Overview

Dataset statistics

Number of variables6
Number of observations1510
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory76.8 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, 가구수 대비 주택수(채), 주택수(채), 일반가구수(가구)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/15110145/fileData.do

Alerts

가구수 대비 주택수(채) is highly overall correlated with 일반가구수(가구)High correlation
주택수(채) is highly overall correlated with 일반가구수(가구)High correlation
일반가구수(가구) is highly overall correlated with 가구수 대비 주택수(채) and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 05:10:37.122681
Analysis finished2023-12-12 05:10:38.750624
Duration1.63 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계연도
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
2017
302 
2018
302 
2019
302 
2020
302 
2021
302 

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 302
20.0%
2018 302
20.0%
2019 302
20.0%
2020 302
20.0%
2021 302
20.0%

Length

2023-12-12T14:10:38.843680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:10:39.011771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 302
20.0%
2018 302
20.0%
2019 302
20.0%
2020 302
20.0%
2021 302
20.0%

시도명
Categorical

Distinct17
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
경기도
255 
경상북도
140 
경상남도
130 
전라남도
125 
서울특별시
125 
Other values (12)
735 

Length

Max length7
Median length5
Mean length4.1258278
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 255
16.9%
경상북도 140
9.3%
경상남도 130
8.6%
전라남도 125
8.3%
서울특별시 125
8.3%
강원도 105
7.0%
충청남도 100
 
6.6%
전라북도 95
 
6.3%
부산광역시 95
 
6.3%
충청북도 90
 
6.0%
Other values (7) 250
16.6%

Length

2023-12-12T14:10:39.184695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 255
16.9%
경상북도 140
9.3%
경상남도 130
8.6%
전라남도 125
8.3%
서울특별시 125
8.3%
강원도 105
7.0%
충청남도 100
 
6.6%
부산광역시 95
 
6.3%
전라북도 95
 
6.3%
충청북도 90
 
6.0%
Other values (7) 250
16.6%

시군구명
Text

Distinct239
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
2023-12-12T14:10:39.715165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.8231788
Min length2

Characters and Unicode

Total characters4263
Distinct characters142
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 (%)
면부 70
 
4.6%
읍부 70
 
4.6%
동부 70
 
4.6%
동구 30
 
2.0%
중구 30
 
2.0%
남구 26
 
1.7%
북구 25
 
1.7%
서구 25
 
1.7%
강서구 10
 
0.7%
고성군 10
 
0.7%
Other values (229) 1144
75.8%
2023-12-12T14:10:40.422313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 530
 
12.4%
군 425
 
10.0%
시 390
 
9.1%
부 240
 
5.6%
동 170
 
4.0%
산 110
 
2.6%
천 110
 
2.6%
주 100
 
2.3%
양 95
 
2.2%
성 95
 
2.2%
Other values (132) 1998
46.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4263
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 530
 
12.4%
군 425
 
10.0%
시 390
 
9.1%
부 240
 
5.6%
동 170
 
4.0%
산 110
 
2.6%
천 110
 
2.6%
주 100
 
2.3%
양 95
 
2.2%
성 95
 
2.2%
Other values (132) 1998
46.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4263
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 530
 
12.4%
군 425
 
10.0%
시 390
 
9.1%
부 240
 
5.6%
동 170
 
4.0%
산 110
 
2.6%
천 110
 
2.6%
주 100
 
2.3%
양 95
 
2.2%
성 95
 
2.2%
Other values (132) 1998
46.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4263
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 530
 
12.4%
군 425
 
10.0%
시 390
 
9.1%
부 240
 
5.6%
동 170
 
4.0%
산 110
 
2.6%
천 110
 
2.6%
주 100
 
2.3%
양 95
 
2.2%
성 95
 
2.2%
Other values (132) 1998
46.9%

가구수 대비 주택수(채)
Real number (ℝ)

HIGH CORRELATION 

Distinct85
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0840464
Minimum0.83
Maximum1.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-12-12T14:10:40.624842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.83
5-th percentile0.9
Q10.97
median1.06
Q31.16
95-th percentile1.36
Maximum1.98
Range1.15
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.1558082
Coefficient of variation (CV)0.14372835
Kurtosis3.7388462
Mean1.0840464
Median Absolute Deviation (MAD)0.09
Skewness1.4659794
Sum1636.91
Variance0.024276194
MonotonicityNot monotonic
2023-12-12T14:10:40.865551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.97 64
 
4.2%
0.92 60
 
4.0%
0.98 52
 
3.4%
0.96 50
 
3.3%
0.95 49
 
3.2%
0.94 48
 
3.2%
0.93 46
 
3.0%
1.13 46
 
3.0%
1.0 41
 
2.7%
1.12 41
 
2.7%
Other values (75) 1013
67.1%
ValueCountFrequency (%)
0.83 2
 
0.1%
0.84 4
 
0.3%
0.85 5
 
0.3%
0.86 11
 
0.7%
0.87 11
 
0.7%
0.88 10
 
0.7%
0.89 15
 
1.0%
0.9 21
 
1.4%
0.91 30
2.0%
0.92 60
4.0%
ValueCountFrequency (%)
1.98 1
 
0.1%
1.95 1
 
0.1%
1.91 1
 
0.1%
1.89 1
 
0.1%
1.87 1
 
0.1%
1.75 1
 
0.1%
1.74 1
 
0.1%
1.73 1
 
0.1%
1.69 1
 
0.1%
1.68 4
0.3%

주택수(채)
Real number (ℝ)

