Overview

Dataset statistics

Number of variables7
Number of observations1700
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory99.7 KiB
Average record size in memory60.1 B

Variable types

Categorical3
Text1
Numeric3

Dataset

Description김해시에서 통계기반 도시현황 파악을 위해 개발한 통계지수 중 하나로서, 통계연기, 시도명, 시군구명, 성별, 고용률(퍼센트), 15세이상인구수(천명), 취업자(천명)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=15110096

Alerts

고용률(퍼센트) is highly overall correlated with 15세이상인구수(천명) and 1 other fieldsHigh correlation
15세이상인구수(천명) is highly overall correlated with 고용률(퍼센트) and 1 other fieldsHigh correlation
취업자(천명) is highly overall correlated with 고용률(퍼센트) and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-11 00:13:18.320089
Analysis finished2023-12-11 00:13:20.168466
Duration1.85 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준연도
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
2021
456 
2020
313 
2019
312 
2018
310 
2017
309 

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 (%)
2021 456
26.8%
2020 313
18.4%
2019 312
18.4%
2018 310
18.2%
2017 309
18.2%

Length

2023-12-11T09:13:20.249985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:13:20.357714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 456
26.8%
2020 313
18.4%
2019 312
18.4%
2018 310
18.2%
2017 309
18.2%

분기
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
2
851 
상반기
849 

Length

Max length3
Median length1
Mean length1.9988235
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 851
50.1%
상반기 849
49.9%

Length

2023-12-11T09:13:20.482886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:13:20.617065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 851
50.1%
상반기 849
49.9%

시도명
Categorical

Distinct16
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
경기도
311 
경상북도
230 
전라남도
220 
강원도
180 
경상남도
180 
Other values (11)
579 

Length

Max length7
Median length4
Mean length3.8335294
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 311
18.3%
경상북도 230
13.5%
전라남도 220
12.9%
강원도 180
10.6%
경상남도 180
10.6%
충청남도 157
9.2%
전라북도 140
8.2%
충청북도 114
 
6.7%
서울특별시 50
 
2.9%
부산광역시 32
 
1.9%
Other values (6) 86
 
5.1%

Length

2023-12-11T09:13:20.744952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 311
18.3%
경상북도 230
13.5%
전라남도 220
12.9%
강원도 180
10.6%
경상남도 180
10.6%
충청남도 157
9.2%
전라북도 140
8.2%
충청북도 114
 
6.7%
서울특별시 50
 
2.9%
부산광역시 32
 
1.9%
Other values (6) 86
 
5.1%

시군구명
Text

Distinct209
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
2023-12-11T09:13:21.091903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0011765
Min length2

Characters and Unicode

Total characters5102
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

Unique1 ?
Unique (%)0.1%

Sample

1st row수원시
2nd row성남시
3rd row의정부시
4th row안양시
5th row부천시
ValueCountFrequency (%)
고성군 20
 
1.2%
동구 12
 
0.7%
중구 12
 
0.7%
성남시 10
 
0.6%
김천시 10
 
0.6%
수원시 10
 
0.6%
장성군 10
 
0.6%
강진군 10
 
0.6%
해남군 10
 
0.6%
영암군 10
 
0.6%
Other values (199) 1586
93.3%
2023-12-11T09:13:21.664497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 822
 
16.1%
시 780
 
15.3%
천 204
 
4.0%
주 193
 
3.8%
구 180
 
3.5%
양 164
 
3.2%
성 140
 
2.7%
산 136
 
2.7%
안 110
 
2.2%
영 96
 
1.9%
Other values (122) 2277
44.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5102
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 822
 
16.1%
시 780
 
15.3%
천 204
 
4.0%
주 193
 
3.8%
구 180
 
3.5%
양 164
 
3.2%
성 140
 
2.7%
산 136
 
2.7%
안 110
 
2.2%
영 96
 
1.9%
Other values (122) 2277
44.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5102
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 822
 
16.1%
시 780
 
15.3%
천 204
 
4.0%
주 193
 
3.8%
구 180
 
3.5%
양 164
 
3.2%
성 140
 
2.7%
산 136
 
2.7%
안 110
 
2.2%
영 96
 
1.9%
Other values (122) 2277
44.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5102
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 822
 
