Overview

Dataset statistics

Number of variables7
Number of observations1510
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory90.1 KiB
Average record size in memory61.1 B

Variable types

Categorical2
Text1
Numeric4

Dataset

Description김해시에서 통계기반 도시현황 파악을 위해 개발한 통계지수 중 하나로서, 통계연도, 시도명, 시군구명, 주택종류, 일인당 주거면적(제곱미터), 일반가구수(명), 가구원 수(명), 가구당 주거면적(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/15110142/fileData.do

Alerts

일인당 주거면적(제곱미터) is highly overall correlated with 일반가구수(명) and 2 other fieldsHigh correlation
일반가구수(명) is highly overall correlated with 일인당 주거면적(제곱미터) and 1 other fieldsHigh correlation
가구원 수(명) is highly overall correlated with 일인당 주거면적(제곱미터) and 1 other fieldsHigh correlation
가구당 주거면적(제곱미터) is highly overall correlated with 일인당 주거면적(제곱미터)High correlation
가구원 수(명) has unique valuesUnique

Reproduction

Analysis started2023-12-12 21:36:02.987011
Analysis finished2023-12-12 21:36:04.797057
Duration1.81 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

2023-12-13T06:36:04.850972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:36:04.955498image/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 
서울특별시
125 
전라남도
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%
서울특별시 125
8.3%
전라남도 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%

Length

2023-12-13T06:36:05.088681image/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%
서울특별시 125
8.3%
전라남도 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%

시군구명
Text

Distinct239
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
2023-12-13T06:36:05.416783image/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%
중구 30
 
2.0%
동구 30
 
2.0%
남구 26
 
1.7%
서구 25
 
1.7%
북구 25
 
1.7%
강서구 10
 
0.7%
고성군 10
 
0.7%
Other values (229) 1144
75.8%
2023-12-13T06:36:05.879272image/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%
성 95
 
2.2%
양 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%
성 95
 
2.2%
양 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%
성 95
 
2.2%
양 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%
성 95
 
2.2%
양 95
 
2.2%
Other values (132) 1998
46.9%

일인당 주거면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct182
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.982914
Minimum22.4
Maximum44.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-12-13T06:36:06.009393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22.4
5-th percentile25.045
Q128.7
median30.9
Q333.2
95-th percentile37.1
Maximum44.6
Range22.2
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation3.5765675
Coefficient of variation (CV)0.11543677
Kurtosis0.098069604
Mean30.982914
Median Absolute Deviation (MAD)2.3
Skewness0.21933603
Sum46784.2
Variance12.791835
MonotonicityNot monotonic
2023-12-13T06:36:06.127752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.6 27
 
1.8%
32.8 24
 
1.6%
29.1 24
 
1.6%
32.2 23
 
1.5%
31.7 23
 
1.5%
29.9 22
 
1.5%
31.5 22
 
1.5%
30.8 22
 
1.5%
29.7 21
 
1.4%
32.1 20
 
1.3%
Other values (172) 1282
84.9%
ValueCountFrequency (%)
22.4 1
 
0.1%
22.7 2
0.1%
22.9 2
0.1%
23.0 2
0.1%
23.1 1
 
0.1%
23.2 1
 
0.1%
23.3 1
 
0.1%
23.4 4
0.3%
23.5 1
 
0.1%
23.6 3
0.2%
ValueCountFrequency (%)
44.6 1
0.1%
43.5 1
0.1%
42.9 1
0.1%
42.8 1
0.1%
42.0 1
0.1%
41.7 1
0.1%
41.5 1
0.1%
41.0 1
0.1%
40.9 2
0.1%
40.5 2
0.1%

일반가구수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct1506
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127029.56
Minimum3516
Maximum4308586
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-12-13T06:36:06.509622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3516
5-th percentile11278.05
Q125453.5
median80064
Q3149443.75
95-th percentile356248.75
Maximum4308586
Range4305070
Interquartile range (IQR)123990.25

Descriptive statistics

Standard deviation266753.9
Coefficient of variation (CV)2.0999356
Kurtosis151.892
Mean127029.56
Median Absolute Deviation (MAD)58092
Skewness10.971623
Sum1.9181464 × 108
Variance7.1157645 × 1010
MonotonicityNot monotonic
2023-12-13T06:36:06.641696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46868 2
 
0.1%
12111 2
 
0.1%
19794 2
 
0.1%
47071 2
 
0.1%
102364 1
 
0.1%
308620 1
 
0.1%
109665 1
 
0.1%
89881 1
 
0.1%
199546 1
 
0.1%
174674 1
 
0.1%
Other values (1496) 1496
99.1%
ValueCountFrequency (%)
3516 1
0.1%
3559 1
0.1%
3573 1
0.1%
3586 1
0.1%
3587 1
0.1%
6641 1
0.1%
6668 1
0.1%
6808 1
0.1%
7325 1
0.1%
7354 1
0.1%
ValueCountFrequency (%)
4308586 1
0.1%
4159483 1
0.1%
4010816 1
0.1%
3863396 1
0.1%
3733558 1
0.1%
1342988 1
0.1%
1320587 1
0.1%
1298131 1
0.1%
1284457 1
0.1%
1277890 1
0.1%

