Overview

Dataset statistics

Number of variables6
Number of observations1560
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory79.3 KiB
Average record size in memory52.1 B

Variable types

Numeric4
Categorical1
Text1

Dataset

Description김해시에서 통계기반 도시현황 파악을 위해 개발한 통계지수 중 하나로서, 통계연도, 시도명, 시군구명, 인구밀도(명_제곱킬로미터), 총인구수(명), 지역면적(제곱미터)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=15110121

Alerts

인구밀도(명_제곱킬로미터) is highly overall correlated with 총인구수(명) and 1 other fieldsHigh correlation
총인구수(명) is highly overall correlated with 인구밀도(명_제곱킬로미터)High correlation
지역면적(제곱미터) is highly overall correlated with 인구밀도(명_제곱킬로미터)High correlation

Reproduction

Analysis started2023-12-11 00:53:34.786397
Analysis finished2023-12-11 00:53:37.270490
Duration2.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계연도
Real number (ℝ)

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.5
Minimum2016
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.8 KiB
2023-12-11T09:53:37.321143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12017
median2018.5
Q32020
95-th percentile2021
Maximum2021
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7083728
Coefficient of variation (CV)0.00084635758
Kurtosis-1.2687907
Mean2018.5
Median Absolute Deviation (MAD)1.5
Skewness0
Sum3148860
Variance2.9185375
MonotonicityNot monotonic
2023-12-11T09:53:37.457543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2016 260
16.7%
2017 260
16.7%
2018 260
16.7%
2019 260
16.7%
2020 260
16.7%
2021 260
16.7%
ValueCountFrequency (%)
2016 260
16.7%
2017 260
16.7%
2018 260
16.7%
2019 260
16.7%
2020 260
16.7%
2021 260
16.7%
ValueCountFrequency (%)
2021 260
16.7%
2020 260
16.7%
2019 260
16.7%
2018 260
16.7%
2017 260
16.7%
2016 260
16.7%

시도명
Categorical

Distinct16
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
경기도
288 
경상북도
150 
서울특별시
150 
경상남도
138 
전라남도
132 
Other values (11)
702 

Length

Max length7
Median length5
Mean length4.0538462
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 288
18.5%
경상북도 150
9.6%
서울특별시 150
9.6%
경상남도 138
8.8%
전라남도 132
8.5%
강원도 108
 
6.9%
충청남도 102
 
6.5%
전라북도 96
 
6.2%
부산광역시 96
 
6.2%
충청북도 90
 
5.8%
Other values (6) 210
13.5%

Length

2023-12-11T09:53:37.619323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 288
18.5%
경상북도 150
9.6%
서울특별시 150
9.6%
경상남도 138
8.8%
전라남도 132
8.5%
강원도 108
 
6.9%
충청남도 102
 
6.5%
전라북도 96
 
6.2%
부산광역시 96
 
6.2%
충청북도 90
 
5.8%
Other values (6) 210
13.5%

시군구명
Text

Distinct236
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
2023-12-11T09:53:38.002974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.9551282
Min length2

Characters and Unicode

Total characters4610
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 row강릉시
2nd row고성군
3rd row동해시
4th row삼척시
5th row속초시
ValueCountFrequency (%)
동구 36
 
2.3%
중구 36
 
2.3%
남구 32
 
2.1%
북구 30
 
1.9%
서구 30
 
1.9%
강서구 12
 
0.8%
고성군 12
 
0.8%
논산시 6
 
0.4%
순천시 6
 
0.4%
부여군 6
 
0.4%
Other values (226) 1354
86.8%
2023-12-11T09:53:38.522319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 636
 
13.8%
군 510
 
11.1%
시 468
 
10.2%
천 132
 
2.9%
산 132
 
2.9%
주 120
 
2.6%
동 120
 
2.6%
양 114
 
2.5%
성 114
 
2.5%
서 96
 
2.1%
Other values (131) 2168
47.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4610
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 636
 
13.8%
군 510
 
11.1%
시 468
 
10.2%
천 132
 
2.9%
산 132
 
2.9%
주 120
 
2.6%
동 120
 
2.6%
양 114
 
2.5%
성 114
 
2.5%
서 96
 
2.1%
Other values (131) 2168
47.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4610
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 636
 