HIGH CORRELATION 

Distinct1497
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115796.9
Minimum2996
Maximum3771104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-12-12T14:10:41.070042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2996
5-th percentile12515.05
Q127531.5
median71545.5
Q3127973.5
95-th percentile322035.7
Maximum3771104
Range3768108
Interquartile range (IQR)100442

Descriptive statistics

Standard deviation235971.6
Coefficient of variation (CV)2.0378058
Kurtosis147.68373
Mean115796.9
Median Absolute Deviation (MAD)47026.5
Skewness10.776684
Sum1.7485332 × 108
Variance5.5682596 × 1010
MonotonicityNot monotonic
2023-12-12T14:10:41.287313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42343 2
 
0.1%
76688 2
 
0.1%
44017 2
 
0.1%
10940 2
 
0.1%
17241 2
 
0.1%
29323 2
 
0.1%
49245 2
 
0.1%
37736 2
 
0.1%
13460 2
 
0.1%
103740 2
 
0.1%
Other values (1487) 1490
98.7%
ValueCountFrequency (%)
2996 1
0.1%
3025 1
0.1%
3044 1
0.1%
3050 1
0.1%
3065 1
0.1%
6646 1
0.1%
6669 1
0.1%
6683 1
0.1%
7367 1
0.1%
7953 1
0.1%
ValueCountFrequency (%)
3771104 1
0.1%
3677426 1
0.1%
3564396 1
0.1%
3407404 1
0.1%
3218565 1
0.1%
1217721 1
0.1%
1210318 1
0.1%
1189929 1
0.1%
1162077 1
0.1%
1142880 1
0.1%

일반가구수(가구)
Real number (ℝ)

HIGH CORRELATION 

Distinct1504
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129868.84
Minimum4061
Maximum4389800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-12-12T14:10:41.470145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4061
5-th percentile11856.05
Q126601
median81726
Q3152717.25
95-th percentile362014.7
Maximum4389800
Range4385739
Interquartile range (IQR)126116.25

Descriptive statistics

Standard deviation271618.97
Coefficient of variation (CV)2.0914868
Kurtosis152.1009
Mean129868.84
Median Absolute Deviation (MAD)58951
Skewness10.977772
Sum1.9610195 × 108
Variance7.3776865 × 1010
MonotonicityNot monotonic
2023-12-12T14:10:41.668080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11518 2
 
0.1%
14044 2
 
0.1%
128012 2
 
0.1%
11723 2
 
0.1%
19431 2
 
0.1%
111810 2
 
0.1%
104775 1
 
0.1%
111383 1
 
0.1%
315696 1
 
0.1%
110955 1
 
0.1%
Other values (1494) 1494
98.9%
ValueCountFrequency (%)
4061 1
0.1%
4072 1
0.1%
4116 1
0.1%
4135 1
0.1%
4145 1
0.1%
6915 1
0.1%
6959 1
0.1%
7072 1
0.1%
7539 1
0.1%
7565 1
0.1%
ValueCountFrequency (%)
4389800 1
0.1%
4235667 1
0.1%
4082640 1
0.1%
3939290 1
0.1%
3806189 1
0.1%
1361719 1
0.1%
1338453 1
0.1%
1314852 1
0.1%
1302500 1
0.1%
1295613 1
0.1%

Interactions

2023-12-12T14:10:38.240057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:37.512851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:37.897808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:38.353950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:37.641748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:38.014741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:38.460924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:37.773328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:38.137598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:10:41.795854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명가구수 대비 주택수(채)주택수(채)일반가구수(가구)
통계연도1.0000.0000.0000.0000.000
시도명0.0001.0000.6300.2900.223
가구수 대비 주택수(채)0.0000.6301.0000.1060.081
주택수(채)0.0000.2900.1061.0000.979
일반가구수(가구)0.0000.2230.0810.9791.000
2023-12-12T14:10:41.942447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명
통계연도1.0000.000
시도명0.0001.000
2023-12-12T14:10:42.061916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가구수 대비 주택수(채)주택수(채)일반가구수(가구)통계연도시도명
가구수 대비 주택수(채)1.0000.4710.5530.0000.318
주택수(채)0.4711.0000.9920.0000.140
일반가구수(가구)0.5530.9921.0000.0000.106
통계연도0.0000.0000.0001.0000.000
시도명0.3180.1400.1060.0001.000

Missing values

2023-12-12T14:10:38.588715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:10:38.706679image/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

통계연도시도명시군구명가구수 대비 주택수(채)주택수(채)일반가구수(가구)
02017서울특별시종로구1.364578762372
12017서울특별시중구1.323951252208
22017서울특별시용산구1.287135691102
32017서울특별시성동구1.486650120957
42017서울특별시광진구1.7584316147846
52017서울특별시동대문구1.4897891145294
62017서울특별시중랑구1.53103740159035
72017서울특별시성북구1.32130766172973
82017서울특별시강북구1.3594526127281
92017서울특별시도봉구1.2104752125974
통계연도시도명시군구명가구수 대비 주택수(채)주택수(채)일반가구수(가구)
15002021경상남도하동군0.952044519457
15012021경상남도산청군0.921720415867
15022021경상남도함양군0.931890217563
15032021경상남도거창군0.982714926508
15042021경상남도합천군0.862352520332
15052021제주특별자치도동부1.14169773194014
15062021제주특별자치도읍부0.976369561757
15072021제주특별자치도면부0.951616115391
15082021제주특별자치도제주시1.12175552197149
15092021제주특별자치도서귀포시1.07407774013