Overview

Dataset statistics

Number of variables6
Number of observations539
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.5 KiB
Average record size in memory52.2 B

Variable types

Categorical2
Text1
Numeric3

Dataset

Description김해시에서 통계기반 도시현황 파악을 위해 개발한 통계지수 중 하나로서, 통계연도, 시도명, 시군구명, 유수율(퍼센트), 연간총조정량(부과량)(세제곱미터), 연간총생산량(세제곱미터)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/15110133/fileData.do

Alerts

유수율(퍼센트) is highly overall correlated with 연간총조정량(부과량)(세제곱미터)High correlation
연간총조정량(부과량)(세제곱미터) is highly overall correlated with 유수율(퍼센트) and 1 other fieldsHigh correlation
연간총생산량(세제곱미터) is highly overall correlated with 연간총조정량(부과량)(세제곱미터)High correlation
연간총생산량(세제곱미터) has unique valuesUnique

Reproduction

Analysis started2023-12-12 13:11:06.932170
Analysis finished2023-12-12 13:11:08.504507
Duration1.57 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계연도
Categorical

Distinct5
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
2011
108 
2013
108 
2014
108 
2015
108 
2012
107 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2011
2nd row2011
3rd row2011
4th row2011
5th row2011

Common Values

ValueCountFrequency (%)
2011 108
20.0%
2013 108
20.0%
2014 108
20.0%
2015 108
20.0%
2012 107
19.9%

Length

2023-12-12T22:11:08.588842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:11:08.713282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2011 108
20.0%
2013 108
20.0%
2014 108
20.0%
2015 108
20.0%
2012 107
19.9%

시도명
Categorical

Distinct9
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
경기도
155 
강원도
75 
경상북도
70 
경상남도
57 
충청남도
51 
Other values (4)
131 

Length

Max length7
Median length4
Mean length3.6011132
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 155
28.8%
강원도 75
13.9%
경상북도 70
13.0%
경상남도 57
 
10.6%
충청남도 51
 
9.5%
전라북도 45
 
8.3%
충청북도 41
 
7.6%
전라남도 40
 
7.4%
제주특별자치도 5
 
0.9%

Length

2023-12-12T22:11:08.869104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:11:09.001375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 155
28.8%
강원도 75
13.9%
경상북도 70
13.0%
경상남도 57
 
10.6%
충청남도 51
 
9.5%
전라북도 45
 
8.3%
충청북도 41
 
7.6%
전라남도 40
 
7.4%
제주특별자치도 5
 
0.9%

시군구명
Text

Distinct111
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Memory size4.3 KiB
2023-12-12T22:11:09.347075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0278293
Min length3

Characters and Unicode

Total characters1632
Distinct characters96
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

Unique1 ?
Unique (%)0.2%

Sample

1st row수원시
2nd row성남시
3rd row고양시
4th row부천시
5th row안양시
ValueCountFrequency (%)
수원시 5
 
0.9%
영광군 5
 
0.9%
광양시 5
 
0.9%
영암군 5
 
0.9%
나주시 5
 
0.9%
순천시 5
 
0.9%
여수시 5
 
0.9%
목포시 5
 
0.9%
부안군 5
 
0.9%
고창군 5
 
0.9%
Other values (101) 489
90.7%
2023-12-12T22:11:09.884837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 383
23.5%
군 171
 
10.5%
주 85
 
5.2%
천 80
 
4.9%
양 55
 
3.4%
산 50
 
3.1%
영 40
 
2.5%
안 35
 
2.1%
성 35
 
2.1%
원 28
 
1.7%
Other values (86) 670
41.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1632
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 383
23.5%
군 171
 
10.5%
주 85
 
5.2%
천 80
 
4.9%
양 55
 
3.4%
산 50
 
3.1%
영 40
 
2.5%
안 35
 
2.1%
성 35
 
2.1%
원 28
 
1.7%
Other values (86) 670
41.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1632
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 383
23.5%
군 171
 
