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, 도시녹지비율(퍼센트), 녹지지역면적(제곱미터), 도시지역면적(제곱미터)으로 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/15110136/fileData.do

Alerts

녹지지역면적(제곱미터) is highly overall correlated with 도시지역면적(제곱미터)High correlation
도시지역면적(제곱미터) is highly overall correlated with 녹지지역면적(제곱미터)High correlation

Reproduction

Analysis started2023-12-12 22:40:02.257790
Analysis finished2023-12-12 22:40:03.528638
Duration1.27 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

2023-12-13T07:40:03.578324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:40:03.658856image/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

2023-12-13T07:40:03.763505image/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
2023-12-13T07:40:04.056874image/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%
2023-12-13T07:40:04.533231image/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%

도시녹지비율(퍼센트)
Real number (ℝ)

Distinct533
Distinct (%)46.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.711684
Minimum0
Maximum92.92
Zeros5
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-13T07:40:04.682747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23.0955
Q154.44
median70.81
Q380.49
95-th percentile89.1065
Maximum92.92
Range92.92
Interquartile range (IQR)26.05

Descriptive statistics

Standard deviation21.339182
Coefficient of variation (CV)0.32975779
Kurtosis0.42070314
Mean64.711684
Median Absolute Deviation (MAD)12.335
Skewness-1.0547512
Sum73771.32
Variance455.36069
MonotonicityNot monotonic
2023-12-13T07:40:04.804407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84.12 9
 
0.8%
80.49 8
 
0.7%
79.46 7
 
0.6%
69.82 6
 
0.5%
70.81 6
 
0.5%
85.37 6
 
0.5%
82.16 6
 
0.5%
68.31 6
 
0.5%
71.78 5
 
0.4%
80.24 5
 
0.4%
Other values (523) 1076
94.4%
ValueCountFrequency (%)
0.0 5
0.4%
0.25 5
0.4%
2.69 5
0.4%
7.89 2
 
0.2%
7.9 1
 
0.1%
7.91 2
 
0.2%
8.8 5
0.4%
10.94 4
0.4%
11.1 5
0.4%
11.92 1
 
0.1%
ValueCountFrequency (%)
92.92 1
0.1%
92.84 2
0.2%
92.82 2
0.2%
92.72 1
0.1%
92.66 1
0.1%
92.53 1
0.1%
92.45 1
0.1%
92.44 1
0.1%
92.28 1
0.1%
92.24 2
0.2%

녹지지역면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct631
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54914216
Minimum0
Maximum4.1030105 × 108
Zeros5
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-13T07:40:04.925826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3316024
Q110910074
median27217556
Q362537245
95-th percentile2.0854577 × 108
Maximum4.1030105 × 108
Range4.1030105 × 108
Interquartile range (IQR)51627171

Descriptive statistics

Standard deviation72581257
Coefficient of variation (CV)1.3217207
Kurtosis6.5508071
Mean54914216
Median Absolute Deviation (MAD)18644858
Skewness2.4834653
Sum6.2602206 × 1010
Variance5.2680389 × 1015
MonotonicityNot monotonic
2023-12-13T07:40:05.067001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33721772 5
 
0.4%
4290109 5
 
0.4%
3584962 5
 
0.4%
25135 5
 
0.4%
0 5
 
0.4%
47964975 5
 
0.4%
22335863 5
 
0.4%
37890936 5
 
0.4%
19818414 5
 
0.4%
25413367 5
 
0.4%
Other values (621) 1090
95.6%
ValueCountFrequency (%)
0 5
0.4%
25135 5
0.4%
383081 5
0.4%
393204 5
0.4%
556776 1
 
0.1%
556919 1
 
0.1%
557173 1
 
0.1%
557975 1
 
0.1%
558104 1
 
0.1%
831254 4
0.4%
ValueCountFrequency (%)
410301049 1
0.1%
408196226 1
0.1%
408153675 1
0.1%
407990645 2
0.2%
359895312 1
0.1%
359448767 1
0.1%
359388722 1
0.1%
359030870 1
0.1%
358663117 1
0.1%
352822754 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
2023-12-13T07:40:05.199383image/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
2023-12-13T07:40:05.338502image/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%

Interactions

2023-12-13T07:40:02.931461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:40:02.498171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:40:02.712476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:40:02.998935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:40:02.564125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:40:02.780333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:40:03.068532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:40:02.632200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:40:02.853601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:40:05.422414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명도시녹지비율(퍼센트)녹지지역면적(제곱미터)도시지역면적(제곱미터)
통계연도1.0000.0000.0000.0000.000
시도명0.0001.0000.6010.6360.597
도시녹지비율(퍼센트)0.0000.6011.0000.4620.405
녹지지역면적(제곱미터)0.0000.6360.4621.0000.967
도시지역면적(제곱미터)0.0000.5970.4050.9671.000
2023-12-13T07:40:05.513051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명통계연도
시도명1.0000.000
통계연도0.0001.000
2023-12-13T07:40:05.623001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도시녹지비율(퍼센트)녹지지역면적(제곱미터)도시지역면적(제곱미터)통계연도시도명
도시녹지비율(퍼센트)1.0000.4980.2800.0000.284
녹지지역면적(제곱미터)0.4981.0000.9570.0000.310
도시지역면적(제곱미터)0.2800.9571.0000.0000.281
통계연도0.0000.0000.0001.0000.000
시도명0.2840.3100.2810.0001.000

Missing values

2023-12-13T07:40:03.167770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:40:03.491599image/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

통계연도시도명시군구명도시녹지비율(퍼센트)녹지지역면적(제곱미터)도시지역면적(제곱미터)
02017서울특별시종로구46.741120386723971709
12017서울특별시중구0.25251359974292
22017서울특별시용산구38.95853016221898766
32017서울특별시성동구25.78433240616804426
42017서울특별시광진구30.89527487317075115
52017서울특별시동대문구2.6938308114245433
62017서울특별시중랑구39.67735218718531520
72017서울특별시성북구26.13643286224621422
82017서울특별시강북구52.381238040223636019
92017서울특별시도봉구52.431084144520679052
통계연도시도명시군구명도시녹지비율(퍼센트)녹지지역면적(제곱미터)도시지역면적(제곱미터)
11302021경상남도창녕군78.833900039549474929
11312021경상남도고성군56.151311158823350496
11322021경상남도남해군87.781035942211801283
11332021경상남도하동군37.87664104617535890
11342021경상남도산청군61.39642538610466483
11352021경상남도함양군82.121592428019391344
11362021경상남도거창군79.652541336731906464
11372021경상남도합천군84.171981841423545887
11382021제주특별자치도제주시79.81183793929230285390
11392021제주특별자치도서귀포시85.39204476225239451968