Overview

Dataset statistics

Number of variables6
Number of observations1140
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory58.0 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 십만명당 문화기반시설 수(개), 문화기반시설수(개), 주민등록인구수(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=15110178

Alerts

인구 십만명당 문화기반시설 수(개) is highly overall correlated with 주민등록인구수(명)High correlation
문화기반시설수(개) is highly overall correlated with 주민등록인구수(명)High correlation
주민등록인구수(명) is highly overall correlated with 인구 십만명당 문화기반시설 수(개) and 1 other fieldsHigh correlation
주민등록인구수(명) has unique valuesUnique

Reproduction

Analysis started2023-12-10 23:00:47.285751
Analysis finished2023-12-10 23:00:48.890803
Duration1.61 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계연도
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
2016
228 
2017
228 
2018
228 
2019
228 
2020
228 

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 228
20.0%
2017 228
20.0%
2018 228
20.0%
2019 228
20.0%
2020 228
20.0%

Length

2023-12-11T08:00:48.956514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:00:49.084081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 228
20.0%
2017 228
20.0%
2018 228
20.0%
2019 228
20.0%
2020 228
20.0%

시도명
Categorical

Distinct16
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
경기도
155 
서울특별시
125 
경상북도
115 
전라남도
110 
강원도
90 
Other values (11)
545 

Length

Max length7
Median length5
Mean length4.1359649
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 155
13.6%
서울특별시 125
11.0%
경상북도 115
10.1%
전라남도 110
9.6%
강원도 90
7.9%
경상남도 90
7.9%
부산광역시 80
7.0%
충청남도 75
6.6%
전라북도 70
 
6.1%
충청북도 55
 
4.8%
Other values (6) 175
15.4%

Length

2023-12-11T08:00:49.238043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 155
13.6%
서울특별시 125
11.0%
경상북도 115
10.1%
전라남도 110
9.6%
강원도 90
7.9%
경상남도 90
7.9%
부산광역시 80
7.0%
충청남도 75
6.6%
전라북도 70
 
6.1%
충청북도 55
 
4.8%
Other values (6) 175
15.4%

시군구명
Text

Distinct206
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
2023-12-11T08:00:49.622595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9307018
Min length2

Characters and Unicode

Total characters3341
Distinct characters132
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 30
 
2.6%
중구 30
 
2.6%
서구 25
 
2.2%
남구 22
 
1.9%
북구 20
 
1.8%
고성군 10
 
0.9%
강서구 10
 
0.9%
완주군 5
 
0.4%
무주군 5
 
0.4%
진안군 5
 
0.4%
Other values (196) 978
85.8%
2023-12-11T08:00:50.118676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 425
 
12.7%
시 390
 
11.7%
구 370
 
11.1%
천 110
 
3.3%
주 100
 
3.0%
성 90
 
2.7%
양 90
 
2.7%
동 85
 
2.5%
산 80
 
2.4%
서 65
 
1.9%
Other values (122) 1536
46.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3341
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 425
 
12.7%
시 390
 
11.7%
구 370
 
11.1%
천 110
 
3.3%
주 100
 
3.0%
성 90
 
2.7%
양 90
 
2.7%
동 85
 
2.5%
산 80
 
2.4%
서 65
 
1.9%
Other values (122) 1536
46.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3341
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 425
 
12.7%
시 390
 
11.7%
구 370
 
11.1%
천 110
 
3.3%
주 100
 
3.0%
성 90
 
2.7%
양 90
 
2.7%
동 85
 
2.5%
산 80
 
2.4%
서 65
 
1.9%
Other values (122) 1536
46.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3341
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 425
 
12.7%
시 390
 
11.7%
구 370
 
11.1%
천 110
 
3.3%
주 100
 
3.0%
성 90
 
2.7%
양 90
 
2.7%
동 85
 
2.5%
산 80
 
2.4%
서 65
 
1.9%
Other values (122) 1536
46.0%

인구 십만명당 문화기반시설 수(개)
Real number (ℝ)

HIGH CORRELATION 

Distinct279
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.562895
Minimum1
Maximum74.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-11T08:00:50.300519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.2
Q13.6
median7.3
Q314.5
95-th percentile28.61
Maximum74.9
Range73.9
Interquartile range (IQR)10.9

Descriptive statistics

Standard deviation9.6203627
Coefficient of variation (CV)0.91076953
Kurtosis8.431635
Mean10.562895
Median Absolute Deviation (MAD)4.35
Skewness2.3132198
Sum12041.7
Variance92.551379
MonotonicityNot monotonic
2023-12-11T08:00:50.814903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.0 28
 
2.5%
2.9 23
 
2.0%
2.8 20
 
1.8%
3.4 18
 
1.6%
2.6 18
 
1.6%
3.3 18
 
1.6%
3.1 18
 
1.6%
4.3 15
 
1.3%
2.2 15
 
1.3%
3.7 14
 
1.2%
Other values (269) 953
83.6%
ValueCountFrequency (%)
1.0 2
 
0.2%
1.2 1
 
0.1%
1.4 2
 
0.2%
1.5 3
 
0.3%
1.7 8
0.7%
1.8 7
0.6%
1.9 14
1.2%
2.0 10
0.9%
2.1 3
 
0.3%
2.2 15
1.3%
ValueCountFrequency (%)
74.9 1
0.1%
74.1 1
0.1%
72.4 1
0.1%
69.9 1
0.1%
68.0 1
0.1%
58.4 1
0.1%
48.6 1
0.1%
47.7 1
0.1%
46.6 1
0.1%
46.2 1
0.1%

