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, 독거노인가구비율(퍼센트), 65세 이상 1인가구(가구), 전체 일반가구(가구)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/15110116/fileData.do

Alerts

독거노인가구비율(퍼센트) is highly overall correlated with 65세 이상 1인가구(가구) and 1 other fieldsHigh correlation
65세 이상 1인가구(가구) is highly overall correlated with 독거노인가구비율(퍼센트) and 1 other fieldsHigh correlation
전체 일반가구(가구) is highly overall correlated with 독거노인가구비율(퍼센트) and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-11 23:45:47.685592
Analysis finished2023-12-11 23:45:49.263363
Duration1.58 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
2017
228 
2018
228 
2019
228 
2020
228 
2021
228 

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 228
20.0%
2018 228
20.0%
2019 228
20.0%
2020 228
20.0%
2021 228
20.0%

Length

2023-12-12T08:45:49.322469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:45:49.419709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 228
20.0%
2018 228
20.0%
2019 228
20.0%
2020 228
20.0%
2021 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

2023-12-12T08:45:49.540362image/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
2023-12-12T08:45:49.852560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9324561
Min length2

Characters and Unicode

Total characters3343
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 21
 
1.8%
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) 979
85.9%
2023-12-12T08:45:50.552465image/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) 1538
46.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3343
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) 1538
46.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3343
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) 1538
46.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3343
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) 1538
46.0%

독거노인가구비율(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct213
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.280263
Minimum3.1
Maximum26.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-12T08:45:50.686227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.1
5-th percentile4.495
Q16.5
median9.5
Q315.8
95-th percentile21.905
Maximum26.2
Range23.1
Interquartile range (IQR)9.3

Descriptive statistics

Standard deviation5.7226984
Coefficient of variation (CV)0.50731958
Kurtosis-0.7335855
Mean11.280263
Median Absolute Deviation (MAD)3.8
Skewness0.65543315
Sum12859.5
Variance32.749276
MonotonicityNot monotonic
2023-12-12T08:45:50.812320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.0 18
 
1.6%
5.1 18
 
1.6%
6.7 16
 
1.4%
6.5 16
 
1.4%
4.8 16
 
1.4%
5.8 15
 
1.3%
8.4 15
 
1.3%
8.8 15
 
1.3%
4.4 14
 
1.2%
6.2 13
 
1.1%
Other values (203) 984
86.3%
ValueCountFrequency (%)
3.1 1
 
0.1%
3.3 4
0.4%
3.4 2
 
0.2%
3.5 2
 
0.2%
3.6 4
0.4%
3.7 2
 
0.2%
3.8 5
0.4%
3.9 6
0.5%
4.0 6
0.5%
4.1 4
0.4%
ValueCountFrequency (%)
26.2 1
 
0.1%
26.1 1
 
0.1%
25.6 1
 
0.1%
25.1 1
 
0.1%
25.0 3
0.3%
24.8 2
0.2%
24.7 3
0.3%
24.5 3
0.3%
24.2 2
0.2%
24.0 3
0.3%

65세 이상 1인가구(가구)
Real number (ℝ)

HIGH CORRELATION 

Distinct1093
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6848.9482
Minimum407
Maximum34661
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-12T08:45:50.933516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum407
5-th percentile1896.3
Q13567.25
median5652
Q39165.25
95-th percentile15478
Maximum34661
Range34254
Interquartile range (IQR)5598

Descriptive statistics

Standard deviation4625.2509
Coefficient of variation (CV)0.67532279
Kurtosis3.6995088
Mean6848.9482
Median Absolute Deviation (MAD)2551.5
Skewness1.5962205
Sum7807801
Variance21392946
MonotonicityNot monotonic
2023-12-12T08:45:51.075011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4996 3
 
0.3%
2625 2
 
0.2%
4531 2
 
0.2%
2956 2
 
0.2%
3907 2
 
0.2%
12188 2
 
0.2%
3690 2
 
0.2%
3721 2
 
0.2%
7220 2
 
0.2%
7236 2
 
0.2%
Other values (1083) 1119
98.2%
ValueCountFrequency (%)
407 1
0.1%
444 1
0.1%
445 1
0.1%
507 1
0.1%
520 1
0.1%
688 1
0.1%
764 1
0.1%
842 1
0.1%
853 1
0.1%
903 1
0.1%
ValueCountFrequency (%)
34661 1
0.1%
30993 1
0.1%
27845 1
0.1%
26737 1
0.1%
26098 1
0.1%
25893 1
0.1%
25880 1
0.1%
25449 1
0.1%
24388 1
0.1%
24010 1
0.1%

