Overview

Dataset statistics

Number of variables6
Number of observations1122
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory57.1 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 공무원 수(명), 공무원 수(명), 총인구수(명)로 구성되어 있습니다. 김해시 중심의 통계지수로서, 데이터 수집, 가공 등의 어려움으로 김해시 외 지역의 정보는 누락될 수 있습니다.
Author경상남도 김해시
URLhttps://www.data.go.kr/data/15110191/fileData.do

Alerts

인구 천명당 공무원 수(명) is highly overall correlated with 공무원 수(명) and 1 other fieldsHigh correlation
공무원 수(명) is highly overall correlated with 인구 천명당 공무원 수(명) and 1 other fieldsHigh correlation
총인구수(명) is highly overall correlated with 인구 천명당 공무원 수(명) and 1 other fieldsHigh correlation
총인구수(명) has unique valuesUnique

Reproduction

Analysis started2023-12-12 06:51:42.567917
Analysis finished2023-12-12 06:51:44.071984
Duration1.5 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계연도
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
2016
225 
2017
225 
2018
224 
2019
224 
2020
224 

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 225
20.1%
2017 225
20.1%
2018 224
20.0%
2019 224
20.0%
2020 224
20.0%

Length

2023-12-12T15:51:44.148631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:51:44.275078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 225
20.1%
2017 225
20.1%
2018 224
20.0%
2019 224
20.0%
2020 224
20.0%

시도명
Categorical

Distinct16
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
경기도
155 
서울특별시
125 
경상북도
115 
전라남도
110 
강원도
90 
Other values (11)
527 

Length

Max length7
Median length5
Mean length4.1221034
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 155
13.8%
서울특별시 125
11.1%
경상북도 115
10.2%
전라남도 110
9.8%
강원도 90
8.0%
경상남도 90
8.0%
부산광역시 80
7.1%
충청남도 75
6.7%
전라북도 70
6.2%
충청북도 55
 
4.9%
Other values (6) 157
14.0%

Length

2023-12-12T15:51:44.413206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 155
13.8%
서울특별시 125
11.1%
경상북도 115
10.2%
전라남도 110
9.8%
강원도 90
8.0%
경상남도 90
8.0%
부산광역시 80
7.1%
충청남도 75
6.7%
전라북도 70
6.2%
충청북도 55
 
4.9%
Other values (6) 157
14.0%

시군구명
Text

Distinct205
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Memory size8.9 KiB
2023-12-12T15:51:44.832321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9402852
Min length2

Characters and Unicode

Total characters3299
Distinct characters130
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
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 row종로구
2nd row중구
3rd row용산구
4th row성동구
5th row광진구
ValueCountFrequency (%)
동구 25
 
2.2%
중구 25
 
2.2%
남구 22
 
2.0%
북구 20
 
1.8%
서구 20
 
1.8%
고성군 10
 
0.9%
강서구 10
 
0.9%
장흥군 5
 
0.4%
무주군 5
 
0.4%
진안군 5
 
0.4%
Other values (195) 975
86.9%
2023-12-12T15:51:45.411730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 425
 
12.9%
시 390
 
11.8%
구 352
 
10.7%
천 110
 
3.3%
주 100
 
3.0%
양 90
 
2.7%
성 90
 
2.7%
산 80
 
2.4%
동 80
 
2.4%
남 62
 
1.9%
Other values (120) 1520
46.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3299
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 425
 
12.9%
시 390
 
11.8%
구 352
 
10.7%
천 110
 
3.3%
주 100
 
3.0%
양 90
 
2.7%
성 90
 
2.7%
산 80
 
2.4%
동 80
 
2.4%
남 62
 
1.9%
Other values (120) 1520
46.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3299
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 425
 
12.9%
시 390
 
11.8%
구 352
 
10.7%
천 110
 
3.3%
주 100
 
3.0%
양 90
 
2.7%
성 90
 
2.7%
산 80
 
2.4%
동 80
 
2.4%
남 62
 
1.9%
Other values (120) 1520
46.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3299
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 425
 
12.9%
시 390
 
11.8%
구 352
 
10.7%
천 110
 
3.3%
주 100
 
3.0%
양 90
 
2.7%
성 90
 
2.7%
산 80
 
2.4%
동 80
 
2.4%
남 62
 
1.9%
Other values (120) 1520
46.1%

인구 천명당 공무원 수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4206774
Minimum2
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2023-12-12T15:51:45.587430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13
median6
Q312
95-th percentile20
Maximum43
Range41
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.1324542
Coefficient of variation (CV)0.72826139
Kurtosis2.3732579
Mean8.4206774
Median Absolute Deviation (MAD)3
Skewness1.3122463
Sum9448
Variance37.606994
MonotonicityNot monotonic
2023-12-12T15:51:45.718812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
3 257
22.9%
4 121
10.8%
2 67
 