16.1%
시 780
 
15.3%
천 204
 
4.0%
주 193
 
3.8%
구 180
 
3.5%
양 164
 
3.2%
성 140
 
2.7%
산 136
 
2.7%
안 110
 
2.2%
영 96
 
1.9%
Other values (122) 2277
44.6%

고용률(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct283
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.783529
Minimum0
Maximum85.5
Zeros12
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2023-12-11T09:13:21.846560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile54.1
Q158.8
median62.8
Q367.225
95-th percentile72.9
Maximum85.5
Range85.5
Interquartile range (IQR)8.425

Descriptive statistics

Standard deviation7.9321504
Coefficient of variation (CV)0.12634126
Kurtosis25.843573
Mean62.783529
Median Absolute Deviation (MAD)4.2
Skewness-3.2628476
Sum106732
Variance62.91901
MonotonicityNot monotonic
2023-12-11T09:13:22.013980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.1 23
 
1.4%
66.1 20
 
1.2%
60.1 18
 
1.1%
61.7 17
 
1.0%
60.7 17
 
1.0%
61.1 16
 
0.9%
65.0 16
 
0.9%
59.0 16
 
0.9%
62.7 15
 
0.9%
61.9 15
 
0.9%
Other values (273) 1527
89.8%
ValueCountFrequency (%)
0.0 12
0.7%
45.2 1
 
0.1%
47.5 1
 
0.1%
48.4 1
 
0.1%
48.5 1
 
0.1%
49.0 1
 
0.1%
49.2 1
 
0.1%
49.3 1
 
0.1%
49.4 1
 
0.1%
50.0 1
 
0.1%
ValueCountFrequency (%)
85.5 1
0.1%
85.2 1
0.1%
84.3 2
0.1%
84.0 1
0.1%
83.7 1
0.1%
82.8 1
0.1%
82.7 1
0.1%
81.8 1
0.1%
81.7 1
0.1%
79.7 2
0.1%

15세이상인구수(천명)
Real number (ℝ)

HIGH CORRELATION 

Distinct1147
Distinct (%)67.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167.89353
Minimum0
Maximum1044.1
Zeros12
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2023-12-11T09:13:22.160754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20.5
Q136.875
median83.75
Q3230.825
95-th percentile618.11
Maximum1044.1
Range1044.1
Interquartile range (IQR)193.95

Descriptive statistics

Standard deviation200.71192
Coefficient of variation (CV)1.1954715
Kurtosis4.1248242
Mean167.89353
Median Absolute Deviation (MAD)55.75
Skewness2.0377691
Sum285419
Variance40285.276
MonotonicityNot monotonic
2023-12-11T09:13:22.316902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 12
 
0.7%
20.6 7
 
0.4%
20.2 6
 
0.4%
35.9 6
 
0.4%
23.0 6
 
0.4%
35.4 6
 
0.4%
39.1 6
 
0.4%
39.2 6
 
0.4%
31.0 6
 
0.4%
26.0 6
 
0.4%
Other values (1137) 1633
96.1%
ValueCountFrequency (%)
0.0 12
0.7%
6.7 2
 
0.1%
6.9 1
 
0.1%
7.0 1
 
0.1%
7.3 2
 
0.1%
7.4 2
 
0.1%
7.5 2
 
0.1%
14.3 2
 
0.1%
14.4 1
 
0.1%
14.6 1
 
0.1%
ValueCountFrequency (%)
1044.1 1
0.1%
1041.1 1
0.1%
1040.1 2
0.1%
1039.3 1
0.1%
1038.1 1
0.1%
1033.2 1
0.1%
1029.2 1
0.1%
1026.2 1
0.1%
1018.8 1
0.1%
922.6 1
0.1%

취업자(천명)
Real number (ℝ)

HIGH CORRELATION 

Distinct1005
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.58153
Minimum0
Maximum631.5
Zeros12
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2023-12-11T09:13:22.477913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.6
Q124.5
median51.2
Q3136.025
95-th percentile372.86
Maximum631.5
Range631.5
Interquartile range (IQR)111.525

