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, 일인당 도시지역면적(제곱미터), 도시지역면적(제곱미터), 도시지역인구수(명)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/15110141/fileData.do

Alerts

일인당 도시지역면적(제곱미터) is highly overall correlated with 도시지역인구수(명)High correlation
도시지역면적(제곱미터) is highly overall correlated with 도시지역인구수(명)High correlation
도시지역인구수(명) is highly overall correlated with 일인당 도시지역면적(제곱미터) and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 03:09:41.901411
Analysis finished2023-12-12 03:09:43.465239
Duration1.56 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

2023-12-12T12:09:43.542064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:09:43.659898image/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

2023-12-12T12:09:43.807722image/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
2023-12-12T12:09:44.210418image/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%
2023-12-12T12:09:44.768667image/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%

일인당 도시지역면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct1120
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean747.58906
Minimum0
Maximum4736.54
Zeros5
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-12T12:09:44.941448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile56.6445
Q1185.63
median557
Q31034.305
95-th percentile1940.253
Maximum4736.54
Range4736.54
Interquartile range (IQR)848.675

Descriptive statistics

Standard deviation713.69027
Coefficient of variation (CV)0.95465585
Kurtosis6.5345518
Mean747.58906
Median Absolute Deviation (MAD)410.97
Skewness2.0136318
Sum852251.53
Variance509353.8
MonotonicityNot monotonic
2023-12-12T12:09:45.124630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5
 
0.4%
1444.78 2
 
0.2%
797.31 2
 
0.2%
976.88 2
 
0.2%
559.83 2
 
0.2%
814.12 2
 
0.2%
1374.06 2
 
0.2%
508.18 2
 
0.2%
1893.17 2
 
0.2%
1317.18 2
 
0.2%
Other values (1110) 1117
98.0%
ValueCountFrequency (%)
0.0 5
0.4%
37.08 1
 
0.1%
37.64 1
 
0.1%
38.13 1
 
0.1%
38.46 1
 
0.1%
39.06 1
 
0.1%
40.63 1
 
0.1%
40.93 1
 
0.1%
41.15 1
 
0.1%
41.36 2
 
0.2%
ValueCountFrequency (%)
4736.54 1
0.1%
4629.76 1
0.1%
4625.26 1
0.1%
4619.82 1
0.1%
4619.52 1
0.1%
4587.74 1
0.1%
4551.9 1
0.1%
4241.32 1
0.1%
4236.19 1
0.1%
4136.67 1
0.1%

도시지역면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct552
Distinct (%)48.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77220269
Minimum3710000
Maximum5.9533366 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-12T12:09:45.283564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3710000
5-th percentile9484653
Q122963322
median41463274
Q392338208
95-th percentile2.7574524 × 108
Maximum5.9533366 × 108
Range5.9162366 × 108
Interquartile range (IQR)69374886

Descriptive statistics

Standard deviation92811415
Coefficient of variation (CV)1.2019048
Kurtosis7.227287
Mean77220269
Median Absolute Deviation (MAD)25075392
Skewness2.5060612
Sum8.8031106 × 1010
Variance8.6139587 × 1015
MonotonicityNot monotonic
2023-12-12T12:09:45.430993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5959271 5
 
0.4%
17306615 5
 
0.4%
207398894 5
 
0.4%
29453071 5
 
0.4%
49850582 5
 
0.4%
13543694 5
 
0.4%
35644901 5
 
0.4%
55061021 5
 
0.4%
108722161 5
 
0.4%
12421767 5
 
0.4%
Other values (542) 1090
95.6%
ValueCountFrequency (%)
3710000 5
0.4%
3720000 2
 
0.2%
3725281 3
0.3%
4466946 5
0.4%
5474447 5
0.4%
5959271 5
0.4%
6196276 5
0.4%
6398307 5
0.4%
7055042 1
 
0.1%
7055185 1
 
0.1%
ValueCountFrequency (%)
595333663 2
0.2%
595333540 2
0.2%
497769781 1
0.1%
494022455 1
0.1%
487842438 1
0.1%
487835918 1
0.1%
487835915 1
0.1%
484020509 1
0.1%
464832544 1
0.1%
464824021 1
0.1%