13.8%
군 510
 
11.1%
시 468
 
10.2%
천 132
 
2.9%
산 132
 
2.9%
주 120
 
2.6%
동 120
 
2.6%
양 114
 
2.5%
성 114
 
2.5%
서 96
 
2.1%
Other values (131) 2168
47.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4610
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 636
 
13.8%
군 510
 
11.1%
시 468
 
10.2%
천 132
 
2.9%
산 132
 
2.9%
주 120
 
2.6%
동 120
 
2.6%
양 114
 
2.5%
성 114
 
2.5%
서 96
 
2.1%
Other values (131) 2168
47.0%

인구밀도(명_제곱킬로미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct1549
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3945.5013
Minimum19.25
Maximum27445.51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.8 KiB
2023-12-11T09:53:38.702408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19.25
5-th percentile38.939
Q1110.895
median677.03
Q36101.925
95-th percentile17182.526
Maximum27445.51
Range27426.26
Interquartile range (IQR)5991.03

Descriptive statistics

Standard deviation5844.6036
Coefficient of variation (CV)1.4813336
Kurtosis1.9902552
Mean3945.5013
Median Absolute Deviation (MAD)631.6
Skewness1.6773556
Sum6154982
Variance34159392
MonotonicityNot monotonic
2023-12-11T09:53:38.846344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
588.54 2
 
0.1%
45.96 2
 
0.1%
141.52 2
 
0.1%
58.29 2
 
0.1%
19.25 2
 
0.1%
44.77 2
 
0.1%
38.04 2
 
0.1%
46.37 2
 
0.1%
78.82 2
 
0.1%
73.98 2
 
0.1%
Other values (1539) 1540
98.7%
ValueCountFrequency (%)
19.25 2
0.1%
19.53 1
0.1%
19.54 1
0.1%
19.8 1
0.1%
19.89 1
0.1%
20.01 1
0.1%
20.46 1
0.1%
20.83 1
0.1%
21.28 1
0.1%
21.43 1
0.1%
ValueCountFrequency (%)
27445.51 1
0.1%
27067.22 1
0.1%
26666.84 1
0.1%
26322.68 1
0.1%
26097.81 1
0.1%
25698.6 1
0.1%
24978.13 1
0.1%
24667.38 1
0.1%
24519.55 1
0.1%
24484.46 1
0.1%

총인구수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct1559
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean233180.19
Minimum8867
Maximum1202628
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.8 KiB
2023-12-11T09:53:39.042852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8867
5-th percentile27534.5
Q162314.25
median188423.5
Q3342149.25
95-th percentile602504.2
Maximum1202628
Range1193761
Interquartile range (IQR)279835

Descriptive statistics

Standard deviation210243.68
Coefficient of variation (CV)0.90163614
Kurtosis3.4716959
Mean233180.19
Median Absolute Deviation (MAD)134374.5
Skewness1.6183189
Sum3.6376109 × 108
Variance4.4202406 × 1010
MonotonicityNot monotonic
2023-12-11T09:53:39.187174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122499 2
 
0.1%
213846 1
 
0.1%
69950 1
 
0.1%
30255 1
 
0.1%
39133 1
 
0.1%
253912 1
 
0.1%
208316 1
 
0.1%
835590 1
 
0.1%
193767 1
 
0.1%
73677 1
 
0.1%
Other values (1549) 1549
99.3%
ValueCountFrequency (%)
8867 1
0.1%
9077 1
0.1%
9617 1
0.1%
9832 1
0.1%
9975 1
0.1%
10001 1
0.1%
16320 1
0.1%
16692 1
0.1%
16993 1
0.1%
17356 1
0.1%
ValueCountFrequency (%)
1202628 1
0.1%
1201166 1
0.1%
1194465 1
0.1%
1194041 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%

지역면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct1557
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0353859 × 108
Minimum2825782
Maximum1.8205158 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.8 KiB
2023-12-11T09:53:39.357977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2825782
5-th percentile16617496
Q152994122
median3.950815 × 108
Q36.6117045 × 108
95-th percentile1.0473901 × 109
Maximum1.8205158 × 109
Range1.81769 × 109
Interquartile range (IQR)6.0817633 × 108

