Overview

Dataset statistics

Number of variables6
Number of observations1138
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory57.9 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, 순이동인구(명), 총전입(명), 총전출(명)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=15110119

Alerts

순이동인구(명) is highly overall correlated with 총전입(명) and 1 other fieldsHigh correlation
총전입(명) is highly overall correlated with 순이동인구(명) and 1 other fieldsHigh correlation
총전출(명) is highly overall correlated with 순이동인구(명) and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-10 23:07:20.343250
Analysis finished2023-12-10 23:07:21.989482
Duration1.65 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
2018
229 
2019
228 
2017
227 
2020
227 
2021
227 

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 (%)
2018 229
20.1%
2019 228
20.0%
2017 227
19.9%
2020 227
19.9%
2021 227
19.9%

Length

2023-12-11T08:07:22.072624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:07:22.187769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 229
20.1%
2019 228
20.0%
2017 227
19.9%
2020 227
19.9%
2021 227
19.9%

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)
543 

Length

Max length7
Median length5
Mean length4.1362039
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
9.7%
강원도 90
7.9%
경상남도 89
7.8%
부산광역시 80
7.0%
충청남도 74
6.5%
전라북도 70
 
6.2%
충청북도 55
 
4.8%
Other values (6) 175
15.4%

Length

2023-12-11T08:07:22.328499image/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
9.7%
강원도 90
7.9%
경상남도 89
7.8%
부산광역시 80
7.0%
충청남도 74
6.5%
전라북도 70
 
6.2%
충청북도 55
 
4.8%
Other values (6) 175
15.4%

시군구명
Text

Distinct206
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
2023-12-11T08:07:22.755250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9314587
Min length2

Characters and Unicode

Total characters3336
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 (%)
중구 30
 
2.6%
동구 30
 
2.6%
서구 25
 
2.2%
남구 22
 
1.9%
북구 20
 
1.8%
고성군 10
 
0.9%
강서구 10
 
0.9%
임실군 5
 
0.4%
구례군 5
 
0.4%
보성군 5
 
0.4%
Other values (196) 976
85.8%
2023-12-11T08:07:23.878399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 423
 
12.7%
시 389
 
11.7%
구 371
 
11.1%
천 110
 
3.3%
주 100
 
3.0%
성 90
 
2.7%
양 88
 
2.6%
동 85
 
2.5%
산 80
 
2.4%
서 65
 
1.9%
Other values (122) 1535
46.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3336
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 423
 
12.7%
시 389
 
11.7%
구 371
 
11.1%
천 110
 
3.3%
주 100
 
3.0%
성 90
 
2.7%
양 88
 
2.6%
동 85
 
2.5%
산 80
 
2.4%
서 65
 
1.9%
Other values (122) 1535
46.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3336
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 423
 
12.7%
시 389
 
11.7%
구 371
 
11.1%
천 110
 
3.3%
주 100
 
3.0%
성 90
 
2.7%
양 88
 
2.6%
동 85
 
2.5%
산 80
 
2.4%
서 65
 
1.9%
Other values (122) 1535
46.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3336
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 423
 
12.7%
시 389
 
11.7%
구 371
 
11.1%
천 110
 
3.3%
주 100
 
3.0%
성 90
 
2.7%
양 88
 
2.6%
동 85
 
2.5%
산 80
 
2.4%
서 65
 
1.9%
Other values (122) 1535
46.0%

순이동인구(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct973
Distinct (%)85.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3170.5422
Minimum1
Maximum62608
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-11T08:07:24.099781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile73.1
Q1365.25
median1051.5
Q34054.75
95-th percentile12392.7
Maximum62608
Range62607
Interquartile range (IQR)3689.5

Descriptive statistics

Standard deviation5324.6112
Coefficient of variation (CV)1.6794008
Kurtosis28.684429
Mean3170.5422
Median Absolute Deviation (MAD)909.5
Skewness4.2956783
Sum3608077
Variance28351484
MonotonicityNot monotonic
2023-12-11T08:07:24.243184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 4
 
0.4%
74 4
 
0.4%
107 4
 
0.4%
478 4
 
0.4%
541 4
 
0.4%
155 4
 
0.4%
457 3
 
0.3%
38 3
 
0.3%
298 3
 
0.3%
62 3
 
0.3%
Other values (963) 1102
96.8%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
10 2
0.2%
12 2
0.2%
13 2
0.2%
ValueCountFrequency (%)
62608 1
0.1%
51838 1
0.1%
45213 1
0.1%
36165 1
0.1%
35171 1
0.1%
29329 1
0.1%
28479 1
0.1%
28377 1
0.1%
28284 1
0.1%
27188 1
0.1%

총전입(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct1128
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31705.251
Minimum1125
Maximum187806
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-11T08:07:24.405181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1125
5-th percentile2717.5
Q15411.75
median19922
Q348369.5
95-th percentile96728.5
Maximum187806
Range186681
Interquartile range (IQR)42957.75

