Overview

Dataset statistics

Number of variables5
Number of observations350
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.5 KiB
Average record size in memory42.4 B

Variable types

Numeric2
Categorical3

Dataset

Description- 제주도 내 연도별 보건소 인력 현황 정보를 제공합니다. - 데이터 제공처: KOSIS 국가통계포털
Author제주특별자치도 미래성장과
URLhttps://www.jejudatahub.net/data/view/data/925

Alerts

구분대분류 is highly overall correlated with 구분소분류High correlation
구분소분류 is highly overall correlated with 구분대분류High correlation
인력 수(명) has 126 (36.0%) zerosZeros

Reproduction

Analysis started2023-12-11 20:11:29.594297
Analysis finished2023-12-11 20:11:31.923197
Duration2.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준 연도
Real number (ℝ)

Distinct9
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.8743
Minimum2012
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-12-12T05:11:31.984514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2012
Q12014
median2016
Q32018
95-th percentile2020
Maximum2020
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5144104
Coefficient of variation (CV)0.0012473051
Kurtosis-1.2119973
Mean2015.8743
Median Absolute Deviation (MAD)2
Skewness0.008130364
Sum705556
Variance6.3222595
MonotonicityIncreasing
2023-12-12T05:11:32.098031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2019 44
12.6%
2012 40
11.4%
2013 40
11.4%
2014 40
11.4%
2015 40
11.4%
2016 40
11.4%
2017 40
11.4%
2018 40
11.4%
2020 26
7.4%
ValueCountFrequency (%)
2012 40
11.4%
2013 40
11.4%
2014 40
11.4%
2015 40
11.4%
2016 40
11.4%
2017 40
11.4%
2018 40
11.4%
2019 44
12.6%
2020 26
7.4%
ValueCountFrequency (%)
2020 26
7.4%
2019 44
12.6%
2018 40
11.4%
2017 40
11.4%
2016 40
11.4%
2015 40
11.4%
2014 40
11.4%
2013 40
11.4%
2012 40
11.4%

구분대분류
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
의료기사
68 
의사
50 
소장
34 
치과의사
34 
한의사
34 
Other values (8)
130 

Length

Max length5
Median length4
Mean length3.2285714
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row간호사
2nd row간호사
3rd row간호조무사
4th row간호조무사
5th row기능직 등

Common Values

ValueCountFrequency (%)
의료기사 68
19.4%
의사 50
14.3%
소장 34
9.7%
치과의사 34
9.7%
한의사 34
9.7%
간호사 18
 
5.1%
간호조무사 18
 
5.1%
기능직 등 18
 
5.1%
보건직 18
 
5.1%
약사 18
 
5.1%
Other values (3) 40
11.4%

Length

2023-12-12T05:11:32.238698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
의료기사 68
18.5%
의사 50
13.6%
소장 34
9.2%
치과의사 34
9.2%
한의사 34
9.2%
간호사 18
 
4.9%
간호조무사 18
 
4.9%
기능직 18
 
4.9%
등 18
 
4.9%
보건직 18
 
4.9%
Other values (4) 58
15.8%

구분소분류
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
소계
140 
공중보건의사
48 
일반
32 
의사
16 
의사외
16 
Other values (7)
98 

Length

Max length6
Median length2
Mean length3.2057143
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
소계 140
40.0%
공중보건의사 48
 
13.7%
일반 32
 
9.1%
의사 16
 
4.6%
의사외 16
 
4.6%
물리치료사 16
 
4.6%
방사선사 16
 
4.6%
임상병리사 16
 
4.6%
치과위생사 16
 
4.6%
일반직 16
 
4.6%
Other values (2) 18
 
5.1%

Length

2023-12-12T05:11:32.377909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
소계 140
40.0%
공중보건의사 48
 
13.7%
일반 32
 
9.1%
의사 16
 
4.6%
의사외 16
 
4.6%
물리치료사 16
 
4.6%
방사선사 16
 
4.6%
임상병리사 16
 
4.6%
치과위생사 16
 
4.6%
일반직 16
 
4.6%
Other values (2) 18
 
5.1%

성별
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
여자
175 
남자
175 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
여자 175
50.0%
남자 175
50.0%

Length

2023-12-12T05:11:32.506629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T05:11:32.607355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여자 175
50.0%
남자 175
50.0%

인력 수(명)
Real number (ℝ)

ZEROS 

Distinct27
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6342857
Minimum0
Maximum73
Zeros126
Zeros (%)36.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-12-12T05:11:32.703635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile14
Maximum73
Range73
Interquartile range (IQR)6

Descriptive statistics

Standard deviation9.2694755
Coefficient of variation (CV)2.0001951
Kurtosis25.38632
Mean4.6342857
Median Absolute Deviation (MAD)1
Skewness4.6378777
Sum1622
Variance85.923176
MonotonicityNot monotonic
2023-12-12T05:11:32.823558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 126
36.0%
1 55
15.7%
4 26
 
7.4%
6 24
 
6.9%
2 20
 
5.7%
5 18
 
5.1%
7 15
 
4.3%
8 11
 
3.1%
11 8
 
2.3%
3 8
 
2.3%
Other values (17) 39
 
11.1%
ValueCountFrequency (%)
0 126
36.0%
1 55
15.7%
2 20
 
5.7%
3 8
 
2.3%
4 26
 
7.4%
5 18
 
5.1%
6 24
 
6.9%
7 15
 
4.3%
8 11
 
3.1%
9 6
 
1.7%
ValueCountFrequency (%)
73 1
0.3%
67 1
0.3%
61 1
0.3%
57 2
0.6%
47 1
0.3%
45 1
0.3%
44 1
0.3%
37 1
0.3%
25 1
0.3%
18 2
0.6%

Interactions

2023-12-12T05:11:31.563365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:11:31.125506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:11:31.665018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:11:31.300842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:11:32.921665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준 연도구분대분류구분소분류성별인력 수(명)
기준 연도1.0000.0000.0000.0000.000
구분대분류0.0001.0000.8470.0000.532
구분소분류0.0000.8471.0000.0000.000
성별0.0000.0000.0001.0000.144
인력 수(명)0.0000.5320.0000.1441.000
2023-12-12T05:11:33.031205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별구분소분류구분대분류
성별1.0000.0000.000
구분소분류0.0001.0000.530
구분대분류0.0000.5301.000
2023-12-12T05:11:33.116584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준 연도인력 수(명)구분대분류구분소분류성별
기준 연도1.0000.0450.0000.0000.000
인력 수(명)0.0451.0000.2600.0000.142
구분대분류0.0000.2601.0000.5300.000
구분소분류0.0000.0000.5301.0000.000
성별0.0000.1420.0000.0001.000

Missing values

2023-12-12T05:11:31.788832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:11:31.878154image/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

기준 연도구분대분류구분소분류성별인력 수(명)
02012간호사소계여자37
12012간호사소계남자2
22012간호조무사소계여자18
32012간호조무사소계남자1
42012기능직 등소계여자1
52012기능직 등소계남자12
62012보건직소계여자6
72012보건직소계남자10
82012소장의사여자0
92012소장의사남자1
기준 연도구분대분류구분소분류성별인력 수(명)
3402020의료기사소계여자25
3412020의료기사소계남자13
3422020의사소계여자1
3432020의사소계남자8
3442020치과의사소계여자0
3452020치과의사소계남자9
3462020한의사소계여자0
3472020한의사소계남자8
3482020행정직소계여자2
3492020행정직소계남자2