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%
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 (%)
550
 
12.3%
530
 
11.8%
425
 
9.5%
160
 
3.6%
130
 
2.9%
120
 
2.7%
120
 
2.7%
120
 
2.7%
110
 
2.5%
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 (%)
550
 
12.7%
530
 
12.3%
425
 
9.8%
130
 
3.0%
120
 
2.8%
120
 
2.8%
120
 
2.8%
110
 
2.5%
100
 
2.3%
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 (%)
550
 
12.7%
530
 
12.3%
425
 
9.8%
130
 
3.0%
120
 
2.8%
120
 
2.8%
120
 
2.8%
110
 
2.5%
100
 
2.3%
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 (%)
550
 
12.7%
530
 
12.3%
425
 
9.8%
130
 
3.0%
120
 
2.8%
120
 
2.8%
120
 
2.8%
110
 
2.5%
100
 
2.3%
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/