Overview

Dataset statistics

Number of variables7
Number of observations1300
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory77.6 KiB
Average record size in memory61.1 B

Variable types

Categorical2
Text1
Numeric4

Dataset

Description김해시에서 통계기반 도시현황 파악을 위해 개발한 통계지수 중 하나로서, 통계연도, 시도명, 시군구명, 노령화지수(퍼센트), 고령인구(65세 이상), 유소년(14세 이하), 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=15110113

Alerts

노령화지수(퍼센트) is highly overall correlated with 고령인구(65세 이상) and 2 other fieldsHigh correlation
고령인구(65세 이상) is highly overall correlated with 노령화지수(퍼센트) and 2 other fieldsHigh correlation
유소년(14세 이하) is highly overall correlated with 노령화지수(퍼센트) and 2 other fieldsHigh correlation
총인구수(명) is highly overall correlated with 노령화지수(퍼센트) and 2 other fieldsHigh correlation

Reproduction

Analysis started2023-12-11 01:02:34.715313
Analysis finished2023-12-11 01:02:36.953678
Duration2.24 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
2017
260 
2018
260 
2019
260 
2020
260 
2021
260 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2017 260
20.0%
2018 260
20.0%
2019 260
20.0%
2020 260
20.0%
2021 260
20.0%

Length

2023-12-11T10:02:37.020486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T10:02:37.145005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 260
20.0%
2018 260
20.0%
2019 260
20.0%
2020 260
20.0%
2021 260
20.0%

시도명
Categorical

Distinct16
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
경기도
240 
서울특별시
125 
경상북도
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 row서울특별시
2nd row서울특별시
3rd row서울특별시
4th row서울특별시
5th row서울특별시

Common Values

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

Length

2023-12-11T10:02:37.288127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 240
18.5%
서울특별시 125
9.6%
경상북도 125
9.6%
경상남도 115
8.8%
전라남도 110
8.5%
강원도 90
 
6.9%
충청남도 85
 
6.5%
부산광역시 80
 
6.2%
전라북도 80
 
6.2%
충청북도 75
 
5.8%
Other values (6) 175
13.5%

시군구명
Text

Distinct236
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
2023-12-11T10:02:37.633245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.9561538
Min length2

Characters and Unicode

Total characters3843
Distinct characters141
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 (%)
중구 30
 
2.3%
동구 30
 
2.3%
남구 26
 
2.0%
북구 25
 
1.9%
서구 25
 
1.9%
강서구 10
 
0.8%
고성군 10
 
0.8%
남원시 5
 
0.4%
덕진구 5
 
0.4%
군산시 5
 
0.4%
Other values (226) 1129
86.8%
2023-12-11T10:02:38.100007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 530
 
13.8%
군 425
 
11.1%
시 390
 
10.1%
천 110
 
2.9%
산 110
 
2.9%
동 100
 
2.6%
주 100
 
2.6%
양 95
 
2.5%
성 95
 
2.5%
서 80
 
2.1%
Other values (131) 1808
47.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3843
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 530
 
13.8%
군 425
 
11.1%
시 390
 
10.1%
천 110
 
2.9%
산 110
 
2.9%
동 100
 
2.6%
주 100
 
2.6%
양 95
 
2.5%
성 95
 
2.5%
서 80
 
2.1%
Other values (131) 1808
47.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3843
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 530
 
13.8%
군 425
 
11.1%
시 390
 
10.1%
천 110
 
2.9%
산 110
 
2.9%
동 100
 
2.6%
주 100
 
2.6%
양 95
 
2.5%
성 95
 
2.5%
서 80
 
2.1%
Other values (131) 1808
47.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3843
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 530
 
13.8%
군 425
 
11.1%
시 390
 
10.1%
천 110
 
2.9%
산 110
 
2.9%
동 100
 
2.6%
주 100
 
2.6%
양 95
 
2.5%
성 95
 
2.5%
서 80
 
2.1%
Other values (131) 1808
47.0%

노령화지수(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct1095
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean208.71315
Minimum34.8
Maximum920.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-12-11T10:02:38.258264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.8
5-th percentile64.09
Q1103.8
median158.15
Q3291.825
95-th percentile494.29
Maximum920.4
Range885.6
Interquartile range (IQR)188.025

Descriptive statistics

Standard deviation141.45418
Coefficient of variation (CV)0.67774442
Kurtosis1.4778702
Mean208.71315
Median Absolute Deviation (MAD)68.5
Skewness1.3156749
Sum271327.1
Variance20009.284
MonotonicityNot monotonic
2023-12-11T10:02:38.637731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
116.9 4
 
