Overview

Dataset statistics

Number of variables6
Number of observations211
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.8 KiB
Average record size in memory52.6 B

Variable types

Categorical3
Numeric3

Dataset

Description김해시에서 통계기반 도시현황 파악을 위해 개발한 통계지수 중 하나로서, 통계연도, 시도명, 시군구명, 건축물 밀집도(동_제곱킬로미터), 건축물 수(동), 행정구역면적(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/15110132/fileData.do

Alerts

시군구명 is highly overall correlated with 건축물 밀집도(동_제곱킬로미터) and 3 other fieldsHigh correlation
시도명 is highly overall correlated with 건축물 수(동) and 2 other fieldsHigh correlation
건축물 밀집도(동_제곱킬로미터) is highly overall correlated with 건축물 수(동) and 2 other fieldsHigh correlation
건축물 수(동) is highly overall correlated with 건축물 밀집도(동_제곱킬로미터) and 3 other fieldsHigh correlation
행정구역면적(제곱미터) is highly overall correlated with 건축물 밀집도(동_제곱킬로미터) and 3 other fieldsHigh correlation
건축물 밀집도(동_제곱킬로미터) has unique valuesUnique
건축물 수(동) has unique valuesUnique

Reproduction

Analysis started2023-12-12 19:27:42.794498
Analysis finished2023-12-12 19:27:45.398986
Duration2.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계연도
Categorical

Distinct5
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2019
43 
2017
42 
2018
42 
2020
42 
2021
42 

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 (%)
2019 43
20.4%
2017 42
19.9%
2018 42
19.9%
2020 42
19.9%
2021 42
19.9%

Length

2023-12-13T04:27:45.463067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:27:45.581110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 43
20.4%
2017 42
19.9%
2018 42
19.9%
2020 42
19.9%
2021 42
19.9%

시도명
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
경기도
55 
서울특별시
28 
인천광역시
18 
경상남도
15 
경상북도
10 
Other values (13)
85 

Length

Max length7
Median length5
Mean length4.1706161
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 55
26.1%
서울특별시 28
13.3%
인천광역시 18
 
8.5%
경상남도 15
 
7.1%
경상북도 10
 
4.7%
전라북도 10
 
4.7%
충청남도 10
 
4.7%
충청북도 10
 
4.7%
대구광역시 10
 
4.7%
전국 5
 
2.4%
Other values (8) 40
19.0%

Length

2023-12-13T04:27:45.713783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 55
26.1%
서울특별시 28
13.3%
인천광역시 18
 
8.5%
경상남도 15
 
7.1%
경상북도 10
 
4.7%
전라북도 10
 
4.7%
충청남도 10
 
4.7%
충청북도 10
 
4.7%
대구광역시 10
 
4.7%
울산광역시 5
 
2.4%
Other values (8) 40
19.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct44
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
고양시
 
5
송파구
 
5
창원시
 
5
성남시
 
5
수원시
 
5
Other values (39)
186 

Length

Max length7
Median length3
Mean length3.6398104
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row고양시
2nd row부천시
3rd row성남시
4th row수원시
5th row안산시

Common Values

ValueCountFrequency (%)
고양시 5
 
2.4%
송파구 5
 
2.4%
창원시 5
 
2.4%
성남시 5
 
2.4%
수원시 5
 
2.4%
안산시 5
 
2.4%
안양시 5
 
2.4%
용인시 5
 
2.4%
화성시 5
 
2.4%
남양주시 5
 
2.4%
Other values (34) 161
76.3%

Length

2023-12-13T04:27:45.855094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고양시 5
 
2.4%
서구 5
 
2.4%
남동구 5
 
2.4%
전국 5
 
2.4%
서울특별시 5
 
2.4%
부산광역시 5
 
2.4%
대구광역시 5
 
2.4%
인천광역시 5
 
2.4%
광주광역시 5
 
2.4%
경상남도 5
 
2.4%
Other values (34) 161
76.3%

건축물 밀집도(동_제곱킬로미터)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct211
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean288.89062
Minimum23.96
Maximum1089.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-13T04:27:46.005280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23.96
5-th percentile51.68
Q197.45
median206.71
Q3399.58
95-th percentile851.895
Maximum1089.63
Range1065.67
Interquartile range (IQR)302.13

