Overview

Dataset statistics

Number of variables7
Number of observations1133
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory67.6 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, 인구천명당특허등록(건), 제1설정등록(건), 공동설정등록(건), 총인구수(명)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/15110112/fileData.do

Alerts

인구천명당특허등록(건) is highly overall correlated with 제1설정등록(건) and 2 other fieldsHigh correlation
제1설정등록(건) is highly overall correlated with 인구천명당특허등록(건) and 2 other fieldsHigh correlation
공동설정등록(건) is highly overall correlated with 인구천명당특허등록(건) and 2 other fieldsHigh correlation
총인구수(명) is highly overall correlated with 인구천명당특허등록(건) and 2 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 23:37:20.599101
Analysis finished2023-12-12 23:37:22.431572
Duration1.83 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계연도
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
2017
227 
2019
227 
2020
227 
2021
227 
2018
225 

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 227
20.0%
2019 227
20.0%
2020 227
20.0%
2021 227
20.0%
2018 225
19.9%

Length

2023-12-13T08:37:22.500371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:37:22.623511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 227
20.0%
2019 227
20.0%
2020 227
20.0%
2021 227
20.0%
2018 225
19.9%

시도명
Categorical

Distinct16
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
경기도
155 
서울특별시
125 
경상북도
112 
전라남도
110 
강원도
90 
Other values (11)
541 

Length

Max length7
Median length5
Mean length4.1332745
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 155
13.7%
서울특별시 125
11.0%
경상북도 112
9.9%
전라남도 110
9.7%
강원도 90
7.9%
경상남도 90
7.9%
부산광역시 80
7.1%
충청남도 75
6.6%
전라북도 70
6.2%
충청북도 55
 
4.9%
Other values (6) 171
15.1%

Length

2023-12-13T08:37:22.746483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 155
13.7%
서울특별시 125
11.0%
경상북도 112
9.9%
전라남도 110
9.7%
강원도 90
7.9%
경상남도 90
7.9%
부산광역시 80
7.1%
충청남도 75
6.6%
전라북도 70
6.2%
충청북도 55
 
4.9%
Other values (6) 171
15.1%

시군구명
Text

Distinct205
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
2023-12-13T08:37:23.047812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9285084
Min length2

Characters and Unicode

Total characters3318
Distinct characters130
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 (%)
동구 30
 
2.6%
중구 30
 
2.6%
서구 25
 
2.2%
남구 21
 
1.9%
북구 20
 
1.8%
고성군 10
 
0.9%
강서구 10
 
0.9%
김제시 5
 
0.4%
진안군 5
 
0.4%
완주군 5
 
0.4%
Other values (195) 972
85.8%
2023-12-13T08:37:23.546216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 422
 
12.7%
시 390
 
11.8%
구 366
 
11.0%
천 110
 
3.3%
주 100
 
3.0%
성 90
 
2.7%
양 89
 
2.7%
동 85
 
2.6%
산 80
 
2.4%
서 65
 
2.0%
Other values (120) 1521
45.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3318
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 422
 
12.7%
시 390
 
11.8%
구 366
 
11.0%
천 110
 
3.3%
주 100
 
3.0%
성 90
 
2.7%
양 89
 
2.7%
동 85
 
2.6%
산 80
 
2.4%
서 65
 
2.0%
Other values (120) 1521
45.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3318
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 422
 
12.7%
시 390
 
11.8%
구 366
 
11.0%
천 110
 
3.3%
주 100
 
3.0%
성 90
 
2.7%
양 89
 
2.7%
동 85
 
2.6%
산 80
 
2.4%
서 65
 
2.0%
Other values (120) 1521
45.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3318
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 422
 
12.7%
시 390
 
11.8%
구 366
 
11.0%
천 110
 
3.3%
주 100
 
3.0%
성 90
 
2.7%
양 89
 
2.7%
동 85
 
2.6%
산 80
 
2.4%
서 65
 
2.0%
Other values (120) 1521
45.8%

인구천명당특허등록(건)
Real number (ℝ)

HIGH CORRELATION 

Distinct498
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0235569
Minimum0.18
Maximum43.69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-13T08:37:23.701803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.18
5-th percentile0.576
Q11.07
median1.89
Q33.34
95-th percentile8.318
Maximum43.69
Range43.51
Interquartile range (IQR)2.27

