Overview

Dataset statistics

Number of variables6
Number of observations1300
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory66.1 KiB
Average record size in memory52.1 B

Variable types

Categorical2
Text1
Numeric3

Dataset

Description김해시에서 통계기반 도시현황 파악을 위해 개발한 통계지수 중 하나로서, 통계연도, 시도명, 시군구명, 인구 천명당 의료인력(명), 의료인력수(명), 총인구수(명)로 구성되어 있습니다. 김해시 중심의 통계지수로서, 데이터 수집, 가공 등의 어려움으로 김해시 외 지역의 정보는 누락될 수 있습니다.
Author경상남도 김해시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15110158

Alerts

인구 천명당 의료인력(명) is highly overall correlated with 의료인력수(명)High correlation
의료인력수(명) is highly overall correlated with 인구 천명당 의료인력(명) and 1 other fieldsHigh correlation
총인구수(명) is highly overall correlated with 의료인력수(명)High correlation
총인구수(명) has unique valuesUnique

Reproduction

Analysis started2023-12-11 00:02:35.377722
Analysis finished2023-12-11 00:02:37.467309
Duration2.09 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계연도
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
2016
260 
2017
260 
2018
260 
2019
260 
2020
260 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2016 260
20.0%
2017 260
20.0%
2018 260
20.0%
2019 260
20.0%
2020 260
20.0%

Length

2023-12-11T09:02:37.545657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:02:37.652797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 260
20.0%
2017 260
20.0%
2018 260
20.0%
2019 260
20.0%
2020 260
20.0%

시도명
Categorical

Distinct16
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
경기도
240 
경상북도
125 
서울특별시
125 
경상남도
115 
전라남도
110 
Other values (11)
585 

Length

Max length7
Median length5
Mean length4.0538462
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 240
18.5%
경상북도 125
9.6%
서울특별시 125
9.6%
경상남도 115
8.8%
전라남도 110
8.5%
강원도 90
 
6.9%
충청남도 85
 
6.5%
전라북도 80
 
6.2%
부산광역시 80
 
6.2%
충청북도 75
 
5.8%
Other values (6) 175
13.5%

Length

2023-12-11T09:02:37.779311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 240
18.5%
경상북도 125
9.6%
서울특별시 125
9.6%
경상남도 115
8.8%
전라남도 110
8.5%
강원도 90
 
6.9%
충청남도 85
 
6.5%
전라북도 80
 
6.2%
부산광역시 80
 
6.2%
충청북도 75
 
5.8%
Other values (6) 175
13.5%

시군구명
Text

Distinct238
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
2023-12-11T09:02:38.128208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.4469231
Min length2

Characters and Unicode

Total characters4481
Distinct characters142
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강릉시
2nd row고성군
3rd row동해시
4th row삼척시
5th row속초시
ValueCountFrequency (%)
동구 30
 
2.1%
중구 30
 
2.1%
창원시 30
 
2.1%
남구 27
 
1.8%
서구 25
 
1.7%
북구 25
 
1.7%
수원시 25
 
1.7%
청주시 25
 
1.7%
용인시 20
 
1.4%
고양시 20
 
1.4%
Other values (226) 1203
82.4%
2023-12-11T09:02:38.634609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 550
 
12.3%
구 530
 
11.8%
군 425
 
9.5%
160
 
3.6%
주 130
 
2.9%
양 120
 
2.7%
천 120
 
2.7%
산 120
 
2.7%
성 110
 
2.5%
안 100
 
2.2%
Other values (132) 2116
47.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4321
96.4%
Space Separator 160
 
3.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 550
 
12.7%
구 530
 
12.3%
군 425
 
9.8%
주 130
 
3.0%
양 120
 
2.8%
천 120
 
2.8%
산 120
 
2.8%
성 110
 
2.5%
안 100
 
2.3%
원 100
 
2.3%
Other values (131) 2016
46.7%
Space Separator
ValueCountFrequency (%)
160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4321
96.4%
Common 160
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 550
 