전체 일반가구(가구)
Real number (ℝ)

HIGH CORRELATION 

Distinct1135
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89240.355
Minimum4061
Maximum480566
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-12T08:45:51.211050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4061
5-th percentile11518
Q122246.5
median59019
Q3133394.5
95-th percentile251695.2
Maximum480566
Range476505
Interquartile range (IQR)111148

Descriptive statistics

Standard deviation84734.837
Coefficient of variation (CV)0.94951255
Kurtosis2.83091
Mean89240.355
Median Absolute Deviation (MAD)42102
Skewness1.5750502
Sum1.01734 × 108
Variance7.1799926 × 109
MonotonicityNot monotonic
2023-12-12T08:45:51.344584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128012 2
 
0.2%
11518 2
 
0.2%
14044 2
 
0.2%
11723 2
 
0.2%
19431 2
 
0.2%
369585 1
 
0.1%
213185 1
 
0.1%
111383 1
 
0.1%
315696 1
 
0.1%
203441 1
 
0.1%
Other values (1125) 1125
98.7%
ValueCountFrequency (%)
4061 1
0.1%
4072 1
0.1%
4116 1
0.1%
4135 1
0.1%
4145 1
0.1%
7539 1
0.1%
7565 1
0.1%
7585 1
0.1%
7661 1
0.1%
7723 1
0.1%
ValueCountFrequency (%)
480566 1
0.1%
466089 1
0.1%
457351 1
0.1%
450819 1
0.1%
445309 1
0.1%
421845 1
0.1%
411137 1
0.1%
409688 1
0.1%
403524 1
0.1%
399353 1
0.1%

Interactions

2023-12-12T08:45:48.728667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:45:47.992250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:45:48.361848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:45:48.841293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:45:48.102829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:45:48.506537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:45:48.936725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:45:48.263422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:45:48.626111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:45:51.431002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명독거노인가구비율(퍼센트)65세 이상 1인가구(가구)전체 일반가구(가구)
통계연도1.0000.0000.0210.1590.000
시도명0.0001.0000.6620.4780.601
독거노인가구비율(퍼센트)0.0210.6621.0000.5490.720
65세 이상 1인가구(가구)0.1590.4780.5491.0000.876
전체 일반가구(가구)0.0000.6010.7200.8761.000
2023-12-12T08:45:51.535442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명
통계연도1.0000.000
시도명0.0001.000
2023-12-12T08:45:51.621207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
독거노인가구비율(퍼센트)65세 이상 1인가구(가구)전체 일반가구(가구)통계연도시도명
독거노인가구비율(퍼센트)1.000-0.515-0.8120.0080.330
65세 이상 1인가구(가구)-0.5151.0000.9070.0670.208
전체 일반가구(가구)-0.8120.9071.0000.0000.284
통계연도0.0080.0670.0001.0000.000
시도명0.3300.2080.2840.0001.000

Missing values

2023-12-12T08:45:49.081034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:45:49.204937image/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

통계연도시도명시군구명독거노인가구비율(퍼센트)65세 이상 1인가구(가구)전체 일반가구(가구)
02017서울특별시종로구6.8425262372
12017서울특별시중구7.2375952208
22017서울특별시용산구6.4581691102
32017서울특별시성동구5.16126120957
42017서울특별시광진구4.66740147846
52017서울특별시동대문구6.59379145294
62017서울특별시중랑구6.510276159035
72017서울특별시성북구5.79926172973
82017서울특별시강북구8.110255127281
92017서울특별시도봉구6.88520125974
통계연도시도명시군구명독거노인가구비율(퍼센트)65세 이상 1인가구(가구)전체 일반가구(가구)
11302021경상남도창녕군19.5528727107
11312021경상남도고성군19.9443522274
11322021경상남도남해군23.9469019606
11332021경상남도하동군21.3414619457
11342021경상남도산청군22.3353215867
11352021경상남도함양군22.5394517563
11362021경상남도거창군18.3484526508
11372021경상남도합천군26.2533320332
11382021제주특별자치도제주시7.013833197149
11392021제주특별자치도서귀포시9.1672474013