Descriptive statistics

Standard deviation117.21918
Coefficient of variation (CV)1.1654146
Kurtosis4.1701904
Mean100.58153
Median Absolute Deviation (MAD)32.4
Skewness2.0527058
Sum170988.6
Variance13740.336
MonotonicityNot monotonic
2023-12-11T09:13:22.612764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 12
 
0.7%
16.1 9
 
0.5%
21.5 9
 
0.5%
16.4 8
 
0.5%
21.4 8
 
0.5%
25.3 7
 
0.4%
14.2 7
 
0.4%
22.8 7
 
0.4%
25.7 7
 
0.4%
14.4 7
 
0.4%
Other values (995) 1619
95.2%
ValueCountFrequency (%)
0.0 12
0.7%
5.6 2
 
0.1%
5.7 1
 
0.1%
5.8 1
 
0.1%
6.1 2
 
0.1%
6.2 3
 
0.2%
6.4 1
 
0.1%
10.1 1
 
0.1%
10.2 2
 
0.1%
10.3 2
 
0.1%
ValueCountFrequency (%)
631.5 1
0.1%
628.5 1
0.1%
627.3 1
0.1%
626.2 1
0.1%
611.3 1
0.1%
608.5 1
0.1%
606.0 1
0.1%
603.9 1
0.1%
596.2 1
0.1%
590.4 1
0.1%

Interactions

2023-12-11T09:13:19.510460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:18.789845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:19.127057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:19.626050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:18.897752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:19.243195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:19.744517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:19.011886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:13:19.396255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:13:22.721849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준연도분기시도명고용률(퍼센트)15세이상인구수(천명)취업자(천명)
기준연도1.0000.0000.4340.1520.1860.172
분기0.0001.0000.0000.0360.0000.000
시도명0.4340.0001.0000.6100.5620.576
고용률(퍼센트)0.1520.0360.6101.0000.4400.431
15세이상인구수(천명)0.1860.0000.5620.4401.0000.993
취업자(천명)0.1720.0000.5760.4310.9931.000
2023-12-11T09:13:23.157244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준연도분기시도명
기준연도1.0000.0000.237
분기0.0001.0000.000
시도명0.2370.0001.000
2023-12-11T09:13:23.325674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고용률(퍼센트)15세이상인구수(천명)취업자(천명)기준연도분기시도명
고용률(퍼센트)1.000-0.662-0.6120.1020.0260.347
15세이상인구수(천명)-0.6621.0000.9970.0780.0000.259
취업자(천명)-0.6120.9971.0000.0720.0000.268
기준연도0.1020.0780.0721.0000.0000.237
분기0.0260.0000.0000.0001.0000.000
시도명0.3470.2590.2680.2370.0001.000

Missing values

2023-12-11T09:13:19.953429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:13:20.102811image/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

기준연도분기시도명시군구명고용률(퍼센트)15세이상인구수(천명)취업자(천명)
02017상반기경기도수원시57.91018.8590.4
12017상반기경기도성남시58.2812.3472.4
22017상반기경기도의정부시54.9363.4199.5
32017상반기경기도안양시60.0500.9300.3
42017상반기경기도부천시58.3729.0425.3
52017상반기경기도광명시56.8278.7158.3
62017상반기경기도평택시59.3398.7236.6
72017상반기경기도동두천시54.583.245.4
82017상반기경기도안산시60.5633.9383.5
92017상반기경기도고양시55.4847.9469.7
기준연도분기시도명시군구명고용률(퍼센트)15세이상인구수(천명)취업자(천명)
169020212경상남도창녕군67.753.236.1
169120212경상남도고성군67.744.330.0
169220212경상남도남해군67.537.925.6
169320212경상남도하동군70.335.424.9
169420212경상남도산청군74.530.022.4
169520212경상남도함양군67.633.522.6
169620212경상남도거창군67.952.635.7
169720212경상남도합천군66.836.824.6
169820212제주특별자치도제주시65.3418.4273.3
169920212제주특별자치도서귀포시72.5151.3109.7