Descriptive statistics

Standard deviation3.7381569 × 108
Coefficient of variation (CV)0.92634433
Kurtosis0.34251579
Mean4.0353859 × 108
Median Absolute Deviation (MAD)3.2810051 × 108
Skewness0.86810914
Sum6.295202 × 1011
Variance1.3973817 × 1017
MonotonicityNot monotonic
2023-12-11T09:53:39.526611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33872729 2
 
0.1%
54947866 2
 
0.1%
24835030 2
 
0.1%
122624800 1
 
0.1%
983525195 1
 
0.1%
940801578 1
 
0.1%
214947693 1
 
0.1%
407251213 1
 
0.1%
81802191 1
 
0.1%
883397742 1
 
0.1%
Other values (1547) 1547
99.2%
ValueCountFrequency (%)
2825782 1
0.1%
2825785 1
0.1%
2825802 1
0.1%
2825996 1
0.1%
2826064 1
0.1%
2829149 1
0.1%
7054680 1
0.1%
7055042 1
0.1%
7055185 1
0.1%
7055439 1
0.1%
ValueCountFrequency (%)
1820515777 1
0.1%
1820343873 1
0.1%
1820310463 1
0.1%
1820278032 1
0.1%
1820182075 1
0.1%
1820144860 1
0.1%
1646099212 1
0.1%
1646079480 1
0.1%
1645196111 1
0.1%
1645179453 1
0.1%

Interactions

2023-12-11T09:53:36.615772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:53:35.149483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:53:35.695380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:53:36.162525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:53:36.722235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:53:35.276615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:53:35.804732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:53:36.256484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:53:36.849715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:53:35.405202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:53:35.931899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:53:36.372709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:53:36.952443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:53:35.529293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:53:36.056586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:53:36.481181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:53:39.643314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명인구밀도(명_제곱킬로미터)총인구수(명)지역면적(제곱미터)
통계연도1.0000.0000.0000.0000.000
시도명0.0001.0000.6950.5710.683
인구밀도(명_제곱킬로미터)0.0000.6951.0000.6580.680
총인구수(명)0.0000.5710.6581.0000.580
지역면적(제곱미터)0.0000.6830.6800.5801.000
2023-12-11T09:53:39.740830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도인구밀도(명_제곱킬로미터)총인구수(명)지역면적(제곱미터)시도명
통계연도1.000-0.007-0.0080.0010.000
인구밀도(명_제곱킬로미터)-0.0071.0000.782-0.8770.358
총인구수(명)-0.0080.7821.000-0.4460.264
지역면적(제곱미터)0.001-0.877-0.4461.0000.348
시도명0.0000.3580.2640.3481.000

Missing values

2023-12-11T09:53:37.107197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:53:37.226466image/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

통계연도시도명시군구명인구밀도(명_제곱킬로미터)총인구수(명)지역면적(제곱미터)
02016강원도강릉시205.552138461040343604
12016강원도고성군45.5530114661099082
22016강원도동해시517.7293297180207886
32016강원도삼척시58.63695991186997758
42016강원도속초시773.6981793105718137
52016강원도양구군36.2824010661839242
62016강원도양양군43.227218630086514
72016강원도영월군35.54400731127615087
82016강원도원주시389.25337979868283081
92016강원도인제군19.89327201645196111
통계연도시도명시군구명인구밀도(명_제곱킬로미터)총인구수(명)지역면적(제곱미터)
15502017제주특별자치도제주시489.13478700978685742
15512017제주특별자치도서귀포시204.69178383871474204
15522018제주특별자치도제주시496.52485946978694846
15532018제주특별자치도서귀포시207.98181245871464625
15542019제주특별자치도제주시500.07489405978668959
15552019제주특별자치도서귀포시208.34181584871558430
15562020제주특별자치도제주시503.2492466978669412
15572020제주특별자치도서귀포시209.02182169871541862
15582021제주특별자치도제주시503.82493096978721709
15592021제주특별자치도서귀포시210.73183663871557015