Descriptive statistics

Standard deviation4.2747486
Coefficient of variation (CV)1.4138145
Kurtosis37.614659
Mean3.0235569
Median Absolute Deviation (MAD)0.97
Skewness5.4466238
Sum3425.69
Variance18.273476
MonotonicityNot monotonic
2023-12-13T08:37:23.835733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.77 11
 
1.0%
0.94 10
 
0.9%
0.78 8
 
0.7%
1.36 8
 
0.7%
1.06 8
 
0.7%
2.13 8
 
0.7%
0.86 8
 
0.7%
2.22 8
 
0.7%
0.66 7
 
0.6%
2.66 7
 
0.6%
Other values (488) 1050
92.7%
ValueCountFrequency (%)
0.18 1
0.1%
0.25 1
0.1%
0.26 1
0.1%
0.27 1
0.1%
0.28 1
0.1%
0.3 1
0.1%
0.32 1
0.1%
0.33 2
0.2%
0.34 2
0.2%
0.35 1
0.1%
ValueCountFrequency (%)
43.69 1
0.1%
42.47 1
0.1%
41.13 1
0.1%
37.95 1
0.1%
33.63 1
0.1%
32.27 1
0.1%
31.9 1
0.1%
31.58 1
0.1%
31.44 1
0.1%
31.33 1
0.1%

제1설정등록(건)
Real number (ℝ)

HIGH CORRELATION 

Distinct544
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean425.71315
Minimum1
Maximum8231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-13T08:37:23.963080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q131
median126
Q3470
95-th percentile1534.2
Maximum8231
Range8230
Interquartile range (IQR)439

Descriptive statistics

Standard deviation896.60775
Coefficient of variation (CV)2.1061312
Kurtosis30.281881
Mean425.71315
Median Absolute Deviation (MAD)111
Skewness4.9866686
Sum482333
Variance803905.46
MonotonicityNot monotonic
2023-12-13T08:37:24.366061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 17
 
1.5%
22 14
 
1.2%
13 13
 
1.1%
8 13
 
1.1%
20 12
 
1.1%
12 12
 
1.1%
19 12
 
1.1%
18 12
 
1.1%
21 11
 
1.0%
25 11
 
1.0%
Other values (534) 1006
88.8%
ValueCountFrequency (%)
1 1
 
0.1%
2 5
 
0.4%
3 7
0.6%
4 7
0.6%
5 9
0.8%
6 5
 
0.4%
7 11
1.0%
8 13
1.1%
9 10
0.9%
10 8
0.7%
ValueCountFrequency (%)
8231 1
0.1%
7922 1
0.1%
7829 1
0.1%
7502 1
0.1%
7281 1
0.1%
6916 1
0.1%
5830 1
0.1%
5701 1
0.1%
5383 1
0.1%
5321 1
0.1%

공동설정등록(건)
Real number (ℝ)

HIGH CORRELATION 

Distinct596
Distinct (%)52.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean482.44042
Minimum2
Maximum8349
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-13T08:37:24.498858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile11
Q138
median162
Q3536
95-th percentile1732
Maximum8349
Range8347
Interquartile range (IQR)498

Descriptive statistics

Standard deviation965.66242
Coefficient of variation (CV)2.00162
Kurtosis26.62046
Mean482.44042
Median Absolute Deviation (MAD)140
Skewness4.6876265
Sum546605
Variance932503.91
MonotonicityNot monotonic
2023-12-13T08:37:24.642744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 15
 
1.3%
26 15
 
1.3%
15 14
 
1.2%
36 13
 
1.1%
20 12
 
1.1%
23 12
 
1.1%
14 11
 
1.0%
11 11
 
1.0%
33 10
 
0.9%
28 9
 
0.8%
Other values (586) 1011
89.2%
ValueCountFrequency (%)
2 1
 
0.1%
3 5
0.4%
4 5
0.4%
5 5
0.4%
6 5
0.4%
7 8
0.7%
8 4
 
0.4%
9 8
0.7%
10 7
0.6%
11 11
1.0%
ValueCountFrequency (%)
8349 1
0.1%
8268 1
0.1%
8082 1
0.1%
7737 1
0.1%
7620 1
0.1%
7071 1
0.1%
6288 1
0.1%
5944 1
0.1%
5883 1
0.1%
5823 1
0.1%