0.3%
110.7 4
 
0.3%
100.5 4
 
0.3%
108.7 4
 
0.3%
170.0 3
 
0.2%
120.2 3
 
0.2%
523.7 3
 
0.2%
82.1 3
 
0.2%
98.8 3
 
0.2%
93.8 3
 
0.2%
Other values (1085) 1266
97.4%
ValueCountFrequency (%)
34.8 1
0.1%
38.7 1
0.1%
39.4 1
0.1%
41.2 1
0.1%
41.6 1
0.1%
42.4 1
0.1%
42.8 1
0.1%
43.0 1
0.1%
44.7 2
0.2%
45.4 1
0.1%
ValueCountFrequency (%)
920.4 1
0.1%
829.9 1
0.1%
773.3 1
0.1%
760.2 1
0.1%
725.9 1
0.1%
711.2 1
0.1%
680.2 1
0.1%
674.5 1
0.1%
670.4 1
0.1%
664.5 1
0.1%

고령인구(65세 이상)
Real number (ℝ)

HIGH CORRELATION 

Distinct1285
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35376.918
Minimum2238
Maximum160521
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-12-11T10:02:38.760907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2238
5-th percentile8431.95
Q116083.25
median29316.5
Q347751.75
95-th percentile79374.7
Maximum160521
Range158283
Interquartile range (IQR)31668.5

Descriptive statistics

Standard deviation25281.163
Coefficient of variation (CV)0.71462311
Kurtosis3.3477487
Mean35376.918
Median Absolute Deviation (MAD)14140
Skewness1.5715292
Sum45989993
Variance6.391372 × 108
MonotonicityNot monotonic
2023-12-11T10:02:38.901925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62009 2
 
0.2%
24892 2
 
0.2%
8898 2
 
0.2%
60663 2
 
0.2%
9286 2
 
0.2%
25257 2
 
0.2%
7945 2
 
0.2%
10423 2
 
0.2%
32983 2
 
0.2%
33834 2
 
0.2%
Other values (1275) 1280
98.5%
ValueCountFrequency (%)
2238 1
0.1%
2290 1
0.1%
2341 1
0.1%
2386 1
0.1%
2390 1
0.1%
4175 1
0.1%
4259 1
0.1%
4324 1
0.1%
4446 1
0.1%
4493 1
0.1%
ValueCountFrequency (%)
160521 1
0.1%
154134 1
0.1%
150905 1
0.1%
150263 1
0.1%
145710 1
0.1%
143096 1
0.1%
140151 1
0.1%
139075 1
0.1%
138091 1
0.1%
135272 1
0.1%

유소년(14세 이하)
Real number (ℝ)

HIGH CORRELATION 

Distinct1281
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29312.464
Minimum602
Maximum173571
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-12-11T10:02:39.113050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum602
5-th percentile2224.55
Q15776.75
median23104.5
Q340917
95-th percentile81223.75
Maximum173571
Range172969
Interquartile range (IQR)35140.25

Descriptive statistics

Standard deviation29562.501
Coefficient of variation (CV)1.0085301
Kurtosis4.3528808
Mean29312.464
Median Absolute Deviation (MAD)17356.5
Skewness1.8113331
Sum38106203
Variance8.7394148 × 108
MonotonicityNot monotonic
2023-12-11T10:02:39.259822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3130 2
 
0.2%
39141 2
 
0.2%
31705 2
 
0.2%
9658 2
 
0.2%
6869 2
 
0.2%
6709 2
 
0.2%
2550 2
 
0.2%
2399 2
 
0.2%
4867 2
 
0.2%
6978 2
 
0.2%
Other values (1271) 1280
98.5%
ValueCountFrequency (%)
602 1
0.1%
621 1
0.1%
701 1
0.1%
706 1
0.1%
727 1
0.1%
1056 1
0.1%
1122 1
0.1%
1140 1
0.1%
1172 1
0.1%
1207 1
0.1%
ValueCountFrequency (%)
173571 1
0.1%
168083 1
0.1%
167701 1
0.1%
167659 1
0.1%
166125 1
0.1%
162591 1
0.1%
161288 1
0.1%
158200 1
0.1%
157968 1
0.1%
157005 1
0.1%