가구원 수(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1510
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean306812.25
Minimum7018
Maximum10663856
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-12-13T06:36:06.777702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7018
5-th percentile23511.4
Q155381.5
median198065.5
Q3349175.25
95-th percentile872237.4
Maximum10663856
Range10656838
Interquartile range (IQR)293793.75

Descriptive statistics

Standard deviation675822.67
Coefficient of variation (CV)2.2027239
Kurtosis160.62365
Mean306812.25
Median Absolute Deviation (MAD)143847.5
Skewness11.380803
Sum4.632865 × 108
Variance4.5673628 × 1011
MonotonicityNot monotonic
2023-12-13T06:36:06.925958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135892 1
 
0.1%
226637 1
 
0.1%
494721 1
 
0.1%
285634 1
 
0.1%
772707 1
 
0.1%
294037 1
 
0.1%
224659 1
 
0.1%
518696 1
 
0.1%
430057 1
 
0.1%
448712 1
 
0.1%
Other values (1500) 1500
99.3%
ValueCountFrequency (%)
7018 1
0.1%
7177 1
0.1%
7278 1
0.1%
7339 1
0.1%
7432 1
0.1%
14581 1
0.1%
14809 1
0.1%
14862 1
0.1%
14990 1
0.1%
15170 1
0.1%
ValueCountFrequency (%)
10663856 1
0.1%
10532860 1
0.1%
10335656 1
0.1%
10168069 1
0.1%
9972502 1
0.1%
3098732 1
0.1%
3065520 1
0.1%
3041429 1
0.1%
3023752 1
0.1%
3002408 1
0.1%

가구당 주거면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct307
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.460861
Minimum46
Maximum94.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2023-12-13T06:36:07.078318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile59.8
Q168
median71.7
Q374.9
95-th percentile81.3
Maximum94.4
Range48.4
Interquartile range (IQR)6.9

Descriptive statistics

Standard deviation6.3990244
Coefficient of variation (CV)0.089545862
Kurtosis1.3834463
Mean71.460861
Median Absolute Deviation (MAD)3.4
Skewness-0.12366231
Sum107905.9
Variance40.947513
MonotonicityNot monotonic
2023-12-13T06:36:07.236571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73.3 23
 
1.5%
72.8 20
 
1.3%
71.6 19
 
1.3%
74.0 18
 
1.2%
71.7 18
 
1.2%
73.5 18
 
1.2%
74.5 17
 
1.1%
74.4 16
 
1.1%
72.5 15
 
1.0%
71.2 15
 
1.0%
Other values (297) 1331
88.1%
ValueCountFrequency (%)
46.0 1
0.1%
46.8 1
0.1%
47.2 1
0.1%
48.1 1
0.1%
48.9 1
0.1%
54.1 1
0.1%
54.4 1
0.1%
54.9 1
0.1%
55.0 1
0.1%
55.2 1
0.1%
ValueCountFrequency (%)
94.4 1
0.1%
94.3 1
0.1%
93.4 1
0.1%
93.2 2
0.1%
93.1 2
0.1%
92.5 2
0.1%
92.4 1
0.1%
92.2 1
0.1%
92.1 1
0.1%
91.7 1
0.1%

Interactions

2023-12-13T06:36:04.302055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:03.319664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:03.639411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:03.988904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:04.382752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:03.396319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:03.733952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:04.065476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:04.462527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:03.477674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:03.808099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:04.141116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:04.552025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:03.562837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:03.902560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:04.220820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:36:07.342224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명일인당 주거면적(제곱미터)일반가구수(명)가구원 수(명)가구당 주거면적(제곱미터)
통계연도1.0000.0000.3100.0000.0000.068
시도명0.0001.0000.6090.2430.2770.618
일인당 주거면적(제곱미터)0.3100.6091.0000.0990.1220.784
일반가구수(명)0.0000.2430.0991.0000.9970.074
가구원 수(명)0.0000.2770.1220.9971.0000.152
가구당 주거면적(제곱미터)0.0680.6180.7840.0740.1521.000
2023-12-13T06:36:07.454973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명
통계연도1.0000.000
시도명0.0001.000
2023-12-13T06:36:07.560091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일인당 주거면적(제곱미터)일반가구수(명)가구원 수(명)가구당 주거면적(제곱미터)통계연도시도명
일인당 주거면적(제곱미터)1.000-0.513-0.5350.6050.1330.288
일반가구수(명)-0.5131.0000.998-0.0850.0000.116
가구원 수(명)-0.5350.9981.000-0.0690.0000.156
가구당 주거면적(제곱미터)0.605-0.085-0.0691.0000.0280.295
통계연도0.1330.0000.0000.0281.0000.000
시도명0.2880.1160.1560.2950.0001.000

Missing values

2023-12-13T06:36:04.653455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:36:04.756081image/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 주거면적(제곱미터)일반가구수(명)가구원 수(명)가구당 주거면적(제곱미터)
02017서울특별시종로구29.25937613589266.9
12017서울특별시중구27.34949511075861.0
22017서울특별시용산구31.28896420190270.7
32017서울특별시성동구25.311909228471360.4
42017서울특별시광진구24.514443833313456.5
52017서울특별시동대문구24.814164532760057.3
62017경기도가평군36.0228545280383.1
72017서울특별시중랑구23.015658738219756.1
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