Descriptive statistics

Standard deviation258.30163
Coefficient of variation (CV)0.89411569
Kurtosis1.0904409
Mean288.89062
Median Absolute Deviation (MAD)134.32
Skewness1.3250361
Sum60955.92
Variance66719.733
MonotonicityNot monotonic
2023-12-13T04:27:46.181131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
213.89 1
 
0.5%
63.68 1
 
0.5%
51.62 1
 
0.5%
42.3 1
 
0.5%
67.37 1
 
0.5%
92.04 1
 
0.5%
71.65 1
 
0.5%
999.15 1
 
0.5%
476.57 1
 
0.5%
286.32 1
 
0.5%
Other values (201) 201
95.3%
ValueCountFrequency (%)
23.96 1
0.5%
24.37 1
0.5%
24.74 1
0.5%
25.03 1
0.5%
25.46 1
0.5%
42.3 1
0.5%
42.73 1
0.5%
43.15 1
0.5%
43.46 1
0.5%
43.75 1
0.5%
ValueCountFrequency (%)
1089.63 1
0.5%
1087.83 1
0.5%
1086.28 1
0.5%
1010.12 1
0.5%
999.15 1
0.5%
990.69 1
0.5%
980.12 1
0.5%
967.6 1
0.5%
966.15 1
0.5%
929.96 1
0.5%

건축물 수(동)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct211
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean373607.02
Minimum12834
Maximum7314264
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-13T04:27:46.369437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12834
5-th percentile23616.5
Q138517
median75967
Q3359111.5
95-th percentile829967
Maximum7314264
Range7301430
Interquartile range (IQR)320594.5

Descriptive statistics

Standard deviation1102198
Coefficient of variation (CV)2.9501535
Kurtosis33.682519
Mean373607.02
Median Absolute Deviation (MAD)50914
Skewness5.7851954
Sum78831081
Variance1.2148405 × 1012
MonotonicityNot monotonic
2023-12-13T04:27:46.556700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57334 1
 
0.5%
523896 1
 
0.5%
636734 1
 
0.5%
805114 1
 
0.5%
710098 1
 
0.5%
170291 1
 
0.5%
7191912 1
 
0.5%
604726 1
 
0.5%
366929 1
 
0.5%
252967 1
 
0.5%
Other values (201) 201
95.3%
ValueCountFrequency (%)
12834 1
0.5%
12853 1
0.5%
12854 1
0.5%
13151 1
0.5%
13157 1
0.5%
21931 1
0.5%
22416 1
0.5%
22988 1
0.5%
23429 1
0.5%
23469 1
0.5%
ValueCountFrequency (%)
7314264 1
0.5%
7275266 1
0.5%
7243472 1
0.5%
7191912 1
0.5%
7126526 1
0.5%
1230057 1
0.5%
1209764 1
0.5%
1193190 1
0.5%
1174833 1
0.5%
1148790 1
0.5%

행정구역면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct210
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9341072 × 109
Minimum29568849
Maximum1.00432 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-13T04:27:46.761957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29568849
5-th percentile34657297
Q11.2107204 × 108
median5.3966349 × 108
Q31.1300636 × 109
95-th percentile1.6829675 × 1010
Maximum1.00432 × 1011
Range1.0040243 × 1011
Interquartile range (IQR)1.0089915 × 109

Descriptive statistics

Standard deviation1.5634183 × 1010
Coefficient of variation (CV)3.1685942
Kurtosis31.046014
Mean4.9341072 × 109
Median Absolute Deviation (MAD)4.8116595 × 108
Skewness5.4864285
Sum1.0410966 × 1012
Variance2.4442768 × 1020
MonotonicityNot monotonic
2023-12-13T04:27:46.948394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33872729.2 2
 
