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, 경제활동인구수(천명)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/15110094/fileData.do

Alerts

경제활동인구수(천명) is highly overall correlated with 경제활동인구수(남)(천명) and 1 other fieldsHigh correlation
경제활동인구수(남)(천명) is highly overall correlated with 경제활동인구수(천명) and 1 other fieldsHigh correlation
경제활동인구수(여)(천명) is highly overall correlated with 경제활동인구수(천명) and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 08:28:36.711675
Analysis finished2023-12-12 08:28:38.547601
Duration1.84 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계연도
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

2023-12-12T17:28:38.605555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:28:38.705891image/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
하반기
851 
상반기
849 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

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

Length

2023-12-12T17:28:38.839077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:28:38.936233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
하반기 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

2023-12-12T17:28:39.054048image/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
2023-12-12T17:28:39.344599image/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%
2023-12-12T17:28:39.787325image/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%

경제활동인구수(천명)
Real number (ℝ)

HIGH CORRELATION 

Distinct1034
Distinct (%)60.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.07159
Minimum0
Maximum655.4
Zeros12
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2023-12-12T17:28:39.961307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.795
Q124.9
median52.4
Q3140.2
95-th percentile388.465
Maximum655.4
Range655.4
Interquartile range (IQR)115.3

Descriptive statistics

Standard deviation122.14999
Coefficient of variation (CV)1.1737112
Kurtosis4.1535459
Mean104.07159
Median Absolute Deviation (MAD)33.45
Skewness2.0508973
Sum176921.7
Variance14920.62
MonotonicityNot monotonic
2023-12-12T17:28:40.202569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 12
 
0.7%
21.7 11
 
0.6%
16.1 9
 
0.5%
23.6 9
 
0.5%
16.4 9
 
0.5%
16.6 9
 
0.5%
14.4 8
 
0.5%
16.9 7
 
0.4%
21.8 7
 
0.4%
25.9 7
 
0.4%
Other values (1024) 1612
94.8%
ValueCountFrequency (%)
0.0 12
0.7%
5.6 1
 
0.1%
5.7 2
 
0.1%
5.8 1
 
0.1%
6.1 1
 
0.1%
6.2 3
 
0.2%
6.3 1
 
0.1%
6.4 1
 
0.1%
10.3 3
 
0.2%
10.4 2
 
0.1%
ValueCountFrequency (%)
655.4 1
0.1%
654.7 1
0.1%
651.2 1
0.1%
650.6 1
0.1%
635.7 1
0.1%
634.5 1
0.1%
632.4 1
0.1%
630.4 1
0.1%
620.3 1
0.1%
612.5 1
0.1%

경제활동인구수(남)(천명)
Real number (ℝ)

HIGH CORRELATION 

Distinct844
Distinct (%)49.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.775412
Minimum0
Maximum384.5
Zeros12
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2023-12-12T17:28:40.365538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.9
Q113.8
median30.4
Q382.025
95-th percentile233.46
Maximum384.5
Range384.5
Interquartile range (IQR)68.225

Descriptive statistics

Standard deviation72.608853
Coefficient of variation (CV)1.1947077
Kurtosis4.1632463
Mean60.775412
Median Absolute Deviation (MAD)19.9
Skewness2.0573113
Sum103318.2
Variance5272.0455
MonotonicityNot monotonic
2023-12-12T17:28:40.515816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 12
 
0.7%
8.9 12
 
0.7%
9.0 12
 
0.7%
14.3 11
 
0.6%
8.8 11
 
0.6%
12.3 11
 
0.6%
11.5 11
 
0.6%
13.1 10
 
0.6%
8.7 10
 
0.6%
7.9 9
 
0.5%
Other values (834) 1591
93.6%
ValueCountFrequency (%)
0.0 12
0.7%
3.4 2
 
0.1%
3.5 2
 
0.1%
3.6 1
 
0.1%
3.7 3
 
0.2%
3.8 2
 
0.1%
5.5 4
 
0.2%
5.6 2
 
0.1%
5.7 3
 
0.2%
5.9 2
 
0.1%
ValueCountFrequency (%)
384.5 1
0.1%
381.6 1
0.1%
381.1 1
0.1%
378.5 1
0.1%
378.3 1
0.1%
375.7 1
0.1%
373.1 1
0.1%
372.6 1
0.1%
372.0 1
0.1%
369.9 1
0.1%

경제활동인구수(여)(천명)
Real number (ℝ)

