Overview

Dataset statistics

Number of variables6
Number of observations1300
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory66.1 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, 세대당 자동차등록(대), 주민등록세대수(명), 자동차등록대수(대)로 구성되어 있습니다. 김해시 중심의 통계지수로서, 데이터 수집, 가공 등의 어려움으로 김해시 외 지역의 정보는 누락될 수 있습니다.
Author경상남도 김해시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15110164

Alerts

주민등록세대수(명) is highly overall correlated with 자동차등록대수(대)High correlation
자동차등록대수(대) is highly overall correlated with 주민등록세대수(명)High correlation

Reproduction

Analysis started2023-12-10 23:22:30.610134
Analysis finished2023-12-10 23:22:32.067669
Duration1.46 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계연도
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
2017
260 
2018
260 
2019
260 
2020
260 
2021
260 

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 260
20.0%
2018 260
20.0%
2019 260
20.0%
2020 260
20.0%
2021 260
20.0%

Length

2023-12-11T08:22:32.127563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:22:32.239829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 260
20.0%
2018 260
20.0%
2019 260
20.0%
2020 260
20.0%
2021 260
20.0%

시도명
Categorical

Distinct16
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
경기도
240 
경상북도
125 
서울특별시
125 
경상남도
115 
전라남도
110 
Other values (11)
585 

Length

Max length7
Median length5
Mean length4.0538462
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 240
18.5%
경상북도 125
9.6%
서울특별시 125
9.6%
경상남도 115
8.8%
전라남도 110
8.5%
강원도 90
 
6.9%
충청남도 85
 
6.5%
전라북도 80
 
6.2%
부산광역시 80
 
6.2%
충청북도 75
 
5.8%
Other values (6) 175
13.5%

Length

2023-12-11T08:22:32.386725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 240
18.5%
경상북도 125
9.6%
서울특별시 125
9.6%
경상남도 115
8.8%
전라남도 110
8.5%
강원도 90
 
6.9%
충청남도 85
 
6.5%
전라북도 80
 
6.2%
부산광역시 80
 
6.2%
충청북도 75
 
5.8%
Other values (6) 175
13.5%

시군구명
Text

Distinct236
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
2023-12-11T08:22:32.713315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.9561538
Min length2

Characters and Unicode

Total characters3843
Distinct characters141
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 row강릉시
2nd row고성군
3rd row동해시
4th row삼척시
5th row속초시
ValueCountFrequency (%)
동구 30
 
2.3%
중구 30
 
2.3%
남구 26
 
2.0%
북구 25
 
1.9%
서구 25
 
1.9%
강서구 10
 
0.8%
고성군 10
 
0.8%
당진시 5
 
0.4%
서산시 5
 
0.4%
서북구 5
 
0.4%
Other values (226) 1129
86.8%
2023-12-11T08:22:33.186295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 530
 
13.8%
군 425
 
11.1%
시 390
 
10.1%
산 110
 
2.9%
천 110
 
2.9%
주 100
 
2.6%
동 100
 
2.6%
성 95
 
2.5%
양 95
 
2.5%
서 80
 
2.1%
Other values (131) 1808
47.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3843
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 530
 
13.8%
군 425
 
11.1%
시 390
 
10.1%
산 110
 
2.9%
천 110
 
2.9%
주 100
 
2.6%
동 100
 
2.6%
성 95
 
2.5%
양 95
 
2.5%
서 80
 
2.1%
Other values (131) 1808
47.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3843
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 530
 
13.8%
군 425
 
11.1%
시 390
 
10.1%
산 110
 
2.9%
천 110
 
2.9%
주 100
 
2.6%
동 100
 
2.6%
성 95
 
2.5%
양 95
 
2.5%
서 80
 
2.1%
Other values (131) 1808
47.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3843
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 530
 
13.8%
군 425
 
11.1%
시 390
 
10.1%
산 110
 
2.9%
천 110
 
2.9%
주 100
 
2.6%
동 100
 
2.6%
성 95
 
2.5%
양 95
 
2.5%
서 80
 
2.1%
Other values (131) 1808
47.0%

세대당 자동차등록(대)
Real number (ℝ)

Distinct129
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0931923
Minimum0.43
Maximum4.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-12-11T08:22:33.375535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.43
5-th percentile0.69
Q10.98
median1.09
Q31.18
95-th percentile1.3605
Maximum4.83
Range4.4
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.29878668
Coefficient of variation (CV)0.27331576
Kurtosis36.641318
Mean1.0931923
Median Absolute Deviation (MAD)0.1
Skewness4.1621047
Sum1421.15
Variance0.089273481
MonotonicityNot monotonic
2023-12-11T08:22:33.540709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.13 49
 
3.8%
1.05 47
 
3.6%
1.11 42
 
3.2%
1.14 39
 
3.0%
1.1 39
 
3.0%
1.09 37
 
2.8%
1.04 36
 
2.8%
1.02 35
 
2.7%
1.08 35
 
2.7%
1.12 33
 
2.5%
Other values (119) 908
69.8%
ValueCountFrequency (%)
0.43 2
0.2%
0.45 1
 
0.1%
0.46 1
 
0.1%
0.48 1
 
0.1%
0.52 2
0.2%
0.53 1
 
0.1%
0.54 1
 
0.1%
0.55 1
 
0.1%
0.56 2
0.2%
0.57 4
0.3%
ValueCountFrequency (%)
4.83 1
0.1%
3.92 1
0.1%
3.83 1
0.1%
3.17 1
0.1%
3.04 1
0.1%
2.74 1
0.1%
2.58 2
0.2%
2.55 1
0.1%
2.52 1
0.1%
2.5 2
0.2%

