Overview

Dataset statistics

Number of variables6
Number of observations684
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.9 KiB
Average record size in memory52.2 B

Variable types

Categorical2
Text1
Numeric3

Dataset

Description김해시에서 통계기반 도시현황 파악을 위해 개발한 통계지수 중 하나로서, 통계연도, 시도명, 시군구명, 인구 천명당 종사자수(명), 종사자수(명), 주민등록인구(명)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=15110109

Alerts

종사자수(명) is highly overall correlated with 주민등록인구(명)High correlation
주민등록인구(명) is highly overall correlated with 종사자수(명)High correlation
주민등록인구(명) has unique valuesUnique

Reproduction

Analysis started2023-12-10 23:30:10.530006
Analysis finished2023-12-10 23:30:11.777282
Duration1.25 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계연도
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
2017
228 
2018
228 
2019
228 

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 228
33.3%
2018 228
33.3%
2019 228
33.3%

Length

2023-12-11T08:30:11.831642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:30:11.925628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 228
33.3%
2018 228
33.3%
2019 228
33.3%

시도명
Categorical

Distinct16
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
경기도
93 
서울특별시
75 
경상북도
69 
전라남도
66 
강원도
54 
Other values (11)
327 

Length

Max length7
Median length5
Mean length4.1359649
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 93
13.6%
서울특별시 75
11.0%
경상북도 69
10.1%
전라남도 66
9.6%
강원도 54
7.9%
경상남도 54
7.9%
부산광역시 48
7.0%
충청남도 45
6.6%
전라북도 42
 
6.1%
충청북도 33
 
4.8%
Other values (6) 105
15.4%

Length

2023-12-11T08:30:12.039330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 93
13.6%
서울특별시 75
11.0%
경상북도 69
10.1%
전라남도 66
9.6%
강원도 54
7.9%
경상남도 54
7.9%
부산광역시 48
7.0%
충청남도 45
6.6%
전라북도 42
 
6.1%
충청북도 33
 
4.8%
Other values (6) 105
15.4%

시군구명
Text

Distinct206
Distinct (%)30.1%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
2023-12-11T08:30:12.303618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9312865
Min length2

Characters and Unicode

Total characters2005
Distinct characters132
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 18
 
2.6%
중구 18
 
2.6%
서구 15
 
2.2%
남구 13
 
1.9%
북구 12
 
1.8%
고성군 6
 
0.9%
강서구 6
 
0.9%
완주군 3
 
0.4%
무주군 3
 
0.4%
진안군 3
 
0.4%
Other values (196) 587
85.8%
2023-12-11T08:30:12.706781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 255
 
12.7%
시 234
 
11.7%
구 222
 
11.1%
천 66
 
3.3%
주 60
 
3.0%
성 54
 
2.7%
양 54
 
2.7%
동 51
 
2.5%
산 48
 
2.4%
서 39
 
1.9%
Other values (122) 922
46.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2005
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 255
 
12.7%
시 234
 
11.7%
구 222
 
11.1%
천 66
 
3.3%
주 60
 
3.0%
성 54
 
2.7%
양 54
 
2.7%
동 51
 
2.5%
산 48
 
2.4%
서 39
 
1.9%
Other values (122) 922
46.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2005
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 255
 
12.7%
시 234
 
11.7%
구 222
 
11.1%
천 66
 
3.3%
주 60
 
3.0%
성 54
 
2.7%
양 54
 
2.7%
동 51
 
2.5%
산 48
 
2.4%
서 39
 
1.9%
Other values (122) 922
46.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2005
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 255
 
12.7%
시 234
 
11.7%
구 222
 
11.1%
천 66
 
3.3%
주 60
 
3.0%
성 54
 
2.7%
양 54
 
2.7%
동 51
 
2.5%
산 48
 
2.4%
서 39
 
1.9%
Other values (122) 922
46.0%

인구 천명당 종사자수(명)
Real number (ℝ)

Distinct627
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean438.17997
Minimum182.5
Maximum3111.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-12-11T08:30:13.079499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum182.5
5-th percentile254.69
Q1330.775
median387.1
Q3455.225
95-th percentile756.425
Maximum3111.4
Range2928.9
Interquartile range (IQR)124.45

Descriptive statistics

Standard deviation260.05478
Coefficient of variation (CV)0.59348851
Kurtosis51.176149
Mean438.17997
Median Absolute Deviation (MAD)59.25
Skewness6.0657657
Sum299715.1
Variance67628.487
MonotonicityNot monotonic
2023-12-11T08:30:13.212915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
415.3 3
 
0.4%
388.6 3
 
0.4%
294.9 3
 
0.4%
391.5 2
 
0.3%
442.2 2
 
0.3%
381.0 2
 
0.3%
417.2 2
 
0.3%
430.9 2
 
0.3%
359.7 2
 
0.3%
380.0 2
 
0.3%
Other values (617) 661
96.6%
ValueCountFrequency (%)
182.5 1
0.1%
186.4 1
0.1%
195.1 1
0.1%
196.1 1
0.1%
203.2 1
0.1%
204.9 1
0.1%
209.0 1
0.1%
212.6 1
0.1%
218.3 1
0.1%
218.8 1
0.1%
ValueCountFrequency (%)
3111.4 1
0.1%
3106.2 1
0.1%
3075.7 1
0.1%
1736.1 1
0.1%
1731.4 1
0.1%
1721.5 1
0.1%
1614.4 1
0.1%
1593.3 1
0.1%
1575.4 1
0.1%
1281.9 1
0.1%