0.9%
268059218.7 1
 
0.5%
1061543886.0 1
 
0.5%
12335125170.0 1
 
0.5%
19032539825.0 1
 
0.5%
10539769841.0 1
 
0.5%
1850159946.0 1
 
0.5%
100378000000.0 1
 
0.5%
605238966.0 1
 
0.5%
769939357.6 1
 
0.5%
Other values (200) 200
94.8%
ValueCountFrequency (%)
29568848.6 1
0.5%
29568889.5 1
0.5%
29568928.8 1
0.5%
32007510.1 1
0.5%
32008762.1 1
0.5%
32008788.0 1
0.5%
33872509.8 1
0.5%
33872729.2 2
0.9%
33874855.0 1
0.5%
33877056.7 1
0.5%
ValueCountFrequency (%)
100432000000.0 1
0.5%
100413000000.0 1
0.5%
100401000000.0 1
0.5%
100378000000.0 1
0.5%
100364000000.0 1
0.5%
19034795138.0 1
0.5%
19034029462.0 1
0.5%
19033343124.0 1
0.5%
19032867577.0 1
0.5%
19032539825.0 1
0.5%

Interactions

2023-12-13T04:27:44.782014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:27:43.954113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:27:44.409151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:27:44.908669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:27:44.093718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:27:44.532662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:27:45.030574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:27:44.258625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:27:44.653961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:27:47.059125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명시군구명건축물 밀집도(동_제곱킬로미터)건축물 수(동)행정구역면적(제곱미터)
통계연도1.0000.0000.0000.0000.0000.000
시도명0.0001.0001.0000.7830.9550.985
시군구명0.0001.0001.0000.9971.0001.000
건축물 밀집도(동_제곱킬로미터)0.0000.7830.9971.0000.1740.416
건축물 수(동)0.0000.9551.0000.1741.0000.980
행정구역면적(제곱미터)0.0000.9851.0000.4160.9801.000
2023-12-13T04:27:47.213148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시군구명시도명
통계연도1.0000.0000.000
시군구명0.0001.0000.930
시도명0.0000.9301.000
2023-12-13T04:27:47.326973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건축물 밀집도(동_제곱킬로미터)건축물 수(동)행정구역면적(제곱미터)통계연도시도명시군구명
건축물 밀집도(동_제곱킬로미터)1.000-0.632-0.8360.0000.4330.879
건축물 수(동)-0.6321.0000.9300.0000.7440.896
행정구역면적(제곱미터)-0.8360.9301.0000.0000.8300.896
통계연도0.0000.0000.0001.0000.0000.000
시도명0.4330.7440.8300.0001.0000.930
시군구명0.8790.8960.8960.0000.9301.000

Missing values

2023-12-13T04:27:45.208263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:27:45.349223image/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

통계연도시도명시군구명건축물 밀집도(동_제곱킬로미터)건축물 수(동)행정구역면적(제곱미터)
02017경기도고양시213.8957334268059218.7
12017경기도부천시803.724295353442446.0
22017경기도성남시389.1655128141659163.9
32017경기도수원시521.1463086121054375.4
42017경기도안산시258.4640112155198095.0
52017경기도안양시424.862485358497532.1
62017경기도용인시109.1764557591350941.5
72017경기도화성시133.7992845693952248.9
82017경기도남양주시112.4651515458070891.2
92018경기도고양시218.1858489268076620.0
통계연도시도명시군구명건축물 밀집도(동_제곱킬로미터)건축물 수(동)행정구역면적(제곱미터)
2012021경기도경기도120.63123005710196731503.0
2022021세종특별자치시세종특별자치시75.6235159464918346.3
2032021강원도강원도25.4642840516829671465.0
2042021충청북도충청북도54.064003867406988695.0
2052021충청남도충청남도67.155538118246960405.0
2062021전라북도전라북도57.124611098072147335.0
2072021전라남도전라남도53.1965739112358940175.0
2082021경상북도경상북도43.7583270719034795138.0
2092021경상남도경상남도68.6972412410541895014.0
2102021제주특별자치도제주특별자치도99.421839511850278724.0