문화기반시설수(개)
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.494737
Minimum1
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-11T08:00:50.988734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q17
median10
Q314
95-th percentile30
Maximum73
Range72
Interquartile range (IQR)7

Descriptive statistics

Standard deviation9.594636
Coefficient of variation (CV)0.7678942
Kurtosis11.736869
Mean12.494737
Median Absolute Deviation (MAD)4
Skewness2.9642073
Sum14244
Variance92.05704
MonotonicityNot monotonic
2023-12-11T08:00:51.181962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 102
 
8.9%
7 96
 
8.4%
8 92
 
8.1%
12 89
 
7.8%
11 85
 
7.5%
5 84
 
7.4%
10 75
 
6.6%
9 73
 
6.4%
4 54
 
4.7%
14 52
 
4.6%
Other values (42) 338
29.6%
ValueCountFrequency (%)
1 1
 
0.1%
2 2
 
0.2%
3 14
 
1.2%
4 54
4.7%
5 84
7.4%
6 102
8.9%
7 96
8.4%
8 92
8.1%
9 73
6.4%
10 75
6.6%
ValueCountFrequency (%)
73 1
 
0.1%
71 1
 
0.1%
69 1
 
0.1%
66 2
0.2%
65 1
 
0.1%
64 4
0.4%
63 4
0.4%
62 1
 
0.1%
44 1
 
0.1%
43 3
0.3%

주민등록인구수(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1140
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean225828.25
Minimum9077
Maximum1202628
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-11T08:00:51.369835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9077
5-th percentile27217.45
Q153266.25
median149942.5
Q3341482.75
95-th percentile651769.7
Maximum1202628
Range1193551
Interquartile range (IQR)288216.5

Descriptive statistics

Standard deviation221006.58
Coefficient of variation (CV)0.97864895
Kurtosis3.2049126
Mean225828.25
Median Absolute Deviation (MAD)108178
Skewness1.6590227
Sum2.5744421 × 108
Variance4.8843909 × 1010
MonotonicityNot monotonic
2023-12-11T08:00:51.546151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
152737 1
 
0.1%
942724 1
 
0.1%
94768 1
 
0.1%
513027 1
 
0.1%
316552 1
 
0.1%
829996 1
 
0.1%
567044 1
 
0.1%
451868 1
 
0.1%
1194465 1
 
0.1%
1066351 1
 
0.1%
Other values (1130) 1130
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

2023-12-11T08:00:48.352788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:47.608252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:47.985799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:48.473817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:47.726652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:48.112830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:48.586175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:47.857490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:48.251485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:00:51.647634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명인구 십만명당 문화기반시설 수(개)문화기반시설수(개)주민등록인구수(명)
통계연도1.0000.0000.0000.0000.000
시도명0.0001.0000.5620.7460.604
인구 십만명당 문화기반시설 수(개)0.0000.5621.0000.4350.538
문화기반시설수(개)0.0000.7460.4351.0000.658
주민등록인구수(명)0.0000.6040.5380.6581.000
2023-12-11T08:00:51.773231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명
통계연도1.0000.000
시도명0.0001.000
2023-12-11T08:00:51.888791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인구 십만명당 문화기반시설 수(개)문화기반시설수(개)주민등록인구수(명)통계연도시도명
인구 십만명당 문화기반시설 수(개)1.000-0.128-0.8310.0000.270
문화기반시설수(개)-0.1281.0000.6290.0000.360
주민등록인구수(명)-0.8310.6291.0000.0000.287
통계연도0.0000.0000.0001.0000.000
시도명0.2700.3600.2870.0001.000

Missing values

2023-12-11T08:00:48.722805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:00:48.842400image/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 십만명당 문화기반시설 수(개)문화기반시설수(개)주민등록인구수(명)
02016서울특별시종로구41.964152737
12016서울특별시중구19.224125249
22016서울특별시용산구6.114230241
32016서울특별시성동구4.714299259
42016서울특별시광진구2.59357215
52016서울특별시동대문구2.810355069
62016서울특별시중랑구1.04411005
72016서울특별시성북구3.616450355
82016서울특별시강북구3.411327195
92016서울특별시도봉구3.412348220
통계연도시도명시군구명인구 십만명당 문화기반시설 수(개)문화기반시설수(개)주민등록인구수(명)
11302020경상남도창녕군13.1861301
11312020경상남도고성군19.51051361
11322020경상남도남해군14.0642958
11332020경상남도하동군15.6744785
11342020경상남도산청군34.41234857
11352020경상남도함양군10.2439080
11362020경상남도거창군9.8661502
11372020경상남도합천군13.6644006
11382020제주특별자치도제주시14.471492466
11392020제주특별자치도서귀포시35.164182169