종사자수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct682
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96848.143
Minimum4352
Maximum698840
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-12-11T08:30:13.333554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4352
5-th percentile9559.9
Q120306.75
median66571
Q3125200
95-th percentile321373.65
Maximum698840
Range694488
Interquartile range (IQR)104893.25

Descriptive statistics

Standard deviation104392.79
Coefficient of variation (CV)1.0779019
Kurtosis6.0866056
Mean96848.143
Median Absolute Deviation (MAD)48069
Skewness2.1511204
Sum66244130
Variance1.0897856 × 1010
MonotonicityNot monotonic
2023-12-11T08:30:13.477859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
108991 2
 
0.3%
20579 2
 
0.3%
268702 1
 
0.1%
178243 1
 
0.1%
20391 1
 
0.1%
12868 1
 
0.1%
206438 1
 
0.1%
70504 1
 
0.1%
260446 1
 
0.1%
392568 1
 
0.1%
Other values (672) 672
98.2%
ValueCountFrequency (%)
4352 1
0.1%
4377 1
0.1%
4473 1
0.1%
4491 1
0.1%
4573 1
0.1%
4879 1
0.1%
6332 1
0.1%
6541 1
0.1%
6717 1
0.1%
6765 1
0.1%
ValueCountFrequency (%)
698840 1
0.1%
694136 1
0.1%
679047 1
0.1%
492031 1
0.1%
467627 1
0.1%
462083 1
0.1%
460383 1
0.1%
452114 1
0.1%
450741 1
0.1%
449870 1
0.1%

주민등록인구(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct684
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean225905.94
Minimum9617
Maximum1202628
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-12-11T08:30:13.616431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9617
5-th percentile27273.05
Q153289.75
median150588.5
Q3344094
95-th percentile651065.05
Maximum1202628
Range1193011
Interquartile range (IQR)290804.25

Descriptive statistics

Standard deviation220994.29
Coefficient of variation (CV)0.97825799
Kurtosis3.220078
Mean225905.94
Median Absolute Deviation (MAD)108500.5
Skewness1.6595777
Sum1.5451966 × 108
Variance4.8838477 × 1010
MonotonicityNot monotonic
2023-12-11T08:30:13.750070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
154770 1
 
0.1%
351350 1
 
0.1%
62455 1
 
0.1%
45916 1
 
0.1%
485946 1
 
0.1%
181245 1
 
0.1%
151290 1
 
0.1%
126171 1
 
0.1%
228670 1
 
0.1%
300889 1
 
0.1%
Other values (674) 674
98.5%
ValueCountFrequency (%)
9617 1
0.1%
9832 1
0.1%
9975 1
0.1%
16993 1
0.1%
17356 1
0.1%
17479 1
0.1%
20566 1
0.1%
21036 1
0.1%
21573 1
0.1%
22441 1
0.1%
ValueCountFrequency (%)
1202628 1
0.1%
1201166 1
0.1%
1194465 1
0.1%
1066351 1
0.1%
1059609 1
0.1%
1057032 1
0.1%
1053601 1
0.1%
1044740 1
0.1%
1044189 1
0.1%
1041983 1
0.1%

Interactions

2023-12-11T08:30:11.337474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:30:10.789839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:30:11.070792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:30:11.432531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:30:10.861701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:30:11.169280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:30:11.519285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:30:10.989750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:30:11.256187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:30:13.835214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명인구 천명당 종사자수(명)종사자수(명)주민등록인구(명)
통계연도1.0000.0000.0000.0000.000
시도명0.0001.0000.4070.5530.587
인구 천명당 종사자수(명)0.0000.4071.0000.6650.189
종사자수(명)0.0000.5530.6651.0000.797
주민등록인구(명)0.0000.5870.1890.7971.000
2023-12-11T08:30:13.929503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명
통계연도1.0000.000
시도명0.0001.000
2023-12-11T08:30:14.015844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인구 천명당 종사자수(명)종사자수(명)주민등록인구(명)통계연도시도명
인구 천명당 종사자수(명)1.0000.3360.0700.0000.197
종사자수(명)0.3361.0000.9470.0000.263
주민등록인구(명)0.0700.9471.0000.0000.274
통계연도0.0000.0000.0001.0000.000
시도명0.1970.2630.2740.0001.000

Missing values

2023-12-11T08:30:11.652600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:30:11.741554image/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

통계연도시도명시군구명인구 천명당 종사자수(명)종사자수(명)주민등록인구(명)
02017서울특별시종로구1736.1268702154770
12017서울특별시중구3075.7386648125709
22017서울특별시용산구581.5133260229161
32017서울특별시성동구548.9167308304808
42017서울특별시광진구349.9125170357703
52017서울특별시동대문구407.4142842350647
62017서울특별시중랑구251.7102758408226
72017서울특별시성북구245.4108991444055
82017서울특별시강북구221.171752324479
92017서울특별시도봉구204.970503344166
통계연도시도명시군구명인구 천명당 종사자수(명)종사자수(명)주민등록인구(명)
6742019경상남도창녕군437.82728862331
6752019경상남도고성군405.12117852276
6762019경상남도남해군388.21693543622
6772019경상남도하동군336.81568646574
6782019경상남도산청군381.01349535417
6792019경상남도함양군370.51468539637
6802019경상남도거창군344.82143762179
6812019경상남도합천군304.71377345204
6822019제주특별자치도제주시438.6214650489405
6832019제주특별자치도서귀포시394.671654181584