Overview

Dataset statistics

Number of variables6
Number of observations1510
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory76.8 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, 평균연령(세), 남자 평균 연령(세), 여자 평균 연령(세)으로 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=15110129

Alerts

평균연령(세) is highly overall correlated with 남자 평균 연령(세) and 1 other fieldsHigh correlation
남자 평균 연령(세) is highly overall correlated with 평균연령(세) and 1 other fieldsHigh correlation
여자 평균 연령(세) is highly overall correlated with 평균연령(세) and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-11 00:32:52.056729
Analysis finished2023-12-11 00:32:53.357083
Duration1.3 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계연도
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
2017
302 
2018
302 
2019
302 
2020
302 
2021
302 

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 302
20.0%
2018 302
20.0%
2019 302
20.0%
2020 302
20.0%
2021 302
20.0%

Length

2023-12-11T09:32:53.435585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:32:53.566046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 302
20.0%
2018 302
20.0%
2019 302
20.0%
2020 302
20.0%
2021 302
20.0%

시도명
Categorical

Distinct17
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
경기도
255 
경상북도
140 
경상남도
130 
전라남도
125 
서울특별시
125 
Other values (12)
735 

Length

Max length7
Median length5
Mean length4.1258278
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 255
16.9%
경상북도 140
9.3%
경상남도 130
8.6%
전라남도 125
8.3%
서울특별시 125
8.3%
강원도 105
7.0%
충청남도 100
 
6.6%
전라북도 95
 
6.3%
부산광역시 95
 
6.3%
충청북도 90
 
6.0%
Other values (7) 250
16.6%

Length

2023-12-11T09:32:53.987228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 255
16.9%
경상북도 140
9.3%
경상남도 130
8.6%
전라남도 125
8.3%
서울특별시 125
8.3%
강원도 105
7.0%
충청남도 100
 
6.6%
부산광역시 95
 
6.3%
전라북도 95
 
6.3%
충청북도 90
 
6.0%
Other values (7) 250
16.6%

시군구명
Text

Distinct239
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
2023-12-11T09:32:54.365943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.8231788
Min length2

Characters and Unicode

Total characters4263
Distinct characters142
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 (%)
면부 70
 
4.6%
읍부 70
 
4.6%
동부 70
 
4.6%
동구 30
 
2.0%
중구 30
 
2.0%
남구 26
 
1.7%
북구 25
 
1.7%
서구 25
 
1.7%
강서구 10
 
0.7%
고성군 10
 
0.7%
Other values (229) 1144
75.8%
2023-12-11T09:32:54.907591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 530
 
12.4%
군 425
 
10.0%
시 390
 
9.1%
부 240
 
5.6%
동 170
 
4.0%
산 110
 
2.6%
천 110
 
2.6%
주 100
 
2.3%
양 95
 
2.2%
성 95
 
2.2%
Other values (132) 1998
46.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4263
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 530
 
12.4%
군 425
 
10.0%
시 390
 
9.1%
부 240
 
5.6%
동 170
 
4.0%
산 110
 
2.6%
천 110
 
2.6%
주 100
 
2.3%
양 95
 
2.2%
성 95
 
2.2%
Other values (132) 1998
46.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4263
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 530
 
12.4%
군 425
 
10.0%
시 390
 
9.1%
부 240
 
5.6%
동 170
 
4.0%
산 110
 
2.6%
천 110
 
2.6%
주 100
 
2.3%
양 95
 
2.2%
성 95
 
2.2%
Other values (132) 1998
46.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4263
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 530
 
12.4%
군 425
 
10.0%
시 390
 
9.1%
부 240
 
5.6%
동 170
 
4.0%
산 110
 
2.6%
천 110
 
2.6%
주 100
 
2.3%
양 95
 
2.2%
성 95
 
2.2%
Other values (132) 1998
46.9%

평균연령(세)
Real number (ℝ)

HIGH CORRELATION 

Distinct213
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.930265
Minimum32.4
Maximum58.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-12-11T09:32:55.053870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32.4
5-th percentile38.7
Q141.3
median43.6
Q348.7
95-th percentile53.8
Maximum58.3
Range25.9
Interquartile range (IQR)7.4

Descriptive statistics

Standard deviation4.7999328
Coefficient of variation (CV)0.10683073
Kurtosis-0.54719434
Mean44.930265
Median Absolute Deviation (MAD)2.9
Skewness0.5647679
Sum67844.7
Variance23.039355
MonotonicityNot monotonic
2023-12-11T09:32:55.188763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42.5 24
 
1.6%
41.8 24
 
1.6%
42.1 22
 
1.5%
41.0 22
 
1.5%
41.3 22
 
1.5%
42.9 20
 
1.3%
41.5 20
 
1.3%
43.0 20
 
1.3%
41.2 20
 
1.3%
42.0 19
 
1.3%
Other values (203) 1297
85.9%
ValueCountFrequency (%)
32.4 1
0.1%
32.9 1
0.1%
33.5 1
0.1%
33.9 1
0.1%
34.5 1
0.1%
35.3 1
0.1%
35.9 1
0.1%
36.2 1
0.1%
36.3 2
0.1%
36.4 2
0.1%
ValueCountFrequency (%)
58.3 1
0.1%
57.9 1
0.1%
57.4 1
0.1%
57.1 1
0.1%
57.0 1
0.1%
56.9 1
0.1%
56.8 1
0.1%
56.6 1
0.1%
56.4 1
0.1%
56.3 2
0.1%

남자 평균 연령(세)
Real number (ℝ)