Descriptive statistics

Standard deviation33698.52
Coefficient of variation (CV)1.0628687
Kurtosis3.4534468
Mean31705.251
Median Absolute Deviation (MAD)15813
Skewness1.7313707
Sum36080576
Variance1.1355902 × 109
MonotonicityNot monotonic
2023-12-11T08:07:24.585848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35389 2
 
0.2%
2559 2
 
0.2%
31899 2
 
0.2%
5688 2
 
0.2%
3516 2
 
0.2%
4058 2
 
0.2%
3848 2
 
0.2%
12978 2
 
0.2%
4014 2
 
0.2%
3489 2
 
0.2%
Other values (1118) 1118
98.2%
ValueCountFrequency (%)
1125 1
0.1%
1142 1
0.1%
1212 1
0.1%
1239 1
0.1%
1243 1
0.1%
1249 1
0.1%
1394 1
0.1%
1462 1
0.1%
1632 1
0.1%
1683 1
0.1%
ValueCountFrequency (%)
187806 1
0.1%
183258 1
0.1%
177079 1
0.1%
177059 1
0.1%
173336 1
0.1%
171708 1
0.1%
170212 1
0.1%
168445 1
0.1%
168204 1
0.1%
167745 1
0.1%

총전출(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct1123
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31808.025
Minimum1188
Maximum193564
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-11T08:07:24.752716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1188
5-th percentile2980.1
Q15682.5
median19737.5
Q347870.5
95-th percentile96654.85
Maximum193564
Range192376
Interquartile range (IQR)42188

Descriptive statistics

Standard deviation33001.035
Coefficient of variation (CV)1.0375066
Kurtosis3.1039312
Mean31808.025
Median Absolute Deviation (MAD)15567
Skewness1.6400689
Sum36197533
Variance1.0890683 × 109
MonotonicityNot monotonic
2023-12-11T08:07:24.940429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4080 2
 
0.2%
3401 2
 
0.2%
5513 2
 
0.2%
4212 2
 
0.2%
5182 2
 
0.2%
3669 2
 
0.2%
3679 2
 
0.2%
4751 2
 
0.2%
4233 2
 
0.2%
6281 2
 
0.2%
Other values (1113) 1118
98.2%
ValueCountFrequency (%)
1188 1
0.1%
1202 1
0.1%
1203 1
0.1%
1306 1
0.1%
1397 1
0.1%
1486 1
0.1%
1649 1
0.1%
1658 1
0.1%
1739 1
0.1%
1762 1
0.1%
ValueCountFrequency (%)
193564 1
0.1%
185355 1
0.1%
184404 1
0.1%
179518 1
0.1%
173747 1
0.1%
168067 1
0.1%
159355 1
0.1%
156393 1
0.1%
155052 1
0.1%
153330 1
0.1%

Interactions

2023-12-11T08:07:21.400787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:07:20.662966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:07:20.998642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:07:21.547409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:07:20.754813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:07:21.093634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:07:21.659891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:07:20.874694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:07:21.256519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:07:25.059156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명순이동인구(명)총전입(명)총전출(명)
통계연도1.0000.0000.0690.0000.000
시도명0.0001.0000.3700.5860.583
순이동인구(명)0.0690.3701.0000.5720.469
총전입(명)0.0000.5860.5721.0000.965
총전출(명)0.0000.5830.4690.9651.000
2023-12-11T08:07:25.164623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명
통계연도1.0000.000
시도명0.0001.000
2023-12-11T08:07:25.255321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순이동인구(명)총전입(명)총전출(명)통계연도시도명
순이동인구(명)1.0000.7430.7490.0390.161
총전입(명)0.7431.0000.9930.0000.274
총전출(명)0.7490.9931.0000.0000.272
통계연도0.0390.0000.0001.0000.000
시도명0.1610.2740.2720.0001.000

Missing values

2023-12-11T08:07:21.816336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:07:21.944411image/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

통계연도시도명시군구명순이동인구(명)총전입(명)총전출(명)
02017서울특별시종로구22062802025814
12017서울특별시중구3302090420574
22017서울특별시용산구14683515436622
32017서울특별시성동구43855041646031
42017서울특별시광진구6375601056647
52017서울특별시동대문구48344789952733
62017서울특별시중랑구35625638559947
72017서울특별시성북구69366128568221
82017서울특별시강북구25784223144809
92017서울특별시도봉구42954096545260
통계연도시도명시군구명순이동인구(명)총전입(명)총전출(명)
11282021경상남도창녕군40164976898
11292021경상남도고성군16643784544
11302021경상남도남해군2537043679
11312021경상남도하동군73136264357
11322021경상남도산청군6834353367
11332021경상남도함양군16134133574
11342021경상남도거창군19051164926
11352021경상남도합천군24436113855
11362021제주특별자치도제주시14856713965654
11372021제주특별자치도서귀포시24322784025408