12.7%
구 530
 
12.3%
군 425
 
9.8%
주 130
 
3.0%
양 120
 
2.8%
천 120
 
2.8%
산 120
 
2.8%
성 110
 
2.5%
안 100
 
2.3%
원 100
 
2.3%
Other values (131) 2016
46.7%
Common
ValueCountFrequency (%)
160
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4321
96.4%
ASCII 160
 
3.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 550
 
12.7%
구 530
 
12.3%
군 425
 
9.8%
주 130
 
3.0%
양 120
 
2.8%
천 120
 
2.8%
산 120
 
2.8%
성 110
 
2.5%
안 100
 
2.3%
원 100
 
2.3%
Other values (131) 2016
46.7%
ASCII
ValueCountFrequency (%)
160
100.0%

인구 천명당 의료인력(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct751
Distinct (%)57.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9523231
Minimum2.21
Maximum63.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-12-11T09:02:38.802576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.21
5-th percentile3.3195
Q14.6775
median6.25
Q38.845
95-th percentile17.6305
Maximum63.45
Range61.24
Interquartile range (IQR)4.1675

Descriptive statistics

Standard deviation6.4948792
Coefficient of variation (CV)0.81672728
Kurtosis25.475411
Mean7.9523231
Median Absolute Deviation (MAD)1.86
Skewness4.3931812
Sum10338.02
Variance42.183456
MonotonicityNot monotonic
2023-12-11T09:02:38.955717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.43 7
 
0.5%
4.67 6
 
0.5%
5.37 6
 
0.5%
5.9 6
 
0.5%
5.35 6
 
0.5%
5.31 6
 
0.5%
6.17 5
 
0.4%
4.5 5
 
0.4%
4.68 5
 
0.4%
4.44 5
 
0.4%
Other values (741) 1243
95.6%
ValueCountFrequency (%)
2.21 1
0.1%
2.27 1
0.1%
2.28 1
0.1%
2.3 1
0.1%
2.32 1
0.1%
2.38 1
0.1%
2.43 1
0.1%
2.51 1
0.1%
2.54 1
0.1%
2.57 1
0.1%
ValueCountFrequency (%)
63.45 1
0.1%
60.2 1
0.1%
58.37 1
0.1%
55.22 1
0.1%
53.84 1
0.1%
52.26 1
0.1%
51.51 1
0.1%
50.96 1
0.1%
49.22 1
0.1%
48.1 1
0.1%

의료인력수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct1054
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1984.4477
Minimum45
Maximum15851
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-12-11T09:02:39.090161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile114.95
Q1327.75
median1327
Q32854.5
95-th percentile6019.4
Maximum15851
Range15806
Interquartile range (IQR)2526.75

Descriptive statistics

Standard deviation2232.6025
Coefficient of variation (CV)1.1250498
Kurtosis6.5661844
Mean1984.4477
Median Absolute Deviation (MAD)1072
Skewness2.1814448
Sum2579782
Variance4984514
MonotonicityNot monotonic
2023-12-11T09:02:39.240739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 6
 
0.5%
215 6
 
0.5%
221 5
 
0.4%
275 5
 
0.4%
160 5
 
0.4%
139 5
 
0.4%
147 5
 
0.4%
238 4
 
0.3%
142 4
 
0.3%
320 4
 
0.3%
Other values (1044) 1251
96.2%
ValueCountFrequency (%)
45 1
0.1%
49 2
0.2%
50 2
0.2%
51 1
0.1%
52 1
0.1%
53 1
0.1%
55 2
0.2%
66 1
0.1%
67 1
0.1%
68 1
0.1%
ValueCountFrequency (%)
15851 1
0.1%
15679 1
0.1%
14515 1
0.1%
13912 1
0.1%
13602 1
0.1%
12916 1
0.1%
11766 1
0.1%
11728 1
0.1%
11117 1
0.1%
11106 1
0.1%