HIGH CORRELATION 

Distinct744
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.295882
Minimum0
Maximum276.2
Zeros12
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2023-12-12T17:28:40.654589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.9
Q111
median21.75
Q357.9
95-th percentile155.025
Maximum276.2
Range276.2
Interquartile range (IQR)46.9

Descriptive statistics

Standard deviation49.797795
Coefficient of variation (CV)1.1501739
Kurtosis4.2131684
Mean43.295882
Median Absolute Deviation (MAD)13.45
Skewness2.0568148
Sum73603
Variance2479.8203
MonotonicityNot monotonic
2023-12-12T17:28:40.776111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.5 16
 
0.9%
7.6 13
 
0.8%
9.8 13
 
0.8%
6.9 13
 
0.8%
0.0 12
 
0.7%
9.9 12
 
0.7%
10.9 12
 
0.7%
7.4 12
 
0.7%
10.3 11
 
0.6%
7.1 11
 
0.6%
Other values (734) 1575
92.6%
ValueCountFrequency (%)
0.0 12
0.7%
2.3 3
 
0.2%
2.4 1
 
0.1%
2.5 5
0.3%
2.6 1
 
0.1%
4.8 6
0.4%
4.9 4
 
0.2%
5.0 1
 
0.1%
5.1 6
0.4%
5.2 1
 
0.1%
ValueCountFrequency (%)
276.2 1
0.1%
272.3 1
0.1%
270.9 1
0.1%
270.1 1
0.1%
262.6 1
0.1%
262.5 1
0.1%
257.7 1
0.1%
252.9 1
0.1%
248.3 1
0.1%
243.0 1
0.1%

Interactions

2023-12-12T17:28:37.976854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:37.147018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:37.570997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:38.100579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:37.290281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:37.690298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:38.245875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:37.452633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:28:37.838253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:28:40.866385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도분기시도명경제활동인구수(천명)경제활동인구수(남)(천명)경제활동인구수(여)(천명)
통계연도1.0000.0000.4340.1660.1580.171
분기0.0001.0000.0000.0000.0000.000
시도명0.4340.0001.0000.5740.5590.554
경제활동인구수(천명)0.1660.0000.5741.0000.9920.990
경제활동인구수(남)(천명)0.1580.0000.5590.9921.0000.972
경제활동인구수(여)(천명)0.1710.0000.5540.9900.9721.000
2023-12-12T17:28:41.011574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도분기시도명
통계연도1.0000.0000.237
분기0.0001.0000.000
시도명0.2370.0001.000
2023-12-12T17:28:41.124844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경제활동인구수(천명)경제활동인구수(남)(천명)경제활동인구수(여)(천명)통계연도분기시도명
경제활동인구수(천명)1.0000.9990.9980.0700.0000.266
경제활동인구수(남)(천명)0.9991.0000.9940.0660.0000.257
경제활동인구수(여)(천명)0.9980.9941.0000.0720.0000.253
통계연도0.0700.0660.0721.0000.0000.237
분기0.0000.0000.0000.0001.0000.000
시도명0.2660.2570.2530.2370.0001.000

Missing values

2023-12-12T17:28:38.368048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:28:38.493007image/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

통계연도분기시도명시군구명경제활동인구수(천명)경제활동인구수(남)(천명)경제활동인구수(여)(천명)
02017상반기경기도수원시612.5375.7236.8
12017상반기경기도성남시485.4280.4205.0
22017상반기경기도의정부시210.0123.386.7
32017상반기경기도안양시310.6176.9133.7
42017상반기경기도부천시445.3265.5179.8
52017상반기경기도광명시165.8100.665.2
62017상반기경기도평택시241.5154.087.5
72017상반기경기도동두천시47.727.420.3
82017상반기경기도안산시400.7244.9155.9
92017상반기경기도고양시487.7287.9199.8
통계연도분기시도명시군구명경제활동인구수(천명)경제활동인구수(남)(천명)경제활동인구수(여)(천명)
16902021하반기경상남도창녕군36.620.016.5
16912021하반기경상남도고성군30.616.514.0
16922021하반기경상남도남해군25.913.412.5
16932021하반기경상남도하동군25.213.212.0
16942021하반기경상남도산청군22.511.511.0
16952021하반기경상남도함양군22.811.811.0
16962021하반기경상남도거창군36.018.717.3
16972021하반기경상남도합천군24.713.211.5
16982021하반기제주특별자치도제주시280.6152.1128.5
16992021하반기제주특별자치도서귀포시111.258.752.5