10.5%
주 85
 
5.2%
천 80
 
4.9%
양 55
 
3.4%
산 50
 
3.1%
영 40
 
2.5%
안 35
 
2.1%
성 35
 
2.1%
원 28
 
1.7%
Other values (86) 670
41.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1632
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 383
23.5%
군 171
 
10.5%
주 85
 
5.2%
천 80
 
4.9%
양 55
 
3.4%
산 50
 
3.1%
영 40
 
2.5%
안 35
 
2.1%
성 35
 
2.1%
원 28
 
1.7%
Other values (86) 670
41.1%

유수율(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.946197
Minimum26
Maximum867
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-12-12T22:11:10.046813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile52
Q165
median76
Q386
95-th percentile92.1
Maximum867
Range841
Interquartile range (IQR)21

Descriptive statistics

Standard deviation36.745064
Coefficient of variation (CV)0.4838302
Kurtosis400.77632
Mean75.946197
Median Absolute Deviation (MAD)11
Skewness18.56719
Sum40935
Variance1350.1997
MonotonicityNot monotonic
2023-12-12T22:11:10.455905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87 28
 
5.2%
88 26
 
4.8%
85 24
 
4.5%
82 20
 
3.7%
90 18
 
3.3%
70 18
 
3.3%
86 16
 
3.0%
89 16
 
3.0%
55 16
 
3.0%
65 15
 
2.8%
Other values (51) 342
63.5%
ValueCountFrequency (%)
26 4
0.7%
37 1
 
0.2%
38 1
 
0.2%
41 1
 
0.2%
44 1
 
0.2%
47 4
0.7%
48 4
0.7%
49 3
0.6%
50 3
0.6%
51 3
0.6%
ValueCountFrequency (%)
867 1
 
0.2%
108 1
 
0.2%
101 1
 
0.2%
100 1
 
0.2%
98 1
 
0.2%
97 1
 
0.2%
96 2
 
0.4%
95 4
0.7%
94 7
1.3%
93 8
1.5%

연간총조정량(부과량)(세제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct536
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26071169
Minimum1969717
Maximum1.7081158 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-12-12T22:11:10.587172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1969717
5-th percentile3492853.6
Q16202766
median13049867
Q330084457
95-th percentile96316706
Maximum1.7081158 × 108
Range1.6884187 × 108
Interquartile range (IQR)23881691

Descriptive statistics

Standard deviation30435916
Coefficient of variation (CV)1.1674166
Kurtosis3.6034177
Mean26071169
Median Absolute Deviation (MAD)8606953
Skewness1.9552951
Sum1.405236 × 1010
Variance9.2634496 × 1014
MonotonicityNot monotonic
2023-12-12T22:11:10.754551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4965434 2
 
0.4%
6328624 2
 
0.4%
8646314 2
 
0.4%
3711732 1
 
0.2%
3521552 1
 
0.2%
4540410 1
 
0.2%
3376229 1
 
0.2%
2760118 1
 
0.2%
6188322 1
 
0.2%
27957109 1
 
0.2%
Other values (526) 526
97.6%
ValueCountFrequency (%)
1969717 1
0.2%
2133802 1
0.2%
2660498 1
0.2%
2704015 1
0.2%
2733323 1
0.2%
2760118 1
0.2%
2822766 1
0.2%
2871191 1
0.2%
2936561 1
0.2%
2967912 1
0.2%
ValueCountFrequency (%)
170811583 1
0.2%
151087812 1
0.2%
151082578 1
0.2%
149174744 1
0.2%
149113058 1
0.2%
143745485 1
0.2%
119249359 1
0.2%
119091788 1
0.2%
115162269 1
0.2%
115100898 1
0.2%

연간총생산량(세제곱미터)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct539
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32263815
Minimum3327718
Maximum1.6052224 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-12-12T22:11:10.906878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3327718
5-th percentile5686838.9
Q19122333
median17216965
Q339790188
95-th percentile1.1856525 × 108
Maximum1.6052224 × 108
Range1.5719452 × 108
Interquartile range (IQR)30667854

