Overview

Dataset statistics

Number of variables6
Number of observations607
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.0 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, 농가수(가구), 경지면적(ha), 농가당경지면적(ha)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=15110103

Alerts

농가수(가구) is highly overall correlated with 경지면적(ha) and 1 other fieldsHigh correlation
경지면적(ha) is highly overall correlated with 농가수(가구) and 1 other fieldsHigh correlation
농가당경지면적(ha) is highly overall correlated with 농가수(가구) and 1 other fieldsHigh correlation
경지면적(ha) has unique valuesUnique

Reproduction

Analysis started2023-12-10 22:58:25.070079
Analysis finished2023-12-10 22:58:26.333740
Duration1.26 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계연도
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
2015
305 
2020
302 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2015 305
50.2%
2020 302
49.8%

Length

2023-12-11T07:58:26.398303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:58:26.497184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2015 305
50.2%
2020 302
49.8%

시도명
Categorical

Distinct17
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
경기도
105 
경상북도
56 
경상남도
52 
전라남도
50 
서울특별시
50 
Other values (12)
294 

Length

Max length7
Median length5
Mean length4.1202636
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 105
17.3%
경상북도 56
9.2%
경상남도 52
8.6%
전라남도 50
8.2%
서울특별시 50
8.2%
강원도 42
 
6.9%
충청남도 40
 
6.6%
전라북도 38
 
6.3%
부산광역시 38
 
6.3%
충청북도 36
 
5.9%
Other values (7) 100
16.5%

Length

2023-12-11T07:58:26.624198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 105
17.3%
경상북도 56
9.2%
경상남도 52
8.6%
전라남도 50
8.2%
서울특별시 50
8.2%
강원도 42
 
6.9%
충청남도 40
 
6.6%
부산광역시 38
 
6.3%
전라북도 38
 
6.3%
충청북도 36
 
5.9%
Other values (7) 100
16.5%

시군구명
Text

Distinct242
Distinct (%)39.9%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
2023-12-11T07:58:27.006180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.8220758
Min length2

Characters and Unicode

Total characters1713
Distinct characters143
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

Unique4 ?
Unique (%)0.7%

Sample

1st row종로구
2nd row중구
3rd row용산구
4th row성동구
5th row광진구
ValueCountFrequency (%)
동부 28
 
4.6%
읍부 28
 
4.6%
면부 28
 
4.6%
중구 12
 
2.0%
동구 12
 
2.0%
남구 11
 
1.8%
서구 10
 
1.6%
북구 10
 
1.6%
고성군 4
 
0.7%
강서구 4
 
0.7%
Other values (232) 460
75.8%
2023-12-11T07:58:27.491084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 215
 
12.6%
군 170
 
9.9%
시 156
 
9.1%
부 96
 
5.6%
동 68
 
4.0%
산 44
 
2.6%
천 44
 
2.6%
주 40
 
2.3%
양 38
 
2.2%
성 38
 
2.2%
Other values (133) 804
46.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1713
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 215
 
12.6%
군 170
 
9.9%
시 156
 
9.1%
부 96
 
5.6%
동 68
 
4.0%
산 44
 
2.6%
천 44
 
2.6%
주 40
 
2.3%
양 38
 
2.2%
성 38
 
2.2%
Other values (133) 804
46.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1713
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 215
 
12.6%
군 170
 
9.9%
시 156
 
9.1%
부 96
 
5.6%
동 68
 
4.0%
산 44
 
2.6%
천 44
 
2.6%
주 40
 
2.3%
양 38
 
2.2%
성 38
 
2.2%
Other values (133) 804
46.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1713
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 215
 
12.6%
군 170
 
9.9%
시 156
 
9.1%
부 96
 
5.6%
동 68
 
4.0%
산 44
 
2.6%
천 44
 
2.6%
주 40
 
2.3%
양 38
 
2.2%
성 38
 
2.2%
Other values (133) 804
46.9%

농가수(가구)
Real number (ℝ)

HIGH CORRELATION 

Distinct589
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7147.598
Minimum28
Maximum119301
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2023-12-11T07:58:27.632822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile121.9
Q11084
median4341
Q37598
95-th percentile20203.7
Maximum119301
Range119273
Interquartile range (IQR)6514

Descriptive statistics

Standard deviation12928.348
Coefficient of variation (CV)1.8087683
Kurtosis32.468471
Mean7147.598
Median Absolute Deviation (MAD)3263
Skewness5.2051061
Sum4338592
Variance1.6714219 × 108
MonotonicityNot monotonic
2023-12-11T07:58:27.796724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
656 2
 
0.3%
54 2
 
0.3%
28 2
 
0.3%
4199 2
 
0.3%
811 2
 
0.3%
842 2
 
0.3%
234 2
 
0.3%
333 2
 
0.3%
654 2
 
0.3%
1217 2
 
0.3%
Other values (579) 587
96.7%
ValueCountFrequency (%)
28 2
0.3%
34 1
0.2%
37 1
0.2%
38 1
0.2%
40 1
0.2%
52 1
0.2%
53 1
0.2%
54 2
0.3%
59 1
0.2%
60 1
0.2%
ValueCountFrequency (%)
119301 1
0.2%
109202 1
0.2%
99093 1
0.2%
94986 1
0.2%
91288 1
0.2%
91069 1
0.2%
80724 1
0.2%
75991 1
0.2%
68908 1
0.2%
59889 1
0.2%