총인구수(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1300
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean233324.55
Minimum9077
Maximum1202628
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-12-11T09:02:39.387979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9077
5-th percentile27805.8
Q162439.75
median188605.5
Q3343434
95-th percentile602504.2
Maximum1202628
Range1193551
Interquartile range (IQR)280994.25

Descriptive statistics

Standard deviation209909.03
Coefficient of variation (CV)0.89964398
Kurtosis3.4692506
Mean233324.55
Median Absolute Deviation (MAD)134512
Skewness1.6149718
Sum3.0332192 × 108
Variance4.40618 × 1010
MonotonicityNot monotonic
2023-12-11T09:02:39.538920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
213846 1
 
0.1%
350759 1
 
0.1%
32373 1
 
0.1%
255402 1
 
0.1%
263185 1
 
0.1%
271392 1
 
0.1%
235633 1
 
0.1%
193807 1
 
0.1%
177784 1
 
0.1%
45204 1
 
0.1%
Other values (1290) 1290
99.2%
ValueCountFrequency (%)
9077 1
0.1%
9617 1
0.1%
9832 1
0.1%
9975 1
0.1%
10001 1
0.1%
16692 1
0.1%
16993 1
0.1%
17356 1
0.1%
17479 1
0.1%
17713 1
0.1%
ValueCountFrequency (%)
1202628 1
0.1%
1201166 1
0.1%
1194465 1
0.1%
1194041 1
0.1%
1186078 1
0.1%
1079216 1
0.1%
1074176 1
0.1%
1066351 1
0.1%
1063907 1
0.1%
1059609 1
0.1%

Interactions

2023-12-11T09:02:36.838574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:35.735612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:36.425067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:36.981122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:35.855283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:36.547364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:37.121387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:35.960288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:02:36.698819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:02:39.631210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명인구 천명당 의료인력(명)의료인력수(명)총인구수(명)
통계연도1.0000.0000.0000.0000.000
시도명0.0001.0000.4860.4730.566
인구 천명당 의료인력(명)0.0000.4861.0000.8070.414
의료인력수(명)0.0000.4730.8071.0000.857
총인구수(명)0.0000.5660.4140.8571.000
2023-12-11T09:02:39.726944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명
통계연도1.0000.000
시도명0.0001.000
2023-12-11T09:02:39.813012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인구 천명당 의료인력(명)의료인력수(명)총인구수(명)통계연도시도명
인구 천명당 의료인력(명)1.0000.7300.4160.0000.213
의료인력수(명)0.7301.0000.9010.0000.206
총인구수(명)0.4160.9011.0000.0000.261
통계연도0.0000.0000.0001.0000.000
시도명0.2130.2060.2610.0001.000

Missing values

2023-12-11T09:02:37.273741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:02:37.416881image/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

통계연도시도명시군구명인구 천명당 의료인력(명)의료인력수(명)총인구수(명)
02016강원도강릉시9.231974213846
12016강원도고성군2.327030114
22016강원도동해시6.4159893297
32016강원도삼척시4.3130069599
42016강원도속초시6.1350181793
52016강원도양구군3.428224010
62016강원도양양군2.948027218
72016강원도영월군4.5418240073
82016강원도원주시8.762962337979
92016강원도인제군2.849332720
통계연도시도명시군구명인구 천명당 의료인력(명)의료인력수(명)총인구수(명)
12902020인천광역시중구17.472441139729
12912020인천광역시강화군4.833269203
12922020인천광역시계양구8.582545296750
12932020인천광역시남동구10.785661525354
12942020인천광역시부평구9.224563494962
12952020인천광역시연수구4.51743387450
12962020인천광역시옹진군4.59220455
12972020인천광역시미추홀구7.723123404343
12982020제주특별자치도제주시9.544698492466
12992020제주특별자치도서귀포시5.531008182169