총인구수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct1299
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean233181.26
Minimum8867
Maximum1202628
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-12-11T10:02:39.428753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8867
5-th percentile27516.1
Q162205.25
median186957
Q3343145.25
95-th percentile603183
Maximum1202628
Range1193761
Interquartile range (IQR)280940

Descriptive statistics

Standard deviation210655.59
Coefficient of variation (CV)0.90339845
Kurtosis3.4788296
Mean233181.26
Median Absolute Deviation (MAD)133194
Skewness1.6212127
Sum3.0313564 × 108
Variance4.4375777 × 1010
MonotonicityNot monotonic
2023-12-11T10:02:39.571903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122499 2
 
0.2%
154770 1
 
0.1%
239413 1
 
0.1%
351888 1
 
0.1%
654915 1
 
0.1%
94353 1
 
0.1%
537307 1
 
0.1%
298599 1
 
0.1%
818383 1
 
0.1%
310614 1
 
0.1%
Other values (1289) 1289
99.2%
ValueCountFrequency (%)
8867 1
0.1%
9077 1
0.1%
9617 1
0.1%
9832 1
0.1%
9975 1
0.1%
16320 1
0.1%
16692 1
0.1%
16993 1
0.1%
17356 1
0.1%
17479 1
0.1%
ValueCountFrequency (%)
1202628 1
0.1%
1201166 1
0.1%
1194465 1
0.1%
1186078 1
0.1%
1183714 1
0.1%
1079353 1
0.1%
1079216 1
0.1%
1077508 1
0.1%
1074176 1
0.1%
1066351 1
0.1%

Interactions

2023-12-11T10:02:36.269725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T10:02:35.050267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T10:02:35.428570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T10:02:35.878252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T10:02:36.353017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T10:02:35.141102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T10:02:35.543599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T10:02:35.964558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T10:02:36.519495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T10:02:35.247961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T10:02:35.658134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T10:02:36.064042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T10:02:36.637355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T10:02:35.344318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T10:02:35.770299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T10:02:36.160016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T10:02:39.663121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명노령화지수(퍼센트)고령인구(65세 이상)유소년(14세 이하)총인구수(명)
통계연도1.0000.0000.1870.0740.0000.000
시도명0.0001.0000.5160.5400.5470.567
노령화지수(퍼센트)0.1870.5161.0000.5820.7000.670
고령인구(65세 이상)0.0740.5400.5821.0000.8820.925
유소년(14세 이하)0.0000.5470.7000.8821.0000.955
총인구수(명)0.0000.5670.6700.9250.9551.000
2023-12-11T10:02:39.761140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명
통계연도1.0000.000
시도명0.0001.000
2023-12-11T10:02:39.834926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노령화지수(퍼센트)고령인구(65세 이상)유소년(14세 이하)총인구수(명)통계연도시도명
노령화지수(퍼센트)1.000-0.563-0.855-0.7660.0790.230
고령인구(65세 이상)-0.5631.0000.8930.9480.0310.245
유소년(14세 이하)-0.8550.8931.0000.9820.0000.249
총인구수(명)-0.7660.9480.9821.0000.0000.262
통계연도0.0790.0310.0000.0001.0000.000
시도명0.2300.2450.2490.2620.0001.000

Missing values

2023-12-11T10:02:36.794983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T10:02:36.904859image/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

통계연도시도명시군구명노령화지수(퍼센트)고령인구(65세 이상)유소년(14세 이하)총인구수(명)
02017서울특별시종로구180.62603014416154770
12017서울특별시중구187.12114811306125709
22017서울특별시용산구157.53648823174229161
32017서울특별시성동구122.04115233732304808
42017서울특별시광진구116.94379037469357703
52017서울특별시동대문구155.95554735637350647
62017서울특별시중랑구142.15918941660408226
72017서울특별시성북구126.36612152354444055
82017서울특별시강북구177.95643731728324479
92017서울특별시도봉구144.95344536873344166
통계연도시도명시군구명노령화지수(퍼센트)고령인구(65세 이상)유소년(14세 이하)총인구수(명)
12902021경상남도창녕군409.419600478860129
12912021경상남도고성군391.716543422350478
12922021경상남도남해군579.216448284042266
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12942021경상남도산청군619.113175212834360
12952021경상남도함양군449.213494300438310
12962021경상남도거창군285.217545615261073
12972021경상남도합천군680.217590258642935
12982021제주특별자치도제주시104.37379770746493096
12992021제주특별자치도서귀포시156.23684823592183663