경지면적(ha)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct607
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8124.341
Minimum9
Maximum176260.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2023-12-11T07:58:27.938264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile72.907
Q1564.456
median3905.995
Q39129.4505
95-th percentile22947.727
Maximum176260.87
Range176251.87
Interquartile range (IQR)8564.9945

Descriptive statistics

Standard deviation17502.016
Coefficient of variation (CV)2.154269
Kurtosis41.777824
Mean8124.341
Median Absolute Deviation (MAD)3555.279
Skewness5.9339706
Sum4931475
Variance3.0632057 × 108
MonotonicityNot monotonic
2023-12-11T07:58:28.065444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135.695 1
 
0.2%
225.443 1
 
0.2%
547.023 1
 
0.2%
673.307 1
 
0.2%
196.114 1
 
0.2%
75.964 1
 
0.2%
401.228 1
 
0.2%
622.179 1
 
0.2%
541.366 1
 
0.2%
315.923 1
 
0.2%
Other values (597) 597
98.4%
ValueCountFrequency (%)
9.0 1
0.2%
12.732 1
0.2%
14.854 1
0.2%
18.657 1
0.2%
19.624 1
0.2%
20.779 1
0.2%
21.609 1
0.2%
21.75 1
0.2%
22.296 1
0.2%
29.013 1
0.2%
ValueCountFrequency (%)
176260.874 1
0.2%
153206.01 1
0.2%
143840.855 1
0.2%
138580.294 1
0.2%
123472.631 1
0.2%
118150.83 1
0.2%
115043.832 1
0.2%
103750.78 1
0.2%
90814.979 1
0.2%
76958.814 1
0.2%

농가당경지면적(ha)
Real number (ℝ)

HIGH CORRELATION 

Distinct223
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4121746
Minimum0.36
Maximum3.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2023-12-11T07:58:28.203112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.36
5-th percentile0.54
Q10.84
median1.24
Q31.915
95-th percentile2.67
Maximum3.72
Range3.36
Interquartile range (IQR)1.075

Descriptive statistics

Standard deviation0.69202903
Coefficient of variation (CV)0.49004494
Kurtosis-0.36576791
Mean1.4121746
Median Absolute Deviation (MAD)0.47
Skewness0.68145427
Sum857.19
Variance0.47890417
MonotonicityNot monotonic
2023-12-11T07:58:28.339595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.14 11
 
1.8%
1.01 10
 
1.6%
0.84 8
 
1.3%
1.87 8
 
1.3%
0.78 8
 
1.3%
0.54 7
 
1.2%
0.73 7
 
1.2%
0.63 7
 
1.2%
1.08 6
 
1.0%
0.79 6
 
1.0%
Other values (213) 529
87.1%
ValueCountFrequency (%)
0.36 2
0.3%
0.38 1
 
0.2%
0.39 1
 
0.2%
0.4 3
0.5%
0.43 2
0.3%
0.46 2
0.3%
0.47 2
0.3%
0.48 2
0.3%
0.49 1
 
0.2%
0.5 3
0.5%
ValueCountFrequency (%)
3.72 1
0.2%
3.41 1
0.2%
3.38 1
0.2%
3.27 1
0.2%
3.22 1
0.2%
3.21 1
0.2%
3.13 1
0.2%
3.11 1
0.2%
3.08 1
0.2%
3.06 2
0.3%

Interactions

2023-12-11T07:58:25.903200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:58:25.333335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:58:25.621575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:58:25.990782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:58:25.429712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:58:25.719489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:58:26.082916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:58:25.537341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:58:25.813497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:58:28.429611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명농가수(가구)경지면적(ha)농가당경지면적(ha)
통계연도1.0000.0000.0000.0000.219
시도명0.0001.0000.2720.1710.593
농가수(가구)0.0000.2721.0000.9590.000
경지면적(ha)0.0000.1710.9591.0000.178
농가당경지면적(ha)0.2190.5930.0000.1781.000
2023-12-11T07:58:28.519789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명
통계연도1.0000.000
시도명0.0001.000
2023-12-11T07:58:28.603534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
농가수(가구)경지면적(ha)농가당경지면적(ha)통계연도시도명
농가수(가구)1.0000.958-0.5910.0000.108
경지면적(ha)0.9581.000-0.7700.0000.066
농가당경지면적(ha)-0.591-0.7701.0000.1650.276
통계연도0.0000.0000.1651.0000.000
시도명0.1080.0660.2760.0001.000

Missing values

2023-12-11T07:58:26.197752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:58:26.295390image/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

통계연도시도명시군구명농가수(가구)경지면적(ha)농가당경지면적(ha)
02015서울특별시종로구59135.6950.43
12015서울특별시중구3821.751.75
22015서울특별시용산구4021.6091.85
32015서울특별시성동구5230.7231.69
42015서울특별시광진구135106.2241.27
52015서울특별시동대문구6653.7661.23
62015서울특별시중랑구315241.4481.3
72015서울특별시성북구8980.5611.1
82015서울특별시강북구7847.9081.63
92015서울특별시도봉구12152.7112.3
통계연도시도명시군구명농가수(가구)경지면적(ha)농가당경지면적(ha)
5972020경상남도하동군63377993.5210.79
5982020경상남도산청군51285057.891.01
5992020경상남도함양군55295217.8851.06
6002020경상남도거창군65036141.451.06
6012020경상남도합천군69357977.8630.87
6022020제주특별자치도동부1521811964.1031.27
6032020제주특별자치도읍부1158518443.3730.63
6042020제주특별자치도면부35625810.5070.61
6052020제주특별자치도제주시1778319554.280.91
6062020제주특별자치도서귀포시1258216663.7030.76