총인구수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct1132
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean225582.87
Minimum8867
Maximum1202628
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-13T08:37:24.762809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8867
5-th percentile27191.4
Q152937
median148113
Q3339996
95-th percentile654602.4
Maximum1202628
Range1193761
Interquartile range (IQR)287059

Descriptive statistics

Standard deviation221903.63
Coefficient of variation (CV)0.98369004
Kurtosis3.2392488
Mean225582.87
Median Absolute Deviation (MAD)107589
Skewness1.6740209
Sum2.555854 × 108
Variance4.9241219 × 1010
MonotonicityNot monotonic
2023-12-13T08:37:24.898027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122499 2
 
0.2%
154770 1
 
0.1%
940064 1
 
0.1%
94353 1
 
0.1%
537307 1
 
0.1%
298599 1
 
0.1%
818383 1
 
0.1%
550027 1
 
0.1%
461710 1
 
0.1%
1186078 1
 
0.1%
Other values (1122) 1122
99.0%
ValueCountFrequency (%)
8867 1
0.1%
9077 1
0.1%
9617 1
0.1%
16320 1
0.1%
16692 1
0.1%
16993 1
0.1%
17479 1
0.1%
20342 1
0.1%
20455 1
0.1%
20566 1
0.1%
ValueCountFrequency (%)
1202628 1
0.1%
1201166 1
0.1%
1194465 1
0.1%
1186078 1
0.1%
1183714 1
0.1%
1079353 1
0.1%
1079216 1
0.1%
1077508 1
0.1%
1074176 1
0.1%
1066351 1
0.1%

Interactions

2023-12-13T08:37:21.920128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:37:20.948149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:37:21.266299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:37:21.598914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:37:22.003004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:37:21.019959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:37:21.358016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:37:21.676612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:37:22.081256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:37:21.096042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:37:21.428295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:37:21.749932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:37:22.160009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:37:21.179394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:37:21.502313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:37:21.824723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:37:25.001359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명인구천명당특허등록(건)제1설정등록(건)공동설정등록(건)총인구수(명)
통계연도1.0000.0000.0000.0000.0000.000
시도명0.0001.0000.3810.3740.4110.608
인구천명당특허등록(건)0.0000.3811.0000.9140.9230.437
제1설정등록(건)0.0000.3740.9141.0000.9790.773
공동설정등록(건)0.0000.4110.9230.9791.0000.786
총인구수(명)0.0000.6080.4370.7730.7861.000
2023-12-13T08:37:25.099819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명
통계연도1.0000.000
시도명0.0001.000
2023-12-13T08:37:25.178017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인구천명당특허등록(건)제1설정등록(건)공동설정등록(건)총인구수(명)통계연도시도명
인구천명당특허등록(건)1.0000.8250.8180.5020.0000.159
제1설정등록(건)0.8251.0000.9980.8920.0000.156
공동설정등록(건)0.8180.9981.0000.8980.0000.174
총인구수(명)0.5020.8920.8981.0000.0000.289
통계연도0.0000.0000.0000.0001.0000.000
시도명0.1590.1560.1740.2890.0001.000

Missing values

2023-12-13T08:37:22.269459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:37:22.383332image/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

통계연도시도명시군구명인구천명당특허등록(건)제1설정등록(건)공동설정등록(건)총인구수(명)
02017서울특별시종로구11.3822927154770
12017서울특별시중구29.3118081876125709
22017서울특별시용산구6.81741820229161
32017서울특별시성동구5.63800915304808
42017서울특별시광진구2.69437524357703
52017서울특별시동대문구1.94311368350647
62017서울특별시중랑구0.82151184408226
72017서울특별시성북구6.0512481438444055
82017서울특별시강북구0.83119151324479
92017서울특별시도봉구0.6193116344166
통계연도시도명시군구명인구천명당특허등록(건)제1설정등록(건)공동설정등록(건)총인구수(명)
11232021경상남도창녕군1.6425460129
11242021경상남도고성군1.17263350478
11252021경상남도남해군0.66141442266
11262021경상남도하동군1.93354943449
11272021경상남도산청군1.66273034360
11282021경상남도함양군0.55101138310
11292021경상남도거창군0.49141661073
11302021경상남도합천군0.77161742935
11312021제주특별자치도제주시1.8402486493096
11322021제주특별자치도서귀포시0.866890183663