HIGH CORRELATION 

Distinct191
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.334702
Minimum32
Maximum55.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-12-11T09:32:55.356807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile37.9
Q140.3
median42.3
Q346.4
95-th percentile51.2
Maximum55.4
Range23.4
Interquartile range (IQR)6.1

Descriptive statistics

Standard deviation4.1767971
Coefficient of variation (CV)0.096384581
Kurtosis-0.43713195
Mean43.334702
Median Absolute Deviation (MAD)2.6
Skewness0.56359579
Sum65435.4
Variance17.445634
MonotonicityNot monotonic
2023-12-11T09:32:55.528141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.3 28
 
1.9%
40.8 25
 
1.7%
40.2 24
 
1.6%
42.3 24
 
1.6%
41.4 22
 
1.5%
42.2 21
 
1.4%
39.7 21
 
1.4%
41.6 21
 
1.4%
42.0 21
 
1.4%
42.7 20
 
1.3%
Other values (181) 1283
85.0%
ValueCountFrequency (%)
32.0 1
0.1%
32.6 1
0.1%
33.1 1
0.1%
33.5 1
0.1%
34.1 1
0.1%
34.7 1
0.1%
35.3 1
0.1%
35.4 1
0.1%
35.5 1
0.1%
35.6 1
0.1%
ValueCountFrequency (%)
55.4 1
 
0.1%
55.1 1
 
0.1%
54.5 1
 
0.1%
54.1 1
 
0.1%
54.0 3
0.2%
53.9 1
 
0.1%
53.7 2
0.1%
53.5 1
 
0.1%
53.3 4
0.3%
53.2 2
0.1%

여자 평균 연령(세)
Real number (ℝ)

HIGH CORRELATION 

Distinct236
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.552119
Minimum32.8
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-12-11T09:32:55.683240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32.8
5-th percentile39.6
Q142.3
median45
Q350.9
95-th percentile56.5
Maximum61
Range28.2
Interquartile range (IQR)8.6

Descriptive statistics

Standard deviation5.4498392
Coefficient of variation (CV)0.11706963
Kurtosis-0.65078484
Mean46.552119
Median Absolute Deviation (MAD)3.4
Skewness0.55795158
Sum70293.7
Variance29.700748
MonotonicityNot monotonic
2023-12-11T09:32:55.843617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43.5 25
 
1.7%
42.2 22
 
1.5%
43.1 22
 
1.5%
42.7 21
 
1.4%
42.3 21
 
1.4%
42.8 21
 
1.4%
41.5 19
 
1.3%
43.7 18
 
1.2%
43.8 18
 
1.2%
44.1 18
 
1.2%
Other values (226) 1305
86.4%
ValueCountFrequency (%)
32.8 1
 
0.1%
33.3 1
 
0.1%
33.8 1
 
0.1%
34.3 1
 
0.1%
34.9 1
 
0.1%
36.0 1
 
0.1%
36.6 1
 
0.1%
36.7 1
 
0.1%
37.0 1
 
0.1%
37.1 3
0.2%
ValueCountFrequency (%)
61.0 1
0.1%
60.8 1
0.1%
60.3 1
0.1%
60.2 1
0.1%
60.0 1
0.1%
59.9 1
0.1%
59.6 1
0.1%
59.4 1
0.1%
59.3 1
0.1%
59.2 2
0.1%

Interactions

2023-12-11T09:32:52.926293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:52.376457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:52.670381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:53.015863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:52.464777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:52.751452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:53.094868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:52.574003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:32:52.843599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:32:55.953889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명평균연령(세)남자 평균 연령(세)여자 평균 연령(세)
통계연도1.0000.0000.2680.2950.244
시도명0.0001.0000.6310.6130.641
평균연령(세)0.2680.6311.0000.9940.994
남자 평균 연령(세)0.2950.6130.9941.0000.981
여자 평균 연령(세)0.2440.6410.9940.9811.000
2023-12-11T09:32:56.065872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명통계연도
시도명1.0000.000
통계연도0.0001.000
2023-12-11T09:32:56.152282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
평균연령(세)남자 평균 연령(세)여자 평균 연령(세)통계연도시도명
평균연령(세)1.0000.9960.9970.1140.304
남자 평균 연령(세)0.9961.0000.9860.1270.292
여자 평균 연령(세)0.9970.9861.0000.1030.312
통계연도0.1140.1270.1031.0000.000
시도명0.3040.2920.3120.0001.000

Missing values

2023-12-11T09:32:53.198821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:32:53.308957image/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

통계연도시도명시군구명평균연령(세)남자 평균 연령(세)여자 평균 연령(세)
02017서울특별시종로구42.141.342.9
12017서울특별시중구42.842.143.5
22017서울특별시용산구42.241.443.0
32017서울특별시성동구41.240.441.9
42017서울특별시광진구40.639.941.1
52017서울특별시동대문구41.841.042.6
62017서울특별시중랑구42.741.843.5
72017서울특별시성북구41.140.341.8
82017서울특별시강북구43.942.845.0
92017서울특별시도봉구43.242.244.1
통계연도시도명시군구명평균연령(세)남자 평균 연령(세)여자 평균 연령(세)
15002021경상남도하동군55.352.458.2
15012021경상남도산청군55.953.258.5
15022021경상남도함양군54.051.156.7
15032021경상남도거창군49.646.552.6
15042021경상남도합천군57.153.760.3
15052021제주특별자치도동부41.340.242.3
15062021제주특별자치도읍부45.643.847.5
15072021제주특별자치도면부48.146.250.1
15082021제주특별자치도제주시41.840.543.0
15092021제주특별자치도서귀포시45.043.546.4