Descriptive statistics

Standard deviation34280402
Coefficient of variation (CV)1.062503
Kurtosis2.3121597
Mean32263815
Median Absolute Deviation (MAD)9931783
Skewness1.7422814
Sum1.7390196 × 1010
Variance1.175146 × 1015
MonotonicityNot monotonic
2023-12-12T22:11:11.063823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120801495 1
 
0.2%
32273340 1
 
0.2%
5871525 1
 
0.2%
5462480 1
 
0.2%
6805264 1
 
0.2%
6047739 1
 
0.2%
4073773 1
 
0.2%
8142529 1
 
0.2%
7614502 1
 
0.2%
8470850 1
 
0.2%
Other values (529) 529
98.1%
ValueCountFrequency (%)
3327718 1
0.2%
3332411 1
0.2%
3483758 1
0.2%
3695966 1
0.2%
3768548 1
0.2%
3862353 1
0.2%
3868396 1
0.2%
3983349 1
0.2%
4005361 1
0.2%
4067519 1
0.2%
ValueCountFrequency (%)
160522238 1
0.2%
160041607 1
0.2%
159028245 1
0.2%
156848231 1
0.2%
153150547 1
0.2%
152829547 1
0.2%
133537435 1
0.2%
131817997 1
0.2%
131745872 1
0.2%
129396593 1
0.2%

Interactions

2023-12-12T22:11:07.834215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:07.198801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:07.501069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:07.943179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:07.281101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:07.613167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:08.081936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:07.391315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:11:07.718579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:11:11.167470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명유수율(퍼센트)연간총조정량(부과량)(세제곱미터)연간총생산량(세제곱미터)
통계연도1.0000.0000.0000.0000.000
시도명0.0001.0000.0000.5880.446
유수율(퍼센트)0.0000.0001.0001.0000.000
연간총조정량(부과량)(세제곱미터)0.0000.5881.0001.0000.920
연간총생산량(세제곱미터)0.0000.4460.0000.9201.000
2023-12-12T22:11:11.272567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명통계연도
시도명1.0000.000
통계연도0.0001.000
2023-12-12T22:11:11.378744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유수율(퍼센트)연간총조정량(부과량)(세제곱미터)연간총생산량(세제곱미터)통계연도시도명
유수율(퍼센트)1.0000.6130.4730.0000.000
연간총조정량(부과량)(세제곱미터)0.6131.0000.9760.0000.223
연간총생산량(세제곱미터)0.4730.9761.0000.0000.221
통계연도0.0000.0000.0001.0000.000
시도명0.0000.2230.2210.0001.000

Missing values

2023-12-12T22:11:08.310589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:11:08.447304image/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

통계연도시도명시군구명유수율(퍼센트)연간총조정량(부과량)(세제곱미터)연간총생산량(세제곱미터)
02011경기도수원시89107235632120801495
12011경기도성남시87107300539123575629
22011경기도고양시9094716015105443548
32011경기도부천시928957469697034290
42011경기도안양시895823949365825224
52011경기도안산시94143745485153150547
62011경기도용인시888653981597877989
72011경기도의정부시933877900541538832
82011경기도남양주시864611151353767042
92011경기도평택시824631515556511825
통계연도시도명시군구명유수율(퍼센트)연간총조정량(부과량)(세제곱미터)연간총생산량(세제곱미터)
5292015경상남도통영시811265641415688493
5302015경상남도사천시821251357815332074
5312015경상남도김해시794666656259270147
5322015경상남도밀양시7770748939203095
5332015경상남도거제시812264072928115822
5342015경상남도양산시742786859437807874
5352015경상남도창녕군6362960899978234
5362015경상남도거창군7143907626159065
5372015경상남도함안군6862068049103957
5382015제주특별자치도제주시4467941259152829547