.107375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:17.772771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:18.555085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:15.503726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:16.081314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:16.604424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:17.204454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:17.883667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:18.642959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:15.588902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:16.167744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:16.705160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:17.306269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:18.006661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:18.761140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:15.690483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:16.257500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:16.797852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:17.406944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:18.125225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:18.864594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:15.799690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:16.350726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:16.891396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:17.528214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:18.247798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:18.972254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:15.892237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:16.448254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:17.006969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:17.658009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:37:18.363800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:37:21.728930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도일반검진(명)특수검진(명)채용신검(명)보건관리대행(명)작업환경측정(개소)
년도1.0000.9070.7260.8860.8140.781
일반검진(명)0.9071.0000.7720.7390.8360.749
특수검진(명)0.7260.7721.0000.5760.5600.696
채용신검(명)0.8860.7390.5761.0000.4610.622
보건관리대행(명)0.8140.8360.5600.4611.0000.760
작업환경측정(개소)0.7810.7490.6960.6220.7601.000
2023-12-12T15:37:21.869768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도일반검진(명)특수검진(명)채용신검(명)보건관리대행(명)작업환경측정(개소)
년도1.0000.8370.3590.962-0.1400.513
일반검진(명)0.8371.0000.2370.8520.3290.680
특수검진(명)0.3590.2371.0000.332-0.0860.148
채용신검(명)0.9620.8520.3321.000-0.0510.594
보건관리대행(명)-0.1400.329-0.086-0.0511.0000.549
작업환경측정(개소)0.5130.6800.1480.5940.5491.000

Missing values

2023-12-12T15:37:19.080622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:37:19.192267image/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.
2023-12-12T15:37:19.294380image/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

년도일반검진(명)특수검진(명)채용신검(명)보건관리대행(명)작업환경측정(개소)
01977339986401817<NA><NA>
119783495102754381<NA>12
2197910708109355049<NA>34
31980230142499515134<NA>79
4198123836189249911<NA>86
5198235311255689067<NA>58
61983565095176211057<NA>348
71984548616309218454<NA>448
81985519797483213907<NA>304
91986601507600420874<NA>395
년도일반검진(명)특수검진(명)채용신검(명)보건관리대행(명)작업환경측정(개소)
3620132011898141678630661973925
3720142093739177374852647163692
38201520878010368974190675083707
39201620920511250568037583653589
40201720045710483259289569363426
41201816351210460175412558243072
4220191656439832571210553372638
4320201452608810054050539112487
4420211540489829967912503162427
4520221519609729465505497262447