주민등록세대수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct1296
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100898.14
Minimum5258
Maximum517822
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-12-11T08:22:33.683160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5258
5-th percentile14540.95
Q129133.5
median81870.5
Q3143060.5
95-th percentile264723.65
Maximum517822
Range512564
Interquartile range (IQR)113927

Descriptive statistics

Standard deviation86782.529
Coefficient of variation (CV)0.86010036
Kurtosis3.295955
Mean100898.14
Median Absolute Deviation (MAD)55048
Skewness1.5817569
Sum1.3116758 × 108
Variance7.5312073 × 109
MonotonicityNot monotonic
2023-12-11T08:22:33.817131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16705 2
 
0.2%
75841 2
 
0.2%
23160 2
 
0.2%
61502 2
 
0.2%
31056 1
 
0.1%
119680 1
 
0.1%
119668 1
 
0.1%
106025 1
 
0.1%
82303 1
 
0.1%
80142 1
 
0.1%
Other values (1286) 1286
98.9%
ValueCountFrequency (%)
5258 1
0.1%
5312 1
0.1%
5426 1
0.1%
5435 1
0.1%
5490 1
0.1%
8827 1
0.1%
8913 1
0.1%
8942 1
0.1%
9055 1
0.1%
9127 1
0.1%
ValueCountFrequency (%)
517822 1
0.1%
506950 1
0.1%
498836 1
0.1%
492939 1
0.1%
483558 1
0.1%
456852 1
0.1%
451940 1
0.1%
448574 1
0.1%
442097 1
0.1%
434028 1
0.1%

자동차등록대수(대)
Real number (ℝ)

HIGH CORRELATION 

Distinct1299
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106717.7
Minimum5462
Maximum619854
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-12-11T08:22:33.943565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5462
5-th percentile15014.15
Q132586.5
median88345.5
Q3142445
95-th percentile297895.2
Maximum619854
Range614392
Interquartile range (IQR)109858.5

Descriptive statistics

Standard deviation95823.371
Coefficient of variation (CV)0.89791447
Kurtosis4.9410212
Mean106717.7
Median Absolute Deviation (MAD)55120
Skewness1.9310533
Sum1.3873302 × 108
Variance9.1821184 × 109
MonotonicityNot monotonic
2023-12-11T08:22:34.074740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86068 2
 
0.2%
104693 1
 
0.1%
23965 1
 
0.1%
21202 1
 
0.1%
145520 1
 
0.1%
141274 1
 
0.1%
143136 1
 
0.1%
128824 1
 
0.1%
99556 1
 
0.1%
98764 1
 
0.1%
Other values (1289) 1289
99.2%
ValueCountFrequency (%)
5462 1
0.1%
5632 1
0.1%
5753 1
0.1%
5809 1
0.1%
5984 1
0.1%
9157 1
0.1%
9460 1
0.1%
9641 1
0.1%
9904 1
0.1%
10139 1
0.1%
ValueCountFrequency (%)
619854 1
0.1%
599336 1
0.1%
563279 1
0.1%
560170 1
0.1%
559516 1
0.1%
550432 1
0.1%
538105 1
0.1%
529578 1
0.1%
509181 1
0.1%
507939 1
0.1%

Interactions

2023-12-11T08:22:31.518409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:22:30.971900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:22:31.225149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:22:31.627573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:22:31.050745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:22:31.311333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:22:31.731286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:22:31.137775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:22:31.411165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:22:34.172193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명세대당 자동차등록(대)주민등록세대수(명)자동차등록대수(대)
통계연도1.0000.0000.0000.0000.000
시도명0.0001.0000.6250.5770.570
세대당 자동차등록(대)0.0000.6251.0000.2740.356
주민등록세대수(명)0.0000.5770.2741.0000.950
자동차등록대수(대)0.0000.5700.3560.9501.000
2023-12-11T08:22:34.263901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명
통계연도1.0000.000
시도명0.0001.000
2023-12-11T08:22:34.333086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대당 자동차등록(대)주민등록세대수(명)자동차등록대수(대)통계연도시도명
세대당 자동차등록(대)1.000-0.1070.1220.0000.321
주민등록세대수(명)-0.1071.0000.9620.0000.268
자동차등록대수(대)0.1220.9621.0000.0000.264
통계연도0.0000.0000.0001.0000.000
시도명0.3210.2680.2640.0001.000

Missing values

2023-12-11T08:22:31.888535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:22:32.002554image/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

통계연도시도명시군구명세대당 자동차등록(대)주민등록세대수(명)자동차등록대수(대)
02017강원도강릉시1.0995963104693
12017강원도고성군0.911551914076
22017강원도동해시1.074073043647
32017강원도삼척시0.913386330889
42017강원도속초시0.973777236601
52017강원도양구군1.021134011621
62017강원도양양군1.061352614302
72017강원도영월군0.992071520466
82017강원도원주시1.1145521160402
92017강원도인제군1.121552317316
통계연도시도명시군구명세대당 자동차등록(대)주민등록세대수(명)자동차등록대수(대)
12902021인천광역시중구1.147106180677
12912021인천광역시강화군1.523466752791
12922021인천광역시계양구2.37127984302708
12932021인천광역시남동구1.31228442298754
12942021인천광역시부평구1.18213372252365
12952021인천광역시연수구1.34155984208616
12962021인천광역시옹진군1.051206112666
12972021인천광역시미추홀구0.88193561169809
12982021제주특별자치도제주시2.5219978550432
12992021제주특별자치도서귀포시1.2487551108162