6.0%
5 66
 
5.9%
12 65
 
5.8%
6 58
 
5.2%
8 55
 
4.9%
13 54
 
4.8%
9 50
 
4.5%
11 41
 
3.7%
Other values (22) 288
25.7%
ValueCountFrequency (%)
2 67
 
6.0%
3 257
22.9%
4 121
10.8%
5 66
 
5.9%
6 58
 
5.2%
7 41
 
3.7%
8 55
 
4.9%
9 50
 
4.5%
10 34
 
3.0%
11 41
 
3.7%
ValueCountFrequency (%)
43 1
 
0.1%
40 1
 
0.1%
39 1
 
0.1%
37 2
0.2%
30 2
0.2%
29 2
0.2%
27 3
0.3%
26 1
 
0.1%
25 2
0.2%
24 3
0.3%

공무원 수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct707
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean989.07665
Minimum339
Maximum4766
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2023-12-12T15:51:45.848226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum339
5-th percentile493.25
Q1620.25
median809
Q31202.25
95-th percentile1983
Maximum4766
Range4427
Interquartile range (IQR)582

Descriptive statistics

Standard deviation543.34257
Coefficient of variation (CV)0.54934324
Kurtosis9.3581912
Mean989.07665
Median Absolute Deviation (MAD)229
Skewness2.478614
Sum1109744
Variance295221.15
MonotonicityNot monotonic
2023-12-12T15:51:45.970844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
631 7
 
0.6%
973 6
 
0.5%
607 6
 
0.5%
559 6
 
0.5%
600 6
 
0.5%
580 5
 
0.4%
666 5
 
0.4%
475 5
 
0.4%
626 5
 
0.4%
610 5
 
0.4%
Other values (697) 1066
95.0%
ValueCountFrequency (%)
339 2
0.2%
353 1
0.1%
362 1
0.1%
368 2
0.2%
369 1
0.1%
379 1
0.1%
380 1
0.1%
382 1
0.1%
388 1
0.1%
396 1
0.1%
ValueCountFrequency (%)
4766 1
0.1%
4601 1
0.1%
4423 1
0.1%
4359 1
0.1%
3995 1
0.1%
3529 1
0.1%
3413 1
0.1%
3234 1
0.1%
3176 1
0.1%
3134 1
0.1%

총인구수(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1122
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean224088.99
Minimum9077
Maximum1202628
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.0 KiB
2023-12-12T15:51:46.101159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9077
5-th percentile27207.55
Q152839.75
median140826
Q3339193.5
95-th percentile652232.3
Maximum1202628
Range1193551
Interquartile range (IQR)286353.75

Descriptive statistics

Standard deviation221892.28
Coefficient of variation (CV)0.99019714
Kurtosis3.2484452
Mean224088.99
Median Absolute Deviation (MAD)101696
Skewness1.68071
Sum2.5142785 × 108
Variance4.9236183 × 1010
MonotonicityNot monotonic
2023-12-12T15:51:46.254967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
152737 1
 
0.1%
451868 1
 
0.1%
650918 1
 
0.1%
94768 1
 
0.1%
513027 1
 
0.1%
316552 1
 
0.1%
829996 1
 
0.1%
567044 1
 
0.1%
942724 1
 
0.1%
58289 1
 
0.1%
Other values (1112) 1112
99.1%
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

2023-12-12T15:51:43.509427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:51:42.887900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:51:43.221345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:51:43.619384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:51:43.005970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:51:43.339988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:51:43.708250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:51:43.103073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:51:43.420260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:51:46.349796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명인구 천명당 공무원 수(명)공무원 수(명)총인구수(명)
통계연도1.0000.0000.0000.0000.000
시도명0.0001.0000.5820.5650.607
인구 천명당 공무원 수(명)0.0000.5821.0000.4520.587
공무원 수(명)0.0000.5650.4521.0000.910
총인구수(명)0.0000.6070.5870.9101.000
2023-12-12T15:51:46.441330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명
통계연도1.0000.000
시도명0.0001.000
2023-12-12T15:51:46.519200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인구 천명당 공무원 수(명)공무원 수(명)총인구수(명)통계연도시도명
인구 천명당 공무원 수(명)1.000-0.642-0.9330.0000.284
공무원 수(명)-0.6421.0000.8560.0000.260
총인구수(명)-0.9330.8561.0000.0000.288
통계연도0.0000.0000.0001.0000.000
시도명0.2840.2600.2880.0001.000

Missing values

2023-12-12T15:51:43.866584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:51:44.025229image/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 공무원 수(명)공무원 수(명)총인구수(명)
02016서울특별시종로구81190152737
12016서울특별시중구91098125249
22016서울특별시용산구51174230241
32016서울특별시성동구41218299259
42016서울특별시광진구31098357215
52016서울특별시동대문구41266355069
62016서울특별시중랑구31205411005
72016서울특별시성북구31441450355
82016서울특별시강북구31125327195
92016서울특별시도봉구31159348220
통계연도시도명시군구명인구 천명당 공무원 수(명)공무원 수(명)총인구수(명)
11122020경상남도창녕군1377661301
11132020경상남도고성군1470251361
11142020경상남도남해군1460942958
11152020경상남도하동군1568844785
11162020경상남도산청군1760234857
11172020경상남도함양군1661339080
11182020경상남도거창군1271261502
11192020경상남도합천군1772744006
11202020제주특별자치도제주시31684492466
11212020제주특별자치도서귀포시71221182169