도시지역인구수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct1120
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean207206.17
Minimum0
Maximum1202628
Zeros5
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-12T12:09:45.609871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10236.8
Q131450
median122518
Q3326329.5
95-th percentile601784.2
Maximum1202628
Range1202628
Interquartile range (IQR)294879.5

Descriptive statistics

Standard deviation222287.47
Coefficient of variation (CV)1.072784
Kurtosis2.9125239
Mean207206.17
Median Absolute Deviation (MAD)106461
Skewness1.5852792
Sum2.3621504 × 108
Variance4.9411721 × 1010
MonotonicityNot monotonic
2023-12-12T12:09:45.820555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
0.4%
12313 3
 
0.3%
75397 2
 
0.2%
18434 2
 
0.2%
102516 2
 
0.2%
15258 2
 
0.2%
12193 2
 
0.2%
29084 2
 
0.2%
16057 2
 
0.2%
472731 2
 
0.2%
Other values (1110) 1116
97.9%
ValueCountFrequency (%)
0 5
0.4%
4204 1
 
0.1%
4283 1
 
0.1%
4388 1
 
0.1%
4430 1
 
0.1%
4447 1
 
0.1%
4539 1
 
0.1%
4559 1
 
0.1%
4573 1
 
0.1%
4628 1
 
0.1%
ValueCountFrequency (%)
1202628 1
0.1%
1201166 1
0.1%
1194465 1
0.1%
1186078 1
0.1%
1183714 1
0.1%
1037968 1
0.1%
1034636 1
0.1%
1030536 1
0.1%
1029383 1
0.1%
1024786 1
0.1%

Interactions

2023-12-12T12:09:42.930594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:42.248193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:42.598038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:43.042515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:42.370716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:42.696842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:43.185222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:42.488414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:42.813773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:09:45.929459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명일인당 도시지역면적(제곱미터)도시지역면적(제곱미터)도시지역인구수(명)
통계연도1.0000.0000.0000.0000.000
시도명0.0001.0000.6250.5970.617
일인당 도시지역면적(제곱미터)0.0000.6251.0000.5570.588
도시지역면적(제곱미터)0.0000.5970.5571.0000.741
도시지역인구수(명)0.0000.6170.5880.7411.000
2023-12-12T12:09:46.044376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명
통계연도1.0000.000
시도명0.0001.000
2023-12-12T12:09:46.146886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일인당 도시지역면적(제곱미터)도시지역면적(제곱미터)도시지역인구수(명)통계연도시도명
일인당 도시지역면적(제곱미터)1.0000.149-0.6850.0000.301
도시지역면적(제곱미터)0.1491.0000.5510.0000.281
도시지역인구수(명)-0.6850.5511.0000.0000.295
통계연도0.0000.0000.0001.0000.000
시도명0.3010.2810.2950.0001.000

Missing values

2023-12-12T12:09:43.309418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:09:43.417058image/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

통계연도시도명시군구명일인당 도시지역면적(제곱미터)도시지역면적(제곱미터)도시지역인구수(명)
02017서울특별시종로구154.8923971709154770
12017서울특별시중구79.349974292125709
22017서울특별시용산구95.5621898766229161
32017서울특별시성동구55.1316804426304808
42017서울특별시광진구47.7417075115357703
52017서울특별시동대문구40.6314245433350647
62017서울특별시중랑구45.418531520408226
72017서울특별시성북구55.4524621422444055
82017서울특별시강북구72.8423636019324479
92017서울특별시도봉구60.0820679052344166
통계연도시도명시군구명일인당 도시지역면적(제곱미터)도시지역면적(제곱미터)도시지역인구수(명)
11302021경상남도창녕군1460.94947492933866
11312021경상남도고성군970.392335049624063
11322021경상남도남해군910.941180128312955
11332021경상남도하동군2122.73175358908261
11342021경상남도산청군1968.86104664835316
11352021경상남도함양군1071.941939134418090
11362021경상남도거창군784.393190646440677
11372021경상남도합천군952.22354588724728
11382021제주특별자치도제주시507.63230285390453648
11392021제주특별자치도서귀포시1504.97239451968159107